diff --git a/.gitignore b/.gitignore index 9aab8f9ac9..da4d902324 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Hidden Files +.* + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -47,6 +50,18 @@ coverage.xml *.cover .hypothesis/ +# Temporary data generated by unit tests +climada/engine/test/data/test.csv +climada/engine/test/data/test.xlsx +climada/engine/test/data/test_imp_mat.npz +climada/entity/impact_funcs/test/test_write.xlsx +climada/hazard/test/data/test_haz.h5 +climada/hazard/test/data/tc_tracks_nc/* +climada/test/data/1988234N13299.nc +climada/test/data/exposure_high_47_8.h5 +climada/test/data/test_write_hazard.tif +climada/util/test/data/save_test.pkl + # Translations *.mo *.pot @@ -107,7 +122,55 @@ venv.bak/ # mac finder files .DS_Store +# directory with input data for hazard emulator tutorial +data/emulator + # climada system data files # they get downloaded separately from the repo data/system/global_coast* data/system/global_country* +data/system/tmp_elevation.tif + +# climada system data files: Nightlight and population data +data/system/BlackMarble*tif +data/system/*stable_lights*.* +data/system/gpw_v4_population_count_rev11_2015_30_sec.tif + +# climada system data files: IBTrACS data +data/system/IBTrACS.ALL.v04r00.nc +data/system/data_master_sl_short/ + +# climada system data files: SPEI data +data/system/spei06.nc + +# climada system data files: World Bank data +data/system/OGHIST.xls +data/system/Wealth-Accounts_CSV/ + +# climada system data files: Credit Suisse Research institute data: +data/system/GDP2Asset_factors_CRI_2016 + +# climada system data files: SPAM data +data/system/cell5m_allockey_xy.csv +data/system/LicenseV*.pdf +data/system/readme_global_v3r2.txt +data/system/spam*csv + +# climada system data files: GPW data +data/system/gpw-v* +data/system/gpw_v*.tif + +# climada system data files: NASA distance to coast +data/system/GMT_intermediate_coast* + +# climada system data files: folders for hazard and exposure data +data/system/hazard/ +data/system/litpop/ +data/system/litpop_2014/ + +# climada data: ISIMIP crop data folder: +data/ISIMIP_crop/ + +# climada data results folder: +data/results/ + diff --git a/.pylintrc b/.pylintrc index c335516839..2c222970e4 100644 --- a/.pylintrc +++ b/.pylintrc @@ -3,7 +3,7 @@ # A comma-separated list of package or module names from where C extensions may # be loaded. Extensions are loading into the active Python interpreter and may # run arbitrary code -extension-pkg-whitelist=numpy,pathos +extension-pkg-whitelist=numpy,pathos,scipy # Add files or directories to the blacklist. They should be base names, not # paths. @@ -130,7 +130,7 @@ contextmanager-decorators=contextlib.contextmanager # List of members which are set dynamically and missed by pylint inference # system, and so shouldn't trigger E1101 when accessed. Python regular # expressions are accepted. -generated-members=numpy.*,pathos.* +generated-members=numpy.*,pathos.*,cartopy.*,scipy.* # Tells whether missing members accessed in mixin class should be ignored. A # mixin class is detected if its name ends with "mixin" (case insensitive). @@ -153,7 +153,7 @@ ignored-classes=optparse.Values,thread._local,_thread._local # (useful for modules/projects where namespaces are manipulated during runtime # and thus existing member attributes cannot be deduced by static analysis. It # supports qualified module names, as well as Unix pattern matching. -ignored-modules=numpy,pathos +ignored-modules=numpy,cartopy,scipy,pathos,xarray.ufuncs,netCDF4,matplotlib.cm # Show a hint with possible names when a member name was not found. The aspect # of finding the hint is based on edit distance. diff --git a/AUTHORS b/AUTHORS index 30619a7040..756ec03bec 100644 --- a/AUTHORS +++ b/AUTHORS @@ -13,3 +13,6 @@ Rachel Bungerer Inga Sauer Samuel Lüthi Mannie Kam +Simona Meiler +Alessio Ciullo +Thomas Vogt diff --git a/Makefile b/Makefile index ba10f69db3..8bfe8eb790 100644 --- a/Makefile +++ b/Makefile @@ -24,6 +24,10 @@ install_test : ## Test installation was successful data_test : ## Test data APIs python test_data_api.py +.PHONY : notebook_test +notebook_test : ## Test notebooks in doc/tutorial + python test_notebooks.py + .PHONY : integ_test integ_test : ## Integration tests execution with xml reports python -m coverage run --parallel-mode --concurrency=multiprocessing tests_runner.py integ diff --git a/README.md b/README.md index 63a41bfbaa..6bdf207fa6 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[![Build Status](http://ied-wcr-jenkins.ethz.ch/buildStatus/icon?job=climada_ci)](http://ied-wcr-jenkins.ethz.ch/job/climada_ci/) +[![Build Status](http://ied-wcr-jenkins.ethz.ch/buildStatus/icon?job=climada_branches/develop)](http://ied-wcr-jenkins.ethz.ch/job/climada_branches/) [![Documentation build status](https://img.shields.io/readthedocs/climada-python.svg?style=flat-square)](https://readthedocs.org/projects/climada-python/builds/) ![Jenkins Coverage](https://img.shields.io/jenkins/coverage/cobertura/http/ied-wcr-jenkins.ethz.ch/climada_ci_night.svg) @@ -57,4 +57,4 @@ CLIMADA is free software: you can redistribute it and/or modify it under the ter CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details: -https://github.com/CLIMADA-project/climada_python/blob/master/LICENSE + diff --git a/climada/__init__.py b/climada/__init__.py index 18613ff62b..2e9822e6fe 100755 --- a/climada/__init__.py +++ b/climada/__init__.py @@ -19,7 +19,6 @@ climada init """ -from .util.config import CONFIG, setup_conf_user, setup_logging, setup_environ +from .util.config import CONFIG, setup_conf_user, setup_logging setup_conf_user() setup_logging(CONFIG['global']['log_level']) -setup_environ() diff --git a/climada/_version.py b/climada/_version.py index 96e3ce8d9f..77f1c8e63c 100644 --- a/climada/_version.py +++ b/climada/_version.py @@ -1 +1 @@ -__version__ = '1.4.0' +__version__ = '1.5.0' diff --git a/climada/conf/defaults.conf b/climada/conf/defaults.conf index 2be6591ca8..b85a116cfc 100644 --- a/climada/conf/defaults.conf +++ b/climada/conf/defaults.conf @@ -1,9 +1,4 @@ { - "config": - { - "env_name": "climada_env" - }, - "local_data": { "save_dir": "./results/" diff --git a/climada/engine/calibration_opt.py b/climada/engine/calibration_opt.py index cc2e4a53f4..1bbbbb07a6 100644 --- a/climada/engine/calibration_opt.py +++ b/climada/engine/calibration_opt.py @@ -25,14 +25,12 @@ import datetime as dt import copy from scipy import interpolate +from scipy.optimize import minimize import itertools from climada.engine import Impact from climada.entity import ImpactFuncSet, IFTropCyclone, impact_funcs -from climada.engine.impact_data import emdat_countries_by_hazard, \ - emdat_impact_yearlysum, emdat_impact_event -from climada.hazard import TropCyclone -from climada.entity.exposures.litpop import LitPop +from climada.engine.impact_data import emdat_impact_yearlysum, emdat_impact_event import logging LOGGER = logging.getLogger(__name__) @@ -40,25 +38,25 @@ def calib_instance(hazard, exposure, impact_func, df_out=pd.DataFrame(), - yearly_impact=False, return_cost = 'False'): + yearly_impact=False, return_cost='False'): - """ calculate one impact instance for the calibration algorithm and write + """calculate one impact instance for the calibration algorithm and write to given DataFrame Parameters: hazard: hazard set instance exposure: exposure set instance impact_func: impact function instance - + Optional Parameters: df_out: Output DataFrame with headers of columns defined and optionally with - first row (index=0) defined with values. If columns "impact", + first row (index=0) defined with values. If columns "impact", "event_id", or "year" are not included, they are created here. Data like reported impacts or impact function parameters can be given here; values are preserved. - yearly_impact (boolean): if set True, impact is returned per year, + yearly_impact (boolean): if set True, impact is returned per year, not per event - return_cost: if not 'False' but any of 'R2', 'logR2', + return_cost: if not 'False' but any of 'R2', 'logR2', cost is returned instead of df_out Returns: @@ -69,37 +67,37 @@ def calib_instance(hazard, exposure, impact_func, df_out=pd.DataFrame(), IFS.append(impact_func) impacts = Impact() impacts.calc(exposure, IFS, hazard) - if yearly_impact: # impact per year + if yearly_impact: # impact per year IYS = impacts.calc_impact_year_set(all_years=True) # Loop over whole year range: if df_out.empty | df_out.index.shape[0] == 1: for cnt_, year in enumerate(np.sort(list((IYS.keys())))): if cnt_ > 0: - df_out.loc[cnt_] = df_out.loc[0] # copy info from first row + df_out.loc[cnt_] = df_out.loc[0] # copy info from first row if year in IYS: df_out.loc[cnt_, 'impact_CLIMADA'] = IYS[year] else: df_out.loc[cnt_, 'impact_CLIMADA'] = 0.0 df_out.loc[cnt_, 'year'] = year else: - years_in_common = df_out.loc[df_out['year'].isin(np.sort(list((IYS.keys())))),'year'] + years_in_common = df_out.loc[df_out['year'].isin(np.sort(list((IYS.keys())))), 'year'] for cnt_, year in years_in_common.iteritems(): - df_out.loc[df_out['year']==year,'impact_CLIMADA'] = IYS[year] - + df_out.loc[df_out['year'] == year, 'impact_CLIMADA'] = IYS[year] + - else: # impact per event + else: # impact per event if df_out.empty | df_out.index.shape[0] == 1: for cnt_, impact in enumerate(impacts.at_event): if cnt_ > 0: - df_out.loc[cnt_] = df_out.loc[0] # copy info from first row + df_out.loc[cnt_] = df_out.loc[0] # copy info from first row df_out.loc[cnt_, 'impact_CLIMADA'] = impact df_out.loc[cnt_, 'event_id'] = int(impacts.event_id[cnt_]) df_out.loc[cnt_, 'event_name'] = impacts.event_name[cnt_] df_out.loc[cnt_, 'year'] = \ dt.datetime.fromordinal(impacts.date[cnt_]).year df_out.loc[cnt_, 'date'] = impacts.date[cnt_] - elif df_out.index.shape[0]==impacts.at_event.shape[0]: - for cnt_, (impact,ind) in enumerate(zip(impacts.at_event,df_out.index)): + elif df_out.index.shape[0] == impacts.at_event.shape[0]: + for cnt_, (impact, ind) in enumerate(zip(impacts.at_event, df_out.index)): df_out.loc[ind, 'impact_CLIMADA'] = impact df_out.loc[ind, 'event_id'] = int(impacts.event_id[cnt_]) df_out.loc[ind, 'event_name'] = impacts.event_name[cnt_] @@ -111,50 +109,56 @@ def calib_instance(hazard, exposure, impact_func, df_out=pd.DataFrame(), ' yet implemented. use yearly_impact=True or run' ' without init_impact_data.') if not return_cost == 'False': - df_out = calib_cost_calc(df_out,return_cost) + df_out = calib_cost_calc(df_out, return_cost) return df_out -def init_if(if_name_or_instance, param_dict, df_out = pd.DataFrame(index=[0])): - """ create an ImpactFunc based on the parameters in param_dict using the +def init_if(if_name_or_instance, param_dict, df_out=pd.DataFrame(index=[0])): + """create an ImpactFunc based on the parameters in param_dict using the method specified in if_parameterisation_name and document it in df_out. - Parameters: - if_name_or_instance (str or ImpactFunc): method of impact function - parameterisation e.g. 'emanuel' or an instance of ImpactFunc - param_dict: dict of parameter_names and values - e.g. {'v_thresh': 25.7, 'v_half': 70, 'scale': 1} - or e.g. {'mdd_shift': 1.05, 'mdd_scale': 0.8, - 'paa_shift': 1, paa_scale': 1} - Returns: - ImpactFunc: The Impact function based on the parameterisation - df_out: Output DataFrame with headers of columns defined and with - first row (index=0) defined with values. The impact function - parameters from param_dict are represented here. + + Parameters + ---------- + if_name_or_instance : str or ImpactFunc + method of impact function parameterisation e.g. 'emanuel' or an + instance of ImpactFunc + param_dict: dict of parameter_names and values + e.g. {'v_thresh': 25.7, 'v_half': 70, 'scale': 1} + or {'mdd_shift': 1.05, 'mdd_scale': 0.8, 'paa_shift': 1, paa_scale': 1} + + Returns + ------- + imp_fun : ImpactFunc + The Impact function based on the parameterisation + df_out : DataFrame + Output DataFrame with headers of columns defined and with first row + (index=0) defined with values. The impact function parameters from + param_dict are represented here. """ ImpactFunc_final = None - if isinstance(if_name_or_instance,str): + if isinstance(if_name_or_instance, str): if if_name_or_instance == 'emanuel': ImpactFunc_final = IFTropCyclone() ImpactFunc_final.set_emanuel_usa(**param_dict) ImpactFunc_final.haz_type = 'TC' ImpactFunc_final.id = 1 df_out['impact_function'] = if_name_or_instance - elif isinstance(if_name_or_instance,impact_funcs.ImpactFunc): + elif isinstance(if_name_or_instance, impact_funcs.ImpactFunc): ImpactFunc_final = change_if(if_name_or_instance, param_dict) df_out['impact_function'] = ('given_' + - ImpactFunc_final.haz_type + - str(ImpactFunc_final.id)) + ImpactFunc_final.haz_type + + str(ImpactFunc_final.id)) for key, val in param_dict.items(): df_out[key] = val return ImpactFunc_final, df_out -def change_if(if_instance,param_dict): - """ apply a shifting or a scaling defined in param_dict to the impact +def change_if(if_instance, param_dict): + """apply a shifting or a scaling defined in param_dict to the impact function in if_istance and return it as a new ImpactFunc object. Parameters: if_instance (ImpactFunc): an instance of ImpactFunc param_dict: dict of parameter_names and values (interpreted as factors, 1 = neutral) - e.g. {'mdd_shift': 1.05, 'mdd_scale': 0.8, + e.g. {'mdd_shift': 1.05, 'mdd_scale': 0.8, 'paa_shift': 1, paa_scale': 1} Returns: ImpactFunc: The Impact function based on the parameterisation @@ -169,62 +173,63 @@ def change_if(if_instance,param_dict): fill_value='extrapolate') temp_dict = dict() temp_dict['paa_intensity_ext'] = np.linspace(ImpactFunc_new.intensity.min(), - ImpactFunc_new.intensity.max(), - (ImpactFunc_new.intensity.shape[0]+1)*10+1) + ImpactFunc_new.intensity.max(), + (ImpactFunc_new.intensity.shape[0] + 1) * 10 + 1) temp_dict['mdd_intensity_ext'] = np.linspace(ImpactFunc_new.intensity.min(), - ImpactFunc_new.intensity.max(), - (ImpactFunc_new.intensity.shape[0]+1)*10+1) + ImpactFunc_new.intensity.max(), + (ImpactFunc_new.intensity.shape[0] + 1) * 10 + 1) temp_dict['paa_ext'] = paa_func(temp_dict['paa_intensity_ext']) temp_dict['mdd_ext'] = mdd_func(temp_dict['mdd_intensity_ext']) # apply changes given in param_dict for key, val in param_dict.items(): field_key, action = key.split('_') if action == 'shift': - shift_absolut = ImpactFunc_new.intensity[np.nonzero(getattr(ImpactFunc_new,field_key))[0][0]] * \ - (val-1) + shift_absolut = ( + ImpactFunc_new.intensity[np.nonzero(getattr(ImpactFunc_new, field_key))[0][0]] + * (val - 1)) temp_dict[field_key + '_intensity_ext'] = \ - temp_dict[field_key + '_intensity_ext'] + shift_absolut + temp_dict[field_key + '_intensity_ext'] + shift_absolut elif action == 'scale': temp_dict[field_key + '_ext'] = \ np.clip(temp_dict[field_key + '_ext'] * val, - a_min = 0, - a_max = 1) + a_min=0, + a_max=1) else: raise AttributeError('keys in param_dict not recognized. Use only:' 'paa_shift, paa_scale, mdd_shift, mdd_scale') # map changed, high resolution impact functions back to initial resolution ImpactFunc_new.intensity = np.linspace(ImpactFunc_new.intensity.min(), - ImpactFunc_new.intensity.max(), - (ImpactFunc_new.intensity.shape[0]+1)*10+1) - paa_func_new = interpolate.interp1d(temp_dict['paa_intensity_ext'], + ImpactFunc_new.intensity.max(), + (ImpactFunc_new.intensity.shape[0] + 1) * 10 + 1) + paa_func_new = interpolate.interp1d(temp_dict['paa_intensity_ext'], temp_dict['paa_ext'], fill_value='extrapolate') - mdd_func_new = interpolate.interp1d(temp_dict['mdd_intensity_ext'], + mdd_func_new = interpolate.interp1d(temp_dict['mdd_intensity_ext'], temp_dict['mdd_ext'], fill_value='extrapolate') ImpactFunc_new.paa = paa_func_new(ImpactFunc_new.intensity) ImpactFunc_new.mdd = mdd_func_new(ImpactFunc_new.intensity) - return ImpactFunc_new + return ImpactFunc_new -def init_impact_data(hazard_type, +def init_impact_data(hazard_type, region_ids, year_range, source_file, reference_year, impact_data_source='emdat', yearly_impact=True): - """ creates a dataframe containing the recorded impact data for one hazard + """creates a dataframe containing the recorded impact data for one hazard type and one area (countries, country or local split) Parameters: hazard_type: default = 'TC', type of hazard 'WS','FL' etc. - region_ids: name the region_ids or country names - year_range (list): list containting start and end year. + region_ids: name the region_ids or country names + year_range (list): list containting start and end year. e.g. [1980, 2017] reference_year: impacts will be scaled to this year impact_data_source: default 'emdat', others maybe possible Optional Parameters: - yearly_impact (boolean): if set True, impact is returned per year, + yearly_impact (boolean): if set True, impact is returned per year, not per event Returns: df_out: DataFrame with recorded impact written to rows for each year @@ -232,53 +237,55 @@ def init_impact_data(hazard_type, """ if impact_data_source == 'emdat': if yearly_impact: - em_data = emdat_impact_yearlysum(region_ids, hazard_type, - emdat_file_csv=source_file, year_range=year_range, + em_data = emdat_impact_yearlysum(source_file, countries=region_ids, + hazard=hazard_type, + year_range=year_range, reference_year=reference_year) else: - raise ValueError ('init_impact_data not yet implemented for yearly_impact = False.') - em_data = emdat_impact_event() + raise ValueError('init_impact_data not yet implemented for yearly_impact = False.') + em_data = emdat_impact_event(source_file) else: - raise ValueError('init_impact_data not yet implemented for other impact_data_sources than emdat.') + raise ValueError('init_impact_data not yet implemented for other impact_data_sources ' + 'than emdat.') return em_data -def calib_cost_calc(df_out,cost_function): - """ calculate the cost function of the modelled impact impact_CLIMADA and +def calib_cost_calc(df_out, cost_function): + """calculate the cost function of the modelled impact impact_CLIMADA and the reported impact impact_scaled in df_out Parameters: df_out (pd.Dataframe): DataFrame as created in calib_instance cost_function (str): chooses the cost function e.g. 'R2' or 'logR2' Returns: - cost: the results of the cost function when comparing modelled and + cost: the results of the cost function when comparing modelled and reported impact """ if cost_function == 'R2': - cost = np.sum((pd.to_numeric(df_out['impact_scaled']) - + cost = np.sum((pd.to_numeric(df_out['impact_scaled']) - pd.to_numeric(df_out['impact_CLIMADA']))**2) elif cost_function == 'logR2': impact1 = pd.to_numeric(df_out['impact_scaled']) - impact1[impact1<=0] = 1 + impact1[impact1 <= 0] = 1 impact2 = pd.to_numeric(df_out['impact_CLIMADA']) - impact2[impact2<=0] = 1 - cost = np.sum((np.log(impact1) - + impact2[impact2 <= 0] = 1 + cost = np.sum((np.log(impact1) - np.log(impact2))**2) else: raise ValueError('This cost function is not implemented.') return cost -def calib_all(hazard,exposure,if_name_or_instance,param_full_dict, +def calib_all(hazard, exposure, if_name_or_instance, param_full_dict, impact_data_source, year_range, yearly_impact=True): - """ portrait the difference between modelled and reported impacts for all + """portrait the difference between modelled and reported impacts for all impact functions described in param_full_dict and if_name_or_instance Parameters: hazard: list or instance of hazard exposure: list or instance of exposure of full countries - if_name_or_instance (string or ImpactFunc): the name of a + if_name_or_instance (string or ImpactFunc): the name of a parameterisation or an instance of class ImpactFunc e.g. 'emanuel' - param_full_dict (dict): a dict containing keys used for + param_full_dict (dict): a dict containing keys used for if_name_or_instance and values which are iterable (lists) e.g. {'v_thresh': [25.7, 20], 'v_half': [70], 'scale': [1, 0.8]} impact_data_source (dict or dataframe): with name of impact data source @@ -289,124 +296,125 @@ def calib_all(hazard,exposure,if_name_or_instance,param_full_dict, df_result: DataFrame with modelled impact written to rows for each year or event. """ - df_result = None # init return variable - + df_result = None # init return variable + # prepare hazard and exposure region_ids = list(np.unique(exposure.region_id)) hazard_type = hazard.tag.haz_type # prepare impact data - if isinstance(impact_data_source,pd.DataFrame): + if isinstance(impact_data_source, pd.DataFrame): df_impact_data = impact_data_source else: if list(impact_data_source.keys()) == ['emdat']: - df_impact_data = init_impact_data(hazard_type,region_ids, year_range,impact_data_source['emdat'],year_range[-1]) + df_impact_data = init_impact_data(hazard_type, region_ids, year_range, + impact_data_source['emdat'], year_range[-1]) else: raise ValueError('other impact data sources not yet implemented.') - params_generator = (dict(zip(param_full_dict, x)) for x in itertools.product(*param_full_dict.values())) + params_generator = (dict(zip(param_full_dict, x)) + for x in itertools.product(*param_full_dict.values())) for param_dict in params_generator: print(param_dict) df_out = copy.deepcopy(df_impact_data) - ImpactFunc_final, df_out = init_if(if_name_or_instance,param_dict,df_out) - df_out = calib_instance(hazard,exposure,ImpactFunc_final,df_out,yearly_impact) + ImpactFunc_final, df_out = init_if(if_name_or_instance, param_dict, df_out) + df_out = calib_instance(hazard, exposure, ImpactFunc_final, df_out, yearly_impact) if df_result is None: df_result = copy.deepcopy(df_out) else: df_result = df_result.append(df_out, input) - - return df_result -from scipy.optimize import minimize, Bounds, LinearConstraint + return df_result -def calib_optimize(hazard,exposure,if_name_or_instance,param_dict, - impact_data_source, year_range, yearly_impact=True, - cost_fucntion='R2',show_details= False): - """ portrait the difference between modelled and reported impacts for all +def calib_optimize(hazard, exposure, if_name_or_instance, param_dict, + impact_data_source, year_range, yearly_impact=True, + cost_fucntion='R2', show_details=False): + """portrait the difference between modelled and reported impacts for all impact functions described in param_full_dict and if_name_or_instance Parameters: hazard: list or instance of hazard exposure: list or instance of exposure of full countries - if_name_or_instance (string or ImpactFunc): the name of a + if_name_or_instance (string or ImpactFunc): the name of a parameterisation or an instance of class ImpactFunc e.g. 'emanuel' - param_dict (dict): a dict containing keys used for + param_dict (dict): a dict containing keys used for if_name_or_instance and one set of values e.g. {'v_thresh': 25.7, 'v_half': 70, 'scale': 1} impact_data_source (dict or dataframe): with name of impact data source and file location or dataframe year_range yearly_impact - cost_function (string): the argument for function calib_cost_calc, + cost_function (string): the argument for function calib_cost_calc, default 'R2' - show_details (bool): if True, return a tuple with the parameters AND - the details of the optimization like success, + show_details (bool): if True, return a tuple with the parameters AND + the details of the optimization like success, status, number of iterations etc - + Returns: param_dict_result: the parameters with the best calibration results (or a tuple with (1) the parameters and (2) the optimization output) """ param_dict_result = param_dict - + # prepare hazard and exposure region_ids = list(np.unique(exposure.region_id)) hazard_type = hazard.tag.haz_type # prepare impact data - if isinstance(impact_data_source,pd.DataFrame): + if isinstance(impact_data_source, pd.DataFrame): df_impact_data = impact_data_source else: if list(impact_data_source.keys()) == ['emdat']: - df_impact_data = init_impact_data(hazard_type,region_ids, year_range,impact_data_source['emdat'],year_range[-1]) + df_impact_data = init_impact_data(hazard_type, region_ids, year_range, + impact_data_source['emdat'], year_range[-1]) else: raise ValueError('other impact data sources not yet implemented.') - # definie specific function to + # definie specific function to def specific_calib(x): - param_dict_temp = dict(zip(param_dict.keys(),x)) + param_dict_temp = dict(zip(param_dict.keys(), x)) print(param_dict_temp) - return calib_instance(hazard,exposure, - init_if(if_name_or_instance,param_dict_temp)[0], + return calib_instance(hazard, exposure, + init_if(if_name_or_instance, param_dict_temp)[0], df_impact_data, - yearly_impact=yearly_impact,return_cost=cost_fucntion) + yearly_impact=yearly_impact, return_cost=cost_fucntion) # define constraints if if_name_or_instance == 'emanuel': - cons = [{'type': 'ineq', 'fun': lambda x: -x[0] + x[1]}, - {'type': 'ineq', 'fun': lambda x: -x[2] + 0.9999}, - {'type': 'ineq', 'fun': lambda x: x[2]}] + cons = [{'type': 'ineq', 'fun': lambda x: -x[0] + x[1]}, + {'type': 'ineq', 'fun': lambda x: -x[2] + 0.9999}, + {'type': 'ineq', 'fun': lambda x: x[2]}] else: cons = [{'type': 'ineq', 'fun': lambda x: -x[0] + 2}, - {'type': 'ineq', 'fun': lambda x: x[0]}, - {'type': 'ineq', 'fun': lambda x: -x[1] + 2}, - {'type': 'ineq', 'fun': lambda x: x[1]}] + {'type': 'ineq', 'fun': lambda x: x[0]}, + {'type': 'ineq', 'fun': lambda x: -x[1] + 2}, + {'type': 'ineq', 'fun': lambda x: x[1]}] x0 = list(param_dict.values()) res = minimize(specific_calib, x0, - #bounds=bounds, -# bounds=((0.0, np.inf), (0.0, np.inf), (0.0, 1.0)), + # bounds=bounds, + # bounds=((0.0, np.inf), (0.0, np.inf), (0.0, 1.0)), constraints=cons, -# method='SLSQP', + # method='SLSQP', method='trust-constr', options={'xtol': 1e-5, 'disp': True, 'maxiter': 500}) - - param_dict_result = dict(zip(param_dict.keys(),res.x)) - + + param_dict_result = dict(zip(param_dict.keys(), res.x)) + if res.success: LOGGER.info('Optimization successfully finished.') else: LOGGER.info('Opimization did not finish successfully. Check you input' ' or consult the detailed returns (with argument' 'show_details=True) for further information.') - + if show_details: return param_dict_result, res - + return param_dict_result -#if __name__ == "__main__": +# if __name__ == "__main__": +# # -# # ## tryout calib_all # hazard = TropCyclone() # hazard.read_hdf5('C:/Users/ThomasRoosli/tc_NA_hazard.hdf5') @@ -414,7 +422,7 @@ def specific_calib(x): # exposure.read_hdf5('C:/Users/ThomasRoosli/DOM_LitPop.hdf5') # if_name_or_instance = 'emanuel' # param_full_dict = {'v_thresh': [25.7, 20], 'v_half': [70], 'scale': [1, 0.8]} -# +# # impact_data_source = {'emdat':('D:/Documents_DATA/EM-DAT/' # '20181031_disaster_list_all_non-technological/' # 'ThomasRoosli_2018-10-31.csv')} @@ -431,7 +439,7 @@ def specific_calib(x): # exposure.read_hdf5('C:/Users/ThomasRoosli/DOM_LitPop.hdf5') # if_name_or_instance = 'emanuel' # param_dict = {'v_thresh': 25.7, 'v_half': 70, 'scale': 0.6} -# year_range = [2004, 2017] +# year_range = [2004, 2017] # cost_function = 'R2' # show_details = True # yearly_impact = True @@ -439,9 +447,7 @@ def specific_calib(x): # '20181031_disaster_list_all_non-technological/' # 'ThomasRoosli_2018-10-31.csv')} # param_result,result = calib_optimize(hazard,exposure,if_name_or_instance,param_dict, -# impact_data_source, year_range, yearly_impact=yearly_impact, +# impact_data_source, year_range, yearly_impact=yearly_impact, # cost_fucntion=cost_function,show_details= show_details) -# -# - - +# +# diff --git a/climada/engine/cost_benefit.py b/climada/engine/cost_benefit.py index fd6a5fa0ca..8f50fdd594 100644 --- a/climada/engine/cost_benefit.py +++ b/climada/engine/cost_benefit.py @@ -34,13 +34,13 @@ LOGGER = logging.getLogger(__name__) DEF_PRESENT_YEAR = 2016 -""" Default present reference year """ +"""Default present reference year""" DEF_FUTURE_YEAR = 2030 -""" Default future reference year """ +"""Default future reference year""" NO_MEASURE = 'no measure' -""" Name of risk metrics when no measure is applied """ +"""Name of risk metrics when no measure is applied""" def risk_aai_agg(impact): """Risk measurement as average annual impact aggregated. @@ -120,7 +120,7 @@ class CostBenefit(): """ def __init__(self): - """ Initilization """ + """Initilization""" self.present_year = DEF_PRESENT_YEAR self.future_year = DEF_FUTURE_YEAR @@ -142,9 +142,9 @@ def __init__(self): self.imp_meas_future = dict() self.imp_meas_present = dict() - def calc(self, hazard, entity, haz_future=None, ent_future=None, \ - future_year=None, risk_func=risk_aai_agg, imp_time_depen=None, save_imp=False): - """ Compute cost-benefit ratio for every measure provided current + def calc(self, hazard, entity, haz_future=None, ent_future=None, + future_year=None, risk_func=risk_aai_agg, imp_time_depen=None, save_imp=False): + """Compute cost-benefit ratio for every measure provided current and, optionally, future conditions. Present and future measures need to have the same name. The measures costs need to be discounted by the user. If future entity provided, only the costs of the measures @@ -185,30 +185,30 @@ def calc(self, hazard, entity, haz_future=None, ent_future=None, \ if not haz_future and not ent_future: self.future_year = future_year - self._calc_impact_measures(hazard, entity.exposures, \ - entity.measures, entity.impact_funcs, 'future', \ - risk_func, save_imp) + self._calc_impact_measures(hazard, entity.exposures, + entity.measures, entity.impact_funcs, 'future', + risk_func, save_imp) else: if imp_time_depen is None: imp_time_depen = 1 - self._calc_impact_measures(hazard, entity.exposures, \ - entity.measures, entity.impact_funcs, 'present', \ - risk_func, save_imp) + self._calc_impact_measures(hazard, entity.exposures, + entity.measures, entity.impact_funcs, 'present', + risk_func, save_imp) if haz_future and ent_future: self.future_year = ent_future.exposures.ref_year - self._calc_impact_measures(haz_future, ent_future.exposures, \ - ent_future.measures, ent_future.impact_funcs, 'future', \ - risk_func, save_imp) + self._calc_impact_measures(haz_future, ent_future.exposures, + ent_future.measures, ent_future.impact_funcs, 'future', + risk_func, save_imp) elif haz_future: self.future_year = future_year - self._calc_impact_measures(haz_future, entity.exposures, \ - entity.measures, entity.impact_funcs, 'future', risk_func,\ - save_imp) + self._calc_impact_measures(haz_future, entity.exposures, + entity.measures, entity.impact_funcs, 'future', + risk_func, save_imp) else: self.future_year = ent_future.exposures.ref_year - self._calc_impact_measures(hazard, ent_future.exposures, \ - ent_future.measures, ent_future.impact_funcs, 'future', \ - risk_func, save_imp) + self._calc_impact_measures(hazard, ent_future.exposures, + ent_future.measures, ent_future.impact_funcs, 'future', + risk_func, save_imp) self._calc_cost_benefit(entity.disc_rates, imp_time_depen) self._print_results() @@ -216,7 +216,7 @@ def calc(self, hazard, entity, haz_future=None, ent_future=None, \ def combine_measures(self, in_meas_names, new_name, new_color, disc_rates, imp_time_depen=None, risk_func=risk_aai_agg): - """ Compute cost-benefit of the combination of measures previously + """Compute cost-benefit of the combination of measures previously computed by calc with save_imp=True. The benefits of the measures per event are added. To combine with risk transfer options use apply_risk_transfer. @@ -266,7 +266,7 @@ def combine_measures(self, in_meas_names, new_name, new_color, disc_rates, def apply_risk_transfer(self, meas_name, attachment, cover, disc_rates, cost_fix=0, cost_factor=1, imp_time_depen=None, risk_func=risk_aai_agg): - """ Applies risk transfer to given measure computed before with saved + """Applies risk transfer to given measure computed before with saved impact and compares it to when no measure is applied. Appended to dictionaries of measures. @@ -287,8 +287,8 @@ def apply_risk_transfer(self, meas_name, attachment, cover, disc_rates, an Impact. Default: average annual impact (aggregated). """ m_transf_name = 'risk transfer (' + meas_name + ')' - self.color_rgb[m_transf_name] = np.maximum(np.minimum(self.color_rgb[meas_name] - \ - np.ones(3)*0.2, 1), 0) + self.color_rgb[m_transf_name] = np.maximum(np.minimum(self.color_rgb[meas_name] - + np.ones(3) * 0.2, 1), 0) _, layer_no = self.imp_meas_future[NO_MEASURE]['impact']. \ calc_risk_transfer(attachment, cover) @@ -310,7 +310,7 @@ def apply_risk_transfer(self, meas_name, attachment, cover, disc_rates, _, pres_layer_no = self.imp_meas_present[NO_MEASURE]['impact']. \ calc_risk_transfer(attachment, cover) pres_layer_no = risk_func(pres_layer_no) - layer_no = pres_layer_no + (layer_no-pres_layer_no) * time_dep + layer_no = pres_layer_no + (layer_no - pres_layer_no) * time_dep imp, layer = self.imp_meas_present[meas_name]['impact']. \ calc_risk_transfer(attachment, cover) @@ -322,7 +322,7 @@ def apply_risk_transfer(self, meas_name, attachment, cover, disc_rates, self.imp_meas_present[m_transf_name]['efc'] = imp.calc_freq_curve() else: time_dep = self._time_dependency_array(imp_time_depen) - layer_no = time_dep*layer_no + layer_no = time_dep * layer_no self._cost_ben_one(m_transf_name, self.imp_meas_future[m_transf_name], disc_rates, time_dep, ini_state=meas_name) @@ -330,14 +330,14 @@ def apply_risk_transfer(self, meas_name, attachment, cover, disc_rates, # compare layer no measure layer_no = disc_rates.net_present_value(self.present_year, self.future_year, layer_no) - layer = (self.cost_ben_ratio[m_transf_name]*self.benefit[m_transf_name] - \ - cost_fix)/cost_factor + layer = ((self.cost_ben_ratio[m_transf_name] * self.benefit[m_transf_name] - cost_fix) + / cost_factor) self._print_results() self._print_risk_transfer(layer, layer_no, cost_fix, cost_factor) self._print_npv() def remove_measure(self, meas_name): - """ Remove computed values of given measure + """Remove computed values of given measure Parameters: meas_name (str): name of measure to remove @@ -350,7 +350,7 @@ def remove_measure(self, meas_name): del self.imp_meas_present[meas_name] def plot_cost_benefit(self, cb_list=None, axis=None, **kwargs): - """ Plot cost-benefit graph. Call after calc(). + """Plot cost-benefit graph. Call after calc(). Parameters: cb_list (list(CostBenefit), optional): if other CostBenefit @@ -373,31 +373,31 @@ def plot_cost_benefit(self, cb_list=None, axis=None, **kwargs): if 'alpha' not in kwargs: kwargs['alpha'] = 1.0 axis = self._plot_list_cost_ben([self], axis, **kwargs) - norm_fact, norm_name = _norm_values(self.tot_climate_risk+0.01) + norm_fact, norm_name = _norm_values(self.tot_climate_risk + 0.01) - text_pos = self.imp_meas_future[NO_MEASURE]['risk']/norm_fact + text_pos = self.imp_meas_future[NO_MEASURE]['risk'] / norm_fact axis.scatter(text_pos, 0, c='r', zorder=200, clip_on=False) axis.text(text_pos, 0, ' AAI', horizontalalignment='center', verticalalignment='bottom', rotation=90, fontsize=12, color='r') - if abs(text_pos - self.tot_climate_risk/norm_fact) > 1: - axis.scatter(self.tot_climate_risk/norm_fact, 0, c='r', zorder=200, clip_on=False) - axis.text(self.tot_climate_risk/norm_fact, 0, ' Tot risk', \ - horizontalalignment='center', verticalalignment='bottom', rotation=90, \ - fontsize=12, color='r') - - axis.set_xlim(0, max(self.tot_climate_risk/norm_fact, - np.array(list(self.benefit.values())).sum()/norm_fact)) - axis.set_ylim(0, int(1/np.nanmin(np.ma.masked_equal(np.array(list( \ - self.cost_ben_ratio.values())), 0))) + 1) - - x_label = 'NPV averted damage over ' + str(self.future_year - \ - self.present_year + 1) + ' years (' + self.unit + ' ' + norm_name + ')' + if abs(text_pos - self.tot_climate_risk / norm_fact) > 1: + axis.scatter(self.tot_climate_risk / norm_fact, 0, c='r', zorder=200, clip_on=False) + axis.text(self.tot_climate_risk / norm_fact, 0, ' Tot risk', + horizontalalignment='center', verticalalignment='bottom', rotation=90, + fontsize=12, color='r') + + axis.set_xlim(0, max(self.tot_climate_risk / norm_fact, + np.array(list(self.benefit.values())).sum() / norm_fact)) + axis.set_ylim(0, int(1 / np.nanmin(np.ma.masked_equal(np.array(list( + self.cost_ben_ratio.values())), 0))) + 1) + + x_label = ('NPV averted damage over ' + str(self.future_year - self.present_year + 1) + + ' years (' + self.unit + ' ' + norm_name + ')') axis.set_xlabel(x_label) axis.set_ylabel('Benefit/Cost ratio') return axis def plot_event_view(self, return_per=(10, 25, 100), axis=None, **kwargs): - """ Plot averted damages for return periods. Call after calc(). + """Plot averted damages for return periods. Call after calc(). Parameters: return_per (list, optional): years to visualize. Default 10, 25, 100 @@ -421,7 +421,7 @@ def plot_event_view(self, return_per=(10, 25, 100), axis=None, **kwargs): meas_val['efc'].impact) # check if measure over no measure or combined with another measure try: - ref_meas = meas_name[meas_name.index('(')+1:meas_name.index(')')] + ref_meas = meas_name[meas_name.index('(') + 1:meas_name.index(')')] except ValueError: ref_meas = NO_MEASURE ref_imp = np.interp(return_per, @@ -439,18 +439,18 @@ def plot_event_view(self, return_per=(10, 25, 100), axis=None, **kwargs): val_i = [avert_rp[name][rp_i] for name in names_sort] cum_effect = np.cumsum(np.array([0] + val_i)) for (eff, color) in zip(cum_effect[::-1][:-1], color_sort[::-1]): - axis.bar(rp_i+1, eff, color=color, **kwargs) - axis.bar(rp_i+1, ref_imp[rp_i], edgecolor='k', fc=(1, 0, 0, 0), zorder=100) + axis.bar(rp_i + 1, eff, color=color, **kwargs) + axis.bar(rp_i + 1, ref_imp[rp_i], edgecolor='k', fc=(1, 0, 0, 0), zorder=100) axis.set_xlabel('Return Period (%s)' % str(self.future_year)) - axis.set_ylabel('Impact ('+ self.unit + ')') - axis.set_xticks(np.arange(len(return_per))+1) + axis.set_ylabel('Impact (' + self.unit + ')') + axis.set_xticks(np.arange(len(return_per)) + 1) axis.set_xticklabels([str(per) for per in return_per]) return axis @staticmethod def plot_waterfall(hazard, entity, haz_future, ent_future, risk_func=risk_aai_agg, axis=None, **kwargs): - """ Plot waterfall graph at future with given risk metric. Can be called + """Plot waterfall graph at future with given risk metric. Can be called before and after calc(). Parameters: @@ -496,36 +496,41 @@ def plot_waterfall(hazard, entity, haz_future, ent_future, LOGGER.info('Risk with development at {:d}: {:.3e}'.format(future_year, risk_dev)) # socioecon + cc - LOGGER.info('Risk with development and climate change at {:d}: {:.3e}'.\ + LOGGER.info('Risk with development and climate change at {:d}: {:.3e}'. format(future_year, fut_risk)) - axis.bar(1, curr_risk/norm_fact, **kwargs) - axis.text(1, curr_risk/norm_fact, str(int(round(curr_risk/norm_fact))), \ - horizontalalignment='center', verticalalignment='bottom', \ - fontsize=12, color='k') - axis.bar(2, height=(risk_dev-curr_risk)/norm_fact, bottom=curr_risk/norm_fact, **kwargs) - axis.text(2, curr_risk/norm_fact + (risk_dev-curr_risk)/norm_fact/2, \ - str(int(round((risk_dev-curr_risk)/norm_fact))), \ - horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') - axis.bar(3, height=(fut_risk-risk_dev)/norm_fact, bottom=risk_dev/norm_fact, **kwargs) - axis.text(3, risk_dev/norm_fact + (fut_risk-risk_dev)/norm_fact/2, \ - str(int(round((fut_risk-risk_dev)/norm_fact))), \ - horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') - axis.bar(4, height=fut_risk/norm_fact, **kwargs) - axis.text(4, fut_risk/norm_fact, str(int(round(fut_risk/norm_fact))), \ - horizontalalignment='center', verticalalignment='bottom', \ + axis.bar(1, curr_risk / norm_fact, **kwargs) + axis.text(1, curr_risk / norm_fact, str(int(round(curr_risk / norm_fact))), + horizontalalignment='center', verticalalignment='bottom', + fontsize=12, color='k') + axis.bar(2, height=(risk_dev - curr_risk) / norm_fact, + bottom=curr_risk / norm_fact, **kwargs) + axis.text(2, curr_risk / norm_fact + (risk_dev - curr_risk) / norm_fact / 2, + str(int(round((risk_dev - curr_risk) / norm_fact))), + horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') + axis.bar(3, height=(fut_risk - risk_dev) / norm_fact, + bottom=risk_dev / norm_fact, **kwargs) + axis.text(3, risk_dev / norm_fact + (fut_risk - risk_dev) / norm_fact / 2, + str(int(round((fut_risk - risk_dev) / norm_fact))), + horizontalalignment='center', verticalalignment='center', fontsize=12, + color='k') + axis.bar(4, height=fut_risk / norm_fact, **kwargs) + axis.text(4, fut_risk / norm_fact, str(int(round(fut_risk / norm_fact))), + horizontalalignment='center', verticalalignment='bottom', fontsize=12, color='k') - axis.set_xticks(np.arange(4)+1) - axis.set_xticklabels(['Risk ' + str(present_year), \ - 'Economic \ndevelopment', 'Climate \nchange', 'Risk ' + str(future_year)]) + axis.set_xticks(np.arange(4) + 1) + axis.set_xticklabels(['Risk ' + str(present_year), + 'Economic \ndevelopment', + 'Climate \nchange', + 'Risk ' + str(future_year)]) axis.set_ylabel('Impact (' + imp.unit + ' ' + norm_name + ')') axis.set_title('Risk at {:d} and {:d}'.format(present_year, future_year)) return axis def plot_arrow_averted(self, axis, in_meas_names=None, accumulate=False, combine=False, risk_func=risk_aai_agg, disc_rates=None, imp_time_depen=1, **kwargs): - """ Plot waterfall graph with accumulated values from present to future + """Plot waterfall graph with accumulated values from present to future year. Call after calc() with save_imp=True. Parameters: @@ -550,18 +555,21 @@ def plot_arrow_averted(self, axis, in_meas_names=None, accumulate=False, combine if accumulate: tot_benefit = np.array([self.benefit[meas] for meas in in_meas_names]).sum() - norm_fact = self.tot_climate_risk/bars[3].get_height() + norm_fact = self.tot_climate_risk / bars[3].get_height() else: - tot_benefit = np.array([risk_func(self.imp_meas_future[NO_MEASURE]['impact']) - \ - risk_func(self.imp_meas_future[meas]['impact']) for meas in in_meas_names]).sum() - norm_fact = risk_func(self.imp_meas_future['no measure']['impact'])/bars[3].get_height() + tot_benefit = np.array([risk_func(self.imp_meas_future[NO_MEASURE]['impact']) - + risk_func(self.imp_meas_future[meas]['impact']) + for meas in in_meas_names]).sum() + norm_fact = (risk_func(self.imp_meas_future['no measure']['impact']) + / bars[3].get_height()) if combine: try: - LOGGER.info('Combining measures ' + str(in_meas_names)) - all_meas = self.combine_measures(in_meas_names, 'combine', \ - colors.to_rgba('black'), disc_rates, imp_time_depen, risk_func) + LOGGER.info('Combining measures %s', in_meas_names) + all_meas = self.combine_measures(in_meas_names, 'combine', + colors.to_rgba('black'), disc_rates, + imp_time_depen, risk_func) except KeyError: - LOGGER.warning('Use calc() with save_imp=True to get a more accurate ' \ + LOGGER.warning('Use calc() with save_imp=True to get a more accurate ' 'approximation of total averted damage,') if accumulate: tot_benefit = all_meas.benefit['combine'] @@ -569,13 +577,13 @@ def plot_arrow_averted(self, axis, in_meas_names=None, accumulate=False, combine tot_benefit = risk_func(all_meas.imp_meas_future[NO_MEASURE]['impact']) - \ risk_func(all_meas.imp_meas_future['combine']['impact']) - self._plot_averted_arrow(axis, bars[3], tot_benefit, bars[3].get_height()*norm_fact, + self._plot_averted_arrow(axis, bars[3], tot_benefit, bars[3].get_height() * norm_fact, norm_fact, **kwargs) def plot_waterfall_accumulated(self, hazard, entity, ent_future, risk_func=risk_aai_agg, imp_time_depen=1, axis=None, **kwargs): - """ Plot waterfall graph with accumulated values from present to future + """Plot waterfall graph with accumulated values from present to future year. Call after calc() with save_imp=True. Provide same inputs as in calc. Parameters: @@ -617,42 +625,46 @@ def plot_waterfall_accumulated(self, hazard, entity, ent_future, imp.calc(ent_future.exposures, ent_future.impact_funcs, hazard) risk_dev = self._npv_unaverted_impact(risk_func(imp), entity.disc_rates, time_dep, curr_risk) - LOGGER.info('Total risk with development at {:d}: {:.3e}'.format( \ + LOGGER.info('Total risk with development at {:d}: {:.3e}'.format( self.future_year, risk_dev)) # socioecon + cc - risk_tot = self._npv_unaverted_impact(self.imp_meas_future[NO_MEASURE]['risk'], \ - entity.disc_rates, time_dep, curr_risk) - LOGGER.info('Total risk with development and climate change at {:d}: {:.3e}'.\ - format(self.future_year, risk_tot)) + risk_tot = self._npv_unaverted_impact(self.imp_meas_future[NO_MEASURE]['risk'], + entity.disc_rates, time_dep, curr_risk) + LOGGER.info('Total risk with development and climate change at {:d}: {:.3e}'. + format(self.future_year, risk_tot)) # plot if not axis: _, axis = plt.subplots(1, 1) norm_fact, norm_name = _norm_values(curr_risk) - axis.bar(1, risk_curr/norm_fact, **kwargs) - axis.text(1, risk_curr/norm_fact, str(int(round(risk_curr/norm_fact))), \ - horizontalalignment='center', verticalalignment='bottom', \ - fontsize=12, color='k') - axis.bar(2, height=(risk_dev-risk_curr)/norm_fact, bottom=risk_curr/norm_fact, **kwargs) - axis.text(2, risk_curr/norm_fact + (risk_dev-risk_curr)/norm_fact/2, \ - str(int(round((risk_dev-risk_curr)/norm_fact))), \ - horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') - axis.bar(3, height=(risk_tot-risk_dev)/norm_fact, bottom=risk_dev/norm_fact, **kwargs) - axis.text(3, risk_dev/norm_fact + (risk_tot-risk_dev)/norm_fact/2, \ - str(int(round((risk_tot-risk_dev)/norm_fact))), \ - horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') - axis.bar(4, height=risk_tot/norm_fact, **kwargs) - axis.text(4, risk_tot/norm_fact, str(int(round(risk_tot/norm_fact))), \ - horizontalalignment='center', verticalalignment='bottom', \ + axis.bar(1, risk_curr / norm_fact, **kwargs) + axis.text(1, risk_curr / norm_fact, str(int(round(risk_curr / norm_fact))), + horizontalalignment='center', verticalalignment='bottom', + fontsize=12, color='k') + axis.bar(2, height=(risk_dev - risk_curr) / norm_fact, + bottom=risk_curr / norm_fact, **kwargs) + axis.text(2, risk_curr / norm_fact + (risk_dev - risk_curr) / norm_fact / 2, + str(int(round((risk_dev - risk_curr) / norm_fact))), + horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') + axis.bar(3, height=(risk_tot - risk_dev) / norm_fact, + bottom=risk_dev / norm_fact, **kwargs) + axis.text(3, risk_dev / norm_fact + (risk_tot - risk_dev) / norm_fact / 2, + str(int(round((risk_tot - risk_dev) / norm_fact))), + horizontalalignment='center', verticalalignment='center', fontsize=12, color='k') + axis.bar(4, height=risk_tot / norm_fact, **kwargs) + axis.text(4, risk_tot / norm_fact, str(int(round(risk_tot / norm_fact))), + horizontalalignment='center', verticalalignment='bottom', fontsize=12, color='k') - axis.set_xticks(np.arange(4)+1) - axis.set_xticklabels(['Risk ' + str(self.present_year), \ - 'Economic \ndevelopment', 'Climate \nchange', 'Risk ' + str(self.future_year)]) + axis.set_xticks(np.arange(4) + 1) + axis.set_xticklabels(['Risk ' + str(self.present_year), + 'Economic \ndevelopment', + 'Climate \nchange', + 'Risk ' + str(self.future_year)]) axis.set_ylabel('Impact (' + self.unit + ' ' + norm_name + ')') - axis.set_title('Total accumulated impact from {:d} to {:d}'.format( \ - self.present_year, self.future_year)) + axis.set_title('Total accumulated impact from {:d} to {:d}'.format( + self.present_year, self.future_year)) return axis def _calc_impact_measures(self, hazard, exposures, meas_set, imp_fun_set, @@ -732,11 +744,11 @@ def _calc_cost_benefit(self, disc_rates, imp_time_depen=None): # npv of the full unaverted damages if self.imp_meas_present: self.tot_climate_risk = self._npv_unaverted_impact( - self.imp_meas_future[NO_MEASURE]['risk'], \ + self.imp_meas_future[NO_MEASURE]['risk'], disc_rates, time_dep, self.imp_meas_present[NO_MEASURE]['risk']) else: self.tot_climate_risk = self._npv_unaverted_impact( - self.imp_meas_future[NO_MEASURE]['risk'], \ + self.imp_meas_future[NO_MEASURE]['risk'], disc_rates, time_dep) continue @@ -744,7 +756,7 @@ def _calc_cost_benefit(self, disc_rates, imp_time_depen=None): def _cost_ben_one(self, meas_name, meas_val, disc_rates, time_dep, ini_state=NO_MEASURE): - """ Compute cost and benefit for given measure with time dependency + """Compute cost and benefit for given measure with time dependency Parameters: meas_name (str): name of measure @@ -760,13 +772,13 @@ def _cost_ben_one(self, meas_name, meas_val, disc_rates, time_dep, if self.imp_meas_present: pres_benefit = self.imp_meas_present[ini_state]['risk'] - \ self.imp_meas_present[meas_name]['risk'] - meas_ben = pres_benefit + (fut_benefit-pres_benefit) * time_dep + meas_ben = pres_benefit + (fut_benefit - pres_benefit) * time_dep pres_risk_tr = self.imp_meas_present[meas_name]['risk_transf'] - risk_tr = pres_risk_tr + (fut_risk_tr-pres_risk_tr) * time_dep + risk_tr = pres_risk_tr + (fut_risk_tr - pres_risk_tr) * time_dep else: - meas_ben = time_dep*fut_benefit - risk_tr = time_dep*fut_risk_tr + meas_ben = time_dep * fut_benefit + risk_tr = time_dep * fut_risk_tr # discount meas_ben = disc_rates.net_present_value(self.present_year, @@ -775,11 +787,11 @@ def _cost_ben_one(self, meas_name, meas_val, disc_rates, time_dep, self.future_year, risk_tr) self.benefit[meas_name] = meas_ben with np.errstate(divide='ignore'): - self.cost_ben_ratio[meas_name] = (meas_val['cost'][0] + \ - meas_val['cost'][1]*risk_tr)/meas_ben + self.cost_ben_ratio[meas_name] = (meas_val['cost'][0] + + meas_val['cost'][1] * risk_tr) / meas_ben def _time_dependency_array(self, imp_time_depen=None): - """ Construct time dependency array. Each year contains a value in [0,1] + """Construct time dependency array. Each year contains a value in [0,1] representing the rate of damage difference achieved that year, according to the growth represented by parameter imp_time_depen. @@ -793,14 +805,14 @@ def _time_dependency_array(self, imp_time_depen=None): n_years = self.future_year - self.present_year + 1 if imp_time_depen: time_dep = np.arange(n_years)**imp_time_depen / \ - (n_years-1)**imp_time_depen + (n_years - 1)**imp_time_depen else: time_dep = np.ones(n_years) return time_dep def _npv_unaverted_impact(self, risk_future, disc_rates, time_dep, risk_present=None): - """ Net present value of total unaverted damages + """Net present value of total unaverted damages Parameters: risk_future (float): risk under future situation @@ -813,16 +825,18 @@ def _npv_unaverted_impact(self, risk_future, disc_rates, time_dep, float """ if risk_present: - tot_climate_risk = risk_present + (risk_future-risk_present) * time_dep - tot_climate_risk = disc_rates.net_present_value(self.present_year, \ - self.future_year, tot_climate_risk) + tot_climate_risk = risk_present + (risk_future - risk_present) * time_dep + tot_climate_risk = disc_rates.net_present_value(self.present_year, + self.future_year, + tot_climate_risk) else: - tot_climate_risk = disc_rates.net_present_value(self.present_year, \ - self.future_year, time_dep * risk_future) + tot_climate_risk = disc_rates.net_present_value(self.present_year, + self.future_year, + time_dep * risk_future) return tot_climate_risk def _combine_imp_meas(self, new_cb, in_meas_names, new_name, risk_func, when='future'): - """ Compute impacts combined measures assuming they are independent, i.e. + """Compute impacts combined measures assuming they are independent, i.e. their benefit can be added. Costs are also added. For the new measure the dictionary imp_meas_future if when='future' and imp_meas_present if when='present'. @@ -842,8 +856,10 @@ def _combine_imp_meas(self, new_cb, in_meas_names, new_name, risk_func, when='fu imp_dict = self.imp_meas_present new_imp_dict = new_cb.imp_meas_present - sum_ben = np.sum([imp_dict[NO_MEASURE]['impact'].at_event - \ - imp_dict[name]['impact'].at_event for name in in_meas_names], axis=0) + sum_ben = np.sum([ + imp_dict[NO_MEASURE]['impact'].at_event - imp_dict[name]['impact'].at_event + for name in in_meas_names + ], axis=0) new_imp = copy.deepcopy(imp_dict[in_meas_names[0]]['impact']) new_imp.at_event = np.maximum(imp_dict[NO_MEASURE]['impact'].at_event - sum_ben, 0) @@ -855,12 +871,13 @@ def _combine_imp_meas(self, new_cb, in_meas_names, new_name, risk_func, when='fu new_imp_dict[new_name]['impact'] = new_imp new_imp_dict[new_name]['efc'] = new_imp.calc_freq_curve() new_imp_dict[new_name]['risk'] = risk_func(new_imp) - new_imp_dict[new_name]['cost'] = (np.array([imp_dict[name]['cost'][0] \ - for name in in_meas_names]).sum(), 1) + new_imp_dict[new_name]['cost'] = ( + np.array([imp_dict[name]['cost'][0] for name in in_meas_names]).sum(), + 1) new_imp_dict[new_name]['risk_transf'] = 0 def _print_results(self): - """ Print table with main results """ + """Print table with main results""" norm_fact, norm_name = _norm_values(np.array(list(self.benefit.values())).max()) norm_name = '(' + self.unit + ' ' + norm_name + ')' @@ -869,28 +886,28 @@ def _print_results(self): for meas_name in self.benefit: if not np.isnan(self.cost_ben_ratio[meas_name]) and \ not np.isinf(self.cost_ben_ratio[meas_name]): - cost = self.cost_ben_ratio[meas_name]*self.benefit[meas_name]/norm_fact + cost = self.cost_ben_ratio[meas_name] * self.benefit[meas_name] / norm_fact else: - cost = self.imp_meas_future[meas_name]['cost'][0]/norm_fact - table.append([meas_name, cost, self.benefit[meas_name]/norm_fact, - 1/self.cost_ben_ratio[meas_name]]) + cost = self.imp_meas_future[meas_name]['cost'][0] / norm_fact + table.append([meas_name, cost, self.benefit[meas_name] / norm_fact, + 1 / self.cost_ben_ratio[meas_name]]) print() print(tabulate(table, headers, tablefmt="simple")) table = [] table.append(['Total climate risk:', - self.tot_climate_risk/norm_fact, norm_name]) + self.tot_climate_risk / norm_fact, norm_name]) table.append(['Average annual risk:', - self.imp_meas_future[NO_MEASURE]['risk']/norm_fact, norm_name]) + self.imp_meas_future[NO_MEASURE]['risk'] / norm_fact, norm_name]) table.append(['Residual risk:', (self.tot_climate_risk - - np.array(list(self.benefit.values())).sum())/norm_fact, norm_name]) + np.array(list(self.benefit.values())).sum()) / norm_fact, norm_name]) print() print(tabulate(table, tablefmt="simple")) @staticmethod def _plot_list_cost_ben(cb_list, axis=None, **kwargs): - """ Overlay cost-benefit bars for every measure + """Overlay cost-benefit bars for every measure Parameters: cb_list (list): list of CostBenefit instances with filled values @@ -905,7 +922,7 @@ def _plot_list_cost_ben(cb_list, axis=None, **kwargs): kwargs['alpha'] = 0.5 norm_fact = [_norm_values(cb_res.tot_climate_risk)[0] for cb_res in cb_list] norm_fact = np.array(norm_fact).mean() - _, norm_name = _norm_values(norm_fact+0.01) + _, norm_name = _norm_values(norm_fact + 0.01) if not axis: _, axis = plt.subplots(1, 1) @@ -916,37 +933,39 @@ def _plot_list_cost_ben(cb_list, axis=None, **kwargs): xmin = 0 for meas_id in sort_cb: meas_n = m_names[meas_id] - axis.add_patch(Rectangle((xmin, 0), cb_res.benefit[meas_n]/norm_fact, \ - 1/cb_res.cost_ben_ratio[meas_n], color=cb_res.color_rgb[meas_n],\ - **kwargs)) + axis.add_patch(Rectangle((xmin, 0), + cb_res.benefit[meas_n] / norm_fact, + 1 / cb_res.cost_ben_ratio[meas_n], + color=cb_res.color_rgb[meas_n], **kwargs)) if i_cb == 0: - axis.text(xmin + (cb_res.benefit[meas_n]/norm_fact)/2, + axis.text(xmin + (cb_res.benefit[meas_n] / norm_fact) / 2, 0, ' ' + meas_n, horizontalalignment='center', verticalalignment='bottom', rotation=90, fontsize=12) - xmin += cb_res.benefit[meas_n]/norm_fact + xmin += cb_res.benefit[meas_n] / norm_fact - xy_lim[0] = max(xy_lim[0], max(int(cb_res.tot_climate_risk/norm_fact), \ - np.array(list(cb_res.benefit.values())).sum()/norm_fact)) + xy_lim[0] = max(xy_lim[0], + max(int(cb_res.tot_climate_risk / norm_fact), + np.array(list(cb_res.benefit.values())).sum() / norm_fact)) try: with np.errstate(divide='ignore'): - xy_lim[1] = max(xy_lim[1], int(1/cb_res.cost_ben_ratio[ \ - m_names[sort_cb[0]]]) + 1) + xy_lim[1] = max(xy_lim[1], int(1 / cb_res.cost_ben_ratio[ + m_names[sort_cb[0]]]) + 1) except (ValueError, OverflowError): - xy_lim[1] = max(xy_lim[1], int(1/np.array(list(cb_res.cost_ben_ratio.values())).\ - max()) + 1) + xy_lim[1] = max(xy_lim[1], + int(1 / np.array(list(cb_res.cost_ben_ratio.values())).max()) + 1) axis.set_xlim(0, xy_lim[0]) axis.set_ylim(0, xy_lim[1]) - axis.set_xlabel('NPV averted damage over ' + \ - str(cb_list[0].future_year - cb_list[0].present_year + 1) + \ + axis.set_xlabel('NPV averted damage over ' + + str(cb_list[0].future_year - cb_list[0].present_year + 1) + ' years (' + cb_list[0].unit + ' ' + norm_name + ')') axis.set_ylabel('Benefit/Cost ratio') return axis @staticmethod def _plot_averted_arrow(axis, bar_4, tot_benefit, risk_tot, norm_fact, **kwargs): - """ Plot arrow inn fourth bar of total averted damage by implementing + """Plot arrow inn fourth bar of total averted damage by implementing all the measures. Parameters: @@ -958,9 +977,9 @@ def _plot_averted_arrow(axis, bar_4, tot_benefit, risk_tot, norm_fact, **kwargs) kwargs (optional): arguments for bar matplotlib function, e.g. alpha=0.5 """ bar_bottom, bar_top = bar_4.get_bbox().get_points() - axis.text(bar_top[0] - (bar_top[0]-bar_bottom[0])/2, bar_top[1], + axis.text(bar_top[0] - (bar_top[0] - bar_bottom[0]) / 2, bar_top[1], "Averted", ha="center", va="top", rotation=270, size=15) - arrow_len = min(tot_benefit/norm_fact, risk_tot/norm_fact) + arrow_len = min(tot_benefit / norm_fact, risk_tot / norm_fact) if 'color' not in kwargs: kwargs['color'] = 'k' @@ -968,12 +987,13 @@ def _plot_averted_arrow(axis, bar_4, tot_benefit, risk_tot, norm_fact, **kwargs) kwargs['alpha'] = 0.4 if 'mutation_scale' not in kwargs: kwargs['mutation_scale'] = 100 - axis.add_patch(FancyArrowPatch((bar_top[0] - (bar_top[0]-bar_bottom[0])/2, \ - bar_top[1]), (bar_top[0]- (bar_top[0]-bar_bottom[0])/2, \ - risk_tot/norm_fact-arrow_len), **kwargs)) + axis.add_patch(FancyArrowPatch( + (bar_top[0] - (bar_top[0] - bar_bottom[0]) / 2, bar_top[1]), + (bar_top[0] - (bar_top[0] - bar_bottom[0]) / 2, risk_tot / norm_fact - arrow_len), + **kwargs)) def _print_risk_transfer(self, layer, layer_no, cost_fix, cost_factor): - """ Print comparative of risk transfer with and without measure + """Print comparative of risk transfer with and without measure Parameters: layer (float): expected insurance layer with measure @@ -983,10 +1003,10 @@ def _print_risk_transfer(self, layer, layer_no, cost_fix, cost_factor): norm_name = '(' + self.unit + ' ' + norm_name + ')' headers = ['Risk transfer', 'Expected damage in \n insurance layer ' + norm_name, 'Price ' + norm_name] - table = [['without measure', layer_no/norm_fact, - (cost_fix+layer_no*cost_factor)/norm_fact], - ['with measure', layer/norm_fact, - (cost_fix+layer*cost_factor)/norm_fact]] + table = [['without measure', layer_no / norm_fact, + (cost_fix + layer_no * cost_factor) / norm_fact], + ['with measure', layer / norm_fact, + (cost_fix + layer * cost_factor) / norm_fact]] print() print(tabulate(table, headers, tablefmt="simple")) print() @@ -996,7 +1016,7 @@ def _print_npv(): print('Net Present Values') def _norm_values(value): - """ Compute normalization value and name + """Compute normalization value and name Parameters: value (float): value to normalize @@ -1006,13 +1026,13 @@ def _norm_values(value): """ norm_fact = 1. norm_name = '' - if value/1.0e9 > 1: + if value / 1.0e9 > 1: norm_fact = 1.0e9 norm_name = 'bn' - elif value/1.0e6 > 1: + elif value / 1.0e6 > 1: norm_fact = 1.0e6 norm_name = 'm' - elif value/1.0e3 > 1: + elif value / 1.0e3 > 1: norm_fact = 1.0e3 norm_name = 'k' return norm_fact, norm_name diff --git a/climada/engine/impact.py b/climada/engine/impact.py index 68f1b7e5de..e7581e789b 100755 --- a/climada/engine/impact.py +++ b/climada/engine/impact.py @@ -69,7 +69,7 @@ class Impact(): """ def __init__(self): - """ Empty initialization.""" + """Empty initialization.""" self.tag = dict() self.event_id = np.array([], int) self.event_name = list() @@ -82,7 +82,7 @@ def __init__(self): self.tot_value = 0 self.aai_agg = 0 self.unit = '' - self.imp_mat = [] + self.imp_mat = sparse.csr_matrix(np.empty((0, 0))) def calc_freq_curve(self, return_per=None): """Compute impact exceedance frequency curve. @@ -101,7 +101,7 @@ def calc_freq_curve(self, return_per=None): # Calculate exceedence frequency exceed_freq = np.cumsum(self.frequency[sort_idxs]) # Set return period and imact exceeding frequency - ifc.return_per = 1/exceed_freq[::-1] + ifc.return_per = 1 / exceed_freq[::-1] ifc.impact = self.at_event[sort_idxs][::-1] ifc.unit = self.unit ifc.label = 'Exceedance frequency curve' @@ -171,8 +171,7 @@ def calc(self, exposures, impact_funcs, hazard, save_mat=False): self.crs = exposures.crs # Select exposures with positive value and assigned centroid - exp_idx = np.where(np.logical_and(exposures.value > 0, \ - exposures[assign_haz] >= 0))[0] + exp_idx = np.where((exposures.value > 0) & (exposures[assign_haz] >= 0))[0] if exp_idx.size == 0: LOGGER.warning("No affected exposures.") @@ -187,7 +186,7 @@ def calc(self, exposures, impact_funcs, hazard, save_mat=False): LOGGER.error('Missing exposures impact functions %s.', INDICATOR_IF) raise ValueError if if_haz not in exposures: - LOGGER.info('Missing exposures impact functions for hazard %s. ' +\ + LOGGER.info('Missing exposures impact functions for hazard %s. ' 'Using impact functions in %s.', if_haz, INDICATOR_IF) if_haz = INDICATOR_IF @@ -198,7 +197,8 @@ def calc(self, exposures, impact_funcs, hazard, save_mat=False): insure_flag = True if save_mat: - self.imp_mat = sparse.lil_matrix((self.date.size, exposures.value.size)) + # (data, (row_ind, col_ind)) + self.imp_mat = ([], ([], [])) # 3. Loop over exposures according to their impact function tot_exp = 0 @@ -206,29 +206,30 @@ def calc(self, exposures, impact_funcs, hazard, save_mat=False): # get indices of all the exposures with this impact function exp_iimp = np.where(exposures[if_haz].values[exp_idx] == imp_fun.id)[0] tot_exp += exp_iimp.size - exp_step = int(CONFIG['global']['max_matrix_size']/num_events) + exp_step = int(CONFIG['global']['max_matrix_size'] / num_events) if not exp_step: LOGGER.error('Increase max_matrix_size configuration parameter' ' to > %s', str(num_events)) raise ValueError # separte in chunks chk = -1 - for chk in range(int(exp_iimp.size/exp_step)): - self._exp_impact( \ - exp_idx[exp_iimp[chk*exp_step:(chk+1)*exp_step]],\ + for chk in range(int(exp_iimp.size / exp_step)): + self._exp_impact( + exp_idx[exp_iimp[chk * exp_step:(chk + 1) * exp_step]], exposures, hazard, imp_fun, insure_flag) - self._exp_impact(exp_idx[exp_iimp[(chk+1)*exp_step:]],\ - exposures, hazard, imp_fun, insure_flag) + self._exp_impact(exp_idx[exp_iimp[(chk + 1) * exp_step:]], + exposures, hazard, imp_fun, insure_flag) if not tot_exp: LOGGER.warning('No impact functions match the exposures.') self.aai_agg = sum(self.at_event * hazard.frequency) if save_mat: - self.imp_mat = self.imp_mat.tocsr() + shape = (self.date.size, exposures.value.size) + self.imp_mat = sparse.csr_matrix(self.imp_mat, shape=shape) def calc_risk_transfer(self, attachment, cover): - """ Compute traaditional risk transfer over impact. Returns new impact + """Compute traaditional risk transfer over impact. Returns new impact with risk transfer applied and the insurance layer resulting Impact metrics. Parameters: @@ -246,7 +247,7 @@ def calc_risk_transfer(self, attachment, cover): # next values are no longer valid new_imp.eai_exp = np.array([]) new_imp.coord_exp = np.array([]) - new_imp.imp_mat = [] + new_imp.imp_mat = sparse.csr_matrix(np.empty((0, 0))) # insurance layer metrics risk_transfer = copy.deepcopy(new_imp) risk_transfer.at_event = imp_layer @@ -308,7 +309,7 @@ def plot_scatter_eai_exposure(self, mask=None, ignore_zero=True, return axis def plot_raster_eai_exposure(self, res=None, raster_res=None, save_tiff=None, - raster_f=lambda x: np.log10((np.fmax(x+1, 1))), + raster_f=lambda x: np.log10((np.fmax(x + 1, 1))), label='value (log10)', axis=None, **kwargs): """Plot raster expected annual impact of each exposure. @@ -385,10 +386,8 @@ def plot_hexbin_impact_exposure(self, event_id=1, mask=None, ignore_zero=True, Returns: matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot """ - try: - self.imp_mat.shape[1] - except AttributeError: - LOGGER.error('attribute imp_mat is empty. Recalculate Impact' \ + if not hasattr(self.imp_mat, "shape") or self.imp_mat.shape[1] == 0: + LOGGER.error('attribute imp_mat is empty. Recalculate Impact' 'instance with parameter save_mat=True') return [] @@ -424,10 +423,8 @@ def plot_basemap_impact_exposure(self, event_id=1, mask=None, ignore_zero=True, Returns: cartopy.mpl.geoaxes.GeoAxesSubplot """ - try: - self.imp_mat.shape[1] - except AttributeError: - LOGGER.error('attribute imp_mat is empty. Recalculate Impact' \ + if not hasattr(self.imp_mat, "shape") or self.imp_mat.shape[1] == 0: + LOGGER.error('attribute imp_mat is empty. Recalculate Impact' 'instance with parameter save_mat=True') return [] @@ -438,7 +435,7 @@ def plot_basemap_impact_exposure(self, event_id=1, mask=None, ignore_zero=True, return axis def write_csv(self, file_name): - """ Write data into csv file. imp_mat is not saved. + """Write data into csv file. imp_mat is not saved. Parameters: file_name (str): absolute path of the file @@ -463,14 +460,14 @@ def write_csv(self, file_name): imp_wr.writerow(values) def write_excel(self, file_name): - """ Write data into Excel file. imp_mat is not saved. + """Write data into Excel file. imp_mat is not saved. Parameters: file_name (str): absolute path of the file """ LOGGER.info('Writing %s', file_name) def write_col(i_col, imp_ws, xls_data): - """ Write one measure """ + """Write one measure""" row_ini = 1 for dat_row in xls_data: imp_ws.write(row_ini, i_col, dat_row) @@ -508,13 +505,13 @@ def write_col(i_col, imp_ws, xls_data): imp_wb.close() def write_sparse_csr(self, file_name): - """ Write imp_mat matrix in numpy's npz format.""" + """Write imp_mat matrix in numpy's npz format.""" LOGGER.info('Writing %s', file_name) np.savez(file_name, data=self.imp_mat.data, indices=self.imp_mat.indices, indptr=self.imp_mat.indptr, shape=self.imp_mat.shape) def calc_impact_year_set(self, all_years=True, year_range=[]): - """ Calculate yearly impact from impact data. + """Calculate yearly impact from impact data. Parameters: all_years (boolean): return values for all years between first and @@ -528,15 +525,15 @@ def calc_impact_year_set(self, all_years=True, year_range=[]): for date in self.date]) if orig_year.size == 0 and len(year_range) == 0: return dict() - if orig_year.size==0 or (len(year_range)>0 and all_years): - years = np.arange(min(year_range), max(year_range)+1) + if orig_year.size == 0 or (len(year_range) > 0 and all_years): + years = np.arange(min(year_range), max(year_range) + 1) elif all_years: - years = np.arange(min(orig_year), max(orig_year)+1) + years = np.arange(min(orig_year), max(orig_year) + 1) else: years = np.array(sorted(np.unique(orig_year))) - if not len(year_range)==0: - years = years[years>=min(year_range)] - years = years[years<=max(year_range)] + if not len(year_range) == 0: + years = years[years >= min(year_range)] + years = years[years <= max(year_range)] year_set = dict() @@ -545,7 +542,7 @@ def calc_impact_year_set(self, all_years=True, year_range=[]): return year_set def local_exceedance_imp(self, return_periods=(25, 50, 100, 250)): - """ Compute exceedance impact map for given return periods. + """Compute exceedance impact map for given return periods. Requires attribute imp_mat. Parameters: @@ -559,25 +556,25 @@ def local_exceedance_imp(self, return_periods=(25, 50, 100, 250)): try: self.imp_mat.shape[1] except AttributeError: - LOGGER.error('attribute imp_mat is empty. Recalculate Impact'\ + LOGGER.error('attribute imp_mat is empty. Recalculate Impact' 'instance with parameter save_mat=True') return [] num_cen = self.imp_mat.shape[1] imp_stats = np.zeros((len(return_periods), num_cen)) - cen_step = int(CONFIG['global']['max_matrix_size']/self.imp_mat.shape[0]) + cen_step = int(CONFIG['global']['max_matrix_size'] / self.imp_mat.shape[0]) if not cen_step: - LOGGER.error('Increase max_matrix_size configuration parameter to'\ + LOGGER.error('Increase max_matrix_size configuration parameter to' ' > %s', str(self.imp_mat.shape[0])) raise ValueError # separte in chunks chk = -1 - for chk in range(int(num_cen/cen_step)): - self._loc_return_imp(np.array(return_periods), \ - self.imp_mat[:, chk*cen_step:(chk+1)*cen_step].todense(), \ - imp_stats[:, chk*cen_step:(chk+1)*cen_step]) - self._loc_return_imp(np.array(return_periods), \ - self.imp_mat[:, (chk+1)*cen_step:].todense(), \ - imp_stats[:, (chk+1)*cen_step:]) + for chk in range(int(num_cen / cen_step)): + self._loc_return_imp(np.array(return_periods), + self.imp_mat[:, chk * cen_step:(chk + 1) * cen_step].toarray(), + imp_stats[:, chk * cen_step:(chk + 1) * cen_step]) + self._loc_return_imp(np.array(return_periods), + self.imp_mat[:, (chk + 1) * cen_step:].toarray(), + imp_stats[:, (chk + 1) * cen_step:]) return imp_stats @@ -599,15 +596,15 @@ def plot_rp_imp(self, return_periods=(25, 50, 100, 250), """ imp_stats = self.local_exceedance_imp(np.array(return_periods)) if imp_stats == []: - LOGGER.error('Error: Attribute imp_mat is empty. Recalculate Impact'\ + LOGGER.error('Error: Attribute imp_mat is empty. Recalculate Impact' 'instance with parameter save_mat=True') raise ValueError if log10_scale: if np.min(imp_stats) < 0: - imp_stats_log = np.log10(abs(imp_stats)+1) + imp_stats_log = np.log10(abs(imp_stats) + 1) colbar_name = 'Log10(abs(Impact)+1) (' + self.unit + ')' elif np.min(imp_stats) < 1: - imp_stats_log = np.log10(imp_stats+1) + imp_stats_log = np.log10(imp_stats + 1) colbar_name = 'Log10(Impact+1) (' + self.unit + ')' else: imp_stats_log = np.log10(imp_stats) @@ -618,14 +615,14 @@ def plot_rp_imp(self, return_periods=(25, 50, 100, 250), title = list() for ret in return_periods: title.append('Return period: ' + str(ret) + ' years') - axis = u_plot.geo_im_from_array(imp_stats_log, self.coord_exp, \ - colbar_name, title, smooth=smooth, axes=axis, **kwargs) + axis = u_plot.geo_im_from_array(imp_stats_log, self.coord_exp, + colbar_name, title, smooth=smooth, axes=axis, **kwargs) return axis, imp_stats @staticmethod def read_sparse_csr(file_name): - """ Read imp_mat matrix from numpy's npz format. + """Read imp_mat matrix from numpy's npz format. Parameters: file_name (str): file name @@ -639,7 +636,7 @@ def read_sparse_csr(file_name): shape=loader['shape']) def read_csv(self, file_name): - """ Read csv file containing impact data generated by write_csv. + """Read csv file containing impact data generated by write_csv. Parameters: file_name (str): absolute path of the file @@ -674,7 +671,7 @@ def read_csv(self, file_name): str(imp_df.tag_impact_func[1])) def read_excel(self, file_name): - """ Read excel file containing impact data generated by write_excel. + """Read excel file containing impact data generated by write_excel. Parameters: file_name (str): absolute path of the file @@ -749,7 +746,7 @@ def video_direct_impact(exp, if_set, haz_list, file_name='', imp_tmp.coord_exp = imp_tmp.coord_exp[save_exp, :] imp_tmp.eai_exp = imp_arr[save_exp] imp_list.append(imp_tmp) - exp_list.append(np.logical_not(save_exp)) + exp_list.append(~save_exp) v_lim = [np.array([haz.intensity.min() for haz in haz_list]).min(), np.array([haz.intensity.max() for haz in haz_list]).max()] @@ -758,15 +755,15 @@ def video_direct_impact(exp, if_set, haz_list, file_name='', args_exp['vmin'] = exp.value.values.min() if 'vmin' not in args_imp: - args_imp['vmin'] = np.array([imp.eai_exp.min() for imp in imp_list \ - if imp.eai_exp.size]).min() + args_imp['vmin'] = np.array([imp.eai_exp.min() for imp in imp_list + if imp.eai_exp.size]).min() if 'vmax' not in args_exp: args_exp['vmax'] = exp.value.values.max() if 'vmax' not in args_imp: - args_imp['vmax'] = np.array([imp.eai_exp.max() for imp in imp_list \ - if imp.eai_exp.size]).max() + args_imp['vmax'] = np.array([imp.eai_exp.max() for imp in imp_list + if imp.eai_exp.size]).max() if 'cmap' not in args_exp: args_exp['cmap'] = 'winter_r' @@ -815,7 +812,7 @@ def run(i_time): return imp_list def _loc_return_imp(self, return_periods, imp, exc_imp): - """ Compute local exceedence impact for given return period. + """Compute local exceedence impact for given return period. Parameters: return_periods (np.array): return periods to consider @@ -827,6 +824,7 @@ def _loc_return_imp(self, return_periods, imp, exc_imp): # sorted impacts sort_pos = np.argsort(imp, axis=0)[::-1, :] columns = np.ones(imp.shape, int) + # pylint: disable=unsubscriptable-object # pylint/issues/3139 columns *= np.arange(columns.shape[1]) imp_sort = imp[sort_pos, columns] # cummulative frequency at sorted intensity @@ -863,21 +861,24 @@ def _exp_impact(self, exp_iimp, exposures, hazard, imp_fun, insure_flag): impact = fract.multiply(inten_val).multiply(exposures.value.values[exp_iimp]) if insure_flag and impact.nonzero()[0].size: - inten_val = hazard.intensity[:, icens].todense() + inten_val = hazard.intensity[:, icens].toarray() paa = np.interp(inten_val, imp_fun.intensity, imp_fun.paa) - impact = np.minimum(np.maximum(impact - \ - exposures.deductible.values[exp_iimp] * paa, 0), \ - exposures.cover.values[exp_iimp]) - self.eai_exp[exp_iimp] += np.sum(np.asarray(impact) * \ - hazard.frequency.reshape(-1, 1), axis=0) + impact = impact.toarray() + impact -= exposures.deductible.values[exp_iimp] * paa + impact = np.clip(impact, 0, exposures.cover.values[exp_iimp]) + self.eai_exp[exp_iimp] += np.einsum('ji,j->i', impact, hazard.frequency) + impact = sparse.coo_matrix(impact) else: - self.eai_exp[exp_iimp] += np.squeeze(np.asarray(np.sum( \ + self.eai_exp[exp_iimp] += np.squeeze(np.asarray(np.sum( impact.multiply(hazard.frequency.reshape(-1, 1)), axis=0))) self.at_event += np.squeeze(np.asarray(np.sum(impact, axis=1))) self.tot_value += np.sum(exposures.value.values[exp_iimp]) - if not isinstance(self.imp_mat, list): - self.imp_mat[:, exp_iimp] = impact + if isinstance(self.imp_mat, tuple): + row_ind, col_ind = impact.nonzero() + self.imp_mat[0].extend(list(impact.data)) + self.imp_mat[1][0].extend(list(row_ind)) + self.imp_mat[1][1].extend(list(exp_iimp[col_ind])) def _build_exp(self): eai_exp = Exposures() @@ -898,7 +899,7 @@ def _build_exp_event(self, event_id): event_id(int): id of the event """ impact_csr_exp = Exposures() - impact_csr_exp['value'] = self.imp_mat.toarray()[event_id-1, :] + impact_csr_exp['value'] = self.imp_mat.toarray()[event_id - 1, :] impact_csr_exp['latitude'] = self.coord_exp[:, 0] impact_csr_exp['longitude'] = self.coord_exp[:, 1] impact_csr_exp.crs = self.crs @@ -933,15 +934,14 @@ def _cen_return_imp(imp, freq, imp_th, return_periods): pol_coef = np.polyfit(np.log(freq_cen), imp_cen, deg=1) except ValueError: pol_coef = np.polyfit(np.log(freq_cen), imp_cen, deg=0) - imp_fit = np.polyval(pol_coef, np.log(1/return_periods)) - wrong_inten = np.logical_and(return_periods > np.max(1/freq_cen), \ - np.isnan(imp_fit)) + imp_fit = np.polyval(pol_coef, np.log(1 / return_periods)) + wrong_inten = (return_periods > np.max(1 / freq_cen)) & np.isnan(imp_fit) imp_fit[wrong_inten] = 0. return imp_fit class ImpactFreqCurve(): - """ Impact exceedence frequency curve. + """Impact exceedence frequency curve. Attributes: tag (dict): dictionary of tags of exposures, impact functions set and diff --git a/climada/engine/impact_data.py b/climada/engine/impact_data.py index f44cffde39..3bc82783b5 100644 --- a/climada/engine/impact_data.py +++ b/climada/engine/impact_data.py @@ -36,64 +36,100 @@ LOGGER = logging.getLogger(__name__) -PERIL_SUBTYPE_MATCH_DICT = dict(TC='Tropical cyclone', - T1='Storm', - TS='Coastal flood', - EQ='Ground movement', - E1='Earthquake', - FL='Riverine flood', - F1='Flood', - F2='Flash flood', - WS='Extra-tropical storm', - W1='Storm', - DR='Drought', - LS='Landslide', - FF='Forest fire', - FW='Wildfire', - FB='Land fire (Brush, Bush, Pastur') - -PERIL_TYPE_MATCH_DICT = dict(DR='Drought', - TC='Storm', - EQ='Earthquake', - FL='Flood', - LS='Landslide', - WS='Storm', - VQ='Volcanic activity', - BF='Wildfire', - HW='Extreme temperature') - -if False: - # inputs - checkset = pd.read_csv('~.csv') - intensity_path = '~.p' - names_path = '~.p' - reg_ID_path = '~.p' - date_path = '~.p' - EMdat_raw = pd.read_excel('~.xlsx') - start = 'yyyy-mm-dd' - end = 'yyyy-mm-dd' - -# assign hazard to EMdat event - data = assign_hazard_to_EMdat(certainty_level='low', intensity_path_haz=intensity_path, - names_path_haz=names_path, reg_ID_path_haz=reg_ID_path, - date_path_haz=date_path, EMdat_data=EMdat_raw, - start_time=start, end_time=end, keep_checks=True) - check_assigned_track(lookup=data, checkset=checkset) - -############################################################################### - -def assign_hazard_to_EMdat(certainty_level, intensity_path_haz, names_path_haz, - reg_ID_path_haz, date_path_haz, EMdat_data, +PERIL_SUBTYPE_MATCH_DICT = dict(TC=['Tropical cyclone'], + FL=['Coastal flood'], + EQ=['Ground movement', 'Earthquake'], + RF=['Riverine flood', 'Flood'], + WS=['Extra-tropical storm', 'Storm'], + DR=['Drought'], + LS=['Landslide'], + BF=['Forest fire', 'Wildfire', 'Land fire (Brush, Bush, Pastur'] + ) + +PERIL_TYPE_MATCH_DICT = dict(DR=['Drought'], + EQ=['Earthquake'], + FL=['Flood'], + LS=['Landslide'], + VQ=['Volcanic activity'], + BF=['Wildfire'], + HW=['Extreme temperature'] + ) + +VARNAMES_EMDAT = \ + {2018: {'Dis No': 'Disaster No.', + 'Disaster Type': 'Disaster type', + 'Disaster Subtype': 'Disaster subtype', + 'Event Name': 'Disaster name', + 'Country': 'Country', + 'ISO': 'ISO', + 'Location': 'Location', + 'Associated Dis': 'Associated disaster', + 'Associated Dis2': 'Associated disaster2', + 'Dis Mag Value': 'Magnitude value', + 'Dis Mag Scale': 'Magnitude scale', + 'Latitude': 'Latitude', + 'Longitude': 'Longitude', + 'Total Deaths': 'Total deaths', + 'Total Affected': 'Total affected', + "Insured Damages ('000 US$)": "Insured losses ('000 US$)", + "Total Damages ('000 US$)": "Total damage ('000 US$)"}, + 2020: {'Dis No': 'Dis No', + 'Year': 'Year', + 'Seq': 'Seq', + 'Disaster Group': 'Disaster Group', + 'Disaster Subgroup': 'Disaster Subgroup', + 'Disaster Type': 'Disaster Type', + 'Disaster Subtype': 'Disaster Subtype', + 'Disaster Subsubtype': 'Disaster Subsubtype', + 'Event Name': 'Event Name', + 'Entry Criteria': 'Entry Criteria', + 'Country': 'Country', + 'ISO': 'ISO', + 'Region': 'Region', + 'Continent': 'Continent', + 'Location': 'Location', + 'Origin': 'Origin', + 'Associated Dis': 'Associated Dis', + 'Associated Dis2': 'Associated Dis2', + 'OFDA Response': 'OFDA Response', + 'Appeal': 'Appeal', + 'Declaration': 'Declaration', + 'Aid Contribution': 'Aid Contribution', + 'Dis Mag Value': 'Dis Mag Value', + 'Dis Mag Scale': 'Dis Mag Scale', + 'Latitude': 'Latitude', + 'Longitude': 'Longitude', + 'Local Time': 'Local Time', + 'River Basin': 'River Basin', + 'Start Year': 'Start Year', + 'Start Month': 'Start Month', + 'Start Day': 'Start Day', + 'End Year': 'End Year', + 'End Month': 'End Month', + 'End Day': 'End Day', + 'Total Deaths': 'Total Deaths', + 'No Injured': 'No Injured', + 'No Affected': 'No Affected', + 'No Homeless': 'No Homeless', + 'Total Affected': 'Total Affected', + "Reconstruction Costs ('000 US$)": "Reconstruction Costs ('000 US$)", + "Insured Damages ('000 US$)": "Insured Damages ('000 US$)", + "Total Damages ('000 US$)": "Total Damages ('000 US$)", + 'CPI': 'CPI'}} + + +def assign_hazard_to_emdat(certainty_level, intensity_path_haz, names_path_haz, + reg_id_path_haz, date_path_haz, emdat_data, start_time, end_time, keep_checks=False): - """assign_hazard_to_EMdat: link EMdat event to hazard + """assign_hazard_to_emdat: link EMdat event to hazard Parameters: input files (paths): intensity: sparse matrix with hazards as rows and grid points as cols, values only at location with impacts names: identifier for each hazard (i.e. IBtracID) (rows of the matrix) - reg_ID: ISO country ID of each grid point (cols of the matrix) + reg_id: ISO country ID of each grid point (cols of the matrix) date: start date of each hazard (rows of the matrix) - EMdat_data: pd.dataframe with EMdat data + emdat_data: pd.dataframe with EMdat data start: start date of events to be assigned 'yyyy-mm-dd' end: end date of events to be assigned 'yyyy-mm-dd' disaster_subtype: EMdat disaster subtype @@ -108,79 +144,80 @@ def assign_hazard_to_EMdat(certainty_level, intensity_path_haz, names_path_haz, # prepare hazard set print("Start preparing hazard set") - hit_countries = hit_country_per_hazard(intensity_path_haz, names_path_haz, \ - reg_ID_path_haz, date_path_haz) + hit_countries = hit_country_per_hazard(intensity_path_haz, names_path_haz, + reg_id_path_haz, date_path_haz) # prepare damage set - #### adjust EMdat_data to the path!! + # adjust emdat_data to the path!! print("Start preparing damage set") - lookup = create_lookup(EMdat_data, start_time, end_time, disaster_subtype='Tropical cyclone') + lookup = create_lookup(emdat_data, start_time, end_time, disaster_subtype='Tropical cyclone') # calculate possible hits print("Calculate possible hits") - hit5 = EMdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=5) - hit5_match = match_EM_ID(lookup=lookup, poss_hit=hit5) + hit5 = emdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=5) + hit5_match = match_em_id(lookup=lookup, poss_hit=hit5) print("1/5") - hit10 = EMdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=10) - hit10_match = match_EM_ID(lookup=lookup, poss_hit=hit10) + hit10 = emdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=10) + hit10_match = match_em_id(lookup=lookup, poss_hit=hit10) print("2/5") - hit15 = EMdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=15) - hit15_match = match_EM_ID(lookup=lookup, poss_hit=hit15) + hit15 = emdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=15) + hit15_match = match_em_id(lookup=lookup, poss_hit=hit15) print("3/5") - hit25 = EMdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=25) - hit25_match = match_EM_ID(lookup=lookup, poss_hit=hit25) + hit25 = emdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=25) + hit25_match = match_em_id(lookup=lookup, poss_hit=hit25) print("4/5") - hit50 = EMdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=50) - hit50_match = match_EM_ID(lookup=lookup, poss_hit=hit50) + hit50 = emdat_possible_hit(lookup=lookup, hit_countries=hit_countries, delta_t=50) + hit50_match = match_em_id(lookup=lookup, poss_hit=hit50) print("5/5") # assign only tracks with high certainty print("Assign tracks") if certainty_level == 'high': - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit50_match, level=1) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit15_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit15_match, possible_tracks_2=hit50_match, level=2) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit25_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit25_match, possible_tracks_2=hit50_match, level=3) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit25_match, level=4) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit15_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit15_match, possible_tracks_2=hit25_match, level=5) # assign all tracks elif certainty_level == 'low': - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit5_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit5_match, possible_tracks_2=hit50_match, level=1) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit50_match, level=2) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit15_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit15_match, possible_tracks_2=hit50_match, level=3) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit5_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit5_match, possible_tracks_2=hit25_match, level=4) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit25_match, level=5) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit15_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit15_match, possible_tracks_2=hit25_match, level=6) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit5_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit5_match, possible_tracks_2=hit15_match, level=7) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit15_match, level=8) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit5_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit5_match, possible_tracks_2=hit10_match, level=9) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit15_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit15_match, possible_tracks_2=hit15_match, level=10) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit10_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit10_match, possible_tracks_2=hit10_match, level=11) - lookup = assign_track_to_EM(lookup=lookup, possible_tracks_1=hit5_match, + lookup = assign_track_to_em(lookup=lookup, possible_tracks_1=hit5_match, possible_tracks_2=hit5_match, level=12) - if keep_checks == False: - lookup = lookup.drop(['Date_start_EM_ordinal', 'possible_track', \ + if not keep_checks: + lookup = lookup.drop(['Date_start_EM_ordinal', 'possible_track', 'possible_track_all'], axis=1) lookup.groupby('allocation_level').count() - print('(%d/%s) tracks allocated' %(len(lookup[lookup.allocation_level.notnull()]), len(lookup))) + print('(%d/%s) tracks allocated' % ( + len(lookup[lookup.allocation_level.notnull()]), len(lookup))) return lookup -def hit_country_per_hazard(intensity_path, names_path, reg_ID_path, date_path): +def hit_country_per_hazard(intensity_path, names_path, reg_id_path, date_path): """hit_country_per_hazard: create list of hit countries from hazard set Parameters: @@ -188,35 +225,35 @@ def hit_country_per_hazard(intensity_path, names_path, reg_ID_path, date_path): intensity: sparse matrix with hazards as rows and grid points as cols, values only at location with impacts names: identifier for each hazard (i.e. IBtracID) (rows of the matrix) - reg_ID: ISO country ID of each grid point (cols of the matrix) + reg_id: ISO country ID of each grid point (cols of the matrix) date: start date of each hazard (rows of the matrix) Returns: pd.dataframe with all hit countries per hazard """ - with open(intensity_path, 'rb') as f: - inten = pickle.load(f) - with open(names_path, 'rb') as f: - names = pickle.load(f) - with open(reg_ID_path, 'rb') as f: - reg_ID = pickle.load(f) - with open(date_path, 'rb') as f: - date = pickle.load(f) + with open(intensity_path, 'rb') as filef: + inten = pickle.load(filef) + with open(names_path, 'rb') as filef: + names = pickle.load(filef) + with open(reg_id_path, 'rb') as filef: + reg_id = pickle.load(filef) + with open(date_path, 'rb') as filef: + date = pickle.load(filef) # loop over the tracks (over the rows of the intensity matrix) all_hits = [] for track in range(0, len(names)): # select track - TC = inten[track,] + tc_track = inten[track, ] # select only indices that are not zero - hits = TC.nonzero()[1] + hits = tc_track.nonzero()[1] # get the country of these indices and remove dublicates - hits = list(set(reg_ID[hits])) + hits = list(set(reg_id[hits])) # append hit countries to list all_hits.append(hits) # create data frame for output hit_countries = pd.DataFrame(columns=['hit_country', 'Date_start', 'ibtracsID']) - for track in range(0, len(names)): - #Check if track has hit any country else go to the next track + for track, _ in enumerate(names): + # Check if track has hit any country else go to the next track if len(all_hits[track]) > 0: # loop over hit_country for hit in range(0, len(all_hits[track])): @@ -224,28 +261,28 @@ def hit_country_per_hazard(intensity_path, names_path, reg_ID_path, date_path): ctry_iso = iso_cntry.get(all_hits[track][hit]).alpha3 # create entry for each country a hazard has hit hit_countries = hit_countries.append({'hit_country': ctry_iso, - 'Date_start' : date[track], - 'ibtracsID' : names[track]}, + 'Date_start': date[track], + 'ibtracsID': names[track]}, ignore_index=True) # retrun data frame with all hit countries per hazard return hit_countries -def create_lookup(EMdat_data, start, end, disaster_subtype='Tropical cyclone'): +def create_lookup(emdat_data, start, end, disaster_subtype='Tropical cyclone'): """create_lookup: prepare a lookup table of EMdat events to which hazards can be assigned Parameters: - EMdat_data: pd.dataframe with EMdat data + emdat_data: pd.dataframe with EMdat data start: start date of events to be assigned 'yyyy-mm-dd' end: end date of events to be assigned 'yyyy-mm-dd' disaster_subtype: EMdat disaster subtype Returns: pd.dataframe lookup """ - data = EMdat_data[EMdat_data['Disaster_subtype'] == disaster_subtype] - lookup = pd.DataFrame(columns=['hit_country', 'Date_start_EM', \ - 'Date_start_EM_ordinal', 'Disaster_name', \ - 'EM_ID', 'ibtracsID', 'allocation_level', \ - 'possible_track', 'possible_track_all']) + data = emdat_data[emdat_data['Disaster_subtype'] == disaster_subtype] + lookup = pd.DataFrame(columns=['hit_country', 'Date_start_EM', + 'Date_start_EM_ordinal', 'Disaster_name', + 'EM_ID', 'ibtracsID', 'allocation_level', + 'possible_track', 'possible_track_all']) lookup.hit_country = data.ISO lookup.Date_start_EM = data.Date_start_clean lookup.Disaster_name = data.Disaster_name @@ -253,21 +290,22 @@ def create_lookup(EMdat_data, start, end, disaster_subtype='Tropical cyclone'): lookup = lookup.reset_index(drop=True) # create ordinals for i in range(0, len(data.Date_start_clean.values)): - lookup.Date_start_EM_ordinal[i] = datetime.toordinal(datetime.strptime(lookup.Date_start_EM.values[i], '%Y-%m-%d')) + lookup.Date_start_EM_ordinal[i] = datetime.toordinal( + datetime.strptime(lookup.Date_start_EM.values[i], '%Y-%m-%d')) # ordinals to numeric lookup.Date_start_EM_ordinal = pd.to_numeric(lookup.Date_start_EM_ordinal) # select time - EM_start = datetime.toordinal(datetime.strptime(start, '%Y-%m-%d')) - EM_end = datetime.toordinal(datetime.strptime(end, '%Y-%m-%d')) + emdat_start = datetime.toordinal(datetime.strptime(start, '%Y-%m-%d')) + emdat_end = datetime.toordinal(datetime.strptime(end, '%Y-%m-%d')) - lookup = lookup[lookup.Date_start_EM_ordinal.values > EM_start] - lookup = lookup[lookup.Date_start_EM_ordinal.values < EM_end] + lookup = lookup[lookup.Date_start_EM_ordinal.values > emdat_start] + lookup = lookup[lookup.Date_start_EM_ordinal.values < emdat_end] return lookup # Function to relate EM disaster to IBtrack using hit countries and time -def EMdat_possible_hit(lookup, hit_countries, delta_t): +def emdat_possible_hit(lookup, hit_countries, delta_t): """relate EM disaster to hazard using hit countries and time Parameters: @@ -288,9 +326,12 @@ def EMdat_possible_hit(lookup, hit_countries, delta_t): possible_hit_all = [] for i in range(0, len(lookup.EM_ID.values)): possible_hit = [] - country_tracks = hit_countries[hit_countries['hit_country'] == lookup.hit_country.values[i]] + country_tracks = hit_countries[ + hit_countries['hit_country'] == lookup.hit_country.values[i]] for j in range(0, len(country_tracks.Date_start.values)): - if (lookup.Date_start_EM_ordinal.values[i]-country_tracks.Date_start.values[j]) < delta_t and (lookup.Date_start_EM_ordinal.values[i]-country_tracks.Date_start.values[j]) >= 0: + if (lookup.Date_start_EM_ordinal.values[i] - country_tracks.Date_start.values[j]) < \ + delta_t and (lookup.Date_start_EM_ordinal.values[i] - + country_tracks.Date_start.values[j]) >= 0: possible_hit.append(country_tracks.ibtracsID.values[j]) possible_hit_all.append(possible_hit) @@ -298,7 +339,7 @@ def EMdat_possible_hit(lookup, hit_countries, delta_t): # function to check if EM_ID has been assigned already -def match_EM_ID(lookup, poss_hit): +def match_em_id(lookup, poss_hit): """function to check if EM_ID has been assigned already and combine possible hits Parameters: @@ -322,7 +363,7 @@ def match_EM_ID(lookup, poss_hit): return possible_hit_all -def assign_track_to_EM(lookup, possible_tracks_1, possible_tracks_2, level): +def assign_track_to_em(lookup, possible_tracks_1, possible_tracks_2, level): """function to assign a hazard to an EMdat event to get some confidene into the procedure, hazards get only assigned if there is no other hazard occuring at a bigger time interval in that country @@ -339,24 +380,27 @@ def assign_track_to_EM(lookup, possible_tracks_1, possible_tracks_2, level): pd.dataframe lookup with assigend tracks and possible hits """ - for i in range(0, len(possible_tracks_1)): + for i, _ in enumerate(possible_tracks_1): if np.isnan(lookup.allocation_level.values[i]): - number_EMdat_id = len(possible_tracks_1[i]) - #print(number_EMdat_id) - for j in range(0, number_EMdat_id): + number_emdat_id = len(possible_tracks_1[i]) + # print(number_emdat_id) + for j in range(0, number_emdat_id): # check that number of possible track stays the same at given # time difference and that list is not empty - if len(possible_tracks_1[i][j]) == len(possible_tracks_2[i][j]) == 1 and possible_tracks_1[i][j] != []: + if len(possible_tracks_1[i][j]) == len(possible_tracks_2[i][j]) == 1 \ + and possible_tracks_1[i][j] != []: # check that all tracks are the same - if all(possible_tracks_1[i][0] == possible_tracks_1[i][k] for k in range(0, len(possible_tracks_1[i]))): - #check that track ID has not been assigned to that country already + if all(possible_tracks_1[i][0] == possible_tracks_1[i][k] + for k in range(0, len(possible_tracks_1[i]))): + # check that track ID has not been assigned to that country already ctry_lookup = lookup[lookup['hit_country'] == lookup.hit_country.values[i]] if possible_tracks_1[i][0][0] not in ctry_lookup.ibtracsID.values: lookup.ibtracsID.values[i] = possible_tracks_1[i][0][0] lookup.allocation_level.values[i] = level elif possible_tracks_1[i][j] != []: lookup.possible_track.values[i] = possible_tracks_1[i] - else: lookup.possible_track_all.values[i] = possible_tracks_1[i] + else: + lookup.possible_track_all.values[i] = possible_tracks_1[i] return lookup def check_assigned_track(lookup, checkset): @@ -369,7 +413,7 @@ def check_assigned_track(lookup, checkset): error scores """ # merge checkset and lookup - check = pd.merge(checkset, lookup[['hit_country', 'EM_ID', 'ibtracsID']],\ + check = pd.merge(checkset, lookup[['hit_country', 'EM_ID', 'ibtracsID']], on=['hit_country', 'EM_ID']) check_size = len(check.ibtracsID.values) # not assigned values @@ -377,392 +421,468 @@ def check_assigned_track(lookup, checkset): # correct assigned values correct = sum(check.ibtracsID.values == check.IBtracsID_checked.values) # wrongly assigned values - wrong = len(check.ibtracsID.values)-not_assigned-correct - print('%.1f%% tracks assigned correctly, %.1f%% wrongly, %.1f%% not assigned' \ - %(correct/check_size*100, wrong/check_size*100, not_assigned/check_size*100)) + wrong = len(check.ibtracsID.values) - not_assigned - correct + print('%.1f%% tracks assigned correctly, %.1f%% wrongly, %.1f%% not assigned' + % (correct / check_size * 100, + wrong / check_size * 100, + not_assigned / check_size * 100)) + + +def clean_emdat_df(emdat_file, countries=None, hazard=None, year_range=None, + target_version=2020): + """ + Get a clean and standardized DataFrame from EM-DAT-CSV-file + (1) load EM-DAT data from CSV to DataFrame and remove header/footer, + (2) handle version, clean up, and add columns, and + (3) filter by country, hazard type and year range (if any given) + + Parameters: + emdat_file (str or DataFrame): Either string with full path to CSV-file or + pandas.DataFrame loaded from EM-DAT CSV + + Optional parameters: + countries (list of str): country ISO3-codes or names, e.g. ['JAM', 'CUB']. + countries=None for all countries (default) + hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.: + Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, + Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), + Storm, Volcanic activity, Wildfire; + Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, + Tsunami, etc.; + OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc. + year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015); + (only min and max are considered) + target_version (int): required EM-DAT data format version (i.e. year of download), + changes naming of columns/variables (default: 2020) -def emdat_countries_by_hazard(hazard_name, emdat_file_csv, ignore_missing=True, \ - verbose=True, year_range=None): + Returns: + df_data (pandas.DataFrame): DataFrame containing cleaned and filtered EM-DAT impact data + """ + # (1) load EM-DAT data from CSV to DataFrame, skipping the header: + if isinstance(emdat_file, str): + df_emdat = pd.read_csv(emdat_file, encoding="ISO-8859-1", header=0) + counter = 0 + while not ('Country' in df_emdat.columns and 'ISO' in df_emdat.columns): + counter += 1 + df_emdat = pd.read_csv(emdat_file, encoding="ISO-8859-1", header=counter) + if counter == 10: + break + del counter + elif isinstance(emdat_file, pd.DataFrame): + df_emdat = emdat_file + else: + LOGGER.error('TypeError: emdat_file needs to be str or DataFrame') + return None + # drop rows with 9 or more NaN values (e.g. footer): + df_emdat = df_emdat.dropna(thresh=9) + + # (2) handle version, clean up, and add columns: + # (2.1) identify underlying EMDAT version of csv: + version = 2020 + for vers in list(VARNAMES_EMDAT.keys()): + if len(df_emdat.columns) >= len(VARNAMES_EMDAT[vers]) and \ + all(item in list(df_emdat.columns) for item in VARNAMES_EMDAT[vers].values()): + version = vers + # (2.2) create new DataFrame df_data with column names as target version + df_data = pd.DataFrame(index=df_emdat.index.values, + columns=VARNAMES_EMDAT[target_version].values()) + if 'Year' not in df_data.columns: # make sure column "Year" exists + df_data['Year'] = np.nan + for _, col in enumerate(df_data.columns): # loop over columns + if col in VARNAMES_EMDAT[version]: + df_data[col] = df_emdat[VARNAMES_EMDAT[version][col]] + elif col in df_emdat.columns: + df_data[col] = df_emdat[col] + elif col == 'Year' and version <= 2018: + years_list = list() + for _, disaster_no in enumerate(df_emdat[VARNAMES_EMDAT[version]['Dis No']]): + if isinstance(disaster_no, str): + years_list.append(int(disaster_no[0:4])) + else: + years_list.append(np.nan) + df_data[col] = years_list + if version <= 2018 and target_version >= 2020: + date_list = list() + year_list = list() + month_list = list() + day_list = list() + for year in list(df_data['Year']): + if not np.isnan(year): + date_list.append(datetime.strptime(str(year), '%Y')) + else: + date_list.append(datetime.strptime(str('0001'), '%Y')) + boolean_warning = True + for idx, datestr in enumerate(list(df_emdat['Start date'])): + try: + date_list[idx] = datetime.strptime(datestr[-7:], '%m/%Y') + except ValueError: + if boolean_warning: + LOGGER.warning('EM_DAT CSV contains invalid time formats') + boolean_warning = False + try: + date_list[idx] = datetime.strptime(datestr, '%d/%m/%Y') + except ValueError: + if boolean_warning: + LOGGER.warning('EM_DAT CSV contains invalid time formats') + boolean_warning = False + day_list.append(date_list[idx].day) + month_list.append(date_list[idx].month) + year_list.append(date_list[idx].year) + df_data['Start Month'] = np.array(month_list, dtype='int') + df_data['Start Day'] = np.array(day_list, dtype='int') + df_data['Start Year'] = np.array(year_list, dtype='int') + for var in ['Disaster Subtype', 'Disaster Type', 'Country']: + df_data[VARNAMES_EMDAT[target_version][var]].fillna('None', inplace=True) + + # (3) Filter by countries, year range, and disaster type + # (3.1) Countries: + if countries and isinstance(countries, str): + countries = [countries] + if countries and isinstance(countries, list): + for idx, country in enumerate(countries): + # convert countries to iso3 alpha code: + countries[idx] = iso_cntry.get(country).alpha3 + df_data = df_data[df_data['ISO'].isin(countries)].reset_index(drop=True) + # (3.2) Year range: + if year_range: + for idx in df_data.index: + if np.isnan(df_data.loc[0, 'Year']): + df_data.loc[0, 'Year'] = \ + df_data.loc[0, VARNAMES_EMDAT[target_version]['Start Year']] + df_data = df_data[(df_data['Year'] >= min(year_range)) & + (df_data['Year'] <= max(year_range))] + + # (3.3) Disaster type: + if hazard and isinstance(hazard, str): + hazard = [hazard] + if hazard and isinstance(hazard, list): + disaster_types = list() + disaster_subtypes = list() + for idx, haz in enumerate(hazard): + if haz in df_data[VARNAMES_EMDAT[target_version]['Disaster Type']].unique(): + disaster_types.append(haz) + if haz in df_data[VARNAMES_EMDAT[target_version]['Disaster Subtype']].unique(): + disaster_subtypes.append(haz) + if haz in PERIL_TYPE_MATCH_DICT.keys(): + disaster_types += PERIL_TYPE_MATCH_DICT[haz] + if haz in PERIL_SUBTYPE_MATCH_DICT.keys(): + disaster_subtypes += PERIL_SUBTYPE_MATCH_DICT[haz] + df_data = df_data[ + (df_data[VARNAMES_EMDAT[target_version]['Disaster Type']].isin(disaster_types)) | + (df_data[VARNAMES_EMDAT[target_version]['Disaster Subtype']].isin(disaster_subtypes))] + return df_data.reset_index(drop=True) + +def emdat_countries_by_hazard(emdat_file_csv, hazard=None, year_range=None): """return list of all countries exposed to a chosen hazard type from EMDAT data as CSV. Parameters: - hazard_name (str): Disaster (sub-)type accordung EMDAT terminology, i.e.: + emdat_file (str or DataFrame): Either string with full path to CSV-file or + pandas.DataFrame loaded from EM-DAT CSV + Optional Parameters: + hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.: Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, - Tsunami, etc. - emdat_file_csv (str): Full path to EMDAT-file (CSV), i.e.: - emdat_file_csv = os.path.join(SYSTEM_DIR, 'emdat_201810.csv') - ignore_missing (boolean): Ignore countries that that exist in EMDAT but - are missing in iso_cntry(). Default: True. - verbose (boolean): silent mode - year_range (tuple of integers or None): range of years to consider, i.e. (1950, 2000) + Tsunami, etc.; + OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc.: + year_range (tuple of integers or None): + range of years to consider, i.e. (1950, 2000) default is None, i.e. consider all years + Returns: - exp_iso: List of ISO3-codes of countries impacted by the disaster type - exp_name: List of names of countries impacted by the disaster type - """ - if hazard_name in PERIL_SUBTYPE_MATCH_DICT.keys(): - hazard_name = PERIL_SUBTYPE_MATCH_DICT[hazard_name] - elif hazard_name in PERIL_TYPE_MATCH_DICT.keys(): - hazard_name = PERIL_TYPE_MATCH_DICT[hazard_name] - LOGGER.debug('Used "Disaster type" instead of "Disaster subtype" for matching hazard_name.') - - - out = pd.read_csv(emdat_file_csv, encoding="ISO-8859-1", header=1) - if not 'Disaster type' in out.columns: - out = pd.read_csv(emdat_file_csv, encoding="ISO-8859-1", header=0) - - if not not year_range: # if year range is given, extract years in range - year_boolean = [] - all_years = np.arange(min(year_range), max(year_range)+1, 1) - for _, disaster_no in enumerate(out['Disaster No.']): - if isinstance(disaster_no, str) and int(disaster_no[0:4]) in all_years: - year_boolean.append(True) - else: - year_boolean.append(False) - out = out[year_boolean] - - - # List of countries that exist in EMDAT but are missing in iso_cntry(): - #(these countries are ignored) - list_miss = ['Netherlands Antilles', 'Guadeloupe', 'Martinique', \ - 'Réunion', 'Tokelau', 'Azores Islands', 'Canary Is'] - # list_miss_iso = ['ANT', 'GLP', 'MTQ', 'REU', 'TKL', '', ''] - exp_iso = [] - exp_name = [] - shp_file = shapereader.natural_earth(resolution='10m', - category='cultural', - name='admin_0_countries') - shp_file = shapereader.Reader(shp_file) - - # countries with TCs: - if not out[out['Disaster subtype'] == hazard_name].empty: - uni_cntry = np.unique(out[out['Disaster subtype'] == hazard_name]['Country'].values) - elif not out[out['Disaster type'] == hazard_name].empty: - uni_cntry = np.unique(out[out['Disaster type'] == hazard_name]['Country'].values) - else: - LOGGER.error('Disaster (sub-)type not found.') - for cntry in uni_cntry: - if (cntry in list_miss) and not ignore_missing: - LOGGER.debug(cntry, '... not in iso_cntry') - exp_iso.append('ZZZ') - exp_name.append(cntry) - elif cntry not in list_miss: - if '(the)' in cntry: - cntry = cntry.strip('(the)').rstrip() - cntry = cntry.replace(' (the', ',').replace(')', '') - cntry = cntry.replace(' (', ', ').replace(')', '') - if cntry == 'Saint Barth?lemy': - cntry = 'Saint Barthélemy' - if cntry == 'Saint Martin, French Part': - cntry = 'Saint Martin (French part)' - if cntry == 'Sint Maarten, Dutch part': - cntry = 'Sint Maarten (Dutch part)' - if cntry == 'Swaziland': - cntry = 'Eswatini' - if cntry == 'Virgin Island, British': - cntry = 'Virgin Islands, British' - if cntry == 'Virgin Island, U.S.': - cntry = 'Virgin Islands, U.S.' - if cntry == 'Côte d\x92Ivoire': - cntry = "Côte d'Ivoire" - if cntry == 'Macedonia, former Yugoslav Republic of': - cntry = 'Macedonia, the former Yugoslav Republic of' - if not verbose: - LOGGER.debug(cntry, ':', iso_cntry.get(cntry).name) - exp_iso.append(iso_cntry.get(cntry).alpha3) - exp_name.append(iso_cntry.get(cntry).name) - return exp_iso, exp_name - -def emdat_df_load(country, hazard_name, emdat_file_csv, year_range=None): - """function to load EM-DAT data by country, hazard type and year range + countries_iso3a : list + List of ISO3-codes of countries impacted by the disaster (sub-)types + countries_names : list + List of names of countries impacted by the disaster (sub-)types + """ + df_data = clean_emdat_df(emdat_file_csv, hazard=hazard, year_range=year_range) + countries_iso3a = list(df_data.ISO.unique()) + countries_names = list() + for iso3a in countries_iso3a: + try: + countries_names.append(iso_cntry.get(iso3a).name) + except KeyError: + countries_names.append('NA') + return countries_iso3a, countries_names + + +def scale_impact2refyear(impact_values, year_values, iso3a_values, reference_year=None): + """Scale give impact values proportional to GDP to the according value in a reference year + (for normalization of monetary values) Parameters: - country (list of str): country ISO3-codes or names, i.e. ['JAM']. - set None or 'all' for all countries""" - - if hazard_name == 'TC': - hazard_name = 'Tropical cyclone' - elif hazard_name == 'DR': - hazard_name = 'Drought' - # Read CSV file with raw EMDAT data: - out = pd.read_csv(emdat_file_csv, encoding="ISO-8859-1", header=1) - if not 'Disaster type' in out.columns: - out = pd.read_csv(emdat_file_csv, encoding="ISO-8859-1", header=0) - # Clean data frame from footer in original EM-DAT CSV: - out['ISO'].replace('', np.nan, inplace=True) - out['ISO'].replace(' Belgium"',np.nan, inplace=True) - out.dropna(subset=['ISO'], inplace=True) - # Reduce data to country and hazard type selected: - if not country or country == 'all': - out['Disaster type'].replace('', np.nan, inplace=True) - out.dropna(subset=['Disaster type'], inplace=True) - out['Disaster subtype'].replace('', np.nan, inplace=True) - out.dropna(subset=['Disaster subtype'], inplace=True) - else: - exp_iso, exp_name = emdat_countries_by_hazard(hazard_name, emdat_file_csv) - if isinstance(country, int) | (not isinstance(country, str)): - country = iso_cntry.get(country).alpha3 - if country in exp_name: - country = exp_iso[exp_name.index(country)] - if (country not in exp_iso) or country not in out.ISO.values: - print('Country ' + country + ' not in EM-DAT for hazard ' + hazard_name) - return None, None, country - out = out[out['ISO'].str.contains(country)] - out_ = out[out['Disaster subtype'].str.contains(hazard_name)] - out_ = out_.append(out[out['Disaster type'].str.contains(hazard_name)]) - del out - # filter by years and return output: - year_boolean = [] - if not not year_range: # if year range is given, extract years in range - all_years = np.arange(min(year_range), max(year_range)+1, 1) - for _, disaster_no in enumerate(out_['Disaster No.']): - if isinstance(disaster_no, str) and int(disaster_no[0:4]) in all_years: - year_boolean.append(True) - else: - year_boolean.append(False) - out_ = out_[year_boolean] - else: - years = list() - for _, disaster_no in enumerate(out_['Disaster No.']): - years.append(int(disaster_no[0:4])) - all_years = np.arange(np.unique(years).min(), np.unique(years).max()+1, 1) - del years - out_ = out_[out_['Disaster No.'].str.contains(str())] - out_ = out_.reset_index(drop=True) - return out_, sorted(all_years), country - -def emdat_impact_yearlysum(countries, hazard_name, emdat_file_csv, year_range=None, \ - reference_year=0, imp_str="Total damage ('000 US$)"): + impact_values (list or array): + Impact values to be scaled. + year_values (list or array): + Year of each impact (same length as impact_values) + iso3a_values (list or array): + ISO3alpha code of country for each impact (same length as impact_values) + + Optional Parameters: + reference_year (int): + Impact is scaled proportional to GDP to the value of the reference year. + No scaling for reference_year=None (default) + """ + impact_values = np.array(impact_values) + year_values = np.array(year_values) + iso3a_values = np.array(iso3a_values) + if reference_year and isinstance(reference_year, (int, float)): + reference_year = int(reference_year) + gdp_ref = dict() + gdp_years = dict() + for country in np.unique(iso3a_values): + # get reference GDP value for each country: + gdp_ref[country] = gdp(country, reference_year)[1] + # get GDP value for each country and year: + gdp_years[country] = dict() + years_country = np.unique(year_values[iso3a_values == country]) + print(years_country) + for year in years_country: + gdp_years[country][year] = gdp(country, year)[1] + # loop through each value and apply scaling: + for idx, val in enumerate(impact_values): + impact_values[idx] = val * gdp_ref[iso3a_values[idx]] / \ + gdp_years[iso3a_values[idx]][year_values[idx]] + return list(impact_values) + if not reference_year: + return impact_values + LOGGER.error('Invalid reference_year') + return None + +def emdat_impact_yearlysum(emdat_file_csv, countries=None, hazard=None, year_range=None, + reference_year=None, imp_str="Total Damages ('000 US$)", + version=2020): """function to load EM-DAT data and sum impact per year Parameters: - countries (list of str): country ISO3-codes or names, i.e. ['JAM']. - hazard_name (str): Hazard name according to EMDAT terminology or - CLIMADA abbreviation - emdat_file_csv (str): Full path to EMDAT-file (CSV), i.e.: - emdat_file_csv = os.path.join(SYSTEM_DIR, 'emdat_201810.csv') + emdat_file (str or DataFrame): Either string with full path to CSV-file or + pandas.DataFrame loaded from EM-DAT CSV + + Optional parameters: + countries (list of str): country ISO3-codes or names, e.g. ['JAM', 'CUB']. + countries=None for all countries (default) + hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.: + Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, + Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), + Storm, Volcanic activity, Wildfire; + Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, + Tsunami, etc.; + OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc. + year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015); + (only min and max are considered) + version (int): given EM-DAT data format version (i.e. year of download), + changes naming of columns/variables (default: 2020) reference_year (int): reference year of exposures. Impact is scaled proportional to GDP to the value of the reference year. No scaling for 0 (default) imp_str (str): Column name of impact metric in EMDAT CSV, - default = "Total damage ('000 US$)" + default = "Total Damages ('000 US$)" Returns: - yearly_impact (dict, mapping years to impact): - total impact per year, same unit as chosen impact, - i.e. 1000 current US$ for imp_str="Total damage ('000 US$)". - all_years (list of int): list of years + out (pd.DataFrame): DataFrame with summed impact and scaled impact per + year and country. """ - - out = pd.DataFrame() - for country in countries: - data, all_years, country = emdat_df_load(country, hazard_name, \ - emdat_file_csv, year_range) - if data is None: + imp_str = VARNAMES_EMDAT[version][imp_str] + df_data = clean_emdat_df(emdat_file_csv, countries=countries, hazard=hazard, + year_range=year_range, target_version=version) + + df_data[imp_str + " scaled"] = scale_impact2refyear(df_data[imp_str].values, + df_data.Year.values, df_data.ISO.values, + reference_year=reference_year) + out = pd.DataFrame(columns=['ISO', 'region_id', 'year', 'impact', + 'impact_scaled', 'reference_year']) + for country in df_data.ISO.unique(): + country = iso_cntry.get(country).alpha3 + if not df_data.loc[df_data.ISO == country].size: continue - data_out = pd.DataFrame(index=np.arange(0, len(all_years)), \ - columns=['ISO3', 'region_id', 'year', 'impact', \ - 'reference_year', 'impact_scaled']) - if reference_year > 0: - gdp_ref = gdp(country, reference_year)[1] + all_years = np.arange(min(df_data.Year), max(df_data.Year) + 1) + data_out = pd.DataFrame(index=np.arange(0, len(all_years)), + columns=out.columns) + df_country = df_data.loc[df_data.ISO == country] for cnt, year in enumerate(all_years): data_out.loc[cnt, 'year'] = year data_out.loc[cnt, 'reference_year'] = reference_year - data_out.loc[cnt, 'ISO3'] = country + data_out.loc[cnt, 'ISO'] = country data_out.loc[cnt, 'region_id'] = int(iso_cntry.get(country).numeric) data_out.loc[cnt, 'impact'] = \ - sum(data.loc[data['Disaster No.'].str.contains(str(year))]\ - [imp_str]) - if '000 US' in imp_str: # EM-DAT damages provided in '000 USD - data_out.loc[cnt, 'impact'] = data_out.loc[cnt, 'impact']*1000 - if reference_year > 0: - data_out.loc[cnt, 'impact_scaled'] = data_out.loc[cnt, 'impact'] * \ - gdp_ref / gdp(country, year)[1] + np.nansum(df_country[df_country.Year.isin([year])][imp_str]) + data_out.loc[cnt, 'impact_scaled'] = \ + np.nansum(df_country[df_country.Year.isin([year])][imp_str + " scaled"]) + if '000 US' in imp_str: # EM-DAT damages provided in '000 USD + data_out.loc[cnt, 'impact'] = data_out.loc[cnt, 'impact'] * 1e3 + data_out.loc[cnt, 'impact_scaled'] = data_out.loc[cnt, 'impact_scaled'] * 1e3 out = out.append(data_out) out = out.reset_index(drop=True) return out - # out.loc[out['year']==1980]['impact'].sum() < sum for year 1980 -def emdat_impact_event(countries, hazard_name, emdat_file_csv, year_range, \ - reference_year=0, imp_str="Total damage ('000 US$)"): +def emdat_impact_event(emdat_file_csv, countries=None, hazard=None, year_range=None, + reference_year=None, imp_str="Total Damages ('000 US$)", + version=2020): """function to load EM-DAT data return impact per event Parameters: - countries (list of str): country ISO3-codes or names, i.e. ['JAM']. - hazard_name (str): Hazard name according to EMDAT terminology or - CLIMADA abbreviation, i.e. 'TC' emdat_file_csv (str): Full path to EMDAT-file (CSV), i.e.: emdat_file_csv = os.path.join(SYSTEM_DIR, 'emdat_201810.csv') + + Optional parameters: + countries (list of str): country ISO3-codes or names, e.g. ['JAM', 'CUB']. + countries=None for all countries (default) + hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.: + Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, + Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), + Storm, Volcanic activity, Wildfire; + Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, + Tsunami, etc.; + OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc. + year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015); + (only min and max are considered) reference_year (int): reference year of exposures. Impact is scaled proportional to GDP to the value of the reference year. No scaling for 0 (default) imp_str (str): Column name of impact metric in EMDAT CSV, - default = "Total damage ('000 US$)" + default = "Total Damages ('000 US$)" + version (int): EM-DAT version to take variable/column names from (defaul: 2020) Returns: out (pandas DataFrame): EMDAT DataFrame with new columns "year", - "region_id", and scaled total impact per event with - same unit as chosen impact, - i.e. 1000 current US$ for imp_str="Total damage ('000 US$) scaled". + "region_id", and "impact" and +impact_scaled" total impact per event with + same unit as chosen impact, but multiplied by 1000 if impact is given + as 1000 US$ (e.g. imp_str="Total Damages ('000 US$) scaled"). """ - out = pd.DataFrame() - for country in countries: - data, _, country = emdat_df_load(country, hazard_name, \ - emdat_file_csv, year_range) - if data is None: - continue - if reference_year > 0: - gdp_ref = gdp(country, reference_year)[1] - else: gdp_ref = 0 - data['year'] = pd.Series(np.zeros(data.shape[0], dtype='int'), \ - index=data.index) - data['region_id'] = pd.Series(int(iso_cntry.get(country).numeric) + \ - np.zeros(data.shape[0], dtype='int'), \ - index=data.index) - data['reference_year'] = pd.Series(reference_year+np.zeros(\ - data.shape[0], dtype='int'), index=data.index) - data[imp_str + " scaled"] = pd.Series(np.zeros(data.shape[0], dtype='int'), \ - index=data.index) - for cnt in np.arange(data.shape[0]): - data.loc[cnt, 'year'] = int(data.loc[cnt, 'Disaster No.'][0:4]) - data.loc[cnt, 'reference_year'] = int(reference_year) - if data.loc[cnt][imp_str] > 0 and gdp_ref > 0: - data.loc[cnt, imp_str + " scaled"] = \ - data.loc[cnt, imp_str] * gdp_ref / \ - gdp(country, int(data.loc[cnt, 'year']))[1] - out = out.append(data) - del data - out = out.reset_index(drop=True) - if '000 US' in imp_str and not out.empty: # EM-DAT damages provided in '000 USD - out[imp_str + " scaled"] = out[imp_str + " scaled"]*1e3 - out[imp_str] = out[imp_str]*1e3 - return out - -def emdat_to_impact(emdat_file_csv, year_range=None, countries=None,\ - hazard_type_emdat=None, hazard_type_climada=None, \ - reference_year=0, imp_str="Total damage ('000 US$)"): + imp_str = VARNAMES_EMDAT[version][imp_str] + df_data = clean_emdat_df(emdat_file_csv, hazard=hazard, year_range=year_range, + countries=countries, target_version=version) + df_data['year'] = df_data['Year'] + df_data['reference_year'] = reference_year + df_data['impact'] = df_data[imp_str] + df_data['impact_scaled'] = scale_impact2refyear(df_data[imp_str].values, df_data.Year.values, + df_data.ISO.values, + reference_year=reference_year) + df_data['region_id'] = np.nan + for country in df_data.ISO.unique(): + try: + df_data.loc[df_data.ISO == country, 'region_id'] = \ + int(iso_cntry.get(country).numeric) + except KeyError: + LOGGER.warning('ISO3alpha code not found in iso_country: %s', country) + if '000 US' in imp_str: + df_data['impact'] *= 1e3 + df_data['impact_scaled'] *= 1e3 + return df_data.reset_index(drop=True) + +def emdat_to_impact(emdat_file_csv, hazard_type_climada, year_range=None, countries=None, + hazard_type_emdat=None, + reference_year=None, imp_str="Total Damages"): """function to load EM-DAT data return impact per event Parameters: emdat_file_csv (str): Full path to EMDAT-file (CSV), i.e.: emdat_file_csv = os.path.join(SYSTEM_DIR, 'emdat_201810.csv') - - hazard_type_emdat (str): Hazard (sub-)type according to EMDAT terminology, - i.e. 'Tropical cyclone' for tropical cyclone - OR hazard_type_climada (str): Hazard type CLIMADA abbreviation, i.e. 'TC' for tropical cyclone + Optional parameters: - year_range (list with 2 integers): start and end year i.e. [1980, 2017] + hazard_type_emdat (list or str): List of Disaster (sub-)type accordung + EMDAT terminology, e.g.: + Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, + Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), + Storm, Volcanic activity, Wildfire; + Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, + Tsunami, etc.; + OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc. + If not given, it is deducted from hazard_type_climada + year_range (list with 2 integers): start and end year e.g. [1980, 2017] default: None --> take year range from EM-DAT file - countries (list of str): country ISO3-codes or names, i.e. ['JAM']. + countries (list of str): country ISO3-codes or names, e.g. ['JAM']. Set to None or ['all'] for all countries (default) - reference_year (int): reference year of exposures. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default) imp_str (str): Column name of impact metric in EMDAT CSV, - default = "Total damage ('000 US$)" + default = "Total Damages ('000 US$)" Returns: impact_instance (instance of climada.engine.Impact): impact object of same format as output from CLIMADA - impact computation - scaled with GDP to reference_year if reference_year noit equal 0 - i.e. 1000 current US$ for imp_str="Total damage ('000 US$) scaled". + impact computation. + Values scaled with GDP to reference_year if reference_year is given. + i.e. current US$ for imp_str="Total Damages ('000 US$) scaled" (factor 1000 is applied) impact_instance.eai_exp holds expected annual impact for each country. impact_instance.coord_exp holds rough central coordinates for each country. - countries (list): ISO3-codes of countries imn same order as in impact_instance.eai_exp + countries (list): ISO3-codes of countries in same order as in impact_instance.eai_exp """ - # Mapping of hazard type between EM-DAT and CLIMADA: - if not hazard_type_climada: - if not hazard_type_emdat: - LOGGER.error('Either hazard_type_climada or hazard_type_emdat need to be defined.') - return None - if hazard_type_emdat == 'Tropical cyclone': - hazard_type_climada = 'TC' - elif hazard_type_emdat == 'Drought': - hazard_type_climada = 'DR' - elif hazard_type_emdat == 'Landslide': - hazard_type_climada = 'LS' - elif hazard_type_emdat == 'Riverine flood': - hazard_type_climada = 'RF' - elif hazard_type_emdat in ['Wildfire', 'Forest Fire', 'Land fire (Brush, Bush, Pasture)']: - hazard_type_climada = 'BF' - elif hazard_type_emdat == 'Extra-tropical storm': - hazard_type_climada = 'WS' - elif not hazard_type_emdat: - if hazard_type_climada == 'TC': - hazard_type_emdat = 'Tropical cyclone' - elif hazard_type_climada == 'DR': - hazard_type_emdat = 'Drought' - elif hazard_type_climada == 'LS': - hazard_type_emdat = 'Landslide' - elif hazard_type_climada == 'RF': - hazard_type_emdat = 'Riverine flood' - elif hazard_type_climada == 'BF': - hazard_type_emdat = 'Wildfire' - elif hazard_type_climada == 'WS': - hazard_type_emdat = 'Extra-tropical storm' - + if "Total Damages" in imp_str: + imp_str = "Total Damages ('000 US$)" + elif "Insured Damages" in imp_str: + imp_str = "Insured Damages ('000 US$)" + elif "Reconstruction Costs" in imp_str: + imp_str = "Reconstruction Costs ('000 US$)" + imp_str = VARNAMES_EMDAT[max(VARNAMES_EMDAT.keys())][imp_str] + if not hazard_type_emdat: + hazard_type_emdat = [hazard_type_climada] + if reference_year == 0: + reference_year = None # Inititate Impact-instance: impact_instance = Impact() impact_instance.tag = dict() - impact_instance.tag['haz'] = TagHaz(haz_type=hazard_type_climada, \ - file_name=emdat_file_csv, description='EM-DAT impact, direct import') - impact_instance.tag['exp'] = Tag(file_name=emdat_file_csv, \ - description='EM-DAT impact, direct import') + impact_instance.tag['haz'] = TagHaz(haz_type=hazard_type_climada, + file_name=emdat_file_csv, + description='EM-DAT impact, direct import') + impact_instance.tag['exp'] = Tag(file_name=emdat_file_csv, + description='EM-DAT impact, direct import') impact_instance.tag['if_set'] = Tag(file_name=None, description=None) - if not countries or countries == ['all']: - countries = emdat_countries_by_hazard(hazard_type_emdat, emdat_file_csv, \ - ignore_missing=True, verbose=True)[0] - else: - if isinstance(countries, str): - countries = [countries] + # Load EM-DAT impact data by event: - em_data = emdat_impact_event(countries, hazard_type_emdat, emdat_file_csv, \ - year_range, reference_year=reference_year) + em_data = emdat_impact_event(emdat_file_csv, countries=countries, hazard=hazard_type_emdat, + year_range=year_range, reference_year=reference_year, + imp_str=imp_str, version=max(VARNAMES_EMDAT.keys())) + + if isinstance(countries, str): + countries = [countries] + elif not countries: + countries = emdat_countries_by_hazard(emdat_file_csv, year_range=year_range, + hazard=hazard_type_emdat)[0] + if em_data.empty: return impact_instance, countries impact_instance.event_id = np.array(em_data.index, int) - impact_instance.event_name = list(em_data['Disaster No.']) + impact_instance.event_name = list( + em_data[VARNAMES_EMDAT[max(VARNAMES_EMDAT.keys())]['Dis No']]) date_list = list() - for year in list(em_data['year']): + for year in list(em_data['Year']): date_list.append(datetime.toordinal(datetime.strptime(str(year), '%Y'))) - boolean_warning = True - for idx, datestr in enumerate(list(em_data['Start date'])): - try: - date_list[idx] = datetime.toordinal(datetime.strptime(datestr[-7:], '%m/%Y')) - except ValueError: - if boolean_warning: - LOGGER.warning('EM_DAT CSV contains invalid time formats') - boolean_warning = False - try: - date_list[idx] = datetime.toordinal(datetime.strptime(datestr, '%d/%m/%Y')) - except ValueError: - if boolean_warning: - LOGGER.warning('EM_DAT CSV contains invalid time formats') - boolean_warning = False - + if 'Start Year' in em_data.columns and 'Start Month' in em_data.columns \ + and 'Start Day' in em_data.columns: + idx = 0 + for year, month, day in zip(em_data['Start Year'], em_data['Start Month'], + em_data['Start Day']): + if np.isnan(year): + idx += 1 + continue + if np.isnan(month): + month = 1 + if np.isnan(day): + day = 1 + date_list[idx] = datetime.toordinal(datetime.strptime( + '%02i/%02i/%04i' % (day, month, year), '%d/%m/%Y')) + idx += 1 impact_instance.date = np.array(date_list, int) - impact_instance.crs = DEF_CRS - if reference_year == 0: - impact_instance.at_event = np.array(em_data[imp_str]) + if not reference_year: + impact_instance.at_event = np.array(em_data["impact"]) else: - impact_instance.at_event = np.array(em_data[imp_str + " scaled"]) + impact_instance.at_event = np.array(em_data["impact_scaled"]) + impact_instance.at_event[np.isnan(impact_instance.at_event)] = 0 if not year_range: - year_range = [em_data['year'].min(), em_data['year'].max()] - impact_instance.frequency = np.ones(em_data.shape[0])/(1+np.diff(year_range)) + year_range = [em_data['Year'].min(), em_data['Year'].max()] + impact_instance.frequency = np.ones(em_data.shape[0]) / (1 + np.diff(year_range)) impact_instance.tot_value = 0 - impact_instance.aai_agg = sum(impact_instance.at_event * impact_instance.frequency) + impact_instance.aai_agg = np.nansum(impact_instance.at_event * impact_instance.frequency) impact_instance.unit = 'USD' impact_instance.imp_mat = [] @@ -774,12 +894,13 @@ def emdat_to_impact(emdat_file_csv, year_range=None, countries=None,\ countries_reg_id = list() countries_lat = list() countries_lon = list() - impact_instance.eai_exp = np.zeros(len(countries)) # empty: damage at exposure + impact_instance.eai_exp = np.zeros(len(countries)) # empty: damage at exposure for idx, cntry in enumerate(countries): try: cntry = iso_cntry.get(cntry).alpha3 except KeyError: - LOGGER.error('Country not found in iso_country: ' + cntry) + print(cntry) + LOGGER.error('Country not found in iso_country: %s', cntry) cntry_boolean = False for rec_i, rec in enumerate(shp.records()): if rec[9].casefold() == cntry.casefold(): @@ -796,15 +917,14 @@ def emdat_to_impact(emdat_file_csv, year_range=None, countries=None,\ countries_reg_id.append(int(iso_cntry.get(cntry).numeric)) except KeyError: countries_reg_id.append(0) - df_tmp = em_data[em_data['ISO'].str.contains(cntry)] - if reference_year == 0: - impact_instance.eai_exp[idx] = sum(np.array(df_tmp[imp_str])*\ - impact_instance.frequency[0]) + df_tmp = em_data[em_data[VARNAMES_EMDAT[ + max(VARNAMES_EMDAT.keys())]['ISO']].str.contains(cntry)] + if not reference_year: + impact_instance.eai_exp[idx] = sum(np.array(df_tmp["impact"]) * + impact_instance.frequency[0]) else: - impact_instance.eai_exp[idx] = sum(np.array(df_tmp[imp_str + " scaled"])*\ - impact_instance.frequency[0]) - - impact_instance.coord_exp = np.stack([countries_lat, countries_lon], axis=1) - #impact_instance.plot_raster_eai_exposure() + impact_instance.eai_exp[idx] = sum(np.array(df_tmp["impact_scaled"]) * + impact_instance.frequency[0]) - return impact_instance, countries \ No newline at end of file + impact_instance.coord_exp = np.stack([countries_lat, countries_lon], axis=1) + return impact_instance, countries diff --git a/climada/engine/test/test_cost_benefit.py b/climada/engine/test/test_cost_benefit.py index f5a8265c78..d24de877ff 100644 --- a/climada/engine/test/test_cost_benefit.py +++ b/climada/engine/test/test_cost_benefit.py @@ -37,7 +37,7 @@ '../../entity/exposures/test/data/demo_today.mat') class TestSteps(unittest.TestCase): - '''Test intermediate steps''' + """Test intermediate steps""" def test_calc_impact_measures_pass(self): """Test _calc_impact_measures against reference value""" self.assertTrue(os.path.isfile(HAZ_TEST_MAT), "{} is not a file".format(HAZ_TEST_MAT)) @@ -55,7 +55,8 @@ def test_calc_impact_measures_pass(self): cost_ben = CostBenefit() cost_ben._calc_impact_measures(hazard, entity.exposures, entity.measures, - entity.impact_funcs, when='future', risk_func=risk_aai_agg, save_imp=True) + entity.impact_funcs, when='future', + risk_func=risk_aai_agg, save_imp=True) self.assertEqual(cost_ben.imp_meas_present, dict()) self.assertEqual(cost_ben.cost_ben_ratio, dict()) @@ -65,63 +66,110 @@ def test_calc_impact_measures_pass(self): self.assertEqual(cost_ben.future_year, 2030) self.assertEqual(cost_ben.imp_meas_future['no measure']['cost'], (0, 0)) - self.assertEqual(cost_ben.imp_meas_future['no measure']['risk'], 6.51220115756442e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['risk'], + 6.51220115756442e+09, places=3) new_efc = cost_ben.imp_meas_future['no measure']['impact'].calc_freq_curve() - self.assertTrue(np.allclose(new_efc.return_per, cost_ben.imp_meas_future['no measure']['efc'].return_per)) - self.assertTrue(np.allclose(new_efc.impact, cost_ben.imp_meas_future['no measure']['efc'].impact)) - self.assertEqual(cost_ben.imp_meas_future['no measure']['impact'].at_event.nonzero()[0].size, 841) - self.assertEqual(cost_ben.imp_meas_future['no measure']['impact'].at_event[14082], 8.801682862431524e+06) - self.assertEqual(cost_ben.imp_meas_future['no measure']['impact'].tot_value, 6.570532945599105e+11) - self.assertEqual(cost_ben.imp_meas_future['no measure']['impact'].aai_agg, 6.51220115756442e+09) - - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['cost'][0], 1.3117683608515418e+09) + self.assertTrue( + np.allclose(new_efc.return_per, + cost_ben.imp_meas_future['no measure']['efc'].return_per)) + self.assertTrue( + np.allclose(new_efc.impact, cost_ben.imp_meas_future['no measure']['efc'].impact)) + self.assertEqual( + cost_ben.imp_meas_future['no measure']['impact'].at_event.nonzero()[0].size, + 841) + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['impact'].at_event[14082], + 8.801682862431524e+06, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['impact'].tot_value, + 6.570532945599105e+11, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['impact'].aai_agg, + 6.51220115756442e+09, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['cost'][0], + 1.3117683608515418e+09, places=3) self.assertEqual(cost_ben.imp_meas_future['Mangroves']['cost'][1], 1) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['risk'], 4.850407096284983e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['risk'], + 4.850407096284983e+09, places=3) new_efc = cost_ben.imp_meas_future['Mangroves']['impact'].calc_freq_curve() - self.assertTrue(np.allclose(new_efc.return_per, cost_ben.imp_meas_future['Mangroves']['efc'].return_per)) - self.assertTrue(np.allclose(new_efc.impact, cost_ben.imp_meas_future['Mangroves']['efc'].impact)) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['impact'].at_event.nonzero()[0].size, 665) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['impact'].at_event[13901], 1.29576562770977e+09) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['impact'].tot_value, 6.570532945599105e+11) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['impact'].aai_agg, 4.850407096284983e+09) - - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['cost'][0], 1.728000000000000e+09) + self.assertTrue( + np.allclose(new_efc.return_per, + cost_ben.imp_meas_future['Mangroves']['efc'].return_per)) + self.assertTrue( + np.allclose(new_efc.impact, cost_ben.imp_meas_future['Mangroves']['efc'].impact)) + self.assertEqual( + cost_ben.imp_meas_future['Mangroves']['impact'].at_event.nonzero()[0].size, + 665) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['impact'].at_event[13901], + 1.29576562770977e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['impact'].tot_value, + 6.570532945599105e+11, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['impact'].aai_agg, + 4.850407096284983e+09, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['cost'][0], + 1.728000000000000e+09, places=3) self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['cost'][1], 1) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], 5.188921355413834e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], + 5.188921355413834e+09, places=3) new_efc = cost_ben.imp_meas_future['Beach nourishment']['impact'].calc_freq_curve() - self.assertTrue(np.allclose(new_efc.return_per, cost_ben.imp_meas_future['Beach nourishment']['efc'].return_per)) - self.assertTrue(np.allclose(new_efc.impact, cost_ben.imp_meas_future['Beach nourishment']['efc'].impact)) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].at_event.nonzero()[0].size, 702) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].at_event[1110], 0.0) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].eai_exp[5], 1.1133679079730146e+08) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].tot_value, 6.570532945599105e+11) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].aai_agg, 5.188921355413834e+09) - - self.assertEqual(cost_ben.imp_meas_future['Seawall']['cost'][0], 8.878779433630093e+09) + self.assertTrue( + np.allclose(new_efc.return_per, + cost_ben.imp_meas_future['Beach nourishment']['efc'].return_per)) + self.assertTrue( + np.allclose(new_efc.impact, + cost_ben.imp_meas_future['Beach nourishment']['efc'].impact)) + self.assertEqual( + cost_ben.imp_meas_future['Beach nourishment']['impact'].at_event.nonzero()[0].size, + 702) + self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].at_event[1110], + 0.0) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].eai_exp[5], + 1.1133679079730146e+08, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].tot_value, + 6.570532945599105e+11, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['impact'].aai_agg, + 5.188921355413834e+09, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['cost'][0], + 8.878779433630093e+09, places=3) self.assertEqual(cost_ben.imp_meas_future['Seawall']['cost'][1], 1) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['risk'], 4.736400526119911e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['risk'], + 4.736400526119911e+09, places=3) new_efc = cost_ben.imp_meas_future['Seawall']['impact'].calc_freq_curve() - self.assertTrue(np.allclose(new_efc.return_per, cost_ben.imp_meas_future['Seawall']['efc'].return_per)) - self.assertTrue(np.allclose(new_efc.impact, cost_ben.imp_meas_future['Seawall']['efc'].impact)) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['impact'].at_event.nonzero()[0].size, 73) + self.assertTrue(np.allclose(new_efc.return_per, + cost_ben.imp_meas_future['Seawall']['efc'].return_per)) + self.assertTrue(np.allclose(new_efc.impact, + cost_ben.imp_meas_future['Seawall']['efc'].impact)) + self.assertEqual(cost_ben.imp_meas_future['Seawall']['impact'].at_event.nonzero()[0].size, + 73) self.assertEqual(cost_ben.imp_meas_future['Seawall']['impact'].at_event[1229], 0.0) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['impact'].tot_value, 6.570532945599105e+11) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['impact'].aai_agg, 4.736400526119911e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['impact'].tot_value, + 6.570532945599105e+11, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['impact'].aai_agg, + 4.736400526119911e+09, places=3) - self.assertEqual(cost_ben.imp_meas_future['Building code']['cost'][0], 9.200000000000000e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['cost'][0], + 9.200000000000000e+09, places=3) self.assertEqual(cost_ben.imp_meas_future['Building code']['cost'][1], 1) - self.assertEqual(cost_ben.imp_meas_future['Building code']['risk'], 4.884150868173321e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['risk'], + 4.884150868173321e+09, places=3) new_efc = cost_ben.imp_meas_future['Building code']['impact'].calc_freq_curve() - self.assertTrue(np.allclose(new_efc.return_per, cost_ben.imp_meas_future['Building code']['efc'].return_per)) - self.assertTrue(np.allclose(new_efc.impact, cost_ben.imp_meas_future['Building code']['efc'].impact)) - self.assertEqual(cost_ben.imp_meas_future['Building code']['impact'].at_event.nonzero()[0].size, 841) + self.assertTrue(np.allclose(new_efc.return_per, + cost_ben.imp_meas_future['Building code']['efc'].return_per)) + self.assertTrue(np.allclose(new_efc.impact, + cost_ben.imp_meas_future['Building code']['efc'].impact)) + self.assertEqual( + cost_ben.imp_meas_future['Building code']['impact'].at_event.nonzero()[0].size, + 841) self.assertEqual(cost_ben.imp_meas_future['Building code']['impact'].at_event[122], 0.0) - self.assertEqual(cost_ben.imp_meas_future['Building code']['impact'].eai_exp[11], 7.757060129393841e+07) - self.assertEqual(cost_ben.imp_meas_future['Building code']['impact'].tot_value, 6.570532945599105e+11) - self.assertEqual(cost_ben.imp_meas_future['Building code']['impact'].aai_agg, 4.884150868173321e+09) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['impact'].eai_exp[11], + 7.757060129393841e+07, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['impact'].tot_value, + 6.570532945599105e+11, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['impact'].aai_agg, + 4.884150868173321e+09, places=3) def test_cb_one_meas_pres_pass(self): - """ Test _cost_ben_one with different future """ + """Test _cost_ben_one with different future""" meas_name = 'Mangroves' meas_val = dict() meas_val['cost'] = (1.3117683608515418e+09, 1) @@ -143,16 +191,16 @@ def test_cb_one_meas_pres_pass(self): disc_rates = DiscRates() disc_rates.years = np.arange(2016, 2051) - disc_rates.rates = np.ones(disc_rates.years.size)*0.02 + disc_rates.rates = np.ones(disc_rates.years.size) * 0.02 time_dep = cb._time_dependency_array(1) cb._cost_ben_one(meas_name, meas_val, disc_rates, time_dep) - self.assertAlmostEqual(cb.benefit[meas_name], 113345027690.81276) + self.assertAlmostEqual(cb.benefit[meas_name], 113345027690.81276, places=3) self.assertAlmostEqual(cb.cost_ben_ratio[meas_name], 0.011573232523528404) def test_cb_one_meas_fut_pass(self): - """ Test _cost_ben_one with same future """ + """Test _cost_ben_one with same future""" meas_name = 'Mangroves' meas_val = dict() meas_val['cost'] = (1.3117683608515418e+09, 1) @@ -168,12 +216,12 @@ def test_cb_one_meas_fut_pass(self): disc_rates = DiscRates() disc_rates.years = np.arange(2000, 2051) - disc_rates.rates = np.ones(disc_rates.years.size)*0.02 + disc_rates.rates = np.ones(disc_rates.years.size) * 0.02 time_dep = cb._time_dependency_array() cb._cost_ben_one(meas_name, meas_val, disc_rates, time_dep) - self.assertAlmostEqual(cb.benefit[meas_name], 3.100583368954022e+10) + self.assertAlmostEqual(cb.benefit[meas_name], 3.100583368954022e+10, places=3) self.assertAlmostEqual(cb.cost_ben_ratio[meas_name], 0.04230714690616641) def test_calc_cb_no_change_pass(self): @@ -189,7 +237,8 @@ def test_calc_cb_no_change_pass(self): cost_ben = CostBenefit() cost_ben._calc_impact_measures(hazard, entity.exposures, entity.measures, - entity.impact_funcs, when='future', risk_func=risk_aai_agg, save_imp=True) + entity.impact_funcs, when='future', + risk_func=risk_aai_agg, save_imp=True) cost_ben.present_year = 2018 cost_ben.future_year = 2040 @@ -200,17 +249,18 @@ def test_calc_cb_no_change_pass(self): self.assertEqual(cost_ben.present_year, 2018) self.assertEqual(cost_ben.future_year, 2040) - self.assertEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.04230714690616641) - self.assertEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.06998836431681373) - self.assertEqual(cost_ben.cost_ben_ratio['Seawall'], 0.2679741183248266) - self.assertEqual(cost_ben.cost_ben_ratio['Building code'], 0.30286828677985717) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.04230714690616641) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.06998836431681373) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Seawall'], 0.2679741183248266) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Building code'], 0.30286828677985717) - self.assertEqual(cost_ben.benefit['Mangroves'], 3.100583368954022e+10) - self.assertEqual(cost_ben.benefit['Beach nourishment'], 2.468981832719974e+10) - self.assertEqual(cost_ben.benefit['Seawall'], 3.3132973770502796e+10) - self.assertEqual(cost_ben.benefit['Building code'], 3.0376240767284798e+10) + self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 3.100583368954022e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], + 2.468981832719974e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Seawall'], 3.3132973770502796e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Building code'], 3.0376240767284798e+10, places=3) - self.assertEqual(cost_ben.tot_climate_risk, 1.2150496306913972e+11) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 1.2150496306913972e+11, places=3) def test_calc_cb_change_pass(self): """Test _calc_cost_benefit with present value against reference value""" @@ -225,7 +275,8 @@ def test_calc_cb_change_pass(self): cost_ben = CostBenefit() cost_ben._calc_impact_measures(hazard, entity.exposures, entity.measures, - entity.impact_funcs, when='present', risk_func=risk_aai_agg, save_imp=False) + entity.impact_funcs, when='present', + risk_func=risk_aai_agg, save_imp=False) ent_future = Entity() ent_future.read_excel(ENT_DEMO_FUTURE) @@ -235,7 +286,8 @@ def test_calc_cb_change_pass(self): haz_future.intensity.data += 25 cost_ben._calc_impact_measures(haz_future, ent_future.exposures, ent_future.measures, - ent_future.impact_funcs, when='future', risk_func=risk_aai_agg, save_imp=False) + ent_future.impact_funcs, when='future', + risk_func=risk_aai_agg, save_imp=False) cost_ben.present_year = 2018 cost_ben.future_year = 2040 @@ -243,34 +295,44 @@ def test_calc_cb_change_pass(self): self.assertEqual(cost_ben.present_year, 2018) self.assertEqual(cost_ben.future_year, 2040) - self.assertEqual(cost_ben.tot_climate_risk, 5.768659152882021e+11) - - self.assertEqual(cost_ben.imp_meas_present['no measure']['risk'], 6.51220115756442e+09) - self.assertEqual(cost_ben.imp_meas_present['Mangroves']['risk'], 4.850407096284983e+09) - self.assertEqual(cost_ben.imp_meas_present['Beach nourishment']['risk'], 5.188921355413834e+09) - self.assertEqual(cost_ben.imp_meas_present['Seawall']['risk'], 4.736400526119911e+09) - self.assertEqual(cost_ben.imp_meas_present['Building code']['risk'], 4.884150868173321e+09) - - self.assertEqual(cost_ben.imp_meas_future['no measure']['risk'], 5.9506659786664024e+10) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['risk'], 4.826231151473135e+10) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], 5.0647250923231674e+10) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['risk'], 21089567135.7345) - self.assertEqual(cost_ben.imp_meas_future['Building code']['risk'], 4.462999483999791e+10) - - self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 113345027690.81276) - self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], 89444869971.53653) - self.assertAlmostEqual(cost_ben.benefit['Seawall'], 347977469896.1333) - self.assertAlmostEqual(cost_ben.benefit['Building code'], 144216478822.05154) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 5.768659152882021e+11, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_present['no measure']['risk'], + 6.51220115756442e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Mangroves']['risk'], + 4.850407096284983e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Beach nourishment']['risk'], + 5.188921355413834e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Seawall']['risk'], + 4.736400526119911e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Building code']['risk'], + 4.884150868173321e+09, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['risk'], + 5.9506659786664024e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['risk'], + 4.826231151473135e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], + 5.0647250923231674e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['risk'], + 21089567135.7345, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['risk'], + 4.462999483999791e+10, places=3) + + self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 113345027690.81276, places=3) + self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], 89444869971.53653, places=3) + self.assertAlmostEqual(cost_ben.benefit['Seawall'], 347977469896.1333, places=3) + self.assertAlmostEqual(cost_ben.benefit['Building code'], 144216478822.05154, places=3) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.011573232523528404) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.01931916274851638) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Seawall'], 0.025515385913577368) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Building code'], 0.06379298728650741) - self.assertEqual(cost_ben.tot_climate_risk, 576865915288.2021) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 576865915288.2021, places=3) def test_time_array_pres_pass(self): - """ Test _time_dependency_array """ + """Test _time_dependency_array""" cb = CostBenefit() cb.present_year = 2018 cb.future_year = 2030 @@ -279,7 +341,7 @@ def test_time_array_pres_pass(self): n_years = cb.future_year - cb.present_year + 1 self.assertEqual(time_arr.size, n_years) - self.assertTrue(np.allclose(time_arr[:-1], np.arange(0, 1, 1/(n_years-1)))) + self.assertTrue(np.allclose(time_arr[:-1], np.arange(0, 1, 1 / (n_years - 1)))) self.assertEqual(time_arr[-1], 1) imp_time_depen = 0.5 @@ -287,11 +349,11 @@ def test_time_array_pres_pass(self): n_years = cb.future_year - cb.present_year + 1 self.assertEqual(time_arr.size, n_years) - self.assertTrue(np.allclose(time_arr, np.arange(n_years)**imp_time_depen / \ - (n_years-1)**imp_time_depen)) + self.assertTrue(np.allclose(time_arr, np.arange(n_years)**imp_time_depen / + (n_years - 1)**imp_time_depen)) def test_time_array_no_pres_pass(self): - """ Test _time_dependency_array """ + """Test _time_dependency_array""" cb = CostBenefit() cb.present_year = 2018 cb.future_year = 2030 @@ -302,42 +364,43 @@ def test_time_array_no_pres_pass(self): self.assertTrue(np.array_equal(time_arr, np.ones(n_years))) def test_npv_unaverted_no_pres_pass(self): - """ Test _npv_unaverted_impact """ + """Test _npv_unaverted_impact""" cb = CostBenefit() cb.present_year = 2018 cb.future_year = 2030 risk_future = 1000 disc_rates = DiscRates() - disc_rates.years = np.arange(cb.present_year, cb.future_year+1) - disc_rates.rates = np.ones(disc_rates.years.size)*0.025 + disc_rates.years = np.arange(cb.present_year, cb.future_year + 1) + disc_rates.rates = np.ones(disc_rates.years.size) * 0.025 time_dep = np.linspace(0, 1, disc_rates.years.size) res = cb._npv_unaverted_impact(risk_future, disc_rates, time_dep, - risk_present=None) + risk_present=None) - self.assertEqual(res, disc_rates.net_present_value(cb.present_year, \ - cb.future_year, time_dep * risk_future)) + self.assertEqual( + res, + disc_rates.net_present_value(cb.present_year, cb.future_year, time_dep * risk_future)) def test_npv_unaverted_pres_pass(self): - """ Test _npv_unaverted_impact """ + """Test _npv_unaverted_impact""" cb = CostBenefit() cb.present_year = 2018 cb.future_year = 2030 risk_future = 1000 risk_present = 500 disc_rates = DiscRates() - disc_rates.years = np.arange(cb.present_year, cb.future_year+1) - disc_rates.rates = np.ones(disc_rates.years.size)*0.025 + disc_rates.years = np.arange(cb.present_year, cb.future_year + 1) + disc_rates.rates = np.ones(disc_rates.years.size) * 0.025 time_dep = np.linspace(0, 1, disc_rates.years.size) - res = cb._npv_unaverted_impact(risk_future, disc_rates, time_dep, - risk_present) + res = cb._npv_unaverted_impact(risk_future, disc_rates, time_dep, risk_present) - tot_climate_risk = risk_present + (risk_future-risk_present) * time_dep - self.assertEqual(res, disc_rates.net_present_value(cb.present_year, \ - cb.future_year, tot_climate_risk)) + tot_climate_risk = risk_present + (risk_future - risk_present) * time_dep + self.assertEqual(res, disc_rates.net_present_value(cb.present_year, + cb.future_year, + tot_climate_risk)) def test_norm_value(self): - """ Test _norm_values """ + """Test _norm_values""" norm_fact, norm_name = _norm_values(1) self.assertEqual(norm_fact, 1) self.assertEqual(norm_name, "") @@ -379,7 +442,7 @@ def test_norm_value(self): self.assertEqual(norm_name, "bn") def test_combine_fut_pass(self): - """ Test combine_measures with present and future """ + """Test combine_measures with present and future""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -398,7 +461,8 @@ def test_combine_fut_pass(self): new_name = 'combine' new_color = np.array([0.1, 0.1, 0.1]) new_cb = cost_ben.combine_measures(['Mangroves', 'Seawall'], new_name, new_color, - entity.disc_rates, imp_time_depen=None, risk_func=risk_aai_agg) + entity.disc_rates, imp_time_depen=None, + risk_func=risk_aai_agg) self.assertTrue(np.allclose(new_cb.color_rgb[new_name], new_color)) @@ -406,34 +470,41 @@ def test_combine_fut_pass(self): cost_ben.imp_meas_future['Mangroves']['impact'].at_event new_imp += cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Seawall']['impact'].at_event - new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, 0) + new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, + 0) self.assertTrue(np.allclose(new_cb.imp_meas_present[new_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_present[new_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_present['no measure']['impact'].frequency), 5) + self.assertAlmostEqual( + new_cb.imp_meas_present[new_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_present['no measure']['impact'].frequency), 5) self.assertAlmostEqual(new_cb.imp_meas_present[new_name]['cost'][0], cost_ben.imp_meas_present['Mangroves']['cost'][0] + cost_ben.imp_meas_present['Seawall']['cost'][0]) self.assertAlmostEqual(new_cb.imp_meas_present[new_name]['cost'][1], 1) - self.assertTrue(np.allclose(new_cb.imp_meas_present[new_name]['efc'].impact, - new_cb.imp_meas_present[new_name]['impact'].calc_freq_curve().impact)) + self.assertTrue(np.allclose( + new_cb.imp_meas_present[new_name]['efc'].impact, + new_cb.imp_meas_present[new_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_present[new_name]['risk_transf'], 0) self.assertTrue(np.allclose(new_cb.imp_meas_future[new_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['cost'][0], - cost_ben.imp_meas_future['Mangroves']['cost'][0]+cost_ben.imp_meas_future['Seawall']['cost'][0]) + self.assertAlmostEqual( + new_cb.imp_meas_future[new_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual( + new_cb.imp_meas_future[new_name]['cost'][0], + cost_ben.imp_meas_future['Mangroves']['cost'][0] + + cost_ben.imp_meas_future['Seawall']['cost'][0]) self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['cost'][1], 1) - self.assertTrue(np.allclose(new_cb.imp_meas_future[new_name]['efc'].impact, - new_cb.imp_meas_future[new_name]['impact'].calc_freq_curve().impact)) + self.assertTrue(np.allclose( + new_cb.imp_meas_future[new_name]['efc'].impact, + new_cb.imp_meas_future[new_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['risk_transf'], 0) - self.assertAlmostEqual(new_cb.benefit[new_name], 51781337529.07264) + self.assertAlmostEqual(new_cb.benefit[new_name], 51781337529.07264, places=3) self.assertAlmostEqual(new_cb.cost_ben_ratio[new_name], 0.19679962474434248) def test_combine_current_pass(self): - """ Test combine_measures with only future""" + """Test combine_measures with only future""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -447,7 +518,8 @@ def test_combine_current_pass(self): new_name = 'combine' new_color = np.array([0.1, 0.1, 0.1]) new_cb = cost_ben.combine_measures(['Mangroves', 'Seawall'], new_name, new_color, - entity.disc_rates, imp_time_depen=None, risk_func=risk_aai_agg) + entity.disc_rates, imp_time_depen=None, + risk_func=risk_aai_agg) self.assertTrue(np.allclose(new_cb.color_rgb[new_name], new_color)) self.assertEqual(len(new_cb.imp_meas_present), 0) @@ -455,21 +527,26 @@ def test_combine_current_pass(self): cost_ben.imp_meas_future['Mangroves']['impact'].at_event new_imp += cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Seawall']['impact'].at_event - new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, 0) + new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, + 0) self.assertTrue(np.allclose(new_cb.imp_meas_future[new_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['cost'][0], - cost_ben.imp_meas_future['Mangroves']['cost'][0]+cost_ben.imp_meas_future['Seawall']['cost'][0]) + self.assertAlmostEqual( + new_cb.imp_meas_future[new_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual( + new_cb.imp_meas_future[new_name]['cost'][0], + cost_ben.imp_meas_future['Mangroves']['cost'][0] + + cost_ben.imp_meas_future['Seawall']['cost'][0]) self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['cost'][1], 1) - self.assertTrue(np.allclose(new_cb.imp_meas_future[new_name]['efc'].impact, - new_cb.imp_meas_future[new_name]['impact'].calc_freq_curve().impact)) + self.assertTrue(np.allclose( + new_cb.imp_meas_future[new_name]['efc'].impact, + new_cb.imp_meas_future[new_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_future[new_name]['risk_transf'], 0) - self.assertAlmostEqual(new_cb.benefit[new_name], 51781337529.07264) + self.assertAlmostEqual(new_cb.benefit[new_name], 51781337529.07264, places=3) self.assertAlmostEqual(new_cb.cost_ben_ratio[new_name], 0.19679962474434248) def test_apply_transf_current_pass(self): - """ Test apply_risk_transfer with only future """ + """Test apply_risk_transfer with only future""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -482,37 +559,46 @@ def test_apply_transf_current_pass(self): new_name = 'combine' new_color = np.array([0.1, 0.1, 0.1]) - risk_transf=(1.0e7, 15.0e11, 1) + risk_transf = (1.0e7, 15.0e11, 1) new_cb = cost_ben.combine_measures(['Mangroves', 'Seawall'], new_name, new_color, - entity.disc_rates, imp_time_depen=None, risk_func=risk_aai_agg) + entity.disc_rates, imp_time_depen=None, + risk_func=risk_aai_agg) new_cb.apply_risk_transfer(new_name, risk_transf[0], risk_transf[1], - entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], imp_time_depen=1, - risk_func=risk_aai_agg) + entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], + imp_time_depen=1, + risk_func=risk_aai_agg) tr_name = 'risk transfer (' + new_name + ')' new_imp = cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Mangroves']['impact'].at_event new_imp += cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Seawall']['impact'].at_event - new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, 0) + new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, + 0) imp_layer = np.minimum(np.maximum(new_imp - risk_transf[0], 0), risk_transf[1]) - risk_transfer = np.sum(imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) + risk_transfer = np.sum( + imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) new_imp = np.maximum(new_imp - imp_layer, 0) self.assertTrue(np.allclose(new_cb.color_rgb[new_name], new_color)) self.assertEqual(len(new_cb.imp_meas_present), 0) self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name]*new_cb.benefit[tr_name], 32106013195.316242) - self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['efc'].impact, - new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) + self.assertAlmostEqual( + new_cb.imp_meas_future[tr_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual( + new_cb.cost_ben_ratio[tr_name] * new_cb.benefit[tr_name], + 32106013195.316242, places=3) + self.assertTrue(np.allclose( + new_cb.imp_meas_future[tr_name]['efc'].impact, + new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk_transf'], risk_transfer) - self.assertAlmostEqual(new_cb.benefit[tr_name], 32106013195.316242, 4) # benefit = impact layer + # benefit = impact layer + self.assertAlmostEqual(new_cb.benefit[tr_name], 32106013195.316242, 4) self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name], 1) def test_apply_transf_cost_fact_pass(self): - """ Test apply_risk_transfer with only future annd cost factor """ + """Test apply_risk_transfer with only future annd cost factor""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -525,37 +611,44 @@ def test_apply_transf_cost_fact_pass(self): new_name = 'combine' new_color = np.array([0.1, 0.1, 0.1]) - risk_transf=(1.0e7, 15.0e11, 2) + risk_transf = (1.0e7, 15.0e11, 2) new_cb = cost_ben.combine_measures(['Mangroves', 'Seawall'], new_name, new_color, - entity.disc_rates, imp_time_depen=None, risk_func=risk_aai_agg) + entity.disc_rates, imp_time_depen=None, + risk_func=risk_aai_agg) new_cb.apply_risk_transfer(new_name, risk_transf[0], risk_transf[1], - entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], imp_time_depen=1, - risk_func=risk_aai_agg) + entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], + imp_time_depen=1, risk_func=risk_aai_agg) tr_name = 'risk transfer (' + new_name + ')' new_imp = cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Mangroves']['impact'].at_event new_imp += cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Seawall']['impact'].at_event - new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, 0) + new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, + 0) imp_layer = np.minimum(np.maximum(new_imp - risk_transf[0], 0), risk_transf[1]) - risk_transfer = np.sum(imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) + risk_transfer = np.sum( + imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) new_imp = np.maximum(new_imp - imp_layer, 0) self.assertTrue(np.allclose(new_cb.color_rgb[new_name], new_color)) self.assertEqual(len(new_cb.imp_meas_present), 0) self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name]*new_cb.benefit[tr_name], risk_transf[2]*32106013195.316242) - self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['efc'].impact, - new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) + self.assertAlmostEqual( + new_cb.imp_meas_future[tr_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name] * new_cb.benefit[tr_name], + risk_transf[2] * 32106013195.316242) + self.assertTrue( + np.allclose(new_cb.imp_meas_future[tr_name]['efc'].impact, + new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk_transf'], risk_transfer) - self.assertAlmostEqual(new_cb.benefit[tr_name], 32106013195.316242, 4) # benefit = impact layer + # benefit = impact layer + self.assertAlmostEqual(new_cb.benefit[tr_name], 32106013195.316242, 4) self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name], risk_transf[2]) def test_apply_transf_future_pass(self): - """ Test apply_risk_transfer with present and future """ + """Test apply_risk_transfer with present and future""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -572,40 +665,48 @@ def test_apply_transf_future_pass(self): new_name = 'combine' new_color = np.array([0.1, 0.1, 0.1]) - risk_transf=(1.0e7, 15.0e11, 1) + risk_transf = (1.0e7, 15.0e11, 1) new_cb = cost_ben.combine_measures(['Mangroves', 'Seawall'], new_name, new_color, - entity.disc_rates, imp_time_depen=None, risk_func=risk_aai_agg) + entity.disc_rates, imp_time_depen=None, + risk_func=risk_aai_agg) new_cb.apply_risk_transfer(new_name, risk_transf[0], risk_transf[1], - entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], imp_time_depen=1, - risk_func=risk_aai_agg) + entity.disc_rates, cost_fix=0, cost_factor=risk_transf[2], + imp_time_depen=1, risk_func=risk_aai_agg) tr_name = 'risk transfer (' + new_name + ')' new_imp = cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Mangroves']['impact'].at_event new_imp += cost_ben.imp_meas_future['no measure']['impact'].at_event - \ cost_ben.imp_meas_future['Seawall']['impact'].at_event - new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, 0) + new_imp = np.maximum(cost_ben.imp_meas_future['no measure']['impact'].at_event - new_imp, + 0) imp_layer = np.minimum(np.maximum(new_imp - risk_transf[0], 0), risk_transf[1]) - risk_transfer = np.sum(imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) + risk_transfer = np.sum( + imp_layer * cost_ben.imp_meas_future['no measure']['impact'].frequency) new_imp = np.maximum(new_imp - imp_layer, 0) self.assertTrue(np.allclose(new_cb.color_rgb[new_name], new_color)) self.assertEqual(len(new_cb.imp_meas_present), 3) self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['impact'].at_event, new_imp)) self.assertTrue(np.allclose(new_cb.imp_meas_present[tr_name]['impact'].at_event, new_imp)) - self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.imp_meas_present[tr_name]['risk'], - np.sum(new_imp*cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) - self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name]*new_cb.benefit[tr_name], 69715165679.7042) - self.assertTrue(np.allclose(new_cb.imp_meas_future[tr_name]['efc'].impact, - new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) + self.assertAlmostEqual( + new_cb.imp_meas_future[tr_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual( + new_cb.imp_meas_present[tr_name]['risk'], + np.sum(new_imp * cost_ben.imp_meas_future['no measure']['impact'].frequency), 5) + self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name] * new_cb.benefit[tr_name], + 69715165679.7042, places=3) + self.assertTrue( + np.allclose(new_cb.imp_meas_future[tr_name]['efc'].impact, + new_cb.imp_meas_future[tr_name]['impact'].calc_freq_curve().impact)) self.assertAlmostEqual(new_cb.imp_meas_future[tr_name]['risk_transf'], risk_transfer) - self.assertAlmostEqual(new_cb.benefit[tr_name], 69715165679.7042, 4) # benefit = impact layer + # benefit = impact layer + self.assertAlmostEqual(new_cb.benefit[tr_name], 69715165679.7042, 4) self.assertAlmostEqual(new_cb.cost_ben_ratio[tr_name], 1) def test_remove_measure(self): - """ Test remove_measure method """ + """Test remove_measure method""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() @@ -631,7 +732,7 @@ def test_remove_measure(self): self.assertEqual(len(cost_ben.benefit), 3) class TestCalc(unittest.TestCase): - '''Test calc''' + """Test calc""" def test_calc_change_pass(self): """Test calc with future change""" @@ -658,31 +759,41 @@ def test_calc_change_pass(self): self.assertEqual(cost_ben.present_year, 2018) self.assertEqual(cost_ben.future_year, 2040) - self.assertEqual(cost_ben.tot_climate_risk, 5.768659152882021e+11) - - self.assertEqual(cost_ben.imp_meas_present['no measure']['risk'], 6.51220115756442e+09) - self.assertEqual(cost_ben.imp_meas_present['Mangroves']['risk'], 4.850407096284983e+09) - self.assertEqual(cost_ben.imp_meas_present['Beach nourishment']['risk'], 5.188921355413834e+09) - self.assertEqual(cost_ben.imp_meas_present['Seawall']['risk'], 4.736400526119911e+09) - self.assertEqual(cost_ben.imp_meas_present['Building code']['risk'], 4.884150868173321e+09) - - self.assertEqual(cost_ben.imp_meas_future['no measure']['risk'], 5.9506659786664024e+10) - self.assertEqual(cost_ben.imp_meas_future['Mangroves']['risk'], 4.826231151473135e+10) - self.assertEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], 5.0647250923231674e+10) - self.assertEqual(cost_ben.imp_meas_future['Seawall']['risk'], 21089567135.7345) - self.assertEqual(cost_ben.imp_meas_future['Building code']['risk'], 4.462999483999791e+10) - - self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 113345027690.81276) - self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], 89444869971.53653) - self.assertAlmostEqual(cost_ben.benefit['Seawall'], 347977469896.1333) - self.assertAlmostEqual(cost_ben.benefit['Building code'], 144216478822.05154) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 5.768659152882021e+11, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_present['no measure']['risk'], + 6.51220115756442e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Mangroves']['risk'], + 4.850407096284983e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Beach nourishment']['risk'], + 5.188921355413834e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Seawall']['risk'], + 4.736400526119911e+09, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_present['Building code']['risk'], + 4.884150868173321e+09, places=3) + + self.assertAlmostEqual(cost_ben.imp_meas_future['no measure']['risk'], + 5.9506659786664024e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Mangroves']['risk'], + 4.826231151473135e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Beach nourishment']['risk'], + 5.0647250923231674e+10, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Seawall']['risk'], + 21089567135.7345, places=3) + self.assertAlmostEqual(cost_ben.imp_meas_future['Building code']['risk'], + 4.462999483999791e+10, places=3) + + self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 113345027690.81276, places=3) + self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], 89444869971.53653, places=3) + self.assertAlmostEqual(cost_ben.benefit['Seawall'], 347977469896.1333, places=3) + self.assertAlmostEqual(cost_ben.benefit['Building code'], 144216478822.05154, places=3) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.011573232523528404) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.01931916274851638) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Seawall'], 0.025515385913577368) self.assertAlmostEqual(cost_ben.cost_ben_ratio['Building code'], 0.06379298728650741) - self.assertEqual(cost_ben.tot_climate_risk, 576865915288.2021) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 576865915288.2021, places=3) def test_calc_no_change_pass(self): """Test calc without future change""" @@ -700,20 +811,21 @@ def test_calc_no_change_pass(self): self.assertEqual(cost_ben.present_year, 2018) self.assertEqual(cost_ben.future_year, 2040) - self.assertEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.04230714690616641) - self.assertEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.06998836431681373) - self.assertEqual(cost_ben.cost_ben_ratio['Seawall'], 0.2679741183248266) - self.assertEqual(cost_ben.cost_ben_ratio['Building code'], 0.30286828677985717) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Mangroves'], 0.04230714690616641) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Beach nourishment'], 0.06998836431681373) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Seawall'], 0.2679741183248266) + self.assertAlmostEqual(cost_ben.cost_ben_ratio['Building code'], 0.30286828677985717) - self.assertEqual(cost_ben.benefit['Mangroves'], 3.100583368954022e+10) - self.assertEqual(cost_ben.benefit['Beach nourishment'], 2.468981832719974e+10) - self.assertEqual(cost_ben.benefit['Seawall'], 3.3132973770502796e+10) - self.assertEqual(cost_ben.benefit['Building code'], 3.0376240767284798e+10) + self.assertAlmostEqual(cost_ben.benefit['Mangroves'], 3.100583368954022e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Beach nourishment'], + 2.468981832719974e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Seawall'], 3.3132973770502796e+10, places=3) + self.assertAlmostEqual(cost_ben.benefit['Building code'], 3.0376240767284798e+10, places=3) - self.assertEqual(cost_ben.tot_climate_risk, 1.2150496306913972e+11) + self.assertAlmostEqual(cost_ben.tot_climate_risk, 1.2150496306913972e+11, places=3) class TestRiskFuncs(unittest.TestCase): - '''Test risk functions definitions''' + """Test risk functions definitions""" def test_impact(self): ent = Entity() diff --git a/climada/engine/test/test_impact.py b/climada/engine/test/test_impact.py index d869b31581..ac9360af68 100644 --- a/climada/engine/test/test_impact.py +++ b/climada/engine/test/test_impact.py @@ -33,10 +33,10 @@ HAZ_DIR = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'hazard/test/data/') HAZ_TEST_MAT = os.path.join(HAZ_DIR, 'atl_prob_no_name.mat') -DATA_FOLDER = os.path.join(os.path.dirname(__file__) , 'data') +DATA_FOLDER = os.path.join(os.path.dirname(__file__), 'data') class TestFreqCurve(unittest.TestCase): - '''Test exceedence frequency curve computation''' + """Test exceedence frequency curve computation""" def test_ref_value_pass(self): """Test result against reference value""" imp = Impact() @@ -81,7 +81,7 @@ def test_ref_value_pass(self): self.assertEqual('USD', ifc.unit) def test_ref_value_rp_pass(self): - """Test result against reference value with given return periods """ + """Test result against reference value with given return periods""" imp = Impact() imp.frequency = np.ones(10) * 6.211180124223603e-04 imp.at_event = np.zeros(10) @@ -110,9 +110,9 @@ def test_ref_value_rp_pass(self): self.assertEqual('USD', ifc.unit) class TestOneExposure(unittest.TestCase): - '''Test one_exposure function''' + """Test one_exposure function""" def test_ref_value_insure_pass(self): - ''' Test result against reference value''' + """Test result against reference value""" # Read demo entity values # Set the entity default file to the demo one ent = Entity() @@ -167,10 +167,10 @@ def test_ref_value_insure_pass(self): self.assertEqual(0, impact.at_event[14309]) class TestCalc(unittest.TestCase): - ''' Test impact calc method.''' + """Test impact calc method.""" def test_ref_value_pass(self): - ''' Test result against reference value''' + """Test result against reference value""" # Read default entity values ent = Entity() ent.read_excel(ENT_DEMO_TODAY) @@ -195,21 +195,21 @@ def test_ref_value_pass(self): # impact.at_event == EDS.damage in MATLAB self.assertEqual(num_events, len(impact.at_event)) self.assertEqual(0, impact.at_event[0]) - self.assertEqual(0, impact.at_event[int(num_events/2)]) + self.assertEqual(0, impact.at_event[int(num_events / 2)]) self.assertAlmostEqual(1.472482938320243e+08, impact.at_event[13809]) - self.assertEqual(7.076504723057619e+10, impact.at_event[12147]) - self.assertEqual(0, impact.at_event[num_events-1]) + self.assertEqual(7.076504723057620e+10, impact.at_event[12147]) + self.assertEqual(0, impact.at_event[num_events - 1]) # impact.eai_exp == EDS.ED_at_centroid in MATLAB self.assertEqual(num_exp, len(impact.eai_exp)) self.assertAlmostEqual(1.518553670803242e+08, impact.eai_exp[0]) - self.assertAlmostEqual(1.373490457046383e+08, \ - impact.eai_exp[int(num_exp/2)], 6) - self.assertTrue(np.isclose(1.373490457046383e+08, \ - impact.eai_exp[int(num_exp/2)])) - self.assertAlmostEqual(1.066837260150042e+08, \ - impact.eai_exp[num_exp-1], 6) - self.assertTrue(np.isclose(1.066837260150042e+08, \ - impact.eai_exp[int(num_exp-1)])) + self.assertAlmostEqual(1.373490457046383e+08, + impact.eai_exp[int(num_exp / 2)], 6) + self.assertTrue(np.isclose(1.373490457046383e+08, + impact.eai_exp[int(num_exp / 2)])) + self.assertAlmostEqual(1.066837260150042e+08, + impact.eai_exp[num_exp - 1], 6) + self.assertTrue(np.isclose(1.066837260150042e+08, + impact.eai_exp[int(num_exp - 1)])) # impact.tot_value == EDS.Value in MATLAB # impact.aai_agg == EDS.ED in MATLAB self.assertAlmostEqual(6.570532945599105e+11, impact.tot_value) @@ -217,8 +217,8 @@ def test_ref_value_pass(self): self.assertTrue(np.isclose(6.512201157564421e+09, impact.aai_agg)) def test_calc_imp_mat_pass(self): - """ Test save imp_mat """ - # Read default entity values + """Test save imp_mat""" + # Read default entity values ent = Entity() ent.read_excel(ENT_DEMO_TODAY) ent.check() @@ -236,17 +236,21 @@ def test_calc_imp_mat_pass(self): impact.calc(ent.exposures, ent.impact_funcs, hazard, save_mat=True) self.assertTrue(isinstance(impact.imp_mat, sparse.csr_matrix)) self.assertEqual(impact.imp_mat.shape, (hazard.event_id.size, - ent.exposures.value.size)) + ent.exposures.value.size)) self.assertTrue(np.allclose(np.sum(impact.imp_mat, axis=1).reshape(-1), - impact.at_event)) - self.assertTrue(np.allclose(np.array(np.sum(np.multiply(impact.imp_mat.todense(), - impact.frequency.reshape(-1, 1)), axis=0)).reshape(-1), impact.eai_exp)) + impact.at_event)) + self.assertTrue( + np.allclose( + np.array(np.sum(np.multiply(impact.imp_mat.toarray(), + impact.frequency.reshape(-1, 1)), + axis=0)).reshape(-1), + impact.eai_exp)) def test_calc_if_pass(self): - """ Execute when no if_HAZ present, but only if_ """ + """Execute when no if_HAZ present, but only if_""" ent = Entity() ent.read_excel(ENT_DEMO_TODAY) - ent.exposures.rename(columns={'if_TC':'if_'}, inplace=True) + ent.exposures.rename(columns={'if_TC': 'if_'}, inplace=True) ent.check() # Read default hazard file @@ -263,21 +267,21 @@ def test_calc_if_pass(self): # impact.at_event == EDS.damage in MATLAB self.assertEqual(num_events, len(impact.at_event)) self.assertEqual(0, impact.at_event[0]) - self.assertEqual(0, impact.at_event[int(num_events/2)]) + self.assertEqual(0, impact.at_event[int(num_events / 2)]) self.assertAlmostEqual(1.472482938320243e+08, impact.at_event[13809]) - self.assertEqual(7.076504723057619e+10, impact.at_event[12147]) - self.assertEqual(0, impact.at_event[num_events-1]) + self.assertEqual(7.076504723057620e+10, impact.at_event[12147]) + self.assertEqual(0, impact.at_event[num_events - 1]) # impact.eai_exp == EDS.ED_at_centroid in MATLAB self.assertEqual(num_exp, len(impact.eai_exp)) self.assertAlmostEqual(1.518553670803242e+08, impact.eai_exp[0]) - self.assertAlmostEqual(1.373490457046383e+08, \ - impact.eai_exp[int(num_exp/2)], 6) - self.assertTrue(np.isclose(1.373490457046383e+08, \ - impact.eai_exp[int(num_exp/2)])) - self.assertAlmostEqual(1.066837260150042e+08, \ - impact.eai_exp[num_exp-1], 6) - self.assertTrue(np.isclose(1.066837260150042e+08, \ - impact.eai_exp[int(num_exp-1)])) + self.assertAlmostEqual(1.373490457046383e+08, + impact.eai_exp[int(num_exp / 2)], 6) + self.assertTrue(np.isclose(1.373490457046383e+08, + impact.eai_exp[int(num_exp / 2)])) + self.assertAlmostEqual(1.066837260150042e+08, + impact.eai_exp[num_exp - 1], 6) + self.assertTrue(np.isclose(1.066837260150042e+08, + impact.eai_exp[int(num_exp - 1)])) # impact.tot_value == EDS.Value in MATLAB # impact.aai_agg == EDS.ED in MATLAB self.assertAlmostEqual(6.570532945599105e+11, impact.tot_value) @@ -285,10 +289,10 @@ def test_calc_if_pass(self): self.assertTrue(np.isclose(6.512201157564421e+09, impact.aai_agg)) class TestImpactYearSet(unittest.TestCase): - '''Test calc_impact_year_set method''' + """Test calc_impact_year_set method""" def test_impact_year_set_sum(self): - """Test result against reference value with given events """ + """Test result against reference value with given events""" imp = Impact() imp.frequency = np.ones(10) * 6.211180124223603e-04 imp.at_event = np.zeros(10) @@ -303,7 +307,7 @@ def test_impact_year_set_sum(self): imp.at_event[8] = 0.569142464157450e9 imp.at_event[9] = 0.467572545849132e9 imp.unit = 'USD' - imp.date = np.array([732801, 716160, 718313, 712468, 732802, \ + imp.date = np.array([732801, 716160, 718313, 712468, 732802, 729285, 732931, 715419, 722404, 718351]) iys_all = imp.calc_impact_year_set() @@ -311,21 +315,21 @@ def test_impact_year_set_sum(self): iys_all_yr = imp.calc_impact_year_set(year_range=(1975, 2000)) iys_yr = imp.calc_impact_year_set(all_years=False, year_range=[1975, 2000]) iys_all_yr_1940 = imp.calc_impact_year_set(all_years=True, year_range=[1940, 2000]) - self.assertEqual(np.around(sum([iys[year] for year in iys])), \ + self.assertEqual(np.around(sum([iys[year] for year in iys])), np.around(sum(imp.at_event))) - self.assertEqual(sum([iys[year] for year in iys]), \ + self.assertEqual(sum([iys[year] for year in iys]), sum([iys_all[year] for year in iys_all])) self.assertEqual(len(iys), 7) self.assertEqual(len(iys_all), 57) self.assertIn(1951 and 1959 and 2007, iys_all) - self.assertTrue(iys_all[1959]>0) + self.assertTrue(iys_all[1959] > 0) self.assertAlmostEqual(3598980534.468811, iys_all[2007]) self.assertEqual(iys[1978], iys_all[1978]) self.assertAlmostEqual(iys[1951], imp.at_event[3]) # year range (yr): self.assertEqual(len(iys_yr), 2) self.assertEqual(len(iys_all_yr), 26) - self.assertEqual(sum([iys_yr[year] for year in iys_yr]), \ + self.assertEqual(sum([iys_yr[year] for year in iys_yr]), sum([iys_all_yr[year] for year in iys_all_yr])) self.assertIn(1997 and 1978, iys_yr) self.assertFalse(2007 in iys_yr) @@ -333,7 +337,7 @@ def test_impact_year_set_sum(self): self.assertEqual(len(iys_all_yr_1940), 61) def test_impact_year_set_empty(self): - """Test result for empty impact """ + """Test result for empty impact""" imp = Impact() iys_all = imp.calc_impact_year_set() iys = imp.calc_impact_year_set(all_years=False) @@ -341,10 +345,10 @@ def test_impact_year_set_empty(self): self.assertEqual(len(iys_all), 0) class TestIO(unittest.TestCase): - ''' Test impact input/output methods.''' + """Test impact input/output methods.""" def test_write_read_ev_test(self): - ''' Test result against reference value''' + """Test result against reference value""" # Create impact object num_ev = 10 num_exp = 5 @@ -353,7 +357,7 @@ def test_write_read_ev_test(self): 'haz': TagHaz('TC', 'file_haz.p', 'descr haz'), 'if_set': Tag()} imp_write.event_id = np.arange(num_ev) - imp_write.event_name = ['event_'+str(num) for num in imp_write.event_id] + imp_write.event_name = ['event_' + str(num) for num in imp_write.event_id] imp_write.date = np.ones(num_ev) imp_write.coord_exp = np.zeros((num_exp, 2)) imp_write.coord_exp[:, 0] = 1.5 @@ -379,11 +383,11 @@ def test_write_read_ev_test(self): self.assertEqual(imp_write.tot_value, imp_read.tot_value) self.assertEqual(imp_write.aai_agg, imp_read.aai_agg) self.assertEqual(imp_write.unit, imp_read.unit) - self.assertEqual(0, len([i for i, j in - zip(imp_write.event_name, imp_read.event_name) if i != j])) + self.assertEqual( + 0, len([i for i, j in zip(imp_write.event_name, imp_read.event_name) if i != j])) def test_write_read_exp_test(self): - ''' Test result against reference value''' + """Test result against reference value""" # Create impact object num_ev = 5 num_exp = 10 @@ -392,7 +396,7 @@ def test_write_read_exp_test(self): 'haz': TagHaz('TC', 'file_haz.p', 'descr haz'), 'if_set': Tag()} imp_write.event_id = np.arange(num_ev) - imp_write.event_name = ['event_'+str(num) for num in imp_write.event_id] + imp_write.event_name = ['event_' + str(num) for num in imp_write.event_id] imp_write.date = np.ones(num_ev) imp_write.coord_exp = np.zeros((num_exp, 2)) imp_write.coord_exp[:, 0] = 1.5 @@ -418,12 +422,12 @@ def test_write_read_exp_test(self): self.assertEqual(imp_write.tot_value, imp_read.tot_value) self.assertEqual(imp_write.aai_agg, imp_read.aai_agg) self.assertEqual(imp_write.unit, imp_read.unit) - self.assertEqual(0, len([i for i, j in - zip(imp_write.event_name, imp_read.event_name) if i != j])) + self.assertEqual( + 0, len([i for i, j in zip(imp_write.event_name, imp_read.event_name) if i != j])) self.assertIsInstance(imp_read.crs, dict) def test_write_read_excel_pass(self): - """ Test write and read in excel """ + """Test write and read in excel""" ent = Entity() ent.read_excel(ENT_DEMO_TODAY) ent.check() @@ -448,33 +452,34 @@ def test_write_read_excel_pass(self): self.assertEqual(imp_write.tot_value, imp_read.tot_value) self.assertEqual(imp_write.aai_agg, imp_read.aai_agg) self.assertEqual(imp_write.unit, imp_read.unit) - self.assertEqual(0, len([i for i, j in - zip(imp_write.event_name, imp_read.event_name) if i != j])) + self.assertEqual( + 0, len([i for i, j in zip(imp_write.event_name, imp_read.event_name) if i != j])) self.assertIsInstance(imp_read.crs, dict) def test_write_imp_mat(self): - """ Test write_excel_imp_mat function """ + """Test write_excel_imp_mat function""" impact = Impact() - impact.imp_mat = sparse.lil_matrix(np.zeros((5, 4))) + impact.imp_mat = np.zeros((5, 4)) impact.imp_mat[0, :] = np.arange(4) - impact.imp_mat[1, :] = np.arange(4)*2 - impact.imp_mat[2, :] = np.arange(4)*3 - impact.imp_mat[3, :] = np.arange(4)*4 - impact.imp_mat[4, :] = np.arange(4)*5 - impact.imp_mat = impact.imp_mat.tocsr() + impact.imp_mat[1, :] = np.arange(4) * 2 + impact.imp_mat[2, :] = np.arange(4) * 3 + impact.imp_mat[3, :] = np.arange(4) * 4 + impact.imp_mat[4, :] = np.arange(4) * 5 + impact.imp_mat = sparse.csr_matrix(impact.imp_mat) file_name = os.path.join(DATA_FOLDER, 'test_imp_mat') impact.write_sparse_csr(file_name) - read_imp_mat = Impact().read_sparse_csr(file_name+'.npz') + read_imp_mat = Impact().read_sparse_csr(file_name + '.npz') for irow in range(5): - self.assertTrue(np.array_equal(np.array(read_imp_mat[irow, :].todense()).reshape(-1), - np.array(impact.imp_mat[irow, :].todense()).reshape(-1))) + self.assertTrue( + np.array_equal(np.array(read_imp_mat[irow, :].toarray()).reshape(-1), + np.array(impact.imp_mat[irow, :].toarray()).reshape(-1))) class TestRPmatrix(unittest.TestCase): - ''' Test computation of impact per return period for whole exposure''' + """Test computation of impact per return period for whole exposure""" def test_local_exceedance_imp_pass(self): - """ Test calc local impacts per return period """ - # Read default entity values + """Test calc local impacts per return period""" + # Read default entity values ent = Entity() ent.read_excel(ENT_DEMO_TODAY) ent.check() @@ -492,14 +497,14 @@ def test_local_exceedance_imp_pass(self): impact_rp = impact.local_exceedance_imp(return_periods=(10, 40)) self.assertTrue(isinstance(impact_rp, np.ndarray)) - self.assertEqual(impact_rp.size, 2*ent.exposures.value.size) + self.assertEqual(impact_rp.size, 2 * ent.exposures.value.size) self.assertAlmostEqual(np.max(impact_rp), 2916964966.388219, places=5) self.assertAlmostEqual(np.min(impact_rp), 444457580.131494, places=5) class TestRiskTrans(unittest.TestCase): - """ Test risk transfer methods """ + """Test risk transfer methods""" def test_risk_trans_pass(self): - """ Test calc_risk_transfer """ + """Test calc_risk_transfer""" # Create impact object imp = Impact() imp.event_id = np.arange(10) @@ -509,16 +514,16 @@ def test_risk_trans_pass(self): imp.crs = DEF_CRS imp.eai_exp = np.array([1, 2]) imp.at_event = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 15]) - imp.frequency = np.ones(10)/5 + imp.frequency = np.ones(10) / 5 imp.tot_value = 10 imp.aai_agg = 100 imp.unit = 'USD' - imp.imp_mat = [] + imp.imp_mat = sparse.csr_matrix(np.empty((0, 0))) new_imp, imp_rt = imp.calc_risk_transfer(2, 10) self.assertEqual(new_imp.unit, imp.unit) self.assertEqual(new_imp.tot_value, imp.tot_value) - self.assertEqual(new_imp.imp_mat, imp.imp_mat) + self.assertTrue((new_imp.imp_mat == imp.imp_mat).toarray().all()) self.assertEqual(new_imp.event_name, imp.event_name) self.assertTrue(np.allclose(new_imp.event_id, imp.event_id)) self.assertTrue(np.allclose(new_imp.date, imp.date)) @@ -530,7 +535,7 @@ def test_risk_trans_pass(self): self.assertEqual(imp_rt.unit, imp.unit) self.assertEqual(imp_rt.tot_value, imp.tot_value) - self.assertEqual(imp_rt.imp_mat, imp.imp_mat) + self.assertTrue((imp_rt.imp_mat == imp.imp_mat).toarray().all()) self.assertEqual(imp_rt.event_name, imp.event_name) self.assertTrue(np.allclose(imp_rt.event_id, imp.event_id)) self.assertTrue(np.allclose(imp_rt.date, imp.date)) diff --git a/climada/engine/test/test_impact_data.py b/climada/engine/test/test_impact_data.py index 3846a890a3..5628bc1717 100644 --- a/climada/engine/test/test_impact_data.py +++ b/climada/engine/test/test_impact_data.py @@ -21,119 +21,263 @@ import os import unittest import numpy as np +from climada.util.constants import DATA_DIR import climada.engine.impact_data as im_d -DATA_FOLDER = os.path.join(os.path.dirname(__file__) , 'data') +DATA_FOLDER = os.path.join(os.path.dirname(__file__), 'data') EMDAT_TEST_CSV = os.path.join(DATA_FOLDER, 'emdat_testdata_BGD_USA_1970-2017.csv') EMDAT_TEST_CSV_FAKE = os.path.join(DATA_FOLDER, 'emdat_testdata_fake_2007-2011.csv') +EMDAT_2020_CSV_DEMO = os.path.join(DATA_DIR, 'demo', 'demo_emdat_impact_data_2020.csv') class TestEmdatImport(unittest.TestCase): - '''Test import of EM-DAT data (as CSV) for impact data analysis''' - def test_emdat_df_load(self): - """load selected sub sample from CSV, return DataFrame""" - df, years, iso3 = im_d.emdat_df_load('Bangladesh', 'TC', \ - EMDAT_TEST_CSV, [2000, 2017]) + """Test import of EM-DAT data (as CSV) for impact data analysis""" - self.assertEqual('BGD', iso3) + def test_clean_emdat_df_2018_load(self): + """load selected sub sample from CSV, return DataFrame. + here: from 2018 EM-DAT version to 2018 target_version""" + + df = im_d.clean_emdat_df(EMDAT_TEST_CSV, countries=['Bangladesh'], hazard='TC', + year_range=[2000, 2017], target_version=2018) + self.assertIn('ISO', df.columns) + self.assertIn('Year', df.columns) + iso3 = list(df.ISO.unique()) + years = np.arange(df.Year.min(), df.Year.max() + 1) + + self.assertListEqual(['BGD'], iso3) self.assertEqual(18, len(years)) self.assertEqual(2017, years[-1]) self.assertEqual(2010, years[10]) - self.assertEqual(475, df.size) + self.assertEqual(450, df.size) self.assertEqual(8978541, df['Total affected'].max()) self.assertIn('Tropical cyclone', list(df['Disaster subtype'])) - self.assertFalse(False in list(df['Disaster subtype']=='Tropical cyclone')) + self.assertFalse(False in list(df['Disaster subtype'] == 'Tropical cyclone')) self.assertFalse('Flood' in list(df['Disaster subtype'])) - def test_emdat_impact_event(self): - """test emdat_impact_event event impact data extraction""" - df = im_d.emdat_impact_event(['Bangladesh', 'USA'], 'Drought', \ - EMDAT_TEST_CSV, [2015, 2017], \ - reference_year = 2017) - - self.assertEqual(92, df.size) - self.assertEqual('2017-9550', df['Disaster No.'][3]) - self.assertEqual(df["Total damage ('000 US$)"][1], \ - df["Total damage ('000 US$) scaled"][1]) - self.assertEqual(df["Total damage ('000 US$)"][1], 2500000000.0) - self.assertEqual(df["Total damage ('000 US$)"][0], 1800000000.0) - self.assertAlmostEqual(df["Total damage ('000 US$) scaled"][0], \ - 1925085683.1166406) + def test_emdat_df_2018_to_2020_load(self): + """load selected sub sample from CSV, return DataFrame + here: from 2018 EM-DAT version to 2020 target_version""" + df = im_d.clean_emdat_df(EMDAT_TEST_CSV, countries=['USA'], hazard='TC', + year_range=[2000, 2017], target_version=2020) + self.assertIn('ISO', df.columns) + self.assertIn('Year', df.columns) + iso3 = list(df.ISO.unique()) + years = np.arange(df.Year.min(), df.Year.max() + 1) + self.assertListEqual(['USA'], iso3) + self.assertEqual(18, len(years)) + self.assertEqual(2017, years[-1]) + self.assertEqual(2010, years[10]) + self.assertEqual(1634, df.size) + self.assertEqual(60000000, df["Insured Damages ('000 US$)"].max()) + self.assertIn('Tropical cyclone', list(df['Disaster Subtype'])) + self.assertFalse(False in list(df['Disaster Subtype'] == 'Tropical cyclone')) + self.assertFalse('Flood' in list(df['Disaster Subtype'])) + + def test_emdat_df_2020_load(self): + """load selected sub sample from CSV, return DataFrame + here: from 2020 EM-DAT version to 2020 target_version""" + df = im_d.clean_emdat_df(EMDAT_2020_CSV_DEMO, countries=['THA', 'Viet Nam'], hazard='TC', + year_range=[2005, 2008], target_version=2020) + self.assertIn('ISO', df.columns) + self.assertIn('Year', df.columns) + iso3 = list(df.ISO.unique()) + years = np.arange(df.Year.min(), df.Year.max() + 1) + self.assertIn('THA', iso3) + self.assertIn('VNM', iso3) + self.assertNotIn('USA', iso3) + self.assertNotIn('TWN', iso3) + self.assertEqual(4, len(years)) + self.assertEqual(2008, years[-1]) + self.assertEqual(2006, years[1]) + self.assertEqual(43, df.columns.size) + self.assertEqual(688, df.size) + self.assertEqual(624000, df["Total Damages ('000 US$)"].max()) + self.assertIn('Tropical cyclone', list(df['Disaster Subtype'])) + self.assertFalse(False in list(df['Disaster Subtype'] == 'Tropical cyclone')) + self.assertFalse('Flood' in list(df['Disaster Subtype'])) + +class TestGDPScaling(unittest.TestCase): + """test scaling of impact values proportional to GDP""" + def test_scale_impact2refyear(self): + """scale of impact values proportional to GDP""" + impact_scaled = im_d.scale_impact2refyear([10, 100, 1000, 100, 100], + [1999, 2005, 2015, 2000, 2000], + ['CZE', 'CZE', 'MEX', 'MEX', 'CZE'], + reference_year=2015) + self.assertListEqual(impact_scaled, [28, 137, 1000, 165, 303]) + +class TestEmdatProcessing(unittest.TestCase): + def test_emdat_impact_event_2018(self): + """test emdat_impact_event event impact data extraction, version 2018""" + df = im_d.emdat_impact_event(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'], + hazard='Drought', year_range=[2015, 2017], + reference_year=2017, version=2018) + + self.assertEqual(46, df.size) + self.assertEqual('2017-9550', df['Disaster No.'][1]) + self.assertEqual(df["Total damage ('000 US$)"][0], + df["impact"][0] * 1e-3) + self.assertEqual(df["impact_scaled"][1], + df["impact"][1]) + self.assertEqual(df["Total damage ('000 US$)"][1], 2500000) + self.assertEqual(df["Total damage ('000 US$)"][0], 1800000) + self.assertAlmostEqual(df["impact_scaled"][0] * 1e-5, + 1925085000. * 1e-5, places=0) self.assertIn('USA', list(df['ISO'])) self.assertIn('Drought', list(df['Disaster type'])) self.assertEqual(2017, df['reference_year'].min()) - def test_emdat_impact_yearlysum(self): + def test_emdat_impact_event_2020(self): + """test emdat_impact_event event impact data extraction, version 2020""" + df = im_d.emdat_impact_event(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'], + hazard='Drought', year_range=[2015, 2017], + reference_year=2000, version=2020) + + self.assertEqual(96, df.size) + self.assertEqual('2017-9550', df['Dis No'][1]) + self.assertEqual(df["Total Damages ('000 US$)"][0], + df["impact"][0] * 1e-3) + self.assertNotEqual(df["impact_scaled"][1], + df["impact"][1]) + self.assertEqual(df["Total Damages ('000 US$)"][1], 2500000) + self.assertEqual(df["Total Damages ('000 US$)"][0], 1800000) + self.assertAlmostEqual(df["impact_scaled"][0] * 1e-5, + 1012894000. * 1e-5, places=0) + self.assertIn('USA', list(df['ISO'])) + self.assertIn('Drought', list(df['Disaster Type'])) + self.assertEqual(2000, df['reference_year'].min()) + + def test_emdat_affected_yearlysum(self): """test emdat_impact_yearlysum yearly impact data extraction""" - df = im_d.emdat_impact_yearlysum(['Bangladesh', 'USA'], 'Flood', \ - EMDAT_TEST_CSV, [2015, 2017], \ - imp_str = 'Total affected') + df = im_d.emdat_impact_yearlysum(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'], + hazard='Flood', year_range=(2015, 2017), + reference_year=None, imp_str="Total Affected") + self.assertEqual(36, df.size) - self.assertEqual(df["impact"][1], 1900000) + self.assertEqual(df["impact"][1], 91000) self.assertEqual(df.impact.sum(), 11517946) self.assertEqual(df["year"][5], 2017) - self.assertIn('USA', list(df['ISO3'])) - self.assertIn('BGD', list(df['ISO3'])) - self.assertEqual(0, df['reference_year'].max()) + self.assertIn('USA', list(df['ISO'])) + self.assertIn('BGD', list(df['ISO'])) + + def test_emdat_damage_yearlysum(self): + """test emdat_impact_yearlysum yearly impact data extraction with scaling""" + df = im_d.emdat_impact_yearlysum(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'], + hazard='Flood', year_range=(2015, 2017), + reference_year=2000) + + self.assertEqual(36, df.size) + self.assertAlmostEqual(df.impact.max(), 15150000000.0) + self.assertEqual(df.impact_scaled.min(), 10943000.0) + self.assertEqual(df["year"][5], 2017) + self.assertEqual(df["reference_year"].max(), 2000) + self.assertIn('USA', list(df['ISO'])) + self.assertIn(50, list(df['region_id'])) + + def test_emdat_countries_by_hazard_2020_pass(self): + """test to get list of countries impacted by tropical cyclones from 2000 to 2019""" + iso3_codes, country_names = im_d.emdat_countries_by_hazard(EMDAT_2020_CSV_DEMO, + hazard='TC', + year_range=(2000, 2019)) + + self.assertIn('Réunion', country_names) + self.assertEqual('Sri Lanka', country_names[4]) + self.assertEqual('BLZ', iso3_codes[3]) + self.assertEqual(len(country_names), len(iso3_codes)) + self.assertEqual(100, len(iso3_codes)) class TestEmdatToImpact(unittest.TestCase): """Test import of EM-DAT data (as CSV) to Impact-instance (CLIMADA)""" - def test_emdat_to_impact_all_countries(self): - """test import TC EM-DAT to Impact() for all countries in CSV""" - impact_emdat, countries = im_d.emdat_to_impact(EMDAT_TEST_CSV, \ - hazard_type_climada='TC') + def test_emdat_to_impact_all_countries_pass(self): + """test import EM-DAT to Impact() for all countries in CSV""" + # ===================================================================== + # emdat_to_impact(emdat_file_csv, hazard_type_climada, \ + # year_range=None, countries=None, hazard_type_emdat=None, \ + # reference_year=None, imp_str="Total Damages ('000 US$)") + # ===================================================================== + + # file 1: version 2020 + impact_emdat2020, countries2020 = im_d.emdat_to_impact(EMDAT_2020_CSV_DEMO, 'TC') + # file 2: version 2018 + impact_emdat, countries = im_d.emdat_to_impact(EMDAT_TEST_CSV, 'TC') + self.assertEqual(142, impact_emdat.event_id.size) self.assertEqual(141, impact_emdat.event_id[-1]) self.assertEqual(0, impact_emdat.event_id[0]) self.assertIn('2013-0138', impact_emdat.event_name) - self.assertEqual('BGD', countries[0]) - self.assertEqual('USA', countries[1]) + self.assertEqual('USA', countries[0]) + self.assertEqual('BGD', countries[1]) self.assertEqual(len(countries), len(impact_emdat.eai_exp)) self.assertEqual(2, len(impact_emdat.eai_exp)) self.assertEqual(impact_emdat.date.size, impact_emdat.frequency.size) - self.assertAlmostEqual(555861710000, np.sum(impact_emdat.at_event)) - self.assertAlmostEqual(0.0208333333333, np.unique(impact_emdat.frequency)[0]) - self.assertAlmostEqual(11580452291.666666, impact_emdat.aai_agg) - self.assertAlmostEqual(109456249.99999999, impact_emdat.eai_exp[0]) - self.assertAlmostEqual(11470996041.666666, impact_emdat.eai_exp[1]) + self.assertAlmostEqual(555861710000 * 1e-5, np.sum(impact_emdat.at_event) * 1e-5, places=0) + self.assertAlmostEqual(0.0208333333333, np.unique(impact_emdat.frequency)[0], places=7) + self.assertAlmostEqual(11580452291.666666, impact_emdat.aai_agg, places=0) + self.assertAlmostEqual(109456249.99999999, impact_emdat.eai_exp[1], places=0) + self.assertAlmostEqual(11470996041.666666, impact_emdat.eai_exp[0], places=0) + self.assertIn('SPI', countries2020) + self.assertNotIn('SPI', countries) def test_emdat_to_impact_scale(self): - """test import DR EM-DAT to Impact() for 1 country and ref.year (scaling)""" - impact_emdat = im_d.emdat_to_impact(EMDAT_TEST_CSV, - year_range=[2010, 2016], countries=['USA'],\ - hazard_type_emdat='Drought', \ - reference_year=2016)[0] - self.assertEqual(10, impact_emdat.event_id.size) - self.assertEqual(9, impact_emdat.event_id[-1]) + """test import DR EM-DAT to Impact() for 1 country and ref.year (scaling)""" + impact_emdat = im_d.emdat_to_impact(EMDAT_TEST_CSV, 'DR', + year_range=[2010, 2016], countries=['USA'], + hazard_type_emdat='Drought', + reference_year=2016)[0] + self.assertEqual(5, impact_emdat.event_id.size) + self.assertEqual(4, impact_emdat.event_id[-1]) self.assertEqual(0, impact_emdat.event_id[0]) self.assertIn('2012-9235', impact_emdat.event_name) self.assertEqual(1, len(impact_emdat.eai_exp)) self.assertAlmostEqual(impact_emdat.aai_agg, impact_emdat.eai_exp[0]) - self.assertAlmostEqual(0.14285714, np.unique(impact_emdat.frequency)[0]) - self.assertAlmostEqual(73850951957.43886, np.sum(impact_emdat.at_event)) - self.assertAlmostEqual(10550135993.919838, impact_emdat.aai_agg) + self.assertAlmostEqual(0.14285714, np.unique(impact_emdat.frequency)[0], places=3) + self.assertAlmostEqual(36925473, np.sum(impact_emdat.at_event * 1e-3), places=0) + self.assertAlmostEqual(5275067.57, impact_emdat.aai_agg * 1e-3, places=0) def test_emdat_to_impact_fakedata(self): """test import TC EM-DAT to Impact() for all countries in CSV""" - impact_emdat, countries = im_d.emdat_to_impact(EMDAT_TEST_CSV_FAKE, \ - hazard_type_emdat='Flood') + impact_emdat, countries = im_d.emdat_to_impact(EMDAT_TEST_CSV_FAKE, 'FL', + hazard_type_emdat='Flood') self.assertEqual(6, impact_emdat.event_id.size) self.assertEqual(5, impact_emdat.event_id[-1]) self.assertEqual(0, impact_emdat.event_id[0]) self.assertIn('2008-0001', impact_emdat.event_name) - self.assertEqual('DEU', countries[0]) - self.assertEqual('CHE', countries[1]) + self.assertEqual('CHE', countries[0]) + self.assertEqual('DEU', countries[1]) self.assertEqual(len(countries), len(impact_emdat.eai_exp)) self.assertEqual(2, len(impact_emdat.eai_exp)) self.assertAlmostEqual(11000000.0, np.sum(impact_emdat.at_event)) self.assertAlmostEqual(0.2, np.unique(impact_emdat.frequency)[0]) self.assertAlmostEqual(2200000.0, impact_emdat.aai_agg) - self.assertAlmostEqual(200000.0, impact_emdat.eai_exp[0]) # DEU - self.assertAlmostEqual(2000000.0, impact_emdat.eai_exp[1]) # CHE + self.assertAlmostEqual(200000.0, impact_emdat.eai_exp[1]) # DEU + self.assertAlmostEqual(2000000.0, impact_emdat.eai_exp[0]) # CHE + + def test_emdat_to_impact_2020format(self): + """test import TC EM-DAT to Impact() from new 2020 EMDAT format CSV""" + df1 = im_d.clean_emdat_df(EMDAT_2020_CSV_DEMO, hazard='TC', + countries='PHL', year_range=(2013, 2013)) + df2 = im_d.emdat_impact_event(EMDAT_2020_CSV_DEMO, countries='PHL', hazard='TC', + year_range=(2013, 2013), reference_year=None, + imp_str='Total Affected') + impact_emdat, countries = im_d.emdat_to_impact(EMDAT_2020_CSV_DEMO, 'TC', + countries='PHL', + year_range=(2013, 2013), + imp_str="Total Affected") + # compare number of entries for all steps: + self.assertEqual(len(df1.index), len(df2.index)) + self.assertEqual(impact_emdat.event_id.size, len(df1.index)) + # TC events in EM-DAT in the Philipppines, 2013: + self.assertEqual(8, impact_emdat.event_id.size) + # People affected by TC events in the Philippines in 2013 (AAI): + self.assertAlmostEqual(17944571., impact_emdat.aai_agg, places=0) + # People affected by Typhoon Hayian in the Philippines: + self.assertAlmostEqual(1.610687e+07, impact_emdat.at_event[4], places=0) # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestEmdatImport) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestGDPScaling)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestEmdatProcessing)) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestEmdatToImpact)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/entity/disc_rates/base.py b/climada/entity/disc_rates/base.py index fb37c63bbb..75166c9757 100755 --- a/climada/entity/disc_rates/base.py +++ b/climada/entity/disc_rates/base.py @@ -38,18 +38,18 @@ DEF_VAR_MAT = {'sup_field_name': 'entity', 'field_name': 'discount', - 'var_name': {'year' : 'year', - 'disc' : 'discount_rate' + 'var_name': {'year': 'year', + 'disc': 'discount_rate' } } -""" MATLAB variable names """ +"""MATLAB variable names""" DEF_VAR_EXCEL = {'sheet_name': 'discount', - 'col_name': {'year' : 'year', - 'disc' : 'discount_rate' + 'col_name': {'year': 'year', + 'disc': 'discount_rate' } } -""" Excel variable names """ +"""Excel variable names""" class DiscRates(): """Defines discount rates and basic methods. Loads from @@ -155,14 +155,14 @@ def net_present_value(self, ini_year, end_year, val_years): Returns: float """ - year_range = np.arange(ini_year, end_year+1) + year_range = np.arange(ini_year, end_year + 1) if year_range.size != val_years.size: LOGGER.error('Wrong size of yearly values.') raise ValueError sel_disc = self.select(year_range) if sel_disc is None: - LOGGER.error('No information of discount rates for provided years:'\ - ' %s - %s', ini_year, end_year) + LOGGER.error('No information of discount rates for provided years:' + ' %s - %s', ini_year, end_year) raise ValueError return u_fin.net_present_value(sel_disc.years, sel_disc.rates, val_years) @@ -183,7 +183,7 @@ def plot(self, axis=None, **kwargs): axis.set_title('Discount rates') axis.set_xlabel('Year') axis.set_ylabel('discount rate (%)') - axis.plot(self.years, self.rates*100, **kwargs) + axis.plot(self.years, self.rates * 100, **kwargs) axis.set_xlim((self.years.min(), self.years.max())) return axis @@ -234,7 +234,7 @@ def read_excel(self, file_name, description='', var_names=DEF_VAR_EXCEL): raise err def write_excel(self, file_name, var_names=DEF_VAR_EXCEL): - """ Write excel file following template. + """Write excel file following template. Parameters: file_name (str): absolute file name to write diff --git a/climada/entity/disc_rates/test/test_base.py b/climada/entity/disc_rates/test/test_base.py index 263319c912..a752e815b4 100644 --- a/climada/entity/disc_rates/test/test_base.py +++ b/climada/entity/disc_rates/test/test_base.py @@ -35,7 +35,7 @@ class TestChecker(unittest.TestCase): def test_check_wrongRates_fail(self): """Wrong discount rates definition""" disc_rate = DiscRates() - disc_rate.rates = np.array([3,4]) + disc_rate.rates = np.array([3, 4]) disc_rate.years = np.array([1]) with self.assertLogs('climada.util.checker', level='ERROR') as cm: @@ -67,9 +67,9 @@ def test_append_to_empty_same(self): self.assertTrue(np.array_equal(disc_rate.years, disc_rate_add.years)) self.assertTrue(np.array_equal(disc_rate.rates, disc_rate_add.rates)) - self.assertTrue(np.array_equal(disc_rate.tag.file_name, \ + self.assertTrue(np.array_equal(disc_rate.tag.file_name, disc_rate_add.tag.file_name)) - self.assertTrue(np.array_equal(disc_rate.tag.description, \ + self.assertTrue(np.array_equal(disc_rate.tag.description, disc_rate_add.tag.description)) def test_append_equal_same(self): @@ -112,15 +112,15 @@ def test_append_different_append(self): disc_rate.append(disc_rate_add) disc_rate.check() - self.assertTrue(np.array_equal(disc_rate.years, \ + self.assertTrue(np.array_equal(disc_rate.years, np.array([2000, 2001, 2002, 2003]))) - self.assertTrue(np.array_equal(disc_rate.rates, \ + self.assertTrue(np.array_equal(disc_rate.rates, np.array([0.11, 0.22, 0.3, 0.33]))) self.assertTrue(np.array_equal(disc_rate.tag.file_name, 'file1.txt + file2.txt')) self.assertTrue(np.array_equal(disc_rate.tag.description, 'descr1 + descr2')) class TestSelect(unittest.TestCase): - """Test select method """ + """Test select method""" def test_select_pass(self): """Test select right time range.""" disc_rate = DiscRates() @@ -147,16 +147,16 @@ def test_select_wrong_pass(self): self.assertEqual(None, disc_rate.select(year_range)) class TestNetPresValue(unittest.TestCase): - """Test select method """ + """Test select method""" def test_net_present_value_pass(self): """Test net_present_value right time range.""" disc_rate = DiscRates() disc_rate.tag.file_name = 'file1.txt' disc_rate.tag.description = 'descr1' disc_rate.years = np.arange(2000, 2050) - disc_rate.rates = np.ones(disc_rate.years.size)*0.02 + disc_rate.rates = np.ones(disc_rate.years.size) * 0.02 - val_years = np.ones(23)*6.512201157564418e9 + val_years = np.ones(23) * 6.512201157564418e9 res = disc_rate.net_present_value(2018, 2040, val_years) self.assertEqual(res, 1.215049630691397e+11) @@ -167,15 +167,15 @@ def test_net_present_value_wrong_pass(self): disc_rate.tag.description = 'descr1' disc_rate.years = np.arange(2000, 2050) disc_rate.rates = np.arange(disc_rate.years.size) - val_years = np.ones(11)*6.512201157564418e9 + val_years = np.ones(11) * 6.512201157564418e9 with self.assertRaises(ValueError): disc_rate.net_present_value(2050, 2060, val_years) class TestReaderExcel(unittest.TestCase): """Test excel reader for discount rates""" - + def test_demo_file_pass(self): - """ Read demo excel file.""" + """Read demo excel file.""" disc_rate = DiscRates() description = 'One single file.' disc_rate.read_excel(ENT_DEMO_TODAY, description) @@ -186,7 +186,7 @@ def test_demo_file_pass(self): self.assertIn('int', str(disc_rate.years.dtype)) self.assertEqual(disc_rate.years.shape, (n_rates,)) self.assertEqual(disc_rate.years[0], 2000) - self.assertEqual(disc_rate.years[n_rates-1], 2050) + self.assertEqual(disc_rate.years[n_rates - 1], 2050) self.assertIn('float', str(disc_rate.rates.dtype)) self.assertEqual(disc_rate.rates.shape, (n_rates,)) @@ -197,7 +197,7 @@ def test_demo_file_pass(self): self.assertEqual(disc_rate.tag.description, description) def test_template_file_pass(self): - """ Read demo excel file.""" + """Read demo excel file.""" disc_rate = DiscRates() disc_rate.read_excel(ENT_TEMPLATE_XLS) @@ -207,7 +207,7 @@ def test_template_file_pass(self): self.assertIn('int', str(disc_rate.years.dtype)) self.assertEqual(disc_rate.years.shape, (n_rates,)) self.assertEqual(disc_rate.years[0], 2000) - self.assertEqual(disc_rate.years[n_rates-1], 2101) + self.assertEqual(disc_rate.years[n_rates - 1], 2101) self.assertIn('float', str(disc_rate.rates.dtype)) self.assertEqual(disc_rate.rates.shape, (n_rates,)) @@ -219,9 +219,9 @@ def test_template_file_pass(self): class TestReaderMat(unittest.TestCase): """Test mat reader for discount rates""" - + def test_demo_file_pass(self): - """ Read demo mat file""" + """Read demo mat file""" # Read demo excel file disc_rate = DiscRates() description = 'One single file.' @@ -233,7 +233,7 @@ def test_demo_file_pass(self): self.assertIn('int', str(disc_rate.years.dtype)) self.assertEqual(len(disc_rate.years), n_rates) self.assertEqual(disc_rate.years[0], 2000) - self.assertEqual(disc_rate.years[n_rates-1], 2050) + self.assertEqual(disc_rate.years[n_rates - 1], 2050) self.assertIn('float', str(disc_rate.rates.dtype)) self.assertEqual(len(disc_rate.rates), n_rates) @@ -246,16 +246,16 @@ def test_demo_file_pass(self): class TestWriter(unittest.TestCase): """Test excel reader for discount rates""" - + def test_write_read_pass(self): - """ Read demo excel file.""" + """Read demo excel file.""" disc_rate = DiscRates() disc_rate.years = np.arange(1950, 2150) - disc_rate.rates = np.ones(disc_rate.years.size)*0.03 + disc_rate.rates = np.ones(disc_rate.years.size) * 0.03 file_name = os.path.join(os.path.join(CURR_DIR, 'data'), 'test_disc.xlsx') disc_rate.write_excel(file_name) - + disc_read = DiscRates() disc_read.read_excel(file_name) diff --git a/climada/entity/entity_def.py b/climada/entity/entity_def.py index 43ae7f9ae9..890f12f017 100755 --- a/climada/entity/entity_def.py +++ b/climada/entity/entity_def.py @@ -25,7 +25,7 @@ import pandas as pd from climada.entity.tag import Tag -from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet from climada.entity.disc_rates.base import DiscRates from climada.entity.measures.measure_set import MeasureSet from climada.entity.exposures.base import Exposures @@ -45,7 +45,7 @@ class Entity(object): """ def __init__(self): - """ Empty initializator """ + """Empty initializator""" self.exposures = Exposures() self.disc_rates = DiscRates() self.impact_funcs = ImpactFuncSet() @@ -102,7 +102,7 @@ def read_excel(self, file_name, description=''): self.measures.read_excel(file_name, description) def write_excel(self, file_name): - """ Write excel file following template. """ + """Write excel file following template.""" self.exposures.to_excel(file_name) self.impact_funcs.write_excel(file_name) self.measures.write_excel(file_name) diff --git a/climada/entity/exposures/base.py b/climada/entity/exposures/base.py index d9844b68be..5e1f32b983 100644 --- a/climada/entity/exposures/base.py +++ b/climada/entity/exposures/base.py @@ -41,33 +41,33 @@ LOGGER = logging.getLogger(__name__) INDICATOR_IF = 'if_' -""" Name of the column containing the impact functions id of specified hazard""" +"""Name of the column containing the impact functions id of specified hazard""" INDICATOR_CENTR = 'centr_' -""" Name of the column containing the centroids id of specified hazard """ +"""Name of the column containing the centroids id of specified hazard""" DEF_REF_YEAR = 2018 -""" Default reference year """ +"""Default reference year""" DEF_VALUE_UNIT = 'USD' -""" Default reference year """ +"""Default reference year""" DEF_VAR_MAT = {'sup_field_name': 'entity', 'field_name': 'assets', - 'var_name': {'lat' : 'lat', - 'lon' : 'lon', - 'val' : 'Value', - 'ded' : 'Deductible', - 'cov' : 'Cover', - 'imp' : 'DamageFunID', - 'cat' : 'Category_ID', - 'reg' : 'Region_ID', - 'uni' : 'Value_unit', - 'ass' : 'centroid_index', - 'ref' : 'reference_year' + 'var_name': {'lat': 'lat', + 'lon': 'lon', + 'val': 'Value', + 'ded': 'Deductible', + 'cov': 'Cover', + 'imp': 'DamageFunID', + 'cat': 'Category_ID', + 'reg': 'Region_ID', + 'uni': 'Value_unit', + 'ass': 'centroid_index', + 'ref': 'reference_year' } } -""" MATLAB variable names """ +"""MATLAB variable names""" class Exposures(GeoDataFrame): """geopandas GeoDataFrame with metada and columns (pd.Series) defined in @@ -116,8 +116,8 @@ def _constructor(self): return Exposures def __init__(self, *args, **kwargs): - """ Initialize. Copy attributes of input DataFrame. """ - if len(args): + """Initialize. Copy attributes of input DataFrame.""" + if args: for var_meta in self._metadata: try: val_meta = getattr(args[0], var_meta) @@ -127,7 +127,7 @@ def __init__(self, *args, **kwargs): super(Exposures, self).__init__(*args, **kwargs) def check(self): - """ Check which variables are present """ + """Check which variables are present""" # check metadata for var in self._metadata: if var[0] == '_': @@ -148,7 +148,7 @@ def check(self): LOGGER.info('%s metadata set to default value: %s', var, self.__dict__[var]) for var in self.vars_oblig: - if not var in self.columns: + if var not in self.columns: LOGGER.error("%s missing.", var) raise ValueError @@ -175,15 +175,15 @@ def check(self): if not found: LOGGER.info("%s not set.", var) elif var == 'geometry' and \ - (self.geometry.values[0].x != self.longitude.values[0] or \ - self.geometry.values[0].y != self.latitude.values[0]): - LOGGER.error('Geometry values do not correspond to latitude ' +\ - 'and longitude. Use set_geometry_points() or set_lat_lon().') + (self.geometry.values[0].x != self.longitude.values[0] or + self.geometry.values[0].y != self.latitude.values[0]): + LOGGER.error(("Geometry values do not correspond to latitude and " + "longitude. Use set_geometry_points() or set_lat_lon().")) raise ValueError def assign_centroids(self, hazard, method='NN', distance='haversine', threshold=100): - """ Assign for each exposure coordinate closest hazard coordinate. + """Assign for each exposure coordinate closest hazard coordinate. -1 used for disatances > threshold in point distances. If raster hazard, -1 used for centroids outside raster. @@ -203,11 +203,11 @@ def assign_centroids(self, hazard, method='NN', distance='haversine', LOGGER.error('Set hazard and exposure to same CRS first!') raise ValueError if hazard.centroids.meta: - x_i = ((self.longitude.values - hazard.centroids.meta['transform'][2]) \ - /hazard.centroids.meta['transform'][0]).astype(int) - y_i = ((self.latitude.values - hazard.centroids.meta['transform'][5]) \ - /hazard.centroids.meta['transform'][4]).astype(int) - assigned = y_i*hazard.centroids.meta['width'] + x_i + x_i = ((self.longitude.values - hazard.centroids.meta['transform'][2]) + / hazard.centroids.meta['transform'][0]).astype(int) + y_i = ((self.latitude.values - hazard.centroids.meta['transform'][5]) + / hazard.centroids.meta['transform'][4]).astype(int) + assigned = y_i * hazard.centroids.meta['width'] + x_i assigned[assigned < 0] = -1 assigned[assigned >= hazard.centroids.size] = -1 else: @@ -215,23 +215,24 @@ def assign_centroids(self, hazard, method='NN', distance='haversine', if np.array_equal(coord, hazard.centroids.coord): assigned = np.arange(self.shape[0]) else: - assigned = interpol_index(hazard.centroids.coord, coord, \ - method=method, distance=distance, threshold=threshold) + assigned = interpol_index(hazard.centroids.coord, coord, + method=method, distance=distance, + threshold=threshold) self[INDICATOR_CENTR + hazard.tag.haz_type] = assigned def set_geometry_points(self, scheduler=None): - """ Set geometry attribute of GeoDataFrame with Points from latitude and + """Set geometry attribute of GeoDataFrame with Points from latitude and longitude attributes. - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” """ co.set_df_geometry_points(self, scheduler) def set_lat_lon(self): - """ Set latitude and longitude attributes from geometry attribute. """ + """Set latitude and longitude attributes from geometry attribute.""" LOGGER.info('Setting latitude and longitude attributes.') self['latitude'] = self.geometry[:].y self['longitude'] = self.geometry[:].x @@ -239,7 +240,7 @@ def set_lat_lon(self): def set_from_raster(self, file_name, band=1, src_crs=None, window=False, geometry=False, dst_crs=False, transform=None, width=None, height=None, resampling=Resampling.nearest): - """ Read raster data and set latitude, longitude, value and meta + """Read raster data and set latitude, longitude, value and meta Parameters: file_name (str): file name containing values @@ -264,8 +265,8 @@ def set_from_raster(self, file_name, band=1, src_crs=None, window=False, ulx, xres, _, uly, _, yres = meta['transform'].to_gdal() lrx = ulx + meta['width'] * xres lry = uly + meta['height'] * yres - x_grid, y_grid = np.meshgrid(np.arange(ulx+xres/2, lrx, xres), - np.arange(uly+yres/2, lry, yres)) + x_grid, y_grid = np.meshgrid(np.arange(ulx + xres / 2, lrx, xres), + np.arange(uly + yres / 2, lry, yres)) try: self.crs = meta['crs'].to_dict() except AttributeError: @@ -306,8 +307,9 @@ def plot_scatter(self, mask=None, ignore_zero=False, pop_name=True, value = self.value[mask][pos_vals].values coord = np.stack([self.latitude[mask][pos_vals].values, self.longitude[mask][pos_vals].values], axis=1) - return u_plot.geo_scatter_from_array(value, coord, cbar_label, title, \ - pop_name, buffer, extend, proj=crs_epsg, axes=axis, **kwargs) + return u_plot.geo_scatter_from_array(value, coord, cbar_label, title, + pop_name, buffer, extend, proj=crs_epsg, + axes=axis, **kwargs) def plot_hexbin(self, mask=None, ignore_zero=False, pop_name=True, buffer=0.0, extend='neither', axis=None, **kwargs): @@ -343,13 +345,14 @@ def plot_hexbin(self, mask=None, ignore_zero=False, pop_name=True, value = self.value[mask][pos_vals].values coord = np.stack([self.latitude[mask][pos_vals].values, self.longitude[mask][pos_vals].values], axis=1) - return u_plot.geo_bin_from_array(value, coord, cbar_label, title, \ - pop_name, buffer, extend, proj=crs_epsg, axes=axis, **kwargs) + return u_plot.geo_bin_from_array(value, coord, cbar_label, title, + pop_name, buffer, extend, proj=crs_epsg, + axes=axis, **kwargs) def plot_raster(self, res=None, raster_res=None, save_tiff=None, - raster_f=lambda x: np.log10((np.fmax(x+1, 1))), + raster_f=lambda x: np.log10((np.fmax(x + 1, 1))), label='value (log10)', scheduler=None, axis=None, **kwargs): - """ Generate raster from points geometry and plot it using log10 scale: + """Generate raster from points geometry and plot it using log10 scale: np.log10((np.fmax(raster+1, 1))). Parameters: @@ -369,7 +372,7 @@ def plot_raster(self, res=None, raster_res=None, save_tiff=None, Returns: matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot """ - if self.meta and self.meta['height']*self.meta['width'] == len(self): + if self.meta and self.meta['height'] * self.meta['width'] == len(self): raster = self.value.values.reshape((self.meta['height'], self.meta['width'])) # check raster starts by upper left corner @@ -384,9 +387,9 @@ def plot_raster(self, res=None, raster_res=None, save_tiff=None, raster = raster.reshape((meta['height'], meta['width'])) # save tiff if save_tiff is not None: - ras_tiff = rasterio.open(save_tiff, 'w', driver='GTiff', \ - height=meta['height'], width=meta['width'], count=1, \ - dtype=np.float32, crs=self.crs, transform=meta['transform']) + ras_tiff = rasterio.open(save_tiff, 'w', driver='GTiff', + height=meta['height'], width=meta['width'], count=1, + dtype=np.float32, crs=self.crs, transform=meta['transform']) ras_tiff.write(raster.astype(np.float32), 1) ras_tiff.close() # make plot @@ -395,11 +398,12 @@ def plot_raster(self, res=None, raster_res=None, save_tiff=None, self.longitude.max(), self.latitude.max() if not axis: _, axis = u_plot.make_map(proj=crs_epsg) - cbar_ax = make_axes_locatable(axis).append_axes('right', size="6.5%", \ - pad=0.1, axes_class=plt.Axes) - axis.set_extent([max(xmin, crs_epsg.x_limits[0]), \ - min(xmax, crs_epsg.x_limits[1]), max(ymin, crs_epsg.y_limits[0]), \ - min(ymax, crs_epsg.y_limits[1])], crs_epsg) + cbar_ax = make_axes_locatable(axis).append_axes('right', size="6.5%", + pad=0.1, axes_class=plt.Axes) + axis.set_extent([max(xmin, crs_epsg.x_limits[0]), + min(xmax, crs_epsg.x_limits[1]), + max(ymin, crs_epsg.y_limits[0]), + min(ymax, crs_epsg.y_limits[1])], crs_epsg) u_plot.add_shapes(axis) imag = axis.imshow(raster_f(raster), **kwargs, origin='upper', extent=[xmin, xmax, ymin, ymax], transform=crs_epsg) @@ -411,7 +415,7 @@ def plot_basemap(self, mask=None, ignore_zero=False, pop_name=True, buffer=0.0, extend='neither', zoom=10, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png', axis=None, **kwargs): - """ Scatter points over satellite image using contextily + """Scatter points over satellite image using contextily Parameters: mask (np.array, optional): mask to apply to eai_exp plotted. Same @@ -443,11 +447,18 @@ def plot_basemap(self, mask=None, ignore_zero=False, pop_name=True, return axis def write_hdf5(self, file_name): - """ Write data frame and metadata in hdf5 format """ + """Write data frame and metadata in hdf5 format + + Parameters: + file_name (str): (path and) file name to write to. + """ LOGGER.info('Writting %s', file_name) store = pd.HDFStore(file_name) - store.put('exposures', pd.DataFrame(self)) - + pandas_df = pd.DataFrame(self) + for col in pandas_df.columns: + if str(pandas_df[col].dtype) == "geometry": + pandas_df[col] = np.asarray(self[col]) + store.put('exposures', pandas_df) var_meta = {} for var in self._metadata: var_meta[var] = getattr(self, var) @@ -456,7 +467,15 @@ def write_hdf5(self, file_name): store.close() def read_hdf5(self, file_name): - """ Read data frame and metadata in hdf5 format """ + """Read data frame and metadata in hdf5 format + + Parameters: + file_name (str): (path and) file name to read from. + + Optional Parameters: + additional_vars (list): list of additional variable names to read that + are not in exposures.base._metadata + """ LOGGER.info('Reading %s', file_name) with pd.HDFStore(file_name) as store: self.__init__(store['exposures']) @@ -464,7 +483,7 @@ def read_hdf5(self, file_name): for key, val in metadata.items(): setattr(self, key, val) - def read_mat(self, file_name, var_names=DEF_VAR_MAT): + def read_mat(self, file_name, var_names=None): """Read MATLAB file and store variables in exposures. Parameters: @@ -473,7 +492,7 @@ def read_mat(self, file_name, var_names=DEF_VAR_MAT): MATLAB variables. Default: DEF_VAR_MAT. """ LOGGER.info('Reading %s', file_name) - if var_names is None: + if not var_names: var_names = DEF_VAR_MAT data = hdf5.read(file_name) @@ -511,7 +530,7 @@ def to_crs(self, crs=None, epsg=None, inplace=False): to_crs.__doc__ = GeoDataFrame.to_crs.__doc__ def copy(self, deep=True): - """ Make a copy of this Exposures object. + """Make a copy of this Exposures object. Parameters ---------- @@ -528,12 +547,12 @@ def copy(self, deep=True): return Exposures(data).__finalize__(self) def write_raster(self, file_name, value_name='value', scheduler=None): - """ Write value data into raster file with GeoTiff format + """Write value data into raster file with GeoTiff format Parameters: file_name (str): name output file in tif format """ - if self.meta and self.meta['height']*self.meta['width'] == len(self): + if self.meta and self.meta['height'] * self.meta['width'] == len(self): raster = self[value_name].values.reshape((self.meta['height'], self.meta['width'])) # check raster starts by upper left corner @@ -548,7 +567,7 @@ def write_raster(self, file_name, value_name='value', scheduler=None): co.write_raster(file_name, raster, meta) def add_sea(exposures, sea_res): - """ Add sea to geometry's surroundings with given resolution. region_id + """Add sea to geometry's surroundings with given resolution. region_id set to -1 and other variables to 0. Parameters: @@ -562,19 +581,19 @@ def add_sea(exposures, sea_res): LOGGER.info("Adding sea at %s km resolution and %s km distance from coast.", str(sea_res[1]), str(sea_res[0])) - sea_res = (sea_res[0]/ONE_LAT_KM, sea_res[1]/ONE_LAT_KM) + sea_res = (sea_res[0] / ONE_LAT_KM, sea_res[1] / ONE_LAT_KM) min_lat = max(-90, float(exposures.latitude.min()) - sea_res[0]) max_lat = min(90, float(exposures.latitude.max()) + sea_res[0]) min_lon = max(-180, float(exposures.longitude.min()) - sea_res[0]) max_lon = min(180, float(exposures.longitude.max()) + sea_res[0]) - lat_arr = np.arange(min_lat, max_lat+sea_res[1], sea_res[1]) - lon_arr = np.arange(min_lon, max_lon+sea_res[1], sea_res[1]) + lat_arr = np.arange(min_lat, max_lat + sea_res[1], sea_res[1]) + lon_arr = np.arange(min_lon, max_lon + sea_res[1], sea_res[1]) lon_mgrid, lat_mgrid = np.meshgrid(lon_arr, lat_arr) lon_mgrid, lat_mgrid = lon_mgrid.ravel(), lat_mgrid.ravel() - on_land = np.logical_not(co.coord_on_land(lat_mgrid, lon_mgrid)) + on_land = ~co.coord_on_land(lat_mgrid, lon_mgrid) sea_exp = Exposures() sea_exp['latitude'] = lat_mgrid[on_land] @@ -598,7 +617,7 @@ def _read_mat_obligatory(exposures, data, var_names): exposures['latitude'] = data[var_names['var_name']['lat']].reshape(-1) exposures['longitude'] = data[var_names['var_name']['lon']].reshape(-1) - exposures[INDICATOR_IF] = np.squeeze( \ + exposures[INDICATOR_IF] = np.squeeze( data[var_names['var_name']['imp']]).astype(int, copy=False) def _read_mat_optional(exposures, data, var_names): @@ -633,15 +652,15 @@ def _read_mat_optional(exposures, data, var_names): pass def _read_mat_metadata(exposures, data, file_name, var_names): - """ Fille metadata in DataFrame object """ + """Fille metadata in DataFrame object""" try: exposures.ref_year = int(np.squeeze(data[var_names['var_name']['ref']])) except KeyError: exposures.ref_year = DEF_REF_YEAR try: - exposures.value_unit = hdf5.get_str_from_ref(file_name, \ - data[var_names['var_name']['uni']][0][0]) + exposures.value_unit = hdf5.get_str_from_ref(file_name, + data[var_names['var_name']['uni']][0][0]) except KeyError: exposures.value_unit = DEF_VALUE_UNIT diff --git a/climada/entity/exposures/black_marble.py b/climada/entity/exposures/black_marble.py index 2e2582c502..b42a18087c 100644 --- a/climada/entity/exposures/black_marble.py +++ b/climada/entity/exposures/black_marble.py @@ -42,16 +42,16 @@ LOGGER = logging.getLogger(__name__) DEF_RES_NOAA_KM = 1 -""" Default approximate resolution for NOAA NGDC nightlights in km.""" +"""Default approximate resolution for NOAA NGDC nightlights in km.""" DEF_RES_NASA_KM = 0.5 -""" Default approximate resolution for NASA's nightlights in km.""" +"""Default approximate resolution for NASA's nightlights in km.""" DEF_HAZ_TYPE = 'TC' -""" Default hazard type used in impact functions id. """ +"""Default hazard type used in impact functions id.""" DEF_POLY_VAL = [0, 0, 1] -""" Default polynomial transformation used. """ +"""Default polynomial transformation used.""" class BlackMarble(Exposures): """Defines exposures from night light intensity, GDP and income group. @@ -66,33 +66,38 @@ def _constructor(self): return BlackMarble def set_countries(self, countries, ref_year=2016, res_km=None, from_hr=None, - **kwargs): + admin_file='admin_0_countries', **kwargs): """ Model countries using values at reference year. If GDP or income group not available for that year, consider the value of the closest available year. Parameters: - countries (list or dict): list of country names (admin0) or dict - with key = admin0 name and value = [admin1 names] + countries (list or dict): list of country names (admin0 or subunits) + or dict with key = admin0 name and value = [admin1 names] ref_year (int, optional): reference year. Default: 2016 res_km (float, optional): approx resolution in km. Default: nightlights resolution. from_hr (bool, optional): force to use higher resolution image, independently of its year of acquisition. + admin_file (str): file name, admin_0_countries or admin_0_map_subunits kwargs (optional): 'gdp' and 'inc_grp' dictionaries with keys the country ISO_alpha3 code. 'poly_val' polynomial transformation [1,x,x^2,...] to apply to nightlight (DEF_POLY_VAL used if not provided). If provided, these are used. """ + admin_key_dict = {'admin_0_countries': ['ADMIN', 'ADM0_A3'], + 'admin_0_map_subunits': ['SUBUNIT', 'SU_A3']} + shp_file = shapereader.natural_earth(resolution='10m', category='cultural', - name='admin_0_countries') + name=admin_file) shp_file = shapereader.Reader(shp_file) - cntry_info, cntry_admin1 = country_iso_geom(countries, shp_file) + cntry_info, cntry_admin1 = country_iso_geom(countries, shp_file, + admin_key_dict[admin_file]) fill_econ_indicators(ref_year, cntry_info, shp_file, **kwargs) - nightlight, coord_nl, fn_nl, res_fact, res_km = get_nightlight(\ + nightlight, coord_nl, fn_nl, res_fact, res_km = get_nightlight( ref_year, cntry_info, res_km, from_hr) tag = Tag() @@ -100,28 +105,30 @@ def set_countries(self, countries, ref_year=2016, res_km=None, from_hr=None, for cntry_iso, cntry_val in cntry_info.items(): - bkmrbl_list.append(self._set_one_country(cntry_val, nightlight, \ - coord_nl, res_fact, res_km, cntry_admin1[cntry_iso], **kwargs)) + bkmrbl_list.append( + self._set_one_country(cntry_val, nightlight, coord_nl, res_fact, res_km, + cntry_admin1[cntry_iso], **kwargs)) tag.description += ("{} {:d} GDP: {:.3e} income group: {:d} \n").\ format(cntry_val[1], cntry_val[3], cntry_val[4], cntry_val[5]) - Exposures.__init__(self, gpd.GeoDataFrame(pd.concat(bkmrbl_list, \ - ignore_index=True)), crs=DEF_CRS) + Exposures.__init__(self, gpd.GeoDataFrame( + pd.concat(bkmrbl_list, ignore_index=True)), crs=DEF_CRS) # set metadata self.ref_year = ref_year self.tag = tag self.tag.file_name = fn_nl self.value_unit = 'USD' - rows, cols, ras_trans = pts_to_raster_meta((self.longitude.min(), \ - self.latitude.min(), self.longitude.max(), self.latitude.max()), \ - coord_nl[0, 1]) - self.meta = {'width':cols, 'height':rows, 'crs':self.crs, 'transform':ras_trans} + rows, cols, ras_trans = pts_to_raster_meta( + (self.longitude.min(), self.latitude.min(), + self.longitude.max(), self.latitude.max()), + (coord_nl[0, 1], -coord_nl[0, 1])) + self.meta = {'width': cols, 'height': rows, 'crs': self.crs, 'transform': ras_trans} @staticmethod def _set_one_country(cntry_info, nightlight, coord_nl, res_fact, res_km, admin1_geom, **kwargs): - """ Model one country. + """Model one country. Parameters: cntry_info (lsit): [cntry_id, cnytry_name, cntry_geometry, @@ -144,47 +151,53 @@ def _set_one_country(cntry_info, nightlight, coord_nl, res_fact, poly_val = DEF_POLY_VAL geom = cntry_info[2] - nightlight_reg, lat_reg, lon_reg, on_land = _cut_country(geom, \ - nightlight, coord_nl) - nightlight_reg = _set_econ_indicators(nightlight_reg, cntry_info[4], \ + nightlight_reg, lat_reg, lon_reg, on_land = _cut_country(geom, nightlight, coord_nl) + nightlight_reg = _set_econ_indicators(nightlight_reg, cntry_info[4], cntry_info[5], poly_val) if admin1_geom: - nightlight_reg, lat_reg, lon_reg, geom, on_land = _cut_admin1( \ + nightlight_reg, lat_reg, lon_reg, geom, on_land = _cut_admin1( nightlight_reg, lat_reg, lon_reg, admin1_geom, coord_nl, on_land) LOGGER.info('Generating resolution of approx %s km.', res_km) - nightlight_reg, lat_reg, lon_reg = _resample_land(geom, nightlight_reg,\ - lat_reg, lon_reg, res_fact, on_land) + nightlight_reg, lat_reg, lon_reg = _resample_land(geom, nightlight_reg, + lat_reg, lon_reg, res_fact, on_land) exp_bkmrb = BlackMarble() exp_bkmrb['value'] = np.asarray(nightlight_reg).reshape(-1,) exp_bkmrb['latitude'] = lat_reg exp_bkmrb['longitude'] = lon_reg - exp_bkmrb['region_id'] = np.ones(exp_bkmrb.value.size, int)*cntry_info[0] + exp_bkmrb['region_id'] = np.ones(exp_bkmrb.value.size, int) * cntry_info[0] exp_bkmrb[INDICATOR_IF] = np.ones(exp_bkmrb.value.size, int) return exp_bkmrb -def country_iso_geom(countries, shp_file): +def country_iso_geom(countries, shp_file, admin_key=['ADMIN', 'ADM0_A3']): """ Get country ISO alpha_3, country id (defined as the United Nations Statistics Division (UNSD) 3-digit equivalent numeric codes and 0 if country not found) and country's geometry shape. - Parameters: - countries (list or dict): list of country names (admin0) or dict - with key = admin0 name and value = [admin1 names] - shp_file (cartopy.io.shapereader.Reader): shape file + Parameters + ---------- + countries : list or dict + list of country names (admin0) or dict with key = admin0 name + and value = [admin1 names] + shp_file : cartopy.io.shapereader.Reader + shape file + admin_key: str + key to find admin0 or subunit name + + Returns + ------- + cntry_info : dict + key = ISO alpha_3 country, value = [country id, country name, country geometry], + cntry_admin1 : dict + key = ISO alpha_3 country, value = [admin1 geometries] - Returns: - cntry_info (dict): key = ISO alpha_3 country, value = [country id, - country name, country geometry], - cntry_admin1 (dict): key = ISO alpha_3 country, value = [admin1 - geometries] """ countries_shp = {} list_records = list(shp_file.records()) for info_idx, info in enumerate(list_records): - countries_shp[info.attributes['ADMIN'].title()] = info_idx + countries_shp[info.attributes[admin_key[0]].title()] = info_idx cntry_info = dict() cntry_admin1 = dict() @@ -208,7 +221,7 @@ def country_iso_geom(countries, shp_file): LOGGER.error('Country %s not found. Possible options: %s', country_name, options) raise ValueError - iso3 = list_records[country_idx].attributes['ADM0_A3'] + iso3 = list_records[country_idx].attributes[admin_key[1]] try: cntry_id = int(iso_cntry.get(iso3).numeric) except KeyError: @@ -220,7 +233,7 @@ def country_iso_geom(countries, shp_file): return cntry_info, cntry_admin1 def fill_econ_indicators(ref_year, cntry_info, shp_file, **kwargs): - """ Get GDP and income group per country in reference year, or it closest + """Get GDP and income group per country in reference year, or it closest one. Source: world bank. Natural earth repository used when missing data. Modifies country info with values [country id, country name, country geometry, ref_year, gdp, income_group]. @@ -247,7 +260,7 @@ def fill_econ_indicators(ref_year, cntry_info, shp_file, **kwargs): cntry_val.append(inc_grp) def get_nightlight(ref_year, cntry_info, res_km=None, from_hr=None): - """ Obtain nightlight from different sources depending on reference year. + """Obtain nightlight from different sources depending on reference year. Compute resolution factor used at resampling depending on source. Parameters: @@ -275,17 +288,17 @@ def get_nightlight(ref_year, cntry_info, res_km=None, from_hr=None): nl_year = 2012 LOGGER.info("Nightlights from NASA's earth observatory for year %s.", str(nl_year)) - res_fact = DEF_RES_NASA_KM/res_km + res_fact = DEF_RES_NASA_KM / res_km geom = [info[2] for info in cntry_info.values()] geom = shapely.ops.cascaded_union(geom) req_files = nl_utils.check_required_nl_files(geom.bounds) - files_exist, _ = nl_utils.check_nl_local_file_exists(req_files, \ - SYSTEM_DIR, nl_year) + files_exist, _ = nl_utils.check_nl_local_file_exists(req_files, + SYSTEM_DIR, nl_year) nl_utils.download_nl_files(req_files, files_exist, SYSTEM_DIR, nl_year) # nightlight intensity with 15 arcsec resolution - nightlight, coord_nl = nl_utils.load_nightlight_nasa(geom.bounds, \ - req_files, nl_year) - fn_nl = [file.replace('*', str(nl_year)) for idx, file \ + nightlight, coord_nl = nl_utils.load_nightlight_nasa(geom.bounds, + req_files, nl_year) + fn_nl = [file.replace('*', str(nl_year)) for idx, file in enumerate(nl_utils.BM_FILENAMES) if req_files[idx]] fn_nl = ' + '.join(fn_nl) else: @@ -298,7 +311,7 @@ def get_nightlight(ref_year, cntry_info, res_km=None, from_hr=None): nl_year = 2013 LOGGER.info("Nightlights from NOAA's earth observation group for year %s.", str(nl_year)) - res_fact = DEF_RES_NOAA_KM/res_km + res_fact = DEF_RES_NOAA_KM / res_km # nightlight intensity with 30 arcsec resolution nightlight, coord_nl, fn_nl = nl_utils.load_nightlight_noaa(nl_year) @@ -325,7 +338,7 @@ def _fill_admin1_geom(iso3, admin1_rec, prov_list): prov_geom.append(rec.geometry) break if not found: - options = [rec.attributes['name'] for rec in admin1_rec \ + options = [rec.attributes['name'] for rec in admin1_rec if rec.attributes['adm0_a3'] == iso3] LOGGER.error('%s not found. Possible provinces of %s are: %s', prov, iso3, options) @@ -356,24 +369,22 @@ def _cut_admin1(nightlight, lat, lon, admin1_geom, coord_nl, on_land): """ all_geom = shapely.ops.cascaded_union(admin1_geom) - in_lat = math.floor((all_geom.bounds[1] - lat[0, 0])/coord_nl[0, 1]), \ - math.ceil((all_geom.bounds[3] - lat[0, 0])/coord_nl[0, 1]) - in_lon = math.floor((all_geom.bounds[0] - lon[0, 0])/coord_nl[1, 1]), \ - math.ceil((all_geom.bounds[2] - lon[0, 0])/coord_nl[1, 1]) + in_lat = (math.floor((all_geom.bounds[1] - lat[0, 0]) / coord_nl[0, 1]), + math.ceil((all_geom.bounds[3] - lat[0, 0]) / coord_nl[0, 1])) + in_lon = (math.floor((all_geom.bounds[0] - lon[0, 0]) / coord_nl[1, 1]), + math.ceil((all_geom.bounds[2] - lon[0, 0]) / coord_nl[1, 1])) - nightlight_reg = nightlight[in_lat[0]:in_lat[-1]+1, :] \ - [:, in_lon[0]:in_lon[-1]+1] + nightlight_reg = nightlight[in_lat[0]:in_lat[-1] + 1, :][:, in_lon[0]:in_lon[-1] + 1] nightlight_reg[nightlight_reg < 0.0] = 0.0 - lat_reg, lon_reg = np.mgrid[lat[0, 0] + in_lat[0]*coord_nl[0, 1]: - lat[0, 0] + in_lat[1]*coord_nl[0, 1]: + lat_reg, lon_reg = np.mgrid[lat[0, 0] + in_lat[0] * coord_nl[0, 1]: + lat[0, 0] + in_lat[1] * coord_nl[0, 1]: complex(0, nightlight_reg.shape[0]), - lon[0, 0] + in_lon[0]*coord_nl[1, 1]: - lon[0, 0] + in_lon[1]*coord_nl[1, 1]: + lon[0, 0] + in_lon[0] * coord_nl[1, 1]: + lon[0, 0] + in_lon[1] * coord_nl[1, 1]: complex(0, nightlight_reg.shape[1])] - on_land_reg = on_land[in_lat[0]:in_lat[-1]+1, :] \ - [:, in_lon[0]:in_lon[-1]+1] + on_land_reg = on_land[in_lat[0]:in_lat[-1] + 1, :][:, in_lon[0]:in_lon[-1] + 1] return nightlight_reg, lat_reg, lon_reg, all_geom, on_land_reg @@ -392,18 +403,18 @@ def _cut_country(geom, nightlight, coord_nl): on_land_reg (2d array of same size as previous with True values on land points) """ - in_lat = math.floor((geom.bounds[1] - coord_nl[0, 0])/coord_nl[0, 1]), \ - math.ceil((geom.bounds[3] - coord_nl[0, 0])/coord_nl[0, 1]) - in_lon = math.floor((geom.bounds[0] - coord_nl[1, 0])/coord_nl[1, 1]), \ - math.ceil((geom.bounds[2] - coord_nl[1, 0])/coord_nl[1, 1]) - - nightlight_reg = nightlight[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[-1]+1].\ - todense() - lat_reg, lon_reg = np.mgrid[coord_nl[0, 0] + in_lat[0]*coord_nl[0, 1]: - coord_nl[0, 0] + in_lat[1]*coord_nl[0, 1]: + in_lat = (math.floor((geom.bounds[1] - coord_nl[0, 0]) / coord_nl[0, 1]), + math.ceil((geom.bounds[3] - coord_nl[0, 0]) / coord_nl[0, 1])) + in_lon = (math.floor((geom.bounds[0] - coord_nl[1, 0]) / coord_nl[1, 1]), + math.ceil((geom.bounds[2] - coord_nl[1, 0]) / coord_nl[1, 1])) + + nightlight_reg = nightlight[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[-1] + 1] \ + .toarray() + lat_reg, lon_reg = np.mgrid[coord_nl[0, 0] + in_lat[0] * coord_nl[0, 1]: + coord_nl[0, 0] + in_lat[1] * coord_nl[0, 1]: complex(0, nightlight_reg.shape[0]), - coord_nl[1, 0] + in_lon[0]*coord_nl[1, 1]: - coord_nl[1, 0] + in_lon[1]*coord_nl[1, 1]: + coord_nl[1, 0] + in_lon[0] * coord_nl[1, 1]: + coord_nl[1, 0] + in_lon[1] * coord_nl[1, 1]: complex(0, nightlight_reg.shape[1])] on_land_reg = np.zeros(lat_reg.shape, bool) @@ -412,19 +423,18 @@ def _cut_country(geom, nightlight, coord_nl): except TypeError: geom = [geom] for poly in geom: - in_lat = math.floor((poly.bounds[1] - lat_reg[0, 0])/coord_nl[0, 1]), \ - math.ceil((poly.bounds[3] - lat_reg[0, 0])/coord_nl[0, 1]) - in_lon = math.floor((poly.bounds[0] - lon_reg[0, 0])/coord_nl[1, 1]), \ - math.ceil((poly.bounds[2] - lon_reg[0, 0])/coord_nl[1, 1]) - on_land_reg[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[1]+1] = \ - np.logical_or( \ - on_land_reg[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[1]+1], \ - shapely.vectorized.contains(poly, \ - lon_reg[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[1]+1], \ - lat_reg[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[1]+1])) + in_lat = (math.floor((poly.bounds[1] - lat_reg[0, 0]) / coord_nl[0, 1]), + math.ceil((poly.bounds[3] - lat_reg[0, 0]) / coord_nl[0, 1])) + in_lon = (math.floor((poly.bounds[0] - lon_reg[0, 0]) / coord_nl[1, 1]), + math.ceil((poly.bounds[2] - lon_reg[0, 0]) / coord_nl[1, 1])) + on_land_reg[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[1] + 1] = ( + on_land_reg[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[1] + 1] + | shapely.vectorized.contains( + poly, lon_reg[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[1] + 1], + lat_reg[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[1] + 1])) # put zero values outside country - nightlight_reg[np.logical_not(on_land_reg)] = 0.0 + nightlight_reg[~on_land_reg] = 0.0 return nightlight_reg, lat_reg, lon_reg, on_land_reg @@ -452,13 +462,13 @@ def _resample_land(geom, nightlight, lat, lon, res_fact, on_land): nightlight_res[nightlight_res < 0.0] = 0.0 lat_res, lon_res = np.mgrid[ - lat[0, 0] : lat[-1, 0] : complex(0, nightlight_res.shape[0]), - lon[0, 0] : lon[0, -1] : complex(0, nightlight_res.shape[1])] + lat[0, 0]: lat[-1, 0]: complex(0, nightlight_res.shape[0]), + lon[0, 0]: lon[0, -1]: complex(0, nightlight_res.shape[1])] on_land = shapely.vectorized.contains(geom, lon_res, lat_res) - nightlight_res[np.logical_not(on_land)] = 0.0 - nightlight_res = nightlight_res/nightlight_res.sum()*sum_val + nightlight_res[~on_land] = 0.0 + nightlight_res = nightlight_res / nightlight_res.sum() * sum_val return nightlight_res[on_land].ravel(), lat_res[on_land], lon_res[on_land] @@ -477,6 +487,6 @@ def _set_econ_indicators(nightlight, gdp_val, inc_grp, poly_val): """ if nightlight.sum() > 0: nightlight = polyval(np.asarray(nightlight), poly_val) - nightlight = nightlight/nightlight.sum() * gdp_val * (inc_grp+1) + nightlight = nightlight / nightlight.sum() * gdp_val * (inc_grp + 1) return nightlight diff --git a/climada/entity/exposures/crop_production.py b/climada/entity/exposures/crop_production.py new file mode 100644 index 0000000000..d1b6c023e2 --- /dev/null +++ b/climada/entity/exposures/crop_production.py @@ -0,0 +1,715 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . +""" + + +import logging +import os +from os import listdir +from os.path import isfile, isdir, join +import math +import copy +import numpy as np +import xarray as xr +import pandas as pd +import h5py +from matplotlib import pyplot as plt +from iso3166 import countries as iso_cntry +from climada.entity.exposures.base import Exposures +from climada.entity.tag import Tag +import climada.util.coordinates as coord +from climada.util.constants import DATA_DIR, DEF_CRS +from climada.util.coordinates import pts_to_raster_meta, get_resolution + + +logging.root.setLevel(logging.DEBUG) +LOGGER = logging.getLogger(__name__) + +DEF_HAZ_TYPE = 'RC' +"""Default hazard type used in impact functions id.""" + +BBOX = np.array([-180, -85, 180, 85]) # [Lon min, lat min, lon max, lat max] +""""Default geographical bounding box of the total global agricultural land extent""" + + +#ISIMIP input data specific global variables +YEARCHUNKS = dict() +"""start and end years per senario as in ISIMIP-filenames""" +# two types of 1860soc (1661-2299 not implemented) +YEARCHUNKS['1860soc'] = dict() +YEARCHUNKS['1860soc'] = {'yearrange': np.array([1800, 1860]), 'startyear': 1661, 'endyear': 1860} +YEARCHUNKS['histsoc'] = dict() +YEARCHUNKS['histsoc'] = {'yearrange': np.array([1976, 2005]), 'startyear': 1861, 'endyear': 2005} +YEARCHUNKS['2005soc'] = dict() +YEARCHUNKS['2005soc'] = {'yearrange': np.array([2006, 2099]), 'startyear': 2006, 'endyear': 2299} +YEARCHUNKS['rcp26soc'] = dict() +YEARCHUNKS['rcp26soc'] = {'yearrange': np.array([2006, 2099]), 'startyear': 2006, 'endyear': 2099} +YEARCHUNKS['rcp60soc'] = dict() +YEARCHUNKS['rcp60soc'] = {'yearrange': np.array([2006, 2099]), 'startyear': 2006, 'endyear': 2099} +YEARCHUNKS['2100rcp26soc'] = dict() +YEARCHUNKS['2100rcp26soc'] = {'yearrange': np.array([2100, 2299]), 'startyear': 2100, + 'endyear': 2299} + +FN_STR_VAR = 'landuse-15crops_annual' +"""fix filename part in input data""" + +CROP_NAME = dict() +"""mapping of crop names""" +CROP_NAME['mai'] = {'input': 'maize', 'fao': 'Maize', 'print': 'Maize'} +CROP_NAME['ric'] = {'input': 'rice', 'fao': 'Rice, paddy', 'print': 'Rice'} +CROP_NAME['whe'] = {'input': 'temperate_cereals', 'fao': 'Wheat', 'print': 'Wheat'} +CROP_NAME['soy'] = {'input': 'oil_crops_soybean', 'fao': 'Soybeans', 'print': 'Soybeans'} + + +IRR_NAME = dict() +"""mapping of irrigation parameter long names""" +IRR_NAME['combined'] = {'name': 'combined'} +IRR_NAME['noirr'] = {'name': 'rainfed'} +IRR_NAME['firr'] = {'name': 'irrigated'} + +# default: +# deposit the landuse files in the directory: climada_python/data/ISIMIP_crop/Input/Exposure +# deposit the FAO files in the directory: climada_python/data/ISIMIP_crop/Input/Exposure/FAO +# The FAO files need to be downloaded and renamed +# FAO_FILE: contains producer prices per crop, country and year +# (http://www.fao.org/faostat/en/#data/PP) +# FAO_FILE2: contains production quantity per crop, country and year +# (http://www.fao.org/faostat/en/#data/QC) +INPUT_DIR = os.path.join(DATA_DIR, 'ISIMIP_crop', 'Input', 'Exposure') +FAO_FILE = "FAOSTAT_data_producer_prices.csv" +FAO_FILE2 = "FAOSTAT_data_production_quantity.csv" + +YEARS_FAO = np.array([2000, 2018]) +"""Default years from FAO used (data file contains values for 1991-2018)""" + +# default output directory: climada_python/data/ISIMIP_crop/Output/Exposure +# by default the hist_mean files created by climada_python/hazard/crop_potential are saved in +# climada_python/data/ISIMIP_crop/Output/hist_mean/ +HIST_MEAN_PATH = os.path.join(DATA_DIR, 'ISIMIP_crop', 'Output', 'Hist_mean') +OUTPUT_DIR = os.path.join(DATA_DIR, 'ISIMIP_crop', 'Output') + + +class CropProduction(Exposures): + """Defines agriculture exposures from ISIMIP input data and FAO crop data + + geopandas GeoDataFrame with metadata and columns (pd.Series) defined in + Attributes and Exposures. + + Attributes: + crop (str): crop type f.i. 'mai', 'ric', 'whe', 'soy' + + """ + + _metadata = Exposures._metadata + ['crop'] + + @property + def _constructor(self): + return CropProduction + + def set_from_single_run(self, input_dir=INPUT_DIR, filename=None, hist_mean=HIST_MEAN_PATH, + bbox=BBOX, yearrange=(YEARCHUNKS['histsoc'])['yearrange'], + cl_model=None, scenario='histsoc', crop=None, irr=None, + unit='USD', fn_str_var=FN_STR_VAR): + + """Wrapper to fill exposure from nc_dis file from ISIMIP + Parameters: + input_dir (string): path to input data directory + filename (string): name of the landuse data file to use, + e.g. "histsoc_landuse-15crops_annual_1861_2005.nc"" + hist_mean (str or array): historic mean crop yield per centroid (or path) + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + yearrange (int tuple): year range for exposure set + f.i. (1990, 2010) + scenario (string): climate change and socio economic scenario + f.i. '1860soc', 'histsoc', '2005soc', 'rcp26soc','rcp60soc','2100rcp26soc' + cl_model (string): abbrev. climate model (only for future projections of lu data) + f.i. 'gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr','miroc5' + crop (string): crop type + f.i. 'mai', 'ric', 'whe', 'soy' + irr (string): irrigation type + f.i 'firr' (full irrigation), 'noirr' (no irrigation) or 'combined'= firr+noirr + unit (string): unit of the exposure (per year) + f.i 'USD' or 't' + fn_str_var (string): FileName STRing depending on VARiable and + ISIMIP simuation round + + Returns: + Exposure + """ + + # The filename is set or other variables (cl_model, scenario) are extracted of the + # specified filename + if filename is None: + yearchunk = YEARCHUNKS[scenario] + # if scenario == 'histsoc' or scenario == '1860soc': + if scenario in ('histsoc', '1860soc'): + string = '%s_%s_%s_%s.nc' + filename = os.path.join(input_dir, string % (scenario, fn_str_var, + str(yearchunk['startyear']), + str(yearchunk['endyear']))) + else: + string = '%s_%s_%s_%s_%s.nc' + filename = os.path.join(input_dir, string % (scenario, cl_model, fn_str_var, + str(yearchunk['startyear']), + str(yearchunk['endyear']))) + elif scenario == 'flexible': + _, _, _, _, _, _, startyear, endyearnc = filename.split('_') + endyear = endyearnc.split('.')[0] + yearchunk = dict() + yearchunk = {'yearrange': np.array([int(startyear), int(endyear)]), + 'startyear': int(startyear), 'endyear': int(endyear)} + filename = os.path.join(input_dir, filename) + else: + scenario, *_ = filename.split('_') + yearchunk = YEARCHUNKS[scenario] + filename = os.path.join(input_dir, filename) + + # Dataset is opened and data within the bbox extends is extracted + data_set = xr.open_dataset(filename, decode_times=False) + [lonmin, latmin, lonmax, latmax] = bbox + data = data_set.sel(lon=slice(lonmin, lonmax), lat=slice(latmax, latmin)) + + # The latitude and longitude are set; the region_id is determined + lon, lat = np.meshgrid(data.lon.values, data.lat.values) + self['latitude'] = lat.flatten() + self['longitude'] = lon.flatten() + self['region_id'] = coord.get_country_code(self.latitude, self.longitude) + + # The indeces of the yearrange to be extracted are determined + time_idx = np.array([int(yearrange[0] - yearchunk['startyear']), + int(yearrange[1] - yearchunk['startyear'])]) + + # The area covered by a grid cell is calculated depending on the latitude + # 1 degree = 111.12km (at the equator); resolution data: 0.5 degree; + # longitudal distance in km = 111.12*0.5*cos(lat); + # latitudal distance in km = 111.12*0.5; + # area = longitudal distance * latitudal distance; + # 1km2 = 100ha + area = (111.12 * 0.5)**2 * np.cos(np.deg2rad(lat)) * 100 + + # The area covered by a crop is calculated as the product of the fraction and + # the grid cell size + if irr == 'combined': + irr = ['irr', 'noirr'] + else: + irr = [irr] + area_crop = dict() + for irr_var in irr: + area_crop[irr_var] = ( + getattr( + data, (CROP_NAME[crop])['input']+'_'+ (IRR_NAME[irr_var])['name'] + )[time_idx[0]:time_idx[1], :, :].mean(dim='time')*area + ).values + area_crop[irr_var] = np.nan_to_num(area_crop[irr_var]).flatten() + + # set historic mean, its latitude, and longitude: + hist_mean_dict = dict() + if isdir(hist_mean): + # The adequate file from the directory (depending on crop and irrigation) is extracted + # and the variables hist_mean, lat_mean and lon_mean are set accordingly + for irr_var in irr: + filename = os.path.join(hist_mean, 'hist_mean_%s-%s_%i-%i.hdf5' %(\ + crop, irr_var, yearrange[0], yearrange[1]) + ) + hist_mean_dict[irr_var] = (h5py.File(filename, 'r'))['mean'][()] + lat_mean = (h5py.File(filename, 'r'))['lat'][()] + lon_mean = (h5py.File(filename, 'r'))['lon'][()] + elif isfile(os.path.join(input_dir, hist_mean)): + # Hist_mean, lat_mean and lon_mean are extracted from the given file + if len(irr) > 1: + LOGGER.error('For irr=combined, hist_mean can not be single file. Aborting.') + raise ValueError('Wrong combination of parameters irr and hist_mean.') + hist_mean = h5py.File(os.path.join(input_dir, hist_mean), 'r') + hist_mean_dict[irr[0]] = hist_mean['mean'][()] + lat_mean = hist_mean['lat'][()] + lon_mean = hist_mean['lon'][()] + else: + # hist_mean as returned by the hazard crop_potential is used (array format) with same + # bbox extensions as the exposure + lat_mean = self.latitude.values + + # The bbox is cut out of the hist_mean data file if needed + if len(lat_mean) != len(self.latitude.values): + idx_mean = np.zeros(len(self.latitude.values), dtype=int) + for i in range(len(self.latitude.values)): + idx_mean[i] = np.where( + (lat_mean == self.latitude.values[i]) + & (lon_mean == self.longitude.values[i]) + )[0][0] + else: + idx_mean = np.arange(0, len(lat_mean)) + + # The exposure [t/y] is computed per grid cell as the product of the area covered + # by a crop [ha] and its yield [t/ha/y] + self['value'] = np.squeeze(area_crop[irr[0]]*hist_mean_dict[irr[0]][idx_mean]) + for irr_val in irr[1:]: # add other irrigation types if irr=combined + self['value'] += np.squeeze(area_crop[irr_val]*hist_mean_dict[irr_val][idx_mean]) + self.tag = Tag() + if len(irr) > 1: + irr = 'combined' + else: + irr = irr[0] + self.tag.description = ("Crop production exposure from ISIMIP " + + (CROP_NAME[crop])['print'] + ' ' + + irr + ' ' + str(yearrange[0]) + '-' + str(yearrange[-1])) + self.value_unit = 't / y' + self.crop = crop + self.ref_year = yearrange + self.crs = DEF_CRS + try: + rows, cols, ras_trans = pts_to_raster_meta( + (self.longitude.min(), self.latitude.min(), + self.longitude.max(), self.latitude.max()), + get_resolution(self.longitude, self.latitude)) + self.meta = { + 'width': cols, + 'height': rows, + 'crs': self.crs, + 'transform': ras_trans, + } + except ValueError: + LOGGER.warning('Could not write attribute meta, because exposure' + ' has only 1 data point') + self.meta = {} + + # Method set_to_usd() is called to compute the exposure in USD/y (per centroid) + if 'USD' in unit: + self.set_to_usd(input_dir=input_dir) + self.check() + + return self + + def set_mean_of_several_models(self, input_dir=INPUT_DIR, hist_mean=HIST_MEAN_PATH, bbox=BBOX, + yearrange=(YEARCHUNKS['histsoc'])['yearrange'], + cl_model=None, scenario=None, crop=None, irr=None, + unit='USD', fn_str_var=FN_STR_VAR): + """Wrapper to fill exposure from several nc_dis files from ISIMIP + + Optional Parameters: + input_dir (string): path to input data directory + historic mean (array): historic mean crop production per centroid + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + yearrange (int tuple): year range for exposure set, f.i. (1976, 2005) + scenario (string): climate change and socio economic scenario + f.i. 'histsoc' or 'rcp60soc' + cl_model (string): abbrev. climate model (only when landuse data + is future projection) + f.i. 'gfdl-esm2m' etc. + crop (string): crop type + f.i. 'mai', 'ric', 'whe', 'soy' + irr (string): irrigation type + f.i 'rainfed', 'irrigated' or 'combined'= rainfed+irrigated + unit (string): unit of the exposure (per year) + f.i 'USD' or 't' + fn_str_var (string): FileName STRing depending on VARiable and + ISIMIP simuation round + Returns: + Exposure + """ + filenames = dict() + filenames['all'] = [f for f in listdir(input_dir) if (isfile(join(input_dir, f))) + if not f.startswith('.') if 'nc' in f] + + # If only files with a certain scenario and or cl_model shall be considered, they + # are extracted from the original list of files + filenames['subset'] = list() + for name in filenames['all']: + if cl_model is not None and scenario is not None: + if cl_model in name or scenario in name: + filenames['subset'].append(name) + elif cl_model is not None and scenario is None: + if cl_model in name: + filenames['subset'].append(name) + elif cl_model is None and scenario is not None: + if scenario in name: + filenames['subset'].append(name) + else: + filenames['subset'] = filenames['all'] + + # The first exposure is calculate to determine its size + # and initialize the combined exposure + self.set_from_single_run(input_dir, filename=filenames['subset'][0], + hist_mean=hist_mean, bbox=bbox, yearrange=yearrange, + crop=crop, irr=irr, + unit=unit, fn_str_var=fn_str_var) + + combined_exp = np.zeros([self.value.size, len(filenames['subset'])]) + combined_exp[:, 0] = self.value + + # The calculations are repeated for all remaining exposures (starting from index 1 as + # the first exposure has been saved in combined_exp[:, 0]) + for j in range(1, len(filenames['subset'])): + self.set_from_single_run(input_dir, filename=filenames['subset'][j], + hist_mean=hist_mean, bbox=bbox, yearrange=yearrange, + crop=crop, irr=irr, unit=unit) + combined_exp[:, j] = self.value + + self['value'] = np.mean(combined_exp, 1) + self['crop'] = crop + + self.check() + + return self + + def set_to_usd(self, input_dir=INPUT_DIR, yearrange=YEARS_FAO): + # to do: check api availability?; default yearrange for single year (e.g. 5a) + """Calculates the exposure in USD using country and year specific data published + by the FAO. + + Optional Parameters: + input_dir (string): directory containing the input (FAO pricing) data + yearrange (array): year range for prices, can also be set to a single year + Default is set to the arbitrary time range (2000, 2018) + The data is available for the years 1991-2018 + crop (str): crop type + f.i. 'mai', 'ric', 'whe', 'soy' + + Returns: + Exposure + """ + + # the exposure in t/y is saved as 'tonnes_per_year' + self['tonnes_per_year'] = self['value'] + + # account for the case of only specifying one year as yearrange + if len(yearrange) == 1: + yearrange = np.array([yearrange[0], yearrange[0]]) + + # open both FAO files and extract needed variables + # FAO_FILE: contains producer prices per crop, country and year + fao = dict() + fao['file'] = pd.read_csv(os.path.join(input_dir, FAO_FILE)) + fao['crops'] = fao['file'].Item.values + fao['year'] = fao['file'].Year.values + fao['price'] = fao['file'].Value.values + + fao_country = coord.country_faocode2iso(getattr(fao['file'], 'Area Code').values) + + # create a list of the countries contained in the exposure + iso3alpha = list() + for reg_id in self.region_id: + try: + iso3alpha.append(iso_cntry.get(reg_id).alpha3) + except KeyError: + if reg_id in (0, -99): + iso3alpha.append('No country') + else: + iso3alpha.append('Other country') + list_countries = np.unique(iso3alpha) + + + + + # iterate over all countries that are covered in the exposure, extract the according price + # and calculate the crop production in USD/y + area_price = np.zeros(self.value.size) + for country in list_countries: + [idx_country] = np.where(np.asarray(iso3alpha) == country) + if country == 'Other country': + price = 0 + area_price[idx_country] = self.value[idx_country] * price + elif country != 'No country' and country != 'Other country': + idx_price = np.where((np.asarray(fao_country) == country) & + (np.asarray(fao['crops']) == \ + (CROP_NAME[self.crop])['fao']) & + (fao['year'] >= yearrange[0]) & + (fao['year'] <= yearrange[1])) + price = np.mean(fao['price'][idx_price]) + # if no price can be determined for a specific yearrange and country, the world + # average for that crop (in the specified yearrange) is used + if math.isnan(price) or price == 0: + idx_price = np.where((np.asarray(fao['crops']) == \ + (CROP_NAME[self.crop])['fao']) & + (fao['year'] >= yearrange[0]) & + (fao['year'] <= yearrange[1])) + price = np.mean(fao['price'][idx_price]) + area_price[idx_country] = self.value[idx_country] * price + + + self['value'] = area_price + self.value_unit = 'USD / y' + self.check() + return self + + def aggregate_countries(self): + """Aggregate exposure data by country. + + Returns: + list_countries (list): country codes (numerical ISO3) + country_values (array): aggregated exposure value + """ + + list_countries = np.unique(self.region_id) + country_values = np.zeros(len(list_countries)) + for i, iso_nr in enumerate(list_countries): + country_values[i] = self.loc[self.region_id == iso_nr].value.sum() + + return list_countries, country_values + +def init_full_exposure_set(input_dir=INPUT_DIR, filename=None, hist_mean_dir=HIST_MEAN_PATH, + output_dir=OUTPUT_DIR, bbox=BBOX, + yearrange=(YEARCHUNKS['histsoc'])['yearrange'], unit='t', + return_data=False): + """Generates CropProduction exposure sets for all files contained in the + input directory and saves them as hdf5 files in the output directory + + Parameters: + input_dir (string): path to input data directory + filename (string): if not specified differently, the file + 'histsoc_landuse-15crops_annual_1861_2005.nc' will be used + output_dir (string): path to output data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + yearrange (array): year range for hazard set, f.i. (1976, 2005) + unit (str): unit in which to return exposure (t/y or USD/y) + return_data (boolean): returned output + False: returns list of filenames only, True: returns also list of data + + Returns: + filename_list (list): all filenames of saved initiated exposure files + output_list (list): list containing all inisiated Exposure instances + """ + + filenames = [f for f in listdir(hist_mean_dir) if (isfile(join(hist_mean_dir, f))) if not + f.startswith('.')] + + # generate output directory if it does not exist yet + if not os.path.exists(os.path.join(output_dir, 'Exposure')): + os.mkdir(os.path.join(output_dir, 'Exposure')) + + # create exposures for all crop-irrigation combinations and save them + filename_list = list() + output_list = list() + for file in filenames: + _, _, crop_irr, *_ = file.split('_') + crop, irr = crop_irr.split('-') + crop_production = CropProduction() + crop_production.set_from_single_run(input_dir=input_dir, filename=filename, + hist_mean=hist_mean_dir, bbox=bbox, + yearrange=yearrange, crop=crop, irr=irr, unit=unit) + filename_expo = ('crop_production_' + crop + '-'+ irr + '_' + + str(yearrange[0]) + '-' + str(yearrange[1]) + '.hdf5') + filename_list.append(filename_expo) + output_list.append(crop_production) + crop_production.write_hdf5(os.path.join(output_dir, 'Exposure', filename_expo)) + + if not return_data: + return filename_list + return filename_list, output_list + +def normalize_with_fao_cp(exp_firr, exp_noirr, input_dir=INPUT_DIR, + yearrange=np.array([2008, 2018]), unit='t', return_data=True): + """Normalize the given exposures countrywise with the mean crop production quantity + documented by the FAO. Refer to the beginning of the script for guidance on where to + download the needed FAO data. + + Parameters: + exp_firr (crop_production): exposure under full irrigation + exp_noirr (crop_production): exposure under no irrigation + + Optional Parameters: + input_dir (str): directory containing exposure input data + yearrange (array): the mean crop production in this year range is used to normalize + the exposure data + Default is set to the arbitrary time range (2008, 2018) + The data is available for the years 1961-2018 + unit (str): unit in which to return exposure (t/y or USD/y) + return_data (boolean): returned output + True: returns country list, ratio = FAO/ISIMIP, normalized exposures, crop production + per country as documented by the FAO and calculated by the ISIMIP dataset + False: country list, ratio = FAO/ISIMIP, normalized exposures + + Returns: + country_list (list): List of country codes (numerical ISO3) + ratio (list): List of ratio of FAO crop production and aggregated exposure + for each country + exp_firr_norm (CropProduction): Normalized CropProduction (full irrigation) + exp_noirr_norm (CropProduction): Normalized CropProduction (no irrigation) + + Returns (optional): + fao_crop_production (list): FAO crop production value per country + exp_tot_production(list): Exposure crop production value per country + (before normalization) + """ + + # if the exposure unit is USD/y temporarily reset the exposure to t/y + # (stored in tonnes_per_year) in order to normalize with FAO crop production + # values and then apply set_to_USD() for the normalized exposure to restore the + # initial exposure unit + if exp_firr.value_unit == 'USD / y': + exp_firr.value = exp_firr.tonnes_per_year + if exp_noirr.value_unit == 'USD / y': + exp_noirr.value = exp_noirr.tonnes_per_year + + country_list, countries_firr = exp_firr.aggregate_countries() + country_list, countries_noirr = exp_noirr.aggregate_countries() + + exp_tot_production = countries_firr + countries_noirr + + fao = pd.read_csv(os.path.join(input_dir, FAO_FILE2)) + fao_crops = fao.Item.values + fao_year = fao.Year.values + fao_values = fao.Value.values + fao_code = getattr(fao, 'Area Code').values + + fao_country = coord.country_iso2faocode(country_list) + + fao_crop_production = np.zeros(len(country_list)) + ratio = np.ones(len(country_list)) + exp_firr_norm = copy.deepcopy(exp_firr) + exp_noirr_norm = copy.deepcopy(exp_noirr) + + # loop over countries: compute ratio & apply normalization: + for country, iso_nr in enumerate(country_list): + idx = np.where((np.asarray(fao_code) == fao_country[country]) + & (np.asarray(fao_crops) == (CROP_NAME[exp_firr.crop])['fao']) + & (fao_year >= yearrange[0]) & (fao_year <= yearrange[1])) + if len(idx) >= 1: + fao_crop_production[country] = np.mean(fao_values[idx]) + + # if a country has no values in the exposure (e.g. Cyprus) the exposure value + # is set to the FAO average value + # in this case the ratio is left being 1 (as initiated) + if exp_tot_production[country] == 0: + exp_tot_production[country] = fao_crop_production[country] + elif fao_crop_production[country] != np.nan and fao_crop_production[country] != 0: + ratio[country] = fao_crop_production[country] / exp_tot_production[country] + + exp_firr_norm.value[exp_firr.region_id == iso_nr] = ratio[country] * \ + exp_firr.value[exp_firr.region_id == iso_nr] + exp_noirr_norm.value[exp_firr.region_id == iso_nr] = ratio[country] * \ + exp_noirr.value[exp_noirr.region_id == iso_nr] + + if unit == 'USD' or exp_noirr.value_unit == 'USD / y': + exp_noirr.set_to_usd(input_dir=input_dir) + if unit == 'USD' or exp_firr.value_unit == 'USD / y': + exp_firr.set_to_usd(input_dir=input_dir) + + exp_firr_norm.tag.description = exp_firr_norm.tag.description+' normalized' + exp_noirr_norm.tag.description = exp_noirr_norm.tag.description+' normalized' + + if return_data: + return country_list, ratio, exp_firr_norm, exp_noirr_norm, \ + fao_crop_production, exp_tot_production + return country_list, ratio, exp_firr_norm, exp_noirr_norm + +def normalize_several_exp(input_dir=INPUT_DIR, output_dir=OUTPUT_DIR, + yearrange=np.array([2008, 2018]), + unit='t', return_data=True): + """ + Optional Parameters: + input_dir (str): directory containing exposure input data + output_dir (str): directory containing exposure datasets (output of exposure creation) + yearrange (array): the mean crop production in this year range is used to normalize + the exposure data (default 2008-2018) + unit (str): unit in which to return exposure (t/y or USD/y) + return_data (boolean): returned output + True: lists containing data for each exposure file. Lists: crops, country list, + ratio = FAO/ISIMIP, normalized exposures, crop production per country as documented + by the FAO and calculated by the ISIMIP dataset + False: lists containing data for each exposure file. Lists: crops, country list, + ratio = FAO/ISIMIP, normalized exposures + + Returns: + crop_list (list): List of crops + country_list (list): List of country codes (numerical ISO3) + ratio (list): List of ratio of FAO crop production and aggregated exposure + for each country + exp_firr_norm (list): List of normalized CropProduction Exposures (full irrigation) + exp_noirr_norm (list): List of normalize CropProduction Exposures (no irrigation) + + Returns (optional): + fao_crop_production (list): FAO crop production value per country + exp_tot_production(list): Exposure crop production value per country + (before normalization) + """ + filenames_firr = [f for f in listdir(os.path.join(output_dir, 'Exposure')) if + (isfile(join(os.path.join(output_dir, 'Exposure'), f))) if not + f.startswith('.') if 'firr' in f] + + crop_list = list() + countries_list = list() + ratio_list = list() + exp_firr_norm = list() + exp_noirr_norm = list() + fao_cp_list = list() + exp_tot_cp_list = list() + + for file_firr in filenames_firr: + _, _, crop_irr, years = file_firr.split('_') + crop, _ = crop_irr.split('-') + exp_firr = CropProduction() + exp_firr.read_hdf5(os.path.join(output_dir, 'Exposure', file_firr)) + + filename_noirr = 'crop_production_' + crop + '-' + 'noirr' + '_' + years + exp_noirr = CropProduction() + exp_noirr.read_hdf5(os.path.join(output_dir, 'Exposure', filename_noirr)) + + if return_data: + countries, ratio, exp_firr2, exp_noirr2, fao_cp, \ + exp_tot_cp = normalize_with_fao_cp(exp_firr, exp_noirr, input_dir=input_dir, + yearrange=yearrange, unit=unit) + fao_cp_list.append(fao_cp) + exp_tot_cp_list.append(exp_tot_cp) + else: + countries, ratio, exp_firr2, \ + exp_noirr2 = normalize_with_fao_cp(exp_firr, exp_noirr, input_dir=input_dir, + yearrange=yearrange, unit=unit, + return_data=False) + + crop_list.append(crop) + countries_list.append(countries) + ratio_list.append(ratio) + exp_firr_norm.append(exp_firr2) + exp_noirr_norm.append(exp_noirr2) + + if return_data: + return crop_list, countries_list, ratio_list, exp_firr_norm, exp_noirr_norm, \ + fao_cp_list, exp_tot_cp_list + return crop_list, countries_list, ratio_list, exp_firr_norm, exp_noirr_norm + +def semilogplot_ratio(crop, countries, ratio, output_dir=OUTPUT_DIR, save=True): + """Plot ratio = FAO/ISIMIP against country codes. + + Parameters: + crop (str): crop to plot + countries (list): country codes of countries to plot + ratio (array): ratio = FAO/ISIMIP crop production data of countries to plot + output_dir (str): directory to save figure + Optional Parameters: + save (boolean): True saves figure, else figure is not saved. + Returns: + fig (plt figure handle) + axes (plot axes handle) + + """ + fig = plt.figure() + axes = plt.gca() + axes.scatter(countries[ratio != 1], ratio[ratio != 1]) + axes.set_yscale('log') + axes.set_ylabel('Ratio= FAO / ISIMIP') + axes.set_xlabel('ISO3 country code') + axes.set_ylim(np.nanmin(ratio), np.nanmax(ratio)) + plt.title(crop) + + if save: + if not os.path.exists(os.path.join(output_dir, 'Exposure_norm_plots')): + os.mkdir(os.path.join(output_dir, 'Exposure_norm_plots')) + plt.savefig(os.path.join(output_dir, 'Exposure_norm_plots', + 'fig_ratio_norm_' + crop)) + return fig, axes diff --git a/climada/entity/exposures/gdp_asset.py b/climada/entity/exposures/gdp_asset.py index 44bf936f07..6802f253af 100644 --- a/climada/entity/exposures/gdp_asset.py +++ b/climada/entity/exposures/gdp_asset.py @@ -29,9 +29,9 @@ import geopandas as gpd from climada.entity.tag import Tag from climada.entity.exposures.base import Exposures, INDICATOR_IF -from climada.util.constants import GLB_CENTROIDS_NC -from climada.util.constants import NAT_REG_ID, SYSTEM_DIR -from climada.util.constants import DEF_CRS +from climada.util.coordinates import pts_to_raster_meta +from climada.util.coordinates import country_iso2natid, get_region_gridpoints, region2isos +from climada.util.constants import RIVER_FLOOD_REGIONS_CSV, DEF_CRS, SYSTEM_DIR LOGGER = logging.getLogger(__name__) DEF_HAZ_TYPE = 'RF' @@ -47,14 +47,14 @@ def _constructor(self): def set_countries(self, countries=[], reg=[], ref_year=2000, path=None): - """ Model countries using values at reference year. If GDP or income + """Model countries using values at reference year. If GDP or income group not available for that year, consider the value of the closest available year. Parameters: countries (list): list of country names ISO3 ref_year (int, optional): reference year. Default: 2016 - path (string): path to exposure dataset + path (string): path to exposure dataset (ISIMIP) """ gdp2a_list = [] tag = Tag() @@ -64,15 +64,13 @@ def set_countries(self, countries=[], reg=[], ref_year=2000, raise NameError if not os.path.exists(path): - LOGGER.error('Invalid path ' + path) + LOGGER.error('Invalid path %s', path) raise NameError try: if not countries: if reg: - natID_info = pd.read_csv(NAT_REG_ID) - natISO = natID_info["ISO"][np.isin(natID_info["Reg_name"], - reg)] + natISO = region2isos(reg) countries = np.array(natISO) else: LOGGER.error('set_countries requires countries or reg') @@ -84,20 +82,30 @@ def set_countries(self, countries=[], reg=[], ref_year=2000, tag.description += ("{} GDP2Asset \n").\ format(countries[cntr_ind]) Exposures.__init__(self, gpd.GeoDataFrame( - pd.concat(gdp2a_list, ignore_index=True))) + pd.concat(gdp2a_list, ignore_index=True))) except KeyError: - LOGGER.error('Exposure countries: ' + str(countries) + ' or reg ' + - str(reg) + ' could not be set, check ISO3 or' + - ' reference year ' + str(ref_year)) + LOGGER.error('Exposure countries: %s or reg %s could not be set, check ISO3 or' + ' reference year %s', countries, reg, ref_year) raise KeyError + self.tag = tag self.ref_year = ref_year self.value_unit = 'USD' - self.tag = tag + self.tag.description = 'GDP2Asset ' + str(self.ref_year) self.crs = DEF_CRS + # set meta + res = 0.0416666 + + + rows, cols, ras_trans = pts_to_raster_meta((self.longitude.min(), + self.latitude.min(), + self.longitude.max(), + self.latitude.max()), res) + self.meta = {'width': cols, 'height': rows, 'crs': self.crs, + 'transform': ras_trans} @staticmethod def _set_one_country(countryISO, ref_year, path=None): - """ Extract coordinates of selected countries or region + """Extract coordinates of selected countries or region from NatID grid. Parameters: countryISO(str): ISO3 of country @@ -108,34 +116,16 @@ def _set_one_country(countryISO, ref_year, path=None): Returns: np.array """ - exp_gdpasset = GDP2Asset() - natID_info = pd.read_csv(NAT_REG_ID) - try: - isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC) - isimip_lon = isimip_grid.lon.data - isimip_lat = isimip_grid.lat.data - gridX, gridY = np.meshgrid(isimip_lon, isimip_lat) - if not any(np.isin(natID_info['ISO'], countryISO)): - LOGGER.error('Wrong country ISO ' + str(countryISO)) - raise KeyError - natID = natID_info['ID'][np.isin(natID_info['ISO'], countryISO)] - reg_id, if_rf = _fast_if_mapping(natID, natID_info) - isimip_NatIdGrid = isimip_grid.NatIdGrid.data - except OSError: - LOGGER.error('Problems while reading ,' + path + - ' check exposure_file specifications') - raise OSError - natID_pos = np.isin(isimip_NatIdGrid, natID) - lon_coordinates = gridX[natID_pos] - lat_coordinates = gridY[natID_pos] - coord = np.zeros((len(lon_coordinates), 2)) - coord[:, 1] = lon_coordinates - coord[:, 0] = lat_coordinates + natID = country_iso2natid(countryISO) + natID_info = pd.read_csv(RIVER_FLOOD_REGIONS_CSV) + reg_id, if_rf = _fast_if_mapping(natID, natID_info) + lat, lon = get_region_gridpoints(countries=[natID], iso=False, basemap="isimip") + coord = np.stack([lat, lon], axis=1) assets = _read_GDP(coord, ref_year, path) - reg_id_info = np.zeros((len(assets))) - reg_id_info[:] = reg_id - if_rf_info = np.zeros((len(assets))) - if_rf_info[:] = if_rf + reg_id_info = np.full((len(assets),), reg_id) + if_rf_info = np.full((len(assets),), if_rf) + + exp_gdpasset = GDP2Asset() exp_gdpasset['value'] = assets exp_gdpasset['latitude'] = coord[:, 0] exp_gdpasset['longitude'] = coord[:, 1] @@ -144,8 +134,8 @@ def _set_one_country(countryISO, ref_year, path=None): return exp_gdpasset -def _read_GDP(shp_exposures, ref_year, path): - """ Read GDP-values for the selected area and convert it to asset. +def _read_GDP(shp_exposures, ref_year, path=None): + """Read GDP-values for the selected area and convert it to asset. Parameters: shp_exposure(2d-array float): coordinates of area ref_year(int): year under consideration @@ -162,13 +152,12 @@ def _read_GDP(shp_exposures, ref_year, path): gdp_lat = gdp_file.lat.data time = gdp_file.time.dt.year except OSError: - LOGGER.error('Problems while reading ,' + path + - ' check exposure_file specifications') + LOGGER.error('Problems while reading %s check exposure_file specifications', path) raise OSError try: year_index = np.where(time == ref_year)[0][0] except IndexError: - LOGGER.error('No data available for year ' + str(ref_year)) + LOGGER.error('No data available for year %s', ref_year) raise KeyError conv_lon = asset_converter.lon.data conv_lat = asset_converter.lat.data @@ -184,7 +173,7 @@ def _read_GDP(shp_exposures, ref_year, path): asset = sp.interpolate.interpn((gdp_lat, gdp_lon), np.nan_to_num(asset), (shp_exposures[:, 0], - shp_exposures[:, 1]), + shp_exposures[:, 1]), method='nearest', bounds_error=False, fill_value=None) @@ -192,7 +181,7 @@ def _read_GDP(shp_exposures, ref_year, path): conv_factors = sp.interpolate.interpn((conv_lat, conv_lon), np.nan_to_num(conv_factors), (shp_exposures[:, 0], - shp_exposures[:, 1]), + shp_exposures[:, 1]), method='nearest', bounds_error=False, fill_value=None) @@ -200,28 +189,28 @@ def _read_GDP(shp_exposures, ref_year, path): gdp = sp.interpolate.interpn((gdp_lat, gdp_lon), np.nan_to_num(gdp), (shp_exposures[:, 0], - shp_exposures[:, 1]), + shp_exposures[:, 1]), method='nearest', bounds_error=False, fill_value=None) - asset = gdp*conv_factors + asset = gdp * conv_factors return asset def _test_gdp_centr_match(gdp_lat, gdp_lon, shp_exposures): - if (max(gdp_lat)+0.5 < max(shp_exposures[:, 0])) or\ - (max(gdp_lon)+0.5 < max(shp_exposures[:, 1])) or\ - (min(gdp_lat)-0.5 > min(shp_exposures[:, 0])) or\ - (min(gdp_lon)-0.5 > min(shp_exposures[:, 1])): + if (max(gdp_lat) + 0.5 < max(shp_exposures[:, 0])) or\ + (max(gdp_lon) + 0.5 < max(shp_exposures[:, 1])) or\ + (min(gdp_lat) - 0.5 > min(shp_exposures[:, 0])) or\ + (min(gdp_lon) - 0.5 > min(shp_exposures[:, 1])): LOGGER.error('Asset Data does not match selected country') raise IOError def _fast_if_mapping(countryID, natID_info): - """ Assign region-ID and impact function id. + """Assign region-ID and impact function id. Parameters: countryID (int) natID_info: dataframe of lookuptable diff --git a/climada/entity/exposures/gpw_import.py b/climada/entity/exposures/gpw_import.py index 880b2e708e..a9cd48e3d5 100644 --- a/climada/entity/exposures/gpw_import.py +++ b/climada/entity/exposures/gpw_import.py @@ -19,7 +19,7 @@ LOGGER = logging.getLogger(__name__) FILENAME_GPW = 'gpw_v4_population_count_rev%02i_%04i_30_sec.tif' -FOLDER_GPW = os.path.join(SYSTEM_DIR, \ +FOLDER_GPW = os.path.join(SYSTEM_DIR, 'gpw-v4-population-count-rev%02i_%04i_30_sec_tif') GPW_VERSIONS = [11, 10, 12, 13] # FILENAME_GPW1 = '_30_sec.tif' @@ -30,7 +30,7 @@ def _gpw_bbox_cutter(gpw_data, bbox, resolution, arr1_shape=[17400, 43200]): - """ Crops the imported GPW data to the bounding box to reduce memory foot + """Crops the imported GPW data to the bounding box to reduce memory foot print after it has been resized to desired resolution. Optional parameters: @@ -43,45 +43,45 @@ def _gpw_bbox_cutter(gpw_data, bbox, resolution, arr1_shape=[17400, 43200]): gpw_data (array): Cropped GPW data """ - """ gpw data is 17400 rows x 43200 cols in dimension (from 85 N to 60 S in + """gpw data is 17400 rows x 43200 cols in dimension (from 85 N to 60 S in latitude, full longitudinal range). Hence, the bounding box can easily be converted to the according indices in the gpw data""" - steps_p_res = 3600/resolution - zoom = 30/resolution + steps_p_res = 3600 / resolution + zoom = 30 / resolution col_min, row_min, col_max, row_max =\ LitPop._litpop_coords_in_glb_grid(bbox, resolution) # accomodate to fact that not the whole grid is present in the v.10 dataset: - if arr1_shape[0]==17400: - row_min, row_max = int(row_min-5*steps_p_res), \ - int(row_max-5*steps_p_res) + if arr1_shape[0] == 17400: + row_min, row_max = int(row_min - 5 * steps_p_res), \ + int(row_max - 5 * steps_p_res) rows_gpw = arr1_shape[0] cols_gpw = arr1_shape[1] - if col_max < (cols_gpw/zoom)-1: + if col_max < (cols_gpw / zoom) - 1: col_max = col_max + 1 - if row_max < (rows_gpw/zoom)-1: + if row_max < (rows_gpw / zoom) - 1: row_max = row_max + 1 gpw_data = gpw_data[:, col_min:col_max] - if row_min >= 0 and row_min < (rows_gpw/zoom) and row_max >= 0 and\ - row_max < (rows_gpw/zoom): + if row_min >= 0 and row_min < (rows_gpw / zoom) and row_max >= 0 \ + and row_max < (rows_gpw / zoom): gpw_data = gpw_data[row_min:row_max, :] - elif row_min < 0 and row_max >= 0 and row_max < (rows_gpw/zoom): - np.concatenate(np.zeros((abs(row_min), gpw_data.shape[1])),\ + elif row_min < 0 and row_max >= 0 and row_max < (rows_gpw / zoom): + np.concatenate(np.zeros((abs(row_min), gpw_data.shape[1])), gpw_data[0:row_max, :]) elif row_min < 0 and row_max < 0: - gpw_data = np.zeros((row_max-row_min, col_max-col_min)) - elif row_min < 0 and row_max >= (rows_gpw/zoom): - np.concatenate(np.zeros((abs(row_min), gpw_data.shape[1])), gpw_data,\ - np.zeros((row_max-(rows_gpw/zoom)+1, gpw_data.shape[1]))) - elif row_min >= (rows_gpw/zoom): - gpw_data = np.zeros((row_max-row_min, col_max-col_min)) + gpw_data = np.zeros((row_max - row_min, col_max - col_min)) + elif row_min < 0 and row_max >= (rows_gpw / zoom): + np.concatenate(np.zeros((abs(row_min), gpw_data.shape[1])), gpw_data, + np.zeros((row_max - (rows_gpw / zoom) + 1, gpw_data.shape[1]))) + elif row_min >= (rows_gpw / zoom): + gpw_data = np.zeros((row_max - row_min, col_max - col_min)) return gpw_data def check_bounding_box(coord_list): - """ Check if a bounding box is valid. - PARAMETERS: + """Check if a bounding box is valid. + Parameters: coord_list (4x1 array): bounding box to be checked. OUTPUT: isCorrectType (boolean): True if bounding box is valid, false otehrwise @@ -90,12 +90,10 @@ def check_bounding_box(coord_list): if coord_list.size != 4: is_correct_type = False return is_correct_type - min_lat, min_lon, max_lat, max_lon = coord_list[0], coord_list[1],\ - coord_list[2], coord_list[3] - assert max_lat < min_lat, "Maximum latitude cannot be smaller than "\ - + "minimum latitude." - assert max_lon < min_lon, "Maximum longitude cannot be smaller than "\ - + "minimum longitude." + min_lat, min_lon, max_lat, max_lon = (coord_list[0], coord_list[1], + coord_list[2], coord_list[3]) + assert max_lat < min_lat, "Maximum latitude cannot be smaller than minimum latitude." + assert max_lon < min_lon, "Maximum longitude cannot be smaller than minimum longitude." assert min_lat < -90, "Minimum latitude cannot be smaller than -90." assert min_lon < -180, "Minimum longitude cannot be smaller than -180." assert max_lat > 90, "Maximum latitude cannot be larger than 90." @@ -103,53 +101,62 @@ def check_bounding_box(coord_list): return is_correct_type def get_box_gpw(**parameters): - """ Reads data from GPW GeoTiff file and cuts out the data along a chosen + """Reads data from GPW GeoTiff file and cuts out the data along a chosen bounding box. - Optional parameters: - gpw_path (str): absolute path where files are stored. - Default: SYSTEM_DIR - resolution (int): the resolution in arcsec in which the data output - is created. - country_cut_mode (int): Defines how the country is cut out: - if 0: the country is only cut out with a bounding box - if 1: the country is cut out along it's borders - Default: = 0 #TODO: Unimplemented - cut_bbox (1x4 array-like): Bounding box (ESRI type) to be cut out. - the layout of the bounding box corresponds to the bounding box of - the ESRI shape files and is as follows: - [minimum longitude, minimum latitude, maximum longitude, maxmimum - latitude] - if country_cut_mode = 1, the cut_bbox is overwritten/ignored. - return_coords (int): Determines whether latitude and longitude are - delievered along with gpw data (0) - or only gpw_data is returned (Default: 0) - add_one (boolean): Determine whether the integer one is added to all - cells to eliminate zero pixels (Default: 0) #TODO: Unimplemented - reference_year (int): reference year, available years are: - 2000, 2005, 2010, 2015 (default), 2020 - - Returns: - tile_temp (pandas SparseArray): GPW data - lon (list): list with longitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) - lat (list): list with latitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) + Parameters + ---------- + gpw_path : str + Absolute path where files are stored. Default: SYSTEM_DIR + resolution : int + The resolution in arcsec in which the data output is created. + country_cut_mode : int + Defines how the country is cut out: If 0, the country is only cut out + with a bounding box. If 1, the country is cut out along it's borders + Default: 0. + #TODO: Unimplemented + cut_bbox : array-like, shape (1,4) + Bounding box (ESRI type) to be cut out. + The layout of the bounding box corresponds to the bounding box of + the ESRI shape files and is as follows: + [minimum longitude, minimum latitude, maximum longitude, maxmimum latitude] + If country_cut_mode = 1, the cut_bbox is overwritten/ignored. + return_coords : int + Determines whether latitude and longitude are delievered along with gpw + data (0) or only gpw_data is returned. Default: 0. + add_one : boolean + Determine whether the integer one is added to all cells to eliminate + zero pixels. Default: 0. + #TODO: Unimplemented + reference_year : int + reference year, available years are: + 2000, 2005, 2010, 2015 (default), 2020 + + Returns + ------- + tile_temp : pandas.arrays.SparseArray + GPW data + lon : list + List with longitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords is 1). + lat : list + list with latitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords is 1). """ resolution = parameters.get('resolution', 30) cut_bbox = parameters.get('cut_bbox') # country_cut_mode = parameters.get('country_cut_mode', 0) return_coords = parameters.get('return_coords', 0) - reference_year = parameters.get('reference_year', 2015) - year = YEARS_AVAILABLE.flat[np.abs(YEARS_AVAILABLE - reference_year).argmin()] + reference_year = parameters.get('reference_year', 2015) + year = YEARS_AVAILABLE[np.abs(YEARS_AVAILABLE - reference_year).argmin()] if year != reference_year: - LOGGER.info('Reference year: %i. Using nearest available year for GWP population data: %i',\ + LOGGER.info('Reference year: %i. Using nearest available year for GWP population data: %i', reference_year, year) if (cut_bbox is None) & (return_coords == 0): # If we don't have any bbox by now and we need one, we just use the global cut_bbox = np.array((-180, -90, 180, 90)) - zoom_factor = 30/resolution # Orignal resolution is arc-seconds + zoom_factor = 30 / resolution # Orignal resolution is arc-seconds file_exists = False for ver in GPW_VERSIONS: gpw_path = parameters.get('gpw_path', FOLDER_GPW % (ver, year)) @@ -158,26 +165,25 @@ def get_box_gpw(**parameters): fname = os.path.join(gpw_path, FILENAME_GPW % (ver, year)) if os.path.isfile(fname): file_exists = True - LOGGER.info('GPW Version v4.%2i' % ver) + LOGGER.info('GPW Version v4.%2i', ver) break try: if not file_exists: if os.path.isfile(os.path.join(SYSTEM_DIR, 'GPW_help.pdf')): - subprocess.Popen([os.path.join(SYSTEM_DIR, 'GPW_help.pdf')],\ - shell=True) - raise FileExistsError('The file ' + str(fname) + ' could not '\ - + 'be found. Please download the file '\ - + 'first or choose a different folder. '\ - + 'Instructions on how to download the '\ - + 'file has been openend in your PDF '\ + subprocess.Popen([os.path.join(SYSTEM_DIR, 'GPW_help.pdf')], shell=True) + raise FileExistsError('The file ' + str(fname) + ' could not ' + + 'be found. Please download the file ' + + 'first or choose a different folder. ' + + 'Instructions on how to download the ' + + 'file has been openend in your PDF ' + 'viewer.') else: - raise FileExistsError('The file ' + str(fname) + ' could not '\ - + 'be found. Please download the file '\ - + 'first or choose a different folder. '\ - + 'The data can be downloaded from '\ - + 'http://sedac.ciesin.columbia.edu/'\ + raise FileExistsError('The file ' + str(fname) + ' could not ' + + 'be found. Please download the file ' + + 'first or choose a different folder. ' + + 'The data can be downloaded from ' + + 'http://sedac.ciesin.columbia.edu/' + 'data/collection/gpw-v4/sets/browse') LOGGER.debug('Importing %s', str(fname)) gpw_file = gdal.Open(fname) @@ -186,30 +192,31 @@ def get_box_gpw(**parameters): del band1, gpw_file arr1[arr1 < 0] = 0 if arr1.shape != (17400, 43200) and arr1.shape != (21600, 43200): - LOGGER.warning('GPW data dimensions mismatch. Actual dimensions: '\ - + '%s x %s', str(arr1.shape[0]), str(arr1.shape[1])) + LOGGER.warning('GPW data dimensions mismatch. Actual dimensions: %s x %s', + arr1.shape[0], arr1.shape[1]) LOGGER.warning('Expected dimensions: 17400x43200 or 21600x43200.') if zoom_factor != 1: total_population = arr1.sum() tile_temp = nd.zoom(arr1, zoom_factor, order=1) # normalize interpolated gridded population count to keep total population stable: - tile_temp = tile_temp*(total_population/tile_temp.sum()) + tile_temp = tile_temp * (total_population / tile_temp.sum()) else: tile_temp = arr1 if tile_temp.ndim == 2: - if not cut_bbox is None: - tile_temp = _gpw_bbox_cutter(tile_temp, cut_bbox, resolution, \ + if cut_bbox is not None: + tile_temp = _gpw_bbox_cutter(tile_temp, cut_bbox, resolution, arr1_shape=arr1.shape) else: LOGGER.error('Error: Matrix has an invalid number of dimensions \ (more than 2). Could not continue operation.') raise TypeError - tile_temp = pd.SparseArray(tile_temp.reshape((tile_temp.size,),\ - order='F'), fill_value=0) + tile_temp = pd.arrays.SparseArray( + tile_temp.reshape((tile_temp.size,), order='F'), + fill_value=0) del arr1 if return_coords == 1: - lon = tuple((cut_bbox[0], 1/(3600/resolution))) - lat = tuple((cut_bbox[1], 1/(3600/resolution))) + lon = tuple((cut_bbox[0], 1 / (3600 / resolution))) + lat = tuple((cut_bbox[1], 1 / (3600 / resolution))) return tile_temp, lon, lat return tile_temp diff --git a/climada/entity/exposures/litpop.py b/climada/entity/exposures/litpop.py index 3711fcb54c..ed41cd5515 100644 --- a/climada/entity/exposures/litpop.py +++ b/climada/entity/exposures/litpop.py @@ -22,7 +22,6 @@ import numpy as np import pandas as pd from pandas_datareader import wb -from scipy import sparse from scipy import ndimage as nd from scipy import stats import geopandas as gpd @@ -30,13 +29,13 @@ from matplotlib import pyplot as plt from iso3166 import countries as iso_cntry import gdal -from pint import UnitRegistry from cartopy.io import shapereader from climada.entity.exposures import nightlight from climada.entity.tag import Tag from climada.entity.exposures.base import Exposures, INDICATOR_IF from climada.entity.exposures import gpw_import +from climada.util import ureg from climada.util.finance import gdp, income_group, wealth2gdp, world_bank_wealth_account from climada.util.constants import SYSTEM_DIR, DEF_CRS from climada.util.coordinates import pts_to_raster_meta, get_resolution @@ -46,47 +45,46 @@ """Define LitPop class.""" # Black Marble nightlight tile names, %i represents the Black Marble reference year -BM_FILENAMES = ['BlackMarble_%i_A1_geo_gray.tif', \ - 'BlackMarble_%i_A2_geo_gray.tif', \ - 'BlackMarble_%i_B1_geo_gray.tif', \ - 'BlackMarble_%i_B2_geo_gray.tif', \ - 'BlackMarble_%i_C1_geo_gray.tif', \ - 'BlackMarble_%i_C2_geo_gray.tif', \ - 'BlackMarble_%i_D1_geo_gray.tif', \ - 'BlackMarble_%i_D2_geo_gray.tif'] +BM_FILENAMES = ['BlackMarble_%i_A1_geo_gray.tif', + 'BlackMarble_%i_A2_geo_gray.tif', + 'BlackMarble_%i_B1_geo_gray.tif', + 'BlackMarble_%i_B2_geo_gray.tif', + 'BlackMarble_%i_C1_geo_gray.tif', + 'BlackMarble_%i_C2_geo_gray.tif', + 'BlackMarble_%i_D1_geo_gray.tif', + 'BlackMarble_%i_D2_geo_gray.tif'] # years with Black Marble Tiles # https://earthobservatory.nasa.gov/features/NightLights/page3.php # Update if new years get available! -BM_YEARS = [2016, 2012] # latest first +BM_YEARS = [2016, 2012] # latest first # Years with GPW population data available: GPW_YEARS = [2020, 2015, 2010, 2005, 2000] -NASA_RESOLUTION_DEG = (15*UnitRegistry().arc_second).to(UnitRegistry().deg). \ - magnitude +NASA_RESOLUTION_DEG = (15 * ureg.arc_second).to(ureg.deg).magnitude WORLD_BANK_INC_GRP = \ "http://databank.worldbank.org/data/download/site-content/OGHIST.xls" -""" Income group historical data from World bank.""" +"""Income group historical data from World bank.""" DEF_RES_NASA_KM = 0.5 -""" Default approximate resolution for NASA's nightlights in km.""" +"""Default approximate resolution for NASA's nightlights in km.""" DEF_RES_GPW_KM = 1 -""" Default approximate resolution for the GPW dataset in km.""" +"""Default approximate resolution for the GPW dataset in km.""" DEF_RES_NASA_ARCSEC = 15 -""" Default approximate resolution for NASA's nightlights in arcsec.""" +"""Default approximate resolution for NASA's nightlights in arcsec.""" DEF_RES_GPW_ARCSEC = 30 -""" Default approximate resolution for the GPW dataset in arcsec.""" +"""Default approximate resolution for the GPW dataset in arcsec.""" DEF_HAZ_TYPE = '' -""" Default hazard type used in impact functions id, i.e. TC """ +"""Default hazard type used in impact functions id, i.e. TC""" class LitPop(Exposures): """Defines exposure values from nightlight intensity (NASA), Gridded Population - data (SEDAC); distributing produced capital (World Bank), GDP (World Bank) - or non-financial wealth (Global Wealth Databook by the Credit Suisse + data (SEDAC); distributing produced capital (World Bank), GDP (World Bank) + or non-financial wealth (Global Wealth Databook by the Credit Suisse Research Institute.) Calling sequence example: @@ -101,7 +99,7 @@ def _constructor(self): return LitPop def clear(self): - """ Appending the base class clear attribute to also delete attributes + """Appending the base class clear attribute to also delete attributes which are only used here. """ Exposures.clear(self) @@ -111,7 +109,7 @@ def clear(self): pass def set_country(self, countries, **args): - """ Get LitPop based exposre for one country or multiple countries + """Get LitPop based exposre for one country or multiple countries using values at reference year. If produced capital, GDP, or income group, etc. not available for that year, consider the value of the closest available year. @@ -148,7 +146,7 @@ def set_country(self, countries, **args): reference_year (int) adm1_scatter (boolean): produce scatter plot for admin1 validation? """ - #TODO: allow for user delivered path + # TODO: allow for user delivered path # self.clear() # clear existing assets (reset) start_time = time.time() res_km = args.get('res_km', 1) @@ -162,24 +160,24 @@ def set_country(self, countries, **args): # bm_year = min(BM_YEARS, key=lambda x:abs(x-reference_year)) # inherit_admin1_from_admin0 = args.get('inherit_admin1_from_admin0', 1) if res_arcsec == []: - resolution = (res_km/DEF_RES_GPW_KM)*DEF_RES_GPW_ARCSEC + resolution = (res_km / DEF_RES_GPW_KM) * DEF_RES_GPW_ARCSEC else: resolution = res_arcsec _match_target_res(resolution) check_plot = args.get('check_plot', 0) country_info = dict() admin1_info = dict() - if isinstance(countries, list): #multiple countries + if isinstance(countries, list): # multiple countries list_len = len(countries) country_list = countries for i, country in enumerate(country_list[::-1]): country_new = _get_iso3(country) - country_list[list_len-1-i] =\ + country_list[list_len - 1 - i] =\ country_new if country_new is None: LOGGER.warning('The country %s could not be found.', country) LOGGER.warning('Country %s is removed from the list.', country) - del country_list[list_len-1-i] + del country_list[list_len - 1 - i] else: country_info[country_new], admin1_info[country_new] =\ _get_country_info(country_new) @@ -188,10 +186,9 @@ def set_country(self, countries, **args): LOGGER.error('No valid country chosen. Operation aborted.') raise ValueError else: - all_bbox = [_get_country_shape(countr, 1)[0]\ - for countr in country_list] + all_bbox = [_get_country_shape(countr, 1)[0] for countr in country_list] cut_bbox = _bbox_union(all_bbox) - elif isinstance(countries, str): #One country + elif isinstance(countries, str): # One country country_list = list() country_list.append(countries) country_new = _get_iso3(countries) @@ -211,7 +208,7 @@ def set_country(self, countries, **args): all_coords = _litpop_box2coords(cut_bbox, resolution, 1) # Get LitPop LOGGER.info('Generating LitPop data at a resolution of %s arcsec.', str(resolution)) - litpop_data = _get_litpop_box(cut_bbox, resolution, 0, reference_year, \ + litpop_data = _get_litpop_box(cut_bbox, resolution, 0, reference_year, exponents) shp_file = shapereader.natural_earth(resolution='10m', category='cultural', @@ -220,9 +217,8 @@ def set_country(self, countries, **args): for cntry_iso, cntry_val in country_info.items(): if fin_mode == 'pc': - total_asset_val = world_bank_wealth_account(cntry_iso, \ - reference_year, \ - no_land=True)[1] + total_asset_val = world_bank_wealth_account(cntry_iso, reference_year, + no_land=True)[1] # here, total_asset_val is Produced Capital "pc" # no_land=True returns value w/o the mark-up of 24% for land value elif fin_mode in ['norm', 'none']: @@ -236,68 +232,75 @@ def set_country(self, countries, **args): lp_cntry = list() for curr_country in country_list: curr_shp = _get_country_shape(curr_country, 0) - mask = _mask_from_shape(curr_shp, resolution=resolution,\ + mask = _mask_from_shape(curr_shp, resolution=resolution, points2check=all_coords) litpop_curr = litpop_data[mask.sp_index.indices] lon, lat = zip(*np.array(all_coords)[mask.sp_index.indices]) if fin_mode == 'none': LOGGER.info('fin_mode=none --> no downscaling; admin1_calc is ignored') elif admin1_calc == 1: - litpop_curr = _calc_admin1(curr_country,\ + litpop_curr = _calc_admin1(curr_country, country_info[curr_country], - admin1_info[curr_country],\ - litpop_curr, list(zip(lon, lat)),\ - resolution, adm1_scatter, \ - conserve_cntrytotal=conserve_cntrytotal,\ + admin1_info[curr_country], + litpop_curr, list(zip(lon, lat)), + resolution, adm1_scatter, + conserve_cntrytotal=conserve_cntrytotal, check_plot=check_plot, masks_adm1=[], return_data=1) else: - litpop_curr = _calc_admin0(litpop_curr,\ - country_info[curr_country][3],\ - country_info[curr_country][4]) - lp_cntry.append(self._set_one_country(country_info[curr_country],\ - litpop_curr, lon, lat, curr_country)) - tag.description += \ - 'LitPop for %s at %i as, year=%i, financial mode=%s, GPW-year=%i, BM-year=%i, exp=[%i, %i]' \ - % (country_info[curr_country][1], resolution, reference_year, \ - fin_mode, \ - min(GPW_YEARS, key=lambda x: abs(x-reference_year)), \ - min(BM_YEARS, key=lambda x: abs(x-reference_year)), \ - exponents[0], exponents[1]) - Exposures.__init__(self, gpd.GeoDataFrame(pd.concat(lp_cntry, \ - ignore_index=True)), crs=DEF_CRS) + litpop_curr = _calc_admin0(litpop_curr, + country_info[curr_country][3], + country_info[curr_country][4]) + lp_cntry.append(self._set_one_country(country_info[curr_country], + litpop_curr, lon, lat, curr_country)) + tag.description += ('LitPop for %s at %i as, year=%i, financial mode=%s, ' + 'GPW-year=%i, BM-year=%i, exp=[%i, %i]' + % (country_info[curr_country][1], resolution, reference_year, + fin_mode, + min(GPW_YEARS, key=lambda x: abs(x - reference_year)), + min(BM_YEARS, key=lambda x: abs(x - reference_year)), + exponents[0], exponents[1])) + Exposures.__init__(self, gpd.GeoDataFrame(pd.concat(lp_cntry, ignore_index=True)), + crs=DEF_CRS) self.ref_year = reference_year self.tag = tag self.value_unit = 'USD' try: - rows, cols, ras_trans = pts_to_raster_meta((self.longitude.min(), \ - self.latitude.min(), self.longitude.max(), self.latitude.max()), \ - min(get_resolution(self.latitude, self.longitude))) - self.meta = {'width':cols, 'height':rows, 'crs':self.crs, 'transform':ras_trans} - except ValueError: - LOGGER.warning('Could not write attribute meta, because exposure has only 1 data point') + rows, cols, ras_trans = pts_to_raster_meta( + (self.longitude.min(), self.latitude.min(), + self.longitude.max(), self.latitude.max()), + get_resolution(self.longitude, self.latitude)) + self.meta = { + 'width': cols, + 'height': rows, + 'crs': self.crs, + 'transform': ras_trans, + } + except ValueError: + LOGGER.warning('Could not write attribute meta, because exposure' + ' has only 1 data point') self.meta = {} if check_plot == 1: self.plot_log(admin1_plot=0) - LOGGER.info("Creating the LitPop exposure took %i s", \ - int(round(time.time() - start_time, 2))) + LOGGER.info("Creating the LitPop exposure took %i s", + int(round(time.time() - start_time, 2))) # self.set_geometry_points() self.check() @staticmethod def _set_one_country(cntry_info, litpop_data, lon, lat, curr_country): - """ Model one country. + """Model one country. Parameters: cntry_info (list): [cntry_id, cnytry_name, cntry_geometry, ref_year, gdp, income_group] - litpop_data (pandas SparseArray): LitPop data with the value + litpop_data (pandas.arrays.SparseArray): LitPop data with the value already distributed. lon (array): longitudinal coordinates lat (array): latudinal coordinates curr_country: name or iso3 ID of country """ lp_ent = LitPop() - lp_ent['value'] = litpop_data.values + lp_ent['value'] = litpop_data.to_numpy() lp_ent['latitude'] = lat lp_ent['longitude'] = lon try: @@ -310,7 +313,7 @@ def _set_one_country(cntry_info, litpop_data, lon, lat, curr_country): return lp_ent def _append_additional_info(self, cntries_info): - """ Add country information in dictionary attribute country_data. + """Add country information in dictionary attribute country_data. Parameters: cntries_info (dict): country ISO3, name and shape @@ -322,32 +325,32 @@ def _append_additional_info(self, cntries_info): self.country_data['shape'].append(cntry_info[2]) def plot_log(self, admin1_plot=1): - """ Plots the LitPop data with the color scale reprenting the values + """Plots the LitPop data with the color scale reprenting the values in a logarithmic scale. Parameters: admin1_plot (boolean): whether admin1 borders should be plotted. Default=1 """ - #TODO: plot subplots for the different countries instead of one global - #one. Countries can be identified by their region id, hence this - #can be implemented + # TODO: plot subplots for the different countries instead of one global + # one. Countries can be identified by their region id, hence this + # can be implemented import matplotlib.colors as colors if not self.value.sum() == 0: plt.figure() # countr_shape = _get_country_shape(country_iso, 0) - countr_bbox = np.array((min(self.coord[:, 1]),\ - min(self.coord[:, 0]),\ - max(self.coord[:, 1]),\ + countr_bbox = np.array((min(self.coord[:, 1]), + min(self.coord[:, 0]), + max(self.coord[:, 1]), max(self.coord[:, 0]))) - plt.gca().set_xlim(countr_bbox[0]\ - -0.1*(countr_bbox[2]-countr_bbox[0]), countr_bbox[2]\ - +0.1*(countr_bbox[2]-countr_bbox[0])) - plt.gca().set_ylim(countr_bbox[1]\ - -0.1*(countr_bbox[3]-countr_bbox[1]), countr_bbox[3]\ - +0.1*(countr_bbox[3]-countr_bbox[1])) - plt.scatter(self.coord[:, 1], self.coord[:, 0],\ - c=self.value, marker=',', s=3,\ + plt.gca().set_xlim(countr_bbox[0] + - 0.1 * (countr_bbox[2] - countr_bbox[0]), countr_bbox[2] + + 0.1 * (countr_bbox[2] - countr_bbox[0])) + plt.gca().set_ylim(countr_bbox[1] + - 0.1 * (countr_bbox[3] - countr_bbox[1]), countr_bbox[3] + + 0.1 * (countr_bbox[3] - countr_bbox[1])) + plt.scatter(self.coord[:, 1], self.coord[:, 0], + c=self.value, marker=',', s=3, norm=colors.LogNorm()) plt.title('Logarithmic scale LitPop value') if hasattr(self, 'country_data') and\ @@ -355,14 +358,14 @@ def plot_log(self, admin1_plot=1): for idx, shp in enumerate(self.country_data['shape']): _plot_shape_to_plot(shp) if admin1_plot == 1: - _plot_admin1_shapes(self.country_data['ISO3'][idx],\ + _plot_admin1_shapes(self.country_data['ISO3'][idx], 0.6) plt.colorbar() plt.show() -def _get_litpop_box(cut_bbox, resolution, return_coords=0, \ +def _get_litpop_box(cut_bbox, resolution, return_coords=0, reference_year=2016, exponents=[1, 1]): - ''' + """ PURPOSE: A function which retrieves and calculates the LitPop data within a certain bounding box for a given resolution. @@ -380,23 +383,23 @@ def _get_litpop_box(cut_bbox, resolution, return_coords=0, \ To get nightlights^3 alone: [3, 0]. To use population count alone: [0, 1]. OUTPUT (either one of these lines, depending on option return_coords): - litpop_data (pandas SparseArray): A pandas SparseArray containing the + litpop_data (pandas.arrays.SparseArray): A pandas SparseArray containing the raw, unnormalised LitPop data. OR litpop_data, lon, lat (tuple): if return_coords=1 a tuple in the form (lon, lat) with the coordinates falling into the cut_bbox are return along with the litpop_data (see above). - ''' + """ - nightlights = _get_box_blackmarble(cut_bbox, reference_year=reference_year, \ - resolution=resolution, return_coords=0) - gpw = gpw_import.get_box_gpw(cut_bbox=cut_bbox, resolution=resolution,\ - return_coords=0, reference_year=reference_year) + nightlights = _get_box_blackmarble(cut_bbox, reference_year=reference_year, + resolution=resolution, return_coords=0) + gpw = gpw_import.get_box_gpw(cut_bbox=cut_bbox, resolution=resolution, + return_coords=0, reference_year=reference_year) bm_temp = np.ones(nightlights.shape) # Lit = Lit + 1 if Population is included, c.f. int(exponents[1]>0): - bm_temp[nightlights.sp_index.indices] = (np.array(nightlights.sp_values, \ - dtype='uint16')+int(exponents[1] > 0)) - nightlights = pd.SparseArray(bm_temp, fill_value=int(exponents[1] > 0)) + bm_temp[nightlights.sp_index.indices] = (np.array(nightlights.sp_values, dtype='uint16') + + int(exponents[1] > 0)) + nightlights = pd.arrays.SparseArray(bm_temp, fill_value=int(exponents[1] > 0)) del bm_temp litpop_data = _LitPop_multiply(nightlights, gpw, exponents=exponents) @@ -405,28 +408,28 @@ def _get_litpop_box(cut_bbox, resolution, return_coords=0, \ lon, lat = _litpop_box2coords(cut_bbox, resolution, 0) return litpop_data, lon, lat return litpop_data + def _LitPop_multiply(nightlights, gpw, exponents=[1, 1]): - ''' + """ PURPOSE: Pixel-wise multiplication of lit (nightlights^exponents[0]) and pop (gpw^exponents[1]) to compute LitPop. Both factors are included to the power of lit_exp / pop_exp to change their weight. INPUTS: - nightlights (dataframe): gridded nightlights data - gpw (dataframe): gridded population data + nightlights (SparseArray): gridded nightlights data + gpw (SparseArray): gridded population data exponents (list of two integers): exponents for nightlights and population data, default = [1, 1] OUTPUT: litpop_data (dataframe): gridded resulting LitPop - ''' - litpop_data = pd.SparseArray(np.multiply(nightlights.values**exponents[0], \ - gpw.values**exponents[1]),\ - fill_value=0) + """ + litpop_data = pd.arrays.SparseArray( + np.multiply(nightlights.to_numpy()**exponents[0], gpw.to_numpy()**exponents[1]), fill_value=0) return litpop_data def _litpop_box2coords(box, resolution, point_format=0): - ''' + """ PURPOSE: A function which calculates coordinates arrays explicitly from a bounding box for a given resolution @@ -444,24 +447,28 @@ def _litpop_box2coords(box, resolution, point_format=0): the bounding box is returned. coordiates (array): if point_format =1 is selected a tuple entry of the form (lon, lat) for each point is returned. - ''' - deg_per_pix = 1/(3600/resolution) + """ + deg_per_pix = 1 / (3600 / resolution) min_col, min_row, max_col, max_row =\ _litpop_coords_in_glb_grid(box, resolution) - lon = np.array(np.transpose([np.ones((max_row-min_row+1,))\ - *((-180+(deg_per_pix/2))+l_i*deg_per_pix)\ - for l_i in range(min_col, (max_col+1))])) + lon = np.array(np.transpose([np.ones((max_row - min_row + 1,)) + * ((-180 + (deg_per_pix / 2)) + l_i * deg_per_pix) + for l_i in range(min_col, (max_col + 1))])) lon = lon.flatten(order='F') - lat = np.array(np.transpose([((90-(deg_per_pix/2))-(l_j*deg_per_pix))\ - for l_j in range(min_row, (max_row+1))]\ - *np.ones((max_col-min_col+1, (max_row-min_row+1))))) + lat = np.array( + np.transpose( + [((90 - (deg_per_pix / 2)) - (l_j * deg_per_pix)) + for l_j in range(min_row, (max_row + 1))] + * np.ones((max_col - min_col + 1, (max_row - min_row + 1))) + ) + ) lat = lat.flatten(order='F') if point_format == 1: return list([(lon, lat) for lon, lat in zip(lon, lat)]) return lon, lat def _litpop_coords_in_glb_grid(box, resolution): - ''' + """ PURPOSE: Function which calculates the coordinates from geographic to a cartesian coordinate system, where the NE-most point is 0,0. @@ -476,21 +483,22 @@ def _litpop_coords_in_glb_grid(box, resolution): OUTPUTS: mincol, minrow, maxcol, maxrow (array): row and col numbers which define the box in the cartesian coordinate system. - ''' + """ minlon, minlat, maxlon, maxlat = box - deg_per_pix = 1/(3600/resolution) - minlon, maxlon = minlon-(-180), maxlon-(-180) - minlat, maxlat = -(minlat-(90)), -(maxlat-(90)) - lon_dist = np.ceil(abs(maxlon-minlon)/deg_per_pix) - lat_dist = np.ceil(abs(maxlat-minlat)/deg_per_pix) - mincol = int(max(minlon//deg_per_pix, 0)) - maxcol = int(max(mincol + lon_dist-1, mincol)) - minrow = int(max(maxlat//deg_per_pix, 0)) - maxrow = int(max(minrow + lat_dist-1, minrow)) + deg_per_pix = 1 / (3600 / resolution) + minlon, maxlon = minlon - (-180), maxlon - (-180) + minlat, maxlat = -(minlat - (90)), -(maxlat - (90)) + lon_dist = np.ceil(abs(maxlon - minlon) / deg_per_pix) + lat_dist = np.ceil(abs(maxlat - minlat) / deg_per_pix) + mincol = int(max(minlon // deg_per_pix, 0)) + maxcol = int(max(mincol + lon_dist - 1, mincol)) + minrow = int(max(maxlat // deg_per_pix, 0)) + maxrow = int(max(minrow + lat_dist - 1, minrow)) return np.array((mincol, minrow, maxcol, maxrow)) -"""def _litpop_convert_coords(box, resolution): - ''' +''' +def _litpop_convert_coords(box, resolution): + """ PURPOSE: A function which fits coordinates to global LitPop grid. Main purpose is to keep track of coordinates cut in reading BM and GPW without @@ -505,7 +513,7 @@ def _litpop_coords_in_glb_grid(box, resolution): OUTPUT: box (1x4 array-like): bounding box with coordinates adjusted to global LitPop grid. - ''' + """ minlon, maxlon = box[0], box[2] minlat, maxlat = box[1], box[3] deg_per_pix = 1/(3600/resolution) @@ -517,10 +525,10 @@ def _litpop_coords_in_glb_grid(box, resolution): +(deg_per_pix/2), max_col*deg_per_pix\ +(deg_per_pix/2), max_row*deg_per_pix+(deg_per_pix/2))) return box - """ +''' def _get_country_shape(country_iso, only_geo=0): - """ Retrieves the shape file or coordinate information of a country. + """Retrieves the shape file or coordinate information of a country. Parameters: country_iso (str): country code of country to get @@ -566,7 +574,7 @@ def _get_country_shape(country_iso, only_geo=0): return bbox, lat, lon def _match_target_res(target_res='NA'): - """ Checks whether the resolution is compatible with the "legacy" + """Checks whether the resolution is compatible with the "legacy" resolutions used in Matlab Climada and produces a warning message if not. @@ -574,13 +582,13 @@ def _match_target_res(target_res='NA'): target_res (scalar): Resolution in arc seconds. """ res_list = [30, 60, 120, 300, 600, 3600] - out_res = min(res_list, key=lambda x: abs(x-target_res)) + out_res = min(res_list, key=lambda x: abs(x - target_res)) if out_res != target_res: LOGGER.warning('Not one of the legacy resoultions selected. Consider \ adjusting it to %s arc-sec.', out_res) def _shape_cutter(shape, **opt_args): - """ Checks whether given coordinates are within a shape or not. Can also + """Checks whether given coordinates are within a shape or not. Can also check if a shape possesses enclaves and cuts them out accordingly. If no coordinates are supplied, all coordinates in the bounding box of the shape under the given resolution are checked. @@ -618,7 +626,7 @@ def _shape_cutter(shape, **opt_args): incl_coords (list): list of tuples of formate (lon, lat) of points inside shape (returned if points_format=1) enclave_paths (list): list of detected enclave paths - mask (pandas SparseArray): SparseArray which =1 where is point is + mask (pandas.arrays.SparseArray): SparseArray with one where point is inside shape and zero otherwise. (only returned if return_mask=1) if only_geo = 0 (default): The shape of type shapefile._Shape @@ -631,7 +639,6 @@ def _shape_cutter(shape, **opt_args): of the shape (array) """ from matplotlib import path - curr_time = time.time() resolution = opt_args.get('resolution', 30) check_enclaves = opt_args.get('check_enclaves', 1) check_plot = opt_args.get('check_plot', 0) @@ -652,13 +659,13 @@ def _shape_cutter(shape, **opt_args): add2enclave = 0 if sub_shapes > 1: for i in range(0, sub_shapes): - if i == (sub_shapes-1): - end_idx = len(shape.points)-1 + if i == (sub_shapes - 1): + end_idx = len(shape.points) - 1 else: - end_idx = shape.parts[i+1]-1 + end_idx = shape.parts[i + 1] - 1 if (i > 0) & (check_enclaves == 1): temp_path = path.Path(all_coords_shape[shape.parts[i]:end_idx]) - for idx, val in enumerate(sub_shape_path): + for val in sub_shape_path: if val.contains_point(temp_path.vertices[0]) and \ len(temp_path.vertices) > 2 and \ val.contains_point(temp_path.vertices[1]) and\ @@ -673,13 +680,12 @@ def _shape_cutter(shape, **opt_args): sub_shape_path.append(temp_path) temp_path = [] else: - sub_shape_path.append(path.Path(all_coords_shape\ - [shape.parts[i]:end_idx])) + sub_shape_path.append(path.Path(all_coords_shape[shape.parts[i]:end_idx])) if check_enclaves == 1: - LOGGER.debug('Detected subshapes: %s, of which subshapes: %s',\ + LOGGER.debug('Detected subshapes: %s, of which subshapes: %s', str(sub_shapes), str(len(enclave_paths))) else: - LOGGER.debug('Detected subshapes: %s. Enclave checking disabled',\ + LOGGER.debug('Detected subshapes: %s. Enclave checking disabled', str(sub_shapes)) else: sub_shape_path.append(path.Path(all_coords_shape)) @@ -687,7 +693,7 @@ def _shape_cutter(shape, **opt_args): incl_coords = [] for _, val in enumerate(sub_shape_path): add_points = _mask_from_path(val, resolution) - if not add_points is None: + if add_points is not None: [incl_coords.append(point) for point in add_points] del add_points stdout.write('\n') @@ -696,29 +702,26 @@ def _shape_cutter(shape, **opt_args): # LOGGER.debug('Removing enclaves...') for _, val in enumerate(enclave_paths): temp_excl_points = _mask_from_path(val, resolution) - if not temp_excl_points is None: + if temp_excl_points is not None: [excl_coords.append(point) for point in temp_excl_points] del temp_excl_points excl_coords = set(tuple(row) for row in excl_coords) - incl_coords = [point for point in incl_coords if point not\ + incl_coords = [point for point in incl_coords if point not in excl_coords] # LOGGER.debug('Successfully isolated coordinates from shape') - total_bbox = np.array((min([x[0] for x in shape.points]),\ - min([x[1] for x in shape.points]), max(x[0] for x in shape.points),\ - max(x[1] for x in shape.points))) + total_bbox = np.array((min([x[0] for x in shape.points]), + min([x[1] for x in shape.points]), + max([x[0] for x in shape.points]), + max([x[1] for x in shape.points]))) if points2check == []: all_coords = _litpop_box2coords(total_bbox, resolution, 1) else: all_coords = points2check del points2check incl_coords = set(incl_coords) - mask = sparse.lil.lil_matrix(np.zeros((len(all_coords),))) - for idx, val in enumerate(all_coords): - if val in incl_coords: - mask[0, idx] = 1 - mask = pd.SparseArray(mask.toarray().reshape((-1,), order='F'),\ - fill_value=0) - lon, lat = zip(*[all_coords[val] for idx, val\ + mask = np.array([(coord in incl_coords) for coord in all_coords]) + mask = pd.arrays.SparseArray(mask, fill_value=0) + lon, lat = zip(*[all_coords[val] for idx, val in enumerate(mask.sp_index.indices)]) if check_plot == 1: plt.scatter(lon, lat, cmap='plasma', marker=',') @@ -743,16 +746,17 @@ def _shape_cutter(shape, **opt_args): return lon, lat, enclave_paths def _mask_from_path(path, resolution=30, return_points=1, return_mask=0): - curr_bbox = np.array((min([x[0] for x in path.vertices]), min([x[1] for x\ - in path.vertices]), max([x[0] for x in\ - path.vertices]), max([x[1] for x in path.vertices]))) + curr_bbox = np.array((min([x[0] for x in path.vertices]), + min([x[1] for x in path.vertices]), + max([x[0] for x in path.vertices]), + max([x[1] for x in path.vertices]))) curr_points2check = _litpop_box2coords(curr_bbox, resolution, 1) del curr_bbox if curr_points2check == []: return None - temp_mask = pd.SparseArray(path.contains_points(curr_points2check),\ + temp_mask = pd.arrays.SparseArray(path.contains_points(curr_points2check), fill_value=0) - points_in = [curr_points2check[val] for idx, val\ + points_in = [curr_points2check[val] for idx, val in enumerate(temp_mask.sp_index.indices)] if return_points == 1: if return_mask == 1: @@ -765,60 +769,60 @@ def _mask_from_path(path, resolution=30, return_points=1, return_mask=0): return lon, lat def _mask_from_shape(check_shape, **opt_args): - """ creates a mask from a shape assigning value 1 to points inside and 0 + """creates a mask from a shape assigning value 1 to points inside and 0 otherwise. - Parameters: - check_shape (_Shape): shape file to check - Optional: - opt_args (keyword arguments): - resolution (scalar): resolution of the points to be checked in - arcsec. Required if the points need to be created first. - Defautl = 30. - check_enclaves (boolean): If activated, enclaves get detected and - cut out from shapes. Default = 1. - check_plot (boolean): If activated, a plot with the shap and the - mask is shown. Default = 0. - shape_format (str, tuple): colour of the shape if it is plotted. - Takes any colour format which is recognised by matplotlib. - enclave_format (str, tuple): colour of the enclaves if it they are - plotted. Takes any colour format which is recognised by - matplotlib. - return_mask (boolean): If activated, the mask is also returned - points2check (list): a list of points in tuple formaat (lon, lat) - for which should be checked whether they are inside the shape. - if no points are delivered, the points are created for the - bounding box of the shape. - point_format (boolean): If activated the points get returned as - a list in tuple format (lon, lat), otherwise, lon and lat - get returned separately. + Parameters + ---------- + check_shape : _Shape + shape file to check + resolution : scalar, optional + resolution of the points to be checked in arcsec. + Required if the points need to be created first. + Default: 30. + check_enclaves : boolean, optional + If activated, enclaves get detected and cut out from shapes. Default: 1. + check_plot : boolean, optional + If activated, a plot with the shap and the mask is shown. Default: 0. + shape_format : str, tuple, optional + colour of the shape if it is plotted. + Takes any colour format which is recognised by matplotlib. + enclave_format : str, tuple, optional + colour of the enclaves if it they are plotted. + Takes any colour format which is recognised by matplotlib. + return_mask : boolean, optional + If activated, the mask is also returned + points2check : list, optional + A list of points in tuple formaat (lon, lat) for which should be + checked whether they are inside the shape. + If no points are delivered, the points are created for the bounding box + of the shape. + point_format : boolean, optional + If activated the points get returned as a list in tuple format + (lon, lat), otherwise, lon and lat get returned separately. - Returns (depending on the chosen options): - lon (list): list of longitudinal coordinate data of points inside shape - (returned if points_format = 0) - lat (list): list of latitudianl coordinate data of points inside shape - (returned if points_format = 0) - incl_coords (list): list of tuples of formate (lon, lat) of points - inside shape (returned if points_format=1) - enclave_paths (list): list of detected enclave paths - mask (pandas SparseArray): SparseArray which =1 where is point is - inside shape and zero otherwise. (only returned if return_mask=1) - if only_geo = 0 (default): - The shape of type shapefile._Shape - if only_geo = 1 - bbox, lat, lon (tuple of size 3) - bbox is a 1x4 vector of the bounding box of the country (array) - lat is a mx1 vector of the latitudinal values of the vertices - of the shape (array) - lon is a mx1 vector of the longitudinal values of the vertices - of the shape (array) + Returns + ------- + lon : list + list of longitudinal coordinate data of points inside shape + (returned if points_format = 0) + lat : list + list of latitudianl coordinate data of points inside shape + (returned if points_format = 0) + incl_coords : list + list of tuples of formate (lon, lat) of points + inside shape (returned if points_format=1) + enclave_paths : list + list of detected enclave paths + mask : pandas.arrays.SparseArray + SparseArray with one where point is + inside shape and zero otherwise. (only returned if return_mask=1) """ from matplotlib import path resolution = opt_args.get('resolution', 30) check_enclaves = opt_args.get('check_enclaves', 1) points2check = opt_args.get('points2check', []) - if (not hasattr(check_shape, 'points')) or\ - (not hasattr(check_shape, 'parts')): + if (not hasattr(check_shape, 'points')) or (not hasattr(check_shape, 'parts')): LOGGER.error('Not a valid shape. Please make sure, the shapefile is \ of type from package "shapefile".') sub_shapes = len(check_shape.parts) @@ -829,20 +833,19 @@ def _mask_from_shape(check_shape, **opt_args): add2enclave = 0 if sub_shapes > 1: for i in range(0, sub_shapes): - if i == (sub_shapes-1): - end_idx = len(check_shape.points)-1 + if i == (sub_shapes - 1): + end_idx = len(check_shape.points) - 1 else: - end_idx = check_shape.parts[i+1]-1 + end_idx = check_shape.parts[i + 1] - 1 if (i > 0) & (check_enclaves == 1): - temp_path = path.Path(all_coords_shape\ - [check_shape.parts[i]:end_idx]) - for idx, val in enumerate(sub_shape_path): + temp_path = path.Path(all_coords_shape[check_shape.parts[i]:end_idx]) + for val in sub_shape_path: if val.contains_point(temp_path.vertices[0]) and \ - len(temp_path.vertices) > 2 and \ - val.contains_point(temp_path.vertices[1]) and\ - val.contains_point(temp_path.vertices[2]): - #Only check if the first three vertices of the new shape - # is in any of the old shapes for speed + len(temp_path.vertices) > 2 and \ + val.contains_point(temp_path.vertices[1]) and\ + val.contains_point(temp_path.vertices[2]): + # Only check if the first three vertices of the new shape + # is in any of the old shapes for speed add2enclave = 1 break if add2enclave == 1: @@ -853,13 +856,12 @@ def _mask_from_shape(check_shape, **opt_args): sub_shape_path.append(temp_path) temp_path = [] else: - sub_shape_path.append(path.Path(all_coords_shape\ - [check_shape.parts[i]:end_idx])) + sub_shape_path.append(path.Path(all_coords_shape[check_shape.parts[i]:end_idx])) if check_enclaves == 1: LOGGER.debug('Detected subshapes: %s', str(sub_shapes)) LOGGER.debug('of which detected enclaves: %s', str(len(enclave_paths))) else: - LOGGER.info('Detected subshapes: %s. Enclave checking disabled.', \ + LOGGER.info('Detected subshapes: %s. Enclave checking disabled.', str(sub_shapes)) else: sub_shape_path.append(path.Path(all_coords_shape)) @@ -867,7 +869,7 @@ def _mask_from_shape(check_shape, **opt_args): incl_coords = [] for _, val in enumerate(sub_shape_path): add_points = _mask_from_path(val, resolution) - if not add_points is None: + if add_points is not None: [incl_coords.append(point) for point in add_points] del add_points # stdout.write('\n') @@ -876,28 +878,25 @@ def _mask_from_shape(check_shape, **opt_args): LOGGER.debug('Removing enclaves...') for _, val in enumerate(enclave_paths): temp_excl_points = _mask_from_path(val, resolution) - if not temp_excl_points is None: + if temp_excl_points is not None: [excl_coords.append(point) for point in temp_excl_points] del temp_excl_points excl_coords = set(tuple(row) for row in excl_coords) - incl_coords = [point for point in incl_coords if point not in\ + incl_coords = [point for point in incl_coords if point not in excl_coords] LOGGER.debug('Successfully isolated coordinates from shape') - total_bbox = np.array((min([x[0] for x in check_shape.points]),\ - min([x[1] for x in check_shape.points]), max(x[0] for x\ - in check_shape.points), max(x[1] for x in check_shape.points))) + total_bbox = np.array((min([x[0] for x in check_shape.points]), + min([x[1] for x in check_shape.points]), + max([x[0] for x in check_shape.points]), + max([x[1] for x in check_shape.points]))) if points2check == []: all_coords = _litpop_box2coords(total_bbox, resolution, 1) else: all_coords = points2check del points2check incl_coords = set(incl_coords) - mask = sparse.lil.lil_matrix(np.zeros((len(all_coords),))) - for idx, val in enumerate(all_coords): - if val in incl_coords: - mask[0, idx] = 1 - mask = pd.SparseArray(mask.toarray().reshape((-1,), order='F'),\ - fill_value=0, dtype='bool_') + mask = np.array([(coord in incl_coords) for coord in all_coords]) + mask = pd.arrays.SparseArray(mask, fill_value=0, dtype='bool_') # plt.figure() # l1, l2 = zip(*[x for n, x in enumerate(all_coords) if mask.values[n]==1]) # plt.scatter(l1, l2) @@ -905,34 +904,20 @@ def _mask_from_shape(check_shape, **opt_args): return mask def _get_country_info(iso3): - """ Get country ISO alpha_3, country id (defined as country appearance + """Get country ISO alpha_3, country id (defined as country appearance order in natural earth shape file) and country's geometry. - Parameters: - countries (list or dict): list of country names (admin0) or dict - with key = admin0 name and value = [admin1 names] - shp_file (cartopy.io.shapereader.Reader): shape file - - Returns: - cntry_info (dict): key = ISO alpha_3 country, value = [country id, - country name, country geometry] - - Retrieves the shape file or coordinate information of a country. - - Parameters: - country_iso (str): country code of country to get - only_geo (boolean): Determines the output mode (default =0): - if =0: returns the entire shape file of the country - if =1: returns a tuple of values: bbox, lat, lon (see below) + Parameters + ---------- + iso3 : str + country code of country to get - Returns: - if only_geo = 0 (default): - The shape of type shapefile._Shape - if only_geo = 1 - bbox, lat, lon (tuple of size 3) - bbox is a 1x4 vector of the bounding box of the country (array) - lat is a mx1 vector of the latitudinal values of the vertices of the shape (array) - lon is a mx1 vector of the longitudinal values of the vertices of the shape (array) + Returns + ------- + cntry_info : tuple + (iso_num, country_name, country_shp) + country_admin1 : list + shapres and records of admin1 regions """ shp = shapereader.natural_earth(resolution='10m', category='cultural', @@ -981,7 +966,7 @@ def _bbox_union(bbox_in): return bbox def _get_iso3(country_name): - """ Find the ISO3 name corresponding to a country name. Can also be used + """Find the ISO3 name corresponding to a country name. Can also be used to check if an ISO3 exists. Parameters: @@ -1015,7 +1000,7 @@ def _get_iso3(country_name): return "" def _get_gdp2asset_factor(cntry_info, ref_year, shp_file, fin_mode='income_group'): - """ Append factor to convert GDP to physcial asset values according to + """Append factor to convert GDP to physcial asset values according to the Global Wealth Databook by the Credit Suisse Research Institute. Requires a pickled file containg a dictionary with the three letter country code as the key. The values are lists, each containg the @@ -1037,7 +1022,7 @@ def _get_gdp2asset_factor(cntry_info, ref_year, shp_file, fin_mode='income_group if fin_mode == 'income_group': for cntry_iso, cntry_val in cntry_info.items(): _, inc_grp = income_group(cntry_iso, ref_year, shp_file) - cntry_val.append(inc_grp+1) + cntry_val.append(inc_grp + 1) elif fin_mode in ('gdp', 'pc', 'none', 'norm'): for cntry_iso, cntry_val in cntry_info.items(): cntry_val.append(1) @@ -1052,9 +1037,9 @@ def _get_gdp2asset_factor(cntry_info, ref_year, shp_file, fin_mode='income_group else: LOGGER.error("invalid fin_mode") -def _gsdp_read(country_iso3, admin1_shape_data,\ +def _gsdp_read(country_iso3, admin1_shape_data, look_folder=os.path.join(SYSTEM_DIR, 'GSDP')): - ''' Retrieves the GSDP data for a certain country. It requires an + """Retrieves the GSDP data for a certain country. It requires an excel file in a subfolder "GSDP" in climadas data folder (or in the specified folder). The excel file should bear the name 'ISO3_GSDP.xlsx' (or .xls), where ISO3 is the three letter country @@ -1072,22 +1057,21 @@ def _gsdp_read(country_iso3, admin1_shape_data,\ Returns: out_dict (dictionary): dictionary which contains the GSDP for each admin1 unit, where the name of the admin1 unit is the key. - ''' + """ file_name = _check_excel_exists(look_folder, str(country_iso3 + '_GSDP')) - if not file_name is None: + if file_name is not None: admin1_xls_data = pd.read_excel(file_name) if admin1_xls_data.get('State_Province') is None: - admin1_xls_data = admin1_xls_data.rename(columns=\ - {admin1_xls_data.columns[0]:'State_Province'}) + admin1_xls_data = admin1_xls_data.rename( + columns={admin1_xls_data.columns[0]: 'State_Province'}) if admin1_xls_data.get('GSDP_ref') is None: - admin1_xls_data = admin1_xls_data.rename(columns=\ - {admin1_xls_data.columns[-1]:'GSDP_ref'}) + admin1_xls_data = admin1_xls_data.rename( + columns={admin1_xls_data.columns[-1]: 'GSDP_ref'}) # prov = admin1_xls_data['State_Province'].tolist() out_dict = dict.fromkeys([nam[1]['name'] for nam in admin1_shape_data]) postals = [nam[1]['postal'] for nam in admin1_shape_data] for subnat_shape in out_dict.keys(): - for idx, subnat_xls\ - in enumerate(admin1_xls_data['State_Province'].tolist()): + for idx, subnat_xls in enumerate(admin1_xls_data['State_Province'].tolist()): if _compare_strings_nospecchars(subnat_shape, subnat_xls): out_dict[subnat_shape] = admin1_xls_data['GSDP_ref'][idx] break @@ -1105,7 +1089,7 @@ def _gsdp_read(country_iso3, admin1_shape_data,\ return None def _check_excel_exists(file_path, file_name, xlsx_before_xls=1): - ''' Checks if an Excel file with the name file_name in the folder + """Checks if an Excel file with the name file_name in the folder file_path exists, checking for both xlsx and xls files. Parameters: @@ -1113,7 +1097,7 @@ def _check_excel_exists(file_path, file_name, xlsx_before_xls=1): file_name (string): file name which is checked. Extension is ignored xlsx_before_xls (boolean): If set =1, xlsx files are priorised over xls files. Default=1. - ''' + """ try_ext = list() if xlsx_before_xls == 1: try_ext.append('.xlsx') @@ -1128,7 +1112,7 @@ def _check_excel_exists(file_path, file_name, xlsx_before_xls=1): return None def _compare_strings_nospecchars(str1, str2): - """ Compares strings while ignoring non-alphanumeric and special + """Compares strings while ignoring non-alphanumeric and special characters. Parameters: @@ -1139,16 +1123,16 @@ def _compare_strings_nospecchars(str1, str2): """ import re if not isinstance(str1, str) or not isinstance(str2, str): - LOGGER.warning('Invalid datatype (not strings), which cannot be '\ - + 'compared. Function will return exit and return false.') + LOGGER.warning('Invalid datatype (not strings), which cannot be ' + 'compared. Function will return exit and return false.') return False - pattern = re.compile('[^a-z|A-Z|0-9| ]') #ignore special + pattern = re.compile('[^a-z|A-Z|0-9| ]') # ignore special cstr1 = re.sub(pattern, '', str1).casefold() cstr2 = re.sub(pattern, '', str2).casefold() return bool(cstr1 == cstr2) def _plot_shape_to_plot(shp, gray_val=str(0.3)): - """ Plots a shape file to a pyplot. + """Plots a shape file to a pyplot. Parameters: shp (shapefile._Shape): shapefile to be plotted @@ -1157,17 +1141,17 @@ def _plot_shape_to_plot(shp, gray_val=str(0.3)): """ gray_val = str(gray_val) parts = np.array(shp.parts) - for i in range(0, len(parts)-1): - x_arr = np.array([x[0] for x in shp.points[parts[i]:parts[i+1]]]) - y_arr = np.array([x[1] for x in shp.points[parts[i]:parts[i+1]]]) + for i in range(0, len(parts) - 1): + x_arr = np.array([x[0] for x in shp.points[parts[i]:parts[i + 1]]]) + y_arr = np.array([x[1] for x in shp.points[parts[i]:parts[i + 1]]]) plt.plot(x_arr, y_arr, gray_val) - x_arr = np.array([x[0] for x in shp.points[parts[len(parts)-1]:]]) - y_arr = np.array([x[1] for x in shp.points[parts[len(parts)-1]:]]) + x_arr = np.array([x[0] for x in shp.points[parts[len(parts) - 1]:]]) + y_arr = np.array([x[1] for x in shp.points[parts[len(parts) - 1]:]]) plt.plot(x_arr, y_arr, gray_val) plt.show() def _plot_paths_to_plot(list_of_paths, gray_val=str(0.3)): - """ Plot a path or paths to a pyplot + """Plot a path or paths to a pyplot Parameters: list of paths (list): paths to be plotted @@ -1182,7 +1166,7 @@ def _plot_paths_to_plot(list_of_paths, gray_val=str(0.3)): plt.show() def _plot_admin1_shapes(adm0_a3, gray_val=str(0.3)): - """ Retrieves the shape file or coordinate information of a country. + """Retrieves the shape file or coordinate information of a country. Parameters: adm0_a3 (str): iso3 country code of country to get @@ -1201,7 +1185,7 @@ def _plot_admin1_shapes(adm0_a3, gray_val=str(0.3)): lon is a mx1 vector of the longitudinal values of the vertices of the shape (array) """ - shp_file = shapereader.natural_earth('10m', category='cultural',\ + shp_file = shapereader.natural_earth('10m', category='cultural', name='admin_1_states_provinces') shp = shapefile.Reader(shp_file) del shp_file @@ -1217,13 +1201,13 @@ def _plot_admin1_shapes(adm0_a3, gray_val=str(0.3)): for i in adm1_shapes: _plot_shape_to_plot(i, gray_val=gray_val) -def _calc_admin1(curr_country, country_info, admin1_info, litpop_data,\ - coords, resolution, adm1_scatter, conserve_cntrytotal=1, \ +def _calc_admin1(curr_country, country_info, admin1_info, litpop_data, + coords, resolution, adm1_scatter, conserve_cntrytotal=1, check_plot=1, masks_adm1=[], return_data=1): # TODO: if a state/province has GSDP value, but no coordinates inside, # the final total value is off (e.g. Basel Switzerland at 300 arcsec). # Potential fix: normalise the value in the end - """ Calculates the LitPop on admin1 level for provinces/states where such + """Calculates the LitPop on admin1 level for provinces/states where such information is available (i.e. GDP is distributed on a subnational instead of a national level). Requires excel files in a subfolder "GSDP" in climadas data folder. The excel files should contain a row @@ -1242,7 +1226,7 @@ def _calc_admin1(curr_country, country_info, admin1_info, litpop_data,\ admin1_info (list): a list which contains information about the admin1 level of the country (is produced in the .set_country procedure). It contains Shape files among others. - litpop_data (pandas SparseArray): The raw litpop_data to which the + litpop_data (pandas.arrays.SparseArray): The raw litpop_data to which the admin1 based value should be assinged. coords (list): a list containing all the coordinates of the country in the format (lon, lat) @@ -1252,106 +1236,102 @@ def _calc_admin1(curr_country, country_info, admin1_info, litpop_data,\ conserve_cntrytotal (boolean): if True, final LitPop is normalized with country value Returns: - litpop_data (pandas SparseArray): The litpop_data the sum of which + litpop_data (pandas.arrays.SparseArray): The litpop_data the sum of which corresponds to the GDP multiplied by the GDP2Asset conversion factor. """ gsdp_data = _gsdp_read(curr_country, admin1_info) litpop_data = _normalise_litpop(litpop_data) - if not gsdp_data is None: + if gsdp_data is not None: sum_vals = sum(filter(None, gsdp_data.values())) - gsdp_data = {key: (value/sum_vals if not value is None else None)\ + gsdp_data = {key: (value / sum_vals if value is not None else None) for (key, value) in gsdp_data.items()} - if not None in gsdp_data.values(): # standard loop if all GSDP data is available - temp_adm1 = {'adm0_LitPop_share':[], 'adm1_LitPop_share': []} - for idx3, adm1_shp in\ - enumerate(admin1_info): + if None not in gsdp_data.values(): + # standard loop if all GSDP data is available + temp_adm1 = {'adm0_LitPop_share': [], 'adm1_LitPop_share': []} + for idx3, adm1_shp in enumerate(admin1_info): # start_time = time.time() LOGGER.debug('Caclulating admin1 for %s.', adm1_shp[1]['name']) if not masks_adm1: - mask_adm1 = _mask_from_shape(adm1_shp[0],\ - resolution=resolution,\ - points2check=coords) - shr_adm0 = sum(litpop_data.values[mask_adm1.values]) + mask_adm1 = _mask_from_shape(adm1_shp[0], + resolution=resolution, + points2check=coords) + shr_adm0 = sum(litpop_data[mask_adm1]) else: - shr_adm0 = sum(litpop_data.values[masks_adm1[idx3].values]) + shr_adm0 = sum(litpop_data[masks_adm1[idx3]]) temp_adm1['adm0_LitPop_share'].append(shr_adm0) - temp_adm1['adm1_LitPop_share'].append(list(gsdp_data.values())\ - [idx3]) + temp_adm1['adm1_LitPop_share'].append(list(gsdp_data.values())[idx3]) # LitPop in the admin1-unit is scaled by ratio of admin if shr_adm0 > 0: mult = country_info[3]\ - *country_info[4]\ - *gsdp_data[adm1_shp[1]['name']]/shr_adm0 + * country_info[4]\ + * gsdp_data[adm1_shp[1]['name']] / shr_adm0 else: mult = 0 if return_data: if not masks_adm1: - litpop_data = pd.SparseArray([val*mult if\ - mask_adm1[idx] == 1 else val for idx, val in\ - enumerate(litpop_data.values)], fill_value=0) + litpop_data = pd.arrays.SparseArray( + [val * mult if mask_adm1[idx] == 1 else val + for idx, val in enumerate(litpop_data.to_numpy())], + fill_value=0) else: - litpop_data = pd.SparseArray([val*mult if\ - masks_adm1[idx3][idx] == 1 else val for idx, val in\ - enumerate(litpop_data.values)], fill_value=0) + litpop_data = pd.arrays.SparseArray( + [val * mult if masks_adm1[idx3][idx] == 1 else val + for idx, val in enumerate(litpop_data.to_numpy())], + fill_value=0) else: - temp_adm1 = {'mask': [], 'adm0_LitPop_share':[],\ + temp_adm1 = {'mask': [], 'adm0_LitPop_share': [], 'adm1_LitPop_share': [], 'LitPop_sum': []} - litpop_data = _calc_admin0(litpop_data, country_info[3],\ + litpop_data = _calc_admin0(litpop_data, country_info[3], country_info[4]) sum_litpop = sum(litpop_data.sp_values) - for idx3, adm1_shp in\ - enumerate(admin1_info): + for idx3, adm1_shp in enumerate(admin1_info): if not masks_adm1: - mask_adm1 = _mask_from_shape(adm1_shp[0],\ - resolution=resolution,\ - points2check=coords) + mask_adm1 = _mask_from_shape(adm1_shp[0], + resolution=resolution, + points2check=coords) else: mask_adm1 = masks_adm1[idx3] temp_adm1['mask'].append(mask_adm1) - temp_adm1['LitPop_sum'].append(sum(litpop_data.values\ - [mask_adm1.values])) - temp_adm1['adm0_LitPop_share'].append(sum(litpop_data.values\ - [mask_adm1.values])/sum_litpop) + temp_adm1['LitPop_sum'].append(sum(litpop_data[mask_adm1])) + temp_adm1['adm0_LitPop_share'].append(sum(litpop_data[mask_adm1]) + / sum_litpop) del mask_adm1 - sum_litpop_adm1 = sum([sum(litpop_data.values[\ - temp_adm1['mask'][n1].values])\ - for n1, val in enumerate(gsdp_data.values()) if\ - not val is None]) - admin1_share = sum_litpop_adm1/sum_litpop - for idx2, val in\ - enumerate(gsdp_data.values()): - if not val is None: - LOGGER.debug('Calculating admin1 data for %s.', \ + sum_litpop_adm1 = sum([ + sum(litpop_data[temp_adm1['mask'][n1]]) + for n1, val in enumerate(gsdp_data.values()) if val is not None + ]) + admin1_share = sum_litpop_adm1 / sum_litpop + for idx2, val in enumerate(gsdp_data.values()): + if val is not None: + LOGGER.debug('Calculating admin1 data for %s.', admin1_info[1][idx2].attributes['name']) - mult = val*admin1_share\ - *(country_info[3]*country_info[4])\ - /temp_adm1['LitPop_sum'][idx2] - temp_mask = temp_adm1['mask'][idx2].values + mult = (val * admin1_share * (country_info[3] * country_info[4]) + / temp_adm1['LitPop_sum'][idx2]) + temp_mask = temp_adm1['mask'][idx2] if return_data: - litpop_data = pd.SparseArray([val1*mult if\ - temp_mask[idx] == 1 else val1\ - for idx, val1 in\ - enumerate(litpop_data.values)]) + litpop_data = pd.arrays.SparseArray( + [val1 * mult if temp_mask[idx] == 1 else val1 + for idx, val1 in enumerate(litpop_data.to_numpy())]) else: - LOGGER.warning('No admin1 data found for %s.', \ + LOGGER.warning('No admin1 data found for %s.', admin1_info[1][idx2].attributes['name']) LOGGER.warning('Only admin0 data is calculated in this case.') for idx5, _ in enumerate(admin1_info): - temp_adm1['adm1_LitPop_share'].append(list(gsdp_data.values())\ - [idx5]) + temp_adm1['adm1_LitPop_share'].append(list(gsdp_data.values())[idx5]) # LP_sum = sum(litpop_data.values) # temp_adm1['adm1_LitPop_share'].append(sum(litpop_data.values\ # [temp_adm1['mask'][idx5].values])/LP_sum) if adm1_scatter == 1: - pearsonr, spearmanr, rmse, rmsf = _litpop_scatter(temp_adm1['adm0_LitPop_share'],\ - temp_adm1['adm1_LitPop_share'], admin1_info, check_plot) + pearsonr, spearmanr, rmse, rmsf = _litpop_scatter(temp_adm1['adm0_LitPop_share'], + temp_adm1['adm1_LitPop_share'], + admin1_info, check_plot) elif return_data: - litpop_data = _calc_admin0(litpop_data, country_info[3],\ + litpop_data = _calc_admin0(litpop_data, country_info[3], country_info[4]) if conserve_cntrytotal and return_data: - litpop_data = _normalise_litpop(litpop_data)*country_info[3]*country_info[4] + litpop_data = _normalise_litpop(litpop_data) * country_info[3] * country_info[4] if not return_data: litpop_data = [] if adm1_scatter: @@ -1360,48 +1340,45 @@ def _calc_admin1(curr_country, country_info, admin1_info, litpop_data,\ return litpop_data def _calc_admin0(litpop_data, total_asset_val, gdptoasset_factor): - """ Calculates the LitPop on a national level. The total value distributed + """Calculates the LitPop on a national level. The total value distributed corresponds to GDP times the factor to convert GDP to assets from the Gloabl Wealth Databook by the Credit Suisse Research Institute. Parameters: - litpop_data (pandas SparseArray): The raw litpop_data to which the + litpop_data (pandas.arrays.SparseArray): The raw litpop_data to which the admin0 based value should be assinged. total_asset_val (scalar): The total asset value of the country. gdptoasset_factor (scalar): The factor with which GDP can be converted to physical asset value. Returns: - litpop_data (pandas SparseArray): The litpop_data the sum of which + litpop_data (pandas.arrays.SparseArray): The litpop_data the sum of which corresponds to the GDP multiplied by the GDP2Asset conversion factor. """ - litpop_data = _normalise_litpop(litpop_data) - litpop_data = pd.SparseArray(litpop_data.values)*total_asset_val*gdptoasset_factor - return litpop_data + return _normalise_litpop(litpop_data) * total_asset_val * gdptoasset_factor def _normalise_litpop(litpop_data): - """ Normailses LitPop data, such that its total sum equals to one. + """Normailses LitPop data, such that its total sum equals to one. Parameters: - litpop_data (pandas SparseArray): The litpop_data which sjould be + litpop_data (pandas.arrays.SparseArray): The litpop_data which sjould be normalised. Returns: - litpop_data (pandas SparseArray): The litpop_data the sum of which + litpop_data (pandas.arrays.SparseArray): The litpop_data the sum of which corresponds to one. """ - if isinstance(litpop_data, pd.SparseArray): - sum_all = sum(litpop_data.sp_values) - litpop_data = pd.SparseArray(litpop_data.values/sum_all) - else: - LOGGER.error('LitPop data is not of expected type (Pandas '\ - + 'SparseArray). Operation aborted.') + if not isinstance(litpop_data, pd.arrays.SparseArray): + LOGGER.error('LitPop data is not of expected type (Pandas ' + 'SparseArray). Operation aborted.') raise TypeError - return litpop_data + + sum_all = sum(litpop_data.sp_values) + return litpop_data / sum_all def _check_bbox_country_cut_mode(country_cut_mode, cut_bbox, country_adm0): - """ Checks whether a bounding box is valid an compatible with the chosen + """Checks whether a bounding box is valid an compatible with the chosen country cut mode. Parameters: @@ -1412,19 +1389,15 @@ def _check_bbox_country_cut_mode(country_cut_mode, cut_bbox, country_adm0): Returns: cut_bbox (4x1 array): the bounding box, corrected if necessary. """ - if (not country_adm0 is None) & (country_cut_mode == 1)\ - & (not cut_bbox is None): + if (country_adm0 is not None) & (country_cut_mode == 1) & (cut_bbox is not None): cut_bbox = _get_country_shape(country_adm0, 1)[0] - LOGGER.warning('Custom bounding box overwritten in chosen \ - country cut mode.') - elif (not country_adm0 is None) & (country_cut_mode == 1)\ - & (cut_bbox is None): + LOGGER.warning('Custom bounding box overwritten in chosen country cut mode.') + elif (country_adm0 is not None) & (country_cut_mode == 1) & (cut_bbox is None): cut_bbox = _get_country_shape(country_adm0, 1)[0] - if (country_cut_mode != 1) & (not cut_bbox is None): + if (country_cut_mode != 1) & (cut_bbox is not None): try: cut_bbox = np.array(cut_bbox) - if not(isinstance(cut_bbox, np.ndarray)) and \ - not np.size(cut_bbox) == 4: + if not isinstance(cut_bbox, np.ndarray) and not np.size(cut_bbox) == 4: LOGGER.warning('Invalid bounding box provided. \ Bounding box ignored. Please ensure the \ bounding box is an array like type of \ @@ -1447,7 +1420,7 @@ def _check_bbox_country_cut_mode(country_cut_mode, cut_bbox, country_adm0): return cut_bbox def _litpop_scatter(adm0_data, adm1_data, adm1_info, check_plot=True): - """ Plots the admin0 share of the states and provinces against the admin1 + """Plots the admin0 share of the states and provinces against the admin1 shares. Parameters: @@ -1464,24 +1437,23 @@ def _litpop_scatter(adm0_data, adm1_data, adm1_info, check_plot=True): spearmanr = stats.spearmanr(adm0_data, adm1_data)[0] pearsonr = stats.pearsonr(adm0_data, adm1_data)[0] # Root mean square error: - rmse = (sum((adm0_data-adm1_data)**2))**.5 + rmse = (sum((adm0_data - adm1_data)**2))**.5 # Relative root mean square error: # rrmse = (sum(((adm0_data-adm1_data)/adm1_data)**2))**.5 # Root mean squared fraction: - rmsf = np.exp(np.sqrt(np.sum((np.log(adm0_data/adm1_data))**2)/ \ - adm0_data.shape[0])) + rmsf = np.exp(np.sqrt(np.sum((np.log(adm0_data / adm1_data))**2) / adm0_data.shape[0])) if check_plot: plt.figure() plt.scatter(adm1_data, adm0_data, c=(0.1, 0.1, 0.3)) # plt.suptitle('Comparison of admin0 and admin1 LitPop data for '\ # + adm1_info[0].attributes['admin']) - plt.plot([0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])],\ - [0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])],\ - ls="--", c=".3") + plt.plot([0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])], + [0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])], + ls="--", c=".3") # plt.annotate(label, xy=(adm0_data, adm1_data), xytext=(-20, 20), # textcoords='offset points', ha='right', va='bottom') - plt.suptitle(adm1_info[1][0].attributes['admin'] + ': rp='\ - + format(pearsonr, '.2f') + ', rs='\ + plt.suptitle(adm1_info[1][0].attributes['admin'] + ': rp=' + + format(pearsonr, '.2f') + ', rs=' + format(spearmanr, '.2f'), fontsize=18) plt.xlabel('Reference GDP share') plt.ylabel('Modelled GDP share') @@ -1489,17 +1461,22 @@ def _litpop_scatter(adm0_data, adm1_data, adm1_info, check_plot=True): return pearsonr, spearmanr, rmse, rmsf def read_bm_file(bm_path, filename): - """ Reads a single NASA BlackMarble GeoTiff and returns the data. Run all + """Reads a single NASA BlackMarble GeoTiff and returns the data. Run all required checks first. - PARAMETERS: - bm_path (str): absolute path where files are stored. - filename (str): filename of the file to be read. + Parameters + ---------- + bm_path : str + absolute path where files are stored. + filename : str + filename of the file to be read. - RETURNS: - arr1 (array): Raw BM data - curr_file (gdal GeoTiff File): Additional info from which - coordinates can be calculated. + Returns + ------- + arr1 : array + Raw BM data + curr_file : gdal GeoTiff File + Additional info from which coordinates can be calculated. """ try: LOGGER.debug('Importing %s.', os.path.join(bm_path, filename)) @@ -1512,45 +1489,55 @@ def read_bm_file(bm_path, filename): LOGGER.error('Failed: Importing %s', str(curr_file)) raise -def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),),\ +def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),), **parameters): - """ Potential TODO: put cutting before zooming (faster), but with expanding + """Potential TODO: put cutting before zooming (faster), but with expanding bbox in order to preserve additional pixels for interpolation...""" - """ Reads data from NASA GeoTiff files and cuts out the data along a chosen + """Reads data from NASA GeoTiff files and cuts out the data along a chosen bounding box. Call this after the functions nightlight.required_nl_files and nightlight.check_nl_local_file_exists have ensured which files are required and which ones exist and missing files have been donwloaded. - PARAMETERS: - required_files (8x1 array): boolean values which designates which - BM files are required. Can be generated by the function - nightlight.check_required_nl_files - OPTIONAL PARAMTERS - cut_bbox (1x4 array-like): Bounding box (ESRI type) to be cut out. - the layout of the bounding box corresponds to the bounding box - of the ESRI shape files and is as follows: - [minimum longitude, minimum latitude, maximum longitude, - maxmimum latitude] - country_adm0 (str): Country Code of the country of interest. - country_cut_mode (int): Defines how the country is cut out: - if 0: the country is only cut out with a bounding box - if 1: the country is cut out along it's borders - Default: = 1 - bm_path (str): absolute path where files are stored. - resolution (int): the resolution in arcsec in which the data output - is created. - return_coords (boolean): Determines whether latitude and longitude - are delievered along with gpw data (1) or only bm_data is - returned (1) (Default: 0) - reference_year (int): Default = 2016 - - RETURNS: - nightlight_intensity (pandas SparseArray): BM data - lon (list): list with longitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) - lat (list): list with latitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) + Parameters + ---------- + required_files : 8x1 array + boolean values which designates which BM files are required. + Can be generated by the function nightlight.check_required_nl_files + cut_bbox : 1x4 array-like, optional + Bounding box (ESRI type) to be cut out. + The layout of the bounding box corresponds to the bounding box + of the ESRI shape files and is as follows: + [minimum longitude, minimum latitude, maximum longitude, maxmimum latitude] + country_adm0 : str, optional + Country Code of the country of interest. + country_cut_mode : int, optional + Defines how the country is cut out: + if 0: the country is only cut out with a bounding box + if 1: the country is cut out along it's borders + Default: = 1 + bm_path : str, optional + absolute path where files are stored. + resolution : int, optional + the resolution in arcsec in which the data output + is created. + return_coords : boolean, optional + Determines whether latitude and longitude + are delievered along with gpw data (1) or only bm_data is + returned (1) (Default: 0) + reference_year : int + Default = 2016 + + Returns + ------- + nightlight_intensity : pandas.arrays.SparseArray + BM data + lon : list + list with longitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords=1) + lat : list + list with latitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords=1) """ bm_path = parameters.get('file_path', SYSTEM_DIR) resolution = parameters.get('resolution', 30) @@ -1563,41 +1550,40 @@ def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),),\ else: country_crop_mode = parameters.get('country_crop_mode', 1) return_coords = parameters.get('return_coords', 0) - cut_bbox = _check_bbox_country_cut_mode(country_crop_mode,\ - cut_bbox, country_adm0) + cut_bbox = _check_bbox_country_cut_mode(country_crop_mode, cut_bbox, country_adm0) nightlight_temp = None file_count = 0 - zoom_factor = 15/resolution # Orignal resolution is 15 arc-seconds + zoom_factor = 15 / resolution # Orignal resolution is 15 arc-seconds for num_i, _ in enumerate(BM_FILENAMES[::2]): """Due to concat, we have to anlayse the tiles in pairs otherwise the data is concatenated in the wrong order""" - arr1 = [None] * 2 # Prepopulate list + arr1 = [None] * 2 # Prepopulate list for j in range(0, 2): - #Loop which cycles through the two tiles in each "column" - if required_files[num_i*2+j] == 0: + # Loop which cycles through the two tiles in each "column" + if required_files[num_i * 2 + j] == 0: continue else: file_count = file_count + 1 - arr1[j], curr_file = read_bm_file(bm_path,\ - BM_FILENAMES[num_i*2+j] % min(BM_YEARS, key=lambda x:abs(x-reference_year))) + arr1[j], curr_file = read_bm_file( + bm_path, + BM_FILENAMES[num_i * 2 + j] % min(BM_YEARS, + key=lambda x: abs(x - reference_year))) if zoom_factor != 1: # LOGGER.debug('Resizing image according to chosen '\ # + 'resolution') - arr1[j] = pd.SparseDataFrame(nd.zoom(arr1[j], zoom_factor,\ - order=1)) + arr1[j] = to_sparse_dataframe(nd.zoom(arr1[j], zoom_factor, order=1)) else: - arr1[j] = pd.SparseDataFrame(arr1[j]) - if not cut_bbox is None: - arr1[j] = _bm_bbox_cutter\ - (arr1[j], (num_i*2)+j, cut_bbox, resolution) + arr1[j] = to_sparse_dataframe(arr1[j]) + if cut_bbox is not None: + arr1[j] = _bm_bbox_cutter(arr1[j], (num_i * 2) + j, cut_bbox, resolution) if file_count == 1: # Now get the coordinates geo_t = curr_file.GetGeoTransform() rastsize_x, rastsize_y = curr_file.RasterXSize,\ curr_file.RasterYSize minlon = geo_t[0] - minlat = geo_t[3] + rastsize_x*geo_t[4] + rastsize_y*geo_t[5] - maxlon = geo_t[0] + rastsize_x*geo_t[1] + rastsize_y*geo_t[2] + minlat = geo_t[3] + rastsize_x * geo_t[4] + rastsize_y * geo_t[5] + maxlon = geo_t[0] + rastsize_x * geo_t[1] + rastsize_y * geo_t[2] maxlat = geo_t[3] else: geo_t = curr_file.GetGeoTransform() @@ -1606,10 +1592,10 @@ def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),),\ curr_file.RasterYSize minlon = min(minlon, geo_t[0]) # Only add if they extend the current bbox - minlat = min(minlat, geo_t[3] + rastsize_x*geo_t[4]\ - + rastsize_y*geo_t[5]) - maxlon = max(maxlon, geo_t[0] + rastsize_x*geo_t[1]\ - + rastsize_y*geo_t[2]) + minlat = min(minlat, geo_t[3] + rastsize_x * geo_t[4] + + rastsize_y * geo_t[5]) + maxlon = max(maxlon, geo_t[0] + rastsize_x * geo_t[1] + + rastsize_y * geo_t[2]) maxlat = max(maxlat, geo_t[3]) del curr_file if (arr1[0] is None) & (arr1[1] is None): @@ -1626,15 +1612,13 @@ def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),),\ nightlight_temp = pd.concat((nightlight_temp, arr1), 1) del arr1 # LOGGER.debug('Reducing to one dimension...') - nightlight_intensity = pd.SparseArray(nightlight_temp.values\ - .reshape((-1,), order='F'),\ + nightlight_intensity = pd.arrays.SparseArray(nightlight_temp.values + .reshape((-1,), order='F'), dtype='float') del nightlight_temp if return_coords == 1: if cut_bbox is None: - temp_bbox = np.array((minlon, minlat,\ - maxlon,\ - maxlat)) + temp_bbox = np.array((minlon, minlat, maxlon, maxlat)) lon, lat = _litpop_box2coords(temp_bbox, resolution) else: lon, lat = _litpop_box2coords(cut_bbox, resolution) @@ -1648,13 +1632,13 @@ def get_bm(required_files=np.ones(np.count_nonzero(BM_FILENAMES),),\ return nightlight_intensity, None def _bm_bbox_cutter(bm_data, curr_file, bbox, resolution): - """ Crops the imported blackmarble data to the bounding box to reduce + """Crops the imported blackmarble data to the bounding box to reduce memory foot print during import This is done for each of the eight Blackmarble tiles seperately, therefore, the function needs to know which file is currenlty being treated (curr_file). Optional parameters: - bm_data (pandas SparseArray or array): Imported BM data in gridded + bm_data (pandas.arrays.SparseArray or array): Imported BM data in gridded format curr_file (integer): the file which is currenlty being imported (out of all the eignt BM files) in zero indexing. @@ -1663,108 +1647,114 @@ def _bm_bbox_cutter(bm_data, curr_file, bbox, resolution): being imported. Returns: - bm_data (pandas SparseArray): Cropped BM data + bm_data (pandas.arrays.SparseArray): Cropped BM data """ fixed_source_resolution = resolution - deg_per_pix = 1/(3600/fixed_source_resolution) + deg_per_pix = 1 / (3600 / fixed_source_resolution) minlat, maxlat, minlon, maxlon = bbox[1], bbox[3], bbox[0], bbox[2] minlat_tile, maxlat_tile, minlon_tile, maxlon_tile =\ - (-90)+(curr_file//2 == curr_file/2)*(90),\ - 0+(curr_file//2 == curr_file/2)*90,\ - (-180)+(curr_file//2)*90, (-90)+(curr_file//2)*90 + (-90) + (curr_file // 2 == curr_file / 2) * (90),\ + 0 + (curr_file // 2 == curr_file / 2) * 90,\ + (-180) + (curr_file // 2) * 90, (-90) + (curr_file // 2) * 90 if minlat > maxlat_tile or maxlat < minlat_tile\ - or minlon > maxlon_tile or maxlon < minlon_tile: + or minlon > maxlon_tile or maxlon < minlon_tile: LOGGER.warning('This tile does not contain any relevant data. \ Skipping file.') - return pd.SparseDataFrame() + return pd.DataFrame() bbox_conv = np.array((minlon, minlat, maxlon, maxlat)) col_min, row_min, col_max, row_max = \ _litpop_coords_in_glb_grid(bbox_conv, resolution) minrow_tile, maxrow_tile, mincol_tile, maxcol_tile =\ - (curr_file//2 != curr_file/2)*90*(3600/resolution),\ - 90*(3600/resolution)+(curr_file//2 != curr_file/2)*90\ - *(3600/resolution), (curr_file//2)*90*(3600/resolution),\ - (3600/resolution)*90+(curr_file//2)*90*(3600/resolution) - row_min = max(row_min, minrow_tile)-\ - (curr_file//2 != curr_file/2)*(90)*(3600/resolution) - row_max = min(row_max, maxrow_tile)-\ - (curr_file//2 != curr_file/2)*(90)*(3600/resolution) - col_min = max(col_min, mincol_tile)-(curr_file//2)*(90)*(3600/resolution) - col_max = min(col_max, maxcol_tile)-(curr_file//2)*(90)*(3600/resolution) + (curr_file // 2 != curr_file / 2) * 90 * (3600 / resolution),\ + 90 * (3600 / resolution) + (curr_file // 2 != curr_file / 2) * 90\ + * (3600 / resolution), (curr_file // 2) * 90 * (3600 / resolution),\ + (3600 / resolution) * 90 + (curr_file // 2) * 90 * (3600 / resolution) + row_min = max(row_min, minrow_tile) -\ + (curr_file // 2 != curr_file / 2) * (90) * (3600 / resolution) + row_max = min(row_max, maxrow_tile) -\ + (curr_file // 2 != curr_file / 2) * (90) * (3600 / resolution) + col_min = max(col_min, mincol_tile) - (curr_file // 2) * (90) * (3600 / resolution) + col_max = min(col_max, maxcol_tile) - (curr_file // 2) * (90) * (3600 / resolution) if isinstance(bm_data, pd.DataFrame): - bm_data = pd.SparseDataFrame\ - (bm_data.loc[row_min:row_max, col_min:col_max].values) + bm_data = to_sparse_dataframe(bm_data.loc[row_min:row_max, col_min:col_max].values) else: - row_max = min(row_max+1, ((maxlat_tile-minlat_tile)\ - -(deg_per_pix/2))*(1/deg_per_pix)) - col_max = min(col_max+1, ((maxlon_tile-minlon_tile)\ - -(deg_per_pix/2))*(1/deg_per_pix)) + row_max = min(row_max + 1, ((maxlat_tile - minlat_tile) + - (deg_per_pix / 2)) * (1 / deg_per_pix)) + col_max = min(col_max + 1, ((maxlon_tile - minlon_tile) + - (deg_per_pix / 2)) * (1 / deg_per_pix)) bm_data = bm_data[row_min:row_max, col_min:col_max] return bm_data def _get_box_blackmarble(cut_bbox, **args): - """ Reads data from NASA GeoTiff files and cuts out the data along a chosen + """Reads data from NASA GeoTiff files and cuts out the data along a chosen bounding box. - PARAMETERS: - cut_bbox (1x4 array-like): Bounding box (ESRI type) to be cut out. - the layout of the bounding box corresponds to the bounding box - of the ESRI shape files and is as follows: - [minimum longitude, minimum latitude, maximum longitude, - maxmimum latitude] - Optional parameters: - gpw_path (str): absolute path where files are stored. If the files - dont exist, they get saved there. Default: SYSTEM_DIR - resolution (int): the resolution in arcsec in which the data output - is created. - return_coords (boolean): Determines whether latitude and longitude - are delievered along with gpw data (1) or only bm_data is - returned (1) (Default: 0) - reference_year (int): Default: 2016 - - RETURNS: - nightlight_intensity (pandas SparseArray): BM data - lon (list): list with longitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) - lat (list): list with latitudinal infomation on the GPW data. Same - dimensionality as tile_temp (only returned if return_coords=1) + + Parameters + ---------- + cut_bbox : 1x4 array-like + Bounding box (ESRI type) to be cut out. + the layout of the bounding box corresponds to the bounding box + of the ESRI shape files and is as follows: + [minimum longitude, minimum latitude, maximum longitude, maxmimum latitude] + gpw_path : str, optional + absolute path where files are stored. If the files + dont exist, they get saved there. Default: SYSTEM_DIR + resolution : int, optional + the resolution in arcsec in which the data output + is created. + return_coords : boolean, optional + Determines whether latitude and longitude + are delievered along with gpw data (1) or only bm_data is + returned (1) (Default: 0) + reference_year : int, optional + Default: 2016 + + Returns + ------- + nightlight_intensity : pandas.arrays.SparseArray + BM data + lon : list + list with longitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords=1) + lat : list + list with latitudinal infomation on the GPW data. Same + dimensionality as tile_temp (only returned if return_coords=1) """ resolution = args.get('resolution', 30) reference_year = args.get('reference_year', 2016) return_coords = args.get('return_coords', 0) bm_path = args.get('bm_path', SYSTEM_DIR) # Determine required satellite files - req_sat_files = nightlight.check_required_nl_files\ - (cut_bbox) + req_sat_files = nightlight.check_required_nl_files(cut_bbox) # Check existence of necessary files for BM-year: - files_exist = nightlight.check_nl_local_file_exists\ - (req_sat_files, bm_path, \ - min(BM_YEARS, key=lambda x:abs(x-reference_year)))[0] + files_exist = nightlight.check_nl_local_file_exists( + req_sat_files, bm_path, min(BM_YEARS, key=lambda x: abs(x - reference_year)))[0] # Download necessary files: if not np.array_equal(req_sat_files, files_exist): try: - LOGGER.debug('Downloading %s', str(int(sum(req_sat_files)-sum(files_exist)))) - nightlight.download_nl_files(req_sat_files, files_exist,\ - dwnl_path=bm_path, \ - year=min(BM_YEARS, key=lambda x:abs(x-reference_year))) + LOGGER.debug('Downloading %s', str(int(sum(req_sat_files) - sum(files_exist)))) + nightlight.download_nl_files(req_sat_files, files_exist, + dwnl_path=bm_path, + year=min(BM_YEARS, key=lambda x: abs(x - reference_year))) except: LOGGER.error('Could not download missing satellite data files. \ Operation aborted.') raise # Read corresponding files # LOGGER.debug('Reading and cropping necessary BM files.') - nightlight_intensity = get_bm(req_sat_files, resolution=resolution,\ - return_coords=0, cut_bbox=cut_bbox,\ + nightlight_intensity = get_bm(req_sat_files, resolution=resolution, + return_coords=0, cut_bbox=cut_bbox, bm_path=bm_path, reference_year=reference_year)[0] if return_coords == 1: - lon = tuple((cut_bbox[0], 1/(3600/resolution))) - lat = tuple((cut_bbox[1], 1/(3600/resolution))) + lon = tuple((cut_bbox[0], 1 / (3600 / resolution))) + lat = tuple((cut_bbox[1], 1 / (3600 / resolution))) return nightlight_intensity, lon, lat - ### TODO: ensure function is efficient if no coords are returned + # TODO: ensure function is efficient if no coords are returned return nightlight_intensity def admin1_validation(country, methods, exponents, **args): - """ Get LitPop based exposre for one country or multiple countries + """Get LitPop based exposre for one country or multiple countries using values at reference year. If GDP or income group not available for that year, consider the value of the closest available year. @@ -1795,18 +1785,18 @@ def admin1_validation(country, methods, exponents, **args): reference_year = 2015 # inherit_admin1_from_admin0 = args.get('inherit_admin1_from_admin0', 1) if res_arcsec == []: - resolution = (res_km/DEF_RES_GPW_KM)*DEF_RES_GPW_ARCSEC + resolution = (res_km / DEF_RES_GPW_KM) * DEF_RES_GPW_ARCSEC else: resolution = res_arcsec _match_target_res(resolution) country_info = dict() admin1_info = dict() - LOGGER.info('Preparing coordinates, nightlights, and gpw data at %s arcsec.', \ + LOGGER.info('Preparing coordinates, nightlights, and gpw data at %s arcsec.', str(resolution)) - if isinstance(country, list): #multiple countries + if isinstance(country, list): # multiple countries LOGGER.error('No valid country chosen. Give country as string.') raise TypeError - elif isinstance(country, str): #One country + elif isinstance(country, str): # One country country_list = list() country_list.append(country) country_new = _get_iso3(country) @@ -1816,70 +1806,68 @@ def admin1_validation(country, methods, exponents, **args): country_info[country_list[0]], admin1_info[country_list[0]]\ = _get_country_info(country_list[0]) else: - LOGGER.error('Country parameter data type not recognised. '\ - + 'Operation aborted.') + LOGGER.error('Country parameter data type not recognised. Operation aborted.') raise TypeError shp_file = shapereader.natural_earth(resolution='10m', category='cultural', name='admin_0_countries') shp_file = shapereader.Reader(shp_file) - for cntry_iso, cntry_val in country_info.items(): # get GDP value for country + for cntry_iso, cntry_val in country_info.items(): # get GDP value for country _, gdp_val = gdp(cntry_iso, reference_year, shp_file) cntry_val.append(gdp_val) _get_gdp2asset_factor(country_info, reference_year, shp_file, fin_mode=fin_mode) curr_shp = _get_country_shape(country_list[0], 0) all_coords = _litpop_box2coords(cut_bbox, resolution, 1) - mask = _mask_from_shape(curr_shp, resolution=resolution,\ + mask = _mask_from_shape(curr_shp, resolution=resolution, points2check=all_coords) # Get LitPop, Lit and Pop, etc: - nightlights = _get_box_blackmarble(cut_bbox, reference_year=reference_year, \ - resolution=resolution, return_coords=0) + nightlights = _get_box_blackmarble(cut_bbox, reference_year=reference_year, + resolution=resolution, return_coords=0) bm_temp = np.ones(nightlights.shape) - # Lit = Lit + 1 if Population is included, c.f. int(exponents[1]>0): - bm_temp[nightlights.sp_index.indices] = (np.array(nightlights.sp_values, \ - dtype='uint16')) + # Lit = Lit + 1 if Population is included, c.f. int(exponents[1]>0): + bm_temp[nightlights.sp_index.indices] = (np.array(nightlights.sp_values, dtype='uint16')) del nightlights - nightlights0 = pd.SparseArray(bm_temp, fill_value=0) + nightlights0 = pd.arrays.SparseArray(bm_temp, fill_value=0) nightlights0 = nightlights0[mask.sp_index.indices] - nightlights1 = pd.SparseArray(bm_temp+1, fill_value=1) + nightlights1 = pd.arrays.SparseArray(bm_temp + 1, fill_value=1) del bm_temp nightlights1 = nightlights1[mask.sp_index.indices] - gpw = gpw_import.get_box_gpw(cut_bbox=cut_bbox, resolution=resolution,\ - return_coords=0, reference_year=reference_year) + gpw = gpw_import.get_box_gpw(cut_bbox=cut_bbox, resolution=resolution, + return_coords=0, reference_year=reference_year) gpw = gpw[mask.sp_index.indices] lon, lat = zip(*np.array(all_coords)[mask.sp_index.indices]) LOGGER.debug('Caclulating admin1 masks...') masks_adm1 = dict() for idx, adm1_shp in enumerate(admin1_info[country_list[0]]): - masks_adm1[idx] = _mask_from_shape(adm1_shp[0], resolution=resolution,\ - points2check=list(zip(lon, lat))) + masks_adm1[idx] = _mask_from_shape(adm1_shp[0], resolution=resolution, + points2check=list(zip(lon, lat))) n_scores = 4 - rho = np.zeros(len(methods)*n_scores) + rho = np.zeros(len(methods) * n_scores) adm0 = dict() adm1 = dict() LOGGER.info('Loop through methods...') for i in np.arange(0, len(methods)): LOGGER.info('%s :', methods[i]) - if exponents[i][1] == 0: # Lit only, use Lit in [0, 255] + if exponents[i][1] == 0: # Lit only, use Lit in [0, 255] _data = _LitPop_multiply(nightlights0, gpw, exponents=exponents[i]) - else: # Pop is used, use Lit+1 in [1, 256] + else: # Pop is used, use Lit+1 in [1, 256] _data = _LitPop_multiply(nightlights1, gpw, exponents=exponents[i]) - _, rho[i*n_scores:(i*n_scores)+n_scores], adm0[methods[i]], adm1[methods[i]] = \ - _calc_admin1(country_list[0],\ - country_info[country_list[0]], admin1_info[country_list[0]],\ - _data, list(zip(lon, lat)), resolution, True, conserve_cntrytotal=0, \ - check_plot=check_plot, masks_adm1=masks_adm1, return_data=0) + _, rho[i * n_scores:(i * n_scores) + n_scores], adm0[methods[i]], adm1[methods[i]] = \ + _calc_admin1(country_list[0], country_info[country_list[0]], + admin1_info[country_list[0]], _data, list(zip(lon, lat)), + resolution, True, conserve_cntrytotal=0, + check_plot=check_plot, masks_adm1=masks_adm1, return_data=0) return rho, adm0, adm1 def exposure_set_admin1(exposure, res_arcsec): - """ add admin1 ID and name to exposure dataframe. + """add admin1 ID and name to exposure dataframe. Parameters: exposure: exposure instance @@ -1894,11 +1882,36 @@ def exposure_set_admin1(exposure, res_arcsec): count = 0 for cntry in np.unique(exposure.region_id): _, admin1_info = _get_country_info(iso_cntry.get(cntry).alpha3) - for idx3, adm1_shp in enumerate(admin1_info): + for adm1_shp in admin1_info: count = count + 1 LOGGER.debug('Extracting admin1 for %s.', adm1_shp[1]['name']) - mask_adm1 = _mask_from_shape(adm1_shp[0],resolution=res_arcsec,\ - points2check=list(zip(exposure.longitude, exposure.latitude))) - exposure.admin1_ID[mask_adm1.values] = adm1_shp[1][3] - exposure.admin1[mask_adm1.values] = adm1_shp[1]['name'] - return exposure \ No newline at end of file + mask_adm1 = _mask_from_shape( + adm1_shp[0], resolution=res_arcsec, + points2check=list(zip(exposure.longitude, exposure.latitude))) + exposure.admin1_ID[mask_adm1] = adm1_shp[1][3] + exposure.admin1[mask_adm1] = adm1_shp[1]['name'] + return exposure + + +def to_sparse_dataframe(ndarr): + """Turns a 2-dim ndarray into a DataFrame with little memory footprint. + + Parameters + ---------- + ndarr : numpy.ndarray + 2 dimensional + + Returns + ------- + sparse dataframe : pandas.DataFrame + """ + + # in order to retain the low memory consumption of SparseArrays + # it seems to be necessary to build the data frame from a dictionary of columns + # and not just a mere list + return pd.DataFrame( + dict([ + (i, pd.arrays.SparseArray(ndarr[:,i])) + for i in range(ndarr.shape[1]) + ]) + ) diff --git a/climada/entity/exposures/nightlight.py b/climada/entity/exposures/nightlight.py index db70797204..54869ce4bc 100644 --- a/climada/entity/exposures/nightlight.py +++ b/climada/entity/exposures/nightlight.py @@ -26,13 +26,12 @@ import gzip import pickle import logging -import math import numpy as np import scipy.sparse as sparse import matplotlib.pyplot as plt from PIL import Image -from pint import UnitRegistry +from climada.util import ureg from climada.util.constants import SYSTEM_DIR from climada.util.files_handler import download_file from climada.util.save import save @@ -42,18 +41,19 @@ LOGGER = logging.getLogger(__name__) NOAA_SITE = "https://ngdc.noaa.gov/eog/data/web_data/v4composites/" -""" NOAA's URL used to retrieve nightlight satellite images. """ +"""NOAA's URL used to retrieve nightlight satellite images.""" -NOAA_RESOLUTION_DEG = (30*UnitRegistry().arc_second).to(UnitRegistry().deg). \ - magnitude -""" NOAA nightlights coordinates resolution in degrees. """ +NOAA_RESOLUTION_DEG = (30 * ureg.arc_second).to(ureg.deg).magnitude +"""NOAA nightlights coordinates resolution in degrees.""" -NASA_RESOLUTION_DEG = (15*UnitRegistry().arc_second).to(UnitRegistry().deg). \ - magnitude -""" NASA nightlights coordinates resolution in degrees. """ +NASA_RESOLUTION_DEG = (15 * ureg.arc_second).to(ureg.deg).magnitude +"""NASA nightlights coordinates resolution in degrees.""" + +NASA_TILE_SIZE = (21600, 21600) +"""NASA nightlights tile resolution.""" NOAA_BORDER = (-180, -65, 180, 75) -""" NOAA nightlights border (min_lon, min_lat, max_lon, max_lat) """ +"""NOAA nightlights border (min_lon, min_lat, max_lon, max_lat)""" NASA_SITE = 'https://www.nasa.gov/specials/blackmarble/*/tiles/georeferrenced/' """NASA nightlight web url.""" @@ -70,7 +70,7 @@ """Nightlight NASA files which generate the whole earth when put together.""" def check_required_nl_files(bbox, *coords): - """ Determines which of the satellite pictures are necessary for + """Determines which of the satellite pictures are necessary for a certain bounding box (e.g. country) Parameters: @@ -90,7 +90,7 @@ def check_required_nl_files(bbox, *coords): """ try: if not coords: - #check if bbox is valid + # check if bbox is valid if (np.size(bbox) != 4) or (bbox[0] > bbox[2]) \ or (bbox[1] > bbox[3]): LOGGER.error('Invalid bounding box supplied.') @@ -106,29 +106,29 @@ def check_required_nl_files(bbox, *coords): min_lon = bbox min_lat, max_lon, max_lat = coords except: - raise ValueError('Invalid coordinates supplied. Please either ' + \ - ' deliver a bounding box or the coordinates defining the ' + \ - ' bounding box separately.') + raise ValueError('Invalid coordinates supplied. Please either ' + ' deliver a bounding box or the coordinates defining the ' + ' bounding box separately.') # longitude first. The width of all tiles is 90 degrees tile_width = 90 req_files = np.zeros(np.count_nonzero(BM_FILENAMES),) # determine the staring tile - first_tile_lon = min(np.floor((min_lon-(-180))/tile_width), 3) #"normalise" to zero - last_tile_lon = min(np.floor((max_lon-(-180))/tile_width), 3) + first_tile_lon = min(np.floor((min_lon - (-180)) / tile_width), 3) # "normalise" to zero + last_tile_lon = min(np.floor((max_lon - (-180)) / tile_width), 3) # Now latitude. The height of all tiles is the same as the height. # Note that for this analysis returns an index which follows from North to South oritentation. - first_tile_lat = min(np.floor(-(min_lat-(90))/tile_width), 1) - last_tile_lat = min(np.floor(-(max_lat-90)/tile_width), 1) + first_tile_lat = min(np.floor(-(min_lat - (90)) / tile_width), 1) + last_tile_lat = min(np.floor(-(max_lat - 90) / tile_width), 1) - for i_lon in range(0, int(len(req_files)/2)): + for i_lon in range(0, int(len(req_files) / 2)): if first_tile_lon <= i_lon and last_tile_lon >= i_lon: if first_tile_lat == 0 or last_tile_lat == 0: - req_files[((i_lon))*2] = 1 + req_files[((i_lon)) * 2] = 1 if first_tile_lat == 1 or last_tile_lat == 1: - req_files[((i_lon))*2 + 1] = 1 + req_files[((i_lon)) * 2 + 1] = 1 else: continue return req_files @@ -136,7 +136,7 @@ def check_required_nl_files(bbox, *coords): def check_nl_local_file_exists(required_files=np.ones(len(BM_FILENAMES),), check_path=SYSTEM_DIR, year=2016): - """ Checks if BM Satellite files are avaialbe and returns a vector + """Checks if BM Satellite files are avaialbe and returns a vector denoting the missing files. Parameters: @@ -153,11 +153,11 @@ def check_nl_local_file_exists(required_files=np.ones(len(BM_FILENAMES),), """ if np.size(required_files) < np.count_nonzero(BM_FILENAMES): required_files = np.ones(np.count_nonzero(BM_FILENAMES),) - LOGGER.warning('The parameter \'required_files\' was too short and '+ \ + LOGGER.warning('The parameter \'required_files\' was too short and ' 'is ignored.') if not path.exists(check_path): check_path = SYSTEM_DIR - LOGGER.warning('The given path does not exist and is ignored. %s' + \ + LOGGER.warning('The given path does not exist and is ignored. %s' ' is checked instead.', SYSTEM_DIR) files_exist = np.zeros(np.count_nonzero(BM_FILENAMES),) for num_check, name_check in enumerate(BM_FILENAMES): @@ -169,22 +169,21 @@ def check_nl_local_file_exists(required_files=np.ones(len(BM_FILENAMES),), files_exist[num_check] = 1 if sum(files_exist) == sum(required_files): - LOGGER.debug('Found all required satellite data (' + - str(int(sum(required_files))) + ' files) in folder ' + - check_path) + LOGGER.debug('Found all required satellite data (%s files) in folder %s', + int(sum(required_files)), check_path) elif sum(files_exist) == 0: LOGGER.info('No satellite files found locally in %s', check_path) else: - LOGGER.debug('Not all satellite files available. Found ' + - str(int(sum(files_exist))) + ' out of ' + - str(int(sum(required_files))) + ' required files in ' + - check_path) + LOGGER.debug('Not all satellite files available. ' + 'Found %d out of %d required files in %s', + int(sum(files_exist)), int(sum(required_files)), check_path) return (files_exist, check_path) -def download_nl_files(req_files=np.ones(len(BM_FILENAMES),), \ - files_exist=np.zeros(len(BM_FILENAMES),), dwnl_path=SYSTEM_DIR, year=2016): - """ Attempts to download nightlight files from NASA webpage. +def download_nl_files(req_files=np.ones(len(BM_FILENAMES),), + files_exist=np.zeros(len(BM_FILENAMES),), + dwnl_path=SYSTEM_DIR, year=2016): + """Attempts to download nightlight files from NASA webpage. Parameters: req_files (array): Boolean array which indicates the files @@ -198,20 +197,19 @@ def download_nl_files(req_files=np.ones(len(BM_FILENAMES),), \ Returns: path_str (str): Absolute path to file storage. """ - if (len(req_files) != len(files_exist)) or \ - (len(req_files) != len(BM_FILENAMES)): - raise ValueError('The given arguments are invalid. req_files and ' + \ - 'files_exist must both be as long as there are files to download'+\ - ' (' + str(len(BM_FILENAMES)) + ').') + if (len(req_files) != len(files_exist)) or (len(req_files) != len(BM_FILENAMES)): + raise ValueError('The given arguments are invalid. req_files and ' + 'files_exist must both be as long as there are files to download' + ' (' + str(len(BM_FILENAMES)) + ').') if not path.exists(dwnl_path): dwnl_path = SYSTEM_DIR if not path.exists(dwnl_path): raise ValueError('The folder does not exist. Operation aborted.') else: - LOGGER.warning('The given folder does not exist using the ' + \ - 'Climada data directory instead.') + LOGGER.warning('The given folder does not exist using the ' + 'Climada data directory instead.') if np.all(req_files == files_exist): - LOGGER.debug('All required files already exist. ' + + LOGGER.debug('All required files already exist. ' 'No downloads necessary.') return None try: @@ -232,12 +230,12 @@ def download_nl_files(req_files=np.ones(len(BM_FILENAMES),), \ path_str = path.dirname(path_dwn) except: chdir(curr_wd) - raise RuntimeError('Download failed. Please check the network ' + \ - 'connection and whether filenames are still valid.') + raise RuntimeError('Download failed. Please check the network ' + 'connection and whether filenames are still valid.') return path_str def load_nightlight_nasa(bounds, req_files, year): - """ Get nightlight from NASA repository that contain input boundary. + """Get nightlight from NASA repository that contain input boundary. Parameters: bounds (tuple): min_lon, min_lat, max_lon, max_lat @@ -247,109 +245,43 @@ def load_nightlight_nasa(bounds, req_files, year): Returns: nightlight (sparse.csr_matrix), coord_nl (np.array) """ - coord_nl = np.empty((2, 2)) - coord_nl[0, :] = [-90+NASA_RESOLUTION_DEG/2, NASA_RESOLUTION_DEG] - coord_nl[1, :] = [-180+NASA_RESOLUTION_DEG/2, NASA_RESOLUTION_DEG] - - in_lat = math.floor((bounds[1] - coord_nl[0, 0])/coord_nl[0, 1]), \ - math.ceil((bounds[3] - coord_nl[0, 0])/coord_nl[0, 1]) - # Upper (0) or lower (1) latitude range for min and max latitude - in_lat_nb = (math.floor(in_lat[0]/21600)+1)%2, \ - (math.floor(in_lat[1]/21600)+1)%2 - - in_lon = math.floor((bounds[0] - coord_nl[1, 0])/coord_nl[1, 1]), \ - math.ceil((bounds[2] - coord_nl[1, 0])/coord_nl[1, 1]) - # 0, 1, 2, 3 longitude range for min and max longitude - in_lon_nb = math.floor(in_lon[0]/21600), math.floor(in_lon[1]/21600) - - nightlight = sparse.lil.lil_matrix([]) - idx_info = [0, -1, False] # idx, prev_idx and row added flag - for idx, file in enumerate(BM_FILENAMES): - idx_info[0] = idx - if not req_files[idx]: + coord_min = np.array([-90, -180]) + NASA_RESOLUTION_DEG / 2 + coord_h = np.full((2,), NASA_RESOLUTION_DEG) + + min_lon, min_lat, max_lon, max_lat = bounds + bounds_mat = np.array([[min_lat, min_lon], [max_lat, max_lon]]) + global_idx = (bounds_mat - coord_min[None]) / coord_h[None] + global_idx[0, :] = np.floor(global_idx[0, :]) + global_idx[1, :] = np.ceil(global_idx[1, :]) + tile_size = np.array(NASA_TILE_SIZE) + + nightlight = [] + for idx, fname in enumerate(BM_FILENAMES): + tile_coord = np.array([1 - idx % 2, idx // 2]) + extent = global_idx - (tile_coord * tile_size)[None] + if np.any(extent[1, :] < 0) or np.any(extent[0, :] >= NASA_TILE_SIZE): + # this tile does not intersect the specified bounds continue + extent = np.int64(np.clip(extent, 0, tile_size[None] - 1)) - with Image.open(path.join(SYSTEM_DIR, file.replace('*', str(year)))) \ - as im_nl: - cut_nl_nasa(im_nl.getchannel(0), idx_info, nightlight, in_lat, in_lon, - in_lat_nb, in_lon_nb) - - idx_info[1] = idx - - coord_nl[0, 0] = coord_nl[0, 0] + in_lat[0]*coord_nl[0, 1] - coord_nl[1, 0] = coord_nl[1, 0] + in_lon[0]*coord_nl[1, 1] - - return nightlight.tocsr(), coord_nl - -def cut_nl_nasa(aux_nl, idx_info, nightlight, in_lat, in_lon, in_lat_nb, - in_lon_nb): - """Cut nasa's nightlight image piece (1-8) to bounds and append to final - matrix. - - Parameters: - aux_nl (PIL.Image): nasa's nightlight part (1-8) - idx_info (list): idx (0-7), prev_idx (0-7) and row_added flag (bool). - nightlight (sprse.lil_matrix): matrix with nightlight that is expanded - in_lat (tuple): min and max latitude indexes in the whole nasa's image - in_lon (tuple): min and max longitude indexes in the whole nasa's image - in_lat_nb (tuple): for min and max latitude, range where they belong - to: upper (0) or lower (1) row of nasa's images. - on_lon_nb (tuple): for min and max longitude, range where they belong - to: 0, 1, 2 or 3 column of nasa's images. - """ - idx, prev_idx, row_added = idx_info - - aux_nl = sparse.csc.csc_matrix(aux_nl) - # flip X axis - aux_nl.indices = -aux_nl.indices + aux_nl.shape[0] - 1 + fname = path.join(SYSTEM_DIR, fname.replace('*', str(year))) + with Image.open(fname, "r") as im_nl: + im_nl = im_nl.transpose(method=Image.FLIP_TOP_BOTTOM).getchannel(0) + im_nl = sparse.csc.csc_matrix(im_nl) + im_nl = im_nl[extent[0, 0]:extent[1, 0] + 1, extent[0, 1]:extent[1, 1] + 1] + nightlight.append((tile_coord, im_nl)) + tile_coords = np.array([n[0] for n in nightlight]) + shape = tile_coords.max(axis=0) - tile_coords.min(axis=0) + 1 + nightlight = np.array([n[1] for n in nightlight]).reshape(shape, order='F') + nightlight = sparse.bmat(np.flipud(nightlight), format='csr') - aux_bnd = [] - # in min lon - if int(idx/2) % 4 == in_lon_nb[0]: - aux_bnd.append(int(in_lon[0] - (int(idx/2)%4)*21600)) - else: - aux_bnd.append(0) - - # in min lat - if idx % 2 == in_lat_nb[0]: - aux_bnd.append(in_lat[0] - ((idx+1)%2)*21600) - else: - aux_bnd.append(0) + coord_nl = np.vstack([coord_min, coord_h]).T + coord_nl[:, 0] += global_idx[0, :] * coord_h[:] - # in max lon - if int(idx/2) % 4 == in_lon_nb[1]: - aux_bnd.append(int(in_lon[1] - (int(idx/2)%4)*21600) + 1) - else: - aux_bnd.append(21600) - - # in max lat - if idx % 2 == in_lat_nb[1]: - aux_bnd.append(in_lat[1] - ((idx+1)%2)*21600 + 1) - else: - aux_bnd.append(21600) - - if prev_idx == -1: - nightlight.resize((aux_bnd[3]-aux_bnd[1], aux_bnd[2]-aux_bnd[0])) - nightlight[:, :] = aux_nl[aux_bnd[1]:aux_bnd[3], aux_bnd[0]:aux_bnd[2]] - elif idx%2 == prev_idx%2 or prev_idx%2 == 1: - # append horizontally in first rows e.g 0->2 or 1->2 - nightlight.resize((nightlight.shape[0], - nightlight.shape[1] + aux_bnd[2]-aux_bnd[0])) - nightlight[-aux_bnd[3]+aux_bnd[1]:, -aux_bnd[2]+aux_bnd[0]:] = \ - aux_nl[aux_bnd[1]:aux_bnd[3], aux_bnd[0]:aux_bnd[2]] - else: - # append vertically in firsts rows and columns e.g 0->1 or 2->3 - if not row_added: - old_shape = nightlight.shape - nightlight.resize((old_shape[0] + aux_bnd[3] - aux_bnd[1], - old_shape[1])) - nightlight[-old_shape[0]:, :] = nightlight[:old_shape[0], :] - idx_info[2] = True - nightlight[:aux_bnd[3]-aux_bnd[1], -aux_bnd[2]+aux_bnd[0]:] = \ - aux_nl[aux_bnd[1]:aux_bnd[3], aux_bnd[0]:aux_bnd[2]] + return nightlight, coord_nl def unzip_tif_to_py(file_gz): - """ Unzip image file, read it, flip the x axis, save values as pickle + """Unzip image file, read it, flip the x axis, save values as pickle and remove tif. Parameters: @@ -375,7 +307,7 @@ def unzip_tif_to_py(file_gz): return file_name, nightlight def untar_noaa_stable_nightlight(f_tar_ini): - """ Move input tar file to SYSTEM_DIR and extract stable light file. + """Move input tar file to SYSTEM_DIR and extract stable light file. Returns absolute path of stable light file in format tif.gz. Parameters: @@ -414,7 +346,7 @@ def untar_noaa_stable_nightlight(f_tar_ini): return f_tif_gz def load_nightlight_noaa(ref_year=2013, sat_name=None): - """ Get nightlight luminosites. Nightlight matrix, lat and lon ordered + """Get nightlight luminosites. Nightlight matrix, lat and lon ordered such that nightlight[1][0] corresponds to lat[1], lon[0] point (the image has been flipped). @@ -427,11 +359,11 @@ def load_nightlight_noaa(ref_year=2013, sat_name=None): fn_light (str) """ if sat_name is None: - fn_light = path.join(path.abspath(SYSTEM_DIR), '*' + \ - str(ref_year) + '*.stable_lights.avg_vis') + fn_light = path.join(path.abspath(SYSTEM_DIR), '*' + + str(ref_year) + '*.stable_lights.avg_vis') else: - fn_light = path.join(path.abspath(SYSTEM_DIR), sat_name + \ - str(ref_year) + '*.stable_lights.avg_vis') + fn_light = path.join(path.abspath(SYSTEM_DIR), sat_name + + str(ref_year) + '*.stable_lights.avg_vis') # check if file exists in SYSTEM_DIR, download if not if glob.glob(fn_light + ".p"): fn_light = glob.glob(fn_light + ".p")[0] diff --git a/climada/entity/exposures/open_street_map.py b/climada/entity/exposures/open_street_map.py index 1f6e454162..33b8cbc8d2 100644 --- a/climada/entity/exposures/open_street_map.py +++ b/climada/entity/exposures/open_street_map.py @@ -13,9 +13,9 @@ import os import time +import logging from functools import partial -import matplotlib #matplotlib.use('Qt5Agg', force=True) import matplotlib.pyplot as plt import pandas as pd @@ -34,10 +34,10 @@ def _insistent_osm_api_query(query_clause, read_chunk_size=100000, end_of_patience=127): - """Runs a single Overpass API query through overpy.Overpass.query. + """Runs a single Overpass API query through overpy.Overpass.query. In case of failure it tries again after an ever increasing waiting period. If the waiting period surpasses a given limit an exception is raised. - + Parameters: query_clause (str): the query read_chunk_size (int): paramter passed over to overpy.Overpass.query @@ -80,18 +80,18 @@ def _osm_api_query(item, bbox): query_clause_NodesFromWays = "way[%s](%f6, %f6, %f6, %f6);(._;>;);out geom;" \ % (item, bbox[0], bbox[1], bbox[2], bbox[3]) result_NodesFromWays = _insistent_osm_api_query(query_clause_NodesFromWays) - print('Nodes from Ways query for %s: done.' %item) + print('Nodes from Ways query for %s: done.' % item) - query_clause_NodesWaysFromRels = "rel[%s][type=multipolygon](%f6, %f6, %f6, %f6);(._;>;);out;" \ - % (item, bbox[0], bbox[1], bbox[2], bbox[3]) + query_clause_NodesWaysFromRels = ("rel[%s][type=multipolygon](%f6, %f6, %f6, %f6);" + "(._;>;);out;" % (item, bbox[0], bbox[1], bbox[2], bbox[3])) result_NodesWaysFromRels = _insistent_osm_api_query(query_clause_NodesWaysFromRels) - print('Nodes and Ways from Relations query for %s: done.' %item) + print('Nodes and Ways from Relations query for %s: done.' % item) return result_NodesFromWays, result_NodesWaysFromRels def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item, save_path): - """ format edges, nodes and relations from overpy result objects into shapes + """format edges, nodes and relations from overpy result objects into shapes Parameters: bbox result_NodesFromWays @@ -104,14 +104,14 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item """ # polygon vs. linestrings in nodes from ways result: - schema_poly = {'geometry': 'Polygon', \ - 'properties': {'Name':'str:80', 'Natural_Type':'str:80', 'Item':'str:80'}} - schema_line = {'geometry': 'LineString',\ - 'properties': {'Name':'str:80', 'Natural_Type':'str:80', 'Item':'str:80'}} - shapeout_poly = save_path + '/' + str(item)+'_poly_'+ str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+".shp" - shapeout_line = save_path + '/' + str(item)+'_line_'+ str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+".shp" + schema_poly = {'geometry': 'Polygon', + 'properties': {'Name': 'str:80', 'Natural_Type': 'str:80', 'Item': 'str:80'}} + schema_line = {'geometry': 'LineString', + 'properties': {'Name': 'str:80', 'Natural_Type': 'str:80', 'Item': 'str:80'}} + shapeout_poly = save_path + '/' + str(item) + '_poly_' + str(int(bbox[0])) +\ + '_' + str(int(bbox[1])) + ".shp" + shapeout_line = save_path + '/' + str(item) + '_line_' + str(int(bbox[0])) +\ + '_' + str(int(bbox[1])) + ".shp" way_poly = [] way_line = [] @@ -121,31 +121,31 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item else: way_line.append(way) - with fiona.open(shapeout_poly, 'w', crs=from_epsg(4326), driver='ESRI Shapefile',\ + with fiona.open(shapeout_poly, 'w', crs=from_epsg(4326), driver='ESRI Shapefile', schema=schema_poly) as output: for way in way_poly: geom = mapping(geometry.Polygon([node.lon, node.lat] for node in way.nodes)) - prop = {'Name': way.tags.get("name", "n/a"), \ + prop = {'Name': way.tags.get("name", "n/a"), 'Natural_Type': way.tags.get("natural", "n/a"), 'Item': item} output.write({'geometry': geom, 'properties': prop}) - with fiona.open(shapeout_line, 'w', crs=from_epsg(4326), driver='ESRI Shapefile',\ + with fiona.open(shapeout_line, 'w', crs=from_epsg(4326), driver='ESRI Shapefile', schema=schema_line) as output2: for way in way_line: - geom2 = {'type': 'LineString',\ - 'coordinates':[(node.lon, node.lat) for node in way.nodes]} - prop2 = {'Name': way.tags.get("name", "n/a"), \ + geom2 = {'type': 'LineString', + 'coordinates': [(node.lon, node.lat) for node in way.nodes]} + prop2 = {'Name': way.tags.get("name", "n/a"), 'Natural_Type': way.tags.get("natural", "n/a"), 'Item': item} output2.write({'geometry': geom2, 'properties': prop2}) gdf_poly = geopandas.read_file(shapeout_poly) - for ending in ['.shp',".cpg",".dbf",".prj",'.shx']: - os.remove(save_path + '/' + str(item)+'_poly_'+ str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+ending) + for ending in ['.shp', ".cpg", ".dbf", ".prj", '.shx']: + os.remove(save_path + '/' + str(item) + '_poly_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + ending) gdf_line = geopandas.read_file(shapeout_line) - for ending in ['.shp',".cpg",".dbf",".prj",'.shx']: - os.remove(save_path + '/' + str(item)+'_line_'+ str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+ending) + for ending in ['.shp', ".cpg", ".dbf", ".prj", '.shx']: + os.remove(save_path + '/' + str(item) + '_line_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + ending) # add buffer to the lines (0.000045° are ~5m) for geom in gdf_line.geometry: @@ -154,7 +154,7 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item gdf_all = gdf_poly.append(gdf_line) # detect multipolygons in relations: - print('Converting results for %s to correct geometry and GeoDataFrame: MultiPolygons' %item) + print('Converting results for %s to correct geometry and GeoDataFrame: MultiPolygons' % item) MultiPoly = [] for relation in result_NodesWaysFromRels.relations: @@ -167,19 +167,19 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item if relationway.role == 'outer': for way in result_NodesWaysFromRels.ways: if way.id == relationway.ref: - OuterList.append(geometry.LineString([node.lon, node.lat] \ - for node in way.nodes)) + OuterList.append( + geometry.LineString([node.lon, node.lat] for node in way.nodes)) else: for way in result_NodesWaysFromRels.ways: if way.id == relationway.ref: - InnerList.append(geometry.LineString([node.lon, node.lat] \ - for node in way.nodes)) + InnerList.append( + geometry.LineString([node.lon, node.lat] for node in way.nodes)) OuterPoly = [] # in case outer polygons are not fragmented, add those already in correct geometry for outer in OuterList: if outer.is_closed: - OuterPoly.append(Polygon(outer.coords[0:(len(outer.coords)+1)])) + OuterPoly.append(Polygon(outer.coords[0:(len(outer.coords) + 1)])) OuterList.remove(outer) initialLength = len(OuterList) @@ -188,15 +188,15 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item # loop to account for more than one fragmented outer ring while (len(OuterList) > 0) & (i <= initialLength): - OuterCoords.append(OuterList[0].coords[0:(len(OuterList[0].coords)+1)]) + OuterCoords.append(OuterList[0].coords[0:(len(OuterList[0].coords) + 1)]) OuterList.remove(OuterList[0]) - for count in range(0, len(OuterList)): + for _ in range(0, len(OuterList)): # get all the other outer polygon pieces in the right order # (only works if fragments are in correct order, anyways!! # so added another loop around it in case not!) for outer in OuterList: if outer.coords[0] == OuterCoords[-1][-1]: - OuterCoords[-1] = OuterCoords[-1] + outer.coords[0:(len(outer.coords)+1)] + OuterCoords[-1] = OuterCoords[-1] + outer.coords[0:(len(outer.coords) + 1)] OuterList.remove(outer) for entry in OuterCoords: @@ -212,13 +212,13 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item MultiPoly.append(MultiPolygon([shape(poly) for poly in PolyList])) - schema_multi = {'geometry': 'MultiPolygon',\ - 'properties': {'Name':'str:80', 'Type':'str:80', 'Item': 'str:80'}} + schema_multi = {'geometry': 'MultiPolygon', + 'properties': {'Name': 'str:80', 'Type': 'str:80', 'Item': 'str:80'}} - shapeout_multi = save_path + '/' + str(item)+'_multi_'+str(int(bbox[0]))+'_'+\ - str(int(bbox[1]))+".shp" + shapeout_multi = (save_path + '/' + str(item) + '_multi_' + str(int(bbox[0])) + '_' + + str(int(bbox[1])) + ".shp") - with fiona.open(shapeout_multi, 'w', crs=from_epsg(4326), \ + with fiona.open(shapeout_multi, 'w', crs=from_epsg(4326), driver='ESRI Shapefile', schema=schema_multi) as output: for i in range(0, len(MultiPoly)): prop1 = {'Name': relation.tags.get("name", "n/a"), @@ -226,13 +226,13 @@ def _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item 'Item': item} geom = mapping(MultiPoly[i]) output.write({'geometry': geom, 'properties': prop1}) - gdf_multi = geopandas.read_file(shapeout_multi) #save_path + '/' + shapeout_multi) - for ending in ['.shp',".cpg",".dbf",".prj",'.shx']: - os.remove(save_path + '/' + str(item)+'_multi_'+ str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+ending) + gdf_multi = geopandas.read_file(shapeout_multi) # save_path + '/' + shapeout_multi) + for ending in ['.shp', ".cpg", ".dbf", ".prj", '.shx']: + os.remove(save_path + '/' + str(item) + '_multi_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + ending) gdf_all = gdf_all.append(gdf_multi, sort=True) - print('Combined all results for %s to one GeoDataFrame: done' %item) + print('Combined all results for %s to one GeoDataFrame: done' % item) return gdf_all @@ -243,16 +243,16 @@ def _combine_dfs_osm(types, save_path, bbox): .. Returns: (gdf) - """ + """ print('Combining all low-value GeoDataFrames into one GeoDataFrame...') OSM_features_gdf_combined = \ GeoDataFrame(pd.DataFrame(columns=['Item', 'Name', 'Type', 'Natural_Type', 'geometry']), crs='epsg:4326', geometry='geometry') for item in types: - print('adding results from %s ...' %item) + print('adding results from %s ...' % item) OSM_features_gdf_combined = \ OSM_features_gdf_combined.append( - globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))], + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))], ignore_index=True) i = 0 for geom in OSM_features_gdf_combined.geometry: @@ -260,9 +260,9 @@ def _combine_dfs_osm(types, save_path, bbox): OSM_features_gdf_combined.geometry[i] = geom.buffer(0.000045) i += 1 - OSM_features_gdf_combined.to_file(save_path +'/OSM_features_'+str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+'.shp') - + OSM_features_gdf_combined.to_file(save_path + '/OSM_features_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + '.shp') + return OSM_features_gdf_combined def get_features_OSM(bbox, types, save_path=os.getcwd(), check_plot=1): @@ -297,32 +297,34 @@ def get_features_OSM(bbox, types, save_path=os.getcwd(), check_plot=1): """ for item in types: # API Queries for relations, nodes and ways - print('Querying Relations, Nodes and Ways for %s...' %item) + print('Querying Relations, Nodes and Ways for %s...' % item) result_NodesFromWays, result_NodesWaysFromRels = _osm_api_query(item, bbox) - #Formatting results for each feature into correct shapes (LineStrings, Polygons, MultiPolygons) + # Formatting results for each feature + # into correct shapes (LineStrings, Polygons, MultiPolygons) print('Converting results for %s to correct geometry and GeoDataFrame: Lines and Polygons' - %item) - globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))] = \ + % item) + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))] = \ _format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item, save_path) - #Checkplot for each feature (1 dataframe each) + # Checkplot for each feature (1 dataframe each) if check_plot == 1: f, ax = plt.subplots(1) - ax = globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+\ - str(int(bbox[1]))].plot(ax=ax) - f.suptitle(str(item)+'_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))) + ax = globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + + str(int(bbox[1]))].plot(ax=ax) + f.suptitle(str(item) + '_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))) plt.show() # Combine all dataframes into one, save with converting all to (multi)polygons. - OSM_features_gdf_combined = _combine_dfs_osm(types, save_path, bbox) + OSM_features_gdf_combined = _combine_dfs_osm(types, save_path, bbox) if check_plot == 1: f, ax = plt.subplots(1) ax = OSM_features_gdf_combined.plot(ax=ax) - f.suptitle('Features_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))) + f.suptitle('Features_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))) plt.show() - f.savefig('Features_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))+'.pdf', bbox_inches='tight') + f.savefig('Features_' + str(int(bbox[0])) + '_' + str(int(bbox[1])) + '.pdf', + bbox_inches='tight') return OSM_features_gdf_combined @@ -358,19 +360,19 @@ def get_highValueArea(bbox, save_path=os.getcwd(), Low_Value_gdf=None, check_plo important: Use same bbox and save_path as for get_features_OSM(). """ - Outer_Poly = geometry.Polygon([(bbox[1], bbox[2]), (bbox[1], bbox[0]), \ + Outer_Poly = geometry.Polygon([(bbox[1], bbox[2]), (bbox[1], bbox[0]), (bbox[3], bbox[0]), (bbox[3], bbox[2])]) - if Low_Value_gdf == None: + if Low_Value_gdf is None: try: - Low_Value_gdf = geopandas.read_file(save_path \ - +'/OSM_features_gdf_combined_'+str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+'.shp') + Low_Value_gdf = geopandas.read_file( + save_path + '/OSM_features_gdf_combined_' + str(int(bbox[0])) + '_' + + str(int(bbox[1])) + '.shp') except: - print('No Low-Value-Union found with name %s. \n Please add.' \ - % (save_path +'/OSM_features_gdf_combined_'+str(int(bbox[0]))+'_'+\ - str(int(bbox[1]))+'.shp')) + print('No Low-Value-Union found with name %s. \n Please add.' + % (save_path + '/OSM_features_gdf_combined_' + str(int(bbox[0])) + '_' + + str(int(bbox[1])) + '.shp')) else: Low_Value_gdf = geopandas.read_file(Low_Value_gdf) @@ -381,11 +383,12 @@ def get_highValueArea(bbox, save_path=os.getcwd(), Low_Value_gdf=None, check_plo High_Value_Area = Outer_Poly.difference(Low_Value_Union) # save high value multipolygon as shapefile and re-read as gdf: - schema = {'geometry': 'MultiPolygon', 'properties': {'Name':'str:80'}} - shapeout = save_path + '/High_Value_Area_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))+".shp" - with fiona.open(shapeout, 'w', crs=from_epsg(4326), driver='ESRI Shapefile', \ + schema = {'geometry': 'MultiPolygon', 'properties': {'Name': 'str:80'}} + shapeout = (save_path + '/High_Value_Area_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + ".shp") + with fiona.open(shapeout, 'w', crs=from_epsg(4326), driver='ESRI Shapefile', schema=schema) as output: - prop1 = {'Name':'High Value Area'} + prop1 = {'Name': 'High Value Area'} geom = mapping(High_Value_Area) output.write({'geometry': geom, 'properties': prop1}) @@ -395,15 +398,15 @@ def get_highValueArea(bbox, save_path=os.getcwd(), Low_Value_gdf=None, check_plo if check_plot == 1: f, ax = plt.subplots(1) ax = High_Value_Area.plot(ax=ax) - f.suptitle('High Value Area '+str(int(bbox[0]))+' '+str(int(bbox[1]))) + f.suptitle('High Value Area ' + str(int(bbox[0])) + ' ' + str(int(bbox[1]))) plt.show() - f.savefig('High Value Area '+str(int(bbox[0]))+'_'+str(int(bbox[1]))+\ + f.savefig('High Value Area ' + str(int(bbox[0])) + '_' + str(int(bbox[1])) + '.pdf', bbox_inches='tight') return High_Value_Area def _get_litpop_bbox(country, highValueArea, **kwargs): - """ get litpop exposure for the bbox area of the queried OSM features + """get litpop exposure for the bbox area of the queried OSM features Parameters: country (str) highValueArea (gdf) @@ -425,7 +428,7 @@ def _get_litpop_bbox(country, highValueArea, **kwargs): return exp_sub def _split_exposure_highlow(exp_sub, mode, High_Value_Area_gdf): - """ divide litpop exposure into high-value exposure and low-value exposure + """divide litpop exposure into high-value exposure and low-value exposure according to area queried in OSM, re-assign all low values to high-value centroids Parameters: exp_sub (exposure) @@ -450,37 +453,39 @@ def _split_exposure_highlow(exp_sub, mode, High_Value_Area_gdf): pointsToAssign = exp_sub_high.geometry.unary_union exp_sub_high["addedValNN"] = 0 for i in range(0, len(exp_sub_low)): - nearest = exp_sub_high.geometry == nearest_points(exp_sub_low.iloc[i].geometry, \ - pointsToAssign)[1] #point + nearest = exp_sub_high.geometry == nearest_points(exp_sub_low.iloc[i].geometry, + pointsToAssign)[1] # point exp_sub_high.addedValNN.loc[nearest] = exp_sub_low.iloc[i].value exp_sub_high["combinedValNN"] = exp_sub_high[['addedValNN', 'value']].sum(axis=1) - exp_sub_high.rename(columns={'value': 'value_old', 'combinedValNN': 'value'},\ + exp_sub_high.rename(columns={'value': 'value_old', 'combinedValNN': 'value'}, inplace=True) elif mode == "even": # assign asset values of low-value points evenly to points in high-value df. - exp_sub_high['addedValeven'] = sum(exp_sub_low.value)/len(exp_sub_high) + exp_sub_high['addedValeven'] = sum(exp_sub_low.value) / len(exp_sub_high) exp_sub_high["combinedValeven"] = exp_sub_high[['addedValeven', 'value']].sum(axis=1) - exp_sub_high.rename(columns={'value': 'value_old', 'combinedValeven': 'value'},\ + exp_sub_high.rename(columns={'value': 'value_old', 'combinedValeven': 'value'}, inplace=True) elif mode == "proportional": - #assign asset values of low-value points proportionally to value of points in high-value df. + # assign asset values of low-value points proportionally + # to value of points in high-value df. exp_sub_high['addedValprop'] = 0 for i in range(0, len(exp_sub_high)): - asset_factor = exp_sub_high.iloc[i].value/sum(exp_sub_high.value) - exp_sub_high.addedValprop.iloc[i] = asset_factor*sum(exp_sub_low.value) + asset_factor = exp_sub_high.iloc[i].value / sum(exp_sub_high.value) + exp_sub_high.addedValprop.iloc[i] = asset_factor * sum(exp_sub_low.value) exp_sub_high["combinedValprop"] = exp_sub_high[['addedValprop', 'value']].sum(axis=1) - exp_sub_high.rename(columns={'value': 'value_old', 'combinedValprop': 'value'},\ + exp_sub_high.rename(columns={'value': 'value_old', 'combinedValprop': 'value'}, inplace=True) else: - print("No proper re-assignment mode set. Please choose either nearest, even or proportional.") + print("No proper re-assignment mode set. " + "Please choose either nearest, even or proportional.") return exp_sub_high -def get_osmstencil_litpop(bbox, country, mode, highValueArea=None, \ - save_path=os.getcwd(), check_plot=1, **kwargs): +def get_osmstencil_litpop(bbox, country, mode, highValueArea=None, + save_path=os.getcwd(), check_plot=1, **kwargs): """ Generate climada-compatible exposure by downloading LitPop exposure for a bounding box, corrected for centroids which lie inside a certain high-value multipolygon area @@ -505,15 +510,15 @@ def get_osmstencil_litpop(bbox, country, mode, highValueArea=None, \ save_path + '/High_Value_Area_47_8.shp' ,\ save_path = save_path) """ - if highValueArea == None: + if highValueArea is None: try: High_Value_Area_gdf = \ - geopandas.read_file(os.getcwd() + '/High_Value_Area_'+ str(int(bbox[0]))+'_'+ - str(int(bbox[1]))+".shp") + geopandas.read_file(os.getcwd() + '/High_Value_Area_' + str(int(bbox[0])) + '_' + + str(int(bbox[1])) + ".shp") except: - print('No file found of form %s. Please add or specify path.' \ - %(os.getcwd() + 'High_Value_Area_'+str(int(bbox[0]))+'_'+\ - str(int(bbox[1]))+".shp")) + print('No file found of form %s. Please add or specify path.' + % (os.getcwd() + 'High_Value_Area_' + str(int(bbox[0])) + '_' + + str(int(bbox[1])) + ".shp")) else: High_Value_Area_gdf = geopandas.read_file(highValueArea) @@ -526,8 +531,8 @@ def get_osmstencil_litpop(bbox, country, mode, highValueArea=None, \ exp_sub_high_exp = Exposures(exp_sub_high) exp_sub_high_exp.set_lat_lon() exp_sub_high_exp.check() - exp_sub_high_exp.write_hdf5(save_path + '/exposure_high_'+str(int(bbox[0]))+\ - '_'+str(int(bbox[1]))+'.h5') + exp_sub_high_exp.write_hdf5(save_path + '/exposure_high_' + str(int(bbox[0])) + + '_' + str(int(bbox[1])) + '.h5') # plotting if check_plot == 1: # normal hexagons @@ -549,7 +554,8 @@ def _get_midpoints(highValueArea): """ High_Value_Area_gdf = geopandas.read_file(highValueArea) - # For current exposure structure, simply get centroid and area (in m2) for each building polygon + # For current exposure structure, simply get centroid + # and area (in m2) for each building polygon High_Value_Area_gdf['projected_area'] = 0 High_Value_Area_gdf['Midpoint'] = 0 @@ -557,21 +563,25 @@ def _get_midpoints(highValueArea): High_Value_Area_gdf.loc[index, "Midpoint"] = \ High_Value_Area_gdf.loc[index, "geometry"].centroid.wkt s = shape(High_Value_Area_gdf.loc[index, "geometry"]) + # turn warnings off, otherwise Future and Deprecation warnings are flooding the logs + logging.captureWarnings(True) proj = partial(pyproj.transform, pyproj.Proj(init='epsg:4326'), pyproj.Proj(init='epsg:3857')) High_Value_Area_gdf.loc[index, "projected_area"] = transform(proj, s).area - + # turn warnings on again + logging.captureWarnings(False) + # change active geometry from polygons to midpoints from shapely.wkt import loads - High_Value_Area_gdf = High_Value_Area_gdf.rename(columns={'geometry': 'geo_polys', \ - 'Midpoint':'geometry'}) + High_Value_Area_gdf = High_Value_Area_gdf.rename(columns={'geometry': 'geo_polys', + 'Midpoint': 'geometry'}) High_Value_Area_gdf['geometry'] = High_Value_Area_gdf['geometry'].apply(lambda x: loads(x)) High_Value_Area_gdf = High_Value_Area_gdf.set_geometry('geometry') return High_Value_Area_gdf def _assign_values_exposure(High_Value_Area_gdf, mode, country, **kwargs): - """ add value-columns to high-resolution exposure gdf + """add value-columns to high-resolution exposure gdf according to m2 area of underlying features. Parameters: @@ -592,18 +602,18 @@ def _assign_values_exposure(High_Value_Area_gdf, mode, country, **kwargs): High_Value_Area_gdf['value'] = 0 for index in High_Value_Area_gdf.index: High_Value_Area_gdf.loc[index, 'value'] = \ - High_Value_Area_gdf.loc[index, 'projected_area']/totalArea*totalValue + High_Value_Area_gdf.loc[index, 'projected_area'] / totalArea * totalValue - elif mode == "default": # 5400 Chf / m2 base area + elif mode == "default": # 5400 Chf / m2 base area High_Value_Area_gdf['value'] = 0 for index in High_Value_Area_gdf.index: High_Value_Area_gdf.loc[index, 'value'] = \ - High_Value_Area_gdf.loc[index, 'projected_area']*5400 + High_Value_Area_gdf.loc[index, 'projected_area'] * 5400 return High_Value_Area_gdf -def make_osmexposure(highValueArea, mode="default", country=None, \ - save_path=os.getcwd(), check_plot=1, **kwargs): +def make_osmexposure(highValueArea, mode="default", country=None, + save_path=os.getcwd(), check_plot=1, **kwargs): """ Generate climada-compatiple entity by assigning values to midpoints of individual house shapes from OSM query, according to surface area and country. @@ -624,8 +634,8 @@ def make_osmexposure(highValueArea, mode="default", country=None, \ Example: buildings_47_8 = \ - make_osmexposure(save_path+ '/OSM_features_47_8.shp',\ - mode="default", save_path = save_path, check_plot=1) + make_osmexposure(save_path + '/OSM_features_47_8.shp', + mode="default", save_path = save_path, check_plot=1) """ High_Value_Area_gdf = _get_midpoints(highValueArea) @@ -635,9 +645,9 @@ def make_osmexposure(highValueArea, mode="default", country=None, \ exp_buildings = Exposures(High_Value_Area_gdf) exp_buildings.set_lat_lon() exp_buildings.check() - exp_buildings.write_hdf5(save_path + '/exposure_buildings_'+ mode+'_'+ \ - str(int(min(High_Value_Area_gdf.bounds.miny)))+ \ - '_'+str(int(min(High_Value_Area_gdf.bounds.minx)))+'.h5') + exp_buildings.write_hdf5(save_path + '/exposure_buildings_' + mode + '_' + + str(int(min(High_Value_Area_gdf.bounds.miny))) + + '_' + str(int(min(High_Value_Area_gdf.bounds.minx))) + '.h5') # plotting if check_plot == 1: diff --git a/climada/entity/exposures/spam_agrar.py b/climada/entity/exposures/spam_agrar.py index 3079606ac8..8441bf20cf 100644 --- a/climada/entity/exposures/spam_agrar.py +++ b/climada/entity/exposures/spam_agrar.py @@ -30,20 +30,20 @@ logging.root.setLevel(logging.DEBUG) LOGGER = logging.getLogger(__name__) -DEF_HAZ_TYPE = 'DR' -""" Default hazard type used in impact functions id.""" +DEF_HAZ_TYPE = 'CP' +"""Default hazard type used in impact functions id.""" FILENAME_SPAM = 'spam2005V3r2_global' -""" """ +"""TODO: Add Docstring!""" FILENAME_CELL5M = 'cell5m_allockey_xy.csv' -""" """ +"""TODO: Add Docstring!""" FILENAME_PERMALINKS = 'spam2005V3r2_download_permalinks.csv' -""" """ +"""TODO: Add Docstring!""" BUFFER_VAL = -340282306073709652508363335590014353408 -""" Hard coded value which is used for NANs in original data """ +"""Hard coded value which is used for NANs in original data""" class SpamAgrar(Exposures): """Defines agriculture exposures from SPAM @@ -62,7 +62,7 @@ def _constructor(self): return SpamAgrar def init_spam_agrar(self, **parameters): - """ initiates agriculture exposure from SPAM data: + """initiates agriculture exposure from SPAM data: https://dataverse.harvard.edu/ dataset.xhtml?persistentId=doi:10.7910/DVN/DHXBJX @@ -100,6 +100,10 @@ def init_spam_agrar(self, **parameters): False: only basics (lat, lon, total value), region_id per country True: like 1 + name of admin1 + haz_type (str): hazard type abbreviation, e.g. + 'DR' for Drought or + 'CP' for CropPotential + Returns: """ @@ -110,6 +114,7 @@ def init_spam_agrar(self, **parameters): adm1 = parameters.get('name_adm1') adm2 = parameters.get('name_adm2') save_adm1 = parameters.get('save_name_adm1', False) + haz_type = parameters.get('haz_type', DEF_HAZ_TYPE) # Test if parameters make sense: if spam_v not in ['A', 'H', 'P', 'Y', 'V_agg'] or \ @@ -133,25 +138,25 @@ def init_spam_agrar(self, **parameters): if save_adm1: self.name_adm1 = data.loc[:, 'name_adm1'].values - if spam_v == 'V_agg': # total only (column 7) + if spam_v == 'V_agg': # total only (column 7) i_1 = 7 i_2 = 8 else: - i_1 = 7 # get sum over all crops (columns 7 to 48) + i_1 = 7 # get sum over all crops (columns 7 to 48) i_2 = 49 self['value'] = data.iloc[:, i_1:i_2].sum(axis=1).values self['latitude'] = lat.values self['longitude'] = lon.values - LOGGER.info('Lat. range: {:+.3f} to {:+.3f}.'.format(\ - np.min(self.latitude), np.max(self.latitude))) - LOGGER.info('Lon. range: {:+.3f} to {:+.3f}.'.format(\ - np.min(self.longitude), np.max(self.longitude))) + LOGGER.info('Lat. range: {:+.3f} to {:+.3f}.'.format( + np.min(self.latitude), np.max(self.latitude))) + LOGGER.info('Lon. range: {:+.3f} to {:+.3f}.'.format( + np.min(self.longitude), np.max(self.longitude))) # set region_id (numeric ISO3): country_id = data.loc[:, 'iso3'] if country_id.unique().size == 1: region_id = np.ones(self.value.size, int)\ - *int(iso_cntry.get(country_id.iloc[0]).numeric) + * int(iso_cntry.get(country_id.iloc[0]).numeric) else: region_id = np.zeros(self.value.size, int) for i in range(0, self.value.size): @@ -159,12 +164,12 @@ def init_spam_agrar(self, **parameters): self['region_id'] = region_id self.ref_year = 2005 self.tag = Tag() - self.tag.description = ("SPAM agrar exposure for variable "\ - + spam_v + " and technology " + spam_t) + self.tag.description = ("SPAM agrar exposure for variable " + + spam_v + " and technology " + spam_t) # if impact id variation iiv = 1, assign different damage function ID # per technology type. - self._set_if(spam_t) + self._set_if(spam_t, haz_type) self.tag.file_name = (FILENAME_SPAM + '_' + spam_v + '_' + spam_t + '.csv') # self.tag.shape = cntry_info[2] @@ -178,44 +183,44 @@ def init_spam_agrar(self, **parameters): else: self.value_unit = 'USD' - LOGGER.info('Total {} {} {}: {:.1f} {}.'.format(\ - spam_v, spam_t, region, self.value.sum(), self.value_unit)) + LOGGER.info('Total {} {} {}: {:.1f} {}.'.format( + spam_v, spam_t, region, self.value.sum(), self.value_unit)) self.check() - def _set_if(self, spam_t): - """ Set impact function id depending on technology.""" + def _set_if(self, spam_t, haz_type): + """Set impact function id depending on technology.""" # hazard type drought is default. iiv = 0 if spam_t == 'TA': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int) + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) self.tag.description = self.tag.description + '. '\ + 'all technologies together, ie complete crop' elif spam_t == 'TI': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int)+1*iiv + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) + 1 * iiv self.tag.description = self.tag.description + '. '\ + 'irrigated portion of crop' elif spam_t == 'TH': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int)+2*iiv + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) + 2 * iiv self.tag.description = self.tag.description + '. '\ + 'rainfed high inputs portion of crop' elif spam_t == 'TL': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int)+3*iiv + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) + 3 * iiv self.tag.description = self.tag.description + '. '\ + 'rainfed low inputs portion of crop' elif spam_t == 'TS': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int)+4*iiv + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) + 4 * iiv self.tag.description = self.tag.description + '. '\ + 'rainfed subsistence portion of crop' elif spam_t == 'TR': - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int)+5*iiv + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) + 5 * iiv self.tag.description = self.tag.description + '. '\ + 'rainfed portion of crop (= TA - TI)' else: - self[INDICATOR_IF+DEF_HAZ_TYPE] = np.ones(self.value.size, int) + self[INDICATOR_IF + haz_type] = np.ones(self.value.size, int) self.set_geometry_points() def _read_spam_file(self, **parameters): - """ Reads data from SPAM CSV file and cuts out the data for the + """Reads data from SPAM CSV file and cuts out the data for the according country, admin1, or admin2 (if requested). Optional parameters: @@ -244,30 +249,28 @@ def _read_spam_file(self, **parameters): data_path = parameters.get('data_path', SYSTEM_DIR) spam_tech = parameters.get('spam_technology', 'TA') spam_var = parameters.get('spam_variable', 'V_agg') - fname_short = FILENAME_SPAM+'_'+ spam_var + '_' + spam_tech + '.csv' + fname_short = FILENAME_SPAM + '_' + spam_var + '_' + spam_tech + '.csv' try: fname = os.path.join(data_path, fname_short) if not os.path.isfile(fname): try: - self._spam_download_csv(data_path=data_path,\ + self._spam_download_csv(data_path=data_path, spam_variable=spam_var) except: - raise FileExistsError('The file ' + str(fname)\ - + ' could not '\ - + 'be found. Please download the file '\ - + 'first or choose a different folder. '\ - + 'The data can be downloaded from '\ - + 'https://dataverse.harvard.edu/'\ - + 'dataset.xhtml?persistentId=doi:'\ - + '10.7910/DVN/DHXBJX') + raise FileExistsError('The file ' + str(fname) + ' could not ' + + 'be found. Please download the file ' + + 'first or choose a different folder. ' + + 'The data can be downloaded from ' + + 'https://dataverse.harvard.edu/' + + 'dataset.xhtml?persistentId=doi:' + + '10.7910/DVN/DHXBJX') LOGGER.debug('Importing %s', str(fname_short)) - data = pd.read_csv(fname, sep=',', index_col=None, header=0, \ - encoding='ISO-8859-1') + data = pd.read_csv(fname, sep=',', index_col=None, header=0, encoding='ISO-8859-1') except: - LOGGER.error('Importing the SPAM agriculturer file failed. ' \ + LOGGER.error('Importing the SPAM agriculturer file failed. ' 'Operation aborted.') raise # remove data points with zero crop production: (works only for TA) @@ -285,24 +288,23 @@ def _spam_get_coordinates(self, alloc_key_array, data_path=SYSTEM_DIR): if not os.path.isfile(fname): try: - self._spam_download_csv(data_path=data_path,\ + self._spam_download_csv(data_path=data_path, spam_variable='cell5m') except: - raise FileExistsError('The file ' + str(fname)\ - + ' could not '\ - + 'be found. Please download the file '\ - + 'first or choose a different folder. '\ - + 'The data can be downloaded from '\ - + 'https://dataverse.harvard.edu/'\ - + 'dataset.xhtml?persistentId=doi:'\ - + '10.7910/DVN/DHXBJX') + raise FileExistsError('The file ' + str(fname) + ' could not ' + + 'be found. Please download the file ' + + 'first or choose a different folder. ' + + 'The data can be downloaded from ' + + 'https://dataverse.harvard.edu/' + + 'dataset.xhtml?persistentId=doi:' + + '10.7910/DVN/DHXBJX') # LOGGER.debug('Inporting %s', str(fname)) - concordance_data = pd.read_csv(fname, sep=',', index_col=None, \ + concordance_data = pd.read_csv(fname, sep=',', index_col=None, header=0, encoding='ISO-8859-1') - concordance_data = concordance_data\ - [concordance_data['alloc_key'].isin(alloc_key_array)] + concordance_data = concordance_data[ + concordance_data['alloc_key'].isin(alloc_key_array)] concordance_data = concordance_data.sort_values(by=['alloc_key']) @@ -310,7 +312,7 @@ def _spam_get_coordinates(self, alloc_key_array, data_path=SYSTEM_DIR): lon = concordance_data.loc[:, 'x'] except: - LOGGER.error('Importing the SPAM cell5m mapping file failed. ' \ + LOGGER.error('Importing the SPAM cell5m mapping file failed. ' 'Operation aborted.') raise return lat, lon @@ -336,12 +338,11 @@ def _spam_set_country(data, **parameters): adm1 = parameters.get('name_adm1') adm2 = parameters.get('name_adm2') signifier = '' - if not adm0 is None: + if adm0 is not None: if data[data.iso3 == adm0].empty: if data[data.name_cntr == adm0].empty: - LOGGER.warning('Country name not found in data: %s', \ - str(adm0) \ - + '. Try passing the ISO3-code instead.') + LOGGER.warning('Country name not found in data: %s', + str(adm0) + '. Try passing the ISO3-code instead.') else: data = data[data.name_cntr == adm0] signifier = signifier + adm0 @@ -349,13 +350,13 @@ def _spam_set_country(data, **parameters): data = data[data.iso3 == adm0] signifier = signifier + adm0 - if not adm1 is None: + if adm1 is not None: if data[data.name_adm1 == adm1].empty: LOGGER.warning('Admin1 not found in data: %s', str(adm1)) else: data = data[data.name_adm1 == adm1] signifier = signifier + ' ' + adm1 - if not adm2 is None: + if adm2 is not None: if data[data.name_adm2 == adm2].empty: LOGGER.warning('Admin2 not found in data: %s', str(adm2)) else: @@ -390,8 +391,7 @@ def _spam_download_csv(data_path=SYSTEM_DIR, spam_variable='V_agg'): if not os.path.isfile(fname): url1 = 'https://dataverse.harvard.edu/api/access/datafile/:'\ + 'persistentId?persistentId=doi:10.7910/DVN/DHXBJX/' - permalinks = pd.DataFrame(columns=['A', 'H', \ - 'P', 'Y', 'V_agg', 'cell5m']) + permalinks = pd.DataFrame(columns=['A', 'H', 'P', 'Y', 'V_agg', 'cell5m']) permalinks.loc[0, 'A'] = url1 + 'FS1JO8' permalinks.loc[0, 'H'] = url1 + 'M727TX' permalinks.loc[0, 'P'] = url1 + 'HPUWVA' @@ -399,7 +399,7 @@ def _spam_download_csv(data_path=SYSTEM_DIR, spam_variable='V_agg'): permalinks.loc[0, 'V_agg'] = url1 + 'UG0N7K' permalinks.loc[0, 'cell5m'] = url1 + 'H2D3LI' else: - permalinks = pd.read_csv(fname, sep=',', index_col=None, \ + permalinks = pd.read_csv(fname, sep=',', index_col=None, header=0) LOGGER.debug('Importing %s', str(fname)) diff --git a/climada/entity/exposures/test/test_base.py b/climada/entity/exposures/test/test_base.py index 5024f4b5af..751e557f64 100644 --- a/climada/entity/exposures/test/test_base.py +++ b/climada/entity/exposures/test/test_base.py @@ -39,8 +39,8 @@ def good_exposures(): """Followng values are defined for each exposure""" data = {} - data['latitude'] = np.array([ 1, 2, 3]) - data['longitude'] = np.array([ 2, 3, 4]) + data['latitude'] = np.array([1, 2, 3]) + data['longitude'] = np.array([2, 3, 4]) data['value'] = np.array([1, 2, 3]) data['deductible'] = np.array([1, 2, 3]) data[INDICATOR_IF + 'NA'] = np.array([1, 2, 3]) @@ -55,13 +55,13 @@ class TestFuncs(unittest.TestCase): """Check assign function""" def test_assign_pass(self): - """ Check that assigned attribute is correctly set.""" + """Check that assigned attribute is correctly set.""" # Fill with dummy values expo = good_exposures() expo.check() # Fill with dummy values the centroids haz = Hazard('TC') - haz.centroids.set_lat_lon(np.ones(expo.shape[0]+6), np.ones(expo.shape[0]+6)) + haz.centroids.set_lat_lon(np.ones(expo.shape[0] + 6), np.ones(expo.shape[0] + 6)) # assign expo.assign_centroids(haz) @@ -69,47 +69,53 @@ def test_assign_pass(self): self.assertEqual(expo.shape[0], len(expo[INDICATOR_CENTR + 'TC'])) def test_read_raster_pass(self): - """ set_from_raster """ + """set_from_raster""" exp = Exposures() - exp.set_from_raster(HAZ_DEMO_FL, window= Window(10, 20, 50, 60)) + exp.set_from_raster(HAZ_DEMO_FL, window=Window(10, 20, 50, 60)) exp.check() self.assertTrue(equal_crs(exp.crs, DEF_CRS)) - self.assertAlmostEqual(exp['latitude'].max(), 10.248220966978932-0.009000000000000341/2) - self.assertAlmostEqual(exp['latitude'].min(), 10.248220966978932-0.009000000000000341/2-59*0.009000000000000341) - self.assertAlmostEqual(exp['longitude'].min(), -69.2471495969998+0.009000000000000341/2) - self.assertAlmostEqual(exp['longitude'].max(), -69.2471495969998+0.009000000000000341/2+49*0.009000000000000341) - self.assertEqual(len(exp), 60*50) + self.assertAlmostEqual(exp['latitude'].max(), + 10.248220966978932 - 0.009000000000000341 / 2) + self.assertAlmostEqual(exp['latitude'].min(), + 10.248220966978932 - 0.009000000000000341 + / 2 - 59 * 0.009000000000000341) + self.assertAlmostEqual(exp['longitude'].min(), + -69.2471495969998 + 0.009000000000000341 / 2) + self.assertAlmostEqual(exp['longitude'].max(), + -69.2471495969998 + 0.009000000000000341 + / 2 + 49 * 0.009000000000000341) + self.assertEqual(len(exp), 60 * 50) self.assertAlmostEqual(exp.value.values.reshape((60, 50))[25, 12], 0.056825936) def test_assign_raster_pass(self): - """ Test assign_centroids with raster hazard """ + """Test assign_centroids with raster hazard""" exp = Exposures() exp['longitude'] = np.array([-69.235, -69.2427, -72, -68.8016496, 30]) exp['latitude'] = np.array([10.235, 10.226, 2, 9.71272097, 50]) exp.crs = DEF_CRS haz = Hazard('FL') - haz.set_raster([HAZ_DEMO_FL], window= Window(10, 20, 50, 60)) + haz.set_raster([HAZ_DEMO_FL], window=Window(10, 20, 50, 60)) exp.assign_centroids(haz) self.assertEqual(exp[INDICATOR_CENTR + 'FL'][0], 51) self.assertEqual(exp[INDICATOR_CENTR + 'FL'][1], 100) self.assertEqual(exp[INDICATOR_CENTR + 'FL'][2], -1) - self.assertEqual(exp[INDICATOR_CENTR + 'FL'][3], 3000-1) + self.assertEqual(exp[INDICATOR_CENTR + 'FL'][3], 3000 - 1) self.assertEqual(exp[INDICATOR_CENTR + 'FL'][4], -1) def test_assign_raster_same_pass(self): - """ Test assign_centroids with raster hazard """ + """Test assign_centroids with raster hazard""" exp = Exposures() - exp.set_from_raster(HAZ_DEMO_FL, window= Window(10, 20, 50, 60)) + exp.set_from_raster(HAZ_DEMO_FL, window=Window(10, 20, 50, 60)) exp.check() haz = Hazard('FL') - haz.set_raster([HAZ_DEMO_FL], window= Window(10, 20, 50, 60)) + haz.set_raster([HAZ_DEMO_FL], window=Window(10, 20, 50, 60)) exp.assign_centroids(haz) self.assertTrue(np.array_equal(exp[INDICATOR_CENTR + 'FL'].values, np.arange(haz.centroids.size, dtype=int))) class TestChecker(unittest.TestCase): - """Test logs of check function """ + """Test logs of check function""" def test_info_logs_pass(self): """Wrong exposures definition""" @@ -128,7 +134,7 @@ def test_info_logs_pass(self): def test_error_logs_fail(self): """Wrong exposures definition""" expo = good_exposures() - expo = expo.drop(['longitude'],axis=1) + expo = expo.drop(['longitude'], axis=1) with self.assertLogs('climada.entity.exposures.base', level='ERROR') as cm: with self.assertRaises(ValueError): @@ -145,7 +151,7 @@ def test_error_geometry_fail(self): expo.check() class TestIO(unittest.TestCase): - """ Check constructor Exposures through DataFrames readers """ + """Check constructor Exposures through DataFrames readers""" def test_read_template_pass(self): """Wrong exposures definition""" @@ -158,7 +164,7 @@ def test_read_template_pass(self): exp_df.check() def test_io_hdf5_pass(self): - """ write and read hdf5 """ + """write and read hdf5""" exp_df = Exposures(pd.read_excel(ENT_TEMPLATE_XLS)) exp_df.set_geometry_points() exp_df.check() @@ -195,7 +201,7 @@ def test_io_hdf5_pass(self): self.assertEqual(point_df.y, point_read.y) class TestAddSea(unittest.TestCase): - """ Check constructor Exposures through DataFrames readers """ + """Check constructor Exposures through DataFrames readers""" def test_add_sea_pass(self): """Test add_sea function with fake data.""" exp = Exposures() @@ -232,17 +238,18 @@ def test_add_sea_pass(self): on_sea_lat = exp_sea.latitude.values[11:] on_sea_lon = exp_sea.longitude.values[11:] res_on_sea = coord_on_land(on_sea_lat, on_sea_lon) - res_on_sea = np.logical_not(res_on_sea) + res_on_sea = ~res_on_sea self.assertTrue(np.all(res_on_sea)) dist = DistanceMetric.get_metric('haversine') - self.assertAlmostEqual(dist.pairwise([[exp_sea.longitude.values[-1], \ - exp_sea.latitude.values[-1]], [exp_sea.longitude.values[-2], \ - exp_sea.latitude.values[-2]]])[0][1], sea_res_km) + self.assertAlmostEqual(dist.pairwise([ + [exp_sea.longitude.values[-1], exp_sea.latitude.values[-1]], + [exp_sea.longitude.values[-2], exp_sea.latitude.values[-2]], + ])[0][1], sea_res_km) class TestGeoDFFuncs(unittest.TestCase): - """ Check constructor Exposures through DataFrames readers """ + """Check constructor Exposures through DataFrames readers""" def test_copy_pass(self): """Test copy function.""" exp = good_exposures() @@ -285,7 +292,7 @@ def test_to_crs_pass(self): self.assertEqual(exp_tr.tag.file_name, '') def test_constructoer_pass(self): - """ Test initialization with input GeiDataFrame """ + """Test initialization with input GeiDataFrame""" in_gpd = gpd.GeoDataFrame() in_gpd['value'] = np.zeros(10) in_gpd.ref_year = 2015 diff --git a/climada/entity/exposures/test/test_black_marble.py b/climada/entity/exposures/test/test_black_marble.py index c435360534..60d118a584 100644 --- a/climada/entity/exposures/test/test_black_marble.py +++ b/climada/entity/exposures/test/test_black_marble.py @@ -29,8 +29,8 @@ _cut_admin1, _resample_land from climada.entity.exposures.nightlight import NOAA_BORDER, NOAA_RESOLUTION_DEG -SHP_FN = shapereader.natural_earth(resolution='10m', \ - category='cultural', name='admin_0_countries') +SHP_FN = shapereader.natural_earth(resolution='10m', category='cultural', + name='admin_0_countries') SHP_FILE = shapereader.Reader(SHP_FN) ADM1_FILE = shapereader.natural_earth(resolution='10m', @@ -42,13 +42,13 @@ class TestCountryIso(unittest.TestCase): """Test country_iso function.""" def test_che_kos_pass(self): - """CHE, KOS """ + """CHE, KOS""" country_name = ['Switzerland', 'Kosovo'] iso_name, _ = country_iso_geom(country_name, SHP_FILE) self.assertEqual(len(iso_name), len(country_name)) - self.assertTrue('CHE'in iso_name) - self.assertTrue('KOS'in iso_name) + self.assertTrue('CHE' in iso_name) + self.assertTrue('KOS' in iso_name) self.assertEqual(iso_name['CHE'][0], 756) self.assertEqual(iso_name['CHE'][1], 'Switzerland') self.assertIsInstance(iso_name['CHE'][2], shapely.geometry.polygon.Polygon) @@ -131,9 +131,8 @@ def test_country_iso_geom_pass(self): def test_filter_admin1_pass(self): """Test _cut_admin1 pass.""" - lat, lon = np.mgrid[35 : 44 : complex(0, 100), - 0 : 4 : complex(0, 102)] - nightlight = np.arange(102*100).reshape((102, 100)) + lat, lon = np.mgrid[35: 44: complex(0, 100), 0: 4: complex(0, 102)] + nightlight = np.arange(102 * 100).reshape((102, 100)) coord_nl = np.array([[35, 0.09090909], [0, 0.03960396]]) on_land = np.zeros((100, 102), bool) @@ -154,46 +153,46 @@ def test_filter_admin1_pass(self): self.assertEqual(lat_reg.shape, on_land_reg.shape) self.assertTrue(np.array_equal(nightlight[60:82, 4:72], nightlight_reg)) for coord in zip(lat_reg[on_land_reg], lon_reg[on_land_reg]): - self.assertTrue(bcn_geom.contains(shapely.geometry.Point([coord[1],coord[0]])) or - tar_geom.contains(shapely.geometry.Point([coord[1],coord[0]]))) - self.assertTrue(all_geom.contains(shapely.geometry.Point([coord[1],coord[0]]))) + self.assertTrue(bcn_geom.contains(shapely.geometry.Point([coord[1], coord[0]])) or + tar_geom.contains(shapely.geometry.Point([coord[1], coord[0]]))) + self.assertTrue(all_geom.contains(shapely.geometry.Point([coord[1], coord[0]]))) class TestNightLight(unittest.TestCase): """Test nightlight functions.""" def test_cut_country_brb_1km_pass(self): - """ Test _cut_country function with fake Barbados.""" + """Test _cut_country function with fake Barbados.""" country_iso = 'BRB' for cntry in list(SHP_FILE.records()): if cntry.attributes['ADM0_A3'] == country_iso: geom = cntry.geometry - nightlight = sparse.lil.lil_matrix(np.ones((500, 1000))) + nightlight = np.ones((500, 1000)) nightlight[275:281, 333:334] = 0.4 nightlight[275:281, 334:336] = 0.5 - nightlight = nightlight.tocsr() + nightlight = sparse.csr_matrix(nightlight) coord_nl = np.empty((2, 2)) - coord_nl[0, :] = [NOAA_BORDER[1]+NOAA_RESOLUTION_DEG, + coord_nl[0, :] = [NOAA_BORDER[1] + NOAA_RESOLUTION_DEG, 0.2805444221776838] - coord_nl[1, :] = [NOAA_BORDER[0]+NOAA_RESOLUTION_DEG, + coord_nl[1, :] = [NOAA_BORDER[0] + NOAA_RESOLUTION_DEG, 0.3603520186853473] nightlight_reg, lat_reg, lon_reg, on_land = _cut_country(geom, nightlight, coord_nl) - lat_ref = np.array([[12.9996827 , 12.9996827 , 12.9996827 ], - [13.28022712, 13.28022712, 13.28022712], - [13.56077154, 13.56077154, 13.56077154]]) + lat_ref = np.array([[12.9996827, 12.9996827, 12.9996827], + [13.28022712, 13.28022712, 13.28022712], + [13.56077154, 13.56077154, 13.56077154]]) lon_ref = np.array([[-59.99444444, -59.63409243, -59.27374041], - [-59.99444444, -59.63409243, -59.27374041], - [-59.99444444, -59.63409243, -59.27374041]]) + [-59.99444444, -59.63409243, -59.27374041], + [-59.99444444, -59.63409243, -59.27374041]]) on_ref = np.array([[False, False, False], - [False, True, False], - [False, False, False]]) + [False, True, False], + [False, False, False]]) in_lat = (278, 280) in_lon = (333, 335) - nightlight_ref = nightlight[in_lat[0]:in_lat[1]+1, in_lon[0]:in_lon[1]+1].todense() - nightlight_ref[np.logical_not(on_ref)] = 0.0 + nightlight_ref = nightlight[in_lat[0]:in_lat[1] + 1, in_lon[0]:in_lon[1] + 1].toarray() + nightlight_ref[~on_ref] = 0.0 self.assertTrue(np.allclose(lat_ref, lat_reg)) self.assertTrue(np.allclose(lon_ref, lon_reg)) @@ -201,46 +200,53 @@ def test_cut_country_brb_1km_pass(self): self.assertTrue(np.allclose(nightlight_ref, nightlight_reg)) def test_cut_country_brb_2km_pass(self): - """ Test _resample_land function with fake Barbados.""" + """Test _resample_land function with fake Barbados.""" country_iso = 'BRB' for cntry in list(SHP_FILE.records()): if cntry.attributes['ADM0_A3'] == country_iso: geom = cntry.geometry - nightlight = sparse.lil.lil_matrix(np.ones((500, 1000))) + nightlight = np.ones((500, 1000)) nightlight[275:281, 333:334] = 0.4 nightlight[275:281, 334:336] = 0.5 - nightlight = nightlight.tocsr() + nightlight = sparse.csr_matrix(nightlight) coord_nl = np.empty((2, 2)) - coord_nl[0, :] = [NOAA_BORDER[1]+NOAA_RESOLUTION_DEG, + coord_nl[0, :] = [NOAA_BORDER[1] + NOAA_RESOLUTION_DEG, 0.2805444221776838] - coord_nl[1, :] = [NOAA_BORDER[0]+NOAA_RESOLUTION_DEG, + coord_nl[1, :] = [NOAA_BORDER[0] + NOAA_RESOLUTION_DEG, 0.3603520186853473] res_fact = 2.0 nightlight_reg, lat_reg, lon_reg, on_land = _cut_country(geom, nightlight, coord_nl) - nightlight_res, lat_res, lon_res = _resample_land(geom, nightlight_reg, lat_reg, lon_reg, res_fact, on_land) - - lat_ref = np.array([[12.9996827, 12.9996827, 12.9996827, 12.9996827, 12.9996827, 12.9996827 ], - [13.11190047, 13.11190047, 13.11190047, 13.11190047, 13.11190047, 13.11190047], - [13.22411824, 13.22411824, 13.22411824, 13.22411824, 13.22411824, 13.22411824], - [13.33633601, 13.33633601, 13.33633601, 13.33633601, 13.33633601, 13.33633601], - [13.44855377, 13.44855377, 13.44855377, 13.44855377, 13.44855377, 13.44855377], - [13.56077154, 13.56077154, 13.56077154, 13.56077154, 13.56077154, 13.56077154]]) - - lon_ref = np.array([[-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], - [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], - [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], - [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], - [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], - [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041]]) - - on_ref = np.array([[False, False, False, False, False, False], - [False, False, False, True, False, False], - [False, False, False, True, False, False], - [False, False, False, False, False, False], - [False, False, False, False, False, False], - [False, False, False, False, False, False]]) + nightlight_res, lat_res, lon_res = _resample_land(geom, nightlight_reg, lat_reg, lon_reg, + res_fact, on_land) + + lat_ref = np.array([ + [12.9996827, 12.9996827, 12.9996827, 12.9996827, 12.9996827, 12.9996827], + [13.11190047, 13.11190047, 13.11190047, 13.11190047, 13.11190047, 13.11190047], + [13.22411824, 13.22411824, 13.22411824, 13.22411824, 13.22411824, 13.22411824], + [13.33633601, 13.33633601, 13.33633601, 13.33633601, 13.33633601, 13.33633601], + [13.44855377, 13.44855377, 13.44855377, 13.44855377, 13.44855377, 13.44855377], + [13.56077154, 13.56077154, 13.56077154, 13.56077154, 13.56077154, 13.56077154] + ]) + + lon_ref = np.array([ + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041], + [-59.99444444, -59.85030364, -59.70616283, -59.56202202, -59.41788121, -59.27374041] + ]) + + on_ref = np.array([ + [False, False, False, False, False, False], + [False, False, False, True, False, False], + [False, False, False, True, False, False], + [False, False, False, False, False, False], + [False, False, False, False, False, False], + [False, False, False, False, False, False] + ]) self.assertTrue(np.allclose(lat_ref[on_ref], lat_res)) self.assertTrue(np.allclose(lon_ref[on_ref], lon_res)) @@ -251,19 +257,25 @@ class TestEconIndices(unittest.TestCase): """Test functions to get economic indices.""" def test_fill_econ_indicators_pass(self): - """ Test fill_econ_indicators CHE, ZMB.""" + """Test fill_econ_indicators CHE, ZMB.""" ref_year = 2015 country_isos = {'CHE': [1, 'Switzerland', 'che_geom'], 'ZMB': [2, 'Zambia', 'zmb_geom'] } fill_econ_indicators(ref_year, country_isos, SHP_FILE) - country_isos_ref = {'CHE': [1, 'Switzerland', 'che_geom', 2015, 679832391757.542, 4], - 'ZMB': [2, 'Zambia', 'zmb_geom', 2015, 21243350632.5008, 2] + country_isos_ref = {'CHE': [1, 'Switzerland', 'che_geom', 2015, 679832291693, 4], + 'ZMB': [2, 'Zambia', 'zmb_geom', 2015, 21243347377, 2] } - self.assertEqual(country_isos, country_isos_ref) + self.assertEqual(country_isos.keys(), country_isos_ref.keys()) + for country in country_isos_ref.keys(): + for i in [0, 1, 2, 3, 5]: # test elements one by one: + self.assertEqual(country_isos[country][i], + country_isos_ref[country][i]) + self.assertAlmostEqual(country_isos[country][4] * 1e-6, + country_isos_ref[country][4] * 1e-6, places=0) def test_fill_econ_indicators_kwargs_pass(self): - """ Test fill_econ_indicators with kwargs inputs.""" + """Test fill_econ_indicators with kwargs inputs.""" ref_year = 2015 country_isos = {'CHE': [1, 'Switzerland', 'che_geom'], 'ZMB': [2, 'Zambia', 'zmb_geom'] @@ -272,34 +284,42 @@ def test_fill_econ_indicators_kwargs_pass(self): inc_grp = {'CHE': 3, 'ZMB': 4} kwargs = {'gdp': gdp, 'inc_grp': inc_grp} fill_econ_indicators(ref_year, country_isos, SHP_FILE, **kwargs) - country_isos_ref = {'CHE': [1, 'Switzerland', 'che_geom', 2015, gdp['CHE'], inc_grp['CHE']], - 'ZMB': [2, 'Zambia', 'zmb_geom', 2015, gdp['ZMB'], inc_grp['ZMB']] - } + country_isos_ref = { + 'CHE': [1, 'Switzerland', 'che_geom', 2015, gdp['CHE'], inc_grp['CHE']], + 'ZMB': [2, 'Zambia', 'zmb_geom', 2015, gdp['ZMB'], inc_grp['ZMB']] + } self.assertEqual(country_isos, country_isos_ref) def test_fill_econ_indicators_na_pass(self): - """ Test fill_econ_indicators with '' inputs.""" - ref_year = 2015 + """Test fill_econ_indicators with '' inputs.""" + ref_year = 2019 country_isos = {'CHE': [1, 'Switzerland', 'che_geom'], 'ZMB': [2, 'Zambia', 'zmb_geom'] } - gdp = {'CHE': 1.2, 'ZMB': ''} + gdp = {'CHE': 1.2 * 1e20, 'ZMB': ''} inc_grp = {'CHE': '', 'ZMB': 4} kwargs = {'gdp': gdp, 'inc_grp': inc_grp} fill_econ_indicators(ref_year, country_isos, SHP_FILE, **kwargs) - country_isos_ref = {'CHE': [1, 'Switzerland', 'che_geom', 2015, gdp['CHE'], 4], - 'ZMB': [2, 'Zambia', 'zmb_geom', 2015, 21243350632.5008, inc_grp['ZMB']] + country_isos_ref = {'CHE': [1, 'Switzerland', 'che_geom', 2019, gdp['CHE'], 4], + 'ZMB': [2, 'Zambia', 'zmb_geom', 2019, 23064722446, inc_grp['ZMB']] } - self.assertEqual(country_isos, country_isos_ref) + self.assertEqual(country_isos.keys(), country_isos_ref.keys()) + for country in country_isos_ref.keys(): + for i in [0, 1, 2, 3, 5]: # test elements one by one: + self.assertEqual(country_isos[country][i], + country_isos_ref[country][i]) + self.assertAlmostEqual(country_isos[country][4] * 1e-6, + country_isos_ref[country][4] * 1e-6, places=0) + def test_set_econ_indicators_pass(self): - """ Test _set_econ_indicators pass.""" + """Test _set_econ_indicators pass.""" nightlight = np.arange(0, 20, 0.1).reshape((100, 2)) gdp = 4.225e9 inc_grp = 4 nightlight = _set_econ_indicators(nightlight, gdp, inc_grp, [0, 0, 1]) - self.assertAlmostEqual(nightlight.sum(), gdp*(inc_grp+1), 5) + self.assertAlmostEqual(nightlight.sum(), gdp * (inc_grp + 1), 5) # Execute Tests if __name__ == "__main__": diff --git a/climada/entity/exposures/test/test_crop_production.py b/climada/entity/exposures/test/test_crop_production.py new file mode 100644 index 0000000000..0c977c516c --- /dev/null +++ b/climada/entity/exposures/test/test_crop_production.py @@ -0,0 +1,107 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +Unit Tests on LitPop exposures. +""" +import os +import numpy as np +import unittest +from climada.entity.exposures.crop_production import CropProduction, normalize_with_fao_cp +from climada.util.constants import DATA_DIR + +INPUT_DIR = os.path.join(DATA_DIR, 'demo') +FILENAME = 'histsoc_landuse-15crops_annual_FR_DE_DEMO_2001_2005.nc' +FILENAME_MEAN = 'hist_mean_mai-firr_1976-2005_DE_FR.hdf5' + +class TestCropProduction(unittest.TestCase): + """Test Cropyield_Isimip Class methods""" + def test_load_central_EU(self): + """Test defining crop_production Exposure from complete demo file (Central Europe)""" + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME, hist_mean=FILENAME_MEAN, + bbox=[-5, 42, 16, 55], yearrange=np.array([2001, 2005]), + scenario='flexible', unit='t', crop = 'mai', irr='firr') + + self.assertEqual(exp.longitude.min(), -4.75) + self.assertEqual(exp.longitude.max(), 15.75) + self.assertEqual(exp.latitude.min(), 42.25) + self.assertEqual(exp.latitude.max(), 54.75) + self.assertEqual(exp.value.shape, (1092,)) + self.assertEqual(exp.value_unit, 't / y') + self.assertEqual(exp.crop, 'mai') + self.assertAlmostEqual(exp.value.max(), 284244.81023404596, places=5) + + def test_set_to_usd(self): + """Test calculating crop_production Exposure in [USD / y]""" + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME, hist_mean=FILENAME_MEAN, + bbox=[-5, 42, 16, 55], yearrange=np.array([2001, 2005]), + scenario='flexible', unit='t', crop = 'mai', irr='firr') + exp.set_to_usd(INPUT_DIR) + self.assertEqual(exp.longitude.min(), -4.75) + self.assertEqual(exp.longitude.max(), 15.75) + self.assertEqual(exp.latitude.min(), 42.25) + self.assertEqual(exp.latitude.max(), 54.75) + self.assertEqual(exp.value.shape, (1092,)) + self.assertEqual(exp.value_unit, 'USD / y') + self.assertEqual(exp.crop, 'mai') + self.assertAlmostEqual(exp.tonnes_per_year[28], 1998.3634803238633) + self.assertAlmostEqual(exp.value.max(), 51603897.28533253, places=5) + self.assertAlmostEqual(exp.value.mean(), 907401.9933073953, places=5) + self.assertEqual(exp.value.min(), 0.0) + + + def test_set_to_usd_unnecessary(self): + """Test calculating cropyield_isimip Exposure in [USD / y]""" + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME, hist_mean=FILENAME_MEAN, + bbox=[-5, 42, 16, 55], yearrange=np.array([2001, 2005]), + scenario='flexible', crop = 'mai', irr='firr') + self.assertEqual(exp.longitude.min(), -4.75) + self.assertEqual(exp.longitude.max(), 15.75) + self.assertEqual(exp.latitude.min(), 42.25) + self.assertEqual(exp.latitude.max(), 54.75) + self.assertEqual(exp.value.shape, (1092,)) + self.assertEqual(exp.value_unit, 'USD / y') + self.assertEqual(exp.crop, 'mai') + self.assertAlmostEqual(exp.value.max(), 51603897.28533253, places=6) + + def test_normalize_with_fao_cp(self): + """ Test normalizing of two given exposures countrywise (usually firr + norr) + with the mean crop production quantity""" + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME, hist_mean=FILENAME_MEAN, + bbox=[-5, 42, 16, 55], yearrange=np.array([2001, 2005]), + scenario='flexible', crop = 'mai', unit='t', irr='firr') + country_list, ratio, exp_firr_norm, exp_noirr_norm, fao_crop_production, exp_tot_production = \ + normalize_with_fao_cp(exp, exp, input_dir=INPUT_DIR, + yearrange=np.array([2009, 2018]), unit='t', return_data=True) + self.assertAlmostEqual(ratio[2], 17.671166854032993) + self.assertAlmostEqual(ratio[11], .86250775) + self.assertAlmostEqual(fao_crop_production[2], 673416.4) + self.assertAlmostEqual(fao_crop_production[11], 160328.7) + self.assertAlmostEqual(np.nanmax(exp_firr_norm.value.values), 220735.69212710857) + self.assertAlmostEqual(np.nanmax(exp_firr_norm.value.values), np.nanmax(exp_noirr_norm.value.values)) + self.assertAlmostEqual(np.nanmax(exp.value.values), 284244.81023404596) + self.assertAlmostEqual(np.nansum(exp_noirr_norm.value.values) + np.nansum(exp_firr_norm.value.values), np.nansum(fao_crop_production), places=1) + + self.assertListEqual(list(country_list), [0, 40, 56, 70, 191, 203, 208, 250, + 276, 380, 442, 528, 616, 705, 724, 756, 826]) + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestCropProduction) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/entity/exposures/test/test_gdp_asset.py b/climada/entity/exposures/test/test_gdp_asset.py old mode 100644 new mode 100755 index d05a648054..f751ee2991 --- a/climada/entity/exposures/test/test_gdp_asset.py +++ b/climada/entity/exposures/test/test_gdp_asset.py @@ -21,7 +21,7 @@ import unittest import pandas as pd from climada.entity.exposures import gdp_asset as ga -from climada.util.constants import NAT_REG_ID, DEMO_GDP2ASSET +from climada.util.constants import RIVER_FLOOD_REGIONS_CSV, DEMO_GDP2ASSET class TestGDP2AssetClass(unittest.TestCase): @@ -48,9 +48,9 @@ class TestGDP2AssetFunctions(unittest.TestCase): """Test LitPop Class methods""" def test_set_one_country(self): - exp_test = ga.GDP2Asset._set_one_country('LIE', 2000, DEMO_GDP2ASSET) + exp_test = ga.GDP2Asset._set_one_country('LIE', 2000, path=DEMO_GDP2ASSET) with self.assertRaises(KeyError): - ga.GDP2Asset._set_one_country('LIE', 2001, DEMO_GDP2ASSET) + ga.GDP2Asset._set_one_country('LIE', 2001, path=DEMO_GDP2ASSET) self.assertAlmostEqual(exp_test.iloc[0, 2], 9.5206968) self.assertAlmostEqual(exp_test.iloc[1, 2], 9.5623634) @@ -101,7 +101,7 @@ def test_set_one_country(self): def test_fast_if_mapping(self): - testIDs = pd.read_csv(NAT_REG_ID) + testIDs = pd.read_csv(RIVER_FLOOD_REGIONS_CSV) self.assertAlmostEqual(ga._fast_if_mapping(36, testIDs)[0], 11.0) self.assertAlmostEqual(ga._fast_if_mapping(36, testIDs)[1], 3.0) @@ -139,6 +139,7 @@ def test_read_GDP(self): self.assertAlmostEqual(testAssets[12], 191881403.05181965) + if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestGDP2AssetFunctions) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase( diff --git a/climada/entity/exposures/test/test_litpop_unit.py b/climada/entity/exposures/test/test_litpop_unit.py index 7985b08770..ac23f0e566 100644 --- a/climada/entity/exposures/test/test_litpop_unit.py +++ b/climada/entity/exposures/test/test_litpop_unit.py @@ -106,7 +106,7 @@ def test_mask_from_shape(self): curr_shp = lp._get_country_shape(curr_country, 0) mask = lp._mask_from_shape(curr_shp, resolution=60) self.assertEqual(mask.sp_index.indices.size, 5591) - self.assertTrue(mask.values.max()) + self.assertTrue(mask.sp_values.max()) self.assertIn(140 and 7663, mask.sp_index.indices) def test_litpop_box2coords(self): @@ -116,8 +116,8 @@ def test_litpop_box2coords(self): cut_bbox = lp._get_country_shape(curr_country, 1)[0] all_coords = lp._litpop_box2coords(cut_bbox, resolution, 1) self.assertEqual(len(all_coords), 25) - self.assertIn(_rnd(117.91666666666666) and _rnd(22.08333333333333),\ - _rnd(min(all_coords))) + self.assertIn(_rnd(117.91666666666666) and _rnd(22.08333333333333), + _rnd(min(all_coords))) # Execute Tests if __name__ == "__main__": diff --git a/climada/entity/exposures/test/test_mat.py b/climada/entity/exposures/test/test_mat.py index 3f9dadc1c6..5aa2c29587 100644 --- a/climada/entity/exposures/test/test_mat.py +++ b/climada/entity/exposures/test/test_mat.py @@ -30,7 +30,7 @@ class TestReader(unittest.TestCase): """Test reader functionality of the ExposuresMat class""" def test_read_demo_pass(self): - """ Read one single excel file""" + """Read one single excel file""" # Read demo excel file expo = Exposures() expo.read_mat(ENT_TEST_MAT) @@ -40,42 +40,42 @@ def test_read_demo_pass(self): self.assertEqual(expo.index.shape, (n_expos,)) self.assertEqual(expo.index[0], 0) - self.assertEqual(expo.index[n_expos-1], n_expos-1) + self.assertEqual(expo.index[n_expos - 1], n_expos - 1) self.assertEqual(expo.value.shape, (n_expos,)) self.assertEqual(expo.value[0], 13927504367.680632) - self.assertEqual(expo.value[n_expos-1], 12624818493.687229) + self.assertEqual(expo.value[n_expos - 1], 12624818493.687229) self.assertEqual(expo.deductible.shape, (n_expos,)) self.assertEqual(expo.deductible[0], 0) - self.assertEqual(expo.deductible[n_expos-1], 0) + self.assertEqual(expo.deductible[n_expos - 1], 0) self.assertEqual(expo.cover.shape, (n_expos,)) self.assertEqual(expo.cover[0], 13927504367.680632) - self.assertEqual(expo.cover[n_expos-1], 12624818493.687229) + self.assertEqual(expo.cover[n_expos - 1], 12624818493.687229) self.assertIn('int', str(expo.if_.dtype)) self.assertEqual(expo.if_.shape, (n_expos,)) self.assertEqual(expo.if_[0], 1) - self.assertEqual(expo.if_[n_expos-1], 1) + self.assertEqual(expo.if_[n_expos - 1], 1) self.assertIn('int', str(expo.category_id.dtype)) self.assertEqual(expo.category_id.shape, (n_expos,)) self.assertEqual(expo.category_id[0], 1) - self.assertEqual(expo.category_id[n_expos-1], 1) + self.assertEqual(expo.category_id[n_expos - 1], 1) self.assertIn('int', str(expo.centr_.dtype)) self.assertEqual(expo.centr_.shape, (n_expos,)) self.assertEqual(expo.centr_[0], 47) - self.assertEqual(expo.centr_[n_expos-1], 46) + self.assertEqual(expo.centr_[n_expos - 1], 46) self.assertTrue('region_id' not in expo) self.assertEqual(expo.latitude.shape, (n_expos,)) self.assertEqual(expo.latitude[0], 26.93389900000) - self.assertEqual(expo.latitude[n_expos-1], 26.34795700000) + self.assertEqual(expo.latitude[n_expos - 1], 26.34795700000) self.assertEqual(expo.longitude[0], -80.12879900000) - self.assertEqual(expo.longitude[n_expos-1], -80.15885500000) + self.assertEqual(expo.longitude[n_expos - 1], -80.15885500000) self.assertEqual(expo.ref_year, 2016) self.assertEqual(expo.value_unit, 'USD') diff --git a/climada/entity/exposures/test/test_nighlight.py b/climada/entity/exposures/test/test_nighlight.py index f109c2d20b..cfa3e754f7 100644 --- a/climada/entity/exposures/test/test_nighlight.py +++ b/climada/entity/exposures/test/test_nighlight.py @@ -30,66 +30,70 @@ class TestNightLight(unittest.TestCase): """Test nightlight functions.""" def test_required_files(self): - """ Test check_required_nl_files function with various countries.""" - #Switzerland + """Test check_required_nl_files function with various countries.""" + # Switzerland bbox = [5.954809204000128, 45.82071848599999, 10.466626831000013, 47.801166077000076] min_lon, min_lat, max_lon, max_lat = bbox - np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox),\ - [0., 0., 0., 0., 1., 0., 0., 0.]) - np.testing.assert_array_equal(nightlight.check_required_nl_files(min_lon, min_lat,\ - max_lon, max_lat), [0., 0., 0., 0., 1., 0., 0., 0.]) + np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox), + [0., 0., 0., 0., 1., 0., 0., 0.]) + np.testing.assert_array_equal( + nightlight.check_required_nl_files(min_lon, min_lat, max_lon, max_lat), + [0., 0., 0., 0., 1., 0., 0., 0.]) - #UK + # UK bbox = [-13.69131425699993, 49.90961334800005, 1.7711694670000497, 60.84788646000004] min_lon, min_lat, max_lon, max_lat = bbox - np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox),\ - [0., 0., 1., 0., 1., 0., 0., 0.]) - np.testing.assert_array_equal(nightlight.check_required_nl_files(min_lon,\ - min_lat, max_lon, max_lat), [0., 0., 1., 0., 1., 0., 0., 0.]) + np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox), + [0., 0., 1., 0., 1., 0., 0., 0.]) + np.testing.assert_array_equal( + nightlight.check_required_nl_files(min_lon, min_lat, max_lon, max_lat), + [0., 0., 1., 0., 1., 0., 0., 0.]) - #entire world + # entire world bbox = [-180, -90, 180, 90] min_lon, min_lat, max_lon, max_lat = bbox - np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox),\ - [1., 1., 1., 1., 1., 1., 1., 1.]) - np.testing.assert_array_equal(nightlight.check_required_nl_files(min_lon,\ - min_lat, max_lon, max_lat), [1., 1., 1., 1., 1., 1., 1., 1.]) + np.testing.assert_array_equal(nightlight.check_required_nl_files(bbox), + [1., 1., 1., 1., 1., 1., 1., 1.]) + np.testing.assert_array_equal( + nightlight.check_required_nl_files(min_lon, min_lat, max_lon, max_lat), + [1., 1., 1., 1., 1., 1., 1., 1.]) - #Not enough coordinates + # Not enough coordinates bbox = [-180, -90, 180, 90] min_lon, min_lat, max_lon, max_lat = bbox - self.assertRaises(ValueError, nightlight.check_required_nl_files,\ + self.assertRaises(ValueError, nightlight.check_required_nl_files, min_lon, min_lat, max_lon) - #Invalid coordinate order + # Invalid coordinate order bbox = [-180, -90, 180, 90] min_lon, min_lat, max_lon, max_lat = bbox - self.assertRaises(ValueError, nightlight.check_required_nl_files,\ + self.assertRaises(ValueError, nightlight.check_required_nl_files, max_lon, min_lat, min_lon, max_lat) - self.assertRaises(ValueError, nightlight.check_required_nl_files,\ + self.assertRaises(ValueError, nightlight.check_required_nl_files, min_lon, max_lat, max_lon, min_lat) def test_check_files_exist(self): - """ Test check_nightlight_local_file_exists""" + """Test check_nightlight_local_file_exists""" # If invalid path is supplied it has to fall back to DATA_DIR - np.testing.assert_array_equal(nightlight.check_nl_local_file_exists(np.ones\ - (np.count_nonzero(BM_FILENAMES)), 'Invalid/path')[0],\ - nightlight.check_nl_local_file_exists(np.ones\ - (np.count_nonzero(BM_FILENAMES)), SYSTEM_DIR)[0]) + np.testing.assert_array_equal( + nightlight.check_nl_local_file_exists( + np.ones(np.count_nonzero(BM_FILENAMES)), 'Invalid/path')[0], + nightlight.check_nl_local_file_exists( + np.ones(np.count_nonzero(BM_FILENAMES)), SYSTEM_DIR)[0]) def test_download_nightlight_files(self): - """ Test check_nightlight_local_file_exists""" + """Test check_nightlight_local_file_exists""" # Not the same length of arguments - self.assertRaises(ValueError, nightlight.download_nl_files ,(1, 0, 1), (1, 1)) + self.assertRaises(ValueError, nightlight.download_nl_files, (1, 0, 1), (1, 1)) # The same length but not the correct length self.assertRaises(ValueError, nightlight.download_nl_files, (1, 0, 1), (1, 1, 1)) - + # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestNightLight) diff --git a/climada/entity/exposures/test/test_spamagrar_unit.py b/climada/entity/exposures/test/test_spamagrar_unit.py index 8216a85047..d9a322b2b1 100644 --- a/climada/entity/exposures/test/test_spamagrar_unit.py +++ b/climada/entity/exposures/test/test_spamagrar_unit.py @@ -42,8 +42,8 @@ def test_all_names_given(self): testdata = self.init_testdata() testdata1, teststr1 = ent._spam_set_country(testdata, name_adm2='municB') - testdata2, teststr2 = ent._spam_set_country(testdata, country='AAA', \ - name_adm1='kantonB', \ + testdata2, teststr2 = ent._spam_set_country(testdata, country='AAA', + name_adm1='kantonB', name_adm2='municB') self.assert_pd_frame_equal(testdata1, testdata2) self.assertEqual(teststr1, ' municB') @@ -73,7 +73,7 @@ def test_invalid_adm1(self): ent = SpamAgrar() testdata = self.init_testdata() with self.assertLogs('climada.entity.exposures.spam_agrar', level='INFO') as cm: - testdata1, teststr1 = ent._spam_set_country(testdata, name_adm1='stateC', \ + testdata1, teststr1 = ent._spam_set_country(testdata, name_adm1='stateC', name_adm2='XXX') testdata2 = ent._spam_set_country(testdata, name_adm1='stateC')[0] self.assertIn('Admin2 not found in data: XXX', cm.output[0]) @@ -82,8 +82,8 @@ def test_invalid_adm1(self): @staticmethod def init_testdata(): - testdata = pd.DataFrame(columns=['iso3', 'dat1', 'dat2', \ - 'name_cntr', 'name_adm1', \ + testdata = pd.DataFrame(columns=['iso3', 'dat1', 'dat2', + 'name_cntr', 'name_adm1', 'name_adm2']) testdata.loc[0, 'iso3'] = 'AAA' testdata.loc[1, 'iso3'] = 'AAA' @@ -107,13 +107,13 @@ def init_testdata(): @staticmethod def assert_pd_frame_equal(df1, df2, **kwds): - """ Assert that two dataframes are equal, ignoring ordering of columns""" + """Assert that two dataframes are equal, ignoring ordering of columns""" from pandas.util.testing import assert_frame_equal - return assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), \ + return assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), check_names=True, **kwds) # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestSetCountry) - #TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestXYZ)) + # TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestXYZ)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/entity/impact_funcs/__init__.py b/climada/entity/impact_funcs/__init__.py index eef7f9739a..9daada52de 100755 --- a/climada/entity/impact_funcs/__init__.py +++ b/climada/entity/impact_funcs/__init__.py @@ -22,4 +22,4 @@ from .impact_func_set import * from .trop_cyclone import * from .drought import * -from .ag_drought import * \ No newline at end of file +from .relative_cropyield import * diff --git a/climada/entity/impact_funcs/base.py b/climada/entity/impact_funcs/base.py index ee6892aba3..7d70846bd9 100644 --- a/climada/entity/impact_funcs/base.py +++ b/climada/entity/impact_funcs/base.py @@ -45,7 +45,7 @@ class ImpactFunc(): intensity (numbers in [0,1]) """ def __init__(self): - """ Empty initialization.""" + """Empty initialization.""" self.id = '' self.name = '' self.intensity_unit = '' @@ -56,7 +56,7 @@ def __init__(self): self.paa = np.array([]) def calc_mdr(self, inten): - """ Interpolate impact function to a given intensity. + """Interpolate impact function to a given intensity. Parameters: inten (float or np.array): intensity, the x-coordinate of the @@ -98,7 +98,7 @@ def plot(self, axis=None, **kwargs): return axis def check(self): - """ Check consistent instance data. + """Check consistent instance data. Raises: ValueError @@ -106,3 +106,25 @@ def check(self): num_exp = len(self.intensity) check.size(num_exp, self.mdd, 'ImpactFunc.mdd') check.size(num_exp, self.paa, 'ImpactFunc.paa') + + if num_exp == 0: + LOGGER.warning("%s impact function with name '%s' (id=%s) has empty" + " intensity.", self.haz_type, self.name, self.id) + return + + # Warning for non-vanishing impact at intensity 0. If positive + # and negative intensity warning for interpolation at intensity 0. + zero_idx = np.where(self.intensity == 0)[0] + if zero_idx.size != 0: + if self.mdd[zero_idx[0]] != 0 or self.paa[zero_idx[0]] != 0: + LOGGER.warning('For intensity = 0, mdd != 0 or paa != 0. ' + 'Consider shifting the origin of the intensity ' + 'scale. In impact.calc the impact is always ' + 'null at intensity = 0.') + elif self.intensity[0] < 0 and self.intensity[-1] > 0: + LOGGER.warning('Impact function might be interpolated to non-zero' + ' value at intensity = 0. Consider shifting the ' + 'origin of the intensity scale. In impact.calc ' + 'the impact is always null at intensity = 0.') + + diff --git a/climada/entity/impact_funcs/drought.py b/climada/entity/impact_funcs/drought.py index ffed4b6d16..65d7530b0b 100644 --- a/climada/entity/impact_funcs/drought.py +++ b/climada/entity/impact_funcs/drought.py @@ -32,11 +32,11 @@ class IFDrought(ImpactFunc): """Impact function for droughts.""" def __init__(self): - """ Empty initialization. + """Empty initialization. Parameters: if_id (int, optional): impact function id. Default: 1 - intensity (np.array, optional): intensity array SPEI [-]. + intensity (np.array, optional): intensity array SPEI [-]. default: intensity defintion 1 (minimum) default_sum: intensity definition 3 (sum over all drought months) @@ -44,7 +44,7 @@ def __init__(self): ValueError """ ImpactFunc.__init__(self) - + def set_default(self): self.haz_type = "DR" self.id = 1 @@ -52,32 +52,32 @@ def set_default(self): self.intensity_unit = "NA" self.intensity = [-6.5, -4, -1, 0] self.mdd = [1, 1, 0, 0] - self.paa = [1,1,0,0] - + self.paa = [1, 1, 0, 0] + def set_default_sum(self): self.haz_type = "DR_sum" self.id = 1 self.name = "drought default sum" self.intensity_unit = "NA" self.intensity = [-15, -12, -9, -7, -5, 0] - self.mdd = [1,0.65,0.5,0.3,0,0] - self.paa = [1,1,1,1,0,0] - + self.mdd = [1, 0.65, 0.5, 0.3, 0, 0] + self.paa = [1, 1, 1, 1, 0, 0] + def set_default_sumthr(self): self.haz_type = "DR_sumthr" self.id = 1 self.name = "drought default sum - thr" self.intensity_unit = "NA" self.intensity = [-8, -5, -2, 0] - self.mdd = [0.7,0.3,0,0] - self.paa = [1,1,0,0] - + self.mdd = [0.7, 0.3, 0, 0] + self.paa = [1, 1, 0, 0] + def set_step(self): self.haz_type = "DR" self.id = 1 self.name = "step" self.intensity_unit = "NA" - self.intensity = np.arange(-4,0) + self.intensity = np.arange(-4, 0) self.mdd = np.ones(self.intensity.size) self.paa = np.ones(self.mdd.size) - + diff --git a/climada/entity/impact_funcs/impact_func_set.py b/climada/entity/impact_funcs/impact_func_set.py index cf652d5969..d954657084 100755 --- a/climada/entity/impact_funcs/impact_func_set.py +++ b/climada/entity/impact_funcs/impact_func_set.py @@ -37,29 +37,29 @@ LOGGER = logging.getLogger(__name__) DEF_VAR_EXCEL = {'sheet_name': 'impact_functions', - 'col_name': {'func_id' : 'impact_fun_id', - 'inten' : 'intensity', - 'mdd' : 'mdd', - 'paa' : 'paa', - 'name' : 'name', - 'unit' : 'intensity_unit', - 'peril' : 'peril_id' + 'col_name': {'func_id': 'impact_fun_id', + 'inten': 'intensity', + 'mdd': 'mdd', + 'paa': 'paa', + 'name': 'name', + 'unit': 'intensity_unit', + 'peril': 'peril_id' } } -""" Excel and csv variable names """ +"""Excel and csv variable names""" DEF_VAR_MAT = {'sup_field_name': 'entity', 'field_name': 'damagefunctions', - 'var_name': {'fun_id' : 'DamageFunID', - 'inten' : 'Intensity', - 'mdd' : 'MDD', - 'paa' : 'PAA', - 'name' : 'name', - 'unit' : 'Intensity_unit', - 'peril' : 'peril_ID' + 'var_name': {'fun_id': 'DamageFunID', + 'inten': 'Intensity', + 'mdd': 'MDD', + 'paa': 'PAA', + 'name': 'name', + 'unit': 'Intensity_unit', + 'peril': 'peril_ID' } } -""" MATLAB variable names """ +"""MATLAB variable names""" class ImpactFuncSet(): """Contains impact functions of type ImpactFunc. Loads from @@ -97,7 +97,7 @@ def __init__(self): def clear(self): """Reinitialize attributes.""" self.tag = Tag() - self._data = dict() # {hazard_type : {id:ImpactFunc}} + self._data = dict() # {hazard_type : {id:ImpactFunc}} def append(self, func): """Append a ImpactFunc. Overwrite existing if same id and haz_type. @@ -131,8 +131,8 @@ def remove_func(self, haz_type=None, fun_id=None): try: del self._data[haz_type][fun_id] except KeyError: - LOGGER.warning("No ImpactFunc with hazard %s and id %s.", \ - haz_type, fun_id) + LOGGER.warning("No ImpactFunc with hazard %s and id %s.", + haz_type, fun_id) elif haz_type is not None: try: del self._data[haz_type] @@ -247,11 +247,11 @@ def check(self): for key_haz, vul_dict in self._data.items(): for fun_id, vul in vul_dict.items(): if (fun_id != vul.id) | (fun_id == ''): - LOGGER.error("Wrong ImpactFunc.id: %s != %s.", fun_id, \ + LOGGER.error("Wrong ImpactFunc.id: %s != %s.", fun_id, vul.id) raise ValueError if (key_haz != vul.haz_type) | (key_haz == ''): - LOGGER.error("Wrong ImpactFunc.haz_type: %s != %s.",\ + LOGGER.error("Wrong ImpactFunc.haz_type: %s != %s.", key_haz, vul.haz_type) raise ValueError vul.check() @@ -341,9 +341,9 @@ def read_mat(self, file_name, description='', var_names=DEF_VAR_MAT): def _get_hdf5_funcs(imp, file_name, var_names): """Get rows that fill every impact function and its name.""" func_pos = dict() - for row, (fun_id, fun_type) in enumerate(zip( \ - imp[var_names['var_name']['fun_id']].squeeze(), \ - imp[var_names['var_name']['peril']].squeeze())): + for row, (fun_id, fun_type) in enumerate( + zip(imp[var_names['var_name']['fun_id']].squeeze(), + imp[var_names['var_name']['peril']].squeeze())): type_str = hdf5.get_str_from_ref(file_name, fun_type) key = (type_str, int(fun_id)) if key not in func_pos: @@ -381,14 +381,15 @@ def _get_hdf5_str(imp, idxs, file_name, var_name): func.id = imp_key[1] # check that this function only has one intensity unit, if provided try: - func.intensity_unit = _get_hdf5_str(imp, imp_rows, \ - file_name, var_names['var_name']['unit']) + func.intensity_unit = _get_hdf5_str(imp, imp_rows, + file_name, + var_names['var_name']['unit']) except KeyError: pass # check that this function only has one name try: - func.name = _get_hdf5_str(imp, imp_rows, file_name, \ - var_names['var_name']['name']) + func.name = _get_hdf5_str(imp, imp_rows, file_name, + var_names['var_name']['name']) except KeyError: func.name = str(func.id) func.intensity = np.take(imp[var_names['var_name']['inten']], imp_rows) @@ -400,14 +401,14 @@ def _get_hdf5_str(imp, idxs, file_name, var_name): raise err def write_excel(self, file_name, var_names=DEF_VAR_EXCEL): - """ Write excel file following template. + """Write excel file following template. Parameters: file_name (str): absolute file name to write var_names (dict, optional): name of the variables in the file """ def write_if(row_ini, imp_ws, xls_data): - """ Write one impact function """ + """Write one impact function""" for icol, col_dat in enumerate(xls_data): for irow, data in enumerate(col_dat, row_ini): imp_ws.write(irow, icol, data) @@ -436,10 +437,10 @@ def write_if(row_ini, imp_ws, xls_data): def _fill_dfr(self, dfr, var_names): def _get_xls_funcs(dfr, var_names): - """ Parse individual impact functions. """ + """Parse individual impact functions.""" dist_func = [] - for (haz_type, imp_id) in zip(dfr[var_names['col_name']['peril']], \ - dfr[var_names['col_name']['func_id']]): + for (haz_type, imp_id) in zip(dfr[var_names['col_name']['peril']], + dfr[var_names['col_name']['func_id']]): if (haz_type, imp_id) not in dist_func: dist_func.append((haz_type, imp_id)) return dist_func @@ -448,7 +449,7 @@ def _get_xls_funcs(dfr, var_names): dist_func = _get_xls_funcs(dfr, var_names) for haz_type, imp_id in dist_func: df_func = dfr[dfr[var_names['col_name']['peril']] == haz_type] - df_func = df_func[df_func[var_names['col_name']['func_id']] \ + df_func = df_func[df_func[var_names['col_name']['func_id']] == imp_id] func = ImpactFunc() @@ -480,4 +481,4 @@ def _get_xls_funcs(dfr, var_names): except KeyError as err: LOGGER.error("Not existing variable: %s", str(err)) - raise err \ No newline at end of file + raise err diff --git a/climada/entity/impact_funcs/ag_drought.py b/climada/entity/impact_funcs/relative_cropyield.py similarity index 61% rename from climada/entity/impact_funcs/ag_drought.py rename to climada/entity/impact_funcs/relative_cropyield.py index ec6640a548..ee2e605e3a 100644 --- a/climada/entity/impact_funcs/ag_drought.py +++ b/climada/entity/impact_funcs/relative_cropyield.py @@ -14,7 +14,7 @@ """ -__all__ = ['IFAgriculturalDrought'] +__all__ = ['IFRelativeCropyield'] import logging import numpy as np @@ -22,22 +22,27 @@ LOGGER = logging.getLogger(__name__) -class IFAgriculturalDrought(ImpactFunc): +class IFRelativeCropyield(ImpactFunc): """Impact functions for agricultural droughts.""" def __init__(self): ImpactFunc.__init__(self) - self.haz_type = 'AD' + self.haz_type = 'RC' self.intensity_unit = '' #self.continent = '' def set_relativeyield(self): - """Impact functions defining the impact as (intensity-1)""" - self.haz_type = 'AD' + """Impact functions defining the impact as intensity""" + self.haz_type = 'RC' self.id = 1 - self.name = 'Agricultural Drought ISIMIP' + self.name = 'Relative Cropyield ISIMIP' self.intensity_unit = '' - self.intensity = np.arange(0, 11) - self.mdr = (self.intensity - 1) - self.mdd = (self.intensity - 1) + # intensity = 0 when the crop production is equivalent to the historical mean + # intensity = -1 for a complete crop failure + # intensity = 1 for a crop production surplus of 100% + # the impact function covers the common stretch of the hazard intensity + # CLIMADA interpolates linearly in case of larger intensity values + self.intensity = np.arange(-1, 10) + self.mdr = (self.intensity) + self.mdd = (self.intensity) self.paa = np.ones(len(self.intensity)) diff --git a/climada/entity/impact_funcs/flood.py b/climada/entity/impact_funcs/river_flood.py similarity index 97% rename from climada/entity/impact_funcs/flood.py rename to climada/entity/impact_funcs/river_flood.py index 28cab03dae..3ed999ff36 100644 --- a/climada/entity/impact_funcs/flood.py +++ b/climada/entity/impact_funcs/river_flood.py @@ -25,7 +25,7 @@ import pandas as pd from climada.entity.impact_funcs.base import ImpactFunc from climada.entity import ImpactFuncSet -from climada.util.constants import NAT_REG_ID +from climada.util.constants import RIVER_FLOOD_REGIONS_CSV LOGGER = logging.getLogger(__name__) DEF_VAR_EXCEL = {'sheet_name': 'damagefunctions', @@ -132,7 +132,7 @@ def set_RF_IF_SouthAmerica(self): self.mdr = np.array([0.0000, 0.4908, 0.7112, 0.8420, 0.9494, 0.9836, 1.0000, 1.0000, 1.0000, 1.0000]) - self.paa = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + self.paa = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) def flood_imp_func_set(): """Builds impact function set for river flood, using standard files""" @@ -167,6 +167,6 @@ def flood_imp_func_set(): def assign_if_simple(exposure, country): - info = pd.read_csv(NAT_REG_ID) + info = pd.read_csv(RIVER_FLOOD_REGIONS_CSV) if_id = info.loc[info['ISO'] == country, 'if_RF'].values[0] exposure['if_RF'] = if_id diff --git a/climada/entity/impact_funcs/storm_europe.py b/climada/entity/impact_funcs/storm_europe.py index 3e77356230..0c2e17f1be 100644 --- a/climada/entity/impact_funcs/storm_europe.py +++ b/climada/entity/impact_funcs/storm_europe.py @@ -45,13 +45,13 @@ def set_schwierz(self, if_id=1): self.name = 'Schwierz 2011' self.id = if_id self.intensity_unit = 'm/s' - self.intensity = np.array([ 0, 20, 25, 30, 35, 40, 45, 50, 55, 60, 80, 100]) - self.paa = np.array([0. , 0. , 0.001 , 0.00676, + self.intensity = np.array([0, 20, 25, 30, 35, 40, 45, 50, 55, 60, 80, 100]) + self.paa = np.array([0., 0., 0.001, 0.00676, 0.03921, 0.10707, 0.25357, 0.48869, - 0.82907, 1. , 1. , 1. ]) - self.mdd = np.array([0. , 0. , 0.001 , 0.00177515, + 0.82907, 1., 1., 1.]) + self.mdd = np.array([0., 0., 0.001, 0.00177515, 0.00367253, 0.00749977, 0.01263556, 0.01849639, - 0.02370487, 0.037253, 0.037253 , 0.037253 ]) + 0.02370487, 0.037253, 0.037253, 0.037253]) self.check() def set_welker(self, if_id=1): diff --git a/climada/entity/impact_funcs/test/test_fl.py b/climada/entity/impact_funcs/test/test_fl.py index 0c7694d170..94d3cf88f8 100644 --- a/climada/entity/impact_funcs/test/test_fl.py +++ b/climada/entity/impact_funcs/test/test_fl.py @@ -16,7 +16,7 @@ import unittest import numpy as np -from climada.entity.impact_funcs import flood as fl +from climada.entity.impact_funcs import river_flood as fl class TestIFRiverFlood(unittest.TestCase): diff --git a/climada/entity/impact_funcs/test/test_imp_fun_set.py b/climada/entity/impact_funcs/test/test_imp_fun_set.py index 397811523e..18bfbdb039 100644 --- a/climada/entity/impact_funcs/test/test_imp_fun_set.py +++ b/climada/entity/impact_funcs/test/test_imp_fun_set.py @@ -51,18 +51,18 @@ def test_add_wrong_error(self): """Test error is raised when wrong ImpactFunc provided.""" imp_fun = ImpactFuncSet() vulner_1 = ImpactFunc() - with self.assertLogs('climada.entity.impact_funcs.impact_func_set', + with self.assertLogs('climada.entity.impact_funcs.impact_func_set', level='WARNING') as cm: imp_fun.append(vulner_1) self.assertIn("Input ImpactFunc's hazard type not set.", cm.output[0]) vulner_1.haz_type = 'TC' - with self.assertLogs('climada.entity.impact_funcs.impact_func_set', + with self.assertLogs('climada.entity.impact_funcs.impact_func_set', level='WARNING') as cm: imp_fun.append(vulner_1) self.assertIn("Input ImpactFunc's id not set.", cm.output[0]) - with self.assertLogs('climada.entity.impact_funcs.impact_func_set', + with self.assertLogs('climada.entity.impact_funcs.impact_func_set', level='ERROR') as cm: with self.assertRaises(ValueError): imp_fun.append(45) @@ -207,7 +207,7 @@ def test_size_pass(self): """Test size function.""" imp_fun = ImpactFuncSet() self.assertEqual(0, imp_fun.size()) - + vulner_1 = ImpactFunc() vulner_1.haz_type = 'WS' vulner_1.id = 56 @@ -249,7 +249,7 @@ def test_size_wrong_zero(self): self.assertEqual(0, imp_fun.size('TC')) self.assertEqual(0, imp_fun.size('TC', 3)) self.assertEqual(0, imp_fun.size(fun_id=3)) - + def test_append_pass(self): """Test append adds ImpactFunc to ImpactFuncSet correctly.""" imp_fun = ImpactFuncSet() @@ -339,7 +339,7 @@ def test_check_wrongMDD_fail(self): class TestExtend(unittest.TestCase): """Check extend function""" def test_extend_to_empty_same(self): - """Extend ImpactFuncSet to empty one.""" + """Extend ImpactFuncSet to empty one.""" imp_fun = ImpactFuncSet() imp_fun_add = ImpactFuncSet() vulner_1 = ImpactFunc() @@ -356,9 +356,9 @@ def test_extend_to_empty_same(self): vulner_3.id = 3 vulner_3.haz_type = 'FL' imp_fun_add.append(vulner_3) - + imp_fun_add.tag.file_name = 'file1.txt' - + imp_fun.extend(imp_fun_add) imp_fun.check() @@ -369,16 +369,16 @@ def test_extend_to_empty_same(self): self.assertEqual(imp_fun.tag.description, imp_fun_add.tag.description) def test_extend_equal_same(self): - """Extend the same ImpactFuncSet. The inital ImpactFuncSet is obtained.""" + """Extend the same ImpactFuncSet. The inital ImpactFuncSet is obtained.""" imp_fun = ImpactFuncSet() vulner_1 = ImpactFunc() vulner_1.id = 1 vulner_1.haz_type = 'TC' imp_fun.append(vulner_1) - + imp_fun_add = ImpactFuncSet() imp_fun_add.append(vulner_1) - + imp_fun.extend(imp_fun_add) imp_fun.check() @@ -403,7 +403,7 @@ def test_extend_different_extend(self): vulner_3.id = 3 vulner_3.haz_type = 'FL' imp_fun.append(vulner_3) - + imp_fun_add = ImpactFuncSet() vulner_1 = ImpactFunc() vulner_1.id = 1 @@ -419,20 +419,20 @@ def test_extend_different_extend(self): vulner_3.id = 3 vulner_3.haz_type = 'FL' imp_fun_add.append(vulner_3) - + imp_fun.extend(imp_fun_add) imp_fun.check() self.assertEqual(imp_fun.size(), 4) self.assertEqual(imp_fun.size('TC'), 2) self.assertEqual(imp_fun.size('FL'), 1) - self.assertEqual(imp_fun.size('WS'), 1) + self.assertEqual(imp_fun.size('WS'), 1) class TestReaderMat(unittest.TestCase): """Test reader functionality of the imp_funcsFuncsExcel class""" def test_demo_file_pass(self): - """ Read demo excel file""" + """Read demo excel file""" # Read demo mat file imp_funcs = ImpactFuncSet() description = 'One single file.' @@ -451,10 +451,10 @@ def test_demo_file_pass(self): self.assertEqual(imp_funcs._data[hazard][first_id].id, 1) self.assertEqual(imp_funcs._data[hazard][first_id].name, 'Tropical cyclone default') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, 'm/s') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity.shape, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity.shape, (9,)) self.assertEqual(imp_funcs._data[hazard][first_id].intensity[0], 0) self.assertEqual(imp_funcs._data[hazard][first_id].intensity[1], 20) @@ -478,10 +478,10 @@ def test_demo_file_pass(self): self.assertEqual(imp_funcs._data[hazard][second_id].id, 3) self.assertEqual(imp_funcs._data[hazard][second_id].name, 'TC Building code') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, 'm/s') - self.assertEqual(imp_funcs._data[hazard][second_id].intensity.shape, \ + self.assertEqual(imp_funcs._data[hazard][second_id].intensity.shape, (9,)) self.assertEqual(imp_funcs._data[hazard][second_id].intensity[0], 0) self.assertEqual(imp_funcs._data[hazard][second_id].intensity[1], 20) @@ -509,7 +509,7 @@ class TestReaderExcel(unittest.TestCase): """Test reader functionality of the imp_funcsFuncsExcel class""" def test_demo_file_pass(self): - """ Read demo excel file""" + """Read demo excel file""" # Read demo excel file imp_funcs = ImpactFuncSet() description = 'One single file.' @@ -528,10 +528,10 @@ def test_demo_file_pass(self): self.assertEqual(imp_funcs._data[hazard][first_id].id, 1) self.assertEqual(imp_funcs._data[hazard][first_id].name, 'Tropical cyclone default') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, 'm/s') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity.shape, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity.shape, (9,)) self.assertEqual(imp_funcs._data[hazard][first_id].intensity[0], 0) self.assertEqual(imp_funcs._data[hazard][first_id].intensity[1], 20) @@ -555,10 +555,10 @@ def test_demo_file_pass(self): self.assertEqual(imp_funcs._data[hazard][second_id].id, 3) self.assertEqual(imp_funcs._data[hazard][second_id].name, 'TC Building code') - self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, \ + self.assertEqual(imp_funcs._data[hazard][first_id].intensity_unit, 'm/s') - self.assertEqual(imp_funcs._data[hazard][second_id].intensity.shape, \ + self.assertEqual(imp_funcs._data[hazard][second_id].intensity.shape, (9,)) self.assertEqual(imp_funcs._data[hazard][second_id].intensity[0], 0) self.assertEqual(imp_funcs._data[hazard][second_id].intensity[1], 20) @@ -583,7 +583,7 @@ def test_demo_file_pass(self): self.assertEqual(imp_funcs.tag.description, description) def test_template_file_pass(self): - """ Read template excel file""" + """Read template excel file""" imp_funcs = ImpactFuncSet() imp_funcs.read_excel(ENT_TEMPLATE_XLS) # Check some results @@ -596,12 +596,12 @@ class TestWriter(unittest.TestCase): """Test reader functionality of the imp_funcsFuncsExcel class""" def test_write_read_pass(self): - """ Write + read excel file""" - + """Write + read excel file""" + imp_funcs = ImpactFuncSet() imp_funcs.tag.file_name = 'No file name' imp_funcs.tag.description = 'test writer' - + imp1 = ImpactFunc() imp1.id = 1 imp1.name = 'code 1' @@ -611,7 +611,7 @@ def test_write_read_pass(self): imp1.mdd = np.arange(100) * 0.5 imp1.paa = np.ones(100) imp_funcs.append(imp1) - + imp2 = ImpactFunc() imp2.id = 2 imp2.name = 'code 2' @@ -644,10 +644,10 @@ def test_write_read_pass(self): file_name = os.path.join(CURR_DIR, 'test_write.xlsx') imp_funcs.write_excel(file_name) - + imp_res = ImpactFuncSet() imp_res.read_excel(file_name) - + self.assertEqual(imp_res.tag.file_name, file_name) self.assertEqual(imp_res.tag.description, '') @@ -664,7 +664,7 @@ def test_write_read_pass(self): ref_fun = imp4 else: self.assertEqual(1, 0) - + self.assertEqual(ref_fun.haz_type, fun.haz_type) self.assertEqual(ref_fun.id, fun.id) self.assertEqual(ref_fun.name, fun.name) diff --git a/climada/entity/impact_funcs/test/test_tc.py b/climada/entity/impact_funcs/test/test_tc.py index c6915bc0ee..502a2339ae 100644 --- a/climada/entity/impact_funcs/test/test_tc.py +++ b/climada/entity/impact_funcs/test/test_tc.py @@ -21,8 +21,10 @@ import unittest import numpy as np +import pandas as pd from climada.entity.impact_funcs.trop_cyclone import IFTropCyclone +from climada.entity.impact_funcs.trop_cyclone import IFSTropCyclone class TestEmanuelFormula(unittest.TestCase): """Impact function interpolation test""" @@ -39,18 +41,25 @@ def test_default_values_pass(self): self.assertTrue(np.array_equal(imp_fun.paa, np.ones((25,)))) self.assertTrue(np.array_equal(imp_fun.mdd[0:6], np.zeros((6,)))) self.assertTrue(np.array_equal(imp_fun.mdd[6:10], - np.array([0.0006753419543492556, 0.006790495604105169, 0.02425254393374475, 0.05758706257339458]))) + np.array([0.0006753419543492556, 0.006790495604105169, + 0.02425254393374475, 0.05758706257339458]))) self.assertTrue(np.array_equal(imp_fun.mdd[10:15], - np.array([0.10870556455111065, 0.1761433569521351, 0.2553983618763961, 0.34033822528795565, 0.4249447743109498]))) + np.array([0.10870556455111065, 0.1761433569521351, + 0.2553983618763961, 0.34033822528795565, + 0.4249447743109498]))) self.assertTrue(np.array_equal(imp_fun.mdd[15:20], - np.array([0.5045777092933046, 0.576424302849412, 0.6393091739184916, 0.6932203123193963, 0.7388256596555696]))) + np.array([0.5045777092933046, 0.576424302849412, + 0.6393091739184916, 0.6932203123193963, + 0.7388256596555696]))) self.assertTrue(np.array_equal(imp_fun.mdd[20:25], - np.array([0.777104531116526, 0.8091124649261859, 0.8358522190681132, 0.8582150905529946, 0.8769633232141456]))) + np.array([0.777104531116526, 0.8091124649261859, + 0.8358522190681132, 0.8582150905529946, + 0.8769633232141456]))) def test_values_pass(self): """Compute mdr interpolating values.""" imp_fun = IFTropCyclone() - imp_fun.set_emanuel_usa(if_id=5, intensity=np.arange(0,6,1), v_thresh=2, + imp_fun.set_emanuel_usa(if_id=5, intensity=np.arange(0, 6, 1), v_thresh=2, v_half=5, scale=0.5) self.assertEqual(imp_fun.name, 'Emanuel 2011') self.assertEqual(imp_fun.haz_type, 'TC') @@ -60,21 +69,91 @@ def test_values_pass(self): self.assertTrue(np.array_equal(imp_fun.paa, np.ones((6,)))) self.assertTrue(np.array_equal(imp_fun.mdd[0:3], np.zeros((3,)))) self.assertTrue(np.array_equal(imp_fun.mdd[3:], - np.array([0.017857142857142853, 0.11428571428571425, 0.250000000000000]))) + np.array([0.017857142857142853, 0.11428571428571425, + 0.250000000000000]))) def test_wrong_shape(self): """Set shape parameters.""" imp_fun = IFTropCyclone() with self.assertRaises(ValueError): - imp_fun.set_emanuel_usa(if_id=5, v_thresh=2, v_half=1, intensity=np.arange(0,6,1)) + imp_fun.set_emanuel_usa(if_id=5, v_thresh=2, v_half=1, + intensity=np.arange(0, 6, 1)) def test_wrong_scale(self): """Set shape parameters.""" imp_fun = IFTropCyclone() with self.assertRaises(ValueError): - imp_fun.set_emanuel_usa(if_id=5, scale=2, intensity=np.arange(0,6,1)) + imp_fun.set_emanuel_usa(if_id=5, scale=2, intensity=np.arange(0, 6, 1)) + +class TestCalibratedIFS(unittest.TestCase): + """Test inititation of IFS with regional calibrated TC IFs + based on Eberenz et al. (2020)""" + + def test_default_values_pass(self): + """Test return TDR optimized IFs (TDR=1)""" + ifs = IFSTropCyclone() + v_halfs = ifs.set_calibrated_regional_IFs() + # extract IF for region WP4 + if_wp4 = ifs.get_func(fun_id=9)[0] + self.assertIn('TC', ifs.get_ids().keys()) + self.assertEqual(ifs.size(), 10) + self.assertEqual(ifs.get_ids()['TC'], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + self.assertEqual(if_wp4.intensity_unit, 'm/s') + self.assertEqual(if_wp4.name, 'North West Pacific (WP4)') + self.assertAlmostEqual(v_halfs['WP2'], 188.4, places=7) + self.assertAlmostEqual(v_halfs['ROW'], 110.1, places=7) + self.assertListEqual(list(if_wp4.intensity), list(np.arange(0, 121, 5))) + self.assertEqual(if_wp4.paa.min(), 1.) + self.assertEqual(if_wp4.mdd.min(), 0.0) + self.assertAlmostEqual(if_wp4.mdd.max(), 0.15779133833203, places=5) + self.assertAlmostEqual(if_wp4.calc_mdr(75), 0.02607326527808, places=5) + + def test_RMSF_pass(self): + """Test return RMSF optimized IFs (RMSF=minimum)""" + ifs = IFSTropCyclone() + v_halfs = ifs.set_calibrated_regional_IFs('RMSF') + # extract IF for region NA1 + if_na1 = ifs.get_func(fun_id=1)[0] + self.assertEqual(ifs.size(), 10) + self.assertEqual(ifs.get_ids()['TC'], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + self.assertEqual(if_na1.intensity_unit, 'm/s') + self.assertEqual(if_na1.name, 'Caribbean and Mexico (NA1)') + self.assertAlmostEqual(v_halfs['NA1'], 59.6, places=7) + self.assertAlmostEqual(v_halfs['ROW'], 73.4, places=7) + self.assertListEqual(list(if_na1.intensity), list(np.arange(0, 121, 5))) + self.assertEqual(if_na1.mdd.min(), 0.0) + self.assertAlmostEqual(if_na1.mdd.max(), 0.95560418241669, places=5) + self.assertAlmostEqual(if_na1.calc_mdr(75), 0.7546423895457, places=5) + + def test_quantile_pass(self): + """Test return IFs from quantile of inidividual event fitting (EDR=1)""" + ifs = IFSTropCyclone() + ifs.set_calibrated_regional_IFs('EDR') + ifs_p10 = IFSTropCyclone() + ifs_p10.set_calibrated_regional_IFs('EDR', q=.1) + # extract IF for region SI + if_si = ifs.get_func(fun_id=5)[0] + if_si_p10 = ifs_p10.get_func(fun_id=5)[0] + self.assertEqual(ifs.size(), 10) + self.assertEqual(ifs_p10.size(), 10) + self.assertEqual(if_si.intensity_unit, 'm/s') + self.assertEqual(if_si_p10.name, 'South Indian (SI)') + self.assertAlmostEqual(if_si_p10.mdd.max(), 0.99999999880, places=5) + self.assertAlmostEqual(if_si.calc_mdr(30), 0.01620503041, places=5) + intensity = np.random.randint(26, if_si.intensity.max()) + self.assertTrue(if_si.calc_mdr(intensity) < if_si_p10.calc_mdr(intensity)) + + def test_get_countries_per_region(self): + """Test static get_countries_per_region()""" + ifs = IFSTropCyclone() + out = ifs.get_countries_per_region('NA2') + self.assertEqual(out[0], 'USA and Canada') + self.assertEqual(out[1], 2) + self.assertListEqual(out[2], [124, 840]) + self.assertListEqual(out[3], ['CAN', 'USA']) # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestEmanuelFormula) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestCalibratedIFS)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/entity/impact_funcs/test/test_ws.py b/climada/entity/impact_funcs/test/test_ws.py index 85f445289c..6518ddc530 100644 --- a/climada/entity/impact_funcs/test/test_ws.py +++ b/climada/entity/impact_funcs/test/test_ws.py @@ -35,7 +35,7 @@ def test_default_values_pass(self): self.assertEqual(imp_fun.haz_type, 'WS') self.assertEqual(imp_fun.id, 1) self.assertEqual(imp_fun.intensity_unit, 'm/s') - self.assertTrue(np.array_equal(imp_fun.intensity, np.array([ 0, 20, 25, 30, 35, 40, 45, 50, 55, 60, 80, 100]))) + self.assertTrue(np.array_equal(imp_fun.intensity, np.array([0, 20, 25, 30, 35, 40, 45, 50, 55, 60, 80, 100]))) self.assertTrue(np.array_equal(imp_fun.paa[4:8], np.array([0.03921, 0.10707, 0.25357, 0.48869]))) self.assertTrue(np.array_equal(imp_fun.mdd[4:8], np.array([0.00367253, 0.00749977, 0.01263556, 0.01849639]))) @@ -45,13 +45,13 @@ def test_default_values_pass(self): self.assertEqual(imp_fun2.haz_type, 'WS') self.assertEqual(imp_fun2.id, 1) self.assertEqual(imp_fun2.intensity_unit, 'm/s') - self.assertTrue(np.array_equal(imp_fun2.intensity[np.arange(0,120,13)], - np.array([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90.]))) - self.assertTrue(np.allclose(imp_fun2.paa[np.arange(0,120,13)], - np.array([0. , 0. , 0. , 0.00900782, 0.1426727 , - 0.65118822, 1. , 1. , 1. , 1. ]))) - self.assertTrue(np.allclose(imp_fun2.mdd[np.arange(0,120,13)], - np.array([0. , 0. , 0. , 0.00236542, 0.00999358, + self.assertTrue(np.array_equal(imp_fun2.intensity[np.arange(0, 120, 13)], + np.array([0., 10., 20., 30., 40., 50., 60., 70., 80., 90.]))) + self.assertTrue(np.allclose(imp_fun2.paa[np.arange(0, 120, 13)], + np.array([0., 0., 0., 0.00900782, 0.1426727, + 0.65118822, 1., 1., 1., 1.]))) + self.assertTrue(np.allclose(imp_fun2.mdd[np.arange(0, 120, 13)], + np.array([0., 0., 0., 0.00236542, 0.00999358, 0.02464677, 0.04964029, 0.04964029, 0.04964029, 0.04964029]))) diff --git a/climada/entity/impact_funcs/trop_cyclone.py b/climada/entity/impact_funcs/trop_cyclone.py index 366fc7265c..c6ef3d9a25 100644 --- a/climada/entity/impact_funcs/trop_cyclone.py +++ b/climada/entity/impact_funcs/trop_cyclone.py @@ -21,10 +21,14 @@ __all__ = ['IFTropCyclone'] +import os import logging import numpy as np +import pandas as pd from climada.entity.impact_funcs.base import ImpactFunc +from climada.entity.impact_funcs.impact_func_set import ImpactFuncSet +from climada.util.constants import SYSTEM_DIR LOGGER = logging.getLogger(__name__) @@ -57,7 +61,7 @@ def set_emanuel_usa(self, if_id=1, intensity=np.arange(0, 121, 5), if v_half <= v_thresh: LOGGER.error('Shape parameters out of range: v_half <= v_thresh.') raise ValueError - if v_thresh < 0 or v_half < 0: + if v_thresh < 0 or v_half < 0: LOGGER.error('Negative shape parameter.') raise ValueError if scale > 1 or scale <= 0: @@ -73,3 +77,203 @@ def set_emanuel_usa(self, if_id=1, intensity=np.arange(0, 121, 5), v_temp[v_temp < 0] = 0 self.mdd = v_temp**3 / (1 + v_temp**3) self.mdd *= scale + +class IFSTropCyclone(ImpactFuncSet): + """Impact function set (IFS) for tropical cyclones.""" + + def __init__(self): + ImpactFuncSet.__init__(self) + + def set_calibrated_regional_IFs(self, calibration_approach='TDR', q=.5, + input_file_path=None, version=1): + """ initiate TC wind impact functions based on Eberenz et al. (2020) + + Optional Parameters: + calibration_approach (str): + 'TDR' (default): Total damage ratio (TDR) optimization with + TDR=1.0 (simulated damage = reported damage from EM-DAT) + 'TDR1.5' : Total damage ratio (TDR) optimization with + TDR=1.5 (simulated damage = 1.5*reported damage from EM-DAT) + 'RMSF': Root-mean-squared fraction (RMSF) optimization + 'EDR': quantile from individually fitted v_half per event, + i.e. v_half fitted to get EDR=1.0 for each event + q (float): quantile between 0 and 1.0 to select + (EDR only, default=0.5, i.e. median v_half) + input_file_path (str or DataFrame): full path to calibration + result file to be used instead of default file in repository + (expert users only) + + Returns: + v_half (dict): IF slope parameter v_half per region¨ + + Raises: + ValueError + """ + calibration_approach = calibration_approach.upper() + if calibration_approach not in ['TDR', 'TDR1.0', 'TDR1.5', 'RMSF', 'EDR']: + LOGGER.error('calibration_approach is invalid') + raise ValueError + if 'EDR' in calibration_approach and (q < 0. or q > 1.): + LOGGER.error('Quantile q out of range [0, 1]') + raise ValueError + if calibration_approach == 'TDR': + calibration_approach = 'TDR1.0' + # load calibration results depending on approach: + if isinstance(input_file_path, str): + df_calib_results = pd.read_csv(input_file_path, + encoding="ISO-8859-1", header=0) + elif isinstance(input_file_path, pd.DataFrame): + df_calib_results = input_file_path + else: + df_calib_results = pd.read_csv( + os.path.join(SYSTEM_DIR, + 'tc_if_cal_v%02.0f_%s.csv' % (version, calibration_approach)), + encoding="ISO-8859-1", header=0) + + # define regions and parameters: + v_0 = 25.7 # v_threshold based on Emanuel (2011) + scale = 1.0 + + regions_short = ['NA1', 'NA2', 'NI', 'OC', 'SI', 'WP1', 'WP2', 'WP3', 'WP4'] + regions_long = dict() + regions_long[regions_short[0]] = 'Caribbean and Mexico (NA1)' + regions_long[regions_short[1]] = 'USA and Canada (NA2)' + regions_long[regions_short[2]] = 'North Indian (NI)' + regions_long[regions_short[3]] = 'Oceania (OC)' + regions_long[regions_short[4]] = 'South Indian (SI)' + regions_long[regions_short[5]] = 'South East Asia (WP1)' + regions_long[regions_short[6]] = 'Philippines (WP2)' + regions_long[regions_short[7]] = 'China Mainland (WP3)' + regions_long[regions_short[8]] = 'North West Pacific (WP4)' + regions_long['all'] = 'Global' + regions_long['GLB'] = 'Global' + regions_long['ROW'] = 'Global' + + # loop over calibration regions (column cal_region2 in df): + reg_v_half = dict() + for idx, region in enumerate(regions_short): + df_reg = df_calib_results.loc[df_calib_results.cal_region2 == region] + df_reg = df_reg.reset_index(drop=True) + reg_v_half[region] = np.round(df_reg['v_half'].quantile(q=q), 5) + # rest of the world (ROW), calibrated by all data: + regions_short = regions_short + ['ROW'] + if calibration_approach == 'EDR': + reg_v_half[regions_short[-1]] = np.round(df_calib_results['v_half'].quantile(q=q), 5) + else: + df_reg = df_calib_results.loc[df_calib_results.cal_region2 == 'GLB'] + df_reg = df_reg.reset_index(drop=True) + reg_v_half[regions_short[-1]] = np.round(df_reg['v_half'].values[0], 5) + + for idx, region in enumerate(regions_short): + if_tc = IFTropCyclone() + if_tc.set_emanuel_usa(if_id=int(idx + 1), v_thresh=v_0, v_half=reg_v_half[region], + scale=scale) + if_tc.name = regions_long[region] + self.append(if_tc) + return reg_v_half + + @staticmethod + def get_countries_per_region(region=None): + """Returns dictionaries with numerical and alphabetical ISO3 codes + of all countries associated to a calibration region. + Only contains countries that were affected by tropical cyclones + between 1980 and 2017 according to EM-DAT. + + Optional Parameters: + region (str): regional abbreviation (default='all'), + either 'NA1', 'NA2', 'NI', 'OC', 'SI', 'WP1', 'WP2', + 'WP3', 'WP4', or 'all'. + + Returns: + [0] region_name (dict or str): long name per region + [1] if_id (dict or int): impact function ID per region + [2] iso3n (dict or list): numerical ISO3codes (=region_id) per region + [3] iso3a (dict or list): numerical ISO3codes (=region_id) per region + """ + if not region: + region = 'all' + iso3n = {'NA1': [660, 28, 32, 533, 44, 52, 84, 60, 68, 132, 136, + 152, 170, 188, 192, 212, 214, 218, 222, 238, 254, + 308, 312, 320, 328, 332, 340, 388, 474, 484, 500, + 558, 591, 600, 604, 630, 654, 659, 662, 670, 534, + 740, 780, 796, 858, 862, 92, 850], + 'NA2': [124, 840], + 'NI': [4, 51, 31, 48, 50, 64, 262, 232, + 231, 268, 356, 364, 368, 376, 400, 398, 414, 417, + 422, 462, 496, 104, 524, 512, 586, 634, 682, 706, + 144, 760, 762, 795, 800, 784, 860, 887], + 'OC': [16, 36, 184, 242, 258, 316, 296, 584, 583, 520, + 540, 554, 570, 574, 580, 585, 598, 612, 882, 90, + 626, 772, 776, 798, 548, 876], + 'SI': [174, 180, 748, 450, 454, 466, 480, 508, 710, 834, + 716], + 'WP1': [116, 360, 418, 458, 764, 704], + 'WP2': [608], + 'WP3': [156], + 'WP4': [344, 392, 410, 446, 158], + 'ROW': [8, 12, 20, 24, 10, 40, 112, 56, 204, 535, 70, 72, + 74, 76, 86, 96, 100, 854, 108, 120, 140, 148, 162, + 166, 178, 191, 531, 196, 203, 384, 208, 818, 226, + 233, 234, 246, 250, 260, 266, 270, 276, 288, 292, + 300, 304, 831, 324, 624, 334, 336, 348, 352, 372, + 833, 380, 832, 404, 408, 983, 428, 426, 430, 434, + 438, 440, 442, 470, 478, 175, 498, 492, 499, 504, + 516, 528, 562, 566, 807, 578, 275, 616, 620, 642, + 643, 646, 638, 652, 663, 666, 674, 678, 686, 688, + 690, 694, 702, 703, 705, 239, 728, 724, 729, 744, + 752, 756, 768, 788, 792, 804, 826, 581, 732, 894, + 248]} + iso3a = {'NA1': ['AIA', 'ATG', 'ARG', 'ABW', 'BHS', 'BRB', 'BLZ', 'BMU', + 'BOL', 'CPV', 'CYM', 'CHL', 'COL', 'CRI', 'CUB', 'DMA', + 'DOM', 'ECU', 'SLV', 'FLK', 'GUF', 'GRD', 'GLP', 'GTM', + 'GUY', 'HTI', 'HND', 'JAM', 'MTQ', 'MEX', 'MSR', 'NIC', + 'PAN', 'PRY', 'PER', 'PRI', 'SHN', 'KNA', 'LCA', 'VCT', + 'SXM', 'SUR', 'TTO', 'TCA', 'URY', 'VEN', 'VGB', 'VIR'], + 'NA2': ['CAN', 'USA'], + 'NI': ['AFG', 'ARM', 'AZE', 'BHR', 'BGD', 'BTN', 'DJI', 'ERI', + 'ETH', 'GEO', 'IND', 'IRN', 'IRQ', 'ISR', 'JOR', 'KAZ', + 'KWT', 'KGZ', 'LBN', 'MDV', 'MNG', 'MMR', 'NPL', 'OMN', + 'PAK', 'QAT', 'SAU', 'SOM', 'LKA', 'SYR', 'TJK', 'TKM', + 'UGA', 'ARE', 'UZB', 'YEM'], + 'OC': ['ASM', 'AUS', 'COK', 'FJI', 'PYF', 'GUM', 'KIR', 'MHL', + 'FSM', 'NRU', 'NCL', 'NZL', 'NIU', 'NFK', 'MNP', 'PLW', + 'PNG', 'PCN', 'WSM', 'SLB', 'TLS', 'TKL', 'TON', 'TUV', + 'VUT', 'WLF'], + 'SI': ['COM', 'COD', 'SWZ', 'MDG', 'MWI', 'MLI', 'MUS', 'MOZ', + 'ZAF', 'TZA', 'ZWE'], + 'WP1': ['KHM', 'IDN', 'LAO', 'MYS', 'THA', 'VNM'], + 'WP2': ['PHL'], + 'WP3': ['CHN'], + 'WP4': ['HKG', 'JPN', 'KOR', 'MAC', 'TWN'], + 'ROW': ['ALB', 'DZA', 'AND', 'AGO', 'ATA', 'AUT', 'BLR', 'BEL', + 'BEN', 'BES', 'BIH', 'BWA', 'BVT', 'BRA', 'IOT', 'BRN', + 'BGR', 'BFA', 'BDI', 'CMR', 'CAF', 'TCD', 'CXR', 'CCK', + 'COG', 'HRV', 'CUW', 'CYP', 'CZE', 'CIV', 'DNK', 'EGY', + 'GNQ', 'EST', 'FRO', 'FIN', 'FRA', 'ATF', 'GAB', 'GMB', + 'DEU', 'GHA', 'GIB', 'GRC', 'GRL', 'GGY', 'GIN', 'GNB', + 'HMD', 'VAT', 'HUN', 'ISL', 'IRL', 'IMN', 'ITA', 'JEY', + 'KEN', 'PRK', 'XKX', 'LVA', 'LSO', 'LBR', 'LBY', 'LIE', + 'LTU', 'LUX', 'MLT', 'MRT', 'MYT', 'MDA', 'MCO', 'MNE', + 'MAR', 'NAM', 'NLD', 'NER', 'NGA', 'MKD', 'NOR', 'PSE', + 'POL', 'PRT', 'ROU', 'RUS', 'RWA', 'REU', 'BLM', 'MAF', + 'SPM', 'SMR', 'STP', 'SEN', 'SRB', 'SYC', 'SLE', 'SGP', + 'SVK', 'SVN', 'SGS', 'SSD', 'ESP', 'SDN', 'SJM', 'SWE', + 'CHE', 'TGO', 'TUN', 'TUR', 'UKR', 'GBR', 'UMI', 'ESH', + 'ZMB', 'ALA']} + if_id = {'NA1': 1, 'NA2': 2, 'NI': 3, 'OC': 4, 'SI': 5, + 'WP1': 6, 'WP2': 7, 'WP3': 8, 'WP4': 9, 'ROW': 10} + region_name = dict() + region_name['NA1'] = 'Caribbean and Mexico' + region_name['NA2'] = 'USA and Canada' + region_name['NI'] = 'North Indian' + region_name['OC'] = 'Oceania' + region_name['SI'] = 'South Indian' + region_name['WP1'] = 'South East Asia' + region_name['WP2'] = 'Philippines' + region_name['WP3'] = 'China Mainland' + region_name['WP4'] = 'North West Pacific' + + if region == 'all': + return region_name, if_id, iso3n, iso3a + + return region_name[region], if_id[region], iso3n[region], iso3a[region] diff --git a/climada/entity/measures/base.py b/climada/entity/measures/base.py index 919f5c8349..d004ad4470 100755 --- a/climada/entity/measures/base.py +++ b/climada/entity/measures/base.py @@ -32,10 +32,10 @@ LOGGER = logging.getLogger(__name__) IF_ID_FACT = 1000 -""" Factor internally used as id for impact functions when region selected.""" +"""Factor internally used as id for impact functions when region selected.""" NULL_STR = 'nil' -""" String considered as no path in measures exposures_set and hazard_set or +"""String considered as no path in measures exposures_set and hazard_set or no string in imp_fun_map""" class Measure(): @@ -66,7 +66,7 @@ class Measure(): """ def __init__(self): - """ Empty initialization.""" + """Empty initialization.""" self.name = '' self.haz_type = '' self.color_rgb = np.array([0, 0, 0]) @@ -78,12 +78,12 @@ def __init__(self): # related to change in exposures self.exposures_set = NULL_STR - self.imp_fun_map = NULL_STR # ids of impact functions to change e.g. 1to10 + self.imp_fun_map = NULL_STR # ids of impact functions to change e.g. 1to10 # related to change in impact functions - self.hazard_inten_imp = (1, 0) # parameter a and b - self.mdd_impact = (1, 0) # parameter a and b - self.paa_impact = (1, 0) # parameter a and b + self.hazard_inten_imp = (1, 0) # parameter a and b + self.mdd_impact = (1, 0) # parameter a and b + self.paa_impact = (1, 0) # parameter a and b # related to change in region self.exp_region_id = [] @@ -94,7 +94,7 @@ def __init__(self): self.risk_transf_cost_factor = 1 def check(self): - """ Check consistent instance data. + """Check consistent instance data. Raises: ValueError @@ -143,8 +143,8 @@ def apply(self, exposures, imp_fun_set, hazard): # cutoff events whose damage happen with high frequency (in region if specified) new_haz = self._cutoff_hazard_damage(new_exp, new_ifs, new_haz) # apply all previous changes only to the selected exposures - new_exp, new_ifs, new_haz = self._filter_exposures(exposures, \ - imp_fun_set, hazard, new_exp, new_ifs, new_haz) + new_exp, new_ifs, new_haz = self._filter_exposures( + exposures, imp_fun_set, hazard, new_exp, new_ifs, new_haz) return new_exp, new_ifs, new_haz @@ -217,7 +217,7 @@ def _change_all_exposures(self, exposures): return new_exp def _change_exposures_if(self, exposures): - """ Change exposures impact functions ids according to imp_fun_map. + """Change exposures impact functions ids according to imp_fun_map. Parameters: exposures (Exposures): exposures instance @@ -228,11 +228,11 @@ def _change_exposures_if(self, exposures): LOGGER.debug('Setting new exposures impact functions%s', self.imp_fun_map) new_exp = copy.deepcopy(exposures) from_id = int(self.imp_fun_map[0:self.imp_fun_map.find('to')]) - to_id = int(self.imp_fun_map[self.imp_fun_map.find('to')+2:]) + to_id = int(self.imp_fun_map[self.imp_fun_map.find('to') + 2:]) try: - exp_change = np.argwhere(new_exp[INDICATOR_IF+self.haz_type].values == from_id).\ + exp_change = np.argwhere(new_exp[INDICATOR_IF + self.haz_type].values == from_id).\ reshape(-1) - new_exp[INDICATOR_IF+self.haz_type].values[exp_change] = to_id + new_exp[INDICATOR_IF + self.haz_type].values[exp_change] = to_id except KeyError: exp_change = np.argwhere(new_exp[INDICATOR_IF].values == from_id).\ reshape(-1) @@ -255,12 +255,12 @@ def _change_imp_func(self, imp_set): new_imp_set = copy.deepcopy(imp_set) for imp_fun in new_imp_set.get_func(self.haz_type): LOGGER.debug('Transforming impact functions.') - imp_fun.intensity = np.maximum(imp_fun.intensity * \ - self.hazard_inten_imp[0] - self.hazard_inten_imp[1], 0.0) - imp_fun.mdd = np.maximum(imp_fun.mdd * self.mdd_impact[0] + \ - self.mdd_impact[1], 0.0) - imp_fun.paa = np.maximum(imp_fun.paa * self.paa_impact[0] + \ - self.paa_impact[1], 0.0) + imp_fun.intensity = np.maximum( + imp_fun.intensity * self.hazard_inten_imp[0] - self.hazard_inten_imp[1], 0.0) + imp_fun.mdd = np.maximum( + imp_fun.mdd * self.mdd_impact[0] + self.mdd_impact[1], 0.0) + imp_fun.paa = np.maximum( + imp_fun.paa * self.paa_impact[0] + self.paa_impact[1], 0.0) if not new_imp_set.size(): LOGGER.info('No impact function of hazard %s found.', self.haz_type) @@ -287,8 +287,9 @@ def _cutoff_hazard_damage(self, exposures, if_set, hazard): exp_imp = exposures if self.exp_region_id: # compute impact only in selected region - in_reg = np.logical_or.reduce([exposures.region_id.values == reg for reg - in self.exp_region_id]) + in_reg = np.logical_or.reduce( + [exposures.region_id.values == reg for reg in self.exp_region_id] + ) exp_imp = exposures[in_reg] exp_imp = Exposures(exp_imp, crs=exposures.crs) imp.calc(exp_imp, if_set, hazard) @@ -301,14 +302,14 @@ def _cutoff_hazard_damage(self, exposures, if_set, hazard): cutoff = exceed_freq > self.hazard_freq_cutoff sel_haz = sort_idxs[cutoff] for row in sel_haz: - new_haz.intensity.data[new_haz.intensity.indptr[row]: \ - new_haz.intensity.indptr[row+1]] = 0 + new_haz.intensity.data[new_haz.intensity.indptr[row]: + new_haz.intensity.indptr[row + 1]] = 0 new_haz.intensity.eliminate_zeros() return new_haz def _filter_exposures(self, exposures, imp_set, hazard, new_exp, new_ifs, new_haz): - """ Incorporate changes of new elements to previous ones only for the + """Incorporate changes of new elements to previous ones only for the selected exp_region_id. If exp_region_id is [], all new changes will be accepted. @@ -329,9 +330,10 @@ def _filter_exposures(self, exposures, imp_set, hazard, new_exp, new_ifs, if exposures is new_exp: new_exp = copy.deepcopy(exposures) - chg_reg = np.logical_or.reduce([exposures.region_id.values == reg for reg - in self.exp_region_id]) - no_chg_reg = np.argwhere(np.logical_not(chg_reg)).reshape(-1) + chg_reg = np.logical_or.reduce( + [exposures.region_id.values == reg for reg in self.exp_region_id] + ) + no_chg_reg = np.argwhere(~chg_reg).reshape(-1) chg_reg = np.argwhere(chg_reg).reshape(-1) LOGGER.debug('Number of changed exposures: %s', chg_reg.size) @@ -343,7 +345,7 @@ def _filter_exposures(self, exposures, imp_set, hazard, new_exp, new_ifs, new_ifs.get_func()[self.haz_type][key + IF_ID_FACT] = \ new_ifs.get_func()[self.haz_type][key] try: - new_exp[INDICATOR_IF+self.haz_type] += IF_ID_FACT + new_exp[INDICATOR_IF + self.haz_type] += IF_ID_FACT except KeyError: new_exp[INDICATOR_IF] += IF_ID_FACT # collect old impact functions as well (used by exposures) @@ -352,22 +354,22 @@ def _filter_exposures(self, exposures, imp_set, hazard, new_exp, new_ifs, # concatenate previous and new exposures new_exp = pd.concat([exposures.iloc[no_chg_reg], new_exp.iloc[chg_reg]]) # set missing values of centr_ - if INDICATOR_CENTR+self.haz_type in new_exp.columns and \ - np.isnan(new_exp[INDICATOR_CENTR+self.haz_type].values).any(): - new_exp.drop(columns=INDICATOR_CENTR+self.haz_type, inplace=True) + if INDICATOR_CENTR + self.haz_type in new_exp.columns and \ + np.isnan(new_exp[INDICATOR_CENTR + self.haz_type].values).any(): + new_exp.drop(columns=INDICATOR_CENTR + self.haz_type, inplace=True) elif INDICATOR_CENTR in new_exp.columns and \ np.isnan(new_exp[INDICATOR_CENTR].values).any(): new_exp.drop(columns=INDICATOR_CENTR, inplace=True) # put hazard intensities outside region to previous intensities if hazard is not new_haz: - if INDICATOR_CENTR+self.haz_type in exposures.columns: - centr = exposures[INDICATOR_CENTR+self.haz_type].values[chg_reg] + if INDICATOR_CENTR + self.haz_type in exposures.columns: + centr = exposures[INDICATOR_CENTR + self.haz_type].values[chg_reg] elif INDICATOR_CENTR in exposures.columns: centr = exposures[INDICATOR_CENTR].values[chg_reg] else: exposures.assign_centroids(hazard) - centr = exposures[INDICATOR_CENTR+self.haz_type].values[chg_reg] + centr = exposures[INDICATOR_CENTR + self.haz_type].values[chg_reg] centr = np.delete(np.arange(hazard.intensity.shape[1]), np.unique(centr)) new_haz_inten = new_haz.intensity.tolil() diff --git a/climada/entity/measures/measure_set.py b/climada/entity/measures/measure_set.py index 7a3c53e404..2386e62e85 100755 --- a/climada/entity/measures/measure_set.py +++ b/climada/entity/measures/measure_set.py @@ -37,49 +37,49 @@ DEF_VAR_MAT = {'sup_field_name': 'entity', 'field_name': 'measures', - 'var_name': {'name' : 'name', - 'color' : 'color', - 'cost' : 'cost', - 'haz_int_a' : 'hazard_intensity_impact_a', - 'haz_int_b' : 'hazard_intensity_impact_b', - 'haz_frq' : 'hazard_high_frequency_cutoff', - 'haz_set' : 'hazard_event_set', - 'mdd_a' : 'MDD_impact_a', - 'mdd_b' : 'MDD_impact_b', - 'paa_a' : 'PAA_impact_a', - 'paa_b' : 'PAA_impact_b', - 'fun_map' : 'damagefunctions_map', - 'exp_set' : 'assets_file', - 'exp_reg' : 'Region_ID', - 'risk_att' : 'risk_transfer_attachement', - 'risk_cov' : 'risk_transfer_cover', - 'haz' : 'peril_ID' + 'var_name': {'name': 'name', + 'color': 'color', + 'cost': 'cost', + 'haz_int_a': 'hazard_intensity_impact_a', + 'haz_int_b': 'hazard_intensity_impact_b', + 'haz_frq': 'hazard_high_frequency_cutoff', + 'haz_set': 'hazard_event_set', + 'mdd_a': 'MDD_impact_a', + 'mdd_b': 'MDD_impact_b', + 'paa_a': 'PAA_impact_a', + 'paa_b': 'PAA_impact_b', + 'fun_map': 'damagefunctions_map', + 'exp_set': 'assets_file', + 'exp_reg': 'Region_ID', + 'risk_att': 'risk_transfer_attachement', + 'risk_cov': 'risk_transfer_cover', + 'haz': 'peril_ID' } } -""" MATLAB variable names """ +"""MATLAB variable names""" DEF_VAR_EXCEL = {'sheet_name': 'measures', - 'col_name': {'name' : 'name', - 'color' : 'color', - 'cost' : 'cost', - 'haz_int_a' : 'hazard intensity impact a', - 'haz_int_b' : 'hazard intensity impact b', - 'haz_frq' : 'hazard high frequency cutoff', - 'haz_set' : 'hazard event set', - 'mdd_a' : 'MDD impact a', - 'mdd_b' : 'MDD impact b', - 'paa_a' : 'PAA impact a', - 'paa_b' : 'PAA impact b', - 'fun_map' : 'damagefunctions map', - 'exp_set' : 'assets file', - 'exp_reg' : 'Region_ID', - 'risk_att' : 'risk transfer attachement', - 'risk_cov' : 'risk transfer cover', - 'risk_fact' : 'risk transfer cost factor', - 'haz' : 'peril_ID' + 'col_name': {'name': 'name', + 'color': 'color', + 'cost': 'cost', + 'haz_int_a': 'hazard intensity impact a', + 'haz_int_b': 'hazard intensity impact b', + 'haz_frq': 'hazard high frequency cutoff', + 'haz_set': 'hazard event set', + 'mdd_a': 'MDD impact a', + 'mdd_b': 'MDD impact b', + 'paa_a': 'PAA impact a', + 'paa_b': 'PAA impact b', + 'fun_map': 'damagefunctions map', + 'exp_set': 'assets file', + 'exp_reg': 'Region_ID', + 'risk_att': 'risk transfer attachement', + 'risk_cov': 'risk transfer cover', + 'risk_fact': 'risk transfer cost factor', + 'haz': 'peril_ID' } } -""" Excel variable names """ +"""Excel variable names""" class MeasureSet(): """Contains measures of type Measure. Loads from @@ -118,7 +118,7 @@ def __init__(self): def clear(self): """Reinitialize attributes.""" self.tag = Tag() - self._data = dict() # {hazard_type : {name: Measure()}} + self._data = dict() # {hazard_type : {name: Measure()}} def append(self, meas): """Append an Measure. Override if same name and haz_type. @@ -152,8 +152,8 @@ def remove_measure(self, haz_type=None, name=None): try: del self._data[haz_type][name] except KeyError: - LOGGER.info("No Measure with hazard %s and id %s.", \ - haz_type, name) + LOGGER.info("No Measure with hazard %s and id %s.", + haz_type, name) elif haz_type is not None: try: del self._data[haz_type] @@ -185,7 +185,7 @@ def get_measure(self, haz_type=None, name=None): try: return self._data[haz_type][name] except KeyError: - LOGGER.info("No Measure with hazard %s and id %s.", \ + LOGGER.info("No Measure with hazard %s and id %s.", haz_type, name) return list() elif haz_type is not None: @@ -275,11 +275,11 @@ def check(self): def_color = plt.cm.get_cmap('Greys', len(meas_dict)) for i_meas, (name, meas) in enumerate(meas_dict.items()): if (name != meas.name) | (name == ''): - LOGGER.error("Wrong Measure.name: %s != %s.", name, \ + LOGGER.error("Wrong Measure.name: %s != %s.", name, meas.name) raise ValueError if key_haz != meas.haz_type: - LOGGER.error("Wrong Measure.haz_type: %s != %s.",\ + LOGGER.error("Wrong Measure.haz_type: %s != %s.", key_haz, meas.haz_type) raise ValueError # set default color if not set @@ -333,14 +333,14 @@ def read_att_mat(measures, data, file_name, var_names): meas.haz_type = hdf5.get_str_from_ref( file_name, data[var_names['var_name']['haz']][idx][0]) meas.hazard_freq_cutoff = data[var_names['var_name']['haz_frq']][idx][0] - meas.hazard_set = hdf5.get_str_from_ref(file_name, \ - data[var_names['var_name']['haz_set']][idx][0]) + meas.hazard_set = hdf5.get_str_from_ref( + file_name, data[var_names['var_name']['haz_set']][idx][0]) try: - meas.hazard_inten_imp = ( \ - data[var_names['var_name']['haz_int_a']][idx][0], \ + meas.hazard_inten_imp = ( + data[var_names['var_name']['haz_int_a']][idx][0], data[var_names['var_name']['haz_int_b']][0][idx]) except KeyError: - meas.hazard_inten_imp = ( \ + meas.hazard_inten_imp = ( data[var_names['var_name']['haz_int_a'][:-2]][idx][0], 0) # different convention of signes followed in MATLAB! @@ -348,8 +348,8 @@ def read_att_mat(measures, data, file_name, var_names): data[var_names['var_name']['mdd_b']][idx][0]) meas.paa_impact = (data[var_names['var_name']['paa_a']][idx][0], data[var_names['var_name']['paa_b']][idx][0]) - meas.imp_fun_map = hdf5.get_str_from_ref(file_name, \ - data[var_names['var_name']['fun_map']][idx][0]) + meas.imp_fun_map = hdf5.get_str_from_ref( + file_name, data[var_names['var_name']['fun_map']][idx][0]) meas.exposures_set = hdf5.get_str_from_ref( file_name, data[var_names['var_name']['exp_set']][idx][0]) @@ -396,7 +396,7 @@ def read_att_excel(measures, dfr, var_names): meas.haz_type = dfr[var_names['col_name']['haz']][idx] except KeyError: pass - meas.color_rgb = np.fromstring( \ + meas.color_rgb = np.fromstring( dfr[var_names['col_name']['color']][idx], dtype=float, sep=' ') meas.cost = dfr[var_names['col_name']['cost']][idx] @@ -404,15 +404,14 @@ def read_att_excel(measures, dfr, var_names): meas.hazard_set = dfr[var_names['col_name']['haz_set']][idx] # Search for (a, b) values, put a = 1 otherwise try: - meas.hazard_inten_imp = (dfr[var_names['col_name']['haz_int_a']][idx],\ + meas.hazard_inten_imp = (dfr[var_names['col_name']['haz_int_a']][idx], dfr[var_names['col_name']['haz_int_b']][idx]) except KeyError: meas.hazard_inten_imp = (1, dfr['hazard intensity impact'][idx]) try: meas.exposures_set = dfr[var_names['col_name']['exp_set']][idx] - meas.exp_region_id = ast.literal_eval(dfr[var_names['col_name'] \ - ['exp_reg']][idx]) + meas.exp_region_id = ast.literal_eval(dfr[var_names['col_name']['exp_reg']][idx]) except KeyError: pass except ValueError: @@ -444,14 +443,14 @@ def read_att_excel(measures, dfr, var_names): raise var_err def write_excel(self, file_name, var_names=DEF_VAR_EXCEL): - """ Write excel file following template. + """Write excel file following template. Parameters: file_name (str): absolute file name to write var_names (dict, optional): name of the variables in the file """ def write_meas(row_ini, imp_ws, xls_data): - """ Write one measure """ + """Write one measure""" for icol, col_dat in enumerate(xls_data): imp_ws.write(row_ini, icol, col_dat) diff --git a/climada/entity/measures/test/test_base.py b/climada/entity/measures/test/test_base.py index 02a366c37b..a345f6e59c 100644 --- a/climada/entity/measures/test/test_base.py +++ b/climada/entity/measures/test/test_base.py @@ -32,7 +32,7 @@ from climada.entity.measures.base import Measure, IF_ID_FACT from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5 -DATA_DIR = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, \ +DATA_DIR = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, os.pardir, 'hazard', 'test', 'data') HAZ_TEST_MAT = os.path.join(DATA_DIR, 'atl_prob_no_name.mat') @@ -52,7 +52,7 @@ def test_change_imp_func_pass(self): imp_tc = ImpactFunc() imp_tc.haz_type = 'XX' imp_tc.id = 1 - imp_tc.intensity = np.arange(10,100, 10) + imp_tc.intensity = np.arange(10, 100, 10) imp_tc.intensity[0] = 0. imp_tc.intensity[-1] = 100. imp_tc.mdd = np.array([0.0, 0.0, 0.021857142857143, 0.035887500000000, @@ -93,14 +93,14 @@ def test_cutoff_hazard_pass(self): self.assertFalse(id(new_haz) == id(haz)) - pos_no_null = np.array([ 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, - 5139, 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, - 5949, 11694, 5484, 6246, 12147, 778, 3326, 7199, 12498, - 11698, 6245, 5327, 4819, 8677, 5970, 7101, 779, 3894, - 9051, 5976, 3329, 5978, 4282, 11697, 7193, 5351, 7310, - 7478, 5489, 5526, 7194, 4283, 7191, 5328, 4812, 5528, - 5527, 5488, 7475, 5529, 776, 5758, 4811, 6223, 7479, - 7470, 5480, 5325, 7477, 7318, 7317, 11696, 7313, 13165, + pos_no_null = np.array([6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, + 5139, 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, + 5949, 11694, 5484, 6246, 12147, 778, 3326, 7199, 12498, + 11698, 6245, 5327, 4819, 8677, 5970, 7101, 779, 3894, + 9051, 5976, 3329, 5978, 4282, 11697, 7193, 5351, 7310, + 7478, 5489, 5526, 7194, 4283, 7191, 5328, 4812, 5528, + 5527, 5488, 7475, 5529, 776, 5758, 4811, 6223, 7479, + 7470, 5480, 5325, 7477, 7318, 7317, 11696, 7313, 13165, 6221]) all_haz = np.arange(haz.intensity.shape[0]) all_haz[pos_no_null] = -1 @@ -131,19 +131,19 @@ def test_cutoff_hazard_region_pass(self): self.assertFalse(id(new_haz) == id(haz)) - pos_no_null = np.array([ 6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, - 5139, 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, - 5949, 11694, 5484, 6246, 12147, 778, 3326, 7199, 12498, - 11698, 6245, 5327, 4819, 8677, 5970, 7101, 779, 3894, - 9051, 5976, 3329, 5978, 4282, 11697, 7193, 5351, 7310, - 7478, 5489, 5526, 7194, 4283, 7191, 5328, 4812, 5528, - 5527, 5488, 7475, 5529, 776, 5758, 4811, 6223, 7479, - 7470, 5480, 5325, 7477, 7318, 7317, 11696, 7313, 13165, + pos_no_null = np.array([6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429, + 5139, 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432, + 5949, 11694, 5484, 6246, 12147, 778, 3326, 7199, 12498, + 11698, 6245, 5327, 4819, 8677, 5970, 7101, 779, 3894, + 9051, 5976, 3329, 5978, 4282, 11697, 7193, 5351, 7310, + 7478, 5489, 5526, 7194, 4283, 7191, 5328, 4812, 5528, + 5527, 5488, 7475, 5529, 776, 5758, 4811, 6223, 7479, + 7470, 5480, 5325, 7477, 7318, 7317, 11696, 7313, 13165, 6221]) all_haz = np.arange(haz.intensity.shape[0]) all_haz[pos_no_null] = -1 pos_null = np.argwhere(all_haz > 0).reshape(-1) - centr_null = np.unique(exp.centr_[exp.region_id==0]) + centr_null = np.unique(exp.centr_[exp.region_id == 0]) for i_ev in pos_null: self.assertEqual(new_haz.intensity[i_ev, centr_null].max(), 0) @@ -166,8 +166,8 @@ def test_change_exposures_if_pass(self): imp_tc.haz_type = 'TC' imp_tc.id = 3 imp_tc.intensity = np.arange(10, 100, 10) - imp_tc.mdd = np.arange(10, 100, 10)*2 - imp_tc.paa = np.arange(10, 100, 10)*2 + imp_tc.mdd = np.arange(10, 100, 10) * 2 + imp_tc.paa = np.arange(10, 100, 10) * 2 exp = Exposures() exp.read_hdf5(EXP_DEMO_H5) @@ -180,11 +180,11 @@ def test_change_exposures_if_pass(self): self.assertTrue(np.array_equal(new_exp.value.values, exp.value.values)) self.assertTrue(np.array_equal(new_exp.latitude.values, exp.latitude.values)) self.assertTrue(np.array_equal(new_exp.longitude.values, exp.longitude.values)) - self.assertTrue(np.array_equal(exp[INDICATOR_IF+'TC'].values, np.ones(new_exp.shape[0]))) - self.assertTrue(np.array_equal(new_exp[INDICATOR_IF+'TC'].values, np.ones(new_exp.shape[0])*3)) + self.assertTrue(np.array_equal(exp[INDICATOR_IF + 'TC'].values, np.ones(new_exp.shape[0]))) + self.assertTrue(np.array_equal(new_exp[INDICATOR_IF + 'TC'].values, np.ones(new_exp.shape[0]) * 3)) def test_change_all_hazard_pass(self): - """Test _change_all_hazard method """ + """Test _change_all_hazard method""" meas = Measure() meas.hazard_set = HAZ_DEMO_H5 @@ -204,7 +204,7 @@ def test_change_all_hazard_pass(self): self.assertTrue(np.array_equal(new_haz.fraction.data, ref_haz.fraction.data)) def test_change_all_exposures_pass(self): - """Test _change_all_exposures method """ + """Test _change_all_exposures method""" meas = Measure() meas.exposures_set = EXP_DEMO_H5 @@ -256,9 +256,9 @@ def test_filter_exposures_pass(self): exp = Exposures() exp.read_mat(ENT_TEST_MAT) - exp.rename(columns={'if_':'if_TC', 'centr_':'centr_TC'}, inplace=True) + exp.rename(columns={'if_': 'if_TC', 'centr_': 'centr_TC'}, inplace=True) exp['region_id'] = np.ones(exp.shape[0]) - exp.region_id.values[:exp.shape[0]//2] = 3 + exp.region_id.values[:exp.shape[0] // 2] = 3 exp.region_id[0] = 4 exp.check() @@ -290,19 +290,19 @@ def test_filter_exposures_pass(self): self.assertEqual(res_exp.value_unit, exp.value_unit) self.assertEqual(res_exp.tag.file_name, exp.tag.file_name) self.assertEqual(res_exp.tag.description, exp.tag.description) - self.assertTrue(np.array_equal(res_exp.value.values[exp.shape[0]//2:], new_exp.value.values[:exp.shape[0]//2])) - self.assertEqual(res_exp.region_id.values[exp.shape[0]//2], 4) - self.assertTrue(np.array_equal(res_exp.region_id.values[exp.shape[0]//2+1:], np.ones(exp.shape[0]//2-1)*3)) - self.assertTrue(np.array_equal(res_exp.if_TC.values[exp.shape[0]//2:], new_exp.if_TC.values[:exp.shape[0]//2])) - self.assertTrue(np.array_equal(res_exp.latitude.values[exp.shape[0]//2:], new_exp.latitude.values[:exp.shape[0]//2])) - self.assertTrue(np.array_equal(res_exp.longitude.values[exp.shape[0]//2:], new_exp.longitude.values[:exp.shape[0]//2])) + self.assertTrue(np.array_equal(res_exp.value.values[exp.shape[0] // 2:], new_exp.value.values[:exp.shape[0] // 2])) + self.assertEqual(res_exp.region_id.values[exp.shape[0] // 2], 4) + self.assertTrue(np.array_equal(res_exp.region_id.values[exp.shape[0] // 2 + 1:], np.ones(exp.shape[0] // 2 - 1) * 3)) + self.assertTrue(np.array_equal(res_exp.if_TC.values[exp.shape[0] // 2:], new_exp.if_TC.values[:exp.shape[0] // 2])) + self.assertTrue(np.array_equal(res_exp.latitude.values[exp.shape[0] // 2:], new_exp.latitude.values[:exp.shape[0] // 2])) + self.assertTrue(np.array_equal(res_exp.longitude.values[exp.shape[0] // 2:], new_exp.longitude.values[:exp.shape[0] // 2])) # changed exposures - self.assertTrue(np.array_equal(res_exp.value.values[:exp.shape[0]//2], exp.value.values[exp.shape[0]//2:])) - self.assertTrue(np.array_equal(res_exp.region_id.values[:exp.shape[0]//2], np.ones(exp.shape[0]//2))) - self.assertTrue(np.array_equal(res_exp.if_TC.values[:exp.shape[0]//2], exp.if_TC.values[exp.shape[0]//2:])) - self.assertTrue(np.array_equal(res_exp.latitude.values[:exp.shape[0]//2], exp.latitude.values[exp.shape[0]//2:])) - self.assertTrue(np.array_equal(res_exp.longitude.values[:exp.shape[0]//2], exp.longitude.values[exp.shape[0]//2:])) + self.assertTrue(np.array_equal(res_exp.value.values[:exp.shape[0] // 2], exp.value.values[exp.shape[0] // 2:])) + self.assertTrue(np.array_equal(res_exp.region_id.values[:exp.shape[0] // 2], np.ones(exp.shape[0] // 2))) + self.assertTrue(np.array_equal(res_exp.if_TC.values[:exp.shape[0] // 2], exp.if_TC.values[exp.shape[0] // 2:])) + self.assertTrue(np.array_equal(res_exp.latitude.values[:exp.shape[0] // 2], exp.latitude.values[exp.shape[0] // 2:])) + self.assertTrue(np.array_equal(res_exp.longitude.values[:exp.shape[0] // 2], exp.longitude.values[exp.shape[0] // 2:])) # unchanged impact functions self.assertEqual(list(res_ifs.get_func().keys()), [meas.haz_type]) @@ -314,33 +314,33 @@ def test_filter_exposures_pass(self): imp_set.get_func()[meas.haz_type][3].intensity)) # changed impact functions - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1+IF_ID_FACT].intensity, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1 + IF_ID_FACT].intensity, ref_ifs.get_func()[meas.haz_type][1].intensity)) - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1+IF_ID_FACT].paa, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1 + IF_ID_FACT].paa, ref_ifs.get_func()[meas.haz_type][1].paa)) - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1+IF_ID_FACT].mdd, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][1 + IF_ID_FACT].mdd, ref_ifs.get_func()[meas.haz_type][1].mdd)) - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3+IF_ID_FACT].intensity, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3 + IF_ID_FACT].intensity, ref_ifs.get_func()[meas.haz_type][3].intensity)) - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3+IF_ID_FACT].paa, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3 + IF_ID_FACT].paa, ref_ifs.get_func()[meas.haz_type][3].paa)) - self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3+IF_ID_FACT].mdd, + self.assertTrue(np.array_equal(res_ifs.get_func()[meas.haz_type][3 + IF_ID_FACT].mdd, ref_ifs.get_func()[meas.haz_type][3].mdd)) # unchanged hazard - self.assertTrue(np.array_equal(res_haz.intensity[:, :36].todense(), - haz.intensity[:, :36].todense())) - self.assertTrue(np.array_equal(res_haz.intensity[:, 37:46].todense(), - haz.intensity[:, 37:46].todense())) - self.assertTrue(np.array_equal(res_haz.intensity[:, 47:].todense(), - haz.intensity[:, 47:].todense())) + self.assertTrue(np.array_equal(res_haz.intensity[:, :36].toarray(), + haz.intensity[:, :36].toarray())) + self.assertTrue(np.array_equal(res_haz.intensity[:, 37:46].toarray(), + haz.intensity[:, 37:46].toarray())) + self.assertTrue(np.array_equal(res_haz.intensity[:, 47:].toarray(), + haz.intensity[:, 47:].toarray())) # changed hazard - self.assertTrue(np.array_equal(res_haz.intensity[[36, 46]].todense(), - new_haz.intensity[[36, 46]].todense())) + self.assertTrue(np.array_equal(res_haz.intensity[[36, 46]].toarray(), + new_haz.intensity[[36, 46]].toarray())) def test_apply_ref_pass(self): - """ Test apply method: apply all measures but insurance """ + """Test apply method: apply all measures but insurance""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) hazard.haz_type = 'TC' @@ -352,7 +352,7 @@ def test_apply_ref_pass(self): meas.haz_type = 'TC' entity.check() - new_exp, new_ifs, new_haz = entity.measures.get_measure('TC', 'Mangroves').apply(entity.exposures, \ + new_exp, new_ifs, new_haz = entity.measures.get_measure('TC', 'Mangroves').apply(entity.exposures, entity.impact_funcs, hazard) self.assertTrue(new_exp is entity.exposures) @@ -378,14 +378,14 @@ def test_apply_ref_pass(self): 1.000000000000000]))) def test_calc_impact_pass(self): - """ Test calc_impact method: apply all measures but insurance """ + """Test calc_impact method: apply all measures but insurance""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() entity.read_mat(ENT_TEST_MAT) - entity.exposures.rename(columns={'if_':'if_TC'}, inplace=True) + entity.exposures.rename(columns={'if_': 'if_TC'}, inplace=True) entity.measures._data['TC'] = entity.measures._data.pop('XX') entity.measures.get_measure(name='Mangroves', haz_type='TC').haz_type = 'TC' for meas in entity.measures.get_measure('TC'): @@ -406,7 +406,7 @@ def test_calc_impact_pass(self): self.assertAlmostEqual(imp.eai_exp[-1], 7.528669956120645e+07) self.assertAlmostEqual(imp.tot_value, 6.570532945599105e+11) self.assertEqual(imp.unit, 'USD') - self.assertEqual(imp.imp_mat, []) + self.assertEqual(imp.imp_mat.shape, (0, 0)) self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) self.assertTrue(np.array_equal(imp.date, hazard.date)) self.assertEqual(imp.event_name, hazard.event_name) @@ -417,14 +417,14 @@ def test_calc_impact_pass(self): def test_calc_impact_transf_pass(self): - """ Test calc_impact method: apply all measures and insurance """ + """Test calc_impact method: apply all measures and insurance""" hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) entity = Entity() entity.read_mat(ENT_TEST_MAT) - entity.exposures.rename(columns={'if_':'if_TC'}, inplace=True) + entity.exposures.rename(columns={'if_': 'if_TC'}, inplace=True) entity.measures._data['TC'] = entity.measures._data.pop('XX') for meas in entity.measures.get_measure('TC'): meas.haz_type = 'TC' @@ -449,7 +449,7 @@ def test_calc_impact_transf_pass(self): self.assertTrue(np.array_equal(imp.eai_exp, np.array([]))) self.assertAlmostEqual(imp.tot_value, 6.570532945599105e+11) self.assertEqual(imp.unit, 'USD') - self.assertEqual(imp.imp_mat, []) + self.assertEqual(imp.imp_mat.shape, (0, 0)) self.assertTrue(np.array_equal(imp.event_id, hazard.event_id)) self.assertTrue(np.array_equal(imp.date, hazard.date)) self.assertEqual(imp.event_name, hazard.event_name) diff --git a/climada/entity/measures/test/test_meas_set.py b/climada/entity/measures/test/test_meas_set.py index 8c870d968a..2be939e7e3 100644 --- a/climada/entity/measures/test/test_meas_set.py +++ b/climada/entity/measures/test/test_meas_set.py @@ -165,7 +165,7 @@ def test_check_wronginten_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): meas.check() - self.assertIn('Invalid Measure.hazard_inten_imp size: 2 != 3.', \ + self.assertIn('Invalid Measure.hazard_inten_imp size: 2 != 3.', cm.output[0]) def test_check_wrongColor_fail(self): @@ -234,7 +234,7 @@ def test_check_name_fail(self): self.assertIn('Wrong Measure.name: LoLo != LaLa', cm.output[0]) def test_def_color(self): - """ Test default grey scale used when no color set """ + """Test default grey scale used when no color set""" meas = MeasureSet() act_1 = Measure() act_1.name = 'LaLa' @@ -329,14 +329,14 @@ def test_extend_different_extend(self): meas.check() self.assertEqual(meas.size(), 2) - self.assertEqual(meas.get_names(), {'TC': ['Mangrove', 'Anything']} ) + self.assertEqual(meas.get_names(), {'TC': ['Mangrove', 'Anything']}) self.assertEqual(meas.get_measure(name=act_1.name)[0].paa_impact, act_11.paa_impact) class TestReaderExcel(unittest.TestCase): """Test reader functionality of the MeasuresExcel class""" def test_demo_file(self): - """ Read demo excel file""" + """Read demo excel file""" meas = MeasureSet() description = 'One single file.' meas.read_excel(ENT_DEMO_TODAY, description) @@ -380,7 +380,7 @@ def test_demo_file(self): self.assertEqual(meas.tag.description, description) def test_template_file_pass(self): - """ Read template excel file""" + """Read template excel file""" meas = MeasureSet() meas.read_excel(ENT_TEMPLATE_XLS) @@ -413,7 +413,7 @@ def test_template_file_pass(self): self.assertEqual(act_buil.name, name) self.assertEqual(act_buil.haz_type, 'TC') self.assertTrue(np.array_equal(act_buil.color_rgb, np.array([0.76, 0.84, 0.60]))) - self.assertEqual(act_buil.cost, 63968125.00687534) + self.assertEqual(act_buil.cost, 63968125.00687534) self.assertEqual(act_buil.hazard_set, 'nil') self.assertEqual(act_buil.hazard_freq_cutoff, 0) @@ -437,7 +437,7 @@ def test_template_file_pass(self): self.assertEqual(act_buil.name, name) self.assertEqual(act_buil.haz_type, 'TC') self.assertTrue(np.array_equal(act_buil.color_rgb, np.array([0.90, 0.72, 0.72]))) - self.assertEqual(act_buil.cost, 21000000) + self.assertEqual(act_buil.cost, 21000000) self.assertEqual(act_buil.hazard_set, 'nil') self.assertEqual(act_buil.hazard_freq_cutoff, 0) @@ -527,7 +527,7 @@ class TestWriter(unittest.TestCase): """Test reader functionality of the MeasuresExcel class""" def test_write_read_file(self): - """ Write and read excel file""" + """Write and read excel file""" act_1 = Measure() act_1.name = 'Mangrove' diff --git a/climada/entity/test/test_entity.py b/climada/entity/test/test_entity.py index 7f3e0ad917..bd19f901fb 100644 --- a/climada/entity/test/test_entity.py +++ b/climada/entity/test/test_entity.py @@ -72,7 +72,7 @@ def test_read_excel(self): self.assertEqual(entity_xls.exposures.tag.file_name, ENT_TEMPLATE_XLS) self.assertEqual(entity_xls.disc_rates.tag.file_name, ENT_TEMPLATE_XLS) self.assertEqual(entity_xls.measures.tag.file_name, ENT_TEMPLATE_XLS) - self.assertEqual(entity_xls.impact_funcs.tag.file_name, \ + self.assertEqual(entity_xls.impact_funcs.tag.file_name, ENT_TEMPLATE_XLS) class TestCheck(unittest.TestCase): diff --git a/climada/hazard/__init__.py b/climada/hazard/__init__.py index 3fc41cb948..983339144c 100755 --- a/climada/hazard/__init__.py +++ b/climada/hazard/__init__.py @@ -23,4 +23,6 @@ from .tag import * from .trop_cyclone import * from .tc_tracks import * +from .tc_tracks_forecast import * +from .tc_rainfield import * from .storm_europe import * diff --git a/climada/hazard/ag_drought.py b/climada/hazard/ag_drought.py deleted file mode 100644 index f202b5bfa5..0000000000 --- a/climada/hazard/ag_drought.py +++ /dev/null @@ -1,321 +0,0 @@ -""" -This file is part of CLIMADA. - -Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. - -CLIMADA is free software: you can redistribute it and/or modify it under the -terms of the GNU Lesser General Public License as published by the Free -Software Foundation, version 3. - -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. - -You should have received a copy of the GNU Lesser General Public License along -with CLIMADA. If not, see . - ---- - -Define AgriculturalDrought (AD) class. -WORK IN PROGRESS -""" - -__all__ = ['AgriculturalDrought'] - -import logging -import re -import xarray as xr -import numpy as np -from matplotlib import pyplot as plt -import cartopy -import shapely.geometry -from scipy import sparse -import scipy.stats - - -from climada.hazard.base import Hazard -from climada.util import dates_times as dt - -DFL_CROP = '' -INT_DEF = 'Yearly Yield' - -LOGGER = logging.getLogger(__name__) - -HAZ_TYPE = 'AD' -""" Hazard type acronym for Agricultural Drought """ - - -class AgriculturalDrought(Hazard): - """Contains agricultural drought events. - - Attributes: - crop (str): crop type (e.g. wheat) - intensity_def (str): intensity defined as the Yearly Yield / Relative Yield / Percentile - """ - - def __init__(self, pool=None): - """Empty constructor. """ - Hazard.__init__(self, HAZ_TYPE) - if pool: - self.pool = pool - LOGGER.info('Using %s CPUs.', self.pool.ncpus) - else: - self.pool = None - - self.crop = DFL_CROP - self.intensity_def = INT_DEF - -# def set_hist_events(self, centroids=None): -# """ -# -# Parameters: -# ...: ... -# """ -# LOGGER.info('Setting up historical events.') -# self.clear() - - - def set_from_single_run(self, file_path=None, lonmin=-85, latmin=-180, lonmax=85, \ - latmax=180, years_user=None): - """ Reads netcdf file and initializes a hazard - - Parameters: - file_path (str): path to netcdf file - lonmin, latin, lonmax, latmax (int, optional) : bounding box to extract - years_user (array, optional) : start and end year specified by the user - - Returns: - hazard - """ - - if file_path is None: - LOGGER.error('No drough-file-path set') - raise NameError - - #determine time period that is covered by the input data - years_file = np.zeros(2) - string = re.search('annual_(.+?)_', file_path) - if string: - years_file[0] = int(string.group(1)) - - string = re.search(str(int(years_file[0]))+'_(.+?).nc', file_path) - if string: - years_file[1] = int(string.group(1)) - - if years_user is None: - id_bands = np.arange(1, years_file[1] - years_file[0]+2).tolist() - event_list = [str(n) for n in range(int(years_file[0]), int(years_file[1]+1))] - else: - id_bands = np.arange(years_user[0]-years_file[0]-1, \ - years_user[1] - years_file[0]).tolist() - event_list = [str(n) for n in range(int(years_user[0]), int(years_user[1]+1))] - - date = [event_list[n]+'-01-01' for n in range(len(event_list))] - - #extract additional information of original file - data = xr.open_dataset(file_path, decode_times=False) - - self.set_raster([file_path], band=id_bands, \ - geometry=list([shapely.geometry.box(lonmin, latmin, lonmax, latmax)])) - self.check() - self.crop = data.crop - self.event_name = event_list - self.frequency = np.ones(len(self.event_name))*(1/len(self.event_name)) - self.fraction = self.intensity.copy() - self.fraction.data.fill(1.0) - self.units = 't / y' - self.date = np.array(dt.str_to_date(date)) - - return self - - def calc_mean(self): - """ Calculates mean of the given hazard - - Returns: - mean(array): contains mean value over the given time period for every centroid - """ - hist_mean = np.mean(self.intensity, 0) - #hist_mean[hist_mean == 0] = np.nan - - return hist_mean - - - def set_rel_yield_to_int(self, hist_mean): - """ Sets relative yield to intensity (yearly yield / historic mean) per centroid - - Parameter: - historic mean (array): historic mean per centroid - - Returns: - hazard with modified intensity - """ - - hazard_matrix = np.empty(self.intensity.shape) - hazard_matrix[:, :] = np.nan - idx = np.where(hist_mean != 0)[1] - - for event in range(len(self.event_id)): - hazard_matrix[event, idx] = self.intensity[event, idx]/hist_mean[0, idx] - - self.intensity = sparse.csr_matrix(hazard_matrix) - self.intensity_def = 'Relative Yield' - self.units = '' - - return self - - def set_percentile_to_int(self, reference_intensity=None): - """ Sets percentile to intensity - - Parameter: - reference_intensity (AD): intensity to be used as reference (e.g. the historic - intensity can be used in order to be able to directly compare - historic and future projection data) - - Returns: - hazard with modified intensity - """ - hazard_matrix = np.zeros(self.intensity.shape) - if reference_intensity is None: - reference_intensity = self.intensity - - for centroid in range(self.intensity.shape[1]): - array = (reference_intensity[:, centroid].toarray()).reshape(\ - reference_intensity.shape[0]) - for event in range(self.intensity.shape[0]): - value = self.intensity[event, centroid] - hazard_matrix[event, centroid] = (scipy.stats.percentileofscore(array, value))/100 - - self.intensity = sparse.csr_matrix(hazard_matrix) - self.intensity_def = 'Percentile' - self.units = '' - - return self - - def plot_intensity_agd(self, event, dif=0, axis=None, **kwargs): - """ Plots intensity with predefined settings depending on the intensity definition - - Parameters: - event (int or str): event_id or event_name - dif (int): variable signilizing whether absolute values or the difference between - future and historic are plotted (dif=0: his/fut values; dif=1: difference = fut-his) - axis (geoaxes): axes to plot on - """ - if dif == 0: - if self.intensity_def == 'Yearly Yield': - axes = self.plot_intensity(event=event, axis=axis, cmap='YlGn', vmin=0, vmax=10, \ - **kwargs) - elif self.intensity_def == 'Relative Yield': - axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=0, vmax=2, \ - **kwargs) - elif self.intensity_def == 'Percentile': - axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=0, vmax=1, \ - **kwargs) - elif dif == 1: - if self.intensity_def == 'Yearly Yield': - axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=-2, vmax=2, \ - **kwargs) - elif self.intensity_def == 'Relative Yield': - axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=-0.5, \ - vmax=0.5, **kwargs) - - return axes - - def plot_time_series(self, years=None): - """ Plots a time series of intensities (a series of sub plots) - - Returns: - figure - """ - - if years is None: - event_list = self.event_name - else: - event_list = [str(n) for n in range(years[0], years[1]+1)] - - self.centroids.set_meta_to_lat_lon() - - len_lat = abs(self.centroids.lat[0]-self.centroids.lat[-1])*(2.5/13.5) - len_lon = abs(self.centroids.lon[0]-self.centroids.lon[-1])*(5/26) - - nr_subplots = len(event_list) - - if len_lon >= len_lat: - colums = int(np.floor(np.sqrt(nr_subplots/(len_lon/len_lat)))) - rows = int(np.ceil(nr_subplots/colums)) - else: - rows = int(np.floor(np.sqrt(nr_subplots/(len_lat/len_lon)))) - colums = int(np.ceil(nr_subplots/colums)) - - fig, axes = plt.subplots(rows, colums, sharex=True, sharey=True, \ - figsize=(colums*len_lon, rows*len_lat), \ - subplot_kw=dict(projection=cartopy.crs.PlateCarree())) - colum = 0 - row = 0 - - for year in range(nr_subplots): - axes.flat[year].set_extent([np.min(self.centroids.lon), np.max(self.centroids.lon), \ - np.min(self.centroids.lat), np.max(self.centroids.lat)]) - - if rows == 1: - self.plot_intensity_agd(event=event_list[year], axis=axes[colum]) - elif colums == 1: - self.plot_intensity_agd(event=event_list[year], axis=axes[row]) - else: - self.plot_intensity_agd(event=event_list[year], axis=axes[row, colum]) - - if colum <= colums-2: - colum = colum + 1 - else: - colum = 0 - row = row + 1 - - return fig - - def plot_comparing_maps(self, his, fut, axes, nr_cli_models=1, model=1): - """ Plots comparison maps of historic and future data and their difference fut-his - - Parameters: - his (sparse matrix): historic mean annual yield or mean relative yield - fut (sparse matrix): future mean annual yield or mean relative yield - axes (Geoaxes): subplot axes that can be generated with ag_drought_util.setup_subplots - nr_cli_models (int): number of climate models and respectively nr of rows within - the subplot - model (int): current model/row to plot - - Returns: - geoaxes - """ - dif = fut - his - self.event_id = 0 - - for subplot in range(3): - - if self.intensity_def == 'Yearly Yield': - self.units = 't / y' - elif self.intensity_def == 'Relative Yield': - self.units = '' - - if subplot == 0: - self.intensity = sparse.csr_matrix(his) - dif_def = 0 - elif subplot == 1: - self.intensity = sparse.csr_matrix(fut) - dif_def = 0 - elif subplot == 2: - self.intensity = sparse.csr_matrix(dif) - dif_def = 1 - - - if nr_cli_models == 1: - ax1 = self.plot_intensity_agd(event=0, dif=dif_def, axis=axes[subplot]) - else: - ax1 = self.plot_intensity_agd(event=0, dif=dif_def, axis=axes[model, subplot]) - - ax1.set_title('') - - if nr_cli_models == 1: - cols = ['Historical', 'Future', 'Difference = Future - Historical'] - for ax0, col in zip(axes, cols): - ax0.set_title(col, size='large') - - return axes diff --git a/climada/hazard/base.py b/climada/hazard/base.py index 8a30f4602a..ba9056880c 100644 --- a/climada/hazard/base.py +++ b/climada/hazard/base.py @@ -47,30 +47,30 @@ LOGGER = logging.getLogger(__name__) -DEF_VAR_EXCEL = {'sheet_name': {'inten' : 'hazard_intensity', - 'freq' : 'hazard_frequency' +DEF_VAR_EXCEL = {'sheet_name': {'inten': 'hazard_intensity', + 'freq': 'hazard_frequency' }, - 'col_name': {'cen_id' : 'centroid_id/event_id', - 'even_id' : 'event_id', - 'even_dt' : 'event_date', - 'even_name' : 'event_name', - 'freq' : 'frequency', + 'col_name': {'cen_id': 'centroid_id/event_id', + 'even_id': 'event_id', + 'even_dt': 'event_date', + 'even_name': 'event_name', + 'freq': 'frequency', 'orig': 'orig_event_flag' }, 'col_centroids': {'sheet_name': 'centroids', - 'col_name': {'cen_id' : 'centroid_id', - 'lat' : 'latitude', - 'lon' : 'longitude' + 'col_name': {'cen_id': 'centroid_id', + 'lat': 'latitude', + 'lon': 'longitude' } } } -""" Excel variable names """ +"""Excel variable names""" DEF_VAR_MAT = {'field_name': 'hazard', - 'var_name': {'per_id' : 'peril_ID', - 'even_id' : 'event_ID', - 'ev_name' : 'name', - 'freq' : 'frequency', + 'var_name': {'per_id': 'peril_ID', + 'even_id': 'event_ID', + 'ev_name': 'name', + 'freq': 'frequency', 'inten': 'intensity', 'unit': 'units', 'frac': 'fraction', @@ -79,13 +79,13 @@ 'orig': 'orig_event_flag' }, 'var_cent': {'field_names': ['centroids', 'hazard'], - 'var_name': {'cen_id' : 'centroid_ID', - 'lat' : 'lat', - 'lon' : 'lon' + 'var_name': {'cen_id': 'centroid_ID', + 'lat': 'lat', + 'lon': 'lon' } } } -""" MATLAB variable names """ +"""MATLAB variable names""" class Hazard(): """Contains events of some hazard type defined at centroids. Loads from @@ -108,8 +108,8 @@ class Hazard(): event at each centroid """ intensity_thres = 10 - """ Intensity threshold per hazard used to filter lower intensities. To be - set for every hazard type """ + """Intensity threshold per hazard used to filter lower intensities. To be + set for every hazard type""" vars_oblig = {'tag', 'units', @@ -136,7 +136,7 @@ class Hazard(): """Name of the variables that aren't need to compute the impact. Types: scalar, string, list, 1dim np.array of size num_events.""" - def __init__(self, haz_type, pool=None): + def __init__(self, haz_type='', pool=None): """Initialize values. Parameters: @@ -166,7 +166,7 @@ def __init__(self, haz_type, pool=None): self.date = np.array([], int) self.orig = np.array([], bool) # following values are defined for each event and centroid - self.intensity = sparse.csr_matrix(np.empty((0, 0))) # events x centroids + self.intensity = sparse.csr_matrix(np.empty((0, 0))) # events x centroids self.fraction = sparse.csr_matrix(np.empty((0, 0))) # events x centroids if pool: self.pool = pool @@ -193,11 +193,11 @@ def check(self): self.centroids.check() self._check_events() - def set_raster(self, files_intensity, files_fraction=None, attrs={}, - band=[1], src_crs=None, window=False, geometry=False, + def set_raster(self, files_intensity, files_fraction=None, attrs=None, + band=None, src_crs=None, window=False, geometry=False, dst_crs=False, transform=None, width=None, height=None, resampling=Resampling.nearest): - """ Append intensity and fraction from raster file. 0s put to the masked + """Append intensity and fraction from raster file. 0s put to the masked values. File can be partially read using window OR geometry. Alternatively, CRS and/or transformation can be set using dst_crs and/or (transform, width and height). @@ -206,7 +206,7 @@ def set_raster(self, files_intensity, files_fraction=None, attrs={}, files_intensity (list(str)): file names containing intensity files_fraction (list(str)): file names containing fraction attrs (dict, optional): name of Hazard attributes and their values - band (list(int), optional): bands to read (starting at 1) + band (list(int), optional): bands to read (starting at 1), default [1] src_crs (crs, optional): source CRS. Provide it if error without it. window (rasterio.windows.Windows, optional): window where data is extracted @@ -218,6 +218,10 @@ def set_raster(self, files_intensity, files_fraction=None, attrs={}, resampling (rasterio.warp,.Resampling optional): resampling function used for reprojection to dst_crs """ + if not attrs: + attrs = {} + if not band: + band = [1] if files_fraction is not None and len(files_intensity) != len(files_fraction): LOGGER.error('Number of intensity files differs from fraction files: %s != %s', len(files_intensity), len(files_fraction)) @@ -226,37 +230,41 @@ def set_raster(self, files_intensity, files_fraction=None, attrs={}, self.centroids = Centroids() if self.pool: - chunksize = min(len(files_intensity)//self.pool.ncpus, 1000) + chunksize = min(len(files_intensity) // self.pool.ncpus, 1000) # set first centroids - inten_list = [sparse.csr.csr_matrix(self.centroids.set_raster_file( \ - files_intensity[0], band, src_crs, window, geometry, dst_crs, \ + inten_list = [sparse.csr.csr_matrix(self.centroids.set_raster_file( + files_intensity[0], band, src_crs, window, geometry, dst_crs, transform, width, height, resampling))] - inten_list += self.pool.map(self.centroids.set_raster_file, \ - files_intensity[1:], itertools.repeat(band), itertools.repeat(src_crs), \ - itertools.repeat(window), itertools.repeat(geometry), \ - itertools.repeat(dst_crs), itertools.repeat(transform), \ - itertools.repeat(width), itertools.repeat(height), \ - itertools.repeat(resampling), chunksize=chunksize) + inten_list += self.pool.map( + self.centroids.set_raster_file, + files_intensity[1:], itertools.repeat(band), itertools.repeat(src_crs), + itertools.repeat(window), itertools.repeat(geometry), + itertools.repeat(dst_crs), itertools.repeat(transform), + itertools.repeat(width), itertools.repeat(height), + itertools.repeat(resampling), chunksize=chunksize) self.intensity = sparse.vstack(inten_list, format='csr') if files_fraction is not None: - fract_list = self.pool.map(self.centroids.set_raster_file, \ - files_fraction, itertools.repeat(band), itertools.repeat(src_crs), \ - itertools.repeat(window), itertools.repeat(geometry), \ - itertools.repeat(dst_crs), itertools.repeat(transform), \ - itertools.repeat(width), itertools.repeat(height), \ - itertools.repeat(resampling), chunksize=chunksize) + fract_list = self.pool.map( + self.centroids.set_raster_file, + files_fraction, itertools.repeat(band), itertools.repeat(src_crs), + itertools.repeat(window), itertools.repeat(geometry), + itertools.repeat(dst_crs), itertools.repeat(transform), + itertools.repeat(width), itertools.repeat(height), + itertools.repeat(resampling), chunksize=chunksize) self.fraction = sparse.vstack(fract_list, format='csr') else: inten_list = [] for file in files_intensity: - inten_list.append(self.centroids.set_raster_file(file, band, src_crs, window, \ - geometry, dst_crs, transform, width, height, resampling)) + inten_list.append(self.centroids.set_raster_file( + file, band, src_crs, window, geometry, dst_crs, transform, + width, height, resampling)) self.intensity = sparse.vstack(inten_list, format='csr') if files_fraction is not None: fract_list = [] for file in files_fraction: - fract_list.append(self.centroids.set_raster_file(file, band, src_crs, \ - window, geometry, dst_crs, transform, width, height, resampling)) + fract_list.append(self.centroids.set_raster_file( + file, band, src_crs, window, geometry, dst_crs, transform, + width, height, resampling)) self.fraction = sparse.vstack(fract_list, format='csr') if files_fraction is None: @@ -266,7 +274,7 @@ def set_raster(self, files_intensity, files_fraction=None, attrs={}, if 'event_id' in attrs: self.event_id = attrs['event_id'] else: - self.event_id = np.arange(1, self.intensity.shape[0]+1) + self.event_id = np.arange(1, self.intensity.shape[0] + 1) if 'frequency' in attrs: self.frequency = attrs['frequency'] else: @@ -286,14 +294,16 @@ def set_raster(self, files_intensity, files_fraction=None, attrs={}, if 'unit' in attrs: self.unit = attrs['unit'] - def set_vector(self, files_intensity, files_fraction=None, attrs={}, - inten_name=['intensity'], frac_name=['fraction'], dst_crs=None): - """ Read vector files format supported by fiona. Each intensity name is + def set_vector(self, files_intensity, files_fraction=None, attrs=None, + inten_name=None, frac_name=None, dst_crs=None): + """Read vector files format supported by fiona. Each intensity name is considered an event. Parameters: - files_intensity (list(str)): file names containing intensity - files_fraction (list(str)): file names containing fraction + files_intensity (list(str)): file names containing intensity, + default: ['intensity'] + files_fraction (list(str)): file names containing fraction, + default: ['fraction'] attrs (dict, optional): name of Hazard attributes and their values inten_name (list(str), optional): name of variables containing the intensities of each event @@ -301,6 +311,12 @@ def set_vector(self, files_intensity, files_fraction=None, attrs={}, the fractions of each event dst_crs (crs, optional): reproject to given crs """ + if not attrs: + attrs = {} + if not inten_name: + inten_name = ['intensity'] + if not frac_name: + inten_name = ['fraction'] if files_fraction is not None and len(files_intensity) != len(files_fraction): LOGGER.error('Number of intensity files differs from fraction files: %s != %s', len(files_intensity), len(files_fraction)) @@ -322,7 +338,7 @@ def set_vector(self, files_intensity, files_fraction=None, attrs={}, if 'event_id' in attrs: self.event_id = attrs['event_id'] else: - self.event_id = np.arange(1, self.intensity.shape[0]+1) + self.event_id = np.arange(1, self.intensity.shape[0] + 1) if 'frequency' in attrs: self.frequency = attrs['frequency'] else: @@ -344,7 +360,7 @@ def set_vector(self, files_intensity, files_fraction=None, attrs={}, def reproject_raster(self, dst_crs=False, transform=None, width=None, height=None, resampl_inten=Resampling.nearest, resampl_fract=Resampling.nearest): - """ Change current raster data to other CRS and/or transformation + """Change current raster data to other CRS and/or transformation Parameters: dst_crs (crs, optional): reproject to given crs @@ -365,14 +381,14 @@ def reproject_raster(self, dst_crs=False, transform=None, width=None, height=Non LOGGER.error('Provide width and height to given transformation.') raise ValueError if not transform: - transform, width, height = calculate_default_transform(\ - self.centroids.meta['crs'], dst_crs, self.centroids.meta['width'], \ - self.centroids.meta['height'], self.centroids.meta['transform'][2], \ - self.centroids.meta['transform'][5] + \ - self.centroids.meta['height']*self.centroids.meta['transform'][4], - self.centroids.meta['transform'][2] + \ - self.centroids.meta['width']*self.centroids.meta['transform'][0], \ - self.centroids.meta['transform'][5]) + transform, width, height = calculate_default_transform( + self.centroids.meta['crs'], dst_crs, self.centroids.meta['width'], + self.centroids.meta['height'], self.centroids.meta['transform'][2], + (self.centroids.meta['transform'][5] + + self.centroids.meta['height'] * self.centroids.meta['transform'][4]), + (self.centroids.meta['transform'][2] + + self.centroids.meta['width'] * self.centroids.meta['transform'][0]), + self.centroids.meta['transform'][5]) dst_meta = self.centroids.meta.copy() dst_meta.update({'crs': dst_crs, 'transform': transform, 'width': width, 'height': height @@ -383,24 +399,29 @@ def reproject_raster(self, dst_crs=False, transform=None, width=None, height=Non 'src_crs': self.centroids.meta['crs'], 'dst_transform': transform, 'dst_crs': dst_crs, 'resampling': resampl_inten} - for idx_ev, inten in enumerate(self.intensity.todense()): - reproject(source=np.asarray(inten.reshape((self.centroids.meta['height'], \ - self.centroids.meta['width']))), destination=intensity[idx_ev, :, :], \ + for idx_ev, inten in enumerate(self.intensity.toarray()): + reproject( + source=np.asarray(inten.reshape((self.centroids.meta['height'], + self.centroids.meta['width']))), + destination=intensity[idx_ev, :, :], **kwargs) kwargs.update(resampling=resampl_fract) - for idx_ev, fract in enumerate(self.fraction.todense()): - reproject(source=np.asarray(fract.reshape((self.centroids.meta['height'], \ - self.centroids.meta['width']))), destination=fraction[idx_ev, :, :], \ - **kwargs) + for idx_ev, fract in enumerate(self.fraction.toarray()): + reproject( + source=np.asarray( + fract.reshape((self.centroids.meta['height'], + self.centroids.meta['width']))), + destination=fraction[idx_ev, :, :], + **kwargs) self.centroids.meta = dst_meta - self.intensity = sparse.csr_matrix(intensity.reshape(self.size, \ - dst_meta['height'] * dst_meta['width'])) - self.fraction = sparse.csr_matrix(fraction.reshape(self.size, \ - dst_meta['height'] * dst_meta['width'])) + self.intensity = sparse.csr_matrix( + intensity.reshape(self.size, dst_meta['height'] * dst_meta['width'])) + self.fraction = sparse.csr_matrix( + fraction.reshape(self.size, dst_meta['height'] * dst_meta['width'])) self.check() def reproject_vector(self, dst_crs, scheduler=None): - """ Change current point data to a a given projection + """Change current point data to a a given projection Parameters: dst_crs (crs): reproject to given crs @@ -414,13 +435,13 @@ def reproject_vector(self, dst_crs, scheduler=None): self.check() def raster_to_vector(self): - """ Change current raster to points (center of the pixels) """ + """Change current raster to points (center of the pixels)""" self.centroids.set_meta_to_lat_lon() self.centroids.meta = dict() self.check() def vector_to_raster(self, scheduler=None): - """ Change current point data to a raster with same resolution + """Change current point data to a raster with same resolution Parameters: scheduler (str, optional): used for dask map_partitions. “threads”, @@ -429,12 +450,12 @@ def vector_to_raster(self, scheduler=None): points_df = gpd.GeoDataFrame(crs=self.centroids.geometry.crs) points_df['latitude'] = self.centroids.lat points_df['longitude'] = self.centroids.lon - val_names = ['val'+str(i_ev) for i_ev in range(2*self.size)] + val_names = ['val' + str(i_ev) for i_ev in range(2 * self.size)] for i_ev, inten_name in enumerate(val_names): if i_ev < self.size: - points_df[inten_name] = np.asarray(self.intensity[i_ev, :].todense()).reshape(-1) + points_df[inten_name] = np.asarray(self.intensity[i_ev, :].toarray()).reshape(-1) else: - points_df[inten_name] = np.asarray(self.fraction[i_ev-self.size, :].todense()).\ + points_df[inten_name] = np.asarray(self.fraction[i_ev - self.size, :].toarray()).\ reshape(-1) raster, meta = co.points_to_raster(points_df, val_names, scheduler=scheduler) self.intensity = sparse.csr_matrix(raster[:self.size, :, :].reshape(self.size, -1)) @@ -443,7 +464,7 @@ def vector_to_raster(self, scheduler=None): self.centroids.meta = meta self.check() - def read_mat(self, file_name, description='', var_names=DEF_VAR_MAT): + def read_mat(self, file_name, description='', var_names=None): """Read climada hazard generate with the MATLAB code. Parameters: @@ -455,6 +476,8 @@ def read_mat(self, file_name, description='', var_names=DEF_VAR_MAT): Raises: KeyError """ + if not var_names: + var_names = DEF_VAR_MAT LOGGER.info('Reading %s', file_name) self.clear() self.tag.file_name = file_name @@ -474,7 +497,7 @@ def read_mat(self, file_name, description='', var_names=DEF_VAR_MAT): LOGGER.error("Not existing variable: %s", str(var_err)) raise var_err - def read_excel(self, file_name, description='', var_names=DEF_VAR_EXCEL): + def read_excel(self, file_name, description='', var_names=None): """Read climada hazard generate with the MATLAB code. Parameters: @@ -488,6 +511,8 @@ def read_excel(self, file_name, description='', var_names=DEF_VAR_EXCEL): Raises: KeyError """ + if not var_names: + var_names = DEF_VAR_EXCEL LOGGER.info('Reading %s', file_name) haz_type = self.tag.haz_type self.clear() @@ -501,11 +526,12 @@ def read_excel(self, file_name, description='', var_names=DEF_VAR_EXCEL): LOGGER.error("Not existing variable: %s", str(var_err)) raise var_err - def select(self, date=None, orig=None, reg_id=None, reset_frequency=False): + def select(self, event_names=None, date=None, orig=None, reg_id=None, reset_frequency=False): """Select events within provided date and/or (historical or synthetical) and/or region. Frequency of the events may need to be recomputed! Parameters: + event_names (list(str), optional): names of event date (tuple(str or int), optional): (initial date, final date) in string ISO format ('2011-01-02') or datetime ordinal integer orig (bool, optional): select only historical (True) or only @@ -519,43 +545,54 @@ def select(self, date=None, orig=None, reg_id=None, reset_frequency=False): Returns: Hazard or children """ - try: - haz = self.__class__() - except TypeError: + if type(self) is Hazard: haz = Hazard(self.tag.haz_type) - sel_ev = np.ones(self.event_id.size, bool) - sel_cen = np.ones(self.centroids.size, bool) + else: + haz = self.__class__() + sel_ev = np.ones(self.event_id.size, dtype=bool) + sel_cen = np.ones(self.centroids.size, dtype=bool) - # filter events with date + # filter events by date if isinstance(date, tuple): date_ini, date_end = date[0], date[1] if isinstance(date_ini, str): date_ini = u_dt.str_to_date(date[0]) date_end = u_dt.str_to_date(date[1]) - sel_ev = np.logical_and(date_ini <= self.date, - self.date <= date_end) + sel_ev &= (date_ini <= self.date) & (self.date <= date_end) if not np.any(sel_ev): LOGGER.info('No hazard in date range %s.', date) return None # filter events hist/synthetic if isinstance(orig, bool): - sel_ev = np.logical_and(sel_ev, self.orig.astype(bool) == orig) + sel_ev &= (self.orig.astype(bool) == orig) if not np.any(sel_ev): LOGGER.info('No hazard with %s tracks.', str(orig)) return None # filter centroids if reg_id is not None: - sel_cen = np.argwhere(self.centroids.region_id == reg_id).reshape(-1) - if not sel_cen.size: + sel_cen &= (self.centroids.region_id == reg_id) + if not np.any(sel_cen): LOGGER.info('No hazard centroids with region %s.', str(reg_id)) return None + # filter events based on name sel_ev = np.argwhere(sel_ev).reshape(-1) + if isinstance(event_names, list): + filtered_events = [self.event_name[i] for i in sel_ev] + try: + new_sel = [filtered_events.index(n) for n in event_names] + except ValueError as err: + name = str(err).replace(" is not in list", "") + LOGGER.info('No hazard with name %s', name) + return None + sel_ev = sel_ev[new_sel] + + sel_cen = sel_cen.nonzero()[0] for (var_name, var_val) in self.__dict__.items(): - if isinstance(var_val, np.ndarray) and var_val.ndim == 1 and \ - var_val.size: + if isinstance(var_val, np.ndarray) and var_val.ndim == 1 \ + and var_val.size > 0: setattr(haz, var_name, var_val[sel_ev]) elif isinstance(var_val, sparse.csr_matrix): setattr(haz, var_name, var_val[sel_ev, :][:, sel_cen]) @@ -568,18 +605,20 @@ def select(self, date=None, orig=None, reg_id=None, reset_frequency=False): setattr(haz, var_name, var_val) else: setattr(haz, var_name, var_val) + # reset frequency if date span has changed (optional): if reset_frequency: - year_span_old = np.abs(dt.datetime.fromordinal(self.date.max()).year - \ - dt.datetime.fromordinal(self.date.min()).year)+1 - year_span_new = np.abs(dt.datetime.fromordinal(haz.date.max()).year - \ - dt.datetime.fromordinal(haz.date.min()).year)+1 - haz.frequency = haz.frequency*year_span_old/year_span_new + year_span_old = np.abs(dt.datetime.fromordinal(self.date.max()).year - + dt.datetime.fromordinal(self.date.min()).year) + 1 + year_span_new = np.abs(dt.datetime.fromordinal(haz.date.max()).year - + dt.datetime.fromordinal(haz.date.min()).year) + 1 + haz.frequency = haz.frequency * year_span_old / year_span_new + haz.sanitize_event_ids() return haz def local_exceedance_inten(self, return_periods=(25, 50, 100, 250)): - """ Compute exceedance intensity map for given return periods. + """Compute exceedance intensity map for given return periods. Parameters: return_periods (np.array): return periods to consider @@ -588,32 +627,34 @@ def local_exceedance_inten(self, return_periods=(25, 50, 100, 250)): np.array """ # warn if return period is above return period of rarest event: - for rp in return_periods: - if rp > 1/self.frequency.min(): - LOGGER.warning('Return period %1.1f exceeds max. event return period.' %(rp)) + for period in return_periods: + if period > 1 / self.frequency.min(): + LOGGER.warning('Return period %1.1f exceeds max. event return period.', period) LOGGER.info('Computing exceedance intenstiy map for return periods: %s', return_periods) num_cen = self.intensity.shape[1] inten_stats = np.zeros((len(return_periods), num_cen)) - cen_step = int(CONFIG['global']['max_matrix_size']/self.intensity.shape[0]) + cen_step = int(CONFIG['global']['max_matrix_size'] / self.intensity.shape[0]) if not cen_step: - LOGGER.error('Increase max_matrix_size configuration parameter to'\ + LOGGER.error('Increase max_matrix_size configuration parameter to' ' > %s', str(self.intensity.shape[0])) raise ValueError # separte in chunks chk = -1 - for chk in range(int(num_cen/cen_step)): - self._loc_return_inten(np.array(return_periods), \ - self.intensity[:, chk*cen_step:(chk+1)*cen_step].todense(), \ - inten_stats[:, chk*cen_step:(chk+1)*cen_step]) - self._loc_return_inten(np.array(return_periods), \ - self.intensity[:, (chk+1)*cen_step:].todense(), \ - inten_stats[:, (chk+1)*cen_step:]) + for chk in range(int(num_cen / cen_step)): + self._loc_return_inten( + np.array(return_periods), + self.intensity[:, chk * cen_step:(chk + 1) * cen_step].toarray(), + inten_stats[:, chk * cen_step:(chk + 1) * cen_step]) + self._loc_return_inten( + np.array(return_periods), + self.intensity[:, (chk + 1) * cen_step:].toarray(), + inten_stats[:, (chk + 1) * cen_step:]) # set values below 0 to zero if minimum of hazard.intensity >= 0: - if self.intensity.min()>=0 and np.min(inten_stats)<0: + if self.intensity.min() >= 0 and np.min(inten_stats) < 0: LOGGER.warning('Exceedance intenstiy values below 0 are set to 0. \ -Reason: no negative intensity values were found in hazard.') - inten_stats[inten_stats<0] = 0 + Reason: no negative intensity values were found in hazard.') + inten_stats[inten_stats < 0] = 0 return inten_stats def plot_rp_intensity(self, return_periods=(25, 50, 100, 250), @@ -638,8 +679,9 @@ def plot_rp_intensity(self, return_periods=(25, 50, 100, 250), title = list() for ret in return_periods: title.append('Return period: ' + str(ret) + ' years') - _, axis = u_plot.geo_im_from_array(inten_stats, self.centroids.coord,\ - colbar_name, title, smooth=smooth, axes=axis, **kwargs) + _, axis = u_plot.geo_im_from_array(inten_stats, self.centroids.coord, + colbar_name, title, smooth=smooth, + axes=axis, **kwargs) return axis, inten_stats def plot_intensity(self, event=None, centr=None, smooth=True, axis=None, @@ -657,7 +699,8 @@ def plot_intensity(self, event=None, centr=None, smooth=True, axis=None, plot abs(centr)-largest centroid where higher intensities are reached. If tuple with (lat, lon) plot intensity of nearest centroid. - smooth (bool, optional): smooth plot to plot.RESOLUTIONxplot.RESOLUTION + smooth (bool, optional): Rescale data to RESOLUTIONxRESOLUTION pixels (see constant + in module `climada.util.plot`) axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use kwargs (optional): arguments for pcolormesh matplotlib function used in event plots or for plot function used in centroids plots @@ -698,7 +741,8 @@ def plot_fraction(self, event=None, centr=None, smooth=True, axis=None, plot abs(centr)-largest centroid where highest fractions are reached. If tuple with (lat, lon) plot fraction of nearest centroid. - smooth (bool, optional): smooth plot to plot.RESOLUTIONxplot.RESOLUTION + smooth (bool, optional): Rescale data to RESOLUTIONxRESOLUTION pixels (see constant + in module `climada.util.plot`) axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use kwargs (optional): arguments for pcolormesh matplotlib function used in event plots or for plot function used in centroids plots @@ -724,8 +768,14 @@ def plot_fraction(self, event=None, centr=None, smooth=True, axis=None, LOGGER.error("Provide one event id or one centroid id.") raise ValueError + def sanitize_event_ids(self): + """Make sure that event ids are unique""" + if np.unique(self.event_id).size != self.event_id.size: + LOGGER.debug('Resetting event_id.') + self.event_id = np.arange(1, self.event_id.size + 1) + def get_event_id(self, event_name): - """"Get an event id from its name. Several events might have the same + """Get an event id from its name. Several events might have the same name. Parameters: @@ -734,15 +784,15 @@ def get_event_id(self, event_name): Returns: np.array(int) """ - list_id = self.event_id[[i_name for i_name, val_name \ - in enumerate(self.event_name) if val_name == event_name]] + list_id = self.event_id[[i_name for i_name, val_name in enumerate(self.event_name) + if val_name == event_name]] if list_id.size == 0: LOGGER.error("No event with name: %s", event_name) raise ValueError return list_id def get_event_name(self, event_id): - """"Get the name of an event id. + """Get the name of an event id. Parameters: event_id (int): id of the event @@ -761,7 +811,7 @@ def get_event_name(self, event_id): raise ValueError def get_event_date(self, event=None): - """ Return list of date strings for given event or for all events, + """Return list of date strings for given event or for all events, if no event provided. Parameters: @@ -774,16 +824,16 @@ def get_event_date(self, event=None): l_dates = [u_dt.date_to_str(date) for date in self.date] elif isinstance(event, str): ev_ids = self.get_event_id(event) - l_dates = [u_dt.date_to_str(self.date[ \ - np.argwhere(self.event_id == ev_id)[0][0]]) \ - for ev_id in ev_ids] + l_dates = [ + u_dt.date_to_str(self.date[np.argwhere(self.event_id == ev_id)[0][0]]) + for ev_id in ev_ids] else: ev_idx = np.argwhere(self.event_id == event)[0][0] l_dates = [u_dt.date_to_str(self.date[ev_idx])] return l_dates def calc_year_set(self): - """ From the dates of the original events, get number yearly events. + """From the dates of the original events, get number yearly events. Returns: dict: key are years, values array with event_ids of that year @@ -824,84 +874,72 @@ def append(self, hazard): "%s != %s.", self.units, hazard.units) raise ValueError - self.tag.append(hazard.tag) - n_ini_ev = self.event_id.size - # append all 1-dim variables - for (var_name, var_val), haz_val in zip(self.__dict__.items(), - hazard.__dict__.values()): - if isinstance(var_val, np.ndarray) and var_val.ndim == 1 and \ - var_val.size: - setattr(self, var_name, np.append(var_val, haz_val). \ - astype(var_val.dtype, copy=False)) - elif isinstance(var_val, list) and var_val: - setattr(self, var_name, var_val + haz_val) - - # append intensity and fraction: - # if same centroids, just append events - if self.centroids.equal(hazard.centroids): - self.intensity = sparse.vstack([self.intensity, hazard.intensity], - format='csr') - self.fraction = sparse.vstack([self.fraction, hazard.fraction], - format='csr') - elif hazard.intensity.size: - n_ini_cen = self.centroids.size + centroids_equal = self.centroids.equal(hazard.centroids) + if not centroids_equal: self.centroids.append(hazard.centroids) - self.intensity = sparse.hstack([self.intensity, \ - sparse.lil_matrix((self.intensity.shape[0], \ - self.centroids.size - n_ini_cen))], format='lil') - self.fraction = sparse.hstack([self.fraction, \ - sparse.lil_matrix((self.fraction.shape[0], \ - self.centroids.size - n_ini_cen))], format='lil') - self.intensity = sparse.vstack([self.intensity, \ - sparse.lil_matrix((hazard.intensity.shape[0], - self.intensity.shape[1]))], format='lil') - self.fraction = sparse.vstack([self.fraction, \ - sparse.lil_matrix((hazard.intensity.shape[0], - self.intensity.shape[1]))], format='lil') - - self.intensity[n_ini_ev:, -hazard.intensity.shape[1]:] = hazard.intensity - self.fraction[n_ini_ev:, -hazard.intensity.shape[1]:] = hazard.fraction - self.intensity = self.intensity.tocsr() - self.fraction = self.fraction.tocsr() - - # Make event id unique - if np.unique(self.event_id).size != self.event_id.size: - LOGGER.debug('Resetting event_id.') - self.event_id = np.arange(self.event_id.size) + 1 + # n_ini_ev = self.event_id.size + for var_name in vars(self).keys(): + var_old = getattr(self, var_name) + var_new = getattr(hazard, var_name) + var_combined = [var_old, var_new] + if isinstance(var_new, sparse.csr.csr_matrix): + if centroids_equal: + var_combined = sparse.vstack(var_combined, format='csr') + else: + var_combined = sparse.block_diag(var_combined, format='csr') + setattr(self, var_name, var_combined) + elif isinstance(var_new, np.ndarray) and var_new.ndim == 1: + setattr(self, var_name, np.hstack(var_combined)) + elif isinstance(var_new, list): + setattr(self, var_name, sum(var_combined, [])) + elif isinstance(var_new, TagHazard): + var_old.append(var_new) + + self.sanitize_event_ids() def remove_duplicates(self): """Remove duplicate events (events with same name and date).""" - dup_pos = list() - set_ev = set() - for ev_pos, (ev_name, ev_date) in enumerate(zip(self.event_name, - self.date)): - if (ev_name, ev_date) in set_ev: - dup_pos.append(ev_pos) - set_ev.add((ev_name, ev_date)) + events = list(zip(self.event_name, self.date)) + set_ev = set(events) if len(set_ev) == self.event_id.size: return - - for var_name, var_val in self.__dict__.items(): - if isinstance(var_val, np.ndarray) and var_val.ndim == 1: - setattr(self, var_name, np.delete(var_val, dup_pos)) + unique_pos = sorted([events.index(event) for event in set_ev]) + for var_name, var_val in vars(self).items(): + if isinstance(var_val, sparse.csr.csr_matrix): + setattr(self, var_name, var_val[unique_pos, :]) + elif isinstance(var_val, np.ndarray) and var_val.ndim == 1: + setattr(self, var_name, var_val[unique_pos]) elif isinstance(var_val, list): - setattr(self, var_name, np.delete(var_val, dup_pos).tolist()) + setattr(self, var_name, [var_val[p] for p in unique_pos]) - mask = np.ones(self.intensity.shape, dtype=bool) - mask[dup_pos, :] = False - self.intensity = sparse.csr_matrix(self.intensity[mask].\ - reshape(self.event_id.size, self.intensity.shape[1])) - self.fraction = sparse.csr_matrix(self.fraction[mask].\ - reshape(self.event_id.size, self.intensity.shape[1])) + def set_frequency(self, yearrange=None): + """Set hazard frequency from yearrange or intensity matrix. + + Optional parameters: + yearrange (tuple or list): year range to be used to compute frequency + per event. If yearrange is not given (None), the year range is + derived from self.date + """ + if not yearrange: + delta_time = dt.datetime.fromordinal(int(np.max(self.date))).year - \ + dt.datetime.fromordinal(int(np.min(self.date))).year + 1 + else: + delta_time = max(yearrange)-min(yearrange)+1 + num_orig = self.orig.nonzero()[0].size + if num_orig > 0: + ens_size = self.event_id.size / num_orig + else: + ens_size = 1 + self.frequency = np.ones(self.event_id.size) / delta_time / ens_size @property def size(self): - """ Returns number of events """ + """Returns number of events""" return self.event_id.size def write_raster(self, file_name, intensity=True): - """ Write intensity or fraction as GeoTIFF file. Each band is an event + """Write intensity or fraction as GeoTIFF file. Each band is an event Parameters: file_name (str): file name to write in tif format @@ -911,28 +949,29 @@ def write_raster(self, file_name, intensity=True): if not intensity: variable = self.fraction if self.centroids.meta: - co.write_raster(file_name, variable.todense(), self.centroids.meta) + co.write_raster(file_name, variable.toarray(), self.centroids.meta) else: pixel_geom = self.centroids.calc_pixels_polygons() profile = self.centroids.meta profile.update(driver='GTiff', dtype=rasterio.float32, count=self.size) with rasterio.open(file_name, 'w', **profile) as dst: - LOGGER.info('Writting %s', file_name) + LOGGER.info('Writing %s', file_name) for i_ev in range(variable.shape[0]): - raster = rasterize([(x, val) for (x, val) in \ - zip(pixel_geom, np.array(variable[i_ev, :].todense()).reshape(-1))], \ - out_shape=(profile['height'], profile['width']),\ - transform=profile['transform'], fill=0, \ + raster = rasterize( + [(x, val) for (x, val) in + zip(pixel_geom, np.array(variable[i_ev, :].toarray()).reshape(-1))], + out_shape=(profile['height'], profile['width']), + transform=profile['transform'], fill=0, all_touched=True, dtype=profile['dtype'],) - dst.write(raster.astype(profile['dtype']), i_ev+1) + dst.write(raster.astype(profile['dtype']), i_ev + 1) - def write_hdf5(self, file_name,todense=False): - """ Write hazard in hdf5 format. + def write_hdf5(self, file_name, todense=False): + """Write hazard in hdf5 format. Parameters: file_name (str): file name to write, with h5 format """ - LOGGER.info('Writting %s', file_name) + LOGGER.info('Writing %s', file_name) hf_data = h5py.File(file_name, 'w') str_dt = h5py.special_dtype(vlen=str) for (var_name, var_val) in self.__dict__.items(): @@ -947,7 +986,7 @@ def write_hdf5(self, file_name,todense=False): hf_str[0] = str(var_val.description) elif isinstance(var_val, sparse.csr_matrix): if todense: - hf_data.create_dataset(var_name, data=var_val.todense()) + hf_data.create_dataset(var_name, data=var_val.toarray()) else: hf_csr = hf_data.create_group(var_name) hf_csr.create_dataset('data', data=var_val.data) @@ -966,7 +1005,7 @@ def write_hdf5(self, file_name,todense=False): hf_data.close() def read_hdf5(self, file_name): - """ Read hazard in hdf5 format. + """Read hazard in hdf5 format. Parameters: file_name (str): file name to read, with h5 format @@ -985,11 +1024,13 @@ def read_hdf5(self, file_name): setattr(self, var_name, np.array(hf_data.get(var_name))) elif isinstance(var_val, sparse.csr_matrix): hf_csr = hf_data.get(var_name) - if isinstance(hf_csr,h5py.Dataset): + if isinstance(hf_csr, h5py.Dataset): setattr(self, var_name, sparse.csr_matrix(hf_csr)) else: - setattr(self, var_name, sparse.csr_matrix((hf_csr['data'][:], \ - hf_csr['indices'][:], hf_csr['indptr'][:]), hf_csr.attrs['shape'])) + setattr(self, var_name, sparse.csr_matrix((hf_csr['data'][:], + hf_csr['indices'][:], + hf_csr['indptr'][:]), + hf_csr.attrs['shape'])) elif isinstance(var_val, str): setattr(self, var_name, hf_data.get(var_name)[0]) elif isinstance(var_val, list): @@ -998,62 +1039,55 @@ def read_hdf5(self, file_name): setattr(self, var_name, hf_data.get(var_name)) hf_data.close() - def _append_all(self, list_haz_ev): - """Append event by event with same centroids. Takes centroids and units - of first event. + def concatenate(self, haz_src, append=False): + """Concatenate events of several hazards Parameters: - list_haz_ev (list): Hazard instances with one event and same - centroids + haz_src (list): Hazard instances with same centroids and units + append (bool): If True, append the concatenated hazards to this + instance, otherwise replace all data in this instance by the + concatenated data. Default: False. """ - self.clear() - - num_ev = len(list_haz_ev) - num_cen = list_haz_ev[0].centroids.size + if append: + haz_src = [self] + haz_src + else: + self.clear() + self.centroids = copy.deepcopy(haz_src[-1].centroids) + self.units = haz_src[-1].units # check for new variables - for key_new in list_haz_ev[0].__dict__.keys(): - if key_new not in self.__dict__: - self.__dict__[key_new] = list_haz_ev[0].__dict__[key_new] - - for var_name, var_val in self.__dict__.items(): - if isinstance(var_val, np.ndarray) and var_val.ndim == 1: - setattr(self, var_name, np.zeros((num_ev,), dtype=var_val.dtype)) - elif isinstance(var_val, sparse.csr.csr_matrix): - setattr(self, var_name, sparse.lil_matrix((num_ev, num_cen))) - - for i_ev, haz_ev in enumerate(list_haz_ev): - for (var_name, var_val), ev_val in zip(self.__dict__.items(), - haz_ev.__dict__.values()): - if isinstance(var_val, np.ndarray) and var_val.ndim == 1: - var_val[i_ev] = ev_val[0] - elif isinstance(var_val, list): - var_val.extend(ev_val) - elif isinstance(var_val, sparse.lil_matrix): - var_val[i_ev, :] = ev_val[0, :] - elif isinstance(var_val, TagHazard): - var_val.append(ev_val) - - self.centroids = copy.deepcopy(list_haz_ev[0].centroids) - self.units = list_haz_ev[0].units - self.intensity = self.intensity.tocsr() - self.fraction = self.fraction.tocsr() - self.event_id = np.arange(1, num_ev+1) + for key_new in vars(haz_src[-1]).keys(): + if not hasattr(self, key_new): + setattr(self, key_new, getattr(haz_src[-1], key_new)) + + for var_name in vars(self).keys(): + var_src = [getattr(haz, var_name) for haz in haz_src] + if isinstance(var_src[-1], sparse.csr.csr_matrix): + setattr(self, var_name, sparse.vstack(var_src, format='csr')) + elif isinstance(var_src[-1], np.ndarray) and var_src[-1].ndim == 1: + setattr(self, var_name, np.hstack(var_src)) + elif isinstance(var_src[-1], list): + setattr(self, var_name, sum(var_src, [])) + elif isinstance(var_src[-1], TagHazard): + tag_dst = getattr(self, var_name) + [tag_dst.append(tag) for tag in var_src if tag is not tag_dst] + + self.sanitize_event_ids() def _set_coords_centroids(self): - """ If centroids are raster, set lat and lon coordinates """ + """If centroids are raster, set lat and lon coordinates""" if self.centroids.meta and not self.centroids.coord.size: self.centroids.set_meta_to_lat_lon() def _events_set(self): - """Generate set of tuples with (event_name, event_date) """ + """Generate set of tuples with (event_name, event_date)""" ev_set = set() for ev_name, ev_date in zip(self.event_name, self.date): ev_set.add((ev_name, ev_date)) return ev_set def _event_plot(self, event_id, mat_var, col_name, smooth, axis=None, **kwargs): - """"Plot an event of the input matrix. + """Plot an event of the input matrix. Parameters: event_id (int or np.array(int)): If event_id > 0, plot mat_var of @@ -1080,18 +1114,19 @@ def _event_plot(self, event_id, mat_var, col_name, smooth, axis=None, **kwargs): except IndexError: LOGGER.error('Wrong event id: %s.', ev_id) raise ValueError from IndexError - im_val = mat_var[event_pos, :].todense().transpose() - title = 'Event ID %s: %s' % (str(self.event_id[event_pos]), \ - self.event_name[event_pos]) + im_val = mat_var[event_pos, :].toarray().transpose() + title = 'Event ID %s: %s' % (str(self.event_id[event_pos]), + self.event_name[event_pos]) elif ev_id < 0: max_inten = np.asarray(np.sum(mat_var, axis=1)).reshape(-1) event_pos = np.argpartition(max_inten, ev_id)[ev_id:] event_pos = event_pos[np.argsort(max_inten[event_pos])][0] - im_val = mat_var[event_pos, :].todense().transpose() - title = '%s-largest Event. ID %s: %s' % (np.abs(ev_id), \ - str(self.event_id[event_pos]), self.event_name[event_pos]) + im_val = mat_var[event_pos, :].toarray().transpose() + title = '%s-largest Event. ID %s: %s' % (np.abs(ev_id), + str(self.event_id[event_pos]), + self.event_name[event_pos]) else: - im_val = np.max(mat_var, axis=0).todense().transpose() + im_val = np.max(mat_var, axis=0).toarray().transpose() title = '%s max intensity at each point' % self.tag.haz_type array_val.append(im_val) @@ -1101,7 +1136,7 @@ def _event_plot(self, event_id, mat_var, col_name, smooth, axis=None, **kwargs): l_title, smooth=smooth, axes=axis, **kwargs) def _centr_plot(self, centr_idx, mat_var, col_name, axis=None, **kwargs): - """"Plot a centroid of the input matrix. + """Plot a centroid of the input matrix. Parameters: centr_id (int): If centr_id > 0, plot mat_var @@ -1125,20 +1160,21 @@ def _centr_plot(self, centr_idx, mat_var, col_name, axis=None, **kwargs): except IndexError: LOGGER.error('Wrong centroid id: %s.', centr_idx) raise ValueError from IndexError - array_val = mat_var[:, centr_pos].todense() - title = 'Centroid %s: (%s, %s)' % (str(centr_idx), \ - coord[centr_pos, 0], coord[centr_pos, 1]) + array_val = mat_var[:, centr_pos].toarray() + title = 'Centroid %s: (%s, %s)' % (str(centr_idx), + coord[centr_pos, 0], + coord[centr_pos, 1]) elif centr_idx < 0: max_inten = np.asarray(np.sum(mat_var, axis=0)).reshape(-1) centr_pos = np.argpartition(max_inten, centr_idx)[centr_idx:] centr_pos = centr_pos[np.argsort(max_inten[centr_pos])][0] - array_val = mat_var[:, centr_pos].todense() + array_val = mat_var[:, centr_pos].toarray() title = '%s-largest Centroid. %s: (%s, %s)' % \ - (np.abs(centr_idx), str(centr_pos), coord[centr_pos, 0], \ + (np.abs(centr_idx), str(centr_pos), coord[centr_pos, 0], coord[centr_pos, 1]) else: - array_val = np.max(mat_var, axis=1).todense() + array_val = np.max(mat_var, axis=1).toarray() title = '%s max intensity at each event' % self.tag.haz_type if not axis: @@ -1153,7 +1189,7 @@ def _centr_plot(self, centr_idx, mat_var, col_name, axis=None, **kwargs): return axis def _loc_return_inten(self, return_periods, inten, exc_inten): - """ Compute local exceedence intensity for given return period. + """Compute local exceedence intensity for given return period. Parameters: return_periods (np.array): return periods to consider @@ -1165,6 +1201,7 @@ def _loc_return_inten(self, return_periods, inten, exc_inten): # sorted intensity sort_pos = np.argsort(inten, axis=0)[::-1, :] columns = np.ones(inten.shape, int) + # pylint: disable=unsubscriptable-object # pylint/issues/3139 columns *= np.arange(columns.shape[1]) inten_sort = inten[sort_pos, columns] # cummulative frequency at sorted intensity @@ -1177,7 +1214,7 @@ def _loc_return_inten(self, return_periods, inten, exc_inten): self.intensity_thres, return_periods) def _check_events(self): - """ Check that all attributes but centroids contain consistent data. + """Check that all attributes but centroids contain consistent data. Put default date, event_name and orig if not provided. Check not repeated events (i.e. with same date and name) @@ -1193,12 +1230,13 @@ def _check_events(self): check.check_oligatories(self.__dict__, self.vars_oblig, 'Hazard.', num_ev, num_ev, num_cen) check.check_optionals(self.__dict__, self.vars_opt, 'Hazard.', num_ev) - self.event_name = check.array_default(num_ev, self.event_name, \ - 'Hazard.event_name', list(self.event_id)) - self.date = check.array_default(num_ev, self.date, 'Hazard.date', \ - np.ones(self.event_id.shape, dtype=int)) - self.orig = check.array_default(num_ev, self.orig, 'Hazard.orig', \ - np.zeros(self.event_id.shape, dtype=bool)) + self.event_name = check.array_default(num_ev, self.event_name, + 'Hazard.event_name', + list(self.event_id)) + self.date = check.array_default(num_ev, self.date, 'Hazard.date', + np.ones(self.event_id.shape, dtype=int)) + self.orig = check.array_default(num_ev, self.orig, 'Hazard.orig', + np.zeros(self.event_id.shape, dtype=bool)) if len(self._events_set()) != num_ev: LOGGER.error("There are events with same date and name.") raise ValueError @@ -1228,19 +1266,18 @@ def _cen_return_inten(inten, freq, inten_th, return_periods): pol_coef = np.polyfit(np.log(freq_cen), inten_cen, deg=1) except ValueError: pol_coef = np.polyfit(np.log(freq_cen), inten_cen, deg=0) - inten_fit = np.polyval(pol_coef, np.log(1/return_periods)) - wrong_inten = np.logical_and(return_periods > np.max(1/freq_cen), \ - np.isnan(inten_fit)) + inten_fit = np.polyval(pol_coef, np.log(1 / return_periods)) + wrong_inten = (return_periods > np.max(1 / freq_cen)) & np.isnan(inten_fit) inten_fit[wrong_inten] = 0. return inten_fit def _read_att_mat(self, data, file_name, var_names): - """ Read MATLAB hazard's attributes. """ + """Read MATLAB hazard's attributes.""" self.frequency = np.squeeze(data[var_names['var_name']['freq']]) self.orig = np.squeeze(data[var_names['var_name']['orig']]).astype(bool) - self.event_id = np.squeeze(data[var_names['var_name']['even_id']]. \ - astype(np.int, copy=False)) + self.event_id = np.squeeze( + data[var_names['var_name']['even_id']].astype(np.int, copy=False)) try: self.units = hdf5.get_string(data[var_names['var_name']['unit']]) except KeyError: @@ -1249,13 +1286,13 @@ def _read_att_mat(self, data, file_name, var_names): n_cen = self.centroids.size n_event = len(self.event_id) try: - self.intensity = hdf5.get_sparse_csr_mat( \ + self.intensity = hdf5.get_sparse_csr_mat( data[var_names['var_name']['inten']], (n_event, n_cen)) except ValueError as err: LOGGER.error('Size missmatch in intensity matrix.') raise err try: - self.fraction = hdf5.get_sparse_csr_mat( \ + self.fraction = hdf5.get_sparse_csr_mat( data[var_names['var_name']['frac']], (n_event, n_cen)) except ValueError as err: LOGGER.error('Size missmatch in fraction matrix.') @@ -1277,14 +1314,16 @@ def _read_att_mat(self, data, file_name, var_names): try: datenum = data[var_names['var_name']['datenum']].squeeze() - self.date = np.array([(dt.datetime.fromordinal(int(date)) + \ - dt.timedelta(days=date%1)- \ - dt.timedelta(days=366)).toordinal() for date in datenum]) + self.date = np.array([ + (dt.datetime.fromordinal(int(date)) + + dt.timedelta(days=date % 1) + - dt.timedelta(days=366)).toordinal() + for date in datenum]) except KeyError: pass def _read_att_excel(self, file_name, var_names): - """ Read Excel hazard's attributes. """ + """Read Excel hazard's attributes.""" dfr = pd.read_excel(file_name, var_names['sheet_name']['freq']) num_events = dfr.shape[0] @@ -1300,17 +1339,17 @@ def _read_att_excel(self, file_name, var_names): # number of events (ignore centroid_ID column) # check the number of events is the same as the one in the frequency if dfr.shape[1] - 1 is not num_events: - LOGGER.error('Hazard intensity is given for a number of events ' \ - 'different from the number of defined in its frequency: ' \ - '%s != %s', dfr.shape[1] - 1, num_events) + LOGGER.error('Hazard intensity is given for a number of events ' + 'different from the number of defined in its frequency: ' + '%s != %s', dfr.shape[1] - 1, num_events) raise ValueError # check number of centroids is the same as retrieved before if dfr.shape[0] is not self.centroids.size: - LOGGER.error('Hazard intensity is given for a number of centroids ' \ - 'different from the number of centroids defined: %s != %s', \ - dfr.shape[0], self.centroids.size) + LOGGER.error('Hazard intensity is given for a number of centroids ' + 'different from the number of centroids defined: %s != %s', + dfr.shape[0], self.centroids.size) raise ValueError - self.intensity = sparse.csr_matrix(dfr.values[:, 1:num_events+1].transpose()) + self.intensity = sparse.csr_matrix(dfr.values[:, 1:num_events + 1].transpose()) self.fraction = sparse.csr_matrix(np.ones(self.intensity.shape, dtype=np.float)) diff --git a/climada/hazard/centroids/centr.py b/climada/hazard/centroids/centr.py index b99e6ace2e..645f1d4a6c 100644 --- a/climada/hazard/centroids/centr.py +++ b/climada/hazard/centroids/centr.py @@ -20,7 +20,6 @@ """ import ast -import shutil import copy import logging import numpy as np @@ -28,49 +27,58 @@ import h5py import pandas as pd from rasterio import Affine -from rasterio.warp import Resampling, reproject -import rasterio -from geopandas import GeoSeries +from rasterio.warp import Resampling +import geopandas as gpd from shapely.geometry.point import Point import climada.util.plot as u_plot -from climada.util.constants import DEF_CRS, ONE_LAT_KM +from climada.util.constants import (DEF_CRS, + ONE_LAT_KM, + NATEARTH_CENTROIDS) import climada.util.hdf5_handler as hdf5 -from climada.util.coordinates import dist_to_coast, get_resolution, coord_on_land, \ -pts_to_raster_meta, read_raster, read_vector, equal_crs, get_country_code -from climada.util.coordinates import NE_CRS, TMP_ELEVATION_FILE, DEM_NODATA, \ -MAX_DEM_TILES_DOWN +from climada.util.coordinates import (coord_on_land, + dist_to_coast, + dist_to_coast_nasa, + equal_crs, + get_country_code, + get_resolution, + pts_to_raster_meta, + raster_to_meshgrid, + read_raster, + read_vector) +from climada.util.coordinates import NE_CRS __all__ = ['Centroids'] -DEF_VAR_MAT = {'field_names': ['centroids', 'hazard'], - 'var_name': {'lat' : 'lat', - 'lon' : 'lon', - 'dist_coast': 'distance2coast_km', - 'admin0_name': 'admin0_name', - 'admin0_iso3': 'admin0_ISO3', - 'comment': 'comment', - 'region_id': 'NatId' - } - } -""" MATLAB variable names """ - -DEF_VAR_EXCEL = {'sheet_name': 'centroids', - 'col_name': {'region_id' : 'region_id', - 'lat' : 'latitude', - 'lon' : 'longitude', - } - } -""" Excel variable names """ +DEF_VAR_MAT = { + 'field_names': ['centroids', 'hazard'], + 'var_name': { + 'lat': 'lat', + 'lon': 'lon', + 'dist_coast': 'distance2coast_km', + 'admin0_name': 'admin0_name', + 'admin0_iso3': 'admin0_ISO3', + 'comment': 'comment', + 'region_id': 'NatId' + } +} +"""MATLAB variable names""" + +DEF_VAR_EXCEL = { + 'sheet_name': 'centroids', + 'col_name': { + 'region_id': 'region_id', + 'lat': 'latitude', + 'lon': 'longitude', + } +} +"""Excel variable names""" LOGGER = logging.getLogger(__name__) -if shutil.which('eio') is None: - from climada.util.config import setup_environ - setup_environ() class Centroids(): - """ Contains raster or vector centroids. Raster data can be set with + """Contains raster or vector centroids. Raster data can be set with set_raster_file() or set_meta(). Vector data can be set with set_lat_lon() or set_vector_file(). @@ -80,7 +88,7 @@ class Centroids(): at least (transform needs to contain upper left corner!) lat (np.array, optional): latitude of size size lon (np.array, optional): longitude of size size - geometry (GeoSeries, optional): contains lat and lon crs. Might contain + geometry (gpd.GeoSeries, optional): contains lat and lon crs. Might contain geometry points for lat and lon area_pixel (np.array, optional): area of size size dist_coast (np.array, optional): distance to coast of size size @@ -91,12 +99,12 @@ class Centroids(): vars_check = {'lat', 'lon', 'geometry', 'area_pixel', 'dist_coast', 'on_land', 'region_id', 'elevation'} - """ Variables whose size will be checked """ + """Variables whose size will be checked""" def __init__(self): - """ Initialize to None raster and vector """ + """Initialize to None raster and vector""" self.meta = dict() - self.geometry = GeoSeries() + self.geometry = gpd.GeoSeries() self.lat = np.array([]) self.lon = np.array([]) self.area_pixel = np.array([]) @@ -106,8 +114,8 @@ def __init__(self): self.elevation = np.array([]) def check(self): - """ Check that either raster meta attribute is set or points lat, lon - and geometry.crs. Check attributes sizes """ + """Check that either raster meta attribute is set or points lat, lon + and geometry.crs. Check attributes sizes""" n_centr = self.size for var_name, var_val in self.__dict__.items(): if var_name in self.vars_check: @@ -118,15 +126,15 @@ def check(self): if self.meta: if 'width' not in self.meta.keys() or 'height' not in self.meta.keys() or \ 'crs' not in self.meta.keys() or 'transform' not in self.meta.keys(): - LOGGER.error('Missing meta information: width, height,'\ - + 'crs or transform') + LOGGER.error('Missing meta information: width, height,' + 'crs or transform') raise ValueError if self.meta['transform'][4] > 0: LOGGER.error('Meta does not contain upper left corner data.') raise ValueError def equal(self, centr): - """ Return true if two centroids equal, false otherwise + """Return true if two centroids equal, false otherwise Parameters: centr (Centroids): centroids to compare @@ -135,17 +143,90 @@ def equal(self, centr): bool """ if self.meta and centr.meta: - return equal_crs(self.meta['crs'], centr.meta['crs']) and \ - self.meta['height'] == centr.meta['height'] and \ - self.meta['width'] == centr.meta['width'] and \ - self.meta['transform'] == centr.meta['transform'] - return equal_crs(self.geometry.crs, centr.geometry.crs) and \ - self.lat.shape == centr.lat.shape and self.lon.shape == centr.lon.shape and \ - np.allclose(self.lat, centr.lat) and np.allclose(self.lon, centr.lon) + return equal_crs(self.meta['crs'], centr.meta['crs']) \ + and self.meta['height'] == centr.meta['height'] \ + and self.meta['width'] == centr.meta['width'] \ + and self.meta['transform'] == centr.meta['transform'] + return equal_crs(self.geometry.crs, centr.geometry.crs) \ + and self.lat.shape == centr.lat.shape \ + and self.lon.shape == centr.lon.shape \ + and np.allclose(self.lat, centr.lat) \ + and np.allclose(self.lon, centr.lon) + + @staticmethod + def from_base_grid(land=False, res_as=360, base_file=None): + """Initialize from base grid data provided with CLIMADA + + Parameters: + land (bool, optional): If True, restrict to grid points on land. + Default: False. + res_as (int, optional): Base grid resolution in arc-seconds (one of + 150, 360). Default: 360. + base_file (str, optional): If set, read this file instead of one + provided with climada. + """ + centroids = Centroids() + + if base_file is None: + base_file = NATEARTH_CENTROIDS[res_as] + + centroids.read_hdf5(base_file) + if land: + land_reg_ids = list(range(1, 1000)) + land_reg_ids.remove(10) # Antarctica + centroids = centroids.select(reg_id=land_reg_ids) + return centroids + + @staticmethod + def from_geodataframe(gdf, geometry_alias='geom'): + """Create Centroids instance from GeoDataFrame. The geometry, lat, and + lon attributes are set from the GeoDataFrame.geometry attribute, while + the columns are copied as attributes to the Centroids object in + the form of numpy.ndarrays using pandas.Series.to_numpy. The Series + dtype will thus be respected. + + Columns named lat or lon are ignored, as they would overwrite the + coordinates extracted from the point features. If the geometry + attribute bears an alias, it can be dropped by setting the + geometry_alias parameter. + + If the GDF includes a region_id column, but no on_land column, then + on_land=True is inferred for those centroids that have a set region_id. + + >>> gdf = geopandas.read_file('centroids.shp') + >>> gdf.region_id = gdf.region_id.astype(int) # type coercion + >>> centroids = Centroids.from_geodataframe(gdf) + + Parameters: + gdf (GeoDataFrame): Where the geometry column needs to consist of + point features. See above for details on processing. + geometry_alias (str, opt): Alternate name for the geometry column; + dropped to avoid duplicate assignment. + """ + centroids = Centroids() + + centroids.geometry = gdf.geometry + centroids.lat = gdf.geometry.y.to_numpy(copy=True) + centroids.lon = gdf.geometry.x.to_numpy(copy=True) + + for col in gdf.columns: + if col in [geometry_alias, 'geometry', 'lat', 'lon']: + continue # skip these, because they're already set above + val = gdf[col].to_numpy(copy=True) + setattr(centroids, col, val) + + if centroids.on_land.size == 0: + try: + centroids.on_land = ~np.isnan(centroids.region_id) + except KeyError: + pass + + return centroids + def set_raster_from_pix_bounds(self, xf_lat, xo_lon, d_lat, d_lon, n_lat, n_lon, crs=DEF_CRS): - """ Set raster metadata (meta attribute) from pixel border data + """Set raster metadata (meta attribute) from pixel border data Parameters: xf_lat (float): upper latitude (top) @@ -157,12 +238,17 @@ def set_raster_from_pix_bounds(self, xf_lat, xo_lon, d_lat, d_lon, n_lat, crs (dict() or rasterio.crs.CRS, optional): CRS. Default: DEF_CRS """ self.__init__() - self.meta = {'dtype':'float32', 'width':n_lon, 'height':n_lat, - 'crs':crs, 'transform':Affine(d_lon, 0.0, xo_lon, - 0.0, d_lat, xf_lat)} + self.meta = { + 'dtype': 'float32', + 'width': n_lon, + 'height': n_lat, + 'crs': crs, + 'transform': Affine(d_lon, 0.0, xo_lon, + 0.0, d_lat, xf_lat), + } def set_raster_from_pnt_bounds(self, points_bounds, res, crs=DEF_CRS): - """ Set raster metadata (meta attribute) from points border data. + """Set raster metadata (meta attribute) from points border data. Raster border = point_border + res/2 Parameters: @@ -171,12 +257,16 @@ def set_raster_from_pnt_bounds(self, points_bounds, res, crs=DEF_CRS): crs (dict() or rasterio.crs.CRS, optional): CRS. Default: DEF_CRS """ self.__init__() - rows, cols, ras_trans = pts_to_raster_meta(points_bounds, res) - self.set_raster_from_pix_bounds(ras_trans[5], ras_trans[2], ras_trans[4], - ras_trans[0], rows, cols, crs) + rows, cols, ras_trans = pts_to_raster_meta(points_bounds, (res, -res)) + self.meta = { + 'width': cols, + 'height': rows, + 'crs': crs, + 'transform': ras_trans, + } def set_lat_lon(self, lat, lon, crs=DEF_CRS): - """ Set Centroids points from given latitude, longitude and CRS. + """Set Centroids points from given latitude, longitude and CRS. Parameters: lat (np.array): latitude @@ -184,12 +274,12 @@ def set_lat_lon(self, lat, lon, crs=DEF_CRS): crs (dict() or rasterio.crs.CRS, optional): CRS. Default: DEF_CRS """ self.__init__() - self.lat, self.lon, self.geometry = lat, lon, GeoSeries(crs=crs) + self.lat, self.lon, self.geometry = lat, lon, gpd.GeoSeries(crs=crs) def set_raster_file(self, file_name, band=[1], src_crs=None, window=False, geometry=False, dst_crs=False, transform=None, width=None, height=None, resampling=Resampling.nearest): - """ Read raster of bands and set 0 values to the masked ones. Each + """Read raster of bands and set 0 values to the masked ones. Each band is an event. Select region using window or geometry. Reproject input by proving dst_crs and/or (transform, width, height). @@ -220,16 +310,16 @@ def set_raster_file(self, file_name, band=[1], src_crs=None, window=False, tmp_meta, inten = read_raster(file_name, band, src_crs, window, geometry, dst_crs, transform, width, height, resampling) - if (tmp_meta['crs'] != self.meta['crs']) or \ - (tmp_meta['transform'] != self.meta['transform']) or \ - (tmp_meta['height'] != self.meta['height']) or \ - (tmp_meta['width'] != self.meta['width']): - LOGGER.error('Raster data inconsistent with contained raster.') + if (tmp_meta['crs'] != self.meta['crs']) \ + or (tmp_meta['transform'] != self.meta['transform']) \ + or (tmp_meta['height'] != self.meta['height']) \ + or (tmp_meta['width'] != self.meta['width']): + LOGGER.error('Raster data is inconsistent with contained raster.') raise ValueError return sparse.csr_matrix(inten) def set_vector_file(self, file_name, inten_name=['intensity'], dst_crs=None): - """ Read vector file format supported by fiona. Each intensity name is + """Read vector file format supported by fiona. Each intensity name is considered an event. Returns intensity array with shape (len(inten_name), len(geometry)). @@ -244,11 +334,9 @@ def set_vector_file(self, file_name, inten_name=['intensity'], dst_crs=None): np.array """ if not self.geometry.crs: - self.lat, self.lon, self.geometry, inten = read_vector(file_name, \ - inten_name, dst_crs) + self.lat, self.lon, self.geometry, inten = read_vector(file_name, inten_name, dst_crs) return sparse.csr_matrix(inten) - tmp_lat, tmp_lon, tmp_geometry, inten = read_vector(file_name, \ - inten_name, dst_crs) + tmp_lat, tmp_lon, tmp_geometry, inten = read_vector(file_name, inten_name, dst_crs) if not equal_crs(tmp_geometry.crs, self.geometry.crs) or \ not np.allclose(tmp_lat, self.lat) or\ not np.allclose(tmp_lon, self.lon): @@ -257,7 +345,7 @@ def set_vector_file(self, file_name, inten_name=['intensity'], dst_crs=None): return sparse.csr_matrix(inten) def read_mat(self, file_name, var_names=DEF_VAR_MAT): - """ Read centroids from CLIMADA's MATLAB version + """Read centroids from CLIMADA's MATLAB version Parameters: file_name (str): absolute or relative file name @@ -300,7 +388,7 @@ def read_mat(self, file_name, var_names=DEF_VAR_MAT): raise err def read_excel(self, file_name, var_names=DEF_VAR_EXCEL): - """ Read centroids from excel file with column names in var_names + """Read centroids from excel file with column names in var_names Parameters: file_name (str): absolute or relative file name @@ -327,18 +415,18 @@ def read_excel(self, file_name, var_names=DEF_VAR_EXCEL): raise err def clear(self): - """ Clear vector and raster data """ + """Clear vector and raster data""" self.__init__() def append(self, centr): - """ Append raster or points. Raster needs to have the same resolution """ + """Append raster or points. Raster needs to have the same resolution""" if self.meta and centr.meta: LOGGER.debug('Appending raster') if centr.meta['crs'] != self.meta['crs']: LOGGER.error('Different CRS not accepted.') raise ValueError - if self.meta['transform'][0] != centr.meta['transform'][0] or \ - self.meta['transform'][4] != centr.meta['transform'][4]: + if self.meta['transform'][0] != centr.meta['transform'][0] \ + or self.meta['transform'][4] != centr.meta['transform'][4]: LOGGER.error('Different raster resolutions.') raise ValueError left = min(self.total_bounds[0], centr.total_bounds[0]) @@ -346,11 +434,16 @@ def append(self, centr): right = max(self.total_bounds[2], centr.total_bounds[2]) top = max(self.total_bounds[3], centr.total_bounds[3]) crs = self.meta['crs'] - width = (right - left)/self.meta['transform'][0] - height = (bottom - top)/self.meta['transform'][4] - self.meta = {'dtype':'float32', 'width':width, 'height':height, - 'crs':crs, 'transform':Affine(self.meta['transform'][0], \ - 0.0, left, 0.0, self.meta['transform'][4], top)} + width = (right - left) / self.meta['transform'][0] + height = (bottom - top) / self.meta['transform'][4] + self.meta = { + 'dtype': 'float32', + 'width': width, + 'height': height, + 'crs': crs, + 'transform': Affine(self.meta['transform'][0], 0.0, left, + 0.0, self.meta['transform'][4], top), + } self.lat, self.lon = np.array([]), np.array([]) else: LOGGER.debug('Appending points') @@ -366,11 +459,11 @@ def append(self, centr): centr.__dict__.values()): if isinstance(var_val, np.ndarray) and var_val.ndim == 1 and \ var_name not in ('lat', 'lon'): - setattr(self, var_name, np.append(var_val, centr_val). \ + setattr(self, var_name, np.append(var_val, centr_val). astype(var_val.dtype, copy=False)) def get_closest_point(self, x_lon, y_lat, scheduler=None): - """ Returns closest centroid and its index to a given point. + """Returns closest centroid and its index to a given point. Parameters: x_lon (float): x coord (lon) @@ -384,19 +477,19 @@ def get_closest_point(self, x_lon, y_lat, scheduler=None): if self.meta: if not self.lat.size or not self.lon.size: self.set_meta_to_lat_lon() - i_lat = np.floor((self.meta['transform'][5]- y_lat)/abs(self.meta['transform'][4])) - i_lon = np.floor((x_lon - self.meta['transform'][2])/abs(self.meta['transform'][0])) - close_idx = int(i_lat*self.meta['width'] + i_lon) + i_lat = np.floor((self.meta['transform'][5] - y_lat) / abs(self.meta['transform'][4])) + i_lon = np.floor((x_lon - self.meta['transform'][2]) / abs(self.meta['transform'][0])) + close_idx = int(i_lat * self.meta['width'] + i_lon) else: self.set_geometry_points(scheduler) close_idx = self.geometry.distance(Point(x_lon, y_lat)).values.argmin() return self.lon[close_idx], self.lat[close_idx], close_idx def set_region_id(self, scheduler=None): - """ Set region_id as country ISO numeric code attribute for every pixel + """Set region_id as country ISO numeric code attribute for every pixel or point - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” """ @@ -406,9 +499,9 @@ def set_region_id(self, scheduler=None): ne_geom.geometry[:].x.values) def set_area_pixel(self, min_resol=1.0e-8, scheduler=None): - """ Set area_pixel attribute for every pixel or point. area in m*m + """Set area_pixel attribute for every pixel or point. area in m*m - Parameter: + Parameters: min_resol (float, optional): if centroids are points, use this minimum resolution in lat and lon. Default: 1.0e-8 scheduler (str): used for dask map_partitions. “threads”, @@ -418,7 +511,7 @@ def set_area_pixel(self, min_resol=1.0e-8, scheduler=None): if hasattr(self.meta['crs'], 'linear_units') and \ str.lower(self.meta['crs'].linear_units) in ['m', 'metre', 'meter']: self.area_pixel = np.zeros((self.meta['height'], self.meta['width'])) - self.area_pixel *= abs(self.meta['transform'].a)*abs(self.meta['transform'].e) + self.area_pixel *= abs(self.meta['transform'].a) * abs(self.meta['transform'].e) return if abs(abs(self.meta['transform'].a) - abs(self.meta['transform'].e)) > 1.0e-5: @@ -426,22 +519,24 @@ def set_area_pixel(self, min_resol=1.0e-8, scheduler=None): raise ValueError res = self.meta['transform'].a else: - res = min(get_resolution(self.lat, self.lon, min_resol)) + res = get_resolution(self.lat, self.lon, min_resol=min_resol) + res = np.abs(res).min() self.set_geometry_points(scheduler) LOGGER.debug('Setting area_pixel %s points.', str(self.lat.size)) - xy_pixels = self.geometry.buffer(res/2).envelope - if ('units' in self.geometry.crs and \ - self.geometry.crs['units'] in ['m', 'metre', 'meter']) or \ - equal_crs(self.geometry.crs, {'proj':'cea'}): + xy_pixels = self.geometry.buffer(res / 2).envelope + is_cea = ('units' in self.geometry.crs + and self.geometry.crs['units'] in ['m', 'metre', 'meter'] + or equal_crs(self.geometry.crs, {'proj': 'cea'})) + if is_cea: self.area_pixel = xy_pixels.area.values else: - self.area_pixel = xy_pixels.to_crs(crs={'proj':'cea'}).area.values + self.area_pixel = xy_pixels.to_crs(crs={'proj': 'cea'}).area.values def set_area_approx(self, min_resol=1.0e-8): - """ Computes approximated area_pixel values: differentiated per latitude. + """Computes approximated area_pixel values: differentiated per latitude. area in m*m. Faster than set_area_pixel - Parameter: + Parameters: min_resol (float, optional): if centroids are points, use this minimum resolution in lat and lon. Default: 1.0e-8 """ @@ -449,20 +544,23 @@ def set_area_approx(self, min_resol=1.0e-8): if hasattr(self.meta['crs'], 'linear_units') and \ str.lower(self.meta['crs'].linear_units) in ['m', 'metre', 'meter']: self.area_pixel = np.zeros((self.meta['height'], self.meta['width'])) - self.area_pixel *= abs(self.meta['transform'].a)*abs(self.meta['transform'].e) + self.area_pixel *= abs(self.meta['transform'].a) * abs(self.meta['transform'].e) return res_lat, res_lon = self.meta['transform'].e, self.meta['transform'].a - lat_unique = np.arange(self.meta['transform'].f + res_lat/2, \ - self.meta['transform'].f + self.meta['height'] * res_lat, res_lat) + lat_unique = np.arange(self.meta['transform'].f + res_lat / 2, + self.meta['transform'].f + self.meta['height'] * res_lat, + res_lat) lon_unique_len = self.meta['width'] res_lat = abs(res_lat) else: - res_lat, res_lon = get_resolution(self.lat, self.lon, min_resol) + res_lat, res_lon = np.abs(get_resolution(self.lat, self.lon, + min_resol=min_resol)) lat_unique = np.array(np.unique(self.lat)) lon_unique_len = len(np.unique(self.lon)) - if ('units' in self.geometry.crs and \ - self.geometry.crs['units'] in ['m', 'metre', 'meter']) or \ - equal_crs(self.geometry.crs, {'proj':'cea'}): + is_cea = ('units' in self.geometry.crs + and self.geometry.crs['units'] in ['m', 'metre', 'meter'] + or equal_crs(self.geometry.crs, {'proj': 'cea'})) + if is_cea: self.area_pixel = np.repeat(res_lat * res_lon, lon_unique_len) return @@ -476,22 +574,30 @@ def set_area_approx(self, min_resol=1.0e-8): LOGGER.error('Pixel area of points can not be computed.') raise ValueError - def set_dist_coast(self, scheduler=None): - """ Set dist_coast attribute for every pixel or point. Distance to + def set_dist_coast(self, signed=False, precomputed=False, scheduler=None): + """Set dist_coast attribute for every pixel or point. Distance to coast is computed in meters. - Parameter: + Parameters: + signed (bool): If True, use signed distances (positive off shore and negative on + land). Default: False. + precomputed (bool): If True, use precomputed distances (from NASA). Default: False. scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” """ - ne_geom = self._ne_crs_geom(scheduler) - LOGGER.debug('Setting dist_coast %s points.', str(self.lat.size)) - self.dist_coast = dist_to_coast(ne_geom) + if precomputed: + if not self.lat.size or not self.lon.size: + self.set_meta_to_lat_lon() + self.dist_coast = dist_to_coast_nasa(self.lat, self.lon, highres=True, signed=signed) + else: + ne_geom = self._ne_crs_geom(scheduler) + LOGGER.debug('Computing distance to coast for %s centroids.', str(self.lat.size)) + self.dist_coast = dist_to_coast(ne_geom, signed=signed) def set_on_land(self, scheduler=None): - """ Set on_land attribute for every pixel or point + """Set on_land attribute for every pixel or point - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” """ @@ -500,9 +606,9 @@ def set_on_land(self, scheduler=None): self.on_land = coord_on_land(ne_geom.geometry[:].y.values, ne_geom.geometry[:].x.values) def remove_duplicate_points(self, scheduler=None): - """ Return Centroids with removed duplicated points + """Return Centroids with removed duplicated points - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” @@ -514,18 +620,24 @@ def remove_duplicate_points(self, scheduler=None): sel_cen = geom_wkb.drop_duplicates().index return self.select(sel_cen=sel_cen) - def select(self, reg_id=None, sel_cen=None): - """ Return Centroids with points in the given reg_id or within mask + def select(self, reg_id=None, extent=None, sel_cen=None): + """Return Centroids with points in the given reg_id or within mask Parameters: reg_id (int): region to filter according to region_id values - sel_cen (np.array): 1-dim mask + extent (tuple): Format (min_lon, max_lon, min_lat, max_lat) tuple. + sel_cen (np.array): 1-dim mask, overrides reg_id and extent Returns: Centroids """ if sel_cen is None: - sel_cen = np.isin(self.region_id, reg_id) + sel_cen = np.ones_like(self.region_id, dtype=bool) + if reg_id: + sel_cen &= np.isin(self.region_id, reg_id) + if extent: + sel_cen &= ((extent[0] < self.lon) & (extent[1] > self.lon) + & (extent[2] < self.lat) & (extent[3] > self.lat)) if not self.lat.size or not self.lon.size: self.set_meta_to_lat_lon() @@ -543,32 +655,33 @@ def select(self, reg_id=None, sel_cen=None): return centr def set_lat_lon_to_meta(self, min_resol=1.0e-8): - """ Compute meta from lat and lon values. To match the existing lat - and lon, lat and lon need to start from the upper left corner!! + """Compute meta from lat and lon values. - Parameter: + Parameters: min_resol (float, optional): minimum centroids resolution to use in the raster. Default: 1.0e-8. """ - self.meta = dict() - res = min(get_resolution(self.lat, self.lon, min_resol)) + res = get_resolution(self.lon, self.lat, min_resol=min_resol) rows, cols, ras_trans = pts_to_raster_meta(self.total_bounds, res) LOGGER.debug('Resolution points: %s', str(res)) - self.meta = {'width':cols, 'height':rows, 'crs':self.crs, 'transform':ras_trans} + self.meta = { + 'width': cols, + 'height': rows, + 'crs': self.crs, + 'transform': ras_trans, + } def set_meta_to_lat_lon(self): - """ Compute lat and lon of every pixel center from meta raster """ - ulx, xres, _, uly, _, yres = self.meta['transform'].to_gdal() - lrx = ulx + (self.meta['width'] * xres) - lry = uly + (self.meta['height'] * yres) - x_grid, y_grid = np.meshgrid(np.arange(ulx+xres/2, lrx, xres), - np.arange(uly+yres/2, lry, yres)) - self.lon = x_grid.flatten() - self.lat = y_grid.flatten() - self.geometry = GeoSeries(crs=self.meta['crs']) + """Compute lat and lon of every pixel center from meta raster""" + xgrid, ygrid = raster_to_meshgrid(self.meta['transform'], + self.meta['width'], + self.meta['height']) + self.lon = xgrid.flatten() + self.lat = ygrid.flatten() + self.geometry = gpd.GeoSeries(crs=self.meta['crs']) def plot(self, axis=None, **kwargs): - """ Plot centroids scatter points over earth. + """Plot centroids scatter points over earth. Parameters: axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use @@ -586,14 +699,14 @@ def plot(self, axis=None, **kwargs): return axis def calc_pixels_polygons(self, scheduler=None): - """ Return a GeoSeries with a polygon for every pixel + """Return a gpd.GeoSeries with a polygon for every pixel - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” Returns: - GeoSeries + gpd.GeoSeries """ if not self.meta: self.set_lat_lon_to_meta() @@ -602,17 +715,17 @@ def calc_pixels_polygons(self, scheduler=None): LOGGER.error('Area can not be computed for not squared pixels.') raise ValueError self.set_geometry_points(scheduler) - return self.geometry.buffer(self.meta['transform'].a/2).envelope + return self.geometry.buffer(self.meta['transform'].a / 2).envelope def empty_geometry_points(self): - """ Removes points in geometry. Useful when centroids is used in - multiprocessing function """ - self.geometry = GeoSeries(crs=self.geometry.crs) + """Removes points in geometry. Useful when centroids is used in + multiprocessing function""" + self.geometry = gpd.GeoSeries(crs=self.geometry.crs) def write_hdf5(self, file_data): - """ Write centroids attributes into hdf5 format. + """Write centroids attributes into hdf5 format. - Parameter: + Parameters: file_data (str or h5): if string, path to write data. if h5 object, the datasets will be generated there """ @@ -624,7 +737,7 @@ def write_hdf5(self, file_data): str_dt = h5py.special_dtype(vlen=str) for centr_name, centr_val in self.__dict__.items(): if isinstance(centr_val, np.ndarray): - data.create_dataset(centr_name, data=centr_val) + data.create_dataset(centr_name, data=centr_val, compression="gzip") if centr_name == 'meta' and centr_val: centr_meta = data.create_group(centr_name) for key, value in centr_val.items(): @@ -635,8 +748,10 @@ def write_hdf5(self, file_data): hf_str = centr_meta.create_dataset(key, (1,), dtype=str_dt) hf_str[0] = value elif key == 'transform': - centr_meta.create_dataset(key, (6,), data=[value.a, value.b, \ - value.c, value.d, value.e, value.f], dtype=float) + centr_meta.create_dataset( + key, (6,), + data=[value.a, value.b, value.c, value.d, value.e, value.f], + dtype=float) hf_str = data.create_dataset('crs', (1,), dtype=str_dt) hf_str[0] = str(dict(self.crs)) @@ -644,9 +759,9 @@ def write_hdf5(self, file_data): data.close() def read_hdf5(self, file_data): - """ Read centroids attributes from hdf5. + """Read centroids attributes from hdf5. - Parameter: + Parameters: file_data (str or h5): if string, path to read data. if h5 object, the datasets will be read from there """ @@ -660,11 +775,9 @@ def read_hdf5(self, file_data): if data.get('crs'): crs = ast.literal_eval(data.get('crs')[0]) if data.get('lat') and data.get('lat').size: - self.set_lat_lon(np.array(data.get('lat')), \ - np.array(data.get('lon')), crs) + self.set_lat_lon(np.array(data.get('lat')), np.array(data.get('lon')), crs) elif data.get('latitude') and data.get('latitude').size: - self.set_lat_lon(np.array(data.get('latitude')), \ - np.array(data.get('longitude')), crs) + self.set_lat_lon(np.array(data.get('latitude')), np.array(data.get('longitude')), crs) else: centr_meta = data.get('meta') self.meta['crs'] = crs @@ -672,8 +785,7 @@ def read_hdf5(self, file_data): if key != 'transform': self.meta[key] = value[0] else: - self.meta[key] = Affine(value[0], value[1], value[2], - value[3], value[4], value[5]) + self.meta[key] = Affine(*value) for centr_name in data.keys(): if centr_name not in ('crs', 'lat', 'lon', 'meta'): setattr(self, centr_name, np.array(data.get(centr_name))) @@ -682,21 +794,21 @@ def read_hdf5(self, file_data): @property def crs(self): - """ Get CRS of raster or vector """ + """Get CRS of raster or vector""" if self.meta: return self.meta['crs'] return self.geometry.crs @property def size(self): - """ Get size of pixels or points""" + """Get size of pixels or points""" if self.meta: - return self.meta['height']*self.meta['width'] + return self.meta['height'] * self.meta['width'] return self.lat.size @property def shape(self): - """ Get shape of rastered data """ + """Get shape of rastered data""" try: if self.meta: return (self.meta['height'], self.meta['width']) @@ -706,49 +818,53 @@ def shape(self): @property def total_bounds(self): - """ Get total bounds (left, bottom, right, top)""" + """Get total bounds (left, bottom, right, top)""" if self.meta: left = self.meta['transform'].xoff - right = left + self.meta['transform'][0]*self.meta['width'] + right = left + self.meta['transform'][0] * self.meta['width'] + if left > right: + left, right = right, left top = self.meta['transform'].yoff - bottom = top + self.meta['transform'][4]*self.meta['height'] + bottom = top + self.meta['transform'][4] * self.meta['height'] + if bottom > top: + bottom, top = top, bottom return left, bottom, right, top return self.lon.min(), self.lat.min(), self.lon.max(), self.lat.max() @property def coord(self): - """ Get [lat, lon] array. Might take some time. """ + """Get [lat, lon] array. Might take some time.""" return np.array([self.lat, self.lon]).transpose() def set_geometry_points(self, scheduler=None): - """ Set geometry attribute of GeoSeries with Points from latitude and + """Set geometry attribute of gpd.GeoSeries with Points from latitude and longitude attributes if geometry not present. - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” """ def apply_point(df_exp): return df_exp.apply((lambda row: Point(row.longitude, row.latitude)), axis=1) if not self.geometry.size: - LOGGER.info('Setting geometry points.') + LOGGER.info('Convert centroids to GeoSeries of Point shapes.') if not self.lat.size or not self.lon.size: self.set_meta_to_lat_lon() if not scheduler: - self.geometry = GeoSeries(list(zip(self.lon, self.lat)), - crs=self.geometry.crs) - self.geometry = self.geometry.apply(Point) + self.geometry = gpd.GeoSeries( + gpd.points_from_xy(self.lon, self.lat), crs=self.geometry.crs) else: import dask.dataframe as dd from multiprocessing import cpu_count ddata = dd.from_pandas(self, npartitions=cpu_count()) - self.geometry = ddata.map_partitions(apply_point, meta=Point).\ - compute(scheduler=scheduler) + self.geometry = (ddata + .map_partitions(apply_point, meta=Point) + .compute(scheduler=scheduler)) def _ne_crs_geom(self, scheduler=None): - """ Return x (lon) and y (lat) in the CRS of Natural Earth + """Return x (lon) and y (lat) in the CRS of Natural Earth - Parameter: + Parameters: scheduler (str): used for dask map_partitions. “threads”, “synchronous” or “processes” @@ -763,13 +879,50 @@ def _ne_crs_geom(self, scheduler=None): return self.geometry.to_crs(NE_CRS) def __deepcopy__(self, memo): - """ Avoid error deep copy in GeoSeries by setting only the crs """ + """Avoid error deep copy in gpd.GeoSeries by setting only the crs""" cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for key, value in self.__dict__.items(): if key == 'geometry': - setattr(result, key, GeoSeries(crs=self.geometry.crs)) + setattr(result, key, gpd.GeoSeries(crs=self.geometry.crs)) else: setattr(result, key, copy.deepcopy(value, memo)) return result + + +def generate_nat_earth_centroids(res_as=360, path=None, dist_coast=False): + """For reproducibility, this is the function that generates the centroids + files in `NATEARTH_CENTROIDS`. These files are provided with CLIMADA + so that this function should never be called! + + Parameters: + res_as (int): Resolution of file in arc-seconds. Default: 360. + path (str, optional): If set, write resulting hdf5 file here instead of + the default location. + dist_coast (bool): If true, read distance from a NASA dataset + (see util.coordinates.dist_to_coast_nasa) + """ + if path is None and res_as not in [150, 360]: + raise ValueError("Only 150 and 360 arc-seconds are supported!") + + res_deg = res_as / 3600 + lat_dim = np.arange(-90 + res_deg, 90, res_deg) + lon_dim = np.arange(-180 + res_deg, 180 + res_deg, res_deg) + lon, lat = [ar.ravel() for ar in np.meshgrid(lon_dim, lat_dim)] + natids = np.uint16(get_country_code(lat, lon, gridded=False)) + + cen = Centroids() + cen.set_lat_lon(lat, lon) + cen.region_id = natids + cen.set_lat_lon_to_meta() + cen.lat = np.array([]) + cen.lon = np.array([]) + + if path is None: + path = NATEARTH_CENTROIDS[res_as] + + if dist_coast: + cen.set_dist_coast(precomputed=True, signed=False) + cen.dist_coast = np.float16(cen.dist_coast) + cen.write_hdf5(path) diff --git a/climada/hazard/centroids/test/test_centr.py b/climada/hazard/centroids/test/test_centr.py new file mode 100644 index 0000000000..dcc29a9aba --- /dev/null +++ b/climada/hazard/centroids/test/test_centr.py @@ -0,0 +1,115 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test CentroidsVector and CentroidsRaster classes. +""" +import os +import unittest + +import numpy as np +import pandas as pd +import geopandas as gpd + +from climada.hazard.centroids.centr import Centroids +from climada.util.constants import GLB_CENTROIDS_MAT, HAZ_TEMPLATE_XLS + +HAZ_DIR = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, + 'test/data/') +HAZ_TEST_MAT = os.path.join(HAZ_DIR, 'atl_prob_no_name.mat') + + +class TestCentroidsReader(unittest.TestCase): + """Test read functions Centroids""" + + def test_mat_pass(self): + """Read a centroid mat file correctly.""" + centroids = Centroids() + centroids.read_mat(HAZ_TEST_MAT) + + n_centroids = 100 + self.assertEqual(centroids.coord.shape, (n_centroids, 2)) + self.assertEqual(centroids.coord[0][0], 21) + self.assertEqual(centroids.coord[0][1], -84) + self.assertEqual(centroids.coord[n_centroids - 1][0], 30) + self.assertEqual(centroids.coord[n_centroids - 1][1], -75) + + def test_mat_global_pass(self): + """Test read GLB_CENTROIDS_MAT""" + centroids = Centroids() + centroids.read_mat(GLB_CENTROIDS_MAT) + + self.assertEqual(centroids.region_id[1062443], 35) + self.assertEqual(centroids.region_id[170825], 28) + + def test_centroid_pass(self): + """Read a centroid excel file correctly.""" + centroids = Centroids() + centroids.read_excel(HAZ_TEMPLATE_XLS) + + n_centroids = 45 + self.assertEqual(centroids.coord.shape[0], n_centroids) + self.assertEqual(centroids.coord.shape[1], 2) + self.assertEqual(centroids.coord[0][0], -25.95) + self.assertEqual(centroids.coord[0][1], 32.57) + self.assertEqual(centroids.coord[n_centroids - 1][0], -24.7) + self.assertEqual(centroids.coord[n_centroids - 1][1], 33.88) + + def test_base_grid(self): + """Read new centroids using from_base_grid, then select by extent.""" + + centroids = Centroids().from_base_grid(land=True, res_as=150) + + count_sandwich = np.sum(centroids.region_id == 239) + + self.assertEqual(centroids.lat.size, 8858035) + self.assertEqual(count_sandwich, 321) + + count_sgi = centroids.select( + reg_id=239, + extent=(-39, -34.7, -55.5, -53.6) # south georgia island + ).size + + self.assertEqual(count_sgi, 296) + + def test_geodataframe(self): + """Test that constructing a valid Centroids instance from gdf works.""" + gdf = gpd.GeoDataFrame(pd.read_excel(HAZ_TEMPLATE_XLS)) + gdf.geometry = gpd.points_from_xy( + gdf['longitude'], gdf['latitude'] + ) + gdf['elevation'] = np.random.rand(gdf.geometry.size) + gdf['region_id'] = np.zeros(gdf.geometry.size) + gdf['region_id'][0] = np.NaN + gdf['geom'] = gdf.geometry # this should have no effect on centroids + + centroids = Centroids.from_geodataframe(gdf) + centroids.check() + + self.assertEqual(centroids.geometry.size, 45) + self.assertEqual(centroids.lon[0], 32.57) + self.assertEqual(centroids.lat[0], -25.95) + self.assertEqual(centroids.elevation.size, 45) + self.assertEqual(centroids.on_land.sum(), 44) + self.assertIsInstance(centroids.geometry, gpd.GeoSeries) + self.assertIsInstance(centroids.geometry.total_bounds, np.ndarray) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestCentroidsReader) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/centroids/test/test_vec_ras.py b/climada/hazard/centroids/test/test_vec_ras.py index e2af94e21c..dd4e2ede4e 100644 --- a/climada/hazard/centroids/test/test_vec_ras.py +++ b/climada/hazard/centroids/test/test_vec_ras.py @@ -25,142 +25,133 @@ import unittest import numpy as np from rasterio.windows import Window -from rasterio.warp import Resampling from shapely.geometry.point import Point from shapely.geometry.polygon import Polygon -from climada.hazard.centroids.centr import Centroids, DEM_NODATA +from climada.hazard.centroids.centr import Centroids from climada.util.constants import HAZ_DEMO_FL, DEF_CRS from climada.util.coordinates import NE_EPSG, equal_crs DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') -VEC_LON = np.array([-59.6250000000000,-59.6250000000000,-59.6250000000000,-59.5416666666667, - -59.5416666666667,-59.4583333333333,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.2083333333333,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.2083333333333,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.2083333333333,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.1250000000000,-60.1250000000000,-60.1250000000000, - -60.1250000000000,-60.1250000000000,-60.1250000000000,-60.1250000000000, - -60.1250000000000,-60.1250000000000,-60.1250000000000,-60.1250000000000, - -60.1250000000000,-60.1250000000000,-60.1250000000000,-60.1250000000000, - -60.1250000000000,-60.0416666666667,-60.0416666666667,-60.0416666666667, - -60.0416666666667,-60.0416666666667,-60.0416666666667,-60.0416666666667, - -60.0416666666667,-60.0416666666667,-60.0416666666667,-60.0416666666667, - -60.0416666666667,-60.0416666666667,-60.0416666666667,-60.0416666666667, - -60.0416666666667,-59.9583333333333,-59.9583333333333,-59.9583333333333, - -59.9583333333333,-59.9583333333333,-59.9583333333333,-59.9583333333333, - -59.9583333333333,-59.9583333333333,-59.9583333333333,-59.9583333333333, - -59.9583333333333,-59.9583333333333,-59.9583333333333,-59.9583333333333, - -59.9583333333333,-59.8750000000000,-59.8750000000000,-59.8750000000000, - -59.8750000000000,-59.8750000000000,-59.8750000000000,-59.8750000000000, - -59.8750000000000,-59.8750000000000,-59.8750000000000,-59.8750000000000, - -59.8750000000000,-59.8750000000000,-59.8750000000000,-59.8750000000000, - -59.8750000000000,-59.7916666666667,-59.7916666666667,-59.7916666666667, - -59.7916666666667,-59.7916666666667,-59.7916666666667,-59.7916666666667, - -59.7916666666667,-59.7916666666667,-59.7916666666667,-59.7916666666667, - -59.7916666666667,-59.7916666666667,-59.7916666666667,-59.7916666666667, - -59.7916666666667,-59.7083333333333,-59.7083333333333,-59.7083333333333, - -59.7083333333333,-59.7083333333333,-59.7083333333333,-59.7083333333333, - -59.7083333333333,-59.7083333333333,-59.7083333333333,-59.7083333333333, - -59.7083333333333,-59.7083333333333,-59.7083333333333,-59.7083333333333, - -59.7083333333333,-59.6250000000000,-59.6250000000000,-59.6250000000000, - -59.6250000000000,-59.6250000000000,-59.6250000000000,-59.6250000000000, - -59.6250000000000,-59.6250000000000,-59.6250000000000,-59.6250000000000, - -59.6250000000000,-59.6250000000000,-59.5416666666667,-59.5416666666667, - -59.5416666666667,-59.5416666666667,-59.5416666666667,-59.5416666666667, - -59.5416666666667,-59.5416666666667,-59.5416666666667,-59.5416666666667, - -59.5416666666667,-59.5416666666667,-59.5416666666667,-59.5416666666667, - -59.4583333333333,-59.4583333333333,-59.4583333333333,-59.4583333333333, - -59.4583333333333,-59.4583333333333,-59.4583333333333,-59.4583333333333, - -59.4583333333333,-59.4583333333333,-59.4583333333333,-59.4583333333333, - -59.4583333333333,-59.4583333333333,-59.4583333333333,-59.3750000000000, - -59.3750000000000,-59.3750000000000,-59.3750000000000,-59.3750000000000, - -59.3750000000000,-59.3750000000000,-59.3750000000000,-59.3750000000000, - -59.3750000000000,-59.3750000000000,-59.3750000000000,-59.3750000000000, - -59.3750000000000,-59.3750000000000,-59.3750000000000,-59.2916666666667, - -59.2916666666667,-59.2916666666667,-59.2916666666667,-59.2916666666667, - -59.2916666666667,-59.2916666666667,-59.2916666666667,-59.2916666666667, - -59.2916666666667,-59.2916666666667,-59.2916666666667,-59.2916666666667, - -59.2916666666667,-59.2916666666667,-59.2916666666667,-59.2083333333333, - -59.2083333333333,-59.2083333333333,-59.2083333333333,-59.2083333333333, - -59.2083333333333,-59.2083333333333,-59.2083333333333,-59.2083333333333, - -59.2083333333333,-59.2083333333333,-59.2083333333333,-59.2083333333333, - -59.2083333333333,-59.2083333333333,-59.2083333333333,-59.1250000000000, - -59.1250000000000,-59.1250000000000,-59.1250000000000,-59.1250000000000, - -59.1250000000000,-59.1250000000000,-59.1250000000000,-59.1250000000000, - -59.1250000000000,-59.1250000000000,-59.1250000000000,-59.1250000000000, - -59.1250000000000,-59.1250000000000,-59.1250000000000,-59.0416666666667, - -59.0416666666667,-59.0416666666667,-59.0416666666667,-59.0416666666667, - -59.0416666666667,-59.0416666666667,-59.0416666666667,-59.0416666666667, - -59.0416666666667,-59.0416666666667,-59.0416666666667,-59.0416666666667, - -59.0416666666667,-59.0416666666667,-58.9583333333333,-58.9583333333333, - -58.9583333333333,-58.9583333333333,-58.9583333333333,-58.9583333333333, - -58.9583333333333,-58.9583333333333,-58.9583333333333,-58.9583333333333, - -58.9583333333333,-58.9583333333333,-58.9583333333333,-58.9583333333333, - -61.0416666666667,-61.0416666666667,-61.0416666666667,-61.0416666666667, - -61.0416666666667,-61.0416666666667,-61.0416666666667,-60.6250000000000, - -60.6250000000000,-60.6250000000000,-60.6250000000000,-60.6250000000000, - -60.6250000000000,-60.6250000000000,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.2083333333333,-59.7916666666667,-59.7916666666667, - -59.7916666666667,-59.7916666666667,-59.3750000000000,-59.3750000000000, - -59.3750000000000,-59.3750000000000,-58.9583333333333,-58.9583333333333, - -58.9583333333333,-58.9583333333333,-58.5416666666667,-58.5416666666667, - -58.5416666666667,-58.5416666666667,-58.5416666666667,-58.5416666666667, - -58.5416666666667,-58.1250000000000,-58.1250000000000,-58.1250000000000, - -58.1250000000000,-58.1250000000000,-58.1250000000000,-58.1250000000000]) - -VEC_LAT = np.array([13.125,13.20833333,13.29166667,13.125,13.20833333,13.125,12.625,12.70833333, - 12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333,13.29166667, - 13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667, - 12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667, - 12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667, - 12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667, - 12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667, - 12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333,13.29166667, - 13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667,12.625,12.70833333, - 12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333,13.29166667,13.375,13.45833333, - 13.54166667,13.625,13.70833333,13.79166667,12.54166667,12.625,12.70833333,12.79166667, - 12.875,12.95833333,13.04166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667, - 12.54166667,12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.29166667,13.375, - 13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667,12.625,12.70833333,12.79166667, - 12.875,12.95833333,13.04166667,13.20833333,13.29166667,13.375,13.45833333,13.54166667,13.625, - 13.70833333,13.79166667,12.54166667,12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667, - 13.125,13.20833333,13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667, - 12.54166667,12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667,12.625, - 12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333,13.29166667,13.375, - 13.45833333,13.54166667,13.625,13.70833333,13.79166667,12.54166667,12.625,12.70833333,12.79166667, - 12.875,12.95833333,13.04166667,13.125,13.20833333,13.29166667,13.375,13.45833333,13.54166667, - 13.625,13.70833333,13.79166667,12.54166667,12.625,12.70833333,12.79166667,12.875,12.95833333, - 13.04166667,13.125,13.20833333,13.29166667,13.375,13.45833333,13.54166667,13.625,13.70833333, - 12.54166667,12.625,12.70833333,12.79166667,12.875,12.95833333,13.04166667,13.125,13.20833333, - 13.29166667,13.375,13.45833333,13.54166667,13.625,11.875,12.29166667,12.70833333,13.125, - 13.54166667,13.95833333,14.375,11.875,12.29166667,12.70833333,13.125,13.54166667,13.95833333, - 14.375,11.875,12.29166667,13.95833333,14.375,11.875,12.29166667,13.95833333,14.375,11.875, - 12.29166667,13.95833333,14.375,11.875,12.29166667,13.95833333,14.375,11.875,12.29166667, - 12.70833333,13.125,13.54166667,13.95833333,14.375,11.875,12.29166667,12.70833333,13.125, - 13.54166667,13.95833333,14.375]) +VEC_LON = np.array([ + -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.5416666666667, -59.5416666666667, + -59.4583333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, + -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, + -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, + -60.2083333333333, -60.1250000000000, -60.1250000000000, -60.1250000000000, -60.1250000000000, + -60.1250000000000, -60.1250000000000, -60.1250000000000, -60.1250000000000, -60.1250000000000, + -60.1250000000000, -60.1250000000000, -60.1250000000000, -60.1250000000000, -60.1250000000000, + -60.1250000000000, -60.1250000000000, -60.0416666666667, -60.0416666666667, -60.0416666666667, + -60.0416666666667, -60.0416666666667, -60.0416666666667, -60.0416666666667, -60.0416666666667, + -60.0416666666667, -60.0416666666667, -60.0416666666667, -60.0416666666667, -60.0416666666667, + -60.0416666666667, -60.0416666666667, -60.0416666666667, -59.9583333333333, -59.9583333333333, + -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.9583333333333, + -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.9583333333333, + -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.9583333333333, -59.8750000000000, + -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, + -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, + -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, -59.8750000000000, + -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, + -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, + -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, + -59.7916666666667, -59.7083333333333, -59.7083333333333, -59.7083333333333, -59.7083333333333, + -59.7083333333333, -59.7083333333333, -59.7083333333333, -59.7083333333333, -59.7083333333333, + -59.7083333333333, -59.7083333333333, -59.7083333333333, -59.7083333333333, -59.7083333333333, + -59.7083333333333, -59.7083333333333, -59.6250000000000, -59.6250000000000, -59.6250000000000, + -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.6250000000000, + -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.6250000000000, + -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.5416666666667, + -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.5416666666667, + -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.5416666666667, -59.4583333333333, + -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.4583333333333, + -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.4583333333333, + -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.4583333333333, -59.3750000000000, + -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, + -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, + -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, -59.3750000000000, + -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, + -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, + -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, -59.2916666666667, + -59.2916666666667, -59.2083333333333, -59.2083333333333, -59.2083333333333, -59.2083333333333, + -59.2083333333333, -59.2083333333333, -59.2083333333333, -59.2083333333333, -59.2083333333333, + -59.2083333333333, -59.2083333333333, -59.2083333333333, -59.2083333333333, -59.2083333333333, + -59.2083333333333, -59.2083333333333, -59.1250000000000, -59.1250000000000, -59.1250000000000, + -59.1250000000000, -59.1250000000000, -59.1250000000000, -59.1250000000000, -59.1250000000000, + -59.1250000000000, -59.1250000000000, -59.1250000000000, -59.1250000000000, -59.1250000000000, + -59.1250000000000, -59.1250000000000, -59.1250000000000, -59.0416666666667, -59.0416666666667, + -59.0416666666667, -59.0416666666667, -59.0416666666667, -59.0416666666667, -59.0416666666667, + -59.0416666666667, -59.0416666666667, -59.0416666666667, -59.0416666666667, -59.0416666666667, + -59.0416666666667, -59.0416666666667, -59.0416666666667, -58.9583333333333, -58.9583333333333, + -58.9583333333333, -58.9583333333333, -58.9583333333333, -58.9583333333333, -58.9583333333333, + -58.9583333333333, -58.9583333333333, -58.9583333333333, -58.9583333333333, -58.9583333333333, + -58.9583333333333, -58.9583333333333, -61.0416666666667, -61.0416666666667, -61.0416666666667, + -61.0416666666667, -61.0416666666667, -61.0416666666667, -61.0416666666667, -60.6250000000000, + -60.6250000000000, -60.6250000000000, -60.6250000000000, -60.6250000000000, -60.6250000000000, + -60.6250000000000, -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333, + -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.7916666666667, -59.3750000000000, + -59.3750000000000, -59.3750000000000, -59.3750000000000, -58.9583333333333, -58.9583333333333, + -58.9583333333333, -58.9583333333333, -58.5416666666667, -58.5416666666667, -58.5416666666667, + -58.5416666666667, -58.5416666666667, -58.5416666666667, -58.5416666666667, -58.1250000000000, + -58.1250000000000, -58.1250000000000, -58.1250000000000, -58.1250000000000, -58.1250000000000, + -58.1250000000000, +]) + +VEC_LAT = np.array([ + 13.125, 13.20833333, 13.29166667, 13.125, 13.20833333, 13.125, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, 13.375, + 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, + 12.79166667, 12.875, 12.95833333, 13.04166667, 13.375, 13.45833333, 13.54166667, 13.625, + 13.70833333, 13.79166667, 12.54166667, 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, + 13.04166667, 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, + 12.54166667, 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, + 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, + 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, + 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, + 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 13.79166667, 12.54166667, + 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, + 13.29166667, 13.375, 13.45833333, 13.54166667, 13.625, 13.70833333, 12.54166667, 12.625, + 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667, 13.125, 13.20833333, 13.29166667, + 13.375, 13.45833333, 13.54166667, 13.625, 11.875, 12.29166667, 12.70833333, 13.125, + 13.54166667, 13.95833333, 14.375, 11.875, 12.29166667, 12.70833333, 13.125, 13.54166667, + 13.95833333, 14.375, 11.875, 12.29166667, 13.95833333, 14.375, 11.875, 12.29166667, + 13.95833333, 14.375, 11.875, 12.29166667, 13.95833333, 14.375, 11.875, 12.29166667, + 13.95833333, 14.375, 11.875, 12.29166667, 12.70833333, 13.125, 13.54166667, 13.95833333, + 14.375, 11.875, 12.29166667, 12.70833333, 13.125, 13.54166667, 13.95833333, 14.375 +]) class TestVector(unittest.TestCase): """Test CentroidsVector class""" @staticmethod def data_vector(): - vec_data = gpd.GeoDataFrame(crs={'init':'epsg:32632'}) + vec_data = gpd.GeoDataFrame(crs={'init': 'epsg:32632'}) vec_data['geometry'] = list(zip(VEC_LON, VEC_LAT)) vec_data['geometry'] = vec_data['geometry'].apply(Point) vec_data['lon'] = VEC_LON vec_data['lat'] = VEC_LAT - vec_data['value'] = np.arange(VEC_LAT.size)+100 + vec_data['value'] = np.arange(VEC_LAT.size) + 100 return vec_data.lat.values, vec_data.lon.values, vec_data.geometry def test_set_lat_lon_pass(self): - """ Test set_lat_lon """ + """Test set_lat_lon""" centr = Centroids() centr.set_lat_lon(VEC_LAT, VEC_LON) self.assertTrue(np.allclose(centr.lat, VEC_LAT)) @@ -173,7 +164,7 @@ def test_set_lat_lon_pass(self): self.assertEqual(centr.geometry.size, centr.lat.size) def test_ne_crs_geom_pass(self): - """ Test _ne_crs_geom """ + """Test _ne_crs_geom""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() xy_vec = centr._ne_crs_geom() @@ -184,39 +175,39 @@ def test_ne_crs_geom_pass(self): self.assertAlmostEqual(0.0001297, lat[-1]) def test_dist_coast_pass(self): - """ Test set_dist_coast """ + """Test set_dist_coast""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} centr.set_dist_coast() self.assertAlmostEqual(2594.2070842031694, centr.dist_coast[1]) self.assertAlmostEqual(166295.87602398323, centr.dist_coast[-2]) def test_region_id_pass(self): - """ Test set_region_id """ + """Test set_region_id""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} centr.set_region_id() self.assertEqual(np.count_nonzero(centr.region_id), 6) - self.assertEqual(centr.region_id[0], 52) # 052 for barbados + self.assertEqual(centr.region_id[0], 52) # 052 for barbados def test_on_land(self): - """ Test set_on_land """ + """Test set_on_land""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} centr.set_on_land() centr.set_region_id() - centr.region_id[centr.region_id>0] = 1 + centr.region_id[centr.region_id > 0] = 1 self.assertTrue(np.array_equal(centr.on_land.astype(int), centr.region_id)) def test_remove_duplicate_pass(self): - """ Test remove_duplicate_points """ + """Test remove_duplicate_points""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} # create duplicates manually: centr.geometry.values[100] = centr.geometry.values[101] centr.geometry.values[120] = centr.geometry.values[101] @@ -226,18 +217,18 @@ def test_remove_duplicate_pass(self): self.assertEqual(centr.size, 296) rem_centr = centr.remove_duplicate_points() self.assertEqual(centr.size, 296) - self.assertEqual(rem_centr.size, 292) # 5 centroids removed + self.assertEqual(rem_centr.size, 292) # 5 centroids removed rem2_centr = rem_centr.remove_duplicate_points() self.assertEqual(rem_centr.size, 292) self.assertEqual(rem2_centr.size, 292) def test_area_pass(self): - """ Test set_area """ + """Test set_area""" ulx, xres, lrx = 60, 1, 90 uly, yres, lry = 0, 1, 20 - xx, yy = np.meshgrid(np.arange(ulx+xres/2, lrx, xres), - np.arange(uly+yres/2, lry, yres)) - vec_data = gpd.GeoDataFrame(crs={'proj':'cea'}) + xx, yy = np.meshgrid(np.arange(ulx + xres / 2, lrx, xres), + np.arange(uly + yres / 2, lry, yres)) + vec_data = gpd.GeoDataFrame(crs={'proj': 'cea'}) vec_data['geometry'] = list(zip(xx.flatten(), yy.flatten())) vec_data['geometry'] = vec_data['geometry'].apply(Point) vec_data['lon'] = xx.flatten() @@ -250,17 +241,17 @@ def test_area_pass(self): self.assertTrue(np.allclose(centr.area_pixel, np.ones(centr.size))) def test_size_pass(self): - """ Test size property""" + """Test size property""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} self.assertEqual(centr.size, 296) def test_get_closest_point(self): - """ Test get_closest_point """ + """Test get_closest_point""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} x, y, idx = centr.get_closest_point(-58.13, 14.38) self.assertAlmostEqual(x, -58.125) self.assertAlmostEqual(y, 14.375) @@ -269,49 +260,49 @@ def test_get_closest_point(self): self.assertEqual(centr.lat[idx], y) def test_set_lat_lon_to_meta_pass(self): - """ Test set_lat_lon_to_meta """ + """Test set_lat_lon_to_meta""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} centr.set_lat_lon_to_meta() - self.assertEqual(centr.meta['crs'], {'init':'epsg:4326'}) + self.assertEqual(centr.meta['crs'], {'init': 'epsg:4326'}) self.assertEqual(centr.meta['width'], 36) self.assertEqual(centr.meta['height'], 31) - self.assertAlmostEqual(centr.meta['transform'][0], 0.08333333) self.assertEqual(centr.meta['transform'][1], 0.0) + self.assertEqual(centr.meta['transform'][3], 0.0) + self.assertAlmostEqual(centr.meta['transform'][0], 0.08333333) self.assertAlmostEqual(centr.meta['transform'][2], -61.08333333) - self.assertAlmostEqual(centr.meta['transform'][3], 0.0) - self.assertAlmostEqual(centr.meta['transform'][4], -0.08333333) - self.assertAlmostEqual(centr.meta['transform'][5], 14.41666666) + self.assertAlmostEqual(centr.meta['transform'][4], 0.08333333) + self.assertAlmostEqual(centr.meta['transform'][5], 11.83333333) def test_get_pixel_polygons_pass(self): - """ Test calc_pixels_polygons """ + """Test calc_pixels_polygons""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} poly = centr.calc_pixels_polygons() self.assertIsInstance(poly[0], Polygon) self.assertTrue(np.allclose(poly.centroid[:].y.values, centr.lat)) self.assertTrue(np.allclose(poly.centroid[:].x.values, centr.lon)) def test_area_approx(self): - """ Test set_area_approx """ + """Test set_area_approx""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} with self.assertRaises(ValueError): centr.set_area_approx() def test_append_pass(self): - """ Append points """ + """Append points""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() centr_bis = Centroids() centr_bis.set_lat_lon(np.array([1, 2, 3]), np.array([4, 5, 6])) with self.assertRaises(ValueError): centr_bis.append(centr) - centr.geometry.crs = {'init':'epsg:4326'} + centr.geometry.crs = {'init': 'epsg:4326'} centr_bis.append(centr) self.assertAlmostEqual(centr_bis.lat[0], 1) self.assertAlmostEqual(centr_bis.lat[1], 2) @@ -323,7 +314,7 @@ def test_append_pass(self): self.assertTrue(np.array_equal(centr_bis.lon[3:], centr.lon)) def test_equal_pass(self): - """ Test equal """ + """Test equal""" centr = Centroids() centr.lat, centr.lon, centr.geometry = self.data_vector() centr_bis = Centroids() @@ -335,10 +326,10 @@ def test_equal_pass(self): class TestRaster(unittest.TestCase): - """ Test CentroidsRaster class """ + """Test CentroidsRaster class""" def test_set_raster_pxl_pass(self): - """ Test set_raster_from_pxl_bounds from pixel borders """ + """Test set_raster_from_pxl_bounds from pixel borders""" centr = Centroids() xf_lat, xo_lon, d_lat, d_lon, n_lat, n_lon = 10, 5, -0.5, 0.2, 20, 25 centr.set_raster_from_pix_bounds(xf_lat, xo_lon, d_lat, d_lon, n_lat, n_lon) @@ -355,7 +346,7 @@ def test_set_raster_pxl_pass(self): self.assertTrue('lon' in centr.__dict__.keys()) def test_set_raster_pnt_pass(self): - """ Test set_raster_from_pnt_bounds from point borders """ + """Test set_raster_from_pnt_bounds from point borders""" centr = Centroids() left, bottom, right, top = 5, 0, 10, 10 centr.set_raster_from_pnt_bounds((left, bottom, right, top), 0.2) @@ -364,17 +355,17 @@ def test_set_raster_pnt_pass(self): self.assertEqual(centr.meta['height'], 51) self.assertAlmostEqual(centr.meta['transform'][0], 0.2) self.assertAlmostEqual(centr.meta['transform'][1], 0.0) - self.assertAlmostEqual(centr.meta['transform'][2], 5-0.2/2) + self.assertAlmostEqual(centr.meta['transform'][2], 5 - 0.2 / 2) self.assertAlmostEqual(centr.meta['transform'][3], 0.0) self.assertAlmostEqual(centr.meta['transform'][4], -0.2) - self.assertAlmostEqual(centr.meta['transform'][5], 10+0.2/2) + self.assertAlmostEqual(centr.meta['transform'][5], 10 + 0.2 / 2) self.assertTrue('lat' in centr.__dict__.keys()) self.assertTrue('lon' in centr.__dict__.keys()) def test_read_all_pass(self): - """ Test centr_ras data """ + """Test centr_ras data""" centr_ras = Centroids() - inten_ras = centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + inten_ras = centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) self.assertAlmostEqual(centr_ras.meta['crs'], DEF_CRS) self.assertAlmostEqual(centr_ras.meta['transform'].c, -69.33714959699981) self.assertAlmostEqual(centr_ras.meta['transform'].a, 0.009000000000000341) @@ -384,13 +375,13 @@ def test_read_all_pass(self): self.assertAlmostEqual(centr_ras.meta['transform'].e, -0.009000000000000341) self.assertEqual(centr_ras.meta['height'], 60) self.assertEqual(centr_ras.meta['width'], 50) - self.assertEqual(inten_ras.shape, (1, 60*50)) + self.assertEqual(inten_ras.shape, (1, 60 * 50)) def test_ne_crs_geom_pass(self): - """ Test _ne_crs_geom """ + """Test _ne_crs_geom""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) - centr_ras.meta['crs'] = {'init':'epsg:32632'} + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) + centr_ras.meta['crs'] = {'init': 'epsg:32632'} xy_vec = centr_ras._ne_crs_geom() x_vec, y_vec = xy_vec.geometry[:].x.values, xy_vec.geometry[:].y.values @@ -400,17 +391,17 @@ def test_ne_crs_geom_pass(self): self.assertAlmostEqual(8.92260922066e-05, y_vec[-1]) def test_region_id_pass(self): - """ Test set_dist_coast """ + """Test set_dist_coast""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_ras.set_region_id() self.assertEqual(centr_ras.region_id.size, centr_ras.size) self.assertTrue(np.array_equal(np.unique(centr_ras.region_id), np.array([862]))) def test_set_geometry_points_pass(self): - """ Test set_geometry_points """ + """Test set_geometry_points""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_ras.set_geometry_points() x_flat = np.arange(-69.3326495969998, -68.88264959699978, 0.009000000000000341) y_flat = np.arange(10.423720966978939, 9.883720966978919, -0.009000000000000341) @@ -419,53 +410,55 @@ def test_set_geometry_points_pass(self): self.assertTrue(np.allclose(y_grid.flatten(), centr_ras.lat)) def test_dist_coast_pass(self): - """ Test set_region_id """ + """Test set_region_id""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_ras.set_dist_coast() centr_ras.check() self.assertTrue(abs(centr_ras.dist_coast[0] - 117000) < 1000) self.assertTrue(abs(centr_ras.dist_coast[-1] - 104000) < 1000) def test_on_land(self): - """ Test set_on_land """ + """Test set_on_land""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_ras.set_on_land() centr_ras.check() self.assertTrue(np.array_equal(centr_ras.on_land, np.ones(60 * 50, bool))) def test_area_pass(self): - """ Test set_area """ + """Test set_area""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) - centr_ras.meta['crs'] = {'proj':'cea'} + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) + centr_ras.meta['crs'] = {'proj': 'cea'} centr_ras.set_area_pixel() centr_ras.check() - self.assertTrue(np.allclose(centr_ras.area_pixel, - np.ones(60*50)*0.009000000000000341*0.009000000000000341)) + self.assertTrue( + np.allclose(centr_ras.area_pixel, + np.ones(60 * 50) * 0.009000000000000341 * 0.009000000000000341)) def test_area_approx(self): - """ Test set_area_approx """ + """Test set_area_approx""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_ras.set_area_approx() - approx_dim = centr_ras.meta['transform'][0]*111*1000*centr_ras.meta['transform'][0]*111*1000 + approx_dim = (centr_ras.meta['transform'][0] * 111 * 1000 + * centr_ras.meta['transform'][0] * 111 * 1000) self.assertEqual(centr_ras.area_pixel.size, centr_ras.size) self.assertEqual(np.unique(centr_ras.area_pixel).size, 60) - self.assertTrue(np.array_equal((approx_dim/np.unique(centr_ras.area_pixel)).astype(int), + self.assertTrue(np.array_equal((approx_dim / np.unique(centr_ras.area_pixel)).astype(int), np.ones(60))) def test_size_pass(self): - """ Test size property""" + """Test size property""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) - self.assertEqual(centr_ras.size, 50*60) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) + self.assertEqual(centr_ras.size, 50 * 60) def test_get_closest_point(self): - """ Test get_closest_point """ + """Test get_closest_point""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) x, y, idx = centr_ras.get_closest_point(-69.334, 10.42) self.assertAlmostEqual(x, -69.3326495969998) self.assertAlmostEqual(y, 10.423720966978939) @@ -475,7 +468,7 @@ def test_get_closest_point(self): self.assertEqual(centr_ras.lat[idx], y) def test_set_meta_to_lat_lon_pass(self): - """ Test set_meta_to_lat_lon by using its inverse set_lat_lon_to_meta """ + """Test set_meta_to_lat_lon by using its inverse set_lat_lon_to_meta""" lat, lon, geometry = TestVector.data_vector() centr = Centroids() @@ -485,20 +478,20 @@ def test_set_meta_to_lat_lon_pass(self): meta = centr.meta centr.set_meta_to_lat_lon() self.assertEqual(centr.meta, meta) - self.assertAlmostEqual(lat.max(), centr.lat.max(), 7) + self.assertAlmostEqual(lat.max(), centr.lat.max(), 6) self.assertAlmostEqual(lat.min(), centr.lat.min(), 6) self.assertAlmostEqual(lon.max(), centr.lon.max(), 6) self.assertAlmostEqual(lon.min(), centr.lon.min(), 6) self.assertAlmostEqual(np.diff(centr.lon).max(), meta['transform'][0]) - self.assertAlmostEqual(np.diff(centr.lat).min(), meta['transform'][4]) + self.assertAlmostEqual(np.diff(centr.lat).max(), meta['transform'][4]) self.assertEqual(geometry.crs, centr.geometry.crs) def test_append_equal_pass(self): - """ Append raster """ + """Append raster""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_bis = Centroids() - centr_bis.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_bis.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_bis.append(centr_ras) self.assertAlmostEqual(centr_bis.meta['crs'], DEF_CRS) self.assertAlmostEqual(centr_bis.meta['transform'].c, -69.33714959699981) @@ -511,11 +504,11 @@ def test_append_equal_pass(self): self.assertEqual(centr_bis.meta['width'], 50) def test_append_diff_pass(self): - """ Append raster """ + """Append raster""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_bis = Centroids() - centr_bis.set_raster_file(HAZ_DEMO_FL, window= Window(51, 61, 10, 10)) + centr_bis.set_raster_file(HAZ_DEMO_FL, window=Window(51, 61, 10, 10)) centr_bis.append(centr_ras) self.assertAlmostEqual(centr_bis.meta['crs'], DEF_CRS) self.assertAlmostEqual(centr_bis.meta['transform'].c, -69.33714959699981) @@ -528,11 +521,11 @@ def test_append_diff_pass(self): self.assertEqual(centr_bis.meta['width'], 61) def test_equal_pass(self): - """ Test equal """ + """Test equal""" centr_ras = Centroids() - centr_ras.set_raster_file(HAZ_DEMO_FL, window= Window(0, 0, 50, 60)) + centr_ras.set_raster_file(HAZ_DEMO_FL, window=Window(0, 0, 50, 60)) centr_bis = Centroids() - centr_bis.set_raster_file(HAZ_DEMO_FL, window= Window(51, 61, 10, 10)) + centr_bis.set_raster_file(HAZ_DEMO_FL, window=Window(51, 61, 10, 10)) self.assertFalse(centr_ras.equal(centr_bis)) self.assertFalse(centr_bis.equal(centr_ras)) self.assertTrue(centr_ras.equal(centr_ras)) @@ -540,10 +533,10 @@ def test_equal_pass(self): class TestCentroids(unittest.TestCase): - """ Test Centroids class """ + """Test Centroids class""" def test_centroids_check_pass(self): - """ Test vector data in Centroids """ + """Test vector data in Centroids""" data_vec = TestVector.data_vector() centr = Centroids() centr.lat, centr.lon, centr.geometry = data_vec @@ -597,11 +590,11 @@ def test_centroids_check_pass(self): centr.check() class TestReader(unittest.TestCase): - """ Test Centroids setter vector and raster methods """ + """Test Centroids setter vector and raster methods""" def test_set_vector_file_wrong_fail(self): - """ Test set_vector_file with wrong centroids """ - shp_file = shapereader.natural_earth(resolution='110m', \ - category='cultural', name='populated_places_simple') + """Test set_vector_file with wrong centroids""" + shp_file = shapereader.natural_earth(resolution='110m', category='cultural', + name='populated_places_simple') centr = Centroids() inten = centr.set_vector_file(shp_file, ['pop_min', 'pop_max']) @@ -621,13 +614,13 @@ def test_set_vector_file_wrong_fail(self): self.assertEqual(inten[1, 0], 832) self.assertEqual(inten[1, -1], 7206000) - shp_file = shapereader.natural_earth(resolution='10m', \ - category='cultural', name='populated_places_simple') + shp_file = shapereader.natural_earth(resolution='10m', category='cultural', + name='populated_places_simple') with self.assertRaises(ValueError): centr.set_vector_file(shp_file, ['pop_min', 'pop_max']) def test_set_raster_file_wrong_fail(self): - """ Test set_raster_file with wrong centroids """ + """Test set_raster_file with wrong centroids""" centr = Centroids() inten_ras = centr.set_raster_file(HAZ_DEMO_FL, window=Window(10, 20, 50, 60)) self.assertAlmostEqual(centr.meta['crs'], DEF_CRS) @@ -639,14 +632,14 @@ def test_set_raster_file_wrong_fail(self): self.assertAlmostEqual(centr.meta['transform'].e, -0.009000000000000341) self.assertEqual(centr.meta['height'], 60) self.assertEqual(centr.meta['width'], 50) - self.assertEqual(inten_ras.shape, (1, 60*50)) + self.assertEqual(inten_ras.shape, (1, 60 * 50)) self.assertAlmostEqual(inten_ras.reshape((60, 50)).tocsr()[25, 12], 0.056825936) with self.assertRaises(ValueError): centr.set_raster_file(HAZ_DEMO_FL, window=Window(10, 20, 52, 60)) def test_write_read_raster_h5(self): - """ Write and read hdf5 format """ + """Write and read hdf5 format""" file_name = os.path.join(DATA_DIR, 'test_centr.h5') centr = Centroids() @@ -687,9 +680,9 @@ def test_write_read_points_h5(self): self.assertTrue(equal_crs(centr_read.crs, centr.crs)) class TestCentroidsFuncs(unittest.TestCase): - """ Test Centroids methods """ + """Test Centroids methods""" def test_select_pass(self): - """ Test set_vector """ + """Test set_vector""" centr = Centroids() centr.set_lat_lon(VEC_LAT, VEC_LON) @@ -702,7 +695,7 @@ def test_select_pass(self): self.assertEqual(fil_centr.lat[1], VEC_LAT[200]) self.assertEqual(fil_centr.lon[0], VEC_LON[100]) self.assertEqual(fil_centr.lon[1], VEC_LON[200]) - self.assertTrue(np.array_equal(fil_centr.region_id, np.ones(2)*10)) + self.assertTrue(np.array_equal(fil_centr.region_id, np.ones(2) * 10)) # Execute Tests if __name__ == "__main__": diff --git a/climada/hazard/drought.py b/climada/hazard/drought.py index de0729180a..d0117e507d 100644 --- a/climada/hazard/drought.py +++ b/climada/hazard/drought.py @@ -65,7 +65,7 @@ HAZ_TYPE = 'DR' -""" Hazard type acronym Drought """ +"""Hazard type acronym Drought""" @@ -80,7 +80,7 @@ class Drought(Hazard): """Name of the variables that aren't need to compute the impact.""" def __init__(self): - """Empty constructor. """ + """Empty constructor.""" Hazard.__init__(self, HAZ_TYPE) # Hazard.__init__(self) #self.file_url = SPEI_FILE_URL @@ -143,20 +143,18 @@ def __read_indices_spei(self, dataset): lat_total = dataset.lat.data lon_total = dataset.lon.data - index_lon = np.where(np.logical_and(lon_total >= self.lonmin, - lon_total <= self.lonmax))[0] - index_lat = np.where(np.logical_and(lat_total >= self.latmin, - lat_total <= self.latmax))[0] + index_lon = np.where((lon_total >= self.lonmin) & (lon_total <= self.lonmax))[0] + index_lat = np.where((lat_total >= self.latmin) & (lat_total <= self.latmax))[0] - lat_vector = dataset.lat[index_lat[0]:index_lat[len(index_lat)-1]].data - lon_vector = dataset.lon[index_lon[0]:index_lon[len(index_lon)-1]].data + lat_vector = dataset.lat[index_lat[0]:index_lat[len(index_lat) - 1]].data + lon_vector = dataset.lon[index_lon[0]:index_lon[len(index_lon) - 1]].data self.time_vector = dataset.time.data self.lat_vector = lat_vector self.lon_vector = lon_vector self.timeforname = self.time_vector - spei_matrix = dataset.spei[:, index_lat[0]:index_lat[len(index_lat)-1], - index_lon[0]:index_lon[len(index_lon)-1]].data + spei_matrix = dataset.spei[:, index_lat[0]:index_lat[len(index_lat) - 1], + index_lon[0]:index_lon[len(index_lon) - 1]].data return spei_matrix @@ -174,28 +172,28 @@ def setup(self): if self.file_path == os.path.join(SPEI_FILE_DIR, SPEI_FILE_NAME): try: - path_dwl = download_file(SPEI_FILE_URL + '/'+ SPEI_FILE_NAME) + path_dwl = download_file(SPEI_FILE_URL + '/' + SPEI_FILE_NAME) try: os.rename(path_dwl, self.file_path) except: - raise FileNotFoundError('The file ' + str(path_dwl)\ - + ' could not be moved to ' + str(os.path.dirname(self.file_path))) + raise FileNotFoundError('The file ' + str(path_dwl) + + ' could not be moved to ' + + str(os.path.dirname(self.file_path))) except: - raise FileExistsError('The file ' + str(self.file_path)\ - + ' could not '\ - + 'be found. Please download the file '\ - + 'first or choose a different folder. '\ - + 'The data can be downloaded from '\ - + SPEI_FILE_URL) + raise FileExistsError('The file ' + str(self.file_path) + ' could not ' + + 'be found. Please download the file ' + + 'first or choose a different folder. ' + + 'The data can be downloaded from ' + + SPEI_FILE_URL) LOGGER.debug('Importing %s', str(SPEI_FILE_NAME)) dataset = xr.open_dataset(self.file_path) except: - LOGGER.error('Importing the SPEI data file failed. ' \ + LOGGER.error('Importing the SPEI data file failed. ' 'Operation aborted.') raise @@ -209,7 +207,7 @@ def setup(self): def __traslate_matrix(self, spei_3d): - """ return hazard intensity as a simple threshold on the SPEI values + """return hazard intensity as a simple threshold on the SPEI values Parameters: see read_indices_spei, just call before Returns: matrix sparse.csr_matrix @@ -226,7 +224,7 @@ def __traslate_matrix(self, spei_3d): one_event_1d = spei_3d[i, :, :] - # get rid of nan's + # get rid of nan's nan_pos = np.isnan(one_event_1d) one_event_1d[nan_pos] = 0 @@ -242,18 +240,18 @@ def __traslate_matrix(self, spei_3d): def hazard_def(self, intensity_matrix): - """ return hazard set + """return hazard set Parameters: see intensity_from_spei Returns: Drought, full hazard set - check using new_haz.check() """ + check using new_haz.check()""" if self.intensity_definition == 2: HAZ_TYPE = 'DR_sumthr' - self.tag.haz_type = 'DR_sumthr' + self.tag.haz_type = HAZ_TYPE elif self.intensity_definition == 3: HAZ_TYPE = 'DR_sum' - self.tag.haz_type = 'DR_sum' + self.tag.haz_type = HAZ_TYPE # self.tag = TagHazard(HAZ_TYPE, 'TEST') @@ -275,10 +273,10 @@ def hazard_def(self, intensity_matrix): self.centroids.set_lat_lon(lat_1d, lon_1d) - self.event_id = np.arange(1, self.n_years+1, 1) + self.event_id = np.arange(1, self.n_years + 1, 1) # frequency set when all eventsavailable #self.frequency = np.array([1]) - #per default equal to event_id + # per default equal to event_id name_list = [] time = pd.to_datetime(self.timeforname) @@ -287,13 +285,13 @@ def hazard_def(self, intensity_matrix): name_list.append(str(time[i].year)) self.event_name = name_list - self.frequency = np.ones(self.n_years)/self.n_years + self.frequency = np.ones(self.n_years) / self.n_years self.fraction = self.intensity.copy() self.fraction = self.intensity.copy().tocsr() self.fraction.data.fill(1) - self.date = np.arange(1, self.n_years+1, 1) - #new_haz.orig = + self.date = np.arange(1, self.n_years + 1, 1) + # new_haz.orig = self.check() return self @@ -315,7 +313,7 @@ def __get_intensity_from_2d(self, spei_2d, intensity_definition=1): #first_month = time[0].month - #index_offset to get index of january of first year considered + # index_offset to get index of january of first year considered index_offset = 12 - time[0].month + 1 @@ -324,13 +322,13 @@ def __get_intensity_from_2d(self, spei_2d, intensity_definition=1): index_offset += 12 - last_year = time[len(time)-1].year + last_year = time[len(time) - 1].year - if time[len(time)-1].month < 9: + if time[len(time) - 1].month < 9: last_year -= 1 - n_years = last_year - first_year + 1 # the first year not counted + n_years = last_year - first_year + 1 # the first year not counted #years_vector = np.arange(first_year, last_year) self.date = np.arange(first_year, last_year) self.n_years = n_years @@ -342,20 +340,20 @@ def __get_intensity_from_2d(self, spei_2d, intensity_definition=1): date_end_matrix = np.zeros((n_years, spei_2d.shape[1])) - time = time[index_offset - 3: index_offset + 12*n_years - 3] + time = time[index_offset - 3: index_offset + 12 * n_years - 3] self.time_vector = self.time_vector[index_offset - 3: index_offset + - 12*n_years - 3] + 12 * n_years - 3] for pixel in range(spei_2d.shape[1]): array_time_centroid = spei_2d[index_offset - 3: index_offset + - 12*n_years - 3, pixel] + 12 * n_years - 3, pixel] list_events = self.__create_list(array_time_centroid) - [intmin, intsum, intsumthr, start, end] = self.__read_list\ - (list_events, np.arange(first_year, last_year), first_year) + [intmin, intsum, intsumthr, start, end] = self.__read_list( + list_events, np.arange(first_year, last_year), first_year) intensity_min_matrix[:, pixel] = intmin intensity_sum_matrix[:, pixel] = intsum @@ -391,9 +389,9 @@ def __create_list(self, array_time_in_centroid): end_time = [] list_events_info = list() - #create a list with every event exeeding the threshold + # create a list with every event exeeding the threshold for time_idx in range(len(array_time_in_centroid)): - #for time_idx in enumerate(array_time_in_centroid): + # for time_idx in enumerate(array_time_in_centroid): if array_time_in_centroid[time_idx] == 0: @@ -481,13 +479,13 @@ def __read_list(self, list_events_info, years_vector, first_year): year_offset = year_start return intensity_min_array, intensity_sum_array, \ - intensity_sum_thr_array, date_start_array, date_end_array + intensity_sum_thr_array, date_start_array, date_end_array def plot_intensity_drought(self, event=None): """plot drought intensity""" - #limits of the plots and colormap settings + # limits of the plots and colormap settings if self.intensity_definition == 1: minimum = -4 colourmap = 'afmhot' @@ -499,7 +497,7 @@ def plot_intensity_drought(self, event=None): colourmap = 'gist_heat' # create boundaries between minimum and maximum - boundaries = np.arange(minimum, -1+0.02, 0.01) + boundaries = np.arange(minimum, -1 + 0.02, 0.01) # create list of colors from colormap list_colours = mpl.cm.get_cmap(colourmap, len(boundaries)) @@ -507,18 +505,18 @@ def plot_intensity_drought(self, event=None): if self.intensity_definition == 2: colors = list(list_colours(np.arange(len(boundaries)))) else: - colors = list(list_colours(np.arange(len(boundaries)-len(index_thr)))) - #set all values above the threshold to white + colors = list(list_colours(np.arange(len(boundaries) - len(index_thr)))) + # set all values above the threshold to white colors.extend(["white" for x in range(len(index_thr))]) - #define colourmap + # define colourmap cmap = mpl.colors.ListedColormap(colors[1:], "") # set over-shoot to last color of list - cmap.set_over(colors[len(colors)-1]) + cmap.set_over(colors[len(colors) - 1]) if self.intensity_definition == 2: self.plot_intensity(event=event, cmap=cmap, vmin=minimum, vmax=0.01) else: - self.plot_intensity(event=event, cmap=cmap, vmin=minimum, vmax=-1+0.01) + self.plot_intensity(event=event, cmap=cmap, vmin=minimum, vmax=-1 + 0.01) @@ -546,13 +544,15 @@ def post_processing(self, date): def plot_start_end_date(self, event=None): """plot start and end date of the chosen event""" - startyear = str_to_date(str(int(event)-1)+'-09-15') - startdate = str_to_date(str(int(event)-1)+'-10-01') - enddate = str_to_date(str(int(event)+1)+'-01-01') + startyear = str_to_date(str(int(event) - 1) + '-09-15') + startdate = str_to_date(str(int(event) - 1) + '-10-01') + enddate = str_to_date(str(int(event) + 1) + '-01-01') - dates = np.arange(np.ceil(startdate/100)*100, np.ceil(startdate/100)*100+400, 100) + dates = np.arange(np.ceil(startdate / 100) * 100, + np.ceil(startdate / 100) * 100 + 400, + 100) list_dates = list() - for i in range(len(dates)): + for i in range(len(dates)): list_dates.append(date_to_str(dates.astype(np.int64)[i])) colourmap = 'plasma' @@ -562,21 +562,21 @@ def plot_start_end_date(self, event=None): index_thr = np.where(boundaries < startdate)[0] colors = ["white" for x in range(len(index_thr))] colors.extend(list(cmap_reds(np.arange(len(boundaries) - len(index_thr))))) - #define colourmap + # define colourmap cmap = mpl.colors.ListedColormap(colors[1:], "") # set over-color to last color of list cmap.set_over(colors[len(colors) - 1]) - #Plot Start + # Plot Start self.intensity = sparse.csr_matrix(self.date_start) - self.plot_intensity(event=event, cmap=cmap, vmin=startdate, \ + self.plot_intensity(event=event, cmap=cmap, vmin=startdate, vmax=enddate, snap="true") plt.ylabel('Date') plt.yticks(dates, list_dates) - #Plot End + # Plot End self.intensity = sparse.csr_matrix(self.date_end) - self.plot_intensity(event=event, cmap=cmap, vmin=startdate,\ + self.plot_intensity(event=event, cmap=cmap, vmin=startdate, vmax=enddate, snap="true") plt.ylabel('Date') plt.yticks(dates, list_dates) diff --git a/climada/hazard/emulator/__init__.py b/climada/hazard/emulator/__init__.py new file mode 100644 index 0000000000..da9f43bafb --- /dev/null +++ b/climada/hazard/emulator/__init__.py @@ -0,0 +1,16 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . +""" diff --git a/climada/hazard/emulator/const.py b/climada/hazard/emulator/const.py new file mode 100644 index 0000000000..5f589030ea --- /dev/null +++ b/climada/hazard/emulator/const.py @@ -0,0 +1,148 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Constants used by the hazard event emulator. +""" + +TC_BASIN_GEOM = { + # Eastern Pacific Basin + 'EP': [ + [-180.0, -75.0, 0.0, 9.0], + [-180.0, -83.5, 9.0, 15.0], + [-180.0, -92.0, 15.0, 18.0], + [-180.0, -99.9, 18.0, 60.0]], + 'EPW': [[-180.0, -135.0, 0.0, 60.0]], + 'EPE': [ + [-135.0, -75.0, 0.0, 9.0], + [-135.0, -83.5, 9.0, 15.0], + [-135.0, -92.0, 15.0, 18.0], + [-135.0, -99.9, 18.0, 60.0]], + + # "Global Basin" + 'GB': [[-179.9, 180.0, -50.0, 60.0]], + + # North Atlantic Basin + 'NA': [ + [-99.0, 13.0, 18.0, 60.0], + [-91.0, 13.0, 15.0, 18.0], + [-83.5, 13.0, 9.0, 15.0], + [-78.0, 13.0, 0.0, 9.0]], + 'NAN': [[-99.0, 13.0, 31.0, 60.0]], + 'NAS': [ + [-99.0, 13.0, 18.0, 31.0], + [-91.5, 13.0, 15.0, 18.0], + [-83.5, 13.0, 9.0, 15.0], + [-78.0, 13.0, 0.0, 9.0]], + + # Northern Indian Basin + 'NI': [[37.0, 99.0, 0.0, 30.0]], + 'NIW': [[37.0, 78.0, 0.0, 30.0]], + 'NIE': [[78.0, 99.0, 0.0, 30.0]], + + # Southern Atlantic + 'SA': [[-65.0, 20.0, -60.0, 0.0]], + + # Southern Indian Basin + 'SI': [[20.0, 135.0, -50.0, 0.0]], + 'SIW': [[20.0, 75.0, -50.0, 0.0]], + 'SIE': [[75.0, 135.0, -50.0, 0.0]], + + # Southern Pacific Basin + 'SP': [ + [135.0, 180.01, -50.0, 0.0], + [-180.0, -68.0, -50.0, 0.0]], + 'SPW': [[135.0, 172.0, -50.0, 0.0]], + 'SPE': [ + [172.0, 180.01, -50.0, 0.0], + [-180.0, -68.0, -50.0, 0.0]], + + # Western Pacific Basin + 'WP': [[99.0, 180.0, 0.0, 60.0]], + 'WPN': [[99.0, 180.0, 20.0, 60.0]], + 'WPS': [[99.0, 180.0, 0.0, 20.0]], +} +"""Boundaries of TC (sub-)basins (lon_min, lon_max, lat_min, lat_max)""" + +TC_BASIN_GEOM_SIMPL = { + # Eastern Pacific Basin + 'EP': [[-180.0, -75.0, 0.0, 60.0]], + 'EPW': [[-180.0, -135.0, 0.0, 60.0]], + 'EPE': [[-135.0, -75.0, 0.0, 60.0]], + + # North Atlantic Basin + 'NA': [[-105.0, -30.0, 0.0, 60.0]], + 'NAN': [[-105.0, -30.0, 31.0, 60.0]], + 'NAS': [[-105.0, -30.0, 0.0, 31.0]], + + # Northern Indian Basin + 'NI': [[37.0, 99.0, 0.0, 35.0]], + 'NIW': [[37.0, 78.0, 0.0, 35.0]], + 'NIE': [[78.0, 99.0, 0.0, 35.0]], + + # Southern Indian Basin + 'SI': [[20.0, 135.0, -50.0, 0.0]], + 'SIW': [[20.0, 75.0, -50.0, 0.0]], + 'SIE': [[75.0, 135.0, -50.0, 0.0]], + + # Southern Pacific Basin + 'SP': [[135.0, -60.0, -50.0, 0.0]], + 'SPW': [[135.0, 172.0, -50.0, 0.0]], + 'SPE': [[172.0, -60.0, -50.0, 0.0]], + + # Western Pacific Basin + 'WP': [[99.0, 180.0, 0.0, 60.0]], + 'WPN': [[99.0, 180.0, 20.0, 60.0]], + 'WPS': [[99.0, 180.0, 0.0, 20.0]], +} +"""Simplified boundaries of TC (sub-)basins (lon_min, lon_max, lat_min, lat_max)""" + +TC_SUBBASINS = { + 'EP': ['EPW', 'EPE'], + 'NA': ['NAN', 'NAS'], + 'NI': ['NIW', 'NIE'], + 'SA': ['SA'], + 'SI': ['SIW', 'SIE'], + 'SP': ['SPW', 'SPE'], + 'WP': ['WPN', 'WPS'], +} +"""Abbreviated names of TC subbasins for each basin""" + +TC_BASIN_SEASONS = { + 'WP': [5, 12], + 'NA': [6, 11], + 'NI': [5, 12], + 'EP': [7, 12], + 'SI': [11, 4], + 'SA': [1, 4], + 'SP': [11, 5], +} +"""Start/end months of hazard seasons in different basins""" + +TC_BASIN_NORM_PERIOD = { + 'WP': (1950, 2015), + 'NA': (1950, 2015), + 'EP': (1950, 2015), + 'NI': (1980, 2015), + 'SI': (1980, 2015), + 'SP': (1980, 2015), + 'SA': (1980, 2015), +} +"""TC basin-specific start/end year of norm period (according to IBTrACS data availability)""" + +PDO_SEASON = [11, 3] +"""Start/end months of PDO activity""" diff --git a/climada/hazard/emulator/emulator.py b/climada/hazard/emulator/emulator.py new file mode 100644 index 0000000000..fde5141a92 --- /dev/null +++ b/climada/hazard/emulator/emulator.py @@ -0,0 +1,310 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Hazard event emulator. +""" + +import logging +import sys + +import numpy as np +import pandas as pd + +import climada.hazard.emulator.const as const +import climada.hazard.emulator.stats as stats +import climada.hazard.emulator.random as random + +LOGGER = logging.getLogger(__name__) + +class HazardEmulator(): + """Draw samples for a time period driven by climate forcing + + Draw samples from the given pool of hazard events while making sure that the frequency and + intensity are as predicted according to given climate indices. + """ + explaineds = ['intensity_mean', 'eventcount'] + + def __init__(self, haz_events, haz_events_obs, region, freq_norm, pool=None): + """Initialize HazardEmulator + + Parameters + ---------- + haz_events : DataFrame + Output of `stats.haz_max_events`. + haz_events_obs : DataFrame + Observed events for normalization. Output of `stats.haz_max_events`. + region : HazRegion object + The geographical region for which to run emulations. + freq_norm : DataFrame { year, freq } + Information about the relative surplus of events in `tracks`, i.e., if `freq_norm` + specifies the value 0.2 in some year, then it is assumed that the number of events + given for that year is 5 times as large as it is predicted to be. Usually, the value + will be smaller than 1 because the event set should be a good representation of TC + distribution, but this is not necessary. + pool : EventPool object, optional + If omitted, draws are made from the events that are used to calibrate the emulator. + """ + self.pool = EventPool(haz_events) if pool is None else pool + self.region = region + + haz_stats = stats.seasonal_statistics(haz_events, region.season) + haz_stats_obs = stats.seasonal_statistics(haz_events_obs, region.season) + self.stats = stats.normalize_seasonal_statistics(haz_stats, haz_stats_obs, freq_norm) + + self.stats_pred = None + self.fit_info = None + self.ci_cols = [] + + norm_period = [haz_events_obs['year'].min(), haz_events_obs['year'].max()] + idx = self.stats.index[(self.stats['year'] >= norm_period[0]) \ + & (self.stats['year'] <= norm_period[1])] + norm_mean = self.stats.loc[idx, "intensity_mean_obs"].mean() + self.pool.init_drop(norm_period, norm_mean) + + + def calibrate_statistics(self, climate_indices): + """Statistically fit hazard data to given climate indices + + The internal statistics are truncated to fit the temporal range of the climate indices. + + Parameters + ---------- + climate_indices : list of DataFrames { year, month, ... } + Yearly or monthly time series of GMT, ESOI etc. + """ + if len(self.ci_cols) > 0: + self.stats = self.stats.drop(labels=self.ci_cols, axis=1) + + self.ci_cols = [] + for cidx in climate_indices: + ci_name = cidx.columns.values.tolist() + ci_name.remove("year") + ci_name.remove("month") + self.ci_cols += ci_name + avg_season = const.PDO_SEASON if "pdo" in ci_name else self.region.season + avg = stats.seasonal_average(cidx, avg_season) + self.stats = pd.merge(self.stats, avg, on="year", how="inner", sort=True) + self.stats = self.stats.dropna(axis=0, how="any", subset=self.explaineds + self.ci_cols) + + self.fit_info = {} + for explained in self.explaineds: + self.fit_info[explained] = stats.fit_data( + self.stats, explained, self.ci_cols, poisson=(explained == 'eventcount')) + + + def predict_statistics(self, climate_indices=None): + """Predict hypothetical hazard statistics according to climate indices + + The statistical fit from `calibrate_statistics` is used to predict the frequency and + intensity of hazard events. The standard deviation of yearly residuals is used to define + the yearly acceptable deviation of sample intensity. + + Without calibration, the prediction is done according to the (bias-corrected) within-year + statistics of the event pool. In this case, the within-year standard deviation of intensity + is taken as the acceptable deviation of samples for that year. + + Parameters + ---------- + climate_indices : list of DataFrames { year, month, ... } + Yearly or monthly time series of GMT, ESOI etc. including at least + those passed to `calibrate_statistics`. + If omitted, and if `calibrate_statistics` has been called before, + the climate indices from calibration are reused for prediction. + Otherwise, the internal (within-year) statistics of the data set + are used to predict frequency and intensity. + """ + reuse_indices = False + if not climate_indices: + reuse_indices = True + elif len(climate_indices) > 0 and len(self.ci_cols) == 0: + self.calibrate_statistics(climate_indices) + reuse_indices = True + + if len(self.ci_cols) == 0: + LOGGER.info("Predicting statistics without climate index predictor...") + self.stats_pred = self.stats[['year', 'intensity_mean', 'eventcount']] + self.stats_pred["intensity_mean_residuals"] = self.stats["intensity_std"] + self.stats_pred["events_rediduals"] = 0 + elif reuse_indices: + LOGGER.info("Predicting statistics with climate indices from calibration...") + self.stats_pred = self.stats[['year'] + self.ci_cols] + for explained in self.explaineds: + sm_results = self.fit_info[explained][-1] + self.stats_pred[explained] = sm_results.fittedvalues + self.stats_pred[f"{explained}_residuals"] = sm_results.resid + else: + LOGGER.info("Predicting statistics with new climate index time series...") + ci_avg = None + for cidx in climate_indices: + ci_name = cidx.columns.values.tolist() + ci_name.remove("year") + ci_name.remove("month") + avg_season = const.PDO_SEASON if "pdo" in ci_name else self.region.season + avg = stats.seasonal_average(cidx, avg_season) + if ci_avg is None: + ci_avg = avg + else: + ci_avg = pd.merge(ci_avg, avg, on="year", how="inner") + self.stats_pred = ci_avg[["year"] + self.ci_cols] + ci_data = self.stats_pred[self.ci_cols] + ci_data['const'] = 1.0 + for explained in self.explaineds: + sm_results = self.fit_info[explained][-1] + explanatory = sm_results.params.index.tolist() + haz_stats_pred = sm_results.predict(ci_data[explanatory]) + self.stats_pred[explained] = haz_stats_pred + # use standard deviation of calibration residuals as "residuals": + self.stats_pred[f"{explained}_residuals"] = float(sm_results.resid.std()) + + + def draw_realizations(self, nrealizations, period): + """Draw samples for given time period according to calibration + + Draws for a specific year in the given period are not necessarily restricted to events in + the pool that are explicitly assigned to that year because the pool might be too small to + allow for draws of the expected sample size and mean intensity. + + Parameters + ---------- + nrealizations : int + Number of samples to draw. + period : pair of ints [minyear, maxyear] + Period for which to make draws. + + Returns + ------- + draws : list of DataFrames, length `nrealizations` + Each entry is a sample for the whole period, given as a DataFrame + with columns as in `self.pool.events`. The `year` column is set to + the respective year and columns for the driving climate indices are + added for reference. + """ + if self.stats_pred is None: + raise Exception("Run `predict_statistics` before making draws!") + + LOGGER.info("Drawing %d realizations for period (%d, %d)", + nrealizations, period[0], period[1]) + year_draws = [] + for year in range(period[0], period[1] + 1): + sys.stdout.write(f"\r{period[0]} ... {year} ... {period[1]}") + sys.stdout.flush() + + year_idx = self.stats_pred.index[self.stats_pred['year'] == year] + freq_poisson = self.stats_pred.loc[year_idx, 'eventcount'].values[0] + intensity_mean = self.stats_pred.loc[year_idx, 'intensity_mean'].values[0] + intensity_std = self.stats_pred.loc[year_idx, 'intensity_mean_residuals'].values[0] + intensity_std = np.clip(np.abs(intensity_std), 0.5, 10) + draws = self.pool.draw_realizations(nrealizations, freq_poisson, + intensity_mean, intensity_std) + + for real_id, draw in enumerate(draws): + draw['year'] = year + draw['real_id'] = real_id + draws[real_id] = draw[['id', 'name', 'year', 'real_id']] + year_draws += draws + sys.stdout.write(f"\r{period[0]} ... {period[1]} ... {period[1]}\n") + return pd.concat(year_draws, ignore_index=True) + + +class EventPool(): + """Make draws from a hazard event pool according to given statistics + + The event pool might cover an arbitrary number of years and an arbitrary geographical region + since the time and geo information fields are ignored when making draws. + + No assumptions are made about where the statistics come from that are used in making the draw. + + Example + ------- + Let `haz_events` be a given dataset of all TC events making landfall in Belize between 1980 and + 2050, together with their respective maximum wind speeds on land. Assume that we expect (from + some other statistical model) 5 events of annual mean maximum wind speed 30 ± 10 m/s in the + year 2025. Then, we can draw 100 realizations of hypothetical 2025 TC event sets hitting + Belize with the following commands: + + >>> pool = EventPool(haz_events) + >>> draws = pool.draw_realizations(100, 5, 30, 10) + + The realization `draw[i]` might contain events from any year between 1980 and 2050, but the + size of the realization and the mean maximum wind speed will be according to the given + statistics. + """ + + def __init__(self, haz_events): + """Initialize instance of EventPool + + Parameters + ---------- + haz_events : DataFrame + Output of `stats.haz_max_events`. + """ + self.events = haz_events + self.drop = None + + + def init_drop(self, norm_period, norm_mean): + """Use a drop rule when making draws + + With the drop rule, a random choice of entries is dropped from events before the actual + drawing is done in order to speed up the process in case of data sets where the acceptable + mean is far from the input data mean. + + Parameters + ---------- + norm_period : pair of ints [minyear, maxyear] + Normalization period for which a specific mean intensity is expected. + norm_mean : float + Desired mean intensity of events in the given time period. + """ + self.drop = random.estimate_drop(self.events, 'year', 'intensity', + norm_period, norm_mean=norm_mean) + + + def draw_realizations(self, nrealizations, freq_poisson, intensity_mean, intensity_std): + """Draw samples from the event pool according to given statistics + + If `EventPool.init_drop` has been called before, the drop rule is applied. + + Parameters + ---------- + nrealizations : int + Number of samples to draw + freq_poisson : float + Expected sample size ("frequency", Poisson distributed). + intensity_mean : float + Expected sample mean intensity. + intensity_std : float + Acceptable deviation from `intensity_mean`. + + Returns + ------- + draws : list of DataFrames, length `nrealizations` + Each entry is a sample, given as a DataFrame with columns as in + `self.events`. + """ + draws = [] + while len(draws) < nrealizations: + intensity_accept = [intensity_mean - intensity_std, intensity_mean + intensity_std] + drawn = None + while drawn is None: + drawn = random.draw_poisson_events( + freq_poisson, self.events, 'intensity', intensity_accept, drop=self.drop) + intensity_accept[0] -= 1 + intensity_accept[1] += 1 + draws.append(self.events.loc[drawn]) + return draws diff --git a/climada/hazard/emulator/geo.py b/climada/hazard/emulator/geo.py new file mode 100644 index 0000000000..c4d310fcc0 --- /dev/null +++ b/climada/hazard/emulator/geo.py @@ -0,0 +1,194 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Georegion objects for the hazard event emulator. +""" + +import logging +import numpy as np +import geopandas as gpd +import shapely.ops +import shapely.vectorized +from shapely.geometry import Polygon + +from climada.hazard import Centroids +from climada.util.coordinates import get_country_geometries, NE_CRS +import climada.hazard.emulator.const as const + +LOGGER = logging.getLogger(__name__) + + +class HazRegion(): + """Hazard region for given geo information""" + + def __init__(self, extent=None, geometry=None, country=None, season=(1, 12)): + """Initialize HazRegion + + If several arguments are passed, the spatial intersection is taken. + + Parameters + ---------- + extent : tuple (lon_min, lon_max, lat_min, lat_max), optional + geometry : GeoPandas DataFrame, optional + country : str or list of str, optional + Countries are represented by their ISO 3166-1 alpha-3 identifiers. + The keyword "all" chooses all countries (i.e., global land areas). + season : pair of int, optional + First and last month of hazard-specific season within this region + """ + self._set_geometry(extent=extent, geometry=geometry, country=country) + self.geometry['const'] = 0 + self.shape = self.geometry.dissolve(by='const').geometry[0] + self.season = season + + + def _set_geometry(self, extent=None, geometry=None, country=None): + self.meta = {} + + if extent is not None: + self.meta['extent'] = extent + else: + extent = (-180, 180, -90, 90) + + lon_min, lon_max, lat_min, lat_max = extent + extent_poly = gpd.GeoSeries(Polygon([ + (lon_min, lat_min), (lon_min, lat_max), (lon_max, lat_max), (lon_max, lat_min) + ]), crs=NE_CRS) + self.geometry = gpd.GeoDataFrame({'geometry': extent_poly}, crs=NE_CRS) + + if country is not None: + self.meta['country'] = country + if country == "all": + country = None + elif not isinstance(country, list): + country = [country] + country_geom = get_country_geometries(country_names=country) + self.geometry = gpd.overlay(self.geometry, country_geom, how="intersection") + + if geometry is not None: + self.meta['geometry'] = repr(geometry) + self.geometry = gpd.overlay(self.geometry, geometry, how="intersection") + + + def centroids(self, latlon=None, res_as=360): + """Return centroids in this region + + Parameters + ---------- + latlon : pair (lat, lon), optional + Latitude and longitude of centroids. + If not given, values are taken from CLIMADA's base grid (see `res_as`). + res_as : int, optional + One of 150 or 360. When `latlon` is not given, choose coordinates from centroids + according to CLIMADA's base grid of given resolution in arc-seconds. Default: 360. + + Returns + ------- + centroids : climada.hazard.Centroids object + """ + if latlon is None: + centroids = Centroids.from_base_grid(res_as=res_as) + centroids.set_meta_to_lat_lon() + lat, lon = centroids.lat, centroids.lon + else: + lat, lon = latlon + centroids = Centroids() + centroids.set_lat_lon(lat, lon) + msk = shapely.vectorized.contains(self.shape, lon, lat) + centroids = centroids.select(sel_cen=msk) + centroids.id = np.arange(centroids.lon.shape[0]) + return centroids + + +class TCRegion(HazRegion): + """Hazard region with support for TC ocean basins""" + + def __init__(self, tc_basin=None, season=None, **kwargs): + """Initialize TCRegion + + The given geo information must be such that everything is contained in a single + TC ocean basin. + + Parameters + ---------- + tc_basin : str + TC (sub-)basin abbreviated name, such as "SIW". If not given, automatically determined + from geometry and basin bounds. + **kwargs : see HazRegion.__init__ + """ + self._set_geometry(**kwargs) + self.tc_basin = None + + if tc_basin is not None: + tc_basin_geom = get_tc_basin_geometry(tc_basin) + self.geometry = gpd.overlay(self.geometry, tc_basin_geom, how="intersection") + self.meta['tc_basin'] = tc_basin + self.tc_basin = tc_basin + + self.geometry['const'] = 0 + self.shape = self.geometry.dissolve(by='const').geometry[0] + + if self.tc_basin is None: + self._determine_tc_basin() + self.hemisphere = 'S' if const.TC_BASIN_GEOM_SIMPL[self.tc_basin][0][3] <= 0 else 'N' + + if season is None: + season = const.TC_BASIN_SEASONS[self.tc_basin[:2]] + self.season = season + + + def _determine_tc_basin(self): + for basin in const.TC_SUBBASINS: + basin_geom = get_tc_basin_geometry(basin) + if all(basin_geom.contains(self.shape)): + self.tc_basin = basin + break + if self.tc_basin is None: + raise ValueError("Region is not contained in a single basin!") + for tc_basin in const.TC_SUBBASINS[self.tc_basin]: + tc_basin_geom = get_tc_basin_geometry(tc_basin) + if all(tc_basin_geom.contains(self.shape)): + self.tc_basin = tc_basin + break + LOGGER.info("Automatically determined TC basin: %s", self.tc_basin) + + +def get_tc_basin_geometry(tc_basin): + """Get TC (sub-)basin geometry + + Parameters + ---------- + tc_basin : str + TC (sub-)basin abbreviated name, such as "SIW" or "NA". + + Returns + ------- + df : GeoPandas DataFrame + """ + polygons = [] + for rect in const.TC_BASIN_GEOM[tc_basin]: + lonmin, lonmax, latmin, latmax = rect + polygons.append(Polygon([ + (lonmin, latmin), + (lonmin, latmax), + (lonmax, latmax), + (lonmax, latmin) + ])) + polygons = shapely.ops.unary_union(polygons) + polygons = gpd.GeoSeries(polygons, crs=NE_CRS) + return gpd.GeoDataFrame({'geometry': polygons}, crs=NE_CRS) diff --git a/climada/hazard/emulator/random.py b/climada/hazard/emulator/random.py new file mode 100644 index 0000000000..2af0a24ec9 --- /dev/null +++ b/climada/hazard/emulator/random.py @@ -0,0 +1,163 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Randomized sampling tools for the hazard event emulator. +""" + +import logging +import numpy as np + +LOGGER = logging.getLogger(__name__) + +random_state = np.random.RandomState(123456789) +# random_state = np.random.default_rng(123456789) + +def estimate_drop(events, time_col, val_col, norm_period, norm_fact=None, norm_mean=None): + """Determine fraction of outlying events to be dropped + + If the mean intensity of events in the given time period `norm_period` + is far from the desired mean `norm_mean`, sampling from `events` will + usually yield draws whose mean is far from the desired mean, so that many + resamplings will be necessary in order to get an acceptable draw. + + Dropping events off the desired mean before sampling can reduce the + necessary number of samplings. + + This function estimates which portion of the events should be dropped. + + Parameters + ---------- + events : DataFrame + Each row describes one event. + The dataset should contain at least the columns `time_col` and `val_col`. + time_col : str + Name of time column in `events`. + val_col : str + Name of value column in `events`. + norm_period : pair of timestamps (e.g. floats or ints) + Normalization period for which a specific mean intensity is expected. + norm_mean : float + Desired mean intensity of events in the given time period. + norm_fact : float + Instead of `norm_mean`, the ratio between desired and observed + intensity in the given time period can be given. + + Returns + ------- + drop : pair [expr, frac] + Only events satisfying the pandas query expression `expr` should be + eligible for dropping. `frac` specifies the fraction of these events + that are to be dropped. + """ + all_idx = events.index[(events[time_col] >= norm_period[0]) + & (events[time_col] <= norm_period[1])] + all_mean = events.loc[all_idx, val_col].mean() + all_std = events.loc[all_idx, val_col].std() + + if norm_mean is None: + assert norm_fact is not None + norm_mean = all_mean * norm_fact + else: + norm_fact = norm_mean / all_mean + + drop_expr = f"{val_col} {'<' if norm_fact > 1 else '>'} {all_mean}" + drop_frac = 0.0 + if 0.98 < norm_fact < 1.02: + return drop_expr, drop_frac + + step_size = 0.5 * np.abs(norm_mean - all_mean) / all_std + drop_frac += step_size + diff = 0.1 + sub_mean = 0 + while drop_frac < 1.0 and np.abs(diff) > 0.025 and diff > 0: + drop_idx = events.query(drop_expr) \ + .sample(frac=drop_frac, random_state=random_state) \ + .index + events_sub = events.drop(drop_idx).reset_index(drop=True) + sub_idx = events_sub.index[(events_sub[time_col] >= norm_period[0]) + & (events_sub[time_col] <= norm_period[1])] + sub_mean = events_sub.loc[sub_idx, val_col].mean() + + diff = (norm_mean - sub_mean) / np.abs(norm_mean) + diff = diff if norm_fact > 1.0 else -diff + if np.abs(diff) > 0.025: + drop_frac += step_size + drop_frac = min(1.0, drop_frac) + LOGGER.info("Results of intensity normalization by subsampling:") + LOGGER.info("- drop %d%% of entries satisfying '%s'", int(100 * drop_frac), drop_expr) + LOGGER.info("- mean intensity of simulated events before dropping is %.4f", all_mean) + LOGGER.info("- mean intensity of simulated events after dropping is %.4f", sub_mean) + LOGGER.info("- mean intensity of observed events is %.4f", norm_mean) + + return drop_expr, drop_frac + + +def draw_poisson_events(poisson, events, val_col, val_accept, drop=None): + """Draw poisson distributed events with acceptable value statistics + + The size of the draw is poisson distributed. Redraws are made until the + draw mean is within the range specified by `val_accept`. + + If `drop` is specified, a random choice of entries is dropped from `events` + before the actual drawing is done in order to speed up the process in case + of data sets where the acceptable mean is far from the input data mean. + + Parameters + ---------- + poisson : float + Poisson parameter. + events : DataFrame + Each row describes one event. + The dataset should contain at least the column `val_col`. + val_col : str + Name of value column in `events`. + val_accept : pair of floats + Acceptable range of draw means. + drop : pair [expr, frac] or None + If given, only events satisfying the pandas query expression `expr` are dropped. + `frac` specifies the fraction of these events that is dropped. + + Returns + ------- + draw_idx : Series or None + Indices into `events`. + If no acceptable draw was among the first 10,000 attempts, the return value is None. + """ + fail_counts = 0 + if drop is not None: + drop_query, drop_frac = drop + while True: + events_sub = events + if drop is not None: + drop_idx = events.query(drop_query) \ + .sample(frac=drop_frac, random_state=random_state) \ + .index + events_sub = events.drop(drop_idx) + + draw_size = max(1, random_state.poisson(poisson, 1)[0]) + draw_inds = random_state.choice(events_sub.shape[0], draw_size, + replace=(events_sub.shape[0] < 1.2 * draw_size)) + draw_mean = events_sub[val_col].iloc[draw_inds].mean() + + + if val_accept[0] <= draw_mean <= val_accept[1]: + return events_sub.index[draw_inds] + + fail_counts += 1 + if fail_counts >= 10000: + return None diff --git a/climada/hazard/emulator/stats.py b/climada/hazard/emulator/stats.py new file mode 100644 index 0000000000..7246c72016 --- /dev/null +++ b/climada/hazard/emulator/stats.py @@ -0,0 +1,278 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Statistical tools for the hazard event emulator. +""" + +import datetime +import logging + +import numpy as np +import pandas as pd +import statsmodels.api as sm +import statsmodels.discrete.discrete_model as smd + +LOGGER = logging.getLogger(__name__) + +def seasonal_average(data, season): + """Compute seasonal average from monthly-time series. + + For seasons that are across newyear, the months after June are attributed to the following + year's season. For example: The 6-month season from November 1980 till April 1981 is attributed + to the year 1981. + + The two seasons that are truncated at the beginning/end of the dataset's time period are + discarded. When the input data is 1980-2010, the output data will be 1981-2010, where 2010 + corresponds to the 2009/2010 season and 1981 corresponds to the 1980/1981 season. + + Parameters + ---------- + data : DataFrame { year, month, ... } + All further columns will be averaged over. + season : pair of ints + Start/end month of season. + + Returns + ------- + averaged_data : DataFrame { year, ... } + Same format as input, but with month column removed. + """ + start, end = season + + if data['year'].unique().size == data.shape[0] and "month" in data.columns: + data = data.drop(labels=['month'], axis=1) + + if "month" not in data.columns: + return data.iloc[1:] if start > end else data + + if start > end: + msk = (data['month'] >= start) | (data['month'] <= end) + else: + msk = (data['month'] >= start) & (data['month'] <= end) + data = data[msk] + if start > end: + year_min, year_max = data['year'].min(), data['year'].max() + data['year'][data['month'] > 6] += 1 + data = data[(data['year'] > year_min) & (data['year'] <= year_max)] + data = data.reset_index(drop=True) + data = data.groupby('year').mean().reset_index() + return data.drop('month', 1) + + +def seasonal_statistics(events, season): + """Compute seasonal statistics from given hazard event data + + Parameters + ---------- + events : DataFrame { year, month, intensity, ... } + Events outside of the given season are ignored. + season : pair of ints + Start/end month of season. + + Returns + ------- + haz_stats : DataFrame { year, events, intensity_mean, intensity_std, intensity_max } + For seasons that are across newyear, this might cover one year less than the input data + since truncated seasons are discarded. + """ + events = events.reindex(columns=['year', 'month', 'eventcount', 'intensity']) + events['eventcount'] = 1 + year_min, year_max = events['year'].min(), events['year'].max() + sea_start, sea_end = season + if sea_start > sea_end: + events['year'][events['month'] > 6] += 1 + msk = (events['month'] >= sea_start) | (events['month'] <= sea_end) + else: + msk = (events['month'] >= sea_start) & (events['month'] <= sea_end) + events = events[msk].drop(labels=['month'], axis=1) + + def collapse(group): + new_cols = ['eventcount', 'intensity_mean', 'intensity_std', 'intensity_max'] + new_vals = [group['eventcount'].sum(), + group['intensity'].mean(), + group['intensity'].std(ddof=0), + group['intensity'].max()] + return pd.Series(new_vals, index=new_cols) + haz_stats = events.groupby(['year']).apply(collapse).reset_index() + + if sea_end < sea_start: + # drop first and last years as they are incomplete + haz_stats = haz_stats[(haz_stats['year'] > year_min) + & (haz_stats['year'] <= year_max)].reset_index(drop=True) + + return haz_stats + + +def haz_max_events(hazard, min_thresh=0): + """Table of max intensity events for given hazard + + Parameters + ---------- + hazard : climada.hazard.Hazard object + min_thresh : float + Minimum intensity for event to be registered. + + Returns + ------- + events : DataFrame { id, name, year, month, day, lat, lon, intensity } + The integer value in column `id` refers to the internal order of events in the given + `hazard` object. `lat`, `lon` and `intensity` specify location and intensity of the maximum + intensity registered. + """ + inten = hazard.intensity + if min_thresh == 0: + # this might require considerable amounts of memory + exp_hazards = inten.todense() >= min_thresh + else: + exp_hazards = (inten >= min_thresh).todense() + exp_hazards = np.where(np.any(exp_hazards, axis=1))[0] + LOGGER.info("Condensing %d hazards to %d max events ...", inten.shape[0], exp_hazards.size) + inten = inten[exp_hazards] + inten_max_ids = np.asarray(inten.argmax(axis=1)).ravel() + inten_max = inten[range(inten.shape[0]), inten_max_ids] + dates = hazard.date[exp_hazards] + dates = [datetime.date.fromordinal(d) for d in dates] + return pd.DataFrame({ + 'id': exp_hazards, + 'name': [hazard.event_name[s] for s in exp_hazards], + 'year': np.int64([d.year for d in dates]), + 'month': np.int64([d.month for d in dates]), + 'day': np.int64([d.day for d in dates]), + 'lat': hazard.centroids.lat[inten_max_ids], + 'lon': hazard.centroids.lon[inten_max_ids], + 'intensity': np.asarray(inten_max).ravel(), + }) + + +def normalize_seasonal_statistics(haz_stats, haz_stats_obs, freq_norm): + """Bias-corrected annual hazard statistics + + Parameters + ---------- + haz_stats : DataFrame { ... } + Output of `seasonal_statistics`. + haz_stats_obs : DataFrame { ... } + Output of `seasonal_statistics`. + freq_norm : DataFrame { year, freq } + Information about the relative surplus of hazard events per year, i.e., + if `freq_norm` specifies the value 0.2 in some year, then it is + assumed that the number of events given for that year is 5 times as + large as it is predicted to be. + + Returns + ------- + statistics : DataFrame { year, intensity_max, intensity_mean, eventcount, + intensity_max_obs, intensity_mean_obs, eventcount_obs } + Normalized and observed hazard statistics. + """ + norm_period = [haz_stats_obs['year'].min(), haz_stats_obs['year'].max()] + + # Merge observed into modelled statistics for comparison + haz_stats = pd.merge(haz_stats, haz_stats_obs, suffixes=('', '_obs'), + on="year", how="left", sort=True) + + # Normalize `eventcount` according to simulated frequency. + # In case of season across newyear, this normalizes by the year with most of + # hazard season, ignoring the fractional contribution from the year before. + haz_stats = pd.merge(haz_stats, freq_norm, on='year', how='left', sort=True) + haz_stats['eventcount'] *= haz_stats['freq'] + haz_stats = haz_stats.drop(labels=['freq'], axis=1) + + # Bias-correct intensity and frequency to observations in norm period + for col in ['eventcount', 'intensity_mean', 'intensity_std', 'intensity_max']: + idx = haz_stats.index[(haz_stats['year'] >= norm_period[0]) \ + & (haz_stats['year'] <= norm_period[1])] + col_data = haz_stats.loc[idx, col] + col_data_obs = haz_stats.loc[idx, f"{col}_obs"].fillna(0) + if col == 'eventcount': + fact = col_data_obs.sum() / col_data.sum() + else: + fact = col_data_obs.mean() / col_data.mean() + haz_stats[col] *= fact + return haz_stats + + +def fit_data(data, explained, explanatory, poisson=False): + """Fit a response variable (e.g. intensity) to a list of explanatory variables + + The fitting is run twice, restricting to the significant explanatory + variables in the second run. + + Parameters + ---------- + data : DataFrame { year, `explained`, `explanatory`, ... } + An intercept column is added automatically. + explained : str + Name of explained variable, e.g. 'intensity'. + explanatory : list of str + Names of explanatory variables, e.g. ['gmt','esoi']. + poisson : boolean + Optionally, use Poisson regression for fitting. + If False (default), uses ordinary least squares (OLS) regression. + + Returns + ------- + sm_results : pair of statsmodels Results object + Results for first and second run. + """ + d_explained = data[explained] + d_explanatory = data[explanatory] + + # for the first run, assume that all variables are significant + significant = explanatory + sm_results = [] + for _ in range(2): + # restrict to variables with significant relationship + d_explanatory = d_explanatory[significant] + + # add column for intercept + d_explanatory['const'] = 1.0 + + if poisson: + mod = smd.Poisson(d_explained, d_explanatory) + res = mod.fit(maxiter=100, disp=0, cov_type='HC1') + else: + mod = sm.OLS(d_explained, d_explanatory) + res = mod.fit(maxiter=100, disp=0, cov_type='HC1', use_t=True) + significant = fit_significant(res) + sm_results.append(res) + + return sm_results + + +def fit_significant(sm_results): + """List significant variables in `sm_results` + + Note: The last variable (usually intercept) is omitted! + """ + significant = [] + cols = sm_results.params.index.tolist() + for i, pval in enumerate(sm_results.pvalues[:-1]): + if pval <= 0.1: + significant.append(cols[i]) + return significant + + +def fit_significance(sm_results): + """Extract and visualize significance of model parameters""" + significance = ['***' if el <= 0.01 else \ + '**' if el <= 0.05 else \ + '*' if el <= 0.1 else \ + '-' for el in sm_results.pvalues[:-1]] + significance = dict(zip(fit_significant(sm_results), significance)) + return significance diff --git a/climada/hazard/emulator/test/__init__.py b/climada/hazard/emulator/test/__init__.py new file mode 100644 index 0000000000..04d6859b9f --- /dev/null +++ b/climada/hazard/emulator/test/__init__.py @@ -0,0 +1,20 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License alon +with CLIMADA. If not, see . + +--- + +init test +""" diff --git a/climada/hazard/emulator/test/test_emulator.py b/climada/hazard/emulator/test/test_emulator.py new file mode 100644 index 0000000000..43c212ede6 --- /dev/null +++ b/climada/hazard/emulator/test/test_emulator.py @@ -0,0 +1,97 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test the hazard event emulator +""" + +import unittest + +import numpy as np +import pandas as pd + +from climada.hazard.emulator import emulator +from climada.hazard.emulator import geo + + +class TestEmulator(unittest.TestCase): + """Test the hazard event emulator""" + + def test_hazard_emulator(self): + """Test HazardEmulator class""" + events = pd.DataFrame({ + "id": [0, 1, 2, 3, 4, 5, 6, 7], + "name": [0, 1, 2, 3, 4, 5, 6, 7], + "year": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "month": [7, 6, 11, 6, 8, 8, 9, 10], + "intensity": [6, 1, 3, 2, 4, 0, 5, 7], + }) + events_obs = pd.DataFrame({ + "year": [2001, 2001, 2001, 2002, 2002, 2002], + "month": [7, 6, 8, 7, 7, 9], + "intensity": [3, 4, 4, 5, 3, 2], + }) + reg = geo.TCRegion(extent=[0, 1, 0, 1]) + freq = pd.DataFrame({ + "year": [2000, 2001, 2002], + "freq": [0.4, 0.5, 0.5], + }) + cidx = pd.DataFrame({ + "year": [1999, 2000, 2001, 2002, 2003], + "month": [7, 7, 7, 7, 7], + "gmt": [10, 11, 10, 14, 13], + }) + + emu = emulator.HazardEmulator(events, events_obs, reg, freq) + + # predict only within time horizon of calibration: + emu.predict_statistics([cidx]) + self.assertEqual(emu.stats_pred.shape[0], emu.stats.shape[0]) + + # predict within time horizon of index data: + emu.predict_statistics([cidx]) + self.assertEqual(emu.stats_pred.shape[0], cidx.shape[0]) + + draws = emu.draw_realizations(10, (1999, 2001)) + self.assertTrue(np.all(np.unique(draws['real_id']) == np.arange(10))) + self.assertTrue(np.all(np.unique(draws['year']) == np.arange(1999, 2002))) + + + def test_event_pool(self): + """Test EventPool class""" + events = pd.DataFrame({ + "year": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "month": [4, 6, 11, 3, 5, 8, 9, 10], + "intensity": [6, 1, 3, 2, 4, 0, 5, 7], + }) + pool = emulator.EventPool(events) + draws = pool.draw_realizations(10, 1.5, 3, 1) + self.assertEqual(len(draws), 10) + self.assertTrue(all(2 <= d['intensity'].mean() <= 4 for d in draws)) + self.assertTrue(all(1 <= d.shape[0] <= 5 for d in draws)) + + pool.drop = ("intensity < 3.0", 1.0) + draws = pool.draw_realizations(30, 1.5, 3, 1) + self.assertEqual(len(draws), 30) + self.assertTrue(all(np.all(d['intensity'] >= 3) for d in draws)) + + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestEmulator) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/emulator/test/test_geo.py b/climada/hazard/emulator/test/test_geo.py new file mode 100644 index 0000000000..592b6cfa4c --- /dev/null +++ b/climada/hazard/emulator/test/test_geo.py @@ -0,0 +1,118 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test HazRegion class and georegion functionalities +""" + +import unittest + +import numpy as np +from shapely.geometry import Point + +from climada.hazard.emulator import geo + + +class TestGeo(unittest.TestCase): + """Test georegion functionalities""" + + def test_init_hazregion(self): + """Test initialization of HazRegion class with different parameters""" + reg = geo.HazRegion(extent=[0, 1, 0, 1]) + self.assertTrue(reg.shape.contains(Point(0.1, 0.1))) + self.assertTrue(reg.shape.contains(Point(0.9, 0.1))) + self.assertTrue(reg.shape.contains(Point(0.1, 0.9))) + self.assertTrue(reg.shape.contains(Point(0.9, 0.9))) + self.assertFalse(reg.shape.contains(Point(-0.1, 0.1))) + self.assertFalse(reg.shape.contains(Point(0.5, 1.1))) + + # restrict region to land area + reg = geo.HazRegion(extent=[6, 11, 3, 6], country="all") + # point in Cameroon + self.assertTrue(reg.shape.contains(Point(10.7, 4))) + # point on Bioko (island) + self.assertTrue(reg.shape.contains(Point(8.7, 3.5))) + # point outside extent, but on land + self.assertFalse(reg.shape.contains(Point(10, 2.8))) + # point within extent, but in ocean + self.assertFalse(reg.shape.contains(Point(9.3, 3.2))) + + # Test for country Malta + reg = geo.HazRegion(extent=[14.3, 14.6, 35.85, 36], country="MLT") + # Test for capital Valetta + self.assertTrue(reg.shape.contains(Point(14.5167, 35.9))) + # Inside of extent, but outside of Malta + self.assertFalse(reg.shape.contains(Point(14.5, 35.95))) + # Inside Malta, but outside of extent + self.assertFalse(reg.shape.contains(Point(14.5, 35.83))) + + reg = geo.HazRegion(geometry=reg.geometry) + # same tests as for Malta + self.assertTrue(reg.shape.contains(Point(14.5167, 35.9))) + self.assertFalse(reg.shape.contains(Point(14.5, 35.95))) + self.assertFalse(reg.shape.contains(Point(14.5, 35.83))) + + + def test_hazregion_centroids(self): + """Test HazRegion.centroids method""" + reg = geo.HazRegion(country="MLT") + cen = reg.centroids() + self.assertEqual(cen.lat.size, 2) + self.assertAlmostEqual(cen.lat[0], 35.9) + self.assertAlmostEqual(cen.lat[1], 35.9) + self.assertAlmostEqual(cen.lon[0], 14.4) + self.assertAlmostEqual(cen.lon[1], 14.5) + + cen = reg.centroids(latlon=(np.array([35.9, 36.0]), np.array([14.4, 14.4]))) + self.assertEqual(cen.lat.size, 1) + self.assertAlmostEqual(cen.lat[0], 35.9) + self.assertAlmostEqual(cen.lon[0], 14.4) + + + def test_get_tc_basin_geometry(self): + """Test get_tc_basin_geometry""" + bas = geo.get_tc_basin_geometry("NA") + polygon = bas.geometry[0] + self.assertTrue(polygon.contains(Point(0.0, 1.0))) + self.assertTrue(polygon.contains(Point(-90.0, 16.0))) + self.assertFalse(polygon.contains(Point(-80.0, 1.0))) + self.assertFalse(polygon.contains(Point(0.0, -1.0))) + self.assertFalse(polygon.contains(Point(0.0, 61.0))) + + + def test_tc_region(self): + """Test TCRegion class""" + # automatically determine basin + reg = geo.TCRegion(extent=[0, 1, 0, 1]) + self.assertEqual(reg.hemisphere, "N") + self.assertEqual(reg.tc_basin, "NAS") + self.assertEqual(reg.season, [6, 11]) + reg = geo.TCRegion(extent=[70, 80, -30, -20]) + self.assertEqual(reg.hemisphere, "S") + self.assertEqual(reg.tc_basin, "SI") + + # init by given basin name + reg = geo.TCRegion(tc_basin="EP", season=[6, 12]) + self.assertEqual(reg.hemisphere, "N") + self.assertEqual(reg.tc_basin, "EP") + self.assertEqual(reg.season, [6, 12]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestGeo) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/emulator/test/test_random.py b/climada/hazard/emulator/test/test_random.py new file mode 100644 index 0000000000..f8b3530392 --- /dev/null +++ b/climada/hazard/emulator/test/test_random.py @@ -0,0 +1,72 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test random sampling functionalities +""" + +import unittest + +import numpy as np +import pandas as pd + +from climada.hazard.emulator import random + + +class TestRandom(unittest.TestCase): + """Test random sampling functionalities""" + + def test_estimate_drop(self): + """Test estimate_drop function""" + events = pd.DataFrame({ + "yr": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "dummy": [4, 6, 11, 3, 5, 8, 9, 10], + "inten": [6, 1, 3, 2, 4, 0, 5, 7], + }) + expr, frac = random.estimate_drop(events, "yr", "inten", + (2001, 2001), norm_mean=4) + self.assertEqual(expr, 'inten < 3.0') + self.assertLessEqual(frac, 1.0) + self.assertGreater(frac, 0.0) + + + def test_draw_poisson_events(self): + """Test draw_poisson_events function""" + events = pd.DataFrame({ + "dummy1": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "dummy2": [4, 6, 11, 3, 5, 8, 9, 10], + "height": [6, 1, 3, 2, 4, 0, 5, 7], + }) + + # reasonable draw: + draws = random.draw_poisson_events(1.5, events, "height", (2, 4)) + self.assertTrue(draws.size > 1) + + # impossible draw: + draws = random.draw_poisson_events(1.5, events, "height", (10, 12)) + self.assertTrue(draws is None) + + # drop all but entries 0 and 7: + draws = random.draw_poisson_events(2, events, "height", (5, 8), + drop=("height < 6", 1.0)) + self.assertEqual(np.count_nonzero(~np.isin(draws, [0, 7])), 0) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRandom) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/emulator/test/test_stats.py b/climada/hazard/emulator/test/test_stats.py new file mode 100644 index 0000000000..8c3f38f3fb --- /dev/null +++ b/climada/hazard/emulator/test/test_stats.py @@ -0,0 +1,164 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test statistical analysis functionalities +""" + +import unittest + +import numpy as np +import pandas as pd +import scipy.sparse as sp + +from climada.hazard import Hazard, Centroids +from climada.hazard.emulator import stats + + +class TestStats(unittest.TestCase): + """Test statistical analysis functionalities""" + + def test_seasonal_average(self): + """Test seasonal_average function""" + timed_data = pd.DataFrame({ + "year": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "month": [4, 6, 11, 3, 5, 8, 9, 10], + "value": [6, 1, 3, 2, 4, 0, 5, 7], + }) + data = stats.seasonal_average(timed_data, [4, 6]) + self.assertEqual(data.shape[0], 2) + self.assertEqual(data['year'][0], 2000) + self.assertEqual(data['year'][1], 2001) + self.assertEqual(data['value'][0], 3.5) + self.assertEqual(data['value'][1], 4) + + data = stats.seasonal_average(timed_data, [10, 4]) + self.assertEqual(data.shape[0], 1) + self.assertEqual(data['year'][0], 2001) + self.assertEqual(data['value'][0], 2.5) + + timed_data = pd.DataFrame({"year": [1990, 1991], "intensity": [0, 1]}) + data = stats.seasonal_average(timed_data, [5, 6]) + self.assertTrue(np.all(timed_data == data)) + + + def test_seasonal_statistics(self): + """Test seasonal_statistics function""" + events = pd.DataFrame({ + "year": [2000, 2000, 2000, 2001, 2001, 2002, 2002, 2002], + "month": [4, 6, 11, 3, 5, 8, 9, 10], + "intensity": [6, 1, 3, 2, 4, 0, 5, 7], + }) + data = stats.seasonal_statistics(events, [4, 6]) + self.assertEqual(data.shape[0], 2) + self.assertEqual(data['year'][0], 2000) + self.assertEqual(data['year'][1], 2001) + self.assertEqual(data['intensity_mean'][0], 3.5) + self.assertEqual(data['intensity_mean'][1], 4) + self.assertEqual(data['eventcount'][0], 2) + self.assertEqual(data['eventcount'][1], 1) + + data = stats.seasonal_statistics(events, [10, 4]) + self.assertEqual(data.shape[0], 1) + self.assertEqual(data['year'][0], 2001) + self.assertEqual(data['intensity_mean'][0], 2.5) + self.assertEqual(data['eventcount'][0], 2) + + + def test_norm_seas_stats(self): + """Test normalize_seasonal_statistics function""" + haz_stats = pd.DataFrame({ + "year": [1999, 2000, 2001, 2002], + "eventcount": [27, 25, 26, 25], + "intensity_mean": [4, 6, 5, 5], + "intensity_std": [1, 1, 1, 1], + "intensity_max": [12, 12, 13, 15], + }) + haz_stats_obs = pd.DataFrame({ + "year": [2000, 2001], + "eventcount": [12, 11], + "intensity_mean": [10, 12], + "intensity_std": [2, 1], + "intensity_max": [11, 14], + }) + freq = pd.DataFrame({ + "year": [1999, 2000, 2001, 2002], + "freq": [0.5, 0.4, 0.5, 0.5], + }) + + data = stats.normalize_seasonal_statistics(haz_stats, haz_stats_obs, freq) + self.assertSequenceEqual(data['year'].tolist(), haz_stats['year'].tolist()) + self.assertSequenceEqual(data['eventcount'].tolist(), [13.5, 10, 13, 12.5]) + self.assertSequenceEqual(data['intensity_mean'].tolist(), [8, 12, 10, 10]) + self.assertSequenceEqual(data['intensity_std'].tolist(), [1.5, 1.5, 1.5, 1.5]) + self.assertSequenceEqual(data['intensity_max'].tolist(), [12, 12, 13, 15]) + + + def test_haz_max_events(self): + """Test haz_max_events function""" + hazard = Hazard('TC') + hazard.centroids = Centroids() + hazard.centroids.set_lat_lon(np.array([1, 3, 5]), np.array([2, 4, 6])) + hazard.event_id = np.array([1, 2, 3, 4]) + hazard.event_name = ['ev1', 'ev2', 'ev3', 'ev4'] + hazard.date = np.array([1, 3, 5, 7]) + hazard.intensity = sp.csr_matrix( + [[0, 0, 4], [1, 0, 1], [43, 21, 0], [0, 53, 1]]) + data = stats.haz_max_events(hazard, min_thresh=18) + self.assertSequenceEqual(data['id'].tolist(), [2, 3]) + self.assertSequenceEqual(data['name'].tolist(), ["ev3", "ev4"]) + self.assertSequenceEqual(data['year'].tolist(), [1, 1]) + self.assertSequenceEqual(data['month'].tolist(), [1, 1]) + self.assertSequenceEqual(data['day'].tolist(), [5, 7]) + self.assertSequenceEqual(data['lat'].tolist(), [1, 3]) + self.assertSequenceEqual(data['lon'].tolist(), [2, 4]) + self.assertSequenceEqual(data['intensity'].tolist(), [43, 53]) + + + def test_fit_data(self): + """Test fit_data function""" + haz_stats = pd.DataFrame({ + "year": [1999, 2000, 2001, 2002], + "eventcount": [14, 12, 16, 11], + "intensity_mean": [9, 13, 11, 11], + "gmt": [4, 6, 5, 5], + "esoi": [1, -1, 1, -1], + "dummy": [0, 1, 2, 0], + }) + + sm_results = stats.fit_data(haz_stats, "intensity_mean", ["gmt", "esoi"]) + self.assertTrue("gmt" in sm_results[0].params) + self.assertTrue("esoi" in sm_results[0].params) + self.assertTrue("gmt" in sm_results[1].params) + self.assertFalse("esoi" in sm_results[1].params) + self.assertAlmostEqual(sm_results[1].params['gmt'], 2) + self.assertAlmostEqual(sm_results[1].params['const'], 1) + + sm_results = stats.fit_data(haz_stats, "eventcount", ["gmt", "esoi"], poisson=True) + self.assertTrue("gmt" in sm_results[0].params) + self.assertTrue("esoi" in sm_results[0].params) + self.assertTrue("gmt" in sm_results[1].params) + self.assertTrue("esoi" in sm_results[1].params) + self.assertAlmostEqual(sm_results[1].params['gmt'], 0.1, places=1) + self.assertAlmostEqual(sm_results[1].params['esoi'], 0.2, places=1) + self.assertAlmostEqual(sm_results[1].params['const'], 2, places=1) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestStats) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/flood.py b/climada/hazard/flood.py deleted file mode 100644 index 6c99b02e76..0000000000 --- a/climada/hazard/flood.py +++ /dev/null @@ -1,609 +0,0 @@ -""" -This file is part of CLIMADA. - -Copyright (C) 2017 CLIMADA contributors listed in AUTHORS. - -CLIMADA is free software: you can redistribute it and/or modify it under the -terms of the GNU Lesser General Public License as published by the Free -Software Foundation, version 3. - -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. - -You should have received a copy of the GNU Lesser General Public License along -with CLIMADA. If not, see . - ---- - -Define RiverFlood class. -""" - -__all__ = ['RiverFlood'] - -import logging -import os -import numpy as np -import scipy as sp -import xarray as xr -import pandas as pd -import math -import datetime as dt -from datetime import date -import geopandas as gpd -from climada.util.constants import NAT_REG_ID, GLB_CENTROIDS_NC -from climada.util.constants import HAZ_DEMO_FLDDPH, HAZ_DEMO_FLDFRC -from climada.util.interpolation import interpol_index -from scipy import sparse -from climada.hazard.base import Hazard -from climada.hazard.centroids import Centroids -from shapely.geometry import Point - -from climada.util.alpha_shape import alpha_shape - -LOGGER = logging.getLogger(__name__) - -HAZ_TYPE = 'RF' -""" Hazard type acronym RiverFlood""" - -RF_MODEL = ['ORCHIDEE', - 'H08', - 'LPJmL', - 'MPI-HM', - 'PCR-GLOBWB', - 'WaterGAP2', - 'CLM', - 'JULES-TUC' - 'JULES-UoE', - 'VIC', - 'VEGAS' - ] -CL_MODEL = ['gfdl-esm2m', - 'hadgem2-es', - 'ipsl-cm5a-lr', - 'miroc5', - 'wfdei', - 'gswp3', - 'princeton', - 'watch' - ] -SCENARIO = ['', - 'historical', - 'rcp26', - 'rcp60' - ] - -PROT_STD = 'flopros' - - -class RiverFlood(Hazard): - """Contains flood events - Flood intensities are calculated by means of the - CaMa-Flood global hydrodynamic model - - Attributes: - fla_ev_centr (2d array(n_events x n_centroids)) flooded area in - every centroid for every event - fla_event (1d array(n_events)) total flooded area for every event - fla_ann_centr (2d array(n_years x n_centroids)) flooded area in - every centroid for every event - fla_annual (1d array (n_years)) total flooded area for every year - fla_ann_av (float) average flooded area per year - fla_ev_av (float) average flooded area per event - """ - def __init__(self): - """Empty constructor""" - - Hazard.__init__(self, HAZ_TYPE) - - def set_from_nc(self, flood_dir=None, dph_path=None, frc_path=None, - centroids=None, countries=[], reg=None, years=[2000], - rf_model=RF_MODEL[0], cl_model=CL_MODEL[5], - scenario=SCENARIO[1], prot_std=PROT_STD): - """Wrapper to fill hazard from nc_flood file - Parameters: - flood_dir (string): location of flood data - (can be used when different model-runs are considered, - dph_path and frc_path must be None) - dph_path (string): Flood file to read (depth) - frc_path (string): Flood file to read (fraction) - centroids (Centroids): centroids - (area that is considered, reg and country must be None) - countries (list of countries ISO3) selection of countries - (reg must be None!) - reg (list of regions): can be set with region code if whole areas - are considered (if not None, countries and centroids - are ignored) - years (int list): years that are considered - rf_model: run-off model (only when flood_dir is selected) - cl_model: climate model (only when flood_dir is selected) - scenario: climate change scenario (only when flood_dir is selected) - prot_std: protection standard (only when flood_dir is selected) - raises: - NameError - """ - if dph_path is None or frc_path is None: - if flood_dir is not None: - if os.path.exists(flood_dir): - dph_path, frc_path = self._select_model_run(flood_dir, - rf_model, - cl_model, - scenario, - prot_std) - else: - dph_path = HAZ_DEMO_FLDDPH - frc_path = HAZ_DEMO_FLDFRC - LOGGER.warning('Flood directory ' + flood_dir + - ' does not exist, setting Demo files ' + - str(dph_path) + ' and ' + str(frc_path)) - else: - dph_path = HAZ_DEMO_FLDDPH - frc_path = HAZ_DEMO_FLDFRC - LOGGER.warning('Flood directory not set ' + - ', setting Demo files ' + - str(dph_path) + ' and ' + str(frc_path)) - else: - if not os.path.exists(dph_path): - LOGGER.error('Invalid flood-file path ' + dph_path) - raise NameError - if not os.path.exists(frc_path): - LOGGER.error('Invalid flood-file path ' + frc_path) - raise NameError - if centroids is not None: - self.centroids = centroids - centr_handling = 'align' - elif countries or reg: - self.centroids = RiverFlood.select_exact_area(countries, reg) - centr_handling = 'align' - else: - centr_handling = 'full_hazard' - intensity, fraction = self._read_nc(years, centr_handling, - dph_path, frc_path) - if scenario == 'historical': - self.orig = np.full((self._n_events), True, dtype=bool) - else: - self.orig = np.full((self._n_events), False, dtype=bool) - self.intensity = sparse.csr_matrix(intensity) - self.fraction = sparse.csr_matrix(fraction) - self.event_id = np.arange(1, self._n_events + 1) - self.units = 'm' - self.frequency = np.ones(self._n_events) / self._n_events - return self - - def _read_nc(self, years, centr_handling, dph_path, frc_path): - """ extract and flood intesity and fraction from flood - data - Returns: - np.arrays - """ - try: - flood_dph = xr.open_dataset(dph_path) - flood_frc = xr.open_dataset(frc_path) - lon = flood_dph.lon.data - lat = flood_dph.lat.data - time = flood_dph.time.data - event_index = self._select_event(time, years) - self._n_events = len(event_index) - self.date = np.array([dt.datetime(flood_dph.time[i].dt.year, - flood_dph.time[i].dt.month, - flood_dph.time[i].dt.day).toordinal() - for i in event_index]) - except KeyError: - LOGGER.error('Invalid dimensions or variables in file ' + - dph_path + ' or ' + frc_path) - raise KeyError - except OSError: - LOGGER.error('Problems while reading file ' + dph_path + - ' or ' + frc_path + - ' check flood_file specifications') - raise NameError - if centr_handling == 'full_hazard': - if len(event_index) > 1: - LOGGER.warning('Calculates global hazard' + - ' advanced memory requirements') - LOGGER.warning('Calculates global hazard, select area with ' + - 'countries, reg or centroids in set_from_nc ' + - 'to reduce runtime') - self._set_centroids_from_file(lon, lat) - try: - intensity = np.nan_to_num(np.array( - [flood_dph.flddph[i].data.flatten() - for i in event_index])) - fraction = np.nan_to_num(np.array( - [flood_frc.fldfrc[i].data.flatten() - for i in event_index])) - except MemoryError: - LOGGER.error('Too many events for grid size') - raise MemoryError - else: - n_centroids = self.centroids.size - win = self._cut_window(lon, lat) - lon_coord = lon[win[0, 0]:win[1, 0] + 1] - lat_coord = lat[win[0, 1]:win[1, 1] + 1] - dph_window = flood_dph.flddph[event_index, win[0, 1]:win[1, 1] + 1, - win[0, 0]:win[1, 0] + 1].data - frc_window = flood_frc.fldfrc[event_index, win[0, 1]:win[1, 1] + 1, - win[0, 0]:win[1, 0] + 1].data - self. window = win - try: - intensity, fraction = _interpolate(lat_coord, lon_coord, - dph_window, frc_window, - self.centroids.lon, - self.centroids.lat, - n_centroids, self._n_events) - except MemoryError: - LOGGER.error('Too many events for grid size') - raise MemoryError - - return intensity, fraction - - def _select_model_run(self, flood_dir, rf_model, cl_model, scenario, - prot_std, proj=False): - """Provides paths for selected models to incorporate flood depth - and fraction - Parameters: - flood_dir(string): string folder location of flood data - rf_model (string): run-off model - cl_model (string): climate model - scenario (string): climate change scenario - prot_std (string): protection standard - """ - if proj is False: - final = 'gev_0.1.nc' - dph_file = 'flddph_{}_{}_{}_{}'\ - .format(rf_model, cl_model, prot_std, final) - frc_file = 'fldfrc_{}_{}_{}_{}'\ - .format(rf_model, cl_model, prot_std, final) - else: - final = 'gev_picontrol_2000_0.1.nc' - dph_file = 'flddph_{}_{}_{}_{}_{}'\ - .format(rf_model, cl_model, scenario, prot_std, final) - frc_file = 'fldfrc_{}_{}_{}_{}_{}'\ - .format(rf_model, cl_model, scenario, prot_std, final) - dph_path = os.path.join(flood_dir, dph_file) - frc_path = os.path.join(flood_dir, frc_file) - return dph_path, frc_path - - def _set_centroids_from_file(self, lon, lat): - self.centroids = Centroids() - gridX, gridY = np.meshgrid(lon, lat) - self.centroids.set_lat_lon(gridY.flatten(), gridX.flatten()) - - def _select_event(self, time, years): - event_names = pd.to_datetime(time).year - event_index = np.where(np.isin(event_names, years))[0] - if len(event_index) == 0: - LOGGER.error('No events found for selected ' + str(years)) - raise AttributeError - self.event_name = list(map(str, pd.to_datetime(time[event_index]))) - return event_index - - def _cut_window(self, lon, lat): - """ Determine size of window to extract flood data. - Parameters: - lon: flood-file longitude coordinates - lat: flood-file latitude coordinates - Returns: - np.array - """ - lon_min = math.floor(min(self.centroids.coord[:, 1])) - lon_max = math.ceil(max(self.centroids.coord[:, 1])) - lat_min = math.floor(min(self.centroids.coord[:, 0])) - lat_max = math.ceil(max(self.centroids.coord[:, 0])) - diff_lon = np.diff(lon)[0] - diff_lat = np.diff(lat)[0] - win = np.zeros((2, 2), dtype=int) - win[0, 0] = min(np.where((lon >= lon_min - diff_lon) & - (lon <= lon_max + diff_lon))[0]) - win[1, 0] = max(np.where((lon >= lon_min - diff_lon) & - (lon <= lon_max + diff_lon))[0]) - win[0, 1] = min(np.where((lat >= lat_min - diff_lat) & - (lat <= lat_max + diff_lat))[0]) - win[1, 1] = max(np.where((lat >= lat_min - diff_lat) & - (lat <= lat_max + diff_lat))[0]) - return win - - def set_flooded_area(self): - """ Calculates flooded area for hazard. sets yearly flooded area and - flooded area per event - Raises: - MemoryError - """ - self.centroids.set_area_pixel() - area_centr = self.centroids.area_pixel - event_years = np.array([date.fromordinal(self.date[i]).year - for i in range(len(self.date))]) - years = np.unique(event_years) - year_ev_mk = self._annual_event_mask(event_years, years) - - try: - self.fla_ev_centr = np.zeros((self._n_events, - len(self.centroids.lon))) - self.fla_ann_centr = np.zeros((len(years), - len(self.centroids.lon))) - self.fla_ev_centr = np.array(np.multiply(self.fraction.todense(), - area_centr)) - self.fla_event = np.sum(self.fla_ev_centr, axis=1) - for year_ind in range(len(years)): - self.fla_ann_centr[year_ind, :] =\ - np.sum(self.fla_ev_centr[year_ev_mk[year_ind, :], :], - axis=0) - self.fla_annual = np.sum(self.fla_ann_centr, axis=1) - self.fla_ann_av = np.mean(self.fla_annual) - self.fla_ev_av = np.mean(self.fla_event) - except MemoryError: - self.fla_ev_centr = None - self.tot_fld_area = None - self.fla_ann_centr = None - self.fla_annual = None - self.fla_ann_av = None - self.fla_ev_av = None - LOGGER.warning('Number of events and slected area exceed ' + - 'memory capacities, area has not been calculated,' + - ' attributes set to None') - - def set_flooded_area_cut(self, coordinates, centr_indices=None): - """ Calculates flooded area for any window given with coordinates or - from indices of hazard centroids. sets yearly flooded area and - per event - Parameters: - coordinates(2d array): coordinates of window - centr_indices(1d array): indices of hazard centroid - Raises: - MemoryError - """ - if centr_indices is None: - centr_indices = interpol_index(self.centroids.coord, coordinates) - self.centroids.set_area_pixel() - area_centr = self.centroids.area_pixel[centr_indices] - event_years = np.array([date.fromordinal(self.date[i]).year - for i in range(len(self.date))]) - years = np.unique(event_years) - year_ev_mk = self._annual_event_mask(event_years, years) - try: - self.fla_ev_centr = np.zeros((self._n_events, len(centr_indices))) - self.fla_ann_centr = np.zeros((len(years), len(centr_indices))) - self.fla_ev_centr = np.array(np.multiply( - self.fraction[:, centr_indices].todense(), area_centr)) - self.fla_event = np.sum(self.fla_ev_centr, axis=1) - for year_ind in range(len(years)): - self.fla_ann_centr[year_ind, :] = \ - np.sum(self.fla_ev_centr[year_ev_mk[year_ind, :], :], - axis=0) - self.fla_annual = np.sum(self.fla_ann_centr, axis=1) - self.fla_ann_av = np.mean(self.fla_annual) - self.fla_ev_av = np.mean(self.fla_event) - - except MemoryError: - self.fla_ev_centr = None - self.fla_event = None - self.fla_ann_centr = None - self.fla_annual = None - self.fla_ann_av = None - self.fla_ev_av = None - LOGGER.warning('Number of events and slected area exceed ' + - 'memory capacities, area has not been calculated,' + - ' attributes set to None') - - def _annual_event_mask(self, event_years, years): - event_mask = np.full((len(years), len(event_years)), False, dtype=bool) - for year_ind in range(len(years)): - events = np.where(event_years == years[year_ind])[0] - event_mask[year_ind, events] = True - return event_mask - - def select_window_area(countries=[], reg=[]): - """ Extract coordinates of selected countries or region - from NatID in a rectangular box. If countries are given countries - are cut, if only reg is given, the whole region is cut. - Parameters: - countries: List of countries - reg: List of regions - Raises: - AttributeError - Returns: - np.array - """ - centroids = Centroids() - natID_info = pd.read_csv(NAT_REG_ID) - isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC) - isimip_lon = isimip_grid.lon.data - isimip_lat = isimip_grid.lat.data - gridX, gridY = np.meshgrid(isimip_lon, isimip_lat) - if countries: - if not any(np.isin(natID_info['ISO'], countries)): - LOGGER.error('Country ISO3s ' + str(countries) + ' unknown') - raise KeyError - natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)] - elif reg: - natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)] - if not any(np.isin(natID_info["Reg_name"], reg)): - LOGGER.error('Shortcuts ' + str(reg) + ' unknown') - raise KeyError - else: - centroids.lat = np.zeros((gridX.size)) - centroids.lon = np.zeros((gridX.size)) - centroids.lon = gridX.flatten() - centroids.lat = gridY.flatten() - centroids.id = np.arange(centroids.lon.shape[0]) - centroids.id = np.arange(centroids.lon.shape[0]) - return centroids - isimip_NatIdGrid = isimip_grid.NatIdGrid.data - natID_pos = np.isin(isimip_NatIdGrid, natID) - lon_coordinates = gridX[natID_pos] - lat_coordinates = gridY[natID_pos] - lon_min = math.floor(min(lon_coordinates)) - if lon_min <= -179: - lon_inmin = 0 - else: - lon_inmin = min(np.where((isimip_lon >= lon_min))[0]) - 1 - lon_max = math.ceil(max(lon_coordinates)) - if lon_max >= 179: - lon_inmax = len(isimip_lon) - 1 - else: - lon_inmax = max(np.where((isimip_lon <= lon_max))[0]) + 1 - lat_min = math.floor(min(lat_coordinates)) - if lat_min <= -89: - lat_inmin = 0 - else: - lat_inmin = min(np.where((isimip_lat >= lat_min))[0]) - 1 - lat_max = math.ceil(max(lat_coordinates)) - if lat_max >= 89: - lat_max = len(isimip_lat) - 1 - else: - lat_inmax = max(np.where((isimip_lat <= lat_max))[0]) + 1 - lon = isimip_lon[lon_inmin: lon_inmax] - lat = isimip_lat[lat_inmin: lat_inmax] - - gridX, gridY = np.meshgrid(lon, lat) - lat = np.zeros((gridX.size)) - lon = np.zeros((gridX.size)) - lon = gridX.flatten() - lat = gridY.flatten() - centroids.set_lat_lon(lat, lon) - centroids.id = np.arange(centroids.coord.shape[0]) - centroids.set_region_id() - - return centroids - - def select_exact_area(countries=[], reg=[]): - """ Extract coordinates of selected countries or region - from NatID grid. If countries are given countries are cut, - if only reg is given, the whole region is cut. - Parameters: - countries: List of countries - reg: List of regions - Raises: - KeyError - Returns: - centroids - """ - centroids = Centroids() - natID_info = pd.read_csv(NAT_REG_ID) - isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC) - isimip_lon = isimip_grid.lon.data - isimip_lat = isimip_grid.lat.data - gridX, gridY = np.meshgrid(isimip_lon, isimip_lat) - try: - if countries: - if not any(np.isin(natID_info['ISO'], countries)): - LOGGER.error('Country ISO3s ' + str(countries) + - ' unknown') - raise KeyError - natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)] - elif reg: - if not any(np.isin(natID_info["Reg_name"], reg)): - LOGGER.error('Shortcuts ' + str(reg) + ' unknown') - raise KeyError - natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)] - else: - centroids.lon = np.zeros((gridX.size)) - centroids.lat = np.zeros((gridX.size)) - centroids.lon = gridX.flatten() - centroids.lat = gridY.flatten() - centroids.id = np.arange(centroids.lon.shape[0]) - return centroids - except KeyError: - LOGGER.error('Selected country or region do ' + - 'not match reference file') - raise KeyError - isimip_NatIdGrid = isimip_grid.NatIdGrid.data - natID_pos = np.isin(isimip_NatIdGrid, natID) - lon_coordinates = gridX[natID_pos] - lat_coordinates = gridY[natID_pos] - centroids.set_lat_lon(lat_coordinates, lon_coordinates) - centroids.id = np.arange(centroids.lon.shape[0]) - centroids.set_region_id() - return centroids - - def select_exact_area_polygon(countries=[], reg=[]): - """ Extract coordinates of selected countries or region - from NatID grid. If countries are given countries are cut, - if only reg is given, the whole region is cut. - Parameters: - countries: List of countries - reg: List of regions - Raises: - AttributeError - Returns: - np.array - """ - centroids = Centroids() - natID_info = pd.read_csv(NAT_REG_ID) - isimip_grid = xr.open_dataset(GLB_CENTROIDS_NC) - isimip_lon = isimip_grid.lon.data - isimip_lat = isimip_grid.lat.data - gridX, gridY = np.meshgrid(isimip_lon, isimip_lat) - if countries: - natID = natID_info["ID"][np.isin(natID_info["ISO"], countries)] - elif reg: - natID = natID_info["ID"][np.isin(natID_info["Reg_name"], reg)] - else: - centroids.coord = np.zeros((gridX.size, 2)) - centroids.coord[:, 1] = gridX.flatten() - centroids.coord[:, 0] = gridY.flatten() - centroids.id = np.arange(centroids.coord.shape[0]) - return centroids - isimip_NatIdGrid = isimip_grid.NatIdGrid.data - natID_pos = np.isin(isimip_NatIdGrid, natID) - lon_coordinates = gridX[natID_pos] - lat_coordinates = gridY[natID_pos] - centroids.coord = np.zeros((len(lon_coordinates), 2)) - centroids.coord[:, 1] = lon_coordinates - centroids.coord[:, 0] = lat_coordinates - centroids.id = np.arange(centroids.coord.shape[0]) - orig_proj = 'epsg:4326' - country = gpd.GeoDataFrame() - country['geometry'] = list(zip(centroids.coord[:, 1], - centroids.coord[:, 0])) - country['geometry'] = country['geometry'].apply(Point) - country.crs = {'init': orig_proj} - points = country.geometry.values - concave_hull, _ = alpha_shape(points, alpha=1) - - return concave_hull - - -def _interpolate(lat, lon, dph_window, frc_window, centr_lon, centr_lat, - n_centr, n_ev, method='nearest'): - """ Prepares data for interpolation and applies interpolation function, - to assign flood parameters to chosen centroids. - Parameters: - lat (1d array): first axis for grid - lon (1d array): second axis for grid - dph_window (3d array): depth values - frc_window (3d array): fraction - centr_lon (1d array): centroids lon - centr_lat (1d array): centroids lat - n_centr (int): number centroids - n_ev (int): number of events - Returns: - np.arrays - """ - if lat[0] - lat[1] > 0: - lat = np.flipud(lat) - dph_window = np.flip(dph_window, axis=1) - frc_window = np.flip(frc_window, axis=1) - if lon[0] - lon[1] > 0: - lon = np.flipud(lon) - dph_window = np.flip(dph_window, axis=2) - frc_window = np.flip(frc_window, axis=2) - - intensity = np.zeros((dph_window.shape[0], n_centr)) - fraction = np.zeros((dph_window.shape[0], n_centr)) - for i in range(n_ev): - intensity[i, :] = \ - sp.interpolate.interpn((lat, lon), - np.nan_to_num(dph_window[i, :, :]), - (centr_lat, centr_lon), - method='nearest', - bounds_error=False, - fill_value=None) - fraction[i, :] = \ - sp.interpolate.interpn((lat, lon), - np.nan_to_num(frc_window[i, :, :]), - (centr_lat, centr_lon), - method='nearest', - bounds_error=False, - fill_value=None) - return intensity, fraction diff --git a/climada/hazard/isimip_data.py b/climada/hazard/isimip_data.py index baca52ca80..a313d7a20a 100644 --- a/climada/hazard/isimip_data.py +++ b/climada/hazard/isimip_data.py @@ -23,7 +23,7 @@ All functions should work for ISIMIP netcdf output files regardless of the resolution of saptial (lat, lon) and temporal (time) resolution and dimension of input data. -Not that ISIMIP data comes in a range of resolutions, i.e. daily (e.g. discharge), +Not that ISIMIP data comes in a range of resolutions, i.e. daily (e.g. discharge), monthly, yearly (e.g. yield) """ @@ -33,7 +33,7 @@ bbox_world = [-85, 85, -180, 180] def _read_one_nc(file_name, bbox=None, years=None): - """ Reads 1 ISIMIP output NETCDF file data within a certain bounding box and time period + """Reads 1 ISIMIP output NETCDF file data within a certain bounding box and time period Parameters: file_name (str): Absolute or relative path to *.nc @@ -41,14 +41,15 @@ def _read_one_nc(file_name, bbox=None, years=None): years (array): start and end year of the time series that shall be extracted Returns: - data (dataset): Contains data in the specified bounding box and for the + data (dataset): Contains data in the specified bounding box and for the specified time period """ - data = xr.open_dataset(file_name, decode_times = False) - if not bbox: bbox = bbox_world + data = xr.open_dataset(file_name, decode_times=False) + if not bbox: + bbox = bbox_world if not years: return data.sel(lat=slice(bbox[3], bbox[1]), lon=slice(bbox[0], bbox[2])) time_id = years - int(data.time.units[12:16]) - return data.sel(lat=slice(bbox[3], bbox[1]), lon=slice(bbox[0], bbox[2]), \ + return data.sel(lat=slice(bbox[3], bbox[1]), lon=slice(bbox[0], bbox[2]), time=slice(time_id[0], time_id[1])) diff --git a/climada/hazard/landslide.py b/climada/hazard/landslide.py index e2205e4c0c..4a9873f0e9 100644 --- a/climada/hazard/landslide.py +++ b/climada/hazard/landslide.py @@ -45,7 +45,7 @@ HAZ_TYPE = 'LS' -""" for future: implement a function that downloads COOLR data by command, not manually""" +"""for future: implement a function that downloads COOLR data by command, not manually""" # def get_coolr_shp(save_path=os.getcwd()): # """for LS_MODEL[0]: download most up-to-date version of historic LS records from # global landslide catalog (COOLR of NASA) in shape-file format (zip)""" @@ -77,7 +77,7 @@ def get_nowcast_tiff(tif_type="monthly", starttime="", endtime="", save_path=os. tiff files (daily) to save_path/LS_nowcast_date.tif """ # the daily one is currently not producing any output - if tif_type == "daily": + if tif_type == "daily": if starttime > endtime: LOGGER.error("Start date must lie before end date. Please change") raise ValueError @@ -93,7 +93,7 @@ def get_nowcast_tiff(tif_type="monthly", starttime="", endtime="", save_path=os. data = resp.json() tif_url = [] for item in data['items']: - #extract json resonse snippet for tiff download + # extract json resonse snippet for tiff download tif_url.append(item['action'][1]['using'][2]['url']) resp_tif = [] @@ -101,11 +101,15 @@ def get_nowcast_tiff(tif_type="monthly", starttime="", endtime="", save_path=os. LOGGER.info('requesting %s', url) resp_tif.append(requests.get(url=url)) LOGGER.info('downloading content...') - open((save_path+'/LS_nowcast_'+str(url[-12:-4])+'.tif'), 'wb').write(resp_tif[-1].content) + with open((save_path + '/LS_nowcast_' + str(url[-12:-4]) + '.tif'), 'wb') as fp: + fp.write(resp_tif[-1].content) elif tif_type == "monthly": - command_line = 'curl -LO "https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004631/frames/9600x5400_16x9_30p/MonthlyClimatology/[01-12]_ClimatologyMonthly_032818_9600x5400.tif"' + command_line = ('curl -LO ' + '"https://svs.gsfc.nasa.gov/vis/a000000/a004600/a004631/frames' + '/9600x5400_16x9_30p/MonthlyClimatology/' + '[01-12]_ClimatologyMonthly_032818_9600x5400.tif"') args = shlex.split(command_line) p = subprocess.Popen(args, stdout=subprocess.PIPE, @@ -114,7 +118,7 @@ def get_nowcast_tiff(tif_type="monthly", starttime="", endtime="", save_path=os. def combine_nowcast_tiff(ls_folder_path, search_criteria='LS*.tif', operator="maximum"): - """ Function to overlay several tiff files with landslide hazard data either by + """Function to overlay several tiff files with landslide hazard data either by keeping maximum value per pixel or by summing up all pixel values. UPDATE: SOMETIMES WORKS, SOMETIMES NOT, ISSUE SEEMS TO BE WITH THE SHELL=TRUE COMMAND Parameters: @@ -144,13 +148,13 @@ def combine_nowcast_tiff(ls_folder_path, search_criteria='LS*.tif', operator="ma /Users/evelynm/anaconda3/envs/climada_env_new/bin """ command_line = 'gdal_calc.py --outfile=%s -A "%s" -B "%s" --calc="maximum(A,B)"' \ - %(combined_layers_path, file, file) + % (combined_layers_path, file, file) args = shlex.split(command_line) else: command_line = 'gdal_calc.py --outfile=%s -A "%s" -B "%s" --calc="maximum(A,B)"'\ - %(combined_layers_path, combined_layers_path, file) + % (combined_layers_path, combined_layers_path, file) args = shlex.split(command_line) - i = i+1 + i = i + 1 p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True) @@ -161,13 +165,13 @@ def combine_nowcast_tiff(ls_folder_path, search_criteria='LS*.tif', operator="ma for file in ls_files: if i == 0: command_line = 'gdal_calc.py --outfile=%s -A "%s" -B "%s" --calc="A+B"' \ - %(combined_layers_path, file, file) + % (combined_layers_path, file, file) args = shlex.split(command_line) else: command_line = 'gdal_calc.py --outfile=%s -A "%s" -B "%s" --calc="A+B"' \ - %(combined_layers_path, combined_layers_path, file) + % (combined_layers_path, combined_layers_path, file) args = shlex.split(command_line) - i = i+1 + i = i + 1 p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True) @@ -180,7 +184,7 @@ class Landslide(Hazard): """ def __init__(self): - """Empty constructor. """ + """Empty constructor.""" Hazard.__init__(self, HAZ_TYPE) self.tag.haz_type = 'LS' @@ -196,20 +200,20 @@ def _get_window_from_coords(self, path_sourcefile, bbox=[]): window_array (array): corner, width & height for Window() function of rasterio """ with rasterio.open(path_sourcefile) as src: - utm = pyproj.Proj(init='epsg:4326') # Pass CRS of image from rasterio + utm = pyproj.Proj(init='epsg:4326') # Pass CRS of image from rasterio lonlat = pyproj.Proj(init='epsg:4326') lon, lat = (bbox[3], bbox[0]) west, north = pyproj.transform(lonlat, utm, lon, lat) # What is the corresponding row and column in our image? - row, col = src.index(west, north) # spatial --> image coordinates + row, col = src.index(west, north) # spatial --> image coordinates lon, lat = (bbox[1], bbox[2]) east, south = pyproj.transform(lonlat, utm, lon, lat) row2, col2 = src.index(east, south) - width = abs(col2-col) - height = abs(row2-row) + width = abs(col2 - col) + height = abs(row2 - row) window_array = [col, row, width, height] @@ -217,9 +221,9 @@ def _get_window_from_coords(self, path_sourcefile, bbox=[]): def _get_raster_meta(self, path_sourcefile, window_array): """get geo-meta data from raster files to set centroids adequately""" - raster = rasterio.open(path_sourcefile, 'r', \ - window=Window(window_array[0], window_array[1],\ - window_array[2], window_array[3])) + raster = rasterio.open(path_sourcefile, 'r', + window=Window(window_array[0], window_array[1], + window_array[2], window_array[3])) pixel_width = raster.meta['transform'][0] pixel_height = raster.meta['transform'][4] @@ -229,27 +233,35 @@ def _intensity_cat_to_prob(self, max_prob): """convert NASA nowcasting categories into occurrence probabilities: highest value category value receives a prob of max_prob, lowest category value receives a prob value of 0""" - self.intensity_cat = self.intensity.copy() #save prob values + self.intensity_cat = self.intensity.copy() # save prob values self.intensity = self.intensity.astype(float) self.intensity.data = self.intensity.data.astype(float) max_value = float(max(self.intensity_cat.data)) min_value = float(min(self.intensity_cat.data)) for i, j in zip(*self.intensity.nonzero()): - self.intensity[i, j] = float((self.intensity[i, j]-min_value)/\ - (max_value-min_value)*max_prob) + self.intensity[i, j] = float((self.intensity[i, j] - min_value) / + (max_value - min_value) * max_prob) def _intensity_prob_to_binom(self, n_years): """convert occurrence probabilities in NGI/UNEP landslide hazard map into binary occurrences (yes/no) within a given time frame. - Parameters: - n_years (int): the timespan of the probabilistic simulation in years - Returns: - intensity_prob (csr matrix): initial probabilities of ls occurrence per year per pixel - intensity (csr matrix): binary (0/1) occurrence within pixel""" - self.intensity_prob = self.intensity.copy() #save prob values + Parameters + ---------- + n_years : int + the timespan of the probabilistic simulation in years + + Returns + ------- + intensity_prob : csr matrix + initial probabilities of ls occurrence per year per pixel + intensity : csr matrix + binary (0/1) occurrence within pixel + """ + + self.intensity_prob = self.intensity.copy() # save prob values for i, j in zip(*self.intensity.nonzero()): if binom.rvs(n=n_years, p=self.intensity[i, j]) >= 1: @@ -271,9 +283,9 @@ def _intensity_binom_to_range(self, max_dist): # find all other pixels within certain distance from corresponding centroid, for i, j in zip(*self.intensity.nonzero()): subset_neighbours = self.centroids.geometry.cx[ - (self.centroids.coord[j][1]-0.01):(self.centroids.coord[j][1]+0.01), - (self.centroids.coord[j][0]-0.01):(self.centroids.coord[j][0]+0.01) - ]# 0.01° = 1.11 km approximately + (self.centroids.coord[j][1] - 0.01):(self.centroids.coord[j][1] + 0.01), + (self.centroids.coord[j][0] - 0.01):(self.centroids.coord[j][0] + 0.01) + ] # 0.01° = 1.11 km approximately for centroid in subset_neighbours: ix = subset_neighbours[subset_neighbours == centroid].index[0] # calculate dist, assign intensity [0-1] linearly until max_dist @@ -284,11 +296,11 @@ def _intensity_binom_to_range(self, max_dist): self.centroids.coord[j], unit='m') # this step changes sparsity of matrix --> # converted to lil_matrix, as more efficient - self.intensity[i, ix] = (max_dist-actual_dist)/max_dist + self.intensity[i, ix] = (max_dist - actual_dist) / max_dist self.intensity = self.intensity.tocsr() def plot_raw(self, ev_id=1, **kwargs): - """ Plot raw LHM data using imshow and without cartopy + """Plot raw LHM data using imshow and without cartopy Parameters: ev_id (int, optional): event id. Default: 1. @@ -307,11 +319,11 @@ def plot_raw(self, ev_id=1, **kwargs): LOGGER.error('Wrong event id: %s.', ev_id) raise ValueError from IndexError - return plt.imshow(self.intensity_prob[event_pos, :].todense(). \ - reshape(self.centroids.shape), **kwargs) + return plt.imshow(self.intensity_prob[event_pos, :].toarray(). + reshape(self.centroids.shape), **kwargs) def plot_events(self, ev_id=1, **kwargs): - """ Plot LHM event data using imshow and without cartopy + """Plot LHM event data using imshow and without cartopy Parameters: ev_id (int, optional): event id. Default: 1. @@ -330,8 +342,8 @@ def plot_events(self, ev_id=1, **kwargs): LOGGER.error('Wrong event id: %s.', ev_id) raise ValueError from IndexError - return plt.imshow(self.intensity[event_pos, :].todense(). \ - reshape(self.centroids.shape), **kwargs) + return plt.imshow(self.intensity[event_pos, :].toarray(). + reshape(self.centroids.shape), **kwargs) def _get_hist_events(self, bbox, coolr_path): """for LS_MODEL[0]: load gdf with landslide event POINTS from @@ -360,13 +372,13 @@ def set_ls_model_hist(self, bbox, path_sourcefile, check_plots=1): ls_gdf_bbox = self._get_hist_events(bbox, path_sourcefile) self.centroids.set_lat_lon(ls_gdf_bbox.latitude, ls_gdf_bbox.longitude) - n_cen = ls_gdf_bbox.latitude.size # number of centroids + n_cen = ls_gdf_bbox.latitude.size # number of centroids n_ev = n_cen self.intensity = sparse.csr_matrix(np.ones((n_ev, n_cen))) self.units = 'm/m' self.event_id = np.arange(n_ev, dtype=int) self.orig = np.zeros(n_ev, bool) - self.frequency = np.ones(n_ev)/n_ev + self.frequency = np.ones(n_ev) / n_ev self.fraction = self.intensity.copy() self.fraction.data.fill(1) self.check() @@ -375,9 +387,8 @@ def set_ls_model_hist(self, bbox, path_sourcefile, check_plots=1): self.centroids.plot() return self - def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", - path_sourcefile = [], n_years=500,\ - incl_neighbour=False, max_dist=1000, max_prob=0.000015, check_plots=1): + def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", path_sourcefile=[], n_years=500, + incl_neighbour=False, max_dist=1000, max_prob=0.000015, check_plots=1): """.... Parameters: ls_model (str): UNEP_NGI (prob., UNEP/NGI) or NASA (prob., NASA Nowcast) @@ -392,7 +403,7 @@ def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", path_sourcefile (str): if ls_model is UNEP_NGI, use path to NGI/UNEP file, retrieved previously as descriped in tutorial and stored in climada/data. if ls_model is NASA provide path to combined daily or - monthly rasterfile, retrieved and aggregated + monthly rasterfile, retrieved and aggregated previously with landslide.get_nowcast_tiff() and landslide.combine_nowcast_tiff(). Returns: @@ -401,18 +412,19 @@ def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", if ls_model == "UNEP_NGI": path_sourcefile = os.path.join(LS_FILE_DIR, 'ls_pr_NGI_UNEP/ls_pr.tif') - + if not bbox: LOGGER.error('Empty bounding box, please set bounds.') raise ValueError() - window_array = self._get_window_from_coords(path_sourcefile,\ + window_array = self._get_window_from_coords(path_sourcefile, bbox) pixel_height, pixel_width = self._get_raster_meta(path_sourcefile, window_array) - self.set_raster([path_sourcefile], window=Window(window_array[0], window_array[1],\ - window_array[3], window_array[2])) - self.intensity = self.intensity/10e6 #prob values were initially multiplied by 1 mio - self.centroids.set_raster_from_pix_bounds(bbox[0], bbox[3], pixel_height, pixel_width,\ + self.set_raster([path_sourcefile], window=Window(window_array[0], window_array[1], + window_array[3], window_array[2])) + # prob values were initially multiplied by 1 mio + self.intensity = self.intensity / 10e6 + self.centroids.set_raster_from_pix_bounds(bbox[0], bbox[3], pixel_height, pixel_width, window_array[3], window_array[2]) LOGGER.info('Generating landslides...') self._intensity_prob_to_binom(n_years) @@ -426,13 +438,13 @@ def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", self.check() if check_plots == 1: - fig1, ax1 = plt.subplots(nrows=1, ncols=1) - self.plot_raw() + fig1 = plt.subplots(nrows=1, ncols=1)[0] + self.plot_raw() fig1.suptitle('Raw data: Occurrence prob of LS per year', fontsize=14) - fig2, ax2 = plt.subplots(nrows=1, ncols=1) + fig2 = plt.subplots(nrows=1, ncols=1)[0] self.plot_events() - fig2.suptitle('Prob. LS Hazard Set n_years = %i' %n_years, fontsize=14) + fig2.suptitle('Prob. LS Hazard Set n_years = %i' % n_years, fontsize=14) return self @@ -446,11 +458,11 @@ def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", raise ValueError() window_array = self._get_window_from_coords(path_sourcefile, bbox) pixel_height, pixel_width = self._get_raster_meta(path_sourcefile, window_array) - self.set_raster([path_sourcefile], window=Window(window_array[0], window_array[1],\ - window_array[3], window_array[2])) + self.set_raster([path_sourcefile], window=Window(window_array[0], window_array[1], + window_array[3], window_array[2])) LOGGER.info('Setting probability values from categorical landslide hazard levels...') self._intensity_cat_to_prob(max_prob) - self.centroids.set_raster_from_pix_bounds(bbox[0], bbox[3], pixel_height, pixel_width,\ + self.centroids.set_raster_from_pix_bounds(bbox[0], bbox[3], pixel_height, pixel_width, window_array[3], window_array[2]) LOGGER.info('Generating binary landslides...') self._intensity_prob_to_binom(n_years) @@ -470,11 +482,11 @@ def set_ls_model_prob(self, bbox, ls_model="UNEP_NGI", fig2, ax2 = plt.subplots(nrows=1, ncols=1) ax2 = self.plot_events() - fig2.suptitle('Prob. LS Hazard Set n_years = %i' %n_years, fontsize=14) + fig2.suptitle('Prob. LS Hazard Set n_years = %i' % n_years, fontsize=14) return self else: - LOGGER.error('Specify the LS model to be used for the hazard-set generation as ls_model=str') + LOGGER.error('Specify the LS model to be used for the hazard-set ' + 'generation as ls_model=str') raise KeyError - diff --git a/climada/hazard/low_flow.py b/climada/hazard/low_flow.py new file mode 100755 index 0000000000..884fa3c1b6 --- /dev/null +++ b/climada/hazard/low_flow.py @@ -0,0 +1,913 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Define LowFlow (LF) class. +WORK IN PROGRESS +""" + +__all__ = ['LowFlow'] + +import logging +import os +import copy +import datetime as dt +import cftime +import xarray as xr +import geopandas as gpd +import numpy as np +import numba + +from sklearn.cluster import DBSCAN +from sklearn.neighbors import BallTree +from shapely.geometry import Point +from scipy import sparse + +from climada.hazard.base import Hazard +from climada.hazard.tag import Tag as TagHazard +from climada.hazard.centroids import Centroids +from climada.util.coordinates import get_resolution + +LOGGER = logging.getLogger(__name__) + +HAZ_TYPE = 'LF' +"""Hazard type acronym for Low Flow / Water Scarcity""" + +FN_STR_VAR = 'co2_dis_global_daily' # FileName STRing depending on VARiable +"""constant part of discharge output file (according to ISIMIP filenaming)""" + +YEARCHUNKS = dict() +"""list of year chunks: multiple files are combined""" + +YEARCHUNKS['historical'] = list() +"""historical year chunks ISIMIP 2b""" +for i in np.arange(1860, 2000, 10): + YEARCHUNKS['historical'].append(f'{i+1}_{i+10}') +YEARCHUNKS['historical'].append('2001_2005') + +YEARCHUNKS['hist'] = list() +"""historical year chunks ISIMIP 2a""" +for i in np.arange(1970, 2010, 10): + YEARCHUNKS['hist'].append(f'{i+1}_{i+10}') + +YEARCHUNKS['rcp26'] = ['2006_2010'] +for i in np.arange(2010, 2090, 10): + YEARCHUNKS['rcp26'].append(f'{i+1}_{i+10}') +YEARCHUNKS['rcp26'].append('2091_2099') +YEARCHUNKS['rcp60'] = YEARCHUNKS['rcp26'] +"""future year chunks""" + +REFERENCE_YEARRANGE = (1971, 2005) +"""default year range used to compute threshold (base line reference)""" + +TARGET_YEARRANGE = (2001, 2005) +"""arbitrary default, i.e. default year range of historical low flow hazard 2001-2005""" + +BBOX = (-180, -85, 180, 85) +"""default quasi-global geographical bounding box: [lon_min, lat_min, lon_max, lat_max]""" + +# reducing these two parameters decreases memory load but increases computation time: +BBOX_WIDTH = 75 +"""default width and height of geographical bounding boxes for loop in degree lat/lon. +i.e., the bounding box is split into square boxes with maximum size BBOX_WIDTH*BBOX_WIDTH +(avoid memory usage spike)""" +INTENSITY_STEP = 300 +"""max. number of events to be written to hazard.intensity matrix at once +(avoid memory usage spike)""" + +class LowFlow(Hazard): + """Contains river low flow events (surface water scarcity). + The intensity of the hazard is number of days below a threshold (defined as + percentile in reference data). The method set_from_nc can be used to create + a LowFlow hazard set populated with data based on gridded hydrological model runs + as provided by the ISIMIP project (https://www.isimip.org/), e.g. ISIMIP2a/b. + grid cells with a minimum number of days below threshold per month are clustered + in space (lat/lon) and time (monthly) to identify and set connected events. + + Attributes: + clus_thresh_t (int): maximum time difference in months to be counted as$ + connected points during clustering, default = 1 + clus_thresh_xy (int): maximum spatial grid cell distance in number of cells + to be counted as connected points during clustering, default = 2 + min_samples (1): Minimum amount of data points in one cluster to consider as event, + default = 1. + date_start (np.array(int)): for each event, the date of the first month + of the event (ordinal) + Note: Hazard attribute 'date' contains the date of maximum event intensity. + date_end (np.array(int)): for each event, the date of the last month of + the event (ordinal) + resolution (float): spatial resoultion of gridded discharge input data in degree lat/lon, + default = 0.5° + """ + + clus_thresh_t = 1 # Default = 1: months with intensity grid cells with a mean discharge below 1 are ignored + ('percentile', .3) --> grid cells with a value of the computed percentile discharge + values below 0.3 are ignored. default: ('mean', 1}). Set to None for + no threshold. + Provide a list of tuples for multiple thresholds. + raises: + NameError + """ + if input_dir: + if not os.path.exists(input_dir): + LOGGER.warning('Input directory %s does not exist', input_dir) + raise NameError + else: + LOGGER.warning('Input directory %s not set', input_dir) + raise NameError + + if centroids: + centr_handling = 'align' + elif countries or reg: + LOGGER.warning('country or reg ignored: not yet implemented') + centr_handling = 'full_hazard' + else: + centr_handling = 'full_hazard' + + # read data and call preprocessing routine: + self.lowflow_df, centroids_import = data_preprocessing_percentile( + percentile, yearrange, yearrange_ref, input_dir, gh_model, cl_model, + scenario, scenario_ref, soc, soc_ref, fn_str_var, bbox, min_days_per_month, + keep_dis_data, yearchunks, mask_threshold) + + if centr_handling == 'full_hazard': + centroids = centroids_import + self.identify_clusters() + self.set_intensity_from_clusters(centroids, min_intensity, min_number_cells, + yearrange, yearrange_ref, gh_model, cl_model, + scenario, scenario_ref, soc, soc_ref, fn_str_var, keep_dis_data) + + def set_intensity_from_clusters(self, centroids=None, min_intensity=1, min_number_cells=1, + yearrange=TARGET_YEARRANGE, yearrange_ref=REFERENCE_YEARRANGE, + gh_model=None, cl_model=None, + scenario='historical', scenario_ref='historical', soc='histsoc', + soc_ref='histsoc', fn_str_var=FN_STR_VAR, keep_dis_data=False): + """ Build low flow hazards with events from clustering and centroids and add attributes. + """ + # sum "dis" (days per month below threshold) per pixel and + # cluster_id and write to hazard.intensity + self.events_from_clusters(centroids) + + if min_intensity > 1 or min_number_cells > 1: + haz_tmp = self.filter_events(min_intensity=min_intensity, + min_number_cells=min_number_cells) + LOGGER.info('Filtering events: %i events remaining', haz_tmp.size) + self.event_id = haz_tmp.event_id + self.event_name = list(map(str, self.event_id)) + self.date = haz_tmp.date + self.date_start = haz_tmp.date_start + self.date_end = haz_tmp.date_end + self.orig = haz_tmp.orig + self.frequency = haz_tmp.frequency + self.intensity = haz_tmp.intensity + self.fraction = haz_tmp.fraction + del haz_tmp + if not keep_dis_data: + self.lowflow_df = None + self.set_frequency(yearrange=yearrange) + self.tag = TagHazard(haz_type=HAZ_TYPE, file_name=\ + f'{gh_model}_{cl_model}_*_{scenario}_{soc}_{fn_str_var}_*.nc', \ + description= f'yearrange: {yearrange[0]}-{yearrange[0]} ' +\ + f'({scenario}, {soc}), ' +\ + f'reference: {yearrange_ref[0]}-{yearrange_ref[0]} ' +\ + f'({scenario_ref}, {soc_ref})' + ) + + def _intensity_loop(self, uniq_ev, coord, res_centr, num_centr): + """Compute intensity and populate intensity matrix. + For each event, if more than one points of + data have the same coordinates, take the sum of days below threshold + of these points (duration as accumulated intensity). + + Parameters: + uniq_ev (list of str): list of unique cluster IDs + coord (list): Coordinates as in Centroids.coord + res_centr (float): Geographical resolution of centroids + num_centroids (int): Number of centroids + + Returns: + intensity_mat (sparse.lilmatrix): intensity values as sparse matrix + """ + tree_centr = BallTree(coord, metric='chebyshev') + # steps: list of steps to be written to intensity matrix at once: + steps = list(np.arange(0, len(uniq_ev) - 1, INTENSITY_STEP)) + [len(uniq_ev)] + if len(steps) == 1: + intensity_list = [self._intensity_one_cluster(tree_centr, cl_id, res_centr, num_centr) + for cl_id in uniq_ev] + return sparse.csr_matrix(intensity_list) + # step_range: list of tuples containing the unique IDs to be written to + # the intensity matrix in one step + step_range = [tuple(uniq_ev[stp:steps[idx+1]]) for idx, stp in enumerate(steps[0:-1])] + for idx, stp in enumerate(step_range): + intensity_list = [] + for cl_id in stp: + intensity_list.append( + self._intensity_one_cluster(tree_centr, cl_id, res_centr, num_centr)) + if not idx: + intensity_mat = sparse.lil_matrix(intensity_list) + else: + intensity_mat = sparse.vstack((intensity_mat, + sparse.csr_matrix(intensity_list))) + return intensity_mat + + def _set_dates(self, uniq_ev): + """Set dates of maximum intensity (date) as well as start and end dates + per event + + Parameters: + uniq_ev (list): list of unique cluster IDs + """ + self.date = np.zeros(uniq_ev.size, int) + self.date_start = np.zeros(uniq_ev.size, int) + self.date_end = np.zeros(uniq_ev.size, int) + for ev_idx, ev_id in enumerate(uniq_ev): + # set event date to date of maximum intensity (ndays) + self.date[ev_idx] = self.lowflow_df[self.lowflow_df.cluster_id == ev_id]\ + .groupby('dtime')['ndays'].sum().idxmax() + self.date_start[ev_idx] = self.lowflow_df[self.lowflow_df.cluster_id == ev_id].dtime.min() + self.date_end[ev_idx] = self.lowflow_df[self.lowflow_df.cluster_id == ev_id].dtime.max() + + def events_from_clusters(self, centroids): + """Initiate hazard events from connected clusters found in self.lowflow_df + + Parameters: + centroids (Centroids)""" + # intensity = list() + + uniq_ev = np.unique(self.lowflow_df['cluster_id'].values) + num_centr = centroids.size + res_centr = self._centroids_resolution(centroids) + + self.tag = TagHazard(HAZ_TYPE) + self.units = 'days' # days below threshold + self.centroids = centroids + + # Following values are defined for each event + self.event_id = np.sort(uniq_ev) + self.event_id = self.event_id[self.event_id > 0] + self.event_name = list(map(str, self.event_id)) + + self._set_dates(uniq_ev) + + self.orig = np.ones(uniq_ev.size) + self.set_frequency() + + self.intensity = self._intensity_loop(uniq_ev, centroids.coord, res_centr, num_centr) + + # Following values are defined for each event and centroid + self.intensity = self.intensity.tocsr() + self.fraction = self.intensity.copy() + self.fraction.data.fill(1.0) + + def identify_clusters(self, clus_thresh_xy=None, clus_thresh_t=None, min_samples=None): + """call clustering functions to identify the clusters inside the dataframe + + Optional parameters: + clus_thresh_xy (int): new value of maximum grid cell distance + (number of grid cells) to be counted as connected points during clustering + clus_thresh_t (int): new value of maximum timse step difference (months) + to be counted as connected points during clustering + min_samples (int): new value or minimum amount of data points in one + cluster to retain the cluster as an event, smaller clusters will be ignored + Returns + pandas.DataFrame + """ + if min_samples: + self.min_samples = min_samples + if clus_thresh_xy: + self.clus_thresh_xy = clus_thresh_xy + if clus_thresh_t: + self.clus_thresh_t = clus_thresh_t + + self.lowflow_df['cluster_id'] = np.zeros(len(self.lowflow_df), dtype=int) + LOGGER.debug('Computing 3D clusters.') + # Compute clus_id: cluster identifier inside cons_id + for cluster_vars in [('lat', 'lon'), ('lat', 'dt_month'), ('lon', 'dt_month')]: + self.lowflow_df = self._df_clustering(self.lowflow_df, cluster_vars, + self.resolution, self.clus_thresh_xy, + self.clus_thresh_t, self.min_samples) + + self.lowflow_df = unique_clusters(self.lowflow_df) + return self.lowflow_df + + @staticmethod + def _df_clustering(lowflow_df, cluster_vars, res_data, clus_thresh_xy, + clus_thres_t, min_samples): + """Compute 2D clusters and sort lowflow_df with ascending clus_id + for each combination of the 3 dimensions (lat, lon, dt_month). + + Parameters: + lowflow_df (dataframe): dataset obtained from ISIMIP data + cluster_vars (tuple pf str): pair of dimensions for 2D clustering, + e.g. ('lat', 'dt_month') + res_data (float): input data grid resolution in degrees + clus_thresh_xy (int): clustering distance threshold in space + clus_thresh_t (int): clustering distance threshold in time + min_samples (int): clustering min. number + + Returns: + pandas.DataFrame + """ + # set iter_var (dimension not used for clustering) + if 'lat' not in cluster_vars: + iter_var = 'lat' + elif 'lon' not in cluster_vars: + iter_var = 'lon' + else: + iter_var = 'dt_month' + + clus_id_var = 'c_%s_%s' % (cluster_vars[0], cluster_vars[1]) + lowflow_df[clus_id_var] = np.zeros(len(lowflow_df), dtype=int) - 1 + + data_iter = lowflow_df[lowflow_df['iter_ev']][[iter_var, cluster_vars[0], cluster_vars[1], + 'cons_id', clus_id_var]] + + if 'dt_month' in clus_id_var: + # transform month count in accordance with spatial resolution + # to achieve same distance between consecutive and geographically + # neighboring points: + data_iter.dt_month = data_iter.dt_month * res_data * clus_thresh_xy / clus_thres_t + + # Loop over slices: For each slice, perform 2D clustering with DBSCAN + for i_var in data_iter[iter_var].unique(): + temp = np.argwhere(np.array(data_iter[iter_var] == i_var)).reshape(-1, ) # slice + x_y = data_iter.iloc[temp][[cluster_vars[0], cluster_vars[1]]].values + x_y_uni, x_y_cpy = np.unique(x_y, return_inverse=True, axis=0) + cluster_id = DBSCAN(eps=res_data * clus_thresh_xy, min_samples=min_samples). \ + fit(x_y_uni).labels_ + cluster_id = cluster_id[x_y_cpy] + data_iter[clus_id_var].values[temp] = cluster_id + data_iter[clus_id_var].max() + 1 + + lowflow_df[clus_id_var].values[lowflow_df['iter_ev'].values] = data_iter[clus_id_var].values + return lowflow_df + + def filter_events(self, min_intensity=1, min_number_cells=1): + """Remove events with max intensity below min_intensity or spatial extend + below min_number_cells + + Parameters: + min_intensity (int or float): Minimum criterion for intensity + min_number_cells (int or float): Minimum crietrion for number of grid cell + + Returns: + Hazard + """ + haz_tmp = copy.deepcopy(self) + haz_tmp.orig_tmp = copy.deepcopy(self.orig) + haz_tmp.orig = np.array(np.ones(self.orig.size)) + # identify events to be filtered out and set haz_tmp.orig to 0. + for i_event, _ in enumerate(haz_tmp.event_id): + if np.sum(haz_tmp.intensity[i_event] > 0) < min_number_cells or \ + np.max(haz_tmp.intensity[i_event]) < min_intensity: + haz_tmp.orig[i_event] = 0. + haz_tmp = haz_tmp.select(orig=True) # select events with orig == 1 + haz_tmp.orig = haz_tmp.orig_tmp # reset orig + del haz_tmp.orig_tmp + haz_tmp.event_id = np.arange(1, len(haz_tmp.event_id) + 1).astype(int) + return haz_tmp + + @staticmethod + def _centroids_resolution(centroids): + """Return resolution of the centroids in their units + + Parameters: + centroids (Centroids): centroids instance + + Returns: + float + """ + if centroids.meta: + res_centr = abs(centroids.meta['transform'][4]), \ + centroids.meta['transform'][0] + else: + res_centr = np.abs(get_resolution(centroids.lat, centroids.lon)) + if np.abs(res_centr[0] - res_centr[1]) > 1.0e-6: + LOGGER.warning('Centroids do not represent regular pixels %s.', str(res_centr)) + return (res_centr[0] + res_centr[1]) / 2 + return res_centr[0] + + def _intensity_one_cluster(self, tree_centr, cluster_id, res_centr, num_centr): + """For a given cluster, fill in an intensity np.array with the summed intensity + at each centroid. + + Parameters: + cluster_id (int): id of the selected cluster + tree_centr (object) BallTree instance created from centroids' coordinates + res_centr (float): resolution of centroids in degree + num_centr (int): number of centroids + + Returns: + intensity_cl (np.array): summed intensity of cluster at each centroids + """ + LOGGER.debug('Number of days below threshold corresponding to event %s.', str(cluster_id)) + temp_data = self.lowflow_df.reindex( + index=np.argwhere(np.array(self.lowflow_df['cluster_id'] == cluster_id)).reshape(-1), + columns=['lat', 'lon', 'ndays']) + # Identifies the unique (lat,lon) points of the lowflow_df dataframe -> lat_lon_uni + # Set the same index value for each duplicate (lat,lon) points -> lat_lon_cpy + lat_lon_uni, lat_lon_cpy = np.unique(temp_data[['lat', 'lon']].values, + return_inverse=True, axis=0) + index_uni = np.unique(lat_lon_cpy, axis=0) + # Search closest centroid for each point + ind, _ = tree_centr.query_radius(lat_lon_uni, r=res_centr / 2, count_only=False, + return_distance=True, sort_results=True) + ind = np.array([ind_i[0] if ind_i.size else -1 for ind_i in ind]) + intensity_cl = _fill_intensity(num_centr, ind, index_uni, lat_lon_cpy, + temp_data['ndays'].values) + return intensity_cl + + @staticmethod + def _intensity_one_cluster_pool(lowflow_df, tree_centr, cluster_id, res_centr, num_centr): + """For a given cluster, fill in an intensity np.array with the summed intensity + at each centroid. Version for self.pool = True + + Parameters: + lowflow_df (DataFrame) + tree_centr (object): BallTree instance created from centroids' coordinates + cluster_id (int): id of the selected cluster + res_centr (float): resolution of centroids in degree + num_centr (int): number of centroids + + Returns: + intensity_cl (np.array): summed intensity of cluster at each centroids + + """ + LOGGER.debug('Number of days below threshold corresponding to event %s.', str(cluster_id)) + temp_data = lowflow_df.reindex( + index=np.argwhere(np.array(lowflow_df['cluster_id'] == cluster_id)).reshape(-1), + columns=['lat', 'lon', 'ndays']) + + lat_lon_uni, lat_lon_cpy = np.unique(temp_data[['lat', 'lon']].values, + return_inverse=True, axis=0) + index_uni = np.unique(lat_lon_cpy, axis=0) + ind, _ = tree_centr.query_radius(lat_lon_uni, r=res_centr / 2, count_only=False, + return_distance=True, sort_results=True) + ind = np.array([ind_i[0] if ind_i.size else -1 for ind_i in ind]) + intensity_cl = _fill_intensity(num_centr, ind, index_uni, lat_lon_cpy, + temp_data['ndays'].values) + return intensity_cl + +def _init_centroids(dis_xarray, centr_res_factor=1): + """Get centroids from the firms dataset and refactor them. + + Parameters: + dis_xarray (xarray): dataset obtained from ISIMIP netcdf + + Optional Parameters: + centr_res_factor (float): the factor applied to voluntarly decrease/increase + the centroids resolution + + Returns: + centroids (Centroids) + """ + res_data = np.min(np.abs([np.diff(dis_xarray.lon.values).min(), + np.diff(dis_xarray.lat.values).min()])) + centroids = Centroids() + centroids.set_raster_from_pnt_bounds((dis_xarray.lon.values.min(), + dis_xarray.lat.values.min(), + dis_xarray.lon.values.max(), + dis_xarray.lat.values.max()), + res=res_data / centr_res_factor) + centroids.set_meta_to_lat_lon() + centroids.set_area_approx() + centroids.set_on_land() + centroids.empty_geometry_points() + return centroids + + +def unique_clusters(lowflow_df): + """identify unqiue clustes based on clusters in 3 dimensions and set unique + cluster_id + + Parameters: + lowflow_df (pandas.DataFrame): contains monthly gridded data of days below threshold + + Returns: + lowflow_df (pandas.DataFrame): As input with new values in column cluster_id + """ + lowflow_df.cluster_id = np.zeros(len(lowflow_df.c_lat_lon)) - 1 + + lowflow_df.loc[lowflow_df.c_lat_lon == -1, 'c_lat_lon'] = np.nan + lowflow_df.loc[lowflow_df.c_lat_dt_month == -1, 'c_lat_dt_month'] = np.nan + lowflow_df.loc[lowflow_df.c_lon_dt_month == -1, 'c_lon_dt_month'] = np.nan + + idc = 0 # event id counter + current = 0 + for c_lat_lon in lowflow_df.c_lat_lon.unique(): + if np.isnan(c_lat_lon): + lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'cluster_id'] = -1 + else: + if len(lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'cluster_id'].unique()) == 1 \ + and -1 in lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'cluster_id'].unique(): + idc += 1 + current = idc + else: + current = max(lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'cluster_id'].unique()) + lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'cluster_id'] = current + + for c_lat_dt_month in lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'c_lat_dt_month'].unique(): + if not np.isnan(c_lat_dt_month): + lowflow_df.loc[lowflow_df.c_lat_dt_month == c_lat_dt_month, 'cluster_id'] = current + for c_lon_dt_month in lowflow_df.loc[lowflow_df.c_lat_lon == c_lat_lon, 'c_lon_dt_month'].unique(): + if not np.isnan(c_lon_dt_month): + lowflow_df.loc[lowflow_df.c_lon_dt_month == c_lon_dt_month, 'cluster_id'] = current + + lowflow_df.loc[np.isnan(lowflow_df.c_lon_dt_month), 'cluster_id'] = -1 + lowflow_df.loc[np.isnan(lowflow_df.c_lat_dt_month), 'cluster_id'] = -1 + lowflow_df.cluster_id = lowflow_df.cluster_id.astype(int) + return lowflow_df + +def data_preprocessing_percentile(percentile, yearrange, yearrange_ref, + input_dir, gh_model, cl_model, scenario, + scenario_ref, soc, soc_ref, fn_str_var, bbox, + min_days_per_month, keep_dis_data, yearchunks, + mask_threshold): + """load data and reference data and calculate monthly percentiles + then extract intensity based on days below threshold + returns geopandas dataframe + + Parameters: + c.f. parameters in LowFlow.set_from_nc() + + Returns: + lowflow_df (pandas.DataFrame) preprocessed data with days below threshold + per grid cell and month + centroids (Centroids): regular grid centroid with same resolution as input data + """ + + threshold_grid, mean_ref = _compute_threshold_grid(percentile, yearrange_ref, + input_dir, gh_model, cl_model, + scenario_ref, soc_ref, + fn_str_var, bbox, + yearchunks, + mask_threshold=mask_threshold, + keep_dis_data=keep_dis_data) + first_file = True + if yearchunks == 'default': + yearchunks = YEARCHUNKS[scenario] + # loop over yearchunks + # (for memory reasons: only loading one file with daily data per step, + # combining data after conversion to monthly data ) + for yearchunk in yearchunks: + # skip if file is not required, i.e., not in yearrange: + if int(yearchunk[0:4]) <= yearrange[1] and int(yearchunk[-4:]) >= yearrange[0]: + data_chunk = _read_and_combine_nc( + (max(yearrange[0], int(yearchunk[0:4])), + min(yearrange[-1], int(yearchunk[-4:]))), + input_dir, gh_model, cl_model, + scenario, soc, fn_str_var, bbox, [yearchunk]) + data_chunk = _days_below_threshold_per_month(data_chunk, threshold_grid, mean_ref, + min_days_per_month, keep_dis_data) + if first_file: + centroids = _init_centroids(data_chunk, centr_res_factor=1) + dataf = _xarray_to_geopandas(data_chunk) + first_file = False + else: + dataf = dataf.append(_xarray_to_geopandas(data_chunk)) + del data_chunk + dataf = dataf.sort_values(['lat', 'lon', 'dtime'], ascending=[True, True, True]) + return dataf.reset_index(drop=True), centroids + +def _read_and_combine_nc(yearrange, input_dir, gh_model, cl_model, scenario, + soc, fn_str_var, bbox, yearchunks): + """Import and combine data from nc files + + Parameters: + c.f. parameters in LowFlow.set_from_nc() + + Returns: + dis_xarray (xarray) + """ + first_file = True + if yearchunks == 'default': + yearchunks = YEARCHUNKS[scenario] + for yearchunk in yearchunks: + # skip if file is not required, i.e., not in yearrange: + if int(yearchunk[0:4]) > yearrange[1] or int(yearchunk[-4:]) < yearrange[0]: + continue + if scenario == 'hist': + bias_corr = 'nobc' + else: + bias_corr = 'ewembi' + + filename = os.path.join(input_dir, \ + f'{gh_model}_{cl_model}_{bias_corr}_{scenario}_{soc}_{fn_str_var}_{yearchunk}.nc' + ) + if not os.path.isfile(filename): + LOGGER.error('Netcdf file not found: %s', filename) + if first_file: + dis_xarray = _read_single_nc(filename, yearrange, bbox) + first_file = False + else: + dis_xarray = dis_xarray.combine_first(_read_single_nc(filename, yearrange, bbox)) + + # set negative discharge values to zero (debugging of input data): + dis_xarray.dis.values[dis_xarray.dis.values < 0] = 0 + return dis_xarray + +def _read_single_nc(filename, yearrange, bbox): + """Import data from single nc file, return as xarray + + Parameters: + filename (str or pathlib.Path): full path of input netcdf file + yearrange: (tuple): year range to be extracted from file + bbox (tuple of float): geographical bounding box in the form: + (lon_min, lat_min, lon_max, lat_max) + + Returns: + dis_xarray (xarray) + """ + dis_xarray = xr.open_dataset(filename) + try: + if not bbox: + return dis_xarray.sel(time=slice(dt.datetime(yearrange[0], 1, 1), + dt.datetime(yearrange[-1], 12, 31))) + lon_min, lat_min, lon_max, lat_max = bbox + return dis_xarray.sel(lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min), + time=slice(dt.datetime(yearrange[0], 1, 1), + dt.datetime(yearrange[-1], 12, 31))) + except TypeError: + # fix date format if not datetime + if not bbox: + dis_xarray = dis_xarray.sel(time=slice(cftime.DatetimeNoLeap(yearrange[0], 1, 1), + cftime.DatetimeNoLeap(yearrange[-1], 12, 31))) + else: + lon_min, lat_min, lon_max, lat_max = bbox + dis_xarray = dis_xarray.sel(lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min), + time=slice(cftime.DatetimeNoLeap(yearrange[0], 1, 1), + cftime.DatetimeNoLeap(yearrange[-1], 12, 31))) + datetimeindex = dis_xarray.indexes['time'].to_datetimeindex() + dis_xarray['time'] = datetimeindex + return dis_xarray + + +def _xarray_reduce(dis_xarray, fun=None, percentile=None): + """wrapper function to reduce xarray along time axis + + Parameters: + dis_xarray (xarray) + + Optional Parameters: + fun (str): function to be applied, either "mean" or "percentile" + percentile (num): percentile to be extracted, e.g. 5 for 5th percentile + (only if fun=='percentile') + + Returns: + xarray + """ + if fun == 'mean': + return dis_xarray.mean(dim='time') + if fun[0] == 'p': + return dis_xarray.reduce(np.nanpercentile, dim='time', q=percentile) + return None + +def _split_bbox(bbox, width=BBOX_WIDTH): + """split bounding box into squares, return new set of bounding boxes + Note: Could this function be a candidate for climada.util in the future? + + Parameters: + bbox (tuple of float): geographical bounding box in the form: + (lon_min, lat_min, lon_max, lat_max) + + Optional Parameters: + width (float): width and height of geographical bounding boxes for loop in degree lat/lon. + i.e., the bounding box is split into square boxes with maximum size BBOX_WIDTH*BBOX_WIDTH + + Returns: + bbox_list (list): list of bounding boxes of the same format as bbox + """ + if not bbox: + lon_min, lat_min, lon_max, lat_max = (-180, -85, 180, 85) + else: + lon_min, lat_min, lon_max, lat_max = bbox + lons = [lon_min] + \ + [int(idc) for idc in np.arange(np.ceil(lon_min+width-1), + np.floor(lon_max-width+1), width)] + [lon_max] + lats = [lat_min] + \ + [int(idc) for idc in np.arange(np.ceil(lat_min+width-1), + np.floor(lat_max-width+1), width)] + [lat_max] + bbox_list = list() + for ilon, _ in enumerate(lons[:-1]): + for ilat, _ in enumerate(lats[:-1]): + bbox_list.append([lons[ilon], lats[ilat], lons[ilon+1], lats[ilat+1]]) + return bbox_list + +def _compute_threshold_grid(percentile, yearrange_ref, input_dir, gh_model, cl_model, + scenario, soc, fn_str_var, bbox, yearchunks, + mask_threshold=None, keep_dis_data=False): + """given model run and year range specification, this function + returns the x-th percentile for every pixel over a given + time horizon (based on daily data) [all-year round percentiles!], + as well as the mean at each grid cell. + + Parameters: + c.f. parameters in LowFlow.set_from_nc() + + Optional parameters: + mask_threshold (tuple or list), Threshold(s) of below which the + grid is masked out. e.g. ('mean', 1.) + + Returns: + p_grid (xarray): grid with dis of given percentile (1-timestep) + mean_grid (xarray): grid with mean(dis) + """ + LOGGER.info('Computing threshold value per grid cell for Q%i, %i-%i', + percentile, yearrange_ref[0], yearrange_ref[1]) + if isinstance(mask_threshold, tuple): + mask_threshold = [mask_threshold] + bbox = _split_bbox(bbox) + p_grid = [] + mean_grid = [] + # loop over coordinate bounding boxes to save memory: + for box in bbox: + dis_xarray = _read_and_combine_nc(yearrange_ref, input_dir, gh_model, cl_model, + scenario, soc, fn_str_var, box, yearchunks) + if dis_xarray.dis.data.size: # only if data is not empty + p_grid += [_xarray_reduce(dis_xarray, fun='p', percentile=percentile)] + # only compute mean_grid if required by user or mask_threshold: + if keep_dis_data or (mask_threshold and True in ['mean' in x for x in mask_threshold]): + mean_grid += [_xarray_reduce(dis_xarray, fun='mean')] + + del dis_xarray + p_grid = xr.combine_by_coords(p_grid) + if mean_grid: + mean_grid = xr.combine_by_coords(mean_grid) + + if isinstance(mask_threshold, list): + for crit in mask_threshold: + if 'mean' in crit[0]: + p_grid.dis.values[mean_grid.dis.values < crit[1]] = 0 + mean_grid.dis.values[mean_grid.dis.values < crit[1]] = 0 + if 'percentile' in crit[0]: + p_grid.dis.values[p_grid.dis.values < crit[1]] = 0 + mean_grid.dis.values[p_grid.dis.values < crit[1]] = 0 + if keep_dis_data: + return p_grid, mean_grid + return p_grid, None + +def _days_below_threshold_per_month(dis_xarray, threshold_grid, mean_ref, + min_days_per_month, keep_dis_data): + """returns sum of days below threshold per month (as xarray with monthly data) + + if keep_dis_data is True, a DataFrame called 'lowflow_df' with additional data + is saved within the hazard object. + It provides data per event, grid cell, and month + lowflow_df comes with the following columns: ['lat', 'lon', 'time', 'ndays', + 'relative_dis', 'iter_ev', 'cons_id', + 'dtime', 'dt_month', 'geometry', 'cluster_id', 'c_lat_lon', + 'c_lat_dt_month', 'c_lon_dt_month'] + Note: cluster_id corresponds 1:1 with associated event_id. + + Parameters: + c.f. parameters in LowFlow.set_from_nc() + + Returns: + xarray + """ + # data = data.groupby('time.month')-threshold_grid # outdated + data_threshold = dis_xarray - threshold_grid + if keep_dis_data: + data_low = dis_xarray.where(data_threshold < 0) / mean_ref + data_low = data_low.resample(time='1M').mean() + data_threshold.dis.values[data_threshold.dis.values >= 0] = 0 + data_threshold.dis.values[data_threshold.dis.values < 0] = 1 + data_threshold = data_threshold.resample(time='1M').sum() + data_threshold.dis.values[data_threshold.dis.values < min_days_per_month] = 0 + data_threshold = data_threshold.rename({'dis': 'ndays'}) + if keep_dis_data: + data_threshold['relative_dis'] = data_low['dis'] + return data_threshold.where(data_threshold['ndays'] > 0) + +def _xarray_to_geopandas(dis_xarray): + """create GeoDataFrame from xarray with NaN values dropped + Note: Could this function be a candidate for climada.util in the future? + + Parameters: + dis_xarray (xarray): data as xarray object + + Returns: + lowflow_df (GeoDataFrame).""" + + dataf = dis_xarray.to_dataframe() + dataf.reset_index(inplace=True) + dataf = dataf.dropna() + dataf['iter_ev'] = np.ones(len(dataf), bool) + dataf['cons_id'] = np.zeros(len(dataf), int) - 1 + dataf['dtime'] = dataf['time'].apply(lambda x: x.toordinal()) + dataf['dt_month'] = dataf['time'].apply(lambda x: x.year * 12 + x.month) + return gpd.GeoDataFrame(dataf, geometry=[Point(x, y) for x, y in zip(dataf['lon'], + dataf['lat'])]) + +@numba.njit +def _fill_intensity(num_centr, ind, index_uni, lat_lon_cpy, intensity_raw): + """fill intensity list for a single cluster + + Parameters: + num_centr (int): total number of centroids + ind (list): list of centroid indices in cluster + lat_lon_cpy (array of int): index according to (lat, lon) in intensity_raw + intensity_raw (array of int): array of ndays values at each data point in cluster + + Returns: + list with summed ndays (=intensity) per geographical point (lat, lon) + """ + + intensity_cl = np.zeros((1, num_centr), dtype=numba.float64) + for idx in range(index_uni.size): + if ind[idx] != -1: + intensity_cl[0, ind[idx]] = \ + np.sum(intensity_raw[lat_lon_cpy == index_uni[idx]]) + return intensity_cl[0] diff --git a/climada/hazard/relative_cropyield.py b/climada/hazard/relative_cropyield.py new file mode 100644 index 0000000000..1603c4c771 --- /dev/null +++ b/climada/hazard/relative_cropyield.py @@ -0,0 +1,720 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Define AgriculturalDrought (AD) class. +WORK IN PROGRESS +""" + +__all__ = ['RelativeCropyield'] + +import logging +import os +from os import listdir +from os.path import isfile, join +import numpy as np +from matplotlib import pyplot as plt +import cartopy +import shapely.geometry +from scipy import sparse +import scipy.stats +import h5py + + +from climada.hazard.base import Hazard +from climada.util import dates_times as dt +from climada.util import coordinates as coord +from climada.util.constants import DATA_DIR + + +LOGGER = logging.getLogger(__name__) + +HAZ_TYPE = 'RC' +"""Hazard type acronym for Relative Cropyield""" + +INT_DEF = 'Yearly Yield' + +BBOX = np.array([-180, -85, 180, 85]) # [Lon min, lat min, lon max, lat max] +""""Default geographical bounding box of the total global agricultural land extent""" + +# ! deposit the input files in: climada_python/data/ISIMIP_crop/Input/Hazard +INPUT_DIR = os.path.join(DATA_DIR, 'ISIMIP_crop', 'Input', 'Hazard') +"""default paths for input and output data:""" +OUTPUT_DIR = os.path.join(DATA_DIR, 'ISIMIP_crop', 'Output') + + +#ISIMIP input data specific global variables +YEARCHUNKS = dict() +"""start and end years per senario as in ISIMIP-filenames""" +YEARCHUNKS['ISIMIP2a'] = dict() +YEARCHUNKS['ISIMIP2a'] = {'yearrange': np.array([1980, 1999]), 'startyear': 1980, + 'endyear': 1999, 'yearrange_mean': np.array([1980, 1999])} +YEARCHUNKS['historical'] = dict() +YEARCHUNKS['historical'] = {'yearrange': np.array([1976, 2005]), 'startyear': 1861, + 'endyear': 2005, 'yearrange_mean': np.array([1976, 2005])} +YEARCHUNKS['rcp60'] = dict() +YEARCHUNKS['rcp60'] = {'yearrange': np.array([2006, 2099]), 'startyear': 2006, + 'endyear': 2099} + +FN_STR_VAR = 'global_annual' +"""filename of ISIMIP output constant part""" + + +class RelativeCropyield(Hazard): + """Agricultural climate risk: Relative Cropyield (relative to historical mean); + Each year corresponds to one hazard event; + Based on modelled crop yield, from ISIMIP (www.isimip.org, required input data). + Attributes as defined in Hazard and the here defined additional attributes. + + Attributes: + crop_type (str): crop type ('whe' for wheat, 'mai' for maize, 'soy' for soybeans + and 'ric' for rice) + intensity_def (str): intensity defined as: + 'Yearly Yield' [t/(ha*y)], 'Relative Yield', or 'Percentile' + """ + + def __init__(self, pool=None): + """Empty constructor.""" + Hazard.__init__(self, HAZ_TYPE) + if pool: + self.pool = pool + LOGGER.info('Using %s CPUs.', self.pool.ncpus) + else: + self.pool = None + + self.crop = '' + self.intensity_def = INT_DEF + + def set_from_single_run(self, input_dir=None, filename=None, bbox=BBOX, + yearrange=(YEARCHUNKS['historical'])['yearrange'], + ag_model=None, cl_model=None, scenario='historical', + soc=None, co2=None, crop=None, irr=None, fn_str_var=FN_STR_VAR): + + """Wrapper to fill hazard from nc_dis file from ISIMIP + Parameters: + input_dir (string): path to input data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + yearrange (int tuple): year range for hazard set, f.i. (1976, 2005) + ag_model (str): abbrev. agricultural model (only when input_dir is selected) + f.i. 'clm-crop', 'gepic','lpjml','pepic' + cl_model (str): abbrev. climate model (only when input_dir is selected) + f.i. ['gfdl-esm2m', 'hadgem2-es','ipsl-cm5a-lr','miroc5' + scenario (str): climate change scenario (only when input_dir is selected) + f.i. 'historical' or 'rcp60' or 'ISIMIP2a' + soc (str): socio-economic trajectory (only when input_dir is selected) + f.i. '2005soc' or 'histsoc' + co2 (str): CO2 forcing scenario (only when input_dir is selected) + f.i. 'co2' or '2005co2' + crop (str): crop type (only when input_dir is selected) + f.i. 'whe', 'mai', 'soy' or 'ric' + irr (str): irrigation type (only when input_dir is selected) + f.i 'noirr' or 'irr' + fn_str_var (str): FileName STRing depending on VARiable and + ISIMIP simuation round + raises: + NameError + """ + if input_dir is not None: + if not os.path.exists(input_dir): + LOGGER.error('Input directory %s does not exist', input_dir) + raise NameError + else: + LOGGER.error('Input directory %s not set', input_dir) + raise NameError + + + # The filename is set or other variables (cl_model, scenario) are extracted of the + # specified filename + if filename is None: + yearchunk = YEARCHUNKS[scenario] + filename = os.path.join(input_dir, '%s_%s_ewembi_%s_%s_%s_yield-%s-%s_%s_%s_%s.nc' \ + %(ag_model, cl_model, scenario, soc, co2, crop, + irr, fn_str_var, str(yearchunk['startyear']), + str(yearchunk['endyear']))) + + elif scenario == 'ISIMIP2a': + (_, _, _, _, _, _, _, crop, _, _, startyear, endyearnc) = filename.split('_') + endyear, _ = endyearnc.split('.') + yearchunk = dict() + yearchunk = {'yearrange': np.array([int(startyear), int(endyear)]), + 'startyear': int(startyear), 'endyear': int(endyear)} + filename = os.path.join(input_dir, filename) + elif scenario == 'test_file': + yearchunk = dict() + yearchunk = {'yearrange': np.array([1976, 2005]), 'startyear': 1861, + 'endyear': 2005, 'yearrange_mean': np.array([1976, 2005])} + ag_model, cl_model, _, _, soc, co2, crop_prop, *_ = filename.split('_') + _, crop, irr = crop_prop.split('-') + filename = os.path.join(input_dir, filename) + else: + yearchunk = YEARCHUNKS[scenario] + (_, _, _, _, _, _, crop_irr, *_) = filename.split('_') + _, crop, irr = crop_irr.split('-') + filename = os.path.join(input_dir, filename) + + + + + # define indexes of the netcdf-bands to be extracted, and the + # corresponding event names and dates + # corrected indexes due to the bands in input starting with the index=1 + id_bands = np.arange(yearrange[0] - yearchunk['startyear'] + 1, + yearrange[1] - yearchunk['startyear'] + 2).tolist() + + # hazard setup: set attributes + [lonmin, latmin, lonmax, latmax] = bbox + self.set_raster([filename], band=id_bands, + geometry=list([shapely.geometry.box(lonmin, latmin, lonmax, latmax)])) + + self.intensity.data[np.isnan(self.intensity.data)] = 0.0 + self.intensity.todense() + self.crop = crop + self.event_name = [str(n) for n in range(int(yearrange[0]), int(yearrange[-1] + 1))] + self.frequency = np.ones(len(self.event_name)) * (1 / len(self.event_name)) + self.fraction = self.intensity.copy() + self.fraction.data.fill(1.0) + self.units = 't / y / ha' + self.date = np.array(dt.str_to_date( + [event_ + '-01-01' for event_ in self.event_name])) + self.centroids.set_meta_to_lat_lon() + self.centroids.region_id = ( + coord.coord_on_land(self.centroids.lat, self.centroids.lon)).astype(dtype=int) + self.check() + return self + + def calc_mean(self, yearrange_mean=(YEARCHUNKS['historical'])['yearrange_mean'], + save=False, output_dir=OUTPUT_DIR): + """Calculates mean of the hazard for a given reference time period + + Optional Parameters: + yearrange_mean (array): time period used to calculate the mean intensity + default: 1976-2005 (historical) + save (boolean): save mean to file? default: False + output_dir (str): path of output directory + + Returns: + hist_mean(array): contains mean value over the given reference + time period for each centroid + """ + startyear, endyear = yearrange_mean + event_list = [str(n) for n in range(int(startyear), int(endyear + 1))] + mean = self.select(event_names=event_list).intensity.mean(axis=0) + hist_mean = np.squeeze(np.asarray(mean)) + + if save: + # generate output directories if they do not exist yet + if not os.path.exists(output_dir): + os.mkdir(output_dir) + mean_dir = os.path.join(output_dir, 'Hist_mean') + if not os.path.exists(mean_dir): + os.mkdir(mean_dir) + # save mean_file + mean_file = h5py.File(mean_dir + 'hist_mean_' + self.crop + '_' + str(startyear) + + '-' + str(endyear) + '.hdf5', 'w') + mean_file.create_dataset('mean', data=hist_mean) + mean_file.create_dataset('lat', data=self.centroids.lat) + mean_file.create_dataset('lon', data=self.centroids.lon) + mean_file.close() + + return hist_mean + + + def set_rel_yield_to_int(self, hist_mean): + """Sets relative yield (yearly yield / historic mean) as intensity + + Parameters: + hist_mean (array): historic mean per centroid + + Returns: + hazard with modified intensity [unitless] + """ + # determine idx of the centroids with a mean yield !=0 + [idx] = np.where(hist_mean != 0) + + # initialize new hazard_matrix + hazard_matrix = np.zeros(self.intensity.shape, dtype=np.float32) + + # compute relative yield for each event: + for event in range(len(self.event_id)): + hazard_matrix[event, idx] = (self.intensity[event, idx] / hist_mean[idx])-1 + + self.intensity = sparse.csr_matrix(hazard_matrix) + self.intensity_def = 'Relative Yield' + self.units = '' + + return self + + def set_percentile_to_int(self, reference_intensity=None): + """Sets percentile to intensity + + Parameters: + reference_intensity (AD): intensity to be used as reference + (e.g. the historic intensity can be used in order to be able + to directly compare historic and future projection data) + + Returns: + hazard with modified intensity + """ + hazard_matrix = np.zeros(self.intensity.shape) + if reference_intensity is None: + reference_intensity = self.intensity + + for centroid in range(self.intensity.shape[1]): + nevents = reference_intensity.shape[0] + array = reference_intensity[:, centroid].toarray().reshape(nevents) + hazard_matrix[:, centroid] = np.array([scipy.stats.percentileofscore(array, event) + for event in array])/100 + self.intensity = sparse.csr_matrix(hazard_matrix) + self.intensity_def = 'Percentile' + self.units = '' + + return self + + def plot_intensity_cp(self, event=None, dif=False, axis=None, **kwargs): + """Plots intensity with predefined settings depending on the intensity definition + + Optional Parameters: + event (int or str): event_id or event_name + dif (boolean): variable signilizing whether absolute values or the difference between + future and historic are plotted (False: his/fut values; True: difference = fut-his) + axis (geoaxes): axes to plot on + + Returns: + axes (geoaxes) + """ + if not dif: + if self.intensity_def == 'Yearly Yield': + axes = self.plot_intensity(event=event, axis=axis, cmap='YlGn', vmin=0, vmax=10, + **kwargs) + elif self.intensity_def == 'Relative Yield': + axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=-1, vmax=1, + **kwargs) + elif self.intensity_def == 'Percentile': + axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=0, vmax=1, + **kwargs) + else: + if self.intensity_def == 'Yearly Yield': + axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=-2, vmax=2, + **kwargs) + elif self.intensity_def == 'Relative Yield': + axes = self.plot_intensity(event=event, axis=axis, cmap='RdBu', vmin=-0.5, + vmax=0.5, **kwargs) + + return axes + + def plot_time_series(self, event=None): + """Plots a time series of intensities (a series of sub plots) + + Optional Parameters: + event (int or str): event_id or event_name + + Returns: + figure + """ + + # if no event range is given, all events contained in self are plotted + # in the case that a specific range is given as input (event) only the events + # within this time range are plotted + if event is None: + event = self.event_name + else: + event = [str(n) for n in range(event[0], event[1] + 1)] + + self.centroids.set_meta_to_lat_lon() + + # definition of plot extents + len_lat = abs(self.centroids.lat[0] - self.centroids.lat[-1]) * (2.5 / 13.5) + len_lon = abs(self.centroids.lon[0] - self.centroids.lon[-1]) * (5 / 26) + + nr_subplots = len(event) + + if len_lon >= len_lat: + colums = int(np.floor(np.sqrt(nr_subplots / (len_lon / len_lat)))) + rows = int(np.ceil(nr_subplots / colums)) + else: + rows = int(np.floor(np.sqrt(nr_subplots / (len_lat / len_lon)))) + colums = int(np.ceil(nr_subplots / colums)) + + fig, axes = plt.subplots(rows, colums, sharex=True, sharey=True, + figsize=(colums * len_lon, rows * len_lat), + subplot_kw=dict(projection=cartopy.crs.PlateCarree())) + colum = 0 + row = 0 + + for year in range(nr_subplots): + axes.flat[year].set_extent([np.min(self.centroids.lon), np.max(self.centroids.lon), + np.min(self.centroids.lat), np.max(self.centroids.lat)]) + + if rows == 1: + self.plot_intensity_cp(event=event[year], axis=axes[colum]) + elif colums == 1: + self.plot_intensity_cp(event=event[year], axis=axes[row]) + else: + self.plot_intensity_cp(event=event[year], axis=axes[row, colum]) + + if colum <= colums - 2: + colum = colum + 1 + else: + colum = 0 + row = row + 1 + + return fig + + def plot_comparing_maps(self, his, fut, axes, nr_cli_models=1, model=1): + """Plots comparison maps of historic and future data and their difference fut-his + + Parameters: + his (sparse matrix): historic mean annual yield or mean relative yield + fut (sparse matrix): future mean annual yield or mean relative yield + axes (Geoaxes): subplot axes that can be generated with ag_drought_util.setup_subplots + nr_cli_models (int): number of climate models and respectively nr of rows within + the subplot + model (int): current row to plot - this method can be used in a loop to plot + subplots in one figure consisting of several rows of subplots. + One row displays the intensity for present and future climate and the difference of + the two for one model-combination (ag_model and cl_model) + + + Returns: + geoaxes + """ + dif = fut - his + self.event_id = 0 + + for subplot in range(3): + + if self.intensity_def == 'Yearly Yield': + self.units = 't / y' + elif self.intensity_def == 'Relative Yield': + self.units = '' + + if subplot == 0: + self.intensity = sparse.csr_matrix(his) + dif_def = 0 + elif subplot == 1: + self.intensity = sparse.csr_matrix(fut) + dif_def = 0 + elif subplot == 2: + self.intensity = sparse.csr_matrix(dif) + dif_def = 1 + + + if nr_cli_models == 1: + ax1 = self.plot_intensity_cp(event=0, dif=dif_def, axis=axes[subplot]) + else: + ax1 = self.plot_intensity_cp(event=0, dif=dif_def, axis=axes[model, subplot]) + + ax1.set_title('') + + if nr_cli_models == 1: + cols = ['Historical', 'Future', 'Difference = Future - Historical'] + for ax0, col in zip(axes, cols): + ax0.set_title(col, size='large') + + return axes + + +def generate_full_hazard_set(input_dir=INPUT_DIR, output_dir=OUTPUT_DIR, bbox=BBOX, + isimip_run='ISIMIP2b', yearrange_his=None, yearrange_mean=None, + return_data=False, save=True): + + """Wrapper to generate full hazard set and save it to output directory. + + Optional Parameters: + input_dir (string): path to input data directory + output_dir (string): path to output data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + isimip_run (string): name of the ISIMIP run (ISIMIP2a or ISIMIP2b) + yearrange_his (int tuple): year range for the historical hazard sets + yearrange_mean (int tuple): year range for the historical mean + return_data (boolean): returned output + False: returns list of filenames only + True: returns also list of data + save (boolean): save output data to output_dir + + Return: + filename_list (list): list of filenames + + Optional Return: + output_list (list): list of generated output data (hazards and historical mean) + + """ + + filenames = [f for f in listdir(input_dir) if (isfile(join(input_dir, f))) if not + f.startswith('.')] + + + # generate output directories if they do not exist yet + if not os.path.exists(output_dir): + os.mkdir(output_dir) + if not os.path.exists(os.path.join(output_dir, 'Hazard')): + os.mkdir(os.path.join(output_dir, 'Hazard')) + if not os.path.exists(os.path.join(output_dir, 'Hist_mean')): + os.mkdir(os.path.join(output_dir, 'Hist_mean')) + + filename_list = list() + output_list = list() + + (his_file_list, file_props, hist_mean_per_crop, + scenario_list, crop_list) = init_hazard_set(filenames, input_dir, bbox, isimip_run, + yearrange_his) + + if yearrange_mean is None: + yearrange_mean = (YEARCHUNKS[(file_props[his_file_list[0]])['scenario']])['yearrange_mean'] + + for his_file in his_file_list: + haz_his, filename, hist_mean = calc_his_haz(his_file, file_props, input_dir, bbox, + yearrange_mean) + # save the historical mean depending on the crop-irrigation combination + # the idx keeps track of the row in which the hist_mean values are written per crop-irr to + # ensure that all files are assigned to the corresponding crop-irr combination + hist_mean_per_crop[(file_props[his_file])['crop_irr']]['value'][ + hist_mean_per_crop[(file_props[his_file])['crop_irr']]['idx'], :] = hist_mean + hist_mean_per_crop[file_props[his_file]['crop_irr']]['idx'] += 1 + + filename_list.append(filename) + output_list.append(haz_his) + + + if isimip_run == 'ISIMIP2b': + # compute the relative yield for all future scenarios with the corresponding + # historic mean + for scenario in scenario_list: + haz_fut, filename = calc_fut_haz(his_file, scenario, file_props, hist_mean, + input_dir, bbox) + filename_list.append(filename) + output_list.append(haz_fut) + + # calculate mean hist_mean for each crop-irrigation combination and save as hdf5 in output_dir + for crop_irr in crop_list: + mean = np.mean((hist_mean_per_crop[crop_irr])['value'], 0) + mean_filename = ('hist_mean_' + crop_irr + '_' + str(yearrange_mean[0]) +'-' + + str(yearrange_mean[1]) + '.hdf5') + filename_list.append(mean_filename) + output_list.append(mean) + + if save: + for idx, filename in enumerate(filename_list): + if 'haz' in filename: + output_list[idx].select(reg_id=1).write_hdf5(os.path.join(output_dir, + 'Hazard', filename)) + elif 'mean' in filename: + mean_file = h5py.File(os.path.join(output_dir, 'Hist_mean', filename), 'w') + mean_file.create_dataset('mean', data=output_list[idx]) + mean_file.create_dataset('lat', data=haz_his.centroids.lat) + mean_file.create_dataset('lon', data=haz_his.centroids.lon) + mean_file.close() + # save historic mean as netcdf (saves mean, lat and lon as arrays) + # mean_file = xr.Dataset({'mean': mean, 'lat': haz_his.centroids.lat, \ + # 'lon': haz_his.centroids.lon}) + # mean_file.to_netcdf(mean_dir+'hist_mean_'+crop_irr+'.nc') + + if not return_data: + return filename_list + return filename_list, output_list + +def init_hazard_set(filenames, input_dir=INPUT_DIR, bbox=BBOX, isimip_run='ISIMIP2b', + yearrange_his=None): + + """Initialize fulll hazard set. + + Parameters: + filenames (list): list of filenames + input_dir (string): path to input data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + isimip_run (string): name of the ISIMIP run (ISIMIP2a or ISIMIP2b) + yearrange_his (int tuple): year range for the historical hazard sets + + Return: + his_file_list (list): list of historical input hazard files + file_props (dict): file properties of all historical input hazard files + hist_mean_per_crop (dict): empty dictonary to save hist_mean values for each + crop-irr combination + scenario_list (list): list of all future scenarios + crop_list (list): list of all crop-irr combinations + + """ + + crop_list = list() + file_props = dict() + his_file_list = list() + scenario_list = list() + + + + for file in filenames: + if isimip_run == 'ISIMIP2b': + ag_model, cl_model, _, scenario, soc, co2, crop_prop, *_ = file.split('_') + _, crop, irr = crop_prop.split('-') + if 'historical' in file: + his_file_list.append(file) + if yearrange_his is None: + yearrange_his = (YEARCHUNKS[scenario])['yearrange'] + startyear, endyear = yearrange_his + file_props[file] = {'ag_model': ag_model, 'cl_model': cl_model, 'soc':soc, + 'scenario': scenario, 'co2':co2, 'crop': crop, 'irr': irr, + 'startyear': startyear, 'endyear': endyear, + 'crop_irr': crop+'-'+irr} + elif scenario not in scenario_list: + scenario_list.append(scenario) + + elif isimip_run == 'ISIMIP2a': + (ag_model, cl_model, biasco, scenario, harm, irr, _, crop, _, _, + startyear, endyearnc) = file.split('_') + endyear, _ = endyearnc.split('.') + if yearrange_his is not None: + startyear, endyear = (YEARCHUNKS[scenario])['yearrange'] + + file_props[file] = dict() + file_props[file] = {'ag_model': ag_model, 'cl_model': cl_model, 'scenario': 'ISIMIP2a', + 'bc':biasco, 'harm':harm, 'crop': crop, 'irr': irr, + 'crop_irr': crop+'-'+irr, 'startyear': int(startyear), + 'endyear': int(endyear)} + his_file_list.append(file) + elif isimip_run == 'test_file': + ag_model, cl_model, _, _, soc, co2, crop_prop, *_ = file.split('_') + _, crop, irr = crop_prop.split('-') + his_file_list.append(file) + startyear, endyear = yearrange_his + file_props[file] = {'ag_model': ag_model, 'cl_model': cl_model, 'soc':soc, + 'scenario': 'test_file', 'co2':co2, 'crop': crop, 'irr': irr, + 'startyear': startyear, 'endyear': endyear, + 'crop_irr': crop+'-'+irr} + + crop_irr = crop + '-' + irr + if crop_irr not in crop_list: + crop_list.append(crop_irr) + + # generate hazard using the first file to determine the size of the historic mean + # file structure: ag_model _ cl_model _ scenario _ soc _ co2 _ + # yield-crop-irr _ fn_str_var _ startyear _ endyear . nc + #e.g. gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-whe-noirr_ + # global_annual_1861_2005.nc + haz_dummy = RelativeCropyield() + haz_dummy.set_from_single_run(input_dir=input_dir, filename=his_file_list[0], bbox=bbox, + scenario=(file_props[his_file_list[0]])['scenario'], + yearrange=np.array([(file_props[his_file_list[0]])['startyear'], + (file_props[his_file_list[0]])['endyear']])) + + # initiate the historic mean for each combination of crop and irrigation type + # the idx keeps track of the row in which the hist_mean values are written per crop-irr to + # ensure that all files are assigned to the corresponding crop-irr combination + hist_mean_per_crop = dict() + for crop_irr in crop_list: + amount_crop_irr = sum(crop_irr in s for s in his_file_list) + hist_mean_per_crop[crop_irr] = dict() + hist_mean_per_crop[crop_irr] = { + 'value': np.zeros([amount_crop_irr, haz_dummy.intensity.shape[1]]), + 'idx': 0} + + return his_file_list, file_props, hist_mean_per_crop, scenario_list, crop_list + # if isimip_run == 'ISIMIP2a': + # return crop_list, his_file_list, yearrange_list, hist_mean_per_crop, file_props + # return crop_list, his_file_list, scenario_list, hist_mean_per_crop, file_props + +def calc_his_haz(his_file, file_props, input_dir=INPUT_DIR, bbox=BBOX, yearrange_mean=None): + + """Create historical hazard and calculate historical mean. + + Parameters: + his_file (string): file name of historical input hazard file + file_props (dict): file properties of all historical input hazard files + input_dir (string): path to input data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + yearrange_mean (int tuple): year range for the historical mean + default: 1976 - 2005 + + + Return: + haz_his (RelativeCropyield): historical hazard + filename (string): name to save historical hazard + hist_mean (array): historical mean of the historical hazard + + """ + + + haz_his = RelativeCropyield() + haz_his.set_from_single_run(input_dir=input_dir, filename=his_file, bbox=bbox, + scenario=(file_props[his_file])['scenario'], + yearrange=np.array([(file_props[his_file])['startyear'], + (file_props[his_file])['endyear']])) + + hist_mean = haz_his.calc_mean(yearrange_mean) + haz_his.set_rel_yield_to_int(hist_mean) + + crop_irr = (file_props[his_file])['crop'] + '-' + (file_props[his_file])['irr'] + if (file_props[his_file])['scenario'] == 'ISIMIP2a': + filename = ('haz' + '_' + (file_props[his_file])['ag_model'] + '_' + + (file_props[his_file])['cl_model'] +'_' + (file_props[his_file])['bc'] + + '_' + (file_props[his_file])['harm'] + '_' + crop_irr + '_' + + str((file_props[his_file])['startyear']) + '-' + + str((file_props[his_file])['endyear']) + '.hdf5') + else: + filename = ('haz' + '_' + (file_props[his_file])['ag_model'] + '_' + + (file_props[his_file])['cl_model'] + '_' + (file_props[his_file])['scenario'] + + '_' + (file_props[his_file])['soc'] + '_' + (file_props[his_file])['co2'] + + '_' + crop_irr + '_' + str((file_props[his_file])['startyear']) + '-' + + str((file_props[his_file])['endyear']) + '.hdf5') + + + return haz_his, filename, hist_mean + +def calc_fut_haz(his_file, scenario, file_props, hist_mean, input_dir=INPUT_DIR, bbox=BBOX): + + """Create future hazard. + + Parameters: + his_file (string): file name of historical input hazard file + scenario (string): future scenario, e.g. rcp60 + file_props (dict): file properties of all historical input hazard files + hist_mean (array): historical mean of the historical hazard for the same model + combination and crop-irr cobination + input_dir (string): path to input data directory + bbox (list of four floats): bounding box: + [lon min, lat min, lon max, lat max] + + Return: + haz_fut (RelativeCropyield): future hazard + filename (string): name to save historical hazard + + + """ + + yearrange_fut = np.array([(YEARCHUNKS[scenario])['startyear'], + (YEARCHUNKS[scenario])['endyear']]) + startyear, endyear = yearrange_fut + haz_fut = RelativeCropyield() + haz_fut.set_from_single_run(input_dir=input_dir, bbox=bbox, yearrange=yearrange_fut, + ag_model=(file_props[his_file])['ag_model'], + cl_model=(file_props[his_file])['cl_model'], + scenario=scenario, + soc=(file_props[his_file])['soc'], + co2=(file_props[his_file])['co2'], + crop=(file_props[his_file])['crop'], + irr=(file_props[his_file])['irr']) + haz_fut.set_rel_yield_to_int(hist_mean) + filename = ('haz' + '_' + (file_props[his_file])['ag_model'] + '_' + + (file_props[his_file])['cl_model'] + '_' + scenario + '_' + + (file_props[his_file])['soc'] + '_' + (file_props[his_file])['co2'] + + '_' + (file_props[his_file])['crop'] + '-' + (file_props[his_file])['irr']+ '_' + + str(startyear) + '-' + str(endyear) + '.hdf5') + + return haz_fut, filename diff --git a/climada/hazard/river_flood.py b/climada/hazard/river_flood.py new file mode 100644 index 0000000000..62f68bbdce --- /dev/null +++ b/climada/hazard/river_flood.py @@ -0,0 +1,373 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Define RiverFlood class. +""" + +__all__ = ['RiverFlood'] + +import logging +import os +import numpy as np +import scipy as sp +import xarray as xr +import pandas as pd +import geopandas as gpd +import datetime as dt +from datetime import date +from rasterio.warp import Resampling +import copy +from climada.util.constants import RIVER_FLOOD_REGIONS_CSV +from climada.util.coordinates import get_region_gridpoints,\ + region2isos, country_iso2natid +from climada.hazard.base import Hazard +from climada.hazard.centroids import Centroids +from climada.util.coordinates import get_land_geometry, read_raster + +NATID_INFO = pd.read_csv(RIVER_FLOOD_REGIONS_CSV) + + +LOGGER = logging.getLogger(__name__) + +HAZ_TYPE = 'RF' +"""Hazard type acronym RiverFlood""" + + +class RiverFlood(Hazard): + """Contains flood events + Flood intensities are calculated by means of the + CaMa-Flood global hydrodynamic model + + Attributes: + + fla_event (1d array(n_events)) total flooded area for every event + fla_annual (1d array (n_years)) total flooded area for every year + fla_ann_av (float) average flooded area per year + fla_ev_av (float) average flooded area per event + fla_ann_centr (2d array(n_years x n_centroids)) flooded area in + every centroid for every event + fla_ev_centr (2d array(n_events x n_centroids)) flooded area in + every centroid for every event + + """ + + def __init__(self): + """Empty constructor""" + + Hazard.__init__(self, HAZ_TYPE) + + def set_from_nc(self, dph_path=None, frc_path=None, origin=False, + centroids=None, countries=None, reg=None, shape=None, ISINatIDGrid=False, + years=[2000]): + """Wrapper to fill hazard from nc_flood file + Parameters: + dph_path (string): Flood file to read (depth) + frc_path (string): Flood file to read (fraction) + origin (bool): Historical or probabilistic event + centroids (Centroids): centroids to extract + countries (list of countries ISO3) selection of countries + (reg must be None!) + reg (list of regions): can be set with region code if whole areas + are considered (if not None, countries and centroids + are ignored) + ISINatIDGrid (Bool): Indicates whether ISIMIP_NatIDGrid is used + years (int list): years that are considered + + raises: + NameError + """ + if dph_path is None: + LOGGER.error('No flood-depth-path set') + raise NameError + if frc_path is None: + LOGGER.error('No flood-fraction-path set') + raise NameError + if not os.path.exists(dph_path): + LOGGER.error('Invalid flood-file path %s', dph_path) + raise NameError + if not os.path.exists(frc_path): + LOGGER.error('Invalid flood-file path %s', frc_path) + raise NameError + + with xr.open_dataset(dph_path) as flood_dph: + time = flood_dph.time.data + + event_index = self._select_event(time, years) + bands = event_index + 1 + + if countries or reg: + # centroids as points + if ISINatIDGrid: + + dest_centroids = RiverFlood._select_exact_area(countries, reg)[0] + meta_centroids = copy.copy(dest_centroids) + meta_centroids.set_lat_lon_to_meta() + + self.set_raster(files_intensity=[dph_path], + files_fraction=[frc_path], band=bands.tolist(), + transform=meta_centroids.meta['transform'], + width=meta_centroids.meta['width'], + height=meta_centroids.meta['height'], + resampling=Resampling.nearest) + x_i = ((dest_centroids.lon - self.centroids.meta['transform'][2]) / + self.centroids.meta['transform'][0]).astype(int) + y_i = ((dest_centroids.lat - self.centroids.meta['transform'][5]) / + self.centroids.meta['transform'][4]).astype(int) + + fraction = self.fraction[:, y_i * self.centroids.meta['width'] + x_i] + intensity = self.intensity[:, y_i * self.centroids.meta['width'] + x_i] + + self.centroids = dest_centroids + self.intensity = sp.sparse.csr_matrix(intensity) + self.fraction = sp.sparse.csr_matrix(fraction) + else: + if reg: + iso_codes = region2isos(reg) + # envelope containing counties + cntry_geom = get_land_geometry(iso_codes) + self.set_raster(files_intensity=[dph_path], + files_fraction=[frc_path], + band=bands.tolist(), + geometry=cntry_geom) + # self.centroids.set_meta_to_lat_lon() + else: + cntry_geom = get_land_geometry(countries) + self.set_raster(files_intensity=[dph_path], + files_fraction=[frc_path], + band=bands.tolist(), + geometry=cntry_geom) + # self.centroids.set_meta_to_lat_lon() + + elif shape: + shapes = gpd.read_file(shape) + + rand_geom = shapes.geometry[0] + + self.set_raster(files_intensity=[dph_path], + files_fraction=[frc_path], + band=bands.tolist(), + geometry=rand_geom) + return + + elif not centroids: + # centroids as raster + self.set_raster(files_intensity=[dph_path], + files_fraction=[frc_path], + band=bands.tolist()) + # self.centroids.set_meta_to_lat_lon() + + else: # use given centroids + # if centroids.meta or grid_is_regular(centroids)[0]: + """TODO: implement case when meta or regulargrid is defined + centroids.meta or grid_is_regular(centroidsxarray)[0]: + centroids>flood --> error + reprojection, resampling.average (centroids< flood) + (transform) + reprojection change resampling""" + # else: + if centroids.meta: + centroids.set_meta_to_lat_lon() + metafrc, fraction = read_raster(frc_path, band=bands.tolist()) + metaint, intensity = read_raster(dph_path, band=bands.tolist()) + x_i = ((centroids.lon - metafrc['transform'][2]) / + metafrc['transform'][0]).astype(int) + y_i = ((centroids.lat - metafrc['transform'][5]) / + metafrc['transform'][4]).astype(int) + fraction = fraction[:, y_i * metafrc['width'] + x_i] + intensity = intensity[:, y_i * metaint['width'] + x_i] + self.centroids = centroids + self.intensity = sp.sparse.csr_matrix(intensity) + self.fraction = sp.sparse.csr_matrix(fraction) + + self.units = 'm' + self.tag.file_name = dph_path + ';' + frc_path + self.event_id = np.arange(self.intensity.shape[0]) + self.event_name = list(map(str, years)) + + if origin: + self.orig = np.ones(self.size, bool) + else: + self.orig = np.zeros(self.size, bool) + + self.frequency = np.ones(self.size) / self.size + + with xr.open_dataset(dph_path) as flood_dph: + self.date = np.array([dt.datetime(flood_dph.time[i].dt.year, + flood_dph.time[i].dt.month, + flood_dph.time[i].dt.day).toordinal() + for i in event_index]) + + def _select_event(self, time, years): + """ + Selects events only in specific years and returns corresponding event + indices + Parameters: + time: event time stemps (array datetime64) + years: years to be selcted (int array) + Raises: + KeyError + Returns: + event indices (int array) + """ + event_names = pd.to_datetime(time).year + event_index = np.where(np.isin(event_names, years))[0] + if len(event_index) == 0: + LOGGER.error('No events found for selected %s', years) + raise AttributeError + self.event_name = list(map(str, pd.to_datetime(time[event_index]))) + return event_index + + def exclude_trends(self, fld_trend_path, dis): + """ + Function allows to exclude flood impacts that are caused in areas + exposed discharge trends other than the selected one. (This function + is only needed for very specific applications) + Raises: + NameError + """ + if not os.path.exists(fld_trend_path): + LOGGER.error('Invalid ReturnLevel-file path %s', fld_trend_path) + raise NameError + else: + metafrc, trend_data = read_raster(fld_trend_path, band=[1]) + x_i = ((self.centroids.lon - metafrc['transform'][2]) / + metafrc['transform'][0]).astype(int) + y_i = ((self.centroids.lat - metafrc['transform'][5]) / + metafrc['transform'][4]).astype(int) + + trend = trend_data[:, y_i * metafrc['width'] + x_i] + + if dis == 'pos': + dis_map = np.greater(trend, 0) + else: + dis_map = np.less(trend, 0) + + new_trends = dis_map.astype(int) + + new_intensity = np.multiply(self.intensity.todense(), new_trends) + new_fraction = np.multiply(self.fraction.todense(), new_trends) + + self.intensity = sp.sparse.csr_matrix(new_intensity) + self.fraction = sp.sparse.csr_matrix(new_fraction) + + def exclude_returnlevel(self, frc_path): + """ + Function allows to exclude flood impacts below a certain return level + by manipulating flood fractions in a way that the array flooded more + frequently than the treshold value is excluded. (This function + is only needed for very specific applications) + Raises: + NameErroris function + """ + + if not os.path.exists(frc_path): + LOGGER.error('Invalid ReturnLevel-file path %s', frc_path) + raise NameError + else: + metafrc, fraction = read_raster(frc_path, band=[1]) + x_i = ((self.centroids.lon - metafrc['transform'][2]) / + metafrc['transform'][0]).astype(int) + y_i = ((self.centroids.lat - metafrc['transform'][5]) / + metafrc['transform'][4]).astype(int) + fraction = fraction[:, y_i * metafrc['width'] + x_i] + new_fraction = np.array(np.subtract(self.fraction.todense(), + fraction)) + new_fraction = new_fraction.clip(0) + self.fraction = sp.sparse.csr_matrix(new_fraction) + + def set_flooded_area(self, save_centr=False): + """ + Calculates flooded area for hazard. sets yearly flooded area and + flooded area per event + Raises: + MemoryError + """ + self.centroids.set_area_pixel() + area_centr = self.centroids.area_pixel + event_years = np.array([date.fromordinal(self.date[i]).year + for i in range(len(self.date))]) + years = np.unique(event_years) + year_ev_mk = self._annual_event_mask(event_years, years) + + fla_ann_centr = np.zeros((len(years), len(self.centroids.lon))) + fla_ev_centr = np.array(np.multiply(self.fraction.todense(), + area_centr)) + self.fla_event = np.sum(fla_ev_centr, axis=1) + for year_ind in range(len(years)): + fla_ann_centr[year_ind, :] =\ + np.sum(fla_ev_centr[year_ev_mk[year_ind, :], :], + axis=0) + self.fla_annual = np.sum(fla_ann_centr, axis=1) + self.fla_ann_av = np.mean(self.fla_annual) + self.fla_ev_av = np.mean(self.fla_event) + if save_centr: + self.fla_ann_centr = sp.sparse.csr_matrix(fla_ann_centr) + self.fla_ev_centr = sp.sparse.csr_matrix(fla_ev_centr) + + def _annual_event_mask(self, event_years, years): + """Assignes events to each year + Returns: + bool array (columns contain events, rows contain years) + """ + event_mask = np.full((len(years), len(event_years)), False, dtype=bool) + for year_ind in range(len(years)): + events = np.where(event_years == years[year_ind])[0] + event_mask[year_ind, events] = True + return event_mask + + def set_flood_volume(self, save_centr=False): + """Calculates flooded area for hazard. sets yearly flooded area and + flooded area per event + Raises: + MemoryError + """ + + fv_ann_centr = np.multiply(self.fla_ann_centr.todense(), self.intensity.todense()) + + if save_centr: + self.fv_ann_centr = sp.sparse.csr_matrix(self.fla_ann_centr) + self.fv_annual = np.sum(fv_ann_centr, axis=1) + + @staticmethod + def _select_exact_area(countries=[], reg=[]): + """Extract coordinates of selected countries or region + from NatID grid. If countries are given countries are cut, + if only reg is given, the whole region is cut. + Parameters: + countries: List of countries + reg: List of regions + Raises: + KeyError + Returns: + centroids + """ + lat, lon = get_region_gridpoints(countries=countries, regions=reg, + basemap="isimip", resolution=150) + + if reg: + country_isos = region2isos(reg) + else: + country_isos = countries + + natIDs = country_iso2natid(country_isos) + + centroids = Centroids() + centroids.set_lat_lon(lat, lon) + centroids.id = np.arange(centroids.lon.shape[0]) + # centroids.set_region_id() + return centroids, country_isos, natIDs diff --git a/climada/hazard/storm_europe.py b/climada/hazard/storm_europe.py index 21ba24bcad..f7a3cdde33 100644 --- a/climada/hazard/storm_europe.py +++ b/climada/hazard/storm_europe.py @@ -41,14 +41,14 @@ LOGGER = logging.getLogger(__name__) HAZ_TYPE = 'WS' -""" Hazard type acronym for Winter Storm """ +"""Hazard type acronym for Winter Storm""" N_PROB_EVENTS = 5 * 6 -""" Number of events per historic event in probabilistic dataset """ +"""Number of events per historic event in probabilistic dataset""" class StormEurope(Hazard): - """ A hazard set containing european winter storm events. Historic storm + """A hazard set containing european winter storm events. Historic storm events can be downloaded at http://wisc.climate.copernicus.eu/ Attributes: @@ -60,14 +60,14 @@ class StormEurope(Hazard): """ intensity_thres = 14.7 - """ Intensity threshold for storage in m/s; same as used by WISC SSI - calculations. """ + """Intensity threshold for storage in m/s; same as used by WISC SSI + calculations.""" vars_opt = Hazard.vars_opt.union({'ssi_wisc', 'ssi', 'ssi_full_area'}) - """ Name of the variables that aren't need to compute the impact. """ + """Name of the variables that aren't need to compute the impact.""" def __init__(self): - """ Calls the Hazard init dunder. Sets unit to 'm/s'. """ + """Calls the Hazard init dunder. Sets unit to 'm/s'.""" Hazard.__init__(self, HAZ_TYPE) self.units = 'm/s' self.ssi = np.array([], float) @@ -78,7 +78,7 @@ def read_footprints(self, path, description=None, ref_raster=None, centroids=None, files_omit='fp_era20c_1990012515_701_0.nc', combine_threshold=None): - """ Clear instance and read WISC footprints into it. Read Assumes that + """Clear instance and read WISC footprints into it. Read Assumes that all footprints have the same coordinates as the first file listed/first file in dir. @@ -98,9 +98,9 @@ def read_footprints(self, path, description=None, defaults to one duplicate storm present in the WISC set as of 2018-09-10. combine_threshold (int, optional): threshold for combining events - in number of days. if the difference of the dates (self.date) + in number of days. if the difference of the dates (self.date) of two events is smaller or equal to this threshold, the two - events are combined into one. + events are combined into one. Default is None, Advised for WISC is 2 """ @@ -131,7 +131,7 @@ def read_footprints(self, path, description=None, if new_haz is not None: self.append(new_haz) - self.event_id = np.arange(1, len(self.event_id)+1) + self.event_id = np.arange(1, len(self.event_id) + 1) self.frequency = np.divide( np.ones_like(self.date), (last_year(self.date) - first_year(self.date)) @@ -143,16 +143,17 @@ def read_footprints(self, path, description=None, ) if description is not None: self.tag.description = description - + if combine_threshold is not None: LOGGER.info('Combining events with small difference in date.') difference_date = np.diff(self.date) - for event_id_i in self.event_id[np.append(difference_date<=combine_threshold,False)]: - event_ids = [event_id_i, event_id_i+1] + for event_id_i in self.event_id[ + np.append(difference_date <= combine_threshold, False)]: + event_ids = [event_id_i, event_id_i + 1] self._combine_events(event_ids) def _read_one_nc(self, file_name, centroids): - """ Read a single WISC footprint. Assumes a time dimension of length 1. + """Read a single WISC footprint. Assumes a time dimension of length 1. Omits a footprint if another file with the same timestamp has already been read. @@ -200,7 +201,7 @@ def _read_one_nc(self, file_name, centroids): @staticmethod def _centroids_from_nc(file_name): - """ Construct Centroids from the grid described by 'latitude' and + """Construct Centroids from the grid described by 'latitude' and 'longitude' variables in a netCDF file. """ LOGGER.info('Constructing centroids from %s', file_name) @@ -228,47 +229,49 @@ def _centroids_from_nc(file_name): cent.set_on_land() return cent - - def _combine_events(self,event_ids): - """ combine the intensities of two events using max and adjust event_id, event_name, date etc of the hazard + + def _combine_events(self, event_ids): + """combine the intensities of two events using max and adjust event_id, event_name, + date etc of the hazard + the event_ids must be consecutive for the event_name field to behave correctly + Parameters: event_ids (array): two consecutive event ids """ - select_event_ids = np.isin(self.event_id,event_ids) + select_event_ids = np.isin(self.event_id, event_ids) select_other_events = np.invert(select_event_ids) - intensity_tmp = self.intensity[select_event_ids,:].max(axis=0) - self.intensity = self.intensity[select_other_events,:] - self.intensity = sparse.vstack([self.intensity,sparse.csr_matrix(intensity_tmp)]) - self.event_id = np.append(self.event_id[select_other_events], - self.event_id.max()+1) + intensity_tmp = self.intensity[select_event_ids, :].max(axis=0) + self.intensity = self.intensity[select_other_events, :] + self.intensity = sparse.vstack([self.intensity, sparse.csr_matrix(intensity_tmp)]) + self.event_id = np.append(self.event_id[select_other_events], self.event_id.max() + 1) self.date = np.append(self.date[select_other_events], - np.round(self.date[select_event_ids].mean())) + np.round(self.date[select_event_ids].mean())) name_2 = self.event_name.pop(np.where(select_event_ids)[0][1]) name_1 = self.event_name.pop(np.where(select_event_ids)[0][0]) self.event_name.append(name_1 + '_' + name_2) - fraction_tmp = self.fraction[select_event_ids,:].max(axis=0) - self.fraction = self.fraction[select_other_events,:] - self.fraction = sparse.vstack([self.fraction,sparse.csr_matrix(fraction_tmp)]) - + fraction_tmp = self.fraction[select_event_ids, :].max(axis=0) + self.fraction = self.fraction[select_other_events, :] + self.fraction = sparse.vstack([self.fraction, sparse.csr_matrix(fraction_tmp)]) + self.frequency = np.append(self.frequency[select_other_events], - self.frequency[select_event_ids].mean()) + self.frequency[select_event_ids].mean()) self.orig = np.append(self.orig[select_other_events], - self.orig[select_event_ids].max()) - if self.ssi_wisc.size>0: + self.orig[select_event_ids].max()) + if self.ssi_wisc.size > 0: self.ssi_wisc = np.append(self.ssi_wisc[select_other_events], np.nan) - if self.ssi.size>0: + if self.ssi.size > 0: self.ssi = np.append(self.ssi[select_other_events], np.nan) - if self.ssi_full_area.size>0: + if self.ssi_full_area.size > 0: self.ssi_full_area = np.append(self.ssi_full_area[select_other_events], - np.nan) + np.nan) self.check() def calc_ssi(self, method='dawkins', intensity=None, on_land=True, threshold=None, sel_cen=None): - """ Calculate the SSI, method must either be 'dawkins' or 'wisc_gust'. + """Calculate the SSI, method must either be 'dawkins' or 'wisc_gust'. 'dawkins', after Dawkins et al. (2016), doi:10.5194/nhess-16-1999-2016, matches the MATLAB version. @@ -343,7 +346,7 @@ def calc_ssi(self, method='dawkins', intensity=None, on_land=True, return ssi def set_ssi(self, **kwargs): - """ Wrapper around calc_ssi for setting the self.ssi attribute. + """Wrapper around calc_ssi for setting the self.ssi attribute. Parameters: **kwargs: passed on to calc_ssi @@ -354,7 +357,7 @@ def set_ssi(self, **kwargs): self.ssi = self.calc_ssi(**kwargs) def plot_ssi(self, full_area=False): - """ Plot the distribution of SSIs versus their cumulative exceedance + """Plot the distribution of SSIs versus their cumulative exceedance frequencies, highlighting historical storms in red. Returns: @@ -374,7 +377,7 @@ def plot_ssi(self, full_area=False): }) ssi_freq = ssi_freq.sort_values('ssi', ascending=False) ssi_freq['freq_cum'] = np.cumsum(ssi_freq.freq) - + ssi_hist = ssi_freq.loc[ssi_freq.orig].copy() ssi_hist.freq = ssi_hist.freq * self.orig.size / self.orig.sum() ssi_hist['freq_cum'] = np.cumsum(ssi_hist.freq) @@ -394,7 +397,7 @@ def plot_ssi(self, full_area=False): def generate_prob_storms(self, reg_id=528, spatial_shift=4, ssi_args={}, **kwargs): - """ Generates a new hazard set with one original and 29 probabilistic + """Generates a new hazard set with one original and 29 probabilistic storms per historic storm. This represents a partial implementation of the Monte-Carlo method described in section 2.2 of Schwierz et al. (2010), doi:10.1007/s10584-009-9712-1. @@ -432,7 +435,7 @@ def generate_prob_storms(self, reg_id=528, spatial_shift=4, ssi_args={}, reg_id = [reg_id] sel_cen = np.isin(self.centroids.region_id, reg_id) - else: # shifting truncates valid centroids + else: # shifting truncates valid centroids sel_cen = np.zeros(self.centroids.shape, bool) sel_cen[ spatial_shift:-spatial_shift, @@ -457,8 +460,7 @@ def generate_prob_storms(self, reg_id=528, spatial_shift=4, ssi_args={}, sel_cen, spatial_shift, ssi_args, - **kwargs, - ) + **kwargs) LOGGER.info('Generating new StormEurope instance') new_haz = StormEurope() @@ -477,9 +479,9 @@ def generate_prob_storms(self, reg_id=528, spatial_shift=4, ssi_args={}, new_haz.event_id = base + synth_id # frequency still based on the historic number of years - new_haz.frequency = np.divide(np.repeat(self.frequency,N_PROB_EVENTS), + new_haz.frequency = np.divide(np.repeat(self.frequency, N_PROB_EVENTS), N_PROB_EVENTS) - + new_haz.tag = TagHazard( HAZ_TYPE, 'Hazard set not saved by default', description='WISC probabilistic hazard set according to Schwierz et al.' @@ -495,24 +497,32 @@ def generate_prob_storms(self, reg_id=528, spatial_shift=4, ssi_args={}, def _hist2prob(self, intensity1d, sel_cen, spatial_shift, ssi_args={}, power=1.15, scale=0.0225): - """ Internal function, intended to be called from generate_prob_storms. + """Internal function, intended to be called from generate_prob_storms. Generates six permutations based on one historical storm event, which it then moves around by spatial_shift gridpoints to the east, west, and north. - Parameters: - intensity1d (scipy.sparse.csr_matrix, 1 by n): One historic event - sel_cen (np.ndarray(dty=bool)): which centroids to return - spatial_shift (int): amount of raster cells to shift by - power (float): power to be applied elementwise - scale (float): weight of probabilistic component - ssi_args (dict): named arguments passed on to calc_ssi - - Returns: - intensity (np.array): Synthetic intensities of shape - (N_PROB_EVENTS, length(sel_cen)) - ssi (np.array): SSI per synthetic event according to provided - method. + Parameters + ---------- + intensity1d : scipy.sparse.csr_matrix, 1 by n + One historic event + sel_cen : np.ndarray(dtype=bool) + which centroids to return + spatial_shift : int + amount of raster cells to shift by + power : float + power to be applied elementwise + scale : float + weight of probabilistic component + ssi_args : dict + named arguments passed on to calc_ssi + + Returns + ------- + intensity : np.array + Synthetic intensities of shape (N_PROB_EVENTS, length(sel_cen)) + ssi : np.array + SSI per synthetic event according to provided method. """ shape_ndarray = tuple([N_PROB_EVENTS]) + self.centroids.shape @@ -521,7 +531,7 @@ def _hist2prob(self, intensity1d, sel_cen, spatial_shift, ssi_args={}, # scipy.sparse.csr.csr_matrix elementwise methods (to avoid this: # https://github.com/ContinuumIO/anaconda-issues/issues/9129 ) - intensity2d_sqrt = intensity2d.power(1.0/power).todense() + intensity2d_sqrt = intensity2d.power(1.0 / power).todense() intensity2d_pwr = intensity2d.power(power).todense() intensity2d = intensity2d.todense() @@ -541,9 +551,9 @@ def _hist2prob(self, intensity1d, sel_cen, spatial_shift, ssi_args={}, intensity3d_prob[4] = intensity2d + (scale * intensity2d_pwr) # 6. minus scaled sqrt and pwr - intensity3d_prob[5] = intensity2d \ - - (0.5 * scale * intensity2d_pwr) \ - - (0.5 * scale * intensity2d_sqrt) + intensity3d_prob[5] = (intensity2d + - (0.5 * scale * intensity2d_pwr) + - (0.5 * scale * intensity2d_sqrt)) # spatial shifts # northward diff --git a/climada/hazard/tag.py b/climada/hazard/tag.py index d4add9ef1b..da0ac0b1c4 100644 --- a/climada/hazard/tag.py +++ b/climada/hazard/tag.py @@ -53,8 +53,8 @@ def append(self, tag): if self.haz_type == '': self.haz_type = tag.haz_type if tag.haz_type != self.haz_type: - LOGGER.error("Hazards of different type can't be appended:"\ - + " %s != %s.", self.haz_type, tag.haz_type) + LOGGER.error("Hazards of different type can't be appended: %s != %s.", + self.haz_type, tag.haz_type) raise ValueError # add file name if not present in tag @@ -81,7 +81,7 @@ def append(self, tag): self.description.extend(to_add) def join_file_names(self): - """ Get a string with the joined file names. """ + """Get a string with the joined file names.""" if not isinstance(self.file_name, list): join_file = os.path.splitext(os.path.basename(self.file_name))[0] else: @@ -90,7 +90,7 @@ def join_file_names(self): return join_file def join_descriptions(self): - """ Get a string with the joined descriptions. """ + """Get a string with the joined descriptions.""" if not isinstance(self.file_name, list): join_desc = self.description else: diff --git a/climada/hazard/tc_clim_change.py b/climada/hazard/tc_clim_change.py index d8c4ea6608..4eb578f60c 100644 --- a/climada/hazard/tc_clim_change.py +++ b/climada/hazard/tc_clim_change.py @@ -26,11 +26,11 @@ from climada.util.constants import SYSTEM_DIR TOT_RADIATIVE_FORCE = os.path.join(SYSTEM_DIR, 'rcp_db.xls') -""" © RCP Database (Version 2.0.5) http://www.iiasa.ac.at/web-apps/tnt/RcpDb. -generated: 2018-07-04 10:47:59. """ +"""© RCP Database (Version 2.0.5) http://www.iiasa.ac.at/web-apps/tnt/RcpDb. +generated: 2018-07-04 10:47:59.""" def get_knutson_criterion(): - """ Fill changes in TCs according to Knutson et al. 2015 Global projections + """Fill changes in TCs according to Knutson et al. 2015 Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios. @@ -40,84 +40,84 @@ def get_knutson_criterion(): """ criterion = list() # NA - tmp_chg = {'criteria': {'basin': ['NA'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['NA'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.045, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) # EP - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [0]}, 'year': 2100, 'change': 1.163, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[1, 2]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [1, 2]}, 'year': 2100, 'change': 1.193, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[3]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [3]}, 'year': 2100, 'change': 1.837, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[4, 5]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [4, 5]}, 'year': 2100, 'change': 3.375, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [0]}, 'year': 2100, 'change': 1.082, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['EP'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['EP'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.078, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) # WP - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [0]}, 'year': 2100, 'change': 1 - 0.345, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[1, 2]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [1, 2]}, 'year': 2100, 'change': 1 - 0.316, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [3, 4, 5]}, 'year': 2100, 'change': 1 - 0.169, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [0]}, 'year': 2100, 'change': 1.074, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.055, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) # NI - tmp_chg = {'criteria': {'basin': ['NI'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['NI'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.256, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) # SI - tmp_chg = {'criteria': {'basin': ['SI'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['SI'], 'category': [0]}, 'year': 2100, 'change': 1 - 0.261, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['SI'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['SI'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1 - 0.284, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['SI'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['SI'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.033, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) # SP - tmp_chg = {'criteria': {'basin': ['SP'], 'category':[0]}, + tmp_chg = {'criteria': {'basin': ['SP'], 'category': [0]}, 'year': 2100, 'change': 1 - 0.366, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['SP'], 'category':[1, 2]}, + tmp_chg = {'criteria': {'basin': ['SP'], 'category': [1, 2]}, 'year': 2100, 'change': 1 - 0.406, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['SP'], 'category':[3]}, + tmp_chg = {'criteria': {'basin': ['SP'], 'category': [3]}, 'year': 2100, 'change': 1 - 0.506, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['SP'], 'category':[4, 5]}, + tmp_chg = {'criteria': {'basin': ['SP'], 'category': [4, 5]}, 'year': 2100, 'change': 1 - 0.583, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) return criterion def calc_scale_knutson(ref_year=2050, rcp_scenario=45): - """ Comparison 2081-2100 (i.e., late twenty-first century) and 2001-20 + """Comparison 2081-2100 (i.e., late twenty-first century) and 2001-20 (i.e., present day). Late twenty-first century effects on intensity and frequency per Saffir-Simpson-category and ocean basin is scaled to target year and target RCP proportional to total radiative forcing of the respective @@ -139,8 +139,8 @@ def calc_scale_knutson(ref_year=2050, rcp_scenario=45): # radiative forcings for each RCP scenario rad_force = pd.read_excel(TOT_RADIATIVE_FORCE) years = np.array([year for year in rad_force.columns if isinstance(year, int)]) - rad_rcp = np.array([int(float(sce[sce.index('.')-1:sce.index('.')+2])*10) \ - for sce in rad_force.Scenario if isinstance(sce, str)]) + rad_rcp = np.array([int(float(sce[sce.index('.') - 1:sce.index('.') + 2]) * 10) + for sce in rad_force.Scenario if isinstance(sce, str)]) # mean values for Knutson values rf_vals = np.argwhere(rad_rcp == rcp_knu).reshape(-1)[0] @@ -152,4 +152,4 @@ def calc_scale_knutson(ref_year=2050, rcp_scenario=45): rf_vals = np.argwhere(rad_rcp == rcp_scenario).reshape(-1)[0] rf_vals = np.array([rad_force.iloc[rf_vals][year] for year in years]) rf_sel = np.interp(ref_year, years, rf_vals) - return max((rf_sel - rf_base)/(rf_end - rf_base), 0) + return max((rf_sel - rf_base) / (rf_end - rf_base), 0) diff --git a/climada/hazard/tc_rainfield.py b/climada/hazard/tc_rainfield.py new file mode 100644 index 0000000000..55de59ac7e --- /dev/null +++ b/climada/hazard/tc_rainfield.py @@ -0,0 +1,221 @@ +""" +This file is part of CLIMADA. +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . +--- +Define TropCyclone class. +""" + +__all__ = ['TCRain'] + +import itertools +import logging +import datetime as dt +import numpy as np +from numba import jit +from scipy import sparse + +from climada.hazard.base import Hazard +from climada.hazard.trop_cyclone import TropCyclone +from climada.hazard.tag import Tag as TagHazard +from climada.hazard.centroids.centr import Centroids + +LOGGER = logging.getLogger(__name__) + +HAZ_TYPE = 'TR' + +class TCRain(Hazard): + """Contains rainfall from tropical cyclone events.""" + + intensity_thres = .1 + """intensity threshold for storage in mm""" + + def __init__(self, pool=None): + """Empty constructor.""" + Hazard.__init__(self, HAZ_TYPE) + self.category = np.array([], int) + self.basin = list() + if pool: + self.pool = pool + LOGGER.info('Using %s CPUs.', self.pool.ncpus) + else: + self.pool = None + + def set_from_tracks(self, tracks, centroids=None, dist_degree=3, + description=''): + """Computes rainfield from tracks based on the RCLIPER model. + Parallel process. + Parameters: + tracks (TCTracks): tracks of events + centroids (Centroids, optional): Centroids where to model TC. + Default: global centroids. + disr_degree (int): distance (in degrees) from node within which + the rainfield is processed (default 3 deg,~300km) + description (str, optional): description of the events + + """ + num_tracks = tracks.size + if centroids is None: + centroids = Centroids.from_base_grid(res_as=360, land=True) + + if not centroids.coord.size: + centroids.set_meta_to_lat_lon() + + LOGGER.info('Mapping %s tracks to %s centroids.', str(tracks.size), + str(centroids.size)) + if self.pool: + chunksize = min(num_tracks // self.pool.ncpus, 1000) + tc_haz = self.pool.map(self._set_from_track, tracks.data, + itertools.repeat(centroids, num_tracks), + itertools.repeat(dist_degree, num_tracks), + itertools.repeat(self.intensity_thres, num_tracks), + chunksize=chunksize) + else: + tc_haz = list() + for track in tracks.data: + tc_haz.append(self._set_from_track(track, centroids, + dist_degree=dist_degree, + intensity=self.intensity_thres)) + LOGGER.debug('Append events.') + self.concatenate(tc_haz) + LOGGER.debug('Compute frequency.') + TropCyclone.frequency_from_tracks(self, tracks.data) + self.tag.description = description + + @staticmethod + @jit(forceobj=True) + def _set_from_track(track, centroids, dist_degree=3, intensity=0.1): + """Set hazard from track and centroids. + Parameters: + track (xr.Dataset): tropical cyclone track. + centroids (Centroids): Centroids instance. + disr_degree (int): distance (in degrees) from node within which + the rainfield is processed (default 3 deg,~300km) + intensity (int): min intensity threshold below which values are not + considered + Returns: + TCRain + """ + new_haz = TCRain() + new_haz.tag = TagHazard(HAZ_TYPE, 'IBTrACS: ' + track.name) + new_haz.intensity = rainfield_from_track(track, centroids, + dist_degree, intensity) + new_haz.units = 'mm' + new_haz.centroids = centroids + new_haz.event_id = np.array([1]) + # frequency set when all tracks available + new_haz.frequency = np.array([1]) + new_haz.event_name = [track.sid] + new_haz.fraction = new_haz.intensity.copy() + new_haz.fraction.data.fill(1) + # store date of start + new_haz.date = np.array([dt.datetime( + track.time.dt.year[0], track.time.dt.month[0], + track.time.dt.day[0]).toordinal()]) + new_haz.orig = np.array([track.orig_event_flag]) + new_haz.category = np.array([track.category]) + new_haz.basin = [track.basin] + return new_haz + +def rainfield_from_track(track, centroids, dist_degree=3, intensity=0.1): + """Compute rainfield for track at centroids. + Parameters: + track (xr.Dataset): tropical cyclone track. + centroids (Centroids): Centroids instance. + disr_degree (int): distance (in degrees) from node within which + the rainfield is processed (default 3 deg,~300km) + intensity (int): min intensity threshold below which values are not + considered + """ + dlon, dlat = dist_degree, dist_degree + + n_track_nodes = len(track.lat) + n_centroids = len(centroids.lat) + cos_centroids_lat = np.cos(centroids.lat / 180 * np.pi) + + rainsum = np.zeros(n_centroids) + + # transform wind speed in knots + if track.max_sustained_wind_unit == 'kn': + pass + elif track.max_sustained_wind_unit == 'km/h': + track.max_sustained_wind /= 1.852 + elif track.max_sustained_wind_unit == 'mph': + track.max_sustained_wind /= 1.151 + elif track.max_sustained_wind_unit == 'm/s': + track.max_sustained_wind /= (1000 * 60 * 60) + track.max_sustained_wind /= 1.852 + + track.attrs['max_sustained_wind_unit'] = 'kn' + + lats = track.lat.values + lons = track.lon.values + + for node in range(n_track_nodes): + inreach = (np.abs(centroids.lat - lats[node]) < dlat) \ + & (np.abs(centroids.lon - lons[node]) < dlon) + + if inreach.any(): + pos = np.where(inreach)[0] + + fradius_km = np.zeros(n_centroids) + dd = ((lons[node] - centroids.lon[pos]) * cos_centroids_lat[pos])**2 \ + + (lats[node] - centroids.lat[pos])**2 + + fradius_km[pos] = np.sqrt(dd) * 111.12 + + rainsum += _RCLIPER(track.max_sustained_wind.values[node], + inreach, fradius_km) + + rainsum[rainsum < intensity] = 0 + + return sparse.csr_matrix(rainsum) + +def _RCLIPER(fmaxwind_kn, inreach, radius_km): + """Calculate rainrate in mm/h based on RCLIPER given windspeed (kn) at + a specific node + Parameters: + fmaxwind_kn (float): maximum sustained wind at specific node + inreach (np.array, boolean): 1 if centroid is within dist_degree, + 0 otherwise + radius_km (np.array): distance to node for every centroid + """ + + rainrate = np.zeros(len(inreach)) + + # Define Coefficients (CLIPER NHC bias adjusted (Tuleya, 2007)) + a1 = -1.1 # inch per day + a2 = -1.6 # inch per day + a3 = 64. # km + a4 = 150. # km + + b1 = 3.96 # inch per day + b2 = 4.8 # inch per day + b3 = -13. # km + b4 = -16. # km + + u_norm_kn = 1. + (fmaxwind_kn - 35.) / 33. + + T0 = a1 + b1 * u_norm_kn + Tm = a2 + b2 * u_norm_kn + rm = a3 + b3 * u_norm_kn + r0 = a4 + b4 * u_norm_kn + + i = (radius_km <= rm) & inreach + ii = (radius_km > rm) & inreach + + # Calculate R-Cliper symmetric rain rate in mm/h + rainrate[i] = (T0 + (Tm - T0) * (radius_km[i] / rm)) / 24. * 25.4 + rainrate[ii] = (Tm * np.exp(-(radius_km[ii] - rm) / r0)) / 24. * 25.4 + + rainrate[np.isnan(rainrate)] = 0 + rainrate[rainrate < 0] = 0 + + return rainrate diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index 2a11596d46..6b718c669d 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -19,57 +19,98 @@ Define TCTracks: IBTracs reader and tracks manager. """ -__all__ = ['SAFFIR_SIM_CAT', 'TCTracks', 'set_category'] +__all__ = ['CAT_NAMES', 'SAFFIR_SIM_CAT', 'TCTracks', 'set_category'] import os import glob import shutil import logging +import warnings import datetime as dt -import array import itertools import numpy as np import matplotlib.cm as cm_mp from matplotlib.lines import Line2D from matplotlib.collections import LineCollection from matplotlib.colors import BoundaryNorm, ListedColormap -import matplotlib.pyplot as plt import cartopy.crs as ccrs import pandas as pd import xarray as xr from sklearn.neighbors import DistanceMetric import netCDF4 as nc from numba import jit -from pint import UnitRegistry import scipy.io.matlab as matlab +import statsmodels.api as sm -from climada.util.config import CONFIG +from climada.util import ureg import climada.util.coordinates as coord_util from climada.util.constants import EARTH_RADIUS_KM, SYSTEM_DIR from climada.util.files_handler import get_file_names, download_ftp import climada.util.plot as u_plot +import climada.hazard.tc_tracks_synth LOGGER = logging.getLogger(__name__) SAFFIR_SIM_CAT = [34, 64, 83, 96, 113, 137, 1000] -""" Saffir-Simpson Hurricane Wind Scale in kn based on NOAA""" - -CAT_NAMES = {1: 'Tropical Depression', 2: 'Tropical Storm', - 3: 'Hurrican Cat. 1', 4: 'Hurrican Cat. 2', - 5: 'Hurrican Cat. 3', 6: 'Hurrican Cat. 4', 7: 'Hurrican Cat. 5'} -""" Saffir-Simpson category names. """ +"""Saffir-Simpson Hurricane Wind Scale in kn based on NOAA""" + +CAT_NAMES = { + -1: 'Tropical Depression', + 0: 'Tropical Storm', + 1: 'Hurricane Cat. 1', + 2: 'Hurricane Cat. 2', + 3: 'Hurricane Cat. 3', + 4: 'Hurricane Cat. 4', + 5: 'Hurricane Cat. 5', +} +"""Saffir-Simpson category names.""" CAT_COLORS = cm_mp.rainbow(np.linspace(0, 1, len(SAFFIR_SIM_CAT))) -""" Color scale to plot the Saffir-Simpson scale.""" +"""Color scale to plot the Saffir-Simpson scale.""" -IBTRACS_URL = 'ftp://eclipse.ncdc.noaa.gov/pub/ibtracs//v04r00/provisional/netcdf/' -""" FTP of IBTrACS netcdf file containing all tracks v4.0 """ +IBTRACS_URL = ('https://www.ncei.noaa.gov/data/' + 'international-best-track-archive-for-climate-stewardship-ibtracs/' + 'v04r00/access/netcdf') +"""Site of IBTrACS netcdf file containing all tracks v4.0, +s. https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access""" IBTRACS_FILE = 'IBTrACS.ALL.v04r00.nc' -""" IBTrACS v4.0 file all """ +"""IBTrACS v4.0 file all""" + +IBTRACS_AGENCIES = [ + 'wmo', 'usa', 'tokyo', 'newdelhi', 'reunion', 'bom', 'nadi', 'wellington', + 'cma', 'hko', 'ds824', 'td9636', 'td9635', 'neumann', 'mlc', +] +"""Names/IDs of agencies in IBTrACS v4.0""" + +IBTRACS_USA_AGENCIES = [ + 'atcf', 'cphc', 'hurdat_atl', 'hurdat_epa', 'jtwc_cp', 'jtwc_ep', 'jtwc_io', + 'jtwc_sh', 'jtwc_wp', 'nhc_working_bt', 'tcvightals', 'tcvitals' +] +"""Names/IDs of agencies in IBTrACS that correspond to 'usa_*' variables""" DEF_ENV_PRESSURE = 1010 -""" Default environmental pressure """ +"""Default environmental pressure""" + +BASIN_ENV_PRESSURE = { + '': DEF_ENV_PRESSURE, + 'EP': 1010, 'NA': 1010, 'SA': 1010, + 'NI': 1005, 'SI': 1005, 'WP': 1005, + 'SP': 1004, +} +"""Basin-specific default environmental pressure""" + +EMANUEL_RMW_CORR_FILES = [ + 'temp_ccsm420thcal.mat', 'temp_ccsm4rcp85_full.mat', + 'temp_gfdl520thcal.mat', 'temp_gfdl5rcp85cal_full.mat', + 'temp_hadgem20thcal.mat', 'temp_hadgemrcp85cal_full.mat', + 'temp_miroc20thcal.mat', 'temp_mirocrcp85cal_full.mat', + 'temp_mpi20thcal.mat', 'temp_mpircp85cal_full.mat', + 'temp_mri20thcal.mat', 'temp_mrircp85cal_full.mat', +] +EMANUEL_RMW_CORR_FACTOR = 2.0 +"""Kerry Emanuel track files in this list require a correction: The radius of + maximum wind (rmstore) needs to be multiplied by factor 2.""" class TCTracks(): """Contains tropical cyclone tracks. @@ -99,7 +140,7 @@ class TCTracks(): - dist_since_lf """ def __init__(self, pool=None): - """Empty constructor. Read csv IBTrACS files if provided. """ + """Empty constructor. Read csv IBTrACS files if provided.""" self.data = list() if pool: self.pool = pool @@ -119,6 +160,8 @@ def append(self, tracks): def get_track(self, track_name=None): """Get track with provided name. Return all tracks if no name provided. + Returns the first matching track based on the assumption that no other + track with the same name or sid exists in the set. Parameters: track_name (str, optional): name or sid (ibtracsID for IBTrACS) @@ -141,47 +184,221 @@ def get_track(self, track_name=None): LOGGER.info('No track with name or sid %s found.', track_name) return [] - def read_ibtracs_netcdf(self, provider='usa', storm_id=None, - year_range=(1980, 2018), basin=None, - file_name='IBTrACS.ALL.v04r00.nc', correct_pres=True): + def subset(self, filterdict): + """Subset tracks based on attributes. Currently only uses exact matches. + Returns a new instance. + + Parameters: + filterdict (dict): Of the form {'sid': 'pattern', ...}. Although + this is not an ordered dict, presumably the filter of greatest + magnitude should come first. + """ + out = self.__class__(self.pool) + out.data = self.data + + for key, pattern in filterdict.items(): + out.data = [ds for ds in out.data if ds.attrs[key] == pattern] + + return out + + def read_ibtracs_netcdf(self, provider=None, storm_id=None, + year_range=None, basin=None, estimate_missing=False, + correct_pres=False, + file_name='IBTrACS.ALL.v04r00.nc'): """Fill from raw ibtracs v04. Removes nans in coordinates, central pressure and removes repeated times data. Fills nans of environmental_pressure and radius_max_wind. Checks environmental_pressure > central_pressure. Parameters: - provider (str): data provider. e.g. usa, newdelhi, bom, cma, tokyo - storm_id (str or list(str), optional): ibtracs if of the storm, + provider (str, optional): If specified, enforce use of specific + agency, such as "usa", "newdelhi", "bom", "cma", "tokyo". + Default: None (and automatic choice). + storm_id (str or list(str), optional): IBTrACS ID of the storm, e.g. 1988234N13299, [1988234N13299, 1989260N11316] year_range(tuple, optional): (min_year, max_year). Default: (1980, 2018) basin (str, optional): e.g. US, SA, NI, SI, SP, WP, EP, NA. if not provided, consider all basins. + estimate_missing (bool, optional): estimate missing central pressure + wind speed and radius values using other available values. + Default: False + correct_pres (bool, optional): For backwards compatibility, alias + for `estimate_missing`. This is deprecated, use + `estimate_missing` instead! file_name (str, optional): name of netcdf file to be dowloaded or located at climada/data/system. Default: 'IBTrACS.ALL.v04r00.nc'. - correct_pres (bool, optional): correct central pressure if missing - values. Default: False """ + if correct_pres: + LOGGER.warning("`correct_pres` is deprecated. " + "Use `estimate_missing` instead.") + estimate_missing = True self.data = list() fn_nc = os.path.join(os.path.abspath(SYSTEM_DIR), file_name) if not glob.glob(fn_nc): try: - download_ftp(os.path.join(IBTRACS_URL, IBTRACS_FILE), IBTRACS_FILE) + download_ftp(f'{IBTRACS_URL}/{IBTRACS_FILE}', IBTRACS_FILE) shutil.move(IBTRACS_FILE, fn_nc) except ValueError as err: - LOGGER.error('Error while downloading %s. Try to download it '+ - 'manually and put the file in ' + + LOGGER.error('Error while downloading %s. Try to download it ' + 'manually and put the file in ' 'climada_python/data/system/', IBTRACS_URL) raise err - sel_tracks = self._filter_ibtracs(fn_nc, storm_id, year_range, basin) - nc_data = nc.Dataset(fn_nc) + ibtracs_ds = xr.open_dataset(fn_nc) + match = np.ones(ibtracs_ds.sid.shape[0], dtype=bool) + if storm_id: + if not isinstance(storm_id, list): + storm_id = [storm_id] + match &= ibtracs_ds.sid.isin([i.encode() for i in storm_id]) + if np.count_nonzero(match) == 0: + LOGGER.info('No tracks with given IDs %s.', storm_id) + else: + year_range = year_range if year_range else (1980, 2018) + if year_range: + years = ibtracs_ds.sid.str.slice(0, 4).astype(int) + match &= (years >= year_range[0]) & (years <= year_range[1]) + if np.count_nonzero(match) == 0: + LOGGER.info('No tracks in time range (%s, %s).', *year_range) + if basin: + match &= (ibtracs_ds.basin == basin.encode()).any(dim='date_time') + if np.count_nonzero(match) == 0: + LOGGER.info('No tracks in basin %s.', basin) + + if np.count_nonzero(match) == 0: + LOGGER.info('There are no tracks matching the specified requirements.') + self.data = [] + return + + ibtracs_ds = ibtracs_ds.sel(storm=match) + ibtracs_ds['valid_t'] = ibtracs_ds.time.notnull() + valid_st = ibtracs_ds.valid_t.any(dim="date_time") + invalid_st = np.nonzero(~valid_st.data)[0] + if invalid_st.size > 0: + st_ids = ', '.join(ibtracs_ds.sid.sel(storm=invalid_st).astype(str).data) + LOGGER.warning('No valid timestamps found for %s.', st_ids) + ibtracs_ds = ibtracs_ds.sel(storm=valid_st) + + if not provider: + agency_pref, track_agency_ix = ibtracs_track_agency(ibtracs_ds) + + for var in ['wind', 'pres', 'rmw', 'poci', 'roci']: + if provider: + # enforce use of specified provider's data points + ibtracs_ds[var] = ibtracs_ds[f'{provider}_{var}'] + else: + # array of values in order of preference + cols = [f'{a}_{var}' for a in agency_pref] + cols = [col for col in cols if col in ibtracs_ds.data_vars.keys()] + all_vals = ibtracs_ds[cols].to_array(dim='agency') + preferred_ix = all_vals.notnull().argmax(dim='agency') + + if var in ['wind', 'pres']: + # choice: wmo -> wmo_agency/usa_agency -> preferred + ibtracs_ds[var] = ibtracs_ds['wmo_' + var] \ + .fillna(all_vals.isel(agency=track_agency_ix)) \ + .fillna(all_vals.isel(agency=preferred_ix)) + else: + ibtracs_ds[var] = all_vals.isel(agency=preferred_ix) + ibtracs_ds = ibtracs_ds[['sid', 'name', 'basin', 'lat', 'lon', 'time', 'valid_t', + 'wind', 'pres', 'rmw', 'roci', 'poci']] + + if estimate_missing: + ibtracs_ds['pres'][:] = _estimate_pressure(ibtracs_ds.pres, + ibtracs_ds.lat, ibtracs_ds.lon, + ibtracs_ds.wind) + ibtracs_ds['wind'][:] = _estimate_vmax(ibtracs_ds.wind, + ibtracs_ds.lat, ibtracs_ds.lon, + ibtracs_ds.pres) + + ibtracs_ds['valid_t'] &= ibtracs_ds.wind.notnull() & ibtracs_ds.pres.notnull() + valid_st = ibtracs_ds.valid_t.any(dim="date_time") + invalid_st = np.nonzero(~valid_st.data)[0] + if invalid_st.size > 0: + st_ids = ', '.join(ibtracs_ds.sid.sel(storm=invalid_st).astype(str).data) + LOGGER.warning('No valid wind/pressure values found for %s.', st_ids) + ibtracs_ds = ibtracs_ds.sel(storm=valid_st) + + max_wind = ibtracs_ds.wind.max(dim="date_time").data.ravel() + category_test = (max_wind[:, None] < np.array(SAFFIR_SIM_CAT)[None]) + category = np.argmax(category_test, axis=1) - 1 + basin_map = {b.encode("utf-8"): v for b, v in BASIN_ENV_PRESSURE.items()} + basin_fun = lambda b: basin_map[b] + + ibtracs_ds['id_no'] = (ibtracs_ds.sid.str.replace(b'N', b'0') + .str.replace(b'S', b'1') + .astype(float)) + ibtracs_ds['time_step'] = xr.zeros_like(ibtracs_ds.time, dtype=float) + ibtracs_ds['time_step'][:, 1:] = (ibtracs_ds.time.diff(dim="date_time") + / np.timedelta64(1, 's')) + ibtracs_ds['time_step'][:, 0] = ibtracs_ds.time_step[:, 1] + provider = provider if provider else 'ibtracs' + + last_perc = 0 all_tracks = [] - for i_track in sel_tracks: - all_tracks.append(self._read_one_raw(nc_data, i_track, provider, - correct_pres)) - self.data = [track for track in all_tracks if track is not None] + for i_track, t_msk in enumerate(ibtracs_ds.valid_t.data): + perc = 100 * len(all_tracks) / ibtracs_ds.sid.size + if perc - last_perc >= 10: + LOGGER.info("Progress: %d%%", perc) + last_perc = perc + track_ds = ibtracs_ds.sel(storm=i_track, date_time=t_msk) + st_penv = xr.apply_ufunc(basin_fun, track_ds.basin, vectorize=True) + track_ds['time'][:1] = track_ds.time[:1].dt.floor('H') + if track_ds.time.size > 1: + track_ds['time_step'][0] = (track_ds.time[1] - track_ds.time[0]) \ + / np.timedelta64(1, 's') + + with warnings.catch_warnings(): + # See https://github.com/pydata/xarray/issues/4167 + warnings.simplefilter(action="ignore", category=FutureWarning) + + track_ds['rmw'] = track_ds.rmw \ + .ffill(dim='date_time', limit=1) \ + .bfill(dim='date_time', limit=1) \ + .fillna(0) + track_ds['roci'] = track_ds.roci \ + .ffill(dim='date_time', limit=1) \ + .bfill(dim='date_time', limit=1) \ + .fillna(0) + track_ds['poci'] = track_ds.poci \ + .ffill(dim='date_time', limit=4) \ + .bfill(dim='date_time', limit=4) + # this is the most time consuming line in the processing: + track_ds['poci'] = track_ds.poci.fillna(st_penv) + + if estimate_missing: + track_ds['rmw'][:] = estimate_rmw(track_ds.rmw.values, track_ds.pres.values) + track_ds['roci'][:] = estimate_roci(track_ds.roci.values, track_ds.rmw.values) + track_ds['roci'][:] = np.fmax(track_ds.rmw.values, track_ds.roci.values) + + # ensure environmental pressure >= central pressure + # this is the second most time consuming line in the processing: + track_ds['poci'][:] = np.fmax(track_ds.poci, track_ds.pres) + + all_tracks.append(xr.Dataset({ + 'time_step': ('time', track_ds.time_step), + 'radius_max_wind': ('time', track_ds.rmw.data), + 'radius_oci': ('time', track_ds.roci.data), + 'max_sustained_wind': ('time', track_ds.wind.data), + 'central_pressure': ('time', track_ds.pres.data), + 'environmental_pressure': ('time', track_ds.poci.data), + }, coords={ + 'time': track_ds.time.dt.round('s').data, + 'lat': ('time', track_ds.lat.data), + 'lon': ('time', track_ds.lon.data), + }, attrs={ + 'max_sustained_wind_unit': 'kn', + 'central_pressure_unit': 'mb', + 'name': track_ds.name.astype(str).item(), + 'sid': track_ds.sid.astype(str).item(), + 'orig_event_flag': True, + 'data_provider': provider, + 'basin': track_ds.basin.values[0].astype(str).item(), + 'id_no': track_ds.id_no.item(), + 'category': category[i_track], + })) + self.data = all_tracks def read_processed_ibtracs_csv(self, file_names): - """Fill from processed ibtracs csv file. + """Fill from processed ibtracs csv file(s). Parameters: file_names (str or list(str)): absolute file name(s) or @@ -190,7 +407,7 @@ def read_processed_ibtracs_csv(self, file_names): self.data = list() all_file = get_file_names(file_names) for file in all_file: - self._read_one_csv(file) + self._read_ibtracs_csv_single(file) def read_simulations_emanuel(self, file_names, hemisphere='S'): """Fill from Kerry Emanuel tracks. @@ -200,14 +417,21 @@ def read_simulations_emanuel(self, file_names, hemisphere='S'): folder name containing the files to read. hemisphere (str, optional): 'S', 'N' or 'both'. Default: 'S' """ - corr_files = ['temp_ccsm420thcal.mat', 'temp_ccsm4rcp85_full.mat', \ - 'temp_gfdl520thcal.mat', 'temp_gfdl5rcp85cal_full.mat', \ - 'temp_hadgem20thcal.mat', 'temp_hadgemrcp85cal_full.mat', \ - 'temp_miroc20thcal.mat', 'temp_mirocrcp85cal_full.mat', \ - 'temp_mpi20thcal.mat', 'temp_mpircp85cal_full.mat', \ - 'temp_mri20thcal.mat', 'temp_mrircp85cal_full.mat'] - all_file = get_file_names(file_names) + self.data = [] + for path in get_file_names(file_names): + rmw_corr = os.path.basename(path) in EMANUEL_RMW_CORR_FILES + self._read_file_emanuel(path, hemisphere=hemisphere, + rmw_corr=rmw_corr) + def _read_file_emanuel(self, path, hemisphere='S', rmw_corr=False): + """Append tracks from file containing Kerry Emanuel simulations. + + Parameters: + path (str): absolute path of file to read. + hemisphere (str, optional): 'S', 'N' or 'both'. Default: 'S' + rmw_corr (str, optional): If True, multiply the radius of + maximum wind by factor 2. Default: False. + """ if hemisphere == 'S': hem_min, hem_max = -90, 0 elif hemisphere == 'N': @@ -215,49 +439,201 @@ def read_simulations_emanuel(self, file_names, hemisphere='S'): else: hem_min, hem_max = -90, 90 - self.data = list() - for file in all_file: - LOGGER.info('Reading %s.', file) - data = matlab.loadmat(file) - data_lon, data_lat, data_y, data_m, data_d, data_h, data_r, \ - data_v, data_p = data['longstore'], data['latstore'], data['yearstore'], \ - data['monthstore'], data['daystore'], data['hourstore'], data['rmstore'], \ - data['vstore'], data['pstore'] - LOGGER.info('Loading %s tracks (each %s nodes), representing %s years.', \ - data_lat.shape[0], data_lat.shape[1], data_lat.shape[0]//600) - for i_track in range(data_lat.shape[0]): - pos = np.argwhere(np.logical_and(np.abs(data_lat[i_track, :]) > 0, \ - np.abs(data_lon[i_track, :]) > 0)).reshape(-1) - if hem_min > data_lat[i_track, pos].min() or \ - hem_max < data_lat[i_track, pos].max(): - continue - datetimes = [] - for month, day, hour in zip(data_m[i_track, pos], \ - data_d[i_track, pos], data_h[i_track, pos]): - datetimes.append(dt.datetime(data_y[0, i_track], month, day, hour)) - datetimes = np.array(datetimes) - tr_ds = xr.Dataset({ \ - 'time_step': ('time', np.diff(data_h[i_track, pos]).min() * \ - np.ones(datetimes.size)), \ - 'radius_max_wind': ('time', data_r[i_track, pos]/1.852), \ - 'max_sustained_wind': ('time', data_v[i_track, pos]), \ - 'central_pressure': ('time', data_p[i_track, pos]), \ - 'environmental_pressure': ('time', np.ones(datetimes.size)*DEF_ENV_PRESSURE)}, \ - coords={'time': datetimes, 'lat': ('time', data_lat[i_track, pos]), \ - 'lon': ('time', data_lon[i_track, pos])}, \ - attrs={'max_sustained_wind_unit':'kn', \ - 'central_pressure_unit':'mb', 'name':str(i_track), 'sid':str(i_track), \ - 'orig_event_flag':True, 'data_provider':'Emanuel', 'basin':hemisphere, \ - 'id_no':i_track}) - tr_ds.attrs['category'] = set_category(tr_ds.max_sustained_wind.values, \ - tr_ds.max_sustained_wind_unit, SAFFIR_SIM_CAT) - if os.path.basename(file) in corr_files: - tr_ds['radius_max_wind'] *= 2 - self.data.append(tr_ds) + LOGGER.info('Reading %s.', path) + data_mat = matlab.loadmat(path) + lat = data_mat['latstore'] + ntracks, nnodes = lat.shape + years_uniq = np.unique(data_mat['yearstore']) + LOGGER.info("File contains %s tracks (at most %s nodes each), " + "representing %s years (%s-%s).", ntracks, nnodes, + years_uniq.size, years_uniq[0], years_uniq[-1]) + + # filter according to chosen hemisphere + hem_mask = (lat >= hem_min) & (lat <= hem_max) | (lat == 0) + hem_idx = np.all(hem_mask, axis=1).nonzero()[0] + data_hem = lambda keys: [data_mat[f'{k}store'][hem_idx] for k in keys] + + lat, lon = data_hem(['lat', 'long']) + months, days, hours = data_hem(['month', 'day', 'hour']) + months, days, hours = [np.int8(ar) for ar in [months, days, hours]] + tc_rmw, tc_maxwind, tc_pressure = data_hem(['rm', 'v', 'p']) + years = data_mat['yearstore'][0, hem_idx] + + ntracks, nnodes = lat.shape + LOGGER.info("Loading %s tracks on %s hemisphere.", ntracks, hemisphere) + + # change lon format to -180 to 180 + lon[lon > 180] = lon[lon > 180] - 360 + + # change units from kilometers to nautical miles + tc_rmw = (tc_rmw * ureg.kilometer).to(ureg.nautical_mile).magnitude + if rmw_corr: + LOGGER.info("Applying RMW correction.") + tc_rmw *= EMANUEL_RMW_CORR_FACTOR + + for i_track in range(lat.shape[0]): + valid_idx = (lat[i_track, :] != 0).nonzero()[0] + nnodes = valid_idx.size + time_step = np.abs(np.diff(hours[i_track, valid_idx])).min() + + # deal with change of year + year = np.full(valid_idx.size, years[i_track]) + year_change = (np.diff(months[i_track, valid_idx]) < 0) + year_change = year_change.nonzero()[0] + if year_change.size > 0: + year[year_change[0] + 1:] += 1 - def equal_timestep(self, time_step_h=1, land_params=False): - """ Generate interpolated track values to time steps of min_time_step. + try: + datetimes = map(dt.datetime, year, + months[i_track, valid_idx], + days[i_track, valid_idx], + hours[i_track, valid_idx]) + datetimes = list(datetimes) + except ValueError as err: + # dates are known to contain invalid February 30 + date_feb = (months[i_track, valid_idx] == 2) \ + & (days[i_track, valid_idx] > 28) + if np.count_nonzero(date_feb) == 0: + # unknown invalid date issue + raise err + step = time_step if not date_feb[0] else -time_step + reference_idx = 0 if not date_feb[0] else -1 + reference_date = dt.datetime( + year[reference_idx], + months[i_track, valid_idx[reference_idx]], + days[i_track, valid_idx[reference_idx]], + hours[i_track, valid_idx[reference_idx]],) + datetimes = [reference_date + dt.timedelta(hours=int(step * i)) + for i in range(nnodes)] + datetimes = np.array(datetimes) + + max_sustained_wind = tc_maxwind[i_track, valid_idx] + max_sustained_wind_unit = 'kn' + env_pressure = np.full(nnodes, DEF_ENV_PRESSURE) + category = set_category(max_sustained_wind, + max_sustained_wind_unit, + SAFFIR_SIM_CAT) + tr_ds = xr.Dataset({ + 'time_step': ('time', np.full(nnodes, time_step)), + 'radius_max_wind': ('time', tc_rmw[i_track, valid_idx]), + 'max_sustained_wind': ('time', max_sustained_wind), + 'central_pressure': ('time', tc_pressure[i_track, valid_idx]), + 'environmental_pressure': ('time', env_pressure), + }, coords={ + 'time': datetimes, + 'lat': ('time', lat[i_track, valid_idx]), + 'lon': ('time', lon[i_track, valid_idx]), + }, attrs={ + 'max_sustained_wind_unit': max_sustained_wind_unit, + 'central_pressure_unit': 'mb', + 'name': str(hem_idx[i_track]), + 'sid': str(hem_idx[i_track]), + 'orig_event_flag': True, + 'data_provider': 'Emanuel', + 'basin': hemisphere, + 'id_no': hem_idx[i_track], + 'category': category, + }) + self.data.append(tr_ds) + + def read_one_gettelman(self, nc_data, i_track): + """Fill from Andrew Gettelman tracks. + Parameters: + nc_data (str): netCDF4.Dataset Objekt + i_tracks (int): track number + """ + scale_to_10m = (10. / 60.)**.11 + mps2kts = 1.94384 + basin_dict = {0: 'NA - North Atlantic', + 1: 'SA - South Atlantic', + 2: 'WP - West Pacific', + 3: 'EP - East Pacific', + 4: 'SP - South Pacific', + 5: 'NI - North Indian', + 6: 'SI - South Indian', + 7: 'AS - Arabian Sea', + 8: 'BB - Bay of Bengal', + 9: 'EA - Eastern Australia', + 10: 'WA - Western Australia', + 11: 'CP - Central Pacific', + 12: 'CS - Carribbean Sea', + 13: 'GM - Gulf of Mexico', + 14: 'MM - Missing'} + + val_len = nc_data.variables['numObs'][i_track] + sid = str(i_track) + times = nc_data.variables['source_time'][i_track, :][:val_len] + + datetimes = list() + for time in times: + try: + datetimes.append( + dt.datetime.strptime( + str(nc.num2date(time, 'days since {}'.format('1858-11-17'), + calendar='standard')), + '%Y-%m-%d %H:%M:%S')) + except ValueError: + # If wrong t, set t to previous t plus 3 hours + if datetimes: + datetimes.append(datetimes[-1] + dt.timedelta(hours=3)) + else: + pos = list(times).index(time) + time = times[pos + 1] - 1 / 24 * 3 + datetimes.append( + dt.datetime.strptime( + str(nc.num2date(time, 'days since {}'.format('1858-11-17'), + calendar='standard')), + '%Y-%m-%d %H:%M:%S')) + time_step = [] + for i_time, time in enumerate(datetimes[1:], 1): + time_step.append((time - datetimes[i_time - 1]).total_seconds() / 3600) + time_step.append(time_step[-1]) + + basins = list() + for basin in nc_data.variables['basin'][i_track, :][:val_len]: + try: + basins.extend([basin_dict[basin]]) + except KeyError: + basins.extend([np.nan]) + + lon = nc_data.variables['lon'][i_track, :][:val_len] + lon[lon > 180] = lon[lon > 180] - 360 # change lon format to -180 to 180 + lat = nc_data.variables['lat'][i_track, :][:val_len] + cen_pres = nc_data.variables['pres'][i_track, :][:val_len] + av_prec = nc_data.variables['precavg'][i_track, :][:val_len] + max_prec = nc_data.variables['precmax'][i_track, :][:val_len] + + # m/s to kn + wind = nc_data.variables['wind'][i_track, :][:val_len] * mps2kts * scale_to_10m + if not all(wind.data): # if wind is empty + wind = np.ones(wind.size) * -999.9 + + tr_df = pd.DataFrame({'time': datetimes, 'lat': lat, 'lon': lon, + 'max_sustained_wind': wind, + 'central_pressure': cen_pres, + 'environmental_pressure': np.ones(lat.size) * 1015., + 'radius_max_wind': np.ones(lat.size) * 65., + 'maximum_precipitation': max_prec, + 'average_precipitation': av_prec, + 'basins': basins, + 'time_step': time_step}) + + # construct xarray + tr_ds = xr.Dataset.from_dataframe(tr_df.set_index('time')) + tr_ds.coords['lat'] = ('time', tr_ds.lat) + tr_ds.coords['lon'] = ('time', tr_ds.lon) + tr_ds.attrs = {'max_sustained_wind_unit': 'kn', + 'central_pressure_unit': 'mb', + 'sid': sid, + 'name': sid, 'orig_event_flag': False, + 'basin': basins[0], + 'id_no': i_track, + 'category': set_category(wind, 'kn')} + self.data.append(tr_ds) + + def equal_timestep(self, time_step_h=1, land_params=False): + """Generate interpolated track values to time steps of min_time_step. Parameters: time_step_h (float, optional): time step in hours to which to interpolate. Default: 1. @@ -268,12 +644,13 @@ def equal_timestep(self, time_step_h=1, land_params=False): time_step_h) if land_params: - land_geom = _calc_land_geom(self.data) + extent = self.get_extent() + land_geom = coord_util.get_land_geometry(extent, resolution=10) else: land_geom = None if self.pool: - chunksize = min(self.size//self.pool.ncpus, 1000) + chunksize = min(self.size // self.pool.ncpus, 1000) self.data = self.pool.map(self._one_interp_data, self.data, itertools.repeat(time_step_h, self.size), itertools.repeat(land_geom, self.size), @@ -285,86 +662,51 @@ def equal_timestep(self, time_step_h=1, land_params=False): land_geom)) self.data = new_data - def calc_random_walk(self, ens_size=9, ens_amp0=1.5, ens_amp=0.1, \ - max_angle=np.pi/10, seed=CONFIG['trop_cyclone']['random_seed'], decay=True): - """ - Generate synthetic tracks based on directed random walk. An ensemble of - tracks is computed for every track contained. - Please note that there is a bias towards higher latitudes in the random - wiggle. The wiggles are applied for each timestep. Please consider using - equal_timestep() for unification before generating synthetic tracks. - Be careful when changing ens_amp and max_angle and test changes of the - parameter values before application. + def calc_random_walk(self, **kwargs): + """See function in `climada.hazard.tc_tracks_synth`""" + climada.hazard.tc_tracks_synth.calc_random_walk(self, **kwargs) + + @property + def size(self): + """Get longitude from coord array""" + return len(self.data) + + def get_bounds(self, deg_buffer=0.1): + """Get bounds as (lon_min, lat_min, lon_max, lat_max) tuple. Parameters: - ens_size (int, optional): number of ensemble members per track. - Default 9. - ens_amp0 (float, optional): amplitude of max random starting point - shift in decimal degree (longitude and latitude). Default: 1.5 - ens_amp (float, optional): amplitude of random walk wiggles in - decimal degree (longitude and latitude). Default: 0.1 - max_angle (float, optional): maximum angle of variation. Default: pi/10. - - max_angle=pi results in undirected random change with - no change in direction; - - max_angle=0 (or very close to 0) is not recommended. It results - in non-random synthetic tracks with constant shift to higher latitudes; - - for 0= 0: - np.random.seed(seed) + @property + def bounds(self): + """Exact bounds of trackset as tuple, no buffer.""" + return self.get_bounds(deg_buffer=0.0) - random_vec = list() - for track in self.data: - random_vec.append(np.random.uniform(size=ens_size*(2+track.time.size))) + def get_extent(self, deg_buffer=0.1): + """Get extent as (lon_min, lon_max, lat_min, lat_max) tuple. - num_tracks = self.size - new_ens = list() - if self.pool: - chunksize = min(num_tracks//self.pool.ncpus, 1000) - new_ens = self.pool.map(self._one_rnd_walk, self.data, - itertools.repeat(ens_size, num_tracks), - itertools.repeat(ens_amp0, num_tracks), - itertools.repeat(ens_amp, num_tracks), - itertools.repeat(max_angle, num_tracks), - random_vec, chunksize=chunksize) - else: - for i_track, track in enumerate(self.data): - new_ens.append(self._one_rnd_walk(track, ens_size, ens_amp0, \ - ens_amp, max_angle, random_vec[i_track])) - self.data = list() - for ens_track in new_ens: - self.data.extend(ens_track) + Parameters: + deg_buffer (float): A buffer to add around the bounding box - if decay: - try: - land_geom = _calc_land_geom(self.data) - v_rel, p_rel = self._calc_land_decay(land_geom) - self._apply_land_decay(v_rel, p_rel, land_geom) - except ValueError as err: - LOGGER.info('No land decay coefficients could be applied. %s', - str(err)) + Returns: + tuple (lon_min, lon_max, lat_min, lat_max) + """ + bounds = self.get_bounds(deg_buffer=deg_buffer) + return (bounds[0], bounds[2], bounds[1], bounds[3]) @property - def size(self): - """ Get longitude from coord array """ - return len(self.data) + def extent(self): + """Exact extent of trackset as tuple, no buffer.""" + return self.get_extent(deg_buffer=0.0) def plot(self, axis=None, **kwargs): """Track over earth. Historical events are blue, probabilistic black. @@ -385,49 +727,37 @@ def plot(self, axis=None, **kwargs): LOGGER.info('No tracks to plot') return None - deg_border = 0.5 + extent = self.get_extent(deg_buffer=1) + mid_lon = 0.5 * (extent[1] + extent[0]) + if not axis: - _, axis = u_plot.make_map() - min_lat, max_lat = 10000, -10000 - min_lon, max_lon = 10000, -10000 - for track in self.data: - min_lat, max_lat = min(min_lat, np.min(track.lat.values)), \ - max(max_lat, np.max(track.lat.values)) - min_lon, max_lon = min(min_lon, np.min(track.lon.values)), \ - max(max_lon, np.max(track.lon.values)) - min_lon, max_lon = min_lon-deg_border, max_lon+deg_border - min_lat, max_lat = min_lat-deg_border, max_lat+deg_border - if abs(min_lon - max_lon) > 360: - min_lon, max_lon = -180, 180 - axis.set_extent(([min_lon, max_lon, min_lat, max_lat]), crs=kwargs['transform']) + proj = ccrs.PlateCarree(central_longitude=mid_lon) + _, axis = u_plot.make_map(proj=proj) + axis.set_extent(extent, crs=kwargs['transform']) u_plot.add_shapes(axis) synth_flag = False cmap = ListedColormap(colors=CAT_COLORS) norm = BoundaryNorm([0] + SAFFIR_SIM_CAT, len(SAFFIR_SIM_CAT)) for track in self.data: - points = np.array([track.lon.values, - track.lat.values]).T.reshape(-1, 1, 2) - segments = np.concatenate([points[:-1], points[1:]], axis=1) - try: - segments = np.delete(segments, np.argwhere(segments[:, 0, 0] * \ - segments[:, 1, 0] < 0).reshape(-1), 0) - except IndexError: - pass + lonlat = np.stack([track.lon.values, track.lat.values], axis=-1) + lonlat[:, 0] = coord_util.lon_normalize(lonlat[:, 0], center=mid_lon) + segments = np.stack([lonlat[:-1], lonlat[1:]], axis=1) + # remove segments which cross 180 degree longitude boundary + segments = segments[segments[:, 0, 0] * segments[:, 1, 0] >= 0, :, :] if track.orig_event_flag: - track_lc = LineCollection(segments, cmap=cmap, norm=norm, \ - linestyle='solid', **kwargs) + track_lc = LineCollection(segments, cmap=cmap, norm=norm, + linestyle='solid', **kwargs) else: synth_flag = True - track_lc = LineCollection(segments, cmap=cmap, norm=norm, \ - linestyle=':', **kwargs) + track_lc = LineCollection(segments, cmap=cmap, norm=norm, + linestyle=':', **kwargs) track_lc.set_array(track.max_sustained_wind.values) axis.add_collection(track_lc) leg_lines = [Line2D([0], [0], color=CAT_COLORS[i_col], lw=2) for i_col in range(len(SAFFIR_SIM_CAT))] - leg_names = [CAT_NAMES[i_col] for i_col - in range(1, len(SAFFIR_SIM_CAT)+1)] + leg_names = [CAT_NAMES[i_col] for i_col in sorted(CAT_NAMES.keys())] if synth_flag: leg_lines.append(Line2D([0], [0], color='grey', lw=2, ls='solid')) leg_lines.append(Line2D([0], [0], color='grey', lw=2, ls=':')) @@ -438,19 +768,19 @@ def plot(self, axis=None, **kwargs): return axis def write_netcdf(self, folder_name): - """ Write a netcdf file per track with track.sid name in given folder. + """Write a netcdf file per track with track.sid name in given folder. - Parameter: + Parameters: folder_name (str): folder name where to write files """ - list_path = [os.path.join(folder_name, track.sid+'.nc') for track in self.data] + list_path = [os.path.join(folder_name, track.sid + '.nc') for track in self.data] LOGGER.info('Writting %s files.', self.size) for track in self.data: track.attrs['orig_event_flag'] = int(track.orig_event_flag) xr.save_mfdataset(self.data, list_path) def read_netcdf(self, folder_name): - """ Read all netcdf files contained in folder and fill a track per file. + """Read all netcdf files contained in folder and fill a track per file. Parameters: folder_name (str): folder name where to write files @@ -466,53 +796,9 @@ def read_netcdf(self, folder_name): self.data.append(track) @staticmethod - @jit(parallel=True) - def _one_rnd_walk(track, ens_size, ens_amp0, ens_amp, max_angle, rnd_vec): - """ Interpolate values of one track. - - Parameters: - track (xr.Dataset): track data - - Returns: - list(xr.Dataset) - """ - ens_track = list() - n_dat = track.time.size - rand_unif_ini = rnd_vec[:2*ens_size].reshape((2, ens_size)) - rand_unif_ang = rnd_vec[2*ens_size:] - - xy_ini = ens_amp0 * (rand_unif_ini - 0.5) - tmp_ang = np.cumsum(2 * max_angle * rand_unif_ang - max_angle) - coord_xy = np.empty((2, ens_size * n_dat)) - coord_xy[0] = np.cumsum(ens_amp * np.sin(tmp_ang)) - coord_xy[1] = np.cumsum(ens_amp * np.cos(tmp_ang)) - - ens_track.append(track) - for i_ens in range(ens_size): - i_track = track.copy(True) - - d_xy = coord_xy[:, i_ens * n_dat: (i_ens + 1) * n_dat] - \ - np.expand_dims(coord_xy[:, i_ens * n_dat], axis=1) - # change sign of latitude change for southern hemishpere: - d_xy = np.sign(track.lat.values[0]) * d_xy - - d_lat_lon = d_xy + np.expand_dims(xy_ini[:, i_ens], axis=1) - - i_track.lon.values = i_track.lon.values + d_lat_lon[0, :] - i_track.lat.values = i_track.lat.values + d_lat_lon[1, :] - i_track.attrs['orig_event_flag'] = False - i_track.attrs['name'] = i_track.attrs['name'] + '_gen' + str(i_ens+1) - i_track.attrs['sid'] = i_track.attrs['sid'] + '_gen' + str(i_ens+1) - i_track.attrs['id_no'] = i_track.attrs['id_no'] + (i_ens+1)/100 - - ens_track.append(i_track) - - return ens_track - - @staticmethod - @jit(parallel=True) + @jit(parallel=True, forceobj=True) def _one_interp_data(track, time_step_h, land_geom=None): - """ Interpolate values of one track. + """Interpolate values of one track. Parameters: track (xr.Dataset): track data @@ -520,147 +806,41 @@ def _one_interp_data(track, time_step_h, land_geom=None): Returns: xr.Dataset """ - if track.time.size > 3: - time_step = str(time_step_h) + 'H' - track_int = track.resample(time=time_step).interpolate('linear') - track_int['time_step'] = ('time', track_int.time.size * [time_step_h]) + if track.time.size >= 2: + method = ['linear', 'quadratic', 'cubic'][min(2, track.time.size - 2)] + # handle change of sign in longitude - pos_lon = track.coords['lon'].values > 0 - neg_lon = track.coords['lon'].values <= 0 - if neg_lon.any() and pos_lon.any() and \ - np.any(abs(track.coords['lon'].values[pos_lon]) > 170): - if neg_lon[0]: - track.coords['lon'].values[pos_lon] -= 360 - track_int.coords['lon'] = track.lon.resample(time=time_step).\ - interpolate('cubic') - track_int.coords['lon'][track_int.coords['lon'] < -180] += 360 - else: - track.coords['lon'].values[neg_lon] += 360 - track_int.coords['lon'] = track.lon.resample(time=time_step).\ - interpolate('cubic') - track_int.coords['lon'][track_int.coords['lon'] > 180] -= 360 - else: - track_int.coords['lon'] = track.lon.resample(time=time_step).\ - interpolate('cubic') - track_int.coords['lat'] = track.lat.resample(time=time_step).\ - interpolate('cubic') - track_int.attrs = track.attrs - track_int.attrs['category'] = set_category( \ - track.max_sustained_wind.values, \ - track.max_sustained_wind_unit) + lon = track.lon.copy() + if (lon < -170).any() and (lon > 170).any(): + # crosses 180 degrees east/west -> use positive degrees east + lon[lon < 0] += 360 + + time_step = '{}H'.format(time_step_h) + track_int = track.resample(time=time_step, keep_attrs=True, skipna=True)\ + .interpolate('linear') + track_int['time_step'][:] = time_step_h + lon_int = lon.resample(time=time_step).interpolate(method) + lon_int[lon_int > 180] -= 360 + track_int.coords['lon'] = lon_int + track_int.coords['lat'] = track.lat.resample(time=time_step)\ + .interpolate(method) + track_int.attrs['category'] = set_category( + track_int.max_sustained_wind.values, + track_int.max_sustained_wind_unit) else: - LOGGER.warning('Track interpolation not done. ' \ + LOGGER.warning('Track interpolation not done. ' 'Not enough elements for %s', track.name) track_int = track if land_geom: - _track_land_params(track_int, land_geom) + track_land_params(track_int, land_geom) return track_int - def _calc_land_decay(self, land_geom, s_rel=True, check_plot=False): - """Compute wind and pressure decay coefficients for every TC category - from the historical events according to the formulas: - - wind decay = exp(-x*A) - - pressure decay = S-(S-1)*exp(-x*B) - - Parameters: - land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry - s_rel (bool, optional): use environmental presure to calc S value - (true) or central presure (false) - check_plot (bool, optional): visualize computed coefficients. - Default: False - - Returns: - v_rel (dict(category: A)), p_rel (dict(category: (S, B))) - """ - hist_tracks = [track for track in self.data if track.orig_event_flag] - if not hist_tracks: - LOGGER.error('No historical tracks contained. Historical tracks' \ - ' are needed.') - raise ValueError - - # Key is Saffir-Simpson scale - # values are lists of wind/wind at landfall - v_lf = dict() - # values are tuples with first value the S parameter, second value - # list of central pressure/central pressure at landfall - p_lf = dict() - # x-scale values to compute landfall decay - x_val = dict() - - dec_val = list() - if self.pool: - chunksize = min(len(hist_tracks)//self.pool.ncpus, 1000) - dec_val = self.pool.map(_decay_values, hist_tracks, itertools.repeat(land_geom), - itertools.repeat(s_rel), chunksize=chunksize) - else: - for track in hist_tracks: - dec_val.append(_decay_values(track, land_geom, s_rel)) - - for (tv_lf, tp_lf, tx_val) in dec_val: - for key in tv_lf.keys(): - v_lf.setdefault(key, []).extend(tv_lf[key]) - p_lf.setdefault(key, ([], [])) - p_lf[key][0].extend(tp_lf[key][0]) - p_lf[key][1].extend(tp_lf[key][1]) - x_val.setdefault(key, []).extend(tx_val[key]) - - v_rel, p_rel = _decay_calc_coeff(x_val, v_lf, p_lf) - if check_plot: - _check_decay_values_plot(x_val, v_lf, p_lf, v_rel, p_rel) - - return v_rel, p_rel - - def _apply_land_decay(self, v_rel, p_rel, land_geom, s_rel=True, - check_plot=False): - """Compute wind and pressure decay due to landfall in synthetic tracks. - - Parameters: - v_rel (dict): {category: A}, where wind decay = exp(-x*A) - p_rel (dict): (category: (S, B)}, where pressure decay - = S-(S-1)*exp(-x*B) - land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry - s_rel (bool, optional): use environmental presure to calc S value - (true) or central presure (false) - check_plot (bool, optional): visualize computed changes - """ - sy_tracks = [track for track in self.data if not track.orig_event_flag] - if not sy_tracks: - LOGGER.error('No synthetic tracks contained. Synthetic tracks' \ - ' are needed.') - raise ValueError - - if not v_rel or not p_rel: - LOGGER.info('No decay coefficients.') - return - - if check_plot: - orig_wind, orig_pres = [], [] - for track in sy_tracks: - orig_wind.append(np.copy(track.max_sustained_wind.values)) - orig_pres.append(np.copy(track.central_pressure.values)) - - if self.pool: - chunksize = min(self.size//self.pool.ncpus, 1000) - self.data = self.pool.map(_apply_decay_coeffs, self.data, - itertools.repeat(v_rel), itertools.repeat(p_rel), - itertools.repeat(land_geom), itertools.repeat(s_rel), - chunksize=chunksize) - else: - new_data = list() - for track in self.data: - new_data.append(_apply_decay_coeffs(track, v_rel, p_rel, \ - land_geom, s_rel)) - self.data = new_data - - if check_plot: - _check_apply_decay_plot(self.data, orig_wind, orig_pres) - - def _read_one_csv(self, file_name): - """Read IBTrACS track file. + def _read_ibtracs_csv_single(self, file_name): + """Read IBTrACS track file in CSV format. Parameters: - file_name (str): file name containing one IBTrACS track to read + file_name (str): File name of CSV file. """ LOGGER.info('Reading %s', file_name) dfr = pd.read_csv(file_name) @@ -668,13 +848,13 @@ def _read_one_csv(self, file_name): datetimes = list() for time in dfr['isotime'].values: - year = np.fix(time/1e6) - time = time - year*1e6 - month = np.fix(time/1e4) - time = time - month*1e4 - day = np.fix(time/1e2) - hour = time - day*1e2 - datetimes.append(dt.datetime(int(year), int(month), int(day), \ + year = np.fix(time / 1e6) + time = time - year * 1e6 + month = np.fix(time / 1e4) + time = time - month * 1e4 + day = np.fix(time / 1e2) + hour = time - day * 1e2 + datetimes.append(dt.datetime(int(year), int(month), int(day), int(hour))) lat = dfr['cgps_lat'].values.astype('float') @@ -682,7 +862,11 @@ def _read_one_csv(self, file_name): cen_pres = dfr['pcen'].values.astype('float') max_sus_wind = dfr['vmax'].values.astype('float') max_sus_wind_unit = 'kn' - cen_pres = _missing_pressure(cen_pres, max_sus_wind, lat, lon) + if np.any(cen_pres <= 0): + # Warning: If any pressure value is invalid, this enforces to use + # estimated pressure values everywhere! + cen_pres[:] = -999 + cen_pres = _estimate_pressure(cen_pres, lat, lon, max_sus_wind) tr_ds = xr.Dataset() tr_ds.coords['time'] = ('time', datetimes) @@ -692,7 +876,7 @@ def _read_one_csv(self, file_name): tr_ds['radius_max_wind'] = ('time', dfr['rmax'].values.astype('float')) tr_ds['max_sustained_wind'] = ('time', max_sus_wind) tr_ds['central_pressure'] = ('time', cen_pres) - tr_ds['environmental_pressure'] = ('time', \ + tr_ds['environmental_pressure'] = ('time', dfr['penv'].values.astype('float')) tr_ds.attrs['max_sustained_wind_unit'] = max_sus_wind_unit tr_ds.attrs['central_pressure_unit'] = 'mb' @@ -702,219 +886,29 @@ def _read_one_csv(self, file_name): tr_ds.attrs['data_provider'] = dfr['data_provider'].values[0] tr_ds.attrs['basin'] = dfr['gen_basin'].values[0] try: - tr_ds.attrs['id_no'] = float(name.replace('N', '0'). \ + tr_ds.attrs['id_no'] = float(name.replace('N', '0'). replace('S', '1')) except ValueError: - tr_ds.attrs['id_no'] = float(str(datetimes[0].date()). \ + tr_ds.attrs['id_no'] = float(str(datetimes[0].date()). replace('-', '')) - tr_ds.attrs['category'] = set_category(max_sus_wind, \ - max_sus_wind_unit) + tr_ds.attrs['category'] = set_category(max_sus_wind, max_sus_wind_unit) self.data.append(tr_ds) - @staticmethod - def _filter_ibtracs(fn_nc, storm_id, year_range, basin): - """ Select tracks from input conditions. - - Parameters: - fn_nc (str): ibtracs netcdf data file name - storm_id (str os list): ibtrac id of the storm - year_range(tuple): (min_year, max_year) - basin (str): e.g. US, SA, NI, SI, SP, WP, EP, NA - - Returns: - np.array - """ - nc_data = nc.Dataset(fn_nc) - storm_ids = [''.join(name.astype(str)) - for name in nc_data.variables['sid']] - sel_tracks = [] - # filter name - if storm_id: - if not isinstance(storm_id, list): - storm_id = [storm_id] - for storm in storm_id: - sel_tracks.append(storm_ids.index(storm)) - sel_tracks = np.array(sel_tracks) - else: - # filter years - years = np.array([int(iso_name[:4]) for iso_name in storm_ids]) - sel_tracks = np.argwhere(np.logical_and(years >= year_range[0], \ - years <= year_range[1])).reshape(-1) - if not sel_tracks.size: - LOGGER.info('No tracks in time range (%s, %s).', year_range[0], - year_range[1]) - return sel_tracks - # filter basin - if basin: - basin0 = np.array([''.join(bas.astype(str)) \ - for bas in nc_data.variables['basin'][:, 0, :]])[sel_tracks] - sel_bas = np.argwhere(basin0 == basin).reshape(-1) - if not sel_tracks.size: - LOGGER.info('No tracks in basin %s.', basin) - return sel_tracks - sel_tracks = sel_tracks[sel_bas] - return sel_tracks - - def _read_one_raw(self, nc_data, i_track, provider, correct_pres=False): - """Fill given track. - - Parameters: - nc_data (Dataset): netcdf data set - i_track (int): track position in netcdf data - provider (str): data provider. e.g. usa, newdelhi, bom, cma, tokyo - """ - name = ''.join(nc_data.variables['name'][i_track] \ - [nc_data.variables['name'][i_track].mask == False].data.astype(str)) - sid = ''.join(nc_data.variables['sid'][i_track].astype(str)) - basin = ''.join(nc_data.variables['basin'][i_track, 0, :].astype(str)) - LOGGER.info('Reading %s: %s', sid, name) - - isot = nc_data.variables['iso_time'][i_track, :, :] - val_len = isot.mask[isot.mask == False].shape[0]//isot.shape[1] - datetimes = list() - for date_time in isot[:val_len]: - datetimes.append(dt.datetime.strptime(''.join(date_time.astype(str)), - '%Y-%m-%d %H:%M:%S')) - - id_no = float(sid.replace('N', '0').replace('S', '1')) - lat = nc_data.variables[provider + '_lat'][i_track, :][:val_len] - lon = nc_data.variables[provider + '_lon'][i_track, :][:val_len] - - max_sus_wind = nc_data.variables[provider + '_wind'][i_track, :]. \ - data[:val_len].astype(float) - cen_pres = nc_data.variables[provider + '_pres'][i_track, :]. \ - data[:val_len].astype(float) - - if correct_pres: - cen_pres = _missing_pressure(cen_pres, max_sus_wind, lat, lon) - - if np.all(lon == nc_data.variables[provider + '_lon']._FillValue) or \ - (np.any(lon == nc_data.variables[provider + '_lon']._FillValue) and \ - np.all(max_sus_wind == nc_data.variables[provider + '_wind']._FillValue) \ - and np.all(cen_pres == nc_data.variables[provider + '_pres']._FillValue)): - LOGGER.warning('Skipping %s. It does not contain valid values. ' +\ - 'Try another provider.', sid) - return None - - try: - rmax = nc_data.variables[provider + '_rmw'][i_track, :][:val_len] - except KeyError: - LOGGER.info('%s: No rmax for given provider %s. Set to default.', - sid, provider) - rmax = np.zeros(lat.size) - try: - penv = nc_data.variables[provider + '_poci'][i_track, :][:val_len] - except KeyError: - LOGGER.info('%s: No penv for given provider %s. Set to default.', - sid, provider) - penv = np.ones(lat.size)*self._set_penv(basin) - - tr_ds = pd.DataFrame({'time': datetimes, 'lat': lat, 'lon':lon, \ - 'radius_max_wind': rmax.astype('float'), 'max_sustained_wind': max_sus_wind, \ - 'central_pressure': cen_pres, 'environmental_pressure': penv.astype('float')}) - - # deal with nans - tr_ds = self._deal_nans(tr_ds, nc_data, provider, datetimes, basin) - if not tr_ds.shape[0]: - LOGGER.warning('Skipping %s. No usable data.', sid) - return None - # ensure environmental pressure > central pressure - chg_pres = (tr_ds.central_pressure > tr_ds.environmental_pressure).values - tr_ds.environmental_pressure.values[chg_pres] = tr_ds.central_pressure.values[chg_pres] - - # construct xarray - tr_ds = xr.Dataset.from_dataframe(tr_ds.set_index('time')) - tr_ds.coords['lat'] = ('time', tr_ds.lat) - tr_ds.coords['lon'] = ('time', tr_ds.lon) - tr_ds.attrs = {'max_sustained_wind_unit': 'kn', 'central_pressure_unit': 'mb', \ - 'name': name, 'sid': sid, 'orig_event_flag': True, 'data_provider': provider, \ - 'basin': basin, 'id_no': id_no, 'category': set_category(max_sus_wind, 'kn')} - return tr_ds - - def _deal_nans(self, tr_ds, nc_data, provider, datetimes, basin): - """ Remove or substitute fill values of netcdf variables. """ - # remove nan coordinates - tr_ds.drop(tr_ds[tr_ds.lat == nc_data.variables[provider + '_lat']. \ - _FillValue].index, inplace=True) - tr_ds.drop(tr_ds[np.isnan(tr_ds.lat.values)].index, inplace=True) - tr_ds.drop(tr_ds[tr_ds.lon == nc_data.variables[provider + '_lon']. \ - _FillValue].index, inplace=True) - tr_ds.drop(tr_ds[np.isnan(tr_ds.lon.values)].index, inplace=True) - # remove nan central pressures - tr_ds.drop(tr_ds[tr_ds.central_pressure == nc_data.variables[provider + '_pres']. \ - _FillValue].index, inplace=True) - # remove repeated dates - tr_ds.drop_duplicates('time', inplace=True) - # fill nans of environmental_pressure and radius_max_wind - try: - tr_ds.environmental_pressure.values[tr_ds.environmental_pressure == \ - nc_data.variables[provider + '_poci']._FillValue] = np.nan - tr_ds.environmental_pressure = tr_ds.environmental_pressure.ffill(limit=4). \ - bfill(limit=4).fillna(self._set_penv(basin)) - except KeyError: - pass - try: - tr_ds.radius_max_wind.values[tr_ds.radius_max_wind == \ - nc_data.variables[provider + '_rmw']._FillValue] = np.nan - tr_ds['radius_max_wind'] = tr_ds.radius_max_wind.ffill(limit=1).bfill(limit=1).fillna(0) - except KeyError: - pass - # set time steps - tr_ds['time_step'] = np.zeros(tr_ds.shape[0]) - for i_time, time in enumerate(tr_ds.time[1:], 1): - tr_ds.time_step.values[i_time] = (time - datetimes[i_time-1]).total_seconds()/3600 - if tr_ds.shape[0]: - tr_ds.time_step.values[0] = tr_ds.time_step.values[-1] - - return tr_ds - - @staticmethod - def _set_penv(basin): - """ Set environmental pressure depending on basin """ - penv = 1010 - if basin in ('NI', 'SI', 'WP'): - penv = 1005 - elif basin == 'SP': - penv = 1004 - return penv - - -def _calc_land_geom(ens_track): - """Compute land geometry used for land distance computations. - - Returns: - shapely.geometry.multipolygon.MultiPolygon - """ - deg_buffer = 0.1 - min_lat = np.min([np.min(track.lat.values) for track in ens_track]) - min_lat = max(min_lat-deg_buffer, -90) - - max_lat = np.max([np.max(track.lat.values) for track in ens_track]) - max_lat = min(max_lat+deg_buffer, 90) - - min_lon = np.min([np.min(track.lon.values) for track in ens_track]) - min_lon = max(min_lon-deg_buffer, -180) - - max_lon = np.max([np.max(track.lon.values) for track in ens_track]) - max_lon = min(max_lon+deg_buffer, 180) - - return coord_util.get_land_geometry(extent=(min_lon, max_lon, \ - min_lat, max_lat), resolution=10) -def _track_land_params(track, land_geom): - """ Compute parameters of land for one track. +def track_land_params(track, land_geom): + """Compute parameters of land for one track. Parameters: - track (xr.Dataset): track values + track (xr.Dataset): tropical cyclone track land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry """ - track['on_land'] = ('time', coord_util.coord_on_land(track.lat.values, \ - track.lon.values, land_geom)) + track['on_land'] = ('time', + coord_util.coord_on_land(track.lat.values, track.lon.values, land_geom)) track['dist_since_lf'] = ('time', _dist_since_lf(track)) def _dist_since_lf(track): - """ Compute the distance to landfall in km point for every point on land. + """Compute the distance to landfall in km point for every point on land. Points on water get nan values. Parameters: @@ -928,13 +922,18 @@ def _dist_since_lf(track): # Index in sea that follows a land index sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] if not sea_land_idx.size: - return (dist_since_lf+1)*np.nan + return (dist_since_lf + 1) * np.nan # Index in sea that comes from previous land index land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 if track.on_land[-1]: land_sea_idx = np.append(land_sea_idx, track.time.size) - orig_lf = _calc_orig_lf(track, sea_land_idx) + orig_lf = np.empty((sea_land_idx.size, 2)) + for i_lf, lf_point in enumerate(sea_land_idx): + orig_lf[i_lf][0] = track.lat[lf_point] + \ + (track.lat[lf_point + 1] - track.lat[lf_point]) / 2 + orig_lf[i_lf][1] = track.lon[lf_point] + \ + (track.lon[lf_point + 1] - track.lon[lf_point]) / 2 dist = DistanceMetric.get_metric('haversine') nodes1 = np.radians(np.array([track.lat.values[1:], @@ -942,453 +941,287 @@ def _dist_since_lf(track): nodes0 = np.radians(np.array([track.lat.values[:-1], track.lon.values[:-1]]).transpose()) dist_since_lf[1:] = dist.pairwise(nodes1, nodes0).diagonal() - dist_since_lf[np.logical_not(track.on_land.values)] = 0.0 - nodes1 = np.array([track.lat.values[sea_land_idx+1], - track.lon.values[sea_land_idx+1]]).transpose()/180*np.pi - dist_since_lf[sea_land_idx+1] = \ - dist.pairwise(nodes1, orig_lf/180*np.pi).diagonal() + dist_since_lf[~track.on_land.values] = 0.0 + nodes1 = np.array([track.lat.values[sea_land_idx + 1], + track.lon.values[sea_land_idx + 1]]).transpose() / 180 * np.pi + dist_since_lf[sea_land_idx + 1] = \ + dist.pairwise(nodes1, orig_lf / 180 * np.pi).diagonal() for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): - dist_since_lf[sea_land+1:land_sea] = \ - np.cumsum(dist_since_lf[sea_land+1:land_sea]) + dist_since_lf[sea_land + 1:land_sea] = \ + np.cumsum(dist_since_lf[sea_land + 1:land_sea]) dist_since_lf *= EARTH_RADIUS_KM - dist_since_lf[np.logical_not(track.on_land.values)] = np.nan + dist_since_lf[~track.on_land.values] = np.nan return dist_since_lf -def _calc_orig_lf(track, sea_land_idx): - """ Approximate coast coordinates in landfall as the middle point - before landfall and after. - - Parameters: - track (xr.Dataset): TC track - sea_land_idx (np.array): array position of sea before landfall +def _estimate_pressure(cen_pres, lat, lon, v_max): + """Replace missing pressure values with statistical estimate. - Returns: - np.array (first column lat and second lon of each landfall coord) - """ - # TODO change to pos where landfall (v_landfall)?? - orig_lf = np.empty((sea_land_idx.size, 2)) - for i_lf, lf_point in enumerate(sea_land_idx): - orig_lf[i_lf][0] = track.lat[lf_point] + \ - (track.lat[lf_point+1] - track.lat[lf_point])/2 - orig_lf[i_lf][1] = track.lon[lf_point] + \ - (track.lon[lf_point+1] - track.lon[lf_point])/2 - return orig_lf + In addition to NaNs, negative values and zeros in `cen_pres` are interpreted as missing values. -def _decay_v_function(a_coef, x_val): - """Decay function used for wind after landfall.""" - return np.exp(-a_coef * x_val) + See function `ibtracs_fit_param` for more details about the statistical estimation: -def _solve_decay_v_function(v_y, x_val): - """Solve decay function used for wind after landfall. Get A coefficient.""" - return -np.log(v_y) / x_val + >>> ibtracs_fit_param('pres', ['lat', 'lon', 'wind'], year_range=(1980, 2019)) + >>> r^2: 0.8746154487335112 -def _decay_p_function(s_coef, b_coef, x_val): - """Decay function used for pressure after landfall.""" - return s_coef - (s_coef - 1) * np.exp(-b_coef*x_val) + Parameters + ---------- + cen_pres : array-like + Central pressure values along track in hPa (mbar). + lat : array-like + Latitudinal coordinates of eye location. + lon : array-like + Longitudinal coordinates of eye location. + v_max : array-like + Maximum wind speed along track in knots. -def _solve_decay_p_function(ps_y, p_y, x_val): - """Solve decay function used for pressure after landfall. - Get B coefficient.""" - return -np.log((ps_y - p_y)/(ps_y - 1.0)) / x_val - -def _calc_decay_ps_value(track, p_landfall, pos, s_rel): - if s_rel: - p_land_s = track.environmental_pressure[pos].values - else: - p_land_s = track.central_pressure[pos].values - return float(p_land_s / p_landfall) - -def _decay_values(track, land_geom, s_rel): - """ Compute wind and pressure relative to landafall values. - - Parameters: - track (xr.Dataset): track - land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry - s_rel (bool): use environmental presure for S value (true) or - central presure (false) - - Returns: - v_lf (dict): key is Saffir-Simpson scale, values are arrays of - wind/wind at landfall - p_lf (dict): key is Saffir-Simpson scale, values are tuples with - first value array of S parameter, second value array of central - pressure/central pressure at landfall - x_val (dict): key is Saffir-Simpson scale, values are arrays with - the values used as "x" in the coefficient fitting, the - distance since landfall + Returns + ------- + cen_pres_estimated : np.array + Estimated central pressure values in hPa (mbar). """ - v_lf = dict() - p_lf = dict() - x_val = dict() - - _track_land_params(track, land_geom) - # Index in land that comes from previous sea index - sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 - # Index in sea that comes from previous land index - land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 - if track.on_land[-1]: - land_sea_idx = np.append(land_sea_idx, track.time.size) - if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: - for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): - v_landfall = track.max_sustained_wind[sea_land-1].values - ss_scale_idx = np.where(v_landfall < SAFFIR_SIM_CAT)[0][0]+1 - - v_land = track.max_sustained_wind[sea_land-1:land_sea].values - if v_land[0] > 0: - v_land = (v_land[1:]/v_land[0]).tolist() - else: - v_land = v_land[1:].tolist() - - p_landfall = float(track.central_pressure[sea_land-1].values) - p_land = track.central_pressure[sea_land-1:land_sea].values - p_land = (p_land[1:]/p_land[0]).tolist() - - p_land_s = _calc_decay_ps_value(track, p_landfall, land_sea-1, s_rel) - p_land_s = len(p_land)*[p_land_s] - - if ss_scale_idx not in v_lf: - v_lf[ss_scale_idx] = array.array('f', v_land) - p_lf[ss_scale_idx] = (array.array('f', p_land_s), - array.array('f', p_land)) - x_val[ss_scale_idx] = array.array('f', \ - track.dist_since_lf[sea_land:land_sea]) - else: - v_lf[ss_scale_idx].extend(v_land) - p_lf[ss_scale_idx][0].extend(p_land_s) - p_lf[ss_scale_idx][1].extend(p_land) - x_val[ss_scale_idx].extend(track.dist_since_lf[ \ - sea_land:land_sea]) - return v_lf, p_lf, x_val - -def _decay_calc_coeff(x_val, v_lf, p_lf): - """ From track's relative velocity and pressure, compute the decay - coefficients. - - wind decay = exp(-x*A) - - pressure decay = S-(S-1)*exp(-x*A) - - Parameters: - x_val (dict): key is Saffir-Simpson scale, values are lists with - the values used as "x" in the coefficient fitting, the - distance since landfall - v_lf (dict): key is Saffir-Simpson scale, values are lists of - wind/wind at landfall - p_lf (dict): key is Saffir-Simpson scale, values are tuples with - first value the S parameter, second value list of central - pressure/central pressure at landfall - - Returns: - v_rel (dict()), p_rel (dict()) + cen_pres = np.where(np.isnan(cen_pres), -1, cen_pres) + v_max = np.where(np.isnan(v_max), -1, v_max) + lat, lon = [np.where(np.isnan(ar), -999, ar) for ar in [lat, lon]] + msk = (cen_pres <= 0) & (v_max > 0) & (lat > -999) & (lon > -999) + c_const, c_lat, c_lon, c_vmax = 1024.392, 0.0620, -0.0335, -0.737 + cen_pres[msk] = c_const + c_lat * lat[msk] \ + + c_lon * lon[msk] \ + + c_vmax * v_max[msk] + return np.where(cen_pres <= 0, np.nan, cen_pres) + +def _estimate_vmax(v_max, lat, lon, cen_pres): + """Replace missing wind speed values with a statistical estimate. + + In addition to NaNs, negative values and zeros in `v_max` are interpreted as missing values. + + See function `ibtracs_fit_param` for more details about the statistical estimation: + + >>> ibtracs_fit_param('wind', ['lat', 'lon', 'pres'], year_range=(1980, 2019)) + >>> r^2: 0.8717153945288457 + + Parameters + ---------- + v_max : array-like + Maximum wind speed along track in knots. + lat : array-like + Latitudinal coordinates of eye location. + lon : array-like + Longitudinal coordinates of eye location. + cen_pres : array-like + Central pressure values along track in hPa (mbar). + + Returns + ------- + v_max_estimated : np.array + Estimated maximum wind speed values in knots. """ - np.warnings.filterwarnings('ignore') - v_rel = dict() - p_rel = dict() - for ss_scale, val_lf in v_lf.items(): - x_val_ss = np.array(x_val[ss_scale]) - - y_val = np.array(val_lf) - v_coef = _solve_decay_v_function(y_val, x_val_ss) - v_coef = v_coef[np.isfinite(v_coef)] - v_coef = np.mean(v_coef) - - ps_y_val = np.array(p_lf[ss_scale][0]) - y_val = np.array(p_lf[ss_scale][1]) - y_val[ps_y_val <= y_val] = np.nan - y_val[ps_y_val <= 1] = np.nan - valid_p = np.isfinite(y_val) - ps_y_val = ps_y_val[valid_p] - y_val = y_val[valid_p] - p_coef = _solve_decay_p_function(ps_y_val, y_val, x_val_ss[valid_p]) - ps_y_val = np.mean(ps_y_val) - p_coef = np.mean(p_coef) - - if np.isfinite(v_coef) and np.isfinite(ps_y_val) and np.isfinite(ps_y_val): - v_rel[ss_scale] = v_coef - p_rel[ss_scale] = (ps_y_val, p_coef) - - scale_fill = np.array(list(p_rel.keys())) - if not scale_fill.size: - LOGGER.info('No historical track with landfall.') - return v_rel, p_rel - for ss_scale in range(1, len(SAFFIR_SIM_CAT)+1): - if ss_scale not in p_rel: - close_scale = scale_fill[np.argmin(np.abs(scale_fill-ss_scale))] - LOGGER.debug('No historical track of category %s with landfall. ' \ - 'Decay parameters from category %s taken.', - CAT_NAMES[ss_scale], CAT_NAMES[close_scale]) - v_rel[ss_scale] = v_rel[close_scale] - p_rel[ss_scale] = p_rel[close_scale] - - return v_rel, p_rel - -def _check_decay_values_plot(x_val, v_lf, p_lf, v_rel, p_rel): - """ Generate one graph with wind decay and an other with central pressure - decay, true and approximated.""" - # One graph per TC category - for track_cat, color in zip(v_lf.keys(), - cm_mp.rainbow(np.linspace(0, 1, len(v_lf)))): - _, axes = plt.subplots(2, 1) - x_eval = np.linspace(0, np.max(x_val[track_cat]), 20) - - axes[0].set_xlabel('Distance from landfall (km)') - axes[0].set_ylabel('Max sustained wind relative to landfall') - axes[0].set_title('Wind') - axes[0].plot(x_val[track_cat], v_lf[track_cat], '*', c=color, - label=CAT_NAMES[track_cat]) - axes[0].plot(x_eval, _decay_v_function(v_rel[track_cat], x_eval), - '-', c=color) - - axes[1].set_xlabel('Distance from landfall (km)') - axes[1].set_ylabel('Central pressure relative to landfall') - axes[1].set_title('Pressure') - axes[1].plot(x_val[track_cat], p_lf[track_cat][1], '*', c=color, - label=CAT_NAMES[track_cat]) - axes[1].plot(x_eval, _decay_p_function(p_rel[track_cat][0], \ - p_rel[track_cat][1], x_eval), '-', c=color) - -def _apply_decay_coeffs(track, v_rel, p_rel, land_geom, s_rel): - """ Change track's max sustained wind and central pressure using the land - decay coefficients. + v_max = np.where(np.isnan(v_max), -1, v_max) + cen_pres = np.where(np.isnan(cen_pres), -1, cen_pres) + lat, lon = [np.where(np.isnan(ar), -999, ar) for ar in [lat, lon]] + msk = (v_max <= 0) & (cen_pres > 0) & (lat > -999) & (lon > -999) + c_const, c_lat, c_lon, c_pres = 1216.823, 0.0852, -0.0398, -1.182 + v_max[msk] = c_const + c_lat * lat[msk] \ + + c_lon * lon[msk] \ + + c_pres * cen_pres[msk] + return np.where(v_max <= 0, np.nan, v_max) + +def estimate_roci(roci, cen_pres): + """Replace missing radius (ROCI) values with statistical estimate. + + In addition to NaNs, negative values and zeros in `roci` are interpreted as missing values. + + See function `ibtracs_fit_param` for more details about the statistical estimation: + + >>> ibtracs_fit_param('roci', ['pres'], + ... order=[(872, 950, 985, 1005, 1021)], + ... year_range=(1980, 2019)) + >>> r^2: 0.9148320406675339 + + Parameters + ---------- + roci : array-like + ROCI values along track in km. + cen_pres : array-like + Central pressure values along track in hPa (mbar). + + Returns + ------- + roci_estimated : np.array + Estimated ROCI values in km. + """ + roci = np.where(np.isnan(roci), -1, roci) + cen_pres = np.where(np.isnan(cen_pres), -1, cen_pres) + msk = (roci <= 0) & (cen_pres > 0) + pres_l = [872, 950, 985, 1005, 1021] + roci_l = [210.711487, 215.897110, 198.261520, 159.589508, 90.900116] + roci[msk] = 0 + for i, pres_l_i in enumerate(pres_l): + slope_0 = 1. / (pres_l_i - pres_l[i - 1]) if i > 0 else 0 + slope_1 = 1. / (pres_l[i + 1] - pres_l_i) if i + 1 < len(pres_l) else 0 + roci[msk] += roci_l[i] * np.fmax(0, (1 - slope_0 * np.fmax(0, pres_l_i - cen_pres[msk]) + - slope_1 * np.fmax(0, cen_pres[msk] - pres_l_i))) + return np.where(roci <= 0, np.nan, roci) + +def estimate_rmw(rmw, cen_pres): + """Replace missing radius (RMW) values with statistical estimate. + + In addition to NaNs, negative values and zeros in `rmw` are interpreted as missing values. + + See function `ibtracs_fit_param` for more details about the statistical estimation: + + >>> ibtracs_fit_param('rmw', ['pres'], order=[(872, 940, 980, 1021)], year_range=(1980, 2019)) + >>> r^2: 0.7905970811843872 + + Parameters + ---------- + rmw : array-like + RMW values along track in km. + cen_pres : array-like + Central pressure values along track in hPa (mbar). + + Returns + ------- + rmw : np.array + Estimated RMW values in km. + """ + rmw = np.where(np.isnan(rmw), -1, rmw) + cen_pres = np.where(np.isnan(cen_pres), -1, cen_pres) + msk = (rmw <= 0) & (cen_pres > 0) + pres_l = [872, 940, 980, 1021] + rmw_l = [14.907318, 15.726927, 25.742142, 56.856522] + rmw[msk] = 0 + for i, pres_l_i in enumerate(pres_l): + slope_0 = 1. / (pres_l_i - pres_l[i - 1]) if i > 0 else 0 + slope_1 = 1. / (pres_l[i + 1] - pres_l_i) if i + 1 < len(pres_l) else 0 + rmw[msk] += rmw_l[i] * np.fmax(0, (1 - slope_0 * np.fmax(0, pres_l_i - cen_pres[msk]) + - slope_1 * np.fmax(0, cen_pres[msk] - pres_l_i))) + return np.where(rmw <= 0, np.nan, rmw) + +def ibtracs_fit_param(explained, explanatory, year_range=(1980, 2019), order=1): + """Statistically fit an ibtracs parameter to other ibtracs variables + + A linear ordinary least squares fit is done using the statsmodels package. Parameters: - track (xr.Dataset): TC track - v_rel (dict): {category: A}, where wind decay = exp(-x*A) - p_rel (dict): (category: (S, B)}, - where pressure decay = S-(S-1)*exp(-x*B) - land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry - s_rel (bool): use environmental presure for S value (true) or - central presure (false) + explained (str): name of explained variable + explanatory (iterable): names of explanatory variables + year_range (tuple): first and last year to include in the analysis + order (int or tuple): the maximal order of the explanatory variables Returns: - xr.Dataset + OLSResults """ - # return if historical track - if track.orig_event_flag: - return track - - _track_land_params(track, land_geom) - # Index in land that comes from previous sea index - sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 - # Index in sea that comes from previous land index - land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 - if track.on_land[-1]: - land_sea_idx = np.append(land_sea_idx, track.time.size) - if not sea_land_idx.size or land_sea_idx.size > sea_land_idx.size: - return track - for idx, (sea_land, land_sea) \ - in enumerate(zip(sea_land_idx, land_sea_idx)): - v_landfall = track.max_sustained_wind[sea_land-1].values - p_landfall = float(track.central_pressure[sea_land-1].values) - try: - ss_scale_idx = np.where(v_landfall < SAFFIR_SIM_CAT)[0][0]+1 - except IndexError: + wmo_vars = ['wind', 'pres', 'rmw', 'roci', 'poci'] + all_vars = ['lat', 'lon'] + wmo_vars + explanatory = list(explanatory) + variables = explanatory + [explained] + for var in variables: + if var not in all_vars: + LOGGER.error("Unknown ibtracs variable: %s", var) + raise KeyError + + # load ibtracs dataset + fn_nc = os.path.join(os.path.abspath(SYSTEM_DIR), 'IBTrACS.ALL.v04r00.nc') + ibtracs_ds = xr.open_dataset(fn_nc) + + # choose specified year range + years = ibtracs_ds.sid.str.slice(0, 4).astype(int) + match = (years >= year_range[0]) & (years <= year_range[1]) + ibtracs_ds = ibtracs_ds.sel(storm=match) + + # fill values + agency_pref, track_agency_ix = ibtracs_track_agency(ibtracs_ds) + for var in wmo_vars: + if var not in variables: continue - if land_sea - sea_land == 1: - continue - p_decay = _calc_decay_ps_value(track, p_landfall, land_sea-1, s_rel) - p_decay = _decay_p_function(p_decay, p_rel[ss_scale_idx][1], \ - track.dist_since_lf[sea_land:land_sea].values) - # dont applay decay if it would decrease central pressure - p_decay[p_decay < 1] = track.central_pressure[sea_land:land_sea][p_decay < 1]/p_landfall - track.central_pressure[sea_land:land_sea] = p_landfall * p_decay - - v_decay = _decay_v_function(v_rel[ss_scale_idx], \ - track.dist_since_lf[sea_land:land_sea].values) - # dont applay decay if it would increas wind speeds - v_decay[v_decay > 1] = track.max_sustained_wind[sea_land:land_sea][v_decay > 1]/v_landfall - track.max_sustained_wind[sea_land:land_sea] = v_landfall * v_decay - - # correct values of sea between two landfalls - if land_sea < track.time.size and idx+1 < sea_land_idx.size: - rndn = 0.1 * float(np.abs(np.random.normal(size=1)*5)+6) - r_diff = track.central_pressure[land_sea].values - \ - track.central_pressure[land_sea-1].values + rndn - track.central_pressure[land_sea:sea_land_idx[idx+1]] += - r_diff - - rndn = rndn * 10 # mean value 10 - r_diff = track.max_sustained_wind[land_sea].values - \ - track.max_sustained_wind[land_sea-1].values - rndn - track.max_sustained_wind[land_sea:sea_land_idx[idx+1]] += - r_diff - - # correct limits - np.warnings.filterwarnings('ignore') - cor_p = track.central_pressure.values > track.environmental_pressure.values - track.central_pressure[cor_p] = track.environmental_pressure[cor_p] - track.max_sustained_wind[track.max_sustained_wind < 0] = 0 - track.attrs['category'] = set_category(track.max_sustained_wind.values, - track.max_sustained_wind_unit) - return track - -def _check_apply_decay_plot(all_tracks, syn_orig_wind, syn_orig_pres): - """ Plot wind and presure before and after correction for synthetic tracks. - Plot wind and presure for unchanged historical tracks.""" - # Plot synthetic tracks - sy_tracks = [track for track in all_tracks if not track.orig_event_flag] - graph_v_b, graph_v_a, graph_p_b, graph_p_a, graph_pd_a, graph_ped_a = \ - _check_apply_decay_syn_plot(sy_tracks, syn_orig_wind, - syn_orig_pres) - - # Plot historic tracks - hist_tracks = [track for track in all_tracks if track.orig_event_flag] - graph_hv, graph_hp, graph_hpd_a, graph_hped_a = \ - _check_apply_decay_hist_plot(hist_tracks) - - # Put legend and fix size - leg_lines = [Line2D([0], [0], color=CAT_COLORS[i_col], lw=2) - for i_col in range(len(SAFFIR_SIM_CAT))] - leg_lines.append(Line2D([0], [0], color='k', lw=2)) - leg_names = [CAT_NAMES[i_col] for i_col in range(1, len(SAFFIR_SIM_CAT)+1)] - leg_names.append('Sea') - all_gr = [graph_v_a, graph_v_b, graph_p_a, graph_p_b, graph_ped_a, - graph_pd_a, graph_hv, graph_hp, graph_hpd_a, graph_hped_a] - for graph in all_gr: - graph.axs[0].legend(leg_lines, leg_names) - fig, _ = graph.get_elems() - fig.set_size_inches(18.5, 10.5) - -def _check_apply_decay_syn_plot(sy_tracks, syn_orig_wind, - syn_orig_pres): - """Plot winds and pressures of synthetic tracks before and after - correction.""" - _, graph_v_b = plt.subplots() - graph_v_b.set_title('Wind before land decay correction') - graph_v_b.set_xlabel('Node number') - graph_v_b.set_ylabel('Max sustained wind (kn)') - - _, graph_v_a = plt.subplots() - graph_v_a.set_title('Wind after land decay correction') - graph_v_a.set_xlabel('Node number') - graph_v_a.set_ylabel('Max sustained wind (kn)') - - _, graph_p_b = plt.subplots() - graph_p_b.set_title('Pressure before land decay correctionn') - graph_p_b.set_xlabel('Node number') - graph_p_b.set_ylabel('Central pressure (mb)') - - _, graph_p_a = plt.subplots() - graph_p_a.set_title('Pressure after land decay correctionn') - graph_p_a.set_xlabel('Node number') - graph_p_a.set_ylabel('Central pressure (mb)') - - _, graph_pd_a = plt.subplots() - graph_pd_a.set_title('Relative pressure after land decay correction') - graph_pd_a.set_xlabel('Distance from landfall (km)') - graph_pd_a.set_ylabel('Central pressure relative to landfall') - - _, graph_ped_a = plt.subplots() - graph_ped_a.set_title('Environmental - central pressure after land decay correction') - graph_ped_a.set_xlabel('Distance from landfall (km)') - graph_ped_a.set_ylabel('Environmental pressure - Central pressure (mb)') - - for track, orig_wind, orig_pres in \ - zip(sy_tracks, syn_orig_wind, syn_orig_pres): - # Index in land that comes from previous sea index - sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0]+1 - # Index in sea that comes from previous land index - land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0]+1 - if track.on_land[-1]: - land_sea_idx = np.append(land_sea_idx, track.time.size) - if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: - for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): - v_lf = track.max_sustained_wind[sea_land-1].values - p_lf = track.central_pressure[sea_land-1].values - ss_scale = np.where(v_lf < SAFFIR_SIM_CAT)[0][0] - on_land = np.arange(track.time.size)[sea_land:land_sea] - - graph_v_a.plot(on_land, track.max_sustained_wind[on_land], - 'o', c=CAT_COLORS[ss_scale]) - graph_v_b.plot(on_land, orig_wind[on_land], - 'o', c=CAT_COLORS[ss_scale]) - graph_p_a.plot(on_land, track.central_pressure[on_land], - 'o', c=CAT_COLORS[ss_scale]) - graph_p_b.plot(on_land, orig_pres[on_land], - 'o', c=CAT_COLORS[ss_scale]) - graph_pd_a.plot(track.dist_since_lf[on_land], - track.central_pressure[on_land]/p_lf, - 'o', c=CAT_COLORS[ss_scale]) - graph_ped_a.plot(track.dist_since_lf[on_land], - track.environmental_pressure[on_land]- - track.central_pressure[on_land], - 'o', c=CAT_COLORS[ss_scale]) - - on_sea = np.arange(track.time.size)[np.logical_not(track.on_land)] - graph_v_a.plot(on_sea, track.max_sustained_wind[on_sea], - 'o', c='k', markersize=5) - graph_v_b.plot(on_sea, orig_wind[on_sea], - 'o', c='k', markersize=5) - graph_p_a.plot(on_sea, track.central_pressure[on_sea], - 'o', c='k', markersize=5) - graph_p_b.plot(on_sea, orig_pres[on_sea], - 'o', c='k', markersize=5) - - return graph_v_b, graph_v_a, graph_p_b, graph_p_a, graph_pd_a, graph_ped_a - -def _check_apply_decay_hist_plot(hist_tracks): - """Plot winds and pressures of historical tracks.""" - _, graph_hv = plt.subplots() - graph_hv.set_title('Historical wind') - graph_hv.set_xlabel('Node number') - graph_hv.set_ylabel('Max sustained wind (kn)') - - _, graph_hp = plt.subplots() - graph_hp.set_title('Historical pressure') - graph_hp.set_xlabel('Node number') - graph_hp.set_ylabel('Central pressure (mb)') - - _, graph_hpd_a = plt.subplots() - graph_hpd_a.set_title('Historical relative pressure') - graph_hpd_a.set_xlabel('Distance from landfall (km)') - graph_hpd_a.set_ylabel('Central pressure relative to landfall') - - _, graph_hped_a = plt.subplots() - graph_hped_a.set_title('Historical environmental - central pressure') - graph_hped_a.set_xlabel('Distance from landfall (km)') - graph_hped_a.set_ylabel('Environmental pressure - Central pressure (mb)') - - for track in hist_tracks: - # Index in land that comes from previous sea index - sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0]+1 - # Index in sea that comes from previous land index - land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0]+1 - if track.on_land[-1]: - land_sea_idx = np.append(land_sea_idx, track.time.size) - if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: - for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): - p_lf = track.central_pressure[sea_land-1].values - scale = np.where(track.max_sustained_wind[sea_land-1].values < - SAFFIR_SIM_CAT)[0][0] - on_land = np.arange(track.time.size)[sea_land:land_sea] - - graph_hv.add_curve(on_land, track.max_sustained_wind[on_land], - 'o', c=CAT_COLORS[scale]) - graph_hp.add_curve(on_land, track.central_pressure[on_land], - 'o', c=CAT_COLORS[scale]) - graph_hpd_a.plot(track.dist_since_lf[on_land], - track.central_pressure[on_land]/p_lf, - 'o', c=CAT_COLORS[scale]) - graph_hped_a.plot(track.dist_since_lf[on_land], - track.environmental_pressure[on_land]- - track.central_pressure[on_land], - 'o', c=CAT_COLORS[scale]) - - on_sea = np.arange(track.time.size)[np.logical_not(track.on_land)] - graph_hp.plot(on_sea, track.central_pressure[on_sea], - 'o', c='k', markersize=5) - graph_hv.plot(on_sea, track.max_sustained_wind[on_sea], - 'o', c='k', markersize=5) - - return graph_hv, graph_hp, graph_hpd_a, graph_hped_a - -def _missing_pressure(cen_pres, v_max, lat, lon): - """Deal with missing central pressures.""" - if np.argwhere(cen_pres <= 0).size > 0: - cen_pres = 1024.388 + 0.047*lat - 0.029*lon - 0.818*v_max # ibtracs 1980 -2013 (r2=0.91) -# cen_pres = 1024.688+0.055*lat-0.028*lon-0.815*v_max # peduzzi - return cen_pres + # array of values in order of preference + cols = [f'{a}_{var}' for a in agency_pref] + cols = [col for col in cols if col in ibtracs_ds.data_vars.keys()] + all_vals = ibtracs_ds[cols].to_array(dim='agency') + preferred_ix = all_vals.notnull().argmax(dim='agency') + if var in ['wind', 'pres']: + # choice: wmo -> wmo_agency/usa_agency -> preferred + ibtracs_ds[var] = ibtracs_ds['wmo_' + var] \ + .fillna(all_vals.isel(agency=track_agency_ix)) \ + .fillna(all_vals.isel(agency=preferred_ix)) + else: + ibtracs_ds[var] = all_vals.isel(agency=preferred_ix) + fit_df = pd.DataFrame({var: ibtracs_ds[var].values.ravel() for var in variables}) + fit_df = fit_df.dropna(axis=0, how='any').reset_index(drop=True) + if 'lat' in explanatory: + fit_df['lat'] = fit_df['lat'].abs() + + # prepare explanatory variables + d_explanatory = fit_df[explanatory] + if isinstance(order, int): + order = (order,) * len(explanatory) + add_const = False + for ex, max_o in zip(explanatory, order): + if isinstance(max_o, tuple): + if fit_df[ex].min() > max_o[0]: + print(f"Minimum data value is {fit_df[ex].min()} > {max_o[0]}.") + if fit_df[ex].max() < max_o[-1]: + print(f"Maximum data value is {fit_df[ex].max()} < {max_o[-1]}.") + # piecewise linear with given break points + d_explanatory = d_explanatory.drop(labels=[ex], axis=1) + for i, max_o_i in enumerate(max_o): + col = f'{ex}{max_o_i}' + slope_0 = 1. / (max_o_i - max_o[i - 1]) if i > 0 else 0 + slope_1 = 1. / (max_o[i + 1] - max_o_i) if i + 1 < len(max_o) else 0 + d_explanatory[col] = np.fmax(0, (1 - slope_0 * np.fmax(0, max_o_i - fit_df[ex]) + - slope_1 * np.fmax(0, fit_df[ex] - max_o_i))) + elif max_o < 0: + d_explanatory = d_explanatory.drop(labels=[ex], axis=1) + for order in range(1, abs(max_o) + 1): + d_explanatory[f'{ex}^{-order}'] = fit_df[ex]**(-order) + add_const = True + else: + for order in range(2, max_o + 1): + d_explanatory[f'{ex}^{order}'] = fit_df[ex]**order + add_const = True + d_explained = fit_df[[explained]] + if add_const: + d_explanatory['const'] = 1.0 + + # run statistical fit + sm_results = sm.OLS(d_explained, d_explanatory).fit() + + # print results + print(sm_results.params) + print("r^2:", sm_results.rsquared) + + return sm_results + +def ibtracs_track_agency(ds_sel): + """Get preferred IBTrACS agency for each entry in the dataset + + Parameters + ---------- + ds_sel : xarray.Dataset + Subselection of original IBTrACS NetCDF dataset. + + Returns + ------- + agency_pref : list of str + Names of IBTrACS agencies in order of preference. + track_agency_ix : xarray.DataArray of ints + For each entry in `ds_sel`, the agency to use, given as an index into `agency_pref`. + """ + agency_pref = IBTRACS_AGENCIES.copy() + agency_map = {a.encode('utf-8'): i for i, a in enumerate(agency_pref)} + agency_map.update({ + a.encode('utf-8'): agency_map[b'usa'] for a in IBTRACS_USA_AGENCIES + }) + agency_map[b''] = agency_map[b'wmo'] + agency_fun = lambda x: agency_map[x] + track_agency = ds_sel.wmo_agency.where(ds_sel.wmo_agency != '', ds_sel.usa_agency) + track_agency_ix = xr.apply_ufunc(agency_fun, track_agency, vectorize=True) + return agency_pref, track_agency_ix def _change_max_wind_unit(wind, unit_orig, unit_dest): - """ Compute maximum wind speed in unit_dest + """Compute maximum wind speed in unit_dest Parameters: wind (np.array): wind @@ -1398,7 +1231,6 @@ def _change_max_wind_unit(wind, unit_orig, unit_dest): Returns: double """ - ureg = UnitRegistry() if unit_orig in ('kn', 'kt'): ur_orig = ureg.knot elif unit_orig == 'mph': @@ -1423,33 +1255,30 @@ def _change_max_wind_unit(wind, unit_orig, unit_dest): raise ValueError return (np.nanmax(wind) * ur_orig).to(ur_dest).magnitude -def set_category(max_sus_wind, max_sus_wind_unit, saffir_scale=None): +def set_category(max_sus_wind, wind_unit, saffir_scale=None): """Add storm category according to saffir-simpson hurricane scale - -1 tropical depression - 0 tropical storm - - 1 Hurrican category 1 - - 2 Hurrican category 2 - - 3 Hurrican category 3 - - 4 Hurrican category 4 - - 5 Hurrican category 5 + - 1 Hurricane category 1 + - 2 Hurricane category 2 + - 3 Hurricane category 3 + - 4 Hurricane category 4 + - 5 Hurricane category 5 Parameters: max_sus_wind (np.array): max sustained wind - max_sus_wind_unit (str): units of max sustained wind + wind_unit (str): units of max sustained wind saffir_scale (list, optional): Saffir-Simpson scale in same units as wind Returns: double """ - if saffir_scale: - max_wind = np.nanmax(max_sus_wind) - elif max_sus_wind_unit != 'kn': - max_wind = _change_max_wind_unit(max_sus_wind, max_sus_wind_unit, 'kn') - saffir_scale = SAFFIR_SIM_CAT - else: + if saffir_scale is None: saffir_scale = SAFFIR_SIM_CAT - max_wind = np.nanmax(max_sus_wind) + if wind_unit != 'kn': + max_sus_wind = _change_max_wind_unit(max_sus_wind, wind_unit, 'kn') + max_wind = np.nanmax(max_sus_wind) try: return (np.argwhere(max_wind < saffir_scale) - 1)[0][0] except IndexError: diff --git a/climada/hazard/tc_tracks_forecast.py b/climada/hazard/tc_tracks_forecast.py new file mode 100644 index 0000000000..d0801ef47b --- /dev/null +++ b/climada/hazard/tc_tracks_forecast.py @@ -0,0 +1,351 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Define TCTracks auxiliary methods: BUFR based TC predictions (from ECMWF) +""" + +__all__ = ['TCForecast'] + +# standard libraries +import datetime as dt +import fnmatch +import ftplib +import logging +import os +import tempfile + +# additional libraries +import numpy as np +import pandas as pd +import pybufrkit +import tqdm +import xarray as xr + +# climada dependencies +from climada.hazard.tc_tracks import ( + TCTracks, set_category, DEF_ENV_PRESSURE, CAT_NAMES +) +from climada.util.files_handler import get_file_names + +# declare constants +ECMWF_FTP = 'dissemination.ecmwf.int' +ECMWF_USER = 'wmo' +ECMWF_PASS = 'essential' + +BASINS = { + 'W': 'W - North West Pacific', + 'C': 'C - North Central Pacific', + 'E': 'E - North East Pacific', + 'P': 'P - South Pacific', + 'L': 'L - North Atlantic', + 'A': 'A - Arabian Sea (North Indian Ocean)', + 'B': 'B - Bay of Bengal (North Indian Ocean)', + 'U': 'U - Australia', + 'S': 'S - South-West Indian Ocean' +} +"""Gleaned from the ECMWF wiki at +https://confluence.ecmwf.int/display/FCST/Tropical+Cyclone+tracks+in+BUFR+-+including+genesis +and Wikipedia at https://en.wikipedia.org/wiki/Invest_(meteorology) +""" + +SAFFIR_MS_CAT = np.array([18, 33, 43, 50, 59, 71, 1000]) +"""Saffir-Simpson Hurricane Categories in m/s""" + +SIG_CENTRE = 1 +"""The BUFR code 008005 significance for 'centre'""" + +LOGGER = logging.getLogger(__name__) + + +class TCForecast(TCTracks): + """An extension of the TCTracks construct adapted to forecast tracks + obtained from numerical weather prediction runs. + + Attributes: + data (list(xarray.Dataset)): Same as in parent class, adding the + following attributes + - ensemble_member (int) + - is_ensemble (bool) + """ + + def fetch_ecmwf(self, path=None, files=None): + """ + Fetch and read latest ECMWF TC track predictions from the FTP + dissemination server into instance. Use path argument to use local + files instead. + + Parameters: + path (str, list(str)): A location in the filesystem. Either a + path to a single BUFR TC track file, or a folder containing + only such files, or a globbing pattern. Passed to + climada.util.files_handler.get_file_names + files (file-like): An explicit list of file objects, bypassing + get_file_names + """ + if path is None and files is None: + files = self.fetch_bufr_ftp() + elif files is None: + files = get_file_names(path) + + for i, file in tqdm.tqdm(enumerate(files, 1), desc='Processing', + unit='files', total=len(files)): + try: + file.seek(0) # reset cursor if opened file instance + except AttributeError: + pass + + self.read_one_bufr_tc(file, id_no=i) + + try: + file.close() # discard if tempfile + except AttributeError: + pass + + @staticmethod + def fetch_bufr_ftp(target_dir=None, remote_dir=None): + """ + Fetch and read latest ECMWF TC track predictions from the FTP + dissemination server. If target_dir is set, the files get downloaded + persistently to the given location. A list of opened file-like objects + gets returned. + + Parameters: + target_dir (str): An existing directory to write the files to. If + None, the files get returned as tempfiles. + remote_dir (str, optional): If set, search this ftp folder for + forecast files; defaults to the latest. Format: + yyyymmddhhmmss, e.g. 20200730120000 + + Returns: + [str] or [filelike] + """ + con = ftplib.FTP(host=ECMWF_FTP, user=ECMWF_USER, passwd=ECMWF_PASS) + + try: + if remote_dir is None: + remote = pd.Series(con.nlst()) + remote = remote[remote.str.contains('120000|000000$')] + remote = remote.sort_values(ascending=False) + remote_dir = remote.iloc[0] + + con.cwd(remote_dir) + + remotefiles = fnmatch.filter(con.nlst(), '*tropical_cyclone*') + if len(remotefiles) == 0: + msg = 'No tracks found at ftp://{}/{}' + msg.format(ECMWF_FTP, remote_dir) + raise FileNotFoundError(msg) + + localfiles = [] + + LOGGER.info('Fetching BUFR tracks:') + for rfile in tqdm.tqdm(remotefiles, desc='Download', unit=' files'): + if target_dir: + lfile = open(os.path.join(target_dir, rfile), 'w+b') + else: + lfile = tempfile.TemporaryFile(mode='w+b') + + con.retrbinary('RETR ' + rfile, lfile.write) + + if target_dir: + localfiles.append(lfile.name) + lfile.close() + else: + localfiles.append(lfile) + + except ftplib.all_errors as err: + con.quit() + LOGGER.error('Error while downloading BUFR TC tracks.') + raise err + + _ = con.quit() + + return localfiles + + def read_one_bufr_tc(self, file, id_no=None, fcast_rep=None): + """ Read a single BUFR TC track file. + + Parameters: + file (str, filelike): Path object, string, or file-like object + id_no (int): Numerical ID; optional. Else use date + random int. + fcast_rep (int): Of the form 1xx000, indicating the delayed + replicator containing the forecast values; optional. + """ + + decoder = pybufrkit.decoder.Decoder() + + if hasattr(file, 'read'): + bufr = decoder.process(file.read()) + elif hasattr(file, 'read_bytes'): + bufr = decoder.process(file.read_bytes()) + elif os.path.isfile(file): + with open(file, 'rb') as i: + bufr = decoder.process(i.read()) + else: + raise FileNotFoundError('Check file argument') + + # setup parsers and querents + npparser = pybufrkit.dataquery.NodePathParser() + data_query = pybufrkit.dataquery.DataQuerent(npparser).query + + meparser = pybufrkit.mdquery.MetadataExprParser() + meta_query = pybufrkit.mdquery.MetadataQuerent(meparser).query + + if fcast_rep is None: + fcast_rep = self._find_delayed_replicator( + meta_query(bufr, '%unexpanded_descriptors') + ) + + # query the bufr message + msg = { + # subset forecast data + 'significance': data_query(bufr, fcast_rep + '> 008005'), + 'latitude': data_query(bufr, fcast_rep + '> 005002'), + 'longitude': data_query(bufr, fcast_rep + '> 006002'), + 'wind_10m': data_query(bufr, fcast_rep + '> 011012'), + 'pressure': data_query(bufr, fcast_rep + '> 010051'), + 'timestamp': data_query(bufr, fcast_rep + '> 004024'), + + # subset metadata + 'wmo_longname': data_query(bufr, '/001027'), + 'storm_id': data_query(bufr, '/001025'), + 'ens_type': data_query(bufr, '/001092'), + 'ens_number': data_query(bufr, '/001091'), + } + + timestamp_origin = dt.datetime( + meta_query(bufr, '%year'), meta_query(bufr, '%month'), + meta_query(bufr, '%day'), meta_query(bufr, '%hour'), + meta_query(bufr, '%minute'), + ) + timestamp_origin = np.datetime64(timestamp_origin) + + if id_no is None: + id_no = timestamp_origin.item().strftime('%Y%m%d%H') + \ + str(np.random.randint(1e3, 1e4)) + + orig_centre = meta_query(bufr, '%originating_centre') + if orig_centre == 98: + provider = 'ECMWF' + else: + provider = 'BUFR code ' + str(orig_centre) + + for i in msg['significance'].subset_indices(): + name = msg['wmo_longname'].get_values(i)[0].decode().strip() + track = self._subset_to_track( + msg, i, provider, timestamp_origin, name, id_no + ) + if track is not None: + self.append(track) + else: + LOGGER.debug('Dropping empty track %s, subset %d', name, i) + + @staticmethod + def _subset_to_track(msg, index, provider, timestamp_origin, name, id_no): + """Subroutine to process one BUFR subset into one xr.Dataset""" + sig = np.array(msg['significance'].get_values(index), dtype='int') + lat = np.array(msg['latitude'].get_values(index), dtype='float') + lon = np.array(msg['longitude'].get_values(index), dtype='float') + wnd = np.array(msg['wind_10m'].get_values(index), dtype='float') + pre = np.array(msg['pressure'].get_values(index), dtype='float') + + sid = msg['storm_id'].get_values(index)[0].decode().strip() + + timestep_int = np.array(msg['timestamp'].get_values(index)).squeeze() + timestamp = timestamp_origin + timestep_int.astype('timedelta64[h]') + + try: + track = xr.Dataset( + data_vars={ + 'max_sustained_wind': ('time', np.squeeze(wnd)), + 'central_pressure': ('time', np.squeeze(pre)/100), + 'ts_int': ('time', timestep_int), + 'lat': ('time', lat[sig == 1]), + 'lon': ('time', lon[sig == 1]), + }, + coords={ + 'time': timestamp, + }, + attrs={ + 'max_sustained_wind_unit': 'm/s', + 'central_pressure_unit': 'mb', + 'name': name, + 'sid': sid, + 'orig_event_flag': False, + 'data_provider': provider, + 'id_no': (int(id_no) + index / 100), + 'ensemble_number': msg['ens_number'].get_values(index)[0], + 'is_ensemble': msg['ens_type'].get_values(index)[0] != 0, + 'forecast_time': timestamp_origin, + } + ) + except ValueError as err: + LOGGER.warning( + 'Could not process track %s subset %d, error: %s', + sid, index, err + ) + return None + + track = track.dropna('time') + + if track.sizes['time'] == 0: + return None + + # can only make latlon coords after dropna + track = track.set_coords(['lat', 'lon']) + track['time_step'] = track.ts_int - \ + track.ts_int.shift({'time': 1}, fill_value=0) + + # TODO use drop_vars after upgrading xarray + track = track.drop('ts_int') + + track['radius_max_wind'] = np.full_like(track.time, np.nan, + dtype=float) + track['environmental_pressure'] = np.full_like( + track.time, DEF_ENV_PRESSURE, dtype=float + ) + + # according to specs always num-num-letter + track.attrs['basin'] = BASINS[sid[2]] + + cat_name = CAT_NAMES[set_category( + max_sus_wind=track.max_sustained_wind.values, + wind_unit=track.max_sustained_wind_unit, + saffir_scale=SAFFIR_MS_CAT + )] + track.attrs['category'] = cat_name + return track + + @staticmethod + def _find_delayed_replicator(descriptors): + """The current bufr tc tracks only use one delayed replicator, + enclosing all forecast values. This finds it. + + Parameters: + bufr_message: An in-memory pybufrkit BUFR message + """ + delayed_replicators = [ + d for d in descriptors + if 100000 < d < 200000 and d % 1000 == 0 + ] + + if len(delayed_replicators) != 1: + LOGGER.error('Could not find fcast_rep, please set manually.') + raise ValueError('More than one delayed replicator in BUFR file') + + return str(delayed_replicators[0]) diff --git a/climada/hazard/tc_tracks_synth.py b/climada/hazard/tc_tracks_synth.py new file mode 100644 index 0000000000..e1c92ea364 --- /dev/null +++ b/climada/hazard/tc_tracks_synth.py @@ -0,0 +1,693 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Generate synthetic tropical cyclone tracks from real ones +""" + +import array +import itertools +import logging +import matplotlib.cm as cm_mp +from matplotlib.lines import Line2D +import matplotlib.pyplot as plt +from numba import jit +import numpy as np + +from climada.util.config import CONFIG +import climada.util.coordinates +import climada.hazard.tc_tracks + +LOGGER = logging.getLogger(__name__) + + +def calc_random_walk(tracks, + ens_size=9, + ens_amp0=1.5, + ens_amp=0.1, + max_angle=np.pi / 10, + seed=CONFIG['trop_cyclone']['random_seed'], + decay=True): + """ + Generate synthetic tracks based on directed random walk. An ensemble of + tracks is computed for every track contained. + Please note that there is a bias towards higher latitudes in the random + wiggle. The wiggles are applied for each timestep. Please consider using + equal_timestep() for unification before generating synthetic tracks. + Be careful when changing ens_amp and max_angle and test changes of the + parameter values before application. + + The object is mutated in-place. + + Parameters: + tracks (TCTracks): See `climada.hazard.tc_tracks`. + ens_size (int, optional): number of ensemble members per track. + Default 9. + ens_amp0 (float, optional): amplitude of max random starting point + shift in decimal degree (longitude and latitude). Default: 1.5 + ens_amp (float, optional): amplitude of random walk wiggles in + decimal degree (longitude and latitude). Default: 0.1 + max_angle (float, optional): maximum angle of variation. Default: pi/10. + - max_angle=pi results in undirected random change with + no change in direction; + - max_angle=0 (or very close to 0) is not recommended. It results + in non-random synthetic tracks with constant shift to higher latitudes; + - for 0= 0: + np.random.seed(seed) + + random_vec = [np.random.uniform(size=ens_size * (2 + track.time.size)) + for track in tracks.data] + + if tracks.pool: + chunksize = min(tracks.size // tracks.pool.ncpus, 1000) + new_ens = tracks.pool.map(_one_rnd_walk, tracks.data, + itertools.repeat(ens_size, tracks.size), + itertools.repeat(ens_amp0, tracks.size), + itertools.repeat(ens_amp, tracks.size), + itertools.repeat(max_angle, tracks.size), + random_vec, chunksize=chunksize) + else: + new_ens = [_one_rnd_walk(track, ens_size, ens_amp0, ens_amp, + max_angle, rand) + for track, rand in zip(tracks.data, random_vec)] + + tracks.data = sum(new_ens, []) + + if decay: + hist_tracks = [track for track in tracks.data if track.orig_event_flag] + if hist_tracks: + try: + extent = tracks.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + v_rel, p_rel = _calc_land_decay(hist_tracks, land_geom, + pool=tracks.pool) + tracks.data = _apply_land_decay(tracks.data, v_rel, p_rel, + land_geom, pool=tracks.pool) + except ValueError: + LOGGER.info('No land decay coefficients could be applied.') + else: + LOGGER.error('No historical tracks contained. ' + 'Historical tracks are needed for land decay.') + + +@jit(parallel=True) +def _one_rnd_walk(track, ens_size, ens_amp0, ens_amp, max_angle, rnd_vec): + """Interpolate values of one track. + + Parameters: + track (xr.Dataset): track data + + Returns: + list(xr.Dataset) + """ + ens_track = list() + n_dat = track.time.size + xy_ini = ens_amp0 * (rnd_vec[:2 * ens_size].reshape((2, ens_size)) - 0.5) + tmp_ang = np.cumsum(2 * max_angle * rnd_vec[2 * ens_size:] - max_angle) + coord_xy = np.empty((2, ens_size * n_dat)) + coord_xy[0] = np.cumsum(ens_amp * np.sin(tmp_ang)) + coord_xy[1] = np.cumsum(ens_amp * np.cos(tmp_ang)) + + ens_track.append(track) + for i_ens in range(ens_size): + i_track = track.copy(True) + + d_xy = coord_xy[:, i_ens * n_dat: (i_ens + 1) * n_dat] - \ + np.expand_dims(coord_xy[:, i_ens * n_dat], axis=1) + # change sign of latitude change for southern hemishpere: + d_xy = np.sign(track.lat.values[0]) * d_xy + + d_lat_lon = d_xy + np.expand_dims(xy_ini[:, i_ens], axis=1) + + i_track.lon.values = i_track.lon.values + d_lat_lon[0, :] + i_track.lat.values = i_track.lat.values + d_lat_lon[1, :] + i_track.attrs['orig_event_flag'] = False + i_track.attrs['name'] = i_track.attrs['name'] + '_gen' + str(i_ens + 1) + i_track.attrs['sid'] = i_track.attrs['sid'] + '_gen' + str(i_ens + 1) + i_track.attrs['id_no'] = i_track.attrs['id_no'] + (i_ens + 1) / 100 + + ens_track.append(i_track) + + return ens_track + + +def _calc_land_decay(hist_tracks, land_geom, s_rel=True, check_plot=False, + pool=None): + """Compute wind and pressure decay coefficients from historical events + + Decay is calculated for every TC category according to the formulas: + + - wind decay = exp(-x*A) + - pressure decay = S-(S-1)*exp(-x*B) + + Parameters: + hist_tracks (list): List of xarray Datasets describing TC tracks. + land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry + s_rel (bool, optional): use environmental presure to calc S value + (true) or central presure (false) + check_plot (bool, optional): visualize computed coefficients. + Default: False + + Returns: + v_rel (dict(category: A)), p_rel (dict(category: (S, B))) + """ + # Key is Saffir-Simpson scale + # values are lists of wind/wind at landfall + v_lf = dict() + # values are tuples with first value the S parameter, second value + # list of central pressure/central pressure at landfall + p_lf = dict() + # x-scale values to compute landfall decay + x_val = dict() + + if pool: + dec_val = pool.map(_decay_values, hist_tracks, itertools.repeat(land_geom), + itertools.repeat(s_rel), + chunksize=min(len(hist_tracks) // pool.ncpus, 1000)) + else: + dec_val = [_decay_values(track, land_geom, s_rel) for track in hist_tracks] + + for (tv_lf, tp_lf, tx_val) in dec_val: + for key in tv_lf.keys(): + v_lf.setdefault(key, []).extend(tv_lf[key]) + p_lf.setdefault(key, ([], [])) + p_lf[key][0].extend(tp_lf[key][0]) + p_lf[key][1].extend(tp_lf[key][1]) + x_val.setdefault(key, []).extend(tx_val[key]) + + v_rel, p_rel = _decay_calc_coeff(x_val, v_lf, p_lf) + if check_plot: + _check_decay_values_plot(x_val, v_lf, p_lf, v_rel, p_rel) + + return v_rel, p_rel + + +def _apply_land_decay(tracks, v_rel, p_rel, land_geom, s_rel=True, + check_plot=False, pool=None): + """Compute wind and pressure decay due to landfall in synthetic tracks. + + Parameters: + v_rel (dict): {category: A}, where wind decay = exp(-x*A) + p_rel (dict): (category: (S, B)}, where pressure decay + = S-(S-1)*exp(-x*B) + land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry + s_rel (bool, optional): use environmental presure to calc S value + (true) or central presure (false) + check_plot (bool, optional): visualize computed changes + """ + sy_tracks = [track for track in tracks if not track.orig_event_flag] + if not sy_tracks: + LOGGER.error('No synthetic tracks contained. Synthetic tracks' + ' are needed.') + raise ValueError + + if not v_rel or not p_rel: + LOGGER.info('No decay coefficients.') + return + + if check_plot: + orig_wind, orig_pres = [], [] + for track in sy_tracks: + orig_wind.append(np.copy(track.max_sustained_wind.values)) + orig_pres.append(np.copy(track.central_pressure.values)) + + if pool: + chunksize = min(len(tracks) // pool.ncpus, 1000) + tracks = pool.map(_apply_decay_coeffs, tracks, + itertools.repeat(v_rel), itertools.repeat(p_rel), + itertools.repeat(land_geom), itertools.repeat(s_rel), + chunksize=chunksize) + else: + tracks = [_apply_decay_coeffs(track, v_rel, p_rel, land_geom, s_rel) + for track in tracks] + + if check_plot: + _check_apply_decay_plot(tracks, orig_wind, orig_pres) + return tracks + + +def _decay_values(track, land_geom, s_rel): + """Compute wind and pressure relative to landafall values. + + Parameters + ---------- + track : xr.Dataset + track + land_geom : shapely.geometry.multipolygon.MultiPolygon + land geometry + s_rel : bool + use environmental presure for S value (true) or central presure (false) + + Returns + ------- + v_lf : dict + key is Saffir-Simpson scale, values are arrays of wind/wind at landfall + p_lf : dict + key is Saffir-Simpson scale, values are tuples with first value array + of S parameter, second value array of central pressure/central pressure + at landfall + x_val : dict + key is Saffir-Simpson scale, values are arrays with the values used as + "x" in the coefficient fitting, the distance since landfall + """ + v_lf = dict() + p_lf = dict() + x_val = dict() + + climada.hazard.tc_tracks.track_land_params(track, land_geom) + # Index in land that comes from previous sea index + sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 + # Index in sea that comes from previous land index + land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 + if track.on_land[-1]: + land_sea_idx = np.append(land_sea_idx, track.time.size) + if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: + for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): + v_landfall = track.max_sustained_wind[sea_land - 1].values + scale_thresholds = climada.hazard.tc_tracks.SAFFIR_SIM_CAT + ss_scale_idx = np.where(v_landfall < scale_thresholds)[0][0] + 1 + + v_land = track.max_sustained_wind[sea_land - 1:land_sea].values + if v_land[0] > 0: + v_land = (v_land[1:] / v_land[0]).tolist() + else: + v_land = v_land[1:].tolist() + + p_landfall = float(track.central_pressure[sea_land - 1].values) + p_land = track.central_pressure[sea_land - 1:land_sea].values + p_land = (p_land[1:] / p_land[0]).tolist() + + p_land_s = _calc_decay_ps_value( + track, p_landfall, land_sea - 1, s_rel) + p_land_s = len(p_land) * [p_land_s] + + if ss_scale_idx not in v_lf: + v_lf[ss_scale_idx] = array.array('f', v_land) + p_lf[ss_scale_idx] = (array.array('f', p_land_s), + array.array('f', p_land)) + x_val[ss_scale_idx] = array.array('f', + track.dist_since_lf[sea_land:land_sea]) + else: + v_lf[ss_scale_idx].extend(v_land) + p_lf[ss_scale_idx][0].extend(p_land_s) + p_lf[ss_scale_idx][1].extend(p_land) + x_val[ss_scale_idx].extend(track.dist_since_lf[sea_land:land_sea]) + return v_lf, p_lf, x_val + + +def _decay_calc_coeff(x_val, v_lf, p_lf): + """From track's relative velocity and pressure, compute the decay + coefficients. + - wind decay = exp(-x*A) + - pressure decay = S-(S-1)*exp(-x*A) + + Parameters: + x_val (dict): key is Saffir-Simpson scale, values are lists with + the values used as "x" in the coefficient fitting, the + distance since landfall + v_lf (dict): key is Saffir-Simpson scale, values are lists of + wind/wind at landfall + p_lf (dict): key is Saffir-Simpson scale, values are tuples with + first value the S parameter, second value list of central + pressure/central pressure at landfall + + Returns: + v_rel (dict()), p_rel (dict()) + """ + np.warnings.filterwarnings('ignore') + v_rel = dict() + p_rel = dict() + for ss_scale, val_lf in v_lf.items(): + x_val_ss = np.array(x_val[ss_scale]) + + y_val = np.array(val_lf) + v_coef = _solve_decay_v_function(y_val, x_val_ss) + v_coef = v_coef[np.isfinite(v_coef)] + v_coef = np.mean(v_coef) + + ps_y_val = np.array(p_lf[ss_scale][0]) + y_val = np.array(p_lf[ss_scale][1]) + y_val[ps_y_val <= y_val] = np.nan + y_val[ps_y_val <= 1] = np.nan + valid_p = np.isfinite(y_val) + ps_y_val = ps_y_val[valid_p] + y_val = y_val[valid_p] + p_coef = _solve_decay_p_function(ps_y_val, y_val, x_val_ss[valid_p]) + ps_y_val = np.mean(ps_y_val) + p_coef = np.mean(p_coef) + + if np.isfinite(v_coef) and np.isfinite(ps_y_val) and np.isfinite(ps_y_val): + v_rel[ss_scale] = v_coef + p_rel[ss_scale] = (ps_y_val, p_coef) + + scale_fill = np.array(list(p_rel.keys())) + if not scale_fill.size: + LOGGER.info('No historical track with landfall.') + return v_rel, p_rel + for ss_scale in range(1, len(climada.hazard.tc_tracks.SAFFIR_SIM_CAT) + 1): + if ss_scale not in p_rel: + close_scale = scale_fill[np.argmin(np.abs(scale_fill - ss_scale))] + LOGGER.debug('No historical track of category %s with landfall. ' + 'Decay parameters from category %s taken.', + climada.hazard.tc_tracks.CAT_NAMES[ss_scale - 2], + climada.hazard.tc_tracks.CAT_NAMES[close_scale - 2]) + v_rel[ss_scale] = v_rel[close_scale] + p_rel[ss_scale] = p_rel[close_scale] + + return v_rel, p_rel + + +def _check_decay_values_plot(x_val, v_lf, p_lf, v_rel, p_rel): + """Generate one graph with wind decay and an other with central pressure + decay, true and approximated.""" + # One graph per TC category + for track_cat, color in zip(v_lf.keys(), + cm_mp.rainbow(np.linspace(0, 1, len(v_lf)))): + _, axes = plt.subplots(2, 1) + x_eval = np.linspace(0, np.max(x_val[track_cat]), 20) + + axes[0].set_xlabel('Distance from landfall (km)') + axes[0].set_ylabel('Max sustained wind relative to landfall') + axes[0].set_title('Wind') + axes[0].plot(x_val[track_cat], v_lf[track_cat], '*', c=color, + label=climada.hazard.tc_tracks.CAT_NAMES[track_cat - 2]) + axes[0].plot(x_eval, _decay_v_function(v_rel[track_cat], x_eval), + '-', c=color) + + axes[1].set_xlabel('Distance from landfall (km)') + axes[1].set_ylabel('Central pressure relative to landfall') + axes[1].set_title('Pressure') + axes[1].plot(x_val[track_cat], p_lf[track_cat][1], '*', c=color, + label=climada.hazard.tc_tracks.CAT_NAMES[track_cat - 2]) + axes[1].plot( + x_eval, + _decay_p_function(p_rel[track_cat][0], p_rel[track_cat][1], x_eval), + '-', c=color) + + +def _apply_decay_coeffs(track, v_rel, p_rel, land_geom, s_rel): + """Change track's max sustained wind and central pressure using the land + decay coefficients. + + Parameters: + track (xr.Dataset): TC track + v_rel (dict): {category: A}, where wind decay = exp(-x*A) + p_rel (dict): (category: (S, B)}, + where pressure decay = S-(S-1)*exp(-x*B) + land_geom (shapely.geometry.multipolygon.MultiPolygon): land geometry + s_rel (bool): use environmental presure for S value (true) or + central presure (false) + + Returns: + xr.Dataset + """ + # return if historical track + if track.orig_event_flag: + return track + + climada.hazard.tc_tracks.track_land_params(track, land_geom) + # Index in land that comes from previous sea index + sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 + # Index in sea that comes from previous land index + land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 + if track.on_land[-1]: + land_sea_idx = np.append(land_sea_idx, track.time.size) + if not sea_land_idx.size or land_sea_idx.size > sea_land_idx.size: + return track + for idx, (sea_land, land_sea) \ + in enumerate(zip(sea_land_idx, land_sea_idx)): + v_landfall = track.max_sustained_wind[sea_land - 1].values + p_landfall = float(track.central_pressure[sea_land - 1].values) + scale_thresholds = climada.hazard.tc_tracks.SAFFIR_SIM_CAT + try: + ss_scale_idx = np.where(v_landfall < scale_thresholds)[0][0] + 1 + except IndexError: + continue + if land_sea - sea_land == 1: + continue + p_decay = _calc_decay_ps_value(track, p_landfall, land_sea - 1, s_rel) + p_decay = _decay_p_function(p_decay, p_rel[ss_scale_idx][1], + track.dist_since_lf[sea_land:land_sea].values) + # dont applay decay if it would decrease central pressure + p_decay[p_decay < 1] = track.central_pressure[sea_land:land_sea][p_decay < 1] / p_landfall + track.central_pressure[sea_land:land_sea] = p_landfall * p_decay + + v_decay = _decay_v_function(v_rel[ss_scale_idx], + track.dist_since_lf[sea_land:land_sea].values) + # dont applay decay if it would increas wind speeds + v_decay[v_decay > 1] = (track.max_sustained_wind[sea_land:land_sea][v_decay > 1] + / v_landfall) + track.max_sustained_wind[sea_land:land_sea] = v_landfall * v_decay + + # correct values of sea between two landfalls + if land_sea < track.time.size and idx + 1 < sea_land_idx.size: + rndn = 0.1 * float(np.abs(np.random.normal(size=1) * 5) + 6) + r_diff = track.central_pressure[land_sea].values - \ + track.central_pressure[land_sea - 1].values + rndn + track.central_pressure[land_sea:sea_land_idx[idx + 1]] += - r_diff + + rndn = rndn * 10 # mean value 10 + r_diff = track.max_sustained_wind[land_sea].values - \ + track.max_sustained_wind[land_sea - 1].values - rndn + track.max_sustained_wind[land_sea:sea_land_idx[idx + 1]] += - r_diff + + # correct limits + np.warnings.filterwarnings('ignore') + cor_p = track.central_pressure.values > track.environmental_pressure.values + track.central_pressure[cor_p] = track.environmental_pressure[cor_p] + track.max_sustained_wind[track.max_sustained_wind < 0] = 0 + track.attrs['category'] = climada.hazard.tc_tracks.set_category( + track.max_sustained_wind.values, track.max_sustained_wind_unit) + return track + + +def _check_apply_decay_plot(all_tracks, syn_orig_wind, syn_orig_pres): + """Plot wind and presure before and after correction for synthetic tracks. + Plot wind and presure for unchanged historical tracks.""" + # Plot synthetic tracks + sy_tracks = [track for track in all_tracks if not track.orig_event_flag] + graph_v_b, graph_v_a, graph_p_b, graph_p_a, graph_pd_a, graph_ped_a = \ + _check_apply_decay_syn_plot(sy_tracks, syn_orig_wind, + syn_orig_pres) + + # Plot historic tracks + hist_tracks = [track for track in all_tracks if track.orig_event_flag] + graph_hv, graph_hp, graph_hpd_a, graph_hped_a = \ + _check_apply_decay_hist_plot(hist_tracks) + + # Put legend and fix size + scale_thresholds = climada.hazard.tc_tracks.SAFFIR_SIM_CAT + leg_lines = [Line2D([0], [0], color=climada.hazard.tc_tracks.CAT_COLORS[i_col], lw=2) + for i_col in range(len(scale_thresholds))] + leg_lines.append(Line2D([0], [0], color='k', lw=2)) + leg_names = [climada.hazard.tc_tracks.CAT_NAMES[i_col] + for i_col in sorted(climada.hazard.tc_tracks.CAT_NAMES.keys())] + leg_names.append('Sea') + all_gr = [graph_v_a, graph_v_b, graph_p_a, graph_p_b, graph_ped_a, + graph_pd_a, graph_hv, graph_hp, graph_hpd_a, graph_hped_a] + for graph in all_gr: + graph.axs[0].legend(leg_lines, leg_names) + fig, _ = graph.get_elems() + fig.set_size_inches(18.5, 10.5) + + +def _calc_decay_ps_value(track, p_landfall, pos, s_rel): + if s_rel: + p_land_s = track.environmental_pressure[pos].values + else: + p_land_s = track.central_pressure[pos].values + return float(p_land_s / p_landfall) + + +def _decay_v_function(a_coef, x_val): + """Decay function used for wind after landfall.""" + return np.exp(-a_coef * x_val) + + +def _solve_decay_v_function(v_y, x_val): + """Solve decay function used for wind after landfall. Get A coefficient.""" + return -np.log(v_y) / x_val + + +def _decay_p_function(s_coef, b_coef, x_val): + """Decay function used for pressure after landfall.""" + return s_coef - (s_coef - 1) * np.exp(-b_coef * x_val) + + +def _solve_decay_p_function(ps_y, p_y, x_val): + """Solve decay function used for pressure after landfall. + Get B coefficient.""" + return -np.log((ps_y - p_y) / (ps_y - 1.0)) / x_val + + +def _check_apply_decay_syn_plot(sy_tracks, syn_orig_wind, + syn_orig_pres): + """Plot winds and pressures of synthetic tracks before and after + correction.""" + _, graph_v_b = plt.subplots() + graph_v_b.set_title('Wind before land decay correction') + graph_v_b.set_xlabel('Node number') + graph_v_b.set_ylabel('Max sustained wind (kn)') + + _, graph_v_a = plt.subplots() + graph_v_a.set_title('Wind after land decay correction') + graph_v_a.set_xlabel('Node number') + graph_v_a.set_ylabel('Max sustained wind (kn)') + + _, graph_p_b = plt.subplots() + graph_p_b.set_title('Pressure before land decay correctionn') + graph_p_b.set_xlabel('Node number') + graph_p_b.set_ylabel('Central pressure (mb)') + + _, graph_p_a = plt.subplots() + graph_p_a.set_title('Pressure after land decay correctionn') + graph_p_a.set_xlabel('Node number') + graph_p_a.set_ylabel('Central pressure (mb)') + + _, graph_pd_a = plt.subplots() + graph_pd_a.set_title('Relative pressure after land decay correction') + graph_pd_a.set_xlabel('Distance from landfall (km)') + graph_pd_a.set_ylabel('Central pressure relative to landfall') + + _, graph_ped_a = plt.subplots() + graph_ped_a.set_title( + 'Environmental - central pressure after land decay correction') + graph_ped_a.set_xlabel('Distance from landfall (km)') + graph_ped_a.set_ylabel('Environmental pressure - Central pressure (mb)') + + for track, orig_wind, orig_pres in \ + zip(sy_tracks, syn_orig_wind, syn_orig_pres): + # Index in land that comes from previous sea index + sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 + # Index in sea that comes from previous land index + land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 + if track.on_land[-1]: + land_sea_idx = np.append(land_sea_idx, track.time.size) + if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: + for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): + v_lf = track.max_sustained_wind[sea_land - 1].values + p_lf = track.central_pressure[sea_land - 1].values + scale_thresholds = climada.hazard.tc_tracks.SAFFIR_SIM_CAT + ss_scale = np.where(v_lf < scale_thresholds)[0][0] + on_land = np.arange(track.time.size)[sea_land:land_sea] + + graph_v_a.plot(on_land, track.max_sustained_wind[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + graph_v_b.plot(on_land, orig_wind[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + graph_p_a.plot(on_land, track.central_pressure[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + graph_p_b.plot(on_land, orig_pres[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + graph_pd_a.plot(track.dist_since_lf[on_land], + track.central_pressure[on_land] / p_lf, + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + graph_ped_a.plot(track.dist_since_lf[on_land], + track.environmental_pressure[on_land] - + track.central_pressure[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[ss_scale]) + + on_sea = np.arange(track.time.size)[~track.on_land] + graph_v_a.plot(on_sea, track.max_sustained_wind[on_sea], + 'o', c='k', markersize=5) + graph_v_b.plot(on_sea, orig_wind[on_sea], + 'o', c='k', markersize=5) + graph_p_a.plot(on_sea, track.central_pressure[on_sea], + 'o', c='k', markersize=5) + graph_p_b.plot(on_sea, orig_pres[on_sea], + 'o', c='k', markersize=5) + + return graph_v_b, graph_v_a, graph_p_b, graph_p_a, graph_pd_a, graph_ped_a + + +def _check_apply_decay_hist_plot(hist_tracks): + """Plot winds and pressures of historical tracks.""" + _, graph_hv = plt.subplots() + graph_hv.set_title('Historical wind') + graph_hv.set_xlabel('Node number') + graph_hv.set_ylabel('Max sustained wind (kn)') + + _, graph_hp = plt.subplots() + graph_hp.set_title('Historical pressure') + graph_hp.set_xlabel('Node number') + graph_hp.set_ylabel('Central pressure (mb)') + + _, graph_hpd_a = plt.subplots() + graph_hpd_a.set_title('Historical relative pressure') + graph_hpd_a.set_xlabel('Distance from landfall (km)') + graph_hpd_a.set_ylabel('Central pressure relative to landfall') + + _, graph_hped_a = plt.subplots() + graph_hped_a.set_title('Historical environmental - central pressure') + graph_hped_a.set_xlabel('Distance from landfall (km)') + graph_hped_a.set_ylabel('Environmental pressure - Central pressure (mb)') + + for track in hist_tracks: + # Index in land that comes from previous sea index + sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] + 1 + # Index in sea that comes from previous land index + land_sea_idx = np.where(np.diff(track.on_land.astype(int)) == -1)[0] + 1 + if track.on_land[-1]: + land_sea_idx = np.append(land_sea_idx, track.time.size) + if sea_land_idx.size and land_sea_idx.size <= sea_land_idx.size: + for sea_land, land_sea in zip(sea_land_idx, land_sea_idx): + scale_thresholds = climada.hazard.tc_tracks.SAFFIR_SIM_CAT + scale = np.where(track.max_sustained_wind[sea_land - 1].values + < scale_thresholds)[0][0] + on_land = np.arange(track.time.size)[sea_land:land_sea] + + graph_hv.add_curve(on_land, track.max_sustained_wind[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[scale]) + graph_hp.add_curve(on_land, track.central_pressure[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[scale]) + graph_hpd_a.plot(track.dist_since_lf[on_land], + track.central_pressure[on_land] + / track.central_pressure[sea_land - 1].values, + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[scale]) + graph_hped_a.plot(track.dist_since_lf[on_land], + track.environmental_pressure[on_land] - + track.central_pressure[on_land], + 'o', c=climada.hazard.tc_tracks.CAT_COLORS[scale]) + + on_sea = np.arange(track.time.size)[~track.on_land] + graph_hp.plot(on_sea, track.central_pressure[on_sea], + 'o', c='k', markersize=5) + graph_hv.plot(on_sea, track.max_sustained_wind[on_sea], + 'o', c='k', markersize=5) + + return graph_hv, graph_hp, graph_hpd_a, graph_hped_a diff --git a/climada/hazard/test/data/TCrain_brb_test.mat b/climada/hazard/test/data/TCrain_brb_test.mat new file mode 100644 index 0000000000..ee23bf2567 Binary files /dev/null and b/climada/hazard/test/data/TCrain_brb_test.mat differ diff --git a/climada/hazard/test/data/emanuel_test_tracks.mat b/climada/hazard/test/data/emanuel_test_tracks.mat new file mode 100644 index 0000000000..13cff9d5c0 Binary files /dev/null and b/climada/hazard/test/data/emanuel_test_tracks.mat differ diff --git a/climada/hazard/test/data/gettelman_test_tracks.nc b/climada/hazard/test/data/gettelman_test_tracks.nc new file mode 100644 index 0000000000..89d0f62fca Binary files /dev/null and b/climada/hazard/test/data/gettelman_test_tracks.nc differ diff --git a/climada/hazard/test/data/temp_mpircp85cal_full.mat b/climada/hazard/test/data/temp_mpircp85cal_full.mat new file mode 100644 index 0000000000..5a97f5dd04 Binary files /dev/null and b/climada/hazard/test/data/temp_mpircp85cal_full.mat differ diff --git a/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.det.bufr4 b/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.det.bufr4 new file mode 100644 index 0000000000..0d5414f564 Binary files /dev/null and b/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.det.bufr4 differ diff --git a/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.eps.bufr4 b/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.eps.bufr4 new file mode 100644 index 0000000000..d555ea0d9e Binary files /dev/null and b/climada/hazard/test/data/tracks_22S_HEROLD_2020031912.eps.bufr4 differ diff --git a/climada/hazard/test/test_base.py b/climada/hazard/test/test_base.py index a97dbb9c5b..57043d0004 100644 --- a/climada/hazard/test/test_base.py +++ b/climada/hazard/test/test_base.py @@ -45,13 +45,13 @@ def dummy_hazard(): hazard.date = np.array([1, 2, 3, 4]) hazard.orig = np.array([True, False, False, True]) hazard.frequency = np.array([0.1, 0.5, 0.5, 0.2]) - hazard.fraction = sparse.csr_matrix([[0.02, 0.03, 0.04], \ - [0.01, 0.01, 0.01], \ - [0.3, 0.1, 0.0], \ - [0.3, 0.2, 0.0]]) - hazard.intensity = sparse.csr_matrix([[0.2, 0.3, 0.4], \ - [0.1, 0.1, 0.01], \ - [4.3, 2.1, 1.0], \ + hazard.fraction = sparse.csr_matrix([[0.02, 0.03, 0.04], + [0.01, 0.01, 0.01], + [0.3, 0.1, 0.0], + [0.3, 0.2, 0.0]]) + hazard.intensity = sparse.csr_matrix([[0.2, 0.3, 0.4], + [0.1, 0.1, 0.01], + [4.3, 2.1, 1.0], [5.3, 0.2, 1.3]]) hazard.units = 'm/s' @@ -91,8 +91,7 @@ def test_check_wrongFreq_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.frequency size: 3 != 2.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.frequency size: 3 != 2.', cm.output[0]) def test_check_wrongInten_fail(self): """Wrong hazard definition""" @@ -102,8 +101,7 @@ def test_check_wrongInten_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.intensity row size: 3 != 2.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.intensity row size: 3 != 2.', cm.output[0]) def test_check_wrongFrac_fail(self): """Wrong hazard definition""" @@ -113,8 +111,7 @@ def test_check_wrongFrac_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.fraction column size: 2 != 1.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.fraction column size: 2 != 1.', cm.output[0]) def test_check_wrongEvName_fail(self): """Wrong hazard definition""" @@ -124,8 +121,7 @@ def test_check_wrongEvName_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.event_name size: 3 != 1.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.event_name size: 3 != 1.', cm.output[0]) def test_check_wrongId_fail(self): """Wrong hazard definition""" @@ -135,8 +131,7 @@ def test_check_wrongId_fail(self): with self.assertLogs('climada.hazard.base', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('There are events with the same identifier.', \ - cm.output[0]) + self.assertIn('There are events with the same identifier.', cm.output[0]) def test_check_wrong_date_fail(self): """Wrong hazard definition""" @@ -146,8 +141,7 @@ def test_check_wrong_date_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.date size: 3 != 2.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.date size: 3 != 2.', cm.output[0]) def test_check_wrong_orig_fail(self): """Wrong hazard definition""" @@ -157,18 +151,17 @@ def test_check_wrong_orig_fail(self): with self.assertLogs('climada.util.checker', level='ERROR') as cm: with self.assertRaises(ValueError): haz.check() - self.assertIn('Invalid Hazard.orig size: 3 != 4.', \ - cm.output[0]) + self.assertIn('Invalid Hazard.orig size: 3 != 4.', cm.output[0]) def test_event_name_to_id_pass(self): - """ Test event_name_to_id function.""" + """Test event_name_to_id function.""" haz = Hazard('TC') haz.read_excel(HAZ_TEMPLATE_XLS) self.assertEqual(haz.get_event_id('event001')[0], 1) self.assertEqual(haz.get_event_id('event084')[0], 84) def test_event_name_to_id_fail(self): - """ Test event_name_to_id function.""" + """Test event_name_to_id function.""" haz = Hazard('TC') haz.read_excel(HAZ_TEMPLATE_XLS) with self.assertLogs('climada.hazard.base', level='ERROR') as cm: @@ -177,14 +170,14 @@ def test_event_name_to_id_fail(self): self.assertIn('No event with name: 1050', cm.output[0]) def test_event_id_to_name_pass(self): - """ Test event_id_to_name function.""" + """Test event_id_to_name function.""" haz = Hazard('TC') haz.read_excel(HAZ_TEMPLATE_XLS) self.assertEqual(haz.get_event_name(2), 'event002') self.assertEqual(haz.get_event_name(48), 'event048') def test_event_id_to_name_fail(self): - """ Test event_id_to_name function.""" + """Test event_id_to_name function.""" haz = Hazard('TC') haz.read_excel(HAZ_TEMPLATE_XLS) with self.assertLogs('climada.hazard.base', level='ERROR') as cm: @@ -227,8 +220,8 @@ def test_equal_same(self): self.assertTrue(np.array_equal(haz1.frequency, haz2.frequency)) self.assertTrue(np.array_equal(haz1.date, haz2.date)) self.assertTrue(np.array_equal(haz1.orig, haz2.orig)) - self.assertTrue(np.array_equal(haz1.intensity.todense(), haz2.intensity.todense())) - self.assertTrue(np.array_equal(haz1.fraction.todense(), haz2.fraction.todense())) + self.assertTrue(np.array_equal(haz1.intensity.toarray(), haz2.intensity.toarray())) + self.assertTrue(np.array_equal(haz1.fraction.toarray(), haz2.fraction.toarray())) self.assertTrue((haz1.intensity != haz2.intensity).nnz == 0) self.assertTrue((haz1.fraction != haz2.fraction).nnz == 0) self.assertEqual(haz1.units, haz2.units) @@ -251,13 +244,13 @@ def test_same_events_same(self): haz2.event_name = haz1.event_name haz2.frequency = haz1.frequency haz2.date = haz1.date - haz2.fraction = sparse.csr_matrix([[0.22, 0.32, 0.44], \ - [0.11, 0.11, 0.11], \ - [0.32, 0.11, 0.99], \ + haz2.fraction = sparse.csr_matrix([[0.22, 0.32, 0.44], + [0.11, 0.11, 0.11], + [0.32, 0.11, 0.99], [0.32, 0.22, 0.88]]) - haz2.intensity = sparse.csr_matrix([[0.22, 3.33, 6.44], \ - [1.11, 0.11, 1.11], \ - [8.33, 4.11, 4.4], \ + haz2.intensity = sparse.csr_matrix([[0.22, 3.33, 6.44], + [1.11, 0.11, 1.11], + [8.33, 4.11, 4.4], [9.33, 9.22, 1.77]]) haz2.units = 'm/s' @@ -267,33 +260,56 @@ def test_same_events_same(self): # expected values haz_res = dummy_hazard() - haz_res.intensity = sparse.hstack([haz_res.intensity, \ - sparse.lil_matrix((haz_res.intensity.shape[0], 3))], format='lil').tocsr() - haz_res.fraction = sparse.hstack([haz_res.fraction, \ - sparse.lil_matrix((haz_res.fraction.shape[0], 3))], format='lil').tocsr() - self.assertTrue(np.array_equal(haz_res.intensity.todense(), \ - haz1.intensity.todense())) + haz_res.intensity = sparse.hstack( + [haz_res.intensity, sparse.csr_matrix((haz_res.intensity.shape[0], 3))], format='csr') + haz_res.fraction = sparse.hstack( + [haz_res.fraction, sparse.csr_matrix((haz_res.fraction.shape[0], 3))], format='csr') + self.assertTrue(np.array_equal(haz_res.intensity.toarray(), + haz1.intensity.toarray())) self.assertTrue(sparse.isspmatrix_csr(haz1.intensity)) - self.assertTrue(np.array_equal(haz_res.fraction.todense(), \ - haz1.fraction.todense())) + self.assertTrue(np.array_equal(haz_res.fraction.toarray(), + haz1.fraction.toarray())) self.assertTrue(sparse.isspmatrix_csr(haz1.fraction)) self.assertEqual(haz1.event_name, haz_res.event_name) self.assertTrue(np.array_equal(haz1.date, haz_res.date)) self.assertTrue(np.array_equal(haz1.orig, haz_res.orig)) - self.assertTrue(np.array_equal(haz1.event_id, \ + self.assertTrue(np.array_equal(haz1.event_id, haz_res.event_id)) self.assertTrue(np.array_equal(haz1.frequency, haz_res.frequency)) self.assertEqual(haz_res.units, haz1.units) - self.assertEqual(haz1.tag.file_name, \ + self.assertEqual(haz1.tag.file_name, [haz_res.tag.file_name, haz2.tag.file_name]) self.assertEqual(haz1.tag.haz_type, haz_res.tag.haz_type) - self.assertEqual(haz1.tag.description, \ + self.assertEqual(haz1.tag.description, [haz_res.tag.description, haz2.tag.description]) class TestSelect(unittest.TestCase): """Test select method.""" + def test_select_event_name(self): + """Test select historical events.""" + haz = dummy_hazard() + sel_haz = haz.select(event_names=['ev4', 'ev1']) + + self.assertTrue(np.array_equal(sel_haz.centroids.coord, haz.centroids.coord)) + self.assertEqual(sel_haz.tag, haz.tag) + self.assertEqual(sel_haz.units, haz.units) + self.assertTrue(np.array_equal(sel_haz.event_id, np.array([4, 1]))) + self.assertTrue(np.array_equal(sel_haz.date, np.array([4, 1]))) + self.assertTrue(np.array_equal(sel_haz.orig, np.array([True, True]))) + self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.2, 0.1]))) + self.assertTrue(np.array_equal(sel_haz.fraction.toarray(), + np.array([[0.3, 0.2, 0.0], + [0.02, 0.03, 0.04]]))) + self.assertTrue(np.array_equal(sel_haz.intensity.toarray(), + np.array([[5.3, 0.2, 1.3], + [0.2, 0.3, 0.4]]))) + self.assertEqual(sel_haz.event_name, ['ev4', 'ev1']) + self.assertIsInstance(sel_haz, Hazard) + self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) + self.assertIsInstance(sel_haz.fraction, sparse.csr_matrix) + def test_select_orig_pass(self): """Test select historical events.""" haz = dummy_hazard() @@ -306,10 +322,10 @@ def test_select_orig_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([1, 4]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([True, True]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.1, 0.2]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.02, 0.03, 0.04], \ - [0.3, 0.2, 0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.2, 0.3, 0.4], \ - [5.3, 0.2, 1.3]]))) + self.assertTrue(np.array_equal( + sel_haz.fraction.toarray(), np.array([[0.02, 0.03, 0.04], [0.3, 0.2, 0.0]]))) + self.assertTrue(np.array_equal( + sel_haz.intensity.toarray(), np.array([[0.2, 0.3, 0.4], [5.3, 0.2, 1.3]]))) self.assertEqual(sel_haz.event_name, ['ev1', 'ev4']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -327,10 +343,10 @@ def test_select_syn_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([2, 3]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([False, False]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.5, 0.5]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.01, 0.01, 0.01], \ - [0.3, 0.1, 0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.1, 0.1, 0.01], \ - [4.3, 2.1, 1.0]]))) + self.assertTrue(np.array_equal( + sel_haz.fraction.toarray(), np.array([[0.01, 0.01, 0.01], [0.3, 0.1, 0.0]]))) + self.assertTrue(np.array_equal( + sel_haz.intensity.toarray(), np.array([[0.1, 0.1, 0.01], [4.3, 2.1, 1.0]]))) self.assertEqual(sel_haz.event_name, ['ev2', 'ev3']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -339,7 +355,7 @@ def test_select_syn_pass(self): def test_select_date_pass(self): """Test select historical events.""" haz = dummy_hazard() - sel_haz = haz.select(date=(2,4)) + sel_haz = haz.select(date=(2, 4)) self.assertTrue(np.array_equal(sel_haz.centroids.coord, haz.centroids.coord)) self.assertEqual(sel_haz.tag, haz.tag) @@ -348,12 +364,14 @@ def test_select_date_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([2, 3, 4]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([False, False, True]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.5, 0.5, 0.2]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.01, 0.01, 0.01], \ - [0.3, 0.1, 0.0], \ - [0.3, 0.2, 0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.1, 0.1, 0.01], \ - [4.3, 2.1, 1.0], \ - [5.3, 0.2, 1.3]]))) + self.assertTrue(np.array_equal( + sel_haz.fraction.toarray(), np.array([[0.01, 0.01, 0.01], + [0.3, 0.1, 0.0], + [0.3, 0.2, 0.0]]))) + self.assertTrue(np.array_equal( + sel_haz.intensity.toarray(), np.array([[0.1, 0.1, 0.01], + [4.3, 2.1, 1.0], + [5.3, 0.2, 1.3]]))) self.assertEqual(sel_haz.event_name, ['ev2', 'ev3', 'ev4']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -371,10 +389,10 @@ def test_select_date_str_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([2, 3]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([False, False]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.5, 0.5]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.01, 0.01, 0.01], \ - [0.3, 0.1, 0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.1, 0.1, 0.01], \ - [4.3, 2.1, 1.0]]))) + self.assertTrue(np.array_equal( + sel_haz.fraction.toarray(), np.array([[0.01, 0.01, 0.01], [0.3, 0.1, 0.0]]))) + self.assertTrue(np.array_equal( + sel_haz.intensity.toarray(), np.array([[0.1, 0.1, 0.01], [4.3, 2.1, 1.0]]))) self.assertEqual(sel_haz.event_name, ['ev2', 'ev3']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -383,7 +401,7 @@ def test_select_date_str_pass(self): def test_select_date_and_orig_pass(self): """Test select historical events.""" haz = dummy_hazard() - sel_haz = haz.select(date=(2,4), orig=False) + sel_haz = haz.select(date=(2, 4), orig=False) self.assertTrue(np.array_equal(sel_haz.centroids.coord, haz.centroids.coord)) self.assertEqual(sel_haz.tag, haz.tag) @@ -392,10 +410,10 @@ def test_select_date_and_orig_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([2, 3]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([False, False]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.5, 0.5]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.01, 0.01, 0.01], \ - [0.3, 0.1, 0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.1, 0.1, 0.01], \ - [4.3, 2.1, 1.0]]))) + self.assertTrue(np.array_equal( + sel_haz.fraction.toarray(), np.array([[0.01, 0.01, 0.01], [0.3, 0.1, 0.0]]))) + self.assertTrue(np.array_equal( + sel_haz.intensity.toarray(), np.array([[0.1, 0.1, 0.01], [4.3, 2.1, 1.0]]))) self.assertEqual(sel_haz.event_name, ['ev2', 'ev3']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -404,14 +422,14 @@ def test_select_date_and_orig_pass(self): def test_select_date_wrong_pass(self): """Test select historical events.""" haz = dummy_hazard() - sel_haz = haz.select(date=(6,8), orig=False) + sel_haz = haz.select(date=(6, 8), orig=False) self.assertEqual(sel_haz, None) def test_select_reg_id_pass(self): """Test select region of centroids.""" haz = dummy_hazard() haz.centroids.region_id = np.array([5, 7, 9]) - sel_haz = haz.select(date=(2,4), orig=False, reg_id=9) + sel_haz = haz.select(date=(2, 4), orig=False, reg_id=9) self.assertTrue(np.array_equal(sel_haz.centroids.coord.squeeze(), haz.centroids.coord[2, :])) @@ -421,8 +439,8 @@ def test_select_reg_id_pass(self): self.assertTrue(np.array_equal(sel_haz.date, np.array([2, 3]))) self.assertTrue(np.array_equal(sel_haz.orig, np.array([False, False]))) self.assertTrue(np.array_equal(sel_haz.frequency, np.array([0.5, 0.5]))) - self.assertTrue(np.array_equal(sel_haz.fraction.todense(), np.array([[0.01], [0.0]]))) - self.assertTrue(np.array_equal(sel_haz.intensity.todense(), np.array([[0.01], [1.0]]))) + self.assertTrue(np.array_equal(sel_haz.fraction.toarray(), np.array([[0.01], [0.0]]))) + self.assertTrue(np.array_equal(sel_haz.intensity.toarray(), np.array([[0.01], [1.0]]))) self.assertEqual(sel_haz.event_name, ['ev2', 'ev3']) self.assertIsInstance(sel_haz, Hazard) self.assertIsInstance(sel_haz.intensity, sparse.csr_matrix) @@ -436,7 +454,7 @@ def test_append_empty_fill(self): haz1 = Hazard('TC') haz1.read_excel(HAZ_TEMPLATE_XLS) haz2 = Hazard('TC') - haz2.centroids.geometry.crs = {'init':'epsg:4326'} + haz2.centroids.geometry.crs = {'init': 'epsg:4326'} haz1.append(haz2) haz1.check() @@ -489,13 +507,13 @@ def test_same_centroids_extend(self): haz2.event_id = np.array([5, 6, 7, 8]) haz2.event_name = ['ev5', 'ev6', 'ev7', 'ev8'] haz2.frequency = np.array([0.9, 0.75, 0.75, 0.22]) - haz2.fraction = sparse.csr_matrix([[0.2, 0.3, 0.4], \ - [0.1, 0.1, 0.1], \ - [0.3, 0.1, 0.9], \ + haz2.fraction = sparse.csr_matrix([[0.2, 0.3, 0.4], + [0.1, 0.1, 0.1], + [0.3, 0.1, 0.9], [0.3, 0.2, 0.8]]) - haz2.intensity = sparse.csr_matrix([[0.2, 3.3, 6.4], \ - [1.1, 0.1, 1.01], \ - [8.3, 4.1, 4.0], \ + haz2.intensity = sparse.csr_matrix([[0.2, 3.3, 6.4], + [1.1, 0.1, 1.01], + [8.3, 4.1, 4.0], [9.3, 9.2, 1.7]]) haz2.units = 'm/s' @@ -505,18 +523,18 @@ def test_same_centroids_extend(self): # expected values haz1_orig = dummy_hazard() exp_inten = np.zeros((8, 3)) - exp_inten[0:4, 0:3] = haz1_orig.intensity.todense() - exp_inten[4:8, 0:3] = haz2.intensity.todense() + exp_inten[0:4, 0:3] = haz1_orig.intensity.toarray() + exp_inten[4:8, 0:3] = haz2.intensity.toarray() exp_frac = np.zeros((8, 3)) - exp_frac[0:4, 0:3] = haz1_orig.fraction.todense() - exp_frac[4:8, 0:3] = haz2.fraction.todense() + exp_frac[0:4, 0:3] = haz1_orig.fraction.toarray() + exp_frac[4:8, 0:3] = haz2.fraction.toarray() self.assertEqual(haz1.event_id.size, 8) self.assertTrue(sparse.isspmatrix_csr(haz1.intensity)) self.assertTrue(sparse.isspmatrix_csr(haz1.fraction)) for i_ev in range(haz1.event_id.size): - self.assertTrue(any((haz1.intensity[i_ev].todense() == exp_inten).all(1))) - self.assertTrue(any((haz1.fraction[i_ev].todense() == exp_frac).all(1))) + self.assertTrue(any((haz1.intensity[i_ev].toarray() == exp_inten).all(1))) + self.assertTrue(any((haz1.fraction[i_ev].toarray() == exp_frac).all(1))) self.assertTrue(haz1.event_name[i_ev] in haz1_orig.event_name + haz2.event_name) self.assertTrue(haz1.date[i_ev] in np.append(haz1_orig.date, haz2.date)) self.assertTrue(haz1.orig[i_ev] in np.append(haz1_orig.orig, haz2.orig)) @@ -525,10 +543,10 @@ def test_same_centroids_extend(self): self.assertEqual(haz1.centroids.size, 3) self.assertTrue(np.array_equal(haz1.centroids.coord, haz2.centroids.coord)) - self.assertEqual(haz1.tag.file_name, \ + self.assertEqual(haz1.tag.file_name, [haz1_orig.tag.file_name, haz2.tag.file_name]) self.assertEqual(haz1.tag.haz_type, haz1_orig.tag.haz_type) - self.assertEqual(haz1.tag.description, \ + self.assertEqual(haz1.tag.description, [haz1_orig.tag.description, haz2.tag.description]) def test_incompatible_type_fail(self): @@ -541,8 +559,7 @@ def test_incompatible_type_fail(self): with self.assertLogs('climada.hazard.tag', level='ERROR') as cm: with self.assertRaises(ValueError): haz1.append(haz2) - self.assertIn("Hazards of different type can't be appended: "\ - + "TC != WS.", cm.output[0]) + self.assertIn("Hazards of different type can't be appended: TC != WS.", cm.output[0]) def test_incompatible_units_fail(self): """Raise error when append two incompatible hazards.""" @@ -552,8 +569,8 @@ def test_incompatible_units_fail(self): with self.assertLogs('climada.hazard.base', level='ERROR') as cm: with self.assertRaises(ValueError): haz1.append(haz2) - self.assertIn("Hazards with different units can't be appended: "\ - + 'm/s != km/h.', cm.output[0]) + self.assertIn("Hazards with different units can't be appended: m/s != km/h.", + cm.output[0]) def test_all_different_extend(self): """Append totally different hazard.""" @@ -566,13 +583,13 @@ def test_all_different_extend(self): haz2.event_id = np.array([5, 6, 7, 8]) haz2.event_name = ['ev5', 'ev6', 'ev7', 'ev8'] haz2.frequency = np.array([0.9, 0.75, 0.75, 0.22]) - haz2.fraction = sparse.csr_matrix([[0.2, 0.3, 0.4], \ - [0.1, 0.1, 0.1], \ - [0.3, 0.1, 0.9], \ + haz2.fraction = sparse.csr_matrix([[0.2, 0.3, 0.4], + [0.1, 0.1, 0.1], + [0.3, 0.1, 0.9], [0.3, 0.2, 0.8]]) - haz2.intensity = sparse.csr_matrix([[0.2, 3.3, 6.4], \ - [1.1, 0.1, 1.01], \ - [8.3, 4.1, 4.0], \ + haz2.intensity = sparse.csr_matrix([[0.2, 3.3, 6.4], + [1.1, 0.1, 1.01], + [8.3, 4.1, 4.0], [9.3, 9.2, 1.7]]) haz2.date = np.ones((4,)) haz2.orig = np.ones((4,)) @@ -584,17 +601,17 @@ def test_all_different_extend(self): # expected values haz1_orig = dummy_hazard() exp_inten = np.zeros((8, 6)) - exp_inten[0:4, 0:3] = haz1_orig.intensity.todense() - exp_inten[4:8, 3:6] = haz2.intensity.todense() + exp_inten[0:4, 0:3] = haz1_orig.intensity.toarray() + exp_inten[4:8, 3:6] = haz2.intensity.toarray() exp_frac = np.zeros((8, 6)) - exp_frac[0:4, 0:3] = haz1_orig.fraction.todense() - exp_frac[4:8, 3:6] = haz2.fraction.todense() + exp_frac[0:4, 0:3] = haz1_orig.fraction.toarray() + exp_frac[4:8, 3:6] = haz2.fraction.toarray() self.assertEqual(haz1.event_id.size, 8) self.assertTrue(sparse.isspmatrix_csr(haz1.intensity)) self.assertTrue(sparse.isspmatrix_csr(haz1.fraction)) for i_ev in range(haz1.event_id.size): - self.assertTrue(any((haz1.intensity[i_ev].todense() == exp_inten).all(1))) - self.assertTrue(any((haz1.fraction[i_ev].todense() == exp_frac).all(1))) + self.assertTrue(any((haz1.intensity[i_ev].toarray() == exp_inten).all(1))) + self.assertTrue(any((haz1.fraction[i_ev].toarray() == exp_frac).all(1))) self.assertTrue(haz1.event_name[i_ev] in haz1_orig.event_name + haz2.event_name) self.assertTrue(haz1.date[i_ev] in np.append(haz1_orig.date, haz2.date)) self.assertTrue(haz1.orig[i_ev] in np.append(haz1_orig.orig, haz2.orig)) @@ -603,15 +620,15 @@ def test_all_different_extend(self): self.assertEqual(haz1.centroids.size, 6) self.assertEqual(haz1_orig.units, haz1.units) - self.assertEqual(haz1.tag.file_name, \ + self.assertEqual(haz1.tag.file_name, [haz1_orig.tag.file_name, haz2.tag.file_name]) self.assertEqual(haz1.tag.haz_type, haz1_orig.tag.haz_type) - self.assertEqual(haz1.tag.description, \ + self.assertEqual(haz1.tag.description, [haz1_orig.tag.description, haz2.tag.description]) def test_same_events_append(self): """Append hazard with same events (and diff centroids). - Events are appended with all new centroids columns. """ + Events are appended with all new centroids columns.""" haz1 = dummy_hazard() haz2 = Hazard('TC') haz2.tag.file_name = 'file2.mat' @@ -623,13 +640,13 @@ def test_same_events_append(self): haz2.event_name = haz1.event_name.copy() haz2.frequency = haz1.frequency haz2.date = haz1.date - haz2.fraction = sparse.csr_matrix([[0.22, 0.32, 0.44], \ - [0.11, 0.11, 0.11], \ - [0.32, 0.11, 0.99], \ + haz2.fraction = sparse.csr_matrix([[0.22, 0.32, 0.44], + [0.11, 0.11, 0.11], + [0.32, 0.11, 0.99], [0.32, 0.22, 0.88]]) - haz2.intensity = sparse.csr_matrix([[0.22, 3.33, 6.44], \ - [1.11, 0.11, 1.11], \ - [8.33, 4.11, 4.4], \ + haz2.intensity = sparse.csr_matrix([[0.22, 3.33, 6.44], + [1.11, 0.11, 1.11], + [8.33, 4.11, 4.4], [9.33, 9.22, 1.77]]) haz2.units = 'm/s' @@ -637,19 +654,19 @@ def test_same_events_append(self): # expected values haz1_ori = dummy_hazard() - res_inten = sparse.lil_matrix(np.zeros((8, 6))) - res_inten[0:4, 0:3] = haz1_ori.intensity - res_inten[4:, 3:] = haz2.intensity + res_inten = np.zeros((8, 6)) + res_inten[0:4, 0:3] = haz1_ori.intensity.toarray() + res_inten[4:, 3:] = haz2.intensity.toarray() - res_frac = sparse.lil_matrix(np.zeros((8, 6))) - res_frac[0:4, 0:3] = haz1_ori.fraction - res_frac[4:, 3:] = haz2.fraction + res_frac = np.zeros((8, 6)) + res_frac[0:4, 0:3] = haz1_ori.fraction.toarray() + res_frac[4:, 3:] = haz2.fraction.toarray() - self.assertTrue(np.array_equal(res_inten.todense(), - haz1.intensity.todense())) + self.assertTrue(np.array_equal(res_inten, + haz1.intensity.toarray())) self.assertTrue(sparse.isspmatrix_csr(haz1.intensity)) - self.assertTrue(np.array_equal(res_frac.todense(), \ - haz1.fraction.todense())) + self.assertTrue(np.array_equal(res_frac, + haz1.fraction.toarray())) self.assertTrue(sparse.isspmatrix_csr(haz1.fraction)) self.assertEqual(haz1.event_name, haz1_ori.event_name + haz2.event_name) @@ -657,19 +674,19 @@ def test_same_events_append(self): np.append(haz1_ori.date, haz2.date))) self.assertTrue(np.array_equal(haz1.orig, np.append(haz1_ori.orig, haz2.orig))) - self.assertTrue(np.array_equal(haz1.event_id, np.arange(1,9))) + self.assertTrue(np.array_equal(haz1.event_id, np.arange(1, 9))) self.assertTrue(np.array_equal(haz1.frequency, np.append(haz1_ori.frequency, haz2.frequency))) self.assertEqual(haz1_ori.units, haz1.units) - self.assertEqual(haz1.tag.file_name, \ + self.assertEqual(haz1.tag.file_name, [haz1_ori.tag.file_name, haz2.tag.file_name]) self.assertEqual(haz1.tag.haz_type, haz1_ori.tag.haz_type) - self.assertEqual(haz1.tag.description, \ + self.assertEqual(haz1.tag.description, [haz1_ori.tag.description, haz2.tag.description]) - def test_append_all_pass(self): - """Test _append_all function.""" + def test_concatenate_pass(self): + """Test concatenate function.""" haz_1 = Hazard('TC') haz_1.tag.file_name = 'file1.mat' haz_1.tag.description = 'Description 1' @@ -699,32 +716,31 @@ def test_append_all_pass(self): haz_2.units = 'm/s' haz = Hazard('TC') - haz._append_all([haz_1, haz_2]) + haz.concatenate([haz_1, haz_2]) - hres_frac = sparse.csr_matrix([[0.02, 0.03, 0.04], \ - [1.02, 1.03, 1.04]]) - hres_inten = sparse.csr_matrix([[0.2, 0.3, 0.4], \ - [1.2, 1.3, 1.4]]) - + hres_frac = sparse.csr_matrix([[0.02, 0.03, 0.04], + [1.02, 1.03, 1.04]]) + hres_inten = sparse.csr_matrix([[0.2, 0.3, 0.4], + [1.2, 1.3, 1.4]]) self.assertTrue(sparse.isspmatrix_csr(haz.intensity)) - self.assertTrue(np.array_equal(haz.intensity.todense(), hres_inten.todense())) + self.assertTrue(np.array_equal(haz.intensity.toarray(), hres_inten.toarray())) self.assertTrue(sparse.isspmatrix_csr(haz.fraction)) - self.assertTrue(np.array_equal(haz.fraction.todense(), hres_frac.todense())) + self.assertTrue(np.array_equal(haz.fraction.toarray(), hres_frac.toarray())) self.assertEqual(haz.units, haz_2.units) self.assertTrue(np.array_equal(haz.frequency, np.array([1.0, 1.0]))) self.assertTrue(np.array_equal(haz.orig, np.array([True, False]))) self.assertTrue(np.array_equal(haz.date, np.array([1, 2]))) self.assertTrue(np.array_equal(haz.event_id, np.array([1, 2]))) - self.assertTrue(haz.event_name, ['ev1', 'ev2']) + self.assertEqual(haz.event_name, ['ev1', 'ev2']) self.assertTrue(np.array_equal(haz.centroids.coord, haz_1.centroids.coord)) self.assertTrue(np.array_equal(haz.centroids.coord, haz_2.centroids.coord)) - self.assertTrue(haz.tag, 'file_1.mat + file_2.mat') - self.assertTrue(haz.tag, 'Description 1 + Description 2') + self.assertEqual(haz.tag.file_name, ['file1.mat', 'file2.mat']) + self.assertEqual(haz.tag.description, ['Description 1', 'Description 2']) def test_append_new_var_pass(self): - """ New variable appears if hazard to append is empty. """ + """New variable appears if hazard to append is empty.""" haz = dummy_hazard() haz.new_var = np.ones(haz.size) @@ -732,20 +748,20 @@ def test_append_new_var_pass(self): app_haz.append(haz) self.assertIn('new_var', app_haz.__dict__) - def test_append_all_new_var_pass(self): - """ New variable appears. """ + def test_concatenate_new_var_pass(self): + """New variable appears.""" haz = dummy_hazard() haz.new_var = np.ones(haz.size) app_haz = dummy_hazard() - app_haz._append_all([haz]) + app_haz.concatenate([haz]) self.assertIn('new_var', app_haz.__dict__) class TestStats(unittest.TestCase): """Test return period statistics""" def test_degenerate_pass(self): - """ Test degenerate call. """ + """Test degenerate call.""" haz = Hazard('TC') haz.read_mat(HAZ_TEST_MAT) return_period = np.array([25, 50, 100, 250]) @@ -773,7 +789,7 @@ class TestYearset(unittest.TestCase): """Test return period statistics""" def test_ref_pass(self): - """ Test against matlab reference. """ + """Test against matlab reference.""" haz = Hazard('TC') haz.read_mat(HAZ_TEST_MAT) orig_year_set = haz.calc_year_set() @@ -781,23 +797,29 @@ def test_ref_pass(self): self.assertTrue(np.array_equal(np.array(list(orig_year_set.keys())), np.arange(1851, 2012))) self.assertTrue(np.array_equal(orig_year_set[1851], - np.array([1,11,21,31]))) + np.array([1, 11, 21, 31]))) self.assertTrue(np.array_equal(orig_year_set[1958], - np.array([8421,8431,8441,8451,8461,8471,8481,8491,8501,8511]))) + np.array([8421, 8431, 8441, 8451, 8461, 8471, 8481, + 8491, 8501, 8511]))) self.assertTrue(np.array_equal(orig_year_set[1986], - np.array([11101,11111,11121,11131,11141,11151]))) + np.array([11101, 11111, 11121, 11131, 11141, 11151]))) self.assertTrue(np.array_equal(orig_year_set[1997], - np.array([12221,12231,12241,12251,12261,12271,12281,12291]))) + np.array([12221, 12231, 12241, 12251, 12261, 12271, + 12281, 12291]))) self.assertTrue(np.array_equal(orig_year_set[2006], - np.array([13571,13581,13591,13601,13611,13621,13631,13641,13651,13661]))) + np.array([13571, 13581, 13591, 13601, 13611, 13621, + 13631, 13641, 13651, 13661]))) self.assertTrue(np.array_equal(orig_year_set[2010], - np.array([14071,14081,14091,14101,14111,14121,14131,14141,14151,14161,14171,14181,14191,14201,14211,14221,14231,14241,14251]))) + np.array([14071, 14081, 14091, 14101, 14111, 14121, + 14131, 14141, 14151, 14161, 14171, 14181, + 14191, 14201, 14211, 14221, 14231, 14241, + 14251]))) class TestReaderExcel(unittest.TestCase): - '''Test reader functionality of the Hazard class''' + """Test reader functionality of the Hazard class""" def test_hazard_pass(self): - ''' Read an hazard excel file correctly.''' + """Read an hazard excel file correctly.""" # Read demo excel file hazard = Hazard('TC') @@ -813,8 +835,8 @@ def test_hazard_pass(self): self.assertEqual(hazard.centroids.coord.shape, (n_centroids, 2)) self.assertEqual(hazard.centroids.coord[0][0], -25.95) self.assertEqual(hazard.centroids.coord[0][1], 32.57) - self.assertEqual(hazard.centroids.coord[n_centroids-1][0], -24.7) - self.assertEqual(hazard.centroids.coord[n_centroids-1][1], 33.88) + self.assertEqual(hazard.centroids.coord[n_centroids - 1][0], -24.7) + self.assertEqual(hazard.centroids.coord[n_centroids - 1][1], 33.88) self.assertEqual(len(hazard.event_name), 100) self.assertEqual(hazard.event_name[12], 'event013') @@ -822,12 +844,12 @@ def test_hazard_pass(self): self.assertEqual(hazard.event_id.dtype, int) self.assertEqual(hazard.event_id.shape, (n_events,)) self.assertEqual(hazard.event_id[0], 1) - self.assertEqual(hazard.event_id[n_events-1], 100) + self.assertEqual(hazard.event_id[n_events - 1], 100) self.assertEqual(hazard.date.dtype, int) self.assertEqual(hazard.date.shape, (n_events,)) self.assertEqual(hazard.date[0], 675874) - self.assertEqual(hazard.date[n_events-1], 676329) + self.assertEqual(hazard.date[n_events - 1], 676329) self.assertEqual(hazard.event_name[0], 'event001') self.assertEqual(hazard.event_name[50], 'event051') @@ -836,7 +858,7 @@ def test_hazard_pass(self): self.assertEqual(hazard.frequency.dtype, np.float) self.assertEqual(hazard.frequency.shape, (n_events,)) self.assertEqual(hazard.frequency[0], 0.01) - self.assertEqual(hazard.frequency[n_events-2], 0.001) + self.assertEqual(hazard.frequency[n_events - 2], 0.001) self.assertEqual(hazard.intensity.dtype, np.float) self.assertEqual(hazard.intensity.shape, (n_events, n_centroids)) @@ -845,7 +867,7 @@ def test_hazard_pass(self): self.assertEqual(hazard.fraction.shape, (n_events, n_centroids)) self.assertEqual(hazard.fraction[0, 0], 1) self.assertEqual(hazard.fraction[10, 19], 1) - self.assertEqual(hazard.fraction[n_events-1, n_centroids-1], 1) + self.assertEqual(hazard.fraction[n_events - 1, n_centroids - 1], 1) self.assertTrue(np.all(hazard.orig)) @@ -855,10 +877,10 @@ def test_hazard_pass(self): self.assertEqual(hazard.tag.haz_type, 'TC') class TestReaderMat(unittest.TestCase): - '''Test reader functionality of the ExposuresExcel class''' + """Test reader functionality of the ExposuresExcel class""" def test_hazard_pass(self): - ''' Read a hazard mat file correctly.''' + """Read a hazard mat file correctly.""" # Read demo excel file hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) @@ -910,30 +932,30 @@ def test_hazard_pass(self): # tag hazard self.assertEqual(hazard.tag.file_name, HAZ_TEST_MAT) - self.assertEqual(hazard.tag.description, \ + self.assertEqual(hazard.tag.description, ' TC hazard event set, generated 14-Nov-2017 10:09:05') self.assertEqual(hazard.tag.haz_type, 'TC') class TestHDF5(unittest.TestCase): - '''Test reader functionality of the ExposuresExcel class''' + """Test reader functionality of the ExposuresExcel class""" def test_write_read_pass(self): - ''' Read a hazard mat file correctly.''' + """Read a hazard mat file correctly.""" file_name = os.path.join(DATA_DIR, 'test_haz.h5') # Read demo excel file hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) hazard.event_name = list(map(str, hazard.event_name)) - for todense_flag in [False,True]: + for todense_flag in [False, True]: if todense_flag: - hazard.write_hdf5(file_name,todense=todense_flag) + hazard.write_hdf5(file_name, todense=todense_flag) else: hazard.write_hdf5(file_name) haz_read = Hazard('TC') haz_read.read_hdf5(file_name) - + self.assertEqual(hazard.tag.file_name, haz_read.tag.file_name) self.assertIsInstance(haz_read.tag.file_name, str) self.assertEqual(hazard.tag.haz_type, haz_read.tag.haz_type) @@ -951,27 +973,28 @@ def test_write_read_pass(self): self.assertIsInstance(haz_read.event_name[0], str) self.assertTrue(np.array_equal(hazard.date, haz_read.date)) self.assertTrue(np.array_equal(hazard.orig, haz_read.orig)) - self.assertTrue(np.array_equal(hazard.intensity.todense(), haz_read.intensity.todense())) + self.assertTrue(np.array_equal(hazard.intensity.toarray(), + haz_read.intensity.toarray())) self.assertIsInstance(haz_read.intensity, sparse.csr_matrix) - self.assertTrue(np.array_equal(hazard.fraction.todense(), haz_read.fraction.todense())) + self.assertTrue(np.array_equal(hazard.fraction.toarray(), haz_read.fraction.toarray())) self.assertIsInstance(haz_read.fraction, sparse.csr_matrix) class TestCentroids(unittest.TestCase): """Test return period statistics""" def test_reproject_raster_pass(self): - """ Test reproject_raster reference. """ + """Test reproject_raster reference.""" haz_fl = Hazard('FL') haz_fl.set_raster([HAZ_DEMO_FL]) haz_fl.check() - - haz_fl.reproject_raster(dst_crs={'init':'epsg:2202'}) + + haz_fl.reproject_raster(dst_crs={'init': 'epsg:2202'}) self.assertEqual(haz_fl.intensity.shape, (1, 1046408)) self.assertIsInstance(haz_fl.intensity, sparse.csr_matrix) self.assertIsInstance(haz_fl.fraction, sparse.csr_matrix) self.assertEqual(haz_fl.fraction.shape, (1, 1046408)) - self.assertTrue(equal_crs(haz_fl.centroids.meta['crs'], {'init':'epsg:2202'})) + self.assertTrue(equal_crs(haz_fl.centroids.meta['crs'], {'init': 'epsg:2202'})) self.assertEqual(haz_fl.centroids.meta['width'], 968) self.assertEqual(haz_fl.centroids.meta['height'], 1081) self.assertEqual(haz_fl.fraction.min(), 0) @@ -980,7 +1003,7 @@ def test_reproject_raster_pass(self): self.assertTrue(haz_fl.intensity.max() < 4.7) def test_raster_to_vector_pass(self): - """ Test raster_to_vector method """ + """Test raster_to_vector method""" haz_fl = Hazard('FL') haz_fl.set_raster([HAZ_DEMO_FL]) haz_fl.check() @@ -989,18 +1012,26 @@ def test_raster_to_vector_pass(self): fract_orig = haz_fl.fraction haz_fl.raster_to_vector() - + self.assertEqual(haz_fl.centroids.meta, dict()) - self.assertAlmostEqual(haz_fl.centroids.lat.min(), meta_orig['transform'][5]+meta_orig['height']*meta_orig['transform'][4]-meta_orig['transform'][4]/2) - self.assertAlmostEqual(haz_fl.centroids.lat.max(), meta_orig['transform'][5]+meta_orig['transform'][4]/2) - self.assertAlmostEqual(haz_fl.centroids.lon.max(), meta_orig['transform'][2]+meta_orig['width']*meta_orig['transform'][0]-meta_orig['transform'][0]/2) - self.assertAlmostEqual(haz_fl.centroids.lon.min(), meta_orig['transform'][2]+meta_orig['transform'][0]/2) + self.assertAlmostEqual(haz_fl.centroids.lat.min(), + meta_orig['transform'][5] + + meta_orig['height'] * meta_orig['transform'][4] + - meta_orig['transform'][4] / 2) + self.assertAlmostEqual(haz_fl.centroids.lat.max(), + meta_orig['transform'][5] + meta_orig['transform'][4] / 2) + self.assertAlmostEqual(haz_fl.centroids.lon.max(), + meta_orig['transform'][2] + + meta_orig['width'] * meta_orig['transform'][0] + - meta_orig['transform'][0] / 2) + self.assertAlmostEqual(haz_fl.centroids.lon.min(), + meta_orig['transform'][2] + meta_orig['transform'][0] / 2) self.assertTrue(equal_crs(haz_fl.centroids.crs, meta_orig['crs'])) self.assertTrue(np.allclose(haz_fl.intensity.data, inten_orig.data)) self.assertTrue(np.allclose(haz_fl.fraction.data, fract_orig.data)) def test_reproject_vector_pass(self): - """ Test reproject_vector """ + """Test reproject_vector""" haz_fl = Hazard('FL') haz_fl.event_id = np.array([1]) haz_fl.date = np.array([1]) @@ -1008,19 +1039,21 @@ def test_reproject_vector_pass(self): haz_fl.orig = np.array([1]) haz_fl.event_name = ['1'] haz_fl.intensity = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])) - haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])/2) + haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]) / 2) haz_fl.centroids.set_lat_lon(np.array([1, 2, 3]), np.array([1, 2, 3])) haz_fl.check() - - haz_fl.reproject_vector(dst_crs={'init':'epsg:2202'}) - self.assertTrue(np.allclose(haz_fl.centroids.lat, np.array([331585.4099637291, 696803.88, 1098649.44]))) - self.assertTrue(np.allclose(haz_fl.centroids.lon, np.array([11625664.37925186, 11939560.43, 12244857.13]))) - self.assertTrue(equal_crs(haz_fl.centroids.crs, {'init':'epsg:2202'})) - self.assertTrue(np.allclose(haz_fl.intensity.todense(), np.array([0.5, 0.2, 0.1]))) - self.assertTrue(np.allclose(haz_fl.fraction.todense(), np.array([0.5, 0.2, 0.1])/2)) + + haz_fl.reproject_vector(dst_crs={'init': 'epsg:2202'}) + self.assertTrue(np.allclose(haz_fl.centroids.lat, + np.array([331585.4099637291, 696803.88, 1098649.44]))) + self.assertTrue(np.allclose(haz_fl.centroids.lon, + np.array([11625664.37925186, 11939560.43, 12244857.13]))) + self.assertTrue(equal_crs(haz_fl.centroids.crs, {'init': 'epsg:2202'})) + self.assertTrue(np.allclose(haz_fl.intensity.toarray(), np.array([0.5, 0.2, 0.1]))) + self.assertTrue(np.allclose(haz_fl.fraction.toarray(), np.array([0.5, 0.2, 0.1]) / 2)) def test_vector_to_raster_pass(self): - """ Test vector_to_raster """ + """Test vector_to_raster""" haz_fl = Hazard('FL') haz_fl.event_id = np.array([1]) haz_fl.date = np.array([1]) @@ -1028,12 +1061,12 @@ def test_vector_to_raster_pass(self): haz_fl.orig = np.array([1]) haz_fl.event_name = ['1'] haz_fl.intensity = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])) - haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])/2) + haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]) / 2) haz_fl.centroids.set_lat_lon(np.array([1, 2, 3]), np.array([1, 2, 3])) haz_fl.check() - + haz_fl.vector_to_raster() - self.assertTrue(equal_crs(haz_fl.centroids.meta['crs'], {'init':'epsg:4326'})) + self.assertTrue(equal_crs(haz_fl.centroids.meta['crs'], {'init': 'epsg:4326'})) self.assertAlmostEqual(haz_fl.centroids.meta['transform'][0], 1.0) self.assertAlmostEqual(haz_fl.centroids.meta['transform'][1], 0) self.assertAlmostEqual(haz_fl.centroids.meta['transform'][2], 0.5) @@ -1047,7 +1080,7 @@ def test_vector_to_raster_pass(self): self.assertTrue(haz_fl.intensity.min() >= 0) self.assertTrue(haz_fl.intensity.max() <= 0.5) self.assertTrue(haz_fl.fraction.min() >= 0) - self.assertTrue(haz_fl.fraction.max() <= 0.5/2) + self.assertTrue(haz_fl.fraction.max() <= 0.5 / 2) # Execute Tests if __name__ == "__main__": diff --git a/climada/hazard/test/test_drought.py b/climada/hazard/test/test_drought.py index 7a359fa454..e1b3663dfd 100644 --- a/climada/hazard/test/test_drought.py +++ b/climada/hazard/test/test_drought.py @@ -25,19 +25,19 @@ class TestReader(unittest.TestCase): - """ Test loading functions from the Drought class """ + """Test loading functions from the Drought class""" def test(self): - + drought = Drought() drought.set_area(44.5, 5, 50, 12) - + hazard_set = drought.setup() - + self.assertEqual(hazard_set.tag.haz_type, 'DR') self.assertEqual(hazard_set.size, 114) self.assertEqual(hazard_set.centroids.size, 130) - self.assertEqual(hazard_set.intensity[112,111], -1.6286273002624512) - + self.assertEqual(hazard_set.intensity[112, 111], -1.6286273002624512) + # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) diff --git a/climada/hazard/test/test_flood.py b/climada/hazard/test/test_flood.py index 9dc91416e6..00136d29e6 100644 --- a/climada/hazard/test/test_flood.py +++ b/climada/hazard/test/test_flood.py @@ -21,27 +21,34 @@ import unittest import datetime as dt import numpy as np -from climada.hazard.flood import RiverFlood +from climada.hazard.river_flood import RiverFlood from climada.util.constants import HAZ_DEMO_FLDDPH, HAZ_DEMO_FLDFRC +from climada.hazard.centroids import Centroids class TestRiverFlood(unittest.TestCase): """Test for reading flood event from file""" + def test_wrong_iso3_fail(self): emptyFlood = RiverFlood() with self.assertRaises(KeyError): - RiverFlood.select_exact_area(['OYY']) - with self.assertRaises(KeyError): - RiverFlood.select_window_area(['OYY']) + RiverFlood._select_exact_area(['OYY']) with self.assertRaises(AttributeError): - emptyFlood.set_from_nc(years=[2600]) + emptyFlood.set_from_nc(years=[2600], dph_path=HAZ_DEMO_FLDDPH, + frc_path=HAZ_DEMO_FLDFRC) with self.assertRaises(KeyError): - emptyFlood.set_from_nc(reg=['OYY']) + emptyFlood.set_from_nc(reg=['OYY'], dph_path=HAZ_DEMO_FLDDPH, + frc_path=HAZ_DEMO_FLDFRC, ISINatIDGrid=True) + + def test_exact_area_selection_country(self): - def test_exact_area_selection(self): - testCentroids = RiverFlood.select_exact_area(['LIE']) + testCentroids, isos, natIDs = RiverFlood._select_exact_area(['LIE']) + self.assertEqual(isos[0], 'LIE') + self.assertEqual(natIDs[0], 118) + + self.assertEqual(testCentroids.shape, (5, 3)) self.assertEqual(testCentroids.lon.shape[0], 13) self.assertAlmostEqual(testCentroids.lon[0], 9.5206968) self.assertAlmostEqual(testCentroids.lon[1], 9.5623634) @@ -71,91 +78,273 @@ def test_exact_area_selection(self): self.assertAlmostEqual(testCentroids.lat[11], 47.2289138) self.assertAlmostEqual(testCentroids.lat[12], 47.2289138) - self.assertEqual(testCentroids.id[0], 0) - self.assertEqual(testCentroids.id[5], 5) - self.assertEqual(testCentroids.id[12], 12) + def test_exact_area_selection_region(self): + + testCentr, isos, natIDs = RiverFlood._select_exact_area(reg=['SWA']) + + self.assertEqual(testCentr.shape, (877, 976)) + self.assertAlmostEqual(np.min(testCentr.lat), -0.68767620000001, 4) + self.assertAlmostEqual(np.max(testCentr.lat), 38.43726119999998, 4) + self.assertAlmostEqual(np.min(testCentr.lon), 60.52061519999998, 4) + self.assertAlmostEqual(np.max(testCentr.lon), 101.1455501999999, 4) + self.assertAlmostEqual(testCentr.lon[10000], 98.27055479999999, 4) + self.assertAlmostEqual(testCentr.lat[10000], 11.47897099999998, 4) + + def test_isimip_country_flood(self): + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + countries=['DEU'], ISINatIDGrid=True) + self.assertEqual(rf.date[0], 730303) + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.) + + self.assertAlmostEqual(np.min(rf.centroids.lat), 47.312247000002785, 4) + self.assertAlmostEqual(np.max(rf.centroids.lat), 55.0622346, 4) + self.assertAlmostEqual(np.min(rf.centroids.lon), 5.895702599999964, 4) + self.assertAlmostEqual(np.max(rf.centroids.lon), 15.020687999996682, 4) + self.assertAlmostEqual(rf.centroids.lon[1000], 9.145697399999989, 4) + self.assertAlmostEqual(rf.centroids.lat[1000], 47.89557939999999, 4) + + self.assertEqual(rf.intensity.shape, (1, 26878)) + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 10.547529220581055, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 938, 4) + + self.assertEqual(rf.fraction.shape, (1, 26878)) + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 0.9968000054359436, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 1052, 4) + return + + def test_isimip_reg_flood(self): + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + reg=['SWA'], ISINatIDGrid=True) + + self.assertEqual(rf.date[0], 730303) + + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.) + + self.assertAlmostEqual(np.min(rf.centroids.lat), -0.687676199985944, 4) + self.assertAlmostEqual(np.max(rf.centroids.lat), 38.43726119999998, 4) + self.assertAlmostEqual(np.min(rf.centroids.lon), 60.52061519999998, 4) + self.assertAlmostEqual(np.max(rf.centroids.lon), 101.14555019998537, 4) + self.assertAlmostEqual(rf.centroids.lon[10000], 98.27055479999999, 4) + self.assertAlmostEqual(rf.centroids.lat[10000], 11.478970999999987, 4) + + self.assertEqual(rf.intensity.shape, (1, 301181)) + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 16.69780921936035, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 40613, 4) + + self.assertEqual(rf.fraction.shape, (1, 301181)) + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 1.0, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 126135, 4) + + return + + def test_NATearth_country_flood(self): + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + countries=['DEU']) + + self.assertEqual(rf.date[0], 730303) + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.) + + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 10.547529, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 38380, 4) + + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 0.9968000054359436, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 38143, 4) + + def test_NATearth_reg_flood(self): + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + reg=['SWA']) + self.assertEqual(rf.date[0], 730303) + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.00, 1) + + self.assertEqual(rf.centroids.shape, (941, 978)) + + self.assertEqual(rf.intensity.shape, (1, 920298)) + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 16.69781, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 480702, 4) + + self.assertEqual(rf.fraction.shape, (1, 920298)) + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 1.0, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 31487, 4) + + def test_global_flood(self): + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC) + + self.assertEqual(rf.date[0], 730303) + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.) + + self.assertEqual(rf.intensity.shape, (1, 37324800)) + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 19.276295, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 26190437, 4) + + self.assertEqual(rf.fraction.shape, (1, 37324800)) + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 1.0, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 3341440, 4) + + def test_centroids_flood(self): + + # this is going to go through the meta part + rand_centroids = Centroids() + lat = np.arange(47, 56, 0.2) + lon = np.arange(5, 15, 0.2) + lon, lat = np.meshgrid(lon, lat) + rand_centroids.set_lat_lon(lat.flatten(), lon.flatten()) + rf = RiverFlood() + rf.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + centroids=rand_centroids, ISINatIDGrid=False) + + self.assertEqual(rf.date[0], 730303) + self.assertEqual(rf.event_id[0], 0) + self.assertEqual(rf.event_name[0], '2000') + self.assertEqual(rf.orig[0], False) + self.assertAlmostEqual(rf.frequency[0], 1.) + + self.assertEqual(rf.centroids.shape, (45, 50)) + self.assertAlmostEqual(np.min(rf.centroids.lat), 47.0, 4) + self.assertAlmostEqual(np.max(rf.centroids.lat), 55.8, 4) + self.assertAlmostEqual(np.min(rf.centroids.lon), 5.0, 4) + self.assertAlmostEqual(np.max(rf.centroids.lon), 14.8, 4) + self.assertAlmostEqual(rf.centroids.lon[90], 13.0, 4) + self.assertAlmostEqual(rf.centroids.lat[90], 47.2, 4) + + self.assertEqual(rf.intensity.shape, (1, 2250)) + self.assertAlmostEqual(np.min(rf.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf.intensity), 8.921593, 4) + self.assertEqual(np.argmin(rf.intensity), 0, 4) + self.assertEqual(np.argmax(rf.intensity), 191, 4) + + self.assertEqual(rf.fraction.shape, (1, 2250)) + self.assertAlmostEqual(np.min(rf.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf.fraction), 0.92, 4) + self.assertEqual(np.argmin(rf.fraction), 0, 4) + self.assertEqual(np.argmax(rf.fraction), 1438, 4) + + def test_meta_centroids_flood(self): + min_lat, max_lat, min_lon, max_lon = 45.7, 47.8, 7.5, 10.5 + cent = Centroids() + cent.set_raster_from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), + res=0.05) + rf_rast = RiverFlood() + rf_rast.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + centroids=cent) + self.assertEqual(rf_rast.centroids.shape, (43, 61)) + self.assertAlmostEqual(np.min(rf_rast.centroids.lat), + 45.70000000000012, 4) + self.assertAlmostEqual(np.max(rf_rast.centroids.lat), 47.8, 4) + self.assertAlmostEqual(np.min(rf_rast.centroids.lon), 7.5, 4) + self.assertAlmostEqual(np.max(rf_rast.centroids.lon), + 10.49999999999999, 4) + self.assertAlmostEqual(rf_rast.centroids.lon[90], + 8.949999999999996, 4) + self.assertAlmostEqual(rf_rast.centroids.lat[90], 47.75, 4) + + self.assertEqual(rf_rast.intensity.shape, (1, 2623)) + self.assertAlmostEqual(np.min(rf_rast.intensity), 0.0, 4) + self.assertAlmostEqual(np.max(rf_rast.intensity), 5.8037286, 4) + self.assertEqual(np.argmin(rf_rast.intensity), 0, 4) + self.assertEqual(np.argmax(rf_rast.intensity), 55, 4) + + self.assertEqual(rf_rast.fraction.shape, (1, 2623)) + self.assertAlmostEqual(np.min(rf_rast.fraction), 0.0, 4) + self.assertAlmostEqual(np.max(rf_rast.fraction), 0.4896, 4) + self.assertEqual(np.argmin(rf_rast.fraction), 0, 4) + self.assertEqual(np.argmax(rf_rast.fraction), 360, 4) + +# def test_regularGrid_centroids_flood(self): +# return +# def test_flooded_area(self): - dph_path = HAZ_DEMO_FLDDPH - frc_path = HAZ_DEMO_FLDFRC testRFset = RiverFlood() - testRFset.set_from_nc(countries=['AFG'], dph_path=dph_path, - frc_path=frc_path) + testRFset.set_from_nc(countries=['AFG'], dph_path=HAZ_DEMO_FLDDPH, + frc_path=HAZ_DEMO_FLDFRC, ISINatIDGrid=True) years = [2000, 2001, 2002] manipulated_dates = [730303, 730669, 731034] + testRFaddset = [] for i in range(len(years)): testRFaddset = RiverFlood() - testRFaddset.set_from_nc(countries=['AFG']) - testRFaddset.date = [manipulated_dates[i]] + testRFaddset.set_from_nc(countries=['AFG'], + dph_path=HAZ_DEMO_FLDDPH, + frc_path=HAZ_DEMO_FLDFRC, + ISINatIDGrid=True) + testRFaddset.date = np.array([manipulated_dates[i]]) if i == 0: testRFaddset.event_name = ['2000_2'] else: testRFaddset.event_name = [str(years[i])] testRFset.append(testRFaddset) - testRFset.set_flooded_area() + testRFset.set_flooded_area(save_centr=True) self.assertEqual(testRFset.units, 'm') self.assertEqual(testRFset.fla_event.shape[0], 4) self.assertEqual(testRFset.fla_annual.shape[0], 3) self.assertAlmostEqual(np.max(testRFset.fla_ev_centr[0]), - 17200498.22927546) + 17200498.22927546, 3) self.assertEqual(np.argmax(testRFset.fla_ev_centr[0]), 32610) self.assertAlmostEqual(np.max(testRFset.fla_ev_centr[2]), - 17200498.22927546) + 17200498.22927546, 3) self.assertEqual(np.argmax(testRFset.fla_ev_centr[2]), 32610) self.assertAlmostEqual(np.max(testRFset.fla_ann_centr[0]), - 34400996.45855092) + 34400996.45855092, 3) self.assertEqual(np.argmax(testRFset.fla_ann_centr[0]), 32610) self.assertAlmostEqual(np.max(testRFset.fla_ann_centr[2]), - 17200498.22927546) + 17200498.22927546, 3) self.assertEqual(np.argmax(testRFset.fla_ann_centr[2]), 32610) self.assertAlmostEqual(testRFset.fla_event[0], - 6244242013.5826435, 4) + 6244242013.5826435, 3) self.assertAlmostEqual(testRFset.fla_annual[0], 12488484027.165287, 3) self.assertAlmostEqual(testRFset.fla_ann_av, - 8325656018.110191, 4) + 8325656018.110191, 3) self.assertAlmostEqual(testRFset.fla_ev_av, - 6244242013.5826435, 4) - - def test_select_model_run(self): - testRFModel = RiverFlood() - flood_dir = '/home/test/flood/' - rf_model = 'LPJmL' - cl_model = 'wfdei' - prot_std = 'flopros' - scenario = 'historical' - - self.assertEqual(testRFModel._select_model_run(flood_dir, rf_model, - cl_model, - scenario, prot_std)[0], - '/home/test/flood/flddph_LPJmL_wfdei_' + - 'flopros_gev_0.1.nc') - self.assertEqual(testRFModel._select_model_run(flood_dir, rf_model, - cl_model, scenario, - prot_std, proj=True)[0], - '/home/test/flood/flddph_LPJmL_wfdei_' + - 'historical_flopros_gev_picontrol_2000_0.1.nc') - - def test_set_centroids_from_file(self): - testRFCentr = RiverFlood() - lon = [1, 2, 3] - lat = [1, 2, 3] - testRFCentr._set_centroids_from_file(lon, lat) - test_centroids_lon = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) - test_centroids_lat = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3]) - self.assertTrue(np.array_equal(testRFCentr.centroids.lon, - test_centroids_lon)) - self.assertTrue(np.array_equal(testRFCentr.centroids.lat, - test_centroids_lat)) + 6244242013.5826435, 3) def test_select_events(self): testRFTime = RiverFlood() @@ -171,20 +360,8 @@ def test_select_events(self): self.assertTrue(np.array_equal( testRFTime._select_event(test_time, years), [0, 3])) - def test_cut_window(self): - - testRFCut = RiverFlood() - centr = RiverFlood.select_window_area(['AUT']) - testRFCut.centroids.lon = centr.lon - testRFCut.centroids.lat = centr.lat - lon = np.arange(7, 20, 0.2) - lat = np.arange(40, 50, 0.2) - test_window = [[4, 24], [55, 45]] - self.assertTrue(np.array_equal(testRFCut._cut_window(lon, lat), - test_window)) - -# -# Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRiverFlood) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + # Execute Tests + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRiverFlood) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_landslide.py b/climada/hazard/test/test_landslide.py index e1068ddd4a..aba3203ed9 100644 --- a/climada/hazard/test/test_landslide.py +++ b/climada/hazard/test/test_landslide.py @@ -20,12 +20,12 @@ """ import unittest import os -import datetime as dt -from datetime import timedelta -import numpy as np -import glob +# import datetime as dt +# from datetime import timedelta +# import numpy as np +# import glob from rasterio.windows import Window -from climada.hazard import landslide +# from climada.hazard import landslide from climada.hazard.landslide import Landslide import math from climada.util.constants import DATA_DIR @@ -33,29 +33,29 @@ DATA_DIR_TEST = os.path.join(os.path.dirname(__file__), 'data') -#class TestTiffFcts(unittest.TestCase): +# class TestTiffFcts(unittest.TestCase): # """Test functions for getting input tiffs in landslide module, outside Landslide() instance""" # def test_get_nowcast_tiff(self): # start_date = dt.datetime.strftime(dt.datetime.now() - timedelta(2), '%Y-%m-%d') # end_date = dt.datetime.strftime(dt.datetime.now() - timedelta(1), '%Y-%m-%d') # tif_type= ["monthly","daily"] -# +# # for item in tif_type: # landslide.get_nowcast_tiff(tif_type=item, startTime=start_date, endTime=end_date, save_path=DATA_DIR) -# +# # search_criteria = "LS*.tif" # LS_files_daily = glob.glob(os.path.join(DATA_DIR, search_criteria)) # search_criteria = "*5400.tif" # LS_files_monthly = glob.glob(os.path.join(os.getcwd(), search_criteria)) -# +# # self.assertTrue(len(LS_files_daily)>0) # self.assertTrue(len(LS_files_monthly)==12) -# +# # for item in LS_files_daily: # os.remove(item) -# +# # for item in LS_files_monthly: -# os.remove(item) +# os.remove(item) # def test_combine_nowcast_tiff(self): # landslide.combine_nowcast_tiff(DATA_DIR, search_criteria='test_global*.tif', operator="maximum") @@ -71,67 +71,88 @@ # self.assertEqual(len(combined_monthly),1) # for item in combined_monthly: # os.remove(item) - -class TestLandslideModule(unittest.TestCase): - + +class TestLandslideModule(unittest.TestCase): + def test_get_window_from_coords(self): empty_LS = Landslide() - window_array = empty_LS._get_window_from_coords(path_sourcefile=os.path.join(LS_FILE_DIR, 'ls_pr_NGI_UNEP/ls_pr.tif'), bbox=[47,8,46,7]) + window_array = empty_LS._get_window_from_coords( + path_sourcefile=os.path.join(LS_FILE_DIR, 'ls_pr_NGI_UNEP/ls_pr.tif'), + bbox=[47, 8, 46, 7]) self.assertEqual(window_array[0], 22440) self.assertEqual(window_array[1], 5159) self.assertEqual(window_array[2], 120) self.assertEqual(window_array[3], 120) - + def test_get_raster_meta(self): empty_LS = Landslide() - pixel_width, pixel_height = empty_LS._get_raster_meta(path_sourcefile = os.path.join(LS_FILE_DIR, 'ls_pr_NGI_UNEP/ls_pr.tif'), window_array = [865, 840, 120, 120]) + pixel_width, pixel_height = empty_LS._get_raster_meta( + path_sourcefile=os.path.join(LS_FILE_DIR, 'ls_pr_NGI_UNEP/ls_pr.tif'), + window_array=[865, 840, 120, 120]) self.assertTrue(math.isclose(pixel_width, -0.00833, rel_tol=1e-03)) self.assertTrue(math.isclose(pixel_height, 0.00833, rel_tol=1e-03)) - + def test_intensity_cat_to_prob(self): empty_LS = Landslide() - window_array = empty_LS._get_window_from_coords(path_sourcefile=os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif'), bbox=[47,23,46,22]) - empty_LS.set_raster([os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif')], window=Window(window_array[0], window_array[1],window_array[3], window_array[2])) + window_array = empty_LS._get_window_from_coords( + path_sourcefile=os.path.join(DATA_DIR_TEST, + 'test_global_landslide_nowcast_20190501.tif'), + bbox=[47, 23, 46, 22]) + empty_LS.set_raster( + [os.path.join(DATA_DIR_TEST, 'test_global_landslide_nowcast_20190501.tif')], + window=Window(window_array[0], window_array[1], window_array[3], window_array[2])) empty_LS._intensity_cat_to_prob(max_prob=0.0001) - self.assertTrue(max(empty_LS.intensity_cat.data)==2) - self.assertTrue(min(empty_LS.intensity_cat.data)==1) - self.assertTrue(max(empty_LS.intensity.data)==0.0001) - self.assertTrue(min(empty_LS.intensity.data)==0) - + self.assertTrue(max(empty_LS.intensity_cat.data) == 2) + self.assertTrue(min(empty_LS.intensity_cat.data) == 1) + self.assertTrue(max(empty_LS.intensity.data) == 0.0001) + self.assertTrue(min(empty_LS.intensity.data) == 0) + def test_intensity_prob_to_binom(self): empty_LS = Landslide() - window_array = empty_LS._get_window_from_coords(path_sourcefile=os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif'), bbox=[47,23,46,22]) - empty_LS.set_raster([os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif')], window=Window(window_array[0], window_array[1],window_array[3], window_array[2])) + window_array = empty_LS._get_window_from_coords( + path_sourcefile=os.path.join(DATA_DIR_TEST, + 'test_global_landslide_nowcast_20190501.tif'), + bbox=[47, 23, 46, 22]) + empty_LS.set_raster( + [os.path.join(DATA_DIR_TEST, 'test_global_landslide_nowcast_20190501.tif')], + window=Window(window_array[0], window_array[1], window_array[3], window_array[2])) empty_LS._intensity_cat_to_prob(max_prob=0.0001) empty_LS._intensity_prob_to_binom(100) - self.assertTrue(max(empty_LS.intensity_prob.data)==0.0001) - self.assertTrue(min(empty_LS.intensity_prob.data)==0) - self.assertTrue(max(empty_LS.intensity.data)==1) - self.assertTrue(min(empty_LS.intensity.data)==0) - - def test_intensity_binom_to_range(self): + self.assertTrue(max(empty_LS.intensity_prob.data) == 0.0001) + self.assertTrue(min(empty_LS.intensity_prob.data) == 0) + self.assertTrue(max(empty_LS.intensity.data) == 1) + self.assertTrue(min(empty_LS.intensity.data) == 0) + + def test_intensity_binom_to_range(self): empty_LS = Landslide() - window_array = empty_LS._get_window_from_coords(path_sourcefile=os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif'), bbox=[47,23,46,22]) - empty_LS.set_raster([os.path.join(DATA_DIR_TEST,'test_global_landslide_nowcast_20190501.tif')], window=Window(window_array[0], window_array[1],window_array[3], window_array[2])) + window_array = empty_LS._get_window_from_coords( + path_sourcefile=os.path.join(DATA_DIR_TEST, + 'test_global_landslide_nowcast_20190501.tif'), + bbox=[47, 23, 46, 22]) + empty_LS.set_raster( + [os.path.join(DATA_DIR_TEST, 'test_global_landslide_nowcast_20190501.tif')], + window=Window(window_array[0], window_array[1], window_array[3], window_array[2])) empty_LS._intensity_cat_to_prob(max_prob=0.0001) empty_LS._intensity_prob_to_binom(100) empty_LS.check() empty_LS.centroids.set_meta_to_lat_lon() empty_LS.centroids.set_geometry_points() empty_LS._intensity_binom_to_range(max_dist=1000) - self.assertTrue(len(empty_LS.intensity.data[(empty_LS.intensity.data>0) & (empty_LS.intensity.data<1)])>0) - + self.assertTrue( + len(empty_LS.intensity.data[(empty_LS.intensity.data > 0) + & (empty_LS.intensity.data < 1)]) > 0) + def test_get_hist_events(self): empty_LS = Landslide() - bbox = [48,23,40,20] - COOLR_path = os.path.join(DATA_DIR_TEST,'nasa_global_landslide_catalog_point.shp') + bbox = [48, 23, 40, 20] + COOLR_path = os.path.join(DATA_DIR_TEST, 'nasa_global_landslide_catalog_point.shp') LS_catalogue_part = empty_LS._get_hist_events(bbox, COOLR_path) - self.assertTrue(max(LS_catalogue_part.latitude)<=bbox[0]) - self.assertTrue(min(LS_catalogue_part.latitude)>=bbox[2]) - self.assertTrue(max(LS_catalogue_part.longitude)<=bbox[1]) - self.assertTrue(min(LS_catalogue_part.longitude)>=bbox[3]) - - + self.assertTrue(max(LS_catalogue_part.latitude) <= bbox[0]) + self.assertTrue(min(LS_catalogue_part.latitude) >= bbox[2]) + self.assertTrue(max(LS_catalogue_part.longitude) <= bbox[1]) + self.assertTrue(min(LS_catalogue_part.longitude) >= bbox[3]) + + if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestLandslideModule) - unittest.TextTestRunner(verbosity=2).run(TESTS) \ No newline at end of file + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_low_flow.py b/climada/hazard/test/test_low_flow.py new file mode 100755 index 0000000000..6b995614b8 --- /dev/null +++ b/climada/hazard/test/test_low_flow.py @@ -0,0 +1,295 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test low flow module. +""" +import os +import unittest +import numpy as np +import pandas as pd +import datetime as dt + +from climada.hazard.low_flow import LowFlow, unique_clusters, \ + _compute_threshold_grid, _read_and_combine_nc, _split_bbox +from climada.util.constants import DATA_DIR +from climada.hazard.centroids import Centroids + +INPUT_DIR = os.path.join(DATA_DIR, 'demo') +FN_STR_DEMO = 'co2_dis_global_daily_DEMO_FR' + + +def init_test_data_unique_clusters(): + """creates sandbox test data for 2D cluster IDs for test of identification of + unique 3D clusters""" + + df = pd.DataFrame(columns=['target_cluster', 'cluster_id', 'c_lat_lon', + 'c_lat_dt_month', 'c_lon_dt_month']) + + df.c_lon_dt_month = np.array([1, 1, 1, 1, 2, 2, 3, 4, 5, 4, 4, 5, 6, -1, -1]) + df.c_lat_dt_month = np.array([1, -1, 2, 2, 2, 3, 5, 3, 4, 6, 6, 5, 7, -1, 1]) + df.c_lat_lon = np.array([1, 3, 1, 3, 3, 3, 5, 3, 5, 3, 4, 5, 2, -1, -1]) + df.target_cluster = [1, -1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 3, -1, -1] + df.cluster_id = np.zeros(len(df.target_cluster), dtype=int) - 1 + return df + +def init_test_data_clustering(): + """creates sandbox test data for monthly days below threshold data + for testing clustering and event intensity computation""" + + df = pd.DataFrame(columns=['lat', 'lon', 'ndays', + 'dt_month', 'target_cluster']) + + df.lat = np.array([-0, -0, -.5, -.5, -1, -.5, -1, -0, -.5, -1, -1, -1.5, -2.5]) + df.lon = np.array([0, 1, 0, 1.5, 2, 0, 0, 1, 1.5, 0, 2, 0, 2.5]) + df.dt_month = np.array([1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3]) + df['dtime'] = df['dt_month'].apply(lambda x: dt.datetime.toordinal(dt.datetime(1,x,1))) + df.ndays = [5, 11, 5, 11, 11, 10, 10, 22, 22, 20, 22, 20, 1] + + df['iter_ev'] = np.ones(len(df), bool) + df['cons_id'] = np.zeros(len(df), int) - 1 + return df + +def init_test_centroids(data): + """define centroids for test data:""" + centroids = Centroids() + grid = np.meshgrid(np.arange(data.lat.min(), data.lat.max()+.5, .5), + np.arange(data.lon.min(), data.lon.max()+.5, .5)) + lat = list() + lon = list() + for arrlat, arrlon in zip(list(grid[0]), list(grid[1])): + lat += list(arrlat) + lon += list(arrlon) + centroids.set_lat_lon(np.array(lat), np.array(lon)) + centroids.set_lat_lon_to_meta() + return centroids + +class TestLowFlowDummyData(unittest.TestCase): + """Test for defining low flow event from dummy processed discharge data""" + + def test_unique_clusters(self): + """Test unique_clusters: + unique 3D cluster identification from 2D cluster data""" + data = init_test_data_unique_clusters() + data = unique_clusters(data) + self.assertEqual(data.size, 75) + self.assertListEqual(list(data.cluster_id), list(data.target_cluster)) + + def test_identify_clusters_default(self): + """Test identify_clusters: + clustering event from monthly days below threshold data""" + haz = LowFlow() + # 1) direct neighbors only (allowing over cross in space): + haz.lowflow_df = init_test_data_clustering() + haz.identify_clusters(clus_thresh_xy=1.5, clus_thresh_t=1, min_samples=1) + target_cluster = [1, 2, 1, 2, 2, 1, 1, 3, 3, 1, 3, 1, 4] + self.assertListEqual(list(haz.lowflow_df.cluster_id), target_cluster) + + # as (1), but allowing 1 month break in between: + haz.lowflow_df = init_test_data_clustering() + haz.identify_clusters(clus_thresh_xy=1.5, clus_thresh_t=2, min_samples=1) + target_cluster = [1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 3] + self.assertListEqual(list(haz.lowflow_df.cluster_id), target_cluster) + + # as (1), but allowing 1 gridcell break in between: + haz.lowflow_df = init_test_data_clustering() + haz.identify_clusters(clus_thresh_xy=2., clus_thresh_t=1, min_samples=1) + target_cluster = [1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 3] + self.assertListEqual(list(haz.lowflow_df.cluster_id), target_cluster) + + def test_events_from_clusters_default(self): + """Test events_from_clusters: creation of events and computation of intensity based on clusters, + requires: identify_clusters, Centroids, also tests correct intensity sum""" + haz = LowFlow() + haz.lowflow_df = init_test_data_clustering() + haz.identify_clusters(clus_thresh_xy=1.5, clus_thresh_t=1, min_samples=1) + centroids = init_test_centroids(haz.lowflow_df) + haz.events_from_clusters(centroids) + target_intensity_e1 = [ 0., 0., 20., 30., 15., 5., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.] + self.assertEqual(haz.intensity.size, 11) + self.assertEqual(haz.intensity.todense().size, 144) + self.assertEqual(haz.intensity.sum(), 170.) + self.assertListEqual(list(np.array(haz.intensity.todense()[0])[0]), target_intensity_e1) + # dates: + self.assertListEqual(list(haz.date), [60, 1, 60, 60]) + self.assertListEqual(list(haz.date_start), [1, 1, 60, 60]) + for date, date_start, date_end in zip(haz.date, haz.date_start, haz.date_end): + self.assertLessEqual(date_start, date_end) + self.assertLessEqual(date_start, date) + self.assertLessEqual(date, date_end) + + + def test_events_from_clusters_parameter(self): + """Test events_from_clusters: creation of events and computation of intensity based on clusters, + requires: identify_clusters, Centroids, also tests correct intensity sum""" + haz = LowFlow() + haz.lowflow_df = init_test_data_clustering() + # set hazard with parameters so that all data si attributed to one single event: + haz.identify_clusters(clus_thresh_xy=6, clus_thresh_t=10, min_samples=1) + centroids = init_test_centroids(haz.lowflow_df) + # call function to be tested + haz.events_from_clusters(centroids) + target_intensity_e = [ 0., 0., 20., 30., 15., 5., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 33., 0., 0., 0., 0., 33., 0., 0., 0., + 0., 33., 0., 0., 1., 0., 0., 0., 0., 0.] + self.assertListEqual(list(haz.event_id), [1]) + self.assertEqual(haz.intensity.todense().size, len(target_intensity_e)) + self.assertEqual(haz.intensity.sum(), 170.) + self.assertListEqual(list(np.array(haz.intensity.todense()[0])[0]), target_intensity_e) + +class TestLowFlowNETCDF(unittest.TestCase): + """Test for defining low flow event from discharge data file""" + + def test_load_FR_all(self): + """Test defining low flow hazard from demo file (France 2001-2003) + and keep monthly data""" + + # init test hazard instance from trimmed ISIMIP output netcdf file + haz = LowFlow() + haz.set_from_nc(input_dir=INPUT_DIR, percentile=2.5, + yearrange=(2001, 2003), yearrange_ref=(2001, 2003), + gh_model='h08', cl_model='gfdl-esm2m', + scenario='historical', scenario_ref='historical', soc='histsoc', + soc_ref='histsoc', fn_str_var=FN_STR_DEMO, keep_dis_data=True, + yearchunks=['2001_2003']) + self.assertEqual(haz.lowflow_df.shape[0], 1073) + self.assertEqual(haz.lowflow_df.shape[1], 14) + self.assertEqual(haz.lowflow_df.ndays.max(), 28.0) + self.assertAlmostEqual(haz.lowflow_df.ndays.mean(), 9.994408201304752) + self.assertAlmostEqual(haz.lowflow_df.relative_dis.max(), 0.4480659) + self.assertEqual(haz.centroids.lon.min(), -4.75) + self.assertEqual(haz.centroids.lon.max(), 8.25) + self.assertEqual(haz.centroids.lat.min(), 42.25) + self.assertEqual(haz.centroids.lat.max(), 51.25) + self.assertEqual(haz.intensity.shape, (43, 513)) + self.assertEqual(haz.event_id.size, 43) + self.assertEqual(haz.intensity.max(), 28.0) + self.assertEqual(haz.intensity[17, 443], 0.) + self.assertEqual(haz.intensity[33, :].max(), 2.) + self.assertEqual(np.sum(haz.intensity[0]), 4006.) + self.assertAlmostEqual(haz.intensity[2].todense().mean(), 0.03508771929824561) + self.assertEqual(haz.lowflow_df.cluster_id.unique().size, haz.event_id.size) + self.assertEqual(haz.date[2], 731488) + self.assertEqual(haz.date_start[2], 731488) + self.assertEqual(haz.date_end[2], 731519) + for date, date_start, date_end in zip(haz.date, haz.date_start, haz.date_end): + self.assertLessEqual(date_start, date_end) + self.assertLessEqual(date_start, date) + self.assertLessEqual(date, date_end) + + def test_combine_nc(self): + """Test combining two chunked data files (2001-2003 combined with 2004-2005)""" + haz = LowFlow() + haz.set_from_nc(input_dir=INPUT_DIR, percentile=2.5, + yearrange=(2001, 2005), yearrange_ref=(2001, 2005), + gh_model='h08', cl_model='gfdl-esm2m', + scenario='historical', scenario_ref='historical', soc='histsoc', + soc_ref='histsoc', fn_str_var=FN_STR_DEMO, keep_dis_data=True, + yearchunks=['2001_2003', '2004_2005']) + + + self.assertEqual(haz.lowflow_df.shape[1], 14) + self.assertEqual(haz.lowflow_df.ndays.max(), 31.0) + self.assertAlmostEqual(haz.lowflow_df.ndays.mean(), 10.588021778584393) + self.assertAlmostEqual(haz.lowflow_df.relative_dis.max(), 0.41278067) + self.assertEqual(haz.centroids.lon.min(), -4.75) + self.assertEqual(haz.centroids.lon.max(), 8.25) + self.assertEqual(haz.centroids.lat.min(), 42.25) + self.assertEqual(haz.centroids.lat.max(), 51.25) + self.assertEqual(haz.intensity.shape, (66, 513)) + self.assertEqual(haz.event_id.size, 66) + self.assertEqual(haz.intensity.max(), 46.0) + self.assertEqual(haz.intensity[17, 443], 3) + self.assertEqual(haz.intensity[17, 444], 0) + self.assertEqual(np.sum(haz.intensity[0]), 5243) + self.assertAlmostEqual(haz.intensity[2].todense().mean(), 0.19883040935672514) + self.assertEqual(haz.intensity[2].todense().max(), 19) + self.assertEqual(haz.lowflow_df.cluster_id.unique().size, haz.event_id.size) + self.assertEqual(haz.date[2], 731519) + self.assertEqual(haz.date_start[2], 731396) + self.assertEqual(haz.date_end[2], 731519) + for date, date_start, date_end in zip(haz.date, haz.date_start, haz.date_end): + self.assertLessEqual(date_start, date_end) + self.assertLessEqual(date_start, date) + self.assertLessEqual(date, date_end) + + def test_filter_events(self): + """test if the right events are being filtered out""" + haz = LowFlow() + haz.set_from_nc(input_dir=INPUT_DIR, percentile=2.5, min_intensity=10, + min_number_cells=10, min_days_per_month=10, + yearrange=(2001, 2003), yearrange_ref=(2001, 2003), + gh_model='h08', cl_model='gfdl-esm2m', + scenario='historical', scenario_ref='historical', soc='histsoc', + soc_ref='histsoc', fn_str_var=FN_STR_DEMO, keep_dis_data=True, + yearchunks=['2001_2003']) + self.assertGreaterEqual(haz.lowflow_df.ndays.min(), 10) + self.assertGreaterEqual(haz.intensity[haz.intensity != 0].min(), 10) + for event in range(haz.intensity.shape[0]): + self.assertGreaterEqual(haz.intensity[event, :].nnz, 10) + +class TestDischargeDataHandling(unittest.TestCase): + """test additiopnal functions in low_flow and required for class LowFlow reading and + processing ISIMIP input data with variable discharge (dis)""" + + def test_read_and_combine_nc(self): + data_xarray = _read_and_combine_nc((2001, 2005), INPUT_DIR, 'h08', 'gfdl-esm2m', + 'historical', 'histsoc', FN_STR_DEMO, None, + ['2001_2003', '2004_2005']) + self.assertListEqual(list(data_xarray.dis.data.shape), [1826, 19, 27]) + # outside bbox: + data_xarray = _read_and_combine_nc((2001, 2005), INPUT_DIR, 'h08', 'gfdl-esm2m', + 'historical', 'histsoc', FN_STR_DEMO, [-180, -90, -170, -70], + ['2001_2003', '2004_2005']) + self.assertEqual(data_xarray.dis.data.size, 0) + + def test_compute_threshold_grid(self): + """test computation of percentile and mean on grid and masking of area""" + perc_data, mean_data = _compute_threshold_grid(5, (2001, 2005), INPUT_DIR, 'h08', 'gfdl-esm2m', + 'historical', 'histsoc', FN_STR_DEMO, None, + ['2001_2003', '2004_2005'], mask_threshold=None, keep_dis_data=True) + perc_data_mask, mean_data_mask = _compute_threshold_grid(5, (2001, 2005), INPUT_DIR, 'h08', 'gfdl-esm2m', + 'historical', 'histsoc', FN_STR_DEMO, None, + ['2001_2003', '2004_2005'], mask_threshold=('mean', 1500), keep_dis_data=True) + self.assertLess(np.sum(mean_data_mask.dis>0).data.max(), np.sum(mean_data.dis>0).data.max()) + self.assertEqual(np.sum(mean_data.dis>0).data.max(), 417) + self.assertEqual(np.sum(mean_data_mask.dis>0).data.max(), 10) + self.assertEqual(np.sum(perc_data.dis>0).data.max(), 392) + self.assertEqual(np.sum(perc_data_mask.dis>0).data.max(), 10) + self.assertListEqual(list(perc_data_mask.lon.data), list(perc_data.lon.data)) + self.assertEqual(len(perc_data_mask.lon.data), 27) + self.assertEqual(max(perc_data_mask.lon.data), 8.25) + + def test_split_bbox(self): + """test splitting the bounding box in parts""" + bbox = [-180, -60, 180, 75] + self.assertListEqual(bbox, _split_bbox(bbox, width=1000)[0]) + + bbox = _split_bbox(bbox, width=90) + self.assertEqual(4, len(bbox)) + self.assertListEqual(bbox[1], [-91, -60, -1, 75]) + self.assertListEqual(bbox[2], [-1, -60, 89, 75]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestLowFlowDummyData) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestLowFlowNETCDF)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDischargeDataHandling)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_relative_cropyield.py b/climada/hazard/test/test_relative_cropyield.py new file mode 100644 index 0000000000..6b73b3bc1e --- /dev/null +++ b/climada/hazard/test/test_relative_cropyield.py @@ -0,0 +1,93 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test crop potential module. +""" +import os +import unittest +import numpy as np +from climada.hazard.relative_cropyield import RelativeCropyield +from climada.util.constants import DATA_DIR + +INPUT_DIR = os.path.join(DATA_DIR, 'demo') +FN_STR_DEMO = 'annual_FR_DE_DEMO' + + +class TestRelativeCropyield(unittest.TestCase): + """Test for defining crop potential event""" + def test_load_EU_all(self): + """Test defining crop potential hazard from complete demo file (Central Europe)""" + haz = RelativeCropyield() + haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=(2001, 2005), + ag_model='lpjml', cl_model='ipsl-cm5a-lr', scenario='historical', + soc='2005soc', co2='co2', crop='whe', irr='noirr', + fn_str_var=FN_STR_DEMO) + + self.assertEqual(haz.crop, 'whe') + self.assertEqual(haz.tag.haz_type, 'RC') + self.assertIn('lpjml', haz.tag.file_name) + self.assertIn('ipsl-cm5a-lr', haz.tag.file_name) + self.assertIn('hist', haz.tag.file_name) + self.assertIn('2005soc', haz.tag.file_name) + self.assertIn('noirr', haz.tag.file_name) + + self.assertEqual(haz.centroids.lon.min(), -4.75) + self.assertEqual(haz.centroids.lon.max(), 15.75) + self.assertEqual(haz.centroids.lat.min(), 42.25) + self.assertEqual(haz.centroids.lat.max(), 54.75) + self.assertEqual(haz.intensity.shape, (5, 1092)) + self.assertEqual(haz.event_id.size, 5) + self.assertAlmostEqual(haz.intensity.max(), 10.176164, places=5) + + def test_set_rel_yield(self): + """Test setting intensity to relativ yield""" + haz = RelativeCropyield() + haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=(2001, 2005), ag_model='lpjml', + cl_model='ipsl-cm5a-lr', scenario='historical', soc='2005soc', + co2='co2', crop='whe', irr='noirr', fn_str_var=FN_STR_DEMO) + hist_mean = haz.calc_mean(np.array([2001, 2005])) + + self.assertEqual(haz.intensity_def, 'Yearly Yield') + haz.set_rel_yield_to_int(hist_mean) + self.assertEqual(haz.intensity_def, 'Relative Yield') + + self.assertEqual(np.shape(hist_mean), (1092,)) + self.assertAlmostEqual(np.max(hist_mean), 8.397826, places=5) + self.assertEqual(haz.intensity.shape, (5, 1092)) + self.assertAlmostEqual(np.nanmax(haz.intensity.toarray()), 4.0, places=5) + self.assertAlmostEqual(haz.intensity.max(), 4.0, places=5) + self.assertAlmostEqual(haz.intensity.min(), -1.0, places=5) + + def test_set_percentile_to_int(self): + """Test setting intensity to percentile of the yield""" + haz = RelativeCropyield() + haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=(2001, 2005), ag_model='lpjml', + cl_model='ipsl-cm5a-lr', scenario='historical', soc='2005soc', + co2='co2', crop='whe', irr='noirr', fn_str_var=FN_STR_DEMO) + haz.set_percentile_to_int() + self.assertEqual(haz.intensity_def, 'Percentile') + + self.assertEqual(haz.intensity.shape, (5, 1092)) + self.assertAlmostEqual(haz.intensity.max(), 1.0, places=5) + self.assertAlmostEqual(haz.intensity.min(), 0.2, places=5) + self.assertAlmostEqual(haz.intensity.data[10], 0.6, places=5) + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRelativeCropyield) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_storm_europe.py b/climada/hazard/test/test_storm_europe.py index 281f74e312..36a52842bd 100644 --- a/climada/hazard/test/test_storm_europe.py +++ b/climada/hazard/test/test_storm_europe.py @@ -33,17 +33,17 @@ DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') class TestReader(unittest.TestCase): - """ Test loading functions from the StormEurope class """ + """Test loading functions from the StormEurope class""" def test_centroids_from_nc(self): - """ Test if centroids can be constructed correctly """ + """Test if centroids can be constructed correctly""" cent = StormEurope._centroids_from_nc(WS_DEMO_NC[0]) self.assertTrue(isinstance(cent, Centroids)) self.assertEqual(cent.size, 9944) def test_read_footprints(self): - """ Test read_footprints function, using two small test files""" + """Test read_footprints function, using two small test files""" storms = StormEurope() storms.read_footprints(WS_DEMO_NC, description='test_description') @@ -64,7 +64,7 @@ def test_read_footprints(self): self.assertEqual(storms.fraction.shape, (2, 9944)) def test_read_with_ref(self): - """ Test read_footprints while passing in a reference raster. """ + """Test read_footprints while passing in a reference raster.""" storms = StormEurope() storms.read_footprints(WS_DEMO_NC, ref_raster=WS_DEMO_NC[1]) @@ -83,12 +83,13 @@ def test_read_with_ref(self): self.assertEqual(storms.fraction.shape, (2, 9944)) def test_read_with_cent(self): - """ Test read_footprints while passing in a Centroids object """ + """Test read_footprints while passing in a Centroids object""" var_names = copy.deepcopy(DEF_VAR_EXCEL) var_names['sheet_name'] = 'fp_centroids-test' var_names['col_name']['region_id'] = 'iso_n3' test_centroids = Centroids() - test_centroids.read_excel(os.path.join(DATA_DIR, 'fp_centroids-test.xls'), var_names=var_names) + test_centroids.read_excel( + os.path.join(DATA_DIR, 'fp_centroids-test.xls'), var_names=var_names) storms = StormEurope() storms.read_footprints(WS_DEMO_NC, centroids=test_centroids) @@ -101,7 +102,7 @@ def test_read_with_cent(self): ) def test_set_ssi(self): - """ Test set_ssi with both dawkins and wisc_gust methodology. """ + """Test set_ssi with both dawkins and wisc_gust methodology.""" storms = StormEurope() storms.read_footprints(WS_DEMO_NC) @@ -124,8 +125,8 @@ def test_set_ssi(self): ) def test_generate_prob_storms(self): - """ Test the probabilistic storm generator; calls _hist2prob as well as - Centroids.set_region_id() """ + """Test the probabilistic storm generator; calls _hist2prob as well as + Centroids.set_region_id()""" storms = StormEurope() storms.read_footprints(WS_DEMO_NC) storms_prob = storms.generate_prob_storms() @@ -138,7 +139,7 @@ def test_generate_prob_storms(self): # but the centroid's location that is decisive ) self.assertEqual(storms_prob.size, 60) - self.assertTrue(np.allclose((1/storms_prob.frequency).astype(int), 330)) + self.assertTrue(np.allclose((1 / storms_prob.frequency).astype(int), 330)) self.assertAlmostEqual(storms.frequency.sum(), storms_prob.frequency.sum()) self.assertEqual(np.count_nonzero(storms_prob.orig), 2) diff --git a/climada/hazard/test/test_tag.py b/climada/hazard/test/test_tag.py index ac82e89f91..a36130dff5 100644 --- a/climada/hazard/test/test_tag.py +++ b/climada/hazard/test/test_tag.py @@ -34,9 +34,9 @@ def test_append_right_pass(self): self.assertEqual('TC', tag1.haz_type) tag2 = TagHazard('TC', 'file_name2.mat', 'dummy file 2') - + tag1.append(tag2) - + self.assertEqual(['file_name1.mat', 'file_name2.mat'], tag1.file_name) self.assertEqual(['dummy file 1', 'dummy file 2'], tag1.description) self.assertEqual('TC', tag1.haz_type) @@ -48,55 +48,53 @@ def test_append_wrong_pass(self): with self.assertLogs('climada.hazard.tag', level='ERROR') as cm: with self.assertRaises(ValueError): tag1.append(tag2) - self.assertIn("Hazards of different type can't be appended: " \ - + "TC != EQ.", cm.output[0]) + self.assertIn("Hazards of different type can't be appended: TC != EQ.", cm.output[0]) def test_equal_same(self): """Appends an other tag correctly.""" tag1 = TagHazard('TC', 'file_name1.mat', 'dummy file 1') tag2 = TagHazard('TC', 'file_name1.mat', 'dummy file 1') - tag1.append(tag2) + tag1.append(tag2) self.assertEqual(['file_name1.mat', 'file_name1.mat'], tag1.file_name) self.assertEqual(['dummy file 1', 'dummy file 1'], tag1.description) self.assertEqual('TC', tag1.haz_type) - + def test_append_empty(self): """Appends an other tag correctly.""" tag1 = TagHazard('TC', 'file_name1.mat', 'dummy file 1') tag2 = TagHazard() tag2.haz_type = 'TC' - - tag1.append(tag2) + + tag1.append(tag2) self.assertEqual('file_name1.mat', tag1.file_name) self.assertEqual('dummy file 1', tag1.description) - + tag1 = TagHazard() tag1.haz_type = 'TC' tag2 = TagHazard('TC', 'file_name1.mat', 'dummy file 1') - + tag1.append(tag2) self.assertEqual('file_name1.mat', tag1.file_name) self.assertEqual('dummy file 1', tag1.description) - + class TestJoin(unittest.TestCase): """Test joining functions and string formation Tag class.""" def test_one_str_pass(self): - """ Test __str__ method with one file""" + """Test __str__ method with one file""" tag = TagHazard('TC', 'file_name1.mat', 'dummy file 1') - self.assertEqual(str(tag), - ' Type: TC\n File: file_name1\n Description: dummy file 1') + self.assertEqual(str(tag), ' Type: TC\n File: file_name1\n Description: dummy file 1') def test_teo_str_pass(self): - """ Test __str__ method with one file""" + """Test __str__ method with one file""" tag1 = TagHazard('TC', 'file1.mat', 'desc1') tag2 = TagHazard('TC', 'file2.xls', 'desc2') tag1.append(tag2) - self.assertEqual(str(tag1), - ' Type: TC\n File: file1 + file2\n Description: desc1 + desc2') + self.assertEqual(str(tag1), + ' Type: TC\n File: file1 + file2\n Description: desc1 + desc2') def test_join_names_pass(self): - """ Test join_file_names function.""" + """Test join_file_names function.""" tag1 = TagHazard('TC', 'file1', 'desc1') tag2 = TagHazard('TC', 'file2', 'desc2') tag1.append(tag2) @@ -104,7 +102,7 @@ def test_join_names_pass(self): self.assertEqual('file1 + file2', join_name) def test_join_descr_pass(self): - """ Test join_descriptions function.""" + """Test join_descriptions function.""" tag1 = TagHazard('TC', 'file1', 'desc1') tag2 = TagHazard('TC', 'file2', 'desc2') tag1.append(tag2) diff --git a/climada/hazard/test/test_tc_cc.py b/climada/hazard/test/test_tc_cc.py index 3d3525ffac..20d9e23adb 100644 --- a/climada/hazard/test/test_tc_cc.py +++ b/climada/hazard/test/test_tc_cc.py @@ -38,18 +38,26 @@ def test_get_pass(self): self.assertTrue('function' in crit_val) self.assertEqual(criterion[0]['change'], 1.045) self.assertEqual(criterion[-1]['change'], 1 - 0.583) - + def test_scale_pass(self): - """ Test calc_scale_knutson function. """ - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2050, rcp_scenario=45), 0.759630751756698) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2070, rcp_scenario=45), 0.958978483788876) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2060, rcp_scenario=60), 0.825572149523299) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2080, rcp_scenario=60), 1.309882943406079) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2090, rcp_scenario=85), 2.635069196605717) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2100, rcp_scenario=85), 2.940055236533517) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2066, rcp_scenario=26), 0.341930203294547) - self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2078, rcp_scenario=26), 0.312383928930456) + """Test calc_scale_knutson function.""" + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2050, rcp_scenario=45), + 0.759630751756698) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2070, rcp_scenario=45), + 0.958978483788876) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2060, rcp_scenario=60), + 0.825572149523299) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2080, rcp_scenario=60), + 1.309882943406079) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2090, rcp_scenario=85), + 2.635069196605717) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2100, rcp_scenario=85), + 2.940055236533517) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2066, rcp_scenario=26), + 0.341930203294547) + self.assertAlmostEqual(tc_cc.calc_scale_knutson(ref_year=2078, rcp_scenario=26), + 0.312383928930456) if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestKnutson) - unittest.TextTestRunner(verbosity=2).run(TESTS) \ No newline at end of file + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_tc_rainfield.py b/climada/hazard/test/test_tc_rainfield.py new file mode 100644 index 0000000000..b0f3fef593 --- /dev/null +++ b/climada/hazard/test/test_tc_rainfield.py @@ -0,0 +1,155 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test TCRain class +""" + +import os +import unittest +import numpy as np +from scipy import sparse +import datetime as dt + +from climada.hazard.tc_tracks import TCTracks +from climada.hazard.tc_rainfield import TCRain, rainfield_from_track +from climada.hazard.centroids.centr import Centroids + +DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') +HAZ_TEST_MAT = os.path.join(DATA_DIR, 'TCrain_brb_test.mat') +TEST_TRACK = os.path.join(DATA_DIR, "trac_brb_test.csv") +TEST_TRACK_SHORT = os.path.join(DATA_DIR, "trac_short_test.csv") + +CENTR_DIR = os.path.join(os.path.dirname(__file__), 'data/') +CENTR_TEST_BRB = Centroids() +CENTR_TEST_BRB.read_mat(os.path.join(CENTR_DIR, 'centr_brb_test.mat')) + + +class TestReader(unittest.TestCase): + """Test loading funcions from the TCRain class""" + + def test_set_one_pass(self): + """Test _set_from_track function.""" + tc_track = TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK) + tc_track.equal_timestep() + tc_haz = TCRain._set_from_track(tc_track.data[0], CENTR_TEST_BRB) + + self.assertEqual(tc_haz.tag.haz_type, 'TR') + self.assertEqual(tc_haz.tag.description, '') + self.assertEqual(tc_haz.tag.file_name, 'IBTrACS: 1951239N12334') + self.assertEqual(tc_haz.units, 'mm') + self.assertEqual(tc_haz.centroids.size, 296) + self.assertEqual(tc_haz.event_id.size, 1) + self.assertEqual(tc_haz.date.size, 1) + self.assertEqual(dt.datetime.fromordinal(tc_haz.date[0]).year, 1951) + self.assertEqual(dt.datetime.fromordinal(tc_haz.date[0]).month, 8) + self.assertEqual(dt.datetime.fromordinal(tc_haz.date[0]).day, 27) + self.assertEqual(tc_haz.event_id[0], 1) + self.assertEqual(tc_haz.event_name, ['1951239N12334']) + self.assertTrue(np.array_equal(tc_haz.frequency, np.array([1]))) + self.assertTrue(isinstance(tc_haz.intensity, sparse.csr.csr_matrix)) + self.assertTrue(isinstance(tc_haz.fraction, sparse.csr.csr_matrix)) + self.assertEqual(tc_haz.intensity.shape, (1, 296)) + self.assertEqual(tc_haz.fraction.shape, (1, 296)) + + self.assertAlmostEqual(tc_haz.intensity[0, 100], 99.7160586771286, 6) + self.assertAlmostEqual(tc_haz.intensity[0, 260], 33.2087621869295) + self.assertEqual(tc_haz.fraction[0, 100], 1) + self.assertEqual(tc_haz.fraction[0, 260], 1) + + self.assertEqual(tc_haz.fraction.nonzero()[0].size, 296) + self.assertEqual(tc_haz.intensity.nonzero()[0].size, 296) + + def test_set_one_file_pass(self): + """Test set function set_from_tracks with one input.""" + tc_track = TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) + tc_haz = TCRain() + tc_haz.set_from_tracks(tc_track, CENTR_TEST_BRB) + tc_haz.check() + + self.assertEqual(tc_haz.tag.haz_type, 'TR') + self.assertEqual(tc_haz.tag.description, '') + self.assertEqual(tc_haz.tag.file_name, 'IBTrACS: 1951239N12334') + self.assertEqual(tc_haz.units, 'mm') + self.assertEqual(tc_haz.centroids.size, 296) + self.assertEqual(tc_haz.event_id.size, 1) + self.assertEqual(tc_haz.event_id[0], 1) + self.assertEqual(tc_haz.event_name, ['1951239N12334']) + self.assertEqual(tc_haz.category, tc_track.data[0].category) + self.assertTrue(np.isnan(tc_haz.basin[0])) + self.assertIsInstance(tc_haz.basin, list) + self.assertIsInstance(tc_haz.category, np.ndarray) + self.assertTrue(np.array_equal(tc_haz.frequency, np.array([1]))) + self.assertTrue(isinstance(tc_haz.intensity, sparse.csr.csr_matrix)) + self.assertTrue(isinstance(tc_haz.fraction, sparse.csr.csr_matrix)) + self.assertEqual(tc_haz.intensity.shape, (1, 296)) + self.assertEqual(tc_haz.fraction.shape, (1, 296)) + + self.assertEqual(tc_haz.fraction.nonzero()[0].size, 0) + self.assertEqual(tc_haz.intensity.nonzero()[0].size, 0) + + def test_two_files_pass(self): + """Test set function set_from_tracks with two ibtracs.""" + tc_track = TCTracks() + tc_track.read_processed_ibtracs_csv([TEST_TRACK_SHORT, TEST_TRACK_SHORT]) + tc_haz = TCRain() + tc_haz.set_from_tracks(tc_track, CENTR_TEST_BRB) + tc_haz.remove_duplicates() + tc_haz.check() + + self.assertEqual(tc_haz.tag.haz_type, 'TR') + self.assertEqual(tc_haz.tag.description, '') + self.assertEqual(tc_haz.tag.file_name, ['IBTrACS: 1951239N12334', + 'IBTrACS: 1951239N12334']) + self.assertEqual(tc_haz.units, 'mm') + self.assertEqual(tc_haz.centroids.size, 296) + self.assertEqual(tc_haz.event_id.size, 1) + self.assertEqual(tc_haz.event_id[0], 1) + self.assertEqual(tc_haz.event_name, ['1951239N12334']) + self.assertTrue(np.array_equal(tc_haz.frequency, np.array([1]))) + self.assertTrue(np.array_equal(tc_haz.orig, np.array([True]))) + self.assertTrue(isinstance(tc_haz.intensity, sparse.csr.csr_matrix)) + self.assertTrue(isinstance(tc_haz.fraction, sparse.csr.csr_matrix)) + self.assertEqual(tc_haz.intensity.shape, (1, 296)) + self.assertEqual(tc_haz.fraction.shape, (1, 296)) + + self.assertEqual(tc_haz.fraction.nonzero()[0].size, 0) + self.assertEqual(tc_haz.intensity.nonzero()[0].size, 0) + +class TestModel(unittest.TestCase): + """Test modelling of rainfall""" + + def test_rainfield_from_track_pass(self): + """Test _rainfield_from_track function. Compare to MATLAB reference.""" + tc_track = TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK) + tc_track.equal_timestep() + rainfall = rainfield_from_track(tc_track.data[0], + CENTR_TEST_BRB) + + rainfall = np.round(rainfall, decimals=9) + + self.assertAlmostEqual(rainfall[0, 0], 66.801702386) + self.assertAlmostEqual(rainfall[0, 130], 43.290917792) + self.assertAlmostEqual(rainfall[0, 200], 76.315923838) + +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestModel)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_tc_tracks.py b/climada/hazard/test/test_tc_tracks.py index 0ca489a5b2..6ef5ccccbe 100644 --- a/climada/hazard/test/test_tc_tracks.py +++ b/climada/hazard/test/test_tc_tracks.py @@ -21,12 +21,12 @@ import os import unittest -import array import xarray as xr import numpy as np +import netCDF4 as nc -from climada.hazard.tc_tracks import TCTracks import climada.hazard.tc_tracks as tc +from climada.util import ureg from climada.util.constants import TC_ANDREW_FL from climada.util.coordinates import coord_on_land, dist_to_coast @@ -34,12 +34,127 @@ TEST_TRACK = os.path.join(DATA_DIR, "trac_brb_test.csv") TEST_TRACK_SHORT = os.path.join(DATA_DIR, "trac_short_test.csv") TEST_RAW_TRACK = os.path.join(DATA_DIR, 'Storm.2016075S11087.ibtracs_all.v03r10.csv') +TEST_TRACK_GETTELMAN = os.path.join(DATA_DIR, 'gettelman_test_tracks.nc') +TEST_TRACK_EMANUEL = os.path.join(DATA_DIR, 'emanuel_test_tracks.mat') +TEST_TRACK_EMANUEL_CORR = os.path.join(DATA_DIR, 'temp_mpircp85cal_full.mat') + class TestIBTracs(unittest.TestCase): """Test reading and model of TC from IBTrACS files""" - def test_read_pass(self): + + def test_raw_ibtracs_empty_pass(self): + """Test reading TC from IBTrACS files""" + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1988234N13299') + self.assertEqual(tc_track.get_track(), []) + + def test_write_read_pass(self): + """Test writting and reading netcdf4 TCTracks instances""" + path = os.path.join(DATA_DIR, "tc_tracks_nc") + os.makedirs(path, exist_ok=True) + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1988234N13299', + estimate_missing=True) + tc_track.write_netcdf(path) + + tc_read = tc.TCTracks() + tc_read.read_netcdf(path) + + self.assertEqual(tc_track.get_track().sid, tc_read.get_track().sid) + + def test_penv_rmax_penv_pass(self): + """read_ibtracs_netcdf""" + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1992230N11325') + penv_ref = np.ones(97) * 1010 + penv_ref[26] = 1011 + penv_ref[27] = 1012 + penv_ref[28] = 1013 + penv_ref[29] = 1014 + penv_ref[30] = 1015 + penv_ref[31] = 1014 + penv_ref[32] = 1014 + penv_ref[33] = 1014 + penv_ref[34] = 1014 + penv_ref[35] = 1012 + + self.assertTrue(np.allclose( + tc_track.get_track().environmental_pressure.values, penv_ref)) + self.assertTrue(np.allclose( + tc_track.get_track().radius_max_wind.values, np.zeros(97))) + + def test_read_raw_pass(self): """Read a tropical cyclone.""" - tc_track = TCTracks() + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', storm_id='2017242N16333') + self.assertEqual(len(tc_track.data), 1) + self.assertEqual(tc_track.get_track().time.dt.year.values[0], 2017) + self.assertEqual(tc_track.get_track().time.dt.month.values[0], 8) + self.assertEqual(tc_track.get_track().time.dt.day.values[0], 30) + self.assertEqual(tc_track.get_track().time.dt.hour.values[0], 0) + self.assertAlmostEqual(tc_track.get_track().lat.values[0], 16.1 + 3.8146972514141453e-07) + self.assertAlmostEqual(tc_track.get_track().lon.values[0], -26.9 + 3.8146972514141453e-07) + self.assertAlmostEqual(tc_track.get_track().max_sustained_wind.values[0], 30) + self.assertAlmostEqual(tc_track.get_track().central_pressure.values[0], 1008) + self.assertAlmostEqual(tc_track.get_track().environmental_pressure.values[0], 1012) + self.assertAlmostEqual(tc_track.get_track().radius_max_wind.values[0], 60) + self.assertEqual(tc_track.get_track().time.size, 123) + + self.assertAlmostEqual(tc_track.get_track().lat.values[-1], 36.8 - 7.629394502828291e-07) + self.assertAlmostEqual(tc_track.get_track().lon.values[-1], -90.100006, 5) + self.assertAlmostEqual(tc_track.get_track().central_pressure.values[-1], 1005) + self.assertAlmostEqual(tc_track.get_track().max_sustained_wind.values[-1], 15) + self.assertAlmostEqual(tc_track.get_track().environmental_pressure.values[-1], 1008) + self.assertAlmostEqual(tc_track.get_track().radius_max_wind.values[-1], 60) + + self.assertFalse(np.isnan(tc_track.get_track().radius_max_wind.values).any()) + self.assertFalse(np.isnan(tc_track.get_track().environmental_pressure.values).any()) + self.assertFalse(np.isnan(tc_track.get_track().max_sustained_wind.values).any()) + self.assertFalse(np.isnan(tc_track.get_track().central_pressure.values).any()) + self.assertFalse(np.isnan(tc_track.get_track().lat.values).any()) + self.assertFalse(np.isnan(tc_track.get_track().lon.values).any()) + + self.assertEqual(tc_track.get_track().basin, 'NA') + self.assertEqual(tc_track.get_track().max_sustained_wind_unit, 'kn') + self.assertEqual(tc_track.get_track().central_pressure_unit, 'mb') + self.assertEqual(tc_track.get_track().sid, '2017242N16333') + self.assertEqual(tc_track.get_track().name, 'IRMA') + self.assertEqual(tc_track.get_track().orig_event_flag, True) + self.assertEqual(tc_track.get_track().data_provider, 'usa') + self.assertEqual(tc_track.get_track().category, 5) + + def test_read_range(self): + """Read several TCs.""" + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', storm_id=None, + year_range=(1915, 1916), basin='WP') + self.assertEqual(tc_track.size, 0) + + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), + basin='EP', estimate_missing=False) + self.assertEqual(tc_track.size, 34) + + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), + basin='EP', estimate_missing=True) + self.assertEqual(tc_track.size, 52) + + def test_ibtracs_correct_pass(self): + """Check estimate_missing option""" + tc_try = tc.TCTracks() + tc_try.read_ibtracs_netcdf(provider='usa', storm_id='1982267N25289', + estimate_missing=True) + self.assertAlmostEqual(tc_try.data[0].central_pressure.values[0], 1013.61584, 5) + self.assertAlmostEqual(tc_try.data[0].central_pressure.values[5], 1008.63837, 5) + self.assertAlmostEqual(tc_try.data[0].central_pressure.values[-1], 1014.1515, 4) + + +class TestIO(unittest.TestCase): + """Test reading of tracks from files of different formats""" + + def test_read_processed_ibtracs_csv(self): + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) self.assertEqual(tc_track.data[0].time.size, 38) @@ -49,7 +164,7 @@ def test_read_pass(self): self.assertEqual(np.max(tc_track.data[0].radius_max_wind), 0) self.assertEqual(np.min(tc_track.data[0].radius_max_wind), 0) self.assertEqual(tc_track.data[0].max_sustained_wind[21], 55) - self.assertEqual(tc_track.data[0].central_pressure[29], 969.76880) + self.assertEqual(tc_track.data[0].central_pressure.values[29], 975.9651) self.assertEqual(np.max(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(np.min(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(tc_track.data[0].time.dt.year[13], 1951) @@ -65,46 +180,113 @@ def test_read_pass(self): self.assertTrue(np.isnan(tc_track.data[0].basin)) self.assertEqual(tc_track.data[0].id_no, 1951239012334) self.assertEqual(tc_track.data[0].category, 1) - + + def test_read_simulations_emanuel(self): + tc_track = tc.TCTracks() + + tc_track.read_simulations_emanuel(TEST_TRACK_EMANUEL, hemisphere='N') + self.assertEqual(len(tc_track.data), 4) + self.assertEqual(tc_track.data[0].time.size, 93) + self.assertEqual(tc_track.data[0].lon[11], -115.57) + self.assertEqual(tc_track.data[0].lat[23], 10.758) + self.assertEqual(tc_track.data[0].time_step[7], 2) + self.assertAlmostEqual(tc_track.data[0].radius_max_wind[15], 44.27645788336934) + self.assertEqual(tc_track.data[0].max_sustained_wind[21], 27.1) + self.assertEqual(tc_track.data[0].central_pressure[29], 995.31) + self.assertTrue(np.all(tc_track.data[0].environmental_pressure == 1010)) + self.assertTrue(np.all(tc_track.data[0].time.dt.year == 1950)) + self.assertEqual(tc_track.data[0].time.dt.month[26], 10) + self.assertEqual(tc_track.data[0].time.dt.day[7], 26) + self.assertEqual(tc_track.data[0].max_sustained_wind_unit, 'kn') + self.assertEqual(tc_track.data[0].central_pressure_unit, 'mb') + self.assertEqual(tc_track.data[0].sid, '1') + self.assertEqual(tc_track.data[0].name, '1') + self.assertTrue(np.all([d.basin == 'N' for d in tc_track.data])) + self.assertEqual(tc_track.data[0].category, 3) + + tc_track.read_simulations_emanuel(TEST_TRACK_EMANUEL_CORR) + self.assertEqual(len(tc_track.data), 2) + self.assertTrue(np.all([d.basin == 'S' for d in tc_track.data])) + self.assertEqual(tc_track.data[0].radius_max_wind[15], 102.49460043196545) + self.assertEqual(tc_track.data[0].time.dt.month[343], 2) + self.assertEqual(tc_track.data[0].time.dt.day[343], 28) + self.assertEqual(tc_track.data[0].time.dt.month[344], 3) + self.assertEqual(tc_track.data[0].time.dt.day[344], 1) + self.assertEqual(tc_track.data[1].time.dt.year[0], 2009) + self.assertEqual(tc_track.data[1].time.dt.year[256], 2009) + self.assertEqual(tc_track.data[1].time.dt.year[257], 2010) + self.assertEqual(tc_track.data[1].time.dt.year[-1], 2010) + + def test_read_one_gettelman(self): + """Test reading and model of TC from Gettelman track files""" + tc_track_G = tc.TCTracks() + # populate tracks by loading data from NetCDF: + nc_data = nc.Dataset(TEST_TRACK_GETTELMAN) + nstorms = nc_data.dimensions['storm'].size + for i in range(nstorms): + tc_track_G.read_one_gettelman(nc_data, i) + + self.assertEqual(tc_track_G.data[0].time.size, 29) + self.assertEqual(tc_track_G.data[0].lon[11], 60.0) + self.assertEqual(tc_track_G.data[0].lat[23], 10.20860481262207) + self.assertEqual(tc_track_G.data[0].time_step[7], 3.) + self.assertEqual(np.max(tc_track_G.data[0].radius_max_wind), 65) + self.assertEqual(np.min(tc_track_G.data[0].radius_max_wind), 65) + self.assertEqual(tc_track_G.data[0].max_sustained_wind[21], 39.91877223718089) + self.assertEqual(tc_track_G.data[0].central_pressure[27], 1005.969482421875) + self.assertEqual(np.max(tc_track_G.data[0].environmental_pressure), 1015) + self.assertEqual(np.min(tc_track_G.data[0].environmental_pressure), 1015) + self.assertEqual(tc_track_G.data[0].maximum_precipitation[14], 219.10108947753906) + self.assertEqual(tc_track_G.data[0].average_precipitation[12], 101.43893432617188) + self.assertEqual(tc_track_G.data[0].time.dt.year[13], 1979) + self.assertEqual(tc_track_G.data[0].time.dt.month[26], 1) + self.assertEqual(tc_track_G.data[0].time.dt.day[7], 2) + self.assertEqual(tc_track_G.data[0].max_sustained_wind_unit, 'kn') + self.assertEqual(tc_track_G.data[0].central_pressure_unit, 'mb') + self.assertEqual(tc_track_G.data[0].sid, '0') + self.assertEqual(tc_track_G.data[0].name, '0') + self.assertEqual(tc_track_G.data[0].basin, 'NI - North Indian') + self.assertEqual(tc_track_G.data[0].category, 0) + + class TestFuncs(unittest.TestCase): """Test functions over TC tracks""" - def test_penv_pass(self): - """ Test _set_penv method.""" - tc_track = TCTracks() - basin = 'US' - self.assertEqual(tc_track._set_penv(basin), 1010) - basin = 'NA' - self.assertEqual(tc_track._set_penv(basin), 1010) - basin = 'SA' - self.assertEqual(tc_track._set_penv(basin), 1010) - basin = 'NI' - self.assertEqual(tc_track._set_penv(basin), 1005) - basin = 'SI' - self.assertEqual(tc_track._set_penv(basin), 1005) - basin = 'SP' - self.assertEqual(tc_track._set_penv(basin), 1004) - basin = 'WP' - self.assertEqual(tc_track._set_penv(basin), 1005) - basin = 'EP' - self.assertEqual(tc_track._set_penv(basin), 1010) - def test_get_track_pass(self): - """ Test get_track.""" - tc_track = TCTracks() + """Test get_track.""" + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) self.assertIsInstance(tc_track.get_track(), xr.Dataset) self.assertIsInstance(tc_track.get_track('1951239N12334'), xr.Dataset) - tc_track_bis = TCTracks() + tc_track_bis = tc.TCTracks() tc_track_bis.read_processed_ibtracs_csv(TEST_TRACK_SHORT) tc_track.append(tc_track_bis) self.assertIsInstance(tc_track.get_track(), list) self.assertIsInstance(tc_track.get_track('1951239N12334'), xr.Dataset) + def test_subset(self): + """Test subset.""" + storms = ['1988169N14259', '2002073S16161', '2002143S07157'] + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(storm_id=storms) + self.assertEqual(tc_track.subset({'basin': 'SP'}).size, 2) + + def test_get_extent(self): + """Test extent/bounds attributes.""" + storms = ['1988169N14259', '2002073S16161', '2002143S07157'] + tc_track = tc.TCTracks() + tc_track.read_ibtracs_netcdf(storm_id=storms) + bounds = (153.4752, -23.2000, 258.7132, 17.5166) + extent = (bounds[0], bounds[2], bounds[1], bounds[3]) + bounds_buf = (153.3752, -23.3000, 258.8132, 17.6166) + self.assertTrue(np.allclose(tc_track.bounds, bounds)) + self.assertTrue(np.allclose(tc_track.get_bounds(deg_buffer=0.1), bounds_buf)) + self.assertTrue(np.allclose(tc_track.extent, extent)) + def test_interp_track_pass(self): - """ Interpolate track to min_time_step. Compare to MATLAB reference.""" - tc_track = TCTracks() + """Interpolate track to min_time_step. Compare to MATLAB reference.""" + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) tc_track.equal_timestep(time_step_h=1) @@ -116,7 +298,7 @@ def test_interp_track_pass(self): self.assertEqual(np.min(tc_track.data[0].radius_max_wind), 0) self.assertEqual(tc_track.data[0].max_sustained_wind[21], 25) self.assertAlmostEqual(tc_track.data[0].central_pressure.values[29], - 1.005409300000005e+03) + 1.0077614e+03) self.assertEqual(np.max(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(np.min(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(tc_track.data[0]['time.year'][13], 1951) @@ -132,19 +314,26 @@ def test_interp_track_pass(self): self.assertEqual(tc_track.data[0].category, 1) def test_interp_origin_pass(self): - """ Interpolate track to min_time_step crossing lat origin """ - tc_track = TCTracks() + """Interpolate track to min_time_step crossing lat origin""" + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.data[0].lon.values = np.array([167.207761, 168.1 , 168.936535, 169.728947, 170.5 , - 171.257176, 171.946822, 172.5 , 172.871797, 173.113396, 173.3 , 173.496375, 173.725522, 174. , 174.331591, - 174.728961, 175.2 , 175.747632, 176.354929, 177. , 177.66677 , 178.362433, 179.1 , 179.885288, -179.304661, - -178.5 , -177.726442, -176.991938, -176.3 , -175.653595, -175.053513, -174.5 , -173.992511, -173.527342, -173.1 , - -172.705991, -172.340823, -172. ]) - tc_track.data[0].lat.values = np.array([40.196053, - 40.6 , 40.930215, 41.215674, 41.5 , 41.816354, 42.156065, 42.5 , 42.833998, 43.16377 , 43.5 , 43.847656, 44.188854, - 44.5 , 44.764269, 44.991925, 45.2 , 45.402675, 45.602707, 45.8 , 45.995402, 46.193543, 46.4 , 46.615718, 46.82312 , - 47. , 47.130616, 47.225088, 47.3 , 47.369224, 47.435786, 47.5 , 47.562858, 47.628064, 47.7 , 47.783047, 47.881586, - 48. ]) + tc_track.data[0].lon.values = np.array([ + 167.207761, 168.1, 168.936535, 169.728947, 170.5, 171.257176, + 171.946822, 172.5, 172.871797, 173.113396, 173.3, 173.496375, + 173.725522, 174., 174.331591, 174.728961, 175.2, 175.747632, + 176.354929, 177., 177.66677, 178.362433, 179.1, 179.885288, + -179.304661, -178.5, -177.726442, -176.991938, -176.3, -175.653595, + -175.053513, -174.5, -173.992511, -173.527342, -173.1, -172.705991, + -172.340823, -172. + ]) + tc_track.data[0].lat.values = np.array([ + 40.196053, 40.6, 40.930215, 41.215674, 41.5, 41.816354, 42.156065, + 42.5, 42.833998, 43.16377, 43.5, 43.847656, 44.188854, 44.5, + 44.764269, 44.991925, 45.2, 45.402675, 45.602707, 45.8, 45.995402, + 46.193543, 46.4, 46.615718, 46.82312, 47., 47.130616, 47.225088, + 47.3, 47.369224, 47.435786, 47.5, 47.562858, 47.628064, 47.7, + 47.783047, 47.881586, 48. + ]) tc_track.equal_timestep(time_step_h=1) self.assertEqual(tc_track.data[0].time.size, 223) @@ -161,7 +350,7 @@ def test_interp_origin_pass(self): self.assertEqual(np.min(tc_track.data[0].radius_max_wind), 0) self.assertEqual(tc_track.data[0].max_sustained_wind[21], 25) self.assertAlmostEqual(tc_track.data[0].central_pressure.values[29], - 1.005409300000005e+03) + 1.0077614e+03) self.assertEqual(np.max(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(np.min(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(tc_track.data[0]['time.year'][13], 1951) @@ -177,20 +366,27 @@ def test_interp_origin_pass(self): self.assertEqual(tc_track.data[0].category, 1) def test_interp_origin_inv_pass(self): - """ Interpolate track to min_time_step crossing lat origin """ - tc_track = TCTracks() + """Interpolate track to min_time_step crossing lat origin""" + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.data[0].lon.values = np.array([167.207761, 168.1 , 168.936535, 169.728947, 170.5 , - 171.257176, 171.946822, 172.5 , 172.871797, 173.113396, 173.3 , 173.496375, 173.725522, 174. , 174.331591, - 174.728961, 175.2 , 175.747632, 176.354929, 177. , 177.66677 , 178.362433, 179.1 , 179.885288, -179.304661, - -178.5 , -177.726442, -176.991938, -176.3 , -175.653595, -175.053513, -174.5 , -173.992511, -173.527342, -173.1 , - -172.705991, -172.340823, -172. ]) + tc_track.data[0].lon.values = np.array([ + 167.207761, 168.1, 168.936535, 169.728947, 170.5, 171.257176, + 171.946822, 172.5, 172.871797, 173.113396, 173.3, 173.496375, + 173.725522, 174., 174.331591, 174.728961, 175.2, 175.747632, + 176.354929, 177., 177.66677, 178.362433, 179.1, 179.885288, + -179.304661, -178.5, -177.726442, -176.991938, -176.3, -175.653595, + -175.053513, -174.5, -173.992511, -173.527342, -173.1, -172.705991, + -172.340823, -172. + ]) tc_track.data[0].lon.values = - tc_track.data[0].lon.values - tc_track.data[0].lat.values = np.array([40.196053, - 40.6 , 40.930215, 41.215674, 41.5 , 41.816354, 42.156065, 42.5 , 42.833998, 43.16377 , 43.5 , 43.847656, 44.188854, - 44.5 , 44.764269, 44.991925, 45.2 , 45.402675, 45.602707, 45.8 , 45.995402, 46.193543, 46.4 , 46.615718, 46.82312 , - 47. , 47.130616, 47.225088, 47.3 , 47.369224, 47.435786, 47.5 , 47.562858, 47.628064, 47.7 , 47.783047, 47.881586, - 48. ]) + tc_track.data[0].lat.values = np.array([ + 40.196053, 40.6, 40.930215, 41.215674, 41.5, 41.816354, 42.156065, + 42.5, 42.833998, 43.16377, 43.5, 43.847656, 44.188854, 44.5, + 44.764269, 44.991925, 45.2, 45.402675, 45.602707, 45.8, 45.995402, + 46.193543, 46.4, 46.615718, 46.82312, 47., 47.130616, 47.225088, + 47.3, 47.369224, 47.435786, 47.5, 47.562858, 47.628064, 47.7, + 47.783047, 47.881586, 48. + ]) tc_track.equal_timestep(time_step_h=1) self.assertEqual(tc_track.data[0].time.size, 223) @@ -207,7 +403,7 @@ def test_interp_origin_inv_pass(self): self.assertEqual(np.min(tc_track.data[0].radius_max_wind), 0) self.assertEqual(tc_track.data[0].max_sustained_wind[21], 25) self.assertAlmostEqual(tc_track.data[0].central_pressure.values[29], - 1.005409300000005e+03) + 1.0077614e+03) self.assertEqual(np.max(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(np.min(tc_track.data[0].environmental_pressure), 1010) self.assertEqual(tc_track.data[0]['time.year'][13], 1951) @@ -222,306 +418,26 @@ def test_interp_origin_inv_pass(self): self.assertEqual(tc_track.data[0].id_no, 1951239012334) self.assertEqual(tc_track.data[0].category, 1) - - def test_random_no_landfall_pass(self): - """ Test calc_random_walk with decay and no historical tracks with landfall """ - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) - with self.assertLogs('climada.hazard.tc_tracks', level='INFO') as cm: - tc_track.calc_random_walk() - self.assertIn('No historical track with landfall.', cm.output[1]) - - def test_random_walk_ref_pass(self): - """Test against MATLAB reference.""" - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) - ens_size=2 - tc_track.calc_random_walk(ens_size, seed=25, decay=False) - - self.assertEqual(len(tc_track.data), ens_size+1) - - self.assertFalse(tc_track.data[1].orig_event_flag) - self.assertEqual(tc_track.data[1].name, '1951239N12334_gen1') - self.assertEqual(tc_track.data[1].id_no, 1.951239012334010e+12) - self.assertAlmostEqual(tc_track.data[1].lon[0].values, -25.0448138) - self.assertAlmostEqual(tc_track.data[1].lon[1].values, -26.07400903) - self.assertAlmostEqual(tc_track.data[1].lon[2].values, -27.09191673) - self.assertAlmostEqual(tc_track.data[1].lon[3].values, -28.21366632) - self.assertAlmostEqual(tc_track.data[1].lon[4].values, -29.33195465) - self.assertAlmostEqual(tc_track.data[1].lon[8].values, -34.6016857) - - self.assertAlmostEqual(tc_track.data[1].lat[0].values, 11.96825841) - self.assertAlmostEqual(tc_track.data[1].lat[4].values, 12.35820479) - self.assertAlmostEqual(tc_track.data[1].lat[5].values, 12.45465) - self.assertAlmostEqual(tc_track.data[1].lat[6].values, 12.5492937) - self.assertAlmostEqual(tc_track.data[1].lat[7].values, 12.6333804) - self.assertAlmostEqual(tc_track.data[1].lat[8].values, 12.71561952) - - self.assertFalse(tc_track.data[2].orig_event_flag) - self.assertEqual(tc_track.data[2].name, '1951239N12334_gen2') - self.assertAlmostEqual(tc_track.data[2].id_no, 1.951239012334020e+12) - self.assertAlmostEqual(tc_track.data[2].lon[0].values, -25.47658461) - self.assertAlmostEqual(tc_track.data[2].lon[3].values, -28.78978084) - self.assertAlmostEqual(tc_track.data[2].lon[4].values, -29.9568406) - self.assertAlmostEqual(tc_track.data[2].lon[8].values, -35.30222604) - - self.assertAlmostEqual(tc_track.data[2].lat[0].values, 11.82886685) - self.assertAlmostEqual(tc_track.data[2].lat[6].values, 12.26400422) - self.assertAlmostEqual(tc_track.data[2].lat[7].values, 12.3454308) - self.assertAlmostEqual(tc_track.data[2].lat[8].values, 12.42745488) - - def test_random_walk_decay_pass(self): - """Test land decay is called from calc_random_walk.""" - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) - ens_size=2 - with self.assertLogs('climada.hazard.tc_tracks', level='DEBUG') as cm: - tc_track.calc_random_walk(ens_size, seed=25, decay=True) - self.assertIn('No historical track of category Tropical Depression with landfall.', cm.output[1]) - self.assertIn('Decay parameters from category Hurrican Cat. 4 taken.', cm.output[2]) - self.assertIn('No historical track of category Hurrican Cat. 1 with landfall.', cm.output[3]) - self.assertIn('Decay parameters from category Hurrican Cat. 4 taken.', cm.output[4]) - self.assertIn('No historical track of category Hurrican Cat. 3 with landfall. Decay parameters from category Hurrican Cat. 4 taken.', cm.output[5]) - self.assertIn('No historical track of category Hurrican Cat. 5 with landfall.', cm.output[6]) - - def test_calc_decay_no_landfall_pass(self): - """ Test _calc_land_decay with no historical tracks with landfall """ - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) - land_geom = tc._calc_land_geom(tc_track.data) - tc._track_land_params(tc_track.data[0], land_geom) - with self.assertLogs('climada.hazard.tc_tracks', level='INFO') as cm: - tc_track._calc_land_decay(land_geom) - self.assertIn('No historical track with landfall.', cm.output[0]) - - def test_calc_land_decay_pass(self): - """ Test _calc_land_decay with environmental pressure function.""" - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) - land_geom = tc._calc_land_geom(tc_track.data) - tc._track_land_params(tc_track.data[0], land_geom) - v_rel, p_rel = tc_track._calc_land_decay(land_geom) - - self.assertEqual(7, len(v_rel)) - for i, val in enumerate(v_rel.values()): - self.assertAlmostEqual(val, 0.0038894834) - self.assertTrue(i+1 in v_rel.keys()) - - self.assertEqual(7, len(p_rel)) - for i, val in enumerate(p_rel.values()): - self.assertAlmostEqual(val[0], 1.0598491) - self.assertAlmostEqual(val[1], 0.0041949237) - self.assertTrue(i+1 in p_rel.keys()) - - def test_decay_values_andrew_pass(self): - """ Test _decay_values with central pressure function.""" - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) - s_rel = False - land_geom = tc._calc_land_geom(tc_track.data) - tc._track_land_params(tc_track.data[0], land_geom) - v_lf, p_lf, x_val = tc._decay_values(tc_track.data[0], land_geom, s_rel) - - ss_category = 6 - s_cell_1 = 1*[1.0149413347244263] - s_cell_2 = 8*[1.047120451927185] - s_cell = s_cell_1 + s_cell_2 - p_vs_lf_time_relative = [1.0149413020277482, 1.018848167539267, 1.037696335078534, \ - 1.0418848167539267, 1.043979057591623, 1.0450261780104713, \ - 1.0460732984293193, 1.0471204188481675, 1.0471204188481675] - - self.assertEqual(list(p_lf.keys()), [ss_category]) - self.assertEqual(p_lf[ss_category][0], array.array('f', s_cell)) - self.assertEqual(p_lf[ss_category][1], array.array('f', p_vs_lf_time_relative)) - - v_vs_lf_time_relative = [0.8846153846153846, 0.6666666666666666, 0.4166666666666667, \ - 0.2916666666666667, 0.250000000000000, 0.250000000000000, \ - 0.20833333333333334, 0.16666666666666666, 0.16666666666666666] - self.assertEqual(list(v_lf.keys()), [ss_category]) - self.assertEqual(v_lf[ss_category], array.array('f', v_vs_lf_time_relative)) - - x_val_ref = np.array([95.9512939453125, 53.624916076660156, 143.09530639648438, - 225.0262908935547, 312.5832824707031, 427.43109130859375, - 570.1857299804688, 750.3827514648438, 1020.5431518554688]) - self.assertEqual(list(x_val.keys()), [ss_category]) - self.assertTrue(np.allclose(x_val[ss_category], x_val_ref)) - def test_dist_since_lf_pass(self): - """ Test _dist_since_lf for andrew tropical cyclone.""" - tc_track = TCTracks() + """Test _dist_since_lf for andrew tropical cyclone.""" + tc_track = tc.TCTracks() tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) track = tc_track.get_track() - track['on_land'] = ('time', coord_on_land(track.lat.values, - track.lon.values)) + track['on_land'] = ('time', coord_on_land(track.lat.values, track.lon.values)) track['dist_since_lf'] = ('time', tc._dist_since_lf(track)) - self.assertTrue(np.all(np.isnan(track.dist_since_lf.values[track.on_land == False]))) - self.assertEqual(track.dist_since_lf.values[track.on_land == False].size, 38) + msk = ~track.on_land + self.assertTrue(np.all(np.isnan(track.dist_since_lf.values[msk]))) + self.assertEqual(track.dist_since_lf.values[msk].size, 38) - self.assertTrue(track.dist_since_lf.values[-1] > - dist_to_coast(track.lat.values[-1], track.lon.values[-1])/1000) + self.assertGreater(track.dist_since_lf.values[-1], + dist_to_coast(track.lat.values[-1], track.lon.values[-1]) / 1000) self.assertEqual(1020.5431562223974, track['dist_since_lf'].values[-1]) # check distances on land always increase, in second landfall dist_on_land = track.dist_since_lf.values[track.on_land] self.assertTrue(np.all(np.diff(dist_on_land)[1:] > 0)) - def test_calc_orig_lf(self): - """ Test _calc_orig_lf for andrew tropical cyclone.""" - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) - track = tc_track.get_track() - track['on_land'] = ('time', coord_on_land(track.lat.values, - track.lon.values)) - sea_land_idx = np.where(np.diff(track.on_land.astype(int)) == 1)[0] - orig_lf = tc._calc_orig_lf(track, sea_land_idx) - - self.assertEqual(orig_lf.shape, (sea_land_idx.size, 2)) - self.assertTrue(np.array_equal(orig_lf[0], np.array([25.5, -80.25]))) - self.assertTrue(np.array_equal(orig_lf[1], np.array([29.65, -91.5]))) - - def test_decay_calc_coeff(self): - """ Test _decay_calc_coeff against MATLAB""" - x_val = {6: np.array([53.57314960249573, 142.97903059281566, - 224.76733726289183, 312.14621544207563, 426.6757021862584, - 568.9358305779094, 748.3713215157885, 1016.9904230811956])} - - v_lf = {6: np.array([0.6666666666666666, 0.4166666666666667, \ - 0.2916666666666667, 0.250000000000000, - 0.250000000000000, 0.20833333333333334, \ - 0.16666666666666666, 0.16666666666666666])} - - p_lf = {6: (8*[1.0471204188481675], np.array([1.018848167539267, 1.037696335078534, \ - 1.0418848167539267, 1.043979057591623, 1.0450261780104713, 1.0460732984293193, - 1.0471204188481675, 1.0471204188481675]))} - - v_rel, p_rel = tc._decay_calc_coeff(x_val, v_lf, p_lf) - - for i, val in enumerate(v_rel.values()): - self.assertAlmostEqual(val, 0.004222091151737) - self.assertTrue(i+1 in v_rel.keys()) - - for i, val in enumerate(p_rel.values()): - self.assertAlmostEqual(val[0], 1.047120418848168) - self.assertAlmostEqual(val[1], 0.008871782287614) - self.assertTrue(i+1 in v_rel.keys()) - - def test_apply_decay_no_landfall_pass(self): - """ Test _apply_land_decay with no historical tracks with landfall """ - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) - land_geom = tc._calc_land_geom(tc_track.data) - tc._track_land_params(tc_track.data[0], land_geom) - tc_track.data[0]['orig_event_flag']=False - tc_ref = tc_track.data[0].copy() - tc_track._apply_land_decay(dict(), dict(), land_geom) - - self.assertTrue(np.array_equal(tc_track.data[0].max_sustained_wind.values, tc_ref.max_sustained_wind.values)) - self.assertTrue(np.array_equal(tc_track.data[0].central_pressure.values, tc_ref.central_pressure.values)) - self.assertTrue(np.array_equal(tc_track.data[0].environmental_pressure.values, tc_ref.environmental_pressure.values)) - self.assertTrue(np.all(np.isnan(tc_track.data[0].dist_since_lf.values))) - - def test_apply_decay_pass(self): - """ Test _apply_land_decay against MATLAB reference. """ - v_rel = { 6: 0.0038950967656296597, - 1: 0.0038950967656296597, - 2: 0.0038950967656296597, - 3: 0.0038950967656296597, - 4: 0.0038950967656296597, - 5: 0.0038950967656296597, - 7: 0.0038950967656296597} - - p_rel = {6: (1.0499941, 0.007978940084158488), - 1: (1.0499941, 0.007978940084158488), - 2: (1.0499941, 0.007978940084158488), - 3: (1.0499941, 0.007978940084158488), - 4: (1.0499941, 0.007978940084158488), - 5: (1.0499941, 0.007978940084158488), - 7: (1.0499941, 0.007978940084158488)} - - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) - tc_track.data[0]['orig_event_flag'] = False - land_geom = tc._calc_land_geom(tc_track.data) - tc._track_land_params(tc_track.data[0], land_geom) - tc_track._apply_land_decay(v_rel, p_rel, land_geom, s_rel=True, check_plot=False) - - p_ref = np.array([1.010000000000000, 1.009000000000000, 1.008000000000000, - 1.006000000000000, 1.003000000000000, 1.002000000000000, - 1.001000000000000, 1.000000000000000, 1.000000000000000, - 1.001000000000000, 1.002000000000000, 1.005000000000000, - 1.007000000000000, 1.010000000000000, 1.010000000000000, - 1.010000000000000, 1.010000000000000, 1.010000000000000, - 1.010000000000000, 1.007000000000000, 1.004000000000000, - 1.000000000000000, 0.994000000000000, 0.981000000000000, - 0.969000000000000, 0.961000000000000, 0.947000000000000, - 0.933000000000000, 0.922000000000000, 0.930000000000000, - 0.937000000000000, 0.951000000000000, 0.947000000000000, - 0.943000000000000, 0.948000000000000, 0.946000000000000, - 0.941000000000000, 0.937000000000000, 0.955000000000000, - 0.9741457117, 0.99244068917, 1.00086729492, 1.00545853355, - 1.00818354609, 1.00941850023, 1.00986192053, 1.00998400565])*1e3 - - self.assertTrue(np.allclose(p_ref, tc_track.data[0].central_pressure.values)) - - v_ref = np.array([0.250000000000000, 0.300000000000000, 0.300000000000000, - 0.350000000000000, 0.350000000000000, 0.400000000000000, - 0.450000000000000, 0.450000000000000, 0.450000000000000, - 0.450000000000000, 0.450000000000000, 0.450000000000000, - 0.450000000000000, 0.400000000000000, 0.400000000000000, - 0.400000000000000, 0.400000000000000, 0.450000000000000, - 0.450000000000000, 0.500000000000000, 0.500000000000000, - 0.550000000000000, 0.650000000000000, 0.800000000000000, - 0.950000000000000, 1.100000000000000, 1.300000000000000, - 1.450000000000000, 1.500000000000000, 1.250000000000000, - 1.300000000000000, 1.150000000000000, 1.150000000000000, - 1.150000000000000, 1.150000000000000, 1.200000000000000, - 1.250000000000000, 1.250000000000000, 1.200000000000000, - 0.9737967353, 0.687255951, 0.4994850556, 0.3551480462, - 0.2270548036, 0.1302099557, 0.0645385918, 0.0225325851])*1e2 - - self.assertTrue(np.allclose(v_ref, tc_track.data[0].max_sustained_wind.values)) - - cat_ref = tc.set_category(tc_track.data[0].max_sustained_wind.values, tc_track.data[0].max_sustained_wind_unit) - self.assertEqual(cat_ref, tc_track.data[0].category) - - def test_func_decay_p_pass(self): - """ Test decay function for pressure with its inverse.""" - s_coef = 1.05 - b_coef = 0.04 - x_val = np.arange(0, 100, 10) - res = tc._decay_p_function(s_coef, b_coef, x_val) - b_coef_res = tc._solve_decay_p_function(s_coef, res, x_val) - - self.assertTrue(np.allclose(b_coef_res[1:], np.ones((x_val.size-1,))*b_coef)) - self.assertTrue(np.isnan(b_coef_res[0])) - - def test_func_decay_v_pass(self): - """ Test decay function for wind with its inverse.""" - a_coef = 0.04 - x_val = np.arange(0, 100, 10) - res = tc._decay_v_function(a_coef, x_val) - a_coef_res = tc._solve_decay_v_function(res, x_val) - - self.assertTrue(np.allclose(a_coef_res[1:], np.ones((x_val.size-1,))*a_coef)) - self.assertTrue(np.isnan(a_coef_res[0])) - - def test_decay_ps_value(self): - """Test the calculation of S in pressure decay.""" - on_land_idx = 5 - tr_ds = xr.Dataset() - tr_ds.coords['time'] = ('time', np.arange(10)) - tr_ds['central_pressure'] = ('time', np.arange(10, 20)) - tr_ds['environmental_pressure'] = ('time', np.arange(20, 30)) - tr_ds['on_land'] = ('time', np.zeros((10,)).astype(bool)) - tr_ds.on_land[on_land_idx] = True - p_landfall = 100 - - res = tc._calc_decay_ps_value(tr_ds, p_landfall, on_land_idx, s_rel=True) - self.assertEqual(res, float(tr_ds.environmental_pressure[on_land_idx]/p_landfall)) - res = tc._calc_decay_ps_value(tr_ds, p_landfall, on_land_idx, s_rel=False) - self.assertEqual(res, float(tr_ds.central_pressure[on_land_idx]/p_landfall)) - def test_category_pass(self): """Test category computation.""" max_sus_wind = np.array([25, 30, 35, 40, 45, 45, 45, 45, 35, 25]) @@ -540,48 +456,87 @@ def test_category_pass(self): cat = tc.set_category(max_sus_wind, max_sus_wind_unit) self.assertEqual(4, cat) - max_sus_wind = np.array([28.769475, 34.52337, 40.277265, - 46.03116, 51.785055, 51.785055, 51.785055, - 51.785055, 40.277265, 28.769475]) + max_sus_wind = np.array([ + 28.769475, 34.52337, 40.277265, 46.03116, 51.785055, 51.785055, + 51.785055, 51.785055, 40.277265, 28.769475 + ]) max_sus_wind_unit = 'mph' cat = tc.set_category(max_sus_wind, max_sus_wind_unit) self.assertEqual(0, cat) - max_sus_wind = np.array([12.86111437, 12.86111437, 12.86111437, - 15.43333724, 15.43333724, 15.43333724, - 15.43333724, 15.43333724, 12.86111437, - 12.86111437, 10.2888915]) + max_sus_wind = np.array([ + 12.86111437, 12.86111437, 12.86111437, 15.43333724, 15.43333724, + 15.43333724, 15.43333724, 15.43333724, 12.86111437, 12.86111437, + 10.2888915 + ]) max_sus_wind_unit = 'm/s' cat = tc.set_category(max_sus_wind, max_sus_wind_unit) self.assertEqual(-1, cat) - max_sus_wind = np.array([148.16, 166.68, 185.2, 212.98, 222.24, 231.5, - 240.76, 222.24, 203.72, 148.16, 138.9, 148.16, - 120.38]) + max_sus_wind = np.array([ + 148.16, 166.68, 185.2, 212.98, 222.24, 231.5, 240.76, 222.24, + 203.72, 148.16, 138.9, 148.16, 120.38 + ]) max_sus_wind_unit = 'km/h' cat = tc.set_category(max_sus_wind, max_sus_wind_unit) self.assertEqual(4, cat) - def test_missing_pres_pass(self): - """Test central pressure function.""" - cen_pres = np.array([-999, -999, -999, -999, -999, -999, -999, -999, - -999, 992, -999, -999, 993, -999, -999, 1004]) - v_max = np.array([45, 50, 50, 55, 60, 65, 70, 80, 75, 70, 70, 70, 70, - 65, 55, 45]) - lat = np.array([13.8, 13.9, 14, 14.1, 14.1, 14.1, 14.1, 14.2, 14.2, - 14.3, 14.4, 14.6, 14.8, 15, 15.1, 15.1]) - lon = np.array([-51.1, -52.8, -54.4, -56, -57.3, -58.4, -59.7, -61.1, - -62.7, -64.3, -65.8, -67.4, -69.4, -71.4, -73, -74.2]) - out_pres = tc._missing_pressure(cen_pres, v_max, lat, lon) - - ref_res = np.array([989.7085, 985.6725, 985.7236, 981.6847, 977.6324, - 973.5743, 969.522, 961.3873, 965.5237, 969.6648, - 969.713, 969.7688, 969.8362, 973.9936, 982.2247, - 990.4395]) - np.testing.assert_array_almost_equal(ref_res, out_pres) + def test_estimate_params_pass(self): + """Test track parameter estimation functions.""" + cen_pres = np.array([-999, 993, np.nan, -1, 0, 1004, np.nan]) + v_max = np.array([45, np.nan, 50, 55, 0, 60, 75]) + lat = np.array([13.8, 13.9, 14, 14.1, 14.1, np.nan, -999]) + lon = np.array([np.nan, -52.8, -54.4, -56, -58.4, -59.7, -61.1]) + ref_pres = np.array([np.nan, 993, 990.2324, 986.6072, np.nan, 1004, np.nan]) + out_pres = tc._estimate_pressure(cen_pres, lat, lon, v_max) + self.assertTrue(np.allclose(ref_pres, out_pres, equal_nan=True)) + + v_max = np.array([45, np.nan, 50, 55, 0, 60, 75]) + cen_pres = np.array([-999, 993, np.nan, -1, 0, 1004, np.nan]) + lat = np.array([13.8, 13.9, 14, 14.1, 14.1, np.nan, -999]) + lon = np.array([np.nan, -52.8, -54.4, -56, -58.4, -59.7, -61.1]) + ref_vmax = np.array([45, 46.38272, 50, 55, np.nan, 60, 75]) + out_vmax = tc._estimate_vmax(v_max, lat, lon, cen_pres) + self.assertTrue(np.allclose(ref_vmax, out_vmax, equal_nan=True)) + + roci = np.array([np.nan, -1, 145, 170, 180, 0, -5]) + cen_pres = np.array([-999, 993, np.nan, -1, 0, 1004, np.nan]) + ref_roci = np.array([np.nan, 182.792715, 145, 170, 180, 161.5231086, np.nan]) + out_roci = tc.estimate_roci(roci, cen_pres) + self.assertTrue(np.allclose(ref_roci, out_roci, equal_nan=True)) + + rmw = np.array([17, 33, -1, 25, np.nan, -5, 13]) + cen_pres = np.array([-999, 993, np.nan, -1, 0, 1004, np.nan]) + ref_rmw = np.array([17, 33, np.nan, 25, np.nan, 43.95543761, 13]) + out_rmw = tc.estimate_rmw(rmw, cen_pres) + self.assertTrue(np.allclose(ref_rmw, out_rmw, equal_nan=True)) + + def test_estimate_rmw_pass(self): + """Test estimate_rmw function.""" + NM_TO_KM = (1.0 * ureg.nautical_mile).to(ureg.kilometer).magnitude + + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK) + tc_track.equal_timestep() + rad_max_wind = tc.estimate_rmw( + tc_track.data[0].radius_max_wind.values, + tc_track.data[0].central_pressure.values) * NM_TO_KM + + self.assertAlmostEqual(rad_max_wind[0], 86.4471340900, places=5) + self.assertAlmostEqual(rad_max_wind[10], 86.525605570, places=5) + self.assertAlmostEqual(rad_max_wind[128], 55.25462781, places=5) + self.assertAlmostEqual(rad_max_wind[129], 54.40164284, places=5) + self.assertAlmostEqual(rad_max_wind[130], 53.54865787, places=5) + self.assertAlmostEqual(rad_max_wind[189], 52.62700450, places=5) + self.assertAlmostEqual(rad_max_wind[190], 54.36738477, places=5) + self.assertAlmostEqual(rad_max_wind[191], 56.10776504, places=5) + self.assertAlmostEqual(rad_max_wind[192], 57.84814530, places=5) + self.assertAlmostEqual(rad_max_wind[200], 70.00942075, places=5) + # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestFuncs) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestIO)) TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestIBTracs)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_tc_tracks_forecast.py b/climada/hazard/test/test_tc_tracks_forecast.py new file mode 100644 index 0000000000..55b23203eb --- /dev/null +++ b/climada/hazard/test/test_tc_tracks_forecast.py @@ -0,0 +1,70 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test tc_tracks_forecast module. +""" + +import os +import unittest +import numpy as np + +from climada.hazard.tc_tracks_forecast import TCForecast + +TEST_BUFR_FILES = [ + os.path.join(os.path.dirname(__file__), 'data', i) for i in [ + 'tracks_22S_HEROLD_2020031912.det.bufr4', + 'tracks_22S_HEROLD_2020031912.eps.bufr4', + ] +] +"""TC tracks in four BUFR formats as provided by ECMWF. Sourced from +https://confluence.ecmwf.int/display/FCST/New+Tropical+Cyclone+Wind+Radii+product +""" + + +class TestECMWF(unittest.TestCase): + """Test reading of BUFR TC track forecasts""" + + def test_fetch_ecmwf(self): + """Test ECMWF reader with static files""" + forecast = TCForecast() + forecast.fetch_ecmwf(TEST_BUFR_FILES) + + self.assertEqual(forecast.data[0].time.size, 2) + self.assertEqual(forecast.data[1].lat[2], -36.79) + self.assertEqual(forecast.data[0].lon[1], 73.5) + self.assertEqual(forecast.data[1].time_step[2], 42) + self.assertEqual(forecast.data[1].max_sustained_wind[2], 17.1) + self.assertEqual(forecast.data[0].central_pressure[0], 1000.) + self.assertEqual(forecast.data[0]['time.year'][0], 2020) + self.assertEqual(forecast.data[17]['time.month'][7], 3) + self.assertEqual(forecast.data[17]['time.day'][7], 21) + self.assertEqual(forecast.data[0].max_sustained_wind_unit, 'm/s') + self.assertEqual(forecast.data[0].central_pressure_unit, 'mb') + self.assertEqual(forecast.data[1].sid, '22S') + self.assertEqual(forecast.data[1].name, 'HEROLD') + self.assertEqual(forecast.data[0].basin, 'S - South-West Indian Ocean') + self.assertEqual(forecast.data[0].category, 'Tropical Depression') + self.assertEqual(forecast.data[0].forecast_time, + np.datetime64('2020-03-19T12:00:00.000000')) + self.assertEqual(forecast.data[1].is_ensemble, True) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestECMWF) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_tc_tracks_synth.py b/climada/hazard/test/test_tc_tracks_synth.py new file mode 100644 index 0000000000..1f2c17585e --- /dev/null +++ b/climada/hazard/test/test_tc_tracks_synth.py @@ -0,0 +1,432 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Test tc_tracks_synth module. +""" + +import array +import numpy as np +import os +import unittest +import xarray as xr + +import climada.hazard.tc_tracks as tc +import climada.hazard.tc_tracks_synth as tc_synth +import climada.util.coordinates +from climada.util.constants import TC_ANDREW_FL + +DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') +TEST_TRACK = os.path.join(DATA_DIR, "trac_brb_test.csv") +TEST_TRACK_SHORT = os.path.join(DATA_DIR, "trac_short_test.csv") + +class TestDecay(unittest.TestCase): + def test_apply_decay_no_landfall_pass(self): + """Test _apply_land_decay with no historical tracks with landfall""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) + extent = tc_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + tc.track_land_params(tc_track.data[0], land_geom) + tc_track.data[0]['orig_event_flag'] = False + tc_ref = tc_track.data[0].copy() + tc_synth._apply_land_decay(tc_track.data, dict(), dict(), land_geom) + + self.assertTrue(np.allclose(tc_track.data[0].max_sustained_wind.values, + tc_ref.max_sustained_wind.values)) + self.assertTrue(np.allclose(tc_track.data[0].central_pressure.values, + tc_ref.central_pressure.values)) + self.assertTrue(np.allclose(tc_track.data[0].environmental_pressure.values, + tc_ref.environmental_pressure.values)) + self.assertTrue(np.all(np.isnan(tc_track.data[0].dist_since_lf.values))) + + def test_apply_decay_pass(self): + """Test _apply_land_decay against MATLAB reference.""" + v_rel = { + 6: 0.0038950967656296597, + 1: 0.0038950967656296597, + 2: 0.0038950967656296597, + 3: 0.0038950967656296597, + 4: 0.0038950967656296597, + 5: 0.0038950967656296597, + 7: 0.0038950967656296597 + } + + p_rel = { + 6: (1.0499941, 0.007978940084158488), + 1: (1.0499941, 0.007978940084158488), + 2: (1.0499941, 0.007978940084158488), + 3: (1.0499941, 0.007978940084158488), + 4: (1.0499941, 0.007978940084158488), + 5: (1.0499941, 0.007978940084158488), + 7: (1.0499941, 0.007978940084158488) + } + + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) + tc_track.data[0]['orig_event_flag'] = False + extent = tc_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + tc.track_land_params(tc_track.data[0], land_geom) + tc_synth._apply_land_decay(tc_track.data, v_rel, p_rel, land_geom, + s_rel=True, check_plot=False) + + p_ref = np.array([ + 1.010000000000000, 1.009000000000000, 1.008000000000000, + 1.006000000000000, 1.003000000000000, 1.002000000000000, + 1.001000000000000, 1.000000000000000, 1.000000000000000, + 1.001000000000000, 1.002000000000000, 1.005000000000000, + 1.007000000000000, 1.010000000000000, 1.010000000000000, + 1.010000000000000, 1.010000000000000, 1.010000000000000, + 1.010000000000000, 1.007000000000000, 1.004000000000000, + 1.000000000000000, 0.994000000000000, 0.981000000000000, + 0.969000000000000, 0.961000000000000, 0.947000000000000, + 0.933000000000000, 0.922000000000000, 0.930000000000000, + 0.937000000000000, 0.951000000000000, 0.947000000000000, + 0.943000000000000, 0.948000000000000, 0.946000000000000, + 0.941000000000000, 0.937000000000000, 0.955000000000000, + 0.9741457117, 0.99244068917, 1.00086729492, 1.00545853355, + 1.00818354609, 1.00941850023, 1.00986192053, 1.00998400565 + ]) * 1e3 + + self.assertTrue(np.allclose(p_ref, tc_track.data[0].central_pressure.values)) + + v_ref = np.array([ + 0.250000000000000, 0.300000000000000, 0.300000000000000, + 0.350000000000000, 0.350000000000000, 0.400000000000000, + 0.450000000000000, 0.450000000000000, 0.450000000000000, + 0.450000000000000, 0.450000000000000, 0.450000000000000, + 0.450000000000000, 0.400000000000000, 0.400000000000000, + 0.400000000000000, 0.400000000000000, 0.450000000000000, + 0.450000000000000, 0.500000000000000, 0.500000000000000, + 0.550000000000000, 0.650000000000000, 0.800000000000000, + 0.950000000000000, 1.100000000000000, 1.300000000000000, + 1.450000000000000, 1.500000000000000, 1.250000000000000, + 1.300000000000000, 1.150000000000000, 1.150000000000000, + 1.150000000000000, 1.150000000000000, 1.200000000000000, + 1.250000000000000, 1.250000000000000, 1.200000000000000, + 0.9737967353, 0.687255951, 0.4994850556, 0.3551480462, 0.2270548036, + 0.1302099557, 0.0645385918, 0.0225325851 + ]) * 1e2 + + self.assertTrue(np.allclose(v_ref, tc_track.data[0].max_sustained_wind.values)) + + cat_ref = tc.set_category(tc_track.data[0].max_sustained_wind.values, + tc_track.data[0].max_sustained_wind_unit) + self.assertEqual(cat_ref, tc_track.data[0].category) + + def test_func_decay_p_pass(self): + """Test decay function for pressure with its inverse.""" + s_coef = 1.05 + b_coef = 0.04 + x_val = np.arange(0, 100, 10) + res = tc_synth._decay_p_function(s_coef, b_coef, x_val) + b_coef_res = tc_synth._solve_decay_p_function(s_coef, res, x_val) + + self.assertTrue(np.allclose(b_coef_res[1:], np.ones((x_val.size - 1,)) * b_coef)) + self.assertTrue(np.isnan(b_coef_res[0])) + + def test_func_decay_v_pass(self): + """Test decay function for wind with its inverse.""" + a_coef = 0.04 + x_val = np.arange(0, 100, 10) + res = tc_synth._decay_v_function(a_coef, x_val) + a_coef_res = tc_synth._solve_decay_v_function(res, x_val) + + self.assertTrue(np.allclose(a_coef_res[1:], np.ones((x_val.size - 1,)) * a_coef)) + self.assertTrue(np.isnan(a_coef_res[0])) + + def test_decay_ps_value(self): + """Test the calculation of S in pressure decay.""" + on_land_idx = 5 + tr_ds = xr.Dataset() + tr_ds.coords['time'] = ('time', np.arange(10)) + tr_ds['central_pressure'] = ('time', np.arange(10, 20)) + tr_ds['environmental_pressure'] = ('time', np.arange(20, 30)) + tr_ds['on_land'] = ('time', np.zeros((10,)).astype(bool)) + tr_ds.on_land[on_land_idx] = True + p_landfall = 100 + + res = tc_synth._calc_decay_ps_value(tr_ds, p_landfall, on_land_idx, s_rel=True) + self.assertEqual(res, float(tr_ds.environmental_pressure[on_land_idx] / p_landfall)) + res = tc_synth._calc_decay_ps_value(tr_ds, p_landfall, on_land_idx, s_rel=False) + self.assertEqual(res, float(tr_ds.central_pressure[on_land_idx] / p_landfall)) + + def test_calc_decay_no_landfall_pass(self): + """Test _calc_land_decay with no historical tracks with landfall""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) + extent = tc_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + tc.track_land_params(tc_track.data[0], land_geom) + with self.assertLogs('climada.hazard.tc_tracks_synth', level='INFO') as cm: + tc_synth._calc_land_decay(tc_track.data, land_geom) + self.assertIn('No historical track with landfall.', cm.output[0]) + + def test_calc_land_decay_pass(self): + """Test _calc_land_decay with environmental pressure function.""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) + extent = tc_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + tc.track_land_params(tc_track.data[0], land_geom) + v_rel, p_rel = tc_synth._calc_land_decay(tc_track.data, land_geom) + + self.assertEqual(7, len(v_rel)) + for i, val in enumerate(v_rel.values()): + self.assertAlmostEqual(val, 0.0038894834) + self.assertTrue(i + 1 in v_rel.keys()) + + self.assertEqual(7, len(p_rel)) + for i, val in enumerate(p_rel.values()): + self.assertAlmostEqual(val[0], 1.0598491) + self.assertAlmostEqual(val[1], 0.0041949237) + self.assertTrue(i + 1 in p_rel.keys()) + + def test_decay_values_andrew_pass(self): + """Test _decay_values with central pressure function.""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) + s_rel = False + extent = tc_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + tc.track_land_params(tc_track.data[0], land_geom) + v_lf, p_lf, x_val = tc_synth._decay_values(tc_track.data[0], land_geom, s_rel) + + ss_category = 6 + s_cell_1 = 1 * [1.0149413347244263] + s_cell_2 = 8 * [1.047120451927185] + s_cell = s_cell_1 + s_cell_2 + p_vs_lf_time_relative = [ + 1.0149413020277482, 1.018848167539267, 1.037696335078534, + 1.0418848167539267, 1.043979057591623, 1.0450261780104713, + 1.0460732984293193, 1.0471204188481675, 1.0471204188481675 + ] + + self.assertEqual(list(p_lf.keys()), [ss_category]) + self.assertEqual(p_lf[ss_category][0], array.array('f', s_cell)) + self.assertEqual(p_lf[ss_category][1], array.array('f', p_vs_lf_time_relative)) + + v_vs_lf_time_relative = [ + 0.8846153846153846, 0.6666666666666666, 0.4166666666666667, + 0.2916666666666667, 0.250000000000000, 0.250000000000000, + 0.20833333333333334, 0.16666666666666666, 0.16666666666666666 + ] + self.assertEqual(list(v_lf.keys()), [ss_category]) + self.assertEqual(v_lf[ss_category], array.array('f', v_vs_lf_time_relative)) + + x_val_ref = np.array([ + 95.9512939453125, 53.624916076660156, 143.09530639648438, + 225.0262908935547, 312.5832824707031, 427.43109130859375, + 570.1857299804688, 750.3827514648438, 1020.5431518554688 + ]) + self.assertEqual(list(x_val.keys()), [ss_category]) + self.assertTrue(np.allclose(x_val[ss_category], x_val_ref)) + + def test_decay_calc_coeff(self): + """Test _decay_calc_coeff against MATLAB""" + x_val = { + 6: np.array([ + 53.57314960249573, 142.97903059281566, 224.76733726289183, + 312.14621544207563, 426.6757021862584, 568.9358305779094, + 748.3713215157885, 1016.9904230811956 + ]) + } + + v_lf = { + 6: np.array([ + 0.6666666666666666, 0.4166666666666667, 0.2916666666666667, + 0.250000000000000, 0.250000000000000, 0.20833333333333334, + 0.16666666666666666, 0.16666666666666666 + ]) + } + + p_lf = { + 6: (8 * [1.0471204188481675], + np.array([ + 1.018848167539267, 1.037696335078534, 1.0418848167539267, + 1.043979057591623, 1.0450261780104713, 1.0460732984293193, + 1.0471204188481675, 1.0471204188481675 + ]) + ) + } + + v_rel, p_rel = tc_synth._decay_calc_coeff(x_val, v_lf, p_lf) + + for i, val in enumerate(v_rel.values()): + self.assertAlmostEqual(val, 0.004222091151737) + self.assertTrue(i + 1 in v_rel.keys()) + + for i, val in enumerate(p_rel.values()): + self.assertAlmostEqual(val[0], 1.047120418848168) + self.assertAlmostEqual(val[1], 0.008871782287614) + self.assertTrue(i + 1 in v_rel.keys()) + + def test_wrong_decay_pass(self): + """Test decay not implemented when coefficient < 1""" + track = tc.TCTracks() + track.read_ibtracs_netcdf(provider='usa', storm_id='1975178N28281') + + track_gen = track.data[0] + track_gen['lat'] = np.array([ + 28.20340431, 28.7915261, 29.38642458, 29.97836984, 30.56844404, + 31.16265292, 31.74820301, 32.34449825, 32.92261894, 33.47430891, + 34.01492525, 34.56789399, 35.08810845, 35.55965893, 35.94835174, + 36.29355848, 36.45379561, 36.32473812, 36.07552209, 35.92224784, + 35.84144186, 35.78298537, 35.86090718, 36.02440372, 36.37555559, + 37.06207765, 37.73197352, 37.97524273, 38.05560287, 38.21901208, + 38.31486156, 38.30813367, 38.28481808, 38.28410366, 38.25894812, + 38.20583372, 38.22741099, 38.39970022, 38.68367797, 39.08329904, + 39.41434629, 39.424984, 39.31327716, 39.30336335, 39.31714429, + 39.27031932, 39.30848775, 39.48759833, 39.73326595, 39.96187967, + 40.26954226, 40.76882202, 41.40398607, 41.93809726, 42.60395785, + 43.57074792, 44.63816143, 45.61450458, 46.68528511, 47.89209365, + 49.15580502 + ]) + track_gen['lon'] = np.array([ + -79.20514075, -79.25243311, -79.28393082, -79.32324646, + -79.36668585, -79.41495519, -79.45198688, -79.40580325, + -79.34965443, -79.36938122, -79.30294825, -79.06809546, + -78.70281969, -78.29418936, -77.82170609, -77.30034709, + -76.79004969, -76.37038827, -75.98641014, -75.58383356, + -75.18310414, -74.7974524, -74.3797645, -73.86393572, -73.37910948, + -73.01059003, -72.77051313, -72.68011328, -72.66864779, + -72.62579773, -72.56307717, -72.46607618, -72.35871353, + -72.31120649, -72.15537583, -71.75577051, -71.25287498, + -70.75527907, -70.34788946, -70.17518421, -70.04446577, + -69.76582749, -69.44372386, -69.15881376, -68.84351922, + -68.47890287, -68.04184565, -67.53541437, -66.94008642, + -66.25596075, -65.53496635, -64.83491802, -64.12962685, + -63.54118808, -62.72934383, -61.34915091, -59.72580755, + -58.24404252, -56.71972992, -55.0809336, -53.31524758 + ]) + + v_rel = { + 3: 0.002249541544102336, + 1: 0.00046889526284203036, + 4: 0.002649273787364977, + 2: 0.0016426186150461349, + 5: 0.00246400811445618, + 7: 0.0030442198547309075, + 6: 0.002346537842810565, + } + p_rel = { + 3: (1.028420239620591, 0.003174733355067952), + 1: (1.0046803184177564, 0.0007997633912500546), + 4: (1.0498749735343516, 0.0034665588904747515), + 2: (1.0140127424090262, 0.002131858515233042), + 5: (1.0619445995372885, 0.003467268426139696), + 7: (1.0894914184297835, 0.004315034379018768), + 6: (1.0714354641894077, 0.002783787561718677), + } + track_gen.attrs['orig_event_flag'] = False + + cp_ref = np.array([1012., 1012.]) + single_track = tc.TCTracks() + single_track.data = [track_gen] + extent = single_track.get_extent() + land_geom = climada.util.coordinates.get_land_geometry( + extent=extent, resolution=10 + ) + track_res = tc_synth._apply_decay_coeffs(track_gen, v_rel, p_rel, land_geom, True) + self.assertTrue(np.array_equal(cp_ref, track_res.central_pressure[9:11])) + +class TestSynth(unittest.TestCase): + def test_random_no_landfall_pass(self): + """Test calc_random_walk with decay and no historical tracks with landfall""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) + with self.assertLogs('climada.hazard.tc_tracks_synth', level='INFO') as cm: + tc_track.calc_random_walk() + self.assertIn('No historical track with landfall.', cm.output[1]) + + def test_random_walk_ref_pass(self): + """Test against MATLAB reference.""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) + ens_size = 2 + tc_track.calc_random_walk(ens_size=ens_size, seed=25, decay=False) + + self.assertEqual(len(tc_track.data), ens_size + 1) + + self.assertFalse(tc_track.data[1].orig_event_flag) + self.assertEqual(tc_track.data[1].name, '1951239N12334_gen1') + self.assertEqual(tc_track.data[1].id_no, 1.951239012334010e+12) + self.assertAlmostEqual(tc_track.data[1].lon[0].values, -25.0448138) + self.assertAlmostEqual(tc_track.data[1].lon[1].values, -26.07400903) + self.assertAlmostEqual(tc_track.data[1].lon[2].values, -27.09191673) + self.assertAlmostEqual(tc_track.data[1].lon[3].values, -28.21366632) + self.assertAlmostEqual(tc_track.data[1].lon[4].values, -29.33195465) + self.assertAlmostEqual(tc_track.data[1].lon[8].values, -34.6016857) + + self.assertAlmostEqual(tc_track.data[1].lat[0].values, 11.96825841) + self.assertAlmostEqual(tc_track.data[1].lat[4].values, 12.35820479) + self.assertAlmostEqual(tc_track.data[1].lat[5].values, 12.45465) + self.assertAlmostEqual(tc_track.data[1].lat[6].values, 12.5492937) + self.assertAlmostEqual(tc_track.data[1].lat[7].values, 12.6333804) + self.assertAlmostEqual(tc_track.data[1].lat[8].values, 12.71561952) + + self.assertFalse(tc_track.data[2].orig_event_flag) + self.assertEqual(tc_track.data[2].name, '1951239N12334_gen2') + self.assertAlmostEqual(tc_track.data[2].id_no, 1.951239012334020e+12) + self.assertAlmostEqual(tc_track.data[2].lon[0].values, -25.47658461) + self.assertAlmostEqual(tc_track.data[2].lon[3].values, -28.78978084) + self.assertAlmostEqual(tc_track.data[2].lon[4].values, -29.9568406) + self.assertAlmostEqual(tc_track.data[2].lon[8].values, -35.30222604) + + self.assertAlmostEqual(tc_track.data[2].lat[0].values, 11.82886685) + self.assertAlmostEqual(tc_track.data[2].lat[6].values, 12.26400422) + self.assertAlmostEqual(tc_track.data[2].lat[7].values, 12.3454308) + self.assertAlmostEqual(tc_track.data[2].lat[8].values, 12.42745488) + + def test_random_walk_decay_pass(self): + """Test land decay is called from calc_random_walk.""" + tc_track = tc.TCTracks() + tc_track.read_processed_ibtracs_csv(TC_ANDREW_FL) + ens_size = 2 + with self.assertLogs('climada.hazard.tc_tracks_synth', level='DEBUG') as cm: + tc_track.calc_random_walk(ens_size=ens_size, seed=25, decay=True) + self.assertIn('No historical track of category Tropical Depression ' + 'with landfall.', cm.output[1]) + self.assertIn('Decay parameters from category Hurricane Cat. 4 taken.', + cm.output[2]) + self.assertIn('No historical track of category Hurricane Cat. 1 with ' + 'landfall.', cm.output[3]) + self.assertIn('Decay parameters from category Hurricane Cat. 4 taken.', + cm.output[4]) + self.assertIn('No historical track of category Hurricane Cat. 3 with ' + 'landfall. Decay parameters from category Hurricane Cat. ' + '4 taken.', cm.output[5]) + self.assertIn('No historical track of category Hurricane Cat. 5 with ' + 'landfall.', cm.output[6]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDecay) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestSynth)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/hazard/test/test_trop_cyclone.py b/climada/hazard/test/test_trop_cyclone.py index 9a3b5c852c..a24fa29c7b 100644 --- a/climada/hazard/test/test_trop_cyclone.py +++ b/climada/hazard/test/test_trop_cyclone.py @@ -22,10 +22,10 @@ import os import unittest import numpy as np -from pint import UnitRegistry from scipy import sparse import datetime as dt +from climada.util import ureg import climada.hazard.trop_cyclone as tc from climada.hazard.tc_tracks import TCTracks from climada.hazard.trop_cyclone import TropCyclone @@ -49,12 +49,14 @@ def test_set_one_pass(self): tc_track = TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) tc_track.equal_timestep() - coastal_centr = tc.coastal_centr_idx(CENTR_TEST_BRB) - tc_haz = TropCyclone._tc_from_track(tc_track.data[0], CENTR_TEST_BRB, coastal_centr) + tc_track.data = tc_track.data[:1] + tc_haz = TropCyclone() + tc_haz.set_from_tracks(tc_track, centroids=CENTR_TEST_BRB, model='H08', + store_windfields=True) self.assertEqual(tc_haz.tag.haz_type, 'TC') self.assertEqual(tc_haz.tag.description, '') - self.assertEqual(tc_haz.tag.file_name, 'IBTrACS: 1951239N12334') + self.assertEqual(tc_haz.tag.file_name, 'Name: 1951239N12334') self.assertEqual(tc_haz.units, 'm/s') self.assertEqual(tc_haz.centroids.size, 296) self.assertEqual(tc_haz.event_id.size, 1) @@ -65,22 +67,41 @@ def test_set_one_pass(self): self.assertEqual(tc_haz.event_id[0], 1) self.assertEqual(tc_haz.event_name, ['1951239N12334']) self.assertTrue(np.array_equal(tc_haz.frequency, np.array([1]))) - self.assertTrue(isinstance(tc_haz.intensity, sparse.csr.csr_matrix)) self.assertTrue(isinstance(tc_haz.fraction, sparse.csr.csr_matrix)) - self.assertEqual(tc_haz.intensity.shape, (1, 296)) self.assertEqual(tc_haz.fraction.shape, (1, 296)) - - self.assertAlmostEqual(tc_haz.intensity[0, 100], - 38.84863159321016, 6) - self.assertEqual(tc_haz.intensity[0, 260], 0) self.assertEqual(tc_haz.fraction[0, 100], 1) self.assertEqual(tc_haz.fraction[0, 260], 0) - self.assertEqual(tc_haz.fraction.nonzero()[0].size, 280) - self.assertEqual(tc_haz.intensity.nonzero()[0].size, 280) + + self.assertTrue(isinstance(tc_haz.intensity, sparse.csr.csr_matrix)) + self.assertEqual(tc_haz.intensity.shape, (1, 296)) + self.assertEqual(np.nonzero(tc_haz.intensity)[0].size, 280) + + self.assertEqual(tc_haz.intensity[0, 260], 0) + self.assertAlmostEqual(tc_haz.intensity[0, 1], 27.08333002) + self.assertAlmostEqual(tc_haz.intensity[0, 2], 28.46008202) + self.assertAlmostEqual(tc_haz.intensity[0, 3], 25.70445069) + self.assertAlmostEqual(tc_haz.intensity[0, 100], 36.45564037) + self.assertAlmostEqual(tc_haz.intensity[0, 250], 31.60115745) + self.assertAlmostEqual(tc_haz.intensity[0, 295], 40.62433745) + + to_kn = (1.0 * ureg.meter / ureg.second).to(ureg.knot).magnitude + wind = tc_haz.intensity.toarray()[0,:] + self.assertAlmostEqual(wind[0] * to_kn, 50.08492156) + self.assertAlmostEqual(wind[80] * to_kn, 61.13812028) + self.assertAlmostEqual(wind[120] * to_kn, 41.26159439) + self.assertAlmostEqual(wind[200] * to_kn, 54.85572160) + self.assertAlmostEqual(wind[220] * to_kn, 63.99749424) + + windfields = tc_haz.windfields[0].toarray() + windfields = windfields.reshape(windfields.shape[0], -1, 2) + windfield_norms = np.linalg.norm(windfields, axis=-1).max(axis=0) + intensity = tc_haz.intensity.toarray()[0, :] + msk = (intensity > 0) + self.assertTrue(np.allclose(windfield_norms[msk], intensity[msk])) def test_set_one_file_pass(self): - """ Test set function set_from_tracks with one input.""" + """Test set function set_from_tracks with one input.""" tc_track = TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK_SHORT) tc_haz = TropCyclone() @@ -89,7 +110,7 @@ def test_set_one_file_pass(self): self.assertEqual(tc_haz.tag.haz_type, 'TC') self.assertEqual(tc_haz.tag.description, '') - self.assertEqual(tc_haz.tag.file_name, 'IBTrACS: 1951239N12334') + self.assertEqual(tc_haz.tag.file_name, 'Name: 1951239N12334') self.assertEqual(tc_haz.units, 'm/s') self.assertEqual(tc_haz.centroids.size, 296) self.assertEqual(tc_haz.event_id.size, 1) @@ -109,7 +130,7 @@ def test_set_one_file_pass(self): self.assertEqual(tc_haz.intensity.nonzero()[0].size, 0) def test_two_files_pass(self): - """ Test set function set_from_tracks with two ibtracs.""" + """Test set function set_from_tracks with two ibtracs.""" tc_track = TCTracks() tc_track.read_processed_ibtracs_csv([TEST_TRACK_SHORT, TEST_TRACK_SHORT]) tc_haz = TropCyclone() @@ -119,7 +140,7 @@ def test_two_files_pass(self): self.assertEqual(tc_haz.tag.haz_type, 'TC') self.assertEqual(tc_haz.tag.description, '') - self.assertEqual(tc_haz.tag.file_name, ['IBTrACS: 1951239N12334', 'IBTrACS: 1951239N12334']) + self.assertEqual(tc_haz.tag.file_name, ['Name: 1951239N12334', 'Name: 1951239N12334']) self.assertEqual(tc_haz.units, 'm/s') self.assertEqual(tc_haz.centroids.size, 296) self.assertEqual(tc_haz.event_id.size, 1) @@ -138,36 +159,15 @@ def test_two_files_pass(self): class TestModel(unittest.TestCase): """Test modelling of tropical cyclone""" - def test_extra_rad_max_wind_pass(self): - """ Test _extra_rad_max_wind function. Compare to MATLAB reference.""" - ureg = UnitRegistry() - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - rad_max_wind = tc._extra_rad_max_wind(tc_track.data[0].central_pressure.values, - tc_track.data[0].radius_max_wind.values, ureg) - - self.assertEqual(rad_max_wind[0], 75.536713749999905) - self.assertAlmostEqual(rad_max_wind[10], 75.592659583328057) - self.assertAlmostEqual(rad_max_wind[128], 46.686527832605236) - self.assertEqual(rad_max_wind[129], 46.089211533333405) - self.assertAlmostEqual(rad_max_wind[130], 45.672274889277276) - self.assertEqual(rad_max_wind[189], 45.132715266666672) - self.assertAlmostEqual(rad_max_wind[190], 45.979603999211285) - self.assertAlmostEqual(rad_max_wind[191], 47.287173876478825) - self.assertEqual(rad_max_wind[192], 48.875090249999985) - self.assertAlmostEqual(rad_max_wind[200], 59.975901084074955) - def test_bs_hol08_pass(self): - """" Test _bs_hol08 function. Compare to MATLAB reference.""" + """Test _bs_hol08 function. Compare to MATLAB reference.""" v_trans = 5.241999541820597 penv = 1010 pcen = 1005.263333333329 prepcen = 1005.258500000000 lat = 12.299999504631343 - xx = 0.586781395348824 tint = 1 - _bs_res = tc._bs_hol08(v_trans, penv, pcen, prepcen, lat, xx, tint) + _bs_res = tc._bs_hol08(v_trans, penv, pcen, prepcen, lat, tint) self.assertAlmostEqual(_bs_res, 1.270856908796045) v_trans = 5.123882725120426 @@ -175,237 +175,138 @@ def test_bs_hol08_pass(self): pcen = 1005.268166666671 prepcen = 1005.263333333329 lat = 12.299999279463769 - xx = 0.586794883720942 tint = 1 - _bs_res = tc._bs_hol08(v_trans, penv, pcen, prepcen, lat, xx, tint) + _bs_res = tc._bs_hol08(v_trans, penv, pcen, prepcen, lat, tint) self.assertAlmostEqual(_bs_res, 1.265551666104679) def test_stat_holland(self): - """ Test _stat_holland function. Compare to MATLAB reference.""" - r_arr = np.array([293.6067129546862, 298.2652319413182]) - r_max = 75.547902916671745 - hol_b = 1.265551666104679 - penv = 1010 - pcen = 1005.268166666671 - ycoord = 12.299999279463769 - - _v_arr = tc._stat_holland(r_arr, r_max, hol_b, penv, pcen, ycoord) + """Test _stat_holland function. Compare to MATLAB reference.""" + d_centr = np.array([[293.6067129546862, 298.2652319413182]]) + r_max = np.array([75.547902916671745]) + hol_b = np.array([1.265551666104679]) + penv = np.array([1010.0]) + pcen = np.array([1005.268166666671]) + lat = np.array([12.299999279463769]) + mask = np.ones_like(d_centr, dtype=bool) + + _v_arr = tc._stat_holland(d_centr, r_max, hol_b, penv, pcen, lat, mask)[0] self.assertAlmostEqual(_v_arr[0], 5.384115724400597) self.assertAlmostEqual(_v_arr[1], 5.281356766052531) - r_arr = np.array([]) - _v_arr = tc._stat_holland(r_arr, r_max, hol_b, penv, pcen, ycoord) + d_centr = np.array([[]]) + mask = np.ones_like(d_centr, dtype=bool) + _v_arr = tc._stat_holland(d_centr, r_max, hol_b, penv, pcen, lat, mask)[0] self.assertTrue(np.array_equal(_v_arr, np.array([]))) - r_arr = np.array([299.4501244109841, - 291.0737897183741, - 292.5441003235722]) - r_max = 40.665454622610511 - hol_b = 1.486076257880692 - penv = 1010 - pcen = 970.8727666672957 - ycoord = 14.089110370469488 - - _v_arr = tc._stat_holland(r_arr, r_max, hol_b, penv, pcen, ycoord) + d_centr = np.array([ + [299.4501244109841, 291.0737897183741, 292.5441003235722] + ]) + r_max = np.array([40.665454622610511]) + hol_b = np.array([1.486076257880692]) + penv = np.array([1010.0]) + pcen = np.array([970.8727666672957]) + lat = np.array([14.089110370469488]) + mask = np.ones_like(d_centr, dtype=bool) + + _v_arr = tc._stat_holland(d_centr, r_max, hol_b, penv, pcen, lat, mask)[0] self.assertAlmostEqual(_v_arr[0], 11.279764005440288) self.assertAlmostEqual(_v_arr[1], 11.682978583939310) self.assertAlmostEqual(_v_arr[2], 11.610940769149384) - def test_coastal_centroids_pass(self): - """ Test selection of centroids close to coast. MATLAB reference. """ - coastal = tc.coastal_centr_idx(CENTR_TEST_BRB) - - self.assertEqual(coastal.size, CENTR_TEST_BRB.size) - - def test_vtrans_correct(self): - """ Test _vtrans_correct function. Compare to MATLAB reference.""" - ureg = UnitRegistry() - i_node = 1 - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - tc_track.data[0]['radius_max_wind'] = ('time', tc._extra_rad_max_wind( - tc_track.data[0].central_pressure.values, - tc_track.data[0].radius_max_wind.values, ureg)) - r_arr = np.array([286.4938638337190, 290.5930935802884, - 295.0271327746536, 299.7811253637995, - 296.8484825705515, 274.9892882245964]) - - v_trans_corr = tc._vtrans_correct( - tc_track.data[0].lat.values[i_node:i_node+2], - tc_track.data[0].lon.values[i_node:i_node+2], - tc_track.data[0].radius_max_wind.values[i_node], - CENTR_TEST_BRB.coord[:6, :], r_arr) - - to_kn = (1* ureg.meter / ureg.second).to(ureg.knot).magnitude - - v_trans = 10.191466256012880 / to_kn - v_trans_corr *= v_trans - self.assertEqual(v_trans_corr.size, 6) - self.assertAlmostEqual(v_trans_corr[0] * to_kn, 0.06547673730228235) - self.assertAlmostEqual(v_trans_corr[1] * to_kn, 0.07106877437273672) - self.assertAlmostEqual(v_trans_corr[2] * to_kn, 0.07641714650288109) - self.assertAlmostEqual(v_trans_corr[3] * to_kn, 0.0627289214278824) - self.assertAlmostEqual(v_trans_corr[4] * to_kn, 0.0697427233582331) - self.assertAlmostEqual(v_trans_corr[5] * to_kn, 0.06855335593983322) - def test_vtrans_pass(self): - """ Test _vtrans function. Compare to MATLAB reference.""" - ureg = UnitRegistry() - i_node = 1 + """Test _vtrans function. Compare to MATLAB reference.""" tc_track = TCTracks() tc_track.read_processed_ibtracs_csv(TEST_TRACK) tc_track.equal_timestep() - v_trans = tc._vtrans(tc_track.data[0].lat.values, tc_track.data[0].lon.values, - tc_track.data[0].time_step.values, ureg) - - to_kn = (1* ureg.meter / ureg.second).to(ureg.knot).magnitude - - self.assertEqual(v_trans.size, tc_track.data[0].time.size-1) - self.assertAlmostEqual(v_trans[i_node-1]*to_kn, 10.191466256012880) - - def test_vang_sym(self): - """ Test _vang_sym function. Compare to MATLAB reference. """ - ureg = UnitRegistry() - i_node = 1 - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - tc_track.data[0]['radius_max_wind'] = ('time', tc._extra_rad_max_wind( - tc_track.data[0].central_pressure.values, - tc_track.data[0].radius_max_wind.values, ureg)) - r_arr = np.array([286.4938638337190, 290.5930935802884, - 295.0271327746536, 299.7811253637995, - 296.8484825705515, 274.9892882245964]) - v_trans = 5.2429431910897559 - v_ang = tc._vang_sym(tc_track.data[0].environmental_pressure.values[i_node], - tc_track.data[0].central_pressure.values[i_node-1:i_node+1], - tc_track.data[0].lat.values[i_node], - tc_track.data[0].time_step.values[i_node], - tc_track.data[0].radius_max_wind.values[i_node], - r_arr, v_trans, model=0) - - to_kn = (1* ureg.meter / ureg.second).to(ureg.knot).magnitude - self.assertEqual(v_ang.size, 6) - self.assertAlmostEqual(v_ang[0] * to_kn, 10.774196807905097) - self.assertAlmostEqual(v_ang[1] * to_kn, 10.591725180482094) - self.assertAlmostEqual(v_ang[2] * to_kn, 10.398212766600055) - self.assertAlmostEqual(v_ang[3] * to_kn, 10.195108683240084) - self.assertAlmostEqual(v_ang[4] * to_kn, 10.319869893291429) - self.assertAlmostEqual(v_ang[5] * to_kn, 11.305188714213809) - - def test_windfield(self): - """ Test _windfield function. Compare to MATLAB reference. """ - ureg = UnitRegistry() - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - tc_track.data[0]['radius_max_wind'] = ('time', tc._extra_rad_max_wind( - tc_track.data[0].central_pressure.values, - tc_track.data[0].radius_max_wind.values, ureg)) - coast_centr = tc.coastal_centr_idx(CENTR_TEST_BRB) - - wind = tc._windfield(tc_track.data[0], CENTR_TEST_BRB.coord, coast_centr, model=0) - - to_kn = (1* ureg.meter / ureg.second).to(ureg.knot).magnitude - self.assertEqual(wind.shape, (CENTR_TEST_BRB.size,)) - - wind = wind[coast_centr] - self.assertEqual(np.nonzero(wind)[0].size, 280) - self.assertAlmostEqual(wind[0] * to_kn, 51.16153933277889) - self.assertAlmostEqual(wind[80] * to_kn, 64.15891933409763) - self.assertAlmostEqual(wind[120] * to_kn, 41.43819201370903) - self.assertAlmostEqual(wind[200] * to_kn, 57.28814245245439) - self.assertAlmostEqual(wind[220] * to_kn, 69.62477194818004) - - def test_gust_from_track(self): - """ Test gust_from_track function. Compare to MATLAB reference. """ - tc_track = TCTracks() - tc_track.read_processed_ibtracs_csv(TEST_TRACK) - tc_track.equal_timestep() - intensity = tc.gust_from_track(tc_track.data[0], CENTR_TEST_BRB, model='H08') + v_trans, _ = tc._vtrans( + tc_track.data[0].lat.values, tc_track.data[0].lon.values, + tc_track.data[0].time_step.values) - self.assertTrue(isinstance(intensity, sparse.csr.csr_matrix)) - self.assertEqual(intensity.shape, (1, 296)) - self.assertEqual(np.nonzero(intensity)[0].size, 280) + to_kn = (1.0 * ureg.meter / ureg.second).to(ureg.knot).magnitude - self.assertEqual(intensity[0, 260], 0) - self.assertAlmostEqual(intensity[0, 1], 27.835686180065114) - self.assertAlmostEqual(intensity[0, 2], 29.46862830056694) - self.assertAlmostEqual(intensity[0, 3], 26.36829914594632) - self.assertAlmostEqual(intensity[0, 100], 38.84863159321016) - self.assertAlmostEqual(intensity[0, 250], 34.26311998266044) - self.assertAlmostEqual(intensity[0, 295], 44.273964728810924) + self.assertEqual(v_trans.size, tc_track.data[0].time.size) + self.assertEqual(v_trans[0], 0) + self.assertAlmostEqual(v_trans[1] * to_kn, 10.191466078221902) class TestClimateSce(unittest.TestCase): def test_apply_criterion_track(self): - """ Test _apply_criterion function. """ + """Test _apply_criterion function.""" criterion = list() - tmp_chg = {'criteria': {'basin': ['NA'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['NA'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.045, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) scale = 0.75 tc = TropCyclone() - tc.intensity = sparse.lil_matrix(np.zeros((4, 10))) + tc.intensity = np.zeros((4, 10)) tc.intensity[0, :] = np.arange(10) tc.intensity[1, 5] = 10 tc.intensity[2, :] = np.arange(10, 20) tc.intensity[3, 3] = 3 - tc.intensity = tc.intensity.tocsr() + tc.intensity = sparse.csr_matrix(tc.intensity) tc.basin = ['NA'] * 4 tc.basin[3] = 'WP' tc.category = np.array([2, 0, 4, 1]) tc.event_id = np.arange(4) tc_cc = tc._apply_criterion(criterion, scale) - self.assertTrue(np.allclose(tc.intensity[1, :].todense(), tc_cc.intensity[1, :].todense())) - self.assertTrue(np.allclose(tc.intensity[3, :].todense(), tc_cc.intensity[3, :].todense())) - self.assertFalse(np.allclose(tc.intensity[0, :].todense(), tc_cc.intensity[0, :].todense())) - self.assertFalse(np.allclose(tc.intensity[2, :].todense(), tc_cc.intensity[2, :].todense())) - self.assertTrue(np.allclose(tc.intensity[0, :].todense()*1.03375, tc_cc.intensity[0, :].todense())) - self.assertTrue(np.allclose(tc.intensity[2, :].todense()*1.03375, tc_cc.intensity[2, :].todense())) + self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray())) + self.assertTrue(np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray())) + self.assertTrue( + np.allclose(tc.intensity[0, :].toarray() * 1.03375, tc_cc.intensity[0, :].toarray())) + self.assertTrue( + np.allclose(tc.intensity[2, :].toarray() * 1.03375, tc_cc.intensity[2, :].toarray())) def test_two_criterion_track(self): - """ Test _apply_criterion function with two criteria """ + """Test _apply_criterion function with two criteria""" criterion = list() - tmp_chg = {'criteria': {'basin': ['NA'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['NA'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.045, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.025, 'variable': 'intensity', 'function': np.multiply} criterion.append(tmp_chg) - tmp_chg = {'criteria': {'basin': ['WP'], 'category':[1, 2, 3, 4, 5]}, + tmp_chg = {'criteria': {'basin': ['WP'], 'category': [1, 2, 3, 4, 5]}, 'year': 2100, 'change': 1.025, 'variable': 'frequency', 'function': np.multiply} criterion.append(tmp_chg) scale = 0.75 tc = TropCyclone() - tc.intensity = sparse.lil_matrix(np.zeros((4, 10))) + tc.intensity = np.zeros((4, 10)) tc.intensity[0, :] = np.arange(10) tc.intensity[1, 5] = 10 tc.intensity[2, :] = np.arange(10, 20) tc.intensity[3, 3] = 3 - tc.intensity = tc.intensity.tocsr() - tc.frequency = np.ones(4)*0.5 + tc.intensity = sparse.csr_matrix(tc.intensity) + tc.frequency = np.ones(4) * 0.5 tc.basin = ['NA'] * 4 tc.basin[3] = 'WP' tc.category = np.array([2, 0, 4, 1]) tc.event_id = np.arange(4) tc_cc = tc._apply_criterion(criterion, scale) - self.assertTrue(np.allclose(tc.intensity[1, :].todense(), tc_cc.intensity[1, :].todense())) - self.assertFalse(np.allclose(tc.intensity[3, :].todense(), tc_cc.intensity[3, :].todense())) - self.assertFalse(np.allclose(tc.intensity[0, :].todense(), tc_cc.intensity[0, :].todense())) - self.assertFalse(np.allclose(tc.intensity[2, :].todense(), tc_cc.intensity[2, :].todense())) - self.assertTrue(np.allclose(tc.intensity[0, :].todense()*1.03375, tc_cc.intensity[0, :].todense())) - self.assertTrue(np.allclose(tc.intensity[2, :].todense()*1.03375, tc_cc.intensity[2, :].todense())) - self.assertTrue(np.allclose(tc.intensity[3, :].todense()*1.01875, tc_cc.intensity[3, :].todense())) - res_frequency = np.ones(4)*0.5 - res_frequency[3] = 0.5*1.01875 + self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray())) + self.assertTrue( + np.allclose(tc.intensity[0, :].toarray() * 1.03375, tc_cc.intensity[0, :].toarray())) + self.assertTrue( + np.allclose(tc.intensity[2, :].toarray() * 1.03375, tc_cc.intensity[2, :].toarray())) + self.assertTrue( + np.allclose(tc.intensity[3, :].toarray() * 1.01875, tc_cc.intensity[3, :].toarray())) + res_frequency = np.ones(4) * 0.5 + res_frequency[3] = 0.5 * 1.01875 self.assertTrue(np.allclose(tc_cc.frequency, res_frequency)) if __name__ == "__main__": diff --git a/climada/hazard/trop_cyclone.py b/climada/hazard/trop_cyclone.py index e06585094c..8f35d0cc0e 100644 --- a/climada/hazard/trop_cyclone.py +++ b/climada/hazard/trop_cyclone.py @@ -27,36 +27,40 @@ import time import datetime as dt import numpy as np -from numpy import linalg as LA from scipy import sparse import matplotlib.animation as animation -from pint import UnitRegistry -from numba import jit from tqdm import tqdm from climada.hazard.base import Hazard from climada.hazard.tag import Tag as TagHazard -from climada.hazard.tc_tracks import TCTracks +from climada.hazard.tc_tracks import TCTracks, estimate_rmw from climada.hazard.tc_clim_change import get_knutson_criterion, calc_scale_knutson from climada.hazard.centroids.centr import Centroids -from climada.util.constants import GLB_CENTROIDS_MAT -from climada.util.interpolation import dist_approx +from climada.util import ureg +from climada.util.coordinates import dist_approx import climada.util.plot as u_plot LOGGER = logging.getLogger(__name__) HAZ_TYPE = 'TC' -""" Hazard type acronym for Tropical Cyclone """ +"""Hazard type acronym for Tropical Cyclone""" INLAND_MAX_DIST_KM = 1000 -""" Maximum inland distance of the centroids in km """ +"""Maximum inland distance of the centroids in km""" CENTR_NODE_MAX_DIST_KM = 300 -""" Maximum distance between centroid and TC track node in km """ +"""Maximum distance between centroid and TC track node in km""" -MODEL_VANG = {'H08': 0 - } -""" Enumerate different symmetric wind field calculation.""" +CENTR_NODE_MAX_DIST_DEG = 5.5 +"""Maximum distance between centroid and TC track node in degrees""" + +MODEL_VANG = {'H08': 0} +"""Enumerate different symmetric wind field calculation.""" + +KMH_TO_MS = (1.0 * ureg.km / ureg.hour).to(ureg.meter / ureg.second).magnitude +KN_TO_MS = (1.0 * ureg.knot).to(ureg.meter / ureg.second).magnitude +NM_TO_KM = (1.0 * ureg.nautical_mile).to(ureg.kilometer).magnitude +"""Unit conversion factors for JIT functions that can't use ureg""" class TropCyclone(Hazard): """Contains tropical cyclone events. @@ -80,13 +84,13 @@ class TropCyclone(Hazard): 'SA' South Atlantic """ intensity_thres = 17.5 - """ intensity threshold for storage in m/s """ + """intensity threshold for storage in m/s""" vars_opt = Hazard.vars_opt.union({'category'}) """Name of the variables that aren't need to compute the impact.""" def __init__(self, pool=None): - """Empty constructor. """ + """Empty constructor.""" Hazard.__init__(self, HAZ_TYPE) self.category = np.array([], int) self.basin = list() @@ -97,9 +101,10 @@ def __init__(self, pool=None): self.pool = None def set_from_tracks(self, tracks, centroids=None, description='', - model='H08', ignore_distance_to_coast=False): - """Clear and model tropical cyclone from input IBTrACS tracks. - Parallel process. + model='H08', ignore_distance_to_coast=False, + store_windfields=False): + """Clear and fill with windfields from specified tracks. + Parameters: tracks (TCTracks): tracks of events centroids (Centroids, optional): Centroids where to model TC. @@ -108,42 +113,64 @@ def set_from_tracks(self, tracks, centroids=None, description='', model (str, optional): model to compute gust. Default Holland2008. ignore_distance_to_coast (boolean, optional): if True, centroids far from coast are not ignored. Default False + store_windfields (boolean, optional): If True, the Hazard object + gets a list `windfields` of sparse matrices. For each track, + the full velocity vectors at each centroid and track position + are stored in a sparse matrix of shape + (npositions, ncentroids * 2), that can be reshaped to a full + ndarray of shape (npositions, ncentroids, 2). Default: False. + Raises: ValueError """ num_tracks = tracks.size if centroids is None: - centroids = Centroids() - centroids.read_mat(GLB_CENTROIDS_MAT) - if ignore_distance_to_coast: # Select centroids with lat < 61 - coastal_idx = np.logical_and(centroids.lat < 61, True).nonzero()[0] - else: # Select centroids which are inside INLAND_MAX_DIST_KM and lat < 61 - coastal_idx = coastal_centr_idx(centroids) + centroids = Centroids.from_base_grid(res_as=360, land=False) + if not centroids.coord.size: centroids.set_meta_to_lat_lon() + if ignore_distance_to_coast: + # Select centroids with lat < 61 + coastal_idx = (np.abs(centroids.lat) < 61).nonzero()[0] + else: + # Select centroids which are inside INLAND_MAX_DIST_KM and lat < 61 + if not centroids.dist_coast.size: + centroids.set_dist_coast() + coastal_idx = ((centroids.dist_coast < INLAND_MAX_DIST_KM * 1000) + & (np.abs(centroids.lat) < 61)).nonzero()[0] + LOGGER.info('Mapping %s tracks to %s centroids.', str(tracks.size), - str(centroids.size)) + str(coastal_idx.size)) if self.pool: - chunksize = min(num_tracks//self.pool.ncpus, 1000) - tc_haz = self.pool.map(self._tc_from_track, tracks.data, - itertools.repeat(centroids, num_tracks), - itertools.repeat(coastal_idx, num_tracks), - itertools.repeat(model, num_tracks), - chunksize=chunksize) + chunksize = min(num_tracks // self.pool.ncpus, 1000) + tc_haz = self.pool.map( + self._tc_from_track, tracks.data, + itertools.repeat(centroids, num_tracks), + itertools.repeat(coastal_idx, num_tracks), + itertools.repeat(model, num_tracks), + itertools.repeat(store_windfields, num_tracks), + chunksize=chunksize) else: - tc_haz = list() + last_perc = 0 + tc_haz = [] for track in tracks.data: - tc_haz.append(self._tc_from_track(track, centroids, coastal_idx, - model)) + perc = 100 * len(tc_haz) / len(tracks.data) + if perc - last_perc >= 10: + LOGGER.info("Progress: %d%%", perc) + last_perc = perc + tc_haz.append( + self._tc_from_track(track, centroids, coastal_idx, + model=model, + store_windfields=store_windfields)) LOGGER.debug('Append events.') - self._append_all(tc_haz) + self.concatenate(tc_haz) LOGGER.debug('Compute frequency.') - self._set_frequency(tracks.data) + self.frequency_from_tracks(tracks.data) self.tag.description = description def set_climate_scenario_knu(self, ref_year=2050, rcp_scenario=45): - """ Compute future events for given RCP scenario and year. RCP 4.5 + """Compute future events for given RCP scenario and year. RCP 4.5 from Knutson et al 2015. Parameters: ref_year (int): year between 2000 ad 2100. Default: 2050 @@ -163,7 +190,7 @@ def set_climate_scenario_knu(self, ref_year=2050, rcp_scenario=45): def video_intensity(track_name, tracks, centroids, file_name=None, writer=animation.PillowWriter(bitrate=500), **kwargs): - """ Generate video of TC wind fields node by node and returns its + """Generate video of TC wind fields node by node and returns its corresponding TropCyclone instances and track pieces. Parameters: @@ -187,18 +214,20 @@ def video_intensity(track_name, tracks, centroids, file_name=None, if not track: LOGGER.error('%s not found in track data.', track_name) raise ValueError - idx_plt = np.argwhere(np.logical_and(np.logical_and(np.logical_and( \ - track.lon.values < centroids.total_bounds[2] + 1, \ - centroids.total_bounds[0] - 1 < track.lon.values), \ - track.lat.values < centroids.total_bounds[3] + 1), \ - centroids.total_bounds[1] - 1 < track.lat.values)).reshape(-1) + idx_plt = np.argwhere( + (track.lon.values < centroids.total_bounds[2] + 1) + & (centroids.total_bounds[0] - 1 < track.lon.values) + & (track.lat.values < centroids.total_bounds[3] + 1) + & (centroids.total_bounds[1] - 1 < track.lat.values) + ).reshape(-1) tc_list = [] - tr_coord = {'lat':[], 'lon':[]} - for node in range(idx_plt.size-2): - tr_piece = track.sel(time=slice(track.time.values[idx_plt[node]], \ - track.time.values[idx_plt[node+2]])) - tr_piece.attrs['n_nodes'] = 2 # plot only one node + tr_coord = {'lat': [], 'lon': []} + for node in range(idx_plt.size - 2): + tr_piece = track.sel( + time=slice(track.time.values[idx_plt[node]], + track.time.values[idx_plt[node + 2]])) + tr_piece.attrs['n_nodes'] = 2 # plot only one node tr_sel = TCTracks() tr_sel.append(tr_piece) tr_coord['lat'].append(tr_sel.data[0].lat.values[:-1]) @@ -206,8 +235,13 @@ def video_intensity(track_name, tracks, centroids, file_name=None, tc_tmp = TropCyclone() tc_tmp.set_from_tracks(tr_sel, centroids) - tc_tmp.event_name = [track.name + ' ' + time.strftime("%d %h %Y %H:%M", \ - time.gmtime(tr_sel.data[0].time[1].values.astype(int)/1000000000))] + tc_tmp.event_name = [ + track.name + ' ' + time.strftime( + "%d %h %Y %H:%M", + time.gmtime(tr_sel.data[0].time[1].values.astype(int) + / 1000000000) + ) + ] tc_list.append(tc_tmp) if 'cmap' not in kwargs: @@ -226,69 +260,88 @@ def run(node): if file_name: LOGGER.info('Generating video %s', file_name) fig, axis = u_plot.make_map() - pbar = tqdm(total=idx_plt.size-2) - ani = animation.FuncAnimation(fig, run, frames=idx_plt.size-2, + pbar = tqdm(total=idx_plt.size - 2) + ani = animation.FuncAnimation(fig, run, frames=idx_plt.size - 2, interval=500, blit=False) ani.save(file_name, writer=writer) pbar.close() return tc_list, tr_coord - def _set_frequency(self, tracks): + def frequency_from_tracks(self, tracks): """Set hazard frequency from tracks data. + Parameters: - tracks (list(xr.Dataset)) + tracks (list of xarray.Dataset) """ if not tracks: return - delta_time = np.max([np.max(track.time.dt.year.values) \ - for track in tracks]) - np.min([np.min(track.time.dt.year.values) \ - for track in tracks]) + 1 - num_orig = self.orig.nonzero()[0].size - if num_orig > 0: - ens_size = self.event_id.size / num_orig - else: - ens_size = 1 - self.frequency = np.ones(self.event_id.size) / delta_time / ens_size + year_max = np.amax([t.time.dt.year.values.max() for t in tracks]) + year_min = np.amin([t.time.dt.year.values.min() for t in tracks]) + year_delta = year_max - year_min + 1 + num_orig = np.count_nonzero(self.orig) + ens_size = (self.event_id.size / num_orig) if num_orig > 0 else 1 + self.frequency = np.ones(self.event_id.size) / (year_delta * ens_size) + + def _tc_from_track(self, track, centroids, coastal_idx, model='H08', + store_windfields=False): + """Generate windfield hazard from a single track dataset - @staticmethod - @jit - def _tc_from_track(track, centroids, coastal_centr, model='H08'): - """ Set hazard from input file. If centroids are not provided, they are - read from the same file. Parameters: - track (xr.Dataset): tropical cyclone track. - centroids (Centroids): Centroids instance. Use global - centroids if not provided. - coastal_centr (np.array): indeces of centroids close to coast. - model (str, optional): model to compute gust. Default Holland2008. + track (xr.Dataset): single tropical cyclone track. + centroids (Centroids): Centroids instance. + coastal_idx (np.array): Indices of centroids close to coast. + model (str, optional): Windfield model. Default: H08. + store_windfields (boolean, optional): If True, store windfields. + Default: False. + Raises: ValueError, KeyError + Returns: TropCyclone """ + try: + mod_id = MODEL_VANG[model] + except KeyError: + LOGGER.error('Model not implemented: %s.', model) + raise ValueError + ncentroids = centroids.coord.shape[0] + coastal_centr = centroids.coord[coastal_idx] + windfields = compute_windfields(track, coastal_centr, mod_id) + npositions = windfields.shape[0] + intensity = np.zeros(ncentroids) + intensity[coastal_idx] = np.linalg.norm(windfields, axis=-1)\ + .max(axis=0) + intensity[intensity < self.intensity_thres] = 0 + new_haz = TropCyclone() - new_haz.tag = TagHazard(HAZ_TYPE, 'IBTrACS: ' + track.name) - new_haz.intensity = gust_from_track(track, centroids, coastal_centr, - model) + new_haz.tag = TagHazard(HAZ_TYPE, 'Name: ' + track.name) + new_haz.intensity = sparse.csr_matrix(intensity.reshape(1, -1)) + if store_windfields: + wf_full = np.zeros((npositions, ncentroids, 2)) + wf_full[:, coastal_idx, :] = windfields + new_haz.windfields = [ + sparse.csr_matrix(wf_full.reshape(npositions, -1))] new_haz.units = 'm/s' new_haz.centroids = centroids new_haz.event_id = np.array([1]) - # frequency set when all tracks available new_haz.frequency = np.array([1]) new_haz.event_name = [track.sid] new_haz.fraction = new_haz.intensity.copy() new_haz.fraction.data.fill(1) - # store date of start - new_haz.date = np.array([dt.datetime( - track.time.dt.year[0], track.time.dt.month[0], - track.time.dt.day[0]).toordinal()]) + # store first day of track as date + new_haz.date = np.array([ + dt.datetime(track.time.dt.year[0], + track.time.dt.month[0], + track.time.dt.day[0]).toordinal() + ]) new_haz.orig = np.array([track.orig_event_flag]) new_haz.category = np.array([track.category]) new_haz.basin = [track.basin] return new_haz def _apply_criterion(self, criterion, scale): - """ Apply changes defined in criterion with a given scale + """Apply changes defined in criterion with a given scale Parameters: criterion (list(dict)): list of criteria scale (float): scale parameter because of chosen year and RCP @@ -304,7 +357,7 @@ def _apply_criterion(self, criterion, scale): if isinstance(var_val, list): var_val = np.array(var_val) tmp_select = np.logical_or.reduce([var_val == val for val in cri_val]) - select = np.logical_and(select, tmp_select) + select = select & tmp_select if chg['function'] == np.multiply: change = 1 + (chg['change'] - 1) * scale elif chg['function'] == np.add: @@ -315,234 +368,195 @@ def _apply_criterion(self, criterion, scale): setattr(haz_cc, chg['variable'], new_val) return haz_cc -def coastal_centr_idx(centroids, lat_max=61): - """ Compute centroids indices which are inside INLAND_MAX_DIST_KM and - with lat < lat_max. - Parameters: - lat_max (float, optional): Maximum latitude to consider. Default: 61. - Returns: - np.array - """ - if not centroids.dist_coast.size: - centroids.set_dist_coast() - return np.logical_and(centroids.dist_coast < INLAND_MAX_DIST_KM*1000, - centroids.lat < lat_max).nonzero()[0] - -def gust_from_track(track, centroids, coastal_idx=None, model='H08'): - """ Compute wind gusts at centroids from track. Track is interpolated to - configured time step. - Parameters: - track (xr.Dataset): track infomation - centroids (Centroids): centroids where gusts are computed - coastal_idx (np.array): indices of centroids which are close to coast - model (str, optional): model to compute gust. Default Holland2008 - Returns: - sparse.csr_matrix - """ - if coastal_idx is None: - coastal_idx = coastal_centr_idx(centroids) - try: - mod_id = MODEL_VANG[model] - except KeyError: - LOGGER.error('Not implemented model %s.', model) - raise ValueError - # Compute wind gusts - intensity = _windfield(track, centroids.coord, coastal_idx, mod_id) - return sparse.csr_matrix(intensity) - -@jit -def _windfield(track, centroids, coastal_idx, model): - """ Compute windfields (in m/s) in centroids using Holland model 08. +def compute_windfields(track, centroids, model): + """Compute 1-minute sustained winds (in m/s) at 10 meters above ground Parameters: track (xr.Dataset): track infomation centroids (2d np.array): each row is a centroid [lat, lon] - coastal_idx (1d np.array): centroids indices that are close to coast model (int): Holland model selection according to MODEL_VANG Returns: np.array """ - np.warnings.filterwarnings('ignore') - # Make sure that CentralPressure never exceeds EnvironmentalPressure - up_pr = np.argwhere(track.central_pressure.values > - track.environmental_pressure.values) - track.central_pressure.values[up_pr] = \ - track.environmental_pressure.values[up_pr] - - # Extrapolate RadiusMaxWind from pressure if not given - ureg = UnitRegistry() - track['radius_max_wind'] = ('time', _extra_rad_max_wind( \ - track.central_pressure.values, track.radius_max_wind.values, ureg)) - - # Track translational speed at every node - v_trans = _vtrans(track.lat.values, track.lon.values, - track.time_step.values, ureg) - - # Compute windfield - intensity = np.zeros((centroids.shape[0], )) - intensity[coastal_idx] = _wind_per_node(centroids[coastal_idx, :], track, - v_trans, model) - - return intensity - -@jit -def _vtrans(t_lat, t_lon, t_tstep, ureg): - """ Translational spped at every track node. - - Parameters: - t_lat (np.array): track latitudes - t_lon (np.array): track longitudes - t_tstep (np.array): track time steps - ureg (UnitRegistry): units handler - - Returns: - np.array - """ - v_trans = dist_approx(t_lat[:-1], t_lon[:-1], - np.cos(np.radians(t_lat[:-1])), t_lat[1:], - t_lon[1:]) / t_tstep[1:] - v_trans = (v_trans * ureg.km/ureg.hour).to(ureg.meter/ureg.second).magnitude - - # nautical miles/hour, limit to 30 nmph - v_max = (30*ureg.knot).to(ureg.meter/ureg.second).magnitude - v_trans[v_trans > v_max] = v_max - return v_trans + # copies of track data + t_lat, t_lon, t_tstep, t_rad, t_env, t_cen = [ + track[ar].values.copy() for ar in ['lat', 'lon', 'time_step', 'radius_max_wind', + 'environmental_pressure', 'central_pressure'] + ] + + ncentroids = centroids.shape[0] + npositions = t_lat.shape[0] + windfields = np.zeros((npositions, ncentroids, 2)) + + if t_lon.size < 2: + return windfields + + # never use longitudes at -180 degrees or below + t_lon[t_lon <= -180] += 360 + + # only use longitudes above 180, if 180 degree border is crossed + if t_lon.min() > 180: + t_lon -= 360 + + # restrict to centroids in rectangular bounding box around track + track_centr_msk = _close_centroids(t_lat, t_lon, centroids) + track_centr_idx = track_centr_msk.nonzero()[0] + track_centr = centroids[track_centr_msk] + + if track_centr.shape[0] == 0: + return windfields + + # compute distances and vectors to all centroids + d_centr, v_centr = [ar[0] for ar in dist_approx( + t_lat[None], t_lon[None], + track_centr[None, :, 0], track_centr[None, :, 1], + log=True, method="geosphere")] + + # exclude centroids that are too far from or too close to the eye + close_centr = (d_centr < CENTR_NODE_MAX_DIST_KM) & (d_centr > 1e-2) + if not np.any(close_centr): + return windfields + v_centr_normed = np.zeros_like(v_centr) + v_centr_normed[close_centr] = v_centr[close_centr] / d_centr[close_centr, None] + + # make sure that central pressure never exceeds environmental pressure + pres_exceed_msk = (t_cen > t_env) + t_cen[pres_exceed_msk] = t_env[pres_exceed_msk] + + # extrapolate radius of max wind from pressure if not given + t_rad[:] = estimate_rmw(t_rad, t_cen) * NM_TO_KM + + # translational speed of track at every node + v_trans = _vtrans(t_lat, t_lon, t_tstep) + v_trans_norm = v_trans[0] + + # adjust pressure at previous track point + prev_pres = t_cen[:-1].copy() + msk = (prev_pres < 850) + prev_pres[msk] = t_cen[1:][msk] + + # compute b-value + if model == 0: + hol_b = _bs_hol08(v_trans_norm[1:], t_env[1:], t_cen[1:], prev_pres, + t_lat[1:], t_tstep[1:]) + else: + raise NotImplementedError -@jit -def _extra_rad_max_wind(t_cen, t_rad, ureg): - """ Extrapolate RadiusMaxWind from pressure and change to km. + # derive angular velocity + v_ang_norm = _stat_holland(d_centr[1:], t_rad[1:], hol_b, t_env[1:], + t_cen[1:], t_lat[1:], close_centr[1:]) + hemisphere = 'N' + if np.count_nonzero(t_lat < 0) > np.count_nonzero(t_lat > 0): + hemisphere = 'S' + v_ang_rotate = [1.0, -1.0] if hemisphere == 'N' else [-1.0, 1.0] + v_ang_dir = np.array(v_ang_rotate)[..., :] * v_centr_normed[1:, :, ::-1] + v_ang = np.zeros_like(v_ang_dir) + v_ang[close_centr[1:]] = v_ang_norm[close_centr[1:], None] \ + * v_ang_dir[close_centr[1:]] + + # Influence of translational speed decreases with distance from eye. + # The "absorbing factor" is according to the following paper (see Fig. 7): + # + # Mouton, F., & Nordbeck, O. (1999). Cyclone Database Manager. A tool + # for converting point data from cyclone observations into tracks and + # wind speed profiles in a GIS. UNED/GRID-Geneva. + # https://unepgrid.ch/en/resource/19B7D302 + # + t_rad_bc = np.broadcast_arrays(t_rad[:, None], d_centr)[0] + v_trans_corr = np.zeros_like(d_centr) + v_trans_corr[close_centr] = np.fmin(1, t_rad_bc[close_centr] / d_centr[close_centr]) + + # add angular and corrected translational velocity vectors + v_full = v_trans[1][1:, None, :] * v_trans_corr[1:, :, None] + v_ang + v_full[np.isnan(v_full)] = 0 + + windfields[1:, track_centr_idx, :] = v_full + return windfields + +def _close_centroids(t_lat, t_lon, centroids): + """Choose centroids within padded rectangular region around track Parameters: - t_cen (np.array): track central pressures - t_rad (np.array): track radius of maximum wind - ureg (UnitRegistry): units handler + t_lat (np.array): latitudinal coordinates of track points + t_lon (np.array): longitudinal coordinates of track points + centroids (np.array): coordinates of centroids to check Returns: - np.array + np.array (mask) """ - # TODO: always extrapolate???!!! - # rmax thresholds in nm - rmax_1, rmax_2, rmax_3 = 15, 25, 50 - # pressure in mb - pres_1, pres_2, pres_3 = 950, 980, 1020 - t_rad[t_cen <= pres_1] = rmax_1 - - to_change = np.logical_and(t_cen > pres_1, t_cen <= pres_2).nonzero()[0] - t_rad[to_change] = (t_cen[to_change] - pres_1) * \ - (rmax_2 - rmax_1)/(pres_2 - pres_1) + rmax_1 - - to_change = np.argwhere(t_cen > pres_2).squeeze() - t_rad[to_change] = (t_cen[to_change] - pres_2) * \ - (rmax_3 - rmax_2)/(pres_3 - pres_2) + rmax_2 - - return (t_rad * ureg.nautical_mile).to(ureg.kilometer).magnitude - -@jit(parallel=True) -def _wind_per_node(coastal_centr, track, v_trans, model): - """ Compute sustained winds at each centroid. - - Parameters: - coastal_centr (2d np.array): centroids - track (xr.Dataset): track latitudes - v_trans (np.array): track translational velocity - model (int): Holland model selection according to MODEL_VANG - - Returns: - 2d np.array + if (t_lon < -170).any() and (t_lon > 170).any(): + # crosses 180 degrees east/west -> use positive degrees east + t_lon[t_lon < 0] += 360 + + track_bounds = np.array([t_lon.min(), t_lat.min(), t_lon.max(), t_lat.max()]) + track_bounds[:2] -= CENTR_NODE_MAX_DIST_DEG + track_bounds[2:] += CENTR_NODE_MAX_DIST_DEG + if track_bounds[2] > 180: + # crosses 180 degrees East/West + track_bounds[2] -= 360 + + centr_lat, centr_lon = centroids[:, 0], centroids[:, 1] + msk_lat = (track_bounds[1] < centr_lat) & (centr_lat < track_bounds[3]) + if track_bounds[2] < track_bounds[0]: + # crosses 180 degrees East/West + msk_lon = (track_bounds[0] < centr_lon) | (centr_lon < track_bounds[2]) + else: + msk_lon = (track_bounds[0] < centr_lon) & (centr_lon < track_bounds[2]) + return msk_lat & msk_lon + +def _vtrans(t_lat, t_lon, t_tstep): + """Translational vector and velocity at each track node. + + Parameters + ---------- + t_lat : np.array + track latitudes + t_lon : np.array + track longitudes + t_tstep : np.array + track time steps + + Returns + ------- + v_trans_norm : np.array + Same shape as input, the first velocity is always 0. + v_trans : np.array + Directional vectors of velocity. """ + v_trans = np.zeros((t_lat.size, 2)) + v_trans_norm = np.zeros((t_lat.size,)) + norm, vec = dist_approx(t_lat[:-1, None], t_lon[:-1, None], + t_lat[1:, None], t_lon[1:, None], + log=True, method="geosphere") + v_trans[1:, :] = vec[:, 0, 0] + v_trans[1:, :] *= KMH_TO_MS / t_tstep[1:, None] + v_trans_norm[1:] = norm[:, 0, 0] + v_trans_norm[1:] *= KMH_TO_MS / t_tstep[1:] - t_lat, t_lon = track.lat.values, track.lon.values - t_rad, t_env = track.radius_max_wind.values, track.environmental_pressure.values - t_cen, t_tstep = track.central_pressure.values, track.time_step.values - - centr_cos_lat = np.cos(np.radians(coastal_centr[:, 0])) - intensity = np.zeros((coastal_centr.shape[0],)) - - n_nodes = t_lat.size - if 'n_nodes' in track.attrs: - n_nodes = track.attrs['n_nodes'] - - for i_node in range(1, n_nodes): - # compute distance to all centroids - r_arr = dist_approx(coastal_centr[:, 0], coastal_centr[:, 1], \ - centr_cos_lat, t_lat[i_node], t_lon[i_node]) - - # Choose centroids that are close enough - close_centr = np.argwhere(r_arr < CENTR_NODE_MAX_DIST_KM).reshape(-1,) - r_arr = r_arr[close_centr] + # limit to 30 nautical miles per hour + msk = (v_trans_norm > 30 * KN_TO_MS) + fact = 30 * KN_TO_MS / v_trans_norm[msk] + v_trans[msk, :] *= fact[:, None] + v_trans_norm[msk] *= fact + return v_trans_norm, v_trans - # translational component - if i_node < t_lat.size-1: - v_trans_corr = _vtrans_correct(t_lat[i_node:i_node+2], \ - t_lon[i_node:i_node+2], t_rad[i_node], \ - coastal_centr[close_centr, :], r_arr) - else: - v_trans_corr = np.zeros((r_arr.size,)) +def _bs_hol08(v_trans, penv, pcen, prepcen, lat, tint): + """Holland's 2008 b-value computation for sustained surface winds - # angular component - v_ang = _vang_sym(t_env[i_node], t_cen[i_node-1:i_node+1], - t_lat[i_node], t_tstep[i_node], t_rad[i_node], - r_arr, v_trans[i_node-1], model) + The parameter applies to 1-minute sustained winds at 10 meters above ground. + It is taken from equation (11) in the following paper: - v_full = v_trans[i_node-1] * v_trans_corr + v_ang - v_full[np.isnan(v_full)] = 0 - v_full[v_full < TropCyclone.intensity_thres] = 0 + Holland, G. (2008). A revised hurricane pressure-wind model. Monthly + Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 - # keep maximum instantaneous wind - intensity[close_centr] = np.maximum(intensity[close_centr], v_full) + For reference, it reads - return intensity + b_s = -4.4 * 1e-5 * (penv - pcen)^2 + 0.01 * (penv - pcen) + + 0.03 * (dp/dt) - 0.014 * |lat| + 0.15 * (v_trans)^hol_xx + 1.0 -@jit -def _vtrans_correct(t_lats, t_lons, t_rad, close_centr, r_arr): - """ Compute Hollands translational wind corrections. Returns factor. - - Parameters: - t_lats (tuple): current and next latitude - t_lats (tuple): current and next longitude - t_rad (float): current radius of maximum wind - close_centr (np.array): centroids - r_arr (np.array): distance from current node to all centroids - - Returns: - np.array - """ - # we use the scalar product of the track forward vector and the vector - # towards each centroid to figure the angle between and hence whether - # the translational wind needs to be added (on the right side of the - # track for Northern hemisphere) and to which extent (100% exactly 90 - # to the right of the track, zero in front of the track) - lon, nex_lon = t_lons - lat, nex_lat = t_lats - - # hence, rotate track forward vector 90 degrees clockwise, i.e. - node_dy = -nex_lon + lon - node_dx = nex_lat - lat - - # the vector towards each centroid - centroids_dlon = close_centr[:, 1] - lon - centroids_dlat = close_centr[:, 0] - lat - - # scalar product, a*b=|a|*|b|*cos(phi), phi angle between vectors - cos_phi = (centroids_dlon * node_dx + centroids_dlat * node_dy) / \ - LA.norm([centroids_dlon, centroids_dlat], axis=0) / LA.norm([node_dx, node_dy]) - - # southern hemisphere - if lat < 0: - cos_phi = -cos_phi - - # calculate v_trans wind field array assuming that - # - effect of v_trans decreases with distance from eye (r_arr_normed) - # - v_trans is added 100% to the right of the track, 0% in front (cos_phi) - r_arr_normed = t_rad / r_arr - r_arr_normed[r_arr_normed > 1] = 1 - - return np.multiply(r_arr_normed, cos_phi) - -@jit(['f8(f8, f8, f8, f8, f8, f8, f8)'], nopython=True) -def _bs_hol08(v_trans, penv, pcen, prepcen, lat, hol_xx, tint): - """ Halland's 2008 b value computation. + where `dp/dt` is the time derivative of central pressure and `hol_xx` is + Holland's x parameter: hol_xx = 0.6 * (1 - (penv - pcen) / 215) Parameters: v_trans (float): translational wind (m/s) @@ -550,68 +564,64 @@ def _bs_hol08(v_trans, penv, pcen, prepcen, lat, hol_xx, tint): pcen (float): central pressure (hPa) prepcen (float): previous central pressure (hPa) lat (float): latitude (degrees) - hol_xx (float): Holland's xx value tint (float): time step (h) Returns: float """ - return -4.4e-5 * (penv - pcen)**2 + 0.01 * (penv-pcen) + \ + hol_xx = 0.6 * (1. - (penv - pcen) / 215) + hol_b = -4.4e-5 * (penv - pcen)**2 + 0.01 * (penv - pcen) + \ 0.03 * (pcen - prepcen) / tint - 0.014 * abs(lat) + \ 0.15 * v_trans**hol_xx + 1.0 + return np.clip(hol_b, 1, 2.5) + +def _stat_holland(d_centr, r_max, hol_b, penv, pcen, lat, close_centr): + """Holland symmetric and static wind field (in m/s) + + Because recorded winds are less reliable than pressure, recorded wind speeds + are not used, but 1-min sustained surface winds are estimated from central + pressure using the formula `v_m = ((b_s / (rho * e)) * (penv - pcen))^0.5`, + see equation (11) in the following paper: -@jit(nopython=True) -def _stat_holland(r_arr, r_max, hol_b, penv, pcen, ycoord): - """ Holland symmetric and static wind field (in m/s) according to - Holland1980 or Holland2008m depending on hol_b parameter. + Holland, G. (2008). A revised hurricane pressure-wind model. Monthly + Weather Review, 136(9), 3432–3445. https://doi.org/10.1175/2008MWR2395.1 + + Depending on the hol_b parameter, the resulting model is according to + Holland (1980), which models gradient winds, or Holland (2008), which models + surface winds at 10 meters above ground. Parameters: - r_arr (np.array): distance between coastal centroids and track node - r_max (float): radius_max_wind - hol_b (float): Holland's b parameter - penv (float): environmental pressure - pcen (float): central pressure - ycoord (float): latitude + d_centr (2d np.array): distance between coastal centroids and track node + r_max (1d np.array): radius_max_wind along track + hol_b (1d np.array): Holland's b parameter along track + penv (1d np.array): environmental pressure along track + pcen (1d np.array): central pressure along track + lat (1d np.array): latitude along track + close_centr (2d np.array): mask Returns: np.array """ + v_ang = np.zeros_like(d_centr) + r_max, hol_b, lat, penv, pcen, d_centr = [ + ar[close_centr] for ar in np.broadcast_arrays( + r_max[:, None], hol_b[:, None], lat[:, None], + penv[:, None], pcen[:, None], d_centr) + ] + + # air density rho = 1.15 - f_val = 2 * 0.0000729 * np.sin(np.radians(np.abs(ycoord))) - r_arr_mult = 0.5 * 1000 * r_arr * f_val - # units are m/s - r_max_norm = (r_max/r_arr)**hol_b - return np.sqrt(100 * hol_b / rho * r_max_norm * (penv - pcen) * - np.exp(-r_max_norm) + r_arr_mult**2) - r_arr_mult - -@jit(nopython=True) -def _vang_sym(t_env, t_cens, t_lat, t_step, t_rad, r_arr, v_trans, model): - """ Compute symmetric and static wind field (in m/s) filed (angular - wind component. - Parameters: - t_env (float): environmental pressures - t_cens (tuple): previous and current central pressures - t_lat (float): latitude - t_tstep (float): time steps - t_rad (float): radius of maximum wind - r_arr (np.array): distance from current node to all centroids - v_trans (float): translational wind field - model (int): Holland model to use, default 2008. + # Coriolis force parameter + f_val = 2 * 0.0000729 * np.sin(np.radians(np.abs(lat))) - Returns: - np.array - """ - # data for windfield calculation - prev_pres, pres = t_cens - hol_xx = 0.6 * (1. - (t_env - pres) / 215) - if model == 0: - # adjust pressure at previous track point - if prev_pres < 850: - prev_pres = pres - hol_b = _bs_hol08(v_trans, t_env, pres, prev_pres, t_lat, hol_xx, t_step) - else: - # TODO H80: b=b_value(v_trans,vmax,penv,pcen,rho); - raise NotImplementedError + # d_centr is in km, convert to m and apply Coriolis force factor + d_centr_mult = 0.5 * 1000 * d_centr * f_val + + # the factor 100 is from conversion between mbar and pascal + r_max_norm = (r_max / d_centr)**hol_b + sqrt_term = 100 * hol_b / rho * r_max_norm * (penv - pcen) \ + * np.exp(-r_max_norm) + d_centr_mult**2 - return _stat_holland(r_arr, t_rad, hol_b, t_env, pres, t_lat) + v_ang[close_centr] = np.sqrt(np.fmax(0, sqrt_term)) - d_centr_mult + return v_ang diff --git a/climada/test/test_LitPop.py b/climada/test/test_LitPop.py index c753b422cf..b99e93bc34 100644 --- a/climada/test/test_LitPop.py +++ b/climada/test/test_LitPop.py @@ -41,9 +41,9 @@ def test_switzerland300_pass(self): ent.set_country(country_name, res_arcsec=resolution, fin_mode=fin_mode) # print(cm) self.assertIn('Generating LitPop data at a resolution of 300 arcsec', cm.output[0]) - self.assertTrue(ent.region_id.min() == 756) - self.assertTrue(ent.region_id.max() == 756) - self.assertTrue(np.int(ent.value.sum().round()) == 3356544363676) + self.assertEqual(ent.region_id.min(), 756) + self.assertEqual(ent.region_id.max(), 756) + self.assertEqual(np.int(ent.value.sum().round()), 3356545986884) self.assertIn('LitPop for Switzerland at 300 as, year=2016', ent.tag.description) self.assertIn('financial mode=income_group', ent.tag.description) self.assertIn('GPW-year=2015', ent.tag.description) @@ -68,13 +68,13 @@ def test_switzerland30normPop_pass(self): fin_mode = 'norm' ent = LitPop() with self.assertLogs('climada.entity.exposures.litpop', level='INFO') as cm: - ent.set_country(country_name, res_arcsec=resolution, exponent=exp,\ + ent.set_country(country_name, res_arcsec=resolution, exponent=exp, fin_mode=fin_mode, reference_year=2015) # print(cm) self.assertIn('Generating LitPop data at a resolution of 30 arcsec', cm.output[0]) - self.assertTrue(ent.region_id.min() == 756) - self.assertTrue(ent.region_id.max() == 756) - self.assertTrue(np.int((1000*ent.value.sum()).round()) == 1000) + self.assertEqual(ent.region_id.min(), 756) + self.assertEqual(ent.region_id.max(), 756) + self.assertEqual(np.int((1000 * ent.value.sum()).round()), 1000) def test_suriname30_nfw_pass(self): """Create LitPop entity for Suriname for non-finanical wealth:""" @@ -85,9 +85,9 @@ def test_suriname30_nfw_pass(self): ent.set_country(country_name, reference_year=2016, fin_mode=fin_mode) # print(cm) self.assertIn('Generating LitPop data at a resolution of 30.0 arcsec', cm.output[0]) - self.assertTrue(ent.region_id.min() == 740) - self.assertTrue(ent.region_id.max() == 740) - self.assertTrue(np.int(ent.value.sum().round()) == 2331978807) + self.assertEqual(ent.region_id.min(), 740) + self.assertEqual(ent.region_id.max(), 740) + self.assertEqual(np.int(ent.value.sum().round()), 2304662017) def test_switzerland300_pc2016_pass(self): """Create LitPop entity for Switzerland 2016 with admin1 and produced capital:""" @@ -97,17 +97,16 @@ def test_switzerland300_pc2016_pass(self): ref_year = 2016 adm1 = True cons = True - comparison_total_val = world_bank_wealth_account(country_name[0], ref_year, \ - no_land=1)[1] + comparison_total_val = world_bank_wealth_account(country_name[0], ref_year, no_land=1)[1] ent = LitPop() with self.assertLogs('climada.entity.exposures.litpop', level='INFO') as cm: - ent.set_country(country_name, res_arcsec=resolution, \ - reference_year=ref_year, fin_mode=fin_mode, \ + ent.set_country(country_name, res_arcsec=resolution, + reference_year=ref_year, fin_mode=fin_mode, conserve_cntrytotal=cons, calc_admin1=adm1) # print(cm) self.assertIn('Generating LitPop data at a resolution of 300 arcsec', cm.output[0]) - self.assertTrue(np.around(ent.value.sum(), 0) == np.around(comparison_total_val, 0)) - self.assertTrue(np.int(ent.value.sum().round()) == 2225855032776) + self.assertEqual(np.around(ent.value.sum(), 0), np.around(comparison_total_val, 0)) + self.assertEqual(np.int(ent.value.sum().round()), 2225854927260) def test_switzerland300_pc2013_pass(self): """Create LitPop entity for Switzerland 2013 for produced capital:""" @@ -115,16 +114,16 @@ def test_switzerland300_pc2013_pass(self): fin_mode = 'pc' resolution = 300 ref_year = 2013 - comparison_total_val = world_bank_wealth_account(country_name[0], \ + comparison_total_val = world_bank_wealth_account(country_name[0], ref_year, no_land=1)[1] ent = LitPop() with self.assertLogs('climada.entity.exposures.litpop', level='INFO') as cm: - ent.set_country(country_name, res_arcsec=resolution, \ + ent.set_country(country_name, res_arcsec=resolution, reference_year=ref_year, fin_mode=fin_mode) # print(cm) self.assertIn('Generating LitPop data at a resolution of 300 arcsec', cm.output[0]) - self.assertTrue(ent.value.sum() == comparison_total_val) - self.assertTrue(np.int(ent.value.sum().round()) == 2296358085749) + self.assertEqual(ent.value.sum(), comparison_total_val) + self.assertEqual(np.int(ent.value.sum().round()), 2296358085749) class TestFunctionIntegration(unittest.TestCase): """Test the integration of major functions within the LitPop module""" @@ -136,7 +135,8 @@ def test_get_litpop_box(self): cut_bbox = lp._get_country_shape(curr_country, 1)[0] all_coords = lp._litpop_box2coords(cut_bbox, resolution, 1) self.assertEqual(len(all_coords), 25) - self.assertTrue(117.91666666666666 and 22.08333333333333 in min(all_coords)) + self.assertTrue(22.08333333333333 in min(all_coords)) + self.assertTrue(117.91666666666666 in min(all_coords)) litpop_data = lp._get_litpop_box(cut_bbox, resolution, 0, 2016, [1, 1]) self.assertEqual(len(litpop_data), 25) self.assertIn(max(litpop_data), [544316890, 594091108.0, 594091108]) @@ -157,29 +157,28 @@ def test_calc_admin1(self): lp._get_gdp2asset_factor(country_info, 2016, curr_shp, fin_mode='gdp') cut_bbox = lp._get_country_shape(curr_country, 1)[0] all_coords = lp._litpop_box2coords(cut_bbox, resolution, 1) - mask = lp._mask_from_shape(curr_shp, resolution=resolution,\ - points2check=all_coords) - litpop_data = lp._get_litpop_box(cut_bbox, resolution, 0, 2016, \ - [3, 0]) + mask = lp._mask_from_shape(curr_shp, resolution=resolution, + points2check=all_coords) + litpop_data = lp._get_litpop_box(cut_bbox, resolution, 0, 2016, [3, 0]) litpop_curr = litpop_data[mask.sp_index.indices] lon, lat = zip(*np.array(all_coords)[mask.sp_index.indices]) - litpop_curr = lp._calc_admin1(curr_country, country_info[curr_country],\ - admin1_info[curr_country], litpop_curr,\ - list(zip(lon, lat)), resolution, 0, conserve_cntrytotal=0, \ - check_plot=0, masks_adm1=[], return_data=1) + litpop_curr = lp._calc_admin1(curr_country, country_info[curr_country], + admin1_info[curr_country], litpop_curr, + list(zip(lon, lat)), resolution, 0, conserve_cntrytotal=0, + check_plot=0, masks_adm1=[], return_data=1) self.assertEqual(len(litpop_curr), 699) - self.assertAlmostEqual(max(litpop_curr), 80313641015.12299, places=2) + self.assertAlmostEqual(max(litpop_curr), 80313679854.39496, places=2) def test_gpw_import(self): """test import of population data (Gridded Population of the World GWP) via function gpw_import.get_box_gpw() for Swaziland""" bbox = [30.78291, -27.3164, 32.11741, -25.73600] - gpw, lon, lat = gpw_import.get_box_gpw(cut_bbox=bbox, resolution=300,\ - return_coords=1, reference_year=2015) + gpw, lon, lat = gpw_import.get_box_gpw(cut_bbox=bbox, resolution=300, + return_coords=1, reference_year=2015) self.assertEqual(len(gpw), 323) self.assertIn(np.around(max(gpw)), [103070.0, 137840.0]) - self.assertEqual(type(gpw), \ - type(pd.SparseArray(data=1, fill_value=0))) + self.assertEqual(type(gpw), + type(pd.arrays.SparseArray(data=1, fill_value=0))) self.assertAlmostEqual(lat[0], -27.3164) self.assertAlmostEqual(lat[1], 0.083333333) self.assertAlmostEqual(lon[0], 30.78291) @@ -191,10 +190,10 @@ class TestValidation(unittest.TestCase): def test_validation_switzerland30(self): """Validation for Switzerland: two combinations of Lit and Pop, checking Pearson correlation coefficient and RMSF""" - rho = lp.admin1_validation('CHE', ['LitPop', 'Lit5'], [[1, 1], [5, 0]],\ - res_arcsec=30, check_plot=False)[0] - self.assertTrue(np.int(round(rho[0]*1e12)) == 945416798729) - self.assertTrue(np.int(round(rho[-1]*1e12)) == 3246081648798) + rho = lp.admin1_validation('CHE', ['LitPop', 'Lit5'], [[1, 1], [5, 0]], + res_arcsec=30, check_plot=False)[0] + self.assertEqual(np.int(round(rho[0] * 1e12)), 945416798729) + self.assertEqual(np.int(round(rho[-1] * 1e12)), 3246081648798) class TestSetAdmin1(unittest.TestCase): """Test adding name and ID of Admin1-region to exposure""" diff --git a/climada/test/test_OSM_unit.py b/climada/test/test_OSM_unit.py index 22efce9e9f..90ae4f1e73 100644 --- a/climada/test/test_OSM_unit.py +++ b/climada/test/test_OSM_unit.py @@ -30,50 +30,60 @@ class TestOSMFunctions(unittest.TestCase): """Test OSM Class methods""" def test_osm_api_query(self): - """test _osm_api_query within get_features_OSM function """ + """test _osm_api_query within get_features_OSM function""" bbox = [47.2, 8.03, 47.3, 8.07] item = 'landuse=forest' result_NodesFromWays, result_NodesWaysFromRels = OSM._osm_api_query(item, bbox) - self.assertGreater(len(result_NodesFromWays.nodes),0) - self.assertGreater(len(result_NodesFromWays.ways),0) - self.assertGreater(len(result_NodesWaysFromRels.relations),0) + self.assertGreater(len(result_NodesFromWays.nodes), 0) + self.assertGreater(len(result_NodesFromWays.ways), 0) + self.assertGreater(len(result_NodesWaysFromRels.relations), 0) def test_format_shape_osm(self): - """test _format_shape_osm function within get_features_OSM function: """ - #define input parameters + """test _format_shape_osm function within get_features_OSM function:""" + # define input parameters bbox = [47.2, 8.03, 47.3, 8.07] item = 'landuse=forest' result_NodesFromWays, result_NodesWaysFromRels = OSM._osm_api_query(item, bbox) # Execute function to be tested - globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))] = \ + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))] = \ OSM._format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item, DATA_DIR) # check that shapes were found both from relations and from way/nodes query - self.assertGreater(len(globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))]), - len(result_NodesWaysFromRels.relations)) + self.assertGreater(len( + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))]), + len(result_NodesWaysFromRels.relations)) # check that geometry row exists and contains polygons / multipolygons - self.assertEqual(globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))].iloc[0].geometry.type,'Polygon') - self.assertEqual(globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))].iloc[-1].geometry.type,'MultiPolygon') + self.assertEqual( + globals()[ + str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1])) + ].iloc[0].geometry.type, 'Polygon') + self.assertEqual( + globals()[ + str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1])) + ].iloc[-1].geometry.type, 'MultiPolygon') def test_combine_dfs_osm(self): - """test _combine_dfs_osm function within get_features_OSM function: """ + """test _combine_dfs_osm function within get_features_OSM function:""" # define input parameters - types = {'landuse=forest','waterway'} + types = {'landuse=forest', 'waterway'} bbox = [47.2, 8.03, 47.3, 8.07] for item in types: print(item) - result_NodesFromWays, result_NodesWaysFromRels = OSM._osm_api_query(item, bbox) #strictly, this doesnt belong here, but needs to be invoked (tested before) - globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))] = \ - OSM._format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, item, DATA_DIR) + # strictly, this doesnt belong here, but needs to be invoked (tested before) + result_NodesFromWays, result_NodesWaysFromRels = OSM._osm_api_query(item, bbox) + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))] = \ + OSM._format_shape_osm(bbox, result_NodesFromWays, result_NodesWaysFromRels, + item, DATA_DIR) # Execute function OSM_features_gdf_combined = \ - geopandas.GeoDataFrame(pd.DataFrame(columns=['Item', 'Name', 'Type', 'Natural_Type', 'geometry']), - crs='epsg:4326', geometry='geometry') + geopandas.GeoDataFrame( + pd.DataFrame(columns=['Item', 'Name', 'Type', 'Natural_Type', 'geometry']), + crs='epsg:4326', geometry='geometry') for item in types: - print('adding results from %s ...' %item) + print('adding results from %s ...' % item) OSM_features_gdf_combined = \ OSM_features_gdf_combined.append( - globals()[str(item)+'_gdf_all_'+str(int(bbox[0]))+'_'+str(int(bbox[1]))], + globals()[str(item) + '_gdf_all_' + str(int(bbox[0])) + '_' + str(int(bbox[1]))], ignore_index=True) i = 0 for geom in OSM_features_gdf_combined.geometry: @@ -85,11 +95,11 @@ def test_combine_dfs_osm(self): def test_makeUnion(self): """test makeUnion function within get_highValueArea function""" - gdf_all = geopandas.read_file(os.path.join(DATA_DIR,'OSM_features_47_8.shp')) + gdf_all = geopandas.read_file(os.path.join(DATA_DIR, 'OSM_features_47_8.shp')) # Execute function Low_Value_Union = OSM._makeUnion(gdf_all) - self.assertEqual(Low_Value_Union.type,'MultiPolygon') + self.assertEqual(Low_Value_Union.type, 'MultiPolygon') self.assertTrue(Low_Value_Union.is_valid) # this takes too long for unit test (loads CHE LitPop exposure!) Moved to integration test @@ -125,24 +135,31 @@ def test_makeUnion(self): def test_get_midpoints(self): """test _get_midpoints within make_osmexposure function""" # Define and load parameters: - building_path = os.path.join(DATA_DIR,'buildings_47_8.shp') + building_path = os.path.join(DATA_DIR, 'buildings_47_8.shp') building_gdf = OSM._get_midpoints(building_path) - self.assertEqual(building_gdf.loc[random.randint(0,len(building_gdf))].geometry.type, 'Point') - self.assertGreater(building_gdf.loc[random.randint(0,len(building_gdf))].projected_area, 0) + self.assertEqual(building_gdf.loc[random.randint(0, len(building_gdf))].geometry.type, + 'Point') + self.assertGreater(building_gdf.loc[random.randint(0, len(building_gdf))].projected_area, + 0) def test_assign_values_exposure(self): """test _assign_values_exposure within make_osmexposure function""" # Define and load parameters: - building_gdf = OSM._get_midpoints(os.path.join(DATA_DIR,'buildings_47_8.shp')) # function tested previously - mode = 'default' # mode LitPop takes too long for unit test, since loads entire CH-litpop exposure! moved to integration test + # function tested previously + building_gdf = OSM._get_midpoints(os.path.join(DATA_DIR, 'buildings_47_8.shp')) + # mode LitPop takes too long for unit test, since loads entire CH-litpop exposure! + # moved to integration test + mode = 'default' country = 'CHE' # Execute Function High_Value_Area_gdf = OSM._assign_values_exposure(building_gdf, mode, country) - self.assertGreater(High_Value_Area_gdf.loc[random.randint(0,len(High_Value_Area_gdf))].value,0) + self.assertGreater( + High_Value_Area_gdf.loc[random.randint(0, len(High_Value_Area_gdf))].value, + 0) # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestOSMFunctions) - #TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestLitPopClass)) + # TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestLitPopClass)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_blackmarble.py b/climada/test/test_blackmarble.py index fbbbbd028d..fc47f9c0ad 100644 --- a/climada/test/test_blackmarble.py +++ b/climada/test/test_blackmarble.py @@ -22,11 +22,11 @@ import unittest import numpy as np from cartopy.io import shapereader -from scipy import sparse from climada.entity.exposures.black_marble import BlackMarble from climada.entity.exposures.nightlight import load_nightlight_nasa, \ -load_nightlight_noaa, NOAA_BORDER, cut_nl_nasa + load_nightlight_noaa, \ + NOAA_BORDER from climada.entity.exposures import nightlight as nl_utils from climada.util.coordinates import equal_crs @@ -43,10 +43,11 @@ def test_spain_pass(self): with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: ent.set_countries(country_name, 2013, res_km=1) - self.assertIn("Nightlights from NOAA's earth observation group for year 2013.", cm.output[0]) + self.assertIn("Nightlights from NOAA's earth observation group for year 2013.", + cm.output[0]) self.assertIn("Processing country Spain.", cm.output[1]) self.assertIn("Generating resolution of approx 1 km.", cm.output[2]) - self.assertTrue(np.isclose(ent.value.sum(), 1.362e+12*(4+1), 4)) + self.assertTrue(np.isclose(ent.value.sum(), 1.362e+12 * (4 + 1), 4)) self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'})) self.assertEqual(ent.meta['width'], 2699) self.assertEqual(ent.meta['height'], 1938) @@ -69,10 +70,11 @@ def test_sint_maarten_pass(self): with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: ent.set_countries(country_name, 2013, res_km=0.2) - self.assertIn("Nightlights from NOAA's earth observation group for year 2013.", cm.output[0]) + self.assertIn("Nightlights from NOAA's earth observation group for year 2013.", + cm.output[0]) self.assertIn("Processing country Sint Maarten.", cm.output[1]) self.assertIn("Generating resolution of approx 0.2 km.", cm.output[2]) - self.assertAlmostEqual(ent.value.sum(), 3.658e+08*(4+1)) + self.assertAlmostEqual(ent.value.sum(), 3.658e+08 * (4 + 1)) self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'})) def test_anguilla_pass(self): @@ -81,7 +83,7 @@ def test_anguilla_pass(self): ent.set_countries(country_name, 2013, res_km=0.2) self.assertEqual(ent.ref_year, 2013) self.assertIn("Anguilla 2013 GDP: 1.754e+08 income group: 3", ent.tag.description) - self.assertAlmostEqual(ent.value.sum(), 1.754e+08*(3+1)) + self.assertAlmostEqual(ent.value.sum(), 1.754e+08 * (3 + 1)) self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'})) class Test1968(unittest.TestCase): @@ -96,26 +98,27 @@ def test_switzerland_pass(self): with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: ent.set_countries(country_name, 1968, res_km=0.5) - self.assertIn("Nightlights from NOAA's earth observation group for year 1992.", cm.output[0]) + self.assertIn("Nightlights from NOAA's earth observation group for year 1992.", + cm.output[0]) self.assertTrue("Processing country Switzerland." in cm.output[-2]) self.assertTrue("Generating resolution of approx 0.5 km." in cm.output[-1]) - self.assertTrue(np.isclose(ent.value.sum(), 1.894e+10*(4+1), 4)) + self.assertTrue(np.isclose(ent.value.sum(), 1.894e+10 * (4 + 1), 4)) self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'})) class Test2012(unittest.TestCase): """Test year 2012 flags.""" - + def test_from_hr_flag_pass(self): """Check from_hr flag in set_countries method.""" country_name = ['Turkey'] - + ent = BlackMarble() with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: ent.set_countries(country_name, 2012, res_km=5.0) self.assertTrue('NOAA' in cm.output[-3]) size1 = ent.value.size - self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11*(3+1), 4)) - + self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11 * (3 + 1), 4)) + try: ent = BlackMarble() with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: @@ -123,17 +126,17 @@ def test_from_hr_flag_pass(self): self.assertTrue('NASA' in cm.output[-3]) size2 = ent.value.size self.assertTrue(size1 < size2) - self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11*(3+1), 4)) + self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11 * (3 + 1), 4)) except TypeError: print('MemoryError caught') pass - - + + ent = BlackMarble() with self.assertLogs('climada.entity.exposures.black_marble', level='INFO') as cm: ent.set_countries(country_name, 2012, res_km=5.0, from_hr=False) self.assertTrue('NOAA' in cm.output[-3]) - self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11*(3+1), 4)) + self.assertTrue(np.isclose(ent.value.sum(), 8.740e+11 * (3 + 1), 4)) size3 = ent.value.size self.assertEqual(size1, size3) self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'})) @@ -143,37 +146,39 @@ class BMFuncs(unittest.TestCase): def test_cut_nasa_esp_pass(self): """Test load_nightlight_nasa function.""" shp_fn = shapereader.natural_earth(resolution='10m', - category='cultural', + category='cultural', name='admin_0_countries') shp_file = shapereader.Reader(shp_fn) list_records = list(shp_file.records()) for info_idx, info in enumerate(list_records): if info.attributes['ADM0_A3'] == 'AIA': bounds = info.bounds - + req_files = nl_utils.check_required_nl_files(bounds) files_exist, _ = nl_utils.check_nl_local_file_exists(req_files) nl_utils.download_nl_files(req_files, files_exist) - + try: nightlight, coord_nl = load_nightlight_nasa(bounds, req_files, 2016) except TypeError: print('MemoryError caught') return - + self.assertTrue(coord_nl[0, 0] < bounds[1]) self.assertTrue(coord_nl[1, 0] < bounds[0]) - self.assertTrue(coord_nl[0, 0]+(nightlight.shape[0]-1)*coord_nl[0,1] > bounds[3]) - self.assertTrue(coord_nl[1, 0]+(nightlight.shape[1]-1)*coord_nl[1,1] > bounds[2]) + self.assertTrue(coord_nl[0, 0] + (nightlight.shape[0] - 1) * coord_nl[0, 1] > bounds[3]) + self.assertTrue(coord_nl[1, 0] + (nightlight.shape[1] - 1) * coord_nl[1, 1] > bounds[2]) def test_load_noaa_pass(self): """Test load_nightlight_noaa function.""" nightlight, coord_nl, fn_nl = load_nightlight_noaa(2013) - + self.assertEqual(coord_nl[0, 0], NOAA_BORDER[1]) self.assertEqual(coord_nl[1, 0], NOAA_BORDER[0]) - self.assertEqual(coord_nl[0, 0]+(nightlight.shape[0]-1)*coord_nl[0,1], NOAA_BORDER[3]) - self.assertEqual(coord_nl[1, 0]+(nightlight.shape[1]-1)*coord_nl[1,1], NOAA_BORDER[2]) + self.assertEqual(coord_nl[0, 0] + (nightlight.shape[0] - 1) * coord_nl[0, 1], + NOAA_BORDER[3]) + self.assertEqual(coord_nl[1, 0] + (nightlight.shape[1] - 1) * coord_nl[1, 1], + NOAA_BORDER[2]) def test_set_country_pass(self): """Test exposures attributes after black marble.""" @@ -181,7 +186,7 @@ def test_set_country_pass(self): ent = BlackMarble() ent.set_countries(country_name, 2013, res_km=5.0) ent.check() - + self.assertEqual(np.unique(ent.region_id).size, 2) self.assertEqual(ent.ref_year, 2013) self.assertIn('Switzerland 2013 GDP: ', ent.tag.description) @@ -190,134 +195,6 @@ def test_set_country_pass(self): self.assertIn('income group: 4', ent.tag.description) self.assertIn('F182013.v4c_web.stable_lights.avg_vis.p', ent.tag.file_name) self.assertIn('F182013.v4c_web.stable_lights.avg_vis.p', ent.tag.file_name) - - def test_cut_nl_nasa_1_pass(self): - """Test cut_nl_nasa situation 2->3->4->5.""" - nl_mat = sparse.lil.lil_matrix([]) - in_lat = (21599, 21600) - in_lon = (43199, 43200) - # 0 2 4 6 (lat: Upper=0) (lon: 0, 1, 2, 3) - # 1 3 5 7 (lat: Lower=1) (lon: 0, 1, 2, 3) - in_lat_nb = (1, 0) - in_lon_nb = (1, 2) - - idx_info = [2, -1, False] - try: - aux_nl = np.zeros((21600, 21600)) - aux_nl[21599, 21599] = 100 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (1, 1)) - self.assertEqual(nl_mat.tocsr()[0, 0], 100.0) - - idx_info[0] = 3 - idx_info[1] = 2 - aux_nl[21599, 21599] = 0 - aux_nl[0, 21599] = 101 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (2, 1)) - self.assertEqual(nl_mat.tocsr()[0, 0], 101.0) - self.assertEqual(nl_mat.tocsr()[1, 0], 100.0) - - idx_info[0] = 4 - idx_info[1] = 3 - aux_nl[0, 21599] = 0 - aux_nl[21599, 0] = 102 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (2, 2)) - self.assertEqual(nl_mat.tocsr()[0, 0], 101.0) - self.assertEqual(nl_mat.tocsr()[1, 0], 100.0) - self.assertEqual(nl_mat.tocsr()[0, 1], 0.0) - self.assertEqual(nl_mat.tocsr()[1, 1], 102.0) - - idx_info[0] = 5 - idx_info[1] = 4 - aux_nl[21599, 0] = 0 - aux_nl[0, 0] = 103 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (2, 2)) - self.assertEqual(nl_mat.tocsr()[0, 0], 101.0) - self.assertEqual(nl_mat.tocsr()[1, 0], 100.0) - self.assertEqual(nl_mat.tocsr()[0, 1], 103.0) - self.assertEqual(nl_mat.tocsr()[1, 1], 102.0) - except MemoryError: - print('MemoryError caught') - pass - - def test_cut_nl_nasa_2_pass(self): - """Test cut_nl_nasa situation 3->5.""" - nl_mat = sparse.lil.lil_matrix([]) - in_lat = (21599, 21599) - in_lon = (43199, 43200) - # 0 2 4 6 (lat: Upper=0) (lon: 0, 1, 2, 3) - # 1 3 5 7 (lat: Lower=1) (lon: 0, 1, 2, 3) - in_lat_nb = (1, 1) - in_lon_nb = (1, 2) - - idx_info = [3, -1, False] - try: - aux_nl = np.zeros((21600, 21600)) - aux_nl[0, 21599] = 100 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (1, 1)) - self.assertEqual(nl_mat.tocsr()[0, 0], 100.0) - - idx_info[0] = 5 - idx_info[1] = 3 - aux_nl[0, 21599] = 0 - aux_nl[0, 0] = 101 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (1, 2)) - self.assertEqual(nl_mat.tocsr()[0, 0], 100.0) - self.assertEqual(nl_mat.tocsr()[0, 1], 101.0) - except MemoryError: - print('MemoryError caught') - pass - - def test_cut_nl_nasa_3_pass(self): - """Test cut_nl_nasa situation 2->4.""" - nl_mat = sparse.lil.lil_matrix([]) - in_lat = (21600, 21600) - in_lon = (43199, 43200) - # 0 2 4 6 (lat: Upper=0) (lon: 0, 1, 2, 3) - # 1 3 5 7 (lat: Lower=1) (lon: 0, 1, 2, 3) - in_lat_nb = (0, 0) - in_lon_nb = (1, 2) - - idx_info = [2, -1, False] - try: - aux_nl = np.zeros((21600, 21600)) - aux_nl[21599, 21599] = 100 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (1, 1)) - self.assertEqual(nl_mat.tocsr()[0, 0], 100.0) - - idx_info[0] = 4 - idx_info[1] = 2 - aux_nl[21599, 21599] = 0 - aux_nl[21599, 0] = 101 - cut_nl_nasa(aux_nl, idx_info, nl_mat, in_lat, - in_lon, in_lat_nb, in_lon_nb) - - self.assertEqual(nl_mat.shape, (1, 2)) - self.assertEqual(nl_mat.tocsr()[0, 0], 100.0) - self.assertEqual(nl_mat.tocsr()[0, 1], 101.0) - except MemoryError: - print('MemoryError caught') - pass # Execute Tests if __name__ == "__main__": diff --git a/climada/test/test_calibration.py b/climada/test/test_calibration.py index e55569a850..1ca78740a9 100644 --- a/climada/test/test_calibration.py +++ b/climada/test/test_calibration.py @@ -31,14 +31,14 @@ HAZ_DIR = os.path.join(os.path.dirname(__file__), os.pardir, 'hazard/test/data/') HAZ_TEST_MAT = os.path.join(HAZ_DIR, 'atl_prob_no_name.mat') -DATA_FOLDER = os.path.join(os.path.dirname(__file__) , 'data') +DATA_FOLDER = os.path.join(os.path.dirname(__file__), 'data') class TestCalib(unittest.TestCase): - ''' Test engine calibration method.''' + """Test engine calibration method.""" def test_calib_instance(self): - """ Test save calib instance """ + """Test save calib instance""" # Read default entity values ent = Entity() ent.read_excel(ENT_DEMO_TODAY) @@ -47,27 +47,27 @@ def test_calib_instance(self): # Read default hazard file hazard = Hazard('TC') hazard.read_mat(HAZ_TEST_MAT) - + # get impact function from set imp_func = ent.impact_funcs.get_func(hazard.tag.haz_type, ent.exposures.if_TC.median()) - + # Assign centroids to exposures ent.exposures.assign_centroids(hazard) - + # create input frame - df_in = pd.DataFrame.from_dict({'v_threshold':[25.7], - 'other_param':[2], - 'hazard':[HAZ_TEST_MAT]}) - df_in_yearly = pd.DataFrame.from_dict({'v_threshold':[25.7], - 'other_param':[2], - 'hazard':[HAZ_TEST_MAT]}) + df_in = pd.DataFrame.from_dict({'v_threshold': [25.7], + 'other_param': [2], + 'hazard': [HAZ_TEST_MAT]}) + df_in_yearly = pd.DataFrame.from_dict({'v_threshold': [25.7], + 'other_param': [2], + 'hazard': [HAZ_TEST_MAT]}) # Compute the impact over the whole exposures df_out = calib_instance(hazard, ent.exposures, imp_func, df_in) - df_out_yearly = calib_instance(hazard, ent.exposures, imp_func, + df_out_yearly = calib_instance(hazard, ent.exposures, imp_func, df_in_yearly, - yearly_impact= True) + yearly_impact=True) # calc Impact as comparison impact = Impact() impact.calc(ent.exposures, ent.impact_funcs, hazard) @@ -88,9 +88,8 @@ def test_calib_instance(self): df_in[df_in.columns[2]]))) self.assertTrue(all(df_out['impact_CLIMADA'].values == impact.at_event)) - self.assertTrue(all(df_out_yearly['impact_CLIMADA'].values == - [*IYS.values()])) - + self.assertTrue(all(df_out_yearly['impact_CLIMADA'].values == [*IYS.values()])) + # Execute Tests if __name__ == "__main__": diff --git a/climada/test/test_centr.py b/climada/test/test_centr.py deleted file mode 100644 index 82e7ebca59..0000000000 --- a/climada/test/test_centr.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -This file is part of CLIMADA. - -Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. - -CLIMADA is free software: you can redistribute it and/or modify it under the -terms of the GNU Lesser General Public License as published by the Free -Software Foundation, version 3. - -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. - -You should have received a copy of the GNU Lesser General Public License along -with CLIMADA. If not, see . - ---- - -Test CentroidsVector and CentroidsRaster classes. -""" -import os -import unittest - -from climada.hazard.centroids.centr import Centroids -from climada.util.constants import GLB_CENTROIDS_MAT, HAZ_TEMPLATE_XLS - -HAZ_DIR = os.path.join(os.path.dirname(__file__), os.pardir, 'hazard/test/data/') -HAZ_TEST_MAT = os.path.join(HAZ_DIR, 'atl_prob_no_name.mat') - -class TestCentroidsReader(unittest.TestCase): - """ Test read functions Centroids """ - - def test_mat_pass(self): - ''' Read a centroid mat file correctly.''' - centroids = Centroids() - centroids.read_mat(HAZ_TEST_MAT) - - n_centroids = 100 - self.assertEqual(centroids.coord.shape, (n_centroids, 2)) - self.assertEqual(centroids.coord[0][0], 21) - self.assertEqual(centroids.coord[0][1], -84) - self.assertEqual(centroids.coord[n_centroids-1][0], 30) - self.assertEqual(centroids.coord[n_centroids-1][1], -75) - - def test_mat_global_pass(self): - """ Test read GLB_CENTROIDS_MAT """ - centroids = Centroids() - centroids.read_mat(GLB_CENTROIDS_MAT) - - self.assertEqual(centroids.region_id[1062443], 35) - self.assertEqual(centroids.region_id[170825], 28) - - def test_centroid_pass(self): - ''' Read a centroid excel file correctly.''' - centroids = Centroids() - centroids.read_excel(HAZ_TEMPLATE_XLS) - - n_centroids = 45 - self.assertEqual(centroids.coord.shape[0], n_centroids) - self.assertEqual(centroids.coord.shape[1], 2) - self.assertEqual(centroids.coord[0][0], -25.95) - self.assertEqual(centroids.coord[0][1], 32.57) - self.assertEqual(centroids.coord[n_centroids-1][0], -24.7) - self.assertEqual(centroids.coord[n_centroids-1][1], 33.88) - - -# Execute Tests -if __name__ == "__main__": - TESTS = unittest.TestLoader().loadTestsFromTestCase(TestCentroidsReader) - unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_cropyield_integr.py b/climada/test/test_cropyield_integr.py new file mode 100755 index 0000000000..a1ac00b850 --- /dev/null +++ b/climada/test/test_cropyield_integr.py @@ -0,0 +1,165 @@ +""" +This file is part of CLIMADA. + +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. + +CLIMADA is free software: you can redistribute it and/or modify it under the +terms of the GNU Lesser General Public License as published by the Free +Software Foundation, version 3. + +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. + +You should have received a copy of the GNU Lesser General Public License along +with CLIMADA. If not, see . + +--- + +Tests on Drought Hazard exposure and Impact function. +""" + +import unittest +import os +import numpy as np +from climada.util.constants import DATA_DIR +from climada.hazard.relative_cropyield import (RelativeCropyield, init_hazard_set, + calc_his_haz) +from climada.entity.exposures.crop_production import CropProduction +from climada.entity import ImpactFuncSet, IFRelativeCropyield +from climada.engine import Impact + + +INPUT_DIR = os.path.join(DATA_DIR, 'demo') +FN_STR_DEMO = 'annual_FR_DE_DEMO' +FILENAME_LU = 'histsoc_landuse-15crops_annual_FR_DE_DEMO_2001_2005.nc' +FILENAME_MEAN = 'hist_mean_mai-firr_1976-2005_DE_FR.hdf5' + + +class TestIntegr(unittest.TestCase): + """Test loading functions from the ISIMIP Agricultural Drought class and + computing impact on crop production""" + def test_EU(self): + """test with demo data containing France and Germany""" + bbox = [-5, 42, 16, 55] + haz = RelativeCropyield() + haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=(2001, 2005), bbox=bbox, + ag_model='lpjml', cl_model='ipsl-cm5a-lr', scenario='historical', + soc='2005soc', co2='co2', crop='whe', irr='noirr', + fn_str_var=FN_STR_DEMO) + hist_mean = haz.calc_mean(yearrange_mean=(2001, 2005)) + haz.set_rel_yield_to_int(hist_mean) + haz.centroids.set_region_id() + + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME_LU, hist_mean=FILENAME_MEAN, + bbox=bbox, yearrange=(2001, 2005), + scenario='flexible', unit='t', crop='whe', irr='firr') + + exp.set_to_usd(INPUT_DIR) + exp.assign_centroids(haz, threshold=20) + + if_cp = ImpactFuncSet() + if_def = IFRelativeCropyield() + if_def.set_relativeyield() + if_cp.append(if_def) + if_cp.check() + + impact = Impact() + impact.calc(exp.loc[exp.region_id == 276], if_cp, haz.select(['2002']), save_mat=True) + + exp_manual = exp.value.loc[exp.region_id == 276].values + impact_manual = haz.select(event_names=['2002'], reg_id=276).intensity.multiply(exp_manual) + dif = (impact_manual - impact.imp_mat).data + + self.assertEqual(haz.tag.haz_type, 'RC') + self.assertEqual(haz.size, 5) + self.assertEqual(haz.centroids.size, 1092) + self.assertAlmostEqual(haz.intensity.mean(), -2.0489097e-08) + self.assertAlmostEqual(exp.value.max(), 53074789.755290434) + self.assertEqual(exp.latitude.values.size, 1092) + self.assertAlmostEqual(exp.value[3], 0.0) + self.assertAlmostEqual(exp.value[1077], 405026.6857207429) + self.assertAlmostEqual(impact.imp_mat.data[3], -176102.5359452465 ) + self.assertEqual(len(dif), 0) + + def test_EU_nan(self): + """Test whether setting the zeros in exp.value to NaN changes the impact""" + bbox=[0, 42, 10, 52] + haz = RelativeCropyield() + haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=(2001, 2005), bbox=bbox, + ag_model='lpjml', cl_model='ipsl-cm5a-lr', scenario='historical', + soc='2005soc', co2='co2', crop='whe', irr='noirr', + fn_str_var=FN_STR_DEMO) + hist_mean = haz.calc_mean(yearrange_mean=(2001, 2005)) + haz.set_rel_yield_to_int(hist_mean) + haz.centroids.set_region_id() + + exp = CropProduction() + exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME_LU, hist_mean=FILENAME_MEAN, + bbox=bbox, yearrange=(2001, 2005), + scenario='flexible', unit='t', crop='whe', irr='firr') + exp.assign_centroids(haz, threshold=20) + + if_cp = ImpactFuncSet() + if_def = IFRelativeCropyield() + if_def.set_relativeyield() + if_cp.append(if_def) + if_cp.check() + + impact = Impact() + impact.calc(exp, if_cp, haz, save_mat=True) + + exp_nan = CropProduction() + exp_nan.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME_LU, hist_mean=FILENAME_MEAN, + bbox=[0, 42, 10, 52], yearrange=(2001, 2005), + scenario='flexible', unit='t', crop='whe', irr='firr') + exp_nan.value[exp_nan.value==0] = np.nan + exp_nan.assign_centroids(haz, threshold=20) + + impact_nan = Impact() + impact_nan.calc(exp_nan, if_cp, haz, save_mat=True) + self.assertListEqual(list(impact.at_event), list(impact_nan.at_event)) + self.assertAlmostEqual(12.056545220060798, impact_nan.aai_agg) + self.assertAlmostEqual(12.056545220060798 , impact.aai_agg) + + def test_hist_mean_of_full_haz_set(self): + """Test creation of full hazard set""" + + output_list = list() + bbox = [116.25, 38.75, 117.75, 39.75] + files_his = ['gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-whe-noirr_global_DEMO_TJANJIN_annual_1861_2005.nc', + 'pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-firr_global_annual_DEMO_TJANJIN_1861_2005.nc', + 'pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-noirr_global_annual_DEMO_TJANJIN_1861_2005.nc'] + + (his_file_list, file_props, hist_mean_per_crop, + scenario_list, crop_list) = init_hazard_set(files_his, input_dir=INPUT_DIR, + bbox=bbox, isimip_run = 'test_file', + yearrange_his = np.array([1980,2005])) + yearrange_mean = np.array([1980,2005]) + for his_file in his_file_list: + haz_his, filename, hist_mean = calc_his_haz(his_file, file_props, input_dir=INPUT_DIR, + bbox=bbox, yearrange_mean=yearrange_mean) + + hist_mean_per_crop[(file_props[his_file])['crop_irr']]['value'][ + hist_mean_per_crop[(file_props[his_file])['crop_irr']]['idx'], :] = hist_mean + hist_mean_per_crop[file_props[his_file]['crop_irr']]['idx'] += 1 + + + self.assertEqual(np.shape(hist_mean_per_crop['whe-firr']['value'])[0], 1) + self.assertEqual(np.shape(hist_mean_per_crop['whe-noirr']['value'])[0], 2) + + # calculate mean hist_mean for each crop-irrigation + for crop_irr in crop_list: + mean = np.mean((hist_mean_per_crop[crop_irr])['value'], 0) + output_list.append(mean) + + self.assertEqual('whe-noirr', crop_list[0]) + self.assertEqual(np.mean(hist_mean_per_crop['whe-noirr']['value']), np.mean(output_list[0])) + self.assertEqual(np.mean(hist_mean_per_crop['whe-noirr']['value'][:,1]), output_list[0][1]) + + +# Execute Tests +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestIntegr) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_drought_integr.py b/climada/test/test_drought_integr.py index 90bce8a281..f434674135 100644 --- a/climada/test/test_drought_integr.py +++ b/climada/test/test_drought_integr.py @@ -19,7 +19,6 @@ Tests on Drought Hazard exposure and Impact function. """ -import numpy as np import unittest from climada.hazard.drought import Drought @@ -30,36 +29,36 @@ class TestIntegr(unittest.TestCase): - """ Test loading functions from the Drought class """ + """Test loading functions from the Drought class""" def test_switzerland(self): - + drought = Drought() drought.set_area(44.5, 5, 50, 12) - + hazard_set = drought.setup() - + imp_drought = Impact() dr_if = ImpactFuncSet() if_def = IFDrought() if_def.set_default() dr_if.append(if_def) - + exposure_agrar = SpamAgrar() - exposure_agrar.init_spam_agrar(country='CHE') + exposure_agrar.init_spam_agrar(country='CHE', haz_type='DR') exposure_agrar.assign_centroids(hazard_set) imp_drought.calc(exposure_agrar, dr_if, hazard_set) index_event_start = imp_drought.event_name.index('2003') damages_drought = imp_drought.at_event[index_event_start] - + self.assertEqual(hazard_set.tag.haz_type, 'DR') self.assertEqual(hazard_set.size, 114) self.assertEqual(hazard_set.centroids.size, 130) - self.assertEqual(exposure_agrar.latitude.values.size, 766/2) - self.assertEqual(exposure_agrar.value[3], 1720024.4) - self.assertEqual(damages_drought, 61995472.555223145) - - + self.assertEqual(exposure_agrar.latitude.values.size, 766 / 2) + self.assertAlmostEqual(exposure_agrar.value[3], 1720024.4) + self.assertAlmostEqual(damages_drought, 61995472.555223145) + + # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestIntegr) diff --git a/climada/test/test_flood_integr.py b/climada/test/test_flood_integr.py index bc929e9058..61e6e13af5 100644 --- a/climada/test/test_flood_integr.py +++ b/climada/test/test_flood_integr.py @@ -15,22 +15,22 @@ with CLIMADA. If not, see . --- - +from climada.util.constants import NAT_REG_ID Test flood module. """ import unittest -import datetime as dt -import numpy as np -from datetime import date -from climada.hazard.flood import RiverFlood -from climada.util.constants import HAZ_DEMO_FLDDPH, HAZ_DEMO_FLDFRC +from climada.hazard.river_flood import RiverFlood +from climada.util.constants import HAZ_DEMO_FLDDPH, HAZ_DEMO_FLDFRC, DEMO_GDP2ASSET +from climada.entity.exposures.gdp_asset import GDP2Asset +from climada.entity.impact_funcs.river_flood import flood_imp_func_set +from climada.engine import Impact class TestRiverFlood(unittest.TestCase): """Test for reading flood event from file""" def test_exact_area_selection(self): - testCentroids = RiverFlood.select_exact_area(['LIE']) + testCentroids, iso, natID = RiverFlood._select_exact_area(['LIE']) self.assertEqual(testCentroids.lon.shape[0], 13) self.assertAlmostEqual(testCentroids.lon[0], 9.5206968) @@ -60,265 +60,26 @@ def test_exact_area_selection(self): self.assertAlmostEqual(testCentroids.lat[10], 47.1872472) self.assertAlmostEqual(testCentroids.lat[11], 47.2289138) self.assertAlmostEqual(testCentroids.lat[12], 47.2289138) + self.assertEqual(iso[0], 'LIE') - self.assertEqual(testCentroids.id[0], 0) - self.assertEqual(testCentroids.id[5], 5) - self.assertEqual(testCentroids.id[12], 12) - - testCentroids = RiverFlood.select_exact_area(['DEU']) - self.assertAlmostEqual(np.max(testCentroids.lon), 15.020687999999979) - self.assertAlmostEqual(np.min(testCentroids.lon), 5.895702599999964) - self.assertAlmostEqual(np.max(testCentroids.lat), 55.0622346) - self.assertAlmostEqual(np.min(testCentroids.lat), 47.312247) - - def test_select_window_area(self): - testWinCentroids = RiverFlood.select_window_area(['DEU']) - self.assertEqual(testWinCentroids.lon.shape[0], 57505) - - self.assertAlmostEqual(np.max(testWinCentroids.lon), - 15.979019799999975) - self.assertAlmostEqual(np.min(testWinCentroids.lon), - 4.9790373999999815) - self.assertAlmostEqual(np.max(testWinCentroids.lat), - 55.97889979999998) - self.assertAlmostEqual(np.min(testWinCentroids.lat), - 46.97891419999998) - - self.assertAlmostEqual(testWinCentroids.lon[1000], - 13.520690399999978) - self.assertAlmostEqual(testWinCentroids.lat[1000], - 47.10391399999999) - self.assertAlmostEqual(testWinCentroids.lon[2000], - 11.020694399999968) - self.assertAlmostEqual(testWinCentroids.lat[2000], - 47.270580399999986) - self.assertAlmostEqual(testWinCentroids.lon[3000], - 8.520698399999986) - self.assertAlmostEqual(testWinCentroids.lat[3000], - 47.43724679999998) - - self.assertEqual(testWinCentroids.id[0], 0) - self.assertEqual(testWinCentroids.id[1204], 1204) - self.assertEqual(testWinCentroids.id[57504], 57504) - - def test_full_flood(self): - """ read_flood""" + def test_full_impact(self): + """test full flood impact""" testRF = RiverFlood() - testRF.set_from_nc() - self.assertEqual(testRF.centroids.lat.shape[0], 37324800) - self.assertAlmostEqual(np.max(testRF.centroids.lon), - 179.97916666666663) - self.assertAlmostEqual(np.min(testRF.centroids.lon), - -179.97916666666666) - self.assertAlmostEqual(np.max(testRF.centroids.lat), - 89.97916666666667) - self.assertAlmostEqual(np.min(testRF.centroids.lat), - -89.97916666666666) - - self.assertAlmostEqual(testRF.centroids.lon[20000000], - 113.35416666666663) - self.assertAlmostEqual(testRF.centroids.lat[20000000], - -6.4375) - self.assertAlmostEqual(testRF.centroids.lon[10000000], - -33.3125) - self.assertAlmostEqual(testRF.centroids.lat[10000000], - 41.770833333333336) - - self.assertEqual(testRF.orig[0], 1) - self.assertEqual(np.argmax(testRF.intensity), 26190437) - self.assertAlmostEqual(np.max(testRF.fraction), 1.0) - self.assertAlmostEqual(testRF.intensity[0, 3341441], 0.9583404) - self.assertAlmostEqual(testRF.intensity[0, 3341442], 0.9583404) - self.assertAlmostEqual(testRF.fraction[0, 3341441], 0.41666666) - self.assertAlmostEqual(testRF.fraction[0, 3341442], 0.375) - self.assertEqual(np.argmax(testRF.fraction[0]), 3341440) - - testRFReg = RiverFlood() - testRFReg.set_from_nc(reg='SWA') - self.assertEqual(testRFReg.centroids.lat.shape[0], 301181) - self.assertAlmostEqual(np.max(testRFReg.centroids.lon), - 101.14555019999997) - self.assertAlmostEqual(np.min(testRFReg.centroids.lon), - 60.52061519999998) - self.assertAlmostEqual(np.max(testRFReg.centroids.lat), - 38.43726119999998) - self.assertAlmostEqual(np.min(testRFReg.centroids.lat), - -0.6876762000000127) - self.assertEqual(testRFReg.date[0], 730303) - self.assertEqual((date.fromordinal(testRFReg.date[0]).year), 2000) - self.assertEqual(testRFReg.orig[0], 1) - self.assertAlmostEqual(np.max(testRFReg.intensity), 16.69780921936035) - self.assertEqual(np.argmax(testRFReg.intensity), 40613) - self.assertEqual(np.argmax(testRFReg.fraction), 126135) - - def test_flooded_area(self): - testRFArea = RiverFlood() - dph_path = HAZ_DEMO_FLDDPH - frc_path = HAZ_DEMO_FLDFRC - testRFArea.set_from_nc(countries=['AUT'], dph_path=dph_path, - frc_path=frc_path) - self.assertEqual(testRFArea.centroids.lat.shape[0], 5782) - self.assertAlmostEqual(np.max(testRFArea.centroids.lon), - 17.104017999999968) - self.assertAlmostEqual(np.min(testRFArea.centroids.lon), - 9.562363399999981) - self.assertAlmostEqual(np.max(testRFArea.centroids.lat), - 48.978911) - self.assertAlmostEqual(np.min(testRFArea.centroids.lat), - 46.39558179999999) - self.assertEqual(testRFArea.orig[0], 1) - self.assertAlmostEqual(np.max(testRFArea.intensity), - 9.613386154174805) - self.assertEqual(np.argmax(testRFArea.intensity), 2786) - self.assertAlmostEqual(np.max(testRFArea.fraction), - 0.5103999972343445) - self.assertEqual(np.argmax(testRFArea.fraction), 3391) - - testRFArea.set_flooded_area() - self.assertAlmostEqual(np.max(testRFArea.fla_ev_centr), - 7396511.421906647) - self.assertEqual(np.argmax(testRFArea.fla_ev_centr), 3391) - self.assertAlmostEqual(np.max(testRFArea.fla_ann_centr), - 7396511.421906647) - self.assertEqual(np.argmax(testRFArea.fla_ann_centr), - 3391) - - self.assertAlmostEqual(testRFArea.fla_event[0], - 229956891.5531019, 5) - self.assertAlmostEqual(testRFArea.fla_annual[0], - 229956891.5531019, 5) - - testRFset = RiverFlood() - testRFset.set_from_nc(countries=['AFG']) - years = [2000, 2001, 2002] - manipulated_dates = [730303, 730669, 731034] - for i in range(len(years)): - testRFaddset = RiverFlood() - testRFaddset.set_from_nc(countries=['AFG']) - testRFaddset.date = [manipulated_dates[i]] - if i == 0: - testRFaddset.event_name = ['2000_2'] - else: - testRFaddset.event_name = [str(years[i])] - testRFset.append(testRFaddset) - - testRFset.set_flooded_area() - - self.assertEqual(testRFset.fla_event.shape[0], 4) - self.assertEqual(testRFset.fla_annual.shape[0], 3) - self.assertAlmostEqual(np.max(testRFset.fla_ev_centr[0]), - 17200498.22927546) - self.assertEqual(np.argmax(testRFset.fla_ev_centr[0]), - 32610) - self.assertAlmostEqual(np.max(testRFset.fla_ev_centr[2]), - 17200498.22927546) - self.assertEqual(np.argmax(testRFset.fla_ev_centr[2]), - 32610) - - self.assertAlmostEqual(np.max(testRFset.fla_ann_centr[0]), - 34400996.45855092) - self.assertEqual(np.argmax(testRFset.fla_ann_centr[0]), - 32610) - self.assertAlmostEqual(np.max(testRFset.fla_ann_centr[2]), - 17200498.22927546) - self.assertEqual(np.argmax(testRFset.fla_ann_centr[2]), - 32610) - - self.assertAlmostEqual(testRFset.fla_event[0], - 6244242013.5826435, 4) - self.assertAlmostEqual(testRFset.fla_annual[0], - 12488484027.165287, 3) - self.assertAlmostEqual(testRFset.fla_ann_av, - 8325656018.110191, 4) - self.assertAlmostEqual(testRFset.fla_ev_av, - 6244242013.5826435, 4) - - def test_cut_flooded_area(self): - - testRFwin = RiverFlood() - winAFG = RiverFlood.select_window_area(['AFG']) - testRFwin.set_from_nc(centroids=winAFG) - afg = RiverFlood.select_exact_area(['AFG']) - testRFwin.set_flooded_area_cut(afg.coord) - - self.assertAlmostEqual(np.max(testRFwin.fla_ann_centr[0]), - 17200498.22927546) - self.assertEqual(np.argmax(testRFwin.fla_ann_centr[0]), - 32610) - self.assertAlmostEqual(np.max(testRFwin.fla_ev_centr[0]), - 17200498.22927546) - self.assertEqual(np.argmax(testRFwin.fla_ev_centr[0]), - 32610) - self.assertAlmostEqual(testRFwin.fla_event[0], - 6244242013.5826435, 4) - self.assertAlmostEqual(testRFwin.fla_annual[0], - 6244242013.5826435, 4) - self.assertAlmostEqual(testRFwin.fla_ann_av, - 6244242013.5826435, 4) - self.assertAlmostEqual(testRFwin.fla_ev_av, - 6244242013.5826435, 4) - - def test_select_model_run(self): - testRFModel = RiverFlood() - flood_dir = '/home/test/flood/' - rf_model = 'LPJmL' - cl_model = 'wfdei' - prot_std = 'flopros' - scenario = 'historical' - - self.assertEqual(testRFModel._select_model_run(flood_dir, rf_model, - cl_model, scenario, - prot_std)[0], - '/home/test/flood/flddph_LPJmL_wfdei_' + - 'flopros_gev_0.1.nc') - self.assertEqual(testRFModel._select_model_run(flood_dir, rf_model, - cl_model, scenario, - prot_std, proj=True)[0], - '/home/test/flood/flddph_LPJmL_wfdei_' + - 'historical_flopros_gev_picontrol_2000_0.1.nc') - - def test_set_centroids_from_file(self): - testRFCentr = RiverFlood() - lon = [1, 2, 3] - lat = [1, 2, 3] - testRFCentr._set_centroids_from_file(lon, lat) - test_centroids_lon = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) - test_centroids_lat = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3]) - self.assertTrue(np.array_equal(testRFCentr.centroids.lon, - test_centroids_lon)) - self.assertTrue(np.array_equal(testRFCentr.centroids.lat, - test_centroids_lat)) + testRF.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, + countries=['CHE']) - def test_select_events(self): - testRFTime = RiverFlood() - tt1 = dt.datetime.strptime('1988-07-02', '%Y-%m-%d') - tt2 = dt.datetime.strptime('2010-04-01', '%Y-%m-%d') - tt3 = dt.datetime.strptime('1997-07-02', '%Y-%m-%d') - tt4 = dt.datetime.strptime('1990-07-02', '%Y-%m-%d') - years = [2010, 1997] - test_time = np.array([tt1, tt2, tt3, tt4]) - self.assertTrue(np.array_equal(testRFTime._select_event(test_time, - years), - [1, 2])) - years = [1988, 1990] - self.assertTrue(np.array_equal(testRFTime._select_event(test_time, - years), - [0, 3])) + gdpa = GDP2Asset() + gdpa.set_countries(countries=['CHE'], ref_year=2000, path=DEMO_GDP2ASSET) - def test_cut_window(self): + if_set = flood_imp_func_set() + imp = Impact() + imp.calc(gdpa, if_set, testRF) - testRFCut = RiverFlood() - centr = RiverFlood.select_window_area(['AUT']) - testRFCut.centroids.lon = centr.lon - testRFCut.centroids.lat = centr.lat - lon = np.arange(7, 20, 0.2) - lat = np.arange(40, 50, 0.2) - test_window = [[4, 24], [55, 45]] - self.assertTrue(np.array_equal(testRFCut._cut_window(lon, lat), - test_window)) + self.assertAlmostEqual(imp.at_event[0], 226839.72426476143) + self.assertAlmostEqual(gdpa['if_RF'].iloc[0], 3.0) -# # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRiverFlood) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestRiverFlood) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_gdp_asset_integr.py b/climada/test/test_gdp_asset_integr.py index 8d22943167..ebe3218d07 100644 --- a/climada/test/test_gdp_asset_integr.py +++ b/climada/test/test_gdp_asset_integr.py @@ -23,7 +23,6 @@ from climada.entity.exposures import gdp_asset as ga from climada.util.constants import DEMO_GDP2ASSET - class TestGDP2AssetClassCountries(unittest.TestCase): """Unit tests for the GDP2Asset exposure class""" def test_wrong_iso3_fail(self): diff --git a/climada/test/test_hazard.py b/climada/test/test_hazard.py index 42bf481245..7c404bb66a 100644 --- a/climada/test/test_hazard.py +++ b/climada/test/test_hazard.py @@ -30,10 +30,10 @@ DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') class TestCentroids(unittest.TestCase): - """Test centroids functionalities """ + """Test centroids functionalities""" def test_read_write_raster_pass(self): - """ Test write_raster: Hazard from raster data """ + """Test write_raster: Hazard from raster data""" haz_fl = Hazard('FL') haz_fl.set_raster([HAZ_DEMO_FL]) haz_fl.check() @@ -46,11 +46,11 @@ def test_read_write_raster_pass(self): haz_read = Hazard('FL') haz_read.set_raster([os.path.join(DATA_DIR, 'test_write_hazard.tif')]) - self.assertTrue(np.allclose(haz_fl.intensity.todense(), haz_read.intensity.todense())) - self.assertEqual(np.unique(np.array(haz_fl.fraction.todense())).size, 2) + self.assertTrue(np.allclose(haz_fl.intensity.toarray(), haz_read.intensity.toarray())) + self.assertEqual(np.unique(np.array(haz_fl.fraction.toarray())).size, 2) def test_read_raster_pool_pass(self): - """ Test set_raster with pool """ + """Test set_raster with pool""" from pathos.pools import ProcessPool as Pool pool = Pool() haz_fl = Hazard('FL', pool) @@ -64,7 +64,7 @@ def test_read_raster_pool_pass(self): pool.join() def test_read_write_vector_pass(self): - """ Test write_raster: Hazard from vector data""" + """Test write_raster: Hazard from vector data""" haz_fl = Hazard('FL') haz_fl.event_id = np.array([1]) haz_fl.date = np.array([1]) @@ -72,7 +72,7 @@ def test_read_write_vector_pass(self): haz_fl.orig = np.array([1]) haz_fl.event_name = ['1'] haz_fl.intensity = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])) - haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])/2) + haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]) / 2) haz_fl.centroids.set_lat_lon(np.array([1, 2, 3]), np.array([1, 2, 3])) haz_fl.check() @@ -81,10 +81,11 @@ def test_read_write_vector_pass(self): haz_read = Hazard('FL') haz_read.set_raster([os.path.join(DATA_DIR, 'test_write_hazard.tif')]) self.assertEqual(haz_read.intensity.shape, (1, 9)) - self.assertTrue(np.allclose(np.unique(np.array(haz_read.intensity.todense())), np.array([0.0, 0.1, 0.2, 0.5]))) + self.assertTrue(np.allclose(np.unique(np.array(haz_read.intensity.toarray())), + np.array([0.0, 0.1, 0.2, 0.5]))) def test_write_fraction_pass(self): - """ Test write_raster with fraction """ + """Test write_raster with fraction""" haz_fl = Hazard('FL') haz_fl.event_id = np.array([1]) haz_fl.date = np.array([1]) @@ -92,7 +93,7 @@ def test_write_fraction_pass(self): haz_fl.orig = np.array([1]) haz_fl.event_name = ['1'] haz_fl.intensity = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])) - haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1])/2) + haz_fl.fraction = sparse.csr_matrix(np.array([0.5, 0.2, 0.1]) / 2) haz_fl.centroids.set_lat_lon(np.array([1, 2, 3]), np.array([1, 2, 3])) haz_fl.check() @@ -100,11 +101,13 @@ def test_write_fraction_pass(self): haz_read = Hazard('FL') haz_read.set_raster([os.path.join(DATA_DIR, 'test_write_hazard.tif')], - files_fraction=[os.path.join(DATA_DIR, 'test_write_hazard.tif')]) + files_fraction=[os.path.join(DATA_DIR, 'test_write_hazard.tif')]) self.assertEqual(haz_read.intensity.shape, (1, 9)) self.assertEqual(haz_read.fraction.shape, (1, 9)) - self.assertTrue(np.allclose(np.unique(np.array(haz_read.fraction.todense())), np.array([0.0, 0.05, 0.1, 0.25]))) - self.assertTrue(np.allclose(np.unique(np.array(haz_read.intensity.todense())), np.array([0.0, 0.05, 0.1, 0.25]))) + self.assertTrue(np.allclose(np.unique(np.array(haz_read.fraction.toarray())), + np.array([0.0, 0.05, 0.1, 0.25]))) + self.assertTrue(np.allclose(np.unique(np.array(haz_read.intensity.toarray())), + np.array([0.0, 0.05, 0.1, 0.25]))) # Execute Tests if __name__ == "__main__": diff --git a/climada/test/test_landslide_integr.py b/climada/test/test_landslide_integr.py index d655a51220..ae88059d0a 100644 --- a/climada/test/test_landslide_integr.py +++ b/climada/test/test_landslide_integr.py @@ -25,12 +25,9 @@ import os import datetime as dt from datetime import timedelta -import numpy as np import glob -from rasterio.windows import Window from climada.hazard import landslide from climada.hazard.landslide import Landslide -import math from climada.util.constants import DATA_DIR LS_FILE_DIR = os.path.join(DATA_DIR, 'system') @@ -38,30 +35,31 @@ DATA_DIR_TEST = os.path.join(os.path.dirname(__file__), 'data') class TestTiffFcts(unittest.TestCase): - """Unit tests for parts of the LS hazard module, but moved to integration tests - for reasons of runtime: Test functions for getting input tiffs in landslide module, + """Unit tests for parts of the LS hazard module, but moved to integration tests + for reasons of runtime: Test functions for getting input tiffs in landslide module, outside Landslide() instance""" def test_get_nowcast_tiff(self): start_date = dt.datetime.strftime(dt.datetime.now() - timedelta(5), '%Y-%m-%d') end_date = dt.datetime.strftime(dt.datetime.now() - timedelta(1), '%Y-%m-%d') - tif_type= ["monthly","daily"] - + tif_type = ["monthly", "daily"] + for item in tif_type: - landslide.get_nowcast_tiff(tif_type=item, starttime=start_date, endtime=end_date, save_path=DATA_DIR_TEST) - + landslide.get_nowcast_tiff(tif_type=item, starttime=start_date, endtime=end_date, + save_path=DATA_DIR_TEST) + search_criteria = "LS*.tif" LS_files_daily = glob.glob(os.path.join(DATA_DIR_TEST, search_criteria)) search_criteria = "*5400.tif" LS_files_monthly = glob.glob(os.path.join(os.getcwd(), search_criteria)) - - self.assertTrue(len(LS_files_daily)>0) - self.assertTrue(len(LS_files_monthly)==12) - + + self.assertTrue(len(LS_files_daily) > 0) + self.assertTrue(len(LS_files_monthly) == 12) + for item in LS_files_daily: os.remove(item) - + for item in LS_files_monthly: - os.remove(item) + os.remove(item) # def test_combine_nowcast_tiff(self): # landslide.combine_nowcast_tiff(DATA_DIR, search_criteria='test_global*.tif', operator="maximum") @@ -78,48 +76,51 @@ def test_get_nowcast_tiff(self): # for item in combined_monthly: # os.remove(item) -class TestLSHazard(unittest.TestCase): +class TestLSHazard(unittest.TestCase): """Integration test for LS hazard sets build in Landslide module""" def test_set_ls_model_hist(self): - """ Test the function set_LS_model for model 0 (historic hazard set)""" + """Test the function set_LS_model for model 0 (historic hazard set)""" LS_hist = Landslide() - LS_hist.set_ls_model_hist(bbox=[48, 10, 45, 7], \ - path_sourcefile=os.path.join(DATA_DIR_TEST, 'nasa_global_landslide_catalog_point.shp'), check_plots=0) + LS_hist.set_ls_model_hist( + bbox=[48, 10, 45, 7], + path_sourcefile=os.path.join(DATA_DIR_TEST, + 'nasa_global_landslide_catalog_point.shp'), + check_plots=0) self.assertEqual(LS_hist.size, 49) self.assertEqual(LS_hist.tag.haz_type, 'LS') - self.assertEqual(min(LS_hist.intensity.data),1) - self.assertEqual(max(LS_hist.intensity.data),1) + self.assertEqual(min(LS_hist.intensity.data), 1) + self.assertEqual(max(LS_hist.intensity.data), 1) + - def test_set_ls_model_prob(self): - """ Test the function set_LS_model for model versio UNEP_NGI, with and without neighbours""" + """Test the function set_LS_model for model versio UNEP_NGI, with and without neighbours""" LS_prob = Landslide() - LS_prob.set_ls_model_prob(ls_model="UNEP_NGI", n_years=500, bbox=[48, 10, 45, 7], \ - incl_neighbour=False, check_plots=0) + LS_prob.set_ls_model_prob(ls_model="UNEP_NGI", n_years=500, bbox=[48, 10, 45, 7], + incl_neighbour=False, check_plots=0) self.assertEqual(LS_prob.tag.haz_type, 'LS') - self.assertEqual(LS_prob.intensity_prob.shape,(1, 129600)) - self.assertEqual(max(LS_prob.intensity.data),1) - self.assertEqual(min(LS_prob.intensity.data),0) - self.assertEqual(LS_prob.intensity.shape,(1, 129600)) - self.assertAlmostEqual(max(LS_prob.intensity_prob.data),8.999999999e-05) - self.assertEqual(min(LS_prob.intensity_prob.data),5e-07) - self.assertEqual(LS_prob.centroids.size, 129600) - + self.assertEqual(LS_prob.intensity_prob.shape, (1, 129600)) + self.assertEqual(max(LS_prob.intensity.data), 1) + self.assertEqual(min(LS_prob.intensity.data), 0) + self.assertEqual(LS_prob.intensity.shape, (1, 129600)) + self.assertAlmostEqual(max(LS_prob.intensity_prob.data), 8.999999999e-05) + self.assertEqual(min(LS_prob.intensity_prob.data), 5e-07) + self.assertEqual(LS_prob.centroids.size, 129600) + LS_prob_nb = Landslide() - LS_prob_nb.set_ls_model_prob(ls_model="UNEP_NGI", n_years=500, bbox=[48, 10, 45, 7], \ - incl_neighbour=True, check_plots=0) + LS_prob_nb.set_ls_model_prob(ls_model="UNEP_NGI", n_years=500, bbox=[48, 10, 45, 7], + incl_neighbour=True, check_plots=0) self.assertEqual(LS_prob_nb.tag.haz_type, 'LS') - self.assertEqual(LS_prob_nb.intensity_prob.shape,(1, 129600)) - self.assertEqual(max(LS_prob_nb.intensity.data),1) - self.assertEqual(min(LS_prob_nb.intensity.data),0) - self.assertEqual(LS_prob_nb.intensity.shape,(1, 129600)) - self.assertAlmostEqual(max(LS_prob_nb.intensity_prob.data),8.999999999e-05) - self.assertEqual(min(LS_prob_nb.intensity_prob.data),5e-07) - self.assertEqual(LS_prob_nb.centroids.size, 129600) - - self.assertTrue(sum(LS_prob.intensity.data). - ---- - -Test tc_tracks module. -""" -import os -import unittest -import datetime as dt -import numpy as np -from netCDF4 import Dataset - -from climada.hazard.tc_tracks import TCTracks, _calc_land_geom, _apply_decay_coeffs -from climada.util.constants import SYSTEM_DIR - -DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') - -class TestDownload(unittest.TestCase): - """Test reading TC from IBTrACS files""" - - def test_raw_ibtracs_empty_pass(self): - """ read_ibtracs_netcdf""" - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1988234N13299', correct_pres=False) - self.assertEqual(tc_track.get_track(), []) - -class TestWriteRead(unittest.TestCase): - """Test writting and reading netcdf4 TCTracks instances """ - - def test_write_read_pass(self): - """ read_ibtracs_netcdf""" - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1988234N13299', correct_pres=True) - tc_track.write_netcdf(DATA_DIR) - - tc_read = TCTracks() - tc_read.read_netcdf(DATA_DIR) - - self.assertEqual(tc_track.get_track().sid, tc_read.get_track().sid) - -class TestIBTracs(unittest.TestCase): - """Test reading and model of TC from IBTrACS files""" - - def test_penv_rmax_penv_pass(self): - """ read_ibtracs_netcdf""" - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', storm_id='1992230N11325') - penv_ref = np.ones(97)*1010 - penv_ref[26] = 1011 - penv_ref[27] = 1012 - penv_ref[28] = 1013 - penv_ref[29] = 1014 - penv_ref[30] = 1015 - penv_ref[31] = 1014 - penv_ref[32] = 1014 - penv_ref[33] = 1014 - penv_ref[34] = 1014 - penv_ref[35] = 1012 - - self.assertTrue(np.array_equal(tc_track.get_track().environmental_pressure.values, - penv_ref)) - self.assertTrue(np.array_equal(tc_track.get_track().radius_max_wind.values, - np.zeros(97))) - - def test_read_raw_pass(self): - """Read a tropical cyclone.""" - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', storm_id='2017242N16333') - self.assertEqual(len(tc_track.data), 1) - self.assertEqual(tc_track.get_track().time.dt.year.values[0], 2017) - self.assertEqual(tc_track.get_track().time.dt.month.values[0], 8) - self.assertEqual(tc_track.get_track().time.dt.day.values[0], 30) - self.assertEqual(tc_track.get_track().time.dt.hour.values[0], 0) - self.assertAlmostEqual(tc_track.get_track().lat.values[0], 16.1 + 3.8146972514141453e-07) - self.assertAlmostEqual(tc_track.get_track().lon.values[0], -26.9 + 3.8146972514141453e-07) - self.assertAlmostEqual(tc_track.get_track().max_sustained_wind.values[0], 30) - self.assertAlmostEqual(tc_track.get_track().central_pressure.values[0], 1008) - self.assertAlmostEqual(tc_track.get_track().environmental_pressure.values[0], 1012) - self.assertAlmostEqual(tc_track.get_track().radius_max_wind.values[0], 60) - self.assertEqual(tc_track.get_track().time.size, 123) - - self.assertAlmostEqual(tc_track.get_track().lat.values[-1], 36.8 - 7.629394502828291e-07) - self.assertAlmostEqual(tc_track.get_track().lon.values[-1], -90.1 + 1.5258789005656581e-06) - self.assertAlmostEqual(tc_track.get_track().central_pressure.values[-1], 1005) - self.assertAlmostEqual(tc_track.get_track().max_sustained_wind.values[-1], 15) - self.assertAlmostEqual(tc_track.get_track().environmental_pressure.values[-1], 1008) - self.assertAlmostEqual(tc_track.get_track().radius_max_wind.values[-1], 60) - - self.assertFalse(np.isnan(tc_track.get_track().radius_max_wind.values).any()) - self.assertFalse(np.isnan(tc_track.get_track().environmental_pressure.values).any()) - self.assertFalse(np.isnan(tc_track.get_track().max_sustained_wind.values).any()) - self.assertFalse(np.isnan(tc_track.get_track().central_pressure.values).any()) - self.assertFalse(np.isnan(tc_track.get_track().lat.values).any()) - self.assertFalse(np.isnan(tc_track.get_track().lon.values).any()) - - self.assertEqual(tc_track.get_track().basin, 'NA') - self.assertEqual(tc_track.get_track().max_sustained_wind_unit, 'kn') - self.assertEqual(tc_track.get_track().central_pressure_unit, 'mb') - self.assertEqual(tc_track.get_track().sid, '2017242N16333') - self.assertEqual(tc_track.get_track().name, 'IRMA') - self.assertEqual(tc_track.get_track().orig_event_flag, True) - self.assertEqual(tc_track.get_track().data_provider, 'usa') - self.assertEqual(tc_track.get_track().category, 5) - - def test_read_range(self): - """Read a several TCs.""" - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', storm_id=None, - year_range=(1915, 1916), basin='WP') - self.assertEqual(tc_track.size, 0) - - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), basin='EP', correct_pres=False) - self.assertEqual(tc_track.size, 32) - - tc_track = TCTracks() - tc_track.read_ibtracs_netcdf(provider='usa', year_range=(1993, 1994), basin='EP') - self.assertEqual(tc_track.size, 43) - - def test_filter_ibtracs_track_pass(self): - """ Test _filter_ibtracs """ - fn_nc = os.path.join(os.path.abspath(SYSTEM_DIR), 'IBTrACS.ALL.v04r00.nc') - - storm_id='1988234N13299' - tc_track = TCTracks() - sel = tc_track._filter_ibtracs(fn_nc, storm_id, year_range=None, basin=None) - self.assertTrue(sel, np.array([10000])) - - def test_filter_ibtracs_year_basin_pass(self): - """ Test _filter_ibtracs """ - fn_nc = os.path.join(os.path.abspath(SYSTEM_DIR), 'IBTrACS.ALL.v04r00.nc') - - tc_track = TCTracks() - sel = tc_track._filter_ibtracs(fn_nc, storm_id=None, year_range=(1915, 1916), - basin='WP') - - nc_data=Dataset(fn_nc) - for i_sel in sel: - self.assertEqual('WP', - ''.join(nc_data.variables['basin'][i_sel, 0, :].astype(str))) - isot = nc_data.variables['iso_time'][i_sel, :, :] - val_len = isot.mask[isot.mask==False].shape[0]//isot.shape[1] - date = isot.data[:val_len] - year = dt.datetime.strptime(''.join(date[0].astype(str)), '%Y-%m-%d %H:%M:%S').year - self.assertTrue(year <= 1915 or year >= 1916) - - self.assertEqual(sel.size, 48) - - def test_ibtracs_correct_pass(self): - """ Check correct_pres option """ - tc_try = TCTracks() - tc_try.read_ibtracs_netcdf(provider='usa', storm_id='1982267N25289', correct_pres=True) - self.assertAlmostEqual(tc_try.data[0].central_pressure.values[0], 1011.2905126953125) - self.assertAlmostEqual(tc_try.data[0].central_pressure.values[5], 1005.706236328125) - self.assertAlmostEqual(tc_try.data[0].central_pressure.values[-1], 1011.6555029296875) - - def test_wrong_decay_pass(self): - """ Test decay not implemented when coefficient < 1 """ - track = TCTracks() - track.read_ibtracs_netcdf(provider='usa', storm_id='1975178N28281') - - track_gen = track.data[0] - track_gen['lat'] = np.array([28.20340431, 28.7915261 , 29.38642458, 29.97836984, 30.56844404, - 31.16265292, 31.74820301, 32.34449825, 32.92261894, 33.47430891, - 34.01492525, 34.56789399, 35.08810845, 35.55965893, 35.94835174, - 36.29355848, 36.45379561, 36.32473812, 36.07552209, 35.92224784, - 35.84144186, 35.78298537, 35.86090718, 36.02440372, 36.37555559, - 37.06207765, 37.73197352, 37.97524273, 38.05560287, 38.21901208, - 38.31486156, 38.30813367, 38.28481808, 38.28410366, 38.25894812, - 38.20583372, 38.22741099, 38.39970022, 38.68367797, 39.08329904, - 39.41434629, 39.424984 , 39.31327716, 39.30336335, 39.31714429, - 39.27031932, 39.30848775, 39.48759833, 39.73326595, 39.96187967, - 40.26954226, 40.76882202, 41.40398607, 41.93809726, 42.60395785, - 43.57074792, 44.63816143, 45.61450458, 46.68528511, 47.89209365, - 49.15580502]) - track_gen['lon'] = np.array([-79.20514075, -79.25243311, -79.28393082, -79.32324646, - -79.36668585, -79.41495519, -79.45198688, -79.40580325, - -79.34965443, -79.36938122, -79.30294825, -79.06809546, - -78.70281969, -78.29418936, -77.82170609, -77.30034709, - -76.79004969, -76.37038827, -75.98641014, -75.58383356, - -75.18310414, -74.7974524 , -74.3797645 , -73.86393572, - -73.37910948, -73.01059003, -72.77051313, -72.68011328, - -72.66864779, -72.62579773, -72.56307717, -72.46607618, - -72.35871353, -72.31120649, -72.15537583, -71.75577051, - -71.25287498, -70.75527907, -70.34788946, -70.17518421, - -70.04446577, -69.76582749, -69.44372386, -69.15881376, - -68.84351922, -68.47890287, -68.04184565, -67.53541437, - -66.94008642, -66.25596075, -65.53496635, -64.83491802, - -64.12962685, -63.54118808, -62.72934383, -61.34915091, - -59.72580755, -58.24404252, -56.71972992, -55.0809336 , - -53.31524758]) - - v_rel = {3: 0.002249541544102336, 1: 0.00046889526284203036, 4: 0.002649273787364977, 2: 0.0016426186150461349, 5: 0.00246400811445618, 7: 0.0030442198547309075, 6: 0.002346537842810565} - p_rel = {3: (1.028420239620591, 0.003174733355067952), 1: (1.0046803184177564, 0.0007997633912500546), 4: (1.0498749735343516, 0.0034665588904747515), 2: (1.0140127424090262, 0.002131858515233042), 5: (1.0619445995372885, 0.003467268426139696), 7: (1.0894914184297835, 0.004315034379018768), 6: (1.0714354641894077, 0.002783787561718677)} - track_gen.attrs['orig_event_flag'] = False - - cp_ref = np.array([1012., 1012.]) - land_geom = _calc_land_geom([track_gen]) - track_res = _apply_decay_coeffs(track_gen, v_rel, p_rel, land_geom, True) - self.assertTrue(np.array_equal(cp_ref, track_res.central_pressure[9:11])) - -# Execute Tests -if __name__ == "__main__": - TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDownload) - TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestIBTracs)) - TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWriteRead)) - unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/test/test_trop_cyclone.py b/climada/test/test_trop_cyclone.py index 8343ac7f8e..4189e7e87b 100644 --- a/climada/test/test_trop_cyclone.py +++ b/climada/test/test_trop_cyclone.py @@ -28,28 +28,32 @@ class TestClimateSce(unittest.TestCase): def test_apply_criterion_track(self): - """ Test _apply_criterion function. """ + """Test _apply_criterion function.""" tc = TropCyclone() - tc.intensity = sparse.lil_matrix(np.zeros((4, 10))) + tc.intensity = np.zeros((4, 10)) tc.intensity[0, :] = np.arange(10) tc.intensity[1, 5] = 10 tc.intensity[2, :] = np.arange(10, 20) tc.intensity[3, 3] = 3 - tc.intensity = tc.intensity.tocsr() + tc.intensity = sparse.csr_matrix(tc.intensity) tc.basin = ['NA'] * 4 tc.basin[3] = 'NO' tc.category = np.array([2, 0, 4, 1]) tc.event_id = np.arange(4) - tc.frequency = np.ones(4)*0.5 + tc.frequency = np.ones(4) * 0.5 tc_cc = tc.set_climate_scenario_knu(ref_year=2050, rcp_scenario=45) - self.assertTrue(np.allclose(tc.intensity[1, :].todense(), tc_cc.intensity[1, :].todense())) - self.assertTrue(np.allclose(tc.intensity[3, :].todense(), tc_cc.intensity[3, :].todense())) - self.assertFalse(np.allclose(tc.intensity[0, :].todense(), tc_cc.intensity[0, :].todense())) - self.assertFalse(np.allclose(tc.intensity[2, :].todense(), tc_cc.intensity[2, :].todense())) + self.assertTrue(np.allclose(tc.intensity[1, :].toarray(), tc_cc.intensity[1, :].toarray())) + self.assertTrue(np.allclose(tc.intensity[3, :].toarray(), tc_cc.intensity[3, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[0, :].toarray(), tc_cc.intensity[0, :].toarray())) + self.assertFalse( + np.allclose(tc.intensity[2, :].toarray(), tc_cc.intensity[2, :].toarray())) self.assertTrue(np.allclose(tc.frequency, tc_cc.frequency)) - self.assertEqual(tc_cc.tag.description, 'climate change scenario for year 2050 and RCP 45 from Knutson et al 2015.') + self.assertEqual( + tc_cc.tag.description, + 'climate change scenario for year 2050 and RCP 45 from Knutson et al 2015.') if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestClimateSce) - unittest.TextTestRunner(verbosity=2).run(TESTS) \ No newline at end of file + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/__init__.py b/climada/util/__init__.py index 79fcf1cfbb..ce70600a69 100755 --- a/climada/util/__init__.py +++ b/climada/util/__init__.py @@ -22,3 +22,6 @@ from .config import * from .coordinates import * from .save import * + +from pint import UnitRegistry +ureg = UnitRegistry() diff --git a/climada/util/alpha_shape.py b/climada/util/alpha_shape.py deleted file mode 100644 index 2eb7202996..0000000000 --- a/climada/util/alpha_shape.py +++ /dev/null @@ -1,81 +0,0 @@ -""" -""" -import numpy as np - -from shapely.ops import cascaded_union, polygonize -from scipy.spatial import Delaunay -import math -import shapely.geometry as geometry -from descartes import PolygonPatch -import pylab as pl - -def alpha_shape(points, alpha): - """ - Compute the alpha shape (concave hull) of a set - of points. - Source: http://blog.thehumangeo.com/2014/05/12/drawing-boundaries-in-python/ - @param points: Iterable container of points. - @param alpha: alpha value to influence the - gooeyness of the border. Smaller numbers - don't fall inward as much as larger numbers. - Too large, and you lose everything! - """ - if len(points) < 4: - # When you have a triangle, there is no sense - # in computing an alpha shape. - return geometry.MultiPoint(list(points)).convex_hull - - def add_edge(edges, edge_points, coords, i, j): - """ - Add a line between the i-th and j-th points, - if not in the list already - """ - if (i, j) in edges or (j, i) in edges: - # already added - return - edges.add( (i, j) ) - edge_points.append(coords[ [i, j] ]) - coords = np.array([point.coords[0] - for point in points]) - tri = Delaunay(coords) - edges = set() - edge_points = [] - # loop over triangles: - # ia, ib, ic = indices of corner points of the - # triangle - for ia, ib, ic in tri.vertices: - pa = coords[ia] - pb = coords[ib] - pc = coords[ic] - # Lengths of sides of triangle - a = math.sqrt((pa[0]-pb[0])**2 + (pa[1]-pb[1])**2) - b = math.sqrt((pb[0]-pc[0])**2 + (pb[1]-pc[1])**2) - c = math.sqrt((pc[0]-pa[0])**2 + (pc[1]-pa[1])**2) - # Semiperimeter of triangle - s = (a + b + c)/2.0 - # Area of triangle by Heron's formula - area = math.sqrt(s*(s-a)*(s-b)*(s-c)) - if area > 0: - circum_r = a*b*c/(4.0*area) - # Here's the radius filter. - #print circum_r - if circum_r < 1.0/alpha: - add_edge(edges, edge_points, coords, ia, ib) - add_edge(edges, edge_points, coords, ib, ic) - add_edge(edges, edge_points, coords, ic, ia) - m = geometry.MultiLineString(edge_points) - triangles = list(polygonize(m)) - return cascaded_union(triangles), edge_points - -def plot_polygon(polygon): - fig = pl.figure(figsize=(10,10)) - ax = fig.add_subplot(111) - margin = .3 - x_min, y_min, x_max, y_max = polygon.bounds - ax.set_xlim([x_min-margin, x_max+margin]) - ax.set_ylim([y_min-margin, y_max+margin]) - patch = PolygonPatch(polygon, fc='#999999', - ec='#000000', fill=True, - zorder=-1) - ax.add_patch(patch) - return fig diff --git a/climada/util/checker.py b/climada/util/checker.py index f270349cac..84fbf8d428 100644 --- a/climada/util/checker.py +++ b/climada/util/checker.py @@ -19,11 +19,12 @@ module containing functions to check variables properties. """ -__all__ = ['size', - 'shape', - 'array_optional', - 'array_default' - ] +__all__ = [ + 'size', + 'shape', + 'array_optional', + 'array_default' +] import logging import numpy as np @@ -47,14 +48,13 @@ def check_oligatories(var_dict, var_obl, name_prefix, n_size, n_row, n_col): """ for var_name, var_val in var_dict.items(): if var_name in var_obl: - if (isinstance(var_val, np.ndarray) and var_val.ndim == 1) or \ - isinstance(var_val, list): - size(n_size, var_val, name_prefix+var_name) + if (isinstance(var_val, np.ndarray) and var_val.ndim == 1) \ + or isinstance(var_val, list): + size(n_size, var_val, name_prefix + var_name) elif (isinstance(var_val, np.ndarray) and var_val.ndim == 2): - shape(n_row, n_col, var_val, name_prefix+var_name) - elif isinstance(var_val, (np.ndarray, sparse.csr.csr_matrix)) \ - and var_val.ndim == 2: - shape(n_row, n_col, var_val, name_prefix+var_name) + shape(n_row, n_col, var_val, name_prefix + var_name) + elif isinstance(var_val, (np.ndarray, sparse.csr.csr_matrix)) and var_val.ndim == 2: + shape(n_row, n_col, var_val, name_prefix + var_name) def check_optionals(var_dict, var_opt, name_prefix, n_size): """Check size of obligatory variables. @@ -71,7 +71,7 @@ def check_optionals(var_dict, var_opt, name_prefix, n_size): for var_name, var_val in var_dict.items(): if var_name in var_opt: if isinstance(var_val, (np.ndarray, list)): - array_optional(n_size, var_val, name_prefix+var_name) + array_optional(n_size, var_val, name_prefix + var_name) def empty_optional(var, var_name): """Check if a data structure is empty.""" @@ -86,7 +86,7 @@ def size(exp_len, var, var_name): """ try: if exp_len != len(var): - LOGGER.error("Invalid %s size: %s != %s.", var_name, exp_len, \ + LOGGER.error("Invalid %s size: %s != %s.", var_name, exp_len, len(var)) raise ValueError except TypeError: @@ -101,11 +101,11 @@ def shape(exp_row, exp_col, var, var_name): """ try: if exp_row != var.shape[0]: - LOGGER.error("Invalid %s row size: %s != %s.", var_name, exp_row,\ + LOGGER.error("Invalid %s row size: %s != %s.", var_name, exp_row, var.shape[0]) raise ValueError if exp_col != var.shape[1]: - LOGGER.error("Invalid %s column size: %s != %s.", var_name, \ + LOGGER.error("Invalid %s column size: %s != %s.", var_name, exp_col, var.shape[1]) raise ValueError except TypeError: diff --git a/climada/util/config.py b/climada/util/config.py index dd48897f8c..07d3a27bf2 100644 --- a/climada/util/config.py +++ b/climada/util/config.py @@ -19,21 +19,21 @@ Define configuration parameters. """ -__all__ = ['CONFIG', - 'setup_logging', - 'setup_conf_user', - 'setup_environ' - ] +__all__ = [ + 'CONFIG', + 'setup_logging', + 'setup_conf_user', +] import sys import os import json import logging -import shutil from pkg_resources import Requirement, resource_filename from climada.util.constants import SOURCE_DIR + WORKING_DIR = os.getcwd() WINDOWS_END = WORKING_DIR[0:3] UNIX_END = '/' @@ -79,7 +79,7 @@ def check_conf(): DEFAULT_PATH = os.path.abspath(os.path.join(CONFIG_DIR, 'defaults.conf')) if not os.path.isfile(DEFAULT_PATH): - DEFAULT_PATH = resource_filename(Requirement.parse('climada'), \ + DEFAULT_PATH = resource_filename(Requirement.parse('climada'), 'defaults.conf') with open(DEFAULT_PATH) as def_conf: LOGGER.debug('Loading default config file: %s', DEFAULT_PATH) @@ -99,8 +99,8 @@ def setup_conf_user(): conf_name = 'climada.conf' user_file = os.path.abspath(os.path.join(WORKING_DIR, conf_name)) while not os.path.isfile(user_file) and user_file != UNIX_END + conf_name \ - and user_file != WINDOWS_END + conf_name: - user_file = os.path.abspath(os.path.join(user_file, os.pardir, \ + and user_file != WINDOWS_END + conf_name: + user_file = os.path.abspath(os.path.join(user_file, os.pardir, os.pardir, conf_name)) if os.path.isfile(user_file): @@ -125,20 +125,3 @@ def setup_conf_user(): CONFIG['cost_benefit'] = userconfig['cost_benefit'] check_conf() - -def setup_environ(): - """ Parse binary environment and correct if necessary """ - if shutil.which('eio') is None: - # correct binary path - os.environ['PATH'] = os.environ['PATH'].replace(';', ':') - env_cpy = os.environ['PATH'] - first_dot = env_cpy.find(':') - while first_dot >= 0: - if not os.path.isdir(env_cpy[:first_dot]): - os.environ['PATH'] = os.environ['PATH'].replace(env_cpy[:first_dot+1], '') - env_cpy = env_cpy[first_dot+1:] - first_dot = env_cpy.find(':') - # add environment bin path - if CONFIG['config']['env_name'] not in os.environ['PATH']: - os.environ['PATH'] = os.environ['PATH'].replace('conda3/bin', \ - 'conda3/envs/' + CONFIG['config']['env_name'] + '/bin') diff --git a/climada/util/constants.py b/climada/util/constants.py index c4066d80aa..b9bdfaf7dd 100644 --- a/climada/util/constants.py +++ b/climada/util/constants.py @@ -30,92 +30,153 @@ 'EARTH_RADIUS_KM', 'GLB_CENTROIDS_MAT', 'GLB_CENTROIDS_NC', + 'ISIMIP_GPWV3_NATID_150AS', + 'NATEARTH_CENTROIDS', 'DEMO_GDP2ASSET', - 'NAT_REG_ID', + 'RIVER_FLOOD_REGIONS_CSV', 'TC_ANDREW_FL', 'HAZ_DEMO_H5', 'EXP_DEMO_H5', 'WS_DEMO_NC'] import os +# pylint: disable=unused-import +# without importing numpy ahead of fiona the debugger may run into an error +import numpy from fiona.crs import from_epsg SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) -""" climada directory """ +"""climada directory""" DATA_DIR = os.path.abspath(os.path.join(SOURCE_DIR, os.pardir, 'data')) -""" Folder containing the data """ +"""Folder containing the data""" SYSTEM_DIR = os.path.abspath(os.path.join(DATA_DIR, 'system')) -""" Folder containing the data used internally """ +"""Folder containing the data used internally""" -GLB_CENTROIDS_NC = os.path.join(SYSTEM_DIR, 'NatID_grid_0150as.nc') -""" Global centroids nc.""" +ISIMIP_GPWV3_NATID_150AS = os.path.join(SYSTEM_DIR, 'NatID_grid_0150as.nc') +""" +Compressed version of National Identifier Grid in 150 arc-seconds from +ISIMIP project, based on GPWv3. Location in ISIMIP repository: + +`ISIMIP2a/InputData/landuse_humaninfluences/population/ID_GRID/Nat_id_grid_ISIMIP.nc` + +More references: + +* https://www.isimip.org/gettingstarted/input-data-bias-correction/details/13/ +* https://sedac.ciesin.columbia.edu/data/set/gpw-v3-national-identifier-grid +""" + +GLB_CENTROIDS_NC = ISIMIP_GPWV3_NATID_150AS +"""For backwards compatibility, it remains available under its old name.""" GLB_CENTROIDS_MAT = os.path.join(SYSTEM_DIR, 'GLB_NatID_grid_0360as_adv_2.mat') -""" Global centroids.""" +"""Global centroids""" + +NATEARTH_CENTROIDS = { + 150: os.path.join(SYSTEM_DIR, 'NatEarth_Centroids_150as.hdf5'), + 360: os.path.join(SYSTEM_DIR, 'NatEarth_Centroids_360as.hdf5'), +} +""" +Global centroids at XXX arc-seconds resolution, +including region ids from Natural Earth. The 360 AS file includes distance to +coast from NASA. +""" ENT_TEMPLATE_XLS = os.path.join(SYSTEM_DIR, 'entity_template.xlsx') -""" Entity template in xls format.""" +"""Entity template in xls format.""" HAZ_TEMPLATE_XLS = os.path.join(SYSTEM_DIR, 'hazard_template.xlsx') -""" Hazard template in xls format.""" -NAT_REG_ID = os.path.join(SYSTEM_DIR, 'NatRegIDs.csv') -""" Look-up table ISO3 codes""" +"""Hazard template in xls format.""" +RIVER_FLOOD_REGIONS_CSV = os.path.join(SYSTEM_DIR, 'NatRegIDs.csv') +"""Look-up table for river flood module""" HAZ_DEMO_FL = os.path.join(DATA_DIR, 'demo', 'SC22000_VE__M1.grd.gz') -""" Raster file of flood over Venezuela. Model from GAR2015""" +"""Raster file of flood over Venezuela. Model from GAR2015""" +HAZ_DEMO_FLDDPH = os.path.join( + DATA_DIR, 'demo', 'flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc') +"""NetCDF4 Flood depth from isimip simulations""" -HAZ_DEMO_FLDDPH = os.path.join(DATA_DIR, 'demo', 'flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc') -""" NetCDF4 Flood depth from isimip simulations""" +HAZ_DEMO_FLDFRC = os.path.join( + DATA_DIR, 'demo', 'fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc') +"""NetCDF4 Flood fraction from isimip simulations""" +HAZ_DEMO_MAT = os.path.join(DATA_DIR, 'demo', 'atl_prob_nonames.mat') +""" +Hazard demo from climada in MATLAB: hurricanes from 1851 to 2011 over Florida with 100 centroids. +""" -HAZ_DEMO_FLDFRC = os.path.join(DATA_DIR, 'demo', 'fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc') -""" NetCDF4 Flood fraction from isimip simulations""" - -HAZ_DEMO_MAT = os.path.join(DATA_DIR, 'demo', 'atl_prob.mat') -""" Hazard demo from climada in MATLAB: hurricanes from 1851 to 2011 over Florida with 100 centroids.""" - -HAZ_DEMO_H5 = os.path.join(DATA_DIR, 'demo', 'tc_fl_1975_2011.h5') -""" Hazard demo in h5 format: ibtracs from 1975 to 2011 over Florida with 2500 centroids.""" +HAZ_DEMO_H5 = os.path.join(DATA_DIR, 'demo', 'tc_fl_1990_2004.h5') +""" +Hazard demo in hdf5 format: IBTrACS from 1990 to 2004 over Florida with 2500 centroids. +""" DEMO_GDP2ASSET = os.path.join(DATA_DIR, 'demo', 'gdp2asset_CHE_exposure.nc') """Exposure demo file for GDP2Asset""" WS_DEMO_NC = [os.path.join(DATA_DIR, 'demo', 'fp_lothar_crop-test.nc'), os.path.join(DATA_DIR, 'demo', 'fp_xynthia_crop-test.nc')] -""" Winter storm in Europe files. These test files have been generated using -the netCDF kitchen sink: ncks -d latitude,50.5,54.0 -d longitude,3.0,7.5 -./file_in.nc ./file_out.nc """ +""" +Winter storm in Europe files. These test files have been generated using +the netCDF kitchen sink: + +>>> ncks -d latitude,50.5,54.0 -d longitude,3.0,7.5 ./file_in.nc ./file_out.nc +""" ENT_DEMO_TODAY = os.path.join(DATA_DIR, 'demo', 'demo_today.xlsx') -""" Entity demo present in xslx format.""" +"""Entity demo present in xslx format.""" ENT_DEMO_FUTURE = os.path.join(DATA_DIR, 'demo', 'demo_future_TEST.xlsx') -""" Entity demo future in xslx format.""" +"""Entity demo future in xslx format.""" EXP_DEMO_H5 = os.path.join(DATA_DIR, 'demo', 'exp_demo_today.h5') -""" Exposures over Florida """ +"""Exposures over Florida""" TC_ANDREW_FL = os.path.join(DATA_DIR, 'demo', 'ibtracs_global_intp-None_1992230N11325.csv') -""" Tropical cyclone Andrew in Florida """ +"""Tropical cyclone Andrew in Florida""" + + +ISIMIP_NATID_TO_ISO = [ + '', 'ABW', 'AFG', 'AGO', 'AIA', 'ALB', 'AND', 'ANT', 'ARE', 'ARG', 'ARM', + 'ASM', 'ATG', 'AUS', 'AUT', 'AZE', 'BDI', 'BEL', 'BEN', 'BFA', 'BGD', 'BGR', + 'BHR', 'BHS', 'BIH', 'BLR', 'BLZ', 'BMU', 'BOL', 'BRA', 'BRB', 'BRN', 'BTN', + 'BWA', 'CAF', 'CAN', 'CHE', 'CHL', 'CHN', 'CIV', 'CMR', 'COD', 'COG', 'COK', + 'COL', 'COM', 'CPV', 'CRI', 'CUB', 'CYM', 'CYP', 'CZE', 'DEU', 'DJI', 'DMA', + 'DNK', 'DOM', 'DZA', 'ECU', 'EGY', 'ERI', 'ESP', 'EST', 'ETH', 'FIN', 'FJI', + 'FLK', 'FRA', 'FRO', 'FSM', 'GAB', 'GBR', 'GEO', 'GGY', 'GHA', 'GIB', 'GIN', + 'GLP', 'GMB', 'GNB', 'GNQ', 'GRC', 'GRD', 'GTM', 'GUF', 'GUM', 'GUY', 'HKG', + 'HND', 'HRV', 'HTI', 'HUN', 'IDN', 'IMN', 'IND', 'IRL', 'IRN', 'IRQ', 'ISL', + 'ISR', 'ITA', 'JAM', 'JEY', 'JOR', 'JPN', 'KAZ', 'KEN', 'KGZ', 'KHM', 'KIR', + 'KNA', 'KOR', 'KWT', 'LAO', 'LBN', 'LBR', 'LBY', 'LCA', 'LIE', 'LKA', 'LSO', + 'LTU', 'LUX', 'LVA', 'MAC', 'MAR', 'MCO', 'MDA', 'MDG', 'MDV', 'MEX', 'MHL', + 'MKD', 'MLI', 'MLT', 'MMR', 'MNG', 'MNP', 'MOZ', 'MRT', 'MSR', 'MTQ', 'MUS', + 'MWI', 'MYS', 'MYT', 'NAM', 'NCL', 'NER', 'NFK', 'NGA', 'NIC', 'NIU', 'NLD', + 'NOR', 'NPL', 'NRU', 'NZL', 'OMN', 'PAK', 'PAN', 'PCN', 'PER', 'PHL', 'PLW', + 'PNG', 'POL', 'PRI', 'PRK', 'PRT', 'PRY', 'PSE', 'PYF', 'QAT', 'REU', 'ROU', + 'RUS', 'RWA', 'SAU', 'SCG', 'SDN', 'SEN', 'SGP', 'SHN', 'SJM', 'SLB', 'SLE', + 'SLV', 'SMR', 'SOM', 'SPM', 'STP', 'SUR', 'SVK', 'SVN', 'SWE', 'SWZ', 'SYC', + 'SYR', 'TCA', 'TCD', 'TGO', 'THA', 'TJK', 'TKL', 'TKM', 'TLS', 'TON', 'TTO', + 'TUN', 'TUR', 'TUV', 'TWN', 'TZA', 'UGA', 'UKR', 'URY', 'USA', 'UZB', 'VCT', + 'VEN', 'VGB', 'VIR', 'VNM', 'VUT', 'WLF', 'WSM', 'YEM', 'ZAF', 'ZMB', 'ZWE', +] +"""ISO3166 alpha-3 codes of countries used in ISIMIP_GPWV3_NATID_150AS""" ONE_LAT_KM = 111.12 -""" Mean one latitude (in degrees) to km """ +"""Mean one latitude (in degrees) to km""" EARTH_RADIUS_KM = 6371 -""" Earth radius in km """ +"""Earth radius in km""" DEF_EPSG = 4326 -""" Default EPSG code """ +"""Default EPSG code""" DEF_CRS = from_epsg(DEF_EPSG) -""" Default coordinate reference system WGS 84 """ +"""Default coordinate reference system WGS 84""" diff --git a/climada/util/coordinates.py b/climada/util/coordinates.py index cfa6a3a190..c0419fadae 100644 --- a/climada/util/coordinates.py +++ b/climada/util/coordinates.py @@ -18,58 +18,264 @@ Define functions to handle with coordinates """ -import os + import copy import logging -from multiprocessing import cpu_count import math -import numpy as np +from multiprocessing import cpu_count +import os +import zipfile + from cartopy.io import shapereader -import shapely.vectorized -import shapely.ops -from shapely.geometry import Polygon, MultiPolygon, Point, box +import dask.dataframe as dd from fiona.crs import from_epsg -from iso3166 import countries as iso_cntry import geopandas as gpd -import rasterio -from rasterio.transform import from_origin -from rasterio.crs import CRS -from rasterio.mask import mask -from rasterio.warp import reproject, Resampling, calculate_default_transform -from rasterio.features import rasterize -import dask.dataframe as dd +from iso3166 import countries as iso_cntry +import numpy as np import pandas as pd +import rasterio +import rasterio.crs +import rasterio.features +import rasterio.mask +import rasterio.warp +import scipy.interpolate +from shapely.geometry import Polygon, MultiPolygon, Point, box +import shapely.ops +import shapely.vectorized +import shapefile - -from climada.util.constants import DEF_CRS, SYSTEM_DIR +from climada.util.constants import (DEF_CRS, SYSTEM_DIR, ONE_LAT_KM, + NATEARTH_CENTROIDS, + ISIMIP_GPWV3_NATID_150AS, + ISIMIP_NATID_TO_ISO, + RIVER_FLOOD_REGIONS_CSV) +from climada.util.files_handler import download_file +import climada.util.hdf5_handler as hdf5 +from climada.util.constants import DATA_DIR pd.options.mode.chained_assignment = None LOGGER = logging.getLogger(__name__) NE_EPSG = 4326 -""" Natural Earth CRS EPSG """ +"""Natural Earth CRS EPSG""" NE_CRS = from_epsg(NE_EPSG) -""" Natural Earth CRS """ +"""Natural Earth CRS""" TMP_ELEVATION_FILE = os.path.join(SYSTEM_DIR, 'tmp_elevation.tif') -""" Path of elevation file written in set_elevation """ +"""Path of elevation file written in set_elevation""" DEM_NODATA = -9999 -""" Value to use for no data values in DEM, i.e see points """ +"""Value to use for no data values in DEM, i.e see points""" MAX_DEM_TILES_DOWN = 300 -""" Maximum DEM tiles to dowload """ +"""Maximum DEM tiles to dowload""" + +def latlon_to_geosph_vector(lat, lon, rad=False, basis=False): + """Convert lat/lon coodinates to radial vectors (on geosphere) + + Parameters + ---------- + lat, lon : ndarrays of floats, same shape + Latitudes and longitudes of points. + rad : bool, optional + If True, latitude and longitude are not given in degrees but in radians. + basis : bool, optional + If True, also return an orthonormal basis of the tangent space at the + given points in lat-lon coordinate system. Default: False. + + Returns + ------- + vn : ndarray of floats, shape (..., 3) + Same shape as lat/lon input with additional axis for components. + vbasis : ndarray of floats, shape (..., 2, 3) + Only present, if `basis` is True. Same shape as lat/lon input with + additional axes for components of the two basis vectors. + """ + if rad: + rad_lat = lat + 0.5 * np.pi + rad_lon = lon + else: + rad_lat = np.radians(lat + 90) + rad_lon = np.radians(lon) + sin_lat, cos_lat = np.sin(rad_lat), np.cos(rad_lat) + sin_lon, cos_lon = np.sin(rad_lon), np.cos(rad_lon) + vecn = np.stack((sin_lat * cos_lon, sin_lat * sin_lon, cos_lat), axis=-1) + if basis: + vbasis = np.stack(( + cos_lat * cos_lon, cos_lat * sin_lon, -sin_lat, + -sin_lon, cos_lon, np.zeros_like(cos_lat), + ), axis=-1).reshape(lat.shape + (2, 3)) + return vecn, vbasis + return vecn + +def lon_normalize(lon, center=0.0): + """ Normalizes degrees such that always -180 < lon - center <= 180 + + The input data is modified in place (!) using the following operations: + + (lon) -> (lon ± 360) -def grid_is_regular(coord): - """Return True if grid is regular. If True, returns height and width. + Parameters: + lon (np.array): Longitudinal coordinates + center (float, optional): Central longitude value to use instead of 0. + + Returns: + np.array (same as input) + """ + bounds = (center - 180, center + 180) + maxiter = 10 + i = 0 + while True: + msk1 = (lon > bounds[1]) + lon[msk1] -= 360 + msk2 = (lon <= bounds[0]) + lon[msk2] += 360 + if msk1.sum() == 0 and msk2.sum() == 0: + break + i += 1 + if i > maxiter: + LOGGER.warning("lon_normalize: killed before finishing") + break + return lon + +def latlon_bounds(lat, lon, buffer=0.0): + """Bounds of a set of degree values, respecting the periodicity in longitude + + The longitudinal upper bound may be 180 or larger to make sure that the upper bound is always + larger than the lower bound. The lower longitudinal bound will never lie below -180 and it will + only assume the value -180 if the specified buffering enforces it. + + Note that, as a consequence of this, the returned bounds do not satisfy the inequality + `lon_min <= lon <= lon_max` in general! + + Usually, an application of this function is followed by a renormalization of longitudinal + values around the longitudinal middle value: + + >>> bounds = latlon_bounds(lat, lon) + >>> lon_mid = 0.5 * (bounds[0] + bounds[2]) + >>> lon = lon_normalize(lon, center=lon_mid) + >>> np.all((bounds[0] <= lon) & (lon <= bounds[2])) + + Example: + >>> latlon_bounds(np.array([0, -2, 5]), np.array([-179, 175, 178])) + (175, -2, 181, 5) + >>> latlon_bounds(np.array([0, -2, 5]), np.array([-179, 175, 178]), buffer=1) + (174, -3, 182, 6) Parameters: - coord (np.array): + lat (np.array): Latitudinal coordinates + lon (np.array): Longitudinal coordinates + buffer (float, optional): Buffer to add to all sides of the bounding box. Default: 0.0. Returns: - bool (is regular), int (height), int (width) + tuple (lon_min, lat_min, lon_max, lat_max) + """ + lon = lon_normalize(lon.copy()) + lon_uniq = np.unique(lon) + lon_uniq = np.concatenate([lon_uniq, [360 + lon_uniq[0]]]) + lon_diff = np.diff(lon_uniq) + gap_max = np.argmax(lon_diff) + lon_diff_max = lon_diff[gap_max] + if lon_diff_max < 2: + # looks like the data covers the whole range [-180, 180] rather evenly + lon_min = max(lon_uniq[0] - buffer, -180) + lon_max = min(lon_uniq[-2] + buffer, 180) + else: + lon_min = lon_uniq[gap_max + 1] + lon_max = lon_uniq[gap_max] + if lon_min > 180: + lon_min -= 360 + else: + lon_max += 360 + lon_min -= buffer + lon_max += buffer + if lon_min <= -180: + lon_min += 360 + lon_max += 360 + return (lon_min, max(lat.min() - buffer, -90), lon_max, min(lat.max() + buffer, 90)) + +def dist_approx(lat1, lon1, lat2, lon2, log=False, normalize=True, + method="equirect"): + """Compute approximation of geodistance in km + + Parameters + ---------- + lat1, lon1 : ndarrays of floats, shape (nbatch, nx) + Latitudes and longitudes of first points. + lat2, lon2 : ndarrays of floats, shape (nbatch, ny) + Latitudes and longitudes of second points. + log : bool, optional + If True, return the tangential vectors at the first points pointing to + the second points (Riemannian logarithm). Default: False. + normalize : bool, optional + If False, assume that lon values are already between -180 and 180. + Default: True + method : str, optional + Specify an approximation method to use: + * "equirect": equirectangular; very fast, good only at small distances. + * "geosphere": spherical approximation, slower, but much higher accuracy. + Default: "equirect". + + Returns + ------- + dists : ndarray of floats, shape (nbatch, nx, ny) + Approximate distances in km. + vtan : ndarray of floats, shape (nbatch, nx, ny, 2) + If `log` is True, tangential vectors at first points in local + lat-lon coordinate system. + """ + if method == "equirect": + if normalize: + lon_normalize(lon1) + lon_normalize(lon2) + d_lat = lat2[:, None] - lat1[:, :, None] + d_lon = lon2[:, None] - lon1[:, :, None] + fact1 = np.heaviside(d_lon - 180, 0) + fact2 = np.heaviside(-d_lon - 180, 0) + d_lon -= (fact1 - fact2) * 360 + d_lon *= np.cos(np.radians(lat1[:, :, None])) + dist_km = np.sqrt(d_lon**2 + d_lat**2) * ONE_LAT_KM + if log: + vtan = np.stack([d_lat, d_lon], axis=-1) * ONE_LAT_KM + elif method == "geosphere": + lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2]) + dlat = 0.5 * (lat2[:, None] - lat1[:, :, None]) + dlon = 0.5 * (lon2[:, None] - lon1[:, :, None]) + # haversine formula: + hav = np.sin(dlat)**2 \ + + np.cos(lat1[:, :, None]) * np.cos(lat2[:, None]) * np.sin(dlon)**2 + dist_km = np.degrees(2 * np.arcsin(np.sqrt(hav))) * ONE_LAT_KM + if log: + vec1, vbasis = latlon_to_geosph_vector(lat1, lon1, rad=True, basis=True) + vec2 = latlon_to_geosph_vector(lat2, lon2, rad=True) + scal = 1 - 2 * hav + fact = dist_km / np.fmax(np.spacing(1), np.sqrt(1 - scal**2)) + vtan = fact[..., None] * (vec2[:, None] - scal[..., None] * vec1[:, :, None]) + vtan = np.einsum('nkli,nkji->nklj', vtan, vbasis) + else: + LOGGER.error("Unknown distance approximation method: %s", method) + raise KeyError + return (dist_km, vtan) if log else dist_km + +def grid_is_regular(coord): + """Return True if grid is regular. If True, returns height and width. + + Parameters + ---------- + coord : np.array + Each row is a lat-lon-pair. + + Returns + ------- + regular : bool + Whether the grid is regular. Only in this case, the following + width and height are reliable. + height : int + Height of the supposed grid. + width : int + Width of the supposed grid. """ regular = False _, count_lat = np.unique(coord[:, 0], return_counts=True) @@ -82,9 +288,9 @@ def grid_is_regular(coord): return regular, count_lat[0], count_lon[0] def get_coastlines(bounds=None, resolution=110): - """ Get Polygones of coast intersecting given bounds + """Get Polygones of coast intersecting given bounds - Parameter: + Parameters: bounds (tuple): min_lon, min_lat, max_lon, max_lat in EPSG:4326 resolution (float, optional): 10, 50 or 110. Resolution in m. Default: 110m, i.e. 1:110.000.000 @@ -100,39 +306,51 @@ def get_coastlines(bounds=None, resolution=110): coast_df.crs = NE_CRS if bounds is None: return coast_df[['geometry']] - ex_box = box(bounds[0], bounds[1], bounds[2], bounds[3]) - tot_coast = list() - for row, line in coast_df.iterrows(): - if line.geometry.envelope.intersects(ex_box): - tot_coast.append(row) - if not tot_coast: - ex_box = box(bounds[0]-20, bounds[1]-20, bounds[2]+20, bounds[3]+20) - for row, line in coast_df.iterrows(): - if line.geometry.envelope.intersects(ex_box): - tot_coast.append(row) - return coast_df.iloc[tot_coast][['geometry']] + tot_coast = np.zeros(1) + while not np.any(tot_coast): + tot_coast = coast_df.envelope.intersects(box(*bounds)) + bounds = (bounds[0] - 20, bounds[1] - 20, + bounds[2] + 20, bounds[3] + 20) + return coast_df[tot_coast][['geometry']] def convert_wgs_to_utm(lon, lat): - """ Get EPSG code of UTM projection for input point in EPSG 4326 + """Get EPSG code of UTM projection for input point in EPSG 4326 - Parameter: + Parameters: lon (float): longitude point in EPSG 4326 lat (float): latitude of point (lat, lon) in EPSG 4326 Return: int """ - utm_band = str((math.floor((lon + 180) / 6) % 60) + 1) - if len(utm_band) == 1: - utm_band = '0'+utm_band - if lat >= 0: - epsg_code = '326' + utm_band - else: - epsg_code = '327' + utm_band - return int(epsg_code) + epsg_utm_base = 32601 + (0 if lat >= 0 else 100) + return epsg_utm_base + (math.floor((lon + 180) / 6) % 60) -def dist_to_coast(coord_lat, lon=None): - """ Comput distance to coast from input points in meters. +def utm_zones(wgs_bounds): + """Get EPSG code and bounds of UTM zones covering specified region + + Parameters: + wgs_bounds (tuple): lon_min, lat_min, lon_max, lat_max + + Returns: + list of pairs (zone_epsg, zone_wgs_bounds) + """ + lon_min, lat_min, lon_max, lat_max = wgs_bounds + lon_min, lon_max = max(-179.99, lon_min), min(179.99, lon_max) + utm_min, utm_max = [math.floor((l + 180) / 6) for l in [lon_min, lon_max]] + zones = [] + for utm in range(utm_min, utm_max + 1): + epsg = 32601 + utm + bounds = (-180 + 6 * utm, 0, -180 + 6 * (utm + 1), 90) + if lat_max >= 0: + zones.append((epsg, bounds)) + if lat_min < 0: + bounds = (bounds[0], -90, bounds[2], 0) + zones.append((epsg + 100, bounds)) + return zones + +def dist_to_coast(coord_lat, lon=None, signed=False): + """Compute (signed) distance to coast from input points in meters. Parameters: coord_lat (GeoDataFrame or np.array or float): @@ -144,44 +362,107 @@ def dist_to_coast(coord_lat, lon=None): lon (np.array or float, optional): - np.array with one dimension containing longitudes in epsg:4326 - float with a longitude value in epsg:4326 + signed (bool): If True, distance is signed with positive values off shore and negative + values on land. Default: False Returns: np.array """ - if lon is None: - if isinstance(coord_lat, (gpd.GeoDataFrame, gpd.GeoSeries)): - if not equal_crs(coord_lat.crs, NE_CRS): - LOGGER.error('Input CRS is not %s', str(NE_CRS)) - raise ValueError - geom = coord_lat - elif isinstance(coord_lat, np.ndarray): - if coord_lat.shape[1] != 2: + if isinstance(coord_lat, (gpd.GeoDataFrame, gpd.GeoSeries)): + if not equal_crs(coord_lat.crs, NE_CRS): + LOGGER.error('Input CRS is not %s', str(NE_CRS)) + raise ValueError + geom = coord_lat + else: + if lon is None: + if isinstance(coord_lat, np.ndarray) and coord_lat.shape[1] == 2: + lat, lon = coord_lat[:, 0], coord_lat[:, 1] + else: LOGGER.error('Missing longitude values.') raise ValueError - geom = gpd.GeoDataFrame(geometry=list(map(Point, coord_lat[:, 1], coord_lat[:, 0])), - crs=NE_CRS) else: - LOGGER.error('Missing longitude values.') - raise ValueError - elif isinstance(lon, np.ndarray): - if coord_lat.size != lon.size: - LOGGER.error('Wrong input coordinates size: %s != %s', - coord_lat.size, lon.size) - raise ValueError - geom = gpd.GeoDataFrame(geometry=list(map(Point, lon, coord_lat)), - crs=NE_CRS) - elif isinstance(lon, float): - if not isinstance(coord_lat, float): - LOGGER.error('Wrong input coordinates values.') - raise ValueError - geom = gpd.GeoDataFrame(geometry=list(map(Point, [lon], [coord_lat])), - crs=NE_CRS) + lat, lon = [np.asarray(v).reshape(-1) for v in [coord_lat, lon]] + if lat.size != lon.size: + LOGGER.error('Mismatching input coordinates size: %s != %s', + lat.size, lon.size) + raise ValueError + geom = gpd.GeoDataFrame(geometry=gpd.points_from_xy(lon, lat), crs=NE_CRS) + + pad = 20 + bounds = (geom.total_bounds[0] - pad, geom.total_bounds[1] - pad, + geom.total_bounds[2] + pad, geom.total_bounds[3] + pad) + coast = get_coastlines(bounds, 10).geometry + coast = gpd.GeoDataFrame(geometry=coast, crs=NE_CRS) + dist = np.empty(geom.shape[0]) + zones = utm_zones(geom.geometry.total_bounds) + for izone, (epsg, bounds) in enumerate(zones): + to_crs = from_epsg(epsg) + zone_mask = ( + (bounds[1] <= geom.geometry.y) + & (geom.geometry.y <= bounds[3]) + & (bounds[0] <= geom.geometry.x) + & (geom.geometry.x <= bounds[2]) + ) + if np.count_nonzero(zone_mask) == 0: + continue + LOGGER.info("dist_to_coast: UTM %d (%d/%d)", + epsg, izone + 1, len(zones)) + bounds = geom[zone_mask].total_bounds + bounds = (bounds[0] - pad, bounds[1] - pad, + bounds[2] + pad, bounds[3] + pad) + coast_mask = coast.envelope.intersects(box(*bounds)) + utm_coast = coast[coast_mask].geometry.unary_union + utm_coast = gpd.GeoDataFrame(geometry=[utm_coast], crs=NE_CRS) + utm_coast = utm_coast.to_crs(to_crs).geometry[0] + dist[zone_mask] = geom[zone_mask].to_crs(to_crs).distance(utm_coast) + if signed: + dist[coord_on_land(geom.geometry.y, geom.geometry.x)] *= -1 + return dist + +def dist_to_coast_nasa(lat, lon, highres=False, signed=False): + """Read interpolated (signed) distance to coast (in m) from NASA data + + Note: The NASA raster file is 300 MB and will be downloaded on first run! - to_crs = from_epsg(convert_wgs_to_utm(geom.geometry.iloc[0].x, geom.geometry.iloc[0].y)) - coast = get_coastlines(geom.total_bounds, 10).unary_union - coast = gpd.GeoDataFrame(geometry=[coast], crs=NE_CRS).to_crs(to_crs) - return geom.to_crs(to_crs).distance(coast.geometry[0]).values + Parameters: + lat (np.array): latitudes in epsg:4326 + lon (np.array): longitudes in epsg:4326 + highres (bool, optional): Use full resolution of NASA data (much + slower). Default: False. + signed (bool): If True, distance is signed with positive values off shore and negative + values on land. Default: False + Returns: + np.array + """ + lat, lon = [np.asarray(ar).ravel() for ar in [lat, lon]] + lon = lon_normalize(lon.copy()) + + # TODO move URL to config + zipname = "GMT_intermediate_coast_distance_01d.zip" + tifname = "GMT_intermediate_coast_distance_01d.tif" + url = "https://oceancolor.gsfc.nasa.gov/docs/distfromcoast/" + zipname + path = os.path.join(SYSTEM_DIR, tifname) + if not os.path.isfile(path): + cwd = os.getcwd() + os.chdir(SYSTEM_DIR) + path_dwn = download_file(url) + zip_ref = zipfile.ZipFile(path_dwn, 'r') + zip_ref.extractall(SYSTEM_DIR) + zip_ref.close() + os.remove(path_dwn) + os.chdir(cwd) + + intermediate_res = None if highres else 0.1 + west_msk = (lon < 0) + dist = np.zeros_like(lat) + for msk in [west_msk, ~west_msk]: + if np.count_nonzero(msk) > 0: + dist[msk] = read_raster_sample( + path, lat[msk], lon[msk], intermediate_res=intermediate_res, fill_value=0) + if not signed: + dist = np.abs(dist) + return 1000 * dist def get_land_geometry(country_names=None, extent=None, resolution=10): """Get union of all the countries or the provided ones or the points inside @@ -204,7 +485,7 @@ def get_land_geometry(country_names=None, extent=None, resolution=10): reader = shapereader.Reader(shp_file) if (country_names is None) and (extent is None): LOGGER.info("Computing earth's land geometry ...") - geom = [cntry_geom for cntry_geom in reader.geometries()] + geom = list(reader.geometries()) geom = shapely.ops.cascaded_union(geom) elif country_names: @@ -247,9 +528,12 @@ def coord_on_land(lat, lon, land_geom=None): raise ValueError delta_deg = 1 if land_geom is None: - land_geom = get_land_geometry(extent=(np.min(lon)-delta_deg, \ - np.max(lon)+delta_deg, np.min(lat)-delta_deg, \ - np.max(lat)+delta_deg), resolution=10) + land_geom = get_land_geometry( + extent=(np.min(lon) - delta_deg, + np.max(lon) + delta_deg, + np.min(lat) - delta_deg, + np.max(lat) + delta_deg), + resolution=10) return shapely.vectorized.contains(land_geom, lon, lat) def nat_earth_resolution(resolution): @@ -300,103 +584,407 @@ def get_country_geometries(country_names=None, extent=None, resolution=10): if not nat_earth.crs: nat_earth.crs = NE_CRS - for idx in nat_earth.index: # fill gaps in nat_earth - if nat_earth.loc[idx].ISO_A3=='-99': - nat_earth.loc[idx, 'ISO_A3'] = nat_earth.loc[idx].ADM0_A3 - if nat_earth.loc[idx].ISO_N3=='-99': - for col in ['ISO_A3', 'ADM0_A3', 'NAME']: - try: - nat_earth.loc[idx, 'ISO_N3'] = iso_cntry.get(nat_earth.loc[idx, col]).numeric - except KeyError: - nat_earth.loc[idx, 'ISO_N3'] = '-99' - else: - break + # fill gaps in nat_earth + gap_mask = (nat_earth['ISO_A3'] == '-99') + nat_earth.loc[gap_mask, 'ISO_A3'] = nat_earth.loc[gap_mask, 'ADM0_A3'] + + gap_mask = (nat_earth['ISO_N3'] == '-99') + for idx in nat_earth[gap_mask].index: + for col in ['ISO_A3', 'ADM0_A3', 'NAME']: + try: + num = iso_cntry.get(nat_earth.loc[idx, col]).numeric + except KeyError: + continue + else: + nat_earth.loc[idx, 'ISO_N3'] = num + break + + out = nat_earth if country_names: if isinstance(country_names, str): country_names = [country_names] - out = nat_earth[nat_earth.ISO_A3.isin(country_names)] - elif extent: + out = out[out.ISO_A3.isin(country_names)] + + if extent: bbox = Polygon([ (extent[0], extent[2]), (extent[0], extent[3]), (extent[1], extent[3]), (extent[1], extent[2]) ]) - bbox = gpd.GeoSeries(bbox, crs=nat_earth.crs) - bbox = gpd.GeoDataFrame({'geometry': bbox}, crs=nat_earth.crs) - out = gpd.overlay(nat_earth, bbox, how="intersection") + bbox = gpd.GeoSeries(bbox, crs=out.crs) + bbox = gpd.GeoDataFrame({'geometry': bbox}, crs=out.crs) + out = gpd.overlay(out, bbox, how="intersection") - else: - out = nat_earth return out -def get_country_code(lat, lon): - """ Provide numeric country iso code for every point. +def get_region_gridpoints(countries=None, regions=None, resolution=150, + iso=True, rect=False, basemap="natearth"): + """Get coordinates of gridpoints in specified countries or regions + + Parameters + ---------- + countries : list, optional + ISO 3166-1 alpha-3 codes of countries, or internal numeric NatID if + `iso` is set to False. + regions : list, optional + Region IDs. + resolution : float, optional + Resolution in arc-seconds, either 150 (default) or 360. + iso : bool, optional + If True, assume that countries are given by their ISO 3166-1 alpha-3 + codes (instead of the internal NatID). Default: True. + rect : bool, optional + If True, a rectangular box around the specified countries/regions is + selected. Default: False. + basemap : str, optional + Choose between different data sources. + Currently available: "isimip" and "natearth". Default: "natearth". + + Returns + ------- + lat : np.array + Latitude of points in epsg:4326. + lon : np.array + Longitude of points in epsg:4326. + """ + if countries is None: + countries = [] + if regions is None: + regions = [] + + if basemap == "natearth": + base_file = NATEARTH_CENTROIDS[resolution] + hdf5_f = hdf5.read(base_file) + meta = hdf5_f['meta'] + grid_shape = (meta['height'][0], meta['width'][0]) + transform = rasterio.Affine(*meta['transform']) + region_id = hdf5_f['region_id'].reshape(grid_shape) + lon, lat = raster_to_meshgrid(transform, grid_shape[1], grid_shape[0]) + elif basemap == "isimip": + hdf5_f = hdf5.read(ISIMIP_GPWV3_NATID_150AS) + dim_lon, dim_lat = hdf5_f['lon'], hdf5_f['lat'] + bounds = dim_lon.min(), dim_lat.min(), dim_lon.max(), dim_lat.max() + orig_res = get_resolution(dim_lon, dim_lat) + _, _, transform = pts_to_raster_meta(bounds, orig_res) + grid_shape = (dim_lat.size, dim_lon.size) + region_id = hdf5_f['NatIdGrid'].reshape(grid_shape).astype(int) + region_id[region_id < 0] = 0 + natid2iso_alpha = country_natid2iso(list(range(231))) + natid2iso = country_iso_alpha2numeric(natid2iso_alpha) + natid2iso = np.array(natid2iso, dtype=int) + region_id = natid2iso[region_id] + lon, lat = np.meshgrid(dim_lon, dim_lat) + else: + raise ValueError(f"Unknown basemap: {basemap}") + + if basemap == "natearth" and resolution not in [150, 360] \ + or basemap == "isimip" and resolution != 150: + resolution /= 3600 + region_id, transform = refine_raster_data( + region_id, transform, resolution, method='nearest', fill_value=0) + grid_shape = region_id.shape + lon, lat = raster_to_meshgrid(transform, grid_shape[1], grid_shape[0]) + + if not iso: + countries = country_natid2iso(countries) + countries += region2isos(regions) + countries = np.unique(country_iso_alpha2numeric(countries)) + + if len(countries) > 0: + msk = np.isin(region_id, countries) + if rect: + lat_msk, lon_msk = lat[msk], lon[msk] + msk = msk.any(axis=0)[None] * msk.any(axis=1)[:, None] + msk |= ( + (lat >= np.floor(lat_msk.min())) + & (lon >= np.floor(lon_msk.min())) + & (lat <= np.ceil(lat_msk.max())) + & (lon <= np.ceil(lon_msk.max())) + ) + lat, lon = lat[msk], lon[msk] + else: + lat, lon = [ar.ravel() for ar in [lat, lon]] + return lat, lon + +def region2isos(regions): + """Convert region names to ISO 3166 alpha-3 codes of countries + + Parameters + ---------- + regions : str or list of str + Region name(s). + + Returns + ------- + isos : list of str + Sorted list of iso codes of all countries in specified region(s). + """ + regions = [regions] if isinstance(regions, str) else regions + reg_info = pd.read_csv(RIVER_FLOOD_REGIONS_CSV) + isos = [] + for region in regions: + region_msk = (reg_info['Reg_name'] == region) + if not any(region_msk): + LOGGER.error('Unknown region name: %s', region) + raise KeyError + isos += list(reg_info['ISO'][region_msk].values) + return list(set(isos)) + +def country_iso_alpha2numeric(isos): + """Convert ISO 3166-1 alpha-3 to numeric-3 codes + + Parameters: + isos (str or list of str): ISO codes of countries (or single code). + + Returns: + int or list of int + """ + return_int = isinstance(isos, str) + isos = [isos] if return_int else isos + old_iso = { + '': 0, # Ocean or fill_value + "ANT": 530, # Netherlands Antilles: split up since 2010 + "SCG": 891, # Serbia and Montenegro: split up since 2006 + } + nums = [] + for iso in isos: + if iso in old_iso: + num = old_iso[iso] + else: + num = int(iso_cntry.get(iso).numeric) + nums.append(num) + return nums[0] if return_int else nums + +def country_natid2iso(natids): + """Convert internal NatIDs to ISO 3166-1 alpha-3 codes + + Parameters: + natids (int or list of int): Internal NatIDs of countries (or single ID). + + Returns: + str or list of str + """ + return_str = isinstance(natids, int) + natids = [natids] if return_str else natids + isos = [] + for natid in natids: + if natid < 0 or natid >= len(ISIMIP_NATID_TO_ISO): + LOGGER.error('Unknown country NatID: %s', natid) + raise KeyError + isos.append(ISIMIP_NATID_TO_ISO[natid]) + return isos[0] if return_str else isos + +def country_iso2natid(isos): + """Convert ISO 3166-1 alpha-3 codes to internal NatIDs + + Parameters: + isos (str or list of str): ISO codes of countries (or single code). + + Returns: + int or list of int + """ + return_int = isinstance(isos, str) + isos = [isos] if return_int else isos + natids = [] + for iso in isos: + try: + natids.append(ISIMIP_NATID_TO_ISO.index(iso)) + except ValueError: + LOGGER.error('Unknown country ISO: %s', iso) + raise KeyError + return natids[0] if return_int else natids + +NATEARTH_AREA_NONISO_NUMERIC = { + "Akrotiri": 901, + "Baikonur": 902, + "Bajo Nuevo Bank": 903, + "Clipperton I.": 904, + "Coral Sea Is.": 905, + "Cyprus U.N. Buffer Zone": 906, + "Dhekelia": 907, + "Indian Ocean Ter.": 908, + "Kosovo": 983, # Same as iso3166 package + "N. Cyprus": 910, + "Norway": 578, # Bug in Natural Earth + "Scarborough Reef": 912, + "Serranilla Bank": 913, + "Siachen Glacier": 914, + "Somaliland": 915, + "Spratly Is.": 916, + "USNB Guantanamo Bay": 917, +} + +def natearth_country_to_int(country): + """Integer representation (ISO 3166, if possible) of Natural Earth GeoPandas country row + + Parameters: + country (GeoSeries): Row from GeoDataFrame. + + Returns: + int + """ + if country.ISO_N3 != '-99': + return int(country.ISO_N3) + return NATEARTH_AREA_NONISO_NUMERIC[str(country.NAME)] + +def get_country_code(lat, lon, gridded=False): + """Provide numeric (ISO 3166) code for every point. + + Oceans get the value zero. Areas that are not in ISO 3166 are given values + in the range above 900 according to NATEARTH_AREA_NONISO_NUMERIC. Parameters: lat (np.array): latitude of points in epsg:4326 lon (np.array): longitude of points in epsg:4326 + gridded (bool): If True, interpolate precomputed gridded data which + is usually much faster. Default: False. Returns: np.array(int) """ - lat = np.array(lat) - lon = np.array(lon) - LOGGER.debug('Setting region_id %s points.', str(lat.size)) - countries = get_country_geometries(extent=(lon.min()-0.001, lon.max()+0.001, - lat.min()-0.001, lat.max()+0.001)) - region_id = np.zeros(lon.size, dtype=int) - for geom in zip(countries.geometry, countries.ISO_N3): - select = shapely.vectorized.contains(geom[0], lon, lat) - region_id[select] = int(geom[1]) + lat, lon = [np.asarray(ar).ravel() for ar in [lat, lon]] + LOGGER.info('Setting region_id %s points.', str(lat.size)) + if gridded: + base_file = hdf5.read(NATEARTH_CENTROIDS[150]) + meta, region_id = base_file['meta'], base_file['region_id'] + transform = rasterio.Affine(*meta['transform']) + region_id = region_id.reshape(meta['height'][0], meta['width'][0]) + region_id = interp_raster_data(region_id, lat, lon, transform, + method='nearest', fill_value=0) + region_id = region_id.astype(int) + else: + extent = (lon.min() - 0.001, lon.max() + 0.001, + lat.min() - 0.001, lat.max() + 0.001) + countries = get_country_geometries(extent=extent) + countries['area'] = countries.geometry.area + countries = countries.sort_values(by=['area'], ascending=False) + region_id = np.full((lon.size,), -1, dtype=int) + total_land = countries.geometry.unary_union + ocean_mask = ~shapely.vectorized.contains(total_land, lon, lat) + region_id[ocean_mask] = 0 + for country in countries.itertuples(): + unset = (region_id == -1).nonzero()[0] + select = shapely.vectorized.contains(country.geometry, + lon[unset], lat[unset]) + region_id[unset[select]] = natearth_country_to_int(country) + region_id[region_id == -1] = 0 return region_id -def get_resolution(lat, lon, min_resol=1.0e-8): - """ Compute resolution of points in lat and lon +def get_admin1_info(country_names): + """Provide registry info and shape files for admin1 regions Parameters: - lat (np.array): latitude of points - lon (np.array): longitude of points - min_resol (float, optional): minimum resolution to consider. Default: 1.0e-8. + country_names (list): list with ISO3 names of countries, e.g. + ['ZWE', 'GBR', 'VNM', 'UZB'] + + Returns: + admin1_info (dict) + admin1_shapes (dict) + """ + + if isinstance(country_names, str): + country_names = [country_names] + admin1_file = shapereader.natural_earth(resolution='10m', + category='cultural', + name='admin_1_states_provinces') + admin1_recs = shapefile.Reader(admin1_file) + admin1_info = dict() + admin1_shapes = dict() + for iso3 in country_names: + admin1_info[iso3] = list() + admin1_shapes[iso3] = list() + for rec, rec_shp in zip(admin1_recs.records(), admin1_recs.shapes()): + if rec['adm0_a3'] == iso3: + admin1_info[iso3].append(rec) + admin1_shapes[iso3].append(rec_shp) + return admin1_info, admin1_shapes + +def get_resolution_1d(coords, min_resol=1.0e-8): + """Compute resolution of scalar grid + + Parameters: + coords (np.array): scalar coordinates + min_resol (float, optional): minimum resolution to consider. + Default: 1.0e-8. Returns: float """ - # ascending lat and lon - res_lat, res_lon = np.diff(np.sort(lat)), np.diff(np.sort(lon)) - try: - res_lat = res_lat[res_lat > min_resol].min() - except ValueError: - res_lat = 0 - try: - res_lon = res_lon[res_lon > min_resol].min() - except ValueError: - res_lon = 0 - return res_lat, res_lon + res = np.diff(np.unique(coords)) + diff = np.diff(coords) + mask = (res > min_resol) & np.isin(res, np.abs(diff)) + return diff[np.abs(diff) == res[mask].min()][0] + + +def get_resolution(*coords, min_resol=1.0e-8): + """Compute resolution of 2-d grid points + + Parameters: + X, Y, ... (np.array): scalar coordinates in each axis + min_resol (float, optional): minimum resolution to consider. + Default: 1.0e-8. + + Returns: + pair of floats + """ + return tuple([get_resolution_1d(c, min_resol=min_resol) for c in coords]) + def pts_to_raster_meta(points_bounds, res): - """" Transform vector data coordinates to raster. Returns number of rows, + """Transform vector data coordinates to raster. Returns number of rows, columns and affine transformation + If a raster of the given resolution doesn't exactly fit the given bounds, + the raster might have slightly larger (but never smaller) bounds. + Parameters: points_bounds (tuple): points total bounds (xmin, ymin, xmax, ymax) - res (float): resolution of output raster + res (tuple): resolution of output raster (xres, yres) Returns: int, int, affine.Affine """ - xmin, ymin, xmax, ymax = points_bounds - rows = int(np.floor((ymax-ymin) / res) + 1) - cols = int(np.floor((xmax-xmin) / res) + 1) - ras_trans = from_origin(xmin - res / 2, ymax + res / 2, res, res) - if xmax > xmin - res / 2 + cols * res: - cols += 1 - if ymin < ymax + res / 2 - rows * res: - rows += 1 - return rows, cols, ras_trans + Affine = rasterio.Affine + bounds = np.asarray(points_bounds).reshape(2, 2) + res = np.asarray(res).ravel() + if res.size == 1: + res = np.array([res[0], res[0]]) + sizes = bounds[1, :] - bounds[0, :] + nsteps = np.floor(sizes / np.abs(res)) + 1 + nsteps[np.abs(nsteps * res) < sizes + np.abs(res) / 2] += 1 + bounds[:, res < 0] = bounds[::-1, res < 0] + origin = bounds[0, :] - res[:] / 2 + ras_trans = Affine.translation(*origin) * Affine.scale(*res) + return int(nsteps[1]), int(nsteps[0]), ras_trans + +def raster_to_meshgrid(transform, width, height): + """Get coordinates of grid points in raster + + Parameters + ---------- + transform : affine.Affine + Affine transform defining the raster. + width : int + Number of points in first coordinate axis. + height : int + Number of points in second coordinate axis. + + Returns + ------- + x : np.array + x-coordinates of grid points. + y : np.array + y-coordinates of grid points. + """ + xres, _, xmin, _, yres, ymin = transform[:6] + xmax = xmin + width * xres + ymax = ymin + height * yres + return np.meshgrid(np.arange(xmin + xres / 2, xmax, xres), + np.arange(ymin + yres / 2, ymax, yres)) def equal_crs(crs_one, crs_two): - """ Compare two crs + """Compare two crs Parameters: crs_one (dict or string or wkt): user crs @@ -405,15 +993,82 @@ def equal_crs(crs_one, crs_two): Returns: bool """ - return CRS.from_user_input(crs_one) == CRS.from_user_input(crs_two) + return rasterio.crs.CRS.from_user_input(crs_one) == rasterio.crs.CRS.from_user_input(crs_two) + +def _read_raster_reproject(src, src_crs, dst_meta, band=None, geometry=None, dst_crs=None, + transform=None, resampling=rasterio.warp.Resampling.nearest): + """Helper function for `read_raster`""" + if not band: + band = [1] + if not dst_crs: + dst_crs = src_crs + if not transform: + transform, width, height = rasterio.warp.calculate_default_transform( + src_crs, dst_crs, src.width, src.height, *src.bounds) + else: + transform, width, height = transform + dst_meta.update({ + 'crs': dst_crs, + 'transform': transform, + 'width': width, + 'height': height, + }) + kwargs = {} + if src.meta['nodata']: + kwargs['src_nodata'] = src.meta['nodata'] + kwargs['dst_nodata'] = src.meta['nodata'] + + intensity = np.zeros((len(band), height, width)) + for idx_band, i_band in enumerate(band): + rasterio.warp.reproject( + source=src.read(i_band), + destination=intensity[idx_band, :], + src_transform=src.transform, + src_crs=src_crs, + dst_transform=transform, + dst_crs=dst_crs, + resampling=resampling, + **kwargs) + + if dst_meta['nodata'] and np.isnan(dst_meta['nodata']): + nodata_mask = np.isnan(intensity[idx_band, :]) + else: + nodata_mask = (intensity[idx_band, :] == dst_meta['nodata']) + intensity[idx_band, :][nodata_mask] = 0 + + if geometry: + intensity = intensity.astype('float32') + # update driver to GTiff as netcdf does not work reliably + dst_meta.update(driver='GTiff') + with rasterio.MemoryFile() as memfile: + with memfile.open(**dst_meta) as dst: + dst.write(intensity) + + with memfile.open() as dst: + inten, mask_trans = rasterio.mask.mask(dst, geometry, crop=True, indexes=band) + dst_meta.update({ + "height": inten.shape[1], + "width": inten.shape[2], + "transform": mask_trans, + }) + intensity = inten[range(len(band)), :] + intensity = intensity.astype('float64') + + # reset nodata values again as driver Gtiff resets them again + if dst_meta['nodata'] and np.isnan(dst_meta['nodata']): + intensity[np.isnan(intensity)] = 0 + else: + intensity[intensity == dst_meta['nodata']] = 0 -def read_raster(file_name, band=[1], src_crs=None, window=False, geometry=False, - dst_crs=False, transform=None, width=None, height=None, - resampling=Resampling.nearest): - """ Read raster of bands and set 0 values to the masked ones. Each + return intensity + +def read_raster(file_name, band=None, src_crs=None, window=None, geometry=None, + dst_crs=None, transform=None, width=None, height=None, + resampling=rasterio.warp.Resampling.nearest): + """Read raster of bands and set 0 values to the masked ones. Each band is an event. Select region using window or geometry. Reproject input by proving dst_crs and/or (transform, width, height). Returns matrix - in 2d: band x coordinates in 1d (evtl. reshape to band x height x width) + in 2d: band x coordinates in 1d (can be reshaped to band x height x width) Parameters: file_name (str): name of the file @@ -424,80 +1079,255 @@ def read_raster(file_name, band=[1], src_crs=None, window=False, geometry=False, transform (rasterio.Affine): affine transformation to apply wdith (float): number of lons for transform height (float): number of lats for transform - resampling (rasterio.warp,.Resampling optional): resampling + resampling (rasterio.warp.Resampling optional): resampling function used for reprojection to dst_crs Returns: dict (meta), np.array (band x coordinates_in_1d) """ + if not band: + band = [1] LOGGER.info('Reading %s', file_name) if os.path.splitext(file_name)[1] == '.gz': file_name = '/vsigzip/' + file_name + with rasterio.Env(): with rasterio.open(file_name, 'r') as src: - if src_crs is None: - src_meta = CRS.from_dict(DEF_CRS) if not src.crs else src.crs - else: - src_meta = src_crs + dst_meta = src.meta.copy() + if dst_crs or transform: LOGGER.debug('Reprojecting ...') - if not dst_crs: - dst_crs = src_meta - if not transform: - transform, width, height = calculate_default_transform(\ - src_meta, dst_crs, src.width, src.height, *src.bounds) - dst_meta = src.meta.copy() - dst_meta.update({'crs': dst_crs, - 'transform': transform, - 'width': width, - 'height': height - }) - kwargs = {} - if src.meta['nodata']: - kwargs['src_nodata'] = src.meta['nodata'] - kwargs['dst_nodata'] = src.meta['nodata'] - intensity = np.zeros((len(band), height, width)) - for idx_band, i_band in enumerate(band): - reproject(source=src.read(i_band), - destination=intensity[idx_band, :], - src_transform=src.transform, - src_crs=src_meta, - dst_transform=transform, - dst_crs=dst_crs, - resampling=resampling, - **kwargs) + + src_crs = src.crs if src_crs is None else src_crs + if not src_crs: + src_crs = rasterio.crs.CRS.from_dict(DEF_CRS) + transform = (transform, width, height) if transform else None + inten = _read_raster_reproject(src, src_crs, dst_meta, band=band, + geometry=geometry, dst_crs=dst_crs, + transform=transform, resampling=resampling) + else: + if geometry: + inten, trans = rasterio.mask.mask(src, geometry, crop=True, indexes=band) if dst_meta['nodata'] and np.isnan(dst_meta['nodata']): - intensity[idx_band, :][np.isnan(intensity[idx_band, :])] = 0 + inten[np.isnan(inten)] = 0 else: - intensity[idx_band, :][intensity[idx_band, :] == dst_meta['nodata']] = 0 - meta = dst_meta - return meta, intensity.reshape((len(band), meta['height']*meta['width'])) - - meta = src.meta.copy() - if geometry: - inten, mask_trans = mask(src, geometry, crop=True, indexes=band) - if meta['nodata'] and np.isnan(meta['nodata']): - inten[np.isnan(inten)] = 0 + inten[inten == dst_meta['nodata']] = 0 + else: - inten[inten == meta['nodata']] = 0 - meta.update({"height": inten.shape[1], - "width": inten.shape[2], - "transform": mask_trans}) - else: - masked_array = src.read(band, window=window, masked=True) - inten = masked_array.data - inten[masked_array.mask] = 0 - if window: - meta.update({"height": window.height, \ - "width": window.width, \ - "transform": rasterio.windows.transform(window, src.transform)}) - if not meta['crs']: - meta['crs'] = CRS.from_dict(DEF_CRS) - intensity = inten[range(len(band)), :] - return meta, intensity.reshape((len(band), meta['height']*meta['width'])) + masked_array = src.read(band, window=window, masked=True) + inten = masked_array.data + inten[masked_array.mask] = 0 + + if window: + trans = rasterio.windows.transform(window, src.transform) + else: + trans = dst_meta['transform'] + + dst_meta.update({ + "height": inten.shape[1], + "width": inten.shape[2], + "transform": trans, + }) + + if not dst_meta['crs']: + dst_meta['crs'] = rasterio.crs.CRS.from_dict(DEF_CRS) + + intensity = inten[range(len(band)), :] + dst_shape = (len(band), dst_meta['height'] * dst_meta['width']) + + return dst_meta, intensity.reshape(dst_shape) + +def read_raster_bounds(path, bounds, res=None, bands=None): + """Read raster file within given bounds and refine to given resolution + + Makes sure that the extent of pixel centers covers the specified regions + + Parameters + ---------- + path : str + Path to raster file to open with rasterio. + bounds : tuple + (xmin, ymin, xmax, ymax) + res : float, optional + Resolution of output. Default: Resolution of input raster file. + bands : list of int, optional + Bands to read from the input raster file. Default: [1] + + Returns + ------- + data : 3d np.array + First dimension is for the selected raster bands. Second dimension is y (lat) and third + dimension is x (lon). + transform : rasterio.Affine + Affine transformation defining the output raster data. + """ + if os.path.splitext(path)[1] == '.gz': + path = '/vsigzip/' + path + if not bands: + bands = [1] + resampling = rasterio.warp.Resampling.bilinear + with rasterio.open(path, 'r') as src: + if res: + if not isinstance(res, tuple): + res = (res, res) + else: + res = (src.transform[0], src.transform[4]) + res = (np.abs(res[0]), np.abs(res[1])) + + width, height = bounds[2] - bounds[0], bounds[3] - bounds[1] + shape = (int(np.ceil(height / res[1]) + 1), + int(np.ceil(width / res[0]) + 1)) + extra = (0.5 * ((shape[1] - 1) * res[0] - width), + 0.5 * ((shape[0] - 1) * res[1] - height)) + bounds = (bounds[0] - extra[0] - 0.5 * res[0], bounds[1] - extra[1] - 0.5 * res[1], + bounds[2] + extra[0] + 0.5 * res[0], bounds[3] + extra[1] + 0.5 * res[1]) + + if bounds[0] > 180: + bounds = (bounds[0] - 360, bounds[1], bounds[2] - 360, bounds[3]) + + window = src.window(*bounds) + w_transform = src.window_transform(window) + transform = rasterio.Affine(np.sign(w_transform[0]) * res[0], 0, w_transform[2], + 0, np.sign(w_transform[4]) * res[1], w_transform[5]) + + if bounds[2] <= 180: + data = src.read(bands, out_shape=shape, window=window, + resampling=resampling) + else: + # split up at antimeridian + bounds_sub = [(bounds[0], bounds[1], 180, bounds[3]), + (-180, bounds[1], bounds[2] - 360, bounds[3])] + ratio_left = (bounds_sub[0][2] - bounds_sub[0][0]) / (bounds[2] - bounds[0]) + shapes_sub = [(shape[0], int(shape[1] * ratio_left))] + shapes_sub.append((shape[0], shape[1] - shapes_sub[0][1])) + windows_sub = [src.window(*bds) for bds in bounds_sub] + data = [src.read(bands, out_shape=shp, window=win, resampling=resampling) + for shp, win in zip(shapes_sub, windows_sub)] + data = np.concatenate(data, axis=2) + return data, transform + +def read_raster_sample(path, lat, lon, intermediate_res=None, method='linear', fill_value=None): + """Read point samples from raster file + + Parameters: + path (str): path of the raster file + lat (np.array): latitudes in file's CRS + lon (np.array): longitudes in file's CRS + intermediate_res (float, optional): If given, the raster is not read in its original + resolution but in the given one. This can increase performance for + files of very high resolution. + method (str, optional): The interpolation method, passed to + scipy.interp.interpn. Default: 'linear'. + fill_value (numeric, optional): The value used outside of the raster + bounds. Default: The raster's nodata value or 0. + + Returns: + np.array of same length as lat + """ + if lat.size == 0: + return np.zeros_like(lat) + + LOGGER.info('Sampling from %s', path) + if os.path.splitext(path)[1] == '.gz': + path = '/vsigzip/' + path + + with rasterio.open(path, "r") as src: + if intermediate_res is None: + xres, yres = np.abs(src.transform[0]), np.abs(src.transform[4]) + else: + xres = yres = intermediate_res + bounds = (lon.min() - 2 * xres, lat.min() - 2 * yres, + lon.max() + 2 * xres, lat.max() + 2 * yres) + win = src.window(*bounds).round_offsets(op='ceil').round_shape(op='floor') + win_transform = src.window_transform(win) + intermediate_shape = None + if intermediate_res is not None: + win_bounds = src.window_bounds(win) + win_width, win_height = win_bounds[2] - win_bounds[0], win_bounds[3] - win_bounds[1] + intermediate_shape = (int(np.ceil(win_height / intermediate_res)), + int(np.ceil(win_width / intermediate_res))) + data = src.read(1, out_shape=intermediate_shape, boundless=True, window=win) + if fill_value is not None: + data[data == src.meta['nodata']] = fill_value + else: + fill_value = src.meta['nodata'] + + + if intermediate_res is not None: + xres, yres = win_width / data.shape[1], win_height / data.shape[0] + xres, yres = np.sign(win_transform[0]) * xres, np.sign(win_transform[4]) * yres + win_transform = rasterio.Affine(xres, 0, win_transform[2], + 0, yres, win_transform[5]) + fill_value = fill_value if fill_value else 0 + return interp_raster_data(data, lat, lon, win_transform, method=method, fill_value=fill_value) + +def interp_raster_data(data, interp_y, interp_x, transform, method='linear', fill_value=0): + """Interpolate raster data, given as array and affine transform + + Parameters: + data (np.array): 2d numpy array containing the values + interp_y (np.array): y-coordinates of points (corresp. to first axis of data) + interp_x (np.array): x-coordinates of points (corresp. to second axis of data) + transform (affine.Affine): affine transform defining the raster + method (str, optional): The interpolation method, passed to + scipy.interp.interpn. Default: 'linear'. + fill_value (numeric, optional): The value used outside of the raster + bounds. Default: 0. + + Returns: + np.array + """ + xres, _, xmin, _, yres, ymin = transform[:6] + xmax = xmin + data.shape[1] * xres + ymax = ymin + data.shape[0] * yres + data = np.pad(data, 1, mode='edge') + + if yres < 0: + yres = -yres + ymax, ymin = ymin, ymax + data = np.flipud(data) + if xres < 0: + xres = -xres + xmax, xmin = xmin, xmax + data = np.fliplr(data) + y_dim = ymin - yres / 2 + yres * np.arange(data.shape[0]) + x_dim = xmin - xres / 2 + xres * np.arange(data.shape[1]) + + data = np.array(data, dtype=np.float64) + data[np.isnan(data)] = fill_value + return scipy.interpolate.interpn((y_dim, x_dim), data, np.vstack([interp_y, interp_x]).T, + method=method, bounds_error=False, fill_value=fill_value) + +def refine_raster_data(data, transform, res, method='linear', fill_value=0): + """Refine raster data, given as array and affine transform + + Parameters: + data (np.array): 2d numpy array containing the values + transform (affine.Affine): affine transform defining the raster + res (float or pair of floats): new resolution + method (str, optional): The interpolation method, passed to + scipy.interp.interpn. Default: 'linear'. + + Return: + np.array, affine.Affine + """ + xres, _, xmin, _, yres, ymin = transform[:6] + xmax = xmin + data.shape[1] * xres + ymax = ymin + data.shape[0] * yres + if not isinstance(res, tuple): + res = (np.sign(xres) * res, np.sign(yres) * res) + new_dimx = np.arange(xmin + res[0] / 2, xmax, res[0]) + new_dimy = np.arange(ymin + res[1] / 2, ymax, res[1]) + new_shape = (new_dimy.size, new_dimx.size) + new_x, new_y = [ar.ravel() for ar in np.meshgrid(new_dimx, new_dimy)] + new_transform = rasterio.Affine(res[0], 0, xmin, 0, res[1], ymin) + new_data = interp_raster_data(data, new_y, new_x, transform, method=method, + fill_value=fill_value) + new_data = new_data.reshape(new_shape) + return new_data, new_transform def read_vector(file_name, field_name, dst_crs=None): - """ Read vector file format supported by fiona. Each field_name name is + """Read vector file format supported by fiona. Each field_name name is considered an event. Parameters: @@ -523,8 +1353,8 @@ def read_vector(file_name, field_name, dst_crs=None): value[i_inten, :] = data_frame[inten].values return lat, lon, geometry, value -def write_raster(file_name, data_matrix, meta): - """ Write raster in GeoTiff format +def write_raster(file_name, data_matrix, meta, dtype=np.float32): + """Write raster in GeoTiff format Parameters: fle_name (str): file name to write @@ -533,30 +1363,28 @@ def write_raster(file_name, data_matrix, meta): meta (dict): rasterio meta dictionary containing raster properties: width, height, crs and transform must be present at least (transform needs to contain upper left corner!) + dtype (numpy dtype): a numpy dtype """ LOGGER.info('Writting %s', file_name) if data_matrix.shape != (meta['height'], meta['width']): # every row is an event (from hazard intensity or fraction) == band - profile = copy.deepcopy(meta) - profile.update(driver='GTiff', dtype=rasterio.float32, count=data_matrix.shape[0]) - with rasterio.open(file_name, 'w', **profile) as dst: - dst.write(np.asarray(data_matrix, dtype=rasterio.float32).\ - reshape((data_matrix.shape[0], profile['height'], profile['width'])), \ - indexes=np.arange(1, data_matrix.shape[0]+1)) + shape = (data_matrix.shape[0], meta['height'], meta['width']) else: - # only one band - profile = copy.deepcopy(meta) - profile.update(driver='GTiff', dtype=rasterio.float32, count=1) - with rasterio.open(file_name, 'w', **profile) as dst: - dst.write(np.asarray(data_matrix, dtype=rasterio.float32)) + shape = (1, meta['height'], meta['width']) + dst_meta = copy.deepcopy(meta) + dst_meta.update(driver='GTiff', dtype=dtype, count=shape[0]) + data_matrix = np.asarray(data_matrix, dtype=dtype).reshape(shape) + with rasterio.open(file_name, 'w', **dst_meta) as dst: + dst.write(data_matrix, indexes=np.arange(1, shape[0] + 1)) -def points_to_raster(points_df, val_names=['value'], res=None, raster_res=None, - scheduler=None): - """ Compute raster matrix and transformation from value column +def points_to_raster(points_df, val_names=None, res=0.0, raster_res=0.0, scheduler=None): + """Compute raster matrix and transformation from value column Parameters: - points_df (GeoDataFrame): contains columns latitude, longitude and in - val_names + points_df (GeoDataFrame): contains columns latitude, longitude and those listed in + the parameter `val_names` + val_names (list of str, optional): The names of columns in `points_df` containing + values. The raster will contain one band per column. Default: ['value'] res (float, optional): resolution of current data in units of latitude and longitude, approximated if not provided. raster_res (float, optional): desired resolution of the raster @@ -567,15 +1395,18 @@ def points_to_raster(points_df, val_names=['value'], res=None, raster_res=None, np.array, affine.Affine """ - + if not val_names: + val_names = ['value'] if not res: - res = min(get_resolution(points_df.latitude.values, points_df.longitude.values)) + res = np.abs(get_resolution(points_df.latitude.values, + points_df.longitude.values)).min() if not raster_res: raster_res = res def apply_box(df_exp): - return df_exp.apply((lambda row: Point(row.longitude, row.latitude). \ - buffer(res/2).envelope), axis=1) + fun = lambda r: Point(r.longitude, r.latitude).buffer(res / 2).envelope + return df_exp.apply(fun, axis=1) + LOGGER.info('Raster from resolution %s to %s.', res, raster_res) df_poly = points_df[val_names] if not scheduler: @@ -583,23 +1414,35 @@ def apply_box(df_exp): else: ddata = dd.from_pandas(points_df[['latitude', 'longitude']], npartitions=cpu_count()) - df_poly['geometry'] = ddata.map_partitions(apply_box, meta=Polygon).\ - compute(scheduler=scheduler) + df_poly['geometry'] = ddata.map_partitions(apply_box, meta=Polygon) \ + .compute(scheduler=scheduler) # construct raster - xmin, ymin, xmax, ymax = points_df.longitude.min(), points_df.latitude.min(), \ - points_df.longitude.max(), points_df.latitude.max() - rows, cols, ras_trans = pts_to_raster_meta((xmin, ymin, xmax, ymax), raster_res) + xmin, ymin, xmax, ymax = (points_df.longitude.min(), points_df.latitude.min(), + points_df.longitude.max(), points_df.latitude.max()) + rows, cols, ras_trans = pts_to_raster_meta((xmin, ymin, xmax, ymax), + (raster_res, -raster_res)) raster_out = np.zeros((len(val_names), rows, cols)) + # TODO: parallel rasterize for i_val, val_name in enumerate(val_names): - raster_out[i_val, :, :] = rasterize([(x, val) for (x, val) in zip(df_poly.geometry, \ - df_poly[val_name])], out_shape=(rows, cols), transform=ras_trans, \ - fill=0, all_touched=True, dtype=rasterio.float32, ) - meta = {'crs': points_df.crs, 'height':rows, 'width':cols, 'transform': ras_trans} + raster_out[i_val, :, :] = rasterio.features.rasterize( + list(zip(df_poly.geometry, df_poly[val_name])), + out_shape=(rows, cols), + transform=ras_trans, + fill=0, + all_touched=True, + dtype=rasterio.float32) + + meta = { + 'crs': points_df.crs, + 'height': rows, + 'width': cols, + 'transform': ras_trans, + } return raster_out, meta def set_df_geometry_points(df_val, scheduler=None): - """ Set given geometry to given dataframe using dask if scheduler + """Set given geometry to given dataframe using dask if scheduler Parameters: df_val (DataFrame or GeoDataFrame): contains latitude and longitude columns @@ -608,10 +1451,77 @@ def set_df_geometry_points(df_val, scheduler=None): """ LOGGER.info('Setting geometry points.') def apply_point(df_exp): - return df_exp.apply((lambda row: Point(row.longitude, row.latitude)), axis=1) + fun = lambda row: Point(row.longitude, row.latitude) + return df_exp.apply(fun, axis=1) if not scheduler: df_val['geometry'] = apply_point(df_val) else: ddata = dd.from_pandas(df_val, npartitions=cpu_count()) - df_val['geometry'] = ddata.map_partitions(apply_point, meta=Point).\ - compute(scheduler=scheduler) + df_val['geometry'] = ddata.map_partitions(apply_point, meta=Point) \ + .compute(scheduler=scheduler) + +def fao_code_def(): + """Generates list of FAO country codes and corresponding ISO numeric-3 codes + + Returns: + iso_list (list): list of ISO numeric-3 codes + faocode_list (list): list of FAO country codes + """ + # FAO_FILE2: contains FAO country codes and correstponding ISO3 Code + # (http://www.fao.org/faostat/en/#definitions) + fao_file = pd.read_csv(os.path.join(DATA_DIR, 'system', "FAOSTAT_data_country_codes.csv")) + fao_code = getattr(fao_file, 'Country Code').values + fao_iso = (getattr(fao_file, 'ISO3 Code').values).tolist() + + # create a list of ISO3 codes and corresponding fao country codes + iso_list = list() + faocode_list = list() + for idx, iso in enumerate(fao_iso): + if isinstance(iso, str): + iso_list.append(country_iso_alpha2numeric(iso)) + faocode_list.append(int(fao_code[idx])) + + return iso_list, faocode_list + +def country_faocode2iso(input_fao): + """Convert FAO country code to ISO numeric-3 codes + + Parameters: + input_fao (int or array): FAO country codes of countries (or single code) + + Returns: + output_iso (int or array): ISO numeric-3 codes of countries (or single code) + """ + + # load relation between ISO numeric-3 code and FAO country code + iso_list, faocode_list = fao_code_def() + + # determine the fao country code for the input str or list + output_iso = np.zeros(len(input_fao)) + for item, faocode in enumerate(input_fao): + idx = np.where(faocode_list == faocode)[0] + if len(idx) == 1: + output_iso[item] = iso_list[idx[0]] + + return output_iso + +def country_iso2faocode(input_iso): + """Convert ISO numeric-3 codes to FAO country code + + Parameters: + input_iso (int or array): ISO numeric-3 codes of countries (or single code) + + Returns: + output_faocode (int or array): FAO country codes of countries (or single code) + """ + # load relation between ISO numeric-3 code and FAO country code + iso_list, faocode_list = fao_code_def() + + # determine the fao country code for the input str or list + output_faocode = np.zeros(len(input_iso)) + for item, iso in enumerate(input_iso): + idx = np.where(iso_list == iso)[0] + if len(idx) == 1: + output_faocode[item] = faocode_list[idx[0]] + + return output_faocode diff --git a/climada/util/dates_times.py b/climada/util/dates_times.py index 3739095614..751f7c73d5 100644 --- a/climada/util/dates_times.py +++ b/climada/util/dates_times.py @@ -20,7 +20,7 @@ LOGGER = logging.getLogger(__name__) def date_to_str(date): - """ Compute date string in ISO format from input datetime ordinal int. + """Compute date string in ISO format from input datetime ordinal int. Parameters: date (int or list or np.array): input datetime ordinal Returns: @@ -34,7 +34,7 @@ def date_to_str(date): def str_to_date(date): - """ Compute datetime ordinal int from input date string in ISO format. + """Compute datetime ordinal int from input date string in ISO format. Parameters: date (str or list): idate string in ISO format, e.g. '2018-04-06' Returns: @@ -51,7 +51,7 @@ def str_to_date(date): return all_date def datetime64_to_ordinal(datetime): - """ Converts from a numpy datetime64 object to an ordinal date. + """Converts from a numpy datetime64 object to an ordinal date. See https://stackoverflow.com/a/21916253 for the horrible details. Parameters: datetime (np.datetime64, or list or np.array): date and time @@ -64,7 +64,7 @@ def datetime64_to_ordinal(datetime): return [pd.to_datetime(i_dt.tolist()).toordinal() for i_dt in datetime] def last_year(ordinal_vector): - """ Extract first year from ordinal date + """Extract first year from ordinal date Parameters: ordinal_vector (list or np.array): input datetime ordinal @@ -74,7 +74,7 @@ def last_year(ordinal_vector): return dt.date.fromordinal(np.max(ordinal_vector)).year def first_year(ordinal_vector): - """ Extract first year from ordinal date + """Extract first year from ordinal date Parameters: ordinal_vector (list or np.array): input datetime ordinal diff --git a/climada/util/earth_engine.py b/climada/util/earth_engine.py index 11e8c0a4d4..8e2c052535 100644 --- a/climada/util/earth_engine.py +++ b/climada/util/earth_engine.py @@ -18,23 +18,22 @@ Regroup methods to obtain images from Google Earth Engine API """ -# This module works only if you have a Google Earth Engine account -# See tutorial climada_util_earth_engine.ipynb import logging import webbrowser +# This module works only if you have a Google Earth Engine account. +# That's why `earthengine-api` is not in the CLIMADA requirements. +# See tutorial: climada_util_earth_engine.ipynb +# pylint: disable=import-error import ee -ee.Initialize() - - - LOGGER = logging.getLogger(__name__) +ee.Initialize() def obtain_image_landsat_composite(landsat_collection, time_range, area): - """ Selection of Landsat cloud-free composites in the Earth Engine library + """Selection of Landsat cloud-free composites in the Earth Engine library See also: https://developers.google.com/earth-engine/landsat Parameters: @@ -47,14 +46,14 @@ def obtain_image_landsat_composite(landsat_collection, time_range, area): """ collection = ee.ImageCollection(landsat_collection) - ## Filter by time range and location + # Filter by time range and location collection_time = collection.filterDate(time_range[0], time_range[1]) image_area = collection_time.filterBounds(area) image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) return image_composite def obtain_image_median(collection, time_range, area): - """ Selection of median from a collection of images in the Earth Engine library + """Selection of median from a collection of images in the Earth Engine library See also: https://developers.google.com/earth-engine/reducers_image_collection Parameters: @@ -67,14 +66,14 @@ def obtain_image_median(collection, time_range, area): """ collection = ee.ImageCollection(collection) - ## Filter by time range and location + # Filter by time range and location collection_time = collection.filterDate(time_range[0], time_range[1]) image_area = collection_time.filterBounds(area) image_median = image_area.median() return image_median def obtain_image_sentinel(sentinel_collection, time_range, area): - """ Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset + """Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 Parameters: @@ -85,13 +84,12 @@ def obtain_image_sentinel(sentinel_collection, time_range, area): Returns: sentinel_median (ee.image.Image) """ -#First, method to remove cloud from the image +# First, method to remove cloud from the image def maskclouds(image): band_qa = image.select('QA60') cloud_mask = ee.Number(2).pow(10).int() cirrus_mask = ee.Number(2).pow(11).int() - mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and( - band_qa.bitwiseAnd(cirrus_mask).eq(0)) + mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and (band_qa.bitwiseAnd(cirrus_mask).eq(0)) return image.updateMask(mask).divide(10000) sentinel_filtered = (ee.ImageCollection(sentinel_collection). @@ -138,10 +136,10 @@ def get_url(name, image, scale, region): path (str) """ path = image.getDownloadURL({ - 'name':(name), + 'name': (name), 'scale': scale, - 'region':(region) - }) + 'region': (region) + }) webbrowser.open_new_tab(path) return path diff --git a/climada/util/files_handler.py b/climada/util/files_handler.py index 600e76182c..134b68fac6 100644 --- a/climada/util/files_handler.py +++ b/climada/util/files_handler.py @@ -19,9 +19,10 @@ Functions to deal with files. """ -__all__ = ['to_list', - 'get_file_names' - ] +__all__ = [ + 'to_list', + 'get_file_names', +] import os import glob @@ -34,9 +35,9 @@ LOGGER = logging.getLogger(__name__) class DownloadProgressBar(tqdm): - """ Class to use progress bar during dowloading """ + """Class to use progress bar during dowloading""" def update_to(self, blocks=1, bsize=1, tsize=None): - """ Update progress bar + """Update progress bar Parameters: blocks (int, otional): Number of blocks transferred so far [default: 1]. @@ -49,7 +50,7 @@ def update_to(self, blocks=1, bsize=1, tsize=None): self.update(blocks * bsize - self.n) def download_file(url): - """ Download file from url in current folder and provide absolute file path + """Download file from url in current folder and provide absolute file path and name. Parameters: @@ -79,13 +80,13 @@ def download_file(url): LOGGER.info('Downloading file %s', file_abs_name) with open(file_name, 'wb') as file: for data in tqdm(req_file.iter_content(block_size), - total=math.ceil(total_size//block_size), + total=math.ceil(total_size // block_size), unit='KB', unit_scale=True): file.write(data) return file_abs_name def download_ftp(url, file_name): - """ Download file from ftp in current folder. + """Download file from ftp in current folder. Parameters: url (str): url containing data to download @@ -96,11 +97,11 @@ def download_ftp(url, file_name): """ LOGGER.info('Downloading file %s', file_name) try: - with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, \ + with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as prog_bar: urllib.request.urlretrieve(url, file_name, reporthook=prog_bar.update_to) - except Exception: - raise ValueError + except Exception as exc: + raise ValueError(f'{exc.__class__} - "{exc}": failed to retrieve {url} into {file_name}') def to_list(num_exp, values, val_name): """Check size and transform to list if necessary. If size is one, build @@ -129,7 +130,7 @@ def to_list(num_exp, values, val_name): return val_list def get_file_names(file_name): - """ Return list of files contained. Supports globbing. + """Return list of files contained. Supports globbing. Parameters: file_name (str or list(str)): Either a single string or a list of @@ -150,7 +151,7 @@ def get_file_names(file_name): return file_list def get_extension(file_name): - """ Get file without extension and its extension (e.g. ".nc", ".grd.gz"). + """Get file without extension and its extension (e.g. ".nc", ".grd.gz"). Parameters: file_name (str): file name (with or without path) @@ -165,7 +166,7 @@ def get_extension(file_name): return file_pth, file_ext def _process_one_file_name(name, file_list): - """ Apend to input list the file contained in name + """Apend to input list the file contained in name Tries globbing if name is neither dir nor file. """ if os.path.isdir(name): diff --git a/climada/util/finance.py b/climada/util/finance.py index 2e430d9c47..b3e022f84d 100644 --- a/climada/util/finance.py +++ b/climada/util/finance.py @@ -44,7 +44,7 @@ WORLD_BANK_WEALTH_ACC = \ "https://databank.worldbank.org/data/download/Wealth-Accounts_CSV.zip" -""" Wealth historical data (1995, 2000, 2005, 2010, 2014) from World Bank (ZIP). +"""Wealth historical data (1995, 2000, 2005, 2010, 2014) from World Bank (ZIP). https://datacatalog.worldbank.org/dataset/wealth-accounting Includes variable Produced Capital (NW.PCA.TO)""" @@ -52,26 +52,26 @@ WORLD_BANK_INC_GRP = \ "http://databank.worldbank.org/data/download/site-content/OGHIST.xls" -""" Income group historical data from World bank.""" +"""Income group historical data from World bank.""" -INCOME_GRP_WB_TABLE = {'L' : 1, # low income - 'LM': 2, # lower middle income - 'UM': 3, # upper middle income - 'H' : 4, # high income - '..': np.nan # no data +INCOME_GRP_WB_TABLE = {'L': 1, # low income + 'LM': 2, # lower middle income + 'UM': 3, # upper middle income + 'H': 4, # high income + '..': np.nan # no data } -""" Meaning of values of world banks' historical table on income groups. """ +"""Meaning of values of world banks' historical table on income groups.""" -INCOME_GRP_NE_TABLE = {5: 1, # Low income - 4: 2, # Lower middle income - 3: 3, # Upper middle income - 2: 4, # High income: nonOECD +INCOME_GRP_NE_TABLE = {5: 1, # Low income + 4: 2, # Lower middle income + 3: 3, # Upper middle income + 2: 4, # High income: nonOECD 1: 4 # High income: OECD } -""" Meaning of values of natural earth's income groups.""" +"""Meaning of values of natural earth's income groups.""" FILE_GWP_WEALTH2GDP_FACTORS = 'WEALTH2GDP_factors_CRI_2016.csv' -""" File with wealth-to-GDP factors from the +"""File with wealth-to-GDP factors from the Credit Suisse's Global Wealth Report 2017 (household wealth)""" def _nat_earth_shp(resolution='10m', category='cultural', @@ -98,12 +98,12 @@ def net_present_value(years, disc_rates, val_years): npv = val_years[-1] for val, disc in zip(val_years[-2::-1], disc_rates[-2::-1]): - npv = val + npv/(1+disc) + npv = val + npv / (1 + disc) return npv def income_group(cntry_iso, ref_year, shp_file=None): - """ Get country's income group from World Bank's data at a given year, + """Get country's income group from World Bank's data at a given year, or closest year value. If no data, get the natural earth's approximation. Parameters: @@ -125,7 +125,7 @@ def income_group(cntry_iso, ref_year, shp_file=None): return close_year, close_val def gdp(cntry_iso, ref_year, shp_file=None, per_capita=False): - """ Get country's (current value) GDP from World Bank's data at a given year, or + """Get country's (current value) GDP from World Bank's data at a given year, or closest year value. If no data, get the natural earth's approximation. Parameters: @@ -162,7 +162,7 @@ def gdp(cntry_iso, ref_year, shp_file=None, per_capita=False): return close_year, close_val def world_bank(cntry_iso, ref_year, info_ind): - """ Get country's GDP from World Bank's data at a given year, or + """Get country's GDP from World Bank's data at a given year, or closest year value. If no data, get the natural earth's approximation. Parameters: @@ -180,14 +180,13 @@ def world_bank(cntry_iso, ref_year, info_ind): if info_ind != 'INC_GRP': with warnings.catch_warnings(): warnings.simplefilter("ignore") - cntry_gdp = wb.download(indicator=info_ind, \ - country=cntry_iso, start=1960, end=2030) + cntry_gdp = wb.download(indicator=info_ind, country=cntry_iso, start=1960, end=2030) years = np.array([int(year) for year in cntry_gdp.index.get_level_values('year')]) - sort_years = np.abs(years-ref_year).argsort() + sort_years = np.abs(years - ref_year).argsort() close_val = cntry_gdp.iloc[sort_years].dropna() close_year = int(close_val.iloc[0].name[1]) close_val = float(close_val.iloc[0].values) - else: # income group level + else: # income group level fn_ig = os.path.join(os.path.abspath(SYSTEM_DIR), 'OGHIST.xls') dfr_wb = pd.DataFrame() try: @@ -199,20 +198,20 @@ def world_bank(cntry_iso, ref_year, info_ind): dfr_wb = dfr_wb.replace(INCOME_GRP_WB_TABLE.keys(), INCOME_GRP_WB_TABLE.values()) except (IOError, requests.exceptions.ConnectionError) as err: - LOGGER.error('Internet connection failed while downloading ' + + LOGGER.error('Internet connection failed while downloading ' 'historical income groups.') raise err cntry_dfr = dfr_wb.loc[cntry_iso] - close_val = cntry_dfr.iloc[np.abs( \ - np.array(cntry_dfr.index[1:])-ref_year).argsort()+1].dropna() + close_val = cntry_dfr.iloc[np.abs( + np.array(cntry_dfr.index[1:]) - ref_year).argsort() + 1].dropna() close_year = close_val.index[0] close_val = int(close_val.iloc[0]) return close_year, close_val def nat_earth_adm0(cntry_iso, info_name, year_name=None, shp_file=None): - """ Get country's parameter from natural earth's admin0 shape file. + """Get country's parameter from natural earth's admin0 shape file. Parameters: cntry_iso (str): key = ISO alpha_3 country @@ -252,9 +251,9 @@ def nat_earth_adm0(cntry_iso, info_name, year_name=None, shp_file=None): return close_year, close_val -def wealth2gdp(cntry_iso, non_financial=True, ref_year=2016, \ +def wealth2gdp(cntry_iso, non_financial=True, ref_year=2016, file_name=FILE_GWP_WEALTH2GDP_FACTORS): - """ Get country's wealth-to-GDP factor from the + """Get country's wealth-to-GDP factor from the Credit Suisse's Global Wealth Report 2017 (household wealth). Missing value: returns NaN. Parameters: @@ -266,33 +265,31 @@ def wealth2gdp(cntry_iso, non_financial=True, ref_year=2016, \ float """ fname = os.path.join(SYSTEM_DIR, file_name) - factors_all_countries = pd.read_csv(fname, sep=',', index_col=None, \ - header=0, encoding='ISO-8859-1') + factors_all_countries = pd.read_csv(fname, sep=',', index_col=None, + header=0, encoding='ISO-8859-1') if ref_year != 2016: - LOGGER.warning('Reference year for the factor to convert GDP to '\ - + 'wealth was set to 2016 because other years have not '\ - + 'been implemented yet.') + LOGGER.warning('Reference year for the factor to convert GDP to ' + 'wealth was set to 2016 because other years have not ' + 'been implemented yet.') ref_year = 2016 if non_financial: try: - val = factors_all_countries\ - [factors_all_countries.country_iso3 == cntry_iso]\ - ['NFW-to-GDP-ratio'].values[0] + val = factors_all_countries[ + factors_all_countries.country_iso3 == cntry_iso]['NFW-to-GDP-ratio'].values[0] except: LOGGER.warning('No data for country, using mean factor.') val = factors_all_countries["NFW-to-GDP-ratio"].mean() else: try: - val = factors_all_countries\ - [factors_all_countries.country_iso3 == cntry_iso]\ - ['TW-to-GDP-ratio'].values[0] + val = factors_all_countries[ + factors_all_countries.country_iso3 == cntry_iso]['TW-to-GDP-ratio'].values[0] except: LOGGER.warning('No data for country, using mean factor.') val = factors_all_countries["TW-to-GDP-ratio"].mean() val = np.around(val, 5) return ref_year, val -def world_bank_wealth_account(cntry_iso, ref_year, variable_name="NW.PCA.TO", \ +def world_bank_wealth_account(cntry_iso, ref_year, variable_name="NW.PCA.TO", no_land=True): """ Download and unzip wealth accounting historical data (1995, 2000, 2005, 2010, 2014) @@ -347,40 +344,41 @@ def world_bank_wealth_account(cntry_iso, ref_year, variable_name="NW.PCA.TO", \ LOGGER.error('Downloading World Bank Wealth Accounting Data failed.') raise - data_wealth = data_wealth[data_wealth['Country Code'].str.contains(cntry_iso) \ - & data_wealth['Indicator Code'].\ - str.contains(variable_name)].loc[:, '1995':'2014'] + data_wealth = data_wealth[data_wealth['Country Code'].str.contains(cntry_iso) + & data_wealth['Indicator Code'].str.contains(variable_name) + ].loc[:, '1995':'2014'] years = list(map(int, list(data_wealth))) - if data_wealth.size == 0 and 'NW.PCA.TO' in variable_name: # if country is not found in data + if data_wealth.size == 0 and 'NW.PCA.TO' in variable_name: # if country is not found in data LOGGER.warning('No data available for country. Using non-financial wealth instead') gdp_year, gdp_val = gdp(cntry_iso, ref_year) - ref_year_fac, fac = wealth2gdp(cntry_iso) - return gdp_year, np.around((fac*gdp_val), 1), 0 - if ref_year in years: # indicator for reference year is available directly + fac = wealth2gdp(cntry_iso)[1] + return gdp_year, np.around((fac * gdp_val), 1), 0 + if ref_year in years: # indicator for reference year is available directly result = data_wealth.loc[:, np.str(ref_year)].values[0] - elif ref_year > np.min(years) and ref_year < np.max(years): # interpolate + elif ref_year > np.min(years) and ref_year < np.max(years): # interpolate result = np.interp(ref_year, years, data_wealth.values[0, :]) - elif ref_year < np.min(years): # scale proportionally to GDP + elif ref_year < np.min(years): # scale proportionally to GDP gdp_year, gdp0_val = gdp(cntry_iso, np.min(years)) gdp_year, gdp_val = gdp(cntry_iso, ref_year) - result = data_wealth.values[0, 0]*gdp_val/gdp0_val + result = data_wealth.values[0, 0] * gdp_val / gdp0_val ref_year = gdp_year else: gdp_year, gdp0_val = gdp(cntry_iso, np.max(years)) gdp_year, gdp_val = gdp(cntry_iso, ref_year) - result = data_wealth.values[0, -1]*gdp_val/gdp0_val + result = data_wealth.values[0, -1] * gdp_val / gdp0_val ref_year = gdp_year - if 'NW.PCA.' in variable_name and no_land: # remove value of built-up land from produced capital - result = result/1.24 + if 'NW.PCA.' in variable_name and no_land: + # remove value of built-up land from produced capital + result = result / 1.24 return ref_year, np.around(result, 1), 1 def _gdp_twn(ref_year, per_capita=False): - """returns GDP for TWN (Republic of China / Taiwan Province of China) based + """returns GDP for TWN (Republic of China / Taiwan Province of China) based on a CSV sheet downloaded from the - International Monetary Fund (IMF). + International Monetary Fund (IMF). The reason for this special treatment is the lack of GDP data for TWN in the World Bank data - + Data Source: https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/index.aspx https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/weorept.aspx?sy=1980&ey=2024&scsm=1&ssd=1&sic=1&sort=country&ds=.&br=1&pr1.x=42&pr1.y=10&c=528&s=NGDPD%2CNGDP_D%2CNGDPDPC&grp=0&a= @@ -391,24 +389,23 @@ def _gdp_twn(ref_year, per_capita=False): Returns: float """ - if not os.path.isfile(os.path.join(os.path.abspath(SYSTEM_DIR), \ - 'GDP_TWN_IMF_WEO_data.csv')): + if not os.path.isfile(os.path.join(os.path.abspath(SYSTEM_DIR), 'GDP_TWN_IMF_WEO_data.csv')): LOGGER.error('File GDP_TWN_IMF_WEO_data.csv not found in SYSTEM_DIR') return 0 if per_capita: var_name = 'Gross domestic product per capita, current prices' else: var_name = 'Gross domestic product, current prices' - if ref_year<1980: + if ref_year < 1980: close_year = 1980 - elif ref_year>2024: + elif ref_year > 2024: close_year = 2024 else: close_year = ref_year - data = pd.read_csv(os.path.join(os.path.abspath(SYSTEM_DIR), \ - 'GDP_TWN_IMF_WEO_data.csv'), \ - index_col=None, header=0) - close_val = data.loc[data['Subject Descriptor']==var_name, str(close_year)].values[0] + data = pd.read_csv(os.path.join(os.path.abspath(SYSTEM_DIR), 'GDP_TWN_IMF_WEO_data.csv'), + index_col=None, header=0) + close_val = data.loc[data['Subject Descriptor'] == var_name, str(close_year)].values[0] close_val = float(close_val.replace(',', '')) - if not per_capita: close_val = close_val*1e9 + if not per_capita: + close_val = close_val * 1e9 return close_year, close_val diff --git a/climada/util/hdf5_handler.py b/climada/util/hdf5_handler.py index 7fbba8e1c5..0ed1e79d40 100644 --- a/climada/util/hdf5_handler.py +++ b/climada/util/hdf5_handler.py @@ -56,7 +56,7 @@ def read(file_name, with_refs=False): Exception while reading """ def get_group(group): - '''Recursive function to get variables from a group.''' + """Recursive function to get variables from a group.""" contents = {} for name, obj in list(group.items()): if isinstance(obj, h5py.Dataset): @@ -86,7 +86,7 @@ def get_string(array): Returns: string """ - return u''.join(chr(c) for c in array) + return u''.join(chr(int(c)) for c in array) def get_str_from_ref(file_name, var): """Form string from a reference HDF5 variable of the given file. @@ -134,5 +134,5 @@ def get_sparse_csr_mat(mat_dict, shape): ('jc' not in mat_dict): raise ValueError('Input data is not a sparse matrix.') - return sparse.csc_matrix((mat_dict['data'], mat_dict['ir'], \ + return sparse.csc_matrix((mat_dict['data'], mat_dict['ir'], mat_dict['jc']), shape).tocsr() diff --git a/climada/util/interpolation.py b/climada/util/interpolation.py index e932450fa6..86b3bff0eb 100644 --- a/climada/util/interpolation.py +++ b/climada/util/interpolation.py @@ -34,14 +34,14 @@ LOGGER = logging.getLogger(__name__) DIST_DEF = ['approx', 'haversine'] -""" Distances """ +"""Distances""" METHOD = ['NN'] -""" Interpolation methods """ +"""Interpolation methods""" THRESHOLD = 100 -""" Distance threshold in km. Nearest neighbors with greater distances are -not considered. """ +"""Distance threshold in km. Nearest neighbors with greater distances are +not considered.""" @jit(nopython=True, parallel=True) def dist_approx(lats1, lons1, cos_lats1, lats2, lons2): @@ -52,15 +52,15 @@ def dist_approx(lats1, lons1, cos_lats1, lats2, lons2): @jit(nopython=True, parallel=True) def dist_sqr_approx(lats1, lons1, cos_lats1, lats2, lons2): - """ Compute squared equirectangular approximation distance. Values need + """Compute squared equirectangular approximation distance. Values need to be sqrt and multiplicated by ONE_LAT_KM to obtain distance in km.""" d_lon = lons1 - lons2 d_lat = lats1 - lats2 return d_lon * d_lon * cos_lats1 * cos_lats1 + d_lat * d_lat -def interpol_index(centroids, coordinates, method=METHOD[0], \ +def interpol_index(centroids, coordinates, method=METHOD[0], distance=DIST_DEF[1], threshold=THRESHOLD): - """ Returns for each coordinate the centroids indexes used for + """Returns for each coordinate the centroids indexes used for interpolation. Parameters: @@ -85,13 +85,13 @@ def interpol_index(centroids, coordinates, method=METHOD[0], \ # haversine formula. This is done with a Ball tree. interp = index_nn_haversine(centroids, coordinates, threshold) else: - LOGGER.error('Interpolation using %s with distance %s is not '\ + LOGGER.error('Interpolation using %s with distance %s is not ' 'supported.', method, distance) interp = np.array([]) return interp def index_nn_aprox(centroids, coordinates, threshold=THRESHOLD): - """ Compute the nearest centroid for each coordinate using the + """Compute the nearest centroid for each coordinate using the euclidian distance d = ((dlon)cos(lat))^2+(dlat)^2. For distant points (e.g. more than 100km apart) use the haversine distance. @@ -131,13 +131,13 @@ def index_nn_aprox(centroids, coordinates, threshold=THRESHOLD): assigned[inv == icoord] = min_idx if num_warn: - LOGGER.warning('Distance to closest centroid is greater than %s' \ - 'km for %s coordinates.', threshold, num_warn) + LOGGER.warning('Distance to closest centroid is greater than %s' + 'km for %s coordinates.', threshold, num_warn) return assigned def index_nn_haversine(centroids, coordinates, threshold=THRESHOLD): - """ Compute the neareast centroid for each coordinate using a Ball + """Compute the neareast centroid for each coordinate using a Ball tree with haversine distance. Parameters: @@ -159,17 +159,17 @@ def index_nn_haversine(centroids, coordinates, threshold=THRESHOLD): return_inverse=True) # query the k closest points of the n_points using dual tree - dist, assigned = tree.query(np.radians(coordinates[idx]), k=1, \ - return_distance=True, dualtree=True, \ + dist, assigned = tree.query(np.radians(coordinates[idx]), k=1, + return_distance=True, dualtree=True, breadth_first=False) # Raise a warning if the minimum distance is greater than the # threshold and set an unvalid index -1 - num_warn = np.sum(dist*EARTH_RADIUS_KM > threshold) + num_warn = np.sum(dist * EARTH_RADIUS_KM > threshold) if num_warn: - LOGGER.warning('Distance to closest centroid is greater than %s' \ - 'km for %s coordinates.', threshold, num_warn) - assigned[dist*EARTH_RADIUS_KM > threshold] = -1 + LOGGER.warning('Distance to closest centroid is greater than %s' + 'km for %s coordinates.', threshold, num_warn) + assigned[dist * EARTH_RADIUS_KM > threshold] = -1 # Copy result to all exposures and return value return np.squeeze(assigned[inv]) diff --git a/climada/util/plot.py b/climada/util/plot.py index 89e6f603e3..90e88dd99a 100644 --- a/climada/util/plot.py +++ b/climada/util/plot.py @@ -45,16 +45,16 @@ LOGGER = logging.getLogger(__name__) RESOLUTION = 250 -""" Number of pixels in one direction in rendered image """ +"""Number of pixels in one direction in rendered image""" BUFFER = 1.0 -""" Degrees to add in the border """ +"""Degrees to add in the border""" MAX_BINS = 2000 -""" Maximum number of bins in geo_bin_from_array """ +"""Maximum number of bins in geo_bin_from_array""" -def geo_bin_from_array(array_sub, geo_coord, var_name, title, pop_name=True,\ - buffer=BUFFER, extend='neither', \ +def geo_bin_from_array(array_sub, geo_coord, var_name, title, pop_name=True, + buffer=BUFFER, extend='neither', proj=ccrs.PlateCarree(), axes=None, **kwargs): """Plot array values binned over input coordinates. @@ -101,24 +101,24 @@ def geo_bin_from_array(array_sub, geo_coord, var_name, title, pop_name=True,\ for array_im, axis, tit, name, coord in \ zip(list_arr, axes_iter.flatten(), list_tit, list_name, list_coord): if coord.shape[0] != array_im.size: - raise ValueError("Size mismatch in input array: %s != %s." % \ + raise ValueError("Size mismatch in input array: %s != %s." % (coord.shape[0], array_im.size)) # Binned image with coastlines - extent = _get_borders(coord, buffer, proj) + extent = _get_borders(coord, buffer=buffer, proj_limits=proj.x_limits + proj.y_limits) axis.set_extent((extent), proj) add_shapes(axis) if pop_name: add_populated_places(axis, extent, proj) if 'gridsize' not in kwargs: - kwargs['gridsize'] = min(int(array_im.size/2), MAX_BINS) - hex_bin = axis.hexbin(coord[:, 1], coord[:, 0], C=array_im, \ - transform=proj, **kwargs) + kwargs['gridsize'] = min(int(array_im.size / 2), MAX_BINS) + hex_bin = axis.hexbin(coord[:, 1], coord[:, 0], C=array_im, + transform=proj, **kwargs) # Create colorbar in this axis - cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", \ - pad=0.1, axes_class=plt.Axes) + cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", + pad=0.1, axes_class=plt.Axes) cbar = plt.colorbar(hex_bin, cax=cbax, orientation='vertical', extend=extend) cbar.set_label(name) @@ -127,7 +127,7 @@ def geo_bin_from_array(array_sub, geo_coord, var_name, title, pop_name=True,\ return axes def geo_scatter_from_array(array_sub, geo_coord, var_name, title, - pop_name=True, buffer=BUFFER, extend='neither', \ + pop_name=True, buffer=BUFFER, extend='neither', proj=ccrs.PlateCarree(), shapes=True, axes=None, **kwargs): """Plot array values binned over input coordinates. @@ -174,37 +174,36 @@ def geo_scatter_from_array(array_sub, geo_coord, var_name, title, for array_im, axis, tit, name, coord in \ zip(list_arr, axes_iter.flatten(), list_tit, list_name, list_coord): if coord.shape[0] != array_im.size: - raise ValueError("Size mismatch in input array: %s != %s." % \ + raise ValueError("Size mismatch in input array: %s != %s." % (coord.shape[0], array_im.size)) # Binned image with coastlines - extent = _get_borders(coord, buffer, proj) + extent = _get_borders(coord, buffer=buffer, proj_limits=proj.x_limits + proj.y_limits) axis.set_extent((extent), proj) if shapes: add_shapes(axis) if pop_name: add_populated_places(axis, extent, proj) - hex_bin = axis.scatter(coord[:, 1], coord[:, 0], c=array_im, \ - transform=proj, **kwargs) + hex_bin = axis.scatter(coord[:, 1], coord[:, 0], c=array_im, + transform=proj, **kwargs) # Create colorbar in this axis - cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", \ - pad=0.1, axes_class=plt.Axes) + cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", + pad=0.1, axes_class=plt.Axes) cbar = plt.colorbar(hex_bin, cax=cbax, orientation='vertical', extend=extend) cbar.set_label(name) axis.set_title(tit) return axes -def geo_im_from_array(array_sub, geo_coord, var_name, title, - proj=ccrs.PlateCarree(), smooth=True, axes=None, **kwargs): +def geo_im_from_array(array_sub, coord, var_name, title, + proj=None, smooth=True, axes=None, **kwargs): """Image(s) plot defined in array(s) over input coordinates. Parameters: array_sub (np.array(1d or 2d) or list(np.array)): Each array (in a row or in the list) are values at each point in corresponding geo_coord that are ploted in one subplot. - geo_coord (2d np.array or list(2d np.array)): (lat, lon) for each - point in a row. If one provided, the same grid is used for all - subplots. Otherwise provide as many as subplots in array_sub. + coord (2d np.array): (lat, lon) for each point in a row. The same grid is used for all + subplots. var_name (str or list(str)): label to be shown in the colorbar. If one provided, the same is used for all subplots. Otherwise provide as many as subplots in array_sub. @@ -226,8 +225,13 @@ def geo_im_from_array(array_sub, geo_coord, var_name, title, num_im, list_arr = _get_collection_arrays(array_sub) list_tit = to_list(num_im, title, 'title') list_name = to_list(num_im, var_name, 'var_name') - list_coord = to_list(num_im, geo_coord, 'geo_coord') + is_reg, height, width = grid_is_regular(coord) + extent = _get_borders(coord, proj_limits=(-360, 360, -90, 90)) + mid_lon = 0 + if not proj: + mid_lon = 0.5 * sum(extent[:2]) + proj = ccrs.PlateCarree(central_longitude=mid_lon) if 'vmin' not in kwargs: kwargs['vmin'] = np.nanmin(array_sub) if 'vmax' not in kwargs: @@ -239,19 +243,16 @@ def geo_im_from_array(array_sub, geo_coord, var_name, title, axes_iter = np.array([[axes]]) # Generate each subplot - for array_im, axis, tit, name, coord in \ - zip(list_arr, axes_iter.flatten(), list_tit, list_name, list_coord): + for array_im, axis, tit, name in zip(list_arr, axes_iter.flatten(), list_tit, list_name): if coord.shape[0] != array_im.size: - raise ValueError("Size mismatch in input array: %s != %s." % \ + raise ValueError("Size mismatch in input array: %s != %s." % (coord.shape[0], array_im.size)) - is_reg, height, width = grid_is_regular(coord) - extent = _get_borders(coord, proj=proj) if smooth or not is_reg: # Create regular grid where to interpolate the array grid_x, grid_y = np.mgrid[ - extent[0] : extent[1] : complex(0, RESOLUTION), - extent[2] : extent[3] : complex(0, RESOLUTION)] - grid_im = griddata((coord[:, 1], coord[:, 0]), array_im, \ + extent[0]: extent[1]: complex(0, RESOLUTION), + extent[2]: extent[3]: complex(0, RESOLUTION)] + grid_im = griddata((coord[:, 1], coord[:, 0]), array_im, (grid_x, grid_y)) else: grid_x = coord[:, 1].reshape((width, height)).transpose() @@ -261,15 +262,17 @@ def geo_im_from_array(array_sub, geo_coord, var_name, title, grid_y = np.flip(grid_y) grid_im = np.flip(grid_im, 1) grid_im = np.resize(grid_im, (height, width, 1)) + axis.set_extent((extent[0] - mid_lon, extent[1] - mid_lon, + extent[2], extent[3]), crs=proj) # Add coastline to axis - axis.set_extent((extent), proj) add_shapes(axis) # Create colormesh, colorbar and labels in axis - cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", \ - pad=0.1, axes_class=plt.Axes) - cbar = plt.colorbar(axis.pcolormesh(grid_x, grid_y, np.squeeze(grid_im), \ - transform=proj, **kwargs), cax=cbax, orientation='vertical') + cbax = make_axes_locatable(axis).append_axes('right', size="6.5%", + pad=0.1, axes_class=plt.Axes) + img = axis.pcolormesh(grid_x - mid_lon, grid_y, np.squeeze(grid_im), + transform=proj, **kwargs) + cbar = plt.colorbar(img, cax=cbax, orientation='vertical') cbar.set_label(name) axis.set_title(tit) @@ -324,11 +327,11 @@ def add_shapes(axis): projection (cartopy.crs projection, optional): geographical projection, PlateCarree default. """ - shp_file = shapereader.natural_earth(resolution='10m', \ - category='cultural', name='admin_0_countries') + shp_file = shapereader.natural_earth(resolution='10m', category='cultural', + name='admin_0_countries') shp = shapereader.Reader(shp_file) for geometry in shp.geometries(): - axis.add_geometries([geometry], crs=ccrs.PlateCarree(), facecolor='', \ + axis.add_geometries([geometry], crs=ccrs.PlateCarree(), facecolor='', edgecolor='black') def add_populated_places(axis, extent, proj=ccrs.PlateCarree()): @@ -341,21 +344,21 @@ def add_populated_places(axis, extent, proj=ccrs.PlateCarree()): PlateCarree default. """ - shp_file = shapereader.natural_earth(resolution='50m', \ - category='cultural', name='populated_places_simple') + shp_file = shapereader.natural_earth(resolution='50m', category='cultural', + name='populated_places_simple') shp = shapereader.Reader(shp_file) ext_pts = list(box(extent[0], extent[2], extent[1], extent[3]).exterior.coords) - ext_trans = [ccrs.PlateCarree().transform_point(pts[0], pts[1], proj) \ + ext_trans = [ccrs.PlateCarree().transform_point(pts[0], pts[1], proj) for pts in ext_pts] for rec, point in zip(shp.records(), shp.geometries()): if ext_trans[2][0] < point.x <= ext_trans[0][0]: if ext_trans[0][1] < point.y <= ext_trans[1][1]: axis.plot(point.x, point.y, 'ko', markersize=7, transform=ccrs.PlateCarree(), markerfacecolor='None') - axis.text(point.x, point.y, rec.attributes['name'], \ - horizontalalignment='right', verticalalignment='bottom', \ - transform=ccrs.PlateCarree(), fontsize=14) + axis.text(point.x, point.y, rec.attributes['name'], + horizontalalignment='right', verticalalignment='bottom', + transform=ccrs.PlateCarree(), fontsize=14) def add_cntry_names(axis, extent, proj=ccrs.PlateCarree()): """Add country names. @@ -367,24 +370,24 @@ def add_cntry_names(axis, extent, proj=ccrs.PlateCarree()): PlateCarree default. """ - shp_file = shapereader.natural_earth(resolution='10m', \ - category='cultural', name='admin_0_countries') + shp_file = shapereader.natural_earth(resolution='10m', category='cultural', + name='admin_0_countries') shp = shapereader.Reader(shp_file) ext_pts = list(box(extent[0], extent[2], extent[1], extent[3]).exterior.coords) - ext_trans = [ccrs.PlateCarree().transform_point(pts[0], pts[1], proj) \ + ext_trans = [ccrs.PlateCarree().transform_point(pts[0], pts[1], proj) for pts in ext_pts] for rec, point in zip(shp.records(), shp.geometries()): point_x = point.centroid.xy[0][0] point_y = point.centroid.xy[1][0] if ext_trans[2][0] < point_x <= ext_trans[0][0]: if ext_trans[0][1] < point_y <= ext_trans[1][1]: - axis.text(point_x, point_y, rec.attributes['NAME'], \ - horizontalalignment='center', verticalalignment='center', \ - transform=ccrs.PlateCarree(), fontsize=14) + axis.text(point_x, point_y, rec.attributes['NAME'], + horizontalalignment='center', verticalalignment='center', + transform=ccrs.PlateCarree(), fontsize=14) def _get_collection_arrays(array_sub): - """ Get number of array rows and generate list of array if only one row + """Get number of array rows and generate list of array if only one row Parameters: array_sub (np.array(1d or 2d) or list(np.array)): Each array (in a row @@ -422,39 +425,39 @@ def _get_row_col_size(num_sub): else: if num_sub % 3 == 0: num_col = 3 - num_row = int(num_sub/3) + num_row = int(num_sub / 3) else: num_col = 2 - num_row = int(num_sub/2) + num_sub % 2 + num_row = int(num_sub / 2) + num_sub % 2 return num_row, num_col -def _get_borders(geo_coord, buffer=0, proj=ccrs.PlateCarree()): +def _get_borders(geo_coord, buffer=0, proj_limits=(-180, 180, -90, 90)): """Get min and max longitude and min and max latitude (in this order). Parameters: geo_coord (2d np.array): (lat, lon) for each point in a row. buffer (float): border to add. Default: 0 - proj (cartopy.crs projection, optional): geographical projection, - PlateCarree default. + proj_limits (tuple, optional): limits of geographical projection + (lon_min, lon_max, lat_min, lat_max) Returns: np.array """ - min_lon = max(np.min(geo_coord[:, 1])-buffer, proj.x_limits[0]) - max_lon = min(np.max(geo_coord[:, 1])+buffer, proj.x_limits[1]) - min_lat = max(np.min(geo_coord[:, 0])-buffer, proj.y_limits[0]) - max_lat = min(np.max(geo_coord[:, 0])+buffer, proj.y_limits[1]) + min_lon = max(np.min(geo_coord[:, 1]) - buffer, proj_limits[0]) + max_lon = min(np.max(geo_coord[:, 1]) + buffer, proj_limits[1]) + min_lat = max(np.min(geo_coord[:, 0]) - buffer, proj_limits[2]) + max_lat = min(np.max(geo_coord[:, 0]) + buffer, proj_limits[3]) return [min_lon, max_lon, min_lat, max_lat] def get_transformation(crs_in): - """ Get projection and its units to use in cartopy transforamtions from + """Get projection and its units to use in cartopy transforamtions from current crs Returns: ccrs.Projection, str """ try: - if CRS.from_user_input(crs_in) == CRS.from_user_input({'init':'epsg:3395'}): + if CRS.from_user_input(crs_in) == CRS.from_user_input({'init': 'epsg:3395'}): crs_epsg = ccrs.Mercator() else: crs_epsg = ccrs.epsg(CRS.from_user_input(crs_in).to_epsg()) diff --git a/climada/util/save.py b/climada/util/save.py index db94981ef6..e465946055 100644 --- a/climada/util/save.py +++ b/climada/util/save.py @@ -41,8 +41,8 @@ def save(out_file_name, var): """ abs_path = out_file_name if not os.path.isabs(abs_path): - abs_path = os.path.abspath(os.path.join( \ - CONFIG['local_data']['save_dir'], out_file_name)) + abs_path = os.path.abspath(os.path.join( + CONFIG['local_data']['save_dir'], out_file_name)) folder_path = os.path.abspath(os.path.join(abs_path, os.pardir)) try: # Generate folder if it doesn't exists @@ -60,7 +60,7 @@ def save(out_file_name, var): raise ValueError def load(in_file_name): - """ Load variable contained in file. Uses configuration save_dir folder + """Load variable contained in file. Uses configuration save_dir folder if no absolute path provided. Parameters: @@ -71,8 +71,8 @@ def load(in_file_name): """ abs_path = in_file_name if not os.path.isabs(abs_path): - abs_path = os.path.abspath(os.path.join( \ - CONFIG['local_data']['save_dir'], in_file_name)) + abs_path = os.path.abspath(os.path.join( + CONFIG['local_data']['save_dir'], in_file_name)) with open(abs_path, 'rb') as file: data = pickle.load(file) return data diff --git a/climada/util/test/test_checker.py b/climada/util/test/test_checker.py index a715109cc8..03a64f2fb7 100644 --- a/climada/util/test/test_checker.py +++ b/climada/util/test/test_checker.py @@ -25,10 +25,10 @@ from climada.util.checker import check_oligatories, check_optionals class DummyClass(object): - + vars_oblig = {'id', 'array', 'sparse_arr'} vars_opt = {'list', 'array_opt'} - + def __init__(self): self.id = np.arange(25) self.array = np.arange(25) @@ -43,57 +43,58 @@ class TestChecks(unittest.TestCase): def test_check_oligatories_pass(self): """Correct DummyClass definition""" dummy = DummyClass() - check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", - dummy.id.size, dummy.id.size, 2) - + check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + dummy.id.size, dummy.id.size, 2) + def test_check_oligatories_fail(self): """Wrong DummyClass definition""" dummy = DummyClass() dummy.array = np.arange(3) with self.assertLogs('climada.util.checker', level='ERROR') as cm: - with self.assertRaises(ValueError): - check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", - dummy.id.size, dummy.id.size, 2) + with self.assertRaises(ValueError): + check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + dummy.id.size, dummy.id.size, 2) self.assertIn('Invalid DummyClass.array size: 25 != 3.', cm.output[0]) dummy = DummyClass() dummy.sparse_arr = sparse.csr.csr_matrix(np.zeros((25, 1))) with self.assertLogs('climada.util.checker', level='ERROR') as cm: - with self.assertRaises(ValueError): - check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", - dummy.id.size, dummy.id.size, 2) + with self.assertRaises(ValueError): + check_oligatories(dummy.__dict__, dummy.vars_oblig, "DummyClass.", + dummy.id.size, dummy.id.size, 2) self.assertIn('Invalid DummyClass.sparse_arr column size: 2 != 1.', cm.output[0]) - + def test_check_optionals_pass(self): """Correct DummyClass definition""" dummy = DummyClass() - check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", - dummy.id.size) - + check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", + dummy.id.size) + def test_check_optionals_fail(self): """Correct DummyClass definition""" dummy = DummyClass() dummy.array_opt = np.arange(3) with self.assertLogs('climada.util.checker', level='ERROR') as cm: - with self.assertRaises(ValueError): - check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", - dummy.id.size) - self.assertIn('Invalid DummyClass.array_opt size: 25 != 3.', cm.output[0]) - + with self.assertRaises(ValueError): + check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", + dummy.id.size) + self.assertIn('Invalid DummyClass.array_opt size: 25 != 3.', cm.output[0]) + dummy.array_opt = np.array([], int) with self.assertLogs('climada.util.checker', level='DEBUG') as cm: - check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", - dummy.id.size) - self.assertIn('DummyClass.array_opt not set.', cm.output[0]) - + check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", + dummy.id.size) + self.assertIn('DummyClass.array_opt not set.', cm.output[0]) + dummy = DummyClass() dummy.list = np.arange(3).tolist() with self.assertLogs('climada.util.checker', level='ERROR') as cm: - with self.assertRaises(ValueError): - check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", - dummy.id.size) - self.assertIn('Invalid DummyClass.list size: 25 != 3.', cm.output[0]) + with self.assertRaises(ValueError): + check_optionals(dummy.__dict__, dummy.vars_opt, "DummyClass.", + dummy.id.size) + self.assertIn('Invalid DummyClass.list size: 25 != 3.', cm.output[0]) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestChecks) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestChecks) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_coordinates.py b/climada/util/test/test_coordinates.py index 6ac275a347..1d4532b1b2 100644 --- a/climada/util/test/test_coordinates.py +++ b/climada/util/test/test_coordinates.py @@ -32,67 +32,169 @@ from rasterio import Affine from climada.util.constants import HAZ_DEMO_FL, DEF_CRS -from climada.util.coordinates import grid_is_regular, get_coastlines, \ -get_land_geometry, nat_earth_resolution, coord_on_land, dist_to_coast, \ -get_country_geometries, get_resolution, pts_to_raster_meta, read_vector, \ -read_raster, NE_EPSG, equal_crs, set_df_geometry_points, points_to_raster, \ -get_country_code, convert_wgs_to_utm, DEM_NODATA +from climada.util.coordinates import convert_wgs_to_utm, \ + coord_on_land, \ + dist_approx, \ + dist_to_coast, \ + dist_to_coast_nasa, \ + equal_crs, \ + get_admin1_info, \ + get_coastlines, \ + get_country_code, \ + get_country_geometries, \ + get_land_geometry, \ + get_resolution, \ + grid_is_regular, \ + latlon_bounds, \ + latlon_to_geosph_vector, \ + lon_normalize, \ + nat_earth_resolution, \ + points_to_raster, \ + pts_to_raster_meta, \ + read_raster, \ + read_raster_sample, \ + read_raster_bounds, \ + read_vector, \ + refine_raster_data, \ + set_df_geometry_points, \ + NE_EPSG class TestFunc(unittest.TestCase): - '''Test the auxiliary used with plot functions''' + """Test auxiliary functions""" + + def test_lon_normalize(self): + """Test the longitude normalization function""" + data = np.array([-180, 20.1, -30, 190, -350]) + + # test in place operation + lon_normalize(data) + self.assertTrue(np.allclose(data, [180, 20.1, -30, -170, 10])) + + # test with specific center and return value + data = lon_normalize(data, center=-170) + self.assertTrue(np.allclose(data, [-180, -339.9, -30, -170, 10])) + + def test_latlon_bounds(self): + """Test latlon_bounds function""" + lat, lon = np.array([0, -2, 5]), np.array([-179, 175, 178]) + bounds = latlon_bounds(lat, lon) + self.assertEqual(bounds, (175, -2, 181, 5)) + bounds = latlon_bounds(lat, lon, buffer=1) + self.assertEqual(bounds, (174, -3, 182, 6)) + + # buffer exceeding antimeridian + lat, lon = np.array([0, -2.1, 5]), np.array([-179.5, -175, -178]) + bounds = latlon_bounds(lat, lon, buffer=1) + self.assertEqual(bounds, (179.5, -3.1, 186, 6)) + + # longitude values need to be normalized before they lie between computed bounds: + lon_mid = 0.5 * (bounds[0] + bounds[2]) + lon = lon_normalize(lon, center=lon_mid) + self.assertTrue(np.all((bounds[0] <= lon) & (lon <= bounds[2]))) + + # data covering almost the whole longitudinal range + lat, lon = np.linspace(-90, 90, 180), np.linspace(-180.0, 179, 360) + bounds = latlon_bounds(lat, lon) + self.assertEqual(bounds, (-179, -90, 180, 90)) + bounds = latlon_bounds(lat, lon, buffer=1) + self.assertEqual(bounds, (-180, -90, 180, 90)) + + def test_geosph_vector(self): + """Test conversion from lat/lon to unit vector on geosphere""" + data = np.array([[0, 0], [-13, 179]], dtype=np.float64) + vn, vbasis = latlon_to_geosph_vector(data[:, 0], data[:, 1], basis=True) + basis_scal = (vbasis[..., 0, :] * vbasis[..., 1, :]).sum(axis=-1) + basis_norm = np.linalg.norm(vbasis, axis=-1) + self.assertTrue(np.allclose(np.linalg.norm(vn, axis=-1), 1)) + self.assertTrue(np.allclose(basis_scal, 0)) + self.assertTrue(np.allclose(basis_norm, 1)) + + def test_dist_approx_pass(self): + """Test approximate distance functions""" + data = np.array([ + # lat1, lon1, lat2, lon2, dist, dist_sph + [45.5, -32.2, 14, 56, 7709.827814738594, 8758.34146833], + [45.5, 147.8, 14, -124, 7709.827814738594, 8758.34146833], + [45.5, 507.8, 14, -124, 7709.827814738594, 8758.34146833], + [45.5, -212.2, 14, -124, 7709.827814738594, 8758.34146833], + ]) + compute_dist = np.stack([ + dist_approx(data[:, None, 0], data[:, None, 1], + data[:, None, 2], data[:, None, 3], method="equirect")[:, 0, 0], + dist_approx(data[:, None, 0], data[:, None, 1], + data[:, None, 2], data[:, None, 3], method="geosphere")[:, 0, 0], + ], axis=-1) + self.assertEqual(compute_dist.shape[0], data.shape[0]) + for d, cd in zip(data[:, 4:], compute_dist): + self.assertAlmostEqual(d[0], cd[0]) + self.assertAlmostEqual(d[1], cd[1]) + + data = np.array([ + # lat1, lon1, lat2, lon2, dist, dist_sph + [0, 0, 0, 1, 111.12, 111.12], + [-13, 179, 5, -179, 2011.84774049, 2012.30698122], + ]) + for i, method in enumerate(["equirect", "geosphere"]): + dist, vec = dist_approx(data[:, None, 0], data[:, None, 1], + data[:, None, 2], data[:, None, 3], log=True, method=method) + dist, vec = dist[:, 0, 0], vec[:, 0, 0] + self.assertTrue(np.allclose(np.linalg.norm(vec, axis=-1), dist)) + self.assertTrue(np.allclose(dist, data[:, 4 + i])) + # both points on equator (no change in latitude) + self.assertAlmostEqual(vec[0, 0], 0) + # longitude from 179 to -179 is positive (!) in lon-direction + self.assertTrue(np.all(vec[1, :] > 100)) - def test_is_regular_pass(self): - """ Test is_regular function. """ - coord = np.array([[1, 2], [4.4, 5.4], [4, 5]]) - reg, hei, wid = grid_is_regular(coord) - self.assertFalse(reg) - self.assertEqual(hei, 1) - self.assertEqual(wid, 1) - coord = np.array([[1, 2], [4.4, 5], [4, 5]]) - reg, hei, wid = grid_is_regular(coord) - self.assertFalse(reg) - self.assertEqual(hei, 1) - self.assertEqual(wid, 1) + def test_read_vector_pass(self): + """Test one columns data""" + shp_file = shapereader.natural_earth(resolution='110m', category='cultural', + name='populated_places_simple') + lat, lon, geometry, intensity = read_vector(shp_file, ['pop_min', 'pop_max']) - coord = np.array([[1, 2], [4, 5]]) - reg, hei, wid = grid_is_regular(coord) - self.assertFalse(reg) - self.assertEqual(hei, 1) - self.assertEqual(wid, 1) + self.assertEqual(geometry.crs, from_epsg(NE_EPSG)) + self.assertEqual(geometry.size, lat.size) + self.assertEqual(geometry.crs, from_epsg(NE_EPSG)) + self.assertAlmostEqual(lon[0], 12.453386544971766) + self.assertAlmostEqual(lon[-1], 114.18306345846304) + self.assertAlmostEqual(lat[0], 41.903282179960115) + self.assertAlmostEqual(lat[-1], 22.30692675357551) - coord = np.array([[1, 2], [4, 5], [1, 5], [4, 3]]) - reg, hei, wid = grid_is_regular(coord) - self.assertFalse(reg) - self.assertEqual(hei, 2) - self.assertEqual(wid, 1) + self.assertEqual(intensity.shape, (2, 243)) + # population min + self.assertEqual(intensity[0, 0], 832) + self.assertEqual(intensity[0, -1], 4551579) + # population max + self.assertEqual(intensity[1, 0], 832) + self.assertEqual(intensity[1, -1], 7206000) - coord = np.array([[1, 2], [4, 5], [1, 5], [4, 2]]) - reg, hei, wid = grid_is_regular(coord) - self.assertTrue(reg) - self.assertEqual(hei, 2) - self.assertEqual(wid, 2) + def test_compare_crs(self): + """Compare two crs""" + crs_one = {'init': 'epsg:4326'} + crs_two = {'init': 'epsg:4326', 'no_defs': True} + self.assertTrue(equal_crs(crs_one, crs_two)) - grid_x, grid_y = np.mgrid[10 : 100 : complex(0, 5), - 0 : 10 : complex(0, 5)] - grid_x = grid_x.reshape(-1,) - grid_y = grid_y.reshape(-1,) - coord = np.array([grid_x, grid_y]).transpose() - reg, hei, wid = grid_is_regular(coord) - self.assertTrue(reg) - self.assertEqual(hei, 5) - self.assertEqual(wid, 5) + def test_set_df_geometry_points_pass(self): + """Test set_df_geometry_points""" + df_val = gpd.GeoDataFrame(crs={'init': 'epsg:2202'}) + df_val['latitude'] = np.ones(10) * 40.0 + df_val['longitude'] = np.ones(10) * 0.50 - grid_x, grid_y = np.mgrid[10 : 100 : complex(0, 4), - 0 : 10 : complex(0, 5)] - grid_x = grid_x.reshape(-1,) - grid_y = grid_y.reshape(-1,) - coord = np.array([grid_x, grid_y]).transpose() - reg, hei, wid = grid_is_regular(coord) - self.assertTrue(reg) - self.assertEqual(hei, 5) - self.assertEqual(wid, 4) + set_df_geometry_points(df_val) + self.assertTrue(np.allclose(df_val.geometry[:].x.values, np.ones(10) * 0.5)) + self.assertTrue(np.allclose(df_val.geometry[:].y.values, np.ones(10) * 40.)) + + def test_convert_wgs_to_utm_pass(self): + """Test convert_wgs_to_utm""" + lat, lon = 17.346597, -62.768669 + epsg = convert_wgs_to_utm(lon, lat) + self.assertEqual(epsg, 32620) + lat, lon = 41.522410, 1.891026 + epsg = convert_wgs_to_utm(lon, lat) + self.assertEqual(epsg, 32631) + +class TestGetGeodata(unittest.TestCase): def test_nat_earth_resolution_pass(self): """Correct resolution.""" self.assertEqual(nat_earth_resolution(10), '10m') @@ -109,7 +211,7 @@ def test_nat_earth_resolution_fail(self): nat_earth_resolution(111) def test_get_coastlines_all_pass(self): - '''Check get_coastlines function over whole earth''' + """Check get_coastlines function over whole earth""" coast = get_coastlines(resolution=110) tot_bounds = coast.total_bounds self.assertEqual((134, 1), coast.shape) @@ -119,7 +221,7 @@ def test_get_coastlines_all_pass(self): self.assertAlmostEqual(tot_bounds[3], 83.64513) def test_get_coastlines_pass(self): - '''Check get_coastlines function in defined extent''' + """Check get_coastlines function in defined extent""" bounds = (-100, -55, -20, 35) coast = get_coastlines(bounds, resolution=110) ex_box = box(bounds[0], bounds[1], bounds[2], bounds[3]) @@ -168,7 +270,7 @@ def test_get_land_geometry_all_pass(self): """get_land_geometry with all earth.""" res = get_land_geometry(resolution=110) self.assertIsInstance(res, shapely.geometry.multipolygon.MultiPolygon) - self.assertEqual(res.area, 21496.99098799273) + self.assertAlmostEqual(res.area, 21496.99098799273) def test_on_land_pass(self): """check point on land with 1:50.000.000 resolution.""" @@ -181,24 +283,55 @@ def test_on_land_pass(self): self.assertTrue(res[2]) def test_dist_to_coast(self): - """ Test point in coast and point not in coast """ - res = dist_to_coast(13.208333333333329, -59.625000000000014) - self.assertAlmostEqual(2594.2071059573445, res[0]) - - res = dist_to_coast(-12.497529, -58.849505) - self.assertAlmostEqual(1382985.2459744606, res[0]) + """Test point in coast and point not in coast""" + points = np.array([ + # Caribbean Sea: + [13.208333333333329, -59.625000000000014], + # South America: + [-12.497529, -58.849505], + # Very close to coast of Somalia: + [1.96768, 45.23219], + ]) + dists = [2594.2071059573445, 1382985.2459744606, 0.088222234] + for d, p in zip(dists, points): + res = dist_to_coast(*p) + self.assertAlmostEqual(d, res[0]) + + # All at once requires more than one UTM + res = dist_to_coast(points) + for d, r in zip(dists, res): + self.assertAlmostEqual(d, r) + + def test_dist_to_coast_nasa(self): + """Test point in coast and point not in coast""" + points = np.array([ + # Caribbean Sea: + [13.208333333333329, -59.625000000000014], + # South America: + [-12.497529, -58.849505], + # Very close to coast of Somalia: + [1.96475615, 45.23249055], + ]) + dists = [-3000, -1393549.5, 48.77] + dists_lowres = [416.66666667, 1393448.09801077, 1191.38205367] + # Warning: This will download more than 300 MB of data! + result = dist_to_coast_nasa(points[:, 0], points[:, 1], highres=True, signed=True) + result_lowres = dist_to_coast_nasa(points[:, 0], points[:, 1]) + for d, r in zip(dists, result): + self.assertAlmostEqual(d, r) + for d, r in zip(dists_lowres, result_lowres): + self.assertAlmostEqual(d, r) def test_get_country_geometries_country_pass(self): - """ get_country_geometries with selected countries. issues with the + """get_country_geometries with selected countries. issues with the natural earth data should be caught by test_get_land_geometry_* since - it's very similar """ + it's very similar""" iso_countries = ['NLD', 'VNM'] res = get_country_geometries(iso_countries, resolution=110) - self.assertIsInstance(res, - geopandas.geodataframe.GeoDataFrame) + self.assertIsInstance(res, geopandas.geodataframe.GeoDataFrame) def test_get_country_geometries_country_norway_pass(self): - """ test correct numeric ISO3 for country Norway """ + """test correct numeric ISO3 for country Norway""" iso_countries = ['NOR'] extent = [10, 11, 55, 60] res1 = get_country_geometries(iso_countries) @@ -244,74 +377,156 @@ def test_get_country_geometries_all_pass(self): self.assertIsInstance(res, geopandas.geodataframe.GeoDataFrame) self.assertAlmostEqual(res.area[0], 1.639510995900778) + def test_country_code_pass(self): + """Test set_region_id""" + + lon = np.array([-59.6250000000000, -59.6250000000000, -59.6250000000000, + -59.5416666666667, -59.5416666666667, -59.4583333333333, + -60.2083333333333, -60.2083333333333]) + lat = np.array([13.125, 13.20833333, 13.29166667, 13.125, 13.20833333, + 13.125, 12.625, 12.70833333]) + for gridded in [True, False]: + region_id = get_country_code(lat, lon, gridded=gridded) + region_id_OSLO = get_country_code(59.91, 10.75, gridded=gridded) + self.assertEqual(np.count_nonzero(region_id), 6) + # 052 for barbados + self.assertTrue(np.all(region_id[:6] == 52)) + # 578 for Norway + self.assertEqual(region_id_OSLO, np.array([578])) + + def test_get_admin1_info_pass(self): + """test get_admin1_info()""" + country_names = ['CHE', 'IDN', 'USA'] + admin1_info, admin1_shapes = get_admin1_info(country_names) + self.assertEqual(len(admin1_info), 3) + self.assertEqual(len(admin1_info['CHE']), len(admin1_shapes['CHE'])) + self.assertEqual(len(admin1_info['CHE']), 26) + self.assertEqual(len(admin1_shapes['IDN']), 33) + self.assertEqual(len(admin1_info['USA']), 51) + self.assertEqual(admin1_info['USA'][1][4], 'US-WA') + +class TestRasterMeta(unittest.TestCase): + def test_is_regular_pass(self): + """Test is_regular function.""" + coord = np.array([[1, 2], [4.4, 5.4], [4, 5]]) + reg, hei, wid = grid_is_regular(coord) + self.assertFalse(reg) + self.assertEqual(hei, 1) + self.assertEqual(wid, 1) + + coord = np.array([[1, 2], [4.4, 5], [4, 5]]) + reg, hei, wid = grid_is_regular(coord) + self.assertFalse(reg) + self.assertEqual(hei, 1) + self.assertEqual(wid, 1) + + coord = np.array([[1, 2], [4, 5]]) + reg, hei, wid = grid_is_regular(coord) + self.assertFalse(reg) + self.assertEqual(hei, 1) + self.assertEqual(wid, 1) + + coord = np.array([[1, 2], [4, 5], [1, 5], [4, 3]]) + reg, hei, wid = grid_is_regular(coord) + self.assertFalse(reg) + self.assertEqual(hei, 2) + self.assertEqual(wid, 1) + + coord = np.array([[1, 2], [4, 5], [1, 5], [4, 2]]) + reg, hei, wid = grid_is_regular(coord) + self.assertTrue(reg) + self.assertEqual(hei, 2) + self.assertEqual(wid, 2) + + grid_x, grid_y = np.mgrid[10: 100: complex(0, 5), + 0: 10: complex(0, 5)] + grid_x = grid_x.reshape(-1,) + grid_y = grid_y.reshape(-1,) + coord = np.array([grid_x, grid_y]).transpose() + reg, hei, wid = grid_is_regular(coord) + self.assertTrue(reg) + self.assertEqual(hei, 5) + self.assertEqual(wid, 5) + + grid_x, grid_y = np.mgrid[10: 100: complex(0, 4), + 0: 10: complex(0, 5)] + grid_x = grid_x.reshape(-1,) + grid_y = grid_y.reshape(-1,) + coord = np.array([grid_x, grid_y]).transpose() + reg, hei, wid = grid_is_regular(coord) + self.assertTrue(reg) + self.assertEqual(hei, 5) + self.assertEqual(wid, 4) + def test_get_resolution_pass(self): - """ Test _get_resolution method """ + """Test _get_resolution method""" lat = np.array([13.125, 13.20833333, 13.29166667, 13.125, 13.20833333, 13.125, 12.625, 12.70833333, 12.79166667, 12.875, 12.95833333, 13.04166667]) - lon = np.array([-59.6250000000000,-59.6250000000000,-59.6250000000000,-59.5416666666667, - -59.5416666666667,-59.4583333333333,-60.2083333333333,-60.2083333333333, - -60.2083333333333,-60.2083333333333,-60.2083333333333,-60.2083333333333]) + lon = np.array([ + -59.6250000000000, -59.6250000000000, -59.6250000000000, -59.5416666666667, + -59.5416666666667, -59.4583333333333, -60.2083333333333, -60.2083333333333, + -60.2083333333333, -60.2083333333333, -60.2083333333333, -60.2083333333333 + ]) res_lat, res_lon = get_resolution(lat, lon) - self.assertAlmostEqual(min(res_lat, res_lon), 0.0833333333333) + self.assertAlmostEqual(res_lat, 0.0833333333333) + self.assertAlmostEqual(res_lon, 0.0833333333333) def test_vector_to_raster_pass(self): - """ Test vector_to_raster """ - xmin, ymin, xmax, ymax = -60, -5, -50, 10 # bounds of points == centers pixels + """Test vector_to_raster""" + xmin, ymin, xmax, ymax = -60, -5, -50, 10 # bounds of points == centers pixels points_bounds = (xmin, ymin, xmax, ymax) res = 0.5 - rows, cols, ras_trans = pts_to_raster_meta(points_bounds, res) - self.assertEqual(xmin - res/2 + res * cols, xmax + res/2) - self.assertEqual(ymax + res/2 - res * rows, ymin - res/2) + rows, cols, ras_trans = pts_to_raster_meta(points_bounds, (res, -res)) + self.assertEqual(xmin - res / 2 + res * cols, xmax + res / 2) + self.assertEqual(ymax + res / 2 - res * rows, ymin - res / 2) self.assertEqual(ras_trans[0], res) self.assertEqual(ras_trans[4], -res) self.assertEqual(ras_trans[1], 0.0) self.assertEqual(ras_trans[3], 0.0) - self.assertEqual(ras_trans[2], xmin - res/2) - self.assertEqual(ras_trans[5], ymax + res/2) - self.assertTrue(ymin >= ymax + res/2 - rows*res) - self.assertTrue(xmax <= xmin - res/2 + cols*res) + self.assertEqual(ras_trans[2], xmin - res / 2) + self.assertEqual(ras_trans[5], ymax + res / 2) + self.assertTrue(ymin >= ymax + res / 2 - rows * res) + self.assertTrue(xmax <= xmin - res / 2 + cols * res) def test_pts_to_raster_irreg_pass(self): - """ Test pts_to_raster_meta with irregular points """ - xmin, ymin, xmax, ymax = -124.19473, 32.81908, -114.4632, 42.020759999999996 # bounds of points == centers pixels - points_bounds = (xmin, ymin, xmax, ymax) + """Test pts_to_raster_meta with irregular points""" + # bounds of points == centers of pixels + points_bounds = (-124.19473, 32.81908, -114.4632, 42.020759999999996) + xmin, ymin, xmax, ymax = points_bounds res = 0.013498920086393088 - rows, cols, ras_trans = pts_to_raster_meta(points_bounds, res) + rows, cols, ras_trans = pts_to_raster_meta(points_bounds, (res, -res)) self.assertEqual(ras_trans[0], res) self.assertEqual(ras_trans[4], -res) self.assertEqual(ras_trans[1], 0.0) self.assertEqual(ras_trans[3], 0.0) - self.assertEqual(ras_trans[2], xmin - res/2) - self.assertEqual(ras_trans[5], ymax + res/2) - self.assertTrue(ymin >= ymax + res/2 - rows*res) - self.assertTrue(xmax <= xmin - res/2 + cols*res) - - def test_read_vector_pass(self): - """ Test one columns data """ - shp_file = shapereader.natural_earth(resolution='110m', \ - category='cultural', name='populated_places_simple') - lat, lon, geometry, intensity = read_vector(shp_file, ['pop_min', 'pop_max']) + self.assertEqual(ras_trans[2], xmin - res / 2) + self.assertEqual(ras_trans[5], ymax + res / 2) + self.assertTrue(ymin >= ymax + res / 2 - rows * res) + self.assertTrue(xmax <= xmin - res / 2 + cols * res) - self.assertEqual(geometry.crs, from_epsg(NE_EPSG)) - self.assertEqual(geometry.size, lat.size) - self.assertEqual(geometry.crs, from_epsg(NE_EPSG)) - self.assertAlmostEqual(lon[0], 12.453386544971766) - self.assertAlmostEqual(lon[-1], 114.18306345846304) - self.assertAlmostEqual(lat[0], 41.903282179960115) - self.assertAlmostEqual(lat[-1], 22.30692675357551) - - self.assertEqual(intensity.shape, (2, 243)) - # population min - self.assertEqual(intensity[0, 0], 832) - self.assertEqual(intensity[0, -1], 4551579) - # population max - self.assertEqual(intensity[1, 0], 832) - self.assertEqual(intensity[1, -1], 7206000) + def test_points_to_raster_pass(self): + """Test points_to_raster""" + df_val = gpd.GeoDataFrame(crs={'init': 'epsg:2202'}) + x, y = np.meshgrid(np.linspace(0, 2, 5), np.linspace(40, 50, 10)) + df_val['latitude'] = y.flatten() + df_val['longitude'] = x.flatten() + df_val['value'] = np.ones(len(df_val)) * 10 + raster, meta = points_to_raster(df_val, val_names=['value']) + self.assertTrue(equal_crs(meta['crs'], df_val.crs)) + self.assertAlmostEqual(meta['transform'][0], 0.5) + self.assertAlmostEqual(meta['transform'][1], 0) + self.assertAlmostEqual(meta['transform'][2], -0.25) + self.assertAlmostEqual(meta['transform'][3], 0) + self.assertAlmostEqual(meta['transform'][4], -0.5) + self.assertAlmostEqual(meta['transform'][5], 50.25) + self.assertEqual(meta['height'], 21) + self.assertEqual(meta['width'], 5) +class TestRasterIO(unittest.TestCase): def test_window_raster_pass(self): - """ Test window """ - meta, inten_ras = read_raster(HAZ_DEMO_FL, window=Window(10, 20, 50, 60)) + """Test window""" + meta, inten_ras = read_raster(HAZ_DEMO_FL, window=Window(10, 20, 50.1, 60)) self.assertAlmostEqual(meta['crs'], DEF_CRS) self.assertAlmostEqual(meta['transform'].c, -69.2471495969998) self.assertAlmostEqual(meta['transform'].a, 0.009000000000000341) @@ -321,11 +536,11 @@ def test_window_raster_pass(self): self.assertAlmostEqual(meta['transform'].e, -0.009000000000000341) self.assertEqual(meta['height'], 60) self.assertEqual(meta['width'], 50) - self.assertEqual(inten_ras.shape, (1, 60*50)) + self.assertEqual(inten_ras.shape, (1, 60 * 50)) self.assertAlmostEqual(inten_ras.reshape((60, 50))[25, 12], 0.056825936) def test_poly_raster_pass(self): - """ Test geometry """ + """Test geometry""" poly = box(-69.2471495969998, 9.708220966978912, -68.79714959699979, 10.248220966978932) meta, inten_ras = read_raster(HAZ_DEMO_FL, geometry=[poly]) self.assertAlmostEqual(meta['crs'], DEF_CRS) @@ -337,13 +552,13 @@ def test_poly_raster_pass(self): self.assertAlmostEqual(meta['transform'].e, -0.009000000000000341) self.assertEqual(meta['height'], 60) self.assertEqual(meta['width'], 50) - self.assertEqual(inten_ras.shape, (1, 60*50)) + self.assertEqual(inten_ras.shape, (1, 60 * 50)) def test_crs_raster_pass(self): - """ Test change projection """ - meta, inten_ras = read_raster(HAZ_DEMO_FL, dst_crs={'init':'epsg:2202'}, + """Test change projection""" + meta, inten_ras = read_raster(HAZ_DEMO_FL, dst_crs={'init': 'epsg:2202'}, resampling=Resampling.nearest) - self.assertAlmostEqual(meta['crs'], {'init':'epsg:2202'}) + self.assertAlmostEqual(meta['crs'], {'init': 'epsg:2202'}) self.assertAlmostEqual(meta['transform'].c, 462486.8490210658) self.assertAlmostEqual(meta['transform'].a, 998.576177833903) self.assertAlmostEqual(meta['transform'].b, 0.0) @@ -352,19 +567,37 @@ def test_crs_raster_pass(self): self.assertAlmostEqual(meta['transform'].e, -998.576177833903) self.assertEqual(meta['height'], 1081) self.assertEqual(meta['width'], 968) - self.assertEqual(inten_ras.shape, (1, 1081*968)) + self.assertEqual(inten_ras.shape, (1, 1081 * 968)) # TODO: NOT RESAMPLING WELL in this case!? self.assertAlmostEqual(inten_ras.reshape((1081, 968))[45, 22], 0) + def test_crs_and_geometry_raster_pass(self): + """Test change projection and crop to geometry""" + ply = shapely.geometry.Polygon([ + (478080.8562247154, 1105419.13439131), + (478087.5912452241, 1116475.583523723), + (500000, 1116468.876713805), + (500000, 1105412.49126517), + (478080.8562247154, 1105419.13439131) + ]) + meta, inten_ras = read_raster(HAZ_DEMO_FL, dst_crs={'init': 'epsg:2202'}, + geometry=[ply], resampling=Resampling.nearest) + self.assertAlmostEqual(meta['crs'], {'init': 'epsg:2202'}) + self.assertEqual(meta['height'], 12) + self.assertEqual(meta['width'], 23) + self.assertEqual(inten_ras.shape, (1, 12 * 23)) + # TODO: NOT RESAMPLING WELL in this case!? + self.assertAlmostEqual(inten_ras.reshape((12, 23))[11, 12], 0.10063865780830383) + def test_transform_raster_pass(self): - meta, inten_ras = read_raster(HAZ_DEMO_FL, - transform=Affine(0.009000000000000341, 0.0, -69.33714959699981, - 0.0, -0.009000000000000341, 10.42822096697894), height=500, width=501) + transform = Affine(0.009000000000000341, 0.0, -69.33714959699981, + 0.0, -0.009000000000000341, 10.42822096697894) + meta, inten_ras = read_raster(HAZ_DEMO_FL, transform=transform, height=500, width=501) left = meta['transform'].xoff top = meta['transform'].yoff - bottom = top + meta['transform'][4]*meta['height'] - right = left + meta['transform'][0]*meta['width'] + bottom = top + meta['transform'][4] * meta['height'] + right = left + meta['transform'][0] * meta['width'] self.assertAlmostEqual(left, -69.33714959699981) self.assertAlmostEqual(bottom, 5.928220966978939) @@ -373,69 +606,90 @@ def test_transform_raster_pass(self): self.assertEqual(meta['width'], 501) self.assertEqual(meta['height'], 500) self.assertEqual(meta['crs'].to_epsg(), 4326) - self.assertEqual(inten_ras.shape, (1, 500*501)) + self.assertEqual(inten_ras.shape, (1, 500 * 501)) meta, inten_all = read_raster(HAZ_DEMO_FL, window=Window(0, 0, 501, 500)) self.assertTrue(np.array_equal(inten_all, inten_ras)) - def test_compare_crs(self): - """ Compare two crs """ - crs_one = {'init':'epsg:4326'} - crs_two = {'init':'epsg:4326', 'no_defs': True} - self.assertTrue(equal_crs(crs_one, crs_two)) - - def test_set_df_geometry_points_pass(self): - """ Test set_df_geometry_points """ - df_val = gpd.GeoDataFrame(crs={'init':'epsg:2202'}) - df_val['latitude'] = np.ones(10)*40.0 - df_val['longitude'] = np.ones(10)*0.50 - - set_df_geometry_points(df_val) - self.assertTrue(np.allclose(df_val.geometry[:].x.values, np.ones(10)*0.5)) - self.assertTrue(np.allclose(df_val.geometry[:].y.values, np.ones(10)*40.)) - - def test_points_to_raster_pass(self): - """ Test points_to_raster """ - df_val = gpd.GeoDataFrame(crs={'init':'epsg:2202'}) - x, y = np.meshgrid(np.linspace(0, 2, 5), np.linspace(40, 50, 10)) - df_val['latitude'] = y.flatten() - df_val['longitude'] = x.flatten() - df_val['value'] = np.ones(len(df_val))*10 - raster, meta = points_to_raster(df_val, val_names=['value']) - self.assertTrue(equal_crs(meta['crs'], df_val.crs)) - self.assertAlmostEqual(meta['transform'][0], 0.5) - self.assertAlmostEqual(meta['transform'][1], 0) - self.assertAlmostEqual(meta['transform'][2], -0.25) - self.assertAlmostEqual(meta['transform'][3], 0) - self.assertAlmostEqual(meta['transform'][4], -0.5) - self.assertAlmostEqual(meta['transform'][5], 50.25) - self.assertEqual(meta['height'], 21) - self.assertEqual(meta['width'], 5) - - def test_country_code_pass(self): - """ Test set_region_id """ - - lon = np.array([-59.6250000000000,-59.6250000000000,-59.6250000000000,-59.5416666666667, - -59.5416666666667,-59.4583333333333,-60.2083333333333,-60.2083333333333]) - lat = np.array([13.125,13.20833333,13.29166667,13.125,13.20833333,13.125,12.625,12.70833333]) - region_id = get_country_code(lat, lon) - region_id_OSLO = get_country_code([59.91],[10.75]) - self.assertEqual(np.count_nonzero(region_id), 6) - self.assertTrue(np.allclose(region_id[:6], np.ones(6)*52)) # 052 for barbados - self.assertEqual(region_id_OSLO, np.array(578)) # 578 for Norway - - def test_convert_wgs_to_utm_pass(self): - """ Test convert_wgs_to_utm """ - lat, lon = 17.346597, -62.768669 - epsg = convert_wgs_to_utm(lon, lat) - self.assertEqual(epsg, 32620) - - lat, lon = 41.522410, 1.891026 - epsg = convert_wgs_to_utm(lon, lat) - self.assertEqual(epsg, 32631) - + def test_sample_raster(self): + """Test sampling points from raster file""" + val_1, val_2, fill_value = 0.056825936, 0.10389626, -999 + i_j_vals = np.array([ + [44, 21, 0], + [44, 22, 0], + [44, 23, 0], + [45, 21, 0], + [45, 22, val_1], + [45, 23, val_2], + [46, 21, 0], + [46, 22, 0], + [46, 23, 0], + [45, 22.2, 0.8 * val_1 + 0.2 * val_2], + [45.3, 21.4, 0.7 * 0.4 * val_1], + [-20, 0, fill_value], + ]) + res = 0.009000000000000341 + lat = 10.42822096697894 - res / 2 - i_j_vals[:, 0] * res + lon = -69.33714959699981 + res / 2 + i_j_vals[:, 1] * res + values = read_raster_sample(HAZ_DEMO_FL, lat, lon, fill_value=fill_value) + self.assertEqual(values.size, lat.size) + for i, val in enumerate(i_j_vals[:, 2]): + self.assertAlmostEqual(values[i], val) + + # with explicit intermediate resolution + values = read_raster_sample(HAZ_DEMO_FL, lat, lon, fill_value=fill_value, + intermediate_res=res) + self.assertEqual(values.size, lat.size) + for i, val in enumerate(i_j_vals[:, 2]): + self.assertAlmostEqual(values[i], val) + + def test_refine_raster(self): + """Test refinement of given raster data""" + data = np.array([ + [0.25, 0.75], + [0.5, 1], + ]) + transform = Affine(0.5, 0, 0, 0, 0.5, 0) + new_res = 0.1 + new_data, new_transform = refine_raster_data(data, transform, new_res) + + self.assertEqual(new_transform[0], new_res) + self.assertEqual(new_transform[4], new_res) + self.assertAlmostEqual(new_data[2, 2], data[0, 0]) + self.assertAlmostEqual(new_data[2, 7], data[0, 1]) + self.assertAlmostEqual(new_data[7, 2], data[1, 0]) + self.assertAlmostEqual(new_data[7, 7], data[1, 1]) + self.assertAlmostEqual(new_data[1, 2], data[0, 0]) + self.assertAlmostEqual(new_data[3, 3], 0.4) + + def test_bounded_refined_raster(self): + """Test reading a raster within specified bounds and at specified resolution""" + bounds = (-69.14, 9.99, -69.11, 10.03) + z, transform = read_raster_bounds(HAZ_DEMO_FL, bounds, res=0.004) + + # the first dimension corresponds to the raster bands: + self.assertEqual(z.shape[0], 1) + z = z[0] + + # the signs of stepsizes are retained from the original raster: + self.assertLess(transform[4], 0) + self.assertGreater(transform[0], 0) + + # the bounds of the returned data are a little larger than the requested bounds: + self.assertLess(transform[2], bounds[0]) + self.assertGreaterEqual(transform[2], bounds[0] - transform[0]) + self.assertGreater(transform[2] + z.shape[1] * transform[0], bounds[2]) + self.assertLessEqual(transform[2] + z.shape[1] * transform[0], bounds[2] + transform[0]) + + self.assertGreater(transform[5], bounds[3]) + self.assertLessEqual(transform[5], bounds[3] - transform[4]) + self.assertLess(transform[5] + z.shape[0] * transform[4], bounds[1]) + self.assertGreaterEqual(transform[5] + z.shape[0] * transform[4], bounds[1] + transform[4]) # Execute Tests if __name__ == "__main__": TESTS = unittest.TestLoader().loadTestsFromTestCase(TestFunc) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestGetGeodata)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestRasterMeta)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestRasterIO)) unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_dates_times.py b/climada/util/test/test_dates_times.py index 687081e8ff..4247925dc5 100644 --- a/climada/util/test/test_dates_times.py +++ b/climada/util/test/test_dates_times.py @@ -25,9 +25,9 @@ import climada.util.dates_times as u_dt class TestDateString(unittest.TestCase): - """Test date functions """ + """Test date functions""" def test_date_to_str_pass(self): - """ Test _date_to_str function""" + """Test _date_to_str function""" ordinal_date = dt.datetime.toordinal(dt.datetime(2018, 4, 6)) self.assertEqual('2018-04-06', u_dt.date_to_str(ordinal_date)) @@ -36,15 +36,15 @@ def test_date_to_str_pass(self): self.assertEqual(['2018-04-06', '2019-01-01'], u_dt.date_to_str(ordinal_date)) def test_str_to_date_pass(self): - """ Test _date_to_str function""" + """Test _date_to_str function""" date = 730000 self.assertEqual(u_dt.str_to_date(u_dt.date_to_str(date)), date) date = [640000, 730000] self.assertEqual(u_dt.str_to_date(u_dt.date_to_str(date)), date) - + class TestDateNumpy(unittest.TestCase): - """"Test date functions for numpy datetime64 type""" + """Test date functions for numpy datetime64 type""" def test_datetime64_to_ordinal(self): """Test _datetime64_to_ordinal""" date = np.datetime64('1999-12-26T06:00:00.000000000') @@ -56,9 +56,9 @@ def test_datetime64_to_ordinal(self): ordinal = u_dt.datetime64_to_ordinal(date) self.assertEqual(u_dt.date_to_str(ordinal[0]), '1999-12-26') self.assertEqual(u_dt.date_to_str(ordinal[1]), '2000-12-26') - + def test_last_year_pass(self): - """ Test last_year """ + """Test last_year""" ordinal_date = [dt.datetime.toordinal(dt.datetime(2018, 4, 6)), dt.datetime.toordinal(dt.datetime(1918, 4, 6)), dt.datetime.toordinal(dt.datetime(2019, 1, 1))] @@ -66,7 +66,7 @@ def test_last_year_pass(self): self.assertEqual(u_dt.last_year(np.array(ordinal_date)), 2019) def test_first_year_pass(self): - """ Test last_year """ + """Test last_year""" ordinal_date = [dt.datetime.toordinal(dt.datetime(2018, 4, 6)), dt.datetime.toordinal(dt.datetime(1918, 4, 6)), dt.datetime.toordinal(dt.datetime(2019, 1, 1))] @@ -74,6 +74,7 @@ def test_first_year_pass(self): self.assertEqual(u_dt.first_year(np.array(ordinal_date)), 1918) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDateString) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDateNumpy)) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDateString) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDateNumpy)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_files.py b/climada/util/test/test_files.py index 3482c5c32c..eca4638461 100644 --- a/climada/util/test/test_files.py +++ b/climada/util/test/test_files.py @@ -27,7 +27,7 @@ from climada.util.constants import DATA_DIR, GLB_CENTROIDS_MAT, ENT_TEMPLATE_XLS class TestDownloadUrl(unittest.TestCase): - """Test download_file function """ + """Test download_file function""" def test_wrong_url_fail(self): """Error raised when wrong url.""" url = 'https://ngdc.noaa.gov/eog/data/web_data/v4composites/F172012.v4.tar' @@ -71,8 +71,8 @@ def test_list_wrong_length_fail(self): self.assertIn("Provide one or 3 values.", cm.output[0]) class TestGetFileNames(unittest.TestCase): - """ Test get_file_names function. Only works with actually existing - files and directories. """ + """Test get_file_names function. Only works with actually existing + files and directories.""" def test_one_file_copy(self): """If input is one file name, return a list with this file name""" file_name = GLB_CENTROIDS_MAT @@ -99,7 +99,7 @@ def test_folder_contents(self): self.assertNotEqual('', os.path.splitext(file)[1]) def test_globbing(self): - """ If input is a glob pattern, return a list of matching visible + """If input is a glob pattern, return a list of matching visible files; omit folders. """ file_name = os.path.join(DATA_DIR, 'demo') @@ -108,35 +108,41 @@ def test_globbing(self): tmp_files = os.listdir(file_name) tmp_files = [os.path.join(file_name, f) for f in tmp_files] tmp_files = [f for f in tmp_files if not os.path.isdir(f) - and not os.path.basename(os.path.normpath(f)).startswith('.')] + and not os.path.basename(os.path.normpath(f)).startswith('.')] self.assertEqual(len(tmp_files), len(out)) self.assertEqual(sorted(tmp_files), sorted(out)) class TestExtension(unittest.TestCase): - """ Test get_extension """ + """Test get_extension""" def test_get_extension_no_pass(self): - """Test no extension """ + """Test no extension""" file_name = '/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1' self.assertEqual('', get_extension(file_name)[1]) self.assertEqual(file_name, get_extension(file_name)[0]) - + def test_get_extension_one_pass(self): - """Test not compressed """ + """Test not compressed""" file_name = '/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd' self.assertEqual('.grd', get_extension(file_name)[1]) - self.assertEqual('/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1', get_extension(file_name)[0]) + self.assertEqual( + '/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1', + get_extension(file_name)[0]) def test_get_extension_two_pass(self): - """Test compressed """ - file_name = '/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd.gz' + """Test compressed""" + file_name = '/Users/aznarsig/Documents/Python/climada_python' \ + '/data/demo/SC22000_VE__M1.grd.gz' self.assertEqual('.grd.gz', get_extension(file_name)[1]) - self.assertEqual('/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1', get_extension(file_name)[0]) + self.assertEqual( + '/Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1', + get_extension(file_name)[0]) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestToStrList) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestGetFileNames)) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDownloadUrl)) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestExtension)) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestToStrList) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestGetFileNames)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDownloadUrl)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestExtension)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_finance.py b/climada/util/test/test_finance.py index 8b658d43a7..82bb3ed13b 100644 --- a/climada/util/test/test_finance.py +++ b/climada/util/test/test_finance.py @@ -25,17 +25,17 @@ from climada.util.finance import net_present_value, gdp, income_group, \ nat_earth_adm0, world_bank, wealth2gdp, world_bank_wealth_account, _gdp_twn -SHP_FN = shapereader.natural_earth(resolution='10m', \ - category='cultural', name='admin_0_countries') +SHP_FN = shapereader.natural_earth(resolution='10m', category='cultural', + name='admin_0_countries') SHP_FILE = shapereader.Reader(SHP_FN) class TestNetpresValue(unittest.TestCase): - """Test date functions """ + """Test date functions""" def test_net_pres_val_pass(self): - """ Test net_present_value against MATLAB reference""" + """Test net_present_value against MATLAB reference""" years = np.arange(2018, 2041) - disc_rates = np.ones(years.size)*0.02 - val_years = np.ones(years.size)*6.512201157564418e9 + disc_rates = np.ones(years.size) * 0.02 + val_years = np.ones(years.size) * 6.512201157564418e9 res = net_present_value(years, disc_rates, val_years) self.assertEqual(1.215049630691397e+11, res) @@ -43,7 +43,7 @@ def test_net_pres_val_pass(self): class TestWBData(unittest.TestCase): """Test World Bank data""" def test_ne_income_grp_aia_pass(self): - """ Test nat_earth_adm0 function Anguilla.""" + """Test nat_earth_adm0 function Anguilla.""" ref_year = 2012 res_year, res_val = nat_earth_adm0('AIA', 'INCOME_GRP', shp_file=SHP_FILE) @@ -54,7 +54,7 @@ def test_ne_income_grp_aia_pass(self): self.assertEqual(res_val, ref_val) def test_wb_income_grp_sxm_pass(self): - """ Test world_bank function Sint Maarten.""" + """Test world_bank function Sint Maarten.""" ref_year = 2012 res_year, res_val = world_bank('SXM', ref_year, 'INC_GRP') @@ -64,7 +64,7 @@ def test_wb_income_grp_sxm_pass(self): self.assertEqual(res_val, ref_val) def test_income_grp_sxm_1999_pass(self): - """ Test income_group function Sint Maarten.""" + """Test income_group function Sint Maarten.""" ref_year = 1999 with self.assertLogs('climada.util.finance', level='INFO') as cm: res_year, res_val = income_group('SXM', ref_year, SHP_FILE) @@ -76,7 +76,7 @@ def test_income_grp_sxm_1999_pass(self): self.assertEqual(res_val, ref_val) def test_ne_gdp_aia_2012_pass(self): - """ Test nat_earth_adm0 function Anguilla.""" + """Test nat_earth_adm0 function Anguilla.""" ref_year = 2012 res_year, res_val = nat_earth_adm0('AIA', 'GDP_MD_EST', 'GDP_YEAR', SHP_FILE) @@ -87,7 +87,7 @@ def test_ne_gdp_aia_2012_pass(self): self.assertEqual(res_val, ref_val) def test_gdp_sxm_2012_pass(self): - """ Test gdp function Sint Maarten.""" + """Test gdp function Sint Maarten.""" ref_year = 2012 with self.assertLogs('climada.util.finance', level='INFO') as cm: res_year, res_val = gdp('SXM', ref_year) @@ -99,7 +99,7 @@ def test_gdp_sxm_2012_pass(self): self.assertEqual(res_val, ref_val) def test_gdp_twn_2012_pass(self): - """ Test gdp function TWN.""" + """Test gdp function TWN.""" ref_year = 2014 res_year, res_val = gdp('TWN', ref_year) _, res_val_direct = _gdp_twn(ref_year) @@ -108,10 +108,10 @@ def test_gdp_twn_2012_pass(self): self.assertEqual(res_year, ref_year) self.assertEqual(res_val, ref_val) self.assertEqual(res_val_direct, ref_val) - + def test_wb_esp_1950_pass(self): - """ Test world_bank function Sint Maarten.""" + """Test world_bank function Sint Maarten.""" ref_year = 1950 res_year, res_val = world_bank('ESP', ref_year, 'NY.GDP.MKTP.CD') @@ -121,9 +121,9 @@ def test_wb_esp_1950_pass(self): self.assertEqual(res_val, ref_val) class TestWealth2GDP(unittest.TestCase): - """ Test Wealth to GDP factor extraction """ + """Test Wealth to GDP factor extraction""" def test_nfw_SUR_pass(self): - """ Test non-financial wealth-to-gdp factor with Suriname.""" + """Test non-financial wealth-to-gdp factor with Suriname.""" res_year, res_val = wealth2gdp('SUR') ref_year = 2016 @@ -132,7 +132,7 @@ def test_nfw_SUR_pass(self): self.assertEqual(res_val, ref_val) def test_nfw_BEL_pass(self): - """ Test total wealth-to-gdp factor with Belgium.""" + """Test total wealth-to-gdp factor with Belgium.""" res_year, res_val = wealth2gdp('BEL', False) ref_year = 2016 @@ -141,20 +141,20 @@ def test_nfw_BEL_pass(self): self.assertEqual(res_val, ref_val) def test_nfw_LBY_pass(self): - """ Test missing factor with Libya.""" + """Test missing factor with Libya.""" _, res_val = wealth2gdp('LBY') self.assertTrue(np.isnan(res_val)) class TestWBWealthAccount(unittest.TestCase): - """ Test Wealth Indicator extraction from World Bank provided CSV """ + """Test Wealth Indicator extraction from World Bank provided CSV""" def test_pca_DEU_2010_pass(self): - """ Test Processed Capital value Germany 2010.""" + """Test Processed Capital value Germany 2010.""" ref_year = 2010 cntry_iso = 'DEU' res_year, res_val, q = world_bank_wealth_account(cntry_iso, ref_year, no_land=0) - res_year_noland, res_val_noland, q = \ - world_bank_wealth_account(cntry_iso, ref_year, no_land=1) + res_year_noland, res_val_noland, q = world_bank_wealth_account(cntry_iso, ref_year, + no_land=1) ref_val = 17675048450284.9 ref_val_noland = 14254071330874.9 self.assertEqual(res_year, ref_year) @@ -163,27 +163,27 @@ def test_pca_DEU_2010_pass(self): self.assertEqual(res_year_noland, ref_year) self.assertEqual(res_val_noland, ref_val_noland) def test_pca_CHE_2008_pass(self): - """ Test Prcoessed Capital per capita Switzerland 2008 (interp.).""" + """Test Prcoessed Capital per capita Switzerland 2008 (interp.).""" ref_year = 2008 cntry_iso = 'CHE' var_name = 'NW.PCA.PC' - res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, \ - variable_name=var_name, no_land=0) + res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, + variable_name=var_name, no_land=0) ref_val = 328398.7 self.assertEqual(res_year, ref_year) self.assertEqual(res_val, ref_val) def test_tow_IND_1985_pass(self): - """ Test Total Wealth value India 1985 (outside year range).""" + """Test Total Wealth value India 1985 (outside year range).""" ref_year = 1985 cntry_iso = 'IND' var_name = 'NW.TOW.TO' - res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, \ - variable_name=var_name) + res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, + variable_name=var_name) ref_val = 5415188681942.5 self.assertEqual(res_year, ref_year) self.assertEqual(res_val, ref_val) def test_pca_CUB_2015_pass(self): - """ Test Processed Capital value Cuba 2015 (missing value).""" + """Test Processed Capital value Cuba 2015 (missing value).""" ref_year = 2015 cntry_iso = 'CUB' res_year, res_val, q = world_bank_wealth_account(cntry_iso, ref_year, no_land=1) @@ -193,8 +193,9 @@ def test_pca_CUB_2015_pass(self): self.assertEqual(res_val, ref_val) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestNetpresValue) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWBData)) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWealth2GDP)) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWBWealthAccount)) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestNetpresValue) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWBData)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWealth2GDP)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestWBWealthAccount)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_hdf5.py b/climada/util/test/test_hdf5.py index 5dec0b7906..c4354cde41 100644 --- a/climada/util/test/test_hdf5.py +++ b/climada/util/test/test_hdf5.py @@ -24,19 +24,17 @@ import numpy as np import h5py +from climada.util.constants import HAZ_DEMO_MAT import climada.util.hdf5_handler as hdf5 -DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') -HAZ_TEST_MAT = os.path.join(DATA_DIR, 'atl_prob_short_name.mat') - class TestFunc(unittest.TestCase): - '''Test the auxiliary functions used to retrieve variables from HDF5''' + """Test the auxiliary functions used to retrieve variables from HDF5""" def test_get_string_pass(self): - '''Check function to get a string from input integer array''' + """Check function to get a string from input integer array""" # Load input - contents = hdf5.read(HAZ_TEST_MAT) + contents = hdf5.read(HAZ_DEMO_MAT) # Convert several strings str_date = hdf5.get_string(contents['hazard']['date']) @@ -47,26 +45,26 @@ def test_get_string_pass(self): # Check results self.assertEqual('14-Nov-2017 10:09:05', str_date) self.assertEqual( - 'TC hazard event set, generated 14-Nov-2017 10:09:05', \ - str_comment) + 'TC hazard event set, generated 14-Nov-2017 10:09:05', + str_comment) self.assertEqual( - 'generating 14450 windfields took 0.25 min ' + \ + 'generating 14450 windfields took 0.25 min ' + '(0.0010 sec/event)', str_wf) - self.assertEqual('/Users/aznarsig/Documents/MATLAB/climada_data/' + \ + self.assertEqual('/Users/aznarsig/Documents/MATLAB/climada_data/' + 'hazards/atl_prob.mat', str_fn) def test_get_sparse_mat_pass(self): - '''Check contents of imported sparse matrix, using the function \ - to build a sparse matrix from the read HDF5 variable''' + """Check contents of imported sparse matrix, using the function \ + to build a sparse matrix from the read HDF5 variable""" # Load input - contents = hdf5.read(HAZ_TEST_MAT) + contents = hdf5.read(HAZ_DEMO_MAT) # get matrix size - mat_shape = (len(contents['hazard']['event_ID']), \ + mat_shape = (len(contents['hazard']['event_ID']), len(contents['hazard']['centroid_ID'])) - spr_mat = hdf5.get_sparse_csr_mat(contents['hazard']['intensity'], \ - mat_shape) + spr_mat = hdf5.get_sparse_csr_mat(contents['hazard']['intensity'], + mat_shape) self.assertEqual(mat_shape[0], spr_mat.shape[0]) self.assertEqual(mat_shape[1], spr_mat.shape[1]) @@ -81,29 +79,29 @@ def test_get_sparse_mat_pass(self): self.assertEqual(0, spr_mat[126, 86]) def test_get_str_from_ref(self): - """ Check import string from a HDF5 object reference""" - file = h5py.File(HAZ_TEST_MAT, 'r') + """Check import string from a HDF5 object reference""" + file = h5py.File(HAZ_DEMO_MAT, 'r') var = file['hazard']['name'][0][0] - res = hdf5.get_str_from_ref(HAZ_TEST_MAT, var) + res = hdf5.get_str_from_ref(HAZ_DEMO_MAT, var) self.assertEqual('NNN_1185101', res) def test_get_list_str_from_ref(self): - """ Check import string from a HDF5 object reference""" - file = h5py.File(HAZ_TEST_MAT, 'r') + """Check import string from a HDF5 object reference""" + file = h5py.File(HAZ_DEMO_MAT, 'r') var = file['hazard']['name'] - var_list = hdf5.get_list_str_from_ref(HAZ_TEST_MAT, var) + var_list = hdf5.get_list_str_from_ref(HAZ_DEMO_MAT, var) self.assertEqual('NNN_1185101', var_list[0]) self.assertEqual('NNN_1185101_gen1', var_list[1]) self.assertEqual('NNN_1185101_gen2', var_list[2]) class TestReader(unittest.TestCase): - '''Test HDF5 reader''' + """Test HDF5 reader""" def test_hazard_pass(self): - '''Checking result against matlab atl_prob.mat file''' + """Checking result against matlab atl_prob.mat file""" # Load input - contents = hdf5.read(HAZ_TEST_MAT) + contents = hdf5.read(HAZ_DEMO_MAT) # Check read contents self.assertEqual(1, len(contents)) @@ -141,23 +139,23 @@ def test_hazard_pass(self): self.assertEqual(27, len(hazard.keys())) # Check some random values - mat_shape = (len(contents['hazard']['event_ID']), \ - len(contents['hazard']['centroid_ID'])) + mat_shape = (len(contents['hazard']['event_ID']), + len(contents['hazard']['centroid_ID'])) sp_mat = hdf5.get_sparse_csr_mat(hazard['intensity'], mat_shape) - self.assertTrue(np.array_equal(np.array([[84], [67]]), \ - hazard['peril_ID'])) + self.assertTrue(np.array_equal(np.array([[84], [67]]), + hazard['peril_ID'])) self.assertEqual(34.537289477809473, sp_mat[2862, 97]) self.assertEqual(-80, hazard['lon'][46]) self.assertEqual(28, hazard['lat'][87]) self.assertEqual(2016, hazard['reference_year']) def test_with_refs_pass(self): - '''Allow to load references of the matlab file''' + """Allow to load references of the matlab file""" # Load input refs = True - contents = hdf5.read(HAZ_TEST_MAT, refs) + contents = hdf5.read(HAZ_DEMO_MAT, refs) # Check read contents self.assertEqual(2, len(contents)) @@ -165,6 +163,7 @@ def test_with_refs_pass(self): self.assertTrue('#refs#' in contents.keys()) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestFunc)) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestReader) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestFunc)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_interpolation.py b/climada/util/test/test_interpolation.py index 8c7af090e6..8ddd4f9255 100644 --- a/climada/util/test/test_interpolation.py +++ b/climada/util/test/test_interpolation.py @@ -25,58 +25,60 @@ from climada.util.constants import ONE_LAT_KM def def_input_values(): - '''Default input coordinates and centroids values''' + """Default input coordinates and centroids values""" # Load exposures coordinates from demo entity file - exposures = np.array([[ 26.933899, -80.128799], - [ 26.957203, -80.098284], - [ 26.783846, -80.748947], - [ 26.645524, -80.550704], - [ 26.897796, -80.596929], - [ 26.925359, -80.220966], - [ 26.914768, -80.07466 ], - [ 26.853491, -80.190281], - [ 26.845099, -80.083904], - [ 26.82651 , -80.213493], - [ 26.842772, -80.0591 ], - [ 26.825905, -80.630096], - [ 26.80465 , -80.075301], - [ 26.788649, -80.069885], - [ 26.704277, -80.656841], - [ 26.71005 , -80.190085], - [ 26.755412, -80.08955 ], - [ 26.678449, -80.041179], - [ 26.725649, -80.1324 ], - [ 26.720599, -80.091746], - [ 26.71255 , -80.068579], - [ 26.6649 , -80.090698], - [ 26.664699, -80.1254 ], - [ 26.663149, -80.151401], - [ 26.66875 , -80.058749], - [ 26.638517, -80.283371], - [ 26.59309 , -80.206901], - [ 26.617449, -80.090649], - [ 26.620079, -80.055001], - [ 26.596795, -80.128711], - [ 26.577049, -80.076435], - [ 26.524585, -80.080105], - [ 26.524158, -80.06398 ], - [ 26.523737, -80.178973], - [ 26.520284, -80.110519], - [ 26.547349, -80.057701], - [ 26.463399, -80.064251], - [ 26.45905 , -80.07875 ], - [ 26.45558 , -80.139247], - [ 26.453699, -80.104316], - [ 26.449999, -80.188545], - [ 26.397299, -80.21902 ], - [ 26.4084 , -80.092391], - [ 26.40875 , -80.1575 ], - [ 26.379113, -80.102028], - [ 26.3809 , -80.16885 ], - [ 26.349068, -80.116401], - [ 26.346349, -80.08385 ], - [ 26.348015, -80.241305], - [ 26.347957, -80.158855]]) + exposures = np.array([ + [26.933899, -80.128799], + [26.957203, -80.098284], + [26.783846, -80.748947], + [26.645524, -80.550704], + [26.897796, -80.596929], + [26.925359, -80.220966], + [26.914768, -80.07466], + [26.853491, -80.190281], + [26.845099, -80.083904], + [26.82651, -80.213493], + [26.842772, -80.0591], + [26.825905, -80.630096], + [26.80465, -80.075301], + [26.788649, -80.069885], + [26.704277, -80.656841], + [26.71005, -80.190085], + [26.755412, -80.08955], + [26.678449, -80.041179], + [26.725649, -80.1324], + [26.720599, -80.091746], + [26.71255, -80.068579], + [26.6649, -80.090698], + [26.664699, -80.1254], + [26.663149, -80.151401], + [26.66875, -80.058749], + [26.638517, -80.283371], + [26.59309, -80.206901], + [26.617449, -80.090649], + [26.620079, -80.055001], + [26.596795, -80.128711], + [26.577049, -80.076435], + [26.524585, -80.080105], + [26.524158, -80.06398], + [26.523737, -80.178973], + [26.520284, -80.110519], + [26.547349, -80.057701], + [26.463399, -80.064251], + [26.45905, -80.07875], + [26.45558, -80.139247], + [26.453699, -80.104316], + [26.449999, -80.188545], + [26.397299, -80.21902], + [26.4084, -80.092391], + [26.40875, -80.1575], + [26.379113, -80.102028], + [26.3809, -80.16885], + [26.349068, -80.116401], + [26.346349, -80.08385], + [26.348015, -80.241305], + [26.347957, -80.158855] + ]) # Define centroids centroids = np.zeros((100, 2)) @@ -91,86 +93,87 @@ def def_input_values(): return exposures, centroids def def_ref(): - '''Default output reference''' - return np.array([46, 46, 36, 36, 36, 46, 46, 46, 46, 46, 46,\ - 36, 46, 46, 36, 46, 46, 46, 46, 46, 46, 46,\ - 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46,\ - 46, 46, 46, 45, 45, 45, 45, 45, 45, 45, 45,\ + """Default output reference""" + return np.array([46, 46, 36, 36, 36, 46, 46, 46, 46, 46, 46, + 36, 46, 46, 36, 46, 46, 46, 46, 46, 46, 46, + 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, + 46, 46, 46, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45]) def def_ref_50(): - '''Default output reference for maximum distance threshold 50km''' - return np.array([46, 46, 36, -1, 36, 46, 46, 46, 46, 46, 46, 36, 46, 46, \ - 36, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, \ - 46, 46, 46, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 45, \ + """Default output reference for maximum distance threshold 50km""" + return np.array([46, 46, 36, -1, 36, 46, 46, 46, 46, 46, 46, 36, 46, 46, + 36, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, + 46, 46, 46, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 45, 45, 45, 45, 45, 45, 45, 45, 45]) class TestDistance(unittest.TestCase): - """ Test distance functions. """ + """Test distance functions.""" def test_dist_approx_pass(self): - """ Test against matlab reference. """ + """Test against matlab reference.""" lats1 = 45.5 lons1 = -32.2 cos_lats1 = np.cos(np.radians(lats1)) lats2 = 14 lons2 = 56 self.assertAlmostEqual(7709.827814738594, - interp.dist_approx(lats1, lons1, cos_lats1, lats2, lons2)) + interp.dist_approx(lats1, lons1, cos_lats1, lats2, lons2)) def test_dist_sqr_approx_pass(self): - """ Test against matlab reference. """ + """Test against matlab reference.""" lats1 = 45.5 lons1 = -32.2 cos_lats1 = np.cos(np.radians(lats1)) lats2 = 14 lons2 = 56 - self.assertAlmostEqual(7709.827814738594, - np.sqrt(interp.dist_sqr_approx(lats1, lons1, cos_lats1, lats2, lons2))*ONE_LAT_KM) + self.assertAlmostEqual( + 7709.827814738594, + np.sqrt(interp.dist_sqr_approx(lats1, lons1, cos_lats1, lats2, lons2)) * ONE_LAT_KM) class TestInterpIndex(unittest.TestCase): - ''' Test interpol_index function's interface''' + """Test interpol_index function's interface""" def test_wrong_method_fail(self): - ''' Check exception is thrown when wrong method is given''' + """Check exception is thrown when wrong method is given""" with self.assertLogs('climada.util.interpolation', level='ERROR') as cm: interp.interpol_index(np.ones((10, 2)), np.ones((7, 2)), 'method') - self.assertIn('Interpolation using method' + \ - ' with distance haversine is not supported.', cm.output[0]) + self.assertIn('Interpolation using method with distance haversine is not supported.', + cm.output[0]) def test_wrong_distance_fail(self): - ''' Check exception is thrown when wrong distance is given''' + """Check exception is thrown when wrong distance is given""" with self.assertLogs('climada.util.interpolation', level='ERROR') as cm: - interp.interpol_index(np.ones((10, 2)), np.ones((7, 2)), \ + interp.interpol_index(np.ones((10, 2)), np.ones((7, 2)), distance='distance') - self.assertIn('Interpolation using NN' + \ - ' with distance distance is not supported.', cm.output[0]) + self.assertIn('Interpolation using NN with distance distance is not supported.', + cm.output[0]) def test_wrong_centroid_fail(self): - ''' Check exception is thrown when centroids missing one dimension''' + """Check exception is thrown when centroids missing one dimension""" with self.assertRaises(IndexError): - interp.interpol_index(np.ones((10, 1)), np.ones((7, 2)), + interp.interpol_index(np.ones((10, 1)), np.ones((7, 2)), distance='approx') with self.assertRaises(ValueError): interp.interpol_index(np.ones((10, 1)), np.ones((7, 2)), distance='haversine') def test_wrong_coord_fail(self): - ''' Check exception is thrown when coordinates missing one dimension''' + """Check exception is thrown when coordinates missing one dimension""" with self.assertRaises(IndexError): - interp.interpol_index(np.ones((10, 2)), np.ones((7, 1)), + interp.interpol_index(np.ones((10, 2)), np.ones((7, 1)), distance='approx') with self.assertRaises(ValueError): interp.interpol_index(np.ones((10, 2)), np.ones((7, 1)), distance='haversine') class TestNN(unittest.TestCase): - '''Test interpolator neareast neighbor with approximate distance''' + """Test interpolator neareast neighbor with approximate distance""" def tearDown(self): interp.THRESHOLD = 100 def normal_pass(self, dist): - '''Checking result against matlab climada_demo_step_by_step''' + """Checking result against matlab climada_demo_step_by_step""" # Load input exposures, centroids = def_input_values() @@ -184,15 +187,15 @@ def normal_pass(self, dist): self.assertTrue(np.array_equal(neighbors, ref_neighbors)) def normal_warning(self, dist): - '''Checking that a warning is raised when minimum distance greater - than threshold''' + """Checking that a warning is raised when minimum distance greater + than threshold""" # Load input exposures, centroids = def_input_values() # Interpolate with lower threshold to raise warnings threshold = 50 with self.assertLogs('climada.util.interpolation', level='INFO') as cm: - neighbors = interp.interpol_index(centroids, exposures, 'NN', + neighbors = interp.interpol_index(centroids, exposures, 'NN', dist, threshold=threshold) self.assertIn("Distance to closest centroid", cm.output[0]) @@ -200,8 +203,8 @@ def normal_warning(self, dist): self.assertTrue(np.array_equal(neighbors, ref_neighbors)) def repeat_coord_pass(self, dist): - '''Check that exposures with the same coordinates have same - neighbors''' + """Check that exposures with the same coordinates have same + neighbors""" # Load input exposures, centroids = def_input_values() @@ -218,31 +221,32 @@ def repeat_coord_pass(self, dist): self.assertEqual(neighbors[2], neighbors[0]) def test_approx_normal_pass(self): - ''' Call normal_pass test for approxiamte distance''' + """Call normal_pass test for approxiamte distance""" self.normal_pass('approx') def test_approx_normal_warning(self): - ''' Call normal_warning test for approxiamte distance''' + """Call normal_warning test for approxiamte distance""" self.normal_warning('approx') def test_approx_repeat_coord_pass(self): - ''' Call repeat_coord_pass test for approxiamte distance''' + """Call repeat_coord_pass test for approxiamte distance""" self.repeat_coord_pass('approx') def test_haver_normal_pass(self): - ''' Call normal_pass test for haversine distance''' + """Call normal_pass test for haversine distance""" self.normal_pass('haversine') def test_haver_normal_warning(self): - ''' Call normal_warning test for haversine distance''' + """Call normal_warning test for haversine distance""" self.normal_warning('haversine') def test_haver_repeat_coord_pass(self): - ''' Call repeat_coord_pass test for haversine distance''' + """Call repeat_coord_pass test for haversine distance""" self.repeat_coord_pass('haversine') # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestNN) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestInterpIndex)) -TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDistance)) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestNN) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestInterpIndex)) + TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestDistance)) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/climada/util/test/test_plot.py b/climada/util/test/test_plot.py index 07c5f91c40..f10337b484 100644 --- a/climada/util/test/test_plot.py +++ b/climada/util/test/test_plot.py @@ -27,20 +27,20 @@ class TestFuncs(unittest.TestCase): def test_get_transform_4326_pass(self): - """ Check _get_transformation for 4326 epsg.""" - res, unit = u_plot.get_transformation({'init':'epsg:4326'}) + """Check _get_transformation for 4326 epsg.""" + res, unit = u_plot.get_transformation({'init': 'epsg:4326'}) self.assertIsInstance(res, cartopy.crs.PlateCarree) self.assertEqual(unit, '°') def test_get_transform_3395_pass(self): - """ Check that assigned attribute is correctly set.""" - res, unit = u_plot.get_transformation({'init':'epsg:3395'}) + """Check that assigned attribute is correctly set.""" + res, unit = u_plot.get_transformation({'init': 'epsg:3395'}) self.assertIsInstance(res, cartopy.crs.Mercator) self.assertEqual(unit, 'm') def test_get_transform_3035_pass(self): - """ Check that assigned attribute is correctly set.""" - res, unit = u_plot.get_transformation({'init':'epsg:3035'}) + """Check that assigned attribute is correctly set.""" + res, unit = u_plot.get_transformation({'init': 'epsg:3035'}) self.assertIsInstance(res, cartopy._epsg._EPSGProjection) self.assertEqual(unit, 'm') diff --git a/climada/util/test/test_save.py b/climada/util/test/test_save.py index cf0238eba0..80b147a949 100644 --- a/climada/util/test/test_save.py +++ b/climada/util/test/test_save.py @@ -46,11 +46,11 @@ def test_entity_in_save_dir(self): with self.assertLogs('climada.util.save', level='INFO') as cm: save(file_name, ent) self.assertTrue(os.path.isfile(os.path.join(DATA_DIR, file_name))) - self.assertTrue((file_name in cm.output[0]) or \ + self.assertTrue((file_name in cm.output[0]) or (file_name in cm.output[1])) def test_load_pass(self): - """ Load previously saved variable """ + """Load previously saved variable""" file_name = 'save_test.pkl' ent = {'value': [1, 2, 3]} save(file_name, ent) @@ -60,5 +60,6 @@ def test_load_pass(self): self.assertTrue(res['value'] == ent['value']) # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestSave) -unittest.TextTestRunner(verbosity=2).run(TESTS) +if __name__ == "__main__": + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestSave) + unittest.TextTestRunner(verbosity=2).run(TESTS) diff --git a/data/demo/FAOSTAT_data_producer_prices.csv b/data/demo/FAOSTAT_data_producer_prices.csv new file mode 100644 index 0000000000..7e1280f5f4 --- /dev/null +++ b/data/demo/FAOSTAT_data_producer_prices.csv @@ -0,0 +1,169 @@ +Domain Code,Domain,Area Code,Area,Element Code,Element,Item Code,Item,Year Code,Year,Months Code,Months,Unit,Value,Flag,Flag Description +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1991","1991","7021","Annual value","USD","213","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1991","1991","7021","Annual value","USD","397","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1991","1991","7021","Annual value","USD","509.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1991","1991","7021","Annual value","USD","197.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1992","1992","7021","Annual value","USD","202.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1992","1992","7021","Annual value","USD","402.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1992","1992","7021","Annual value","USD","184.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1992","1992","7021","Annual value","USD","202.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1993","1993","7021","Annual value","USD","145.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1993","1993","7021","Annual value","USD","416.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1993","1993","7021","Annual value","USD","221.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1993","1993","7021","Annual value","USD","148.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1994","1994","7021","Annual value","USD","162.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1994","1994","7021","Annual value","USD","425.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1994","1994","7021","Annual value","USD","177.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1994","1994","7021","Annual value","USD","149.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1995","1995","7021","Annual value","USD","191.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1995","1995","7021","Annual value","USD","564.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1995","1995","7021","Annual value","USD","226.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1995","1995","7021","Annual value","USD","169.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1996","1996","7021","Annual value","USD","159.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1996","1996","7021","Annual value","USD","386.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1996","1996","7021","Annual value","USD","251.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1996","1996","7021","Annual value","USD","161.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1997","1997","7021","Annual value","USD","129.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1997","1997","7021","Annual value","USD","329.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1997","1997","7021","Annual value","USD","231.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1997","1997","7021","Annual value","USD","132.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1998","1998","7021","Annual value","USD","122.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1998","1998","7021","Annual value","USD","329.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1998","1998","7021","Annual value","USD","182.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1998","1998","7021","Annual value","USD","118.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","1999","1999","7021","Annual value","USD","119.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","1999","1999","7021","Annual value","USD","297.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","1999","1999","7021","Annual value","USD","181.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","1999","1999","7021","Annual value","USD","115","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2000","2000","7021","Annual value","USD","95.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2000","2000","7021","Annual value","USD","243.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2000","2000","7021","Annual value","USD","190.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2000","2000","7021","Annual value","USD","93.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2001","2001","7021","Annual value","USD","94.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2001","2001","7021","Annual value","USD","230.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2001","2001","7021","Annual value","USD","210.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2001","2001","7021","Annual value","USD","96.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2002","2002","7021","Annual value","USD","93.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2002","2002","7021","Annual value","USD","223.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2002","2002","7021","Annual value","USD","190.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2002","2002","7021","Annual value","USD","91.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2003","2003","7021","Annual value","USD","149","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2003","2003","7021","Annual value","USD","232","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2003","2003","7021","Annual value","USD","249.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2003","2003","7021","Annual value","USD","129.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2004","2004","7021","Annual value","USD","116.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2004","2004","7021","Annual value","USD","161.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2004","2004","7021","Annual value","USD","258.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2004","2004","7021","Annual value","USD","119.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2005","2005","7021","Annual value","USD","128.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2005","2005","7021","Annual value","USD","201.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2005","2005","7021","Annual value","USD","262.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2005","2005","7021","Annual value","USD","116.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2006","2006","7021","Annual value","USD","164.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2006","2006","7021","Annual value","USD","259.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2006","2006","7021","Annual value","USD","270.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2006","2006","7021","Annual value","USD","152.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2007","2007","7021","Annual value","USD","256.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2007","2007","7021","Annual value","USD","348.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2007","2007","7021","Annual value","USD","475.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2007","2007","7021","Annual value","USD","259.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2008","2008","7021","Annual value","USD","180.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2008","2008","7021","Annual value","USD","429.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2008","2008","7021","Annual value","USD","568.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2008","2008","7021","Annual value","USD","212.5","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2009","2009","7021","Annual value","USD","170.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2009","2009","7021","Annual value","USD","352.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2009","2009","7021","Annual value","USD","447.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2009","2009","7021","Annual value","USD","155.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2010","2010","7021","Annual value","USD","247","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2010","2010","7021","Annual value","USD","401","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2010","2010","7021","Annual value","USD","552.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2010","2010","7021","Annual value","USD","246.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2011","2011","7021","Annual value","USD","259.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2011","2011","7021","Annual value","USD","415.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2011","2011","7021","Annual value","USD","504.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2011","2011","7021","Annual value","USD","268.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2012","2012","7021","Annual value","USD","276.1","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2012","2012","7021","Annual value","USD","355.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2012","2012","7021","Annual value","USD","595.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2012","2012","7021","Annual value","USD","285.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2013","2013","7021","Annual value","USD","227.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2013","2013","7021","Annual value","USD","356.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2013","2013","7021","Annual value","USD","590.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2013","2013","7021","Annual value","USD","244.4","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2014","2014","7021","Annual value","USD","194.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2014","2014","7021","Annual value","USD","421.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2014","2014","7021","Annual value","USD","475.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2014","2014","7021","Annual value","USD","216.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2015","2015","7021","Annual value","USD","173.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2015","2015","7021","Annual value","USD","372.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2015","2015","7021","Annual value","USD","385.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2015","2015","7021","Annual value","USD","172.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2016","2016","7021","Annual value","USD","175.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2016","2016","7021","Annual value","USD","361.3","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2016","2016","7021","Annual value","USD","380.2","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2016","2016","7021","Annual value","USD","159.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2017","2017","7021","Annual value","USD","160.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2017","2017","7021","Annual value","USD","333.8","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2017","2017","7021","Annual value","USD","390.9","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2017","2017","7021","Annual value","USD","157.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","56","Maize","2018","2018","7021","Annual value","USD","186.6","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","27","Rice, paddy","2018","2018","7021","Annual value","USD","372","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","236","Soybeans","2018","2018","7021","Annual value","USD","383.7","","Official data" +"PP","Producer Prices","68","France","5532","Producer Price (USD/tonne)","15","Wheat","2018","2018","7021","Annual value","USD","195","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1991","1991","7021","Annual value","USD","214.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1991","1991","7021","Annual value","USD","178.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1992","1992","7021","Annual value","USD","216.7","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1992","1992","7021","Annual value","USD","206.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1993","1993","7021","Annual value","USD","161.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1993","1993","7021","Annual value","USD","164.3","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1994","1994","7021","Annual value","USD","169.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1994","1994","7021","Annual value","USD","160","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1995","1995","7021","Annual value","USD","204.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1995","1995","7021","Annual value","USD","172.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1996","1996","7021","Annual value","USD","191.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1996","1996","7021","Annual value","USD","173.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1997","1997","7021","Annual value","USD","137.6","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1997","1997","7021","Annual value","USD","136.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1998","1998","7021","Annual value","USD","131.6","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1998","1998","7021","Annual value","USD","126.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","1999","1999","7021","Annual value","USD","126.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","1999","1999","7021","Annual value","USD","119.7","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2000","2000","7021","Annual value","USD","109.6","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2000","2000","7021","Annual value","USD","106.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2001","2001","7021","Annual value","USD","103.7","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2001","2001","7021","Annual value","USD","99.6","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2002","2002","7021","Annual value","USD","101.6","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2002","2002","7021","Annual value","USD","95.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2003","2003","7021","Annual value","USD","141.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2003","2003","7021","Annual value","USD","124.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2004","2004","7021","Annual value","USD","170.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2004","2004","7021","Annual value","USD","154","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2005","2005","7021","Annual value","USD","119.4","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2005","2005","7021","Annual value","USD","123.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2006","2006","7021","Annual value","USD","171.9","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2006","2006","7021","Annual value","USD","139.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2007","2007","7021","Annual value","USD","242.3","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2007","2007","7021","Annual value","USD","239.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2008","2008","7021","Annual value","USD","262.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2008","2008","7021","Annual value","USD","272.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2009","2009","7021","Annual value","USD","168.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2009","2009","7021","Annual value","USD","157","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2010","2010","7021","Annual value","USD","207.9","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2010","2010","7021","Annual value","USD","198.7","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2011","2011","7021","Annual value","USD","286.4","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2011","2011","7021","Annual value","USD","287.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2012","2012","7021","Annual value","USD","269.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2012","2012","7021","Annual value","USD","281.4","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2013","2013","7021","Annual value","USD","258.9","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2013","2013","7021","Annual value","USD","269.5","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2014","2014","7021","Annual value","USD","220.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2014","2014","7021","Annual value","USD","224.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2015","2015","7021","Annual value","USD","174.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2015","2015","7021","Annual value","USD","179.7","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2016","2016","7021","Annual value","USD","168.1","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2016","2016","7021","Annual value","USD","154.9","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2017","2017","7021","Annual value","USD","176.9","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2017","2017","7021","Annual value","USD","170.2","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","56","Maize","2018","2018","7021","Annual value","USD","194.8","","Official data" +"PP","Producer Prices","79","Germany","5532","Producer Price (USD/tonne)","15","Wheat","2018","2018","7021","Annual value","USD","198.8","","Official data" diff --git a/data/demo/FAOSTAT_data_production_quantity.csv b/data/demo/FAOSTAT_data_production_quantity.csv new file mode 100644 index 0000000000..4e9f96b93a --- /dev/null +++ b/data/demo/FAOSTAT_data_production_quantity.csv @@ -0,0 +1,21 @@ +Domain Code,Domain,Area Code,Area,Element Code,Element,Item Code,Item,Year Code,Year,Unit,Value,Flag,Flag Description,Note +QC,Crops,255,Belgium,5510,Production,56,Maize,2009,2009,tonnes,807866,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2010,2010,tonnes,745891,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2011,2011,tonnes,859692,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2012,2012,tonnes,701700,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2013,2013,tonnes,826984,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2014,2014,tonnes,662700,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2015,2015,tonnes,597169,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2016,2016,tonnes,480496,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2017,2017,tonnes,608671,,Official data, +QC,Crops,255,Belgium,5510,Production,56,Maize,2018,2018,tonnes,442995,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2009,2009,tonnes,244911,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2010,2010,tonnes,196903,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2011,2011,tonnes,204400,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2012,2012,tonnes,191363,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2013,2013,tonnes,185284,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2014,2014,tonnes,173066,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2015,2015,tonnes,121114,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2016,2016,tonnes,84641,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2017,2017,tonnes,116711,,Official data, +QC,Crops,150,Netherlands,5510,Production,56,Maize,2018,2018,tonnes,84894,,Official data, \ No newline at end of file diff --git a/data/demo/WS_ERA40.mat b/data/demo/WS_ERA40.mat deleted file mode 100755 index 3758d01539..0000000000 Binary files a/data/demo/WS_ERA40.mat and /dev/null differ diff --git a/data/demo/WS_ERA40_sample.mat b/data/demo/WS_ERA40_sample.mat new file mode 100644 index 0000000000..d39c04f3fb Binary files /dev/null and b/data/demo/WS_ERA40_sample.mat differ diff --git a/data/demo/atl_prob.mat b/data/demo/atl_prob.mat deleted file mode 100644 index 6719ca15ae..0000000000 Binary files a/data/demo/atl_prob.mat and /dev/null differ diff --git a/climada/util/test/data/atl_prob_short_name.mat b/data/demo/atl_prob_nonames.mat similarity index 100% rename from climada/util/test/data/atl_prob_short_name.mat rename to data/demo/atl_prob_nonames.mat diff --git a/data/demo/demo_emdat_impact_data_2020.csv b/data/demo/demo_emdat_impact_data_2020.csv new file mode 100644 index 0000000000..55c72eaf4a --- /dev/null +++ b/data/demo/demo_emdat_impact_data_2020.csv @@ -0,0 +1,1076 @@ +Source:,"EM-DAT, CRED / UCLouvain, Brussels, Belgium",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,www.emdat.be (D. Guha-Sapir),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Version:,2020-06-15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +File creation:,"Mon, 15 Jun 2020 05:32:00 CEST",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Table type:,Custom request,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +# of records:,1069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Dis No,Year,Seq,Disaster Group,Disaster Subgroup,Disaster Type,Disaster Subtype,Disaster Subsubtype,Event Name,Entry Criteria,Country,ISO,Region,Continent,Location,Origin,Associated Dis,Associated Dis2,OFDA Response,Appeal,Declaration,Aid Contribution,Dis Mag Value,Dis Mag Scale,Latitude,Longitude,Local Time,River Basin,Start Year,Start Month,Start Day,End Year,End Month,End Day,Total Deaths,No Injured,No Affected,No Homeless,Total Affected,Reconstruction Costs ('000 US$),Insured Damages ('000 US$),Total Damages ('000 US$),CPI +2000-0097-AUS,2000,0097,Natural,Meteorological,Storm,Tropical cyclone,,Steve,Declar/Int,Australia,AUS,Australia and New Zealand,Oceania,"Cairns, Tablelands districts (Queensland province), New South Wales province",,Flood,,,,Yes,,120,Kph,,,,,2000,2,27,2000,3,13,1,,200,,200,,10000,90000,67.35575898 +2000-0557-CHN,2000,0557,Natural,Meteorological,Storm,Tropical cyclone,,Maria,Kill,China,CHN,Eastern Asia,Asia,"Hunan Sheng, Guangdong Sheng provinces",,Flood,,,,,,160,Kph,,,,,2000,9,1,2000,9,6,47,,46000,,46000,,,169000,67.35575898 +2000-0214-AUS,2000,0214,Natural,Meteorological,Storm,Tropical cyclone,,Tessi,Affect,Australia,AUS,Australia and New Zealand,Oceania,Townsville district (Queensland province),,,,,,,,130,Kph,,,,,2000,4,3,2000,4,3,,,400,,400,,,60000,67.35575898 +2000-0843-AUS,2000,0843,Natural,Meteorological,Storm,Tropical cyclone,,Sam,Affect,Australia,AUS,Australia and New Zealand,Oceania,"Bidyadanga area (Broome district, Western Australia province)",,,,,,,,280,Kph,,,,,2000,12,7,2000,12,7,,,750,,750,,,,67.35575898 +2000-0211-BGD,2000,0211,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Alpara, Netrokona Sadar, Kendua, Purbadhala areas (Netrakona district, Dhaka province), Rangpur district (Rangpur province)",,,,,,,,120,Kph,,,,,2000,4,21,2000,4,21,20,300,,62500,62800,,,,67.35575898 +2000-0229-BGD,2000,0229,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,Bangladesh,BGD,Southern Asia,Asia,"Maulvibazar district (Sylhet province), Netrakona district (Dhaka province)",,,,,,,,80,Kph,,,,,2000,4,11,2000,4,11,,12,,7000,7012,,,,67.35575898 +2000-0713-BGD,2000,0713,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Barisal, Barguna, Jhalokati, Bhola, Pirojpur districts (Barisal province), Khulna district (Khulna province), Noakhali, Lakshmipur, Cox's Bazar, Chandpur districts (Chittagong province), Dhaka, Mymensingh, Shariatpur districts (Dhaka province)",,,,,,,,100,Kph,,,,,2000,10,28,2000,10,28,15,200,,,200,,,,67.35575898 +2000-0642-BLZ,2000,0642,Natural,Meteorological,Storm,Tropical cyclone,,Keith,Kill,Belize,BLZ,Central America,Americas,"San Pedro and Belize cities, Cay Caulker and Ambergris Caye islands (Belize province), Corozal, Cayo, Orange Walk provinces",,,,Yes,,,2628,215,Kph,,,,,2000,9,30,2000,10,3,14,570,62000,,62570,,,277460,67.35575898 +2000-0533-CHN,2000,0533,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,China,CHN,Eastern Asia,Asia,"Leqing Shi area (Wenzhou district, Zhejiang Sheng province), Fuzhou district (Fujian Sheng province)",,,,,,,,,Kph,,,,,2000,8,23,2000,8,23,11,47,,4475,4522,,,69443,67.35575898 +2000-0556-CHN,2000,0556,Natural,Meteorological,Storm,Tropical cyclone,,Prapiroon,Affect,China,CHN,Eastern Asia,Asia,"Xiangshui Xian area (Yancheng district, Jiangsu Sheng province)",,,,,,,,,Kph,,,,,2000,8,,2000,8,,4,80,,37690,37770,,,10,67.35575898 +2000-0605-CHN,2000,0605,Natural,Meteorological,Storm,Tropical cyclone,,Wukong,Affect,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,,,,,,,,Kph,,,,,2000,9,9,2000,9,9,6,,2400000,,2400000,,,,67.35575898 +2000-0831-LKA,2000,0831,Natural,Meteorological,Storm,Tropical cyclone,,04B,Affect,Sri Lanka,LKA,Southern Asia,Asia,"Ampara, Batticaloa, Trincomalee districts (Eastern province), Anuradhapura, Polonnaruwa districts (North Central province), Mannar district (Northern province)",,Rain,Surge,Yes,,,282,150,Kph,,,,,2000,12,24,2000,12,28,5,,375000,,375000,,,,67.35575898 +2000-0178-MDG,2000,0178,Natural,Meteorological,Storm,Tropical cyclone,,Hudah,Affect,Madagascar,MDG,Eastern Africa,Africa,"Antalaha, Sambava, Andapa districts (Sava province), Maroantsetra district (Analanjirofo province), Bealanana district (Sofia province)",,Rain,,,Yes,,,300,Kph,,,,,2000,4,2,2000,4,2,23,1,369271,,369272,,,,67.35575898 +2000-0107-MDG,2000,0107,Natural,Meteorological,Storm,Tropical cyclone,,"Eline, Gloria",Kill,Madagascar,MDG,Eastern Africa,Africa,"Marolambo, Antanambao Manampontsy, Mahanoro, Vatomandry, Brickaville districts (Atsinanana province), Ambositra district (Amoron I Mania province), Antananarivo Avaradrano, Andramasina, Manjakandriana districts (Analamanga province), Ambatolampy, Antsirabe II, Antanifotsy districts (Vakinankaratra province), Antalaha, Sambava, Andapa, Vohemar districts (Sava province), Morondava, Belo Sur Tsiribihina, Mahabo districts (Menabe province), Morombe district (Atsimo Andrefana province), Maroantsetra district (Analanjirofo province)",,Flood,,Yes,,Yes,8840,,Kph,,,,,2000,2,17,2000,3,11,130,,736937,,736937,,,9000,67.35575898 +2000-0257-PHL,2000,0257,Natural,Meteorological,Storm,Tropical cyclone,,Biring,Affect,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region III (Central Luzon) provinces",,Flood,,,,,,,Kph,,,,,2000,5,18,2000,5,18,12,4,235885,,235889,,,1201,67.35575898 +2000-0572-PRK,2000,0572,Natural,Meteorological,Storm,Tropical cyclone,,"Prapiroon ""12""",Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Kangwon-do, Hamgyong-bukto, Hamgyong-namdo, Yanggang-do, P'yongan-bukto, Hwanghae-bukto, Kaesong-si provinces",,Surge,,,Yes,,466,,Kph,,,,,2000,8,31,2000,9,4,46,180,480000,147000,627180,,,6000000,67.35575898 +2000-0323-IND,2000,0323,Natural,Meteorological,Storm,Tropical cyclone,,,Waiting,India,IND,Southern Asia,Asia,Andhra Pradesh province,,,,,,,4812,100,Kph,,,,,2000,10,17,2000,10,17,,,,,,,,,67.35575898 +2000-0785-IND,2000,0785,Natural,Meteorological,Storm,Tropical cyclone,,03B,Affect,India,IND,Southern Asia,Asia,"Nagappattinam, Thanjavur, Sirkali, Papanasam, Tiruvallur, Tiruvarur areas (Cuddalore district, Tamil Nadu province), Andhra Pradesh province",,,,,,,,120,Kph,,,,,2000,11,29,2000,11,29,,,30000,,30000,,,,67.35575898 +2000-0447-JPN,2000,0447,Natural,Meteorological,Storm,Tropical cyclone,,Kirogi,Affect,Japan,JPN,Eastern Asia,Asia,Tookyoo province,,,,,,,,126,Kph,,,,,2000,7,8,2000,7,9,4,,900,,900,,200000,300000,67.35575898 +2000-0576-JPN,2000,0576,Natural,Meteorological,Storm,Tropical cyclone,,Saomai,Affect,Japan,JPN,Eastern Asia,Asia,"Aiti, Mie, Gifu provinces",,,,,,,,160,Kph,,,,,2000,9,11,2000,9,14,9,41,180000,,180041,,1050000,1400000,67.35575898 +2000-0550-KOR,2000,0550,Natural,Meteorological,Storm,Tropical cyclone,,Prapiroon (Typhoon n°12),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Chollabuk-do, Chungchongnam-do, Cheju-do provinces",,,,,,,,209,Kph,,,,,2000,8,31,2000,8,31,25,,,300,300,,,11300,67.35575898 +2000-0601-KOR,2000,0601,Natural,Meteorological,Storm,Tropical cyclone,,Saomai,Waiting,Korea (the Republic of),KOR,Eastern Asia,Asia,"Kyongsangbuk-do, Kyongsangnam-do provinces",,,,,,,,,Kph,,,,,2000,9,16,2000,9,16,8,,,411,411,,,71000,67.35575898 +2000-0643-MEX,2000,0643,Natural,Meteorological,Storm,Tropical cyclone,,Keith,Affect,Mexico,MEX,Central America,Americas,"Puebla, Campeche, Quintana Roo, Yucatan, Veracruz, Chiapas, Oaxaca, Tabasco, Tamaulipas provinces",,,,,,,,150,Kph,,,,,2000,9,29,2000,10,3,23,,30000,,30000,,,1000,67.35575898 +2000-0107-MOZ,2000,0107,Natural,Meteorological,Storm,Tropical cyclone,,"Eline, Gloria",Kill,Mozambique,MOZ,Eastern Africa,Africa,"Cabo Delgado, Gaza, Inhambane, Manica, Nampula, Niassa, Sofala, Tete, Zambezia, Maputo, Lago niassa provinces",,,,,,,,,Kph,,,,,2000,2,18,2000,2,23,17,,,,,,,1000,67.35575898 +2000-0178-MOZ,2000,0178,Natural,Meteorological,Storm,Tropical cyclone,,Hudah,Affect,Mozambique,MOZ,Eastern Africa,Africa,"Angoche, Moma, Mogincual districts (Nampula province)",,,,,,,,,Kph,,,,,2000,4,2,2000,4,2,1,4,,300,304,,,,67.35575898 +2000-0644-NIC,2000,0644,Natural,Meteorological,Storm,Tropical cyclone,,Keith,Affect,Nicaragua,NIC,Central America,Americas,"Léon, Chinandega, Managua, Granada, Rivas provinces",,,,,,,,,Kph,,,,,2000,9,29,2000,10,3,1,,2300,,2300,,,1000,67.35575898 +2000-0396-PHL,2000,0396,Natural,Meteorological,Storm,Tropical cyclone,,Kirogi,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,National Capital region (NCR) province,,,,,,,,185,Kph,,,,,2000,7,5,2000,7,5,11,,,120000,120000,,,7500,67.35575898 +2000-0397-PHL,2000,0397,Natural,Meteorological,Storm,Tropical cyclone,,Kai-Tak (Edeng),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Caloocan, Manioma, Quezon cities (Metropolitan Manila district, National Capital region (NCR) province), Mindoro Oriental district (Region IV (Southern Tagalog) province), Cavite, Rizal districts (Region IV-A (Calabarzon) province), Iloilo, Negros Occidental districts (Region VI (Western Visayas) province), Region I (Ilocos region), Region III (Central Luzon) provinces",,,,,,,29,200,Kph,,,,,2000,7,7,2000,7,7,75,11,1483310,,1483321,,,25763,67.35575898 +2000-0706-PHL,2000,0706,Natural,Meteorological,Storm,Tropical cyclone,,Xangsane (Reming),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cavite district (Region IV-A (Calabarzon) province), Sorsogon, Catanduanes, Albay districts (Region V (Bicol region) province), Metropolitan Manila district (National Capital region (NCR) province), Samar districts (Region VIII (Eastern Visayas) province)",,,,,,,,140,Kph,,,,,2000,10,28,2000,10,31,154,314,2435942,,2436256,,1500,17000,67.35575898 +2000-0715-PHL,2000,0715,Natural,Meteorological,Storm,Tropical cyclone,,Bebinca (Seniang),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Metropolitan Manila district (National Capital region (NCR) province), Rizal, Laguna districts (Region IV-A (Calabarzon) province), Cagayan, Nueva Vizcaya districts (Region II (Cagayan Valley) province)",,,,,,,,120,Kph,,,,,2000,11,3,2000,11,5,94,,1747872,,1747872,,,31000,67.35575898 +2000-0788-PHL,2000,0788,Natural,Meteorological,Storm,Tropical cyclone,,Rumbia (Toyang),Affect,Philippines (the),PHL,South-Eastern Asia,Asia,"Autonomous region in Muslim Mindanao (ARMM), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces Central Philippines",,,,,,,,135,Kph,,,,,2000,11,30,2000,12,3,48,26,164067,,164093,,,1000,67.35575898 +2000-0037-REU,2000,0037,Natural,Meteorological,Storm,Tropical cyclone,,Connie,Affect,Réunion,REU,Eastern Africa,Africa,"Arrondissement du vent, Arrondissement sous le vent provinces",,,,,,,,153,Kph,,,,,2000,1,30,2000,1,30,2,,,600,600,,,,67.35575898 +2000-0576-RUS,2000,0576,Natural,Meteorological,Storm,Tropical cyclone,,Saomai,Affect,Russian Federation (the),RUS,Eastern Europe,Europe,"Primorskiy Kray, Khabarovskiy Kray, Sakhalinskaya Oblast, Yevreyskaya A. Oblast provinces",,,,,,,,,Kph,,,,,2000,9,13,2000,9,19,9,,,,,,,50,67.35575898 +2001-0054-PHL,2001,0054,Natural,Meteorological,Storm,Tropical cyclone,,Auring,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas), Autonomous region in Muslim Mindanao (ARMM), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,,,,,,,45,Kph,,,,,2001,2,17,2001,2,17,55,84,,100000,100084,,,6000,69.25933995 +2000-0706-TWN,2000,0706,Natural,Meteorological,Storm,Tropical cyclone,,Xangsane (Reming),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Taipei, Pingtung, Kaohsiung areas (Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",,,,,120,Kph,,,,,2000,11,1,2000,11,4,89,2,,,2,,60000,150000,67.35575898 +2000-0645-SLV,2000,0645,Natural,Meteorological,Storm,Tropical cyclone,,Keith,Affect,El Salvador,SLV,Central America,Americas,"Ahuachapan, Cabanas, Chalatenango, Cuscatlan, La Libertad, La Paz, La Union, Morazan, San Miguel, San Salvador, San Vicente, Santa Ana, Sonsonate, Usulutan provinces",,,,,,,,,Kph,,,,,2000,10,1,2000,10,1,1,,100,,100,,,,67.35575898 +2000-0539-THA,2000,0539,Natural,Meteorological,Storm,Tropical cyclone,,Kaemi,Affect,Thailand,THA,South-Eastern Asia,Asia,"Tha Tum, Chom Phra, Samrong Thap, Sikhoraphum, Sangkha, Muang Surin districts (Surin province), Ubon Ratchathani province",,,,,,,,,Kph,,,,,2000,8,21,2000,8,21,2,,41219,,41219,,,,67.35575898 +2000-0517-TWN,2000,0517,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,322,Kph,,,,,2000,8,22,2000,8,23,14,100,,2000,2100,,3000,135000,67.35575898 +2000-0652-USA,2000,0652,Natural,Meteorological,Storm,Tropical cyclone,,Leslie,Declar/Int,United States of America (the),USA,Northern America,Americas,"Miami-Dade, Monroe, Broward, Collier districts (Florida province)",,,,,,,,,Kph,,,,,2000,10,4,2000,10,4,2,,14418,3015,17433,,,219000,67.35575898 +2000-0518-VNM,2000,0518,Natural,Meteorological,Storm,Tropical cyclone,,Kaemi,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Binh, Quang Tri, Thua Thien - Hue, Quang Nam, Quang Ngai, Binh Dinh, Da Nang City provinces",,,,,,,,,Kph,,,,,2000,8,20,2000,8,20,15,4,,,4,,,5000,67.35575898 +2000-0582-VNM,2000,0582,Natural,Meteorological,Storm,Tropical cyclone,,Wukong,Affect,Viet Nam,VNM,South-Eastern Asia,Asia,"Thach Ha, Cam Xuyen, Ky Anh districts (Ha Tinh province)",,,,,,,,120,Kph,,,,,2000,9,10,2000,9,10,5,29,,6000,6029,,,21000,67.35575898 +2001-0198-BGD,2001,0198,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Mymensingh, Netrakona, Tangail, Gopalganj districts (Dhaka province), Naogaon district (Rajshahi province), Maulvibazar district (Sylhet province), Chuadanga, Jhenaidah, Meherpur districts (Khulna province), Gaibandha district (Rangpur province), Patuakhali district (Barisal province), Noakhali district (Chittagong province)",,,,,,,,100,Kph,,,,,2001,5,11,2001,5,11,12,200,,,200,,,,69.25933995 +2001-0202-BGD,2001,0202,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Patuakhali district (Barisal province), Satkhira district (Khulna province)",,,,,,,,,Kph,,,,,2001,4,1,2001,4,1,193,2000,500,,2500,,,,69.25933995 +2001-0671-BHS,2001,0671,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,SigDam,Bahamas (the),BHS,Caribbean,Americas,New Providence island (Administrative unit not available),,,,,,,,,Kph,,,,,2001,11,9,2001,11,9,,,,,,,15000,300000,69.25933995 +2001-0553-BLZ,2001,0553,Natural,Meteorological,Storm,Tropical cyclone,,Iris,Kill,Belize,BLZ,Central America,Americas,"Toledo, Stann Creek provinces",,,,,,,,233,Kph,,,,,2001,10,8,2001,10,11,30,,20000,,20000,,,250000,69.25933995 +2001-0736-BLZ,2001,0736,Natural,Meteorological,Storm,Tropical cyclone,,Chantal,Waiting,Belize,BLZ,Central America,Americas,"Belize, Corozal, Orange Walk provinces",,,,,,,,100,Kph,,,,,2001,8,21,2001,8,21,,,,,,,,,69.25933995 +2001-0292-CHN,2001,0292,Natural,Meteorological,Storm,Tropical cyclone,,Chebi,Kill,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Zhejiang Sheng provinces",,,,,,,,,Kph,,,,,2001,6,23,2001,6,25,125,,2895000,213000,3108000,,120000,470000,69.25933995 +2001-0322-CHN,2001,0322,Natural,Meteorological,Storm,Tropical cyclone,,Durian and Utor,Kill,China,CHN,Eastern Asia,Asia,"Hepu Xian area (Beihai district, Guangxi Zhuangzu Zizhiqu province), Shangsi Xian, Fangchenggang Shi areas (Fangchenggang district, Guangxi Zhuangzu Zizhiqu province), Ningming Xian, Long'an Xian, Heng Xian areas (Chongzuo district, Guangxi Zhuangzu Zizhiqu province), Tianyang Xian, Tiandong Xian, Pingguo Xian, Baise Shi areas (Baise district, Guangxi Zhuangzu Zizhiqu province), Nanning district (Guangxi Zhuangzu Zizhiqu province), Zhanjiang, Yangjiang, Maoming, Jiangmen, Yunfu, Zhaoqing, Shanwei, Shantou, Jieyang, Chaozhou, Meizhou, Huizhou, Dongguan districts (Guangdong Sheng province)",,,,,,,,169,Kph,,,,,2001,7,1,2001,7,1,33,8298,14990000,,14998298,,20000,2743000,69.25933995 +2001-0405-CHN,2001,0405,Natural,Meteorological,Storm,Tropical cyclone,,Toraji,Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,,,,,,,,Kph,,,,,2001,7,25,2001,8,1,100,,,,,,,40000,69.25933995 +2001-0419-CHN,2001,0419,Natural,Meteorological,Storm,Tropical cyclone,,Yutu,Affect,China,CHN,Eastern Asia,Asia,Guangdong Sheng province,,,,,,,,150,Kph,,,,,2001,7,24,2001,7,24,,,23250,,23250,,,85000,69.25933995 +2001-0475-CHN,2001,0475,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,China,CHN,Eastern Asia,Asia,"Zhaotun borough (Qinpu Qu area, Name Unknown (13243) district, Shanghai Shi province), Huangdu, Anting borough (Jiading Qu area, Shanghai district, Shanghai Shi province)",,,,,,,,,Kph,,,,,2001,6,19,2001,6,19,,1,11125,,11126,,,,69.25933995 +2001-0796-CHN,2001,0796,Natural,Meteorological,Storm,Tropical cyclone,,Fitow,Affected,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Hainan Sheng, Guangxi Zhuangzu Zizhiqu provinces",,,,,,,,,Kph,,,,,2001,8,28,2001,9,9,4,,,,,,,213000,69.25933995 +2001-0659-COK,2001,0659,Natural,Meteorological,Storm,Tropical cyclone,,Trina,Affect,Cook Islands (the),COK,Polynesia,Oceania,"Rarotonga, Avarua, Mangaia islands",,,,,,,24,130,Kph,,,,,2001,12,2,2001,12,2,,,744,,744,,,,69.25933995 +2001-0612-CUB,2001,0612,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Michelle,Affected,Cuba,CUB,Caribbean,Americas,"Matanzas, Cienfuegos, Villa Clara, Sancti Spiritus, Isla de la Juventud, Pinar del Rio, Ciudad De La Habana, Ciego de Avila provinces",,,,,,,,250,Kph,,,,,2001,11,4,2001,11,4,5,12,5900000,,5900012,,20000,100000,69.25933995 +2001-0611-CYM,2001,0611,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,Kill,Cayman Islands (the),CYM,Caribbean,Americas,,,,,,,,,,Kph,,,,,2001,10,30,2001,11,5,,,,,,,40000,60000,69.25933995 +2001-0570-DOM,2001,0570,Natural,Meteorological,Storm,Tropical cyclone,,Iris,Affect,Dominican Republic (the),DOM,Caribbean,Americas,Santo Domingo province,,,,,,,,,Kph,,,,,2001,10,4,2001,10,9,3,,,,,,,10,69.25933995 +2002-0233-BGD,2002,0233,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Gaibandha, Lalmonirhat, Nilphamari, Rangpur, Kurigram districts (Rangpur province), Bogra, Sirajganj districts (Rajshahi province), Netrakona, Kishoreganj districts (Dhaka province)",,,,,,,269,,Kph,,,,,2002,4,22,2002,4,22,31,400,100000,,100400,,,,70.35781897 +2002-0705-BGD,2002,0705,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,Barguna district (Barisal province),,,,,,,,85,Kph,,,,,2002,11,14,2002,11,14,49,,,,,,,,70.35781897 +2002-0617-BRB,2002,0617,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Regional,Barbados,BRB,Caribbean,Americas,"St. Andrew, St. George, St. James, St. John, St. Joseph, St. Lucy, St. Michael, St. Peter, St. Philip, St. Thomas, Christ Church provinces",,,,,,,,110,Kph,,,,,2002,9,24,2002,9,24,,,,2000,2000,,,200,70.35781897 +2002-0599-CHN,2002,0599,Natural,Meteorological,Storm,Tropical cyclone,,Hagupit,Affect,China,CHN,Eastern Asia,Asia,"Jiangxi Sheng, Sichuan Sheng provinces",,,,,,,,,Kph,,,,,2002,9,13,2002,9,13,25,,180000,,180000,,,,70.35781897 +2002-0604-CUB,2002,0604,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,SigDam,Cuba,CUB,Caribbean,Americas,"Isla De La Juventud, Pinar del Rio provinces",,,,,,,,165,Kph,,,,,2002,9,18,2002,9,18,,,42500,,42500,,1000,23000,70.35781897 +2002-0636-CUB,2002,0636,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Affect,Cuba,CUB,Caribbean,Americas,"Sandino, San Juan y Martinez, Mantua, Guane districts (Pinar del Rio province), Isla dela Juventud province, Batabano, Guira de Melena, Nueva Paz, San Nicolas, Melena del Sur, Quivican districts (La Habana province), Sancti Spiritus, La Sierpe, Fomento, Taguasco, Trinidad, Jatibonico districts (Sancti Spiritu province), Rodas, Aguada De Pasajeros, Cumanayagua districts (Cienfuegos province), Guantanamo, Maisi, Baracoa, Manuel Tames districts (Guantanamo province), Guama, Tercer Frente, Palma Soriano, Santiago de Cuba districts (Santiago de Cuba province), Niquero, Media Luna, Cauto Cristo, Pilon districts (Granma province)",,,,,,,,173,Kph,,,,,2002,10,1,2002,10,1,3,,281470,,281470,,1000,23000,70.35781897 +2002-0626-CYM,2002,0626,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Affect,Cayman Islands (the),CYM,Caribbean,Americas,"Cayman Brac, Little Cayman",,,,,,,,128,Kph,,,,,2002,9,30,2002,10,1,,,300,,300,,,1000,70.35781897 +2002-0656-CYM,2002,0656,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Regional,Cayman Islands (the),CYM,Caribbean,Americas,"Little Cayman, Cayman Brac provinces",,,,,,,,,Kph,,,,,2002,9,14,2002,9,26,,,,,,,,500,70.35781897 +2001-0614-PHL,2001,0614,Natural,Meteorological,Storm,Tropical cyclone,,Lingling (Nanang),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Camiguin, Misamis Oriental districts (Region X (Northern Mindanao) province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,,434,215,Kph,,,,,2001,11,8,2001,11,8,290,147,1060000,,1060147,,,22700,69.25933995 +2001-0758-PHL,2001,0758,Natural,Meteorological,Storm,Tropical cyclone,,Kajiki (Quedan),Affect,Philippines (the),PHL,South-Eastern Asia,Asia,"Cebu district (Region VII (Central Visayas) province), Leyte district (Region VIII (Eastern Visayas) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2001,12,5,2001,12,7,6,8,54832,,54840,,,96,69.25933995 +2001-0522-TWN,2001,0522,Natural,Meteorological,Storm,Tropical cyclone,,Nari,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Taipei, Keelung, Chiayi, Miaoli areas (Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,,,,,2001,9,16,2001,9,20,80,200,650000,,650200,,500000,800000,69.25933995 +2001-0223-FJI,2001,0223,Natural,Meteorological,Storm,Tropical cyclone,,Paula,Waiting,Fiji,FJI,Melanesia,Oceania,"Western, Central, Eastern provinces",,,,,,,,220,Kph,,,,,2001,3,1,2001,3,1,1,,,,,,,,69.25933995 +2001-0570-GTM,2001,0570,Natural,Meteorological,Storm,Tropical cyclone,,Iris,Affect,Guatemala,GTM,Central America,Americas,"Peten, Zacapa, Quetzaltenango provinces",,,,,,,,,Kph,,,,,2001,10,8,2001,10,11,8,11,6435,,6446,,,100,69.25933995 +2001-0603-HND,2001,0603,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,Kill,Honduras,HND,Central America,Americas,"Colon, Atlantida, Yoro, Cortes, Gracias A Dios, Islas De Bahia provinces",,,,,,,,219,Kph,,,,,2001,10,30,2001,11,4,21,,61051,25270,86321,,,5000,69.25933995 +2001-0611-HTI,2001,0611,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,Kill,Haiti,HTI,Caribbean,Americas,"Grande Anse, Centre, Sud, Nippes, Ouest, Artibonite, Nord Ouest, Nord, Nord Est, Sud Est provinces",,,,,,,,,Kph,,,,,2001,10,30,2001,11,5,,,,,,,,20,69.25933995 +2001-0584-IND,2001,0584,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,Andhra Pradesh province,,,,,,,,,Kph,,,,,2001,10,,2001,10,,78,,25000,2000,27000,,,,69.25933995 +2001-0729-IND,2001,0729,Natural,Meteorological,Storm,Tropical cyclone,,01A,Waiting,India,IND,Southern Asia,Asia,"Gujarat, Goa, Maharashtra, Kerala provinces",,,,,,,,80,Kph,,,,,2001,5,28,2001,5,28,,,,,,,,,69.25933995 +2001-0615-JAM,2001,0615,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,SigDis,Jamaica,JAM,Caribbean,Americas,"Clarendon, Hanover, Manchester, Portland, Saint Andrew And Kingston, Saint Ann, Saint Catherine, Saint Elizabeth, Saint James, Saint Mary, Saint Thomas, Trelawny, Westmoreland provinces",,,,,,,,120,Kph,,,,,2001,11,6,2001,11,6,1,,,200,200,,15000,55487,69.25933995 +2001-0454-JPN,2001,0454,Natural,Meteorological,Storm,Tropical cyclone,,Pabuk,Affect,Japan,JPN,Eastern Asia,Asia,"Siga, Aiti, Oosaka, Ehime, Mie provinces",,,,,,,,109,Kph,,,,,2001,8,21,2001,8,23,9,40,7000,,7040,,500000,800000,69.25933995 +2001-0511-JPN,2001,0511,Natural,Meteorological,Storm,Tropical cyclone,,Danas,Affect,Japan,JPN,Eastern Asia,Asia,Tookyoo province,,,,,,,,126,Kph,,,,,2001,9,10,2001,9,13,7,15,1200,,1215,,300000,500000,69.25933995 +2001-0488-MEX,2001,0488,Natural,Meteorological,Storm,Tropical cyclone,,Dalila,Affect,Mexico,MEX,Central America,Americas,"Guerrero, Chiapas provinces",,,,,,,,,Kph,,,,,2001,7,25,2001,7,25,,,100,,100,,,,69.25933995 +2001-0562-MEX,2001,0562,Natural,Meteorological,Storm,Tropical cyclone,,Juliette,Affect,Mexico,MEX,Central America,Americas,Baja California Sur province,,,,,,Yes,,120,Kph,,,,,2001,9,24,2001,10,2,3,,3000,800,3800,,150000,400000,69.25933995 +2001-0570-MEX,2001,0570,Natural,Meteorological,Storm,Tropical cyclone,,Iris,Affect,Mexico,MEX,Central America,Americas,Oaxaca province,,,,,,,,,Kph,,,,,2001,10,4,2001,10,9,2,,,,,,,1000,69.25933995 +2001-0572-MEX,2001,0572,Natural,Meteorological,Storm,Tropical cyclone,,Lorena,Waiting,Mexico,MEX,Central America,Americas,"Colima, Jalisco, Michoacan, Nayarit, Sinaloa provinces",,,,,,,,,Kph,,,,,2001,10,3,2001,10,3,,,,,,,,,69.25933995 +2001-0735-MEX,2001,0735,Natural,Meteorological,Storm,Tropical cyclone,,Chantal,Affect,Mexico,MEX,Central America,Americas,Quintana Roo province,,,,,,,,100,Kph,,,,,2001,8,21,2001,8,21,,,2500,,2500,,,,69.25933995 +2001-0611-NIC,2001,0611,Natural,Meteorological,Storm,Tropical cyclone,,Michelle,Kill,Nicaragua,NIC,Central America,Americas,Atlantico Norte province,,,,,,,,110,Kph,,,,,2001,11,1,2001,11,4,16,,24866,,24866,,,1000,69.25933995 +2001-0319-PHL,2001,0319,Natural,Meteorological,Storm,Tropical cyclone,,Utor (Feria),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV (Southern Tagalog), Region VIII (Eastern Visayas), Cordillera Administrative region (CAR), National Capital region (NCR) provinces",,,,,,,,140,Kph,,,,,2001,7,,2001,7,,232,241,1902413,,1902654,,,68565,69.25933995 +2001-0711-TON,2001,0711,Natural,Meteorological,Storm,Tropical cyclone,,Waka,Affect,Tonga,TON,Polynesia,Oceania,"Vava'u, Niuafo'ou islands (Tonga province)",,,,Yes,,,868,260,Kph,,,,,2001,12,31,2001,12,31,,,16500,,16500,,,51300,69.25933995 +2001-0293-TWN,2001,0293,Natural,Meteorological,Storm,Tropical cyclone,,Chebi,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,225,Kph,,,,,2001,6,24,2001,6,25,9,116,,,116,,,5000,69.25933995 +2001-0359-TWN,2001,0359,Natural,Meteorological,Storm,Tropical cyclone,,Utor,Waiting,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,140,Kph,,,,,2001,7,6,2001,7,8,1,6,,,6,,,2000,69.25933995 +2001-0405-TWN,2001,0405,Natural,Meteorological,Storm,Tropical cyclone,,Toraji,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Hualian, Nantou areas (Taiwan Sheng province)",,,,,,,,150,Kph,,,,,2001,7,30,2001,7,30,100,,,,,,20000,240000,69.25933995 +2001-0415-TWN,2001,0415,Natural,Meteorological,Storm,Tropical cyclone,,Trami,Affect,Taiwan (Province of China),TWN,Eastern Asia,Asia,Kaoshiung area (Taiwan Sheng province),,,,,,,,,Kph,,,,,2001,7,11,2001,7,11,5,17,,2000,2017,,,,69.25933995 +2002-0557-KOR,2002,0557,Natural,Meteorological,Storm,Tropical cyclone,,Rusa,Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Kyongsangbuk-do, Kyongsangnam-do, Chungchongbuk-do, Chungchongnam-do, Kang-won-do, Cheju-do provinces",,"Slide (land, mud, snow, rock)",,,,,190,204,Kph,,,,,2002,8,31,2002,9,6,184,,88625,,88625,,170000,4200000,70.35781897 +2002-0146-MDG,2002,0146,Natural,Meteorological,Storm,Tropical cyclone,,Hary,Waiting,Madagascar,MDG,Eastern Africa,Africa,"Antalaha district (Sava province), Sainte-Marie district (Analanjirofo province), Toamasina I district (Atsinanana province)",,Rain,,,,,,300,Kph,,,,,2002,3,9,2002,3,11,1,,,,,,,,70.35781897 +2002-0657-HTI,2002,0657,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Affect,Haiti,HTI,Caribbean,Americas,"Grande Anse, Centre, Sud, Nippes, Ouest, Artibonite, Nord Ouest, Nord, Nord Est, Sud Est provinces",,Flood,,,,,,150,Kph,,,,,2002,9,30,2002,9,30,4,,250,,250,,,,70.35781897 +2002-0627-JAM,2002,0627,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Affect,Jamaica,JAM,Caribbean,Americas,"Saint Thomas, Saint Andrew And Kingston, Saint Elizabeth, Clarendon, Westmoreland provinces",,Flood,,,,,,100,Kph,,,,,2002,9,30,2002,9,30,4,,1500,,1500,,,30,70.35781897 +2002-0126-FSM,2002,0126,Natural,Meteorological,Storm,Tropical cyclone,,Mitag,Affect,Micronesia (Federated States of),FSM,Micronesia,Oceania,Yap Island (Micronesia province),,Surge,,,,Yes,,212,Kph,,,,,2002,3,3,2002,3,3,,,,175,175,,,,70.35781897 +2002-0811-SLB,2002,0811,Natural,Meteorological,Storm,Tropical cyclone,,Zoe,Affect,Solomon Islands,SLB,Melanesia,Oceania,"Tikopia, Fataka, Anuta islands (Temotu area, Solomon Islands province)",,Surge,,Yes,,Yes,584,340,Kph,,,,,2002,12,28,2002,12,29,,10,,1100,1110,,,,70.35781897 +2002-0397-FSM,2002,0397,Natural,Meteorological,Storm,Tropical cyclone,,Chata'an,Kill,Micronesia (Federated States of),FSM,Micronesia,Oceania,"Weno, Tonoas, Fefan, Udot, Uman, Siis villages (Chuuk island, Micronesia province)",,,,,,,138,112,Kph,,,,,2002,7,1,2002,7,1,47,148,1300,,1448,,,500,70.35781897 +2002-0849-GTM,2002,0849,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Affect,Guatemala,GTM,Central America,Americas,"Retalhuleu, Suchitepequez, Escuintla, San Marcos provinces",,,,,,,,,Kph,,,,,2002,9,20,2002,9,24,2,,1500,,1500,,,100,70.35781897 +2002-0432-GUM,2002,0432,Natural,Meteorological,Storm,Tropical cyclone,,Chata'an,Affect,Guam,GUM,Micronesia,Oceania,Guam province,,,,,,,,177,Kph,,,,,2002,7,5,2002,7,5,,,4044,,4044,,,60000,70.35781897 +2002-0753-GUM,2002,0753,Natural,Meteorological,Storm,Tropical cyclone,,Pongsona,Declar/Int,Guam,GUM,Micronesia,Oceania,Guam province,,,,,,,,180,Kph,,,,,2002,12,8,2002,12,8,1,,6000,5100,11100,,,70000,70.35781897 +2002-0204-IND,2002,0204,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,India,IND,Southern Asia,Asia,West Bengal province,,,,,,,,,Kph,,,,,2002,4,3,2002,4,3,9,50,,5000,5050,,,416,70.35781897 +2002-0702-IND,2002,0702,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,"Baleshwar, Bhadrak, Jagatsinghpur, Kendrapara districts (Orissa province), East Midnapore, North 24 Parganas, South 24 Parganas (West Bengal province)",,,,,,,,60,Kph,,,,,2002,11,13,2002,11,13,124,,,,,,,,70.35781897 +2002-0656-JAM,2002,0656,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Regional,Jamaica,JAM,Caribbean,Americas,"Westmoreland, Clarendon, Hanover provinces",,,,,,,,,Kph,,,,,2002,9,18,2002,9,19,,,,,,,,1000,70.35781897 +2002-0430-JPN,2002,0430,Natural,Meteorological,Storm,Tropical cyclone,,Halong,Affect,Japan,JPN,Eastern Asia,Asia,"Okinawa, Kagosima, Isikawa provinces",,,,,,,,108,Kph,,,,,2002,7,15,2002,7,15,,9,570,,579,,,,70.35781897 +2002-0431-JPN,2002,0431,Natural,Meteorological,Storm,Tropical cyclone,,Chata'an,Affect,Japan,JPN,Eastern Asia,Asia,Hokkaidoo province,,,,,,,,,Kph,,,,,2002,7,5,2002,7,5,5,18,100000,,100018,,,5000,70.35781897 +2002-0004-MDG,2002,0004,Natural,Meteorological,Storm,Tropical cyclone,,Cyprien,Affect,Madagascar,MDG,Eastern Africa,Africa,"Morondava district (Menabe province), Morombe, Toliary-I districts (Atsimo Andrefana province)",,,,,,,,150,Kph,,,,,2002,1,2,2002,1,2,2,,1900,,1900,,,181,70.35781897 +2002-0281-MDG,2002,0281,Natural,Meteorological,Storm,Tropical cyclone,,Kesiny,Affect,Madagascar,MDG,Eastern Africa,Africa,"Antsiranana II, Ambilobe, Nosy-Be districts (Diana province), Vohemar district (Sava province), Fenerive Est, Maroantsetra districts (Analanjirofo province), Atsinanana province",,,,,,,205,180,Kph,,,,,2002,5,9,2002,5,9,20,1200,520000,5000,526200,,,,70.35781897 +2002-0609-MEX,2002,0609,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Kill,Mexico,MEX,Central America,Americas,"Campeche, Quintana Roo, Yucatan provinces",,,,Yes,,,,180,Kph,,,,,2002,9,20,2002,9,20,13,30,500000,,500030,,280000,640000,70.35781897 +2002-0647-MEX,2002,0647,Natural,Meteorological,Storm,Tropical cyclone,,Julio,Affect,Mexico,MEX,Central America,Americas,Acapulco De Juarez district (Guerrero province),,,,,,,,,Kph,,,,,2002,9,25,2002,9,25,,,500,,500,,,,70.35781897 +2002-0669-MEX,2002,0669,Natural,Meteorological,Storm,Tropical cyclone,,Kenna,Waiting,Mexico,MEX,Central America,Americas,"Nayarit, Jalisco, Sinaloa, Colima provinces",,,,,,,450,225,Kph,,,,,2002,10,25,2002,10,26,3,200,8800,,9000,,,200000,70.35781897 +2002-0039-MUS,2002,0039,Natural,Meteorological,Storm,Tropical cyclone,,Dina,Affect,Mauritius,MUS,Eastern Africa,Africa,Port Louis province,,,,,,,10,206,Kph,,,,,2002,1,22,2002,1,22,3,50,,1000,1050,,,50000,70.35781897 +2002-0850-NIC,2002,0850,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Affect,Nicaragua,NIC,Central America,Americas,Managua province,,,,,,,,,Kph,,,,,2002,9,22,2002,9,22,2,,300,,300,,,1000,70.35781897 +2002-0285-OMN,2002,0285,Natural,Meteorological,Storm,Tropical cyclone,,,Waiting,Oman,OMN,Western Asia,Asia,Dhofar province,,,,,,,,100,Kph,,,,,2002,5,10,2002,5,10,7,33,50,,83,,,50000,70.35781897 +2002-0408-PHL,2002,0408,Natural,Meteorological,Storm,Tropical cyclone,,Chata'an,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,National Capital region (NCR) province,,,,,,,,170,Kph,,,,,2002,7,7,2002,7,9,33,41,700000,,700041,,,1000,70.35781897 +2002-0423-PHL,2002,0423,Natural,Meteorological,Storm,Tropical cyclone,,Halong (Anday),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV (Southern Tagalog), Region IV-A (Calabarzon), Region V (Bicol region) provinces",,,,,,,,160,Kph,,,,,2002,7,13,2002,7,13,62,,11000,,11000,,,5664,70.35781897 +2002-0528-PHL,2002,0528,Natural,Meteorological,Storm,Tropical cyclone,,Rammasun (Florita),Affect,Philippines (the),PHL,South-Eastern Asia,Asia,National Capital region (NCR) province,,,,,,,,,Kph,,,,,2002,6,28,2002,7,3,7,,,3000,3000,,,,70.35781897 +2002-0561-PRK,2002,0561,Natural,Meteorological,Storm,Tropical cyclone,,Rusa,Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Tongchon, Anbyon, Kosong districts (Kangwon-do province)",,,,,,,133,,Kph,,,,,2002,8,31,2002,9,6,3,,7401,,7401,,,500,70.35781897 +2002-0043-REU,2002,0043,Natural,Meteorological,Storm,Tropical cyclone,,Dina,Affect,Réunion,REU,Eastern Africa,Africa,"Arrondissement du vent, Arrondissement sous le vent provinces",,,,,,,,250,Kph,,,,,2002,1,,2002,1,,,300,,2800,3100,,,50000,70.35781897 +2002-0635-RUS,2002,0635,Natural,Meteorological,Storm,Tropical cyclone,,Hygos,Kill,Russian Federation (the),RUS,Eastern Europe,Europe,Sakhaline Isl. (Sakhalinskaya Oblast province),,,,,,,,,Kph,,,,,2002,10,2,2002,10,3,6,,,,,,,,70.35781897 +2002-0851-SLV,2002,0851,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Affect,El Salvador,SLV,Central America,Americas,Ahuachapan province,,,,,,,,,Kph,,,,,2002,9,21,2002,9,21,,,500,,500,,,,70.35781897 +2002-0590-SYC,2002,0590,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,Seychelles,SYC,Eastern Africa,Africa,"Praslin, Anse Aux Pins, Anse Boileau, Anse Etoile, Anse Royale, Au Cap, Baie Lazare, Beau Vallon, Bel Air, Belombre, Cascade, Cerf Island, Conception, English River, Glacis, Grand Anse Mahe, Les Mamelles, Mont Buxton, Mont Fleuri, Plaisance, Pointe Larue, Port Glaud, Roche Caiman, St Louis, Ste Anne, Takamaka, Therese provinces",,,,,,,20,120,Kph,,,,,2002,9,5,2002,9,5,,,6800,,6800,,,,70.35781897 +2002-0415-TWN,2002,0415,Natural,Meteorological,Storm,Tropical cyclone,,Nakri,Waiting,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,65,Kph,,,,,2002,7,9,2002,7,9,7,,,,,,,,70.35781897 +2003-0024-FJI,2003,0024,Natural,Meteorological,Storm,Tropical cyclone,,Ami,Kill,Fiji,FJI,Melanesia,Oceania,"Northern, Eastern, Western provinces",,Surge,"Slide (land, mud, snow, rock)",Yes,Yes,Yes,2254,150,Kph,,,,,2003,1,14,2003,1,14,17,,30000,,30000,,,30000,71.95500655 +2003-0114-MOZ,2003,0114,Natural,Meteorological,Storm,Tropical cyclone,,Japhet,Affect,Mozambique,MOZ,Eastern Africa,Africa,"Inhambane, Sofala, Manica, Gaza provinces",,Rain,,,,,,130,Kph,,,,,2003,3,3,2003,3,3,11,10,23000,,23010,,,,71.95500655 +2003-0100-IND,2003,0100,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,India,IND,Southern Asia,Asia,Gujarat province,,,,,,,,,Kph,,,,,2003,2,18,2003,2,18,5,,140,,140,,,,71.95500655 +2003-0065-MDG,2003,0065,Natural,Meteorological,Storm,Tropical cyclone,,Fari,Affect,Madagascar,MDG,Eastern Africa,Africa,"Analanjirofo, Atsimo Atsinanana, Atsinanana, Sava, Vatovavy Fitovinanay provinces",,,,,,,,,Kph,,,,,2003,1,29,2003,1,29,,,,500,500,,,,71.95500655 +2003-0057-SLB,2003,0057,Natural,Meteorological,Storm,Tropical cyclone,,Beni,Affect,Solomon Islands,SLB,Melanesia,Oceania,"Rennell, Bellona islands (Solomon Islands province)",,,,,,,,110,Kph,,,,,2003,1,26,2003,1,28,,,275,,275,,,,71.95500655 +2001-0242-USA,2001,0242,Natural,Meteorological,Storm,Tropical cyclone,,Allison,Kill,United States of America (the),USA,Northern America,Americas,"Texas, Mississippi, Louisiana, Florida, Pennsylvania, South Carolina, North Carolina, Georgia, Pensylvania, New Jersey, Virginia, Iowa provinces",,Flood,,,,Yes,,,Kph,,,,,2001,6,5,2001,6,17,41,,102000,70000,172000,,3500000,6000000,69.25933995 +2001-0318-VNM,2001,0318,Natural,Meteorological,Storm,Tropical cyclone,,Durian,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Thai Nguyen, Tuyen Quang, Vinh Phuc provinces",,,,,,,,,Kph,,,,,2001,7,4,2001,7,4,30,3,117450,,117453,,,25000,69.25933995 +2001-0436-VNM,2001,0436,Natural,Meteorological,Storm,Tropical cyclone,,Usagi,Affect,Viet Nam,VNM,South-Eastern Asia,Asia,"Ha Tinh, Nghe An, Quang Binh, Thanh Hoa provinces",,,,,,,,196,Kph,,,,,2001,8,11,2001,8,11,3,3,,10000,10003,,,3200,69.25933995 +2001-0624-VNM,2001,0624,Natural,Meteorological,Storm,Tropical cyclone,,Lingling (Nanang),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Phu Yen, Binh Dinh, Quang Ngai, Quang Tri provinces",,,,,,,,,Kph,,,,,2001,11,12,2001,11,12,20,83,60000,13100,73183,,,55000,69.25933995 +2001-0093-VUT,2001,0093,Natural,Meteorological,Storm,Tropical cyclone,,Paula,Declar/Int,Vanuatu,VUT,Melanesia,Oceania,"Malukulan Ambrym, Paama, Lopevi islands (Malampa province), Efate, Shepherd islands (Shefa province)",,,,,,,,200,Kph,,,,,2001,2,28,2001,2,28,1,,,,,,,,69.25933995 +2001-0143-VUT,2001,0143,Natural,Meteorological,Storm,Tropical cyclone,,Sose,Affect,Vanuatu,VUT,Melanesia,Oceania,"Santo island (Sanma province), Malekula island (Malampa province), Ambae island (Penama province), Shepherd, Efate island (Shefa province), Erromango, Tanna, Anatom island (Tafea province),",,,,,,,,100,Kph,,,,,2001,4,7,2001,4,7,1,,505,295,800,,,,69.25933995 +2002-0724-USA,2002,0724,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Regional,United States of America (the),USA,Northern America,Americas,"St. Tammany, Terrebonne, Lafayette, Acadia, Evangeline, Rapides districts (Louisiana province)",,Flood,,,,,,,Kph,,,,,2002,10,3,2002,10,3,,,,,,,750000,2000000,70.35781897 +2002-0652-USA,2002,0652,Natural,Meteorological,Storm,Tropical cyclone,,Isidore,Affect,United States of America (the),USA,Northern America,Americas,"Louisiane, Mississippi, Alabama, Tennessee provinces",,,,,,,,105,Kph,,,,,2002,9,26,2002,9,27,1,,13200,,13200,,200000,300000,70.35781897 +2002-0653-VCT,2002,0653,Natural,Meteorological,Storm,Tropical cyclone,,Lili,Regional,Saint Vincent and the Grenadines,VCT,Caribbean,Americas,"Charlotte, Grenadines, Saint Andrew, Saint David, Saint George, Saint Patrick provinces",,,,,,,,,Kph,,,,,2002,9,24,2002,9,24,4,,,,,,,11000,70.35781897 +2002-0826-MNP,2002,0826,Natural,Meteorological,Storm,Tropical cyclone,,Pongsona,Affect,Northern Mariana Islands (the),MNP,Micronesia,Oceania,"Saipan, Rota islands (Northern Mariana Islands province)",,,,,,,,,Kph,,,,,2002,12,8,2002,12,8,,,,300,300,,,,70.35781897 +2003-0211-BGD,2003,0211,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Noabadi, Nayabadi, Merashani villages (Brahamanbaria district, Chittagong)",,Hail,"Slide (land, mud, snow, rock)",,,,,120,Kph,,,,,2003,5,4,2003,5,4,23,400,,,400,,,,71.95500655 +2003-0114-ZWE,2003,0114,Natural,Meteorological,Storm,Tropical cyclone,,Japhet,Affect,Zimbabwe,ZWE,Eastern Africa,Africa,"Manicaland, Masvingo, Matabeleland South provinces",,Rain,,,,,,,Kph,,,,,2003,3,7,2003,3,7,8,,,,,,,,71.95500655 +2003-0448-BMU,2003,0448,Natural,Meteorological,Storm,Tropical cyclone,,Fabian,SigDam,Bermuda,BMU,Northern America,Americas,,,Rain,,,,,,240,Kph,,,,,2003,9,5,2003,9,6,4,,,,,,125000,300000,71.95500655 +2003-0443-HKG,2003,0443,Natural,Meteorological,Storm,Tropical cyclone,,Dujuan,Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,Rain,,,,,,180,Kph,,,,,2003,9,2,2003,9,2,4,22,,,22,,,,71.95500655 +2003-0487-CAN,2003,0487,Natural,Meteorological,Storm,Tropical cyclone,,Juan,Affected,Canada,CAN,Northern America,Americas,"Nova Scotia, Prince Edward Island provinces",,Flood,,,,Yes,,143,Kph,,,,,2003,9,28,2003,9,29,2,,200,,200,,,110000,71.95500655 +2003-0599-DOM,2003,0599,Natural,Meteorological,Storm,Tropical cyclone,,Odette,Affected,Dominican Republic (the),DOM,Caribbean,Americas,"Barahona, Pedernales, Baoruco provinces",,Flood,,,,,,,Kph,,,,,2003,12,6,2003,12,6,8,,10000,,10000,,,,71.95500655 +2003-0073-COD,2003,0073,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Congo (the Democratic Republic of the),COD,Middle Africa,Africa,"Yumbi area (Plateaux district, Bandundu province)",,,,,,,,,Kph,,,,,2003,2,2,2003,2,2,17,2500,,20000,22500,,,,71.95500655 +2003-0779-BGD,2003,0779,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,Barguna district (Barisal province),,,,,,,,,Kph,,,,,2003,12,20,2003,12,20,28,,,,,,,,71.95500655 +2003-0468-CAN,2003,0468,Natural,Meteorological,Storm,Tropical cyclone,,Isabel,Kill,Canada,CAN,Northern America,Americas,Ontario province,,,,,,,,,Kph,,,,,2003,9,18,2003,9,18,,,,,,,,,71.95500655 +2003-0346-CHN,2003,0346,Natural,Meteorological,Storm,Tropical cyclone,,Imbudo,Kill,China,CHN,Eastern Asia,Asia,"Guangxi Zhuangzu Zizhiqu, Guangdong Sheng provinces",,,,,,,,,Kph,,,,,2003,7,24,2003,7,24,20,20,7400000,,7400020,,,100000,71.95500655 +2003-0443-CHN,2003,0443,Natural,Meteorological,Storm,Tropical cyclone,,Dujuan,Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Fujian Sheng provinces",,,,,,,,,Kph,,,,,2003,9,1,2003,9,3,38,1000,,68925,69925,,,241000,71.95500655 +2003-0594-CHN,2003,0594,Natural,Meteorological,Storm,Tropical cyclone,,Nepartak,Affected,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,,,,,,,,Kph,,,,,2003,11,18,2003,11,18,,,1720000,,1720000,,,196938,71.95500655 +2003-0577-FSM,2003,0577,Natural,Meteorological,Storm,Tropical cyclone,,Lupit,Affected,Micronesia (Federated States of),FSM,Micronesia,Oceania,"Ulithi Atoll, Woleai Atoll, Fais Island, Rumung Island, Yap Island areas (Micronesia province)",,,,,Yes,Yes,10,240,Kph,,,,,2003,11,21,2003,11,25,,,,1000,1000,,,,71.95500655 +2003-0346-HKG,2003,0346,Natural,Meteorological,Storm,Tropical cyclone,,Imbudo,Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,,,,,,,,Kph,,,,,2003,7,26,2003,7,26,,11,3500,,3511,,,,71.95500655 +2004-0153-FJI,2004,0153,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Fiji,FJI,Melanesia,Oceania,"Central, Western, Northern provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,90,Kph,,,,,2004,4,8,2004,4,8,16,,5000,,5000,,,4000,73.88141244 +2004-0312-GUM,2004,0312,Natural,Meteorological,Storm,Tropical cyclone,,Tingting,Affected,Guam,GUM,Micronesia,Oceania,"Agat, Merizo, Barrigada, Mangilao areas (Guam province)",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,137,Kph,,,,,2004,6,27,2004,6,28,1,,300,,300,,,,73.88141244 +2004-0473-HTI,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,Haiti,HTI,Caribbean,Americas,"Artibonite, Centre, Sud, Nord Ouest provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,40208,,Kph,,,,,2004,9,17,2004,9,18,2754,2620,298926,14048,315594,,,50000,73.88141244 +2004-0462-CUB,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Cuba,CUB,Caribbean,Americas,"Pinar del Rio, La Habana, Ciudad De La Habana, Matanzas, Villa Clara, Cienfuegos, Sancti Spiritus, Isla de la Juventud, Ciego de Avila, Santiago de Cuba provinces",,"Slide (land, mud, snow, rock)",Surge,,,,1703,,Kph,,,,,2004,9,13,2004,9,14,,,3245,,3245,,,200000,73.88141244 +2004-0473-BHS,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,Bahamas (the),BHS,Caribbean,Americas,"Abaco, Andros, Berry, Bimini, Eleuthera, Exuma, Grand Bahama, New Providence Islands (Administrative unit not available)",,Flood,Surge,,,,,,Kph,,,,,2004,9,25,2004,9,25,9,,1000,,1000,,,550000,73.88141244 +2004-0473-DOM,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Santo Domingo, Maria Trinidad Sanches, Espaillat, Samana, Duarte, Monte Cristi, San Pedro de Macoris, El Seibo, La Romana, Azua, Hato Mayor, La Altagracia provinces",,Flood,Surge,Yes,,,79,,Kph,,,,,2004,9,16,2004,9,17,11,9,14000,,14009,,,296000,73.88141244 +2004-0462-HTI,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Haiti,HTI,Caribbean,Americas,"Cap-Haitien district (Nord province), Cayes district (Sud province)",,Rain,,,,,,,Kph,,,,,2004,9,13,2004,9,13,3,,4000,2500,6500,,,1000,73.88141244 +2004-0435-CHN,2004,0435,Natural,Meteorological,Storm,Tropical cyclone,,Aere (Marce/20W),Affected,China,CHN,Eastern Asia,Asia,"Zhejiang Sheng, Fujian Sheng provinces",,Flood,,,,,,140,Kph,,,,,2004,8,20,2004,8,23,2,7,,41350,41357,,,5000,73.88141244 +2004-0415-CYM,2004,0415,Natural,Meteorological,Storm,Tropical cyclone,,Charley,Kill,Cayman Islands (the),CYM,Caribbean,Americas,Grand Cayman province,,Flood,,,,,,,Kph,,,,,2004,8,13,2004,8,13,,,,,,,,5000,73.88141244 +2004-0131-BRA,2004,0131,Natural,Meteorological,Storm,Tropical cyclone,,Catarina,Kill,Brazil,BRA,South America,Americas,"Torres district (Rio Grande do Sul province), Ararangua district (Santa Catarina province)",,Surge,,,,,,150,Kph,,,,,2004,3,27,2004,3,29,4,60,150000,,150060,,3000,350000,73.88141244 +2004-0462-BRB,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Barbados,BRB,Caribbean,Americas,"St. Andrew, St. George, St. James, St. John, St. Joseph, St. Lucy, St. Michael, St. Peter, St. Philip, St. Thomas, Christ Church provinces",,Surge,,,,,,220,Kph,,,,,2004,9,8,2004,9,8,1,,,880,880,,,5000,73.88141244 +2004-0150-FSM,2004,0150,Natural,Meteorological,Storm,Tropical cyclone,,Cosme (Sudal/03W),Affected,Micronesia (Federated States of),FSM,Micronesia,Oceania,Yap area (Micronesia province),,Surge,,,,Yes,7494,240,Kph,,,,,2004,4,3,2004,4,18,1,8,6000,,6008,,,,73.88141244 +2004-0004-ASM,2004,0004,Natural,Meteorological,Storm,Tropical cyclone,,Heta,Declar,American Samoa,ASM,Polynesia,Oceania,American Samoa,,,,,,Yes,,310,Kph,,,,,2004,1,5,2004,1,5,,60,20000,3000,23060,,,150000,73.88141244 +2004-0455-BHS,2004,0455,Natural,Meteorological,Storm,Tropical cyclone,,Frances,Declar,Bahamas (the),BHS,Caribbean,Americas,"Abacos, Andros, Berry Islands, Bimini, Eleuthera, Grand Bahama, New Providence islands (Administrative unit not available)",,,,Yes,,,,,Kph,,,,,2004,9,2,2004,9,3,2,,8000,,8000,,230000,1000000,73.88141244 +2004-0462-BHS,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Bahamas (the),BHS,Caribbean,Americas,,,,,,,,,,Kph,,,,,2004,8,25,2004,9,8,1,,,,,,,,73.88141244 +2004-0412-CHN,2004,0412,Natural,Meteorological,Storm,Tropical cyclone,,Rananim (Karen/16W),Kill,China,CHN,Eastern Asia,Asia,"Taizhou, Wenzhou, Ningbo, Shaoxing districts (Zheijang Sheng province)",,,,,,,,165,Kph,,,,,2004,8,6,2004,8,12,188,4000,8590000,468000,9062000,,,2190000,73.88141244 +2004-0415-CUB,2004,0415,Natural,Meteorological,Storm,Tropical cyclone,,Charley,Kill,Cuba,CUB,Caribbean,Americas,"La Habana, Pinar del Rio, Cienfuegos, Ciudad De La Habana, Isla De La Juventud provinces",,,,,,,120,220,Kph,,,,,2004,8,14,2004,8,14,4,5,202500,41500,244005,,,1000000,73.88141244 +2004-0462-CYM,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Cayman Islands (the),CYM,Caribbean,Americas,Grand Cayman province,,,,,,,,240,Kph,,,,,2004,9,12,2004,9,12,2,,,,,,1500000,3430080,73.88141244 +2004-0455-DOM,2004,0455,Natural,Meteorological,Storm,Tropical cyclone,,Frances,Declar,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,,,,,,,,Kph,,,,,2004,9,2,2004,9,2,,,250,,250,,,,73.88141244 +2004-0462-DOM,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,,,,,,,,Kph,,,,,2004,9,9,2004,9,9,4,,,,,,,1000,73.88141244 +2004-0462-GRD,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Grenada,GRD,Caribbean,Americas,"Name Unknown, St. Andrew's, St. George's, St. John's, St. Mark's, St. Patrick's provinces",,,,Yes,,Yes,18254,,Kph,,,,,2004,9,8,2004,9,8,39,,60000,,60000,,,889000,73.88141244 +2004-0445-GUM,2004,0445,Natural,Meteorological,Storm,Tropical cyclone,,Chaba,Affected,Guam,GUM,Micronesia,Oceania,Guam province,,,,,,,,,Kph,,,,,2004,8,30,2004,9,1,,,,,,,,1000,73.88141244 +2004-0144-IDN,2004,0144,Natural,Meteorological,Storm,Tropical cyclone,,,Affected,Indonesia,IDN,South-Eastern Asia,Asia,"Cijeruk, Cipelang, Warung Menteng villages (Cijerik area, Bogor district, Jawa Barat province)",,,,,,,,,Kph,,,,,2004,3,30,2004,3,30,,,1315,,1315,,,,73.88141244 +2005-0151-ASM,2005,0151,Natural,Meteorological,Storm,Tropical cyclone,,Olaf,Affected,American Samoa,ASM,Polynesia,Oceania,Manu'a island,,,,,,,,,Kph,,,,,2005,2,16,2005,2,16,,,,,,,,,76.38802721 +2005-0190-BGD,2005,0190,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Manikganj district (Dhaka province), Natore, Sirajganj, Pabna districts (Rajshahi province)",,,,,,,,,Kph,,,,,2005,3,31,2005,3,31,24,500,,10000,10500,,,,76.38802721 +2005-0102-COK,2005,0102,Natural,Meteorological,Storm,Tropical cyclone,,Percy,Affected,Cook Islands (the),COK,Polynesia,Oceania,"Pukapuka, Nassau islands",,,,,,Yes,,249,Kph,,,,,2005,2,28,2005,2,28,,8,600,,608,,,,76.38802721 +2003-0459-KOR,2003,0459,Natural,Meteorological,Storm,Tropical cyclone,,Maemi,Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,"Kyongsangbuk-do, Kyongsangnam-do, Kang-won-do, Pusan, Chollanam-do provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,230,216,Kph,,,,,2003,9,12,2003,9,12,130,,65000,15000,80000,,504000,4500000,71.95500655 +2003-0605-IND,2003,0605,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,"Krishna, Guntur, West Godavari districts (Andhra Pradesh province)",,Cold wave,Rain,,,,,100,Kph,,,,1214,2003,12,17,2003,12,17,50,,,40000,40000,,,28000,71.95500655 +2003-0388-JPN,2003,0388,Natural,Meteorological,Storm,Tropical cyclone,,Etau,Kill,Japan,JPN,Eastern Asia,Asia,"Hokkaidoo, Ehime provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,145,Kph,,,,,2003,8,8,2003,8,11,20,80,2100,,2180,,47000,55000,71.95500655 +2003-0258-PHL,2003,0258,Natural,Meteorological,Storm,Tropical cyclone,,Linfa (Chedeng),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Benguet district (Cordillera Administrative region (CAR) province), Bataan, Bulacan, Zambales districts (Region III (Central Luzon) province), Mindoro Occidental, Romblon districts (Region IV (Southern Tagalog) province), Batangas, Cavite, Rizal districts (Region IV-A (Calabarzon) province), Iloilo district (Region VI (Western Visayas) province), National Capital region (NCR), Region I (Ilocos region) provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2003,5,26,2003,5,30,51,16,11345,2548,13909,,,4000,71.95500655 +2003-0290-PHL,2003,0290,Natural,Meteorological,Storm,Tropical cyclone,,Soudelor (Egay),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Sorsogon, Albay, Catanduanes, Camarines Sur districts (Region V (Bicol region) province), Biliran, Leyte districts (Region VIII (Eastern Visayas) province)",,"Slide (land, mud, snow, rock)",Flood,,,,,140,Kph,,,,,2003,6,15,2003,6,18,13,3,46472,80655,127130,,,2455,71.95500655 +2003-0474-MEX,2003,0474,Natural,Meteorological,Storm,Tropical cyclone,,Marty,Affected,Mexico,MEX,Central America,Americas,"Sonora, Sinaloa, Nayarit, Jalisco, Colima, Baja California Sur, Baja California provinces",,Rain,Flood,,,,,166,Kph,,,,,2003,9,22,2003,9,22,2,,6000,,6000,,,100000,71.95500655 +2003-0314-SLB,2003,0314,Natural,Meteorological,Storm,Tropical cyclone,,Gina,Affected,Solomon Islands,SLB,Melanesia,Oceania,"Faea, Ravenga areas (Tikopia island, Solomon Islands province)",,"Slide (land, mud, snow, rock)",,,,,,111,Kph,,,,,2003,6,5,2003,6,5,,,,150,150,,,,71.95500655 +2003-0182-IND,2003,0182,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,"Chirakhawa, Chirakhawa Topopara, Peepulbari part 1, Peepulbari part 2, Bhurakata, Baliabeel, Bengerbhita, Baushkata areas (Dhuburi district, Assam province)",,Rain,,,,,,100,Kph,,,,,2003,4,22,2003,4,22,45,4000,,600,4600,,,,71.95500655 +2003-0459-JPN,2003,0459,Natural,Meteorological,Storm,Tropical cyclone,,Maemi,Affected,Japan,JPN,Eastern Asia,Asia,Okinawa province,,Rain,,,,,,200,Kph,,,,,2003,9,12,2003,9,13,1,93,130,,223,,,50000,71.95500655 +2003-0218-MDG,2003,0218,Natural,Meteorological,Storm,Tropical cyclone,,Manou,Kill,Madagascar,MDG,Eastern Africa,Africa,"Vatomandry, Brickaville districts (Atsinanana province)",,Rain,,Yes,Yes,,1294,210,Kph,,,,,2003,5,8,2003,5,8,89,86,114500,47500,162086,,,,71.95500655 +2003-0688-MEX,2003,0688,Natural,Meteorological,Storm,Tropical cyclone,,Ignacio,Kill,Mexico,MEX,Central America,Americas,"La Paz, Agua Escondida areas (La Paz district, Baja California Sur province), Ciudad Constitucion area (Comondu district, Baja California Sur province)",,Rain,,,,,,,Kph,,,,,2003,8,24,2003,8,27,2,,3000,,3000,,,,71.95500655 +2003-0388-RUS,2003,0388,Natural,Meteorological,Storm,Tropical cyclone,,Etau,Kill,Russian Federation (the),RUS,Eastern Europe,Europe,Kuril Islands (Sakhalinskaya Oblast province),,Rain,,,,,,,Kph,,,,,2003,8,8,2003,8,11,,,,,,,,,71.95500655 +2003-0443-TWN,2003,0443,Natural,Meteorological,Storm,Tropical cyclone,,Dujuan,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,Rain,,,,,,155,Kph,,,,,2003,9,2,2003,9,2,3,,,,,,,,71.95500655 +2003-0437-VNM,2003,0437,Natural,Meteorological,Storm,Tropical cyclone,,Koni,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Giang, Bac Kan, Bac Ninh, Cao Bang, Dien Bien, Ha Giang, Ha Nam, Ha Noi City, Ha Tay, Hai Duong, Hai Phong City, Hoa Binh, Hung Yen, Lai Chau, Lang Son, Lao Cai, Nam Dinh, Ninh Binh, Phu Tho, Quang Ninh, Son La, Thai Binh, Thai Nguyen, Thanh Hoa, Tuyen Quang, Vinh Phuc, Yen Bai provinces",,Rain,,,,,,90,Kph,,,,,2003,7,23,2003,7,23,,18,,5000,5018,,,,71.95500655 +2003-0346-PHL,2003,0346,Natural,Meteorological,Storm,Tropical cyclone,,Imbudo,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Maguindanao district (Autonomous region in Muslim Mindanao (ARMM) province), North Cotabato, Sultan Kudarat districts (Region XII (Soccsksargen) province), Isabela district (Region II (Cagayan Valley) province), Ilocos Norte district (Region I (Ilocos region) province), Romblon district (Region IV (Southern Tagalog) province)",,Flood,,,,,,200,Kph,,,,,2003,7,19,2003,7,23,21,,14280,,14280,,,26468,71.95500655 +2003-0599-PRI,2003,0599,Natural,Meteorological,Storm,Tropical cyclone,,Odette,Affected,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,Flood,,,,,,,Kph,,,,,2003,12,9,2003,12,9,,,,100,100,,,,71.95500655 +2003-0468-USA,2003,0468,Natural,Meteorological,Storm,Tropical cyclone,,Isabel,Kill,United States of America (the),USA,Northern America,Americas,"North Carolina, Maryland, Virginia, West Virginia, Delaware, Pennsylvania, New Jersey, District of Columbia provinces",,Flood,,,,Yes,,170,Kph,,,,,2003,9,18,2003,9,22,16,,225000,,225000,,1685000,3370000,71.95500655 +2003-0782-USA,2003,0782,Natural,Meteorological,Storm,Tropical cyclone,,Bill,Declar,United States of America (the),USA,Northern America,Americas,"Orleans, St. John the Baptist, Terrebonne, St. Tammany, Tangipahoa districts (Louisiana province), Hancock, Harrison, Jackson, Pike, Walthall districts (Mississippi province), Bay district (Florida province), Mobile, Baldwin, Lee districts (Alabama province), Sumter, Bulloch, Fulton, DeKalb, Cobb, Clayton, Gwinnett districts (Georgia province), Wake district (North Carolina province)",,Flood,,,,Yes,,95,Kph,,,,,2003,6,30,2003,6,30,4,4,,,4,,22000,50000,71.95500655 +2003-0234-IND,2003,0234,Natural,Meteorological,Storm,Tropical cyclone,,,Affect,India,IND,Southern Asia,Asia,"Mekhliganj, Haldibari, Fukaldabri, Nijtaraf, Kasiabari, Bholarhat, Beltali areas (Kochbihar district, West Bengal province)",,,,,,,,,Kph,,,,,2003,4,12,2003,4,12,,60,,400,460,,,,71.95500655 +2003-0602-MDG,2003,0602,Natural,Meteorological,Storm,Tropical cyclone,,Cela,Affected,Madagascar,MDG,Eastern Africa,Africa,"Antalaha district (Sava province), Ambanja district (Diana province)",,,,,,,,100,Kph,,,,,2003,12,9,2003,12,9,,,141,23,164,,,,71.95500655 +2003-0135-NCL,2003,0135,Natural,Meteorological,Storm,Tropical cyclone,,Erica,Kill,New Caledonia,NCL,Melanesia,Oceania,"Noumea, Bourail areas (South region), Kone area (North region)",,,,,,,300,200,Kph,,,,,2003,3,13,2003,3,13,2,100,,1000,1100,,,40000,71.95500655 +2003-0822-PHL,2003,0822,Natural,Meteorological,Storm,Tropical cyclone,,Gilas (Koni),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region VIII (Eastern Visayas) provinces",,,,,,,,,Kph,,,,,2003,7,15,2003,7,19,8,1,116601,,116602,,,1499,71.95500655 +2003-0823-PHL,2003,0823,Natural,Meteorological,Storm,Tropical cyclone,,Ineng,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region V (Bicol region), Region VIII (Eastern Visayas), Region X (Northern Mindanao) provinces",,,,,,,,,Kph,,,,,2003,7,30,2003,7,31,,,3748,,3748,,,146,71.95500655 +2003-0824-PHL,2003,0824,Natural,Meteorological,Storm,Tropical cyclone,,Kabayan,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,Region III (Central Luzon) province,,,,,,,,,Kph,,,,,2003,8,4,2003,8,5,,,155147,,155147,,,661,71.95500655 +2004-0218-PHL,2004,0218,Natural,Meteorological,Storm,Tropical cyclone,,Nida (Dindo/04W),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Region V (Bicol region) province,,Rain,"Slide (land, mud, snow, rock)",,,,,260,Kph,,,,,2004,5,13,2004,5,19,31,14,,700,714,,,1000,73.88141244 +2004-0080-VUT,2004,0080,Natural,Meteorological,Storm,Tropical cyclone,,Ivy (P13),Affected,Vanuatu,VUT,Melanesia,Oceania,"Malekula, Ambrym, Paama islands (Malampa province), Epi, Shepard islands (Shefa province), Ambae, Maevo islands (Penama province), Erromango, Tanna islands (Tafea province)",,Rain,"Slide (land, mud, snow, rock)",Yes,,,123,190,Kph,,,,,2004,2,25,2004,2,27,2,8,54000,,54008,,,,73.88141244 +2004-0428-KOR,2004,0428,Natural,Meteorological,Storm,Tropical cyclone,,Megi (Lawin/18W),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Chollabuk-do, Chollanam-do, Kyongsangbuk-do, Kyongsangnam-do provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,75,Kph,,,,,2004,8,15,2004,8,17,8,,,2400,2400,,,1000,73.88141244 +2004-0435-PHL,2004,0435,Natural,Meteorological,Storm,Tropical cyclone,,Aere (Marce/20W),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Benguet, Ifugao districts (Cordillera Administrative region (CAR) province), La Union, Pangasinan districts (Region I (Ilocos region) province), Nueva Vizcaya district (Region II (Cagayan Valley) province), Bataan, Bulacan, Nueva Ecija, Pampanga, Tarlac, Zambales districts (Region III (Central Luzon) province), Rizal district (Region IV-A (Calabarzon) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,"Agno, Pampanga, Kalaklan, Camiling, Chico, Tarlac, Sinocalan",2004,8,25,2004,8,26,35,,1058849,,1058849,,,,73.88141244 +2004-0602-PHL,2004,0602,Natural,Meteorological,Storm,Tropical cyclone,,Merbok (Violeta),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Aurora district (Region III (Central Luzon) province),,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2004,11,23,2004,11,28,29,14,,2000,2014,,,,73.88141244 +2004-0609-PHL,2004,0609,Natural,Meteorological,Storm,Tropical cyclone,,Winnie,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Real, Infanta, General Nakar areas (Quezon district, Region IV-A (Calabarzon) province), Isabela district (Region II (Cagayan Valley) province), Bulacan, Nueva Ecija, Aurora districts (Region III (Central Luzon) province), Rizal district (Region IV-A (Calabarzon) province), Camarines Sur district (Region V (Bicol region) province)",,Flood,"Slide (land, mud, snow, rock)",Yes,Yes,,17278,,Kph,,,,,2004,11,29,2004,11,30,1619,1023,880000,,881023,,,78200,73.88141244 +2004-0309-TWN,2004,0309,Natural,Meteorological,Storm,Tropical cyclone,,Mindulle (Igme/10W),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Chi-Ci, Guo-hsing, Ren-ai villages (Nantou area, Taiwan Sheng province), Hoping village (Taichung area, Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,,,,,2004,6,29,2004,7,4,28,6,1165,,1171,,,400000,73.88141244 +2004-0580-VNM,2004,0580,Natural,Meteorological,Storm,Tropical cyclone,,Muifa (Unding/29W),Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Ngai, Quang Nam, Thua Thien - Hue, Quang Tri, Quang Binh provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2004,11,26,2004,11,26,56,,500000,,500000,,,23000,73.88141244 +2004-0276-JPN,2004,0276,Natural,Meteorological,Storm,Tropical cyclone,,Dianmu (Helen/09W),Affected,Japan,JPN,Eastern Asia,Asia,Murotosi district (Kooti province),,"Slide (land, mud, snow, rock)",Rain,,,,,285,Kph,,,,,2004,6,16,2004,6,19,6,56,700,,756,,,,73.88141244 +2004-0492-JPN,2004,0492,Natural,Meteorological,Storm,Tropical cyclone,,Meari (Quinta/25W),Kill,Japan,JPN,Eastern Asia,Asia,"Niihama, Saizyoosi districts (Ehime province), Miyagawamura district (Mie province)",,"Slide (land, mud, snow, rock)",Rain,,,,,220,Kph,,,,,2004,9,23,2004,9,25,25,89,10000,,10089,,,740000,73.88141244 +2004-0468-TWN,2004,0468,Natural,Meteorological,Storm,Tropical cyclone,,Haima,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Shichih, Wufeng, Nankang areas (Taiwan Sheng province)",,"Slide (land, mud, snow, rock)",Rain,,,,,,Kph,,,,,2004,9,12,2004,9,14,9,,6000,,6000,,,2710,73.88141244 +2004-0258-THA,2004,0258,Natural,Meteorological,Storm,Tropical cyclone,,Chanthu (Gener/08W),Kill,Thailand,THA,South-Eastern Asia,Asia,"Phrae, Nakhon Sawan, Sukhothai, Phichit, Mae Hong Son, Tak, Nan, Phayao, Phitsanulok, Loei provinces",,Flood,Rain,,,,,,Kph,,,,,2004,6,4,2004,6,21,1,,4000,,4000,,,,73.88141244 +2004-0524-JPN,2004,0524,Natural,Meteorological,Storm,Tropical cyclone,,Tokage (Siony/27W),Kill,Japan,JPN,Eastern Asia,Asia,"Ibaraki, Kanagawa, Nagano, Toyama, Kyooto, Okayama, Tokusima, Ehime, Kagawa, Saga, Miyazaki, Okinawa provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,230,Kph,,,,,2004,10,15,2004,10,19,89,342,84450,,84792,,1100000,2300000,73.88141244 +2004-0435-TWN,2004,0435,Natural,Meteorological,Storm,Tropical cyclone,,Aere (Marce/20W),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Hsinchu, Nantou, Taipei, Miaoli, Taichung areas (Taiwan Sheng province)",,"Slide (land, mud, snow, rock)",Flood,,,,,130,Kph,,,,Tansui,2004,8,24,2004,8,25,32,2,,,2,,,5000,73.88141244 +2004-0462-USA,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,United States of America (the),USA,Northern America,Americas,"Alabama, Louisiana, Mississippi, Florida, Pennsylvania, Maryland, New Jersey, Ohio, North Carolina, West Virginia, Georgia, Tennessee provinces",,"Slide (land, mud, snow, rock)",Flood,,,Yes,,,Kph,,,,,2004,9,15,2004,9,16,52,,,,,,12000000,18000000,73.88141244 +2004-0462-JAM,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Jamaica,JAM,Caribbean,Americas,"Clarendon, Westmoreland, Saint Catherine, Saint Elizabeth, Saint Thomas, Saint Ann, Trelawny, Saint Andrew And Kingston provinces",,"Slide (land, mud, snow, rock)",Surge,Yes,,,3345,250,Kph,,,,,2004,9,11,2004,9,11,15,,350000,,350000,,200000,595000,73.88141244 +2004-0235-MMR,2004,0235,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Myanmar,MMR,South-Eastern Asia,Asia,"Myebon, Sittwe, Ponnagyun, Pauktaw, Mrauk-U, Minbya areas (Sittwe district, Rakhine province), Kyaukpyu, Ann areas (Kyaukpyu district, Rakhine province)",,Flood,Surge,,Yes,,264,160,Kph,,,,,2004,5,19,2004,5,19,236,,,25000,25000,,,688,73.88141244 +2004-0004-WSM,2004,0004,Natural,Meteorological,Storm,Tropical cyclone,,Heta,Declar,Samoa,WSM,Polynesia,Oceania,"Savaii, Upolu islands (Samoa province)",,Flood,Surge,,,Yes,,310,Kph,,,,,2004,1,5,2004,1,5,1,,,,,,,,73.88141244 +2004-0103-MDG,2004,0103,Natural,Meteorological,Storm,Tropical cyclone,,Galifo,Affected,Madagascar,MDG,Eastern Africa,Africa,"Antalaha, Andapa, Sambava, Vohemar districts (Sava province), Mampikony, Antsohihy, Bealanana districts (Sofia province), Morondava district (Menabe province), Ambilobe, Ambanja districts (Diana province), Mahajanga I, Mahajanga II districts (Boeny province), Vatomandry district (Atsinanan province), Maroantsetra district (Analanjirofo province), Morombe district (Atsimo Andrefana province)",,Flood,Transport accident,Yes,Yes,,12266,300,Kph,,,,,2004,3,7,2004,3,12,363,879,773000,214260,988139,,,250000,73.88141244 +2004-0580-PHL,2004,0580,Natural,Meteorological,Storm,Tropical cyclone,,Muifa (Unding/29W),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Mindoro Occidental, Mindoro Oriental districts (Region IV (Southern Tagalog) province), Region IV-A (Calabarzon), Region V (Bicol region) provinces",,"Slide (land, mud, snow, rock)",,,,,,215,Kph,,,,,2004,11,14,2004,11,21,104,240,838434,,838674,,,6000,73.88141244 +2004-0276-KOR,2004,0276,Natural,Meteorological,Storm,Tropical cyclone,,Dianmu (Helen/09W),Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,Munkyong area (Kyongsangbuk-do province),,Rain,,,,,,,Kph,,,,,2004,6,18,2004,6,20,6,,,522,522,,,,73.88141244 +2004-0473-PRI,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,Rain,,,,Yes,,130,Kph,,,,,2004,9,14,2004,9,14,2,,3500,,3500,,30000,100000,73.88141244 +2004-0455-USA,2004,0455,Natural,Meteorological,Storm,Tropical cyclone,,Frances,Declar,United States of America (the),USA,Northern America,Americas,"Martin, Palm Beach districts (Florida province), North Carolina, South Carolina, Ohio provinces",,Rain,,,,Yes,,,Kph,,,,,2004,9,5,2004,9,5,47,,5000000,,5000000,,5000000,11000000,73.88141244 +2004-0415-JAM,2004,0415,Natural,Meteorological,Storm,Tropical cyclone,,Charley,Kill,Jamaica,JAM,Caribbean,Americas,Saint Elizabeth province,,Flood,,,,,,,Kph,,,,,2004,8,13,2004,8,13,1,6,120,,126,,,300000,73.88141244 +2004-0445-JPN,2004,0445,Natural,Meteorological,Storm,Tropical cyclone,,Chaba,Affected,Japan,JPN,Eastern Asia,Asia,"Oosaka, Hyoogo, Okayama, Ehime, Kagawa, Miyazaki, Kagosima provinces",,Flood,,,,,,126,Kph,,,,,2004,8,30,2004,8,31,14,50,180000,,180050,,1200000,2000000,73.88141244 +2004-0511-JPN,2004,0511,Natural,Meteorological,Storm,Tropical cyclone,,Ma-on (Rolly/26W),Waiting,Japan,JPN,Eastern Asia,Asia,"Tookyoo, Sizuoka, Aiti provinces",,Flood,,,,,,260,Kph,,,,,2004,10,3,2004,10,8,11,44,5904,,5948,,241000,603000,73.88141244 +2004-0029-MDG,2004,0029,Natural,Meteorological,Storm,Tropical cyclone,,Elita,Affected,Madagascar,MDG,Eastern Africa,Africa,"Antananarivo I, Antananarivo II, Antananarivo III, Antananarivo IV, Antananarivo V, Antananarivo VI districts (Analamanga province), Mahajanga II district (Boeny province), Fianarantsoa I district (Haute Matsiatra province), Toamasina II district (Atsinanana province), Toliary-II district (Astimo Andrefana province)",,Flood,,Yes,Yes,,428,210,Kph,,,,,2004,1,26,2004,2,4,32,100,,44190,44290,,,,73.88141244 +2004-0309-PHL,2004,0309,Natural,Meteorological,Storm,Tropical cyclone,,Mindulle (Igme/10W),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cagayan district (Region II (Cagayan Valley) province), Ilocos Norte, La Union districts (Region I (Ilocos region) province), Autonomous region in Muslim Mindanao (ARMM), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,Flood,,,,,,230,Kph,,,,,2004,6,25,2004,7,2,28,12,385000,,385012,,,19667,73.88141244 +2004-0535-TWN,2004,0535,Natural,Meteorological,Storm,Tropical cyclone,,Nock-ten (Tonyo/28W),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Juifang, Taipei areas (Taiwan Sheng province)",,Flood,,,,,,205,Kph,,,,,2004,10,22,2004,10,25,7,100,1600,,1700,,,,73.88141244 +2004-0448-USA,2004,0448,Natural,Meteorological,Storm,Tropical cyclone,,Gaston,SigDam,United States of America (the),USA,Northern America,Americas,"Richmond, Lynchburg, Chesterfield districts (Virginia province), South Carolina, North Carolina provinces",,Flood,,,,Yes,,,Kph,,,,,2004,8,29,2004,8,30,7,,,,,,62500,62500,73.88141244 +2004-0459-JPN,2004,0459,Natural,Meteorological,Storm,Tropical cyclone,,Songda (Nina/22W),Kill,Japan,JPN,Eastern Asia,Asia,"Hukuoka, Kagosima, Kumamoto, Miyazaki, Nagasaki, Ooita, Saga provinces",,Transport accident,,,,,,230,Kph,,,,,2004,9,3,2004,9,4,41,900,40000,,40900,,4700000,9000000,73.88141244 +2004-0459-RUS,2004,0459,Natural,Meteorological,Storm,Tropical cyclone,,Songda (Nina/22W),Kill,Russian Federation (the),RUS,Eastern Europe,Europe,Kouriles Isl. (Sakhalinskaya Oblast province),,Transport accident,,,,,,,Kph,,,,,2004,9,10,2004,9,10,3,,,,,,,,73.88141244 +2004-0415-USA,2004,0415,Natural,Meteorological,Storm,Tropical cyclone,,Charley,Kill,United States of America (the),USA,Northern America,Americas,"Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Jefferson, Leon, Liberty, Madison, Okaloosa, Santa Rosa, Taylor, Wakulla, Walton, Washington districts (Florida province), South Carolina, North Carolina provinces",,Transport accident,,,,Yes,,230,Kph,,,,,2004,8,13,2004,8,13,10,,30000,,30000,,7600000,16000000,73.88141244 +2004-0428-JPN,2004,0428,Natural,Meteorological,Storm,Tropical cyclone,,Megi (Lawin/18W),Kill,Japan,JPN,Eastern Asia,Asia,"Kagawa, Ehime, Hyoogo, Kooti provinces",,,,,,,,126,Kph,,,,,2004,8,17,2004,8,20,11,,8502,,8502,,,500000,73.88141244 +2004-0435-JPN,2004,0435,Natural,Meteorological,Storm,Tropical cyclone,,Aere (Marce/20W),Affected,Japan,JPN,Eastern Asia,Asia,"Kasarityoo, Nazesi, Setootityoo, Sumiyooson, Tatugootyoo, Ukenson, Yamatoson districts (Kagosima province), Okinawa province",,,,,,,,,Kph,,,,,2004,8,23,2004,8,23,2,2,,,2,,,500,73.88141244 +2004-0459-KOR,2004,0459,Natural,Meteorological,Storm,Tropical cyclone,,Songda (Nina/22W),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Cheju-do, Chollabuk-do, Chollanam-do, Chungchongbuk-do, Chungchongnam-do, Inchon, Kang-won-do, Kwangju, Kyonggi-do, Kyongsangbuk-do, Kyongsangnam-do, Pusan, Seoul, Taegu, Taejon provinces",,,,,,,,,Kph,,,,,2004,9,6,2004,9,9,,,,,,,,250000,73.88141244 +2004-0462-LCA,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Saint Lucia,LCA,Caribbean,Americas,"Area Under National Administra, Region Number 1, Region Number 2, Region Number 3, Region Number 4, Region Number 5, Region Number 6, Region Number 7, Region Number 8 provinces",,,,,,,,,Kph,,,,,2004,9,8,2004,9,8,,,,,,,,500,73.88141244 +2004-0004-NIU,2004,0004,Natural,Meteorological,Storm,Tropical cyclone,,Heta,Declar,Niue,NIU,Polynesia,Oceania,Alofi area (Niue province),,,,,,,,300,Kph,,,,,2004,1,6,2004,1,6,1,2,200,500,702,,,40000,73.88141244 +2004-0618-PHL,2004,0618,Natural,Meteorological,Storm,Tropical cyclone,,Nanmadol (Yoyong/30W),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,Region V (Bicol region) province,,,,,,,,240,Kph,,,,,2004,11,30,2004,12,3,8,8,70480,,70488,,,34000,73.88141244 +2004-0455-PRI,2004,0455,Natural,Meteorological,Storm,Tropical cyclone,,Frances,Declar,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,,,,,,,,Kph,,,,,2004,8,31,2004,8,31,,,,,,,,,73.88141244 +2004-0388-RUS,2004,0388,Natural,Meteorological,Storm,Tropical cyclone,,,Declar,Russian Federation (the),RUS,Eastern Europe,Europe,Ustordynskiy Buryatskiy Okrug province,,,,,,,,100,Kph,,,,,2004,7,22,2004,7,22,6,62,,,62,,,6000,73.88141244 +2004-0455-TCA,2004,0455,Natural,Meteorological,Storm,Tropical cyclone,,Frances,Declar,Turks and Caicos Islands (the),TCA,Caribbean,Americas,"Grand Turk, Providenciales and West Caicos provinces",,,,,,,,,Kph,,,,,2004,9,1,2004,9,1,,,200,,200,,,,73.88141244 +2004-0004-TON,2004,0004,Natural,Meteorological,Storm,Tropical cyclone,,Heta,Declar,Tonga,TON,Polynesia,Oceania,"Tafahi, Nuiatoputapu islands (Tonga province)",,,,,,,,,Kph,,,,,2004,1,6,2004,1,6,,,,,,,,,73.88141244 +2004-0462-TTO,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Trinidad and Tobago,TTO,Caribbean,Americas,"Caparo, Tumpuna villages (Couva/Tabaquite/Talparo province), Caroni village (Tunapuna/Piarco province)",,,,,Yes,,,120,Kph,,,,,2004,9,9,2004,9,9,1,,560,,560,,,1000,73.88141244 +2004-0618-TWN,2004,0618,Natural,Meteorological,Storm,Tropical cyclone,,Nanmadol (Yoyong/30W),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,90,Kph,,,,,2004,12,4,2004,12,4,3,,,,,,,,73.88141244 +2004-0473-USA,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,United States of America (the),USA,Northern America,Americas,Florida province,,,,,,Yes,,,Kph,,,,,2004,9,25,2004,9,26,6,,40000,,40000,,4900000,8000000,73.88141244 +2004-0462-VCT,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Saint Vincent and the Grenadines,VCT,Caribbean,Americas,"Charlotte, Grenadines, Saint Andrew, Saint David, Saint George, Saint Patrick provinces",,,,,,,,,Kph,,,,,2004,9,8,2004,9,8,,4,1000,,1004,,,5000,73.88141244 +2004-0462-VEN,2004,0462,Natural,Meteorological,Storm,Tropical cyclone,,Ivan,Kill,Venezuela (Bolivarian Republic of),VEN,South America,Americas,"Aragua, Distrito Capital, Miranda, Vargas provinces",,,,,,,,220,Kph,,,,,2004,9,8,2004,9,8,5,5,1645,80,1730,,,,73.88141244 +2004-0473-VIR,2004,0473,Natural,Meteorological,Storm,Tropical cyclone,,Jeanne,Kill,Virgin Island (U.S.),VIR,Caribbean,Americas,"Saint Thomas, Saint John, Saint Croix provinces",,,,,,,,130,Kph,,,,,2004,9,15,2004,9,15,,,,,,,,,73.88141244 +2004-0258-VNM,2004,0258,Natural,Meteorological,Storm,Tropical cyclone,,Chanthu (Gener/08W),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"BinH Dinh, Da Nang City, Quang Ngai provinces",,,,,,,,65,Kph,,,,,2004,6,7,2004,6,11,14,5,900,,905,,,7000,73.88141244 +2005-0048-MDG,2005,0048,Natural,Meteorological,Storm,Tropical cyclone,,Ernest,Kill,Madagascar,MDG,Eastern Africa,Africa,"Tulear town (Toliary-II district, Atsimo Andrefana province) Tsihombe, Ambovombe districts (Androy province)",,,,,,,,100,Kph,,,,,2005,1,22,2005,1,23,78,8,,7977,7985,,,,76.38802721 +2005-0120-PHL,2005,0120,Natural,Meteorological,Storm,Tropical cyclone,,Roke (Auring/02W),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Leyte district (Region VIII (Eastern Visayas) province),,,,,,,,120,Kph,,,,,2005,3,15,2005,3,17,18,,11,,11,,,,76.38802721 +2005-0102-TKL,2005,0102,Natural,Meteorological,Storm,Tropical cyclone,,Percy,Affected,Tokelau,TKL,Polynesia,Oceania,"Nukunonu, Atafu, Fakaofo islands (Tokelau province)",,,,,,,,249,Kph,,,,,2005,2,28,2005,2,28,,1,,25,26,,,,76.38802721 +2005-0151-WSM,2005,0151,Natural,Meteorological,Storm,Tropical cyclone,,Olaf,Affected,Samoa,WSM,Polynesia,Oceania,Samoa province,,,,,,,,190,Kph,,,,,2005,2,16,2005,2,16,9,,,,,,,,76.38802721 +2004-0312-MNP,2004,0312,Natural,Meteorological,Storm,Tropical cyclone,,Tingting,Affected,Northern Mariana Islands (the),MNP,Micronesia,Oceania,Saipan island (Northern Mariana Islands province),,,,,,,,,Kph,,,,,2004,6,26,2004,6,27,3,,200,,200,,,,73.88141244 +2005-0419-CHN,2005,0419,Natural,Meteorological,Storm,Tropical cyclone,,Matsa (Gorio/09W),Affected,China,CHN,Eastern Asia,Asia,"Shanghai Shi, Jiangsu Sheng, Shandong Sheng, Anhui Sheng, Zhejiang Sheng provinces",,Rain,"Slide (land, mud, snow, rock)",,,,,165,Kph,,,,,2005,7,30,2005,8,4,10,,1240000,,1240000,,85000,850000,76.38802721 +2005-0497-JPN,2005,0497,Natural,Meteorological,Storm,Tropical cyclone,,Nabi (Jolina/14W),Kill,Japan,JPN,Eastern Asia,Asia,"Okinawa, Ehime, Kagawa, Kooti, Tokusima, Tookyoo provinces",,Rain,"Slide (land, mud, snow, rock)",,,,,144,Kph,,,,,2005,9,2,2005,9,4,32,140,270000,,270140,,550000,1000000,76.38802721 +2005-0510-CHN,2005,0510,Natural,Meteorological,Storm,Tropical cyclone,,Khanun (15),Kill,China,CHN,Eastern Asia,Asia,"Shanghai Shi, Zheijiang Sheng, Jiangsu Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2005,9,11,2005,9,13,25,8,1350000,,1350008,,78000,1750000,76.38802721 +2005-0585-CUB,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Cuba,CUB,Caribbean,Americas,"Santiago de Cuba, Granma, Guantanamo, Matanzas, Sancti Spiritus, Villa Clara, Cienfuegos, Havana, Ciudad Havana, Pinar del Rio, Isla De La Juventud provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,,,,,2005,10,19,2005,10,24,4,,100000,,100000,,,700000,76.38802721 +2005-0597-DOM,2005,0597,Natural,Meteorological,Storm,Tropical cyclone,,Alpha,Kill,Dominican Republic (the),DOM,Caribbean,Americas,Puerto Plata province,,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2005,10,22,2005,10,24,9,,1000,,1000,,,,76.38802721 +2005-0640-HND,2005,0640,Natural,Meteorological,Storm,Tropical cyclone,,Gamma,Kill,Honduras,HND,Central America,Americas,"Atlantida, Colon, Cortes, Gracias A Dios, Yoro provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,507,75,Kph,,,,,2005,11,18,2005,11,20,47,,90000,,90000,,,15500,76.38802721 +2005-0597-HTI,2005,0597,Natural,Meteorological,Storm,Tropical cyclone,,Alpha,Kill,Haiti,HTI,Caribbean,Americas,"Dame Marie, Les Irois villages (Anse-D'Ainault district, Grande Anse province), Carrefour, Gressier, Port-au-Prince villages (Port-Au-Prince district, Ouest province), Leogane district (Ouest province), Anse à Pitre village (Belle Anse district, Sud Est province), Jacmel district (Sud Est province), Anse Rouge village (Gros Morne district, Artibonite province), Hinche district (Centre province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2005,10,22,2005,10,24,12,17,2175,,2192,,,,76.38802721 +2005-0585-JAM,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Jamaica,JAM,Caribbean,Americas,"Saint Thomas, Saint Catherine, Trelawny, Saint Andrew And Kingston provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2005,10,19,2005,10,24,1,,100,,100,,,3500,76.38802721 +2005-0351-JAM,2005,0351,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Dennis""",Kill,Jamaica,JAM,Caribbean,Americas,"Portland, Saint Andrew And Kingston, Saint Mary, Saint Thomas provinces",,"Slide (land, mud, snow, rock)",Rain,,,,,,Kph,,,,,2005,7,7,2005,7,7,1,,8000,,8000,,,30000,76.38802721 +2005-0492-CHN,2005,0492,Natural,Meteorological,Storm,Tropical cyclone,,Talim,Kill,China,CHN,Eastern Asia,Asia,"Anhui Sheng, Zhejiang Sheng, Fujian Sheng, Jiangxi Sheng, Hubei Sheng provinces",,"Slide (land, mud, snow, rock)",Flood,,,,86,,Kph,,,,,2005,9,1,2005,9,1,159,,19624000,,19624000,,,1900000,76.38802721 +2005-0565-CHN,2005,0565,Natural,Meteorological,Storm,Tropical cyclone,,Longwang,Affected,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Guangdong Sheng provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2005,10,2,2005,10,2,95,,2460000,,2460000,,,150000,76.38802721 +2005-0351-CUB,2005,0351,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Dennis""",Kill,Cuba,CUB,Caribbean,Americas,"Cienfuegos, La Habana, Ciudad de la Habana, Matanzas, Sancti Spiritus, Ciego de Avila, Camaguey, Santiago de Cuba, Granma, Las Tunas, Guatanamo provinces",,"Slide (land, mud, snow, rock)",Flood,Yes,,,,235,Kph,,,,,2005,7,8,2005,7,9,16,,2500000,,2500000,,5000,1400000,76.38802721 +2005-0567-GTM,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Guatemala,GTM,Central America,Americas,"Escuintla, Jutiapa, Santa Rosa, Suchitepéquez, San Marcos, Quezaltenango, Huehuetenango, Solola, Totonicapan, Retalhuleu, Quiché, Sacatepequez, Chimaltenango provinces",,"Slide (land, mud, snow, rock)",Flood,Yes,,Yes,25257,,Kph,,,,,2005,10,1,2005,10,13,1513,386,474928,,475314,,,988300,76.38802721 +2005-0464-JPN,2005,0464,Natural,Meteorological,Storm,Tropical cyclone,,Mawar,Affected,Japan,JPN,Eastern Asia,Asia,"Nagano, Sizuoka provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,108,Kph,,,,,2005,8,25,2005,8,26,3,4,90,,94,,,,76.38802721 +2005-0351-HTI,2005,0351,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Dennis""",Kill,Haiti,HTI,Caribbean,Americas,"Sud, Ouest, Nippes, Sud Est, Grande Anse provinces",,Rain,Flood,Yes,,,,,Kph,,,,,2005,7,7,2005,7,7,40,36,15000,,15036,,,50000,76.38802721 +2005-0567-HND,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Honduras,HND,Central America,Americas,"San Pedro Sula, Potrerillos, San Manuel districts (Cortes province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2005,9,28,2005,10,10,6,,2869,,2869,,,100000,76.38802721 +2005-0540-CHN,2005,0540,Natural,Meteorological,Storm,Tropical cyclone,,Damrey,Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng, Guangxi Zhuangzu Zizhiqu provinces",,Rain,,,,Yes,,198,Kph,,,,,2005,9,26,2005,9,26,25,,5719000,,5719000,,,1040000,76.38802721 +2005-0625-HND,2005,0625,Natural,Meteorological,Storm,Tropical cyclone,,Beta,Affected,Honduras,HND,Central America,Americas,"Gracias A Dios, Atlantida, Colon provinces",,Rain,,,,,,,Kph,,,,,2005,10,30,2005,11,15,,,11000,,11000,,,,76.38802721 +2005-0381-CHN,2005,0381,Natural,Meteorological,Storm,Tropical cyclone,,Haitang (Feria/05W),Affected,China,CHN,Eastern Asia,Asia,"Wenzhou Shi, Pingyang Xian areas (Wenzhou district, Zhejiang Sheng province)",,Flood,,,,,,260,Kph,,,,,2005,7,15,2005,7,19,9,,1000000,13000,1013000,,85000,1000000,76.38802721 +2005-0382-HTI,2005,0382,Natural,Meteorological,Storm,Tropical cyclone,,Emily,Affected,Haiti,HTI,Caribbean,Americas,Saint-Marc district (Artibonite province),,Flood,,,,,,,Kph,,,,,2005,7,17,2005,7,17,6,,565,185,750,,,,76.38802721 +2005-0567-HTI,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Haiti,HTI,Caribbean,Americas,"Dessalines, Saint-Marc districts (Artibonite province)",,Flood,,,,,,,Kph,,,,,2005,10,,2005,10,,1,,10000,,10000,,,,76.38802721 +2005-0585-HTI,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Haiti,HTI,Caribbean,Americas,"Sud, Sud Est provinces",,Flood,,,,,,,Kph,,,,,2005,10,19,2005,10,24,12,,,,,,,500,76.38802721 +2005-0585-BHS,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Bahamas (the),BHS,Caribbean,Americas,,,,,Yes,,,,,Kph,,,,,2005,10,19,2005,10,25,1,,1500,,1500,,,,76.38802721 +2005-0585-BLZ,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Belize,BLZ,Central America,Americas,"Belize, Cayo, Corozal, Orange Walk, Stann Creek, Toledo provinces",,,,,,,,,Kph,,,,,2005,10,21,2005,10,21,,,,,,,,,76.38802721 +2005-0640-BLZ,2005,0640,Natural,Meteorological,Storm,Tropical cyclone,,Gamma,Kill,Belize,BLZ,Central America,Americas,"Belize, Cayo, Corozal, Orange Walk, Stann Creek, Toledo provinces",,,,,,,,,Kph,,,,,2005,11,14,2005,11,21,3,,,,,,,,76.38802721 +2005-0625-COL,2005,0625,Natural,Meteorological,Storm,Tropical cyclone,,Beta,Affected,Colombia,COL,South America,Americas,"Providencia Isl. (Santa Catalina, Santa Catarina districts, San Andres y Providencia province), San Andres Isl. (San Andres y Providencia district, San Andres y Providencia province)",,,,,,,,175,Kph,,,,,2005,10,29,2005,10,29,,,3074,,3074,,,,76.38802721 +2005-0567-CRI,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Costa Rica,CRI,Central America,Americas,"Quepos (Aguirre district, Puntarenas province), Guanacaste province.",,,,,,,,,Kph,,,,,2005,10,1,2005,10,16,1,,1074,,1074,,,20000,76.38802721 +2005-0585-HND,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Honduras,HND,Central America,Americas,Omoa district (Cortes province),,,,,,,,,Kph,,,,,2005,10,21,2005,10,21,,,,,,,,,76.38802721 +2005-0382-JAM,2005,0382,Natural,Meteorological,Storm,Tropical cyclone,,Emily,Affected,Jamaica,JAM,Caribbean,Americas,"Trelawny, Saint Catherine, Saint James, Manchester, Saint Elizabeth provinces",,,,,,,,,Kph,,,,,2005,7,18,2005,7,18,4,,2296,,2296,,,1000,76.38802721 +2005-0419-KOR,2005,0419,Natural,Meteorological,Storm,Tropical cyclone,,Matsa (Gorio/09W),Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,"Cheju-do, Chollabuk-do, Chollanam-do, Chungchongbuk-do, Chungchongnam-do, Inchon, Kang-won-do, Kwangju, Kyonggi-do, Kyongsangbuk-do, Kyongsangnam-do, Pusan, Seoul, Taegu, Taejon provinces",,,,,,,,,Kph,,,,,2005,8,2,2005,8,7,5,,,,,,,500,76.38802721 +2005-0497-KOR,2005,0497,Natural,Meteorological,Storm,Tropical cyclone,,Nabi (Jolina/14W),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Ulsan, Ulleung areas (Kyongsangnam-do province), Pohang, Gyeongju areas (Kyongsangbuk-do province)",,,,,,,,,Kph,,,,,2005,9,8,2005,9,8,5,,1100,,1100,,,5000,76.38802721 +2005-0662-MAR,2005,0662,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Morocco,MAR,Northern Africa,Africa,"Guelmim - Es-Semara, Laâyoune - Boujdour - Sakia El Hamra, Souss - Massa - Draâ provinces",,,,,,,,,Kph,,,,,2005,11,28,2005,11,28,1,,,,,,,50,76.38802721 +2005-0382-MEX,2005,0382,Natural,Meteorological,Storm,Tropical cyclone,,Emily,Affected,Mexico,MEX,Central America,Americas,"Playa del Carmen, Cozumel areas (Cozumel district, Quintana Roo province), Merida, Tizimin districts (Yucatan province)",,,,,,,,,Kph,,,,,2005,7,18,2005,7,18,2,,,,,,250000,400000,76.38802721 +2006-0251-CHN,2006,0251,Natural,Meteorological,Storm,Tropical cyclone,,Chanchu (Caloy),Kill,China,CHN,Eastern Asia,Asia,"Shantou district (Guangdong province), Fujian province",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,25.97,118.77,,,2006,5,18,2006,5,22,23,,3150000,,3150000,,,475000,78.85225551 +2006-0388-CHN,2006,0388,Natural,Meteorological,Storm,Tropical cyclone,,Kaemi,Kill,China,CHN,Eastern Asia,Asia,"Jiangxi Sheng, Fujian Sheng, Zhejiang Sheng, Guangdong Sheng, Hunan Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,26.39,117.28,,,2006,7,24,2006,7,28,109,,6531000,,6531000,,,367000,78.85225551 +2006-0504-JPN,2006,0504,Natural,Meteorological,Storm,Tropical cyclone,,Shanshan (13),Kill,Japan,JPN,Eastern Asia,Asia,"Nagasaki, Hukuoka, Miyazaki, Okinawa, Hirosima, Okayama, Simane, Tottori, Yamaguti provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2006,9,15,2006,9,20,10,448,12000,,12448,,1020000,2500000,78.85225551 +2006-0362-CHN,2006,0362,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Hunan Sheng, Guangdong Sheng, Jiangxi Sheng, Zhejiang Sheng, Guangxi Zhuangzu Zizhiqu provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,24,112.11,,,2006,7,16,2006,7,19,820,,29622000,,29622000,,,3325000,78.85225551 +2006-0410-CHN,2006,0410,Natural,Meteorological,Storm,Tropical cyclone,,Prapiroon,Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,21.11,110.11,,,2006,8,3,2006,8,6,89,,10000000,,10000000,,,900000,78.85225551 +2006-0155-AUS,2006,0155,Natural,Meteorological,Storm,Tropical cyclone,,Glenda,SigDis,Australia,AUS,Australia and New Zealand,Oceania,"Exmouth, Ashburton, East Pilbara, Port Hedland, Roebourne districts (Western Australia province)",,Rain,,,,,,235,Kph,-25.68,115.13,,,2006,3,30,2006,4,5,,,500,,500,,,,78.85225551 +2006-0687-MDG,2006,0687,Natural,Meteorological,Storm,Tropical cyclone,,Bondo,Affected,Madagascar,MDG,Eastern Africa,Africa,"Mahajanga I, Mahajanga II districts (Boeny province), Antalaha district (Sava province)",,Rain,,,,,,150,Kph,,,,,2006,12,25,2006,12,25,1,,,304,304,,,,78.85225551 +2006-0043-AUS,2006,0043,Natural,Meteorological,Storm,Tropical cyclone,,Clare,Affected,Australia,AUS,Australia and New Zealand,Oceania,"Wyndham-East Kimberley, Ashburton, East Pilbara, Port Hedland, Roebourne, Upper Gascoyne districts (Western Australia province)",,Flood,,,,,,,Kph,-23.26,116.94,,,2006,1,9,2006,1,31,,,1500,,1500,,,2354,78.85225551 +2006-0139-AUS,2006,0139,Natural,Meteorological,Storm,Tropical cyclone,,Larry,SigDis,Australia,AUS,Australia and New Zealand,Oceania,Cairns district (Queensland province),,Flood,,,,Yes,,290,Kph,-17.91,145.54,,,2006,3,20,2006,4,2,,30,,7000,7030,,335000,1180000,78.85225551 +2006-0510-BGD,2006,0510,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Hatiya area (Noakhali district, Chittagong province), Bagerhat district (Khulna province), Patuakhali, Barguna districts (Barisal province)",,Flood,,,,,,,Kph,19.73,83.75,,,2006,9,18,2006,10,5,115,,9135,,9135,,,,78.85225551 +2006-0738-BGD,2006,0738,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Tangail district (Dhaka province), Sirajganj district (Rajshahi province)",,Flood,,,,,,,Kph,,,,,2006,4,8,2006,4,8,22,500,,1000,1500,,,,78.85225551 +2006-0437-CHN,2006,0437,Natural,Meteorological,Storm,Tropical cyclone,,Saomai,Kill,China,CHN,Eastern Asia,Asia,"Zhejiang Sheng, Fujian Sheng provinces",,Flood,,,,,,216,Kph,,,,,2006,8,6,2006,8,11,441,1350,5920000,2000,5923350,,,2510000,78.85225551 +2006-0272-CUB,2006,0272,Natural,Meteorological,Storm,Tropical cyclone,,Alberto,Affected,Cuba,CUB,Caribbean,Americas,Nueva Paz district (La Habana province),,Flood,,,,,,75,Kph,,,,,2006,6,11,2006,6,11,,8,260,,268,,,,78.85225551 +2006-0073-FJI,2006,0073,Natural,Meteorological,Storm,Tropical cyclone,,Jim,Affected,Fiji,FJI,Melanesia,Oceania,"Lautoka, Ba towns (Ba district, Western province), Rakiraki town (Ra district, Western province)",,Flood,,,,,,,Kph,-17.82,177.68,,,2006,1,29,2006,2,6,,,168,,168,,,,78.85225551 +2006-0466-HTI,2006,0466,Natural,Meteorological,Storm,Tropical cyclone,,Ernesto,Affected,Haiti,HTI,Caribbean,Americas,"Sud, Grande Anse, Ouest, Nippes, Artibonite provinces",,Flood,,,,,,,Kph,,,,,2006,8,25,2006,8,26,5,,15000,,15000,,,,78.85225551 +2006-0510-IND,2006,0510,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,"Andhra Pradesh, West Bengal, Bihar provinces",Monsoonal rain,Flood,,,,,,,Kph,19.73,83.75,,,2006,9,18,2006,10,5,114,300,,150000,150300,,,,78.85225551 +2006-0737-BGD,2006,0737,Natural,Meteorological,Storm,Tropical cyclone,,,Affected,Bangladesh,BGD,Southern Asia,Asia,Dhaka province,,,,,,,,,Kph,,,,,2006,4,5,2006,4,6,9,65,,1400,1465,,,,78.85225551 +2006-0517-CHN,2006,0517,Natural,Meteorological,Storm,Tropical cyclone,,Xangsane (Milenyo),Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,,,,,,,,Kph,,,,,2006,10,,2006,10,,1,12,,,12,,,,78.85225551 +2006-0089-MDG,2006,0089,Natural,Meteorological,Storm,Tropical cyclone,,Boloetse,Affected,Madagascar,MDG,Eastern Africa,Africa,"Androka town (Ampanihy Ouest district, Atsimo Andrefana province) Itampolo town (Toliary-II district, Atsimo Andrefana province)",,,,,,,,160,Kph,,,,,2006,2,4,2006,2,4,3,,6112,100,6212,,,,78.85225551 +2007-0095-MDG,2007,0095,Natural,Meteorological,Storm,Tropical cyclone,,Indhala,Kill,Madagascar,MDG,Eastern Africa,Africa,"Diana, Sava, Sofia, Analanjirofo provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,Yes,,18703,230,Kph,-14.84,49.94,,,2007,3,15,2007,3,17,80,16,203182,12000,215198,,,240000,81.10165893 +2007-0151-FJI,2007,0151,Natural,Meteorological,Storm,Tropical cyclone,,Cliff,SigDam,Fiji,FJI,Melanesia,Oceania,Lau district (Eastern province),,"Slide (land, mud, snow, rock)",,,,,,120,Kph,,,,,2007,4,5,2007,4,5,1,,,,,,,,81.10165893 +2007-0032-MDG,2007,0032,Natural,Meteorological,Storm,Tropical cyclone,,Clovis,Affected,Madagascar,MDG,Eastern Africa,Africa,"Mananjary, Nosy-Varika districts (Vatovavy Fitovinany province)",,Flood,,,,,,90,Kph,,,,,2007,1,3,2007,1,3,1,,7313,,7313,,,,81.10165893 +2007-0136-MDG,2007,0136,Natural,Meteorological,Storm,Tropical cyclone,,Jaya,Waiting,Madagascar,MDG,Eastern Africa,Africa,Sambava district (Sava province),,,,,,,,,Kph,,,,,2007,4,4,2007,4,4,3,,,,,,,,81.10165893 +2005-0540-VNM,2005,0540,Natural,Meteorological,Storm,Tropical cyclone,,Damrey,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Yen Bai, Tram Tau, Nghia Lo districts (Yen Bai province), Nghe An, Phu Tho, Hoa Binh, Lao Cai, Thanh Hoa, Nam Dinh, Quang Ninh, Quang Nam, Da Nang City provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,,717,133,Kph,,,,,2005,9,27,2005,9,30,75,28,337632,,337660,,,219250,76.38802721 +2005-0467-USA,2005,0467,Natural,Meteorological,Storm,Tropical cyclone,,Katrina,Kill,United States of America (the),USA,Northern America,Americas,"Mobile, Bayou La Batre, Dauphin Island, Coden areas (Mobile district, Alabama province), New Orleans city (Orleans district, Louisiana province), Slidell area (St. Tammany district, Louisiana province), St. Bernard district (Louisiana province), Biloxi, Gulfport cities (Harrison district, Mississippi province), Pascagoula city (Jackson district, Mississippi province), Waveland, Bay St. Louis cities (Hancock district, Mississippi province), Georgia, Florida provinces",,Flood,Broken Dam/Burst bank,,,Yes,,280,Kph,,,,,2005,8,29,2005,9,19,1833,,500000,,500000,,60000000,125000000,76.38802721 +2005-0540-PHL,2005,0540,Natural,Meteorological,Storm,Tropical cyclone,,Damrey,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV (Southern Tagalog), Region IV-A (Calabarzon), Region V (Bicol region) provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,95,Kph,,,,,2005,9,15,2005,9,22,16,,20000,,20000,,,2000,76.38802721 +2005-0585-USA,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,United States of America (the),USA,Northern America,Americas,"Florida Keys, Naples areas (Collier district, Florida province)",,Rain,Flood,,,Yes,,165,Kph,,,,,2005,10,24,2005,10,24,4,,30000,,30000,,10350000,14300000,76.38802721 +2005-0567-SLV,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,El Salvador,SLV,Central America,Americas,"San Salvador, San Marcos districts (San Salvador province), La Libertad, Santa Tecla districts (La Libertad province), Lourdes city (Colon district, La Libertad province), Chaparral village (Chilanga district, Morazan province), Ateos city (Sacacoyo district, La Libertad province), El Chaparral area (Ciudad Barrios district, San Miguel province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2005,10,1,2005,10,13,69,,72141,,72141,,,355700,76.38802721 +2005-0382-TTO,2005,0382,Natural,Meteorological,Storm,Tropical cyclone,,Emily,Affected,Trinidad and Tobago,TTO,Caribbean,Americas,"Arima, Chaguanas, Couva/Tabaquite/Talparo, Diego Martin, Penal/Debe, Point Fortin, Port Of Spain, Princes Town, Rio Claro/Mayaro, San Fernando, San Juan/Laventille, Sangre Grande, Siparia, Tobago, Tunapuna/Piarco provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2005,7,14,2005,7,14,,,,,,,,,76.38802721 +2005-0540-THA,2005,0540,Natural,Meteorological,Storm,Tropical cyclone,,Damrey,Kill,Thailand,THA,South-Eastern Asia,Asia,"Lampang, Chiang Mai, Chiang Rai, Phayao, Mae Hong Son, Phrae, Yasothon, Ubon Ratchathani provinces",,Broken Dam/Burst bank,,,,,,,Kph,,,,"Ping, Chi",2005,9,26,2005,9,30,10,,2000,,2000,,,20000,76.38802721 +2005-0567-MEX,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Mexico,MEX,Central America,Americas,"Chiapas, Oaxaca, Veracruz, Puebla, Hidalgo, Tabasco provinces",,Rain,,,,Yes,,130,Kph,,,,,2005,10,1,2005,10,13,36,,1954571,,1954571,,,2500000,76.38802721 +2005-0585-MEX,2005,0585,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Wilma""",Affected,Mexico,MEX,Central America,Americas,"Benito Juarez, Isla Mujeres, Cozumel districts (Quintana Roo province)",,Rain,,Yes,,Yes,,,Kph,,,,,2005,10,19,2005,10,24,7,,700000,300000,1000000,,1800000,5000000,76.38802721 +2005-0625-NIC,2005,0625,Natural,Meteorological,Storm,Tropical cyclone,,Beta,Affected,Nicaragua,NIC,Central America,Americas,"San José de Bocay village (El Cua district, Jinotega province), Wiwili district (Jinotega province), Waspam district (Atlantio Norte province)",,Rain,,,,Yes,37,,Kph,,,,,2005,10,30,2005,10,30,4,,,5763,5763,,,,76.38802721 +2005-0536-VNM,2005,0536,Natural,Meteorological,Storm,Tropical cyclone,,Vicente,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Thanh Hoa, Nghe An, Ha Tinh provinces",,Rain,,,,,,,Kph,,,,,2005,9,18,2005,9,19,8,,8500,,8500,,,20000,76.38802721 +2005-0524-MEX,2005,0524,Natural,Meteorological,Storm,Tropical cyclone,,Bret,Affected,Mexico,MEX,Central America,Americas,"Naranjal, Chinampa De Gorostiza districts (Veracruz province)",,Flood,,,,,,65,Kph,,,,,2005,6,29,2005,6,30,2,,15000,,15000,,,10000,76.38802721 +2005-0567-NIC,2005,0567,Natural,Meteorological,Storm,Tropical cyclone,,Stan,Kill,Nicaragua,NIC,Central America,Americas,"San Sebastian De Yali district (Jinotega province), Leon, Chinandega, Granada provinces",,Flood,,,,Yes,,,Kph,,,,,2005,10,1,2005,10,13,3,,7880,,7880,,,,76.38802721 +2005-0381-TWN,2005,0381,Natural,Meteorological,Storm,Tropical cyclone,,Haitang (Feria/05W),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Hualien area (Taiwan Sheng province),,Flood,,,,,,220,Kph,,,,,2005,7,18,2005,7,18,12,34,1500,,1534,,25000,100000,76.38802721 +2005-0419-TWN,2005,0419,Natural,Meteorological,Storm,Tropical cyclone,,Matsa (Gorio/09W),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,Flood,,,,,,,Kph,,,,,2005,7,30,2005,8,8,,,,,,,,1000,76.38802721 +2005-0547-USA,2005,0547,Natural,Meteorological,Storm,Tropical cyclone,,Rita,Affected,United States of America (the),USA,Northern America,Americas,"Louisiana, Texas, Mississippi provinces",,Flood,,,,Yes,,280,Kph,,,,"Vermillion, Calcasieu, Mermentau, Amite, Sabine, Neches",2005,9,23,2005,10,1,10,,300000,,300000,,11300000,16000000,76.38802721 +2005-0732-SLV,2005,0732,Natural,Meteorological,Storm,Tropical cyclone,,Adrian,Declar,El Salvador,SLV,Central America,Americas,"Ahuachapan, Cabanas, Chalatenango, Cuscatlan, La Libertad, La Paz, La Union, Morazan, San Miguel, San Salvador, San Vicente, Santa Ana, Sonsonate, Usulutan provinces",,,,,,,,,Kph,,,,,2005,5,19,2005,5,19,,,,,,,,,76.38802721 +2005-0662-SPI,2005,0662,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Canary Is,SPI,Southern Europe,Europe,"Tenerife, La Palma Islands (Santa Cruz de Tenerife)",,,,,,,,152,Kph,,,,,2005,11,27,2005,11,29,19,,,,,,,375000,76.38802721 +2005-0492-TWN,2005,0492,Natural,Meteorological,Storm,Tropical cyclone,,Talim,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,227,Kph,,,,,2005,9,1,2005,9,1,3,59,,,59,,,38000,76.38802721 +2005-0565-TWN,2005,0565,Natural,Meteorological,Storm,Tropical cyclone,,Longwang,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2005,10,2,2005,10,2,2,46,,,46,,50000,100000,76.38802721 +2005-0351-USA,2005,0351,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Dennis""",Kill,United States of America (the),USA,Northern America,Americas,"Florida, Georgia provinces",,,,,,Yes,,,Kph,,,,,2005,7,10,2005,7,10,5,,,,,,1115000,2230000,76.38802721 +2005-0382-VCT,2005,0382,Natural,Meteorological,Storm,Tropical cyclone,,Emily,Affected,Saint Vincent and the Grenadines,VCT,Caribbean,Americas,"Cannau, Union, Petite Martinique, Carriacou islands (Grenadines province)",,,,,,,,,Kph,,,,,2005,7,14,2005,7,14,,,530,,530,,,,76.38802721 +2005-0611-VNM,2005,0611,Natural,Meteorological,Storm,Tropical cyclone,,Kai Tak (21),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Son Ha district (Quang Ngai province), Phong Dien district (Thua Thien - Hue province), Quang Nam, Quang Tri provinces",,,,,,,,,Kph,,,,,2005,11,2,2005,11,4,20,,15000,,15000,,,11000,76.38802721 +2006-0483-MEX,2006,0483,Natural,Meteorological,Storm,Tropical cyclone,,John,Waiting,Mexico,MEX,Central America,Americas,"Comondu, La Paz districts (Baja California Sur province)",,Flood,"Slide (land, mud, snow, rock)",,,,,215,Kph,25.35,-111.81,,,2006,9,2,2006,9,4,7,,10000,,10000,,,,78.85225551 +2006-0505-MEX,2006,0505,Natural,Meteorological,Storm,Tropical cyclone,,Lane,Affected,Mexico,MEX,Central America,Americas,"Culiacan, Elota, Mazatlan, Escuinapa, El Rosario, San Ignacio, Salvador Alvarado, Concordia, Cosala districts (Sinaloa province)",,Flood,"Slide (land, mud, snow, rock)",,,,,250,Kph,24.47,-107.04,,,2006,9,16,2006,9,18,4,,240700,,240700,,,2700,78.85225551 +2006-0251-PHL,2006,0251,Natural,Meteorological,Storm,Tropical cyclone,,Chanchu (Caloy),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,185,Kph,,,,,2006,5,11,2006,5,12,41,,42000,,42000,,,3328,78.85225551 +2006-0362-PHL,2006,0362,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), Region I (Ilocos region), Region II (Cagayan Valley) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,120,Kph,24,112.11,,,2006,7,11,2006,7,17,37,,51680,,51680,,,3000,78.85225551 +2006-0517-PHL,2006,0517,Natural,Meteorological,Storm,Tropical cyclone,,Xangsane (Milenyo),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV (Southern Tagalog), Region IV-A (Calabarzon), Region V (Bicol region), Region VI (Western Visayas) provinces",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,160,Kph,16.68,107.21,,,2006,9,27,2006,10,6,228,406,3842000,,3842406,,,113000,78.85225551 +2006-0600-PHL,2006,0600,Natural,Meteorological,Storm,Tropical cyclone,,Cimaron (Paeng),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cagayan, Quirino, Isabela, Nueva Vizcaya districts (Region II (Cagayan Valley) province), Benguet, Kalinga districts (Cordillera Administrative region (CAR) province), Aurora district (Region III (Central Luzon) province), La Union district (Region I (Ilocos region) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,230,Kph,17.02,121.82,,,2006,10,30,2006,11,1,34,58,282963,,283021,,,9077,78.85225551 +2006-0362-VNM,2006,0362,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Kan, Lang Son, Vinh Phuc, Cao Bang, Thai Nguyen, Ha Giang provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,24,112.11,,Lo,2006,7,11,2006,7,19,17,,,2000,2000,,,,78.85225551 +2006-0648-PHL,2006,0648,Natural,Meteorological,Storm,Tropical cyclone,,Durian (Reming),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Albay, Catanduanes, Camarines Norte, Camarines Sur, Sorsogon districts (Region V (Bicol region) province), Mindoro Occidental, Mindoro Oriental, Marinduque districts (Region IV (Southern Tagalog) province), Batangas, Laguna districts (Region IV-A (Calabarzon) province)",,"Slide (land, mud, snow, rock)",Flood,Yes,,Yes,14414,195,Kph,10.33,106.74,,,2006,11,30,2006,12,8,1399,2143,2560374,,2562517,,,66400,78.85225551 +2006-0610-PHL,2006,0610,Natural,Meteorological,Storm,Tropical cyclone,,Queenie (Chebi),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Isabela district (Region II (Cagayan Valley) province), Aurora, Nueva Ecija districts (Region III (Central Luzon) province)",,"Slide (land, mud, snow, rock)",,,,,,130,Kph,,,,,2006,11,13,2006,11,13,6,10,21250,,21260,,,,78.85225551 +2006-0388-PHL,2006,0388,Natural,Meteorological,Storm,Tropical cyclone,,Kaemi,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Region III (Central Luzon) province,,Rain,,,,,,,Kph,26.39,117.39,,,2006,7,24,2006,7,28,4,,200355,,200355,,,471,78.85225551 +2006-0415-PHL,2006,0415,Natural,Meteorological,Storm,Tropical cyclone,,Henry,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Tarlac, Zambales, Nueva Ecija districts (Region III (Central Luzon) provinces)",,Rain,,,,,,,Kph,15.86,120.74,,,2006,6,30,2006,8,2,8,,476027,,476027,,,645,78.85225551 +2006-0604-MEX,2006,0604,Natural,Meteorological,Storm,Tropical cyclone,,Paul,Affected,Mexico,MEX,Central America,Americas,"La Reforma area (Culiacan district, Sinaloa province)",,Flood,,,,,,,Kph,24.93,-107.53,,,2006,10,25,2006,10,28,4,,20000,,20000,,,,78.85225551 +2006-0241-MMR,2006,0241,Natural,Meteorological,Storm,Tropical cyclone,,Mala,Affected,Myanmar,MMR,South-Eastern Asia,Asia,"Hlinethaya area (Yangon(N) district, Yangon province), Ayeyawaddy, Rakhine provinces",,Flood,,,,,,240,Kph,18.37,94.95,,,2006,4,29,2006,5,5,34,31,60075,,60106,,,,78.85225551 +2006-0466-USA,2006,0466,Natural,Meteorological,Storm,Tropical cyclone,,Ernesto,Affected,United States of America (the),USA,Northern America,Americas,"South Carolina, North Carolina, Virginia provinces",,Flood,,,,,,119,Kph,34.74,-77.87,,,2006,8,2,2006,9,7,6,,140,,140,,,32860,78.85225551 +2006-0648-VNM,2006,0648,Natural,Meteorological,Storm,Tropical cyclone,,Durian (Reming),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Ba Ria-Vung Tau, Ben Tre, Binh Thuan, Vinh Long, Tien Giang, Khanh Hao, An Giang, Tra Vinh, Long An, Dong Thap, Ho Chi Ming City, Can Tho City provinces",,Flood,,,,,467,,Kph,10.33,106.74,,,2006,11,30,2006,12,8,95,1360,975000,250000,1226360,,,456000,78.85225551 +2006-0410-PHL,2006,0410,Natural,Meteorological,Storm,Tropical cyclone,,Prapiroon,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Quirino district (Region II (Cagayan Valley) province),,,,,,,,,Kph,21.11,110.11,,,2006,8,2,2006,8,6,6,,15000,,15000,,,135000,78.85225551 +2006-0667-PHL,2006,0667,Natural,Meteorological,Storm,Tropical cyclone,,Utor (Seniang),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,,,,,,,150,Kph,,,,,2006,12,11,2006,12,11,42,42,327500,,327542,,,,78.85225551 +2006-0362-TWN,2006,0362,Natural,Meteorological,Storm,Tropical cyclone,,Bilis,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2006,7,17,2006,7,17,3,,,,,,,,78.85225551 +2006-0388-TWN,2006,0388,Natural,Meteorological,Storm,Tropical cyclone,,Kaemi,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Pingdong area (Taiwan Sheng province),,,,,,,,,Kph,26.39,117.28,,,2006,7,24,2006,7,28,,,800,,800,,,,78.85225551 +2006-0251-VNM,2006,0251,Natural,Meteorological,Storm,Tropical cyclone,,Chanchu (Caloy),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Quang Nam province,,,,,,,,,Kph,,,,,2006,5,17,2006,5,17,204,,600000,,600000,,,,78.85225551 +2006-0517-VNM,2006,0517,Natural,Meteorological,Storm,Tropical cyclone,,Xangsane (Milenyo),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Ha Tinh, Thua Thien - Hue, Da Nang City, Quang Nam, Quang Ngai provinces",,,,,,,,148,Kph,16.68,107.21,,,2006,9,27,2006,10,6,71,525,1368720,98680,1467925,,,624000,78.85225551 +2007-0133-NZL,2007,0133,Natural,Meteorological,Storm,Tropical cyclone,,Becky,Kill,New Zealand,NZL,Australia and New Zealand,Oceania,Bay of Islands area (Northland province),,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,-35.35,173.98,,,2007,3,29,2007,3,31,,,300,,300,,,,81.10165893 +2007-0085-REU,2007,0085,Natural,Meteorological,Storm,Tropical cyclone,,Gamède,Kill,Réunion,REU,Eastern Africa,Africa,"Arrondissement du vent, Arrondissement sous le vent provinces",,"Slide (land, mud, snow, rock)",,,,,,205,Kph,,,,,2007,2,25,2007,2,25,2,90,,,90,,,,81.10165893 +2007-0591-AUS,2007,0591,Natural,Meteorological,Storm,Tropical cyclone,,George and Jacob,Affected,Australia,AUS,Australia and New Zealand,Oceania,"Darwin district (Northern Territory province), Ashburton, East Pilbara, Port Hedland, Roebourne districts (Western Australia province)",,Flood,,,,,,275,Kph,-13.67,131.92,,,2007,3,2,2007,3,16,2,,730,90,820,,,100000,81.10165893 +2007-0556-BGD,2007,0556,Natural,Meteorological,Storm,Tropical cyclone,,Sidr,Kill,Bangladesh,BGD,Southern Asia,Asia,"Bagerhat, Khulna, Satkhira districts (Khulna province), Patuakhali, Barguna, Pirojpur, Barisal, Jhalokati, Bhola districts (Barisal province), Madaripur, Gopalganj, Shariatpur districts (Dhaka province)",,Flood,,Yes,,,214000,250,Kph,22.61,90.15,,,2007,11,15,2007,11,19,4234,55282,8923259,,8978541,,,2300000,81.10165893 +2007-0523-BHS,2007,0523,Natural,Meteorological,Storm,Tropical cyclone,,Noel,Kill,Bahamas (the),BHS,Caribbean,Americas,"Abaco, Long Island, Exuma, Cat Island, Andros, New Providence islands (Administrative unit not available)",,Flood,,,,,,,Kph,18.53,-70.06,,,2007,10,28,2007,11,2,1,,7000,,7000,,,,81.10165893 +2007-0080-MOZ,2007,0080,Natural,Meteorological,Storm,Tropical cyclone,,Favio,SigDam,Mozambique,MOZ,Eastern Africa,Africa,Vilankulo district (Inhambane province),,,,,,,,180,Kph,,,,,2007,2,22,2007,2,22,10,70,162700,,162770,,,,81.10165893 +2007-0085-MUS,2007,0085,Natural,Meteorological,Storm,Tropical cyclone,,Gamède,Kill,Mauritius,MUS,Eastern Africa,Africa,"Black River, Flacq, Grand Port, Moka, Pamplemousses, Plaines Wiljems, Port Louis, Riviere Du Rempart, Savanne provinces",,,,,,,,,Kph,,,,,2007,2,25,2007,2,25,2,,,,,,,,81.10165893 +2007-0080-ZWE,2007,0080,Natural,Meteorological,Storm,Tropical cyclone,,Favio,SigDam,Zimbabwe,ZWE,Eastern Africa,Africa,"Vumba, Odzi, Marange areas (Mutare district, Manicaland province), Penhalonga, Stapleford villages (Mutasa district, Manicaland province), Chimanimani town (Chimanimani district, Manicaland province)",,,,,,,,,Kph,,,,,2007,2,27,2007,2,27,,,,,,,,1200,81.10165893 +2007-0227-BGD,2007,0227,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Cox's Bazar, Chittagong districts (Chittagong province)",,,,,,,,88,Kph,,,,,2007,5,15,2007,5,15,41,,,225,225,,,,81.10165893 +2007-0360-BLZ,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Belize,BLZ,Central America,Americas,"Corozal, Sarteneja, Consejo cities (Corazal province)",,,,Yes,,,,,Kph,,,,,2007,8,21,2007,8,21,,,20000,,20000,,,14847,81.10165893 +2008-0604-ATG,2008,0604,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Omar""",Affected,Antigua and Barbuda,ATG,Caribbean,Americas,All country affected (no more data),,Flood,,,,,,,Kph,,,,,2008,10,15,2008,10,16,,,25800,,25800,,,,84.21522909 +2008-0648-BGD,2008,0648,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Reshmi""",Kill,Bangladesh,BGD,Southern Asia,Asia,"Barisal, Patuakhali districts (Barisal province)",,Flood,,,,,,80,Kph,,,,,2008,10,27,2008,10,27,15,200,,,200,,,,84.21522909 +2008-0352-BHS,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Bahamas (the),BHS,Caribbean,Americas,,,Flood,,,,,,,Kph,,,,,2008,8,26,2008,8,26,,,,,,,,,84.21522909 +2008-0233-BLZ,2008,0233,Natural,Meteorological,Storm,Tropical cyclone,,Arthur,Affected,Belize,BLZ,Central America,Americas,"Corozal, Orange Walk, Stann Creek provinces",,Flood,,Yes,,Yes,362,,Kph,17.3,-88.1,,,2008,5,31,2008,6,5,7,,10000,,10000,,,,84.21522909 +2008-0644-BGD,2008,0644,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Jamalpur, Mymensingh, Netrakona, Sherpur districts (Dhaka province), Bogra district (Rajshahi province), Gaibandha, Kurigram districts (Rangpur province), Sunamganj, Sylhet provinces (Sylhet province)",,,,,,,,100,Kph,,,,,2008,3,22,2008,3,22,12,200,,,200,,,,84.21522909 +2008-0384-BHS,2008,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ike,Kill,Bahamas (the),BHS,Caribbean,Americas,"Great Inagua, Mayaguana, Acklins, Crooked, Raaged Islands (Administrative unit not available)",,,,,,,,215,Kph,,,,,2008,9,7,2008,9,7,,,3000,,3000,,,,84.21522909 +2009-0204-BGD,2009,0204,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Aila""",Kill,Bangladesh,BGD,Southern Asia,Asia,"Khulna, Satkhira, Jessore, Bagerhat districts (Khulna province), Patuakhali, Bhola, Barisal, Barguna, Pirojpur, Jhalokati districts (Barisal province), Lakshmipur, Chittagong, Noakhali, Cox's Bazar, Feni, Chandpur districts (Chittagong province)",,Broken Dam/Burst bank,Tsunami/Tidal wave,Yes,,,11808,90,Kph,22.16,89.09,,,2009,5,25,2009,5,26,190,7103,3928238,,3935341,,,270000,83.91580741 +2009-0048-AUS,2009,0048,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Dominic""",Affected,Australia,AUS,Australia and New Zealand,Oceania,Queensland province,,Flood,,,,Yes,,,Kph,-20.11,120.56,,,2009,1,26,2009,2,20,,,400,,400,,,,83.91580741 +2009-0157-BGD,2009,0157,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Bijli""",Affected,Bangladesh,BGD,Southern Asia,Asia,"Banshkhali, Anowara, Sitakunda, Mirsaharai, Sandwip, Patiya, Boalkhali, Satkania, Chandanaish, City Corportaion of Chittagong (CCC) areas (Chittagong district, Chittagong province), Sadar, Ramu, Chakaria, Pekua, Maheshkhali, Kutubdia areas (Cox's Bazar district, Chittagong province), Hatiya area (Noakhali district, Chittagong province), Charfashion area (Bhola district, Barisal province), Pirganj area (Thakurgao district, Rangpur province)",,,,,,,,100,Kph,,,,,2009,4,19,2009,4,20,7,84,19125,,19209,,,,83.91580741 +2009-0204-BTN,2009,0204,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Aila""",Kill,Bhutan,BTN,Southern Asia,Asia,"Chhukha, Dagana, Gasa, Haa, Paro, Punakha, Samtse, Thimphu, Tsirang, Wangduephodrang provinces",,,,,,,,,Kph,,,,,2009,5,25,2009,5,26,12,,,,,,,,83.91580741 +2007-0457-CHN,2007,0457,Natural,Meteorological,Storm,Tropical cyclone,,Wipha/Goring,Kill,China,CHN,Eastern Asia,Asia,Zhejiang Sheng province,,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2007,9,20,2007,9,20,9,,,,,,,638000,81.10165893 +2007-0523-DOM,2007,0523,Natural,Meteorological,Storm,Tropical cyclone,,Noel,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Santo Domingo, Distrito Nacional, San Cristobal, Peravia, Azua, Barahona, Pedernales, Independencia, Baoruco, San Juan, Santiago, Puerto Plata, Espaillat, Salcedo, Duarte, La Vega, Monte Plata, Monsenor Nouel, Hato Mayor, El Seibo, Dajabon, Monte Cristi, Santiago Rodriguez, La Altagracia, San Pedro de Macoris provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,18.53,-70.06,,,2007,10,28,2007,11,2,129,,79728,,79728,,,77700,81.10165893 +2007-0612-DOM,2007,0612,Natural,Meteorological,Storm,Tropical cyclone,,Olga,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Bonao city (Monsenor Nouel district, Monsenor Nouel province), Nagua district (Maria Trinidad Sanches province), Arenoso, Villa Rivas districts (Duarte province), Santiago, Barahona, La Vega, Puerto Plata, Monte Plata, El Seibo provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,18.93,-71.03,,,2007,12,11,2007,12,17,33,,61605,,61605,,,45000,81.10165893 +2007-0360-GLP,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Guadeloupe,GLP,Caribbean,Americas,,,Flood,"Slide (land, mud, snow, rock)",,,,,160,Kph,,,,,2007,8,29,2007,8,29,,,,,,,125000,300000,81.10165893 +2007-0439-GTM,2007,0439,Natural,Meteorological,Storm,Tropical cyclone,,Felix,Kill,Guatemala,GTM,Central America,Americas,"Puerto Barrios, Morales districts (Izabal province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,14.62,-83.64,,,2007,9,4,2007,9,12,,,3905,,3905,,,,81.10165893 +2007-0439-HND,2007,0439,Natural,Meteorological,Storm,Tropical cyclone,,Felix,Kill,Honduras,HND,Central America,Americas,"Santa Barbara, Cortes, Choluteca, Valle, Paraiso provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,14.62,-83.64,,,2007,9,4,2007,9,12,1,,19500,,19500,,,6579,81.10165893 +2007-0464-MEX,2007,0464,Natural,Meteorological,Storm,Tropical cyclone,,Lorenzo,Affected,Mexico,MEX,Central America,Americas,"Veracruz, Puebla provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,20.93,-97.85,,,2007,9,28,2007,10,1,5,,33000,,33000,,,,81.10165893 +2007-0360-MTQ,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Martinique,MTQ,Caribbean,Americas,"Fort-de-France, La Trinite, Saint-Pierre, Le Marin provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,160,Kph,14.64,-61.02,,,2007,8,16,2007,8,24,1,6,,,6,,125000,300000,81.10165893 +2007-0439-NIC,2007,0439,Natural,Meteorological,Storm,Tropical cyclone,,Felix,Kill,Nicaragua,NIC,Central America,Americas,"Puerto Cabezas, Waspam, Siuna, Bonanza, Rosita districts (Atlantico Norte province)",,Flood,"Slide (land, mud, snow, rock)",Yes,Yes,,,,Kph,14.62,-83.64,,,2007,9,4,2007,9,12,188,,188726,,188726,,,,81.10165893 +2007-0164-OMN,2007,0164,Natural,Meteorological,Storm,Tropical cyclone,,Gonu,Kill,Oman,OMN,Western Asia,Asia,Muscat province,,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,731,260,Kph,,,,,2007,6,6,2007,6,10,76,,20000,,20000,,650000,3900000,81.10165893 +2007-0346-PHL,2007,0346,Natural,Meteorological,Storm,Tropical cyclone,,Chedeng and Dodong,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region III (Central Luzon) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2007,8,8,2007,8,13,7,7,921455,,921462,,,492,81.10165893 +2007-0463-PHL,2007,0463,Natural,Meteorological,Storm,Tropical cyclone,,Lekima,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ifugao, Kalinga districts (Cordillera Administrative region (CAR) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,17.07,106.9,,,2007,9,29,2007,10,12,8,,2000,,2000,,,,81.10165893 +2007-0576-PHL,2007,0576,Natural,Meteorological,Storm,Tropical cyclone,,Mitag (Mina) (23),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Isabela district (Region II (Cagayan Valley) province), Camarines Sur, Camarines Norte districts (Region V (Bicol region) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,170,Kph,17.08,122.01,,,2007,11,25,2007,12,2,29,6,443109,,443115,,,5000,81.10165893 +2007-0578-PHL,2007,0578,Natural,Meteorological,Storm,Tropical cyclone,,Hagibis (Lando) (24),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region VII (Central Visayas), Region X (Northern Mindanao) provinces",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,129,Kph,8.796,124.83,,,2007,11,18,2007,11,23,20,11,35322,,35333,,,1000,81.10165893 +2007-0667-PHL,2007,0667,Natural,Meteorological,Storm,Tropical cyclone,,Hanna,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region V (Bicol region), Region X (Northern Mindanao), Region XII (Soccsksargen) Region XIII (Caraga), Cordillera Administrative region (CAR), National Capital region (NCR) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2007,9,29,2007,9,30,11,,12515,,12515,,,260,81.10165893 +2007-0523-CUB,2007,0523,Natural,Meteorological,Storm,Tropical cyclone,,Noel,Kill,Cuba,CUB,Caribbean,Americas,"Granma, Holguin, Las Tunas, Guantanamo, Santiago de Cuba provinces",,Flood,Broken Dam/Burst bank,Yes,,,,,Kph,18.53,-70.06,,,2007,10,28,2007,11,2,1,,192488,,192488,,,500000,81.10165893 +2007-0245-PAK,2007,0245,Natural,Meteorological,Storm,Tropical cyclone,,Yemyin,Kill,Pakistan,PAK,Southern Asia,Asia,"Balochistan, Sindh, North-West Frontier provinces",,Flood,Broken Dam/Burst bank,,,,35000,,Kph,25.26,67.9,,,2007,6,26,2007,7,6,242,,1650000,,1650000,,,1620000,81.10165893 +2007-0262-JPN,2007,0262,Natural,Meteorological,Storm,Tropical cyclone,,Man-Yi,Affected,Japan,JPN,Eastern Asia,Asia,"Okinawa, Hukuoka, Kagosima, Kumamoto, Miyazaki, Nagasaki, Ooita, Saga, Aiti, Ehime, Kagawa, Kooti, Tokusima provinces",,"Slide (land, mud, snow, rock)",Rain,,,,,250,Kph,31.69,130.9,,,2007,7,13,2007,7,16,5,12,40000,,40012,,67000,60000,81.10165893 +2007-0360-JAM,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Jamaica,JAM,Caribbean,Americas,"Clarendon, Saint Thomas, Saint James, Saint Andrew And Kingston provinces",,Surge,Flood,Yes,,Yes,,,Kph,,,,,2007,8,20,2007,8,20,4,,32000,1188,33188,,150000,300000,81.10165893 +2007-0360-CUB,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Cuba,CUB,Caribbean,Americas,"Pinar del Rio, La Habana, Isla de la Juventud provinces",,Flood,Surge,,,,,240,Kph,,,,,2007,8,20,2007,8,20,,,,,,,,,81.10165893 +2007-0380-CHN,2007,0380,Natural,Meteorological,Storm,Tropical cyclone,,Sepat,Kill,China,CHN,Eastern Asia,Asia,"Hunan Sheng, Jiangxi Sheng, Fujian Sheng, Zhejiang Sheng, Guangdong Sheng provinces",,Flood,,,,,,,Kph,,,,,2007,8,18,2007,8,21,39,,8000000,,8000000,,,890555,81.10165893 +2007-0552-CHN,2007,0552,Natural,Meteorological,Storm,Tropical cyclone,,Krosa,Kill,China,CHN,Eastern Asia,Asia,"Zhejiang Sheng, Fujian Sheng provinces",,Flood,,,,,,240,Kph,,,,,2007,10,2,2007,10,8,,,,,,,,1077788,81.10165893 +2007-0360-DMA,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Dominica,DMA,Caribbean,Americas,"St. Andrew, St. David, St. George, St. John, St. Joseph, St. Luke, St. Mark, St. Patrick, St. Paul, St. Peter provinces",,Flood,,Yes,,,,,Kph,,,,,2007,8,21,2007,8,24,2,30,7500,,7530,,10000,20000,81.10165893 +2007-0360-DOM,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Barahona, Distrito Nacional, La Altagracia, La Romana, Pedernales, Peravia, San Cristobal, San Pedro de Macoris, Santiago, Santo Domingo provinces",,Flood,,,,,,,Kph,14.64,-61.02,,,2007,8,21,2007,8,24,1,,1600,,1600,,30000,40000,81.10165893 +2007-0360-HTI,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Haiti,HTI,Caribbean,Americas,"Sud, Sud Est, Grande Anse, Nippes, Ouest, Artibonite, Centre, Nord, Nord Est, Nord Ouest provinces",,Flood,,,,,,,Kph,14.64,-61.02,,,2007,8,18,2007,8,24,9,6,3960,,3966,,,,81.10165893 +2007-0523-HTI,2007,0523,Natural,Meteorological,Storm,Tropical cyclone,,Noel,Kill,Haiti,HTI,Caribbean,Americas,"Port-au-Prince district (Ouest province), Gonaïves district (Artibonite province), Jacmel district (Sud Est province), Cayes district (Sud province)",,Flood,,Yes,,,,,Kph,18.53,-70.06,,,2007,10,28,2007,11,2,90,133,108630,,108763,,,,81.10165893 +2007-0612-HTI,2007,0612,Natural,Meteorological,Storm,Tropical cyclone,,Olga,Kill,Haiti,HTI,Caribbean,Americas,"Nord, Nord Est, Nord Ouest provinces",,Flood,,,,,,,Kph,18.93,-71.03,,,2007,12,11,2007,12,17,3,2,2090,260,2352,,,,81.10165893 +2007-0164-IRN,2007,0164,Natural,Meteorological,Storm,Tropical cyclone,,Gonu,Kill,Iran (Islamic Republic of),IRN,Southern Asia,Asia,"Ghalehganj villages (Kahnug district, Kerman province), Chahbahar, Zarabad, Konarak, Kahir villages (Chahbahar district, Sistan-o baluchestan province), Sarbaz, Iranshahr villages (Iranshahr district, Sistan-o baluchestan province), Zahedan, Khash, Saravan, Nikshahr districts (Sistan-o baluchestan province), Jask, Bandar-e abbas, Geshm districts (Hormozgan province)",,Flood,,,,,,220,Kph,,,,,2007,6,6,2007,6,10,12,9,185000,,185009,,,,81.10165893 +2007-0457-JPN,2007,0457,Natural,Meteorological,Storm,Tropical cyclone,,Wipha/Goring,Kill,Japan,JPN,Eastern Asia,Asia,"Iwate, Akita provinces",,Flood,,,,,,,Kph,38.57,125.83,,,2007,9,17,2007,9,25,4,,,,,,,,81.10165893 +2007-0479-JPN,2007,0479,Natural,Meteorological,Storm,Tropical cyclone,,Fitow (9),Affected,Japan,JPN,Eastern Asia,Asia,"Tookyoo, Hokkaidoo, Nagano provinces",,Flood,,,,,,140,Kph,,,,,2007,8,28,2007,9,8,4,82,900,,982,,700000,1000000,81.10165893 +2007-0470-KOR,2007,0470,Natural,Meteorological,Storm,Tropical cyclone,,Nari (11),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Cheju-do, Chollanam-do provinces",,Flood,,,,,,,Kph,34.78,126.59,,,2007,9,16,2007,9,19,20,2,,600,602,,,70000,81.10165893 +2007-0360-LCA,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Saint Lucia,LCA,Caribbean,Americas,"Region Number 1, Region Number 2, Region Number 8 provinces",,Flood,,Yes,,,,,Kph,14.64,-61.02,,,2007,8,17,2007,8,24,1,,,,,,20000,40000,81.10165893 +2007-0360-MEX,2007,0360,Natural,Meteorological,Storm,Tropical cyclone,,Dean,Waiting,Mexico,MEX,Central America,Americas,"Yucatan, Quintana Roo, Campeche, Veracruz, Hidalgo, Puebla provinces",,Flood,,Yes,,Yes,,265,Kph,14.54,-61.02,,,2007,8,21,2007,8,24,9,,140000,,140000,,475000,600000,81.10165893 +2007-0439-MEX,2007,0439,Natural,Meteorological,Storm,Tropical cyclone,,Felix,Kill,Mexico,MEX,Central America,Americas,"Veracruz, Tamaulipas, San Luis Potosi provinces",,Flood,,,,,,,Kph,14.62,-83.64,,,2007,9,4,2007,9,6,,,,30000,30000,,,,81.10165893 +2007-0380-PHL,2007,0380,Natural,Meteorological,Storm,Tropical cyclone,,Sepat,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region III (Central Luzon), Region IV-A (Calabarzon), Region V (Bicol region) provinces",Heavy rain,Flood,,,,,,,Kph,27,119.91,,,2007,8,17,2007,8,24,3,,380000,,380000,,,492,81.10165893 +2007-0457-PHL,2007,0457,Natural,Meteorological,Storm,Tropical cyclone,,Wipha/Goring,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Negros Occidental district (Region VI (Western Visayas) province),,Flood,,,,,,,Kph,38.57,125.83,,,2007,9,17,2007,9,25,2,,30000,,30000,,,,81.10165893 +2007-0668-PHL,2007,0668,Natural,Meteorological,Storm,Tropical cyclone,,Goring,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas), Autonomous region in Muslim Mindanao (ARMM), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,Flood,,,,,,,Kph,,,,,2007,9,18,2007,9,18,1,,64000,,64000,,,,81.10165893 +2007-0655-FJI,2007,0655,Natural,Meteorological,Storm,Tropical cyclone,,Daman,Waiting,Fiji,FJI,Melanesia,Oceania,"Cikobia island (Lau district, Eastern province)",,,,,,,,250,Kph,,,,,2007,12,5,2007,12,7,,,69,,69,,,652,81.10165893 +2007-0556-IND,2007,0556,Natural,Meteorological,Storm,Tropical cyclone,,Sidr,Kill,India,IND,Southern Asia,Asia,"West Bengal, Orissa provinces",,,,,,,,,Kph,,,,,2007,11,15,2007,11,15,,,,,,,,,81.10165893 +2007-0523-JAM,2007,0523,Natural,Meteorological,Storm,Tropical cyclone,,Noel,Kill,Jamaica,JAM,Caribbean,Americas,"Saint Catherine, Clarendon, Manchester provinces",,,,,,,,,Kph,,,,,2007,10,28,2007,11,2,1,,,,,,,,81.10165893 +2007-0671-MEX,2007,0671,Natural,Meteorological,Storm,Tropical cyclone,,Henriette,Affected,Mexico,MEX,Central America,Americas,"Acapulco De Juarez district (Guerrero province), Oaxaca, Michoacan, Colima, Jalisco provinces",,,,,,,,,Kph,,,,,2007,9,1,2007,9,1,6,,,,,,,,81.10165893 +2007-0560-PHL,2007,0560,Natural,Meteorological,Storm,Tropical cyclone,,Kabayan (Peipah),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Isabela, Cagayan districts (Region II (Cagayan Valley) province)",Tropical storm Peipah,,,,,,,25810,Kph,17.57,121.76,,Cagayan river and tributaries,2007,11,4,2007,11,6,8,,33884,,33884,,,2971,81.10165893 +2008-0155-CHN,2008,0155,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Neoguri"" (Ambo)",Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,175,Kph,,,,,2008,4,19,2008,4,19,25,,274000,,274000,,,49000,84.21522909 +2008-0338-DOM,2008,0338,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fay""",Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2008,8,18,2008,8,18,4,,,,,,,,84.21522909 +2008-0304-GTM,2008,0304,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Dolly,Affected,Guatemala,GTM,Central America,Americas,"La Union district (Zacapa province), San Pedro Soloma area (Soloma district, Huehuetenango province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2008,7,21,2008,7,21,17,,,,,,,,84.21522909 +2008-0184-LKA,2008,0184,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Nargis,Kill,Sri Lanka,LKA,Southern Asia,Asia,"Colombo, Kalutara, Gampaha districts (Western province), Ratnapura, Kegalle districts (Sabaragamuwa province), Puttalam district (North Western province), Nuwara Eliya district (Central province), Galle district (Southern province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2008,4,27,2008,5,3,9,,50000,,50000,,,,84.21522909 +2008-0304-MEX,2008,0304,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Dolly,Affected,Mexico,MEX,Central America,Americas,"Tamaulipas, Veracruz, Yucatan, San Luis Potosi, Nuevo Leon, Coahuila provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,160,Kph,,,,,2008,7,20,2008,7,21,2,,500000,,500000,,,75000,84.21522909 +2008-0249-PHL,2008,0249,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Fengshen (Franck),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Aklan, Antique, Capiz, Iloilo, Negros Occidental districts (Region VI (Western Visayas) province), Cebu district (Region VII (Central Visayas) province), Leyte, Eastern Samar, Samar districts (Region VIII (Eastern Visayas) province), Marinduque, Mindoro Oriental, Romblon districts (Region IV (Southern Tagalog) province), Masbate district (Region V (Bicol region) province), North Cotabato, South Cotabato districts (Region XII (Soccsksargen) province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,4636,170,Kph,13.16,122.6,,,2008,6,21,2008,6,23,644,826,4784634,,4785460,,45000,284694,84.21522909 +2008-0234-CRI,2008,0234,Natural,Meteorological,Storm,Tropical cyclone,,Alma,Kill,Costa Rica,CRI,Central America,Americas,"Parrita, Aguirre, Puntarenas districts (Puntarenas province), Canas, Bagaces, Abangares, Nandayure, Hojancha, Nicoya, Santa Cruz districts (Guanacaste province), Perez Zeledon, Tarrazu, Leon Cortes (San Jose province)",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2008,5,28,2008,5,28,4,,55000,,55000,,,,84.21522909 +2008-0197-PHL,2008,0197,Natural,Meteorological,Storm,Tropical cyclone,,Halong (Cosme),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Pangasinan, La Union districts (Region I (Ilocos region) province), Zambales district (Region III (Central Luzon) province)",,"Slide (land, mud, snow, rock)",Flood,,,Yes,,95,Kph,,,,,2008,5,18,2008,5,18,64,33,1496635,,1496668,,,99174,84.21522909 +2008-0514-CYM,2008,0514,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Paloma,Waiting,Cayman Islands (the),CYM,Caribbean,Americas,"Cayman Brac, Little Cayman provinces",,Surge,Flood,,,,,220,Kph,,,,,2008,11,8,2008,11,8,,,,,,,,,84.21522909 +2008-0384-CUB,2008,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ike,Kill,Cuba,CUB,Caribbean,Americas,"Guantanamo, Santiago de Cuba, Camaguey, Holguín, Granma, Las Tunas, Ciego de Avila, Sancti Spiritus, Villa Clara, La Habana, Ciudad de la Habana, Pinar del Rio, Isla de le Juventud, Matanzas provinces",,Flood,Surge,,,,,,Kph,,,,,2008,9,8,2008,9,9,7,,,,,,,1500000,84.21522909 +2008-0352-JAM,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Jamaica,JAM,Caribbean,Americas,"Saint Catherine, Saint Andrew And Kingston, Portland, Saint Thomas, Saint Mary provinces",,Flood,Surge,Yes,,,,,Kph,,,,,2008,8,28,2008,8,29,12,,4000,,4000,,,66198,84.21522909 +2008-0352-DOM,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Dominican Republic (the),DOM,Caribbean,Americas,Santo Domingo province,,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2008,8,26,2008,8,26,8,2,6255,,6257,,,,84.21522909 +2008-0426-PHL,2008,0426,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Hagupit"" (Nina)",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Itogon area (Benguet district, Cordillera Administrative region (CAR) province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2008,9,22,2008,9,22,37,17,41630,4485,46132,,,7420,84.21522909 +2008-0369-CHN,2008,0369,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Nuri"" (Karen)",Kill,China,CHN,Eastern Asia,Asia,"Guangzhou, Shenzhen districts (Guangdong province)",,Rain,,,,,,,Kph,,,,,2008,8,22,2008,8,23,4,,900000,,900000,,,58000,84.21522909 +2008-0378-DOM,2008,0378,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Hanna,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Monte Cristi, Puerto Plata provinces",,Rain,,,,,,110,Kph,,,,,2008,9,3,2008,9,3,1,,10745,,10745,,,,84.21522909 +2008-0249-CHN,2008,0249,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Fengshen (Franck),Kill,China,CHN,Eastern Asia,Asia,"Sichuan Sheng, Guangdong Sheng, Jiangxi Sheng provinces",,Flood,,,,,,,Kph,23.542,112.3,,,2008,6,24,2008,6,27,14,,340000,,340000,,,175000,84.21522909 +2008-0292-CHN,2008,0292,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fung-Wong"" (Igme)",Kill,China,CHN,Eastern Asia,Asia,"Yuexi Xian area (Anqing district, Anhui Sheng province), Jinzhai Xian area (Lu'an district, Anhui Sheng province), Fuzhou, Quanzhou, Putian districts (Fujian Sheng province), Wenzhou district (Zhejiang Sheng province ), Jiujiang district (Jiangxi Sheng province)",,Flood,,,,,,155,Kph,32.56,114.41,,,2008,7,28,2008,8,8,1,6,93000,,93006,,,73000,84.21522909 +2008-0329-CHN,2008,0329,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kammuri"" (Julian)",Kill,China,CHN,Eastern Asia,Asia,Zhanjiang district (Guangdong Sheng province),,Flood,,,,,,,Kph,22.33,113.15,,,2008,8,8,2008,8,11,,,42000,,42000,,,80000,84.21522909 +2008-0352-CUB,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Cuba,CUB,Caribbean,Americas,"Island of Youth, Pinar del Rio, Havana, Matanzas provinces",,Flood,,Yes,,,,240,Kph,,,,,2008,8,29,2008,9,1,,19,450000,,450019,,,2072000,84.21522909 +2008-0051-FJI,2008,0051,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Gene""",Waiting,Fiji,FJI,Melanesia,Oceania,"Central, Eastern, Northern, Western provinces",,Flood,,,,,,177,Kph,,,,,2008,1,28,2008,1,29,7,,,,,,,30000,84.21522909 +2008-0426-HKG,2008,0426,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Hagupit"" (Nina)",Affected,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,Flood,,,,,,,Kph,,,,,2008,9,25,2008,9,25,,58,,,58,,,,84.21522909 +2008-0352-HTI,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Haiti,HTI,Caribbean,Americas,"Sud Est, Sud, Nippes, Ouest, Grande Anse, Artibonite, Centre provinces",,Flood,,,,,,,Kph,,,,,2008,8,26,2008,8,26,85,36,72970,,73006,,,,84.21522909 +2008-0378-HTI,2008,0378,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Hanna,Kill,Haiti,HTI,Caribbean,Americas,"Gonaives, Saint-Marc, Gros Morne districts (Artibonite province), Port-au-Prince district (Ouest province), Sud, Nord, Sud Est, Nippes provinces",,Flood,,,,Yes,,,Kph,,,,,2008,9,2,2008,9,3,529,,48000,,48000,,,,84.21522909 +2008-0384-HTI,2008,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ike,Kill,Haiti,HTI,Caribbean,Americas,Gonaives district (Artibonite province),,Flood,,,,,,,Kph,18.46,-71.69,,,2008,9,6,2008,9,8,74,50,125000,,125050,,,,84.21522909 +2008-0043-MDG,2008,0043,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Fame""",Kill,Madagascar,MDG,Eastern Africa,Africa,"Melaky, Boeny, Analamanga provinces",,Flood,,,,,,130,Kph,,,,,2008,1,27,2008,1,27,12,,5457,3156,8613,,,,84.21522909 +2008-0070-MDG,2008,0070,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Ivan""",Affected,Madagascar,MDG,Eastern Africa,Africa,"Analamanga, Betsiboka, Vatovavy Fitovavy, Analanjirofo, Alaotra Mangoro, Atsinanana, Atsimo Atsinanana, Boeny, Sofia, Menabe, Bongolava, Haute Matsiatra provinces",,Flood,,Yes,Yes,,28487,230,Kph,-18.69,46.9,,,2008,2,17,2008,3,3,93,580,332391,191182,524153,,,60000,84.21522909 +2008-0184-MMR,2008,0184,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Nargis,Kill,Myanmar,MMR,South-Eastern Asia,Asia,"Labutta, Mawlamyinegyunn areas (Myaungmya district, Ayeyawaddy province), Ngapudaw area (Pathein district, Ayeyawaddy province), Bogale, Dedaye, Kyaiklat areas (Pyapon district, Ayeyawaddy province), Kungyangon, Kawhmu, Twantay, Kyauktan areas (Yangon(S) district, Yangon province), Bago (E), Bago (W), Kayin, Kayar, Mon provinces",,Flood,,Yes,,Yes,603184,215,Kph,,,,,2008,5,2,2008,5,3,138366,20000,2400000,,2420000,,,4000000,84.21522909 +2008-0292-PHL,2008,0292,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fung-Wong"" (Igme)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Piddig, Pasuquin areas (Metropolitan Manila district, National Capital region (NCR) province), Ilocos Norte, Ilocos Sur districts (Region I (Ilocos region) province), Abra district (Cordillera Administrative region (CAR) province)",,Flood,,,,,,,Kph,,,,,2008,7,28,2008,7,29,10,2,22079,,22081,,,40,84.21522909 +2008-0329-PHL,2008,0329,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kammuri"" (Julian)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Quezon city (Metropolitan Manila district, National Capital region (NCR) province), Cordillera Administrative region (CAR), Region I (Ilocos region), Region III (Central Luzon) provinces",,Flood,,,,,,,Kph,,,,,2008,8,4,2008,8,4,2,,4498,,4498,,,,84.21522909 +2008-0540-PHL,2008,0540,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Maysak"" (Quinta-Sony)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,Flood,,,,,,,Kph,,,,,2008,11,6,2008,11,11,19,14,300,160,474,,,,84.21522909 +2008-0609-PHL,2008,0609,Natural,Meteorological,Storm,Tropical cyclone,,"TYphoon ""Rammasun"" (Butchoy)",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Lambayong, Esperanza areas (Sultan Kudarat district, Region XII (Soccsksargen) province)",,Flood,,,,Yes,,,Kph,,,,,2008,5,13,2008,5,13,1,40,8350,,8390,,,280,84.21522909 +2008-0621-PHL,2008,0621,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Helen""",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Nueva Era, Paoay, San Nicolas, Bangui, Batac city, Dingras, Pasuquin, Marcos, Piddig, Laoag city, Bacarra, Sarrat, Currimao, Pagudpud areas (Ilocos Norte district, Region I (Ilocos region) province), Luna area (La Union district, Region I (Ilocos region) province), Santa Terisita area (Cagayan district, Region II (Cagayan Valley) province)",,Flood,,,,,,,Kph,,,,,2008,7,18,2008,7,18,2,1,31129,,31130,,,147,84.21522909 +2008-0514-CUB,2008,0514,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Paloma,Waiting,Cuba,CUB,Caribbean,Americas,"Santa Cruz del Sur, Najasa, Guáimaro (Camaguey province), Amancio Rodriguez (Las Tunas province), Sancti Spirtitus, Ciego de Avila, Granma provinces",,Surge,,,,,,230,Kph,,,,,2008,11,8,2008,11,8,,,49445,,49445,,,,84.21522909 +2008-0338-HTI,2008,0338,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fay""",Kill,Haiti,HTI,Caribbean,Americas,"Grande Anse, Centre, Sud, Nippes, Ouest, Artibonite, Nord Ouest, Nord, Nord Est, Sud Est provinces",,Transport accident,,,,,,,Kph,,,,,2008,8,18,2008,8,18,10,,190,30,220,,,,84.21522909 +2008-0426-CHN,2008,0426,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Hagupit"" (Nina)",Affected,China,CHN,Eastern Asia,Asia,"Maoming, Yangjiang, Zhanjiang districts (Guangdong province), Guangxi Zhuangzu Zizhiqu, Sichuan Sheng provinces",,,,,,,,,Kph,,,,,2008,9,24,2008,9,25,12,,,,,,,824000,84.21522909 +2008-0441-CHN,2008,0441,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Jangmi"" (Ofel)",Affected,China,CHN,Eastern Asia,Asia,"Ningde, Fuzhou, Putian, Xiamen, Quanzhou, Zhangzhou districts (Fujian Sheng province)",,,,,,,,,Kph,,,,,2008,9,28,2008,9,28,,,,,,,,,84.21522909 +2008-0338-CUB,2008,0338,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fay""",Kill,Cuba,CUB,Caribbean,Americas,"Cienfuegos, Sancti Spiritus, Guantanamo, Santiago de Cuba, Granma, Villa Clara, Ciego de Avila, Camgguey, Las Tunas, Holguín provinces",,,,,,,,85,Kph,,,,,2008,8,20,2008,8,20,,,,,,,,,84.21522909 +2008-0352-CYM,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Cayman Islands (the),CYM,Caribbean,Americas,"Grand Cayman, Little Cayman, Cayman Brac provinces",,,,,,,,,Kph,,,,,2008,8,26,2008,8,26,,,,,,,,,84.21522909 +2008-0329-HKG,2008,0329,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kammuri"" (Julian)",Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,,,,,,,,Kph,,,,,2008,8,5,2008,8,5,,37,,,37,,,,84.21522909 +2008-0369-HKG,2008,0369,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Nuri"" (Karen)",Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,,,,,,,,Kph,,,,,2008,9,22,2008,9,22,2,112,,,112,,,380,84.21522909 +2008-0234-HND,2008,0234,Natural,Meteorological,Storm,Tropical cyclone,,Alma,Kill,Honduras,HND,Central America,Americas,Choluteca province,,,,,,,,,Kph,,,,,2008,5,28,2008,5,28,,,,,,,,,84.21522909 +2008-0338-JAM,2008,0338,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fay""",Kill,Jamaica,JAM,Caribbean,Americas,"Clarendon, Hanover, Manchester, Portland, Saint Andrew And Kingston, Saint Ann, Saint Catherine, Saint Elizabeth, Saint James, Saint Mary, Saint Thomas, Trelawny, Westmoreland provinces",,,,,,,,,Kph,,,,,2008,8,18,2008,8,24,1,,,,,,,,84.21522909 +2008-0111-MDG,2008,0111,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Jokwe""",Kill,Madagascar,MDG,Eastern Africa,Africa,Nosy-Be district (Diana province),,,,,,,,,Kph,,,,,2008,3,5,2008,3,6,,,400,,400,,,,84.21522909 +2008-0111-MOZ,2008,0111,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Jokwe""",Kill,Mozambique,MOZ,Eastern Africa,Africa,"Mossuril, Angoche, Nacala, Moma, Ilha de Mocambique, Mogovolas districts (Nampula province), Zambezia, Sofala provinces",,,,,,,8166,,Kph,,,,,2008,3,8,2008,3,9,9,13,165000,55000,220013,,,20000,84.21522909 +2008-0234-NIC,2008,0234,Natural,Meteorological,Storm,Tropical cyclone,,Alma,Kill,Nicaragua,NIC,Central America,Americas,"Leon, Chinandega, Rivas, Carazo, Masaya, Granada provinces",,,,Yes,,,,,Kph,,,,,2008,5,29,2008,5,29,13,,25000,,25000,,,,84.21522909 +2008-0155-PHL,2008,0155,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Neoguri"" (Ambo)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Autonomous region in Muslim Mindanao (ARMM), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,,,,,,,175,Kph,,,,,2008,4,13,2008,4,13,1,,,,,,,16000,84.21522909 +2008-0369-PHL,2008,0369,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Nuri"" (Karen)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region I (Ilocos region), Region II (Cagayan Valley), Cordillera Administrative region (CAR) provinces",,,,,,,,150,Kph,,,,,2008,8,22,2008,8,22,38,13,429450,,429463,,,33870,84.21522909 +2008-0623-PHL,2008,0623,Natural,Meteorological,Storm,Tropical cyclone,,"TRopical storm ""Higos"" (Pablo)",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Nueva Ecija, Pampanga districts (Region III (Central Luzon) province)",,,,,,,,,Kph,,,,,2008,9,29,2008,9,29,1,,27683,,27683,,,,84.21522909 +2009-0204-IND,2009,0204,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Aila""",Kill,India,IND,Southern Asia,Asia,"Sagar, Namkhana, Kakdwip, Pathar Pratima, Canning, Basanti, Mathurapur, Kultali villages (South 24 Parganas district, West Bengal province), Kolkata, North 24 Parganas, Haora, Hugli, Darjiling districts (West Bengal province)",,Flood,"Slide (land, mud, snow, rock)",,,,,110,Kph,,,,,2009,5,25,2009,5,25,96,,5100000,,5100000,,,,83.91580741 +2009-0174-PHL,2009,0174,Natural,Meteorological,Storm,Tropical cyclone,,"Typhhon ""Dante"" (Kujira)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Region V (Bicol region) province,,Flood,"Slide (land, mud, snow, rock)",,,,,148,Kph,,,,,2009,4,29,2009,5,5,29,8,383457,,383465,,,25810,83.91580741 +2009-0165-PHL,2009,0165,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Emong"" (Chanom)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Aringay, Agoo, Bacnotan, Bagulin, Bauang, Burgos, Caba, Naguillian Pugo, Rosario, San Gabriel, San Juan, Sto Thomas, Tubao, San Fernando areas (La Union district, Region I (Ilocos region) province), Anda, Agno, Bani, Bolinao, Dasol, Infanta, Lingayan, Mabini, Sta Barbara, Sual, Alaminos city, Dagupan areas (Pangasinan district, Region I (Ilocos region) province), Ambaguio, Bagabag, Bambang, Bayombong, Dupax del Sur, Solano, Sta Fe areas (Nueva Vizcaya district, Region II (Cagayan Valley) province), Aurora, Cabaruan, Cordon, Delfin Albano, Ilagan, Luna, Quirino, Ramon, Reina Mercedes, Cauayan city, Santiago areas (Isabela district, Region II (Cagayan Valley) province), Cabarroguis, Saguiday areas (Quirino district, Region II (Cagayan Valley) province), Rizal area (Cagayan district, Region II (Cagayan Valley) province), Sta Cruz, Olongapo areas (Zambales district, Region III (Central Luzon) province), Guagua area (Pampanga district, Region III (Central Luzon) province), Kiangan, Asipulo, Lamut, Lagawe, Hingyon, Hungduan, Tinoc, Aguinaldo, Alfonso Lista areas (Ifugao district, Cordillera Administrative region (CAR) province), Pinukpuk, Lubuagan areas (Kalinga district, Cordillera Administrative region (CAR) province), Baguio city (Benguet district, Cordillera Administrative region (CAR) province)",,"Slide (land, mud, snow, rock)",Flood,,,,,139,Kph,,,,,2009,5,7,2009,5,10,77,53,400954,,401007,,,30342,83.91580741 +2009-0239-PHL,2009,0239,Natural,Meteorological,Storm,Tropical cyclone,,"Topical Storm ""Feria"" (Nangka)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Metropolitan Manila district (National Capital region (NCR) province), Pampanga district (Region III (Central Luzon) province), Batangas, Cavite, Quezon districts (Region IV-A (Calabarzon) province), Romblon, Mindoro Oriental, Mindoro Occidental, Marinduque districts (Region IV (Southern Tagalog) province), Albay, Camarines Sur, Masbate districts (Region V (Bicol region) province), Antique district (Region VI (Western Visayas) province), Cebu district (Region VII (Central Visayas) province), Region VIII (Eastern Visayas) province",,"Slide (land, mud, snow, rock)",Surge,,,,,83,Kph,,,,,2009,7,23,2009,7,26,16,5,110400,,110405,,,4281,83.91580741 +2009-0047-MDG,2009,0047,Natural,Meteorological,Storm,Tropical cyclone,,Cyclones Eric and Fanele,Affected,Madagascar,MDG,Eastern Africa,Africa,"Ambositra district (Amoron I Mania province), Menabe, Sofia, Sava, Atsinanana, Analanjirofo provinces",,Flood,,Yes,,,861,210,Kph,-17.69,46.42,,,2009,1,19,2009,1,22,12,,58493,4012,62505,,,,83.91580741 +2009-0123-MDG,2009,0123,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Izilda""",Affected,Madagascar,MDG,Eastern Africa,Africa,"Androy, Anosy, Atsimo Andrefana, Menabe provinces",,Flood,,,,,,140,Kph,,,,,2009,3,17,2009,3,27,,,3376,,3376,,,,83.91580741 +2009-0123-MOZ,2009,0123,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Izilda""",Affected,Mozambique,MOZ,Eastern Africa,Africa,Zambezia province,,Flood,,,,,,130,Kph,,,,,2009,3,26,2009,3,27,,3,600,6500,7103,,,3000,83.91580741 +2009-0137-MDG,2009,0137,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Jade""",Kill,Madagascar,MDG,Eastern Africa,Africa,"Soanierana Ivongo, Maroantsetra, Mananara-Avaratra, Sainte Marie districts (Analanjirofo province), Nosy Varika, Mananjary, Vohipeno, Manakara districts (Vatovavy Fitovinany province), Port Berge (Boriziny-Vaovao), Mampikony districts (Sofia province), Amparafaravola, Moramanga districts (Alaotra Mangoro province), Vatomandry district (Atsinanana province), Vangaindrano district (Atsimo Atsinanana province), Antalaha district (Sava province)",,,,,,,,160,Kph,,,,,2009,4,6,2009,4,6,15,10,60818,4090,64918,,,5000,83.91580741 +2009-0265-PHL,2009,0265,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Isang"" (Molave)",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Dingras, Nueva Era, Paoay, Batac, Adams, Pinili, Piddig, San Nicolas, Banna, Marcos, Solsona, Sarrat, Pasuqion areas (Ilocos Norte district, Region I (Ilocos region) province), Naic, Kawit, Noveleta, Bacoor, Cavite areas (Cavite district, Region IV-A (Calabarzon) province), San Mateo, Rodriguez areas (Rizal district, Region IV-A (Calabarzon) province), Passig, Las Pinas, Marikina, Quezon, Taguig areas (Metropolitan Manila district, National Capital region (NCR) province)",,,,,,,,,Kph,,,,,2009,7,18,2009,7,18,5,1,248057,,248058,,,,83.91580741 +2007-0552-TWN,2007,0552,Natural,Meteorological,Storm,Tropical cyclone,,Krosa,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Yilan, Pingdong, Tainan, Hualian, Jianyi, Taidong, Nantou, Gaoxiong, Yunlin, Xinzhu areas (Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,23.76,121.02,,,2007,10,6,2007,10,10,18,67,1000,,1067,,,35000,81.10165893 +2007-0557-PNG,2007,0557,Natural,Meteorological,Storm,Tropical cyclone,,Guba,Kill,Papua New Guinea,PNG,Melanesia,Oceania,"Rabaraba district (Milne Bay Province), Northern province",Tropical cyclone Cuba,Flood,,Yes,,Yes,1300,,Kph,-9.03,149.22,,,2007,11,12,2007,11,16,172,,162140,,162140,183000,,,81.10165893 +2007-0612-PRI,2007,0612,Natural,Meteorological,Storm,Tropical cyclone,,Olga,Kill,Puerto Rico,PRI,Caribbean,Americas,San Juan province,,Flood,,,,,,,Kph,18.93,-71.03,,,2007,12,11,2007,12,17,1,,,,,,,,81.10165893 +2007-0457-PRK,2007,0457,Natural,Meteorological,Storm,Tropical cyclone,,Wipha/Goring,Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Hwanghae-bukto, Hwanghae-namdo provinces",,Flood,,,,,,,Kph,38.57,125.83,,,2007,9,17,2007,9,25,,,,1649,1649,,,,81.10165893 +2007-0439-SLV,2007,0439,Natural,Meteorological,Storm,Tropical cyclone,,Felix,Kill,El Salvador,SLV,Central America,Americas,"Ahuachapan, Cabanas, Chalatenango, Cuscatlan, La Libertad, La Paz, La Union, Morazan, San Miguel, San Salvador, San Vicente, Santa Ana, Sonsonate, Usulutan provinces",,Flood,,,,,,,Kph,,,,,2007,9,4,2007,9,4,,,2800,,2800,,,,81.10165893 +2007-0359-USA,2007,0359,Natural,Meteorological,Storm,Tropical cyclone,,Erin,Waiting,United States of America (the),USA,Northern America,Americas,"Texas, Oklahoma, Missouri provinces",,Flood,,,,,,132,Kph,,,,,2007,8,16,2007,8,19,7,,,,,,,,81.10165893 +2007-0463-VNM,2007,0463,Natural,Meteorological,Storm,Tropical cyclone,,Lekima,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Binh, Ha Tinh, Quang Tri, Quang Ngai, Quang Nam, Son La, Yen Bai, Hoa Binh, Thai Binh, Thanh Hoa, Nghe An, Ninh Binh provinces",,Flood,,Yes,,,,117,Kph,17.07,106.9,,,2007,9,29,2007,10,12,96,150,637755,47525,685430,,,191000,81.10165893 +2007-0380-TWN,2007,0380,Natural,Meteorological,Storm,Tropical cyclone,,Sepat,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Yunlin, Changhua, Kaohsiung, Pingtung areas (Taiwan Sheng province)",Heavy rain,,,,,,,,Kph,27,119.91,,,2007,8,17,2007,8,24,,12,,1800,1812,,,25000,81.10165893 +2008-0295-TWN,2008,0295,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kalmaegi""",Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Yilan, Hualien, Pingdong areas (Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",,,,,138,Kph,22.94,120.79,,,2008,7,18,2008,7,19,26,8,,,8,,10000,16000,84.21522909 +2008-0329-VNM,2008,0329,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kammuri"" (Julian)",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Lao Cai, Yen Bai, Phu Tho, Bac Kan, Quang Ninh, Ha Giang, Tuyen Quang, Lai Chau, Son La provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,22.05,105.21,,,2008,8,8,2008,8,11,162,90,52630,4910,57630,,,120000,84.21522909 +2008-0426-VNM,2008,0426,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Hagupit"" (Nina)",Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Lang Son, Son La, Bac Giang, Lao Cai, Quang Ninh, Vinh Phuc provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,21.9,104.59,,,2008,9,25,2008,9,28,46,61,51755,6695,58511,,,63000,84.21522909 +2008-0442-TWN,2008,0442,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Sinlaku""",Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Nantou, Taizhong areas (Taiwan Sheng province)",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2008,9,16,2008,9,16,22,20,,,20,,,26400,84.21522909 +2008-0304-USA,2008,0304,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Dolly,Affected,United States of America (the),USA,Northern America,Americas,"Panama city beach (Bay district, Florida province), Aransas, Bexar, Brooks, Calhoun, Cameron, Hidalgo, Jim Wells, Kenedy, Kleberg, Nueces, Refugio, San Patricio, Starr, Victoria, Willacy districts (Texas province)",,Flood,Transport accident,,,Yes,,160,Kph,,,,,2008,7,23,2008,7,23,,,,,,,600000,1200000,84.21522909 +2008-0352-TCA,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,Turks and Caicos Islands (the),TCA,Caribbean,Americas,"Grand Turk, Middle Caicos, North Caicos, Providenciales and West Caicos, Salt City, South and East Caicos provinces",,Flood,,,,,,,Kph,,,,,2008,8,26,2008,8,26,4,,,,,,,,84.21522909 +2008-0378-TCA,2008,0378,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Hanna,Kill,Turks and Caicos Islands (the),TCA,Caribbean,Americas,Providenciales and West Caicos province,,Flood,,,,,,,Kph,,,,,2008,9,1,2008,9,1,,,750,,750,,,,84.21522909 +2008-0184-THA,2008,0184,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Nargis,Kill,Thailand,THA,South-Eastern Asia,Asia,Tak province,,Flood,,,,,,,Kph,,,,,2008,5,2,2008,5,3,,,,1000,1000,,,,84.21522909 +2008-0329-THA,2008,0329,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Kammuri"" (Julian)",Kill,Thailand,THA,South-Eastern Asia,Asia,"Chiang Rai, Loei, Nakhon Phanom, Nan, Nong Khai, Phayao, Phitsanulok, Sakon Nakhon, Udon Thani, Uttaradit provinces",,Flood,,,,,,,Kph,17.31,103.66,,,2008,8,11,2008,8,20,,,,,,,,,84.21522909 +2008-0292-TWN,2008,0292,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fung-Wong"" (Igme)",Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Nantou area (Taiwan Sheng province),,Flood,,,,,,,Kph,,,,,2008,7,27,2008,7,27,2,,,,,,,10000,84.21522909 +2008-0441-TWN,2008,0441,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Jangmi"" (Ofel)",Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Yilan areas (Taiwan Sheng province),,Flood,,,,,,227,Kph,,,,,2008,9,28,2008,9,28,3,60,,,60,,65000,90000,84.21522909 +2008-0338-USA,2008,0338,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Fay""",Kill,United States of America (the),USA,Northern America,Americas,"Florida, Georgia, Alabama, Mississippi provinces",,Flood,,,,Yes,,100,Kph,,,,,2008,8,20,2008,8,28,12,,400,,400,,,180000,84.21522909 +2008-0384-USA,2008,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ike,Kill,United States of America (the),USA,Northern America,Americas,"Galveston, Brazoria, Harris, Chambers, Jefferson districts (Texas province), Louisiana, Tennessee, Arkansas, Ohio, Indiana, Illinois, Missouri, Kentucky, Pennsylvania, Michigan provinces",,Flood,,,,,,200,Kph,35.05,-90.45,,,2008,9,12,2008,9,16,82,,200000,,200000,,15000000,30000000,84.21522909 +2008-0437-VNM,2008,0437,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Mekkhala""",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Binh, Ha Tinh provinces",,Flood,,,,,,102,Kph,,,,,2008,9,30,2008,9,30,18,,30860,820,31680,,,6500,84.21522909 +2008-0540-VNM,2008,0540,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Maysak"" (Quinta-Sony)",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Ho Chi Minh City province,,Flood,,,,,,,Kph,,,,,2008,11,5,2008,11,5,11,,,,,,,,84.21522909 +2008-0552-VNM,2008,0552,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Noul""",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Phu Yen, Khanh Hoa, Binh Dinh, Lam Dong, Ninh Thuan, Quang Nam provinces",,Flood,,,,,,,Kph,15.7,107.71,,,2008,11,17,2008,11,20,17,8,8585,235,8828,,,1000,84.21522909 +2008-0384-TCA,2008,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ike,Kill,Turks and Caicos Islands (the),TCA,Caribbean,Americas,Grand Turk province,,,,,,,,,Kph,,,,,2008,9,6,2008,9,6,,,750,,750,,,500000,84.21522909 +2008-0426-TWN,2008,0426,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Hagupit"" (Nina)",Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2008,9,23,2008,9,23,1,,,,,,,,84.21522909 +2008-0352-USA,2008,0352,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Gustav""",Kill,United States of America (the),USA,Northern America,Americas,"Louisiana, Mississippi, Texas, Alabama provinces",,,,,,,,,Kph,,,,,2008,9,1,2008,9,1,43,,2100000,,2100000,,3500000,7000000,84.21522909 +2008-0378-USA,2008,0378,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Hanna,Kill,United States of America (the),USA,Northern America,Americas,"Florida, North Carolina, Maryland provinces",,,,,,Yes,,,Kph,,,,,2008,8,28,2008,8,28,7,,,,,,,160000,84.21522909 +2009-0321-CHN,2009,0321,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Morakot (Kiko),Kill,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Zhejiang Sheng, Jiangxi Sheng, Anhui Sheng, Jiangsu Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,83,Kph,,,,,2009,8,9,2009,8,10,8,4,11000000,,11000004,,,1415594,83.91580741 +2009-0399-CHN,2009,0399,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Koppu""",Kill,China,CHN,Eastern Asia,Asia,Guangdong Sheng province,,Flood,,,,,,126,Kph,,,,,2009,9,14,2009,9,14,13,58,1000000,,1000058,,,295001,83.91580741 +2009-0621-CHN,2009,0621,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical cylone ""Goni""",Waiting,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,Flood,,,,,,,Kph,,,,,2009,8,1,2009,8,5,9,,,,,,,7000,83.91580741 +2009-0554-FJI,2009,0554,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Mick,Affected,Fiji,FJI,Melanesia,Oceania,"Central, Western provinces",,Flood,,,,,93,100,Kph,,,,,2009,12,14,2009,12,15,2,,3845,,3845,,,13300,83.91580741 +2009-0342-CAN,2009,0342,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Bill,Waiting,Canada,CAN,Northern America,Americas,"Nova Scotia, Newfoundland and Labrador provinces",,,,,,,,,Kph,,,,,2009,8,23,2009,8,23,,3,,,3,,,,83.91580741 +2009-0422-CHN,2009,0422,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Pepeng"" (Parma)",Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng provinces",,,,,,,,,Kph,,,,,2009,10,13,2009,10,13,3,,,,,,,35000,83.91580741 +2010-0484-CHN,2010,0484,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Fanapi,Regional,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Guangdong Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2010,9,20,2010,9,20,75,,1000000,,1000000,,,298285,85.2920606 +2010-0571-DOM,2010,0571,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Tomas,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2010,10,29,2010,10,29,,,12000,,12000,,,,85.2920606 +2010-0468-ATG,2010,0468,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Earl,Affected,Antigua and Barbuda,ATG,Caribbean,Americas,All country affected (no data),,Flood,,,,,,142,Kph,,,,,2010,8,29,2010,8,31,,,5000,,5000,,,12600,85.2920606 +2010-0106-FJI,2010,0106,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Tomas""",Declar,Fiji,FJI,Melanesia,Oceania,"Central, Easter, Northern, Western provinces",,Flood,,Yes,,Yes,,250,Kph,,,,,2010,3,14,2010,3,16,2,,39101,,39101,,,39427,85.2920606 +2010-0120-AUS,2010,0120,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Ului""",Affected,Australia,AUS,Australia and New Zealand,Oceania,"Mackay, Whitsunday districts (Queensland province)",,,,,,,,200,Kph,,,,,2010,3,21,2010,3,21,1,,,,,,,,85.2920606 +2010-0171-BGD,2010,0171,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,Bangladesh,BGD,Southern Asia,Asia,"Rangpur, Dinajpur, Nilphamari, Lalmonirhat, Kurigram, Gaibandha districts (Rangpur province), Sirajganj, Bogra districts (Rajshahi province)",,,,,,,,120,Kph,,,,,2010,4,13,2010,4,14,8,200,246910,,247110,,,,85.2920606 +2010-0205-BGD,2010,0205,Natural,Meteorological,Storm,Tropical cyclone,,,Affected,Bangladesh,BGD,Southern Asia,Asia,Lalmonirhat district (Rangpur province),,,,,,,,,Kph,,,,,2010,4,17,2010,4,17,3,,10000,,10000,,,,85.2920606 +2010-0503-BLZ,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Belize,BLZ,Central America,Americas,"Toledo, Stann Creek provinces",,,,,,,,,Kph,,,,,2010,9,25,2010,9,25,,,,,,,,,85.2920606 +2010-0571-BRB,2010,0571,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Tomas,Kill,Barbados,BRB,Caribbean,Americas,"St. John, St. Andrew, St. Joseph, St. Michael, St. George provinces",,,,,,Yes,,,Kph,,,,,2010,10,29,2010,10,29,,,2500,,2500,,,,85.2920606 +2010-0308-CHN,2010,0308,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Conson (Basyang),Kill,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,,,,,,,,Kph,,,,,2010,7,16,2010,7,16,2,,,,,,,500,85.2920606 +2010-0543-CHN,2010,0543,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Megi (Juan),Kill,China,CHN,Eastern Asia,Asia,Fujian Sheng province,,,,,,,,,Kph,,,,,2010,10,22,2010,10,22,,,,,,,,420000,85.2920606 +2010-0691-CHN,2010,0691,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Meranti,Affected,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Zhejiang Sheng provinces",,,,,,,,100,Kph,,,,,2010,9,9,2010,9,9,3,,,186000,186000,,,,85.2920606 +2010-0078-COK,2010,0078,Natural,Meteorological,Storm,Tropical cyclone,,Pat,Affected,Cook Islands (the),COK,Polynesia,Oceania,"Aitutaki, Rarotonga, Palmerston islands",,,,,,Yes,,200,Kph,,,,,2010,2,11,2010,2,19,,8,2194,,2202,,,,85.2920606 +2011-0070-AUS,2011,0070,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Cyclone ""Yasi""",Affected,Australia,AUS,Australia and New Zealand,Oceania,"Cassowary, Innisfail, Silkwood, Mission Beach, Cardwell, Tully towns (Cassowary Coast district, Queensland province), Townsville town (Townsville district, Queensland province), Ingham town (Hinchinbrook district, Queensland province)",,Flood,,,,,,290,Kph,,,,,2011,2,2,2011,2,5,1,,7300,,7300,,1300000,2500000,87.98460292 +2011-0328-CAN,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,Canada,CAN,Northern America,Americas,"New Brunswick,Nova Scotia, Prince Edward Island provinces",,Flood,,,,,,130,Kph,,,,,2011,8,28,2011,8,29,1,,,,,,,,87.98460292 +2011-0279-CHN,2011,0279,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Kabayan"" (Muifa)",Kill,China,CHN,Eastern Asia,Asia,"Shanghai Shi, Shandong Sheng, Liaoning Sheng, Jiangsu Sheng, Zhejiang Sheng",,Flood,,,,,,,Kph,,,,,2011,8,4,2011,8,8,,,3649800,,3649800,,,482585,87.98460292 +2011-0379-CHN,2011,0379,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Pedring (Nesat),Kill,China,CHN,Eastern Asia,Asia,"Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,Flood,,,,,,,Kph,,,,,2011,9,29,2011,10,3,3,,1000000,,1000000,,,219000,87.98460292 +2011-0328-DOM,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,Dominican Republic (the),DOM,Caribbean,Americas,"San José de Ocoa, Peravia, San Cristobal, Santo Domingo provinces",,Flood,,,,,,,Kph,,,,,2011,8,24,2011,8,24,4,,37000,,37000,,,30000,87.98460292 +2011-0328-BHS,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,Bahamas (the),BHS,Caribbean,Americas,,,,,Yes,,,,,Kph,,,,,2011,8,23,2011,8,26,,,10000,,10000,,,40000,87.98460292 +2011-0203-CHN,2011,0203,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Dodong"" (Sarika)",Kill,China,CHN,Eastern Asia,Asia,"Xianning district (Hubei Sheng province), Guangdong Sheng province",,,,,,,,,Kph,,,,,2011,6,4,2011,6,11,23,,,,,,,,87.98460292 +2011-0303-DOM,2011,0303,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Emily""",Affected,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,,,,,,,,Kph,,,,,2011,8,4,2011,8,4,3,,7000,,7000,,,,87.98460292 +2009-0373-PHL,2009,0373,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression Maring,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Botolan, Iba, San Antonio, San Felipe, San Marcelino areas (Zambales district, Region III (Central Luzon) province), Abucay, Bagac, Dinalupihan, Hermosa, Morong, Pilar, Samal areas (Bataan district, Region III (Central Luzon) province), Apalit, Arayat, Bacolor, Floridabanca, Guagua, Lubao, Masantol, Mexico, Minalin, San Luis, Sasmuan, Stanta Ana, Santo Thomas areas (Pampanga district, Region III (Central Luzon) province), Balagtas, Calumpit, Guiguinto, Marilao, Meycauayan, San Miguel areas (Bulacan district, Region III (Central Luzon) province), Calamba city (Laguna district, Region IV-A (Calabarzon) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2009,9,8,2009,9,9,15,,388373,,388373,,,6303,83.91580741 +2009-0374-PHL,2009,0374,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Depression Nando,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Santa Teresita area (Cagayan district, Region II (Cagayan Valley) province), Silang area (Cavite district, Region IV-A (Calabarzon) province), Santa Cruz, Bay, Los Banos, Santa Rosa, Calamba, Banan, San Pedro areas (Laguna district, Region IV-A (Calabarzon) province), Pasil area (Kalinga district, Cordillera Administrative region (CAR) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2009,9,12,2009,9,13,3,3,48330,,48333,,,,83.91580741 +2009-0422-PHL,2009,0422,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Pepeng"" (Parma)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Isabela, Nueva Vizcaya, Cagayan districts (Region II (Cagayan Valley) province), Quezon district (Region IV-A (Calabarzon) province), Albay, Camarines Sur, Catanduanes, Sorsogon districts (Region V (Bicol region) province), Negros Occidental district (Region VI (Western Visayas) province), Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region III (Central Luzon) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,146,145,Kph,17.57,121.42,,,2009,9,29,2009,10,10,512,207,4478284,,4478491,,,585379,83.91580741 +2009-0477-SLV,2009,0477,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Ida""",Kill,El Salvador,SLV,Central America,Americas,"San Vicente, San Salvador, Cabanas, Cuscatlan, La Paz, La Libertad, Usulutan provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,Yes,Yes,8531,,Kph,13.77,-89,,,2009,11,7,2009,11,9,275,,75000,15000,90000,,,939000,83.91580741 +2009-0321-TWN,2009,0321,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Morakot (Kiko),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Pingdong, Hualian, Gaoxiong, Taizhong, Tainan, Nantou, Taidong, Jianyi areas (Taiwan Sheng province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,,3511,148,Kph,,,,,2009,8,7,2009,8,8,630,46,2307477,,2307523,,,250000,83.91580741 +2009-0384-MEX,2009,0384,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Jimena,Affected,Mexico,MEX,Central America,Americas,"Loreto district (Zacatecas province), Comondu, Mulege, Baja California Sur districts (Baja California Sur province)",,"Slide (land, mud, snow, rock)",Flood,Yes,,Yes,,206,Kph,,,,,2009,9,2,2009,9,3,4,,72000,,72000,,,40000,83.91580741 +2009-0321-PHL,2009,0321,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Morakot (Kiko),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Benguet district (Cordillera Administrative region (CAR) province), Tarlac, Zambales districts (Region III (Central Luzon) province), Pangasinan district (Region I (Ilocos region) province)",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2009,8,4,2009,8,8,26,18,94211,,94229,,,25000,83.91580741 +2009-0360-PHL,2009,0360,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Labuyo (Dujuan),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Botolan area (Zambales district, Region III (Central Luzon) province), Pilar, Morong, Hermosa, Dinalupihan areas (Bataan district, Region III (Central Luzon) province), Sibalom area (Antique district, Region VI (Western Visayas) province), San Erique, Pontevedra, La Carlota City, Valladolid, Hinigaran areas (Negros Occidental district, Region VI (Western Visayas) province), Bayawan city (Negros Oriental district, Region VII (Central Visayas) province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2009,9,2,2009,9,8,1,,95700,,95700,,,,83.91580741 +2009-0478-PHL,2009,0478,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Mirinae"" (Santi)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Aurora, Bataan, Bulacan districts (Region III (Central Luzon) province), Mindoro Occidental, Mindoro Oriental districts (Region IV (Southern Tagalog) province), Albay, Camarines Norte, Camarines Sur districts (Region V (Bicol region) province), National Capital region (NCR), Region IV-A (Calabarzon) province",,"Slide (land, mud, snow, rock)",,,,,,140,Kph,,,,,2009,10,28,2009,10,28,39,20,802155,,802175,,,15194,83.91580741 +2009-0521-PHL,2009,0521,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical depression ""Urduja""",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Agusan del Norte district (Region XIII (Caraga) province), Region VII (Central Visayas), Region X (Northern Mindanao) provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2009,11,25,2009,11,25,4,13,48129,,48142,,,12,83.91580741 +2009-0316-JPN,2009,0316,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Etau""",Kill,Japan,JPN,Eastern Asia,Asia,"Hyoogo, Okayama provinces",,Flood,,,,,,,Kph,,,,,2009,8,10,2009,8,10,12,,2000,,2000,,,,83.91580741 +2009-0414-LAO,2009,0414,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Ondoy"" (Ketsana)",Kill,Lao People's Democratic Republic (the),LAO,South-Eastern Asia,Asia,"Attapu, Xekong, Savannakhet, Salavan provinces",,Flood,,Yes,,,9966,,Kph,,,,,2009,9,29,2009,10,1,16,91,128796,,128887,,,100000,83.91580741 +2009-0477-NIC,2009,0477,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Ida""",Kill,Nicaragua,NIC,Central America,Americas,"Atlantico Sur, Atlantico Norte provinces",,Flood,,,,,,140,Kph,,,,,2009,11,5,2009,11,5,,,19897,,19897,,,,83.91580741 +2009-0414-PHL,2009,0414,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Ondoy"" (Ketsana)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Bolinao, Sual, Dagupan areas (Pangasinan district, Region I (Ilocos region) province), Rizal, Cabarroguis areas (Quirino district, Region II (Cagayan Valley) province), Mandaluyong, Manila, Marikina, Muntinlupa, Navotas, Paranaque, Pasig, Las Pinas, Caloocan, Makati, Valenzuela, Malabon, Pateros, Quezon, Taguig, San Juan, Pasay areas (Metropolitan Manila district, National Capital region (NCR) province), Balanga, Orani, Mariveles, Hermosa areas (Bataan district, Region III (Central Luzon) province), Angat, Balagtas, Baliwag, Bocaue, Bulacan, Bustos, Calumpit, Guiguinto, Hagonoy, Malolos, Marilao, Meycauayan, Plaridel, San Miguel areas (Bulacan district, Region III (Central Luzon) province), Arayal, Bacolor, Lubao, Mabelacat, San Simon, San Luis, San Fernando city, Sto.Tomas, Floridablanca, Sta.Rita areas (Pampanga district, Region III (Central Luzon) province), Licab area (Nueva Ecija district, Region III (Central Luzon) province), Magsaysay, San Jose areas (Mindoro Occidental district, Region IV (Southern Tagalog) province), Naga City, Sangay, Pasacao areas (Camarines Sur district, Region V (Bicol region) province), San Pascuan area (Masbate district, Region V (Bicol region) province), Pioduran area (Albay district, Region V (Bicol region) province), Pilar area (Sorsogon district, Region V (Bicol region) province), Zamboanga city (Zamboanga Del Sur district, Region IX (Zamboanga Peninsula) province), Kalamasig area (Sultan Kudarat district, Region XII (Soccsksargen) province), Kabugao area (Apayao district, Cordillera Administrative region (CAR) province), Kabayan area (Benguet district, Cordillera Administrative region (CAR) province), Region IV-A (Calabarzon) province",,Flood,,Yes,,,95446,,Kph,,,,,2009,9,24,2009,9,27,501,529,4901234,,4901763,,,237489,83.91580741 +2009-0606-PHL,2009,0606,Natural,Meteorological,Storm,Tropical cyclone,,Goni (Jolina),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Panay area (Capiz district, Region VI (Western Visayas) province), Negros Occidental, Negros Oriental districts (Region VII (Central Visayas) province), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,Flood,,,,,,,Kph,,,,,2009,8,1,2009,8,3,14,10,221412,,221422,,,2888,83.91580741 +2009-0477-USA,2009,0477,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Ida""",Kill,United States of America (the),USA,Northern America,Americas,"Kent, Sussex districts (Delaware province), Atlantic, Burlington, Cape May, Cumberland, Monmouth, Ocean districts (New Jersey province), Virginia, New York, North Carolina, Pennsylvannia provinces",,Flood,,,,Yes,,,Kph,,,,,2009,11,9,2009,11,10,6,,,,,,,600000,83.91580741 +2009-0423-JPN,2009,0423,Natural,Meteorological,Storm,Tropical cyclone,,"TYphoon ""Melor""",Affected,Japan,JPN,Eastern Asia,Asia,"Mie, Aiti, Gifu, Siga, Wakayama, Miyagi, Saitama provinces",,Surge,,,,,,220,Kph,,,,,2009,10,7,2009,10,9,4,119,5000,,5119,,625000,1000000,83.91580741 +2009-0342-USA,2009,0342,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Bill,Waiting,United States of America (the),USA,Northern America,Americas,Maine province,,Surge,,,,,,,Kph,,,,,2009,8,23,2009,8,23,3,9,,,9,,,,83.91580741 +2009-0399-HKG,2009,0399,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Koppu""",Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,,,,,,,120,Kph,,,,,2009,9,15,2009,9,15,,50,300,,350,,,,83.91580741 +2009-0609-IND,2009,0609,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Phyan,Kill,India,IND,Southern Asia,Asia,"Gujarat, Madhya Pradesh, Maharashtra provinces",,,,,,,,,Kph,,,,,2009,11,11,2009,11,12,20,,,,,,,300000,83.91580741 +2009-0414-KHM,2009,0414,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Ondoy"" (Ketsana)",Kill,Cambodia,KHM,South-Eastern Asia,Asia,"Kampong Thom, Kratie, Mondul Kiri, Preah Vihear, Ratanak Kiri, Stung Treng provinces",,,,,,,,,Kph,,,,,2009,9,29,2009,9,30,17,91,178000,,178091,,,,83.91580741 +2009-0478-KHM,2009,0478,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Mirinae"" (Santi)",Kill,Cambodia,KHM,South-Eastern Asia,Asia,Kaoh Nheaek district (Mondul Kiri province),,,,,,,,,Kph,,,,,2009,11,2,2009,11,3,2,,,,,,,,83.91580741 +2009-0477-MEX,2009,0477,Natural,Meteorological,Storm,Tropical cyclone,,"Hurricane ""Ida""",Kill,Mexico,MEX,Central America,Americas,Tabasco province,,,,,,Yes,,,Kph,,,,,2009,11,8,2009,11,8,,,40000,,40000,,,,83.91580741 +2010-0503-GTM,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Guatemala,GTM,Central America,Americas,"Peten, Isabel, Suchitepequez, Huehuetenango, El Progreso provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2010,9,25,2010,9,25,,,,,,,,,85.2920606 +2010-0501-JAM,2010,0501,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Nicole,Kill,Jamaica,JAM,Caribbean,Americas,"Clarendon, Saint Catherine, Saint James, Hanover, Saint Mary, Saint Elizabeth, Saint Ann, Saint Andrew And Kingston, Westmoreland provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,65,Kph,18.19,-77.29,,,2010,9,29,2010,9,30,15,26,2480,,2506,,,150000,85.2920606 +2010-0571-LCA,2010,0571,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Tomas,Kill,Saint Lucia,LCA,Caribbean,Americas,"Region Number 1, Region Number 2, Region Number 8 provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,150,Kph,,,,,2010,10,30,2010,10,30,14,,181000,,181000,,,500,85.2920606 +2010-0104-MDG,2010,0104,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Hubert""",Kill,Madagascar,MDG,Eastern Africa,Africa,"Farafangana, Vangaindrano districts (Atsimo Atsinanana province), Ambatondrazaka district (Alaotra Mangoro province), Vatovavy Fitovinany province",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,,,,,2010,3,10,2010,3,12,120,132,192000,,192132,,,,85.2920606 +2010-0503-MEX,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Mexico,MEX,Central America,Americas,"Chiapas, Veracruz, Tabasco, Oaxaca, Nuevo León provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2010,9,25,2010,9,25,3,,1075,,1075,,,,85.2920606 +2010-0479-PRK,2010,0479,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Kompasu,Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Hamgyong-bukto, Hamgyong-namdo, Hwanghae-bukto, Hwanghae-namdo, Kaesong-si, Kangwon-do, P'yongan-namdo, P'yongyang-si provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2010,9,2,2010,9,2,20,,40000,,40000,,,,85.2920606 +2010-0211-SLV,2010,0211,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Agatha,Kill,El Salvador,SLV,Central America,Americas,"San Salvador, Sonsonate, Ahuachapan, La Libertad provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,,Kph,,,,,2010,5,29,2010,6,1,10,3,8717,2800,11520,,,20000,85.2920606 +2010-0426-MEX,2010,0426,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Frank,Waiting,Mexico,MEX,Central America,Americas,"Oaxaca, Tabasco, Veracruz provinces",,"Slide (land, mud, snow, rock)",Flood,,,Yes,,,Kph,,,,,2010,8,23,2010,8,24,6,,154000,,154000,,,,85.2920606 +2010-0554-MMR,2010,0554,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Giri""",Kill,Myanmar,MMR,South-Eastern Asia,Asia,"Kyaukpyu, Manaung areas (Kyaukpyu district, Rakhine province), Minbya, Myebon, Pauktaw areas (Sittwe district, Rakhine province), Salin area (Minbu district, Magway province), Seikphyu, Pakokku, Paukkhaung areas (Pakokku district, Magway province)",,Tsunami/Tidal wave,Flood,Yes,,,,193,Kph,,,,,2010,10,22,2010,10,22,45,49,260000,,260049,,,57000,85.2920606 +2010-0044-PYF,2010,0044,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Oli""",Affected,French Polynesia,PYF,Polynesia,Oceania,"Tubuai island (Australes archipelagos), Bora Bora, Maupiti, Raiatea, Tahaa, Huahine islands (Iles sous le Vent group), Tahiti, Moorea, Maiao island (Iles du vent group)",,Flood,Surge,,,Yes,,260,Kph,,,,,2010,2,4,2010,2,4,1,11,3400,,3411,,,11000,85.2920606 +2010-0120-SLB,2010,0120,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Ului""",Affected,Solomon Islands,SLB,Melanesia,Oceania,"Isabel, Malaita, Guadalcanal, Temotu, Makira/Uluawa, Rennel/Bellona areas (Solomon Islands province)",,Flood,Surge,,,,,,Kph,,,,,2010,3,15,2010,3,15,,,,590,590,,,,85.2920606 +2010-0211-GTM,2010,0211,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Agatha,Kill,Guatemala,GTM,Central America,Americas,"Guatemala district (Guatemala province), Alta Verapaz, El Progreso, Zacapa, Izabal, Sololá, Quetzaltenango, Retalhuleu, Suchitepéquez, Sacatepéque, Escuintla, Totonicapan, Huethuetenango, Quiché provinces",,"Slide (land, mud, snow, rock)",,Yes,,,,,Kph,,,,,2010,5,28,2010,6,1,174,154,397808,,397962,,,650000,85.2920606 +2010-0543-TWN,2010,0543,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Megi (Juan),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2010,10,22,2010,10,22,32,,,,,,,10000,85.2920606 +2010-0198-IND,2010,0198,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Laila""",Kill,India,IND,Southern Asia,Asia,"Krishna, Prakasam, Nellore, Guntur districts (Andhra Pradesh province), Tamil Nadu province",,Rain,,,,,,110,Kph,,,,,2010,5,20,2010,5,20,32,,,,,,,,85.2920606 +2010-0260-SLV,2010,0260,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Alex""",Kill,El Salvador,SLV,Central America,Americas,San Miguel province,,Rain,,,,,,,Kph,,,,,2010,6,27,2010,6,27,6,,500,,500,,,,85.2920606 +2010-0468-USA,2010,0468,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Earl,Affected,United States of America (the),USA,Northern America,Americas,"North Carolina, Virginia, Maryland, Massachusetts, Rhode Island provinces",,Rain,,,,Yes,,,Kph,,,,,2010,9,3,2010,9,3,1,,,,,,,100000,85.2920606 +2010-0503-HND,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Honduras,HND,Central America,Americas,"Paraiso, Atlantida, Islas De Bahia, Colon, Yoro, Gracias A Dios, Comayagua provinces",,Flood,,,,,,,Kph,,,,,2010,9,25,2010,9,25,4,,,,,,,,85.2920606 +2010-0571-HTI,2010,0571,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Tomas,Kill,Haiti,HTI,Caribbean,Americas,"Port-au-Prince, Leogane districts (Ouest province), Cayes district (Sud province), Jacmel district (Sud Est province), Grande Anse province",,Flood,,,,,,130,Kph,20.48,-75.88,,,2010,11,4,2010,11,5,21,,5020,,5020,,,,85.2920606 +2010-0693-IND,2010,0693,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Jal,Kill,India,IND,Southern Asia,Asia,Andhra Pradesh province,,Flood,,,,,,100,Kph,,,,,2010,10,31,2010,11,3,22,,,,,,,,85.2920606 +2010-0260-MEX,2010,0260,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Alex""",Kill,Mexico,MEX,Central America,Americas,"Nuevo Leon, Coahuila, Tamaulipas, Oaxaca provinces",,Flood,,Yes,,Yes,,135,Kph,,,,,2010,6,30,2010,7,7,22,,170000,,170000,,,2000000,85.2920606 +2010-0494-MEX,2010,0494,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Karl,Declar,Mexico,MEX,Central America,Americas,"Campeche, Veracruz, Tabasco, Puebla provinces",,Flood,,,,Yes,,195,Kph,,,,,2010,9,15,2010,9,17,12,,230000,,230000,,150000,3900000,85.2920606 +2010-0210-PAK,2010,0210,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Phet""",Kill,Pakistan,PAK,Southern Asia,Asia,"Sindh, Balochistan provinces",,Flood,,,,,,,Kph,,,,,2010,6,6,2010,6,7,23,,4000,,4000,,,80000,85.2920606 +2010-0308-PHL,2010,0308,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Conson (Basyang),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Manila City, Muntinlupa City, Pateros areas (Metropolitan Manila district, National Capital region (NCR) province), Dingalan area (Aurora district, Region III (Central Luzon) province), Abucay, Bagac, Balanga, Limay, Mariveles, Morong, Orani, Orion, Samal (Bataan district, Region III (Central Luzon) province), Malolos city (Bulacan district, Region III (Central Luzon) province), Masantol area (Pampanga district, Region III (Central Luzon) province), Basud, Capalonga, Daet, Jose Panganiban, Labo, Mercedes, Paracale, San Lorenzo Ruiz, Santa Elena, Talisay, Vinzons areas (Camarines Norte district, Region V (Bicol region) province), Bombon, Caramoan, Lagonoy, Pasacao, Siruma areas (Camarines Sur district, Region V (Bicol region) province), Bagamanoc, Caramoran, Gigmoto, Pandan areas (Catanduanes district, Region V (Bicol region) province), Region IV-A (Calabarzon) province",,Flood,,,,Yes,,120,Kph,17.1,121.47,,,2010,7,12,2010,7,15,146,91,585383,,585474,,,8675,85.2920606 +2010-0484-TWN,2010,0484,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Fanapi,Regional,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Hualian, Gaoxiong, Pingdong areas (Taiwan Sheng province)",,Flood,,,,,,220,Kph,22.429,120.7,,,2010,9,18,2010,9,20,2,100,,,100,,,63100,85.2920606 +2010-0522-USA,2010,0522,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Hermine,Waiting,United States of America (the),USA,Northern America,Americas,Texas province,,Flood,,,,,,,Kph,28.89,-97.06,,,2010,9,7,2010,9,11,8,,,,,,,,85.2920606 +2010-0383-PHL,2010,0383,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Ester (Dianmu),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Sitio Sabitan area (Santo Rosario borough, Malolos city, Bulacan district, Region III (Central Luzon) province), Navostas area (Metropolitan Manila district, National Capital region (NCR) province), Region IV (Southern Tagalog) province",,Transport accident,,,,,,95,Kph,,,,,2010,8,9,2010,8,9,31,,1045,,1045,,,,85.2920606 +2010-0260-GTM,2010,0260,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Alex""",Kill,Guatemala,GTM,Central America,Americas,"Santa Lucía Utatlán district (Solola province), Suchitepéquez, San Marcos, Jutiapa provinces",,,,,,,,,Kph,,,,,2010,6,27,2010,6,27,,,2180,,2180,975000,,,85.2920606 +2010-0332-HKG,2010,0332,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chanthu,Kill,Hong Kong,HKG,Eastern Asia,Asia,Hong Kong,,,,,,,,,Kph,,,,,2010,7,21,2010,7,23,1,,15000,,15000,,,,85.2920606 +2010-0211-HND,2010,0211,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Agatha,Kill,Honduras,HND,Central America,Americas,"Copan, Santa Barbara, Cortes, Yoro, Paraiso, Francisco Morazan, Choluteca, Valle, La Paz, Intibuca, Lempira, Ocotepeque, Comayagua provinces",,,,Yes,,Yes,,,Kph,,,,,2010,5,29,2010,5,29,18,4,16673,7998,24675,,,90000,85.2920606 +2010-0171-IND,2010,0171,Natural,Meteorological,Storm,Tropical cyclone,,,Kill,India,IND,Southern Asia,Asia,"Bihar, West Bengal, Assam provinces",,,,,,,,,Kph,,,,,2010,4,13,2010,4,14,114,,,500000,500000,,,,85.2920606 +2010-0479-KOR,2010,0479,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Kompasu,Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Chungchongnam-do, Chollanam-do, Kwangju, Inchon, Seoul provinces",,,,,,,,120,Kph,,,,,2010,9,2,2010,9,2,12,,41500,,41500,,,,85.2920606 +2010-0260-NIC,2010,0260,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Alex""",Kill,Nicaragua,NIC,Central America,Americas,Atlantico Norte province,,,,,,,,,Kph,,,,,2010,6,27,2010,6,27,5,,,,,,,,85.2920606 +2010-0503-NIC,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Nicaragua,NIC,Central America,Americas,"Atlantico Norte, Atlantico Sur, Boaco, Carazo, Chinandega, Chontales, Esteli, Granada, Jinotega, Leon, Madriz, Managua, Masaya, Matagalpa, Nueva Segovia, Rio San Juan, Rivas provinces",,,,,,,,,Kph,,,,,2010,9,24,2010,9,24,,,,,,,,,85.2920606 +2010-0210-OMN,2010,0210,Natural,Meteorological,Storm,Tropical cyclone,,"Cyclone ""Phet""",Kill,Oman,OMN,Western Asia,Asia,"A Dakhliya, A Sharqiya, Al Batnah, Al Dhahira, Al Wusta, Dhofar, Musandam, Muscat provinces",,,,,,,,,Kph,,,,,2010,6,6,2010,6,6,16,,,,,,,1000000,85.2920606 +2010-0543-PHL,2010,0543,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Megi (Juan),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Aringuay, Bangar areas (La Union district, Region I (Ilocos region) province), Bolinao areas (Pangasinan district, Region I (Ilocos region) province), Aparri, Claveria, Gonzaga, Iguig, Lasam, Rizal, Santa Aba, Santa Praxedes, Santa Teresita, Santo Nino, Tiao areas (Cagayan district, Region II (Cagayan Valley) province), Dinapigue, Divilacan, San Mariano, Santa Maria (Isabela district, Region II (Cagayan Valley) province), Casiguran, Dilasag, Dinalungan areas (Aurora district, Region III (Central Luzon) province), Bagio City, Itogon, La Trinidad, Tublay areas (Benguet district, Cordillera Administrative region (CAR) province), Pinukbuk, Tabuk areas (Kalinga district, Cordillera Administrative region (CAR) province), Tadian area (Mountain Province district, Cordillera Administrative region (CAR) province)",,,,Yes,,Yes,,260,Kph,,,,,2010,10,18,2010,10,18,35,42,2008984,,2009026,,,275745,85.2920606 +2010-0503-SLV,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,El Salvador,SLV,Central America,Americas,"Ahuachapan, Cabanas, Chalatenango, Cuscatlan, La Libertad, La Paz, La Union, Morazan, San Miguel, San Salvador, San Vicente, Santa Ana, Sonsonate, Usulutan provinces",,,,,,,,,Kph,,,,,2010,9,25,2010,9,25,1,,,,,,,,85.2920606 +2011-0228-MEX,2011,0228,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Beatrix""",Affected,Mexico,MEX,Central America,Americas,"Guerrero, Colima, Jalisco, Michoacan, Oaxaca provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,110,Kph,,,,,2011,6,20,2011,6,20,3,,450,,450,,,,87.98460292 +2011-0385-MEX,2011,0385,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Jova,Affected,Mexico,MEX,Central America,Americas,"Puerto Vallarta district (Jalisco province), Manzanillo district (Colima province), Michoacan, Nayarit provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,160,Kph,,,,,2011,10,10,2011,11,1,5,,50000,,50000,,,27700,87.98460292 +2011-0157-PHL,2011,0157,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bebeng (Aere),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Bulacan, Zambales districts (Region III (Central Luzon) province), Cebu district (Region VII (Central Visayas) province), Eastern Samar, Leyte, Northern Samar, Samar districts (Region VIII (Eastern Visayas) province), National Capital region (NCR), Region V (Bicol region)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2011,5,8,2011,5,11,37,11,430081,,430092,,,31259,87.98460292 +2011-0203-PHL,2011,0203,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Dodong"" (Sarika)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region III (Central Luzon), Region IV (Southern Tagalog), Region IV-A (Calabarzon) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2011,6,9,2011,6,10,9,1,1151,,1152,,,,87.98460292 +2011-0231-PHL,2011,0231,Natural,Meteorological,Storm,Tropical cyclone,,"Topical storm ""Meari"" (Falcon)",Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Pangasinan district (Region I (Ilocos region) province), Batangas, Cavite, Rizal districts (Region IV-A (Calabarzon) province), Albay, Camarines Sur districts (Region V (Bicol region) province, National Capital region (NCR), Region III (Central Luzon) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,120,Kph,,,,,2011,6,21,2011,6,25,20,4,1700085,,1700089,,,12869,87.98460292 +2011-0272-PHL,2011,0272,Natural,Meteorological,Storm,Tropical cyclone,,Topical storm Juaning (Nock-ten),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"La Pinas area (Metropolitan Manila district, National Capital region (NCR) province), Nueva Ecija district (Region III (Central Luzon) province), Cavite, Quezon districts (Region IV-A (Calabarzon) province), Marinduque district (Region IV (Southern Tagalog) province), Iloilo district (Region VI (Western Visayas) province), Siquijor district (Region VII (Central Visayas) province), Leyte district (Region VIII (Eastern Visayas) province), Ifugao district (Cordillera Administrative region (CAR) province), Region V (Bicol region) province",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2011,7,26,2011,7,27,84,53,1108171,,1108224,,,63258,87.98460292 +2011-0279-PHL,2011,0279,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Kabayan"" (Muifa)",Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Mandaluyong, Marikina, Quezon, San Juan cities (Metropolitan Manila district, National Capital region (NCR) province), Japalit area (Pampanga district, Region III (Central Luzon) province), Paniqui area (Tarlac district, Region III (Central Luzon) province), Calatagan, Malvar areas (Batangas district, Region IV-A (Calabarzon) province), Antopolo City, Rodriguez, San Mateo areas (Rizal district, Region IV-A (Calabarzon) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,165,Kph,,,,,2011,8,2,2011,8,2,11,5,8418,,8423,,,59,87.98460292 +2011-0241-MEX,2011,0241,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm ""Arlene""",Kill,Mexico,MEX,Central America,Americas,"Isla, Tamalin, Tamiahua, Tampico Alto districts (Veracruz province)",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,22.11,-98.75,,,2011,6,30,2011,7,1,22,,300000,,300000,,,70000,87.98460292 +2011-0328-HTI,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,Haiti,HTI,Caribbean,Americas,Nord province,,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2011,8,24,2011,8,24,2,4,1000,540,1544,,,,87.98460292 +2011-0378-PHL,2011,0378,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Quiel (Nalgae),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon) provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2011,10,1,2011,10,1,25,12,1113763,,1113775,,,2655,87.98460292 +2011-0352-JPN,2011,0352,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Roke,Affected,Japan,JPN,Eastern Asia,Asia,"Aiti, Ehime, Siga, Nagasaki, Kumamoto provinces",,Flood,,,,,,215,Kph,,,,,2011,9,22,2011,9,22,13,308,,,308,,1210000,1820000,87.98460292 +2011-0057-MDG,2011,0057,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Cclone Bingiza,Affected,Madagascar,MDG,Eastern Africa,Africa,"Vohipeno, Mananjary districts (Vatovavy Fitovinany province), Vatomandry district (Atsinanana province), Mahajanga II, Ambato Boeni, Soalala districts (Boeny province), Port-Berge (Boriziny-Vaovao), Mampikony, Befandriana Nord, Mandritsara districts (Sofia province), Iakora district (Ihorombe province), Miandrivazo, Manja districts (Menabe province), Sambava, Antalaha districts (Sava province), Ambohidratrimo district (Analmanga province), Morafenobe, Maintirano districts (Melaky province), Ambovombe-Androy, Bekily, Tsihombe districts (Androy province), Anosy, Atsimo Atsinanana, Analanjirofo provinces",,Flood,,Yes,,,,180,Kph,,,,,2011,2,14,2011,2,16,35,73,89297,25845,115215,,,,87.98460292 +2011-0267-MEX,2011,0267,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Dora""",Kill,Mexico,MEX,Central America,Americas,"Guerrero, Oaxaca provinces",,Flood,,,,,,150,Kph,,,,,2011,7,20,2011,7,20,3,,200000,,200000,,,,87.98460292 +2011-0328-PRI,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,Flood,,,,Yes,,130,Kph,,,,,2011,8,22,2011,8,22,1,,1500,771,2271,,450000,500000,87.98460292 +2011-0279-PRK,2011,0279,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Kabayan"" (Muifa)",Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Byoksong, Haeju, Chongdan, Baechon, Bongchon, Waudo districts (Hwanghae-namdo province), Sohung district (Hwanghae-bukto province), Kaesong-si, P'yongan-namdo province",,Flood,,Yes,,,,,Kph,,,,,2011,8,7,2011,8,9,10,887,5612,,6499,,,,87.98460292 +2011-0070-SLB,2011,0070,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Cyclone ""Yasi""",Affected,Solomon Islands,SLB,Melanesia,Oceania,Solomon Islands province,,Flood,,,,,,,Kph,,,,,2011,2,2,2011,2,2,,,,,,,,,87.98460292 +2011-0272-THA,2011,0272,Natural,Meteorological,Storm,Tropical cyclone,,Topical storm Juaning (Nock-ten),Kill,Thailand,THA,South-Eastern Asia,Asia,"Phrae, Chiang Mai, Sukhothai, Nan, Nakhon Phanom, Lamphun, Lampang, Mae Hon Son, Uttaradit, Phichit, Phitsanulok, Udon Thani, Nong Khai, Sakon Nakhon Loei, Phetchabun provinces",,Flood,,,,Yes,,,Kph,,,,,2011,8,4,2011,8,6,18,,1000000,,1000000,,,,87.98460292 +2011-0328-USA,2011,0328,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Irene,Affected,United States of America (the),USA,Northern America,Americas,"New York, New Jersey, Pennsylvania, North Carolina, Virginia, Maryland, District of Columbia, Connecticut, Florida provinces",,Flood,,,,,,252912,Kph,40.186,-70.06,,,2011,8,27,2011,9,13,46,,370000,,370000,,,7300000,87.98460292 +2011-0267-GTM,2011,0267,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Dora""",Kill,Guatemala,GTM,Central America,Americas,Petén province,,,,,,,,,Kph,,,,,2011,7,20,2011,7,20,5,,,,,,,,87.98460292 +2011-0303-HTI,2011,0303,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Emily""",Affected,Haiti,HTI,Caribbean,Americas,Sud province,,,,,,,,,Kph,,,,,2011,8,4,2011,8,4,1,,1500,,1500,,,,87.98460292 +2011-0303-MTQ,2011,0303,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Emily""",Affected,Martinique,MTQ,Caribbean,Americas,"Fort-de-France, La Trinite, Saint-Pierre, Le Marin provinces",,,,,,,,,Kph,,,,,2011,8,3,2011,8,3,1,,,,,,,,87.98460292 +2011-0414-OMN,2011,0414,Natural,Meteorological,Storm,Tropical cyclone,,Keila,Kill,Oman,OMN,Western Asia,Asia,"A Dakhliya, A Sharqiya, Al Batnah, Al Dhahira, Al Wusta, Dhofar, Musandam, Muscat provinces",,,,,,,,,Kph,,,,,2011,11,3,2011,11,3,14,200,,,200,,,,87.98460292 +2011-0341-PHL,2011,0341,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mina (Nanmadol),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Quezon City area (Metropolitan Manila district, National Capital region (NCR) province), Zambales, Bulacan districts (Region III (Central Luzon) province), Iloilo district (Region IV (Southern Tagalog) province), Benguet, Abra, Mountain Province districts (Cordillera Administrative region (CAR) province), Catanduanes district (Region V (Bicol region) province), Zamboanga Del Sur district (Region IX (Zamboanga Peninsula) province), Region I (Ilocos region) province",,,,,,,,,Kph,,,,,2011,8,27,2011,8,29,43,37,403193,,403230,,,34452,87.98460292 +2011-0379-PHL,2011,0379,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Pedring (Nesat),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Mindoro Occidental, Mindoro Oriental, Romblon districts (Region IV (Southern Tagalog) province), Albay, Camarines Norte, Camarines Sur, Catanduanes districts (Region V (Bicol region) province), Antique, Iloilo districts (Region VI (Western Visayas) province), Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV-A (Calabarzon) province",,,,,,,,,Kph,,,,,2011,9,24,2011,9,24,103,91,3030755,,3030846,,,344173,87.98460292 +2011-0384-PHL,2011,0384,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Ramon (Banyan),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Mindoro Oriental, Romblon districts (Region IV (Southern Tagalog) province), Capiz, Iloilo, Negros Occidental districts (Region VI (Western Visayas) province), Cebu district (Region VII (Central Visayas) province), Southern Leyte district (Region VIII (Eastern Visayas) province), Misamis Oriental district (Region X (Northern Mindanao) province), South Cotabato district (Region XII (Soccsksargen) province), Dinagat district (Region XIII (Caraga) province)",,,,,,,,,Kph,,,,,2011,10,13,2011,10,13,11,6,75632,,75638,,,,87.98460292 +2011-0070-PNG,2011,0070,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Cyclone ""Yasi""",Affected,Papua New Guinea,PNG,Melanesia,Oceania,Alotau district (Milne Bay province),,,,,,,,,Kph,,,,,2011,2,2,2011,2,2,,,,,,,,,87.98460292 +2011-0303-PRI,2011,0303,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Emily""",Affected,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,,,,,,,,Kph,,,,,2011,8,4,2011,8,4,,,,,,,,,87.98460292 +2011-0091-TON,2011,0091,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Wilma,SigDam,Tonga,TON,Polynesia,Oceania,Ha'apai island (Tonga province),,,,,,,,,Kph,,,,,2011,1,25,2011,1,29,,,,,,,,3000,87.98460292 +2009-0414-VNM,2009,0414,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Ondoy"" (Ketsana)",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Binh Dinh, Da Nang City, Dak Lak, Gia Lai, Ha Tinh, Thua Thien - Hue, Kon Tum, Lam Dong, Phu Yen, Quang Binh, Quang Nam, Quang Ngai, Quang Tri provinces",,Flood,,Yes,,,,170,Kph,,,,,2009,9,28,2009,9,29,182,860,2367820,108635,2477315,,,785000,83.91580741 +2009-0478-VNM,2009,0478,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical storm ""Mirinae"" (Santi)",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Binh Dinh, Phu Yen, Khanh Hoa, Ninh Thuan, Dak Lak, Quang Nam, Quang Ngai, Kon Tum, Gia Lai provinces",,Flood,,,,,1585,140,Kph,,,,,2009,11,2,2009,11,2,124,145,500000,,500145,,,280000,83.91580741 +2009-0422-VNM,2009,0422,Natural,Meteorological,Storm,Tropical cyclone,,"Typhoon ""Pepeng"" (Parma)",Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Giang, Bac Kan, Bac Ninh, Cao Bang, Dien Bien, Ha Giang, Ha Nam, Ha Noi City, Ha Tay, Hai Duong, Hai Phong City, Hoa Binh, Hung Yen, Lai Chau, Lang Son, Lao Cai, Nam Dinh, Ninh Binh, Phu Tho, Quang Ninh, Son La, Thai Binh, Thai Nguyen, Thanh Hoa, Tuyen Quang, Vinh Phuc, Yen Bai provinces",,,,,,,,,Kph,,,,,2009,10,14,2009,10,14,,,,,,,,200,83.91580741 +2010-0503-VEN,2010,0503,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Matthew,Kill,Venezuela (Bolivarian Republic of),VEN,South America,Americas,"Distrito Capital, Sucre provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2010,9,24,2010,9,24,8,,,,,,,,85.2920606 +2010-0332-VNM,2010,0332,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chanthu,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Cao Bang, Ha Giang, Lao Cai provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,22.11,105.95,,,2010,7,23,2010,7,25,10,,12400,,12400,,,,85.2920606 +2010-0571-VCT,2010,0571,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Tomas,Kill,Saint Vincent and the Grenadines,VCT,Caribbean,Americas,"Charlotte, Grenadines, Saint Andrew, Saint David, Saint George, Saint Patrick provinces",,,,Yes,,Yes,,,Kph,,,,,2010,10,29,2010,10,29,,,6000,100,6100,,,25000,85.2920606 +2010-0308-VNM,2010,0308,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Conson (Basyang),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Thanh Hoa, Quang Ngai, Quang Ninh provinces",,,,,,,,,Kph,,,,,2010,7,17,2010,7,17,11,,,1500,1500,,,500,85.2920606 +2010-0432-VNM,2010,0432,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mindulle,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Nghe An, Thanh Hoa, Ha Tinh provinces",,,,,,,,230,Kph,,,,,2010,8,24,2010,8,25,14,20,20560,120,20700,,,44000,85.2920606 +2011-0454-JPN,2011,0454,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Talas,Kill,Japan,JPN,Eastern Asia,Asia,"Wakayama, Nara, Ehime, Kooti provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,90,Kph,,,,,2011,9,2,2011,9,4,68,,1300,,1300,,430000,650000,87.98460292 +2011-0070-VUT,2011,0070,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Cyclone ""Yasi""",Affected,Vanuatu,VUT,Melanesia,Oceania,"Banks, Torres Island areas (Torba province)",,Flood,Surge,,,,,,Kph,,,,,2011,1,30,2011,1,31,,,,,,,,,87.98460292 +2011-0432-DMA,2011,0432,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Orphelia,Affected,Dominica,DMA,Caribbean,Americas,"Canefield, Massacre, Mahaut, Cochrane, Campbell villages (St. Paul province), Coulibistrie village (St. Joseph province)",,Flood,,,,,,,Kph,,,,,2011,9,25,2011,9,29,,,144,96,240,,,,87.98460292 +2011-0071-VUT,2011,0071,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical cyclone ""Vania""",Affected,Vanuatu,VUT,Melanesia,Oceania,Tafea province,,,,Yes,,,,,Kph,,,,,2011,1,12,2011,1,13,,,32000,,32000,,,,87.98460292 +2011-0576-CHN,2011,0576,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Haima (Egay),Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces",,,,,,,,,Kph,,,,,2011,6,19,2011,6,24,,,,,,,,,87.98460292 +2011-0566-IND,2011,0566,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Thane,Kill,India,IND,Southern Asia,Asia,"Cuddalore district (Tamil Nadu province), Puducherry district (Puducherry province)",,,,,,,,140,Kph,,,,,2011,12,29,2011,12,30,47,,,250000,250000,,,375625,87.98460292 +2012-0260-CHN,2012,0260,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Gener (Saola),Kill,China,CHN,Eastern Asia,Asia,"Hubei Sheng, Fujian Sheng, Zhejiang Sheng province",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2012,8,8,2012,8,8,34,,183630,48540,232170,,,124000,89.80529293 +2012-0585-KOR,2012,0585,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Tembin,Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Cheju-do, Chollabuk-do, Chollanam-do, Chungchongbuk-do, Kwangju, Kyongsangbuk-do, Kyongsangnam-do, Pusan, Taegu provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2012,8,30,2012,8,30,2,,,,,,,,89.80529293 +2012-0410-HTI,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,Haiti,HTI,Caribbean,Americas,"Ouest, Sud, Grande Anse, Nippes, Sud Est, Artibonite provinces",,"Slide (land, mud, snow, rock)",Flood,Yes,,Yes,,,Kph,,,,,2012,10,24,2012,10,26,75,20,168500,33330,201850,,,254000,89.80529293 +2012-0425-IND,2012,0425,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Nilam,Kill,India,IND,Southern Asia,Asia,"Andhra Pradesh, Tamil Nadu provinces",,Flood,Surge,,,,,,Kph,11.82,78.08,,,2012,11,4,2012,11,8,40,,70000,,70000,,,,89.80529293 +2012-0259-CHN,2012,0259,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Vicente,Affected,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Fujian Sheng provinces",,Flood,,,,,,,Kph,,,,,2012,7,24,2012,7,24,8,,58500,,58500,,,329000,89.80529293 +2012-0283-CHN,2012,0283,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Damrey,Affected,China,CHN,Eastern Asia,Asia,"Jiangsu Sheng, Shandong Sheng provinces",,Flood,,,,,,,Kph,,,,,2012,8,2,2012,8,8,15,,3790000,,3790000,,106000,600000,89.80529293 +2012-0410-CUB,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,Cuba,CUB,Caribbean,Americas,"Santiago de Cuba, San Luis, Palma Soriano, Mella, Contramaestre, Segundo Frente, Tercer Frente, Songo - La Maya, Guama districts (Santiago de Cuba province), Rafael Freyre, Banes, Antilla, Baganos, Holguín, Calixto Garcia, Cacocum, Urbano Noris, Cueto, Mayari, Frank pais, Sagua de Tanamo, Moa, Jibara districts (Holguín province), Guantanamo province",,Flood,,,,,,175,Kph,,,,,2012,10,22,2012,10,22,11,,,162605,162605,,,,89.80529293 +2012-0410-BHS,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,Bahamas (the),BHS,Caribbean,Americas,,,,,,,,,,Kph,,,,,2012,10,24,2012,10,24,1,,,,,,,,89.80529293 +2012-0282-CHN,2012,0282,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Haikui,Affected,China,CHN,Eastern Asia,Asia,"Jiangxi Sheng, Shanghai Shi, Anhui Sheng, Jiangsu Sheng, Zhejiang Sheng provinces",,,,,,,,150,Kph,,,,,2012,8,8,2012,8,8,3,7,6000000,,6000007,,230000,1500000,89.80529293 +2012-0294-CHN,2012,0294,Natural,Meteorological,Storm,Tropical cyclone,,TYphoon Kai-Tak,Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Guangxi Zhuangzu Zizhiqu provinces",,,,,,,,,Kph,,,,,2012,8,17,2012,8,17,2,,107500,,107500,,,262000,89.80529293 +2012-0406-CHN,2012,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Son Tinh (Ofel),Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangxi Zhuangzu Zizhiqu provinces",,,,,,,,,Kph,,,,,2012,10,28,2012,10,28,1,,126000,,126000,,,197000,89.80529293 +2012-0313-CUB,2012,0313,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Isaac,Kill,Cuba,CUB,Caribbean,Americas,"Gantanamo, Santiago de Cuba, Granma; Holguin, Las Tunas, Camagüey provinces",,,,,,,,,Kph,,,,,2012,8,25,2012,8,25,1,,45496,,45496,,,,89.80529293 +2012-0313-DOM,2012,0313,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Isaac,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Monte Plata, Sanchez Ramirez, Barahona, San José de Ocoa provinces",,,,,,,,,Kph,,,,,2012,8,25,2012,8,25,5,,20515,,20515,,,,89.80529293 +2012-0410-DOM,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Barahona, San Cristobal, Santo Domingo, Duarte, Monte Plata, Peravia, San Juan, San José de Ocoa, Distrito Nacional provinces",,,,,,,,,Kph,,,,,2012,10,24,2012,10,24,3,,22000,,22000,,,30000,89.80529293 +2012-0498-FJI,2012,0498,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Evan,Kill,Fiji,FJI,Melanesia,Oceania,"Bua, Macuata, Cakaudrove districts (Northern province), Ba, Nadroga & Navosa, Ra districts (Western province)",,,,Yes,,,,,Kph,,,,,2012,12,16,2012,12,18,2,,8400,,8400,,,8400,89.80529293 +2012-0313-HTI,2012,0313,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Isaac,Kill,Haiti,HTI,Caribbean,Americas,"Grande-Anse, Sud, Sud Est, Artibonite, Ouest provinces",,,,,,,,,Kph,19.1356,-72.28,,,2012,8,25,2012,8,29,13,7,8000,,8007,,,,89.80529293 +2012-0410-JAM,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,Jamaica,JAM,Caribbean,Americas,"Clarendon, Hanover, Manchester, Portland, Saint Andrew And Kingston, Saint Ann, Saint Catherine, Saint Elizabeth, Saint Mary, Saint Thomas, Trelawny, Westmoreland provinces",,,,Yes,,,,,Kph,,,,,2012,10,24,2012,10,24,1,,215850,,215850,,,16542,89.80529293 +2012-0414-JPN,2012,0414,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bolaven,Kill,Japan,JPN,Eastern Asia,Asia,"Okinawa, Kagosima province",,,,,,,,,Kph,,,,,2012,8,25,2012,8,25,,,,,,,,86000,89.80529293 +2012-0451-JPN,2012,0451,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Jelawat,Affected,Japan,JPN,Eastern Asia,Asia,"Okinawa, Kagosima, Mie provinces",,,,,,,,126,Kph,,,,,2012,9,29,2012,9,30,2,180,18045,,18225,,,27400,89.80529293 +2012-0588-JPN,2012,0588,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Sanba,Affected,Japan,JPN,Eastern Asia,Asia,Okinawa province,,,,,,,,,Kph,,,,,2012,9,15,2012,9,17,,,25250,,25250,,,31000,89.80529293 +2012-0414-KOR,2012,0414,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bolaven,Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Cheju-do, Chollabuk-do, Chollanam-do, Chungchongbuk-do, Chungchongnam-do, Inchon, Kang-won-do, Kwangju, Kyonggi-do, Kyongsangbuk-do, Kyongsangnam-do, Pusan, Seoul, Taegu, Taejon provinces",,,,,,,,,Kph,,,,,2012,8,28,2012,8,28,20,,,,,,,450000,89.80529293 +2012-0588-KOR,2012,0588,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Sanba,Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,Cheju-do province,,,,,,,,,Kph,,,,,2012,9,15,2012,9,17,,,3120,,3120,,,349000,89.80529293 +2013-0396-JPN,2013,0396,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Wipha,Kill,Japan,JPN,Eastern Asia,Asia,"Tookyoo, Saitama, Ibaraki, Totigi, Hokkaidoo, Tiba, Kagosima provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2013,10,14,2013,10,16,39,107,19182,,19289,,,200000,91.12079403 +2013-0429-CHN,2013,0429,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Fitow,Affected,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Zhejiang Sheng, Shanghai Shi provinces",,Flood,Surge,,,,,160,Kph,,105.73,,,2013,10,7,2013,10,8,8,,475000,,475000,,,6700000,91.12079403 +2013-0401-IND,2013,0401,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Phailin,Kill,India,IND,Southern Asia,Asia,"Orissa, Andhra Pradesh, Jharkhand, Bihar, West Bengal, Chhattisgarh provinces",,Flood,Surge,Yes,,,,200,Kph,,,,,2013,10,12,2013,10,14,47,,13230000,,13230000,,,633471,91.12079403 +2013-0008-AUS,2013,0008,Natural,Meteorological,Storm,Tropical cyclone,,Ex-Tropical cyclone Oswald,Affected,Australia,AUS,Australia and New Zealand,Oceania,"Brisbane, Ipswich, Bundaberg, Rockhampton districts (Queensland province), New South Wales province",,Flood,,,,,,,Kph,,,,,2013,1,23,2013,1,30,6,,7500,,7500,,1000000,2000000,91.12079403 +2013-0272-CHN,2013,0272,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Utor (Labuyo),Kill,China,CHN,Eastern Asia,Asia,"Hunan Sheng, Guangxi Zhuangzu Zizhiqu, Guangdong Sheng, Hainan Sheng provinces",,Flood,,,,,,98183,Kph,22.7696,113.69,,,2013,8,15,2013,8,21,88,,8000000,,8000000,,,2120000,91.12079403 +2013-0306-CHN,2013,0306,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Trami (Maring),Affected,China,CHN,Eastern Asia,Asia,"Fuqing Shi, Fuzhou Shi areas (Fuzhou district, Fujian Sheng province), Ningde, Putian, Sanming district (Fujian Sheng province)",,Flood,,,,,,,Kph,,,,,2013,8,22,2013,8,22,,,189800,,189800,,,376000,91.12079403 +2013-0341-CHN,2013,0341,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Jebi,Affected,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,Flood,,,,,,,Kph,,,,,2013,8,2,2013,8,3,,,5000,,5000,,,20000,91.12079403 +2013-0515-IND,2013,0515,Natural,Meteorological,Storm,Tropical cyclone,,Helen,Affected,India,IND,Southern Asia,Asia,"Krishna, Guntur, East Godavari, West Godavari, Vishakhapatnam districts (Andhra Pradesh province)",,Flood,,,,,,65,Kph,,,,,2013,11,22,2013,11,22,10,,,,,,,262000,91.12079403 +2013-0415-JPN,2013,0415,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Man-Yi,Affected,Japan,JPN,Eastern Asia,Asia,"Aiti, Kyooto provinces",,Flood,,,,,,,Kph,,,,,2013,9,16,2013,9,16,6,141,30147,,30288,,,2000,91.12079403 +2013-0138-BGD,2013,0138,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Mahasen,Kill,Bangladesh,BGD,Southern Asia,Asia,"Patuakhali, Bhola, Barguna districts (Barisal province)",,,,,,,,,Kph,,,,,2013,5,16,2013,5,16,17,65,1498579,,1498644,,,,91.12079403 +2013-0249-CHN,2013,0249,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Soulik,Kill,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Fujian Sheng, Zhejiang Sheng, Jiangxi Sheng, Anhui Sheng provinces",,,,,,,,120,Kph,,,,,2013,7,14,2013,7,14,9,150,390000,,390150,,,460000,91.12079403 +2013-0258-CHN,2013,0258,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Rumbia,Affected,China,CHN,Eastern Asia,Asia,"Guangxi Zhuangzu Zizhiqu, Yunnan Sheng provinces",,,,,,,,110,Kph,,,,,2013,7,1,2013,7,1,7,,21000,,21000,,,177000,91.12079403 +2013-0261-CHN,2013,0261,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Ciramon,Affected,China,CHN,Eastern Asia,Asia,"Guangdong Sheng, Zheijiang Sheng, Fujian Sheng provinces",,,,,,,,101851,Kph,25.8633,118.14,,,2013,7,13,2013,7,15,1,,3000,,3000,,,253000,91.12079403 +2013-0373-CHN,2013,0373,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Usagi (Odette),Kill,China,CHN,Eastern Asia,Asia,Guangdong Sheng province,,,,,,,,,Kph,,,,,2013,9,22,2013,9,22,20,,506000,,506000,,,,91.12079403 +2013-0419-CHN,2013,0419,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Wutip,Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng provinces",,,,,,,,,Kph,,,,,2013,9,30,2013,9,30,74,,,,,,,,91.12079403 +2013-0433-CHN,2013,0433,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Haiyan (Yolanda),Kill,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,,,,,,,118,Kph,,,,,2013,11,11,2013,11,11,5,,1800,50000,51800,,,,91.12079403 +2013-0138-IND,2013,0138,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Mahasen,Kill,India,IND,Southern Asia,Asia,Andhra Pradesh province,,,,,,,,,Kph,,,,,2013,5,16,2013,5,16,8,4,,,4,,,,91.12079403 +2013-0413-JPN,2013,0413,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Toraji,Affected,Japan,JPN,Eastern Asia,Asia,Kagosima province,,,,,,,,,Kph,,,,,2013,9,4,2013,9,4,1,19,4317,,4336,,,2000,91.12079403 +2013-0429-JPN,2013,0429,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Fitow,Affected,Japan,JPN,Eastern Asia,Asia,Okinawa province,,,,,,,,,Kph,,,,,2013,10,7,2013,10,7,,,4392,,4392,,,,91.12079403 +2014-0092-COM,2014,0092,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Helen,Affected,Comoros (the),COM,Eastern Africa,Africa,"Ngazidja, Moheli, Anjouan provinces",,Flood,,,,Yes,,100,Kph,,,,,2014,3,27,2014,3,29,,,9511,,9511,,,,92.59898057 +2011-0519-PHL,2011,0519,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Washi (Sendong),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cagayan district (Region II (Cagayan Valley) province), Negros Oriental, Cebu districts (Region VII (Central Visayas) province), Zamboanga del Norte district (Region IX (Zamboanga Peninsula) province), Misamis Oriental, Lanao del Norte, Bukidnon districts (Region X (Northern Mindanao) province), Compostela district (Region XI (Davao Region) province), Surigao del Sur district (Region XIII (Caraga) province), Lanao del Sur district (Autonomous region in Muslim Mindanao (ARMM) province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,,Kph,8.526,124.88,,,2011,12,15,2011,12,18,1439,6071,1144229,,1150300,,,38082,87.98460292 +2011-0555-PHL,2011,0555,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chedeng (Songda),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Taguig city (Metropolitan Manila district, National Capital region (NCR) province), Isabela district (Region II (Cagayan Valley) province), Albay, Camarines Norte, Camarines Sur, Catanduanes, Sorsogon districts (Region V (Bicol region) province), Zamboanga del Sur district (Region IX (Zamboanga Peninsula) province), Bukidnon district (Region X (Northern Mindanao) province), Compostela, Davao del Norte districts (Region XI (Davao Region) province), Lanao del Sur, Maguindanao districts (Autonomous region in Muslim Mindanao (ARMM) province)",,Flood,,,,,,,Kph,,,,,2011,5,20,2011,5,29,,,446907,,446907,,,431,87.98460292 +2011-0456-USA,2011,0456,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Lee,Affected,United States of America (the),USA,Northern America,Americas,"Louisiana, Mississippi, Alabama, Texas, New York, Pennsylvania, District of Columbia, Georgia, Maryland, Tennessee, Virginia provinces",,Flood,,,,Yes,,75,Kph,,,,,2011,9,4,2011,9,9,15,,600,,600,,560000,750000,87.98460292 +2011-0460-MEX,2011,0460,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Nate,Kill,Mexico,MEX,Central America,Americas,Tabasco province,,Transport accident,,,,,,100,Kph,,,,,2011,9,9,2011,9,9,10,,,,,,,,87.98460292 +2011-0576-PHL,2011,0576,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Haima (Egay),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Autonomous region in Muslim Mindanao (ARMM), Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV (Southern Tagalog) Region IV-A (Calabarzon), Region IX (Zamboanga Peninsula), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas), Region X (Northern Mindanao), Region XI (Davao Region), Region XII (Soccsksargen), Region XIII (Caraga) provinces",,,,,,,,,Kph,,,,,2011,6,14,2011,6,20,,,,,,,,,87.98460292 +2011-0576-VNM,2011,0576,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Haima (Egay),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Giang, Bac Kan, Bac Ninh, Cao Bang, Dien Bien, Ha Giang, Ha Nam, Ha Noi City, Ha Tay, Hai Duong, Hai Phong City, Hoa Binh, Hung Yen, Lai Chau, Lang Son, Lao Cai, Nam Dinh, Ninh Binh, Phu Tho, Quang Ninh, Son La, Thai Binh, Thai Nguyen, Thanh Hoa, Tuyen Quang, Vinh Phuc, Yen Bai, Binh Dinh, Da Nang City, Gia Lai, Ha Tinh, Kon Tum, Nghe An, Quang Binh, Quang Nam, Quang Ngai, Quang Tri, Thua Thien - Hue provinces",,,,,,,,,Kph,,,,,2011,6,19,2011,6,24,,,,,,,,,87.98460292 +2012-0410-USA,2012,0410,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Sandy,Kill,United States of America (the),USA,Northern America,Americas,"New York, New Jersey, Pennsylvania, Connecticut, Ohio, Delaware, Rhode Island, Maryland, Massachusetts, Maine, New Hampshire, North Carolina, Vermont, Virginia, District of Columbia, West Virginia provinces",,Flood,Fire,,,Yes,,,Kph,,,,,2012,10,28,2012,10,28,54,,,,,,,50000000,89.80529293 +2012-0056-MDG,2012,0056,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical depression ""Irina""",Affected,Madagascar,MDG,Eastern Africa,Africa,"Vatovavy Fitovinany, Sava, Diana, Atsimo Atsinanana, Anosy, Analanjirofo, Analamanga, Alaotra Mangoro provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,,,,Kph,,,,,2012,2,26,2012,2,26,77,15,85000,,85015,,,,89.80529293 +2012-0259-VNM,2012,0259,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Vicente,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Kan, Cao Bang, Ha Giang, Lang Son, Tuyen Quang provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2012,7,26,2012,7,27,10,3,,,3,,,,89.80529293 +2012-0406-PHL,2012,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Son Tinh (Ofel),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region I (Ilocos region), Region III (Central Luzon), Region IV (Southern Tagalog), Region IV-A (Calabarzon), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas), Region XIII (Caraga) province",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2012,10,25,2012,10,25,36,19,108021,,108040,,,1339,89.80529293 +2012-0540-PHL,2012,0540,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Wukong (Quinta),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,Flood,Surge,,,,90,,Kph,,,,,2012,12,25,2012,12,27,24,3,241603,,241606,,,5526,89.80529293 +2012-0260-TWN,2012,0260,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Gener (Saola),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Hualian Xian, Taibei Xian, Yilan Xian areas (Taiwan Sheng province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2012,8,2,2012,8,2,6,,,,,,,27000,89.80529293 +2012-0294-VNM,2012,0294,Natural,Meteorological,Storm,Tropical cyclone,,TYphoon Kai-Tak,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Yen Bai, Vinh Phuc, Quang Ninh, Hai Phong City provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2012,8,17,2012,8,17,17,14,59320,1145,60479,,,6800,89.80529293 +2012-0056-MOZ,2012,0056,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical depression ""Irina""",Affected,Mozambique,MOZ,Eastern Africa,Africa,"Cabo Delgado, Sofala provinces",,Rain,,,,,,,Kph,,,,,2012,3,3,2012,3,7,3,13,4945,,4958,,,,89.80529293 +2012-0005-MDG,2012,0005,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Funso,Kill,Madagascar,MDG,Eastern Africa,Africa,"Atsimo Andrefana, Melaky, Menabe provinces",,Flood,,,,,,,Kph,,,,,2012,1,24,2012,1,24,,,300,,300,,,,89.80529293 +2012-0043-MDG,2012,0043,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Giovanna,Kill,Madagascar,MDG,Eastern Africa,Africa,"Brickaville, Vatomandry districts (Atsinanana province), Moramanga district (Alaotra Mangoro province), Mampikony district (Sofia province), Antananarivo I, Antananarivo II, Antananarivo III, Antananarivo IV, Antananarivo V, Antananarivo VI, Antananarivo Atsimondrano, Antananarivo Avaradrano districts (Analamanga province)",,Flood,,Yes,,,4262,200,Kph,,,,,2012,2,14,2012,2,15,35,284,250000,,250284,,,100000,89.80529293 +2012-0005-MOZ,2012,0005,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Funso,Kill,Mozambique,MOZ,Eastern Africa,Africa,"Nicoadala, Cidade de Quelimane, Chinde, Pebane, Maganja da Costa, Namacurra, Gurue, Mocuba districts (Zambezia province), Mossuril, Nacal-A-Velha districts (Nampula province)",,Flood,,,,,,212,Kph,,,,,2012,1,20,2012,1,22,10,,65000,,65000,,,,89.80529293 +2012-0006-MOZ,2012,0006,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Dando,Affected,Mozambique,MOZ,Eastern Africa,Africa,"Chokwe, Xai-Xai districts (Gaza province), Cidade de Maputo district (Maputo province), Zavala district (Inhambane province), Nampula, Zambezia, Sofala provinces",,Flood,,,,,,,Kph,,,,,2012,1,18,2012,1,18,20,,40000,,40000,,,,89.80529293 +2012-0005-MWI,2012,0005,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Funso,Kill,Malawi,MWI,Eastern Africa,Africa,Nsanje district (Southern Region province),,Flood,,Yes,,,,,Kph,,,,,2012,1,,2012,1,,,,6000,,6000,,,,89.80529293 +2012-0260-PHL,2012,0260,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Gener (Saola),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Sur, Pangasinan districts (Region I (Ilocos region) province), Cagayan district (Region II (Cagayan Valley) province), Bataan, Bulacan, Nueva Ecija, Pampanga, Tarlac districts (Region III (Central Luzon) province), Batangas, Cavite, Laguna, Rizal district (Region IV-A (Calabarzon) province), Mindoro Occidental district (Region IV (Southern Tagalog) province), Aklan, Antique, Iloilo, Negros Occidental districts (Region VI (Western Visayas) province), Cebu district (Region VII (Central Visayas) province), Lanao del Norte, Misamis Oriental districts (Region X (Northern Mindanao) province), North Cotabato, South Cotabato districts (Region XII (Soccsksargen) province), Benguet, Ifugao, Kalinga, Mountain Province districts (Cordillera Administrative region (CAR) province), National Capital region (NCR) province",,Flood,,,,,,150,Kph,,,,,2012,7,31,2012,8,7,58,35,949051,,949086,,,9821,89.80529293 +2012-0296-PHL,2012,0296,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Igme (Tembin),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,La Union district (Region I (Ilocos region) province),,Flood,,,,,,165,Kph,,,,,2012,8,20,2012,8,21,11,1,5606,,5607,,,99,89.80529293 +2012-0374-PHL,2012,0374,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Lawin (Jelawat),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Muntinlupa city (Metropolitan Manila district, National Capital region (NCR) province), Zamboanga del Norte district (Region IX (Zamboanga Peninsula) province)",,Flood,,,,,,210,Kph,,,,,2012,9,21,2012,9,24,4,,7921,,7921,,,,89.80529293 +2012-0414-PRK,2012,0414,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bolaven,Kill,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Hamgyong-bukto, Hamgyong-namdo, Hwanghae-bukto, Hwanghae-namdo, Kangwon-do, Yanggang-do provinces",,Flood,,,,,,,Kph,38.19,126.24,,,2012,8,28,2012,8,30,59,1,10960,33500,44461,,,,89.80529293 +2012-0313-USA,2012,0313,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Isaac,Kill,United States of America (the),USA,Northern America,Americas,"Mississippi, Louisiana provinces",,Flood,,,,Yes,,83938,Kph,30.503,-89.35,,,2012,8,28,2012,8,29,1,,60000,,60000,,,2000000,89.80529293 +2012-0406-VNM,2012,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Son Tinh (Ofel),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Nghe An, Thanh Hoa, Ninh Binh, Nam Dinh, Thai Binh, Hai Phong City provinces",,Flood,,,,,,,Kph,,,,,2012,10,28,2012,10,28,11,90,,278400,278490,,,336000,89.80529293 +2012-0498-WSM,2012,0498,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Evan,Kill,Samoa,WSM,Polynesia,Oceania,Samoa province,,Flood,,Yes,,,3458,,Kph,,,,,2012,12,13,2012,12,13,12,,12703,,12703,,,133000,89.80529293 +2012-0006-ZAF,2012,0006,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Dando,Affected,South Africa,ZAF,Southern Africa,Africa,"Limpopo, Mpumalanga provinces",,Flood,,,,,,,Kph,,,,,2012,1,,2012,1,,,,1350,,1350,,,,89.80529293 +2012-0425-LKA,2012,0425,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Nilam,Kill,Sri Lanka,LKA,Southern Asia,Asia,"Central, Eastern, North Central, North Western, Northern, Sabaragamuwa, Southern, Uva, Western provinces",,,,,,,,,Kph,,,,,2012,10,29,2012,10,31,,,,69000,69000,,,57000,89.80529293 +2012-0276-MEX,2012,0276,Natural,Meteorological,Storm,Tropical cyclone,,Ernesto,Affected,Mexico,MEX,Central America,Americas,"Tabasco, Veracruz, Guerrero, Quintana Roo, Puebla, Oaxaca provinces",,,,,,,,,Kph,,,,,2012,8,10,2012,8,10,12,,,,,,15000,300000,89.80529293 +2012-0401-MEX,2012,0401,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Carlotta,Waiting,Mexico,MEX,Central America,Americas,"Puerto Escondido city (Dist. Pochutla district, Oaxaca province)",,,,,,,,150,Kph,,,,,2012,6,15,2012,6,15,7,,87000,,87000,,84000,555000,89.80529293 +2012-0294-PHL,2012,0294,Natural,Meteorological,Storm,Tropical cyclone,,TYphoon Kai-Tak,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), National Capital region (NCR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV-A (Calabarzon) provinces",,,,,,,,,Kph,,,,,2012,8,17,2012,8,17,4,,,,,,,3000,89.80529293 +2012-0434-PHL,2012,0434,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Karen,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region III (Central Luzon) provinces",,,,,,,,,Kph,,,,,2012,9,13,2012,9,14,1,,1234,,1234,,,,89.80529293 +2012-0463-PHL,2012,0463,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Marce (Gaemi),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Zambales district (Region III (Central Luzon) province), Mindoro Occidental district (Region IV (Southern Tagalog) province)",,,,,,,,80,Kph,,,,,2012,10,3,2012,10,3,,,322,,322,,,,89.80529293 +2012-0500-PHL,2012,0500,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bopha,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,Region XI (Davao Region) province,,,,Yes,,,,260,Kph,,,,,2012,12,4,2012,12,4,1901,2666,6243998,,6246664,,,898352,89.80529293 +2012-0500-PLW,2012,0500,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Bopha,Kill,Palau,PLW,Micronesia,Oceania,"Angaur, Peleliu, Babaldaob",,,,,,,,,Kph,,,,,2012,12,2,2012,12,2,,,,1250,1250,,,,89.80529293 +2012-0313-PRI,2012,0313,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Isaac,Kill,Puerto Rico,PRI,Caribbean,Americas,"Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces",,,,,,,,,Kph,,,,,2012,8,25,2012,8,25,,,,,,,,,89.80529293 +2012-0585-TWN,2012,0585,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Tembin,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Pingdong Shi area (Taiwan Sheng province),,,,,,,,,Kph,,,,,2012,8,24,2012,8,24,8,,,,,,,8300,89.80529293 +2012-0176-USA,2012,0176,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Debby,Affected,United States of America (the),USA,Northern America,Americas,"Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Jefferson, Leon, Liberty, Madison, Okaloosa, Santa Rosa, Taylor, Wakulla, Walton, Washington, Brevard, Citrus, Hardee, Hernando, Hillsborough, Indian River, Lake, Manatee, Marion, Orange, Osceola, Pasco, Pinellas, Polk, Seminole, Sumter, Volusia districts",,,,,,,,67994,Kph,28.9635,-82.5,,,2012,6,22,2012,6,27,9,1,17000,,17001,,200000,210000,89.80529293 +2012-0498-WLF,2012,0498,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Evan,Kill,Wallis and Futuna,WLF,Polynesia,Oceania,Wallis and Futuna province,,,,,,,,156,Kph,,,,,2012,12,15,2012,12,15,,2,1250,,1252,,,,89.80529293 +2013-0335-MEX,2013,0335,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Fernand,Kill,Mexico,MEX,Central America,Americas,"Yecuatla, Tuxpan, Atzalan districts (Veracruz province), Oaxaca province",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,,Kph,,,,,2013,8,25,2013,8,26,14,,5000,,5000,,,2000,91.12079403 +2013-0358-MEX,2013,0358,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Manuel,Kill,Mexico,MEX,Central America,Americas,"Guerrero, Oaxaca, Jalisco, Michoacan, Sinaloa provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,,Kph,,,,,2013,9,13,2013,9,20,169,,105000,,105000,,685000,4200000,91.12079403 +2013-0406-MEX,2013,0406,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Ingrid,Kill,Mexico,MEX,Central America,Americas,"Veracruz, Hidalgo, Puebla provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2013,9,12,2013,9,17,23,,50000,,50000,,230000,1500000,91.12079403 +2013-0373-PHL,2013,0373,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Usagi (Odette),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV-A (Calabarzon), Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas) provinces",,"Slide (land, mud, snow, rock)",Flood,Yes,,,,,Kph,,,,,2013,9,21,2013,9,22,5,,72696,,72696,,,,91.12079403 +2013-0438-SOM,2013,0438,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 3A,Kill,Somalia,SOM,Eastern Africa,Africa,"Bandarbeyla, Caluula, Qandala, Qardho, Iskushuban districts (Baria province), Eyl, Garoowe districts (Nugaal province)",,Flood,Surge,,Yes,Yes,,,Kph,21.68,57.43,,,2013,11,8,2013,11,19,162,,142380,,142380,,,2000,91.12079403 +2013-0032-MDG,2013,0032,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Haruna,Kill,Madagascar,MDG,Eastern Africa,Africa,"Morombe, Toliary-I, Toliary-II, Sakaraha districts (Atsimo Andrefana province), Menabe, Analamanga provinces",,Flood,,,Yes,,,177,Kph,,,,,2013,2,22,2013,2,22,42,127,40154,,40281,,,25000,91.12079403 +2013-0036-PHL,2013,0036,Natural,Meteorological,Storm,Tropical cyclone,,Tropcal storm Auring,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Palawan, Mindoro Oriental districts (Region IV (Southern Tagalog) province), Zamboanga Del Norte district (Region IX (Zamboanga Peninsula) province), Compostela, Davao del Norte districts (Region XI (Davao Region) province)",,Flood,,,,,,,Kph,,,,,2013,1,3,2013,1,10,1,,10597,,10597,,,,91.12079403 +2013-0430-PHL,2013,0430,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Nari (SAnti),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Nueva Vizcaya district (Region II (Cagayan Valley) province), Pangasinan district (Region I (Ilocos region) province), Region III (Central Luzon) province",,Flood,,,,,,,Kph,,,,,2013,10,12,2013,10,14,20,154,871601,,871755,,,96723,91.12079403 +2013-0437-PHL,2013,0437,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Krosa (Vinta),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte district (Region I (Ilocos region) province), Cagayan district (Region II (Cagayan Valley) province), Apayao district (Cordillera Administrative region (CAR) province)",,Flood,,,,,,,Kph,,,,,2013,10,31,2013,10,31,4,,220443,,220443,,,4729,91.12079403 +2013-0026-SYC,2013,0026,Natural,Meteorological,Storm,Tropical cyclone,,Topical Depression Feleng,Affected,Seychelles,SYC,Eastern Africa,Africa,"Anse Aux Pins, Au Cap, Pointe Larue provinces",,Flood,,,,,,,Kph,,,,,2013,2,7,2013,2,7,,,3000,,3000,,,9300,91.12079403 +2013-0433-PHL,2013,0433,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Haiyan (Yolanda),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Samar, Leyte districts (Region VIII (Eastern Visayas) province), Cebu district (Region VII (Central Visayas) province), Iloilo, Capiz, Aklan districts (Region VI (Western Visayas) province), Palawan district (Region IV (Southern Tagalog) province)",,Surge,,Yes,Yes,Yes,844063,315,Kph,,,,,2013,11,8,2013,11,8,7354,28689,16078181,,16106870,,700000,10000000,91.12079403 +2013-0138-LKA,2013,0138,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Mahasen,Kill,Sri Lanka,LKA,Southern Asia,Asia,"Central, Eastern, North Central, North Western, Northern, Sabaragamuwa, Southern, Uva, Western provinces",,,,,,,,,Kph,,,,,2013,5,13,2013,5,16,7,,3478,3861,7339,,,,91.12079403 +2013-0026-MDG,2013,0026,Natural,Meteorological,Storm,Tropical cyclone,,Topical Depression Feleng,Affected,Madagascar,MDG,Eastern Africa,Africa,"Analanjirofo, Atsimo Atsinanana, Atsinanana, Sava, Vatovavy Fitovinanay provinces",,,,,,,,,Kph,,,,,2013,1,30,2013,2,2,9,,4598,,4598,,,,91.12079403 +2013-0188-MEX,2013,0188,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Barbara,Affected,Mexico,MEX,Central America,Americas,"Chiapas, Oaxaca provinces",,,,,,,,,Kph,,,,,2013,5,29,2013,5,29,4,,12000,,12000,,,,91.12079403 +2013-0258-PHL,2013,0258,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Rumbia,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region III (Central Luzon), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,,,,,,,,Kph,,,,,2013,6,30,2013,6,30,7,4,3592,,3596,,,1000,91.12079403 +2013-0272-PHL,2013,0272,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Utor (Labuyo),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon) provinces",,,,,,,,,Kph,,,,,2013,8,12,2013,8,15,18,7,395723,,395730,,,32431,91.12079403 +2013-0378-PHL,2013,0378,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression Crising (Shanshan),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Mindoro Oriental district (Region IV (Southern Tagalog) province), Leyte district (Region VIII (Eastern Visayas) province), Zamboanga del Norte district (Region IX (Zamboanga Peninsula) province), Bukidnon, Lanao del Norte districts (Region X (Northern Mindanao) province), North Cotabato, South Cotabato districts (Region XII (Soccsksargen) province), Agusan del Norte district (Region XIII (Caraga) province), Maguindanao district (Autonomous region in Muslim Mindanao (ARMM) province), Region VII (Central Visayas), Region XI (Davao Region) provinces",,,,,,,,,Kph,,,,,2013,2,20,2013,2,21,6,4,262880,,262884,,,1680,91.12079403 +2013-0433-PLW,2013,0433,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Haiyan (Yolanda),Kill,Palau,PLW,Micronesia,Oceania,"Ngercelong, Ngaraard, Ngardmau, Koror, Airai, Aimelick, Ngatpang, Ngiwal, Melekeok, Ngchesar, Ngeramlengu areas (Palau province)",,,,Yes,,,,160,Kph,,,,,2013,11,7,2013,11,7,,,,,,,,,91.12079403 +2013-0249-TWN,2013,0249,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Soulik,Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Bailan village (Xinzhu Xian area, Taiwan Sheng province)",,,,,,,,220,Kph,,,,,2013,7,14,2013,7,14,3,54,,,54,,,43500,91.12079403 +2013-0306-TWN,2013,0306,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Trami (Maring),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2013,8,22,2013,8,22,2,,,,,,,12000,91.12079403 +2013-0373-TWN,2013,0373,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Usagi (Odette),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2013,9,22,2013,9,22,,,,,,,,6100,91.12079403 +2013-0341-VNM,2013,0341,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Jebi,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Giang, Bac Kan, Bac Ninh, Cao Bang, Dien Bien, Ha Giang, Ha Nam, Ha Noi City, Ha Tay, Hai Duong, Hai Phong City, Hoa Binh, Hung Yen, Lai Chau, Lang Son, Lao Cai, Nam Dinh, Ninh Binh, Phu Tho, Quang Ninh, Son La, Thai Binh, Thai Nguyen, Thanh Hoa, Tuyen Quang, Vinh Phuc, Yen Bai provinces",,,,,,,,,Kph,,,,,2013,8,2,2013,8,3,7,11,5000,,5011,,,1000,91.12079403 +2013-0419-VNM,2013,0419,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Wutip,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Binh Dinh, Quang Binh, Quang Nam, Quang Ngai, Ha Tinh, Quang Tri, Thua Thien - Hue, Khanh Hoa, Nghe An, Da Nang City, Phu Yen, Thanh Hoa provinces",,,,,,,,194,Kph,,,,,2013,9,30,2013,10,15,31,330,1835255,,1835585,,,663230,91.12079403 +2013-0430-VNM,2013,0430,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Nari (SAnti),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Da Nang City, Quang Nam, Thua Thien - Hue, Quang Ngai provinces",,,,,,,,133,Kph,,,,,2013,10,10,2013,10,15,20,154,109600,,109754,,,76000,91.12079403 +2013-0433-VNM,2013,0433,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Haiyan (Yolanda),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Ninh, Hai Phong City, Bac Giang, Quang Ngai, Thanh Hoa, Nghe An, Ha Tinh, Quang Tri, Thua Thien - Hue, Da Nang City, Quang Nam provinces",,,,,,,,102,Kph,,,,,2013,11,11,2013,11,11,16,89,12630,375,13094,,,734000,91.12079403 +2014-0020-PHL,2014,0020,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Depression Agaton ( (Lingling),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"North Cotabato, South Cotabato districts (Region XII (Soccsksargen) province), Autonomous region in Muslim Mindanao (ARMM), Region X (Northern Mindanao), Region XI (Davao Region), Region XIII (Caraga) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2014,1,19,2014,1,20,79,86,1148621,,1148707,,,12591,92.59898057 +2014-0055-PHL,2014,0055,Natural,Meteorological,Storm,Tropical cyclone,,Topical storm Basyang (Kajiki),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Antique, Negros Occidental districts (Region VI (Western Visayas) province), Cebu (Region VII (Central Visayas) province), Biliran, Leyte, Southern Leyte districts (Region VIII (Eastern Visayas) province), Agusan del Norte, Dinagat, Surigao del Norte districts (Region XIII (Caraga) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2014,1,31,2014,2,1,6,,47740,,47740,,,3200,92.59898057 +2014-0096-VUT,2014,0096,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cylone Lusi,Affected,Vanuatu,VUT,Melanesia,Oceania,"Western Pentecost, East Ambae, Maewo areas (Penama province), Western Ambrym area (Malampa province), South Santo area (Sanma province), Gaua, Vanua Lava areas (Torba province), Shepherd area (Shefa province)",,Flood,,,,,,140,Kph,,,,,2014,3,9,2014,3,12,12,6,20000,,20006,,,2000,92.59898057 +2014-0092-MDG,2014,0092,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Helen,Affected,Madagascar,MDG,Eastern Africa,Africa,"Betsiboka, Boeny, Diana, Melaky, Sofia provinces",,,,,,,,240,Kph,,,,,2014,3,29,2014,4,1,17,,,1736,1736,,,,92.59898057 +2014-0016-REU,2014,0016,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Beisja,Affected,Réunion,REU,Eastern Africa,Africa,"Arrondissement du vent, Arrondissement sous le vent provinces",,,,,,,,151,Kph,,,,,2014,1,2,2014,1,2,1,15,250,,265,,49000,85000,92.59898057 +2014-0001-TON,2014,0001,Natural,Meteorological,Storm,Tropical cyclone,,Ian,Affected,Tonga,TON,Polynesia,Oceania,"Mo'unga'one, Ha'ano, Foa, Lifuka, Lofanga, Uiha islands (Ha'apai island group, Tonga province), Vava'u island group (Tonga province)",,,,Yes,,Yes,,270,Kph,,,,,2014,1,6,2014,1,11,1,14,4000,,4014,,,31000,92.59898057 +2015-0479-BHS,2015,0479,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Joaquin,Affected,Bahamas (the),BHS,Caribbean,Americas,"Exuma, Long Island, Mayaguana, Rum Cay, San Salvador, Samana Cay, Crooked Isl., Ragged Islands (Administrative unit not available)",,Flood,Surge,,,,,,Kph,,,,,2015,10,1,2015,10,4,33,,6710,,6710,,61000,90000,92.70882199 +2015-0309-BGD,2015,0309,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Komen',Affected,Bangladesh,BGD,Southern Asia,Asia,"Cox's Bazar, Chittagong, Noakhali, Feni, Bandarban districts (Chittagong province), Patuakhali, Bhola, Barguna districts (Barisla province)",,Flood,,,,,,,Kph,,,,,2015,7,29,2015,7,30,45,,2600000,,2600000,,,40000,92.70882199 +2015-0053-AUS,2015,0053,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Lam,SigDam,Australia,AUS,Australia and New Zealand,Oceania,"Northern Territory, Western Australia provinces",,,,,,Yes,,185,Kph,,,,,2015,2,20,2015,2,20,,,390,48,438,,,78000,92.70882199 +2015-0079-AUS,2015,0079,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Marcia',Affected,Australia,AUS,Australia and New Zealand,Oceania,"Queensland, New South Wales provinces",,,,,,Yes,,210,Kph,,,,,2015,2,22,2015,2,24,1,,,6000,6000,,396000,546000,92.70882199 +2015-0135-AUS,2015,0135,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Olwyn',Affected,Australia,AUS,Australia and New Zealand,Oceania,"Carnarvon, Ashburton, Upper Gascoyne, Geraldton-Greenough, Irwin districts (Western Australia province)",,,,,,,,160,Kph,,,,,2015,3,12,2015,3,15,,,600,,600,,49000,57000,92.70882199 +2016-0175-BGD,2016,0175,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Roanu,Kill,Bangladesh,BGD,Southern Asia,Asia,"Barisal; Noakhali, Lakshmipur, Chandpur (Chittagong); Cox’s Bazar, Bhola, Barguna, Patuakhali",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,88,,2016,5,21,2016,5,21,28,,1083855,119700,1203555,,,600000,93.87843648 +2016-0355-BHS,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Bahamas (the),BHS,Caribbean,Americas,"New Providence, Nassau",,Flood,,,,,,,Kph,,,,,2016,9,28,2016,10,10,,,,,,,,600000,93.87843648 +2016-0284-BLZ,2016,0284,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Earl,Kill,Belize,BLZ,Central America,Americas,"San Pedro, Caye Caulker, Belize city, Ladyville, (Belize province), Orange Walk province, Belmopan (Cayo province)",,,,,,,,110,Kph,,,,,2016,8,4,2016,8,4,,,10355,,10355,,,,93.87843648 +2014-0227-CHN,2014,0227,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Rammasun (Glenda),Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Yunnan Sheng provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,200,Kph,,,,,2014,7,18,2014,7,19,71,99,9960000,,9960099,,,4232973,92.59898057 +2014-0183-GTM,2014,0183,Natural,Meteorological,Storm,Tropical cyclone,,Boris,Affected,Guatemala,GTM,Central America,Americas,"Alta Verapaz, Huehuetenango, Petén, Quiché, San Marcos provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2014,5,30,2014,6,6,5,,100000,,100000,,,,92.59898057 +2014-0236-JPN,2014,0236,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Neoguri,--,Japan,JPN,Eastern Asia,Asia,"Ehime, Kagawa, Kooti, Tokusima, Hukuoka, Kagosima, Kumamoto, Miyazaki, Nagasaki, Ooita, Saga, Hukusima, Nagano, Hokkaidoo provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,130,Kph,,,,,2014,7,7,2014,7,10,7,66,600,,666,,,156000,92.59898057 +2014-0397-JPN,2014,0397,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Phanfone,Kill,Japan,JPN,Eastern Asia,Asia,"Okinawa, Sizuoka, Aiti, Kanagawa provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,110,Kph,,,,,2014,10,6,2014,10,6,7,60,8706,,8766,,,100000,92.59898057 +2014-0183-MEX,2014,0183,Natural,Meteorological,Storm,Tropical cyclone,,Boris,Affected,Mexico,MEX,Central America,Americas,"Chiapas, Oaxaca provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,40,Kph,,,,,2014,5,30,2014,6,6,,,,,,,,,92.59898057 +2014-0333-MEX,2014,0333,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Odile,Affected,Mexico,MEX,Central America,Americas,"Cabo San Lucas city (Los Cabos area, La Paz district, Baja California Sur province)",,Flood,Rain,,,,,205,Kph,,,,,2014,9,10,2014,9,17,6,135,75000,,75135,,1200000,2500000,92.59898057 +2014-0314-JPN,2014,0314,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Nakri,Kill,Japan,JPN,Eastern Asia,Asia,Kooti province,,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2014,8,3,2014,8,6,,,4401,,4401,,,,92.59898057 +2014-0417-MEX,2014,0417,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Trudy,Waiting,Mexico,MEX,Central America,Americas,"Tlacoachistlahuaca, Huamuxtitlan, Tlalixtaquilla, Malinaltepec districts (Guerrero province), Oaxaca province",,"Slide (land, mud, snow, rock)",Flood,,,Yes,,,Kph,,,,,2014,10,18,2014,10,20,6,,30000,,30000,,,2000,92.59898057 +2014-0385-MEX,2014,0385,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Dolly,Affected,Mexico,MEX,Central America,Americas,"Tamaupilas, Veracruz provinces",,Rain,,,,,,75,Kph,,,,,2014,9,4,2014,9,8,1,,1500,,1500,,,500,92.59898057 +2014-0213-CHN,2014,0213,Natural,Meteorological,Storm,Tropical cyclone,,Hagibis,--,China,CHN,Eastern Asia,Asia,"Shantou district (Guangdong Sheng province), Fujian Sheng province",,Flood,,,,,,80,Kph,,,,,2014,6,14,2014,6,16,,,5000,,5000,,,131000,92.59898057 +2014-0390-CHN,2014,0390,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Kalmaegi (Luis),Kill,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng, Guangxi Zhuangzu Zizhiqu provinces",,Flood,,,,,,,Kph,,,,,2014,9,10,2014,9,16,9,,394000,,394000,,,2900000,92.59898057 +2014-0325-MEX,2014,0325,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Marie,Affected,Mexico,MEX,Central America,Americas,"Oaxaca, Guerrero provinces",,Flood,,,,Yes,,220,Kph,,,,,2014,8,22,2014,9,2,3,,30000,,30000,,,14000,92.59898057 +2014-0386-MEX,2014,0386,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Norbert,Affected,Mexico,MEX,Central America,Americas,"La Paz district (Bafa California Sur province), Manzanillo district (Colima province), Mazatlan district (Sinaloa province), Baja Peninsula, Chihuahua, Jalisco, Nayarit,",,Flood,,,,,,205,Kph,,,,,2014,9,2,2014,9,11,3,,7500,,7500,,,25000,92.59898057 +2014-0392-IND,2014,0392,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Hudhud,Affected,India,IND,Southern Asia,Asia,"Vishakhapatnam, Gangavaram towns (Vishakhapatnam district, Andhra Pradesh province), Baragarh, Bolangir, Boudh, Gajapati, Ganjam, Kalahandi, Kandhamal, Khordha, Koraput, Malkangiri, Nabarangpur, Nayagarh, Nuapada, Puri, Rayagada, Sonepur districts (Orissa province), Chhattisgarh province",,Surge,,Yes,,,,215,Kph,,,,,2014,10,12,2014,10,12,53,,920000,,920000,,530000,7000000,92.59898057 +2014-0240-CHN,2014,0240,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Henry (Matmo),Affected,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Jiangxi Sheng, Shandong Sheng provinces",,,,,,,,120,Kph,,,,,2014,7,22,2014,7,24,14,,,,,,,500000,92.59898057 +2014-0330-CHN,2014,0330,Natural,Meteorological,Storm,Tropical cyclone,,Tropical strom Fung-Wong (Mario),Kill,China,CHN,Eastern Asia,Asia,Zhejiang Sheng province,,,,,,,,,Kph,,,,,2014,9,18,2014,9,24,,,1250,,1250,,,,92.59898057 +2014-0316-JPN,2014,0316,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Halong,Kill,Japan,JPN,Eastern Asia,Asia,"Ehime, Kagawa, Kooti, Tokusima provinces",,,,,,,,160,Kph,,,,,2014,8,10,2014,8,12,10,96,21654,,21750,,,200000,92.59898057 +2014-0396-JPN,2014,0396,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Vongfong,Waiting,Japan,JPN,Eastern Asia,Asia,"Makurazakisi, Kagosimasi districts (Kagosima province), Okinawa, Sizuoka provinces",,,,,,,,290,Kph,,,,,2014,10,12,2014,10,13,9,94,1104,,1198,,,100000,92.59898057 +2014-0314-KOR,2014,0314,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Nakri,Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Chungchongnam-do, Chollabuk-do provinces",,,,,,,,,Kph,,,,,2014,8,3,2014,8,6,14,,,,,,,,92.59898057 +2015-0375-DMA,2015,0375,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Erika,Affected,Dominica,DMA,Caribbean,Americas,"Petite Savanne, Pichelin villages (St. Patrick province), Good Hope, Petite Soufriere villages (St. David province), Bath Estate village (St. George province), Dubique village (St. Andrew province), Campbell village (St. Paul province), Coulibistrie village (St. Joseph province)",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,,Kph,,,,,2015,8,27,2015,8,27,30,20,28000,574,28594,,,482810,92.70882199 +2015-0261-JPN,2015,0261,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Nangka,Waiting,Japan,JPN,Eastern Asia,Asia,"Aiti, Akita, Aomori, Gifu, Gunma, Hirosima, Hukui, Hukusima, Hyoogo, Ibaraki, Iwate, Kanagawa, Kyooto, Mie, Miyagi, Nagano, Nara, Niigata, Okayama, Okinawa, Oosaka, Saitama, Siga, Simane, Sizuoka, Tiba, Tookyoo, Totigi, Tottori, Toyama, Wakayama, Yamagata, Yamaguti, Yamanasi, Ehime, Kagawa, Kooti, Tokusima provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,185,Kph,,,,,2015,7,16,2015,7,17,2,56,789,,845,,127000,207000,92.70882199 +2015-0016-MDG,2015,0016,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Chedza',Kill,Madagascar,MDG,Eastern Africa,Africa,"Ambatondrazaka district (Alaotra Mangoro province), Ambatofinandrahana district (Amoron I Mania province), Ambohidratrimo, Andramasina, Ankazobe, Antananarivo Avaradrano, Antananarivo Atsimondrano, Antananarivo I, Antananarivo II, Antananarivo III, Antananarivo IV, Antananarivo V; Antananarivo VI districts (Analamanga province), Maevatanana district (Betsiboka province), Ambato Boeni, Mahajanga II districts (Boeny province), Tsiroanomandidy district (Bongolava province), Ambanja district (Diana province), Ambalavao, Ambohimahasoa, Fianarantsoa I, Vohibato, Lalangina districts (Haute Matsiatra province), Soavinandriana district (Itasy province), Besalampy district (Melaky province), Belo Sur Tsiribihina, Mahabo, Miandrivazo, Morondava districts (Menabe province), Mampikony district (Sofia province) Farafangana, Vangaindrano, Vondrozo districts (Atsimo Atsinanana province), Antanifotsy distrcit (Vakinankaratra province), Ikongo, Manakara Atsimo, Mananjary, Nosy-Varika, Vohipeno districts (Vatovavy Fitovianny)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2015,1,16,2015,1,17,89,37,,173970,174007,,,36000,92.70882199 +2015-0105-CHN,2015,0105,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon' Maysak',Waiting,China,CHN,Eastern Asia,Asia,"Hunan Sheng, Jiangxi Sheng provinces",,Flood,,,,,,,Kph,,,,,2015,4,4,2015,4,15,6,,12000,,12000,,,209000,92.70882199 +2015-0252-CHN,2015,0252,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Kujira,Affected,China,CHN,Eastern Asia,Asia,Hainan Sheng province,,Flood,,,,,,,Kph,,,,,2015,6,22,2015,6,22,,,193000,,193000,,,11000,92.70882199 +2015-0339-CHN,2015,0339,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soudelor' (Hanna),Kill,China,CHN,Eastern Asia,Asia,"Zheijiang Sheng, Fujian Sheng, Jiangxi Sheng, Anhui Sheng provinces",,Flood,,,,,,230,Kph,,,,,2015,8,9,2015,8,9,18,,1580000,,1580000,,,1282690,92.70882199 +2015-0375-HTI,2015,0375,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Erika,Affected,Haiti,HTI,Caribbean,Americas,"Ganthier village (Croix-Des-Bouquets district, Sud province), Port-au-Prince district (Ouest province), Gonaïves district (Artibonite province)",,Flood,,,,,,,Kph,,,,,2015,8,29,2015,8,30,5,3,,1966,1969,,,,92.70882199 +2015-0244-JPN,2015,0244,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chan-Home,Affected,Japan,JPN,Eastern Asia,Asia,Okinawa province,,Flood,,,,,,250,Kph,,,,,2015,7,9,2015,7,11,1,27,,,27,,,,92.70882199 +2015-0456-JPN,2015,0456,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Etau,Affected,Japan,JPN,Eastern Asia,Asia,"Ibaraki, Totigi, Miyagi provinces",,Flood,,,,,,,Kph,,,,,2015,9,8,2015,9,10,8,46,60000,,60046,,250000,,92.70882199 +2015-0093-KIR,2015,0093,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Pam',Affected,Kiribati,KIR,Micronesia,Oceania,"Tarawa, Arorae, Tamana, Onotoa areas (Kiribati province)",,Flood,,Yes,,,,,Kph,,,,,2015,3,12,2015,3,13,,,1500,,1500,,,,92.70882199 +2015-0470-MEX,2015,0470,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Patricia,Waiting,Mexico,MEX,Central America,Americas,"Manzanillo district (Colima province), Coahuila, Nuevo leon, Tamaulipas provinces",,Flood,,,,,,270,Kph,283.812,-101.77,,,2015,10,22,2015,10,28,14,,15000,,15000,,343000,823000,92.70882199 +2015-0244-CHN,2015,0244,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chan-Home,Affected,China,CHN,Eastern Asia,Asia,Zhejiang Sheng province,,,,,,,,,Kph,,,,,2015,7,11,2015,7,12,,,10800,3000,13800,,,940000,92.70882199 +2015-0278-CHN,2015,0278,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Linfa (Egay),Affected,China,CHN,Eastern Asia,Asia,Guangdong Sheng province,,,,,,,,,Kph,,,,,2015,7,9,2015,7,9,,,56000,,56000,,,213000,92.70882199 +2015-0458-CHN,2015,0458,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Dujuan,Affected,China,CHN,Eastern Asia,Asia,"Fujian Sheng, Zhejiang Sheng provinces",,,,,,,,,Kph,,,,,2015,9,28,2015,9,28,,,,,,,79000,661000,92.70882199 +2015-0490-CHN,2015,0490,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mujigae,Kill,China,CHN,Eastern Asia,Asia,"Guangxi Zhuangzu Zizhiqu, Guangdong Sheng, Hainan Sheng provinces",,,,,,,,,Kph,,,,,2015,10,4,2015,10,4,20,,52500,25800,78300,,,4200000,92.70882199 +2015-0473-CPV,2015,0473,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Fred',Waiting,Cabo Verde,CPV,Western Africa,Africa,"Boa Vista, Brava, Cima, Fogo, Ilheu Branco, Ilheu Raso, Maio, Rombo, Sal, Santa Luzia, Santiago, Santo Antao, Sao Nicolau, Sao Vicente provinces",,,,,,,,140,Kph,,,,,2015,8,31,2015,8,31,9,,,,,,,1100,92.70882199 +2015-0375-DOM,2015,0375,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Erika,Affected,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces",,,,,,,,,Kph,,,,,2015,8,28,2015,8,28,,,33,2100,2133,,,,92.70882199 +2015-0105-FSM,2015,0105,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon' Maysak',Waiting,Micronesia (Federated States of),FSM,Micronesia,Oceania,"Chuuk, Ulithi Atoll, Yap areas (Micronesia province)",,,,Yes,,Yes,,260,Kph,,,,,2015,3,27,2015,3,30,5,,35000,,35000,,,11000,92.70882199 +2015-0176-JPN,2015,0176,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Noul (Dodong),Affected,Japan,JPN,Eastern Asia,Asia,"Amagityoo, Isentyoo, Kasarityoo, Nazesi, Setootityoo, Sumiyooson, Tatugootyoo, Tokunosimatyoo, Ukenson, Yamatoson districts (Kagosima province), Okinawa province",,,,,,,,180,Kph,,,,,2015,5,12,2015,5,12,,6,39,,45,,,23200,92.70882199 +2015-0371-JPN,2015,0371,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Goni (Ineng),Kill,Japan,JPN,Eastern Asia,Asia,Kumamoto province,,,,,,,,198,Kph,,,,,2015,8,25,2015,8,25,,70,,,70,,,,92.70882199 +2015-0458-JPN,2015,0458,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Dujuan,Affected,Japan,JPN,Eastern Asia,Asia,Okinawa province,,,,,,,,,Kph,,,,,2015,9,28,2015,9,28,,,,,,,,,92.70882199 +2015-0074-MDG,2015,0074,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Fundi',Affected,Madagascar,MDG,Eastern Africa,Africa,"Androy, Anosy, Atsimo Andrefana, Menabe provinces",,,,,,,,,Kph,,,,,2015,2,7,2015,2,8,6,,,8430,8430,,,10000,92.70882199 +2016-0361-KOR,2016,0361,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chaba (Chyba?),Kill,Korea (the Republic of),KOR,Eastern Asia,Asia,"Busan city (Pusan province), Ulsan city (Kyongsangnam-do province), Cheju-do province (Jeju island), Chollanam-do province, Gyeongju city (Kyongsangbuk-do province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2016,10,5,2016,10,12,9,,1500,,1500,,,126000,93.87843648 +2016-0433-PAN,2016,0433,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Otto,Affected,Panama,PAN,Central America,Americas,"Veraguas, Chiriquí, Colón, Bocas del Toro, Panama Oeste (Capira district, Panama province); Panama, Panama Oeste",,Flood,"Slide (land, mud, snow, rock)",,,,,120,Kph,,,,,2016,11,21,2016,11,22,7,,12000,,12000,,,,93.87843648 +2016-0284-MEX,2016,0284,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Earl,Kill,Mexico,MEX,Central America,Americas,"Puebla, Veracruz provinces",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2016,8,6,2016,8,7,54,,11000,,11000,,,,93.87843648 +2016-0041-FJI,2016,0041,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Winston,Kill,Fiji,FJI,Melanesia,Oceania,"Savusavu (Cakaudrove district, Northern province, Vanua Levu Isl. and Taveuni Isl.), Bua, Macuata districts (Northern province, Vanua Levu Isl.), Western, Central provinces (Viti Levu, Yasawa, Mamanuca Isl.), Lau district (Eastern province, Vanua Balavu, Nayau Isl.), Lomaiviti district (Eastern province, Koro Isl.)",,Flood,Surge,,Yes,Yes,,325,Kph,,,,,2016,2,20,2016,2,21,45,144,540414,,540558,,50000,600000,93.87843648 +2016-0350-CHN,2016,0350,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Megi,Affected,China,CHN,Eastern Asia,Asia,"Quanzhou district (Fujian Sheng province), Sucun village (Quzhou district, Zhejiang Sheng province), Baofeng village (Lishui district, Zhejiang Sheng province)",,"Slide (land, mud, snow, rock)",,,,,,119,Kph,,,,,2016,9,28,2016,9,28,35,,36000,,36000,,,830000,93.87843648 +2016-0382-JPN,2016,0382,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Mindulle,Affected,Japan,JPN,Eastern Asia,Asia,"Saitama, Hokkaidoo, Tookyoo, Tiba provinces",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2016,8,22,2016,8,22,2,70,3633,,3703,,,100000,93.87843648 +2016-0256-CHN,2016,0256,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Butchoy (Nepartak),Kill,China,CHN,Eastern Asia,Asia,Fujian Sheng province,,Flood,,,,,,,Kph,,,,,2016,7,9,2016,7,9,69,,,24900,24900,,,1511160,93.87843648 +2016-0342-CHN,2016,0342,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Ferdie (Meranti),Kill,China,CHN,Eastern Asia,Asia,Fujian Sheng province,,Flood,,,,,,,Kph,,,,,2016,9,15,2016,9,15,,,205500,,205500,,101000,2300000,93.87843648 +2016-0433-CRI,2016,0433,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Otto,Affected,Costa Rica,CRI,Central America,Americas,"Upala, Bagaces, Osa, Golfito, Corredones",,Flood,,,,Yes,,175,Kph,,,,,2016,11,23,2016,11,25,9,,50000,,50000,,,,93.87843648 +2016-0141-FJI,2016,0141,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Zena,Affected,Fiji,FJI,Melanesia,Oceania,"Central, Western provinces (Viti Levu Isl.)",,Flood,,,,,,,Kph,,,,,2016,4,4,2016,4,7,2,,5000,,5000,,,,93.87843648 +2016-0485-IND,2016,0485,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Vardah',Kill,India,IND,Southern Asia,Asia,Chennai district (Tamil Nadu province); Andhra Pradesh,,Flood,,,,,,140,Kph,,,,,2016,12,12,2016,12,14,24,,,,,,200000,1000000,93.87843648 +2016-0324-JPN,2016,0324,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Lionrock,Kill,Japan,JPN,Eastern Asia,Asia,Iwate,,Flood,,,,,,,Kph,,,,,2016,9,4,2016,9,4,22,,,,,,,,93.87843648 +2016-0390-JPN,2016,0390,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Malakas,Affected,Japan,JPN,Eastern Asia,Asia,"Hyogoo, Miyazaki, Oosaka province",,Flood,,,,,,185,Kph,,,,,2016,9,20,2016,9,20,1,,6000,,6000,,,,93.87843648 +2016-0355-LCA,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Saint Lucia,LCA,Caribbean,Americas,"Gros-Islet (Region Number 1 province), Castries, Bexon, Marc, (Region Number 8 province), Dennery (Region Number 3 province), Laborie, Vieux- Fort (Region Number 5 province), Micoud (Region Number 4 province), Choiseul (Region Number 6 province)",,Flood,,,,,,,Kph,,,,,2016,9,28,2016,9,28,,,25000,,25000,,,,93.87843648 +2016-0319-MEX,2016,0319,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Newton,Waiting,Mexico,MEX,Central America,Americas,"Cabo San Lucas city (La Paz district, Baja California Sur province), Oaxaca, Colima, Jalisco provinces",,Flood,,,,,,,Kph,,,,,2016,9,6,2016,9,7,11,,10500,,10500,,,50000,93.87843648 +2016-0363-CHN,2016,0363,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Karen (Sarika),Affected,China,CHN,Eastern Asia,Asia,"Hainan Sheng, Guangdong Sheng, Guangxi Zhuangzu Zizhiqu provinces",,,,,,,,,Kph,,,,,2016,10,16,2016,10,19,,,,,,,,890000,93.87843648 +2016-0364-CHN,2016,0364,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Lawin (Haima),Kill,China,CHN,Eastern Asia,Asia,Guangdong Sheng province,,,,,,,,,Kph,,,,,2016,10,19,2016,10,21,,,12000,,12000,,,,93.87843648 +2016-0379-CHN,2016,0379,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Dianmu,Kill,China,CHN,Eastern Asia,Asia,"Zhanjiang district (Guangdong Sheng province), Hainan Sheng province",,,,,,,,,Kph,,,,,2016,8,18,2016,8,19,,,40000,,40000,,,,93.87843648 +2016-0355-CUB,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Cuba,CUB,Caribbean,Americas,"Baracoa, Imias, Maisi, San Antonio del Sur districts (Guantanamo province)",,,,,,,,,Kph,,,,,2016,9,28,2016,10,7,,,190000,,190000,,,2600000,93.87843648 +2016-0355-DOM,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Dominican Republic (the),DOM,Caribbean,Americas,"Azua, San José de Ocoa, San Juan de la Maguana, Pedernales, Barahona, Independencia, Dajabon, Monte Cristi, Bahoruco, Elias Pina, San Cristobal, Peravia, Santiago Rodriguez, Puerto Plata, Valverde, Santo Domingo, Distrito Nacional, Monsenor Noel, Sanchez Ramirez, Monte Plata, La Vega",,,,,,,,,Kph,,,,Yabacao,2016,10,3,2016,10,4,,,,,,,,,93.87843648 +2016-0284-GTM,2016,0284,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Earl,Kill,Guatemala,GTM,Central America,Americas,"Petén, Izabal provinces",,,,,,,,,Kph,,,,,2016,8,6,2016,8,6,,,55,,55,,,,93.87843648 +2016-0284-HND,2016,0284,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Earl,Kill,Honduras,HND,Central America,Americas,"Islas De Bahía, Colon, Gracias A Dios, Cortés",,,,,,,,,Kph,,,,,2016,8,6,2016,8,6,1,,151,,151,,,,93.87843648 +2016-0355-HTI,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Haiti,HTI,Caribbean,Americas,"Grand’Anse (Jérémie), South (Les Cayes, Les Anglais and Tiburon municipalities), Nippes, South East, West (Leogane, Port-au-Prince) and North West departments, Les Cayes",,,,,,,,,Kph,,,,,2016,9,28,2016,10,7,546,439,2100000,,2100439,,,2000000,93.87843648 +2016-0355-JAM,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Jamaica,JAM,Caribbean,Americas,St. Thomas and Portland,,,,,,,,,Kph,,,,,2016,9,27,2016,10,3,,,125000,,125000,,,,93.87843648 +2016-0361-JPN,2016,0361,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chaba (Chyba?),Kill,Japan,JPN,Eastern Asia,Asia,Hokkaidoo province (Hokkaido island),,,,,,,,,Kph,,,,,2016,10,5,2016,10,6,,,,,,,,,93.87843648 +2016-0433-NIC,2016,0433,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Otto,Affected,Nicaragua,NIC,Central America,Americas,"Costa Caribe Sur, Chontales, Zelaya Central, Río San Juan",,,,,,,,,Kph,,,,,2016,11,17,2016,11,17,,,10570,,10570,,,,93.87843648 +2014-0240-PHL,2014,0240,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Henry (Matmo),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Region II, Region IV-A, Region IV provinces",,Rain,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2014,7,22,2014,7,23,2,,683,,683,,,,92.59898057 +2014-0227-PHL,2014,0227,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Rammasun (Glenda),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital region (NCR), Region III (Central Luzon), Region IV-A (Calabarzon), Region IV (Southern Tagalog), Region V (Bicol region), Region VIII (Eastern Visayas) provinces",,Flood,"Slide (land, mud, snow, rock)",Yes,,Yes,,150,Kph,,,,,2014,7,15,2014,7,15,111,1250,4653716,,4654966,,,820576,92.59898057 +2014-0227-VNM,2014,0227,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Rammasun (Glenda),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Mong Cai Township district (Quang Ninh province),,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2014,7,19,2014,7,20,27,,36000,,36000,,,6200,92.59898057 +2014-0479-PHL,2014,0479,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Hagupit' (Ruby),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Bataan district (Region III (Central Luzon) province), Agusan Del Norte, Agusan Del Sur, Surigao Del Sur, Surigao Del Norte districts (Region XIII (Caraga) province), National Capital region (NCR), Region IV (Southern Tagalog), Region IV-A (Calabarzon), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces",,"Slide (land, mud, snow, rock)",Flood,Yes,,,7951,210,Kph,,,,,2014,12,12,2014,12,12,18,916,4149484,,4150400,,,113878,92.59898057 +2014-0390-PHL,2014,0390,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Kalmaegi (Luis),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte, La Union, Pangasinan districts (Region I (Ilocos region) province), Cagayan, Isabela, Nueva Vizcaya districts (Region II (Cagayan Valley) province), Negros Occidental district (Region VI (Western Visayas) province), Rizal district (Region IV-A (Calabarzon) province), Apayao, Benguet, Kalinga districts (Cordillera Administrative region (CAR) province), National Capital region (NCR), Region III (Central Luzon) provinces",,Transport accident,Flood,,,,,130,Kph,,,,,2014,9,10,2014,9,16,4,1,431085,,431086,,,19183,92.59898057 +2014-0330-PHL,2014,0330,Natural,Meteorological,Storm,Tropical cyclone,,Tropical strom Fung-Wong (Mario),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Dagupan, Lingayen areas (Pangasinan district, Region I (Ilocos region) province), Pampanga district (Region III (Central Luzon) province), Metropolitan Manila district (National Capital region (NCR) province)",,"Slide (land, mud, snow, rock)",Transport accident,,,,,85,Kph,,,,,2014,9,17,2014,9,22,22,16,2052141,,2052157,,,75783,92.59898057 +2014-0497-PHL,2014,0497,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Jangmi' (Seniang),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Palawan district (Region IV (Southern Tagalog) province), Antique, Capiz, Negros Occidental districts (Region VI (Western Visayas) province), Bohol, Cebu, Siquijor districts (Region VII (Central Visayas) province), Leyte, Samar, Southern Leyte districts (Region VIII (Eastern Visayas) province), Zamboanga Del Sur district (Region IX (Zamboanga Peninsula) province), Bukidnon, Camiguin, Lanao del Norte, Misamis Oriental districts (Region X (Northern Mindanao) province), Compostela, Davao del Norte, Davao Oriental districts (Region XI (Davao Region) province), Region XIII (Caraga) province",,"Slide (land, mud, snow, rock)",,,,,,210,Kph,,,,,2014,12,28,2014,12,31,72,,578549,,578549,,,17688,92.59898057 +2014-0474-PHL,2014,0474,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Sinlaku' (Queenie),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Palawan district (Region IV (Southern Tagalog) province), Negros Occidental district (Region VI (Western Visayas) province), Bohol, Cebu, Negros Oriental districts (Region VII (Central Visayas) province), Southern Leyte district (Region VIII (Eastern Visayas) province), Misamis Occidental, Misamis Oriental districts (Region X (Northern Mindanao) province), Davao del Sur district (Region XI (Davao Region) province), Dinagat, Surigao del Norte, Surigao del Sur districts (Region XIII (Caraga) province)",,Flood,,,,,,,Kph,,,,,2014,11,27,2014,11,28,12,,4695,,4695,,,,92.59898057 +2014-0240-TWN,2014,0240,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Henry (Matmo),Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,Flood,,,,,,160,Kph,,,,,2014,7,22,2014,8,24,,,,,,,,20000,92.59898057 +2014-0330-TWN,2014,0330,Natural,Meteorological,Storm,Tropical cyclone,,Tropical strom Fung-Wong (Mario),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,Flood,,,,,,,Kph,,,,,2014,9,18,2014,9,24,,,,,,,,400000,92.59898057 +2014-0423-PNG,2014,0423,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Ita,Affected,Papua New Guinea,PNG,Melanesia,Oceania,Milne Bay province,,,,,,,,,Kph,,,,,2014,4,7,2014,4,9,,,12346,,12346,,,,92.59898057 +2014-0310-USA,2014,0310,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Iselle,Affected,United States of America (the),USA,Northern America,Americas,Hawaii province,,,,,,,,95,Kph,,,,,2014,8,8,2014,8,10,1,,834,,834,,,66000,92.59898057 +2014-0390-VNM,2014,0390,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Kalmaegi (Luis),Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Bac Giang, Bac Kan, Bac Ninh, Cao Bang, Dien Bien, Ha Giang, Ha Nam, Ha Noi City, Ha Tay, Ha Tinh, Hai Duong, Hai Phong City, Hoa Binh, Hung Yen, Lai Chau, Lang Son, Lao Cai, Nam Dinh, Nghe An, Ninh Binh, Phu Tho, Quang Ninh, Son La, Thai Binh, Thai Nguyen, Thanh Hoa, Tuyen Quang, Vinh Phuc, Yen Bai provinces",,,,,,,,130,Kph,,,,,2014,9,16,2014,9,16,11,,11325,,11325,,,4500,92.59898057 +2014-0474-VNM,2014,0474,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Sinlaku' (Queenie),Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Phu Yen, Binh Dinh provinces",,,,,,,,,Kph,,,,,2014,11,30,2014,11,30,,,750,,750,,,,92.59898057 +2015-0017-PHL,2015,0017,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Amang' (Mekkhala),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Albay, Camarines Norte, Catanduanes, Sorsogon districts (Region V (Bicol region) province), Cebu district (Region VII (Central Visayas) province), Eastern Samar, Samar districts (Region VIII (Eastern Visayas) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2015,1,19,2015,1,19,2,,21687,,21687,,,1000,92.70882199 +2015-0371-PHL,2015,0371,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Goni (Ineng),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte, Ilocos Sur districts (Region I (Ilocos region) province), Region II (Cagayan Valley), Cordillera Administrative region (CAR) provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,252,Kph,,,,,2015,8,22,2015,8,22,40,24,318359,,318383,,,30299,92.70882199 +2015-0462-PHL,2015,0462,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Koppu (Lando),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Camarines Norte, Catanduanes districts (Region V (Bicol region) province), Cordillera Administrative region (CAR), National Capital region (NCR) province, Region I (Ilocos region), Region II (Cagayan Valley), Region III (Central Luzon), Region IV-A (Calabarzon) provinces",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,210,Kph,16.691,121.24,,,2015,10,14,2015,10,20,51,83,2898507,,2898590,,,210985,92.70882199 +2015-0543-PHL,2015,0543,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Melor (Nona),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Nueva Vizcaya district (Region II (Cagayan Valley) province), Quezon, Batangas districts (Region IV-A (Calabarzon) province), Marinduque, Mindoro Oriental, Mindoro Occidental, Romblon districts (Region IV (Southern Tagalog) province), Catanduanes, Albay, Masbate, Sorsogon districts (Region V (Bicol region) province), Biliran districts (Region VII (Central Visayas) province), Samar, Northern Samar districts (Region VIII (Eastern Visayas) province), Quezon City area (Metropolitan Manila district, National Capital region (NCR) province)",,Flood,"Slide (land, mud, snow, rock)",,,,,185,Kph,,,,,2015,12,14,2015,12,15,46,24,287227,,287251,,,135217,92.70882199 +2015-0278-PHL,2015,0278,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Linfa (Egay),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte, Ilocos Sur, La Union districts (Region I (Ilocos region) province), Palawan district (Region IV (Southern Tagalog) province), Benguet, Abra, Kalinga districts (Cordillera Administrative region (CAR) province)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2015,7,4,2015,7,5,,,55567,,55567,,,2218,92.70882199 +2015-0093-SLB,2015,0093,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Pam',Affected,Solomon Islands,SLB,Melanesia,Oceania,"Temotu, Malaita areas (Solomon Islands province)",,Flood,,Yes,,,,,Kph,,,,,2015,3,6,2015,3,6,,,44096,,44096,,,,92.70882199 +2015-0494-SOM,2015,0494,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Megh,--,Somalia,SOM,Eastern Africa,Africa,Bari province,,Flood,,,,,,,Kph,,,,,2015,11,8,2015,11,8,,,,,,,,,92.70882199 +2015-0339-TWN,2015,0339,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soudelor' (Hanna),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,Flood,,,,,,,Kph,,,,,2015,8,8,2015,8,9,6,380,,,380,,,,92.70882199 +2015-0459-USA,2015,0459,Natural,Meteorological,Storm,Tropical cyclone,,Joaquin,Kill,United States of America (the),USA,Northern America,Americas,"South Carolina, North Carolina",,Flood,,,,,,,Kph,335.033,-804.93,,,2015,10,1,2015,10,13,21,,800,,800,,400000,1700000,92.70882199 +2015-0093-VUT,2015,0093,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Pam',Affected,Vanuatu,VUT,Melanesia,Oceania,"Torba, Penama, Sanma, Malampa, Shefa, Tafea provinces",,Flood,,Yes,Yes,Yes,,250,Kph,,,,,2015,3,12,2015,3,14,11,,188000,,188000,,,449400,92.70882199 +2015-0494-YEM,2015,0494,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Megh,--,Yemen,YEM,Western Asia,Asia,"Hidaybu, Qulensya Wa Abd Al Kuri districts (Hadramaut province), Aden province",,Flood,,,,,,,Kph,,,,,2015,11,8,2015,11,10,18,,15000,,15000,,,,92.70882199 +2015-0093-TUV,2015,0093,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Pam',Affected,Tuvalu,TUV,Polynesia,Oceania,"Nanumea, Nanumaga, Niutao, Nui, Vaitupu, Nukufetau, Nukulaelae atolls (Tuvalu province)",,Surge,,Yes,,Yes,,,Kph,,,,,2015,3,10,2015,3,11,,,4613,,4613,,,,92.70882199 +2015-0105-PHL,2015,0105,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon' Maysak',Waiting,Philippines (the),PHL,South-Eastern Asia,Asia,"Isabela district (Region II (Cagayan Valley) province), Aurora district (Region III (Central Luzon) province)",,,,,,,,200,Kph,,,,,2015,4,6,2015,4,6,,,2761,,2761,,,,92.70882199 +2015-0176-PHL,2015,0176,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Noul (Dodong),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Benito Soliven, Dinapigue, Dilvilacan, Maconacon, Palanan areas (Isabela district, Region II (Cagayan Valley) province), Aparri, Buguey, Calayan, Gonzaga, Santa Ana, Santa Teresita areas (Cagayan district, Region II (Cagayan Valley) province)",,,,,,,,220,Kph,,,,,2015,5,11,2015,5,11,2,,523,,523,,,348,92.70882199 +2015-0244-PHL,2015,0244,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chan-Home,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative region (CAR), Region I (Ilocos region), Region II (Cagayan Valley) provinces",,,,,,,,,Kph,,,,,2015,7,12,2015,7,12,5,,10800,3300,14100,,,1500000,92.70882199 +2015-0339-PHL,2015,0339,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soudelor' (Hanna),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Janiuary area (Iloilo district, Region VI (Western Visayas) province), Valladolid area (Negros Occidental district, Region VI (Western Visayas) province), San Lorenzo area (Guimaras district, Region VI (Western Visayas) province), Bayawan, Siaton areas (Negros Oriental district, Region VII (Central Visayas) province), Kapatagan, Laia, Sapad areas (Lanao Del Norte district, Region X (Northern Mindanao) province)",,,,,,,,,Kph,,,,,2015,8,5,2015,8,5,,,3843,,3843,,,,92.70882199 +2015-0490-PHL,2015,0490,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mujigae,Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Benguet district (Cordillera Administrative region (CAR) province), La Union, Pangasinan districts (Region I (Ilocos region) province), Aurora, Bulacan, Nueva Ecija districts (Region III (Central Luzon) province)",,,,,,,,,Kph,,,,,2015,10,2,2015,10,2,2,,3500,,3500,,,1300,92.70882199 +2015-0093-PNG,2015,0093,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Pam',Affected,Papua New Guinea,PNG,Melanesia,Oceania,"West New Britain, Madang provinces",,,,,,,,,Kph,,,,,2015,3,13,2015,3,13,,,9199,,9199,,,,92.70882199 +2015-0281-SLB,2015,0281,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cylone Raquel,Affected,Solomon Islands,SLB,Melanesia,Oceania,Solomon Islands province,,,,,,,,,Kph,,,,,2015,7,5,2015,7,6,9,,,400,400,,,2000,92.70882199 +2015-0484-SOM,2015,0484,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Chapala,Affected,Somalia,SOM,Eastern Africa,Africa,"Baargsaal, Bander, Bareeda, Butiyaal, Caluula, Murcanyo, Qandalla, Xaabo villages (Bossaso district, Bari province), Biycad, Bulahar, Ceelsheik, Shacable villages (Berbera district, Woqooyi Galbeed province)",,,,,,,,,Kph,,,,,2015,11,2,2015,11,2,,,4000,,4000,,,,92.70882199 +2015-0244-TWN,2015,0244,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Chan-Home,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2015,7,12,2015,7,12,,6,,,6,,,,92.70882199 +2015-0458-TWN,2015,0458,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Dujuan,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,Taiwan Sheng province,,,,,,,,,Kph,,,,,2015,9,28,2015,9,28,3,376,,,376,,,26000,92.70882199 +2015-0252-VNM,2015,0252,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Kujira,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,Son La province,,,,,,,,,Kph,,,,,2015,6,24,2015,6,29,7,,,115,115,,,,92.70882199 +2015-0484-YEM,2015,0484,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Chapala,Affected,Yemen,YEM,Western Asia,Asia,"Abyan, Hadramaut, Shabwah provinces",,,,,,,,,Kph,,,,,2015,11,3,2015,11,4,8,65,110000,,110065,,,200000,92.70882199 +2015-0339-MNP,2015,0339,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soudelor' (Hanna),Kill,Northern Mariana Islands (the),MNP,Micronesia,Oceania,Saipan island (Northern Mariana Islands province),,,,,,Yes,,170,Kph,,,11:30,,2015,8,1,2015,8,2,,10,350,,360,,,,92.70882199 +2016-0379-VNM,2016,0379,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Dianmu,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Hai Phong City, Quang Ninh, Yen Bai, Phu Tho, Lai Chau, Dien Bien, Son La, Hoa Binh, Than Hoa, Lao Cai, Bac Giang, Nghe An provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,"Luc Nam, Thoung, Ma, Buoi, Tao river",2016,8,18,2016,8,21,11,15,11075,,11090,,,157,93.87843648 +2016-0322-USA,2016,0322,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Hermine,Affected,United States of America (the),USA,Northern America,Americas,"Georgia, Florida, South Carolina, New York, New Jersey; Cedar Key, Florida, Aurora (North Carolina); Virginia Tidewater region",,Flood,Surge,,,,,85,Kph,,,,,2016,9,1,2016,9,6,3,,13500,570,14070,,270000,600000,93.87843648 +2016-0456-PHL,2016,0456,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Marce (Tokage),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Romblon, Mindoro Oriental (Region IV (Southern Tagalog) province), Antique, Capiz, Aklan, Iloilo (Region VI (Western Visayas) province), Leyte (Region VIII (Eastern Visayas) province), Surigao del Sur, Surigao del Norte, Dinagat (Region XIII (Caraga) province)",,"Slide (land, mud, snow, rock)",,,,,,55,Kph,,,,,2016,11,23,2016,11,25,,,14309,,14309,,,,93.87843648 +2016-0350-TWN,2016,0350,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Megi,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,,,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2016,9,27,2016,9,27,7,160,,,160,,,110000,93.87843648 +2016-0490-PHL,2016,0490,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Helen (Megi),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Meycauayan city, Marilao, Sta. Maria (Bulacan province)",,Flood,,,,,,,Kph,,,,,2016,9,28,2016,9,28,,,1559,,1559,,,,93.87843648 +2016-0342-TWN,2016,0342,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Ferdie (Meranti),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,,,Flood,,,,,,305,Kph,,,,,2016,9,14,2016,9,14,,,,,,,,70000,93.87843648 +2016-0362-VNM,2016,0362,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Aere,Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Thua Thien-Hue, Quang Tri, Quang Binh, Ha Tinh, Nghe An provinces",,Flood,,,,,,,Kph,,,,,2016,10,13,2016,10,15,34,,610000,,610000,,,350000,93.87843648 +2016-0256-PHL,2016,0256,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Butchoy (Nepartak),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Zambales, Battan (Region III); Rizal, Batangas (Region IV)",,,,,,,,,Kph,,,,,2016,7,8,2016,7,8,2,2,3357,,3359,,,,93.87843648 +2016-0342-PHL,2016,0342,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Ferdie (Meranti),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte district (Region I (Ilocos region) province), Batanes, Cagayan districts (Region II (Cagayan valley) province)",,,,,,,,,Kph,,,,,2016,9,16,2016,9,16,,,16648,,16648,,,4913,93.87843648 +2016-0363-PHL,2016,0363,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Karen (Sarika),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Pangasinan district (Region I (Ilocos region) province), Nueva Vizcaya, Quirino, Isabela districts (Region II (Cagayan valley) province), Aurora, Bulacan, Nueva Ecija, Zambales districts (Region III (Central Luzon) province), Quezon, Rizal, Laguna, Cavite, Batangas districts (Region IV-A (Calabarzon) province), Albay, Catanduanes, Camarines Sur, Camarines Norte, Sorsogon districts (Region V (Bicol region) province)",,,,,,,,210,Kph,,,,,2016,10,16,2016,10,19,,,52270,,52270,,,11490,93.87843648 +2016-0364-PHL,2016,0364,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Lawin (Haima),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos Norte, Ilocos Sur, La Union, Pangasinan districts (Region I (Ilocos region) province), Cagayan, Isabela, Nueva Vizcaya, Quirino districts (Region II (Cagayan valley) province), Aurora, Bataan, Bulacan, Nueva Ecija, Pampanga, Tarlac, Zambales districts (Region III (Central Luzon) province), Batangas, Quezon, Rizal districts (Regio IV-A (Calabarzon) province), Camarines Norte, Sorgoson (Region V (Bicol region) province)), Abra, Apayao, Benguet, Ifugao, Kalinga, Mountain province (Cordillera Administrative region (CAR))",,,,,,,,215,Kph,,,,,2016,10,19,2016,10,21,8,,981154,,981154,,,50690,93.87843648 +2016-0503-PHL,2016,0503,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Nina' (Nock-Ten),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Batangas, Cavite, Laguna, Quezon, Rizal (Calabarzon); Mariduque, Mindoro Occidental (Mimaropa); Albay, Camarines Norte, Camarines Sur, Catanduanes , Masbate, Sorsogon (Region V, Bicol region); Northern Samar (Region VIII, Eastern Visayas)",,,,,,Yes,,185,Kph,,,,,2016,12,25,2016,12,26,24,,1893404,,1893404,,,103661,93.87843648 +2016-0004-TON,2016,0004,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Ulla,Waiting,Tonga,TON,Polynesia,Oceania,"Vava'u, Ha'apai islands (Administrative unit not available)",,,,,,,,150,Kph,,,,,2016,1,2,2016,1,2,,,392,,392,,,,93.87843648 +2016-0041-TON,2016,0041,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Winston,Kill,Tonga,TON,Polynesia,Oceania,Vava’u Isl.,,,,,,,,,Kph,,,,,2016,2,19,2016,2,19,,,,,,,,,93.87843648 +2016-0141-TON,2016,0141,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Zena,Affected,Tonga,TON,Polynesia,Oceania,"Eua, Tongatapu, Ha'apai, Tongatapu, 'Eua, Vava'u islands (Administrative unit not available)",,,,,,,,,Kph,,,,,2016,4,7,2016,4,7,,,,,,,,,93.87843648 +2016-0256-TWN,2016,0256,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Butchoy (Nepartak),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,,,,,,,,,,Kph,,,,,2016,7,9,2016,7,9,3,300,,,300,,,,93.87843648 +2016-0355-USA,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,United States of America (the),USA,Northern America,Americas,"Florida, Georgia, South Carolina, North Carolina, Virginia provinces",,,,,,,,,Kph,,,,,2016,10,7,2016,10,9,49,,,,,,5000000,10000000,93.87843648 +2016-0355-VCT,2016,0355,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Matthew,Kill,Saint Vincent and the Grenadines,VCT,Caribbean,Americas,Layou (Saint Andrew),,,,,,,,,Kph,,,,,2016,9,28,2016,9,29,,,,,,,,,93.87843648 +2016-0268-VNM,2016,0268,Natural,Meteorological,Storm,Tropical cyclone,,Storm Mirinae,Waiting,Viet Nam,VNM,South-Eastern Asia,Asia,"Nam Dinh, Thai Binh and Ninh Binh provinces",,,,,,,,88,Kph,,,,,2016,7,28,2016,7,28,1,5,191745,,191750,,,191000,93.87843648 +2017-0410-CRI,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Costa Rica,CRI,Central America,Americas,"South Pacific, Region 8- South Zone – Central Pacific: Region 6- Puntarenas (Parrita)- North Pacific: Region 5- Guanacaste (Hojancha, Sardinal de Carrillo)- Huetar Norte: Region 9- North Zone- Central Valley: Region 1, Cartago (Llano Grande de Cartago), Alajuela (Sarchí de Valverde Vega,Athens, ), San Jose (Puriscal,San Marcos de Tarrazú), Esperanza de Santa Cruz, Mollejones de Cabagra",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,,Kph,,,,"Tempisque, Bebedero, and Sierpe Rivers in Catsa, Taboga, El Viejo, and Osa, Aranjuez River",2017,9,21,2017,10,6,13,,11500,,11500,,,185000,95.87816577 +2017-0383-DMA,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Dominica,DMA,Caribbean,Americas,All island,,Flood,"Slide (land, mud, snow, rock)",,,,,260,Kph,,,,,2017,9,18,2017,9,19,64,100,71293,,71393,,19300,1456000,95.87816577 +2017-0383-DOM,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Dominican Republic (the),DOM,Caribbean,Americas,"La Altagracia, El Seibo, Hato Mayor, Samaná, Espaillat, María Trinidad Sánchez, Puerto Plata, Santiago, Sánchez Ramírez, Monseñor Nouel, La Romana, Montecristi, Duarte, San Juan, Valverde, Dajabón, Santiago Rodríguez, San Pedro de Macorís, Hermanas Mirabal (Salcedo), La Vega",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,9,20,2017,9,20,5,,26000,,26000,,,63000,95.87816577 +2017-0105-AUS,2017,0105,Natural,Meteorological,Storm,Tropical cyclone,,Debbie,Affected,Australia,AUS,Australia and New Zealand,Oceania,"Logan region; Queensland; Nouvelle-Galles du Sud, au sud du Queensland, Sydney; Bowen (Whitsunday), Mackay, Proserpine (Whitsunday), Airlie beach (Whitsunday Isl.)",,Flood,,,,,,263,Kph,,,,Logan river,2017,3,27,2017,4,6,12,,45000,,45000,,1400000,2700000,95.87816577 +2017-0352-CHN,2017,0352,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Hato',Kill,China,CHN,Eastern Asia,Asia,"Southern, Guangdong, Guangxi Zhuang, and Fujian Provinces (Guanghai, Guangdong towns, Zhuhai, Shenzhen), Guizhou and Yunnan",,Flood,,,,,,,Kph,,,,,2017,8,24,2017,8,24,8,373,,21900,22273,,250000,3500000,95.87816577 +2017-0410-COL,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Colombia,COL,South America,Americas,"San Andres, Providencia and Santa Catalina",,Flood,,,,,,,Kph,,,,,2017,10,4,2017,10,4,,,,,,,,,95.87816577 +2017-0381-CUB,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Cuba,CUB,Caribbean,Americas,"Habana del Este, Habana Vieja, Centro Habana, Plaza, Playa municipalities (Habana province); Sierra de Cúbitas, Florida, Nuevitas, Esmeralda municipalities (Camagüey province); Martí, Cárdenas, Matanzas, Los Arabos Unión de Reyes municipalities (Matanzas province); Jobabo, Manatí, Jesús Mendez, Puerto Padre municipalities (Las Tunas province); Gibara, Frank Paí, Banes, Mayarí, Rafael Freyre municipalities (Holguin province); Encrucijada Caibaríen, Sagüa la Grande, Santo Domingo, Santa Clara municipalities (Villa Clara province); Bolivia, Moron, Chambas, Venezuela municipalities (Ciego Avila province), Pinar del Rio, Matanzas, Artemisa, Mayabeque, Cienfuegos, Sancti Spiritus, Granma, Guantamo",,Flood,,,,,,,Kph,,,,,2017,9,8,2017,9,10,10,,10000000,,10000000,,200000,540000,95.87816577 +2017-0381-DOM,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Dominican Republic (the),DOM,Caribbean,Americas,"Veron, Higuey (La Altagracia), Samaná (Galera), La Romana, Puerto Plata (Motellano, Sabaneta de yasica); San Cristobal, Peravia, San José de Ocoa, Azua, Santiago (Santiago), Valverde (Mao, Esperanza), Monte cristi (Montecristi), Dajabón, Espaillat (Gaspar Hernandez), Maria Trinidad Sanchez",,Flood,,,,,,285,Kph,,,,,2017,9,6,2017,9,6,,,6300,,6300,,,,95.87816577 +2017-0381-AIA,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Anguilla,AIA,Caribbean,Americas,,,,,,,,,,Kph,,,,,2017,9,6,2017,9,6,4,,15000,,15000,,6700,200000,95.87816577 +2017-0381-ATG,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Antigua and Barbuda,ATG,Caribbean,Americas,"Barbuda, St John and St George districts (Crosbies, Fort Road, Clare Hall, Grays Farm, Pigotts) (Antigua)",,,,,,,,295,Kph,,,,,2017,9,6,2017,9,6,1,,1800,,1800,200000,6800,250000,95.87816577 +2017-0152-BGD,2017,0152,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Mora,Affected,Bangladesh,BGD,Southern Asia,Asia,"Swandip, Anwara, Lohogara, Bashkhali, Sitakunda, Mirsarai, Chandanaish, Karnaphuli Thana (Chittagong district); Cohokoria, Teknaf, Moheshkhali, Kutubdia, Pekua, Ramu, Ukhiya, Shah Parir Dwip, Saint Martin’s island (Cox's Bazar district); Rangamati district (All upazillas); Nikonchori (Bandarban district); Bhola district. Khagrachhari, Feni, Noakhali districts",,,,,,,,135,Kph,,,,,2017,5,30,2017,5,30,7,12,3300000,,3300012,,,,95.87816577 +2017-0381-BHS,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Bahamas (the),BHS,Caribbean,Americas,"Inagua, Mayaguana, Crooked Island, Acklins, Long Cay, Ragged Island, San Salvador, Bimini",,,,,,,,,Kph,,,,,2017,9,8,2017,9,9,,,,,,,398,2000,95.87816577 +2017-0381-BRB,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Barbados,BRB,Caribbean,Americas,"St. David's Christ Church, Weston St. James, Cattlewash St Joseph",,,,,,,,,Kph,,,,,2017,9,8,2017,9,9,1,,,,,,,,95.87816577 +2017-0281-CHN,2017,0281,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Talas,Affected,China,CHN,Eastern Asia,Asia,Hainan Island,,,,,,,,,Kph,,,,,2017,7,16,2017,7,17,,,30000,,30000,,,3600,95.87816577 +2017-0326-CHN,2017,0326,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Nesat & Haitang,Affected,China,CHN,Eastern Asia,Asia,Fujian province,,,,,,,,,Kph,,,,,2017,8,3,2017,8,3,,,13200,600,13800,,,57000,95.87816577 +2017-0485-CHN,2017,0485,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Pakhar'/'Jolina',Affected,China,CHN,Eastern Asia,Asia,"Guangdong, Fujian, Guangxi Zhuang, Hainan, Yunnan",,,,,,,,,Kph,,,,,2017,8,27,2017,8,27,12,,300,,300,,,56000,95.87816577 +2018-0170-CUB,2018,0170,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Alberto',Affected,Cuba,CUB,Caribbean,Americas,"Ciego de Avila, Sancti Spiritus et Villa Clara, Matanzas, Pinar del Rio, Cienfuegos",,Flood,Oil spill,,,,,,Kph,,,,,2018,5,29,2018,6,2,9,,40000,,40000,,,,98.21999062 +2018-0285-CHN,2018,0285,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Maria' (Gardo),Affected,China,CHN,Eastern Asia,Asia,Fujian province,,Flood,,,,,,,Kph,,,,,2018,7,10,2018,7,11,,,45000,,45000,,,490000,98.21999062 +2018-0305-CHN,2018,0305,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Bebinca',Kill,China,CHN,Eastern Asia,Asia,"Guangdong, Hainan Provinces",,Flood,,,,,,,Kph,,,,,2018,8,15,2018,8,15,2,,,300,300,,,188000,98.21999062 +2018-0347-CHN,2018,0347,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Yagi,Affected,China,CHN,Eastern Asia,Asia,"Zhejiang, Anhui, Jiangsu, Shandong provinces",,Flood,,,,,,,Kph,,,,,2018,8,12,2018,8,12,3,,2400,,2400,,,367000,98.21999062 +2018-0348-CHN,2018,0348,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm Rumbia,Affected,China,CHN,Eastern Asia,Asia,"Shanghai, Jiangsu, Zhejiang, Anhui, Shandong, Henan",,Flood,,,,,,,Kph,,,,,2018,8,15,2018,8,17,53,,39600,,39600,,,5400000,98.21999062 +2018-0145-DJI,2018,0145,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Sagar',Kill,Djibouti,DJI,Eastern Africa,Africa,,,Flood,,,,,,,Kph,,,,,2018,5,20,2018,5,21,2,,25000,,25000,,,,98.21999062 +2018-0201-CHN,2018,0201,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Ewiniar',Kill,China,CHN,Eastern Asia,Asia,"Guangdong, Jiangxi, Fujian, and Zhejiang provinces, Hainan Island",,,,,,,,,Kph,,,,,2018,7,19,2018,7,19,14,,16200,,16200,,,570000,98.21999062 +2018-0287-CHN,2018,0287,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Ampil' (Inday),Affected,China,CHN,Eastern Asia,Asia,"Shangai, iJiangsu, Zhejiang, Shandong, Hebei",,,,,,,,,Kph,,,,,2018,7,22,2018,7,22,1,,18000,,18000,,,240000,98.21999062 +2018-0309-CHN,2018,0309,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soulik',Waiting,China,CHN,Eastern Asia,Asia,,,,,,,,,,Kph,,,,,2018,8,22,2018,8,23,,,,,,,,79900,98.21999062 +2018-0341-CHN,2018,0341,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mangkut (Ompong),Kill,China,CHN,Eastern Asia,Asia,Shenzhen,,,,,,,,,Kph,,,,,2018,9,10,2018,9,18,,,,,,,,770000,98.21999062 +2018-0373-CUB,2018,0373,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Michael,SigDam,Cuba,CUB,Caribbean,Americas,"Sandino, San Juan municipalities (Pinar del Rio province)",,,,,,,,,Kph,,,,,2018,10,10,2018,10,11,,,540,,540,,,,98.21999062 +2018-0280-DOM,2018,0280,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Beryl',Affected,Dominican Republic (the),DOM,Caribbean,Americas,San Cristóbal and Santo Domingo provinces,,,,,,,,,Kph,,,,,2018,7,9,2018,7,11,,,,8000,8000,,,,98.21999062 +2019-0387-CHN,2019,0387,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Lekima (Hanna),Kill,China,CHN,Eastern Asia,Asia,Zhejiang and Shandong provinces,,"Slide (land, mud, snow, rock)",Flood,,,,,175,Kph,,,,,2019,8,10,2019,8,12,72,,108000,,108000,,,10000000,100 +2019-0165-COM,2019,0165,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Kenneth',Kill,Comoros (the),COM,Eastern Africa,Africa,"Grande Comore, Moheli, Anjouan islands",,Flood,,,Yes,,,185,Kph,,,,,2019,4,24,2019,4,25,8,180,345131,,345311,,,,100 +2019-0164-BGD,2019,0164,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Fani',Kill,Bangladesh,BGD,Southern Asia,Asia,"Barguna, Kishoreganj,Noakhali, Laxmipur, Feni, Chandpur, Bhola, Patuakhali, Barishal, Pirozpur, Jhalokathi, Bagherhat, Khulna, Satkhira districts",,,,,,,,200,Kph,,,,,2019,5,4,2019,5,4,39,45,,10000,10045,,,,100 +2019-0412-BHS,2019,0412,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Dorian',Kill,Bahamas (the),BHS,Caribbean,Americas,"Great Abaco, Grand Bahama",,,,,,,,298,Kph,,,,,2019,9,1,2019,9,4,370,,15000,,15000,,,7000000,100 +2019-0424-CHN,2019,0424,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Lingling',Affected,China,CHN,Eastern Asia,Asia,,,,,,,,,,Kph,,,,,2019,9,5,2019,9,8,,,,,,,,131000,100 +2019-0472-CHN,2019,0472,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Mitag',Affected,China,CHN,Eastern Asia,Asia,Zhoushan,,,,,,,,,Kph,,,,,2019,10,2,2019,10,2,3,,,,,,,263000,100 +2015-0620-KHM,2015,0620,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Vamco,Affected,Cambodia,KHM,South-Eastern Asia,Asia,Battambang province,,Flood,,,,,,,Kph,,,,,2015,9,19,2015,9,20,,,6300,,6300,,,,92.70882199 +2015-0620-THA,2015,0620,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Vamco,Affected,Thailand,THA,South-Eastern Asia,Asia,"Rayong (Muang, Bang Chang districts), Chonburi, Phang-nga, Chumphon, Trat, Saraburi, Ranong, Satun, Tak, Chanthaburi, Surin, Si Sa Ket, Trang, Chachoengsao, Nakhon Nayok, Krabi, Phatthlung, Nakhon Si Thammarat, Prachuap Khiri Khan, Prachinburi, Surat Thani, Phetchaburi (Ban Laem district), Sa Kaeo Provinces",,Flood,,,,,,65,Kph,,,,,2015,9,15,2015,9,21,3,,,,,,,561,92.70882199 +2017-0410-GTM,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Guatemala,GTM,Central America,Americas,"Ixcan, San Luis and Coban communities",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,Chixoy River,2017,10,5,2017,10,5,8,,,413,413,,,,95.87816577 +2017-0410-HND,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Honduras,HND,Central America,Americas,"Ocotepeque, Copán, Lempira, Intibucá, Santa Bárbara, Comayagua departements, Francisco Morazan (San Ignacio), Atlantida (El Porvenir), El Paraiso (Trojes), Valle (San Lorenzo, Nacaome Paso Hondo in Goascoran)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,10,5,2017,10,6,3,,120,,120,,,,95.87816577 +2017-0152-IND,2017,0152,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Mora,Affected,India,IND,Southern Asia,Asia,"Manipur, Mizoram",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,5,30,2017,5,31,,,1970,,1970,,,,95.87816577 +2017-0432-JPN,2017,0432,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Lan'/'Paolo',Waiting,Japan,JPN,Eastern Asia,Asia,"Honshu Island: Fukuoka, Mie, Osaka, Toyama, Wakayama, Ibaraki, Nagano, Hokkaido",,Flood,"Slide (land, mud, snow, rock)",,,,,216,Kph,,,,,2017,10,22,2017,10,22,8,210,18600,,18810,,,1000000,95.87816577 +2017-0468-JPN,2017,0468,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Talim',Affected,Japan,JPN,Eastern Asia,Asia,"Oita (Bungo-ono), Kagawa (Mitoyo city), Okayama, Ehime, Kochi, Kyoto, Miyazaki, Hyogo",,Flood,"Slide (land, mud, snow, rock)",,,,,160,Kph,,,,,2017,9,17,2017,9,19,2,56,21600,,21656,,330000,500000,95.87816577 +2017-0508-LKA,2017,0508,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Ockhi',Kill,Sri Lanka,LKA,Southern Asia,Asia,"Galle, Matara (Southern), Colombo, Badulla (Uva), Gampaha, Kalutara (Western), Nuwara Eliya (Central)",,Flood,"Slide (land, mud, snow, rock)",,,,,130,Kph,,,,"Nilwala, Gin, and Kalu Rivers",2017,11,29,2017,12,1,27,77,160000,,160077,,,346000,95.87816577 +2017-0485-MAC,2017,0485,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Pakhar'/'Jolina',Affected,Macao,MAC,Eastern Asia,Asia,,,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,8,27,2017,8,27,,,,,,,,,95.87816577 +2017-0168-MEX,2017,0168,Natural,Meteorological,Storm,Tropical cyclone,,Beatriz,Affected,Mexico,MEX,Central America,Americas,"Sierra Madre del Sur, Sierra Madre de Chiapas Mountain ranges, Oaxaca State, San Francisco Ozolotepec, San Marcial Ozolotepec, San Pedro Quiatoni, San Carlos Yautepec, Jamiltepec, Juquila, and Pochutla, Tehuantepec in Istmo Region, Juchitan district, San Pablo Topiltepec",,Flood,"Slide (land, mud, snow, rock)",,,,,65,Kph,,,19:00,Los Perros,2017,6,1,2017,6,3,6,,,600,600,,,,95.87816577 +2017-0334-MEX,2017,0334,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Franklin',Affected,Mexico,MEX,Central America,Americas,"Quintana Roo State (Yucatan Peninsula); Campeche, Tabasco and Chiapas, Lechuguillas (Veracruz state); Puebla, Hidalgo states",,Flood,"Slide (land, mud, snow, rock)",,,,,140,Kph,,,,,2017,8,7,2017,8,7,,,300,,300,,,2000,95.87816577 +2017-0326-PHL,2017,0326,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Nesat & Haitang,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"National Capital Region (Valenzuela, Quezon, Manila, Malabon, Paranaque, Makati), Ilocos (Pangasinan, La Union, Ilocos Norte, Ilocos Sur), Central Luzon (Balagtas, Meycauayan, Marilao-Bulacan, MacabebeBrgy. Sto. Rosario - City of San Fernando -Pampanga), Calabarzon (Cavite, Rizal), Cordillera Administrative Region (CAR) (Benguet, Baguio city, Abra,Mt Province, Ifugao)",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,7,25,2017,7,27,,,7339,,7339,,,175,95.87816577 +2017-0422-PHL,2017,0422,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Damrey' / 'Ramil',Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"southern Luzon: Batangas (San Juan town), Palawan (Busuanga town), Camarines Sur (Barangay Sibaguan near Sagnay town, Lagonoy), Albay, Camarines Norte (Vinzons, Mercedes), Oriental Mindoro (Calapan city), Laguna - Calabarzon, Mimaropa, and Bicol Regions",Precursor of Damrey,Flood,"Slide (land, mud, snow, rock)",,,,,75,Kph,,,,,2017,11,1,2017,11,3,6,,305,,305,,,,95.87816577 +2017-0383-PRI,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Puerto Rico,PRI,Caribbean,Americas,"Humacao town, San Juan, Bayamon, Guajataca (north-west), Tao Baja, Culebra island, Vieques,Naguabo, Fajardo , Juncos, San Lorenzo, Ceiba cities (Humacao), Arecibo, Yabucoa Caguas towns (Guyama), Cataño (Bayamon), Aguada (Aguadilla)",,Flood,Broken Dam/Burst bank,,,Yes,,240,Kph,,,,"Rio Guajataca, Rio Culebrinas, Rio Gurabo, Rio Grande de Manati, Rio Cibuco, Rio Guanajibo",2017,9,20,2017,9,20,64,,540000,210000,750000,,30000000,68000000,95.87816577 +2017-0432-PHL,2017,0432,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Lan'/'Paolo',Waiting,Philippines (the),PHL,South-Eastern Asia,Asia,"Mimaropa (Puerto Princesa City, Palawan), Western Visayas (Negros occidental), Central Visayas (Negros oriental, Siquijor), Zamboanga peninsula (Zamboanga city, Zamboanga del Norte, Zamboanga del Sur, Zamboanga del Sibugay), Caraga (Agusan del Norte), the Autonomous Region of Muslim Mindanao (Maguindanao), Soccksargen (Sultan Kudarat) regions",,Flood,Storm,,,,,250,Kph,,,,,2017,10,18,2017,10,20,9,,163349,,163349,,,3400,95.87816577 +2017-0515-IDN,2017,0515,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Cempaka',Affected,Indonesia,IDN,South-Eastern Asia,Asia,"East Java’s Pacitan Regency (Klesem village in the Kebonagung Sub-district, Sidomulyo Village in Ngadirojo Sub-district)",,"Slide (land, mud, snow, rock)",Flood,,,,,65,Kph,,,,,2017,11,28,2017,11,29,11,35,,2000,2035,,,,95.87816577 +2017-0392-MEX,2017,0392,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Katia,Waiting,Mexico,MEX,Central America,Americas,"Tecolutla, Xalapa, Jalcomulco",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2017,9,7,2017,9,8,3,,1110,,1110,,,,95.87816577 +2017-0406-PHL,2017,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Doksuri',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Central Luzon, Calabarzon (Laguna, Quezon, Cavite, Rizal), the National Capital region (Metro Manila)",,"Slide (land, mud, snow, rock)",Flood,,,,,,Kph,,,,,2017,9,11,2017,9,15,26,,7465,1645,9110,,,5300,95.87816577 +2017-0383-HTI,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Haiti,HTI,Caribbean,Americas,"Limbe, Cornillon (Croix des Bouquets), Saint Marc (Bocozel), Grand Saline (Artibonite), Saint Rafael (North)",,Lightening,Flood,,,,,,Kph,,,,Artibonite,2017,9,21,2017,9,21,3,,,,,,,,95.87816577 +2017-0383-MTQ,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Martinique,MTQ,Caribbean,Americas,"Le Morne-rouge, Le Carbet (St Pierre), Le Marigot, Gros-Morne (La Trinité), Northern coast, Fort-de-France, Schoelcher (Fort de France)",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2017,9,18,2017,9,18,,2,,,2,,,44000,95.87816577 +2017-0410-PAN,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Panama,PAN,Central America,Americas,"Ngäbe-Buglé, Veraguas and Chiriqui, Panama Bay, Panama West, Cerro Hacha",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,"Cobre, Tribique, San Pablo, Tabasará, Santamaría, El Pavo, La Playita, and Quebro i; the rivers Punta Peña, Changuinola, and Sixaola in Bocas del Toro; Fonseca, Chiriquí Viejo, Cobre, Caldera, Risacua",2017,10,6,2017,10,6,7,,12000,,12000,,,,95.87816577 +2017-0410-SLV,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,El Salvador,SLV,Central America,Americas,"Chalatenango, La Libertad",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2017,10,5,2017,10,6,10,4,530,50,584,,,,95.87816577 +2017-0383-GLP,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Guadeloupe,GLP,Caribbean,Americas,La Desirade Island,,Flood,,,,,,,Kph,,,,,2017,9,20,2017,9,20,4,2,80000,,80002,,,120000,95.87816577 +2017-0352-HKG,2017,0352,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Hato',Kill,Hong Kong,HKG,Eastern Asia,Asia,"Tai O, on Lantau Island",,Flood,,,,,,,Kph,,,,,2017,8,24,2017,8,24,,129,27,,156,,,755500,95.87816577 +2017-0381-HTI,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Haiti,HTI,Caribbean,Americas,"Caracol (Trou du Nord), Feroer, Malfety (Fort Liberte), Ouanaminthe, Cap Haitien; Port de Paix, St. Louis de Nord, Anse-a-Foleur, Jean Rabel, Baie-de-Henne, Detipotpe, Ile de la Tortue, Bassin Blue, Chansolme, Mole St. Nicolas which were supported",,Flood,,,,,,,Kph,,,,,2017,9,7,2017,9,8,1,17,40075,,40092,,162,,95.87816577 +2017-0508-IND,2017,0508,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Ockhi',Kill,India,IND,Southern Asia,Asia,"Kerala (Thiruvananthapuram, Kozhikode,Ernakulam),Tamil Nadu (Kanyakumari-Munchirai, Thiruvattar,Killiyur, Kurunthancode, Rajakkamangalam-, Tirunelveli, Tuticorin districts), Andhra Pradesh (Chittoor and Nellore), Lakshadweep Islands",,Flood,,,,,,130,Kph,,,,"Amaravathi, Bhavani, Suwarnamukhi and Kalingi Rivers",2017,12,2,2017,12,2,884,,60000,,60000,,,,95.87816577 +2017-0352-MAC,2017,0352,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Hato',Kill,Macao,MAC,Eastern Asia,Asia,Fai Chi Kei,,Flood,,,,,,,Kph,,,,,2017,8,24,2017,8,24,10,200,,,200,,,1420000,95.87816577 +2017-0075-MDG,2017,0075,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Enawo',Kill,Madagascar,MDG,Eastern Africa,Africa,"Antalaha district (Sava province), Analanjirofo, Alaotra Mangoro, Atsinanana, Analamanga, Vakinankaratra, Bongolava, Itasy, Ihorombe, Amoron I Mania, Haute Matsiatra, Vatovavy Fitovinany provinces",,Flood,,,,Yes,,300,Kph,,,,,2017,3,7,2017,3,10,81,253,434000,,434253,,,20000,95.87816577 +2017-0376-MEX,2017,0376,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Lidia',Waiting,Mexico,MEX,Central America,Americas,"Cabo San Lucas, San Jose del Cabo (Baja California Sur), Comondu, Mulege, La Paz, Los Cabos, Loreto",,Flood,,,,Yes,,,Kph,,,,,2017,8,30,2017,9,2,20,,,1000,1000,,,,95.87816577 +2017-0051-MOZ,2017,0051,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Dineo',Affected,Mozambique,MOZ,Eastern Africa,Africa,"Vilankulo, Massinga, Maxixe, Jangamo, Morrumbene Inhambane City, Funhalouro, Homoine, Jangamo, Inharrime, Panda, Zavala (Inhambane province); Chibuto, Guja, Chokwe distrcits (Gaza province)",,Flood,,,,,,100,Kph,,,,,2017,2,15,2017,2,15,7,102,750000,,750102,,,17000,95.87816577 +2017-0381-PRI,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Puerto Rico,PRI,Caribbean,Americas,North. Culebra and Vieques Islands,,Flood,,,,,,,Kph,,,,,2017,9,6,2017,9,7,2,,,,,,,,95.87816577 +2017-0381-TCA,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Turks and Caicos Islands (the),TCA,Caribbean,Americas,"South Caicos, Salt Cay, Grand Turks, Provo (Five keys)",,Flood,,,,,,,Kph,,,,,2017,9,8,2017,9,9,,,,,,,14900,500000,95.87816577 +2017-0406-THA,2017,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Doksuri',Affected,Thailand,THA,South-Eastern Asia,Asia,"Uthai Thani, Phitsanulok, Phrae, Chaiyaphum, Chiang Rai, Kalasin, Lampang, Loei, Phangnga, Phetchabun, Sakon Nakhon, Satun, Uttaradit",,Flood,,,,,,,Kph,,,,,2017,9,17,2017,9,17,,,28500,,28500,,,,95.87816577 +2017-0485-HKG,2017,0485,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Pakhar'/'Jolina',Affected,Hong Kong,HKG,Eastern Asia,Asia,,,,,,,,,126,Kph,,,,,2017,8,27,2017,8,27,,,,,,,,,95.87816577 +2017-0327-JPN,2017,0327,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Noru',Waiting,Japan,JPN,Eastern Asia,Asia,"Wakayama prefecture; Mie region, Kagoshima (Yakushima, Minami-ko towns), Miyazaki, Oita, Fukuoka Prefectures, Hyogo Prefecture",,,,,,,,120,Kph,33.2,134,,,2017,8,5,2017,8,7,2,51,1338,,1389,,,2000,95.87816577 +2017-0381-KNA,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Saint Kitts and Nevis,KNA,Caribbean,Americas,,,,,,,,,,Kph,,,,,2017,9,6,2017,9,6,,,500,,500,,2300,20000,95.87816577 +2017-0281-LAO,2017,0281,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Talas,Affected,Lao People's Democratic Republic (the),LAO,South-Eastern Asia,Asia,"Borikhamxay province (Thongsaen, Napae villages)",,,,,,,,,Kph,,,,"Phao, Kata, and Nam Thern rivers",2017,7,17,2017,7,19,,,,,,,,,95.87816577 +2017-0152-MMR,2017,0152,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Mora,Affected,Myanmar,MMR,South-Eastern Asia,Asia,"Chin, Ayeyarwady, Magway, Sagaing, Rakhine (worst is Buthidaung, Maungdaw-Arakan ) state",,,,,,,,,Kph,,,,,2017,6,1,2017,6,9,,,107520,,107520,,,,95.87816577 +2017-0410-NIC,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,Nicaragua,NIC,Central America,Americas,"Rivas, Chontales, Madriz, Boaco, Rio San Juan, Nueva Segovia, Carazo, Granada and North Caribbean (Bilwi, Waspam, Prinzapolka, Siuna, Rosita, Bonanza)",,,,,,,,,Kph,,,,,2017,10,5,2017,10,6,15,,39200,,39200,,,,95.87816577 +2017-0142-PHL,2017,0142,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression 02W (Crising),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Cebu Isl. (Carmen, Danao City)",,,,,,,,,Kph,,,,,2017,4,15,2017,4,15,10,10,850,,860,,,2000,95.87816577 +2017-0485-PHL,2017,0485,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Pakhar'/'Jolina',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos, Cagayan Valley, Central Luzon, Cordillera Regions",,,,,,,,65,Kph,,,,,2017,8,25,2017,8,26,,,,,,,,,95.87816577 +2017-0281-THA,2017,0281,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Talas,Affected,Thailand,THA,South-Eastern Asia,Asia,Nan (Mae Charim district),,,,,,,,,Kph,,,,Nan,2017,7,17,2017,7,19,,,60,,60,,,,95.87816577 +2018-0008-MDG,2018,0008,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Ava',Kill,Madagascar,MDG,Eastern Africa,Africa,"Antananarivo Atsimondrano, Antananarivo Avaradrano, Antananarivo Renivohitra (Analamanga); Fenerive Eet, Manama, Maroantsetra, Sainte Marie, Soanierana Ivongo (Analanjirofo), Befotaka, Faraganga, Vaingaindrano (Astimo Atsinanana); Antanamabo Manampotsy, Brickaville, Mahanoro, Toasmasina I et II, Vatomandry (Atsinanana); Mahanja (Boeny); Miandrivazo (Menabe); Ambohimahaso (Haute Matsiatra); Antalaha, Sambava, Andapa, Vohemar (SAVA); Mampikony (Sofia); Ifanadiana, Manakara, Mananjary, Nosy Varika, Vohipeno (Vatovavy Fitovanmay)",,Flood,"Slide (land, mud, snow, rock)",,,,,165,Kph,,,,,2018,1,5,2018,1,8,73,,161318,,161318,,,,98.21999062 +2018-0044-PHL,2018,0044,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Basyang' (Sanba),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Palawan (Mimaropa); Capiz, Iloilo, Negros occidental (Region IV - Western Visayas); Bohol, Cebu, Negros Oriental, Siquijor (Region VII - Central Visayas); Biliran, Eastern Samar, Leyte, Samar, Southern Leyte (Region VII - Eastern Visayas); Agusan del Norte, Dinagat Isl., Surigao del Norte, Surigao del Sur (REgion XIII - Caraga)",,Flood,"Slide (land, mud, snow, rock)",,,,,75,Kph,,,,,2018,2,12,2018,2,16,,,254859,,254859,,,3070,98.21999062 +2018-0225-PHL,2018,0225,Natural,Meteorological,Storm,Tropical cyclone,,"Tropical Storm 'Son-tinh' (Henry), 'Amil' (Inday) and 'Josie'",--,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative Region, National Capital Region, CALABARZON, Western Visayas, Ilocos Region, Cagayan Valley, Northern Palawan",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2018,7,17,2018,7,21,,,1677993,,1677993,,,25944,98.21999062 +2018-0455-PHL,2018,0455,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression 'Usman',Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Regions IV-A (Calabarzon), IV-B (Mimaropa), V (Bicol), and VIII (Eastern Visayas)",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,,Kph,,,,,2018,12,28,2018,12,31,182,105,1015958,,1016063,,,106753,98.21999062 +2018-0280-PRI,2018,0280,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Beryl',Affected,Puerto Rico,PRI,Caribbean,Americas,,,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2018,7,9,2018,7,11,,,,,,,,,98.21999062 +2018-0385-IND,2018,0385,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Titli',Affected,India,IND,Southern Asia,Asia,"Mandasa, Godalpur, Gajapati, Ganjam, Rayagada, Puri, Kandhamal (Andhra Pradesh & Odisha)",,Flood,Surge,,,,,126,Kph,,,,,2018,10,11,2018,10,12,85,200,300000,,300200,,,920000,98.21999062 +2018-0341-PHL,2018,0341,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mangkut (Ompong),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Apayao, Benguet, Cagayan, Kalinga, Isabela, Abra, Ilocos Norte and Ilocos Sur",,"Slide (land, mud, snow, rock)",,,,,,240,Kph,,,,,2018,9,16,2018,9,16,84,138,3800000,,3800138,,,32033,98.21999062 +2018-0399-PHL,2018,0399,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Yutu' (Rosita),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Ilocos norte, Ilocos Sur, la Union, Pangasinan (Region 1 - Ilocos); Cagayan, Isabela, Nueva Vizcaya, Quirino (Region II - Cagayan Valley); Aurora, Nueva Ecija, Tarlac, Zambales (Region III - Central Luzon); Northern Samar (REgion VIII - Eastern Visayas); Abra, Apayao, Benguet, Ifugao, Kalinga, Mountain province (CAR)",,"Slide (land, mud, snow, rock)",,,,,,210,Kph,,,,,2018,10,30,2018,10,30,12,2,253298,,253300,,,305000,98.21999062 +2018-0101-FJI,2018,0101,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Josie',Waiting,Fiji,FJI,Melanesia,Oceania,Nadi,,Flood,,,,,,100,Kph,,,,,2018,4,2,2018,4,2,7,,89950,,89950,,,10000,98.21999062 +2018-0326-JPN,2018,0326,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Jebi,Kill,Japan,JPN,Eastern Asia,Asia,"Osaka, Wakayama, Hyogo",,Flood,,,,,,220,Kph,,,,,2018,9,4,2018,9,5,17,600,3300,,3900,,9000000,12500000,98.21999062 +2018-0353-JPN,2018,0353,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Trami,Affected,Japan,JPN,Eastern Asia,Asia,"Tokyo, Okinawa, Wakayama",,Flood,,,,,,216,Kph,,,,,2018,9,28,2018,10,1,4,200,18000,,18200,,,4500000,98.21999062 +2018-0227-LAO,2018,0227,Natural,Meteorological,Storm,Tropical cyclone,,Tyhoon 'Son Tinh',Kill,Lao People's Democratic Republic (the),LAO,South-Eastern Asia,Asia,13 villages across Sanamxay district,,Flood,,,,,,,Kph,,,,,2018,7,18,2018,7,19,,,120000,,120000,,,,98.21999062 +2018-0305-LAO,2018,0305,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Bebinca',Kill,Lao People's Democratic Republic (the),LAO,South-Eastern Asia,Asia,"Attapeu, Khammouane, Savannakhet, Champasak and Oudomxay",,Flood,,,,,,,Kph,,,,,2018,8,13,2018,8,16,,,615145,,615145,,,225000,98.21999062 +2018-0086-MDG,2018,0086,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Eliakim',Kill,Madagascar,MDG,Eastern Africa,Africa,"Mandritsara, Soanierana Ivongo, Masoala, SAVA, Analanjirofo, Sofia,Alaotra Mangoro, Antsinana, Diana, Vatovavy Fitovinany regions",,Flood,,,,,,105,Kph,,,,,2018,3,14,2018,3,20,21,,50872,,50872,,,,98.21999062 +2018-0421-MEX,2018,0421,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Vicente',Kill,Mexico,MEX,Central America,Americas,Michoacán and Oaxaca,,Flood,,,,,,,Kph,,,,,2018,10,19,2018,10,23,14,,,,,,,7005,98.21999062 +2018-0029-MUS,2018,0029,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Berguitta',Affected,Mauritius,MUS,Eastern Africa,Africa,"Rodrigues island, Rivière du Rempart, Pamplemousse, Port Louis, Flacq, Plaines Wilhems, Black River",,Flood,,,,,,120,Kph,,,,,2018,1,15,2018,1,21,,,30000,,30000,,,,98.21999062 +2018-0177-OMN,2018,0177,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Mekunu',Affected,Oman,OMN,Western Asia,Asia,,,Flood,,,,,,,Kph,,,,,2018,5,23,2018,5,23,6,,,,,,,,98.21999062 +2018-0394-OMN,2018,0394,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Luban',Kill,Oman,OMN,Western Asia,Asia,"Dhofar, Al-Wusta Governorates",,Flood,,,,,,,Kph,,,,,2018,10,14,2018,10,14,,,,,,,,,98.21999062 +2018-0227-PHL,2018,0227,Natural,Meteorological,Storm,Tropical cyclone,,Tyhoon 'Son Tinh',Kill,Philippines (the),PHL,South-Eastern Asia,Asia,,,Flood,,,,,,,Kph,,,,,2018,7,17,2018,7,31,16,,2231101,,2231101,,,88000,98.21999062 +2018-0309-PRK,2018,0309,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Soulik',Waiting,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Kangwon, South Hamgyong provinces",,Flood,,,,,,,Kph,,,,,2018,8,23,2018,8,24,86,,,,,,,4640,98.21999062 +2018-0029-REU,2018,0029,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Berguitta',Affected,Réunion,REU,Eastern Africa,Africa,Saint-Pierre,,Flood,,,,,,,Kph,,,,River D’Abord,2018,1,18,2018,1,18,,,200,,200,,,,98.21999062 +2018-0469-SLB,2018,0469,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Penny',Affected,Solomon Islands,SLB,Melanesia,Oceania,"Malaita, Western, Guadalcana, Isabel, Makita provinces",,Flood,,,,,,,Kph,,,,,2018,12,29,2018,12,29,,,1000,,1000,,,,98.21999062 +2018-0145-SOM,2018,0145,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Sagar',Kill,Somalia,SOM,Eastern Africa,Africa,"Somaliland, Puntland",,Flood,,,,,,,Kph,,,,,2018,5,21,2018,5,21,53,,228000,,228000,,,,98.21999062 +2018-0194-FJI,2018,0194,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Keni',Affected,Fiji,FJI,Melanesia,Oceania,"Sigatoka, Nadi, Lautoka, Ba, Tavua, Rakiraki, Nalawa, Labasa, Suva- Kadavu, Levuka, Savusavu",,,,,,,,,Kph,,,,,2018,4,9,2018,4,11,,,89250,,89250,,,50000,98.21999062 +2018-0341-HKG,2018,0341,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Mangkut (Ompong),Kill,Hong Kong,HKG,Eastern Asia,Asia,,,,,,,,,240,Kph,,,,,2018,9,17,2018,9,17,,300,,,300,,,,98.21999062 +2018-0431-IND,2018,0431,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Gaja',Kill,India,IND,Southern Asia,Asia,"Tamil Nadu, Nagapattinam, Tiruvarur, Thanjavur, Pudukottai, Dindigul and Ramnad, Chennai, Sivaganga, Theni, Madurai districts",,,,,,,,120,Kph,,,,,2018,11,16,2018,11,16,45,,500000,,500000,,,775000,98.21999062 +2018-0473-IND,2018,0473,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Phethai',Waiting,India,IND,Southern Asia,Asia,"Andhra Pradesh, Odisha",,,,,,,,100,Kph,,,,,2018,12,17,2018,12,19,8,,10000,,10000,,,100000,98.21999062 +2018-0424-JPN,2018,0424,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Kong-Rey',Affected,Japan,JPN,Eastern Asia,Asia,,,,,,,,,,Kph,,,,,2018,10,6,2018,10,7,3,,4200,,4200,,,,98.21999062 +2018-0406-MEX,2018,0406,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Willa,Waiting,Mexico,MEX,Central America,Americas,"Sinaloa, Nayarit, Jalisco, Durango, Zacatec",,,,,,,,195,Kph,,,,,2018,10,23,2018,10,23,6,,10000,,10000,,,536800,98.21999062 +2018-0469-MHL,2018,0469,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Penny',Affected,Marshall Islands (the),MHL,Micronesia,Oceania,,,,,,,Yes,,,Kph,,,,,2019,1,5,2019,1,5,,,,,,,,,98.21999062 +2018-0004-PHL,2018,0004,Natural,Meteorological,Storm,Tropical cyclone,,Agaton (01W),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Aklan, Capiz (Region VI); Bohol, Cebu (Region VII); Camiguin, Lanao del NOrte, Misamis Oriental (Region X); Agusan del NOrte, Dinagat Isl., Surigao del Norte , Surigao del Sur (Caraga)",,,,,,,,,Kph,,,,,2018,1,1,2018,1,3,3,9,83908,,83917,,,12292,98.21999062 +2018-0347-PHL,2018,0347,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Yagi,Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"I, III, CAR, NCR, and CALABARZON",,,,,,,,,Kph,,,,,2018,9,19,2018,9,19,5,,1709511,,1709511,,,19000,98.21999062 +2018-0376-PRT,2018,0376,Natural,Meteorological,Storm,Tropical cyclone,,Storm 'Leslie',SigDam,Portugal,PRT,Southern Europe,Europe,"Coimbra, Leiria dsitricts",,,,,,,,176,Kph,,,,,2018,10,14,2018,10,16,2,28,60,,88,,60000,115500,98.21999062 +2019-0110-MOZ,2019,0110,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Idai',Kill,Mozambique,MOZ,Eastern Africa,Africa,"Beira (Sofala province); Zambezia, Manica and Inhambane provinces",,"Slide (land, mud, snow, rock)",,,,Yes,,140,Kph,,,,,2019,3,14,2019,3,15,603,1500,1500000,,1501500,,150000,2000000,100 +2019-0492-JPN,2019,0492,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cylone 'Hagibis',Kill,Japan,JPN,Eastern Asia,Asia,"Tokyo, Fukushima, Miyagi, Shizuoka, Kanawanga, Nagano, Saitama, Gunma, Ibaraki, Tochigi",,Flood,,,,,,160,Kph,,,,,2019,10,12,2019,10,17,99,470,390000,,390470,,10000000,17000000,100 +2019-0418-MEX,2019,0418,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Lorena',Affected,Mexico,MEX,Central America,Americas,"Baja California Peninsula, coastal Baja California Sur, Jalisco, Sinaloa, Nayarit, Michoacan, Colima States",,Flood,,,,,,130,Kph,,,,,2019,9,20,2019,9,20,1,,200,,200,,,,100 +2019-0519-MEX,2019,0519,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Fernand',SigDam,Mexico,MEX,Central America,Americas,"Coahuila, Nuevo León, Tamaulipas, San Luis Potosí states",,Flood,,,,,,,Kph,,,,,2019,9,5,2019,9,6,1,,,,,,25000,383000,100 +2019-0124-FSM,2019,0124,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Wutip',Affected,Micronesia (Federated States of),FSM,Micronesia,Oceania,"Chuuk, Yap Isl.",,,,,,Yes,,,Kph,,,,,2019,2,,2019,2,,,,10000,,10000,,,,100 +2019-0164-IND,2019,0164,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Fani',Kill,India,IND,Southern Asia,Asia,Odisha province,,,,,,,,,Kph,,,,,2019,5,3,2019,5,3,50,,20000000,,20000000,,,1810000,100 +2019-0424-JPN,2019,0424,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Lingling',Affected,Japan,JPN,Eastern Asia,Asia,"Nagano, Yamaguchi, Hiroshima, Tottori, Okayama, Okinawa and Miyazaki Prefectures",,,,,,,,,Kph,,,,,2019,9,5,2019,9,8,,,,,,,,10000,100 +2019-0443-JPN,2019,0443,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Tapah',Affected,Japan,JPN,Eastern Asia,Asia,"Okinawa, Miyazaki Prefectures",,,,,,,,,Kph,,,,,2019,9,24,2019,9,24,,21,2000,,2021,,,,100 +2019-0424-KOR,2019,0424,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Lingling',Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,Jeju,,,,,,,,,Kph,,,,,2019,9,6,2019,9,7,3,33,38,,71,,,,100 +2019-0443-KOR,2019,0443,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Tapah',Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,Jeju Island,,,,,,,,,Kph,,,,,2019,9,24,2019,9,24,2,,83370,,83370,,,,100 +2019-0472-KOR,2019,0472,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Mitag',Affected,Korea (the Republic of),KOR,Eastern Asia,Asia,South Jeolla Province,,,,,,,,75,Kph,,,,,2019,10,2,2019,10,2,15,11,1400,,1411,,,553000,100 +2019-0110-MDG,2019,0110,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Idai',Kill,Madagascar,MDG,Eastern Africa,Africa,Besalampy (Melaky),,,,,,,,,Kph,,,,,2019,3,15,2019,3,15,3,,1100,,1100,,,,100 +2019-0450-MEX,2019,0450,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Narda',Affected,Mexico,MEX,Central America,Americas,Oaxaca state,,,,,,,,55,Kph,,,,,2019,9,30,2019,9,30,2,,1000,,1000,,,,100 +2019-0165-MOZ,2019,0165,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Kenneth',Kill,Mozambique,MOZ,Eastern Africa,Africa,Cabo Delgado,,,,,,,,300,Kph,,,,,2019,4,25,2019,4,25,45,94,400000,,400094,,,230000,100 +2019-0021-PHL,2019,0021,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression 'Amang' (01W),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Agusan del Norte, Agisan del Sur, Dinaga Isl., Surigao del Norte, Surigao del Sur (Region XIII - Caraga)",,,,,,,,,Kph,,,,,2019,1,18,2019,1,25,,,13160,,13160,,,,100 +2019-0349-PHL,2019,0349,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Danas',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Cordillera Administrative Region, Ilocos, Cagayan Valley",,,,,,,,,Kph,,,,,2019,7,18,2019,7,18,4,,2000,,2000,,,377440,100 +2019-0434-PHL,2019,0434,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Podul',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,Aurora Province,,,,,,,,,Kph,,,,,2019,8,27,2019,8,28,2,2,61500,,61502,,,,100 +2019-0424-PRK,2019,0424,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Lingling',Affected,Korea (the Democratic People's Republic of),PRK,Eastern Asia,Asia,"Yonggwang, Yodok, Jangjin Counties; Tanchon City (South Hamgyong Province)",,,,,,,,,Kph,,,,,2019,9,6,2019,9,7,5,,27801,,27801,,,24000,100 +2019-0009-THA,2019,0009,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cylone 'Pabuk',Affected,Thailand,THA,South-Eastern Asia,Asia,"Nakhon Si Thammarat, Surat Thani provinces",,,,,,,,,Kph,,,,,2019,1,4,2019,1,4,7,,720885,,720885,,,,100 +2015-0620-VNM,2015,0620,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Vamco,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Da Nang, Quang Nam (Duy Xuyen, Nong Son districts), Quand Ngai (Ly Son District), Thanh Hoa",,Flood,,,,,,,Kph,,,,,2015,9,14,2015,9,14,11,,,,,,,12800,92.70882199 +2017-0383-VIR,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Virgin Island (U.S.),VIR,Caribbean,Americas,"St Thomas, St John, St Croix, Water Island (St Thomas), Estate Frenchman's Bay",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2017,9,20,2017,9,20,3,,,,,,,,95.87816577 +2017-0091-ZWE,2017,0091,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression 'Ex-Dineo',Affected,Zimbabwe,ZWE,Eastern Africa,Africa,"Chitungwiza (Harare), Makoni, Nyanga (Manicaland), Bindura, Centenary, Mazowe, Rushinga, Shamva, Mbire (Mashonaland Central), Goromonzi, Hwedza, Mudzi, Murehwa, Seke, Uzumba-Maramba-Pfungwe, Chikomba (Mashonaland East), Chegutu, Kadoma, Makonde, Kariba (Mashonaland West), Gutu, Zaka, Chiredzi, Mwenezi, Chivi (Masvingo), Tsholotsho, Bubi, Chipinge, Umguza (Matabeleland North), Matobo, Bulilima, Gwanda, Mangwe, Insiza (Matabeleland South), Chirumhanzu, Gweru, Kwekwe, Shurugwi, Zvishavane, Gokwe, Mberengwa (Midlands)",Torrential rains,"Slide (land, mud, snow, rock)",Flood,,Yes,Yes,,,Kph,,,,,2017,1,,2017,3,,251,128,100000,12895,113023,,,189000,95.87816577 +2017-0362-USA,2017,0362,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Harvey,Affected,United States of America (the),USA,Northern America,Americas,"Eastern Texas (Rockport, Corpus Chrsti, Port Lavaca, Cypress area of Houston, Houston metro area, Beaumont, Port Arthur, Angelina, Aransas, Atascosa, Austin, Bastrop, Bee, Bexar, Brazoria, Brazos, Burleson, Caldwell, Calhoun, Cameron, Chambers, Colorado, Comal, DeWitt, Fayette, Fort Bend, Galveston, Goliad, Gonzales, Grimes, Guadalupe, Hardin, Harris, Jackson, Jasper, Jefferson, Jim Wells, Karnes, Kerr, Kleberg, Lavaca, Lee, Leon, Liberty, Live Oak, Madison, Matagorda, Montgomery, Newton, Nueces, Orange, Polk, Refugio, Sabine, San Jacinto, San Patricio, Trinity, Tyler, Victoria, Walker, Waller, Washington, Wharton, Willacy, Wilson, San Augustine), Southwestern Louisiana (Acadia, Forrest, Iberia, Lafayette, Vernon, Beauregard, Calcasieu, Cameron (Hackberry), Jefferson Davis, Vermillion, Allen, Natchitoches, Rapides, Sabine)",,Flood,Oil spill,,,Yes,,215,Kph,,,,"Sabine River, Brazos River, Navidad River, San Bernard River, San Jacinto River, Trinity River, Clear Creek, Cypress Creek, Davidson Creek, Lake Creek, Menard Creek, Peach Creek, and Buffalo Bayou while the Colorado River, Guadalupe River, Lavaca River, Tres Palacios River, Bedias Creek, Caney Creek, Garcitas Creek, Sandies Creek, Sandy Creek, Spring Creek, Brays Bayou, White Oak Bayou, Greens Bayou",2017,8,25,2017,8,29,88,24,555000,27000,582024,,30000000,95000000,95.87816577 +2017-0326-TWN,2017,0326,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon Nesat & Haitang,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Kaohsiung City, New Taipei City",,Flood,,,,,,150,Kph,,,,,2017,7,29,2017,7,31,1,131,,,131,,,17200,95.87816577 +2017-0381-USA,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,United States of America (the),USA,Northern America,Americas,"Keys islands, Monroe, South Florida, Jacksonville (Duval), Marco Island, Naples (Collier), Fort Lauderdale (Broward), Lakeland (Polk), Orlando (Orange), Clay (Florida), Savannah, Tybee Island (Chatham), Brunswick, St. Simons Island (Glynn), McIntosh, Camden (Georgia), Charleston, Folly Beach, the Isle of Palms, Sullivan’s Island (Charleston) Hilton Head, Beaufort, Edisto Beach (Colleton) (South Carolina)",,Flood,,,,,,300,Kph,,,,,2017,9,10,2017,9,28,58,,,70000,70000,,29000000,57000000,95.87816577 +2017-0381-VIR,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Virgin Island (U.S.),VIR,Caribbean,Americas,"St. John, St. Thomas, most of St. Croix",,Flood,,,,Yes,,,Kph,,,,,2017,9,7,2017,9,7,4,,,,,,,,95.87816577 +2017-0422-VNM,2017,0422,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Damrey' / 'Ramil',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Khanh Hoa, Phu Yên, Binh Dinh, Dak Lak, Gia Lai, Dak Nông, Lâm Dông, Quang Nam, Quang Ngai, Kon Tum",,Flood,,,,,,130,Kph,,,,,2017,11,4,2017,11,5,123,,4330000,,4330000,,,1000000,95.87816577 +2017-0381-BLM,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Saint Barthélemy,BLM,Caribbean,Americas,,,Flood,,,,,,,Kph,,,,,2017,9,8,2017,9,9,4,,,,,,1048800,,95.87816577 +2017-0383-USA,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,United States of America (the),USA,Northern America,Americas,"Jersey shore, Seaside Park, Point Pleasant Beach (Ocean), Long Branch (Monmouth) (New Jersey), Fernandina beach (Nassau-Florida)",,,,,,,,,Kph,,,,,2017,9,26,2017,9,27,4,,,,,,,,95.87816577 +2017-0410-USA,2017,0410,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression'16/Hurricane 'Nate',Kill,United States of America (the),USA,Northern America,Americas,"Mexico Gulf, Louisiania, Mississippi (coastal areas, Biloxi), Alabama (Dauphin Island, Autauga, Chilton, Lowndes Counties), Florida, South Carolina (Pickens, Laurens, Spartanburg, Newberry, Greenville, Union), North Carolina (Polk, Wilkes, Ashe )",,,,,,,,140,Kph,,,,,2017,10,7,2017,10,8,,,60,,60,,,250000,95.87816577 +2017-0381-VGB,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Virgin Island (British),VGB,Caribbean,Americas,"Anagoda, Tortola, Necker Island (Virgin Gorda)",,,,,,Yes,,,Kph,,,,,2017,9,8,2017,9,9,9,,,,,,,3000000,95.87816577 +2017-0383-VGB,2017,0383,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Maria',Affected,Virgin Island (British),VGB,Caribbean,Americas,,,,,,,,,,Kph,,,,,2017,9,20,2017,9,20,,,,,,,,,95.87816577 +2017-0281-VNM,2017,0281,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm Talas,Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Thanh Hoa, Nghe An (Hon Ngu beach, Cua Lo district), Ha Tinh, Ha Noi city (Nhà Xanh Market in Câu Giây District)",,,,,,,,95,Kph,,,01:00,,2017,7,17,2017,7,18,9,,21000,,21000,,,71000,95.87816577 +2017-0352-VNM,2017,0352,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Hato',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Lao Cai (Sa Pa district),,,,,,,,,Kph,,,,,2017,8,24,2017,8,24,1,1,,,1,,,1430000,95.87816577 +2017-0406-VNM,2017,0406,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Doksuri',Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Nghe An, Ha Tinh, Thanh Hoa, Quang Binh, Quang Tri, Thua Thien-Hue, Hoa Binh",,,,,,,,185,Kph,,,,,2017,9,15,2017,9,16,14,112,691900,,692012,,,484000,95.87816577 +2017-0231-VUT,2017,0231,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Donna',Affected,Vanuatu,VUT,Melanesia,Oceania,"Torba, Malampa, Sanma provinces",,,,,,,,215,Kph,,,,,2017,5,7,2017,5,12,,,2564,,2564,,,,95.87816577 +2017-0381-MAF,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Saint Martin (French Part),MAF,Caribbean,Americas,,,,,,,,,,Kph,,,,,2017,9,8,2017,9,9,7,,,,,,1231200,4100000,95.87816577 +2017-0381-SXM,2017,0381,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane 'Irma',Affected,Sint Maarten (Dutch part),SXM,Caribbean,Americas,,,,,,,,,,Kph,,,,,2017,9,8,2017,9,9,4,,,11400,11400,,500000,2500000,95.87816577 +2018-0227-VNM,2018,0227,Natural,Meteorological,Storm,Tropical cyclone,,Tyhoon 'Son Tinh',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Chuong My,,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2018,7,18,2018,7,22,34,,96000,,96000,,,220000,98.21999062 +2018-0305-VNM,2018,0305,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Bebinca',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,"Nghe An,Son La, Thanh Hóa, Yên Bái,Bac Giang provinces",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2018,8,17,2018,8,19,13,,50000,,50000,,,2000,98.21999062 +2018-0042-TON,2018,0042,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Gita,Affected,Tonga,TON,Polynesia,Oceania,"Tongatapu, Ha’apai and Eua islands",,Flood,,,,Yes,,285,Kph,,,,,2018,2,12,2018,2,13,,,87000,,87000,,,,98.21999062 +2018-0313-TWN,2018,0313,Natural,Meteorological,Storm,Tropical cyclone,,,Affected,Taiwan (Province of China),TWN,Eastern Asia,Asia,"Chiayi, Kaohsiung, Tainan counties",,Flood,,,,,,,Kph,,,,,2018,8,23,2018,8,26,7,140,6000,,6140,,,34000,98.21999062 +2018-0342-USA,2018,0342,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Florence,Kill,United States of America (the),USA,Northern America,Americas,"South and North Carolina, Virginia",,Flood,,,,Yes,,,Kph,,,,,2018,9,12,2018,9,18,53,,1500000,,1500000,,5000000,14000000,98.21999062 +2018-0201-VNM,2018,0201,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Ewiniar',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,,,Flood,,,,,,,Kph,,,,,2018,6,6,2018,6,19,1,,,,,,,,98.21999062 +2018-0042-WSM,2018,0042,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone Gita,Affected,Samoa,WSM,Polynesia,Oceania,,,Flood,,,,,,,Kph,,,,,2018,2,12,2018,2,12,,,,,,,,,98.21999062 +2018-0177-YEM,2018,0177,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Mekunu',Affected,Yemen,YEM,Western Asia,Asia,Socotra Isl.,,Flood,,,,,,,Kph,,,,,2018,5,23,2018,5,23,24,,750,,750,,,,98.21999062 +2018-0394-YEM,2018,0394,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Luban',Kill,Yemen,YEM,Western Asia,Asia,Al Maharah governorate,,Flood,,,,,,,Kph,,,,,2018,10,14,2018,10,15,25,124,15000,,15124,,,,98.21999062 +2018-0373-USA,2018,0373,Natural,Meteorological,Storm,Tropical cyclone,,Hurricane Michael,SigDam,United States of America (the),USA,Northern America,Americas,"Florida, Georgia, Alabama, North Carolina, Virginia, Maryland",,Surge,,,,,,250,Kph,,,,,2018,10,10,2018,10,11,45,,5000,,5000,,10000000,16000000,98.21999062 +2018-0170-USA,2018,0170,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Alberto',Affected,United States of America (the),USA,Northern America,Americas,Southeast,,,,,,,,,Kph,,,,,2018,5,27,2018,5,30,5,,,,,,,125000,98.21999062 +2018-0484-VNM,2018,0484,Natural,Meteorological,Storm,Tropical cyclone,,Tropical depression 'Toraji',Kill,Viet Nam,VNM,South-Eastern Asia,Asia,Khanh Hoa province,,,,,,,,,Kph,,,,,2018,11,17,2018,11,18,19,28,10000,,10028,,,17200,98.21999062 +2018-0399-MNP,2018,0399,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Yutu' (Rosita),Affected,Northern Mariana Islands (the),MNP,Micronesia,Oceania,"Tinian, Saipan",,,,,,,,290,Kph,,,,,2018,10,25,2018,10,25,2,,,,,,,,98.21999062 +2019-0541-PHL,2019,0541,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Kalmaegi' (Ramon),Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Romblon Province (Mimaropa region), Camarines Sur Province (Bicol Region)",,Flood,"Slide (land, mud, snow, rock)",,,,,110,Kph,,,,,2019,11,17,2019,11,17,,,3000,,3000,,,,100 +2019-0634-PHL,2019,0634,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Phanfone' (Ursula),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Salcedo, Eastern Samar; Tacloban City, Leyte; Gigantes Islands, Carles, Iloilo; Ibajay, Aklan; Semirara Island, Caluya, Antique; Bulalacao, Oriental Mindoro; Cabucgayan, Biliran; Cagayan Valley, Cordillera Administrative Region",,Flood,"Slide (land, mud, snow, rock)",,,,,150,Kph,,,,,2019,12,24,2019,12,28,63,369,3296877,,3297246,,,15722,100 +2019-0600-SOM,2019,0600,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Storm 'Pawan',Affected,Somalia,SOM,Eastern Africa,Africa,"Las Qoray (Sanaag Region); Nugal, Bari provinces",Heavy rains,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2019,12,7,2019,12,10,6,,30000,,30000,,,,100 +2019-0534-VNM,2019,0534,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Matmo',Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Quang Ngai, Binh Dinh, Phu Yen, Gia Lai, Thua Thien Hue province",,Flood,"Slide (land, mud, snow, rock)",,,,,,Kph,,,,,2019,11,3,2019,11,3,1,14,20000,,20014,,,,100 +2019-0601-MDG,2019,0601,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Belna',Affected,Madagascar,MDG,Eastern Africa,Africa,"Soalala distric (Boeny), Diana, Besalampy districts (Melaky);",,Broken Dam/Burst bank,Flood,,,,,180,Kph,,,,,2019,12,9,2019,12,10,5,6293,14000,,20293,,,25000,100 +2019-0417-USA,2019,0417,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm ' Imelda',Affected,United States of America (the),USA,Northern America,Americas,Texas,,Flood,,,,,,65,Kph,,,,,2019,9,18,2019,9,24,5,,1000,,1000,,1200000,3500000,100 +2019-0110-ZWE,2019,0110,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Idai',Kill,Zimbabwe,ZWE,Eastern Africa,Africa,"Chikomba, Mudzi, Mutoko, UMP districts (Mash East pronvince); Chipinge, Chimanimani, Buhera, Mutare (Manicaland); Gutu, Bikita, Zaka, Masvingo (Masvingo); Murambinda, Nyanga, Mutasa, Checheche, Biriwiri, Chibuwe, Chakohwa, Nyanyadzi, Berzely Bridge, Mutimurefu, Ngundu, , Mutasa",,Flood,,,,Yes,,,Kph,,,,,2019,3,14,2019,3,14,628,186,270000,,270186,,,,100 +2019-0549-VNM,2019,0549,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Nakri',Affected,Viet Nam,VNM,South-Eastern Asia,Asia,"Phu Yen, Binh Dinh Provinces",,Flood,,,,,,,Kph,,,,,2019,11,12,2019,11,13,3,,2150,,2150,,,4000,100 +2019-0387-TWN,2019,0387,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone Lekima (Hanna),Kill,Taiwan (Province of China),TWN,Eastern Asia,Asia,,,,,,,,,175,Kph,,,,,2019,8,10,2019,8,10,2,,,,,,,,100 +2019-0165-TZA,2019,0165,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Kenneth',Kill,"Tanzania, United Republic of",TZA,Eastern Africa,Africa,"Mtwara, Lindi",,,,,,,,,Kph,,,,,2019,4,26,2019,4,26,,,2000000,,2000000,,,,100 +2019-0335-USA,2019,0335,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cylone 'Barry',SigDam,United States of America (the),USA,Northern America,Americas,"Louisiana, Mississippi, Arkansas, Oklahoma, Great Lakes region",,,,,,,,,Kph,,,,,2019,7,13,2019,7,15,1,,,,,,300000,600000,100 +2019-0412-USA,2019,0412,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Dorian',Kill,United States of America (the),USA,Northern America,Americas,"Florida, Georgia, South Carolina, North Carolina, Virginia",,,,,,,,150,Kph,,,,,2019,9,4,2019,9,6,9,,,,,,,1200000,100 +2019-0550-BGD,2019,0550,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Bulbul',Kill,Bangladesh,BGD,Southern Asia,Asia,"Satkhira, Khulna, Bhola, Bagerhat,Patuakhlai, Barguna, Pirojpur districts",,,,,,,,130,Kph,,,,,2019,11,9,2019,11,10,40,71,251435,,251506,,,5785,100 +2019-0601-COM,2019,0601,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Belna',Affected,Comoros (the),COM,Eastern Africa,Africa,,,,,,,,,,Kph,,,,,2019,12,5,2019,12,8,,,,,,,,,100 +2019-0642-FJI,2019,0642,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Sarai',Affected,Fiji,FJI,Melanesia,Oceania,"Ba, Nadroga, Lau, Kadavu",,,,,,,,,Kph,,,,,2019,12,26,2019,12,26,1,,7780,,7780,,,,100 +2019-0550-IND,2019,0550,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Bulbul',Kill,India,IND,Southern Asia,Asia,"South Assam, Meghalaya, Tripura, Mizoram, West Begal, Odisha state",,,,,,,,,Kph,,,,,2019,11,9,2019,11,10,12,,130000,,130000,,,,100 +2019-0521-JPN,2019,0521,Natural,Meteorological,Storm,Tropical cyclone,,Typhoon 'Faxai',Affected,Japan,JPN,Eastern Asia,Asia,Chiba prefecture; Tokyo,,,,,,,,170,Kph,,,,,2019,10,8,2019,10,8,3,150,120000,,120150,,7000000,9100000,100 +2019-0549-PHL,2019,0549,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Nakri',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Cagayan province, north Luzon Island",,,,,,,,,Kph,,,,,2019,11,10,2019,11,10,19,3,670,,673,,,36000,100 +2019-0573-PHL,2019,0573,Natural,Meteorological,Storm,Tropical cyclone,,Tropical cyclone 'Kammuri' (Tisoy),Kill,Philippines (the),PHL,South-Eastern Asia,Asia,"Aurora, Pampanga, Bataan, Bulacan, Zambales (Region III); Batangas, Cavite, Laguna, Quezon (Calanarzon); Marinduqe, Oriental Mindoro, Occidental Mindoro, Romblon (Mimaropa); Albay, Camarines Norte, Camarines Sur, Catanduanes, Masbate, Sorgoson (Region V); Northern Samar, Eastern Samar, Western Samar, (Region VIII); Surigao del Sur (Caraga); Mountain province (CAR)",,,,,,Yes,,210,Kph,,,,,2019,12,2,2019,12,3,4,318,2305075,342165,2647558,,,109151,100 +2020-0218-SLV,2020,0218,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Amanda',Kill,El Salvador,SLV,Central America,Americas,"La Libertad, Santa Ana, San Salvador",,Flood,"Slide (land, mud, snow, rock)",,,Yes,,,Kph,,,,,2020,5,31,2020,5,31,31,,119872,,119872,,,220000, +2020-0218-GTM,2020,0218,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Amanda',Kill,Guatemala,GTM,Central America,Americas,"Chiquimula, El Progreso, Jalapa, Jutiapa, Santa Rosa",,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2020,5,30,2020,5,31,2,,306000,886,306886,,,, +2020-0218-HND,2020,0218,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Amanda',Kill,Honduras,HND,Central America,Americas,,,"Slide (land, mud, snow, rock)",,,,,,,Kph,,,,,2020,5,30,2020,5,31,5,,1200,,1200,,,, +2020-0219-MEX,2020,0219,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Cristobal',Affected,Mexico,MEX,Central America,Americas,"Ciudad del Carmen (Campeche State); Quintana Roo, Yucatan, Tabasco, Chiapas Oaxaca, Veracruz states",,"Slide (land, mud, snow, rock)",,,,,,75,Kph,,,,,2020,6,3,2020,6,3,,,606,,606,,,, +2020-0132-SLB,2020,0132,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Harold',--,Solomon Islands,SLB,Melanesia,Oceania,"Espiritu Santo, Malakula, Ambae, Pentecost, Maewo Islands",,Transport accident,,,,,,215,Kph,,,,,2020,4,5,2020,4,6,28,,,,,,,, +2020-0211-BGD,2020,0211,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Amphan',Kill,Bangladesh,BGD,Southern Asia,Asia,Cox's Bazar,,,,,,,,,Kph,,,,,2020,5,20,2020,5,20,26,,1100000,,1100000,,,, +2020-0015-FJI,2020,0015,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Tino',Affected,Fiji,FJI,Melanesia,Oceania,Viti Levu Island,,,,,,,,155,Kph,,,,,2020,1,17,2020,1,18,2,,3115,,3115,,,, +2020-0132-FJI,2020,0132,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Harold',--,Fiji,FJI,Melanesia,Oceania,,,,,,,,,,Kph,,,,,2020,4,6,2020,4,9,1,,1837,,1837,,,13000, +2020-0211-IND,2020,0211,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Amphan',Kill,India,IND,Southern Asia,Asia,,,,,,,,,,Kph,,,,,2020,5,20,2020,5,20,103,,14000000,,14000000,,,13500000, +2020-0217-IND,2020,0217,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Nisarga',Affected,India,IND,Southern Asia,Asia,"Raigad, Pune Districts (Maharashtra State)",,,,,,,,120,Kph,,,,,2020,6,3,2020,6,3,6,,7500,,7500,,,, +2020-0211-LKA,2020,0211,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Amphan',Kill,Sri Lanka,LKA,Southern Asia,Asia,,,,,,,,,,Kph,,,,,2020,5,20,2020,5,20,4,,,,,,,, +2020-0203-PHL,2020,0203,Natural,Meteorological,Storm,Tropical cyclone,,Tropical Cyclone 'Vongfong',Affected,Philippines (the),PHL,South-Eastern Asia,Asia,"Calabazon, Eastern Visayas, Cordillera Administrative Region provinces",,,,,,,,185,Kph,,,,,2020,5,15,2020,5,17,,169,250000,,250169,,,31000, +2020-0132-TON,2020,0132,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Harold',--,Tonga,TON,Polynesia,Oceania,"Tongatapu, 'Eua",,,,,,,,,Kph,,,,,2020,4,6,2020,4,9,,,1289,,1289,,,111000, +2020-0015-TUV,2020,0015,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Tino',Affected,Tuvalu,TUV,Polynesia,Oceania,,,,,,,Yes,,,Kph,,,,,2020,1,18,2020,1,18,,,,,,,,, +2020-0219-USA,2020,0219,Natural,Meteorological,Storm,Tropical cyclone,,Tropical storm 'Cristobal',Affected,United States of America (the),USA,Northern America,Americas,"errebonne, Plaquemines, Lafourche Parishes (Louisiana)",,,,,,Yes,,80,Kph,,,,,2020,6,7,2020,6,7,,,,,,,,, +2020-0132-VUT,2020,0132,Natural,Meteorological,Storm,Tropical cyclone,,Cyclone 'Harold',--,Vanuatu,VUT,Melanesia,Oceania,Pentecost and Espiritu Santo,,,,,,,,,Kph,,,,,2020,4,6,2020,4,9,4,,,,,,,, \ No newline at end of file diff --git a/data/demo/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-whe-noirr_global_DEMO_TJANJIN_annual_1861_2005.nc b/data/demo/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-whe-noirr_global_DEMO_TJANJIN_annual_1861_2005.nc new file mode 100644 index 0000000000..f3edc0a3e9 Binary files /dev/null and b/data/demo/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-whe-noirr_global_DEMO_TJANJIN_annual_1861_2005.nc differ diff --git a/data/demo/h08_gfdl-esm2m_ewembi_historical_histsoc_co2_dis_global_daily_DEMO_FR_2001_2003.nc b/data/demo/h08_gfdl-esm2m_ewembi_historical_histsoc_co2_dis_global_daily_DEMO_FR_2001_2003.nc new file mode 100644 index 0000000000..c1d248a585 Binary files /dev/null and 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b/data/demo/histsoc_landuse-15crops_annual_FR_DE_DEMO_2001_2005.nc differ diff --git a/data/demo/lpjml_ipsl-cm5a-lr_ewembi_historical_2005soc_co2_yield-whe-noirr_annual_FR_DE_DEMO_1861_2005.nc b/data/demo/lpjml_ipsl-cm5a-lr_ewembi_historical_2005soc_co2_yield-whe-noirr_annual_FR_DE_DEMO_1861_2005.nc new file mode 100644 index 0000000000..7952fd63dd Binary files /dev/null and b/data/demo/lpjml_ipsl-cm5a-lr_ewembi_historical_2005soc_co2_yield-whe-noirr_annual_FR_DE_DEMO_1861_2005.nc differ diff --git a/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-firr_global_annual_DEMO_TJANJIN_1861_2005.nc b/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-firr_global_annual_DEMO_TJANJIN_1861_2005.nc new file mode 100644 index 0000000000..2fe5b53700 Binary files /dev/null and b/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-firr_global_annual_DEMO_TJANJIN_1861_2005.nc differ diff --git a/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-noirr_global_annual_DEMO_TJANJIN_1861_2005.nc b/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-noirr_global_annual_DEMO_TJANJIN_1861_2005.nc new file mode 100644 index 0000000000..108b68b8e6 Binary files /dev/null and b/data/demo/pepic_miroc5_ewembi_historical_2005soc_co2_yield-whe-noirr_global_annual_DEMO_TJANJIN_1861_2005.nc differ diff --git a/data/demo/tc_fl_1975_2011.h5 b/data/demo/tc_fl_1975_2011.h5 deleted file mode 100644 index 8416b7843e..0000000000 Binary files a/data/demo/tc_fl_1975_2011.h5 and /dev/null differ diff --git a/data/demo/tc_fl_1990_2004.h5 b/data/demo/tc_fl_1990_2004.h5 new file mode 100644 index 0000000000..dc46a5610f Binary files /dev/null and b/data/demo/tc_fl_1990_2004.h5 differ diff --git a/data/system/FAOSTAT_data_country_codes.csv b/data/system/FAOSTAT_data_country_codes.csv new file mode 100644 index 0000000000..b79cade059 --- /dev/null +++ b/data/system/FAOSTAT_data_country_codes.csv @@ -0,0 +1,200 @@ +"Country Code","Country","M49 Code","ISO2 Code","ISO3 Code","Start Year","End Year" +"2","Afghanistan","4","AF","AFG","","" +"3","Albania","8","AL","ALB","","" +"4","Algeria","12","DZ","DZA","","" +"7","Angola","24","AO","AGO","","" +"8","Antigua and Barbuda","28","AG","ATG","","" +"9","Argentina","32","AR","ARG","","" +"1","Armenia","51","AM","ARM","1992","" +"10","Australia","36","AU","AUS","","" +"11","Austria","40","AT","AUT","","" +"52","Azerbaijan","31","AZ","AZE","1992","" +"12","Bahamas","44","BS","BHS","","" +"13","Bahrain","48","BH","BHR","","" +"16","Bangladesh","50","BD","BGD","","" +"14","Barbados","52","BB","BRB","","" +"57","Belarus","112","BY","BLR","1992","" +"255","Belgium","56","BE","BEL","2000","" +"15","Belgium-Luxembourg","58","","","","1999" +"23","Belize","84","BZ","BLZ","","" +"53","Benin","204","BJ","BEN","","" +"18","Bhutan","64","BT","BTN","","" +"19","Bolivia (Plurinational State of)","68","BO","BOL","","" +"80","Bosnia and Herzegovina","70","BA","BIH","1992","" +"20","Botswana","72","BW","BWA","","" +"21","Brazil","76","BR","BRA","","" +"26","Brunei Darussalam","96","BN","BRN","","" +"27","Bulgaria","100","BG","BGR","","" +"233","Burkina Faso","854","BF","BFA","","" +"29","Burundi","108","BI","BDI","","" +"35","Cabo Verde","132","CV","CPV","","" +"115","Cambodia","116","KH","KHM","","" +"32","Cameroon","120","CM","CMR","","" +"33","Canada","124","CA","CAN","","" +"37","Central African Republic","140","CF","CAF","","" +"39","Chad","148","TD","TCD","","" +"40","Chile","152","CL","CHL","","" +"351","China","156","","CHN","","" +"96","China, Hong Kong SAR","344","HK","HKG","","" +"41","China, mainland","1248","CN","","","" +"44","Colombia","170","CO","COL","","" +"45","Comoros","174","KM","COM","","" +"46","Congo","178","CG","COG","","" +"47","Cook Islands","184","CK","COK","","" +"48","Costa Rica","188","CR","CRI","","" +"107","Côte d'Ivoire","384","CI","CIV","","" +"98","Croatia","191","HR","HRV","1992","" +"49","Cuba","192","CU","CUB","","" +"50","Cyprus","196","CY","CYP","","" +"167","Czechia","203","CZ","CZE","1993","" +"51","Czechoslovakia","200","","","","1992" +"116","Democratic People's Republic of Korea","408","KP","PRK","","" +"250","Democratic Republic of the Congo","180","CD","COD","","" +"54","Denmark","208","DK","DNK","","" +"55","Dominica","212","DM","DMA","","" +"56","Dominican Republic","214","DO","DOM","","" +"58","Ecuador","218","EC","ECU","","" +"59","Egypt","818","EG","EGY","","" +"60","El Salvador","222","SV","SLV","","" +"61","Equatorial Guinea","226","GQ","GNQ","","" +"178","Eritrea","232","ER","ERI","1993","" +"63","Estonia","233","EE","EST","1992","" +"209","Eswatini","748","SZ","SWZ","","" +"238","Ethiopia","231","ET","ETH","1993","" +"62","Ethiopia PDR","230","","","","1992" +"66","Fiji","242","FJ","FJI","","" +"67","Finland","246","FI","FIN","","" +"68","France","250","FR","FRA","","" 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b1d70a36d3..f254c62a92 100644 Binary files a/data/system/GSDP/AUS_GSDP.xls and b/data/system/GSDP/AUS_GSDP.xls differ diff --git a/data/system/GSDP/USA_GSDP.xls b/data/system/GSDP/USA_GSDP.xls index 9a60be6f6e..f910cf1700 100644 Binary files a/data/system/GSDP/USA_GSDP.xls and b/data/system/GSDP/USA_GSDP.xls differ diff --git a/data/system/NatEarth_Centroids_150as.hdf5 b/data/system/NatEarth_Centroids_150as.hdf5 new file mode 100644 index 0000000000..c42dac6ba4 Binary files /dev/null and b/data/system/NatEarth_Centroids_150as.hdf5 differ diff --git a/data/system/NatEarth_Centroids_360as.hdf5 b/data/system/NatEarth_Centroids_360as.hdf5 new file mode 100644 index 0000000000..6b0eba2ece Binary files /dev/null and b/data/system/NatEarth_Centroids_360as.hdf5 differ diff --git a/data/system/NatRegIDs.csv b/data/system/NatRegIDs.csv index d2b7033604..f0fd13edd4 100644 --- a/data/system/NatRegIDs.csv +++ b/data/system/NatRegIDs.csv @@ -140,7 +140,7 @@ MOZ,138,5,SSA,1 MRT,139,5,SSA,1 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+NA2,80.50000000000078,1000827798849.3094,1003120744701.8477,0.0022884288726663198 +NI,63.70000000000054,126746456379.99149,126560705107.34152,-0.0014666091937892772 +WP1,60.70000000000049,27180582530.706223,27166156987.460518,-0.0005308706175343171 +WP4,169.6000000000021,100555602505.43022,100576382012.8803,0.00020662559039291492 +WP2,167.50000000000202,39580910159.29215,39540714298.26242,-0.0010160525586619114 +OC,56.80000000000044,25250367735.06945,25229905386.510567,-0.0008107067737265066 +SI,48.50000000000033,5610589205.660851,5590051012.149456,-0.0036673290331080514 +GLB,98.90000000000104,1812186524820.4053,1815464200189.393,0.0018070517309236817 diff --git a/doc/climada/climada.entity.exposures.rst b/doc/climada/climada.entity.exposures.rst index 7ee9a9d59a..cbbe34e259 100644 --- a/doc/climada/climada.entity.exposures.rst +++ b/doc/climada/climada.entity.exposures.rst @@ -17,6 +17,14 @@ climada\.entity\.exposures\.black\_marble module :undoc-members: :show-inheritance: +climada\.entity\.exposures\.crop\_production module +--------------------------------------------------- + +.. automodule:: climada.entity.exposures.crop_production + :members: + :undoc-members: + :show-inheritance: + climada\.entity\.exposures\.gdp\_asset module --------------------------------------------- diff --git a/doc/climada/climada.entity.impact_funcs.rst b/doc/climada/climada.entity.impact_funcs.rst index 4a56398f19..159655543e 100644 --- a/doc/climada/climada.entity.impact_funcs.rst +++ b/doc/climada/climada.entity.impact_funcs.rst @@ -1,14 +1,6 @@ climada\.entity\.impact\_funcs package ====================================== -climada\.entity\.impact\_funcs\.ag\_drought module --------------------------------------------------- - -.. automodule:: climada.entity.impact_funcs.ag_drought - :members: - :undoc-members: - :show-inheritance: - climada\.entity\.impact\_funcs\.base module ------------------------------------------- @@ -25,18 +17,26 @@ climada\.entity\.impact\_funcs\.drought module :undoc-members: :show-inheritance: -climada\.entity\.impact\_funcs\.flood module --------------------------------------------- +climada\.entity\.impact\_funcs\.impact\_func\_set module +-------------------------------------------------------- + +.. automodule:: climada.entity.impact_funcs.impact_func_set + :members: + :undoc-members: + :show-inheritance: + +climada\.entity\.impact\_funcs\.relative\_cropyield module +---------------------------------------------------------- -.. automodule:: climada.entity.impact_funcs.flood +.. automodule:: climada.entity.impact_funcs.relative_cropyield :members: :undoc-members: :show-inheritance: -climada\.entity\.impact\_funcs\.impact\_func\_set module --------------------------------------------------------- +climada\.entity\.impact\_funcs\.river\_flood module +--------------------------------------------------- -.. automodule:: climada.entity.impact_funcs.impact_func_set +.. automodule:: climada.entity.impact_funcs.river_flood :members: :undoc-members: :show-inheritance: diff --git a/doc/climada/climada.hazard.emulator.rst b/doc/climada/climada.hazard.emulator.rst new file mode 100644 index 0000000000..82e910c956 --- /dev/null +++ b/doc/climada/climada.hazard.emulator.rst @@ -0,0 +1,43 @@ +climada\.hazard\.emulator package +================================= + +climada\.hazard\.emulator\.const module +--------------------------------------- + +.. automodule:: climada.hazard.emulator.const + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.emulator\.emulator module +------------------------------------------ + +.. automodule:: climada.hazard.emulator.emulator + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.emulator\.geo module +------------------------------------- + +.. automodule:: climada.hazard.emulator.geo + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.emulator\.random module +---------------------------------------- + +.. automodule:: climada.hazard.emulator.random + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.emulator\.stats module +--------------------------------------- + +.. automodule:: climada.hazard.emulator.stats + :members: + :undoc-members: + :show-inheritance: + diff --git a/doc/climada/climada.hazard.rst b/doc/climada/climada.hazard.rst index 418cbbd734..f10f49774a 100644 --- a/doc/climada/climada.hazard.rst +++ b/doc/climada/climada.hazard.rst @@ -4,14 +4,7 @@ climada\.hazard package .. toctree:: climada.hazard.centroids - -climada\.hazard\.ag\_drought module ------------------------------------ - -.. automodule:: climada.hazard.ag_drought - :members: - :undoc-members: - :show-inheritance: + climada.hazard.emulator climada\.hazard\.base module ---------------------------- @@ -29,14 +22,6 @@ climada\.hazard\.drought module :undoc-members: :show-inheritance: -climada\.hazard\.flood module ------------------------------ - -.. automodule:: climada.hazard.flood - :members: - :undoc-members: - :show-inheritance: - climada\.hazard\.isimip\_data module ------------------------------------ @@ -53,6 +38,30 @@ climada\.hazard\.landslide module :undoc-members: :show-inheritance: +climada\.hazard\.low\_flow module +--------------------------------- + +.. automodule:: climada.hazard.low_flow + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.relative\_cropyield module +------------------------------------------- + +.. automodule:: climada.hazard.relative_cropyield + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.river\_flood module +------------------------------------ + +.. automodule:: climada.hazard.river_flood + :members: + :undoc-members: + :show-inheritance: + climada\.hazard\.storm\_europe module ------------------------------------- @@ -77,6 +86,14 @@ climada\.hazard\.tc\_clim\_change module :undoc-members: :show-inheritance: +climada\.hazard\.tc\_rainfield module +------------------------------------- + +.. automodule:: climada.hazard.tc_rainfield + :members: + :undoc-members: + :show-inheritance: + climada\.hazard\.tc\_tracks module ---------------------------------- @@ -85,18 +102,26 @@ climada\.hazard\.tc\_tracks module :undoc-members: :show-inheritance: -climada\.hazard\.trop\_cyclone module -------------------------------------- +climada\.hazard\.tc\_tracks\_forecast module +-------------------------------------------- -.. automodule:: climada.hazard.trop_cyclone +.. automodule:: climada.hazard.tc_tracks_forecast :members: :undoc-members: :show-inheritance: -climada\.hazard\.water\_scarcity module ---------------------------------------- +climada\.hazard\.tc\_tracks\_synth module +----------------------------------------- -.. automodule:: climada.hazard.water_scarcity +.. automodule:: climada.hazard.tc_tracks_synth + :members: + :undoc-members: + :show-inheritance: + +climada\.hazard\.trop\_cyclone module +------------------------------------- + +.. automodule:: climada.hazard.trop_cyclone :members: :undoc-members: :show-inheritance: diff --git a/doc/conf.py b/doc/conf.py index ceb842bbab..8d422b93e3 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -11,19 +11,18 @@ # All configuration values have a default; values that are commented out # serve to show the default. -import sys, os - -sys.path.insert(0, os.path.abspath('../')) +import os +import sys # If your extensions are in another directory, add it here. If the directory # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # sys.path.append(os.path.abspath('sphinxext')) +sys.path.insert(0, os.path.abspath('../')) # set version -__version__ = None -# Sets the __version__ variable -exec(open('../climada/_version.py').read()) +from climada import _version +__version__ = _version.__version__ # -- General configuration ----------------------------------------------------- @@ -38,7 +37,8 @@ 'sphinx.ext.inheritance_diagram', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'nbsphinx'] + 'nbsphinx', + 'readthedocs_ext.readthedocs',] nbsphinx_allow_errors = True @@ -204,7 +204,7 @@ # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ - (master_doc, 'climada.tex', u'CLIMADA documentation', + (master_doc, 'climada.tex', u'CLIMADA documentation', u'CLIMADA contributors', 'manual'), ] @@ -245,4 +245,3 @@ def setup(app): # improve parameters description napoleon_use_param = False - diff --git a/doc/guide/Reviewer_Checklist.md b/doc/guide/Reviewer_Checklist.md new file mode 100644 index 0000000000..46f99e770f --- /dev/null +++ b/doc/guide/Reviewer_Checklist.md @@ -0,0 +1,14 @@ +# Reviewer Checklist + +* Include references to the used algorithms in the docstring +* If the algorithm is new, please include a description in the docstring, or be sure to include a reference as soon as you publish the work +* The code should be easily readable (for infos e.g. here [here](https://treyhunner.com/2017/07/craft-your-python-like-poetry/?__s=jf8h91lx6zhl7vv6o9jo)) +* Variable names should be chosen to be clear. Avoid `item, element, var, list` etc... +* Avoid as much as possible hard-coded indices for list (no `x = l[0], y = l[1]`) (see also [here](https://treyhunner.com/2018/03/tuple-unpacking-improves-python-code-readability/)) +* Avoid mutable as default values for functions and methods. +* Use pythonic loops, list comprehensions etc. +* Make sure the unit test are testing all the relevant parts of the code +* Check the docstring (is everything clearly explained, are the default values given an clear) + +* Did the code writer perform a static code analysis? Does the code respect Pep8? +* Did the code writer perform a profiling and checked that there are no obviously ineficient (computation time-wise and memore-wise) parts in the code? diff --git a/doc/guide/coding_conventions.rst b/doc/guide/coding_conventions.rst new file mode 100644 index 0000000000..eb60632ac4 --- /dev/null +++ b/doc/guide/coding_conventions.rst @@ -0,0 +1,82 @@ +.. _Coding Conventions: + +Coding Conventions +================== + +Contributions are very welcome! But we need to keep a certain order. Please earnestly consider the following guidelines. + +Unit Tests +---------- +Each method/function should have its dedicated unit test suit. +Excepted are methods/functions that are only called in a particular, isolated context and not meant to be called from elsewhere. +For these cases it seems sufficient to test the calling method/function. + + +Python Style +------------ +Follow `PEP 8 `_. + +Linter +------ +The PyLint configuration file is `here `_. +As a general rule, aim for no warnings at all and always make sure *High Priority* warnings are immediately eliminated. + + +Best Practices +============== +Coding can be good or bad, this much is clear. It's not so clear what exactly distinguishes the good from the bad. +This question has probably only one correct answer, the universal: it depends. + +In the context of CLIMADA, we consider code to be good if it adheres to the following commitments at best effort, i.e. reasonably rather than dogmatic. + +Correctness +----------- +Methods and functions must return correct and verifiable results, not only under the best circumstances but in any possible context. +I.e. ideally there should be unit tests exploring the full space of parameters, configuration and data states. +This is often clearly a non-achievable goal, but still - we aim at it. + +Tightness +--------- +- Avoid code redundancy. +- Make the program efficient, use profiling tools for detection of bottlenecks. +- Try to minimize memory consumption. +- Don't introduce new dependencies (library imports) when the desired functionality is already covered by existing dependencies. +- Stick to already supported file types. + +Readability +----------- +- Write complete Python Docstrings. +- Use meaningful method and parameter names, and always annotate the data types of parameters and return values. +- No context depending return types! Avoid None as return type, rather raise an Exception instead. ??? +- Be generous with defining Exception classes. +- Comment! Comments are welcome to be redundant. + And whenever there is a particular reason for the way something is done, comment on it! + It *will* pay off when maintaining, extending or debugging. An extensive guide is `here `_. +- For functions which implement mathematical/scientific concepts, add the actual mathematical formula as comment or + to the Doctstrings. This will help maintain a high level of scientific accuracy. E.g. How is are the random walk + tracks computed for tropical cyclones? + +Performance +=========== +C-like data types +----------------- +- Use arrays, implicitly (DataFrames) or explicitly. +- Initialize arrays and DataFrames not by appending to an initially empty array or DataFrame but + by using concatenation (`numpy.vstack`, `pandas.concat`) of lists or maps. +- Avoid loops (`for`, `while`) around arrays and DataFrames in favor of + vectorized operations. +- Mind the creation of temporary arrays in vector arithmetics. + +Parallelization +--------------- +Don't parallelize inefficient programs! (Unless they're not yours and you cannot change them.) + +Cython, Numba, ... +------------------ +- First try to exploit Numpy vectorized operations to speed up your code before you resort to tools like Cython or Numba. +- When using Numba, make sure to avoid Python objects as, otherwise, Numba will + use the less efficient `object mode `_. + +Configuration +============= +- URLs of external resources and locations of data directories should always be defined in the config.py file and not declared as constants. diff --git a/doc/guide/developer.rst b/doc/guide/developer.rst index 67e403ea32..e683036704 100644 --- a/doc/guide/developer.rst +++ b/doc/guide/developer.rst @@ -5,56 +5,144 @@ Contributing Contributions are very welcome! Please follow these steps: -0. **Install** `Git `_ and `Anaconda `_ (or `Miniconda `_). +0. **Install** `Git `_ + and `Anaconda `_ (or `Miniconda `_). -1. **Fork** the project on GitHub:: + Also consider installing Git flow. This is included with `Git for Windows `_, and has different implementations e.g. `here `_ for Windows and Mac - git clone https://github.com/CLIMADA-project/climada_python.git +1. **Clone (or fork)** the project on GitHub -2. **Install the packages** in ``climada_python/requirements/env_climada.yml`` and ``climada_python/requirements/env_developer.yml`` (see :doc:`install`). You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. + From the location where you want to create the project folder, run in your terminal:: -3. You might make a new **branch** if you are modifying more than one part or feature:: + git clone https://github.com/CLIMADA-project/climada_python.git - git checkout -b feature_branch_name + For more information on the Git flow approach to development see :doc:`install`. - `About branches `_. +2. **Install the packages** in ``climada_python/requirements/env_climada.yml`` and + ``climada_python/requirements/env_developer.yml`` (see :doc:`install`). You + might need to install additional environments contained in ``climada_python/requirements`` + when using specific functionalities. -4. Write small readable methods, classes and functions. Make well commented and clean **commits** to the repository:: +3. Make a new **branch** + For new features in Git flow:: + + git flow feature start feature_name + + Which is equivalent to (in vanilla git):: + + git checkout -b feature/feature_name + + Or work on an existing branch:: + + git checkout -b branch_name + + See CLIMADA-python's branching policies in :doc:`git_flow`. + + `General information about Git branches `_. + +4. Follow the :doc:`coding_conventions`. Write small readable methods, classes and functions. + Make well commented and clean **commits** to the repository:: + + # get the latest data from the remote repository and update your branch git pull - git stats # use it to see your locally modified files + + # see your locally modified files + git status + + # add changes you want to include in the commit git add climada/modified_file.py climada/test/test_modified_file.py + + # commit the changes git commit -m "new functionality of .. implemented" + Usually you will want a longer commit message than the one-line message above. In this case ``git commit`` will open your terminal's default text editor for a more detailed description. You can also create your commits interactively through your IDE's version control GUI (Spyder/PyCharm/etc). + + 5. Make unit and integration **tests** on your code, preferably during development: - * Unit tests are located in the ``test`` folder located in same folder as the corresponding module. Unit tests should test all methods and functions using fake data if necessary. The whole test suit should run in less than 20 sec. They are all executed after each push in `Jenkins `_. + * Unit tests are located in the ``test`` folder located in same folder as the corresponding + module. Unit tests should test all methods and functions using fake data if necessary. + The whole test suite should run in less than 20 sec. They are all executed `after each push + in Jenkins `_. + + * Integration tests are located in ``climada/test/``. They test end-to-end methods and + functions. Their execution time can be of minutes. They are executed `once a day in + Jenkins `_. + +6. Make sure your changes are not introducing new test failures. + + Run unit and integration tests:: + + make unit_test + make integ_test + + Compare the result to the results before the change. Current test failures are visible on + `Jenkins `_. + Fix new test failures before you create a pull request or push to the develop branch of + CLIMADA-project/climada_python. See `Continuous Integration`_ below. + +7. Perform a **static code analysis** of your code using ``pylint`` with CLIMADA's configuration + ``.pylintrc``. `Jenkins `_ executes it after every push. + To do it locally, you might use the Interface provided by `Spyder`. + To do so, search first for `static code analysis` in `View` and then `Panes`. + +8. Add new **data dependencies** used in :doc:`data_dependencies` and write a **tutorial** if a new + class has been introduced (see :doc:`tutorial`). + +9. Add your name to the **AUTHORS** file. + +10. Merge any updates to ``develop`` into your branch. + + There may have been changes to the remote ``develop`` branch since you created your branch. You can deal with potential conflicts by updating and merging ``develop`` into your branch:: + + git checkout develop + git pull + git checkout feature/feature_name + git merge develop - * Integration tests are located in ``climada/test/``. They test end-to-end methods and functions. Their execution time can be of minutes. They are executed once a day in `Jenkins `_. + Then `resolve any conflicts `_. In the case of more complex conflicts, you may want to speak with others who worked on the same code. -6. Perform a **static code analysis** of your code using ``pylint`` with CLIMADA's configuration ``.pylintrc``. `Jenkins `_ executes it after every push. To do it locally, you might use the Interface provided by `Spyder`. To do so, search first for `static code analysis` in `View` and then `Panes`. +11. **Push** the branch to GitHub. -7. Add new **data dependencies** used in :doc:`data_dependencies` and write a **tutorial** if a new class has been introduced (see :doc:`tutorial`). + To push your branch ``feature_branch_name`` for the first time call:: -8. Add your name to the **AUTHORS** file. + git push -u origin feature/feature_branch_name -9. **Push** the code or branch to GitHub. To push without a branch (to master) do so:: + or, if you're updating a branch that's already on GitHub:: - git push + git push - To push to your branch ``feature_branch_name`` do:: + Only push small bugfixes and comments directly to ``develop`` - most new code should be pushed as a feature branch, which can then be reviewed with a pull request. Only emergency hotfixes are pushed to ``master``. - git push origin feature_branch_name +12. Create a pull request. - When the branch is ready, create a new **pull request** from the feature branch. `About pull requests `_. + When the branch is ready, create a new **pull request** from the feature branch. `About pull + requests `_. + + To do this, + + - On the `CLIMADA GitHub page `_, navigate to your feature branch. Above the list of files is a summary of the branch and an icon to the right labelled "Pull request". + - Choose which branch you want to merge with. This will usually be ``develop``, but may be a feature branch for more complex feature development. + - Give your pull request an informative title, like a commit message. + - Write a description of the pull request. This can usually be adapted from your branch's commit messages, and should give a high-level summary of the changes, specific points you want the reviewers' input on, and possibly explanations for decisions you've made. + - Assign reviewers using the right hand sidebar on the page. Tag anyone who might be interested in reading the code. You should have found someone who is happy to read the whole request and sign it off (this person could also be added to 'Assignees'). A list of potential reviewers can be found in the `WIKI `_. + - Contact reviewers. GitHub's settings mean that they may not be alerted automatically, so send them a message. + +13. Review and merge the pull request. + + For big pull requests, stay in touch with reviewers. When everyone has had the chance to make comments and suggestions, and at least one person has read and approved the whole request, it's ready to be merged. + + If ``develop`` has been updated during the review process, it may be necessary to resolve merge conflicts again. + + Merging the pull request is done through the GitHub site. Once it's merged you can delete the feature branch and update your local copy of ``develop`` with ``git pull``. -Notes ------ Update CLIMADA's environment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Remember to regularly update your code as well as climada's environment. You might use the following commands to update the environments:: +---------------------------- +Remember to regularly update your code as well as climada's environment. You might use the +following commands to update the environments:: cd climada_python git pull @@ -62,5 +150,66 @@ Remember to regularly update your code as well as climada's environment. You mig conda env update --file requirements/env_climada.yml conda env update --file requirements/env_developer.yml -If any problem occurs during this process, consider reinstalling everything from scratch following the :doc:install instructions. -You can find more information about virtual environments with conda `here `_. +If any problem occurs during this process, consider reinstalling everything from scratch following +the :doc:install instructions. +You can find more information about virtual environments with conda +`here `_. + + +Continuous Integration +---------------------- +The results from the Jenkins server are to be taken seriously. +Please run unit tests locally on the whole project, by calling `make unit_test` and if possible +remotely on Jenkins in a feature branch. + +Before pushing to the develop branch they should run without errors or (novel) failures. +After pushing, check the CI results on Jenkins, if the commit causes an error there, revert it +immediately. +If the commit merely introduces novel failures, fix them within 3 days, or revert the commit. + +Similar rules apply for the Pylint results on the deveolp branch. Novel high priority warnings +are not acceptable on the develop branch. +Novel medium priority warnings should be fixed within 3 days. + +Tolerance overview +~~~~~~~~~~~~~~~~~~ + +======= ===== ======= ==== ====== === +Branch Unittest Linter +------- ------------- --------------- +\ Error Failure High Medium Low +======= ===== ======= ==== ====== === +Master x x x \(x\) \- +Develop x 3 days x 3 days \- +Feature \(x\) \- \- \- \- +======= ===== ======= ==== ====== === + +x indicates "no tolerance", meaning that any code changes producing such offences should be +fixed *before* pushing them +to the respective branch. + + +Issues +------ +Issues are the main platform for discussing matters. Use them extensively! Each issue should +have one categoric label: + +- bug +- enhancement +- question +- incident + +and optionally others. When closing issues they should get another label for the closing reason: + +- fixed +- wontfix +- duplicate +- invalid + +(Despite their names, `fixed` and `wontfix` are applicable for questions and enhancements as well.) + + +Regular Releases +---------------- +Regular releases are planned on a quarterly base. Upcoming releases are listed in the `WIKI `_. + diff --git a/doc/guide/git_flow.rst b/doc/guide/git_flow.rst new file mode 100644 index 0000000000..7370193de8 --- /dev/null +++ b/doc/guide/git_flow.rst @@ -0,0 +1,229 @@ +.. _Git Flow: + +Git Flow +======== + +In general, our policy and naming of branches follow broadly the conventions of +`git flow `_. + +Philosophy +---------- + +We use a *merge only* philosophy in all shared branches. This is safer +when working with people with various levels of git-skills. + +We use rather long-lived feature branches, as we are scientist and not +software engineers. The creator of a feature branch is the +owner/responsible for its content. You can use the workflow you prefer +inside of your feature branch. It must be regularly merged into the +develop branch, and at this point the branch owner must organize a code +review. This allows for a smooth development of both the code and the +science, without compromising the code legacy. + +Scientific publication should, once accepted, be made into a minimal +working example and pushed onto the Climada_papers repository. + +Do not forget to update the Jenkins test and the CLIMADA tutorial. + +Release cycle +------------- + +When a new release is made, everything in the develop branch is merged +into the master branch. + +Fork or clone? +-------------- + +Core developers should clone the project. + +External developers can fork the project. If you want to become a core +developer, please contact us. + +Branches +-------- + +The branching system is adapted for the scientific setting from + ++-------------+--------------------+------------+---------------+ +| Branches | Purpose | Code | Longevity | ++=============+====================+============+===============+ +| Main | Releases | Stable | Infinite | ++-------------+--------------------+------------+---------------+ +| Develop | Between releases | Reviewed | Infinite | ++-------------+--------------------+------------+---------------+ +| Feature/ | Scientific Dev. | Anything | Max. 1 year | ++-------------+--------------------+------------+---------------+ +| Feature/Fix-| Minor changes | Anything | few days | ++-------------+--------------------+------------+---------------+ + +- The ``master`` branch is used for the quarterly releases of stable code. + +- The ``develop`` branch is used to gather working/tested code in between + releases. + +- Development is done in feature branches. + +- Feature branches are used for any CLIMADA development. Note that a + feature branch should not live forever and should contribute to the + ``develop`` branch. Hence, try to merge your working code for each + release, or every second release at least. Ideally, a branch is + cleaned after max. one year and then archived. + +- Small fixes, bugs, code updates, improvements etc. are done as feature branches and should use a clear prefix such as 'Fix-'. These branches are thought of to be short lived, but still require a review! After the review, these branches are usually deleted. + +- Code for a scientific project/paper (i.e. CLIMADA application, not + development) is not pushed to this repository. A minimal working + example should be pushed to the climada\_papers repository. + +What files to commit? +--------------------- + +- Git is for the code, *not* the data. The only exceptions are small + data samples for the unit tests. Small means (<1mb). A very very + strong reason must be given to commit larger files. A more systematic + way to handle data will be developed soon. + +.gitignore +---------- + +See +`here `__\ for +details on how to use the .gitignore file. + +- If your script (not a paper script, but a core CLIMADA script) + produces files, add these to the gitignore file so that they are not + committed. Add .gitignore to a commit. Do only add your file. + +- Remember to remove a file from the gitignore if it is not produced by + the code anymore. + +Don'ts +------ + +- Do not rebase on the ``develop``/``master`` branches, or on any branch that has already been pushed to GitHub. +- Do not use fast-forwarding on the remote branches. + +Creating feature branches +------------------------- + +Before/After starting a new scientific feature branch, present a 2-3 mins plan at the +bi-weekly developers meeting. + +Naming convention: feature/my\_feature\_name + +For small features (fixes, improvements,...), use a clear prefix such as 'Fix-'. + +Naming convention: feature/Fix-my\_feature\_name + +How to use GIT for CLIMADA +========================== + +How to use Git? +--------------- + +Please check the `Git +book `__ +tutorial to get a basic understanding of git. + +Recommended reading (to begin with): + +- Chapter 1 Getting started +- Chapter 2 Git Basics +- Chapter 3 Git Branching, +- Chapter 6 GitHub + +Also checkout this +`cheatsheet `__ +for git commands. + +GUI or command line +------------------- + +- The probably most complete way to use git is through the command + line. However, this is not very visual, in particular at the + beginning. Consider using a GUI program such as “git desktop” or + “Gitkraken”. Your python IDE is also likely to have a visual git interface. + +- Consider using an external merging and conflict resolution tool + +Commit messages +--------------- + +Basic syntax guidelines taken from +`here `__ (on 17.06.2020) + +- Limit the subject line to 50 characters +- Capitalize the subject line +- Do not end the subject line with a period +- Use the imperative mood in the subject line (e.g. "Add new tests") +- Wrap the body at 72 characters (most editors will do this automatically) +- Use the body to explain what and why vs. how +- Separate the subject from body with a blank line (This is best done with + a GUI. With the command line you have to use text editor, you cannot + do it directly with the git command) +- Put the name of the function/class/module/file that was edited +- When fixing an issue, add the reference gh-ISSUENUMBER to the commit message + e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see `here `__. + +Git commands for CLIMADA +------------------------ + +Below should be all the commands you need to get started for working on +a feature branch (assuming it already exists). More features are +available in git, and feel free to use them (e.g. stashing or cherry +picking). However, you should follow the dont's (do not rebase *on* the +develop branch, and do not fast-foward on remote branches). + +A) Regular / daily commits locally + +0. ``git fetch --all`` (make your local git know the changes that + happened on the repository) +1. ``git checkout feature/feature_name`` (be sure to be on your branch) +2. ``git status`` +3. ``git add file1`` +4. ``git commit -m “Remove function xyz from feature.py”`` +5. ``git status`` (verify that there are no more tracked files to be committed) + +B) Push to remote branch (at least once/week, ideally daily) + +1. ``git fetch --all`` +2. ``git checkout feature/feature_name`` (be sure to be on your branch) +3. Make all commits according to A +4. ``git status`` (check whether your local branch is behind the remote) +5. ``git pull --rebase`` (`resolve all conflicts `_ if there are any) +6. ``git push -u origin feature/feature_name`` if this is the first time you're pushing to the remote repository. Or just ``git push`` if the branch already exists there. + +C) Merge develop into your branch (regularly/when develop changes) + +1. ``git fetch --all`` +2. Make all commit according to A +3. ``git status`` (verify that there are no tracked files that are + uncommitted) +4. ``git checkout develop`` +5. ``git pull --rebase`` +6. ``git checkout feature/feature_name`` +7. ``git merge --no-ff develop`` +8. resolve all conflicts if there are any +9. ``git push origin feature/feature_name`` if this is the first time you're pushing to the remote repository. Or just ``git push`` if the branch already exists there. + +D) Prepare to merge into develop (ideally before every release) + +1. ``git fetch --all`` +2. ``git checkout feature/feature_name`` +3. ``git status`` (see how many commits the branch is behind the + remote) +4. Make all commits according to A +5. Merge develop into your branch according to C +6. Push the branch to GitHub. If parts of the feature are incomplete and not everything is ready to go into ``develop``, create a new branch + ``feature/feature_name-release`` with + + - ``git checkout feature/feature_name-release`` + - Clean the code so that only changes to be pushed remain + - Check that the code on the new branch passes unit and integration testing. + - Commit all changes according to A) + - ``git push -u origin feature/feature_name-release`` + +7. Make a pull request +8. Find someone to do a code review on ``feature/feature\_name-release``. + Implement the code review suggestions (once done, redo steps 4 - 6)) + diff --git a/doc/guide/install.rst b/doc/guide/install.rst index 346adbfc76..f2a56c5289 100644 --- a/doc/guide/install.rst +++ b/doc/guide/install.rst @@ -85,4 +85,7 @@ FAQs where ``library_name`` is the missing library. + Another reason may be a recent update of the operating system (macOS). + In this case removing and reinstalling Anaconda will be required. + * Conda right problems in macOS Mojave: try the solutions suggested here `https://github.com/conda/conda/issues/8440 `_. diff --git a/doc/guide/install_cluster.rst b/doc/guide/install_cluster.rst index 9444a7f6a7..a5f3959da0 100644 --- a/doc/guide/install_cluster.rst +++ b/doc/guide/install_cluster.rst @@ -55,7 +55,7 @@ During the installation process of Miniconda, you are prompted to set the workin cd /cluster/work/climate/USERNAME/climada_python conda env create -f requirements/env_climada.yml --name climada_env -(You might need to restart the terminal for the command "conda" to work after installation of miniconda) +(You might need to restart the terminal for the command "conda" to work after installation of miniconda. Alternatively, execute the command *source activate base* before executing the above comments.) To include *climada_python* in the environment's path, do the following. In your environments folder, for example /cluster/work/climate/USERNAME/miniconda3/*:: diff --git a/doc/index.rst b/doc/index.rst index 0a3cd805a3..ddcf38b517 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -16,6 +16,7 @@ User guide guide/data_dependencies guide/configuration guide/developer + guide/git_flow ---------------------- diff --git a/doc/tutorial/0_intro_python.ipynb b/doc/tutorial/0_intro_python.ipynb index 5c6c0b809d..cf07bcef94 100644 --- a/doc/tutorial/0_intro_python.ipynb +++ b/doc/tutorial/0_intro_python.ipynb @@ -199,6 +199,8 @@ "metadata": {}, "outputs": [], "source": [ + "# Note: execution of this cell will fail\n", + "\n", "# Try to modify a character of a string\n", "word = 'Python'\n", "word[0] = 'p'" @@ -295,6 +297,8 @@ "metadata": {}, "outputs": [], "source": [ + "# Note: execution of this cell will fail\n", + "\n", "# Tuples are immutable:\n", "t[0] = 88888" ] diff --git a/doc/tutorial/1_main_climada.ipynb b/doc/tutorial/1_main_climada.ipynb index b760102db3..9bddc495fb 100644 --- a/doc/tutorial/1_main_climada.ipynb +++ b/doc/tutorial/1_main_climada.ipynb @@ -62,7 +62,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:22,664 - climada - DEBUG - Loading default config file: /Users/aznarsig/Documents/Python/climada_python/climada/conf/defaults.conf\n" + "2020-09-16 14:51:40,441 - climada - DEBUG - Loading default config file: /home/tovogt/code/climada_python/climada/conf/defaults.conf\n" ] } ], @@ -92,14 +92,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:24,844 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", - "2020-03-13 16:28:24,845 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", - "2020-03-13 16:28:24,845 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", - "2020-03-13 16:28:24,846 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2020-03-13 16:28:24,847 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2020-03-13 16:28:24,847 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2020-03-13 16:28:24,849 - climada.entity.exposures.base - INFO - region_id not set.\n", - "2020-03-13 16:28:24,850 - climada.entity.exposures.base - INFO - geometry not set.\n" + "2020-09-16 14:51:41,572 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", + "2020-09-16 14:51:41,572 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", + "2020-09-16 14:51:41,573 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", + "2020-09-16 14:51:41,573 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 14:51:41,573 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 14:51:41,574 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-09-16 14:51:41,574 - climada.entity.exposures.base - INFO - region_id not set.\n", + "2020-09-16 14:51:41,575 - climada.entity.exposures.base - INFO - geometry not set.\n" ] } ], @@ -129,17 +129,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:24,858 - climada.util.coordinates - INFO - Setting geometry points.\n", - "2020-03-13 16:28:24,870 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + "2020-09-16 14:51:41,579 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2020-09-16 14:51:41,588 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n", - "/Users/aznarsig/anaconda3/envs/climada_env/lib/python3.7/site-packages/contextily/tile.py:199: FutureWarning: The url format using 'tileX', 'tileY', 'tileZ' as placeholders is deprecated. Please use '{x}', '{y}', '{z}' instead.\n", + "/home/tovogt/.local/share/miniconda3/envs/tc_env/lib/python3.7/site-packages/contextily/tile.py:199: FutureWarning: The url format using 'tileX', 'tileY', 'tileZ' as placeholders is deprecated. Please use '{x}', '{y}', '{z}' instead.\n", " FutureWarning,\n" ] }, @@ -147,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:26,645 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + "2020-09-16 14:52:05,354 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" ] }, { @@ -186,7 +186,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -305,15 +305,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:27,630 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/atl_prob.mat\n", - "2020-03-13 16:28:27,670 - climada.hazard.centroids.centr - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/atl_prob.mat\n" + "2020-09-16 14:52:06,149 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/atl_prob_nonames.mat\n", + "2020-09-16 14:52:06,172 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/demo/atl_prob_nonames.mat\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, @@ -326,7 +326,7 @@ }, { "data": { - "image/png": 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\n", 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dwiVcxh52XfhEvqLqx8XzkvXQTwjqa4lsTZHdZ2mRapyqU/V6u85qnKw9y9E61DDZO898NT5ESRJUU0+RmXaQeW34euppiZz6O84aOlVDypeojnN12sm6mVO1KtNtlhztw8FBUcZZvIFjXXFZy0b28lynl5JjgaKpD1EXypuuIUSb2cZB9vIMjy5aUrSWjZxjjDN6sv2LQ0Li2T5CfnbdVzQMAugbQhQ6XocEKSQ+oa9ReJ7qi3yKvPA6P/Yn8k2/5U8UmsIix+nQr8hTnGrgj+SEwRGtPqmqmQccD8snKVkHEuSoWYk0TfZ7GBG6pOksJD2BvxCxn5Gb9CUKA1FGfkXGtyjhT+RK0ndIGvgTGd+jZj5Eytw/sYnIlSLyuFXOiMgvi8gyEblbRHab40jrf6yLGyLCVrazk4fZo892ejmzxkau5BAvMaWTnV5KjgWIZj5E3ShvuoYQrZC13M4bmeAsj/Jt9uvzi44YOeJyCZdf+FOb3+RPz/IH0rQGKCQ8WeYzc01aW1QTk8g2qdX5DMKIzhLNR2QiC7VKoZYoYRrzwnua8endaOn1ZZkD0+9Fqq9GW2Tmtrfj2yYstU1Z6fN6/kSJuEUp01mNSc3yMWoIwWuzNIOqPqeqN6jqDcBNwDjwOeBDwD2qejlwj6kvGayXLdzGD3KIl/ie7uCILj7/xbL0sIoN7Of5Ti8lx6JE98mbriFEAK4UuJE7Wc9mXmYPe+fC/HSRsY7NnOIY43q2vQvTu8jS2+zTxSJFmc7TGSayGvOZdZ4gRSnUOluHZCg2e9nEyPZPiuIUEV5PbEazNTxp36J6aECQss1myf6YBKUJEoTO1nFqkthkBtT3J4Jaf6Ias1nzr6oCMyptlTbxOuAFVX0J+FHg46b948CPtTvZYkdZeriF17GSdXyfJzis+zu9pLZxKVfwMi9S1ZlOLyXHIkM3ypuuIkQQaFlWyyVs5wfYzwuMzcb81EEUpMB6trSnJUr9yGtKyxPlxErU4zEaOk/bW+3teaF291lGX9qnqN6us5oYRaG2SGMCFG7Pz/QvCklRyuFaUiSpVb+iehqhzF1nWCTIlZgEhT5FofbH9ieCBDnK9Ceq52SdHtfozwBm88Q2KiKPWOXnG9zincAnzfkqVT0EYI6LP6/FLFCQIutkEzdwB8/xvUXnpNwr/SxjJQd4sdNLybHA0Eocom6TN4sql1k7KEsPa3Uj+9jNNr2RghQ7vaSWcQmXcx9fZVzPMcJg9iBDPtTLjoWSbk/URWLfoOgHWwPnXd8BDFlKm3880+YCvgNonGLC9IU+QzimLkKQC00RCVQqKiCORvOGOcUgyBUmZq5w51bkX+QpARNRJNTQmJbIjEXggB1eG+4Mq0eKJLhtbXvWeEujlNAMqblvaFqL4hYF7eoEuc4UJyB3osHbq8F7qQr4JndZSDajBXgBkWwhMGNwedtPYcdV9RXNBolICfgR4NfavcFSwIAMM6Ir2M/zbNKrMhM1L1RsYhuP8R3W6+ZOLyXHAkKzXGbQffKm6zRENi7hMnw87uXLHNfDnV5OyyhKiQ1cxh5m6bDp1/n1DxGSKdt0ZkxZmqUByjSl+UltUdSXNJHV+BPZgRyNM3asMSIOuGj7F/nxvAmNUUorFEa6bitOUeqtqtUWpd6L9Bhrd1iczwzAIkWhpigrDpFtVktriqjTVu9jZVZPbK3izcBjqhpG8zsiImuCJcoa6JK8FheALVzDGCe5ly9xTsc6vZyW0S9DLGNl7kuUoy10o7zpakJUkgrXy+2s5hKeNbvPVHVRhKy/hMs5zmHG9Xz2gIaBF/3G/RaJ0SiVBzH5COfPcrS2+4IJaglTGjYp8onmi/OamV1olinNjlQdao9ssmWTopZ8i5qg4bb7BmPsnGeJ9B1gmc4MSbL9iWbrZN0AijCjblulDbyLWH0N8AXgPeb8PcDn25msG9EnA9wkdzHAMM/xOAC++otic8dmrmIfu3NfohwtoxvlTdeazGxsYitHOcC9fJmVjHKEY5S0TJUZtrKdFbK200usQUGKXKpXsI/drOKyZGcTQqe+Io6fqkuiHptmBPUMd3ccQp8iEScYE/6g+37SsdfH6tMktVZjtvIlMJ0ZSHi/dLsXalACgqRmHYjpc2NtkBqOoL4goYkNUGOyUidYjzjGnuaYvtTTSajpzdQIpRBvyye02CVh7qnmnuIEa1dHEB/UUcQ3pkFz7+D9VvM6wnmsugP4hkCFZswmjy/hE9tcQ0R6gTcAv2A1fwT4tIj8DLAP+Mk5v/EixeVcx8N8g0f127zIC/TQF/3NXMutDC7ACAW9MsAKXcdB9jLAqk4vJ8cCQKs+RHONTsqbphoiEdkgIt8UkWdFZKeIfMC0Xy8i94vIUyLyRREZtK75PeMwdZepbxQRFZH3W2M+KiLvnYfXlPUqmGGazWzjVt7ATdzFJrbh47OThxesxmgDWzjPGKf0WGSiUs9rWtLjauqA+l7gQ2TiFKnnxbGIPCuYox2fKBwTaoSiOEd+PFeY+8zEKJKq8SmqxjvcxPdNbCE/Ea9IqkFMI6n6cdyi0IxWteIXeYp4Vp60aip2UYO4RSESSWVTZrGsWEXYpCg1Bkgkeg1jEMW5z6y6HYcozH2WilcUa4rCNhOTyGn2hCV46rRVWoGqjqvqctXYDqSqJ1T1dap6uTnOye6FbpA3HlUU5TKu4XW8jWt4JZu5ignO8zxPXYwlzAqb2cYRDjBRTyudY0mheS6zxS9v0mhlhVXgg6q6DbgVeJ+IXAX8FfAhVb2WIE7ArwKIyFZz3Z3A+6x5jgIfMM5SFxUV6eFO3sp62YKI0C+DrJYNXMetVJnhDAtzJ5orBTaylcf0W22p3bWpD5Efj7O20qu9ZT/LfGbGZZZw3oSpTY1fjx+bzLykGS6RLFZrx4bxiMJ72zvSQj8j24xWkwKkTtyi2Wy7zxxjmSGj9B2G+KhlKkv31ZjMIidsstN7hPVGHyswg9tWWYBY9PJmmFHu5K0MyXJEhCFZxhq5lCu5gZMc5Xy7ITUuEirSyyrW8xQPdHopORYBukTeJNCUEKnqIVV9zJyfBZ4F1gFXAt8xw+4G3mbOXQjTdib0accIAiq9hw6gJOWathFZwWauZt8CdiZcxkr6GWK39wTqa0sF9VsYE/yqR+YziEkRJEiRpp2nM5yxY4JhEyI/0W/7DNkl4WStqbFeXE/nREunAGnLt8hCq9vua8hSBvFMxyaKnaxp7GRdE4+IWofqZj5EOj9PbBcT3SBvRCRT3qxnC2u4lEO8dLGX1DLWcClTTPKy7un0UnIscHSDvEmjrRWKyEZgO/Ag8DTBtjgI7HkbAFR1J9AL7AD+NDXFR4APiiycPak99HKUA8zodKeXkgkR4Tpu4yB7mPTPR6azhgWajrG1Q1EKD4jNZ5AgPjW7z7JIUYisaNFh6o8M8lNDjOpokCJtUagJ8pREQEc/RYqaxS1qVyNU45huXWOPs2MP2U7UTraTdcuRrFvZdo+0VRYyuk3eiAglygs6YGxRSlzDLexlF55WO72cHB1EMx8i6C55A204VYtIP/BPwC+r6hkR+bfAH4nIfyLwAI8Yhaq+P2sOVd0jIg8BP93KPUfW14nBcwEYXJW0iw7rNZznOA5VRmR0zuadKwTzruFGvZUxXmY124N4Phc8by+hZ7S4wa924HhtBQSMfoitnU6A2DFx7CjKIgwOlwidtU1j7Q96aqu6TRogPTZoGhwoIp4aXxyLcFixhmJzFFF/6LsTQqN7B+sY6i/WmsDSyGhquCNNhKHBYnStTfRi0x2W5ssQuZT5MDBBYmnVgN21a4mXKUxrd+yTuNjy5mLIGoAb9ZX4TFDAZ0CG52zeucLgqn42s4Wqnuccx9goV87ZvPOBfN75m3dodBC+V7+/m+RNiJZejYgUCYTTJ1T1swCqugv4QdN/BfDDLd7zd4DPEKu/6+LUgTMtTtke0vOu0i3s4B6GWMa1vBJXZvchz+d6+3SUnTyBR4G1snHO5gWCpKFikaIwiahITH6cmBSJHUUZgvEWSTt9dKK238wHxATKkVpSZJErICA04XqPTwXOxxCnyrDJlUOijsTXh0Qp7I/MWr5y8rSlHczQENWgwU40te5x6tR00pwXabeM2c/PIES26c/zkoSoiW+YAn4XRNLohLy5WLIGYLlewtf5IitZxzZumtUDznytN5x7SNfwAHejFBm+gAfF9LzzgXze+ZlXqo1lSbfIGxut7DIT4K+BZ1X1D6z2leboAL8J/FkrNzSC7RngLbNZ8HxgQIa5nTdynjOcZ2E6PDricDU3s5unmJzj9AAJ89lc+BRBql+T9dSYRIyitMNz1E5sNgvHR35FxGY0y38onSw2KxlsutTuMLNMen4y4WxUwvWBpb1q8IZH8YmafDC2Sa1FeCptlYWGpSBvRmVNlBjWIzvSfKdRkjJb2c4zPJKbznLUxWKXN2m0Qu/uAN4NvFZEHjflh4B3icj3gV3AQeBjbdz3w8D6tlc7T1BVvscOJjhPkYu+KaUlTOsUDi7r2RwFmZwztOtTlBXROpEwNulMjR9enzXWIkUQOFGb+0Y+RECUNNYK6hht0bcdrqu+RXg01sJEu9CsHWqGIMXERmt2odkkK0o0G5m94l1uolpDguKgjRKZ/mre+qxdZlloaZeZ4OG0VRYgul7eVLXKfXwNRXEX6M6bCT3PICMMMMLzPN3p5eToAJrHIeoKeZNAU9uQqu6g/rPsH7ZyE1XdC1xj1Z9gAUXJHuNEtPVesxxGFgAO8RK7eZJLuYJznOZl9rCeOcw9pD7qAaKI6wbBGsMoiE5AksRx4gCNPqjjg2dMbhDHMbLJmqdBHB3f5E3zCExovh/7Cpkgj1HgRg+TIyyoSxi/yFOjLdE4N5oZLyZ3mkqYC43YrOYHuc/QgIAIau4drDMiOgah+StOVms6fIJrSfarFTsoMsUZp+kwYGR8kcSBIsVMnuqPAjRGJkZTb0KCFZhZ5Db9pSBvjrAfj0Dr4uMvSFL0Ajs5zD42sZX9vMAKXcMyyQM2LiU0y2XWDfImjQUjJDqJEpXofCEKJ4B+hqjQy1FepkwPL/A053QebNHWTrVZxykKj7bvS9qEVm8Msc9NpBWy5oxIQWqLfm1etNqYRVnaothUpsniWWswJT0mJFg1SGtypM648OXZu8oaodm2e9pTXy8GFXY3wrHE7kJNAjvEMgYYYR/PM8AwO3mEaZ3q9LJyLCB0o7zJCRHQK/1czx0IzoK16Q8ywiTjTHCeS7mCy7mOJ7lvXsIFzIlPUSO/IjvXWtpnyCY5Vh0rvlGtH1EqL5rlW2RvvY+33GvCt6gV8iP2Vvuw2N9vSR0zzFt2oMa6sE1os4CP01bJcfGxmkvYRpDwe6FGyR9ghLOcwqPK5VzHGi7lKR5YsOvN0Rl0m7zpLn3XBWCY5Sg+FXo7vZRMFCji4OLjUaEXRRnnHA/zDW7nTXN7M9t85hiTVD3zGQBuZD6LyJBI3O+6kfkrMqGpMaH5TsI8FiT6CuxbQT4yoznxUqRJjAlJjZkKDcxuOCZXmJo8Zg5RcliLiIS3kWoypUejHWR2f7CbzImcnqOAjI45D+9BaCIjeJ1efH8cED94rYoEZj+bj9vEyZjNGn5sKu0mUMzRAYgIvdpHD/04sjB/JPoYiM4dHIZYxl52sZOHuZZXdnBlORYKulHe5ITIYJpJehewgBIRtumN7ORhnuFRzhOYy8Y5xxE9wCqZB59R9VHfQVzihLGGwKjvx1vyQ22NI7Gzt9YhGSKBpsUx1xHOKXXGGaVKdI8MwhItN/ALspPIitimrdhfJyQqkeYpnCs9fZbfTlrDk9YOWe2amjPyYcrMEhv2h+eWH1MLUFgU0WBzQJWZBOlYaChKiY26lb3s4gnuY4Igv9kR9rNZr6JPFu7ac1wcdKO86a5XcwGYYRqXYqeX0RB9DNDHIFNMMMoaNnMVwPzmHpqtT1HahGauyfQrsk1oWeM05VsUjiF5nk4LkpX6QzytMaPV22Yf7TKzSgQ7OGQaNcQo1GJltLeyu6wNM1q37froVgTyZmE/j/YzyCirmeA869nCWjYC8FyjaH05lhS6Td4s/BVeJIxxgmGWd3oZDW2/XVEAACAASURBVNHPEBOcw6PKddzKIfZRoQ+AMT0xb/edlU8R1G7LN9ck4wxZpCjdXzOmQcyisD8jJ5qd+iOdD83Odh+SpQQBSq8FYnJjf3vS8YfqbbGv940LfYsu4BupCL62V3J0BmOcYGiBy5tBlnGak/TSzxau5igHATjJUabmOBZajoWHVrbdd5u8WdiPKBcRU0wu2BhEIRxxGdRlnOcsh9iHg8N5zlCmh3OcmT8B245PkR/EMFK8wAwWbssPSUUY9To8D7fdm630hPNizGqRP5LGJrTIHyn2xYnmg6RvUbg939Tj7flGK+SFu9FqX7YkSJCgLpbGhjj6teU/VFP31RjIzA01NptFPkaOIpbvUGQ2C7fbt/ox0X3bYLsVU0wywopOL6MheujDwWGC8xzlAIMMc4pjOLiMc44yPZ1eYo55RL7tfomiqjMc4EVWLZzYbXWxhksBOM3xiMANMxqps+cVYQBHLK2R0d5E5rO0uQtqTWGtaIvSgR8zTW2NNUbp3GCZiWKBzG339uuBSONTz/SV1ReRokSbZEefljiW0ewheG2WHBcf53SMUxxjlDWdXkpDiAhruAQHl9OcAON3dwmXMyILm8zluBjoPnmTEyLgAC+ygjX0ydwneJxrrGAtVWY4zH7UONRcwXVzkvC1JTTzKYIk4bG35adNaPVIVOSYXYcUpYhR1jb8LJNZom75FknmOoj8lSLyktillk2C0uOCev1xme0hYWrzM1XAV6etkuPiYx/PcwmXU5CF7bMIQYgAxecI+5khiEO0Ra7u8KpyLAR0o7xZ+Cu8CHiepxb801qIkpRZxyYU34qqfXGZdyOfokR6Dqst068IaklR5piUhik8jxYUnGc6XYekx/ItkjQxSscg8qwxjZymM+IQNfYhIjtIY+hcnQWbHDULzGi2wbZTWoGIDIvIZ0Rkl4g8KyK3icgyEblbRHab40hLky1xeFrlIHtYzYZOL6UlDMgwgyzHx1+gMfxzzBea+hB1obxZ8oRoQoPtpIMsHnm+kSsBOMcYkIx8e1GgPup5QY4zc06Y/8zzUd9us5ytPS/WFnmWhsj3g7HhuYb9anySvHhMeF1YvGQRk+JDPDVkJx4rvh+0VeO6qDmGJVwzxm/ICqao0TE0e1ntTnJcTH6SfRHC3Ga2lkgs8hPuPmtTS+Sp01ZpEX8IfFVVtwLXA88CHwLuUdXLgXtMPUcTnOEUwKLyv9nMNgCmGO/wSnJcTAyM9Dcd023yZkkTIl99dvE9NnDZojCXhahIL0PmqQ3oXHTtLJ8iiLQzNdvyG2mL0uPSfki2RqhdjVHKryjZTup+ZJKQ6LucafoyY7J8iOq0ZWmJ6pKfFk1oCvhIW6UZRGQQuJMgAz2qOq2qp4EfBT5uhn0c+LGmky1xzOg0z/E4V3LDojCXhVgmKylQYoa5j4qfY/GiG+XNkiZEZznNCQ5HGpfFhGu4JTof52znFpL2KSKZxiNBilJb82tSftikKKzXOFJbDtcX4HSdaLdMZYlvRESC2vUhSr1FGc7V0RzpZltL1DZkNk9soyLyiFV+PjXpZuAY8DER+Z6I/JWI9AGrVPUQgDmunMWClxSOc4jznL04GyDmGNdwc3Q+mW+5zwF0o7xpSohEZIOIfNPY8naKyAdM+w0i8oCIPG5e2C3WNb9n2u4y9Y0ioiLyfmvMR0XkvfPwmlpGkRIlyhQpd3IZs0KP9DHKagBOcayja6nxKUo5Q6tNclJBHDXtOG2PqedwrfH9zCTxMe13hLV9PosU2T5FlpYpdqTOeMFpokRMjJr7EGU4Utdxrk4To2Y+HAqzsekfV9VXWOUvUtMWgBuBP1XV7cB55tE81t3ypswgw7iy+LYqj8oawgAQZzjZ4dXkuBhoHodo8cubNFrREFWBD6rqNuBW4H0ichXw34H/qqo3AP/J1BGRrea6O4H3WfMcBT4gIgsm2E+FXqaZYprJTi9lVtjGTQDs4dnOLiThU6QxKQr9ilQT/Ul/IKMtCsdGvkG2/5Hta2T7IBk/pXCuBv5FsY+Q5VtkfKCi+dNRocMs9NF50q8oMonZPkJhHCKw/I4yzGeCZQozb2PqPvFYaz2NPob5CZR2ADigqg+a+mcIBNYREVkTLE/WEHy/5wJdK2966WeMk4s2QepNvBqAXXmk6iWBZj5EXSJvEmhKiFT1kKo+Zs7PEjg4rSOQ+aHjzRCYMKbgYp7FSf4EHCNwhnrPnKx8DjDFBCUqlKjwiH6Lkzov7/G8oSw9DLEMYGGsPfQpMseEtgiS2qIE0bF8i+rFLLL1I+ndaObeNf2NNEa2GS28rEE6jszo1Km+bB8iyWzL0hLVEJ5ZbL+f6+zTqnoY2C8ioV35dcAzwBeIv8vvAT7f8iIb369r5c0E5xhmlGmmeES/xVk93ekltYVhCQK/TjOZR6rOASx+eZNGW7pbEdkIbAceBH4Z+JqI/A+Cn4nbAVR1p4j0AjuAX01N8RHgKyLyNxe27LnBaY4zzHIE4TTHeYzvsFov4WpuvnhxfS4Q13E79/IlHuM7vFZ/YgEkp/XjRLAQJIcNk8JCMjFsSHjCJLG+oo6PWGODKVO+RsnbWSQleZ9GY2s+3QZ+Qll5y2KClPF3kkGCsv6aVGoz3KsD4gs1BjKRzMS2ifkUZvx5+fzfD3zCaFteBP4NwTv5aRH5GWAf8JNzfdPukzcnEvLmQb7OFr2aTbKt00trGbfwWh7iG9zLl3mdvm3RyMkcc49ukzfQBiESkX7gn4BfVtUzIvLbwP+pqv8kIu8g8Ap/PYCqvj9rDlXdIyIPAT/dyj1H1s/9zq/BVYEacELHGeMQV3IVDjOsj6JU+7zE01zNzZxnjAq99Ehfy/PO13objOBavZFTHOUYe9kqN8zRvLNDMG/wJRFDFsQNvzQhWQnNUhbhCImcaRebjIgwONoTX0uyL243bVhtaaJjOzKLMDBSju8vAq61rd4QNXUtzY5rmcwcM57glr5LZO4a7i9YCWOJz/1kHrVQWxVFzlas5LOBeS/wgdKmTkSBCnvuBZSqPg68IqPrdXN+M4OLLW+WXdI47Ia4BdSrtvUaBlb0Ef7Nn+IoM3qWbXIzZznFeg3kzRRjHOA5tslNjHGcAYYpUWk4r/0dy4JTdPFnZrfzdHBVb8P+EQY5aNY+zknWy6YW5+2UfMznnS3cYmNZ0k3yJkRLhEhEigTC6ROq+lnT/B7gA+b8H4G/avGev0NgF/xOs4GnDpxpccr2cOrAGb6unwFgCzeyn/0c4AArWccwo7zA05zkNOcZw8HlDnlzy/PO13obYUTX8RSPcYAD9LGcfhmak3lni2hesYiROBFBCnKghWTJBOuKiI2TIEsSjlXl9JHx5LjwOpvsOGkC5KSIV3KsinD6xGR0Xy2YY0iOnIAEJciQE5ChoGDmwfQbXyBVTp2ZiclQSIRC0qNEASBDQoSvUTsRQbL6szRkKSyG8PjN0Al5M3aq2uT9nWnxdjGkUmXsZLBV/evnvwDAtt7XcHB6FweqB1hfuII+Z5Bd049wzD3OOf80A84ybqw0lvtOZSaad85gXrtTdJvKhbV6JQ9xDwc4wKsZbDmEQKfkYz7v7LBs9XDTMd0gb2y0sstMCJ7GnlXVP7C6DgJ3mfPXArtbuaGq7iKwCb6lvaXOLYYZZYBhChRZL1t4NT9KHwN8nyfw8Jhmkg1cho+34G39JSlzCZcDQVqABYOEX08cswiId5nZEa2jsZrw+cmMO+TX8RlqNW6RaRfV2jlS80qGG1N0nvEbKmqUUPZUWXIj7Twdjm3BgToLgQJqcWef7pS8kVIJSkUoFOauuAWkWAwKwobiVpxCgW29t/Lq/p9imimem34EgGmd4rLyjZz2jjEjM4jr1i24TsP+WZVCASkUwHFjbW0dDMpIlJj2MPta+RhydCG6Qd6k0YqG6A7g3cBTIvK4aft14OeAPxSRAjAJpOMJNMKHobNbFdaykWd4hB18mVv1BylKiXW6mb08x0a2sodnUdazgnUc5WUGaM6WO4mNbGU/z3OQPVxldp8tCKhv/IjEOvcDoWuy12f6FBnNiDp+oCVKExWRpP9R2AZmXtOGfS+pnYNgCK5pI7gu8OKR2FU3MnGB+hKPN75P4giqGmSvr/O9VzHaMi/FohxMVntNjZeYsLUEodpiePwFjM7Im1KxNqHvBUIKLlIqgq9s7LmePROPMy1TXNf/GsoyyCXO1Zw/P8aAu5zD0y9QKFQYKq7gOIdZW7ys/sSFAhTneOt++NBQcHGKBfyZavKBJoUruYEHuJtdfI/1bJnbteRYJOgKeZNA02+Vqu4g2ycUaO2XV1X3AtdY9SfocFDI1VzCMzzCFJN8my9wp76VfexmPVvYIlezSbdxgsPs43lu5FWdXGpLKEmZ9bqF/TzPET3AKlnf/KKLCDWkIRCybnBshRQhJCJbh9oTm9CE5+EPmSEpEQFK3Ssak0myguUF9yMYHxIlC6KKIgFBSn871B6X8WZk+EzXhbQ+OHA5WvhPYY3QMXlTLNQG+rThukGYhnr1LBTcgLz4Ppv7b2TPxOMcmd7DQ2fOc/PIW3lpcieX9F7Lpb3X4KvP/omdnPfGWF7ZgLj1RXOo0amLVtYWInyd4d+844Lr4kBDUtQvQwzrKKc5znk9S58MtHa/HIsGzXOZLX55k0antyR1DI44vI63RfXv8EXGOMmAyWl2gsM8y2Ncz20MyMLWDoXYzFUAPMUDeNqhdB5ZMNGlw6346nnJVB9WrKJgvCZjCfmKqrUtPxGs0Yoj1CyQo51aJCtqtR2gMTSXZZjNojbbbGb8f2LTG3FdtYYY2fnOItj50lIRs1vdft9t2acvGnp7oK8nOIalpxKXUrFxPasUS1AuQ08Pxb4h3nDJLwEwVj3KjpOf5qx3gsGeNVAq8dLUM+yb2MnNK3+CSs8IlEr1S7HYuN91G/fbpVxO1KVURMplpFzGqZRxSvXDOF3JdgDu52voHGnVciwctJLLrNvkzeILmTqHEBGu19t5gvso00OBAqOs5qQe5Wke4npuY1hGO73MlhGY/TbxMns4zD7W0doOkIuGUFNjzmvNZ5LUFNkaoDBWUda2fIh9ipzUl84nNp/ZmqNIq2SvT+Nr3FgLFIxRRAXF0iQZ7VFkJjOKJLHJEHW0RPZtpdZcNhsoQnURCJ2FCC26FpE2pNe7wACKxUKgJQJQB1ddLl/2A+w+uYOCU2KgtIKh3rUcOPcMe84+yi1r3kFfaXnzeV2jeboQZGl+VMF1kWIhthRD8P3MGD8gQwzpMsY4yWmOR35FOZYGulHeLGlCBDDKGrZwNWu4lIr0MqWTPMUDXMsrWSarOr28trGJq3iZPezh2YVHiODCfIrCKdT4FbViQqshQTT2J0q3Zc5L/GsRXhZa61ImNFuLFJ3X8SMi/ZsjGLNciyYzWBSOiwsRESEyZEhVwZXaz6SdOV3HIkTBZ3jpspvx8Ng88koKTomxycPsPvVdblr3dvorLaZnKrjxvG0vKvojzGgDXAccJ0GKnKJX13x2KVfyJPdzkL05IVpi6EZ5s+QJkYiwiSAwmqryCN9kOasZDaKELzpUpAdXi0wyHjj5LtDAaS37FEGs/QlNW47EpChsD8mLTaAySVHKn8gyjakv4NgO1cFPgqjW7AYT42SNZjhRWw7YQT3V72u2l4yY/9IEyARlbMUKuhjU0gsRNiGKNEWeE5FRdQWpxoRAC06ingnXCcI4QPSZuk4Pl69+DaJKtTrJ/fv/X65c8RqG+ta2vlhHAuJS77Wk1pa5Vj+DZBsNEYUCVKsRKWrkU7Tc5FM8xEtcbSWAzbH40cyHCLpP3ix5QgSwV59jJeu4j68CLMps1DaWsYpjHOAYB1nJuk4vpxZRIthAU6SeZ+IUpUiR58VxisKcY44TPbXPznxmTsL7hOa4tKbJJ4gMHR5VAj8iITCbRQnIiMxmYezEBNexNEfx9n/TJ4EJLmuLvmR5XjfLZabdp8K+WPB6CvFnGR49jT8/30fdpFYmXU9DSwX8SikRQ8rzptl39EFWDV3Njt3/C4CBwfVoubVYPsG8RbTcmB3XrDX9XcjSOqoGZr5yKSBcnvVQ4nk4xlnbn45jILni0qsDjHOWc3qGfpn7YLo5OoOBkX44VL+/G+VNToiA53mK53kqqocxfRYrVrGWYxxgF48tTEIUoqn5LOVTFF0Xa32ibfmJXWVhvx888WaaxGx/JksTlN52b2D3peeKzGS+MbMQ+hDF/fF9rTbbDJjlRxTuMGvDvajbVNgXC37RIXSY19Bs5ljO8J5Emr9WoQWjIYoc7RVfhN0Hv8Hug9+Ixi0b3tyeU7JrgoW2BWt8Sllkm2TVleBBolAgyLNbCMgg9X2KVrKOvexiL7u4hlvaXFeOxYxukzc5IQI2sS3KGH8Hb8aVxf22rGQ9Do8yzdTC3xKbIkX1zGexNsfS/oQ5xhr5FNU4Z2ONsX4ZQjJjj/dBHE3GHbK32ttb9NN9BtEPqq/ZBMl+K+rEHWo1HlE32vQvFvyCGEJkCLEK4mv0JyKOxtqjFqGuoAUJ/MCMKc51e1g+fDknTgdxJe+66UMBaWrDV0kdp6l2Ko3E30/4UB+aA7G+H47xT6oSk6ImPkUbuIy97OIw+9iq21uOXJ1jcaMb5U136btmic1chWu44YN8fWFkjr8AOOJEZr+Q6C10hFGs4633yS35QZMfP22H5+HuM/XjeEVp8pAVV8Z6ak+cZ11vtcXb74Mt+pEvESSPmiJDBrFfkSbr0QCyTWMmZUgzdFvk2IsFvyhoUczRwS8IfsHBN23VHte0O6greGU3IDympOtxcVA3mC/UGF1/zbuj+97/5Ec5N30i6puTUnJj7ZQribqa9QdkzdQdJyZZofap4MaaokIhIkW4Jk5RsRBpWMtSYYRgN+4hXurI55dj7tGaD1F3yZucEBE4Vt/KGwDw8HiZFzu8ogvHZVyDIBxmH562l5TyosOKUwTUjVOEakyKQm1RdK7xWDv+kO2QbY/1U9dF7fFYsY8+FuGxNAWhBsEiP1Hqjug+JAmSTYaiviQzinKitfM2mm2w7ZQcAao9DtWKVXqC4vW4eD0uakiR1+tS7XHxCwEJCktIiuzil1y8soNXdvDLLl7FtPeU2H5TEGjbcYscOrUTv+Lil00pOY1LURr3F+L+cG1+ya1b1Cp+qYCGpVwI4i2VS0EplZByKTNO0VZuBOA5Hs9jEnUJmsUh6kZ5s/BXeJHQI328glej+BzhAFM62eklXRAKUoy0RAcWC8Gz852F5zYpiobZu87sc421RGFbeox9TI9Lt9UZm85tFmqJbL+h+q8xRYaoJUMJiNSP21wzd/c9sV0s+AUx2iBMEaMlyii2NqnQpBQDjYxvSqidGRzdzBVX/QSTk6fYu/ceqo7G1xitUt3iNOl3TUJjUzRMUhyWaJ6gbr+2IIlxQKIIj6GmyA2OoaZICkZjJA59MsgQQQylUxzr8KeZ46KgC+VNTogsDMsoN/NaAKrMcTbpi4wpnYhyDO3myUX11GaTosQxbT4L4Wc4YKS3FWeRIj/FYGxtUHosKeKS1ZZ1b9tvKMNMFl3vW+cZcqOVSNWhTb+bBNTFgl8Ii018iEs9clQQfJfs4gTHTKLkCqs3vpIrrnk7AOpoRJpaIlr1iiFcfsGJSuTLFJYUMbLJk1/AmNViUpQwn1lxikJS5BQLTDAehS95jO908qPMcZHQjfImJ0QpDBJkcr6ff2VGFy8pep6neZCvR/WTLBK/qGhLfsqniCY+RVCrJfKThKaGFIWoF5PFum2c0qP+mMS8dZyok4EaU9cT980u271Q9Z22So4AAbkhVWLSU+1Jao+qPU6i3ytbvjluWCfWDhVCoiIJkrTiku24bol77/7NgIyEhCat1bFKkNqlTgkJlUNUvKKTmM8vOXXJU+CrJglS1IpP0aP6bR5nR/R+TupEBz/NHHOBprnMulDeLPwVXmSICNdxGxDkM1us2Mp2lhFHvv0+T3RwNW2ijk+Rel7Ql+VTFOY0g6STte07BMl6VJLjmuY2S5nNQufqOGVH2G5phkK/o7TPUIJwhf3mfmFOszbIkaq0VXIE8CpQrQheRRLHsPgFaus9YoqT2e+VBK8czOWVJaiXBK/kUC07eCUH7S2z7VU/B8B4dczyOXLwKk5wXbqUatv8UlzU8iEKyU9mvRw7jcfFGldy8cvFlnyKbu1/C73WbtaXeK5TH2WOOUIrucy6Td40JUQiskFEvikiz4rIThH5gGn/lIg8bspeEXncuub3ROQREbnL1DeKiIrI+60xHxWR987Da7pgFKXEBi7jFMc7vZRZw5UCN8qdvI63sYJ1eMzg18lcvWCR8CnSRHuNVift8+Onjml/orAtPS5h7kppcGp8h6z7Z2mJrHqmf5FNhjLH26ql5u5JwZTSVmkF5vv9lPmuP2LalonI3SKy2xxHWpqs+b06Im8CU1FSQ6R1tEX1tElZu8xs36FobDE598DKTQyv3srxwztjTZIxu0VzJTRB1GiGfPteTmodRlMUzWlpjzRj3X7BMeaz0AepuU9RuTjAqwbezut7fpp+hjnH2Fz8OeRY4Fjs8iaNVjREVeCDqroNuBV4n4hcpao/pao3qOoNwD8BnzUL32quuxN4nzXPUeADIlI/ffICwgYu4ygHFrXZDAKN1/VyGyUqHONgp5czK9Q4WocINUjNnKYbmcSyrmuw7b6Vsc2Su9oEKDqv52Nkc9imkarn1ab/GvN9f4Wpfwi4R1UvB+4x9blAR+SNWr4/aWKUJkjajCBZPkSB2czUC0miFJ0XhdVX3cnRPQ/iO17SdFZIjtUswmORnpD4JEhPilQ1Jk/BXDYpasenyC2WubX0Rs5zljN6ao7+JHIsRHSJvEmgKSFS1UOq+pg5Pws8C3H4YwmSZb0D+KRpcok3KdvvwDGCF/KeOVn5PKNX+llhIrCGmNSJReWcbGMzVy1ONXbKp6jG0TrtZD2bXWcZ89WYzaDmiEVmEjvN6kCsuRJO1Jk+RrP5OxM832mrXAB+FPi4Of848GMXMlmITsmbBOmxSIyaNq+cJEdeJUmQvEqSIHmlIJhnSJC8kiRJl5skTwNrr6BQ6ePI3kcikjQxdQrP1YaaqxrS4xrTmUV6vFKS+Ng+Rr4Lnu1z5BKYyyxS1K5PkVMoscm9ipfYfcF/Dzk6h+ZxiBa/vEmjrZDMIrIR2A48aDW/CjiiqrsBVHWniPQCO4BfTU3xEeArIvI3s13wxcQWruYB/pV1uple6WcHXwbglfp6+hkCYILzPMfjXKPXU2ThRoRezmp28T1O6wmGZXmnl9Me1Af8KCFsSI7E8YPH8DADR5jmw07w6pPMeRZGo04RpyB7pWE0dXObQd3cZmIlgbV9hiBp60r7DIVrICNOkR+f2xqkhm/V/NjpFfhXEVHgz1X1L4BVqnoouKceEpHMVO0ish54J4GcWAtMAE8DXwa+olrfjnsx5Y1XISa04dG340sFZMEe4ztEeejED8gFGA2hHxANryTRHF6YbsMmzpb5dd2tP8Lzd/81w1fcgOuWefzzH6Z3dAOb73gnPcOrUd9n4vQRTuz8Nu6qmxhcc3mmHVUU1NaLqeKVUv2p1B/h2iKTXclBPI1zoPmK4wjqO8HXzVNwJcj3BjW5z9Y6V/LCuaeZLlYpzSzuyP9LFQOjQ3Cg8ZhukjfQBiESkX4CVfUvq+oZq+tdxE9rmAW/nwyo6h4ReQj46VbuObJ+7hMFDq5q7ihmjeYmvY19PM1WtvNGfpydPMzL7KJCL5OMA9BDgeWrluNKpcPrrY8pnWSUZbhUGZHBOZs3jfmcd5oZJjiHj48jDj1uP2XpQxwHT6u4TjF4og1NSxLvmpGoTeICDI5WojGIE5kAwojRQbZy4zfhSkB4HIIfCgme+gOZYLY0m7hBQ4NF68eukVao1sfIjoYdYh5Td4yGdnqDvzACyMYdqnrQCKG7RWQXLUBEPkag3fkS8LsEZqwKcAXwJuA3RORDqlqzT/tiy5vR/mINUUmYK33rM0qPs8daBGeoXMAvWu1WX+K1mr6RkS1w8AaOf/NP2fymn+XmH/tZDj3yVU4/+knGB5YzffYEAKtWraJv/Sg9gxkpMtJ/JlmEqcE1gxXzk+Arjh+8LmcmeEgQTxHfx5kpI76PeD54wEw1OPd98DyYmeHclMfqsZUUyrDcWcXgil6ccgXpqaATcxfjbXBlH055fuTudEmZ0LMo4ODQKwOUnAqqio83qxRPgyv7cCo9c7/e1X04vb1zOufQisaEqJvkTYiWPlERKRIIp0+o6met9gLwE8BNrcxj8DvAZ6B5sIpTB840GzIrtDNvRYc5YP69Xt7Oar2MR/gWy1nFCY6wknVs4RpcqSyI9dbDpI5zjBOMM82ArJqzebMwH/M+p4/z5IHHMvv6ZZhzOsZydzXbKrfS5w4HHU5MiIAg35mTJEQ4DqePTcbtRv0f1tVsRdaQEDlOpD0K4rhIQIjEIkQACqdPTgXnaedpSxNka4VqiJDa12jz3WYKXvsC6rhlp8+eVvWgOR4Vkc8BtwBHRGSNeVpbA5lxHX5fVZ/OaH8a+Kzx77kk3dkJeXNYpwNmm9YSpc5tjVHDcX5g0jo2MxOs3SJEETEirofz6lU/wIFH7uX45/+SK975QeTsefbf80mGtixn7MABVlz/avpv/UHG/Apnp60I9Fl8uY72qNEYdeDUVBXxFacaEiIfxwM8xfEUZzogQ+JpcJzyEN+Hqh8cpzzGxid5+chB3N4XKfWvRioeY6c9mPJgwmOuID1m3jmEqvLw1Od4cX+2ya/PHWbcO8Pqyha29t9O2W2diEivz9iZud/cIqNlxsaap9poGao4lSbr7BJ5Y6MpITI2+78GnlXVP0h1vx7YpapNFGsxVHWXiDwDvAV4qNXrOoWCFLlLf4Qd/Aun9BjDYoxFSAAAIABJREFUjLKJbVSZoY9BTnJ0YSdPNahIL7fpG7mXLy78VB4ZGGYFV3A9I7ISxylwUPdwWo9xRk9xTk9ToMgJ7xA7zn+O6yuvZnVpU+MJGxEMTbujxIiy3mfMI54GuWnDZKx+8rrAJCeWSczSAPmYfixNkZ802zVBYFWbWxW2iPQBjqqeNec/CPwW8AUC/5yPmOPna9aTIZzM7pANqvqkqk4Dz6f6OyJvgp1fPjIjEXnRouJMCaighcDE6UwH/X4hGONOm7EFcMx52O874Jfi+d2p5P0cUxc19WkorljBZf/7r/Dip/6Y8bHDDF77Cob2P0vPhk0oHtPjp3CHBpiZngnuHc5XAGcmPgdwZ+J7gVkfwZqc6ZgceSWiufyiMNMvFCYCE6F4ild2KUz4iAfqC17FoTDuGULkoJUC7ngVKXiBKa1cZKiwgduL7+axw5/jyuU/DD0CPR5UykGZNC++Ug6Odn3SeqOa1QcHwCZE7V6frvdUYHyC1X1X0L92K/2VVTA5yb4zj3O2epKxyYOc904jOBya3M2hyd3cuuadDJfXoL09yHgcfymrTsW8D3X6265PBGvXnhK6YgQZN/Xe4H216+F5S/VyibMzjbXS3SBv0mhFQ3QH8G7gKWur66+r6r8Q2Oo+WffK+vgw8L1ZXNcRFKXElXo9j/NdXFymCf5w7uDNHGlmZF1AKEiBAR3hFMcZZVlH1vCiPsMIKxiRFW1dt0rWUZIBEAcR4Qpne9AhMM45qlKlzxlix+Q/88Tkt5jSCS4tXxXsivE10hIBtQQjiwD5gY9EiBoiZEHU8iPyDKkJzWNpbZAd08j2EQrnSuRWI/BXahnzEg12FfA5Y3IsAH+vql8VkYeBT4vIzwD7gJ+suyqRbwE/Yq5/HDgmIt9W1X+fMbwj8sbvMc77Rev99sEvKJG/GOCXzOfoiyFN1tgiCS1TtQjVKLN82B9UQxIU9gVzB0e3fy3DN9/OC5/8f3B6+vDOnGbq7AlWvPqHOHHfPVR7gj8Nv0RCuxTWo+WXkvN7RevetuS36tUyeDPguxJohXzBnVF81wm0RZ7iTCv+QAHHC7SazrSPug7iBxojZ8aHSoEebxPeEWXcPc/I8Gr86UIc16sntfnPrjfqS9W1XERHBjL7VZVnD36NS5ffTF9leeb1NXUR6CmxYcUdnDo5GbVdtWZLNOfZicM44lIs9PLtJ3+fBw79Azds+SlW9mxFe5JmzJr6cBm/Wqjf305dBL8vID5aKeL3+tCbem12vVFfWLe02X0rBuAZGqAr5E0CTQmRqu6gzuOyqr632fVm3F7gGqv+BIssKORKNvAMj7KOTUxwngq9nOU0A8a5erFgnHNk69fnH1Wd4UWewcHhDn0zZWndln5GT3GPfp5X8RbK9ID6ETnqk6GI8NzR86N8c/xT7Jp6kAk9x5W9t0b+Q6o+gtveolvUzkjMcxA0JkMZcYWyTGPBGGuSWWrV53oTpKq+CFyf0X4CeF2L0wyp6hkR+VngY6r6n0XkyTr364y8KdW+4eoTkCHV4H01ZEeVyGSmEJES1dChPiQdikdcDyaNTapQ64sUnvdfv52TO+5h+NV3MP78c/RfeTUTxw9QWrsOvwS+R2KezB2OdUxmNc02iTLRuEVBPfO3KoJ4AVlXLzARiw9aDZ2qHXDVEKTAlAwwMzPO9MxZ/J4ifrmI3+vhl13c8ZnMj2A20EoBvyfDlwo4N36U/Sce4fi5F7jtuvcFPobNYL7rRyde4N4nPsFdt/w64jiJRMt9fbHV5dabPsADj/4hj7/wKa7Y/MNsWHdbw+m9ShGvd45MZhKb671yAa/nAudNySqv0lxWLnZ5k0bu/t8iClIIhA4Ol3ENIBxiL4MpTYuvPqc5xhgn2STbOrPYBhhgiJl5ytM2rmepaP0v0fe4F4A1bOQhvsFavZR+huhlgD4GOc1xzjFGmQqTTOBR5SgvM80kq1mFYrZ1ZcFogYpS5urKHbw0/QyHZl5k4vx5ruq9nZL0xo7VIexvc6gRaoUA+QSbvdX8ADhJUxhhFMWUjxCkyJBNhMI26wlNWyBi6ZfjL8zw+AVj938H8BudXkwmCrU/JsFnZzQ+BR+ddg0xIvjcwXzOYsiTRoRHfNCC4ovGZAkySDBx3WpzVwdypbB2FcuuvILCwADHv/A5+q+9wUTBNqRreprzz+5Ep2cYuvGWxq8x9eOVFSLCLxrNktmoGf69igvixZsIHC80qQVvkfiCeoHjNYZQSbmPnt5RJmWSao+L11vAKzq1341Z4uy5Q1RYjl/ODjV1/4N/TH/fGsqlAR7c+ResWbWdSmUZ/X2r6OlZxrHjzzLjTeCIS7U6ydTUGY6feJaZ6gSrVo1Q9SbxKgXEaIqzvo+Vyho2bLyLs2de5vt7vsL56mk2bX4DbqFcK28Ar+ziVS7wZzf8Uwp9FwWmhwtUJ2fvS1Ujp5SmhKgb5U1OiNrAKKt5iec4x2lOcIRhRllL7Ksyoef5Ll8BAuIUJjtcSOhlYN4I0V6eYx9V0BKXc13NLoyNbOUJ7qNEmeu5ncPs4xD7GOccE5zHQRhlLVWm6WWAKjOMsgYHl3Ws4RRnOM0xVrGh4TqWu2vYrY8yrZMcndnL0bG9rCpu4obB19eaz+xficwo0xZJsslS9OOn0Y+CvVVfVCPzV10SlNIGtbKTrBkWaALF3wK+BuxQ1YdFZDMsrCA1pd7GWgv1BS16RgskETHSUGvkC/EvVVD3HRff8ywtjiFLFvlJa4yiY18Rd2SI41/6Z/yzZ9HpGQqrVjKwdgivx6da9Jl6aR+Hfv+PAKhccTm9d94cz59YfOO6Pb5agBmzu0z8gPA4MxgNUehkLVGfGJOamEhQoXYhmNOlPDTKeHmSmQGXKb+I+IpXcVLjZocnH/0ko0d7KA5ex9rNr8Jxkj/gl13/Np5/4p9Ye8WdlHuGOPby45w8+RLnXzrC1MQYpXI/AyOXoupTrgziyTSjl24HhPXr13Pk2D9y2jlB79Bq80YJWV+voc3X8vK37wP1eXnfd3l533e55NofYt3W19aMnRlwmdJZ/uyGfypO8hhoiBymls1y3owQIeLD9FDz+bpN3uSEqA2sZRPHOcylXMkJjiAIR9jH/8/ee4dJcpX3/p/3VHX35LQ7Oxtmo7TSKuwqIAmktQQSAkw20SbaYIN/FxuDL742DvfCNQ6ysTE2tjHBxjbBBgy2SQIECAkJoYgECqu0Wq1Wm9Pkme6q8/7+qHSqunumR9rVjPbu+zw9XVUnVnXNqW9937SStYRqUzB0Bs9gBWsXeLaNRZCYaTn2sonzOcSj3MXt7GI7m/WZDEkGXqaYoJ0u1nIavpTooR9VZYoJjnCACUZRlIAq+9iFJaSHfip0cIQDjHGUKZ3goN3DUrMiGzhWnyXSbrq4vOsX2Fnbxo7qPUzZMfr8QabCcdq9boQCuClKy2oy167IYYui3bm9xopG186482WH3KkvFhGR1wHfVtUvAl9Mjse0+KsWbGINpFKa3dEgtE5OJhwgFIMjjd+UU6BkBREbxcqC+EHjgJ8CINLCAwkV2s85k/Hv38TSX3k9Bz/5OfwlfUz8+MfomadSGzmagqHB//EW2s84A2sSNqr5eTQCIK5hrBqNVGYJ4PGiaaeAKGWEsmPRS0DSv8Zqxvg6+IbAt4SVKMebaPLCEJ2jhI3n1Iqcc+VvcvD+L/Lwvd9g1/YbOO2Zb6B30LH1GdtNz+ApDKw/H2M8uleehqplcnQfIwcfZmbiCGoDJkf2Mrr7EUQMnQPDlNp7OLj3KEEww5GRh6n5AV1L18SMDHWgqH14Pee//k/Zeet/M7p7G9OjB/B7+5linFJ73r4paBOC2hMEETlmyJmHRIFDgyeiiYzvy1zYj3g7qLTgyHGCrTcnAdE8ZJCVrGAdPURpVI5wgBKVeDvyAlzH6aySOTycFlAsNjIAPg5ixHCanMM0IfdzJz/lZnbro/QywAiHmWCUc9nKDFPs0u2McIgRDiEIvSyli148DAMso5s+KrSntHO/9GDo4CfcBBaeZ36+wclFaq8kQOPy0gZ2VO8B4P6pW7h/6hZK0sZF/S+l23eMLFtyac/YIiFmBEwODpGAIqA+qCIFMETzY9kFnX1KdVNEFhuFvRb4YuxG/13gauAWXYTh3stzACKNUw+kpJ416X4EimwOKFkreBLiJcY+TtuUWSLbr2OKEAbe8CLs9CTtF58FnwQ7PUV1924oWcZvjmJVDvzSq2l/xqYYfNucrVP9STQ4nOr9kmlqZDhuIxVZXmUWfScqMwyYOCBl9kB12ihYGxD6mgIiWwZ/CodNKjBm85G2djY+5y2M82X2bbuee677KP2rN9O5ZDVHdt2NhiGnX/E2Rid2M7L7fsYP7mD84KP45Q66lq2nvWcQ45fpXXsW7QMrM/AiMNDlM1kZ5NHb/otyZz+bf/4PYhAiaZ28CEs3b+Xwjsh2f/sNnwWgY3A1G678Jcpd0TPDxol55ysJCErGdvdVIvASBPPrt5n9WvIdVuaY0wm43pwERPMQEWFIV/EDvs5qTuUxHsIS8B39D4YZBmAVGxZ4ls1FVdnFw1zAc47rOKvlVEpa5m5uYYoJOulmKSvYxHns5EH2spPlrGGIYU7jHNpoYN/TQJaygpWsZ7V3akvzKFPmZ7pfzbQdZ1LHeKy6jf3VHdxy5Kssb9vAmd2XNW/c0POMzHaIDBQR10xAUapec/4Nm6rI5hFnqJX/6sWENFT1KuAqEekmcpl/K/APInIf8E3gW6q6byHnmEibPzsgavNrTNYS7yUhtFGyShcoJQySVcFawSekpLGLNfUgSB12KF8WHy8Z2k5Zyf4/+ShtZ6xn+r5HMAM97P79DzGyK/Ju7bz4LKSUhkxPwVXdI6Buv/5eE40AkYaaRuaWEBKQIybqxwipwbValwmNPhJvV0cOM/b4A6y68rUE5SgaeOKJ58ZryjGn8xStGFZd8jK8zk523341QTDFTHWUZWdfRseSlTx651cZ2/MgA6c8gyVnPIu1Q7+QAp8cCytRjMnk0tiyoecZz6J/ZA8rn/XiCMQUWJniJfSXL2PTW/8P1ZFDzBw9wP5br2Fy/2Pc9+W/YPD8K1h2wXMJyhA8kTiSDVghBztT64JwPtFUkpc0Zz/H1CkctjPMJSfaenMSEM1TfMqEBHTGaTqWsZo97ABgM8/iYe7mbJ65gDNsLgE1PHz6ZOlxH2u5rKFHB7iHWznAbtroYDv3sIK1PJMraZP5R1U1YjhTLpwdPBWAhVGwhEzbCabDMU5pP5917Vu44egXWVZ+jH7Oytohde72KQgiVpHFb8aoRvPQJBijRuoFzRatIgiqz4GmWUqQ2cSNsj2bFB+si0TinGT/GX8QkTOBFwL/CrxgAaeWytL2iTnrdJUi2zurQqAml7AysCYCQyQASegJoSrTaRtwbwdJf6tiWbQdHZvuVA4/vIsVb3sBe+57hM7TVsKuKADf8l97ORPf+h5L33ClY7SdADPnXijcE7OCJfVQL8TGDBFWsDWJmM/UpkhyIKiR/QkKU2NjtK1YDasHCCpQ9aKy0AkXkNRNnvDzVZ/VOoSaGJZc+jw6TtvEzm9+huq+cSZHdjN98z6WnncZp175cvz2rvRSJBaUdUM5lynoAPE6WfmKNwJQTRiZgtqqninymZmYpFauMTNygJU/90Y6Vm/gob/9AJ3nn0tf13Lmk8mkDoA1AmQCtgLVvlY7jZu5v1uDYz3dc1JEJ9x6cxIQzVN6Y6+ybXFYk8jTrJ8OuvgpPwJgtZ5K7yLMFxZQo0RLyb+PiXRIFxfoc5hglGkm6aL3CQGhJyJpXjPgronvM24P41HiwvbN+FJixk7Q5ffP3kmS0yz1p49dsGO1mMsW4VbDAUPN2CBoDQzNU9K5LTIRkS3AOrI15xFVXRRgCKDDn5+jQWA9LBmgCWIVWgKIrAptUqLTm0kBEpBr4z5MMlCUB0nLn38Gez7+TfZ84lsAjP1oG8suOZeKnWDv30Wx6Za99Dz8gT6n34ytAkcL0ujhVUAFxlpEwyjOkifR/SQm8iCzEiWsjZmiDABJ/oEaA5sgmEY62qMwAWUIG6Q/SQFRAobmad4YlkjztJVXr+aUX/4dpvc9Rjg9RdvQMH5HBIRS/6viJagn5qL65ejfv1VA4sqj//KRaD6DQ3Sdfz7Vg/sxbW1If1d0HeYB+mYbM2dDVHbOcc5OG4BYV20Zl9sWHhUn2npzEhDNU0SEy/Xn2MlDPMK9LGUlmziPZSxlgNXs4H72sYteFh8gmmKCMnOg/mMsIkIXvWky3IWQczov57bxqzm945n4Ev2XGzzGg8NgHePsBAA1EjegonMolSQBLMTJX7U5KwRPOM7QXLL4rHMgTq66BbiH7MwV+HLTRk+xdM4TEIUpcDEOY5QHRR2UqfnVHJPkAqJG3+62qkCf4ZIvvYMdn72ZPV/5MUMv3MKmt72IZaUqYw/sY+c/Xcf0Hfcz+KLz41suU9tFfeTBkStFDyFVKNkQjxAbmuhhF8bhBbxIp6SO6iyRxJA6xxAB1YlDeL3dhG0aAReNAlv6U5IHQZADRPOxKbIlJyYTAIbK2rVZOTQFPRTK3G1bABhz2fC4bVf+j3ey+6MfYejNv0RYAdsRPWanju4jXNLVOnARnR2ESVbP+hZrWlhUXFu1JmAoORZW5v4RTrT15iQgegLiic96NrGeTekxEaFLepnRKSYYW8DZNZdxRuriJp0oUtMZQkLa6Kwr6/L66PR6sbE9h4hhZWUj28Z/yMZEZeaKo3ZLGSEDJKk0ok7yAAgHKCXqhWYqsqTfnA3Dk3/TUhYnhQ08S1XPXOhJzCad3uz2Ej3+NKMNjD+sGkIkZYwSoBRYjw7KhP5MDIhMXH92YNRwe6nHlndtZcu7tgLQFSgzJqBzyxLue2gvncO9dJTOTvts92uMVyspC+Uag7tju5LcN6UwoCQhoRfd9zY0hEL0IA2jeEuqea+kiCXQ7CEbl8/s20153TBhm2I9ixWNglW6LJF1VGXOd6vqMy1F/5az1mkCehrtJ3VtCWqVOHVLRPnmwEgwOgLlEl5HRw6wAFROX4fX20sQTOBXFG95P35/HyM/+gH2jA3YuU4sLi8yUCpZWR4QgXaE2Mk5+tX4Tw4AJeuccyzeP8IUs8mJuN6cBETHWLroZT+PL/Q0GkqNKt4J+pM/Fj7AQ8FdXNz2Yrq9PDs3FYxyONjDmR1bsRqya/peds1soyzxA66RQbMDYpI34CjTvab1JUkSG8brShygUVQRtdlKXVSbNQM/T9LlPuettLjkJhE5U1VnTQSwkLKqcnTOOt1e4yztIUItzn0RgSJDTT0qM0pPeTQ+bhxWKQNIbh9JWfSdlbsAJlShq2ap+JNYFfo39OGNjzLUMZ71jdDh13L2TW4/zcAXQHdNmLABoRVq1sNaoVrzo3op2+Q8CBOw5RzTOGhlWJug3L8C7QlQMdgwQG3kEZU+gBOVSwEgQfKQnv1+rpWUWgtMhouuZu0yLgtKFhvaSG2U1E/j/igHPv0lpu66h1V/+X/wejrTtiIw/cAjhCMj+BuXEjDG6LdvoLp7D6avE+0MsbaJ9XMd2HH2ARFtsg1Gynil5iynOoAn8YRMt5NyN46WQm/HHOYNJ+B6c2I+HRdQNrKZXTxMqEFdYMKFlhpV2hswKCeCdEqkkjMNbumKdNBuupmxk9wzegNGfAwep3U9a/ZOVSM1WtRx5lkGdTnG0jInDlF9lvvjzy/rcVLFPUn5F6JFai8wQ3y5VHXLwk4rk64mYKdVsUYIMSljVFOPsi1T9qO37FBNCkjCAlsEEDq62oxFyo65YKpDy+BPEyJc+b6L+Oo7vkdvaSplqBLVXdSHpACsGTvlSrvWaNcaNevhW0sQGkTUeXDmvercfupiM02MU1rSgSmHCCHGDyKwlD6QY5bVAVUuQFKV2JCn+XVX32Kb6Z+bMUO57ULnyfC+oskLTQI8TAZQSquWMnUXSJsicdqXBKS0nTIInoedOsr+D32G0qpBpFyi7+WXIuUA0yy5tgNwEI36S/apB0QpQSRKCUOZxoGIkthZyXa0kQdDRQN8VTDluZV7J9p6s7ie2CeAJMoTM9+cWU+BTDLGUpYv9DSOiwyaVTy78ioqpv6tZjQ8RKBVDtR2UjbtnNP13LqotilzozayGnWiTgOpAXROLdYUFJFXmSX9H3dpbCuyCOSfiBO2ctysp56cdJvZ1QOtSMQCRcCnqj6+aaPiTQKkYCmRjBEyuX33GDjgySkv2QomBlrTdor2bp+e0nROXZf0mYKhFIzlwVE292i/w9bookrVegTWUPM8yjbMGYGHNgNWjQBSGDNStb0H6VjXT6kSUMLD15BKKWBqupRjk9KHsyYvHJIyRuoySA1EPUW9uQFRHenqAqEGYEmMjVheSIFJxABFoKT/lZfR/8pL8boqiATpGCLK2G3b8Jf0MPq179N18Zksff2Vafc+Fr+BFVECcDIQpOmcTe5YBpLc4+1q6JB6hsgFrdl7WWMPRxf0KlCqzOXHf+KtNycB0TEWTzw89ahRfcoNmOeScUYX1Lj5eIoRL0r62kBEBKshE+Eoy8sb5o555IKiqINoxQgVPJOqxVJQ5ACnHIsEefuhRMU2H5lv/UVo5AjsVNWvLPQkZpO+GLg0kx4zzaidPYBMohYLMdTUR7x2grjfBCxldV01WAKK6lmi/LFou+S1UYoBkayE8QOTdJtJNI7W3uVNc6TWmbYN4vyCoWYAKQ/Asu0220aXzFC1PtXQw2pA4DBaVrOwAinIsll5ApCCiRns6CS9azpBqlSsRztVKn6YA1Ap0EreO1IGKYkA7gCmBiLGIk0AkTRhf+rKGgAnIyHt5RlqNa+OoRFRqPgxgAnSsgSkTJcVYyB4fB9DLzuP9koGVCpqaG8AXJK2ybwMjQFRcdvEF27IC5gJ80CrkUejy+a5YCip69YLzWjdPOvkBFtv5gREIrKayH9/ORHa+riq/nVc9k7g14EA+Lqq/nZ8/IPA5cB7VPU6EVkHPAL8hqp+JK7zt8BtqvrPT2Tii1kU5Xq+ypW8eqGnksqMTmEJm4KGE1km7AghAYeCx1nbFhlRq1pETJ65UY18g5PIcyogJst9ZkzsOxzti2cy+yHI3PBFIvsha5vaDM07cWsr9ZXF6ga7TUQ+B3yViMIGQFXrvD4War1Z5bdgQ2RaU6tFbJAwEwp+eX/MGtWzQ00BUlw3X54ci9RIYeUwVg26TJkeDfjkq7/LH33t3JShSuaazMVlqGrqMkhFt0oP8aYJrJfOM2GcWlXJBWo48ODj9KzvZ1XPGFaFnjDEM1ME1tBZnkkBVRFMFQNeJgApHa9wf1eA9uyWqgNB7r+NqSvTJtvQqcqMqdHRVk1BR5GVyQGYFJxYJg7uobr3COIZVqyC9u7MyaY/DPEd8G0cAGQcwJPbxj1mc+MldbprXayN7ciS6+kCokZG/UVbMtfA3yIMVjq5iVnkBFhvitIKQxQQLTR3xBEgbxeRa4Ah4OXAFlWdEZFlACKSuF5dBvwzcF28vx94l4h8TFWPT3bRRSIrWMsuHqamVUry1MX9mU12cD+9LGkpIvSJIjN2ih21e9hT284l3a/gzonvNlSpAXlglHqPxTtJePriG6fLCuWy3UNqmTibHJeo94vy920nWpie7xxTGrvBLsh6023mE+Z3brEKU1KjbGZS1ZUrKSiikT1RPXvkAqqq1wVmMn3grVxXZtcDU/SaCUSEagx4IiYoYq2sA7ISw+/i2CEGvGlqrt1TCqYknYfLNAHU0oetSUHTrf92OyvOHaSvPEWghq5aCetP5wJaFgNcWnUAEtm+q5YLCwCuTT3aJbOdKQIiQ2sAyW1nRGm3Hh1eNQdKpAFgccsndx3m4c/cztH7D/C8z7+B77358wws8ymVMsDWKT6hP+P0YetAUVRm8eoAUVbXI9+2QzxKpYn09yyqYouG/K5HZDP7sp5SK2YfT/v1JidzAiJV3QPsibfH4jDYq4C3AVep6kxctj9u4hGbypG/WgeAG4FfBD4x17hPZ9kk51HTGe7nTs7SOSIrP0XSywDTzK4WOJHkULib+2ZuodPr5aLOF9Lh9VG105SlAUOWGE8aZ7EVceISxcFXEvbILYfGBtfzsRk6lvfHIqSwVfUt86i7IOtNnzm2/6OhKr5R2o0Sxgg7bIC5wX1o1R9rpNqqminwptJjX7h2JW95yW6+8ne7+MXfWJIBorhtVRMPuEylZx01nQuYrDeF5485YMmkTFIyl6JHXQKakvnU1GP1Ob3UpkL6SlOECO34iD9FT2maAzNdOTbJjd+UBrh09pN+rWbjJaqdivXp8DK8W2SB6gGRNqxbbNdpPTraRxmP07XUAaACQ/PoN+/nrr+7mVVb1/CCv3sBRix+xaO/q4YnGcvUJR5eaTLuIwI0XiPAI4ohY4MAPOL6CUtEtl1Rn47SeM7mrGi87/5GOcaRehBlVejxW+BLnubrTVHmZUMUU9HnATcDHwQuFZE/BqaB31LVW1X1HhHpAG4A/lehi6uAq+PASSe0bOJ8ruMrtNPJcl1Dp3TP3eg4ikVPWJf7RjIaHmZSR2nTDqbtBB304UuZQGuUXbWh6wpvbR4UQWpMnT7CXNsiyAGnlCmy5J94x0paAVmLaIESkT8A/l5VDzcpvwLoUNWvNSlfx1O03nTNweSWxKOmLYfUAwFjfDpNCYgAUuRUFf1AYeGHSo/Hv7FNyzWtmwCqaQkd5ik69odX9fK6lx5gaU/Ii1/aRsdgWwagUoAUgZrcQzM1BI+OVc00HTEgSh6WNc0DoNDLg6BajpGKHqzlsEpHt2FJOXpIl22JUnmCNlMjLGcMU8ULGK21peCtZr0UDEURwL1c9O+gwCB1hh5Vh8mBPAiqA0iz7LvbnYGH9WdyKqpuf5qpMAFINgbuT4dDAAAgAElEQVQtUZt9P3iY6UNThCMTlMdH6F/TQW2ySq8/iW+SeVna1adcjpicUpQoriHAMWJzQAkyVshzgFKy3YPliJ+B5KLtWSMj/nS7iQdkpRVHgxNovYF5ACIR6QK+BLxbVUdFxAf6gWcBFwJfEJENGsk7G/Whqo+IyC3A61sZs3+4p9XptSw9Q13HvM9G/c7oNMMMU2OMIzxGG2vpZxmezM/77FjNd0Z7acenX3qOab9FOf79GsRhadLtZAEwkeFyPxdzNudzKNzL7tpDbOjcyMruFSzp7KHiZ3PsWeoYyiZGz2mfkj8mznF3P6kjWT89/c4D1jXOptDGrZPuxgaOIvWG3bPJ4tPp/xT4qohMA3cQsTZtwEbgXOA7wJ80avhUrzcHJ499wNKp6X5mYhCV4GPr/IZ5ligGQlpfZnMsEQTVpTHmzgBRbSZgeLjCv/0j3PPjMi/7hS5OP7uNUllyarfU8yy5xzCoRjdfqIJX7UURfCJfWatCKf72AC9hhRw1oBcDphBBEUKEbjNA39I22qs9+CbEt22Ug4BAuxhwAU/oscRhKQKN5hOBLwjVy9sUxWMkY/eIT4eU0usjTcBQM/UYOABKs+1OLTETRC4oJg5m6AU+3c5+yhqhvOHPns/k/knu/9ZjbP/XXbzgd7ewatUwQ7YMKjHYUcr4WFuK5+rn2SHy6jMXKCXnYApgKD3fag9LEyayoOJMrq0LevL3lMMQOoCpQ+Z4/p5A600iLQEiESkRLU6fdQyTdgFfVlUFbhERCyyNJzGb/AnwH8D1c417ZFcLVu5PQJ6KfkMN2cWudH8bUYyoAYYYZgPLZNUT6veJiNWQH/Adhhim3xn3aXl9xQVE+W0gB1TEGNpYyuHJ27nOfI0DtV2s75thysGkIsLRPRNZu9S1w+T3XVBUHKtRHWDkwHTeGDpx5U/qFgMxuuVxnVxZK29jx+GNTUQ84DbgcVV9iYgMAJ8H1gE7gNeq6pG6qaj+N/DfIrIR2AqsAEaBzwBvV9WGr6ALsd7UKrvn6GZ+EgU2t4SVvSm4CQvAhsLxRuqxnD0RBg8bsTdt0WmrRgCkb6Vl164oIOzjj8M3Yh+bZ7+ok1e8dQmbzu9M+3AZIVeFFmIQSlTjYJJFeyNbYIxcVZrLNowfrfH1j93M8357C+OlftpMDd96TJVHqVmv0JeX83xzI35bNQUPN5PaHEEMEAJlzM+MiesZINu8rCl4sohYpkpjdfY7RfVWZudjKa9Wzn7TAJ949Y+Z+JPDjNYOUquMABELZMXiS0hQiQiMkoSERKqwKMWIImnwMoumgRljhihhlEjCAWRsEVhoO0gYR2JLfk+PRC1mMPHvE13RPFCuV6sZ1F8YRvqpXm9cacXLTIB/BO5T1Q85Rf8FXAF8X0ROA8rAwbn6U9VtInIv8BLglrnqP13FE4+z9ALu4TY2splBVvFDvslh9nGYfbRrF6dyNkMyfNzn8hB3AyzalCLHU0SEc9qfwyO1n3J+1/OiXGbO00gbxQpKQEhx32qU0ylN6OS0c4GMm/cs/tZin+4ck2ZubCMndUha1oo0cU9+kvIu4D4geWV8L/BdVb1KRN4b7/9O0ympPgg82MpAC7XeHLWlZkVPSDyUQA0zNgMPrtQbNDvAp5F3GRKpTLCEtkzNltOHc4jQt8Lwpl/v59N/e4T3f2wVXb0e7/n5nVz3jQmu+8YEG85u5+3vW8nG87pzAAgcrzM1iC1RtaXc/NyAk259qyYHmpJz+fLv3wbA+OGZtF9TMOROzs0FQwmLkYChtJ7jxWZV8myPw54k4oKgujKKgKlxO0/AlzBXbzYwlKqvKh6v++gl3PKZh3jj3z8z7qtef16SMKf6ggjoFNVnCSPkOeqxTIWWGV5HmXIVjzACQBLNy6qJ09ApoQpGbHRPqcFIiEUykI3BEIVFiEjweeRGO7bylK03RWmFIdpKHORIRO6Mj/0eUfCjfxKRu4Eq8IuqLVuS/jHE6eJPYFkh6+jVpbTTGT2YdSvbuIMZpphinLu5mWmdZK2cdtzmoKppKpFOjr0K8qkUVWWacdq1a16G6mXTxuntz5ylX4tYx70e6u2J0sCNyXeDAI6pPVE9+BFco2uaAqOGkbCTuEdznaimL5PHTERkGHgx0f/s/4wPvxx4Trz9L8D3mWWBmqcsyHpz1M4ejuJo2JGLVVTcdyUDLhU8G8Uia9Xt3gUMRY+q5GEpWqIW95scC9XwuncPcdkrl9C7poeyhPzGB5V/+uPdjB8N2X73FH/whu28869P5ZznLk3bFD3OjPWZ0VJLACg6lnmhWQzTo1V23hqxV/3reqLjzv9q0Vst67s+BEDmfZYZXTd2Rc/b2iTSil1RsY3r2WVDy8T+SXpXdubq5vu1FFVzg2vaefHvbW5oEyS4wCoDQtDIpigDPJFBtc3VTwyvPbGohKQRu8VQohZd2/RtK8nPa4g5OmzKIBmI2aYwBlDR+c8RiuLEWG9y0oqX2Q009617YyuDqOoO4Gxn/y6Ok9PxYpMOyexVBmUFg7yYMT3KI9wHCA/yE1brqRg5Ppdjigk0/kfpPQaJXVWV7/GfdNPHRXLFk+5vPjLJODfptzhVt7BeznhinRSfoVbBKmoSUOSWFUBRzvOM6L8i2S9GtVbyehE0U++pZF8JOHIZITc/GlBMHttc5Im8sS0Vkduc/Y+r6sed/Q8Dvw24XgFDsTcYqroncYE/FrJQ682O6uCc/R4NO2fdLz7I/LAHDWpOXCEHGKTHMiNhV9xgjQlzkDxg/bALCbOYSJnaBDpWG6atMEmFc17WxV+/bA333DzKt//xccaP1Pjob23nqtvW1BnXJt8V2854EKRzmK/b/c47D7P0jH723HGA3jU91NTDqBJYL1KPOe3Ta5GAHqTOqLo6FfLlKz7BKa/ZzDnv/pkcEEq+Xe+s6PrPZlTdGAAV24lYdv3wca7+zet46d88h/U/s7whK5Rc/6L3V9S3C2I0BUUJ+ChLkK/rAJ2sX1sHgJL6KWOEYglBstARIQZPbHZ/SXTdS0lZpEAjxESquxjQRtvRPdfhzfUC/fRfb4ry/47b0SKSbuljCxfzmD7MYfZxhP0sOQ4pNUb0MHdxIwaPLnpYzalPus+dPIhiGaWhIf9xlQ666GGAh+xPWCUbGrvQN5NmKbEdgKQaEcgNVWCNWKBEdZYDSbMsEGk7p/805lETxijxWjt+NkQHVfWCRgUi8hJgv6reLiLPmXfPWT8DzTw/FouMhHMkspxD3Lf3RH1SthWqcb+tRqGOjkt6LHmIJg9GI4pvK2jYllPFuA/zLPBiBGTWXLiMt16wnC//0TamJi0P3DHO8LkDdV5GoRqwPtO21DIAclVcj167gx9+4Ad4vmHTSzcwtHkpxCqYNK5QDgiZHBhKx3HA4Q9+65sA7Pzm/Zz37q11bJAbG6h4HZqrw2ZToWUs0LqLozX5q7/xfX79ltdSKidt8kxNcXyXFYrq5cfwyAO4BAzNBoSScT33PnPAVF26egc4hZjYCJyYFQoJYzVZolZDTKpuS9RqrsqwqZxg681JQLSAMswGZpjkx9zApfpiKvN5wLcg+9nFEKt5jIcAjkk8pA4ixutsLnrSfc1XRITzeXbMrh1DiWLXxytGQeXlskRFmyJobE+UfLtslGuHBGAcNjsFPXPYFz31bvdbgZeJyIuIvDV6ROQzwD4RWRG/ra0gCoI4m9wcq78+BVw9D1XXUyYj4ZP730vUHS6AKYXt1GzE5NQHymusGnM9gRJWCKBkgvTBWrFlbKwyK6pRojEMjRig5757E6Pvv4ePv+VWfuemFyKlyG7KVVthS8xYPwNErn2PA4jcyMcJ2Hn4Gw9z/q9dwM1/fhNjeydidVnERKTnV9dvHlS59awalj1zDQfu2M3WP39hWlZUXbXKCLUChNw+SmXhrd96OXd+ZhvGKLHfXX5syUDJbGDIvS+yuK+aA0NFtZgLhJIxXFbIc1zzQfEkTO+vVBUWXc2c6gzIAaBoDsaxNzKgNndPNZUTbL05CYgWUESEU9nMlE6yi+2cwlnHrO+jepDd7OBCLmcV65FjpKEclJULmpLElxKnmXNabzCnq7rzT281DvPnqMHcftz9Yp1GbWabRxrfqGhUTZ6RwqnTgts9x9ANVlV/F/hdgPiN7bdU9Y1xqoxfJIrz84vAf8/R1WnAlcBbgY+IyOeBf1bVB47ZZJ+kjASzA6KxoI1uf7rhfqLKcOPClExIxZaZCdtmzW6fUx0V/kfdh28pBi2eWNSWCGwWDwfyD97JsEIpjrxt1TAWtkXxf9oqvOyqi9j/C9/nJ9/ez6YXrq1TYYn1UkDUkBFyAJB77PFrH+Lg3fvZ+r7LGDqtl77hjtjmSAklszEqBv8rqgqDwlhnvPFcznjjuTHw0JbAUDMQVKxfrFtkeowoPcvauOx/nhupQB3GxGV5WgFD+TG1DmzMpR4DKJG3MUps1QBEgthDLbYFc0CQBxjVyIDaYYyihsnLXlbfYPEwqM5tQ3SirTcnAdEikHWczp3ceEwDOO5iO+s5g47jEBCyqjOUZXElrm0k2kxNlquUZ4FULUdq+zBi6C0vQ4yXqds8rwBKEtCSjOM1Z4gajasSA6iIlZLkGA3si1p8yZHWqj1ZuYooDtAvAzuB18xWOX5Duwa4RkQuJ3KDfYeI3AW8V1VnTZn0VMjuqb4564zV8sldJ4Jy+lD0XePZODZNV62bcSczPFCnMsq2s3oGxTfJwy+JiJy5fXfUOpmWoKB2KUQ2tuWca7sLcra8+Wxu+dw2us9eRefynhxD0x1UGIkfvEWD5uxY9gHwjWXb537Chb/3HLzOCgNbllMSG1moqBCoh4eXnrvLOIWFMYo5t0Qt1bEZ2vuciNG5uDxJBOm8eimRPGBq7oLvFeo1ihgd1XPiAElebdUMCBVjDUms+krYoXtvPMrAUmHjGeWIKWqiFkv7iedQTllJpUriFeeyRJkNUUnCzG4NJ7J4cl+KwXXDD7G0eXMnAj/R1puTgGgRSBe9KJab+BZX6CuPiYH1DFO00Xqso1Yl0Bo38g0u1MvpkrkfIk8bSUCRVW4ZjYK4nNf9fIY6TmngYVa0JWqw34oU86c5fYlqY4+zOftsvep8RFW/T+TdgaoeAp7balsRWUJkEP0mYB/wTuArRMHSvgisP7aznb8kKRpalUaJON1UDr4JMbbERFjOsSmJ5JmhDGyk/Vg/92D23QeuLeUiJidzgMx4NxsnU3kl+8suHGbfe2/gK2//Dq/8r9fmYv3MWJ9q6M8KgNz6RhQvDJg+PEX78u4csMsC/tm0ftH+qAi43GtjRDn64AFu+J1reP4nX0rHYGedUbW416WBSmw2z7PkemVlecBUBJmNWKF0uwUwlLRPRjRYNAi56i33A/A3V5/OKaf7dWqxpJ+8DZE6v7nN51lD02voqtGyUzVZbCMS1ZpN2aMIiJlUDTyrnGDrzUlAtAhERNioW7iX2wgJMDy5hLBH9ADTTDLAEDM6zT3cyrlcgplnlOxG4kuJc3RrZHy8WGU+JipWHUBjqZkQD5+QgJ3T97KsbT1iTJ5JqlOdNdhvxBA1Uqc1TSpbMKpODswhT9Eb23zlJuDTwM+p6i7n+G0i8g8LNKecTMwTEHkme1C6gCg75lMKS4xr7HZf8MZplEwzz3jkAZfLQFWsz1SYxU1yGRLIWKV0rKKNTmeJs95yLo9c/RBT06B+lpqjZj2q4jkAKlORuR9VQUTxjeXRq++nY3k3nWsHOHjvfrZ/8S4u/8DPRGBMNGeUnTJA5A2s07EcMAQwcPpSnvGeZ2F8kwOcybXwpLltUDNGqBkIcusZZgdD7ncrYCg3PwdMPfbAFAPLSxzeW+NH3zrCKacPpvUSVihvQ5TdE6l6DaVEFtcIKNg1xucdgyMPi3GCcyIWXHsjaDkO0Ym23pwERItElrOGIxxgB9vYyJYn1dcM05So4IvPDt3GYfYdMxsigIHj5/U4q6gqNaaZ1HH6ZW436Xl2DiJsn/oxIQFlaedQbRffOvAxLh34BTpL/fX1oRDEsWBg3WiMRlJUiTnAKG943cLqc3wCpT1Z+QNV/YJ7QEReo6pfVNU/W6hJuTI5ByCarvm0lSK7HCNKNfRoL9XSNBDVwKejVE3f1I0olbDEpEbApehmDqQJShNx3/J9F3AVQFIlKDEp5QYu6FGbqvVp82pp+XRYouwF6biTQZnTf+ki9t25nwev3s6aF5+VqdesSQFRygjZRJ2cgaNEfWdVmNw7RsfKHsQIt/7pdYw8dIjL/vBSjGgORBWDUwJ1kaiL52JEWX3ZuoZAZ76M0GxskCtJv6KW0b2TVEdmWH5GX4NUGq4qrR4M5cttw3H+/c93cnhvDWPgcx/ez/e+dIS//tJalg6axjZDDivkgqNSHOMokZIEKRvnxZc98iqL2SMn7pCJ5xLGr7kJY6RzxSGCE269OQmIFomICCt1PfdxO6fq5iflEdZGO6McJtCAHWyji95j4mG20PIAd/GYPkRH2M1W/0WNK7UCGlxRTW2NxBgO1naxxF/JsvI6xoJD7Krezw8O/zuburaytmNztKQ3SgALWRRra9L4RnOKq2KrU8lBzr5oznPhuFHYT1LeC3yhcOx3iejrRSEjk21z1qnW/BQAGaPM1HyMKBJ/qrGnWnLM0wqHc3b5jUBR9FOnjBMaZ4DRHEDyTMYqiC0zYtvSsRp9Twd+br9qM5BjEabVZ+gFZ/H4jdsZeP55KetTsiXGQpvONznuztf3LCoaM5mW8pJOHr/mfiaOzDDy0CHOfNMWAuuBgRKueiwfidoNvJhII6PpIkhygycatC6idCKtAKB6m6Ooni8hn/rFa3n8J0c471Vredn7z8m7uRfGaKYic8dM4klpzP7YmYD7bh3n+a9bwupTKmz/yTjXfmWUn7/oIf744yt4zvPbUhBUBEDJWMmxGZRKfB1S4JlTnQpe4u0nmR1RSWz822aqtShso6Fs5jCJOAHXm5OAaBFJD/1MMMoP+Sbr9AyWsfIJ9WNiddYD3MUZXMBK1h7LaS6YbOBMVsp6uszcxn4ti6MyU2vZ2vVz0XFjoCJs6rqEm0e+wrbxG9k2fiNLysMMtW1gsH097X4cuKxRFOv5jO9KI4JpPiqzeQx9vEVEXgi8CFglIn/jFPUAQeNWCyNBdY6lMAYoQJyGTiH5xrVpyQBNVUtUJTNQTiTDv5LWD8WkwArAM9m2QfFiEC6i1NRnJszm66rrkjrF6MnJvsvY9DzjVO656hpu/fXPs+o1F9L7jA1USz6BBDkA5CZXNaJ4qhCzPwbB7+1gas8oD/zL7Vz0Ry9gw3PXQBzfxsbMQzMjalcaBVhsBIaKARTd6w/zB0Ku0bRb71UffhbTh6dZvrHLadeavVCjMZNzIjamLlUM/3bfeZQlgiBl6eXX3jfEq5/xIL//9j0AXP6zHVx2ZRtXPK+N/n7JjROBpWQcR3OvWXkGjsiLEscjEsfVPi4TG5W3ojI7wdabk4BoEckoUb66KSZ4kLu4nzs4Vy+iX1e1zPBM6Ch38AO66WM3j9BD3wnBDgGUpExJ2hqfz3xASCMpgpr4mGd8Lu59BUeCPdw9fj2Hqrs4VN0Fo9dz2dCb6fB7HZWX216bz2k2tZoTp6h+jq2cRwt1njrZTZSk8WXA7c7xMeA3F2RGTSSszq5SljTHnAuGkkKN49TkgVINQzV+OUnZoQIYSuoKOIBLsU5/IorYjDWqxl5jad5fp91s+wmQSNRUB255FICxe3fz0IevIZyuct7bXwovPCPHCtnYuD+Zr1VBVPBEOfLjndzzF9+lbaibR754F8uftToFQsl4EcuUGVG7kuzX2dm4824AhlwmyWVM0j6eBBBK5tA/WILBUro/HzBUF3zRMYh255hjkVD6B4TvPnQq139jlA/+3gGu/eYk135zkv8L3PTT5fT0mnSsUvq7Qs1hizxxWKJG4AgcF/vEANumwMjEffmtoJ0TbL05CYgWkexhB6eymV08zDSTnMWFTDPBfdzBmTyjYZtAA8Y4QokyHj73chsCjHGUTnoYllOe2pN42olt7B3meJ2JEZaUh3n2wOvBM4wFh7jx0Be4ft+/csWKt1E2bXkvs0SKsYvS43MsNG7063m43Ed9t171eEucMuMuEfmsqi4qRqgoWpsFEAkQCJSjWC0qoIEgpSQGjBKGBlMK0weNDQxBxSNQLwulUPhtREitUouAKrQZSHJZJ4AAj6rxsj7Ig6Ba4FHxg7S8Gnq0pfuR/VPJWPZ9627W//bL2PGRqwlGJtnw/p/n6NfvZXJshMFXbk1BXGglVetVj04yuWsfbYNdUKuy7c+vJhyfIRyfYeWVpzF40VqsZtGpQxWMg+6LXmhF4FIEQyXjqMMKdjnHAgjVe51lQEvRNAxCK2CoJEHqpVXsL5u/ghN0MZlPTg3nCc9/WScvfnk7Hpabr5/iHW86yJtffZAvf20ppbYk0W9yblCRvJVolM4jA0dJ/eR9qyRKLfVEU0z8eySsUbQG1SWUr5cTbL05CYgWkUwzyRJW8Eyu5FEe4B5uZQOncoi9jevrJHdyI+OMNCw/l63Hc7rHRayGHGAP7XTS3QK7pfO1GWrUPk3UGqnNJLERSkERUa4zibzNur0Bzu19PneOfJsb932WZw6+OmKKIAu46NoFtSLF6NcQBfhN2KQW3PlFF5fXh4h8QVVfC/xYJDczIQoX8uS8B46heBP1XpOplisGQQSZ+kEEqBmQyI1aDYTxflJurU9oE3ucpNO4vcM4RTYdeRWcGE3BVQKcEmA0Iz5TkhmBJwyTux+ExtmHMLev1PCY3jeKt2YVp/zNr3HoSz9g+/s/z4YLtzB6+w56XvrsCBBpBNZEQmqPH2L7ez9FODbV8Bqe8e7LCazBN5m6LLAenhc9fGvWczzWTE5FlvfQq7cVispsHQhywxEk7d22Uftm3maW6dEq22/az9DpvQyu73LqWULqI5An2+4cmhlPF1mhJO6QpDZBNo07lNRN3evJ7IZ+5tkVfv093fztX47xltcf4mOf7GdwicFzXGUCoE0kPdMQTf2ASyihJuq6TAPv3jSRGjQCT1G+M4tvCo4kBTkR15uTgGgRyQBD7GEHg6zgVDkbX0t00047/dyu1+FTYglDDLGaR7iPnTwIwKW8hNu5jknG0r7O5ALapbPZUItW9rCTB/kJPiX6WMKMTtNNH8/Qi+srP1k1WbGvOLlrCopcUJOAohg4LW87hbW1c3h08i6u3/evnNn7bFZ3bUbEa92gOjd+g/quPZJt0UvwGEaOPQbyrvj7JQs6ixZEqoXrFoOaBBRFuFQiAOQAJZA4vmYMYFLKRuPX86gTgRQMRU+SqB0pAIr7RiIAltw/ad+Zii0QQ4iXexolYCnZzubngCzy+53nnMLha+5i6ZtfwNJffglWfMq9S+gaaOPR3/kE/kAPXZecTde569n39//F+C1RvJwzP/kOtr3zk9ipajr+hf/weryuNiLbobw3WpiozHJu9a2DobqErA3UZmlZEyPpIhBK5EefeoCffu0xbKhseu5K9j84wmmXDXHF687Dq+Td6t3t+ajIitGxk3b1QR+1ji1KvPne8c5O7rmzyrXfneEZ5+znEx/v48UvbEtty4wInsMmG4TcVXOvS6qV17ROwiRFKrRsPnPKCbbezLnKishqEblWRO4TkXtE5F3x8feLyOMicmf8eZHT5oMicpuIPDveXyciKiLvdOr8rYj80hOd+Ikow2ygyjR3ciM/1R+xn10cZj/3cTv9DDLFJNv4MdfxFY5ykNM4h+fyKirSxsU8P9fXEoYW6CyenPTQj4fPhVyOh88QqznEPo55/jJHGrFMaZRrN9p1Ui82xN7UdTFbeqN4YfeOXMd9R68vqN109s+cE1MHlNmW2Kbkra3Vz/GUJEM1cBB4TFUfBSrAOUT6/vr5L9B641Ul/ZiaYKqCxN/RPkiN9JgEUbkEgok/6bGaIFUDQfQd7UflOB8JBAkMEkR1qRk0MGggaGAgNBBG2xoabGCwtWRbsLX4WPwJa4YwMISBF31qXrwffYLAEIbZp/+VlzN28zZ2f+g/ePzPPs/UvY8ycds2Dv7LN+i85Bym7nmEfR/+Ag//0p9hp6qses+rOOu//jel5QOc9Yn/L712lWXdtK8bzMcscvKUNZKiF9lcYCip0woYinyk4o9oQzCUHF9+Rh/9wx289V+3EtZCLnj1Gn74zw9x+5cei0FbwtborGDI7b+oJsu28+ldojnbXERq9/rkVIFG+Pgn+/g/74syD7zt7Uf51KcmMcSG7fF3SQxeDI4MpB8PST9lkTh+E5TErRONWZLsM5c83deborTCEAXAe1T1DhHpBm4XkWvisr9S1b9wK4vIpnjzMuCfgevi/f3Au0TkY6pa5aTUiSc+5+uzeZztlCjTThfDrOIUJjFi2MCZAFi1FKNZ7+HRdPsSfvaYJ4p9qqRb+ujUbo5wgDMkspua0FHEWVhnU5NN6CgH2UNVZwiIbrMVso5eBiKVV1FikKFWEZOxRInXmXheLiCjqk37EWBl++n0l1dy3YFPc7S6J9dnUxuiROYCRW6S2Whj9votVlkAuR64VET6ge8SGT7+PPCGBnUXZL0xtehbJSV+0n0EJBRsEgtRQAKwZTKGJzDYsqbqNROASAyCiOprKWFnBKygvubGSFVxIqnqLWGgiJOLSnIsMU5O5pn88AKEgvhJgD1BQ4PxM5drGxi8kkV6+1n5h7/K6Pd+TGlZH/6yfpYvX4l4glcWen/2EsQohhpeKfKCi8YJ2fvFLPvBeR99M6bkYTWLkAyuR1uSsELm9CIrzRFHqJFbvssK+RLWBXcsur+7ctaVQ3zvw/cwM1rjFX8Y5Ui8+5u78UwBkDgqs2TfaMjOu8fYduNhZiYDxg9W6ez1ufgVy1l9eptj47uaD2QAACAASURBVFMfN8mLAVvuXB21mXvOiTeZ7wu/8rZOzj6jxGt/4TB33lnDi28AE4OdrF10rW1i34RQUxeoOQySKB5CGKvWkuuqtpU4RHNXWQCZz3qTkzkBUYy69sTbYyJyH8yaE8IjUlMmBHEiB4AbiZKzfWKucf9fFU881rAx3S9JGSPTuTpFMDSho9zLbQBcxkufFnnGZpNlrOIQ+1jOGgD6WMJUog5soiZTVe7lNg6zn6Usx6dMJ93sYjuP63Y28yyGGK5vl9gQAWpNHhRFHUff1oIXqcNc1RmqtMWqyZK05b3V3PatSBE8FQFTC8ldF5MbrCOiqpNxLqKPqOqfi8iPG1VcqPXGHycFMynISO6B+Jip5ctMgKP6AhODn2TfF8GfiVVgBgiIAmzGqjINxOkv207LHRuiVL0mgG8g9PJnmwKoxOjbZK/kAmGQeaUhSpi0L/fS88LnxA1BTBnRmfgFQaPOPB+rGpvGKSO3b+fAf9+KVHzO+/d3YzoNoVoSdVkSyDGxJQpU0zxZjTzI6tRcDuhpxggV2SC3rNQkLlHUrsDe+MKZzx3ikR/tZ83mHoxYVm/uYWTvJNDVlBWy1YC/+5U7GdlfZdPWfjp7PFZv6uC//upRvvPPj/N/v3Iuaze1p3NMx8cikrcdSqNSJzZEMYtUSvdjYCQR0Nm4IULmK4Z8SmIwGGriUYqfCyUg1MhQGkhBkSdeZKsWH8tsjCStX3JsjIzpJ0od1kROgPWmKPOyIRKRdcB5wM3AVuDXReTNRAjsPap6RFXvEZEO4AbgfxW6uAq4WkT+aT7jnpTmMqFj3MS3AbiUFz/twRCAh880k+l+SMgUkxzW/bNGyd7LTtroYIyjjMaed930cSqbGWRF03Yae5LlJPYYyxlZOznLMlAEB6oRO9dTHswDobkYorqJHIPXrcX5xiYicjHRG9ovx8fmXHueyvUmAjfO5YtBjTrf4gAWl9WBDDyl9QSMBxISM0z5MhVJ+1MhNbLO+pVc/2I0AlOAIEjogC+yerjzJANfOIfUvScdIOW2SQyqs/s5GmvsjofZ9YHPAbD5s7+JqfhAmMYkyqX8cGINiUQeZEkUbciDoUbu9Olv4wKkgrF0M5VYsY9m9jyeWModHkd3T6XjaM3y2E+OcmB7G0OndDVUkY0frfHgrSOs3NjJQ7ceZfeDk3T0eGx+dj9XvnkFw6d3ggPWvBjkCHmVmZuiwz2Hov1OovYywBf+I1obTz+lRASHEvWY86KcM/SOAE/uvGNGKKvvFD79GekntN60XCkeoQv4EvBuVR0VkY8CHyC6JB8A/hJ4K4CqvrNRH6r6iIjcAry+lTH7h3tanV7L0jPUNXelp0m/I3qEx7iHYYa5gOdQktbzMS3m6/CojnEuF9Iv0e/fp5uxQzPcxa1s4JSmnmc/p29kmikmGKOfpQBzAsSeoY50OzJslRgcxYuL53h5iYBkuczEM9Rslf0jP2W4a5jzB7ZS8tsBoWfpE1RZzgagVGFX82I4/nr6JyjvIooU+58xgNkAXDtbg6d6vVlWLuXBRRHwkJW5ICmtTz27NOD70et2EUi1sp2wRM64yRhLjJ8Zs6a3iwNs1Gmj7tw1/wCLy936SyjFXJumzgQSWsTAxA9/wuQXv8/w8DDrP/SrlEoGAqWkIV5s1G2MpRyG+CbypvLFUjYhlbCdWugh1qPNjwBRApIgAyqSqpdIj7vBF/MpPBRqnXjqBKmsAxJZaITk0rh1wjDkkW9XeeWfXYCd6saI5bm/soTtw2Nc/w+P8JoPrI+cPSUCMxDpdLt6lKuu/TmO7p3m8OPTnHpBL56xdPSUMKIE0y4zYwnicf1aL2gU4dyP84/5MTvkE6asmC8BfgyikojcFVG2P1jj05/zGB7u5fKfXcrByQgQHZjqZlCDdMwicWMdQOSWpYxRYd2wgJolzCUnynqTSEuASERKRIvTZ1X1ywCqus8p/wTwtRYn+yfAfxDp+WaVI7tGW+xyfvJ071dVGeMot/BdVnMqGziTcZkGpht30GK/x0qeTL+qyoNso40+PMnASr/0cGDXIR5gG8tkNg0KlOlmghmA9HvO+SZ2QSYCPck3kIIiSRK7OgleDwS72DW2CxBWT+6mtzwUlaMc3T0etS+m+ihKLh9aYT/njr84V5+5RFWvx/l/V9XtwG80q78Q683h0RoSgvVJQYkJIcmhmrxw2xIp44OA9eJ9Gz1srVPf64CDtVq+finrWwXUjxkiC2nIong/mUvSny3F4CGEwxMBtpSp1UwgUbmQ2i1l+1EdLQGi+XLI2CZfoQz7aiEYRcsRELLVGjMPPMz+v/o0A298Ed3PvYi9RpEgiOL11IS2SrTtqcWGQme5hlHFMxYbCCulxqjMMI0PAXT4Ua616TjidpcfmXlNhxFY6Ij3q9YHhQ6vihFlOv5BOvyZiHXRTkZD6Pbj//cwyvHWGe9PheV020MZD8t0edWU7RmZ9nh0+04OmbNQr8REUKGnMsPQlQP8+998i3UPrmb9pra4r2jsHj9aZ6f6y9BfZtXZJaawTNrohbTXn8JDGQvb4v1pPJSRsJ12oM2fwcMyaSsYgUF/AhPvD/pjqffZpK0w4I2nkaz3aRvX37qbAwePMjOt7KgZTu2t4aHMhMK+0n76YmO4QzZ6EewzVazCoTjVS5+JrsWhOM1Mnxedy+GwgxBhiZkkRDgUdtJj5nj7WqQy3/XGlVZoawH+EbhPVT/kHF/hWHW/Ari7xcluE5F7iVzjbmmlzUmJZEonsITcxLfpopfzuJQl8vT0Jmskqsrd3EwH3SxrYDaylo3s5MGGZU9+8LwdkVoDhJFRdWjz6rPE88zzUJvYKyihDeK3apt/5bLF97UWxXX9h6etDZGInAb8FrAOZ81R1Ssa1F2Q9cabicFG4DJAQmKLrCY6lgCZBDR5TrkAxjqgJgRvmpz6zXPai4Amt09qY1To22GcvBi4eCpR32GeTTQ1yTFNpiZ59qpGCrBVwHPKEYWaYKzBxJblMzv3Yz1h7x//BR3POp+h9/4alY1rwCjVicjGSIxifMtEWIm2RSn5IbXAw5gIEJW9kBHbzpEgS0eSpB6JGCDN5V4zokwGpZw6rJibbTKIwEl/WGKSMpNBOacimwwztnwyyLajviOwEIxN8YPf+TYrL1lNsGoV++N3p4mwTLd2sfFVZ3LHZx6g7Q8ujdtGKr6JsJJT8Y07Y3soE0ElF1ZgPIzG80QJwzYOzCyJUndIxAiNh20YlJIJGLNJ/rKo/GjYgRdvlyRk12gHM9NRwMTD1Xa21zojV/mwHRN2cii+nxJV5eEweqkMY8b7qO1I7bkshsM2YvTD+MY56tQfZxC4n6ZyAqw3RWmFIdoKvAn4qYjcGR/7PeB1InIu0YvRDuBX5zHnPwZaMnI6KZHs1ce4m5sBOJ1zWc6aeanIng5ymP3s53G28qKGarFBVvEw97JXd7Jc1hy3eaQeZ2IioJREjvYiUKSiXDP+aXwp0+0NpO26TX+9DVGr0khVVgRSrSZ4XXzyReAfgE8C4Rx1F2S9cexwE+euKIGpOAxOoipNjhVUZJAd1xgQmRjUJIBJydq5dkmp01ihbxdcpfPzMq+4RFwD7OwkGhwv1M8AUbRhPEEsHPrG1xm59lrKa9cw8JpX0HXJRUjFhxqo2pgpVdSQ5WQzYE2ktDESpTOxJsqDVsMwrX4aRduNsD1bypFMlZavk0i79ZmklO4Xk9wC9Wq2WHZcfR8T+ye57JOvYbyWV62XwhLDL9/Cd974eR678whLzh6q84hrFEfJLa+PiaTYoMK4tKWAKQE/RpSKCdJ4Rp5YKiZif47unuBPn3cTA6va6OqLHtk9S0uEfb0cCmOPNdsONmPU3RQpCRgKNbsJbCHiTujUT8BUSTuYU57+601OWvEyuwFHU+3IN1odRFV3AGc7+3fRQgykE1VqWuUoB1nKiqb2MBB5j23TH1FTGOYUVnMqw2ygU469bdVCy4SO8VN+xDlspa1JyAAjhs36TO7gelBhiOFZr98TkhgAJaAo9TwTEzFFnkFUWFM6g521+5ixUwyW1tDtDVAylSzlRjNA1Gy+LYGnpy0gClT1o61UXKj1xjRYNl22JlKfJSrNPIsUdRAXFdqkjFODchHHfMfkwYvLItXNdRZAROFbBWojR6gePUTnulNnBU6TOx5i5zVfYWLjGbSvXIs8+3J6Lr0Ub0lvNOcgtlNK6TBFjYKa1AtOYgBkYvYoFENoLTV8qhIBomh8TefhAqEMAMXnWgBJ7jGAKVtiSusBUfSTZNvF9odve5T7/+U2zv/zVxC0dxMUEj1UwhKjlaWc+VtXcNP/voZzfvd5LDl/OAe46hLoNgBexaCSEpaZMOV0fkn8JU+UqZgxKqXfMfPUX2HjZUM8eP0+upf1s3pLL1tfv5pR2xmpJbFM1QZo9/MmE6Fzy7vhEOrLYsDk/NtFCXhP/PWmKCcjVT+FYtVSY4YaVe7ihwywjI26hW7pS+s8ro9wn5OXbphhlrOGfhmkn8GFmPZxF1XlPm4joMZOHmApy5vW7ZY+tujFbOMOxjjKRjYfhwnNrT7bVL6QdeWzuXPqWo4G+1jqr0LDEIxE3meqdZaKU+EYILR7cxieFz3e0nnNvvoIx57CFpE2In18hWi9+A9VfZ+IDACfJ6KldwCvVdVmyY++KiLvAP4TMqMuVT18bGf7xMWbbnLhHLf6lKVJAYtELBLkXPST+l5J8aZjlslkqqqiB1vSvmjUrU1uA8/EqrhmEgMtG9aw01PMPLadXVd/lqUXXkH/WRdSXjqUjrvv+q9z8Obvpk2Hh4fpWX82bcOr6d24JZrXRDw/E7NCnkTnayIbJvXilwCjWAPqW0JDDJAU4ylTpsSkRobWYazOSqN7p5c6S0HiJrpt9g5hRKlohRGHZXLbRtv5+gC2FvDT938dOznDYzc8zvJVa+uvsVaYEsU/fwur3l7hjj/6Nqvf9DMse+E5aV/Nkufms0bkx6+FHRyp1TNZJjZAT4CWLxbfhCnQuviDL+acHQe5+je+QzAVMNWzjIenB1Pw1B60s3cm/3w4tGOM9t4ybX2R/ZDLCoWFmysfPyqq1+HNkbqDE2+9OQmIniI5qge5je8DkVv5EpZziL3czHfYqFtYwnK6pCfNSzbEGoZZzwY5hSNyfIyfF4NYtdzPnRzlEBCpzSZ0dFYWrF8GuUAv53q+xgY9A0+O0238/7P33tGx5Nd95+dWdQAajQw8hAc84OWc5s1LM5wcRIo0SVGkuaa8CraPTO8RxbUlmdTqKK2OfChxj7y0qF2LtiVStqwVFSiOSIma0Qwnv5xzjsg5d6q6+0dVRzTQDbzGewCmv+c0uqvqV7/6VaH71rfu/f7u1QwPUWr4zIASKWWVbxPnQ+8StUPxE0ING1V1QgsuJqwRjox8F4A1pbtpLdmMx5gh5DlDwkbN9TimLMSsjzDwvKqOu2Lnd0Xk74FPAa+r6ldE5MvAl4EvzdDHT7nvqdPiFVhT8NHOE9k8RA40y6yyZNmNRKjMyNyWEjIDx5NCumcpszRI5kyzmXyfEpvuIUpvAH1n3qbznb9JrCptWEX/sTfoP/YGjU9/gqpNu/GWVTDV6eSZqT/wMpXrd9DS3kZvJIpGAcMlQmYKYTME28ZJA2AIuOmO1CV9EvcYGa63yFBstVEjWek+tbRIam6k1G3TypC47TMRFYOIm1Enk0xN2x+wxibp/Por2JPOfbLzW29S+fJezGC6VzqiXsKG4zYq27+F9roabv/Wn1P5UrLI9mwerUzEx1Fqe5m0fdM8X/EwWTJrd5IgeQz3vbWJtg9v4OK3TjM8AmOxkkQ2b8vyMxZNnkPH8W5e/YW38JR62Pdzu1j3kXVpqUVSM4jHyVFqmM1WwdKSrOeSwDK0N0VC9BBgq80FjiWWTTxEXOJaTT0RwsSrymyUXazXHdOSLy5HjOogFznJJGNUUM0oDuG/wzW2sCfH3iAIBtOLchYaWcNnrqao0WjjCscYjvWSmqcoE7bGUGzKzVq6IzeIaYR1gdznOPfBFrg7VQXc6XJ43ZcCnwCeddd/C3iTGQyUqq4u7KgKD7FmvnDi/sk2NT6+Lu4BSpAkASOmGPEZXikao0x9EgJqZ+QlmiUSbMRI9JsNVjScRoZMfwBvSZApoH7nM0QH+5xyJFFY94l/g60WYjiJGg3LIVvqkqH4c0B8WQ1HOI4hyXU2DjGKLyvOzTdOmhTUMFB1ypJIPPSYkiognSTNTIIyr0vMMIlpehgus118/cSpq/T+1++h4SjelXVEO/oB6Hv1HNX/5Im0fqNiELGSt8dIRJBACaGIZxr5kYz32cYbUg9hwzOtvSMuT3qeDFG8hpX4bIjiMyxW/thjXPzWabouj1D/9IaEF4lYCWMkvxRjEwa2Qnl7NSf/+CIhSmj/kXVAkvikeYUyLrSTVyoPjeoyszdFQjQPqCqv81cEqeSAvDRtu6UWg/Rwk0uMMYSJB8v9sq5iPRvEcbtO6BilBDAk/ab+QSBDAFGijDPMAV7iMK8l1ndyKychGmOIMioKryHKxGyaIlUMMXmu7LPJ2WfxKfkZ9crKjRqeqvgsFyffoyd6C78E8qtnlho+e0QxfRExgRPAOuAPVPWIiDTEZ32papfIzBkz3cSJ/w5Ypao/KyLrgY2qmu/U+QWHEc0RjrQU2yvu/0MzCI44Gc8lfZ0RS/Ybn1LvkCtJJ1QpBCmxfwqiU2Oc/bPfoH7TE6x64scdopUyXisaYuD6CfouvUdouCdt37YPfYbaTU5h5KmhLkqq42khcGadgWN/4okjXbKV9AjheoRSSJHrMRKJE6I4OYpriKZ7jDAN57cTE+fhz2WZSRKYoc9y1mb5R6QvqinYqYWPsxCh+Lpw1yix3mGaf/Nn6fz1byTajR+7QsWHn0rrNyYm4agX0+e4+CaudONrbSTmaqAyiVymFZopbBYVw0k9QHadVDxEaIgSERMRMF0PUdiwMYI+nn393zmz02JWQoc0FSojWpIkROV7N/D0Nxs59VuvMXpnhP4bo9REkh6fbAQo9R1gIpKHHnmZ2ZsiIZoHRIRabSRGdr/1ad5liL7EcgtraGY1AYJpN/AyKV/wsS5m1EoDm3Q3J3mbVaynhhVc4gR7eSHnvpXUEmaKMR1O02A9CMI6xfv8Awd4iVK3HAcws6YIE9DsU/KziKp94mdn4DleHblFb/QOfZG71HtbZx/ULJ6LbJhHTL9ORI6nLH9DVb+R2kBVLWCXiFQB3xGRbcwNf4xj4OKP4fdxZoIsGkJkhnMbfyNeegPnRgxk6IlIrMMQzBLF42qT1EjdrtM0RXGvUjZ4CVISrINwBO+U4vEo3qnk9+LEn/1KWvuWbS9Tv2Yf/jLnd6GTbp2wkkaYSicfiTG4yx5T8YTc8bikyPaQDJ+BkzvJSLaxPfHxO8TI9sS9Q67nzFTEKxgxN2QWNdOPnTqeacwi+zWJ76OmJ1mIOfVcMtoBBJ58gkjXCL1/8FdUfPxFvPW1jPztP1L/8/+K2GT6rdBy+425Y/Zv2crg//c6U71RzGAZqd6txKFmix/FSZn4CLksInzzPt1f/RNav/aLmCW+tPGKO0sv7jGLz9ozEqRJ8Zh2wrNUa/sYsDLuJ+XlrPmtn+DEJ/8vrv/3E/gP7qK0PckjUkmRZtEU1Xhz35+Wm70pEqJ5YhdPzrhtM3tQ9ANPePJBi6ylVINc4TSjDLGN/ZiYHNHX2cbeGbVEpnio1QZGGKCcwhAiA5MAQexZZmrOOCXfIEmKRJxtWfIPCbDB/zhXw8c5OfEqL5X/VP4ewVzeMGU+T2z9qvp4Pg1VdVhE3gQ+DPTEcwOJSBNOMdWZsFZVPysi/8ztZ0oW3LU3N0gsP8seH7amZIrW+BR0SV2nGDFFou5NLVMjNC2ENvtxd7/0i05YK+L0aUSS/+hdL/0ipseHP5Ahgo23SeQeyiBCKULw+FjMGJhRTXqDRByNUGLZ/ZpJcp2Rsl0McOfdp4XURCSZJynNi5N+/BkxbZvblwqk1G2TzLYpXzMBaj7xMcabmhn+zt/ha2uh/l//NFbfKH1/8Mc0fukLGKWOF0VscbOBO249T3kNnsZ6Ijd6KNmyNrE+LW9q1nGnr1XDwHZDfHhKMasqsEOS9Nql9GeYdvIYLiESV6guQNSwk94kPIRStJRxgqPqofbj+xl45QgX/rf/xrbv/howfdZZMltIcn0kFz1YhvamSIjmidmub0AWpizGckWtNHBQX6aLu1zkOCEmUXRGD1yvdnCby0wxSTsbCzYOr/jYz4szN5gtfJaSp0jiCRVnCHGt9m2j3mzhvcm/4Xb4HGv8O9IbzDg1P/dNu9AiRxGpB6KucSoFXgR+B3gFR7z4Fff9u7N0E3H3VbfPtZBHCvGHCInle+HcG2/cw2M4tsDR/aSTJLFsjFhcK6RJMiUQj5JnEoSZYSS8hXHSEkd5qfvUP0PYT1O/FKmerMRYU44SdcJx8WKzjig6xcPlftXTCFLKMi4hygypiSFIVDBjYPtSBObumFLeZiZGmR4ZXKIVixOXjH5nQPnuPZRt3cHYu+/T83//IRoKo9EohEFcz594BYk56QTG3n6PsfcOY4dC+JpXIhEjuzcrbXDxa57RyGPgTMEDT10jTb/6bx2PYTS9qQjYlqQcR7FNTczME3EIU3w5jIdQSj6mVNT85Efxrmuj+/e+zcB71wju25wgMqkEKD0HrBDNQ5+53OxNkRAVsSggIjTTRpOuQrE5zpv0001QqzBTNFan9B0G6GEXT1JFHR7JbgQWDKmkyMz0GDmias0jM3WZUUmJlHEtcpIaTxNVZsqU2RmIlOahIVqAzLFNwLfcuL4BfFtVvycih4BvuxWl7wKfmaWP3wB+ALSKyJ/iJF/8mYKP9AGQy0MkloWaKTeIRF4hScwYS3hiXJIkMRsjaqd5kcD5rLam7D/XsbqepxlgRm0sb9LrmNa9aJouLZOQGTHFjDhEKEFy1JktlvBsaYr3Jx5Gm8VjpHYyd5KTqFKmHTv1Gkzz8mSOP3VRQKIpC1n2z3Z9TcNH1dPPUvmhp0GVu7/260ydPEdw396El9eYhNu/+WsYpSXU//PP4WtZifi8MIugPXnc6QfN9GglxynpuyTeU4X36uq81P3uOUkv4yG1qOUl5okr+KcPy79tMwBdv/tntP6nX8JTW5UeJsvYR1UYisw2ldEd4jKzN0VCVMSigoggmGzRx7nKGa5zjvW6g37t4pbeYoAe2tnEKEOc5j1QeIqP4ZccU0QLibimyHI8Bdk0RVi2k5doFmz1HeBE+HVOTL7KFt8BGsy26Z7HtOWHL6pW1bM4Fecz1w9AHmIvp+2rInICOIBj7r+oqv0FHegDwghFcraRaMb/Mzk33CU8yXVqGJhTJuZUNLk9lTSZM93xc8PjN/BOzHZXBiOS/U6V6ZVJEhLng1li45mwnBuxmaIJcvVBaiY1Q/GX7ckgSJ50gqQeMAFPOBmWix8701s1DTOFyuLXwhY8kezXMj+i6ZDc5s/9C3q//x1kyqJ8xx5CHVcYuHQBe3KS2pc+ysQ7R+k6eRQzWE77l34V8XhyHyMbEfMZeKIy7TwyPU7ZEm3GUzfEP8d1ZyoKpoE9NdvDoZeaf/ZJBv/sb+j67W9S87kfo3TTuvQTSLUdClWlZZmdTMcyszdFQlTEokRQKlmn2zjHEYboZxWtRNzitd3cJcQkJQTw4WeKCfwsHCGa0gk8eLOWSplRU5QHas1mms01DNhdXIocZdwzzDrfrowDaPbP2TC/mP6CQ0ReV9UXgO9nWbcokK+GKBtUxNHOiCSJhqFIzHZIlEjCk5TIjZc6M2rOY1UkOr/xTnNgpHi1AIyohRm1HS+W5YTOnDxDzme1SITE1NUJoZr8PENIzfA4npyZSotMH+Ds6xL5mzKydk/zNOWJspVrqT34HENH3mbo7TdYs2MX9qRjb0aPHSbceY+SVatRy0JHJzDLK2ftbyaiZBhgxAlc5jVw3yN9vZjV1RhebzpJcgsJJ8KzRjxHliAeJ8Q384CgfP8TTBw5gzU6St//8y1qP/Mpgvumz+ZNSM08Oa7gMrQ3RUJUxKJFOdUoyjjDxGhkjBH28jwjDGARY4h+BunhCqfZo08vWPjsAscYpp/n9VPpAuhcmqI8iNEmz+Ncih6nS29yM3aOWqOJamOmGaU5poZT+Jj+g8DNOhvAmVlSTdK8VwDNj2xg2RCbU8mjNDhRD3E8g/E7vmmDFUsQosQrEWqbPwEzoiZGZHYPUe5Bp4bNXEKHqyEKW27SRXGntYs7W8wNg8U1RYY6M9CQlBCbODKijJBaPL+RYbmz1uZCiLK0i0eaDA8YkfT95hqCjKO8bTNd3/lTvBXVhDruYVbU0fq5zxPq6UA37mD86nnCHXfpfeWvWPnpn3JE7jOMMesQxL0GKXqhtASc7oe7v/cVxONh7a/9TpoujVh6ugY1SYjSRR3R+uwwafjcT9D/ne8w2X+B/j/7NqUtbXjrUsP1KcPN4cVcjvamSIiKWLQQER7X53mX7zHJOCEmOMYbAHjw0s4mVrKacxzmLIfZrR9akLxEW3ic9/kBb/DXvKA/nn6MmTRF2OQjgvbgYZNnN8ORXqqMek5H3uIx77NUGrXT2ualIVpEBgqnAOv/jmOMTpA0UKPAHzyqQWVFNIdeImaBZxaRqRhphEgsA4kmPURpXqQHHqsHmYUQSdRCvTkEsamzr+LeK8CIxjAiFmoYCUKEx3ByD9lOODiV8IgtSIqHKE2EnTLLjFhGQsm4Iy0KVqbjdSZSlGWd4UnxEM3h0mYjTYYnwOrPfZFb//Nr2BVlTN25yfDpw07XXh8rX/gM0Q076fnhKwy88Q+sePpHc44581hiZB9vqpeo9Z9/nnv/4z9z9/d/l9X/5kvpYbOU0FpC+C7giQpGtpPK8cWFjAAAIABJREFUuGi+QA31H/kkd69do3znY3T9v/+Zlf/6C3irqtNth8JEJLcOebnZmyIhKmJRo0RKeFyfpZvrRHF0Hj5KqKeJO1zBcB+5Rxmkm7s00VbwMQQkyH59kV46sjfIoilSWxPC2Zlgq80d+xL37RuUSIB6WYktMW7GzrPL83S2A+Ue7CIyUKr6NeBrIvIFVf39Rz2eWRHKYxJKZgXQbIhPnzYMmBKYmHLq24lAgRKuSkCQ8dmKmYGEcwtiEzAk4eWSoIExFgFTwDBQUzC8ZhpBsj1GQl+khqBeSSFAgu3N0BiZ4DUV76STmiAzy7YRncWrk8ODZJrgmZql/RxRWdVG63OfRa+9R3ioFzFMfJV1lDWtpuO1P8cbdFJ8DJ06RGXLFsoa2/MbtwsP4J10PmeGyuI/3cr6DZg//nnCgz14J5L7ThOhG8l1Ril4U78SWeyAFQ7R/+6rjJw7Tln7BiratxLp6GT8nfdY8dzHpu1XU+bPfhKpWGb2JichEpFW4E+ARsDGSaT0tZTtvwh8FaiPC5dE5KvAc8AvqOpbItIO3AJ+Pj5QEfk6cFxVvzmfgRfxwUE5VYxTiZc+DAy2c4Bh+okSYR3buMUltrOfCxyjTpuyan0eeAxSRTlVhHQSW+0ZUyskPESqOT1Etka5bp/Fi58JHeG+fZUpnWCKiby8S9kHML/dFhKq+vsi8gTQTorNUdU/yWz7yOzNA4TM0mElPUWW5Xie4rmpCoWoByK5ReB5I0UcTtSHRNwxm4ZDjGwQ00YNwwmjqKJWSkjNrV3mkCQlMQvNDbGhkqy/NtvU+Fm0QjO1M6M6e123eaBixXpCfdcwunvxldew5kf+BR2HX0GtGPVbnqD37Fs07/8YHa/9OZv+6S8hcaKbS3ITD5mlhPiyicpVoKJ5A6zcQKinB9NfijdYMa19KikyTDBycPro8BiDR97EE6wgdP8Ow+Eooa77iIKZua+CkY8CYYnbm0zk4yGK4RiakyJSDpwQkddU9aJrvF7CmQYHgIhscj8+DXwTeMtd7gW+KCJ/qKoF/DUXsdxhiocmVnGZC7SyjgqqqaCKQXq4znnWsIVaaSSg5YwxTA0zZnV/YNzlGne5xlP6UfySXhAyNXyWDzzi5UnzRxnUHhpkVYLIzRQayxkyUxZiGuwDQ0T+O7AWOA2JrJeKQ3wy8UjsjRaSYLgESGMxNBp1bphGAQlRLJY7xDcfiDj9hiMuGTLd7OuAKUjcY6TpHiNsHDKUKsI2HI+ps6wOcXGn9CdCZFlI4lyJkmTkZCoEAiXV+BvWY58/ScPmpwmU1LL6ic9yoecuHYdeYfUzP0Ft22N0HHoFe3gUX1ll3rPNTJ/mDJml7tfxj99movsWO372dzE8nvT2KSE0wwtmjq9woKyedf/LvyXU10nVxj2J/lQ1mbog5VLmJJrLw96kISchcuuHxGuIjInIJWAlcBH4j8C/Jz1JkonzE1LS/719wHs4SZX+S67jFlFEKmpoZBdPco4jAKxnB4NustJ2nHtiCQHHu7KAaGMDd7nGVc6ynf3TG6SQonyIUSlBVhJ0Zu6kEJ6s5GfpaYjieBzYonmIoB6VvdEc4TCNxhDvHBQGcXIRiSYzWBcIGvGis4T4NBxG/HmEOzJhGGgo4nifTNNRQJsGovrAHiOJOmTIiLqVYJ2RYsQUy5tLjUwyq3YqxCFDZqRwX/o4Malds48N416uvv8/8OClqnkT0alRPP4yVrQ8BhHwB6qJDg9Q4pthxlmWc5EYyfFmeomyEKSWxz/Kle99ncEz77Fi+zPT93NfXlXMFAJjhhXLn9qh81Ze2Up5ZatDExJO0ewXfXJ49rAsLH17k4k5aYhcV/Ru4IiIfBzoUNUzqSJTVb3gFld7F/iljC6+Avy9iPzRXAdaxAcbhgh10sQOPcAp3mU9O2hjA020JWZ+WTgV5RcSfimlSdvo4g6bdHf28JzmL6ouOBangTqPEwLrmstOD9Pe2FNTuZqgsbl5ZexJD9b4eO6Gc4Q9IVhjY7M3mqfHSysN7LEQYpoOKTINxOt1CZKBJJYNx0tkCuI1E3ojNQwMr+FqihyCpKbg9SreccsJ72QUDTVmGmoWr1qmJ8ZjKt7xOfzO8uSmftNHQ8MOYts+QeeZf6ChegtN7Qdp3fgi3nFn/HY4hDkewVfqnk8O4qsCXtPGN2ZPJ0Mp+6cSpJpAO96SCu4f+S4r255w/i9k7CuCWQ6+sfQff4J4zZ0XAFBTkUcqk2Vkb2AOhEhEgsBf4ai4Y8CvAC9na6uqX5hh/S0ROQp8Lp9jVrdkr2P1IKhoWJiyGsV+H06/FRqkj9tUEGCffCixfUxHKMHEJkT1DPXPZut3Ljigz3Cdc1RRjk+yP4kv1HXg3izbFqkLG6gDLrq//YRrQ1U/PtMOD9veFG2Ng/L6UsQ0HFGK+y5ej5tAx30llh0ChMd0PEcSJ0Cmox8SlxR5hMpyz+w35hnIRK5p+ZVlHiTPEPVcUBn0Igql67YRGT7BiuoyVnzos86YgJGBm9RW+TCj96gt3zrj+Kb1G/Amf6M5Qmbxs3r+U/+euxf+gboyn5tF292e4jWrKjXnfB1y2Yqq0ty1zJaLvYkjL0IkIl4c4/SnqvrXIrIdWA3En9ZagJMisk9Vu3N09x+AvwTeznXcofuj+Qxvzij2u7T7HdQRuuimXKqw1SZGlLf5WwBMPKySrbN1M2O/c0EDa5kgzMQsJXIW6jrMisX5xPYbc2n8KOzNUv0tLASGuyYSHiIxTfB5nXfD0RXh9SQ8RpiGM8Xf9RBhGNheMxE+ixMisZXhoQhiuaG0bAQob1F1cqXYMDRceEmq2DA8GCYmATq7eujrGcX0+LFiEWKxEEd++J8AGJvyU1n/3PQOZtCMicKwO97MsimZWcST5+6nZu3HGR6JzehREgsGR6OJYzwwVBHNQ1W9DOxNKvKZZSbAfwMuqervAajqOUgqV0XkNvB4PumxVfWyiFwEPgYcnee4i/gAo5o6LnGCkE4SIYyPEppoo5Ia7nHjUQ+voLA0xhB91ElTzrbC4ozpq+pbuVs5KNqbR494yRnBFWZZZrpAy8gQ9BgGYCMYKDZiC4qRbG+BWIpYTrJShwc4U/DFdgvJJvpK71ogC3nS5GBUF8RDFO/XNE3KK1o4f/yPGRu5j21Hqa3fTH3jTsrKGxkf7cjuJbE1O5mzNFGkN+203NQHzj7p21OJUuK04+3EuRRGVDGs5HWIE8/0c8p92tHwOJNjvVTWrWF8NEdqB5a+vclEPh6iJ4H/FTgnIqfddf+Hqv7dfA8K/DZw6gH2L+IDjLVs4xyHibjemQghurhDF3dYxfpHPLrCYoAeznJoekLImbCIDJSIjJF9RAKoqmaLUxXtzSKAWhZqWQnNipgmatqOpgic5EJW6rIjpBbb+Y6KK6rGduaFi6XJ8ijxwrbg3OQzCE3WrNRu20wYETu9bluB9OtG1EYsxbSULRs/zfETf4BtOx6Ygb5LAPR1n2HTph9PHj+PNFNGzMZMKbuSJHtJkjet3hzpBCjZWYqHyHZIUdqxrLkbg46bR+i5f5K9z/4ilWV5pC9Z+vYmDfnMMnuXHF8zVW3Psf02sC1l+Qx5fX2KKGI6fOKnQVsZoi+xbhcf4hyHaaDlEY6s8KijiYP8SN5kaEGelucJVS2fxz5Fe7OIoJaFWFa6h8jMcIm4y45HyXDIkOsxAhssI+EhmuYRQqcRHWGGWWXZ2j6Ah2jWqfKaLL1T6q+itmYT3T0nE5t37/xXnDrzX6mv3Zw8fh7DSHjK4svE8zK5g0lKhNJ0RQLTw2WuF0ri/cbyvw4zeXZWthykoWGXUy8vV2quZWBvMlHMVF3EkkQXd9KWz3KIAEHKqX5EI1oYGGJQRv6/88Xowi5iaUMtKxk+A9Qy0hmrlbxzCqC2JMNnOMXPEsTF0ixsV6eFxcQmOy3O9B654bj5YDY+5Hhc7ETYqaf3dNr2M+e+SX3tZnxm6bSahfkSrfSxuGQvRXAtGdclnv/RWZcMlznj1Xl5hDLhNfx4/X4ntJlHf8vN3hQJURFLEnvFETKGdIowU5RShhffgtQymwsmdYzLnKJdV1OlK9OLweaBw/oa69lOrTTObwDLzEAV8ejxoJoiLEloZ8S20RRnndhuPiPU8Y5kal+y6IfSyNMMBONB4XhykjmTXjj4m4itjIf6QASvJ4DfDKBZSMNsFsip5zZD4lUjnp8oJRSW6hVLpC9KXoPh0dvcuPYDNm/aS2nwsYT9k5iiqdXqs+icYrEQR498jZ27foaysunJbMeHJ2c5k/igczdZSigSoiKWNEqklBJKczd8SAgxxSC9rGAFJ/lrdutT1EpDXvsOag/jjHCKd3lWP4FH8smdn45FOg22iCWOB9IU2SAxt9AtIFZ6LCZt2cjyHc4SUouvN6IWRrRQZVdShhG1XFLk9B0nIEFvXbJRfPscnnnEspAZysRkaqXSiF82XZEBE4P3GR25w+BAFdeO/CUf2v8l/P4KRxSe47J03nufcHiYo0f+I89/6P9MliBxURnMnYdoudmbYly9iKzo1y4imkfByyLSMM6I+z4MwBVOEdLcSf8ASihLfD7Nu9hzTeyojgt7Lq8iipgL1LLAJUdYlkOCbNv5bNspyzZi2wmvELazLLGY857ywrLoHjiLWlGHOMX1RvFXzE7Ozkp9xdyagW7/BX1l9CuWNW3ciVfMzj7u+b5idvrLthHLRmzHayWW7YS0YsrERA8AluXY6guX/wIrNDlL38lXTXBN4v967frfQdRO254zZLYM7U3RQ1REVpzmPQBe5NOPeCRLC62so5l2yvAzwQ8Zoo93+T7rdQdtsmHWfQMSZJ++wCVOMMwAVznDJnbPbQBLwOgUsbQxd02R+8d2SMO0bPKqnL33N5R0v8EzG3/O3TGLVyhbONwlDQ+EbP1a6ni00sKB07VO6cjjxxcncrlgpJxzQleU9BzFQ4ybVn2EDa0v4y+boKdrgKGRm7x59LfZvu4zNNZum6l3AKrKmnls009y9tq3udd9mIC/mlWNBxLbxc5jnMvM3hQ9REVkxfN8imrqOTH/lA4fSIgIHvESkCC7+RDxOmvXOMsNvUA0R53RCqlmAzsJUE4Xd3K2Tzs2hX9iE5FWEfmhiFwSkQsi8kV3fY2IvCYi19z35aVmL2JGaIqHSF0vkSY8RK7XKMVjlOqhyFyWiIVhw3Prv4AhHq51/nBmr1AsmyenAN6ghLcr5aXu8VPbRZ0ZdwlvURZvV64X+baNe5zihC/l/BNjiNmYtoEXH5XeBvZt+hlWrdgHwLnrf8H9rqPYkbDTR9ia7nmK2dSVrWZDy0tUBJq41fG2c47utvHB2TVEy9HeFD1ERWSFIQbb9QCT5KiZVMSMMMRkHdso10rOcYRbXOIWl0DBh582NtLCGkxJ/xlWUE2EEBYxRhmilvw0SLAg02CzVp8Hfhp4XVW/IiJfBr4MfKnQBy9icWIumiJUIWZB1C2gm+l5sG38lLCn8VMoyXYzeoVSZ15FbSRSeA2RRFytT4z0rNPTnCZz805JNIZEZi8knGycRVxuZNkuArYHn+1jc8NLlBrlXOl+nUt3v8+lu98HIOivp71uP83V26dphepK2rg4+T1ACU8MUOqrAqCiPB8N0fKyN0VCVMSM8IkfH/Ooml1EGhqklQZaiWiYu1xligl6uM81ztJLB3t5Lq29KR526EFO8janeIfn9JPTSFNWKAV3Yc9Sff4TwLNus28Bb1IkRB845JWnSFxtkWWDbTl10rKgzHSrxlsZ+2crgxEnA3HvTaGR2q9NdmI2r351+vnlQjxDtZEZPtPkFHzLToTi2mv2016zn8nIEHcGjjIRGWRg4hbnO77HRKifjSvS7U3AKGdzw4tc6nmNYzf/lKfXft6ZrZYrtLcM7U2REBVRxEOCT/ysYzsA24EpncAm+9Ntjaxgg+7kKmc4yyF281Rex5jHrI86ETmesvwNVf1G1r5F2nGrzwMNrvFCVbtEZPq83SI+EMipKbLdEJRtzZ0MwMyERIwk0So0LBui0XTyNkN9sjnhQQicneIRct8Tk88se9rstTKjgi31LwKgqkxEBjANX9brtapiN2NTPdwfPcvN/vdZW3Mwr3EuN3tTJERFFPGIUCpls25vZR0D9DBAN2EN4ZcFmQbbr6qP5+w3pfq8qo4+6nxPRSwe5MxTJJLiIZoeLksTLs+2LJKx3dX/xPIMQeWDOOnRLEQr6oYA49/9mAWeFMKUuZwNcc3SfJEa7jJSCFI0mn5tYzGnAG98N8si6HFlN3GdVOp2YFPtM3SPX+Fa/9usrtjD+OB47uEsM3tTJESLHOM6yml9l9VsoVJqHvVwiniIEBFW62YG6KaPDlpYO/sOiqPXKPw40qrPu6t7RKTJfVprAnoLfuAiHjqGtI+zepK1bKNM8s+QPqumSNW5QUedWmDTCMFclkXSl1P7LSQiXqdvO2PeUSahi+RYzkQ0CpEHHK9keIoAYiaEMtKkZOY7yrHsQdhc+TTnhl5jaPQ261q3zD6OZWhvioRojlD3CxBnrGEN4WHuCfSyYVLHGKKfJtoSGY5jROmnm366KdUyWllHBdWEmKKCakooZYh+qqhFEAzJ8YRSxJKApRaTjCVKlMTIz4gWOtdHturzLl4Bfgr4ivv+3cIeuQhw7E3c1qgqYaawH7xkEwCjOsQkYzTQmjhGhAi9dDBEPwEtc1NIVDLFOLU0YmAwyiBV1AEyLRN7Vk2RJHMTpZKmeSNN5LzAGiLbLpx+CJIz8AoBkaS3TB/sOkTtEGFrio6Ji86yFcovZLbM7E2REOWAqjLBKD3cY5xRhujDxKRUg0wwho2FFx979QnGdIoSAik3LyfGGyWCYFBJLZ4s4tiwhniPv0/oSWpYQambpK9KajnIy5zmPaaY4CpnEvu1sZEwk3Rzz2lLHVv0cTx48UlRDL2UcZ1z3OM64HyLQuSX3HEB8oJkrT6PY5i+LSL/ErgLfKbgR/4AwlabUYbo5T4TjDJEP34twU8p44ygKGtZj6kBSijFT+k0exMmjBcvldRmLR0zoaMc4tXEcj0rMXFISoOsZC/Pc4wfMkKYEQYT7Taxm/vcTCQfbWY1bboBPyVpWdWnaYpE0PjUfJcsPRAskpXebTsRsisk1LbRSNQJkRWyX8tCHzDEl5glZkiCtGgk+kChw5ND32co2uX0j0EoOpJff8vM3uQkRCLSCvwJ0Igjb/uGqn5NRH4LR/lt47ivflpVO919vgo8hzN97i1XHHUL+HlV/X23zdeB46r6zUKfVCFgq00v97nCaUw8NNBCI6vYyC4UZZJxyijHTylD9DLBGHe5zRTjaTOzFHWrPisRwuzSJ/FTyiRj9NJBN/cI4eR72MweGmnFwGRSxzEwOKXnuM6VRH9b2Ucl1YQJc5XTjDHMJh4jRoTrnOd9fkAJAdbrds5xhB0cxMamUVof9iUs4gFQS0OCECnKfW4Q1MpZ93mQyt8zIUf1+RcKejA+yPbG4h43uMlFSiilgVZaWMtW9hIhQoQpglThxYcyxUXOE2aKEJP4KCF+Z1IUA5MYUQRhtz6FIEwwShd36OQ26rbdyRPU4tTMm9BRDEwu62Hucz8xrsd5Dg8ebGyO8joAOzjIGEPc4jKd3KKRVoJaxXXOsYsnkZhQx8okKRJxBdVWgizFkekxynvZwvGOhCNpWh+NRhGvd8blvJHNk5Ohu5nXcqoIPFf7GZBIbJmYii8QiyWJoa1zHluzuSZBiBSby+PvE7g3e0mk5WBvMpGPh2imvABfVdVfBRCRnwd+Dfi8iGxy93sa+CYQz+zXC3xRRP5QdQ7Z5h4yVJV+urjOOQw87OZDVGTR7pSmlFmooYFqqaAmB+k4o4c4wVvEiBKgnEpq2MZ+OrlNP534KcUUD+f0MEP0EyFECy20shYTD2MMc5vLxIhSShljbnmIlaxGRPBpKUEqKKeKCUYBOMthQLmqp9nBQQIE8eUhzi3i0aJOmnhOf4yLHKPHvUFd5mTO/ZZCevwc+EDZG1tterjHNc4RpIL9vEhAgmltHMJTkViukUa2SWDWflWVI/wjh3kVC4syyqmmnsd4mg5uMUgPpZQhCEd5nSgRQkzSQgvtbMIixgSjXOQ4NlbC3vkpZYWspF6b8VFCHU2UEGAQp4TEGQ6h2ARjlWyNPU7ArMRrByASc7wYgGaIlR9o2fKhGdoZtWZfzgthX7I8SSoedDkaRSOR/Nvni5gXnQplrIvlvbzSWMOK0pUcC73KuDr3lZOjr+c87DKwN2nISYhmygugqhdTmpWRdJ6ZOE9xaWFkoA94Dyf+918efOj5Ix728uLD1nLu6jXACWVFCGNiYhFjiklGGKCUMtrYRBOrClY9XVUZoAsbm4P8SJpgsYpahrWNM7xPja5glCGe5MOMMsQg9xhihAqqaaCVIJV48THJOCYmlzhJN3dpoo1maUv0GaSSp/Vj9NNNH51ECHOCt1HsYjmOJQJTTLZzgM0aZZwRxhjmCqdn32mJG6jlYG8stQgxSZmWENUIHdzCwCTMFBZRDEyiRJhighEGqaSGLeyhTpoKNwZiidDWs3wyLVRfTT3deo+TvE0FNZiY7OMjdHGXKQYYZYhyqmimnSCVGJhMMc5qNnGeY4zoAJVSSyvrEn3W0sgT+mEG6aWbu/jwc4TX8VulPG18ErWtacLrQiDeb6GhtoVGCzh7Ld5vLFbwfsVIeuAeBF48POH7USIaYkyHiQUmOTNyePadlri9ycScNEQZeQEQkd8GfhIYwXFZo6oXRCQAvAv8UkYXXwH+XkT+6IFGPQtUlT66uMtVwMkIPMIAFhYxorTQwigTriHwUE4lFjFMPNTTTDmP46OkYEQoDhHhSf0IIFmnT1dJHfv0BXq5zxq2YoqHaupZI2sZktFp7eNPbFt0D6d5j1Edopp66mlmkjEucZJh+gGnDIchBv3azVCKOD8uEFc0q9agiMUBj3ipoo4q6riisxAiJXdBxiWEpWJvOrlFJ7cx8WBgMsIAUSK00EIX3VRRSyllePFTQoXrsamgkVVUUI1XfAUfl0e8PKkfwYM3q26xUVop03IG6KGZNkSEZtqolu1Z7U0Ax2u1QXdwjiM0aAt1NFEt9QxpH1c5yxhDGBg8L58CoFNvEyPqEiEbOxYDtVG1p2VLni8S2qQCQy0bjUXTp7kXqt8Cj1ctsKNR7AIRLQ8eqqmjpqxydkK0zOwNzIEQZeYFAFDVXwF+RUR+Gfg54Nfd9V/I1oeq3hKRo8Dn8jlmdUtF7kYpmNIJrnMOC4ud7OEGFwB4iU9SQinHeYu6hhp2sTZnDpi5oqIhmLsRs59PNRU0k/6UmKvfaiqo1RpXiNlBH2EEIUgJQVoAmGKIe1xjJasJ4CPEML0rBrmhVzEwsbHYwzMFEWLndx2K/c4b93JsXyb26WHbm7naGnBmad3gAh58bGEn97hGCQGe4HlMTO42XKCEMraxt6CkJ9/vVnUe9mYVq+bUdzXbaNImxhihh7soIaJEqKSMSvchbYgOerjHKtbTSwcwxc0VN+jRnkQ/+3kJowAPnRUNAeZaPmN59lt4e2N68/j/LBN7E0dehGiGvACp+J/A93ENVA78B+AvgbdzNRy6P/1JJRW22ig2Hdx232/RwhpWsRkAxUuQKkISI8QY63iMaqlgqGOUELP3PR/kGu/C9SsEqGGlBunhHjGiDDCUEE/28D5NrKKTLkYY4CqX2MnjhO5b9Lv6lEquMc4wK2h54HxHj+46fDD6nQ3LIab/KOxNPv8rW21sLO5wFS8+bnOZTeyhniZsLMLY1FHPpDialTWyk6GOUcYJAaHZO58jFvK7lbtvL0HqMLWUPrqIEaGXPky8WK7taWQVt7jFGEPc5S4b2UzkvroECVZwgy7usJatlOTQQz34eIv9zgc1jVU52ywHe5OKfGaZZc0LICLrVV0xDnwcuJzPAVX1sohcBD4GHJ37kB03dYhJTvFuWvHRUpw8PfFw12b2zKf7JQuv+BLJ+9p0g3tthADBtBDgPb3OIL0J4TXAOMPc4Sp3uMoefYZqqX/Ywy+iAFiAYosPFYvR3thqM8kYx/ghFjGCVDLOCM20s0KaATDxsJMn5tP9kkWplLHK1RG16yYmGcPAdMTaKfbmip5mglFGGUqsG2WQLu7QxR0O6suUydw9dEU8eix1e5OJfDxEM+UF+JcishHH/3cH+PwcjvvbwKm5DFRVGWGAEQa5xlkEoY4mtrMfL378C6D7WcoQEcpmcJm3yjpK8GBynltcZAUtlFPFHp7hJG/Tw31u62VWsjZh8ItYAliAYouPAIvC3thqM0QvQ/Rxmyv4KKGZdhppJUA5HrxFe5MCQwyCZE8LsVF2ualr/dzkIu1sJEAFOzjAWQ7TyW3GdJi1bCtm419EGBvKUbpjedibNOQzy2ymvAB/l+9BVPU2sC1l+QyQU61mq80wfUwyzj1uAI4XyMDkGT6OWczKPG+USoDVsokGbUmIQsNMsYYtiSSUA/SwXQ/QIC2PerhF5AHByQ2ylPEo7Y2lMYboY4JR7nKdEkoRDMoo5wAvFwnQAyAoFayRLdRqI73c5zaXMDBZwxZucwUbi0HeYJ8+nzXNSREPH+XVQXe+Z3YsB3uTiUWdqfoN/ppSglRSwwZ2UENDwY3SpI65ZTBqss7GWO4ISJB1bEdV6eIundxigjGe5ZO8x99xjsPc1Vo2sINKqX3Uwy0iFxagisEHAR16k0ucpIJqglSxg4ML4q0Y02EsYlRQ/YEss1MpNVRSg6pymyv00UEJpezled7iFY7yBnXaxAZ2TsvHVMQixDKzN4uaAaxnB820L8i0VIBxHeE4b1JGOeOMENByLCy8eJlgjDLKiRLFgwcPPiKEUGw8eKmmnloaiRBmglGqtIL7eg8DgzqaqaNpSRGs+LTbZtpbwefIAAAgAElEQVQ4ov/IKIM8yUe4wDH66OQYP6Ra69nOgWmz0fq1i6ucIUA523U3Jg8mkixintDlF9N/WOjgFhvZzUraF4yo9GknlzjhZqofp0zLibkpP0JMEKCcKGG8+DExCRPCmRrip4YGaqhniknGtIT7eo8IYbz4WMFKamhYUqkzRITVbKJdN/IWr2AR4yk+ymneo58u+umiSdvZzO5p/4+7eo1OblNGOTt1DxSolmQRc8QytDeL+o7dJhsWrG9VpZdO6mlmq+zFUosxhjDx0M09ApQ7ZAwfMaLEiOJ3XegRQgzSwzXO4qeUAOWUEWQFLVhE6eIOlzhBk7axnu2YS4gYATTRxlkOsZ0D7JQnUFWucoZ7XOdt/pYX+TTX9Bx3uMJBXsbGZpJxJhnnGibrdW8xnPlIoAtSffqDgL08v6AhMacUUCetrGO1bCamUcYYxoOXe1ynlgZqWIGfEsLug5ePEqeuFBP0080VzhCgjCBrKKcaLz7CTHGby5znKKt0A6vZtKRCeyJCk7ZxlDd4jKfYLy9iq81p3qWL2wzTx5N8hKP6OqMM8SyfJIaTqHScES4hbGT/oz6NZYmcGqJlaG+W1p26QAjpJGc5hEUsMRPNFNOt4Awdeosu7rBV9s7QQwU1rEhbUy0V+N2EZitZQ0TDXOUMR3mDtbptSYmTV8l6glrBRU7whH4YQww2souVujqR26mBFnz4HTIoFbzIp7E0RiVBxiX/KcZRjRQFqgXEcpsG+7CwkN+/MR3mLIfw4Wc1TqURjzheZoAh7WOKCdaJI3vKnAwRpIK6lPxk1VKRljxxFeuZ0gkucpxBelirW5fULNGNsosyLecyp9ijz2CIwW59in66GHQTya5hCzGieMTDGrawhi1ENUINVYy5tSBzQVWJEsGLr2hv8kAuDREsP3vzgSJEQ9pHj97kMudpYyNtbMj6wyinMs0AzQc+8bNV93KP65zlfV7QH19SP8IaaaBcqzjHYeq0kSbaCUplYmpxhVRTQXXaPqZ43PBmkhB1qeMts91gcxV1NNCCYDDKEN3cxcZijW5hjWx5aOe3LLEMM8cuVagqA9rLGT3GKENsZBeNsipr23qa3Tr180eplPGYPs0lTnKZUxzk5Qfq72GjmdXcx9Fx1Wg9DbRSL83U4zxIZitr4hXfNFnCNT1HT0rB7Foa3esLfS7BsrGKk0UKgWVobz4QhCiiIS5wjEnG2cuT7OOFWTNVr5Q1rGTNAx9XRCjVMgKULykyFMc29nGHa3Rwy3mCZfuc+wgTcrNhO4TIwKSPTkoI4MHLU3yUO1zhJhdp1FVFIeWDYpm5sJciJnSU8xyjmSaaaGcLe7OW64ljg+wsyHFFhDINEmUexUwfMeJeoXtc5wYXsbFppn3O/UQJY5AarneKdfvwU04VOzjASd7hHIep0Y8vmD71A4NlZm+WPSFSVU7xHjXUs5MnqZWqrLV65oqQThFiIvHjszVEn/YjGBgYDNPPEL1MMDYvIrEYYIqHNWxmpa7mKK+DCmvZOidy1y4baWcjttp0c48B9ylNMAgzxVu8QjubCFDO+/yAvfocE4wTpIJyqpYkkXykWF72ackhplFO8g6r2cQ2djEsY7l3ygOTOk7EfbjwqE2vOoWiPXhQlEF6GWGAScbZtkQ1NX4pYR3baNAWTvIOttqsZPWcbMAWeRxwUih0cIsh+himn0pqGWeUW1xiI7sYpp8j/CPbdT+TjFNBdTE5ZAZya4hYdvZm2RMip+iiwTq2F+zmOqA9nOUQQSqwsVGU1ayhmx532aacKtawhSCV+KW0IMd9VPBLCfv1Bc5ymOO8SZXWUkdTQtQZJYIgDNBLo65gSEcpoRQvJXjcr1iAIM3izGKLaJhhBphijAF66OI2pQQT2YAd8bpQSS1bde+Smj2TDZM6TgmlD2Wa9XLLC7LUcItL1NJAi6wtmL25rze5wXkCBLGwWUkTAwwjGFhEE7+VdWwnSGVBahI+SpRLFY/rs5zlEH10Uqk1VFOPnxJGGcbAIEqEYfpp0iaGdZQSAnjx4cNPlAgV1LBK1if0VWMMM0gvowy6M9QqmGA0YW8Um2ZtZ50szYfXVEzoaEGiEvlpiJaXvVmWhCisU4wyhI8SbnCBnTxRUE+DINhYmHjYwV78Ukq1VNBQAM/TYoVPSnhMn6aPTvrp5iyH3Bl4Mcoox8CkjkbKqGCIUUYYJEKYQZckAmzU3axgJX4pYYWrDViljo5rXEfo5h63uYyByR6e5gLH6OAWrW45kqWK4/yQWhrZykwi/QJBgWUW018KmNAxphhHMOjkNgd4qaD9C0KUCGVUsJ4drJC6gni5FzPKpJx9+jw93KePTu5zE8PNrVlCAB9+ammgjCCDDDFEHxHCDNCd6GOr7qWeZkqljFLKWMFKNuouRIRh7aeTO3Ryiwqq2cRuTvAW1bqCWml4VKddEBziVTaye+Ht5jK0N8uOEI3rCId5jRICmJi0s6ngCdZqZAXP6Me5zEkO8xpN2sZ23V3QYyxGGGLQQAsNzCxGdGbbJVP4X9Oz3OEqzbQzQj83OE+V1rKN/XgkObssKJWs1jLuc4MpxrnECQShm7v0aQd79CC2mpznqDOGJSSI3MFBYkQX/DiCFvyJTUT+CKcOWK+qbnPX1QB/DrQDt4F/qqpDM/WxnDGgPZziHQKUY2CwmT0F9wivlNXUagOXOMn7/IA9ehCPlj1wUdTFDlM8NNM+q5bIsTfJIqQn9C2G6KONDfRwn8ucokFb2MRjGGIk7E2V1OHXUjq5RR+diRQHXdzhjl7hgD5DVCOc4zBr2bqkktLu4CB+Fj4qsRztzZIlRJZaAGn5biZ0lJO8TSvr2MDOBdWfeMTLNvYT0knuc4NLnKBfB2llHU3SNuf++rSTy5xiAzuX1M0+F9aylXqaqaQWEcFSi2uc5Siv06YbqaAaP6X4xI8pHp7lE4zqIL10MkQfIwwAcJmTbGAfA3TRy33K9UcISPkjPrv8UCV1xDRKt96jUVoX9mCFd2F/E/g68Ccp674MvK6qXxGRL7vLXyr0gRcTYhrFxJNmUwa1h/McYT3baZONC3r8Egmwmw8xriNEGecEhwhqJe1spFYa59zfXb3GPa6zlX1ULaGbfS7s5EkmGE08BMc0yjmOcII3adX1BKlwJnSIl1Ip40U+zYD2MEQvUSJ0cxeAe9ygllUM0ssgvTyrn8AjSyMB5ApZyZROMKDd8/puxJGfhmh52ZslKc6w1eYor3OE1xLrerWDQ7xKM6vZKLsemhi3RAKsk+08xtOsZSvXOMegOrkzxnWEkE7SqXcI6RQxjTKlE1n7CRPCxOQCx4jpwnsTHhYMMamSusT/wxSTTbKbDexkkB5O8jZv87dp16VCalgn29grzyWm+SvK6/wVJTizA69xDl1C8etxRrjIMUI6tXAHibuw5/LK1aXq28BgxupPAN9yP38L+GRBz2ORIaZR3uS7nOdoYt0dvcpJ3mE9OxacDKUiKJWskc08xUdpYQ1nOcyUTqCqjOkwkzpOl94hqhHCGiI8w/ctxCQGJuc4vKR+R7ngEU9aRMAjXnbxJCtZSw/3OMxrvMl3iWks0aZWGlgn23mKj7IRx9M/RC9v8jeU43if7ru1NJcKBujhHEewXcfBfFBenWPG7zK0N4vaQ3RVz6A4Ve5jRKmkli7uAE7ysknGeU//Hh8ljLrXMJ747GFDRKiVRjbrY5zjMFGNAODFh59SrnAKy03TX6IBSiljPTsoc70cFVRjYKLYZK9tOTPu601ucYkKqtnGviWRGbtOmqijKZGV9iyHWKXrqaY+LRQQf2LbwUHu8xdOmRTq6KOTTm6zktWP6hTmhBIC2Njc5jKbWLjw6jxc2HUicjxl+Ruq+o0c+zSoaheAqnaJyIoc7Rc9RnWILu4gCD3cp5Qy/JTSwz0Ayqlyih6rk116hEFKCNDE3L3BhYAhJg20EtYQx/ghETf3l9dNl3qRE4Am7E2QSjawE5/4UVUqqGaYAaKE5/zweF3Pc58bNNPOenYs+pmgqWWJohrhOG9yjkM0azvVrEiI0EWEu3qVMirYxGPc5z5jDBMgyHXOU6m1SybhZSkBYkTppYNGsue/KgSWm73JeecUkVYc91UjTim3b6jq10Tkq8A/ASLADeBnVHXY3eerwHPAL6jqWyLSDtwCfl5Vf99t83XguKp+c8ZjI/gpxYsfm5hbIqOcEgI04IQeJhkjQpgSArPmFnpYqJdm9ukLjDHipNl39TRhDRFmigBBppigk9vc5jIbdRcXOE4fHZh4KKOSIXoTCcnyQQSn7z6mOM8xtuv+JTMzK55/pJPb9NLBFU6DQgtrWMNWNrCLSSY4yyEAaljBIL3s5CBBqnL0vnhQIgF26hMEqczd+EEwdwPVr6qPL8RQ5oNHZW9O8Q6rWI+BSTPtCEIpZZRRTgXV1IlD3icYxSLmEKZFMHt0laynRlcwwRiV1CQeJiZ1HEXx4iVMiJtcooObrNQ1nOJdxhiimnoUGNdRgnOYcj7BKDGi3OUaHrys1s2LnhTF4RUf+/QF7nGdLu5wkeMYarKWraxkDbt4kpO8w2VOAhCkkhhRtnNg4X+7BUQNDWxjP7UssEB8idubTOTjSojhGJqTIlIOnBCR14DXgF9W1ZiI/A7wy8CXRCTuonkaJx74lrvcC3xRRP5Q1XWf5MB62ZGzTRkVPHoalI74rIZUTDHu5BARL+VU0aYbOcyrCY9XCQFCTLKWLVzhNOM6SjNtkJHG33JdvQZmwgitZvP/396ZB8d1Zff5O2j0hn3fdwIguK+iKC6SKEoZzXgWa0ayZ5w4U+VMVRw7k0zKccWOXXa5Uk6pPC6X46qUXc44FVcymXiZTePZKImUxEUgIW4iQQAkQIBQEyBWAiCxA33yx3toNkgsDaDR6G7eD9UFvNe3f+/2Q7/T95177jkoSjtN9HGXGzTEVC4SEaGYSoqpRFUZYZAGTuHjNmXUsJejdHMTgEF6cZBIJ61kkke+luBnllSJ/sFR7rqXb4lYbaEeESm079YKwa6vEB42xN7kUEilbFmyTYIkBKZQookUSX/iy3qMhySRgks8uPBQobVc4jRtNOLGShI5xADVbOc65ynSigW9XTM6gyDzYjV38hyNNHCPTm5zAycuSqle3zcZRhzioILNYOdH66OLa9TTQQsV1HGA43xilyh6yDCJdn3LcUbJ02IUDXj2oxURoYC1xStGUS2z9bQ381h2QGS7qubcVQ9EpAkoVtUTQc3qgdftvx1Yd3bK/LmfPuAs8FXgf6y967FFK9cZop8X9RdJlEQ84uWQvsoEoySTxlW71lGuFCGaQD9dfMgJyrWCfh1ghhkmGcePNSesKIlqBfn58TPLoznxuRpJsYiIlVPlmL5GH120co1EXORTxk17SX4iTpJIZZRh6mlC8bNTnyNPije6+xtL5JbBvoV1Hb9p//5huIQ3yt5ME9I9WsxwlXMofl62T1O6ZHNQX7G9Wym8z1sUU0kZNbjx0k8XbTRSqVX0aT/TTDPJOGr/iIIDJ6D4mWWWR7Ep6cRuUPbcytks/Ty93OUWH+PBSy5FdNAOQDLpJJLICIO0cg1BOKDHY+ImbC0sm4coDuzN46wo2MR2Re8Bzj/21K9hLYtDVRtFJAk4A/z2Y+3eBH5qL61bEwut+ohmdnGIhwzjCEor7xI3Lqz5670cDezPkQJyKKBGd5GMm3vcw0GiPX3oCqzWmrGTss0lKhvhPu008ZARVDVmzs1COMRBAaWkaSYXeJfNbKWETfhoY5JxssknT4rxq59GGphgHYOVY4h1WAb7HeBFrLl/H/CHWNfx34vIvwI6gTfCetBHx64gQvZmiP5Fn1NVZpmJmVVGAEf49BPXRHBIwUu8Fvi7gFIKKGVWZ0jGTQY9JOLCgxeH/RVh3XLN2PbGwSRjPGCIm1xlmMEn6hrGGk5xUUwlXk2mkQY+wxu00cogPQwzwFb2kyyp+NVPA6eYZILo9hFFhniyN7CCAZGIpADfBb6hqiNB+38Py8397bl9qvr1hTRUtV1ELgC/EsoxM0sWntee0HEuUw/As7xCQohf/He1A817wJT6ceGel9V1SPvx0U4xFasKnEvLX74GVx45q9L1hphvpIhCqrWGG1xkhHuLFq+d010Pwq2bSRoH9Ai9+bfZzi5cwBST3MfHGAN4ScaJUk01CSSGfK7Wq7/rrWvH+C5OmA2Uqn5lkaeOh/VAjxFpe/O5kq88USh0jiHtp8WOKXlOQiua6lflQV4fg9prZavHQwrpgUFVr96ln25KqV6xpyG0z9bqylBY9iaUIIQsoIQKreIGHzHJ8JIpJWLlOsskDXSSnvx2DnGMK5wBwMcNkrCOlU4y5ZSjgHuFWcFj5TwApOUsn6k6XuzNHCENiETEiWWcvq2q3wva/1WsJErHNfS1m/8V+Efgg+Ua3vctno21QncywzTDIdYKmtAxznGSEkpQn5NxxnjAfRJxkkkuKWRwk2s0cY2X+OKqgpKX6u9aWKlugVZzidN00U0NOxa9s42W/i5HEtn4meBd34+ZZpJEnDzLK7RwiVZukkE2P+EfEYQqti4bC7Le/V1v3UVRhVl/ZI+5DmyEvRnxjS5686DqpIxtgIScIXpI+7lHKz6fjyIqGGeUEe7jIYks8nDhpo1GWmjiRflCiG/lEev52VqNvWngA4qpooyaefFGa9ENlXDrZlHKBPd5x/cWE4yRRzG17OQCJ5lignxK+SHfxkEideylUFa2iitWzsPDvrGlG8SJvQkmlFVmAvwN0KSqfxa0/1Ws5EgvqOoyZ+4RqtosIjewDNuF5dovxkqytE7oGFc4Rzm17OdwYBClqowywgA9tHKNY7xGAgkxPdUElvt3nz5PM5c5zY8p1HKKqIjpYqnZkkcJVbTTRAIO6jnBM7zEZvYgCGf4CRnkcJsbZGl+2LOTxwwxnlNmo+zNUteFiJC0ggmSEb3PNc5zgKNs5tmAtl/9PGSIe3xCDz6O86WYvR6DSZIU9uuLNHGRDpop11ryKYnpYqmFUk46LUwwxhADNHCKA7yEIEwwRg+fkEYmrXxMumaRJOvkEd5AQqllFuv25nFC8RAdBn4VuCYiV+x9/xn4C8ANvG1f1PWq+ushHvePgcsr7OuqaeU62eRRzY5502siQgrpONVNK9eYZgpPFCylDQdWJu0DTOsUbTRynfPMMkuhllMt2ze6e6uiiq2UUYNTXDTpJWsZMZUkk8YRPkMzl1GUBk5SpJXUsTsiBVWjitg3UDFvb5q5TCVbKJCSeR6lBEkgjSxmdZYefFZcErETl7QUVibto4zrKLe5wSVOk6AOyqmlRKo2unurYjvPsoV9JIqTi/o+PfjIoZA0sniOT9HIBSaZ4Bw/o1p3LBmiELfEvr2ZRyirzM6wcKbAn4R6EFXtALYHbV8lglmyZ5gmm4JFP6xu8bBJt3Oet6nQOsqlNlJdW3ec4qKOPagqYzzkI07h1WQ8WsOQ9ltp3KJ8CekcIoITFwBl1HCTKzRwCj+zVhqCoI9pF+1000GRVrKJbTFfATwkVGF29Zlpo4F4sTdLLc/PIIdcCvmQn1Oju9a/nEsE8Uoy23gmkD7jIu/jVg9J6mJA75FKBi7xbHQ3Q0JEAgPWuSoEbTSi+EkgYd5Ku1au8QmtFGgZVWxddNowrogDe/M40Z/SOAwUUUEbjeTr4jXCKmQzWZrLBU5SolXrnu35gQ4xxAClEplK7iJCMqlkaT5NXOQBPfjwkUwaW3U/E4yRSW7MDBySJZU9HEVVAwOibu7wgCE+oRWAYqoQhAZOUqd7Y76KdUjE2R1bLFJEOe3coFwXzmItItSxl3TNppVra84XEwqD2sM00xGrkziXPsOhTq5yjgE68WHFU+VrKX5mySQ3ZlbuZUgOz3AMv/qtNAQIndximknu2DnSSqjiAcNc5D3qdC9pEtsr7zaoltmG8lQMiHIp4i636eQW2TyzaLtRrNiiBFY3uldVhhnEgWPRlSMzOm3XmanHiZtSIjMgmmMbz5BHETVsIYkrtpflJNnkc4OPUFWcuNjL0ZiIARCRwNLguarYosIQ/dyjkzr2kkkeTVwkWwvIo4gMcmKivMmKUcAfXwYqFimjli7uMEgfCSzuDRnlQSCv2GpQVQbpxUvyojEs0zqFjzbaaCSXIvKJbOHow7zKAD1sZgvnOEUXHdyjkwxyuMZ5UPCSzH5exCmuiPZtNQQvtrGSO1pFxaewzvN2nmWUB1zmDCVaRQY5ZJIbM5UDggkpD1Gc2Zs4/Faw8Kuf2zRSRi0ucbNZ99iegq2LvmYuu3Q7TVSxeLtgRnWEW1wjX/No5CoAFdQ94TL3q587tNBmZ0B1kMhhPr2at7YmrERkpTgkgTKpJlvzmWKCTMkNeFvu0sEFTpKuWdynjyN8ZkVlCvzq5z59jDPKIL2AUs32iE3NFVNJL3eZYZrrnGc/x9jDUXy0cZkzbOMZCrQsPuf74+yOLVaY1Al8tFFBnVXAWPfQQTMlumVRr2sK6UwxSbfeoVBCq4k2rIP49AaDOkQvXQBs40BgSfgc0zpFB80B70UWeezg4Bre4epIFCf5lCAibJX9lKh1A5gmmba3xU8r1znHz0jRdB4yzBF+YUVTTrM6wyC9jDPKAD248VDN9ohNzZVRw3UuMMUklzjNPp5nH89zh5tc5jQHOB7zeZoWJc7sTdwOiEDpoIUOWjigx0mTTNI0iyucJUcryJGCJ16RITns0INco55irVxyEDCrs1zlrP2FD2V2Ab069lCywDTYBGOBwdBujix4/I0gWaz6cPDI21JGNQVaSjd38DNLPW+TqC5mmaaCOgooCxj5WZ2hmzsM0mslK1MvfQyQSCJpZJNKOm000stdjmtkVtUkixVkfUHfZYT7fMQpAGsgiINGGujBx059bt37ElkU/PG1DDZWUPy008Q9OtmnL5BJLn208wE/4pC+uqAHp0BKGdURGmlYdoA+pZOc5x0mGaeEErz2AGgPRxecCh5hMDAYOsDxqJm+Ce6H5TVJYDO7KdfN3OU200xxlp+SoJZHZRPbyaUwMLU2rVPc5TbDDDLOKJmaxj168ZJEKpmkkk4HLYzygGc4FpH3lCX5PM/neFe/i+LnI95DEIqxgskv8C6lWk0tuyLSn8gRf/YmbgdECeKgWCvpppOLvIdXraKqXgpIXaBIn1/99NONjzayKcC1hKsbrAKHg/RSwiY2sY08ySFrieDIJEmJ2IAgHLjETTm1lFPLhI4zywyzzNBOE7e5gUvdJOBgnIdkkkcBZThxUkwJffSTRCoOcTCuo4GB4HXOs1X3R2y6ah8v8AltdHKLJFLw4GWWWarYxgiDNHGJwxoZoxkRlLgzULGCR5LI1FyGGeQ87+DCTaZtZzw8eWM1q7P04qOXu5RSvaxdGGaAScapZgc72cuIjFLD4rUes6UgpuyNR7xsYhtVupVxRgGYYJQOWmjmEh5NAtTOC1Ri2xsXRRRRRD8ppCMiDGk/HbQwzAA39So17IzYOTjKZ+nkJp/QRi5FTGBlh9jBQdpp5g4tZHEgIn0JB8vGEMWhvYnbARFYdxeTjDPJJJvZRTJp5EnOvKWwszpLN3e4QwsuPBRSRhGVy15EaZIZqBMUKrFinB4nOBXBLg7hVz9jPEBRvCTPC4z0StK8+CmvJPMyrzOrMzRxiRtcZEeECs86JJEKNtOnXSSRSj6ldHKLcmoB5SPex8dtUmK49tsTxJkLO5bYwj6ucx4XHiqpo5Ry6mT+l8qMTuOjjU5aSSWDKraQF0JcT64UBexNqNNJsWhvrJxPlvcriRSyyGdWZxjjIYLgJWXe+3/c3mRIDi/zOlM6ycd8yG1usIltEem7S9xUswOf3iaZNFJI4yHD5EsJGZpNA6co1VIcUVeOfGFMHqI4wyVuduohrnKWS3xACulU6iZGdBQnbh5wnz66SCebrexfVcmOp5EESXiiuvZyOCSRLbqPs/yUUX0Q0WrRZdRwjXq6aJ+3JHa3HqaLFnzqo5RqkkiJyS+RR8Rf5thYIklS2KvP08ApLvIBD9nKoA6RSjpCAsMMMEAPuRSxl6OkyMquoacVhyQumcZgIVziZrs+Sz0nqNDNEV1EUckWbvExAFvZD4BbvOzWw3TQgqiHIipw4zX2JsqI6wERWF/eu/UIs8wwwiBpJNPPANNMkUomm9geN8kYox2HOMjSPPrpDsQtRYJ8KSFHX2OWmXkBrm7xsINnuUg9lznNBGPs0IPkURybhkpBNb4MVKyRKE4O6ivMMI0TRejmPv0kkkg2BWxhX0yspooHPOIlSVO4Tx85FEbsuOVSS7FWAszznqdIOjkc4CIf8iEnmGUmEN8ak8ShvYn7ARE8SrCVRT6ZkkZiHKZZjxXKqOFjPrSnrSKHQxw4Fkin4BQX5dQywwzddHCNeuuzovnkURx7SfPibBlsLDKXQDRT0kDcgXQQhshTRg2d3IrogAhYNL+SV5Ioo4YZpumjiwu8i1Pd5FBIIaVkRVGutJDyEMWZvYm95AiGmCaVDGaZYSL0clTrzj066aYDsAKxi6lkhEGuc55OvYVf/YFH1KO6sofBEMdkkccI96Pq2u3kFn12yoTn+RyZ5DLAPS5xmgHtsdKfqJ/Q6xevD6mZITgO4szePBUeIkP0ICKkaxZn+AmH9dN4ZeMDDIuoREhAEDLI4SLvB567yVVu2vmlAFBw4aGETZRRHV2ZduMwlb7BsBZc4iFBHbzHD3hBvxAVJTUqqCOZNDwk4cRFL77Ac1c5i59HgzdRwUMSZdRSTGV0JXiMQ3tjBkSGiOPEiuNZaAprI5hLMTDHi/oF+uiikYYn2h7ls0wzSTvNnOcdyrSGfEqjpuSJxtkyWINhrThxMsPUvFqHG4mV+21zYPt5/Rz36OQmV+cNhhJxcpTPMsIg7TTRRTtlWkMuxSRGSab9eGA39d4AAAm6SURBVLM30XFWDU8VORQyykjUFnlMFCeFlJOvpZzke4H9c7lP5oKxB7WXLtppo5EcLaSOvRtsqGLDLW0wRJICyhllJLq8K0G4xE0ZNeRrCaf5cWB/CZtwiINMcsnQHHq5Szd3aOEKxVpJNTvWdfHH8jFE8WdvzIDIEBLTOmUHJxaQLtlr0sqjmGYuM6FjeCQpTD0MPwmSwMu8zqROkEDCE6uDsiSPLPKY0Wkuc4Z7dJKjBXgkyaprpwM4cdPJTWsghZcU0smQnPXpsLIuLmwReRX4b4AD+Jaqvhn2gxgMQYzrKF20U0D5mlN0lFDFaX6Mqkb16lG3eHmZ15nQMZy45qUKEBHyKSGfEiZ1ggZOkkkeqZqBWzzMqp8htSoE3OEmSaTgxEUGOatO7xBSLbMw25uNtjVmQGQIiWmmaKeJdprYrs+uafWViOBWD1NM4iF6B0RzuJfxZCWKk2zNp5lL1g6FSqroootJJsiliBlmmGKK2zRRpjUUUBr2waACGuZVHyLiAP478ArgAxpE5C1VvRHWAxkMQUwwRjvNtNPMM3psTTdhTnGBXafREQNfecvZBbd4yNBsrnDG2mHbm7t0McUEZdQwygMScNBGIzW6k1yKwp7uIdz2JhpsTfR/OgxRQZKksEsPcZVzTDK+Zj0PSfTiI1UzovquLVQq2EwexUwwxhXOUslWKtnNEP1kkht4j+maxT0+oZNbHNSXwzttqArhX01zAGhV1dsAIvL/gC8AZkBkWDcyJZdK3UI7TUwwRjpr80p7SKIHX9ykQKhjL1VspY8u2mhkM7upZDfDDMxLMHxbvXRzh05u8Yy+FN6g8vDbmw23NVE9IBIRXJ5Hq3imJqbXvu124va6VvX6YB5/TpCAbtj66nEG+ruS16/XeSihghIqmJqYpjXhGu0zzaQnZLFfX8Tjtb7YXW5nSP+3re49XJ2qp8l/kT3eQ+tyHkLZXu7/FsxyWo6JRLI9uRRTToYnld6EQQo9JfPaV3prqaSWy5Nnuetvp867K2T9lKxkghakLEi4PURAMfBJ0LYPIlR7JYIs9NkKJtK2Zqntha6xNfd1Ce1w2t2VvHYre6ie2I7L4+T0+E8Z0fsUU8ku78HADUaodnfn7LN8PHWeKSbZ7N2xbPv1Pg9r1XPjIplkMsmmlh0ki5fRpHG8FM9ru4XdqCqnx3/GkKuXosTykI917CtH+Ps/+xZLEWZ7s+G2RjY618FiiEh0dsxg2FjuqGrFQk+IyM+AlQYoeYCJoO2/VtW/DtJ8A/iUqn7N3v5V4ICqfn2Fx4lajK0xGBYlYvYmGmxN1HqIVDX251EMhgiiqq+ug6wPCA4YKwE7q1ycYGyNwbBy1sHebLitic51iAaDIVpoAGpEpFJEXMCXgbc2uE8GgyH+2HBbE7UeIoPBsPGo6oyI/Fvg51hLYf+nqjZucLcMBkOcEQ22JmpjiAwGg8FgMBgiRUxPmYnIfxCRRhG5LiLfERGPiPwXEflYRK6IyAkRKQpq/00R+UhEXrC3vy8ivxj0fIuI/H6Q7rCInLZ1vykizbb290UkI0y6b9j7/CKy/7H3txbdLBF5W0Ru2b8zV6G72T6PQyLSLiIjIvINEdklIh+KyDUR+ZGIpK1Q+89t3TntMVt3t4jU2/s/EpEDYdL9u6D9HSJyJRy69vNft9s0isifrPQcB21/V0S+iCFqEWNvjL0JTdfYmxglZgdEIlIM/Dtgv6pux3KxfRn4pqruVNXdwD8Bf2C3r7Nf+jzwm/bf54BD9vPZwEPgxTldYBS4b+u+DWxX1Z3ATeB3w6R7Hfgi8MFj72+tur8DvKuqNcC79vZKdJ9T1Rb7PI4Bh+3f3we+BfyOqu6wt397hdo1qro7SPuhrfMnwB/Z+//A3l6zrqr+ctD+74JVj2OtuiJyDCtPxk5V3Qb86UrPMY94zm5jiEKMvTH2JlRdY29il5gdENkkAl4RSQSSgC5VHQl6PhkroSZYBsxvb8+tKjmL/aGxf/8TkG3rbgbGsc5Rl6qeUNUZu209VgR8OHSbVLVlgfe2Jl2sC+dv7bZ/C8zdLYSqmysWlbbudqBNVe/Yx5ozqG8DX1qDNsAtW1eBubu/dB6tMFirLgAiIsAvAd8Jk+6/Ad5U1UkAVe1dg+64qt7DEM0Ye2PsTSi6gLE3sUjMBlWr6l0R+VOgE+sCOqGqJwBE5I+BfwkMA8fs9o0ikgScwb7DAC4C28WKaD8EvA9UYSWHqrfb1M/pBvFrwN+tg27w+1uTrojkq2q3rdUtInmr0N0C7MG6uL7Mowv7OvB54IfAG9hLJVepPRak+w3g5/b/NcFuHw7dOY4CPap6K0y6tcBR+/M2AfxHVW1Ywzk2RCnG3hh7swLdOYy9iTVUNSYfQCZwEsgFnMAPgH/xWJvfxXKJLqVzFjgInLI1fwtoAv4G+I3HdYHfw3K3Sph138Nyxy/3vkPSBYYee939Fer+BvA1rNoyvwn0A/l22zrgBNZF94fAwCq1/xLLlTun+xfAl+y/fwl4Jxy6Qe3/EvitVZzjxfp73e6zYKWdb1/qc7HMOf71jb6mzGPJz4SxN8beGHsT549YnjJ7GWhX1T5Vncaapz30WJv/yyP36mKcw5p7TVXV+1juRwewFzgdrCsiXwU+C/xztT9x4dBdIaHq9ohIod3vQqB3YblFdettnUO2/iVV7QFQ1WZV/Wequg/r7qVtldqfAj6e0wW+avcf4B+wLvpw6GJPc3wR+047TLo+4HtqcQHLbb1U5talzrG5Y4tujL0x9sbYmzgnlgdEncBBEUmy52qPA00iUhPU5vNA8zI6Z4F/DVy1t88AZfajMUj3VeA/AZ9X1bEQ+heSbgg6q9V9C+uCx/79wxXqfox1d1GGdQEF3MFz7nARSQB+H/irVWoXYwVMztEFvGD//RJwK0y6YH2hNavqMtXAVqT7A7ufiEgt4MK6s12p7tz/zhC9GHuztK6xN/Mx9iYW2WgX1VoewB9hGaDrwP8G3FhR/dex/vk/AoqX0cjDCkb7WtC+DixXZbBuK9bc+RX78Vdh0n0Na+Q/CfQAPw+TbjbWao9b9u+sVei+hxXEOACkB+3/91grX24Cb7K8O38h7Q+Aqcd0j2C5xa8C54F94dC19/8vQnQTr6C/LuD/2Of9EvDSKs/xkv9z84iOh7E3S+oaezO/rbE3MfgwiRkNBoPBYDA89cTylJnBYDAYDAZDWDADIoPBYDAYDE89ZkBkMBgMBoPhqccMiAwGg8FgMDz1mAGRwWAwGAyGpx4zIDIYDAaDwfDUYwZEBoPBYDAYnnrMgMhgMBgMBsNTz/8HMh5HPJ9lzP8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -342,6 +342,9 @@ "from climada.util import HAZ_DEMO_MAT\n", "tc_fl = Hazard('TC')\n", "tc_fl.read_mat(HAZ_DEMO_MAT, 'Historic and synthetic tropical cyclones in Florida from 1851 to 2011.')\n", + "tc_fl.event_name = [\"\"] * tc_fl.event_id.size # storm names are missing from the demo data set\n", + "tc_fl.event_name[11100] = \"ANDREW\"\n", + "tc_fl.event_name[11690] = \"ANDREW\"\n", "tc_fl.plot_intensity('ANDREW') # plot intensity of hurricanes Andrew\n", "print('Two hurricanes called Andrew happened in ', tc_fl.get_event_date('ANDREW'))" ] @@ -378,31 +381,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:37,505 - climada.entity.exposures.base - INFO - Matching 50 exposures with 100 centroids.\n", - "2020-03-13 16:28:37,509 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:09,044 - climada.entity.exposures.base - INFO - Matching 50 exposures with 100 centroids.\n", + "2020-09-16 14:52:09,047 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", "Expected average annual impact: 6.512e+09 USD\n", - "2020-03-13 16:28:37,562 - climada.util.coordinates - INFO - Setting geometry points.\n", - "2020-03-13 16:28:37,571 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + "2020-09-16 14:52:09,092 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2020-09-16 14:52:09,098 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2020-09-16 14:52:10,856 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", - " fig.tight_layout()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 16:28:37,934 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n", + "/home/tovogt/.local/share/miniconda3/envs/tc_env/lib/python3.7/site-packages/contextily/tile.py:199: FutureWarning: The url format using 'tileX', 'tileY', 'tileZ' as placeholders is deprecated. Please use '{x}', '{y}', '{z}' instead.\n", + " FutureWarning,\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -455,7 +454,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:38,412 - climada.util.save - INFO - Written file /Users/aznarsig/Documents/Python/climada_python/doc/tutorial/results/impact_florida.p\n", + "2020-09-16 14:52:11,205 - climada.util.save - INFO - Written file /home/tovogt/code/climada_python/doc/tutorial/results/impact_florida.p\n", "Data read: \n" ] } @@ -510,19 +509,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:38,423 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,425 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,453 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,455 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,485 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,487 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,521 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,523 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,573 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,574 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,603 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:38,604 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:38,633 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", + "2020-09-16 14:52:11,217 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,219 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,244 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,245 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,278 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,279 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,312 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,313 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,365 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,366 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,394 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,395 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,426 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", "\n", "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", "----------------- --------------- ------------------ --------------\n", @@ -542,7 +541,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -551,7 +550,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -599,39 +598,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:39,015 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", - "2020-03-13 16:28:39,015 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", - "2020-03-13 16:28:39,016 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", - "2020-03-13 16:28:39,017 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2020-03-13 16:28:39,017 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2020-03-13 16:28:39,018 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2020-03-13 16:28:39,019 - climada.entity.exposures.base - INFO - region_id not set.\n", - "2020-03-13 16:28:39,020 - climada.entity.exposures.base - INFO - geometry not set.\n", - "2020-03-13 16:28:39,033 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,034 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,058 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,059 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,086 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,087 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,114 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,115 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,168 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,169 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,197 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,199 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,231 - climada.entity.exposures.base - INFO - Matching 50 exposures with 100 centroids.\n", - "2020-03-13 16:28:39,235 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,261 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,264 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,300 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,301 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,331 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,333 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,385 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,387 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,413 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,415 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,449 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", + "2020-09-16 14:52:11,713 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", + "2020-09-16 14:52:11,714 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", + "2020-09-16 14:52:11,714 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", + "2020-09-16 14:52:11,714 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 14:52:11,715 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 14:52:11,715 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-09-16 14:52:11,715 - climada.entity.exposures.base - INFO - region_id not set.\n", + "2020-09-16 14:52:11,715 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-09-16 14:52:11,724 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,726 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,752 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,753 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,783 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,785 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,811 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,812 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,860 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,861 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,890 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,892 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,925 - climada.entity.exposures.base - INFO - Matching 50 exposures with 100 centroids.\n", + "2020-09-16 14:52:11,928 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,957 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:11,958 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:11,999 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,001 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,036 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,037 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,094 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,096 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,127 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,128 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,163 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", "\n", "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", "----------------- --------------- ------------------ --------------\n", @@ -646,21 +645,21 @@ "Residual risk: -36.6389 (USD bn)\n", "-------------------- -------- --------\n", "Net Present Values\n", - "2020-03-13 16:28:39,499 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,500 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,525 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,527 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,561 - climada.engine.cost_benefit - INFO - Risk at 2018: 6.512e+09\n", - "2020-03-13 16:28:39,561 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,563 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,589 - climada.engine.cost_benefit - INFO - Risk with development at 2040: 1.302e+10\n", - "2020-03-13 16:28:39,590 - climada.engine.cost_benefit - INFO - Risk with development and climate change at 2040: 3.440e+10\n", - "2020-03-13 16:28:39,600 - climada.engine.cost_benefit - INFO - Current total risk at 2040: 1.215e+11\n", - "2020-03-13 16:28:39,601 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2020-03-13 16:28:39,604 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", - "2020-03-13 16:28:39,630 - climada.engine.cost_benefit - INFO - Total risk with development at 2040: 1.775e+11\n", - "2020-03-13 16:28:39,632 - climada.engine.cost_benefit - INFO - Total risk with development and climate change at 2040: 3.611e+11\n", - "2020-03-13 16:28:39,653 - climada.engine.cost_benefit - INFO - Combining measures ['Mangroves', 'Beach nourishment', 'Seawall', 'Building code']\n", + "2020-09-16 14:52:12,201 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,202 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,233 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,234 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,271 - climada.engine.cost_benefit - INFO - Risk at 2018: 6.512e+09\n", + "2020-09-16 14:52:12,272 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,273 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,304 - climada.engine.cost_benefit - INFO - Risk with development at 2040: 1.302e+10\n", + "2020-09-16 14:52:12,304 - climada.engine.cost_benefit - INFO - Risk with development and climate change at 2040: 3.440e+10\n", + "2020-09-16 14:52:12,315 - climada.engine.cost_benefit - INFO - Current total risk at 2040: 1.215e+11\n", + "2020-09-16 14:52:12,315 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 14:52:12,316 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 14450 events.\n", + "2020-09-16 14:52:12,348 - climada.engine.cost_benefit - INFO - Total risk with development at 2040: 1.775e+11\n", + "2020-09-16 14:52:12,349 - climada.engine.cost_benefit - INFO - Total risk with development and climate change at 2040: 3.611e+11\n", + "2020-09-16 14:52:12,364 - climada.engine.cost_benefit - INFO - Combining measures ['Mangroves', 'Beach nourishment', 'Seawall', 'Building code']\n", "\n", "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", "--------- --------------- ------------------ --------------\n", @@ -676,7 +675,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -805,34 +804,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "2020-03-13 16:28:40,338 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", - "2020-03-13 16:28:40,338 - climada.entity.exposures.base - INFO - tag metadata set to default value: File: \n", + "2020-09-16 14:52:12,842 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", + "2020-09-16 14:52:12,843 - climada.entity.exposures.base - INFO - tag metadata set to default value: File: \n", " Description: \n", - "2020-03-13 16:28:40,339 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", - "2020-03-13 16:28:40,339 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", - "2020-03-13 16:28:40,340 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2020-03-13 16:28:40,341 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2020-03-13 16:28:40,341 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2020-03-13 16:28:40,342 - climada.entity.exposures.base - INFO - region_id not set.\n", - "2020-03-13 16:28:40,343 - climada.entity.exposures.base - INFO - geometry not set.\n", - "2020-03-13 16:28:40,452 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/WS_ERA40.mat\n", - "2020-03-13 16:28:40,652 - climada.hazard.centroids.centr - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/WS_ERA40.mat\n", - "2020-03-13 16:28:40,938 - climada.entity.exposures.base - INFO - Matching 6186 exposures with 6331 centroids.\n", - "2020-03-13 16:28:41,566 - climada.engine.impact - INFO - Calculating damage for 6186 assets (>0) and 1755 events.\n" + "2020-09-16 14:52:12,843 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", + "2020-09-16 14:52:12,844 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", + "2020-09-16 14:52:12,844 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 14:52:12,844 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 14:52:12,845 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-09-16 14:52:12,845 - climada.entity.exposures.base - INFO - region_id not set.\n", + "2020-09-16 14:52:12,846 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-09-16 14:52:12,932 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/WS_ERA40_sample.mat\n", + "2020-09-16 14:52:12,948 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/demo/WS_ERA40_sample.mat\n", + "2020-09-16 14:52:12,965 - climada.entity.exposures.base - INFO - Matching 6186 exposures with 6331 centroids.\n", + "2020-09-16 14:52:13,496 - climada.engine.impact - INFO - Calculating damage for 6186 assets (>0) and 100 events.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -841,7 +840,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -897,7 +896,7 @@ "from climada.engine import Impact\n", "\n", "# Put any absoulte path for your files or set up the configuration variable \"repository\"\n", - "FILE_HAZARD = DATA_DIR + '/demo/WS_ERA40.mat' \n", + "FILE_HAZARD = DATA_DIR + '/demo/WS_ERA40_sample.mat' \n", "FILE_ENTITY = DATA_DIR + '/demo/WS_Europe.xls'\n", "\n", "# Define hazard type\n", diff --git a/doc/tutorial/climada_engine_impact_data.ipynb b/doc/tutorial/climada_engine_impact_data.ipynb new file mode 100644 index 0000000000..5ee70afd06 --- /dev/null +++ b/doc/tutorial/climada_engine_impact_data.ipynb @@ -0,0 +1,424 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Impact Data functionalities\n", + "\n", + "\n", + "Import data from EM-DAT CSV file and populate Impact()-object with the data.\n", + "\n", + "\n", + "The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it.\n", + "The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", + "\n", + "\n", + "## Data Source\n", + "The International Disaster Database EM-DAT www.emdat.be\n", + "\n", + "Download: https://public.emdat.be/ (register for free and download data to continue)\n", + "\n", + "\n", + "## Most important functions\n", + "- clean_emdat_df: read CSV from EM-DAT into a DataFrame and clean up.\n", + "- emdat_to_impact: create Impact-instance populated with impact data from EM-DAT data (CSV).\n", + "- emdat_countries_by_hazard: get list of countries affected by a certain haazrd (disaster (sub-)type) in EM-DAT.\n", + "- emdat_impact_yearlysum: create DataFrame with impact from EM-DAT summed per country and year.\n", + "\n", + "\n", + "\n", + "## Demo data\n", + "\n", + "The demo data used hee (demo_emdat_impact_data_2020.csv) contains entries for the disaster subtype \"Tropical cyclone\" from 2000 to 2020.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"Load required packages and set path to CSV-file from EM-DAT\"\"\"\n", + "\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from climada.util.constants import DATA_DIR\n", + "from climada.engine.impact_data import emdat_countries_by_hazard, \\\n", + " emdat_impact_yearlysum, emdat_to_impact, clean_emdat_df\n", + "\n", + "# set path to CSV file downloaded from https://public.emdat.be :\n", + "emdat_file_path = os.path.join(DATA_DIR, 'demo', 'demo_emdat_impact_data_2020.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### clean_emdat_df()\n", + "read CSV from EM-DAT into a DataFrame and clean up.\n", + "\n", + "Use the parameters countries, hazard, and year_range to filter. These parameters are the same for most functions shown here." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Dis No Year Seq Disaster Group Disaster Subgroup Disaster Type \\\n", + "0 2005-0540-VNM 2005 540 Natural Meteorological Storm \n", + "1 2005-0540-THA 2005 540 Natural Meteorological Storm \n", + "2 2005-0536-VNM 2005 536 Natural Meteorological Storm \n", + "3 2005-0611-VNM 2005 611 Natural Meteorological Storm \n", + "4 2006-0362-VNM 2006 362 Natural Meteorological Storm \n", + "5 2006-0648-VNM 2006 648 Natural Meteorological Storm \n", + "6 2006-0251-VNM 2006 251 Natural Meteorological Storm \n", + "7 2006-0517-VNM 2006 517 Natural Meteorological Storm \n", + "\n", + " Disaster Subtype Disaster Subsubtype Event Name Entry Criteria \\\n", + "0 Tropical cyclone NaN Damrey Kill \n", + "1 Tropical cyclone NaN Damrey Kill \n", + "2 Tropical cyclone NaN Vicente Kill \n", + "3 Tropical cyclone NaN Kai Tak (21) Kill \n", + "4 Tropical cyclone NaN Bilis Kill \n", + "5 Tropical cyclone NaN Durian (Reming) Kill \n", + "6 Tropical cyclone NaN Chanchu (Caloy) Kill \n", + "7 Tropical cyclone NaN Xangsane (Milenyo) Kill \n", + "\n", + " ... End Day Total Deaths No Injured No Affected No Homeless Total Affected \\\n", + "0 ... 30.0 75.0 28.0 337632.0 NaN 337660.0 \n", + "1 ... 30.0 10.0 NaN 2000.0 NaN 2000.0 \n", + "2 ... 19.0 8.0 NaN 8500.0 NaN 8500.0 \n", + "3 ... 4.0 20.0 NaN 15000.0 NaN 15000.0 \n", + "4 ... 19.0 17.0 NaN NaN 2000.0 2000.0 \n", + "5 ... 8.0 95.0 1360.0 975000.0 250000.0 1226360.0 \n", + "6 ... 17.0 204.0 NaN 600000.0 NaN 600000.0 \n", + "7 ... 6.0 71.0 525.0 1368720.0 98680.0 1467925.0 \n", + "\n", + " Reconstruction Costs ('000 US$) Insured Damages ('000 US$) \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "\n", + " Total Damages ('000 US$) CPI \n", + "0 219250.0 76.388027 \n", + "1 20000.0 76.388027 \n", + "2 20000.0 76.388027 \n", + "3 11000.0 76.388027 \n", + "4 NaN 78.852256 \n", + "5 456000.0 78.852256 \n", + "6 NaN 78.852256 \n", + "7 624000.0 78.852256 \n", + "\n", + "[8 rows x 43 columns]\n" + ] + } + ], + "source": [ + "\"\"\"Create DataFrame df with EM-DAT entries of tropical cyclones in Thailand and Viet Nam in the years 2005 and 2006\"\"\"\n", + "\n", + "df = clean_emdat_df(emdat_file_path, countries=['THA', 'Viet Nam'], hazard=['TC'], \\\n", + " year_range=[2005, 2006])\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### emdat_countries_by_hazard()\n", + "\n", + "Pick a hazard and a year range to get a list of countries affected from the EM-DAT data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['China', 'Dominican Republic', 'Antigua and Barbuda', 'Fiji', 'Australia', 'Bangladesh', 'Belize', 'Barbados', 'Cook Islands', 'Canada', 'Bahamas', 'Guatemala', 'Jamaica', 'Saint Lucia', 'Madagascar', 'Mexico', \"Korea, Democratic People's Republic of\", 'El Salvador', 'Myanmar', 'French Polynesia', 'Solomon Islands', 'Taiwan, Province of China', 'India', 'United States of America', 'Honduras', 'Haiti', 'Pakistan', 'Philippines', 'Hong Kong', 'Korea, Republic of', 'Nicaragua', 'Oman', 'Japan', 'Puerto Rico', 'Thailand', 'Martinique', 'Papua New Guinea', 'Tonga', 'Venezuela, Bolivarian Republic of', 'Viet Nam', 'Saint Vincent and the Grenadines', 'Vanuatu', 'Dominica', 'Cuba', 'Comoros', 'Mozambique', 'Malawi', 'Samoa', 'South Africa', 'Sri Lanka', 'Palau', 'Wallis and Futuna', 'Somalia', 'Seychelles', 'Réunion', 'Kiribati', 'Cabo Verde', 'Micronesia, Federated States of', 'Panama', 'Costa Rica', 'Yemen', 'Tuvalu', 'Northern Mariana Islands', 'Colombia', 'Anguilla', 'Djibouti', 'Cambodia', 'Macao', 'Indonesia', 'Guadeloupe', 'Turks and Caicos Islands', 'Saint Kitts and Nevis', \"Lao People's Democratic Republic\", 'Mauritius', 'Marshall Islands', 'Portugal', 'Virgin Islands, U.S.', 'Zimbabwe', 'Saint Barthélemy', 'Virgin Islands, British', 'Saint Martin (French part)', 'Sint Maarten (Dutch part)', 'Tanzania, United Republic of']\n", + "['CHN', 'DOM', 'ATG', 'FJI', 'AUS', 'BGD', 'BLZ', 'BRB', 'COK', 'CAN', 'BHS', 'GTM', 'JAM', 'LCA', 'MDG', 'MEX', 'PRK', 'SLV', 'MMR', 'PYF', 'SLB', 'TWN', 'IND', 'USA', 'HND', 'HTI', 'PAK', 'PHL', 'HKG', 'KOR', 'NIC', 'OMN', 'JPN', 'PRI', 'THA', 'MTQ', 'PNG', 'TON', 'VEN', 'VNM', 'VCT', 'VUT', 'DMA', 'CUB', 'COM', 'MOZ', 'MWI', 'WSM', 'ZAF', 'LKA', 'PLW', 'WLF', 'SOM', 'SYC', 'REU', 'KIR', 'CPV', 'FSM', 'PAN', 'CRI', 'YEM', 'TUV', 'MNP', 'COL', 'AIA', 'DJI', 'KHM', 'MAC', 'IDN', 'GLP', 'TCA', 'KNA', 'LAO', 'MUS', 'MHL', 'PRT', 'VIR', 'ZWE', 'BLM', 'VGB', 'MAF', 'SXM', 'TZA']\n" + ] + } + ], + "source": [ + "\"\"\"emdat_countries_by_hazard: get lists of countries impacted by tropical cyclones from 2010 to 2019\"\"\"\n", + "\n", + "iso3_codes, country_names = emdat_countries_by_hazard(emdat_file_path, hazard='TC', year_range=(2010, 2019))\n", + "\n", + "print(country_names)\n", + "\n", + "print(iso3_codes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### emdat_to_impact()\n", + "function to load EM-DAT impact data and return impact set with impact per event\n", + "\n", + "##### Parameters:\n", + "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", + "- hazard_type_climada (str): Hazard type abbreviation used in CLIMADA, e.g. 'TC'\n", + "\n", + "##### Optional parameters:\n", + "\n", + "- hazard_type_emdat (list or str): List of Disaster (sub-)type accordung EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", + "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", + "- countries (list of str): country ISO3-codes or names, e.g. ['JAM', 'CUB']. Set to None or ['all'] for all countries \n", + "- reference_year (int): reference year of exposures for normalization. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)\n", + "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", + "\n", + "##### Returns:\n", + "- impact_instance (instance of climada.engine.Impact):\n", + " Impact() instance (same format as output from CLIMADA impact computations).\n", + " Values are scaled with GDP to reference_year if reference_year not equal 0.\n", + " impact_instance.eai_exp holds expected annual impact for each country.\n", + " impact_instance.coord_exp holds rough central coordinates for each country.\n", + "- countries (list): ISO3-codes of countries imn same order as in impact_instance.eai_exp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-07-10 14:18:25,584 - climada.engine.impact_data - WARNING - ISO3alpha code not found in iso_country: SPI\n", + "SPI\n", + "2020-07-10 14:18:26,995 - climada.engine.impact_data - ERROR - Country not found in iso_country: SPI\n", + "Number of TC events in EM-DAT 2000 to 2009 globally: 533\n", + "Global annual average monetary damage (AAI) from TCs as reported in EM-DAT 2000 to 2009: USD billion 38.07\n" + ] + } + ], + "source": [ + "\"\"\"Global TC damages 2000 to 2009\"\"\"\n", + "\n", + "impact_emdat, countries = emdat_to_impact(emdat_file_path, 'TC', year_range=(2000,2009))\n", + "\n", + "print('Number of TC events in EM-DAT 2000 to 2009 globally: %i' %(impact_emdat.event_id.size))\n", + "print('Global annual average monetary damage (AAI) from TCs as reported in EM-DAT 2000 to 2009: USD billion %2.2f' \\\n", + " %(impact_emdat.aai_agg/1e9))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of TC events in EM-DAT in the Philipppines, 2013: 8\n", + "\n", + "People affected by TC events in the Philippines in 2013 (per event):\n", + "[7.269600e+04 1.059700e+04 8.717550e+05 2.204430e+05 1.610687e+07\n", + " 3.596000e+03 3.957300e+05 2.628840e+05]\n", + "\n", + "People affected by TC events in the Philippines in 2013 (total):\n", + "17944571\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'People Affected')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"Total people affected by TCs in the Philippines in 2013:\"\"\"\n", + "\n", + "# People affected\n", + "impact_emdat_PHL, countries = emdat_to_impact(emdat_file_path, 'TC', countries='PHL', \\\n", + " year_range=(2013,2013), imp_str=\"Total Affected\")\n", + "\n", + "print('Number of TC events in EM-DAT in the Philipppines, 2013: %i' \\\n", + " %(impact_emdat_PHL.event_id.size))\n", + "print('\\nPeople affected by TC events in the Philippines in 2013 (per event):')\n", + "print(impact_emdat_PHL.at_event)\n", + "print('\\nPeople affected by TC events in the Philippines in 2013 (total):')\n", + "print(int(impact_emdat_PHL.aai_agg))\n", + "\n", + "# Comparison to monetary damages:\n", + "impact_emdat_PHL_USD, _ = emdat_to_impact(emdat_file_path, 'TC', countries='PHL', \\\n", + " year_range=(2013,2013))\n", + "\n", + "ax = plt.scatter(impact_emdat_PHL_USD.at_event, impact_emdat_PHL.at_event)\n", + "plt.title('Typhoon impacts in the Philippines, 2013')\n", + "plt.xlabel('Total Damage [USD]')\n", + "plt.ylabel('People Affected')\n", + "#plt.xscale('log')\n", + "#plt.yscale('log')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### emdat_impact_yearlysum()\n", + "\n", + "function to load EM-DAT impact data and return DataFrame with impact summed per year and country\n", + "\n", + "##### Parameters:\n", + "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", + "\n", + "##### Optional parameters:\n", + "\n", + "- hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", + "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", + "- countries (list of str): country ISO3-codes or names, e.g. ['JAM', 'CUB']. Set to None or ['all'] for all countries \n", + "- reference_year (int): reference year of exposures for normalization. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)\n", + "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", + "- version (int): given EM-DAT data format version (i.e. year of download), changes naming of columns/variables (default: 2020)\n", + "\n", + "##### Returns:\n", + "- pandas.DataFrame with impact per year and country" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-07-10 14:36:28,646 - climada.util.finance - INFO - GDP USA 2019: 2.143e+13.\n", + "[2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014\n", + " 2015 2016 2017 2018 2019 2020]\n", + "2020-07-10 14:36:29,099 - climada.util.finance - INFO - GDP USA 2000: 1.025e+13.\n", + "2020-07-10 14:36:29,539 - climada.util.finance - INFO - GDP USA 2001: 1.058e+13.\n", + "2020-07-10 14:36:30,302 - climada.util.finance - INFO - GDP USA 2002: 1.094e+13.\n", + "2020-07-10 14:36:30,754 - climada.util.finance - INFO - GDP USA 2003: 1.146e+13.\n", + "2020-07-10 14:36:31,204 - climada.util.finance - INFO - GDP USA 2004: 1.221e+13.\n", + "2020-07-10 14:36:31,648 - climada.util.finance - INFO - GDP USA 2005: 1.304e+13.\n", + "2020-07-10 14:36:32,098 - climada.util.finance - INFO - GDP USA 2006: 1.381e+13.\n", + "2020-07-10 14:36:32,549 - climada.util.finance - INFO - GDP USA 2007: 1.445e+13.\n", + "2020-07-10 14:36:33,037 - climada.util.finance - INFO - GDP USA 2008: 1.471e+13.\n", + "2020-07-10 14:36:33,485 - climada.util.finance - INFO - GDP USA 2009: 1.445e+13.\n", + "2020-07-10 14:36:33,927 - climada.util.finance - INFO - GDP USA 2010: 1.499e+13.\n", + "2020-07-10 14:36:34,785 - climada.util.finance - INFO - GDP USA 2011: 1.554e+13.\n", + "2020-07-10 14:36:35,227 - climada.util.finance - INFO - GDP USA 2012: 1.620e+13.\n", + "2020-07-10 14:36:35,674 - climada.util.finance - INFO - GDP USA 2014: 1.752e+13.\n", + "2020-07-10 14:36:36,125 - climada.util.finance - INFO - GDP USA 2015: 1.822e+13.\n", + "2020-07-10 14:36:36,701 - climada.util.finance - INFO - GDP USA 2016: 1.871e+13.\n", + "2020-07-10 14:36:37,145 - climada.util.finance - INFO - GDP USA 2017: 1.949e+13.\n", + "2020-07-10 14:36:37,591 - climada.util.finance - INFO - GDP USA 2018: 2.058e+13.\n", + "2020-07-10 14:36:38,029 - climada.util.finance - INFO - GDP USA 2019: 2.143e+13.\n", + "2020-07-10 14:36:38,861 - climada.util.finance - INFO - GDP USA 2019: 2.143e+13.\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total Damage [USD]')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"Yearly TC damages in the USA, normalized and current\"\"\"\n", + "\n", + "yearly_damage_normalized_to_2019 = emdat_impact_yearlysum(emdat_file_path, countries='USA', \\\n", + " hazard='Tropical cyclone', year_range=None, \\\n", + " reference_year=2019)\n", + "\n", + "yearly_damage_current = emdat_impact_yearlysum(emdat_file_path, countries=['USA'], hazard='TC',)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axis = plt.subplots(1, 1)\n", + "axis.plot(yearly_damage_current.year, yearly_damage_current.impact, 'b', label='USD current value')\n", + "axis.plot(yearly_damage_normalized_to_2019.year, yearly_damage_normalized_to_2019.impact_scaled, \\\n", + " 'r--', label='USD normalized to 2019')\n", + "plt.legend()\n", + "axis.set_title('TC damage reported in EM-DAT in the USA')\n", + "axis.set_xticks([2000, 2004, 2008, 2012, 2016])\n", + "axis.set_xlabel('year')\n", + "axis.set_ylabel('Total Damage [USD]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb b/doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb new file mode 100644 index 0000000000..88987ec76a --- /dev/null +++ b/doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to use polygons or lines as exposure\n", + "\n", + "Exposure in CLIMADA are usually represented as individual points or a raster of points.\n", + "See [Exposures](climada_entity_Exposures.ipynb) tutorial to learn how to fill and use exposures.\n", + "In this tutorial we show you how to use CLIMADA if you have your exposure in the form of shapes/polygons or in the form of lines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The approach follows three steps:\n", + "1. transform your polygon or line in a set of points\n", + "2. do the impact calculation in CLIMADA with that set of points\n", + "3. transform the calculated Impact back to your polygon or line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polygons\n", + "Polygons or shapes are a common geographical representation of countries, states etc. as for example in NaturalEarth. Here we want to show you how to deal with exposure information as polygons." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets assume we have the following data given. The polygons of the admin-1 regions of the netherlands and an exposure value each. We want to know the Impact of Lothar on each admin-1 region." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-18 09:27:55,885 - climada - DEBUG - Loading default config file: /kp/kpbkp/tgeiger/code/climada_python/climada/conf/defaults.conf\n" + ] + } + ], + "source": [ + "from cartopy.io import shapereader\n", + "from climada.entity.exposures.black_marble import country_iso_geom\n", + "\n", + "# open the file containing the Netherlands admin-1 polygons\n", + "shp_file = shapereader.natural_earth(resolution='10m',\n", + " category='cultural',\n", + " name='admin_0_countries')\n", + "shp_file = shapereader.Reader(shp_file)\n", + "\n", + "# extract the NL polygons\n", + "prov_names = {'Netherlands': ['Groningen', 'Drenthe',\n", + " 'Overijssel', 'Gelderland', \n", + " 'Limburg', 'Zeeland', \n", + " 'Noord-Brabant', 'Zuid-Holland', \n", + " 'Noord-Holland', 'Friesland', \n", + " 'Flevoland', 'Utrecht']\n", + " }\n", + "polygon_Netherlands, polygons_prov_NL = country_iso_geom(prov_names,\n", + " shp_file)\n", + "\n", + "# assign a value to each admin-1 area (assumption 100'000 USD per inhabitant)\n", + "population_prov_NL = {'Drenthe':493449, 'Flevoland':422202,\n", + " 'Friesland':649988, 'Gelderland':2084478,\n", + " 'Groningen':585881, 'Limburg':1118223,\n", + " 'Noord-Brabant':2562566, 'Noord-Holland':2877909,\n", + " 'Overijssel':1162215, 'Zuid-Holland':3705625,\n", + " 'Utrecht':1353596, 'Zeeland':383689}\n", + "value_prov_NL = {n: 100000 * population_prov_NL[n] for n in population_prov_NL.keys()}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assume a uniform distribution of values within your polygons\n", + "This helps you in the case you have a given total exposure value per polygon and we assume this value is distributed evenly within the polygon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now perform the three steps for this example:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-18 09:28:12,270 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2020-05-18 09:28:13,329 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n", + "2020-05-18 09:28:13,330 - climada.entity.exposures.base - INFO - tag metadata set to default value: File: \n", + " Description: \n", + "2020-05-18 09:28:13,331 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n", + "2020-05-18 09:28:13,331 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n", + "2020-05-18 09:28:13,332 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-05-18 09:28:13,333 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-05-18 09:28:13,333 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-05-18 09:28:13,334 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-05-18 09:28:13,334 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-05-18 09:28:13,335 - climada.entity.exposures.base - INFO - region_id not set.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/kp/kpbkp/tgeiger/code/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from pandas import DataFrame\n", + "from climada.entity import Exposures\n", + "from climada.util.coordinates import coord_on_land\n", + "\n", + "\n", + "### 1. transform your polygon or line in a set of points\n", + "# create exposure with points\n", + "exp_df = DataFrame()\n", + "n_exp = 200*200\n", + "lat, lon = np.mgrid[50 : 54 : complex(0, np.sqrt(n_exp)),\n", + " 3 : 8 : complex(0, np.sqrt(n_exp))]\n", + "exp_df['latitude'] = lat.flatten() # provide latitude\n", + "exp_df['longitude'] = lon.flatten() # provide longitude\n", + "exp_df['if_WS'] = np.ones(n_exp, int) # provide impact functions \n", + "\n", + "# now we assign each point a province and a value, if the points are within one of the polygons defined above\n", + "exp_df['province'] = ''\n", + "exp_df['value'] = np.ones((exp_df.shape[0],))*np.nan\n", + "for prov_name_i, prob_polygon_i in zip(prov_names['Netherlands'],polygons_prov_NL['NLD']):\n", + " in_geom = coord_on_land(lat=exp_df['latitude'], \n", + " lon=exp_df['longitude'],\n", + " land_geom=prob_polygon_i)\n", + " np.put(exp_df['province'], np.where(in_geom)[0], prov_name_i)\n", + " np.put(exp_df['value'], np.where(in_geom)[0], value_prov_NL[prov_name_i]/sum(in_geom))\n", + "\n", + "\n", + "exp_df = Exposures(exp_df)\n", + "exp_df.set_geometry_points() # set geometry attribute (shapely Points) from GeoDataFrame from latitude and longitude\n", + "exp_df.check()\n", + "exp_df.plot_hexbin()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-18 09:28:20,586 - climada.hazard.storm_europe - INFO - Constructing centroids from /kp/kpbkp/tgeiger/code/climada_python/data/demo/fp_lothar_crop-test.nc\n", + "2020-05-18 09:28:20,610 - climada.hazard.centroids.centr - INFO - Setting geometry points.\n", + "2020-05-18 09:28:22,033 - climada.hazard.storm_europe - INFO - Commencing to iterate over netCDF files.\n", + "2020-05-18 09:28:22,131 - climada.entity.exposures.base - INFO - Matching 40000 exposures with 9944 centroids.\n", + "2020-05-18 09:28:25,230 - climada.engine.impact - INFO - Calculating damage for 9660 assets (>0) and 2 events.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/kp/kpbkp/tgeiger/code/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.hazard.storm_europe import StormEurope\n", + "from climada.util.constants import WS_DEMO_NC\n", + "from climada.entity.impact_funcs.storm_europe import IFStormEurope\n", + "from climada.entity.impact_funcs import ImpactFuncSet\n", + "from climada.engine import Impact\n", + "\n", + "### 2. do the impact calculation in CLIMADA with that set of points\n", + "# define hazard\n", + "storms = StormEurope()\n", + "storms.read_footprints(WS_DEMO_NC, description='test_description')\n", + "# define impact function\n", + "impact_func = IFStormEurope()\n", + "impact_func.set_welker()\n", + "impact_function_set = ImpactFuncSet()\n", + "impact_function_set.append(impact_func)\n", + "# calculate hazard\n", + "impact_NL = Impact()\n", + "impact_NL.calc(exp_df, impact_function_set, storms, save_mat=True)\n", + "impact_NL.plot_hexbin_impact_exposure()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0\n", + "province \n", + " 0.000000e+00\n", + "Drenthe 6.852537e+04\n", + "Flevoland 2.716078e+05\n", + "Friesland 7.782136e+05\n", + "Gelderland 4.056456e+05\n", + "Groningen 1.150089e+05\n", + "Limburg 2.559739e+05\n", + "Noord-Brabant 7.391625e+05\n", + "Noord-Holland 5.908293e+06\n", + "Overijssel 1.588620e+05\n", + "Utrecht 4.846288e+05\n", + "Zeeland 4.069767e+05\n", + "Zuid-Holland 2.893966e+06\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "### 3. transform the calculated Impact back to your polygon or line\n", + "impact_at_province_raw = pd.DataFrame(np.mean(impact_NL.imp_mat.todense().transpose(), axis=1),\n", + " index=exp_df['province'])\n", + "impact_at_province = impact_at_province_raw.groupby(impact_at_province_raw.index).sum()\n", + "print(impact_at_province)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the LitPop module to disaggregate your exposure values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of a uniform distribution, another geographical distribution can be chosen to disaggregate within the value within each polygon. We show here the example of [LitPop](climada_entity_LitPop.ipynb). The same three steps apply:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-18 09:28:33,013 - climada.entity.exposures.litpop - INFO - Generating LitPop data at a resolution of 60 arcsec.\n", + "2020-05-18 09:28:57,503 - climada.entity.exposures.gpw_import - INFO - Reference year: 2016. Using nearest available year for GWP population data: 2015\n", + "2020-05-18 09:28:57,504 - climada.entity.exposures.gpw_import - INFO - GPW Version v4.11\n", + "2020-05-18 09:29:22,763 - climada.entity.exposures.litpop - INFO - fin_mode=none --> no downscaling; admin1_calc is ignored\n", + "2020-05-18 09:29:22,774 - climada.entity.exposures.litpop - INFO - Creating the LitPop exposure took 53 s\n", + "2020-05-18 09:29:22,775 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n", + "2020-05-18 09:29:22,775 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-05-18 09:29:22,776 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-05-18 09:29:22,776 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-05-18 09:29:22,777 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-05-18 09:29:22,777 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-05-18 09:29:24,433 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2020-05-18 09:29:24,908 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n", + "2020-05-18 09:29:24,910 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-05-18 09:29:24,910 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-05-18 09:29:24,911 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-05-18 09:29:24,912 - climada.entity.exposures.base - INFO - category_id not set.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from climada.entity import LitPop\n", + "from climada.util.coordinates import coord_on_land\n", + "\n", + "\n", + "### 1. transform your polygon or line in a set of points\n", + "# create exposure with points\n", + "exp_df_lp = LitPop()\n", + "exp_df_lp.set_country('Netherlands',res_arcsec = 60, fin_mode = 'none')\n", + "exp_df_lp['if_WS'] = np.ones(exp_df_lp.shape[0], int) # provide impact functions \n", + "\n", + "# now we assign each point a province and a value, if the points are within one of the polygons defined above\n", + "exp_df_lp['province'] = ''\n", + "for prov_name_i, prob_polygon_i in zip(prov_names['Netherlands'],polygons_prov_NL['NLD']):\n", + " in_geom = coord_on_land(lat=exp_df_lp['latitude'], \n", + " lon=exp_df_lp['longitude'],\n", + " land_geom=prob_polygon_i)\n", + " np.put(exp_df_lp['province'], np.where(in_geom)[0], prov_name_i)\n", + " exp_df_lp['value'][np.where(in_geom)[0]] = \\\n", + " exp_df_lp['value'][np.where(in_geom)[0]] * value_prov_NL[prov_name_i]/sum(exp_df_lp['value'][np.where(in_geom)[0]])\n", + "exp_df_lp = exp_df_lp.drop(np.where(exp_df_lp['province']=='')[0]) #drop carribean islands for this example\n", + "exp_df_lp.set_geometry_points()\n", + "exp_df_lp.check()\n", + "exp_df_lp.plot_hexbin()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-05-18 09:29:30,053 - climada.hazard.storm_europe - INFO - Constructing centroids from /kp/kpbkp/tgeiger/code/climada_python/data/demo/fp_lothar_crop-test.nc\n", + "2020-05-18 09:29:30,067 - climada.hazard.centroids.centr - INFO - Setting geometry points.\n", + "2020-05-18 09:29:31,446 - climada.hazard.storm_europe - INFO - Commencing to iterate over netCDF files.\n", + "2020-05-18 09:29:31,544 - climada.entity.exposures.base - INFO - Matching 17576 exposures with 9944 centroids.\n", + "2020-05-18 09:29:32,834 - climada.engine.impact - INFO - Calculating damage for 16834 assets (>0) and 2 events.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/kp/kpbkp/tgeiger/code/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.hazard.storm_europe import StormEurope\n", + "from climada.util.constants import WS_DEMO_NC\n", + "from climada.entity.impact_funcs.storm_europe import IFStormEurope\n", + "from climada.entity.impact_funcs import ImpactFuncSet\n", + "from climada.engine import Impact\n", + "\n", + "### 2. do the impact calculation in CLIMADA with that set of points\n", + "# define hazard\n", + "storms = StormEurope()\n", + "storms.read_footprints(WS_DEMO_NC, description='test_description')\n", + "# define impact function\n", + "impact_func = IFStormEurope()\n", + "impact_func.set_welker()\n", + "impact_function_set = ImpactFuncSet()\n", + "impact_function_set.append(impact_func)\n", + "# calculate hazard\n", + "impact_NL = Impact()\n", + "impact_NL.calc(exp_df_lp, impact_function_set, storms, save_mat=True)\n", + "impact_NL.plot_hexbin_impact_exposure()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0\n", + "province \n", + "Drenthe 6.397671e+04\n", + "Flevoland 1.666099e+05\n", + "Friesland 4.602394e+05\n", + "Gelderland 3.284452e+05\n", + "Groningen 1.563104e+05\n", + "Limburg 3.730663e+05\n", + "Noord-Brabant 6.247242e+05\n", + "Noord-Holland 1.819026e+06\n", + "Overijssel 1.074240e+05\n", + "Utrecht 4.484917e+05\n", + "Zeeland 7.632003e+05\n", + "Zuid-Holland 2.898284e+06\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "### 3. transform the calculated Impact back to your polygon or line\n", + "impact_at_province_raw = pd.DataFrame(np.mean(impact_NL.imp_mat.todense().transpose(), axis=1),\n", + " index=exp_df_lp['province'])\n", + "impact_at_province_lp = impact_at_province_raw.groupby(impact_at_province_raw.index).sum()\n", + "print(impact_at_province_lp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparison of both modelled impacts:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'litpop disaggregation')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.plot(impact_at_province[impact_at_province.index!=''],impact_at_province_lp, '.k')\n", + "plt.xlabel('uniform disaggregation')\n", + "plt.ylabel('litpop disaggregation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Further statistical analysis of hazard on polygon level\n", + "imagine that you need access to the hazard centroids in oder to provide some statistical analysis on the province level" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 18.4 s, sys: 3.24 ms, total: 18.4 s\n", + "Wall time: 18.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# this provides the wind speed value for each event at the corresponding exposure\n", + "import scipy\n", + "\n", + "exp_df_lp[:5]\n", + "l1,l2,vals = scipy.sparse.find(storms.intensity)\n", + "exp_df_lp['wind_0']=0; exp_df_lp['wind_1']=0 # provide columns for both events\n", + "for evt,idx,val in zip(l1,l2,vals):\n", + " if evt==0:\n", + " exp_df_lp.loc[exp_df_lp.index[exp_df_lp['centr_WS']==idx],'wind_0']=val\n", + " else:\n", + " exp_df_lp.loc[exp_df_lp.index[exp_df_lp['centr_WS']==idx],'wind_1']=val" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot maximum wind per province for first event\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot mean wind per province for second event\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# now you can perform additional statistical analysis and aggregate it to the province level\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "exp_province_raw = exp_df_lp.copy()\n", + "\n", + "def f(x): # define function for statistical aggregation with pandas\n", + " d = {}\n", + " d['value'] = x['value'].sum()\n", + " d['wind_0'] = x['wind_0'].max()\n", + " d['wind_1'] = x['wind_1'].mean()\n", + " # one could also be interested in centroid of max wind with respect to province\n", + " #d['centr_WS'] = x.loc[x.index[x['wind_0'].max()],'centr_WS'] \n", + " return pd.Series(d, index=['value', 'wind_0', 'wind_1'])\n", + "\n", + "exp_province = exp_province_raw.groupby('province').apply(f).reset_index() # Result is not a GeoDataFrame anymore\n", + "# add geometries to DataFrame and plot results \n", + "exp_province=gpd.GeoDataFrame(exp_province, geometry=None)\n", + "for prov,poly in zip(list(prov_names.values())[0],polygons_prov_NL['NLD']):\n", + " exp_province.loc[exp_province.index[exp_province['province']== prov],'geometry']= gpd.GeoDataFrame(geometry=[poly]).geometry.values\n", + "exp_province\n", + "print('Plot maximum wind per province for first event')\n", + "exp_province.plot(column='wind_0', cmap='Reds', legend=True)\n", + "plt.show()\n", + "print('Plot mean wind per province for second event')\n", + "exp_province.plot(column='wind_1', cmap='Reds', legend=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lines\n", + "Lines are common geographical representation of transport infrastructure like streets, train tracks or powerlines etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# under construction. here follows an example on how to deal with lines" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/tutorial/climada_entity_LitPop.ipynb b/doc/tutorial/climada_entity_LitPop.ipynb index 9b738e3840..04647bd8b8 100644 --- a/doc/tutorial/climada_entity_LitPop.ipynb +++ b/doc/tutorial/climada_entity_LitPop.ipynb @@ -11,13 +11,20 @@ "source": [ "# LitPop class\n", "\n", - "This class is a data model of economic asset exposure. It models countries' gridded asset exposure by disaggregating a macroeconomic indicator (e.g. total asset value or GDP) proportional to the product of night light intensities (\"Lit\") and gridded population count (\"Pop\") per country. Asset value is distributed to the grid proportzonal to $Lit^m Pop^n$, computed at each grid cell:\n", + "The modeling of economic disaster risk on a global scale requires high-resolution maps of exposed asset values. We have developed a generic and scalable method to downscale national asset value estimates proportional to a combination of nightlight intensity (\"Lit\") and population data (\"Pop\"). \n", + "\n", + "Asset exposure value is distributed to the grid proportzonal to $Lit^m Pop^n$, computed at each grid cell:\n", "\n", "\n", "$Lit^mPop^n = Lit^m * Pop^n$, with $exponents = [m, n] \\in \\N_0$ (Default values are $m=n=1$).\n", "\n", "\n", - "For more information please refer to the related publication (under review): https://doi.org/10.5194/essd-2019-189\n", + "For more information please refer to the related publication (https://doi.org/10.5194/essd-12-817-2020) and data archive (https://doi.org/10.3929/ethz-b-000331316).\n", + "\n", + "How to cite:\n", + "\n", + "Eberenz, S., Stocker, D., Röösli, T., and Bresch, D. N.: Asset exposure data for global physical risk assessment, Earth Syst. Sci. Data, 12, 817–833, https://doi.org/10.5194/essd-12-817-2020, 2020.\n", + "\n", "\n", "## Input data:\n", "\n", @@ -66,14 +73,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2020-01-31 10:59:32,239 - climada - DEBUG - Loading default config file: /Users/eberenzs/Documents/Projects/climada_python/climada/conf/defaults.conf\n" + "2020-05-11 10:21:54,916 - climada - DEBUG - Loading default config file: /Users/eberenzs/Documents/Projects/climada_python/climada/conf/defaults.conf\n" ] } ], @@ -84,7 +91,7 @@ "from matplotlib import colors\n", "from iso3166 import countries as iso_cntry\n", "\n", - "from climada.entity.exposures.litpop import LitPop" + "from climada.entity import LitPop" ] }, { diff --git a/doc/tutorial/climada_entity_MeasureSet.ipynb b/doc/tutorial/climada_entity_MeasureSet.ipynb index f8a6c7451b..37e3a7a283 100644 --- a/doc/tutorial/climada_entity_MeasureSet.ipynb +++ b/doc/tutorial/climada_entity_MeasureSet.ipynb @@ -81,7 +81,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:50,842 - climada - DEBUG - Loading default config file: /Users/aznarsig/Documents/Python/climada_python/climada/conf/defaults.conf\n" + "2020-09-16 09:45:15,661 - climada - DEBUG - Loading default config file: /home/tovogt/code/climada_python/climada/conf/defaults.conf\n" ] }, { @@ -96,7 +96,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -162,19 +162,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:53,024 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/tc_fl_1975_2011.h5\n", - "2019-10-29 22:08:53,048 - climada.entity.exposures.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/exp_demo_today.h5\n", - "2019-10-29 22:08:53,099 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2019-10-29 22:08:53,100 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2019-10-29 22:08:53,101 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2019-10-29 22:08:53,111 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n" + "2020-09-16 09:45:16,998 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/tc_fl_1990_2004.h5\n", + "2020-09-16 09:45:17,014 - climada.entity.exposures.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/exp_demo_today.h5\n", + "2020-09-16 09:45:17,050 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 09:45:17,050 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 09:45:17,052 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2020-09-16 09:45:17,062 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:318: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, @@ -182,16 +182,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:53,746 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2019-10-29 22:08:53,747 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n", - "2019-10-29 22:08:53,766 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2019-10-29 22:08:53,767 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n" + "2020-09-16 09:45:17,641 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 09:45:17,643 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", + "2020-09-16 09:45:17,656 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 09:45:17,657 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -200,7 +200,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -293,20 +293,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:55,685 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/tc_fl_1975_2011.h5\n", - "2019-10-29 22:08:55,710 - climada.entity.exposures.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/exp_demo_today.h5\n", - "2019-10-29 22:08:55,729 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2019-10-29 22:08:55,730 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2019-10-29 22:08:56,046 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2019-10-29 22:08:56,051 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n", - "2019-10-29 22:08:56,068 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n" + "2020-09-16 09:45:20,276 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/tc_fl_1990_2004.h5\n", + "2020-09-16 09:45:20,289 - climada.entity.exposures.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/exp_demo_today.h5\n", + "2020-09-16 09:45:20,299 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 09:45:20,299 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 09:45:21,431 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2020-09-16 09:45:21,436 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", + "2020-09-16 09:45:21,445 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:318: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, @@ -314,16 +314,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:57,003 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2019-10-29 22:08:57,004 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n", - "2019-10-29 22:08:57,022 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2019-10-29 22:08:57,028 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n" + "2020-09-16 09:45:22,024 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 09:45:22,025 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", + "2020-09-16 09:45:22,039 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2020-09-16 09:45:22,045 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -332,7 +332,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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0bkET6mgLgA8BWA/gmwB+/DhD4SyIJwVDpLHmwkux+UWvxj0f/xPM7d+11M3JOIkYl8cAADfd+oElbklGRkZGxvGCiM5GExPwMmZ+Bhr64UcB/D8A/gszX4gmBuJPnKw2LG+GSFZLWhk+UPDrWlVvuPgKEAzu/tgfYMfr/h1Wr9wSKOq2H4MliD3ay4YcU+L1mESXh9V1q+Rc1w1rY9NSxU4/hzRV1cbgTOswU9KVZapMrdM4xijQOwLAVdXkB8Dj0vc5LELYHmPAcwX46BxImC6lByT6Rp5lMlE6MoXPIyyTYqQ0c0SAZ49Ed4kIsxjiFc/5FVz3z/8vcPgoCtNPhqgNHLCAxaxB7+g4vk7wukqW4WKtCC7dkvOe6KYQancNcV4356y+Ue1/72KCEl0iuTWhDpG0ZdzOECWr9jpYFMtQ6JXvFNC6Sp2KoZFuj7S1Q29FshjAEIFKz6Zopm1SwxIWwqrDOZ0ppwsT6wWh8DovjhFSStX+utfd8O8X+y5oYwDYMyyREvmC0zZmg6iixHAkUSxPdMKCgdavlzq+Fy5lwAo5BlEYIhDMmNL6FEvIJmaL5FpU0aT5kijE2YPSn2mFno9dSadlktCiFwS0MkDRdX3OFBjMxL9pYwd5b8t7h9gndt2SfxZiToMq66losVOOHoBZIhoDWAHgEQAvRxMIGgDehyao9R+djMqflAyRYP3FL8BZz305dt7wmaVuSsZJRGH6WDV7Fh47/OBSNyUjIyMj4zjAzA8B+G0A96MRhB4D8A0AB5hZlmsPAjj7ZLVhWTNEei/eEx9eAyJZPavjhqdejj03fxl3X/cBPOXF/xOMKYKVvpW8a7V80OaQAFhWhFaqN8IQOesyzwYBAMra6xBVdcPGyPlINveVhN7G8HTpELWhiwlS1x0LFLBCXFqGRKzMKJaV3QodAM/1UR8+4nUgFCNEzvxeXZfzwnSzSa5CKStgjJSOEgXM08bZ87Bv353Y0JvuWfFWLwRaaWCOjqJ6I50l7U5AGCN3bpkhaz1IPYIRtigwxW/Kgr1uq6v8XBTrsloxULJsqRXbE+psyJQ1Y8CMWlbgFm3qeNrqUmOS1V7ntNT1kr/mmI3uYps8piEDzZgChivWzZqY3xEL6nnWzJBj8+wzXLBbgXvdIUTnRpk7k4FftcuzIo+u3DfL5JF7hzTHOnwLaxcHAmlzxBDJ/7Z8YWAda23boS0MpyEGbNmOzK7IMYiuXiLQmPw9cfPVs6+uvi6msmtJ3jrpYqZIW0UmlnpR1o5OT5qEC0xQZ1FZoYURovhczQVw+psrV21U1I4ZCue+vKdUUxfBdC2RldlGIroxOP9TZv5TACCiMwB8P4CnADgA4FoAr2op46RRW09qhggAisEMnvbGX8D8oT04+PB3l7o5GScJK/rrMDd+bKmbkZGRkZHRjT3MfFnw96fBb9cAuIeZdzPzGMDHALwQwDoikmXDNgAPn6zGLWuGKPGjoa0ogjSTVrGmN8Ca7U/D4d33Yu25Tw8IE1mJq3K1DhEHjVAWRokfHfExROQZmWEPPNP3PoMqoQsm6Px0WYwJ2pZB2qpMmA1VT8LgFIX3lVKpVYPSJQIRaDAADfqBDpG2KrP+liZZMZgOWVzrEoV9q+wSVS8zC4NZswrHRgdApVY466rf3zcqa0DujesK+f14x1Tq8RRdJhm7gC0QozU9paR88YUjzvPqQEfA3j/n3NEWIuxkYrnGgW4L21Vol+5EcD1xbihJ9C3v8qEUlt9VRrio79Iv0mUFbaWq0W1KLP4WYLVA7CxCvT6Vet6dvoe9b+KXiIDapimN9bFl3z2FvT4um+vVWI7GN0aYJm1BiebmsGJ7IigLKk6UtIL+uTHuGASdZ5pHQ5w3OlZL2KD0norPKzhrS/tzG2M0Qb8oqjd812seQL/rO5w7tqlfOh9zwuRIvYV/CMjUINS+HkkrbGSt2Empfkwwyhlnl35QdN7CIALw/vekftfvYL7qaaGfnQXAYFTLL7D7/QCuIKIVAI4BeAWAGwFcB+BH0FiavRnAJ05WA570DJFgdsPZOLL7vqVuRsZJwprhJhyc34WqHi+cOCMjI+M0Rw0+5X+TwMxfB/BRNKb1t6CRT/4UwC8BeBsR3QlgA4B3n6wxWdYMkfOp0LaSmlLfQSToNdufhoe+9jfYe88/Y+O5zwbgVzRaUnd+PZxUz94CQK+WZXXU995om7IYLPpE/QI86HkX+h26PeGWrkvTFYajtfMdOkkSjkPOQ2YIaPwEia+gSi1LBCFjMxzAzM52W5eJPlWXSRBRQGmooy6zzZeSsEDOT1WNPnpYO9iE3YfuxOZVF7XXG6IO6qlqzywF/WTnZ0mPvWXAHNsjjJzcX/LzswxYoyAPqxUzGwILo+jmkG2q6Lo5gsqudgOi0c3HqmFVElZHr1QDJM+M63+cN7II6liRJoRGSI50TN2uZ5YNQD2ro6FX0x06UoK6IBcSJAkPoVkKPUYVgS1bVLppwtF5zAw1hZieWJHa4rWVJTNgONENC+teEG2MOLUftaXhxJA2KnyKY1BCC7Y2nR0Dp8MkDCYSi0p4n3JKv8jNcWnHIJioXbS/9mUkeSd54ha9J7FslPYIyd2rG4/axAEjY5laYYKL4PnWkO6V8Xm3lVkL6SdJ5R/1feKwTEVyLk+DscWDmX8NwK+py3cDeMGpqP+0YYhMb4Ad17wFD17/cey796ZTUicPptD8PJHon2L5djA4tfUtQPFuXX0xHjp06wmrburttxMEMzq1bzU6td075fUtOhbW40TRP7VKqhND1DwJ6ptoTn8yqpvaqeGJwSl/HgIwgAp8yv+WO5Y1QyT7u+wsch6f/LZi4zZc8Kp/i7s/+x7sv+9mnHnhlVi9+fzAY6xmWOQyBSt5OYoOSOxHh9wqovE9xIMCdb+Helg5JkGYo04P1TU6LcScFVsbmBuhSDEWbsmR+DuydfQKUGkHOYlllo45DfrA0ApDg4Fnl7p0l8L2uUIWYIa6EJbhWJ2mvi0zF+KOfV/CoWM7sXqwcbIA5eo3TTnS7zAulDGNUCQrRLe3LwyOjKtljOqWe6RWiFArcM8CNdWYESeWks7K0g2rnUcuL7nyjPXIrsKCRf6HgiIbNglxWv3hc0UFDETXx7Hz9jn2pSVPR1oYgEqtQ6QbpbI69iVgh7qoKSlCdIuC2GrCEEn5XpeoOa9GMQVnJFBsvw4Cpdp5I/OBG99kpPUPg/4sVubgSLeHwyYFMcc4rCLOX8TvCseGKn21pmjrc8j4ejggQ41W8SN/7oKsuvsYM0Zu3kgZffUOC5AwQ451SUfPsWOWGSKlv1W7QLg2Vl1R+1B08k7X7+dJu/KLZIpan4eOue51jXw/k6m9wFzPmIxlLRAZO0lr+fD0/AvLoYsO78CKjdtw8Q++HXvvuBH3fe2jmD1jC3a88I0o+jPBR1IVxkiUALkvD5R9aGRrpA6O8sHqG9SDwgs1+sPpHhr/4LH9yCbba5V6ONtM9N1DJ3niF6NPHwg7Rra7VLR7DTJAfwAMKi8IdQWR7RJIQsFMh/3oCjvS1k+BFWYKY7B95aV48ODNePq6q+M0XUrpVDf5yzL93bVJuxWIX9bOEZ8jCOpAgTfe3ura8inqQIlaBSOVeSLm2awEJDB7hWzZMtPzVZEXFNQtSLZy1Ms8UqrV2zRQ5xwf24JZdiHcMjNVIxRpQVJvXTvhMRTqgg/3RHB6dG2we2Q16f0LO972HVX0a/TslpkzyZctFyuwm7pGwTVqvw8X9rq9Te68pRM6S7JFZq9LW4P76rYT3RS3bVZuDSgQZNqCYstfWK/Eg3bzqQjmozIU0MKT75sBBnrS6veXNKQlmVyzW5qi9ExaaLPvn7pXgHsEZkIhW58uODC1VTMZXYLRBBIxXKQ09UuH1LGeMKcXQeMtU8eMS4rTZsssRDGYxVmXXIWLf+Ad6A1W4KaP/l+48x/fh4du/izmDu1Z6uZlPA5sXfFUPHL0u6h5CfnojIyMjIwnHJY1Q+ToW1EqlVVNxYGk3SERT1o1WJiih3Nf/HpsffYrcejRO3F09wP4zmf+AJe85m3oz67222CBmaMEhEyCZ4pynqNkvSJ2PTCoBybYNrCrMemPWz14NsStqGq7JecC+2l2Kcgj10ycFpr6dWMUnLutsY4pEdL7gx4wM0hZnC7zdA29dQcszCq1/dYSpHYFVmFlcQYePXw7ts5OoVxtTMOKjVVQWwDoxfbt+h54xk9Cocj2qWdsfLBHmTcxy+RWyjW7uePCfajh9A5K47xgf81UDFNyYp6ut8VCdIbDUOxStGJVq9aEndBbBcHzt+AaNtheSM3u47Y50/qe+j3aSuooXyHaOlOhLMRPrjznnhlqBO9er8Kg3yQS03xjE5disl/XKOA13nlCKA3uYD/aGx63P9k6U6wd99hvWTlmyKZxjiYp+p1ruLGmcGyCuaDvjdtCq5Fu/2hmD8LE2etlkGigJrHcJ7VV5uZa5YPOkmKGJFyMm0/iOHVkUPcMqrpAIW4TToQOk3LC6dBStN+qs3nUuctKbVtli2sWA8vR7H7JcVoyRBqDlWux4fzn4ZwX/ABWnnkuDj56x1I3KeNx4PxVz8Vdh7/hBZeMjIyMjIwFsKwZomSPOAiq58wn21aiLeeNXoDW2bHHYFXb68+Ax2NQHfwefFfrAUV53H6vrFoC00znYK9vUA2NZ3B6akUjTEPY1kTxWqV1ukrSME6U/zSL5ASEZGs+FRxYb+oHojPPDlDP1qmukkug2Kuk8HBAFyhjGh0ilXbD8AIUR27APuzChplzJpcLNPpTOlAtAPTs42GZInG66Y4LOZecgGR8ETCEyW6fnS8yQUQHpSe6KkFZtcwJDnK21DepifpZ0ib8LaovmoVI2CD2+etIUXdCM6hZydcF0qCgiM+1g1YO/CQmahUxkZK2uSKwGz+lP9JhicRMjtVpI0Cj8iX8h71/dcQU2XuNgJkBAiVkT7HosdBKt9qhbcQ0Bixc035532iWKdA/ciFCgnKJ4wDMLQh9LCb6aJpRrP2Ru/RhdCFJoYBmYNr8WgLwoXNqSykyobbX+tZqsDdoOj6Wd/xA8tj3AIev1PY209yEnzn+rTOos9JDjCtorXYiliRwxzJHZogCMDOO7HsIM2vOXOqmZDwOEBE2zJyDvXMPLHVTMjIyMpYdeAlM7rPZ/eOED0oYszJmbGBGzTWtxd91JObgf/WbLXfPHTegnDuM1evPbczmQ1JCWUU46zIntXtmCEBk6VP3gbpPncyQYwBaGKK0zTGb5AM5ItJBituk6luEXoI/9xfqYb9hiAQJ8zY9M+SK73JB0JI+KV9b4tXAZnomvvXgR3H+1pfBUJHmEQeUVdW4Khgqn0pEzgKO+0V8dGySrdaFLpFz+PHS4RuUdVmiexP85h3haSZImKSQOfLPSOjIMLGYjPooxbWzrcmtCNuu9RpaGKGoLIMk5Ein7lKQp+4h1kGRJujQHW7MfENYX1PlOzZGO341fjwdU6T1V5wei2eQWOl4VRKeRTEdoqPi+kIUsCHC5rrG2ETC8tgfCnb6MZooSazBHIPjWSevoxj3y990TaNRMtZsky1ETkwMDqzaHD2myRyOr2vfrZHOlG6rYjKTNtX+z9/H5liIx8lh87EZK5K5hmmxCPPfAQCJFeQkSNpa68Vp544tqJf1F335Iw9fgNkztoJMMdWWR8byxprZzVg52IiHDtyM7Wc8Z6mbk5GRkbF8wN6DS4bHshaI6iq2xjDWGZoZeV8XwhCJFcEkXaIuZkjOV67ZjNGR/eDxGKY3iFYXrToKQOKvI7I6kxVEn1ANvJDl6+3Yb+aQ7dH9YHVuf68CBqyO04QsUlxP9xOhGQ13TkC5okA53+tmhDo2p6N0Xf1BfN3lrVt+c/2zViHqCT9/+8tw890fxdbNz4OhmGJwaesaPDsA+rEfIiYCeoohCgP3Ih6T8DqIUn0bPY5yWXTFgrbXeidbmEV3Q0UZR/oPyGBQjVYdg9aVccIexUxRgoC56fKRoudlFL5BWSEtyBQRwD1PAEZt66g/YoU6gn8m+ipKZ4uZPJuinwP1bHHAGFV1fLM102AMw4BhLOMQOgCsVVpP3llfbOq/lSYAACAASURBVCrkRauln5x2MUWORfTvCufryr0jpD9Shp/j7P+N25CwyerYBnUf297bbPT909SQqid8vbS1dRKE7mJyY1KreyH+pXrDZsLIG6MGIIp2bsir+H4loWYmtEdbTCbPc8vzmQR8zjgu5OELUJcjFP0Z3HPjX+HwvgdQ19mXzRMZ61Ztx8qZjXho77eWuikZGRkZywaMaJfwlP0tdyxrhsjt8Vtvo2JZZkaEYtQkcf4lhCESq682pkivDN2x+aHfX4Hnvu5X8eCtn8XtX/xzrNywHRde9WZQ0UtXs4opcjpFwQpSVleVZohU2zSitiasVru+R+i7KGGGdNpOM5gAojPVsgKvhgblrGlhdSb0J0SwQk3STGCtWlkxBGOehCZhPOUp1+BfbvsQtpz9AhiTegtHxeBBH/VsPyoTQGJVthAzJL6HmNK0mhlKvJUHbXfXZMyd51q97G3SmdJbHJmKYSr27EQHk9J+Ta/I1VwLdDY0U5pYmWnrLxNc69AlSla3liHiQB9Pt10zt7GnamlLzH7o8A2JDhEHllOiu6MZExnfQJdIW5nJea+wvoqoRp+qlCGqCZW1WKqVblLtPCrbdpTBPUoYMDU/NFMkqANmyPVP+iPPh2JhuKU+yw7pezCRjGnzaYVgHoW3qnNeIkrsLAGj9inqyT2Hcb3hkWHnttIT03PN2JBSRaB26MagksDPdlyVp3rfdsXWhvWosUjf30FazQyd4vhvTzZkhkih6A9x7rNfg+e87j8CINx1/QfBmSl6wmLtmu2YnVmPnXtuXuqmZGRkZCwTEKol+FvuWNYMEQeMUHQcN3pEQMAQSZxPzRQFLEkXM6RXUGwAMgUuvOrH8d0v/Dnu/uqHcN6L3gBT9BMp3jEAbvXrV1ZutZMwRDFzoyGBFMM0C/lZIqaA/ZD+UXSeRPychI4VFRugmiGUo1SWXmivO2KKunSHOvI25av+SRrNFKmx2b7jZbjru5/Cmec8z/lZCVmleqZAPdss98hZn3Ggr7EAM5T46SHvKVpbR6m2JoxV2AG9mnaBZ6H+Yfe/szJzfnTitlF4X7v0GfTKXMVOI/b90i5gEvYnOF+QGVLPVpRGkx8TVvppPyyEEQosQcNjmI/Vc8baCksxKHXt9YDk+e672GbWnw1V6FOFgmKGqDIEIz7LhFUWaqgyUdpQ70u/e1zwaDVPE0uuwEeaYy40A6YZotr/FjJCTG1tQwSS/Zngt+TxVvMo0llyR9tm9xrTVFhQsfaj5K7Hv4esF0kRwT2N64/LMOKTalChlMDjwuQV8b1JdIiC/5O53Lk74M+dnpH+HmWK43HhCTt88hIzi9iYTCfnZMHAFH1c+JK3oq5KfPtvfxf3fvVaPPKNz0ytW6S3csxi1PqPh/lUL8LFPBzicLIeTB6TEOUMRcfxikXknTU2z/SNHK808XF2urxnbLgQKArs3Xs7ylWau+5GNdOLjvXM9HnrQdO2ajB9/1jM/NuioS+U9zheiOkC4fjzTpXHLVLshUXMcR0tfFHQoR2mqU8ecR03YQIqG7pFhBn3kZwCYtptFrHlwQMrRMlxuIgOagFoqgrtUbKcqq+HFkqnwXG0jVU9i6mP7DaaCy81BWrlcHERcVkD44Hp80T50Wztnuq/5Y5lzRCRXtEpq5BQKNIvW1KraYnWHB0rAERIdE8Cyb3oDXDBS/5n7L//FoznDuHAQ9/Bwb/7Y1z4ip9AMZhx+9bJKjeMgwYlFHURNXrCBOfJChnpeeOOxi5v7UqHjU8scXu6CKJQKArZjrB+EFD3POPVJhR16RbFDJESirpeBiFxUjPGKw2oboQiqhqhqJM5CvSRtl1wNe6794tYd84lKFcV/qNcMcrZHsar7Go+tPqquVUommSBF45Vm1BEStcs1IeimluFIq+HQOo3tJ8HFo7pHBPmyD9TTPGz0aWr4SmHOG17HlU9Ne2iyh5r387E0XAwX6hGOn/qIA+mRCgUtc1HBIJaReAeN23tAajJsxOlvtfeuqgYVKhKg6JXo6oNDDHKysDYuisyKMk4i7GZnrdsrJhQGJvPeErFdEhxFQDMFeABg0aEetD4TauHrKwPfX+L+eCeF9Jo+36V/ji/RHHeSKeIfP9hkOrLaFZvmpvUwS45hMKK8pvFmtUKhQXtXVtitbk89rowZjVAhWXRCm7Kdu9Am1cxRqZgVGiEIh6bJlaceCHv+TGPuiPVh0IRI2Zu1bhp3aJIKFpo/DKmwrIWiPysaQ7RB0/TiXoFKVlN8CJUSoGsHnpfrxytEMPAxrMvBQBs3nEF7rnx47jj8+/G01/6b2H6/aipfoXuHTuaAZwSeCs6HoCoSXrrpaX/yUdWjYUbk0K9ONrqU3kigagA6p5/2JNVygTBLmp78FsrpawgArLRTiu1wKzuHzGw7sJn497bP4O9x+7H6jPPiwTncoXBeFTYsqVML7j6oLmpcNiGJuRE+xjr7UvXjpL9/dNbcXac5b4ldHyxODZJ6nfjpALBJua+zjFc+PzEY5GQrbKVLU7mgGQeSLGs5nK4HSdbgIkhwmK62/FcdSkBN3Wqr7nqoC/SuHNZg7mAzIr1GZsCI/QwHDQ+Q2SLLRJ6TDzo4uRxOJCymuM89VHZfLV1FOrD+MQCg5m3QltAcLrtLxeYWfLGZUTjLsUW/iIbBmQ+yvzRi1EcH/ywqDvU5RogZHRUW/Vcdw433XuA3J+4ueCqfY7LAqhnA/sSsQvDwjbch6h7cN/eow4F7ahtam677qtvXOS9xL2LOCl3ITwRdHpONZ6wW2ZLBSKDp1z2g+j1Z3HPjR/LAUSfICBTYMvTrsYj37luqZuSkZGRsaRoBPisVK2xvBkii8TRWJ1eS5yQaUVX9pK90eEvtEzTQjnHrAPhoue/CTf/w7tw4O6bsHHbs9qV54SqHjOKUSo4TaXvodgszQRE7VJjkm7jxef1JGdeajUUXuceoe6RU+zTLuYXNMcPt8EUQ5Tct6A54WquOdpzTe+7MYkLWXfp5Xjw25/FwdEurFi7yeUdryCMrf6Hr8MzKG77Va/gJt2/lpVg2DbfB8tCFoQulwN6y0yPN5uWbcquZunVKAJmUTFFXdtgzXaZYk5atrvCokzwv4O0oVDnYRm1ZWz0NsFJRviuAZAqtAt7IBnYuP8reb/Y8awtQ1QPCDW8eX4VdCbZGrNMkA5wSpaVKAxjzjTsdClbVtr+3TJDzlS/Zb5qVon1uy+ADzBrz2HngnvPTKDvFnvfWnYB3E/qPQC9/dVSb6Jzo86d4ndFjhnyHYv7bSwLRLL95gqA25JzzFCpHoRwHHT/ksC6qisd77Xo0vKXOZY1MkN0nCh6Q+x49g/i3ps+iaqcX+rmZEwB0xvgrGdcjUe/+fdL3ZSMjIyMJUXNdMr/ljueEAyRBgWMjWNDlMSfKPbVQdxHbYbeFQ4jTK9X7wScsf589PqzOLb3IazZ8JSo3qicWYYpVf4w2aR5ovVJnAKjSsbTMEPtOjCN4q6uVx2DttYFmpAKwhBpawm94g/a6AtS5Tqlx/Y+AAFTY/tptCJ9l05YUMa6578Yu977mzh47BHMbtwKqoFyljB2CvxSBwU6SbG+waTyNZJVnm67PdaR3kpHWaKPo3SIprH00mlCZWUffibUEfLMVKpL5NP4EBeSJmZOEj2h4Ddnwa6YovA5dEGSp32XTphjx6XM4nQ/bGaZJ0V8bnm+5lpfnlE7b/qi2WsP4sZAjmDUaNEngtd1EYjbhMLUTtF7vmgeRFEuHs9bAwDl+iFxDhj105av9PC8OwV29yfUIQKzZ687jAsiUmSh+xjdvxbdoKhR9qif3RCKzfUuT+QZCua8ZaV82A3Ngtr76RxCwp2T+s2Z34tukZ5HTIniuo6QnLynodKFjeg6z1gUMkP0OLFq7VYc2H3HUjcjY0oUgyHOvOzl2Pm1/7HUTcnIyMhYEmQdonYsb4aoa0Un+70IVzD2Ny1Vh4K5Zn60HpK7zun1xIlCc37uBdfglq//GQ7vewCbtl2GNRt3YDBcFaU0Y8CMWzrTxQRQmkYzXm0u4Tv9WSQsU3rU49dpZYbAykzqE50WYTCUjotrRtv9VDpEXQwfKGWCqg62JalCjeeay6/E7m9eh0OPPYDZTdtRzgLjlhWyDsLbqavU0ac2hIyQbntXefp+LoYZanPqBqi6hMAQhkauaz2h4LTTMaJStArPxGmqZoRkIe4W7+E8XojV6dAzaU2jkIx3ONcTZi9WknKBUgOmyK/gbVobBDRkhBg+CGwR0NqFfaElTFGXHgmAwgYk7lkfOMfmY50i1zZlFj8JCTMkz2XhmSHf57R9XZiWGQrfzY556mKak/d2UJn2y5C81zQbys27J3RTIsxTXzFD8k0JWKE2x5kAHFNU9/QuhM+j9cR0RyfvIEz4LWPRyAzR48Tsyg247Oq3YcOmi7HzwRvxjeveiZ0PfGOpm5UxAaY/wMYrr8HuL/3dUjclIyMj45SDQahgTvnfcseyZogoCd0Bd3SrTelBF6MR6qB06BS4wJjjeFVBgb+bztU7Mwx62LLtcmzZdjmOHHoUN3/9T7HxjKeiP1jRlFtyK0OUMjnBwfm8idkqxwzZZVMUxNMvOOO+q5Ad2uWICXQYWOtxtDABwrbplZpmLqaxOktWpB1jwiYozwU7tdVTfJ4guC7lr7riCuy54Toc2n0PNlx4kWObIkvGxDmd6s9x6KRox6Em1H/osu5q6Ye0UfIKq1kOCeWYUiaojRnqWD13+oQK0nffpwlspJqfuj5Xb7B4j3yOBUW4pkzoZ+LfSPdDd0LGM6iI9LzU3iPlPGRqZWXvdFGaYxPew6C2zjerSpibGjVZP1hKF6XQOkUtc65nLdJmh2ObpqlPDFtFmXUaQpHKjsl3HHPdWaG2MN7uVN4VbV6bNSMl5co9lrSqcA6VTDvqTZTcDDf+vMDeeaOr3/qA6jXHwp5LaJaqNokOUfL9sX6kOPAR5eenmkuaHWz5lmWcHCx/ke0JhhWrzsJ4fAS7Hr1pqZuSMQGm18OGq/8V9lz36URxNSMjI+PJjmxllmJZM0TFnN1rn0N8HAcrbGEJjqMnOiCr6HUUis1pVqntH01yS2O7MhiXICpwaP99qDY+E/3+CtC4hhm1rM906IVwvnR4mdY+WxyLRRSstOOJ5xYesnop4h846B93eFR1JVLDzhVjrwvimCa3Io3PJzIqKq27j5O8X0+7YmphhkIdpdXPfT72feU6HH30PlRnnd1kCX1PLcQQTYFaWfYkwYcDS7kkHMUC/YusscTCaJYwAnW3NbwnCzFECia08utgZBO2J2SDVNpa6Z65esognV3sd6j7JCyQ9+ZLyco78WcmWRUrSmXwo5r/rn/OSiloSD9uk7NWEqvAqvkoVC5gq7C/5JioJKZZhz+iML/4VRvaUCDFbJPniGkCFlt3RKhMt5kZj03cZqnDjWfA5oQkWe3nnhzdfZW4Xm3zWHToxKOzhLoQC70+O2s9YWj8nBNK39YjFl2u0UH7Vd0+OkHMxpABqFc33sXlmi23sFaCPcsQDazulht/4m42Z6gmW1C/m7tKP21BdmsCFgjP6cDInqrbkBmiE4yi6OPyF/8C5uYew55HbwEAzM3tx85H/xkPP3QD7rn7czhw4F6XfjQ6jPHoyAmpe5Jp/8mAGZ/S6hYVSHSq8ooC66/5Xhz4yhdaPY5XwxNb30IoV5za+sanuD4tHJ5sLCpw6YnAKa6vUxn3pFV4iuvrneAHfgE4wetU1dc/tfVlLIwnFkNkP8Cm9B9/0X1xe/1T9EhWdbUOdupWkFbydx6KORHavX8jjo8lY0WxBuee/SLcetu1uOe+z2Pr2Vtw5NAAvd4MesUQ37n1w+j3V2LlyjPx6M6bMByuwZlnXozDh3fi2c/5NzYAqOxF65Vo3ORQL4NNMy6JD6YOPQjjWKFgdVe0r+rCPXFTMahsVtZm7Nvkxj5YVUZtDs/1eKq0jjUQvSEDF4R0QR2btnvVwVysfNazwN+9FYfuugUrn3Fp7A2bG6Eo8T80xXfB6UKoJUcnQ2StzMoVLfoHYT8CRFZ3tm3V0IYRm6Bb487ZCkUdbI9GGEy5i8Fssw4MURepvoguP/Q6L7pVQgokYqtmH53XYvaBS6F+kywy14WdtEyOqf198o3rqFff58B/lY/tZZ/h2oBrg9qyupVjJzhhvHqRj++UoCZqmAlm8qHL7KAPLYNRWJbpiD3OFX1oVJYZklhbekx0INUmUF8wBsGz7HzuCNvjLFeDxsu7pxS9JsmDqH7u2zd7r3aeoX0RNo34yXKDYg8tFG4SADZ56QNAYVki+/63AlIXM+SqjfwQNdeKQbvJq7ecoyaAbL9uobYmMGvBzxPTLAhCNVWohNMLy1sgOtYcRZlaqGwqwxeW3zICgriGLT1zgpC8+GzehLHUwUJHKWfvPLWXNpEIUSVjfnQI9z/wZTz1vO/D+rXnY/OWjTiwb+wUXy/cdg32H7wXc6ODOG/zi7H3sbtw6MjDOHDgHlBZgoLG+20ueamqTrmPGLv9Q1IvaS8YyRaZLTNQyGat2JeUH1wqm23FWj3I7qUtLzf5eRqBSP2TmGCzb8uCyoa6zYFCtj5SQVjz4qvw4Ec+hNlLLgYNZZK0tHsBxPXJSztO48zs7QfBHQOz+0kuD0JEW0DiimAAlIHpsEvbZna/CAFP2ggAdRUIL6rNnQrZFKTp2kYMFNqB5pEmq9zumqoEo8SFRnDsCjPY5TjUS5V++zlhQQMzdOlX03YKnqG4OCeUVQTUhFoUl0nCxTCMCCI2MKy83grVCQoeImeib6+IjkZlyxjIFprbhvMvj9HYOm+spKPyQovb7h9IpM8DNzdVhJh6KNKcOhI7gYTGSsCTd1AR56Eeo7CCkARRdS3qCE6tHSYCXtBzAl8d1+/N5RnGFCi4ctuVctSCULot5QXa3qBsa1qQ0h5r8m3SFiVdq2855em3xjIWh2UtED1RcWxuLw4cvBcHDt6Llz7/fwdR/FU0VGDD2vMd+7Nq1WYAwMHDD2HX7n/BWZue5YSXjJOPmR07UKxZg4NfuR5rX/bipW5ORkZGxkkFA6izxkyCZS0QyepMVtWykjRlGApDSdUqPAUHTgJF2c8pYpOszq3kL6sJyyyH2yduS0ycNpaSR85twL+qxrrZbbjsaW/F6pmz0Oc+zLiCGVfJktUxNnbFs2XDM3HrbR/BsFiJM9afH/VH6BhPD6dMDgfKm02f4+02vTIOzeNllULOFDguPmQrmvFHsNyxK0S3FRc1udVcXS+CRKFdK6aGTvySrb8FtpTCbbJOp5QFgIKw4Ud+EI+86w+w4vnPRG/NGnBEZSt0yaqFHztnKqy2ICrHDNkGyHmwbdnpkK1j26ZxHNr8WBaMshcwmpqlCO5JwkgtgEgB3LkNiOuZtNXaVV5X2AjJFuqOJUyRvkeq39FPYkad5InHpi6cLnPCFElb3HAGjIpm4byJNbvLzHAsUC3vs4JRyyDIM1rHzjFk7sePpX3uFEMrz7I8j4OiqWitVbaeG/dRVk1FJB21A5qMkWJyos4XDW1U2y0lPefFTB0M1GVh26YGVtqstttMr0ZhzdoH9piEx7BwYxNcF+eX0k9hY6pa5/Xv1R5V6KNy18ThZRczJPVVTBgOxq1pNNx7rTaujTqUi0ub5M2L5JONLCKeBBARzlh9Hvq92UXmM1i9YgvWrtp2klqW0YXBpk1Y86IrseuP/xzVwUNL3ZyMjIyMk4ocuiPF8maIWhRP5frUps9KFwVIHfkJU+NDdlCUp+5TrE8EhMuxqB4BRUtXq9Cgl7PCTFnJ/9yzrsSRo7vx9Zv+EM+48PVYs+psV7Df644VnrzOBvk2aD0HzQj1hEVL9QO8OX9cX2h+b0rAjBgYtFMWeiXT6mrfsRvtNIVWsq4ZngzsYIo8e9ZShu6flAsCmeb+nvHK78WRm27B6P6HMbvmqS0MjbrXypybg2QJYSKrTacTpVfi5ANN6n4Eeg4RnP06PDtVINUzA8B1XB/X4X1J2cZp4apaiLULrpNilbrYHqaGKKl7SEO6THLLIPV0MGCd91UaWbMP2ir3SznUTML9BHpcTl9GmL5QobcmR3vKlnkNgDhmhJwxgXretd5Q229FB7U5tEzRqOwFDiDjrtMwDkTr4d8vYqIvjgy7mKHCHqtQqU0HgFVGDqLQTMTuf4HoRukAuNoZIlHIuth6kkkgh4CVZQq0gdL6NENVyfMfFK3bJijshBlbTfNGn8s66NRuIKTcuKm+3jLlMbKGxYnBggwREW0nouuI6DtEdCsR/Qd7/VlE9FUiuoWIPklEa4I87ySiG4noant+HhExEf1skOb3iegtJ6FPT1gY08MzdvwQtp75HNx+76eXujmnHYgI/Y0bcPBLX0Z58OBSNycjIyPjpIC5sTI71X/LHdMwRCWAtzPzN4loNYBvENFnAfwZgHcw8xeI6H8B8AsAfpWInmbzvQTAewF8wZ7vAvAfiOhPmHk0TeNkFeZ0iCovfachAuJzV0YQuFLMQLtiHMqeuzell3ZQoFdE0dGvjOMVULwgoUiEF30j1xfFlpyx8lzs3ndbnC7R6RGdH1uGIecAzjMXinURB5RyvfDptOWZy6NXkgxQxbHzSsUUiSmtccvAuH+hvoXXu4iX860WQoox0YyQpmUiqy9teReEASkAFFaXZ/Ob3oK9f/e32PPuD2LLz/z79gZIsSOv/wMEdRTe9UEtjtn6imkTB3hR6I72OeW617EMpJpiM/gWx4nqVkSEokuygFuUSIcotgoPTKzV0bU9KEe6qSzSSFmfMTVuBCpCyhAFC/u4kS116vmh4N8ZwnSQf1c4VlW1VTNG5N9BzlmqtFXucdXcd7mPrlkBg+kVXS2jaDQj5NkgTw5PS5d3oz9jA8X24oC0Ya3lqBmE0oUkaWG9fRM7rfymAnlmbST6R6IHVMUvw7agq8Iu6Txc6wcBLm9ZGJTBB0Rbm2mGqI0Z0sb2A+UCYWQTlFWRWJklnpz1hoIdB3GVkHHiseDIMvMjzPxN+/8hAN8BcDaApwL4ok32WQA/bP8v4I2Wwzu8G8DnAbz5hLT8SYzDx3Zh2F+91M04LUG9HjZ832sx3rMb4927l7o5GRkZGScFNeiU/y13LEqHiIjOA/AcAF8H8C8AXgfgEwBeD2A7ADDzrUS0AsCX0bBGIf5vAH9HRH8+VX1Kd0jAxq/+unQKtPVLDTidGjHgIFJHSSuu/HvByk6sS3pxxWYkFhCKAQj2v5maVSGJKZW2NnPb9tZNfznGnsfuwB33fQYXbb3GdkgSq0bDX3e6TyZO49kQ2x9hivrG/671jlR1oe5NUcKGIlGD3sEUJSETONAPc7otC+hKcNCPuNbUIkbPDbQwRIHjR0OBFVEFAAVWX/ocHPzsdTjrh94IDUlbCM8pK2JXNgVeru0YV+mqEgiYhlKxPEFi7WDT/d5mlcVoDXXhE6ij/j9oU5I1vI82j1hupkGW4xaEoTtkHorVaNW1LCOgHjbEWWolqHSwhLlNlLfi8qI2Kd873tmq963jVu+K1UoYotrPaXm/OGaIgpdQFbwrgmZ5XRb7DhCrL/t8SJltrJC2UtJMwyTrpIH1mzNjraRmrO8iXcaxcR9HJOSIWKIJE7MAA9dc15Os40bJs1sTyrG1hHPXmqPzndSp++YZIjm6PNrRZBBItaoKlHXPjW2tgrxKUFet20TwzJDklcCvEpxXfEEJUzQu/RhzYm0Wd6u2OkO1HQ+ewBBN7TMNeEJEnz/VmFogIqJVAP4KwM8x80G7TfYuIvpPAP4GgNsGY+afbSuDme8hohsAvGmaOjeuaPapxMGqM3UPKGan+CyKwjo8QCjkhJGUEUweO5sLMYMdWKrUnhdDH61ejtIWM66itHCK2f6rsmbNIL6mtsy8e9PmuO6Mq7BmfQ+H5h7FGRtnbKMkLfQ/tk/kYyYpoUk7yxOBSQSiSXHQ9MuOAKxd3QdqRm23gWTs3bn2RBwIU3KuBSJXhnxgpS/uHN18ZodAhFAg6lLKNcD6YS/xnrzh1a/Fwx94D1Y//ABmz9sRVWdMfNQCEUzjIBEAqr5swwQDGMI5IWwTiGy5WoiZIBBt6PXa8yiE3q1FQNbOFlvzSDotECVbZkogCn6T+vT4pRUC6/u2P8mWWbydqAWius+TY2khFYh8tHvjp5Bsg2nXAMqJJAceuCUelxsTe+/XF3awgq1VwH5g3aMYm3oXsnUmiwu3Hc0+VqC9VliJobDvmX5tY3DZMsX79aAeYAUPwuZjaBs/qNu3zOa4h5Vo8pSm6ccGkhezTeS2kqwjSBEsiMA2jhoH2/TNBXn3yb0ofBlqa8ovgpOXoToNFLKdV+uFBaIN1AdMIGxq79PqXiTVBgX3WcbabkXaMZm3L7R5FG57VIRhd1Tdqt17XMZwYYHovs4UGZMwlUBERH00wtAHmfljAMDMtwF4pf39IgCvnrLO3wTwUfjttk7sPdSsWrwnW/ny+BeSfIQ79bUCwckHHbTHeFHm/R45BsC+aMZAMS8CkH3Y5bd5ey5+iETYYXirtYpxYO+8E4SoEkpIXgKpmU15cAUe3PkAtvUfgzGBEx4t7IQOSmw5ia6JYpccM2QfrLpvUoFIC0KBXhWNajy2bx7VwOa3x0oEokEsGGmGiGoO2D/70pS8TshCVCb30hdFV1iKNq/Jk/wQmVlg7/zYnTf1G1SXXYVb/uI9OO+nfwHFzKzzmC7MkPNirITzaghUVo6tbFr5SPrGx0fRLwnbL1+8TuEmLDK4X7vG45QN0WVwcF/cvbAfK3nedJNlalMgqMpzqEM+6HaFP2rrnC6GgZrwFjuLoD96TFxSkdDs+dgzlMm7wai2uijFIhiS+2D7MbJCnPadJL+Pg4WAHT8XqDRYpO0qSy+oOcvAFkstiFBhjyIQGS/8+EffQFJmugAAIABJREFUfoTtjexZNkLO+7bRwlIcMTXq4TwAYKZv37H9ZqIOemM7JE3hR8tG6Hns6Ao8ZvOPWAQhxk6/DvaCgrxP7eRgJtR2cLw+lW28HGUgxeQq1INT84YredfqMuCPzlO8jFsd1e8bHTBtPYOdlY/Z4oQquSeWIRIruvA16wRYm0Ys+mbteMrYH7PKqCPTw3xlvYUrL9oC8WjOlhmSoabxBGW/KRkiIIfuaMM0VmYE4N0AvsPM/19w/Sx7NAD+I4A/nqZCK0h9G8BrjqfBpws2rtqB2f5a3PLQJ1sDj2acfKzccRFWXnQxdn/mE0vdlIyMjIyMk4xpGKIXAfhxALcQ0T/ba78C4EIi+l/t+ccAvGcR9f4GgG9Nm9jT5CF9rWjL9lh6jqWpQYGHalue2iZxhk5KX4hqBikrM79Cjilgt/KJ/A41/5P2XeS2ILSSFINqxrM2vQY3PvQR3PbQZ/D0TdeAyHjqV/spqclTvFJMB1NkbId9MFYGC2ukWRfnK0nazI3n7VHbKqVdp8ixSwErFLJF7Yhpg5op3ep0zJBa8autwSi4a8u9Nz049keuU9mUs/nFr8Hdf/5OzN38Haw7++lNLxWDKPVWMx17Mwjmhe5eeFn6I22UidqxQ+DL4njbsGVI20iYhfw46a3IkFVz20KOdW3fnoqYHJmfwVbmJDABdY9RV3U6Borl8b5+bLKa0m2RjjIcixA8ulqPy83D6B3k62MK5mWoFxY10ur5KVc/hICtlrFRrIF4sva7e8bHJpOuT7LQDLByMI8VlrmQ4+p+E0F7ZdEwRwfLhuIcVY1xR2HYsRyRDlHAsBC1z4G0t20/2+sjryio/W9RwOABcMyQY+2c9ZvPo9li0uxgaP3L5D3IA35+SKw22da3+kghUyRetcUizcWPE6/XdsIU5MfOe7oWfbGmWtEZEl0hsmMi80msW1sx5dqZEVo0ZgimsTL7MjMTMz+TmZ9t/z7NzL/HzBfZv1/mCTQGM9/LzM8Izm9iZsPM7z3ehi+4VdaCRA9gEcSLfFyrxBnhpDyyhSVbVNPnRWFQmD6eu/31ODy/B5+/4/dww/3/HXPjbi/K3LaNNm1bj8Ozl1C3Tr+qVUhqh3E6WNPfBJdHHacBubAvU2dBMQKK4Qy2v/SNePhz1wKHji0qLwDY78tUcFvDx9FW97Isp7+P8mIt7DEJZDop71jqmz4PaSXYUwWn2zX9fHFK1YvJs5BO1ATI/eNF3L9ahKUpBaEQ7kNtOlaSLRhaBeyejeSuo9BPg8W8Zkhvry1ibMjN6UXkUQGXF1Pf8cBtlclW+SJCc7jnbxH9y1gYy9pTtY4pFnpcprpDKNIvhUAXgG1eNsExXFyr1URkrVQ1QhFVrISidqmDyhpUsheKjAFVFbgg75VWVjFqZQmihvUoDPqYwQvO+9eYr47h7j3X447d1+HSba/zekdKCJooFOk87kOh8sCzLpp9ESsmqhjcI9C4Bg8KmHGjZG1GtRMAleFMwBQpwaZNKGqhKySOWpS35AlWZn6VyIYc62PKgKTjhrkySq1LxqYYAau3X4Q151yMB7/6CZx79Y963bKRarfUS4RqpslbDRuhSFtudSlKm9KyDWFb29gkNUZNHgIG9iWpWZ4O5qZNKNIWVJqZq2GZtTGh7jdx7WpNMyWsE9vn7jhe4CfinR8IRf75VoyVnDOaiPcFg0rrQbxQAxrca6B5pOSdEiaL/ufgD4EQBABSV48boagnrIEILLK4am5OzY1QU9cGRVGj5sYvEbO/FyIoybmw2D3NXpjK6SHJtTW9hjE62m/MJY+WfcyVPQwHJcqysEJRD0W/ivz/RGPjfADFN5AI6T0tW+aPscIQcSQUOcHFMUbxeALNs08jaqxIx55ddu+6NkG3HwtFbLipzwmcdn705B5YpqhFMNTCjfg3Eu/W4gW7VSjiuH9QzJhb/IRC0XEI4dUiBLDTBctaIBJLnYSKRvqBSQWhOC+1hPtwSrhTTKbKCmdGKR37j1UsbJiCHLVcDwvUM4WjY0keIMdUqQYwJ93p0xDr6wtw366vo54d+i0gEwgfXay0EoBCZWrAUsEqj3vZyHaCbG1ZwbDuG3dNFM11gMhOgaiGC1sSOtsM2wrHVvjthsTpphKE3P3UZtSAu8nOvFkJ124+yc8cM4pbr3gNbv/wO3Hwvm9j/aanJ2UAnhViwz7Qrv5AJvMGCRKBXN8bVteD342xTI8IPErxu63MrhA5boEgQZFlq/CYX6ywszBM+xHVYwjVcBF8PtB8zGqCqUwi5OqBI71lxvABQ+XeOku4WBAS5sjPHx+qx61d9PxwQkBwvUtgNXEav60edNltvcs8tR9d20jjzskV6bZyJCi1PH+2P5VS0h0HPjWOWWXpOavYe7TXvGyP2Jfu+v5RAMAZgyMAgFFduLAT4uRwhgvM0jhxWCjM1bx15Ng6Nq7j0u/o1J7EaZxgoAWhNvcsHM8Ht/0FaZKS8uVHTp8/bwlqk7nQLPa8ImcVK2NT2fNj4/jBmBPnlmWBchSbRBt7PysJsC1bk/I+DQNB60UgOs4zFoW8ifgEwtH5vVg5s2Gpm3FaohjM4Nyr3oD7vnItytH0W2cZGRkZyw0MQgVzyv+WO5Y1Q+RWnZqtDn2oLMAMORPQIlhBLCBF69WKbM0B4TYMRWn8UtGadxrjGaKBQT0sQKJ4qrS4vbTvGxYyYEeP7cHu/d/F4dEurFq9FdWKnqd+A99DpJS1HZRSuvPZJMeWeeodBsbdI8sOcY+8NzLFFGnTfa+A6hkip6ytHBbqcxNRNtIPaWTwG8K54FfRQGMOTWqlGIbJCP1aRTsiai6t2XIR1m6/GPff+AnsuPJHU0V+lnEgcafSyRC5LOH5QuxR17wlX4/pNUyV2xITVxLOT1Zatt4iS3wMyVCJX64xgDmVRjtmlOrEFcEASEKTaOj+EVDUhKIMtzxsUr3Ad9sM0h4OGAWbROaJ1inSTgIDv0DaLDzcIgO6Gas4jz2t/F/4A7MfJ0dEyTMkTlRtg8QMn4m94rVta+22Y+LBatsa4dL6wxk3x2PWMePRXvPSLS0btLbfCP8bhkdQWuZHmKfZymClGfngqraeI/ODeBiiOR6/XNP3d/CyV0ObMMJqqyx8HjXDpwvxr4ru+wb97CpmKtw2FUVrcT8xsuOqw6qM7fVyVHhmTcz6xYmkC8GkBifUfVXs9PGgzmb3CfKIPAHwrVvfh527b8YjO7+FY3P7l7o5pzW2Pf81OPjoXdj/4LeXuikZGRkZGScQy5shGqgLweKhS4co0TeSlULtJfvEsELroEg9vejnJo1RyxUd20JWcoa9F+ZhgWqmiBiSJotmNlRRAB68/yuYHx/GqnXbMFMew4MPXY/zL/1+rycjLA+1rKC0apJb+ccr9SasgtLJEKZGmAdR+qu40SEqyJn8hw4oATgnlaJc7cNzwJ27AJjCLoku1DhOW7sx8ywB63vgOmiPsucuyqQBgxI6F3R5OLj3wRiWRw/j9vf/Fs44/7k4+4rXouA+er0ZnH/FG3Hn9R/E2pf/PAYzq/yYBYyEBL/ttCxsubwQM9RlFckmYIj6jQuBhCGq4nPiIE3pywE826P18k3AJGmFcj2nxEmhe4Zb9LR0/wShLpghy3g5vQ7FaupnV9wm1OTmlrunMoedboZuj7CT5K91MEOpvhAnlprunojVIBPMiILn3x5qQj2InzenZCuKtFDXyUQK1q7PAEqrK+SenZaJI2yOeE4eVU2eOcsQyfnI3sh1g6NYPzwaXVtdGZTFMcccHRkN4/pCFtKxSNr/RdwufysofkaDo2eclS5RqD+ml/qd980WXdj/CYHOl+qH0mViF9iXvAK5daJYkf8OhAj1hrpCghir41kpVjd8v+vv32KZIkYO3dGGPCLLHIcOPoS6HmPf3juw/dwX4/kvfvtSN+m0QTGcRV2OMT76GL77N/8Vc481wV7XbDofG5/yPNz1zWuz08yMjIyMJwmWN0PUoZcQWVS1SM8AklWYCfbuWVslTMEMuar1SrVrxWH8j2UfKIdBOADFELms4an9/6zzno/aMFas2Yx77/oczl0xxOCszd7pYOAsL9lbd2MSs1l69cQGyRg79ket6sk0rBT3jQ9YWqs8yqy+zbkjKbbMOZ5zjF6sQ8E9dFtf6XOldGZATjfDezwI9B447IMvgqjAzPrN2PTsV+DIo/fgtk+9C4OV67H1qS/B9md8D279zLuw654bsPm8y+NxCMoTJsWZ7nbpz4TNFnTMbfez8UdhRkwZW5lFej8IfEaVSPQs/POm5okqy5QB6zGO762ztpFYbgFrVmmGqIMZcv2jxjAsYoiCPkdpdZzCGs66zDlTVeeOoVVhR5qAqpP1nbQOE1NCsnrzaGHn2N4bdV9rBM+szHcVTsXrvFhdlap2FTp1J4hfIm5tutb1Ccsdl5YRsnqATl/IDnRZG6wZNIpjwhTNjvoY90Y4qJghfQSxY4ZYz2mN4LpW7epiwJMQNK0vbptV3lHiaFPe9aZx68Hk07axgNFp+K6yFsTOx65Y3CWmzc3BFOzYI3+0Y0/xfOw8LvTbBDAom923YFkLRBnA/l23Y/dD3wIeImy74GqM57sdM2aceMxs2IJj+x7BWc98KbZc+GIceODbuP+Gj2Pjec/FRc9/E2754h9h7YYdmF195lI3NSMjIyPjcWBZC0SdK+NQip8SDWOjylMrCx2A2bUjKigoD3DLMxfZWlZ9hdcVqIcG1Yz32+NZrI599OB840VXYM+uW7F641PAgwKlqVDOmogd0P1LPHELO9Gxr962UJBQJaI75PQRSkI1oFbGS7M+4mvIRVN3v7NfrVeKmnH9UQyR8WlSB5qT9XTaLbkCBoDQOp5UNwLR3N6Hm6SmQF2XGK5cDwBYsWYTznn6K3HbP70fl77sZ0DF0OV1rIvoEMVRFpL2ROhaRbvrdqXvLA2DlXcZO3PU3q+dHl3pmTtvqWjTiO5LrDIR+CvywXm9FZvoP0gbYxqILBM3EYoRYNPUaUZImKEk7I5j/mwZdcgMxfPSh/mg6FyCdzaMm+TRz466Lk1mgF1fEaXpfL5DPUfXprgM1pZ5ohdFFOgT2Wuib+T0nTp0YIL2CFvkyNuqiPIWQXBZcdq4qt+4Xx8UJWaKMUZFk2dsj6XVgSnteTjFE0NfmWxaB6btmZU5Zy+7qD8cM2U0hcf1Vn09w/5vEaCKgjA79v4py165Xlinjqaone6QHE0nbabaaoLnXeZ0EZ9Pgxy6I0UekWWO2VUb8Zzv/SWcf/kb0JtZhaP7H17qJp1WWLV1Bx67/9vgusLuO27A3V/8ALY87Wr3++YdV2Lluq2488asT5SRkZHxRMayZoh6h+0/ahUd6kwspDsUZ2wORlslaL8SWkycsGer97G5CJKKD5Ze4+naWSF1lOEzcqLHwQbYeef12Hb596McBuxAyFR1jEGX/sUkb8l6bLx3WMZ4JWEUeA9OmKkOxig8aj0j13XHAFhmKtIhUno4igFKwmMEbIJmbEKGoe55n1fi3JdsuTObtqEez+PI7vux955v4Jwrfgjrtj8DTIQm/iXh3Ctfj2///e/joTu/gM3PeBnqHjnvzhPnUge0CkHCkGofLtxdrp+finkgBH5W7L3o8LnjjPoCK01TxeV5n1O2LInlFlhhdsXL09adHFp/sT0soCOR+J3hdN7p8dQ6RbJipqKGC5VhE5Gcl/EYhb5qSOJKCdvSF7ZA5nIz19wzFcyN5D3S9Y5wg8QBu9OhfKa8srtfWxgiDbnuwoCAnF7R0bJREFsLhgFj3aDxVSQMh9ch8vXVs83J3Kh50ERnSayudDwvIk7f+zZNJR7/bfDTeiQMtJ2vc+RiCCasf8CyROcU/NXqHrsG6K0DTv71AcJtm1QwWTIpQ9QrmglRKBapEt02679O5lNzgqjP7ts2ZTxCZq8fluFxWo1IsYjglScC9WKCuU4B0xu6F3QbquEJrW5BjFadWqW88ewpVgI0AJclyrkjMEUPoyMHsGbrhW5LwiXr9XHh1W/Bzlu/gEOP3nXc1YXC9KnAooINnwAksd9ONk41Yfck11E9WnbEZzlJWLFqEZGRTwRO8XwRQShj+WBZM0Q2nI5DuOKvNUOkdTT05BaVlEAoCvUpwvI1sxIuFPRKLtQZakNdELhPqPuU6hB1rXop9blTcYnRsQMo1q1DNaRE50UYomqYrpYT1x/ai/I0DJG0uSKMVxDGdmUyWkUTmCKtYyRHDqKlawULpSck/mzsTB3P+vo0M+TGV+kJsUGi6xUyRNwHam1xaIsxwwHOvuqH8NDXPonezCocO7gLw3Vn2TGxYzB3EJUZYdtVP4J7v3otLvrRd4D6Epk47l6nh9mgv52WOF3ntUrL3c9B6PGZ2ApF+r7JcyGecym+V5G35Q7IvZD4biBCNWiEImfpI7pQImDqeVnD+S+aNFfjen06p1endHqcTlEVz7VwGlMRP0QSW8wxReq9g8pGx2ppn8RUqwtGzewYo06/TBPg5kZoaqtM3ljpb7WyQEp3KK0nZmwqG0T2aNl3rNEc+jhKA6zqNYKLZorkWDO5gLL1TJP36LhhmSTWl8RjEy/Y0q5QKJI4YaVYdI2FXbJ5RN9xYMDHmmvFsfjmhgGfo3PF5ESJNCbcL69zZst1TJEt0pZZGM8ODaxg5B6DgEUCfMxJ2ZKvg1vvmF/7PqXRtJOJfEDmDIflLRAdjj9wLuREEZgILyBcRHS8/pC6j7w9ajNkWbFPUMhOVvWKrZbyuAhW5B10ePix0orDo7ljYADDbdtQqbQunWbK24Qm3S9pn2aDky0zf17OAOM6rWfBLTS35ULeQWA5+aUTbXsp2jsVtOIPXFhGlzAownXb9qG8lDdeciX23no9Vm05H/d9+VocvuRBrDhzO2bWnYXh6g144Jufxv7b/wnFzApUc0dx3+c/gHNf+9ZwKHS3/Ie07YOk3s2dupbBBzUU1BsF3/byI5a8Y04nzwWp85qTxUPXu9VtjY4DwcdVZLtRyEcpFRSisCph06d4lyfzwjkQpdamO2EjFEo7BCMXemKcNkS3LQySywynYMvTCERdi6ZgXGIhKUjC+ryl3AWqLa2wMUc9FxpEyj1mejiKPoxNvarfmOWvG6iVLICeKGeLkDRshKfDdvtNtuNKJxD5D7aUP28D0c7bsCPSNtl+E4FpPOih7DdpuNdcM3PBXnh4lNOaGkmjDh8mNRg6r/qeJOUhELSUQn9dG/Ts6rynlKrlvJQg4GhW7LW1WGBj/L2324Y9kRunZLkYecusDctaIMrw4PEIpndqKeuMBmQKbLj4SsztewTnv+6nsf+7N2LPt6/H/IHdGB0+ANQVTH+IuhxjzY5LcfDOWzC352HMbNy61E3PyMjIyJgSy1ogGhwWZbLmvA62T8S8d5LJukaqjDuZuncsatGy6uvS92hZMdfBFl/0m2Z02hgieyxpDDMYOId3iYK0CcrRzFAXO+JYEk7SOjZATHkDpqgaAiGxk7ArXUxR6NhvHF9LoMc76J933a+q7VKqDhJps21w0369WApZlmoI9M/ciMfuvxWDs7dg09bXAgCO7XwQD/79X8IMZ3D2NW/A6PB+PPIPHwcA3P/pD+D8N78DVMQFKx+ZKcPZho6tspDp1FsfWtm5dq4PggRa+V7llbRGOeAEtayiu1wfWDTb0sIE2fqkTXprWjEfx8Xsh8ySYrx0ga1bZu5/eSfYMZB5JPN2gX5HsCw199QNBLonQMe9B3umxjsZna4ZXYrUISRY6ch+Ipgp2UabNz0cqz1DJAzH6hamqO8Yojo6r+z4jizlL84Cy7rwCt0cp5mzDNGcZYzG9uU6stePDvs40msUKqtec63uNWkKYYq0aT2H7Kpi8ltCkSwEv2vZvivA8NuDRb95Gfattc9MP1Z2nTeWEZNnmRhstw2NY4ZkLkzfxhy6I8WyFogyPOpyBNPTwd0yThV6K9dgfOQgAIDrCofu+Q4e+dIncebzXoYzLr0cRAQzM4tq7giGZ27F/J5H8dht38S6Sy5b4pZnZGRkZEyDZS0Q9easUpkVjUXXoO6T0ydKwlNYtFpM6uCLSvJ3ugwixYuZNwer2ilXCW0hPbrCfCR70iZYUNg7NHdgF3pr1ibm3BHj0cEQeTNmjs49YxQyRLGehQu3ETBFVZ8b9/Ka2UqOcV4f7oESx36dYxOeK4eTCVPUwRA5c9rwWoB6EChVh1WK/MlAsXkjRgf3YdQb4ehDd+GR6z6K1Rc9E2suu9wpHR+687uY2XoOqNfD8KzN2H/r17H6ubFAlBgBBCvHTqdqHau+0L2CDFfdb8JmuN+Uw7bIaWCH7lDKusZMLQVODx27owwG0sa2dKvjWQqZry4domngLOPtva2J4h/kXByzur4EbUiYsG7ayudRSTp0ikKWgpPyO6ppKTNhfI5zvEIIQzQJx6o+jiaRsj2jIzpFa3pzjj3q2wdfn0seYS1KLiK2qPmNXL0AMGePmjlaPSywr9eUe7jfMEXzRfMwV5YpMkfFAaVttFDCnN7iJMisPg/Zpk43BnLs/oDImAxs20XHR8LfSCBvwLseOF4wyI15hkfmzJ4gOHj7TVjz1GctdTNOW5jBEMMzN+PYw/fj6AN3Y92zrsCWV/6QM8FnrrH3q/+A9ZddBdQ1Vj/tUszvehSj/XuXuOUZGRkZGdNgWTNEZAMNisM46ntWKLR4kWsAOnWKGoaI/f+AWx6IxZpz0OYMS2z62rNFnQj0jXzlvg0UWGW5n7XjuIDRcJZuttz5XQ9j9uU/0GmujdA0Wa34aRQnrmY5TecutS9nQ8eTjWUWp6tavXo18fVa7uOYHAOVMEQTVtXa4WSXWb/LE7J6yUrbz4VxnzEaqHkUNarB8IIdOLTrbtCKHmquMV7t23PoX24GZgYYPvNpGH/508CmtVj57Odg361fxYZXvcZXm/TBny9oZh83PWJ45Np4CIxNMCbKSjCqV6tGdOkhJSFa2tg5SdMyjhqOgbXHIn4O3fPI/r47J4Ydzhbbnk7WeoaKnYN6Xnw+9gFfhTHVZtnOclP0oihhhhLrS+KYjTX6BnR0xLYpagexZ690+x/Pyt+W5RhT0bM05AP1in4R9zBHPWcZ5nR51LFPNdZYtkhYkMLehILUzbCmdzXI3fS+9d0wXsBR15krmziPJReYKRo6el9vBQDggLXcOkoNY1Q7J47BS4KpmcfybpAh0Nar7sGx4wF4vbCOiU8mvn8UjEXo2gAAxjZ8iljRyXWBKWrUMu+K+LgY33dZhyhFHpEnCOpynK3Mlhiz552PuXvuxmDTJhy94/YoVMf+L3weG17+StTz8xjt2Y3h1m1Yd/XLcPCfbsBo56NL2OqMjIyMjGmwrBkisW4Rfx0+SKMBOVbHXivj1dJEr+suLISUJ+c2r9OxsSuDyqftgtPHCVfT4V4zA0azO2q1a8Z+tamtvVBVKOoi1fsI3KIkIUG6LLnsyqZcEe5925+SjsX9qwc1uFejpsAboLRVr3iTFZU9q8nrhY2VfxDlOM23mb2beh00U7uxl1rD9ujb5+oD6qJGpZWIwgbYvL1nnoO5D9+Hjf/+LRh//FrM00EUq1ejfuwQxgf2ov/ci3D4tu9icM42VOsMyv+fvXeNtuSozgS/iMxz7rnn3qq69a6SSlKVVJIoJAHiKRACIUCAMfZ42cZ2z3gYt9t2j3uwPctrpt3dXvPwY7W72+7V7m73MtNtTxsPYNONsXH7BcbmITBCIAEChND7gapKUqlet+4952RmxPyI2Dsidmaee0oq3bqScq91KyrzZEZGRkZG7v3Ft/cuSwAGZilHuZhmEK4l4I04Pa3oSguCpCy4T6oeUGbRbzKOVIxIyTbI68j2NCBE9Vhe6f1Nuw8GOGTQTOLJWfiULek4b6q/Ru9QU87hIHnN7YobR0iRJktcoMeIeUki+J4MBshcNppLov1tWSFkwt1GVIma35C8dXZJzw1zruKyUqn9XEKhhGYkg+MCeYRj7GMB5dog91yjrbmblDh4Yw0q1by/h9T7qjDNCNHuOYcM7ei5srAZ5j30PPTXIzSN2jj2qAsh8HqkeZ7md5TmorSHooS70S/0PuRyYKZjgerU0cdAIkAck6mqI0OAf840DsQ7Y2a0mS0A08UhqsmGVog6cXL60Xug+3PIeoNaUuhO1k+y4RBz+y/CylfuRLZtCeUJpxCNH34Yc5dcDKU1xvc/gMFll2L1rm/j2Ac/jKV3vR359q3nu+mddNJJJ5EoJql3EmRDK0SE+pB2byMCK3s9iQSiuhTkgjjwCwEMPhQ6osShcR2WvFIibywl1tRJ2GhR6c9x5GiZ6kAiOTLOiyoifk5h8MinPox9N/wA8ipD5flAFI8otn6Z3iA8uOoIEbWROEUIVqtoI69RU2LBOQNoC52V0T0LpIitIZNuR8cRn4jC73NCSOntFiWKNJG12li2oUCxCHNPaetc5hVZktGhEmWsFDa97dU4/ddfRLZ9M8rJU+hv2o2iOAW9YzPsphKjB+/D/DVX4sn3fwBbf/i7sHjDy0GRZl0l1Ga3aSLUqzWVSxsnJOYFEQ8hMyj7Jhp7xPtIt5u82pq4SU37E4RIcJUk6tmIFEmQQzyTOC2P1W67ljKnxTJois1Uj2+k0h0t7QMQcT/ohvy5hF7T71G6HTmG+X3LLSwsv2t2Cupc+yWTnTYLV2iGDx6PofQd4nmM0PMGrzMDnfBQKoFsENKRKcORqsmrbKvPy8QcotjFzx9Hv2lxLsmFc8cBANuyMwCAnbkLi1HYDEOfXZjOpZhF48KVJyn1kHUTqbF+no48DAl1DXO6f9Y8Xn2fwUbfDn+O5BSJ+cxaxf1E+9jTTiDdmVhasFYxcmk8IsXIkOlQn2ciG1oh6gRYPf4YJsvHsWX/i893UzoBMP+SK/Dkf/xa212/AAAgAElEQVQIhi9/MarjJwEAdjSGHszBFiXG9z+K8bcfxI7/+UewcN1L8TTXLzrppJNOnjXplsyaZUMrRKoSVhlp2UZBURwiQldqJansweSxFDWYUCWPFDHykKVljBhxTCLS8AlV4rbRtlxxBrSxjAK5k5PbaeB5WOYbPXzLR3Dguh9ENhYoma/KRBnuZbJaKtsRItpWqIbpejl7APV9Q+a8tdaroJRGr1dHiHQDqtO83wZD21tJWqBJ7I2iwv5KeGPIhJCGLbpghUlhjhe1WVsMACzqEW/H1weQciQWgYWXXgZUI1SPH0Z/cQJlV5FvzmGffAi9XUsonzyJ7W95EYBxNB6ccD4o9mikbR0QKYlMtcSZSTkjND41rCnZumXPPkKGJFKEGEERHJQWVEYZNZsXmyzFfclzpAeXtc7jzObh2rVSgj3R9WtAkLy+Fbun6K6SQ2SYwxGV1Kc0/qJI8HxfmQ0eY9N4ibVpRDY2/k22v3m8JNsyuascA4bmAz+O+AaCGGiYBr8cI7yjlLKc3LXvJ6U5X27tnUnO7cUIEehddPvIy2xv7wSAgAhtz5bddjbyx4X7XzEOATpTuYlypXRQCvGR6B0qAJiRRaUtspHggPGmeL7+GsooHheMMHMcMD9/icjYpgEhQswRQshpJqUyGtojQ7YXEGYAzLPt5OnJc1ZF1OMqLSe03ZYLAlClP4bc+Se+LNYeRJzpmzNlI9luPIeUoLaPx9TrAcvHHkY5Oo0dB17Jy1+6aD8n82HcKcM4b4+bj0+uN/YfTpp3hVtnk/R8ADEKJMbbvTVSoQMY5O45LQ5c4xbnJr5sb+y2eQezb/Xl0nA1KacJXWfTvJs0twxHa54rr0Pn7P3uazC6/zCWP38nymOnYFbH0IM+qpUxsk3zgFbYPEyvQ+Vmf/0mGcy7PhgMXTk3XyTb06Tvj8kHrl/zBbedLbQPGDP0AeCGJtmmcpqUC8aXNi2H7eOlnHdlNfDbw7ScJrREbGipeBZTTqWlDD46VYTiao3UPtY8NZCpZ5llRVtnOqdGoj6LNrYpQtPOIWWwIkJyqtRPE8puf6pwD/9UOVjznJ635GipbClz7+P2fHnNcwd+ohz4OkgRmybWh95geoA0CKaJUO7tDHN8WGJMlbM2RSiW3M+xmhLA9me4oLy+5xGt599Glw2NEJHiwjnH6P33CM5UpcgHEyLEyGYKtpdBlRVspqELA5tr6ImByTV0YYNnRXQOn5sp6NI6PoOxsJmLtmxzrxSJNlJ03xo/KFKKZMRlyYc6evct2HPZ9chLhTJ3ypDJfOZwGYWaaFEzKEV0Du2zGVBlTikyczYEbY2Uopjjo1RAUqYpRRIZIo8S6qNpSlHOyJA7hyzMbfMrKI3G1vkVVFZjabiKyoQSQC0CK03+bUrRpsEYJndKDyFSuTYwVmHr/AoKk2FpuIqiyrBlOEL58ksx3LcV1gKnP/Y3yKAw2LyIud4YWRbQryalqDIKm+dHtSi0xmhURiVKkbUqUYokupVkH7deKbID9IYFrHVKkak0soWCuQ2WsolXCjBe+bEK1dC49GbDCrBhfyKxJ5l1SpGqFMoFx6EoFyxgnFLUGCPKOKVIWaEUWVc2cZVMzytBJkTgNr60eTi2pvxIBaNBKaqtGHCblavYuvJcKUVWW1hrk1hCQVSzUtSmsKh4v21WiqTSk5zTrBSxQST5M8YhWta4OcBU2nloCqQzrp8Up1FmMMhLrBR9DPMCp4oBFvIxTpUDLPpJiBQYyReaphQtaf9/j+5u0+56J73HaJNS1PcEy4W+j21U+ijYpQZGgJ0zwIqG7Vmowo1zKPeoWMmR36M4o330zFxfpX1togjTWW5QGY08MyirDJkuZ1KKtLYoK428V6EY59A9AzPJgL45qzHaSV02tEKkCloDooR8fn9MUCYUxpOpFUVVpJKDZ0W+u/I6rMSkS2Z8tK1P1sFJlI6lj30Et0YTu6pCHUzWTpHZ0B4DTEan8cTDt+OV135vY5v52EjBalsq4/AF1BeSAFshuLL733IfsMxMfOmtpqpvYLIeRvkcev5jTe6gPJ9Hy1FA85IZifzIZzo9JvPlsDfhfUGZkGXak6bBIulnfqL1pXPvrdi1lxSvSZVxu+d8kLeQxDLDgZ98I77+v/8BVu89jE3XHkB/mCFHBa3ceOp7ZU+61PY91F155bfyz6TSFpn8UIk+IpFLktaGY+ZMjoEqImUJSTvYuq90qxUr3bfDtq+zipYIaiEQCMKv/67KFIVsXZojMQplHyhUfQlOhhXgtkdODhwqgt43P4YpiJ7JZUf7tk7CM6MjKtE4Sq4JHzZCTXQdEaqiB0T1e+U1rQx1MaGvG0XZQN6W3uhtilC0bMPBBsVJtEQmg0zG72zl79n23NiQy0HTIBUtfjtWLABw6T0AYOgVJGN1SPPhB9PJykGMR4stAICteUqm3pU5t/sTZoi7R3sBAPeu7AIAPHTGeXqW4n2MHTUUzfMiNMYa8SBnkwaFqC7uc7wwR4Tw+jIb4IbTxGc3r/zca/w3chqinzTHqo5D1CBdj2xQOXH02wCA5aceOc8t6aRJFi7dheH+Xeht34Tjn7kLc3u3wlYG5fIIqncuZtBOOumkk07WUzY2QkQu9A0WBycdJSSIl6Yku42sQtTJxV40EZVFqH8iNts8GHmaTSZ/eUINxP44UKKu3F8tlD9atgEsbt2HvDeP4ZY9zW3mMADRvhZkiK3qiJQe71cm1GeFJd477REwT94zfQXd01Aqx8Rb/Nm8R1AIKZJWpdhWiJa1hBXLAdt85w/6ru7CZDWytJElEYsFomKs4uU5QoZiRKhfVRzqn9INFKaOEM35OgiV3PuOq/DwB74AANh08SYsf+MURt95Cnou53MlG4fuYeADxpUR2iXRJIkUtZHUtbLcB/NVhgU9qSFE1DeBIJ7VuB+yf2v9GC2P8NKbWGJhyF6EF7CVgiLruAUhqolVqHKDMjdRUE7f1prbv1gm1YjSGvh9eYoQ1d474gMa5WApAGQz1sA0QpF8abVlDgohMYSIhRAhfqkv9TBP203vJqFpbQiRVrCeP9IaWFMgQ/R+GuiAsNE7I09uiAFh/PIS3Y9VGsZoZH0iDtfdw11TLaRzwfGJQ4aI20OEaRq3w2zCnJMeKDCj+1wd96SzI2OHFD3e3wwAOJo7hOhkNcS9KzsBAA8vO2SI3i3pws5tNco9ZCqBgHYy6bnx1OkinwVVXWat79nEc4ra+JTWKkwq1yfEP+Jnm82OcVQdQlSTrkc2oFRlgbu/8H5c8pJ3YW64dL6b00mL7LrpEE94R/7sa7DGYHjJdvQ2z5/fhnXSSSedTBELZySt999Glw2NEMFziJq6UQVyhCtryBCSbQUEV/k2hwPSmLPUylVWweQijHq4gLs8cYjI+os4RFx9Wm09PL9v68mjd6M3t4hd+19VayKhPjS4dGSBcCBGSeSW0QskwbtCxCdKeUaMFHnHDtNT6M0p5Faj8sdyGo55v+ZPhEC+L9EPFhHx0F9Pp/2Xe2SI0wHodvOMUQqBGNH+ubxC7pEhQnuIXKmVRc9WjNRQ2oHKaA6xUHjTkBAiQptKrXHwx16Db/7LT+CR938OV/2ztyEf5ChPBs81QnMk0ZssZnI/rpRGqZvtE8klCOR0V+ba8DELZYZJNqqhZ1agaJXRfK+y/jYkLg57wNwLidIZcU4ZOEtGIkStE2T0YmgN26/C681cN4EYMQoT6qx5SvbECykl5s0QSlAENCQ5lBAkukbPQpGnD0PD/tz4vq1KOUXUVjqVjiWESAIaPIfYdi5SCzLEaUhggrs8vecSKZLvbKlD0EFCiLSGrTQ/iswHBwwBWUMDaYydGDs2PXH56D2cePSHxw00B1c0itJt5El5snCGx5MebXq877Itnynn8NBpzxny73Xewh9NbxKAiebHFiQzEPZn4+v4g+uXq1L0kT5LE99/Y+3uk5CieC7pZx7J9v1YETI0I4eok2bpEKINKE995+vYuvdQIGh3smFl941XYG7nIgBg5dETmNu+gMmJlSTxayeddNLJxhKFyup1/9vosqERIlV4nbnudhP+X5GJIzpbN8ATMmoWWTySIeAfHFVpM0DbaP09biMZig0cIht5k1mF1AJFZND57WK0jK/96b+AynLsv+Zd6e1IMopNrXhgCjIkgtjVEnOa9nMSFAlAbwTkJdCrAO2t2JJdVz06QHFsuKuE6WoA6frLCJHnRZA3FKEm5ZS1cYkIscdVxBuSyBBZqJMqc0HS/DNPOD3UavJ48zwEOneQlUAGvOgnXos7/q+/xAO/dyv23HQ5srkeipOr6C+tHWCHrL48L9ETKFINGRLhC3TEISLvuE0qh81HjATVeFbRNYgv1YZAkTD/KAprIJGnuN+AgOwRH6KsMt7XlnJC8kysUehBITeTwFky4TdXRmEEEAHFEffGNqS9cD/IbZoPLJKxCkBNxMk0NxCPp2c4JgyNZUbEONCkhdUmICzsdqoCZ0hwh+i9qwV5jNpbmx9bkKHMb1fQ7LnFnkYM6goulr8HVakouCehugqq0txV9L5kfeIBBiTz1MghQ8zd820hTg+NRcPjKcM2H7Rx6GOIEM+oNGl5qnBBF58au/dttewx+tnGGZomcg60wQ3Zl1NOXistTHKsL0qBFOWu7fTuTHzakc1zzhPPWMX9R2VJKTzKs7/fToJsfJXtBSSnn3gQUBpXv+Vn0JtbON/N6WRG2fPGgxjsdM/r1LefwOJlO3DmvsfPc6s66aSTTprFIhg26/m30WVDI0SY+OiCMhp0bBExRGPTbTbvA6mHPNAsKGS62CYdneN7RHCP5xBR8ljDdaRN4+1MQT7/2ngQnCJTTbB5z2WYW1xyseQRUB8JtlDMJGOC1dKK8lAYfuoqjlfk64iSzxK/SiJDMqBk70yEPHHcDhFscD41b5PjhTMLR4clagNFBvfcoaK0NW6LFHrhiC8Ue7vFaAoQ0I6JyVCYjHlC5LVRGs3XKwXXJnjC+fZohSt+6vW4+7dvwejJZWy66kKc+tZRbL720lZLVXJvYpSHRCJBJHRcjBjlOsRX6mclnyuRotBXGgObkunqiFSztWmsZkRIIk+URFNat0WV8T55HSkcb8poDKzG0E5CrCkRJbmWAoWQokqHQIvyAi2XZ/QJyqNE0dkCZQ3IkH8GPcPRgwkhothFhrwCrXH8GkKvY05O5Inm9qXlTKsNzMNL+XiZSB+hbfBKJPSIuyTyCuS2+bYqyWsyAKroWH9fhIzpvmv8aNLjZ1547gu9oxR3bO1EtXUOEaFKNK7OTBxSZKxaExmSMdGUsu7ZqoZ5ehaRyJD0op22hE6nUMoT338TzyEa+efX046ktak/Zm9ZQojY81TGhOrkrGRjK0QvMOkvbsX49FPnuxmdPA3Z+5YrcP8f3IHi1AijwydRnF473UYnnXTSyfmSqlsgqsnGVogqoeXHWjYHBkrXwPkZN7k2mdQqIOKrYj4OGrdhVUgBotJjaigJ81hs4AFY/0fWlxiHtH9xaR9Gp59AceY0+nPOY4LW9CnfGnnRUJRdjjirYk6Q/60ld5qM4Jtl9TZR6g/OoRbFOFI9V3cdRUp5D2oiKuX2qbqVTv0m4r6snYEoqtdbRxSOfzxx2/1eidXcWVccj8gjKgYKE5OxlwtZm0UZ4h7xW1Kmr8uyt0jpnB0/ciMe/OUPY/n+J6Hmejh+Zp4t4H7PXZd4SJW3YDMPS4yrYCXHSTFj4YS3/lyOoK0se9Hkpo9TZlDzbpMIUWk1xv5+2q7D/CZOzFlxXTL2EyFuhIhR8sxJGbZNNFZjqfkORB5r1jrypxGRlesxruhUFX5fw+OmFgGcrGursFayTKYsRpa5EpHZ2cuN4w45lJPePxV5v1lCayl2UUscojiWkvK8EXmfoW/E/vh3Ge08kCH5ftx2QEHZa47alLtrEO+P2pN7ZIh4gMqaCOKWsbbcOeRFyshilvEYWvaJWekdXSn7vnTv9MiXFB3eJU5N84MZGhcRIguEOGeTfg5bOY6X7fv3UDSZOaEChXHRrf1vhPAxbyw6BpiaWoOPqdJ3luYD6o9RlWPi311OPUL8vC51xzOSF5aKuN6eP2d5OZ33sO3il+A73/zk07qcnqzv/c3iyXoupZysbwRommxmlc2vvhzzl+1BtTxCeew0iidOntX5y+P+WR3/TOXEaH3jJRGper2EE1+u1/XOyg37mQsrROsla4UtOMdCis96yXr3Z2vQzXUQi/XnD3UcomcqwW3IlcbUfyNpQ4p4UVw5c6gJZeL6hTeBCdZm0PRpTZ0sw9SSZA6A9iwE6y1DY/lYqUjEyM7qiSPYc8UNHDOJruuXjJFNvDVGVgQ9QTLwJraWJFYJK1d7PhSHVtIKIkhyDRniZNEe6Yp5SpL3oGm1qC1sQIQQKfkI6BjmqLjN0rp7TJQiRoao9Jww8lTxz68oMmR+shvltPYe1uDnbQ8Tf2VSggghipUiynG3Wng0yZdknVWVxpYfuBmr/+L9AIDHP/pF7P0Hb/Nd4Xkl3rrMeBylluzyuA8juFiyGxkh8hbxXK8MUbVNH6erqh5LSHCJxr7tsVIkI2BnApGSUb7j+siltqhSS5+UIBN5n9H9ZPy80vuL2+7+wn20gEkRX8yXeeD21ETwPWSSU2tUQJqi3GhNwqhQhA7R/w2hyFFcHq1tbQKwRvFMzJ5pnHOu+cL08Va5CUiTPKYlUjyUCn1M90lIkOwuQrM0HM+pFyXFzQ2gDbcl66XIkGGEXEdhKNIJmnaTFykjiyZDX1dYKfuR55k7hpChVUKGPFK62JvwcWcKp0zReGRkSLwH1A+9XgUUgOoZmJ7nQBFPTcazojsgNM9E8DzfJnU60jJGyZEqRYFLqpOS5hdGX3WGQnhzEoJK6FwnT0/WVIiUUhcBeD+APXCj+f+x1v6mUuplAH4bwABuZeOnrbVfVEppAP8ZwEEAP2Gt/YZS6kYAfwvge6y1f+rr/W8Aft1a+6nWi0tFSCowQHC7J2lTjIxF/eubKkC1pbOIhM3jWQZV42NF06LL8baAU0lUpLONzxzHpr0HYTJSnuhlTBWvjJfQAqTNipBoEylvdBMZESnJKzgDMrqeP0UqRHFwMlouo/rpGFvwHWFNqZEOfV1+s0YCjqvldZOwXAEAKkth4ziNROXRM3JpJQVJZwZD9LEqPlJugknbMJq4CbgqPExd0McrXK93+YuRbV9CdewETv3F57HzPW+D0hoT/6oRwZVI3GGIB2d3IyY5mXmc7nvcc3X2i4qX5Ia2hzOqShK/ApHCEil8MnVHW/oUWvajtvezipUj6X5PEzVD+VGKDyOWAi2R7cV1w9KhQoVwXnyuaHIY2zlHGqy78de8HND4e61fouu26fgqUoRI0eN6aCkJFXKUjd7/PGa9AkfEWpncNb4HTUrflMzocdvTfb6Uyz9TUC4KkMiGXW6grKkpQnwv0XuhI5d/X5v7DemHnZa6RtHyNCnzTJ72yg4tGQ99GpzFHiWGVexUcAZeMSK3fpWO1zi5tO4ZZLZC2U+NMU7ZQdMoKULRHBY+LemLF5bKUDun9g2hR0u0iCp9lzIOVZDzPnr/ekToPwuUUoaQ6WS2JbMSwM9baw8BuA7AP1JKvRjAvwTwf1trXwbg//DbAHAzgFsBfB+An4/qeRTAPztXDX8+iqkKVMUIeT9Y7dYaVEVzTptONp4opbD9x76ft8/cdtd5bE0nnXTSSV2sdZHn1/tvo8uaCJG19jCAw/7/p5VSdwG4EE6v3ewP2wLgMf//DE79N0hN7K8C6Cml3mqt/cRMrSP0h81osiCDRaQk3EIikSJlIkwyXRoIqI5Ajgh5sNGSGZGr6Tfe9r+TxWFsgKr9shKfIyxQatbp449gfmkv0O+FfUfux7f++rdx9Vt/BsOlC9DzbquEFDFvKENYxouW4NzBadcQ9Ks9KtREqpbIUFynNha6CqRx3epQNQtS5Ate2vHX5yW0+vPlfqTlSkb2BHoXB/GjZSiP6hiCv7XFRPcw9ut9vPwQsdRpmY6SW1qftkH5uthi9OfOXXEF5g5ejPG9D+PIb/wB9n/gV0C2R5wgNb5vWNUadBBiPwf+89cvexmTlzdlPawgpPKgZTeyvDmVRqnZzZceQnDXphK+9JapRzomOqsFhiORlmucyoMRu5j4jAgZEste1irnAh9ZvTWEiJZCOLHwdCTH1Su302eRZXE9EkFpr5eQIUKKenmKXvdtjjlVNiJXlUiWm+eEkhE7t/26ekZX6+Se0piR0VJ1+w0y8kXvvUdUGBmKECF3YFg2oveYAkISUpT5bRnIE+i3Jj2d7zlEiJGh3BmMC3mEEImxtVwIpEg4G1jrxplCIIkzekTdy8iQ345oA5bpEOlY5g7m70K0zf8H9xOAKHRE+g5VUbBaGhd9P8Y0L8uik2cgZ4WZKaX2A7gWDgH6OQD/Sin1CIBfB/BP/GF/BeCNAD4G4F+LKn4FwC8+/eY+v+X00fuxafeBZN/i7ksBAF//xL/FF//LL+DYY18/H03r5CxEKYWlH3gbb5/8k0+fx9Z00kknndSlI1XXZWZStVJqEcBHAPyctfaUUupXAPyv1tqPKKXeDeB3ALzFWlsC+OGmOqy1n1VKQSl1wyzXXNo9pBNlPWGBlzRzQoRkGRicweT1PBJ2I6VjKRCjkueCkztaz0RmTo8viYgX7ye39i3DHDBgojTxgxiV8Zc5lS1j68FrsXlLL4ArpofLDr0MWy98MSYrJ3H66K04MXkE+w6+AXPzS8Edv7IBzWGkqIFzBcD0PVow8KjJnELVp/vwF6ZEjZLtbIGluRyoAENWrD+W3FIJeDib1DXUVwzuCNTAGhtgI8E7spD3q5LDVPwiEvJFxrsGtme9EJwzdjemeih9AYceIIQoWMBxOyws7ItejIUbrsPkgUeBz9+Fza95OXp7d4Q6axwqVUPLmHAZB8KLhfcbKB+iYrGXY2x7bM1X0nqn7VIzCZ7dijnVAu1P4UFFwTIzy3wGJhNnKUHaRmk+3HaYEBmp8JdhRErsh7LY5jkgrUgNvcq8P7S5zVquOZvKibrhXEk4b5ascZOCPC7ZDD1Vn3ItFCMMMg2NbFtTAMOz9Y0yyiFvQEA2eHvKdVh8Z281Pddem9YByO3QB8yt8fxMfoweOskmnrifG8wZ7zTgyyXvRDD0UWvnfZXzvs5F/00wVmFonKv+onKT04oPcnjGT1YT4132/aQ8QYbN6DvvK0oT1Es5gjy/ilgPqgpzHwQ/M7xDad/F2WF4xUDMZwQZa79S0ispIW6JrcQfU9SPadDTr6KTpyMzKURKqR6cMvQBa+0f+d3vAfCz/v//BcB/mvGavwrHJVozxMyJIy6XjYxUba2pK0QZzYyk3NBsFClElDGdFCIi4UqFyG9zbI5M8TGsEHllgLZJITK8X0WKj8JTpwr4d7BRIbKmwj13fgnXXHEziuWC58EzRx7EfXd9BTvHPRhTYs+BN+Mbf/tbOHLkCVzxih9Bj6L/llMUItF/1cArRPPeE2igUM155aYvXv5avCVXHFsu+NjKP0m6PwofZc5ipja0OkpKoyC4GxspRDypBAUEQBpHBggxQSoV4HteQvWln8COYuJ3R9oW1UPxVES+KS0UIkMKkXLxTEavexkev+VWwFoc/Y3fxQW//NNQ/Tnf9CkKkfRA4QzvaZ+FrOomfGCsxeN2zEsRlZ+pjViGs5UO9Yslx7AKLRQiWoawFplvDJFXMybJUl/49yH6OLJCRM+Lu8+KMt3/BMbRc0FyDIlUWNQ0hai2nR7YdO5sClGzxLnhjqlR7XeDoKAYqZjI3GINy9Bna3kbqOS5JKVQyqeJtcDjdlJ3YhCkaijxXiH6kPvdNJ5yX/ZVhYFftx94D4/5zJVlPklK45fKsjzk+lr21stp5d63ZetL4xTskY/SvuoVpjFyFDbH43aMiqwzIxQimWeOt8Ncw8v3Yo6CmJtcPkffPVIhytMJNLNeIfKfzIEqQkwylS6VzRr6wb2PHalayixeZgoO/bnLWhsvgT0GtzT2KQA3Abhnlgtaaz+ulPplABes3Tr6OqYuXCrid5BSExQfoRjFSgF91Vu4DMEz1A8yUoJsA9pi0gmkyduMx6bzGw4vkqAFjFdP4oHPfhCDpd3I5xeSRLCTM8ehshxP3H8b+gtbsePClwEAzpx8DLf++f+JbbsP4fKXfD962SBcj79z/sNdkqu+f4lG4stqNUjLkIpPcg9+O+9buLlJXFDItHeT9YEUrIseDf2HzEADkFeX8CBUgvNlxamxstEYYt9G90C7y+ieInfl9AaRtIcUJOcRpzF/4DIMDl2G0TfvRfnkCRz7vT/D9h/7gaQdcvwkbRRKXI17lnCk6CPknjslvuRjpQIW/1/0jY2etaszfZcqY/nampJKRkgQEHlYRcopu5TzMxaKLO2PXNkrZKiaeIKyDohtZdk7jkQqDuyBx/wd/740cJZkGINZFAapPA1VD2dgmHdFHKPRpFdTRLgUdTZed0YlhlA9G9cv5jEr4280CHHK1nj9Oa2Jneig5GcBxUnqkujaFJHJh0m5yvz+CgqrHgka+ZJc9SnI6LBXJz72UKKPEgWHyPCcQTKcyWAuyaD1vKhSMXrMyhMbR1S7MMisqgfMpddjrKMaGuwgZbE48EqgQFfXOxbW801mQYiuB/CjAO5USn3F7/unAH4CwG8qpXIAIwA/eRbX/VUAf3I2DX2+iqlKfO3DvwQAOPCGv5f89vg3b8Hh2z+OC1/xXVjaeTnml/YgLxRe++5fhy4sqqrAPV/8IL7zwC3Yf+lbAABnlo/i8MNfQK+3gIv3vxFab+xQU8932fLfvRWjb94LW1ZY/syXMDh0EAvXvex8N6uTTjp5gUs1i9PLOotS6koAfxjtuhTOi/39fv9+APMctVMAACAASURBVA8CeLe19vi5vv4sXma3oNUGwCtmuYiPNfSpaPtjU+oM0ob2WBs80GpLZYQiCEQn/j8nRCUzN0CfQEAtUjTIH0sWgE7rr3mbVdH/4aztEN+ILGOFJ+7+PDdvaf9LGB2ySiEbLCCf34THv/lZ6BcpDLbshPUKjtWA1j3suvgVOPLgF2B7CqefegR33vo7uOCS1+HkiYfwwP2fwKVXvKN2/wyuSaQIgXPShAy56zpryHGXBNrit2cxUjgMfgq4RdCvK4uhP6BnQc+ALCgZ/MyKVdKkeQLxsgFGS9tMHmSVitb9UwRDJuVVohtUGc4dXHIAg6uuhF4cYuXWO3Ds9z6K/oED6G3d6q/bbvHXgrzJfjXR72TplxmMyaK3S5wkPWeahPuCUENCE3xfZbaOCLF3HpL98TKKZYRIIHm1/vUograotEaJcD+1ZbDaUlmo09S8SdOTCcUqCxqMdG57vRJJmSX4PZ1bZjkKVMj8EpCNUMLCB8qsLVm1oD42GtON6B/AfUYonoo6zwqEku9nyjJ3m0Nv7Ti6HnlDAgC9s4SuMDDkeYxnkUVAoiHaL4uRGKuxUqXpPRgZ8p5pTcuMfVslXoAlJeklz0nmhxJi5O+v0NC02lA2D1Re1m94l3nlivhH1DcTom64zcqfNFE5J3zdPD9q7JPnqlhr7wbwMgBQSmUAvgPgowB+AcAnrbW/ppT6Bb/9j8/19Tv44DzLsQfuAADsetH10D7fFsm2y16ObftfhpVjj+LIlz+OI9/8NHYeeBW2XXQNdGmxevpJlKunkPcXAACHH74VF19+E/btvwEnjz+I+77xsXW/n07qsvSut+KJ9/0+BlddjtE378XkoUdZIeqkk046WW+xOHvu2XmQNwO4z1r7kFLqewHc6Pf/HhzA8gJTiIhDRCozsXWtjUxB4g5NQYZIJDLEqrcRdaQkFGVMzQtJoj0hZlGwvBI+jo3QibgZ1mDbgWux9+o3czOtCgiD0goLOy/C5W/+cZw59gieuvd2fOMT/w6mcpbO9gtfgvlNu2AyhfH4FLbtfTFsprC4dR9WzjyBolhBhkG9L6K256tVtC+59ahNvtQWurQOIeJjCHXwltUa4J9pGHXycVbehYSSWSI3/COhDnosTFbZZiat25plxqAORcyShMnEgiPOgEA2JBIVLhcsYaUxt28/+vsuQL59G0b6fmBsQkwTvki9zwJKJ7dTK1tVCATQXAOVDtWJ9A1J3aI+Hssi/pESXCYYpAk+EdAjGX2XPfQqFVnPok3CIyceaybLYJC134dMTxHHUuIYSelJhIIajgYtSH0E6TbULxGwGt8raVw6N0Ckjotj4DCfaZKmcqnHH4r2i+dXE+IM+YGaZDeXfCqeWpvraot2raJmUJoUmc4EiBwsxj6VBnOXfD+L+ETux/R6gdjeS7bD7z6mkckYGaJjCBka+qzVpmGOmrcFFvQE2tdfULJoQrooHlAmESML43O88DxG8wldRkQcVzZ41iIT40Si15JTpIBx5r3mMoeELQ1W/fVmRYieE6TqHwbwIf//3T4mIqy1h5VSu56NC274Hnkuyyxjc2HbPgy3XYj+cMvax26/CJe84nuwefdB7Nz/Krzye38J/fktyHoOMjamAr1MOuth5+6r8djDf/dMbmGq6HJ94dl1T744OXcW1Jbvvhkrd9yJhVe8FE/+7gdQnji7xK+ddNJJJ88D2aGU+lL018g9Vkr1AXwPnAf7usnGRogIsQlkA79t6qiO9AJrisEjiQCMGKXbzDkg4CQL6EfwwKFz0rX42Jq3vnSRqkNSRLaOtMXWfVfhsbs+hflNuzC3bTcGm3cAysIQiUaFYwHAZgr9TdswN7+EbGGIuU3bceLwt7Dn4OuxZcelOP7Et7Fj5yEAwMX7b8RXbnsf9l9ww3RytbWMElWMEPnrMTpCiIfjD2WFRdVTQilyx+RoRorYoyz6Pz8CejTeiiYXfus9VfJ+BVNoqNxwrCSyOgkpUoJTpCKAQHqesVVbKigo6EnqZacqd46aqDDssvRZSC5RerOhfgAY7L0Ic5dcDFu4fl7+7K3Y+l1vS06pKdCyYjnGmGugog5UQKmTRJ5xW5NHQvUx8iP6gH8XqI9RgfdD3JM8fR9qrsmmgV8hECG5H1pB5QowOrw7WfSCAYELxm7OwQOqYlIZvc9+3IgEqrUIxKoBDZwW0oHuWz4/6pO5iqt3IdTcOYGqqOpeX6VArahp8bNoQZHoWRBniCJJa2Vqx0tEyAqkq8bvahDKZaZjdA4pgiO9D5V8ZxmtaECKvIxFG+oIkUdyrEKuUs7QwLvsx9GsgeCxppVFzxQYZhN+d8ZK5B/0XCK6z4r4T9pGzmMMd7r7Y89iMX4yG41zG/YhQpX4Wfj9k4A6lx6lYqRIe6RofhWzShNKtg7ypLX2lTMc9w4At1trj/rto0qpvR4d2gvg8Wejcc9dhCgXH3gjZ6MGaSPuzULokwpWJbabpC3ZanTK4o79WD1xBPd86nfx9T/65zh1+J7kHMsfY19lBswtbMVk5QQAYPel18FUBb74J7+Ih+/6K8wNAzdlYXEX8t48Vlefmvn+spHzH1WFDwo2aUdlKMEsKUWURkSvGWEKTGBnD16K0dRrPh6IIHmvJIHcevO1n5+KM1MjuMhPQ4Ey/5umcpyW0y/oS3r2pcLSO27G6J77AADLn/9C/RypRHAgyBkuV6Tnak+I5lhJTWLFOSLO0tTr0TFEvCaFUi5jRqLb+nMGJI6fE9U/WXvqotQHtAxlaDlqsraLN3+w6Xrjtc9hpVoqWGeT7JikaQlOttH3CQcIlUraFKml25jhHCmcbHQG5JbfXX8sGTrThNLsVFFCYgAYF2vb8ZTodegVoM15Pf5T/Ry3nLap71OB9N02pQqZJtorvZjz9zeYAc3muZ0USb89QyoW4x0BJhPXF6NiysT53JQfQVguA1zmi/f4/78Hz5KX+sZGiHIJI0TcH2OdUhS7RTQpRQQjZLFbU8PL72MFhWOib5qFU9vp3Fgp0io5N/U2U4lSZP0tWA0Qp6jXm8e+l74dD37xIwCAez/5/2Lnodfigle/E0pnLkyOBkdDnZx4Ckfu+jQuu+FHYTMFNejh0M0/DV1YqEnlcrt5RWX59BEUxQp6vYW0+9q+JZFSZLVySpFS0BMTEKJMQVUWqjCwPZ0gRSZX0BML01fQZUCKOM5Z1O82C/ekjYu1EStFbOX2XaPzXoWyyKB7BtVYO6VorJxSVCnY3PIHViqTUMqhPeT9JZWiQvFHmRQ1ZdzjyybKefRROVaORzVWwRux7VsilKK53RdicNmlqE6cwPihh2GXx9ADwe+aphS1oQUeAVOFAvr+vpRXipSFLlQdxSLUbIpSxEhRHO8I7plZbaFK1ycoNJDZRCnic8u0X/XEt22iXNvGoX8ZcSN0VDvul6o0bGadAqDhlCJtgYkOHxN+dcn7yzpPsylKkQy0KREiNdaA8kqRRqIUKal8kLE/0aFvMpsoRTES5NoonkmDUsTKjk2P4etHSpHNrFOKMtcu4g4xeEjKj02Ro1QpojE1g5EIIPOxlLLccHT0jDlEYW6eTHL/7mZQuYGdZLA9GxROVvojpCizqAqNrGdQlRmyXoWizNDLK4yLvBYjSvKFmpQiQoj4avG51RCb+2OslD1s6o+xWvSw0J9gXOWY7xXoaXevlA+NFLQy0yjGOfRc5cbXnHFjc2BYgadxwsijCDJrM4d1Ku0+aVZHITgFYqQmGsgrmCJDlpWYTHLk8xOMih5W8rrncJNQcteNKEqpIYC3AvipaPevAfiwUurHATwM4AefjWtvaIXIDHzY/lpQRBMRPhsI13EZu+czJC9c9OW5pFhl0YCRKSxaDMYQ2TlM8JJUzeRV/5LsPvhawBg8+KWPwpRjLB++Hw988v04cNP/CPT8RO4jYB/51mew7YpXYWHfZagAaB7/CtpmvEx35tQRfOP238HBq74X+cImHyywuc2x0CRKy1L8caJJNVMwfY1qoEN0bopQTUtZflRR9Gv+oEYoF7vd65ZSLI1oFdJFGEoySW2jY8UaFida1OHWGWmI+kJZlSpCUeku5PcR1zgjxUss+TSI/K5su+lteOx9vwUAOPXJT2PbzW8PP0qFp/bBpB+QXleFj4LSXnni38T9xufIxop7Z7if+yJukND25FILTd42bMdLmK79/hiE5wREiJhWUEqliIdAFGsIX0TQ5lAA1Bap9NH9NcQpVWKpOoz/9KHUgoBOEUoeSqRu/qBOMlbUCPniD6hHUhixarhOUAbFM2kRHSXkrZGoOVp5ilpAWV4qKr1iWOoMhc2hfZ9UIrkt1VEWGapxis6xIlTz3qAyGJ6NwUujtlM5rtzEE3tPtRGH5z25ejGLEsJmBYpsUvO+oner8IEaJWJrLVDx3JS65NOyKY+T+B1oGzRNUeyT7fpvMlntc1mstSsAtot9x+C8zp5Vee4umT3PZPcV1+PyG/8+AODMkw9jfPopHL79r5JjTFXi+H13YMeV102ta3TmGL7x+f+ES1/0Tuy6oAsCuJFkbs9eDC+/Enp+Hsc/+XFUy8vnu0mddNLJC1CM1ev+t9FlYyNEQ2cGKuFK7xKZes2b8nYREiSQHEKQrNaR9Uimvl/XjvPLACEq4FlIzaXe2sQ1X1U2oNHCaqdzt+67Codu/ke46+O/BVNOcOye27By7DFsuehF2PGS16MqRjCmhN60yChFamUoPH7vF/DIHX+Gi656G3Zc8gp/SwLimCLcFX2P/lBuNkZwXN6zstT8G+doo2SvPk5axQliqR98HVmoj5fTOEFsCiWTNZtnJgr37585BwNM0YhaOABjGR3IRMR+k7vhIJGhBCEShjfzIymg57ThkgJdgAW233gzVu7+FgDg8T/4IC58z0+KdotzZJU8jkL7CG3RmYIuVYIeuWPraJZKuy/sJ0RFLpkJRAeoLxnV0KuoDokQ8XKBvF605Kl8IFBqk2nhnkiulFWKkUNVI3qnbW9MoUD9RUT6PL3BRkNcPjA+xi8dV87VeUJLVbQEWeiwHEfT2CDl3PDcFHGnapcTc1BAFghpCc+P81cTOifronEToT7F2CMwtOyYZbAmQ8VobhqahJGjiW5HhnjZWSBxttZ9tWCSNB+MyvQz1ouW6iRiQh9litI89GTrxXwCo0uUWVEjGxPxelSlncSLEFrzvdJ8xalriFNE70mcsLmG2qZ9ECZdiFLxs5ULIiuT5x2XaF1l46tsLzDZtOtSLO66FOOTT+DCV74TWy4+hO/c9ucAgHxuHkpnMMW48dzVU4/jka/8Oa6+6b3Yc/D69Wx2J2ch/Z27sXDli9HfcwFW7vkWznz7rvPdpE466eQFJJRseb3/NrpsaISoGrrmEVGZCcrGBquO99H6bsoHYs61RjB7ZDb4KKt9o3iiYqNQFcJ6QBWQILaOBVDDLvxkqXoLYdtFV2Pl2CN46p4vYf9NP4rv3PqnOHr732D5yP0Y7tyHfLg5ICz+GqePPoDv/N0fY8/Vb8Lctp3OAFkj/P80CeiP5BQ5blBVKuYK0W+MDPX9/j5VBu4Hd78Rh4gC+9W2Uw6D1gaZR2TKVmQo3abdulDwVAFo0iV9l+jcAQla8EgSXom05KTFJrs34ovF+2LZ8Yab8dD7XK7kx//kv+LS9/5TqCxrPkciOE0cIvpv7hAQGTSSEaQGgKVteEiEKNBdVa2/WuuM+kzGQ5WH1MIKaIfe6VK1IkO1NhPJGAhIL9VHHCJquwwvELWdE3nKh0CPaAZPIBZCgiY+rQrVHJNkiTM08C7sfV96ZIoIsIzyxNypGblDMdJCyBAhQFwDuaNLHtA4q6M8PQWUOqScoD4hZIjQrkJF3D3BrWF0qanB6T3LtCbEwZrY9L0psjpC1Maxoe35rMAwn8Dm4zpCpHqN55DbfWYMMs9DpXmT3ztChGg8cRJmVUvWXOsDDgeTvvDKRv5DvqTUL2aG5LydtEvXextQlva9GEpnOH3kflhjsP9N/wPK0TI277sS+29+T5KTCADK0Rnc+5fvw46Dr8KeF7/hPLW6k7OR/vadWDh0NVSvDxiD5Xu+eb6b1EknnbyAxECt+99Gl42NEA1oXT0gQ64M6E5Aj3zcFQoUyO7uxJ1QzCeiMPEEs9i1XEQa1EYVe7wBISBXhFYk3nHW1pESySXy+webdmLzhVfi+INfxeipw9h60VVY2n+1qypz1yBDYLzyFB76+O9j6+Uvx46rr3c5agrRxhrphNrYft8SIaoinlDVVyhLFbzLBCJESBFt13g5EZ+EPc8IbSJ3e8+dSDhEwvoJfC3akZaeHoBsHJAh5hCRwZY5oz/zvBIbvxEMMaXn1BLFCuO3KZmsrFNZYNf1b8fpO+/AYPeFOP2VL2HLZddgqsjHR4ajDr/pyv3VOERThriq/Uf8Ljg22WyevUkbU68+/x+qp40LYwCV1X+fRbIYQRFIUDwOk/ZQc7KIp1KDBelc6nDR6KZjiB+UaaBqdt03xBny8XoyjxDlFLeHqEScNFdzmIPQ8LSUyGl8D/zu+O2QZiNFhhLekEeGON6S9Xy1KEyCazySbVVEECahSYQAtyFE8bYE3z0HqxTxsuieysgT0AhkjfdDIkcaA7+ss5A1UxLo2JJ4SL4sq8CnJJS/8nMVhTbgEAf0HCsdvB/XQopod8P8QvGIOJRC3nKyEBvdTydBNrRC9EKWA6/7IWw/cC0Wd+1vPaaajHDvH/8Wdlx9PXa/5MYolkcnzwXpb92ObLiI8bGjMJPJ2id00kknnXTyrMmGVojKAYV3D8gQbbOVJ9Cj4HWWIkW6tDWkhL1maH23zdUi3sXcJGoMJfVrMHMjC5gC/bk2ITlHpvRQxiLP5rBt3zUOCTJg8yAOtnbsq7dgYe8B7HrFTU7jp2tzehHRfhlwdYrlbRmxoZIQI7evGqyNDJk+m9ni/lCL+xOSuKY8BI6Gm2RbTbkhJOy44nULRoXGYR8Zf1SFzl2IKorXZuL7JgNc0EhkBhjJgUkTp6Ztk23edNlVOH3PnahWloFRCZ2FV7LVgJPtUFEjeoJDdDboSsv1uK+akCFZfwuKhbiNM/YN8unI1loSvMl8KZFKiRgRamjBM6OK4nwlbdTp2LY6oCDsBSkimqtccQwkIBrGc4YDkGY+4nG/7xrf84H2yJovCAkwAS2aGo08bYG/YIwUpQ+lERkCgHFWjzAOF9A0xBTz8yqjPtF2GzIk4jw1Jif1v1UeDeFHIpE94j9lBiOxT07tNYQICkPTxxk9h3ntJkoqib9Vaoq/pJOyynWgp/p5siQUyyOCnFCYEsRWNqSS4eTCLUhRw+NlHhd996jeGSKckzwX3ODXW7oeeY6KqUoc+9at2HHN6893Uzp5BrJw0WXobdkGABgdfuQ8t6aTTjrp5IUrGxohkpGOg6WnItQooCoAYMs6mgQ4y4S8ubT3DuAYH5xIMbVsEvHeaxwZm66rojhHQGT2BURKWZvEJQoIkW+Pb3NwiFMwlHSQLBn+zWJy5gQe+MyHMFjag8Xd+wHjuDCE2IQgIimqxAYBNyTcJ/MrfB8zIkTWX1RS6pFzIeXQ3+c45SxRFOyCcvX0cky8VwnlOFIiX5cmBExyRmx9PASwzse5odQS0aNn5C518oiIF6LkE7E2+uG3h/sO4PAnPwoAePLLn8ZFFx9oR1lIRDsShEi7dqv6I65LSxtrhmqT0d5yP4SwTWujEbNOGwdM+3QlqkItZhAjwWQRc9RpKsN4kPkAAyLrrxdli6D9nLxVPnPum/QdS+6VCV0zviTKneWul55DCINJ0mvQHOivR6jqOXgn+TZnABpUqZznHkfvT58JO3/ZmO9HiI3kEPm5ivs1upDweFtLTKVRNtOAwjH+AqXn+pRGY4g+TmPAKM8iR7NuXs6OOTiUtoS4QwWvHLjfQ+yksKIQ0H5/7zNyemS09PTHmaoAniNu8OstLyiEiEna6yWzJI09S6mKMY587W+x/Ni92HnV69If13l8S7Ltsy3FaH31d5nS4dmQ3uatyHp99LfuxOlvfw3l6pln/6LnSdZ7/tXrTMtaj/ESi51b3xdw3b+fZ7H8cy5kpeyCGr7QZWMjROylJJCiyEuJUSMqKc8Ue5n57dJFi64GGWxBPKMUMWoTVZnAHQJ5WJBWT8cIc9v4xJfkXWYQxR9KDyULK0aKAjKUIkXf/vP/gJVjjwIAnrjzs9h8ySFXlw5WOPUbI0NkpVAbZa4eG/Wfb1RAhlJrz3n4ReiLSevxnVSrP9lvAzJE3jW2l/ITbEbr8+7Co9xNVsUod5F9kSZnBQJnRCJFae4f0TZ/P8SP4XypynVT/JGrxfap5V/D05L5Cw/AViUmx5/Aia9/Edtf+yZXX9v3oAmxUunvtXPbnknDMTN9hlp4QDz8W2YWF+PHH9MXjZB1FAplH1DL0fgkXhBFCeco1G6/FvOBnoDNvnLeXyfAg+6yYiyrCjVeURufg6186/5RVbiPgMyGjrXiObnjIlCXAC+PNHBTiWcSI2LaOqWIDpJRw6mNszxXvr58oIi2fQT0+DflkTwgSf7r2uqPi+K7yb7md7QJksqtu982ZIhOYWA/IC3UX21IESFEVR4Qos0KWC76tWMJIZr3bqvEvSGERSuLiV85mFQppzSOZg0AFQcQOntlj/LazeYyukZdiD0oOyHZ4AqRVITCcpicCOupF8S5mYWmTPQE7TIMTTNwen26nrY2uMhTtuLazGjTMiJ+OxK4jZbK/KE0CdE6TaQYHX34dtxz24fwmu//Negsx/Ejd2P5iYdYGdp9zY3Y+7K3pjC/WMrhwIh0PeGmGpbJbIAKVTqpyeUFqwAmiTcoRWmfNO+uhpYVIcy7B1iRK6+Hq8m114xdJUUWFCFOdEmB9si93iMCNTJtrMDVlnrccmZQCEMpM53UlAwZaDMKJdCqzMjra2B40QEs3+9SeRy//fPY+to3QikdljhlXU0KEQ0/uWQmzpFjvFGkMtBwD23LXPI9jJfQQpiA9D20Mugi1dVTMLlBWVlk4/qSWHwdTthKSvEkhF2g61LcSyb/k5If9R3gn724D34WtJ/aGj2DWmqTShwjJDw/G9y1y3TQ8YdVLpnNmbrSYsQx0561UCLayiYtOY73GitzrBhxf/r2WBvIzTKpM1sgYgxk0bXXkgbFiN3QWxQjDq5oAjF6nGUYIUdO6UpEB5JCtJCnlWllkXuFKPfnnPFGWklpTLhOP6+dhS7CS6K0dNjpMc+abGiF6IUs/cFmAMCTD98BpTTu+9KHYU2AK1aefBRZbzDT962TjS0L+y/HE5/7OLL5BZhijDP33Y3Fg4fOd7M66aST57F0HKK6bGiFqKoF9guWT4C3UyJfbSmHlOsKMIQoUCI+QozK1BKhOrMJmxy8dKQ4I2Wz9ZIsnZnIdLFhWwlr07LLa0CKlrYeAADcd9sfYtfFr2Bl6Mo3/xROHbkH/S3bebmHmiNTI5AVRv2oBeKhOW5RIHEzeiXSHoQjwW7l/Dq1LJ8kyQgBmHkTSp+ioD/vrK7SL42Zni8pQOPIBzTLM05sSehRNkoRIi7pvgg9iJYxGsnN0ZJaTK6uRWFIb6dGmEyW6DJxkqwkekb9Hbsw2H0hqtUzUFmO41/+HBauOBSSn7Yu1/jdVtSr6rcpSdbxKa0gfttKRcMSZBxMEUCCXAIuXAOPS9k3RBLtp+q9KTRMZlEZy+eQ63cNGfbPnMMrTAKHiFEJ2qb3QiJFMTLWch9heUpsq3g8pD1LyBF68pxoKa1KJwXD2Vd9HbSE5vtIKRtIxnRdThOh46qa4QgxhrlJYn/Tsl5trIn99CxC2AoVtSWd6+RYCMvPM6JDDfcEC0iicg0pEoC+VhZj5BjZHvQasR6akCJCk0jJIJJ1KAkpovu3Yby0XahHz1rc3zlgz1t0ClGTvKBI1c8lUUrj0qu/B4DC6vKT0FkPOp/Dpl2X4qJr34mdV1x3vpvYyTmU7a99E4pTJ5DNDzF69CFMjh87303qpJNOOnlByYZGiCg1RFPIfQ4SRyHTW4OuhTV4JRKIMmLk0QgtXPaZFJmpwDcSppRq8ySjdB2+/c6qTq0W/p3W/mOkyAB7L74OTx6+E2WxCgDYuudKoKqgVBbI4nH4AckTkUiR709NVjtZR/HBIjOqNEqst55jV/Yal0h0SRUjQwD0fMnI0MLAme2j3D0USlJYZoQUeZLiSAf3eo8MyWCLWpCqY8K9rtJnm8AjNnqO9GwijpSUWgYUSa5GsDxNiwUskaL5AweRL27CmQfvweZrXo6Tt38eO972rsbrSGiHeF1pI9Njnok08YVqFJOWki9vYy6E3yWSghLiQWkrTKahtQZQwRDqQUgtBQkUIRcIFdJFRK6nJKPyOdF7MXAl8Z3i1CcM1EhOkaS+xGOCnwuhWTQY3DwiE5qqUoV+Io4Jhfmg2+5HhDg4pIEDQJIjBKeJoG0ayxHM468bkKC0Di22GWlRthWpbCOex5winvJormNAzNdPxOuIQD0TGbxJovehFSkSHohjBRTQmJgMmXYDQSJFgUztSkKIFvIxTOEGEfGPcokQqXS7UhqGJmKVemNwOBj5jPTT7ZBm6RCiunQI0QYWneW45vX/EACQ5QNUxRhf/4t/jdNPPHCeW9bJuRalFLa/4a1QWYZsuICTd9wGU8jQ4p100kknnTxbsqERIpY2jor4/9MVkwlNWXBflFUcKFBb8s9eq1K0qpshMW1ymXDd6FytFHZd+DI8eNdfohidxkVXvwP3fPb3sbjzYuy/7t3IFhZcXTqyuuR9tEmDgcABGeXIiIzMqg9UVUPqDpmyg5pBXn2csNUi9yk5+j5T6IqvJCRB9LhVZOUqiQaIkpGiIu1fXQUrPXgUBUvV/U6NDbBLLSCjlDYUJuLwxDQKoI4wxEEBNx+8Bkf1R7D8ra+jt2UJY5g+oQAAIABJREFUy3d+BVte+qrZLMIIpVMm4sWIJiZViQCWa3PBov0N7yKAJIAngCQkQUjk608inpj3MtN+fBjvTWgmGUyWAWNbSxtBqAffJ6G+hCqXLi2LbEN8DI1xRmymWcyyD2h/hIrIZxxCOQQU26Xw8QfS+4+GoZSn0BRhSHUvsAbhY6hOCSlGt9WS/LS236qaNx6jqy3ofMLLIxBExmqK03u0CaHzdMqsAS+bhMA6781XRs9vghyjhmBSIQFsWpLMZ5Oa55k8phmNSSdZpqUJ78EaElg+cxzDogvM2CQdQvQckH0Hb8TSritw5uRjqCYruPa7/gn6wyXc//kPne+mdXIORWmNHTe8DWYyRnlmGSdu+9z5blInnXTSyQtGNjRCVIsdE5fSCmnhDsXn8j7hIcKWY+yCA0DxmnFk6ZM1NGkJS9uU/oNIJSKeEvOQaJ2bgiDqgGC43zUOvvT78aVP/HPc/+WPYHHTBbjoqptxx8d+FaqooHQGq23w3FhLzZX9ichaJgeVXrodI0SmD1QmcC8IGaoGbaQbXxLqlZmaFwbF6aC0HJaDLXpkoFSBL0JxhkT8oWzkOSjMIbLJ8dR+d1CwllVlA98h4rdElIvG++FnJLhbVkXjpWE4+Ms6iTzJrAa2XvVqPPGZvwSMQVE8ifEjj2Bw4UVJ/bUGxRY/IYXpUK55yOkieg/k7c2AGLUBFEzBEoiN6dl6Al/yoslSZAhj90LqsYbONfSqDnGIJMKG9HqKPFMrzvvMx0pEUyJFMd9Qz9AHiSjUxjmPn8gjTsX8pAhmqj0nrti/D76SENxVtaNEMvBqQ2oIjpkV8d6AkBzUNtw3B5iMAy9qCxTpYKshRg190wBW1YTRYZoTiFsTedpx/bE0IGAS9SchtKVSQKE0Jshq/UrBG9sQIsChREA9RpEULdA717Q8aSvzuQRniGJUTUvxwZ6GM0gXmLEuHUL0HJHBcCte+ob/BQDwtU/9O0xWT6E/3IKV44+d55Z1ci5F5z1sf/UbkQ3mYasKx7/coUSddNJJJ+shGxshaokFEkceriNFKTKUpPsgq4T5DSmiEiwDldTh+AqpKaXJhCq8tTIlbxlxBxgZoiazBZBaMZTGQsqmrRfjmtf/Q9x5y2/ja3/x69h36C04etctOHjdD8MqFe4jS6oLfdNiEJi8zmmRfAvuM+Xit1QIcVyqOWGptXaFfzY6cIh6nmiRyQSOFJWaIhBHXkMyMnU2cmVOCNGEkMCoISLBbbCQFXQZPAwZpbMNlBJGhtL7sSKYizJojUoumpOgSnTM9mteh2N/9zcwkzFOfvU27H7Tu5DNLwR0gK7TgPDJtqYtjVJbRLG8WqUFFWkCJmoeSIQUeX6QzS1HpLaMDPlt4kSMCBnyz3yioHoK+Yqqe3dNGcuAQ4pklGlGhiR3iL3d/LOvABAKuVYfxFOGROVknxjlPc3ScaMiFpE8l6cgejH9/ixf6+E1SNweql9wheqcInpvbC2ivtVIuGGM3DYgRK0eam1SKvakDfCVTjYZKWqYcNqQIY7iLcdpqVDqDKXNak0kJEUiQ4T2xN5osyJFro3+tkyK/EhPv4p5lSop08oIRpqxg23nZdYkLyiEKF6GWg+ZpiQ9Xdmy4zK89I0/gwuuuBEXXHEjTj72LaycOAwguJavl7QukT1Losq1jzmXosv1vT+SbDDE1pdch/723QAsTtx523lpRyeddPL8FAunEK3330aXDY0QtcU4SSILc9mMDNUi6eqApMiS85URkhEn7eE2pZGqmWLTwilS1kIZA1U1WHSk6XM7BO8oLqOfNm29CIvbL8J9d/wRivEylh9/CPPbLkCVOaWIcpYpcX9SKB6TjS1GyQHJ02NN5jlEvo6noxQpZWtxObKM4q6Qte77VyZyLYM3WS6QoXzV85EKQVBQwYpVETIEANa6PHMhQSUhRa4vdGlr6FL9fsR2/KhbEKKmqNPx0Nr50jfg+Nf+DgBw7HN/jR3XvoEjEMvrmTy9Tow2sSHMkd3ppBkQIm6cP3caMrRWFXngEHGcFbJmR4EzBKRxpnKjHCLYgkC1iek1IESMDFGb/H7OaWZ5P8XwIRRyLc872Khpfp8RbdWVj5jPXmb+elA1FlHttWf0yqMVSiPzD5O8yNj+4klQDIJpryq9MnLO4xg4KiBpfKxDWqUXn0SKrIrmoilNAAJvKEEw6XrkAStRrD7xq2LyEJU0oaWlpfvi/tawpYIxGjLYheTsyDhFTZGt10KKjFXMTapsaulRfUXpY7FxHClxT0BA6zjH38ZXOjayPHcRoikTU5vIj3wo1x5Exi9j0bHWu+GTO36TqMK/qBRkTZTPVMarJwAAc8OtAKLAhIJMPMuHz7BLtCutKBvlHLx75HbP5OqsvbHsVs8BGb0iNFpbKWNX/JL6Ji0bz+H+8+fKpYBp16Mgn/RMRDnteroAepuWsOWyl2Cwex+q0QrOPPjtqddrW7KaFh6inhA5LWe63lmI8ktlvES24hWh1RmmoafRxkDodmUY02uPF14ybkvB0iC1JX6RXmSqyGTUM3zYjFg6OZuknzIw4ywilxg5rcjTSbMxgxCRnlL0cKqeyQw3KhShWcjGtBRFROuicA+fFJNpMqpyX7rBtkoM/inSz0pfuoHSy31A0hleLjJ66V1ST8MVv0OI6rKxFSLb/NcW76LmhWaDpSGtxCalyOTwf8r99dxkyrF5pihFppe5PFxKAUpB+ezH05QiZQwf93T6Zt8Vb0JvsBkLm3bXPKqmKUUmS//k5N+kFPHxPfc7I0PPklJkM+v+vNcU3cc0pSgbGfc3cX+6tNClDX3TqhSBj+U/kRetUSnyY0xX1v1F58f1NylFSvzpErBVherUKd9WYOfL34Ti5HEAwFNf/qxvp/vQkrdSE7rXpBSphr/a/YiS/+zZo0NJ26I2TlOKshWFbEUhXwXyVfeMVdGgaDS1seX+pilFNMbg/3jMiVhKiVIk5yKb9s9Upcii+VkwategFJXuz5ba/VXujxCsWZQipWztLz42VooYfPc3pSgiNqFDNd5V2E/vKns6Rly6tYTGtB4r6LEKytAUpUiNNdRYw04y92eU+5MoV6QUhT73UcONQ7kYaZmiFNHfpMwxKXOMK/cXlKF2pWghH2MhH2OYTzDMJxiwMtSuFNGfpT9/f4yYNSlFfrx08vRkQy+ZcXoOMdkkBOWG39KSXtwwkNpKJtT6cyhgo8oimJgUKP/iaCLEeavTCmgWgCM8xzMVL2Xp9LoxyZuCRfLHjaBe8DGbdh7A3oPX41tf+D3su/a7sHDR5VBKcbvlEgGl7mCJPpzSvZ4+AhIOdxOfm/xqaJxYViBCLSVjrXzi1mKUYyVzk8XpnpsQRhPXuHLsGq1o+cRPiLpY+xnLjxF4KQSw/JBnmyyUsXWIOkvHnLbR80KkG8ZLVkKTkEtZsUL5+J2fxne++N+wdOlLse/678P80h4s7NmPM0cfxOkH7kJx/Cn0N28L/RwFdZRKQrgPX1b1claiqxwDTcczp5MSpnqivaGErXMmfPDp2VKSXvroiVQsVoUPZY2c3tbmuLvl6o9UVqKln+Rci9pYXiv4YHysvD4kkb2SB8QKie8juWgml9WVgtHRAEBw0+aphh0I5FwSGivJxbRJS9icmNkaXrK1kWs+Eatd23wpxomqov9TkyuxbYUy2LSk24YSUuBGUgxyWx8fchmR90dlpLgDkUu+79fCe6vINCd6zYXA4DxS+om1tBqV7zAKdUBKEX1bDH8PfElKqLFR+AV/ARkBdg3pAjM2y8ZGiDpZUy540U2YrJ7Ctz75Phz+2ifPd3M6eQay49DrML9tL07c/1V844O/gvv/9H0wVYlqdQUAcOwrt5znFnbSSSedPH9lQyNE7VC5bSdPt6EGkWtrXdLlH7Z4CHbPAU7AyhCxSo5pWqO2uebS5rp2ea6DyjxsW2ERxshQsr+vcNXbfxZ3fPSX8Njtf4Hdr3xrLfAcu8dT0Dph0FiFWjA56WIeLyPYDLCRB52sryK0zKfwYERglSyuHCNv7ZzOXONGq75xnmCbjTx6ECfrFK7Q9aB5z5zLEMaTZZI93asViFCw2Ojk8KwkAjQNGaLfe3oOV779p3H/Zz+E5cP34vTDd/Pv+XAznrz9U7jguu+G6qWQlEFE7pcIEVniZbodB29sk7aUF9POreZdn1QD34hhlJ9mkoZSaEvBoiNWa5b5tgdghKubKiq0m8cLo2P+ZOoTCRpUDSiEQNokKKBshLLSPh4XYTtGS9LBIJBEPongVtF2rUIKBzo1T5EiI8YlotAWWoS5qKUE8ecQp89azci5yqIXz1pGYnkeE8CXsqgjQnR7Lc8gQd5SsCoJvwKAl460X96yuQXmKW9PigzLcVMLjQDAVmn0SAoWS30TkCIk+6dJ6QcHL6v5ZTcAmO+7AZ8JSKweCiE0i1Ic8fsgx/YMMi3A4wtVNrRC1Mls0h9ucW/Gs+Dm38n6Sj43xMG3/QOsPPkIxmeeQm9xCd/+6G+iXHHcotVjj2F+z77z3MpOOumkk+efbGiFKBASBdpj61ZCbNnH2zGpkSz61pDlNQTHl0ZxQkpGihgZEmvTwhpz9SiH/ggziFAeQ6HhI8RI8gIlMmT6riRe0KEf/Me4+4//DcbFaWQLm9xvhAz5FBu14H2x/rQmQhSRKbVNdS8ywjxnCHOu0zURaDNvuXmkSK1mqHx9q55LVHlkSI3q3CHAEWybEIRpwm7UM7jf1DhpFuyaK9HHENwxRYzowCR1Rg0ZEmMg+p3q0bBY3LoPwx0uZcehd/8C7vnT38KOq6/H/OLOmlWtYu5D5a1u6QIdI0P+gq0GIjkgyEStEVIkPbWYvzrXgAwBwESH5LyTtOT7EUR2andWRH0uXjMp/L5kqCGIfHuUdJUDmarkOEe4DddvKmsybYhFKFMctDM0OqrAJjvDfvk+lprHt3RDtwIpki7mMbG6rflNCUY5kCai9yKzUWBSf50pyAM9WyOfY8MpbaFTmG9EjhaT9HfTU+AYuPOpS74V3ZrkaPGBM2W8BNosi/RcnupnQIhygRBNqhzzPdfwQe5uJFeExglEKLQwCCND/tiK80/NLF3qjrp0HKLniQyWdmHbodfg6G0fP99N6eQcy2BpFy5+4w/hyW98Hg/+9f8H+3Q9EzvppJNOOmmVDY4QkcXht5s8ECQiNM3LjOpV0kxo3iSxGZJUD65s9loI4fij+pQ7TiYDlRyi1OtL8FUkMkQJVSkUQA/Y/sobcO8H/w12vfP7oJSuJV81/RTx4Nu2dWRIWqTggInwanSoxFAqBl9mHiHq9VxZeMuy8kgRRhpYdf+faA9xraZpG9hFnThEZfR/7z6fFc2WGSND0ywgQh2hnIdxmfaNiihnvE5P3jOSu1TjOITr1r2ion6Md6tojDHMEY7desEhbP6hX8TdH/tNrDx6HzZdeHnyrJguQl5ZNmwDaEYARfewe3lL4NKmRK21mD7zdWQIAFSh6giRsPCzCA2kNurSIYI1LhuE0L3Ez4geaRvnjIPZiWcfc4hELKFG/p0v16Jk1L3Mph6dNpbewziRqrw/fxCryy2cohhRlNwhJVAeuicdud4bS8ivhYUNU6DogKZAuq3coSl9J5FZRogJYZQIYBE4OwEp8nNUGwRQaecNXKmwCsB9RNt+7i2kt9nacYpy3/ljzxsa9oLr/TB3N5TrdGBwyhAqIyRQ+U4pK/FCzOplZmeLd/RCkw4heh5Jf/M25PMLWHnk/vPdlE6eBdFZji0XX4Xj995xvpvSSSedPMeF4xut499Gl42NEE1BfWoWsA2/AXVUCZGXWboPCGZLe1tCrCCJDKVr7ekNNCNQVJcRXhlJPCJGaNZGhtzvjjO0/bo34ehn/hwX//33hnMGLdZ83LTUGSPct0CIoD2fyFqAECEfOj/35VzfWT6DnrN8KObQJHPDrVA5LCEHlLZhkqbo0JF3GeBQBEKEKHkrBWVsQ4rCTaD+fOixldb9CTQSNkKG5KPmBLA2+T1O/dLGdeH9NG6jA0LqjvRhxDyuPYduwNf/6NcwevEbMdi5mw9jSkTlgzyK2DBNyB/fVwsiJOuYJVErV0qpVqJSIkP8jMWzjjliui8QIgKgRKwb4gNNiyVUo+fU5pDQPsm9kkhRWxqXWaSGKDYdxI0VB0UImBWoVbg/gY4Q/9EHPk2CM9JVWoI1hhGt+EIyllBr25teSzHlagEoThPmlvnx0cYxMnmokGP90LtEnCJ6DyjNkVFQSkFVOnj08ftPneL7ldCZYm1kiKT0fT/v58RhXmDgIdF5X+YqjVQtEZyYWzTxXCSOMUW8qrXpTJ1MkTVfa6XURUqpv1VK3aWU+oZS6mf9/j9USn3F/z2olPpKdM6/Ukp9SSn1Rr+9XylllVLvjY7590qp/+lZuKcXtGy55pWwZYHlb371fDelk2dB8rkhdr/kzTh8+1+e76Z00kknz1lZ/7Qdz4UlulkQohLAz1trb1dKbQLwZaXUJ6y1P0QHKKV+A8BJ//8X+d1vAPCfAXzabz8O4GeVUu+z1k4wg8i4M4z+xJbJmshQelzymxI/zaBd16KysjVNJk/Q4kNcDscfqiFDIv4Q79cRiuSRIPImC2lEkJRVj7zINHa+/Xtw9E/+EBe9/CqoPA98j54wJaP7JetDe4uJvOpiZAiA62DlQReKYeLRAeIqUJTbvg9DXxhXVhWlOjEcnRUrFJvGX6atLGydz0Elp8hI+WLBskNk+tbvvVHid/dskkRJkeOzxjkLDWHkgK2+lG9Ez2jX5a/B177yCZSnT6M3v8kNuQh5UmWEPohQNcn9z3hbxD3juEs9G5AhQopEjKEmjzLmDPmkvBSRmjlEfr8uw30rYxs9u1oTtkZlm5cce8vRGFdpe+J0GlTqtjhEETJVQ1lFKeMQxXGmWx8F/SD7QCFKSeF3SRSQuC809p4JqhWR6uh9V6VL6xE8YcX7F18vQiYBJP0GnB2y0RYtnOd+jVYOGKW1qCF8dHsx34nfXfE+ClSmKjXog9aG6gw8T8j4gRQrCIReaeKAEcLnO60yms8BgEG/hDFhLgVCdGs7Q17OTtplzVfEWnvYWnu7//9pAHcBuJB+V26GfzeAD/ldGdw7LqfcJwB8EsB7zknLnwOSZK9fj+v5j8Pw0svR37UXJz/32dZjxw8/ivL4iXVq2bkRPRMZ9dyJ6a8vxY7JvWtI1htg6aKr8NS9tz+z662zs1q2ur7XM2vn1zynMi1x77Mi67w8ovU6X3C9ZZ1vb6Vc5wEqpOMQ1eWsOERKqf0ArgVwa7T7BgBHrbX3AIC19htKqSGAWwD8b6KKXwPwF0qp353lepwcU0aNRdDwa5aFsNLaHMqeqbAVRNGlGVmhtVzFx9nc/TECRElhGRmSdYb/c/JZyRkiZIg8yHzMIZs7C3jHO74bj/zHf4/FV78Kan7B/RhFbX3qA/8Vk0e+g23//fdj0+uvC95dE2pLWnK/mgrjRx5GtbSAfOeQ64uF4nJQslZO2qoj0gNzeJQokZQ1pKiKrXTbWAZLPXoGbJkKC85QUkoV3ybnkjN9DZCiIjlfgYAhtqO+EGPYigEbA0ZWeaVIgHPctohbtOuy1+D+L/wh9lx5PbTKAwrhkQq+Hl2HYyeFbavccZIjRaURnmQxb0h5Dybro/hmKwIhImSFECLigq1GyJAv85FHGKVC4a11ZW19MhXjk2Ju1VCgfrzP+vuiTvFVjdM2q2iMkRLO41LMRQEdcW3SZfTu0GXoOp6bZAT/SakWBC8pU5QCRjmvL4sQKIcRRkKECCGi0kfOVwbqLL/+jCDHSpH2D4j4Y9IzNs4lKJ5XG5I/lZu1RpObvrdyH0XNZ35QP3qQjBCJj4d8BjKCtQ4fezlOiYuV0UOPxvggKxOlqPSdQbGKVgs3yU9Kdy7FK+rnoRJqGtHuuoAcz0xmVoiUUosAPgLg56y1p6KffgQBHQIAWGvfiwax1j6glPoigL83yzV3zPtEn7IeIHzYGC4Vg7d2A+EDWQs814LkNMG4vPQhs8oz0ZdYgkFZ2rzUdx9lrwhVDPPT0lnaDqsDSZSX01gxcqVUhEwuJqJL9mHw5pugbv8yNr/zJl9XuI9t7/1xPPFvfxf49K3YdvBFGGzdDTMeI9fOV79iwnD4KJanz+DER/8M+coYW294BYa7r3T37l/knv/qejUJQ/92LljX2FXlGj9GD5V1/9c+YWTu+yLz95f7+/P5DtEzIYu86qcfUO3TRFDf03M2vr9trsJk3TCYNm/qNYQ5CIfUUgVI+D8diq5N9EEtW8Zl05ATSlltWTZSmHdccjnMkcuRP3UPlg5cwwrz5n4OVOFZ1+4nVubo/8LdnpZnKQ2H9cEWSTFSmeFEmpyg1X8ctFyiiG6NcpFS9IWM6punG07PsQpYmnfKHicspvQh1EZ6L+g9iNKL8BKfDC5K3zuvrGW+booAoaJvPE0ZGX3IhaKZkNSbQhtE5dJc7uaFBtK6XEpqX5oP9xSI5bQvvb/QSr+8aQ0fnpnUJZ8ULTJmaLmGQlc0hS3ZnOVuyazv52k/aeWU+DrSK6RhVVvK4sr/f/beO96S4joX/aq6dz75TM4DDDOkIUsasjACgQhKKCdsPclX8V6HZ1tyeO/Z0sO28DW2JVlWsiTr2QaEjCWhgBAIEIgsEIgchsnp5LDP7lDvj65VVb26+5wzwzBzBvb6/Wb6dO8OVdVV1Wt99a210vVNSdHyGu83vkUHqX/wJUezvEfLsyWFflkCPDhu9vpkL92+WWRVQUS6bXVhiCBdDpKClFQyWZU0naAURzbDfaidUrTBWNHXdOhtl76G3PRL0sOon7z8iXKyndJs+0BvN/IiZkrcdrvPk1kpREKIEhJl6NtKqeud4z6ANwM4eS+e+VkA1wG4baYTB0YSvTcv9gcNUM4vmlYhYh89wz/YB4XIeMToASWnkk7rN7MKkZICA0MtqxAR2mNyl+lLHA5RbHgODBnSA12PIQNexCVkrOPg1FOw+cq/QbevUDt6LfwVC2xFejswdcm52PXFb2Lz//0Z+D09CIeGcMRfXgUhBEaffBzBzp0Q3TVMPvIYms88i7jZRGnBPASVGtSalRiNEzjJ0xNtWdspHSIx/ac0BDCuJ4txPTE3lUKoC+5pTw1ff5x8zSPxJ9Lb8rgy8YdMm+ut19QKkW57g64RElcSGevViG6/geFW6vfUB4Dx06xCRDN++niqjEFBv5xOIWIfBZvtminHfUfj0bt/inXz1yFyIpfvngjSecfcersfbaYQ0Zb6FOWki31SNHU7iNjke5IhvT/a19Uz/C792MCij75BiBgyxMe5LuvuiaBwHBjljcYBKSyeozxQ/ZhXIHk0egzFklPpHHrJObo+3CsqTyGaxrNv10SQy2nKyxmY2nKFKBUbTR8rJNWkEVqhFDw9FilTO33IaZ/HwHFjetG5gfKxI25B6fEt2VgmTti0ChFfBs8xSIxwhYhvabfk9OGc2wBOO1J9hQI8YEcYZOIQUfvZ/p8tmBBpBdPXUFBFpLdlbTGUZISKVnBI0SGFaCRO9sei5FxC2MkbrSwjDOs2H9PPnUQyEAK1V4s+bWEyY+tpjtBXATymlPo79vN5AB5XSm2e7QOVUo8LIX4D4GIA90x3LueM5C1JZOKb58JJSAVOezEus4UKl/lYaRg1UvZDVpJQJem4yqc/bK4iBCTKEEePCpEhJ+gin0RlrYHu887B4He+h8HvfA9LPvMHKC2eb5qsfuxx6Hv35Rj49rUIh4YgqzVMPPwwWgO7MXTfL1BZshQo+ygvWQIFYPLRx6DCED1vvxDD5RJsxkY9mcr0hEEDnKxOg/aHwgmKh/SWuTu7igVXhARXNhhKKJwlpoz1TiK0Auok1gXSfYQmyIzSnaMIAdr13dQrrUwViRKwszgp+yaBcLr9gqkJPHPfNRgdfAHzV5+aDiSokr/N8/iSBBVd2DrStVbZ0B8JTdiEXipzl8nkhP74jWlUQCuuRe7qMrIKUUYRojLlWP4CSP7jSAmdwxU9s4SmHMte/0aKEAX0433QKXuGlMvfp3n31DeKFSIybmg500bRdLZ0P/6R5+dSJWJljzFlwy5v60vZ0lnKODQKUb6YR0jbkUxgW5m478eGTK37DSFuzrvKC67rbkncoZVZ7ipQgKajQ9CyLBdjNEW2PUXVBmV0n0fLYOQowDuqksKUiVzyKflqqBtBSrZk5iO1fAY4wRs1rEVEbArcSEtm5K6fKgMr64yisp/OtswOITodwHsB/Npxrf+UUupGAO8AWy6bpXwGQDu63AGQzrPOwOANPwAA7Pn6tVj0qY+kfz/tVeg6+gRs/+KXUFt9BLZe9y3Ulq/CvIvfiMZRx2LovjsweNNNqB9/LJb86R/CX9CFmlfG8OwcBdvyEkjQHMXAtkdRqnai1r3wYBenLfsgSimMPPUoxp5/HFN7diAYG4IQMtEipICQEl69A5Wly1BdugLlFcvhd/eAvv5KKUw8+hv4vX0orVh0cCvTlkNS2rnMsjKjQqSUugMFOrhS6gOzeYhS6nkAxzr7D2E2MZDIMjaWlLNMlrOMlrqWWfH7IhlidurHfPXaWofCeClFfrJMZmF/hhAZZMiiQnxZzwRkrPB93UZlleVKeAqi4qHn7Rdj6D+/j6mnNyIcGYZf7QUASCLEyhr6Tj8Pu35yA4746J/Br3cgbnjYdeN/Y2LTc1j2oY/B08ttcRRbi5YsbnK35yRq4iPohqQw8yqWFlxiJFxOZrWkartkJhhSJAO9FMDDKbgcbrKgWJoUJZP3kn0nOUtmBilCamvOI/QgUjYUQJz+rUgEYFEOY+EzxEjvdtQXYMPFn8Hg8HN49r5rUe6eh/rSVeZ5rtt4kXuzi4CZvqS3JjwDIUM6BYvSSyJy3IM/nkaG/HH9uIL3KCJ3qbMbFYTwAAAgAElEQVSgEQqaKAlbweqT4VexekkbSNL001a6r/Gyusez/TD9Ps3cIOwcZX/T49inc3SZ9LsZfeEJbL35OrSG9yS/+z5kqYKoOQEohcqipeh91RnwGh2Y3LYJIw/cg+b3rgMA+L198BodiFtNRBPjiCcnUZo/D11nnYnaicdCCGH6vX33hDBSP1LGTdvwZUx/IAQsjfoCyCQxlZ6CVDHiAq4WjbUU4jYDQmREIfOOjRSsArj7tFxXJIbwbcohIKSADIR1HiAENUg/kCeLFlJBUWEJoaT+IinNhz6Zvrihne9JTPBGQoRMao/E+GzobU0GNnijUZDbys3+kPaC4ytAul53JuqnrMfQNd/DxL2/RteZZ2XO6Vx3LMYefxjDD9+Deae/zvlFWe+mtswZ8bwSehetw4LDXo3BF35tFKK2zG2JwwDPX/8lAED3USejY8UayEYDslGDV60jFjHGn30Su26+ER1rj0Ht8DWoLFiErldvgFerJwr8+BjiqIX68ccBQmD8N49g6Cc3YfSXv0T/Oy+HN7/7INeyLXNdFNpKVJ7MaYVIcEvHSdSaQY/2x/M4F4Ufh2NNOmlEABj+hw0fL1JebMoTlqPhpbect+IGlbMBEaffuhwpblkJCZT6u9HYcDKGb7wFXWedmaqYEgIQQO+Gs7Hl219B/4ZzAXiY97qLMXD/Hdjy5c9j4QevQHX16nSjaE+jSG9bQdKdSpqES4kMm/p42NIIUUvCo0B+LBVDUfqI2BNZ/kYB6ZgHvHQDXfJAmspLLHkiJed7maURGp5YmApkUk44/BxD1MUshHGSDErF3P1hvOiArqVr8dy912Fx6dLkmJegXDMlak3Vj6EhhB5A8yuIQA3NG/Kawljg/mT62iJ3dRnlB1jMqz9J7NG7c8aMw7MDcvqN6xFEKA5LI5IhUTPitAycv1sqdU5R8MUUSZ24NPqAGcIx4IkS1n/i7zJldwNM1uYtQc8Jr8LA3bdh7DcPAwqIgylMbd0Mr9FAfd3RqB5+BJrPbcToffdi8sknIKtVTD7+JLb85ZWY/398ALWj12V5TkSXgYTU5KSISicIZSX+j663GziUh4yge5p5Jz0e3ICUhgtVFDBRpH+fFbJfYKfN2M/cc505UsQJt5GHBlCy+HuQFENYviILrkpbCkprxPny8uSw5H3GOZiykMDalv0lc1ohasv+leoxa7Dna9dg7O4H0HjVidnfFy2FV62huXMryquWQ0iJnjPOQjQ6gubTz6QVorbMCenoW4bW+BCC8RGUGl0Huzht2U/i1eqYf87rU8vqKo7R3L4J44//BsO334q42UT9mGPQ8/GPIRobw+idv8DYvfdj5xe+gr53XY7Os151cCvRljksh0YqjQMtc1shIkQgk3xVWSuEeZAUxqjIeffcayE3NYh7D9i4QzYeUXrfojTCQSooTpBFJZJtFhkCyMssXS8wdCK7FeYkE8qfKk0Wsapgwe9+EHv+41oMf+8mVFavRNd5Z6PSmAfhVTH++KMo9c3D5M5N8NcsT+5VUkDZQxyHSaA+X+lYHZajE08lhSWaNbckJ5vl1HmyKa3FzTgE5D0XkfWukRtZAczLJvdX4uUYrxdqP92uTgBMGwwv3eaxn3CIFEftcjlEep89x/Stii5rKCz3hEIFUGLaqWlCp83gdi8ZDylBtXx0LFuD4Z1PoveoU5Ikv5H1Pixyu08O6kOE4pA7fCaZpT6uAxi6CFFhHBmOVrq8vxnGqKm/p5FV3xkPfnqbSd3h8vKMF5lIlZ8nlfVy9k0CYeNuzzhEDLl1vy2m7xjujr6/DsxowASqt5cBwdPoit4XQqK2ZCVqS1bqsAL0PAX09KP6rpUoLVyEwRt/iKH/+j6igQF0X3xBJq6OQIISATBIUWxQJMszcsvhCh/f2RN0uRwEzLR52jHVSXKst8587k73qXOKuGb74j3s1k+7AdJ4MPejdBgOXwzssiJWAaHn/PcU8sa+N552qfdFemtd66UJ6EjBG5sajfe9dmjGFyNzWyFqy36XyqoVmPc778Tgd25EadEC7Lj6S4gnJlFesBCtHdsBAH53Dzqda8KBAZTXHnZwCtyWGaVz6RqMbXoKvUedcrCLMudl4Jab4Fca6D55A2a3LnNoSc9vnYtwZAjB9p2YfPwphAOD6H//WyH89lTflrS0qaFZmdOjhCxi8+KMBSCQcSPj3JpphFt5mfgyHDFKcYi0xZ9ZA2fWvWB/yzQfxn1+Bp3wXCs5vQVHhowVrMArb7gTUfraaMsgmo88juoRh2Hxp38fmAwwec9DkPUGdn/3Ovi9vcZDJy4pTG3ehM7XnwV4Ckr/g1CWo6DLYJAi9g6Cpu5mTe1xEQoDOxR5mxikyAnsx9+18VYy7aytMRPd23KKuFXnxqZRIutd5lqbPAgof1/cmo9UNh6PFxDvh3gsxbMRR4a4h1om7lGjjnBnC2EFiKqJA58JzMgs7/SNWPkp+epU+vn0or0mbW29zC0YUpKXaNR4ZPFyMETMRRho3GQTs/J9gmHSSA6AlBfZ8D13QfolBLt2Y8kZCe/KcIhMslll3o99j+m5gPO5XDERjckTiIoWJbG0aKnCDRLN6ZIR84LKxBxKjRf9oy5j3xsuxpbPXYWe15+PiYcexo6rv4oFH3o/ZFcShd6dKWwCWD12YuI/aY6fSPNYphU2Z5n4S54ttyBkuChSNYl06sjnds7h2RtkqOg7IQAZCBOs071vTEifHrs87YkC0pM9YHh3in1bIjM+lBkPIcu6S166LYoyqvtgqMOly0hhIiRkKNmWyEOt1A6H8mJkX0DGQ1b2JqPy/pADndx1tols6sccBQAY/fmd8Lu7UJo/D73nvg6dJ52CBZdeju7XnGlvOTmJcGgYpcU58W4OcLJHCjlwoCSe0+ZCIlHYwrY7f4COVUce7KLMeYnGxxBPTWLlez+BsacexcjTvz5oZdnz5L145Bt/gdHNT+73e8tyGfPf8Q4M3vB99L/9LSgvWYTtV30e4Z7B/f6sthy60k7umpU5PeUb5MYeSTZKGc8bu0BPp6SRGnOl0uvSyrFqM0iRPTe175TDes+w9eQiBEIK8y/22G9s310/nymEP/fWQAzAS7Zk5UieMJW4CyUfXkcnosEhyHEFVRUJIlQqofO0DclzNELUfGYTysuWQJQlAAXhKQiZhKpXsUii1VKdyaOCcYoUcYccxCrDgSqwGE0k7kBABgpRxSqaFLDVRCB2uCdADsfHbT+HS5SgRIQqpc+LfdtuUdG7YJarUDboNCF4sS4rpZogTyeK2uyKiZFCbZLDU7Enx4hbU/Dn9SMuJ+0VKQchKlBak1he6fYxHjcMKaKOSRwjETl1LkCGMvsSNj5OjnWeXJNG52LtXaZix8uMc4a4NyYhps4zRCSglMLum36AjrXHoVxuYPF5b8b2n/4Xelcc56QUsXwv44HmxJYCchAiFy3gcw9HnMMEjWoO7cSWu25AtXsBguHBhFfE2415ZalsV3aem93WVq1G48QTMHjDjeh/z9swcsvt2P7XX8CCj1yB8qol2TYnZChmDyLvKC+GV2RR0nGW88vN0cg5kZJ5I+blGOQxp2aKPTetFJxr2jVMkCzhxMiyfUiPYeJo+qyN3I+KnnMNBcugTDJ1TR4Pi44FeqIhrzKKdh0qaY5PamTI1x5pDY0MUVTrtuybHLoIEZ+ocpSXjLBs6JyoPJ2YNCK0jMc/hnmSM1EBziQ+jRQqRNNc60+K1NbTLtF+TpAywymQ6YFKaRvIfbm1aRMqq5bnPk/Qx5aWCGgijGaeqfgyHleM8sTk6+LbyszPKyTBTnMpEXRDWr6rprfTCZ0T6sSlgc54GzSKHxiXp/vq5YtXqmLx6W/A0IN3J/fQimxU02TZSvF4oHcdmVQdyfHZpEMqSmA6bXua5Ui95YrrdM/bi3FA4i6V7bnxe5jasQ0LLnozRAQ0Vq4BADx/49cxsvkJqDg9CfA0MSYn3TQiWzowqT5XjU9g53P3YPMDN2Lj/Tdg68M34flfXocnf/RFLDnxAkStSdT7lyZlLDDGpq0fX25mWe97LjgfE488inDPALrPPQu9l1+KHf/wZUw+8oRVTmmJJ57FOyjI5C68mdumKEzCdJINcTLzNdmb7MWpJghs+vlyNjoGzXnGCJ35weSKH7JtK5q5c1MetN5KMsn3lGeIRMlEqTZClCdzGyGiTOE0eBxrUEmRKCciUYqUJ5IBI7RSVJjdXAFCGMSIFtRFDBRxh4wTk6sUCfaR5c9jsTjylKIMh4gjR9Nss8kS08pQrlLkKB31lYdjPHwC3oSAigTiskLcnIQKQqCzUz9HYer5TWiccrxRkIT2xBCegopEEqVVI0WI7X5r8y4MfOM6VI9eg54LLki3Zx66xY+zyS+qJJ4qUVlABsnWC5TeV4lSlIm6i9T+dEpR5jfmzZSnFHGr18Yn0ihRNalHWEsm2aCeTK5Bw4lJZRAjvV/WMbb2Yu6oLVyOnQ/93OwbRcdRijKxU5QAVHKuCBOlSIZJuWSQ1NvwnzQyZPq/HjfGE8gZR2ZbiBRNoxSxMRKpAK3JScSlKgAx7TjIeCsJezwcHcHwbbeicfhaDN97J0rrNsCr1LD2Lf8Lex77Jbbd/QO8MDGM/hUnYN7KE4EghgcPja7FAGRKKVJxjO2b78PQnqcxf9F69M1fh+bkIHZuexBKxajWeiGkh8HdT2Fg1xPomX8E6r1L4fsdqNZ70ejyMe+IV6Fj3gpsfeBHKHk1g+6KOKkTAQ4itqiaaT849RPONXqLWCRITQx41Rqqh63G1JPPoNzXj/r69fA6u7HrK/+K7ovORce5GxIvTj0RqlhYLhHl5DLouYD0FJQSKXTDVYpiJ0K++26VByBIlCLp7CsJCOOaqjcOr0rJ5P1R/UBtsrdmvDOWOBKXUnY8vS+0N6DzfBnYiyxSxJ4T6XkwTN6BCIXlAeo2Ib0zBOCXIkSRhO9HCCOJkt76XoxW5BkFgjhFUUy8LgUpVK5S1FmavWLUdrvPypxWiDJEQkfZsMkr06Q1c45nTk1+dpQW+2UGO4ndy90nt/pSuhNl+pRZehE2K7kk+N+ZIJBdnklN9Jy4WxRgjxK5BnbSMBYOT7TpLFksfv3leOKv/wgDP7kR/a9/AyCArV/8F0xtegGV1asw/4r3wu/pRrB9J0qLF2XgaunFhnhJ7RiOTWD4e7dg/M5fIR4ZAwBUjzvazgImkaLIKJkcceBLL7EHaK9TR9i7oGCIocr8zBXVVMJP9wOes2yZsW45oddJ3pn8gAwKqfS7iDNu48Ict+T+5JgNdkj7hE4m+2FN16VaQxy2EFUTBSdygxJytC6njDwlCT+XlDX3HWWW8Qq2KXL1DNfwJKy7Hvw5hl54GINeHUve/B749Y7cwJKuCGfI0t+lehdWfOT3EW7ahvHnnsSTv/gMjnj376Pu9WLBMWdh2aozMTmyEwNPP4Bn7voPSK+EOAzQmhxCo2sJ6h0L0KjNR7nSgeHB5zEyvBmLlpyMLc/djsce+neU/BoWLD4BfqmK0cEXEEcBevsOx+HHXIJypdO8456+MvzOIFGCA4V5h52CnY/cjmWnX2YNKhpSrP04oV+pLEpmVtOdi4Rfwtj9DyIcGsHwLbei65wzsej3PoI937oGo7fchdpxR0LWqqifejTKK5fYRMLpoZ3cl/oltbVUEFAGGbaIsS6jb5FHWr4nA4DmJl4H9/1yow98WOcSzKdXmEyXo+c7FRROypakPunb29umFSMoYRQeE5CRLZmZz5NZVhWIKCAjH6KE2unvhRc7RgMSBYmUIRO0kcjv7eCNL0rmtkLUlpdMpJfMTIN3354oRAC6Tzsdw/f6qB+1Dtv/8YtY+sd/kJwczRz6tbV5B3Z87hsoLZyHeGQMXn8Paiccg64Lzgbay9ovqQghoWbxjg416T/hDEyN78T4Q/fhqb//Cyz4rUvQveGMfXIhryxeikb3UkSTExh5/CGoKLSIM4Ba1wKsOOYCrDjmAohIQUYKYTCJsT2bMTm+C5OjOzE6shlCSBx34gdQqXRi2YrTEMchhJAQ0n4uDQpRco4phTgOEUyNQ3o1CCmx6Oiz8cj3r0oUopdI5l/+Noz9+iEEO3ei702XYvC/f4DacWsx/2MfwNa/+BxGf/oLAIDX34XyyiUvWTnaMvek7XaflTmtEMV6Qslzhc6suZP1zJYvbPoKYV0gM/B9umdkAvEpOGkgSFunk/MRo7jkpIMoJclDyTqK2HJJJpBZKuga27LjJuCZmzCTdXS+9Ee/dx1/KkYeuhfRZBPSq6LzuJMweMvPUJm3EM3+fozf9yvUjz4K43fej96VlyQX6TJLT4Fsp+bz27Djb76JrtefgbE7H0LP2y5E90VnA1My04607y4vJTdMt4lBjtwlkaIBzN6BcaN2FLFCt3tDek+fp9xltBzSu3sPk9CxpJyLddEoeCVRtqZB/AipISJ5hnAepcdBSwemDuBBIUarJ4YqyaQ/E1pIwQgD3gDu2NHbAg6FEbfsHJHlW6qX2+dm4HFlUE+vimUXvQeD5QYG7v059tz5Mww+cCfmX3ApquuPSaxtHiyPtgommSshXK3t27H7jp9g3bv/GOWOXsiJND9IOEFXRahQElX09R4O9B6eIVcrvfXgWQRZAnEcYaI5gLGRrRgZ3YzR4c2YGN+JKGxi2bLl2LZ9JyAkVh1/Caq9CyEEBSmletAElt82fLyk6p7h4Ql4fhVdp7zKtGs0NIKxO+9D37veCFmroX7q8WicdhKqRyzV7yvdT9zlMUIj3CKmzjbofPqdxAAiQonZuOB6PB+HgOVA2rRIrL6sr6m8ZTXe51iR6f55S3I0HjJIEaE9Stl248v1lCyXvkfG714ipnQp+psSMfd7umcex66pkonSJHk120OXFjwXZE4rRG15aaVvw9kYeehejDxwN3rOOhvC8zDv0suw+7vXw+vvg6xW0HXuWdj62c+h6w3nwOvqzNwjHB7D9r/5JrouOA2jN9+NjnNORdeF2eSxbXnpRHhyVijeoShSelh07mWorToM239wLTqOOha7f/oDePfcjv43XIrKstmhGtFUE8//x+ex6Nw3otzZu8/loUTHCkAQTGB8bDvGxrdhbGw7xse2Y3x8J8qVLnR0LkJn9zKsOuI81LsXwy9V0bewgZXDAUbHtuHJu/8NreYoVp38xn0uyz6VPwigwhDC89D3tkux55vXoPOcVx/QMrRlbsihQHI+0DKnFSI3sJ4rPFAd4KA6ej8XKeJWJPE5TLh65G4Vkbjh8kks9yM5OV3G2LdB+KISEJWTf4ANOmivdcoI5BKuLZKgLVNKx+EGLuTWD0OE+PHKguRjsvuHN6D3tLMBAPXVayFrdTSffBLz33I5/M5u1I85GhMPPoLOszckbvdCaaKfxMiNv0D1yBUY+cld6L38fHSefRLiKamt5nS7psjTzFLkZGbDNaDvvCwOsxQzbpE0aJ5uqyBrGbptbnhE+jm05QTMvPAI7nHDKSrFBu2xpHc6R6TO9VyDjtCkcnrL70HPb2n9NCwh8ZIqxxq9IWazg7CZJKvOPYnywTlnDNGDY3kDSb9VvL1miRi5x4razw15EPtJdTqOOg5L6nVsve4bmH/xmxBOjWPb176EjpNPRt/Flxgr2/KehEFPMd7Cluu+iq7VR6PvyFMgm+l6c48yEakEKVAKzZGdGB7eiOGBjRge2YiJiV2mGp5XQUdjETo6FqG7cxmWLDkVjcYi+OWKrp8DpcTanb8Vo7OxCCe99veSlDFKYWDjo5gY24HW2BD6jz0dtd5FWV4JtY2LOHKeVqFYTlH3htOw+aqrEOwcQO2odeh5y0XYceU/o+PsU1FdexhKyxfA6++G1B2TFEAhY4MWSWeCFLABWk1csgoVkl6Jh9ig7npOLPDgcgEOSg+TCWJKPB2av/n8Ih20iHPawI7vhXDivnXRt4gwPDYQqFsycjxiixbFUqZ+MxwiVTTjAZ5ua0KKFEOK2rJvMqcVora89HLY//xzTO3ZbvaFEKguX4mpzS9g5M5foO/NF8Pv70M0PJq5VkURRm79Ffre+lqEg6PoPPukA1n0tpB4Eohe/jmM6isPx5L3fwhbv/UV9J1/AZb/wR9h6798EcO334aes8+GimMIZ9lBRREG770Dg3f9HPVlq7H0vLdPe/84CjA6vAWju5/DyOBGjAxuhPRK6O5egZ7OlVi29DVoNBZkspMDAIT1KJqNEO9o6xO3YvMTNyNqaU+hNdmky/tT/J4e9Fx4PnZ87p9QP+l4+Iv60H3Z+Zj89WOYevJ5hLsGEAcBOk8/Ht0XnwFvafdLWp62HBxRODTc4A+0zGmFKGYeXbOKzWESwqbX5IVUGZdfY1HQ2jiziF2eEE+nwBO0xmyOjH3LGVIlmKB5gEWKjIWTwxdIJW50nke8COEiQ3pb1L8zSJHj4VVp9KDc2WMSwioFqKkWOl/1Gow9cD/qxx+N1vYdqJ5wJJSn4GmEyPdijN7zJMoLeyEqJZ3sVbcNSyti03TQ1nmXDCEyiJ5BW+he1gBl4VYyCJg0bWdREhMbJg/tcTxaUqgQR4S4h1PRcQHEVWMeJ4dC1m+Mx6NTf44QEdeszCquGyLs0q7R45MQ5RKU1KlVYL3MuAs0oSMitGUzCBH35uHjgc5Xtu9ydJMHLuReNqlraMvTcjjefMpLnkvPqy5dhmUf/Ci2/OuXEE1OYMFvX4FtV/8DRm6/HeHQEKqrDkPHMeshWhGG778bpc5urLrkCtQWLjeIg9SIQzgyhOHtT2Fq11aMDGzE+MhW1BsL0N2zEgsXHI8jj7wUtVJC1BIpDpGtTZoDlm4vyw0U5lrjAZnUFL19h2MLbgEA9C4/Dl3zVgOhyiLOBoHQdYhcl/j088yWH9djvPuMM1FZsxrNJ55GuH03gt17EO4eQLRnEJW1q1FduxrBtu3Y8ukvou/NZ6Lnog06oTNxiOxWQEF6xIVJtjYNiC1GbOYEjYbo8UBzIRd/0km6S+k+qF+S9xpHilwuEedapQFEC8Dx+WAWYuZevR8DjkeaReNMWegkwPKOYmU8zuIwjRAJgwwVFyrWN6RgmVP6Ux5zaLEteyVzWiFqy4ERFUdo7tiK6pIkAGP/a8/Hxn/6W/Rd+AYM3vhjhMPD6LrknMx1wzc/iOrhi7HnWz/G4k+99wCXui0AEE+1sOdr/4nyyqUHuygHTMrz5mPJRz+GbV/+EsLRESz+2McgAHjd3Wg+/BgmnnoCIhZYdN6b0Fi5Bj4jlCulsOs3d2Dbgz9B16I16Kwvxqp1F6C7sQyeX0lI1aSB5CzP70/p6FmK1cdfimcf+i8sW3/BS/osV8rLlqK8bClQ0h/fkoIKAozf/xAmH3oCzUefRvdFp2H8vicweucjWPj+89BzQn6A1rYcmvLS9uxDU+a0QmSCWk0XfbpIDKfIsa45ImMQIjZhcjRIwmr2RQlaDQ/CcovI+on8hEfELX9u2RirwuE7WWRIb4OCbTj7Ds69h4KBXdj45f+NJW9+HzqPOwHljl5UV60GohitrVvReeZpGPruj9D52ldj6Lob0fJqGF5Ywdj9z0CUfcz/yFtRXrUM0VQWCcrbQuUcY+8kE1zRswBJoRA6Qb3ajWlCCAkhRU4gTdejzEWQMhyXQu6QA51Ao3oEZ+kPTkwpJaaM26PeOpWirsqQIWUS7erGoUSmvkK4azeC7buw+M8+atOqOAgRLyuhL6Wm45lYwBkqQhaTKLf60pmQIieejemfHBliZePelhSkzz3H7+3Gko9+FLuvvx7bPv959J/3enSe/Gp0H3k8uo88Hl4TSaqRwAks2Yyw+4lfYs8T90DFEY5/7SdQbfTDm6QI0zEQxgkKZDgvLjJk9+3cZFgpmTY3q3cmDliiaKU8QgEsXHoS5q06OTnVJCxOI9w2xpB9Bk/tMrPoMnsq4/WkPAV4JXScfjI6Tj8ZIzf+DNHgCJb8+RUYvf0hbPvC97BnfieW/6/LUF7YAAD4UsEXMYSGTEyf0AgR8VlCABHxZSiSs/bAjZmHo68DycrAjlVCdy3vUN+DkCKD6NNqQM6Y5R6jSN1yn8REsoYDAFG7srLavmG5VDxoqWkbQywsRooMR4rmHDM2Z9kZ1F6c+wqSto9eW1DqTrxuBu+9wxxrHH0sJp9/FipW6DzrDPh93Rj54W0Itu0BAIze/wz8+d1Y+ZfvR8drjj0o5W4LEE804fd0wevOegC+3MVrNLDgfe/FwiuuwPAv78TO7/wH4iAb9EophYHH78Wj112Jged+heXHX4hjLvgkqo3+g1DquS+tzTswevM9qJ98NISU6Dr7RBz2Dx9F45gV2PhX19jwJW1py8tM5jRCRByiTE4bZ73eoB3KWj+pcyluiCesh4/xQtKXMjJkHkLEEaBsFGOLDNHvqairbmwhs3UQISBtZTNPBpuwlfaR2Z9J4TfIiWPpA4DnV1DuX4DJTc8iGBpAqbcP9ROPx8DPfgLVbCJqjqP/d96MeHIKm373/0L1qJWYf+oydL/9AgjfQ9CUaD71AmS5gdLiBTafT8zenxPfhkfRNkI0Gb1rqEa+E59npvl42nZIo1fZYCqzuA87bvIWORYf70OyklTU8J8M38O5iDgRBhHS/YM4RGXN0SBuViQQjzUhajVr6SNJxSIYb8sk03Ta3XhhUVEKEKFs2o+s7coTFxtxqFTmXO5VxjlETjRv5SfX2mOMU9cUqC1eieVXfBTbv/sfeOZzf4GOw9ahPn85SqUOTA3vwsSW5xC1mjjstHegY/5qlJoAQkAGDjIEQNA2VhlEyCydMd6OsfKVLZMgDz9Cl9zOHFvU2saXUhAONyhpR/3edONT9GnqLlI46MpsDX0zpwhbJsMv0mjOyAi2/9UX0POW81E5ag1iTdYXpRL63/ZajNz9NIYe3IiukytZ29gAACAASURBVA5DyQ9REaHhrVBqiVgThoy3VCwQUzJs6tt6P9bzmslJ6vQ5HieL+Fcxm8fM3E8Jaj1h31MBUmT6KXE/Na/R5almPNQYP85IjEwspCxSRCdbZDrDYxLpWS/OzIJ2cEnmiRZTLKO9ibbY1msz8opCiILGy7u6Xmvmc4qk+9gEst/6nW8hHB2BV6th4fvej8arToLX1QEASXRfAFAK0XgTU9ut59muq7+NrZ++at8LMAdln5JJvgiJq3s/Q8UTk5D12j49z5s6sDNi/BKaX7JcwZK3vx+Hf/hPUF+1BsH4CMY2PgHEMfrXvhprL/skOhccllke35+SFw7kpZQZDYNZSDgygj3fuQE7P/91ROMTiMcn4HV3oev80zLnBsPJ2mMwMPbiHzwLobArB0okN85eaplFAti2HFiZ0wgRZQa3uZyS44kFp09iVmwmyjRb8w8a0qI8Wj/ia/rZuCgi4xEzHTLkbmcl3PVBuRGGqR66rDNwibyWcx8tpmx0+xwvqZ5TTsPgA7/A1M5tePYfP4vamjWorFgBr7sTWz71GXSceyq633AW+t5yNmpLlsLbtRlb/vSfUT9pLcqrVqJ63FqM334fJu5+FI1j1qfLmhMziSNcbpRZt8wmzI1ny889+riYmD8O70RQkla6r/N81zIUDgJgPIeEPZb8wfapjIHlRxiLOLBWMgDIuuZb6LX/WLvEKZGgOXFVGa4QIUOCkCG6v+YhyQjA8BS8Ug1yIrmP9CVk6BlkyB/X24mkjL6b7BeJUsTjfNkxxbgbTlwdHpPF2LLEf/LTbURtHPs544shQy5CFPtAJJXDC9Nt0kqPD9P/y53oP2YDvDU2jo0/pYAoG2/IRKjWyJB0ECIYhCgdQEa5nCHAIDsKQreLKkaKlIJQytzD9DWHU+d6gNJvgG0zg0QkYcDS14r8bSYRdGwR2j3fvhZTzz2HeLKJ5iNPo/n0U6gcvtKglYb7JgVGb38YXncDHacdhyD0oHyBGAIl7V1GebQo4rLJfebFENoTLfa1Bxrxf/z0/CmcPiCD5PeIRW43nnoq/S4IqZJKZdF/RtlTbjvCQRxDZ97gnESOFCF9PO83m8Ban+TMXcpTiVLEEHt4MyNFFB+KPO2kHhe+N3utrs0hysqcVohaHckLoxQM7tIZX2qxcH96ScRViHh2chnyi2HOTbbO+bTUMYMi5CZsNRMjwb9MeVMqf4R5UyLrcsqWmDJLZ4FTByrrDBaWS7iVHQ0s/8DHsP3716C1YxsgJaLJCSgvQvfF52Houz/E6I/vxBHX/CU6UUFYWoPauWdh9Kf3YOqpTRi/637UT1iPgWv+Gx1/dAyE71tFKEdpo/LTu43ZxEQS0b4z8fOQBKba1IxsWVP5VhEikb4tS1QGxGT6Hm6mcb5MUuheTf0qsksBtOxrVl70jO81kopbkqy0YQvKtEyhH0RuuaQIkWIUAvFYE36pDn88+c0rS/gtacpoFCGmEHlTyrwPHtrBCEMgKH1FLESG5EtilFHuOOAonRmFiClCZgyVFOKSSlIjUNuapWO2FMgUbK9lA/rR2CB3e6sYpRUh2XJiT7AlMlO/9K4JV5GE9dDvKUq3pzAdJPlnQoKY3BYKgpQHIv2bQJ66PmaJyyqljK+bFT69OEuhpDD3XXgRtn3+8wCA3V/7N1SPWoP5H3wvlFkG1sqNUIimYpSWLUQkK4hCIBQSoZDG9dsn93u2XNSSnk71AyhfK/eGvpBWjGhcxl62P5BiZILSRmwAUru6qXhojmBj1MzxZIApAKGeZ0k50pea4V6gGCUEZaSEjHhunNkTs4qQYMtg1PkUM0yUUqZeJqGuLuXexMJqS1bmtELUlgMr5b55WPaB/4GJZ57ErltuRLBrF/xF/Ri/51eY9/F3o37SUXC/kl6jhp7LzkY0UULvu98EL6pg+//+J0w++wzqR649eBV5BUk8MQG/3nGwi9GWQ1QqS5ZgwXvfi4nnnkawZSsWfOKDAABVFBe+nRH0ZSPtV5mVOa0QhYl3J2Jm/YlIZKz0QkIoWd06AB8AJ+Q6szA4nOpAz3bJbGZkiI7nJWvNEyq7WSZrOa7ChBQxErlBioxrqr1fhnQ4g7ikcXgC9SPXYsmJa9B8+hmEzSH0v+ct8ObXk3tHEWIhEMUSMQVZiwGvVIYMJMrLliHcshNy1bosiuUkorXHaOmBEBVdBwddo2cohtRwpMG4rVNb6eORhwwRUhKCIYGokvwDHCTODbTHCfym4fSWL1EIW79Y348Ip1SOSJvCvl5Ci4R9jqQlM40MUZJc2dJb6ich0Hz6Gcx/3SXwCPmJk+UwjhDxoIRey3KIbF9OI6NcWGy5zN/uvglUR2lVfHsDNzUHkIMQEZncV1C+Qhy7qJhFx9xttq8pMyaongYxasWprSFTB/oFx7FdIqNJQ3IIQJeDcpvFAsJY6flIkYUSROp3+ML87fE24n3dWQoyS25sRSWTCDZnjnQDBtaPXAd4AkMbNxtytWBLTTEk/MULMPLzX6E1MA6/pwNhJBHAM++8qh9Q0ss2tGTmezECXbGIoC4ix/M+QMmdAxchYqi/IuSPxkF6ICrfSbZKCA1fFZbpvg8lICO9ZGaQ5vQ1RUiRO8/y+ZmHYDCpNZQCGIJvkCJChvQTzTKiPi4Rm/tQEEdazqZ5pi37JnNaIWrLwRMhBGprjkBcM4vgs7pOlstQYdb1uS37X6LxMQQDu1FbedjBLkpbDnXxfKgZEgTXTliL+uMb8fzH/x69l56Oxe983QEqXFv2tyigzSHKkTmtEAUaIcryZZAlVfOLuVUEB1XR+zFX8dk6s0WMsqTqImQo5XbvnBv7WSTD5QwBFkHxphyEiPGnpEkhoPedBJWz9srIQ444AZOEiArk1h0n7aNiBzYjN/CpEKO/vBs9J29IUCBDqtb1cpAistYNh4hIgmQNppfGE95DZs09XR3bvsmRFG+INQ1xfAghCp22BzTZmqw78t7jIAE/7CKKZL2TbmjctGXqXGo6rx4YDlEcpJEhQZaw5k4Qv6w1NAq/sxt+5NnAdrHmCenneQ55GgD8KToem4B3xMeJWZqPDOfOIU8YAivRYKjqVL0ovR+XHDQg416vzDnuPqUikUEOEpQTRiD1e+ggQ4SiTmlkaCo5mThDBhkK2ASBNAKU/JYPuwrnnCKkKCGWW8KvQYZDZWP7EDJFHBEa08L2VwBQwqYiEk6/c++r2DxjxBlL9Ft53nwE27cj2rkHpf5+4+Zvx42EEkD3W1+PiQceR3nlMkSRh9BBiChEQE3D2YQU+V5s0nvQNtLk6tineup7OEixcbgghIS5rlPhqV8SUpR86PXfrG1MExgEmtAnZZDrFK8IGRpZLlLEnQxMSR1ACLDdRylhuEGGO0jjgBBEleYQCc2/ipU04WUk9QvDP0JbXoTMaYWoLYeWRJOTiCcmIMoFCYrasl9FeD5UGM58YltethK1mmgO7kZt/rIXeaPki67CGbyUohjBjj2oHP4in9eWgysK2XXEtsxthShssLVi11W6ICGlEYYiuMEAjYXGPMYyXkNO8DJuaUyHDNE2k4KAcV4k5wM51q3kFnHIkCEnqJsRXm7GhTJNxX5P1Y8OUfsazpTehhJKSkTCs3wDvfUbnagduRbSK0HEWc8fF+GzyJeuF3EyeJvQWnxkPc4Mhyi21p1bLwr+Nt14ty7PSRJe4hCl0LsCa2tW0whDLiWrD1m75NasIpt9mrhDIiDuUBpBNN5ho5OQpTJkK819kiEs7ynjGafbWzntaJIbp+EelQazUkHmKMGneQfC/la0NQgQ5+plApVSXxcQQkCEwqBYPMhnUT0JjUnOTW/NWNJeZkIrAwYNct8wtRftcqTIgQD4ORmkyHja0XP0rYLYcQ+nNtDjjZAFSWWH+V15wNCzj+L5W76NeevPxMKzL4H0fIdrk96qvL6tBFQUYce3voWe174W5XkLgciplrMVUkBIH13nn4bd//ZjLP74OxEpafqC1BOqpwtQKdHYVsVjhvoReWPSvOA73mXEQ2MhD6ZDigx3B+xUuoLfSwmIMEm+a1EWXSa9V4QUwXfamK8usHEBd5UgMzHr5/D3ZX7W/chThl9EqV0I0YsEL2WxtNGkrLQZWG3ZrzLvsjdh4PabEYwMHeyivOxl/PknUV/W5g+9kqXSvQCyVMH41mex9Zbr9+kezU0voLVlCxrHrc/9vbV5K3Z/5d+x6RN/ha1/fjVUECAeG38xxW5LW+akzGmEKK5oLZjQiRIhRcJYyUa48cARIicYGecdmC27NqXt53gUADnI0DSpO2KGDBlLnHEqRIQMR8okeWRxNdzIu9wrg8fx4Ell3d85f8N48xhPFo1eCAnlycSbwaTogLmm0rsAXetPwdBdt6O+4RJTH3crI5tckkxRahPiFoFxGFxPuJhbXSzuCcwaPKy4iXPd+xI6ZqCcZBM2YPtQwSpCxirMiVvFEcSwrnkCVc2pqOpAjbGACpKOIFiMHZFp52Tr+VWE4UCShsP1ulS2LJFZveQms2OgZtLRZM/NVr5gv+gagYzXjkHLWB8zl2iemjseTB9yPBbd5+Z5hhLRydaT+rI+wWMF2xfTWYhCNEm4EI1S6YSwdIFinYk8DunMKM33Uh6gQoXO3mTpanL3FvQff0bKGzODnrnzmxPMsb5iNbrPPAfDP7sF8y6/XD84QY4Gvvt9jN/3ILovOAtebxdGf/Rz+H3d6P/tt0BFgFLCoBKhfkBLBxul5g1CD5H2SI0J/STyHKG5lHaDvMxCYYOzcg6RYO1H4z207y2TEJy4NgzuNPGCeJBLwASaLESK6FUJO6dy4XOEQfg855Xrbazy62WEDseA0i+Z+EaWQ7QXy2BthCgjc1ohasuhKdIvAbINPr7UEk2MwWu88pK6tiUtcTCFRSdfgL5jXr3P96gsW4axB+6392wF2Pm1f4VCjCV/+ofweqqIvRg9bzofstoe2215ecrcVoiceCTJVpit+bvIMs3lEDEeAreg2HG7yO+sBRdwlqaLVB37OrQJQ4YyiBRZQjkIkfHmYZaOQYwUENE6fE4KhNT9CQ3y7H5cTrc1IUPGApcUsRYQvoCSEkLHxeHtOrVzO7qPPTnjCWS4GzGsRY+0GPSMIUWpBLs8jQFL7phnJJk2MMia40LieA6FdecZDCHiqToKeQNuXCd6b+U0MoSqJrGSh0koDWeI0lJwpIjzyqZ2bUPXkSckv7tGpYOOCI4U5QQR4olZuRdU5iWJgr/zzqH6O5wQF7UFXCs9/c5FpC13J+4Y91g03CkH8QAIpaOxorK/AVDa08lyh+he8d6jRBr9cYWBEUnqDtfLzEWKeOwg2qf60bh0vEwJLVr/zr9Aqd6FyEEfkufpfYakJogUlSHZRkPDkJUqRJR4P+361jfh1Wrof987IDwPKlIQUibzgL6hiiXiWJqKRvr9BSIHIaKYWlRnisJuEsDqd6D7QhwAgmKS0RyUeSU0PrLvl/OnnCA/qWttGh6VxCGKHMSGzqS51kGEUsedc+mLysDqjLgRsUlsZGyGFDGukYqF4/2oUs+NeOj4QhF7hya9QuQVpeqHHbOLpbO/hJSLA/e8A/o4Q3bPHPd9mwh2fz7vRSSv3ReJGge2v+ytNPfsQLV/4cEuRlsOspTqXS/q+smNz2HwZzeh+1UbAABTWzajtWMH5r/znRB8ObEtbXkZyxxHiNgHyXjhCESc5zADUgRtZYYdcSbPjOWipC3yVCxCvuxfhBSRle3EWCHvkiJkipdV+QlK5D4nY5UwxMjcwkcuRwjIIkOEGsRlZT2z/LQlFVOsE0InhIBQAkJDDzIQmeStpUYPoqHhLEfLqW8GcWPvzXJFkhM8KRBWEqUog2QQwkHtypfe3V5OXjyEzIik2cmj0c2TJpRWinh/iXhh7TXJM3IQIurLFOiS+jKhQoGwiFArveWeVSIC4jBEMDKAaveCpAKGL6MRqnQJs0gRhEFOLKdG/8K4DcYa3YtM8YYK4yKmhPQadIKQPapXugAiSt6tiGx8KL4tQnXzUDrLmdLxXWg8cO6PEtnkroUVdVrakEw40VDYc5Xlz7kxvzJxbMx907GMCA1Rgb3expHSt6CtYPsMRUtuBAze9jP0nXsBaktXASHQfOJJNI4+BhKemYeSRMdpFAdeAkXSWCKekOU3whynayiWjmJ8GZtkVdel5NSrlDrViEwPJSsOOmi4Qpm6p+c5EQqdNFxZVJ4FdqK5VrD65cUh4sZp3vfC8InI45QntOboGc1ZDv/IBkUycDlmLW0OUUYOXYSorntgrWCbI3EjSm0jjRiFs0ACoppKb6u0neYiPuGzJZjpJKPUMNJ27uMKlsrMhDLd80ghoszqehtzpdSRTPJWjeCUGt0IxmbwMuPLTGybJyYVQ0Hi2+mEfxwNzF8vrh/9FuttxLbTCTkEmCWyGs16xRWklBwUqNNjwSJdaQ3uRLm7H9JLXrKZD+mEWbRnVEl/cHjQ0emEpy+YzfvLOB6YxJ4zz8w85MBs0EKe8dwmFJ2FYmeyss9+ihSGqJvWKGdUqgD74mJ2j2kkE9bChBWYRVkdRbJj3XEYuPUmBIN7AADh8CBKff3Zi0gRoj5MSgw3EPLKqucRqd+1N828QlI0n80u7oW+Zhb9kkRQoFLTrvvQnmw7feHS5/L3Oe3zTAobWpdsL3/tD5nTCJHJ9m3mF7JMkAzKaZUitmYcCSAWiBsRRJRsEYtEKYoSpYgsVrMkTQqNvm1US6yHqJZYeVFVAUogqmbRHuWppIyxADxnoLicpoI5wVjoPiB0Th8RJkqRjDWCxK0ULdMpRS5nCEAGFXKVIhGKRCkKBeJSbDyARCgAKSAJTctRikodPRjf/IwTVZhxe2iNXyCFbqQmL0LCyApsJcqnFyQxg7wWENaS50aVpH2MlxJZdGYC1c8vUIriWoyIzDST+T15f1E9QRTjetJPaD+q2/5i7m+QIn3f6ZQiys1FsYYIHZpGKXKjNE/t3IZq36J0++UpRSr9MTDDoUxt5yhFKukjQqWVogzXgFvBOe+P89Z4lGrlJ/1f+QqIE6WIjzcRCggpMhHP3b4mnSjQ7tZFipKtgICC8pJ4M8q3HIq4pFEYQgDiOFFIfAnEMZQvswpKhk+m0tsoeRaUAoRIlKJYXyfTZTbIm6sUCVKKtCJB8arooy0FIpm0RVxO+kbsJR/xyBNJTi5ChljUcOKYiQiABHpOeBUmnnsaE089iZ5TNyAcGkJ97bqEu0Xxj6T2BoxEUn5XKZKJUsRj4oT6gYQcyVIMKJFSiriHlaJo05EA9NznhXrb0v0osnVwpSgHH3GKlGlT3QhOoxtwN0gigMsgeRZlAEjGXhopdTlFSs/xymdbjz2OdhliJKKk/Pa5DmpGEezNSoZK7h8KKE+ZLQIxrcGVbpSccd2WOa4Q0YeG3CpdJYe7n9MkYz52KnUPAFDav9cNhpf8oTuccW8mqNIZ9AVoj+sKmToe75UhkxV6tFZeDIGYrAlGLIaaZomsIHmmSaJZUlBa+STyZv60ocVTEC2BTMBFvV+ftwzbtl2POAohpGfcuO3ymA1+xtMOkGS+wa67MifBs29V7lKJIVHrumvlRBCpmt1MOssepp9E6f5jlnjoVCewoHkvpGDRNc4SmXsPEVilKGNtskSmADA1sAOVeYuSurofB4G0E0DaprDjJc5BDs0SWf5+6p0UvK9MsEWzr0zgRXsPKgztZ5UbcoU2pOqCFDYZFNZxuzZLSRSOwtRPf6idfgkAiIQNDskDMRYsSeQeNg4BtLYFvaTJvtx5E4Vg8w3V16NyqYwSmFmCLFASXacGk7qjtx+Dv7gVvSdvQDA0CL+rJ9XuKhZ23DlLOCoSdnWKPtyU9JnsVy/OLpExe9WQqWk5UypHmab5Wu+zJXOLbNI97LyfCXHAQ3I4WzNN8FfNxwML8yFiZx6m/sjmtVxHhYLxZqSAdK2UQJyJDcPKPBvZm3MPARFCLAPwDgBnAlgCYBLAIwB+AOCHShn3gkI5dJfM2jJnpdzVB+H5eOjL/yd2PnTLwS7Oy1Imtr+A2vylB7sYbTnERcURdv3sBxi86+eoLVsBpRRUGEC20++05RASIcTXAXwNQAvAXwN4J4CPAPgpgNcDuEMIcdZM95nTCFE8pfkRXIMWMMtphBoptnxByydeSSdyFAqxcRfVS2PakjGWjWYfKhbsTThLOEYhd4mScCwCWtOVMMkyI6kQQVkEwLjdzq4dgCxqEDPL2BWy/DNbkzQzXT9IZV2eM66e6a1rQRVaGJ5AY9kRGHrsXowPbEZUo4JRmUXG9ZoklyCpJdTpNYi3ZbZ67iar3kUlzD5DLIxF56mk/iDEKG2JJ9eQpU8ok0wdN0WlayUKYJUcca1EuoSWNrlFqrcTO7ZjYscLWLFqjUWIaJmOAoAyC5VbtXDCCvA0B0XJc1PFLkKIWELYlDXPjHWDwlBwOSqyIdgm18Ul63odmXGgly3CdCEtUiQgA3r/FjkAAM+0px6fFXK/p3sou7SiUeWiQH+uSZ4hSxuESM83VT+Xf6aEKDRNOZHd8KCkcOqljzEOmE0Eq4vjIiwCUFGILdd9A3EQYPUnPgW/0ZEsd4YBBHHT8hBcg0SJBIExS3/piS2Ocy5mHcfzNa/TjC07Zk2f5qRqFkbATdBKbWPRMkIS0+gZh1KUFIhLAlFZmOLGJm0RlSO9n7tEx6tM1eKhA9zH07ksXEIqTEKqrDnIJfv+zU5eVktmVymlHsk5/giA64UQZQArZrpJGyFqy0siXauPAgBMbNt4kEvy8hKlFJ759lVYdObFkKXKzBe0pS05ouIIW6//NygAy971QfiNDgBAND6GuDUFv6f34BawLW3ZC8lThoQQvUKI9fr3llLq6ZnuM6cRIjTTlptBacqxsTTsT+lFabI8fG0OSqEsJ0lfGxInhPaJKmLyHmgkQDi6NA/2xngtKaWbEApfQQllkpNaa4FxUMwznEqT9VOUZmQa4ZwhsyZPVphrzZCB4biOTysq+zdZTFEV6Fh/AtatPx5PXf3nmGwNotTVm+Z78PVzEm4NufXhCBHpA7zMXrovQCrnHJU6R8g4+WeQxWTredaSN5Y4bU27FSFEyrrIxuwdF26z1hoP9pn0MYHOw4/F1PBue9ypqwkIWmT8cesTMO3Gjcxp+Qh5CBCQIU+ng1WyPm3GDBvgvMjSooDGwcGgHdzyTzZeoCyaYjgvup5kiUs7vt1iiBiO272yx1zhiDGyfdkgizSvVD2ENS9/vDO0sRBYNOhFtq0tkpk+1+VXNffswI67foTmjk0oz1+EJW/7AIRvO9LUjq2oLFwCSJEtpkGC9AHNrVSGN0PzCnF+HOiR3Uz6sf3NLavZV4jL6d/oDx50kTvBKKkcxDDdPwRDldxOHnsCsS8y3CSeCokjQ6lUPbzNWb80NVHO9M+CxtobF1zrcMAMK4YH9pyNvMw4RAAghLgVwKVIdJtfAdglhPi5Uur3ZnP9jAiREGK5EOIWIcRjQohHhRCfdH77uBDiCX38b5zjfyuEuE8IcbbeXyWEUEKIjzvn/JMQ4gOzrWhbDj0RQqDrqBOw++42j2h/Sv+p52B804zGTlvakhKlFHbc9j2Ue/qw9G2/jWVv/x1IP20TR1NNyHr9IJWwLW150dKtlBoB8GYAX1dKnQzgvNlePBuEKATw+0qpB4QQnQDuF0LcBGAhgMsArFdKTQkhFgCAEGKdvu4sAP8K4Od6fyeATwohvqSUmlXMYTmVDvRFHJgk6SNbA6Yw7oQIaWvU9xLzxXO8hsiSKWnNn8Kdm3AvmksU6VKKUEKBWYpsjXja9VgKlkfIBVnPBVYnhHKshYI18PTP0x4jd3oT74V7QEmLnnGPH8PJyvOJZ5yXiFs6Aug7//XY+IWrgGoJ88++ENL3kzpwCwr2mjxRwiJExnI0LvLpclj+ikWDFOcGuYiQUBlkKOVlRu+AJdg16Qio6E7Qx4xLawEa6HqY5Hm+pOplwvO3IKvV3JhUynPQmdSD9NZpb4MEMa5JoaXq3CvjyccQIh4/i7zfkpN0O5mBnS4i5zQBjrVOXpeM52eRIXqusJwSQooIXSqzCrJ7JB5cjIMyE0KUE7SS82+iikBUdaJm5lFrOGdoGqTIcIe4txW1H6XSGd6FwcfuxcS2jYia41h26fuAejkJd8Cu8ao1xM1mdhzGTtFc1NPxsLLJbOme2cKTy70NMJtGZs2YLiuTtiTT9ByVZx6GCYcItoxwERuVPu6My9hL+q1tzwJkiFdLOK90hvnZ7WomMGjRffk93f7CvNb2JR/xyxEhAuALIRYDeBuAT+/txTMiREqpbUqpB/TfowAeA7AUwP8AcKVSakr/tlNf4iHpogrpV7wLwM0A3r+3hWzLoStevYGVH/49DN55C4YfuudgF+dlIXGrCVmeLiJoW9piJRgfxq57for64pU47B2fhCwVe5DJag1xc/IAlq4tbdmv8v8A+DGAp5VS9wohDgPw1Gwv3isOkRBiFYATAdwN4G8BnCmE+AyAJoA/UErdq5R6VAhRB3AHgD9kt7gSwA+FEF+bzfMo6B9xYFLasFHJtdWuOUOeXpsu6f2yRoiEcKwRijWjtxF5kph17OR4oLchfIPQGOsrLDLdpquQRmoMQsRRGWcr05aM4aJwyeHyFApxiRhaIhwPIOF4ebhbE0zOib0zHTKUbHWByh2oLFsO1EsJ/ydGdo0d7FomSlhkKMNbMdt02V3PMhuULm2RCpm8d44MpThEhvqi+yNxYBhphLxqUujQvqzpM46EuT8hREETslpNc4hIJNIIUV7fom3GK0rv83OR/d32i/Q9OO/J8Nc8lS3DXnhZkpj70zfdSWcC2OB1Ms72j6g6PfpiOUQWXSpCZjMJfb2c35hEZYGwKjg9UN8nPb/MFJfLlZilXpEsXk/n4iOwHUMdXgAAIABJREFU5Ow3Y+e9N2Hg13ehtnAZqouXo/eEDfD6e1PncoXIRS25J6+JpWOOp8vuIkSWM8TGH4v7pbRXcCxgvTiJu2fIblTx9LuJnP5cjBClkVq3fnFZmEClbpsUITh5/LFCz2GGxiLHE7QosCS/h/sck9haqcw5M97rZRSYUQjxTgA/UUpdC+BaOq6UehbAW2Z7n1krREKIDgDfAfA/lVIjQggfQC+A1wA4FcA1QojDVCIfz7uHUuo5IcQ9AN41m2cuKCeYolnq8Z3JVdLMR+XTJGo9Wsoauy/rmUooVyGia7SCQktm+pqW/pKGtHQmPdN5aJAL1pmEvak9qAd5v16nV5zsy6FQ95acJFc0WPZGIXLydAFIE6hN2+jQBFKHINCB0gRlKodEv+9DlADJAusVKkSxQM+734dd//UdtB55ENHYGDrXn4T+s163dwoRg5gVWzJDRiGyv9u6avd6RznuFyV4FBGYItc6X2s7Z4rcLQlF6I2FsMH3qN9QO5ouINJbISCpHgUKER2v1StoLVmMedVsTpa+sp97jc3XZ++dl1PLKfK0ChG4EsU+HjZIqLNsyQMzUt3NOND79HGWAn0lH6LifNAo3xNzUDC/O8FCTeqFgjQKxQoRsktlhcvb+l5OGxZ9Z3oafuo5+00hYu7gfDwqAcx7zWuxbsM5CCfHMDm4HRM7NmHsp9dg6eVXwKs3zFJn0NFAvGCBmXvd0BXcOOrz0sm3TGRsNtaS6tG4S/bteEu2kZ6cIhOqwDNLVsat3qf+kmw9fZz0JkpvKZ0l+Ww4Bnbc2e+tpcdORiFiwoOhuvfNnqy3zj3NUtxepMyh8/g4M+9Jb5+bzX1eXktmKwFcK4QoIVmJ+iGAe5Tau1rOSiHSD/kOgG8rpa7XhzcDuF4/8B6R9Ph5SJbGppPPArgOwG0zPXfXZEIIiMtacTHcAwVFA0xbHjTAfD0jVvRMWdVb18tMsi8NISUtPds29ezQ1F/gUHqIyUXMoFQUn0QP0pzoxaaTRsCOKLAXs86b52FhBlhmTTp/Zsx97QVdgfuPCFgvK/op1jCWoqizlFpCR1reFQTZSdve0NYDQGvbNuz42r+i1NeH3osuwu5rr8VUrYYp1QLGxuE1Oma0jhRg3EnMHEZNTVwRVgfD/XEIOjQxk0UqZQwJhT2yCcAiQ74XZULNkDVGiKJBHkwE9GQbQdrEjPokY8kRGsn6iwxtGxdxiej4nl07oVpTiMIg1Q5AgpDsjAKzn7FcyY6IUKiQc05R1vK31/KckvQODFXDHbOMDwcWU8hsQ3tcCWB3K8h4Wcowf9+Nnk4R1HO9uhzJepllo0Dzd8DjLEHktBd7bhQK7A4KElVxVIUbGQVlT25cXKbsfhXx/FVQi1dh8IXn0XzgXvSecrpRiJqjw9i2ZwDlKd23zMdapf6m++4IQ+vVSZ3AZ40llFF8aOuJ9JaiUUdaGw7hITawVXpLhpjUvxOY67lerBmvMt0GjK/mvqMoBnZHQbFSMwuEKCN0XJ8bOwiqTVuiT50tYhrY92KS7xIS+zLQcoQQPQC+AuBYJC342wCeAPCfAFYBeB7A25RSg3SNUupKAFdqnvN5+pp/FkI8BuBHAH6slNox07Nn42UmAHwVwGNKqb9zfvovAOfqc44EUAawe6b7KaUeB/AbABfPdO7+lpHJA8u7mBEC3c8ipg7sA2e7bKj0OkZ5yRJsvfpqCOmh8+RTsev6a/DsZ/8crT27ocKZMxpSQt0DJbVSMPNJ+1HCjtnNiPFUE7Ly4vuyCZh5gGSvYsbtBwkaB/h5nQd2CaLV+eKu7zvpdOy562eInbEXT00VRqlWs0jCuz9FerPVEPaPhAfYue5A98+MqIPwb3ZyNYAfKaXWATgeCW/5jwHcrJRagwQB+uPcKik1qpT6rlLqw0qpEwH8FYD5AL45mwfPBiE6HcB7AfxaCPErfexTSMJkf00I8QiScNnv3wt46jMAHpzpJBOvh1lNZGsAMMGDKOo05SOLysl+EOolM6EgRKIUcS4RxSqic6daSbOErWQ/Dhwsk3g3ZI5QjIowjaRQZugkSrJKzAHHqwtAJt9VymzR9TFZjDNeS+ldY4xNpxTRLdwEpgAgPWc5Q//W0vUxyVv1vgaGZJhY7yIUDsJFSA09L/mjvHQRVKsFr6cLANDauQNb/uWfEI2Oou/8C7Hx7z4LAKgefjg6N7wGjRNPgPDy6xFVnaUXHndItz1N3CY5sFTW+1AjilJf48kYHiKU/aSzUV8g7pmrFBlkiCw7XXGKZxVqFDH0pPFAEyztdRRQP6W+pftPS0KEAmFHnOE5cHQiipuQXT0IOp1OoC8JSwph2TnOLWWDxiTjy1WKzHsrQIjcXGoZ7hD3dPJZH6Nxo5yKcC+kPETK/ef8ZpaHKAo7vWr9ukQpqV/QcFCeWS9DiQxCVMQh4mhM0CkyiBNJUBcIhMhFrIqcEnOX0x1xlaLpkSFY5FTHMKqsOQyVxUuw644fou/iS5L7NYcgahWDymeWn31n/PkKELHZNx6cfEnefbZBTtMV4lwiz48RBRLSiy2C4kTpTvZV6nio+7Ib9T2DDPG25++oA5lxZws5w37eNRwhYsmOg4Zt430yGFhMuUOdFiSE6ELiof4BANAe6S0hxGUAztGnfQPArQD+qOAe65EgSTRLPKeUumA2z59RIVJK3YHC4Yj3zOYhSqnnkcBftP8QZoNOsUnOLYZBsmkBmZZTwvRHyigqDk+GhBNpSakiRUi1HEWILcdI+tga92KaJPRgjWz4eCJJZycOujlTiJxs6CBFpMBYyh1EBeQIO4nTR9F+rGwQNV3GgJZy9LkmgauAlCLJLp9ZStHnssEpSz4WvOc92P7Vr8Lv74OoVFFZvhx9F14IWaqguvZIbP3HqxGODGPX//fvGLjxRvS+8SI0H38K3ZddAL+7S98o/ZGldlQmJQr7EtHkWopNoE5Omi75EcrKR8VLvvZlfV5JRvBkutFjNpmTQhSQIqSjBbYiD0KHCCgxAkszSL7gLa0QhVrZjqY801fBlmFJVKwwdP33MXLnnej/wDsQ15N7K6dvx55EFFnNxdyD+iIt0QUCSr/TDEGYf0iZQpRCBjPpUtg98ngRzK/efhDoXrr+sYIJQUGPLOBZ8ITGInSUIzZGZvxoCNghWaQQ5RFuWb/nzw2rejjzD27OtUXvILfsXJkqUITc3+mcee98G7ZcfTVUZwnlxYux+7rr0P+utxo01rqyK9v2pOj4iZFnnTPS61F5ChEPP1GoGHlxZqnaVcoAmDkrlOkKphQi86EoeOnOpaEPBJ7z3goUotkoHUXKjUms7SNDi3hRCOoMfKdcmZva02FIaDdfF0IcD+B+AJ8EsFAptQ0AlFLbKMwPF+2wtR7Ao0gzya7PO5/LAV7UacsrVWpHHIHGSSdAVqvoOOlETD71FGQl0RqqK1Zg9d9flSwDxTHqJ63H0A9/ita27dh59ZcRTzYPcunnhozefBsmHkwi1Mtq2+2+LS9evEYHFn/owwi278Ce62/A/A+9H/Xjj535wra0Zd9kng7aTP8+xH73AZwE4It6yWscBctjBfIapdQpSqn3K6Wu0P9+e7YXz+nUHQai99P7Qgi7isZhYlo2iWgpzUEtGHIQk+cP7WsLXQVpPTEnvpixHjyNFJG7Py2VqEg4rvIx4MXWkOOQMpWdlslaEoKWrMK0RTVdKPhMEdnSlWDWp/FqiISzbGcRBCAHKQoB4SWk3MySCnsuPY9q3n/Rxdh05ZUYuumnmPf2yxPvL7pGKvRc+Drs/PLXgThCuG0HvP4exCNj2PRn/y+qR6xA9chVaJx+IsrzkrxLFv0jKCq9FGmCLfoRfJbKxV0aq8QhKiVNxtdIUdUL4WuLNy6wpOh4y9NLrTr+QAMtlPSSW1Xfj86dCBMIYzIo621yzVSphDBMk7NN+o9IYPLRpzFy821Y+OkPY+sf/i1kTwmirh0GpDJ19qSErwLzEky6iijdt2NPmqUGi9QwpNKMLX1cXysjZfpF1sssPcbSTHsOVbBd1m+UXl1yE8NmEs8WICqi5KBFs7S8pzOYi+6Rl7y2ECEqFSAQIqceeel1nG1uWfn7KggGCmkDvypPQS7vw7zffXey7ysoxBkERyj7bg3C7SXxMyxCpLdmbnZDVzC0k6GtPK2SgF3WVroT01xO7mRxlcF2br0L5smMOPWLvBhh2Vmy5sIpAdNIESqZWu5n6NWMyb7d5xbVS86ys+eU8QDJbqXUKdP8vhnAZqXU3Xr/OiQK0Q4hxGKNDi1GEug5T+4SQhytlPrNvhRuTitEbXl5iddoYPmf/AnCiWGUFy7O/F5euRy9b70Ywz+5FQAQDY0CUYTS4gVoPvQEgs07MHzjbeh7++vQ+dpTD3DpD65M3PcIui480yq3tTZC1Ja2tOXlJUqp7UKITUKItUqpJwD8FhInrN8gCep8pd7eUHCLbyBRirYjST4hktuq9bN5/pxWiAwflVmOCTcgjQrYIF50rbZACDGSKkPCjQu4KLYAyUYpmHgxXIynKbn/a+QhjoWJbyRiH1JFmUSfGWTKEJmlg8zoB8XWkknuSQXILVa6/Ny6NOEAqG1A2UqsdUsIkYMMAQkyJCOkOURg13KrlqoAAb/UgDevjthYMsKUyevtROcFZ6Fy7JFQzVHIzga2ffrvEQ2PYP6H34hg03aM3vEwhm+4Fa1nt6DvA2+EkNJxu9dlzUHgCBGq+FmuUCX20SgleVqqDkIkZzDZyC041FuJZGmv7IUoy3T4B5LRMFFkxoJkuXCklWwnS2U0w2Q4Ur8hHlwUS4Sbt6D7jHVQA9uS+/ZKVOoJSUbK2KSmqQuFDmGXGInoHQRE+NZkbs9D7KfZnDx4ZYbjRvyHQBiuHCGHGQv4xYiLUEmNWnBkiEpWgKDEcpZjJO+5jhShPblIThFCpLeRrxGIaThN/L5Zork9nnXJTyNDRQRiJe39eSobQroVQ5eUsgElzfjyY0gVZ4IsWoTI5RDlvwRzlCNIzv3MnEqOFrXpPVIVQYtA7jtNP9giqbHwEEeR5dtx4emN6LAorF6mHG56H4v6E1pWcI/ZyN7ygRye1RyUjwP4thCiDOBZAFcgmQ2uEUL8DoAXAFxecO3XoJ3AsA+hX+e0QtSWV6aUly6CKCecueWf/yPs/MK12PXP30VpYR+i4XFUj16N1padGPnhHeh+w1kHubQvvTSf2Ypwzwiq61Zi7JcJEizrlYNcqrbsL1FxDCHlzCe2pS2vAFFK/QpA3rLab83i8heUUv+9r8+e0woRR4gMKd93PM/IBZO7CpMmblAQYb0UTGhTldpy7yhXheYGB18Dp98JifA8a+n7cQwfFiEiPkdsAtSxYH2xrbtBaAoCxLn7RUHcTJlpn8rx/7P33vGaFFX+8Lequ59w49wwOTIzzMA4DENSohIVEFFYBET9rWBg14RhdfWn7/u6wQ2usrrKYthVDOgKiBHRldUVAxkEBskTYHK4E254UnfV+0fXqaqu7r73ucPM3OvlOZ/PvfV0qNjp1Le+5xzLs6xGrxyEjdvOzqDaRe3LmZ1nzaCsoiEYM78DB43gyQ4GfZ2Y9d5L4Xt1bP7s99H3xrNRPn4lwm0DeP79n0XpyMNQWLQwOSguomg55TTcIYUUeREKUYTIQobitDEmQkTiqbJtVKiN1/Xv+Jy4rN1h7HxknxcjRW1+fN6+eknziyJ1M5P12uCTT6Pv1GXo6o4QFuMbv6OHISjH4YgDLrRFXCckGtwgRJV6XGaVEaEmTkIGjdKlZvgOUuQGsxUe1/csuQ0glwdZ94KUEo2Nm+HP6I3J4O7M1DHDt7lM0pOx0zn9vLl5k2XYiJFwjuXKaJPrnGcqxfHhMs0JdGAswRUCYe2PBoew6QN/i8Li+Zj5wavA223bcQdlscpmTKbuc+m8E1yUh8TmnBHPh/g65JJEF2WV4Yam8biEZzt1tZ433UZHUkGPmxAag0J5dL9g4ylZ94/ehYLBkx58GabG0R1nU2HGfTWW41y6NsLmmBKsmdPY0Tq23yhPFsQ4JeQJxti3AfwYVrx2y6H0qDKpFaKWtAQAvO4OBIUQ8z5xJQCgvqeKrZ++EeVjjoA/q3+CW3fwRUYRWDFWaMqLZmDa6SvBCj4mM+ZNIhsNbPzoJyGGhtF9wdmYduErJ7pJk0qY74N3tiPctgu7b/4Z+t5y0UQ3qSUvFpn8r4/9kTJiRch+0Ug0aXY/qRUiol+4Ls45YPyd0EUlBIMmNOTLRG8DkhAnQkYo/IBGkZwZsg2xaCsI3Tq7WkSOlYQ9owoFRyi9NIeIkKmSskSi+GGM60CGOj6QRmqcMrJmFSmkS+2m2Ynlel/XQWOrxpwcs5nZkRkL6ccWPNK5e/L4HAosMduhFQ9SowLUT0Lv1H61XasG+lrWntmCxqYd6HnjheClNuOHiPKSOx9tacg1l2aoqng/QZwWgxBFEWCYxVZfoTC8ID7GG4MQHbKiI3SpwCONFpW9eFbb7is0R11IF33iTGqrNhMWQyIarmPffc+i/6yXQOzei1JfG5Z++DUADG/I48L4O1J5h+txf6oKISKno9rZaMiNJ2CH3+ByQeg+DopxnyKPa96bUJwkl39Hebf+4xcghobj8XroUUx7/dnmRUyIg0huw9oUnoQIRfrlnYq3YcaMtjOtQxONHOX65uV1UCduIzk5yKjU7y0OT4aJdwQv+Zj5oauw5W//HUP/ew86T1+N0rIF+bwcu80OIuNKFsqjs/Jk+33tky3JIdI+tyQzPC5VXACOAsJ00GynbMqfaNsL+BjvD8pE4lqz6bA7gsGXEXxYPrxy8qaDOqeXO1O3a4ZjXY24OQ5f89uO/POmpnIzbpFSXvlC8rcWrlvyJyflo5Zi+nsux97v3zHRTTloUt22Dw+89Wu459LrUZw9DV2r5uPhN30Bm75x50Q3rWmJhkdQf24zgvmzAc9D+ynHTnSTJqUUFs1B/1tjZGjnl74HGeVEo21JSw6kyAn4O0jCGPs4Y6x3lONnMsbGDBc2qREilzdD2puUBi0y0e7VMe7khdmvXb5rZEgdJBTGsbRKIC16QqrK0BUoSyBCsXSgQ7M+LeHpYKlJoXASaqavkKKIeaBJB4XOsPlFVH5qOw8ZEoksKXf8kMx49SW0hTwfE/VEz4oYhJdEiHRE8xwOEVl/eXWzj/FkPRrgIKslj9AdFSHcQunAgfIxR2HP936JrX97PbyuLrSfcjzaVrwkUT9FmBeRRLWajAxPiEkk62iTAUYQIyqRjRDlzLwryts0oTDE+SELtqIXaj9EBdWxNj8e4O5CRe03QYcp1bN1LrDt9kfQe+wCLH33GRDwcP+VX42PtRcSyBAQW8oRSlVpBBhmkUGGGmlkCIjRSZdH4qIQkesPSaWFQqit1SKy2CRSsFXGyENPxm14fgt6/uxM9Fx8CoAwVV4eUMMAcM7BAjNrz0MHXMQjC+EZEzHSJ+a/ufMsqbL4Mm51ATwECDPRAv/MVag+8gSG7lqDaNMmFA+f49SXrJczqdEce58tIo93ZYm+lyjYKlm+OnkFmObK0X3XHjFUeU2fy53j9pjYSFOi66OgPVljOlpZ4xE3/E4kOAoiRIk1UuOp31HqDUp5stAzt43utt0nG53KyjNq+3P7NSV5Qc3IowB+zBirAngQsbfrEoDDAawGcAfiwPKjyosKIeKjW2secBH1LCXoINZXGOXNdzDqCw5tfaiY8WSeh5kf/Ut0v+6V8Lq7MPLgmgNe3XAjO8jloZDK1n1oP6xfLykGXTEJ2ytN6jlMQooLZ6PthBXgXe2oPLF+opszqYUxhpnXvB4LP38NiovTPrpa0pIDLlMIIZJS/lBKeQqAv0ActsMDsA/AtwC8VEr5finljrHKmdRvV82bIU4IKTTSQotIIXaszDLj+bBYKSJkSAcLdBAjqk+jTVxqlEOvoxPHh6AqzXlxZml1D9JjcWyz1MSQEAw1O1Ner/1SqI12oFAO1wM3xaRyrc0SSpF00yRXioQ3DDLkBtQVtOZOQTSZhAikvhYikBop0twkh2chWRLG43UkvI7H/VLbdN0qjjJJVjUVT19rr9iO8vJl8Dt6sPUz/45o5174PdNM/cQhqqYVU82BEQxtKGBYKLQnSCJEtlJEcci05VYtTumeIB8uBT9CQJ6x1Wy5pILEEu+oqxBbgvlqZm4jRPU9I9h59zosffupcX4B9J+8BPse34LuI2bCV+gT5RVguk284aOKAHUnQDHdN8Rb454JeOtaCWnrR+3tmumxilOOUiHuT6juT0KMbFjEX9SDeR+5TO9m6qI3O8NnTCqeStq6yJ1xMyvPaOVl7x+1GZmShYLklUvXtSw52lk9E7nRnJ0F3QCERn8851nVvnmY1G3Is+qi/W4MvtH6Q0iRG6/PY0Lfy5R2hhyhV8m9jlme3sU40B0bPc3KG2Vwd5oVGxkiaZccNV4z9SI5ru6YaITH6ks+Epas377noiauT1aZiWP7gTJNRZFSPg3g6f3NP6kRIm3yHSW3eWTM7ukDzpxtfdxKbeeCiWNqKcej5SlyikjLSA2mQ1noNpHjQpUHdZ5KmUP2lU4KZ5vM8TmX2sSUKyIrSqrisgroqbZlUehUFtRvCn6oUlqto6Utrehpp4vMjKM293fOISUykDqVOlChTB6jtinX+uRiPyqrpZiitfrl1Mur6gXsphUOrgjRlDKVFvpmojB/DmpPr40LofGtqI6OePEf/QYgR2KFoT4SKxIjlVjxGa5RSI1AKxl7RmJT6D3DcTo0GKM19cH43IZKa0NFfXzvYDLPrqHY3H77cBx2ZMtwHLB2Tz0+zpnUL+B137oPs85YhrYZcR6fCyy4/ASc/ov3o/dli7UiRFKpBxhRy3eNlCKUDE9jUpHaJ52XqiY708qq5WKCfpcKyk2BcgdAilK5WFdpvN1WqqOtVE+cS9sdyn2Am7Zbx91jnW3V5HZZbZdqOqXfnZSXylNpF+UtpVN3X7vqj7vdpvrbVmgkfgNAe4HOSaZdpWqirI5i3fxWaVktE5aDRiJvZ0HVX6ihQ/3uKlYTx7qVst0ZJLenFSs6pd+UlxyTdqiUtnvUeb2lCnrpd3EkUa7Zr8pX+7tU/V1BTZfbRW1WaYdTr31c/w5qmXm7nbbbada+rJT6P61Y0f2hPlP5VJ8ee5W3zUrpd9k5RhOhciGZ2nmo3I5iMnXvG/s43Vvm/kiWP6ZIxBOeQ/03yYXJF+Qe8+AJY0yufue1AJCMFwRAehn7cpAiO9aS8QKbPMfdr+k+diRvOtdRKjTa4kaCBvSHZKZfwLaonub22DGBAHBCiAJDqtTcD4pzRh8t7Z+IGmYV7O7L8bzK60ynxO8hxcSNHq6VHV9ihh9gRzVMKVzQiphjvVRTikydFBpTH41jVHSnUOn2pq6fNdaVJ5/Cru/cjLkf/TBQbiKIFS33FSPM5AXsKsQv+aJSQEuFhlZQasqDtOblVJVH6RqRzlR1qkzmCXDyXK5SQozo5dbufOAKXoRK3cMT1/8W2+/ZgBP+9WIUelXMNnXjhNoaJTk4w/WCVoim1duxHbUMhUghGhohEtr3jOaLkW8hK4YaYBQl+/7Unr5VSl61SbI4L5qb5Mzs87haANAvS9jFK7nHdRmj4PF5CNBo9bri8mRGQ4bc8uncflnCgNWXLOTG3eciUaQMM8sq0edJBXks68hEv5wHTaOOqn67Djrmq1lhR6MT+/wh7bHd7YON7Lj15CFF9jVxkRoqw80b7gdS5LaHQ6JXlLHHG8m9L3R95KE+AyFyr59bj13fWG0aDRFykSh3+77z/hFyFLiouGC+nP3X78st/2DJhnf/1QNjxDKbUPmTWDLLfL71x16dm6EAAUbpYTxDacpJPUJDiIRtKWC6Pq2c0cdDJvLoJR6qL7KCfzj6ilk2UlAs8+AppSggk3ztzJHMneljRYoSLLN+9SJ2tgk50YFbLcRN/9ZLZslUUBsZAM7ipTJS6BxEivlGMdB5YPsd4/qCecp1ll9xIGb3UWZIX1trrDsWLse+GTMx8uCj6Dz+uJxCjAi6XoIBnocwUstfFtGRXi6NmqMAqdSrUYPUy8gah5CC/SolKVSKA5G5tZKlltDaCzWMrNmAbb9fjxOvvxSFrpIxHFBCHydy2Eik7pF6QZOno4Zy8aCWBN1Awpp87JklOgO3w4wJbKQonUbqt0dIUZAkiTNnuYNZS4KujPYB7xAMdV4bUwEZj3IznnNdGQ+R113yKQsP7V591I9hnqJA157y+lyk9rn9ynLt0Gw/7KVcICbu++olEahjJfjwgpFUmaQg2R/4yD2WozBk9YVrJS07r1GMmudsmmtj+tkZcsCvpJfokCzfVYyEZKnrlqfcZN17efd/njIVt8UZiyaX3Wx5AY/BpBXGWK+UcmB/80/qJbOWtGQ80rZ8Oarr1k10M/ZbhjfuQc/KWSh0tQK3tqQlLTnIIifg7+DLPYyxmxlj5zM2fnbgpEaIIsVn1U4YmdlOWYwlJ+up/anfzYh1vg4VQmRj7TBRoQKE2DjLOnExTHNxAKOZa6K2CpQpFKFX+kITW2lZQ2i+Ea0xOZ3xDCfEhWRch2qECHBaFhPWuYS+6CUzVT8tnXkS8CRYlYHXWOJcTarWSIlarqElsxqhQkzzttzwLCRZ11Fzn9xrrZCi8vwl2PvrO1F9/BmIkWG0rzgKzEvOGg1/yt5m+loQ8hb53JCIKfgumazTtXSugXZvIEzjJJHP1ZoqXb6648TO4wLVKoPwC5rInQeH0/IUkbwbDU+jWFJySMHBaNlSw5A0duatZMjUzE7M+Joz4/oVMlYPOaJAmfMrnzkNnxxOJhGixo494OUCgq5yPvl3lDdlScZuBNxXW00hbeRQMHCWjWwhNCtylnby2mHXtT+MAs8xxad+F2UBg1GUup4lv5FTL1b5AAAgAElEQVRaYnGvPS0zavIzF7q9nrtkNsaSD/F1RjuHyP+2KwgXNeoO27G34eWiI+Rqwj624WdPYWjDbrTP7ULnYX2YduQMMMZQVe41bCQlrx956EjeEmKW2Egb1VeMAoywQup+NAhRsl576WwspCYPCbOlpsaArmczLgnyls5exLIMwNkArgLwecbYdwHcIKV8qpnMLYSoJVNGinPmom3Zcgz84nZsu/m/MPz4gTfFP5giGhGYN7UeyU3X344/XvEZyGjcgadbMgXlob+/A09/8wHsfHATfnP1LRh4dOtEN6klU0hkLL+QUr4BwNsA/DmAexljv2aMnTRW/kmNEAlCiCwyNWChNVmSgxTZ22Mp0e5x3jB1ak4SoSIWzyjRVouIzaCs1GhCrkNMKPTFJzRBaf0FpucPEZnxk5M8F5UoJMmy8UlJPhPTjVbHBfFn1Om+mcFoUrW2JqP+SNN2zsAi0w/iAel+aIRIdbOWPM+rwXCxchCiTMRoLO4XY5h5/iXYeuu3EQ0No33eEnPdHD1DB6sV6o9QINX2sOEZiMRBhnRed5JHAJ20UCTtrkFxirQnUU8dJr5XhO2/eQYzzlutrdsihzOhncmpmWpNWZQ1ar4hTwcROI80qduEbXBRIENy1rNN432URinRL4161bhGXQi5FEG6fACY8b7LIa/7Pp75xE2Y//E3J/vsEK+zpB0BRiCMSwIdgiTuO7kOKChrtyxzeArbEjqOJrU4bW6GY+TOxBmTCbQvq38lUcAgE7o9dD1rBR8FRVzMcohot52uPecijUTlIG10H9E1GykUtIVTnow0jLUlEI+J67yxHpWxi3spFI0I93XVT86k7lfXitk47K2nIhyqYe/z+1BYvgBDDabRkVrD1/0ei1tDqBmFraF6XcQskdfh8lBenwuUhI8RBLkIUR6ZO5I8ZYI/lmsJe0wI7aT7QrtTIKOejGFwr/2BcFY5FYQx1gfgTQDeDGAbgPcA+BFi54w3AzhstPyTWiFqSUv2R6qbN6L/zHPhtXdMdFOalm3fuwtgDH2nr8gNev2nKLxUQP9bzkW4ffdEN6UlEywijDC0dgc6l83Eph/8AUFHCSPPDaB9Ud9EN+1FKVORVA3gLgDfBPA6KeVGa//9jLEvjpV5UitELofIRobyJrHmBCd1fwMp0/xUcEbLp5EboFQ65unS4eCAMV2+Jxm8OrMCtNK5xBOKt6m/MQWFJ8rToTuocYQM0WzCk2nnXyAkSM1ayOU8XXVtDWKs5lL+htz6o/hcFlm+i1R/fGVRrPlUdN3Iz1PN1GEjNHHbHBmFQ+QihkknnByzLrgMm2/6Kvy2LrQtXJJcGCY0QCNUMeKl3Rk01JhwaSFESc6Qtt5zNRdhxggu8kUm85RVzfBC7kGGEbbfeg+WfuZKVEVBowA0o9/1o7ux/YafY/ktfxPnVfs1byhi4Moq0Q8YfETwvKT5tBvgM+ux0KiKHuBk9zQXTQJQzi4JGdLIpfsMcQm09yFY3IuGfoayuURZSFEDPhosjBE7AKJBln5qjAjRKyhOUUFxmTxzccgyMyJnnGSBp18AyUFhTBo+nnNKSigPNwiRG86Dxr5NFDAspeGkkRVjw0egrPTIjYEbXJUQIu2yQJq6x+IMuY41a/UAlWKM/JAfG1cIGdIoZMNLhSvxUcQeGOSb7rmiQuts9wpCMgw/vQXFmdPQCDoQSh8D963HA09tw6rrrwKmxf646gohqodeyvlm6h5W7Yh8g9QAxrlkYixyODx1RaT0vQjt0kdFBKn7Mo8PFAlzTTRi6hzL46BJyUzA5TDpWoU5CFEW9zUvoO4k9aJzKOXjUsqb7B2MsddLKW+WUv7zWJmnFmGhJS0B0LbgMMy++M3YfPPXUd+9a6KbM6ZEe4fAfA/F2dmxCbd/7ef5H+SWtORPRPbc9TS6j10EAOg7YwWW//2l6D99BdZffweqGyf/czrlRLJD/3fw5SMZ+z7abOZJjRCJ2PFvmjNkm/C5KUmSOhEjDDlIkCspb9f1tH8e4XjR1kiRZ52n2sQl4FXNuS5vJtLRIcwsW+j4FM5sRYXmIM6QTnm+1wrNc+LJWYy0nCqRNRRzUBfXWziLGJjHYt9FlN3xAM40p0h1oar2O7wdu9zxWJnZ1oZ2W231vmveMgy/5BgMPnQv+s48zwytDhGiZp+ejNtDGS0v4jr4qBtYV/fDaWQk9f5U++kyckL+FLLBAFHsRDRUxfCWYQT93YmZfWNb7FKj47SjtRdqE2pGIYyB0M48PT9GiAhpEClug8orWYrvIyVPnZNou30r0uSVQqwESYSI+qfpa1xmILFpZMaViHPUGwWNCDHl3JM8xdPzIQrx/kZRcbMKUdoDt7YWVNeTkCK6rlbbNbjBsmfpGs3Ss3ipUT+NLikhRCiSAUIhDeKoTms0OEJltddQCBehLVSS64dM2n7HnPHTTlxda1Oytgs8hKpNtSC+pwjVIbGRIQCI6h4koXOqfw0WoC6ktmiMgiQqKaznQ9RD7PzVHzHvvRdg+73PQYYRSgtnoP9Ni7DlP+/Akx/6FrpPX4Wey88BD3xEDQ9Rw0Uq1XUjBIW4itoSL07tcCd5ITTo+lEYHC/yUEGAER6lOEgphMh5lmIOEfXZsYDLCakRhtyEDwppiYAgPxuKRRqttPa5aCTn6WfoxSCMsfMAnA9gLmPs36xDXQCajmI6qRWilrTkhUj3Ucdj0/e/GStEk1h4IUDnqauw9Ys/xvyPvylxbOj+OGJ85yuOmYimteQQiQxD7Pjizei75BUoLpg50c05oCJqDTz5F9ch3DWIDf94C0oL+sGKAaprt4F5HKWF09F+9GIM/PhuDN79BGZ/+Ar4c+ZMdLOnttigwtSQzQDuB3AhgAes/YMA3t9sIZNbIcqx4hnVc7WbJvgl6lAOUpSKf6Y4Dzy0/RCphPz05CBF9j5PAF7FbNMEJOVxXqNazCBR1GYXGSJLFsvzcL4kZ8okGoXi0FMm6cRfg8UdAtRYcMUDcmLMuXHQ/Fqyv9SZBLpEnKUMRMjdTnGIXN9JTlrun4toeBByaBisI44LRqhSAu0RZsaoywyti+Nys5x7jDmIkWRW86lcshrS1zjJKWoMDKHjxJcgDD09q6xv2oWdN9wOFvgoLD8cUT15w9ihNHwdSoOjwCJttSQV2S3LYk1YVjKZovvpwncZY1Ijslmy37Bnt85MN20J6iBGDQ7p+WAV411dh5vR1oMKHVChX4Q6TxY4ZNG5uejebiTvcW09aFOoHKQr1WZ63qzwPtoSk84JnXrBwUJP1ysqFTz3N38HWavpMeh5/QVoDAcIA0JBFFLkxDuEZCYWos2HA8xN5uynZsmAo6HQl0ghUoQEkbUe8VoitV+GHNDxG9VDVOJA6BkeWYZHcwAIB4YRDVbRdfrR6LvkFSjMjknUUkqE2wdQ37gTlQ07UF4xhMqadXjug9eh5w2vRseZp4JxDkZIkQ40TYgQWVKqfikr1zAjfFKWVSBguFkeF6ixABVm+Heux2pdpFOmlCwXIaL6I0L4LJ9m+l1L/XMRIv0+k4kUzEKN9KObjU6+WERK+TCAhxljN0opm0aEXJncCtEBFulZSzRTUMK6p0mlh0KEn+EgcxIJ4xylWfMw8txatK84avz5I2bCkxxkifYOYuQPa9F5yiqgEK8VV9Y8CwCY97kPgzF2wCd0nAv9Aj8kIhhwKF/YkuFQmtKM936RUYTy4UsRVSvg5SK6X3MO/N7u5vMLaGOIQyGsznUA6fFI0N+Nw7/z8bgMuzzGUJjVi8KsXpSPOQK9rzsNe379GHZ84bvY/Z3bUHnoSfS+9fUIOrO5dQdawogf0i8iL0YmHNBEyBTSnRhjN0kpLwXwEEuuvTMAUkq5qplyJrVCFKkIBpKizhNPx7f26bhjKvVyUot3krRKykCEaFt97L16uh4TtDJOCSniMnkeEL+Ts97LLghin69nd9qfDfWDUAhjIQZYs7F6/gOmZ27EQ7BnKKRH0bq80zgTy0wiCiREGCNBwjd91VZnRO9wJuh6hsqSv5OdH2M7Iw8NbcpyLQL6j3kFNv30JswOBbqOOFrfDwbZY/AFA9dRZlX/IwkZyHh2nUIykv3M4quZ9quZG90fCuGguEzU9pkfege2/9vXseeOh9B9buw/rPOsE9F51omQgikUILvjUrJE3DHBGKoKqnStXWwFqFhogHOBUlHFSCOrF+UcMvKSs1rNdeDWc0A+rVwrMxdZ4TK+zwSDQx2yUCUqQ50QMgAs5q2FSWRID4H7nOv3gLKplMwgKMQJc1JdlvVecJEh3VT9wLFEvUzGfWa2nzDNVaIHgoEJpsvw2jsw86qrIH1prDoFwcfZaIsbw1EKqx8pzpeDitB5DYCgBVe9IUPAiIIDE29Io0PcXL9QXRcH7aQy9VBYPq90W9z3i3ontZ2wGjM/Ng07Pvct1Dduwdb/77OYdtEF6HjZcWCKZyUFoSHJsdFWWtL0KuW52R0T8grtxc95I/RSvop0Gx3uUF5fsoQ4fhT4Omp4ECEDL0bGwz8heS5/TX9zLJg5h180HoRoipndX6PSC15IIZNbIWpTJD0Nk5uXEC1Nucs1uYpR/G41v5FBnnZSz1K6KEQH9MMSb9I7hu5hveTCTN3jFgF49OGkpTn90CVNdskxHvOySKvpcgGYEBTW0oF+IesI6OpcGk8aKw5IX0JEUr+QaBypjaQI5So3LEOpcM/JkLx3G9dLA8n9jAHTFq5E8JoOPP+zGzH09OPofsmxKE2fA68z9lEko/ha+rTkSQF2I2uZIqAK3H6Q8shS+/OWZbViRNcPHFJKDP76PoRbd6G4dClGHlmPaHAYbccuBy8EyFsqltYLWjt5a/iowuSRzkuctoPAIIlEIi0Ukw71GirsSajMmkP1tYwaXF97/eF2XuL6pU7LSHVull/d/uQolDyKryELzT3mLosKJ7AwKfR2n1NuEtwPgdN2+11hCkucYpoe0jtJpj+2rpsGFrch897Qik5yMNxAu4n9rgLUpDAJreDo94na1PG0SREiEnudJfNTvdK8Ryy7/Lgsen9aH+m8IL16KTDiKC1ZjFkfehcGbrwV1aeewcCNN2PgxpvRff7ZKC1bimDhLPBiEdCBtWmSQZV4ufWkTfnpfSohPAYBBqnGJOcW0GKTr/Vz5pxDJGeunXXG+30vMsuSZHZPCjRPPkOktOnlNotS4ZKrhb3U/yISKeUW9XMngIqUUjDGlgE4AsDtzZYzqRWilrTkhcrw5rV49pYvAADqg3tQ2fY8GoO70bH4SHQtPwqFGbMg582bsPZJIbDrpu+hvmkTZv0/78LwvY9g8H9+j2jXHnSdfxp63zC5CeEtacnBkGDmdMy85mrUNjyPod/di6Hf3Y29P70De396R+K8wpIF6L3i1SguXTBBLf0TlqmFEJHcCeA0xlgPgP9BTLS+DMAbm8k8qRUiUSJsWWnGhJaEBi3SSx+uGbdLtGVmnzYTrzupiwxpx4NSI1FSTTtpCUkjQy5CxS1o1VoCG02oDzy0lqF0PmcWRCnNrDwgz0RYiwV1A2YGKwOpoVxGM2wNySfhadFgMUIUSD2D0aEzqi5S4rTVmuS6y02ppo4GQ1M1zrXnDkIkRIjnfvYtk09EaJ+zGG2zFqC6Zzv2rXkIlW3Pgy87Avykc+F3dCXcCwi6/kSIDBwiLUvO6BJtz+mfRq9EfC/t/v4P0di8DbPe/ZeobdmEfT+7E+VVR6KxZTu6XvnyGM2jMtwlHmupgGbYIThC6cE1madtvxh3Kg7dobrjBAnt8ONz6p5ykqcQoxoRUT1PL81q1wSEGLn3oDZtN0tFqck7leEgbixkYIyBh8aNgUZd6Ro4gYU1QiWzlpmc8ctZXkgsd7suFqgaByVExNJLKY4xAXwk74eMezyFnrnLX1Y7WOpYMk/ukohk2mkrIUWEiugsDjLEpFWP1TYmrTxEDtbr/RaalQtm0fjxRHuUd1oUFyxAad4C9F92CUTUQH3TFtQ2rMfIH9agvuF5eNO6sP1fv4GZH3wrCocpyzQGyCwPpIlak/ccZBxWJ5JeyjGiu+xGTj/Nfpk6R5vzOwgRCWPGCSc5o4xUcG/X6aJxcKmQooinzO01euQG/X7xCZNSjjDG3grg81LKTzHGHmo284sTX2vJi0J2P3k/Sr2zcPS7rsWSS64B8wMMrLkbA2vuxuAza7Dgwitx+Ds+hvLshVj/7evQ2LfnkLZv4Oe3o7p+AzqOOQa7br4VO754A/ouvxiVB9eg/+1XwOvuPKTtaUlLJrOwIEBx0QJ0nXUaZn3wLzHjw3+B+obNCObMwI7rvoVoaHiim/inJXIC/g6+MBXE9Y0AblP7mgZ+JjVChLKarquZubCcstHM0J7RAxaxl9ACi0Nkk6SBDPJ0BjIEJM3EtbNFbcpO5TtcCg4QP5I4ELmcIkI47ACymiORPJVpVID6R8Qdabgs+mSrPFiTd+JtEhrkSRMc1iXpJakGmhslhTTIkEOodegOqfYw+1izExoLdXGRodS2Om/PMw+j74gTwRtAV99CzD7+PGx94OcIhwfRPnsReANgnofpq07F9r3DWH/jdVh02V+i0N2bcCvgmmcbpEj103Hsl+kElIaVJsJ79mHf73+Hvgsvwo7v3YT+174Oveefh6H7H0Rh0UIEHX1ATd1j7rWhoomHZBHpJfMghKcbQKgFd6wPGZPWTFSdo7Z9NZDFYuxRkwJvEoJUb/ioUdgECqmhCaHqWW0kZ6xxuJY0whD3Q7cqmYQMjCt0iIinLvLrJe9XWHydBAnVkjwvA7ak0Cxn21xfphPmQkSO40T7Hk4U5u638upnmt4zNio0FprkfoAcpNZugkaKWAYylFcf3egWKgdAG2tosnUWEseSP5gTHicTUbSMJQCgNHcBZr737dj6qS8gmDMTO6//L0y/5iowj4O5BP0xkGgpGSQYhGRgxCtyUH1NwM7hCwGA71EAXJHcZmmEiIRcZNR1cFeFxBJqpi15DVJE6BXxj6RLrH/xyjWIPVN/X0r5GGNsMYBfNZu5hRC1ZEqKiEIMb1uPzrnL9L7pK09F26zDEI4MwWvrRH3fgDl2wpnoP/bl2HDT9ZAiyipyv0RKCVFPRhaXUYQdN9+EjuOOQ/uRK1CYMROiVkNt40bs+81v0Xf5nx2w+lvSkqkswfR+9F5xEcKdA4gGR7D72z80lnotyRWyfD7UfwdbpJR3SikvpLhlUsq1Usr3Npt/UiNEhbYYMqFZqDYD9nnKmRWhOZqp7yJHDYPAMIcrRIiRCUGhZjwWAkHl6wm/M/szzrOgU6rbC+O66DnVJstItkPPViyzZm1B5cyOtHWLFTIkhUq4kwba1sgQ5TUIkXYE585QaaLFJBgXkAVhwjbkIEGjbudNZEaZ4OQjQ8nrBQDh8BA8v4AiL0GGEmAAh4dlr3oHRnZuxM6n78HT/3Ut2qbPQ+dZrwPvmoXpK0/Dzgd+jfqmzSjNnm/uB5VG2jll3Mio4Mw+VVpZtxY7vvNfCAcG9JS+79JL0Ni6DdV169G+ahXC3QOYdeWVYNxDx3HHY/iRh9H4352YfeXbUOjoBerWDJX4OWRRRahIzbo2dJE9DkQGMWLkN8ZB/JiFoLicBUp9dfOVVOwVQo6qXGhOBAXH1CEeyDpJm7ar+iIT3DjPai41mVdvUenLBPIKIG0Wr59HG3VxbiYat7wXM2WNmIVKJPvhtjXxjOUgtIkKrK9CAkl10dzMk6w2yvQ+2nY/PJkfIgc21udkIUNUh4voyWSXdDXaEaY63x6XvOdb863MeKeuQQpFi5P2o49G7Ym1aGzbjsam7dh1/bfR/9bLwYLAsgB1B0UltqsFxpSLiyTaqVPdP6cMJg0S5CBEAU9uJ5rgtKmoAvzWw+zPMlk6R4zrQMV6hUIkEdkXqyjLsr8CsAiWfiOlPLOZ/C2EqCVTUxggRJSJ9rT1z8P8U/8Myy96HwY3PoWBpx5AVK9ix4O/AgNQnr7/YQOklNh5y/fQc/65WPQv/4zpb7oCALDrpluw787fgAUBdt8WL20zz0N9xw7svuO/UXtuA/rOfzVKC1rWMi1pyXil5+LXQIxUUF55JMAYtn32PxANj0x0sya3SHbo/w6+3AzgIQAfB/Ah668pmdQIUWdbPDOt1gMAQMNXflEaHgTNRLVLe0KGaC06TrhGUpieYqQQE5VqDo8TroIJy+GaO2Hjyf2aYxRJjQ6wKEaftNNGmkTzRJYk0pDTxlzujWApyxfX14/mDqUyG0smTYdxp30ZfIR8i5g4icrJejVCIIxFmOZv1ZJ5M8VBy7KQoXgHUGjvRrGzD3s3P4XuBUdm+AoCmO/BK7VjaONT2HTfb9AxbykWXXw14HlgMG2k6+QGovUUQiOlRHXDOgw9sQa1Tc9DVqroOvJYsAZDee5iBNNnoLFjOwCgtnYtSkuWoPrss9j1gx9g6JFHIBsNdB73UnQfeyIQ2TNHBxmi+1HfsC6hxRrDHG5IpMpuMM84ngtGdzcu1D0eKpgmEiwVosBXyKKn0lBZqBENQnBovlsqKK47ebeeP8kUWuo6q3PymiGznnFCP8aoT9drIRKpe9ZBVGhz4Pbbse+u38Pv6UVhzmx0rFqF8ktWZNcjWYLPpK0TpZnpM+c5Z0jWa/ePOfsQJNFdbWHoIB52+enOq+MabcpA1eg3lzFaSdVprptznkDq/bg/Yt4fdP+bMhkPMP3KN2PLtZ/HjKvfguEHH8HWT34efX9+GUpLDjNjREg+cdLssXE+2tJBNIX2AWd3DJDKl5gtKTqZa6mWAdvR9SwoK0+ukN4sn0q+cqZKPsKiMOmrqCk5BEtYEyChlPL6/c08qRWilrTkhciMI0/F1kd+ia75R2Tg7UChowdHXfV36C1x9FQbYJ6nldX6vgFs/e1P4JU70HfKWfA7upCH99e3bsbG//gCCrNmo751C2ZcfJleJgh6ejH7rVdDhFUEff1Y99G/Rn3rNpSWLMXe3/4GPWeeg713/w7957/mYA1DSw6SiGoVe++8E3M/8AGIkRHUNm3Erh/9CO0bN6LnVa+Mj//qfxENDaHr5acB8yfO39WLQYLp/ei/4lJs/9IN6Hnt+SgtPQw7v/JNtL/0WEy78FVgQTDRTWzJwZcfM8beCeD7AHSQQCnlQH4WI5NaIaL110JZWbsEcXOrdR9h4Hj5JJ86lFK4AcU1YhYvxyMPp443azuYK2AjRCzl48Zwh9S2TOaV0sofSfBIGmTGmdihmCzL5hCluRPZ9TLYCr+a9ag92iO1g+DYPo20HxJCW3Jme1KwGG3JsDrRLlmoP0QxipLHmQCkGms9nuRyysS5TEkzyJBdZu/SY7H10V9ixxN3YfpLTomP8eQ5ksdIEQtMYbsfvx+bf3Ur+o97Bep7duHp6/4GpVnz0f+KVyKqVdGx/CXgxRLE7t3YcttNCAf3AgAOe9sHwRiPy66aNvmlnvia1iU6Vh8H5gfoXHU0duwbRNcxJ2DP7+6EX2zXqGbKQsbhBbizz1Et9Wg2XU+GarAxIR2SQCFFepueEzVVJm5DPfISQTEBY02j/R2pbfJlFHoepJ/0sp7y20NoLj2zHmK+m0AKmUlZ77koJbNPUrui5PPnSiL0i8sd0veW2s8A7hfhtbcD9QZKCxaitHAh2o9ahU3/ei2i3btReeYZlA47DMH06djyhevQefbZkCecAF4uJ8YBTKYs0lwUUD9Luh3Sgn7VPnpWPctdPszzkvCVk2d95Yxjwpea3keou4z9/TheyVPWbsmXU7Zk8Z8y/B4Blr8xXa9BjNpXrETw7j5s/+a3wTyO7nPPQfXJp7H5k59F32UXo7RiSVyP5nNaSBGLETzt4ZzQOLIoVgfID1GkLgr3hClHtVl/Y9R25FjajYYQlRykiEVe6jyyRCspHiOF3SGfRs3IoSA5T4D8uUrtZTIJYHEzmSe1QnSgRRQFeG0ckOILFMkzPtoHUZjMgMKnkDAhzRJlM+dzjiVnX4ln/vsraIzsxezjzwN9AcLqMMKRQdQre8HFMDZuWAuICGF1GCNbn8PSS9+LYv8sSAZ0rzoBG757PTZ+9z8BAH53D0qz5qKycQN6X/pyFHr7wbwAbIxIm/XtWzD0hwcAAIP3342+cy/Atpu/jY6VR+/fgLRkQoVxjo7VqzF4//3ou/BCAIDf2Yk57343Rv74R3QcfwLKi+P3cNfLToS4525s+fKXMefd7wbzJjCo5xSXwuxZmPOh96Gy5o/Y/dOfI5g1E92vOgs7v/VdtJ+wGj0XnT/RTWzJQRIp5WEvJP+kVogMUz+eEpT8mORT9ANUGjH8GZGHXkopQCV50tX+SzjQYBBFYWLgUEoxuSg+muOfiEXGQozEtQYhiyQdY8lj4GFsHcNkrBhxmZzt0cyG1qpDy2LMnfW5AWkzqRQMCc+x5M075UPJmcFJaTnSJsIMjRvFNrORIpmsn36H5SSqZAKAUtmq6HpaqXEDs+YhRUzIMZEhezZfnDYDh7/2Gqz9+X9i6Kcb4JfbMbJtA8J6BUF7F/y2TrQdsRKlvplgng8vKGPu6ZcgaOuCVH3rmH84Vrz/XyCYhKhVEA7uQ2PPLsw84VUoT59r2jFitcNpk/SAAGW0LV6OkbVPYtYlb8TA//4CbctXoP+VFyT6pLlLGg5IcoXIIlDP1D1rlu6MiZYMpCiXOeRwiggxCtW90Qi9lFUNPaOEguqYacobb9ULNAfQ9ZGkeQ/6JlNoU6S8otvXXD8XSX6QBoaIf2RxiAxSQucgsZ8kcQ86NC5nU293n3gqnv/cpzHtjDPgdcaONIPePnSfeloCVfE6OlGYNRviwYcgQwHGPIMQCwtCobGhh5SQKjps+V0yHMEkTMYcr90pLlETcwrNibHrd1E4L96QDpdtVOQhZ8aWQviaEO1x3+UwMQbOPLSvOArl5ej8SSoAACAASURBVEdg1023YOT+hzDnw+/D5n+6Fu3HrUZh4ez4VG1NG78vpGVhKJ2LzYj7KQnBpOfQeI4mdI4Qd6HK1zHNyPt0Vn/I6hOEEMX7Ay95NodBiOgbWQ7ij1bNH8cnfQoiRIyxNgAfALBASvkOxtjhAJZLKX/STP5JbWXmOSaLJQUh9pZG0FOOvz5dajmtoxR/QUvF+MYISvG5ngpYiWIEqUKBiKK6KdUHPGqL07ANmWlUMstAlAq1HK0fmowlLqNwxbv0eyvKTrn1HRJaaXIGxX1x2O8+eiadpb9U8FpnP2swHYWd21GtrVS71KcOC/PiiErxD1GicVXjrFLalgrejQpSj58o5KTquFkeG1sRIsXWpPH+oK0DS1/7l+g74qXoWrQCSy64Givf/kkcccVHsOTid2HGCWdj+srT0LfyJPQsWY2gvSs2+7WuC/M8FBo+/PZOdHbORdeyVejqiJWhoBKf51upslSHb6VeqQ2FchcAYMdPf4ju407EzLMuBONcuXZQ3XLuBzIM0Aq6E3qFhayp0DDxwBrFiAwTGjW1FFY3S9KAcRRHkw2C5QM/QkEpTfRMFvwosV30k8c7y1W0leMbs6TSQIUR8ZTTSEYBZynQMDnADIQJPpxSep3+aUXC9DX9vCXH030+7GuRJvIn06C7Bx1HH4u9d96ZcnxqtuP6Kk8/jd7zzofnFZLnRda1zElB19wKhZJa9kql6tnRYXlUhb6I/+i3nXp0rspLS8mB0O46pD43Z9nNbUeGODpc6uOcUMDGEP28NMzzoPd5AfouvQS1Dc9BVKooLlmE+qYtifdYXIblFiK0niuYa6AdTpISFRlTd6JqRDpgKxkgqNR5hhqhp3+7zxc9d3Wig6i2Fj3zrNFyWkHtKyuwYFq50tygTV35GoA6gJPV9kYAf99s5kmtEAH5ShGA8StFwH4rRcB+KEUwShEpOAdTKbJ/j1cpAjB+pcjqz3iVIgDjVori30hKk0oR9wvoWX4cepcfj2LvjJgf4M4AnWth/6bx8h3lJ3BS33of2UrR0Ian8ORnPoo9j90HAJh59uvQf+zpcdnWNdhfpQjA+JUiYL+VIgDjVooAjF8psia841aKrL6OVykCmleKpp10KgYfvB9SiFGVotK8+dh7568RDQ9rJcm+n8etFCUqQCKVIkL1qfUYeWANRv7wJKp/fAL7fvF77P729zHwjR+i8shTkIQ8NKsUAWmlyD94SlHuvqyyHKUosc8L0H78sRi6536IoWF4nR2q7KSCA5jnarxKUfx7fEqR/Xu8ShGAF6YUKZR/qjlmBLBESvkpAA0AkFJWMOpdmJQx8TXG2HwA3wAwC/Gr4ctSys8xxj4B4O0AdqhT/6+U8qcqz78AOAPAB6WUv2aMLQKwDsB7pZSfV+d8AcD9Usob8uomGJ4cxJFjuIIXoU1d/JEw/mqOeHHqBtPTjuO4p53GEbQqKGgsmQMH6ianwLEq5b71IaIXnUWeBgxBmXvWfoJeOYv/LDN8AOChaofvQPoh9JVxwwyknKBZ4pLBufuBJyJhBrpED5zWPUiBozz0Uvaknr2RUumGltDwMRENG7RME6ccNmRML5hkG92gml7N6nGOEpS7dMad35aQAzh3WZFJq5Fq+S7LYact9rKmIW/HFY+sewYA0HXYSsw84RyU5s4HqtABZEUAs5TqXlx9vZz1G7qQnvW8e4jHcKypjmBAjUjO8a68JTRSSmj5y+dCw/jusrYmZDtaigDTChO95GuOA7pQn0t94SpcgjRBhqmvdD86yzU0NIylFZq0s0gktu11sbSDRJU4TlUlgML0mfBKZdQ2bkRp/kKkROXtPP5lkHfdhZHHHkPX8S81BG1p9UNPrqh/Tv32/ZlcgYSs1zFy3yOoPvEMKo8+Bb+3G17fNCBsQAqBYGYvCnOmQwqJ3d+5DfxHZZRXLUV55RKUljntpvqEuZ7GbJ8GJ34RmGWn5LVoSrKQISC+VnkKU96ym3U96Z1K41qcPx+VJ55EtHcfvK5uc09YZTLJjEJkN5Haod/t6rpZS2bm/lQphf8gmkRG+JKUew2V2ApTfJ76YSlA+pvofBuzyNovMqkzxspQV5YxtgSWtdlY0syCY4hYsXmQMdYJ4AHG2C/UsX+VUn7aPpkxdoT6+XIANwD4tdreDuAaxtiXpJTJWAYtackUli0P/QLbHroDALDw3P8D7vlNgzkt+dOQ0sLDUHv+uWyFyJLGzh0oLz38gNdfffxp7LrhJhTmzUb5uBWYdvE58Gf0ADBWUUTRkhLoOvck7PvvezDwzduw72d3YeGXP3bA2zSZRIyMgJdKiPYOwuvqmujmTA6ZmrrTJwD8DMB8xtiNAE4BcGWzmcdUiKSUWwBsUb8HGWOPA5g7Shaao0okoaodAH6H2CzuK800znVmldivDhFSZNrLzDmOkKZNzun0R0mNApE7JSE2CjESIUstVdC2cJY2RGi2aeYZFoFQWDC9YzbqLsVFReigrs0q/ExkQPo5y3LSOS4CWMtGarbnuCTQ7RGIZ1KCmVk0DSQtU7mIEZlRE/zcYBra1kuJhIjpa5HcD7AkStSMZNw+WaiAHdYlwZ7Vyx1JZIiuXwo1sCfIMl622LPpCWx54HYAwGGvugrccx45a4lFXx8agzy0jlDJBE5hcyGM6bAbEDYhNLOnJQMKHKxTgvFVLerDKqQp3/eSg+wiRPa2r1Cl9kI8HzJobpzW1Wy3oVBdEUlwFoHz0CAVRPqn+5SuY5Tsg+RmmZE5YyFdpI3EmbADSC2Hp/artDh/Pqob1uU8sPFJ9Z074Hd1we/p0YR9qtZFfvW2XnpLru9Kq5HRwC7s/NKN6HvHG1B+yTJDqpaAFMIgUVzGTkQfW4u9P78L1SfWo+u8k9Hz2lfod6OnUDwhktcR1jXXaDFBxbSZg9AyZu0kxMRFfWiT0Bd7HB1kSLpuN0QyL6RBZGhXY9cueJ0dEI0GeEcZo0rGfZDsmNMHFYQ4PiKTbdQhnfKfQ+09Qd3/ghEhO84TqtSj8BySIVI3RigcpCgjRMiLSaSU/80YewDAiYiv4DVSyp3N5h+XlZla+joGwD2INa93M8b+D4D7EaNIu1WE2TYAv0XaZfY/AbidMfbV8dTbkpb8qYkI63js1k+hPrQbALDsovehfcaCFjI0RcXv6UX4yB9GPSca3Avm+6ht3oTinNHmlOOTcNcAmO9BDFcw8sAa+H2dKCyej3DrLmz+yGdQPupwtB13JGTYwOAv7wcAdL/qRMx41+vBS8UD1o7JLI1tO4AZ01FcMC8ZW+3FLFMQIWKM/Y+U8iwAt2XsG1OaVogYYx0AvgfgfVLKfYyx6wH8HeJh/TsAnwFwFQBIKd+TVYaUch1j7F4AVzRTZ4+K/eArVZx4CvY6qad+d6q0omaXFTUTrypoo+H5aCi4I0SSO6T5JTRDV4Q2aRPrNKqSnImagLA0Q6dtaA5Kl+8jLKc5PYS+RIpELIlk7FtIRs5N60QSiCdqLiJEbgRCc47VXeNZn1sTONcBZHJiBykl+uCDSWaeJ42YaOgiUYZBxlQqAKmJ02r2o64FcbAISGEU/kMax5kaKUpxhZI8Eps3ZNCdZBshgWmBB4/4UJTXXsZ3HUtSEY4rBC0c2LNxLWb2TQOfMR0LTr8cxd7pcR7btYKV2nykvHPynHMKbmagvdyPkTon2HBecMv4HDWbJR6CGhwKJqnvaTUb5VzAJ7Nftc8LydxYlemY33MmEID4RvEN6StCblXNiOsqbXDlQkMw9ILBF55GiCIdxFLVQxfBQbkQMjAKfqtDhiSHQpOWMzhEemyc5yL18LD4mfC3b0XHwoXoKwQZyEKc9C5bhupRR6H6i9vhL1iI3rPPjR152hw3hyTtct4Mx0gawOKIFZj+xstQffpZSCkR3rULzA/gDQ1hxdVvBm8LUHt2I7gHLPyLy1FYOh+uRwdPoUq+n0Ti9PWUzCCHKnOf9BNIjgkZ4qB1iYIc1C7voywzfrtEdPdBt2E2Hd5DYN9v7kL/0DC8IED55Jehq2B5rLba0cv9OCSKjfQCBmXNcydilaORIf1uUgei5HW1xQ2qrJ8ptfzgR2pbmvPINJ++hZwny2hGphLdiDFWAtAGoJ8x1gMz0l0Amg5O2ZRCxBgLECtDN0opbwUAKeU26/hXADRl5w/gHwDcAuDOsU7c7cVseYIBCxkKER2jfSOIb/ZhZapUYfF2jfmoq+6GmvCWZCxqmF+t45iYZtx8FF0LEMcKyChI5jcLgB3VRor0rBUi9WAJK9V5x6EQpTxsu5Zr9EFwyhC+tUzjLF3pj6/+OMcVbo8aaW4jkQydlzktj2krtropT6ixp7hg5H+ILLTscaBjfjVZT0ohSu23FKLQgdvV+A0MNpJttxQid5lNK0R0jjUOjZF92LLmV9j+WHx7Lz73bRjyp2FwOC5fW9bReJLiZ1sXBsljrmLkXhNBHp3Vvu2NhuV/i9o4mkJEHBN68aptSunbom4gD0KTOHWq8rrLeaQYeUzo55esYihPRZpn1E5p4rIdDV1OpJ/d5LJeSiGKGDhZYzmKj1lSTk5iEgqR820fTSEaevgh7L7vHsx6+9XY3mjkKkQsCBCecRa8agPrv/LvGGjrRPcJJzqk/+YUIulJy8WHBFYuB45epoYgQu2JZ+F1d2B4/uzYemz1UniBQOxTvW4+4KQQkbKqqO06bpfuArMML8xsaZu0yspTiKRdULMKEUsoK3Z57nPo+m6KDSLifbt+8H3UNmxAY89udJ9wDORJx6ISWtfIfg94DNvCRr5CpEnUSLQj3lB5SGlJPXejKES0dKzyeupj46s0UDeoZylO9Ju+e2TcMB6FaIrJ1QDeh1j5eQBmpPcBuK7ZQpqxMmMA/hPA41LKa639sxW/CAAuArCmmQqllE8wxv4I4AIA945+bvLuqQvzlaIbQQeXJLN8Lxl+IItL5CnrMu5o5sTuj6L4BgwbRjGiGZNwzZ3VtrufhUZJijwg5EhbJzkcIqE+ajzD0sGV1HOWxSFyLbcchEPrDcIoY8aqTGV1P8YeA2css43EVXCDPBpzfjov/ZEXDppFY8K06b2t6KiPbNV5+DNm+EA83l6DxjbZT0gJryHB1XFtnWKVkacI6T6o9oiogR1rH9DK0MIz3ojuRXGgzzwHm+QewVaI9LtaJtOkQzwjHEYBYaEqy7WE0W4IrFk0iaNBRsT5srgngMUr8Uxw10jdOKHDXdDIEClZXr4FWnch1n5HeHyxyeljGHGUJUdZNnTd+hml51HNJuj5pLYj9FLBjVPKxSjeF92PYTqvKWLvPXdh2jmvhD8tJuumJivO/cj9Avpe+Wrs/NGt6D7uZWCMpfLkbSe5foRqJivgnofy8mXadUGqydZuCsbrK+5QISAOESm4cdmxWXjScSaTElxKoyDYYTBgngsGZhE23Q6l26TPc5Uo4ZRPpzovNikZICTCPXsxdN8DmPc3H8Pmf74WpZXLwXw1brp6Ut6smt1Xm/s+CZ37RUIPjQ4krMdC7c/ysO8lzyF00OXw1ckNiv5eGQXIdfj4YlWIpJSfA/A5xth7yJJ9f6QZhOgUAG8G8ChjjBbJ/y+ANzDGViO+HdYj1tCalU8CeGgc57ekJZNaRFjHH274CErTZmDJuW9H0NGNcm/TSG1L/oTF6+hAdf06dKxuPgRLefFSwPMw8vQTaDviyIPYuhev1J5dj+LSxeDlEsqrVmD4rvtQeP2rJ7pZLTmIIqX8PGPsZACLYOk3UspvNJO/GSuz3yIT6MNPm2wjpJTrAay0th9GE04hadbbcILb2UIz0wLBjCyJFIUWM59mniSuVh2SM6wMZ3RmRkqIEE+kNGuhbRYyjRqFXCIsSLP8QxYyNIsgFISseZiZ1eZByim/OcI610WKXIjXKZNLc0w4y3nMWdoRHsC5QntygSzqnyqfkCGrLOmEBtHLh0U1G7Ks2qjNnoOu0AzUd5EiOs3y9+SR4706LZnRoDDwRgaqltE3occg2T+JCH+44SMAgIVnvwlt/SaqeQoZUuI6zmQRzLV0ESFnecP1fQVYbmwEy0QP3DLjZRqZOEb+t7TFDM24aSwiulZMI080m414conFQPjqubAGVOTcONOKCilS1mj1yENbxNHB6vrZbCgkw0WKGgrNpWdOcGnxOJL15CJFFizjInm5S2YAeEcbhu67D30Xvy6ZR6M5lMdAT4wx9Jz8Cuz53Z1oW35kLrpJ4k78JSyU2mm/vuekc31HRYaSTjQjuq5Z0Jjax6UEh7DuSwdd1c2T+lk1ZSQ7lN1tZyAdxEh3TDfRGsSIob5lIwqHzYYoR+h61cux+RPXovuSV4H55nvC7F9cxn/CKZ9EI0PJpTsAFm+SHs7ks+Rae0r7/qTnzbGkDB3HYNoajUmEPHuVY1x88SkIJjHGvglgCYA/wGK3IvalOKaMqZS0pCUtyRYRhZAiQm3fLgDA8ovfn1CGWjL1RVSrGHl0DWa9653jztux6hjUtm5GbeuWsU9uybgl3LYTrFDA1r//PFihAF4qItq7b6Kb1ZKDK8cDOEVK+U4p5XvU33ubzTypg7sSMjSa902abbpcIiJwkp+ighelmflOqt2lK5II1d8Qnvau26Bjjst1odLIQo6IlC2kj6gQpV3wJ/gAY0gOemAjG7ncIXe/M5y2p+aULxxteaeO+zFC5CXdP8Xn5Ky9pyzXPJkiCGuv14SWOd61mWDpmWeKUxRvEnGa0JeYI6SONRyuixdzIcwYGeQoTV5menvfxqfw7G1fxJyTXoPpx56Bo991rd3c3Jk+EcNdb+V2yBaXLKrROwcZSjA6iIMVxWXpwK9uQGEL4tAevgml0lPQZEO0VQ2hQpKBEceEgld6SU6DiwhIydLeq3P8jE1TnKK68NAVeqiwmn429fOnosVTkGd6Du17Q1oz6uRBJPpl7TH7XQ5RRlOllNh39+9RXLwIhYVz0kQYTTK2ipfWc+/56H7pKdj7uzsx45LLVJkOKkLi8lgsio1GnH0nT454vkghQ0UddiV+UPQ4ay6haYDmh0EohIiQDXVc3zc2okL8Hqcx40AptI8ibVGlDmTwZ0TEIaoj2HNLvJARVXZDjIzA6yobP01W9Yz+8XQjXc5Qwpu2Pim7Pylk0TObpu4kmhtpzlDyeTRBfPP73jRCZN2HU0zWII6qsV+zjBZCdBCl0HFoHXJHh9iliBu64mDLeCICHGgJaxVIpR3uWb8Gz972RQBA75EvO3CVHOL+peLCHWSpj7L0fVCk7eDdoMMPPYznP/L/Yt///gZdp58W7xTjv4DdLz0ZQ489gnBocNx5D/UHrR4e2uvnKtDjle5Xn4rOs08EKxXByyVASrBiIT/DBL5fWnLApB/AHxljP2eM/Yj+ms08qRGivBcot9RwT62lEmfBePCM9xe8tKl+nriIke0fgvhH5UI8g6qrOEwN7c03iRyR345CRx1e5MGXDc1voICAwg3WSF6wI2l8IGmkJIkWZFqZRbFS5Pof0uiO+wF0uSqIfQQlynVPceu1vzk5LxQ52jvIbZPmrdAsyd7PbBpGKiVzdbKAI3RECKZnfSbuksrrsdhqSrsdcFAEIbDt8d/i+Xt/iO6FR2HJOVei3DMLC896E6YtXQ3GuYGFrKzu3cY1IqROjZLIEI8UR4lZ6IqDhLmewbUFC4NxC0FIocu30KihOuAxSD9GxrSVUsr8UF0DmsEys9vkoWIVhyhniiUlQ+BHiWdauF6unRuoza+jhALaWENzA+vko0VtU7BL/cyqvHUmIWo+0Bbp58w1305abAG8ZpBbc78nx4aGqPbsenSefCJ6X3NBvMN5Tu1z7ZScO9N20NaBjpVHY/ev7sD0Cy5KoRCjvrWYaus4p7VRgxvLWkUS1K4VlFknvddSQUctpSiKOCLJE96sAQspkvkPhEaZPJE+N+O8uI3ZoxFu24locATBrD54nW26P6XDF2DgW7ej67xTUN+wGYVFcw2nh8ZZ862oEqk5PboNmhekduRxjGBdCzc2nfuutbO6ntM1JSyZ2bwTZeJZTB5LNSlfpiZC9IkXknlSK0T0MI42UyCI3gSZFIltIgVySP1A0cvTDYxH/on00lmkQghIZswc1V3UqQigtKxWU54EQ18pO9bLvS3k6GBVa5ktGQ1ZE7P1shszy2hkxt8wL+v4ZOfBFrAiM6uxcR0y5ilEMB9sO/RI4hxr+UsyJJckXCUNyTyp+gUD0/6AkgoK3DLtPBnLhHFj44QQMrMcR2Vby0OuMuUxRAWGyFm25CEQVkfwyA//CVE99oe1d8OjYFKi1NmPUme/6oMhjOplKZahG+q2q3vQUVKFn/YBJZzU/Uja/bcJvCyyhp7y6Be/URb1PUo+jLh7wWgz+4UNGGVbk7d1eAwzMQDiZ8h1hRE44SHcJTTOJIqRj7biILjyL+ZOWijPdH8IALDXiz15DvsFVBRxmJazpaMIaeeDFdVWuq/qzFy/jOdNNBqorduAzhNfBmYtHefOuWgywyyF1ZL+c16N5/79M2hfegTajjwyMSZmOSUtZik6e1kvl1QuGBrV+KaKAnKfoNIoqQjp/aHRujwK4RIxCMkhyNWB677AHTtY96nn3nOmbWbDabelEAAAuEQ0XMFz13wWwbwZiAb2AR5HMHs6gtnTEe7ag+KyReg69+XY/LHPofdNr036RkqMkYT2P+WOl+c0RK9dWyfSTzdUh0vAbgb1cscvtfxs1edOmsYuPV3PFBIp5a9fSP5JrRC1pCUTKYPb1yKsDaOtfz7K02Zh9jHnTHSTWjLB0hgYwPYbvo7CzJnofOmBWS712tox8/VXYOt/fRPz3/MB+F3dB6TcF4PwthLaT1oFMVzBrI9eBQCob9qJcMsO+DN70Xn2ydj7w1+idORitK0+IuFvqCVTRxhjg8hW8RgAKaVsKqLvpFaIjPM3ltgGzCyBECKhZhqRs4QWWB483aUw16kczTZtZIjKcj2CErpEpO2qykPokpRM19MTMMAbQY3OCZPnZhG0hbOsllpmcwmbkUGVaDYS5YQmSDktDpkJUtsMUsTi2WlqRpyzrZd4YJWpXQ+oY84MS4u1VOgiQzo4bo4HZ0NoZClyo+2RWgTA4KbnsWf9GiBsYP4JFwIS6Jm3Ei9902eS5Ym0t2tNIrXJ1+5E0FlitJEh3Q61XCcsFwfxj2RRKUeDwkLow6RLBL18QW3Tw8s02ilcaN5LbJp6adIrYRkCOLNXTZ5VKZGufUOq1o7nqL+OYYQh7UqUIg/1KECbH/PxeJREilxUqdQec3H2+iXs80sAzPNF5xIKUtcoiao/InNypsPE2Ia70fAwtnzhOnSfehqmnXp67OIgx1AhU7zkeTbC17ZoKbpPOAlbv/0NzLnyHWA6xlgSKSKRHgwD1H12cgKJMuuVoR1Z1gidVmMQJI1EhIWccsfVAoS6pnTdyKFnFhriIEO5QYczUCX3mH52VTv63nYp9tzyc2z80GdRPvoIdJx6HNpffqImJVcefgp9V14St5feiRo5NWgodSW1NEffDQcpstEs5lyDTDcXgAXZWhB73s3jIrMaIk8jblZjs8vKOGsqkaqllJ0HopwWqbolk16iSgXbbvnOfhFPx5KwOoSN996GJ3/4WWx7+A4Mbn32gNfRkqkhI489htL8BZh2+ukHJUBo7xmvRNA/Hc/926ex957fQ4oWnNGMMN9Dz+XnY+4//xWKi+dj940/xo7Pfh1SRRyQtTp42xgR7lvSEkxyhMhFhvQM0tbMlUYeKXacXt92ECPOpCZienqdNzlDjZx6bL4C5dX7HBN+21SYyqBjXRGH9Ks6D6FJlBJy1LAcQuaZ97u8I5rJQZrZjwnDYWZyqqMJ0ZwjAe0UkmaEZKYuMvhIkqsZjzMLctEPlw+kHTRyM2MypEAkd1j7OTgGH7wPgw/eh7mXXYWuJbGPT1k05dllmJAa5rhXj1GL4d2bMbTlWUghEVb2YfvDv8K8efNQ7puLxae/EW0dM5EySaX267AfMtFfg0yp8fRlKs6Zi26lnDx61r4gkTUlbiwlu63cj8dZj6eD9jjBF+L/eWbpLneCNoWLV6TPMWRVQn9gkATqc04oDxKmOEQ1L9DPUkn5e+CMUN3sOV2pbQhF5WSwpg0g4sEYrMY3TqRRVzWQhBRJqdtKYWdYBFSeehr+tB489w//gBkXXYq2JYen+WyjCPPgEN5NykSM2c286DJUn1uPHT/9AWrr1mH66y8H8zzQtdF8IY40d8jhFLlBQ01DrMo1EqRQcR1Y0bkmXFqBZx3kh1Bch++Y4Jq5ZGM7f5bI5Hs+WW0GpwYAb+tE55mnouO0k7D9M1/B0G8fRscpx0HWG4BXgIxYCr1K3PqBevBdZdcBQ5mnoWGrbYSMZpN5spytptGdbNHnaaeebsOTbWlaphBCdKCkhRC1ZNILLxYx/+prAACbvvtVDD37+LjyDz33FJ750fV49D8/hie/9xls+v0PsOXen2D7w79C57zlWHze27DidR9AadrMg9H8lkwBqTz7NCrPPoPqurUIB3Zh520/OCj1MMZQXngY5r79XYiqFWz9xlc10tGS5oT5HrpefQaGfvV7AIBohGB2hPuWtCRHJjVC5CJDhF5Iwa2geXEqCRkinoA6oINLcolIIUIeRRVWMxDfMf91+UIeE2nLNMdCzdeOIGuqHVzPYrtCDh6MWKhSvL8SxfbohCrVheEW0T6a3dYcjlK9oWa9xI8QzATfJOsZbZ7tIGsiOZ4s5MZsW3MJCClSea0QEzIAEFooT8bsB7AQDCegqmSwZrNm3+AfH0Zp3nxUNj6HynNr0X38SSjOmg0AKM9diL4zzsXAb+7A5p98B9Nedip2/fJnWPjOD6Iwe66qQJVloTNSSqy79Yuw5bDXvB3tMxaB+wE86aPYHmB4b93pgzSRARpOfwghciaKOtyJP8aE7gAAIABJREFUAJiF/NhiOEPJ43HE+mQ9uUIz1YzvJIvi8dbsBDKIcfJyax8nDg8cBED3iwoxiGKu6OuqztURwjkiCs45xltHWghtQ/ioRr6F1saVE1KUhxABwKxyfM5QGCNCA9V42aToUxgQZe1JvBmLlyeleYaGHnoIAz/+CaLhIUSDsafj+tYtiPYNImiLqQtMQA9oKiCrEuYjEcRX75fWuaoMzy9gzhVXYtPXv4zB++5B18knxQdspCWHO6QjrbscoywkkJANsiIjQh5zymLW+8Pi8MR/MGNgp/Tu4NCBZjUokYtopPfnOq419J9UmeWly7Br23cQ7RwCDwJgpAFWZGlUxLrXGfEwHdAlZa1H++sj2H7dzQh37kH3Baeh87TVKe4cPYBZyFHeezN3bCwOXx4y1DQvyEXBWwKghRC1ZJJIY88ANt/yday//tMY+M0vEA4P4fmvfQHbbrsV6z73j5BRhL5Tz0Jx9jyAcww//QQAYNtPbgUA1HdsRziYdsvPGMOKD1+Lo95/LXqPPgXTjz0TXQuPhF8sg3uTej7QkgmWaGQE0dAQGjt3orx4KWa98S36WGnhYjz/xc+hsXfPQaufeR7aDl+Oxq6dB62OqSrM81BevRJDd94Fr6vzgIfskFGErZ/+FryeTvS95dXYfcsdGPjuf0OMVA9oPS05tDKpvwh6DVxtc5rESGuddww1V6NMAuDEGSLeEfGM1GzQ5Rhp4dCaf5jDIeIiackSH1PWLMJDLWOWW1YRPgMe5/Uj8nVkOBMUgqQoknyImhUAE4g5RjQL0VwoQoI0wuZYkJAFW2AcQWp+irIYkdoCh9AkIAwkImFxaGhGGOWk7pI7s1AVlVa3bYrrqVVR3z2AxuA+iEoFg488CFEZwbaffA/lWfMx+3VvxPrrP4Xq4HrMe8s7wThHY+cObPi3f0LPqWdi+isvyAxdwgQw7/Q/020ijo8nEF9f8llk8cvIeaIrbgzL9AnpXXrW7MzWEw7VMtCDTHH4SYk20czPQRzcMbGtBPU1p23Hv5OpZJQ2uc4OM6xsmObspdufKMp6hmrSR5XnL3eM5nBVP6POs+oK8RB5Mb45BI//dv7Hd1B5+HHwjnb0nH8++PQuFGbPQcfq1eg87mXY9ZMfYvDJRzHt5NOSVpAUuJcQPAvR4xaqZ4MGuseOo0BZq4P7hbQzQMGSv4Hc+0eCfKOp7YgbK0/9XKsyCNEjzpIVIFb7RjJeDJF8mJMd0+CdJxPcJ8AghyxlbSZ0m7UPJs1RdG6YHL4ORHxs2rnnYMs/fxZe7zRUHn4cxfkL0+id7YyU+FA01NypXz8fDLW1W1DftB2z/votYIGPOZ/4C+z82o/w3Hs+hY5Tj0HvFa8G870UqpaAblMBb53N0Z43lwOVU8ao0kKIUtJCiFoyKWTzd7+mf8t6DaXZc9HxkqMx5w1Xom3pEQj37kZ951YUevpQmj0PYAxRdQTlhYvBS2VMP/8i9J157gT2oCVTReobNmPnl76FxpbtmPWhazDzPVej66QTUV23HoXZs9G+ejW8jg7wUgm1LZsOqjVYY2An/J7eg1b+VBa/rxf9b7kCXlsbqk89c0DLLiycg9KyBdj41/+Gff9zL3h7CTPfdwXmXftXaGzZiX0/+90Bre+giJyAv0kukxohIiEuj7BsZMh3Ctfr5tmzQLIkAQBBKA6dm4MYuRwjxjzdBvJmHSq4Snu3Vi7wadu2MhOijD2R1DPVkrJ+KXBK1cw0Y8pMs+WSG6LA8djbsNpokKI4JesaQoyId6S91IYcILcnlJcQIppJkjfakEH4AiET2oothQhRCJLQ2W+hE8ZzsoCMQsy+4ips+fZXdb9ZsYjZV/w5IIG5i5eAk3+dCOhcuhI9q05E16KjwKqA53WguPq0GNmoW+EwaKbeMDNzl3ISBUDkG8suG3k0PBhqvxp7d9anxA7/QfXwhplh22OhLdJoRu4ZlCpPNN/I2aYQDvstLh2heXpHBgLloAMaEbBm2mqMxf/P3pvH2XFUZ8NPVfe9c2fuLBrtu2VtyJYt2+AFLxhswCwGjInZzZ4ACdkg5IXAmzcry5fwQhIIvGEJYHZwSMxmMMGQOBhssLGF8W5ZsiRr3zWau3XX90fXOVV9qnsWWxqN7Xt+P6mm+1ZXV3dXV5/z1HPO6RTbYy4eEdBEhFFVCaJck5ShPloZ5hlxsmZRkvTXs2WOThLh/t/7GFrb9mLGSy/B7Le8BMpGvm4+tB0Hrr8es698BTZ/4P3oXXsSZr7kMuz+4lex+8ffw8xLL2WPNBJKNuy/HyopRhgDdo8B0k4HR+67B7Mueq5Lw+NHGjdiDNG4ZPTH7qekr8RbS5QX1d4eQ2gI8cXGGE85PkxBPWrDWN4QtGtf2ejhiiL/x3llkr1mVRqko+B7JJEiRl28XfbvvjVr0LdmDYyXwNn/nRo3CtlY9rvD0cLF+QEoHWP2216L5r0P4sB//AiH/+d2zPn9VyMaHED9nNPRuPOBjJcl+UDsuTmJF1YMDqVNwA9FvkpXHqE8JhSioyVJotktfyrkQKMXQ7XRKTtfLW6j0Xl03hQmTbH3S9+Hqvdj4DnnQ0flicjSvgT6yMQTPh66az2gFHRvDY1d29C7eBke/vrn0Tmwj+vEM2Zi6JzzMHzBMwo6B0ABs8+5KNs8xm9/GiOXhb4rj3/pP3MVWrsPY+gFF0IpBdMG2jt24+G//QiGnnMRRu++FwDQfGgztv7NB7OD1LEB2hsPPYjK8CxUZ85+LBjX01qORdwopRRqT1qOnj9dhgPX/Ajb/vJjmP07L0M8exidPfvGb+A4S5dUHcq0Vog4UjR5fUUeuYEMG45YLdGRfG4ekiTRSCmfFMcqIrSFPNPs+ciK0QYR9YU8uKI8/4jO1/aSV2plcKDRi4qpYb82qFjeD8UfItSHEtCy5wxUYAkHSJHgRVBfa3E7iOIr+0gJGjnRJqFDANJGCw/+4GdAkmL0lvVY/IHfy/b78Y+0RholUIlC2pewRUqltpZp2lYwxmD013chGhrC1qs/h8rsOVBRjNaObVCVCky7jcqcuehfuw59a05Gz7ITsskrtfqPjJRtnMUtORp0yziRKnmHJSEyJCUN3oSMQ5TGHjeEoi4TDymIHuzQEYpm7TzUiENBfA+ymN2xpiR6rwM07PlpP58XoWk43mSnJqBQjoUYMW9MIA0+xAE4PolxegNxWhTFEqJcfoQ4EloRK3QQo4E4eB9IJELk4hV10LI3t5Pmxz/JUF8G4dRstPmO0Rj87XOw/p3fwIGvfwdzXncJkrgKzKxAaYVDP74B9QvOwoyXvwCj6++GGW2itnoF6k89I+PhEaVH5K8ipCjjqHi3yLskyd9SCqgOzER7316glUBVIteGva80BxgJOdEmhRSiZ0O8HT8eD707pZwer+8lP/EjIASzSoG7vDapbzTXWtQqEggR6S2pF1WaVgb4XaG6HEW8hKfj9w2yrsm3ZeC8zKiqOJZ/EQiVgsaMS5+D6uIl2PXxL2Pmqy5HZ9c+oK1DXhA93zjfz7HEeQ0SxIowxx0De12M6NHItOYQuVD/+W5GUepc4gXiI5MR8kvV0e5vSmDYjnKlTGzoZ7AnN3dSIhqW3HxgNEsPcNCWhxsZonJotIcDwI3aY0daVVtm+w+1s/Kg3d7fyuD5XCJMMcBZMbLLbpQ6pF5p8d9UzrAJaId7MpRq0G4P1rJyZt8RAMDs+ghm10eyv2d2cOo3/wwDpyxGc8M2DPZndXvr2fpTtZ61Hdfb0PZv9Fmtpc8u/fWmGLntduy94T+x418/g+1XfQZbP/rh7N4ePIj2vj2oLV+B+S9/LZb97ftxwtvfjeEXPA+9JywLAgnmyLkCbeaAj3ZpLKKyafd7iTdJxvP29ROqUugBSjHhu8hn2ypX0n7dMXzuyC6ZkYIUbDddGbXs3y36cNn2eMmlbD+Ce1MmYypB4oMSLKF5awcuTUx+2TRQlPxtoTzRMqwsU3ovW/b9a8dotrIHM9qu2DJ7lwgRpZKUn1Ya5f72ZVZfNtZn9WblHK+Meqs444OXofPgZmz70NdRUSOIButY8vdvw4K//WPMfv1z0bNqGToP70Bn1x4MXHIuqosWwFQMUrtExKUdS4kAWQP3dM9BgZeZO0B1eBZ6Zs/DwfW3uGVgrx6FxCD3dv/Y3P5WWLICxku2tLwlS+RLIPzI07G1rHPKktOJpK6iJtDOvLzinqxzUZz9Ftv5m8rIK2kpUEdCwYpFn8eAOlw4A3rB89vKL43cZ+uSAtTSuVI1vbKl0XfayRh42vloPvgQkBo0N27OlCKAS9Xxxvo4ixWsnIrrVDp194J/Q67OhMQch3/TXKa1QgSUK0UAJq0U+X9PVikCMGmlCHBRcY+0sgl7okoRgEkrRf7fk1WKALBSVNu9GYfu2IInve9lWV2hFFV63TpSkVJk0hQ7P/sF7L/mWhy56y70nrwGvWtPQnXpUsx9w+twwl/8FRa95W3oO3UtdE9P8CFxmaKzIveOT6FSlPXpkSlF/rknqhRl/X9kSlHWCVtnKpQiTF4pAjBppShpu/d+skqR//dklKLKUC9Wv/+V6KlH2PTez0If2YPqglnoXVwHAPSdvAAzX/8izLzy+YhnzuAYOwAesVKU3ZOs9BWbORc+D3v+6zqYpBMoRcAjV4oATF4p8mUSStHOj30Dm37n/UCSpd6ZqFIEYEqVIv/vySpFtK8yZzaS3Qcx/Fsvws6PfhaNBx4sVYpyfSmRMqUIwNFRirqSk2m9ZEYQOs1vvGShDb88qVgi4+CNQvGBAZP0SDjcughcyARtD/IlKJ4TsIo0Iuzi7i3R0UvcimM0UGEFrk37rWJFS2bsSh9FvI9EphORS2ipl6zTBZG0k5LdL5Nolrosr61iwQ1vAwAc6WQfilH7sWm2Y/Qbg3rS4XvRSejjZcnbFYW5f/p6qGovqgvnQvX0ZfeRFNSOClME0NxLE3yB4qPEnMCBE8vc/Cei7ivkoWuaMyveB4rnUXvf+H7mhRWclrdkZkuQC79MREuXn4Ldpimdh3xubqmMbo7bz30hS0wqPUVKkFR6eL8p3vaUmZBQT43lJ2d21VZgd21e4qEhTjdYuGYjBozW6JgoWL6gbZoHKIlz2zLgtTLs6EAy2yo9lCiWlqj7rRbdtieu6gTVqIZZf34x7rnqV9j0J/8Psy8/F7MuPROdWmaw6PPWZJfQsvmy4JYCeTVKLLUaBZgKoGkJzVOKgiCOdru+cDmi3jpGN25E/YSVThHxFW+TP8hPWeO3Zfh+u9fOkd7Fx5Xvt7vxQYoVnV11VLFKEDkSRESctuN3fxarKdmwAfWzVmd1BA0iWJ5Syr2LIlAoLbGWLqEpJVfGXLvypfUVfO21ATjESAZALZg2qZlkzwEcufU2tLdtR8+SJdj1ic9gwR//Aarz5+evM4V7QHHJ+6bz23Q//VQsvATHiaDHsnbENXT1pkCmPULUlcem9K1bjdqqE6Dr3aSKXXlsilIKi195Ltb+3Stw5J6tuPvNH8e+a2+GaRcz7ZORIzj805uPej96Zs9He/9jNzjj7JdfhPqTV6K27PGfGmfoomdgyV/9BWZfcQVMu4200cDB/7rheHerKxOUaY0QEdqirSmVeDay5pQAsKUqLNlU8C0DBiXylge5qTKp0wuFnwrStkSVXKj2cHmunUZomxiJtYY6tu+xRYE6kbbbdvkuihgtqlj4IxZu/5QypFPg4SLrEtIQ67FRJ8CFAhis5NEmv6ylMWq6g5YNKMkGnK1DCXVTIldTRE2G5jUHhQQnSqXnZncT+sHbKEY5PAninHn8B4kWKTEegt+Nh+IIq9wlW7TNy6S2xngXIhCTAI3xdkhUnNoVSTWLgjvyMDgWlh+/Q94yQkkwzoDgypk3PbRI3D8IRI+vJdVApGGSiAE2+X6ndtmF3q1K6n6n8U3LxxTegt6dHjvW6b1oJ8X2Yd+y2Tjhz67Akfu3YeeXfoJ91/wUs1/+dAw94zR0bNDI0T178fCffwzpkQb6Lzgr65OwN1PjgELALWsVBdgk0R2gc+gAdE+5YeGQkXxzcrmTk1lXvU7QfaX5i5Z3CtZVc/EYAaCSHe+CD+bnZHZ9P30l+s9YYX/Lo54qFc8zdYi/n6oJ8KgPNAbEkq2/JCbROfdu5vvmi4qQD4uQltf1xb9VSmno+iAq9UH0vPG38dBf/xUO/eznmPGc5yAeHPSSWSt+v11yXjsXOniVWrX3geoVfG9SUWcC0l1ZC2VaK0Rd6UpXujJdpG/lAqz865fj8G82Y8vn/hu7/+2n6D/3FKStNg7dsB59Tz0djTuPTgDApDGK1r7dOPjrW9Datwv9K046Ku12ZepEVyoYetrTsO+667DvO9/FnFe98nh3qSvjyLRWiNhdlNwurfqrlPKCtxXDBswPYhNI58jVtsH8JqVv4AR9DjFSFuWQCJHkwPhiLLfGIEKaRo7XREhJTNaQRXQsryaJFapkKVn0SAZkJCvXX09uWXd+GbSRUpKMFcSOrGcZNJKT11IbyAJMamUC9IifFhEhbZsJ3yrPhBUJdU2brtciQ5RKJHbr+IQqUSYHsrCJvMwEaonGeIY61fGHjR8oki3IxOMiMQJG9fMWnBEIh9Ee/0GEhXIpQqivZMEaN4YCU3wC4lm+yhQMx8lYg3xzxPvhEUwld0ha0+5+evdKvCpkCTMSK1BCRnXbmoE2aRmnHq8QAJLYOkOkEWCJzGOl9wCAEZv8tZFkD2t/q4ZDzcxBgpwh+qrZwIl0itppCzDjQ6/AoTs2Y88vNkEP92DoPa9G61AbnW3bmVjsqFKWa5cadLRxQTkL0ni0D+zD9q9+Hs2dO1CdORt9C07AiW94B3RP1d4rcBmgc2LcKwH2OFd7n59j99m5aqzEvcFo7I2AJEZKwRYjQoRtW9rjEnGf8vOYyr9Kbn5NlIe+2wuhiUQEleS0IyQGBRw3+5P8BPjfhFQBqSp96+QwYkTT51mJY3qWLEVl/nyM3ncfGvc/gNrqFbYt43GfClYx4M0zfM+890UgQnwvJjFldDlEoUxrhagrXelKV6ajKKUweOpSVNYsBwA0WxU0fnYvVE95INOxJBk5jNaeXdj5rasxcOoZWPr6P4DSEXtNdr9dj03pXbUKSin0LF6MnV/+Mha98+2I+vuPd7cAdJfMimRaK0QuMFcmGhTAzXj8jrxK7JChUFV23Axh8pMQd0jlNXNoEyAIwZptkWZOVk9bZwkVyUWbgvRRIENr1abkOZdqTqqaiEVhxahPHp0BgDYFh+zkvWvIepaWMrVV0SmqFhki9+SqzoYGebv5ASC1MYh0CpXqXDvkMRLEdmMug31+UQhhpBF5oNl7QVafg5fYpThlZIhQJLtffItMATKEAl5AljbD1kvc74FFKDgZSmz7aT+SCvFlBBKm8+WxioTNSM0E90+2jQAZkmgaHeQjRfw8RMNGvG8ekqFSlSFSTXr37XtBHpP0DhE6YX+vVBI09NjT2xER1Z22R1o9GGlmg6mvaj3SLA+JgquSOz/xAOMoRbJqNnZ/agtwYDuqC2ahTV5WmlLnpEg6KUw1j2yMPrQJ2/7po6guXoz+M56MoadfDNXMI4lBKpQUQWociWRKxMj3OiM0XI8VIgHeGFBhexoaUVtzaAqXIsReN6HAyiFDYwb79MV4HCER+JXTjpShWWmIYI739Vdp9u4LmmVx1wIAVXH7wWmiGHNf/Ro8/M8fxcBZZ2HXF7+CeW9+U+ZZWZaChDYZ4c5fg9Eq4EixlAXW7MqEpOtl1pWudKUrR0Eqc4cw/NwzseV/fxpb3/8FtDZvn9BxulKBrtWw4G2/h+FnPPOYpJnoyvGT6rx5iIeH0btqNdJmAwd+9OPj3aVMzHH4N81lWiNEjMLYTeIaGKM4UCP/JrwVxhQyVoinIi1/YY0ZrZzmTcfSGjU1WXBadg6CtWYSDyEBGDEiDhEhHdl6uk07kBBXyHZJcIl8DhHFA+JYScJrRgnzhRCdSpw4dMl6upElXEmy0keKtDJQMIxSGRFYzU/O6UsRl4NBOz81CFDIH2DUyCJDqbW0ZXA6aWnpFjigIt1jl/fC8jH4ubqy1AKlccGJTMlys12tKpcINuAdkNUrfyg51wSl1AAeq92JnrOgnkxWyzwW5kaJY5X3t88ryp3G3hu6n/a/zKtNkF7odSQengjOqrRhpFSLCyjL90eBVkebFfT2ZIhQb9VFggc8D047LjvGeYNWowqG3nguDr/8POy99lbs+NBnceIn/gRJZM+XVIA0QUJB/RKFzr792HvdtYjnzsnGppfiJTkygoO/uAkjD9yNtN3C0OlnY8ZTzsuuqRPee+fBRDehfH/ghSU9JaUoD62y7cRKIW4qJPY9NCIopePcmRAZmgBC5MaYQIRk6gw5PlMfGRrnNF41P2L4mHXFaoEpOpFAjGY881nY861rMO933oRtH/1nRDOGMHD2U0T7JZ0tQPiCeEP8feoq049GprdC1JWudKUrjzHRlRizX3Q29vzoDozeuRHVJ63i3zq79mLk57+BaXeQ7DuII7euR+2k1aguGsT2j34cMy++BL0rV+HQL36J3dd9B2nT5ltbtBQ7vns1qrPnoe+EFcfr0rryCKV/3WkYWX87Dv3s55j/1jdj51VfxOGbbsKM516C3lUrp75DjxHEZqplWitELnJ0ftt4FBR/X/aHQIrYIjEItHiiCDH/gazQ/LbSxq3j+havJxJ98fsC0uiFxcOcIuLAwPWd+h9F+etgZCg8m4vhYS1jF7VbIEURebPZMtWMKiUVm+rAok0UAdiPi9RJFbSXrDYSMZq4P3Y7FtZ7EYpH1nzixSHJrsGVhBCZKqV3sGgSeaIxb8deJ3OMlPNiEZwv1Ub2XDicv93vxdopE7aYKeg2vU3G4w5JlGwCHIXyE9q+0bV426ag3kRlMrwirigqyzgvAVLkIUSu/wJGEvuVAqAtj4gOovMQp8gibcbyxxgY0JqjVpM0O8XTHZ2PxmCtp83elr0iFU7VRobvWK6d49wlfAy9D0NnLEH64IPoPeMEmCTF6I/XY9vnv436OadA13tRWTSMuZe8Cdv//KPoWXEiWps2Q/XVsO2zn4Y50gAU0H/yaRg67SyM3HcXkKao9s3I0KGOxyESY9qntuT2E8LgeVBK78AyjzUor10KK6aBuOH4eZzmhktqVAWoFYvcNuHfQd/KxH/ncvN+CJzIz0NRHwKPSdlHbwUgfHcEigWDOZe9BJs/8iHU156Cxe/4Exz+1a3Y/ZVvoHfFCsz+rd+CqsTFnaK+Cp6e/5uMjdaVRybTWiE62mK0CUlox1JSTClLq9OKEFcfzRd3ekul1kG7MXVD1sRwwfOmQJR5fCPeuoNcnrhjLZ1OhDieuvehk2peUgOAvpXzsfdHd2D4JcCWv78ac6oDWPKBN0PPnAMgWx42xmDwec9A8+4HMftNr0Xv8uXYrzWaO7djxlOfhjkXXQqlNQZWnoyk2UB6eITbT+NjR8ovkqkenypxS91TIRNK9fMoJOrvx6zLX4JdX/0qFrz1rRh4ylmor1uH7Z/+NA7+/OcYfNoFx7YDniiMv2r5RJTprRAxb4CgnKwwqWZPrXCd3CIocp3Xi+GgRCwY4hLR5BLkANIILSYSRoYkIkXXYP9O3cF8CFvVxIdyAW1k3BUZnbgQkUKmFDF3SOR1484Td4kjwSpGWYibFXFUbetFQ5wJHaEvrSC1nIoiScVNYq4R36qCvpNxJNEsKskjrtbhRLwptUMeaoQIWeSIl9VT43LOUXvkqZZmt9+IvEip5a6YCbwhRnCm8jGFaN/47QAC7ZGW6sSaKK48kYOllSmvocgKlQgYefPJXG32HdId7yMnOUW8373blF+K0doypIFjttix3c6Mg04nYrSxVOzpKhWHglKEeIrPJZEhigJPY522O6nmRMrxhUuw5RM/QM/erWjevxmL/va3cXhJHY2WzZ1mk0UPv/wSoGHHdNNg7tt+B2pfC1FvL0wr696Bm3+GPddfC0DhxLf+KSrV/iw2VwxEFFuLrkciQzQ1UW4+441HiRTRFETPwkeIiGtn6+h2xs8rH1v2vYgmzyHa8uEPo3/daZhx8TN5X1l+Mnc6N27KkhKHMZny+40OkZgw4ne+LLyekk4OnLIOnd27seXDH0Zt+Qr0r1uHWS+6DNs/82kMnHkWdE9P+K3hedO9A8xbnOD97MrEZForRDwwBaHSpIaXhyK77ENut6Ts8HKR1YxSD64hxUPRiOY0EllBQQF9N0epJLlO5ndIgm1WxcL+JS+yCyxGwR9dxVDxK9mGIyTzfaIkiPJDxsuMdG9cELSUFSNSmuxE7ylIDROjiRiJ+CiECTiL39Ki/dRGKpbMuF+J5qSR9Mz5WG6XrjevgGXXRF+9rOAggDGQ9gAdb5IHAB2Pr8Twx9+I7WIMPasaKPDuEOqwXG6TqRAKt719hYEZg46Ef8v2g7qeQi/tAPqAaqsnKxkk0zMqKFgloUX8nvMSBb272fusOsqF06D2vOVJAC7lhHe+pENzAltFKBLKuu6njZGJkPc2swTFRKZuJ3nFKEk1+oSRMGtGglPfeg7uePsXkTY76EEDrdEWdn77N9j7X3di4Py1mPuyC7P2xOVgVg86JkFr6yYc+vlNaNx7Pxb87u/i4H/fgN23/ARznvUCvtdlH32eI2RwQsC578vzWqFnk/v4i/bT2NYb52NsdJGClX+eUn/o7NuLvdd+D5VF89G3dm2+veAEBScNFKL8GHBDwQ1ko0qM30ARKjjhRBUSA8y46GLUTz8dzY0bse8//xP9p5+O2vLl2Hvt9zDrhS+CigUsFvTDCwPDoWHE9gT70pW8dN3uu9KVrnTlGMmJl52Mc//fy7Hkxevw4Cf/G7+48lMY3bQLoxt3Yd+PbgcAmDTFkV/egR0f+izm11o3AAAgAElEQVS2ffBj2PLu96Px4Cbs+MSnsfsrX0Nl/jws+qM/QnX+fAw/8xIcvPkm7PiPrx/nKzu2Mv933woA2PHpf0UyMjJO7ceeVIZnov+MJ2POS1+KI3fdhVkvvhytrVux84tfhEmmZpmXDJup/DfdZVojRMyQFGkATKpcwj+LoDB6wEtKKvc7EFmUCE7VJ7NMiHRJZcsBKIVG3TIAWdEmnzrCGwzMY+Igknaz7Ro3lK6EXPRlwEm2YjxTXYa2Dyz9fOeZdJwaHglEaub0AhRkzUNw2ogyhIieASMLKl9i4iJTohBSZJLwmetYIEVs2VNb9IfT9xPhaU1u8WlboVMxSAQxWlfKESK2zEsSRqq2QH5yf4h+0DGpCZZSefmXn0lxG4VStLw17jFu7PrHGoFY5RIYC5OKkBtG2hJXj7silmcCXhH9rgAd2WU2gtRi8bAZobXvBxHvtUJK71CJ1aw4QXS5eb+/mSVVpbHOpH9aIrMIKqX2AMLwEguXVfHrXz2EOUNzceofXoD9W0ewF8Bp//xaNLduxsP/cB1G792aO+bIvXdA98WY8zfvgIoioKOQIIVBD3pOWIzWvl1IKiZDnyUAJpGhTm53nhNfghQxCmTfGxO5SvTM0yyKAEsZcTk7Tx45d33Nz4H0Q3XZIsx542ux61+vwkP/+/9g1qtfjoFzzsKERSKwfGEmt228K09jg9SYYN4sXCIb57yBiDmCUeVaDcmREcQDA5j/lrdgx+c+h51f+hLmXPlqhxRJlMv/WyBDhehVVyYsXYSoK13pSleOsVz8z5di7jlLsee2rTCdBHOfsRqbP//fuOvPvoahp62F7u3BvD+8AkOXX4yhyy/G4Z/cjMEXXJwpQ1Za23Zg24f+0f69DRve/+c48sB9x+uSjrnUT1+H4Rc+HwCw50tfQ3vnruPco6Mrh9evx66rv4HeVasBZAE6573+9UibTey95ppj3wFzHP5Nc5neCJEIGsbJ7pRi5CdltEWE0qew+R7Hx1gT20zUM4N4JUSMRtG6srCeeU1XOc1eq4yzUmKGScI02h7fSFjC0rLyOUzMRQpMtZLrY3RCAYT2xPn7qtI8w9BECgkitEzsyNNktMvgmGO5pAgzzCXtzG/7/DFp/RBSpANOESFUNhxABYHlxqEPYoU0TpEQr8zew7StAoSIUA/Hbct1mYnwMAq6Pc7b7yFDgIWUJTIkkTbBMXJcIpNHcQpOXcQPKiVry/EpECMow2kZJJcoJS4RfcftPYt81IzHS/58TJfzUASV2PefAmvKoI6y8zRu29oNv0jUJb6hONhHOA82araPFhEihIjCQNi6PZWJTSaVehWnvXw1jnx1Nzb99xYc3LAXc85fgfM/+yo8+L370XfyYuz61LdQf/IqVGYOYNH/eS1qJ85Bp92GSRIcvP5X2H/1dZhxxfOR7DuIeNZM1Necgu1f/xJqf/hnOTIuI9xeGhr/+pU3vZQhRRCoSBojcDAwFe8Z5hoT4r17/A4zp0jCMbaw43jweRehZ80KdPbug5416ILainZLzymHCb+sdpwQsqgUTMW+PvI6jgbq4r2zJk1x+Je/xN5vfRszL70U9VNPBRHBVbWCOa9+NTa/732Y8dznIqr3ORTN//ZIhI0SwEZHoa9PYJneClFXutKVrjyOZOMPH8T8C07Emtecgc7AEO76h5/gwL27MPfKi5DsH8Hid14BIAsZMHrXRuz6/HVIDhxGNGsG5r3rTajMX4Q9n/sGaouXoe9Ja1BbdiIO/OKnGL7g4uN8ZcdOepYtRc+ypce7G49a2nv24tCNP8PI+vWIevuw4K1vRc+iRUG9qN6H2vLlGL33HvSfccax61BXdwpkWitE0hJ3YfsVZGoHYyun1rysWM8R4pmMmdJDmrkFP9M6PBuotLxb5iGj4YIoanu8QJdI3Nq0Z7UQF0LwjTicgBLmNhCGcx8HGcpJW3hopXnTKuH9QKIUOka7ey9QnYB7Moaw91Aqbk6BhyHzmWQbdr9EinKjW94a4iZVNKAjpPQgiX9VUczdIc6Q88SjRsYoKbCmQIqCIHP+MYQW0RiCqDMJEc6IYzYxPlJEFe1mFB7EbvGRV8crVTsMJBgEBZScInKB9lAvft9EWA2JgpjUoX0cukKOjwLUDABGGtWQy8acxazOROJ9yZQhADD/KfOx55aHsO+2zTiw5TCWPmsF6vOWYetnf4jlbzwPw/XMZb/RidHcsx3NB7agftqJWPDe10MphdZoCtMYhRqswVQNZlx6Cbb908fRd9ppqA7Pyq5TIkNSDAJvWYkUyWSy8DzFCAVMKkBiCs5TcN5gKiDOi0Q0eDJRwTMNpGR6MSocDxBotmFkxaunTdifRyrBZSXY+ncfQv3Jp2POla9Ez9ITYBpNHLjp52ht2YLWw9uQHDwIKIXq/HmASTF6993of8rpxdws+S3h1Yij0/0nqkxrhagrXelKVx5Psu5NZ2DDNXdiYOEAOj19+NlfXI+ll52C0z54OQZWzEHDW4HrHDgCABi++HTnJNJJ0Ny4FUPPuwQAUJ0/DzOf9Rxs/8K/Ysnv/wmU7n4Rp6MorTFw3jlobtgIpCmO/PoO7Ln6m+hZtgy15ctRP/0MxDOGAGPQ3LwZR37zm2PrbVakyHZleitEQYI+tl4Umzjs5cBpNrKSvD8obYTWqfM24XVst36cbdv2iavhpwWxlmFEaAHVFekbcl4Utt1IKUQtF74+8Fij/rC27/hGSlpsQWh95doILLVyxKtUyNONziO5RUbB6AhJEpWiOuOGs/U6YNyFZIVMOumv+QszNuUteji2qITuYTKQZaJdG0qnTIYwyvugECpIKVbomdOztvcordJJROltEFLE40O2RSiiLwL9GCsOkV+nKLhjaVlwvjJh61kpZ4kyx42uT9wr4qBEQNyySB57gtlDRYwvNtojj0MkOWDSu8xHV4EMRaa6cR6F5HeKx1y2o9VyiFLAYSM+lUwi640r8i6jdB5S2iYC+nqx5tWn48jOEdz9hV9h8VlzccEfnIojnR4AR3C4nQ2mI1EFvZedhNH7HsaOq36I9kf+Has+9cfY97WforpkLirL5yJtZOfrv+hcHLj5Row+vBG1ZSfyvMlIm+AU2RvlXxaLRMB9Dyv+u+LQHRObUiQz8H4rOC/H4xJzh/JQQUk+G73rPrQ2bcHgsy/MSOdybBQdK8d2MEUZG9/HBOcrlTFXHfJNGAXMuPwFOPzTn2P3174BXevF7Ctfgd41q4PsCZX5s9F/1hmZd7PXlvNaNoFXGX/bulrOo5JprRB1pStd6crjTUxqcPULvwYAWPfqNaX1KoO9WP3nL8HIAztw7we+jW2f/B7ae0ew4L1vyGVGV0qhZ/EitHftQm3Zice8/8dbGnfei4PX/Rea9z+Iub//xuPdnQmL0hqDF5yHwQvOe0TL4EddpkMfpplMa4VI8nNcAk7loi3bXTRBOK+yvDdIpFNUq5kJ3rL7EhH7hoEOjpTrLNqIrHeJFEmL319zJ4tXAVEjtL4ClMdDGGTsi/AYu+ld7kTzDI1lRHCfyYonlC4xrow0UkQOwRDcIdeIyW16PQg6UYgI+W16VpS7dvvMbURuQoooVkyOJyG92ji+kYaB5m1GJScgSU++aR4D7YJxYVGmqCWRFFu2DFt7hCY5JMPk+ub2u/Pnkjs+2oluHM5GzttPjF3JM1EUl8jj0EmEVI6PqOna0NrGIaK5QPC3GMkhT1R6TzyQJkAj+Bc/ir3rkEmVOyjNVfV4gSa3HekUo518sCtK/0H5zaqmioNpDfW4iTPecT4e+t5duP8HmxBFCmteeSr6ZvdC20mPjtHKIEED7R370N65H8v+7++iOlRBq5Fw15KKAgZr6CRHkPSlGS8OjvvGCaJlDCB3yaXCEasjICWkzeOHmdTN04Fnl4+YCpSfpWW5izI5cCGHKGuk/6yzcfiGm9G4+wE079qEnpWhEjgu/6hAFBR0x09ES0imJARNfI4I4i35nKUgNwd3JH+sXEHwuE7sVRblx2VXHpmMu+CslFqilPqxUuoupdRvlFJ/JH5/p1LKKKVm222tlLpKKXWjUmqt3fcMW+eF3nHfUUo94yhfz5gyEZLv0ZR4dEpP56UAmaLzNaeYrzAJZaUrXZmuopTCqitOwYu/8EI87+OXIO2k+NoLr8bovkZh/Y1fugmqEmPWM09BdeHswjqVubPQ3rbjWHZ72kh1/jzUn3I6dG8NB6770fHuzmNWupGqQ5kIQtQB8CfGmFuVUgMAblFK/dAYc6dSagmAZwN4yKt/CYCbAPwpgA8AIExzC4D3Avj2hHsn+CQ+f4a9WqiqSApqJLchUtA6i9cSW6SI6rAnCRMXs1KTSdlROfTGaxaRncOCNXdrFcWjWS7VaLQYReK6/n6F0IomJEFyijhGhco4Fx3lkTBQLEX7JeIk+StsiRuoWIHywKmmDtbAcxG6/TZz1SR5wZaSS+BRNowGkCgvD5gYA5S7TSBTmRWfP18uAa5RwTWMJYQM8fo9I0SE+hSMB94mpCjfhtGK42PRMQ4psu3ysyB0wqEz41mGkodUxDMq9TYTko/Ya2vzdbp74O/PxnTJGLZC94S90eIsp1zU8iImi3EScPc4ia9xnEAxf/CTZsRIvDDGIdAQ3AzmO1Gf7e+j7Qqj0cQligjtsbyjIR2jgRi1OLvAdhqhfsIsrHvDabj9y/fg4evvx6rfWptr44Gv3obdN24AFLD8dU9Fw4uInVpPt6QTobZyLo788nagP0FCKCQlPeZ5RiDR8u8xJM2NaXrmFqmgdgVSxB9AbQJQRbXyiGwRMlyK8ihg6KnnY+SW2zB6971oP/hw5r5eALiURfFm8R+vzp47zx90iJexYNIi3T1zZQmCLusKhMhow7w4CA7RpBCix4CCMtUyrolvjNlmjLnV/n0IwF0AKHjCRwD8L+RvbYTs1fDCGQIAbgdwQCn17Il2jl8WmsgYI1YuOScv6dDySfbWJhaKTa07eacdodPJXy65ziqb5RpVe4JKfqCOyZ2jJTSLBhEqFDecskQB/WgpgErdLN4fNb06lAKhVbJtJxbdVu5vSrCZjFN6bs/jZb9WfF7/fFYB6eQnN0rXwPsJufJLUm65FBOkJFcnXqBEbt9O+DQGpIs0BdHraFaAWBESqUEmIkktGxdpzYZ4sNuJLHuc0pTUwPv8ssP7s/N3ehSSqrLH2DHbk19OCALsHaulsfFEmcBt2il+YruIlCtmnZjGfYNKk99uecqSfA9E6ca4cmPKmzdyZUds27FhfKQ1p1SDb7q2S1qNVmZTjjYraDQzre1IwxKjW1nZsolgSWGibSJfR0N9uOKLz8P6r9+Hjd+9CwAwEGcXX1Ft9M7rxzkfvQI9s/sx1Jvtn1EfRX9fduP6+ppob9+N6ryhrL2+7GakfVZhonHZ68pOX/Z3p3dipakY54QSPOP8sk2QDFW5+6Z4/hBzB+/PjtEtBd20xoModVOhumABhs45D0hTHPjh9byfjqW6lJ5It/PtkwOD9qgQbg6kceOUM2CS71uQdNWWvpIjg1Rymb+f7MxglSAVu2MpZZVbwp1EH7sSyKTWPJRSywCcAeAmpdSLAGw1xtwuqv0AwNMBfAvAh8Vvfwvgf0/qnGVKkffbRJUiAI9YKRqzjyVKEZCf1IGJK0W5OhNUivy/J6sU5f4eRynyc649YqUImLRSlOvjJJUiAOVK0WTQoQkqRcDklSIAjx2lCJi0UlS2DxhDKfLfh0kqRQAesVKUkwkqRQDGVYoo4rVUiuacNBNLzl+EwztGUbc3YyBu4OQr1+Hya16B4VMXol7JLpyUIgCsFJmd2xHPGUK1ntUZTykCMGmlKGv4kSlF/n2bqFIEYEylaPjiZ0PXahhZfxs6W3fl6vt1J6wUYYqVIr/OJJUi/9hHqhR1l8xCmTCpWinVD+DfAPwxsmW09yJbHsuJMaYD4BVFbRhjblBKQSn1tImcc16cTS40GFIeWMZLamqF0yjkX0jl8ilw0EFyyI3shJbaD1JKrtiWualj+wJVNY9DytShvSSggA/3e4qU7e9QFCMqWkZhayFfGq//qXiJguU1sUSXq1M0KflCy21e+yz52+g1BsxSsZ04CEoWleV5XSPeuUv6FIkJyDs0XCY0uboGpvjYgjeR3f1jYJaOXRXvmSi6PnKrFxMcu43TJEoTe4/iSZYnYVoGkh/xxG27Cdrkf6Ntct2394iIy2lF8dga6s0GJpNhqSQdMHal/M0Rok3u2CLIvizIqLI3J+blo2x/rIBIjH++R2I5Snt9nVGJoEy+3/m+0j3IX28aA6lIKBqQ/OX4of2xCUJ0kDJESpCy7vE8u7CPtJPYau09NrNwHRptHaGusmP7TKYZV6IEVd1BPRnC/lv3Ix4ZwhxrmNVNduyAXf6ivMLtKEXTKm+tisHCy8/EPR+8FgsPHUA6OCfbb29oam+8qhwl+MDez1lRnH2wpeHCN4UmYcN/h8PG7lf5/cqbPyVpm59TtYKBl16JPT/+Purrb8fsZz2fWy1Lxh0GiHT1Z0ZRdi0BLYHK/AMunFclAZqkYDtYRqRxKBUblZ93oEwWKsRrl++fPXZDQdceK6KU2gjgEDKzt2OMOVMpNRPA1wAsA7ARwMuMMfuO9rknpBAppSrIlKEvGWO+qZQ6FcCJAG63PIbFAG5VSp1tjNk+TnPvQ6ZMjZsEaGcr+2pw3jKaDI1xHz9pkZj8tltTNW5Ss8SDyL7JBGUndpZNbepzbdGHqKUDC5aRHYn8ePFm+COkFXaPtAtje2SNif2ecpMKLkYpN8VXiErqBqK8D0SZQiQnN7u9s+OSUxk5CZQpRN6EUjYZyPP43ifMF+Es5XnryqjiSSIzT8TlJeJ6EquhcGRsZ7nKDypbbbKP5O3WVjnoH/A+/hLlsPujllOIaAw5pcoq8C16D6xCRB//inJ/R9lYIwUhUCC8/axE0HhhZSJ/fSaw+ssVIrL6Y3vd9L7Eow45DRSijrjeTr7vuxsdjvUkr4sVokT8ngKpKb4O5+kjnqdy+10UI1LW7DOw7mzMY6O2TfiFi+0U12u14F4dYa8eRctOFomdVKpRB52ojRkXDuHnX7sJF/WcAWMR65FOduGHbBsNlV1gK4nQsNN3EzGwuIYDsyvY8MvfoO/ic7M69oEmCeXpm9SCQLl4Hnw7Ou1ShYi5N9p4S9/IlQ6JQVjK90sqRADMipPQ2LED6azZSJsOWgo4QwXH5vbrzMDa2W6XKkTS22xCClGJMjWmQkSGuzAA/Yrah/ZzpwljsBWKQaDATzO5yBiz29t+N4AfGWM+qJR6t91+19E+6bgKkco0ns8AuMsY82EAMMb8GsBcr85GAGeKCygUY8x1Sqm/AbBw3HP7k032l/uNlstoF1sTBOHYSY5euMiAkn2mgs0sIUcIy9JEJiCEBkqMeDl0x03WOjHQHZeAU6YdKGqL0Yck/C3rU7gtA91Jwmk4KTjrrFxBCbd1ZD/0PFEQUpQ/T5AU0TcDZd2C8wC+QqTcTvEVJuVY8SAQH3Clgn3+pK2UCt38DTgAHX8wdX6iYpdX0a1UKW82o2Pzz57Ltrs8TojKdYuP5SWFImy8SLlFwYcAAUAS7hc/FC4fB1Zs+fWWKULhcq7hpnQ7e3dkcmMZzJGPjdy2IrSRQkbIYI45OAIBmbZIeLlVzk0GgRcrRY1uW4SmrTRaaYSmhZdbkbMJNQz0QB8GlwyihQrPb71R9rAJRavam9eKYlSTCg5s2IMDt27DQ9fejf1378KT3nIeErukdlhlCFSTx2V23ol+M8sk54AwAdBJdVSe2wVvSV2ksPEVosAVX5wzu90Ks8+5KNvuuN9Vro47hkdAAcqrrHMFzydG1JFscU9ReySEa0n4DpU4gQxRvzwFKUCRHr9yGYBn2L8/D+AnOAYK0URMhvMBvAbAxUqp2+y/54930DjyPmSoUle60pWudAVA/6JBHN4+gk5jXPAcALDnju340Wu+jvUfuQH7796F6qw6Hvzcjce4l1153Ig5Dv8m3rPrlFK3KKXebPfNM8ZsAwBbzi09+lHIuAiRMeZ/MI4dYIxZNs7vP0Gm0dH2t8ZrEwDSCi0R2OMIytcSOYAzZyWYlDuLqCuEkCJjida0rJKmGklA9KVO5rc5KKBxqFJaUUirylnItFySCCTKt6rFspNc+nDLR247COIorOjgNhQte5QgNf5g5mB5YmlKtkfXU7SOLy2bIFq+dLs3fkPS4rdC90QkvlWqoK5/XQaOKJ6zCksglBJxCJJy6I7gRDHAIJ4vFAIuWWqPZS9F8XxzIQnKmJQTseDp+giNJMSE+Hfe0ge3KS1TMQaKlix42Y7q2OvjxMkCXYKy/Ki2N879JWKvfRcewm0HiWDpNzrWT80D8FJQERYppwyZCiYn9qdOIz+9jpoII0FqYmCgatfbKxUMnjgDW+48iKVPzhK1xrbzo4klZ1vYuZFUsH/DfkS9MU5961Ox4iVrceD+Pbjpr36EGb3Z2iSFBDhky4Ytk2aMgm6MK+x80CZkWWXoD90KsfzlL6WVeriKtCKurfz4zn60/ZDTStE8FKDV9jfJS/LacKiUGFRiDuT3wyOUy3kr4KlJiKpoF49LatfuYE4RjVfjKsm58Citih5Dma2U+qW3/UljzCdFnfONMQ8rpeYC+KFS6u6p6ty0jlTdla50pStPJJm1di5237GTFSIpd395PeY+ZSH6Vi7AootXYt65J2BwTuaueOih/RhYOmMqu9uVx6goHLdVtt3GmDPHqmCMediWO5VS/w7gbAA7lFILjDHblFILAOw8Fp2b1goRkz6F66FKVM6SADyNXCrkHh9iosu8xA0xPRYxShVSi+YkHunWL0MLVTGHKImBpOIRsCngXsAP8kaoQBBo3T5Airx1aIkelV1waZAyoBwhok0NqChDuYJ1+AAhGuP8jJSofFckCFTII5Br+XlTS8nGVBgYjmukCgraEaijRzFLsDXqcV7oHgi+kfMedJadnwgVcN5WRtRlJKrg3hC3t5Q8WoAGym1p4RthzRuN0POszNplNAgB743fGUbGxD2zCFFace9SKYdOvv8eRC/HsmH0UaLKDjGSyBBxk/iWlXgxFQkhRS3EaHoEntQenEIhrWQo0awzl+Kuz/wSC847ATtueRgH7tuNtJ0gSRT2378HBx7Yi9PffTEWL1+EWn8M06sdv6hPo3O4gdm1kWzbEnXIm/YQhQmIUrQbxCea4KxoFJQlzFNaH2iLEAWoOaHkdn8yBiIkw3/ItoDwHhegj7aL2WYBQhQgfgIpolWHfCDY/HXwYJAcUKNKEdPSAeLtLvN8c2TyPFKU9T0PeyrBmZ2QTEPakVKqDkAbYw7Zvy8B8NfIwvi8DsAHbXnNsTj/9AfYutKVrnTlCSILzl+GWafOx0/efi12r9+OmatnoTbci92/3o4DD+zF4ktWY9GzVqF1qIGb//KH+Pen/wtu++jPAAB7796N+sKB43wFXZkqSQ4cwq5PfB3tbbuOd1eOpswD8D9KqdsB3Azgu8aY7yNThJ6tlLoPWXaMDx6Lk09rhChAhnzLg/4mS5XDq4+/ts+b42jIFOvBVBW79Spr2stkpLJE6qxYnWZeFeQ+rYVbtfHcWLNr8P7Wwpql6yyIIcNoUekFlWz7B0gDR/TJ6CwGk/YSmAbu/gIxCvgeykMdJmrQFFxUEFJfWGm+F5HsS46r4LWfow0ECErRDSvpLo9Zu23y44VpOzZgnml5HDPyIgvCFthjpeWalsPfcnehZ5l85lQnFWPP3y9d1QN34zwfKI3BXCGZtFkJ1IfQGKgMWU2qCD0xZUgJOcZ8DlGaf+iO1iFgLc885CoCGXLIkWhzzJQJ9h60NJI0AsWaZK9Tozj0x0BVYfUfXYx17+jgzk/dhJv//kb0zq1j/tNXYukLTkZ9yTAe+u6duP9Lt6B/4QBmnjwX935lPZIjTWz+8UZc8ukXIbYXPrPnCACXKJaQosNRiiO2v63m2EgRJ89taY6pxYEOIwXdLkCIJFqegpEf6V1WhhB5ty3cnghiJJ8pvTsS+abHSG7+adg8CaPZfKz3XihRZ7xr8I4XAGWAeAepkLSCQYrDP70dIzfehtpJKxHPn2t/mzjso8b7AB4HMcZsAHBawf49AJ55rM8/rRWirnSlK115wor9Xp38xjOx4PmnYu+vt+F/3vx1HLx/N1SsgSTFyt9ai9Gdh/HANffg4n96HgaXDrkDu/K4ltpJKxDPmYnedU863l153Mi0VojIq0yJIHpInVXC3jUSHgmggDHOI7gFgUQGpppVShmpyZ+nyEri4Hwq+9sFcbQIUZJrotA0SYVHEZUuEJ214NKCtWiS8VCY8Y3bnCWuK3kvszKEqNSbzeO4lAeNLO+jfKRh+yFyFHBcSLQpvD3KHJ3Pioybw22KxpOagWnl+13msRV4ynhoCFvHY9w/YJzrK+US2bGujEMURFRd4jdR8Egb+gYq8ZBQAbSxwS3jaCHjEaZJOLYKA5N6bfthq5wXXfEgUIxm8Z6wb9RlI66b+6omMNdkDyZpEqfISeqhRQCQVptY9sYLMLBmAW55z3cQf/Rn6Iy0MLB8JvoWDuKUN5+F5c86ATrWWPeqNfjB734PptlmdAgAB++b2ZNxiggpquiE/x6JMu+1JiNF2pb2umwaJNVWzLPj6Ot2jguQIQ+5BLLfJSKkE1Gn6NYFN1/8XoQM2VLGenPfi/z5uEn7+JTx0GvZn2CMeYhRGdojj81t59/3IAm25COxtyegoNGzYDEWfsCG4iGPZdnnMjGTqfzEkWmtED3WRSdepOkpkKhllxemSHTbEd+nQqb6+nwofCok7TG5XEzHXKb4+kyEfJ6xx5ukKgxG+iikM9rGLe/5Tvb3SAu1eQM4tGEvnvG5V2D2qhnQNrBj1BOjUq+6yO3HSDKj6rQAACAASURBVFSKcgPm8SBTrCDkjISuTAuZ3gpRWWI9X7stiy1SEpkY8Pkck1hvjS2fyENkACC1VmcivCcotYdOMgtUJYa5GGSt6Bb9kf8q+dYFRe0NOEt8b6xlYGO6RL7ZKdsrsbRy1pmwyop4QrpjOUQW+Qr4HdSW9ADyrHcl25dSCNtk1yc9iwLvNrnfvzyxHp/aMN0B4uibkB4yAjhEUaYUKOy7QE6CnGl+x0ymFAUdF5tacHFgXLup5dwEcVbKZCKvgOASmQjllr1ITknj0kQAbDwh6WXmvL/sfp/LE9m8a2UR4sXzLEJbmbtU8pxMEMPFuJx6tIf6Frkquf0GmTKUqnJ0TljlSdMmePWqksfY3hvvxd1/+R8AgNnnLUdycBStg1l8oZ+8/qtY8uyVOOsd56A23Isdt2zFvvv3YvFTF+RSOkTi4QxXj2A8abdtVG1CjMR8E0SQLkJuxXytEpRyhfQY47TUc5h2y3kMon5BW/ye0zEFcyO3IxB8WTeHBonBNSGkSJnsvsg60gNXntcY9lCk6PyUhWEy2e4f/8GtJy/TWyEiyetFObdK/m4J4qQkpkEjF2odcIpRYicBTURYPoY+gN5HihNE5rfpd0YwjMsYkEZZYMbEg5Czrlo3VqEY5QaqICOCUz3YZTcirY71ZSN35pKnbZSnpLECZn8LPkAKumqDS6riOmMtlfF+8UxLkQrvHOMpQGN9FEsntST7hkd2GcAPa2Bi+pjbZ07BOWVOM5Hra6zrKVvJ9UMuMOomZ9MyRdYjVRtt+152PwuGiUwhMJ4ULrep/L2goJKKgql2XL614KMkPqTay3AfuELTfog/6AdvDJZ+IKXiTNVo/KQuWS47L5ChRUohXYuvGOWnAtdn1q0VTOII/hgjxEM8awZmXbQWQ+sWQfdUgEOHsONHd2PwSXMxsmkv4uE6vv3Sb6A6oxdpo40z3/10oNqDjnev0pIvXifVSOzSWCIeem8tU8+0HY9t28dEGyQ2BYmxEUI7kUEnNqVu97HNY2eUUySl270ZCzEc5x0KiPSe0iVTgnApg5r6RlpiqQBiHguUmyIlp0TxGVsxEpV4ThTfoWCeU2zMu5AYdnw+nhHYKZDHhkLUla50pStPIKmvmo+V73ohe4bV4jYWPGct7vyb7+CkP3w6lr3gJKx9w1Mwsu0gZq+eCaUVJpAvuytdcdJFiAKZ1goRkfeIXM2iCjRuuZQEQnJov/c3WYKd/BpPQkklK3lWndImFwARQODGTVZ9zjqkdrVFjkoyO1Mj5I6fbRSbmbwk15EmiFdXIjclFjFLCrc0J6BtDg3AbRnotkHUMgXLafmWyyyqXGqSsSBl/7z+MaLueAEixzqP0dl4oHQqbLlGipNFymCYZGUSYsSWvpdNXVqG4xHBiwJCSrI/bbNx6y3d8RJubF3cj7elSPeCstLHLn2DTBvBSeebBb+bPEIUWPzS7b5oSVIs4QTLwnIcefeTkKE0FhMAbfqolxyHEjikJMJjIEMdm9uFSkJqklQDtR6c8v+9DADQSBJU++roXVHPgj0mjpgds7dGVtAyHP3eSiO0BblxoJbd/DhKbOmCOAJZMtk2EeYJKVIR0krqBanN2o8sMsQoofZueUlgxglJyZwRBCj1lpBlShB+p7xxY9IE7b27cCRtAPNOYNQ9CBdCzQtEuhAhEnUDRLOk3bTVxMh9d6N+0lqoapTrK9fzxprvaORf50Sku2QWyrRWiLrSla50pStdOVbS2LUND171j9BxBUuXr8Ch4fmY8/TnHrf+NLc9jB1f+jz6152Oua++kvOXdWVqZForRNq6IXNcuNgz9SY6UBjCgdOqW8XHMv/B6uI6zswLpYxn/eWRJ3bHJy2eDS9H1E1VnuiaBNp8Pgy7bhu+aEWoi+RZdBwakbXgrkmmgCjj9vi8IS3dYQmRojrEXdJZ6pEMIcqbOAGRnZoqQmwEcZAlWC939QMibQkSVrSeP1YqCx05dMJPIsqcF4sEcXJS2k/nJQTJR4zG8cYJUnp4qIER1mzKz96OS1HP59kQh6iUkzURq7CsziOwKPnZxAaG0qMI1IwDURYQtbMgrN4+Qdz3uVNA8WWXJSoOuFP03sQ+QmTrElJE+wn58q/Tex75DuQ3XR5qNwgTiwglaX7gMLpD57edraGNjq1btaiO287KOOgIuB45CAz2NHJtULqPCpdZG6NRglHBKwIiIPXib4xaRIO4d/R1oaCH8J5TMF9OXOT85X5wZUDwLkCRAKC9axdqcxZi2St+DzN1Bzd/8u/ROXgA9RNWojJjFvoWLSsMLAu455hDiMZBvMf06lRAe9t2AMDh9behfso69K87HUqudmiEwRuLkNHxpIsQBfJ4dqLsSle60pWudKVUBlashUkSbL/+PxDXB3Dia9+OytBMbP32l7DxC/80pX1p7dqBnd/6Bm/v/tY3kRw+PKV9eKLLNEeI7B/EoSDrrGLKNWEZnt+znFUJMsSHElPfnii1kICKXTx39jCq2jrseWR/J5QABoqCrCELLpcIK8VZwgLqQArdEvBAEIDSogXEc4HxuDRicboEGfLX8d3feWTIWVaGz6fbBlHT4xCVXUYpUuShWQJVKg3Ap1WpN1lZChHZr3wf7DZsoElCKTzOFAUVdDwYe6hAjMizw/eiChL2isSszEMiz8bIN2uzgwkN4Ow0Jt8WPxs/cCFxiMYKeDdBKUzzMdGDxMPPIW7UN8HbCsYcEKYlKUFfZOC9wi6VoAV8iAfUOu8yu4vQLH4W+WdjYhQEicw/aw78J/qXJgppkp1QiQdG84oM3JgYhVrcye3rWDSnYwcDoT1xgW/7cE/mwu+QoU7uGNp/mDlFVeYVHdGZK23VaPSghdZoRqBM2SuTeFf2GlIVXLRMhl0o8kbRs07y20VlWWgH+V60RxsYXHkqdv70e1DPexmq1X7MO/vZqA3NwebvfgHNbQ+jNndh1oSc16gtXTDuyvomf4c3jzVbqM6Zh9auHQCAvpVrsPtb/475r3xNVoF4eal3LyiETNncVyYGXQ5RgXQRoq50pStd6coTUrZe91Xs/On3AAAPfPkfsOmaz2DfHTdjcNWpWHzpldh09b9gdMfWKelLbeESLPv9d2H5e94HFcfoP+U0NDZuwOE775iS83dluiNE1mrn0EK8jg4YGSF5HHWXws6PdYhhayUf7ArQUNY64gCNpJETIkSeCXwe7dApk3EPpAWcCI84HwJRtjOqM44az95nii0z/kmmsED+/D4apKWXGVtUeZiAkmZGbeOhO+I8JUDcWPGBZLJOQrlySEvAGcrXker9WOfjbQ2ojkPaZFwSwKVJoQB/EjGi+658pEgkKmWnHk454SFDQJ5DpPO/8bgky1u5Zw5kt58Rksj2T8T6KYR5JmAhNrdtxZ7rf4B5V74+/5wl6jgGRyv73bjkrdTvEk6PHGs+4sanKUEWxsSABSIU8Eq8oIEMxIp3VqJchNykxriLZW9Diep6/wBOJWKSiC8+RBpMrgn2XE2VS/NhkZnIdjaxRCfy8ortfuIUzag2UI0IEcrKHlv2RtmkSxwiOkYrw3wmCvgYNauIkaJDc2Js502KPWX7lU3Y+csywTMfn/wyHjLke5cG47JkjC1+1suQNBuI+/oxu17Bhjtux747fo69t9+Ixc95FRY84yXYdPW/4IQXvwm9C0/IDpXerN6CRSnfaCwukTgm2b8fptPBzmu+AaUj7Lz6qzDPfxEGzjw7q6D9CxJtlCTpLZQuQhRIFyHqSle6Uiq6tw8jd96BDe95J0xaooV0pSuPUYn7BtAzPAdRTy/inl4Mn/QULLvi9zDj5LOw4esfRVTrw6Jnvwyb/uMz2PK9L6N1YO8x71N1zjws+6P3Ytnvvwtps4EFr3wd9vzwWhy+4/Zjfu4nukxvhIj4MQTC0A9KhVFYy7zOGHkI1eEQLRBmhMcF4MSWbL079AjwLB5qK3EWcWoyhEDTNsUsYhSGOCEOsSH0SAdISTHqk+sEyAInPpVHmPHLMUTyZVwcD4NUO6s015eSEL3kueasKNcBR40S10lWtheDR4byl6k0guS8BQhRACQqIKq4lCf+Gjx78BHyQ89PxFAJvM86XqRjEbGZo1CLGEZ+rC1l42/JZJqM+hAyRfGyEufRmERZxoFSDxw6R+rFmBoD3YmHh7Hk7f8Lmz/yd9j9zW9izhVXoFDomRA6YDtAqQVSKGjyliF0Jc1fV+DRSH3zESl7ugDRG8O0C7hn+Vc34JfoxAOgxH1jmhEhAB3arziXGEe1jvNjGBGyZ0XbOY9VJoXkrosRxraNT0QdUUCTeG9WiCtkLO+HvGU7VpElXpD/W5mMJtlAbVhuUyuJ0E6yzvRVspelJ+2gptrsIccxm6gkFLShvVRD+fPQu0t8UZ2gXMQ4kYlilZfSJkC6BbqUyvFiUaUshpjCrDPOR232PDz07aswuHodBletAyKNB77wfzHzyRdg7tOelxs340XRV4D4w/0t0SUdafTMmAWjgYFTn4zRDQ9g4aveiK1f+BRqC5YgnjUzeB8m5V1mq3c5RKE8sRCiSQ6aRyuP98R9aTS1N3TC+bmOkuiCvHBPRKnOm48l73gX+lavRnvvsbeQu9KV6SD1JSux9LI3ADAwaYKRB+/Bkhe/Aftu/Sla+/dMTR9Wn4TGlodQW7gEQ2efhz3X/2BKzvtElWmNELmYO1nJEXoVwoBVpWiPO4aszbLowdLaZI+y1B1EViAjRIQYkdVObcWGkQKTZsoR5TkLLHIR60Ql3sdf9EkqWYWRXuV1jWMJjGUpMBrho1mRYmUojVR434R1xuchBMxvX6A+rswjKDAZOqY8r6Pw2PwJ3fq9ChLr5uIQdQxHCXfJXe1vLbDZQNY6I0YikjVzjCKUcqJU1d63CiFDtj+JuysUhVwJDh0/e0IW2LMsdXFydIpUpyGXQICfuqU8zpy7F/698cvqvHkY3bYJm9/3Pgy/+AUYfM7TMZYw/4mTTiqkdCMJJZNeZaSACivaKA8ZIlSgna8rOVo5EbGLeFuiuvScU3ceSS/ibgnU2hgwn9BHhgHwLKs6KpsTBOctN5UJhFRmIU1tdP1EPF4ASG3cNI78rW08ooj2e++sPJa5UNkxDYEQNZOYkaGa5R9V0jbqusWxk6iNhCJad4hTFMGMZn+roo7DQ+1o7BcYP2VRrovKABkibz1x7/0xz7HOvGmkb/GJ6F16IgBg369vwuZ//1cMrFiLTV/5OBY++6UYWLYm4xCpsL3CEuF2GboEANUZs9DeuxswwNAZ5+ChT/2jRaTC+bHoNGPKJJKbP1FkWiNEAcnSg0hpQiRliQnYwq0yl3BQwrX8YaGvRP7EPMF1NL+oHFzOwsTsJsuKEc1GhiFz+vilViGiMukR2/R7xS1J0RJLWqElpUkM+RLFSInSlzJ3WOlKa2Kvb2VqdRmpu2NyyxNZaYq3PRjcEb9l3eKSxkvUNrBcUVe2ykrDJf2teZ+tY1MTUDDHuCn2N4Bo1P5mE4zHDVnHLoc13bIYLY25NCL0zJEvWQGlsQakNbtcQoFCe9J8WTVBW4GSXTYb2EExcPZZmPu616LnhCUlFQsOZYKtASq0pGP3kVIoPiZFHxUaB/QM+P2nZ0LvP42XBE7hKlHQgyCgfknvAc0vYllGzju67S37cN/yim2Quoc+/h3lRXgsuRni96StOSl1u5OVLbvd7GQvZNt+/Vu8besnESs6DfubU4Aquf0tu0w2UG1isJoN9MFqNoh74+zCeitZOdCX7e+v29/rWf2orw30W6WMxmEkxqNYdobxnmVZWaIQaW85uMyg5KC1nuFSFvCVtofWnAHTaaO+aDlmnHYuNv3bJ7Hhax/D/jt+gXS0UdwX2Ud/TMolQDleU6A6PAtpo4HGw5sRD86AabeRHDzs2p0MiVoIhYGYyn/TXaY3QoTsJhqaoFQ2WMiq1m3kJkvdzga4sogMHUvbgGuHZKJKEYBMKdJZxF0VG5gk8z5TymTWlzZAmv3GHiSxAUz2QdJthbSa9TO1GeOTHiAezbajRqYU0cczjRV0YgqVIvb+Gk8eoVIU3DdYpcgY+GmQfKWocElLZQ/PKIRGkqcUGZ0pPEarcLvjJkqd2LaSzCxzdV3JbSrkELQipcgo9wGj7ahluLOUs023DEyksjo6e1ZpnClFJs6UotTup3sWjQJQmVJk4uy5JvY5p5VMKUorBrqpvDEdKkUmNuMrRXBKUdpjgFQoRaniMZjLtTZJpah++mlZfybhoqK8KNWopEBTw0QGKlWZUtRWhXqAEu/qWEqRibPnmkb2g0nH2ZgtKsnPCa5zAIw4nzTEOln7OnHtmzjrj4nsvKPzfdHtbHzoVvaMVRtAbJ9nZPJKkd/XiG6CyZcqe6bQ/s0BknaEqJKg3YkQRwla7QhxlKLZiVGJE7RTjUgZtDoxKlGCdhqxB1kjiVHVCRqdGNUoQSOpZNsFShHFPMopRaN11Cst7E960Vtpo5VEGOhroJNo9NcbaHci9NabaDbtJNGfAKNRphQlVikydjzS87HveiQU0MkqRTRecjkQx1GKgHDc+UqRiitY+PxX4cAdv0ByZARx3wCObNmAI1s2QF1/NQaXrwWiGEg6iPuHUF+8HPUlKxHVet1cpMQ8mXr7aM717odWMeY9/yXYetUnsfDK30bPvAVo7diG3sFV2XiMM6WoKBdiVyYv01shoklD7u94ULUYuC5Am7W+fCxR+GDyi8CWASEQcnaGmxwjtysTmgnzSJGJDWPmxhikqiBwohUO2OgtnZHCI+uOSTosI5ZL5UeWkxATZe79PuzOypm0tCUiR9CwVmE4AXqgXuoTvxHd8QIz8nPLw8USiuJV09S4ibIAJYjaJp9Y1+4PXP+TvFXLCA4p5EXWJkH17HKelUmSbyP1wki4JTJpRee3+Z5p49z34zRjfROxl4Mg6twx2VJuyfLFeEbnWOOGfhNjQMWeCkVjWyYolgE2VbZPdZwSS88pWDKjd4iDA7rXoTRQqFAyvSgbgXu/I0/bY+gZ0dKacj8aiQaQg0SsrIIkr9d9gcnhgNPC8Kul8jsApHz/8muCzhEhKyO7vM+pYCLFpGpeKhM3p2UHbL+3TFazD4Fc85OojU7cQrXPKViAQ6iadvtInGA0yuDvts72pQ0biJLunxc2IiuVQ4fF/YRnSPn7faWoNMCrTGdkhR+BRriSREBsqjC8+gwMrzwjO38KjGzbiB0//wEOb7kPB+69DfVFK9DYux2Dy9di7+03Ysv3v4LBVesw5+xnomd4jndCBPNlQMC2MrjmdOz/5c+w73+uh9IRDv1mPXpXrMpdM7cxUd6q903ripPprRB1pStd6UpXujJNpb5gGZZf9hY0D+5B68Bu9C1ajh03fg/777kVUa0P9UXLcfC+9dj/m5ux8jXvRG3Owkd0ngUvfAV23/ADtHbvRGvfbhz45UIMnXnuUb6arkxrhci5etsdPtIhyHKsIZO1wFo3mZBuGYSBFLKiybqWrqEe8iD7IkEQIlU7LlHqcX0NTJTC6LyV7jptt9gdGUgschCJJKtB4C8UyGSXlR+BtWC0hzgF2Q7tprzf/n67j3hB5bCSVxIyQxawQIyUHwAPvmXpLVEUXCelIvG7kQs+GFiX1uKX5GafnyOCRlrDmNFAGfQxSeECPpYk5XUdFmikNo7LZhfrFbnkt4kJ7l0XsmcSpjgpfn6FEqCOSuwIhfl1At2RFjK1oDsALDokkaGISfC2CU6YnO1PU5VfJi+/BAew+EiRGLs8ftqirjdGZJBROY8wz4jGL3Go7FJ07hiZvJPmFb4WR4onl3wIZIjQn5j201IRXFDHsi+AQ4baXBIy1GfLqOcw+iqHMGohL3LVbxE/yZaHoh5O+zGiM+JkixJpW6TIWL4TURRSL51QJJfFyjg3PloinmnwTomxT2E9ckv7kvRPyFQB16fWPwu1/sxVfuEFl2HB+S/E6O6H0di9FZX6EPbe8TNs/s5VWP36d/P5+DwCIZUzIgBUB4YxfNaFOHzPJzDrwmdjz39ei94lJ6I6f35WgdDqSRB1ptpr97Eg01oh6kpXutKVrnTlsSZKa/TNXYy+eYsxc+05WPjMK2DSsfgO40tt3kIsetkb8fA3r4KuVrHrB9/Cote9+Sj1uCvANFeIaK09WOsXrpHZj1mhxDYH/mort95PFhwhC4S+SCu6gGujJDeD0Saq4Drgp14wBuzGH1yPQBNS7RAFCnJGHk9SctwpRpOstcyICvJ9TvPWJqVI8K/90UjZ9fnWNlGjiIuk2vkOFPKd2Ii2/WcCSfH5HQKhuMHgLhJSIlAKKFWKDMlxIhG+DM0Sdl6JRwlbnW1w1D0tzidThDiXfeINGRfgERom0W4MW+4QBXvUXtDHgIhZhOQBzrX3UXi0AICypnUaWTSAUIKIEDd7mqrXndha9yJ4aSoGasroWh4NAUIEgVOICNQgnQD/QqIS/pxUApSyaD9FDOACGVYMp2Xh3yQCx37+HipI49EOIv7e8vuWlYmAQ9JUI7GIDbvK22ciXeh9d3xyyad9laSCw7qKVio81SyHiAJFDtdGEdm/Ke3HYZsgtmmvJyFuEd9P5SYJCjlAAW1LiNTS09CXMrqhnwSWwmXkEEr/PNRY0dwkvhlG1I1SBSBmdDQg9nt94fdd/K4MUF98IobPuRC7/vPbaO/bi5H77kF95ZO8OWgS72iXQxTItHa770pXutKVrnSlK076V56E+Ze/CgDw8Of/pZtS5yjKYwohykf0y9ctC9aXQ0GIu0Dr1NLtki1HwU8wboPdnP0okQCjT3xeD3WANpnJR+dL81ZsEMQvBhJ5ffZ80RjRk0s5V+QFRsEBA0TD5JEmry/lJ0PwDByHIo+ksIXD3lkIzqftj+x9xp5r3kMP+Ft5REzWK+IDqYLfDBwa419L4OIujS+5La16fyelQCFrk6g9NCY7PjpljxT8I+aAWJTHxbVSjmMCBdXRjm/FbvwOGQIydCnwSJG3kdAIGfxtIvOvJEIo46W9EQ+SAvlVCAVy50ltslrmy/DDyLdRhAzJ/vJ7kBQfk0uQW2ZoyzY9LiNb+OLyGBntZM/bPw8AKKMc6if4Pka+j/RctUP4lIj3RTwchwxZLzO7rZXh1EB0GxMOqmhRoAkQEU1aQSOpBEEcq9b9q+q5xM7syQJzUfoQQowO22ffsGXbvjypjmDsnMDvQVu8Qz66Cm9eQwGaUyb+u0aJkeUULzlKBe+5RIY4PJ0XRw0Q77iYV+R8ydMaHWs3a7PmozpvPpAabL/mKzj0q19g8MxzcsdORB4LcYGmWroIUVe60pWudKUrjzGpr3wSAGDP9d+f/MEGmTY81f+muUxrhKgsZgRMgXFeYq37JWvEFOCNOQUliJF/d9j6IHOPziv4LGQppI63Qu0EKUEkJ4V4EAWWh0wAW4QUBRGhSxCjMBaPx7EpigUzQaHo2iGHyBaEUEUFFjZRJNiaNuIaDN9jCT7QBQbcL99yVvkffa4QlCpGf6SJT6cb554o466LY7/QOKHnKMagf0F8/2i/4Bawx5qHOjHvJtLQbecCqCwyROlAUoocHSHkENH10f4ov82xsYwKeFulYvuho9QhQwJ5ImSIeVaeBZ5WgKQS/sbICT1jiXYZb4yZ/FhyFr/JHesjRhwxebxnzePYuPQs4t5QE1ErC+QZzDMeAs0xijj/Rr5NPyYVc5ZoTPE7m+90Yk+kKR6Rh9YZQhvtsovkFpGkRgW8ItOp4AiqHLMotp0k7lBcEDRtRnU0V5eQIm3LUfuQ2togtWQ6Q2Ulj7YS6uliXWWlhgcgSqRdIDkkhAgXIeP8HSKOn5hHlRkDGaLo+oJjmvNiFRxSOeYSkQzc2LEd9w4AANJmE83ND6G2aOmkEKKuhDKtFaKudGUqZes9P8bhPQ+hPmMRFjzpQmgbTK4rXelKV6abNHc8jHhoBuqrT8bo5k2ZQjQJ6S6ZhTKtFSJnLeX3qyL0rcQSKEJF+LeSSKeMGPFavztYgA9w3CK7HXm/EyihVBayn01FQmOshUzJH2ld39fy6YTslZS3GMu8z/xj3b0QqEvBera4rDzKUiLOw8ceq6XFk7eSVOohJTKGDyFFFOvHQ4qYMxQ8BO86chfhkwrGvg5jDLbdewPajYM4tGcjNv/m+zjx7Cswd+VTw1xX8lh577w+Sq8ymYNvLE5BwCWyxxZFvaaI11pn0ZD5GOIM0ZjzUJ/AGmZkiJBNe7/JJPbRT4FcBiL4Qkq7iMmU/JMs/jTNt6+YVJHNAWnFR3ts+yJOD4t33xkZSvO/cVwgAjCY10Xvo3IxwcpmSDnfpB7ixAiRvffUvk3XowRClHEUXTuAQ80MR6y3Tcbuuo2YtxxIrXPHMifSNqK08SLh22umqNaCW0QiEaOsbpYTLUCGVL7U3peX2mGkSBwT2f6MaAMCgOhRU2Ru8pxklJB5cbYexkDHrQS/AxyHKODSSa5nAafIIUMm95uWaKT2nh+/5+IZiw7wkK+4X40Gdv/X9zF8zoVIRg7DtFqF19mVyckTi0M01YOlJLPz40VKPxbHSI5lIDGlFJ586Xuw8KSL0VOfCQA4uHMD7r/xy9jz4K1IOs1jd3IrUWOKB+gU5z9ixWqqZIpPN+WB7qZ4fhntVMavdBSl1jeGB8njQCKZLmgS0j64H6ObN2LGmec98g6Y4/Bvmsu0RohYYy5Cg8oQIapSsM4rj2UrXkSflZZXGvtRb602jzzC4cxnQkdMpsonKis7ygEaZMkRn8MeSgZHBMV/h54ytq5AirTMDVYkAhlyiIZxnip8QoeoGJPi8O6HoJRGfc7SHMqSxu6+cX4zGSWWgw65a+BYSNR/ne+UEQgKW15eFOGxuEPt1hFsue967NpyG2AMTnvW21Hp6Xcdz/GLsn86irH0tOfzfdj4q29hxz03YM/GW7NDcd+AowAAIABJREFUohhD81ejd+ZCVPuGUBkYxt4Nt+LQtvuw6pK3oD5jATd/4OF7sPeh9egdmo/5a54G1TLoNEdQ6e3P9dn1J9sXNUwBjyuPuNmQMS7qdcfFoIlswllGGDzOUFbBQ81om9GjPKJAKA9zTziSu2feOpKUPcY2z8fm21DaILJRtBMPCcqaojg3tg0bbTqthohvwEGT+/3IzyXvkOMU5a16P6mxkh6hEpQUUEo2tunvvOUf9dhkwDKze+r6KD2oGZmh5+jFPzMR3PwCMDeS+85cNCYb2UI5DhGjaHkEw4gLNQboqXQw2qlwn/qVgVYmQHmqFv7siQoCAgkhnpHkFEUqxeFGD2p9LTR1poilzYj7//+z997hlhzlnf+nOpxwz81h7r0zd3JUTigLRYIRImMbMNE2DoBxwDZre3e9a8zP/tlm7TU2BrwEA16SCAYsAQZLAgkBQggFNIojTc5z79x4QnfX/tFV1dXV54xGSKARnPd5Zup2nw5V3VXV7/ut7/u+QBbVWiPsGpWBjhyi4/kou8ipQWRd7pflQdYJGdIR1jMvs2z8ST9VijIvMxtjJjdHJXHEgW9eT9/ak6iu20Bz/17K48vx/RJeqUK8uNiNPP0kyAmtEEV9eg1L7bAGabugibn9WnSgvxjs5KlmH1ZnNeTm/EdEZ2FOz1HnyvxkZwKrmeUFq67ukplDsM2WwfKKA7QJRuYqRnoitWD+YkLD/HJR0mjQOHqQxsx+Zrbfw9GdWyn3jdA7toa+4VWEPQPU5w4yd/BRFo/sprl4lHLvEHGrQe/Yagae/QqQYcdn34nEnZFlpflbf7h1O91gaHrba4nOSp81d0uZsOvBG9lxz3VmX7k2jCiVkb6wSI6WYmCl9sjmIsHyLVcgEkljcZr5w9tp1eeZ2X0vM7vv1QeZxiaNurlGffYQ93/9/ZT7Rjn44LfZeceX8LyAuFXnpOe+hb5la4vLlo9DjuX+rzJ3GNJ9okP6O3B/miVb7dMTsbNkJq0lJCDftw2On19i6WSYpE4m7lpE+1KPv7gEcSjTvuEcoxWHLIGvfgD6MEEusXOb0lTV7M/q18lJw+zWyr7a49mDQbejnaHQgbhrxPkmmqVVZ26QgfU6XDf+OP8+TaiQwI5CmL9uht5pxTXdH8XpRX1Pmr918MaG8KnLwLTPTRR7PK77WhE61rFBKe20ulm6TNz3pt30bT3M6Tfu/JLrE+5yWTtj2irtcBwFZdsx+IoJsLMxlNEvjEWnrpsdsO/W65jbuZXpe77L6DMuo7p6Hc3DB0mW6lRGxpn+wbexU848lgi7XV0xckIrRF158mTpyD4O3X8rM4/eQ7Q0R7l/lMrAMvomN7Dq/JfQnJ1m4dB2ju69n1Z9nnLfCAOTm1h+6lWUaoP45Spx1GTPnV/hwa9/kAUxQM/YKsJqH0Gtl7BngLB/GJnENOePAumHVgiVB04IRKK2EwmxZOHgdmKRUO4fJaz2UuodekJtPLJvK1u/9UEAPC9gfN2FjK48k9rIKjPBH9ezmjvI/JEdHNx+O0f3PwDA8pOvYuWZzyOJWhzZdTdhtY/D23+AF5QYXnsmvWNrSOKY1sIMcatObXiKsNJHqWeAuf0P45V6CKv9lHtHnlAbu9KVrvzsyYE7b2Tk9EuY3/kg+2++jvCuIfo2nsrOT/wzEz/3Mup7d5FEEcLvftKfiJzQT0/0pWtZ0oHWyWy/zGLqFJxPnxEL0KQ8g/Loa+QtYBcpEi2B5yxRFcxMkyPEsgZ1lTSM7lo0BrLPlwgMQdBA5C5SZCFDkEK0woJwk6jJgR/ezPzebTSOHiSJGgyfdD7rX/DrVPpGEZ6XW0qoVfqoLVuFt+mZ+eZZFpQfllh19gvo9eZ5+N47WTy8m9n6HK36PM35aaRMkEmEX6pazZMaHlCPSf0tBJXBZYgwJFqapzk3zdDGs1h+wTV4QamIEIUWIbmVIkH12YPU5w+xOHeAHXf/u34xnPOC/0apOpBfetLXcT2BhUJVEknUXGL7D69n3yO35g4ZnjqdPfd+naktVxEEJcZWnw3AwMQm84xaC3Pc8Zn/QVDuJWoscPrVb6NnYCL9rT7H4sw++sfXc3jnXey+66vUZw+Y65/x8v9OqTYAQGN+msXp3fSOr8Hv6cu9C1fake9FEzyL7iTcoI6670dWHzNIqF6a04iCKjUhVI8HXxaQocIYMilY7G2ZP8dpiEl2alnOMpAkJWmO0e/PpNnQrtbO6oxIrKUiO2KfVbYNz6B/d5fGXOKyvr+FFHnOpCPtuQA6unW3RSVcBENPLwZxEyQF1/z8+9NLSjrRr1la9qw51YEJEh28UZOtdfqPRLDkkAaXvJCFOEGqRLAFpEi/szakah200XN4ZTbKVArTl6qny6afPvSmijsRWUEc7fYlsYW+6FLP/Q4SXEjDY6M9DvpYQD/1uJEgzdykxoiZi/NIkR2o1PRD9/oFxFJy0ovext57vk5jen/6kx8wcuYlHLr9BvZ/9XOUR8eZ+fY3GH7mlRyXPE3iAv2k5YRWiLryo8nioV08csPHqA5PMLLlPEqDY1SGliHUhPJE15rLtSHGN19sxW9JFZ3G4gxeEBLU1Ic8Hyg3Bx+bmEWqB0aNJXbddC1b/+9fsu75b6TH4uPYIpOE3ffdyJ77byQIq1T6xpjZdx8Ap175W/SNrD5u2NiWfdtu5eEffBaAUnWQgbH1eH7Akb1bmTv0KBsveDV+0NkNPyjXGFxxMo35IwghCMKq+S2s9DEw2U99/jAP3/wxACr9yxhaeQpBpZew0qeewSJ3X/vn+OUeKgPL2Hj1b+AFJWa2/5CekeWEfU8MQetKV7ry9JSekeWsee5rmd99EQ/923toTh/koQ/+JRPP+3mSKGJp1yNM3/YNmtMHGbn02U91dZ+2ckIrRL39dQArzHy+BCtImGOxunyFJEqTXkJmORlr2eHeGFKpsipoCWvBOF/HjNSpf9fQDdmH2RcQC8vFPM8hcq2HJMSCgPLHZNyb/P1FJJl/6F7mtt/H0ft/wIpnvpihTeeYdkmrioUEo9KqaxtkqHCs4qlIy4ISCCpqycvmjdh11mhBEoosgaG28MMqq57/Gnbf+FmO7rqX8sQkzdkj1PfvojFzkMbhA9Sn91Of2U/P4CQnP/936Kmk3mD7HryFAw99h3u/8T4mNl7M1JlXpyRxneQyTt2MpZQc3nU3czO7WFo4RLVnmErvKPVawsM/+BzV2ihbznsttf4JVWEBZ6RBFYUQSONWrd5f6JlHlMQxQngsHd3HhoteTalngJxISblnkLUX/iI9g5P0jEyl17S6qV+qMrL+HOYPPEp9Zj8Pffn9jJ50MY/e8FFq42vYcM1v4gVtPH1kStaFtKv5FkKkydaGX2KhQgV+kS4NOql/zyNFBDLbp/unQUrz4yRDirJ9BS5G6GjoxlL2FEKEUap1e4STrkFfwbeSZ5pHK45dulwiC8zKOGcOb8TwPCykSPNgzLFuSh49jbj1sMRwhtw0DraLPgpk1clhTeDXPGJkiNh6ntPtS4QVFkHtc5DTxHCL0rLeLPa7hh+yJGIzzqul9OG7XCIPaf7WyFC7wI9pu9KyEkSIUOaOaarloAU9J1nTM1iRGCILIVJzuAmAapLk6m3rBpJ8wFG3v+rNNgR7g9TrfqlIbsfiEHkueqSJ7SIPI5okzxH0jW/gtDf/NYfuvZW9N3yWfdd/2hy36lW/ydzD97L9n/+W45Euh6goJ7RC1JXjk7hRZ/q2m5i59/sMbj6Lza/6Q8KevsJAfrqI8AOWDu5m2+feS21iDeWBUWoTaxjbeB6VwXFKfi09UMHTExsvZmLjxbQaC2z9z/fiBSVWnFa0kg7uuJ1dW7/GshVnMTp5KvXFafZt/w6Mj/KMZ72dSi3P74mjBguz+1iY28fi0b0szO5l8eheolYdzw+oDkzQMziJEIKZvffTXDxKWO5lZOXp7dslPMbWn6c22v0uWHPJK5jeeQ/bb/4U8/u2sXhoJ8vPfT5L03vZ+um/orZsFb1Tmxg56fwf/QF3pStdedqK8HxGzryE3jWb2Xfzl5h78G68coXp22+hPLGciat/nt3XfvixL/Q0/T78OOWEVohGe+eBzGqIlNkkrTDy+jeNIsXGdTePGEWxT6QQojhSx0Q6wJfS7vW6eaRMABUALBc4rUMnMkEdbfdjkf0mYiyLw0GGlJgEj/a5ymow7bESUUbzcxy56WvM/uC7VFasZuUrf43S0AhE0MLyqNBWpa6rE+hP2ADYsZAhda30ecjMK84BxzohAcajLLCt2qz9UiYs7H2Evo2nceSB2xk+82LGL/q5tK4WRyrSAQrVNTQq4Ae9bLrq19j61X/Ar9aY2HhxWr0YRCtmzwM3seGUFzG8bIuxxsYnz2L/I5/GSwQLh3cyfehB5qZ3sjC3j2Z9lp6+ZfT0T1Lrn2B48mR6+icJe/qIowbzc3tZnNkLwMS6i/CCMg/c+hEe/NbH2HjhqxGeVwh0WXgX9iMSqdI0tOZ0wmof04/eRbl/hAM//CZ+qULY08/0Q3eAHzBy8vnM3Pd9ZKvJ8CkXZBwTP5/WxeXc2IiRyy8yXlEaOTLelvnlTRmLjF/kIEUGPWsTMqOA8Lpu/uTPkSJFp5JAmrpqnopOX2LyFqtTTeiKFlmARCdtSycukf2OhMs1s3hNAInzBqWfVcJ0+8cR5a2ADOm667HqcF5S33L1p/Ey0883z/8xTbCQFRcVM3U3SJyeG/WcW6xzM/RpEBQQoWqYR4ogS+paQIYcy6AWZp3XcybbehDk9i+qhgsr3QeAjD2TkkR3/HZcIVv0vJZL2OoClw4yZFNBi4m61RzfcpAifbE4KfKLjCjOl0q0q4Orer41HgVU+8ZY/YI3IJOY+txB6gf2UD+0h5nvf9u9YFeOUx5TIRJCrAQ+AkyQvqn3Syn/txDiHcCL1L4DwOullHuEEB7wYWAD8EYp5Q+FEJcDNwAvlFJ+UV33S8DfSClvfNJb9VMmUkqiuTmi6Wmiw0eYv/dumgf3E80epf/0c1jz1j8mrPYViKVPNzly163UD+xm/pGtzPzwu6x52a8/7muUevrZfNWvs/Wr/0hUX2Biy6WUKPPQnZ+lXB1iaGxz7vhKdZCR8VO44+Z3Uyr3MTCynmUrzqLWP0GlNoLwfCuMgfrgAH5Qpn90Lf2ja3PXO+2qt/LDm97LgW3fZXzDBT/ScwDoHV9L70R67dGTL2Zu9wMsHN7JxDnPpXfVRlqLc+z88scIewfxSlUGN5zxI9+rK13pytNXhOdTHp2gPDpBf5A6fGx95+899nldhKggx4MQRcDbpJTfF0L0AbcLIf4D+Gsp5X8DEEK8FfjvwG8AzwG+A/wB8BfAL6vr7AL+BPji8VZuqpa6b5u4FsZ7wTNokd4XKUum0/5GHJg4Gk1VtiKnbCn1XiEOdqyVbJ332HUWKAstbiIq5XSfTF3OJZKk2eDwF/6NeH4eUSoRjo0CUH/0URrbHgHfxyuXEKX0n4wi4ulpRKlMMDxEMDREdfNGBp59BX5vH2FfylWJ43SNGSxUQG87sTc00lKIwWHta4cMpful+medYwWLyx3sJKy0eUMGjbB6YP+pZxPHDZJGg1W/+BuUJ6ayjCVBin6AZYWZtBSqnWoJrdI3wknPeRN77vgKd37+nZSrg8gk5sxnvkU9m7yHxeo1l9E3VIz4KiWKN5PDcQrb0nLp97yAdWe/lK03vZ/hlacRlmv5M9oslWlx451kXkEe/Su30Ld6i/lZJ7ptzc+w48sfZeSNqUKkAzOaNjjcIRsx0vFaXD6Xub9BivR2drxJMaH5Kr5TaROILkOOTDqR4PhY/TEqgGAgkQ4Jv5DWx0GKEml5nrmvrUOZeZ0W6+KiSBmQk128U/DG3HY73lBcRIYM+mAjQpBLdWO4el7uVHOyiS2kL2YCN1qJYTXvyalfoudC04hivSPp05KBhfypQ9V2OcgCND4WMtQTpMhQb5DdyEWIFqNSbr/vIES6va16QBLraI3OGH2sYL/2fv0sTGqlfGl4bdYxidM/NVLlNTVSJE0pTBA5h6emJjodd02qec3TXnxB5szs9uFOmXS6cnzymAqRlHIvsFf9PSeE2AqskFLeax1WI+tKPqqPkB/+dwKhEOLZUsr/eDIq/1RJXF9CRhFBb5/ZJ6UkOjpNY+8eZr5zM0uPPETPhk3IJCGqVpAvehn+QC/TX/4y8cICfeeeS9xsEB08CELQf/FFVH/ldSAlcauJbDZJmk1EEBAMDeKHqXJlSKwa0v8pik7qV6qMnn+cbqOPIZW+UTZc+CoaCzNEc9P09UymXmLHyv32JEltcDkjq85kxw++xLrzfsFEAT9emT+4g9k9D+CXyoxuvgBRKnq3BdVeznzjX7M0vZeFfdufrKp3pStd+VkQSbbe3BUjj4tDJIRYA5xFigAhhHgn8FrgKHCFOuwrwMfU/l9zLvHn6t9xKUSeQ7iwrQod8yPqsFDvxsJot8/ExFDxLoyBqDlGJXVcLBAxNHbvZvq661h65BGE7xGOLWP8la9h/s7vc/RbtwCS8sRyaiefyopX/TJHv/8dpm++gaivl5l/+xyj17yYudtuY+Uf/BeC3r7MK8SxBr1KT2bZWOvU1qFtLc1OYixIjdRopEhHh26Bp/g9mo9j4mjI/DsQiSAqCeKyKCZzdZNWHmMJr2BF++2Pyw6AWNffSZCq+SSe9vTQ+33wK0OU+oeQTUmElUrCinqdhJ7hNxkRtEEWVDudJTT3/SFg6vTnsvXG93PP195NbXiKnsFJasNT1EamEI4XSRw12fWD6xG+z/zB7TQXphlYeTJ7vn8LzaVZlp9/Tb5qho/gUxuaojY4ZaWfsCxPMoTGcNwM6kSW1NQh4LgpZjDn6mvJ7G/dTzXSoJ+Fsdo1QiQMLyUIXYKOOsftFOUIT/gQx9nc7ZBBsphhqrQS4Bb6susRp6Ndu6k77O+Ew/lyvb6y2E12v8iX5lK++uc8XwkZh8f9zeX4WHwvN+lv5tlH7mTN6zJ93AJItZeZe24h71wbaFNKkb5X7e2m0HjN51xqhea4IsrvzME6LpGVENbEMVKVc68RG36Tmrs0YlSJssSw6hOn7cZIPchgyTEshfUv/0iK48F5N3kOpjMnOCiX19KwUpZg14xXjXKqiOLSiRjvKcTWb0pigwLm6/g4ba+uOHLcCpEQohf4DPA7UspZACnlnwB/IoT4I+AtwJ9KKSPgFe2uIaX8phACIcQzj+eefa0075MeCDZhWg+KlibNqXO0gqQHS6QGZyR9GkmAh6SlZhU9OHw1CzRVqvqWWl9IVNyepNVk4cabiR64n/UXXkzPr/4awvPZ/d5/RH7qXxkcn2DtW95KaWxZLpfNxDOvZG58nODO22D/fpLPfYIt17yQgeHUXVw6JM88PO4QQLVosp7+KFrh+vXfJvigA/Vmof3zpRdmHw3fXV5zFCISGOwJ0liHVYHfkIXs9q5CZMcr0vVK2kzwxxSZfvREYi3/dErzYVyzpWmTCZgWy9yxAAN9bVzZhYKmWxTeT2LeUweFCICQZS99G7OHttFcnGFp9hAL22+gubvMuvN+Hq9cMec25ufYP7cNgPGRMcYufh7C8yjPbWdyzToGayqXk4yRcWTQQnuJTb+nwWo2pJvz0zQW56kOTyJKIa3FWerTB6mOrcDrqWSvVMP7rnLhkq3NtsyW15xgpoW8aNq920tMhYMOsKZ0OnsiBCMiTAPvqY+EDnXgq4+Dpz8Iuk9YfdzrlPbGVYi0tqWPy1cqL867ttPiuEtmbp8equgB6VzL/ruTQqS37Xeiu58bLsEZb5ocn1jvzIRNcJbkzHsrRCEsfmlHhA9epsSE6gGGqgL6jHISESorLFCs7bJavwvVdmgUouL9tUIUKovIj1PEtJSkZU2m+yNNcMejKTSZWjU60POmUoickCsAwyXnc2iMwA7bRqHO+mGgnnmgnrnO2xeU1bzT1NZFli7INmAA4rL67pTVt6ysvn96O7CMdWe528yxxyNdgKggx6UQCSFCUmXoX6WUn21zyP8F/h340+O43DtJuUSPSQE+7C8B2SDJeEG+UZI0H0grQoZLpFTnWPXUVuzTVF9orRB5qtNW1Fr3kho8S2qgLR5cZPoT17P0g/vofcbZDL7h1cxXaswjiaYPsnvvHgafdRXT/34d0bOuIkiaeGrNWERq4KzexMiaDbTuuJ1wbIzWyjUcWlQRuF2r0P64tEEd0h1q053kY2EUEM9ReMygcT0dtIUpCvNiliPK8VQTSYqYHWpFWQ4lVxFSHBZtubrIURLk/253TNvB6njiuHmsvA6KkReBH+UVIVtBikOY1u/EmvcLOYwMOpBHxIofryy+kOxbDX2rqYxDWUp2fe+LfPPzf8/Ks59PWOlTSrFg6NzXMHdwG/Mz+zl02w3UZ/bTmDvMfHgba8dPBuDhL/8fZnfeR//KLYyfcQW94+sQQrDjpk8ikaw8/8Uw2MuBmQUe/vqHmd1zv2nL2GmXcvCHN1MbX01UX2DTL/0hQmWJdXkP7iTr8r2kTxaTyVKS0nPyvAgT3TqR5oX5qmN2Qoq0JLGHh+RQqUmsb674fp6qu7aaXbqMn1gKc5s+nCtdAyGW2TjroBdIpw8cCyGSljJzsNFqq+y4SJB7PxMR24pZ5imHLDPOHB6Lu232hzLv0QoZB8xVcF2kCLLx4AfsT5p4Ormr8iTznCzupTA2ClApSI8pKQJbSSlRptSKEbLIIVJK1ZyqwIIqW8qKC1V/aiU+DaUhN0k7daxfVKznfqVYL+VR5QMNDbdl4ipAhf4TQZCGzCNY0qVS/pcSp1Tta8RZbCIT6yot4x5V54rKHVdR37JSpiBFmkGhFKPYGbtd+dHkeLzMBPABYKuU8n9Z+zdKKR9Umy8E7jueG0opv6o81JY/1rH1OKTit2jGASU/IpEenkhISBEiDavaZZR4BF5CLD18kRAnHr6XlvrvMIhpRT61UpOFZolaqcFCs0xPqcViM6RciljYv8T+v/gAPeefwdS7/gue14toCRI/wWt6iOEaQggae3YjwhARKnhYqElUw68SgqDMwFnnpsoE6cSrE8bKIB2MSQAiEshApsHEBOpvhYzoMnEsT31DqQqR7YpVdm1dJmWJ1xDZ/gr4dYh60joEC9CqQbgArR4IFyGqpoNcnxMpYCOupEhJYF3ftN10krT9uiz83raDpMdL33pOqkSov730Oej6m7IK/lJxf1QFGQiCJUmrKgiXJK0eQbgoiXqyScZvSJKSwGumJTKFp5NQ4LUkiS/Sj6Vuj6ffR5qfLSvVb7HbDsGK815Aeeut7L7rq8TNJTUmEuJmnbWXvpLRjeel71dCLCOQEpnEzO1+gNbCDMtOv4xy/yg7bvokXlihb8VGDj3wHWpjq9l2w0eZePFvAjC7536E8AhrAzTnp2ktzkGSsHR4L54fIFoq2YSf9SlTT2dbvxOzPwHctiUC6ck0ibFO7WFty1jRwD1JEnl4QZIqPH7SdhwnUuD5CcQqHEY5Jm74UI1hyScpJ3gNj7ia4C95xFWJvyRyfV4K3e/Ba6TJYv2mVYbpslpSSpULXcaldInci9KI6l6UvXudIFi3PfEzUnTWH5xSH2tZ8V7s7BdqjLfp9/Z98IBELXO3UiVWzx92G3KlOk633WumfTspSTx1bT33EAkIsveWe5/q/WVzjgVxCYhbPn4Y02oGhKXIlPVmSJx49JSa1KOAShBRj0IqQYulKKQatFiMQnqsspn4eEJS8mKaiU/Ji4mkRyASWolP6MX4niROhDWPp2VvuYEnJEuNkHKlSaNewq9GxEsB9MSw6JNUErx61m+SoC0IVpzT7flMjwVrrjePxp0bTL+xEky3YmTotykTZOjhtRKS0DPzkd9MiEteisoLkb7LlpqfdB+Ijx8l6nqZFeV49MmLgdcAdwshfqD2/THwK0KIzaTf+O2kHmbHK+8E/u14DqzHIYFIaMaBmigzbaDTerRGifQSW5xk59hKEUCt1FRlg8VWiZ5Si7mlEgff9zl6LzqN4V+8irgewFJqWZFAUkoQBIy96ZdZvP0ulr/99/B7e8092kXJFXH6t548RayOi9T+SO2PRBYxO1IfGXWMKd0VB2sku94wcTlfJuV01Jr9lcwyjmrpPVq1dBJt9aRlVE3rHZdJP9RlDFoTWdc3bbLr1mkJ4Fii2+CiRvY+z6p/lJZelCpFXsvarmQIQVRNUbRWNf3ItXpE2q6qB/UMmtZWF6iPY6L4F9LyqnKXTsyyYXZuITaJD0IKxk6+iGVb8l5tM3vv49FbPk1Y7aN/xSaCai9SJiwd2cvRnVsp9Q4xsuV8hjY+g6BcZWTL+czveoiFA4+y7orXMrjyVO75zF+wcHAHQWWSqbOvplQbZHD9mTTmjtBsLTDz8B0kzToTF1yD5wfp3K76YzQ3iz/Qn1M4zUc5yN6J3o8+11WK9Ec0UB9R+6MKRlFKIg+vFKdKURAbNMAubaUIwLeVosVUKRKRIK4meC1BXJWIllKKFHoSl5XCUAaSVCEQuoxTpchLUsVBxFkJkARpYuIkyL97Fx2yP0AdUSIHEU3a9e0OaGnhPpZB5Fl8OukpJUgpR1IrSSoPoFQxqjQS6DUFMpTZnKTmGyIBSoE3Sq3u1okzkNsoRQCtZmBKXyE3i80SlVKLehRQ8mPqUUjoxyxFIaEXsxiFlHxVKqSoqRqtS70SoKkSvkKwsnm8aY6pllvUm0GqFDXCVCmqK6VoqagU6ea0UxSOqRS5ee7azg3S6jfWfN2Ki6VWinwfr5UQl/2CUgRKwS2nSlFcUkpRSDFfYyfp5jIryPF4md2M841Tct3x3kTFGrrR2v5Ch2vmxCyHqeUvd7KEItEuC9SYV4ZiKUxwsIoKHFYN0nLXx7/Dgx+/g+GzV7Hh1y4h3tXA2Dj5AAAgAElEQVSktecQK97+SkQQ0bCIgyYMfiwor52iMrUy3a8JnO0aoiwI1zPMuNDqb6y9BGUGlcgdc6ynZgaigU/1soXar/kB0tGaZAbFC+0mqidavYxgEVTjEkRRNlHbAR4fq45ZZTuU7mH2ElqbpQaARKFWrht57mOh6RsmFIF+jxCVBa2qard+R1Z93KUWk6jW5RBZE2En4rWGyY3Sq645sGILp778vzC750Hm9z9CfWY/CEHP2CrGz7iSyuCyrN0ShPDoX7GJ/hWb8OKUj7Dy3Bey586vMXTOq1h+ylXpsyAN4Fbxxpg6/8Uc3bWVpYN7eODTf8uqy3+R6shyZh65h+3//kE2vPr3qYwtz0jWTr+0lduMPCrzx+pnEFsfSnWYmX/1crZaxtQ8I22kGElAIBGexNODRivgugs380vjmT5aVEw7LZHpcAImYKlfPKaQXsflvgnrnRc+iNa5nrOtj+9kMDjX0oqe18xI47qfm+US/WycpZ3EpsoZI1JtO2EMzLvS/dQjWz6zx6wkC2TbQWRCIbVSHDrzs+9wP33PIlqnJ+nvgeYbVdT8red1W4lyuUi6XzbUts7KZNzUhYfUyr+zxNqRTE22v0B+78CRzBxQLKUo0hOXukSrHZkNQD/nJEvv0S4xcVd+ZPmZW3GM5us89LEbiOYaiGaTFc8/hW2fuZvL3vsyHvziA9zyqg8BsP5PXoIIjj3Qu9KVJ1OE5zMwtYWBlSrekHBn3mPL0KrTmJt+kAdv/BCrznkh1YFl6XWkZPf3rmffXV9PL+eHyLhlJtNdX/8kALu/9ikmL38JldWrn8RWdaUrXTkRpbtkVpQTWiHSMGkBTm9DuOuEGGkX0JIfUwla7PjKbbT2HGHy8g3c/df/ycFbt/HMP7uM5esr1H7tbKoVycCVZ1BdPsjRehaCvqktXq2lZ1HQckWhhEJSR41CGAvOsQJtNKQY9DD/jGyLMlGeDBmpUhNfZa7qmVuu9Qy1Ja/Ta2iyqrFCFaISpTBtbJFWO36wOyA6rntrW3GQo2MRzc3ymSbw6oCNenkissnU2T5Il0viCkTNPEIkIgoedm4CUxc1sBEB1/MuexYify03l8fjEOG8P4FgzfkvZ/G2/2TrV94NEsJqP5WBZSypFCOrL3o5C4d2MXHGFZQGxiCGkS3nceDOm0iaDR759D8ydfUvMbDpjCxZpkvsByswoQVbgfEU055BJmgf6XJh+mP64BLj+JAf3zoMBqSkap+EWN1RqEHj6SVgxyJPDKSRVdiQqwueme1LGefRIsiQRS1uwESdQNndl9vWbvfu71lVOyJEOtimIVJH+b9z7XPSmrRNqWP6tJ4v1dwR6v6p3l9i9WPX86LlQew95lhO2iBE2kVfqhBbsZ6vFdIXSY9I/a0RoED1mx4HGQpUw0vqQTSVN3FaVadUfayu6tzSLv3CJ44S4iDBW1Joo5NAuF3KDv1sOqGCnZbUpSfQqTnMtNhSL7Khx1An8c1SYTuHjq786HJCK0RPhrRmFpi94xHiI3OIZp09X7qLZ7zz+QydMsGay1bSWmiybE0VAL/kc/KvnseRevUprnVXuvKjiecHTJ5yBRObLyFqLPLgLR+lOjhO38Q65vZvY2zzhYxtzn/Al1/0AnrGV7Pv9q8g44idX/wXam96B15Ye0rb0pWudOXHJJZS3JVMTmiF6OBCSlR214M9pNGQqyoZoCbZ6fQccSJ49O/+nUNfvYv+czfSs3qYUtVj7Rsvo+/k5UQSSoM1/MEa84rLYCwNxyLxvcQkoDTuxiYUvK6bXhvWFgdZUDpfoRXOurVnJeFMr4G6tsUx6bB+7QJT0sMsjLuBzLRIN7aRg3g4B3f+jbQ9Ojp+gbPjxEMy8YgsjoVrPT/W2rtypDN/21V0o+ZppEy/I89rcz1d9zjlRMWVbBtStMAE8nPj18jsmPT3/P1sa9rlhxlpR9z0nGfdCWHTx1s8nlw/ESArIX5lgM3XvAWAo3sfZP6HN7K4dIRy/wh4GQ8mCaHvpNMpr1vLoe/dSHX1OsRAjchPaBzch4hiyhMrMthE0Aa5bI8UCStmgU46aggcqr06EKpGijQyoAP6xYmXJZxN8naz8FVF9ExmjssQQpPFwQpRkZb5/TZPrhCs0kZvac8ZKcQMcvuyr565cZrI398+1xVPIUSGN9QEv6E5ger6hqOY77eGW2TF09HnJO4418e0G4cahbPJ3XGbCru7pJdxO/UuZ16J1HuM9NybeERqImmpQVlW7vVmpcDJ7OtZcepMGBbtWKNK/Q2plPPu9S1AtiSJlVZG83yyuFXOfGCVLvHdc8MymLGmUUsPqXhUspW+IGFCWagYSjpAoyoTVXpRglT9QXOJ/OON59aVY8oJrRA9HokWGhz59sMc/tbDREcX6dm8gkNfvQuAqbdcTW1ZjyFTZ8nKutKVnw3pX7GJ8dOv4L7PvAsvCAl6eqkMTzJ2xjOpTKWcoaDWx8RlLyAJYXbrD9j7lWuRzSYyjgiHx1j1hrcQ1Poe405d6UpXTnQROEvuXQFOcIVoqaEikDbzifqEkGlsEsCbb3D4C99m+mt30HvSFAMXbSEc6ePoHY8y+tILGbrmQhjooxHFOQ4SWEleE9d7IbVEsuSBWIkSndKgQK73UBZvIvYlMdJ4NplYZ/pYRz8TNoLyGAiR7bZrvNQcC1ijHiZBpt95IAgnV1qBX6F4EImVoBXFA7C9uuxSOPv1Nez6u27I7SwdYwk7XnpuWgqDxGmkyM84UAXPqSR1yY7dIH6xdYz76tV+k0S1kNKjzX2cc11+UhIIm5jjNNwp9SVt9EzzqEqCqCwKz1d6MHLOMxk65yKixXla9TkW9j7CI9d9kP5TzmHZJT+HKJfUNaBZPwpJglcqUx5dxeLObez88D+y+vVvxav10Fky1ArIc6YcdCXzwHO9znSlU05QYqX9MHdxPJ6ywJDq4cWe6e/mGZc0oqJdnfJ9Xad+8ZsWqumOA5vTZpW5d3MMhEjHFIM8MuUiiDL/GM3Y0n3Ob8pi3zJ9TiHELqJhBcrOkCFVOqiHQR8tVEgjbmYq9EQuTEhHkVmD9O1iByHSdY1j7WWW5NAiyDzQEuXSq8MgBMpFVm9HiUcjUoEZVak5pTrhrBZhrz40SpDEmeeZ8nzL3o06VqfQWMrGvXnHHaKFmwSx6tw49BAlX+1Lb6ARVKkjVZd0qephBXDU3z8a+cnwuMKadKWjnNAKkSsySYj2HyFZWCSZnWfh+w+w8O17GLz8dE559y9THh8wMYh6ztgA5GMQdaUrP+siPJ+wd4Cgf4DK+BQDm85iz02fZduH38XkC1/J0btuo3FkP4MXPJOVr30TpbFxAkJ2fPy9LDzyADs/8X5Wvvo3ED2Vp7opXelKV56ItM+g8zMtJ7RC1Gqk1YvnE6LDMxx67yeIZxfw+3vx+nspr1/J8v//9/D7e1mqRCzNQ7mUWgBRnK0nA0iZWVBNi2cEmceDDkFv4lqouCi+lxiNPNGaudazDDKkzVDNYZAZh8hL72/W+B20x3i0WEhRIR+S6+Hg6nkW6GPYHA5nIrFQHru0rdtOPAvb4kl8SFK3plxdEoMUiVx7hIMgmQjIdl3aed6QR2UK8T8cZMjsN4kP1bspSSt3WpE/koRZ4EpjRbdDiHSAwGa+7gVEQOQRKH2ftD1tkCF1LYP4OHwVIy7FyKAVwsSY0XyoTs817wkHotLL5C+8lqN33sb2f/l7+k47i9rmU5m+5UbWvOG304MjWHbVi9j9uX+hvnsHOz7490y+/LWUl01kdSpMrvqhWOMlcRqgE0CpcacRI8MXkoJYeIZjZD8b4SBQZnxo9DOQhu+nJUuWqeuct/izVCXCvOMCUuoiRO0s8k5jN8n+6W1dGo9N5zkahEjVx2/K7FouypO/nYUU6WtbiJmLWDoxmdz+Inzbk1ed7KcIkYmn1i7NByg+mfqtqflh6abhFOlxqTmYicihRQCx5tRoPpIKPKaRfc2ZjBKPhiKO6ftUFV0iVHN70ubFhSKiRIuW7lsq7Erizol1VQ+vOHYNFcvJrZc4iadlmKGeIs57LhtkKPBy+4XlNig0mqqBPJ23sYsQPSE5oRUigPoDOzj43s8SHThMzzNOZ+KP35ouU+kls9LxhuXsSle60kkGzjiX3tPPxAtDkkaTwzd+maTZwCulmmJlbJK1v/o2Hvk/7yKJWuz8yHvwKz3UtpzM8GXPxuvpemZ2pStPJ+lyiIpyQitEhz/6FeZv+T7Dr3gxfl+NcHAZXsPHawkLlUj/aDQVH6icNiksp6q4tjJkIMzymTTIkLZIFUKkLJF6oJMVZkkLs6zd2srUBIA8T0Ffm0Rk6Wt1YktjRTicE8uinH3oHo587xvE9UXKw+PUptZTm9pAaXgMITJuiG0VSilJogZ+Ob+MYaw+lx9gWTZpKQuRk02sJCfKdcqDkGmd9bNwOC6xShyZ6GjQJgZQZiW5yJN0UJFCdOiEjh53hQBjxjMoe846maXLO0oRIknseByJ2KqD5phoZMjxtnHrlUMBTHs6I0PpNln/6ODVVhBdjzCzSJMwRYkKCFEbxMjlPXg6m3hYojK+gqWd26mt25ShFH7I8he8kr3Xf5ry6AS19ZuYuePbTN9yI6t+821UJlcUqiisODYa1Ul0/zfhhvQxCtVV1q+UAikEUooCh88AQ8bLM48YSV9mCUu1WP0dQJTyCIpoqfnBFwax8BsabXQbVmhqQaTT15Ck/bgdf0fz0lrOObrqjkeZSDDwR6cEtBkCpblE+nhR8EAz2y5CpPsVFhqh4zupPIyZx2ied2TVyFRKmjp7+Udgcg3peUbk0CK7NOiS3q9jp6lrtBLfoEhlFdNKe6iVHITIs/pTmRZVkbn+RnrO0/dZUqiTfvdWRuFOiHeGDKm2KCRXxAKhfhOh4hKZiOJq2wRtct59LEl0vRONxusfOT6xUMKuZHJCK0TRoWmW//nv4Jf7ARCNJ8YHaiyUKNeaj33gkyWeLC4THEMWdm1j71evZfkVL6HUP8zSwd0s7NrGwe9+DZnE1KbW49f6WNq7A4mkOjFFMDDE0Xu+R2vmMH61RnVqDeHQCNHsDK2j00Rzs5TGllFZvgqv1kM8P0drdoZobhaZRJggkJ5HeeUUPRs2UVmzFk+UO9ZTNhq0po/Qqs8hj84TLy4gmy28WpXqKSfj/5jQgkJi2x+z6BxQP7H7eWb16CciOhFqO6muXsfijoeprduU3798Navf8DtMf/cmZr7/bfOB2/FP76Jn4xb6zzqX3tPOpN3MbBKIdqrP/CLJ4hJBf4hXrYD/BKcnnaz0JyQ6r9tPSnRCz5/W+8VNH/8nuAJQCaJsDe8nIDIQZumrKyeGnNAK0divvgbIFCGvmVltGnXQ662xWptOymnZrCgtWw0ozUNoLJQyJcXwfdSm+tjqNeuWQoq8IMFXf2urRXug6XNNXA372g0vVYqUgdQp9o4uj97/A4afcSn9m87Ai6BndCUjWy5ASklz4Qjzux4mqs/Tf8nzwfeo799Fc/YwE1e+mJ51m2jNHGZp16O0Zqcpr95MODhEUOuncXAv9X27iOYP4Nf6qK3aQNDbD2UNDQiSJKK+41Gmv/YfNPbsobpuHX3nno8oBTT376d58ACtA/tpHThAa9k4++Zm8ft68Xp78Xp78EoloodmOPypz1HZsIaec06nesbJ+LUe81GSqkxaIot87eQOK1jPnRAjKH5z2yA1pjSeRuo9aj6AhCSQJlKvjUh5UcrNMeCVawW6fI8298vW/dVl2yFD9raXoRHyMZ6Jtjall10nLqVRxB+To6W90soUk0FK6Fm9jkP/+WXGLnm2BROmhe8FjFx0FSMXXYX0oL53Fzs+/A94YZnpb/wnS9seYtk1L0N4Xo4nlyYzFlk8MW1Fx4J4YZFdv/9nuBJvWs+RoSqjb3wxfq2a9wqiiM4VysBCJzQyZCK362V3NZYViigbnomirZ+fQYoeQ2HNeX06vCM0cuhyiMj3O8gQBTcKtYsKeRFZv3C4dBnqm+/bIrHqpvlTettJPmvCEom0j3mRtU+NY4OUasTY5R/JLLmvQQy1x5bmFJmYQhYq5Enipm/QOjN2NJdJx+dBc8CyCUHHp3PznOltLTnP42aZahhlec+89GE06ulAM7HoTJmhn52S+RbQZM2VCsCLJTIQVnRw7dUmc6V55yaBX/abF+WjvrsR1TuLhO6SWUFOaIWIln7JGhbMlmDcoGrZhJJXcmjqGU3mgral5+iPssiOSe+UXsqqig4Ip5fVTJA5PXpMoDo1oKWfn5z1PyhkvNbEu1Z9jsq6dSkpVkPnSXoBv2eEgbGRXMC06vp1SA/mHvghycI85f5RyiePZpVW96sMLWNg0xlmu52ru/Sgtm4zI1c8l6TRYO6+u5m95RbwPUrLllFZMUXfWWcTTo4zOTREIFvWJJDBtUm9ztI9W1m4/U6OfPLfqGxcS+2cs+k55wyy5ITWc9V1aKfEYE+qxY9Rx0SYztJILmiePld//BLSJUAdXNEoMsKQR80kbgVzzN1WrxRoxVpY8LcT57+TImQTM+0Aerk6kd+fq0Onuc3ZL6xrZi7W+sesPdXV6xBByIPvfgfjV76QgVPPyZ6vR65vV5ZPsfKX38yej3+Qyup1HP3erYRjYwxdfHnunLjV5ODnr0UKSXT4EONv/Q28MARP4vdVmfyz32bh1juI5xdIFutE+w5ClLDwnXvou+o8qqeuN5XVy1za0DHJZK3kyy46JPSHM99cpNEOLWPGWS7Vopdej+XebOYV/Tz1NZwArdJ6/kaR6kBqzmKqWgqFs8xdQE871NGLrd+cZUzTl50+l7RZYhFxOhf7ps6ZggC2UpAppTjtcfu6nV5Ip1hJdMBFTX/QbvZOeiZNibCVHmOoOl86nf6jopbSEikIk5iSF2XXVW73LV+79WtFOt++uCItzbyTZL0t2/baVa1NAFg1D2l0NZEmqGNigjfmlcau/GhyYitEP0MSLy3R2LuLkYuvfNzn7v7kByiPTbD+DX/4pNTFK5fpP+sZ9J/1DGty01YR6eDv8AH2KhVq551F7byzSJpLLN55L3M33MLcN29l7JdeSTgy/KTUsSs/XvFKZVa/7i3U9+1i96c/jExiBs84r+PxleUrWf2mP+DIrTcifJ9D13+B2pZTKI2OmWOS+hLz3/8eAOHYGCLITz+llZOUVk5aX+OEype+xdjV51M5Zd2T38iudOVnWLrJXYtyQitEnoMM5dzSOy4FKG1au+pqC8SXhr8gnCWzzFrRCJI6zkktAGTBwEruWk/e+gSZkaVF3grUyzZIqG97lLkbv8nSfffTe9Y5+OuniCLLRd2x/tohKuHIGI2D+5BJgvC8zrC+YzzYgEQhgaGLxmjY2k9JlSIWWe9x3c817B9U6T3nHHpPO5vZG77Bnv/194y+7GX0nXxGgZhooGXH6rWRv8K+DsaQQeCsJLcmGKV5KWo7kMpy19aXdW0HjTC/qTACiXbVVzC1X1fbHgVCtvt820LqnVCBJLdZvLb1rrxYEV0dRMrdRhb7lrm/BZSWl0+x8hVvZPtH/oGeDZsJ+wbapiZBgN/Tw+hzrmb4yuew85/+ltbCDMHkqDnOH+ln+VveQiJiKhvXI0OJxBqXxog2nYOBF1xJg0aaAkRmSJBGDaRGDQyBX50bCTN/mCUzveSgyb/O0plJzdAUZnleXyMJLIPAelbtvioG0TNBPtU1vLQvCWeMJTK7jOmzjuOFJs2a5U0psGAju5mZOKiF/bvpOx3GmxsENUfC1Y8xSENQdFwu0tfwRIaGaWXXd96Bi97JLNWLWeJU58YK9TdUB42O6ACOgUdT5bLQaJEOvtsT5jmk9pJaIBJCLzbIkL6Gp5f7dFJi9XtSVQ+x5Zm2+47DSnFZ33qvZux4uXPdZdFsDsyWPg2R3CzBO/NaV34kOaEVop9mkVIyd+PNzPzH1xl67s8x8pKXEFb6f6RrCTVwo4U5wr6BJ7OaT4oIz2PgqsuprlnPgY9+lOajO+g/70KC4ZHjdoroylMn5dFx+k8+k5nv3cLYFVc/5vFeELDyd/9AbeUn6MratWbZqStd6cpTKF3lqSAntEJk3LVdopj1FXWXdAxZVqNJGvXxJWhGv4sGGE8whzOSs8b0sekxukp+2AECENY+PyWwxWXFP4oaHP6Xz9DafYCJP3kz4WBqRcctZYG0Mgu1iBBpMp22hKF5YF96m8G+lLiqg8q5SJFrSVp8ATeZbIFXYrmj+wL8SBjkxA1XjxOkUr+T6opVTP3Wb3PwM9ey693/i+qmTYy/7nVt2+eiIYaQqv7O/eaIjQyZa6oG2Rwa01CJhR6q3RYxOnM9VlZZKX8/N4SA37DQAZcz4TzXXMoS91gX1dGnagTOun8uXEA7TxkXbRIWMuSircLZL2HowsvZ/oG/o//scymNjBX4Ki551KTSsBEVE/BOlaEiuDtpOLKGCiB1uzccIQOxqcIgQ9ojIkN2RARJq4XwA4QQuTAMkCEMJtVNAjKOqW99GLEUUZqYhGXDqs7qWI1WCGc79zDyCLNJoUOKEBmumfZpSDIU0oBzLuqiB2ZLz3OYwdrxu9ZhLOeCuBqkyEHP80XK4ZPZ3wBemIaicJGhQloQ21FAvwOdpkJXzgknIn1p+FJZehZdV3WfRCNFGtlXyE0i8HUwR4XmSO3uLkJVx/Qcnfy15MWEfkzJjw0XSQd81A40sf5uVNPBbfpkKA1qpJEvnXTV5QMZqpRH9v50Mw1al293dg0LIdKomG6XIeN3TcwnIie0QvTTJMniEvPfv4Pmjt007n+Y8obVjP/xm/BK4RPONTt8+bPpmVqHKARLOfHEr/Uy8drXM3f3Xcx959anujpdOU4pDY0wetXV7Pro+1j5hjcTDA091VUqSHPPPhoP7WDhu7cz9qpX0tq5l/0f+iB+f3+asmRygvLUFH0XX0QwUERj44UF9v/TPyOkwO/ppbFnF16txsjzX0D11C1PQYu60pUfk1hL5l3J5IRWiExCTsdCsMUgQ1Fm5YGFFGnLy7fWW03wLwcV0Mq17ali3R/IUnXoQ421pKxdbVkJCb4knltg9rbvsPtzX6Z66noqJ62l/8rTqJ60Vt0wIlbedIn2qmtmiSmzwHb5dhovNwTDz30eXgvjmaYttYLbr0aE9LOzQC03JUjBacL6XfiK42TQlDzPwvAD9LV0uPqMGsLStgepbt6U90Sx7l8I+pjIzpaTE+tJOu6xSNHGdb39en2OS+QAieilHufZGNdlbR36WXwfN6zAsZL0usExO4mLCHjk33XufMtKt+8nhPUIrDFil24KioFzLyRpNXn0PX9FMDiMVyrRe9qZDF18WTFYnUGfsvdq+CNB/jnKDrG6hHkBWWWl884zRFgw/ckvUL//wfS4hQb7P/RBwpFR/P4BRp5zNfHiPEsPP8juv/4bKqtWI0olkqUlRClABAH1R7fTd/Y5jDzramSYjrHpG77Gvg/8M8v/6PcpTU5kfcrtp77M+oVuj373OqCn4ksZRMNCIA0wY4JVqm3nvemB6bWkxScqPLh0t4sM2eKc43KKzGG6XjJ7tYZDpLiOBh3MU30sDpHVp8x928BVTmXNu9bzmYUW6+sCSIMUKYQ9FsQqTEqiEHzhwMkm/YdCgyLfoxeJhzTpPXRg3jDIXyOxkCiAOPKJFW0hEXkCVezMWW2TARfiJTjt1UeZa0nzLcuOcebP45HukllBTmiF6OksSaPJzOdvYv6Gb7PhOZcz8advprx8ECAjCf4Mi4wi8Itu+F05sWXoosvoP/McmrMzyGaDfdf+K6VlE/Rs2vxUV43Kxg3ER2bov+yZlCcm8XpqadDQOGbm5puYeM3rqZ1yGgOXXkpz716SVhOvWiWJW8g4ZuDSy6msWoVoCSQSIQT9z7yEmeu/zOI991KanHiqm9iVrnTlxyhPC4XI5VDYYmIUuV4ShXg2wgQdM5wQjRhp/kjsWHb6Hpb+krhohEl4mIoXJtQf3sWBv/sklY1TrPyrN7NsYhRo4KvcD4GJZZSe22ilFYqaOqmfT6IClpk66bVhVVfNZ/GUZZAoLkyuzfoZOB5r7fgkhYSUWL+R/S5EGjfJa2J6jx27J62/2u/yLYxFF9F49FGqJ2+2uCYdLG9rW1vWwkGmMu+LPJqW4w251pZtxdq/6yoHSebp4wTtdtEzzWORmq9gpX7wHD5XwWq3rXkLLcrfoH3dbSCoEILG9RZyEgvTDiHS13W4S259vL5eKv29ACx7+SvY/+mPs+ptb8crVzp6Q9qpNDLUz4If2oiMBRKBjDyrPfqdq22NDMeCoauexcilz0q3W4KJl76SyuQUB677HI19e5j5+teprllHaXyS8sbT2icUjlKk06QzKVdY8d//iP3/+D4IBAM/d1n+HI3c+hbJRntMqTGcjROZptlx4qAlZMiM4drodDEaMSogtiLPJ2pzTAGF1LGGbGSggBQ5KKidPFcDwPoQjRDpr4iLDFn9yPCKXKTI4YSZk3zrhhoddLwhDWKi5wpNYAsSc79YvZNmh0+d9hpOECwlAYsyNJ5nOt2HV9Foj4pLpL3ZVNls+TSU62ms+WGG7+hCs22roZrpoD6Fcd8G8dbInvFeexzGdtcuL8jTQiF6Okn9/h3se9e/MvrLL6LvwpOe6uqccBJNT3PoU5/CHxyksnHDU12drjxB6dmwiZ71G9n/8Y+x7BW/hNdbeeyTfkLSu+lkACZ/4bUsPvwA8/f/kIPXfZ7mgf1UVkwx9pJfoDS2LHeOjGMW7r2HOGrSd+4zAAhHRxj/3d9k/7vfj2w1GLjm2Rzzy9aVrnTlaSkntEJ0POuhWQh6VTroh+1xY7yBtPeHNiPciLbH0JyNA5UTJlYKj6TeYP/ffYLRN76c2jmb0GaslMrSdQKFJAXryCkBdFoB/aZ0ZNb3b/EAACAASURBVF5tSWpLtkXemsMyvrQhpT2f8oZXDi3QUoj1Y19Lps9Sh81wKSAGQWnGLGz9IbN3fI/W4UNE00cQYUjfM57B0AuuScMFmEyNysJxyDaaRyKxeUDuc3MroP+wzs3vylvP9unW/o5Lm64lXuCTiAJSYnJAtXue+n7Hm0fJxNZS15BWwF+h6uPwEAxHzGqSoSy46JizfayozADLXvoLHLzuC+z+h79j/HWvpzQxUbQ+JVkkaNeK7XAD4UvlaCaLY1Z7K6kErSZRrLayPWl1REH1pM1UTtms2hsze+u32PXe/004Po7f14fwfZJGnebuPcQLCxDHlDZOEUylgSVlI6H/6suZ+eSXCMZH6L347PTSfvbQhEue0e9cowSJejc6wnEpQzbdecxNrpxoj1s93iPwdOJQ7ZTh8NQ6xieyx5IWd+45xjWMl1mc/jNAnz4mds4RuVeRL515W8+JIrG4Vq4HmqmjgwTbSJiTKDhR76YVqVRP6p0Ehi/k00fIUhLTSvIIkeYOtUQeKjXovC9MNOtYuwc6Ma5ild5R92PfjuWVd1AzUkSKsvftWWk8cs/kcaA+3Wz3RTmhFaInXSwU9schs9d/g/KWtVTP2AIkP/HkhHFV4i89MctVSsnCjgeZu/9ukqiJCEJEEOAFAcIPwfOIRZNtd34fv6eGCEvEi/O0ZmcoLRtPXbKTmObhQ7QOH6K8Yor+884nHB4hXDGJKJUQvnjMj2xbaYmM2PyTkB9zf/lpEREEjL3opczefht73vseRl/6Mmpnn/FUV6ujCM9j4OJL6L3gfBqPPkpcX0LGEV61RDA0xO6/+TtEucT8d26n9YUDNB/ZgYxiwhXjJItLNLbtwKtVqN/3MEt33EuwbBg8gT/QS+8lZ1E9pYt8dqUrT0d5WihEx1oXdeNmuB8wFyWxrftC5OPCjdt8tU2MEbWp0KVoepa5r9/K5H/9baj7xL6HV4qJmz5R4BFJ36xXa+tE19Vwh1rKW6JlhzpW91PWpFEINKKi7h/7Aq/lEVel5bml1sdV3BxtfZqIyra3hmrqnq98hsXtDzF06nn41V5k1EJGLZI4Mn/XNmxk1eZziVuLJFGE19dL0NdP/fA+opnD4HmURpYRDo/iV6qZlQmg4pYUEqQ6niRZbA7L4gdoiWPE6ci/L52vDCmLFpTtBeVlyJSweU8yLfUz7rRK4ubEE3F2n7is2+FyM/JV9pqi4OnXUZx6JDLj0MXlzPMxvS5ZnZz7215/7epkbufmlUvyf+tTB888l/LkJPs++iGWHnyAgUsvhcBLkwD3VEyfTTTKU8oPvFy4r2aLaN8+Gn6JqCzxB/vxqpUsrphj3YqystZbWTyixI3JYp6vhjh8Smeuz/HVGo/shCQhnBpHJnV6LzqZ8mufi+gfZv8734sIA+ZvuJWlu7dSu/Aslv3uq5m74buU108hG00Of+BzlDeuYvSNLwEvffkm7lKSRuZ2LX4ZZvtMzCLNjXSQo8TiFmluoEaIPIcj2BkhaoOCO8h6W66bU0ed1LTA1xT57RwK2wHMNSiJiemVxTnL84rsyorcz2auFMJCX9T8qJEhFdncU+888nWU6IS6F7IoYspOElnfa/+BSDpNCPYxZQfC0bC6JwxnyEWGtLgIkeGTxVnd9HXtqODHLV2EqCAntkLkvq92bvfH6apsi+loxxnEKkeq1ks5TrDImc98hb6LLiCsjUA9rasO3CUrPkliZW027uDqmkoRQpMwPZm5vxqyZXqwp1xAjau7DkgXCpKmXrJRcLEejKoeekBpMrlvEX6lgKTVZObOb7Pxd/8nYVjLtdNWVPqrIY16ywqAmJa1qXUwtS5P6O0Q2t8QeZ10KWZAuyRTS0d0U64UJnf9s1YCrA9Ozo8YkH6a1sPAx/bamrMEaQjuzqVMmAbroxJX8pB5keCqmqlDS1ik8eLHKN9APZEaO8GHWC+tlPM+ASb1g3rXnrWc4QZidB5Npqw6Hzj7I+h+bCuTU6x88+8xfcuN7H3PexBhCEIw/opX45d7qO/cTjg1TmlykgSf2Ru+yfx3vkv1pI30nHsG0aEjLH7vLpbufYBgbJjqypUc2LaNePooCIE/NEAw1I8/1I8/NIA/2AdxgmzUqZ6+idLqcWav/xZefz+V007B7+3J+olL3DcfUDW2fEnPqROs/+Sf4SslJlJjs3Vkkea2nQD0nHsa9fu3EU4MU14zTvkNLzBLe31XnMXBf/ocB971UZb9/uvTZWHboPNlFtDPCj9gwmpo5Vop84mtZFu/e1E2Nk2anw70gWMqRB2WSd1+2i65cuKlgTgLNAUnwKhxXLCPcYDzdgT3jJ6Q//ibSjlOG0ZBismcNDQB2mTsyC+hCTVXxn5CK/Bz5Gt9X18vme09jEwSKlNWAm19VUfJpdJeO9UBHKXnWe8l/4IK7vbt0jg581nc1p2/K49XTmyF6GkijR07qd/3ACv+5O1PdVWekHhhidq6Lez69IfpXb2R0uAIg5vPeaqr1ZWnmfi1GiPPu5qR512NFDB/153s++iHSOp1aptPpnnLjbQOHcIf6CdpNBl53cuo3/MARz76GfyhAapnnMzwa16G119l1C+RiAZ4MclinejwHPH0UeIjs0TTs0R7D4LvkywusfD+a5n8b29k+lNfBSCY/AbLfu9XCIYGH1f9U4+d/FfJ76ux+iPvJJ6r4/f20HjoUfa9432U103i9/YQjqYpc7xSyLK3/Dx73/khZr96KwPPu+RJeaZdeerl0X/6GjO3beO8699uvLqetiLJu6l2BTjBFSJjkeRXmPJLCsoS0GQ8z7Vq7Qtqg8JNBaJPeQwEUcYxrf0H8Gu9eIN9AESHZzn0kU8w/OznEdIDi+rYQJoAXggPIp+kpKw9N3VIS1sN1nKcY7IJx5oNygopCjNIONbQv1qKSJylHAPDlxSCVM+QIv2sV7zi9cxtvYvG3j3sue6ThIMj9KxYY3KVmBD+0rIMXVKnY3UWEpoGxSUzg464aJKVDLIQNqDj+1LttpagTGgFDTGbZy2doG32kpkFwVvtMEtj+hRt8etAbRUrmayTzsRco5lHwqQF8xeWdjogRibkQ5D9FpUlkYVIFJJN2gEjneua9+XcrgDAJUU0qUCSNX0PBjeeycDvnkY8N0fYP0gSQtJs0mjMEPT1wUCZ2uZTkWVnCc26sxACv1Yl7K/A2rEi2i8Sdv7W3yAXZln197/Drj96Hz2nrWX/X7yH/qvOpXbBqZSnRoBsucSgrnpMKWKs7yc09fK1DraZpMR8MVAFJPPf/B4Ae//n+xGBz+ivvJC+i05N6+ILRl9/NXv/v3+h/+cuMgRfKdIlMz3+zVKsJ7N+EGpHDP0882NYk8qTRFgIkYsikd9uN046IEOFZS8LLSwA9L7qV491DWsS7jT3FpwQ1PXBDiSbXy4tpP3Q40IIK4RK/kMgnbGlUUMZCCLhEUkPocjT+vqe+qgMPfdsZm7bxsztjzJwzrqs7hYEF1RauTZnwRzTHZGK/ZKIrNHZ+3GQZp002iGp2wiRG+jxeAMzCixUvCtGTmiF6EQSKSV7r/0Yi488BDLB7x/Ar1ZpHtjP4KVX0Hfu+U91FZ8UEUFA/2lnI046m+ryVez41PspDY0xdMp5DJ15ke2v1ZWuHLcI3yfsz5Aar1Si1J96cCVPAnNdCEHt/FM5cu0NDDznPITvMfyK59J78ZnMf+su9v75B/B7q9TOP4W+i06hNDX2hHqyX6uC79N32TnEs/Mcvf7WTCECSqvSII7x9Bz+4ImX5qQrj18GLtjExCsuoTR+4iXQ7sqTIye0QpRZJ5YlpcROywCZtactOtdyFbK4bt1RnPk5Xlrkob/6rwCs+vXfozyxnMbeXSStFuHgMOHgEKKePyfxMytFeB5eyzN8gAKZtIAESFzukF6X9xyrNiynZkQUxMQK+dHPSxMIXb5R3NABIBVS1BT49byV6QG9p57OulXraB7cx4H/+CKzD93FyDMuZ2TLqenzfAxkyLjSmoCMqpTZ34kT1M0lFptkkFYqDS0dXYc1quZZ1qHuHxopsp+5/WW0LDvpQicuUuNwhxKNcAQZQiR852Fozpdul21Fu5wh1+pz7m+STXoysx5DmXHUoEAuNeTVBgXEzXkERajIsrIL9AeHZ6TRT5NeIikiGC7hQYeykM74yAXOVBKE+cEsPMnApaew4+3vZ+HbP2TZa6+idwQYGWf4zGcjf+NZ1B/cwdFb7mPvX3yEcKSPDW9/IeWJQUOa1WWjFWQB+1Tn9XQwVTUQh37+SsprJ6mctIo9/+MDVDavovHgdvyBXrxqBb+/RrBsiNbuvfhDg0x//Dp6JlcQn7oWv28o1/r6tkdobN9B9ZRNlKbG1YPLw3WJm04oFqDQYR16AJdvpLmD1jsAZ9x04hDprmWR8R3A3vDQ2tHvcte0Eg53UkJNvE19X7LrFlZ2OgRzNHO/kNBwoBJ37Or5wPJaTYRHLH0rRIVGiBSS7nmMvvJKPC+hEYFvfY+qBhnKd1ThoLl1dU7Tal+s+73+zCnEz3ecGWw3/U7I0OMyLboIUUFOaIXoRJCk1eTwzV8nHBxm7W/9MSglorJi1VNcs5+MBD29BKs3sPY1b2Xmnts4eMtXEHd/k4WBSQZPOZfKSDedQVdODKlsWMHmz/wp9Yd207Npee434QlqJ62kdtJKpn71KvZ/8mYeesdn2fiOX6A62vO47+VVSvRecgZCSKb+8k1MX/ufHPzgdSQLS8RzSwQj/ZQ3r+HQ//k3Rn+9zPx372Ku+gh7P/1Fxv/wNwknsoCQRz7+eVo79zADjL319fSc2Q3o2pWuPBVyQitEBq0wloJR3Q2U4HoeuVav8UI7HnTIIU00pw+z61/fR3l8OStf9+Y0m/yx1uWtcz2ZXU8nQdR1TrQJol3N9al2yglt9ehEgjomvInxr9uVHud70qyta0TG09vK9ChVUysmVjyFuKSQorqHVIqep7gtUvMS9Dp+4NN//gUMnHMBA4tHeeiWb/Dote8l6Btg6JTz6N9yJkGllnsGBiXQaJDDMyF7JNk5HdbAc67eHZGhVGZuvZWjt3yTyprVDF9zDWKwxwoKqE6x0A+Z/perkBQWv8CxgM2hmtOgK6SRmUCac4Ux87L75Uqy7cRNHusmuC1UgOx3CwGT1gNKnHdh0kWE2XU8h6PQyV3bRgvcunQM7Gm5ZBe8kdywF+5YjtJnnHrVqXHgRKUUqnMJgxQLSutXKdd+jfw4VrsnGH/FJQgk977lg6z+9SsZufwkAlWRyJMZEquur1GCuI1V7fdWGXvD8y2+CCzefh8HP3gd8ZGj7P+LfwYgOG89YW+JxgOPpAqR4gFOvuO3mL/xOxz58Oc59P7/y9Tf/iHhoIrkpxEGzQPU7kSJyDhr+rm6vJkCQpQhK524Q/pFGo9U+/3pcayuF2uPxg4Ice7aecDLiEFXLX4hpMix/rvQLx0vQXcseS1RHDPCOdRBpJAeUnjIxNN0SeMRpvuY5+fRed03NPcMsLie5I6NHZhLCIkbMcZ4BRtg2mmY9fA6hUd4XPHdughRQY6TgvXTIUmz8biO/3/sfXecHcWV9anq7pfnTY4aSaMsJJSQQCCiRDA5OxAN9jruOts47X7rvLbXEdvgNTbYaww4YsA20SazBkQWYFDO0sxo8swLHer7o+tWd1fPKGAhRva7P+nX0/06VFdXV9c9de6523/7c9QsORoT3nwFrOoKD4AsUdeA5uVnYcb7/gNNx5yK4S1rsfq6L2PHg3dACG/PJ3idreeeu9Bw1rkAgM6bbopp1lSsYowxtF18DKb9+/nY9psn8Px7rsf66x9B18OrUdjUDeG48MpjMID34tzZJYdgwn9/CDUXnohERxuSs6bA2dYJuC4ySxfF9186HzANpGZNwdCjz+yPW6zY62yvXPJVFDd2vtHFqNh+tHGNEBly/EI8E+WhmAwejcQ1L0hPOeFKUUJ3xy5s+M6X0XTKuahbetyo11OfTQ44gwMod+9EzZKjRp34ViNx7VvLQ55xxOsSTLkjRlHOGVNEheRMKM4LF3HeiEooSJwauZSaQg4TKpLB07hD3KTz+9dPpX1RDkdyjmzThCfRIsUvKpOXHvUYmcPgJgBHhoGk5h2ClkMPgTsyjK03Xofulx5F3ZJj5b7+MfQcYwlVQ9vGkqAPowe6QOBYE+ZWXT3gCjRccCG2fu9qDD30KHLL/fBnRnokyoWTqIlHkX7yZzOg+lL9Ub0qjgE0Ny0cCahFBcb4SEoMUHr5yZDbrhAiRI8hl9LVzhXhH4W2h0+mRe+5yRCfiOpER7E0E+FnoiFDAcoplyryRy5DUW2eOfoyaOt0Mp+sxByuNHuovbsKuoj6dMpDD2l5KQ9fiy4zuAc2dSqmfvNdGH5xE0aeXYP+u15GafNDKHUOQDgepn37XUhPbVURnAp11fPVjGI8mUDt+StQffZJGHpoJXDX4+BVOQzeex8Sk1qRXzYLhZc3IjtvCkQKyMyZDG9oAKXnX0bqQj+PGiEMjmx7hPYqpChktI34RgpV8qLrqm4Rf//UdhW5RksEaJ/c5liAbWLMdzlAsbHHyF7VF0YiUfVGJiL7BgK08rlS1F6orxhLCJVeYoWIe8JvS66h+D6u4v8RUhS0H+G48EZKGFmzE4mJLbEoYHV/oUkNIBCIFA5Xdax4TJTmg0f7Ii65UUaIS7THpNF7sgBArVjIxvWAaH+ZcF103fV7VM1dhO4H70LNoqXgieSo+3bffyf6n18Jp78XdcecCG4lDnBpD04zMlk0LD8NnXfdipoFS/dYb+XeLpR2dSIzfSa4ae3XsuQOnY/hVc8hfegsNJx3PrZf9yM1IKpYxXRjjCF36GTULmwHAKRMB4Uiw6p3XIO1H7kOM374b2CNja/5/MJxMPTAk2g6+Wj0b90CcI7eW+5C9w9/DWE7yMyfhpFV69FxzYew8UPfhyjZGHzyVVQdPnN/3WLF9rMx00Dtecei88d/QP7I2TAyle/EP4KN6wGRnnbAC303BWcQjgO7qxvJppYYR4iQIUcU0P3rX8FzbDScdAYGX3wG3Q/dg5rFR8Oqro0IbLnDg+h57C8wMllkZ85B/Um+sNye9InCZYTumcjfmAuYIQ8GCHnRxBOiaBEeRYuAAO2BF/VQhYpkE4HXSF6eEfWM1dy3XGZTfgXbpotS2ZR/+0uRlF6KVM+mlAjcFXBNDw4TMe5AYu5MJF+ejC2/uR6tl74ThmVBeB4KO7Zg572/hyjbyEycBs4M9P3taVj5GpTv+hWqZsxF1ZwFyEyZid4nH0HvXx9E7pD5aFh+Kgwjoa6hkCEdfdAsM3kqOp97xveeyjYSLS3gWvqNSLJKM0CIlEYMOJiGDBFSFHjaGiEp5KUFSIW/ySuV4Y0UwbM10etTZBoTgUetc4i06KFR75u851DUT6SIBC6lAq6RQmwSUW95LE6ROqWNeNSQziXSkCLBQufVPHydj8TCit2ejNZUqEcUKYqpeNMfLPDowaN5YShizJEeuSPXKfGnY3EkLRezvvQ27LjzeXT94i9o/eib/fNTOgdNX2YsK23Yhq5rfwuzoRb5Uw5DI5uHZNLG0LxWbP7CLwAAI8+vBQBU511M/fCpWPu1O7D5yzdj+lVnoPnkOX6ZXKmY7QXIENcgUtuLpqfYHaqkRJ81mXIV2SXfd4pMZU7wDBR3yfDgJLwxkVoy5oY0k/TsAFpfqNKcRPSNgn4RQCwqN9BoQmQZPn9QmNGXrMzADAbmMAgFndI7pV1f9gNDK1+FKJbR9euH0XjpyaNeRyHEVGekOedwFW3MQtGi/r3LtkWJYmkZ+lbpCvRjKcfvzio6RHEb1wMissL2TSh2bgMsAzyZBMuk4KVM9Nz9RxQ3rMeEf3k/3L5+2D09sHu7YffsQmHjOmRmz0Fp+1akZ81EyxUXw/IsTHrPRzCw8glsvP7bEI4DM18DnkzCHRmGMzSIumUr0LDiNAD7SFCrGBhjaDz/Lej69S3Y8PUvINHUAsYNjKx7FamWdjQedTLK/T2A66Lt1LcgN/UQlAa6MPjqKmy7/SaIchlWXQNaz7sEvU8+gjVf/w8/9QEAq6Ye1YctRfX8JWCZ1O4Lwg3Vidrd3bCaXrt3//fats9fB6M6h/KmHbC3d6P9u5/1001UbNxbenIDWi45Dmuu+hm2fP1XaLxkBYyW5n06B7MsOF09qDn/JDDO1Qc6t2g6Ft72Kay67Go4AyOY/v8uhFmdQcPyuShu7cXWGx/Bmm/8CZlJdaiaVYnkHI+WnNwKe0snhh55Pj4gqthBaeN6QMRLAjse/QP6Xn0W2UnTIZiAWyrCc0rwXBvpjqlIT5yC7j/dDqu2HlZdPZJTpiC7eAmcO34Hlk6g8V8uRXJqB2BzuEXAmjIRTW0T0XT2BcBAAfZAL7xyCUY6CyNXBSOVDhx+3esN2ZgRMzToDnGIjLKfUDXGlyHHlbwH4kglENOeYJrnrTxmSkTIgxxJTNPaUYq8WuSMIQuYTtsoWn5TKMjpK4e8ZS3xrGtzeIaAy0OeIXGVXAGAofGyi+H09sLe2QWnrw/ZQ+ai+vBlMDwDGYQ0cASQqG1E/dLlyE6dCWewH7npvkecPXsKvNPKgO3DCoXtm7HriftR2rIZredcFKk/xVtxHAjPA2PMJ1J7DEYmB6e7R3lSlAeMhZETxoJnLT1YFt6XnGjiq8n69WT9kffHQpwexgTcwWEU12xGwxVnILP4ENg7urHt37+DukvORHbZQugW5r8AIc6S/D2MfoxpIf6SWgfg6bnVjCASjlBAijSkCMMYxyD0InCt/avLaUF1YaRIUYM0blTA9wjasroV2U7ULVHZdB7VaBZDq2RdGFEEwJPvA+k6uR6H5/mQRiLHMOtbl2PH75/Bxk9fD7MuD5ZKoGrZXFQdMw9G3h/cCsFCSsp0o0CirRETPnMpdv7gVgz39yF7/iJkk2V4ZQfZlIPjfv8edN7/N6y+9i5s+LaNKRcuxIJ3HYGWGRkUdgyipRXIpIdRdglOk7cUum/6uywRorJCk+RSrlNS6TAwQJG7SvVcLkmpW/UDthHkTaT7BIfwQuQgvV1SM3IYGCFOxE3SotqEho5AsHh/qfrEOGcougxzpbQixcoo1CHMZn6EmrpeFKVXibXlSWovPgMsnUbqkMmKG6SfNyhHtN0yy1MINCHPBiGVXKL01A9ovCcwFtMV05GivbIKQhSzcT0g2nLvLSj1dmLaZR+Fmc6qaTAvgWgG99DgnPZp/9RVct+ghXhJAV4K3ggjlfazsUvb34iQUQDc9J7321/G2AFu4xkXGDFG/cmsrYWV9yPzVJ3v5mVNNU8AmidEtnErocL+cx0zkaptwpqf/jdaXAfMCJqucF103X0bBp5/Gsww0HzeW1VFZGYdgu7bb0Vx4wakJnfs0+0xhyt4/LUYz2WQPWIuBh94Go3vvRBVxy7E8F9fhDs4POr+VsqBXTxwr6SZseGM7F/+1u5MmGOTa18P4yUeiGX+HWakEmi84GjUnrgQhc5huAPD6H/wOez65QNglgkjl4ZRk0P9ZW9CauqE2PHpOR2Y+LX3ov+rv4ZtlFCakMPab90JI8Ex5zOnoWXFLLSumIFXr30EPc9uBQBMWDHDv4fdDPg6sj3YMFz3d9/f3lqmqoiRwT2gs/vRRMIDK/M977ifjO0FUT5sRlUW9ZefFYiv7qMd6Pc9aqIyIBrFxvWAaGT7Bsx880eBhC94QZFOwkCcMyQ50sTDoYEQ5UfyGHWQItAsoalbFXmEyPawVk3M+9G1U0R0O+1uFHwHI4wQBdFy0fWwlhJpUug5vmJysV6o0JZQWbeBEEIk1wNFXhFZMiZQkyoAANKWr1VUsP0PZTnhF7Jk+0unbIAzBpiOf+2Mq1AVpcRNKIsbLTspyYIDnCLPxqjHWN4yD7CqapCqb8HI6ldRNW2OUrsubN2IkdWvoOM9H0PvE4+g98E/A0LI6CkTdSecjN6778aEK98bzQcEAIYfjRRofhBvB4AhB0VaGm/ShFKcIoUQQT0LqvOWD16AgXufxLYvXIcJn3snwARGnngOzC0if+ISmNVSu0malXJivBRScFZbd9NvM4Zo4FWaYEr5rFW+Ln/dzNhwTRn5YkkkQX6EKN+a0i4iBXDBFJ8vlmld9+rD7xD8QZEYre2G9omkhxHMv76G2gZ1raEEmvESj0X0BXnmCDGK8pM8l6lnSjychOUgUZcGr84BAHKLpvm5DftG4A4V0Xf3kxj8y9ORAVFS6n5lUmUgw9D0qVPx+A/+gO0/2QBmcMy/cgm6//gUDj2mBu5IGY/8+WWc9J2TUJ8eVBwlnSfEZVukgVJHtkf9VpIdCi0dWdFl118nBAkIkKCx1kcc/wEX5bLkmCiWTWSqiorPZIEhAZVGPh5hJZeuy+E6US6i4iZRHxED/IT/L+HFPVXqX3SUPtT2xhxHahxPhQIDgCn5ToRyUuSbRfvSMjpIC8t6BHXAIgvVRxAqJLXgrJQDy/RfHlMmLaN+m5AiRR+lHGss3qdSWXnwOCr2GmxcD4hqOubDZEkQKqvgQSf4m5AiPZw5SH0hfzeCdArBVAvT1qPLUQm4+od6rKUd7MNNSURF9FhDc17DIaHqbxo0kVAjfQBGC7fUPhIqVYcslK0lLSQLd4ZJw6/sAF6n6bxgis6AgMmdYIrAjV6P3mBaD15aCidlKoQ0IFvKdS20PrwuGFA1awG6/+8+GJksRnZuQvcDdyLTMQ1mdQ2smlrULj0G6x67H4mmgHeRX3g4eh/6C4ZefhHZOXMj1/MgyZRqgB0iLqrvtCy3NhpVfSuP975UN4xx5E9ZCnCOzh/8Fu1ffjf6bnsYPbfch55b7kPHNR+F1VijptmA0OBMIeTas495pKF1HtZ7gPJedVjeCDU+mkqlpLCeJCELCv+lAVJoGpdCmKlxDgAAIABJREFUhlUZneA3/1i5DE9b6cVmYyxDHzrG5bn1qY9YFYwySlTfpuhAKGiP0YER5ODOsxhs2aZdS75DcmBkyZQhjAkwk8Oqy8Oqy6P6xMXY/O8/Rv89TyB31By0ffytSCb8SkmZ/rJmUg5Lv3E2Ou9YiRevfwaAQNezO3DjkT9BpimDxnmN2PrwZnQ9txMzT52EdE0KljYHEh4QGWN89Wmfkhft3hPy3fYEg6dlAdUHRLszT/h1kAFDjpViA6HRyN5qCs4KpuCA0ECBdlYDaRZ65lqfZ9CgSh5LfYUKPhBjgx+7aYOeKeCFBzdaCpTYFKw2pR7eNuaFdoP4BVOuY0wF0qol4mC7fsyeTKCCEI1i43pAlKrZNwJjxQ5OE56Lkc0bkWxqg2ntHpKvXXgUBDxsvf1GOEMDaDn9zSh1b0f1Ij+5rlnlJ14UbgAhMtNE8wUXYfvNP0XqqQ4k2yYgO2suUm3tr99NaZY/aQkG7n8ahVXrUH/RycgsmIatn7sB5U07/QFRxQ5qS05pxdTrrsLwU6+g88d/wPoPfA+p1mqkJjVi+nuPjUSzVnfUotRTwNaHN6GqvQqHvn0+Nj+4EV0vdqFmUh4DWwfx7I+fw+nfW4Gu5zrBDYY5506DkRh9erpiFavY/rE9DogYYxMB/C+AFvhj9B8JIb7LGPtvAGfBz1O3FsCVQog+ecx/A1gO4GNCiAcZYx0A1gP4oBDie3Kf7wNYKYT46VjXLvbt9POcagiR4EEkoiImk7dA6wRWSLiTeSyYI9amJGi6LZYoL+REmcN0nvhv+2pqSk6uh6cCaUnhqSRAGdN51wiGYAyQCVqJFOtRsleCq+WyLD1ig1AD7iFNYfYyYWTJ8ZtGkcLx5ZSZEL4zwrmAYUTnLcmLpqVCiCiJrLo/pkK9mQcMPvUMun55C4x0Bq1vuRyZjukRcjrVmV/nFmpOOAHVxx0HwQHGOXL03Fyg76m/AgAy02ZEoO7kjKlo/+gnUFy3BqWtW7Dtxp8g0dKKmiOWwZs+A0hYfhRQBPIW8ApFOAMDcPv74Qz1w+0fAAyAZ1LgVWnwbBo8mwLPpMGTFoTjAI4D4ToQjgthO4AQSE5tQ+bwQ7HjGzfDbKyF09Xrly2RgWMb/rPQovjJFNpEaSpGQaTIGETEY9eRIUKDhGBqak6R7rUpQBXa7hFC5S9dk4FRYlG5VAJ3emh06D1RqJIi0CKyDPaTS5eBMZ/oGhP/I9vNeozXSglbtet68v1X0d2Ood4Z15FT7nKqh9LdWBL9Cc/wsFQKuaMXILfsUJQ27gA6d6Lzlw+j568TUHvkdL/OBcOwl0K6JY95XzoXW/70ItY/vAFVkxux6EtnIlXtF6burpfwu8vuRNtR7QADnr55NRa8dwk23bcWXS904oSvrkDzvIbI/ZUlIkRTZI7WSSmRR48Hofg6Wiw7GJoyL6qps+BTQdPqSWEgze04QqStm66nthGdkNqhapdE/g8R03XEhH5ztf5MkBBtSJCSjRHeH7MQQiQMD57lBX0OfVN08rZGCIeHeKeuT+XSPRAy5nAl/Oha0TQ0Tkn2tYTM6olqrSCARn+H9okxVxFmjNneIEQO/IHN04yxKgBPMcbuBXAvgE8LIRzG2NcAfBrAJxljs+VxxwH4KYAH5XongA8xxv5HCLFXM5110xbteaeKHfSWbGsDM024w0PoefjPyHRM3+MxjPNRE88km9tQe/xJqJofbztmrgq5BQuRW7AQdSedhsFnVqLv0QdhPf4INq1dAyOThZGvAk+m4A4PwukfAIQHo7oaZk01eE0eZk0egnmwd3RCFApwRwrwhgvwRgoQZQfMNMAs01+aJphlQLgenM4eGHmfL+QODCO7bD4a3nkueHp0gdCKHbzGDI7U1DakZjeCJyxsuPZupCfVA5PqAQC189vQsHQyHv/Ab7Dkm+di2lsXgbs2Nv7hJTQd1oaqjjp0nD4LE46fgmyV38g3PbQJf7v5BdTNrMfiDy7F/R+/D+f+8lykag4cybliFftHtz0OiIQQ2wFsl38PMsZeBjBBCHFPaLe/ArhQ/m3AH3sKROmfXQAeBfB2ANftTeFe/u03cOhln4OVyQOIIimCuCeauBslBNRF3uAhNiJWInX63DB9aOU5zGEWkEd1hGhfkCKdOEyrtB66J8WhUaGWdD0ROVXYEaF75YQQkYidHeVDELGPPBQj4QZpDciTIxK1Fk7KuQCHAONCJTUkUqDy4KTHRkJ3towIo/QHnsUjBHKjqgntX/gP2Fu3weBJOFmh7iHMJwm8oehzU2JvHpCa3IHUlA5/NcQZ00UGmWGgatlS5A8/Ek0JC4mhItzhYdgjA/CKBfDqHIy6arBUSqGOCv1QaSSivLUwqVonPjt9Ayg89SJS82Zg5PHnMfTYMxh67EXkVxzmnytEBNU5BHrqFd0jDxuDABcBEVVHhsg8j6nnROkoFHpEyX/1MHK6hiWC9BASRfKs6A3raRe4G9xPmNAeOa+WLoK78n+Ij6f23QNSxNwQlYqaB6HHso9wk/J6So0wODcrRCUIhOISkVinv91KSqQohLip7kMwVC2dDTbQh5c+eQtaf/xBDCT9AUzb+06De8PDeP5bj2DWZ8/Cq1+5G7sefhUAMPPDJ2HqeXOARBKulJ3IHzkLRxw5S91f7versf6ZQTQvqw/C7iXZOUyeBgCTkpBS8lrB1b6ezluRpiNDQgCZhCSJW74/m/YMZIxyDBHS18vcUNuoLJZEly2uRcdI88BiZSNeo5IVIEkAQqZVImym0KNArHbUy0TfU2YAnhtImjhRxJ36GcV7IuSUQ7Vz1R/HgkWC/s6/CR6k6iDuJb0PMrUT15AhkaD+LCSZEZJq2VerCDPGbZ8mfuTU1yIAj2s/vQPAnQAghHgRQAbAIwCu1fb7KoCPMcb2ejLczFRE7P4ZzMikkZoxDcmJE9+Q6zPDgJnPIzmhHelpM3x163Q6wv34e8yszaPqpKNgNTeg+uwVqH3b6ej7zT0ob9qxX85fsfFrzWcehmRLDfqe2qC2McbQ9rajMLx2J558y/ex69HVAIC6IzrgDBV3ez63aGNg3S7kJlcSTlesYvvT9ppUzRjLAfgtgA8LIQZC2z8Lf1rtF7RNCPGB0c4hhFjPGHsCwMV7c83jP/BVmOlEzBv0eIAEkTNEfBFHbScUgbxrBpAXq2YpNKhG+/ZxOVI3DYBkb3SESHm7VvT3cALMupTpn1qF10evF5sHNuP3p+5dR0nCCJkGJqmQVgolpyV5NtLj4K4LU3IGTPgeW5rmuhV/Re7LBOpgweAuTEbht9LbI80eWXYJMqEouRtugjwsRUtR3hZxNKDmzeVdUOSIQCz6jxA1ZpIHJ89J/DGqIwvQQ7sVGmAx1JlBJZPD6hkBZyaGBlLEn4YsRPgI+jhKD0letAAt706h/2f3oPZfzkVyUn2Qw5VOJOuPvGsdIQrHs9BzqhMmjBAUyilijG6b+F0ei3mxQTJUQvLk81L+S+geyCNNkjdNIaBRLkWEhzFGuo2A/8cip2AMqDNNcAvB89I9b0TXWeheVP1o3EAKviKtQ1fxnQK+XvAOqU3yMtF3icmIKyPpqgS+TP6WkC8nL6Yw5/Kz4f7xOaQOmQ6W8isvyYCl3/8EvIEhJOozsLsGsOHzt6Dj/UfAKvrh/YYsgBHqnHbd9xJmHrMI+boOoBBUgSFvkKTPTC1EP+AQMSRlY6YQetWUCVmU20kUImU6SMv6ycjzVDMTGWbFECGmPRybGbDNaJQeIUOWMTpCRHyrcJlcWciSbAMSPIMjt7uyU7FdDkfJJsjfRkmG65dV3rfHUCM4uDAgKMUMcZMkF1NxiWhJiCpDCN6lNjCG0SvAGEDXoeoiaQwNMVKINH1jTA+qMxrjC75+rOuHbZwiRBIwWQlgqxDiTMbYFAC3AKgD8DSAy/aWdrOvtlcDIsaYBX8w9AshxO9C298O4EwAJwqx17X7FQC/AfDQnnbs8xLAsB0fEBnBAMiTjYpeKzV7Qp264WH4iWfQff1N4LkcUtOmou6cs2DV1alGPFbOJkN+nM0SYMqOZ8wBkQr9DO0X+gZ3jdghvaHo9cIZngH/3iiZN91ffN8oZAoWnDcoA4vsSzoy9KgETad4Liz4owka3NigD2m0k6eQ2m6UYMkLJWipwd8E3Y/IgjkqfD0MhxPcrb5O/vWcaCe02wERTbHoAyKVlwzx/EcacbKr7B+sBkRecIyqY40wTB/FWFb6vRkQMQHMnYJCoR/PfvEHaPvM5UhNafWvrc3ZqgER9GWwlwg13k6U1N8cQR0AgaaRJ3h8KkxEz0/l8LxRBkTqSyKfo5qa0AZEbvAcA10qbYqFpsocmvKVx8p67yrZ0fdKK0rkeqGBk57fiZwH+Y1UfYYaENGURUiLRmmGqcFq9N1h8oNuCke1ZSYbTFK+U5wJYEYdvKY0XviPH6Hp7Scje2iHvCbA6yx03XIvBp9aC9cextb3fA9t5x2GVHsdxNAIJpy7EMwMuurnfvkgpv/bCvTw0GgIcRI1DYhIf0wNiBiHK/e1RdTxofeypHliae4ga/jtyjb9bxF3BfrN4fiUmfZwyp6h+gLqI9TSGF2pMywNQGWi+yuSrhKiityubNtlz1ASI9SPOWNMhgT+EYMrODpFKagLkh6hNqx01qLvpwgLH2kadzH1bNVHs/gUMk1dk3NIRHO6jso84Kl+Uim276sJBI16/NmHALwMIC/Xvwbg20KIWxhjPwTwTsRnn/aL7U2UGQPwEwAvCyG+Fdp+KoBPAjheCDGytxcUQvyNMfYS/IHUE7vdlz7+sY47+HDRaD7mQYaWZlMjzIZ6ON27MPLc88guWwKjpSb+IZPGpfoydcy8DBh29LzhpJX+TnI9/N4JwCkMo+vFp7Fl63YkcjWwcjVIZGuQyNXArKrx1ZhV3QTHqQ8AA7xyCVtv+jFK27ciO3suqo8+DsnJMmScOnsW/hhE56t5Qa4Tt0iKPiqv1xBKF0QfFIaRIQDg3AODB4OLmMAjdbxp068sQhiSVlTbyPWCjzF1zIUSCUH6SzcpO2qJ0iEUNaI6Jq7P5dMHV+4YGsAEmjNyW6h+hSkgLOp8gsEVCfYFPRet6pVEmwMEJJ4iIDo4pZNll84HwLD9K/+L1s9cjmRHq0o+OlrdA0F9jxaRw4WIfJBiyJBasnjKEdWna6igqXnxDLH78zRFb0KMlGKK4LFBjBrsytMrLSo3WDKpbq0PiEYT7gRCY1IvOIbaA0WTxtLtUBERvDcs/OECIJt0oJZPH0u5nyMYeCKaFbdE0UJyvfHsFcg15LHlG79F3cWnoupYn/jv9vZg502Bf5iZ34GywzH42Dp4w0UMbB5A05mLsflH96HhlAUY2bgLm+9YBXPuDIxmpE/kUnsRUQ6R7Rmhd5GpbX5ZESkz8YbSVhk5yR3KWf7AKMNMuGZpFA5RtC04nqH0zdKyU6X1JIUUSnNDAxeFEMltlNg2JkBJKUsoys4z1KCJOFBKnFLyjqj9Oh4hZRwJ10QSdlA3FGlIaDmltiEkx6YBMEKdqYISR12yEOKtUh7ZdGj0pSJEiPospYjNhUoM+49mjLF2AGcA+DKAj8rxxwoEs0o/A/A5vFEDIgBHA7gMwAuMsWflts8AuBr+5NO9kmfxVyHEe/fyul8G8Mw+lvU1W3JSO9r/89MxlOBA2MCa58H/9iR6t2wBM0xUT18Ae7AP9lAf7OF+8EQKmZaJyLRNQbp9CtItEwErypBjpgWvXEaypQ12fx+2/ODbqDlhOepPP/PA3UjFXjfLLp0HZgo1KCKkqGL/WMZMA1XHLkRyygRs/8pPIUo28icdAXfA1/SoPnkxJrzrFPCkpQIVMDyMVe+5Dv0r16G8sx8Dz2xA3fK5qDlqpv/zq9uw/aZHkGyqRtsVJ8DIVKIWK7Y3Jt6oKbMGxtjK0PqPhBA/Cq1/B8BVAIg8XA+gTwgKo8IWAPH8OPvJ9ibK7BGMPoT4095eRAixAcChofXnsBeEbpoWG02xU81yjeHtBTo2Ic9cGxAFEKTcV9euCF1MiQfHPH95Lm1JuxmpjCp269FnomHhcQEMDw/28ABGOjdieNt67HzwdhS7dyLd0o7M1BnITJmJ1OTJ4Iyj/bJ3o//pxzG89lWkJk9BoqXNvze6UTNcKXTFaGEMlaIkirB4BoenIbCkicHD8+TwPWMBDo8xFeUxlhFSVJ3wSaJOCAInz4wQIj1yqkxermyiwmGBF0avhpoeJaRBzWv4x4T5VYQWGdFnLuS2ALmhU7BgCk5Pn0Ln0LarpsGC5xKbjo2hSf7BmSXzwZkcFH3sLUjP6RgTGaK6ouJ4UjOJ7ijM4fC0hJ5hFDIAvqJeLQugIn/diF6XbjG8jTh2KiKI4H6VbsALKl1TGI69O2HUlSHy/HSUiYfQJGCMKTPFKZO/Ke4ZrUcRMXhMnRdaf2KEciH6P1P9smAGIqYTJd8zZsD1TBgNrWj+1Lux82vXgedrwaQGWHlHP2yWBspB5JSZzKPtg+dg4+dvQrKjGW3/djbS09sgBLDrmY3Y8rVfoeGiFSiu24aXP/RTTPjk25Ce5OsTGdo7TLwdgwuFtig0REVq+WVNSZ0lSieRMFwVIRbcO/PbHgEXSkuIR66XNEtIypdJX6YkPEKoEh3rhqB2xamTz68oPwyOfMFLbhQx4sxDQUJ5RVdGyxFiREiRlgDXcQ2kXAtpZqu69yQiVKaoXEKMDEKM5Dsemv6K6WVpqLLiJXoslAyapozlrvQu6dy2cLLu3SU1Ht/WLYRYMtoPjLEzAXQKIZ5ijJ1Am0fZ9XW7+XGtVL3fzWOjdFavn+165mHsevZhHHbBO1Bb1RLPDM64P41WU4PqmQt8blS5hOEd6zG06VXs+OOvACHQesGlSLa3o+6YFag9foV/K6OMRUbL8fZ62shIEplMac877iczMjbcA5iMNCYc8Tpb7qhDwdJJbP/mL1H9piNQfeyhSLQ17PnAih10ZjXVo+7is9D7qztRfc4JSC+cCaezF723PYKas4+O7Ft12HRMvfp9SDTXgif99i8cFxs/+1MkO5pRc/JhYHwJev74OLZ/93eY+s13vxG3VLGDzcYfqfpoAGczxk4HkILPIfoOgBrGmClRonYA216vAozrAZGTkX+MNerGbrgFmmcn6OMWHhSRS2qECCUAvHSUxIdQXp2xdIhogGLIiNnhzWvR+de7MfWtH0SmeQKGR6Jz5WMZTySRnTYb2emz0bT8LPStehLbf/cLdHzgk9H70rhLkDo9Yc0dFZVE89YEDGlIkWBcHROk9IoiN2E1Y4dxRcYcGUmqSjYtSlIY1SeixLEUUeJ4AanT1TxVpU6rJf40sw6Ey2Bk7JhCradyGBGCFOVQCUMEqI6ekNVjAA+jP/KyhEKJYI4/iAqMIkNxFEiEeDiI7BuQeqjhSI/RAhwBJA+ZjfYvvhu9tz+MTf/5U/BkAvXnH4vqFYtAD5+kAMJ8D6FBk+q5qWWc98R49NlCbzfq2Og5I/dKRgiKlivNkUrPrm0EHjVx2YiDQfVJnnFIqdpNAG6ID6R4cZQcmLZrxQkjYHSMoSVxjqF2PGg3AYcoelqKUFOaNGGNKhblmMQItgb3ybByPX3ooRi876/oufGPyB27BHWXn4+uq/8X3Tf/GUZ1Fs0fuQiZWT5XkLe0+cCovAfPBqpOOAylDdux42d/Rv3FpyB97DJ03fQXDGwuwazJh56vLLN8Jpl0ST1T0k5SfB2NW0TvpeNxxccZsn30xXXT6HWCNqG0hmTlZcwA/aFtxB1KaEiRbmEFbZ2jFBC/SZE7ihSZ3EXWLKlrR5fRMpZl9IrNPCTgIsUdMCt6PdJkI/X+siS4u6GITYjoOxnLP6b3ayKI8hR6hK0ehawhp4yJQF/pQHpsr7MJIT4NX+AZEiH6uBDiEsbYr+HrHN4CX8fwtterDON6QOTqIqyhxqYPjvSOSwkyqnjS0PQSfazkB5tpQn9EWCMI3BVcJdwcK8qMBhlCCHQ/dg96n3kM7W+6GMm6plChEL2Oxr+LREAxgIEhN2U2uu67Pbg/Opb63PDAgcpkhu6ZfguXVd6/SrfAQtA/nV6fVqQ6Egwe43ANrpIrCjl15hZlh0HRa7Ic1HHk0/5o0eJuLKRWC05SnXdCCt8lLCfonAnep4GRFx0YCS2zti/qGP34BwJqwh9EmkLt6/8BNaVIg1BFfiTurIbSRZLz6gMhfZpNRWcFELoS9qttRN3bz0fjuxwUV29G5zW3YviF9ag68hCkprch0ZCXdcZUnQWXFjJ9B0a18P2L6OsQpAbRB0D6ICt0vrGSgtJgmJZl01PEfY+S/FL0Iz0nNcgI3jXXEnAhAuJ1iabx5PU1OYxREdIxBkb6MwonFB4LaVXjWjXIkBsYU1FmKlpNe5mYwYKQavj12HjF5SiuWY3UglngCQvN//5BCNtBYeUz6Ln5PiQ+/c7orSgBQAP177wQbv8QOr93E7Z/7SY0/MtFyJ94NHZ8/keov/wtSM2cJgvpF4SS97o2RzItBytE0Pf09zE6ICq5JobLSbmNBnop9LCgvdAUXYqCKojoz0QwEJHCljQg0iNTw+3JpCk+bUDkqr4jiCoDAnK1Kbg6JicfNiXJpYFQkUmZETkgKnMPeV6EZxSQMKJCsyOmv29BilWWLSJzB4PHWGLWMUxNxwmm3gc1BWcEfQEQ7ov0jx2Cl5Cqbx+S8yobfwjRWPZJALcwxr4En3v8k9frQnvk8VRs32xky3r0vbgS0y75CKqmzvm7zze89m/ITJq2H0pWsYPNGGNIz5yEiV99N6zWOvTf9xQ2fvyH2PCJH6Hn9sdQWL3Fz5VWsYPWjEwamUXzwGV0JWMMPGEhs3QBSms2+bnxdnd8dQ4tn3onjJo8Oq/+GarPOhG1bzkH3T+5CT2/uh1eee+Q6YpVbDyZEOIBIcSZ8u91QogjhBDThRBvFkK8bjyNcY0QORnN05LGRoHD1bSQGjGH9gXUlBIQRwv0pJZKNFAGe7ke1BRHQKSVZFKNZDmyeS2qZsyDVVWzz9SvSCixPK1VVYPCts2wd+1CorY+IAxrMCo4QvpG0TLqmjGeNo1ilJnah5AiaMgGpXMA4xCc+zo2BNsS5EtpDqT77kjoeUg+GyJh5zNFNX2me1bkSaYkNykpEYaMVVYESAqdpWk3XbqfrkPhsm6Zg6T2dMl7ZghJUoQse7Aci0ivphx1hCH0LGLoA7UbQhSUsE0IJiTSudyFPoVmKo26C1aAcQHheii8uA6Dj65C3wPPw+nqQ+tH34yqRaMPmhVyRO2VhRABhTbI90CfOtME/cK2J0+YLIwUlSTzWj0fQvIIMSLEj9ZdBmF6cIVQaKYKpdeXmpxCOFXImNN72hRaGCEay5QkFT3HcvD8PK1PiodgMzCb6ZI1PoJJ071qajcFs6Eew0+8isySebF7EQi3SwN1l56P7h/8At0/vBmMmcgvPx69v78DhedeQtMllyA1eXJALnc4CrKuExl/wKSmTQkVJ2RIPqtC2VLIEE2rpZHEIIJ2ktDlNeQFDebFxCE58ztXQoj05K4m92LCkmRBOH4U1SKidMLgoakx//zxKTT5u+xT8sxDjeshYQ0pNInOP+z4ZaWwfhXKL4+1XSMgfkvTtZjIiIZRsk2MGP55bZpeJqRIocijT6ExFtQ5Tc2LfUWKBMazDtEbZuN6QHQwWmnXDmQnj64P8los1zETDctOwsaffhctZ7wVuTlz99u5K3bwGTM4sgumIbtgGoRgGFm1Hjuv/i1y//NhMKMC+P4jWd1F56D7+lvQ/8e/IDljCpydXag6+ViYDbVwdnbD6ewGDAPpebNgNdWj/sqL0HfbXRBFG0NPrESitRUwDOy84QbkDjsM1Scuh1FVSYVUsYqNZeN6QOSllWJadBn6OxC6ItdGm3/1Qp6XSu5IHBdJANUQIqHxj0RCKNl4EgHkGjJETkymfSpGNq9D/Zwj93yDUYpPCOFBxKutn78MyaZWbL3t5+h76lFUzZ6H/BFH+T+GEQg9PYUebqxxqJQSKg/4RGQxUUwijDIPwmA+EVvjfijFaEKKJMGcuEZFlzxMhpxEgAzy2KSHWp3xDyIeQlaKwWVMW3lX5Anq6+SRFmUaEvJuS2ULjsY3ovBXz5X1pksVcEBHFnRJBT4GFwU8VOcxpIjWWXTdDeraGwMpMqTwHw/xKTOHTgFLWihv6UJycrPvPSLwgAWFSqv7lkVkIZRsjJQgMeRIGvFN9sUShqu4GcrDlggeecYuIUchxWhwA3BFUE+yMkg0k0LnSdldSVp4QayEerJ6P6JxiojnzoAxwV015KQqCJ+T0E569mH0D4DhMR+N1dEeI4xeB9yydMcMTPjCp1FatxHF1WthzWlE789vBRiH2VgPq7EewnXRf9t9yCyah6ojjoC9eTuKr66JlLnx9PNQ2rkNW778X0i2tiM99xCkj1wIs7YGZYmuGmm/YolHRiheuWzK+mQKxaL+scxNlIQHwxod7aX2bHAxJtqopAEQbYMm91SYv4626MgQmRJZFAFCRMfSes6McooIMbK4hyrbQzYxqBT4yQpSF25EhvATUkQkbloPn28sG5Fo05CRUDIaI1yeV+dzaUhRkJVAhPpwRJZ7uHzIROiEFSMb1wOig9Gqps9F18N3wikMw0xl93zAXlqmfQqmXPkxDG9+FZ333o5EeztSbW9MItSKjS9LtDei8OoW9P7pCaCqBkPT65CZ3gpmGhh4Zh12XPcnTPvRx2Fk9SiFio13Y4aB1IypSM2YCgDILw+F5MuPYM15p6P/jnuw/ZtXI7t4ERrf9jb03nsvnJ4eQAh0/elWNJxyFqZ+4nMYWb8Gg2teRN9Xv4X6S9+KzNLZb8BdVWxc2MFDqj5gNr4HRCkt1CM0F6+ikoiRT3PwEsFR67SfKSAkEkST91SeAAAgAElEQVQ8EkKISHyMECSyQFwuQBSIL6P4RppnatbVID9vMXY89ke0vekt/j6GHw0TQ4LItFG+CCFE4cSvRlUWVfMXwR7sQ89jD6D1LZeFUAkRuK/6zIkmrhi/MEAVpRIYKgiOeEG0ysAEg2BBbqpIAs/QktIUORLS8CTxosyAYVkmSutBKQIIGUpb/pJCdzNmOeAKcAq3lQgRjyJGgQBdQi6FCp21taS1wmO+V2wRt0guRXiKXSJfck1/fCosP0TD0PeN0XBi60yFVCtuCIuiOiptkUVIkX+S2rOXYdtXboRXKCPX3o6e1RZ2bt8Fz3aQntoKUbTh9vTBqmoK8jSFbkj33vVwezLlsYYSb8aEEpUYIEWsBeekkGtDCU1KD1veh63CxCUHxTbAmAckvCCSMBG9blwAL1gqQHRPvHO5XzgCfKwoPT1FUEQIkp4/H72MnPltJRap6rIYv9EztX5NF/oE1OtrJrKov+A8VB++DDyTQSJVjeoFR2Dtf34aolxCqnUihl9ehcbFy1E9aS5ys+ZiaOlS7Lzhetg7j0X+pBPgSt4fJ96kHbwf6sIKIZKbEhzC44gH5EU/K6MJeuptjdolIcZhVMnQBCH1vGv6esYqB/wjEOJESXJlvyPbInGMLOYixRNgvKzEIl1Ey0T7FiRSpPhJ4dyBOgKmECr/HANGSpYnQMDo/gqSS6RztfR1Aahcna5D3ygVQoyKvXarkA5eB2s87lQMrX0JI1s3vC7nF8KL68r8E9jApn489Nm/YNV1T77RRRlXlpkzGa0fOA8wOMz6Kkz+wmWYff2HMOfnH0PH5y5Benob1n/4Wrgjfsc9+OQr6L39MQw+tgru4F6nIazYOLdESwvMfF6t80QCUz76H5h0+b+ivKsT5d5d6rfUpEmY8MEPYeTZVdj53f9B8dX1b0SRK/ZGGZGqD/T/cW7jGiHi2tz0aKk7hBK4k+tu1GsgvhAEU8iQISOmTPlbwiJEIbpekvPnjuPBNclDlUspt++Voku3xIB8Gg2nn41tf/4NJr3vI3BSFuyQwHJM4ySMDIVvDoEXSWlMRta9gu77/ogJn7kKTt6LeIwKLSIBPCPaAFW28Rg8wQIEiLhSxL/QPGHh+iQVBqY0MJQnrkX4BdnpRWSdc6E8z+Fh32OiSDFb1quS1Pc4hBB48X+fxSs/XQmv5KD+cBft5VRIB0QiQxLpU9mvQ7pFpIWUSkbDkB2LIwnAlNFQDomuJThYmfSMosgXcaQIUSAuUTj6TCmJ6xFoZLtZV3o8VH9Kn0eWR6tPxj1kDp+L6TfPQYuRRKdXAr0QBhcobur09y+VMbJuB3b+8A5UHX0oRl5Yh85rb0OirR7puR0wMkkw0wBPGDAbqlF1xCwwg8NxCb7wF15Id4W8eEsT5aQl8YaShhPz9F1OekRR3RelX8MdmDBgcEdptXgUJUjHSs4Nl4iKSiFiBuiqStKskmhqSy26bAytwMgxSss1pH+k3hkj+ptCoIQUbtW5fTyEumi8oyB9w+ickbAFCK2/np1+CAaeegJNR78JNQuWYuf9t6P17IsAI+Ujzg21aPn4+zD8xDPovvYmZJctRs35J4GZZowjJVym+JgsVFa/P5DPjZA9eWxRkJSAiPHS9FQ9SpiVrsGDY/gYCFFYhwsAUhJlNrgZQz058zWUBuw0gECPqNryRWNN7sJxMhhhjhKLpIhbPTUIIURkJndV9CbXpqE8hVT5Za2R1zOYiEXR0fuhi9UqhEghVkLNkKgUJE6QgqRir90qCNHrZLn5i2Bksuh7/NH9el6WTIIlEkg0N+155zfAhOvCs/ev9snO+1/FmpufgZVNoP1Ns7DoK2fv1/P/o9hYqOHU/7oCNacchrUfvAbbvvUbNFy0Ak1XnoYJn70U0264Co1vfxOMqjSE48IdKsDu6sOu3zyMrpvvP8B3ULH9aXVHLUffU49BCIGGY98EblpY+70vYvCFZ9U+zDSRW3Y4Wj//IdjbdmLHF69FeePrlhmhYuPJhDjw/8e5jWuEyEzo0QvxfYjzodI3kLS+RElI8Th8PJdeJulmpMwot4GMthcdU6FFrqaKHCBFMhqjFERe1V50HnZ85/soHn847Opk4GFpSMqoHCIVnRRFe0qDnRDlMkY2rUZy5lSlrwMW7KOScdKNeFFPio5RSFH4erQP18pKnqsLiLINYQlw0iFyGQqrV2Posb9i6LlnkZ23AC2Xvj2meq2QIo+hPOSTFZSqtPRuqX57V63H8EubUTOnBa9+8U4AwOQrj8HEi5diyDOBUhwZIrPdAF2ic2ZSPnyTNKPuv+txZDyOfML/vWDJiI+yqdBApY9ja4gRRdXJiEOjKJflUH2OhRDthZGIb6DALdscpVVRkY884J4IHmhJARBCwJrSjrb3T0DTJStQ3NCJzJxJwXO2TGTmdiAzt0N54kIw2F19WP/xHyE1dzoSs6f6vB7BIDwP7lARRpWfV8dIRBEhlbZFnouejRHiTJCZmiKwq7Sp5H17HBwCVsJR77EtX3CFFCnVaw0xkrw9ADAkxcPQ07KEovUiy/A+msUyoBBSZIUQIkKcQnwmKhO3Q8hVGEnVXFP1G0WdKQ4fXVjE2pRCLOUyVd8Mnkih84E/INUyAfUnnobao0/A5v+9BhMWzoCRSatjjVQeje+9AkOPPoHOb90Alk7D2dGJ3PJlqLv4XP9eQn0AADBT8ggpSbQTR3lUmTVESEeIKBUFqZZDMHUM06Ic9RkDIymRYdm+SjxQxi7KbZRupCzRHurrh2QEWT5RBNw0BoWr+EWkiE1RZHQsGfGSPI/FuEpBYWUfRdwouV+dNaw4SFxxlKQeEemredFGEY66I7SI9qE+j9Cs1ajYa7FxPSAazyY8D6V122BNalckUN0SzU2oOvoo9P/5fuCMU/fLdbNLF4Fxhl0//w2czl2YdM1/gRkHDiYVtoMd11yH3qZ65I87Fl03/Bw1p5yCrp/fCAAw6+tRd+rpr+3cjguvUILTO4TN3/g90lOb0fnLRwAAi394GTLTm/fbffwzmlmdRXb+FADYY8St1ViD5vecja4b/gSnZwCpQ6ag9vwVKLywBj03342JV38CZkPNASj1nk24Lkqbt2Hw//4Kp7sXVYsOQ37OYagA4ED7m69E/wsrMfjKKuy861akJk6W/JF4A2CMoerYpcgtWwJ720503/BLmE2V5ML/sHYQIDYH2sb1gIh4HbtTxfVEVEPI0BKL0jJ8HvJIKRqJIpt0hIgsZdooynw2NGdb6B/Aus/+D6qOmIW2D5wDUe2H2Nsyn5fvLTHkLzwepe/dguEXnkRm8XwwywxFwEUjSKJRZlG0hxRsORPInTAPRkse3d+/CUh7qt8nPSVVN672QdA8VrqgMET8NzWB7w/+Rp59CUNPP43Spo1ozVXB2dULI5WBvbNTDYbqzjgD1cefAMY4BERM2kfpsgyZAR+H5sddhm1fvBr2ji7AdcFSCRTWdaL13aeh7tTFECkPQyVCkfwloRHQ5s2Jj0TLTKKsotnSKuGkXzjH48i6HHnm6x8RglRIWCEOmaasTJGGpJBtyXVaFliMr6J/m0fji42Vp1FF/hkqRMc/ByGnTIAjUNf1BA+iMFUOOtl+wtFfGg+Iolgomiy9eB7aF8+D3TOE4ZUvYfuXr4fwPKQXzkb39b9H61WXgLQg6T0jzhChPypCh3uxvFWu/CgLDRlS98UFTOHBgBdErxkehp9dg8En18LtH4LT3Y/y5u0wamqQWXQocrOno//+h9H/f4+g5ZIrYFbXBEljCT2WiBEhKep1CIMTtMmJvptkKnkvoSWujxL55ZbbNC4d93zUL9CbkvvxEJKonptcasr04US0MRpaKCExnSPR0oLG1jMBAPbIENZ9/f8BAHb9+neoO/csDD35FIx8FdKzZ8GsqZb8QI5EczvqLjwPXT/+GTKzZsFqaYpxiOACcHxNsugNURnD69rHVy88odhhbTMtGk8Z3V9StnnZxh2T+DRCtbuCLftlJ8q1ofZUSPoPbcSxYHITfZ4by2VW1voXQmoIpeEIEKnwtohRG5A3YwpD8Yn0hLOOdsOmeocoIs6DrVAkI3JdHcWq2L7ZP1XtNWaH0DWc2y/nshrzSHU0obh+B9Z++Fo0v/ccZA+LKlSnciUUkUTtRWdj02e+il0//iVq3nw6qk864e++fnLaRFjtzdjxxWvR8L6LYDXVwbA8uPb+9Yrtrm70/OFPKK5eC8Y53MFBmFOnova0E2FW16Dtox/GyPOrkJk9G6kOH33Y15QlZG5vP1jCBByG6pMPR+2ZRyHbkpG/ehgpJtTU14GwutwIeoYye97xH9yM6irkT1yKzBELANeFkU2i6+obseWT16Bq6WzwVAIY7Ee5awCwbTSePB/Ny+Nq7Sb3YtMAujkDIyj12hCuB3vXIJyntmLzps2wGvKwmmvhll303/sUqk8/BsmpE8BzNUhMbgM3pebXsIfMoXPRf/f92P7zn6D9vR/CgermzIKAk34Nc6Ov0bgTTAvujXmlIphpouaE5XDsIrb855fAs1mkZ89E7+//gNTM6cgvPxbJKR0AY0hOmwKeTmPbF/4bLJVE9XHHIn/mKWAHCHkzChxueg9Q5n60nkIGPHfgoi4dYahpswNvBwen50DbuB4QpRJ7TlxJaAGN6hPKU43ygwB/xN+YHYpFs9Cxuqpp2EgXR7H6PQPt5y1E77Nb0HTqfKz95u1IvmM5qo6e5//uGPA8jmS2CHfH5qC8u3aCVxF8IDeOwg8YK79U4B0xNL//HGz812+Cl/thpf1wW8PyFDIUoAY8cgrlwYVfCLqMrE/XKWPgvofQf/vd/nbLQu7oo5BbshjNHR3Y6dmACyQnTkSyfWL0fqCtEodiyK875gJci4hxHY72j18FL8vAM2mwnIAHYKTg70Boz0gxoVBAdXqNY0CK1dmkP3hKmXZIzyhK+PbAkHcM2NxHiOiDXbRMFB0LdbkRpays1HulphFFrjkJiSBZ0lszDcUnUgrgGiiobLTv51jfVIo2U5ojhCJCqVsL+BpBjNaVXLBEY2RbCLdxldmcEC/ighB3idSMM/7gkFsuJnz2YhRWrUfppbUQw8NIN+WRnzMBpuVh/bV/RuHljZhwzkKkJuXBGAt5uV7w/sn3zim76Pzzy9jy65Uodw/CrMkCnMOszmDq0gXIN0wBS5iwuwfACjYmfeI8JA/xc7eRrpRT8s/V9ZNbMPLEc0jNmo7ylq0Y6d6IzARf1JD0gSgPmaEjRWFEQuMX6ZFnQu8jmP9szYIIeD+E+hDA4Mj2vjsOkXoPIetIrscQoni5mIYu6e3IamhA8/kXo2/loxCug4bzLgAMhuSEdjSefyEGn3wS3T//JYxMBo2XX4qhJ56E09WNqmVHofrEFei68RcQv3dQd/ZZ/ukdJpEcqiR60RFZj2onjf4R1jXNqH8wCjymvuwlJRqjIg39dcpdaLNAd0xvy8RzIl6SLfcr2hYmWUDXSA7ZhN8wdCVsHRlS0ZBMBL+5wTZ/KbfTDIZCpj0YhgNHGKi3hgEgppBNPLywVhItbfmQqQy0bou9pE+MMW36z27jekCU0MiyegMFgIwcNBERLqUNhFJG8AEMBkI8uj7GF2i0BH2OERDerKUt2HjDw1j4yROQ/PSpePFzd6C9rx9Ny2ejKp9GacTD+u/djVonhaazFqPzjqcw6e3LYFT5UKkeuhwmbI42KAuXubi1B5u+dDNarjwJtfOaAZTVoIGSZQYS8PSyasxpaUKwYKAlzz+y8nk1GKo++yRULT8GRjYDuMwXuSwh1ANHT6uK7kbJxkGnFxCGA4Irg5Gs9hPC2oBXlCGossg2E6EUEvSxl9fVBkjhUG/AbwthgcdwPTqCw+Kums6hjs3kHnIybUhRwtAFR06bUuoJIjImZKcqp1VdS8CV8L0oRYnXY87+MoxOrg8fQ2MrNYUGVQ9MTbH4YnnB191fuLKjJHKyCAnfqalAaj8OTYGw6NII2iljDJl5U1F72CQAQEpOSVqGi/yhk7Dt5kfx3Cd+DcYZ6hZPQuPhE1E3ow7JmjQ8MJT7Cuhd14feZ7dgx4NrkZ3SgCn/ehKqF06CI2EPVzA0iBQsVoxNd1NCE5qqK1E6nrR/n8XV65CY3I5yz05YE5rBM2lFvKYQfaGRrlUqFoS+FWqaSN67JjURcVTUs9U9neAY5iE2UBnVaGyhnUrNpnjxbfrUUmxalgFVCxYiO38++h9/DIVXXgFLJNB3732oPfEU1Bx5HKqXHoP+Rx/Gli99xT/GMFB/xlngiSRarngHtn3/e7Bq65Fftgxw/QGRnp4maL/R/k2uRAuptWnVR9jBoEhNPxvRpWr/FGwgB6Il1wqS5VIHQu8MDfLlqh1yAgaTBvrA1Hud1IJtVDJpL+gjADkg8qIh9PrS1Ed1CIQfydLyW2XpAyA56h19QCRJ1SKavqhir83G9YBovFumLY+6Re3429UPYOoHTsKCb74ZG3/xBDbf/Djc4RKE46HhxLmY9pGzsMsso+HsI5BoyMN9jQNzIQSKm7oxsHINOn/7f2i59ATUvWnx/r0pablli5Bbtkh1LIr3VLGK7cGsmgym/+sKTHv/cthbu7Fr5UZsu+dvWHtDH0o9BQgIJKrTyLTXoHbRRMz/2gXIdjQo/ai/xxrfcz5qzzkexU27YG/tQWHVK+i79S40XPZWZGfM2Q93d/Ab4xw1Rx2DmqOOARiw6547Udq2Nfjt2ONhVOXQefMv0HDe+eAJX8PHyGbR8s5/wbYffB9GTQ0aFy54I2+jYn+vVabMYjauB0Q0VfH3GE2BhAmdesgieZ3hEX94v9GQIjpm7sdPxKr/uhcr3/VzHPLp0zDns2cA8KfYCK3KlMswjBE4uQSAIeWB6AhR2KuITGm4HrY/sAYbbl4JZ7CEmoXtWPyN85Gb1giTD/hlZ96YyU4pJFOJnxGpk0jVoVQM9BsJfNE0iqem0gyf05t0Qu8TuYTyvDSIKlO4enR6jBuBV0cBei7B4JIoCbkkcU7P4QqoILjbMAgBIxK1RMKssQdvZS8KNTuCA24SfSGSNeCjTIQq5S0fQqA2RMkYTYkcKURKoiTDVgI2CTxakmRP6ESZGLfxsgltelRohPpYODfVK2eK0CogxfIoHJ1QHUpULIIpA0LcCBlSXrUmDqim28rUJpiSSSDZCUIniehO57aaJiB/+gTUnXUkMhJxU9MLgjx6E15o3RVBuxSCRbxeanNU54aU1yCkqMAtYEoeyfY6OGUDwDIUVm1C1zU3Qlx8ITIL5gYih0ROp6k0Il3bob8pmSyJcZLIo0PTQVEUyD+fXMrxHY3zhOkLrOoooQiRqnW5BrXPvkyt7o3JY7vvugODzzyN1ne8O/JzbsFCdN/6W2TmzI3sbzU0oPmKK7Hj+p9gQnMzWF19TM4jlmQ6hArFp4w1eNkLNisBSG2crKbV5LNXQQ70vvCgf2EaMhQLGiFhXQbYhgFbGChIdjzRMSgJKxG16XaCFDReLJmsHqBDbZtmLhLcVd+HYYkMkSAkEaYNhS4FZGrAJ1frU2XOfnAmKjbOB0QHg5nZJBZ+6Uxs+ctavPCZWzH/6xciN23/iCa6JQfb73kZ629+Com6LKa/Yxlql075p0zbUbGKvVZLzZyCpg9egc7v3ACrrQXJ6kooOZk7NIyaY45DsqU1sr28cyeMXA5mVVXsmNTkyWi88M3o/t3vIM6/AGZD3YEqbsX2p1UQopiN6wERhT1zvu8PThftK3MjNo8b4xDJn0dDikZDicLnaFs+HVy4ePFzt+Hway6GWW8qvkozH0LWGlTS7+UxRvMqFHy4hDW3/g0v37IK9bPqccLnlqFxQau83qAso39f4XnnYD7ZX9L1yHsoayEp4fvX0TIqI6FNFM5ZsC1UCQ8DjhMLk1ZeC4lUSn0kJZ5H6IgR4rzIInkJ6XGPggz5PzCQe0cIkZrOo5QsRGYdRciTpmNi6SM8DtNLYlCumyHeGrUXSuWST/jEa2ofRdeN1FFK1mHSdDBk+tMMRZkmhhAjrySJ5dIzHZVwqiX6jMkyqHDtwAtWKVYYA3N5EAJNyBDxrKhORPC8FHmaIsAUMiS9XiNKDEXRUCigTeKX8jnRviR+SvWdDAVIGIosGkWKAgQzaE+eYJE3Tw+eIHSVnhsRUQumi5Lhe/q2IZCZ24r86ceh93e3oen9V/rnl+1SAn6KAM/tkMihRISYlv5D8VvotlicsxNDiHgc7aDtY6Z4eZ0tv/Qo7PjFz5CYMAHu0BASrS3gVgI9d/4RmdmHjHlcdt48ZDwPr/zkx2j7tw/ASKcDLiG1S3pPQ8fFqJHEM9LI5PDY2KKmtK9CSOUh1FcwAUZ/7ymIi05e4vA4h+sZsLWEs4FUhb8rtV/6LvFQEIyh3aCrFb7oyHZqBAKQGdk3EAKtI/1hXiMwujAjGaFOFXttNq4HRAebtayYhYHVXXjy/Tch25qHZXhoWtSK3Bnz4dQ58DgHM0bv8dyyi22Pb8bG+9Zh+/9tRttR7Tjx26egYVYtgOD7VrGKVey1WdXJyzB49yMob9mORHvrng/4J7DUpMnILz4cvffcheLGDQAAnk6javHhqD3p5N0em1uyBJmNG7D9h9ei+e1XwKqvIEUHj4nKR2UUG9cDIgqppZG4LuEeNvKaaUROniKNtk3mBZ7pGPO7njYVFUaKxopE08816z3L0HLcNFjlAlKigM0PbcJfPvMg1q/ZCOF68ByBWSe3Y8aKNgx2lzDUWcDg9mFseqITjTOqMftN7TjnUyci35CUZ+yLlJHEJDMyJCYjVeYs5sJFNOKAEKOihGFsiRDRvbihaDt1jJbQsCCzyg7afhLWITuBGteBLWzlQVEdj5R9GfyC9JLK8nm5nJAiibiVufLqFE9AIkPMIvQnGukkBNRUIYWSB4lnJceFkA6NQ+S4RiAUSLwZeuYeQ0okUDbjnlVZnpfQMkLl8paPFCUktFA2oqlC0sJWYp8Dll9vI6ZfNyWZGsSTIfooUVy1iHGHwjIM/lKuK75FUA9KxoD7kT+Kg6KAIrmBEv8KEfCKxkKGZOQWCX4qrpHlgRVluSmcmZZUduIJJeIuuhHydP11KhPdVvDMhfB5UUr4UZPV0KMxyetOmA4K8pmPGH7dc8NE7qg5KK55BckZjRAyEtAjrpuMPvOcQFhToUYkuii3exqCxN14VKAe/h7hCoWN7yH6cF+2j7aPVi7d6k45DWCnYeTVVzCy+hXULl8OntP02kbregVQd+bZ6H/oAWz97neQX3ok8sccA7M6L38mjmS4TEI/hW+66COPp6Aeq/wqQs0OccMcbecxonbDBREOh3A5HJkeRghCISWSSKH6Et5WvCHuxYLnPNVvRX8oy/ZrGJ7iExHHk2YzVDoOSnDtRtFtg3sKTSWj70JxlH6sYntv43pAdDAaYww1c1qQMW3krSImLJuIdmZgxBpAhpfhlFw8cO0rWP2Xbcg2pZFrTKNldg1OvGoh8s3+x9OAh9F7oIpVrGJ/jyUmt2DkhY1vdDHGpWVmzkJm1kwAe9/7MMZQc8JyZOfNR/9DD2Lz178Gq7YWifZ2pKZNRe7wJWDGaKPAir2hJgCxp/w9/4Q2rgdEroxq8RRCJH8IzdmKkKfvL6VmBKXwkNsdLiIjbCDwUPUoL93MUBRBWIwrfA4yQl9M5qlRuyl8BKfaLAAmcNlVbX4ZJRrjD4BGYDGfyWIwL3ZeS4ZoZSUiRMsM85GiBHNR1lCeYN0vU1H4njKhaHR9T/DYscOej1D1u34CyH7HF+UbdpLI2GWYzFbI0EA5pe6Zyg8AI+RRyWfhGIFwIXnlKgqEkCFaJ49RcQuYWhEqSoiiSqSHr/W7hGgw7sHmUbRRgSHcgwOmoqTCmim2vI+yPNaUnmNCIkTVUnpfcbVCcvq6EGSf6dcjJZMsyKXiFpUN5WUyXYwT2jqhPoTGeAGHiLRhAo0iqkd6XyQSZ4a8Vzo98X+0xMgkhOkSUuQxhTQxiaAIKVqpEKgEtbG4hbkX/nqUoxFO7ioEh5UI0inogqtjcfsShqveP0KgCoYL84iJ6Ln5Xhh2H0QmL+9LtktKG2HzAC0ifpHiEBFvS66HkCLapuvxkI2FEEV5ZKPezj7ZHnlI+5unJACrrh4N556PujPOQnnHdpS3bEHf3fdg5PkX0HzFFYCpKUzqAWljRJRFLhOLUNNWHRbbvi+cLOYx/12S7cBVXD2JdGvvZ/C+BgMLxV/UIzWlOTLa1DA9FcFLPDya1aDoNkKIXC0VEedCvSN6ypz9IV3xz2yVoXvFKlaxfxpLNNUgf9wCdN147xtdlH9I45aF1KRJyC9bhqrDD8fISy9h/VVXof/hh+H09UEIAVFRSB4f5okD/3+c27hGiISKOiFPOECKSP9DCfKSJo0coRukmyPRA8MIUB5CMJS+hIYc6ak9TOHFlIx11ewYcsSE0o8wwWAxVymNVht+vpyEXCd5d0OGuVnMVWW0pI+tkCEZ7pKR5cnKYiQZhy2k5LwsU1nWG63bChmSHoh0V10whRaR9UlkqM/zkaF+MyO3Z2BxF2mzgH7H3ych3WTyUiyV4JMifnw3u2RQslRPoUVCJUolDkEUIYrok1D0Cm1T/JjQPgA8SnCokBYeeHVa5JQwGFwYcHTfwASYPI/OPSu6/v1YpnwmcklRfHZIEyqcsgJADLUgFK3MRShKRi407pCOiqoIMk8ED1kIgIvAWybEhngWpPINL5Y4WK8bQ1uqaDPOISiRMCXLtLX3UaUS8S2MFOnpaOidYdrSMl0w4cFgIvYMqF51VFe9hxAwZRmDyDTJJbpsKZ6/4hq0v/90cMtAiTSjJGfRtTkEpXyQ90XoESFCTEOMuBVHjXRTUWb78l3QwUGdRxb+kVZFfJf/396ZR9lx1Xf+86vlbd2tVner1Wq1ZMmWbfbvN34AACAASURBVFmWjG1sDNiOPSyxDSZAAnggwx6SsGQyZJkMcw4H5gROToAhzAAhDIQtA8MSICQTJzExWw5e4tgGgwEbm/Emy7ItS62l1/de3fnj3t+t5b3X3TJanlB9z3nnvqpXVa9+dW9V3fu939/vlztGl33S/yvcY0tAumxnWi1mf3o3rf37MU2XRWDzZhYfsowRxhDU64xccQW1zZsJx0aRMOye7HipP84tm9w5d7s0pkhJ+RPuctBCotk0jYsy1IWdJUiPU2BdixRVorHS2olnkTwjpEy6Rs9Wz99CLDiTCJF64eo2kbJah8FxlG73HejrDtGRxkIz8sHzfhFx0MDQkabDl8CW2mP8bP7IxFxaCUycIEc4ee1SmGvGPoddiV8cREN1ouEGzcenqa4fO96n8wuDZHGRh//8gwS1OvH4WhZ37wJg8jd+k6Bew7TbtKb30Xz8cQ7ecgv7rruOpNlkzTUvY2DHjuN89iVK9HmHKKrZl1FRWyFiWJip5Lb1SfU0j5fqHnR02w4IApv0rxhHwsd1cf/jGSMdqYZtP2odcon/VBtSZIbS5d608P62ZVvyGqL0GCFJyhCJepXltUMDidUOjTq2aXXQBoSDGWZykXxU5o7ljG5IWaPQDW3Gwhl3DdwoO5NHJ2hXCCP7+5baY+yNrEfKvtixSLHTy1ScZ1rL1tVM0+lnFmPmm5ZlmV9wsWLmXVP0EWXzjINfjpOclgxSbZmPc6T7KIsYmDTaszIY6pEWGdoSpB546rFmhDBMmGvGfqSmpXqdtQreZ2m8p9B7ivjYUhojynuO5Ed9QZQQuphFGtOn6GXWoZvLMmXKyCR2fVBsfl5vpcNogTl3vRptt687bkFP5b37vDeapDmnVO9TcW14Md9hNV6nltEzBXl7ivXpR7+LAU0iDrUrVJxXnuaRW3Ad1V4aPoC6u0eV3R1wkbIBVm2doHn3fQxvWuXXtdxFMxWh5epAWSPjl1Vn5K6Vz7clPs9ZUIhq7a9FjM2RVmA4gybLMiPKSGWTvHryQZt9watN8rdSuj6zriNpbA8ipRuCQAiakobyue9BSAzrX/vbHLj1Zg7ecjPx6BhRWIcmQEQwPE5leNynUZl/4H4e+cTHqP/BHxKtHlmZ5qeDIZKOnzts7mFn6gFo7HVsSWbbvFYxl0eO9L6wC5n7is7nlt9Mo6Mn4rVBeg4djFA7vad1H7+9vqt8ZPi8fnNZGEOZ3LUTfd0hqvsOUf4BOjNbTV+cDin96DomhQ6SBInvALVUhFukNwsUvm9smaeDukBStx2RpQJhZYXXgRgvUF6O1syKqjWxn7rZa4doPLIC7CGXpX3eJMwX3ex1SkxfxuRTenj3+8x0kZ+2c8cYdx0jnboLSDBB3XeIAEbd92nX0dsXD9iyZcsDzmX/YMsKtQ9VqhxctN8PxracCW256DpG/qWcnR4qdhD0QeXE936aqHCfSyBeIekvfajtJSEJAlqigm/3wCKddvVpSzTwpO8QuWkxDfGgKUSSwNe9TtdokDV1sdUOkbaxWjXtdC9qpu6Ca60U3lItn+rC2GkzPXN9uGftLbjhB/OB79T4fk7dXSPt3Ke5Uuz6jHDbP/irOvWYn54NFrT+7HJigpSh9+dWmDpzAtG2pB3CNgHNduSvRbMQ4qBjGtGhGrX89GTRAaIeNZl4+kYevfU+pq7e7u+DUAM1ivHTGFoXrVY6nQb46U0tTUv8M8mnlnAJfUMnPE+i7h0i6DLNVpga87OnzfT3onjbBzvVstBhynYkluosrRSSgLTTfRrrN5HML/DA+95NZXwCgOELL8nZtv+OW5m+6V+YfMVrmXvgPg7cehPJwjyzd9zJwLYdRKOjy57ISkIU+K/aPgqdHG+/diKN7eBIS/KdJEgdPIIlTirI/4+/VZP8Pt4hRMQ7sOi7S88p8YFSi5WU3i/6m08eq9PbTyKIcYkUfd0hKlGiRImjgVVb1/LTj93Izi/dwuDF26lOrD7ep3TCI4grrP/11wFCdd16Zu+5i8aWrblt5h+8n4VdO7n//e+mvuVMxHlbHrrrx+z99vXEa8YZufw5zD/0AI0ztzJ7909szKwwQiL9hEgcU924icrEujKV0ZNFqSHqQF93iAZrC7nl/bOWaUhaqejRww+hlEu0y20vxBbvvl90iSwmWZViYLzM8f1Uh9tnrGHZkVqPQHEAh9pVZls1HyhRp6eKwuws9DjVAkM0Etv/S5khW4WxSfz32aSS+y0N2OiWC273WfjpOw2a56fQrIt5KAmtsEE1POj30W3HQ5todm9op9CecFNph1xwwr2OMZpu1tkX2e+q0Zl2TIqmvJibdwEM2+koqjjF0taAejoy98LsglFtOuhvZYhMBCYMMJJO7YBlX0yUFy62Cu6vylqE3l02ZS3iMCsjTn9TZqPoLluNW77O51WE3soHZitOyxo3tWRMOqokoTCdkleaeuZmUfzUjl7QRFmyutvXD2YLgRkNSFXnh1SInWeIvHODY0kCI+kUn2et3P2oSTr1h8w0RyJCOwn8NGK7IDwNCgxRxWkEoyBIr3khrEYkCSOnj/GUt1/J7m/+lLvf+ime+onXUxkazG0HkFRs+1zQqTrHGDWVMWqlbbCYJLftpvmMJhDFkGRfQtn7X5vnCiVrQSvdx5OorkpMkSkqMES5v+3BFBUV2d2m0CS055tlRRprNth9mzC0eZu995J0m/rUZvb/240AVEfHqaxbT3tmhqlffz0SRhy441Ye+au/BGD6u99m5NJnEcQxptXEzM2TtNuYdotkYYF93/hnKmvWsubKF1CdnMqcWO4su9qXTn+l7wdCyYX3SA1z2xZjSGSrspCuxO/jp+9dkfrupwFm/fnmGakOKizD+CUFQbY+g6OweJIlDgd93SEqUaJEiaOFh6/9Ebu//VPCegUJyggkxwLD517I8LkX0jq4nz3fuY49119LMjfLve96G5MvexXD51+EhBG7v/xZwnqDkUv+HUG1lj+I7wC22ffdb7Hrs58kHBpi3Uv/A5U148feqBMUZfiDTvR1h2jQCZj3zlltio6Uk1aQ6kUUkhmmkGGBMmLSjqSfheB/frq3w905XbdQ0C4pRpymSMXWgRif7iJpNThokow7f55l6gY/mnU6CA0C2AgtUzRv4lxZMy3mNXmsGxpqIMZ5F12uGHyx6/+SF5RX3LAzduV4OM9CsMhcOOvZpCJUkD2eWMZItUWPh1bAuiccYtAlvn1i0f6mwldlVmLHFM3Mu2CSrSANnb+gAmzHdqhmQwdHRW1YBqlAWEeBAWLEi391n7ZJg5/5hKWBaoqCXFkMLBgHnaM0ZSu8C3jVuSRroMEwyxDZNjTXcnXsBOjFBLx+lChhXicSmFSroCNIFfouKnODFwHrtsZ1CrymSNOctPPXJqi0O1z0fd14zZCz2+nWwgUhUM2EMkOFWH0aViFppUybCSS9l4GWY2qSlipbbaFCbC+AN+KF7BquwzM/zpzZXdNs/JUdTF61neHRgMV2eu8GBVYpcYEmtU5U3N3KsIeeQXTsUTN2jGxF2aSQVpwQqKekfxAJRc1MUVNUJCswKZvkk6D6YJyu1DAUXcTWHeRDkSHK/E+uzO7Tsp8O2Upxny56p7gxzOTzrmHs6c9m5r67mb7tRh756//NY//4NYIoZvj8pyNhyP0f+BOqE+uRIGD8yhdRddoki5A1lzyX0Yufzf5bb2Lnxz/E2LOuYtW5FxJU8043HWb5Nu7afmj1WdLEt8Ni6pOlWLOiVq9XGAG/PpEO3VEHM1X8o0wiXO8A4dt7njEt8eTQ1x2iEiVKlDhaGNiwmqFTR1l99rrjfSonLSqja6iMrqG6dpIHP/Nh2ocOEq/fyNjlVxDWG1TG17H3u9fTOniAVU+5oNAhspAgYPVFl1DbuJk937iWPddfy8DW7aw69wIap56OhGX05k6YUkPUBX3dIWoVtBupTkI6vMy69aLttloGqUi/wAwVg591umamDaft/nfBj7byI8mgnm7rR/StKtPttvdmKQZ+XAqqTaqHdjiobI8vHUM0k7T991lTzW2jaTiaS8XFd1C3zfnEjrhnxAWzSzR4pGHRBIwFCx37FnmnUTeEfdxtWws0qOSC10TVtXT2qb17Q9WC2P89NF9hcdElXfQByuz/eM1QMUGkott9n2GRxAjSKS7wTJ4fjXm9TD44p9a96oK6QZmvhnP9Viax5hmipg/VMOu88ZQ1UxapyE60nVdUK0y8Z5YJ8g55nhnyHk9u/aL9AJmRcL4GVVMkdfv/obrWh4nXSviAiWH+oi96NjZlRfUcVL+lKI7Wva4FwZggdffPnpujPTTdSKJegxmdVyvIa69M4QUwefU53PVn32DDC88hiMIcu1tkiLQcdPWnXoMagqHZDr33oXoSzsSa0Nc9ZpsRJC3aLqGvLGTs8nWQZyeCZvH3TFnYJnXPdqXe7t71z62XLtqh4nkoMpesSD4ELfvfHaTEChiiogfcwPrTWHflS1nYsxuTJDzylc8RRBGtQwcYfspFjD7zWYS1OmSZswKR0xif4pSX/xatQwc58OPv8cT1/8DuA9Os2n4eqy+8lMrYeLqPXiO9JgkEoQsj4JnLHoxNV+Y5fzwpMkUFL0+7bVF3VGSm8uvT5MHG69T0GdT2mqIVTv0aMnE7Sij6ukNUokSJEkcLI+dtoLZ2iF3X3cWGq7cf79M56THy1Itt56ndYvd1XyWo1dn4798A0eG9pqLBIUYvuozRiy5jYe9jHPjBbTz4yQ8y8vTLGL3suaVXWome6OsO0eqq9aTSkZfOl84mAa1CfIdiEDvfy5513f1EOhghv68yDgWPnHTqWDqHQQUPNe2pz7fSS6rK/wSxn8K2K9EQ6TYzLrihemEdTFxsHxfbKAwTzwRpuWBUO5Sv5l7anyyUbYoTOyRTXUyNFk1C5k3gPdA6zz2/rPGQmj4+UuTZqol4vz2nZQKKxWHbj7jnXUoGr9Fw3jzJgvMY0ZH3EgOgHEuY3c7rL9IAbZ6JKng06XofD0g9n8IkTQirSYYde6RxdJRFmHdJShtR6Ot81jFBc047tNDOxyWquH20DMOEOdXyLEaEpkmiCUvVE8+xSSZ2jFEk6EyCkn2alFzT4Bgf5FQ3tEUSSJrwUneqOEZPYwlVUi2PXivPuOkgVtOKxJpw1sU0aqX1Z0Kxo+HiveJjxGhcIvc/jsUypPWkbI62sWwA1rGrzufh63/IyBXnp8mYM8mc9R4u3o+LBTYom1Sz5jwntX5mXbutzsfUWGRRE/pqu50P02SymnUm0npyy5o41i0nrZQZUsma13718DILXT0HSY68yX9ZgiHqfG6Si0O0pMZGGZmMjin7f+1KuixRxLoXXNNbf9MD3Z7v1ZG1jF/+PFaf/wwe+PSHGNq6g+rayc74S4ndXwwZb7KicGsJO70kUd8leTv13eIDM0qGRSqQOh3JgTuumXjPTA0cqs+VxcOZHiyz3XegdK0oUaLEEUGy0GT+/keZ+9mu430qK8bg2VMcuvex430aJY4y4uERGpu2MPvg/3tS+ycLCzz+91/j0a98gcU9j2NaLdpzsyTz8x1TsSVOXPQ1Q1RzupLhimWK0hQK4vVEnQkwbdmes6b5uA/d5sIL8Yg0KrJG3c0yRaYw+W0KpY5CA5eeom0Cr/0wxrJDOrpsrXSeNwMdXc9otOfIMUShLQNJmE3y2iFleRSeGXJDk5UwRZ7Vcd5C80lEOwmZN6FniDo8G4z+n8kdQ73cmiZkMt4HpLqiXt4Rmjh2tlJh2tk8ox5oLlmnJudsuuSxJnbXd0HZwc7j5vRHkraTNOS+6dBCmAIr2S4spxFnA1quDbUcc6Nxa5qOQWm5ZIzKLCy2U4ZIGSGNeaPaM/VQKyYpHqnPUnGRvmthTD3DQrQcU2QcG9FeUJYmSLUJSgA5TVHYwRA5A9XrLJTUy8y5qiVG2PmeLzL7/XuJxlZR3TTBxO+8mKBmAx4mCV73oMyQluITxea9Bi2rK0g25ljBM8e3Go1TpHFnTHpvFlP1ZCPRt6tDtGcWmVuM/Si7GrbSfXokjy3qhSDjIVlIBO1TiEjMYjDPbFxIWxNGJKorcu1CGSPPHKknZYYx8mxRIf5Q0assnHeLXkPZRffTQ1OUY12Kv7Uznp26Tbd9M7sWNTW6rGyXe4TlWZOV6pyW0DthIB4aoT1ziGRmhuk7b0WCkKGnPJWw3rBeZo7xWty9i303fhvCkFVPuQAj8OjffIH65i1Eq4Z56H++F4LQJqVN2kgQsurcCxi/8oUE7r7rjHvUyRx51kgKv/VgjrwpAT5Zs2rn1AN7sb0yhshAJrp9CUVfd4hKlChx4mDizS9k9we+wtw9O2ndfg8P/tHHmPzjNxGtHjrep9YTQS0mabYw7QRKZ6RfaNQmJnnsW9ey77YbGdiyFZKEJ276FlPXvJYDd91BMNDg0T17OPSTOxm59NlIGLLzUx8BYPKVv8HgNqszG77g6YQDgwTVKsYY2gcP8uhXP88T37qONb989fE0scTPib7uEClDpKPcltMUtdpBZ54nV2ouLB2N+iSXifQewRS8MPRoWaaoY57claobWVAPKF2fiB9xDknIgol65l1SBkD1CsWcVVmoHudQO68hCjA+QnXTdK9WjT8Uq6vGCpgijWatrFMctEmcBqgt6fXpdhx1kEmZIXte6+L9NCSfrFb31VK90PZH1r4DrbqPwTTt1lWUMXIajTkX4bmp+adUk9IMO5ItikaJbdv5+PR3d2kS6dA7eB1EIbK5wnu/mcB7wyQaxVrn/AvRrptJqi3ycYha+bakzFBYSOCoDEQUJNScXiWSCs1gzrNn86FeE3cejmJIBC/2Mgs6QnXtUD3RPFNUuBChSXOYeY8/IRpZzdQ7X8e9L/9v1sY9+3nozX/Kls++HRM3UlZXI8Fr/WicI2WGNIGqAUGQpqT3Inrt3TUv6kyUaTAhLb0XNZ+h6oLcscK2u56NKrPTTWqjLtdgEni2xzNFGT0g5JkhsOxQR73osmMJV9VnCcJZDqoO0JWzYYVF13bVAy0pMEXKmmUZI2WL0nhSuUuUehEWGRSTell5b6uiTqXbI6jIECXpf8MS3mbdDuGZk8Jy5tHUkZOteKwej8nc+ozNQ1vPo3XoIJWxCQZdSpGf/a/38MDHPwDA1IuuobpmktFXX051jXXvD4OY+cd2MbTlbK8tqqwes18S2z6DwVWMXX4Fu//2i4S1Bs3pvax+2qU2REDBzixzZArxjorR5bMxkvL2mTSHnj5X3HJzpRoiY0oNURf0dYfoSEPanY3raGKuGfvUFMcCs0mVRhd3+KOF+SSi1pGV8uhhqj7Nw3PHMOdUl2mCo4nZxZhG5di1l6SWEMwfWRmhhAEb3v0G9n7lOyRzC1Q2rrVRoBOQOME0j+z/LYVkISKoLt8+qxvWsPDQHmqjG3+u/6uE7RVPWRwJtOuGcO7YNdCglYq+T0SICKNPuyy3buIFL2PuofsxiwuMnH8pzaSV61CtPv8ZacdsiWPXpk6hunYdzf3ThI0BHvr0h5l8yasY2HJmz32CJiRxz59LHAf0dfPW2DQt1Vm4clVtviNj/KE5O9pKijoEn+eKXAnZ+XHXE1eGxm+gI+cuehId6btRmI8Oqx5kUWC9f5oxcxIzG7S910nRY2Wp6KLF2EWKpmPIDrZTDRHYTlGwjMeWig+yTNFK9ERgdUnGxMybmICE+STyI+Mmyjjl7VH90VTkdEPSpOFEAwOuQnQfZYym2y7vWWgjWR+Ma+xpDjFVn2Ygsts0XD401RapB97MYl5btLgY0V4sDDd9HBLHPuhoKRvrSus9yY/k0hFdQciS1Ropc6JtqO3q3LXLdqx6OFdGASKG2cXYr6u6vFxhQfuSZYbAMhH6fbg2SxTNMO20ZTNOWzSz4K6J5uQTYD4gqSU+QnXqneS0N4v55dyIPcMMQRo1G6B6xiYm3/Zqv2yw0bzb7RCJE88Q6T2knSTRmEmtzHUV5x2a5O/RNGt5kfZ1+y4KRIZkIQLH0Eghd5raVT1rMw9/4nqSN15N/fT1RGHiveNiZeWCfOwpRZG1q4Rtzwwps6fLjahJK1rw8Y6q7nlwIGwz47RlmsdOo7GbSGOZdTJGQVNo103KHmlsqHm3TYEFypWFNuu1QEWGqNDfCloZ3c8yGqIlexB6+AI7oqSJyTBEPQclXZivXr8VPdD0/wbXn8bg+tPQbPfd4ip5ZrjoxaePBQMiIVMve62/xo2p03jka5/n1Df8IVFjIBNrK7XJiNNOdTBEeU2Rz1Hnsy6In9718YhUQxevfMBRaog60dcdokHnJ5p2iNJw+YNV+9v+OZceI/siA/9i8wRG5oHdO5BYj44RkjZGfVhrIDyl5YsdIiOekl8MI+Yl6njIHolEfOq+rtNl9ly6j1J9R0V0GiATlewwhNZgE8emCWDToI0A7cIx1rmkrwOiiWoNNfd/DRf4UafQVie2PBDaVCjTbkpwOmn4hLZ7QqtJ0Q5zw02hTUe283RApyLcVNpsHLPghKzNRU39kD6BJUjQHpIPlpZIKsj30wq6j05tuvV+6lWn49LvvtSpnFg7Eq796AMtDrzLurro68u++NLt5hLug0MGbSpBm7V1mz5luiDE9tNyAk1x10I7KOqaH+Y7SJqgtV11dqeXK9Mx6v4gzjrgBHGCabVp7W8iIkjFdmi1I+ST8+o9K/pf4m9aHyKjmMy540VuoKmdUWeIm6LThM8qSB3+1ecSjt7OQ+/6P6x+4cWMvfiSNDGsdmo0fELh3lUB9VIdIU2/E5qESBKqkQbn1I59k32hbcPZaTTAByNNmtohSjuR7TDfgVS2z1+aHp2BblNm/tHQ65EknV+LU2adkoTDeOH6e8sdqpV+b1cL2/487/H8IzD3LigGmkzTbLhtM49LyE+DSeG4g5vPZNW289j9j19mw6++xnccs6LyDuF15rfc8QuBS00IgSZE9lOprkNUTHq+FMopsw70tdt9NYiZqp9KLYg4pbGJWhBx6sAp1MKIehhx5tAUjShk2/B6GlHI9pF11OOQHWMT1OKQHeNrqVYitk+spRZF1KL0e0e5LrMcZtavtcu1MGLH+FpXTthyzQS1ILL/F0TsGF1HLYjZPrKOahBTDWLOXj1JHERsG56kGsSctWo9lSBmqyvPHJqi2qWsBDFnDG6gIhVOH9xAJBVOHdhIJFU2NTYRSpUN9VMJqLG+dhohNSKpsq52+pLlRKYMpcra6hmEUiWUKuPVMwikxlj1TAKpMVrdSiA1RqpbEamxurKNgBpD8WkINQKpMVQ5G6HGUGU7IjUGM2XgSqhTj3dgqFONz8FQx1Ajis8hoUYQ7SCQOlF8DqHUqMbnEEmVRryDWCoMVc4mlgoViVlTPZNqELGutoVaELHBtY9NjU3Uw4jTBjZSDyPOGNxAPYzYOjRFPYyoRxFnr56kHkVsH1lHLXL1FUacMTxm63Nswq53ZS1ydRxmyrElyihd9vsWj5FrLxPUgpgdI7bdaHupBnG+vUjM1qEpKlLhjKG0TVSCmC2DG6hITEUqnDawkUhiTh04hUpgy3oYcfrgBhphyNZV66lHIduGJ6lHIfUotOeQsXnHmglqccSOtWupxe4+iO39UQ/T+6MWZOxxdmwfsdezWO5wJQ8/zv2vfgc73/IuHnrzH/PEhz7N1upg/lqNu/9ba++/08fGutx/a/N10qPsqLcgU1+j6T3bqNV55jUvYst73kJy6z3s/fTXbV0Ma12s73nvVoLY14nWR+zqInb3rC1PISJmc+MUIqlwSmMTlSD27bYRhvZ5Foac5eqpa3stts/x7HUL2T6xlqqrr2ol4uz1ttw25coN9vdqJWLbRrvurFO6lHFnWVxXicL8NsV9K733LZa1KOKsjbbctiF9Xp+93j2P1xWe05O2nZxdLNfbMvu9o5zqXlaj0H/flv0typTrC+WkO9cu57Dtwoto7n0s//+Zfbsdp1h22O3KWmDrvF64D6ti78MSTw7SrzEUZCllcYkSJUqUKFGiFx4wxmzu9aOI/BOw5tidjsceY8xVx+F/V4S+7RCVKFGiRIkSJUocK/T1lFmJEiVKlChRosSxQNkhKlGiRIkSJUqc9Cg7RCVKlChRokSJkx5lh+hJQER+T0R+JCJ3isjnRaQmIp8QkTtE5Aci8mURGXTbDorI34nIN0VkvVjsEZER9/ukiBgRuTRz/MdFZOw42/M5EbnbrfukiMRu20BE/kpEbhSR7W7d90TkPPc9EpEZEXll5vi3ichTj6MtvyMi97rrvCazbV/bsoQ9p4rIv4rIPSLyRREbu6Df25qIbBWR72c+B0TkrSJyrojcJCI/FJH/KyKrMvu8T0RuFZHL3fLfiMiLM7/fLSJvzyx/RUR+7Tjacp6I3OzW3SoiF7nt+7qtLWHPFzPr7heR72f26cu6Wcoe99t/dOf2IxF574lgT4ljBGNM+TmMDzAF3AfU3fKXgNcCqzLb/BnwNvf9jcDVwDnAn7p11wLPd99fAtwO/JFb3gr8pA/seT42QoYAnwfe5H6/CngLMAF80q37c+DN7vsFzp6PuOUBYB8QHkdbzgc2A/cDazLb960ty9jzJeDlbt1HM3XT122tYFsI7AY2Af8GXO7Wvx54l/t+FvA+oAF8ya37z8B73fcx4Dbg2sxxdwHrjqMtXwee59Y/H/j2idDWetlTWP9+4B0nUt10qZ9nAdcDVffb2hPNnvJz9D4lQ/TkEAF1EYmwN9AuY8wBABERoE4aPizEhvZyedUBuAG42H2/GNuBemZm+cajbUAB3ez5B+MA3AJscNuuxJ6PAue55YuA240xP38UypWhmy3fM8bc32XbfrcFOu15BHg28GX3+2cAHcWeCG1N8RzgZ8aYB7Ads39x6/8Z23GD1B5Db3v+Hhh3bNipwJwxZvcxmLG2LgAABHFJREFUOP8ssrYYQBmuYexLE06MtqbI2gP459o12MERnDh1A3l73oQdLCwAGGMec9ucSPaUOEooO0SHCWPMw8B/Bx7Evpz2G2O+DiAin8KORM4CPuR2+Rzwu8CHM+tuJL3RLgK+BmgipYuxN+IxwVL2AIidKnsV8E9u1XXA5cDfYV+ukLfnYuzLbUFEhjiG9ixnSxf0rS3Q3R7sKHXaGE2DyU4skwR93tYKeDnpy/VO4IXu+8tw52eM+RG2E/hd4C/c77cBO9w04cXATcDdwDaOnz1ZW94KvE9EHsLW3X916/u6rRWQtUfxS8Cjxph74ISqG8jbcybwS2KnnL8jIk+DE86eEkcLx5uiOtE+wAjwTWAciLEvmFdmfg+BjwCvW+IYDSwdPgDc7NZ9CTgd+BFwVh/Z83Hgf6zgODuBdcD3sCOs9wLPxU7ZXNUnttxPZsqsn21Zwp5XAfdmttkI/PBEaGuZc6oAe4AJt3wWdqrpNuCdwBPL7H8D8AzgW+4avRl4A3YK6o3H2ZYPAi9x368Brj8R2lovezLr/wL4gxXs3zd106N+7nR1JNgBwn24eHwngj3l5+h+Sobo8PFc4D5jzOPGmCbwVdJRHsZS3F8kpf07YIyZBe7F6iVud6tvxmoO1mJHIccKPe0RkXdiX8a/v4Lj3AS8FHjE2CfJzcAl2IfOzUfjxLtgybo5DPSDLdDbntVuCg3sVOauXgfos7ameB52OuhRd453GWOuMMZcgB3J/2yZ/W8ELgOGjDH7sPZczPEZtedsAV6DrSeAv8a2maXQL21NUbQH19Z+DftcWw79VDfQac9O4KvG4hbsNNlSEZv7zZ4SRxFlh+jw8SDwDBFpuHn15wA/EZHTwc+1/wpw1zLHuQFLr9/klm8C/hN2FH8sw4f3sucNwJXAK4xZURbAG4DfI2/Pq4Hdxpjpo3De3dDVlidxnH6wBbrb82PsaPWlbpvXAH+7zHH6pa0pXkFmSkZE1royAN6O1dIshRuA3wbucMs/wI7iT8GyXscSOVuwndPL3fdnA/css3+/tDVF0R6wHfO7jDE7V7B/P9UNdNrzNWy9ICJnkjJIvdBv9pQ4iig7RIcJY8y/YgWttwM/xF7DjwGfEZEfunWTwB8vc6gbgNNIH4S3Y0f7x1TkuoQ9H8V6xNzk3FbfscyhcvYYYx7BTh8eM3t62SIivysiO7HX9wci8pfLHOq42+L+t1fd/Bfg90XkXqz3yyeWOVRftDUAEWkAv0zKogC8QkR+ih1E7AI+tcxhbiRfPy3gMeDWFXbejwh62PKbwPtF5A7gT4DfWuYwfdHWoKc90F1T1At9UTfQ055PAqeJyJ3AF4DXLDMo6Bt7Shx9lLnMSpQoUaJEiRInPUqGqESJEiVKlChx0qPsEJUoUaJEiRIlTnqUHaISJUqUKFGixEmPskNUokSJEiVKlDjpUXaISpQoUaJEiRInPcoOUYkSJUqUKFHipEfZISpRokSJEiVKnPQoO0QlSpQoUaJEiZMe/x+6xItTWDa4RQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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ijSPOPQPH/sZ5uOY178f8lh3LXZ2MJUS5t2KGbvmHf1rmmmRkZGRkjMF6Iro8+HuhnCCidQCeDOBuADYDWAXg8Yk8lmwkvKIZItEQyQzSiwRh/bFCCUNFcLruggcAZHDtH78HJ7762egcvzmYpYtII9ZkQK9RhwyIPifaEC+iVvRBmFzYm3Gz2v25zZrZGZFPY0Ll2xBqkSSxujb4xxjADKghDtfi44Z2COTT6XNNMXW8m+w6ArozR+CEv/kr3Pj7rwbvG4K6TU1DEgGpRQxQqRtTt0Mqx0o0rhkk3x9UM4qePVJ9459Ffw1BnKRqZoj842mTx2MNEdWaklSz5RoEt9rPWmNmqJzgedRapUmEoaSZjba8iQNWJRYKj2OIbKCzqDVKTjNkRCAdz6pDRqMnzFBDqyFMRyyMroSuWsdRaz5CCKsSish7YzQ8XuDLtW6oVcCuNC/Cck3CbGiErJDWLE3ZAnNlp6mdEo1UQvOi9UUC/xy5Z3AUeyL9mWK8wjJGQdieSdJq6H6P803fixRT1MYeCTrKeMIzf8TJ92oxqDREy6LJ2sHMZ7ecOx/ANcy8HQCI6MMAHgLgSCLqOJboeAA3LVXl7pIMkWDd+ffD+qc+FDs+8MXlrkrGEsL0euhuPhbz11y/3FXJyMjIyNg/XA/gwUQ0Q9Vs89EAfgjg8wB+xaV5LoCPLVUFVjRDJKN3X0k3fBta4y0OtLVHrauotmvPuz9uu/Qb2PrGD2HjS54G6hRgNfP3VkSyaZivc2BuJayAy0IG2az2o3NUsTGJNOG1rfuLRDP/mLXyzZfJkZ2gTmH+FjDDFAMk+7U+Jzwf7murq8ZkS1FGKVYpPDd9+hmY/9HPMHPKqWOJOA0CgYZNHRQrJpG0Vspre1Q6E1g3WqUz8n0hWpvgeQ3YIn8M9bPNbmaqrdBsyBChwICamhufl7BQIF+c1hlN4p3Es0hj0qXn387qasxzbsAY2oqZsA1dSgs7EDy4WmMjeXhNUYt5+rQpvTbI5+utv5TLg2DbMGFX90lm9rpe2t2AbkfVFhNtpV+A5v2rtUOapZh8/jsMyvHlqWOlNRiSaZqAc1x+yGik9EVhHXV7U5iEZbqzDMooeL2etvJDu5VlSls3llW1at9LxfiAWNytNCszZr6MiP4NlWn9EMC3AbwdwCcBfICI/sIde9dS1eEuzRABQDEzhbu98bcw2HYbZr/38+WuTsYSobv+aAxvv325q5GRkZGRsZ9g5lcz8z2Y+T7M/Bxmnmfmq5n5HGY+lZmfzszzS1X+CmeI0pYI4TmBZhzCfdPvYvX9TsXcT67D6gee1vSBIzNw0biomQdHtIFcm95yQCL4XAyDC/YsDCvGppF32A7FQI00TJD8Kd7XfpU8exHqd7S1nK9AohgDhG5D2pigSSZpTWaoZZ+D/DTjBKCzbh0Gt94OME1Ubp0/R3mzOhUWpDVEEJ0QC4MTpOe4slzKqTQrCUMoQ7YIlVYLqK2hGoxRoGUywTNsieJ7i8AAyZNZHDipSz9UmokKIde2+VcSpGZco5zjhbDEDb9K2inlKHjmpSWppfg9FL3MPEKfQsJGdKJyQ4eMALBgO75d/aIyiBFrNumErinRM0MYxNZaybqJD6WW86n2N33TaJapyU5oeOeNE7BJQ2swQNF4LmvGTTRENUPUZPpMXK6vezt70Whn4ga3+qly7e4oSzUDRpcY3bBcl4V3UCrunsq4DdZSzUKqcrnB0iWYIs0QOetqVj+kUbpFsuAaDPa/Nxk17vIMkaB/yibM/TT7JrqronfccVjYsgV2YbDcVcnIyMhY8bDgg/630rGiGaLaykx5MQ3W4SddK56536nYftGnsedr38fqc88EUK+hem0GYqZIEDJGfoav2RDlyTYcfLNxEy5OnEwgtEjzKSdppkqj2SXtP6dmkChiYqpjI4oxQOR+RGlqoBioBlL3bAQz1IDotwKCozM1g/4JJ2DfD3+EVfe9b3vlG/lT/aer5lkXt2sVU6TZOz/740QT1cxRxDcSEoVRezB3DZMZnFHMlEhGKGBppK9LIpRc6zoowSYBsfan7R3y74MUn7if42ZUnnhcrLALVd2tNSjJeIsi8SWmtYIaHRP4vmlJ42fcnkVz2hA2nvnRTIVmiCR8RsnkQ4I0NTW1RWyHrNf4mIQPn8VA32N/vKE/ilmKFBp6KtH2BIxOsz8r7ZowYfU1TQ1RyBaFedR9VeXRK5rhRTQmCTPS6APXdm1p2PUWhxaFKb2OLKx/GzMU5c/yzsZMkX7uRzFEDd9hyj+YIPz+ZRxYHDYMkel3sen/PBO3vPNS7P7alQen0OIgj4gP8t3kg92+McWtfsADsOeb3zyA5R3cHx0uD255VjujXGKUB7m8pRTWptBPxAq7K2Fyp3+5vElwsN+HEAygBB/0v5WOFc0Qyejd+30ox890BKkZ6dQpm3Dcq56Nm/7vB7Dn61di7QXnYPpeJwd6DqF9YgYgZoy0jiS+1M+i5d4X7KyP6lhVDXF/w6otqIPCyN94pmhQFAWnTV3rWS6uY5WpWGbJYgqufe8U3GR3Jqh7Q74y6W8Dt/yPakB0+6c+hcFNW9HbtCmdLsE8kUXznpD7YwqYQ1V5jp+bUEvEPoaZ5Jdg5VCng7MU45IAFTtNZoOkZozecg2BRsh5wNWMUBubYG3TY27t08ulUX0WWXKNu3F+RlztLu4jQBgyoSQTMF2iRYn7sz3YZdNnS5tVT33cYMgxE6TThswQUA+G+mZYW6QphqhnSvSLoWdUwvho7f5q2rw1N9+xtnaOYtPrwLqOKdHaKfHV5FiJ2CN33L/SLq0pKsgmAr7GTJTGVDGI6jNRO8OYd6o/RR+2oLxt+wDDnar9BTiwfIst+yYZYHumSGIjcvq+pt6HWpcnvyfCho7QEikc7EnAXQ0rekAk7vM75Jx1yQBJO9EbgXBgxEzo3+04nPiGl2D3F76D7e/4OPonbsQxL3oqzHQ/WC2Kl6tSS2byBSL1wxwJmuVjUFQiNv+sSvX1hxXBrlqGqU+qbQglnm64Cmh7V6IBgxrwpZIrUbW+tDlAiqqhLpqgTqn9sDz5KBZdrH3wubjj65dh/ZNbwt2kBoUMNH6XKaiv3PMi7l894PQDT1sPSBouHhpLk/X5+nGIBzz6wZE8/VtAtfAaRMkBfEpArZfA9I9p6ZcmFaUPapjxt0E+TJMsmaUGKpYNBtYEour0wE6WF4qUCXtiCSdZbrB05p05qgGRjoQeDoT0kpkX7LqPY9cMnaja1Se4F5MKzVPtarTHz3Tar/UDoRbXA8IehktnetDp6yHtceeHZTwwsoaiwVFYnqXmPQeqfu+p/tRoWwqsnB7GJvESjkb2pc5z1I3aHZZn3PdHAtHqwfEosB8YpeuYeh+kv9rcKOilsyjNBIMljUNB03OwcdgsmYUoVk3hyCc+GCe+/rdgVk3hupe8ATf/zQdw+yWfx+DmW5e7ehl3Aqsf8EDs/c63weUk3nQyMjIyMjIqrGiGSEwghyYO8toh25hZ7A9Mt4ONL3oyjnr6edh35TWY+9kW3PSn78Rxr30JOkeuDmbzVAtL/fKaLI/Es7GIAQiuoXDJTDMKKTZkFHsU5RHM+NWqjD+ul/l8ZtQ4PsYSu0pTuIq00MD1gfa87gyz23YtMdA7ej26x2zA3u9+F2vu/wBXjxEV4OpfYYhChs8L54vGJXFiaXgQPFiHiWkyRnEeRMExWYZNCK+BWnwdLqVJXa2plsB8i1XbkySdEl4XjWlSzIoQ1TMpmT3XgZKbs/Ww7im0sUdMVZklTL1UZmOWolO0O0ocZ4behqrOskziWGlvQl/th8wQAHSNRVcCwvolM1c3U7g0JYZm6JfSpO8sEzot71vbktmosBF+X0z31YPbMbbhGFGboWtRdYeoFlhz3MfCDGmhe7hsVJimSLsqLy1aN8T1UmNLwNu29ls29dKYq4Pe94yYrcqfK7sYcAfztuPZogMRPFbuQVuQ5QhexB2HfCl18F7YdpZzQjCQze4TOCwZIo3OUWux5uFnYf3znoCpU4/H3JXZgeOhjHWPPh+3f+4/wXZleWLNyMjIyFi5WNEMUanXhmVGBfIj/jbzRo3o/IiBsZmZgl0Q52TN2a2s5/rZumKKJKRHVIapaAitedHimmjArgS7gkYewVZn67Nvy2MRa8hR9xUMy3Xb6xMTZ9dksRrsUot2Col2qjym7nE66FMd7LvmZ5g+/fR0xcL8CE0/dJGGKK6ST2sW0eBJoBknip8LL+6uvStWW2v8rJKt0xC1MH1hjWv9W5yoTTtEwcMXskWpPBqPBlMg1o71Dm2m84YYBTOKwIS+jRhuiKkj8e/o+6Sd6hlidBy7M/SBWIWlNsltU5UPlCLKbZl3CqMUnte/eZJvGES2qo9tBMcdK6KmOF3cjipfrWUK80gxbotj3ZqsixYdh9u6b2vT+LDOiyl33L5lgwETBmz8Mbk/wgLaor2u47AYtxOFet9IsWnMNLFj1FHI08UmMkMUgJkxf+1W9DYdvdxVybgTICJMn3469v08M30ZGRkZGrwMJvfZ7P5OQkbeA8UGLZSFtxoYKOdm2qolugWJWStQW1Tc8flvobxjL/qnnRTphoCm+bSYzHqLID/ZDo5LKAZjQbDtITtk9psKEdKYais2KfIAGdelzSJtoqXjUROaohLdNIPgtqQfVV6bLkddNHIipPIgBlY98Cxse9dFOPLx54M6icdcyrGoHWcCcbvlHo9jhjQbQ9xgldoa4HVK4bOmGaA2q8dwI8+Ff+ziZy01k+RGvq7YIAUQaFHkaMD2tN1c//4JW2Fsw0Ks22JyHcLAomtsMxhwCwuS0hC1+ZcZKid6XqNoLIautcIUtVk0CaNTcuUeoKpcfI2gYj2ooVEpOXA069OKGSuitKMYsEkZCwtqBn71eqYRZv4c/D8Ckwb+Dcst1P5iNDE6mG4Y6knrp0a5JmA2sO6vqos41KyuqZmiZh81NGwucrHof2jMs5jKq1CWgDoMSHUw7qeuycYkdwaZIQrQP/FYUGHaefmMQwb9E45Hb+NG7P7G5ctdlYyMjIyVBQbKZfhb6VjRDNGQFTPk3eUXmHcu2GUE3uYIK8XK1LNXOeDWjE84FsMdu2DnBzD9nk9fWQDpdd3qnDFq9O6dagX5U+V0T2tumuvK3Pi3ycIk2CRJr3RHSRYpumbEwK9FfwBUGiIuOahkezbjymlqh1oYh1Q+LUyY7B7xxPOx/d3vw6pzz26wRP5eGFSMVyKC8KKZoUmgm+A1Z/UJCQ3SNhFnzf6Ava6Iidwzk37WorG+Yo/YswXuuNIpeKNEitmiKI3WqwXvi54le/dO6h0Kr+1YjnwLtTEHjfAVlAo1UUEzQzrI6tAa76dH3mfPGggbk7jptU4mPi56j2mq2C7jdUHSHwTr2CVJK9ZtYmkk4YuELVhI9EFbffS+KWpGqm5fmjEKkQqoHTJxoz54Rj0fmhscxW4ZZQFXX50OqJqq6ygm0dfBMWch6xei65/DZu/r/ETfJc9ukbAya2PaNDM0CWum/Ull7B8yQxTAzi2Apvu49d0fx/zVN4KHmX48lNE/5WR0j92APV8/gOE8MjIyMg5xMKph5cH+W+lY0QyRjNCFGZobVtv5Ycd7Qx0ohmhUfCbNDLFnlar9YtUMTnzz72PnRz6Pba9/L/qnHI+Nv/csoGsazJCG+CcqEjNlA4bh+nHws+qGuKeZObdYcqX0OzUbNoJFGn2giUaV2Pm9samTaaRYnwYzND6vtr7w2qnEG3fkky7A9ne8D6sf/gugotabhWFUuGCIhX44wfJVUsFVPRr7dbqGdqiNDNS6L4TPpzunQnmkQsvUFmjuOfCeqxsdjSbi59EzUC1WaKELKuW4vWHhFIZxaPM27dMqL9OGGB0qURjb1NhIzUcwAdrXzlBpEWUWr621OkF5rdqhlKWRS6Kty7re436JDpV+X2BBXnBqPEOjvBUn/PWMs6LTYSS852imhj+eBgMWWF9ptJUn97dMpNPPxThmsUqrmaHJYMh6WnechiiFNms9uefCFFluxq7zIUHEl1DLPUpp+rR2SDNDqT6StPpdytg/ZIZIwUz1cdSvPQ4n/N0rAQJueYJc6hwAACAASURBVPPF2evxIYz+KSehc8zR2PvN7yx3VTIyMjJWCKqlwYP9t9KxohkizwyVtXYIqCzLxMrMx4rxTFG8HzIqbcxQ0ykOQN0CG373V7HtDf+C7W/5MDa86MkwvW6dRM38NYPEQaaGObYuWISHUD0rYE4fr6zi0pqeduu2EVDZhzMaIgtT2Inzm9gaLZU2rIO/TrWnTTvlsPYJj8TOiy/FzEPv533rRKxSUV8UBeBt0wiNYIaCrNNoY4aCOouVGbcFHfaBYYP6eFFP9eezU9oh37/RTFL+V0xRYLkWNE81P549C/tjfGwxYU6bVmYpFknSSvnjrHLaZuAxOyGMjWKGWqzMDHFjdt7Q2iT8EXlWh0UDIgFDq/2eGYLNoNEmS+S9P3vPxj5NodoZ+yNKtV3Dy9SCdjbb09a+9ofZgCNrt1HTxjbtUBiPL2xL0tO4sibTWiI5PUnw71F9NomPKSCOfzYQRqoUpi/ephicNnazjREKjwszlApmnLH/OGQZIv/iTuIS3UGbzo8zJjO9Lja+7NfBgyFu+MO34Za3fwy3Xvz5ibVF3oGeWgKZ6NpFmJ0KKP3FmuxaCSBYTP5CFV2b3E4C49KazuTl1ddU/U+dycqbuvfpQEGY+/6PQd1F/GC4urHaTgKpGy3iGvn95bYlulHltS3NjSww/uFdjCd/3o/3TyYrbRHAJypvP94LwWKulcGSXk4bBTH0GChjkEmgg55OgqlimNxOgsU4FRToZbaD/fGdZKAj2B+RsRaUL4bREBcSvUXcg44KmzKJSb5AO21cLBiVHcnB/lvpWNEMkX5ptUfZcFCkmSEdwbqKJUYgcj/8xEC43wLT72HDS38Ve7/xQ5S79mDft3+CLX9+ETa98tdhZqYag5AGGbJEg6LG2rtuD9Vl+mvGfGjDQVFjsBjNbOAtZVKDolajNtUe07UAUzUomuDFZkvummpQxEzVwEOxZr6UoLlHPOGRuOM/voCZB96jGhSFbJqxqD09y7VUVSk1KBphgQcE8p3UoEgxQjUjJayQOx7eq8ZgJ94nHQE+vPdpcilgnQggeTeqeHvaIi2lLSLEg6I6fpXUUc2ujUFBFqUlFKYuzzI1dEEm+DCFPnMmGRSN+6iMigHGwe9Mx1gM2aBDlUfoMAp7G/pmiPmyioU14AJdKp22SAZXBQa28HlNmUF1IRtYUNU/7MqSB9FVd6bQpQGzwz6miiEWbIGpYgjL5Ldhu+QDLxpMy1X0edE9WaY6CrwwVdrqLOgbrV/STJ+wFxPF71okLCjQb6nvgXoRO2SxwIXTommrwXQfpQZFpRvgSgw6+bDXXsNLzNsOumSxgGpQJKsZ3aKM8hP4WHyJQZFnitSPMKnnNBwUZWbowGCFD4jiBz509pYKDQAE34HUR6v+ba7ytXKtUUnlAyB5Gaz6hfsAANacdw5u/adPYOv/ez+O/cPnwXRdFyZG7FKnkgzKRbibT6HNMV19IOgT3XS9vDdByIlRInJ5af2HeExeqdJazbNH5OM/ZmoA1FhGTCwzrn7ovbDzkk9j4ZprMHX6ifU5SyCyIKG/E3k0fmsalVSjD3BjKbWuSzxC8cJ3y83RtFxr4n3fV8GgabGusyJHkEi/O3UaGWgGAyPdKaruyYGRevHEv51a+PBOEYHqnnNiUNQ2M14Mi9AGcaAI1B8lmYyJWHbUwMgHbVWhT7rcwTx30Uc1EJLfhCIQAdeWAfHym2eQXDDbAuzFxvvKrq93tY1/N0VykBI5t271kqEbRAFxINhIgG7cPSrje7Q/bHdYtjZ/9+L4Rh0ncJjYwm5Vjhmr+y7OfuvlqFjkXPdDnYcsn0mw2J7bH5rxrgHaBvjeWaUsR/ufjva8FsMyHQqanoONQ3bJbLlAxuDo5/8SzMwUdrz7403Lp4wVCSoKHPHEh2HXJ7603FXJyMjIWFYwkEXVCaxohkgjnBG0OWD0K1ipmbkPhikHx4wHJbklRXMUWP/iZ2Drq9+CPV+7EqsedGYtWg2n6sJc0ISzpDCJJh3azKgn0X94tiI+bPRSy7g6ubQVwV/TtJ0iNv30VWtp86jZ0qi1cb0capUmxRNkLeUeecFZ2PmR/4K95Sb0jt9YpbWEAhYFl/G1CcZtHDsRoaXPtfA9dKjYehMVs1cvkcn5BLvUgjiAcLRpMEWNhAFTVC/VBnWoGhJdGTNFMUVr3dqDd1WRuPfWmjokRgJ3RlOkQQkGxetmXJ9PwhR58TbiwLAzstxDcTqw8QyQXhkTVkDYpD5VGhVDDFPG2hO/zOOZoYo5Mj5AbVNz12BZEsyQlKd1ObJcNomm6E4FIW35nW5jxCyoIWpuY1BClmZoDYa28O0UoXQhzJC7tkuxTqgg9stpEjpDtESydNbhpqZJm/Xr/TaMXDLOS2d3Cpkh2k+YqT6Oeu6TcNv7L4Wdm1/u6mRMANPvYd2F5+K2D31xuauSkZGRsayQpeGD+bfScUgwRFoXEAo/a7FomlmozY25Kbj2GapdxTql0gDA1Bl3h5mZxvw1N2PqjJMb5ft8jImscRY1W9IzcAkNknq2NKvUlqXuK0r0W8s11TmOzKi1iXVb+yYRieuwDuGMxwvnZSas3Cck75vChic9ED/5X28Gb9mCqZM3wjKhiwJdGzNEHObT8nxMgjbWzHqni+645fEMlGaK9kejkRR0xae0Q09u/FefH2ui7+tmIfMvYcmEdYF2yChXMKG0BGto0TPf8B0/EJZp3uEexMljO1Mk1wgzFDplBWrHjV0EgttQT4TamaMwQxLKwwdDJevN+TXDsK+swg4tCIMrJtojzHzatEQp8XQdnNZWovMRDB6wuN+7sPyaGWo6sqzSpN0nhGkERvVFqn3sRPxaYA402R0gCLgbmOVrsbRoiZJiflVHrzlrYbF8ukNgYHGoIjNEdxK9kzZj7sqfLXc1MiZEMd3DMU97CLa9/wvLXZWMjIyMZUHWEKVxSDBEGqEprkBmm20u0ktrAo2J3qaZgNpmOXHOnTriSY/GLa9/NxauvhGrHn42ps64G4q1q5UeiCJtRfuMvn0mxZ4ZEr1F3L6wTn63QfNw8nhottnGsIUwzsIl5Xwv2lezsWS7FCui3deHjs1KNRPUAX3LES4NwnZs/qWz8L2Pfg3lNddj+vTN6HOBaWf5E+qT2h2jxXVfDCQPEoucwNy+Lb921nOCAhfBbjWtK+UirdFqslOjTPSBSgvUYIa0fZkP8tpa/QOC/Zlhe6YI7UyRsESWNHNTP7fh0kGpWKHUsfAcgEj32DeVTkV0K3vKKQDAgGOLJ81ajEKbr6EO2ZrxarkrbY42R2kG2xBZ+rW8h21uWVLlNL4XCaszQuxAV5iinnKG6bchM5RwmAnUbFoPzbY02LgxzFDWBy09MkN0J9HdcDQ2/8VLMf2Ae2Hvly/HTX/weuz57yuWu1oZI2D6XWx6xkOw5V+yxVlGRsbhBwahhDnofysdK5oh8iE7JJCr2w5L4zUY45ghwajZdD3bVTeMg21jlhNoarpdrHnkg7DmkQ/Cwo03Y9v/fQemz7wXitUzLikBKfai4SsplSZugMy8GzPx4FizhnJe2I9qX9zCgJu+YNpzqY5YkNeHiDRBwmJ4aVLLTHyUH41RegPvi0XaLPobn1/6hQvzlHKOu/De2HbJ17Fw1bXo3fM0b70Tsk1N7Vp6xqoR+fhRbE6df6x/sgGDOSl/UTM25HcKZq8t0XUJy0+hzfdUSquU9PMV5tHIs3b22cYkjPKhojUfbezdKO3QuP0wT7HUanseU0xRRwXlFIahG+wPuEAXMXNquXbWKM+45dgXzihImtXF3Mj2TQKxitofaItfYXm7o+4r4rSh1sczUuoedLy1os+k2k142Z889ItFtyjRK4Y1o+bK73pdl9tKu0TnBZNgc+QdrPqz59i80GKuzaFkW90zQ7T0WPlDtkMM3c0bYPfsxexlOZjoSobpdXDCs8/F9Rd9ecK1p4yMjIy7DrKVWROHBEM0N6z8acw71/PDsqg7d8TsIESSfPHEgsw6ZbrepikK0HJz7YIFigJzP78e079wPxSrZyoJUXL5PjbJSVqOKesyX3xK09TQDI3Oy+tZ2Hq2qFRaJR8cNCy7QX+mtSDQQXR96oTFhZpVik8aG7AHi/HCGrUhcaxDFpsec09s+eBlmL3qBvTvc1RUt1G+ru5MHYQZKlWek+qfQoSsk+TT5SF6GI5lhlJtGRcfSSwlGU0GqJlHtZ9i/ozSmOn7KjoacpY/YVu0t+A2y0LLTYavbUYuzI5oUAYBSyJ1czE7/b7Xx7mGdoytw16o90vyK21Rhe9wzIIxzXIavmgm0P3U/nGqfNcW+9xx7TeoPS+vf2rxQxTqNkNdTkrrI30zXTRjPkpddaw4uQdildUzpdc8iVZKIP6B9G9/6vdBsypDtQoQ+inq2iF6ZuitxyQ4rzBDfedbqE+DRjlt5U0VcdpU0NxJGaIDCUb2VJ1CZogOMEyvi82veyXK23Zi3xU/AAAMd9yOvZd9B3u+dBl2fuyzmP/ptT59uXsPyj17D0jZvASxg0ahLA9uefrH7M7CdAqc/LyHYMsl30p6HF9MoMYDgenuwS2v32v/YV8KiAPPg4WDPSM92OXd2XBAi0Wbg8SlQv8gv389M1nQ7gOF6eLgvn8Z47GiGaJ9jhmSoISDoYthY8lb58gYQGbYhUnPWONj7h8TMxskOh0vziC/1d/LhqVPcE3nyHVYe8EjseNdH8CuT3wWOHYT9s30YGamQVN97HjHv6JYsxrdTRuw97JvozhyLaYfcB8MbtyKDa94odfihAVFXomrf6KNTNurQRHHSbTuopGXQaksgLxWSa4N2m5BsFT5hilL4/ugUP3H6l5oK60QbOJr5eOpA38O2UBbiIzTewBNjYKk3Xzeabjhv27A7suuwsaH3z0KIGyZokHRYhgiXZ5g6Bkit7XxNhwUjfPfEjJY0m9TXGCGFvysc5ylXL83aGWgtIUOO+sla02TyVB1btMjdQpbBwHV7FnD6qaqv6Xa87AnceX5nNACKQXtk6bjrdzqeFY6jpYwlvJA2vBZJG0dJX5yqjQDJgzYoCP6oOBF8IFDXbvER5Ges+p+L9l4Fkn4pq5jVI5Q1m7CIIWYd8zQUMXv0uSBeH+2bHybxW+PQAYU0o+dBAMo6UUXKvBBTv21VV79YthgiIxosxTjNYoBs0rPJfe19iXEgO2jb4a1nsvVu40Z8s9+gv5vG+zoZ2O6GDQ8jOv3YmlAB31AfShgRQ+I5gbVgGhYykdDlhcCUbVLa2VQEwQe1PDjAHFs53/bXOgJH2pcNu6KxMRBRymnYL/cdQfu+MyXsO4ZT8L0Gadh4/r1IFu/1Oue8kTM/eTnKG/fhbXn/yLmfnwVFq7fgvmfXAMaMKhj6hU5v3KVXjrzbaOgEpMsrwVbZngHgf4DogZeUVgDoqr/ffTBZrzycD+1rNEUtLprJU/X56GzRz0AmhSFsQ3hbm3eD5z0K/fD1970SWw892TM9GpHaosVPfrjxPXHoGVAJFsRsabW2MeZ3YYffenbKdvBtBm0CobD44sKRYJQCF42XCC0ieJTfaQnLaUOwRIMeqRtsqKoB0aCthAU0TE/0Er369D/hrBvx1B9uH373CMug4zU/dPlMhtYNn7wUYT1EHbCvYgDd0oGRrJELYJ5yxQPihA+27Ho94jOrLt2yhc3cIMy2XoBsxsg6SW8VHsEfhBD8WAmDPUh1/ilORPn1StkqWzot/K/ZoqMcg0gSP3my2+F9HnbMlEBRs+UGIYDIrdtHQglyp4Zw/qEg37NuEndCvV85qWtg4cVPSA6VDHYfivmf3o15n96NU74mz+vfM4EkwgqCkzf83S/3zv+WADA/HU3YPaK72HmnPu1C0cyDjjWnXUc+kfP4PqPfh+nPuPM5a5ORkZGxpKCUZMIGTVW9IBIaFUfckNmqJZgS8VKyPhB6Vo6RW26yVrs62edFRqBNzmYDZYUnfNiYxvvwwL9u52Mjb/3YvQ2b4LpT1VVDFlVzcK43VXnPBA73vWv2LBmLabvefe4XS2sCIfVkmylQZox8nmpZTGwX4JsW5ILzfstVS7MrFc1ShoVmqFliYwTLEVjxi8zSNSMH6uZ2ThmI1y2Ch08yjF/zgBnvvSR+OpvX4ITzz8FU0fPoGfKpvv/MSLH0Ey40+KoTWaqMlMOxaVty1Bt5rY2mm1W/68qOxgU8+3i2BEs3TjIvRqyaTjFbKu7IHWvJA9d1/CZqJfMXJ0bTFH70lgbk9GWTsy5rakZN3bPobj8gGpnWH6jz9WkRhgvEQXL0o8hBkTI3cIUeTYoYIra+lyYh8KLgoUpqsTWc7br+17YCC8kVqEmDMV5xvlXoTvkN7bjzdJjtscy+aW5NrbVs0uBibvUW8TNwo71pasaDFF9f/2SJ8nyWpWHMGL1NXUevXKIMloyS7O8zfIMpmkhmUZD+rWEaTyfbQOUxTLiGfuPPERcAhARpk47BWZmenHXGYPeCcehf7cTl6hmGW1YfdI6nPTk++Drr/wk5m6bXe7qZGRkZCwpcuiOJlY0QySsgQ/qKTOvlImyIjbqPNxMx9TBJbW2RSQw3mxW2J9AJCxCZx9MVk8aRu0zJRkeEu2EO7X2vEdgsHUbtv7l32L9bz4L/ZOODxI3Lq8OhxWROnphJIWb0RojpSsaWS5VM10DmeErAbQi7xom3+ExX33HlLi9QlNGxibE2Kp9klRm054JZC8E1SEXJN2QDU75zXOx9Ys/x21X3Yqpc09ozrxbbkLDBQRx7YHAnRMdhFUz8VB2pcMm6Pb4fdF3BObdfkZvOWLEpMqTaGvGwZtIw/r2SR93FfuSYoRS9wUIRPhKn2OIUbCtNGAjgv6GSLVlXNpUf2txuBaYy1YYPuJaJ6Pvo7CC1omQa7F1IK72z0OaKRLhbsgUFYj73Ds5dPvyAZIn3bMkFJj7e2enVb5tYuCQpRB2s3DOE9uYIV8eili8HCAVQkPqpZmahqdjJWb2zixDvaN/7tPidEFBDAtTpZN8Vd830c4n6JAr0u6B3B22Pv9a2yaOPeN8pe9CPdQoAXnG/mMsQ0REJxDR54noR0R0JRH9b3f8LCL6GhF9n4g+QURrg2v+moguJ6JHuv2TiYiJ6HeCNG8mouctQZsOWVCng/XP/TWsfvDZuP3ijy53dQ47EBFWHXcErv3w9zB3a2aJMjIy7ppgrqzMDvbfSsckDNEQwMuZ+VtEtAbAFUT0WQDvBPAKZv4iEf0mgP8D4FVEdA933SMAXATgi27/FgD/m4jexswTLbhq7VA4ufCyGK+bcTMLNVP1a+XGBudkZC4Fuey99VnT6kxYFwl/0WCKvD4n2Pf/c0Qp0QgnfADQP+0UzH7/ylh31MbchDMdqb9inlhd06oxQqDl0e0JwIbAJQXkxmimqGGuzu3aIVYsnuRWsXTuiJ7xi1VSi8WTQR02olTWXyVbLNjCu3a496svxE/f8d+47NWfxbl/98vNxgcQx6GCMOyAzAhnOtWMW5gEzRqEGqJxDJFnKRL6IP1/K8skfZVgisYh1BCx6uuu8uEyiiHSei5yD2SDFQTQtSV6Rdm4p8JKNHReizBZbvRr2IcSfFf6xk3stXYq3Pr7V8g9jvdL6yzM1CPeNWXNIsnM3z/+oktDVMcUC1LciRWJ6aL6SRYT95SeZbZ0Vr+BiX5KV9fGyuwvZm0vzrflwxqyJs17Kpos5Zgx6MfCFpgP3mlvrefdB6RZnxSkXHkvhDESDVOoIZJ+atMKeQefHOsPMw48xj6xzLyVmb/l/t8N4EcAjgNwBoAvuWSfBfA093+B6nUOF2IAYDuAzwF47gGp+V0Yg603o1i7dnzCjAMO0+vgtBc9HLM37MTeG3cud3UyMjIylgR+Cfcg/q10LGqoSUQnA7g/gMsA/ADAkwB8DMDTAZwAAMx8JRHNAPgKKtYoxOsAfIqI3j1Jed7qRB03hr0lkw4m6a9tsDCmtjoywgCRP+fqHu5GVmf6nLY2g9LtxPEN3F9LSJA6B/ffQom5H16F2z9yKY568hPjYvxFiUy8pVicr6ax/ETctYVCvzCUrqMHu3xsHUp2HFPUDBsB71W7Dj/RpheocyfFkDSq5q174soTsfd9I7OwOgSDwcAWWHCvQunYwWMedQZ+8r5v456vuMDno0MxiI8aPWsvjMVUJ9Zi9Ir0zDT0S+RZJK2zEKu9Nj9Eif4Y69MI7B8QXZc2B5Shs0fN1HRVmIYUU+PLkXfWxnmk0LcdTBWDVq2Jr9si9VBRXkpvZQKfV4XRjE210dZtQzZec6jvi3b2qc8PAk1I1xVTSt38My3PRt1X2jfR/kA0Q1Om2vZNOtTEnO1i4MNsONYKTq82Rs9imSa2lArz2ucYKc32iPZK3kNdfhFo6ORcG0MUWpkNbMdbw8XXxv6V/LUjGKJCabMkLzk+bzsNZqiNAZM6S90OBEPESGiyMiYfEBHRagCXAHgpM9/hlsn+noj+BMDHAfhlMGb+nVQezHwNEX0DwLMmKfMY9AHUN04GDAwKTOYVDS+C6YTJdE3Jy1eYXP4xdek9h1Jtns8+NHxdh2pPDSD0gAnA0YX7mRMnjvr3Q7Jw243nPQp3gLBw4004ptONztXXJAZEbb85esVK6hg4PWwVVWswcLTpVu0Lr09svWm0yrTSmMeDXRmHyY87+fvqfki4PtZaNRHDJxTucu8J8bawjCO5D2PjAdm6556PH/zJx0HfuR1HnnkcgHqJVX6Qy+BDB8Qf2L77Me87mXhXCTP1IKTk2nNsm3PDSQZEa8oppKA/KuF1pR+UjX4IwvR6QNRBun0h5JpJywOAI+wUAPZLRpMOiLoo0bWjzab9Mpx6TgfW1INedY9l0KqXzAwsOs6Tdw/xx69wD85qrpZ+xPw//GiSNzeXgVhsyu7dOCRMwduis5O6N97zMncxLHtRX4QOEUOwjxvWwbCsfo/tsLp2ZjgTlSNLdgXFdR+yQdd7/o4HM/LMS19McdfVox586GdX8pB7Q40BUbgsKwY0MiCiRlqBtEdAqu+124ZRS2Y9ivuzNqKoPrlcdhvfm7ZBvTxz0ocDzktmS4WJepaIuqgGQ+9j5g8DADP/GMBj3PnTATxxwjL/CsC/oV5ua8U2W42x/Mw00rq4HyJhe+SEGmz4cYrlOqik/Li4tX1hk+TBkwGYD8RHJpodu8pE+1TGxytTqroS24aDWjs0gcxh9uh12Pn1b6Dctw/USdwmPcghpAdJqX0JkxEMjJqhQRRC3zAFVe2RgLpee+U+Vu44BaEQdB5+TCqDxCLOozEwCix/GlVrmX2G4SOMZhT9h4cx4AK3u2dNiij6hKlfOwtffu0luN/bfxOdVX3vi2bB3etSfuNEw+Ha2+2UmJIPDLstah9FEdw9GNr6GRunIRKEP5zhuZ0uuGeVffvD5hkMsXYx8QCpLTgqiloboYNyakQfbjUIbM0/SN8vh5gv9jbarsNu6O0+1N6P26z0dNBTed8XbFEzdyoIqWbRwkGi1ov0lH8eYxm7in0Nj86h3yrfryqwaErP0uYvR6y7JIQHizdt1+552/P6SM0MTSmvzHNugDI7nMGcGzDsM1OuncBcd3eiX2tfQkDFcOgQIUPf19VxGTgsRAOh2gcZ0PQ6Lc+t1zRFz1rcJ1p/p2HAWDAFdpu5IA/Xn2PuSQgfZsRtjetXctfOOz3UvO02+kSbpctxSSf3YqHV6m0xyKE7UpjEyowAvAvAj5j5b4LjG9zWAPhjAG+dpEA3kPohgAv3p8KHC6bveQ90jlqHHe/7INhmE8vlwJH3PwnrHnR3XPvW/1ruqmRkZGRkLDEmYYgeCuA5AL5PRN9xx/4IwGlE9BK3/2EA/7SIcv8SwLcnTay1MAxqLIuM0w5YIPArJEyFmz0Ic+TPC4MirIVtjXPmjXdEhpCaKIsfIk3gtE3emUAocMyzn42b3/p23H7Jx3DUU58CMibog1Rerg9auqIhdwoYLB+IVTv18Wnq9hIIVNZ3QGXb0BQ1ViotoemHKE7TiN1kwga4pAnrtahCskv1op1fzjPCDjKGXGChKNz5Kl3BVTDLzc87Dz/4rXdh61dvwPT9qnArpcTWU17RixHR3HW8qbBuuj1Wz2pb/OUIwlhRrKzMRnnb1kyJ97/lH+qYdaljPNkG+9ErJmCI/NJOzO60wRBjGkMMAz2WZnUEns0NmJsG49TCDPmlHZeuQ1RPFZV2qC5P3gdhqtn3NXkmw0TXlmKJpvLuBF7Ra42be8b8vZClVXlPm/5yTCroYgIzZqHBCE35/Yop3Wur5TFhRYvAc3QoU6h0ROOXS4XlaYtfX7NzYT5p30G1H6c0exdqwBpbxO9F+D70yg7mqJtgDt29McKoynJxzfAJMxS+I2Ee2t9TAdvoE62REg3VQinaoSLapjCxxSgOnBXgXQmTWJl9hZmJme/LzPdzf5cy898x8+nu7w+YEzazdR7XMvN9gv3vMrNh5ov2u+IyWFnENVoIuZjo5d68v2htZuIi2aolqomuZZheFxtf8Hws3LwN1/3Rq7D1H96C4a5d48tzWETzFteRcomY9/ulwEVkImnHuCCILhEHnW4gwou4dn9Et/PDDoqZPja95Em48c2fxPwdk7nnB4DB0P14Ddt/vDTkB170FYMRP3waEuYmdDg59hr5gS3vRHmLeKgXk1ZjMeb0jWuVxmYStOlGJilnVDiRNtQf98nvwcH+oMlHX0z0u6ZteHNg4F0RqKWySSAuMbRrjFEogyXTajv5tYsNg1OV5zRr+/FeLJTxu5txYLCi1VmdIh6h1xZJFcMQDopkVttKujABxN5CxjJV8bECi5n2bcWGEFV1qAZFIt42vg5h+WRRfezDQRET2NSi6rZ3iAIKp5iZxqbffhGGs3ux6zOfw22fuBTHPOfXwsT64rjd4b58VPRAPOhNWAAAIABJREFUze+HF8YZhGxS5A/KErioYqFR4dro26cYOa2hCgZFjfvmTglTZLjSEKUGRXoo3oiLS9WfDe49Ox0QG4sS8L6uasbGWXYMO1h131Ow6oGnYftF/4FjX/IUlEMT1UFjQAW6nRKDYYF+Z4iFYYFOi5OY8ENPxNGgyBBXW9c7+nOuNVWpQdG4+GipQZGfEavBlS+/qGbAC2WBXlFWdS6b7dHlGde+/R3cHKhBkWYLvP8o72q8+vh2yGLIphoUKaao4bHaVr6ZokGRe9baPpZ+gGireg3ZVGUGvoqMZ4Fkv85LvCtrZqgW6YpWS977WFw9CqvMPABg3ulW5niAOdtF3wwxbzvVoKicRt8MWxki3+4JPvjewlLp4sQrczgoajBDal+unSs79bbBDqbfh2EwKDLEWLAd7xtK+tVbRbriOij970sbxIeQZwKVhVk4KBqogaAWXevBUDgoWszALMdIa2JFD4i6nTjQoB4QjcKoa0aQWa0wMjhrsBItAyMKPsymSlE7gFRZqOqk2mbWzqB/j1Ox+/NfBnfCEUmwbVkyqtMqpkoGlIab19i4r+plr2pQ5z1NBWm1pVpjwJAUnMeDJK8XVPWxSKzmNZbMXJ3VshhzvcTqlzqDwRoHyyveSDB4RpgJx/zGY3DNS9+CPVf8FFP3vUdUZ19HWUozDCqlDqNncI3BGwLrpwkHAOGP+wIXmEfHHxtMMIOUfpQQOdrazVP7th4kypKATFoK5fDS1y1wwjjVmYxRCAdxvbKDWXQbH7K2UBrhB8GzOy3vQ3P5rV7WbJjkj4Ehbhi/Nz6+bqNn9dYEgWETJuSA++giZIXqdMZbyUY/OE33De78PBv/gR4YsVyK92fcgGiNExmXbGALE6WdLhZAnfmG2b987COzeY77pK1bw6Ws+l7GIvy2gVD4DLQ9L9oaMirbDV47Xrje8owlljd1OJZBi+5zthRRdWziH9axqwLsyjOxgObAZzGrHIcCiOgMAB8MDp0C4E8A/LM7fjKAawE8g5lvP9Dl50XEQwjDW3agu/GY5a7GYYliuo+NL34ybnnbx1Hu3Tf+goyMjIwVCgahhDnof2PrxXyVSHMAPBDALICPAPgDAJ9j5tNQOXj+g6XolxXNELWt3zMHpvgtI2TNEFlrkqyR+29kPUwglpUgrz6wppqx1QwE+eEmE4PBfr8x8W8Uz9Fa12Dbduy78sdYuOkm9E7cDHQ4WOYKlsHGTWaFyBEBtX8+Exe6yYifsIbsmuFKhK2W02QZilQeofsByUsLrb0/pyD2ob5W3C40mSLdTHdvhNkOHU9KXUMqD0F9hLCKGMVqO3Pm3bHqAWfg1vd+Chte9MsJhsrNKEvyPqzKcsyzlWCBJmWGonz8sprxs1SgZn1G6YOsTb9LXiTu3kPJaxgwHMIMdVoE5SGTtNjZLBFjviwwFzBebctmehbfMda7WqiXYxxLQDFTVTvnc8wAU80aqZArhoP3bQw0yzTkyvlminEIXRpU5bhyxaRcHItGImvHWsuz5sXOwiZJOfJS1fdNWJ45W7E4805UPceD6LwwRGuKOf9Bk3P9YgqdYq7htkBYkJANamPa2hyIWtTLj20soGaGwiXeNh7IjnA8K8u67X6/HAukCikK9ibzwhrNOxebmqEVVihkh7QZv9QxDMIbttMGv013xvtzWwiUFYRHA/g5M19HRE8G8Ch3/D0AvgDg9w90gSu+RzKAW970Tuz95new92uXY7jjtuWuzmGN9c95DOauvBp7r/jxclclIyMj466MXwXwr+7/jcy8FQDcdsNSFLiiGSIdDiCEnlm0WXbIhK407Ge4zcCMcR6CImCofNrGENIxQ+LczlWZA8aGyDkVbEQyjctXJwEAuz/33yjv2I3uycejs3cvdn/uKzjq2RcGjhTD9JqxUFkqVmkUE6HDe4Sm+kRcWf0m9EVAze74Oup2cn2snkS6Onn1aHRpXDdhulT9/f0zcV5VPZvaodS+eKMuiLGwax9+9uI3Ye3Dz8Qxv/EYcGcKNDWFY170K7jlTR/E5r84EcURq4MK1PUQPdFwDNsTPuHjmCH9fIbif+nrAQoscOHPDV09RDQ+TAjBvQm52/fOTmMRXBBsmfxsXNiCiIUL8vCGESgbfd7WrrB9C7aLOXRrdwkt2h6ZKReBqbRmhrQGpKN8ZAhTZEPNyzj2eMQ900LdQVlgAUVD/2GZPDPU4ZhtkbARg0CAXWVeL0DUWhbHLCDWBaVCe+jlC2Es+qZiNkQvJHqyGTPvhdYD56Kia+bRLeZqZohj79ej0MZORL/fWkfYwoqUSlMEhBZ/ciOVqweVd+SOYqxrB7cVnV5g+CAokM5jn2fPbKvTzQHH1+p2Mzfjgi1WU8RYttAd64no8mD/7cz8dp2IiHqoQoP94UGrGTJDtOKxcO0W8MIAc1f+BGsueDg2/9XLlrtKhw2KVVPghQGGt+3G9f/fu7Gw9VYAwPS97obVj7g/drz7I/sl0M/IyMg4TLGDmc8O/hqDIYfHA/gWM29z+9uIaBMAuO0tS1G5lc0QtfgNIWcuH0KbJmrGqGBGSbLOa1TaOl8A6BQxa1DBreHbeNbsNSfKUoWDOhJZECU0FD6+lm9Zo62rHvEAMCx6x2/Ero9/Fmb6cegef4zXAfnZdkJD1HB+2MKsJItWGp6IBTLVAQqPIZiNyabFzJ44ccwXF4t8xDcbJxpYa4rUcSlXWAtr/AzRM0Uyg6Q415AtpILQP2ED1j314Zj78fW48Y/fju76I7H28Q/Ful95NG561Vux5wuXY/WjzonyrDRS8rxMPufQbJXGKIbIB6+FwZBMwBC5sABl/MzbkhqMjYRcgcRX88XFGiLL5PtYXBD4OomT06LJxnJntB+glJ5kgQvMcW02Xejn3sFbuQW/A23WQdrBX5ea1m/j4p/JrR6l4dAOA4dsIrcG4f20rv6hc0Gg1hI1HETa+v+u4xmFGdJm9UN3XOKgpfwXzbv4WNo83FufcYE1hdMTOV2RpSEKGmCW+619IPVZUH2vkbr3Y53taquv8F1TFobE6fJ9WBXRD41k/DRjVJcvDJsweUULk1nnxQ1myMdt9Nqp2Louhbbv3TgwJg+4u0z4NdTLZUAVL/W5qALEPxdVUPkDjswQrXDMfe8nmP3qt7HzQ5/G6kecjXLXnuWu0mGF/kkbsHD9Nqx70kNwt7e/Euue9kjc9oHPgDodrH/xM3D7hz6Nwdbty13NjIyMjLsEiGgGwAWoImAIXgfgAiL6qTv3uqUoe0UzRK2B+PZjlaLSWcTsgF6D17POVFiFwg8hnXbIxkyRBDStNDZ1ecZwg0HReUcn3L9rzjsbs9++ElNnnATqEmAHKLrWi2+0dqPKT+fbbE98vHnM5+GDWTr2wlLFeAW6BB+g1dNlqjmKQeJIQ8RhkoDFivcBbrBYETuWOu4PcPNcICUIL/ceyYkBJkydtAHz11WsLRUFeFiis2EdAKC7+Vgc+dTzsf3N78emP3kRsKrryzOKydAzt9ST7dmUxLnwvM6jmv06FofioI02YHWA+l6xNbUlnwtJICynf6RdWiMWlfIsWKot09TWWzC2aPxGQTuRtMQonWWW3BcrlmCKMarDnshzlWCIFDNkVZDMMMyCUXXpKAanbpjbUqAZspqBTvdFysqsta7+Oar36+tjS9c2y60y4cem3hcLqtiyKQzYapzATrREXTOENYPayaC3tJL9mC0ZhRSzodmaoWL2JRixxqAs6j4fU3ZovUgqFEnDwWQLhmzqfvLPYawDEr2QeEvvUumZIdGyidZLO/VIhfsxwXMHNAnESbBSQ3cw8yyAo9WxW1FZnS0pVmaPZHh0jz0ax//1y3DMC38ZxdpVWLhu63JX6bDCzL1Pwt7LfwIuS9zx+W9h299ejCMe/1B/fvWjH4zeSZuw452XZD1RRkZGxiGMFc0Q7V2IrRbCkXKhmJGG36FEUEu91hz6KAIm8/8ybtYXsi1iaWOYYWCbzE3AILUiYIJ2/+dlOOZ5j0PRKRt6oLA2nphRzJekMcq/U4ohqn3TiH8ll6cldFCgw009SFv7GsFdAw2K1hBJIs80eOaNA9YozeS1a2zQ0FyZoF8NbK0lUH0zdffNsHPzmP/pFuz+8ndxzP+4EKt+4Z4gAkynSnvMC56Mm17zduz+1Jex7kkPAxn2mpZJLaqierekHRWeJmSXGPU9bT6nkhmDjHpn3D0p1My4VCyTtdTUh0n+ktZXumYvUhZuSZh6I/6gGvdY1ZHU1oSsS0M7FPeJ1324dP0gRpdtmYKn8pIwKA1WSfbZomvK2r9O8NS3WVAtBtpyS1spjUor7I7WH4VhQCQf8V0049IIYzSybkV8rfbHMypOmbRD/GvN+2CncdBTYebmyg72DZ2X7BafQimtkGiI6r6JLf7a6lWlCShnIHgvYoZP+rdrrGeGusra0bNIbiv7PdHUdkY8yxPGNmNGxCRnVDisemTiH+QDhf3/bUvC9Hvgst31fHeMaPVAo9db2uCOGkX34LbPEIMHQ5R3zII6BYa37sL0macE5uguXa+LY1/269j57/+NfT+8dr/L2x+HjHcKB7m8xQjMDwT2J+DmoYSD7VhvzsU1O1g4qrf3oJZ3sJ8XPRDKWH6saIZo33z1Asr3p57VWxTKt0ibbiYEEWNYmoYfotpyzM2SEpYs4yx/qE3YRFV9K4bIT9snqmtQJWA4wPC2XegfswZFYSP2A6j7ptspG1qMZrBauHaG8ZDi+mtLvNCbcY876HI1GOr1ho21dn0vtDYm1KDoeGeaBdJanKJbtrMuTZfV/lrdF+E9Lphrr8wKnakONr7gCdj+3s+gWDuDwU3b0T9ufVRuuesOkJ3DxhdeiFve/lGc8sYXwXSqV6vp7TZd1br9Tb1Tm3ZoUZ6f5XGi+oAEINX+oqzXDrk6y0Am1JUpDUOD5ZS0zlSthEHRsSMHRca3t2ZQvLGjKk9f44sNtEQF4uCqmoUZNiy45ANVeM0HdER3FWvPB9wcwXD0/EzfVh60J9CmtGqKAo/IwmaUqg+0lZJGOWKmJudEX1LrgQgG1aDIa4NsB6XtBnHP9rny24PIHlFUaWZtxf4LY+TZSMS/O+GgyDNEJmaXhOETlmmu7Hp2bm6YjhfWFuw1vidx/40LlBxdr5gibdElFmVdKhsDI9kXBkmeH1sMfXn62ZHnT9iz8Wj6MspY4QOiwaCqnv+QG3l4TRC4E247+c3VH5rmtbFjOhMNjOLyJonATfKnBjncslQWppVtuWcWAGPN6RsAlMkBkbwkXf3xVy99PShoBuQMwwyE7ZQPWWkJPS4wTYOE2M+1Sy1bahFpWZo6kKj6QOo74Ze4ggHnuAGYvjasm17KKgyjYJu8j0Jzr3vsA7HzP76J6XufjFve+jEsXL0FU6duRm/zMehuXIft7/8Mdn3+uzBrZmB3z2Lr316Ck/7w6Y06APWSzqjntSHAbnz04+NRsGN1D/yVjYELhWejtP69cB9/uUX1oLi16g34pTXUgmcN/y77urtJRtC+xbJnlgmFGrQ0Qz9InAy5SOrJfrmi68MpyJJqvNy1MPEHqFoC6bmo9mE9JvkNWQxGDXjCckfmofpq3nZ9iBDZ9rkKEzPvmKM+VWE/ZhJLaOIwU34z1riBkQyIBs7sP6x727LefOGusSowrdvuHfbRc4OHPaZyCTA7DALNJrYAwG6QoEOGLAaNCVCw5Bi2wcL4Pim0g0bvJDMejHuxd1kzdbJsOOeew8nN7vOSWQorekCUUcPOD2B6B5eyzqhAhcGRj30g5q/bjhP/9LnY9cXvYudnLsfCllsxuPUOoCxhpnvghQHWPPgM3PG1H2Pu2m2YOnnjclc9IyMjI2NCrOgB0XChGvF7Qayp2YLavDd97UiT83GDaC2yTgyktfg2NeOvxczWU6Qp6ImzIW4wRHY4DzPd9TohmcWEDJEWDOv9QpmThuLPtlAIqQCKM7bAKrNQ1xdxeRoyExHBX1kYn29p0zcj1Se+bjKzblmaq/Oo66WXFiP2CJxgYWpWotspMXP8Ouy9/CdYfff1WH33yvpz9mc348a//xiK6R6O/50LMdi+C1ve9mkAwA1v+Aju8ab/CTL64RnPbrWZKWsWyAZMCgfLsAwKnj1ppDsfOKbUwuuakUG0hQp5QxSumMX5tiEMZ+KrpFhev8zmr3EcaiLrFrKpUSaQWCoTyksxQ7WYm/0xmZXLbF2WZxZs86czJZZOwS+BBPd8UpYoEjlPaBa+P/DMkGN/Smsav4Pz3MWAu43+m3G/DeLIsWTyz7Qsp0m/rjGxib5npmAaDIZngowwQrEzSdnOmAX0h9MAaseLsoQmTJFup/xvmVq/D+PCuIxCSgRdh0Wp+ks/a9In+ve6IPZLY3Mq38WslCxT6I4VjRU9IMqoYecHMP3MEC0XOketxuD23QAALi12f/On2HrR57D+qefi6MeeBSJCsaqPcvc+TJ2yEXPX3oLbv3gljjrvzGWueUZGRkbGJFjRAyIe1mEwgGBGabi25tXXjBjEN2aVWhwn+gCZiIuWtKQGIzQOWr8yatyeZC2U+He45Rb0169p6IFCTYwWDGqNkGeEEkxRx8Rr/KJzGFLMEA2NQa8ssarTZIh0e7SjumHHza7Lwq//aw1R20w5DNeiZ0PeLNz1ckrblOpjQdfWgRYjOImJZULn+LUYbNuJnt2HO75/PW5626VY95AzsOkJ9wG5a3f/4GqsPmMTqNvBqpOOxm2f/Q42PeaeUZbNNsDXfVEi6UQedVvCQKo2SpNi07SWrRliptoI2cXMgeCnRbQ0oimR88sR21FoE1mHkOdBdFtjmSEfVDM0TIjz0iEZUlqUSdmeTkLD5/NoC/mQCBq6FNZmwraEfTTn/i/db0LHdjFf9v17oklw0RSFZvl1/zn9jPpd9cYcMJ4tqoXW7veDxzNE2mRdWJee0xTtGfRdngmGSOFAa7yApsgaCE3yq7pOaXcRnuZejHg6DUa6rYc7Mmd2iOC2r/wY6x52xnJX47BFMd3D9Enrseeqrdhz5Q045rFn4cQXP8YL79kybr74a9jwpLPBZYmjHno69l27HXM371zmmmdkZGRkTIIVzRDVVE21EZ0CM/uQD1Az31FohnRwM0hhhrwJvWNdQo3GmLHjKKszrquZvEZmRakZMjtz8H1X34KZF17QsOCqHdEFkzmlLRAWRq5Z1XVr1kGtGuaiCnJthywKspgqhg1GStAwS+3EDs4WbIEFpycaNpzJqWvDOrY42GtzUpaavafKWV0aDArtML+Z3/r7bcbgx1djaorBdojV/Wrmy0zY/sWr0JnuYPNDjsPWf/4C1h47hWPPOwM7P30FTn3Bwxp56ryZqdG+cUgFdJwqO5gxg/ret2gLLFODTW1zNqrrzlyb4rNOq97HFNvTcBhq4vsUWhSSK7cOzumO+xAd1X6K9Kn/b3lORmhFQr0GUJtAtzk/NGQa751mkQhpNjZM04aUjmScpuXOOXmstTwAUDLX/SgWdraDedupA8Eqbc+CM48viD1L5JkhsbCSkDOJ39dad1S6NKNZ+iOLWZ9X31TslGx3mZkordzHPYPa+S9jzLtHQULXGpebZ93bkLp/0gelZ7ZdP4o7ASuBduO+KYLnU2ukNOM2CllD1ETukUMEdjDMGqJlxpH3PQ47v7elWg67/LooVMf1778MJ//Gg1HOLmDfjbdjzWkbcOIzH4gtl16JPdfeuoy1zsjIyMiYBCuaIfKxF9VsECbUTcQz08akyc8GubaEkZG+ZMg6qTA2dTozxvmKt9Dxs4hQo0GRFU7bzDtkxBq+koYWQ+qhq1zse5YAwXxFBeMclumAjcIUAQn/GWo2LTORqWKIKQyBzsII7VA8S9FWUyWTt9LRLvt1YMV0nZTTyBZmJcVgaZ2RIcaaYQHqzqqymnOFmQccjSv/Yise/tePxVVv/CymZneiv24Gc7fNYm7bLpzysE245RvX4ch7HIOjVg+wd9ccyFqsW2uxqjeXrGu4bQTcnNCfTHjd9LCDVcV8Q2el/UpVDFHLc+igH/nQr1TKYWeYR8p6rs3/l/Y1Fc5yC66YkPoalYey7AqZonFBRUexAaJBaYZ+iIN0ht7v/fPG8TWhNWfHBGFigvek7f3TTICuV3TNnWCESvVueV2VbxNhoAK/dthgwIW3HvV+gbxFXrVfwKJLThfTwqQIUxSyMIVKa1rm78IMrTXVO7bAhS+vR7Evn6HXG8W/IbPDbquGqIEEU2TVfdLQv4kGNnB+66xw3TnvfBNp9txQWT8Hqtx+MVn0AEb6N+5wx4oeEGVU2Pv9q2Gmeiime+MTZywZemv7WHevDdjy+Z9jZuMazG3fi/66Gez80Tasu+dGkCHc+t2bcPRZm7DtGzfgW6/7Au79wnOw6tg1y131jIyMjAA01oHn4YgVPSAimX0KCeQZHaqtzLSljVVskk8W3Hzv18id00N+zRhR+2yS/GxWMVUU5MppN+ladxHOtr3/IS5x8z9+AhtfeCGG6MGUzn174TQN4czYXSMzNpm9an8eWqu0urvQZCeEGTI1MwQAPTNEn4coipBdSrM6hZ9dq9kRsWeGBi0aIu3235BtenvVjFEioG/qeHjOkMUqMuj2ZhtpBGE5Zz3jVFz5bz/C6g1TKHbehiN7q3HLHXdg7cYprO3OYef3t+D4c4/DFX/2nzj7d8/GGReeAqDWJ+mZeMhytT1jbTO5yELGtXE1dVF25hvskbcaVH6l9P9AgsFU/R0yRI1AyS3eyVMEa5vfqCLQ9JEPjKzzlWvc88/xvS5Cq0TPMKc1RG26LqBmguR5FNZpqGbmw4QeSG8J7PRJNro2hWZ4iDjtJGxQypJJQz9bWnsWevPuOi2PMEV9mMgaTFt7ydaQRbd0/eg0kcLctL2jPRrW1rKu7ZqhEmZojdMnrXJWbSUIU2X1vzBPC2KRJvqcTvOdsqX7nZ7QqCxkm41bzvBaSxOH4ygSv2tW9ZsIIrx2yGvR5PdVyjVe0+YDvxbN9zpj8cic2QrH/PXbMNi+C6vPPn25q5IB4ISHHIcdP9yB/hF97L2l+kEezA7QmemiXCix44c78K23fAvnvPwcnPrEU5e5thkZGRlNyJLZwf5b6VjRDJEPSOlZl1CHI/+42Z+N97WmiAiNtV/W+QtjZFVC05zh1p6y47S1LImCKjgNUYtVT8NHjK31Hbe87RM45oVP9jPxwTCeJfUCpqh053xARtcOrSESyCx0lhgznUF0rGaGquPiXXe6WMA09zAsFhqWOD5ftDFGwfHAx0+qTjr2kbQxbF9bQMhREIuNsI7T3MVUZ7ZRvi5nwAXQBU4+dyMwmMe+a7fjyO6J6C7MYu0aYHjNFhyxeQa7b57FL1x4LIDZhofy2monnoGXE/gFaczmg1m15LeKu+DunD9eM0PxrHPIppUVbNsPY3BpVkX3Vcpn1KQapdrzOaFg64K0xtfI1vuxEsutQGPkuVsW1ggTIcW+dJR36ZQforagn6ElUMeUwX4z2nmbFds4PVSIccxQWEabbs0zmN6XWOF/H4UpGrLBIBGsd175ByrI+oClhWrPDIn1mej96nSeYUZ8zZEuiOwa5+F5xuW5Srxe///svXm4JVdZLv6uVVV77zOfnod0hs5ABjKQkJAmIQlIggQBlUkUeVCv+EMRQbheFP2BA3C5ch1wRFRQFAQFhOAABCTImEAGEpIQQkLGTo/p6fQ5e6ha6/5R61vDV1V71+nxdLq+59lP7ZpWrVo1fetd7/d+WgHIn4GuUdruGjSpZ3Onhe9EpSWiNEU/HhTUrPl7uywHXVVePOL0cM6Xf/wqfipP+kqWQVvdtLFoEKxrEKKDs6XvslWYMFm0aQo7HbITvSFpWI2GqLLRN5HdhpO3h9yA5FhpRjytIxGgtUDvvoeR7d2PySsvhKKs4UOyhVMoOzlNNE2z0Ze5zz6YXLyxzEjsjOBbEmLjSQnLrGPCYcfN0NtknL/ASO6/zChT9oxxXmbicH6YTUb5i3faljF63+Xx/mDbZUk+v+mlJ2DrXbtx7+cewty2eQz2D5CMx+jPDdCZaUEIUXKcheD4ZTZt2qBqOsxmE/ORSMJ9ZpLq400mvaHTYTZpCPk0nTL7+ER9buNmXScx90mSBtNhZoX2IgovHu0gVMku1BHa48MWB5PoczHCfrKEcF1l3PGpM0RGVghIqDEEx4d4+JD1MKPs9vtVLohISV2HGQ/R79jksYPKfchaoKE5StI7+h5rWVpA+BGp1Tao3xZuH9PJZc5ZUuIoc2t7FAZg+PBrlWWGR3Qkf0vdljZCxPNc0SyNyQ9zihhypCVy7hCFYykBSA2hBLQwDo+PJgEMMdLQmciRIQ2XLFzkx7L+EZVBUWBDnKIyZCj/k0/2fPYbmL7mUkBHABRUJhDFGpmSoM4DfRjoeHWcosj0pPrC5IoTGrHso68idKIUlM/Hd4r8qIZ8Psy7U+YUFaNEwg/DMKeoEKlm9p2JFpBBYCaeh9ISM3E4D1Rnca5yiibjLjLz3/bIzfGWx/sx0JE9zrJkPyYvW41Vp4xDaeDb778jL/OEFsbQRSy1VXQuc4oyLTEdLTjtFosQ5YjNMKeI65GQ0YdwNlkA+i3MJnlbTMfdvG2ShQJnS2mJVMvACVJasPlyfgmhS8Q9o+lU0kOqJSY8Tpo/1VpgPOlDQaCT5FpJvlNUFvWWKIWWyJApgURmUBCIo5xTVOYUVeXAG+YUWf0gnysl8msTR5mdD3OPcV6cAlBEYv3jk5p8GbKalZTt8+aA8g+enyeMl0M2ioPmO0V8mS1PmNemFpBCY6AjdLyy/WPYD5+pVlclaMsB5lULM9EC9qs2OtEAXZWgY559iyBx7uAQp6jDkKFxYfJ7IQOgSp0icozGzbuAnJGe6gODHBGfU220ZGY7ibY9aMCA6U3RMgUBCZ32GHvXAAAgAElEQVS3BWkMMb6a5UwqIIpSKEhIZBjoKHhfljlF9F6TyJDqCO0oxXyWoCXT/F4VquDMNbY4W+IOEZsXhT/OiWHOBDehSsjTZt6mBLEkbnIy6GbW9k2rC+8lImiTo+Mge7uFyB0lK95ITgxVuaTO2Z592P/V27DilW8pPyHazjqNwobZ0zJHeGU9SSbUqLVw6TUM05xgY3rAujHNp8jSMfTluP1Y8xc8EQyrhsz8dWXwc15WOGRGL7B8H06uLneEynp2iX05koOnEENBevNA/qK0Uvq0jsqTwPP+55PwNz97E7bctRunblqFsfEZSJXl5yGc/D6vMy2neZsuBqJY/8LHLQy1Ldu2JQfQHoxuP3CGdElDHAMd1SZx848jOVNldbRDcyXyAim776rP0y0fzyJMyF7hnq0amvPT1UTeUJU/bdm0DuE95gsOkklR/opcyFpm28juw4fIRiELwxAd3o4ocWpsB6PiI2jvMTZMNVCRu4fMxA5XVqBZfmeG2keZXj93Yvh8uC58gRJiRJ0jGo7Lr13obO03zstulSdunTJh9vvUwMznzlVXS2zJ8sjO7ek0AGBLOhOUReajXMIM63Jn/kAQvipLfTTIpN/IZHgfULLXYR3KHsKOGzlQXOi2yvIh7GN2gOiwWdMiS9QW7vg+AKB3/6NHuSaNldm6M6ex9klTmF7dwZ2f3YxlG8ahMo2FvQNEydKHhhtrrLHGGgttSSNEdihs2PeFE6MtRBl69Vp4hGdOmiakiITjCO1xsb2ORE2oSgVSVOZjaiGgtSxqAQyx1sYTICfG0NqwpnS9DXP2OEU0LGZ7z2yYzgnjIVge9IpYD3yfSYLYNb2ZVpQBWRtdMW57scRb4WH2RdTHg/b5cCjbhqByGk7j4+x5HUNkqKpHnmnpeqAlCFEiMihBYnKxLZsuMd+HzvNpLzoBX3jvfQCA9ae08cCt89j5wH4kHYcu8XFzF74dIlJKy6LYXsV9X+g5ekMr7aiPOOpWomWZHI0QuX3CMnwiOJc+sPuQyBwbSkm1LBC7ed3s+XiIzWQaYxD1CnIBtE+ZjACAIEUGT29QOazgoSU+SlRmPjJEdW4xAi0ntBOx3J1vSei3Nyzp78tNCm35I8UyypEhGtaGdEO1o5Ai/5ntMTHVgYrRV7Gtx3BkKFy3T3UAuGeKnrtxE0LfwcDSQSOGuBGqtNcgRdOGZL1XEUKUWERoh0GI6H6tqqOCMAiRLCBEZMOQoqo0KnQtIkaQ7qkYoOvHviX2fcPalZZnEGgzaYPYHCcW5fdLmVVRC45na1pkCZrqD7D9Tz6MZT91LeIVM0e7Oo1V2AXPW2dfgzf+y8NQSmP1aZMYX9akWGmsscaWrmkYztgR/i11W9oIUdh5KQdYyItn+ogOKaKJZnhQWXi9QVI8ZIiKsvuyzl0lUuRfe6oDE0S0q0X4Z+H2exFNT2Dyyqfywl1CWMsPcmVVIUM8zYkrw5VlUzxYHlI47Q4MhyhOkagW9umO4x2ZnsZEnPfQYtYbqpOGg6NKRNa2PVl4yRArOS7l/n0isgLKQ+RKKTRikWGBwuGJ7KwjVyfqNdvyTM8uiXHtL5+Kj/zWXbj+z7+Pn373OeiMS8zvHh3xQWUnhCSJ+mP6ZYRzSckdxQCRdJFeHKHy+VckUscRJ24cgcu8XjRPL8AjkXwyd8pQvioODaESmRaYQIRB1CvhJIX3nmL3L4AShGgU4ZQe6iKfiN/DPjIE5BFKFikhlJOhMCozCARLKeOjRtReXEjT1tDs2xLZyMgmjgwl/nU2u1QhRfwe66m40NZ9FaGXuU9IFVLk84Y4MmSTu0p2DtJLu2Hv2XA6b5CifVmOFFEC2a5OsD2dMnUN3y/DTDEEj3OIuCxEeRmhXAFtW3avE+KmeFsb3pp9Vxn+pJMhEIX3WWqQod4BRJs15qxBiJagzd98N8YuPMuq8Da2dO0pz12L2bX5i3nHA/OYXtXC3M5+4Kg21lhjjS0ty4M4jvRvqduSRoisXhDNl3xjCik6KgsTLhTeLjH70AKWTNYiO1I71IUixBhk5IQi3dTyjsTwmpFle/fj4Tf9EUQSY9lPPi88LxtlY1ALit6U/jblyFD1vFtOvb+ISdDz1AtzvTY6uoW5rG1vcNqXes2TsRFbYxyGQgJJFHthhAw5boHpwWpdgFyrHjAq05Yl0wIy5JevvIgLOp7/ANtUEiyVQCJSIAZe+IZT8f5fvROf+fMHcOG1q5GMRdi/a4DJ5S733KgeaiIyW/6ofQKBS7asFXWRRQuFtilDcnivnEcAccu8qB8e4Ufmt58/P9CRRYgWo5czjgQwUgtUb/+4ZVwl2o6WVXFtyBxvxokv8iivbpYjpE5A0CFDQC6QxxESu62NMMzQkmkR7YJA3yAtHBmieXe8UCCyzKxmE0OGHCrpWRVSZKzn1Yu3yUBFpVyrMUkirx4ylDFkyD5DxIthKCSkRXw6gniEMZvm9SFNI0KfeioZyRkaZmWSEcF6xrOikHtgOHpUZS4UPz8vQjIJQUrse6xvjuEto1QkZp9ENQjRwdjSd9mOI+t+70FASqz/ndcgmho/2tVprKZd8JxVmF2TOz8P370P68+cxObv7j3KtWqsscYaKzcNb4jwCP6Wui1phKhCuTwEW1gb8ybXZWvZeDlPIgsu+KZcslXSMnIoU8g/Ik6RGCYax+ts5nW/j7FzNiJeOWvL4TwnrUPuEMm+Cz+ZpdVkCtEdOo7lfxBPSEv3X5RziCyPxKQV6aqkVGvGn47HoWpxmbKt1c+hCB2KlvCQIZryB4qjHmRc8t7vJVKvjvYd6BjQsae+W4xYK4twy4+T11nLCC/4n6fjU+++D3u39nDKU5bh4e/sxWmXrSryOywvqWhOFLK8l+cS3zIOkVC2ZxqLXFOJt0GBdzVkOHZUL7dMM8m1Z3mCz4GKC+1Yh7vUyfpAvGDL5yhTlXpyqqMCElXFtfG5PAByUVKbqqM8iq3FlIJbMrWIZCGVjUWqMrSjtKCCnapo5LNURwuHI0MOwTBojOFUDZTjxcVwkWf5upD75Ue78ci+zOhRxUynihCPtjnuvGo5DSTGHaI6D1UxNnWj8t3Ue4bh1LAzLQs6YNyKApAauiT9CjD6GpBY5WKtgK6aqUWGBCFFSTA/LvsF/aHIvCdH8+QaG2ZL2iE63ixeuQzptl1HuxqNHYBd9CNr8V/vfxjzewZ4/JEFzO9rXkyNNdbY0rWqDuXxbEvbIapwun1nnAVQFQhCARjEl1V1SkqPa/hMVomatiU0poTM5POKtLCoToH3ZOZbp6zHYMtOZLvnIKengm2U0RhySWXNcl93iUeVcc6QPRVTlu1BRoXIN9I0sqklMkKMBFKjUMsj3lI27Wfh7eWjLoWecEUPcTEwK/VCezZ6ys1TT6qtw96X0hICsoBodFXikJiK90bXlEU9uMt/8Rz886/ciM3fm0PcibFrMOFpipheu41CkUE9oKNKXk5VGhNfS8lyiFSC1ETf+FaGrlUhX4XeM+N7+OXxl2oZMkTzxWs5/IVMMDulNQnqyDWuXM6cvGRd0tuvQKSK3DSPQzRCuTmyqJ0uaGhRb52iOSU0JHQhLUMsM4fIyFB/iCsPU8RcK0rt/6rcV04XrHgOtK6gAWX5RmZeuzIsUmJSlCTIOVGEkrXZ1CZ31dpT0zb3HB3WPmIhgtTXMRJtVPLN80X3Wo8Sttopi8oSmbu3g9xNKNwDpHO2kLWQGY5XJwrRJZ7XkaIXyYbpuHHNqWHvM0UoJ2u/Mj6ee75oOly3qrF6dny5iEc48KdOElffZCvBxKVPxu5P3nBAx1M1ktQeSqsSVzxc1hshlneorcphqLIzrlqLtWfPoLt3gLltC9j72Oiks77NZ0VH5nDanvTI8tQW254Ha3WSDB9KkyNI6YfajvT5HUgC0YOxI32/HOk8YHXTbBwO0zjy/KGGQ3SIrNDB8RqWR45VIUVu34plwa68kJJyLRrDo87Mai0sryfPY4YiggO3LS3vP7wN0899egEJ0sbZsVMRIlaWH+QlqbUOGT+e2Zj4QqnQkCziyHKHDDLkJ4fVELaNMlVEivo1eilVyFDXxMCUPTxSaPRUXFhHPTcf9QGAnim7h9j21tsyHI+PhEKmkkKPK9URJFS+nL4DpgnmTR4r6qkOrFqyxCW/eCE+/Ss3AAC+8Q/34er/dYE54bCuNlKHvfTns3aBD1OVE86PorPK2KqFgWqBG8//RsiN7xRV5U7iEUE+UsRRrSJC5JLX0nKneTM6qo0SjlYltrV1tRpVRrnXXm/nNIzKtu0QEIkoGo4M8eM6zSrlcb3YVGaIZVZA4DIIiwqkhfuw/LzJOWnLtDZvhBw2KQQUOz3LO+LtTOdlosxioSBN2yQ6Q0tmNoqNI0MOZVP2/reaOxRYZa4J5TALOFsifD4IKSHEiHNrKPEr4DSK6mZYH4v66CPPIt/Kwkg+rmdVppJeTBAc6hFxJXAy//pSGal9/sP2tGirh4Bx7uNYFPI2G1ucjXSIhBAnAvgggLXIb+v3aa3fI4R4CoD3AugASAH8ktb6JiGEBPB3AE4H8Gqt9Z1CiGcC+CKAF2qtP23K/TcA/1drfUPlsfmwFFnwMBvHYIRjJPxiNNunzjNT5WAVa12sJA2fMSfKrTf1EUC6YxfGztnoOURsqI5QIBtiT5uJ4smzoTN7ONrXOj0aAyb5nmbmQeOh/DC9i2DIjD6ypgz6Bg25u0b3Fkb76i6ZrHkZCf6ycQTcyHwBeiysXwqN8ayDQZbL/9OHd6AkJCtvPjWOkHkxUcixT5KdvuRUTKz9JvZv2Y9vf/geXPGrF0BGEjCOGJFYudOjIAtEYe7E2PM2F7RtoP22GthhE60S9EqQJj9UGsiduUGF6B8f+uEOZyKyyuS1VY5Q4NRYaYfyUG9XZtEZKqAwIhyKSBih2LeqO8onU9O5VIVtV0khBFnsEX446Tp1sjRIvEvmk7/tR1CGToVd790L5AhxAn0d4yH47h4rP7+2UAX5jJbM0PbI5AVHyL6HpCUM0/EG5jAJI3PTe6irE1sVG+hh7gMaKqPzbpswf98hIqNtrWNV8UxROZkcYCEKleb5vVRII+OhH3zbYcuLiWbN+bL2TLQK5gc6cu1llrVt2pj6KN6oTsbxaHVaJAXwJq312QA2AXitEOIcAL8P4He01k8B8FYzDwDPAXAjgB8H8CavnEcA/OahqvgT0VR/ALXQg5wYs8u0UlDd3pC9GltKJoTA09/yDDt//w1Nct7GGmtsaZnWuWN7pH9L3UZ2w7XWjwF4zPzfJ4S4G8AJyMGCabPZDIDN5n+E3LdXCPGUbwNIhBDXaK2vr1W7IcgQT+sxEinyF44gUw8dOuOIzTBydQV5unBeRP69/2G0NqxB1Inszgt334/H3v4BrP/d16B18joIgzTYcHzl9W4Lw3kV50k9nMxqBBTahJLG2iK9OtOQGQ2RVSJDi6A4FMTPhjw8FrpmYcURC//14fc+DZ9lofhZJDR02kZXdkxZxeu3oAmiD5EhLqZH9Vp1yQasOm8Vtt+xHf/xa1/Fa771U7bIQUEg0fVYR4kO8t5mWxGJ1Q0JCpVgwRsy42Hp1HvvqdiW7xChsD350I+PSCRWNM4Qh1kdM0Yi9RGwzCBDUcXQGe9N1+n1Ukg5R7XKrHBv0aNkr42qPOawcrlgJg0DwUxbIoUqQTEgiu3VRogUVdYd7tzrWkhI56Tj6v0olYZF4ww6RGhLmXAjQKT/sGCOFMVROByW7xgef8DI04QIUeJmSgzrIy+EHlNIvmLPVJm46xhD8Xx5hLLlKnh2QyqAtPdWEf0tbGueM0uoN88hEc0pcWsuPCnNOYdDZAcS/t+Ys0VhZkKIUwBciBwBegOAdwshHgbwfwH8htnsswCuAnAdgD9kRbwdwG8deHWf2Na9+wF0zjo5WNY56xQAwOa3vhcPvOptmL/5rqNQs8YWY0IIXPSLF9n5Wz5w51GsTWONNdZY0RpSddFqk6qFEJMAPg7gDVrrvUKItwP4Va31x4UQLwPwtwCu1lqnAF5eVobW+stCCAghrqhzzNUt01vg0bMaBbTHtrXwNwqRItsZ4NuWJWTlZZPnzcqwZVJKD387838F4nwHm9wV4bZU5M4FTF1+HsZF2wlBtgRw8fkYv+hspI/vwcL130L83Ucw/dwrEa+Y8djUooQLRWgWW6zDnrlQClKFBNpKQrYWWIYWNASiLOyVx5GZakpKWL/nasPSM9O7NikK4syJygmLYITTyBJaGbGRRDR1sQdHFkkFmY4DNlknhUwrZESEND1TaXhVkenBxYQM0TkQuTRKMXP+WbjgOduw867t2PypnZi7MsLsSdNWWJPqZvuc2gvJt9etyFXwTRhkQIsMwnKI2tBZy7YNHU+YOmubLFciNgifFOG9G5s2EDb1Csx25hhCW3JsRolTRUhEJXKu3zOOWP35dSwQlqHQGUyizIooEuf6jO4pD3tBF8o/iAgrm3YjG0MrLcKmoRzF6PBsvg8WSSHyeVlVgpB1rJ1O5iKdDBkijk1sjpEnZiZEFmYacr0iKypp2kcowHBpMrNszCBCxFkixIqmbZFTDTQEhHlmI4MMxWae0FNp5um5h0oQpRMAXDqMDuPppAwR9wUiI/usEJpK8yFq6LezRnmbE/LbyvLzJRFQl8JjYEUv+X3P5SgaW5zVcoiEEAlyZ+hDWutPmMWvAvB68/9fAPxNzWO+AzmXaOSAyra+gS4PxiHyllc6RHxbhOtLHSLJh8zMR8VXqPY+NFt130WE0dQ7MZ1leOBrN+Pkn7oKc+jZdfPfexiPfut2TE7EQJph8kVX4qHfex/Gd+zAiv/vZZCJGR5RoiSfWoVDRAvooZIKUocqybrgHSJYvg09u61VnWVRSIsJY7WKq5J0VugF6eZlwSEKoz8KLx3vhVP1oaFhoLl4LigrkZl94dkhMkFDZlEwJaPz7ZjM52tetB63f/42aKXxiTd/Gi/6h+ch6rSCupFlJXVMK4bMeN2J3AoASZZiPtln26ZsqIzKTr0hDb/9XHuyl7z3EXP6SuUOEX1wfaJ4lb4SJ3Pb9ea+WmgV06BURd5VrS+zxThEo1S1h5n/0eq19hTr4d0LfOixyg5GVM93iKqI/LXKgcD+ZF/lUJmfh80fogaKatopOT9+0IMZCqNpZByi2Mzb4UczL8y8hoQm7SITbZbaiFAKjDAaRma6oFqIVIT5ZJ99RtwQefgMuXlpz5OeGRuRJvm7yQzz+7n2Khyijhmyo2lmppaQL/uFTiBZ3fs0D7tvSNXc6kSZCeToz91aa38IbDPyobEbAPwQgHvrHFBr/TkhxO8BWD9yY/vhpp2Fm3A+UIkDlC8oFlfKL6prBR7QkBevZv/5vhS1sWsvtv7Zx9E6cTWiqfEAOUp37IFIYsx96RbEK2cwful5AID+Q5vxyGt/D2NPORsrfvZFkJ0OhFWeNIchnhMlyaVnJaW6Uy/Rq2aBt1XkI2UijPwptMAiOEQRR9YUVTF0QCdlzzomFoWgl7ggpKOqN1Y9Xu+v8/fpZdWPBkc0eELOvhnrnz1/A9ZevA6P3bQZc1vm8eV334yn/2YIjlbJD1DdyrZx50/noFzvXMucG6RD9KdK+LLMXHvQeZkPnTmvRGY2ok8ZRzqT5R9WPylryiQGRnGWpFCIdBzoT8nCQ5xbMV2GGukUVTlvkdC2/CouUZ0IHb5vlrWwkLULYp1dlRQ5dKz8YYTUuqiOzzWqG9FYZnUTppJgY1/F9tmNLbKYHyep+ICXppZxFK/h8/BFG8MQfao7597k+2fIPFFGd1+aToR1ejJ7XgDQz6IgqTDg3gVUJ7qDy3hH7nh53SiR8DCbjvKo2IJUxhHWinqiWR2E6HIArwRwhxDiNrPsLQBeDeA9QogYQBfALyziuO8A8KnFVPSJanqQ4oHXvBsAsOaXXxys2/2fN2Lnv3wRK37yGrSffCpaJ64BdISNH3oHVCah+gPs+MuPYu/nvoLZF14NAOg/uhVzX7oRcnIc09c+EyI+JqSmnrB2wasvxGM3bUY2yHDvdfdi7cXrsfGHTzva1WqsscaOc6ur0XQ8WZ0os6+gGkt5ap2DGK2hG7z564aU6fYjj9+iQdqbhCjLMGSoMF/YxiAoXJfIA6g0O57dkzqsHP3RunCG2l9ntt3zuW/a9ZNPP8chJQKIpscQz05i939+HTMQSNathIjyXrYQGlE7xuTlF2LfF28CJNC77yFs+8MPYOrZT0fv+w9izyevx7KXXlvsz9ECDymifoUoaCTxnQHIvKV4L5YDQlxo1u/02d67Ir5K2KMShtM0HptxdG/4jXpQNrqLIoxYFJZvBXjau8Y+4c/xIFzlqUdYhU5wFKafOfHB2XPXY92mDWjPtPHAZ+/D19/1NSw/fx3G1kyZ+lQjDcU68Qg1t37gwfk+olLFKQhJjoQmZaXHU7adzfpMOESIevwlUTR+WQMVBUMovvEhUD/qLcoS9KK4GhkilM6W5cQHRyEZpMNUJoRZpT9Uhnxxq0KmCO0ifhyZ0sJeM34/DPto1R3msvevRRR9RHT0+ZDVjWCi4VsfraBntk/CmYQwioq6S6ACnKtEhih9S6aFHQqja0t1oci00iFHOQBk30UqEq+QhvXsMHSIvsZCoZuFw2vEw7OIEY+69NrZvr8Yl7XPju+niZkX+dDfTDxvlzV28NbAB0fZ5r56OwBg5rmXQrZCqHTqGedj8rLz0Lv/Mez85y9i979/FVNXXoSJp50LrYDBlh1Id80hmsrVhue+9E3MPP9ZmP7hK9C990Hs+tB1AK490qfUGLNzf/6p+OpbPo+1l56Ard/cjMfv3o4T1kyN3rGxxhpr7DCYxuII9MeLLW2HqAL1yQEizrSuKMKBMcVlFQcsQ4rs3wJxmabEtfFQLEHLhE3lwffRSmHy8vMx+6NXlp6LkAKd09dj3Zt/Gr37H8Xe/74Dm9/2XmhDOB+/5FwkJ6wChEa2ew/GLjoLEED7lPUYbNmObP88ZGeivHECpIh0hyoaR7idiJBHPalRSBG3SCrXtpKioMJtJpJ8jL9VkoKBerXzqel1ZqHiMZ/6VjVuz/lJ/nYp6xHyaZH/IWydAGDq7PWYOWMFJtZNY+vNj6HfF/Z4w2yUNpOPZhHnoq8SdLNkZBTWsONR+3JehI+gWMSNpTPgGlCOX1UfIfI5WrHKUZUi4boCGaLoPe0i4co4ZYAjzXK9J+mpMle1GycQDzOefJXQCemhsbxOVZyvrATdqbqn6bhlOjoceRqFNtXhpsQ2qCIkEvvlOqQoRB1R9jxwJIjVxXLNKAGtckitTSzLAj5Is6jMVNSHjHqFyDCrFE16QKQgbbbriWLduZq1PcYQZMgisOy6lnGKYpVvS/kPCSmqbw2pusyaFjmcVgPFbJ96Atob1yFePl1r2xWvfB46Tz4Vk1ddhJP+6jcRr5iB7OSigjrNYJ26VoLxi8/Dvv/6+sGcwVBT2ZG9fY50MsvuEGL1Yu3cn78Yj95wP0589mm48W2fx8L2/Yes7MYaa6yxxg7eljRCxDlE1rcWblmBD3S4h1JLeEBDD6zZDwgQoImLz8bu6/4b8bpVaJ+4Eq21KyCEdjpEFDpP1BepkayaRbxyGeLpccSrl2Hh29/D1HM2oX3WRnTv+B7GLzgbADBz7VXY8n/+CtPXPBMiGXKpNYABoVnsdOi4AVon8vOWGiqTHu9IhmUwc6iF9P4Thyg/QCcKQ/ZJgyOWGVIVoS1T1+Mldds0RHf48YYhRX2TF4gQHY6KdLO4ENZfB32x5ZtyJ85Yi9mz1yAd5Nve98m7cNb/uLSwX1kZVfO2FyoESNFoTEUB+sSlCoLybDiiKU+Xo2T2OIY3FEuVJxEGoCQPtw/RCD8CL1XlSJuta4kuUSuLA8Sr6nxsbi6rYq4CtAhwSBBFu/WY0riPEFUdryrij0cr5u0URpOVSQ/kZUnvvwjqVoUUVZXlH7cs+qrKOOeLrA6yyDlDXDU8KJ+hHqT2rhiKFtSD6IXmOlLEWBWXSGmXbNVpFIWq1rbtiH8EhUwMEMu+Q55A9w3xnlhuMSsd4M6T/rvzo2TDIacolqqADPF3oq2cWUxlSmjEwkTNEYplEtJORV3UtcVEnh4vduwiRAdyLdnHXrD5WvsekLmhM38KAO0zTkL/kW3Y+u5/xEO/8seY/8794Z5EJDfh6VJqxKuWId2xGwAw/UOXQPcHeOgXfhd7Pn494lXL7L7xujWQ42NId+ysX1NDtBY8NUiZ2SSx5qOQ0fzo28p+SMy29BLlwwu+0YvX6e8YAbOoeh8yqxnCpsNQIHqZ0TacOFnHfMforJ+7BDtuzXObPXhdUXHchu1zZ6IGtE11JaIwzdepq9VVysLpMKM69Vk6k2Ehw7wdF9Oe9gOj6u9DTtkCac1kLTMdHdZMx+PT4fuU161ODqfCEFaNFxxvR394cpSRU2gzqx/AC9UOZ0ejkVt6dmnbVo19+kwPiKZBeo8KI/I0hdcPGyojs84TpQKJesF0mE3E+TZcQ2iY8Q4A73gNM7qXyXGe99L1NHbgtqQRIs4h8tGLnKMzZFdOMdKefpE3DZazA5dGnY3iKvmcoqAS+UGIS6S1ALSGHB/D8pddjR1/ex0A4LHf/xBmn3MJVr7i2RCxiyjTWkBIjf6W3djz71/B6tf/BCA0ZDvG+rf+PLQCsgEgpLQdjP4jW6Dm5iEny9V+q0ykIm8bUr9WAtpGvwloIXJnSGo71Sqvn8oEZKShlURm3nnSOHPK69FJqW1Prswpoh4VOT1tmVqEaJC1csQok2jJFN0sQSvK7IfBqSU7PpSErnSK+ipCF+G+mZIQQtuIsW4W22ksFfoqqtV79sscO3UNlp9/Arrb92H3XVvRnUsRj4cvsmFOUQEx8pxVCV1waKju/gfaf9n6/8ucomp3BcAAACAASURBVAIKotx8LBVSLU35OYrmO0WFttbOAeXtSXUsCm9qDFRkEaJultjjSGGuAX1MzHnQZyiCMNe82iniueicRfZ4Ejo4vm37ighA/3xiz7k/UKeoX4EU+QgmTemaxEIhVZF97jL7PDCuX4lTFGgx1TDf0eF55Ii3A+Tt3pYpFlSCdpRiIUvQilIXMVrixCUyQ1/FaMkUPRWj7U1Lc54pqpNxSkqcIlpGzUmCtBIKPbNPVydoywEGOsJ41HNTyzkLESMpNHoqxkTcw0KWoBMN0DVTMqveXRBsLDpF9FwNQ4rG4z4WshaSuIteFiOOM8yrllWzHmWU3LWx0Ja0Q6Qj9lDqkv92CIttylAgLVCpUF1VRplVE7LD+mitS1JmlE+nr74UWmns/MCnobt9zN/9IDb/4cew7o0vRRQb0rEpbM9/fg3Tz7oQk+eeDEB5H0UJGRunC0D/ka3Y/kd/i+WvfCGimXEQGbquCdP2vgwATYXUgFBuG0KvIlK+zuejKEwDQmVJqa0itWTCjLy3FHkvV8XE3NJK0mxx6MwPiff3oe19RwgwHyBGlFfsOPEikmrSPqe8ahNu/tWPAQDu/afbcNrPPn3kPlVW5twMVIS+cI6CExgsH54qO14x7D483xTO6aZlXRYiXK4WPuL+M6v9obWBzofabLnmHmhJ2tZcV3ZesXTK5nwYr0xp2DcJXfxQ8dDogzA7ZGfabCFLHPphHAQa0iEkxS2v/wy7trdaFgDMkCDfthA4UBTLpGXkVHazGAsyCbYBHIKbUjLSLPYc0nqIW9l5jjp3clSgUHCSSAiSlrdITZ+kJoQEZAolBwUSt7UKkrcSAilLWGwV9tlwmH8OLhBgcfdUoGxecJSP3UGfpWBN6y0Rm3nOJqz5tZ8GAPTufQTptl3Y+c9fDLZRgxT7vnwHpp89XP5psO1xbHv3+7HsJ34EE5uectjq3NjibXLjSix/6kmIJ9v4wQdvRH/3YqNDGmusscYO3nLe25H9LXVb2ghRwuAYb2pRB822MVaG5FR1MGxneTEIYhX64w+v+ciThheGz/YxCyYuOhvr3/ZqbP6dv4bqDbDvhtvQf2ALJi48HcuuvRRqvgedpmgtG3OoDB1C5N2ivV/4Jh7/yOcw++JnY+KyCwBoiBBxHWqWPE3oHHGYPKRICAUpHUIkGUJkk7wabg/nTkmp7HEIKYpYDiCHFHn5s8y6nqo31u73xtxQUBycTz68IG0otA0f93hQhAxRT05XscZH1CHfV+Ckn74MO256AABwxzs/h/P/94tL9qzmdVA9aEAiSPWiJFIhC21TaKshp8CRNk6ujqVLFcI5WLy3GySztKhRveNKoZEpmQ8/eTntyozzdqQ3nMbrz1OtlCGKdYnf/vIqBINQAwWBDALzDO1ZyFoFBIiGW4hzw8nIZelmRplLW1NWf5acl6E+UqgCB6uVJViQrYKgZszQpr6KC8hQVV7C0nrT+YlwnvK+9RinyB+qs0Rru7OZmJcicYs6cgAhBtBigIyJRbqcjaZNKF+lLUtgQMlpSehShtckZihlnWtWlbqnbH97f9TgVy11E0LMIs+Nei7yN9XPAbgHwEcBnALgAQAv01rvOtTHXvou23FmnbNOQeeskzHYvAMrXnENJi46Azs+/AUAQDTZgYgk1EI5ya+/eTse/+jnsO63X4Ppa6qHYho7ujZ+0gqs2HQaJk5dhV3ffAA7b/rB0a5SY401dhyZNrnUjvSvpr0HwGe01mcBuADA3QB+HcAXtNZnAPiCmT/ktrQRoti44BYNohVwIo0VvBzN9sk5RAbtIN6NRXUOghdgdw0RKw3hkASN8h45r7OZHb/kHPTu34y9N9yGdb/6Umz/4Oew85NfwcJdD6Jz2nrEy6YgKYzT9HL3f+9BbP/AZzD7wivQWr8iOKA9zWH3I0e4qOdm0B7baTKSABIKMgp7eUmcBVPqzWcq7OFoLaz4GdWJostc1uhw7D0RmX2giEO0YKpUhWjQvr00ttEYPIIqkQo9FWMQh8uDsX4ztaKDIuwhllkVCZZswysux22v/SAA4N73fB6TH3g1ZDw6OggoIkM+KqO0QKZkKXpU16jdAj6V+QcYFGpEX4ojEEq77N6sg23bhicWVkIjVSLnEHE+YYX5iBXdS5w/xo9XFmpuEUTGHRom/lmFHpF440Lawn7RdudHEYgqtqgVIUNjEWV2N89QFPKBullciRIsJmKMp3yIGGJD57KQJQWUp6NIEiEkCg8s2VjZ86tChuxzXvKCpOctrjidAeNikfkIkU1JwjhEyv0x+6SIkQEiRUeG6wr1IjkHr43omMSbkub6peweHBD/sSRIgownPVa6+t1E/52g57GNcQghpgFcCeBnAEBr3QfQF0L8KIBnms3+HnkqsDcf6uMf2633BLWJi86EiCN0734QOlNY+/qXINuzHxNPOR3rf+1lVrOHLNs3j0ff/g+YuupCzP5IgwwdCza2YRlWXHYGZCeBzhR23Xj/6J0aa6yxxg6RKYgj/qthpwLYDuADQohbhRB/I4SYALBGa/0YAJjp6sPRJksaIRIxg4Fsz664zGlaiWAfiyDlAVj+LoUgswLNos71q+ASCf/YmiKWaGU5l4h6pcnaVRi74HTs//p30H9oGyYvORtTl54VlEvbpjsex+Y/+DimrzgPy573tLwJMpZSg85zWFAUO1fiKNmcqxYp0oigEAlleT8cESIOkeU/sPDtTEnbO+fcIVuGFdrL5yNPLI9slHChRYXS2On0pEwjJlLo6diG3SdMQA2AlRwQ7IbR5jpyFEaX9ODK1q1/xRXY/sW7MX7aWmz7/F2Y3nQWhlkVZ0p4nJeBijBAVEwTU8MsMsT2cecgS9f72wwTreRcEGrHqjYSQiPV0aIiq8j6WYS+ifyh8ng0ma44vhQuCpJQgoJQI6WLqECF8nIJzTERYx5S4h831RLjcR4nN2YRonye348qcrySUUrqhfb26iVFyMWKZTmHiFChhazlySoYXaAsRle4xLvFUHKHEFWJEMZe5Fv1eYTCmRwR4tsNArkDq7xoNjLLWTLpDBIds0lH9IN1kQ7vBcs3NGVkQjoEylw3eveSeKML1TdIkhGE9a0KEXLn571XSMCTJQVWumbYPYr3/RGylUKIb3nz79Nav8+bjwFcBOB1WusbhRDvwWEaHiuzJe0QHc+2+jU/hoXLz0PnzBMrt8kWenjot/4Oy669BLMvuPywi3Q3dmits24WycwYFh7aAdUdLeTWWGONNXaM2w6t9cVD1j8C4BGt9Y1m/mPIHaKtQoh1WuvHhBDrAGw7HJVb0g6RsBwiM7FokChEaNleuxqBHPkLyatnSFEtsxt7kJC/XGhXJxCqE+7iymJTAYh2B+MXPxlCaoOIhXXXWuDxf7sJ4+echJUvuhwAkFHXnYAhHg1VdfwScwiRCuYjqSCh0IoyiwxZdCcO1Wg7ZlqWJJTzU7hgGQm+WbFGkaGLvLdalZiSzEeGgBwtIGQoZfnXZCbRh0t3kVI9Iiet71JchL1n26ct4cA4ngpKjcqcftoZ2PON7yHdPY9+T0MmUSXvhwupUf2EEJYzpLRABllI/VLHRvGNyuK7ePlVZfgISlaBzPCy4hoK5MOMeuB0r3FEqOra2CgiuAjKqrQjPmJUFdVG1spizMMhRLT9eNy3AqRjJuqp7QmSAkUOkYrcEASlrqkyF5Xl7l9aFrE60/M2n4VClt0sKVUYJyFKoIgQ+fyrKmQoGcIh4maT8FYhRPQu8ZDAjLNCIrbcQ4wEEmjdQmIUq2nqxCpNneFpFyGPKKN1hATRvRcbRI/SfWT23lAWLbLI1oiwdGq7VEkn2En3NCF8i+CPLcUweK31FiHEw0KIM7XW9wB4NoC7zO9VAN5lpp86HMdfei3SWC3TgxR7vnArll17ydGuSmMHYVPnn4zWmhkAwPy9m49ybRprrLHGjrq9DsCHhBC3A3gKgHcid4SuEULcC+AaM3/IbUkjRBRZ4lSnvV6v5cWwHipTE3V5R7WLLiMODw93GWI8Mo0DQmWoj/D+B7pETPioyO3REEbHQhGHgVZJYLBzD7b+6SfQOnE1Ok86CcqkwiBlaLs1jUlTjjFR7v8GPWU6Py93GuD1iKWGRLXmymJtIsnH3G3eMJnfki2Z91CpZ9kWLTtezvNnkQZNERFw5+f+M1RQAoCwkXCWJyMcd4hzvFx7EQ9qOLcpWMd6cJNPPhEPv/dzAIDtn7wRU0/eUNlb5lGCPupic96pEKUZhvpwRKYKTaqDNnGEYdjxubZO1fEHKoLWuX6R1RASIVLD0RhfoybNXOobwPHVBI8yY4iR0sJuY3lH9tGqeFkIV05VTirBlvn/eaoMywmj9BqkXwNXLzpenVx+B2rDuD1c84p4NDa6j9pDOkV9Wlel+lx2bBv5VrPOqY6wMKJJbJJXcz+15QBCJchUC21KNC3DhLBlCtVkA4SRdTwxrL0vUS+SdJgtRiG/0hYXBn9ETWt9G4CyYbVnH+5jH18IUVz9cThWTHX72PXJr2DhOz/A7HNDdKhOUsBDWpcj/EDVScp5KC0bltj2EFlr1TSidoL2Ccux+6v3IN37xFWujod8XA+HDdSRfb0d6eehTgLRY9nqJKk9lFZF1m7s+LElfQdEcah0TL0M4fX4CckgpEgzFEb4CqVaALG2PCOOGFWaf7zCuiFcIj8qSTtdooLfQohV8L0gDk+IFD36/78fvR/kQyu7/u0bmLzwjGB7KTSkaTfHlzGIScUHKUdQGE/GcoiIp+LQEe2d52LQELtcC4sMjcf5S70vw8zyBYRIOpXcXhYiQ4QODLKQJ+TrHnFkyK+LgrB6ToL0kVSehNd3ikjugDqftmds7qe6CTG5TTz5ROhBit6jj+Px62/HupdcGpTPjaMuHCka5hgPvV4l0VZAiT5QSRk+ugIASQVqEXvRgnTtq8rtZjHaWYI98BAioxETa6eJBBTVpwklHChpe+vtJOSlFfSISqIg7TqKKKxoP6WF5eaQRlOMcqSIzOcjce2ZlEUaEW9uYJWOJaRQgVPElY0XY44TFaoyu/WqsC0ZPXeEwFmkSDqE2qIaBLhlIfelrD6UnDeKUraOrl8xBx0ZtV8VUkTvBkoEmymBSeRKzzYXo7mpiUtUQIooUE1rmySWECFCjembxZW/68WChVYnIq+uaSyOb3S82JJ2iOjG1Mw50EoUhkBsegqWysO+2IQbZisMq6mKDw99DJVzbioTpNoPrldWyTBasG0h34hzjOa+eht2vPefccrf/w5EHGPhO/ehe+9D1hla9sLLsfwlz3RFa3eDRx4xOC+WIG1ZXi0lrMNlB/OsQ8m21SIXnfROlV+LqhcyLZ9M+vZjOB7nqts0/EUvzvk0dIgWZBoQPAE3zMYdIQq1zbiTzM4jPz9zXelS+8lyWbg9J+Fa39uKvlUnaxyWhmPqyRuw91v3AQC2//stWP/iSyCkKCS85eavp7YVRjizanirLMlklQ3bhreF73z6+9JL3HfiODG5wz54/lDLuEgwJgb2WvOhM+4IkcPQTyN7P9DlMd81dJJBUDfu7FDKEL8udh1JSLC6+tfAH17m51t2Lf37JUhQ6hmhJXS+LZkWwtv5sNqhQKyikuvGja4ttX1UMmRm01wUAhPK6xgLZYnmZC4BbegYlJGDqU1HOUb0rujIATo6xkDH6Frv3mxTYwjNpffIpxQAQrIhFObvzl/ReP1IowS/hyKxcGPDbUk7RMezRcumAABzX70dkAI7/uZfgdQ91d37N0OOtVErZKyxJW3TF5yMzR/+CuLpMWTdPvbcfD9mLzntaFerscYaewLbUuUQHU1b0g4RkYQ5AqGFRwjzidYl29pRK6ndsJodKjMTctQZghMMk9l7pwIm4KuFWyd0EIVftJIhtPaTTgUA7HjfxzHxjIusM7TuN38OC3d8H8naZQat8c8/bAubfsCQ0zMGYtnevHRkssJQZOl5GpSI9h8BvfrIEJCHGU8YIbqpuAvA8YNsEk/TO+x6y7kwHE0HjFRNKI/t5StRGALhxhOPCq0L4fbcighSfrxIutQPVb06FzIPjJ24AhOnrUG6dx4yibD107dg2dNOLZRfJXqoIAooBAfVy857FFna3gsl4Ce/5kWCcjjUlHhDZS49S9jT55yYvooxLhJMij66IrzmXMqBrjURqQfKSS3Q8EVKSIbZhsjdNgWDl+aB2qYQsi/Ce8Im2tWiMJTJCdmUz4mH7ANeyDUbjuHrSbhRQhfSa1CaiAETMiz78HF0KWL3GB+WqUpREiBjRCSmkHMPKVIyRIY48ZrMDq0NeaUcCqTIhasbVF0o9BChr+PKa0DLWwb1aXnilgPzKaVwfhpuc+3LRSt9mYby82xZqYLwWhzo0LxvGo1DVGbHF6n6GDIhJZa94vmAEEi37IBoJxCdFjpnnozlP/nDmLl6mLZVY8earXvppeht34doagxz392M7pbdR7tKjTXWWGPHlS1phCixpOp8Xnnj+hTpzHv+FuRhJFqlBLTHQSqbOiKJDw2ZKSdeWySowlvX3v6aflRX4ucQRBXynvJlAlPP2oT5b34Haj5HUcbOfxK0UkFaEH7efvUtpdpyiox4lyWeuu0UQ8k4yuSXr8GIyva45TwSHxkCgImojwnDHZqJ8xStXIiO+ELUk+xmiSVR+6kDACdMlxGHiJLJ+jwQxndylqNdhfQGShZ6/NwIJXFh3dqtGcErsqvNvjMXnIjW8nHsve1BrHrW2dj277di46uvCrZxqFKI7JShUJwrVCd0vopf5CgVwltWde3DeeHdCw4RCs8nZigFiRP2hMK4GmBc9guoUpcl6+1ZtNDcA5n0EEIEbRDZ0P183w6RuwmR08KiRST9kTGytuXl0XYeBFwQRPUy6PgIEVmqZRGVsClD8j+EDBEpV5aksiG+SsxCvQkd4cKeZRYxbNG/t0aRw33hR8DjFEkFnYX3YRVidDC2GKSIp2TpqgRtFaOnEvscy6G5jkKkSFlkKEStLFJkjm+5RkJXEqzbnETOCPeHyhqEqGgNQrSETSQx1vzGq/P/nTZUt4dHf/3P0L3nwaNcs8YOtQkhcOJPXQaRRIinx7Dts9+B6h9ILEpjjTXWWGMHYksaISqI6R0mc6H7ZoFk/CPtopEsEaeOVQEMHK2wSUK99TbsPcLEpguw+2OfRbZ7DrMvuwZb//if0D59A1b9wouQzOYpCXVUGf9WQHuGmYuQ4QJ77n8EhUS41B0Uxkw9m3Ycfsht79728jOXmsP0suZ0fh5W+p74DzaqRzgBRhtVFkaXEWfEohUWKRJFXpgFBTWUEAVemS6CRgXj6y0XQEnbfpaDUhDpI8SG6iMws+lMCHk9Hv/699FePY3tN9yD1dec6/rs9v4oR2fof8BTqUCKpFe3snKC5RV8obJ9Ii/FC+BSM8RSOX4YJfCV4ZR4Y6QHs5AliFWMri6ig0EEE1yYP52TlAqSkCAmMkp19Ovml8nbM2gL7d0gCNER+3xZ4DcsL9WyGg3iQoVmnpS3eDJW33j6DUIe+HWrwz3hqS78MjhXiO41224sus22lZIOKTVVoKjAOpwhQpp4mo9C2P2wBLHsAIQU9TwCT6ryhKtdhHpniiWCde8kE6EmBuiIQbBNJQ8J3nous2Tue84N4xIIo1J81DGNpSvMeDStQYiOAZv+kavQOe8MDB56DGpuARv+6E2IV8xi+19+7GhXrbFDaCKS2PDKZyCb72Owez+2fPrWo12lxhprrLHjxpY0QkRWJrFflzsUoEtsXaFHRz2tLOwRCMDqD9lOFvUAFkH4t/vaaLmwDO3Vx24rcoL1ip/5cTz6pt/Hzvd/Cq2T1mP2xc/Gw7/ybmQDBRFFEEJYWQutw/MaZdpDFFxkmumlsLQUUmi0dIyWTG1PvwoZ4kY9n0RkdizdSd3nU5tklfRkPK5IPwujyQYssog4RHT9CPHzU704XpiDirTwtGjsvYDq9uNlMO6IgvDSQ8Auy8sNK+JziYTQmL36KXjkg1+GzhS63V3Ye882TD5pjTlM+c1WGkVU0LgKt/V79tzKOEP58mrkhBvn/LRk5pAh4ZYBjjdG135/2rLzSZZgHqqADHGziTcjxzvkyKg9vplavS6mR5QqOTJyksrO/GPY5zpEpHxuHQmB+stTVHOInPhfyLPyjXODePLjQpJi79iEcmSauDy0voge8qStsVSIpUIvDa9NQdQScCRFWS78WGY2kpCJb/qRdmEdi/17fh3p3UHvnb6nTt3VCeZVq1BGJqlRzMQ+tDRRlk+0KKSI1jGkqCrCL/VEOatsUcldG2HGgjUI0TFi8cplWPvWXwIAPPbbf4ls1z7Ey6fRf3DLUa5ZY4fSZCvGmh9/GqLJDlSqsPXTtxztKjXWWGONHRe2pBGiquSLfoTTKGQo3Mds7KMAQKGn75LImvXSK9cWYXbmQ/s+7OMjQD5KQWUwhMqygCqQgPZpJ2LNW16Nre/8azz65vdg5seehT2f+QZWveYlea+FcRfqwlcuKazPIQp5Fn4C00TlvX2ODI1SQfa1Tmhb4hDxyCOeuLVvxvcBxyEizhBNs9QgQ5TM1s9FxptEeFMhciVzwKmFC12Z0cUpqIfXS3vtzlWSC2Wwa6yE0y5afu1T8djHvgG10Mf262/Hhp//IcRTY5UIEeDQjlyfRheiypxejtPVKUbclVud1B0cYbSoDPGDoqygMdVmnKH5tA0g1JlKsgT7PU2oUWq9dL+qSCDToUo2oUecO8Q5RLFUFm2s0lfibeH3LOlp4jpEqTbJUCPq8TseTYpwW44wFHSChrbCgRu/tn7kWEHZW7B7TYVt5beN0/cyPKcR4ESqJZQKOYExQ0aqkKIyyzSvm4FlzL2Qqgh9FaGnYofCESJEzzlrdMkiyAAcEFJkIxitJhKVm+9Ezwchf2XIDp1f7fxvuokyK7PjCiEalbLskNsihtPqWuesU7H2t1+L6R+5AjPPuwLzt30P/Ye35oc7AslIfRtLjmxySXKEjpTpI5wclCyeGsPK5zwFnRNXAhrYcf0dR6UejTXW2BPTNEJS/JH6LXU7JhCiQ1OYx9ngCsAsEZ9gPRGdOTVr6+PYPxVIEd+uzDmi86PTjOp5UO1TN6B96gbs/LtPQu3dj+49D6F90hqISOV5ySgJqTkfUqrm7Rl5iVtdz57WqWCb2ON/tFVsVYUP1CkiZIi4BFZDhamzWoTIy1vWz8qRITWgXja7b0puIx8t1NLTVPK4RUIwp8iiLrRriBTxXnBQBY4IlXCKnCK0wvIXbMKOz+ak6s0f+RpWvPBSxBX+oI/eSWiLEvl1GrDettLCokVVdV0MMjTKYqFsTiZCFh0yRDnqaD6P8ullMdoqwUBk5bo/QyyJsgKHqIAMiQqESCukRryIoq5GtUWm3PVzqu+mrQW1eb4fj7BKlXT/K5AiSdwXepFIoC0WJ8vg69kUEEpbV4NWUF5ETyfIJm210zxykOpOiFxmOEU+F62uunLqaYdxrlWsVOk+ZUgR7cuRE46uEB9nACBTEVIVoUflmOeNR2NyXleZ1UGKqI0Ti1aFZaQ65AxZVfaAyxfyq44Fp2Mp2zGLENUiUTMTNKxAYbjGURCyxsPKsr9rcl6G7csJ2Hx6IOYdLtuZqxnHq5blxXLByQqYv8yK4dIqmD9cxqXth5EteXh9SmH2gxq3MW97dt+UmSNlh/uoGkgcHzYoC+ku7GPKTTOJZOU0pjedjbHT1yHbt4B9t94/9HhVkgrDrn1V3QofgBKr6wj5ZsPqjeMzZ4bIyAEaZoUUGrXu6ZBoHbEhsmEWs/u/jmQF3ScZTx1To64p2zatEVpdNTxSh7B8IObLZgDFYbxDbSS4SqTtLhNiHWajHKEyo20orL1n08SMRqa7OjH7mqH+GvtQJ5CSxSZDpBW48WS/tYfKPGsQoqItaYeIGjGPGHHcoTr8Ib6PRYWGOEWSfpGCjBSE1PaXbzzEKZLIf0KHHKBhTpFG8EHmu1aa2Wb6Bc9ENDuF5IQ19uNdxymSUkFKhSjKf1yfpcwpot5gEmWIpUJnRETZYqzMKSKeESEehGYMdYqy/Kczkf+U0RciB2aIU0Tb+j9guFNk7y0dlqEquBQ8wktpAaWk/ek0Q3/nnD3PlT9+Gfpbc6d323XfyvNzqQiZlsi0hBAwvxDx5OY/N/QrciqKTtGol9liPoS07TCnaG7QwtyghW6aoJsmSLMIWSYLjpoua0f2o22HOUX2J4o/2sbfR5i8XX5b0HvGn8/rWuYUCRdp5v9AH++iU5Qa1II0jAbmZz+IQ5wi+wwJtWiV4wi5knIsMsQis/tzp8hXH6dnlRBnhfzc6nDV6Py6WWx/wHCnqJsl6GYJFsyPjjcMHaq+Z0LdszKniJ6/gYoxMKrWPZVY7bRhThFpFSUisz9guFOUmec0Q/5TWsKPMCtzivoqDiLnGlucLemWK6Sn8ByhOiTq/A8bCgkOQBMDrZPDo8N5aK88CsWnVVS8ZAs0GHG35ATteuNg2SEsf3tduo8QGmNnnoyp5zwd297zj1jx8udg/PxTIISwdXGCdOYlFoUvRR9i5qkkXDqK8KNAL1lfap9/LHl4Lr3EWjZRa4a2zIdJOip/EXSVGSYhMnUWkqoHWWRfVu4a0zRczi0X1qy6D0xj83sB2htGQ7CvkOQUmcUl17awrKJz5I+87vr0N7DjH67H5NPPwZr/8Ty0NqzG2FknYuGeR7DvW99Hd8setFbP2nvQEl69ToL9IKhyKD3zHOW6Do1LeTEaEaDhMHKYx01ajE40sC9wSthLQ588SW8/dR+SzHNuAFhi+SjERmsnlMgFEiunQ1AEfo/VQRyoijbxLJgwo7FYKnt/8OE0JwUQDplFkXbh9SyUvMp8wT8/BQgtC+tEQpdmWEcoO5zmht7Cd0FK7wiWrVTBXUMnZGnmrG0/PwAAIABJREFUSaqCXQvtOYq0jIJduAOZmhQsNNRFdfePNwqdkMH7zA2j5edDAp9RUGebpmNEig/AE8/0zomOQ21BTpEyQSG0DzlJdC9EEFD2AxTWX9ZEYRphxnJb0ghRY6Nt5gVXIXt8Lx57x/ux61+/dLSr09hB2OwPX4LWyWsw9/W7cP8v/jEe+t1/hB5kyPbNAwB2/ts3j3ING2usscaeuLakESIS5CobGqtCBexy5bYFADu+AFhxMHhoQL4pR4pMER7qoqnzYZmTNGXeto/ycISogAyZWSpT6iKi4CFD/nwUASe8/Rfx0Gv/Dx7/yBew8qVXumGByIU85/PlPRm/dygYChCxHmQsFCJGyiz2LvN9iHhNiMDcIB8ekdBIPJFGANib5qk79pkhlDlDtLWQdSbtcFmlHUIqQ2nIviVG0jUQ4dTfn7VJVYSjn65FtDvY8LafxdY//wTm73wA+2+9z24XL5vCjk9+A6teeQ3iJKxQ3mMnMqzMicAsdYidapIDGN1YHBGKmbBimRHJfjIOE/oC5VIKgEOGUia8CeRDFZnXeO55H967FcLV34ko0tCCu5fzY5QNVbF2U2EZ3JQWBb5dMQGzGTKjsHU7pCMLqGrKeFyE9lJdpddGMQ2/0LZ0feyLxdURbBvJ0D9uPjpBpHQnBZAPL/I0MWX3lhtKZOeF6n2KZYSIERm1Sd+8O1oyw7hJHl1ATthtU4Z20nBZZBtuZNVGQgsDc5i+GU7rajfURsgQ1dXeY5aAnV9Z5SNRDCW20g6FfCDVdrhTYh2LtqQdosbqWbx8Ov8C1BWWaWzJWjQ1jvW/8dPo3rcZ2fZdiFdO46Ff/2uku/YBALoPbsXk6WuOci0ba6yxxp54tqQdojJkiOaruEKO/BquF9AOQGAy8p4yYmAWAJDaraPwewrRr+KKaLasJBpN+4gQ4IV8ewVUIENEBpcG9TnlT34ZD735fdB79yJZOQbAIUMtm4S1OoqB99B4r88XsYuUDiJ1qMdGInw2JD/K0QHqBVLSxPmsVQiv32/JtSZtgxdmD+SogU3RUVcfyKKDdTZm94DPASOhTpYAtpDCIyiOI4blldABOuem7Y0nQJy2HgBwyp+8Dg+/9QNYdu0lSNassEhZbLqFmZCW0KQgggSdDhmi+9VcVwwRnqzgkQWJWj0hSN+IM+QjQ0COEnJkqHiNTV09JFBpiRQySMY7zOz5aZ8DxjgtHhIEFDkvqZIFNILzWXjfY5jGWSB14EMJ9q97lspC8gHvedSEFGm7m0XQKGEyFV+BFA2LQiMxQIKrqe6JhwrS8YiwHVdwh8hyVCw8VSHYNSipk88nAnz5gnzK7ye7XET2P92HFgmqOPVIaKRKYqDcvpanY8Psdek00qogk1Ao32zgkldHaBtkiELzq7h59n1H4E/mPtsSxE87gCizJnVHwRoO0RPEWutXYvbZF2LHRxse0RPNWiesxJpf+lHs/sw3sfmPPg6dHV4phMYaa6yx49GWNkJUCB2Hm6+KLCogRzQRHleIti2Dc4omhAYkP14IP5QOUTMHvEA/YGKIZTyjUcgQTSOpsPrHLsa9b3w/kl++GkIKiwx1TK+dEKNh0QU8hQZHivzIEiqHyieBtDHT86GII4cy5cfvZgnmBjlniLgLXJyv5yV1BXLUIGXJW1UVp2hYZGFhW+8HhLeCzz/zF3PkqQwl5Ieuqotftpc2BPB6KwKYuPBMnPIXb8TDv/E+7P/Owxg/byMy/7gkIgdh+C4hMuS4Gm4X3iPliBDnkQ1L1EpWhgwBeS+erqWbOn5Y2RQAUgikiFy0UAVPhYsi+u3topSoLRx3p6wdUiUtclEI9WcaVHW4L349lHaJf52Ap7SJgcGQIS4aaflOmYAixI7dn8pcE0qNwpEiX5iRp4ng5tpG2XYjtIg4RP75AcWo4DLjKO8w5I9fvwFDFjl6V4aEE2rNz4vaZqAixCYKULFnhppmaIqQEuHF4BxMHXsmmrYjUosQtYzApuVr0b0mQymALiGCUtjItoWsFWxTF/XRuhFxLLMGIXoCWWv1LOLpcczd+fDRrkpjh8FEEmPikrOw9ytNKo/GGmvs4KxMn+xw/5a6LW2EaIi2UBVXqFKx2gXiWKTIokx1KsPQnGIiVs4ZYf+DeVOWDOd97aHFIENAHkHWijKse8ml2Pz3X8S5f/QKqwXTMUgN9erJaiFFJYhRIjP0hELHlEdcIeqRElI0LvPlMRMhS4TCftOz2c+QIUIPUjt1QowcGaIkroVUHXWMI0EqvNd8KyQBrkKM7A4lx6uqYsl9AcaVEFYYEJi59ul46A3vQff5l2HspBVmR+lSWwgTZaZCjk+VzpS/LGK8II4UlSZqNfcWR4p8ZAjIryuPKhtk5VFmPnqgkItQcmTITzuTW/i8KAjbnKPSRhREKbX0kCGYOoWoRxXnDhiBJHhlZD6X0XJsQn4jjzazaSyk460owfq1Fq2Izbbh8zfMHNfG8NM8bgpxj9SQKMO6xqPORnHD/G1dSovy5zCVUaDL5U/HoxDB9EUbM5ULXlqEqMAhCqO/qD0ioS16U4UUKfNnXObRb744I0WZEc8ok8NxCqUFFlSIDPnrGjtwG4kQCSFOFEJ8UQhxtxDiTiHE683yjwohbjO/B4QQt3n7vFsI8S0hxFVm/hQhhBZCvM7b5s+EED9zGM7puLYVzz4Pqpdi55fvOdpVaewwWDQ5jmU/dgV2fuS/jnZVGmussWPWjnzajmPBWauDEKUA3qS1vkUIMQXgZiHE9Vrrn6ANhBB/AGCP+X+WWXwlgL8DQCzfbQBeL4T4K6116KZXmB2vr4ggy5eJwrLAfACHgTk67JDXM84bGYYekA4KUZ44MiTDXq2fdNZqIVHvZAgyBABJnOW9Zgmc+ppn4t4/+CxOuvJEyFZcSGbJz8XvZRBCw6OI/DF34hElNlIsX0epPiwSRFEvpkeXSoqwyNDS+TriEqVMtZV65KRFRSlcALh0GlxrinOHLA/LWzbC/MsXPR6H5bNrXni+Szhg1QcK66MFoNoGCepJuyw4jtln7MJn4PF/eSfm7leIpiehpYYwCGInbuFxlWDFqr0AqtGKQMm5gktE5iNDNE+oA6Xi6FcojPsIkeWFpSEaSGlYaOoiOA0iAVHouVFAU8KeB4duaXBOFN3/PKcZ3b9UHz/1B92P/n2YHye0TLnIqYIGlcdd1NrxuiLSklFON6oKKeLRbqmSthIUbZhyBMPcE25f4iXVz5lVZsQh0l56EKCIPooS1KzqXVvrY1nBG3UK9sQ1UgVeUSGykD0XZalULIXIlkFDDISeGV5QFlsOXxVSRHwhigpLvGzg9n5g72OKSKTj0L04HvXttaR1hLK2DvLaHu82EiHSWj+mtb7F/N8H4G4AJ9B6kb8FXgbgn8yiCLCZuvy7fDuALwB41SGp+YHYEXZQa+RnPKRGL/rZC0/C+MaVeOgT367cdtc92zG/de5IVe2QmK4bbn+ojrf4SNaDs5pDf7LTwdj552D/jbcc1OGO9Jh+v0ZSzkNpROo/UlYn6e8hPd4Rvn6LzYfW2HCj3GdHyxoOUdEW9YYSQpwC4EIAN3qLrwCwVWt9LwBore8UQowD+AqAX2NFvAvAfwoh3l/neBwB8Bu0dnbvYb34kRWo3tgqV1tUwqzw8p8BxikSGhC6BBkiFIghGtJpxNRBhgDXe49kriR92i9cgdve8BFsuPZsxCtDLoHSEje/60vY/b0deOr/ugKn/dg5NuN43+ZFCnkjZCpV2HnXdqgNCmOryx9onrneql3DLafelktMaLhCjFdiuRt2WuIU2Z6hmWWK44FV8Xv4/US3XgRMPMzLZ2Vxulqde4zQBHM6C+tVXq4SaO8wSrldVhfp5lee8XRs/o+PYvWTrgLi2Dpv+iST2JZ6tXQfsTxQQH6N/ReV69wankpURIbyaWqdjQWmQk7XsV/BE+qnMXoMESLukC7wuER+LUURpOOohJ+IOG/efHk7SguIEEdKqa6ZF1lGvXaXpDVEIagdHRqSH1spUbKO0IkIAxG5OnoUlYwhQjwHl9PGce9EKUyyWcs5MyiW2ddFGJrzslpG9fPYcfOdIiEVEun4ZHzqlMKLHDayrBC1V78unD8zLOKP2o+iWakeFH2mSnqvVRFbtJxQUR9tyiTbx/KPis5kIrLAKSLUh9SsrWaROY6fAJYrph8Lw1HHgtV2iIQQkwA+DuANWuu93qqfhEOHAABa69ehxLTWPxBC3ATgp+occ7Vum/3CF0xYsXBa5YUG+5YNqdQ0966mD5rZOSa43LtRzXFWRkm+OX2gOSG0QLL24GdK0GpfNuYDEFGopoFKRWprF+sYq05cD/Hjl2H/J+/DKT9zodnHOTfPf/uP4oY3fhZbP/wDnPTkMzGxYTnS7gBJkg9htQ0hu02ETJmhu2sBt//FrUh2CKx76UmYvHK5WWcSeWb5SOhY1DL75g+wNqGmwkx11sbApOpQg3xbMTCJX80La5wNvaQqCj6UeduHIdEiomuAYjtXOC8AsEIm3nJ3b4h2Xoexifw4k48Ri5WVxR2iGkAW1XF+Tb7xQqShzcew3crPeXy3uZ5d82Eh6QcJ6Kkz0DnxdMx++3uYPfU8zK/N100IAUhgWuXinIVhDG+8gaod6XAbcnbGtBGOM9eAYP4WMpuAtW8cochcPxrciswJtjLn+BJBfsxMs7ScHGufSwDL0YLQbvjLPgd0r5s6JeaD04IJNYdLOkpkVXIK6bFzw3nSnKeTASAHjgj85OjxN5CLqdC2bQsSAMZmRQItUEisKuA5eCT2J8NOBI2oxZ7DF6vQ8eDyCDR0TfMRzYuisKY9DnPEkhLBP9pHp+OQ0IhMgETLPN9tM53wPvZ0hckvqEqoa9uwcNRi2+sKhyWGQmKuZcu8T8DAQis2SimEZIbJbKy0XKIGRBUImYSjD0QypAtEJqRemnlhO5gKMNcjNVNBYfdZx5xg3n7TdN1A34AULW2CUrL8+WsbknW3ZqZ7jcaJKrNarSeESJA7Qx/SWn/CWx4DeBGApy7imO8E8DEA/z1qw60q/8CWOkT8WpagSL75H0XBHi1RFloElHb1C/mJqIdDPcis6BAJQediyS75cvpgI3xRA25cntZFCHtfpF1Bvfa2TG370Mtu5kUb8aVXfggLkwtYu+kkrDxtytXtJIGNrzsLX37z5/HIq/4GndWT6G6bw5XXvxFCCOy/5fuYe3AXJmYTbPnag3j825sx2N/H1IkzmBUzOG3TeZhL9pk6mIc9yiMoIjbtmYeWIiP2px3sM2+ovSp/+OdonREd7AqjR2Ru0VTHHocoRIso2kxkzCHSXjsPcYgAd68FDlGa/59YyI/T22s+RsRhOhiHyGwzN53/mR9oG70zZogIE3PG+V0oOkQAoFacgztv+jzOXH0W5rr5umklsDXroy9yeIk7RH5vlj7cPJqMes09ihYkHStzndtRavlAezVdx/waW86N5Ww4FerUePwD85Eldd2qFzPVbzt6lc8BffT954DOiSNCHFWykY2mHn24nnnf1DtjdS44RHaq3XNslhV1nhR2iG7BgRFeByhiSFchl6B3rWLmALn5sE2swrgX5Rnbj/xwh2hQMm5sM7enbcwl+7BPmPyDpgNLz/KCch0wuv+4AnjGrv0wrlEVd4hbIlVwr5aZr6nlT/fG+wvlUllVOfyk0Pa+o3bVhDwZFWpt1isz1SKzDhFNpXmnD4xj1qNIMrNe2+1T9M3z1jXO0zy9Y+XRHYY71m2kQ2Q4Qn8L4G6t9R+y1VcD+K7W+pG6B9Raf1cIcReA5wO4aei2DE61QyH+i4aPdLAH77BZ1ZvRGzILh8i0XUfDXzzsXnqIhkOGzIuwYoiMHvh2nAYJWAEgXt7GGa+4ELf/6ddx+59+HT/6zy/GzMkz9mW05hmn46m/PsDN7/oSutvmEE+2sfW/78PCo7uw9d9uxeTpq7GnLTB12koIAFu+9iCyfobzXn8x4k4MgMJFw7pWDZnZj6SKbLgrJ1MXwnGt8+MJMdK15e+nEPgLX6AcAeI7MkROCG15rfRNoM7X9H37y49fyxHKN5o7OX/p0TtWKLihP38ZgPEtubMWLRgCc7aAu+77V+zZ9wjWrb0I0cDfR0AoYfksdkSXJTaW0AXnPuJDZFGYioU+rL0sxl5Dhp8zCN+CmfIkqPbD5wlrUmoOzrkpPrO6IHjq0puEDh53KDpRWupEAEDXcJn8MPu87k7MUvGhMqoRS99iASLACS56ZHDAPdfkNrkMFwZNgwu7rxKRpFvNdx4LxGE+dEah8yVDZ5I7IqzpafjGlyzg4o3CkOsLjhlz6oI6M0eIX3N/iHdRwS4V1svKP3GDLD+v1DDbUykxoZ0gJ+A7hzSUFT7gNnEstHs5UPnk5JhAEkL+hqX44E4oT+3hD5mRZVU9vFGmiw5mY/UQossBvBLAHV5o/Vu01v8B4OVgw2U17R0Abj2A/RpbpJ364vNw5198HQDwtbd/Gdf+9fOD9RtfcDaWXXkWbnzjdZi9YAPuecd1mD7nBJz+2mdi5WWnY/t1N+P7H7wJ66/ciKs//HLMru1gOp3EHPYdjdNpDEB/MIftj38XrWQSE+Orj3Z1GjsA01pj9zfuwb5b7kf34R0Y7NgLIQVEJAEpIKVAPDuO6SetwdSZazF95mq0V08F+2/92g8wtnYKy89YfhTPpLFj1ZpcZkUb6RBprb+CioEGrfXP1DmI1voBAOd6899Grf607evXOUy4Zw2k6IB6ICOqEqThCDhDuoAMkeCeZKRqKTzOhFk3DBkC8h4x5wXEMgPGgae+4VLc/Mc3Yvvt29DdOQfMLgPghgxUJ8EJL9+EH/zVF3H+378W8cw4xsY07v2rL2Lurkdx4R+/DCtOnQEApGpQEIuTnKshwin1wF1iQ1kgUxfDnEUw1UoUkCE+XFmZikULt4zTVUx4tktq6ZbT9bOEZeIomV5lfF8OjOpsdKiriMwQjEnYatF3muoiMuS2MejHozsBANPzXVy9/Gexc3WKu+7/FE49ZR3ESRvdqetytAUIhfDoHuNDKy0WUk/DAZZAnbYtMrS/b6D6gbmX7PUUwbyGQ/gWHY0lPETIG4YOp+F6KbQVJCXrM1SycO9ZVMhL7sqQSqp7WVJnd/uJoC6UlkMbhGjvzd/Ho3/5n+hv2Z3v2ooRdRKkc11AaYyfvgZrX3ghktlxzN/7GLZ85ju49z1bAGiMrZ1BMjsGtdDHYM8CBnM9TJwwg40vPh8bnrURQjhSt0OQQrJ1HloexoWTuKM0nCw+hOYbPecpNCIUw+6Lw7QoCF06ccqwPW2thIeqVFghdN5DzLqD4UNHFr3y9ukhQjeLC4EkfTZs6MQrvRB+SlNklsU6RIYK56JQ+ALSNWkzJKiIEKUFUrUto+EFHZQtaaXqxg6Nnf3yc3HyD23ELe/5Bh78woM4+cXLCtssv+wM7PzK97Djc7dj/U9eDstC1Bq6Iot1Y0fPIhFj5exGnLD6qdj10B1YdvHGo12lxmqY6qf4wdtyUH3Zs87F5AUbkcx00JpsI54ag0SG3Tf/AA994MtYvuk0zF50EsZPXoH1LzgfyVQbUgL93QsQgwHWXrERkAI7vnIf7v2Hb+Hhf78LF775mZhaM36Uz7KxpW4aR4BWcgza0naI7NA7IQCup8WJ0Qd3nPIbo6yDUkxcGJIJrNij9HgpxAmy6EOIDDlyNcxyVSC8kg1LGcCX+evGV0/glOedgTv/7tvY+JLzS7db/+JLcNdvfRzrXrYJSIBTXv1M7Ljum7j1jR/DJe98Hpafty44jk3BYMbJeyYqIlFhqoCeGV+naV/FgQief14cGQsT31Ljhr1KK4RH8xLBvOVw+cfxUTnh4kr8NBmZEbaj8uw0shcHAJDt3Im6Fk9NAgA6EznCMhg33Jtxgaydl9vOgQOM7TCRhNtzzlL22Jb8+AaRih+bwOp0Ne54+EtYt/OF+bJlEZJ+jH2t8bANOIdKAKuNeGPGRAdTO0/IniF+m2iiuUELXSPTYPk4FciQ7bEeAGdBSg1J4n/EqeOk4wridCxdAtMuF4tkUgAFqYdMWkkAyyVi/LWC+KIo/rdAKrWNBHSc4NxPvTU4FwrZB3LOzpqT12D1D5+Px/71m9jx5XsBraG6fczduw3JzBhWbNqIFReegMfv3IrNn7kLO7/1EOLxFrbd9BCuf/mHsOl/PxdrN51keXrF0G+HeBCOkhGSQbwndn5RgTAHI8qo/h977x1nx1XejX/PmZlbtmqllbS76tWyJKvYlrFlGxs3bAwxxtTQTQ8hhISXACHwIy/thZCQNyEBkpAEMD+aARs3MMEF44JtuQk3SVavK6202nbLzDnvH3OeM2fOzNx7V1ZZ2ff7+UizM3fKmTPteb7n+zxPXEuD5HulEjjRfWHpnhLlmQzNm7TeCY3CFmqnwirsyyERIBT+Z52H3tRiAMGSyRuTCSFTkprZeiLSHalZ6nPHYuCbOHaY2AZRE0cVvWfNwH1/eze2/HIj5ly2IPF764JpcNsLGHtuHwpLp4JxhtmvXY3ywAgOPLozYRA1ceLRkZuOcmkQ1ZHD8Fo7TnRzmjhKcNsLmPW282MGnxQSwxt248B9z2Hbj9bBHylj2rkLsPif16BycAzbbngcO257Bvf+5c1Y/VcXYNFVS+ocpYkXL06OUhrHGxPbIEpEckVMkZ0rwvYiGqED7dIdaaVBEvuy19EaEEtbwEwWQqkHtJebzgzZSd7Szkt7807cm3eZiLyRqKJgOE/UBgfO+bsr8eiX78T6f3sYk0/rweI3rQSmTobrcRy4dxOKvZ0obdoFb3ko1MzxAJ7HwAI/LOrJA7hcwGVC73fEz8faaEejDKuQUFpv1M9FyfAsT400UhT9EaipcCPdg3Z0dcAPRQJRv1JDIg2XXSzXToLJEGftwFKYIT098heJv2UrACDqsW41zSEohPtt3xZqBYpbBgEA4tlN4XEtrZK/LdQwTSnOgP/Ik+juOx1eq4f2EWBEaXyykkaW+6o6LxUxJMT6ZHnGlLyzHLiaGbJZH1vTo2U1khksSm2Pn5nXBhKcC4MRUnqqjDB1kynyLW+9pEuGuLHztkuIBAGPmCFdTiTRytjEpIikZh1C6GhFwSHAtaaIIr3CtluJGS0GxQcHGNB+Sh/aT+kL8xAZ0XO5ni6sWNqD1jlT8Oy/34f1/3I/RvcMY+l71oBTYkjNGAWAnV9InR+V+6ATPpIEjrp8CoveYbrUBL2/DL1W+nFkFKXX4HEbYZLqFd4FkuU93Aw9VS2DQli6yVrRZfZvFJ2rUz7YGiYpdEJHmpZV7ieP19czNpGNiW0QNXHUMXnpdJz9/70Mj33992ifMwl3f+hGVIbKaJ3TjZEt+wEAuWlxpmFsz2FMP73JDk1UTCnMwsDAJvT0nX6imzLhseV794O3t2H6lStPdFOOCRb88Rmo9A9haOtB7HtwO8b2DuElnzwPjne869A0MdHRDLtPYmIbRFlRQ4wZiyJdUfjT+K+yzQzVKhgr7YgmPehtt9H4k4c/2yyFzQwRO+FwYRSmTB9H154Wj8aqNVukPMdRFRVke8qHto1h973b0b2qD5d+9/UojUlsuf05eB0r8PQ//AYt09tiuWgGn9mLZW85Lcxwy8LSIJwJffPQfm2miDCmtCcl1Z5K4CQy1QZW5FrEFMW1HAB0Ikadl8ihflQrWPmdUkt40Coq2opb/cyAZJRZo65qA0hjioKCyuRchxmywQdGIVsOo/DMHhS7ZqN6sAJeUeoQfZuqa9SnFjtRdl06Z99iimwQw1KqegkGVt+ftiZMa25gS8ASSBSZVf8cHl0fradIJCOMT81ISDPX1a6bngDLeRjZcQg9114GIM4M0ZTuLbuQcCJJbMqrQmuIdE4hFdHEwwgwzWBq1onDpgmiBIVxloRyDXGZZCjotyXvX4u7r/0Blly7Brvu2IQ7//w2nPOFl6Ol042OS4WZrbIeVdVvniaVG2ccbGbRzEfEVX9WrX7Uecc0W2buUarm1mZkG2GxbGYorRCtHzCdPNRch96xFH2Wdryo9IdiXa1Cu7rkCmVS50YBWit/FCUBtct9UGSZw4RmjTQzxCgxaTWjB5poBEfxFd9EAsd5iLbRMeHpa+cCADZe/wcUu1vDsN23nIXey07Foj+/FDNevVqvWx0uo7RvBB1zk5Fpx5uedZzjKyqsZUhNFATSx0Y8ge7C7BPdlAmP6uAogpEyTv3qW3Ho/g0YvO+ZE9aWwTsexYZr/w4jjz931PftFDys/MQl+MPX78Wqj12AzvmTcecHfo6RPSdXMecmji2axV2TmNAMUaQVsfU5EomxeyMCre5+yXPLyE1Tkymyl2UyQ8l9J1iIFGaIljuWR5xVHDGKYuBaR0RaDzvPCnkkPufIdbVgbO8wDpccFHLK+2nhmHPVMgBRDppDz+xD5+JuuF64B87CyB+PhR6Ox4Oo7lMGUzSiGCLSDQmwZNt0XpK4jU75l4RgCAIOxxGRt0WXgkp2WNQby2DXzP4M9UMREp6jWfbjGMBkimQx7LdGmSEg1E0F8JEfqMA/uAP56S0o7B0FL08NV1AnOjRb1QVU2jPmCK0hYiLu2ZO2hrL8RhmeVS06oztt/R2tSx5/jHHL6kdrefRcCDAI9TxYGiJLO0THNZkhc5mUEs/9+z2YvHYReEcrZv3J5dj2jdvRsmapzqAtdI01niwsrRnKOLORBmLjmI7cojxAoZDV330A/f91G3IzulHZN6jYT67OXd3v+jjEpKqjm6m7rYKwJus6aXkfei9ehPX/+gBO/8SFKPzocfz6vTfivC9fjilLJic0Q9G2xKbRCRL9mh3lpMt/2GydjFi8iqUt030ueGx5QDm4GEtlcUw0kkeuEc0QEEYY+tLR77LwfNK3tfMUmevRO84l1ocR46bYJTOSKsbIAAAgAElEQVRjtc0U0uVU/Ujsj84RReyoFJnMEOUsauLIcPIyRAkBtDWEVWsbmq0xNJbc1jaErGkKsoS8jVC8tiFkfwDSQIbQmA6JDqejJLA126Y0BSzOUeuHnV5qg0/txeSl6dmQ6WXjWVRyI0yVXdPIptDTQAwRVy9xnejSaeAC2terkaHV4/x0jMcQIrjMw0Kchp3YAiAKzc89HQqueTl7XzoBoxNPzGgWAc5C8uM0/mtQy2C1YYuos+pKmTATMG785j0Y3dyPeR+8BIHgaF81FwCw/Us/wshjmyCD+P7senloJJmklSg0GCph6K6HceD7v8L+79yMg9ffif5/uxE7P/ufmPL6iyBGSsjPC7V5iRqJDTxDdgg7GYN03ovffhb23PMcRncdxsI3rMSqD6/F3R+5Gbvv2663jZyZ+jd7YD0QZsh6PdgGbSPGTPLd0Lhn0qghZEIPmVrOmmkkZSG615RQv4Eiq5SGJKpqHx8GqwUyhNqcsGZhOy/V3caElE2GKA0TmiGyNUSRLcLCD5pE+HKlacwossb4JQtfvBIxI8Zcnn3BLGOL9pFmFNkvfW0Uxdtj5laxI8qy2CHTKMoqltiIURQ4HB0rZmNw3RaMVT0EUiDHA1SHyxCVAPnJYQ4blwsMPLUPsy5eEBXTVFFmPhMQMszZYTJFnEkIyXBw8yBu/cJv0feSGVj0jpcAMPPbWN5vilFk6wZcR8BXDFEgOLgjIQIG7giIgIdGkd3nNmoYRbYX6ugItfRdHW34W7aCOc4RGUUd6MI2bIj2tW0H3GlTkXt6B/z5feDlIKGDIiMyxwP4kiPnBPAF19fRcwL9QRjTzFD8moTPDYvNRxobdTzjtpf0hzE1jSJm7AcAmF+FGK6AdZN2ImkU2Ua4jkwiJkJylAdGsPWHD6PzjHnYc9Nj6LjsTKC1gLlfeS8O/Xod9n/v1/AHDqP1nBVoO2cFhC8B10NuVg8Y5zGjSAqB4d8+hNJTm9B65mkorlwCf/9BjNz/KKQQcLu7wDjD2JMbUXr8GRSWLURuTg94RyucrsnwWhxMf9npKCyciQM//A14SxFCMjjMri0X9qXOeaP6JjBYIc7CZ89B+MxxJuELHj6fksNtK2Dyij70P7ILxb5J6LlwAc7ubsMDn7wNy96+EoteuxQ5N/wQ57kPIbnWEiVqnSHcfwAeY4pMo0jruay8RMy4br7gcLiEHyAmBdX3DZ2vysVG50Xvaf2+bgANRZQFTmKeMRm+Y9TU4SLVKKLz1H2v3oe+eg9WhKuLvabVPfRYgLJwkec+qtKBx9SzxwOUhaeNHqpX5pE+CQ4cyFSjqDAODVEz7D6JiW0QZcF8IHSxRZuNsd7IMIZUaFUyKhLJFWswR4kPZPpDFyZjVJswmVqOQ9P9ViFXh4mEmNoeGtChrWp5NXAi8WiiHEbSw5r9Z1fi4T/6Mjb9x71Y+O5zwZnEAx+9AYef2ouu03px1t9eiuLUNhzecgid85O1kjwm9MuTvCIxNIpHv/04Nt72HEoD4UPad96cVE/WHhKI+i3erbr8ARcpN2ucc5aqw2uxTImwcEqJkPF7zZ0owbc7c0b2uuOA6A8TPI7HGAIAFx4CWFW93bC3mP7KWhuZj5BlmNpTaQ1jMsPfyBois8XVaR8y2wCCNX/gxvtw+I4NGJzqYdHHXwWvsyXxPNjnkMZSul3tWP2Nt+HQpoM4vG4zdr7761j4tfcAk6Zg0ivOQdsl56Oyqx9D9zyO/m9cD5bzICs+/IFDyM3qg9c3DV7PVPCOdpQ3bEFl83a0nnsmDv/qHvR/8wfgrUW0vmQVeEsR5Y3bIKs+CksWYPKbXgmns12/m9pcD+2yAsYlRAC0vXQVBm56ANOvvTwaqkrcn3HmlAjdQPAoOaS+PNbQGWNgOQ/bb9+IkX2j2PzDRzD/dStwwb9chYe/cAeevf4pzDhnJrxWD/MvmonuU6Ykhs64HsLjCYE1h4TDpFGqJ/4e0xXlnSBZQNeJhsLD84vfL4wlmXTaNrr26lpb4fmMyYRDZRtHQQrjJ8gApfu+nuFlvH6o3+zySYlwe2G0RyfMtI6j1qGwezJ8hDHvsPG9I5poDCenQdTE8wZXQ2a7f/YwFr77XADA7KtXYqe3HtPOnoN7PnQDLvrOGwEAwq8/PDGw6RB++ZH/QcesDpQGSmjtbcWM8+fi1DefhsqxO40mAJW3/YWXxXbKlWtQ3nQIQ/c+gnVv+ifMftfLMOPqVfreHQ/aFk6DO2cG/MNjGPjt05DV+Acl1zcVXddcgq5rLtF188TIGMqbd8Lf04/qrn4EW3YCjGHqh98Jp7Md7Revhaz6gMPBeGQ0RtGOhjMmJUTVRzA2Aqe9AMY5Jl15Lnb81T9j+rWXH1H/NILlH7sYe+94FiPbDmL5h87Fk/96H2acMwsv/crLccubfoynf/gHAEDH9CK6T5lyzNrRxMRDM+w+iYltEFkJDGNGv2ZzaD5O1UeMUTYtGCuzgTRmyBoiM/ab0AdZYDwq5sodAY4ouZyrvCPXipoyhwTqMUK2ANakdBPp8C2PnzD10uXov309/JEynHYP3RcuwebrHkJx9hS09HVi2+0b0bN2DjbfsgFdi88BEBWVdXkAqGP2P9OPWz98B05786nYeNsWnPmhM7H8rcsx6uchEHmBJlNksxBm6QIgoux18UeHaQ7EvmmZvsbKQ7ZEsuFKNgtBf9B8Gkuh7gs9Yqr61Q2npeUzw3keX34kYL5E7mDIxPHHwuEvUW5MF8DAdVJKZ+5suK0ljM4PowKdkrpfVOfRVPiOHl7VxXeDeLLMSP+prpHBAOjfdP/FqT3N1JjpMNKG3BBnBcxtnbYcev7XqzH0HQ/7fvZ77PrR/dh3yzrMf++F6D1/Tkx4SzAZorIfT8A4tOUAdn3/d1j49T+FO7UT1SoxYKrlppBaAryliOKpi4BTF0UBHnQOJP5VTJx+HwUBqv0HUNm2C5Ut21HZvAPVXfsgSiWIWbOwq38fwBkm//ErkJs1FXCc8PmgfqRnRbEBdM8TYyyo0C+TWkyth5koaMJk9PJ5zHhFGCjhMoHR/lFsvuUZTPnLtfDacphzyTzMv2Ihpi+bjEBGAnBYGi2eMkxmz9tFnc3iugnWke6fBPtovV8BVKqUBDS+rX2/SC09SN4XgXkfZkCAIVDDl7FtRWJYIA6eZIT08TULpNJBEPMmebRMvUdJ4F5Vm5pDZDbofHRJlCOqVN6EjYltEDVxTNH32jXov3099ty2HnNetxrcdbDkQy/FU/9wB1p6O+C25LDwDSvx67f+EEvfthKFycmikaMDJdzy4Ttw2htPwZM/eRZLXr0IS9+y7ASczYsXDAziBcgQAQBzHcx8zyWYtGIGnvvaLzFl7UJs+fZvsfvnD2Pxn7wUnYu66+8EQDBaxsa/+h5mvvdSeFM7j7g90nCrxfAoqjt3obJjN6o79qC6Yzequ/fBmdQBb2YP8vNmofOqS5GbPR28pYCefBGOqKC6fRf6/+UHCA4NYfLbX3nEbTkSiIoPUQnAXQer/nwtHvrCXVh89anHtQ1NTAxMVJEzY2wLgCGESZ18KeWZjLHJAH4IYC6ALQBeL6U8eLSPPaENIh09ZF83iWQ0mc0Y2aH6QKbOKFmyw2KKWDRjFwdlGc4D4yJiiLiAA6FDyCmqx9YJcYMhymKEzIKJQLJApblOFuh8i3PD6LFN/3onZl5zBgCgbdU8OO33Y/9D23HqX14CZ3Irpq2di513PYdFr1kKV41fOywAOPD4dU+id2U31v/oWaz5k1U45VULMBY4oTA6S5NiRBzYLJbWB1gaikBwwCEvK0TWzeuTroWmAc/UqWgNkdW/jCEhACZh8sFFKoSdx5cfSQJHkgJwH8gPhmfUiUXhsgaZIg4OyRncxQsxvKgLh6dXcXiW0luo8UqaEkMkfZYoYRFIo68NRBHYkdGVpfWqpyWKnbvFDNmaOocLOFLAYRLd5y6C117As5/7ORZ+8CLIkRE88rGfou+yJTjlA+cZDFWISuDotAGlUYGNn/kp2tcsQscFK1GpxstHJJKtivBvKSWqe/pR3rgF5Q1bUd60Ff7ufVH7CwXkZvbAm9mL3LzZaDv/JfBmTAdX6RMi9hHR/iWDN6sPvZ//czAmACEx/OCzqO7aC//AICZdvgb5WVFUJ93jutyHFvuLKOyewGmbuJZIM0YcmPlHK/C7d30fQzuHMP0lc7Dqg2vwy/fdhEWvXoye1T2YtrAdrdNbNQNhPmN2sdGwIEd0TxCzUnCq6vhR++x3gNYDCYuJMu6TsmKG9P2o5XDxch/6sqkH0OHCKBIb7yL64UhKktjPhalLshmh5DVQekMlsvaFk2CiuC5jUlv/ZCIhoX7h+EUvk1LuN+Y/DuB/pJRfYox9XM3/1dE+6IQ2iJo49lj9vQ+gvK1fzzPG0HFqL4ae3oNtP38Mp31wLVp6O1A6MJbYVvgCT960BWe9ZxlG+sdwyquSBWObOPZgYJDJYlsvOHSumIXTvngN1n/qZ1j4zrNx7nfehoc+cj22/vhRzH39ashAxNJICD/A3hvXYdf1D6Jt2SzM+NPabIysVFHeshPlZ7eGBtDGrWA5D/mFc5BfMBftL1uLXF8PmJMcwoj7Z/U/toxzMM4weNtvMfiLOyFGwuer47zlDfXFkaIwrR2nvPMs3PH+n2PmRfPRObMNK957Bnb/bhv2PboXwzuHEJR9LLpiLla9dSmmzioc0/Y0cWIgcXKEwRu4CsCF6u//BnAnXnQGUcKKb+ACWjqgWKQAsUaZGYit/RuMUlZOIVqHW/tkXBrRFmFIKmmGaEr5XmwPwC7cCEQhniXy6i1myBcRCxJYN7o9bk+QkiHX3Yn81A74VMwREn7JR9+Vy7H79qcx8/w5GN4ygLnn9ekwZ/q3/Xe7MGlGK7yiC7DII9SlQqzoMjM02g6T1t2bpSliEiAWjJiKjMzVXAlcqyxKKiftqBIWTRiS0WVmVCAxP4fnq36zQvIjpkhG8/VyI6lwIorKZT5D6TC1sQ3AOJiihbPAt+VweHk33LkcB0scozPUvaXYEF5Wx1NMEatyXYrDLrhJsF+YxMxJLjVLQPdulpaIrmN4jHQdVxozBITXnksZSzPRsXgaVv396/HEX10Pf6iEMz9/Je79wI+x5SePotQ/jM7TZmDq+YvgV4A9tz6OXHc75nzyDSgu7A1LdPjReVb2D2PsiU2obNmL8satqGzbDa93GnIL56DlrFXo+uOr4HaFWiwtqZEs7oUTy2X2G+J9oAlowSCDqHSHBFBYuhCDN98FAGg5axkKp8yFFJEmRGvoLJ0cwMHVzWPn6bGLO9uJG+desxLTVvWgf91OHN52CMO7hjCyawgje4bRs3o6elZPx+BzA7j+rbfhzHedilVvWgyvRmocioIixjuwnnvzb3o3VIUKKbf0aoSy7+q8QCKInx9FZUkrOjeKaOQJ9j1R+si6t53Mb0IS1Ga9TwEIm+0hzZcR8RfOU5JOqd/htC7lI3Jk/P2SpiXSkXV6f/HpSQ4J4Fcs7OBvSim/BWC6lHI3AEgpdzPG0pPjPU9MbIOoieMCGQgMb9yDtsU9AIA5bz0HD7/nv7Dg3efh6f/4PUr9w5j0jqTn+sTPN6NnaRfu+YdHcdU/XXCcW90EEJbueHz3TWidNPNEN+W4oWVmF17yz6/FQx/9OSr7h3H2P10DyRjy3W3Y9bttGHhoKwQ4Zn/gMrSvnINKEP+aSykxeOsDOPDjO1BcvhDezF50XnMZ8nNmgxfy4cc5MYx+bJCb04fJb74SA9+9CZNfe/GxPZiBSYu7MWlxt85I7zKBoOxj552bsP2eHdj9+z1Y8eYl2HzXTmz41TZc+OHlWHBWMwrthYRjfGtnoZsx9pAx/y1l8Jg4V0q5Sxk9tzPGnj5ejZvYBpGlUxjXpvqFxoxJXNeg55MDzbHjMyajv61Mq4lIGcNrIc2Q63B4LNDaIYrUytIHUXkMcxmlhCfvxDeYoXC5KXqpDYoqooiVyrb9WP/B72LhJ6/CtAtPgds9CZOW9wFC4PDG/Zh39XI89o2HsPg1S/DoP/8ebaIT+blVbLpnL9yCg0s+ew66T+3GmIh7hrZXaC63vVqKkrKXm5oiOxllFogZihgIqQt4Cu1xm9dNJrxMxqTWsIl5owCMfFF23ijN/EXRg15GZnFi9LQGTLXLDzhKh6nkCSXRrM0UuacsxFi5H0N7D2LOGz+Cg20c7bN9jIoqevpCveGY0mGUVLFXv6Ie+QrX0Tt29KGt6yJwfW2ELnhpn58dXWZGndlJE7mOCkJsX7FoS4QMkWdlUHentuGs/3sNnvranbj/wz/FvHesRe8Vy9B57lJ0nrsUFd9BJXBQFVGZCL8CDP7Pwzj8m3WQvkDvZz8Ib9pkSF+xFAELo8kkM94fNLV0hRrGedI7QWtdrPeMMrSIraRfW885He3nrwr7TVjaOUsvw7XuUernmIrHwsrToyOPbO0Lk+BUroUKiToSLO9h/hULMf+KhXjyO49iZN8YXvPNi/D0LVvwy79dh86eAl71+TMxtddTbRTwWKAzxyfLf0T3D70DCkb2cCCZM41YSz/gUXRqYFBqxnH0eVNgHE2V/gtIPqtHlGE9A4GhzbI1Q7XYcZrX2cKlzehRMkcndn7EFIXbWdGA4608LU+YqHq/lPLMWitIKXep6T7G2M8AnAVgL2OsV7FDvQD21drHkeIFwa818fyQmxZG3ey98WG9bOq5C3Do8Z2QQmL+a5ejZXor/vC9J3Bo2xAAYNM9e9HR24I3/NtLsfCS2Sek3U0AflBGvtAJt639RDfluCPXWcTqz1yOMz73Cuy84TE89eVfQVT8xHpSShy64zFs/fD/xfC9T6Dr9ZdhxufeD29aMuFoE8DBTQfx5PUbMPfCmWCc4dRXzsM7f3Y5Zp3RjZ98+H5ttDXRxNEGY6yVMdZOfwO4DMB6ADcCeLta7e0AbjgWx5/QDBFFaem6QuZzSPKQhCeH+Lo0ZQYRpPMaWQ+2xfqkMkSaKYrrHpjliXAeleagjK52PTJbJ2SzQkDkWdlRZaQt8K36O7VADIbO/UPHLeRQmDUFQ+t3oLRnEIWeTvS+bBE2f+cB+CMViMNjuOBT56AyUsV3L/w+ZqyeipkXFbDmT1fD8TjGAo49j/dDtLShc+4kHVGhayuleIPUfp0xlhLbiUgjYcLjQc06biZsr68CkwVRbEAD++nuCquDJ64bi8/T9TJrg0U14cLprpGO1LaZKJMGSd3bVA5JKqEwz4WeuaOm1WntKB1gYLKIcle4bXdHGUWUMa8zzHo9XM2racg6jVTC+VLV1ewUwfYYEy3V3c+NmTgr4bD4cs0KcaH1YY2WqPGcAC4LdLkYwGCI1DajvofC4plY9tU3YcNXbsWDb/g6Os+Yj/zCPqC1DZXdAxh9ajvEaBlTP/Aa5E+ZAxk4IVkTRMwQAJ0DKBbFSp1Ap2VrCHUnsUSPRatYnrjNOsHQuBGpozqDngIiAExNEdklth4mGX0Wz1fkC67fNa6lLxruL+GWd9+CMz6wGj1rZqIqQwOTOxJr378UG+7ag03378eCtdNQ5D4k9/U1DiwmmDItC8lQVkNzFRbe1PR8VHlcS6T7w2CRkwV2qQPpHhPGXNitek3SHxk5ioBklJmQSmgMBjsHdP2oXaavF6vBCAHRdfWFEzU4I5t1FlMU/hln5ZLMbAOYmHbtdAA/U9fJBfB9KeVtjLEHAfyIMfYuANsAvO5YHHxCG0RHG8wLIKvjz3J7pJAyZTTuGIKqwR8Jui9ahh3/fTee/eIvsOQzr0ZbTx4r//ZK7LnxEZV/SEJUScQpUR6q4sCuMqbNKQIAbv/Y3RjdP4a33P+uo3U6JxzH+/rlt+RRnlse1za+X4KTLx7R8Z7P/XIk4FwmhNtHC04xhyWfvgoj/WUcun8DRrYNoLSxH7nudnRefDra1y5DAJUu4Zi0AOpLfPy+MkKwRDDHeFE6MIpnvrsOY7sH8ZJPXYDgcBnF7hYse0MyN9HwYPj8D/WPr5DokYLqFB4vBIIhJQfiMYMvnKjWWRMAACnlcwBWpiw/AOCYi+wmtEHkeIoh0h6CEZGQoXPIHPMnB8wLEpmv7QikJEME2BmqazFD5ryUYVOoujCQ1GwQtHUvI82QjiKzsglXLU2RrtSc8gKhj56ty4mKLwLTX7kKe29ah9HN/Vj3jn9D95mz0LlkOopTirj56u/h1KsXYcXbluGsdy/FjL6p2LevjJ++/ZeYc/4MdC2dhhnnzMSGX2zAc3dsR/d5oebFPgdqsx9w429HtU15qpw8KyuLt9KTAJFXq/vN1roYGhQAcCTT+yWWjI4b9hczdAmGhkllZLZ1TTpixCr4qPVBIkhUkqciu7u2hcJU5rP4NGDwEBpFHZvD9k96JtQuOeufC9s6FA5XOu3h8BivBAjKo3ALRTgV5Xn6DqrSSTBDY2paUroh0g8FAU9EXWY9UyLmwaa7t9zS+ph19IQMP+BmzT7AYIqIGeKBnnelQM4JosKhPB5taWvqWGc7ul5+Otp8BxXSjQXhnS5VX+vn3WaG9BTJ90gkDMoGQ6pRFNMzGr/rjMvGPUa15+hdF+iV4iwPFR0Vgmldkf1pZdY9zVP0K4988U4ceGI3/OEKdj24Bwce3o7u06br66azKEPiqVu2oTi5gFMumx0WhEW4H4oyiyIO1fFU233uwFPtzyn9JL0bKNKW3gdRNn+Biu/EjCKd3Tog7ZR6f8n4vWjmENOfAYoMozWt97WU4ShAIFgU0ZehjyPo9wGSdpTNCNH5mjXPXCaOmCmid2FUxy3OGDWCkyzs/rhgQhtEuVz48GhxnYyG0BL0vvWiklY4J+MSdiK4LG/VLv9hDnPUM4TMyvX1QjmzPuSlwNUf0Ko2GMJ1dJFEywBKO5d63qOZ+t7rLOC0r74Jm752G0Y27wdjgD9UgicDLLt2NR7/1wex/gdP4c/WvREt1Q4szHdi0euX48nrN2Dv+gPYfMsG9F24AI987V6cv2YRuOdkJo+sCifRfl280rqQNKwXVgS3zs8SoxNsyt5hEo4X15bQtakyB64UqWVOmKbBVX/Rb0FtN7IqHD0UUFHhxWNVJURVhkvrNiVmVc1iAZAfrG0IpcEPSnCdIqjItahyBMJJGEKjSlRtisup75mI38OELMNIAlq0GhmL4Tzdn2TAkLjc/NA41nWzDSH6SLpcwENoXNqGkB6O1UVD40PIVd/RH87AEE2HJ0+GEdT5k4FE5xmJqqP4iwzDKDF0lrLQ2JRJYwgthU2y5QEkFKbQ8+ielLrv6dwpBVNWgII5mkL9t+g9a3Hgz64HADzw6dsx/cyZOOdzl2hDKCo9wVEpCUyePwlBroBAAgXhoCqc6N1nCXypMGwZQjMhrhoSJ4ehwpVhRENojELRBbg6eS2dsITDtiNLYu7wXa9OWb/LqW8Q+0Hfx1wgYBw+HGNonk6+tmEEmDKE+Dy9A+3EjRDR3/r7ZpVLsQ0j/buI/iZjmATX9juyifFhQhtETRxfFPq6cNpXXo9D67Zi+7fvxsiOQXTOasPW2zfhov9zIea8dBbM3Kj5jjxWv3M5hvwC1nzsXJSdIu5+/08x8OhOdK9pCq2PByr+GJxc14luRhMnKToWdGP1py/Hoce3Y3DDAVzwD1eoX5LidCAyIJo4+dG8lElMaIOoJRdmkSOGQWghLs9kd6Iw7vgwUViiIYQWotG8tQ+bSTIZonrMkDlkpocEUJ8tAiIWoRy42rPwNQNkha6L+NQcKhuvLoQZIe0OA6acMQdzzn4dDjy6Ezg4iDUfPx+Tul1IAFUp4EsOCR4lGZMMPO+hWnHRsXgaBjcfRMfp8xKlIXxjeM+3mC1pXQ3SWJvMjT3kaCetJNDwWCAj8bOd/FJT9EwgJx14Ij6UxoyhCPK4dZszhIvmEGRgMRiVKjFCqu0qQeKkzWq4wZfIDYT6oUaYIcLBw1swY9kKMGWnyqoDKRzNDFHYPTFDmpnzHYN1IC9Z7TRDB6OvEKf/AJvnZyw+VKavkSNizyKQZIaIBcoZ854Ih8x0OLYl0PdThmOB8L4Slmhas8b2UBklxzRF1dQVatoQU5TotlT6KNpUGj/bbA7i+5ckUKa2hnPh3zqJo/rNiTObxGdGSVCj4qe+5Og6ax64Axx8ql/fr9wKBXeYROe8Ljx186MY7K+gZUoBvuSoSkcPjTmaFVTMvtqHxwI9rOZZwQZ2gAKxg4Fk8AOLjVfvNWGqi5FkilSnmN2XHMa0GBsJDsHCIUBiohzNgqqNshLBGn9rBl/vnoYRZWIjev60BEA/UiodBl1lFn9OwlXjzBAlZAwmqFL6ZMGENoiaOHFgjKF79Uy0eVPVkkTVnFQ4BQ+i0hQKHg9UqiMYKw2gfdp8DJ/oxjRxUoN7DkS1tiM1+7w+7H5kH/77qptw+luX4OXvOus4ta6Jo41w2Ls5vmZjQhtErRZDZBahzEr5ntQWRV4peY/k3Rp5tWJIlOkwltVjhkzBsmOG3RvlB+w2jim9UEV5wZXAiSXsAyJWJNIYxBkiGbCoGC6du+4D1IWt5yCQlkDwKGRfSAZfcJ1QTIfZVwJsv/lJ9Fy5MtTRWMyQeU62GJwKR0pmnV+KEJRlPMhRqRQS4FYyz9cJIq1XTgTIK9ebWMEg4EYhysZCT8yINGpLlc5dMTWkIVL1L8ErYTtanumH7A9D5RthhgCgUh1GPtcOxo32+QwImGaGTPE0EDJDACB8FjEmxIhSUkDNFMWPZ3JBEUFkC1qJGYrrW1xHxvR1QOTxkvA8ZydfZGFCxkrgaOZC30tW+omIMTUZojgzpEXUpCVKC7e3Yb0K6jJFqTD2L5M7k8IgMPS7J/68i8Bm4mS88DGiZ1dabdP6FqNFNstamDkZQ1sHMLhjGK19HTphYySjCcW/qz94Jrb8dg+qwVMAACAASURBVCemnNoNH06cIVKMbJ4YDYMV8tRvrogzQ3TtK5ao2mFSB0Bo9lu/qOszRfblsKNFpaUh4lJCcI4A3NAIxftcDyI6URsJgfUs6ZZaKVWInRSSRcwQTei6RZSeefjYvKuTNMbF9zyDvW6iMUxog6iJkwvV4Qqqh0vghRqFj5o4amDMhRBNNu7FjGC0hPK+ARTn9z6v/QhfABI6tUYWZCAxuH0IU5c1y3ic1DCGaZuIMKENolYv9PB1+DaPQtB9K9InK9zQjM4iz5QyrWrvUrnEduBIWmHARgpSAkpDRMsgYjoWWp5MWBh51YGIe3869FnEvd5aRc6j88g+H2qrrbHRHnli6iCQoWeoNUTqSM6kNkxZMwfwXASCR5FAgX1+3PDe6Roob53Hz5tKDARGf5rhw+a2dA65Grk97CSYAJBDoGs6ESqoz6zZmhgdGZRyK+qSIRYboeVPQQCoKurOpEk1j8vaWgEApbwP7uVQbWXwW9RvRR9M+EY6A2ZNzROwzscKxWEZ9IiUMhGFlUhNoPsmWs4M5gdISUxqacJ8yRFIDh/RvaQ1GiJ+70XaQdUewSPGgIKf7Kgy6ns6Teuc0lA3+sxshA39EVLPoW4rizZh8SnpZ+jaSP0OYwDjOPzgRuz9x5+g65UvQd87Lw6HvtKuNUztJNOnLiSD8AM89tlbMe9NZ6A4awp8YURFkbyFSXApAMfB8jcswb1fexRX/00YjUbvvKqIp/NweP1hdvuZ9akwM3fgqL8jTaTN5WcwRQFLsJvUf2nsP+2JEjNmsZ90fCIYzegzWjOw3gl0f9AbyTFYIf3+opQg9BtF2XOrkSkRarETAODUKyptoCmqTsLu1iaaeF5Y/KELsfMHD6C8v7FhnyaOHAcHNqKza96JbkYTJxC53ingxRxGn9yKXd+89Yj2cfipvTi8oR89FyxI/f3QxgO49zN34AeX/wA3vuVG+JUApcHjk5yxiSaOJyY0Q1RwVSVm5Xn4PBpvrup0/5ZuxnLvTA2KTsZHTIWVat7ORWNHm6UhrSAlTbXnKwQcJrQH4Gv9E7O2iaIbhE4YJmJtJAtWqvMXRuPIk9KsVUrRUbOtFNHh8SCW+8VsY4WKPzJPHV8iJxxUhKuZO7NER37GFHRfshy7frYOU992KQDDm9esFjP6WJ0PraO8I/KodDQMk3CINbKLSVp6o1pILaTLksV0i141M5Gm7VmZ+ZyiZRbr4ak8KznVdk/tOxdOK/OmggXd4bZ+bXGr36pyCo1uR3lgEENzAH92+IFqaQXamPGxyoiMCYzyG3aiUjtRo43xZO+OIu9kIhqQ7ht9jVPmA6Uds4uAVq35ZCONYsCaIo1+i51IDQnRMYEVScYMXZFdEFYXgtVRlwZDzAMUFswAwFB+bg+6r1yjojHVPWazZwajSpdYSIaO5TMw+3WnY9P312HZR8NkwFxICF9g/TfvxfbbN+DUN69AS3cLnrzuCbROa8H5nzpXRZlxcC3GVEPl1i1XFY6+Xr6MJ5rV0WZ6Poo209fcoQz5tMd0ekRr/SISLgHNFFlRfJBMCY2h81TV08ml5SmqV+Yj0hqxGFsUQ0b4c8QoBZG+kRLZ2jmMGkGTIUpgQhtETZyc4DkXzGmSj8ca/tgw3OKLr6hrE3GIsTKmvOFCdF2y+oj30bF4Onb/+ik9H5R9PPSpW8CkxKXXvRFtk1w4CLDy/WegpaC+pOmpippo4qTFhDaISNeRU99WimZyhUDFKj9RD77k8B0rh4mMMzWayUgREWRlQyakFQAlD9XlEi4XCR2EvQ8dVSGYtvw5rRNPAqsLO5pmh87XYbUlKqIZZ4bMNtsRPtSvdnFZDg+5wEUlcFFR+TKIKaLzGt2yH5MvWh6L+DHPN3Xs2tRGGGdm6p4YZbG1Sj/YWqK0WyJLr0IMEW1TVLo1zqTRXhbriyzY7TKX5VWm7IqrztMN9zk0U0WfCReMcsyoJCY844Pjt6htd+1G2/JV8GeX0NYeskIM+XhmdfojhSki7Rxpe2ytWc2wXJt0qeMZO1wkIhipf32L+TOzUJv/AOhyHDqvkp2R3myPpcex265ZGco+XyvarB5S+yplRwYbFN/GYIuAKMqMqByaiGg5RV/N+7f/BberHX5A0V3x62eXpTHzENFvpf0jcFry8AWHlBKPfPo2uO1FnPHXF4G7HL4IwB0J5nGtFZIyzEVW1dWIwwlXWk+KPqsqzSEQRQfaOj/SI5KGyOdca0aJOZfqHo4ei/j7Rd+3sQtui4msdWS0PGSuudZpJQkiS0NkaIuoZI3era0lsmAym8KOrM2INtMlg8A0I2TnKmq8uGuy2kMTLzINUZuXHYZ9LGDX3DrWqEfXHm1UMkpYsJwLWSda5Ujgp9RpO5boyI2v0OrxRmXvXuSmTz/RzWjiBMPten4s4eAfdmHzd+/HjFetAAAMPbsXw1sHcNonLgV3X1SfiCZe5JjgDFH8oxpFpwTIWblh6hkDvgwjVdq8SuSZWkyNbzFEZi4cm4kytUlpbdRMERcQfpi7x/YAsix01xEJS19nItURMuQ1xb1BzqTWCumplQXWzhBccPxELpgKsTHCYopU/aJSoHInBVHNMvJ6vCkdKO8fRsFmhrQnB2iNRGoPGNGBhhGU8/ywMKxqm63T0dfC6laXiUR0mX2/tHvl2HKaduTKibpZtnYplXWyjpNXerjDXjg/2keshGpywAyGiMV+s5kivwhI34c/OAB+egfa2ktGVAtifUOgXuRu9Exp9i1LM/Q87Osor5ViSZnQ91hC+2UzRTy6nlKyMNIsMyN1PELU1KZFjAGxjHHWJeoiFlvPrGU2nj7IjECLFYFL29Bcl2CxVsJYF4jqsgGJOmeB1Y+2NlJKBp+K8UJi6///IOa+bS3alsyAkBIHHt6OaecuABwHQlK+G6YZetpvVbIwF5kVBMaN3D4AUBau3jZv3cz0nos0RWoUgIsoa7X1XnbVAStUsLURosNi3qJOURNhaIg0ExM7Lf2uTbLYgWbHIeKsfC0Euv0W3a8OTO67jkIzGHFdBNvQFZnzDaGpIUrgpDX/W9yKNS3Hpmno8MJhhTb18euwprVQ9KrxqRufpiHxwrdChmvBsT4onNd/0FxbVG0ZZ7Wgq7OrF1ZBZQ6kj1gaqNBm2SrR4UzpQHX/4brHNJGVEsAEfRSjsiZxQ6wWsgrB1rr2dJ8Qs2hPa6FF3Rc0pSGz9t7s6DsxbwwAUJ0X3qflueHxx+Ynj1fp3wd3aheYa/k0ViBArf4k0b2TMZxYCzzjetU6nnZorLD7RphUXSjYj1/7ccESj9cCGU11RklTcURErW2AZZQmim2SSMGh3jcNbGuK1KecuxBbvnc/xnYPAgDK+4ZQ7O1MbGMHL1CJi0aGacgQcq3SHbVgF/ul+XrDs+FK9Efj1xyW4za+/lTGqFVsuBaixK+UniRu9NcCPQ8kVyD5QhPPDxO6F7O8iTzCm6aWUeRYD01VhPk5OrwSfMnR5pUhJEeHV1bLy5kMAGVJNo0iIRmKbnxqgjRDiZveMIrqjeE6TMKXDA6XCAQD5wJCcDhc6ppR+mElvVINoyiNGQKSmYJz3IcvOQpOFRXhIucEEVOkIn/oQUwzirzuDow8sTXy3hP5etLPO80ois4vHKf3AweeG2YvLrhhvh3XFQgET3zI6cObxuCY3lGrW4HU2qHooy3A0OaV4QsHbV5F3Tchw9jmVRJRUfbUNIoOlovIez5KVQ/tvUOJPFMi4JABD42igIVGUcBCo0gwjM2vaFaAFwKM7diG4tzuODuU6MfQ4zV/p7733AB+wGNGkTS0JZzJug5kLaPINsZNbZ2QTPevy4XO2ms7Dr7KZUX6kzSjyK4OX1cXwWUoAmGRsRNlATLYA6mMIskgedLIqfdNZjKD9DG3swikVKOIDDNiErX0JcxMLwVNw6gzKRjgyDBTt46WIu0QtS269ziTmHrZShx6dDsOPLwN+d4VKO0dwpQ1c+AbzxQXEjkjuiliyDkcFoS6Fs30UZ1AqdcBwvd5AJZqFFH+Hp9yzfHw2ns8jDbznAAV39GZy836i4m+ZzK8Dxj1pQw7W89HHSlN5s1RRhALp4xLPRWCaQbOTk8kGAfnAoFQ72rJ4bBw3rWrB9hR0Na18AMHDhfh98rOt0a6UhWxWQkcuFygImjqNh5tJht4Vl6EmNAGEbcvruGukbFkr6Opej1koQwHzhEg/sDSDRdYD3JkGEUGUuLmzJpmDKWNF3YZCpvut5NKCsk0i6QLKDrWUJmVLt8sppmzPLjoYadxm6htjpCoCCdZaFNNC/P6MPqN2yCrPuA6EaVtfAD08ELGhyXxspORcUTb2B9QKnRYewgrPJ8WNyohUJQ5CGVM00vcZUF0/VWxzKp1f/iJYdvIAKPjUX/Oax8AAFRa3dg+TE/PLktRTTBi0TDRyL5dKMyZmvgwMITvej2MmFFgFIjuD4Kwu7yBzG32cEWUqJSmEQtkX5fEM8Piz06gHIrAcCxiBVGNef1yTxHu6+SGOoRdmruILBctYGYxsa29v9g2tZCwoox/9vIs6PNCfJrStmQSTnsILYgtB6JhoUJfF3b++EH0XLkSpX2HkZvaocv0ABnvQGUE6UK+OnGhGlLTDkMygaGwqDfPkEOEU0e/n+y0JHqY2RryjVITREauNI3ccCM1j/h8lgDenLWvBYvexdpY0oex+r5GUI5+n1Fbbcabzks5RA4XmaH54xrzeYENmTHGZgJ4I4DzAfQBGAOwHsDNAG6VslYa4xAn7ZBZExMX3vRJYJ6LZ9/4v3Hwht+d6Oa8IDG6YSeK83pOdDOaOMkhA4Ft374Lu37yINqX9EJKCVH24TTL7zRxEoEx9p8Avo1QevV/ALwJwJ8A+DWAywHcwxh7ab39TGiGaCzIAUhnW/JquIdYI24IzgBoryuv9QmRcVhlJEBTHrktFNDsSOTJ+mbSLwO2t2sOo5AHnpMB8o6vEwsGxOqMoxCfnZgu0DVEItEjwWaGvIyQetNriULG1dBYA23Kolw5B1pPm4NDv3kclc07kXPjgj95BHQtYxKeaxUDVUJl0nFlCad1aL2xLBpOE2BMaA/VZHk8Fu6f7g9iPzjNWy5WdAyh+zHIOE+bWTSh0wkoityz1hne1o+xDbvQvnquYohMZlTEw/7peDWYIi1OpzWs49nzaUMVtMzWq5nXoq42yfKUHS7AZVjk0yNG1CFdhx7vokbG2iqloa2xPHpiBTQ54FoskIzGu7LF1eNwr4lYcJEo/hk/SPa2GloHJQ1mJD50aU9ttpDJsG9FNcCGz/0colzF6f/1XniTWsCYhKj4YF62r0zvzYDSISRE1RQCntxHYJ0QFYDVz13K/ULvL7r2OYsF1Pcnfc0Yh1lmJvzDWtfucoaoT+kTovWbaupE7wYgPjSdhah0lDVUlvLup1GNtDQJuo1q3k41Uut9ko0X1JDZV6WU61OWrwfwU8ZYDsDsejtpMkRNHBN0nLkIADD6zI4T3JIXFqSU2PDhf0fvOy4CL+ROdHOaOEkhA4GNX7oRAHDK/34dvElhMbzq4CiCsSoK0ztOZPOaaGJcSDOGGGNdjLEV6veKlHJjvf1MaIZo1E9/4eccP7K4tYcYZ4rI83BZJNxzQOHoVhinjCf8q6gpCYXBorFtu6BpLc0Q7S/HAxQcHz6LJx+rZc0ndBZW0dM0D8PeH3lWWjOUWVRTJDy0LAjJEmvYoum862PahYsx7cKP49E3/iNwaAC5qR2xtAP1RLBpLASdB0VsEUuYZH0shggyxQOl+0TAY1KXgrGZIgDIa/ZR3WNqagv3zX1rL9q6T7Jg6jUIdD7SDLFlQOfZi1DdMxDTAOkSGTLUL0VSCanXCFeIjkdusl16JEtLlOYJ296yZ+k+WOwa1L+30uAygUCda6DYh0CFNzOtK1NTo+RFQO3W1Jea0GEswSszk/XJ+MrZWrfo78zivlrrInQwhHXgxpbFjmswGZaWhluRqKQ3ZIyhtH0/9l13J0Y37kZxTjeW/M1V4Lmo8PXIpn60zp9qlKeIoFlwRO8vIbnBpFPgSP0oKS/rWTXuFwrysN9JdqFiWzMFJhKMUJbWLNonwJzw+bGZnyxmKA32PR7Yow8KwghgcKy+SJ6npob0/JGULUrgBaYhAgDG2J0A/gihbfMogH7G2F1Syr9oZPu6DBFjbBZj7A7G2FOMsT8wxj5s/PYhxtgzavmXjeVfYYw9xBi7QM3PZYxJxtiHjHX+mTH2jkZPtImTD4wxTH7pqdj7k/tOdFNeUJj+mpdg6PGtJ7oZTZxkkFJi17d/jXzPJCz+zGtwymevAc/FfeJgpAy3o3CCWthEE88bnVLKwwBeA+A/pZRnALik0Y0bYYh8AH8ppVzHGGsH8DBj7HYA0wFcBWCFlLLMGJsGAIyxJWq7lwL4LwB3qfl9AD7MGPumlLKhlNEU0k2wE7ulgTwP8lQ9kyGi3xDEtqlaYaKOZTpXhaO9IhowV057zfwPtoaFitXaCSCztjOht3HqewL2fu2yHG6KXsbRbU2yRwBQKyGLjiSychZxJjH/2nOw7n3fgVdwMPNt54PnXJVyoO5pJGAzQ1nlOOzzdHmQWMc8X1NDREygyf7oKBrSOyiWglsnQfdNAKZZFjuappGxfps1o/7V25QrcFvzCYaK2mDmqtJ6HMRT+3Pzb8vzTduvDZ7BLrk12MgE+xhjq2rDjFYDooKfZpQlEEUiBoxFnjxFaJKj7aR7+qa+JPpbrZvpgdMxUqRAFoMDJlP1QrF2NOjok3YMiBghe1M6h/KuAzj4m8cx9swO+IfH0PPJq+EVXAgJONa70GvPIxgpp0YE2teJltkshR11ZoKeL3qv2O9cWp5zfIOxj0cFJ9mRePRcYBSPjpjDeJ+kRZARi5lg3DKYIfPeZ0Y/AVFEow2TTY+0X9DL0mBrisJ7zXqPjONZihrT+KonEVzGWC+A1wP46/FuXJchklLullKuU38PAXgKwAwAHwDwJSllWf22T23iILQaJOLPaD+A/wHw9vE2somTF15HESu//nbs+vHv0X97muatifEiGK3AaWnqh5poDNWBYfT/6B60LJ6BhX/3TvB8dgSZ25aHPzyxS9Y00UQN/C2AXwLYKKV8kDE2H8CGRjcel4aIMTYXwGoADwD4CoDzGWOfB1AC8FEp5YNSyj8wxloA3APgf1m7+BKAWxlj327keMQQ5VLSkkeed+RRAEYeC+XNpzFEBFtvRMkHeSK5gz5MItV8o8VlzXUpIaK9j3jUl50jJp1pqAV7XDnHszQ3IiUyi37LHgMnj6ZgRXlF56B0Jd0e2k+ZjnyRoeBWw9IBVmHdLJhek60ZqqsdMqauZg7j9wexd7bHamrPuNYBxbVDxBBFHmsyb1U9mF5vImmbtQ9iSeRYCW5LLjUDOZcytpxZnnIUQZmtgapXCsH0iJMaomSOK1qvXoHkRhCxkcQEhcsjLVFUDkFJ9bSWiLvpz2oiYahk2dFJGdumXW67SgRjQucKS1svnKn9PkllESyGwc5rU1w6Dz3vvQL7f3Q3DvzyEbQs7EHroh5MvWIVeE8oniZmz2nNIzAMIpOBsK+XBFO5iNR9b2mJhNFWep4c+z2D6B0ERPnlHCZRtlgju00JFkhFobIgmfTWzmOV0OdAhhGaxrs/GbWHTGRqlSyk7avRDPFRKSnAHigYt4ZIIqGjOpnBGHsTgF9JKX8M4Me0XEr5HIBrGt1PwwYRY6wNwPUA/lxKeZgx5gLoAnA2gDUAfsQYmy9DfChtH1LKzYyx3wP440aO2RWEkQ+eqqfjqaGKvPS1EUMixYJQWZdJSKxCpvM8Kq1hfwBoW3qJQg1/cTWExkToSTnCi4asNC0cT0ZY1RXfo5vMUQZWuyio48c/3LQPlmoQ1f54NPIxkdZDYqe+p5pATAmLAcChISPKPC3DaZ7mBUd7UESVOagEJGRPbzPN+4Lj3I++Gs/842+w9ZbnUD04iqkXL8XMPz57XAZRzkq86Mj40BjtSSd0M7Il07lGv0X7KvqtybIShkEUUfOWUZpB4fuSI1DCUl0HTW1btdLzU0h/NXCS19iuZ0f7Zy1o652BKaIIG5NknDmyX9R0f9r3BhDdh/VGzLgxXGN/NFz1rLrqgPTsOkIiL+PJHOxniNpmLu8QBQgWGdAUbl81Qr/N8/QN45XOMcgw6vV5kyFh9EnKwFbGxg0YRGp+CktnZuIGUa2WphtE2pmh55muo7HO1FecjyWvOA/BoRFUdu5D6dkdGPzy7ej99GvgdhT1s+LnAvR2T9fvXldG4nj7OWuTeQDJEkH2sxaukz6U6qpn2lHvGVe/Rx1dr5KuMb2DaErZ8suC3lXR+knnIoQeOtNnEa03GTbrSv2YPiRJCCP1M4ZfLUTPmIzeX6KxgBa9DyYTzxntyxN1cw8abWx41ZMBcwD8mDHmIRyJuhXA72Uj2WUNNGQQqYNcD+A6KeVP1eIdAH6qDvh7FloX3QiHxmrhCwB+AuDuesc9wMPaTnZpiYLja7aDWINAzftU7oOHMiWmPQ6hc+vYHofWfigDyBf0wOXU1MWYKmYaRVLEsxXbmYfD46iPhBAYckdStTtAbYbIZoZsjIcpqmawQB4TOsqKfhvjnj53ACixqJgrABxkZe1xZ+fjCaeHNh7AQ5+5BcXeTix+z1qs/7vfoG2axLB7GOXBMnKdxfo5agCULc1QVtScqR2i9tHftlbIYwFcJlDNHwIAyKjCatR/Vh8HRpQNEBlKVf2xdvR9UbXukwqPv8x1FmrOY/cOkHyp0nXcc2A3RFBFhzesz4+QE0UMqefG3MYukCklM6KPwnWztBL2tWEG2xOxSuHUjmg05wNiRhMZv60pLVeGzAArRSVvEJ9SF1HB3+h3ZqwT78d6tbCkZAlmyK5nlebp19uvC4EBZAxHWY9xZlmKGvtnGR/w2PXrcuF098BbPRWHt2zFs799BNNfdbp+HiqjA9gz2I+DzmhsW5eLBBPrCoHBlPea/fw5TBqMffL5M+Ebxg29e6q6TJCnpuHyUT+cL6tnqlQN58PUSHEGKIshMuEKgYOslFiehbRrFEWEpW9jlsohB53yxTVUo43aSu9uq+qAyxo3iF5IkFJ+CcCXlM75EgDXAvgGY+wpALcB+KWUcm+9/TQSZcYA/AeAp6SUf2/89HMAF6l1FgPIAdjfQMOfBvAkgFfWW/doYyg4vtETjRStPJqgF8bxgv0Bz4IMFFO2sBv3vf+HYA5D72VL8fRXf417X/OvGNt1CKJav9hjS41CuscCLc7x1VJ05Bo7XjBydDRExdzx7c/nW85mvCAR/nE7Xu74Hq+l0FBsSiamvvIM7Prx/RCVqN3+aBVOMf3eOt7vM5fVfyccTRRzz68/x4vWBp/3YwZ5Av4d61OSckhK+TMp5fuklKsBfA7AVADfaWT7RhiicwG8FcATjLFH1bJPIkyT/W3G2HqE6bLfPg566vMAHqm3UlbVX9PjIc+xqosCxqdFpxLbxjSKHMQ9HqJciYIlVogyZgPRsFNe8U1lFq9NRRa6nucCTMhYLhzb04rOK9KxVHUVYxVh0SBTVMsoytIHCSMPEXkt5J1ViJ42am5VhaO990rgJMfAZfx4nQsmIyhVUZwS0vAj2w7ikY/8COWBUSx411o88NZQUjZ51QzMedVS9F28GIynfzxb3GqqBgqImIdIRxbphmp5qDnmg7OqXpeWA6FRZPc9Ufj28KmrWApPCp0N3R7UonurrFnIqJ8rwkFHrpwZiaYzDZfG0NLbgs5c0pttDRxUnaQGRGvfDDYmkDxmFNlsj15OQ7xGjSVbQ5QV6RfNRzpAff/p6D1rH1a2YsaiCDWd6dnevxoyoBxfVeFACIa8548r1xXBZhbqMUTUjnzOz4xey0kXeZZuNNUq0FsLplHErP7L2pazUGc2ZWUf9i+Yit3fuxNLPnAeAGD04CHkWlzN7EU1+SI2Vj9XCGsg2s+jl5E/qBFQdDAEUJZu+Nyq205rLvVzTs99OG1RBZrNQrP2dbQRuzeEg9Z8JcEm1epHG/WMfl1XUrW9NVfW53EkDoP9zBxvo3UiQiVjnIvIvtkspXx5I9vWNYiklPcgm6l9SyMHkVJuAbDcmH8MDbBTgV3kTsF80OijHg1JqKEKJ6414EwmwuntEGsa8iADqFwjpN6u2EzHJwNJGMJhl4vU0G/bCKHzKgtXGzZRodnaD3at3+wH1y7hIXiQEE9XrHF6mlIFclOXITI+oPq43MOKv7kc6z7xC7T0tsNtzWPSkmlY/O5zgEIe3WfOwgMf+CHK+4fx6Bd+jWf+/T6seP9LsPehnVj+njNR7G5V7bZf0ormJ+2X1Y86MSWPNGeeppYNg4gHcNTQqkdTFkT3i+pWu+yAnXyxrIZcq0KgqIYT6bh62JLuE6noftXPY46n/46Ml7hDEAjg8a/fj+03PIE1n74IbV453s8AWn0Xwo0MJVvwbQ7tVqz91xrCtbfNNIRsjUgKhW8bmIl9yWie/mlfizRg1j49uuZKbeoGQr8/6rlpjZVeiLc5zfioN+ybDxwUeTorlzYsWWt5rW1twzat9Apdn5Ufvwj3vf+H2N7G0bFgCp786h1Y/fELNRsrDKM1MRQmQ+PIHo5OFOU2QM8KGcOUNkGLq0FGFSCsWpzkcNh6vzTmOEvflwUhGYqBg1annDCm6g1lcSYbNmbMIeRE8t0GhdEcMrFuo8LsGF5AomqCCthaAeAPiEvHfpq5kYFm6Y4mjgumnD4LMy5eBLctj76LF2H/w9vhFkOjoPPUHrzy7j+F25KDDCRmvmwBnvzPdRjcPIC7PnIzqiPHl8qeqNjww8ex667nAABeM+y+iaOA3KQi1nz11RjaMoD1//hbnP3Fy9F3/rwT3awmmjhSnC2lPFNK+XYp5TvVv2sb3XhCl+4gD48sdJqnRF1AWDI7RwAAIABJREFU0jJOiJ2dZEi4pnQVs2CzPGMpw05ZUV8UJhqJ26J9kRXvBYEWhMeObyeAJA9cODFGxjxerVIhWchqu9lntiefxgwB4TUQkqEqnchzsobIyJu3FQAL33cefvvW72LDdx7Cyo9dFHqdRkTKkmvX4IGP3wLpBzi85SBae9pQOjiGm197HaatmIaeldOw8BUL0D4tr9qtSq5Y4bkJpogF0XXSTFEQ+00oz13/zvy63hb1I0XiOUYRSE+HGcf7tUTMkGKTSkq8nhc5LQ4NLIZIgGHX73dj4w8ewxXfugLXX3092jsl2l1iiCJvsx0OPG80KhmiI98UM6VYKJe5UXRLBsNgM5nExOV4kGSXLMrevreF5DWZg3pwLGbIvocJFC3p8aBmmZtaSBvq8bNYLYONqXe/FP2QgSCYDEQW42Yfr1Yb05IpmtCliriIrdsypxV9n39Z+BsTCBUQahvjPrKH+nM8LFptprAwYTPytZBWCJbepST+9yxWvqVGfzf6fjTPr6XqoepWGmbajwQmc/p83ulZGM8w5VE4nYmI+xhjS6WUTx7JxhPaIGrihYVcZxEvve5tEANDaJ07JfF716nTsPJD5+CZ60Kp2tj+UQhfoHNuJ3b+bicObTqIJ76zHmf96WqcevXC4938E4rNv9mKZW9epudzrU2GqIkmmmjCwn8jNIr2ACgjFDxIKeWKRjae0AYRWcxaS0ROhEE9ZAkydSFVQ0Pkak/XGovWzEy8O9KsbcqV4jJqo8qRoUKKPcUIBNzXHn7BrUC45YQmRIuBJbExkdBWh2NnaIjscW4TWWPemhFikbaJzlNYImY7LDww8i1VhYMAPCkeJSbPbodxvVhrC/LtBXArgoQziZbuIpa9eRlmnNOHYHAYhUkF3PCmGzC2fxQv+5uzMLDhIJ65bSse+c/12P/kfpz7ibVgnCWuPbe0DJxJzdjkU7RCHvMBiyHKMb++t0Uh35Rrx0j0SVqInHWexBCNiJDlIqZoVFQ1WxQxT8QQcQxu2I+ll/Yi2BlmtZjSGaDDK6nzNhgi6SLnjerj0T6I9SRv3hUBXNJeZWiGEskqg0i4TNv6Ip05ORKkPcuMSeScoK73bN8DABoqc5O5P5vhsjUbCRYtyfLYbW5jHgI3KXhP27beNUn7LQt2O7ghUqd92AlLo22TLJtmXmWU/gRIlt9oRHRM79PUMjTUNtLhKbFXu1s7Oms8jGCg36ccrfAg3VLd7Y9ELK63Ne6No8kM1Qu6SUDihVq649tQQWAAxk1JNzVETUw4TJrfhd4zetG1oAtvu+016JrXid989gFsvnsXxgbK6OhrxcHNg3j8uj+c6KYeF+x9cgBDe0fRt7obpcPhcEau7fimWGji2EGKF+aXqYkmTgC2SSlvlFJullJupX+NbjyhGSKd9dmONuPQrEO98F+zPEZWAj+uo5Tiv0eFMKXhzakmkUWutqG2EgPhMkOPEgTwjUgn7fkTE6B1OkYkl5XoMStJn8kUZWl6CKTtEZYH63Khw4o1w2Zpl0ymSpJ3Y+k4qA+idlBMsmqAyezZ4bwsrj0h77BtagGXf/FceLKKm//6AZz7odMw76K5GNwxhO9dfRN6T+9B19Jp4bYpnjDtK/KELS0RC8K/1XyBwu+Zr9sQ1PHgCjzaBgh1QybTZKKkMknT75Tw0mM+8jye/JN0QI89vAvLL+3BlJYKdqmEo13tAgWnpPdF925RePCcKMqsJOKGk/l8eJamx77/bY+f2ANfOjG2CDAi/VLcTiklDm/cj7YZ7fAaGOrTGhUWwBMqzNu6BLaGKE1zk8WgHImHn8XUmIyOrbkSFrvblsJAlA6O4adXXIfuZd245B9fjkKHyo6fwniZ+05rEyGpHbJZPJG4xnbErX3Pp5VNckV0n5vtidqeEmFYJ2t4GnShbn70gisiZihiiAoyB3ilVEYtDWkaKTsS1YZ5PLsYbj2Wx7z2yXXHm7OJvSCjzAA8zRj7PoBfAFEWVCOhdE1MaIOoiSYAoGVyAXnHxWu/FQo/hw9WcPNf3I3Z589E56z2E9y6Y4/AF3DzoWE8bVEHll85E26e42TgvIOyj5uuvg7lQyUse9fpOO3dZ5zoJk0oODkH+a4CDm8fwrqvP4S1nzjnRDepiRcLJv7r40hQRGgIXWYsk2gw7H5CG0SRdoiSdRlWsZYVkUcTn09jjuzkiaSbiRijbM9AW+dkpNPxdR20ZPuJ8ZHCQVm4CQ0ReWWtRrQQTW0Wy9YSEUxNUaOagkTyPOPJoOOYRTntbV0WwEOQKC5qe7N0DSinEWkAfMGj/apdENNA/elqHY3SLvk5vc3uJ/fj4ObDOPujZ8PrKKBs6a30PlXfVVmgc5gM+2FiTmLyCrwKKV1IxaRQGz3mJIoB2yD2hUpEROxSENMThb+F3q2jPXObnZFwdCa48DcpGErDPjb+di9WvaoPw/vG0DE1h2u+dDqASBfFmUh4q6Oq7Ay10U426ksnER1k78NmOooqYtMXQm9L+7WjFGmfN117A8qHQsZq5x3PYc37ViSYkywtBWcSLcyLJZokCK0NrB0pl/bbeJAVGWczOOYzm2xreF5FmYNwrUSfHcAr/uli3PDOW/Hsz57Bqa+ai54VUzNZurRzStPfANnMppBMsy5RcVXrvtS5t3hsO3PdgshDOkatSHtfmj2vzwrV0tFkabKeF6zzE5IhH/iQTjVT15Ra9BvpEXKEWtfAZoKPBMc7+/tEh5Tync9n+6aGqImTDjPP7sUlXzwf6771aP2VT1Ic3DmGv/+je/C5C+7A5JktmLdmMr568e34zT89c6Kb1jDKh8sYeOYAuhZNBnc5FrxiwYlu0oRE9ylTcMFfnw0AuOuz90H4zWzDTRwHyBPw7xiBMfYpxtjkGr9fxBirWy5sQjNEpGuhavRS5XmRMplFVHsRludmMke0LjFDiXIbVvRVTItDXm2Gx0jLq9L0qNTfgauLpcY3gjqeKvOgvC1T8+JapUGy8hKZGG+EDJDMN0Smspk/KfxdwpWhdsAx0vnb+0trBzFF4OF+zOPQcbnKNEPsjsmqmRqNvgvm4cFvPYEbr70V+SktmHflYkxfOz/83QnXq2q2yYHw47Y/RQVWpYNCkAcUo5JX90YAritI26BIsWGrNl4UyVbVnj3pigo83H+L0kGQN61ZKGmwAJLjwet3YPHZk3H1X58CHy7+7pW/Dbdv5zFmiI5L99qY8FAWOc0MESNETA5FNLosqMtC2B4sabSKTgWu2g/pkChDO4EzgV0PbQMAHNwwgDPfdxrOfucpACqJHEm1tBMtogqRElVk6vvs40Z/H9kbuJH8OQm9FZM1I6YAoCiqYG4lVZ+z/FWzsON3s7Dh9u0Y2rgPM5aH73ablYgxQ0h/7rKYmTQ2QWvrWDxSMrEtmKHvC9fJOwW4Tkmv6xgMd9g+9Q5OeUeNJyoqq9B1I8xTPZjldwq8Chg6JZsh1pn9rfMJZJJF07CYKJMZzioSfSSop3N8EeAJAL9gjJUArENYZL4AYBGAVQB+jbCwfE28qBiio/EAjQdpCR6PJQrO8S0uSWnojxdG/UiQy12Oy791JVa+dzWKU1qw484tR/94KjT+RGBgZwm9i9vA1BBeS6cSXxcbK6g7ETBlcRfmvmwWCl157F6370Q3Z0KDMYbLv7gWb//5KzD91ExHt4kmjh5eQAyRlPIGKeW5AN6PsGyHA+AwgO8BOEtK+REpZX+9/Uxohog0RJEHpKxrzgGVZZc+GNLyktKYI84kAskhgzi7ZGuJakawkE0lrLpTVrQGYSzw4Eonlemg0XcuydNXkUI8WXDTrm+VpSkyjaKsOj52McGKcDRDYxfUNbPS0ryHIIqicoJE/arM6B61vBy4iTp1xM7ROqNBMhqJQ2LU0BLxNg/dZ86GO6UTd/zJjVi0ZwQt09uj4o+qXSN+0rDJi7CfApdBiBygjkdGs8d9OJAxo6ikcwaF65LBa+eiclkQi2IDoki0Eg/ZDs0UaW9aoKrYsdGDJTx1dz+u/IuFeh+nXTwV2x4/hNnLO2PMEB2f2iaFi5LwMpkhkx00I4RoGRDdYzaTQ96uANftryptVpqWpzi/iGv+IRIJc1T09mFf1/dqizIP7kVRc/Y2jeo8auFImCSbnRGSJSLhbC1N3snDMRgVgu5fB2iflwdQzWR99L4hMxmZLNTSqtRjCU2dmr7/eDkW0WjrutIQRefWbGoMmQzR89LeJPU7RSd+PlnnYW+bxnaNx/mul/doPBqs4MXFcSQgpdwAYMORbj+he4+GzKKK02rYKOBGqDp9yKm0BNPrAECVBLeBo/+mbfWUKrmraSmlsCkdx6z6Hs7Hq5ZTUdZS4Gmjxa6OnigvIuMfIM6ETvFPgmsaTmtR8y1O+HEpqOUFp6r/zumippHRYk6jD180DEd9ERglOmLrWGLrHA+S+6eU+qpteupWY9OiW00IaWn/o76XOU0uCw2TwuxuTFrUjf2P7bH2ldNT+ntYGUcjygAaUiLrw35Yl56MnbLwtJExqH476LcAAAYqrbHpoWr4+4FKi54/VG2Jb+Orbfy2cBqE8yOGwUUf2V99YwtWXT4dk3sio/Cid83FV9dfiiUvnZowZEr/j70vj7OjqvL/3lv13ut9SyfdnXQ6+0ZWQiAhgbAJCEFEARVREVR0xkEdRB2VGdzGH+jghuuMCOOIG7IoCrKHXZYshJCNkH1fOr33W6rq/v6oOrdu3ap673XI0gnvfD5JddWr5dat7Zzv/Z7vcRIhErVeoFgviGugsJCgPqQlPwSCyRcvXeMqI+jo0Xy5N1/Os3Ldcm0dmtaa/ZHTKiONKu9DFfUbANQY/aF96vuPO061tw91GrVMncrnz3N0K4xs+L73jpfy1qFAR99XlZlGlRm3X71f/W1pmT/N5J1W87Scqn8H1/WO7yUIyOPxNKq9PqZtKvX9er/TcnVaGdMm6ht9WsEzcp24bfW2qtOoZerUP05WngMlPtA56/0bt20Fz8q/U9p1o+sYNa2Q92X08WTbQvdeJrR/ec8VK00g4KbdH+l/g9yYKFQK+igZY0xMvvdrAADOgy9uxkTkMiAq0wlyPR01onlCEvTfVcRD592E9HNYdIQFADVWFbrMnliODSELSU0jBwhH+BQh60iRanFZPLqlLd/xoxpX5AjJWl+yRpunvMxt1FqV2M/7/T7Q+kJVrwVUJ9GUUzoeRbn68FtU1B6lDkzTPS9vxfLvLMZZ//dBpMp5YP1A33h9Qc5juZFDVa4adqodgO+AlvGc3J6uATkdhDjpQ6LkbKS4JdE+dRmgvOy0D50BBxmb4/7vvolVz+zHP/9qNmq9mm2FIuM+OyXbxtJD0Jfokg4R8aj09hgQITRTd4BymiMkNba4HcjSA+KRRdUKZR1FZfWlsrXIJDsj1w8cbwB4fKHswUNl+rugPFuLbLIjtJ56PaM0g4AwgqOiQVxBGd96m6NRcgOOj0gRlyjTIJ8boDiFaP3axyEa6rnE3Tc6B2cgph/XgAMzUw8rdaBgtqC+rXrN9GczH1cqDo0rZPnuF+qjm6Y/ABFVxsCzVNtI0fKlzxV1vENpm//lhiVCiDlH/MBF2qAeMqPr6RDXlJBlHl5GQ2f0ipdOD3witj68Rg6QYDEE7TzS+hLZ0IZ6IlPZHQ7LMUIPGn2Q6GMit+H+x6fK9IYmCAkTQdKx+tGSH7SYiN9yguiWStTWkaGoshvURlswmNyRiJApZf+DH11d+C/KyDHq10jPUR+4fCKcVSeORUXbCmx9ciOGnze54HF9dMwAs5KwTG/IjJArg8mhnT7P8SFHSHfwqJ/JqTOZI51C3SHKGO425GRlDHdfFTyLzUv3YeWT+/D535+EyrqEci9HDxn02W57+pykdM4MO4F+ngw5QvqH1OR2SIQvbiiAPnS0Ty78wpT0AqYIlSwqMCg0pBO1TYKXB4Yx4mwghUSLOe6ASyEUsf8Es8B4LtJxiXMM9GEodaguRMyn4x0CsoahpeUbyvFkf/EMYPRHOOxRQ0jhYddCFhr+jNlWR+AHYmpfJYwsnDz3mn4cmrcVsclQaZI8QpSh+6BA86P69a2k7r8FNYpBa4yxBiFEe+E1o21QD5mVrGQDscZTRuHAazuOdjMO2vZu6sOYE2tRWVcqy1GykpXsMNtxRKpW7EXG2N2MsQsZYwP2Egc1QmQYXnTCg0NajPn8It3eighbPiOeEbUhVCA1ApWR5GnHQFZBiCTypJF/LcPbp+PIwpQ60VUfxiBLKMMfesQv2+RtQhGO6cFsDmcQXoFSOr+Exg8ylaGthHBgOVwiJD5SFBw6I3QkNGRmmZJ7FUKkYixqyFMX4aye2opNf1yOXS9vh9WVRuPp48ENfQgiuK3FOWzBJQdMTSOn/ss6mvSBJoFApg5R6iR4/fhS2M/xh0L6cxw8aUoidxz8TpGpSu6mIbKUYMgJHlGWI2oI0huKQJAYbYvgfM47/5x3PEdwiVTqUzKKfjt39iFZYaKyLlFwqCoK2Ug5CWTsMCmejqcLYEZZsQjCW0FWotLS5X4pWUKYyDqJ0HVV2x43POo4OlIkiiZT6whjSknaiLOMNhzskqqD7zrTroRl+cigjo5EXZMX/rIHu97sQ2NbOYZPqMSo6dVgjCnljMLEbEPep9FDVdRHutRFlOnp7+qQoOOYsJ3wfSqRoJghOhs8FqmJQxo585Mo/CHq4HOXz3RJg0OJaB7jNhHAOwBcA+A2xtgfANwphFhXzMalXizZcWNVE4ah4eRR2HjH81j1nYex77k3j3aTBmR21gE3Bz/xcCD28H8ux/cX/g2OfWTCw5INbvvfL67Bw7/YgnUvduC771uKDcu6jnaTSnYcmXDtUSHEFQA+DuAqAC8xxp5ijBWsizOoEaKEqZUWkBFrYS2WOJJ1MaZvk3P8DDXZFi11nWvIkco7ygmOrGPEpsETwqJncgE+UpOLIVETVyWqVIGjcUQ4FXel40ToCJEYoUqiVucth8MWDBZ4KPMujmRNyBDxhbKWKbMB40Qjo8zgGoFeJ9QzjrHXnYu1tzyEVEcaVdPbIvhZTuC4Lr+LSySHBAZNbuctvhvVdp/DxULIEK3rl+6g+9NDPx0brz66FydfNlJmiulcBTKKJNXUekIOqz3Cs44QqRmMajvUv6n91DZLQ33o9147iaSwgn3Co6Pb8285DeymF/Hbf3kRl/5kYeCcyfKhusJJoN/xs+2oTcTrIhRCiprm4ecQb6uQGUwMuEwDhxNAG4DweTl2Cmm7LFTcOcUtSU6PEkRUl+vvG7e9Yd5ivn2U8VzBbCQ9a9FgTggBM61KZHgYjaECymURSM2oWbW44HMT0N9lYfemNIbPGIKM4yNE6r1f7PlIiQvui60Wa6rIaUKYyIlErCCrjxCFuXZxSE2+BAJbQ4Yk6hlz/5CpZZri0Ky3qzHGhgD4EIAPA9gN4DoAf4Erzng3gDH5tn97917Jjj9jDD1v7Maojy5AorbiaLemaHvujvVgDJh+wfCj3ZRDaolyEws/fyLmfmzK0W5KyY6y2TkHO9Z2Y8TUGuzZ0IPyGhN7NvQe7Wa9bY2JI/+vqHYxZjDGljHG/urNj2GMvcgYe4Mx9gfGWFikzrcXANQAuEQIsUgIca8QwhJCvALg54WOPagRIl03x1Z4Q3pGoc8viu51xgbOL5LIgG3ILC8ZJXnFSAkpMrTsMrUdGdtAWpihKI+MCptKsUXD/13XLCKjtHE9nTrKfK2fYGSqCsLJdb1IR0eGskoBVUtwOGC+VhFxbSiVnvqEOYHlWYukA/yMuEKqDyotzhbBqEiVVHDn3enYf70Aa79+L3h9DWqmj3R/Jx6a8JEhwEWxBML8oKyCJuhZeXFIn6Ps22HRv0npACWN2s45eP7ON3HNb89AjqckP44ixlf+bx2e/N4KfH7Z+7x+cH9XJRkoiy0pbFjcksU7/b6LbnOUFcOD6PMy7gihzIf08SEp1DXWos8OomPy9zycCeaJTOqoWFbTVyqXMgpZb59CIl96hiZtG1/EUxTF46Dj0DlFlfNQj5O1ytDHciEeYLmRRYYThy34HOu8FRVd0o+jm37NVQHRtFdKiOQf9GscLgqcDJ1fhV2OPisXQLoAIEVt5MF9bl/TifoRFWDl5bCZibXP7sePX1+C6+49HWWN5YHjWsLIWxgVgLzHcyR+GlPOBYi/P1Vh0QphICcMye0JHzeG3yV4bAaa3mbV6FpSH/sIkf9ucKfh8/GLC8dzWI9h+yyA1XAdGwC4BcD3hRC/Z4z9HMDHAPwsZtsbhRB/VBcwxi4XQtwthLil0IFLCFHJjjurntqKCV++GOu+eT/Su8K6L4PN+trTMBIcDSMrI39/8tYVRypDo2QlO2y2+sldGH/qUADAzEXD8ZGfn4wZFwzH325ehf2beo5y696GNgiFGRljrQAWAfilN88AnA3gT94q/wvgkjy7+LeIZV8utksGNUJEooB2DGcjygg50lEE1eJQJDUrDFBUrh0ula/JTMqA86J5HTFiTMi2ZO0E0sKS56GjIkIj0qr8I0IlKKJJeiKOfvTgZ9nEZRroEaTtlSqh0qRZxwypV6ucIbUdpFlkMS6vB50nnR8hQcT5IXTJdvz1/W10pC/yFMCZ8DlRjPpc4xApXkPljLFoOPME7Pn7SrR99DSJDFEM4POpmPznnp937ZkTiqx1ZCiE9FH/i3CUqpeHUYU8eU0l0l1Z7N2RRU1zheSp5ByOzm3uh2LSotGSN6Mjfklu+aVUHAu2Mk9GeihRWZBOjLMV97yp+6ASKzq3Tc/mc+/pIOoShczoZtgJdKBConSUsajz1gj1oWg7xS2JmuoZmjJr0AkipoFrUiQHUQpdMhFA/VSj61RmpdDNcvJcyDKOKdEVQrgI/dBRHhUhiiuVoYu46vdvyrEkIqX2l94mfSpVz4nLmS1Ht/BFOn3dqvC9ZmVsLP/rTiz6+my8/mwHHMvG0PH1WDirCY/duhK/uuo5TL2oDXM/PRNm0kDGMQMorXo+hIrnBGl8Bfsowew8uk7Ba018nQS3wb2MxkLZkFE8Lx39L1S4NWP7vL+Mdj+q9xSAyPtKL8zsLz8yoqOH0X4A4IsAqr35IQA6hBB0g24DMELfiDF2AYALAYxgjP1I+akGQNFFPksIUcmOW2s8Zxr2P7XqaDejoJkpA5MvGIVHvrk09NvGp7cDACZfNPZIN6tkR9DsnI3Hv/I09q8f/IjmQC2XtvGzix5Bx7Ze3Pv5F/Hsf6/By7/dgDuufAo/u/gxdGzvw+h5w/DKXevxf5c+hH1vHH99MOhMHKV/QCNj7BXl37XUJMbYRQD2CCGWKC2NisqiopQdAF4BkAawRPn3FwDnF9stgxohItMj8SjeiVzmrepo2hSROiwa98Sve0YRuhdp2WGESCIcXnRiEL9FQTyoDVnHjXhUhEQ9bj6jYq26AnZCIkP+GDLPo8UCQBaRJVMjdC6RKE3N2glGOm5GFoPNw+rWdH4ULWVzXnSk7UMIf1lcD+hPgQ0FCfKQIYc0fHgQDSSUrnxME3IHepHrSoPXemUwvP2pNdRUTpNeW01dppdCyYcYhTKMEIxMfYTPfQS792Uw/uyRXsaY2/cHNnfj6f9aBiNlYOhJI5DVdI+Iv5NSESJuw4lAiKKylQpF0aHlgZIBwXWoVlwIAcuDEMWVsiHLOiaElUKXI/z6gt4zShmLMpPR9DKbPCXwMsOSPDt/f8FswdA9rnBQotTQo87LlCiNCLU/VLPQSqELljxepjuLJy67A3a/384Tr5uLjlyFz8eRmVNB3lFO+JmR4ftR4yRq92dZHoSIECqVO0TzPkrnYsumYyJtJ0LPBXFr6N3Y3d6H/q4cTrhoFOZdewLq29zgXwiBrm3daN/Yjd3ru9G6L4OtL+3Bb973MOb+6xyMv3wGGGc+X4z02qhYrs51814AxAtSzznOJA/JNmB4KJiewRWXuaXuWyJ3WuFi/Zr4fCEeQirlaIAdRpED84H7M5g1eihUyg+z7ctTumMBgIsZYxfCHcCogYsY1THGTA8laoXr/ARMCPEqgFcZY3cpaNKA7ZhwiA6VGdwJVVk/nkyF3o+EJbhdlATCoTJ1GLKo9Q2OygnN6HptK4aeNn7Ax7McIy9Z/VBa375+bPrHHkx65yigzO3TbS+5xWqveOBSMMYO+avOgHNEq2M7gh8PkH6sWYJLB60YE5aDxpNaYfekkaxMYNrHZ6OyKZpHNhgsbSdCTmYxVt1cgc/+49LQcsYY6kdWoX5kFdpOb8XJV0/Byoe24bGvPIcXv/8KNj6zEwv+43QkGmsPRfMLWlYc2c9hpZGVRaaPig0y30kI8WV4fB/G2JkAbhBCXMkYuxvAZQB+D1dX6M/6toyxPwoh3gdgGQsiDczdtZhRTBsGtUNU5lVHp4ifnJkcM2A5QVa9jvZIvRct28bgTkjHJh8yRMvpOPQ59rkv9EGh+j480A5qoxpdF4MQ0fEoIiajD4quqZITHJyJSK0VPUrSx6yzjqloIAUdKhmJKESTJBx3nN7hSHBbyfDzzt3rCtsJqnurpDpZR07PFgy1Pmh5+4y0jUh7iAsMefc8bPzRX+FY56P+tMmSdyRRLcFR7pjos7zsFiXjL8ntQA26KNQjytTlevYHIR26nfvTd+LJGx7Hivs2YcYVkwAAUy6djCmXTvaUrxHKuglE/sxf5giGjPdyz8dlSDG3rlZC1qJz+42iZh+FcX9X7xtuaM+fVLuOjmppO7XmXlR/qdsSepe1DYmqSF0p7fn21dHDWZeWhq7oiIo8bgDNir7Gchvmn5O/jRNAFvXI3xbMRVjpWasrx0n/eRFM7kgk2BHBrL24unJOJIIZncWqI0Rx96BqOofIV5v3ESFS4C9kBkSIxOE7xsEMwFHnjsdFw2rw6A1PonP9fvztyvsw7dOnou2CSUjED5VqAAAgAElEQVQmvPPTsvdkBq7XJUbEEEJsrS96bTOGFICMSMD0qgjG6RkVun+jzNTqPmYcE5ZjoNLISu0znS8ahwLZgsXyiwaSST3ApOujaV8C8HvG2LcALANwe8Q6n/WmF72VAw1qh6jCc4iyTpDga3BHOjE6OVcfNlEdJf2D5qfVBwUGaTiHljuCQdBwFz0LsuSEE5gXwj8efXwHakIwvy08+uHTX35RQxK66VA6pcPTxx/wPyz0wePaBwgAEsJ2nQXqWyf4APvlOOh8tLaDyWVvxQGi3/QhOTIhBKrmTMaor1Ri6/fvx4GXN6DhzKkoH9OEVF2Ztw5DzjHQz8IOEf2tC1jGfSz136PWifooCSGw6d430bmlGw0ntmLzy/uQPpDGqNNbYaZU+F9P5fU/9OTopEVCiuqpx9O3TRlWaGiAhtnoQ6A7QrR+lonQx0jeLzHOIsk1qG3yOwDBbRQCv+UR+OnZ12Ut4sRAg+eenwwfNyymWpyTob5TTO03vcQLOatRfRQadooZplWXH0wxU9q2kFOkO0JRwrJ0PtIJjBG+jJQx0O5HIoJnbBN104bj7F9cgle+8wz2LtmOpTcvxtKbF2PK1Sdh6OzhGDKhDonKJMxk0EGSQ4gDQOkoxd4QApbgrkRDkaBplHinHrQkJNUh+D1KMDsgAqu2PyTfQN8r6TDx2HWKFR8d7CaEWAxgsff3BgCnFFh/p/fnPgD9QgiHMTYRwGQADxV73OOj90pWshjrXbUFG798JwCgfU8n+tbtQG5fF2rnjEXd/EmoHN2IhtbWo9Y+4Qgs/+5T6F6/Bxfe8S5senQj1ty9Cr27ejHjw1Mx9zMnHbW2laxkR8uqR9bijB+9C+2r92DDX9Zg019WYfUdS7D6jiWB9RqnDcWp15+MYdOHHqWWHsN27CBEA7GnAZzOGKsH8DhcovX7AVxZzMaD2iGqSriiYUQ+I9Qk6zgyqhVaNGYrKBIQjOAkeVpLq1cFGAF/qExGhUIZ5vLuIvpJrqsN2TEGCI/ELFDcvadC0XFDSoQ9y7R477xN5hTNz/DJfDRMZocgXT0FlAjZHAJJDyHSI92MFYyayaLOJXbIrAgcV0eiCG3S92XlbGy99V5/ge2gYmobKiaOQHb7fuxfvBpb1+9EZtoklH9sHhINVbANfyhNF8xM8mikKKqcgo5G6tG85XAIIbDqtufQtfEATvvhe9D+5m68ftdKjFgwEp2bOjDpiukeybNAGjAYejxSs7BN9HO/MKc+1FLulYhQo1tJ1KeSC3DXIZE8WRqBECNmy0hUFx/UU+nVsidxxXBlO7Rts47hkocVNMHQECG6Jvq1CQ4jRst20D7CaffqMF8MqqWZI1gIWSiESEXNh4e/dJJu4aGyuCHBKItDiuQ7V5EmiCXb0ztYSlp4UxXxKCAJkrGDw0aEgNVNacKMSS2Y8YWzwHJZdL65Hx0rd2L7U5vQvmYvyodW4pHrn8A5Pzwfw6bUuzsVPFasUjcpv+ENd+aEgQjgJ7iNvE/88yeENk4wMVToloUR2Rw3Ivchh+6IisBEKN0+F3OPvw2NCSH6GGMfA3CbEOI7jLFlxW58/DKMS/a2t67FryI1chim3HcTRt/yMbBUAu2PLEP7I8vQ+Y+1GP3VyzH19k+jYkIL1nzpLmT3HdlCk2/e/jwOvL4TLe+YiFe/9zSe+9LDmH3Dadj61GYs+MZZKB9y7JQeKVnJDrcZKRMNJzRh4vtn4KyfXox3/GwR2tfuR+3oWjz95SeQ7kgf7SYeWyaOwr/Db8wr4nolgL95y4oGfgY1QlRtughRv038AG/M1XEkV0At0qlOKVXSUMZadSRIrmsHpyoyBLhkXZ8I7S5jcp7IMMF5l9ru/mQzDgtGiOBNJs/B9rk3FFkIjUNEbTOpCKsTjJjpXNWpvq1EhgwfFTI14h68iFuPrrkhkBA2yrglERQiJOvGNfQgH7l6IKbLF4iYSLzrhdWofcdsOA5HanwbGj9wFvb/cTFyB3pRMXkkcpYBgzMMXTQX9blerPniXRj/7SuRaqqD7XAfLaJ7y8iPFAUQIl0wjea9Jmb292Lb/a9i4nVnYvWtj2PKdQsx+eNzse3Rdag/oQnGsAak6b4nlC4mKszZXF4n006gn4c5ROUR2UFxAnSESFUb7gcmLczA+irpOKMlN5ARJyQQ8cdcJ3+bIE+IREC5IXzZCT3tXXum/H1zUEg9EIRG30+hNgfaryFgkvek3B+RKGK+RAGdVK2hQAdrOiqmI0UqMqRO1b8FovtFLzPEmd8vIWkCmmoSBQ7CiJR+7rWTmnHGDxbh8WvvR82Yeiz+92dw5vfOh2kwidj7bYiBfSTKY8B2DJf4z6KR7rCUBd2v/r6Jd6cKPkadt3rsFIJFaWOLmMv8HcMXx5RCnTx6m7effRZuptp9QojXGWNjATxZ7MYlhKhkx6U5OQv9a7agYsZYuazugrkonzQSdkcvjLpKZPf4AnDD3nsqGi+ei/Vf/S2EfehSw4UQsNNBZ8SxbLz+3cfQct4UDD11LCrb6mH3ZdGxbi823vMaTvzCwkN2/JKV7Hi2qtZanHTDAvTu7EamI40lt74A5xA+v8erMTF4i7u+FRNCPC2EuJjqlgkhNgghPlPs9oMaIar0ECKKkKU8v20iy4NjzjpCpGe05GxD4QwFs8hyVjAaUpEhwCs1QWn2jLLN6OpqyBAhRVxASME3Dhvcz6zSUCYVGQLcyERmUGkRmiFIgsDjEBGnQoQRIjmvRScSGaJUZaamXlNEE53i6ggOEw6S3IKlaWjEFdiNyjbzy36gKFM1iEKIUASHyO7sBy9LgpdXwHHgXiYzgeE3XoX0hp3ofmIJNlz/3ygb14KyKy+ENXEI6i6ch733v4juN/agcmKLPGdb4xLp2Wc6atH+6nasuPkx9O3qBjwEZfoNZ6J7YzsOvL4LzWeMR3pXF2b/5yLAMDH8/CnYtfgNbNjeidk3XwzeWI+0pWROKTwxwH8eKAPI7QuvTZ5YHpmuGxOVnlsQKWIuUqRyiRIsyC/S07RzGsJgaVwg9TfdVBFLzlx0KJyKLCK3kSYAivf87LXIw4X2EYVmxSIyyj7zcZFUyycSq/+Wr8BpIe7QQNAtsmKQIb2f4q6FFZGhGmdx+448Hw3BGX7WBOxeugvdmw+gY2MHnrlxMebddCaMlKlc+yD6KI8r+93x5C2YXJeURkwNLY8SwDU1np2euZmPB0h8oASCJVB0RRdpPFx2JqfdC29X8zLLbgAwGop/I4Q4u5jtSwhRyY5bE5YNYYdfXmVjW9D0iUUYdcu16Ht1A7qefg12Xwb77n8eYED52OaDP6YQWPn9pzDp4/Nw4WOfwqwbzwUAvPZfi7HpnhXgSRNrf/E8AICbBnq3HsCG/30Rnat2YeInF6B2ysEfu2Qle7vajOvmI9eTRcv8NoABiz/3ELJdmaPdrMFtgh35f4ff7oarVXQjgC8o/4qyQY0Q1Zr9APxsF4o+k9xEvyx0qUXvmhiaXgQyn0ndIdLR8XSEAgiRZz53KDgvUQyH+ePTzIs8pD4PRSUat0axgUqw540MWHAdHlfNEwCkxln+40dFzDqCk/LKKchsMO5va2v8LULp8h5T238UMkRmNtQg0dSAvlffROVJE+Vyta+ZacCoLkfvqxuw/dHnUTl9NMZ8/UowgwMQso2+9k0Qcch6vpbjAF0rt2Pf8+vRvW43cj0Z1J1xAtJgqJw6EhUj69G39QAA4MCKHWiYNQLty7dj1W1PY/fi9bAzFlreORVN75wOy0HeyFBdTlFnVFFfX+8oyO+KjFS9zWnNENtI3j/etRKGnxkjgmKOnOUCfcS94+v8GbWNuqkoL2MibykNHT3gynMZx0GJM5VjF4e26Oew7vYXsPXPr6G8uRrVYxvRcuY4DDt1TOz+o9qgagqFEanomNVtR1DckFCKYrLOCqE2ev8G9kdotYfe6WWF4q7NQC0OpaPMMHkcxgDTwJxvnI+nPnkPTr3lAmx/fD0evuZ+nHzjWWic0RIvGCq7gcMWLiJHIo7EpwzpG9FE7oshQWKOiH6Pce0ZVVFZ/YkMZaRFvA/o3UrfRFnwlg8A4zgCQ1hHwSwhxM8OduNB7RCVrGRvxeoumIv2+59BxewJcihTtURjLSb++otozCVQzdOug0TV0/d0YNedT8CorcCI97sp+XHW++ZeLP/XP6BybCN6N+zD5C+cK49X3lyD2d99D5x0FhXDa/HYeT9Bz6Z21J/Yii1/Wo4xHz4F2/68AuM/VeINHWtm9Waw+e5lmP/LK5DrSqN73R6s/smz6Fy7FxM+egqsviw2/G4pMh39GHPpTNSNLGUNHk6rHFGLE//tLLzwpYcw/Z/mYeis4fjHjY+i7bwJmH7tHBip0ufubWAPMMb+GcB9ACREKIRoL2bjQX2HkKec8hSrMx5S1GcnQxoOPldBL/7o62lQBEMfPYMHtVQoi4iQIlXnRs/2isuOkqrJHCFUiSIcWkrZUuTUG0qkpfKJoqb68dWxfD2alarXWsSVFPF1uvSISo06Y7kK3nIquaKjZlJt1+F+uRStzXoRXTIhWEFkSO+T6tNn4MD9z6DzkZdR/86T3XW0dRkDeJLDYFzu+MCTK7Djv/+Ooe+ei8zOA1jx4R+jYmILWq9cALsvg4Z542BUpNC9pw8bv/8Qsu09AIBpP7kGzLtP0pbSloZGGNxBWgg0nTsZPGGi6cyJyOzvw7DzpmHLPcvBqyqgc0H18hgyi4kmMaiFarStX2LB532FtpP71TgMnklUyDHkc6ZrFNkeikY19VSOkcwMLZBllvWeWe4IJBynqMysKI2jOKSiEHfJESy0vyhjZSkkasthZWzUnDAcdVNbMOyMCXj+479D3+5utC/bhvrpw1E5sg4vfOYenPjeBai+bDwSVW6hYalRJZjkxdBzlpV8lbACd5zR+ZosWP5D1ytS9xtncl0FXXa0oIIU6/V3hdIif9sYDpau01UMqhQqcaEgRkPnj8P8H9Vi6TceBTcNTLp6Dva+sg2PfPQezPr8QrTMafHWtQPHdbXm3GxNn6sUPC8dQeXetgnYvuK1zlGKeUZzwijI3YtDirgQctsyT1eMOHsZEZ3xG2VHguR8FOwqb6oOkwkAYyPWDdmgdogOtVWYudgU8cNhAy1G+lYtiuB4PB0PAoVrfSjGDI6WL16BHd/+Dez2Lgy54myJ3NjdfbA6emAf6IK5L4Md6zdA2A7s7j70rduOsTdfg/JRjWBMYOh50/HGl3+LdTf9CQCwtakGleOa0LN6B5ovmYOyEfVgCVM6Q3HWt3Efdj+6BgCw88GVGHftaVh9898xdOGEg+qOkh1dY5yh+eyJ2PHwakz6Z1cpOdVQgbk/vgx7nt+IEe+cgoaZIwAAre+ahs4/vIFXbrgfc398ObhZom8eLqsZMwRn3P5+7HpuI9be/hKqR9dj0kdmY8m3n0TbueMw45/mHu0mluwwmRAiery6SBvUDpHuKScMPxolXpGOENGYqsyWsr2oidvI2iYqzFxs/SOpYM01nSJFh4hMRz9sjR/EvN/IKRJg/pgtcWkITSJOj+F4+wgjQYY2Tk+mRyBqZKVrMoU4OLQPhDM6pHK1ltFBmRjq+lLrxgzyR6hf9Yy/rO0rccNyr5dj2l7bvMV2DKdIFKFurfycHN6Ikd/+OHbc/Fuk122FUV2B/je2welNw6yvRqK+EjWzTkCqbShYwoRRkULLJy+EUV8DwIEQDFUzxmDWn/8NgAO7J43s/l5kd3Wg8QNnonxMkzxWvxc6qlmCZAYTsMqqUHvSaHQu2YRJX16ELXe9gCFzx2LsJxYGImiZfUj3h3cNdJRA9jOcgo6prg2jIkV6NC4j0yBFRT5rNngoqyahR9zavvqdpOQ56IU2qW16HSYOAwluI2nYsYhNHKdINTXTTZ2P1bkRqu5Y/n5tvWQW/vHx32D0FXOQqneHxCqG12L0ZbO8Z8NdL1FXicoxDbCez8G2BGCwIPpCz4bQiuRSGwkdUDMaZfNj7osQpyg+E1XvA6rQHMU/kppX3IGpIEShfRRhA1HV1k2/JyRPVLj9M3TBeDSePAqv3boYWx9bjzNvvxxPfvQPaD1nPBonuarW6nvNgpsJKd+piL4GhsZlAvcLyhKvyPaeWZllFkHYkbpD+vWTxf2CKKv6u68J5k5THlKUcvQyunnsOESIGGMVAK4H0CaEuJYxNgHAJCHEX4vZflCHKQQ/yrRE78aoNfpQa/a5f3vTCiMLwBegoyml7pcbOZmCTJWlqXhsecJb7s1XJLKB5SnTRsKk4Tt3W9NzXjgPOiyqQ0SkbN95cs+LhtL0F7OtPOC6I6RbyKlRXqrSsdNKlFh29PKs7Vetpqla3Vpd7h/Pbyv1Y5XXb1VmNrjcm6d+Txq2TPNPev1J89TPhvJyo+lAHCH190RtJdq+fhVqz5mN6pMnoO2mD2PSXV/CuB9/GqO+9VEMvex01F84Fw3nzUbV/GlINFRDCEWQ0eZgpoGsKEeivgp8ZCvqFkyG0epG/5msR/rPegUxsybS3rJszluWM2FWl8GoqwEAvPmzJ9F84Qy0fvwcMO6SzG3NgY2TkqBroS4v9oPiKPvo92QTyBGhRAWZOi+HyIKp9AYcCdXTlMp+0IuZnlX6vcbol89ijemm8Vd4z2NKbuvdH3Rv0P3CLblMlzggi/oox0lw6P2ql/CxhU+qjpN6kMPOQ2vQfPYkbPqjXx0gztFqX7INEz4xHyyZCO0rq4nF6m2XySKKcKFuep8QFUD2r+FP5fOmTWkdU5ZE8a8JOeRJ7UM90AQQOmd1Wmh5PqO+UvuQ+o0lE5j++TNxYNUe5HqzGDKjBZ0b2v1kHKVf5fOu9bV+DeT7U1meUxIOAutKgdLwVNI7RDC496Ur3CkFECnlWdMDkTJvmJS+h29juwNAFsB8b34bgG8Vu/GgdoiAeKcIwICdIgAH7RQBGLBTBBxZp0jdz0CdIgADdopUG6hTBGDgTlHEORfrFPFUArULp6P2zJlIjRgCxpjkaVHv+tfA34nqFAGQjk7ac37I4dGdInXdbM5Ez/INWPqeW9H++AoAQNsn34HGd5/qbqOoBB+sU6S2v5CpzuzBOkUABuwUARiwU0SosLqsWKcIiNclK+QUASjaKRpxySzsenQ1hJK9GfWhr57UhE1/XIZsZ3/kh3+gTlGUSU6O5WD3q3uwdfEmbHluG/a8uBmr/rgaS259Fs9/5x/Y9sJ2yTUq1ikCEHKKkvmUmAtYIadoIKY7RQACTtGIcydi80NrkensR7KuwjtOsF/VvwfqFKl/F+sUqX8P1CkCwujsgJwicXwKMwIYJ4T4DjyqlxCiHwMgWhQcMmOMjQTwawDNcL8d/y2E+CFj7GsAPgFgr7fqV4QQD3rbfBfAWQA+L4R4ijE2GsBGAJ8RQtzmrfNjAK8IIe6MO7YuaiVFr5QINa4AJQnHUZSUYLaEkE2HpjS85mUW8aBDkPDWy3EjLDTnfSBJoJGeYYPTVXfkkJifmu9OpUCjNyUBSJranMUkbyqpp1RkVh0i0wrcCu0DHwdPq9GmHDIjyJwHhxtM5rgfD+47N3ppCVnQ0FueiSkgqZowtTY6weK9th12FP0F0cuZsn7c0IcQwTI7an/S9ZDXmj6MUrBTa0ZUO7xlncu3AACqT5mExvctROXEZqRzvlNtcsd3ADXytNAOJD/8ajqu97ftCSAWRlA4stLPTsplkUZp+cp1JudHD1TkcJg2XJNgttwmp4k4xt3sXHAkmBUg7oYIwvQMKWKOZHFOkHR8Y4aSo66f3yYE1nEEQ3lbI8zKFDrW7MmrI9Vy4TSsemwJ9jy3EcMvmCqXZ4URcvIiU8uhDAs54RRyO53F5sc3Ydcr27H9he2oaqpEVXMlnJwNx3JQO7IK9aNrIWyBl37wMlLVSYw8tQWtpzSjeWawWrzez2opjDixyHwWV+Q4fLzwu6hYwUl67znML/VC/VY3aRj2vrwF6X19KBtaEdq3e175h8wcRkOFdmA5eNQwGg07ayn7eYxrpHW5L29T+rYkmC2fO7oHaNjtiHI6B6dlGWPl8N4MjLFxULLNClkxHCILrmOzlDFWDWAJY+xR77fvCyH+S12ZMTbZ+3MhgDsBPOXN7wHwWcbYL4QQ2WIbWLKSHevW/qfFaL/3GQBA6xcuB0sY0HkfJTu2rXbacHSt2ZVfWJMx9G87gIbZIw/58Xe9vB3/+NbTqB/fgFFnjMSJnzwRDSMqAURo7wCYccUkrPzjOjx36xK89ru1uPqJyw55mwaT5XrSMCuTSO/vRdmQyqPdnMFhx6fv9DUAfwcwkjF2F4AFAK4uduOCDpEQYieAnd7f3Yyx1QBG5NmE3vYCwfhqL4Dn4KbF/U8xjbMlGhJvEo7XCKBkMorgCfilBIMmYWAp5hgsC5LlRmiIQi/8SdOcMk8oR9KxkWKWj9RoBOxkwufWAEDS9Dk2xRZxdMBC0D4dLxQJa8N3puHzj+JKgkiCNOMoc9z+kNGrHYzOpdS9t1wfcss6Rkgw0xbB4rKGQSVKlAguJiV/ICaHPQjlAWRpFfX3QIagiN6WTB8OBTwyvWWjd/kb2P/7JwAAw790BWAakeVKhPCRKIoEpQAeRbvecUPpuhzy/rccDsvhIbG86GGIINk2VEjU+90Xf1OGrOTz5j1TIgjz6MRpR3D5Ya4wMoHzIKFQLqNcuvcMlDkWckYWvigkl/sDFORSokAKx8YL0i0SLvT6xJIIKsE9XhvplNQ0fynKGf31oHVrJzehc+WOvIVi01v2I9VYhYrmagAiMiXbJzV7C7TdqdILhFCkd3fg2a8+gQXfPAstc1v9JBHhQDgCSYNS+G0IIbD95d1Y+Ye12LFsD2Z8cDJOvNpHqyQlgfqEUGclkYIr/ZngdqCsD6DcRwqKJste6Gn2Eciev01+flG+VH0dcevd3oVUfQXsjI1kTQpRph8zLmVeN7cgbBCxoWeVa0hOvn0RMdvw5DZCxV6JcA8Gh/Yr+5H69+0daAkhHmGMLQEwD+5d/FkhxL5itx9Qlpk39HUigBfhel7/whj7CIBX4KJIB7wKsxUAnkVYMvtmAA8xxn41kOOWrGTHmjmZLDb/649h7XULyLbdci3Kx49ACRk6Pq2suQa7n1yXd53M/j7whIHu9XtQPX7YITt2745u8KSBbFcGW5/ciKph5WicOhRdW7tw7+X3onXecIw+sw1OJovV978JAJj+/ok455vzkag4cjIkR9N6tnSgamQt6icP9aQ3jk94ZEB2HHYBY+xxIcQ5AP4WsaygFe0QMcaqANwD4HNCiC7G2M8AfBNut34TwK0ArgEAIcR1UfsQQmxkjL0E4IPFHDORcVMj/WwGn7TsA0LuVS2jeeHxgmjqkDCjiazwRRqBMB9HIkIeqSHndY/NuF/gE0R6ZfI3wEVOAJ9zk3MMGSVUOxxJZrip9/DRJJ9r452vSfwdDsdJBtqoW5SgmSPRqWAEpQpMun9QFOohOsIvnmkafh+7bQlGNiZ3UG6XRSIOhoySglNZssRD3gyHo9w7XkoK93nokSd/RtfANgjhY8hRH8cgRUx7wv0WCkDjaalcoTokJUKk8o7k1oSs0bwmRRCFHvSu3oyW6nqwIUPR/C+XIDW8AQBgyixEDxHzLopt++2RZH0pJBo8jpRgsIl/JOSyCrsMtmDyWhAqoveNakSiTTpErnXneU6/ntQuB4xQKg8NFKH9B+8PE8KXcvDOvcK79vRcZpRnFXCfxzI7iTJeFuYDEXJBUgASxfOJrUQQtzVEQWj7CN3LQv4HkpaKJQ0LF2nqXtWNtjFtaHDKw6t4+6+cPhYdc6Zg721LkD2hBaOvXgAw9xhxwqtxXDAG/9rWTJ+M5GeS2Ld0B4QQ2LtjOzYnNyHd3ocLblyEVKWBvSv3wuApLPryuWiaNsRXbvcAdomSS1HH4DPmIJy1V26XwWC27HP5vtGuhSyaGtEncRaFEIkYRIh6SF2b3oEMApvvew2pHQ5MK4cZ58xCg+1fI38fDFVOGbgdRu7o2ZHPkhPmb5JJ1FMii4R8x5+roSKvAJI2PSdBsrs05vjPm+SYFX7OdTue6EaMsTIAFQAaGWP18G+HGgDDi91PUQ4RYywB1xm6SwhxLwAIIXYrv/8PgKLy/AF8G8CfADxdaMVc6oB3AJ9MDQRhR13DISdoeIayZfysGWLyE8lX1qwBQZTBIQL5YnYMJcsgJgOEaUNnMORH1hYM+1naf2FoNbGSWjZHgts+QXAADpE8XozTJj/opJitOD2SfO61RXeIDE27qdsIZzLIYRrt5U7DY2nvdsvB1yEiYnba+yimRbBGD+l5CDCpBmtjYA4Rg/BfpvD3p9oekfHOO7wvIT+k3nzMCxMArAPdOPDnZ9HxtxcAAMO/ciU6WqrAPV6fqemHUL/bCqHS1H4ztH6VSuvMvybqEFmH0RfS2sqXBUSZlyklzR1AiLgp65XB8ZMXYrKM9PuWQ8Dx9ivksIznEEU8q4A/RNCf7JFD0frQmSUdouCwTdYx0OeRxS3tWZJOlfbMqsbinPsI2/XEWmxavAwn33oJOiKeC/lhL6tA4ydmorp/OpZdfzd6H05g+KLpgTpvoWmMQ8QV4jCHQPU5Tag/t9Ftu3Cw+5UdqGxIoX5CAxLcxpBz6uV17UNPqI2SFM+jHSJbMCVryv+tN9EdGioLDaGJsENUKJNMVYyWywqQqwOEeu+arvrRU+hYtRv9PZ2Y+J42NF7ahi7eG2oH7bvT7A3tTwaLWpZj1DnoAaT+nEeZqTlEliZr4PAg1cNgfgCr6xHpStlvI/skgM/BdX6WwH/9dwH4SbE7KSbLjAG4HcBqIcT3lOUtHr8IAN4DYGUxBxRCrGGMrQJwEYCXitlGl5G3lWuuj0GXMVLH89ZVH0TPdzLNoMdNTlUo5VFJn1Q1J5V+g4AAACAASURBVID4YrIy/dMx5N8Vlok0z4aywOjDljKCHKJsnCihYvrLQS1toX+4w/PBaU4wCMMJrEuoBDlThiBOj4OsYyDNwreO5ILEOER+OZX4gpDUJ9QXhLgJ4X+ksoTcaUhRnJieI/zivHIbpW9sxiCk8h052Aq+pDtChNjw4HFELouup5dLZ6j5s5ei2iss65cKoam7DRW1VblS8voYQWdf6C9VNcvMM8qU0Ufmoj7sulNNHzIKBHT+A3GNOHPgcO1e1g5ITistT/Fo/h4AVPBM4Dj0krcFR8rIwuFZpBjdqx73TD6b3roItl3luOnnS5c2X8HnOAdFN0cwbP/raxj/0VNQPqQCkeMQzF/XEQxGysS4j5+GtT98Ai0XTsubFKyj2FHCqz43inh5wLCTXS5RvjrOgFIeSZM90D/2ORih+40cOVkGQ+MYEa/GVjhE0pQ+iTTmxDpRevHaqDJDDmfo39uL7Q+vxXn3fBiLr/4DWua2evwctVOC2Yrq/uLmLS3j2AHzr48Mdol7yQJ9Edwv3X/edfMQIdo/cYlCgqVMhMCBuDIgbxcTQvwQwA8ZY9dRJvvBWDEI0QIAHwbwGmNsubfsKwCuYIzNgnsrbYLroRVr/wlgWcG1SlayY8ScTBZvXvktJEc0YsSNH4LZUIPUqKbCG5bsmLdUXQUOvLYTw88cX/Q2dbNawRIG2l/ehKFzRx++xr2Nbd+KnRhy4nAkKpNoWTAam/62Fg2fOeVoN6tkh9GEELcxxuYDGA3FvxFC/LqY7YvJMnsW0THMg0W2EUKITQCmKfOvIn/yGAAVGaKIMYychNj1Xkt1sSrwMLxI5g+ZBYW0coqwVlhsKwjZ5zSkKGub8u9KlkDOyIRQJH3IzNKjqAgLjasrsHFc9kWcsJy/Ex9dsj1ERmY8ME0PiHFkHRMZBSGKi54papIKx7KorhMa0jFlWQFP2yiidIBesD7HgmKTusnzd5jkHdFhZOFdBoAxSavyNYDCEZeODEkkyrbx5pWuGGrL5y5D2dgW7ze/X0Jt95AhRxHo1K+Tbn4UqN0nepYZ45HRPBAc1tQjcIl2IrgNPS9UiNdktkSL5P0SQmOC3Am33AsNrYjAlM6rmrtijWrh5gTPgRnp2GeTuH0ZZXhbbbv6t64voyNFKuIQ0gWKQ4jAkKxJYfvfV2HqdadHHi+0jWAAGFovPQnb/rQUQ04ZUzCTaSBIkf7ejLI4ZIiydm05NOlpskFB1b3dm8wdOqXj+cO/en9z6KVAijFd78hHmbRSFvrws4dI9a7fg6GT6lFhZnHCh6bhoQ/+CbM/fRK46et0+bglh4sTh58LMl0lXB2yC+tIETIURIzI3Pdb8CElZI36jxB1fVvOhP+8CWp94Sy2kB2Ho2uMsf8DMA7Acvg3ioCrpVjQ3noec8lK9jY1kbMgbBu5XS7XbdQt16JsbMtRblXJjqRZfVnsfnYD5v3gvQPedtiZk9C7cR96NhadFVyyAVjXlk4YZSb+fs2fYaRMmBVJ9O/tLbxhyY5lmwNggRDin4UQ13n/PlPsxoO6uKstuQ3xnBpdj4HM5y54ZQSEEgtoXrTOIcp5BF/KcMoJwy9doEWqPkIUjKiyhimj2HIk4ZjpEN9IV9BV26flFeRFhmi+EHfI0ZarRm1ghFgQhygCMbIEiy++qliImKpkaxACRFFtmRerEWdIzyZSozB//9o5eG0SWkaZY3NfXdoObsS4cJGCYMAFCOa3X8oTQ55P76tvYts3fo2hHzkXje+Zjyn33eRtGIzmYpEhBb1S2+odwTshryl0/WSqCnE2/H2p/RYgCUvZGhFonkrKzcpIVOdOUNQZ1JCxOfOLWGqJCT7xFAdthBTlhAGDZ2DwjHwm5fPnoYOkVJ9BMJrmCBOVQ0aLlesat23UPoQQ2PaXFWiY3oyGSUOgX/so8m9AKydpYvjFM7HtnqWo+cK5kU0M6fMoSFEsqTim72m9FLdikSG9OgCdUg6GT+RVULMoDhFiEKOotuQzs8Aqkj8T4rpx2GCwe9NY/mOPpnqgE7nuDCrqkoEiyZTxaCnnE9J+iikKHIkQxbzzuHaNLOXY9JASz0jn8Pnn6yh/B889ap28Jo6vLDPFVsKtqrGz0IpRVkKIDqPVJY5sob2kqbtRh9ds5y189Q7CDqZe0qEyu7cfwkuH7X5pDbZ9w0Vg694x+6i16a1aXAX5w2W5PIHN4TCqbXg4bPuTb+LBC36FDXevwNjLZwI4uP5suWgm9j3zBrIHBv6uGMjw06Ewndw72G3mh6fghMsnIVGZQLIqASEEzPL4czjSZS+O9PV7m1gjgFWMsYcZY3+hf8VuPKjvcErD1U39MDoyIgxmIOjaRUA4Eya0X+nVhzlLpM1S5gl3hDgNLDjvGDnkHI66RB+SIgme6A/zj6Ryc1AZ23I4LI1XpL9sdX6QAxcZSJo2bIciM+IF5ec0qIiRU+Cl7oBBQFW2Du9ToiIUFRlBRy2K/xDHu4jKFAprtri/GRoXhNGUK1GfnuzC3VBJpttr3CHhODjw0AvYd+ffUXnKFAz/4hVItQ1Dy+cuQ82CE8AM7temi0QRaKqhdLouksNDx5Zt1M6X+B10P6sZhlLzRavbpdd2okw/VdU6xLGRStbw9uHXJ6NoVvIqCDGi86IOVWgSCW4PyClKMNvVaOE5v7YXZbp5U1nfMCK9uddKocLIwuL6sxOtaZT2kDIHrCC/Yu/ynWi7eCqmfGq+ty93eaDYbswHT71WZl0lhp4xCRvvegkTPn1W0R9lVenZHGCGUV7Hxmt+FI+StjXkteeBwqi2xq2Rbc3DjSwG0ZAq1zFqzx1bu5HuyKC+rRLldb4KdfP0Rjz//WWY8cHJ2L+2HY2Th3jZsyLUpiBfTsti0zhn4XOIQBQ17lksn0zJUNM5UxJ9lfsgpWoh7zdH65N8oykhOz4Roq+9lY0HtUOkl5yIMj3tUC8yaQj/RicdG3UZ4L9cKFVYd3aCx3O3qeKaY8SDVcEdMFm0MuGkUJbojUznd+fDxNC4tP5Y2FYVTtMqpuvOk27qMIst0+uDLwxVKI55TokueCf7SBuTiyqkmNXIr3E6Nup56n1AH195D5jBtpMbxhiTH2giV0unzXOG6LrSedo2h93Tj63/+n04fe4QTu9LqwEAieYGJJobXGfGRmjIJZ9FOUKA64jR0JzUfCKBRs2hjSpzQM6fLdwyJHTutC8aKpQFZBUiNpmehp6vdIF8eTNa17uesrgl7cSbcO5fF6ZNOT1/weeQ9ivLSUSZJAcHh3xMZsvhIN0B0LWM+qxU4HzTthlKC1fvaTtj4cCq3Wi9aKq8j/OZfw8joMlDy8dccxqWfOrXaJgzGkPnjS64P7Kw5k3++48rTmy/HSzo62jvPmqb/q5yd+RplYHBBpMBXTwJOj7QKfdQPF3LKMr0e4wzB5nuLH5zyQOoH1uL3j194CZH3aga1I2uQffOXjTNHIYTrpyO+674C+Z+YW7wPJR2RDkHeiki/VyciOH8OKdN3yafhdfRHlS1i0TwQStm//62xa96rJgQ4qm3sv2gdohKVrKjaek1m+B09yE1bgSSI5sw5PKFR7tJJTvK1rezC0v/40FUjR6C1kVTD8k3JVFbjilfvgCrvvUgan7+QaQaqw7BXt8elqxKYNx5o5DtyeGin74DANCxqRMdm7tQ01qNye87ActuX4HmOc0YedpIWG9vuZ7j1hhj3Yh28RgAIYSoKWY/g9oh0qMUXT0V8MWsJGpAQoIaYgRhKIhQkIhN+6UI1Y+SfKjW0AiFZBU8G9gmFxHNlhn9qDB75TpEBM1H0I5DkSgai4pS9OG1uDR83SzHj97lPuygYCDtgzGXjMeYkPL/FLHpBRvJ9OGinG2ECdcxUa5KPNfJ4oZEVILq2rpApGVzgBEypJGeuStyllm/DT0vrYbIWmj8yAUAgMo5UzD2998KiCkKJ4y4yeKuzB+i04fP4kQxuVIqhZAgfxoUy5R9Iq+Fh/pA+NdPuXfU/pPIkHr6Man5EvHSUbvA/RQcTpO71IpbSmROOBIJksuYdn8yyQD3zstxyz8oKJFP7CVSd/CdUG+6WUQJZqOPuyhIWht6p+esx/KUrLkmtCdYGPlhQLazH//4zD0YddkstF52kpfe7d/rhYnCLBKldcBQO6MNLYtm4LVv/A0zb74UiYroV7P6nMQP0xROwab3Sc5DimieULUoZEgS5jWSuC45kg8NobYlmR27DhBEm/QhMx2VXPj1hXjlJ0vx+8v+gpELRmL8ReMx9pIp8vnY9vx2zP3K6W4R1hixRw73mXX7LNprMkPHD5OcVWJ3YP807JWXYB5Ni5DImNxWaZ/WFzpKF2cMxxepWghRfSj2UyJVl2zQW64njTXfeRjZA4c+Zdbu6sW+3zyCrf/2Cxy492n0v77pkB+jZMeH7XluI2qnNGHM+2f7tcAOoY360DxUtNbj5U/8GtsfWAFRSGa6ZAAAbnKc8tk5uPSeS9E4rREvfe8lPH7943A8OMjqt5Cqjq5wX7KSqTaoESIdGbI1pAHwicOymGUexIiT164TsYnUphGz1fowftRFy4LTMo1TZIP76/AsEjwjUauslkJM5HGJUDlmLHpEYl05GYH4fIh44bBoLlaAxK2hSzkS49PKjQhvvJwzIUtJ6FGJHgX7nB9vH4zFpuSTqQiLzUzsevh17Hr4dUz82ntRO3cSAF/QUkdjKM3XksiOALMFhBDo37wXfa9vAhwHdkcP2u9/Dq2trUiNaUHz5y6D2TzMRWSUc6C9y9R9EfxB1oYjPTzhgMeEGpJio4k8ciYkIiR5P7HRngj8bqnPg+CwHO6jVjYPrEu8tiikiJA1vSadH+XSpuHCm3JXdM/JlGuad/zni5ZRJ+l6k8p8QiRgOQn5W4pK83iWCAlUuFZm5tDtuCWf9Wepx/KWU8FmI4hwJJXzyyplGva9shWpYbV46or/xaTr34GG2W0Faw6qRoT3cEIE3JDdMDDphvPQ9foOrP/pYnS8th1Tv3QuuOmjNMVIA8SWLFGM3peE7hCnSK8Np67vI4R0T3GoNcf8JJEguuYIJu+pZJEE8MhaZvQ9iCEqm7UmJl4+HePfMxWP/stDWPfQJoxdNBFWxgZLmYG+j0OdokxH3hLaOajEaLks5t0Y9y4uxlTEyH82o9cpykr+dshKCFHJBr2Z5UmceNsHAQDrvnYvOl5+c0Db97y6EZtv+jXWfegWbP78z7D3Vw9h728eQ/v9z6Fi5jiM+OqVaPvuPyE5YujhaH7JjgNrX7YV7cu3oWPFdvTv6MT6n74l7masMcZQO20EZn//clg9Gbx64wNw7BLxZSDGTY6pH52FdX9aBQCwMhaM1JGVfCjZsWmDGiGKQ4ZywgiNzcrsshjEKKfwEKS4mYYYkemVg4McomD0rBfVo0KVqplGBsJIy/lquH9nZYaaJjrHjVDGG6FIOSOYDpsRCdlHFKHp6a96Boc+5h/MavOmnCqRG4HltuBIOLbHISIEJsgl0o9P108oGV5cQW/I9j+zBtWTmtGzZie6V25F06KZqB7rVvCumjIcbVctwLbfvYhN3/srWi45EVvufA4n/uIjqBkXdGQozZrbBoQQ2Pjvvwn83vYfV6JsQit4KgHHSCCBFOAENWsCvB0nGNFLZEgt/wGAeRsxwSEoE4YTmoPAvCz/AVruSE6UjgzF8ZEsm4V+twXzngHiDlF+LnGVFJ6O9p2VyIKG2BB3QuWTxJZrUYQRAR8xAg/ziqTyJPfuYUdpG9zUegYOR5h+ZhHoeY8vFkvWYLhV3ekZ6bbL3Xnv3k54HBDTceeTSjaRL4IJ7HhiHVb/9DnkOvqR3e8O2/Zs2If+/f0w610CdEgQM8LcItERmauAJHRIXloqialfvxiv/du92P631zHq3dMC5x9dXsSJnA8djzkSbZFtk9zB4Dn4Kf2OLC1BGJ1BiFdMFXqZSaqgWYUsigNjSTSHsuSi+1nlIzXObkX3tifQvScNM2Wivw9gVVGfO58LJzROWKHsPbu3H4//x/Po2dmLWR85ARMvHKNwkmjvwXevQd+pCI5UIVNRyPxlYYowcXxxiA6VlRCikg0Ky+zuxBvf+jNe/eQd2PbbF5Dt6MPKz/8e6297HK9cdTuE7aD1inmoHD8MzOA48NJGAMCbP3ocANC3tR3Z9jDHiDGG2Q9+BTMeuBFDFs3BkPcuQNXs8TCqysASpaixZPGW7Uoj29GPvm2dqD9xJKZ97SL5W+30EVh23W+R2dt92I7PTQMNJ49C//aOw3aM49W4aWDEwjF48/5VSDVUIL3v0PIPHcvB3z//FCqHlmPhF07CSz9fgRd/shyZnsMnBlqyw2+DGiHyC7YGx10NCBla61lfulkRGTc6qqQXTpRG0ZHgyt9ulyW86FZmuTAq/+F3qSF5OIY77iszY9w2JaWonIc2eRGrIRzJM6I2qgUvASDD3Kg3ISji4LJ8g41gf0mNEeIHcS2DjSv8IyOaD2ApWWjltokULP94Gs+Isi8o2iyGX9H35m53H31ZpHd1ILu/B1Z3GnufXA2rK403fvQ4KsY3Y9wX34UVn7wd2f09mPZf7wczOLq3dWLp1Xeg9f2nYMwnFkaWNXEcjpZrL5TLpG4gc3WVJMpDUy58BCjOigjG4sqp+CiMjwzEj4xofA6e554XTHKaAJ9jQICHmuXmyCw1WjeY9ZXPQvwOiboGnzG1BITMsNG5QrACy9NqPzim+8zpPCPQeQ18OEkiKAhyQrjporvcToAzgZe//Ri2P7sFqboyTPzEqahtLkP1uEY0nz0Rze+cijd++jQOPLcWI997ImyHgxvufrNWMBOUTIhoXpgDX0RUZvh5XWX1W2CphM/T8TrAhONfLxZ8ZqO4WOp8zuP+AAjpnekZaip/zH9PumaLaERHFzRUS/WE9XqC14/yAXPKkWR5DY03GXdcauekq+fgyWv+hPKmaux4bgvqTmgOZ30pbXWz5tR9B3XqVA7d3rXtaN/QiQtvOwdG0sAlvzofz97yMu5adB8mXDgWp15/MrjJwxptisijLdsyMA0htQDtwXCRpJUQopCVEKKSDQpb+/X75N9Ofw6VE5ow5PSJmPL1d6P+5NHI7O5E/+Z9KGupQ+X4JoAzWN1p1ExrhVmZwphPn4NRV80/imdQsuPF9q/bj2e/+jg6N3XgrF9eitN+eDFGvWsqDqzciaqxjWg6eyKS9RUwK1PoWb8X4jByfPp3dKCsuSgJlZJpVtlSgzk3vQPJmhT2Lt1+SPc9ZNIQNM8chj++/69Ydc86pGqSOO87C/GB+9+Dzi1deO23qw7p8Q6LiaPwb5DboEaIyGQk57lvthCAFtEkKDNGevVBVMRdV0GYEEaMDHkcX7vI3acI6xp5RmgMoTu6LgoApOwkLLtMzku0R6I/8XwIamNIrVfhWbjrhY8rM95IY4SxwPIAUmTQ+dD4v8drCiFFBiqQQI2RCaBGaltlpppBWkZhTQ7JPxIOnJyFqV9/N16/6c9+H5WbmPa1iyAEQ8P/a4UlOCzbgBBA7bxJGHL+LFTNnYKsxYCqGjRedIqbVWb52kmy7IBlKOU1tG7kwr2+dN/IFbjMopNRPX33dJRHQZXoGDLLS2bnIdAHNIBP3BgRoV2km4L7AECAc1Ro28NlFKnqJViiOER6gVn/Hg4ul5lpgkHYCTh2ys9IQ3DbuJesAUc+M3pWp87jqTH73eOC438ufhgHtvZizj/Nwtk3zYOTcnlHezbsxYa7luCkG8/Bs1fciab5ozH1ugVY9u0nsOXOZzDp2gWy9Ae9VdM5F++g07MFC5S6Uaub+BmY3ruJCThZC+2vbEbbVach5+mC5VMaJ/N1nYIIh0XvPfh8Q8nP8X5LxpSPKcboOJRRpqJC9J42tUwtPWPLls+Fo+iyuVO1ECsQfuepqtN0XsPmjsKwuaMgBCFAMYgK8zI0BZd9IXlWIcTNBgwDZ9xyNnYv241Xf/kq1j64CWfdfBbKh1Rg9PljsfOlXcg4ZjgTjgdHBQZigXIgtLl2Om8JMSrZseEQHSqzHCMkw344rccuQ5VCpj7cluK52PpvxZpwBJ7/wVIkaysw9YopQDKeZ1OTTKMrWxb7u27tz64FOEOiMom+TXtRfcIIrP3G/cjs8XkYZc01aHnXTLRdflJoe5LIb750LoCwc3OojXOhVaEv2fFuY09vRueeLGZeNQ2MMWQcoGtrFx656j5M/tAs7H5pKwDgwOrdeOKKuwDgsGgSAUDX69tR3lKL8uF1OCbC60Fsh+MaMcbQPLsZTT9uwvJfLscDVz2A079+Oqqaq9Czq+eQH+9QW4lUHbZB7RARykOojylREgPQPG2uhUt+ZprHbwGDAeFmVHnr6OiSrGnmRWtqRpn8jfbnDSgbocwqPxvLYA567DLknHJk7EyID0TIUMIrDCsz5fKMJZd5Oiy+DpJ7fKrTluI5uSzUJkXnCFAiZcXnSffaWPmHtXAsgS1PbsQH7jofgFJnTRioYiayPAMHDDXJdCjazNo0b0MIgV3PbUaqsQrrvnk/ylvrwRMGejfuA0+ZcDIWKtoaMPT08WicNwZ1U5vd2mOCARBSb0nlBckabZqzIgscOpSFZcj1CiEonGvoheFAOAycC58HpKE++kdKLRBL6zi2lpmmcZVkROfEI0SybVTTzAmGrHk5RXLNIDoKROvTABE6NnnUdeOmoQwdR/k7xCFyzUIQ9XU4gyFM2BHFSO2I81G3dQX7g221tXC61kOGUoqG2KLPjMH/XPMKlv3oRZxx/UyYPAleJ8A5w/q7X8O4d03ESZ+di23PbUWurwrDZrdg9PkTkDTCgZZteM+D5Z6XUDKyvAV+u2kbyXETSDTVo39nF7JZBjOhoZRASOmbriehdRKp8Z4XmS3oaVW5/eUhQ4YVmNfNEVxmSOnIjCwgTCiQEVQVVxEimf3rvb9T3nEln0bjPaqdY2tIbRxSFNW2sKZR8D6lZWqtOZ9nxAPH0zPxAGDKx05BzcQmLP7KUzj5hvno3tETXedORZmKtBAfz51zJ/LxCp9PyQZug5pDZEc9HHAdI7WIo2pxJS8cwXy5ejkNih3SR19OhV9iI1z1mVJ5XYSk06rwpi7E3mOn5G/9trtun5PUpiltmpTnEfcA05BcQitqmeI56SzRsmruolOEUtF8tTdfa/YBABrMXjR4JQ+aajP40svvxqiTGrBn9QHUJ9x16hLux6PaTMt5+rs64R3HI6VWJTLYsXg9tv3uRSz98gN49at/wUuf/C0AILu/B+mdnaibOQIn/PsinP3gP2HenVdhyrXzUDt1OAzS6os4/1AJCxoas4zIqR3x4ooz+dIJCCV6Q2BaSQ2qSi+FGOXU+5DbXDpCUswxZupY3rzNZOFZRxNTdPRt5fkTmT26JETg/IpAGIpxhAD3WoRkGgoUH7YEDxF4aT72+VOm9DeV4aDnL+0Nh6UjhsXoWdGHselerzW8e9ubNhi9SFWauPa/Z2Lvqn144AvPI5XtRXl9GT74+/Nx8V0XY+H1szB0ZhO6Nh5Az7YuTH3fZNSNb0CS29Ip8qfuJ5SK9JLp5VvUoTTfmecoa65D+ahG7H1sZai0Ts4xQsWcs3pg4s2nPZI3DemlLTPkxMhEE5IkoDIdMeRnt/1BR6vCe/7LDff9U+nNl4k+sG4XMSmX7yuvb+R7zAsOyYnjthxOM7XkE1l0NU/b4ourxtyfDpffG71f/f40Y6dZx8SI00dh7HunYd/reyFsgf2r9ubdxhdpjHYwdUeIzj/BHOX6BIeoB2QlDlHIBrVDBMQ7RQAG7BSpfw/UKQIwYKdI/a3P1p2hQ+8UAThopwjwPxTZLXuweUk7PvKLUwAg5BTRiw5ApFMkHIGl//F3rPz5i9j7wiY0ndqGpvmjUDulCbO/eSHOvP8TmPODy9G8YAzMiqSvYmsEH/Ko8fAj6RSpfw/UKQIwYKfI3ebgnCJ13djzO4ROEYABO0XBbYpzitQoe6BOEYCDcooq65K46n/moaKC4XcfewrY34G6tmoMb3X3P2pGDRZ8aS5OvX4OKpuqAvpFB+sUAYh0ikZ8eCG23fU8nJwdcorcfjw4pwgIIzuFnKIoc4pwih776vO445w/gfe5fV6sUwTgiDpFQIQzVKRTRH9XtdaiZ3c/Znx2AZ7+wsPY9+rOvNscrFME4NA4RSUL2OAeMtNe8FyBBUM3O4IvWnJ4rEghNPrguvMEhUrCGxUr9W5CmzH/QSQSs0ba1oelAN9hMUQCfU5SpsjLITOuzStDZ/oDbsQwKJPesJsBRxK/9QeDnCR/KC6olaGmLtuCo2Eyw0/WnOmen3BfYvLj4ySQchKA2R8iqdIHK8UNvPOH54BXplA7phZGpesoZuWwm4DluG2KQhJU08scRC0jJ4mIpzF8w0jjTEAdcZIvF0P4BFitPAM5RY52TaQT4zA/ZZ+mWqOE5DQIeS5yyExvOKFmTmBWG0Jz5H4FCg8Rqr8PBBkCgkMuulMUJlWrwyb0XMUNr9HHzX+mTDuBrJ1UxEWD94ee7EBleByI0PWpNVyHnp4HKrdDQ9d0jASzkagow0dvmYiHfrENv77iUcy9aiJOev9YpFJuwDL5nOEAgIwTToiQ97QRHGY3bQcJ7kjnRJpgcjhWt4qpbTBrK9D5+g7UzBgl73mDO/6oiSbQGXc9LcV50cuzmDHvjnwOscldpIKcFV3OhOb797rvkI4VOzD0jGa3/ZrAbdh8WkTo1eedNwWofpspwSTina/1AZnurAdJ1UHEqBjr2tWPbY+vR+eGdtROGobnv/ggFvz0MlSPbnD3yf173+EU0OuSBNFOUEBwk+5VHhR6tKKG6aLsGEFsjrQNeoSoZMemjZw/AsNmDCsVVSzZMWuMMZx57Th8/FenYPuKdvzsokew9PfrYeeig5NsVwYb/rrmkLejqwoNfgAAIABJREFUrK0RmZ3Hrjjj/E9NxZgFzRg6sfZoN+Ww29gPzMY5916D6TecBTtjw+rJYuPdy492s0pWpA1qhIiQByrgSMThBOzQsEqUQJi6PIp4p6MwNCwRksJnIkTetBH0yH3StRJxeIhP0jGQcUxJGs3J0gHxiJEfZQURKV9WIDh85h47SAaX5xUnPKls50faHuLkzdPwhOo6J7mFFLf86F8bx6K+MqmPtAjIElz2jS7zr5talFSWndCMMkj0WD0gvpgn0mUI/+4iNrQfitBco/6UxGg9Ey0q+ioCrpJlGyQDm34IDpPpxWUZE/7QGnP79GDSeguZKgY3UFI1hwigRW5b9XWDKeAOt5EThkQeo4zQCSriLKecyWeUEKGkdm/rBZqjhuUBoGl8NS7/3inYuaoDT/14FV759VrM/+QJmHZRG7jhOvy9u7rw4AcfRLY7iw+8a2LkfhKOjST371JCl6OeS7q3bMGQ29cNXpFyr7tyH+lChLQf29ud7EcdMTJFCH2n+bi+jhq+rpC/ETpoR86Pnt+M0fObvd+I9E7wVlCuRH2f62R4P2Eljgfkv4uLGR7TjUp3FCNMqu5LRbk4F0gMqUJiSBVm33wxnnzv7djywCpMuHoeUkMqA2KaEvVk2vPu742O5E4kqMxD35tiRR1VK/GvwzaoHaKSlaxkJRss1nJCHa782TxsWbofj922Fi/+ai3GndsGK2Nj7YObMfq8sdj18o5DciyrJ43MjgPY9+TryGxvR93J4w7Jfkt25MxImRh9+Sysv+NFrPnF85j5lXOPdpNKVsAGtUMki7l6yIohK2QCqSKKOwL++KvlRHj+miiZn44fJBdyIXyRM61Qaj4Pvd+LWFK2iX6ekARBGfGQUCJxhxTEiAiGhkYaNDQ0RkW5dME5Q0O68pU58SUAgqiVjKaF3w7mCVXKYrHecSUqIfuNIJrgsbgyTk9mymKy7i1peoSZLGVuCRYSfDQ9hWBKgzWN4IGYNvYO+LyDYDeKUBFW2+GKmKPP81Hn9XIfsmQGgxLJa32uoU6SU/QW9I404GDAFocWxK1nOTzEHSLCbz4kTgr3xSkKknmHt7xMqrSdCKEh9AwlPKTB8kRGpdipMPD/2XvzeDuKMn38qeo+5+5rcm/2FQIhhLAlAVEBAUFkEVHHDXRwm+8oOn51/DnjiOOMOozj12XcZmTUEdxG1BEREAUFFFC2QFgCIZCE7Pt6t3NOd9Xvj663uvrt7nPOzXoD5/l8kr7dp7u6uru6+q2nnvd9W71I4GvbPQsISRhxvEmBKAmsdXowzhCtXqS7k0JhzsJOzP7vxVj9yA48c98OtPf6uPxrr8DAHo2B1TvQ5ic1etSfNIUBWryKfaeKTp9BbXRkyx4s++dbMPjCdjRP7kbbsVMw72tXodDiA3ATKsesIL/3NvwE3U62vRx69nxlkRRh19MWqJxmVcReNNt3l95ZG87EWedaId438f7TTS9i9aAZSamBJHNJS65JzGvTrj4oUBJBDgudhWrhKAhdcyegfdY4bFuyFtuWbkD/SZPiOrJZhrjcpJ7LhiJgIQKA9LWPCg2GKIUxbRA10EADDYxFCCEwa+E4TDolmgoaVkXs+N0mFJr3LWFwZfcQhtbtxIqv3In+V83FvC9dAeFJJ+ZN4+t1JGLcqdMgpEDXMf14/LO/wZnffjOK3S21DzwEaEyZpTGmDSLyELMaFRqJwLWQ67foVY35Vm6Zk9u0G1gsLqv6yCOqGzEoUZwKCp1PgdJIR0MjLOt9I6Udzdq0Iuz8VEcvgyGqpEYc2WUQIs1SMjgkZ4wIHiI9VeTpYJghNsctXaYE8fOzo0ItLQNGoGdtWR+TqqHojALdgI8AUCZWgKUIIbhshQ3SSN5YznPzdOwBRSPGrEScnClKJYSl3YXOdVfI0irVQl7akdG402UFZtxXZGo0Yve5aGE9NdNeZgGSzGweY6QgbDoFZWJ5cWaW3qGKZVfNjfdiz8jUszCnG0EyqjsxRUNhk2WIWmXE9lAYCxv0VBBDFTOp049rxd3LdqCyfhu6p3ckUy0AaA7LCP0Syl4yHc6eZRtx/wd+hq45fZhw7rGY8daFKKuoopz9odQdUTJgYueIuST2DInzElzGNI8Zqqa14bqwVl3AgC7aEAM2MCOxva5+LDO4YD6UlimWhzNEeWxWkn1Jej/mn09A6X1kWhykzuP7OOFTF+Khq2/E5Ncch0c/dwcWf/4SQOazO5Kvp7wwYw1Yml06sq0cIUQzgD8AaEL0qf+Z1vofhRCzAPwPgF4ASwBcqbUu55e0b2h4mTXQQAMNHAB0Tm7FSX8xGz+96k7c/KF7sOO5nXUdJ5t8FNqKOP2rl2Pm2xYftFQgDRwetM/oRfOEDoxfOB3BYBnP/+iRw12lCPow/KuNEoBztNYnAjgJwGuEEKcD+DyAL2ut5wDYCeDd+3PpeRjTDBExHaTlSYwAyJukDu8BAImEeKlRLe2TFxcF2gkpn2PVV5lPDpVMWPJ5yRfj+CzKptOQOh4VuMiK42EDUlqmKI8JMxopRy9E99hNY5DcJ63ZohGwq7UCkGYszKDQ84yWIqNOFJjPZ54qcaRxaRkhupe0Hieezb7eSuhBy+Rz087bKaEy4x3lsTfW2SP1g9F3SGE9xXggSas3ou0HeECnTTOP6559Aq1FTWYpTkTL1p2/bSobriGy7CDMfsKyRT5LIGoZI+aFRiPhQMnYM42YIssQRWU1xfRktFBxwl6rITO/UQBHDmKUhsKi1QxRrCLyVEt5twondpEo4rUfnI1XvGcOlvx0FW7/0J14968vhl8wLFLYDFUYieNxKQ9DWwaw8rv3o31aN4q+gjb3pqI0KruHsfm2p7Dr4ZVQIxX0XzAf/RedYu93yNss07zZdBgg3VGsWSSWRbP+k7OshCxPzTZRxF4VohCyYJQ8YKDQKbasHuTpgPLe8ywvyNFAQVZNmxTfxyo6uZzvwqwrFuPpr/8Ri/71Yvzpgz9Hc187pl4wN7FP6nzM+8z1wkx9u14kU6o6opYpEVzB/NMAzgHwNrP9egCfBvAfB/r8Y9ogaqCBBho40uAXPSx++9F4/FfrsP6RrehfOMX+tnfDXqz83VqEpRCDW4ex7q6VmLB4KrrGteHeq2/CzHecjt5TpmHjr5dh1XX3IByKDLP2uZOw8qt3oHl6HzpPmHa4Lq2BfcTEs+dg8z0rsOZXT2Lx/3sdHvun27Hm1mU45qrF6D9lSu0CDjTqZ2wOOYQQHoBHABwN4BsAngewS2tNo/J1AA7KTRvTBhGPMeTbkWocmTfeNx0pmiMvlHsqym5GXJTaI5v03LirM3JHDiNmlOtbDwtzncRiOPPsNB/PG6/HRg9AzAhVS10CxOHwScdTkSGaQEkWTSwjFgGYNEZSKEiTKBcyHeHXhS3Lq60b8EVy5E/X4K43qWREbPLSKYcUATs5kiTmyNUU8JFwqKPIzsTYZDJFOXXmbAgxYFrH5aVkP6OIejtWUFUnxxlaun821pDDEph7wt83yxhlkFqanT8+X9ReiixJptXWQVvvTXo1q8UzAuI0PS2ynMgRCLhRrU1MNNM+LUMmZOxdZfY56rRe7HhyE44+fRxUqLHits24/Rt/wFHnzUBTZxHjZrRiwTdfjZuuuAXjFkzEzqe3YE5bAY/+/S9R3lsGhMC4Vx6LvvPnY+eDq6BDDa+307JD1IaV1RAlRWU2lhHdG4c54syQTQ2To5tzWREqd0T4GBYFq+GrKNL2JRmjLPaiHk0PB48ynbev0qIm61JPmx5NXWtFd5dC47i/ORt/etcP0f/Ko3HGd9+OjXcux9LP/x69J03F8R95FfxC8vrqiSAvWR80Vo0cB+OFEA8769dpra9zd9BahwBOEkJ0A/gFgOMyyjkoVzqmDaIDjSyq8WCi2otyMFBSfkqs/GJCi1fGcFisveMBgpQqkSvsoENj//znxzgqyrPTq4cCh/p9UDo2igBg8rxuLL15LRYD+OXH/ox2OQ5vueHVaJnSDSAKMaG1xrwrF2Djks047bPno+fEKRBSYOiFHZh82UmYdtXZEJ5E9+KjEQyWUN4d5xH0pMqd4joYSKSXOQRQEId0Kuhgn6vY3YrjPnw2nrj2Diz68usx6YJ5mHDmUXjk727G2l89iVmXLzio53chcNi6mm1a64X17Ki13iWEuBvA6QC6hRC+YYmmAjgwAb8YxrRBFI/qWb4woVORqW1HxOIEuVGnlfaSHg/sBSiz3DiJOfCcPEH2/DmjCmUYCNcQswwGixtk95EBYEaznPEiL5c4ZlIyjkdJ+Y6GKOmVYc/DtEu+69FhdDmk5bHRwd28a9qvysRx1NPRxLmo2GiPvIbM423xyqmYRTaOFEvAaEe/UiT+BpznGHqQWsOzcYjiYDVaR0YRBx9Fh0wEG0W+Tu7LtUS5qMMoyvI6455v+wKuGaq2X94Imz7QPDeXMO9QRXmOp1T2CNh9P21+qZx94/WoTGKBpFBoEgol5cdMUQ3Y5KJOpHi+tIyQZaS5l5a0iZQXntOFX3/ucVTWbcWmJ7fjvd84De2zQgyFkVFTMmWefvVJGAxM3KMgxBlfugR7d2v47U2ohBpAiHW3PIE137sXEAILvvUueB3tCLWAJxUU8zTimmzLDNlYV8Ia+oq1Tx5rSzjRxCn2Ee0bCK+ue+tqxjhDYz16WaO7/z0/xsRXzcHsty+sTx/qlJl1PrtuhVZVfuf3j2mHan0LsupG6D9zDgbX78b97/4xek6cgglnz8GxV5+FJR//JaZcOA9+i+P5WKV4notuXzRaYxFCiD4AFWMMtQA4D5Gg+i4Ab0TkafZOAL88GOcf2wYRFzI70x40HdTkUQBDooNNwzAfNhusUCHD/dZkH0ZyeoY+rFmiQEJew7MNNaM157m0ZqWt4EaLPW+qHvHLn2cIpc7HpqNoqi7aZu4biUXN9dgpJiEB5aOi4heXCxH3RdDIPyxZU2b840QidBKgw0yd2XvnCqnNPkTj07SNJxWalI8WGDG5jAXata4jDJPXfaBG6ymRLP1hDRad2j9v5F63IeYg5YItstutizgtS7TO27Rw3iHfJD0lw4imazzBOnmhI4cEIVPvG7nqx9OjfuJYd3qMB0TlsO3J6W94WI1dYatZT07Nx+J/z7roE3raA7z+IzNx3RV/QnkkhBdWoHYNYclPN+CJ29bj2Aum4Yz3Zc0GAIVeH1qPYPOyrVhzy1PYtmQ9Tvv312PVT5di+01/wsx3n2mnigm8b3DTf5gt8b5kNLDExbDPyDwLmf7Q2hAAWuWG8kjUS2grD3C3VcPwpj149rr70TWrB31nzK55jqhi+eWnpsrMz9Rfk3Dfkwoi1+093+io1xBRWmDWWxdi4quOwa6nNmLlDQ9i0jlz0HviFDx73f2Y+4FXwver9zuJAXpq8F5fWAMAY3V6bRKA642OSAK4UWt9ixBiGYD/EUJ8FsCjAL5zME4+pg2iBhpooIEjGa988yQc/bJxuPcn6/HrLz6D5U+swrGvmogtz+1FeeQFnPG+46CVxurfv4DlN63A8N4KhrcOYtFnzsfT33kYgxv2YvrF8zDnr16Bpp5WzHrH6XjgPT9AaXcJs/7mwsN9eQcNp3/pdfjje2/EQ39/K867+T0odo2NYIYHCi0TO6N/Ezqw/Bt/wCn/cgke+9RtWPrPv8bJn7oA0t+3AJ+jwVgkk7TWjwM4OWP7SgCLD/b5x7RBRCyQNGJVYj6k1AgFbUsyGTF7kJxqgaPDVjXCs2eJrWuJ9OzvKv0bTTHUSjDoioBhmC83OGTyvJSOIxae88Bl8fVkX2/ZmV60I2IzbRBPxZnRtYzZAqk8BMpLTxfUCGvlTnPy5KPphI1pt3tlg+ElReH2nlfpQ4pIugLTsy0rDy2hj2bDENHUYEV5+c8pTKb/oOlSG4xwNAyZdpdMGZnUyKYPZdMbtE0jCjIHZJCi5hgvYxtnhPLgXp91DBBUF9OGDetDLJpw3LaJjShQXWSyDN8yOnE6hZQAO6fuLmvi2fcvu136deiZ9qrmRBmhwwi55yU3fRfEnkyf4eG5P+9Ad8ckvPETR2HL2gqeAPCBG1+OXSu24H//6SlsfGJH4tjt9z+P5haJc358OaRPYTtK8LsFeo7rx8j6HfBFACEElJfNaLiOA3nIY4p4IEjP6QPpNz9Utt+thXpYFhd988Zh0WcvwEOf/A3uvPTbOOnvz8H01x5X9VhXwMzPy3/jfbwvlf2XW/cDQamwR1FoK6K8exhNva1Y+MXL8Og1t+Kxz/wWJ11zvjWKqt27+L3IF7A3UD+OPJeXBhpooIEjDB+7YT6OP7MXKx7ajTBQOOnCCbjza8vxvfc9gONeMxXF9gIu+NzLcPJ7F+CU9y7AipuexQlXnQjpx1307lU7cc/7fgYAGFi1Hfdf/k3sfHTN4bqkg44pZx+Fef/ndADAY9f+HgNrdh3mGh1YbLrnOTz5hd9h/MLpAKJksCd/5iKEQ2U8/fV7D34F9GH4N8YxphkinvyO5rMTqQNAoyCynqNjSRQcj4Bgzb9a7rcEGoGXlJc7SvCYuE05IkQSgoZaJIKJ1WIQAkirh+Fu93GwrmT6A8DVDNXHgBHKoQ/Fgl/G6UTSCWk97aOiC7msDqGa8JqnkAiRXZZNf5LhoUQqpgKbN7dsljm2mOFp5KYGaUUB7Ub/QSxdQYWpAHA2UChzzY+F2ARpGZJcpDoHkd42Gmao+tkyUSudR146Bym0HUnTNsHYHmlDQJhjQ5lKfaINs+GzY91hWqApWF7yfPY9I1Eu2x4oD6UcoWk9nm57Q2KGDNNlKhVYd/toe71ebC3tPl57ZRd+dcNGPPL7nVj/7CDmn9uPv/vlYtz7i22YdlIP7vqXhzDr5RPR3t+C13/zLPTPbUVJjUAFCstuWYVHvrkEJ119Goa2DqJ1Ugd6z5iDpz5/Gxb997vgtcTelza9iU1Dk2yvQDq4IGeKiCS0wVe9MCUqLogw8W7Vw4xaRoMlrY7LcPpJCMy78iT0nzoZQ5sG0D6xBb5U+SxIhoYoxQyxl8ptxwUZoijD1DEHQqjsisi10lh7+3I8/R/34pj3nYEJrzzK/iabPJx4zQW4+y3X49h3L0axszlbs+Q4CyXXjwCrYwxjTBtEDTTQQAMvJjxw23YsOGc8XvtXU+H3tOFnn1mONcsGcNbV8zC4vYRL/y1iRErKx/olW/DHLy/F0I4RtE1sw4XfPB8tsyfgz5/7A7qPm4zxi2ege/5kbLh5Kaa9edFhvrKDh955E9A7b8LhrsZ+Y2jjHrxw85PYdM/z8DuaseiLr0fnnL7UfsXOZvQumIxtD63F5HPnHLwKNWynFMa0QWQ1PDJp3VeUZ7eRlsim1rDBFJlHkpSohPsmVNNapNJg1OMhQ39XlIey8FLWOx/durCeYCx8gD2WeRVksUK1RmzuaMm6qqfSiaTTi/jKQzn0U6xOfF7uuZIGvx6etJN7+SiIpB7MQcEGxCOGIRq1Zo3eYw1RHOSxRcepGiiZbNnREBFrZEfaMslscETpM8zoPKz+DKold+XeZiRaykzyWoNNit3y978XdNs2Z49izYlKrIehn8tYkAamQOSnw4ApJaGksAmDU6wVD9PgbA8Yi8o9nTjLTMuBsNm+72mvsiQz5KE+DQ1h7uJOLP/TDqx4cCc2rylh0UXj0TNxHH7/xSdw4YeORk9hEECURmTV+u3Y8tR2zDi9H5d+41UQQmAwrEANjKC100OzF2Deuxfi3qtvwsSzj0LzpCi+URDW7tazWEYgZorsc5SxboiYNWJVigjR5AWZgRGBHG8sxmyknolzzL54q9J54zpQWcm2wNMlAUBBKxS9cJ/PWw1CK/zhqh9j8jlzcMonz0PncRMRDFWw/pYnsHv5VuxduR0j2wYhPIGOGT3QSmPLAy8kDKKsbwvHqLzMGkhhTBtEDTTQQAMvJlx29RTc85Mt6JvaBNlaxH/97bN4+Zun4L3fOgVT5nZgxPnODe2IjPQTLp1h4wGpQGHbM9sx911RbLuOmb2Y/c7T8fg1v8Ki666AkAf+Y97A/kNIgRmXzMOOJzZBhRqb712FJ750N3rmT0LvgimYdO4xaO5rB7TGnmc2YfN9q6GCg2jc6LHpZXa4MaYNIj4KdLVEPBVDSktE+gg7F66sVqJgA/uZIIQyW59AoxVPKijDLgVhcpRrEymKOK5NdL64tZWVRBkZDFFGILqo8DR7lJrPtpqitJao3mjcVZkpuhdMWxRKgYLyUFJ+LqvDy69nXrvmiN/xMrPIYYrq0XXQyBeI0j/whJRArMmipWUFmUYjyzOFQ1G7ofJFsv1oDSfOEF1n9fuWFaARmlKRVD9WaVHNKS+1L5B8T/KCONKIP6D4NXapoAIW/8qmnDDr1jOU2AnDtmkRe7ExhogSw1JcIqv7crxKrR6OtVdJfQbFLgrjYKjU1/BjCowJrna/yqZ7LZrEyIH2UGhvxoXvmYwdm8q47bp1OP60Dlzx0YkoaR/AMAomvldBhDjzzROx8amduOcrj+OWTzyEv779Qjz4XyswbnYXJhzTgcEguq5j3ngcNtyyFIPPrEf3/MlQOtpOfZRHHn+2zcV1zWUaMoIRck8/Tye9svKYlWqpO+pLpZF80bc8tBY7ntmKuW9bkBCdZ9Ujr0+qp24HGgs+cDpW3vw0Hv/CXSi0F3HyP5yH/kXTUnXqmt6JaecfY9bSQYGj4MLpGQkg7b3bwOgwpg2iBhpooIEXG5TS+PCZjwIAXntVvjamtbuAt33lVGx8Zjd+/LGluOPax7BnWwmXffNclB2rRgiBrmP6MLh2J7rnTz7o9T/c2PTgOjzzw8exbekmnPnF1xzu6tQN4UnMumw+Zl02H8C+TwkeMDRspxTGtEFkNQAqOdpUENbjJx1oBYntxBxJKBvVmvQAoZfNbJC1XRYUtVhb/ZHVMQXJGBGCWejuKKOsCxjRQWrfPM81IdKjAs5CVEsvYm/FfrxwI0ZT5Jt7QyN/X0sUVQHDYSHF0sXnpfhN+VG7OXJ1SA5zRI01sJlYzQbWBmQGQ8Y9qSpOnKOKw3gEGVHD89DsV2zdANg24gkJaSJeh6HRLLHYMBRfSgtajwkicA81zZdJPZDWIhWzKC+UUD2Rqzlbl2p7jkaD71vm8WvsUiJgpJKwdU1WiuI8eVqjoj2UAw+eTLJ02rAgNrYRS8niRidPtZOcdeXo8SrMY1PmeEW5Wr4RHbVQilFE+1J6iyZVQKCKaBEab7tmNu7/3024/9Yd8H2NC66ajO6+YvxemwflQcMPyti9fhB7NgziyhsvQEeXwEAQAIUoRUhZ+Wjt9OENDaKzUMKINBH4TVT2YRmxTl6Qfvi1cpO5mjBqB77zXife7Zw2Vy31ESW6zmKI8/qvKRfOw/O/XI7Nj2zE+iXb0HfSxNQ+o9E10e+BkgnP0gPpZcb7awVRkx2rFo3aZwyR1XeJ2h6UDeSjZhwiIcQ0IcRdQoinhRBPCSH+hv3+t0IILYQYb9alEOIGIcT9QojjzbazzT6XOMfdIoQ4+wBfz5jCSHBo7c3yPorG9xXUmR0q5GW7bqCBIwlCCJx7xSR87qb5+MT1cxFUgA+dtRR7tlcy97/zW6vgFyUWXDINvTM6MvfpmNqJPat2Hsxqjxl0zOzFlPOOht9exLM/eORwV+eIhdCH/t9YRz1f7ADAR7XWS4QQHQAeEULcobVeJoSYBuDVANzoYOcDeADAxwBcC+BdZvs6AP8A4Ff1Vo6PQmPGyMsf7RmQPsFzvBooqippTGz5XnIUWBbJvEgDqphmX0z5Nno2We/mHDT6HQl8lBFpbqqxSNEx8XbOIgUqe87YLcMXCuUwrVWyda4WsTaHXSIjKzBMhq8lmkIfI2bU6RpFKb0Tn6/fBy2R2wZ8qaI2UGPEn04EqlLapMBqpSS0SSBaL4hp5NdDnlUV6cELkyyjTZZptpNeRlNIaSGhTZsizzS7nuNtFlNKuqZqzHqBifQ27sFV6zlV8zIjFqFCrK6jRYkThma3f2JdSafnewqB9lDxPSgeh4h5taW8pjyHWfboHWKsMms3WVHfSX/E31mu1SipQiqnn829Rx5p2kdF+6gYdqugA0w8qhWvf/9k3Pa9LVhy+1ac/fbJibr97vq1eOqurRACuPADsyAoX5oPNKmo3Ir2MHVuG9b+7nm0F0ZQ9KL+i97NLCZ6tFnrfRmzElZDJKJ4VDw/GMF6yGaw1xRRnOdbdJfVtJBTXncS1t35HLY8uA5bnt6Z6b6eqEMdLDXFi6uVlWA0yPUOhk69b+mE5PG3ix+bxwyNqo5HgIFyqFHzK6C13qi1XmL+3gvgaQBTzM9fBvD/IXlrPUSvs0KSRF0KYLcQ4tX1Vo4bQu56oJICzYrjRg3EbuS0XtGeFU0Smph7NglreSC/RKh0Jjakb1PZdOblijl/xUO5Yjp48xt1+CWzJAaJjA67DDyUzW8V+5u5njC5veSUUXZcxt37Zu8fROZ6ItAlN0hoKkQl6zgS+vHfpg5EN9ulzl9mPdPEkpVFlHZyn+R1Uhtwp8Oi372EAQTEItnRGELNxhBqNVNltKTtzb5pT16AIv3NlsWC6cBY8kzPV5C0zTdtzGM9Vt7U2WEAd2W30ylsWsWdFounyrINIXqHKN1H/E551kiybYDWw2RCY+1+UO00KGv//P1QyTIClR8iww6wjMVC/ctQWMSwioIj0nLILEtGKE0CcAryWDFTbE3dTbj2puPw6x9sw/0/3wgAaJOl6D7qCnomN+GDPzgVnf3N6PCiabIefxA9hSHz9xB2rdmLnslRMMkOP9qHQklQO20pxEvehmstizJMBVXkzzzXScT5m/qRkZD1fWx7Vv/i9pcds8dhyqULoEOFFYYlcvs/KsdN0QNU75OyBmFZy3pQKxm4FCpOt5G6r6Hdx/3dNZSq6120AAAgAElEQVT2yxBqIBejmoMQQsxElHjtASHEpQDWa62Xst1+A+AsADcD+BL77bMAPjmac+YZRQBGbRQB2GejyEW9RlH0d9IYqtcoisobnVEUXfO+GUVARgeQYxS5MZn21ShK1KFOoyiz3DqNomifHKNoFK9BvUYRgFEbRQBe1EYRgFEbRZXQeX6jNIoA7LNRBGR8xGoYRQBqGkWkL+JG0azj23DSWV3YvqmCZhEZMm2yhNe+Zwq+fPcpmH1KN5pk1NbIKAJgjaLhF7ajc1IbugvD0T41jCJ3W71L954cCqPI/S3LKJp55enw25qw+e4V2PnCnkQZbjn1GkVANjOdtawHtYwiAPtsFAH7yQ6hMWWWhbpFLkKIdgA/B/BhRNNo/4BoeiwBrXUA4C1ZZWit/yiEgBDilfWcs6PSBgAoeizYm0gHCYvryaxp6rSlgieyG1+zNajMaI9cX4OoM2sKfTTbDo06MvNyWXd/0xmHDnVv3p0OUUAFMjVlEIfHT9YdQtvfaEpAkBEG2s7LcoSLoJcIievlcLfzDxQPGOh2CE2qGW1apihffu+zzuNeg/sblUV7pkSREPEHScTPNFpPCnj5s4/qlmwnvnleTRBoC1oTzAItaQqkh66P2hKSz4S4j4pR8VaEtEJamjKryGRnHPrk8k0fZGGDitJUWWiExJp0kkxM7bIvdK29JqEJtQGPgjmapW8K80JtgwpagabdJ3lfCyw1iRTaXjt/fq3mozNo3pc2HS1L8OPpQ7pvgTFSVPLe0/ULrdGNIrQWkKYOBdMXCGLWzHafXPU9Wg8z+w0g3W6y2i/1FTTFSOs0YALlFXbaqWBTPDRVVjCGjFRN0bScqJhrMNNfQkGJEDIYwbMPDKH8l31oKZjrNAaTNPeR2k9J+QjMbwEkXv2GefjJJ55A0+UB2vs7Evd+WEf92IhpGwLZ/UEtWDf+MKpbu2qCFyr7nnCjgc7iaY0wSP4WskGPslVKs9a8L7J9lQQWffgyrL3+Pgzdugb9755u90lPkfE+CWxdo0M1QWuR/obY9x+J9ax+ld/bqvvWmppm/Ziw7RjwVLYR1Ejuun+oyyASQhQQGUM/1Fr/rxDiBACzACw1MUSmAlgihFistd5Uo7jPITKmagaL2elFo52iR5ZwPBopWoMoqarPU+YXZJiypG0Ua6eTAYDBMOpABnQTAGAIBTvSswwMfehAhpExiChbtPsxhsYWlOKGjewXzDVurEHEPLW4oeQeG+sbskdrHImpQLZPnpcGLfd6Q7kxkmrTxenz1hqJuQZRHhPBMz4nz5P8qPM4M3u8QXNeZ4rOehhx4zrZjuKI1s70pWkfJfI8ImOJDGliOcgggrSsAbUh+mhobiiQMWztZ5XoPLegZI0d3haozXtS2+vwmUHJ7yu9f/G91Kl7TPeARuUDlimJjh1RPkqmu6H2H0eIZh9Sc91CK0AA25zrKbB32BptMtkGCjLM7Deyri9rFM61Q9R3BIw5dj1Uee4+7RlmxRg+zWELysVd8M160UyLSRFCixALXh3g1h+uhGjtAowxpxV5YUXL0LafgmWXKtpD5xygOLWEJx9YgblvmAsAGDQDukFh+jFRtNe3L7Beh867tMcfrKoDAqL77U59Aw47I5Lb7cCkHoMIgDhrGgpbj0J5Sid2yGG7Pctz10VWnySFxk5vqIp3V/YyUU6NjALV6pBXFi/DEzqXFcrLS5iCRkNDlIGaBpGILJ7vAHhaa/0lANBaPwGg39lnNYCFWutttcrTWv9WCPEZAHUHzMgKAsepb0JKiEZPXcFSMVZ0a9apk7UfujD54ZVCx0yNFcmCLdmHXUk7FaJFFCyPZn4E/9hnMEf2Q2M+gjbAndlO0z2u2DPPBZlP8fF7xf9OXAerqzbajFIQi8TzOog8A8kNPJkyiHIDM4p0AE0+5aXovMqex54jL3S/FuaaWKcO4QRrTH4w8xJTZsK8YZ5KGh00HZowgo3dQYYRyA1ckEFk6kgs5Ch6tP3RGHDBdBbswCRMOiQkPl62TWd/BOn6YkJKxu8OJW0lwbVlQbkxFS9TgVZz2k3cCSafcxYqLHCpPdLRoBDo42SNKeWhonxUzHpZJ7vf5u4mTJjejFAUUNaGXRJMz0h1lBoVHWL9s4N49sFd+PNNm/HCkwO45COz0exMqSWONdeVpZvLS6WThbzghnnHlEMvNQ3OmaKUQWTey8zzsecz4Q1R/rdKGP9OJnwea611un0mAo4C8femxquzr+9WnhNDNUPI/d2F6zzUwL6jHobo5QCuBPCEEOIxs+0TWuvb9uO8nwPwy/04voEGGmjgRYX+6U3YvrGM8ohCoaX2/isf3YN/e+tjdr2zvwm//frzuPTa3oNYywZeNGgwRCnUNIi01vcCVYYL0T4za/x+N4C7nfWba5UJZE+V0ZKzRrVo4EgTkm2RE8jKbvGSAfcCJVOjS+v57IGtC7uBpjYogBm55JMINA6m5+iOzCXxEQ4JcIlNitmneKRDIwjugpx3ve7oiDM1hKxRWln5qMDLDTBZDz2d9xuh6hQac5umdlG2AfGSI+CEsDODkaLku+72etJxcNi0FVnh9s3SuuzTvSJGBWndGLEhWS760QZ6zqOuanQ4Y1B4WgzOrBBk4vrS+qKs9YhlpfZh2FbFzs9YUdrmeqh5LCVIfC1I1r2a5yS7HmKKbJgICdu2bLJRxi7XMxIfNtNdJA5oCosYDpvSOxIh2ORh8lEteO7JYcxb2GbqnGzb7jTZ+ueHUWyVeMNHZ+Lst03CuuWD+PZHl6PHj6Z/LfNN7v+mzgNhEVnJoJNVSr+XdlqU9JTKSyRBznOAcT1ENduH1sMUa5hmnKhOIW/3rF8PEzogsGPoHTJlOhMIKYaIrpu7w+v0u51ynWf9DA9Bkriu1DvDtJD2fYzbnhPsJHE9qPFcG6iOMR2puoEGGmjgpYTZJ7bjuccGrEHE8Zvvrsfc07oweV4XTnlNH44/qxd9/ZGxtHnVMCbObj2U1W3gCIXAkeH1dagxpg0iLup0rW4acXB3zwNyXnK9N/P4zZ4Xi2DJ88dPjjpd7QItpa2bggdlXYlt2gY2ahEZQ31imayHB4ktzShFW22TSLBFQD5rVg+jUC3FQ6AFKk6yWs4U1WSIoGvuk8VUue73pqBo37C6xqCWiLtiku8CSWYonuPHqODLMMEuuuVSmeSCT/ehLPxYQyRZu8jRFtHjTYhOhU5oi+oJwJcXoDGPWYlGxNnMEF/GwUhVfF22nGxGihgwISJPOJdd5W3NY/XIikNUi/FKaTkcvSFplcgdn7xbrSZtFEzRiCpg2DhsRKeh+ki0GoH1nDPG4davv4ATzurF03/ejXXPDCAoa4QKWPvMINY/O4QrPncsJszrQVs70NIirK6ouUViaHcFHTLSEHmI+x++TvWoNxF0qAVGDLtFbbqiJMqhV9NVPVSxvoozQaFljpL3hAupgVi0btu0Je/SzFEeM8QDalpGRwNKp9tGJpi2SGmRave1dE+J37gQO+fYWMsoAfJgdK4kUbd60DCIUmjwaw000EADYwQLXjUOs0/uwpffuwzPLdmDGfPa0Dm+gOeW7MH6Z4ew6NIJWHhxP4Z2V/Cdjz6ND8z/I37y+VUAgNVPDqBvWvNhvoIGDhWGtw/jrmv+iF2rdx/uqrxoMKYZIq4hcuem80Z/sdWbFJrsC4NEc+7NXiUVyNCORL1shkhpYbUgKojC0Nt4KxRsjgY6WcwQbVLEBOnE5dmyrW5GW7ao1jChGkOUxwy5xwTaQ+DEVeIhATg7QHoo6azzkX5Y4/EoLexo3bq5h8m0KYSqDFGGl1nFTQVzgMBDBNCommsNKJCj68lIATfz7itpi2ybO0BDPapTnpbIXoMTLLJWqAXL5Ehtia04oGlS56EcZsiWAwVPqDjMBMUfYkEfeZvTjpeSvT7ulcgYBttOHA2R1Q653qpwWZekviQLxFKUVCHWFeWgVQJvvOZYvPVTIW7+99W44dOr0DOxiJMu6McZb5yEvpmt+NPPN+KO615A39RmzFzQgd9+dwNKAwEe/u0OXHPjfHtvKICjfQamjgUZ2jAFQya9R15S4zgoq58K+krau7S+Ms0shkxDRKmV0l5m6Tqk+iuuGWLMkdQ6pTPi+kr7eBPvo/E4zbwTzj1h55fCScOR04FmuujXYJPydEi0fcXtq/Hc7avQv2gqumd2Ue1q1D6GqCfT80sMY9ogaqCBBhp4qYIMxkuuno7T3jgVzy/ZjS+86RGsf2YAXkFAhRrnXDEJOzeO4J4bt+Bj/30cJs6swz2tgRcFJiycjPYpHZjysqmHuyovGoxpgyiLGaJl3rx1vV5n1ZC22BWayfMsR1/BvbGkjr3JlIqC7pGGSDMNEYcQzpjfDnFohBOtkjcbjRa0VAhzRnm8KI5qA4WsEbjWUdDCVMTtGhomdz/OHhGqMXk0sne9kLLPU7+GaF+8yUaD3BhNSNaj2QtSbIs0nm88hKngo10t4mdIJMd+aOqyonYDyRE/MQpFLzvvnw326AR95IlY6V2l8j2byiQuz9cKBSePFm9zHnt+WTo2fh0142U5GqLYk9HEEmIeaa7nD9encIQ6YosGjH6HmA0FaZkS2tYqy7jgb47BtAXduO79T+DGf12FkYEQk+e0Yfy0Zlz24RlYdEEv/ILEa/+yH//y9mWojIRWT+SiwwQsdINbxmlXousaDJqSdSJWy9S17OQWswmflUQFaQ0Rbz9aC8sIcS2R7c+qaHdoH/5ep73QYK/BaobA3hW2r8sUKUTfFdfzrBqSmrok88uR9T2qxSZVbacK6DiqH5f+/M0AgJKiAKW8R82BRkNDlIExbRAdaCgtD2ngKqVEyj34YCJUMvWBOJgIQg++V+cLeASez42OfShQlGEiF9OLDZ6IpzJejAi1qGkUjQaloRDXvf8JAMDIQIieSU3YsGIQn/jlQsya24yCCBFqoNgk0dLuxWEZDhIO9ftwqFGXoPoAItAyEfW7gcOPMW0QVYtaXG2beyz3EFJa2lFdPPKtLSCxHm80UjTDWNIQUVkFldS3KCVirZBizJDKfgGTmiKqa7QmPBpZmQ3kdUY51bgnlgPmHJF9bu75BqpqPPIOIIyGKDJS7OgoZxRvRzrOSG9fPOCk0AhCryoD5J4/63ceK4n0aFmMox016uR4Mm6X9Xsa1cPYKB1Hx07AvKXkbcbTHbg2d0EpFERYdcRdL6p6aTF2J51GJbkMtUIl9OAJndI8ce9Kt+6ejvIW5rKQOc/e3c7fCZXzLCxbmKEhIlYyle7D5reLto3G4KNULx50nHvOlPvM7zbhhg89CgA4/lXjMbSrjMFdERv3L697GIsv7sMVn5yBzt4Clv15N9Y9O4z5r+hKlM+Ns46cCNb2AuCkLzKMUYodRHasnmpw369Yawm2niwzyzuS900xcxSfh0BPnNpaHEvI7Mv2o3W3bVutUsqLLV23vHuSpxOi36pFDecMUoI5qnUhdeAA2u4vGoxpg4jD7eRqBVuLO2/z8XA+wjZAo2lFJU2u0Tw/jCuczMl7xJYUQBGIX2Cpo3N7ftJd2n5o+cviNlSbtoHWE5vj1B6oAntM0shxYeltMtqQ00EJIISHUMhMY8mtGyHro5XnDp5nEMmMY7jomJ/PnZZzBd0uQq1QVp7Nom2fo1S5H/cgldMsmRYkqy61Osx6DSYXrquy/ViYoIn6IAsm86YGbH40+06RqDxjyszAQ/IelR0DJgqoKuOEsPYjaNppapo2QqiFbZfVpqZd2KCBSsbPXCcNPUqLU7RVpDYhqwbfi+rkIVDxAIISxmahfWIbTrxoMmYt7EGh2UNp5wiW3LoR047vwOaVg2gf14SPv/pRtPcWUBlWeMdnjkahpZD4HtIgSlpDnqblZJyChD3Hdq9k6sbbuI7fB7NsEQW0eeVc+cJwULDHSuZub5/fPhjuef2AK9CmKTqaf63VfqLjImmDZuWnDGg2Le2+a6lpvSru9zVd8+kSkO5D8r5/9YZRaCAbR5RB1EADDTTwUsDkeV140+dPtgOwZhFg0WWT8f2PLMXlnzgWr3zTBFz0/mnYvm4Es+a1QMp40NZAA3WhwRClMKYNIhq1ZwVmTCUjzHGxdS1oPtVRYnRlnM05eT6pdWyt54zoCyzTehTSzYyWaRrD/MIH7yrIYYoyYBkce34KB5DuDO00AwtqZ8uybtuwL0dK5GgFsPFIXAkB5bjdc4qGn4dfVVbCz9y6O9NxqXJzplFiBlCn9uUIlURFeRgx2cSJxQiVQsBZAsWYImJBLJMUt5uUy2wOU8RFyEDMQvDRXyHFb0VwE5l6xk29npG3PeYgcOd0790p5oJ5j7n4lhAGntkeb9Oa3ndTZ3rmOnsqJDGtkSPytXVk100pUjypUsfYZ8LK8EX8fDl7nDWNryAyP0TE3lAAyAqSISWUlBCdRbz3u4sAACWlUewUmDSvGWWhAQ0UkUwLY6+L8cdKCyueJtB0GqX78CwbFNrtg7LJ1Cn6rRVFBH451dcOGWbIsmoiFjnbALNmWW2KnyOPRbWsE001IW4HxBTZaVmZZOm1BnSoMLJ+B7wdAYKTJsBjOou8KXg6h3Le97w+nM9OZJWrtEA4XMGuh1ei74zZ8ImG5EXqrGlsEzg48+zZaEyZpTGmDaIGGmiggQYaOFgYXr0VT3/kesiij+lzZmPkqR5Mu/IVh60+Ayu34olP34oJrzoGCz71mszsBQ0cPIxpg4h73IwmTUeWBZ0SU+ewSpRioElS0DwVh+pnQdyajdsxCV0Dx2eYRkO+H6IgwtTIOA5MRwEcScHpCvvM0gaeNCMtxXdwRlrEYhEzxG8Og9Zx0EjN3GEtLDMlLEPEzxePeGhUln2+xMioimYoOi0vO5s9SpWbc/4sPVOgZZxI1Vx/KBU8HbNFANKMEYWFMM/edxijvDaa0pw5It3Rusq77cmmw1A6U7i8P8gUhNYoP3WdUiFQJEROvpsUiDIzbYMWiUSfeUxRXsLPqDx2PeT6zRkjx0Vb57B0irEHtO5LlQqcVwuWxUCceHpIFxP7eKzPIp1Ms6w49U4GLOUsT+q8zrvbzpihgmXHVWp73Gajbc26CbowEtfdCLF9J9QCEAX6jHNeJ+8r15VxJslFHsMNnv5DuBols832l8kAqUPrdqJlZj+O/tcr0Tuocd///U+Utg2gY8EMNE/sRPtxU8H7M65bgxYp3VFeiIBE28hofwMrtwMANt/1LDa88mhMOPvYdGgQkf6GxcFGRxFhtsEQpdBI3dFAAw000MBLEl2nzYEOFdZf91sUutsw99+vQnFCF1Z/4Zd45qPfP6R1GVqzHSu+coddf/Zrd6O8a+iQ1uGljjHNENmRo7GCi0ZDkemanAN3frsc+vbvzH1pzpv9XhAqpQ8oEnvE5voluXb4zigoVPClQmg0FHzESiM2CqWuo8pEP+YwKPHoyG6JdU7kh82O5aMvq2lSsZeSZoORWEsUr2spk+6pPOlicrNz/owLyRlZUegB67nm+JZTMTXd/DPAPUaIIarIJBvpaQHPYYuibTmMEUvcqqTIDfjImSHrlZXQEBmtTS12wmENiCHypE5oYPKg9IGJmVMvG+ILlbpPgXkfeVoHt+5xCo4czVBOwk8XeSk8+HtoPTeh0iEHZLYeifokpeNnbllkxiaT1ovfs0B5mSyceyyx1qHVDAo0GZaItGWuF5m73cvQF1ICWM4MFUTUrxXNclA02d89zhqpEoQ/Yl30bVuWyUTJWd5RHluvBs4eWW8y8ztPji21toxNzDqa81mmO1oPBsroPP1YbPr+3Qje9zqIjnb0/8UrUZw0Di984RcYWLUNLTP7o/JzPNWi8qozRFx3lLVPeThEy4xxGH4hYol6F87As1+/G/M/+dpEGe518YTXdbPMuqEhykKDIWqggQYaaOAliXVfvQWbvn83AGDFR76LVZ+5ETvueAxdZ8zFzL99HZ775I8w9PymQ1KX9mMm4pRvvwsvv+kDEAUPfWceg12Pr8fW+54/JOdvYIwzRCUzguTeNVkB7Gp5IJR1HRGA84JbyQCUkjGhKwIQsPQigYjPQ3qigh+iKIP8YGSsagoyjgNUr8Gv46jYnInJ9S5T8egpFTSSnTex3dk/sU8Nb7PEWg67FDND5ldHh2D3iU+QOJYzR9l14XWMRtQh9zhUAgGxOUYAUTdjpFXKIy0duDDplegGdyTPJcVi31iPNZPGxSZb1dqyLL4XoiDTejUOD0jvk3HfBp7fitXX348T/umSVDvNQl6SVyC+B+Q9ypEZeDV1DcmRf7y5HqaBloJt5+uIkynzd5Zpi2w9HfbOsoNWz5SsR8z8ZXu7AnF74Cyee4+4nsjqf1j5xPrQs2iTJWdfE3CWMUQeo89clslqrUSAUAYIDCNUoT7QrFP/lxV8ME+/WQ1xe81uAy4L4+p7ACcQK2Mjp3zgYky8agR+VxvG62asfPgJ7LjjUWz79RJM+7+vw+S/eg2e/9T/YOY1f4GOuZOjMthzlUgnk00loGXX53pB8voPbRmEroR49iu/g/AEnv6336L8f87ClAvn2X1tChn+vo0m/liDIUqhwRA10EADufA7mrHtvudx13lfOeipIRpo4FDD725D0+Rx8Nqa4bc1o+fsEzD7c1eg99wT8fzHr4ff3oypH7wIqz9zI1740s0ob9l90OvUOn0cTr3hvTj1O3+JcKiM4//xYqz67/uw5Q8rDvq5X+oY0wwRjXqz2J/Utpy+mkcbrgc8LL8ntKNdiLYVaESfE0Ld1zLBCvhCIZDEOCQ9K7jXGTwNZdgHzbREXFOTxQDYOW67zliY1P7pMnj5rkZDSB0ta2iGeMqSxH52Hp7qZM4DVkca0Qkds0b2liSPJYYjlQbA2cavXSBi5MhTzG53zkejSvLaq4cxIraINGXUFoo8knOGbo20buRlSUubJoPiwPgRMxAoaVPIFCshmryg5ohbaWFj3lRDc38HTv/ulfjzu76PZV++Cyd89FVV9/e5vs+kgoAX3wuqKz2T+P6ZdZaaJAuUKDkvjpX7G283QHbbtvog5eTuIK9RxXMk0Fr6/MSIFFnk+0DJRKRq8rLL7N/o2k0ZlOaDTi+1tmyAx4R/liGi2Ddm3WXaQ8seZesxKZJ1Wftm3bfbWk0064oMoGUFgZfNEMmCYQTDQkrrwtkdYvD5fi5sJHFKhKuSS+r3Qi1jJo0x33Gb4JqfSMMZPQuB8Rediubp4/HCv/4c3S+fi+4zjoXwBJ754LfRd8mpmHzlWfbY0El07bJG0W+MObIXk9aS0b6eBJom9kAIjfGvOg47l67HvH++DE994udomzMBbZM6c+Oc1QuBhoYoCw2G6CDixZ64TxzCxLVAHBLgUIFc8V/qaJ81Di/73jvQt2g6hjbuOdzVaaCBQ4L2E2Zg5j+8CVpr6CDEniWrMPuTb8DWW5agtGnXIalDz2mzsfeZjeg4ZiImX3IiXrjhz4fkvC9VjGmGiMCjmUpolOnvGvFeXE+dvCjT1spmXiG0dEfScXyO5JIzRb5QKMOzMWk8qXIztSvGXmgNCBqZMmYo5blVbSKYyWb2KW+QTI58aGhB27OMotR52Ly6Gxo71q8gZ0nnId2TdOKAILFMh8ROnS4ziaSCsKyEux2IjCIbCVu5DALgeUlmgxijwDxvtxwbs8hLsgdxzqz4/JQYOBWHKyePXqtfsaPmZhVE6znGI70PZScpL4EnvnV/b5vRi8HlG3D3W67H8R84A8e//YTM8m1ducZPS1B3Q/oq5SW96bgBKoWG1MTWJTUgqX1lus6cFUhr7LLfnSguF3uhazBFCsL2Ux5jo+m5hVoi0NJJ3hm3xfiZJvVHPLGwZYoyQG2YWB9lme70e18r59WIilSTxApVtIdWWTblRzojX1bgybJlm0LD/FGfWHFY0KGwaK4nrRNz68NjUiWuj+VDy2KGou2x1yyfZajmkSqhU+9Ex/FT0Tl/CgBg22+XYuVnfoauxXOw4uM/wJSrL0LnqUdFO3LNUI73o8so5rFHNqYYIqZoeP0uKC3Q/5oFWPqhH0JpYd/v0cx8pHCQ8x0eiRjTQ2DrKskad0V58d9hkmpNucu6qTtquLam3GGJAtbSfpwClaSHCQXuRi1D21gpZUGTCeJY9M3SbC/4xj2WplmkhvCSBgd9rw9X4FJrL5ARInXaeKHVHOPNQsMxjpJLO73GxazKeX/tNvPMraHCpiBpeyjsFItiS9oeBtLuS+t8n8C0tYpJMVGu0DL6SJVoexgniy2Z30ZMOgNKeEnpDUZC+t23x/Apsqwgh0DSQGr1o48VtTGaTqNls2lz7jHxlHB2ahcCnXfqa47Dyf90IXqO6899lxR7L9ywAhSqgto9d1OPDVz+8dL23qcCaCr2HFX6fY+nwrgxzJfx9cehAdh5dfI8bj9EA6eS80yB+HlWyJhTcb9CS/qb37+4P6PzG4NW+TYzfd7SGjN8XXuo6PS2aJm9vUlW0CqjqTIyjJpFtKQptC5/GADQaZeRa393YRidhWhbaiDA2rLnuOpT307L1DN3wk5E2+MBCzeEaLrbM/0pH7C4RnfetFrvmfOgSgFaj5+O8a89Gav+8cd47u9uwPY7HkcwWErWIWW0sbpqmQoQag0751vWPKkbwWAZA89uQqGvE6oUYHjnSK5hORoIfej/jXWMeYZI66jDIoYnVNLJNxV3RkJoBCoazdO+dok491lWHBCgtlEERB2bLxQC5cGXISpaxgwRIqOooiV8GdoO1JcKCKOPQDn00OQFqCgPRT9AOfBR9EKMBD4KfohyxYMnlTW6hKehlcg0ig6HcS+kOa+jAUoYRU6dhNBRJ8C2J+AaQ4LWnWs259Kuh5lpDwmjSGgoFe0TLdNGEYBso0jEHlphEGkByCiifYTQ0bEi+kBKoVEJPEipzDPTKFd8SKlQCv0RozgAACAASURBVDzb6Y4EPiQ0ShUfntQYCQrwvRDDQQEFGWIoKETPP/TtByHLKHJzo+UZRQAio6jcjGajIXKNIgWBZj9AOTRtmHmp1GsUTTr7aGPQqKpGUcJrToa2TRdlgJHQuW6pUAm9mkYRkGGcOB9IaZ6RlAqhaQPu/aM2pFTsjZk8H7UtnYo1FITmvQwlfE8h1BKeiM7jyaj9UD0rKuqLSqGPgoze7aIXxnqw0DNRu6M6u/1LoCV8ocz9ixm2uD+L7muo4xhSJeWjSQYoKR8FESaXMkRFR/e2onwUZICK9iyDVNE+CiLeFi/TRhEd4xpFZbO+VzWj1SuhpAro8odRUR46/WFUtIdOfwTDhh3qLAxjKGhCUYbRtTpGEbXLMuJ7DWCfjSK3XdRrFLl/Z7U/0eRjxkcvxvY7n0SwZwh+TzsGn1yDwSfXYP1/+Og87RiIogddDlHo7UD7/OloP2EG0N5s26BbVwAJo0hC2/7Nfud8H7M/eB6e+vufYd5nL0frrD4MrdqGlp6pqe9cA/uPMW0QWSEzd7N0OiBCTEUmKUTOFLnbuMaHOnE+KktsY1MrxLGRYUQdVTQiNsyQDNHsBakpOV626+JrP+hs333R0XDbJSsAXr0QUlsBYnwCkThP/iyeU5O8fSjkvop3BRDdZ3t4koESLOWDvWfufWWMk3s67RpQDgUXT8UlO0faJxZu0u+x0JdT8tQZF4lZ8JIjR+UlP4BAmkHhRlFsHIV2n2avjMAvWQMkniLz2TH5Bg0hNaW2H52uL0P7atJ7QVODHFzkHIRebMzS/QpZ3c1roZ3UDHaajQVzJOeFVAoYZ5rDTlSxkT6B3OxDrZ1j6EPL3llTRgUeyk5YDvc5cpFsPWJZesZePB9jriNpNFjhtDPbp/Jf0qiuxigiNqgoglQQRyED+LISSwvMvpxdKojQMoXUTw4aI8m2U3M/iT0MlbQBJvnUmDUi+HYVG0Vpwzq5zsMZRPm7o2eYG7hXe+h65QnofMUJ9nxDy9dh84/uweATq7D7j8vQNn8GRtZsReeiOdh22yNY8+Wb0XXGXPS/6eVomtybYPh5T55KR2Oup+cV89B6y1Ksu/EhCE9i6x+eRe/J00ydzDsy2ik0p09sIMaYNogaaKCBBhpoYKyi9dipmPHpKxBs3oHyph1oPX46tnz/Luz6w5Pw2pvRNm8adt//DHbeuRRzvvpetM6esE/nmf3Ri7DhB3/E8NrtGNm4Cxtv7cOkixYc4KtpYEwbRHb0bCxnzzFpUy75bFRJdrKb/M5nSRBp2sAKXpmINYtVskwUFzcypoi0HABQCEMUZZwcUYa1GaJ4JJwcBe0PqjJCoyzfnVqoZ9/E+Z1pNJrSsiMnm3aEptO03Y+76OcxRmBaIujk3y60MKlIVMzuxL+xqrCgkSA38qxRKBuRSsG1BMnQC4GWKDB9RR4jlBXMUdqRbzT6LXjRdBnpSYKMMg4kA8RB7CqfOgNiYTAXuqbckJWMEp+GMsUMKet2b/alRMnUXqROlR/TPqZupFdhiTfdZs2ZIprKocCFbvoYXhf7XhvWI4BExYluaaeMnOnHmCmSifLjduuIrM356Blb93pihHLIZKVErrs9sTI0Neam9KDgjTYQpBwBvKFc3RG57BdEiEJICWeTbXeYia2pLy54Ydq93uqsiJlNvufudJmrx3TXPdbWqKyI9Y6WfEYij4ly+22vvxct/b2AAPqvugAT3nkeRlZvxsiqTfDHdWHnbx7Bms//L475j/eb86WDOdony5giBYHC+C5MeN0i7PjTjzHt7Wdg1X/fh7bjp6JzZq/ZOZ0mpRZe5E7Q+4QxbRA10EADDTTQwJEG4Um0HDUJLUdNQs95J2PK+y+EDvbPAmmd3Y9j/vENeO7amyCbC1j9rbux4NrLD1CNGwDGuEHER8p5I0nAsYwZm+TOyVMgNBp52KB4MqnZSJWJtOVtXYVJZ8S8cT2h7YjYkwq+DO3oNm/k7zIMNKKx2hbSTDB9jIuUF5YlTjgtkr3/gQalmEgnjAUgY70GgHhYxB9tHAs/dqO2UR3ZvuxYl1FKBXFMnYYf7GxjTBHXFCWuyxzAz2dvNdOIhJYhS3vGxKPapEcOaXCKXtyOKSHsiPIxHBYsi0RsDIl6yastUDJXb1At/QYHf++qgXtiSpFkiuh63cClntaQUlntHHm/a0aZShYGIpEIk7FL9r1jrIyuQ38R65qS7VeIdDk2+ScJg4WXYIjcxLE8iaxNjJrqM6hPjN33iY2mMuw5siMERGk/DGNpl+a8PE1SmEUzmXID7UOpQi5DRHXukCO2P44T3ybDlsTX1WSP5f0jF1THyY2T7F0Qeqlgt3npadwQCZICM1oGmO4BkmXY9Xg77wv490JrD5AelCksoSUitor0fab9c6YIIgoDMPGyhVjz7btR2rQb2x95AT2nzrSu+qPyOmtoiFIY0273DTTQQAMNNNBAjO7FR2HOx18LAHjy4z+DDhtzXwcKY5ohovg9WcxQLUuYjwiUjgOnWe8TyRgjlpDTRYohsp5Gybl+6zXh1FUa74WUy7RKMgDxSFlDs/QeoPl07l1TB/JGR9plkuxAf/+HDTaGkj0fxQsgNgh2eJUKzJj3bjt1TGzDKFifnH2iRIu0oeYhNRFdWpLNosskRoiYDusuHMSek7ELuvEisu7CJuCd2R5oE9dHhjawY2vooSzi15qn/3C9UfK8Hgnctd+N6TVaeEKnUuLwWDSkoXJTe3hawYOOE3qa+8aTWGYxQ5yVow2hfXcZU+swRrXaDmf6BOJ3XvL745FGUaKiPXsMpdxQOrSJcxWLY5PyNHTqSEybDRxr7o0PCnhJcduQ+F05Xp62H6NnS2lTyJvWGTKnEtpqD6GT0oMzQ0UR6yg7vGHzW7LPs0lmmZeuFMUUQ0ReiTEzlHTNjxmcMFffqNl77s4+SK1ZUE3SLiU1RVntPz4fY4pIf8T2F3C+QzyIowFniujnlul9aJ85DjoEnvt/t2Hzb5/C5Avn59YtDwdQMviiQYMhaqCBBhpooIEjDN0LZwIAXrj+vtEfrBFZh4f63xjHmGaIuIYiYfXXaQi70Wqthc+8FjymJeIaIwp+lVWu1RIpGh3FIzquxaARXZ6WKC+1B9U/WsaMV7Sef+126pkxKZmxeLKGMEjOddeC9Eg7QccSO2Lur+NRFtfN7EMeI9aDjGmKRPw3Z4TyWK3EfrUYMFbn5DZa58xXNqI4UsljpYh/A+Igg8rJIJ9OOJnNFBXM0kY69gIbv6WsPIyEfjyqdgICAknGgTOhqfbIND9ufK56R6JxXK4wrSEy3pZFxgwpp50WVIiCCOP2TtodFuuHs1waaWbIjXoOxBofilvlMkY8FUjee+YmDbYcQQ6LW4GHsvZSqV60F8cdI1bcsjk5fUVRhqk+iLQ0kpghUFGmPua6pXACO3JPLqYtIighM7RIPgJdSMQbAgAPSVbLRYccSewTM0SMrUNGMFIWJ470cJZxs2mU4qBlo2FMPKlSaVcAwKPYYawzdOO5pRIE27aW1+fqVOea9/77XrINKggILeB1t0f1Gipj9zOb0DF3ktUSHakQQkwDcAOAiYha7HVa638XQvQC+AmAmQBWA/gLrfXOA33+BkPUQAMGu2/5AzZ/5UfY+Yu7oErl2gc00EADDRwmDK3cgmJfB/rOnYc9T28c9fFCH/p/dSAA8FGt9XEATgfwASHEPAB/B+B3Wus5AH5n1g84xjRDRJoCQmJ2t8bNpVGTlzFKsZFj+ahIJxmpTO8zstJZLKOsOX5ii2xSRz7iYRoKpfMZotDojehqrIdDHZGruXdWzF5oW6aN9gz6jRVSpTXHzBAVnCyfCqVRaZQ8kzFB7DlZzzT7e4IiSu6bpyFK5BLJqbzdrrDnN/ch3LkXpWdfwK6f3Ylx77kMnecsyjkwfY/c+8qjgdP1kNeOYpSDW1Q6fYA51rTTgEe9VhKhivQaJfgYQcwQ8Qi2vuOxxhkizgjFWh9iiKjuqqaeyMZUchgArhMpkncUK8suPQFPKPhKQfns3Qiz2338ftQeKce5zcw768Qwst5JOZ5n/G3QSth2aNkrFkMsEFEcIhvlWsbvI2fHqC/gGiKKpRZomfYi456u7Lrsdq1t4leC1VWlguI4ZZq/aR+pPVS0Z9kezvpYbSSU1TMROryRxL5cW5SlOSsa4d1IWEjcK8tuUT+k4nvuWXIsuz3E7JK2rD6PW+VJFnuK4o85ZeYxQ+l+ziBDp0aMJe0ZsCZPmiKpo/5l7Q33YvLrF6KyewjhSJSuJyuR75EErfVGABvN33uFEE8DmALgdQDONrtdD+BuAB8/0Od/STFE+5MIb1/AAz2+2EDZ3g8dDt7zE0Jg2lf+Fl2XngW/rwcAMPL0Kmz5xo0YuG8p1MjBZ4zCnI/8wQJPXXOwUajDrf2A4hBLFg61RKJWxvoDjRF9aMfPlBz2xQq1H+97aeseDCxbjwmXnLTvFdCH4d8oIISYCeBkAA8AmGCMJTKa+kd/wbUxphmiqpFzHe+xLBAzFEeuzh/R8rgWcZ4iMyJXKsXmBCxmTOYcODQC5SFQEoHyUtdDH6RmJ6o1h62TGSFbZsh64pjrHIX3GQsCDa21ZYiymCGtFEor1gGeRMvRUxJeWZ6nUowQFUGRgPnoCTrJFgHOyDQ5aM9hihgyvkThwBD23HoPBv+8FNAakz97NbzOdue60sUI30fvW843RWrs+P6t2HP7nzB439Lo94KPlhOOQnHGRPi9nSj0d2PvH5di+MmVmPzJd6I4baIta2jpCgw++BSKUyeg68KXIQwBtXcIXmdb4l4lzi8io4gzbfHv1MZMe6VcVTr2qhkRPoZ1wTI0NrM8i2XkMkY0EucMkav/ARyGCMLqVQg8MjX3YLMRj6VC0XjHhRmMEJVP230douCFTrs3S2JqmRdPihnSrtaD7cO2W48xrexQ0c2NVg+0jt9FnvMuFBIBpGUNSKekvaRuCoDtmelYeo7uvaHE1XTvbS9imRx6BoptV5ZJiOO10W/suTrrBRkkjKIWKHhQTpT0bA+y6DzZ7DdppuyxTryigbAJnf6ILX8waDJ1pX5ZJspwE+WmNDv7YKzaOpGWlRhZiqHmeARyRoiYe+q/snSbEEmjiGsvXb5CV0Js/J+70HHq0eg+aRqGVm9D6+wJEMUiZEsTKntGIuZ9NKLPw4PxQoiHnfXrtNbX8Z2EEO0Afg7gw1rrPeIQXdeYNoi6i8kRQlYqjVqsj53acoSgectqGcUD9hKm0ihwF14RiwLtlBkLThYLsnkoeKcT8pKGkOuSDMC662qZFvJaA4XNL6rhMsobtqOyfisGHnwGg0uWozChF83HTEfT0dPh9XSgsmkbRpavQXn1RgQ7dqPQ1w01UkbznGkY/543AG1x8LO8QIn1JJGl6TZJ11GlY6k1ohLGeNtz2x+x8ye/sdv9vh7IlgKE1Lb8xGhFuB9JKgvouvgsQAPB9l0YWbEWavcAhpYsx9CS5fFOlIhxsBRPxW7ajk3XXg9/4jjs/d3D2PGj30AUfKihEUz+p/eh+djp8b07SL6v5IptRdzUlnUc8oEHPrUGEXMqoA8AT4gJuNNo9A5l10dpad2Xa4FE1vCBJhGioMK0sWTTKZCVQ40+aWgfKPDnFSeENZCI/aMN6L1zp0ATUzIZsK7Ytv7RdZYpP6uM7wMfwFlDORXskFzoI5PJg4jTQrCQH7BGr5mGcvoqetZDOkq3IXUBFV1IBXMkuE4rboZ3FwWRPxgkNJl609dKMmaTG6uByjeM6L3gQVClM8jjx3Lxesq4Qbrv5sZ3nKLItE8n9IF95ta/Pj1fueX7d2Do0eew47ePYej1p6PrhCkYXrcdlYEymqePx9bbH4PSom5RtcC+GYkHANu01gur7SCEKCAyhn6otf5fs3mzEGKS1nqjEGISgC0Ho3Jj2iBq4MChtGYLdt/xMAYefAbhnkEUJvaiOGk8Wk6YjfHvugiVLdFHf2jpCoR7BlCY0IPWBUeh57Iz4Y3rgt/eDFUqY8dPf48N//ZD7JrQjuY5U+F1t8PvaoU/rguF/k7oIES4fQ8gBITUECLKIR0lCwIgjEdNqDG8Yi2gQhQn9sLrakOhr3u/rnHoseXY8sXrAURsTvs5i9H+shNRPGqqjftTDyqbtqH03BoM3PsYRp58DgDQ9bqzMO4t50OVKxh88Cn43R0Y+NPjEMUCOl4+H83HTIeqhAh27EY4NILirMnwuzvg93ZiZNkqyPYWFLo74Pf37Nc1NtBAAy897Lz5fvS8dhEGH1+FTTfchR19neh52TFYfs1PMevq8zG4YjNUOYDXfGSrYEREBX0HwNNa6y85P90M4J0A/tUsf3kwzj+mDaKe4hCA9Fy5S9fzESqHSzFTGoNaDBEXMgZaohx4VY+xxzoJZGOGSCBQaVE1H3m410BTHjZxofmi+0y3Q7S7UiIxSlGlMnbd/gCGl72AysbtUCNldJ97MqZ/+goUJoyD8GTCZdjvakfLnKlQr+HUbjyMkE1FjHv7hejdPoyhRx7HyKqNCHcPQO0eQGXbbkAp6EoI2dYMU5GodAVomBEYxaMQAsUp4yGLPoLdgwi27kLHK05A/zteDdlUgJA0soqvjwZOKhTQSqGycRuCzdtR2bAVO//ndlvPaV//OPyeTucikHZ/1c5vhvJWg8PY9bPfYO/vHkjcg9bF87H7l/eg+3VnQzYV0f7yaN6+Zf5Rdp/KjgGs+etrITvboPYOYcrnP4ji1AnRNNjuAZTWbELLvFkYfPBJ7PzZXahs2GqPnfEfH4PfG9W3sm0Xyqs3oOXY6fC68qfX+HabtiH0UJbxax0S3W+YIRLnB1JZtqgWQ5Ral2HqvYvbcDJQqSta5XWNy0iW777uBaXQ5Mcsgn0fvPg9AwDFp1aFjkXOqaib2vk/diiIwyrEu7rviFtC6l0G4hkONvKnY4TQmWEfhIj7mjhRqWHAqHyRbL9Ki5gtYn0RZ4yKhmFxg8ZaEXNKeE3BHilVigkGqpJpRwDAVwWUwia0eiVTD3r2UZk0ZUZu+S6s44oNfkiMVNyeiBlqMpOBsVs/TdHRMygmrp9CTFA50bUbhl8ng/Mmg6Em2TvOCHFmyLPMu7L6vxQDzZbpNhmznXYfc//itCkK07/wfuz8xT0or9sWHV/0Me7iRVC/+BNW/+edaJk+DhtvegTT3rwYdWHsxgV6OYArATwhhHjMbPsEIkPoRiHEuwGsAfCmg3HyMW0QNbBvGFm5EZu+8lM0Te9H17mnoGlSD4pTx0P6NM2wf9MJ/vhudJ1/WtyZGK+cYPtuiKKPQnf0IeeGn2uM8MjC4eAwNn3rVqy8+muY9sm3ozB9IrKglcKum+/Fntv+ANnagsKkPgwvjaawJn36r9F09LR9moba+/sHsON7NwEAvHFdaD5uNkTBx/Cjz6C0Yg36rn4LZFMx93ivsxWtpxyLytZdEEJAtjbHv3W1o/WEo1HZsgNbvnojAKAwuQ+tp86F39UWxxMZGMYL7/8iZHsLilP6MOVT74BsKmLgoeVomjURhfFdo76uBhpo4MhH08yJmPK3b8LQU4uw5prvobR+B57+q//EzA9dCF0OMPDUOmy66WGUNuzA1Le+7HBXd5+htb4X+d4z5x7s849pg6i3OAggzf64LpwpYSabqw4d9qWkKLGlGSkhe4RFI1Ub1C7wUsyQrQs71vXbs1ohLVLHJeuc/K3ghSljImDXFY+wzO+hwO4Hn8PeR57H7nuXYeK7X4POM08AEGuK4jKjddJfhBmu++nEpVwgJJheQzhTXtV1HFJqO/KkUZbf2YQZH7scG667HUNLlqNvdh/KW3Zh6LlNKG/YjtLa7Sit24rK+m0oTpuAqdf+Nbxx4wAAe+74M/b+/mFsuvY76Dz/dPS+9Xy4IjwiqrTWGHpoGUor1yHYvA1+fy/8CePRXNLY8aOb4E8cj74Pvh3FqcYYEwCuMqJzISxbZdM9eo5WIVCA56GydjP6rn4z/N6u+NzmGfjju9H3f96A4rQJKM6eBCGSSSFlWzM6zjwRI8vXorxuCzZ87vvovGAxNn/5p2g+dhqmffqdkMVC5j2lUXFFR8H/CDwIoMsYEVtkA8BxnVEVxigvnQcPEugLumMindLCgBiMLBRkiKIIEZDrM0tjQpcagAJdRuuJYHmIDfG6kbOvZWjMo3cZI8vdCvZbXhLdDDaCCuFBI+Mgp9U0RMk+I2a6k6k9JHT8LEivRae3zzGpJSrpdJ9bVAUMq3iQ0CwrqAUrwM7N0ROhSVZSIVMKopAoQ7IcXnHQSi/Vbw7p5LFaJAdkEUSiz8rrn60Y3jIsEoHVi5ljSVjPQjtQh0B9ZrSSvHbSSlICXGLchBRonjcLx/38Guy+4yFs+M/bsfqrv7bHHXvtW7H7oRV4/IM3oB4cJg3RmMaYNogaqA/hUAmbf/EQdt71JLrPnI+jvvrX8LvbxygjWhui4GN45Sas/tT30TJ3GoqTetEydxo6zzkVxal9EG0RA0WZoztffTo6X306wj2D2PQv38Wu5gJ6Lj8nVe7gfY9i1013oe1lJ6F14XwE23Zi4O4H0TluPCZ/4WMo9Pcm9lcjJVTWbUZ57SZU1m5CeV20VEMjEAUfhan9KE6fCAiB4SeeQ7h9N2RnG9pOm599XVKi46xTzErW1IlA//svx+BDy7DlP2/G8LIXMPL8Boy/4jyU1mzB6g9/A81zpqLtxFnoPveU/bjDDTTQwJEK4UmMv2ghOk6ejU3X34nd9z8Lr60JW25Zgraj+jD7Qxfg2c/cVLugI/T7cDAxpg2i8YW9iXXF5pkT2xyXYHedUNEeAjOcLJmQ7xWmYaB59ZLVGpkAYDkMT7JuybnqqDzSECW9zBLB1RyQa60btp72aTKu+aQtCrVEeecg1v/wAWz57ZNonzsZx372L9A8qceM4CoIwuQUmZ03p7qa34X7GyeCspghEOuS1GTEcRiTmgwqw47spIqDjDkecFppDD+zBj0vPwZ7/vA4xl+8EBPedpYtg+pLzAYdS/P3fncrJv79O7Hh0/8F2dGKrlefbsoVUIHC7tvuRe/bL0XLgmPtOdtedjLCb/0MAgKl59dhZNlzKK1ci8q6zQh37UFhUj8K0yaiOG0CWk6Zh+K0CfC72yNjae1GlNduAgB0nncaZHMTtvz7j7Dlaz9B/4feDOEoubkHIL+/sUegRMfpx8PvbsfAn5ehMLEXu2790//P3nuH23FVd/+fPeWU27uupKveLFldLnK3seWGC73jEIPBgQR+lCSE0F5KQggkxAnlhTf0YjAGbBNsgo0bNi5YxrbkomJbvUtXulf3njIz+/fHzN4zs+ccFddrOOt57jN35kzZs9us9d3ftRZWcwGnq5Wh3z2CcC06zl7KvttXE5SrdK1YkgoUmuR6mEEAk4iRb3iZqcCkXlS/2tXbQCICBF69oI2GohePLT9zzAy8Wkvy0gsRIiPFhEpwK3X/iVyvo3eXfpatE6NxdbhEScQmqD3edftpByBN/Mjg/PW5X9ljJmcoG0Q1jRhBllekn6eaX6fqkJl91W46Vppui3SC1hE/i0iq/lAIXEYTv6u5Nx8hRcl5WgdgrOORpqQgqnpreo+ZAR91ihDD47dqWTFf1HdrnpPppwj9pyTDGRIKUTU5RXHKDzVGEKoRTBQosW94pBlnaDQw9rSN+WSBsHD7e5j6kdch/YDqtl2MPrmT8tM72HHjwzTkmclhFaJD5Bb5NGH0yIDQBe5tUsqtQggL+DYwE7hCSrlaCHEmcCtwiZTyhui+vwS+IKW87Tl/qz8xkVJS3nuQ0o4DjGw9wO671nFww14qu4fpPXseS7/9Duz21ppLXy8l2XPTSkbXb+fAH9ax9+aHmP7pNx31PZzOVsZ/5G1s/T//j+DACO0XnAz5Inu/fR1OVzuFBbPT53d3UFgyj7Wf+jJ2eyuFudNpXr6Y3EA/Tn83wraJWbLqKolVyFOYM4XCnCmp+0341F+x7bP/zdCtf6Dt7CMkONaQ4jFTKM4N791x3nGMPPwk5fVb6H7tmTQvmoY3OMzmL/4Mp7sNuylP68nHPuNnNaQhDXnpirAtipN7KU7uxbXmAnDv+Z87/HUNhCgjR4IQqdwiK4UQrcADQojfAP8qpfwYgBDivcDHgSuBcwkjS/4t8M/A5dF9NgP/CNxwpIXrddIIURIFMhGgOCWCMPYjizIKMw9Qslx9DMggR+VDlOlwhGRfCqSUyHIVtyn2atMW/GiVVf91O5X9o9h5h+aBdgSwb/V29j68FcuxsIsuTsHBKbr4ZZ+RHUM4RZdifyuFcW30LR2g/c1LyXU1ketuDe8bVGLrWcXasJRVnUbNfM1TSBT8KJAhCWkLR1+jbpVWIJT1pBAd2wr0/0mUoPfMYxClEv5ImVmffSNNM8dBgmsQI0IReqYQLuVpF+2747qY8PG3s/ea37LxfV/E6W5HVn36P/Ge0KMohW5B+wVnMem8U9Lvm7Sq48ys0fum+R6punNcuv/yErZ/7ls0Hz8fu62JmmLEzTmUCNuieclMWpbMDPeF1I3n7TnApi/8jNnXhHwxH0unkwEyQQCTiJFGi0yvRyttAR8qhpGyqM2o17W8zRQfJX8IzlD4DjEPyRY+jgj0/XWCWyM+l4qrIGVcDh352+ASmV5nmZgxySYxxoHmnmRCxcQJZEwPpsNJMhin7hYmh8hAjAJpxzwq5dWlUZ8IlYuSgpr7yAQvRnOH0i80WgMZMqXsu4xaMYdIz73RvZT3mZtCB9Pzs0JsFP+oQJaHpLg0uShmkWXGWfLT9Tzqu3GybSP2mzCRIoMzVGslwDYCzMZIePSPZcXJgPU4i9pLlc2IR5ScgzLdRJ2iEaLoniqWkSX0nJ4irSbeqyHPTA6rENXLLSKlfDRxWjPxd9EmHFUB6U/FQ4ArhFgh4OvRSQAAIABJREFUpfzNc1H4F0u84TJB1SPX2ayPSSkp7xxieP1OtvziQfY/tJmuZZMJ/IBuq4XxHz6FYncT67/5eyr7R5l43lyC0QoHNw+CgCmXzuf4z5wPUhKUq8hSBW/UQ7g2xXGtWIVw4tFBHDWZ9YV//+dLnJYC/a8Jl7mebVoCt7+bvve8Dm/PINVdQ7gDE0IvsRcge0R+ynhali9k749upOedr+Joo6yW1m5m5OH1WMU8bSuWYeezw9Rpb2b2jz9OeeNORtdseq6K3pCGNOTPQSR/Wh+P50iOikNk5BZBCPFZ4DJgP3BWdNqvge9Hx99p3OIz0d8RKUSaZV9LC1aeYDIdW8hEhpLr2PWipeajdWu19q64RV4UPtmzQw7QgbW7eOIbv2fw4S0Ix6J5UifzP3YB2295gs2/eBiQNE/vpe/UGcz79CvZeeMjbPrx/TTnAx7/z9uZ/Z4z2HLjo5z83b8g19mUiWWirEC3uVAjZhHGfv2PbDY5aLS2H/GQFJKkeDyeb+FFXkoaQTCX3xJWtE3oJab5P8phxXju0eSOM3WGWkl57QgV0NyXyExXcTw8xYXRa+4Ce1wbbk9HFELfj6NdJ29vySPDj7X1LmvvJ87reO3Z7Pjct9n2if9Lfup4cpP7yU2bQH76xJBblOSClCvs+8nNCNuitHYT3u79NC+dzf6rb8EbHKL3Leekbh+nd7DJTx9PbtqEFMqRTHYbmEhGKmuncjsKjJNrnGoer3Usea66tYp1hKVt/8MhROpWti1pCqo0WVnMVqfZMNrcFjFKqrlRRgyqOLaQ4m2lrfcUQnSYGFA6GjWxN5np1anEliEal03qKeteYyIa6jzbilNmVNXYjd5LRY7OpP1I7Cu0QXsLCrWf5vvVkqRHYTKCdWCg9CN+mGqjYFUJonZJokWHk3qxisz4Vfq9FcfIicte7z1GPIWApTmflpCZvlWna8dGW6q/RBuR3teiUB6ZuM6IjabBO7NfKmPYT942zWvyRe1vXEOOTI5YITJziwBIKf8R+EchxD8Afw18QkrpAW+odQ8p5Z2hq7E47UieaZW71X/RDeJuoCiMjkxPBpahEKmcPRKBH7hYSOzoGkefE3X8SLmSiojnR3Dw/oBN16xi930bmPeKpfR+4nVYjs0f3vcTNv3jrTRP6+KcL7yVpoHOlLtn98WnMr63n5Hrn2DHuh1s+9TtLPuLcxjX1gN+VoFI5n/SgKgZyyc6rkimelkjsFLPhlhRUB8EpWSofe2yL61YkdSB0WoPLImgixxSgGP7qXQaMVxbe4lC/W5LiYi+Ok6U1ftI9BHbkviBSJU7rINIITLKrp4fCBHPOVa2L3VbtZcHhBXoSShZyKwCVIMV2ZJj/KfeS+nxp/D3HqC6bTfln9+FyOfoueIVcfBKwBsawXtoIwCd47tpf9UKhGOxa/VWumZMo418dKJP4HnYhXz0fqTeE6CLeAmjuns//oEh8pP7sPMO3r5hqtt2U5w2HreloNtABW1UwfnifFJp93tLt1kQE1sjTV0toZn5n1RqBldIRPRb7gjI1BD2+aLXTItwcaKxmIuWtd1ojKrlbq0U6zFgJ4wis56Mpd7EHAGZVbKaohUUvQWhPk7qHKNbdJIDmVyyU5IY78ZvwnxO9LOdCCKoUGPVbo4mMEf1TbptXJnNP5bZP8TSixp/TUHYDxWJOudH+fP00paMfvd0mRzdp7zU8+wailJGoYyI0m7k6p9XTi9RX3ASipmawx1jq/pPPtom57n2IByTpiGXUaT1smW8RFiJrGlF6leKSWDMNzIK1ZFWiNLvrUJJCKVga0U7nkdt3dfUcnZ6LB+RNACijByRQlQnt0hSfgj8D/CJI7jdZwm5RIdNYlPO7a95PMkhUh/yqlaM0shQLQ6R4tioAafjZwTRJOuFA2PfVo/7/vMBNt65mYFz53DMv56J01qkwgijO4bYum0rM996HI9//ff0XbkI3x3W8WD8IESVOHki4xdNYujuh2ge6KQwbwK7RCn1/Fpbc0LSa9NGvBetBAkrwyFSaI/tpD9smWt9WytJmn/kp5Wp5KRgE7CXcjhJ23HZTIvcNxVO4kk3mxAy2jditiS5TEJIsOLnVCMEz9fvYen3gXjN3Q+sOEeajigb3T8QCAJ2UiObfSKmDWQ/ZHW9iNQ/Foj5E4AJ4a6U7Pner9n86a/S9aZzcdpbiL6QWH/7WkqPP83wll3svOkOKpt3U92xl8G7HmDCSWFE7C2f/wEHH1xH85KZdF16Cvm5UxFCsOOr14GU9L7tAmShne3lYbZ/4UeMPLROl6nzouXsu/E+irMH8A+MMOM/342j+ls0mbpGUtdMcstEgthknj91LL1vJpX19X1V3KEmO80XySaMjaK9O8N4kUJUiRJ8liPuiooV5hl93/NtKqp/1EGI4ijXseKsfzc/UhlvwFgRghAdMlGebDTkgL0im8HdSqBGGYWozj0tITUypDhj6mOolVSV2Dc6rhP+Cj+T5Fd7wEbnmF5ZtSQX+Ay7Q1RURGmVK80YF3lR1b+5VjrqtLm1DeUtKWo1oBQpRAqBqjp2+hppIaJzlLLkRf2m4oXHy3Z4XCFFaq7aa43q55kBZevt+1JQEWHZKiKtGAWG0SYV79FP9DHzVSOES8U5EnobtZUttQKZnFPDS48cgWtIVo7Ey0xQI7eIEGKWlHJttHsJ8PiRPFBK+b+Rh9qEw51blTau8AkQWEi9TUoyRQaEipCNJJAWlgj0vi8tfGlhiwDX8qkGNk12mRE/T5NVYSTI6f2iXWX3do8br7yFqedO47U3vAa/2EbJd3Asn4pv09KTR1iCoXU7sfM2bi4bnl7Dr4UC486dHyksvi6HH1jYVkDVt3Ht8L65aGsJSc4K3Y1VcllHBJk6SL6/+k0IiZSCvO1R9p1463iUvXi/6FQZ9Vyac2Wqgc1oxaXoVhmtuhRzVUYrLoWcR6nikHM9KtVwSzWP6/hYVkCl6uA6PlXP1s/VfYSjN0JUQD1Vd7YV15N6X/WcglulVHUpOB4lzyHv+JQ9m7zrUa7GZXadcInA82xs18ev2thugF+1sNwAfBC2RPoitQWQvsCyg1CRil5Iv6feD61GnTw2+sDJwNK/WZYkCAQ9l53H/l/fz+C1t+IfLCGQSD8gGC3T/zevou2speHHVoL0KshAIH2fkYefxNtzgM6LTyLX38X2r1yH1ZSnacF0DtzyAIXZk9j27z+h7x+uAAiVIdvC6WrD2zVIdXAY/IDyhh1hotlqgG+FxNwgEFiW1P1SZ1I3tqptFPk0+ZsXWDhWop9G40/338AmEBLX8vECG8fyqQYWrhVgERBg4Qpfj/mqtBMGQkDRrjLquxTsKiXfpcmpMuLFW90Hor6di9J9JMeU6qdq69g+nm/j2AGeb+mtbYV1ELZl1MZR2+pt1LctKyCI2lnVh0jUlZRCI5u2FYBE92k1D9kiVKZ8KXR/UdfUGgd+YIUpWqJ3U2WvBnZ0z7BeK15YD+q4mgtKvkMOP5xfjHZTbaO3iXkHSM3Dii+s+kA5cMhbHiO+S5NdjbdBDh+LJqtCOXDJW9W6W9X+FelgE+jEsLYIUn3DFb7+Tc3bLXaJYb9Ak13GFgHDfoEWu8ywn6fNKXHAK9DilhiuFmhxKwxXc3rrWj6Wwakx27PefnLO1XOfMUfEWxlDkL4AWyICgbTiLYEI4UW1jc6TvoWwgzDoo7Cw7XjsxlurbiBQUxpeZlk5EoSoXm6Rtwsh5hDqtxsIPcyOVD7LESZnq8pwwoiXcrJ4tglxmuiQySNSShHEnhBNVoWSdGiyywxWi9z66XuYdd4Ulr97AcN+jhEPCraHJ61wTd62OfkLF7L5lvWc9a3X4XQUqfX5j1GT2IK1rXAgW8j4uB8qFEoZgtD6tYQMYxhF21p1kPKUUMtUCq6OeEN663gEkbIEUHSq+vpirkrVtym6VfzAopir4gUWhZyHHwhyroeUAteJrZCcG1l8kdIRPjetFB2thBNJHIspiRqZlnIhKmvB8aj6NnknnOTzblxmhRg5jh9+VFw/5Be54YQiHKmVIoi3+n8Jlh3EE1zi+SZnQCTWSWKoOyq7FU6SHeefQPt5aZf8kYfWsuOr1+F0ttK0cDp2ewsEPuUNOzi4ci1ubzvtZy+l7YyF2M1F2s9eysFVTzP6xCb6P/B6mo+by4b3fYnyus2IGb10vXEFTnc7baceS3XnIMHQMMO/W0UwUqb3racjXAei/mdbAeU9Byn2NOFrhUZgq4+xoRSpmfRQSlFO+BmlCML+n7c8vCA0AqqBRT5SiiCNGCSVIoCiXeWgl6dgVxnxcjQ5VSqBrbcFx6Pi2+RtTyNFOdsP+4EdGlau4+s+7AciXPYNQoUikCLcam8emW5zhQrVQIfq9U+h+3C6L+slxcTyoa2vIXWNOQ6UUgRxhHLPDz2d1PxSDSxsIal4TlTP4fGy7+hnVgJbK0VqflFKUdietm6/pNGV3PpS4Ii0UgRx/KIR39Xo0EiQoxApP67w62y9VLvrlQCZXglQW4WMqHm8xS7p31rsEiN+jha7zKifo80pMeznaHFLjHh5WtwKI56rtxArfMn2PJxSpJDNWtyw8B+MbWKOjCJaiyDeZpQiO6sUQYjiK6XItiOlyJZxbrTDyUs1cu/zKEfiZVYvt8ivjvQhUayh2xL719e5Z0pUx/ZJN7BNkDlWP2VHrESoyVYtkam179u+sZ7f/PdmZp3UxcUfnMmuzfsZ3DDEK/79FGy7QlVammDtKP5KYNN7TDfts/sAqETzmp44kpaALqNatkkv2ZlwuZRSuyub6+j1SILKUoHYJbnesoZZHgAvglo9O1bQILGEphU6C0f65ISnn2cGxTQJ0s8kt1ita00YX39s7PRHpuqny6ysbcgupwVS4ODHAQZrBOQz3W2zhTQ/gjUmQ+N91M/q3s2LZ9F81fsYeeRJRp/YQGXzLhCQnzFA1ytOJTexJy5P+FI0LZxB08IZoRt1IOh+6/nsvfY2xPtfTcelZ0SVJnH7exDju+l52wWMPLiW0ad38NTffYPx776YwtR+hu5by6Z/uppZV11Bcdo4TdBU6R30EoFKISBFHIA02iSTGkPM74pTRCS5ehGpPxrfasnOJFu7+DiEaWwUUbjZSROsnah/OtGHOMnDU6L6gV7iiPa1a7sK56CXzmI+nuoPmSUzQ3lJLnuZSlDc5jJCE+JrlNQbI+YcUfFU+iErs6xt67QQpN5L7Tt2wnBS7xcd07wu3ayqrYQuh2UsY0lpEUhLK0H1JOnOrraqrRXXxlfOHMTv4hpcJDWXK65UwapEv6eV6UCKjFPGoUji+hxfhnNIUNvwrEeyxlCgQHOnE+lj1HygfkgoRWbRVFgUlf7DeIrU5kOycOnnNeSZyZiOVP18yOiBKjf916OMHqjilzyWv2YCt35/K3939WLuuHYXn15xNwCv/7dl2G6Dsd+QF06EY9O8ZBbNS2eljx/hJNd8wjxY+RTbvvgDut96AbkJvUCoYO/50c3s+/kd4f1yLrJS1QrB1v8MwdotX/4VE644l7Z545+rV2pIQxoyRqWhO2VlTCtEVamsvnSo9oAEpFoHKTJTerjCJ29Vuffap9i3cYgl5/Xxo48/werbdvFXX5zJtBk2Xe+dRCEnWXDpZLonNTHsV6JyxB4rOq2Hdk89vNIklPVoWNwm1Cq1dSFTaFHq/eogRpaQMYnSQIRyBlEyrqvsUps6pgipytJX0LwT2OSDkIOgXfO18XPoEXYoK02aZpAhycB/SWJpcpszQvtrmN23U2gRJNCAwMKVQQIhSqMJycKZwSrNdBxJVEijAuZ76pcwDjyLySlGLwQ977iUwRtvZ+vHvwFI7I5WchN7qWzaAUDvOy+hvH4rna84FXdCJ4GE9rOXsPf63+OPVFj/ke8x+QOX0nHq3AQSFxU2hQoZiJAah4bpmkwbYSJ8nk7BECEqaplXhcEQ4VJNkqSrUBCFFFlGagYtCbDJE+m5QC9BHQIxSqJF4TkmWpB+Pz3GyfaH2CMt/E0HKq0xHswxpJfPE8gQqFAZ6b6q5ooY2Uunk1BjXEqh5wpTMolhE++QRIsgSiQc2ECWP5m+p5V1lVderVFb6/kn4Z6v0CKFDiokqCVC9m1jmdUc90nRvMvoi6fuZes5JMDxq5TtauySb2TwrRdGJIkIZ4JyGstlKXd83dbR+PWN+UX3myxSFHtEptHbhtv9s5MxrRA9FzK8p8yTv9/F8K5R/NEK9/xkM+/8r/lMW9zO8Ss6GB32mDQlcufMWVz83qkMBYXD3LUhDRmbIlyXjotPo+3ckwiGR9l51dW4A30U5k6h9NgG2s85Hs5RE3Y4xfZddi7F2QPs+cntyIrHhs9dS8uPPojbnjv0wxrSkIa8NCUe/g1JyJhWiPZ6YSRo0wXTQupjigcUGKQ7X1r84uN/5MGfb2LW6ePom9FCsSi48INzmLy4Ex9o6szT1JlnJLq96QKqOTki5phUtZWZ1sSV1ZS0rDzl/hr4oReDSbCOUBflEl6Lj1A3UJsmHcZ1oi0YI7mjEtPFFn1afTpXPavItX3NVarF2QHwdOoQZYFHZbdkbCUrdMDkW6j6TIQKqOcCbJaxEHkY6ZQFSF0WywxAKQNc6ZGTCiEKdJnNlA5KTLdbc5FfecnVuiaznzztMNyremhTisiuDOGiDcUWJn3m7QCMPLKe/b+8C3/3Htxxnak+5tgBXafOoXPheHb+/F7a5g9QbM9h4zGyYTclT9I0YxzYMYcIAxHSqTxUShEV/DM67mFlOEQKbdUpGFR7WYnktFLgJzgovlELrg4smObLKRJ1qmwyXTYz1USyH8eB7ojKFFVvhksU/S6yARdtY6w6BCkStcltSp6bfP9kmateTKL2jeTNGmE2uEQmtygpmVAE0Zj2DDQtmXBaBXr0AitODltDYueQuqfouUrVhUI4qsKOOUFGUMdAk2wUpyh2tw9/F3rlQHNJFTcpOrdoZ1OE2E4Vzynrcpf89OfR5EGpMpekG895ag4wCfiKz6W8VwMQitOlIz7WRpX0cStxrUKG/DSKdZRB8RtiyJhWiI5GSsMej922k1W/3cXBfRUGFnby4M/DlAYXfnwxXeNycbyhGvlyGtKQP2VpWjiDzktOYcOHvorIuTjtzeSn9NH18hNpnRtyhtzOFiZefja2CNh752Ns/K+b8EtVZNWnMLGTY//tzbgdzYd5UkMa0pCxLuGKXQMiMmVMK0TDUeAtZUkmPQeUVenvPcD931/HIz9/ikmLuzjm7PG0jSuw7vd7WP62WSx98ywKvUWq0k9E5I28kYxgjqa3QjKKq8lz0IHMlIVqKc8cZaWJ2FqVQRgvJIh/U/cNy5Hm4kgZWwDKnjwcUpTkHZUjM6VWUk7I8gPMBIeQiHZtWNEq0WYS8VJWrxkY0gycGCNIQQYZsrWLerpeU+VS1nhMS8mek3ivJHfKDtIWb9LKc4OAglBpTeL3NXkjpjWtrHVVfdqwS6AFipMkjXuYYJew5CFwuvi+qX11T4S+n20H2MSBL5OoXO8lx9N30VK8wYP4+4cYeXwTmz93NV1nHcuEt55OLoql5dg+cnA/0g+wizmKc3oYWrWJ1R/8Icf++2XQlgeN8KWt5gwylKj3GG1Ie1nGSJFCkOL3dLHxAjvhNRrxSgxkSCc2TfRbsw+ZCW7Nvq7GezWwtTeiGu++0fb1Umok38Mco0KG/cJEhpJR5s176H7qpxEiPxEh3kQyhYn6BKoPxMdlhE44pidsIt6Q+Q4KGVKTki+tlEu+KXo8ykR6jzqIs/Iy86LAho7wU2gRxPNy3kpzi8zUHekgvI4ua3ht2pPRTvqK2VV8Jw7QaiKLZh8fiSJl21aArTz91DVGUmXt8Re1hbAT/FBdhKh9VNgPO31tPIkIHVhW9TvppzlYDXlmMqYVIlMCP+DAlmFK+yuU9o3y9J1bWXfzRo69aDJX/uR0OiY06QE9eXlo9Zqk64Y05M9ZhG3hdreS62mhaWY/HafNY9vXb+Kx9/w3Mz70cnbf/AilTbsZ/8plzPvXN1Gc3I10cjzxkR9x4MGnefxjP2HeP78Ou+Xw2dAb0pCGjGE5iiwffy4yphUiZTkOjQgObj/IbR+7k/K+UQpdRQqdBXrm9/Gan76SYmcB366wpxqvDSttXqNACRMuyTNK7mfCyatQ9AQxz8GIcxKHulfWRGwJKcvTDcKosCaCYVqZOuZPjXOUhZrx/NGWegKJid4ztpYjS0pZYQm0R5WnXkqQOGlmIm0DYeqGTHj/iA+g0ynYacTI0xwNceTIUAI3CUwoxqgTJRqViCysgu3pkPqqzBrJEAE54el0EprLEEhdX7Ehl0aGVEA+EzVIHjsSZEiVy0SATN6RKfpXIXV6FluEsXt0UL86CUdF5L2V7y4w5x8vYddvHuHRD3yP3rOOoeukmWy95j6O/dJlUV3ApHeew/rP/oyDj29l1fu/z5yPXELT1J6sd1kNZAiUl1J67FgqhU4drzN1n6oVGzV143Ip7lR0D0/ITNwty+hjJkKkEMWS71A2+H6K36TkUB6V9RAi6YkQnZFGv5JCIz71EBTtUaZ5Q8k+pcZFmiuki6gB6KxxGBhlsS2D76c88pJ1qZJgB1YqkGzdthHo9qkYMYs0z0t7A0fIvyVSaBFkPajqJYytSodykFbYFV3ikMllnTLCKen+OOrH/EWI62okSv+h+lc1SMSWMrmQ9bhEUsRQd1SfuvYUMqSQIqNPCJngg6k4WToXZUPLeTYyphUigO0P7+KWT97D0OYhJp89neXffAV2IshbLXJcQxrSkKOT3hUL6H/ZbKycQ3XUY+N378IvVbAL4eTfNLWXBV+5nEfe/U2Cssfqv7sap6VA50kzmPTmk3AaiFFDGvKSkgaHKCtjWiG6898fYt2v1rHsg6eQ7yjiTuymHOSoVGPPBuVR1BQl61PxSZSipL0grJjPUdVr+GlPBJ0t2lJr1XEiwmReJYg5C4qPFCNDCa+o6NkCk0NhcAsS1vv+e9aw4xf34w+Pkp/UQ8v8yTQvmEphoAshhPYkUWvfoTeURI5WcJrzNesxjnuURrk0xyiB9pjr5HbiHFVGWwbkLL+uZZj0QoEYMdLJNqWV8TyrhwyZCRXD36h5rhI10JNeZoUoVYmKSaO8zgIpyPm+bmvtfRZYcXoC9R4aGYraTRck3Y5BILLoziGQIXXPrIdRdOlh5i074bXnioBcor8qZM9EiGwr0HWvOWB2xOXJ52ie0cfQ6m20L50aIw6uy7QPXcyGq26kaUoP7UunsvOmh9h6zf0s+dpltMzs07FbzEjrqf4fGL9ppNZK7UPW28kmPQ59mUaZLI1k+hnUs17iWR17K8Fl0h5SKmK7SHtTHVHkYzXOdIJPkY7aTIzOmFwhE1kMfGMcJPqX7h9mn1M8RB2fKItgao5L9HrKI01H2068p3ofxbLRHCLS9zfnkmR9qojmJlKkOESKE1aVlkaLqjpzfHpO0HygqLuY2QkgiwyZ0baTIkQVrIr+Kqp+or4hB6MEw+a8l+rbenxFdaB4owZiJAOJ6lIxohe1sSq+5oSp/URhjdhTGig94rQdZOakhoxxhWh42zCX/vBSRHsbACX/2Vmhg9UiHe7o4U98jsSKklweqQyv3sTGL9/IhHeeT66vg9Ent3Nw9QZ2/uQupB/QMn8ybmczB9dsRQQBTbP6yfe1s/uWVZS3D+K0FmmdN5H8+A4qu4ao7NxPZe8wxUldtM4Zj9uWp7rvIJVdQ5T3DIPnRwRuiWVbtM/po3PZZDoXTAC3vjutP1rh4P5BggMHGdlTojo0il/yyLXmGXfyVHKttRWzZytmIt/nW1TyzRdKVKLQF0pUItRa0rZgEkOrNtK+dGrqeMucCRz7pcvY/ov72XnjQ5ok+uCV36XzhGn0r5hH75lzarr/qmSk9aS8v0xluELQKsi15LCdZ1cXyaXgF0JUstEXSmSgPc9fEFFJYl8oGQ1citYLtwJQtKuUXrCnkVpKbMjYkDGtEC3/zAogVoRUXIiS5+gPVcmPslo7durcpshboGiH23IU6XWwWtRcG6WsmJwaNehHNacojmTsJng34T2yVgmE/KeKDJMjKguiXuJHdXzwztWMe8UJdJxyDEFgUZgxgfZzloW5zXbtZXjVBvwDB+m/7Cxsx2Jk3TbKOwYZeOcK2pdMobx9kIOPbqay6wBtS6aSG9eB29XM6NO7GFm3nfKGQXKdzbQsnEJXdwt25FVkWxKqVQ48upX1372P4XW76Fg4kYkXzsctWAxv2MvIhr0Mb9zHwY376O8Zx57SPvKdRfIdRfLteeyCy56dwzz8b3fSs2gck142nYHTppBry2trMBn9umLELlJSDxlKeXrU8chRoiMSK48/4txb6oPsyBgdyEk/RpAU0mcFeFFGciWKX6HW6ZNoUlhWdJkz3KFIaiFD4b2jfmUF+hplnZv30NGEo3dJxnVypZ9C70yOlokc5W2vptLQtmCAjd+5iwlvPAUi5CiJFE143UlMeN1JCCE5uHY7qz/0Q6x8jo1X38/eP25m1vvOQVhCW7CBgbik6gCbyoES11/wo0w5ph8zDQZ8zvzoieRbc5lrbX3/dCyaJAprGchvzJ1TsczSyJElAo2IlkQ05/jp8V5PkjHT/AQypOov5BBhHE8ci65VXCGdWy8TxyraBrU5bMlz0EhnohwKwTBiFAUG94woTyC+rZPEKqkEFhWyHKJ68c4gjmiuf1OR/1WOyERb2UIyGriaQ6RReDvN91PX5hPKU95AhtQ2p7xJ1XytNiKAKjTZldh7NrpmmNDAs/wsMqTepV6kamGMc43oWDL0DBOxUivtdH1lkKEED1HNMDpitbrmiDUseXjo+c9QxrRCpD+k0SRaSSQcNT+UZiZ4tT+qYc4gowCZyowiXmcCHBKQk052AAAgAElEQVRPJpqUp111w07lZ2DiIH2DhJhu7xq6HxymeeHEKGN7+n72QBvNAwtS7updCycghGTfPevwBkfIje8iN74r87zcxB7aT5mL6eouEh9NRwR0LJvG5Leegj9aYfdda9n4i4ewbEHzlC5aZo9j3DlzaZ/aQV9bN0PuSM3w/tWDFbbf/TSbf/skK//tbvoW9zNxxWwGzp4ZB7NMjEMzjYHp4ioT7Vtv4jfFFtlymQRbXedChERxVQ6ZLkeqDQw3XGmnlY1ALwPGGdPNuay+IiQTdZB+H6k/WmlFKX1O/DwpyaQbqHWNSfJXz/GBtvkDWDmbP172FQbe8TK6z5pfN3Bo6+xxLPjiG3jsEz+nY8EAO/7nIZondTDpNcelzpPlCo//+28RMmBk635Ou+oS7LyDY/k4HS4Xf/8VPHnTeir7R6kMVdi/YT+BF/D0zRs59tWzGDihX5ddLWNU9Zi2U8c9mUjQqisjulh9BHWjqPqIl9J1igzD0DkUwmXWq3qebms7NKyEWgLy42WrOHhitFWKibqnKntSMdI+BnXGQx3dTQaWVur1/Y2UpYFMz13a9TshnrSpBnZqnIXXpvuVYwVZQr3qj6ruo7pIJlnWy+hG+/haAUrPFck0TYdzLlaKkUvcnlL4BDXc/U0qhQ5fEpWv6Faz6YSMsAWVGp9a3aQqdYdR6HgpNL0VCYVWI4RW4seGPGMZ0wrRn5N4wyWG1+1g4utPOOprn/jEtRSn9DL3q1c8J2Wxizn6V8yjf8W8msqbmXMnKW5zjinnzmLKubPwR0ps+d1G1ly7mid/vpolH11B0/i256SMDXl+xS7mmPeFt3Bw3XbWfPrnyGpA73kL657fMrufJV//S7b99D6Ea/Pk126n56QZNA106HO84TLb/vcxAJondWDl0h/dzlldLJvVpVE5IQPWfv1pZlw+iYnHj3se3rIhDfnzlYbulJUxrRBpZCiIkSElpkauYXBFZFRE3ihBrGP5GRJlNlhYmhDq6+O21s4ViqRJuHU4A7ZBtLOE1M8vuHFwsD2PbGPdTx5h1/0b6T9nLr3zuvCCin5X09oyU1540qIwsZPRDbuQfoCwrcwSixKd9iCycGxFRgwsnSTQDAWgSJ4KdfGERdl3qFj2IRNBAljFIgMr5tB71lzW//iP3PHOn3LsB86k+7Q5NYmJyWvNpTNXxsteh0OKzKS2OTteQjIlZ3m4lq/PNdHD5P9SptEBlbpEk64TYQWksZxmShIZUvuZkAqRxO1poGhBYqu9360w9laNNgYSCYSTfKw00pV0sW6b3ccxn3kdj37w+3QeP5Vcd2sqNUkqcF9bnulvP4Wpbz2Rle/5Id6e/eSmtOp75fqaOPHLr8X2q/QsmUg+Wvowl7B8jWpZLL58ESPuASRBSD42iLMaIUogQxCiy2aKDkWod4wlci+BDEG4NK9DRxiBJh0TfTwE8qaI+/oeUQofJW4NYrRGrYgRzOiXcD8ZkM8I7aCkbvoGzbyt5fqvTkkfVyFJFBqa/M3HwpN2NkisclJJzLeZZTUzaKYqW5DYT4NWun9UjRAFcfJahez4uGYolWi+VgFY1b3jhOECCJDC1wRvdQ9Nl9CBbcP9pujrmSKWG+MirjNjzkq0kU7QSlpUUtfE+nv8m6obg6zd4CQ9OxnTCtGfskgpefKah1nzvZXMunw58957BqKj9fAX1hArIkBXBw+S635m93g+RViCmW9cQsfCAR785E30PbqTiRctoDihvS6s35CxI8VJ3XSfMZcdN6xk0tvOOOz5Vs5h+X+/JdpLz9Cd88dr5bMhDWnIiygNDlFGxrRC5Mm0haUkaZWZloe29pT1rBCWwE6lckj+FhiaeOx6HlmJiXMckU73kdfBGw3NP8mTidJdFHJR0MjRCvf8813sf3ofF/z3xeT6OwGoBCEBvGLZdS1UM7AgAYw8vRuAQlczQkg8HVTRIPQqzoH2D4/3pWHZmClDlKVqCalJ0bFlpuqmtvWsytpyzHhO/NobWP3F33Lfu35I93GTWfapC2peU4tTlHSVT25NKz2Z4NP83QzuGERcE6dOGAWICdhKXINHEocoiDhvnh0TuzWZMnqsoQAqNCaJKJqggzSUCpUTshpx1IJAxP1diKg9VP9Q6E9k+SdurrlPltF+ColK9Inxrz6eVe/9LuPOm0/TQEc2CB+12y/ZJ9QxNQ6dzHg00bQAP0KFAh1MNV2BGgmO+IYaIfJtPGnhlz1wbUQCEcugPolgkoEXsP2BLZRHA1qmddM60JZ6j3r8v3Spo/qUMaoK4Pghl02jMwkOTMzRSSc0zY7hGBVUV2gkoQ4KmkFSE/tmEMd66LLnx8hzEHXAKjaVBEJkm2M20a/MMAxZ9Cd93ELW53ypSw1kryrj1B4qqGM1QoiaomsUf1Ld240QI0tIEGEAXsUrMgnZqr82R4EHdD0EdqZ/KMk4VYjsKocZcLLetSTayAz0qE8JardfQ45MxrRC9KcklaEyT928hr1P7GHnym30LuzjvK9fjFNwDp0N+ghk8luW0zJvEsIa+4Mh11Fk0acuYtcda9j8y1UvdnEacoRSGN/B5L88jUc//BMW/NsbKI4be0jk/if3smvVLjbe9AQLP7KCwXV7WfmRX5LvaUbYFq3Tummb08eUS+dT6M4mqa3sL3HPh25AInDamxhau4Nce5HZV55K//LJL8IbNaQhz5PIZBy1higZ0wpR0kUX0i6tSswkktrLzOAPOJEbdfIaM3lfEjUAKNWIQRPUgfvNpIEWIWeoNFji6f95kju/cxcDJ/Qzftk4Fl48iYlL+/DxAE9btyomTMVydIgBEwXRyFDCqJh5+UlUA5tqFGo+XuOP1v8NDorJJxHIjIuuQjJMpEgIiRfYVHA0p6aWRZgqs2H5O1bAvgc30XvcpKyrrslDsmKrs17b1vJqSe4nzzFFBcrT/UR7n8UxbJRLfh2nQV2ekkJ7LIdKFOZBWfjZpJnpe9iJ4JiiTlmVxO0ZJbPEjr3aAhGiebrKI8831fYyWQ6Rup9CLEzPP9VPxl+0iKBc5cErvkWhvx0r79B31hwmvXpppu5rISrxOX6qLuq7sltIaaW4gyaqGyNENiu/9Ht2/WEzAKMHfVZ+5JcUJ3aQ72lhxttPoTI4wr4HN3HnX/6I9mPH4+QdqsNlnLyF5drsW72dCSuOYcrlp+LaEiklT3/vXlb+/XW87LtvoG16Vw3kK/F+Bv/NM+YPO/JmrJcmB2IESPktmHyyIDHwtedZXWTI3FdoZba+Y6/d2ucKGYdQUOJj4QlLj1GVtgUD0ZdC6D5l3iOFCIUvGO4fgkMUXxrVdxRqJZ+oTxUeRY3Zcp1AkIpX6eKTJ0yMrOpY8Y7c6NoWOwz6G+iURNHWsjNlO5L4V4fKIgK1EKJ4HhdG2iCFDB3OA9d4wJGf+2ciY1oheimLV/J48JsP8cS1j3Hiq07gkm+/nK5JIXDrNlRzgqqPeJaB9xrywsvEVx9H/4q5VHYewB+t8vg//w/NU7roOX7Ki100epZOZGTHMNNes4jWaT24bQWqB0YJqj4bf/oACz55Mb2nzmTKa5cwtH43slLFackjKhWCasDU1y+jfe44Kr4AJEIIpr5qEWu/eS/b73qatunZkBYNaUhD/nTkJaEQHSo6sY4XYnggmWkXbCvQCJMOzhfURoo86n+ozTQRpuQtjx2r93DTP9xF//xu3vijC5jY28eIewBXhHFQ1Zq0Hz1HWS+5iIMy6ruZsPEmymKurwdSJJKRRpa+SmHhK96BipOjLH+ifZFFggzeShJZ8KQIUQkZW1eqDJBAiuqgPngeg6u20X/ipLqcDMvkIyFji1olqVWeeIalnU2BIWr+X/PcBB/KOsI8eTqwZwJ5VH2tYiB99Z6b9NxSuEI9KzO2HFWf8DWfSDVkYCBTcaC4+LTYsycde8aMu5Tx6GwvUugoADD378/jsc/9mq7vXIbTlKvLBXMSwU1Nb6R6EkiBh5WOKWR4ASZ5hjPecjyT3rQcCNHl2X9/IS0z+1j/5d8yvH4XT/3gftoXDtAyrZuuk2fV5KtV/LD9NM+pw2HFj9/M3e+/HtuBuW9eFB43UC4VSBCyPKck+uiIIJOCAjtRX4l0MxDzS1QbeBpViOeDLE/F5AyR/b3elKp4KpmLpH6OSm7sWWH72EZwR2nw5uwEzyXQfS59+wyHKIle1OEQxe0X9d8IKXKETWAg9vGgSh9WXK1AWNjSoSodfT81r7U44bwdc5UiPpuVnr+T55jotTmWhTiEN2AkdZM7yyzqr252VKBPAyDKyEtCIXopybaHdvGrD97B6R85gVkvmxQebDjVaBndOcSqz99Csa+F7iUTX+ziNORZStfSyXQumcSqz9zI/I+cj9M2dpK8dp04HYBjPnYxgys3sPfudaz/8q0c3LiH1tnjOOYD59A8OY36BH7Ajt+tQ5TKDJx/DADNE9s548uXcuf7fok/WuXYty97wd+lIQ1pyPMvY1ohOpK8VYdDhpRmLqTAMjgolm3wcgypZcEeam24OlLlxr//Had94lSmnjqebGSJQ0ute+ciS8dRZRfKOjNinIhApxeohT5ANsVEUjLpL1QdmQiKCK1HXwptsfkGr0lZd6US7L57Pdt/8xijWwYZ3X4AO+8w4bxjmHvlKViORWB4cCl0LmFzRS9BBiWoJyaXI5nT6lCJGZPXhlbiYZ6jlj4NTxmLBNqjUmpQu02SzzZjFtXr/0HGtIwjDwsZPrtWeojoSPT8xNiI7mc/Q5PxmA+tYN1X7+Dev/oRSz59ES3TurP8GBEjejnbq3cr460kpveaCY3mhG/8jt5PchD7jp9E/wkDYVECn03XPcLKv7ma5ild5LuasBwL72CFA+t2UdlfQnoB/Qu6aZsWkseF7TH/sgU8cNV9tE9qYdaFM6LnxF6CKsWEpeOZRchFhCbZQqY4RAq1CGSgPdEUj0shX6pPxEh3NGdZcYJkMyGs2Ttq8UrqoQ9Hew+JyMQwinlIau5F84p0tHft6ZqO/ZbkfJqewhluoAKbVILfBMqkEgTrrZrPVILpaF+h9VVhY0mXcpDLeJcpsk9gjI/YG1Mk4h2lPdKc6HlNTjVVxmR9xn239ufYnK/9wErkeInGt45vdHjukpJGtvusjGmF6KUmq7//COOX9jPplAHApxw4GbL18ylNTpUR79lZ6FJKhh96mn13P0FQrmLnHITrYOUcRM5GWILR/ZL19zyM017Ezjt4gyOUdw/RNKWb4kAnwvMY2TLI6JZBWmePY+KFx1Kc0E7bjG7soottJZbPjkK8wNYTTUPGjliuzez3nsW2X6/m/v/vWua+/ywGzprxYherrgjbYvKrFjHponkMrt6GN1RCVgPcJodifyt3vuPHOEWHDTeuYdWGvex5dBdBNaBjeieVA2X2PLqLQqvL9ge2sen2DbQOtGJZgubuArMunMaEExrIZ0Ma8lKUl4RCdCikSP1mrttrDbyGJVQ3DpEhNdEghYIECg0ILYCRncM8fs1jnPedV3HQy+GIgKJdDZO8BjblwMGLzq1aac6NWoNW20rgZCxjpVj50VZzizRSZFPyHZqcKhUVpVhxpgxrs+KluTfhe4TP2fCVmxh6aANd5y7GaWvCL3vIioeshlu/7NG6cCYzL5qHHB4lqHrkOwrkulupbNpBadt+hG0xYaCT/MQunOZ83EZIfB9EEOcWM72vHMOCVPnPkhHIa0WTTu4r5C8ZmdzkrSSRoGQU8UNFHs4eT/O7kv1J/a+iMdeLyK2kEtjZWC11RHuhJfhjir+SjLoNsadmrThIGY8+47n1IoIHCIxwLgCMO3c+rdN7ePjjN7D3gY1Mfe1iHFuS72wiaMlnxlvO9MxMPMcreQw9tZeqV6LcdpBCTwu5llzCEzSNLBaia3UC4cDW/ysxk45aOUnniX2J3332PLoL6Us6Z3ZiVUrMWTFA3/sX4/S1ceM7b8TO26y55lG2/G4DM86fwXlfPIvHfr6GcfO68UpV7vinexm3oIczProcN59GOGyDQ5TMdWbG9DK5kb4xhquWrec8TyeCrY1oHg1CdDiuW1iGqF8gUx6qMRop0/sJ/p/uu+q9jDIm52Yd06dOzKJMtHCNGIkaMYqiOc9XyHfknRz1EYsA23cp+bnEXJtGTn3jO3EoDpx6rumhmuQZqjmnZBix9eYbX7czsWeh5oFGW/vQc0dKGghRRsa0QpRxp64VBE0FKjuEAlTvvodyxT6cmB+0lV9dyZRLjkX0dDHiRW7+Knhj4DBqudq7zDeWE1QCWhWMMbmMo8nF0fOKEdG3qhQhGac1UehJKSIX6gzuRqLKspV28/cDK1xWKlfZfdNDLL36bxDNoUecb3zsLSFplwWqoqQHt4LBnXlTaJqXnmw9PzvZiQRB04TDFUxufqRT7sp12k8rO0pZtWLCq5qg0ktiYZ+yRZCp71DBSJONTWK7GVAwGQ5AQeQZYrnxgamZ/b3G8k/yfbU7vB+Tn1Ub5KVHIZH1W51bNZZeLCEP66Krn6f2E/fUdW3comXWOI7/2pvZ/NM/cN/7rg2RRUsw/6MXIJqLHHhsO50zOmid3k1L0WHdTx5m468eY9xxE5l8zgwObj3Apt8+xbZ7N9M6sZWBSQNsWPc0IztHEBY09TbR3NdMU18Tzb1NFHuKSD/AO1hl4OSJ9Mxs45EfPY7b3cyEU6aRb8/XqN9kG6eJ0e2LWvibB16vU54oI2V4b5ndq8IAqNPOmcL2lTvoGGihZ1Y7p/3d8VhCYiM59uJp3Pype/n1B2/jvC+dg+VYqT7sWH7mQ2ou20JWITKVbs+OwxGoj3qc5DS9THokCpFW2ozzkn1PjU1VN0Kml9aS9IRwPzpPCL2M7huKkPmczJJa8v4iJqdD1hkmdhJItLVSiHQKonSbq2CONpK8dChLV3d01U5qOWzfxiGCQNI9tX4+RlX3TU459TztlHIIwyvzvkZ76rnRCrTyq20jO7sU15CjlzGtEL1UZPDxney6bxMv++GbX+yiPCux8i7ty6ax5lM/o3XRVPLjO+k4c8GLXayGvMTEbS8y64pTmHXFKQBsv20tD33sBryDFbqXT2fzNXsY2bKfYk8T3miVE/7hNLbfs5k//MudFPuamXjqFI7/8Gk0tbu0ei0sy+3HwacyXGV45wgHd40wEm33b9iP5VhUDlS4/ZN3cck3VnDfl/8IQPvU1bzsqvNp7ssGYTyUhNyX9Iel0FHgHfe9ldKBKvn2PDsf2sEN77iJvrmd5NtztPWHBoRTcDj70ydzw7t/y6ofP87CN8979hXakDEhN/zzY6y5cxeffuTCTHyol5zE9l5DEvKSUojMFBGQtRIUXJsk1JpSL+Hm4dyApR8wvGkvufYi9BQBGNo5ysrP3ML0y0+mmm+m6sXlUstZJZljRORjgrQB5yaRIUijT7Zh1SppccLw8cp6KAeODiCWM5bkYtfk8DkllazQD7dl39F1MveTl7LnzjUMrd/N01/6H+aM76DlmIkonEAHMRQx4qohe6P+TAvVTlqBarnHTLFSBw5PBmY8HLKXSSMhpUbPzACMKqCeiRok/w+MdtHu/ur9DeSoYHs1AhSmbWLljq8kSfxOHqtXF5BI+0GMcBUCR6OIyTLbKoBhjeVSc243260eUpS8jxlUUie6lRbtp87juOVzKe8dId/TSs728EtV2DdIvquJtnaL3pNnZJbQkk8SQpBvzdHcbsOs1mx/kQHfveg6/H3DXPaLl/Pjy37DpBP6+N8rrufYV81k+jlT6JkaEqSdBMk5fK+01e4KXyNDMQId9vliew6QrL1hLQDXvePXWK7FmR8+nlnnTgmvtwWnf2gZ1//1rSx842y97KuXzNRYTgQlNINWmqElMghRYOFF6IZnp9E/k5BdS+ohRObSarwvdIHVHGsHMhUs1yTwJ5fOdEYJY3lNidnXkgmGpXnMmDvMlYNAiMQYrTOmFCqYSMFUCewQNTdc9FXogyWvmcqaO3ex5q49zDylL3VfNZe32JXoObW1DfcIECLTMUhGYzYZOsOcU48WGRLIBqm6hrykFKIXU6SUPP7ZXzL4x40gJfnuFpyWPCMb9zLl9cuYcOGxL3YRnxOxXJvel82l8wybltnjWfvRH5Of2EnXOYvoefkyGtlYG/JMRNgW+Z443YddcClM6oz2jize0yHvLwQzzp7MvV9fzYLXzMSyBSf+9WJmXTCVdTdt4IYrbybflmPmOZOZs2IiXdPbnlVXzrflsByLYy6ewei+Eg9d/YRWiAB6ZnUAMLJ7FLc392xfryFjQOac1c/p75pNx4Smw5/ckJekjGmF6FBJPJPWP9RYTzZSFUBsOdUNeFVHvKES9736KgAWfeUymqf3MrxuB0HZo9DfTr6vjZKxKO6IQFtuJRxGcPV+TtYmkyaRDZM7lOUURZwRK3YRDRS/KEgjUFWFEEVcmFzEMVLbkudr77RkGofe02fRuuBdjG7YzYZv/Jb9v3+Cvlcup3fx3DCJqEwzDuq5elsqzHwiYJtp9SWTxybfuxan4HAcL0+72ibqU6YRm2R9ihrWXDIQZOY3Rcg0rFDF2XIsP4MMKUuuXAMZ0mWsgwiZfUCTkh0/c5+C72r+UvK47aeDLFY8J8OpzHCUMhytSBIkdSVqnPmkkQx1Dz/IInyZ+9uqHg+vICniq5WwlI+9cIAfvPlm1t28iTPeN5+edp+epa3MXjof+eFj2fHILh7/zVau++vbaesr8Jp/WUrnQJNGOVS7VgMnRoZ0QuGI/xc9b/m7FtA3p5MJi3v52RU3M2FxDzse3kmxK0++JUexM0/rhBb2rhtkQl8b937pfiZMnETbaR3keltTFbrn4a3se3wX40+cSOe0UJFSoQmUy34WIbLrErDNpNhmfSf7dd22MI8HoFJzKORCWOkQD2a6j7jtwdQ+zecdMv1HLWiSxPegRjBHFb6jHh9PjalcolyetKhKS7voY7nRfVX/sDnp3QuwCSgneEo+Fs2KM2QU0kR1lSQdL8zfFLqq+mCclDu+Zz3OUCN1x7OTMa0QjQXxy1U2X30P+f52ln7rCiwn7JWtc8a/yCV7YcRtb8JdOJljvngZe25+hG0/uAP/mpUcnNNN19kLKUzufbGL2JCGANB/bBcfevDVbF+1jwkLOlK/CUswaXE3kxZ3c96H5nHH19dw9fvv5y1fOZGOvqNHcNyiw5wLpgLw+h9ewP1ff5jbP/8ApaEKpcEKLeOa6F8yjjv+6V7O+D85nrrlaQadA+z+xm7O+dpFtE2Jy/fAF+9mcO0eAM78wgoGTp38zCuhIQ1pyDOWMa0QKS8K0yMpsETMk6nDK4mDzqW9Gw4lpjVR2jbIo//wE5qm9zHvX9+EtOzI2qkvZqJMCIN+VUQ2M2i89h0HHwwvBmX2aFtZ5Sq0lHdE+n6WkKAtGZG6SAWkbLXDEPSKqxFvXZ3ORCWVVYhCHBDOZuJFC+i/cBHNm0s8etM9PPnRH+D2tNJ9zkI6z5iH3RJCyYpbo/lcgWHiWRZxZsrI2hSGhahfIR247VCi6n7LDQ+z6WcP0jW/n9nvOgWrM6fDIyjLSqMhUuBLOxHsMX6+CrRnWqim9amCAybTOTgauattMdZCgXJGaosj9TZTPCgAV6bd7rOeeeFv0hZ10YF6VmbyvHrDwESXkq72JnJgjl0z8GYlSuzqBXYNV7dwo9JGBCLu+z3ze/GRCcve5KcITn/XbATwtdfdwQV/N5eFF0zQQfUCEXuGaWRR1WlgjruApnaHM/42TnDrB/DUHVu5/fMrObhjhF9d+WsApp42HW/EZ+eD22mb0qGRsEu/fxHrfv44d33uXu76+K286fpXUuxMK2kxH1Bxs2KejOK8mFzBeghS8jfz/kmOEsRt5Ukrgfgo71mPguVl+Eem+LLW3FuHD5fgGdoGD8/sl/XGRyWwM17AdcdSog+WfZdRK5dA1tMBGpNoJMR9wxVZpNaUWuPdPFYwQnQQLSRI20CQyNZ1Ej06YmkgRBk5vJbwJyR+qXJU56/5pxvov2Qpsz/2SvJ99V0t/9ykMKGDgcvPYv53/prxbzmd4VUbWX35V9jyzVuQwYs/yJ781t3Mfs+ZADz82f/NZthuyJ+9CCE488rZvOHfj+N333qS/3zlHdz0H2t55Dc72Ll+CL8a4JWfWRBQIQTTz5jI66+5iOOvXEj3MV30LxnH/qcG8as+U8+bkTl/xrnTsFyL8Uv6WPOrJ5+LV2zI8yz/ceov2LVu/4tdjIY8hzKmESJPBZWzshal0oRN66dqeGwpq+/AtiH+cNn/Y8qVZzP+lcfVfF7S8q7sGWZ00x7GvXzRUcWMSO4ny6u9s0DzdZTlY1oGlghi3oiy3FQSQuVppzgFVlxmZRn62jIM9xXfQlk2bVGyQnV81PI0qpCL1s0rduSV5Ke9XCq+Td63KUaxblpOnkT/SZOpHhjl0X+8lv033ce4S8JcTzoQZPRdORK9JEYPIlEUhsBKWVe1RNVNcUIHUkrmfeAs7nn3NTz9s0eY9dr5UVmyHJ5qYCc8/cIyOyL2hKuHFCneQ624MvW8A+NzFHoRBXBzYl5ZPd6YtvwNtzDT2kw+sx7aVHCq+lj5CBPQVv0YsT0cqqQTw2oOioU0eGGKu2PyusxkvCXf1f3di87N1+N3GQE3IQ6sp/lcUvWjgM4F43nbj3rZ+MAeNvx+O/f8fDt7nlzLvq2jBJ7k8qvPon9uB4GZjsPwFLVEkKk/t+hw3BULWPj2Jay5fi1PfWsjxa4ia7/3AN2zOznmZePZsnIXU07sQ7T5DCztpbxvlK13beL0v5galT3iNal4Y7K291TyNxMhqhpct1ocong/7fma5CWZKFLRs2m2y5l7mF6KvrQyqZRMMdEg2woy/c/sy2a/SSJgmkeY8ZRM7ye35cCh5LuxJ6ruc2puiJ/nVwMqwy/t0LIAACAASURBVB5bHj1A98xOzelUU5NZ9lpBeNVcFCPDUR1E55jzgJ2IaaZ5RjX4tUckkvow75+xjGmF6LmSwA948qu30n3mXDZ/9076LliIXajNG9j0nTvYfctqyjsOMPENy7HzYydZ5VgWt63I5MtO5amv3kLPuQuxC4eut9LWvZQ27aFz2VSs3HPbDftOm8nO29cyfvkk5r3vDP7wt9dphaghDTFFCMGU43qYeULo9Za3qoyUba664Ga++YZbufKGFTRrj7ijF7/q88Qv1rLoDUvZvHkzli2496qV3PqJEn7ZZ/LycWy6fyfv+OWFfPuVN1Et+ay7fRszz/jz4Cm+FMV2LY77y7nc9i8PcMzZEyg0ZykRDXnpyZhWiJQWbKt4E2ot1Q6tgaDqM7RpH81Tu/U6tdKUdcqE0iiPfP4WqFSZ8o7T2HPbY2z+/t2Me/kScuPaUwG2vP3DbL3mPty2Ip0nzmDK5admLN+6SRFNjzjSFlkgRcaTyzY84xRKYCUQMNPbLIiiE6uw8mkLNe1VFnvipD3TlNXZ7ozq82K0KFQUdRoRhRRF+55t0eI5lK1yhgNSWD6BfXf0s+5T1zL/M5fiujmkHzCyZg8b/u/N+CWPlgVTwLYZvH0Vud42nr7qRjqXz6L7tDm0LZnK9htWsu1n99N96mwmXXYqIp/TdSZqWEq1pG3hANt/+wSBFHjlgNZp3amI0MlrY4QofD/N67LI8FUU5yqOcptFCfT9DeuuMhoGFrS7IutPIUM1YgbVsy7jVB3ZlW4rwbOo6Y0Z3TsZQVtzoKw0f+xwVmfVtzPJf01kL4NMWUGm35tRvPV4SYxJicCTlo51ZXIH60kaNYu4HkHam0zxu8oKdYq8P31pUchVedvXT+Den27hji8/yss/d1J0bZqfd6hEw4EU7H5iL7d+8ve09Dez+JVTmFPopmhXmLaknZ+++3cAbLxnBwAdxSqv+D8LuObv/8i1772b1/7zQo67ZAKQRYjUOyalHopUC1Uyy22mpVDjQaGHnrQyyFOLcPEj76rk/WtF3TYRJ1OS0bwhHIdHGpdLe9PV4JN6Rj8142bpPudHCJFw9DHlgeYZ41KtOjx5x1aqBz3u/sbjnPLeRel3rwPYJONKKU/CnIHgO35tD2OVvBti9E2NC/X9Oxovs0YcoqyMaYVIycE1Wxl5aheOK7CbcuRaHPJ5WP//7mLwka0s/rfXMrprhNK2QUrbBhndup8Dj2ym56RpDK/bSc9xk1n04fPx7AKLv/wWtt74KKve9x2Cik++tw2rKYe3f4TqvoNMeO2JTHnbqUAjDPrRihCCuR86h9Wf+w33vOEbNE3tQdgWgys30Dyrn/43nkp5+yDSl0x+34W0HzeD6tY97Pv9Wtb/6y/xy1UK4zuY+XcXseOGldz/2v9EONGyX387fecvovecY7FbDo0+CcvS63MjWwZpntRxyPOfT/nFO/6Xpu4ie9YOMrjhAK/71eto6mnEMXkpSN+MVk5/9zF857I7ue5Dv+e0v55P65Sj60t23mZoyxBL3rkYkSDHTju5n3944BL+45ybGNlX4Y1XHU9zV45FL5/Ing0H+e1X1nLtRx+hf3ozA/Pbn+tXa8hzID1zOtn31AGeuGmDVoga8uxECPFN4CJgp5RyfnSsC/gxMBV4GnidlHLf8/H8Ma0QVaoWO75zM/t/t5qWhVOxCPBHysjRMrJSpWPRAC3HDrDua3dQ6G+nMKGDrgXjKZ57DGuuOkiu6HDiZ86le34/FR9Kvkd+Xg/Ns8+G951N6UCVys4D+KMVnLYm3I4mnJYCh1pcNZUkc21cKd1Jr5py4FCSbuKc2giRsl4KtpfhEGX4JH6aU5GMdOzU8VaqF9+m3RmhEHGHFFJkJpxNJp5tEi5lq5IptxdY4MCyj53D6M4hDmzcT2nnED0nz2D8RQupirwuuy8tpIT8hC76X30iHcumUN09TMcJIeG0ae5kJpeqBNXwXYbXbWfnT+9heM12Znzw5an601Fuqz7SD0CE9/YCC7utiYPbhrSFqiRZB9XAii3XJE9IxzdJc3hij6Po3FpxjETA6GCJHav2cPrfLmPqGQMMbhzi+jdfz/IPHs+M86anz0+YlJbBU1FIlM6xdBSKehYZijziEhybUtT2ytPQ5I0pSXp5moho/LxIonsnI+rquFSG5aue46DqOcEFCaw4GSoxiqX4HRo5rYEYZfPh+anjpidgEmFR/ze1VHn790/l7h9u5Ad/cSut44o4BYdZ505izvlTaO4uZOohib50Tm3n0qtO4zefvI/ChgqLruijxSnhVXyacgGfuesM/njjNq7/p4e57mM+Z751gFf+zTSmznLZu7XE+D6fdnsk41WafC8lMRJkpfZ9zSXKTvdmvSmkQY33amSQhEmqo9yH0bs2SxfplmqUKz0evAQCWy9GkokWpspozoGk+83hchwmn1fPUzNAUJUhkmXOa2Z/UXLi+0/AbS0wYXFP3D5R8W0DIlJtUNbZBDzd70wk3xGHn7/jvh312aPgaWoZmwjRt4H/Ar6bOPZh4BYp5eeEEB+O9v/++Xj4mFaItlx1PeUtu5n9H+/AaWvSE7Zr+xpuNF061VLZmd8L84qpayBUNEoJUq3TXMCZVnjeyl/ynBRZ9k9NWtwSw9Xa9Vfsa8XtDS3bshfV+SGcdpqnj4Pp41LH7IKLFXG42hZPIz+xh8ff8w2Cqo/lxh8I6Qc89bVb2XXLowjXZs4HztW+/90nTGXNf93G4OptdBx7dJwML5Ew95lIoT3PjJdN4rHrn+SsT57E7Aunsf7mjZT2lWueX7QrOtHvCyFtTokD3vPX/01JJvV9IWTESwepfKaSa3JYfvkcFlw6hcGdFUb3lln9Pxu452urcfI2hbYcTd0FTv3/FtM3rytz/cCyPt74g3P5zV/9kXLhAOMmOfzsY6twcoK3fH4BSy8cx5IL+rj+X9ex9v5BLgCWXRCmhjAD/SWl1znALu+F837tyQ2zu9Lygj0vZ3lakXohxA+sbHbbQ0iho8DJHzrhGc8RzU6Zg17+8Cc+LyLHpEIkpbxDCDHVOHwpcGb0/3eA2/hzVIhGHt/M1C+8C6vFJghi3owVWPhWbUVIeWwpRUhxNCwkJd+hYHsZC6LiKV6H6ZEQSpKXYaI8sgYyBOioqSXPoSJtSoGTsapNhCj5e8bzTCcBMixjEfNXtIeG4V0T76e9e/QWSbsd8omarDA0wUgQfphLQaiQJJGiZhw8u4QvBS1uKV4Xt9JxUExPD4UWVC1b17mqNz9IW2W1IufmetooTO5h38qn6Th+po6UffDRLez7w9Ms+urb2HbdSjZefR9SRt41js3kNy1n7TfvZdHnX63vl+Qs+NLKchusKNp4MqaJ4WWmLDuTf6GtQwHnf3Y5j/x0Hddd8Rte9Y1zEEie+t/1UCpz7Ctn0NyVVoCKdiW28PX9FIfIT9VvLXEIUvwL1f9NNETttzmlDCqoEKPD8T4g9gStFwcsOYbU1sxbpa+pgfJITPQlgUZSn+eU9OjU6INC+FQdKI8qxaWKOERJj1C1dYVPW3dOI0LTT+4j8AKG91YpHajwx2vWs/q6J1MKkfLmbLIrtHfBm/9lPj/+l7u5+Qt7sBzBxX81hft+/P+z9+VhdlRl+u85VXW33jvdne6kk3TS2feEECBhCxgTWQRkEXAZR8YVFfcR0XEcHRWdGccRdFRQkfGHI+ggq5CwRfYACSQESEJC9n3r9S5VdX5/1PlOnTp1b3cHQtLR+z1PP9X33lpOVZ069X3veb/3ewMnnToeua4CXrhnB77wiwmosXpizywpgJsOUqPdof4viCiCQ5/puuZF6eHevBdZQc+9VLP3HRSEhYZEl+onGThwnO7YviwTufK52oZ0lHRuElCaN5fgbozvZCJD5jZ9KdmX7KcaH0mplBvoFZlqqx9eX5PPVSoMpmeNlhV2LswCRmmUzLRSCvSudxge3fFjQ4UQOwBACLGDMdbU3wZv1ga1Q1R58mQgmYQnyZDM06Y55I1PxlLWow4EDX4+98IBkZNEf3GSLiuxL6A4cU83oQajKOzvMh57yYs+CJlkplgfmdk2Llg4xUMp5FoKcvBPMLh55vSQZiQylpRp9YTA6UUubeartHsAKJAjRFMOJRwiuhdZz1bfUVvofnIltBdtF0dwX+pOnYgdv3sSTnUaPWu3Y+stf0H1jBFINlYhNbQKwy6chedufxaZtiFq2+ZFU7Dlf5/F3idfR8O8YEouUiTT5+H0kG8cFFADX6nv+4oQBbMw9dIJYJxhyT89hct+/Q48f8urePrGF/H0jS/iw/edh+qWoBo7vUgKitcdda7J8TKPp7+AbN9XpE8ghN/DopKSUKwN8moqyYuuw2VplzyLBgycCXAv+oyo+2c8Q8VE9Ur1ZdNcwdVfqefNNztKEcsbz71K1za+Dx17C64VThUB4QssUjjX5qhsSqOyKY0pF43B7z/0EFbdvh7j3tGKc38wX3v5BdvUjUjjU7+Yjqd/twl337gFDALrnuvAh8Y/hbqhDsbOrMSLDx/A+uc7cOp5daiud0omRABxx0N9bzzYJBlAz7aOqisn1LyMRkKB/p0jg82Mn4UjEzN0i5G9uRVOwVH5IC4dohLOve4ce8rRCSUAACCPeL/Ufy+63z6kJUxRUzV+ieh+1fgqZTc8weJBkUmtUI71W891p2D5LZnAsUKIGhhjz2mffy6E+PmxaEgxG9QOUWJ4uSzE34IJz0fn6u3IjGkCT/cNITecMxvM97Hh+rtQONCFtk8vRnbzbgxdNA0AkBgSQPrC1TK+HAsTv7wIL3/zbuy4fxiqxjVhyCntqBr3tgUaMZt6cTvW3LUBW5/bjXmfmo6RJw/FHz/6CPatP6QcorIdv9Y0oQ7/sPRCbFy2HY9+9zncctF9qB+eRmN7Nc7/8thINuuwMWl07C1gxUP70TQyifM/1oLnlhzAhpe6MGx0Cru3ZPF/N2zHtb8ch7UvdMKyGM66dAic5N+Ujm7Z/jptrxCiuBBgadvFGGuR6FALgN1vR8OAAThEjLERCAhOzQhihJ8LIX7EGPsBgPMRKIm/DuDvhRAH5TY/ALAAwBeEEI/JOcGNAD4jhPixXOcGAM8JIX5d6tj5bXsAAJ4XRRo8LhSPlaZpLBIBZNFIVZezN+F2MkKZYsiQ5uX3FIKpDVXY8C1koJlIEe2TCke6nKvzMdM440RRiYCBIe9ShCunVqziafgq3VhGaQ7zkLHycp0gms0VmSoDgijNFww291TEmZbnYQrD0XEVMsQc1XY9fXr7g6/i1e8ugVOdwoSvXYCaGSOKknY5E4ANVF0+G6MumwUufDCLg7HxwfUTAjvufxEAUDtzpEJKOBNomDkMp/zyAzj44hZ0rN2N1V+7ExVjGjDs/BnITGyHVyfArChc7voche483P2d6N3TjfzeLvTu7YFlCSQqE8jU2EhUJZCuSSBRmYCdsuEVPIi8B7/gwSv4EAUXQgg0ThqCtrNG4d4vPI6qYRXo3B5MNbBMCjnfjsj/+yWmBEyUp5hZzAuFJKH35Tgy5Bn7p/6hjk/3SH5OSD5cwreQ5yTHIJc8+twVe4bMaS4TOVTracRbz+fIw+oXkS1mJkLp+lEkmJ6dDAv6PJVkybtJxV2hlGhCjBTZ2iIENTx+IuNgwuJRmPDOVuxdexDdmw/iiZ+/itdOqsHEM5vgg8MDR54nUD88hatunIGn/rADyx/ej6Fjq3DVj6ejujbY/9g7d+CrF72C6afXAgy495Y9uPjzI/HMvXux/oVOXHPjeLTPiPJ5CAlSZGoDKaEp8ryw+5xWAopPmZGl5BiR4C6gIcUmkVh9L3x1bNqPSjG346RswJgmBaWW0/gt0TuLUKeo/EVEIqCf89T7Z4bZcO0wWUQXo9X3ZZKui0svFJdl6NbGUxqfcmpcDj4Tj7BbLrMSqdXJ3WYxc1dSJ5KHgx4dP8KMdwH4OwDfk8s/vV0HGghC5CJwbF5gjFUBeJ4xtgTAEgDXCiFcxtj1AK4F8I+MsYlyu9MRMMYfk593A7iGMfYzIcSAamhUnTrtME6lbMerVY1tBE/YKBzsxdbfPY2aGSP63YZxBlZkzq+ivQkjrzwJTQsmxn5L1GXQvGA8mheMR9vfzcPOpa9i6x3Poyv/Mjat3QCnOoXkkAzsTAL5gz3I7ukGhEC6sQKphgpkGjNIN2TAINCx6SC8rhxyHTnkO/PId+bg5jxYjgUrwcFtDisR/C88Hx1bu5CuC/gnvQdyGLtoFM647iQkKsrCn39txi2Opkn1yEyphJPiuPu7L6FxTAVqJBLYfkINJp9Wj/98/wpcfdMMnP33I+EXPDx5xw5MPKkGLe0ZzLtoKGYtHILqquCFt+Kh/fjzL7dj5OQKXHFtG3748dfw3fumo6qu3H/K9tdjjLHbEBCoGxhjWwF8A4Ej9HvG2FUANgO49O06fr8OkSQzEaGpkzH2CoDhQogHtdWeBnCJ/N9C4HsKRGem9wB4AoGH94uBNG7T53+CsTd9AVZdkElBxFvm8VjR1oKBDPk87s2bBLq0kYFSqjREdyGhOBIUpVhFIuDDtTC6Deei6Zw8RvPkRtsoskI0QtD/JwFIit5pvj4vowdboQUS4eGF2HmYZGoyik48jWiueEeSSljgxCEqXkYlwe0IF6t2YgY1//dBdKzfCy+RQlUiF4vKhGAxrpcpzCaEQO3kFtRNaVZtpfVi5TDSHG3nT0LruVNQ52Uw0u9B4WAPvINdcLtyyAxJId1QAbsiAccqjmhQpmMpQcXgu2Ddnj092PToJgw/eTg2LtmIDX/egLVLtmLyBTL9vo9A1hQS7Ms4iwoz0r1xDGJowedFBSUBDSkqwZdL2B4SKnqWfasEQkSW98ICrfHSJMFSoU3avXd9hkIRhKi/bDXP57Hn3ZRp4BIZMvft+ly1IWEIkxJJXZWv0D6bBYI9cLQvaEVubzdu+vByXHvnu+E7QfHQRdfNxJIfvYbb/nUDLv/BDPz+Syux6sFAoPE935iE0y8fBmSS6PaD+zd+QQvGLwizJFt+vwerVxQw9czqWHkPImBTexzmRr4vaMWMTe4LWV/IECVeOPDAmVsSsQzbZSMrouUoHAMVMU1HVjwDiaE2lRKgdH1LoUZuEbmCYsYhkBQOXDsXSwqh5536Z6wocZFr6BvHNbfp8RJqfMqxKBeRECFakkVEK0l+RQm8Hj6vaDAKMwohrijx09lH4/iHxSGSU1+zADxj/PRhBMJJEEK8zBjLAHgcwJeM9b4H4H4pvjQgs+qOXppn2Y6dJaqSaJg1PCKLcDSNWxzJIZWwGwPRRJv7mmP81geOTGMGky6dBACY8eEZaJhQj8e//SSGTq7DkHFvvixE2Qa/nXT5KKx+YDvWPbEH7YsCjhxjDGd8dCxueM9f8J3TH0a2M3A2Jp7WgGxn3y+3fK+H7Wu7MXRMWeCzbGU7kjbgtw9jrBLAHwB8VgjRoX1/HYJptd/Sd0KITxfbhxBiI2PsWQBXDuSY82/5GiwekmyVsJsvlLy5LWQELKLy+2mZtJwSwfYF30LK4DBQEGsiQybSwl0HCfmiJpTK4tFsGqa2jfM/av0kwDQxO/m9Skn2jewXV8CRoj22T2vT/HE02g1/D4/pGW0gZMYyImVbRhUOL8Dy8vJ8ZAqojOgyRUTRUoUK2PBCpERGe2b2E0VaFNFR2q/PtLRmydnJEiImkYacXLdAAm4yuy26fxnlqgK/1Nbgd4o+beaHiB6iqFJBcFSLMPXdLVAmlwgRKSOjSl1HtzhiFVzH4k4UHX/C3EpUfqUaK/71JZx+3SmoGV0T471lSiApJoKpR6gJNx25TqX4OUzEERRzvym5X3JSrSJgAt3xAosiALSgjENPcHUvzd2oNHEjzd8HQ5WfQkGTrFDyFqU4RaDfi5wfIW2WHCskokljhi6toeQfXOMZIqRNPjtC9v0UL8SE/NKSl+eiGu/81BlY85udGDpnOBLp8Hp++o7L0HMgh5oGBx07e/G7Tz2N6f84HSIrUZAizviKu7bjhAWTUNvYhkJvKM/gGlPIIadHtpn6grDAZFtpGct4JQ6VhoSlGHGHgufFcdPweMh+sEogRQVhISlI8oDGa7pWxSVPIk1BdFxT4wihQIiiXS44XCkoRL8RUhQrxKyJPCZFAhk/qZ4RT26TN0qieC6PnK/F4+ddTDZEXwow1c8J+aF3iC2fA4ICiG9J7wRHuMjLMbU/jlSfNggRomNtA3KIGGMOAmfot0KIP2rf/x0Cme2zhRjw1f0OgDsALOtvxf3VFoBQxE5oA46aCmA0DRPVHcrJZcYuYNOD6/DMPz+MRG0a9TOGY/LV85Bpri5JAKVO1iVflD1+Ar2SEOyx6JQZM14mCq7WHjzBGPawrGp/qYFa1xFSToaRtqxgUyOFWN+vqdNB18bcVsH/Vh5C1iTyDE2a2LSDXOYSHera00vB59HIlpRxe/1ofTR9eoEGsV4ehYmJgK2mT1DEIVLVqKNLVaPLCh0iUwmXBiOCwQ+wbGQfNte2KUFkN6ttF6syb5pOjKw5owajCsPxu6vvwAU3LkDThEDIkgZeC9H9mveATCf2+oKhxwm1aYpNldExYve2hLou3Ztiad50bJpSMp0qXaE4b6Tmk7lGQkQB0ZfXfoQV1UspZJMpXSvwWN0qSznw0bHC1PzSHSLzGTL1zVwiV/OCul50HZlW56t2JkfiUR+/+sQ9OPPz0zByTphBa6WBJf+9Aq8/sQu9fg7/fsmdOPUDo9AwKoPejjzmXzkSdiJ0WB74zQt491cno5CMVi8w1aypHUIpH4dTa+bzZ5J/s8YLl1t5WNL54TzUdvOS+8ONSjhErrDDtqm2SIdSaXbJacYiBGXTISoIaptBItcUuc26bq4imhdPjy/IKdYepzM23UaaSabattKzKiK7Edc7io7JfhGHiJ4dEyUPtfXC2mc0TvZVS69PEwCJ15YttIFkmTEANwN4RQjxH9r3ixGoRZ4hhOgZ6AGFEK8yxtYgcKSe7WtdQmGoE+lRt5l55osop0ZfVo6oQ8WwanRv78DOx9aj/bxxqB+eKfoiA4COfEB+Jd5QwbNQcE1OS9RRoaxafbAXAnA7erBnyUps3b4NTkMNnIZqWENq1P886ai4TkXBEBGtIK83j1e+/segLtspozHi4lmonxSkjFMExCHic9sG0hU6QlHui8182H70oS7lCHH4wR8TGvIUCjwCYaaaz8LoGYgOvmYmTK8lnU/5oPca8+h531btJSeG9EH6K02i82qUE8M1no0fOj9keiZiDEGRqxK6FR5X28ZwDMzyETSQtS9sA2PAXZ96BBfceAYax9cpR7PYtde/D48VDr4282NOEBB1hILjs5DjpbSfDMdL3c+oo1vM2TMz1FTmD7XNR6QEBxAvEULXnD4LwQI+HUK0xzMcIzLV5/3w91ixUZvWlS9fQqII+lK3N3xZ0T4Up8gnccwwexUACpalECG6X6YK8ayrZoCP4fjTl5/FydfMwoRzA/5YdmcHlv30VbXeqLmNyHscqx/dh2xHHrs25TD38tH48/dXY/Z7RmLX69146vZtGDa3uPJ6KNpqCAqSw6A7RCLeL3TTs0/pua7ggaPHuQufF1S/NI2ugyM8LYCVL3Xq432kOvmIvuzzqo08+hnxc6EsOdMxMsuXUP8sMAs2L0BYeSTl/sgRIlSLxkjlGBXJZDMdlHhGWrg0gzJzzCDn2xy7LCYiRaHLduRsIAjRfAAfALCKMbZSfvdVAP8FIAlgidTYeFoI8fEBHvdfAaw4zLa+aaub2IhFt79PfTY73ttph556FWzpSziwdStYwkb1vEko7OlAYW8H3P0d4JkkMuOGoWLyCFRNaUVmXAuslFF3K+nAzxZQ2d6I3J4uPPeJ29B2xQkY/7FTj9p5lO3tszHvaIPDfPzp6seUU1S2vz7jNsOEc0ZjyKQhuPvqh+FmPUy5eBx6ZSmX6RePwTu+NA1O2lZBRO5QDjdc+CjWPb4bB7f14vWn9mL6ecMxcUGQOLBt9QEs+++1qGlJ4+xrJiHZT+HjspUtMFGeMitiA8kyexzFc2DuG+hBhBBvAJiqfX4RQL9Yn2UR8kDb6fs05mRNSB3RZVDMsrgEu5lFUCwCDqfGipup0AuSfa9MK55F8wfPxpDzTwrb7Au4+zuRW7cZ3a9sxbablyK7aS8yY5tRM2sUama3oWZyCxjnmPLdi7Hr/lU4+PwbqJ46DJVjGuMaPcb/ZrSXM6BYdf6+pVSJyWL8A3UhuIrIVAaKeVHkJhRRZowsF1/wGFRPKFOoyBu9BzbzY1MgSnPKLz7lqSNGJrytR2429yMKtWSEEJrZbTFkqEiWWX9qzIRQEZIy6h1tOEM6Red+fx6Gz24siQyZ5Rw4Cwv7EoKnzsGIqsMINbxpCsUxsqTo62LZbbHMImOE4EYkrgchsXINApHPSm9MQ4ZDFDVYKr6IF6JJ+vkJAfiKfyb3S4rUchuLR5GpSPmGEuNJzu2fs+EafUlN03gOungCqZENWPTTxXjgkw8g0ViNREJODW7tgZtIw/W0Plzl4F3/Mge3f/IJNE2oweJvnICWKYHDvHb5XvzpC89i/tVTsGvNAfz8isdx4Q9PQeOYquD8jH5DKI3F/BhCa2ZqETIUZjh6SLDiZG8TyTFRnxQrKGRIoUbys1OiwGGxzK2U/Io4RCkYSJHWp0PejyxHQ4gRNxAjuXSYB24VwKy84rIlZRZyTm7rSN5qjqYetVJF4dgUHV8IFPUNxNTXhDpNqoM5FR9my9JUvQfe/+uzbG/CBrVS9ZE2X/A+Re2OtO29ezn23v0sZn/xfagbVxeD+RlncBqqkWqajJr5k2FbPrxsHr1rNqNr5UZs/M8/A76PcV89HzXjG9H63rkYdXkg8mlO8QCy9tabnVN+E9bhF48CkAAAIABJREFUplS9pqNhVXYWnUexGOnRtnELRyJR4eDeLz2B6ZeNxaR3jUT9qKpj3ayyvQ1W3VqNuZ+fi+d+/BxmXzUNI+cPQ8e2Ljx/yyuY/cGohtaY+c348B8WonZ4Bo4kZHsFH7d9+DE0jq/BjPeMBrtkDF64bT3uvW45PnTbWcfilMp2vFkZIYrZoHaI0k7goZfiCwS/Rb8rxaPxwWQkrXNDSKMlGqVQheyixyHipBHNEjJEXIOu1Zuw67ZlaP/+h5AZ1oJulHYcdG6UlUqg5oQxqJ0zBsOvOgv7l76Idd+7B7Nv/nBwvBLEbCowGEFSrCiBmLgihHxk+9BQMrOydC0cy7fUPHqHm0I3Aq4ERTJJQ2Okxg4oZoT6eIiTqlWkamSMEDpR6eRREBxVdrZkzTRTP0QVZ9TQQVXbTkVqgYpuTPlVi8AIWTPJuaU0d4ohReq4Rr+0VVsDXaCmk0bhkl9V4oVbXsXv/+FROGkLJ101CVMvGA0IQm7imYxmlG5aWBOLtvHDQsEluEPmtsXOTx1fZTSSxpXUqvHCaNrkYCjUTmZKhpyK8HPSc5DihRiSkStIgr4MwT0vRIaCpf7sSvRD8gApOCfAI5ZlChbyi0wSrjw/UpJXhH7Ow2K4JYKSvO9EtGWaTm1H8o61ePLfn8O488di7j/Ow6NffhhP3bgK6fokFn3/dLROD9CgytFD4AJw5VBVcAUmv3s09qw9iId+uAbzPjMTbRdOxbIbXsbW7QyZhkzsvtLzWGnlYmhqmAwS5eP42pIQYXreHS+Fghem/puFZ1Ma+dqSSRIhl8gqus3hmCpmayh0c/jq2ObuVYFrA4n2GAOTytuks0RjUNKXiSYyk5IQI5UBG0G8+0YQdXK34kKR/paZyNIHtSMkmvePWJZt4DaoHaKUIZwoIi+A4lNmxUjVgIQoDWje1iDI4Ifo8SOijqUyxORO6aUphMDO2x7HnntewIjPvRup1iGxh5KsVCaSDyadF4aqE8Zi0y8eCc9PjuasSLaNmh40Xu50XubDmtcqI5d6qetZUcEXrpLGJweE5OhNAUHahgYqcoz6KlOhhC/lcTNO4EgmWUENfDmD1GgORvS7PsCYYpSedm8tTXNId3LC6c9gocQ55UuwryLAZlagOc2mspe0qUMl9jdsCE65dj4WXJfDzpf2Yek3n8GmZ3dj/NmtaJ5Sh5qhKdn20k6Q6dz4xnUNDlvcETJfpGR9ZZmZlpQp2rYtScjCKjnlYNP9IhIrDx2iNCsgwwvKyci58iVoGY6tKvJa+gVhOkZkZlCjE7JLmac5QkHbw6w90+kmczxbZU+SnfDNxTjwwhYMnzccdsrGol9dCC/nYcvS9Xjqhpdw3k8XFm+ABcz/+mno3d+LpV9Zhrs+twzzv7kAEy+bjDuvegAnXncmmmYNk+cXDQZytq2Q3ZhjZIwr5BhlhY0emS1K3yW9FHKu5hDJ/ScNZ4QzoZ4hckRMkjWZ3qfV9HmJcVLJNVAavspC5rCkZ0yOEX22ZFBBS5VQIBhslkOSZ8MiuDTeyHuWUtNtebmMBmb6NqUsfKdw9c7olY4mScnQebgG2ToShCsg4C3MCJQRopiVJyKPsHW/vAX7H1qN8f95FapPHPeW93fouQ2onj7qCLSsbMebMcbQMqMBl9+6ELUjqrDqjxtw6+VLcOuVD2H5rWuxY9U+eIXiHIyyHR+WqEpi+JljYKeClyNjDHbKxuh3jsbuVbv7vb/p+jTO/clCVDRk8MgXl2L6h2di1ufm45lvPIQVP3oSbu4IVEYvW9n+RmxQI0SVTuCJxzQdtMjLTI/tcyk3I40fQgucEhGxntoYm+qQUWxvIRrxda7ajJpTxsNpqD5sBzyESkPUI9FYja7XtqN7WwdSLbVqXW5IEgCIFCbV20hRkJo6M6bS8p5VEiEytX8AwPEt5HxbQ2JIUC8RWY90M8KprGBZbfUq6X7TKArK2L0Awum3lKbzQqmzKmVWIUVRBElHijx1bXWEREoOSBmBgVrB0NMxRRg5C0Udhcci65iIkX6vSqES6Qpg7kenBlO+no8ty3dj7QObsfruzejc0Y1zrp+HMfOG9tnm8PxInI9rEWfxKTEzMtfJsv1N0ZEpyQXhxdKXVZFOA8HQp0BTcJGyXGQlMuTyaIRPU1ceCaXS6YVBdAwzUsRsL6p/RAhHMS0cc1sut5HajvAthoQUiXVZdHu6rylZEsQUcHQZV1OnCrV2OCqHV2P9ozsw8qzRRdui7ikHTvzHU/HwtY/i0a8vAywL7ZfPxOobnsTWv2zCjK8tQu2UFi15xFbPYoUVZLg5BqpsTkP1uIkIugEAaS+FXi+v+oUpz+ArXTC/5DRsqdIdFsKCsNwYSE3kzZQOcJgX9g/ZthSTkggkBEnoKKEv3IdtZeFbPbF+T6heLIWfpuwsFkNqSnFV9fT/HhaMl5xEhhUhW54Pj05bRvZvoLmuUVakXxNAWYcoboPaIToeLbt5L6pmth2x/VXPbEPLe+fh5c/9BmM++y40zGs/Yvsu2/Fn3OIYdXIzRp0cpF1veW43HrjuaXzk/nPBrTLg+9dkJ3xhPp7+5iNY9esX0TRzKDo3d2DiFVNQ0VyJzi0d6NraAW4zDD+lFVWt1TjpnxZg9c+WI5/1sPn+V1E5Zgi4Y+GFr92LYQsnYNyVs5CsK5f7KFvZStmgdogyBoeoGF/GRG502X/zd6XYbHCHVFqjwdPxeMjzqDSL87nF9T4qp4xA16rNqFs4e8DnWazIa5iVyTHknDnIjGnChu/9CbvueQH188dj2HnTAYQF+vTUa/PcS6k060G+zicCNB4Ej0ccngiKJ5pk5ryx5PIauU40ein4XHEYTBHCOqsbQMhDoHIBOrGWUmfNKNYs/qiQIuGo+XqTb0SqyCZvRhdzVGng8vwpYKXzNNXKLa6lwbMwAgVCxMgykaIBRHYk/EfGmcCIOU1wUhYObDyExrE1kXYHbYoSsSn69USRNpZAhEz07M2UC0jxAlKyyIQjI26zqKVJui4wH0nfjSiN20pUUS4VYiS5hLIbF4SlpC9U62PJGcGS0vA9NoDzklF7sZks83krJnuRLTLk6uigUkoXHNUzRmLx7e/DvtU7sWfFDjSdUo9nrn8KjAOVrTWoHF4Nv+DhxZtWYviZozHi3MnYv34/9j6/LbL/0VefjZ4Ne/DwFf+DynFD0XTKKLQvGo1MUyVyVtCeKsnVIySHnp1eTRW6YBCtheeg200ohJ2QIzoXQoz0kh4KOVGcouLIkQWh/V8cbTH5OmGiBg95mzTUyX0porfsRxWyEoLFBBhPg1k9sRIkBSqObSBDpnTBQIzS/3PM0QRtid9ICGWUQ2SOWRZEKLpZQgiyfxNhkkbZlA1qh+h4tJqTx2H7rcvQ0tEDq+rIRWNVk1sx5Ya/R+fKjdj8i4dRPb4JleObj9j+y3b8Wv2Yaux4aT9euG09hmQaUTOTo3lSLbjDsfHJ3Vj6vRX42IPnI1mV6H9nZRtUxm2OxpnD0DgzIEiPvSSQc9Od1OmfPBmrb1qOxz7yB7QuHIdp1y7E6795Fj07OgBfYOOND6Hto2fgpP/9OA6+uAUdz6zDwx+6HSd8dQHazxh2TM6rbIPAyqTqmA1qhygjawGVkkMH4ox8QjTM1Feb+Yo7FKaFG/PYA3CuKROG0CaS/ad2VDRVouGsydj5m4cx6jPnAAgiJJuFUSA3+qEZmOoZTnr2U6ImjaFnTYC77xB23LEck647t2iKd18lLOQXkI1Wn80SBUoNU88UAcAFh2dkmcVKLxhLs0QKR4hiUIqrXiIAiCJDQJC1RJwER0Sz1xxDDE1lsMjIzvE92CyKGukChQmuF6qN7qtPk91GpSjTJj5As1cqBlOonB/57Gv3zzxmqbT+kKsR7Gv2BybiT59Zhny3i9bWAqzXcjiwuQtuzsPQSXUo9Hro2tWNdJX2uGs8l8NFhnzwWDZS2NeKR52c+RoaSNlDMtvMJw5PNNvNZh6SfgEJ5sY5gcZzZ5qekarK4NCpG+dF63pGfy1mVEuZjq/kOCymRAbN41Eb876FnDbkKuTR54obFZN/YMXbFPlcmcHUz56BlvNnIlGVhKitQePCqXjs3Bvg9RZQMa4Z+554HY0XzUPFCRMx5OR2jDh3C57/6t3o2jQN46+cCc6CVHpCIQsSNSuWJk7Pe1JyCUH9kYRfjbGjWPtLFYINM6r8CFqkm2fcc7MMScbKhWn1EgUxRWTN0iEW88GYC87cGK9JCVzKexem7MvzLdIFTQ4RbdPtJ+XxXQ2Vlu8hnzJgo0i4yd3yBFMIl9o/PX9+FOkv2+FZmXTwNljrB09Dx/L16Hpl69uyf+EJoMhU1l+77dzYixuvWYs7f7TpWDdlUNnw2Y1417dOBLcZKptSeO/PT8OnHliMzz56Li776alonlKHX1+yBLmuwLlc/9h2PHfra1i7ZAuyh3L97L1sx4tVtdUjOaRCfeYpByf+9qOY/G/vQ+/WfcjuCIvB1kwailP++73Y9uhGLPv03di1YuexaHLZjpURqfpo/w1yG9QIUSxrochcbcHIzjB5NHaR+WslIGhFEaNQRyNa8V3PqHItKW3vh9kXQFihOGvZQBIY+/HTsfkn9+OEn7wfGdtCJQ9fPGYESWbq2uhtosjx0IpN2HTzMpz12ytRmYyKPZpV2M0qzKauhVK79sPrRRlo5nVUaA8suH7AHypVlLNY6QzzvGidA1LHJCwZIOfpeThfL4TAspu34b6fbEIh62PC/Hp0+imY4o4USZqFHAu+pSJdQqTIktxFWtgoWMG1pPuc4I7isuRlmwg9o89hlp5cz6VSHyJ2L01Uoi9T15M0bnyD50SCinTeQmDUmaNw9TMjUeNVoRcdqvgwZwJ71x8KrlO2gC2vHsSD334BE945Apue2Y2l33wWdaOq0DqnEalKG5bDYTkWqlsyGHtGC7jNUazwp/ksJkmI0RA7VeUiIEJNJLlUei/EmyGEhROSGpRUqbKzIc+IRwsV0zIr+UhKOJEJ5Ilr5knunGdmAkXvDf2qr1fqvvkiikh7wlfPteKLGahWwbeQ53bsuBb3lY6Seu54lI+kNNQ0tMREXaifUDtqT2zHjj+vQcuVp6Fh0UxsvelhtH/xXFiJQMS1YmglFvz03dj8wDos+9ojaD9nHGZ9bAYsJ0QZdJ0wE433RIASxxBGyvyTWVSWpptTCkE0+xhnIla6xjQz0zEt+2DOd0LuEHEslV6cE/lMCLTDXFh+AvCTsfMpxR1S5ws/tg2hPIQMUVupMK6lTVfFRCKNskahaG2YnRgKM0Yza822le3wrIwQvU3WtGACnOo0tv1pZf8rH4ZZaQdWykblyMFZANR3PXj5I6t9svLPu7D05i1IVdqYe0EzPvKTGUd0/38txkqQgt/36zMx4+LRuPk9S3D3tc/g1E9OwYIvzcKFPz4dH3vkQpzx+RlI1yThF3xkD+XRsaMbT9/0Cp746ZqjfAZlO5I27NK52HXvCgghMPx9p4Inbaz44E+x+9HX1DqWY2H0eRNx3q0X4uDGA7jvqvuwb+2+Y9jqsh01E+Lo/w1yO+4RIjuWTSajTSNCLfZb0uCpmPO+9H3Wd2IeOEVJNOfe6xFSFEQged/CCV+cj7988v8wZ+EU1DWIWAacacWiP1oSt6Vr5254WRedL72BplktkZdgf8iQ+l6eJpWn0Oui9ZutB4Z8rwuXI6Kls/eFLdh89xrsfHgtms4Yh+n/fG7sXNQxwHCwECBDZqYaRVJrn9iLLSv2YfT0Ktz6hVUAgEWfGYcFHx2DPOPI++H9VPdNKcxG0S0PXEWPStJfmic4klYevhOoaNN9TvJEXM/Iit57isizXnDcnOQs5bwQBVCFYUvI8PfFV9FROQBwWYh+AGH06zMflPPoCo6Cz8FZiJo1TGrA4n+qw+mfmoJd6zrQOqtB7cdKcIyY04QRc5oikXjHjh7ccsVSDJ/bjKGzmsGtoDyB7/nIdrpI1QTnmrYowi6ODOlLxZUwztMzdXuoXzEOm/nIWAVtP1F+U5gxKpEiUrtmPixO2kUSaZOIkev1H0UrNEwuS01QE7IjBFPIlsVDVCVynuBwfR4iQ5oytpl1aKJLZJFxweg61F+Ik5Ua0QgrncC2Xz2MivahGPWhUzH80hOx5ku/w6hTPgCnKqm2tWorMf/6Rdhw12tY8tmHkKhw0LHpEMZdMhknfnFegBDFVOUDlFjxVog350WfywjiXeI58BTPSmaDChYrH0Rmjp9pVSIk5AMRxyYnuUM9CMZnGr+VEr5EbDI8D0ek4PvpGIfI1B8i0zXNaDYohmYZfZtrSFHITYqO9WGhXbMYd5iVphfKBsLrR+dbtjdng9ohGswmfIHdr+xH3cQGsBJ8nqpRdWh792Ss++0KtF4z84gcd9SisWCcYfl3/4KurR247C9XgdtHD+jz8h4ev+ZusPEJtF0yA89940G0f3AuVv7LAwCA9LAajP2HeW9q377rI58voHtvFnd+5TkMnVCDx38WRLOf/cPJaJlY288eytaXZeqTGDW3CQBKTEKEVt2SwTu+fiIe/cEKdO3qwbDZQ3HCR6dh69M78PSPV+LKey5CVXNFP3s5Oua7Pg6t3Y3N96xB145ONJ89EUPOnHysmzUobNw/XYx9D6/G/ifX4Y2fLEXVpGEQCAQ+TWOMof2CiRh97nh0vrEfT/3LY6gaUX0MWl22o2LHAWJztG1QO0QmQlTMdOY9UCQyNdCSYF0eWYcym2LRC4P6nTxvihL27srh9g8+gHELWvCub85BWqbY90qEyBUWPMEw9yOT8ew1z2Lf0lVoPbsdVsKKoVnFlILNqIHOg0Ng6jkjUD8siYe/8hgqnLwS5LONyIZ4M+BRZWATKQoiHBY5Zz1aEb7AtsfewPalr+HgK7vQkKhFfkcevCKNzjcOKGdo/MfmY/R7Z0FwC7HwFeExDuXTcf6RbeGBD9yOzk0H4bs+nIyNna91YOG1MzHz0jHIOC56ZHspAlbXhJBDTr/bkfUyPB9mq3FT24rD4WlYss4a3d8kd2MIkeKRGTWMCBXskfwWy/XVb3QfB8IhKoUWqWtF/cXgFFm6crsIOD8OzH4fFr6kJT073OA5UNHKEWe2YcSZbejYk8cbj27GXZ94CMITGHlqKx7/ztM474dnwLGjOlLm86dn0ig9GepjVMCXnnOitJEOGARsFihcc01La9NTO7H+L7vQs68XHTt7cWD9fmQaK9G6oA1NJ0zA67e/iK13rsSUfz4fqcYqFCSyZ7lBG/M8yiky7w1jItQwIgRI/WhcVg3JsSjDtUjWaHA+HC7jSmUeimsUcpIU/8jQp+KKXxK20ewv1OeUXg33UTW6HlVXnR6061A3nr30RgDAqh8uw5RPzsP6Ja8hWZ9B09wRSDdWBvvkQHpMM6Z/9jQ8fd0DGHJiG2pG1cbQYtcPsk3VWOGZ3MFwXCtVj4wr3h/x5cI6haq/l0CVqJKAWW8x6zvqux6RlPuPquVTn6+0gmc2a+VQ7Ttw/WRM9yiGDKlsSHpumHZ/SO+LOK10nvSsEqLjK3RKPcekraVmQvKRfem13cJajAbPyPrbS7Y5kjaoHaIjbXV2Nw64RyaqrW5Oo2l8NXa9dgi/vGQpzv76iRh9aktknSGJHuxDBid+/iT89gO3At9chlmfnosJV751DkzT1EbUtdfinqvux5nfPh3VrVWRl/iRsq6tB/HKz57GvhXbwCyO3P4eVJ7ShtZPTkGisQqn/vxS7Hj8DTSc1Ia6qcH5v9nAo2d3F6ykBWYxTLt4LGa+fwIam8Pz6fJSqLSyfezhyFqd040DhcGBghxLywxJY/LFEzB64Wj4nkCq0sZDX3oEv7vyfkw4qwVOxkZ2bw86dvbCz7uYfeEIzFrcFNuPw7x+SZ89B/M4uL8XvidwaHcOOx55A1s2b0F1cwVqhmeQzwOr/vA6pr9/MhonD4FdX436CfVARRCQdPdyNJ86Gq/euhIvf+1OzLrhSsA6OkRT17VUMdujYQXfKhrwlTK3JweesNF2xWygJ4sll/wGidoUmuaMwOobn0TjCa0Yd9k01E9rBmMMQ2a0wKlMYsmVv4OdcTDm0hmY8g9z4johb5P1uE5MnPfttA4vgwwO9L/iETIfvCRh/O2344PTc7RtUDtEpE0zECXQEBFyI59Jz4Y89zq7u2TdGTMy0CMw2o/KNOAWTr1yBF5fvh9zLh6BO65bjsTnJ2HCopEAAjTBB0e9042Nrx9S+8lt2Ye6RIBGmNlYZDpSZfKByHyL4Z3/NAe/Pu9usI5OVFjBHHmSu+qlY6t4MooUKWRKE0Qy5+X9Hh9rb1uF1b9YHuwhaaPtwikYtnAiRo4Zhe5EF/IeUD2hGdUTmuU+otfN1I7pyAdV2l3BFTLkaRlqp97yAVQmXSSqU6hIFOAC6Haz6nyBwClSSJg8IL0UzBpnuraRqWtE5gkOi+fBebRuXoq5yAobdU53iA5S7S1Bn4O29/Lg2tO9SlieUjKnGlx96sgM0FSWoOIQEdTige6xR/pQsotbGs8o2DaKpAJxZMjkyalMzqrg/iWtAi788anYunw3djy3A/lDOdS1pDBqZh0c28e916/B1hV7Me/KEWhpS4IxFq1yLqLHzec9rLhnO5b9+g0c2pVFRX0S3GJI1yUx49SJSLY2wkra6NjZC7fHxUXXz8GwOUGfC7h7Ajk/iLaf+u7j2LRkA5rmDMehdXtRWL8VVVNbg2tBzzkpDzOzWjmheSEQRHfJ96PPR8wnkJ9d1wKjumq0LSFEnMmxLCqjLJgAk0cSHmWqRZ+hWLaiCL7TK60LDRnSjbatGl6DCV9ejJ33rIRf8DDpcwvAOUP1+EZM/MLZ2Hb/K1j+7UfgVKcw6xuLsPW+V9C99RBGXTAV7VfOxopvPYiX8h4mfWJ+cD6SQ+QbmVsx3bE++rqq5xZDiILPPa4T41GlZHaw4pHJ546CQYsJlb1q9uEQIRKRbXr8BBoAHHArYpmo8TaHyBAQvFtUpphChqLHoc7g0TUChwUPPniYeWa8f8wsO6sIhyjMQIvOlPRrAlCiWmVTNqgdIhN+H8g6DnOLfm8xP5a+SGY6XEVFw+QmuijgtPlVePC/1uJ935qAK6+fit9csxI9u7sx89wW1NYkkM36uPNfXobTW4O5V7Th2dvewKKrxyAtp2dMK5ZuX6qNe9/oxp+ufhJnf34qxs6sBJBTD4lD5Q0UtC0/G5A3me4g0rXZ8vgG5QxN/4dZGHvpNCSqk/AFR9p10csEEpZn7Ce6XxrUiOiqO0E0AJJDxJgAamrg2QX0+gDtWofd1f9K9JDRP/L3aCqtLvKoCzwGm4RwvMU8QPYbuoacCSRlqYmCns4LoCCkeJ0gYr3ctxvso4snYkKPdA1Mi5bZMF8oxZeuMWUGP3zpFgQLBn81/URwu3SEpJNcQJgu3Z8jFJtqRcA3GTF3KCacHGQ7KrFT7qJtTh2W/Ww9fvbh58AZw/h59Zg8vw6tEzOorEuAMQ9d+wvYsq4X65cfwIsP7kHz+Eqc99VJaD9pCFwqFwGGZL4GuUQYUITPQfQ575GFhVOBz4Y9K7ajflID3G17YLfXIlGVjF1fci7krVf9UwhfBc9C9RN5fJWSb77ktb6v/B3aSSjiKKARpv3wZyXkyaL7oKQJUUS+wfzOlHowp5oYExi6YDyazhiHHXevxJ5nN8NKO1j/m2cx5u9OxvCLZmHYBTOx5Y8r8Ojlvwm2cSy0f/x02GkHs75zPp6++nYkm2sw8sLpKMhitaX7aziulJr2Ci+ZJFWTtIVvxUjilkmol44fOVFEuO8SLDatHRagjgYVYbJMDo6XRi8vICunvs1kG0s5M9HpqmD6OXSOdLMM56kv099V+rZ07RJF3oNUbiYUhy1Pmb0VG9QO0WC3htY0xs6twx++/Rou+PpkfPzXc7D0vzfi0Zs2INvpwnMFZp4/DO/5zmzk04dw0vvHoLo5jcKbdMyFENi7oRPr/7ILT/1qHc741GTMumT0kT0pae2L29G+uD2mRl22svVnlfVJXPDViXj3tROwb0MXXntyP5bftRP3/bgHHfsKgBCorE+gYWQG406qw0d+cQKax1WG02lvAclf8I2TMfvDU7B3Qzf2burG9qe2YOV/P4cTvroAtSeWCyMDAOMMrRfOROuFQaLHhl8+ic51u9VvIy+ZjWR9Bqu+9WdMuuZM2OnAQUjUpDHn+nfjmU/dgVRTFepPKhPXj2srT5nFbFA7RNkBpBBSyq5XItOLPPYEQoI2edOlvHklqlVCXl63y781Cb/9yhr88KKncMX10/D+fw+KrjrMU9M0IpuFm+xB/RgOoLskj0KfKtOREd8TWPngbjx00xvoOeRi7Nw6fPKXszFsQhU461BtL1XstFQRQj1qURGGiEY6YaQV7quCMwirV9vWRIaiZGMVnbFQcNCi/clpi5TtRpcSdSH0pSB4GJkZab2+THM25eyVabMUeRbt8r5gsH0HvV5annc4pUT3j0q+UP+gadOssNW6gIZMeSmFmHS7AanTltNqZhHdSDONyNcki5pTkRQZ+1qZAxLLU2VOSNBQQ4aAYCqN0KNSyBD1HxIwpYQBX3C4VFbAYpF9OH50ijDTlsKstgbMff8YpBhNdfiR/ReEJYVsB4bgAlo0La9VmO6fQNXoJBpHVcjofxzeeH4/HvvKwzjhKx6Gn9YWE3UkNI/QhAK34ErIRhV+JQkEYyqLTEdu6BciglPQbjMBm/kxEndEyFObtjtcezPTsOt//hfsWvoqZn3/wsj3zQvG45X/eBiN80ZH9p0ZXotZ3z4Xz197N4b9Rwv8samBObD9nE+sNItgITncGIdDQVSJXMrfqVgwZ36RskLR5y58hiRi5DnI+jZ6PCfG6wlJzSL6WStUq9CjEuVGyJT0BPOQkM8DodajZET2AAAgAElEQVSEAJEQZFjKJno8fawPz6f4O61sh2eD2iE6HixVaeOqG6bjufv34OaPv4CP3jwHwyZWHZF9F3Ielv9pJx6+eROqGhI45zPtmHh6Y0kBvrKVrWxxGzqzGWf9+0I89PklqBlTD7u5/lg3adBY4WAvRlw8C5WjGyLfd72xD4m6DJL18aSC2iktmPrFs7HmPx/BiK/NR0XzkRnvynaUrYwQxWxQO0Q5vzghtahFeYphpI+wWGepYpXhLqLFQvXItD+0aPbioWC+wC2fWYnP/X4uUnVcIQYJuxfC7lZIiim4ZR6jt8vFo7/biQd+vROjJlXgY98bjQlzqmQbu+VpBuumlGRAXKyLIo1YamaRCDzcNooQmaJkOd9BEhyW3RO7frQOIQkqDVYhRSGXyCxaSURJQoYIYelLqC1EGoyIrYgRElWsUGRCJJH1oymwwbpRMiORLQuC0v2jSIeOLlHUR+eR9Agxigp49sUhsjWpBd0UUsSIQ2EpvkbBt5D37fi1IGUCjWRNiFspZEihS4yWIfLnKnQnWBKSZxmolpK2EJbi9lsDHIg9wWNigDpXST+eyRns8RJwtFT9ihnVmPr+KVj1X49j/vcXyzZGEaK8hhARWuRZUcI5IUYk7khk6whCJB8LSyFERLKOp8rT74eLDL0ZNKjYtsPOn4bV/3wPqsc1In+wB5VjGsCTDtb9/Ak0nNRWch9DT29HZaeN5//xLpx0w6WByGMfTSqVQGL+rqf295eIEBKxoxw3Llg41pgJNKbIo0bmLvg28r6NXlViJipLYabbq886z7EfEUm9nxLHkVBrQow8A9k3j6vzYU1EiPZRtjdng9ohOt5s1jnN2PpKJ3542bNoaE3C4QIT5lbj7PMzyDT5EJzBsouPeIW8j5ce349n7tuHlx47gOmn1+KLv5iAkZPKad9lK9uRsEmXTcKa29bg4Pr9qB1bRokAoGZSC1oWTcHrv3oKh17eAQCwq5IYtmgS2v/upD63Hb54EjZv3ITln/sjZn373DJSdFyZCEWSyqZsUDtEVA7DTN8sJfIFhBEbcSh8hUT4KjI1o+eYQKLcvZ7dVir13xSbe/cXxmDGwgagNwfLzeOFhw7gx9esx4Z1m+F5Ap4LzDunBicvrMGBPS727Spgz7Y8Vj3VhVETUjjt3Bp84mvj0NBI4oZdRY+nIg1EMxP0tqq0ZoUUxUsJmOdmbqP4MiSB7ydgezYcJ2wXRTI9XjLSNrp/dL9sqY6bZ7ZKeQ4Roihfx0QrfMFUJhWZQojMOX3DCsIKrxuPon++YEi5SXi2RAfVdRAoyMeDMgsdrRAkAFgqmy8arenRHx2PkI2EwS0ipEhvv23099j5Gv2VMx7JyMn7FmwDaqAInKJoC0yhRTHOkGyjifIQn8wVXLXbNbJ31DYyhaqYuGopToa6jvo9l3+KI2TIapiInx6B9/hRhMupdDDuHa048PwmjJhYgZz6PYoQ5bmlZSVJyQH6TEWHLdlmKnGjpeWHRVuN+yeiCJHOG3o7Z8H7EgXlTGDsVacAOAX7lm/C/uc3YfQVc5CoTUfWM9FgIMiWG//J07H59y/gyY/+DiPOm4qRF89AakgQxKnCqtrxSyFFJofI5sWf5WJmFlnmTBx2EogvGLK+jV4vFHU0i0aXeg9x+KXPx0BwbK1/hiKwkifJomi8yQHV+UleCeSJ9lG2N2eD2iE6Ho0xhrYZNUixAjI8hxln1KIiXwWk6pDiBeRzPm79z714eskhDBnqoL7JQfuUND7yT8PRMFTvzGWNiLKV7Uhbw/habF6+91g3Y1DakBNHoXHuyMPahjGG0ZfPxtDT2/HG71fg8Q/cinRzNarHN6F+egtaF00ESqDiZTuGJgJ5ibJFbVA7RCGHyJzDDb1xyi4j/QUlk0/ZZyzMGFMedgmkCBRVGKpsEaRI06mJbhvPTLD0SIKJQA8nBXz2K8XqA+U0/Yl4RKeQFIlOJBRSRFFK3IUKdYein009Ij3aCLlDwbLbl0iG1ObICgfwHFTYh7RsK0e2hfgbyUibbRWJO+qzTQVTZVSXMMqLxIrKCh7rByRMqKOAuumFHBUyYvBXOHzYgodZKFpAR/tT+5H3OGlwikwZ/YKwFMpBmWqdPBW5RmYWWq/nFC15AMQRUYuFvLjg/H3V/0kbxqfnAuE6+ucEd2NRZilkiLg4CUEcB6ZlEka5GrZP+kuluQwmJ8L83kQjHK4hbobgailzmFu0wOzk+XV46oaXYHceRLKmUu0fCIryAkCe2zGESEePgvMkIcyQW0T33MwYo2vOICJoTX/aPEDfXKFSyM9A9nskzRcMqZZaTLxmAcZ9/DT0bNyLQ2t3Y90tz2LnstdxwrfPAbf7Ri764hgN9HwUKqStfjhcKyHRSOJAJo2yKcVKLAHFx+tSmm+klZS03Bg/0+Sumr8Xu0bxUlVlDtFbsXKOXtnKVra/GasdlsHkc0dh2Y9eOtZN+as0K2mjZtJQjLxgGlrfNRl7nnoDfz77J3jjDy+id3cXhBAQZe7K4DBfHP2/QW6DGiEilIKKV3KjDAEAeH5xhIg+69/3pyfhG0qkOlJUKqpVxqK+JYdQ6zIIMPgqkkgRn8QowFks4iAEyFFqpZTJRFl0FF1weHR91By4mfEgv6fsKO03c90eeV0rZCSe1dAgz0qjYHco9IiWKguCMn/keZrcopxvK34R8TdcI5vILK8ScEmM0hXG/STEyOSzWEyAG2gjRWo29+DACrWhtGxFyiJTHAJCFlX/ieqHhLL8fqgDRBlpdB8pkvPCtlG7VDFe1YS+OUR0fj50zRYBm/sK2dB2Fjk/rmU8qWyyEshQyHuQaAmz1bq2bAuVKBkI96MkF8NAWZPcBWcCAkLdg5DP0f/gSpl+oXp90A8Xfnw0/uOdS/Gur/fCcrhCMBNU0sO3YwrHlLVECJGJIBUsCwUvihqR6f1HR/sIrOyLP9QX54aOYiJFZkHYw7FiXKHD2j7vYvdzm5Hb0wUvF1zX2qktOLh2D9b9+lkIX8CpTGLc389F3dQWpJurIuhRsTb3d6+LIYvmd+pziWtTTK2/t5+SJMW4rJ4xfpnXkzS9PDD1PNE6igdkZAdThqOezap4fcRpo2e0n1qBESun3cdsUDtER9qyvhOrZfXXZDl4SOLokeoarU7s8Y5eZknCctUU29GwHi+JjJU7ascr29GxdE0CFXVJHNrejfpR5cyoI2VetoBnrv497IoEKkbWo+v1gKs1+/oLkKhMwHc9ZHd1onvLQWy772Ws+9Uz8HIupn3xLAw9dcwxbn3ZyjbIHaJKWZyzWJHATlcWLZJflUKIiHXvMA+cCWR9J6YjYUYRlkIRKLoPI1fijZgcBqVyXWQW0mI+mLaPrIw2s8bl1zPFzEiY0CRCNlLEYzGilJyGOoWIkfysEKJo+3zEMyqqlG4NaXuEUW6BF+Cw4Do0Wp2qOKHiG4kA/aH6Ut1GLbCs76BHZpVRFNYtUSSKzH0DLVDcF8stGbEp5WZCiqRmjM382LqEMnpgsKV2DwCleu35PMhU8pIoSJ0QxQsg9NGINlU9JmHHI1OlQRWtsUTRn8N8xeExlcxjHAVVwyxUqvYVyuPD5n7s+DHtJjBkZQHaSicooGvyqLiKIKNcBk9TEaYlVSVXRW0RvRfFrlcpbqBCvnwO+A5yXlIdmwIaM/PGRBQBxHhHSS0YGjGtGrtX7kLr6KT6jtbzrYLiExGPkTLSSMGaHHMdKcpbBu/IcN5t5keq0ysOn4nmFbGCH0egCVkya7OFY6C8fyK6nifCe0Ct6SsTrZTlPY68FoAdWL0NwheYdv0l2HHfKmy7ZzVSLTVAOo28B4BZSLTUI9FSj7q5gQN0aM0OvPDl/8P8X70fqaYSzqnRtDh/0/h9ACiTqQoPSH003yqybgkkusj+4tp2puZbiGJTFmIprpD5Wc8uJcV0uwhPbkAmBMrFXeM2qB2iCjtaBZg63cFCJlLlGQiL3FmGQ6QKYsKKTKEA2nRbzCGKdja9DT5VYZSH74/cqRtNO5WUVxfRYwHx9Ho1PWMM9g6EcoAKhuMTOkRRkjWM7/V16eGvoOtK6f0QyMOFr1WErkDwfxUPXqzkGPXwYJnxg98pdT8rHHR5KblO4DTR9EyXFC50tZevaaZAY04JPkYHEDL93nMlxxA6BgnfVvsgR8zjLDbtSoNbgWBqEZ2G0yHtsBBk9LeCH+0DSSONPNhvdIA0z5uuSeS+iZC4q0+HmUuyHjehSh+oaujSMfKNqUeQg6INzHQ+GVsGLeaUq1u67E6pF45K0Sd/j/lwBEfOt0sSUEsJpjrMCwsxy+/ouUryAiafWodXn9qNeRe3hIU5tZI9aekYhw5RdOlK5yenkcpdY5otK6dF6To7cJGwwtcoFXktsHC6rZQJ5TyREGT8OqqkDCPdn5wdoa0fk7AoMb3W1xSaDx6R7KiYNBxudx5PX3kTMqMC5evm82dFxuo9S17G1j8+jyn/fAEOrd6GHfe+CK87j+2PbcCQee2oaCmWcFLcBjJtyks4hYp+oY39ngiKTsemqvsRlQx+Kx5km466mm63hKKEeL7hRNHzZ4yB6t0mLPgiWk5I3fsBlJsqW2kb1A5R2cpWtrK9HTZyahXu+eHreOyXGzFu4QjUDs8c6yYd92YlHUz+5oXwwVDR3oSDyzeids7oyDqHXt6OrnW78cz7foHa2SNhJQPHed9TG7Dpt88i01qLUVeeiI41O1F/4ijse2YjhC/AExa4Y4M7VvCXtFE7eSgq2oaUSxm9WStziGI2qB2iKiuIWMn7PlAIBL8K0pOPGMGJhI7wqFfNEXrkZGYxyVJQPhDC+bHCrAZSpKM7tJ+cbwO+Eyut0ZfphfyAMCKm6amUSoEO1i/AV8hQgb4zkAQzzbpY9OdpYmMA1EOTIcQNgXwBKyIRXyG/IyJ2twjaWiVT9hVy5CfDdHQ/mo5OqBylo5v3DAjviynPbxKzddOnzwDAJeRQcNg6QqRNpSlkSKIFijRLx+EhhB0cN2xriNxB7Q8I+1pIXA7TZamPEZJWCikyZV30MgecCfUHaNN58ppkPSodYqupHXM6sYJQH0PewNUgfBLSdEqUF6HzJLJ1McJrKEQZfVbVemBwBQ/OX/5EKEzSKp5eTERqzjXxOkNOw4KPERMq8IHvT8GK+3bhpisewxfuPg0pKUaoT5GbU3R0b6i/pBVaGKJm9BuJVxJalkASCSs+ZaZbwUDtFPKnro18lj0em+4KpRaiUzzkMIgiCAdtS/vqr7yRbq5vxfpmcswwCMHg+UDVCePgCcDX5ugrJrcC97wIAEgNr0dFexPyh7KY9K2LwG0Lu5euwUtf/RMAYPPtz2PEZSfCSlvw8x4K3QX4hSxEwYXXW8DGW59BprUOYz56Oirbm2LnVMxKIac286WcBC+5bl8cdUKe+yOy0xjFfUsVWjZJ2gUD6Q77AtT6pmhjEtHCyWV7czaoHaKyla1sZXu77Ok7tmHln3cjUWGD8TLKcDSs6R1T0PSOKcjv68SW/3kCm25eBrcziyfP/REmXncehi6aCuZYeO0798KuSqH10jmwM4mi+xKejy3/uxyrv34nEvUVmPiVc5BprTvKZ3T8mihziGI2qB2iSokQHXAJGaLSAVbIbzBMReu+ifpo8+dFCgnq65Qqphm0IYpYqDl0uwdAlFPUIxERLlLw/XTJ4qp9GSEJGYUMyYKARHTVuD0mMpSPFWylZWmEyjJSzcOoWnKKOAfjXJH6ItvKlatkmzKSw5EVUSJ6ihVU5O14UQ6NueySZGs9UqUIvKAIrVEya59RLShl3VfrFjyNVK1FWAohEmGKPiDJvtCFH6OIYjFio2cgQ0TON4m/+ne5EkiR6keyfTkWFnMN0CE/5EoR0duLCinmPDtG5o1Fz4p4ET2XtFXQSoBE0R4ys4/3uI46tkoUIIIxkbiLkLo9P/qsEy5Ez6F5zW0rPG+FXtC1NZCivZt7Me+yYTjxomGoqwN8EfLi1DXhUfRPlVmg/kK8Mg0toSWhnCRSmRBJeHZOXYe++ml/RGtfsFhhWRIdVWOffEapyCxxUfTxzZxuEgYyVcxopsUTDB54bObFvPfR4wVLq64GbZ8+By2XnIyDz7+B3feuwKv/eg9e/+kj4AkbTYungdkWnv3gzciMbgSzLbR9bAEyI4eECBDnGHbFKWh578nYee9KrPzs7zDig/PRtHAKrFS8eHLQ5mibdHSt4FvIMwsmz6iU6TMLpbh6Md4R8eMiQrNRdCn2fog1g6v+n0T0OS8mElm2gdugdojKVrayle3tsqa2NFrGVaBtRs2xbsrfrKWG1aF5WB0ybQ1Y88X/h8L+blSMb8GI98+HXZVCpq0B2257Gvl9Xeg+ezIyI4fE9sE4Q8v5s1A1aRg2//Iv2PTLZag/uR2NZ09G/eyRYFbfzuXfpokyh6iIDWqHyIyIXZWqbMU4RCFXorh4n+/zkshQKdO9e5OLYZpi+Vtaer0sacG9BFyZVQX0jdCQmRGpEu1CyFkIlhJ50Ar+mbLxeWObPo8bizSiiJEFAVcI1A6giGBSXqoeiRQlkJXn4iPhExIko2ePsuiKI0ZdXlKJOepFRiNLf+AInO8p6Aue4CpF2tfSoh2VXRVmj9E1AOI8NRtxZEjti5AhKxv5TOeb1JBFyl4LhSyjSJHiGGmSEnkpDcBYtIAocXgo80mVp3BtlcptpmubUW0VC9qc0DLiTGSmv4wfX7AQITKKftJnV0k7hDyugmBF+60vERrKTKPMqjAzx9M4goR2Ei8u+P7kS4fj9994Fadc1grL4aovRrmDchvZXTIIkFoz09CzQkSqYLSNkCLLT4M5vUhIbhFxjLgX3jMmIQy6t67irYXZZdRGlR0n76Og6yevBX02BSB1LhEzEAVat9gzJIyxzxNBUeFSY2Ix4cmYRAAhz1NGYdSnFqF3014Iz8dr37sXPGGhsK8LDQunoeXSk2BXpOBpQKTJFUqPacGEb1+G/IFu7HvsFWz61RNY94P7MeT0iRj67tlID68PpQfkNoSeCS/gq7mMx56DUudXnBdXHGU15WACJJcXXSfcZ7Tcjt4eNSZRpiKVkhno1K9APN24bIPbISpb2cpWtrfLxs6tQ11LCsvv2omTLx52rJvzN29N584GAPgFD5t/8iDsyiRav3kpuHN4YrOJugq0XDgHLRfOQe/W/dj70Gq8/Nn/QfNFczDiypPLWWllK2mD2iEiXRvS/lGIkVaqgKyUd91VCJAZv8g2prhcqchAj8ZKoQ+EYhEq5IGH3BJa5zCyzMw2Kg0fZiyVHoVQnKGCiF+vwzVClUIBN0KfBDwIFIQPa4ADC83b68gV/U/oAyFrpggamcNdFXEnpeCj4maoZTTy7lNDRbv3vmCx++oLFmogseg2ltE/FHIpI74cs4sUXaTyHlEuESEBnlb6hfoQ3T9CiKiPUUmUGh6WpCAdJ+FmADursuno2tA1Ie6UxXxYGloEaBk3qmRHsOxlCfk7ZehZkf8BIG0RJ8qV1ybaF30rfP7M/VMbKXOMUKyAB2QrfpdupgaVb6CjQRvktTYy4XStllmXtGHlXVsx66JRIdqlZYqWet4VMkQcIi3bivhxKZMf5mVgO73qfOn8s8xRRXHpu7z8XKBMPEJBJWJU4BwW8aVMLhGPnic9pjk35NqZWWu0TimMs9jsiicYPMZiyFG4Tfh9qIVEn+l+BUb3yOICPMEx5rOLtTb2jWSY47V+3NTwIWj94BloetdMvPy5/0H9vHHItDXG0R8GWW6r/3cLmb6e1w/aWgw56m8dM3Nab0/B0MlS40mRZ6Wklavdx6w8uVq2spXtiFgh62H32oPYuebAsW7KgG3kzDpsW9NxrJtRtrfZkk3VqJ4xEh2rtr6p7d3ePNbd+Che+cGD6Nl6AH7eRaEzC7c7p6Ymy3b826BGiMwMq4IV6jOUiv7Ju6Z5+2LaNKWQolL70nVeTFMF+Ixo3mdcizg5PIQ6JQNBbMxsAZXNIiNTM6MlCxHjCnklkKjDUTMNC+zqHKKAWeSrYqd9Gx2toJ1/lbynVJLEKhGtUFuzvoOU5Hj0+EHknZTIRVKqXfcqpEhmt/WBFNH1NDPTFO+ECfW/xaJcImL7cOM+6tEZISc5KgMjz4/m+pVCNZXrYFaoQ0T32EA/lDozFc+Vn1O2i07JwRJ2Fo7Tq/ZvFtHVlz0KUZCohESKLGNdk4/n6qVQpJq8D447P/843nhyJyqHptE4rhaL/+VEpGuq1LVzjSKyCR5Fz8KCquF19HweyTJT2Wx06eVjQqiSrmxOfAqzwK6eFcgqGbLdLrK+E3LChAjLeKC41lQx9NW8L2EhYYkO2hVI2d2q39L3vTyhStjkOWkYSY0teW3yCoUMESPFL/KjKA/dT6XX5cannPwSWWwmL6cU+hP8xlURY6AfTmYJ1EXpIDEaZzz1/UCUqPszhfIDSDRWo3CoB4WOLPYuXQVmW2g6axLsqlQwnknl7c7X92DbHc/Bsjkaz54EDuC1f3sANdOHI9lQiWeuuhXMYuC2Bd/1wW2OpndMxuiPnamm98hJ8k3URzv//vhGJTPXINQzQqWcQk7bwKYXBQBR5hDFbFA7RGUrW9mOH3vnN+bg3mufxo6X9mPjrh34nyuX4j23LEamIX2sm1bSnLQFN+fBc304xeVuyvZXYpnRTdjyq0ex+54VqDlhNITnY8cdz2DC1y/EvsdfQ49diR0H9mDfE+sx/LK54A7Hqi/+HgAw9dsXouGUoP5ayznT4NSmYaWSEEIgv78br/3gAWz+zZNou+q0Y3mKZXuLNqgdIsr6SHGabw74Jp7gKnIzLayFFY383SIRUTHeCADYiKIVnIkY3yisTUXzvFFtkSR3VcTJRVzRVd9HeJz+kRviIVEBVeIQAUCelIT7w2xkYHA4SJGeweaDIS8ELIN3YJXkW0T5HVU8jwTVlSqh+aHQD5l9leUOUoQMSX0nxc9RGWtUxFOiI4QceU7pe804ksJFd6wGUcgrinOJrKJt1tfPqfOQEZygGnRRzklSq82lo2HFrkWoW+Ibv7uot7sAADm7Egm7Bw6T14hH64R1qZpuSRV5quwv2TEo4y5rIA3E/XEhlFJ1yJ/ykGqsxMU/X4D/mnM7AKBzZw9uWfRHfOKJS5BMWPCtKFeP2kQ8CFUoVXt2C76FvFZwMxywDKRImo4uqWefRRWBzQy5ZKWDQwcFWIOjroNCi1RNuuhz7xmfHebF7pNSmSdkzOqBa3eHmaM87K+2rPvXq7heVActOB/iGOmIEaFFdL1sxRmS91UqZFO/FBrHJuTbRK+fUrXuAzxQfCNRvNZgX6iSWsfgEoX7Dg6csLwBF5wtdrxiswC1p01C/kA3Uq1DUCdLirz00V/gpU/9BgAw9GPnIVXdhMkXzkWa0vsTCfRs2I2qE8epWpGJ5kD80Q8aDGdIFVrfdwrW/8cD4JVp5HYdQvO7Z0c0k2LK8XoG8wD4Rub31KeJS0TPYY4N8JUuRJlDVMQGtUN0pM0VXA0aR8N6vCQyVq7/FY+QFWCpIrBH5XgCcI5iwka91YX9XuVRO54P1q8U/5G0Hi+BjBUXB3y7LGPn0eMeWViEWxyX/fpsPHvTGuS7CqhvrwGXtUaSlqumto6G9XoJRfbuy4aMrsK+jZ2obXhrKsd6SYWjYWmngN5C6SK6R9qEYAN2Ugbj8RhjGHrBibR3AMDoaxaja802+NkcGs+fg4TTG9mmafEMbcqqdFsqJ7Yg09aA/J5O2NVprP7C/8P4a89H3QmjSm5T8C3lFJctMMbYYgA/QlAU6yYhxPeO5vEHtUNEWRo0300RWJWVjUX8h9wAllcaNYZ6salZo5uZZabUjCN1yaIPg9JGkRwNxT2RmTKeH3CIerwkuO8g5yUVX+StqImqSueUfSait5B0ivo22fY3gRQVBIMrmMYL0rPoKLvFaLNco0qef4oJOIoXI9vrE68kQAGzUpG7ggUvtG6RQLefRL3VpVAjpeEjESOKvHs8+ix/5wn0qkwtgyskfDjcU5k/4fccnHnwwVRfCvkAoU6Obr6GbCiFaPlbGOFHOUSKg8Y8WPDR4yXU9TIruus8Lr0d+rop3gvP6g4/S1Stk4U6WGRZz0HGzkfqOQXfh2rWwXFE5BxSthvLugzPHWiaNhTn/Who5FhJuOj1HSQtV9Wry5m16IhDpFV+9wy+YPjcc/VNMcv7Nmzmo9dLKFSFnFulOC7RiWGzm7Dk31aDXTcLzVPq4cj7DmjonHGeZMXQO7P+oKW4RC5sVgjrwClEs6BlJRJSFOV+JeRxdcQo71tIO4VYTbr/3965x9hx1Xf88507r3vX6zi2Y3vt7Mtee+21rRoiTDCFiBAgCWqb8lKiJqSg0PIIlId4ioJUJFQlAaG0DVEKQaCgPJqmNG0qAoiSqn4EEofECSSNQ0JwkxDeiARC7D3945yZO3fuvXt3XfbuLHs+0ujMnJk593znnHPnd57zjBsLVmwRKrrTRnkLUPncTEgG03Jv95Z6mHlumLqN25zOxhQZgtoxjFE+JiqjPJus0+yytriUIpOl78DUCANTI85T+TcPi2Sz9srviaKxplrI5F+fk1+zbGqEBy+7hZ1XvJFoeb2tpSi7/9lC62d59eyZWo7y1trp1paicJZjiKB6Y4gk1YB/AF4GHAG+JelmY8x3+hWHShtEcT41ubX7ZFrKP32QG0KFRRuh8wc/ey3EWKaTYZT9zm/c8XQXC/9Y0Pw4aOw+Htr2J1t60R0P2cvzt7MoCGXDZ7pgGM3FKAJrGGVrgDW7zLI/jtZrVwRZF4XTT41I2bN1XUZZ9wnZ5z7cdHGTvdiPtnz6w4bXuqhh6l4mzcHX2aDro/lU/addl0Ru5CDi6WP5AN/sMx1HTftCnnnTdbeF2grLCkyXu2xLXXLNpRGyBTibywqUX9kFgJ0AAAsfSURBVKTlrrJyl0wgU3jpHiPQMVbWngI6G0JZGLWyIaTWzx1kU7+zFyxhZjQ2y1JuoM9iokAcHOXYs9M89SsDgaBh0yt7yZe7zLL9FoMoX10wV9Ll16bzMlHuUsrTyYW16027SNc+xI0X7+W5F2zl+W/YkperfLBvaYB0p4HT3Qyhli4PTRfCyNK73rY8Q3lQfGY85oPip2vE+SB06z5d6iJr7+Zvdoe1GUvuqXUzKjJjqIjJtjn+rxZ/Lw/fDQw/5gySo4VBx2WD6P9DblCXImBcmbWTX0oVnbaP5dI1XtmzGHzOOCtfvI2HL7+VTR/6U8rLG0C7YdXtuGwohcF0s6LfNv1+Ds+qel1mu4HDxpjvAUi6DvgToG8GUaWn3YeKOSHeRqSYE5NJQsWsSrYQKSZWxNp0giSIWF/fSBKEDNfHSIOQ0cYoaRCycWCYei1kYtnJJEFEEkRsdvud3LjgTpTcOIjYNDBMpJjx3B0hUsxoY5RQMSPOHa6PESomVMKG+jihYtbXNxIqYSjdRE0J69JNhEpYl04QKmFtya0pYU06QU0JJyWbCUhZlWwhUMrKZBJRZ0W8DVFneTyFlBIoZTCe6uqKlMF4O1LKsng7gXOlFJEyEO9ASmnEOxApjWgHok492gHUSaOdQJ042gzUgZQk2omca8/Z46JrSIminUBK6I4NKUG4A0OKQvs7gfu9MNpJoDpRtJNANuyaUmpKGIh3ECphMJ4iUswKlz9WJpNEilnt8seadIJYEevSTcSKiBWxoT5OEkQM18eIg4iRxiixIobq64mDiNHGKHEQMdYYIXFpPj4w0nQVM9awaT7WGCFW1Nltu3eYWFGeX7IwmvlmjFBJIb8kHfKLzQtrnbsm2ZznjZoSAmX5w5aRmhJWJVuIFbEm2Zw/iySI2FDfmJeHkcYoSa45ZHxgmDQI2bRsmNTl/7QWsmVwA7FiJpadTKQ4z/9hriNhuD5G5PJ/J/cXh3/FVadeyzVnXMc1p1/Lbe/5KmuPnpT/buLKWPa7Nn7rWspfrJiNA8Ntz7VTWnVKt9HGKJErq5F79vW0zlmvfzl/ds0refQbT3LbZYdcWtg0WJ+2l91aqczWXJnN0mN1IQ2absTKZJJAKScmk9SUsDKZdPkzZm06QRxEDKWbiIPW/FpMq1HnJu4ZNJ9byOZlJ5MGNr3qtZCty9dTr4Vsc+7UCUOktZC0FjK1YqjF3Z6769rdICxdM0Rcq818T8HtdU0SREytGCIJIradMJSXv8nl64kVsWVwA0nQ7k4ObiB1buKuT4KINLDakyBi6/L1pLnb6p8U/GOF+f42d8+2DtcW3ex3k1Ic0iBk+wtP4ZkjP+16bzG+kwU9nXRm7uZB+z6KFLNpYJiwVA5rrhwuUjYAPygcH3F+fUNVXUNB/eys9ng8Ho/n94fvG2PGup2U9GVgdf+ik5PS7GABuMoYc5WL02uBVxhjLnLHFwC7jTFv71fkKttlZo6nHdbj8Xg8Hs+MGGPOXOg4dOAIMFw4Phl4rJ8RqHSXmcfj8Xg8niXBt4DNksYlxcC5wM39jEBlW4g8Ho/H4/EsDYwxRyVdDNyKnXZ/tTHmvn7GobJjiDwej8fj8Xj6he8yOw4kvUvSfZLulXStpFTSZyXdLekeSTdKWuauXSbpZklfl7Relh9LOtGdH5JkJP1hIfwfSVq1wHq+KOkB53e1ZOdkSwokfUHSPknbnd9dkna5/VDSU5LOL4R/p6TnLqCWiyUdds95deHaSmuZQc+4pNslPSjpete8XPm8JmlS0rcL2y8lvVPSH0jaL+mQpH+TtLxwz6WS7pB0mjv+F0nnFM4/IOnDheN/lvSqBdSyS9IB53eHpN3u+krntRn0XF/we0TStwv3VDJtZtLjzr3dxe0+SZcsBj2ePmGM8dscNuw0wIeBuju+AfhzYHnhmk8CH3D7bwZeCewE/tb53QKc7fZfDRwE3ueOJ4HvVkDP2djFMwRcC7zFnT8TeBuwFtukCXYxrbe6/VOcnivc8QDwM6C2gFqeA4wBjwCrC9dXVksPPTcA5zq/KwtpU+m8VtJWA54ARrFjB05z/m8EPub2twKXAg3gBuf3XuASt78KuBO4pRDuY8C6BdTyFeAs53828I3FkNe66Sn5fwL4yGJKmw7p8xLga0Dizq1ZbHr8Nn+bbyE6PkKgLinEFqDHjDG/BJAk7CI9WV9kDfdxeJpfXtoL7HH7e7AG1AsKx/vmW0CJTnr+wziAb2JH/MPs9FwJ7HLHu4GDxph+rVHfSctdxphHOlxbdS3Qrudx4HTgRnf+80BWi10MeS3jpcBDxpjvYw2z/3L+X8UabtDUY+iu59+Bk1xr2Djwa2PME32If5GiFgNkLVwn0JwlsxjyWkZRD5D/r70OWzmCxZM20KrnLdjKwjMAxpgn3TWLSY9nnvAG0RwxxvwvcBnwKPbl9AtjzFcAJH0OWxPZCvydu+WLwDuAvy/47aNZ0HYDX6I53XAPtiD2hZn0AMh2lV0AfNl53Qqchh39/0nnV9SzB/tye0bSIH3U00tLByqrBTrrwdZSf25M/lXf4uJllc5rJc6l+XK9F/hjt/9aXPyMHVDZAP4b+LQ7fyeww3UT7gH2Aw8A21g4PUUt7wQulfQDbNp90PlXOq+VKOrJeBHwQ2PMg7Co0gZa9WwBXiTb5XybpOfBotPjmS8WuolqsW3AicDXgZOACPuCOb9wvgZcAbxhhjAa2ObwAeCA87sBmADuA7ZWSM8/Ap+aRThHgHXAXdga1iXAGdgumzMrouURCl1mVdYyg54LsMvbZ9cMA4cWQ14rxCkGfgysdcdbsV1NdwIfBX7S4/69wKnAf7pn9FbgImwX1JsXWMvlwKvd/uuAry2GvNZNT8H/08B7ZnF/ZdKmS/rc69JI2ArCw7jJRYtBj9/md/MtRHPnDOBhY8yPjDHPAjfRrOVhbBP39TSb/dswxjwNHMaOlzjovA9gxxyswdZC+kVXPZI+in0Zv3sW4ewHXgM8buw/yQHghdg/nQPzEfEOzJg2c6AKWqC7nhWuCw16LF5WsbyWcRa2O+iHLo73G2Nebow5BVuTf6jH/fuAFwODxpifYfXsYWFq7S1agAux6QTwT9g8MxNVyWsZZT24vPYq7P9aL6qUNtCu5whwk7F8E9tNNtOKzVXT45lHvEE0dx4FTpXUcP3qLwW+K2kC8r72PwLu7xHOXmzz+n53vB/4K2wtvp9rIXTTcxHwCuA8Y2b1FcC9wLto1fN64AljzM/nId6d6KjlOMKpghborOc72Nrqa9w1FwL/2iOcquS1jPModMlIWuPcAPgwdizNTOwF/hK42x3fg63Fj2BbvfpJixascXqa2z8deLDH/VXJaxllPWAN8/uNMUdmcX+V0gba9XwJmy5I2kKzBakbVdPjmUe8QTRHjDG3Ywe0HgQOYZ/hVcDnJR1yfkPA3/QIai+wkeYf4UFsbb+vg1xn0HMldkbMfjdt9SM9gmrRY4x5HNt92Dc93bRIeoekI9jne4+kz/QIasG1uN/tljbvB94t6TB29stnewRVibwGIKkBvIxmKwrAeZL+B1uJeAz4XI9g9tGaPkeBJ4E7Zmm8/07oouVNwCck3Q18HPiLHsFUIq9BVz3QeUxRNyqRNtBVz9XARkn3AtcBF/aoFFRGj2f+8Qszejwej8fjWfL4FiKPx+PxeDxLHm8QeTwej8fjWfJ4g8jj8Xg8Hs+SxxtEHo/H4/F4ljzeIPJ4PB6Px7Pk8QaRx+PxeDyeJY83iDwej8fj8Sx5vEHk8Xg8Ho9nyfN/541nqfDvJtEAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -454,20 +454,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:08:59,986 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/tc_fl_1975_2011.h5\n", - "2019-10-29 22:09:00,010 - climada.entity.exposures.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/exp_demo_today.h5\n", - "2019-10-29 22:09:00,032 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", - "2019-10-29 22:09:00,033 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2019-10-29 22:09:00,035 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2019-10-29 22:09:00,042 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n", - "2019-10-29 22:09:00,068 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2019-10-29 22:09:00,071 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 2405 events.\n", - "risk_transfer 1.11e+08\n" + "2020-09-16 09:45:25,729 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/tc_fl_1990_2004.h5\n", + "2020-09-16 09:45:25,743 - climada.entity.exposures.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/exp_demo_today.h5\n", + "2020-09-16 09:45:25,755 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n", + "2020-09-16 09:45:25,756 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-09-16 09:45:25,757 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2020-09-16 09:45:25,762 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", + "2020-09-16 09:45:25,778 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2020-09-16 09:45:25,780 - climada.engine.impact - INFO - Calculating damage for 50 assets (>0) and 216 events.\n", + "risk_transfer 2.7e+07\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -765,7 +765,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Read measures of an Excel file\n", + "## Read measures of an Excel file\n", "\n", "Measures defined in an excel file following the template provided in sheet `measures` of `climada_python/data/system/entity_template.xlsx` can be ingested directly using the method `read_excel()`." ] @@ -779,7 +779,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Read file: /Users/aznarsig/Documents/Python/climada_python/data/system/entity_template.xlsx\n" + "Read file: /home/tovogt/code/climada_python/data/system/entity_template.xlsx\n" ] } ], @@ -798,7 +798,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Write measures\n", + "## Write measures\n", "\n", "Measures can be writen in Excel format using `write_excel()` method." ] @@ -837,7 +837,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 22:09:00,717 - climada.util.save - INFO - Written file /Users/aznarsig/Documents/Python/climada_python/doc/tutorial/results/tutorial_meas_set.p\n" + "2020-09-16 09:45:26,216 - climada.util.save - INFO - Written file /home/tovogt/code/climada_python/doc/tutorial/results/tutorial_meas_set.p\n" ] } ], diff --git a/doc/tutorial/climada_hazard_Hazard.ipynb b/doc/tutorial/climada_hazard_Hazard.ipynb index 2b654efff8..1522b6b96b 100644 --- a/doc/tutorial/climada_hazard_Hazard.ipynb +++ b/doc/tutorial/climada_hazard_Hazard.ipynb @@ -40,15 +40,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:09,546 - climada - DEBUG - Loading default config file: /Users/aznarsig/Documents/Python/climada_python/climada/conf/defaults.conf\n", - "2019-10-29 21:57:12,428 - climada.util.coordinates - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd.gz\n" + "2020-09-16 09:43:43,430 - climada - DEBUG - Loading default config file: /home/tovogt/code/climada_python/climada/conf/defaults.conf\n", + "2020-09-16 09:43:44,439 - climada.util.coordinates - INFO - Reading /home/tovogt/code/climada_python/data/demo/SC22000_VE__M1.grd.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:318: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, @@ -66,7 +66,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -135,19 +135,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:18,056 - climada.util.coordinates - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", + "2020-09-16 09:43:48,636 - climada.util.coordinates - INFO - Reading /home/tovogt/code/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", "\n", " Solution 1:\n", "centroids CRS: {'init': 'epsg:2201'}\n", "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 978, 'height': 1091, 'count': 1, 'crs': {'init': 'epsg:2201'}, 'transform': Affine(1011.5372910988809, 0.0, 1120744.548666424,\n", " 0.0, -1011.5372910988809, 1189133.7652687668)}\n", - "2019-10-29 21:57:20,357 - climada.util.coordinates - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", + "2020-09-16 09:43:50,830 - climada.util.coordinates - INFO - Reading /home/tovogt/code/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", "\n", " Solution 2:\n", "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 501, 'height': 500, 'count': 1, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(0.009000000000000341, 0.0, -69.33714959699981,\n", " 0.0, -0.009000000000000341, 10.42822096697894)}\n", "intensity size: (1, 250500)\n", - "2019-10-29 21:57:22,513 - climada.util.coordinates - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", + "2020-09-16 09:43:52,994 - climada.util.coordinates - INFO - Reading /home/tovogt/code/climada_python/data/demo/SC22000_VE__M1.grd.gz\n", "\n", " Solution 3:\n", "raster info: {'driver': 'GSBG', 'dtype': 'float32', 'nodata': 1.701410009187828e+38, 'width': 20, 'height': 30, 'count': 1, 'crs': CRS.from_dict(init='epsg:4326'), 'transform': Affine(0.009000000000000341, 0.0, -69.2471495969998,\n", @@ -208,7 +208,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:22,545 - climada.hazard.base - INFO - Reading /Users/aznarsig/Documents/Python/climada_python/data/demo/tc_fl_1975_2011.h5\n" + "2020-09-16 09:43:53,023 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/demo/tc_fl_1990_2004.h5\n" ] } ], @@ -217,7 +217,7 @@ "from climada.util import HAZ_DEMO_H5 # CLIMADA's Python file\n", "# Hazard needs to know the acronym of the hazard type to be constructed!!! Use 'NA' if not known.\n", "haz_tc_fl = Hazard('TC')\n", - "haz_tc_fl.read_hdf5(HAZ_DEMO_H5) # Historic and synthetic tropical cyclones in Florida from 1975 to 2011\n", + "haz_tc_fl.read_hdf5(HAZ_DEMO_H5) # Historic tropical cyclones in Florida from 1990 to 2004\n", "haz_tc_fl.check() # Use always the check() method to see if the hazard has been loaded correctly" ] }, @@ -225,7 +225,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Define a Hazard by hand\n", + "## Define a Hazard by hand\n", "\n", "A `Hazard` can be defined by filling its values one by one, as follows:" ] @@ -238,7 +238,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -247,7 +247,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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E7bwqrVw6R1UVMZmkqoqYVi6dk9acpXQD0hFHHKFTTjlFjzzySC5L9wUTjwEACJCRTsTOZKVa713JP/zhD2ddr58IOQAARES6AWnJkiX61Kc+VYCK8ovhKgAA0Mf8+fO1a9cu7dq1y+9SskLIAQAAfZSUlOjcc88N/C0eCDkAAOAdwrCUnDk5yJnhLhcOILr4+RA8ixcv1le+8pVA3+IhmFWj6PReLjzenpDTXy8XnssbxwEIJn4+BNPxxx+vSZMm6ZlnnvG7lBEj5CAn0r1cOIDo4edDcAV9yIqQg5zI5n4qAMKNnw/BRcgBlN39VACEGz8fguvss8/Wk08+qf379/tdyogQcpAT2dxPBUC48fMhuMrLyzV//vzA3uKBkIOcyOZ+KgDCjZ8PwRbkISuWkCNnRno/FQDhx8+H4FqyZImuuOIKv8sYEc7kAACAQZ1yyil67bXXFI8Hb8k/IQcAAAyqpKRE5513XiBv8UDIAQAAQwrqvBxCDgAAGNLixYu1YcMG9fT0+F1KRgg5AABgSFOnTlUsFtMrr7zidykZIeQAAIBhjR49OnBnclhCDgBAwBXiLu89PT0ys5weM98IOQAABFjvXd57b4Lae5d3STkNOs45jRoVrAGgYas1s+PM7EEzazWz58xsWcpzV5tZm7f9W4Ps/7+957ea2S/NbKy3vdLMms1snZlN8LbdZGZvm9mUlP3fyr5NAADCqVB3ee/p6QlfyJF0SNJ1zrnZkk6X9AUze6+ZnSPpQklznXPvk/Sv/Xc0sypJX5R0qnPu/ZJKJF3sPf1FSVdL+omkS1N22yvpuhH2AwBApBTqLu9BHK4aNuQ453Y75zZ5n3dIapVUJelzklY55zq95/YMcohSSTEzK5U0TtIub3uJpB7vI/WrdoekvzezIzNvBwCAaCnUXd6DOFyV0ZwcM5smaZ6kJyXVS/qQma2QdEDS9c65p1Nf75yLm9m/StohKSHpt8653qsJ3Srp55L2SbokZbe3lAw6yyTdOFxNHR0dmbQQSEG9xX0uRLl3Kdr9R7l3Kdr903tm6hZN1eoN29R56K8rn8aUjtK1i6bm9HfklClTdODAgUD93k075HjzZtZIusY596Z3ZmaSkkNYp0n6lZnNcM65lH0mKTmkNV1Su6T/MLNLnXN3Oee2SzpzkLf7nqTNZnbLcHWVl5en20KgRaXPgUS5dyna/Ue5dyna/dN7+moXlEtlsT6rq67Nw+qq3bt3a/z48YH63qQVcsysTMmAc7dzrsHbvFNSgxdqnjKzHknvkvSnlF3Pk/Syc+5P3nEaJJ0h6a6h3s85125mv5D0+UyaAQAgigpxl/dQzsmxZEe3S2p1zq1OeapR0iLvNSdKGq3kpOFUOySdbmbjvOOcq+ScnnSslnSlWOYOAIDvgjgnJ51qF0q6TNIiM9vsfZyv5LyZGWa2VdI9ki53zjlvafivJck596Sk+yRtkrTFe7/b0inMObdX0lpJYzJtCgAA5FYQz+RYyhSawDEzF+T609XR0RGoMdBcinLvUrT7j3LvUrT7p/fse8/HFZAnT56sbdu2afLkyVnXNxgzk3MuZ0mKoSAAAEIkX1dADuKZnGANrgEAgCHl6wrIQZyTw5mcYRTipmcAAORKvq6AzJmckOk95RdvT8jpr6f8GlvifpcGAMCA8nUF5LDeuyqyCnXTMwAAcqWuplqxspI+22JlJaqrqR7xMR966CGNGjVKo0ePzra8gmK4agiFuukZAAC50julIldTLZqamnTZZZdp3bp1GjMmWFd1IeQMobIipvgAgSbXNz0DACCXcnUF5HXr1ukzn/mMGhsbdcYZZ+SgssJiuGoI+TjlBwBAENx777268sor9Zvf/CaQAUfiTM6Qcn3KDwCAIPjZz36m5cuX67e//a3mzp3rdzkjRsgZRiFuegYAQLH40Y9+pBUrVqi5uVmzZs3yu5ysEHIAAIAk6Tvf+Y6++93v6qGHHtJ73vMev8vJGiEHAADoG9/4hn7605/q4Ycf1tSpU/0uJycIOQAARJhzTv/0T/+khoYGPfLIIzr22GP9LilnCDkAAETY97//fa1fv14PPfSQjjrqKL/LySmWkAMAEGHPPvusrrrqqtAFHImQAwAAQoqQAwAAQomQAwAAQomQAwBAhJWXl2vTpk1+l5EXhBwAACLsa1/7mjZs2KA777zT71JyjiXkAAAUscaWeF7voXjkkUdq/fr1OvPMMzVz5kx98IMfzNmx/caZHAAAilRjS1zLG7Yo3p6QkxRvT2h5wxY1tsRz+j6zZs3SXXfdpb/7u7/TK6+8ktNj+4mQAwBAkapvalOiq7vPtkRXt+qb2nL+XkuWLNHy5ct1wQUXqKOjI+fH9wMhBwCAIrWrPZHR9mxdffXVOuOMM/Txj39c3d3dw+9Q5Ag5AAAUqcqKWEbbs2VmuvXWW9XR0aGvfOUreXmPQiLkAABQpOpqqhUrK+mzLVZWorqa6ry9Z1lZme677z6tWbNGP/vZz/L2PoXA6ioAAIpU7yqqfK6uGsjkyZO1fv16nXXWWZo5c6YWLlyY1/fLF0IOAABFrHZeVd5DzUBmz56tf//3f9fHPvYxPf7445o2bVrBa8gWw1UAAGBAH/7wh3XNNddo2bJlfpcyIoQcAAAwqEsuuURPPPGEnHN+l5IxQg4AABjUu9/9bnV3d2v37t1+l5IxQg4AABiUmemUU05RS0uL36VkjJADAACGNG/ePEIOAAAIH0IOAAAIpXnz5mnTpk1+l5ExQg4AABjSCSecoL179+qNN97wu5SMEHIAAMCQRo0apZNOOkmbN2/2u5SMEHIAAMCwgjgvh5ADAACGFcR5OYQcAAAy1NgS18JVzZp+w/1auKpZjS1xv0vKuyBeK4cbdAIAkIHGlriWN2xRoqtbkhRvT2h5wxZJ8uVGmoXy3ve+Vy+//LLefvttjRs3zu9y0sKZHAAAMlDf1HY44PRKdHWrvqnNp4oKY/To0Zo1a5a2bNnidylpI+QAAJCBXe2JjLZno9iGxU455ZRAzcsh5AAAkIHKilhG20eqd1gs3p6Q01+HxfwMOkFbYUXIAQAgA3U11YqVlfTZFisrUV1NdU7fpxiHxYIWcph4DABABnonF9c3tWlXe0KVFTHV1VTnfNJxIYfF0jV37lw9//zz6urqUllZmW91pIuQAwBAhmrnVeV9JVVlRUzxAQJNrofFMjFhwgRNnTpVra2tmjt3rm91pIvhKgAAilA2w2L5nLAcpCErzuQAAFCERjoslu/r+Jxwwgl6+eWXsz5OIRByAABFrbElnvf5L8VqJMNiQ01YzsXXbfTo0Uok/JsXlAlCDgCgaEX16sLZyPeE5dLSUnV1deXkWPnGnBwAQNEqxmXUxS7f1/EpLS3VoUOHcnKsfCPkQFLxXVUTAKTiXEZd7PJ9HZ+ysrLAhByGq8DpYABFqxiXURe7fF/HJ0jDVYQc5H2SGgCMVF1NdZ//hEn5ubpw2OTzOj6hGq4ys+PM7EEzazWz58xsWcpzV5tZm7f9WwPsW21mm1M+3jSza7znKs2s2czWmdkEb9tNZva2mU1JOcZbuWkVg+F0MIBiVTuvSiuXzlFVRUwmqaoippVL5/AfMB+FbbjqkKTrnHObzKxc0kYz2yDpaEkXSprrnOtMDSa9nHNtkk6WJDMrkRSXtNZ7+ouSrpY0Q9Klkn7kbd8r6TpJXx5xV8gIp4MBFLNCXF0Y6QvScNWwZ3Kcc7udc5u8zzsktUqqkvQ5Saucc53ec3uGOdS5kv6fc26797hEUo/3YSmvu0PS35vZkZk0gpEr1M3mAADBF6Thqozm5JjZNEnzJD0pqV7Sh8xshaQDkq53zj09xO4XS/plyuNbJf1c0j5Jl6Rsf0vJoLNM0o3D1dTR0ZF+AwG1f//+vB7/3JlHaOUFM3X7oy/rTx2dOqp8jD71wek6d+YRvn998917sYty/1HuXYp2//Re3EpLSzVu3Djffz+kI+2Q482bWSPpGufcm2ZWKmmSpNMlnSbpV2Y2wznnBth3tKSPSlreu807o3PmIG/3PUmbzeyW4eoqLy9Pt4VAy3eftQvKVbtgZl7fY6Si8j0eTJT7j3LvUrT7p/fiVVZWpj//+c9FX6eU5nVyzKxMyYBzt3Ouwdu8U1KDS3pKyWGndw1yiP8haZNz7vV03s851y7pF5I+n87rAQBAYQRpuCqd1VUm6XZJrc651SlPNUpa5L3mREmjlZw0PJB/UN+hqnSslnSlWOYOAEDRCFXIkbRQ0mWSFqUsBT9fyXkzM8xsq6R7JF3unHPe0vBf9+5sZuMkLZbUMNDBB+Oc26vkSqwxmewHAADyp6ysLDCrq4Y9S+Kce1R9Vz+lunSA1++SdH7K47clTU6nGOfcTf0eXyvp2nT2BQAA+Re2MzkAAACSCDkAACCkgnTFY0IOAABIW6iueAwAANCL4SoAABBKQQo5XIMGAAAMqLElrvqmNu1qT6iyIqa6mmq9b0JwlpBzJgcAALxDY0tcyxu2KN6ekJMUb09oecMWNbftDcyZHEIOAAB4h/qmNiW6uvtsS3R168ePbifkAACA4NrVnhhw++tvHWK4CgAABFdlRWzA7cdOGs+ZHAAAEFx1NdWKlZX02RYrK9GyxdWBCTmsrgIAIEIGWjFVO6/qHa/r3db/tYtPrNBnAzJcRcgBACAieldM9U4o7l0xJWnQoNN/e2dnJ2dyAACIinTPjvh93MFWTNU3taV9XC4GCABAgGQTJjI9O5JJTbk+7mArpgbbPpCSkhI559TT06NRo4p7am9xVwcAQJ4NdtG7xpZ4WvsPdXYkG/k47mArpgbbPpignM0h5AAAIi3bMJGLsyOFOu5gK6bqaqozOg4hBwCAAMg2TOTq7Eghjls7r0orl85RVUVMJqmqIqaVS+dkPPxVWloaiAsCMicHABBplRUxxQcINOmGibqa6j5zZ6SRnR0p1HEHWjGVqbKyMs7kAABQ7LIdwsnV2ZFCHTcXgjJcxZkcAECkDXbRu0zCRC7OjhTyuNliuAoAgIAo1jBRrBiuAgAAoRSU4SpCDgAAyEhQhqsIOQAAICNBGa5iTg4wgHzdhwYAwiAow1WEHKCffN2HBgDCguEqIKDydR8aAAiLoJzJIeQA/eTrPjQAEBZBmZNDyAH6ydd9aAAgLBiuAgIqV3fpBfKpsSWuhauaNf2G+7VwVbMaW+J+l4QICcpwFROPgX5ycYl3IJ+YHA+/BWW4ipADDIBLvKOYDTU5nr+3KASGqwAAecHkePgtKMNVhBwACBgmx8NvQRmuIuQAQMAwOR5+C8qZHObkAEDAMDkefgvKnBxCDgAEEJPj4SeGqwAAQCgFZbiKkAMAADISlOEqQg4AAMgIZ3IAAEAoBWVODhOPAQCIoMaW+IhX6AVluIqQAwBAkcomiAx33Gzuf8ZwFQAAGLHeIBJvT8jpr0EkF3ecH+r+Z+kIynAVIQcAgCKUbRAZSrb3PwvKcBUhBwCAIpTPG7Fme/8zhqsAAMCI5fNGrNne/4zhKgAAMGL5vBFr7bwqrVw6R1UVMZmkqoqYVi6dw+oqAACQf/m+EWs29z8LynAVIQcAgCJVrDdiDUrIYbgKAABkpKysLBDDVYQcAACQEc7kAACAUApNyDGz48zsQTNrNbPnzGxZynNXm1mbt/1bg+xfYWb3mdkfvWN8wNteaWbNZrbOzCZ4224ys7fNbErK/m9l3yYAAMiVoAxXpTPx+JCk65xzm8ysXNJGM9sg6WhJF0qa65zrTA0m/XxX0n855z5mZqMljfO2f1HS1ZJmSLpU0o+87XslXSfpyyPqCAAA5FVQzuQMG3Kcc7sl7fY+7zCzVklVkj4jaZVzrtN7bk//fc3sCElnSrrCe81BSQe9p0sk9XgflrLbHZKuMLNvOuf+MrK2AADIv3zdQLPYhSbkpDKzaZLmSXpSUr2kD5nZCkkHJF3vnHu63y4zJP1J0k/N7CRJGyUtc87tl3SrpJ9L2ifpkpR93lIy6CyTdONwNXV0dGTSQiDt37/f7xJ8E+XepWj3H+XepWj3H5TeH2h9Xas3bNOhQz2aEpMOdb6t1b9+RupK6NzZR4/omEHpvaSkRBMmTCj638Fph0JCvKcAABpRSURBVBxv3swaSdc45940s1JJkySdLuk0Sb8ysxnOOdfv+KdIuto596SZfVfSDZK+5pzbruRZnoF8T9JmM7tluLrKy8vTbSHQotLnQKLcuxTt/qPcuxTt/oPQe33z04p3OPUdjHCqb96h2gUzR3zcIPQ+evRo/elPfyr6WtMKOWZWpmTAuds51+Bt3impwQs1T5lZj6R3KXnmRimv2emce9J7fJ+SIWdIzrl2M/uFpM+n1wYAIGiCPtSTzxtoFrugDFels7rKJN0uqdU5tzrlqUZJi7zXnChptJKThg9zzr0m6VUz673RxrmSnk+zttWSrhRXZQaA0GlsiWt5wxbF2xNykuLtCS1v2KLGlrjfpaUtnzfQLHZBWV2VznVyFkq6TNIiM9vsfZyv5LyZGWa2VdI9ki53zjlvafivU/a/WtLdZvaspJMlfSOdwpxzeyWtlTQmg34AAAFQ39SmRFd3n22Jrm7VN7X5VFHm8nkDzWIXlDM56ayuelR9BxxTXTrA63dJOj/l8WZJp6ZTjHPupn6Pr5V0bTr7AgCCIwxDPfm+gWYxC03IAQAg1yorYooPEGiCNtRTrDfQzLfS0tLQDFcBAJBTUR7qCYOysjLO5AAAMJChhnqK/dorkA4cOKCysjK/yxgWIQcA4IuoDvWEwebNmzV37ly/yxgWw1UAACAjmzZt0imnnOJ3GcMi5AAAgIxs3LhR8+fP97uMYTFcBQBAgBX6ytHd3d165plnAnEmh5ADAEBA9V45uvfCir1XjpaUt6Czbds2TZkyRRUVFXk5fi4xXAUAQED5ceXooAxVSYQcAAACy48rR2/atImQAwAA8suPm4Ru3LgxEPNxJEIOAACBVegrR/f09KilpSUwIYeJxwAABFShbxL64osv6sgjj9TkyZPzcvxcI+QAABBghbxydJAmHUsMVwEAgDQFadKxRMgBAABpCtKkY4mQAwAA0uCcC8w9q3oRcgAAwLBeeukllZeXa8qUKX6XkjZCDgAAGFbQJh1LhBwAAJCGoE06llhCDgAAPIcOHdLu3bu1Y8eOPh/bt2/X448/rnvvvdfvEjNCyAEAIMJ+8Ytf6Ic//KF27Nih3bt366ijjtLUqVM1depUHX/88Zo9e7Zqamq0cuVKzZkzx+9yM0LIAQAgwtavX6/zzjtP//iP/6iqqiqNHj3a75JyhpADAECEtbe367TTTtP06dP9LiXnmHgMAECEtbe3q6Kiwu8y8oIzOQAAREhjS7zPDT1ffW1vaEMOZ3IAAIiIxpa4ljdsUbw9IScp3p7Qa3v/rCd2JvwuLS8IOQAARER9U5sSXd19tvUc2K/bnnjdp4ryi5ADAEBE7Grve8bGHToo19Oj1/b3+FRRfhFyAACIiMqKWJ/HPZ37NWrseFVNGudTRflFyAEAICLqaqoVKys5/LjnwH6VjJ2guppqH6vKH1ZXAQAQEbXzqiTp8OqqyWWHNO7Ydx3eHjaEHAAAIqR2XtXhUNPU1KRbXpzic0X5w3AVAAARtW/fvtBeI0ci5AAAEFlhvtqxRMgBACCyCDkAACCUCDkAACCUCDkAACCU2tvbNXHiRL/LyBuWkAMAEED97yZeV1M94PVuhnpd2M/kEHIAAIGV7i/6sOm9m3jvzTbj7Qktb9giSX36H+51YQ85DFcBAAKp9xd4vD0hp7/+Am9siftdWt4NdDfxRFe36pvaMnpd2EMOZ3IAAIE01C/wYj2bk6szT/3vJj7Y9uFeF/aQw5kcAEAgpfuLvljk8sxT/7uJD7Z9uNcRcgAAKELp/qIvFukOMaWj/93EJSlWVvKOu4kP9brOzk51dXVp3LhxGb9/UBByAACBlO4v+mKRyzNPtfOqtHLpHFVVxGSSqipiWrl0zjuGvoZ6Xe99q8xsBN0EA3NyAACB1PsLPSirqyorYooPEGhGeuYp9W7iI3ld2IeqJEIOACDA0v1FXwzqaqr7LOeW/D3zRMgBAAA5UWxnngg5AAAgZ4rpzFMUQg4TjwEAiCBCDgAACCVCDgAACCVCDgAACCVCDgAACCVCjiQzO87MHjSzVjN7zsyWpTx3tZm1edu/Ncj+r5jZFjPbbGZ/SNleaWbNZrbOzCZ4224ys7fNbErK697KrkUAANBfFEJOOkvID0m6zjm3yczKJW00sw2SjpZ0oaS5zrnO1GAygHOcc3v7bfuipKslzZB0qaQfedv3SrpO0pcz6AMAAGQgCiFn2DM5zrndzrlN3ucdklolVUn6nKRVzrlO77k9Gb53iaQe7yP1xhl3SPp7Mzsyw+MBAIA09d67KswyuhigmU2TNE/Sk5LqJX3IzFZIOiDpeufc0wPs5iT91sycpH9zzt3mbb9V0s8l7ZN0Scrr31Iy6CyTdONwNXV0dGTSQiDt37/f7xJ8E+XepWj3H+XepWj3T++FMXbsWI0ZMybUv0fTDjnevJk1kq5xzr1pZqWSJkk6XdJpkn5lZjOcc67frgudc7u84awNZvZH59wjzrntks4c5O2+J2mzmd0yXF3l5eXpthBoUelzIFHuXYp2/1HuXYp2//Sef3/84x917LHHavz48QV5Pz+ktbrKzMqUDDh3O+cavM07JTW4pKeUHHZ6V/99nXO7vD/3SForacFw7+eca5f0C0mfT6c+AACQvoMHD+rgwYMaN26c36XkVTqrq0zS7ZJanXOrU55qlLTIe82JkkYrOWk4dd/x3mRlmdl4SUskbU2zttWSrhT31wIAIKf27duniRMnKvkrPrzSOZOzUNJlkhZ5y8A3m9n5Ss6bmWFmWyXdI+ly55zzlob/2tv3aEmPmtkzkp6SdL9z7r/SKcxbjbVW0pgMewIAAEOIwsoqKY2zJM65R9V39VOqSwd4/S5J53ufvyTppHSLcc7d1O/xtZKuTXd/AAAwvKiEHK54DABAxBByAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABAKBFyAABA6Bw8eFCdnZ0aP36836XkHSEHAIAI2bdvnyoqKmRmfpeSd6V+FwAAAPKnsSWu+qY27WpPqLIipo/PLovEUJVEyAEAILQaW+Ja3rBFia5uSVK8PaFv/d8tKh0d/qEqiZADAEBo1Te1HQ44vRJvdajzYDR+/UejSwAAQqj/UFRdTbVq51Udfn5Xe+Id+/R07ldXydhClukbJh4DABBAvUNR8faEnJJDUcsbtqixJX74NZUVsXfs19O5XxOOmFjASv1DyAEAYBiNLXEtXNWs6Tfcr4WrmvsECb8MOBTV1a36prbDj+tqqhUrK+nzmpKut3X6rOMKUqPfGK4CAGAIA03eXd6wRZL6DA2N9Nh9hpsWTVXtgvK09h1oKKr/9t76Ut+jctp4zT3+XVnVHRSEHAAAhjDUGZNsQs5A4Wn1hm1SWSyt41ZWxBQfIOj0H6KqnVfV53hXXdUQmSXkDFcBADCEdM6YjMRA4anzUE+f4aahDDQUFSsrUV1N9ZD7ReWWDhJncgAAATDcKqJ8SveMSaayDU8DDUWl83Uh5AAAUCTyOScmHXU11X3eX0rvjMlwchGe+g9FpSNKIYfhKgBAUUtnFVE+1c6r0sqlc1RVEZNJqqqIaeXSOVkHrIGGm8aUjso6PA0nSiGHMzkAgKKWrzkxmRjJGZN0jin1HW66dtHUvJ+dIuQAAFAk8jUnphj0D08dHR15f88ohRyGqwAARW2kq4jwTl1dXTpw4IAmTJjgdykFwZkcAEBRG+kqIrzTvn37NHHiRJmZ36UUBCEHAFD08jEnJoqiNFQlMVwFAEBkEHIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAoEXIAAEAo9d67KioIOQAARARncgAAQOg45/TGG28QcgAAQLhs3bpVkydPVnl5ud+lFAwhBwCACGhoaNDSpUtlZn6XUjCEHAAAImDNmjX627/9W7/LKChCDgAAIffCCy9oz549+sAHPuB3KQVFyAEAIOTWrl2riy66SCUlJX6XUlClfhcAAEChNbbEVd/Upl3tCVVWxFRXU63aeVV+l9VHLmtcs2aN/uVf/iXHFRY/Qg4AIFIaW+Ja3rBFia5uSVK8PaHlDVskqWiCTi5rfPXVV/Xiiy/q7LPPznWZRY/hKgBApNQ3tR0OD70SXd2qb2rzqaJ3ymWNa9eu1QUXXKCysrJclRcYhBwAQKTsak9ktN0PuayxoaEhcquqehFyAACRUlkRy2i7H3JV4549e7R582YtXrw4F2UFzrAhx8yOM7MHzazVzJ4zs2Upz11tZm3e9m8NcYwSM2sxs/9M2VZpZs1mts7MJnjbbjKzt81sSsrr3hp5ewAA9FVXU61YWd9VRrGyEtXVVPtU0TvlqsZ169appqZGY8eOzWV5gZHOxONDkq5zzm0ys3JJG81sg6SjJV0oaa5zrjM1mAxgmaRWSUekbPuipKslzZB0qaQfedv3SrpO0pcz6gQAgDT0Ttwt5tVVuapxzZo1+uQnP5mPEgNh2JDjnNstabf3eYeZtUqqkvQZSaucc53ec3sG2t/M3i3pf0paIenalKdKJPV4H6nXmL5D0hVm9k3n3F8y7ggAUBSKeZl27byqoqllMNnW2N7ert///vf6j//4jxxWFSwZLSE3s2mS5kl6UlK9pA+Z2QpJByRd75x7eoDdviPpS5L63xHsVkk/l7RP0iUp299SMugsk3TjcDV1dHRk0kIg7d+/3+8SfBPl3qVo9x/l3qXg9/9A6+tavWGbDh3q0ZSYdKjzba3+9TNSV0Lnzj56yH2D3ns2ctn7/fffr9raWknR+F05kLRDjjdvZo2ka5xzb5pZqaRJkk6XdJqkX5nZDOecS9nnI5L2OOc2mtnZqcdzzm2XdOYgb/c9SZvN7Jbh6orK3VSj0udAoty7FO3+o9y7FOz+65ufVrzDqe+Jeqf65h2qXTBz2P2D3Hu2ctX7fffdp9ra2kh/LdNaXWVmZUoGnLudcw3e5p2SGlzSU0oOO72r364LJX3UzF6RdI+kRWZ213Dv55xrl/QLSZ9PqwsAQFEJwjLtMNu/f78eeOABXXDBBX6X4qt0VleZpNsltTrnVqc81ShpkfeaEyWNVnLS8GHOueXOuXc756ZJulhSs3Pu0jRrWy3pSnFVZgAInCAs0w6z3/zmNzr99NN15JFH+l2Kr9I5k7NQ0mVKnoXZ7H2cr+S8mRlmtlXJszSXO+ectzT819kW5pzbK2mtpDHZHgsAUFhBWKYdZlG+AGAqS5lCEzhm5oJcf7o6OjoiO6Ya5d6laPcf5d6lcPQ/0tVVYeh9pHLRe2dnp4455hi1trbqmGOOyVFlhWFmcs7Z8K9MD0NBAIC8CMIy7TB6+umnNXPmzMAFnHzgtg4AAIRIW1ub3ve+9/ldRlEg5AAAECLbtm3TiSee6HcZRYHhKgBAKBXzFZfzadu2bfr4xz/udxlFgZADAAidxpa4ljdsUaKrW5IUb09oecMWSSq6oJMaxt4/ZbQ+dc57s6qxra1N1dWsYpMYrgIAhFB9U9vhgNMr0dWt+qY2nyoaWG8Yi7cn5CTt6ejU8oYtamyJj+h43d3deumllzRz5vBXlY4CQg4AIHSCcsXlXIex7du36+ijj1YsxkUXJUIOACCEgnLF5VyHsW3btjFUlYKQAwAInaBccTnXYYyVVX0RcgAAoVM7r0orl85RVUVMJqmqIqaVS+cU3aTjXIexF154QSeccEIuSgsFVlcBAEIpCFdc7q2vd3XVlPIxWp7F6qrOzk6NHTs2lyUGGiEHAAAfpYaxbO9dNWbMGHV2duaqtMBjuAoAgJAYO3YsIScFIQcAgJAYM2aMDhw44HcZRYOQAwBASDBc1RchBwCAkCDk9EXIAQAgJMaOHctwVQpCDgAAIcGZnL4IOQAAhAQhpy9CDgAAIUHI6YuQAwBASDAnpy9CDgAAIcGZnL4IOQAAhAQhpy9CDgAAIcEVj/viBp0AgCE1tsQP3yW7siKmuprqor+791AK2U+hv3bcu6ovQg4AYFCNLXEtb9iiRFe3JCnentDyhi2SFMigU8h+8vVeQwUnhqv6YrgKADCo+qa2w7+keyW6ulXf1OZTRdkpZD/5eK/e4BRvT8jpr8GpsSUuiZDTHyEHADCoXe2JjLYXu0L2k4/3Gi44MSenL0IOAGBQlRWxjLYXu0L2k4/3Gi44MSenL0IOAGBQdTXVipWV9NkWKytRXU21TxVlp5D95OO9hgtODFf1RcgBAAyqdl6VVi6do6qKmExSVUVMK5fOCeSkY6mw/eTjvYYLTgxX9WXOOb9rGDEzc0GuP10dHR0qLy/3uwxfRLl3Kdr9R7l3Kdr90/vQvQ+1uurQoUMaPXq0uru7ZWaFKDmnzEzOuZwVzhJyAAACpHZe1aBng15//XUdddRRgQw4+cBwFQAAIfHqq69q6tSpfpdRNAg5AACExKuvvqrjjjvO7zKKBiEHAICQIOT0RcgBACAkCDl9EXIAAAgJQk5fhBwAAEKCkNMXIQcAgJBgdVVfhBwAAELg4MGD2rt3r4499li/SykahBwAAEJg165dOuaYY1RSUjL8iyOCkAMAQAg89dRTmj17tt9lFBVCDgAAIXDbbbfpiiuu8LuMokLIAQAg4F588UU9++yzWrp0qd+lFBVCDgAAAXfbbbfp8ssv15gxY/wupahwF3IAAAKss7NTd955px577DG/Syk6nMkBACDA1q5dqzlz5uiEE07wu5SiQ8gBACDA/u3f/k1XXnml32UUJUIOAAAB1dbWptbWVtXW1vpdSlEi5AAAEFC9y8ZHjx7tdylFiYnHAAAEVENDg9avX+93GUXLnHN+1zBiZhbc4gEAQH/bnXPTcnWwQIccAACAwTAnBwAAhBIhBwAAhBIhBwAAhBIhBwAAhBIhJ8/M7H+b2XNmttXMfmlmY83sSDPbYGYveH9OGmC/ajPbnPLxppld4z1XaWbNZrbOzCaYWYWZ/dnMzHv+A2bmzOzd3uOJZvYXMyvo93ukvXv7VpjZfWb2RzNrNbMPeNsD0bv33tn0/4qZbfG+939I2R6I/rPp3du/xMxazOw/U7YFonfvvUf6736smT1lZs94+3895blA9J9F78eZ2YPev/fnzGxZynOh7t3b9w4z22NmW/ttD0Tvg8n0a2Jm9Wb2BzM7y3u81sxqU55vM7P/k/J4jZkNeuv1ovgihJWZVUn6oqRTnXPvl1Qi6WJJN0h6wDl3gqQHvMd9OOfanHMnO+dOljRf0tuS1npPf1HS1ZJ+IulS51y7pNckzfaeP0NSi/enJJ0u6UnnXE/uuxxYNr17vivpv5xzsySdJKnV2170vUs56V+SzvH+Dpyasq3o+89R78v01+95r6LvXcq6/05Ji5xzJ0k6WdKHzex077mi7z/L3g9Jus45N1vJ2r9gZu/1ngt775J0p6QPD7C96HsfTKZfEzOb5e16pqQveJ//Xl5vZjZZ0luSPpDyNh/wXjMgQk7+lUqKmVmppHGSdkm6UNLPvOd/Jmm463GfK+n/Oee2e49LJPV4H+Zte0x//Ut+hqRv93s86F+CPBpR72Z2hJJ/yW+XJOfcQe8fthSc3qXcfO/7C0r/I+7d+x/p/1Tyh3qqoPQujbB/l/SW97DM++i9zkdQ+h9p77udc5u8zzuUDLlV3tOh7l2SnHOPSPrLAE8FpffBZPI16e3VafBe/1PSUZY0XVLCOffaoO/unOMjjx9K/o/0LUl/knS3t62932veGOYYd0i6KuXx8ZIekbReUrm37QpJd3ift0gaK+lR7/EGJf93GIjelfwf7FNK/s+mRclfduOD1Hu233tJL0vaJGmjpM9G5Xvvbb9PybOXZ0v6z6D1noP+SyRt9vb/ZtD6z6b3lOenSdoh6Ygo9e71vbXftkD0nquviaTvK/lzb5H3eIykdkmjJa1U8mzXzyW9V9LHJf37kO/v9xcgzB+SJklqlnSUkv8ja5R0aYZ/6UdL2ivp6GHe6wRJf5Q0XdJab9tjkiYo+b+DCUHpXdKpSp66/hvv8Xcl3RyU3nPxvZdU6f05RdIzks4MSv9Zfu8/IukH3udnKyXkBKH3XHzvU56vkPSgpPcHpf8c/cyboOQvuaVB+t7nqPdp6hdygtB7Pr8mKb2d7v17mCTp85I+Len/k/S/htqX4ar8Ok/Sy865PznnuiQ1KHm67XUzO1aSvD/3DHGM/yFpk3Pu9aHeyDn3gpLf/AskPe5t3ijpE14Nbw22b55k0/tOSTudc096j++TdMpgb1SEvUtZfu+dc7u8P/coORdrwWBvVIT9Z9P7QkkfNbNXJN0jaZGZ3TXYGxVh71Ju/t3LJYdoH9LA8zR6X1Ns/WfVu5mVSVqj5P/4G4Z6o7D1noki7H0wufqa/F7JKQzlzrk3JD3hHecMJQPQoAg5+bVD0ulmNs6bCX+ukuPM/1fS5d5rLpe0bohj/IOkX6b5fo8reWrw8ZTH18if8dkR9+6S46uvmlm1t+lcSc8P837F1LuURf9mNt7Myns/l7RE0tb+r+unmPrP5nu/3Dn3bpe8d83Fkpqdc5cO837F1LuU3ff+KDOr8D6PKflL4o/DvF8x9Z9N76bkPLxW59zqNN8vFL2PUDH1PphcfU0ek3Slkme1JelZJc/sTJX03JB7+n06K+wfkr6u5A+prUqOI46RNFnJGeUveH8e6b22UtKvU/YdJ+nPkiam+V51kg5KinmPpyk5gesfAtj7yZL+4P1lbpQ0KUi9Z9O/pBneP+ZnvH/AX43S9z7lGGdrmOGqYuw9y+/9XCXnWDzr7ftPQes/i94/6NX9rJJzkjZLOj8KvXuPfylpt6QuJc9mfypIvefiazLEMaZ4vX06ZdtDkpqGe39u0AkAAEKJ4SoAABBKhBwAABBKhBwAABBKhBwAABBKhBwAABBKhBwAABBKhBwAABBK/z+mlO3UViRv4AAAAABJRU5ErkJggg==\n", 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\n", + "image/png": 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o0YLp06cTHR3Nvn37GDZsGIMHD859TWCTToBGQJP/fyTYmXX9/f39Wb16NZ5plzgzoz/nV00gYccKMs6fJCcjlSvLPmbYsGFMnTqVSZMmER4eTgmvDIe4FGRcb/0OjOouLuZzFxfzuYuL9JopFAEBAVSvXh2At99+G6UU837bT+kWL4Cm4eF79ZqXK+r6+/v78/WCJZR9eig+5euQcfYv4pd8xKmJXWlQqxJbt25l9erVrF+/niZNmlDu1Drd9BjQW78Do7qLi/ncxcV87uIivWbs3mumXqWSlA/xZ+n3/yXz4mlqPNzOZXX9a4cGUqlCeQ5n3UNOhQeo1bILE0d/yOi3+mCxXN0baprGgQMH8MhKpc+zHaTfQSHn66mniuRijhwlF3OfYyQX6TVz0+O7BcVaXf9Zs2bx8ssvk5Fh/ZKHnnjyySfx8fHh+++/t+u67t7vwBaSi3UkF+tILraRbKzj7rkYsteMI+jatSuZmZlcvnzZ1Sp3ZeTIkWzbts3VGoIgCILgMHS9EcnJybH7mn5+fnh4eLB37167r21vypQpQ2JiItnZ2XefLAiCIAgGRNcbEXuWdb9OdHQ02dnZhIaG2n1te1O6dGnq1q3L+PHjXa0iCIIgCA5B13VE7r33XruuN331Vl7t/gSeweXoufgUQ9r46bqcrqZpzJ49m/saNOSbU4Ek+JW7ae7FixdZtmwZ999/P6mpqTRo0IAiRYrw5bJNjIz6gIt/7eKBwbN5O6K+bkseO3O+uOjfxcju4mI+d3Gx782qtnCbp2aW7oqh34vdSDt/mtIvjCUZH0OU0/3tWDI/n8zi2JJxeAWX5fzxg/zwzUyWL1pA1Htvk5CQwJQpU5gxYwbvvfce8xct5Zsvx6GKhZIRE02KxY9f9p2kcuUqpiszbNRS5pKLOXKUXMxdylxykRLvdmfMT4cp+sDTkJ1F0r6fAWOU0x3z02G8ajQjuGU/Lv/xLckHN6CFVOB8SH22bNnCypUr+fvvvzl//jzx8fFkVniQkHZvULzpc/hUvp9L62dwce+vpiwzbFR3cTGfu7iYz11cpMS73TlzORW/ao0JbvNvLq79kqSdKwluM4AztZpZnWtrDWePXx/zqxmOX81wAHIy0zi9YiwDB+6iQoUKVKhQgY4dO1KzZk2o2x4/QLN4UKxJZ7IuxRL0aC9Tlhk2qru4mM9dXMznLi5S4t3uXC9VG1D/ccq/8T0WH38ub5yv+3K61sYy40+gLpzklVdeoW7dupw7d45mzZoRFBTEiXEdODWhM2dmDSR+2WiKPdwND98AU5YZNqq7uJjPXVzM5y4uUuLd7txYwtbi7YPFNxCvosV0X07X2lxLykXCwuoSERHBgAEDmDhxIqdPn+bIkSMs2nKM6oO+I6TtQEo/9xn+dVuatsywUd3FxXzu4mI+d3GREu92v1n1egnbPacu8desIWScOcTwTybwSsTDt71OT+V0rc1tWyadzWsWs2rVKmbOnEmvXr346KOPmDVrFgNffIbGYTWIvuJJiuZr6jLDRi1lLrmYI0fJxdylzCUXKfHusBLvnTt3ZuXKlWzbto26desWan1XoZTixx9/xMPDg6ysLP78809OnDhBw4YNmTJlCp07d6Zx48Y88sgjpKWl8dFHHzFkyBAqVqyIUordu3cTFBRExYoV0bTbqu26Le5eftkWkot1JBfbSDbWcfdcbJV4d5uNSEZGBq+//jr/+c9/WLt2LS1btrSHpq5QSrFy5Uq2b9/O5s2b2bx5M56enly+fJlatWoxe/ZsunbtSnZ2NtnZ2YSEhDB58mQaNGhAsWLFXK3vctz9JGELycU6kottJBvruHsubt1rZsCAARQpUoSvvvqKb775xpSbELj6S46IiCAqKoq1a9dy/vx5VqxYwd69e+nWrRtNmjShatWqbNy4kZiYGF566SXefPNNypQpwyOPPMLKlStd/SMIgiAIboauNyJvvPEGaWlphV4nPj4eX19fvL29WbVqlR3MjEGRIkVo2rQpYWFhjBw5kvT0dH755RdCQkLQNI1Bgwaxa9cuLly4wKuvvsrLL7/MlClTXK0tCIIguBG63oicPn2ajh07UthLNN9//z0pKSnMnz+fBQsWMHr0aDsZGgtvb2+r4z4+PnTp0oVNmzYxefJkhg4d6pCGg4IgCIJwK7ouaPbtt98SHh7OV199Rf/+/Qu9nmfVhwis3oj3J0xnBY0NU9ffmS4lOn/CsqUf4uExnE8++UTXueR3vrjo38XI7uJiPndxkV4zkR988AFNmzbl+eefp2PHjgQHB+frKQ9rdf0v/7OHzPhjaGFPGKKuv7P7HSTneGCp3Jjt30/i2F9HefTRR/Hy8tJdLo7IUXrNSK+Z/MyXXMzdU0VykV4zudSuXZtRo0bRsGFDatasybFjxwq0zvVa+hb/IFTm1ftOjFDX3xUumV7+lOsxgfj4eBo2bMjGjRtd5mLkHAvrkp6ezkcffcRrw0Zw7n+LyUo47zIXPc4XF/27GNldXJzXa0b3GxGAl19+mYsXL/Laa68RHh7OM888w8KFC/N1H8OZy6lkXDhN4vZlFClf96Zxa3NtreHscVe5xKV78O233zJy5EieffZZxo4dS8ylFJe4GDnHwrhkZ2czYsQITq2bxaX1M4j5sudN90uZLZf8jouL/l3yOy4u+ndxBIbYiAB4enry6quvsnbtWjp06MCgQYMYN25cnl/vc3YvsTP741msNPc8MTh3XO91/V3pomkaXbt2ZfPmzcybN4/LS0aSefmsrhzzOm5EFz8/P5RSPPTxWkp2/YjABzvddGnSbLnkd1xc9O+S33Fx0b+LIzDMRuQ6YWFhPP/88wQGBvLAAw/YnLd48WJeeeWV3PLnx777kICaD1PmpUlYvH0AY9T114NL+fLl2bFjB4+3epQLS0ehcrJtztWbuxlchj5+L8HVGxLUoqddXdLT03WVS37ni4v+XYzsLi7Sa4aoqKjIkSNHcubMGVJSUvDx8cFisZCamkpmZiaTJ09mwIABBAcH3/banJwc6tati1KKo0eP8ssvv9CsWVOiJn3NgdhEQ9X110u/A4vFwpOPteCjEe9Qo9WzJGfi8n4HRu2poodc9u/fz5AhQxg+oKducjFajpKLuXuqSC7SawZN01T37t356aefsFgsXLp0iWLFipGamkpgYCCJiYkcPHiQChUqWH19nz59WLt2LV988QVPPvmkk+31TUHLDI8YMYI9e/awfPlyB1hZ58KFC+zbtw9/f3/uv/9+LBbHfYinx/LLSimH9QPKysrC0/PuT/DrMRc9ILnYRrKxjrvnYqvEu67riOzZs4fTp0/j4+NDdnY2Fy9e5NKlS/z666/ExcXZ3IQAzJgxg8GDB9OhQwd+/PFHWrdu7URz+6CU4sqVKxQpUgRfX+dcq7PFn3/+yfTp09m9e7fTjjlp0iTee+896tWrx8WLF0lKSuKhhx6iQYMGKKU4fvw4iYmJNGvWjAsXLnDy5EksFgt16tRB0zTuueceypYty969e8nMzKRNmzYopcjOzqZixYoEBASQlpbm8mxvJCEhgRkzZjBo0CDWr1+f244gIiLC7hvAvGxCBEEQHI2uPxFZsWIFTzzxRIHXSExMJDQ0lMcff5yFCxfa0c42WVlZ7Nu3j1KlSlGmTJkCrZGTk8PSpUuvPjFx6hQZGRn4+flRpkwZKlasyPDhw5k8eTKtWrXipZdeyv2UIC0tjbi4OBYuXEhAQAAnTpygZ8+eVK9e/ab187srT0xMpGHDhowePZqOHTsW6GcqCM2aNWPQoEF06NABpRSHDh1ix44d7Nq1C09PTypVqoSPjw+///47JUqUoEqVKmRmZrJv3z68vLw4deoU586do379+jd1LNY07bbHwN98800OHjxIyZIl0TSNp59+mvbt29usRmsPFi5cyDvvvMPKlSv5+eef+euvv9i8eTNbt24lKSmJOnXqcOLECZo1a8b06dOpWdM512tvxd3/L84WkottJBvruHsuhvxEpDCbkOsUK1aMH374gRdffJG5c+fawerODBs2jAULFpCens4jjzzCt99+m3tNMC8sX76cgQMHEhwczMSJE2nVqhVw9RJFTEwMa9asoXnz5nh6enLs2DFmzJgBwN69e8nKyiIkJIRq1apRsWJFSpcuTZMmTWjRogVt27YlODiYc+fO8dtvv5GcnMzTTz9Njx497vqX7QcffMBDDz3k1E3I2bNnSUtL4+TJk8DVN3Dt2rWpXbs2zz///E1zX3rppXytrZTi0qVLzJw5k6FDhwJw+fJlunfvzpUrV7h06RLjxo3L/XljYmIoU6YM8fHxBAUFFfiThFsvhaSnp5OZmcmECRP4z3/+kztetGhRYmNj6dChA23atKFt27YFOp4gCIIhUErp8p+raoUjISFBZWZmqhkzZiiLxaLatWunzp07V+h1byUrK0v99ttvasOGDapq1apq+fLlKj09XUVERKhRo0bleZ0DBw6oEiVKqI0bN9qck5OTozZt2qSOHj2qsrKy1LfffqvWrFmjEhISVE5Ozm3zT506pebNm6eef/551aFDB9WzZ0+1cOFCtWjRItW6dWt1//33qwsXLtz0mh07dqigoCD15ptvqj179qjHH39cTZs2Le+BFIKkpCQVERGhihcvrgYNGqRSU1Mderz4+HiVkpKilLr6frlOdna2GjVqlKpbt64CVMuWLRWgZs6caXWdnJwcq/krpdT8+fNVWFiYAhSgfv75Z6WUUrNmzVJ16tTJHR88eLB69913ba7jKm7MRfh/JBfbSDbWcfdcrv29ftvf97r+RMQeeHp60rt3bypXrswzXZ6lVKlS+FSox/39J+S7rv8TYaXYsGEDISEhhIWF4eHhwZEjRwgPD8cnoDiX0zW8azbjk/1+ZJeL57HHHmPPnj15Wv+15hWY/8nbvPDCCzRt2vSuLg9Xu+rdtWvXPLg/yrwbPkW4/vFgx44dGTJkCP9q9zR+T75PbEI6ZYr78rDHUUqWLElKSgrdunWjZs2adOnSxaaLPfsXrNwTy8pVqynZ9UP+LPEgP0ZfcEmvBovFQu22LxKoPUiFc+c5dW4/wSX2U65cOavzw8PD+fPPPylatgb3PPsZZUMCctd/6623OHv2/+uvtGrVirade/Djwjm5Y75FAyhRogTDhg1j2LBhBAbeXuLZXXpYGNVdXMznLi7SayaysG43Piq157I3W/zDSYk7QeqxXVgadsp3Xf9ty2YxZdwnLF26lK+++opatWrxxBNPEPHiAOIa9sM7rA1Fyt1LYvrV+avGD6LTMx1p0qTJHddPSEln4YTheGSlMWv6V/j4+NzVxR79DjRNIzGoJjNmTOdKagbepauRmJbFr19/QsfnevLluE8YMGAA3bp1w8fHxyn9DkasOIQqGkL8sk9JSMtkS2ppl/dU0Ty9yS5Wjku711G+7oM82rjebS7JgZX4cdE8MhMvUDSsFcn45K4/fUwkw4YNIyIigoyMDJIyFKeL18dSvDTepaqCyqFCr0l0e7o9M8d9cNs1ZFf106hesijLly/nl19+oVSpUgQFBemq54WrXaTXjLl7qkgu0mvmJv7++2/i4uI4fvw4v/zyC2vWrGHFihUsXbqUZcuWsWLFChYsWMDIkSPp378/EyZM4NKlSzetMeanwyReiifl8P9yC0Plt67/0qU/MG7cOKKjo3n88cfp168fEydO5HCJR6zOD2zckbfffpvExEQyMzOZNWsW/fv355VXXub0iolcWj+TK5u/59x3I8hIukyRx4dQrFixPLnYq2fA+F/+pliLXiRsWQRAdmoiaWf/Yqf37X/ZOqvfgX9YK8r0nkLitqUkp6TopldD+oUYPhz4AllZWbfN//4fC6G9plD2lZl4FS990/qapuHr68uDDz7InDlzKPXiBLxrPUJQi5cIbv0KoT0mkuHpx5ifDlO0aFGHuOc3l8/WRNO7d29ee+01JkyYkHtzr556XoiL/l2M7C4uzus1o+tLM6tXryYtLY3Bgwdz7NgxypYtS/Xq1fHx8cHDwwMPj6uV4HJycvDx8aFWrVqEhYXx559/8uGHH9KlSxciIiJo164dZy6ncnHtVDz8gwls9HTuMVpUqHUAACAASURBVPJT19+79r+YMWMGHTp0YOzYsYwdOxaAd99eZXV+amB50tPTCQ8P5/z584SFhfHkk0/y/T8WvDWNrCtxZCVewLfK/QQ0eIJzVlq5OKOXQFZCPFmXz3Jq8vPkpKfgV+thYhMzXeJyHa/gsniVqETClsWcadq90OsX1CUnI42kPT+SHP07ZF/NxFr9jTOXU/EuUdGhLo4YtzaWuGs1m9dOZTPg4eHBiBEjch8j1ru7uOjLJb/j4qJ/F0eg643I5MmTsVgsJCYmsmfPHurVu/3/0q3x73//m7Nnz7JixQreeecdpk6dSslGL3Mm5QoeAffcNNdWXf8YK7+AauFt+X3cc1y+fJnixYvfdX6VsMZsGHmIlJQUihUrRpUqVQBYkLTe6vz8uNypN0B+50enXAHAUsSPnPQkUg78yoWEs2R8+NhNT9Q4w+X6fJWTTeaFUwS16GmX9QvicvpSMvFLR6F5eFEsvCuahzdV73uQIkWKsGzZMj7//HOio6PJyMgg555qpKalcU+7N/AsVtLuLo6af+vcnIw0Lq6dCkDfvn0ZNWrUTU996dldXPTnkt/54qJ/F0dw10szmqZ9rWlanKZp+28Yq69p2p+apu3WNG27pmmNb/jemGtjza99XUnTNKVp2sAb5nyhaVrPux17zZo1rFq1ivj4+DxvQq5TunRpunXrxs6dOwkNDSX9p3EEVmtE5oVTuXPyW9e/W21fq8WvbM0f2rYWNWvWpEGDBrmbkDvNd1WPgRIPtKNIhTDQLHj6h4BmIelUNEuWLCnQ2kuXLqVt27akLv8AS0JsgdyzLsWiWTwoXqmuy3o1ZB7aQE5aEiU6vItftQcJrvEAda9swWKx8PTTT1O3bl06dOhAUFAQl49sJf3kXjLO/uUQF2f1sLB4+1Bz+CoWbTuBn58f1atXZ8uWLYZwFxf9uRjZXVyc12smL5+IzAa+AG4swvEZEKWUWqNpWrtrX7fQNK3Wte8/cu11G659HQe8rmnaNKVUhj3E84qnpydfffUVbdu25dzhDVhQaHDHu4Kvj916B/GC0YMYPHjwTZ+G3Gm+rTuO8zPfkWvfOP+zgHEcP3yAkkH+vNOlBTknthEeHl6gtRctWsTvv/9OamoqI5/vx7oE33y7H969D2//YnzSMcwlOZZMPYHngZVUbNaRTItH7vzKllBWz/+KwMBANm3axKOPPspnn31GaGgoZ4uUZ+zaI7r5nRY0lwapu+jUqD1wtajcgQMHcuvZ6N1dXPTlYmR3cXHeUzN5relRCdh/w9c/AV2v/flZ4L/X/lwH+BTwBRbe+FpgGtD32tgXQM+7HLPQzyzf+Mz2+fPnlbe3typWrJiaN29evms1LFiwQNWoUUMlJSUV2svVOPpZ9qSkJPXWW2+pwYMHF3iN4cOHK0AdOXLkjvPS09NVVlZWgY9zIzfm8uyzz6r27dur7Oxsu6xtJP7444/c2iaAGjp0qKuVdIm714S4E5KNddw9F+xcR+QN4CdN08Zy9fJO+LWdwwFN0/yATcCQW14zGlijadrXeT1IYmJiAfWukpycnPtnb29vYmNj+eOPPxg/fjwHDx7knXfeydM6MTExjBo1ijlz5pCTk1NoL1dzYy6OYuTIkUDBf4eHDh2iXLlyZGVlcfToUfbv309QUBC1a9fGz88PuFpttlmzZmiaRo8ePQgNDSUtLY3SpUtz6NAh9u3bR3BwMKNGjSI6Opq4uDgeffRRfHx8cpvJJSQkEBsby969e0lPT8fLy4t169YRExPDlClTnJKV3ggLCyMuLo6dO3eyc+dOunbtavj3vCNwx/dGXpFsrCO5WCdPvWY0TasErFRK1b329SRgg1JqsaZpXYB+SqlWd3utpmlzgXXAg8B2pdTsOxxT5cXtTtiq65+QkECFChXYt28f5cuXv+s6LVq0oH379gwZcuveypgYod9BiRIlOH/+PCEhIWRlZVG/fn2uXLnCuXPn8PT0xNPTk7i4OAICAhg/fjzR0dGcP3+enTt34ufnR+vWralXrx5Llixh4cKFhIaGUqJECa5cuUJqaip///03r7/+OnPmzKFs2bLUqVOHypUrExsby2OPPUanTp3yVZrfzFx/vyilWLt2LRUrVqRq1ap4eXm5Ws2lGOG/I1ch2VjH3XOxd6+ZHsDr1/68EJiRx9eNAhYBvxfwuHYhMDCQzMzMu55IMzIyGDp0KKdPn2bQoEFOshPgarffbdu28eijj+Lt7U1QUBBKKWbMmMG5c+fo1q0b99xzz23369xKu3btGDZsGJUqVULTNNatW8fGjRtZvHgxPj4+HDx4kNDQUEBOEncjISEht+/NsGHDGD16tIuNBEEwAwXdiJwBmgO/Af8CjublRUqpQ5qmHQSeALYW8NgF5sYStv51WtB/WBQ/zPnS6twlO07R7dEGeIRUoO5zn7Ji71kp7et0l5L4nMni6QalgKu76b59++ZrfU3TqFGjxv/P3evDGdWUMr1a06RNzdxNiLN/ViP+TosVK8a0adP477Kf+MWzEZXfXmUYd3GREu/iYuAS75qmLQA+AipERUW9HBUVdQWYBUyIiooawNUNySuRkZGx1l4fFRVVHOgeGRk59drXR4ChwLLIyMjdto5r7xLvt5awzdK82L9+CXVbPmO1nG7f14eS9NdWSnX7mDTv4lJm2ME/px5ydFbJ7utz487FonkVITE92zC5nLKUZtnlclzJ8rC7iyPcHTlfSryb+xwjueioxLtS6lmlVKhSykspVU4pNVMptUkp1VApdZ9S6kGl1I47vP749XtLrn29RylludP9IY7gxhK22cmXifvuPXKyMhnw+pvMnDnzprLdHy/8g0s7VlGm7zQ8i139v3Ep7asvF6O6Hz16lF7dO3H8m3eJ+fIlzs4dROLuHzmzdjp9ejzHK6+8Qnp6utPc9ZKL0d3FxXzu4uK8Eu+G6DVjD24sVat5eOJ3b3OKlLuXFM2XmTNnMnTo0Nzv/715DX7Vm+AVXNbmGs4a11NpXz255HdcLy4bN24k+WIcRe9tQbkBc/GpeB8Xf/qChK1LuLBvA9OmTeOff/5xmqNecgHYvHkz+xZ9Tk5mWp7m68ldXMznLi7OK/HuNhuRG0vVWnz8KRExhJA2A7j38R65TfO2bNmCUor06A0UrfPoHddw1rgrjmkEl/yO68UlNjaWjLh/uPTrTGKm9Sb12E58qzehWHg37h88D6UUtWvXdpqjXnIBCA8PJ3HHck6N70TizpV3na8nd3Exn7u46KjEu1m4Uwlbb29vRo8eTd++fZkyZQqlivtRvEo9q3Pzu3Zh5+uptK+eXIzq3r17d6K+/JYqL39J+TcXUabXF5Ts+B6h/+rBiO63b34d7X59rsrJ5vzKcaTHRLvsd1Sy5NUePaXaDuDy/74lI/5Entwd4ZLf+eJiPndxcV6J97verOoq7H2zaq3QQMoF+bIv5gpJaVmULe7L+xH38nSDsoSHhzNt2jTCwsKYMmUK/9u4gQY1Klqda407rV3Y+Y5YO6+5OPPn1EOOBc0lP/ODgoJo/kBdKpQKZn9Mgm5y2XviAidXf0npqnX5tG97p+cCcPz4cbZt28aQtwZxKC6N1MtxVA17QLfvR2flkt/5enAxwzlGcrE9v6DYulk1TwXNXIEjC5rdyJUrV3JrUYwfP57OnTtTrly5Qh1X70i9DOtILtZxVi779u1jzJgxrFq1irS0NN59912GDx/u8OMWFHm/2EaysY6752LvgmamITAwkPHjx1OhQgWeeeYZV+sIgq6Ij4+nYcOGrFixgvvuu8+hxwoLC2Pu3Ln8+eefrFmzhpdfftmhxxMEQR+4/Sci7ojkYh3J5XYuX75MWFgYp0+fJjs7G4vFbW4ruyvyfrGNZGMdd89FPhERBCHfFC9enOnTp5OQkIDFYiEnJ4fffvuNpk2b4u3t7Wo9QRBMgPzvjSAId+Thhx+mS5cuADz11FO0bNmSevXqsXr1ahebCYJgBtzqExGj1vUXF/O5G9UlOjqa1z6YyLq/U3jq+b6UqvcIX0wYawh3cZH/TsXFoL1mXIWje80Ypa6/9Dswb68ZI+aSVaImo98bzJX40/hUvI/zf3zPNo86VCxTUpfujpwvvWbMfY6RXHTUa8YsGLWuv7iYz93ILotPeBHaeypFaz6Myrp60vpnam8i56zRvbu4yH+n4iK9ZlyKUev6i4v53I3uonl641/vMYJb9qXEM+8D8NcvC/jll1+49Uk3vbmLi77HxUX/Lo7AbTYiRq3rLy7mczeTi2/VRgS3HUj6sZ307duXefPmFXj97Oxs1L4VXNmyBJWTfdf5ZsrRrC75HRcX/bs4ArfZiBi1rr+4mM/dTC6apuGZk0HzVm0YNGgQGzduLND6SimGDRtG7C+zSdm3lpNjnuL8ynEOdbfnfHExn7u4SK8Zp/aauRU91fWXfgfm6jVjxlxefbw+30/9lKVLl/Laa6/dVIE1ry5btmyhd+/ejBo1ijbdX+GnRfPIjD9O0o7l/Gfsh7rMUXrNmPscI7lIrxmprOpAJBfrSC7WyWsuSik07baiiXni1KlTVKhQgZSUFLy9vdm1axeNGjXKXVePyPvFNpKNddw9F1uVVd3m0owgCI6loJsQAB8fHypUqECzZs3w9/enUaNGBAUFsWDBAjsaCoKgR9yqoJkgCPqkRIkSHD58mG3btlGyZEm+//57OnbsSJ06dVytJgiCg5FLM26I5GIdycU6kot1JBfbSDbWcfdc5NKMIAiCIAi6w60uzRi1rr+4mM/dHV2O/32EIC2Fj/p3NZy7uLinu7hIrxnpNeOgtc2QiyPcpdeMY3OJX/IRsb/NZ2fRBylfsrjhc5ReM+Y+x0gu0mvG7hi1rr+4mM/dHV1y0pLIiD9O0br/4uxv8wzlLi7u6S4u0mvG7hi1rr+4mM/dHV2yUxOxePngXbo66bGHDeUuLs4bFxf9uzgCt9mIGLWuv7iYz90dXTyLl8a3xkMk/LkQ79I1DOUuLs4bFxf9uzgCt9mIGLWuv7iYz90dXTRNI+Sxf1NuwFzKtX/VUO7i4p7u4iK9ZqTXjAPXNkMujnCXXjOuzcVoOUou5j7HSC7Sa0YKmjkQycU6kot19JRLZmYmcXFxlC3rnMcK74SectEbko113D0XKWgmCIKhOXz4MHXq1KFcuXKMHTvW1TqCINgJ2YgIgmAInnvuOQYMGMDp06f5/PPP2bVrl6uVBEGwA7IREQTBEFgsFry9vSlbtiwPPfQQ0dHRrlYSBMEOSIl3A5TTFRfzuYuL7flTl23kvXfehtA6VH2wNcM7h/N0g7J07tyZN954g/79+1OjRg1++OEHrhStwLz9qcQmZty09pUrV/jqq69o0qQJzZs3d7sc9eRiZHdxkRLvUuLdQWubIRdHuEuJd9fmAvDDztP0694RLaQCGeePc/rH/7By/SbK1G7IFx8P54033qBp06Y8+OCDfP6fOUz/9D1it6zE656KpPqWYMOReLSEWB5pUJOff/4Zi8XCU0895RB3KfFu7nOM5CIl3u2OUcvpiov53MXF9vxPV+wm/exfBLXsS8kOwyk7YA5Jh/7gpVb1OXXqFMOGDQOgaNGieD3+NhWHrSS4dX/iFo4k7oePSYiP4ctVWwkODmbt2rXMnDnTKe56ylFPLkZ2FxfnlXh3m0szRi2nKy7mcxcX2+Oxl1PRvH3JTrqEZ0AIFi8fyg2cz+nJz5GVlcXWrVupVasWS5YsYe+Cb0mPOURWQhyeQWVIPbIZD79ieLd5lQsXLjjVXU856sklv+Pion8XR+A2n4gYtZyuuJjPXVxsj5crFUKR0Oqknz6QO+bhVwwsFj7++GO6detGyZIlWblyJSUq1SLkiUGUf/1byvb7DxUG/0DwYwPcPkc9ueR3XFz07+II3GYjYtRyuuJiPndxufN8/8oNSD9z+Kb55StVIzw8nGPHjpGSksLixYsZHzmU4uVronlc/WBX8/DCz9vT7XPUk4uR3cVFSrxLiXcHrm2GXBzhLiXe9VHi/a/o/Rw7FYNXpYa5870Sz5CWlkaTJk2wWCy6yFFKvJv7HCO5SIl3KfHuQCQX60gu1nF2LosWLWLSpEls2LABTbtaDfrtt98mICCA4cOHO83jbsj7xTaSjXXcPRcp8S4IgiFo3749MTExLFmyBICkpCRWrlzJfffdd9vcnJwclixZwvr1652tKQiCnZCNiCAIusLX15c5c+bQqVMnXn/9derVq8cjjzxC+/btb5s7cuRIoqKi6NWrF4sWLXKBrSAIhUUuzbghkot1JBfruCqXZ599lujoaMaPH8+//vWv275//vx5SpQowcmTJzly5Ajdu3fnzJkzeHh4WFnN/sj7xTaSjXXcPRdbl2bcpo6IIAjGYsGCBSiliImJYerUqXTt2pWQkJDc7wcGBlKuXDlWrFhBdHQ0fn5+ufeUCIJgHNxqI2LUuv7iYj53ccnbfL+4AxyaO5yc7CweffTRmzYi3t7e9Bz6MSOmTic7sCzVu49h+Z5YyVFnLkZ2FxfpNSO9Zhy0thlycYS79Jpxfa+ZW+dnFAkioOr9zJj2Fc3r17ht7uTtyViqPYxPxftIUV5OzVF6zZj7HCO5SK8Zu2PUuv7iYj53ccn7fM3TC0tobSb+etzlLneaLy7mcxcX6TVjd4xa119czOfu7GOqnGyyLp3hDOVd7mLPcXHRv0t+x8VF/y6OwG0+ETFqXX9xMZ+7s4+ZdfksqX9v04WLPcfFRf8u+R0XF/27OAK32YgYta6/uJjP3dkuXsFlKfVwZ124FHb+li1bePbZZ/nuu+9c7uKstY3sYmR3cZFeM9JrxoFrmyEXR7hLrxl99Jq50/x+/fpx5MgRfvjhB7b/vIxKHhehRDVScjyk14wT1s7rfDOcYyQX6TUjBc0ciORiHcnFOnrKRdM0WrduzdSpUzl69ChfffUVZcqU4csvv3S6i55y0RuSjXXcPRcpaCYIguFp3749bdq0oVq1alSrVo3GjRtTt25devfuzQMPPOBqPUEQCoDb3CMiCILxeeqpp5gxYwY5OTkAhISE0KtXL5YvX+5iM0EQCopsRARBMAx9+vTBw8ODyZMn544FBARw/Phx10kJglAoZCMiCIJh0DSNGTNmMHHixNxPRVJSUoiPj3exmSAIBcWt7hExal1/cTGfu7gUfH6DBg3w9/dn06ZNXAyoyn8PJHMuJouHR6+XHHXoYmR3cZFeM9JrxkFrmyEXR7hLrxn99ZqxNl/TNHbu3Mmfh07xzfGiXDh2kOzkS2SVbyi9ZnTiYoZzjOQivWbsjlHr+ouL+dzFpfDzX3rpJX5Y9D3n/reYhO3L8Kl4n0vd9ZKL3lyM7C4u0mvG7hi1rr+4mM9dXAo/7ufnR2rcCTJ3rqLYQ13wq/2ISx31koveXPI7Li76d3EEbvOJiFHr+ouL+dzFpfDjYWFhVOs0hNIvjMW/Xms0TbM51xnjeslFby75HRcX/bs4grtuRDRN+1rTtDhN0/bfMBapaVqMpmm7r/3T7obvjdE0bbumac2vfV1J0zSladrAG+Z8oWlaTzv/LHfEqHX9xcV87uJS+Pmenp6Mefd1/IsFu9zF0Wsb2cXI7uLivF4zebk0Mxv4Aph7y/gEpdTYGwc0Tat17Y+PXHvdhmtfxwGva5o2TSmVUWDbQnD97t+83BWcn7mOni8u5nMXF/O5i4v53MXFeU/N5KnXjKZplYCVSqm6176OBJKsbETqAC8CkcBcpVTn668F/gC2K6Wma5r2xbU/z77DMaXXjIOQXKwjuVhHcrGO5GIbycY67p6LrV4zhblH5FVN0/Zeu3QTBKCUOgD4AZuAW7tQjQbe0jTNA0EQBEEQBAr+1MyXwIeAuvbvcUAvAKXUQGsvUEod0zRtK9A9rwdJTEwsoN5VkpOTC/V6syK5WEdysY7kYh3JxTaSjXUkF+sUaCOilDp3/c+apk3n6qWXvDAKWAT8npfJ9vgIy50/BrsTkot1JBfrSC7WkVxsI9lYR3K5nQJdmtE0LfSGLzsA+23NvRGl1CHgIPBEQY4rCIJgjRMnTvDkk08SEBBA48aNWb16tauVBEHII3f9RETTtAVAC+AeTdNOAyOBFpqm1efqpZnjwMv5OObHwK58m9oBo9b1FxfzuYuLfd13zXiboDKVmbpiM8US/uH111/n448/5ssvv6RevXpOddFTLnpwMbK7uOjoqRlXYO+nZq7X0r+xjK2vlwefdAy7Lez8zHX0fEesbYZcHOFe0Fwc4eKOuRTG/eLP08hKiKdC5/cY3ak+T4SV4vPPP2fs2LH8+9//ZvHixfSLmszk7cl2czFCLq5yMcM5RnKxPb+gOOKpGUNh1Lr+4mI+d3Gxv3tQi17kpCRwfvfPjPnpMJ6enrz11lu0atWK999/nz179jBr6zm3yFFPLkZ2Fxfn9Zpxm42IUev6i4v53MXF/u6apxdeJSuRef7kTeNz516tw1ihQgXOZ1svV222HPXkkt9xcdG/iyNwm42IUev6i4v53MXFMe4B9R8naf8v+J0/mDt2vQfNyZMnucdi/dFJs+WoJ5f8jouL/l0cgdtsRIxa119czOcuLo5x9y5ZmbJPD+XM0jE899xzPPbYY8TGxpKQkED58uUpuuc7fG4pp2jGHPXkYmR3cXFerxmPyMhIpxwov0RFRUUW1i0jI4MiRYoAUCs0kHJBvuyLuUJSWhZli/vyfsS9Vm/Eyc9cR893xNpmyMUR7gXNxcg56ikXe7h/9GJL3uzZiblz5/L7778TEBDAY489xvPPP8+r/XrRvFoQlrL17OJipFyc7WKGc4zkYnt+QYmKiiIyMjLq1nG3eWpG+H8kF+tILtYxWi7du3dn+fLlVK9enQ8++ICIiAgAGjduTIkSJVi1apVdjmO0XJyJZGMdd8/F1lMzBS3xLgiCoEs8PT15+OGHeeihh9iyZQvR0dFcvHiRihUr8ueff7paTxCEW5CNiCAIpuKLL76gb9++JCUlERAQwO+//577KUj16tVRSuXexCoIguuRjYggCKYiMDCQ77777qaxAQMGMHXqVGbPni2bEEHQGW51j4hRy+nae22z5GJv98LkYm8Xd83F3u43opRi2e4zdnMxSy6OmG+Wc4zkYt+bVW3dI+I2T81cL2F7MSUDgMS0LDYciadckC+1QgNvel1+5jp6viPWNkMujnAvaC5GzlFPuTg6x2W7z9jVxSy5yDlGzr35nV9QbD014zZ1RIxaTldczOcuLuZzFxfzuYuLlHi3O0Ytpysu5nMXF/O5i4v53MVFSrzbHaOW0xUX87mLi/ncxcV87uIiJd7tjlHL6YqL+dzFxbnuOTk5unFxxnw9uRjZXVykxLuUeHfg2mbIxRHuUuLd+CXeb527ZcsWKlSogMVi4eVuEXZ1MXIujp5vhnOM5CIl3qXEuwORXKwjuVjHqLnk5ORQv359mjdvzty5c/nnn38ICQmx2/pGzcUZSDbWcfdcbD2+6zaXZgRBcC8GDRpE0aJFmThxIlWqVOH48eOuVhIEwQpSWVUQBNNx4MABFixYwKFDh/Dw8CA0NJTY2FhXawmCYAX5REQQBNPRv39/3n//fYKCggBISkrCz8/PxVaCIFhDNiKCIJiK6Oho/v77b1555ZXcsUOHDlGzpnOeABAEIX+41aUZo9b1FxfzuYuL49xr+1nw8PDAw+P/H0dMT0/Hz8/PbXLUk4uR3cXFvk/N2MJtHt81al1/6Xdgvp4qkotj3YO0FFZ/N5vBgwfj4eFBTEwMkydPJuyJl3hv2UHpNeMEFzOcYyQX6TVjd4xa119czOcuLo51//ZINi1atOD+++9n4sSJdOjQgZ49ezJx/TG3yFFPLkZ2FxfpNWN3jFrXX1zM5y4ujnWPTchg6dKlTJ48maVLl7Jt2zbGjh3rNjnqySW/4+KifxdH4DYbEaPW9RcX87mLi+PdLRYLLVq04Mcff+Sff/6hSJEibpOjnlzyOy4u+ndxBG6zETFqXX9xMZ+7uDjP3cfHh8qVK+vCxVnz9eRiZHdxkV4z0mvGgWubIRdHuEuvGfP1mnHkfMnF3OcYyUV6zUivGQciuVhHcrGO5GIdycU2ko113D0X6TUjCIIgCILukI2IIAiCIAguQzYigiAIgiC4DNmICIJgeDZt2kTFihWpUaMG0dHRrtbJJTXVOXUYBMHISK8ZA9T1FxfzuYuLfd13fjWIso26UNP7Et988w0ff/yxy1yuz61wfBXfThvPuHHjGDRokEtdjPg7FRf9utgbt3l816h1/aXfgfl6qkgu9nX/a8UUUqI34N/2Tc6cv8ylY/vo/WJ3h7vcLZc/p7/HB1Pm8en7Q+nTpw9+fn5OzUV6zegvRzPkUhik14xB6/qLi/ncxcW+7imH/6Bklw+xePuSnpnFkbhkl7nchIcXS//OomPHjowcOZIbyxHI71T/7uIivWbsjlHr+ouL+dzFxb7uKisDz6BQADLjjpEdaP3jZGfnaClSlNi4C4wePZqtW7cSERHBli1bXOJilHFx0b+LI3CbjYhR6/qLi/ncxcW+7oGNnubc/KEkbFtK0r6fqfTAv1zmciMWH3+KW9K455572LRpEy1atKB9+/b8+uuv8ju1MS4u+ndxBG6zETFqXX9xMZ+7uNjXvdhDXbgnYjCpf2+lVOt+jHyxjctcbqRo+drUyPwbAG9vb1544QWCg4PJzMyU36kB3MVFes1IrxkHrm2GXBzhLr1mjNtrJtU7iEr1mvDpgC666TXzZsdmzJ/4AWPGjGH37t0MGzaM3r1789JLL1Hb5O8vM5xjJBfpNSO9ZhyI5GIdycU6es/lypUr1KhRg7i4OHJyctC021pZOIS85KKUYufOnXz88cf06dOHdu3aOcXN1ej9PeMq3D0XW71m3KqOiCAI5qN48eIAfPrpp07bhOQVTdNoLUU4sAAAIABJREFU2LAhS5YscbWKIOgWt7lHRBAEc/LHH3/QqVMnhgwZ4moVQRAKgHwiIgiCoQkPDyc8PJx//vmH4sWLExwc7GolQRDygVttRIxaTldczOcuLvZ3PzmhE/0GR/Llx8Nc7qKnXFztYmR3cZES71Li3UFrmyEXR7hLiXdjlni/PtenUn1iA2pRLrioU1yMkouUeNdPjmbIpTBIiXeDltMVF/O5i4tj3IuE1iAtR9OFiz3XNrKLkd3FRUq82x2jltMVF/O5i4t9xk+eOMHZb4aSnXzJ5S56ykVPLvkdFxf9uzgCt9mIGLWcrriYz11c7DOeuXMJ6TEH0TyLuNxFT7noySW/4+KifxdH4DYbESOU0/13eCgrVqzQhYuecjGbu7jYZ/7nEydQffAiLEX8XO6ip1z05GJkd3GREu9uWeI946//0aVLF0qWLEmjRo0c5mK0XMxWylxyKfj8S5cuUbVqVSpVqkTnx5pSvkQxl7noKRdnrZ3X+WY4x0guUuLdLUu8//DDD/z73//Gz8+P9u3bM2bMmNw3rT0xWi7OQnKxjp5ySUxMpFevXrzyyiu0bNnS5S56yUVvSDbWcfdcbJV4d5tLM0YgMDCQlJQUunTpwokTJ+jRo4erlQRBVwQEBHDhwgXmzp3rahVBEOyE221EcnJyGDVqFC+++CK//vorp0+fJjY2lpycHFer0bJlSw4ePMiPP/6Ir68vK1euZOHCha7WEgRdoJRC0zR+/fVXkpKSXK0jCIKdcLtLMxEREaxcuTL36zJlypCZmYmfnx+TJ08mIiKiUMe0B0lJSbneYWFh7N27167ru/vHg7aQXKyjp1w0TaNMmTJMnjyZjh07utRFT7noDcnGOu6ei1tfmomLi2P8+PHs27fvpk3IRx99RExMDHFxccyZM4cBAwZQq1Yt+vbty759+8jKynKJr7+/P8ePH+ett95i+PDhhVpr4cKFdOrUic2bN9vJThBcR05ODjExMS7fhAiCYD9M32smNjaW2rVrc+XKFfyLFQfNQpFy91K5RRfqtOuZO6958+ZER0fzzz//MHv2bNo88RQXLicQ+EhPajzczul1/StWrMjYsWMLvXb2zjWc/99PbNu2jT59+jBixIhCueut34FR3cWlYPM1TcvzXGe7i4v53MVFes0U+vHddevW8cADD9C3b18yLL7EZ3pTsnMkxR58hqyA0Ntq6Xt7e1OqVClSS9Zlk3cjtNB7ubB6IhklavLHmRxj9juIOQoeRfjsq7kMf7UXr732GkC++x2MGzeOZ7p2Z905PxK9itv959RDjtJrxti9ZpztIrmYu6eK5CK9ZvLNwYMHeeuttxg7diy9evUiPDyc/v37A/D111/j1ep1Sj77CV4h5XNfc7e6/kVCqxPQMIKE7csM22PAr/pDpMWfYHjUxzzwwAO33fya1/WjoqLIKV2bU99Fcn71RDLO/YPKyZYeFuLi0PkZ8cdJiI/Rvbu4mM9dXKTXTL558skn+e677zh58iSNGzfm/Pnz1KtXj2XLlpGSksKJ6N1WX3e3uv4BDSNIP3WAtFP7nVLX/8CBAxw+fDhPc/My7lmsJKWeHcXZneu477776N+/P59++ik7duwgKysrz+u0b9+eC4e24RVSjqwr54hfOoqTY54i60qc7vog6L1Xg7jkbTxh+3LOffseZ6b14a8dv+vSUVzsOy4u+ndxBKbZiMyaNQsfHx8mTZpE9+7dOXr0KNWqVaN3795ERUVRsXZ9q6+7W11/i7cvQY/25tL6mQ6t36+yMkheN4k2bdrQrFkzGjZsyKpVq+yytodvILWfG8nOnTspVqwYAC+++CIhISFcXhJF+pnbd723rvPf//6Xev3GEVC/HZlxxynZOcrmXD2Oi4v+XW4cTzuxl4Q/FxL64jj86z+Ob9IZ3Tm6+phGcMnvuLjo38UR3HUjomna15qmxWmatv+GsTGaph3SNG2vpmk/aJpW/Jbvbdc0rfm1rytpmqY0TRt4w5wvNE3rac8f5L777uPs2bPA1cJgmzZt4uLFi8ybN4/333+fdzs2LnBdf7+a4eQkX6Jt8Hmrxy5MXf/MC6dJObqF+O+GUz3EmyNHjnDq1Ck++ugjevTowUv1A+zSM+CjVzqzYcMGqlWrxqVLl/jtt9/4+++/eaJtSy6v/w9K5dw0/9b1NU3j/edbUeL+x7D4BZJ1+WrW/iGlpIeFuNh9fuLOlQQ26YRnsVJYsjN4/P6qunYXF/O5i4vzes3ctY6IpmmPAEnAXKVU3WtjjwHrlVJZmqZ9CqCUGqZpWi2gNzASmK2U6qJpWiVgC5AI3KuUytA07Qtgu1Jq9h2Om686Ips2baJr167ExMTkjt36zHZh7lD+V9HTzJ8QyV9//YXFcvv+raB3KG8e+QQB5WrSpVNHpn0y/Ka1IyMj2bt3Ly+OmGy3u5+Tk5P54IMP+PLLLwkPD+ezzz6ja48+nCeQoq1fo2yw/11z6fJIGMrLl6LBpZm9cHmeXYqlnmFkj8fpcH+5ArkXdv7d5hbm/WJvF3fNBWD+7wd5sXVDyg34hnIlg9j9cQf+/N8mwsLCnJrL3eY7Oxcjvb/see41U45myaWg2KojglLqrv8AlYD9Nr7XAZh/7c91gE8BX2Dhja8FpgF9r419AfS8yzFVfnjggQfUt99+e9NYQkJCvta4EydOnFBFixZVGRkZdlvz4MGDKiAgQKWmplr9fmpqqqpRo4ZatmyZ3Y6p1NVckpOT1eeff65CQkJURESEAtScOXPy9PpOnTopQI0ZMyZfx61Vq1aej+EK7Pl+MRPOzuXKlSvKy8tLnThxQo0YMUIVLVpUJSUlOdUhL8j7xTaSjXXcPZdrf6/f9ve9PeqI9AK+u7ZzOKBpmh+wCRhyy7zRwBpN0762wzFvYtWqVVy8eNFhRY7++OMP+vTpw/vvv4+Xl5dd1szIyOCJJ55g0qRJ+Pj4WJ3j4+PD1KlT6d+/PxEREbfVUCgMfn5+vPbaa3Tu3Jl169bRq1cv2rZtm6fXjhgxgtOnT+e7Cu2DDz7IgQMHckt1C4I1qlevTmZmJhUrVgRg/fr1FC1a1MVWgiA4ikJtRDRNGw5kAfOvjymlBlqbq5Q6pmnaVqB7XtdPTEy86xylFJ988glTpkwhLS2NtLS03O8lJyfn9VBWSUtL491332X37t2MHDmS9u3b58kpL6xfv54aNWrwzDPP3HHNRo0aUbx4cfbv30+lSpXscuwbc/H396dDhw4AZGZmkpmZedfXV65cmbVr1wJ5+x1dp3Xr1rz99tv07NmTcuVsX55xFYV9v5gVZ+eyfv164uLi8PDwoHbt2vj4+Njtvzt7Iu8X20g21pFcrFPgjYimaT2AJ4CW1z5yyQujgEXA7c/iWSEvNflXr17NmTNnaNOmjdX/yy5oXf81a9bQq1cvHnvsMTZu3GjzU4uCMn36dF544YU8+QUEBHD27Fmr18gLiiv6HTz33HM899xzTj9ufvg/9s47LIqz68P3LL0XlSoKqICKJXaNHWs0alCjibG3RI01lpio6KvGYNfEEo3dRPNGY8UYO/aosZeIWAFBqnQW2Pn+4JXPAkrZZWeX+V0XV8KzZ8/c+5vd4XH2ec7RZh+IkJAQ3Nzc1P5eU4dK0pfq1atTvXr1EjtecVSa+4a8S7I3eUv25U0VafuuIAgdgMlAF1EUUwv6PFEU7wC3yJnAFEvh4eEMGzaMPn36sG7dOrXe6v/333+ZPHky8+bNY+PGjWr/wyCKIvv37y/wxMLY2FhrfW+krCNHjiAIAoIgMHjwYG3jFEvBwcF4eXnRqFEjoqOjtY0jS5YsWSWmd94REQThV6AlUFYQhDBydsR8DZgAh/43ATgniuLnBTzmHOBykWiBxMREAgIC2Lp1K0OGDOHWrVs4OzsX6LnvWhWclZVF37592bZtG4JCwaonTgRM2a/2FceiKGJjX5bOXy1B8Z7/O+PjM0TGbT3PiBMqra+WltLK7UqVKlGrYTNuXr/KrsdG3Jl3lEbibb4a4J/ne+Ll3Ib3T+Jr9pya7g5MnTo1t+xySbG/Hvtsy3i8vLxITk7mt99+Y+TIkVpj0fZqfF1ll1n0j11mKZleM+/cvqstvdi+m5WVxbVr11i4cCFPnz7l9u3bdO7cmfHjx1O1atW35nh5q9SLWvovl7E1MzLg85pGhJ7czaFDhwgJCcHY2Ji0tDSsqjbFvsuUV2K/86+R54nJL/fb4set+IOHa7/EonorynwwFnMT4zzjd10OZ8iXX5GZnYVdiwFqYSmIL/mxFPZ1ajr+qw1HefrnKjAyQcxIJe3eeZzKV+Tpk4dvzR2+5nOy4sIAuHnzJtWqVSuyL+rwUYy4RbX0mziYiUydOhVvb+8i5VYHi7reL1Jg12S87It+X2NkX/KPL6ry274r6cqqS5cuxdbWljZt2tCwYUOmTp3KiRMnWLNmzTsnIa/rRS39zPinxB9bR8S6Udz5rgtjP27Df//7X7y8vPjss89wdXXFsrw3Vk37vvJ8ddf1F8t4YGjnTMrNY2TFRby1x4Bxlcak3D75Yluz3O8AOHfuHB/VKU/osn6k3j2DKi2JtHvnAVB4NnxnbpeByynzwTgqfZT3hLakfRRcqpFYpx8bN258ZRKiDRa5L4nMIrPLLCXZa0Yd23c1pkuXLnHixAlq1aqFoWHRUMPCwli7di0XV20hM/YJYpYSwcQchakVpm6+GFiVwds+k+DgYGrXrs2qVasY/GcSGLy5TVfd9fvt24/i2bZvMLApl2/8vUvBpIfdRMzMICP8Nqblq2mEpSDjUup3ULNmTWxb9MesUn2My7m/8lheq4VezyEYGmFZw49s3mwtr2l2KfkoJZbCjsss0mcp7LjMIn0WTUjSd0Q2bdpE3bp1izQJWb16NfXq1cPNzY2lS5diqABjFx9MXKuCKGJk64xRGTccPKoxbdo07ty5w7Fjx/Dz88O1TN5tj9Vdv18QFCjMrBEMc9YnOJrncE+YMAFBELC3tyf+z6UknvsdVWoCKTePaYylIONS6ndgbm5OtQ793piE5BcvJXaZRf/YZRb9Y5dZJNRrRhf16NEjRowYwfvvv0+PHj2wtrbGWJmIqb0L1g17UH7UZhx7z8a14xcsCpiEn58fjo6Ouc8vqbr+WQmRGNo4EndoJWHL+3DpPx9x8OBBypYty7Jly9i+fTtbj/xD5bG/AJByOxhVRorc70DH2WUW/WOXWfSPXWYpuV4zBgEBASVyoMJq5syZAUVl69ChA2ZmZsTExNC2bVvmzZvHvO/mUrVRa+6kWZGSCa62Zkz/sFqeC3F8nK0pb2fG9fDnJKdnvTW2OPF3Mux4Hh9H+u3jNGvVhmsXz9K3b1+aNWtGw4YNqVSpEtVd7ajgaM++ffvJykjD2sSAhaM/KRaLUqnM3SVSGPaS8kUT8Zr0RZd9lJIvuuaj7It+X2NkX/KPL6pmzpxJQEDAzNfHJb9rprA6f/48jRs3ZufOnfznP//h0qVLGqBTr77++mvmzZsHwKxZs2jWrBlZWVl4eHhQqVJO19Fp06Zx5swZrl69yoULF/Dw8AAgIyODuLg4nJycEASB5ORk5s+fT3BwMD179qRChQr4+flhZvb/t9heb7wkK0eyL3lL9iVvyb7kL9mbvFXafdHJXTNF0WeffUajRo1o0aIFKSkpZGdnv/tJWtaIESMYM2YMANOnT2f8+PH06tWLypUrs337dgD8/f25cuUK06dPp3379pw6dYrly5fj4OBAzZo1sbGxwdfXFzc3N+7du8eoUaM4ceIEixYtws3NjZEjR5KRkaHNlylLlixZsmS9IUnvmimsduzYQWhoKEeOHMHOzg4HBweuXbvGe++9p220t6p9+/bcvn079/dt27bh5eXFL7/8wldffcWFCxf4/fff6devH6NHj8be3p6hQ4fi5OTExYsXqVKlCvHx8YSFheHo6IiDgwMA3bt3B+DBgweMHDmSSpUqUbNmTSwsLAgLCyMlJQVnZ2dWrFiRe+dFlixZsmTJKknp1VczDg4ONG3alJ07dwIwbtw4PD09+fLLPPvwSUZ9+vQhPDycGjVq0KBBA3r37p3b5ffOnTtMmTKF1q1bM3r06GId599//yUkJAQjIyMsLS2xsLDgyJEjLF++nL1796q1l40uqrTfNs1Psi95S/Ylf8ne5K3S7kt+X83ozR2RRYsWER8fz6ZNm3LHGjduzI8//pg7EZFKOd309HSGTpzJ0auhxD2+iyIjkTW/BfFpM583Yn18fBgw40fmH/yXxcUsN+/t7Y23t/crH4batWvzKMWAOo2bYVi2IrYunsz8ZgrDOr1ZFEzTvmg6XmaRPou22K9du0ZGRgb169fXOouUfCnt7DJLyZR414tdM1lZWbRr145hw4bRtWvX3HEXFxcWLlxIzZo1uRJvxNc7rxOXqgQgKT2LE3ejKW9nho/zq3VDXpS7LUhsQeIzMjKIjo4mOzubn376iY969OJG+HOyrZwxca2Grd8wzj5KLhEWeHXl9q7L4fx0U4VJrY4YmFmT8jSU/b/+jEPVRtT1Kq9xlpKKL4ov2mQpjb6UtI8x92+yY91ylsz6mgWB33P16lWGDh1a4Pz66osmrjG6xC778u74oiq/XTN6sVh1/PjxQM5dkZdlaGjItGnTmDFjBoF/3imxcrqiKObGP3/+HFNTU1xcXHB2diY4OBgn/6mU6TYVm0Y9sKzhh2BgpPXSvgojU8y9GlPmg7GYeTdlZM+2jBw5kvj4+BJn0US8zCJ9lpJiF1XZJJzZRvQfc8CyHHZ+Q/Hy8qJChQolzqKJeCmx6DK7zCKXeC+wkpOTWblyJd9//32eFVj79OnDnDlzSLx2HtOKtd54XB3ldMPjksmIvEf6o6ukPbyCMuIOBtYOPE6Jx+U/Oetcjh07RvPmzVEoFHhM2V/g/CVdwlcQFNg0/hirWu0Rs07RoEEDLl68iI2NjeTKDOtqiWSZRXvsyuhHxAYtRmFijkOv2ajSErn737lU9XRjypQpBc6jDhZNjUuJpbDjMov0WTQhnb0jolQqUalU9O/fHzs7u9y7Iq/L0NCQuXPnknhkFSpl+huPF6ecbmxsLOPHjydseR9iDywjOyUe63pdcRm+FoePvqHexM0kJycjiiItW7ZEoVAUKn9hY9U57ubixIoVK+jUqRPVqlXDyMgIg5CjWmHRtxLJmsotiiJ79+7FyerNPkklzVIS44XNYRJ+iaht32Ds4ImBhT2RG8cSsyeQKt0ncOPGDerWrVtiLFLyRUrjMov0WTQhnZmIJCcnM3r0aD788EMcHBwwMTHB0NCQnTt3snLlyrc+t0ePHjRs0IDEY2t5eSdOUcvppqenExgYiI+PD+np6azedYxKn6/Cvs1wzKs0xNDSHmtnd6b6N8izoZoulfZdsmQJQUFBmJubUzEzTFJlhnXJR03nTk5OZsaMGXTp0gXbq1t10pfCxhe6LPW1PahSE0gNOYdR2QqUH7WFKmO3Mm/84BJnkZIvUoqXWaTPognpzGLVVq1aceDAAUxMTGjTpg379u3D39+fwYMH065duzxzvLwwyL9zB9b9MJ/0yFAMHL0oX86u0OV02/vYs3XrVrp37052dja//PIL/fv3p05lF70uM+zk5ESrVq1YuXQhARNGcDdWKYkyw7payrwoue0Mlfxz/Taxd//B0dqM//Rukht/+fJlqlevTmpqKn/++SffThzHvKnjC3SepOSLpn387JOPybRxQ1mnN6ry71G+rLVc4l1D8fpQylz2peRKvCOKoiR/ctBydOHCBdHAwEBcvny5WBglJia+8ntsbKw4dOhQsUyZMuL3338vpqenFyiPUqkUFy9eLNrZ2Ynt2rUTg4ODC8UhNb3uS0E1ZswYceDAgWqmkY6K6osmdfHiRbFt27aig4OD6OLiInbs2FF0cHDIfVylUomNGzcWZ86cmTvWq1cvccWKFWpjkKIvUpDsS/6Svclbpd2X//1df+PvvU4UNPPy8qJMmTKcPXu2UDleLx5z48aNV4p2tWrViqNH8173ABAeHs7PP//M2rVr8fb2ZtmyZVStWrWQr0R6KmpRnefPn1OlShVOnDihFz68LikWG/ryyy9JTU1l4sSJ+Pj4kJ2djZeXFw4ODsyZMwcvLy/c3NxQqVS5XwMeOXKE4cOHc+vWLYyNjYvNIEVfpCDZl/wle5O3SrsvOlvQLDQ0lJCQEHbv3l3sXFZWVpiamuZ+tfPiFpkoiixbtozg4GAiIiJQqVQkJSXx9OlTevfuzZ49e6hdu3axj6/rsrGxYcKECQQEBOT2wCntUqlU/PLLLxw6dAiA3r17U61aNSpWrFjs3MnJyfzwww+sWbMGIyMjRFHEwMCA7777jnv37jFu3DgePHjAiBEjcichKpWKbdu2ERoaSnBwMG3atCk2hyxZsmRpVHndJpHCDyCmpaWJnp6eoo+PT5FuA+V1G+zQoUNirVq1RJVKlTuWnp4uAqKNjY04e/Zs8dy5c+K1a9fE1NTUIh1X6irq7cGxY8eKZmZmIiA+fPhQzVTaV1F8CQwMFK2trcXFixeLy5YtExs0aCACYkxMTLF5UlJSxPHjx4uNGzcWnZ2dRQsLC7FixYqihYWFaGdnJ27YsOGN50ydOlUExK5du4oJCQnFZhBF+XZyfpJ9yV+yN3mrtPtCPl/NSPqOSLly5TAxMeHGjRtqy1mmTBkyMjJe2c1iYmJCdHQ05cqVY+HChXzzzTdqO56+SBRFrl69Slpazr7yhg0bcvfuXayt1Vd1Txfl4+ODQqFg/vz5NGjQgL///hvI2dpdpkyZYuU2Nzdn4cKFub8nJiYSFRVFxYoVuXfvHnXr1mXPnj34+fkRFBSEkZERJ06cIDIyEkdHx2IdW5YsWbJKSpKeiPj6+nLy5Mk8C5UVRbsuhxN4IIaQsGfUHruWgP4dc1cFly1bFgsLC06cOJEbK5W6/lJgEQSBo0eP4telF8GHDxD9PIV2i08ypUvtUt3D4sMPPyQ+Pp5bt26xMegUbkk2ZJV/j4E7w5jY3kLtLGHP4rDJOMTkXq04ffo0devWxdDQkP79+yMIAmPGjMHR0VHrvpRU/OuxX7XzorGLIWfOnOHJkyeEhYWRlpaGv78/rVu31rn3lz6w6DK7zCL3mgl48OBBsSYhedX1j0/LQlRl8+zSQS4I3swd2J55c+ewadMmwsPDad68Ofcy7SRT119q/Q6+nzQcpwHLsW3Wl5Rshdxr5n86HaZkxZUMsp2qYmBpr7FzGv3HXML/WsORy3dp1a0Pv6yYj6mpKT///DOenp588MEH7LseJRlfNBWfkJBA18+GsmLjdqJCr5P+6BpRN8+w9bsJrFq5gtiYaCBn6/m+fftQqVSklquuVhYp+iIVFn3oqSL7IveaAVDbnRB4tZa+db0uZCXG8PToRjLtPcnIyGDt2rUsWbKEzp07S6quv9RYDO1cUGWkIBgYapWlKOz6wGLTpDe2zfth2bg38w/+i0KhYNOmTQQFBTFkyBAGDBhQbJZUZdZbWVKVWWQ8vUv88fXEH1tH3L9/M33tbi5fvkxERMQrRQMLwiKKIhkZGZw+fZrZv516I/55xH2+XryeVatWMWTIEARBwM7Ojsv3o1CU80AwNAYDQxQmFjh+8h01v95JUFAQy5cvp3Xr1kRFRTF16lTJnlN9ZtFldplF7jWjdr1cM18wNMbx41lE7wkkJS6Mig7lEEWRMWPGvBGbX46SGpcai4mbL+kPL2PiVFmSjPmN6wuLiYs3Ji7er4yvW7cOR0dHVq1ahbu7O6dCQ8iIuodFtZZvzZ+dnc3jsAgUFrYIgoAqM52E4M0kXdzN80Y96XtzPcnJyZQvX55WrVqhUqm4umUFGeG3QRAw92kO2Zkk/r2ThIxUBp7+iYiICLKzs6lRowa+vr7cvq3E0NYJA6syKAxNMLAphzLqPjeOnWP8s30cOXKEa9eu5TKZezelXLecni+iKJJwcgvJVw+S4FSZc4rqeHp68tFHH9G3b1/GnTME4c1/S714nWfOnKFXr16sXLkSe3t7yZ5TfWYp7LjMIn0WTajUTERcbM0If8lUAwtb7FsPIWbHTFJTU19ZWPh67MvjBcmtznipsaRVbU7cXyuwbtgjd8GvNliKwh6ekJZzN8fIFEFhoHWWwuZ+fn4HljXaYGBukzseHx+Pra0tP/74Iz/++CMAGxt3I+bcbpQxTzCv3ABjx0oIBoa5+e/du8emTZs4duwYYadOAaAwsQDA1LMu9m0/xygxgoYNm+Pi4kJ0dDRr1qwBwLFKLdIa+GNUzv2VBd+utmacntIagKioKK5fv86NGzcw+SeY5w8vk5Uch5ilJDspBoWJOS6Nu+Lq6srkyZNJT09n4sSJxMXFUat/ABHPX/SEEkm6tBdRmYZhdjotWrSgb9++uXdKF/x7NF8f79+/T9OmTVmyZAn+/v7F8r0g8VJ8r0uBpbDxMov0WTQhSX81o07lVUs/499gmrVqQ6tWrV5pmieluv5SY7H1qIGoyib9wT8lypKRkUFoaCj379/n0aNHPHjwgK6uqWQ/vEjyjSMkXtpLwpltJJ1Yj8XfP9OnTx/69evHoEGDmD17Nh86JZMUvIGwH/oRvnIQiZf2YpieoDPnNCv0HAnH1xO+egjP/htAxo1DJP8xgzJlylCtWjUaNGhA06ZN8fLyIvvhRSr0DURUphF78AeeLPuEqE3jEA8vpH///jRq1Ijz58/j7u7OjB+34txtEnZtP8fxs/mU6zIJh4ZdWbVyBaNGjcLf359PP/2UAwcOcODAARZO/wob18qvTEJeZ3d0dKRNmzaMHTuWlT8up8InszCv0hCFkQkKYzOMrMvikvWUDRs2MGzYMGbNmsWYMWO4e/cukzr45HojCArcRm+l0uhNjJkwkQ0bNuDo6Ji7c+ttPt64cYMmyktwAAAgAElEQVSOHTsyevRoyZ7T0sCiy+wyS8n1mik1d0RerP59eVWwSVmB9u/XYf78+XzxxRdvjX3bCmJNxkuR5evHwwk9sBbPWo2Y/EF1jbE4W5vQ1CiUbd9vYNBff2FjY4MgCGRlZaFQKChXrhyOptY8TlagVJhgbW2D33veNK7qhqWlJSqViqysLC5dusTfu3fjZmhOpSm/Evb4Ecp//iDqwnYW/VODJz170qNHDywtLUvUx8Lk/rOKOVtcvbDr8jWGjy+QdGE7HQf2p3xZa27dukWdOnVo1KgRTZs2pWbNmuy9Fsn8g3WJSEjDwTSb7p4C7qZpxMbGMnv2bNzc3HLz127cosiv09Fc4PP3nenk65Bv/PH9O/gp5Axl2o/GqVwZ/KtaUNkyCx8fHypUqIC9vX1ufJX//fdFftcy1kxsX59u77miGvkZrq6urF+/nhEjRrzVx5vGCdy4cSOnfPT/Jk1SO6elgUWX2WWWkts1oxMl3ouqt5XTFUWRmjVrMmvWLPr378+BAwd4//33i3U8XVFxywyLokjnzp2pVKkSS5cuzbPD8NuUnZ3NzZs3iYyMJCYmhqdPnxIeHk54eDhpaWmYm5uTlJTE7du3sbe3Z8SIEbRr147y5csXmTkvRUVFUbVqVeLj4/nvf/9L+/btJVt+WaVS0aRJEzw9PTl06BDu7u4kJCRw7949zM3N6dOnDzt27MDS0hIrKyu6dOnC2LFjcXDIe4JQGCUlJZGdnc2iRYs4duwYLi4uuXelnj9/jpWVFcnJydSrVw9fX1+eP39Oo0aNuHfvHlWrVmXGjBn8+eef1KlTp9gse/bs4ccff+TgwYNvjRNFEU9PT4KCgjTWjqC0l+t+m2Rv8lZp90VnS7xrSqGhoSQkJNCtWze2bt1Kjx49mD59+it3RmTlLUEQ2LJlC35+fnzyySf0798fIyMjVCoVZcuW5b333iMlJQVjY2POnz/PxYsXiYqKIjY2loiICM6ePYuDg0Puv4ZdXFxwcXGhfv36mJubk5qairm5OZ6envj6+hZ6olNQ3bhxg/j4eIKDg2nWrBlJSUkaOY46pFAo6N69O5MmTeLo0aPUqlULOzu7V7xZtmwZkZGRxMbG8vPPP1OjRg2++OILIiMjmTFjBs7OzkU6dmJi4iuTwBYtWtC7d2969uyJs7MzCoWC1NRUgoODCQkJwcjIiMuXL1OxYsXc3SvqmIQAmJmZkZmZ+c64w4cPk5GRga2trVqOK0uWLM2p1E5E4uPjMTExQRAEPvzwQ1auXMmKFSvkiUgBZWdnx4kTJ1iyZElu9U9BEHjw4AHZ2dmEhYWRlZVF1apV8fPzw9nZGQ8PDxwcHFi7dm2R/yiqU/Xr18fX15c//viDxo0baxvnnZo4cSLNmjXDz88PAwMD3N3dGTFiBI6Ojty8eZOwsDDq1KmDs7Mz3t7eWFpaMnNmzpb9UaNGFcnz06dP07t3byDnDtLmzZs5deoU48aNIz4+Pje/ubk5HTp0oEOHDup7wXnozJkzrzSuzE9hYWG57ztZsmRJW6VuIqJSqdiwYQPjx4/nyy+/zB1/+PAhHh4eWiTTPVlZWTFt2jSmTZuWO6ZSqbh58yaOjo5s2rSJMWPGYGRkpEXK/GVtbc3x48fp06cPfn5+bNq0SZK3TUVR5OTJk+zfv5+NGzeycOFChg8fzl9//cXWrVt5/vw5Pj4+VK9enVOnThEXF4erqysBAQE0bdoUd3f3It9VsrCwwMrKipo1azJu3Dg8PT25cuUK1tbWzJo1C19fX3r27KnmV5y/9u7dy/z5898Z5+vry9KlS0uASJYsWcVWXg1opPCTg1Y8vd5gKDU1VezQoYNYs2ZN8dKlS688FhQUJLq5uYlPnjwp9nGlrtLeeOl1ZWdni19//bVYp04d8cqVK9rGyVVWVpbo7u4uAmLlypXF6dOni1evXi1RhnPnzonly5cXgVd+ypQpk/v/Bw4cKDGeDz74QFy8ePE749LS0sRy5cpp9HzKn6P8JXuTt0q7L+TT9K5ULVZt59+Hf0IjsGw/Dld7yzdWBc+cOZNbt26xfft2SdX1V3fu133R5X4H6mTfsmULffv2ZdasWUyePJmgm9FaYwkODmbdunVs3LgRgBkrfuHQc8cSZ8nMzMTT05OwsDAOHTpEYmIiGQ7VWR78hHOzuiFmpADwts+qOs/pf/7zH27cuMH27dvfGbt161YmTJjA7t27adiwodpZivM50nS8tln05Roj+6LeXTP5LVaVdK+Z4rK9XNd/6W+HWb1oLvY9ZiEYmeRZS9/X15eJEyeicvBm4elYSdT1l/sdlJyPzs7OzJ8/n2PHjjF79mz2Hz9Lmo07CjOrEme5ePFibhfoRq07csm2pVZ8iYuLY/Xq1SQmJtKkSROsfFszfd+/xKdlYupWg5R/T2NSriK+ft1L5JwaGxuzceNGPv/883fGmpmZsXbtWoYOHYqDg4PaWeReM/p9jZF9kXvNqF0zp3+LdaOeKEzMc8der6VftmxZ5s2bx/ffzZVMXX8p9RiQEosm2E1NTTlw4AAjR44EIPnfs0T8NJQnyz4l7eGVd/ZgKQqLqMpGpUwn/v5VPuvUEkEQqFOnDn/88Qfu7u4sXLgQsfkIrfmSkpKCi4sL/fv3p1OnTq/Em7j6YF2vCybu7xGwISi30FhRWfKKz0qMIWzPYkaNn8TChQuZMmUKbdq0KVDuQ4cO0aVLF3x9fdXC8rZ4XXuvSzVeZpE+iyZUKharbty4keTocJw7T3njsddr6Xfv3p2hX04gY/f32LcZhoGFXb6xJTEupR4DUmIp7HhBY1/s/PhvpD1xR9eSFf8UVVoisfsWojC1IqXehySOqIe1tfVb8xSUJfHc7ySc3AxAmY6jCRjvRpkyZXj27BkLFizAycmJ5VP2Fzh3cVjyGnd3d+fw4cO5t5MjEq69EpedkoAyMoSrF3bh+dt0VqxYwUcffaQWRlEUSbq4G+Wz+6RYlSUiIoKPP/6Y4cOHFyjHs2fPXincJoX3V2ljKey4zCJ9Fk1I7ycioaGhfPXVV1TrO48Ewzd3b7xeS9/GxoZ6Ezdx589NRP7yNU6fzsPAwjbP2JdzlIYeA1JiKWx8YXNXrteS8Mo56wperH9If3yNrGv7cXV1pWHDhnTu3JnGjRvj6+tbZBbBKOc2rbFjJbybd+Wrr1oXm70kz6m5T1OSrxzA2qMWO7eupGfPnpiamtKxY8ci53/06CGpIedJvR2MKiOFcv7f4u5ZmYVTWr8Rm19uURSJjIzMvQ2ujtf6tnhdfq9LKV5mkT6LJqS3X82IosjPP/9M27ZtGT58OAH9OxS4lv7XXevg0nYwZp51iT/281tjofT0GJASS0myC4KAIAjYV67Dz1t+4+nTp4waNYqbN2/yxRdfUK5cOe7/OIjYXXNJOLWV1LtnyHr+7JX8oihy//594uPjc3OrMtNJuX2Ssl2n4Dn0B0n6Eh0dzZUrV9i3bx/nz5/nq3Zer8SbVayFuXstEh9cpXHjxixZsoQFCxYUiSU9PZ0KD/cTuWkcymcPsG7UA+dBP2DtWKFQ7EPq2tGuXTtWr16NmZnZO+O1/f7SZxZdZpdZ5F4zxda6des4cOAAW7dupVGjRrl1FApT13+e4RAufNebcopUvvFvUup7DEiJRdvs3bp1o1u3bgBkZWUREhLCz7uPs+3gKWL/CSL90RUAzmRMxCCsGQsWLODu3bskJydjbGyMkbkVMdHRmFZpRJUGrZnUsZokfVm6dCkbN24kLCwMAA8PD2q835Y9W35CYWqJjbsvqQ+v5v7B/+CDDxg0aBBxcXHY29vn5vk+6CZPEzNwtbN4g0UURWbPns306dMBsC/nSHrMfZ4/u4/w6CIjvvyCuMsHGbHmIg4ODsTHx5OamkrZsmXxDo/n+NkLpGcosXEsj51hGuOWX2Hs2LEcOHAgt1Ovtn0syXgpsegyu8wi95op9vbdDh06MHz48Fe+ry6KevXqRceOHRkwYECx8khJpb3fQX5Sly+iKNKiRQtOnjwJQJ06dRg2bBhDhgxBEARiY2NJSEjA3t6eMmXKFPt4mlR2djYbNmxgyJAh+ca8//777N27Fzu7nPVU/fr1Y/Pmzezbt49OnTpx4cIFmjdvTo8ePRg9ejTvvffeKxOEhISE3OcOHz6cQYMGYWxsTGZmJvv372fHjh34+PjQpEkT4uPjsbe3x8zMjNjYWBQKBbVq1cLIyIj79+/j7OxM06ZNc/NpUvLnKH/J3uSt0u5Lftt39XIicuvWLVq2bMnly5dxdS3ejG7FihXMmzePv/76Cx8fn2LlkopK+4chP2nCF6VSibGxsVpzlrSSkpKwtrZm1KhRBAYGYm6es/MsOzs792url3Xjxo3cMuyiKOLl5UVISAiWlpY4Ozvz7Nkz2rdvz6hRo2jWrBkAcXFxKJVKnJycSvbFFUPy5yh/yd7krdLuS6lqerdy5UpGjhz5xs6Goqhbt25cv36dFi1acOrUKapUqfLuJ8mS9T/p+iTkhV7+R0FMTAyiKKJQ5L3E7EW3248//hilUsmcOXP4+OOPSU5OxsbGJrfHT79+/WjSpAmrV6/G3t6+RF6HLFmypCe9W6x67tw5tm3bRv/+/dWSz8XFhZUrVzJz5ky6du1KSkqKWvLKkqWrKlOmDGXLls338dTUVNzd3QkNDcXW1pbbt2/ndt+9ePEiv/zyC1OnTmXp0qWYmJjQoEED7ty580aerKwsAgMD2bJlC0qlUmOvR5YsWdqVXk1E7t27R7du3di0aRPu7u5qzf35559Tt25dRo0apda8smTpm6ysrLh//z4uLi6kpaXx559/0rZtWypVqgTAjz/+CEDXrl1p3LgxEyZMoHnz5pw+ffqVPJmZmUyePJm+fftStWpVDh48mGfRNFmyZOm29Oqrmblz5/L555/n1jB4XcWt6+/j48OCBQtYv359sXKrg0Vfei+UVnZ9ZxEEgcWLF3Po0CG8Gvqx/shFMt1aQGgol+5Hk5mZSUhICJ6enpiYmODk5ESvXr1wc3Pj3Llz2Hk3RKhYl+rDFpN+cj3xUWH4+/uTmpoK5KxDqV69ut77qA8suswus5TMrhm96jVz48YNQkNDc3fKqLuuf12Pcpw/so927dq9sqhO7negH71mSqp3SGnxxd7enjPX73Hk2mPib5zAvu0XJF3cxWNDV3zqNKZZDc/c3TNeXl64urqyZMkSAEx8mpP57AFPT24nIeYZqclJPHnyhNWrV6NUKlmxYgVOddsx5/ATrfko95rR72uM7Ivca6ZIGjVqFCdOnGDNmjVvPKaOuv67IyyYNGkS3377bZFzq4tF7negu+yliSVEdCL+wm5M3HwxtM5ZV6JMTcwzvlevXlhVzOkL8/z0r5TpPIHyIzfjPHgFdSZuwdHRkfj4+Nz4MZ91JfFZmMbYpeSjrrLoMrvMUnK9ZvRqImJjY8Phw4eZNWsWGzZseOUxddX1f/bsGdWrVy9ybnWylPS4lFgKOy6zaIfleaYCFAbYt/0cUZVzoRMMTfKMF0WRpEc3cn9P+mcfgiBgaF2OOEVOmwUDAwOSkpL4999/sWrci6hfvyYj4tWLpT76qKsshR2XWaTPognp1UQEoHLlyhw+fJhJkyZx//793PG31dgvyNiL8ZiYmDcWwhYmtzpZSnpcSiyFHZdZtMNSpWEbKny1C0OrssQeWA6AuXeTPOPv3bsHgPPAnLjM2P+/2/FyvKWlJV5eXni39Me+7Rc82zGLpCt/lthrKu3ntLSwyyxyr5liydvbG1EUc0twg/rq+gcHB9O0adMCx+clXe0xICUWXWYvbSzmxjnrQAzMrbFrNQjbClXzjHd3dydw8z5sXCsDIBgYvpO9TLUmOPUJJO7gD0SsG4X49DajW1QgLi6O1wsi6rqPusiiy+wyS8n1mtGrxaova/LkyVhaWtK9e3dsbW3xcbamvJ0Z18Ofk5yehautGdM/zLu/R36xNgl32bZtG3Pnzn2lmFNhchc2XhO5X14wpW2WkorXpC+67GNJ+pLlXAPP6nXyjTcwMOD9ml6UtzPj4jMwqt6W8uXs3sl+K04kKT2L1DsnER//w9Yfv2fFihXs27cPIyMjqlWrhoGBgdp9LKn3S2HjpcCiD9cY2Zf844uq/Bar6mWJ9+fPnzNgwAAuXrzIt99+y/Dhw9XCNHfuXCIiIvjhhx/Ukk9bKu1lhvOT7Eve0hVfRFFEqVQiiiJGRkb88ccf9OzZky5durB79261H09XfNGGZG/yVmn3Jb8S73r51UxYWBi7du0CcgqRffXVV6hUqmLnbdiwITdu3Hh3oCxZskpcgiBgYmKCqakpBgYG9OjRg4YNG7Jnzx46d+5MSEiIthFlyZKVh/RyIlK9enXGjh3L9OnT6d+/PwsXLsxdbV8clS1blri4ODVRypIlS9M6fvw4UVFR+Pn50axZMx49eqRtJFmyZL0mvfxq5oVedA2FnMmJQqFg/vz51KpVq0hdPuPi4vDw8ODZs2e53/Ppokr77cH8JPuSt/TFl4ULF7Jq1SpOnTqFo6NjsfPpiy+akOxN3irtvpSq7rsv6+TJkzg5OVGpUiW6DxxJhw4dsK7fDV//L4tUTtfb25vz58/TvHnzAsXnJ10t7SslFl1ml1lKnn3s2LHsPHicen0mYVjvY732UUosuswus8gl3ou1awZyVihXqVIFe3t7dl+JYFuEHfGX/8LE2YtMx2qFKqd78OTf/H3gN/4OPsKkSZOwtbV9a7xcZlha8XKJd+n7omkff//7AYunjiJTYYxFtZZyiXcNxuvDNUb2RS7xrnYFHrhN+IGVZCdFY1a5IVDwcrqpd8/weOtUjl66w7Fjx94oaFZaSvtKiUWX2WWWkmVXqVRcunSJEcOHAGDbor/WWEoiXkosuswus8gl3tWuuyf3kHRpD5BTWOmF3lVOVxl1n9g/f8ChxwzMWn1OrVq13hqv7nEplfaVEkthx2UW6bMUdrwgsXfv3sXPz4/evXujNHfAuoE/sfsXk3b/UomzlNS4lFgKOy6zSJ9FEyo1E5HKjTvg2Gc+AJkxT3LH31VON+XWcSxrtMHEuUqpL+0rJZbCjsss0mcp7Pi7Yg8fPoy3tzd3794lKyuL5L93kPj3TpRRocQeWFqiLCU5LiWWwo7LLNJn0YSKNRERBGGMIAg3BEG4KQjC2P+NuQiCcFQQhN2CIFj+byxAEIRUQRAcXnpucvHQC6cpH9bCyqE8gokFgok58PZyuorEp8T+uZyU28FY1eksl/aVGIsus8ssmmUf36Yyn3zyCW3btgXg2bNnrFq1CsScWkLluk/HedCPJcIiJV90+ZzKLNJh0YSKvFhVEARf4DugAfAjMGvmzJlngS+AecADoGlAQMDFmTNntgS8AauAgIDDADNnzpwaEBAwN7/86lqs+nI5XWuDLHZvXoUq9TlVGrVjxofVX1kVHB0dzW+//caGJXO4d2gL1u41sO44jgrlXeQywxp+nVLwUS7xrl1f1MX+7MJ+vv/+e9zd3VEoFKSkpLBlyxaMjIxo698Hy3rdSMlWyCXeNRivD9cY2RcdKPEuCEJPoL0oikP+9/s0IAMoB6wDPIEKoiiuFAQh4H9PGwDUEUUxThCEZFEULd+SXy11RF7fsx0dHU2TJk2ws7OjdevWuLi4EBMTw/Hjx7l69Spt27bF398ff39/TE1Ni3V8qaq072XPT7IveUuXfBFFMbcPVM+ePbl79y5Xr17NfTw7O/uVPlHFkS75UtKSvclbpd0XTdQRuQHMEQShDJAGfABcBBYAm4HnwKcvxSeTM0EZA8woxnGLpXLlynHr1i2OHz/OuXPnuHfvHjY2NkyZMoXWrVvr7eRDlqzSIEEQGD9+PAcOHHhlEmJmZkZaWhpRUVE4OztrmVKWLFkvq8gTEVEUbwuC8D1wiJxJxlUgSxTFR0DzfJ62DLgiCMLCghyjuCXZU1JS8n2sUaNGNGrU6JWxzMxMMjMzi3VMXdDbfCnNkn3JW7rmiyiKpKamkpSURPny5XFycmLFihX4+/uTlJSEpWW+N2ILJV3zpSQle5O3ZF/yVrEqq4qi+DPwM4AgCHOBsHfEJwiC8AswoiD51XELqzTfBnubZF/yluxL3tIVXzIyMlixYgXOzs6IokhiYiJnzpxh0aJFeHt74+Xlpdbj6Yov2pDsTd6SfXlTxZqICILgIIriM0EQKgD+QOMCPG0RcKG4x5YlS5as13X79m3MzMx4+PAhtWvXxsbGht69e2NgYED37t1ZuXIlhw4dIioqitOnT2sbV5YsWRR/MrDjf2tEMoGRoijGv+sJoijGCILwBzCumMcutHS1rr/Mon/sMotm2D/1McLAIGcb4pUrV/Dw8KBGjRqEh4ezbds2bMpX4Z9HKUT/c4baY38moH8HvfRRSiy6zC6zlEyvGb3vvvviNtiLWvovl7E1MzLgO/8ab5hdmFhNx2sitz74ogn2ovqiCZbS6Is62J8fWY1zdhShN69gbGxMixYt8Pb2pmrVqlhVb0lA0D3SMrOJP/ozyuiHuH82t8gsuuRLSbPowzVG9iX/+KIqv10zpaayqq7W9ZdZ9I9dZtEMe9qjqyTeOEbIv3cIDAwkIiKCffv2sXDhQoYMGcKyE49JVWYRd/gnki4HkfH0LonPwvTORymx6DK7zFJyvWYkvU4jPT1dbdtpdbWuv8yif+wyi2bYE05sRFRlU673HMaMGZ1nfMqt4yRfP4SYlQFZGYBIeFzeRZ511UcpsRR2XGaRPosmJOk7Ips2bVJbLl2t6y+z6B+7zKJ+dlGVjTLyHmYedfHw9gVyuu6Kopj7U84wg7hDqzC0dqD86F8xKV+dpxvGkLT/e7WyaHtcSiyFHZdZpM+iCUl6IjJ79mxUKpVaculqXX+ZRf/YZZaCxyf9s4+oLRNwDtlFREQECQkJ3Lt3j/Pnz9PKPAwh5gGiKJIV/xTB2BQFKhJ3TMPU1BQDAwNcXV2xs7PDysqKqws+RVSmkRnziLAf+qKMDAFRRYPqVV5hOHr0KDVq1ODe8gFEbhxD1LapRP8xl4TTv2KQFCV5H6XEosvsMosO9JrRtGbOnBmQmZnJwYMHGTRoUJFy6ENdf7nfgf71VJF9KXj884PLqeLmhJOlISNHjmTBggVs2bKFrVu3cv/6BWIuHSDu/E4SLwdhYl2G1Mc3SUtKoEGDBpiammJubk7fvn2xsbHB0sKC1IxM0tPTUZhZI4giQydMY8uP3+eWfY+OjqZTp07MnTuXad98jUu1BjxUOJFpXhaT1GieH/0JC2Uc5cqVw9raGnNzc508p5qO14drjOyLDvSa0bQEQRBv3bpF7dq16dixI7t27Sp0jtJe1z8/yb7kLdmXvKVNX+7evcu4ceM4fvz4K5WPFy9ezJMnT9i/fz+1atVi586ddO/ene3bt9OmTRsMDAw4ePAgCoWCUaNG0aBBAypUqICbmxuurq4YGRlx4cIF+vfvT1hYGLa2ttSoUYOoqCiaN2/OokWL8uSJj49n1qxZbNmyBVNTU3766Sc6duxYkpbohOTPUt4q7b7kt2tG0hMRURQ5f/4877//PgMGDGDt2rWFylHaT3p+kn3JW7IveUsKvsTGxjJnzhx27drF8+fPSUpKIisrC1EUsbW1JSEhAWNjY5RK5SvP69SpE/v27cs3ryiKJCQkkJCQwD///MOjR48YPnw4FhYWb+WJi4ujVq1ahIWFsWvXLrp27aqW16kvksJ7Rooq7b7o7EQE4MCBA3Tu3JkJEyYQGBhY4Byl/aTnJ9mXvCX7krek5Et6ejqBgYEcPnyYjIwMHj9+TGRkJD4+PlSqVInevXsTFBTEr7/+CkBISAiVK1fWCMvGjRsZMGAAAE+fPsXJyUkjx9FFSek9IyWVdl90uo5Ix44d2bRpEwsWLGDy5MlqW8AqS5Ys3ZKpqSnTp08nODiY33//HUEQcHNzIy0tjV27dvHZZ59RpUrO4tPs7GyNTUIA/P39GT58OADt2rUjOzub5cuXM2nSJBITEzV2XFmy9E06cUfkhVavXs2XX36Jm5sboaGh78xR2mef+Un2JW/JvuQtKfvy7Nkzvv/+eyZMmICLi0vu2MGDB+nbt69Gj/3Cl7Zt23L48GGaNGnCmTNnsLGxwdbWloEDBzJp0iTMzEpmC6SUJOX3jDZV2n3R6a9mXlZcXM6K9WPHjtG8efO35nj9pOtqXX9159YXX9TNXhxf1M1SWn1RN7sm41/4olKp8PHxISQk5JXnVq3dACvjnHpI3t5vboPUV19e9kYX2WVfNNdrJr+JiKS37+bFZmJiwqxZsxg6dChubm5vzfHyVqkXtfTjUnMWsyWlZ3HibjTl7czwcbZ+5XmFidV0vCZy64MvmmAvqi+67KOUfNE1H1/4IggCFt5N2LN1DWW7TsGiWnNS75wkJjKc2Lh4Nm/eRFhYGFWrVsXW1lbvfQH9uMbIvuQfX1Tlt31XJ9aIvKyYmBhEUaRWrVqFep6u1vWXWfSPXWbRL/abN28SMH85iKAwMcfcqwnlR/+CacVapKelEhsby/Lly6lTpw79+/cnPj6+VPii6+wyS8n1mtG5iYiDgwP29vYMHTq0UM/T1br+Mov+scss+sF+7do1rKys8PX1JSz4N6zqdsbMow4ABmbWOPaeg9Nn82nVqhWQU4MEoHbt2ty/fkFjjNr2pTjjMov0WTQhnZuIAAQGBrJ9+/ZC7Z7R1br+Mov+scssus+uVCr59NNPadGiBRkZGTSeuQ/7NsPfiPWsXoejR49y7tw5IGe9yIoVK4jdG0jcoZVkpyWpnVFK56iw4zKL9Fk0IZ2ciAwcOBCFQpFbK6Ag0tW6/jKL/rHLLLrPHhISgkqlYs+ePRgbG78zd8OGDbl//z6Qc0dk7e4TGAgQsfYLkq78iajK1gtf9IldZpF7zd88DnMAACAASURBVOS7WBXg+vXr7NixA3Nzczp37pxvDn2o6y/3O9C/niqyL7rv46RJk0hJSeHzzz8vcG47OzsCAgKwtramlocDPvVbcEuowNMTv5Jx/SATPunAoHZ1dNqXF9KHa4zsi9xrJt/tu71792b79u0oFIp3buEt7Xu285PsS96Sfclbsi852r17N/379yc9PR2lUsnMmTOZNm1asfOKosimTZv46quvmD59OqNGjUIQ3tjhqFOS3zN5q7T7kt/2XUNtwBRVV65cYefOnSxcuJDx48drG0eWLFmlQJGRkYwePZodO3bQu3dv/P39cXR0LPTOvfwkCAL9+/enadOmdOnShfT0dCZOnKiW3LJk6YJ0ZiISFBREp06daN26NWPHjtU2jixZskqBkpOTqVatGoaGhqxcuZJhw4blPpaUlPSWZxZelSpVIigoCD8/P9LT09Vyt0WWLF2QzkxEKlSogEKhoFOnTigUOrnGVpYsWTqkoKAgevbsia2tLaGhoZiammr8mBUrVuTUqVO0bNkSGxsbRo8erfFjypKlbenUGpFOnToRERHB5cuXC5RDX8rpymWG9a+UueyLtH0MCQnB29ubpu27ktlsJJGJGfmWeNcEy8OHD2natCmLFi3i448/lowvBY3Xl2uM7Itc4v2NXTN//PEHkZGRjBw5skA59KGcrlxmWP9Kmcu+SM/HoF/X0qZNG2bNmsWcOXNYunQplavXIq31ZOLTMvPMr0lfbG1t8fPz45NPPqFevXpcTTDWqfeXPlxjZF/kEu956uTJk7Rs2bJIz9XVcroyi/6xyyzSYp/z39NMnDiR4cOH88cff/Drr79y5coVyvVZqFUfa9Wqxfbt2+nVqxdTl22Wz6nMIgkWTUhn1ogAREdHF/huyOvS1XK6Mov+scss0mK/smo8Xl5eLFq06NX4X/aXOMvr461atWLnzp207NgV+zafY+7dRGssJTEus0ifRRPSmTsiycnJiKKYZzvtgkhXy+nKLPrHLrNIi12VEke/fv0kwZLXeNOmTanWdyaxf60gKzFaqyyaHpdZpM+iCenMROT+/fsIgoChYdFu4uhqOV2ZRf/YZRbpsKdf+xOVMo2BAwdqneVt8bOGfoR9/Q+J2j6N1Ht/v7UkvJTOUWHjZRbps2hCOrNY1cbGhjlz5tCuXTvc3NwKlEMfyunKZYb1r5S57Is0fLROvM/9X2fi5+fHiBEjCp2/pH2p6FWD336cQ+rtE/DsLgsmDqNHA/cS9byg8fpwjZF9kUu857l918HBgcGDB/Pdd98VKEdpL6ebn2Rf8pbsS97SR1969+7Njh07aN26NQcPHixSDm34EhwcDMCaNWtQqVRs3bq1RI9fUOnje0YdKu2+6HyJ9+TkZGJiYvD399c2iixZsnRYISEhbN++nTVr1jBo0CBt4xRKL3pr1atXj5o1a7J3714+/PBDLVPJklU86cwaEZVKhSiKZGVlaRtFlixZOiw3NzcMDQ0JCwvT2SrN5ubmrF+/niFDhvDw4UNt48iSVSzpzKfQ2toaPz8//Pz82LJlCyqVSttIsmTJ0kGZmpryzTffMGvWLD766CNt4xRZzZo145tvvqF9+/aEh4drG0eWrCJLZyYiAH/99RedOnVi4MCBGBoaYmlpiYWFBQ4ODmzfvl3beLJkydIRBQQEMHnyZPbs2cPRo0e1jVNkjR49moEDB9KyZUsiIyO1jSNLVpGkU4tVX0ilUnHp0iWuXbuGkZERe/bsYdeuXVy5cgVfX9/cOH2p6y/3O9C/niqyL9LwMfbXSajSErl7926h80vJl8mTJxMVFcWGDRu0zlJcb7TNLvsi95rJVV69Zl5IEARcXV2pU6cOtWrV4uOPP2bevHk4OTnRrFmz3Dh9qOsv9zvQv54qsi/S8THxeTxh/xxjxowZhWaRki92dnaMHDmSgIAArbOAflxjZF/kXjOFVmZmJnXq1Mn3cV2t6y+z6B+7zCINdpUyneijG3B+v7vWWYobf+3aNcmwFCdeZpE+iyakNxMROzs7/vzzz3wf19W6/jKL/rHLLNJgz05NAFGFYb1euWORkZG5O/N0yUdXV1fatGkjCZbijMss0mfRhPRmItK9e3fWr1+f724aXa3rL7PoH7vMIg12I1snFOY2RG0YRWRkJK6urjg7O2Nubo6LiwvZl3eWGEtxx83NzUlLS5MES3HGZRbps2hCejMRWbx4MampqQQGBub5uK7W9ZdZ9I9dZpEOu+ewVVibGuLs7ExycjIpKSksWrSI9u3bE3ZoA5HrR5cYS3HiLSwsSE1NlQRLceJlFumzaEI6uVg1LxkaGhITE8PixYsZOXIkpqamJVLXf+PGjWz54XseHtpAxKF1JARvIuniH1QxjOHQLyuJiIigVatWRc4v9zvQv54qsi/S8TGgex3WBE5DqVQyZ84cPDw8aNCgAd26dWPw4MHMnzODCjUaoTSx03qvmbfF37t3j+PHjzN48GCtsxTHGymwy77IvWZy9bbtu/kpKysLLy8vHj9+zKRJk/j6669f2Sp18eJF1q9fz82bNzlx4gTGxsa89957iKJIjRo16NWrF3v37qV58+bExsbi6elJ27Zt8z1e//792bx5M97e3tSvX5/69evTs2dPpk+fzsmTJ7G3t+fs2bNs3ryZPn36FNkLdau09zvIT7Iveau0+hIYGMiUKVNISEjA2vrNnQNS8mXVqlVcvHiRtWvXahsFkJY3UlJp9yW/7bt6NRF5oR9++IGxY8dSsWJFLCwsaNKkCbGxsfz+++8A+Pj4IIoiPXv2ZMuWLSQlJZGdnU1CQgLOzs7ExMRgaGhIWloaQUFB1K1bl82bN3Pp0iXKly/P48ePOXToEElJSWzfvv2t1RknTJjA4sWLMTExoW7dumzbto3y5cvnPh4REcHVq1epX78+ZcuWLdLrLaxK+4chP8m+5K3S6Mu4ceNYtmwZgYGBTJgwIc8YqfiiVCrp1q0bHTp0YMSIERgYGCAIb1zrS1RS8UZqKu2+5DcRQRRFSf7koBVdSUlJ4rp168TOnTuLbm5uoqurq7hkyRLx+fPn+T4nMzPzld+rVKkiGhoaioIgiAqFQvTy8hKdnJxEb29v8csvvxTj4+MLxHLmzBlxyZIlopubmygIgti0aVNxxYoVopubmwiIRkZGoiAI4syZM4v1mguqxMTEEjmOrkn2JW+VJl+ys7PFxYsXi4A4ZcqUt8ZKwRelUim2bt1a7Ny5s3jo0CHR3NxcBMT33ntPq1xS8EaKKu2+/O/v+pt/7/MalMJPcScioqi+k37s2DExPDxcLbmOHDki+vj4iGZmZqKfn58YFRUliqIorl69WhQEQdy2bZtajvM2lfYPQ36SfclbpcWXU6dOiY6OjqKRkZFoZ2cn3rlz563xUvBl48aNYosWLcTMzEwREB0cHERLS0tx3rx5Ylpamta4pOCNFFXafclvIqI3u2Y0qZYtW+Li4qKWXK1bt+b27dukpqZy+PBhHBwcABg2bBijRo3is88+IyQkRC3HkiVL1tulVCrZunUrrVu3plmzZvj6+hITE0NcXBze3iWzY6A4Cg4OplevXrm1T+zt7REEgbVr1+Lo6MiIESOIiIjQMqUsWW+XobYBSlJSr+u/bNkyzp49S6NGjXj69CnGxsZaY5GSL/rILrOUHPuzZ8/46aefGDBgABejhdx447BL3Ns2C0MDAypVqsShQ4fw8/PTKIs648e29mDfvn1MnDgRIyMjmjZtSr169QgMDGT/jWfM2XGOrX9tZY1XdXyqVqVOtSp4eHjwySefvDHJ0rVzKrNo/3OqTunN9t28pIt1/edOGsnChQvZu3cvQ4YMkfsd6GFPFdmXkvVx7ty5zJo1i6VLl/HbuhWEn92NwtSKsN3zsarWiq37j7F89td4enoWKr+2ffnr2ElMY+8xa8Y0NmzYwD///MO2bdvYdz2Kr3de53mWAWYedTAqW5G42GjqN2yMmBrP6NGjCQ0NpW3bthgaGsrXGLnXjNxrpqSkK3X9TU1NOXv2LBcuXGDChAmS6jEgJRZdZpdZNMeekpHJf7YHs3XrVqZPn87AgQM5fPgwZmZmuHf5Euv3P8HIvjyxB5ZgXM4Dmw6jddbHDGUmMRkCSUlJfPvttyxZsgSFQvFGrJlnXWzbjeKy2XvMnz+f0NBQEhISaNq0KTExMZI/pzKLtFg0oVLz1Ywu1fX39vZmw4YN9OvXj7LdzDD3aiwJRm37UpxxmUX6LIUdf31MpVLxdO0XPIkP5zPA1NQUJycnFAoFR44c4dPdcdgANg38Nc5SEuPZyXEoVQLfffcdbdu2pX79+gXKYW1tzbZt25g6dSpNmjQhpcV4jMq4lSh7fuNSfn/JLJpTqbkjomt1/T/77DMGDx5MzO55ZD2PkgSjFHwp6rjMIn2Wwo6/Pha7N5DspBjqffM78fHxpKWl8eDBA0JDQ2ncuLHe+aiMCsXGsTyrV69m7ty5hcohCDkTmClTphC9/VuUUfdLlD2/cSm/v2QWzanUTER0sa7/mjVrcPOsTOTmCahUWVpl0XTu0sQus6ifXZWeSuqd0zh3+4pvujfC1tZWayzqzp1fvKGBgqi/9zNs2DBcXV3fGptf7kGDBjF22hye/XcGymcPSoxdSj7KLHKvmXyl7sWqulrXf+igASxaMJ/MR1cxr+4n9zsoYryUeqrIvqifPfzk7ygj/2Xjxk0lxqJtX6qpHnLn6kW+/fZbKleuXOTcbd+vR2y2KSdWT8PYsRIV3T3ka4yGcuuDL8VRfotVtV64LL8fJFTQTNu6evWqaGBgIH777bdqyacvvqhbsi95Sxd8mThxomhsbCxevny5xI6pbV8mTpwoAuLp06fVku/48eOio6OjuGTJElGlUhUrl7a9kapKuy/IBc10VzVr1mTVqlXMmTOHgwcPahtHlizJafbs2dSoUYP27dtrG6XEFBMTA+QsylWHWrRowdmzZ1m/fj1DhgzJLZImS5amJU9EdERDhgzh008/pWvXrnKlRFmyXpOxsTFBQUFER0cTFBSkbRyN6/r162zYsAGAMmXKqC2vh4cHp0+f5smTJwwcOBCVSqW23LJk5Sd5IqJD2rJlC+7u7jRo0EC+QMiS9ZrKli2LkZERd+7c0TaKRpWenk7NmjVffIVNxYoV1ZrfwsKCXbt2ce3aNfbt26fW3LJk5aVSU0cEdLec7suxDr0DeRj4CR07dsz3a5rSVGZYV9llFvWzWyU/JjMzk7Fjx2qdRZO+PAy9C8CS7YcY3dPvrbHFYZk4cSI//PADH3zwAf/++y/e3t4YGhpq/LVK9f0ls2hOpWbXjK6W0309NiVLgaXHe1z+YyWGhoY0b9680LlLypevF6/jqxGDibr9Nxnht0nKhFNPBdzszbXuo1ziXXq+FJc9ITyUlFsnqPXhYKq62GicRVu+GJjbYNv0U67GQHm7Nz9L6mJp27g2G5d9x+rVq/npp59Yvnw5pqamPMaBb3bdlMQ1RtufjcLG64MvxZFc4l1Hy+nmFYtDZSp2GsG0adM4fvy4VlnyilUqlXh4eDBv/BBEFGTFR5Dy7ymebf+Wu9915uOWtRg0aBBKpVJy7DKL7pZ4N6lYG8HAiH5dWmudRZ25tcWy5NhDRowYwf3797l//z67d+/m999/p19XP55HhEqaXWaRS7xLUrpaTjffErvVOvCRUQQdO3bkyZMnlC1b9q3xjyMimTBhAk5OTgwePLjYLKos5f+xd97RUVVfG37uzKSH9EIgJBCKtJCAoUuRolKliBQFBCkiWBBRmggooUgHlSaEKj8DSK8CgjQJUqRJE0JIDwnpfc73B5CPSAIpU+5k5l0ra5GTffd55p07dw637E3Kud1k3LtErFLF6OidVK5cmfDwcIKDgwkPD8e99zdYVvb//23UarLCr5F69SibN29m7969XLx4ETc3t2KzlIZd3+MmFu2wKxQKXLqNI3bLNL2z6HNck7lHfjGSGjVqYGtry8svv8yhQ4dw7TSa6J8n4NRuODa1W8mW3cSivXFNy2jOiBhqOd3nxQYHB1OhQgUCAgLybl4tKD793iUilg5m1apVfP311/j5+WFpaUmtWrWIXDGMyDWjSQrZRk7qw7xtzO+H8O2339KpUyccHR2xsLAgcvlQYrfPIjLoU8Lm9eThH+vISYqFpEiCg4P56quvWLNmDfXq1eP06dNU9c/fI0ehUGBZqQ71eo8hIiICW1tb3N3d6dKli8b8KmzcUN5TY2Yp7vh/xzKjbhG/bwkKlYXeWfQ5rsncTk5O9O7dO29MkiReatkV976BJBxeSeq1Y7JlN7Fob1zTMpqFiKGW031erEKhICQkhJiYGNq1a8fAgQP5a1pnwha8TWbULQBSrx4lZtME6jd5hQcPHpCSksLu3btZuHAhfn5+NH+lBUpzSx4eW0f4kncJW9SPiOXDuPnzNyxYsIB79+7xySefsGHDBho1bkx27B0kMwtcuk/Ca8xWqg7/gQ17/uD+/fskJSURHR3N9u3badCgwXPZbW1tGTFiBAqFgm+++UavPpY2t4lF/+w5aYnEBE9BoVTx4YTpemXRdG45sthXqIpb729IOLSC9DvnDIrdxFL6eE2rVJdmJElyAFYCdQEBDAZCgfVAMvCOECJFkqQpwBdAZSFEzONtU4QQtqWZvzh6cvdvUe4KLk6stuNfFOvk5MSBAwdo164dDg4OfDVxIhu27OCfDV9gWbk+6bfO0G3AcH5d82NezipVqlCvXj2GDx8O/P/d0vejHpBxYjXOuQ/Y/r/fqV69ej6Wt956q1h3Vr+IPS0tDUtLS+rVq6d3H3UZb2LRLHu/N14hPeYuZjYOLN+yn/fa+umNRU6+aJ/FHHXnMSTsX8Ti0ScNjN3EUpp4TUt68ix6iTaWpDXAH0KIlZIkmQPWwDhgHeADVBRCLH28EBkM/CyE+PLxts9diEiSJErDBpCcnEy5cuVKlcMQpVaradeuHf/88w+LFi3irbfeyvd3ufiSkZGBs7Mzbdq0YefOnfrGkY0vcpOcfdmyZQtvv/02YWFhVKhQQadzy9kXXapNmzZ89NFHdO/ePW/M5E3BMnZfJElCCCH9d7zEl2YkSbIDWgI/AQghsoQQDwEloH788/SEq4DekiQ5lXROk4omhULB4cOHiYiIeGYRIidZWlqybds29u7dy48//vjiDUwy6Snl5OQwePBgevToofNFiEn/L2traw4cOJD3e3JyMuvWrSM4OJioqCg9khVdGRkZREdHEx0dTVJSEtnZ2ZT2P8ImFV2luTTjA8QCqyVJ8gP+Aj4BlvDojEgi0O+p+BQeLUY+Ab4uygTJycmlwIPU1NRSbV9WJSdfVCoVlStXJjQ0tNTvd2klJ1/kJDn6cv/+fYYNG0b58uVZtmyZXvYdOfqiD128eJGLFy8yY8YMTp48ybRp0/D39yc1NZUpU6agVqsxMzPD3Nwca2tr7Ozs6Nq1K7169corkFYSHTt2jF27dvHgwQPS0tJITU0lMTGRnJwcLC0tsbGxwdraGhsbG2xsbHBycqJ27dp07do1rz/PkSNHmDNnDuHh4djY2ACQmZlJZmYmarUaS0tLLC0tqVSpEpUqVaJatWp4eXlRqVIlvLy8sLMrXo0N0z5TsEp8aUaSpADgNNBcCPGnJEkLgSQhxFcFxE7h0UJkJXABqAdEmC7N6Edy8WX8+PHMmjWLRo0asW/fPhwcHPTKIxdf5CY5+lK7dm0iIiI4f/48VapU0QuDHH3Rh86dO0ezZs2YOHEi69evZ8qUKXTu3Jly5cohhODhw4dkZWWRmZlJamoqkZGRfPvtt/z999/4+Pg8umm+eXNatGiBj48PkvTMmftntHTpUmbNmsWECRPw9vbG1tYWW1tbnJycMDc3Jy0tjeTk5LyflJQUIiIiOHDgAJcvX2bUqFGMHj2aPn36EBYWxsGDB7G1zf91lJOTQ3p6Oqmpqdy+fZtbt25x7dq1vH/funULCwsL/P39adKkCU2bNiUgIAB3d/dCuY19nyns0swz7XiL+gOUB+4+9XsLYHchsVOAzx//O5BH95GkvCB/qVsOG3vL5cIkB1+CgoKEJEli3bp1+kbJkxx8kaPk6MvYsWOFubm5uHXrlt4Y5OiLPhQZGSmcnJyEs7Nz3vtRFG/Cw8PFyZMnxeLFi0Xv3r2Fh4eH8PLyEi1atBCDBg0SGzZsEDExMc9sd+vWLeHi4lLi9z4kJES0bNlSODs7Cx49ZCEAYW9vL5YsWSJ27twp2rRpI5YuXfrcPGq1WkRGRordu3eLSZMmibZt2woHBwdRsWJF8c4774itW7eKlJSUfNsY+z7z+Hv9me/70t6s+gcwRAhx/fFZDxshxNgC4qY8XnjMkSTJBQgBPIQQhfav1sYZEUOt66/p3Pr2JfXq7wwYMIAxY8Ywe/Zsrb5WXfmiaRZdxsvJl+LEBwQEcPXaP9QZvZa4HAuds8jVF12zWNw/y4PfltOnVw8WL15cYm+EENy4cYPgPy6xascxoq6dIev+Zdzc3OjYvg3dunWjefPm3Lt3D39/f8aOHcvs2bNLxD573z+EhUdQwcWBi7N6k5lRcOGutUcusfR0TJFz/3ruPt9u+p27F0+Re/s0mZHXad60CbVq1aJr1640adKkTHwnlVSFnREp7ULEn0eXW8yBf4FBQoiEAuKm8Hgh8vj3ecDogoCe2kajC5EntfSfLmNrZaZkRg/fZ8wuTqy247WRW5++SPGhhK4cxdixY5k1a5bWX6sufNEGizH6Utz4zSGh9GvfmNzsDCqOWI1CZa5TFrn6omuW2F8DyY69y8TAuXz9QR9Ac8cYoc5FmRhBK9tI7l04ztmzZ6lRowZnz57F29ubBb+eKBW7UOdy77s36TP8M9YsmkFKSgp3797lpZdeYtfFSKbsvVUqH83VGbztmYrywW2mTZtGdHR0XiVpOb+nL4ovqTT+1AyAEOKCECJACFFPCNGtoEXI47gpTxYhj3//7HmLEG3IUOv6lzWW8O1zsK5Q/ZlFiCGwm1jkFT//0G3KD/4ehCBy1UjUarXJRx2zCHUuGfevkpOayK57yhfGF5ddUihRO1bihmsrDh06RHh4OIsWLWLo0KGEhoYyffOpUr1WSaHE+8td3KvcCXNzc5ycnGjQoAE2NjYsOnav1D5mKSw5kubJ1KlT6dGjBxs2bOD333/n7t27zN57lbSsnBL5UhIWOfeaMZrKqoZa17+ssWTHhmLTtF8B0fJnN7HIazziYToKc0s8Bi8hNyWB6A1j9cqi6znlwKLOTENkpSOy04nNLbgcuCbZbW1tadq0KfPnzwcg7ObVUs9ZUpbijqtUKg4ePMg777xDixYtODXxNe7N7kLK3wfyPSqs7/e0KOOaltEsRAy1rn9ZYlGr1SDU+Pg1LVK8JsfLko9llaW440/GVLZOlB+4gKyo28RsnW70PupyTqVVOSy96yEpzajoaF3q/EWNffJkjatTwU/ayc3Hy5cvc+jQIe7du8eFCxcICwuj3sgfcGg1kOTze4j530QyI64jhND7e1qUcU3LaBYihlrXv6yxALwfUHBNO7mzm1jkFf90rLmzJ+59A0m/9Sf259fqlUXTueXOItRqlFbldMpubW3N6dOnmTash0H4ePDgQXx8fOjVqxeurq4ATH2/K+Vb9KH8u3OwrtGMuB2ziV7zCfWSQ0hPTy9y7tK+zpLEa1rKKVOm6GSi4mrq1KlTSsuWlZWFhcWjTpw1PezwdLTiUngiKRk5VHSwYnKX2gXeiFOcWG3HayO3vnzxdLQm5mQwbV6uRaNGjWTnY0l90bWPZdWX0rJ7e3nR741XWLPgW8zNzWnRooVWWQzFF22yJMY/4OHhlTRr+Sqzxg7Li9HFMcbT05NaBuLjqlWriIyMpHPnzvj6+uaLvxyRTJajDy+1eZuBrzfm+ok9TJgwARcXFzw9PbG2tpbV/lUaTZ06lSlTpkx95g8FPdMrhx9MdUS0Jn36EhAQIDw8PPQ2//Nk2l8KlqH58v333wtJkkRQUJBW5zE0X7Sh999/X3h7ez/jtcmb/AoICBDe3t7i5MmTRYo/duyY6NChg7C1tRUKhUL4+vqKUaNG5dU82bp1q0hOTtYyteZFIXVEjObSjEnyUGhoKH5+BXdHNckkTejDDz9k/PjxDB48mP379+sbp8xq2bJlrFu3DkdHR1n3tNK3wsPDuXr1KlZWVtSqVatI27Ro0YI9e/aQmJhIeno6K1euxNPTM++R6B49euDt7c3YsWNJS0vTJr5OVKo6ItqUqcS79qQPX9LS0mjatCmXL18mPDyc8uXL63T+osi0vxQsQ/XlvffeY8OGDYSEhODv76/x/Ibqi6bk4OBA165dCQoKQqHI/39aY/fmiRITE+nQoQNRUVF8++23dOnSpdS+bNq0CTMzMxo1asT48eM5evQonTp1wt3dnaFDh+Lp6akhes1LK3VETDLpRXrSIdXOzo7IyEhCQ0NluQgxqewpKCiIV199laZNmxIaGqpvnDIltVpNYmIi33zzzTOLEJP+X4GBgdja2pKenk7Pnj01krNPnz707NmTSpUqsX79enbt2oWXlxe3b9/Gz8+PiRMnkpGRoZG5dCXTGREjlC59qV69OuHh4Xz00UcFFjGTk0z7S8EyZF/UajUNGjTg7t273L17V6ONFQ3Zl9KqV69e7Ny5s9AvPGP25olu3LhBQEAA5ubmrFu3jg4dOmjdl/v37zNy5EjS0tLYtm1bXkdhuUgrJd61KVOvGe3l1pUvc+fO5ctx42gwPpjYLDPZ+2jqNaN/XzTNDo/OylWtWpXs7GwW/HqChUfumHrNlCJ+5MiR/PDDD9QZModU55p6PcZoO76kuUPv3iFy9Uc42NuzNXgTrVq10pkvubm5DB06lCMn/sT21eGkOFSVfa8Zo3l890kt/fi0oqrq2wAAIABJREFULACSM3I4eiMWT0cranrY5duuOLHajtdGbl35MmTUaJIVtlDnDYPwsaS+aNtHfbPoyhdt+ahQKHj//feZMes7gjdtQF3zNSRJKhZLTlIcqcI8X7yh+1LS+LGTphEXFU5STDiWVRqQKsz0doyRo493/9hCzOapqJwrUXHoUl6pX1unvigUCtSVXmbntUTubJ2Ndc1XSMVCoz6WVIU9vms0F/fk2quhLLM8MHMj80G4QbKbWOTfa6Y4sXZ2dtQcuYycxNi8UvBFYYk4spbQ794k/Mf3iFw3hntbZzFr7zWdvs7ixmubxbH3DJT27mTev0LEiuEa7fFj6D5G/LaKhN+WY9esNxUGLSIzV+iFZc6BG5i91AK7xj2JCZ5C9oP7su41o9LJLDKQXHs1lGUWi6bvoj6zi6wH9zF39nxhvJzYTSzyHy9ujji1DeXfW0jkqpGEzuoMgGRmwX0re8YnDiY8PJzatWuzZs0asrOzCc+xJSP0bxzbvA8KJYnHN5AVcZ3rv/nAhHY6e53FHdfFnFZVXiblwh5EThaxW6bi3muqwbBri+Xm2aMknd6Mc8dPsfVtq1eWJ+P2jXuisLAhauOXuHQZS0Tlgp8eM/Wa0ZHk2quhLLN4ODy+UUqdv8OkIbCbWOQ/XpIc5s6euPX+FoW1IwBmrpWx86zOihUrOHXqFBMnTiQmJgY/Pz+ktHhcuo/HrmE37F7uQqVPNmFduxXJf2551DdJR6+zuOO6mDM79u6jXySJzPtXNZbfkH1MObYaC886+RYh+mJ5eryc/xu4vjmOuJ3fYRlxrtQs2pDRLETk3KuhrLK0dYgFhRJz18oGx25iMaxeM8XJbeXlS6WP1mHXvC/ZsaGsWv8/4uLiuHnzJomJiURHR7NlyxY27j+FS538ZeIrvPEhKpFDhQoVOH36tE5eZ3Hjtc3ySStvMiOuYevfAYW5Fc4dPzUYdm2xTJw4kcwHEZRvP0TvLAXFW3r5UqnPNKL3LmHdunWlzq9pGc3NqnLt1VCWe83Ur+HNd7NnY2drjaJ8Tdn7aOo1Y9i9Zoqbu0rN+oQfWcc7nVpTo0YNAMzNzfPqYhSUf0qP+iz5dhzHjh1j1apVKBQKmjRpUqZ8eVH8jvXLOH7yJPVHLkHh343K1V6STT8rffi4d+9ePvzwQ6ZPn867/frK4thbUPw3/VowdkgfBg0aRFJSEi1btszrYqzvXjNG9fiuSY+kS19eeuklnJ2dOXnypE7mK41M+0vBKsu+NG7cGIA///yz2NvWr1+fFi1asGjRIk1j6VU5OTmkpaVhZ2dHVFQU9+/fJyAgAHj0RdqvXz+2bNnCnDlzGDNmTIE5yvI+87RSUlJwdnamX79+rF69+oXxcvDl/PnzvPrqq1SrVo3NmzdTuXJlnc1tqqxqkl6Umpqad7A3ySS56auvvuLs2bPP3PPxIuXk5BAXF8fw4cO1RKZ7qdVqOnXqhJmZGfb29tja2uLh4UHDhg2xs7PDzMwMCwsLdu/ejaurK9nZ2fpG1rsWLFiAubk5P/30k75Riqz69euTkJDAu+++S7NmzUq0CNe0jOapGZP0o4SEBLy9vfWNYZJJBapjx46o1Wpu375N9erVi7xdXFwcAHXq1NEWms508+ZNTp06xY4dOzh8+DBHjx6lcuXKbNiwgaFDhxIbG8vGjRtp3bo1vr6+uLm56RtZNnJwcCA3N9fgytxLksSnn36Kj48Pb775JgMHDiQhIYFx48bh4+Ojex7TpRnjk658uXfvHt7e3qSnp2Npaan1+Uor0/5SsMqyL8uWLWPUqFGkpqZibm5e5O0mT55McHAw1649W1PEkJSWloaTkxO5uY9qSKxfv57evXuXOm9Z3meeSK1WY2NjQ82aNTl//nyRtpGjL5IkIUkSkydPJigoiNu3b6NUKl+8YQnnKujSjFGdETG2MsP6Zrly5QoKhZJX5x0nKinToNhNLPph0SW7Q3YclxaO5MMPPyx0EVJY/pCQkGfO9BmiL40bN8be3p4f95xl7sGbjDufzqI7hw2CXQ4smZmZLFy4UBYsJY1ftGgRXbt2ZduZW4SGTsXCwY3a/acxbWg3jd+sWpiM5qkZYyszLIcS7xcfmvPL8vlkuddGZe8uex9NJd7LZon3guJzUh9ya+kIzF29GfvdimKz7P/fKsqXL0+vXr0M1pfZs2cTHBzM3E2/Mf23ewZ5jNGnj5IkERgYSLdu3ahRo4bB+tK4cWP2X41jSIeGAJR7uQvh+1fwl2UDKrnam0q8a1LGVGZYLixzH5fCNnMob3DsJpayVeL96fjU6yeIWDoYpZUdrn1nlojl2rVrtG7dWievs7jxRY09f/48SqWS7/dfNDh2E4tmWUaP/QIAz0824dhyANY1mnB37Vgm/fhLgfGaltEsRIylzLCcWO7euASAys5VZ4yFjRuyj8bCUtzxYsVGRPDPb5sIW9KfuG0zsarRHI+hy1CozEvEolAo8mowFJdF2+NFjV22bBmVKlXiyvppOmcsbNzQfFSpVMycOVMWLKUZjzz5K0p7d5SWtgA4vTYS+6a9+efn6cyYMaPAbTQpo1mIGEuZYTmxpB5fi5lrFZ0yFjZuyD4aC0txx4sae+jQISpWrEjC0TVYevlScdQ6XLuMyXvSobgsDpkxpKSk5NXWKEkOOfhiZ2fHhg0byIkPJ+XKEZ0yFjZuaD5u27aNkydPcujQIb2zlGbcp9unuPf+Ju93SZKwqfkK9T/6gXXr1rFgwQIAhBCkpqYWmKM0MpqFiDGUGZYbi1V2ItaeNQ2S3cRSdkq8f/rpp7zyyitsPfMvXj3Ho7JxKBXL/U2TaNiwITVr1nxhrJx9AWjYsCF1GjQm5cxWg2OXA0v79u1RKBSYmZnpnaU08XMnf46dW6Vn4if2as6+ffsIDAzk5s2bvP/++9jb2/P222+Tnq65hnhGc7NqWS8zXJx4Xfny7bQpdO/WjWyXGgbho6nEe9kr8a5Wq/n4449Zu3YtrzfxLRWLu2Uu5a9u4lLICc6dO5dX4MsQfXmilJQUxn72Cb36D0HtXtPgjjFy8HHp0qVkZGQwdtg7ZdIXe3t7QkNDOXv2LFu2bOH+/fvs3r2bVatWMWDAgALzFabCblZFCCHLn0dopVNSUlKpc5RF6cKX5ORkoVKpxKZNm7Q+l6Zk2l8KliH7MmPGDKFSqURubm6pc1WtWlXY2tqKIUOGCCEM25cnCgwMFDY2NhrPWxa8KaoaNGggOnXqVKRYQ/VlxYoVokaNGiIgIEAIIURCQoIAxNWrV4uV5/H3+jPf90ZzacYk3WrlypXk5OTku45ukkmaVps2bahUqRLNmzfnl19+ISkpiblz59KrVy/8/PyYMGEC06dP10jly/DwcKpVq8auXbvYv38/d+7cYcaMGcTHx2vglehHs2bNynsE2aTiKyMjg8uXL/Pqq6/qG0WrevXVV7lx4wZJSUnAo4qygYGB+WqolEoFrU7k8IPpjIjWpAtfnqyYH1fINQiZ9peCJWdfLCwshIuLi2jevLlQKBQCEDY2NqJu3bqiY8eO4sCBAxqba8GCBcLe3l5YWFgIQHh6egqlUinMzMxEuXLlxOLFi8WlS5c0Np+2tW3bNqFUKkV6errGc8t5n9Gkdu/eXawzbobsi62trXj6e/nQoUPC19dXZGVlFTkHhZwRMZV4N0Lpyhe1Wo2ZmRljxoxh9uzZWp+vtDLtLwVLTr7Ex8ezfPlyhBAolUoWLlyISqUiNDSUnJwckpKScHJy0iqDWq0mJyeHiIgI3NzcGDVqFNu2bSMhIQFJkhg3bhyBgYFaZdCE/P39yc3N5dKlSxrPLad9Rpu6ffs21apVK3K/GUP2xc/Pj7///vvJiQIyMjKwsrJi2bJlDBs2DHjUMmD06NHY2Njw9ddfY29vny9HYSXe9X7mo7AfTGdEtCZd+hIQECB8fX11Nl9pZNpfCpZcfNm2bZuQJEnY2dkJJycn4eDgIPz9/UVISIheeAry5dtvvxUqlUr4+vqKsLAwPVAVTUeOHBGSJIkbN25oJb9c9hldSJKkIu+DhuiLWq0WmzdvFoCoWbNmvr81a9ZMNGzYUNy+fVsIIcSGDRtEkyZNxJAhQ4SXl5c4ePBgvngKOSNi6jVTBvodyJmlzisdWLt4BlXG7TY4dhOL7lmeFz9r1iyaNWvG8ePH88V+sv86EZvlsX/tUTbFtd93XFo7mgnf/cjahdP1xvK8WBcXF4QQef1y5LQPGBqLu7s78+fPZ8OGDXpn0Ub8u+++y8aNG4FH9xQ9rZ9//hlvb2/Cw8Px8fHh/PnzdOnShQkTJrB//34GDRrEiBEjmDBhQqHzghE9viuXuv5yYNGlLzMW/EB2QiR2Td6SvY+67jUTeesy2Q+jSLdwNPlShPid65dRt25dunfvrneWmh52hfry4OCP5DyMIq3JMLzcnWT5nqrVaubOncvEiRPZdSnaoI8x+j722tjYMHfuXHw7Dy6Tvlz6Yx+vvvoqwcHBNGrUKN92YWFh7N27l7lz5yJJEufPn+fBgwe89tprVKtWjX79+jFq1CisrKx4+eWXTb1m5FTX35hY0u5dxtLnZYNk1yZL2I55RK37jOiN4whb8i5JseFG78uT+NTMTBJPBRO7cw4PT/6PqBPBDB82jFu3btG2bVtZsj8dm/7vX9jWa0+2hb3eWQqKzcjIoFGjRvj4+GBpaSlbHw2FZfjw4eTk5DAlaI/eWbQRv2nTJhYvXoyXl9cz29WuXRtXV1eWL18OQLVq1bh582be38uXL8/+/fuZMmUK27dvL3BuwHguzciprr8xsSgsy5Gb/EBnjIWNy83HjH//olyDzji0eJeonycQ+dOHKEeu1QuLrud83vj9qAeELx+KOjsdVTlX0m+dAXUuCksb+vTpw8CBA/XO+EIf1TlYevvJg+U/41lZWdSqVYu0tDRu3bqlN8bCxg2RJS4uDoCYxDTMC6igXtZ8eVoKhYI1a9bQokULatWqxVtvvQVATk4OKtWj5UW1atXYvn07HTt2LDzPc2cpQ5JTXX9jYrGs7E92bKjOGAsbl5uPKidPUi4dIvWfE5QfuADJ0pbkvXMLzFHezkKrLJrOPWHCBDw8PPD390dx/RBqtbrA+CtXrjB58mR++eVRh8/Lly8TueoDJKWSSp9souKwZXiN/gWvMVtoPDGY9evXa529pOP5xiQlGaEXUedk6Z/lP+Nt2rTh4cOH3LhxAwcHB62zFHfcEFn++uuvRzdchp9Hrc7RK4uuxp9WrVq1CAgIYNOmTdjY2ADwxx9/5Itp2LAh3333XaE5jGYhIqe6/sbE4uLfHnVGCvGHVxocuzZZvPt9g1XVAOIPLCH57DbcWr5L0u1zfPHFF3Tu3JlatWrh6OiIUqnk9MT2hM7qQuh33Qhb1I/o4CkQe1t2vpw7d47GjRszc+ZMmjdvjpubG6G7FnN//lvE7pxDTkp8XrzTpZ/x9fXlhx9+oF+/fiiVSnx9falY3p0qHyxHoTLXKXtp45+OdXx1EKmXDxE2vxfv1DR/JlaXLP+NvXDhApMmTcr3iLNcfTQUlg4dOjB06FAijmzg4a65L4zXJouu4v8rBwcHoqOj8+7f2rBhwzMx7733XqHbG83NqoZS118XuXXpi3cFd84lWhJ5YAUvtenNlB7+svVRl71mKjnb8q+tL/GRYWT8c5Q1a9cTcTWE3377DUmSqFOnDm+//TZff/01CxcupHyDdty18CFTZYs6+gYPTv7Ca43rUr9+fdn40qpVKxITE5k1axazZ8+mf//+TJgwgX8fpPP3sb08PL6R9Mu/kXJ6Ezf+PktQUBDr169nwoQJvPHGG8ybN48vP/8ML1d7g/icFuZLtlNVqjbvROQfwUz9/EMqVKigU/bnxQYHBxMVFUX//v21ymJsx94uXbqwatUqXqrmg23N5mXal4I0adIkbt++zZIlS9i3bx9Hjhxh/PjxeZdnnsjUa8akPOnDF2dnZ9GtWzedz1sc6cOXGzduCEmSxPHjx4u13ahRo4S1tbWWqPKrIF8SExPFF198Ibp16yb69esn5s+fL6ysrMTs2bMLzXPmzBkxZMgQMW7cOJGQkKBNZJ3oeftLaGiokCRJTJo0SYdEL9agQYOEj4+P1ucxxmOvJEnizJkzz40pi76sXbtW2NjYiKZNmwohhNixY4cAxLJly56JpZA6InpfcBT2Y1qIaE/68KV///7Czc1N5/MWR/raX5o3by6qVatWrG2ys7OFQqEQe/bs0RLV/6sgX9zc3ES5cuVEvXr1RI0aNUS5cuWEl5eXSE5O1jqPXPSi/WXVqlVCkiRx6NAhHRG9WCtXrhSWlpZan8cYj73Ozs6idu3aIjs7u9CYsuTLxYsXRaVKlYQkSeLNN98UkZGRQohHBdAkSRKASElJybdNYQsRo7lHxCT9ql27diQnJ+sbQ5basGEDd+/epVu3bgXe2FmQVCoV3t7ebNq0Kd/49u3bmT59et4jdPHx8ezYsYOHDx/mi9u/fz8uLi54eHjw1VdfFXneJ0pJSWHOnDlcvHiR69evk5SURGhoKLa2tsXKU5Y1aNAgatSoQYcOHRgxYoS+cQDo27cv2dnZfPHFF/pGKXO6cOEC//77L9OmTdM3italVqtp0qQJFSpUICIigm3btlG+fHngURn3P//8E3jU/LRIKmh1IocfTGdEtCZ9+BISEiIkSZL1KXl97i9Hjx4VVlZWxSqHP2TIkLyzTJcuXRIVK1YUCoVCODo6CiCvOZuZmZkARJ8+ffK2dXV1FS1bthTDhw8X5ubmwsnJqdCW3v/1JSwsTEiSJC5evFiCV1p2VJT95eLFi8LS0lIARW6Mpm2tWbNGKBQK0a5dO60xGeuxd/jw4UKlUhVaOr+s+BIUFCTMzMyeu/8AYuLEic+MCdOlGZOE0J8vHh4eon379nqZuyjS9/4SFhYmzM3NxQcffFCk+MjISKFSqYSzs7OQJEk0bdo0b6EXGhoqNm7cKKKjo4UQjzplmpmZCV9fX7Fp06Z8C4nU1FTRqFEjYWlpKWJjY5+Z57++LFiwQNjY2JTmpZYJFWV/ee+994SlpaXYtGmTDoiKrpCQEGFlZSU6duyolfz6/izpU7Vr1xZeXl4FdjUuC77k5uYKT09P0bp16+fGJSUlCbVanW+ssIWI0RQ0A8Oo61+WWZw6jeHgyrHEx8cX2iFVruy6YfFk3bp19OnTB5VKxeLFi1+Y+/z586xbt45OnTrRsmXLvPhzD5QsCXVnwryQvPgrV67QuXNn3nnnHVq1akW9evUAOHA9AcWb08m+/g5etV9m4/6Tz32d5lUCSE1NxWPAXHxq15ehj/Jh2bdvH8OGDaN37956Z3k6NiAggEOHDtG8eXM2b96MqmpTWftoSCwWb3zJtcXDcavViLVb95Y5X9q1a8eDBw+4ePFiobmAYnUZNprHdw2lrn9Z6zXzdHyWtQtJp4MRzlVo3+zlF8aX1Z4qz4vt2a4pNWvWZMKECWzZsoW+ffuy79qDQuNb+lWjffv2ec3Lnpe/XlVPvp8+gcmTJ+c90/8kNiE9G+sazYn/Yz07du6hapPXqFvJuUBfAg+FkXBuH7kp8eRWblymPhvFiX/R/rLnwG/E/bWPWrVq0rVrV9n5UqlSJe7cucO3gTM469CqTBxj5LB/JWOJhWcd4o9vJDhoGanmzrRvHmDwvsTFxdG6dWv+/PNPQkJCqFy58jN5XiRTrxkDqetf1lkUNo58v3Auq1ev5vfffzcodl2x9O7dm1u3bhEfH4+LiwuD+nQn+UGU1llUdi54vLeQzPhw3m3bgKSkpELjbXzbkXH3vEZZSsOu6dylZUk6t4uw9eOwKF+NBQsW6JXlebHLly8nPT2duEu/a4VFm+xyZrGsVIeKozZg4e3HnPEjiYmJ0RuLpuJHjBjBtWvXOHnyZN7ZVE3JaBYihlrXv6yxuLz5Belx4YwcOZI2bdrw+uuvazR/YeOG5qO3tzdhYWEsW7aM5Oh7hC8bQlZ8uNZZzF0rU3HUWlCZU79+/WeepnkSb+lZB3VWeqF5dDEu5/c09dIhLLzq4dxnZoFPEsnFF3Nzc5RWdmRF3ixSvLbH5fyeFndcZW2HW/cJSOZWBZY3NyRfxo8fz+bNm+nUqRMNGzYsMLY0MpqFiKHW9S9rLJYVatLk6+2kpaVx4cIFfvvtN3799VeDYNcHy/vvv0/jcRsxd/Mhau1nqLPStM6iUKio/8kKoqKi8i0Un47Pir6N9FQZdrn7qEuWmM3TyIq6iXWNpnpnKcq4lXMFUv85Ts5/mlMaArshsDhWa8CGDRueOcNoSL78+OOPTJo06ZlyAZqS0SxEDLWuf1lmqVevHk2bNuWbb74xOHZds1R+bw4KMwvu//AeOYnRWmeZ2LMxJ06c4MiRIyxbtixfvFlmMol/BmPlE1Akdjn5qK34EU3L82D7DO7Ne4v0u+dxf/c73Jt0Mwhf5ny/HAlB+A8Dif5lMmp1juk91WDueQuXEBkZib29PYsWLdIrS0ni7927R2Ji4nN7xZRWRnOzqqHW9S/rfSAqV67M7NmzGT16NH6VXY2m10xxc1dytuWOW0ui/jpI5tUj/DhjotZZypcvj62tLQsXLuTo0aNkZWUxd/IYLm9dhJmlDe79ZuLpZFumPhvFiX96f+nZvhkpUaG4N38Lh64TqFzFx2B8CahRiXpv9OOy2pPI3zdgK+Uw77MBZeYYoysfC4vv3awaH3/8MSEhIYSGhjJgwACD8qVx48a4u7szadKkArctjky9ZkzKk9x8cXBwEJ999pm+MWTnS0EKCgoSVlZWOp1z69atwtvbW1hZWYmqVauKy5cv63R+uerp/cXGxkYW+3BpNW7cOAGI7t27lyqPIXyWdC03Nzfx1Vdf6RujWFq3bp1QKBQiPDxcI/kwlXg3Sa7q27cvK1asIC4uTt8ostfRo0d1Xka9Xbt23L17l7S0NG7dukWdOnV0Or8haObMmcyfP5/4+Hh9o5RKM2bMyLtv6/79+/rGKVNKT0/H09NT3xhFVkZGBv3792fAgAEFdpDWpEwLEZP0rgULFuDq6oqbmxtffvmlvnFkq8uXLxMUFMSsWbP0jWLSf9S2bVuEEKSlpb04WOa6dOkSlpaWWv/yMTYplUqD6rc1ffp0zMzM+Omnn7Q+l2khYpLeZW5uzu3bt1m7di2zZ88mIyND30iy1KpVq3Bzc2PQoEH6RjHpP/rjjz+AR80IDV23b9/G0dERhSL/10N8fDxqtZqcnJy8J91MKpp27drFw4cPn3kKTa6Ki4tjxowZTJs27Zn9QBsyLURMko2USiUKhQJLS0t9o8hSfn5+JCYm6hvDpALUr18/4NFNmoau7t27ExUVhY2NDR07dsTf358vv/wSZ2dnlEolTk5O9OjRg127dukb1SCUkZFBt27dGD58eL4KyHLWm2++SYUKFRg3bpxO5jP85XsxZIh1/Y2FJSYmhsGDB9Oq01s0n3nYoNh1xfLDHUcyMjJoOGkrE3s2LpO+GCp7nz59UKrM6Lk8hLicSwbvy6YTNzm/YyU//PADVlZWzJ49mwYNGtCvXz9iYmL4dc8B+nzwOa7vSrJjlxtLxKntKJVKli5d+sylGTn6cvPsUWJPnmLOxj2FxmpaRvP4rqHV9ddmbrn5kpWVRd26dTGzLkfWaxNJSM/Wi49y6DXzvNwPsyDpz81kWLtxOtGuzPmiKx813Wvm/v37fDhyJG4dPibTpUaZ8OX4vwkMfKsLaxYG8vnnnzN58mQ++OADmjVrRqpLbVYHrSEnJwdb/zcM4hijLx+3/RxE8KKpfPzxx7z++uuy9yUuJYOodWOw8nmZf91bFRpfUpl6zRhYXX9jYRk8eDCWlpakpKTgNWgBmbnCYNj1wSKZWZKTGC0LFn3Hy4Vl3LhxqGwdsajTVu8s2sr99H0C3+2/Tm5uDkKdW2i8nNj1xZKTFEv0/h/xeKUX8+bN0ytLUeLD93zP/flvgToHl65jnxuvaRnNQsSQ6vrre05dsXTs2JF169axZcsW4uPjic0y02h+TeeQA4vIyURVzlUWLPoelwNLWloav/zyC9a1274wVlfjOplTrUZhaVv0+CKOy+E91UQOtVpN5NrPUDlVxLzZAL2yFGX87p1/ST63i3INu1Nh+E8oHrdvKCxe0zKahYgh1fXX95zaZilvZ0Hjxo05fPgwp06donv37hrNX5Z9FDnZqJwqyIJF3+NyYAkPDyc7OxtP3yZ6Z9HlnMpyLmSGXSLprx0azS+H91QTOVLO70ad+hCP/nP0zvKi8Xv37hG1aiRmbj44tuyPysbhhXk0rRIvRCRJspQk6YwkSRclSboiSdLUx+MVJEk6LEnSdkmSbB+PTZEkKU2SJLentk8pPX7RZSh1/cs6i4WUy73lH3DlyhUuXbpEQECAwbDLgkWhJDv2rjxY9BwvB5YVK1YAMHFgR72z6CL3k3hbb18QgqTTmw2OXRcsGXfOAwLi7uqd5UXxq1atwsLCnCpDFhc5v6ZV4ptVp06dmgv8LIRYNHXq1BVA4NSpUy8DA4GZwB3glSlTppydOnVqa+AloNyUKVN+e7z9hClTpgQ+J7+p14yWcuvLF3fLXO4vH05ORirXr19/5lE2ffso514zT+LjI0JJ/msHsyd8St/mNcqUL7r0URPxsbGx9O7dm2nTpjHy3R5G5Ut13wb8cS2CpFt/Ub1VD6b2bCCLY4xcfMz1akjiqf/R6/UWfDGgs6x9iYuLI/iXX1g+f0aR85dUhd2sKj0q/146SZJkDRwHRgBvAasAH8BLCPGjJElTHoe+BzQQQsRLkpQihCi0VrUkSaK0bMnJyZQrV65UOcqi9OFLREQEdevWxdramqtXr2Jnp7k7sTUlQ9hf1Go1Pj4+uLq6EhISopM5DcEXfejrr79myZIlPHjwQN+JxpXvAAAgAElEQVQoelFSUhL29vYEBgYyfvz4fH8z9n0mLS0NOzs7Jk2axNP/oZajL3379uXgwYM6abEhSRJCCOm/46WqIyJJkhL4C6gGfC+E+FOSpChgHZAI9HsqPIVHC5RPgK+Lkr+05XBTU1NLtX1Zla59uXPnDm+99RZ169Zl27ZtSJIky1LHhrK/BAcH07NnT7Zv306bNm20Pp+h+KJrpaenU6tWLVnuy7pQSEgInp6etG/f/hkPTPsMeHh4kJSUlM8bufkSExPDiRMnWLJkiV7341ItRIQQuYC/JEkOwK+SJNUVQlwGWhayySLggiRJc4uSXxMrR7mtPuUiXfkSEhLCK6+8Qv369fn99991Ui64NDKE/aVhw4a0a9eO3r178/DhQ51UojUEX3StrKwswsPDjdYbSZK4f/8+bdu2LbDir7H68kRmZmaEhIQ844OcfOnWrRtKpZI+ffrolUMj3wpCiIfA78AbRYjbCHyoiXlNkrd+//13mjVrRtu2bTl9+rTsFyGGpFWrVmFlZcWIESP0jWK0unTpErVr19Y3ht7Upk0blEolXbp00TeK7JSWlkZYWBg9e/bUN0qhysjI4Pjx41SvXl3fKCU/IyJJkiuQLYR4KEmSFdAOKEpb0HlASGnmLqnkWE63LLO8/fbbVK3tz22HAFy7fkGNZm8YDLshsAQGBvLRRx+xePFibG1tXxhvCL4YEvvdu3eZNGmSLFj05YtaacHh0Ex+3HGCEV2bGxS7NllCQ++Sk5NDDPaFxuqKpbD4Ll26YGFhkddPprj5NakS36wqSVI9YA2g5NGZlV+EENMKiZ0CpAgh5jz+fR4wuqCbVp7aRqM3qz4pYft0tTkrMyUzevg+Y3ZxYrUdr43cuvJFkh6/vQolqHNROVZAaWZBowZ+HNv7bPdOfftYUl+0wVLU+GGv16dNmzZs2rTJ4H3RBrs24728vNizZ8+je5+M1Jf4QytJPrsNgAW//MYnvR4VdjP2Y2/qP8eJ2z6TKiOWM29oR50fe18UHx8fj4uLCzt27KBz587Fzl9SFXazaonPlQsh/hZC1BdC1BNC1C1sEfI4dsqTRcjj3z973iJEG5JbOV1jYDGzdaRcw+54j92Ox5ClqBw9wMKGP/bvYNmyZbJmNxSWyZMns23bNlmw6Dpe3yxCCJycnGTBoqvc/413ajsEx/aPrrSP7tuBtLQ0g2HXJsvDo0GYV6yN2q6C3lkKih89ejQuLi507ty5RPk1LaO5aC+ncrrGwlJx5Dqc2rwPgLmzJ+69plK+30wsKtRk48aNsmY3FJZhw4aRk5PD6tWr9c6i63F9sqjVaoC8hYgx+2LXoCO2fq8jcrPJysoqVf6y4OODAz+SkxSL65tf6p2loHG1Ws2mTZv48ssvS5xH0zKahYhcyumaWMCpck1OnjxJvXr1+PnnnzWe31h8rOBghbm5OZ6enhw7dkzvLLoe1ydLRkYG8P/N4IzdF8fXRmBm50zbts/23JE7uyZZ0u9dIuX8Hlw6jUFVzlmvLIWNz58/H4VCwejRo0ucR9MymoWIXMrpmliULJg7hxEjRuDu7s67776bV0jHENjlyJKQkIC/v78sWHQZr08Wa2trrK2t+fzzz/XOosvchcXbWFgwcuxkzp07R9++fQ2KXZMsCXsXYuHli03tlnpnKSi+V5VcvvrqK5o0aZLvKcbi5te0SlziXdsylXjXXm59+9IzwIsOHTowYMAANm3axKRJkxg6dCgB1SuYSrwXM3daWhpTp05l9OjRVK1a1aB90aePJYkPCwtjyZIlTJw4kVoV7I3el1E9X2XevHkkJibywQcfGNyxd/ny5XTo0IGvvvqKSZMmFTt33O2/+WnpEup9MJ8MLGR57PXIjSYoKIjt27fj7u5e4vwlVWEl3hFCyPLnEVrplJSUVOocZVFy86VOnTrC3NxcJCQk6JVDbr4URb169RJOTk4iNzdXa3MYoi+6UEhIiDAzMxNff/21vlFko5UrVwpJksSJEye0NkdiYqJIT08vdZ6NGzcKR0dH4e/vLzp16iQUCoVQKpXCxsamRPmaN28u/P39nxuj789SWFiYkCRJ7Ny5Uy/zP/5ef+b73mguzZgkX12+fBkvLy/c3d35+++/9Y1jUPr111+ZN2+eqVicHvTSSy/h5+fH4cOH9Y0iG73//vuoVCp69+7Nrl27NJ5/z5492NvbY21tne9my6IoLi6OGjVqYG9vzyuvvMI777xD7dq1uXDhAqdOneLIkSN8/PHHpKenF5s9KSmJU6dOIdcrDE80ZMgQvLy88p6WkY0KWp3I4QfTGRGtSY6+5Obmipo1awpXV1dx69YtvTDI0ZcXSaFQiKpVq4pmzZqJjz/+WPzzzz8an8MQfdGFkpKSxNatW4VCoRCJiYn6xpGNxo0bJzw9PcXixYs1nrtNmzbi5ZdfFqtWrRJKpVL4+vqK+fPnF+lsau/evYWTk5MYNGiQ8Pb2Frt37xZCCJGcnJwvbujQoUKhUIjg4GCRmZkpxo0bJxo3biy2b99eYN7s7GxRvXp14eHh8UIGfX6Wtm3bJiRJynvd+hCFnBHR+4KjsB/TQkR7kqsvYWFhok6dOkKlUomOHTvq/FKNXH15nlauXCm6du0q2rRpI9zd3QUgrKyshJubm3B0dBQqlUoolUqhUqmEhYWFcHJyEjVr1hT169cXTZo0ET179hSpqanPncMQfdGFnvhiaWkp1q1bp2caealnz57CxcVF43mrV68uBg4cKIQQ4uLFi6Jp06bCxsZGqFQqcePGDREUFCSaNGkirKys8sbNzc2Fg4ODkCRJjB8/vkjzfPTRR0KSJCFJkrC2thZ+fn5CqVTmi0lISBD+/v7CxsZG2NnZicjIyBfm1ddn6dChQ0KSJPHhhx/qZf4nKmwhUuLKqtqWpiurmvT/krMvarWaFStWMHnyZJRKJRERETqbW86+FFVpaWkEBQURHh6Ora0t/v7+2NnZkZaWRkpKCleuXOHq1aukpaWRnp7OwYMHCQwMzCvzXJDKgi/a0BNfVCoVP/30EwMHDtQ3kmw0Z84cxo0bx7lz56hXr55GciYlJeHq6kpgYCBjxozJ97f69etz4cIFLC0tqVOnDkOHDkWSJLy9vUlJSeH27dv4+fnx+uuvF3m+mJgYQkNDadiwIWvWrOG9994jNjYWFxcXAAYOHMivv/7K+PHjGTlyJHZ2di/M+d/P0p07d9i3bx9Dhw5FpdJO15OsrCy8vb2pXbs2hw4d0socRVVhlVWNaiFibP0OCos3BF/u3rxGxKpRrPv9Cu+2KryxmCbZS+OLpll0Fa9QKJj3836C7yhl4Utx4/XJ8sQXe3t7bGxsGD55Hgceuhm9LwCJiYl07NiRf/75hwcPHpSKfXgjV0b3ak1KSgpOTk6Eh4fnuyfqSfz9mAQ83Ry14uO1Axt5eGwNCzYdYO6Y94iJiSE7O5tJkybluy+kOMeYEydO0KJFC4QQ2NRuhV//yYWyjB07lh+XraDqwJkk2VQq8ns0dub33P51PgqVGUF7T2vsWFpSFbYQMZrHd5/U0o9Pe1T5Lzkjh6M3YvF0tKKmR/6VbHFitR2vjdyG4EvcnoXkJMVwxb09lZxtdeJjSX2Rs48vip86dSrXXVuQJKz07ouh+fjEl6FDh7Jl92/8b9l8Ym6eR2FtR4a1m9H6ApCdnY2/vz9LliwhKsuCn64rSswePGs0UkYin378EUFBQfkaPD4dLylVWvNRaetE0vk97PvfKpJTUsjOzsbMzIyDBw/m9dQqzjGmS5cujB8/Husq9VFnZ5IZ/g8xN85xLEKNs4MDflUePVobExNDr169WLduHWpzG6LO7MK6ZgtSJcvnvs5vVgTz8aDexJ7dg02tVri9M5PT91I1tg+UVIU9vms0t9obY78DQ2bJuPMXTu2GkymUBsduKCzx8fEIIchUK14YKzd2ObG4uLhg0+1rnDp8jDo9kdgt01DnZBm9L40bN+b1119n2YyJPPjndInZ00L/JkOtxMPDAzc3N52w/zdWZe+O95iteH+5iybfHuC1114jKysr31N+Rcl96dIlFAoFu3fvpmqvL3HqOYXygxbj3HE0uWmJhG2cxMC29fDx8cHPzw9PT08uXbpE9d4T8Ri2ApVjBSKWDyMrNrRQ7i+++ILJw3qD0oIKQ5fj0mk0CoVKo/uApqWdi1IylLH1OzB0FpWzJ2nXT1LOv4PBsRsKy6efforC2h4zh/J6ZynJuNxYytVrj03dtoR91xVyskBlbtS+KBQK9u3bh5mzJyl/H8S6asMX5ilorFzD7qTfPsMnn3xCbm4un3zyidbZXxR7fedO2rRpQ8OGDalZsyZvvPEG/0a5Y1GxVoHx9+7d4/r160ydOhVbW1suX75Mqx8uoQAU1vbY+rbF1vdRefycxGheuhuMubk5ffr0Yfz48VQZtxsFIKksAEHsthmIrDTuZabT7+9urF+/HoVCwd69e1m4cCEOrd/DvnFPrfiiDRnNGRFj6ndQFlhsXmpBZvg1jeU3Vh+fN3748GFc6rwiC5aSjMuRJTcxGgCFpa3eWeQy7lq7KZn3nq0PVFR2x5b9aTh6Ja1bt2bevHl5fX60zf68WHNzc44fP868efOoXLkyq1evJmr9F8TtXUTq9RPEH1xK3N6FqNVqEncE4u3tzWuvvYatrS2nTp3Cy8ur0Pze3pXZu3cv27dvZ/z48flYlNb2KCxtUVjYYF2jGZXa9Wfbtm0olUpsbW3p2LEj1apVo2b7flrzRRsymoWIMfU7MBSWCxcu0LdvX1QqFd7e3jTlel68XZOeiNwcwn94jx7ez3b01De7obGY5WZgeWo5VatWpVq1alhZWREZGcnHwwbJxpfixsuRRWllD0BOUqzeWbSRuyTx0ydPQJ2VTsLRNaViX716NcnJyfj4+JCTk6N19qLEfvTRR+zcuZO4uDg+mDCDjFt/ErfjO1KvHyf18hHifh5H4o0/Wbp0KTdu3CA4OJg6deqUmMWtx0QqfbIJjwFzqdhxJAunTyYtLY3Q0FBmz57N0aNHuXLlCl92qKXV91TTMpqbVQ2l34Eucuvbl86+7gwePJgPP/yQyMhIpk2b9qiV/ffzGNq9DfEqF1KzBD7NOpB+5TeS798s8NFIQ+2pouv9yyriPLdXfkxiTDjt2rXDy8uLDz74gA0bNtCuib9sfJG7j/+NL8iXP/++SvTpHVR95U2+6dPMKH35rzf1q3oQlWnGyf8twbpaU7w8K5SI3d7enpEjRxIYGMi2bdsYPHgwtSs6yMbHzm1foV7H/oR5d0BRrysV6wSQfukADfzrsWTJElxcXLR27LW3t6dhw4Z4e3tr7T3VhEy9ZkzKk6Z8KUl/k/DwcFGuXDlhY2Mjtm7dmu9vb775pqhQoUK+sU6dOr2wf4OmVBb3l7FjxwpJksT7779f4hxl0RdNqCBfdu7cKRQKxQuLxJU1paamivDw8LzfC/KmRYsWwtnZWYSEhIhbt26JBw8elGiuq1evCmtra9GvX78S8+pLxv5ZwtRrxiRNKisrC5VKxVdffVXkbR4+fEjdunWpUKECSUlJdO/ePd/fly5dSmRkJCEhIXljv/32Gy1bttQYd1lWfHw8UVFRPHz4kKioKJo1a8a8efMICgpi5cqV+sYzCrVu3Rq1Wk1sbKy+UbSmrKwsrly5glqtJisrix49elCuXDk8PT1ZvXp1odvt27cPe3t7GjVqRPXq1XF2dqZBgwakpaUVa/5atWrh5eVV7O1Mkq9MCxGTSqTAwECEEEyfPh0/P78XHhTUajX169fHxsaGv//+u8AmbeXLl6du3bqMGDEib8zBwYH9+/ejVqs1/hrKgtRqNb169cLMzAxnZ2c8PDxwdHTEw8ODO3fucOnSJQYMGKBvTKNRZGQkQN4p8rKmc+fOUa5cOerWrYubmxsODg4cOXKE//3vf3To0IHJkycX+lm1trbm9u3bxMTEcOLECW7cuEFYWBg2Njbs3bu3WBzZ2dk4ODho4iWZJAcVdJpEDj+YLs1oTZrwxd3dXQwcOFCcOXNG2NnZiQEDBjw3fsCAAcLCwkLExsY+N+7ixYtCkiSxZs0aIYQQt27dEmZmZmLGjBmlZn6RDHF/adWqlbC2thYbN24s0aWyosgQfdGFCvKlU6dOonLlynqg0b4+/vhjoVQqxeuvvy6Sk5NF3759xahRo/L2u7CwMGFjYyOsra1FvXr1hJubmzh+/LgQQojMzEzx2muvCS8vL6FQKIQkSaJy5crC0tJSSJIkAGFhYSEA4eTkJLZt2yZSU1NFenp6gSyNGjUS7dq109lr15SM/bOEqemdSU9UWl/OnDkjJEkS0dHRQohH3Tbt7e0LjQ8ODi5W18fPPvtMqFQqcffuXSGEEEOGDBHOzs6lYi6KDHF/USqVYufOnVqdwxB90YUK8qV169bCy8tLzJo1q9AvUUPUrVu3BCAWLVr03LjMzEwxa9YsMXXqVNGmTZu8BYalpaVwcHAQQ4cOFatWrRJ3794Vbdu2FRMmTBC5ubkiMjJSbNy4Ufz777+iZ8+eeYsTQDg7O4vhw4fn3VPyzz//CGtra/HBBx/o4qVrVMb+WSpsIWLqNSPTXg3azF1aX/q93pT02DCafLOfLzvVoXUVG5ydndm8efMz932sOvAXQzo2wca3PfV6f15k9pB5QyAjiU1HztO6ig0uLi78+eefNGzYsMB4Y+w109XPA6VSyYMHD3BycioTvmiaXZvxBfny5Zzl3AyejQSoszPYuXMnnTt3NnhfunXrxrlz57h3716R4p94Ex8fz/79+9l7+jI3nVsQlZZbJPZZe68R8TAdF5KoGHGUU/t/JS4uDnNzc7Kysqhfvz5nzpxBpVIZlI9l5TuppDL1mjGwXg0virUVqWxbu5Rjx47h4uKSV/pYF71mMnMh498QEmIj+TPXh+oVnMiKucPcuXMZMWIElpaWAExesp7PB3ZH5eCBe68pxfLFplZrHp76H3uOhdCgbVcuHtvL2bNn6d+/v0Z9NOReM7lxdzmwZT0zZ87UKoup18zze808HZthWwH7pm+jcvEm7doxWvUbRUCNSgbty6+//so333zD6tWrqVWrVrH2GSsrK27nOLH2X0sSMnOLzJ6Qno0kSaRLliQ512Fh4Ncs+XY8/v7+LFu27FFVYIXCoHx82hc5sJQmvqQq7PFdozkj0nzmYcILKFdb0cGKE+Pa5BsrTqy245/EZsXeJeHwT4jsTNRZ6WTH3sXBwR5JkkhISMDW1pZ3332Xo1lViM9+qjDNYw/dylmwrH8AarWanJycvJbTw9f9RVxyxlPhj240c7U1Z0m/BvlYRm08R2zKox31we755CbFYF2rJf4Dvubo5y2pWrUq0dHRVKlShYiICJKSkrGqGoBLj4koFP/fTaCovqT+c5y4HbMImLCZ4ZXj+fDDD8nIyMi70VXT72lJ9xdtsBQlPutEELk3jhEdHa1VFl35og12bcY/z5eo9V+QFXuH2gO/ZWR9K/r375+vWZuh+LKqe0V8fX0ZPHgwS5cuLXL+snTs1WTusuBLaVTYGRFTrxkZ92oAOP/TBNJvnwEhUDlXQmntgNLWCcfWg4j+5dGjs1lZWUyZMoWffvqJmLgHeYuPpxUNtFj+6P2vWLEi4eHhAOSqC17sxQDtV+d/siUn99nYtGvHiHiYjkql4s6dR2dFzp07xzvvvMPyuOp5pa5L8vptar5C/G/LuLp1IYMvH2HkyJGsWLGC4cOHFytPcWO1Pa6p3MnJKbhYW8uCxZB91AaLS7dxRK35lCsrP2e0uTlffPEFkZGReYsROfly5/pV4g/+SGbkdVDnIplZoiznTLhaTe2J0TRv3jxvESI3dhOLfsY1LaNZiFRwsCpwxVdYj4Gixmo7XpmZhMLaAfd+MzF3+v/rdRWfijU3NycwMJDAwEC9rMqfcCsUCsaOHZs3vvcF8QWN/zfe1rc9qRd2o1AoaN26Nd9//33eQsRQ31NN5bZSqvNakOubxZB91AaLytYJz5FrqehgxR9ftMbDw4OuXbty+PBhnbP8N1atVtOiRQtcXV0JDw8n8uxZVC5euHQag5mrNxlhl8i8fxVrcxU/TVtK165dS8yiaXZdxptYTL1mNC5D7dXg4+GChWP5fIsQY+ph4fZKL3IzUrl8+TIBAQH5LkPInf3/2jvz+Jju9Y+/v5HElthqjaAUpRS1VC039rVFLW1vW6Xlpy4tem8XW2nituVSVd1QS+1VVWIpRSl6UbUvUVTsobWLRCpivr8/MtyomcgkM5mzPO/Xy0vmO895zmc+Mzke55x5Hl9qyaluEr//J9q1a+d3LWb2MTu0BAQEsGzZMjZs2MDjjz+eLVpSYn8m5eoFt7GbNm1i+/btXL9+nXHzVlH+H5PI+1AEwUXKkK/mE5TuPJjJX868qwjJDu1meE/trMUX2OZmVbPNarjFgnmzyQEUq93GMrNmPImP7FiDtQtnERQURLFixVi+fDlDhw71iXazzJoJuXiIM7PeIE/unKxatequ5nBm9SW7ffTFrBl3sSVLluSJJ55g4MCBBAYG8vLTj/tMe9z+rXw26P+4tnc1KQmXuBGzipb3BzOwe2pRcenSJUaPHs0vv/zCsGHDeKzqA5abZ2XUz5cVfMkKMmvGpDRu3Fg3bNjQqznN5svDDz+sO3bsqJOSknRQUJB+5513fLIfo/ly8+ZN/f333+srV67cXps+fbpWSulmzZrpS5cuZYsOo/liFDLjy5AhQ7RSSj/44IN61qxZXtc0a9YsrZTSrVu31p06ddIlSpTQZcqU0YAeOXKknj17tq5WrZouUaKE1/edFvnMuMbuviANzcxJo0aNdEREhFdzms2X8uXL6xdffFFrrfVrr72mCxUq5JP9GMmXrVu36oIFC2pAFyxYUGud2uhNKaWHDRuWrVqM5IuRyKwvU6ZM0aVKldIhISFe64Z748YN/dNPP+lq1arpIkWK3PX8c889p0NDQ3WuXLl0eHi43rlzp1f26w75zLjG7r64K0Rsc7OqWdFau7wh0U5cuXKFUqVSezGEh4eTkpLiZ0W+4/Llyzz55JNs2LCBpk2bki9fPjZt2kStWrXYs2cPU6dO5aWXXvK3TCEL9OzZkzZt2lCuXDk6duzI4sWLs5Tvt99+o1KlSiiluO+++xg/fvxdMXPmzMnSPgTBl0ghYnC01i4HxNmJxMREypYtC0D58uW5evUqU6dOpWfPnn5W5l0cDgcPPph6c+O6deuIiIggNDSUChUqsG/fPmJjYyldurS/ZQpeICwsjJkzZ/Lss88SExNDlSpVMp3rypUrOBwO4uLiCAsL86JKQcgebFWImLGdrtaa8wnJNBi11u9a/OXL9evXqVChAgAdOnSgd+/evP766/Ts2dPw2j3RErt2HpcvX+bSpUvkcfYHyZEjBzt37qRj9z48O/cwpy/vtcR7akXtnuYOrtCAvKWrUrVqVQLz5Kdylao80TyCli1b0rBhw9tNB++Vv3bt2pQsWZK33nqL2bNnG84XX/soWrJfi7exzbdmzNpOd/T4CRy/dJ2b5SO8lttMvvx44A/Ob0htY543b14AcubMybRp06je/v9M2crcVeyCyeNYMm0cA/r3v2M2SYcOHbiZvxTbCjTyWxtrafGe8RbvnuYOqtyUkOqtcCT/ybkzJ9ix+SemTv6CqKgo5s6dS5UqVShbtuw980+ePJmcOXPStWtXv/sC5jrGSIt3/7d4t805/zErD5J04+Yda0k3bjJm5cEsxfo6Pu5SIg5UhmJ9rcUfviRePg9we5YOwOeff879999veO3u4geOnkjspL7Eb40m+WIccRN7cmHjPMq0fpmxY8feEVu5cmX2hNax1HtqRe1ZyR0Yeh+Fmv0fxbqOpepb80lJSWHnzp2UKlWK5s2bs3r16tvxDoeD09P6cXzMkxwc3YkuDSoTEBDAwYMHGTlypOF88TRetBhfiy+wzaUZs7bTTU5xEJAjY7G+XvfHPm9euwJpCrGEhASCg4M5evQo10+cJDBf4QzlMcJ7umXLFqZPn86heZMILlGBS+umw9opBBUpS8m+MyBvgWzXaARfMrtuZS01atRgzZo1NGjQgDfeeIP4Nu8DcGHZB9y4eIrC7d/k5tXz3Iw/z3O1ivHQQw9Ro0YNn2jJznXRYnwtvsA2Z0TSa22blVhfrwflANTdb5M/tPjl9ecvCmg6dOhA8eLFCQ0NZdmyZeTNm5czU3qTfO64YbXfWk9OTqZt27bUq1ePhQsXUrpNL0p0+5Ayb0ZT6vWFhPX4hMC8BWzznnpr3Q5aRo8eTUxMDHGfvcCJj57m2oH/UrTTMPI+2IB8tTtQtdOrTJgwgX79+vlcS3asixbja/EFtilEzNpOt3i+nOTIEZChWF9r8YcveUPzU7NhU3799VeefvppDhw4QGJiIleuXOHBKtU5M70/SSf2Gk67w5FCwqY5qB8/omzZsqxdu5bNmzfzxx9/MP694bdjAwKDfa4lvXgjfdbNrN1XuRs0aMCRI0do8cSTFKrbkVL955K7XE2/aMmOeNFifC2+wDY3q5q1ne7ML6dSuFB+kq5c5OLutdxf5REiO9awVZvhcUNepV+/frRp04bChVMvxSileOUfvVizeTu/Lp5AUIESlK34kGFamSet+phLO1aQO4cmX758fPnll0RERPjVRyP4YvQW3J7EZ5cv+fPn5/kuHahYoy77/kgyvC9Z8cYI2sWX7G/xrrSLkfFGQCmls6ot7ZRZs1K7dm22b99OcHAwN2/eRClFQEAAycnJhISEULZsWeLj42nXrh2BgYFMnjyZSpUqMWvWLCpXruwypxV8ScvAgQMZM2YM77//PoMGDcp0Hm/6EhoaypAhQxg8eLBX8vkTq31evIX44h7xxjV290Uphdb6rg6dtrlZ1ax8+OGHfCz00JoAACAASURBVPvtt7z33nvExMSwcOFCHnvsMWrWrMmsWbPYsWMHoaGhTJw4EYBBgwYRHR1N1apVWbp0KW3btvXzK/A9//nPfyhTpgyvvvoqJ06c4PPPP/e3JJKSkm6fAREEQRDcI2dELEJycur3v4ODU+856NWrF1OnTuXatWvkypXrjlir+rJ48WI6d+5MmzZtWLp0qcfb/9WXlJQUunXrRnR0NMnJybdb7ffp04ePP/7YbZ7ff/+dEiVKcPXqVUJCQjx/IQbDqp+XrCK+uEe8cY3dfXF3RkQKEQtTsGBBHnnkEdauXXvHupV92bJlC40aNaJq1ar8/PPPd3WnTI9bviQkJNCvXz/mzp1Lrly5ePfdd6lZsyYJCQkcO3aMvn37MnbsWF577bXb2+7Zs4cJEyZQokQJvv32W86ePcuZM2d88RKzHSt/XrKC+OIe8cY1dvdFLs3YkBYtWhAdHc0XX3zByy+/7G852ULdunWJiYmhZs2alC9fnn379nl0VmLXrl3Ur1+fXLly8f777/PPf/7zrlk/+/fvJyoq6nYhcmt/RYsW5fr164SEhLB69Wqvvi5BEASrYqszImbt65/Z3KcuXiXu4+cIyqFISkywnC/pxV+8eJGqVaty/fp19u7dS1hY2D1znzhxgsqVK1OnTh3Wrl3Lkt1nXMbPmTOHHj16cP36da5du0bx4sWpXr06r388z5Q+3is2K58Xf2v3Zbz44pvPjL+1iy++mzXj7oyIbb6+a9a+/lnJrVQAV7YsJHfF+lSu29Ry8w7Si3+kXDH69evH7NmzGTFiBI6iFfno58suY3eu+45p06YRGRlJnjx52LZtG0t2n3Gbe0jf7pQrV45evXrx0ksvERsby3uzVzE0OsZ0PsqsGf/OmrGiL2CNY4z4IrNmvI5Z+/pnJNbhcLiND8hbgBsJlyw57+Be8cHBwezbt49WrVox/OVnODKlP+eWjuHUhB7EffEyl47H0K1zW1544QXmz59PvXr12L9/PwEBAW5z9+3/GidPnmTRokVA6j0pbdu2ZdyaWFP6aCQtZtYuWqynXbRk36wZ2xQiZu3r7y721MVEvvzyS4oVK0aOHDkoWLAgMTExd8SnJF4m5cJJgotXtOS8g4ysBwQEsGjRIop0HoYKykXy6YPkLFWVHHkL8sesN0g4eYBdu3YRFxfH+PHjb3/ryF3uP7Z9z4ABAwgLC2PLli2pXS9btDCtj0bS4um6aDG+Fk/XRYvxtfgC2xQiZu3rn3bNkZLM2UXvc2HlZ5z9si+9evWiTp06xMXFUaVKFWrVqkVo/DESYn7EkXyNgNz5CC5egSub51E8j4vJeVnQYrb1CrUbUfy5kZTsPYUiT/yL4s//h1JvRlM36juqVq2a4dw5gnOyf/9+ZsyYQdOmTWnUqBHdu3c3rY9G0uLpumgxvhZP10WL8bX4AtsUImbt6/9mqwfJqW5yZfM3nPr4ef48vpukQ5twXD3P7t27WbZsGWFhYWzYsIE6deqwd8KrXFg2llOfv8j56Pe5ceEkOG7SpYJLKab1xRva8+bMycA2rrvPusv92tD3WLVqFb169aJdu3b88MMPXtFiJF/M/J6KFmNpMbN20SKzZmw5a+Z+dY4OHTowatQoPv74Y4YPH868iWM5u34OyacPkKdSQ2r2+YiPR0Ux57P/ULRo0ds5lVL06NGDHj16EHdVczBmDylX/qD4o23p2rUb7w140bS+GGmmSr8uTRg2bBjDhg2jS5cut5ucmdVHmTVj/FkzZvMlK94YQbv4IrNmbmPHhmZDhgxh5MiRdOnShTx58lClShUqVapE9erVKVOmjNf2YzZfsgvxxTXii2vEF/eIN66xuy/S0MwEXL9+nbCwML755ht/SxEEQRCEbME294iYgaSkJIKCgvwtQxAEQRCyDSlEDIQUIoIgCILdkELEQPz555+3+1gIgiAIgh2w1T0iRu/r764QMdKMASNpMbN20WI97aLFetpFi3e/NeMO23x91wx9/RP3rSFYOejVq5dPtZjNF6vNVBFfrOGj+GLtY4z4IrNmvI4Z+vrH/n759ofU31qM5IvVtIsW62kXLdbTLlpk1ozXMUNf/+vXr991acZIMwaMpMXTddFifC2erosW42vxdF20GF+LL7BNIWKGvv6BOuWuMyJGmjFgJC2erosW42vxdF20GF+Lp+uixfhafIFPCxGlVGul1EGl1GGl1CDnWphSaq1SarFSKsSX+0+LGfr635cngFy5chlCi5F8sZp20WI97aLFetpFiwVmzSilcgDfA62AkcDHUVFRG4CXgVHAUaBhZGTkNlfb23HWzPqFMyldujSdO3f2qRaz+WK1mSriizV8FF+sfYwRXywwa0YpVQ+I1Fq3cj4e7HyqEDANKAeU1lpPcLO97WbNPPDAAzRt2pTJkyf7dD9m8yW7EF9cI764Rnxxj3jjGrv74o9ZMyWBk2kenwLqAmOAWcAV4Dkf7t903Lhxg9y5s+eanCAIgiAYAV8WIndVPYDWWh8HIjKS4OrVq1kSkJiYmKXts5v77ruPQoUKZfl13wuz+ZJdiC+uEV9cI764R7xxjV19SU5OJjDQfbnhy0LkFFAqzeNw4LQnCbxxCstMp8FOnTqFUipbNJvJl+xEfHGN+OIa8cU94o1r7OZLUlIS+fKl3xTNl4XIVqCCUqosEAf8HT9fijF6O12tNUrdfSLJSK19jaTFzNpFi/W0ixbraRctWb9ZNXfu3GzZsoW6deu6jfHZt2YiIyMdUVFRvwFzgH7AbK31txnd3o4t3oOObyb20AH69OnjUy1m88VqrczFF2v4KL5Y+xgjvnivxfupU6eYO3cuN27cyP4W71rr5VrrilrrB7TW7/lyX/fCDO1087X+F/v37+f555/3uxYj+WI17aLFetpFi/W0ixbvtHhPTk6mS5cuPPnkk25jbDN91wztdC/nLMry5ctp06YN165dY9GiRYZq7WskLZ6uixbja/F0XbQYX4un66LF+Fo8ZcmSJZQsWZIVK1a4jZEW7wZrp9uiRQs2btzIkiVLeO655whNOOE3LUbYp7fWRYvxtXi6LlqMr8XTddFifC2eMnLkSB566CHatm3rNsY2hYiZ2unWrVuXadOmsWHDBvZ+/goJm+b4TUt25baTdtFiPe2ixXraRUvWW7zHxsZy5MgRVq1axeuvv+42zmc3q2YVO7Z4Txtfo0YN/vWvf6GAlbM/J2+gJkf4w9JmOJPxRmplLr5Yw0fxxdrHGPElay3eL168SJcuXShQoABFixYlLi6OrVu3Zm+L96xixxbv7hg1ahRDhw6la9euTJ48meDg4Czls4ov3kZ8cY344hrxxT3ijWvs5EurVq04fvw4Bw8eJCgoiBdffJHJkye7bPFum0szZmbQoEEsXLiQBQsWkCdPHho3bszFixf9LUsQBEEQ7iIpKYldu3ahteaBBx5gz549fPHFF27jbfOtGbPToUMHEhMT+fbbb+nXrx9VqlThzJkz/pYlCIIgCGitef755wkPD+fnn3+mRYsWjBkzhujoaIYOHUqxYsXcbiuFiMno3LkzrVq1Il++fGzbto3atWv7W5IgCIJgc06cOMFXX30FwPDhwzl9+jSVK1fmypUrAAwbNszttnJpxoSEhISQO3duNm/e7G8pgiAIgkDBggUB6NOnDzNmzODmzZv89ttvt58PCwtzu62tblY1a19/V7ETBvXg4MGDHDt2zOPcVvHF29qz4ou3tdjVF29r92W8+GL9Y4z4kvFvzbz88sucO3eOjRs3MmnSJDp27MiMGTN48cUX+eWXX6hTpw5KKZc3q9rm67tm7evvKvbHA39waOkkalR7mK5du3qc2wq++EK7zJqRWTOexIsv1j7GiC8ZmzWjtebtt99myZIlxMfHM3z4cJ57LnW+bVBQEM888wz169cHICoqKvtnzRgJs/b1dxV7fucqLl88z/z58/2uRWZYiBbRLlqsqF20ZGzWzKRJk1i8eDGDBg3ivvvu46WXXrr9XJUqVYiIiLhnDtsUImbt6+9qLWeJCqA106ZN87sWs6yLFuNr8XRdtBhfi6frosX4WtKSkpLCiBEjmD17NpMmTeKtt95CqbuuvNwT2xQiZu3r72otuMj9lG7Zw2XLXDvNOzCrdtFiPe2ixXraRcu9Z83s27eP/PnzM336dPLmzZvuPJn0sE0hYta+/u5i/z1wAA6Hg9mzZ/tdi8ywEC121y5arKddtNx71syxY8fInz8/K1asYMmSJQQEZK6ksM3Nqmbt6+8u9pn65Tl8+DAffPABQ4YM8Si3FXzxhXaZNSOzZjyJF1+sfYwRX+49a2bnzp388MMPdOrUiccffzzd2IsXLzJ69GiZNWM1Nm7cyN/+9jc6dOjAokWLMryd1X3JLOKLa8QX14gv7hFvXGM1XzZv3kynTp0YMGAAgwYNSjd2/fr1NG7cWGbNWI0GDRrw3XffsWzZMpo1a+ZvOYIgCIKNKFOmDPHx8eTKlcttjMPhYPny5SxcuNBtjLR4Nzlt2rRh+fLltGzZkoSEBEJCQvwtSRAEQbABW7duJSgoKN25Z1OmTKF3797p5pEzIhagVq1aACQkJPhZiSAIgmB1UlJSGDx4MP369WPYsGEsW7bMZVxCQgKRkZFs3bqVffv2uc0nhYgFcDgcKKVYs2aNv6UIgiAIFiYhIYGBAweyfv16Fi1axNy5c3nxxRddxn7wwQc0adKE2rVrU6VKFbc5bXVpxqx9/e8VW7hwYbp3707fvn15/vnnTTV7ITvjRYvxtZhZu2ixnnbR8r/4mzdv0qdPH+bNm0fDhg0pWrQoTZo0YciQIbzxxht35Tty5AiffPIJ27dvd7vPW9jm67tm7euf0djw8HA+/fRTqrXryZBF+2wx78CsM1XEF2v4KL5Y+xgjvtwZv2fPHkaOHMnevXs5fPgwq1at4vjx4zRp0uSubqoHDhygTZs2DB48mJYtW5KSkkJ0dDTz58+XWTNm7Ouf0dixY8dy//33M3b1b37XYsR40WJ8LWbWLlqsp1203BkfGBhIUFAQnTt3Zt++fezYsYM8efLclWv16tVEREQwdOhQEhISUEoRFBTEK6+84nLfYKNLM2bt65/R2OTkZEJDQw2hxYjrosX4WjxdFy3G1+Lpumgxrpby5ctz9OhR8ufPz7p16+7qopqQkMC4ceP47LPPWLBgAQULFqRp06bMmDGDo0ePMmDAAAoWLOhyP7Y5I2LWvv4ZjT127Bjh4eGG0GLEddFifC2erosW42vxdF20+FdLSvxZrmxZyPUzv90Vf+jQISB1ou5fi5CVK1dStWpV9uzZw4IFC9i4cSPNmjXjww8/pFu3brzzzjsUKFDA5b7BRoWIWfv6ZzT22LFjPPbYY4bQYsR40WJ8LWbWLlqsp91OWl5vUQHHyd38Pvstblw4wdkFkSRu+YauD+UkJSWFtWvX0rhxY3Lnzk3//v1vb6e1ZvTo0Tz77LM888wzpKSk8OSTT3LkyBHWrl3LCy+84HJ/f8U2N6uata9/RmPfffddOnXqRNcnmthm3oFZZ6qIL9bwUXyx9jHGDr4sX76c4cOHM+3DfxPw+6/c1+AZctf9O2VqNKR8cizzJ/yHwYMHs3LlSqpXr063bt146qmngNR29T179mT27NkkJycTFBREkyZN+Prrr+nYsSNFixa9S1dUVJTMmrEqycnJ5MqVix07dlCjRo17xtvFF08RX1wjvrhGfHGPeOMaI/ny0Ucf8emnnzJ48GDq1atH5cqV7/r2C6Se9VBK0bNnT0JCQqhZsyZLly5l3bp1pKSk0Lx5c7744gsKFSp0z30qpVzOmrHNzapW5rvvviMwMDBDRYggCIJgP/78808OHjzIr7/+yqRJk/jtt99Yu3YtFStWBODkyZNcuHDhrn9HbhUnI0aMoFevXuzfv5/8+fPjcDiYMGECzz77bJa1SSFiAVavXk3hwoX9LUMQBEEwGOfOnaN///4sX76cYsWKUbBgQTp37sy4ceO4dOkSc+bMYfv27cycOZOUlBRWrFhBvXr17spTsmRJPvroI1544QWUUuzZs4fw8HCvaJRCxAL8+OOP1K9f398yBEEQBIMxffp05s2bR61atThw4AAXL17ks88+Y/r06eTNm5dy5cpRrVo1fv75Z6Kjo5k+ffrtQkRrzbZt2zh//jyLFi1iwYIFjBgxgr59+971zZmsIIWIBTh58mS6zWIEQRAEe9K+fXvi4+OpWbMmTZs2JX/+/G5jAwMDOXv2LJ988glRUVFcu3aNUqVKUbJkSZo1a8aBAwdc3oSaVWxViJipr39GY5OTk0lMTKR9+/Z+12L0eNFifC1m1i5arKfdOlqaUOv+B9MtQqJ3xvH6wCHkfqAO3//0Hz6YsogXmlYnNDTU5U2s3sQ2X981W1//jMb+8MMPzJs3j7Fjx2Y4txV88YV2mTUjs2Y8iRdfrH2MsaMvVw5sxpEUT4GWr7I3qSAPFC9AZRfxmcXd13dt09DMbH39Mxq7atWqO25Utfq8AytoFy3W0y5arKfdjlpKdBtLie4fkSu8crrx3sY2hYjZ+vpnNPaXX36hfPnyhtBi5HXRYnwtnq6LFuNr8XRdtBhfiy+wTSFixL7+3tBy+PBhHn30UUNoMfK6aDG+Fk/XRYvxtXi6LlqMr8UX2KYQMVJff29qOXfuHK1btzaEFiPHixbjazGzdtFiPe2ixX28t7HNzapG6Oufmfj0Yk+cOMGHH37IlClTyJEjR4ZzW8EXX2iXWTMya8aTePHF2scY8cV9fGaRWTMW5NNPP2XQoEEkJCR4tJ3Vfcks4otrxBfXiC/uEW9cY3df3M2asc2lGSuyYcMGSpUq5W8ZgiAIgpBppBAxMXv37uXhhx/2twxBEARByDRSiJiYkydP0rhxY3/LEARBEIRMY5sW78nJyUS0as+Wdaso/MS/uP/hxxj6VD3TtvZtW6UIiYmJPPHEE37XIq2jRYtoFy1W1C5avHuzqjts8a2ZU6dOUbRYceLi4tDawbWDGzn9329YOP8r8j9Qk7oPlb1ju/Ta41YsFkLfvn357rvveOyxx0hISGD1ocvZ3mb4wqHtbFq15HZrd09yG73NsNVbmYsv1vBRfLH2MUZ8cR+fWWzb4t3hcNCiRQtyhBSiZP+vKP78aApEdKdk3xkQmJMBTzUlZ86cbNiwgdjYWMB1e9zEpCTeeG88I0eOZOLEiSxcuJAiRYpQrFgxer3ULdtb+06Zt+iO1u6Zye0tLdI6WrTYXbtosZ520ZJ9Ld4tfWkmKSmJGjVqcPr0aYq8NIGAgAByhlUkZ1hFAMJ6fIpOSebG3Fdo1KgRALt373bZ1jZuwks4ricybGHqmxUTE0NAQAA7d+6keYuWXLtxk/sef52AgP/Vdr5s7Xvx6H4erVAhS7m9pcUM66LF+Fo8XRctxtfi6bpoMb4WX2DoMyJr1qzh+PHjHm0TGRlJtWrVqF+/Ps8++yxnzpzh5MmTlC5d2mV8eOH8VHD+g16mTBmaN29OWIHcJB3dQeL+9Vz6cRp/fBOJIymeR4cv5vz58xw4cIDChQtTqFAhmjVrRuXu73Ht4EZOjf87f3zzDn+ejAF829rXceUMdevWzVJub2kxw7poMb4WT9dFi/G1eLouWoyvxRcYuhBp3rw5ZcuW5d13381QfLNmzXjvvfcoW7YsuXPnRmvNzJkzKVy4cLotbL/77jtmz55No0aNOHfuHHtHdeHs/OGcXzqGqzuXk3z6ICWfGcHgdtUpVKgQDz54Z9vb9/u/QMXXvyb0kTYknz7E+SWjfd7a9+a1+Dtau2cmt7e0SOto0WJ37aLFetpFi7R4JyoqKrJt27b079+fQYMGMWLECMaPH8+KFSvo2rUrAQEBLF68mPr16zNmzBimTJnC7t272blzJwMGDKB79+506tSJRx55BEi/hW1wcDDVqlUjR44cbNq0iTatW/HWJ19xulx7Amt1oXLrF3ive/N02+OWKpyPw0HluJ6/FFd3reCx8FxcPbyNbdu2ERERcVd8Vlr7/qNWKEvnTGbq1Kl3XArKaG6rtBk2aytz8cUaPoov1j7GiC/S4h2llD537hyFCxcmJSWFTZs2sW7dOkaNGkX+/PkJCQkhNjaWsLAwunfvzpYtW3j77bfv6Kvhr3a6vXr1YsaMGdy4cQOlFCEhIbz66qu8//77Xsn/ySefMGTIEK5evZqp7e3eZtgd4otrxBfXiC/uEW9cY3df3LV4N3Qh4krb77//zuuvvw5A+/btadeuHXny5HGZw19v+okTJ+jduzdly5Zl+PDhTJw4kREjRlClShXOnDlDo0aN+PbbbzOd/6mnniImJob9+/dnanu7/zK4Q3xxjfjiGvHFPeKNa+zui2UKEU8w0ps+bNgw1q9fz9atW/nzzz9p3bo1Q4cOpWHDhh7nqlSpEjVq1GDevHmZ0mIkX4yE+OIa8cU14ot7xBvX2N0XGXrnZ/7973+zYcMGypQpA6TOiWnRogUOh8PjXKdOnbr9dWNBEARBMDOW7iNiRPbv34/D4UjtaZIzJxERETzwwAO0bdsWpRRPP/10utsnJyeTmJhIu3btskmxIAiCIPgOWxUiRujrHxAQQEBAANE74whr0YOfN0azceNGZs6cSUBAAF26dLnrmzBpc+c+u48cOXIQHh7ulddpFF/spl20WE+7aLGedtEis2a8NmsGjNnXP6VIRfLV6YBOSSZPqcoknz5EfHw8rVq1cpv7j5+XcOPK71Rr09X28w7MOlNFfLGGj+KLtY8x4ovMmvE6Ru7rX7Dxi+Rr9BIBefMzf/78dGOvnzlEYMGSMu/AxNpFi/W0ixbraRct2TdrxjaFiNH7+if+toUb8RdZuHBhurEpl06Ts0RFmXfg4bpoMb4WT9dFi/G1eLouWoyvxRfYphAxcl9/hyOFC0s/oHC1xtSpUyf92KR4cpWrJfMOPFwXLcbX4um6aDG+Fk/XRYvxtfiCLBciSqlcSqlflFK7lVIxSqko53qYUmqtUmqxUirEufagUmqdUmqXUupXpdQXWd1/RjFyX/8LSz9AKcWEKV+mG3vj8u+gHRQoV03mHZhYu2ixnnbRYj3tosVEs2aioqJuAl9prT+OioqaDLwfFRW1D+gOjAKOAg0jIyO3RUVFzQU+0Vr/MzIy8rOoqKhjkZGRZ93k9erNqkbt638+di+X1k5jyIdTeLXT3b1B0sb+vn0V10/tZ/KH78u8Ax9ol1kzMmvGk3jxxdrHGPHFpLNmlFJ5gP8CfYAuwDSgHFBaaz1BKbUHeElrvT0DuSzVWdUVDoeD4sWLU61aNX744Yd7xnfp0oX9+/dnurX7LYzui78QX1wjvrhGfHGPeOMau/virrOqV/qIKKVyANuB8sBnWustSqnfgVnAFeA5Z+g4YK1SahOwCvhSa33ZGxrMyGuvvUZ8fDzR0dEZit+7dy81a9b0sSpBEARByD68UohorW8CNZRSBYBFSqmqWut9QMRf4r5USq0EWgMdgN5Kqepa6+uu8mZ2uuwtEhMTs7S9Lzl+/DjR0dFMnDgRrXWGXqvD4aBp06aW9sWfiC+uEV9cI764R7xxjfjiGq8PvVNKvQMkaq0/yEDsPqC7q0s1Vr80U758eUJDQ9m5c2eG4m9dWzx58mS6XVUzgpF98Sfii2vEF9eIL+4Rb1xjd198NvROKVXEeSYEpVRuoDlwwE1sa6VUkPPn4sB9QFxWNZiNUaNGcezYMVasWJHhbX744QcCAwOzXIQIgiAIgpHwxqWZEsAM530iAcB8rfUyN7EtgfFKqT+dj9/UWv/uBQ2m4ezZswwbNozhw4dTvHjxDG+3evVqihQp4kNlgiAIgpD9eP3SjLew6qWZ2rVrc+HCBY4ePerRdg0aNCAoKIh169ZlWYMRfTEC4otrxBfXiC/uEW9cY3dffHZpRsg4M2fOZMeOHSxfvtzjbQ8fPsyjjz7qA1WCIAiC4D+kEMkmrl27Ru/evenduzeVK1f2ePsLFy7QunVrHygTBEEQBP8hhUg20bFjR/Lmzctnn33m8baxsbHcvHmTiIiIewcLgiAIgonwSh8RIX1WrlzJ6tWr2bBhAwEBntd+S5cuJTQ0lMBAebsEQRAEayFnRHxMSkoKzzzzDB06dKBhw4aZyvHTTz9RqlQpLysTBEEQBP8jhYiP6dmzJykpKXz99deZzrF3716qV6/uRVWCIAiCYAykEPEhu3btYtasWcyYMYPg4OBM54mLi6NRo7sn8wqCIAiC2ZFCxIc8/vjjNGjQgM6dO2c6R3JyMteuXaNdu3ZeVCYIgiAIxkDufvQRAwcO5Pz588TExGQpz63W7mFhYV5SJgiCIAjGQQoRH3D8+HE++OADxo8fT4ECBbKUa+XKldLaXRAEQbAscmnGB7Ru3ZpKlSrx6quvZjnX1q1bqVixohdUCYIgCILxkELEy4wfP55Dhw55NFk3PWJjY6lbt65XcgmCIAiC0ZBCxIvEx8fz5ptv0rx5c0qXLu2VnBcuXKBNmzZeySUIgiAIRkMKES8SHx9PeHg4a9asoWjRonz11Vd3PH/58mU6depEREQEy5Ytu2e+W63dM9sITRAEQRCMjhQiXiQ8PJwjR45w8eJFGjVqRNeuXSldujRjx45l2bJllClTho0bN5KcnEz79u3p1q1buvmktbsgCIJgdeRfOB+QL18+vvnmG86ePUu3bt2IjIwkKSmJBg0asGbNGgIDA1mzZg2tWrWiSJEijB071mWeDRs2SGt3QRAEwdLIGREfUrRoUb7//nuuXr1KSkoK69evv312o1mzZkydOpVx48aRkJDgcvt9+/ZJa3dBEATB0kgh4ke6d+9O7ty5mT59usvn4+LiaNy4cbZqEgRBEITsRGmt/a3BJUopYwoTBEEQBCEzHNda3//XRcMWIoIgCIIgWB+5n6VPzwAABVdJREFUNCMIgiAIgt+QQkQQBEEQBL8hhYggCIIgCH7DNIWIUupBpdSuNH/ilVKvKaXGKKUOKKX2KKUWKaUKpNlmjFJqm1KqkfPxIqXUk2meP6iUejvN42+VUp2y95VlHaVUAaXUAqcPvyql6imlqiulNiul9iqlliql8qWJt60vzvV+ztcYo5QanSbetr4opf7t/B3apZRapZQKSxNvZ18KKaVWK6V+c/5dME28LXwBUEodcx5LdimltjnXvk5zPD6mlNqVJt4W3rjyJc1zbyiltFKqcJo1W/jiKaYpRLTWB7XWNbTWNYBawDVgEbAaqKq1rgYcAgYDKKUqOTeNAF5x/rwJqO98/j4gAaiXZjf1nDFmYzzwvda6ElAd+BWYAgzSWj9Mqk9vgviilGoCdACqaa2rAB+A+AKM0VpXc/5+LQOGg/gCDALWaK0rAGucj+3myy2aOI/BtQG01s+kOSZ/CywEW3pzhy8ASqlSQAvgRJo1u/mSYUxTiPyFZkCs1vq41nqV1jrFuf4zEO78OQfgADSgnGsbcb7pzr+XAUVUKmWBJK3179nyCryE80xHBDAVQGudrLW+DDwIbHCGrQY6O3+2uy99gFFa6+vO9bPOTWzti9Y6Pk1YXlJ9AJv7QmrROsMZNgO49b9XW/iSEZRSCngauDVcS7yBccBb/O/3CMQXt5i1EPk7//vQp6UHsAJAax0D5AH+C0xwPr8dqKqUCib1Td8MHAQqOx9v9K1sn1AOOAd8qZTaqZSaopTKC+wD2jtjngJKgfgCVAT+ppTaopRar5SqA+ILgFLqPaXUSeB5nGdExBeKaa3PADj/Lur82S6+3EIDq5RS25VSL//lub8Bf2itfwPbeXOXL0qp9kCc1nr3HYH28sUjTFeION+w9sA3f1kfCqQAc26taa37aa1raa3XOh9fB2KAmsBjwBZS3/j6zj9mPAUWSOrrmaC1fgRIJPX0cQ/gFaXUdiAUSL61gc19CQQKkvo63wTmO/9HZ3df0FoP1VqXIvV36NVbG9jdF3fYxJdbNNBa1wTakHpciUjz3LP85T+GNvLGlS9DcRbyf8VGvniE6QoRUt/wHVrrP24tKKW6A08Az+t7d2jbROop2FCt9SVSL+fcetPNWH2eAk5prbc4Hy8AamqtD2itW2qta5F6kIi9Rx5b+OJcX6hT+YXUU6WF3eQA+/iSlrn871KeO+ziyx9KqRIAzr/Putn+FlbzBQCt9Wnn32dJvefsUQClVCDQCfg6A2ks540LXxoBZYHdSqljpN4qsEMpVTydNJbzxVPMWIjcUX0rpVoDA4H2WutrGdh+I9AbuHXabA+plWhpUitTU+G8fnhSKfWgc6kZsF8pVRRAKRUAvA1MvEcqW/gCRANNAZRSFYFg4Hw6qWzhi1KqQpqw9sCBe6SyhS/AEqC7c607sPgeqSzlC4BSKq9SKvTWz0BLUi/9AjQHDmitT2UglaW8cePLVq11Ua31/c5W5qdI/Y9hevd5WMqXzBDobwGeoJTKQ+qdyL3TLH8K5ARWO8+w/6y1/kc6aTaRej14JIDWOkUpdRY4qbV2+ES47+kHzHFetjoCvAR0U0rdujN7IfDlPXLYxZdEYJpSah+pl6u63+Msml18meL8R9gBHAfS+x0C+/gSQOrlu56kfgPiqXvksKIvxYBFzuNrIDBXa/298zl39+u5wmrepOeLJ1jNF4+RWTOCIAiCIPgNM16aEQRBEATBIkghIgiCIAiC35BCRBAEQRAEvyGFiCAIgiAIfkMKEUEQBEEQ/IYUIoIgCIIg+A0pRARBEARB8BtSiAiCIAiC4Df+H5rr3tCvTva2AAAAAElFTkSuQmCC\n", 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" ] @@ -429,12 +429,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of total events: 2405\n", - "Number of synthetic events: 1924\n", + "Number of total events: 216\n", + "Number of synthetic events: 0\n", "Number of historical events between 1995 and 2001: 109\n", "Number of events in 1999: 16\n", "Year with most hurricanes between 1995 and 2001: 1995\n", - "2019-10-29 21:57:25,316 - climada.hazard.centroids.centr - INFO - Setting geometry points.\n", + "2020-09-16 09:43:55,053 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", + "2020-09-16 09:43:55,596 - climada.util.coordinates - INFO - dist_to_coast: UTM 32617 (1/2)\n", + "2020-09-16 09:43:56,705 - climada.util.coordinates - INFO - dist_to_coast: UTM 32618 (2/2)\n", "Number of centroids close to coast: 41\n" ] } @@ -470,7 +472,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize Hazards\n", + "## Visualize Hazards\n", "\n", "There are three different plot functions: `plot_intensity()`, `plot_fraction()`and `plot_rp_intensity()`. Depending on the inputs, different properties can be visualized. Check the documentation of the functions:" ] @@ -500,7 +502,8 @@ " plot abs(centr)-largest centroid where higher intensities\n", " are reached. If tuple with (lat, lon) plot intensity of nearest\n", " centroid.\n", - " smooth (bool, optional): smooth plot to plot.RESOLUTIONxplot.RESOLUTION\n", + " smooth (bool, optional): Rescale data to RESOLUTIONxRESOLUTION pixels (see constant\n", + " in module `climada.util.plot`)\n", " axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use\n", " kwargs (optional): arguments for pcolormesh matplotlib function\n", " used in event plots or for plot function used in centroids plots\n", @@ -541,36 +544,33 @@ "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:27,657 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 10 50 75 100]\n" + "2020-09-16 09:43:58,296 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 10 50 75 100]\n", + "2020-09-16 09:43:58,657 - climada.hazard.base - WARNING - Exceedance intenstiy values below 0 are set to 0. Reason: no negative intensity values were found in hazard.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/aznarsig/Documents/Python/climada_python/climada/util/plot.py:318: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + "/home/tovogt/code/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", " fig.tight_layout()\n" ] }, { "data": { - "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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LopVCaZARaAmMDGLUzo65xCgdKzUO4HggJ7FONUhdQ7FAi6o6qtVGVRvUqTqAZNxMN54UZajz447OiVLAQfLbCDu/53u456f+HaOTJyiXlizoyF/vmNgnSoOmTE4eqIvH2Q4ECRkoguac6MuWlqVu5rW49QKWjx9g40WXx+K4RKz02wGh2qUX0tJmeScBofCNNsuS1r2lMmy+EpvC3Wx931neadla6rIxYKVb4qOTn4d47d+5iqwdXxsANuTXAGraiB863QC142fuUWc+50B5G6nA0voOfpW+swb5zXktIYqrsaijBhARLtrxDO584H2cHR6LYMgkoZA2GCqN07FLIwSJUTrh+ckySC8IomT8dfo8yduXx9ozaVCXefsmcSojqexkt339ZTx08kvoaGQnRR8qa7ui9SOIrDvq2EnenumRpgRYSNEBTFvxpfs3+CVKUJs1JqGgKiX0lb32g8gl4WyRIjNK+9z3q0SJXapCTclIlPCY5P2k7EE9m4SGlKjXs9fBPslEUFjXdjxMobW2YnusaOzEpyBlycZnPYtD7/i/VGfONGxffJyxCay2ibsm767JPQVu4/LLAIsv4qY9V3H8a7dOLO44qVaQIhiZDBa6yrOKuF15y4TnLfWat2lK2q0rNBOZsZx5H3WBoTxuCnTygud9mpalcyxMb9SWem1COVvqtwkAMqe1xm/Oa0CkjUmt2UOX7Pp6dm+5hk/c8SbufOC9HDt9f3si8St0L9kJhrgmBEy8H34nICidKGOgEwR1gQszsqo+Gak1+q4tQPJgiErZsnAhhZQcPnM31JWVCI1GFiA90o4zU6nZODD0aDvrzI2RM/ugVrkycOFtIhrUwfgb/5M+a4Ai33+JKhWhYT8W7ImCXVBH+dL/rfpaMG4NyZPgbKUoEpVar2eDB2EpKJrSLwpU6KrCnDoo70aF7d/+HZRbt3Dfz/4sRz/wAQb797dA0cR5SrPQFUWy39L+n6v2Oss8pSxbrngGpw/cw/LJw813JQolGtKbvJwmMaKeFUQk6bbsjZI2kTG/u9qsBYK8eCm1gRrXRuNoXMOtBii0QKutaBt4dKC6cdKpVH3GGEA3qQ4doKiVRvZsWl3XIr85rwFRmMQ6JhoR4fILv5kXPO1HUYVbvvYODh7/UpTaBAlREwzVpYm2Pn4SLD1IioAnB0FROkQjfWtMLc3Bk0i2ACsh8kbfzsbJgyGp7W6zvZtu4N7jt1gpQFVZMFQ9SuJIX76u3k9VdjNMwudEIoiXFJnYv5KpK7UjNFSVHWUL6sy0D0Oa7n8GBn0fhv9FHENBSpQaO2d1af3PpVpFgRZFUzrkDPopCrTng7Ml6jlpkd+55kHRFImdAkOtVxXmNJ4aEomiZMff/E4u+if/lNGRI+z7lf/J4KEH25KUc/1cMvCe3h8neWiAJLL3OtL2ZHp9tl71LA7f9vFm2jlQaaUTeZ0HHDpuBhmHK7ru5yq1Ce+3AGUOipJyBePwHIi0CpXcXxXCo9lvXem7Co9VpbXAU1cajC9/ql7rGitd+cqYkNVjFhuitcZvzm9AlA7yIgKbYBhtDAsLSzz1ileybfPlnK1Po/2CerFAy4K6b3d61aVBe4a6Z99LVWW1Dz2x8d07ftIKcU0ETcGeJR+EHsClVRDwhrdeXSajCIa8DcneDU/j8PJ9DEZn7MT3SKjJxDRDkahjlFAeL7mKhX4MkbzP138r46QsEJhd03ZI2+4TEkoBkqb9k6o2vV1S6EfH9L0Ex0uJTNKGqfTGgyUvjUxUtdovLdhZKKj9715h7/dL6sWSeqGHLvTQxdL9t8FKjcpEgjTd5K9eZZiTo3zidGowqaWhil3YuYsd3/ld9LbvoD5xqjupCdLLaWVoSIWSRVhq7DxWcuTTyNIjue/j73z6N3Hkzk+jWk+0zWmUxYW6yEBHqnJO887SDIbZdfzdUpOlYKxDOtRpIJyDIQGMS8ho/J2Bh676dtIUoNkCQ6FPtP08gMlMVdYhAWq8GyrbUZ7sujE2aP/uCg3g6H4vrZtsQwRrj9+c14DIT3JBddaQ9lgD3Lpv/egcPXUPCxu3US0U1AsF1YLdtaMJ8PFb6eNWeRqTa5AgJb5u4s6vNG+6P5IWI9TuFVGrokJRLLB7/RXce+ZWvEH1wyUxErZzS89u7bZgyIIIKgfSqgjSfHkeVVK1Rtt1bVWEXvKxColUSw8+zabB2y359L2haZUAKrVlCDsUJb7XcMGQ+ggyYsHmhHxTyWNQ2yauGuKYdEA8Vfs6CdMsfaKrFF8/EUTYjznlahoHjFL9gg6HrNx/H73du7uT6OiqFFTlxr+dq/oxk1wXGOoCRZ07pJL4/S076G/ewbGvfj4+S/Jv2KAk0pZ00m+Vqavu0gQxqX+lzp1lHe3WaoekvONtv2bgYeNA0bR3cxCR3Ivto519O9Hoeqa5IqY/8TlZGfPyTni3Zac2htYivzmvAREQSuh3A2nh1Q4eDFnws3nb5Xzxjt/nyOl7qPqGasFJhlIgE3aI5SstO/F4sBPUaenElTkATAdW09jPgY2ZV4VxYr1iy/O459QtDIdn7Vb3qnrYkqIGGPI2MN41gWoTFA0TcPJogqKqCtv5/c661M+Q/93yM5Q3RUM9SgSwaRSvAQwMQZJnMT9v1xX6MlWX+p1v/QQI+fY0yXb5jvxDOf34S106pHZr3vdSstOu4Z/Jl2kSOY3sasKcLI0DMfYhjbFn+n16u3ez/82/zsqhg6vWssxcngxsxIJlz/JJmfaz1kQssPs5L+fBz7zP+htLn2V51YWTChVdYExaE2hrQk2BYB3/TxIZNOb7aZPzI9X+s0iKxrVnI94EabV/ngG7VlYd4LmrjLmdZCxnhzRpDIgcO06m0RrkN+c1IGrtHqO5wq4cGKoXDFc/+3u47LpX8cWbf5sDB29lhbPUC8ZKhjzICcZ27iNO9OE2v5h+HWyLUnshAhgKgGm1H2M66UNj0ttQbmFH/2LuO3sbOhzZUD98OyLxjh6LIgEfDojUNYwsGJKqspKiWSfgcyGtI9jzbgZSh5odX81EB4ypvVbHKigXBXeuysJK1UkkE9uIlqPGVFXmbXxS8ORtjsYZhksypvzY9BLL1O1AEt++32HM3aqKMFxlmFM3pX5yWpOSwN43/HM2Pv0Z7H/Tr3LmS1+iHq5M5AX5s7GSj1ZBYug0dB0z5ltp0BJ0semSp2J6fY7ffVsrnl80WvVYslg0WfrTypdKgDJQJEr0JKFZmNQusw7biSh3+rs5KGtJwTr7I1OT5fEaq+eOsk6Qlo0dJGOlRREUjbNxGitJBI4vLzOJ1iK/Oa/9EPkVSBgghZ20aqfSqhaSbfPGsP2a51Bs3cJ9t/wpX731/3HdN/4w65d2Bz8ciFvlGHuhqiCCVBoBEj47cc4I/QetAQwhtAet4iQMierF/+9aCVWJmgjssQ4rAy7rXcfnTr6XvXIJBkGrEToadiQwpemKAil7dgL3EqJ00k7jqlr/PM4HEapIr4xmNbXaCdmtJIMbgNUayWlNPRzZnXbGtr8s9IMjShEBU1u/K5j4gRoikKsF7XIjlX7M6cre32vEFaSL6/h6BRWtIoWgGIQa1FjfRCKIGmDUAI9NMCRt4+3c+D7oN9T6nKmx/VAIonZci5/BTDbrdTUvD1ug+KSlcca+MUIWv1ey5aUvpdi6hcN//P9AlYv+wY/S27ipzRu6+IXLcxyQEX/thAneQSKaAPYUrEkSv25/D7ntjR16wq5nvYQDN/85my+7HiP2q1AjTdWbXxy4RD0/65a2E3Z5pe3oAZCK+5+yjmlsZMKEPlbS4pmXzzDcP8dJWWnWsbMs2gZCabb5FvucPzUGwwxl6hpX7p5PSpJMtOu1DASnddk8xYZoLfKb81xC5H6k48SrHhK1Vl0S7ISW9lzNda96Axff+Cpu//ibqUYrjRWMJunGCVfCdWMVlUqQRIKH1rDtO3VZn4Mhv73eO5TsUkUNRxYIDYcwHKHDIZuqTWyULeznbibapsxChUH6fSsh6vXi7qVCQBIv3ekOLr/TqSyRvn1Heva3lD17FlmvxASQlRluYyaX2zl51NpKhsJuOu+Dydvw1M4APVyPSS8YamqYDFr2GlloeDEPzCkxLM9smRq2bN7mJxinR+mWONBMJt3Rxs64pOxhfDcnmnAv37E2A1XIqsKcEsrHSTqG8nju/6ZnfR0X/8RPsPGGG3jod95iHZmOS37c6rxL0kB8NlZKkkzQnbYsZspzYPPlNzBaPsPp/V9tAJwAukx2Le1neTnC76xuk+qRh1nsWHLQ5f97R46NiJ0GPV2JTrk3DgxNoHESu0b6HdKxVZncTAKNWb5jVZznQGuN35zXgAianWxFuFF0a9VaHgwRpD9qYMeVz2bD1j08dM+nk4+0Y4JJQVHXR5mt6qPUxwGjOrF1SUCS+CMwai/ZiBNrg4bu0NDB0Iaq4inmOr6md1A/Al5ApSiiw79wRIRpHlPiVZJewpGepVaW9rytXs+Con4/PPcAyXqfjmEahWNAtLZ1Tx1SejCUuieYVL9ga+RBUTvYPvKTW+yDIL3xz+oIluJOmKTP/NfijaxTm6HcTUGq7pWOtMY2Tlq5DtXZlFeHalYV5tSmsdvOU8qeb73pFdQry5z58p2TX8vBQWNVHq9Xo5bKQU9j7u8CTWmahWHXs1/Cg595X+CxdAChHBQ1gFNH2QIYGkOT2ndieSfRagDOpP4N4ESmj4NzoY5FWgsITYuTRpeO35NAKOMx4mpoLfKb87+EJB+633JfSgBDdQcY8mHnlV/P/bd/gK/e/E6Gg+S0+MQeKE3f/9bs407vBylECnSqJhiKkyPRcDk5+8r7Rwo0HKKDgbWrAbaanaxnI/v42iPTgAv9ICFKDXtbB5imBsMePPUyMNR3IMhtB5feOWhdnYRIHYDRKjqjlJVBExRBBBLjAEXojwhk4sGTNPsE2qO+BaCS+9AAJY3fpWmeOVdrPAsOYnuOkzAAQTqldNfPEMfKFImhsvZWbI8ZjZMGORq7E8p3tTEsff3zOfDOd3DoPe+ifji7RCd1yzgJShcoycCQehCTvbP12mczOHWMEw98ecLW+o7QBdRy4DLLEJsA9sZKyDxNAQuNOEyJN4Um7YabCTnNAuh8qLPrafWckHZQ1/l4nUGbATi+PN0P0VrjN+e3DZEnsaCn7vmJ3B6/UfUksQlq0+a913D9Ta/nKx9/G3d/4V1c9Zy/Yx8EeyC1q3fCgiCAJWv81xx9YUx12ApJbT1Rh7O2oLFzyk6MSXr5LqKgbjGIqblCrucL+nGepk+frYlyp4Fi3Jlhrs36vXBoqncSSCG2SLUbqCVQCfStjQ91aSU0I2uMTWVVWJSj4EAy1KnWAOhmIq3RUY0OC9v+Xq1o3Dk9vRLt2/aw29BxKj6C5K7L43bnWPAqqVRVZohAKqg3PQDK3jWK1vE8NK1dGR2oFJHgT0pGoBRRslUpUmDtolQcIIbGwIPQjmGXYlLu1u9xTYpQPTHWOOcv5WC1g3LbH888Nn/9C1h38WXs/63fpFhYxwUv/9bYx9KMPovqxRdFlMk2Q57N+N/u6LvUXqhh2JzaGClgCnZ9wyt48FN/xmXXXktdZnmk+ZB/H83/re35PptZrmX8/0nvthos/53cmyqZmkAtVZMZh54nlG8qeIsvt8ZZR1Tx6Sb5ZsMNb1OU2k12pZu3zebFaTZEa4/fnNeAqDEg/MRuaPkC6jJa9lKcdet3sOfqF3PnX/0W6zft5uwmw+HjQxY3bAOELduvYKG/0Q10CfmGVbtTjaV2Qbk0KKhavNQIwnZ9VBBRN9+JnewK0KpChmIlDCNbNzHGDtkKNst2NulWHuJe1rFthsaK0hpZWEAHg6YjRqcSax9fIeCwVDAervxE776QorDgpzDOwFqikW/lr92Ev0qqV5YxLMZ5o+dAlTFQ12jZC8AD7xwznMqd7PLz0r6yWYZgXDhSTOaDKDRd5iYhqNXGSKQkBblGrSG0kejbyM9OdW37VN2c4o34FRSdDnI8OPKqwBmofjgy8DmNpQgM7Gpp3GS1cNEeNj//mzj8nndzYsd2Dtx1F/2du5Ben2L9etZffhVmYTFOXn585t2bAFqOJ7oAACAASURBVJawQJMIplJb4TAx5r9NEscDIZ92MokKsOWpz+Tgpz/IqX13wdZL2io9SYBVNgfOBCTG0bhnHUBolrw8aMujzfJZBOAZ8tcmCJJ4f2yZSfrGFSSMlVxPNcbYu+nGpV3+7uy12a++PBr/r5o1zPDCWuM3U+GdiFwsIh8SkdtF5DYR+TF3/xki8gkR+WsReZeILCXv/BcR+YyIvNBdXyYiKiKvT+L8DxF53dQS+m3uQgMM+YHbUpF0hM3bnkJRLnDy8N30FzezcvoI99/5Ib706bdx350fbPd7FxjSBPQ4aVA4k8zbsSRqsTAwG35mrMds6wW76D7IE/D2NVdyHfu4m6EOprSRCafZS79v1VuLC9YouiiakqiwC8qConBcRXo6e2o47LeZF0X0uePK6o/f8M4fZ3UgmJOOhmiwJRpFAFC7/i+c36nkyJU69S3VsCuDqmelh97LuGdKQAO0pFI+3++NMeCNuYPjRm2Bqa5de8F9QJ2ko3EszdYocVwFKqapzISBFqsK5xs9XvymC5CMtbPQuHDqmrzWX3E10u+zcvgA5fbtnL33bo5+9EPsf9v/5sTnP9dIO02yldckUCDxf3ovpJ0ZQOf2P2lQAGPY/fxv5dDNH7G2i+mzjvxbx2KMKWsXTbNzmWmOHRcnbHhJwmooURs17KUyMJTbtkaXLlmGGpNtUUent3xfJe/PZNuWltdfp//HvZanO0MnrAV+k9MsEqIR8EZV/ZyIbAI+KyLvB34D+HFV/YiI/CDwE8C/FZFr3HvfDLwF+Ii7PgD8mIj8muq0Gd5S14cTP3bp3s7uX6nBjCyIKc0C2y+4nkuf9gp2X7CL3pJVQ+372se56wt/xGXXvZKi6BHVFr4AhIlMvIG0N6YeVVaK4t8Z1XGyNDZ+8LBd2LKm6hztFWhVIKPSGhP7yS5RO22UzQzYxZ3cwvU8d0xFHRgqCqsiE7HG0O4/pVOPNY6h8ADJpyFRJG80bvd2mjIJ6NN/nRUNKZFaaY6Vcq1+0AdVmxFrR1SWVj3nAKY6iZaXDgUJYe4HSmj5hzKpmiw1bB/HWFID+HTnWBrFSxJ9/JB/ytjsu6rJfVWkFss003udzDKTCvkdbRNIgfqJL8J+3PjNJPKSh+RO+B78BOwlA8XSEhufdgPbXvRy6mQmevB3f5uDf/z7LD3vG5AJM1Qq/WmoQVLRR3qfmD8AJQ21mBowVVL+RHKR0qYrr2ew/04OfObP2fW8m7rLZpJ0zkE40Jqoc8rvZ6CxcW9cGhofzaReS+Oo/T41uR8AqMkkgwEoaUwnnTvSvKQJavJySR5/XFm7yuyv04QlGzPp2JkGqlwas9gQrQF+06CptVHV/ar6Off7JHA7sAd4KvAXLtr7ge90vwuiSVjabQeBDwI/MHPpJJn4vMqnsJOeX3n7re1mpJhhEgZWimNGSlkVXHv9d7Ou2ByfV3DBnmeztPVS7r71TxoGuQ3DaacOC6fVjxQZVfbA1sHIOjQcjIC42yxKBJqSgQaAC7u6EmlLB13ClTzIvRzTQx3tY+LRHGE3WGnTXLcO1i3CukW0LINUKhwsWoCW7qy3Xiq9Sg8bLZLDR8vgLyhIjNwuNO/40XvDbtkzzUBaVVbNV6sFhbV13Gi9aLtVWZk4zEzOo/MhnEtXQt2DakFCX6Lq3jHhgN/OIecN4N3hu1QKozraBCW7cKaSd3Q5cvVwrgaCvVKXEw8PwoKvF0HLIp5nNi1LnthGjo8rv8kmpRwwtFw51F1iHSg3bmL3d70G0+8nL8OOV30HvW3bOf6Jv+yU1DSkNvkqP7sOC8Mxxs6pUTT+OgsND9QFUArbbnwBBz7+Z6wcOxB3m/n8fJqlC+m7Pm5BQ3o0yd6oJcmYMBzHgqGud6ZJh8aBIZr3U0lbS5qVgiH/XG2EsVKcMRKfcRKtidKgjvihPF5KJbEvGu3bBcZ8Z9WRZ27pTz/L7InOb3JaFbwTkcuAZwKfBG4Fvt09+i7gYgBVvQ1YD3wU+F9ZEv8JeKOIzDRjTtY9p0sHu3wzoyR4sDTS8NtUqYRHKemxe++zOXbwy9RVFQeomwi9ZEg0TmDidz+lE2bYEZCs6sNOJex7wxqzMsIsV5hBhaw4ILWyYv0QQQQT3r8P0JM+1/Nc7uAWa4Myjox7tyztrjJ3TEc4vy2csm4akpYgKRKcTyen3ksAVLBBCmDI3zNRJecNrGfcETWWfBt6L9ZB/ahxknI+h7okJgHYZhOadeRprPdyd6xLKiHz/8MuMhJpEsTdhO53p1+krDypPyXCmKobRtwTmZ5XcXoJ2YSoNnthqMWqwvlMjym/ySclF6apMFajlimXlthw/dM58+U7pkdOJuDWJOyfZ8AoeG3vSCelMMl3AKrFPZew4/kv56GP/dnk3WVJGRoALp94x03GOVDqSGMi/5/6MWTNkPZXmm/6SirtYXy81kuTJDpdQK9rrI2hmVVlSXFa+eZtmY/hjvuzjOu1xm9gFUbVIrIR+APgDap6womt/5uI/CTwx0AQS6vq67vSUNWvicingNfMkue2Lf0wUavBnVZvt9r7nV2hfA7kpNdm6AGQAy0Gljb1GnlsuPIGzh79JJsWVlhc3NwcLFXdsP8wwyr6x/H/beOwtLm5GlQxEW7WOKkD4R1ZABYKWN+HqoznlqnCaGS34dfK0u71XKPXMeI0A05wgVyclN4ghbESmsWFCIjWL9r2Lg0Ye9K632Jv28+waWvfgqVEzedVRfFcL1f3gZOQqGuPYd8BQgdaBgN0UEKtLO1Yb90IVM754qrOODaYDeuir6NeD920iC4UbFzquYNPo0NO7ZgAaudXydublaW1JPXXqGJqKFYUM6hY2lQmdmIOtGjSxw0/Q7UFetk9qy7VoDZEhKUtfcJuRucIk54tcDBs933iNZJVArhVrWTKD0gvNbp9fOsprJldH481v9nV77VtXCW77ixoV8b23zbvkiKJs+nGGzn4zi+zvRBMGdnvRKlC9nxrr2yq1fJ4eVpeelF3PM/ibl0oufbFL+e+P3gz6489wPo9l8U4HZNqXrZWudy9rQtld5vm0pfEqLnTRiqjbb2yVZZOGyV/LwMrLfslJ13ZXpYt6VDTYCzNx3+jTjqUzPsNA2uX7kw0bjyMGYetdDWLnNlWhSdBr9oE/6KwdYpEei3xG08z9Y6I9LDM6W2q+k4AVb0DeLl7fjXwqhnz/Fng94ni77F05Pgg2ImogWrBBEAENFbYUkExiFunpQYzcKtxvx3e2GM6jh0bJruUFpGFq/nYB3+Dp1z9rWzdfqVNULUBqGRUYwZV9I8zrOw2dk9ac/SIk/QYrPTEpYOqmzTraJ8yGMLyilMP1eggOZ6jrtGVlWBbc+yBk2zVPfwVH+a5vJR1ssHW0aupyhKzrgLnWVo3uLydVKjaYMGausNER4sF2jMcOTFsfKzRr1IdJGnUVjVIpUhlHSfKcOQAkVVr6anTVt3lJDtHHzhpVWCrPeJDDMXGKvg7ot9Dz0K9rkddGo6eHFlQbCScTWffi/1Q+zPr3EGUxYB4bQQzVIqBYlZqihVbz6PHVsLuwVQC2PCD5AFSaYIkyavWrETRtUfS78cOnA1+iqykrmgc7qqlcRIpVwfVJggfpVKyWZaIQvUEcH42jR4PfnNwuX08Tkvi0fXcl1nb1wAHBsOmtOfCvRzdtoMjb/51tr7sFSzuTRc47ffDdfYJHRgMO8FSmNhyKUVNY8LrysMrHQ+pobrxG7nlHW/mKT/0E5T9xVD+PI3W73Ekro1zQGSS317a5eJ3GY935X1gJfZda1eWya6T8rTuiwNELr8D1bAJ2HKD6RwUdRkGeZCU9MtD1eSjmKZJg8aB84eyI55iX0kbENVpR7THjCgcq6bx7bXBb1KaWhsREeA3gdtV9ReT+7vcfwP8G+BXZ8nQMbYvAt82vXSSfChxy32LSaWdqSBDb+tjVVVS1WFXWNg+75/XcPnlN7HrgmfwxZt/hzMnHsKMaqdmc6fAB/sPJw2oKitJGSVqM7WAoaFS87YjqR3JqLLemVcGEQy5098bp8B726DCgBg2yBKX8lS+yGdQ1XBWmfT7iPdE7SVEhaD9MgYj1L2Cul9Q9d2OrZ5Q9w1135+yTpS+9IyN73d1lcaCq14BvSLmJc6YOuzAil9UsG3yoG0WFVrj4FfnubqOdkQWqBFVTqHPOziI+8hHi2LDgjBaiEDJqwzCkSWFdE98iT2ReKlNZQ/CDVKbtP7e47YPQ+uFWwYVOHszGVYhmGFlx+goWr2mxviduxDHNR88KiJsEbnb7e66RUQ+4+5tE5H3i8iX3f+tMyU2Pa/Hj98kEwY0J5BOdVqyk9XHbxnNZhOviLD7e17DwkV72Pem/0k9HMR4GVhI1WWdqitnu9PyGO0XAMkJ9Vrgjjgi2P/UpQvensjZBtUlbLjqGtY/5WoOfPhdDTuhTs/VeUiBZGpvJ1m9xkl/OiQ3jT7yfdP1OH+3K34XOJIsUkf+ndq0FHTkD1SiTari/L0JUstYVVgT3OYNFuN07YrsGkOT6hPGcALYvMkBNSxNsSFaC/wmp1ng3QuA7wde4gp4i4i8EvheEfkScAewD/jfq8j3PwJ7p0VqrRDGrRLCvewIB0P0X+TULDZBB2BcMArrFrYxGp2lrE2iMiL6qEm3XCeToD9vi5rgYTmoTrzdTgIo1B+d4cmf6VXXNoSJ1npy1sqqqhDDZcU11FQ8YO4OBsx4g2ofjEH7Zcy7MM5/TwYoiW0b3RlI2NHVaDtjInAIvoyksZ3fArPSGf86I2t33tmqjKyr2qoMR6MIhkbWDscayTuwmmiTohuBfDwQDOu9bVkEQ95eyN+TJJ1Y14a7Aoh9ne5CC0eG1DGg1oWAA73hbLZ0jKQ2RjUJ8HNHmPiyzEjeUdpqwiroxap6o6o+213/K+CDqnoV1nj5X60msQn0+PCbbHU8FRSNebeRRvKsMdeWJcXSEjoYIEXZAgOTwFGXnU0KkAIA6ohLGnccoPELBQM7X/HtnPrqHZy6+0udaeW2P51xaNYjtyFKv9kuKc44YDSuLJ12S1maY/MbR/nYyO5L8j+CCmncT1VTeeadAGfmwnVQXscx6DFXkTXKR3fzpbRG+E2DpqrMVPWjjG+b/zpLJqp6N3B9cv15ZgFjyWQ1bXyEDvVqNHWTv7MdCt7xiJIFVZCRsrx8jNv++m0AjIZnYXFiZeJvLxExBHCltdjMK0V7xqrVEiAlVHZjcVeaiZTFAqNovfuQ3sdX61t5Wu+5fH74UbbLXjYW24LxdAilsxdKJT4pGCoy8ODAAbU6ibmAKIoE5iiClTphEK1ce7oERKy/o6q24KssIwBStVv3B7PvetbR0Kk2CwcovETGSesq7/BSER1jaOyBLPG/GrWrXG2vUL26Sg3WXQFKcLqYDDwtCHY+Go8AbwZPoyoamqtaH04ePKp3fOnSMW5nm0sv7KoT+yx4xp6B6sdOhP0dwIvc77cCHwb+5cNN9PHiN10r9TDEm8PAJZrF7Vqxp3ElpnP2rrs49Ed/YB+NRkjRp5PGtYIDNmm6IasMCIjD6XndxgEDD5IOf+QDnPnKHex6xXew/12/y2X/8McpF9fbuB35ptS5Vs1BX9f//PckmvUdz1q7wFmjDNq87kzLVroxHtK2GNMeIV4LdDQ7qinFygZgB+XjsqHS9P00qTxkcXKANOl9R090fpPT+a8A9GBowko5nfwa54wpURViJOloL92xYTQ4HdJa7G9uHOeQboPuPJwV3FbxmlRi5NVnwVakV0Sbo/wAVK92Sg899SfC+yhUnOEknxt+mFJ63Dr4GLWRaEhtiuhrqHA+e5xUrHbGyNbZogNGJIuWIA3KwIJbMWo43Z2m6sY7bnT+jqQsrZSoVybqu3PYgj8cocOhBYXLK07lqFFa5H33JDZkuf1GYyxUTko09HZmJHWN9bbpSFOVlkqJiPeldmquKuk7HxwY0rPL8eDaOkqKgkdzP16qqH71dkNd0qmp7QaP1opNgfeJyGdF5Ifdvd2quh/A/d81a2LnJXVJgDLQMy4ALW/njVV3ltbw8MEgJZZ+ssmjleiETEWDFCQ6BcRtPtGmGi1VYxmnGstUbblmpl4+y9l77mL/H74dgIf+7A+opem5ubWlOwUZ+T064iT/ZzGg7kqjS3rWpZZLqQWGuigHBZlkp9W3edfUEoI/GqkhbST7PY6mGRQ1izVeJdmVrLZ/p+U5MdNZZmuL35zXR3dA8qFnK6500vMqrQB2PHhxDMDvprKO/mgdGrq+2EJZrEO1YrR8igWzHr/dOz2PTEWsBMFLDgxNXzKVU5WhFvxUauP24oQKzXfUL98cGNL0f0Kb2Moi6wFhZ7GXM5ziS4PPcO2GF1qbnoWeVckVhbMZsqoyC4Zo2GMB0a+Tk5gFZ5fqvok0+1rsSBkZwB1HUShCGe2JnIG1PwzWG1jruR5yORqhZxVZWLCgaLBo7W9KY0GRSGh+SXyf5Ktl442lFVQ0GE+Dq7/zZSS4/nXSHxENErH07LKmxEaJ0iGvBnN2YaPSApvhMI6hqnYuCwqoDaLRs7eKWDMrDzgdKJOkLtMWz4qcy9bWHV5P7+hNqvqmLM4LVHWfs+N5v4jMsG/8CUYd8860RXaI576fIJlOvrMGYHcsad0lT8H0+9TLy3a8lGVr4tPEl0yrqI4ndtrOebDiP2SRxkbPxiIoKX947myJFi+7jMX7LmP53rvZctMrOfGZv+LY5z7Olue8oAkc80bSdppdgKmlKpsFDJHFa9R3TPysiaLRtufpSdJpnZJ77VZ2AKeVuC8cYwFxwLvpOFGX5kSDonOnHPBI3kc5OHK0NMNZZmuN35zXgCgaUDdX8X4HlBkRgRBu0kgmrbDSJxu8TkXiqewt8o1f90Y+9Ml/z8LCZsvc6iSOB0FebQQoNdT2nDK/vbpxtELiQyaApMQA2wK3OtqfGEGH+QcWOdnGYgvLozMAXLnuWbDQ4y+P/B5bl/dywdK1YQt3AIASbWR823Tr1C0oslIPDTzUf6N4ECi4r1kCMAyTtEjiNygxsvbPnDdtrZP2mWXnWa12N56INUz2hsi+3GpXYFol3qn9YPBSuCoRcfckqAdxk5iXoImxUkGtPBMUxCgME1Ccgu1Zdn15lwhFAhrVIXnfFu6MNgGnbq2tT6kKB7zTvCbnaTfCrVroeyjR049JV/e5/wdE5A+B5wIPiciFqrpfRC7EeoZ+wlJL7eQmxknGuxMp6zJJ0u3v3MWFP/QjPPjW37DguCvtNP08r0mgQSyo8buKNNWPjamMpmphJzXqX3ghy/feDcCW5z6f9ZdfxX1v+q8s7rmYxYsuaWkEQ/LSvNe6n6j6WnWdUKeZKYur6X0v4UrjSYyoJAAoBQlJYunYiBNSklkOhBqFSG6l74wFcxMGYJpG9n7LHii3X5qFZoi7FvnNea0yS0W4nqL3aPvbVMQzxcS/kxhR+/eTnQ4xMR+EleFJFvqbMKXfoo5Lzw96d86XP+9LxKmm4sGjQcWReKD2fmYCMPNAKPU75D0+F/ZYdC8liuU0mLLHpeW1AHz2zAf4xNE/ZFgvc8vhP2XAijWiTvIM0rGcQXStOPMdfCbWubECk0R15uufgqEU4NUapV/GgqJmR5r2vQ7SwcCqnYY2xB1aTn02rDEDF7y38pFalwtDt7sw9whtkr5znq+th2sTVI1WJWCaX0iO4SaAIjEZGKrreFabV6MNh1GyVkXj6sbOvQYIm9ZaQr3KMDVFkQ3uCA1EZAN26/utWF9AP+Ci/QDw/6Ymdj5Tul7KV84dobXKZsL7Hffr06fp7dztbNbo/jATsDBWHZQFKz1SKBK1mVEXGBM0BlEwSrlzB4uXXgbAA7/16zzwlv+FDgbc+2u/jNZVNMjO85as7OPKnVd5Giia9mwSJfVveNNOJMu5VAhItA8xBKev6TNv6rlaMJTH65rwUrCat2NCuba1VZd03Kq0AHsad9x1m9YevznPJUTgbYjUgBmCFzGKWseLCNFHR6V2oqujVMCvThSJzMeTV1cYGDGkLNfb88DCc8AqlYL9kBp7Iryawtp7gP3gvE8ZX/aGlEbQksRRowUPqlaFImKsj5qqQrRA61TNZMKOsp3mUo7qITYUmzk1PMLm/m6ODx7i5gf/iOds/kELirydkM8bWjZLmjGkuvQ7IsLSh/RkbKu6cUbHNdZWyeAMgROpUGoL5euZgAZJyhFUgmIa4E9rtSow9x+wPppGIxgOkbMCsoCRClVjjdf7hWVslWCGNXW/aIBZLSMwzhmyGrvlWJwBvpaR24hzzKji6+qBiSu7JvWEMJ7CNvmesw9xYEiXV6BX2v5WtXVaxEoJCsutBW9oHftL8vzGkAIDfcQ/6d3AH7pvpwTerqrvEZFPA+8QkR8C7sV6j35iUzo5JJdxk0YzrmTXXZNqeKa4zRdu3A0GFOvXt4uQAhxoT54dz4IEVHC8SGEkUKg7O48mmO+4Fn+EouAkTMrGr3s2ZtMSunwWHVX0d1/I4KH9HPnon7P9hTc5vhrYZCxXWlx/PwVIHXXpqutU9VlOAvksnoK2vG0bVsmR9YWkYiJJdJNc+w07WbyQwDRAMa5uU8WPXe+M+e2vpwjku8DQibPTbYjWGr85rwERxEEc/CXUcTu8KNbYNpUA1HESs8ap9nbe4RGw2OuTZx5k/frtweA4RrR5xU1qzrakru12+mBsLdZpXygHUdIgIIi17ylGdvI1xoIvL9UZjZB+3/NN6uEIKQpMr2dPr+/12GR2cubMcU5UB/n6C7+Xe07ewtnqBEfP3s/QjCh66+J2eM8IxpFEwIk4UFBZFaBRoJAACLzNlAKUWBsat0xpqJNSe6tgG+WARFGEDz2AnQ5bKRchAiUPAqrK+m4qS2Rl6EBGYZn5ch3BZyGYlSrUsVrvPOR6Q/J0l51j/lpIFJPXBmPEHvkiNVpZkIKaxsG7AQSlIEUkgqCyBFYs6Klrayju/FeplyAWBlkmSpH6PaCw9ku1y9MzshlUdIpQnwsznZSm6l3AMzruHwZe+ohm9jhS18q+hUWqZK4Sh+MlXDYnepordk0iCLCyfx+9HTtDXpjMYFmaabUk257VJGAodSqoPcUPW/GmjSllmjotbSG1jPXqXbCL4Uf/gtHRo1z8z/4FD73trQAc+csPsvXFL0M8upkgCWu0ly9zqjnP67Wa++m9MWCqKcVqq8zs3BHV/yngQe1CMKXcQWZL8+/Tn/65dlQkSyMZlBM/6w5pT3g9kQbZ+TP6QBr3nu/PWWyI1hq/Oa9VZp7CIO2YFFqMzNsHubgRPPkJm4bkCedTZ3nlGOs37E788STBq1bSXT/J7y5fMXb3UBWcQgLW0Do9A6woIiBKAIXWivgdZEYscCoMC+UGemYdhZSYouTQma9RKyz2NrPv8M3djRdUfsT6SneZJ/aBayfbJlY9Z504FhYE9KIfpHAmm5F46Kv77Q+kHUeSqtfSYzIyNaTt26zz077wdU3bQIkH7qaM3AVRovH5pHZwbTEzdYG+VsIzSIFm6LNH0S/I2qZsUh9H+Sawiaq2rjwcjY4eob9z92zlmqV8irN7JKhvUolJl8qtFYz/EOy7/UsvYXj0KNLrMTp+hHqwAv0+UpSc/sqdmbQlhphe874HKGPVfkk5G2mOu5+mAQHwtOySJn7Pro2y9BVoTy5TaEaWsJpku9Rjs6QfwNC50pR287TW+M35XcJ0AvBzW5jA1Nqp5J2WAqEw0SUnjKfphq3mwqHDd7B+0+7G6en+qIv0ENTWduwu8luqR7W1dxlZmxfr6yaZ1BMwpMGBny+js0vyzhcdeNq9/imUZoF9p+9k4+JORvVZRvUyO7Zc3V2Ujg/K2wp5Q+SWXVywQYrqtwA2fJukh7+WRdh6b0GPrVtwyhi8VUvT8LyLHBgK6rUu+6yknztFwQ4MpbZbeGebYWeiNvTuktg+2XxTABmlT+12WiWwDGrF6HeoHanjXqoCHZc21i/IasKcHOXAY1JI49O837DjyO57qlcGnPnql+nvvmBsWcZOmq0FYPZettW7k3IgIvkzCxBMv8e6q65CRyNO33G7lWgNBpjFRRYvvjRKYXIe0wWOVgNUsmdTARI0G0yy/13pJ9QFzKAJ3h4x6hgPY6POWH4PfppjT7pB9CNYl7XIb85rldlqdcgtPehIE/sL4kQPjYn+yJGvMBicZMdFT88+MoERWPsZgdJYYAMWSlYuDto9MTpQBCB19EvkHSg2TnT3v41TR0kCnLyvHxEu3HAN952+jWMr+1BXt2dd+X2sX9w2fgHpwE+otzMobDPCpD5pNQxW1Oru1z2T+Hoy6ELPAgoR5wKgb42gXacIpWt+VwbvVLGikyIYsvUPYKoh/XO/M1st/KGvrv/sga22v9SIO49Moo1ZAo5sP2Vd6NIV1wadjhKNuDykqUpLgY2/TsdJrVZ1UdudZVbd2+7FWaV557gNdk6OpM4mXy9lgfhJ+M89eRzUYUn8FjASwg6mE5/7FP2du1jcc3H7m03z7LrXNRTSMipB9V27TP33EExm/Jb+vE61XygRvomNX/dMlr/6VZbvvSeojPf+kzdQrF8Xi+Yk+KEuknxHXQCoA+w07KCSxs3ngPBt+zYxsaGn2l8l1DKlcMZOwU4yHwePFGUAtnUvxOtaFE1IV7H8ycepY5ue6+796TZEa4/fnN+QTWhLL2gym5b42jOFxDmjN+6NolwJdjYqwpfu/GMufsqLEHf4ZlSRYXcg+VPW3XXLzgjihJfZ0gDOoaDbIXV2YMGClxL4XUchsgNDXlVmonSIXsnShj2s623m2Jn7A8BYt7C1s71y4+mGu36IZxR1rJjCyigcRgpaJG1RCHXfno+mpUHX9dHFngVDZekOmu0554zJjquG0bqJKrJ015kzMg+qqfS/p1wylKsuvTG0kwwZaRczFAAAIABJREFUF4DkIFftGDv+UFtCvaPrBJplaPx2Zc+ONAn3ArByQNdkA9u7LMjJOdoMYQpVyKrCnCy1ziPLdxN5SnlM4DUJT8qkSK2dP1XNwXe/k20veXnIL5QhX9BNkhQ1xJueCaZlFLcbV0IcLdQBCOK4To2OC7/TjABG1t14PWIMK1/7mpVuAsWmDW3HjqaZ3iTHj10qs4Z6jZh/Q8oUFjoagJCrauv91Up1gsowKXv+/FGjFBgJNGyd0vsd1BofjnflO+XSeF3Sui6braV1k22IYO3xm/NbQuQHecaAvAFcQ/yoEM6ZmkhucndShZqKs6cP0l9cckyiOem6QyycpbO0B2fuIyYtQ3I6sgycSqyqrddifzp6crhr+t+sW+eOwXA2R04tJcZw1e6XcPN972BQnbWJlyVaFtGvTs+49pMIaCDsigg7SrKPKawkHRhQ4+riwJA4wGB9F9lVqAhI7VYJ4g6C7ZUuTwf0nM2QAKq1BZ5gDatFkNEoGFdLeJe442+KXU2qvmzZhzUq2Fw1dTI5GbOkStMyNJ01hsTTOOJOuu/Zo0d8PRy4ky6XA373Wa1xK+8qFmCq8oQQS5+3NGbF3jCMzuJLdj+Mq0w65Kk6dRKA3ubsbMp8Kd8lKZL4MC9LMP51BfKOHSOISN7vqMc4SYjp9dj8spdy9F3vto4kfVFJPiD3bkPq0lV2oZlPx/eXT8xNdZl2xsl5cpeKLa/vTFNzm33MHHdaBuOMxh8Jakgm0/+roFlUhWuR35zXgCiKctuPIijSAIaiB2oC6JFw4SYjN7mrszE5uP9WlrZcyvYLnhZ2IKX5i3vXOiN0jMjgnOdlOp/UZwyEc82krq1UyAMhb0fjdh9RVWEXEg4wpHZD4eBWd07Zzi1XYe4vObNy2OZTGOpSYv28YXAhbYlRugLOVVZ+dSRJ3UXtVnTfrn7jl8vOGIM6o3GR2paxNMk3PmoAGqlAiyI+9+DI236VZWzLSZRKbZLyt0gt89baOlpUv6T0Kr703TTLdLdJrhbtKpuR6GwRCCpPA2ClXVJrU9KVq9BCmePRHratZ+OYCmtOhP2YUTpJp92bgZ1GdD8s0msmgG3g+Gf/ik03Ppvetu3j56kUoMw4WXbZ09jFQTb7J8CicXMCGFx6wfM5+q53Mzp0qBFdGn+bxc9Sj+9JvO5ekGR16gJCHYColda0dssLHdL2zI2m+mxSJVeRd8s4vDP/R4Dydbo8ckmnWaw1fnNeA6JUkiHOgLo19lLJUDjY1YOfZMIR54CvZ6zaR+DsylFu/8L/4dpnvxZ6HR3r+YRJQJFx34aok5oIEvzwEHez1Q4I+QNKh8O4wyz1z1NV6GBIvbKMlD17anxZ2jOOek4FtShWHRVUegXXXPE3+OJX/hCAs3KG9YubiZIRrGrPz7s5k3Mes1sOC8H5LXEMwdvoJG2d9oBUGuyAZGSQqqbul2i/hEKtzdWogJWhBYPGQFFbr9WVCfY2srhgfwcv0gmASneahX4VmnZDxN+pdKgxmXlQZMeJGonHeHhmJykTlvA/Na6WIOXx5XXPVJscxxnDA+FYFlKJV2pTlKrX0rqqxq3eMy3E5Fw8x86JbOJjyurYsZbWnJgCoY73z9z1ZQ5/6L1c8qNvjDdTKZNGO73xBrVJrmO+7xAvL4sDF6lNkS/DpEncLCyw8XnP49QnPwlAPRpiej23uHC+jhp5Z+DRL7RMcj22fs3L4EogeZa+23AQ6evSlZYrUAOshoVzfB6QQwoqPUbK22scqErL3wWWOgHRmH7vaqecbY9rTG1HC/BVkuKfM1Bae/xmam1E5GIR+ZCI3C4it4nIj7n7N4rIX4nILSLyGRF5bvLOf3H3XuiuLxMRFZHXJ3H+h4i8bmoJk3lmLMJNpDLxvQQcAV5y4ncfUQjD4WnWbdzF9j3Xd+u3kwNPfWu1dpd17f6p1XlWHkaPxKnjwgQ0edsRKQpnTyNRKuS357v/HgxRCBdc+HUhu5On9wWPy/j/CXUtCD0PyM0Qgh8h115pqAvv0TkJBdSFPxPMhDLaQ2aTXWje+aADhVIUSM8eBht+BxuipPxTtrd37vbzkjLXNw0v4dlYCm+OGVst3yk5eGl49e4Ivg5iXP3HxPPpdBhiB8A/hXEpUKusKpxv9LjymxRrTGrrnB9px70OGp08wYZrrmPhwj3tuKuZlMYCIHVAfTwY6pa4jM/cx9n+t14d7q3se6BpwBzSjflNshvq2o7fTCsN8Vlud5TnGes5po3SOqfXHeXvymO1UqiJdkCzgKEp1Oq2R3Q8TX9tLfCbnGaBdyPgjap6LfD1wD8WkacBPwf8tKreCPyku0ZErnHvfTPwj5N0DgA/JiL9mUuXMyglOcWccCr9VBLCNnq7w8r+X7/5As6eOsDy2WOdE1lzQCeT6izk1WPeMd9giA6H1rngcAiDIbqygg4G1IMBWqs7ssMBp7qOk2QXKCgMNzz9tQDcf+/HYpMJmIFax4LDeLSJqbT9USbU9LmEddDmgzO+blx7Wx3/XpCoEPwtqfe75HfVlWU0tPZhYSGCP7D1Dy4InH1VAzw22z8/V2zsYskBI7/1PhhP++34it2VGM7Jq+PONA9ivCdwD1THUUc5w/10vKZ9HWzLEluzKj6X/N0xtAb8gjwu/KYFbjTynFZYTXoJLV60l9N33GbHNB0Tpi9CDmby3yFi9jwHGv7agaTGMRXe0LoT1CT3se+bhT5bXnETAGduvTUChPA/ATFmfNDS5WvGhA7gNJOaaZwF+kTLdN/H0t23OSgjA2RpOTqAYP6sDboSSdSsYGuKdCg30p9S/aa07RzwyhrgNw2aWkJV3a+qn3O/TwK3A3uwXbPkom0G9rnf7mjBlkDxIPBB4nkkM1Eq5fEASOroU2Zi2QU7aadgKPlfUWNMSX/DZk6dfLA5mBur+0mZTJj40km9qiwIGgzQlUEDDDUonfjzCTAYDtv6bNtlzzY7dXIfo1HcIul3VkllgVEIVfeXURceKPog7l7mmDKAHxoMEce8om2WBDCkxkQpUVE4YFRYw+uiKTWiMEGK1gBCORhKJSe+zuNASEbRJ5UbTx5kjxwQqhQzqpOz8GLd614RJHSdfT6uDHk/pvHCuXYeBMXrKBnSeD2BFGGkxarC+UaPJ7/pWnHnu878/U6J0JThVw8G9HdZ30ODgw/NVKYu26Cx+TTAUAJOUtBi7EaITvCTik2T+xacKJtf9hIAjv/5h+yOsxQUjdtNlu1Ea52Blu1OmyiV6fyd88gEAWQdOtOEn65XUuAS2iWdI5plmghq8n5MgNBESdKs42tSPJWpwKhrjjuxPH3b/ROd3+S0KhsiEbkMeCbwSeANwHtF5Oexw/n5AKp6m4isBz4K/ESWxH8C/kxE3jxTflknW0NgxdsUTaOgwvFAqJR4VIPAqSMPsH7LHmpRbvngLwBw400/zoZNu4MBMva7dwPXgqPGxOQNbnH3R1G6wWhkjaZHI3tIKYSDWzU3yE7LXddIVTccNcYz0YwLtizXP+t13Pq5t3Dzx/47z3nhj2NcG5lh/Ii9vyWpBO0ZRgtWk+zVYnWP6KcoYVpx917eD25FVSiqUUVXQ7DTMm6Xmrjt4uqN3usaSX0wBQeFzlaoKGxbVTVa1Yg4+wQPHCrvswfCafDOP5D/8DWpc7ttbVnNqHYSo9pKg3wFwLkWcG3SMxZAGXtOGoXlYNZgP09cm79TUKt1E9h5SZiPG3xUFfG5Mda1gvMOPm0FoArVE0AsPSs9pvzG2RC1+9T9cw+6bFBmpeV997Fw4V5Gx49xzy//Z8otW9n7I6+n3LwlJD5REuLtW7LyBQ6kBElQKKx7P2iVvS2NZ2oCqslmAw9O3ELU3xcFWeyx+Vtu4vh73s+Rd72L7a/+Dvt+Vs/WrjxxCy2/Y9V0RPYvJGCz1bZd4HAcTVNDSVZGjfwv/PdgRW16QZrk7jXGwczl7XAmTMyrtUMs+93kxdJ+lmLBNKPMUaevRi5l1CSPqUd3rDF+A6sARCKyEfgD4A2qekJEfgb4Z6r6ByLy3cBvAi8DUNXXd6Whql8TkU8Br5klz22bbPEE3OqdpjGwElbRXkLgB4UaLHDwnqYLa++ytNSz0g+Uo1+9jauvexZbNhn27t0LwKHbf5fFp93Ejj1Px1QJsk6lLrVihrU7nkOhUpY2ucNe/Sp/aGDUTwCRPeMqnt3VdqQjhUGcrY0sLli/Q9vtf13fd/Ux1D3rAwhg65anc+yALXuvfpDNm/baiTth1nbMRjufakFY2tizE21BlJp5BuDAQMOppQdHgFR+1WyNfs1QMU79tLSxjF7BFfe7dn1UWweGdeX6sW4Co6qGlZ5VLY4qG6/XA1Owacc6y8XXLQbfRloW9vy4IMWJANjfa1HS7KKxvL6ewc+UszULANxJjizYdOUfePBCE9wBS9sXXX4Jl0odcaLxGuJ/LykTY305eZuyGfnOE0FPPws91vxm5/peRyGStDKg0lApkUzyEq+39ctwr9aK4d1f5oIbn02PipHjN6PfeTObv+3VrH/qUy0g8gmaLM8kr+1l0QJMajT6ACoSiQYgOXjIJ3KVAKi2Sa8JSsK3b1/a+apvYf+tt8NX7mLr6ZP0tmylExVledkyN8vVSeorNC5eM6PtZdkdbxJYHSfVSdppe1EG9Ct5e6SJdNW7ka827neWNxlTnYCoI6lGmyvsKEqkR+TTDV1Yq3rjnyXXW7o2GmW0VviNp5kAkYj0sMzpbar6Tnf7B4Afc7//L/AbM+b5s8DvA38xLeLRY0Obv598hxEMRelFAoj8LjPshF73TZQMCdYI2MDhUyO+8unf4+Ddn+aGl72BWz7xLh68/362XngtSzuv4OaP/TEXXH6AA1/7FJu3X8HVN/wtqJRipQ4qOzNwk2JVOSDU5/ihlajyWF5xQGgYJESaSAtyCVF+zIWsq6Dfh16fY8dH6EIVzg7TsqDuWw6sImzb8TK+cMtbuP/+X+KbXvBvWKz60Xlk4p9HS7vDrlo0SA3HTg5RY9tF/dmrYoFjYyWnaXsTPPpKrZgRFINoeyMKx44No32XOnWdB4+1Pc4ELzHyUrWqtkvI5RUYVVZKpAr9OhhdH33gBLJuGM5O07KAXhHVc6VJpIL+6JVsUGWACODo0UGoZ1ARBlBI0ue1dbJZuf8rw+bOwlHSpyIcO3CWsAOtVgvwUjuxdAyoWvDsxgKFsf3vwVHumLKD1orn2MeD3xw6M2zNdSm1AI8HKeOuHTg5sDIEgS/9zL9ARyMuf+V3su/tb2b5/vtZet7z6W3dyl+/5TfZ9q2v4tiHPsjSC17Alhe9OIzbTpsV4KFqGC88CHJSU02X+QKS+ENr1C8FRH7SN8KBahDuA/GbUUCEU8+4jmN/8l7u//9+ikt/+ecwXT610rYTQHq2zEGclZUlRHa3xwCsLrXUQ6NhZ9xOyiVD4X67jR6qhg0wmAtoW9KajgddeKGzvGm8MXWXpL8a8RxfPjD05U0kRNrxfpZPF+AThbrsWCQ0Xl8b/CalWXaZCXY1druq/mLyaB/wQvf7JcCXZ8lQVe8Avgh829S8k8koVd1MMxJr7o7y9xyTclKRg3d/GoANO/ay9/qbuPSZ38HpY/u45wvvZuX0YQ7e+zk277ySA/d/lsrZ54gm9kujREIU8pb2LiJ/7YHOpF1T6XEd+e4jl1eYkL0H5lHN9qUrWVywYvdD+28NUjRb1tqepVY5wFj5iV0xPgwVM1CKgZX2FEMLdMyIzIg9gqGghiyIO828ui21N/I2WI1t8qZ5LRIn/l4PygLpW4/X4o4sUa8+rJ3xcV07CVMVgVUWOm1upox4ce95G6PG2DJREhXqUWT16NpC7/vRFN270kIGttw6GFggPRza3Yrem/lwMuNXnvi7Ph43fqMJ7tAsMGHlPoEXpXF0NELKknLDRi78Oz/Atpe8nJOf+SSH3/Mn1MtnOfGJj7Hhuus48YmPo1MTbUodcmqoS5RonzMr+YYIdkjNe0s3vThEHTzYtL3sCg2boMwbdGdcJqT1CNAsYKhp32TbQbN2bNpK+dWiji9v1hbNQmX/V0Nd+WS/J4KhMfem2xA98flNTrOYfb8A+H7gJW7L6y0i8krgHwC/ICKfx67CfngV+f5HYO/UwrnJ2u+Y8kawEQVnvWzc1m8/cZVi1Utui3jYQWXgouteBsDRB++kWFzPBdd+Eze84p8BsGHLHs4c309RLrB11zU8dP/nbPqKAyJ+Z1AyAadb/73hcFkivehbaOrBpuBOti+aPmxGVdhxJMMKWR5hzsbQOzPi+svsttjb7/ojC9SGVQyjGjOoKFYqiuUR5ZmR/X96RHmqojxTUZ6p7f+zNeVZddc15RkLmsJONQhtqMYCId/GVS/+9rZEqcSGRK1lXQsYCyZK74DS7URbXIB1i4g/L2mU2GGNRhYkrAxgZWilc64vvKouqOxqb7fkui+TsDSu0zkmPQjWqcw0BTdCsOXqBEU+XkjQgycTD8FNXSpA9FI+HKJVZY3uT59BT55CT52G02fRs8vTRs9aOGzxceE3Tb5C2FwEjJdadE1i+W93vfFpz0BHI1YOPEi5tJntL/kW9vy9HwGg2LSJwYP76V9wIWZhgeWvWKzXsFHx6XasBhvzjJMKhMWjIVbIT/BjGwFng5Ts+PKAoHDBgCwUbPu+vw3Awd94a8PuUAudGII0K00zCa130rRzg+wpoStuw1C6y9K4YeDtymmwpvumXa48v3H3bMh31SVxfT+n3aHjixn6K30vjzNWzEaQ9DfGVkbTbIjWCL9p0FSVmap+lPHri68bcz9P427g+uT688wCxvzZUwrR1gJ3z/1IJ7uGsZ5fcSRG1UWUIO248rnsu+0D3PnhX+eSZ307F177Qo4/+CU2bNnD1d/wWo7c/9ds2XY5X/38O8fWPtrYZNKIWi0nEmcAbKZsh+sif+Cnc/oo6pwgFi5fr25xSW9fvJiNi7s5tfwQK8unWOhtyBJ0kg9nmG2lQrWzszHhTDOtNNlV5j5wIpCMK6Ik5fA7+nhSJ90LHqXde2IUaolGfQbnMFGcyF+sx2cviVWNdllu95l44FFXUJexfYJ0SIKjPVXwnqpXcwwGdABuXwfnrDH9T66WCO9kEqG6tmPDH+rqfVPVaqVgYVwrWg0saKqqJkgeQ6owfAIwnUn0uPEbbWYajKghGiynE1BXCcfdB7Y87xv5/9l773hLrqvO97t21Tk3p85RauUsW7JkOUgWCs4ZGMCGAcMAnjcef+ADDMHM400CPPYAA8+PZIZsmyTAYYyNbGzjIFtZtnKrJbW6pc753u57zzlV6/2xQ+1dp869tw2o2/3e/nzq3jpVu3aqXXv/9lq/tfbsww+w/QPvY8Pbfoixyy7n+LatjF/xQqZvvJkTT25laONGyhPzzYk0pTtIypBIZhQRtwGR6oAHU/xeR4KWWOyNCGwlx196DYdu+zi9/QdQ7Vk171JlbZKMNIXQ6HGl/E9JAOBSkqMmHnoflyluFg+E/LlG8Y2kwFSrthlcl1peSRvY9yK+32j/Y99U6KtvdG0JEH8y+Z4J4009nN61cR0nAJsE6UsFgtwRO+LTEI9kQvYqnKGplVz6uh8H4Jl7P8ZTd/4VB3d8nbXnvZSR0RWsPesanrjvrxifWM/6jS+uVHelWmmEl0z0yqCOolc4ac5gC7KBVfW8kSYnfaUFBVKWjq9UuA1jvdrOqpAu2fBqAL7y6G/a69ER0ovVSf68dIThnpUCeXN9UxA2Rc06iuk6qZ1Tp9nDXhcXVwqQnkaDb72irm71nherF+tqRadSk3YbaTkP3sYEkOS5SThpkDj12iA3A3EI/SU44fQuAyKp0MCXVgPhNd5Wo7SoKZ67FjZ+rWfj3RIsw63AmbZiO11DEwk1CbXfY2efx7rv/NcAPPeR3+fg529n9qGvM3ndyxjeuInRSy5l95/8EZMvfSkjF1xQy6yWaQ0QJNeXBBzLQSTNUROQlWfMvPX1AOz/4z9PAY8/Yh9E7neQDsW+ibKG6yGNAWkOEp/Ur8cqrHracR6xVKgRHLp7EXl9yTLFwOr5CBHoqmO0ARSngdKh5YYzbbw5vUsY+CextEcqCYWkICgVU6agyFtTxaLUsdWb2fACu+v03q13cGTvNkbH11B2Ozxyxx8wOXMWF1z2HRiVoD4JAMMDkhgE9Qqr0lnoQN2/0GLB7fTu1WVSV5n5rUGcpCQGY/QcECiUFaObGBtaRbc4wZG5ZxMJROrTxv13YtOgZnLcJA+CvP8i07Vqs6xrgVHW8TwjB5KKCEg1CMPi92XrK31Gdv0qJ0PimNLzibzqse7UMHo31tFiPyhLJD4eRGcRAPKSsQgg9ZWRSiKWqtFqYG4QGIp/++09ZMBmr/V2XAJoKyenz/9W0OmfzuFkV/GTV1zFyDnnA3DgM39H7+hh2jMr6R0+zO4/+n2mXn4D0zffsmhmlconAkPx5Az9/XaJ97yYC60+MBaVZ/yGa5HhIY7fdR/FsWNROSrgUVd3SZaqjWiVFTCK1Uh1UFRXL9WBRh2AJHEHACHov+bnixooskYpmtavXq6ljkHtXx8iBvWrQe+xAQAtK/S/0iQsxw/RmTbenNZ7makAfpd1iKy0XMMq6VcskTjVTW52WwkqArAhWFFJCasufinPPfD3bHnpv2JqcgtjI6t4+Mu/R7s9wfkXvpHMeXq2YKEysw8+hwIIEjh+3PJdum5DUyf1CeLkPhWMCdZldu+yaNJvuVcTbQQbQgHSa55oL155E/c895c8s+8urtzwBtsmTj0j4r+cIhC0MSA933YOMLq93vAeqCGY4lvrPQcYIjcHQCBq94UYrEoETFTtvah5pPDAwr3noSH76PAwjLg2cBviogqdLuQeANmE1Bj7OzfW95LjL/mgWQR2/GDnL3jQE2MdV0dBq7r4wVANSolo5JsoDJIRoAvvTgOp3PpU0sr3kNv0Vkv7XvBuiro96HQoi6VVr+Xzthz9/2YQpW+1rWDHE6WaY6T/PsDqV72RZ37n19jwfT9Me+06zPAIO3/n1xm95DKmrr+xIUP6J7wmMORVttG9pQScAHVLpJSvFP1Xm6YSfTsmY+LWGzj6ic8wd+8DTNzy8qrS8eckipgSEevUTWrk5MQKzn3WlD7TKsmqfFGZ64Txepn7JNH1MThtijjEbezbUrPoJdeEU7HVXl/QWjmXExrTWSKRtNn6O+ygxxoknEtziM688ea0BkQ+hMkpWFy51RIQu8drtFKo+dkJvjrcTNcam+RF7/gfGBXyE8q+bXfR6xznipf9EFkh0XYOWhGbkz3J3Hm3az1Qd52ZfamVGqyJ+xGBIYykkqF4awhjpUf92zbUfru2WTm8GYDnjj7Ileteb+vrHRf6UDow4/wDBeeSkQpIxDlWhDAaVBIUg/gqldUAcdJ676YR20j8wq2fIXBernMreXMOGlXduygLW6fCAh8Rx2FyUisrkVKSLUaopI5xOeIVuC+jerhkbDP5fpRwiAKQqjlsrKv/MvcuwwjrpF0mNDxijAVFYInWXkq4hDNSBXrlmWUG+y0Tmia8htc1vGEzF/znXwnS7wOf+Tvaa9ez+nVvcnyUSsWTSFx8aAJDtXGvuqf9ZTqZmTmOGoGiKiMYu/5ajn7iMxy+7ZNM3PqyEFficvtxN26UpiK4/ARBa4NJE48pBIkuxek28fqaQM+gELx6RwDPATK7poucLDpSpPg28sBtifyXC1hSkDMgirCsMXi58ZZTpDNtvDmtAVHggIRd0P2N6prGH4P3thx9GFJiRbOJVAlELUcGoOh0kGyIA9vvZ+s9f86GLS9DMFb94k3tg68j+iU9ywl+9e/UIwJWeuTVZC23n5e3RvITpLe+ikPdmR+ECdaYnImhtRxb2MPxhYOMtmdsXp6M6EGOU/nhygRYr9IqWCKOVN5N3dcjReXsUItKGpeukmI3CZHF16A281KiJpl9DCYa3BWIt9jK8xR0qtqyS9xOFahzzREml0S1Hfche+Z281ZLRi9LN0i6d2lsn7Dn7okyAllNICauZ11foRW5OvFbtZw9+75FxNJncvBzYZ3Wom5OVQHtdDBDQ+z52F9x5K6vsP7t71h+BnWpRFP+vj/7wbEutnLpLDe/8KhE11whWqumkZFh9MQ82u1ghtopEBP/gD36fCJFRbPfDS5e2o+9byUfJ2zUXAOETTwrGIA5akAzCUaRBLz5cztWxa4NYr9P4dNWqbntkLQtNfpfC/9sgKWWzj9XunGCZ9p4c1oDotBZCq0vTOx5pE4L98IKSlIRNh7dR/+dx+W7PvIexleexeyBZwBYv/k6G6ckcqSn1X5Sda6LlM3SDl+uorDfVCt3BFmHxPLckmmzzBKFvQm6n+ih2hgV0om0CWS4MqwdO49jC3vYdvAOrljzGqtu8z6Q1Pn08Q4koZICqXESDuMWPFqt7JwUSbwax/sYCm2r9tlAPvcAclA5a6AgrlccrwlIiVRenD0Yiq0Ok04RnUq1zUhdtVqlXcvL2G1HAjE/jpsL0nV9wUTALiZkN5XfX6tJpxK1aL3KRpYERbbLnlkD1LdMiCc79zuok6PuXszNsu39v8Dw5i3M73gagJGzz10i7WXMZJHQRNwC0HfbpGC+TD5dGs6XSL8ehi45n/l7H2TujnuZuPm6KF4EKMSWS4wmnCZb1qqIQWrrgVz0qaDiFrce6zkA1ZRGHBYBPn3Dtl/81V6eeE5UUdXHV1Hi9gxDlgbHrhWArI0zi4CVMP6GVWcavy9I7bxv4pNFQVhTesvyQ3SGjTenNSCq81H8lgwhiOUEVfddh4xX+UowwaawFvCmZ69b/zpgslYAQxu3XM/o8MrAGzI9O6nH0pRQAm/5E5wvGqQwVqVkrOdl7TgEkTkwlefIiNvWwU/mmakAUeZ88zi36drK0XbDa/JSBc/GcDTFAAAgAElEQVRniq5vmLycJw7ewbPHHuTSmZvIxJluO6KyANLJkPmaoz9jtw/RwkmufJI1y7CsW1jnhO59qKk+tmw+x3Qqq7aEZB27J4g4MokfJ29+H4OhuH4tx6/ypGtjEiBhze+r//aihEFMHajxKtSiZT1110MFwDXwj0Rd0TymVev3CrA8rLC3WplY8fXVQVy5jVhCm/dw7aVDzmt3P4l6aUDUiz+I/z/8k8NSc0ff5KvRnBODImP/ZSPWHYYHQytveS3Z2HiIn8wvcZeJ5sZKHdM/0Wuk5qkEG5F0wj07UMg9CCQF0VdaRhGYvPk65u99kIN//DeM33ytFW4ncRQRv5j0YKN6PpbuJIBJsechuiLRPdTWV2MpmETp1sMA6VEaxyXuJUTObUEMNuO1jrp9HW05bQP5d+7ZFL4dpdSq/bSWnwd3VPcCKKp+LB78MDwI+MT3G5qlHpbcy4wzb7w5rWuT7Nq+mJrKW5+ZymNymRN2b7edw0qZvDMqUayEqFRedOtPI8aCjrLooUUvqMo816aef2KS3XCuRVEdvW44x//3YMiRqC3wydBWtR2FZgK5VBu6eg6P95bsnRt6B4cujOZTTA6tBWDX3GPBbD94PO45UrLjOyXXekUw7Q+7rhdFMPn3h+n0kIUC6fQwnaLyoO3j1S3ONBq0llI5Nnlz9hZcnnSeZ6lkqAmARPn3O2X0wChuU1Lg5MB15aG6+l9mQvBx5Qnpg+oWu09IpItpPYMfIq8i07J2LL20O9OsPk6LEE3ay2qyGAhFh51XhbPf+ZNV1KJAe0Xf5NVPcpWBE5cvY8o7cofjvflyJWBIq2v1vJoZxlVaIk66I8rw5edjJscB6Gzb7kCDO2rWXcknHZmqiymdBAlMpkGaJEaRLE5Hq3qFfPCGun3pVhZh0XOhPDrgIORn62B/m6xsOGz5TFamabpnxJUz5XxF59ELT/iL9f426OULfWnVMW3glcXPNCXbAKCWCmfaeHN6AyKvfgEnQYgGmtp2HrZTODDiidPe3NPdS75zNyqIwtDINOdc/BoAjh1+hmee/Fzlqdipf8LWGc7UW4LJt9uHy63stSjs5OUIJWLs11rfq6wCQ96rtUHz3IKbVoZm7hC7oav18Fxdpw6Msgg4jbS5ePXNADx44O8jEJFjt4/IcCNPNZJEHBytT9i+vWrSjphTFW90G7YMiY9SIz9KTuLmJFyJl+96aOBJ9QHQ+uGuJ2b4vgx+c15nPdgI0uKi1AencEj636vJInJ1Y7kWATXSatm+kee2v+StvmOxcCaawZ6KUJv7l4wH1Ca3Re4Bw+s2Mn7plQAce+gBjj5wdy3hhvO+CTMFGj6OBIkD1eHTqUlXGoHPMkJd+iIiTL3Zugs4+OFPpmb+NVBYFjEgq/JXd55QCQcWTav7dWATA58ms3iiuA0AMoA4LDCrJE7qBLsR2BOc9RwBRFXSpArAVZzDuNFqdehvkuTaskIfyKrf1764SX5LlKMezsTx5rQHRElQku076o734gkrgCC/wm9A0nH/2HDuDUxMbmL22HPs2/MgZdGrnDD6yT5SU7HQrTwoFwXqOTm9XiBJS5YFMBR86bTympTDBBCEB0Me2Cx2xEDIb27qgJZmGTPTWxhrrwLgMAcq4NVyfnyMOAu4iMQdOwas/6+rfgYQg8Wr8Xpl/9EtnCPLonJqGauXCr9fWUMedb5N/bqXsvhz73/JSa6M86nknU7afeCqfGKwPXAxpq5fBZWshH5m1Z1RWWLuVVA51upQa0PJc2i3LDBq5Ui71XcsFUrkpI7lBBHJROQ+EfmE+71CRG4Xka3u/8yyEjqdQzw5JCDD/qtPHI2AZ6kJKQrrnZPG7r49zD70gI1ek9b08SOXSLNeH/ugPzzwsOdSSp9g5JsKLp+Jm16MDA/R2baD4vAsqpIchP+2PImRblmBoT4QNzD4j5XqCOAnSqj2YdelV4MOL9WJreViaZeYGiiKwJD0ga0qHY37SL3fRC9Ca31J4/tNR62x6tuC1PtuXZqoUTx/bykOEZx5481pDYjqq+lkMivU+cmppECaWVVZGXwQEdRmYTd3ah+/2nwMwtUvfhcXXvhmTpw4wPFju91kavcDo7DqIVnoIZ2uNfXuWAeMurBg/5clMjSUHm03uXkw1GpZQvBQCx3KKYfb6HCbcrhFMZJTDuWUbbubfTmUUbbcf3+0DZoL2jKULati01ZG2c7Df3KDDrW4aLN1OnnfztvQkbY9hhwHJwJI2m5ZlZ1T24WJ3fOIvCSoYRPVuiqNXomZ7yCNRw+z0LVtuNBDuj0Lgrq9yrllXfXly+H4VZhalzWSgjsI4CscnR5moYdZKMjmC7L5sjo6DhgpzpJw8ckncIvC6tOr3Ewi3bNStxrgrEuLfMhzGBmG4SFk2O7hJmOjmPGx6hgbxYyMLPm59EpzUscyw48Bj0S/fxb4rKpeAHzW/f6WDhpNDGF6GTRxxWEQcFoCwEieccEvvI/p62/m+OOPUnYX0vFJo3RqPoa07i05Kn+QfEQTpJYSwAel29amlPTw/T+SxCdAqc6/qSEoyQxTb7ZS6YN/8fcV8CnFWqT6/+rO/eF+l0X1m7T4fe0dQIuvbwxgTGlVbJli8pK62s2fm1wxWXTkZXVkJVmmGK8mMxokQyY+ghpNybLSqc4iUJVIoqr/fe+q76hJcvx7lPTob5tIOhb6Svrbx+nbHy767+NPjizBIToDx5vTGxANCI1bMtRUGMkAF3cg/107IBTOARFh7ZorAHj2ubuqndSDOiiSAnmHjF5KlBm3rYQFPuLIv0EN4jwSex9D6lRk5BbY2M1no41p/XkeHU7C4OMQdpS3kiJ/lEM5T+79Elt3fQ4Rw0L3GAvMB6mSV7V5XlL8bDJxe2ASq7RitwMxePHqw54HIe5IpETOkaVXN/pz34Z+N/s4Xc8d8u/WS1k8qKhZpUkZpeVUmeEd9kq39UiJ6ZbWw3bkiTuVSNUGnXqXUychSjhGjsvl+UX9uoWq3J4nlVUbAeP6D60chtrudyuVKC4R/rlF2CKyCXg98HvR5TcDf+TO/wh4y5IJne5Bolc8YPXsw0lLawbElTxn8oXXAHDsnrub4zdOfPX/g0Sa0bmXDHlpUSyNCdeWWaEGcvK+3/4zFh59EoC5f7ybstBAOE4HZBJJUd8BARTVwZGX7Pi8B0p6Iu5SIhHyfKAADDx3qbRgx1RHn8QoqmuSnvuNWHUaIY+0rPhPvwkURXHiNl6qnyVzW9wXahylPsmmB0X1Pl6fL5fRHc608ea0BkTi+SnLIJP2oefa951KF7VPmkpp82nlo1x5ydt5ds9dHJ59JpKOuIl6kOmzibZgEIl2um85NUheTW4tb10mbsf0CNzkEck3jyZcE11zYKkMhGt3zR1lLoyPb+DY8V2oWmbxg9s/FqRJGpGSNc9TzlIElOr8pD7QlLwsSc8b/Ab1qYycQ8pEyuPTDv8lpKl1MBGfxwApISJQAd9ojzPPJ/KWcOJdO2ikknUE/DAge19YZTSIJasv/9vVM+YVNe1PVw/eiafneWVRW8TOOgeEfyGd/v8Efpp0s5W1qrrLVkd3AWuWk9BpH2pACPrBT+MkNeheE5CqTTRD69Yxff23se9jt1HMzS1atjCZJXlEi7oY7HiEp9X/WJPUFzT633hf+s+jeO2zNnDigcfC72N//5Wo4A3J1VRqlWqNIFkKEqZ6nFreA8vsQ/JeHWiJeUDhSEGUEW0EPzGfyJgySIwCCIoAieUjVdIh8eUxESjqI11XZT0ZcJLOf4OkTw2SIn/dNMRbLL8zcLxZEhCJyGYR+ZyIPCIiD4nIj7nrfy4i97vjaRG5P3rm/SJyt4jc6H5vEREVkXdHcT4gIu9YNPNoQ9U+UFT7uRwdeAWw4smu/8E1Ky4F4PDRZ1LP1NBvARVLLPzE7uO6Fb60WtBuBwlAygVywCaQpCuLuVgi5K3nYgkSEViK08AIK1dfzPXX/SyXXvDttrgO6JStjHKsbSVTTs1GLGHKK/J2sGBz3rMDaIo5TH1AKAIBfkKP48R8Je+EMvLQrZ7j5NNz//uI3k2Aqx4iS7MK/EQSKA9wPEAuK8AcuGqkwDm+Bk5K1MRj8sDIW6H5uvpyhfxr0rCgGpQKFNWlYouEQs1JHcAq963640er1ylvAPaq6j1LN/Y/TzhV400iHfLXaufNaopa3AYwtNQ8sOqVrwNg4blnF4/Yl3cNHKnj4ZTiDEGcL6JS+gHTIhKaClQtEsLsa5+beu2NbHj/TzP1llsBKI7MQuMsXs8//a2FSVRpTWVM1G01kJQAq0HNFgGh8DuyKvNNGkCQO4yTJPXbdFTvwf/ulzBpZRkXuUZIAM8AUJRKlAY3a/31VOrTWv9MQJGG3/XjyMLSHKJv9fGmHpbjh6gH/KSq3isiE8A9InK7qn63jyAivwIccecXu8uvAP4Q+IL7vRf4MRH5HVVd9s6nQT2mRKvx6u1LJAwQTb+DAJLChObAUM/1g+CB2k2GPcV0rWrn6vO/lyee/QfOnbq2UunUg/NDFEjTYK3MSr8SECsNGhm2k307C+qycigPgEhzoWh5EEDgRgGULbd3mEQDW6nOq3RV27iuAOTQak+xduxaJqfP5q57f5O54jAjoysB6I3mFPOla4/SPhc+Ggu+AoAsFa0DR686c8Rlm7diuTOS4swyHrMlqH5E1anssmp0UkUxYZAJ4MJg0y1cWqr9Eqc4RJZw6vds0wjAqbGGdk51FoRKhuAlV2uDn61LrZNBMG1WA6hU7z74qYrq5dOIgZmI6xdZRTAv1a4c/dYe3gx/kWCjLWsVFof9qnrNgHsvB94kIq8DhoFJEflTYI+IrFfVXSKyHvtt/3OFUzPe+JVyyGRAvMUAT8OkFObqhonM35M8Z9Ub38qxe+5k9MILB5cxmTCriSzcAwuCIIAN8QApXIt+U3s2joP4E3tW/bFDRQTC7AWltWoF02++leFLzmPfr/8RU2+6xXqujtMOKSYFSO/X+/Cgd+EL4oCQjyvifBkJiWfsAFY8iGkAQFXcEqFSe/n0B9l1GFNCadDM+1FSZ1FnPdhr6b1sVxOWuCYOHklKqRb+MVi0BajGVFe3vuaoizfcnAd27gikdV8MHwdt3JR7crTdfzFO/swYb5KwpIRIVXep6r3u/BiW7LTR3xcRAb4L+Ii7lOGmS9LXtg9LiPqBky2kRJOvBTf+448/5IYlQbLqiYCPA0MhxFZNalUpK8bOoSg67Dv2BJE5hCVTN+XlraNi1VqkuonBULAkcyqw0klngrrMk928T6XIB44likt1P9rEtsyspKhspdKkkYk1bN70Mp7e8YWIl+RVbLHazZ6XuZD454nUcd4FQGz2H7w31yQl8eiR+OrxJL5MrPoulhjFUpW6tZZ7Vmtp96Uf94nCWbE51wn+/VZSv0iF5gCydQVQlySm80ZjiCRETZKjSmrkfgeOlJcIkUqT4mcH7YlXC4NUEQNVFIumpT+nqptUdQvwPcA/qOr3AR+j+o5/APjokgVbZjhV482gFXgiGWqahJruxc803GsKEy+8muNbH6ezZ/dyitsPhuLzGAw1HdT6ciztoXadhvhEw3E8Sbtj+IJzGb78Qo7d/pV0DGZAeTxIi9LQ+PcASVEK3qKxJlar1fOnAkP+ybpqzBjtG8IqYFSrd0iToGYzUTqWj1QGX0aNZvmxxMgsk3RdeyF9n7LULdrStFLJFJVhUv3eEuFbfbyph5PiEInIFuAq4GvR5RuAPaq6FUBVHwJGgS8Bv1VL4r3AT4rI0iM7DvhEKNcHlRRJB8mfc7QYrCXiw92v/OZgfdH0Kg6J9VxtibiZwkVrbuKxfZ+zJviqPLD3f/PM0a/3EXlny8PMa03/H6s5ggSgUpNpJpS5CRZxKQiqrpU1a4CgTovVbA5MVRyfSpVmv3jYuPll7Nv3IJ1yvroXgIetR+WewE/s/jrRyFC9gwTwZA0AwE/2mSRSngoEOGBlImAWfDR5NZ5Xp2UJsNBQxgg4mYZ27wNFrj+VON9EpLyiCBhJT60LAX90rSQt+Mfy4NxPx8n7pypnre/6flNdTB8OUqKTUJX5l/M8+QV5L/BKEdkKvNL9bi6RyCYR+SkR+aiI3CUi/ygivykirxeRRcef53W8Cf26NinU7vedD0hnsbhNzZ6NjjFz0y0c+OTHbd6qPPurv8aJrU/0xe88u5vi2OzgvHUxFVnl4DHhVdIUN7rm4jcBpyaQMPmqVzD7hTurTYqT9BqOWlr+PGm0Qc/FwMidN6vTfLr9dQiSooZ3s5gQOi5v4qPIFSeo2AIgaVCjhevVuQZ/Sj5eEzjSRQ4SUBRUZDUHlTGHqM9n05LDw5k13sBJbN0hIuPAbcCPq+rR6NbbqFZrAKjqu2kIqvqUiNwJvH05ec6saNvVMgSAECbBxGtzBBZy/4Il+aK8qmx6LMc4p4umqwS+SE/JWiA9A0UOWjI9cRmdoe082fsUV698I6uyaQ51HudBfZxLpm/kWGc/Bxd2cjw/wtTMy5jONgawJMNt7G7tObRza/LuuDeaC0U7sx3Sez72b8J/q2LBzPR4boGB+9A9SGwScSYqQj9he/BW5Jx/wZUU3cdZveHFTI5bnzYWEMbLqLjTuro4UNk4cReKaDXoTc60/ct2kSQdNWJAYEBzgxQagILmBtPtheJoK0N6JZPTrSid2ugbj1jeq3ksoYukMpaj5IAiwvhECymtuwZcvwIPuEjz8K733XtI1Lmo3RLG1X1yMreqxLK6RllU/qz6ymdS/pkS8kpmiG0MDAoU/0Ku9FX188Dn3fkB4JalnhGRP8BKdz4B/HesqHsYuBB4DfDzIvKzqvqPDc8+r+PNqtGaj6dljt19EqIIWK0YyvuuV5NTNBFhd3BZdeNN7Nq2Ffno37L6X30HvSxDP/pxiuEhpl//Wrq7nmPu/m+QTUwy84oXM3L5ij6uCApSSNV3fKaiKVCqB/f8Cuexv29CjH5rfYNW6f+pW86mvXoNU48/y8il57HStGoNFoobF735Rn8WIayUPKisqzJqeEgS9OejOOkvVpoT1EbuXARW0G5UBAwM7rkKk0k4AEpj562VtGwhykiiXQNzaKr29MVvknb53yvzVn9fi0EotTkj7hu1dH2TtRbZW9EncaaMNz4sCxCJSAs7OH1IVf86up4D3w68aDnpuPBLwF8BAwvlw6EDC46861VElXQFwW2fgJWWCMleZvF35/lDpqdkHeXwwQWkhGyhCMRt6an1kdOLTLU7XSaOX8CDuz7Mrmd3c/2a72Xb3g9xvHeYnTs/FNJfO3wewzNrObJvvsq0VVoS9RBoG8quoWw5K7CWoeyUkRVZtZ+W1wP7/2VL2D/fq9R8SpCENQXvXTsQhJ3k4+ihZ3j0kTtZt0lprbgKzYRDx7pIoWSdcjAe0kpiYq9VE7bEQMffF+HQoYUUsHgujbsfk44r4nhah5CX2m1D5ub385k7fo/5zpEQb83kRUyOrmcon+DIiV2snb6YlSNn2UHPu0moASdtZ+HcV+TwwW6w9qsAEQGUppy1CLBoZJkW1LoOLOkQhw+4diixKrpe5ITS84jiUGsnu9dZBIaWGqGXEeV5Dr+iqg82XH8Q+GsRaQNn1W+eivFmn9/X7yQWsX0SJKndE5tuopqIgZGprmkBGGXussvY/1d/yb7cMPPv3sn2n3kPADt+/QMh7ZG3vIH5887iaNFJAZGb/KSIJ1eQmENEtHBqqovCnrJXRaxLC0RTrkqd2O3C7B33cPDhR1nxokuYuMi+4j2FbeNF++igew3vxX+Wu126iSQmPKMVMBL3bbp4iYoLD4bs/7lHdnDPf/og2rFtYcaHGXvh+Qyfux4z0qazYx9TN7+QkfPWJ2WXqL3K0oEiIUiljCr7zYJd1HtLOqL/ccXCosgDpHgcqrVVAXvKLgmRNpaExfEbgHEiJXTxVw8v4Qj2DBlv4rAkIHI6+/8FPKKqv1q7fSvwqKruXGaBUdVHReRh4A3Anct9zhaGBjNnCR3H94WwiR7xi66BBccTCWqyIjLD9pNpqUzlqzl77AVsn3sA1ZIbVn8vdx34W9aMnMPWo19j48jFXDp9I61sGCRSm7ld6qttNyK/Ql7lZRwnyEjiOLJSV1FJvwARiYBQ/wgh6v6IbQwFR76GyZmzeMF1/5ZH7v8wpbErUs3dh+hVkItMBoFkHbgv7gP2z2aAagIe/IedqNcMFuQ6wFG2aiBA4wFXMN0SRHj4mf/NfOcIEyNrybNhDs1uZ+/Rx9h71Jr65tkwOw/czUh7mped92/IZciSo2t+kwIB3Jen5/hEPZwkzpXV+E4jVZtCMgLUwZDE4KWI4zWMGiaaHQOoLCOgFsUro/ZZIpxOu083DU7Oy+xmVf26Izs/Ubt/asabOpihGTjU45zsPZ9XP+fD/pu45lqO3XUnc/fdx8q3vImz3//LPPvL72P69a9h/598hBXf/Vamb7nRTn59uqsm3pD0RWsqoFDr1/gJOp41q/sVgIoG3ije2EuvhqJk7o57mbjpJVXW3+wE2jDsBTJytEaRIMINFwLY8KAnxHPlVgc6Ahmbkt2/+XG002P4/A2IMZx4fCfHvvQgx77kunRuOPTJOxm/5kI2/cx3IQ0+wmLCtmK/zUpm5MeWqCKljRzIz35u86U3GtVV0i/dg8H4d2gHqQrh5ghL8I7ftX9Oq39Lkia/9cebeliOhOjlwL8GvhGZur5HVT+JJT59ZOCTg8MvAvctGavEdVLAi/Zcp/H9iFIhs95X1bhuEgGkEAKZ1oEiP4F5InXdIWAgUiuXTN3AbOcgd+z7C9YPX0i3nOe544/x0pXfyZ0H/5ZLyxsqPzF+0sqd+XhunCm7JNweLxmyahoLiqACemGudIBIFAcUbM9uUpmFj1uqNvLXBGFyxRbEZByf3cOKFZvtMw5kKtI8cUMCVJJ8w2IiWtk4TpBPKzFBB44vHGZoZMptayIBGFVAyv4P/DEHDq+55B2ct2ohSFi6vRPMndjHQneWA8e20Ss6jAzN8NTuL/LZR36FGy96NyNm3AGbCFT4/x50+fevcT9Ta5XhrMPsMJa2TfMqW6oaOOAnqiiltSDJojxcP4zbN5zD8twK1PNH/sVE2P+UICKfB96EHW/uB/aJyBdU9Scaop+y8aaOE755ygPVt5F26+Y8o7iSZ2x417vY8f738dxvfICxF1xJMTvL/JNPsfpHf5Cjn/083HKjeyCMBva8AQwlc+Ri81sMbJIFZg0UuRsJKOpLy443o9deyaGPfIxyfgGaLJaWKk9TwfvAXRxfU1AEdPceJF+zIm0n/zWXBhWn1jZVhUQM5/zGuxjTTgA13UNzdPccoLvnEHP3b0PyDMkzDn/6brb9+/+Hc37tnWQjQ0EDbtNJC1x1BVtK9WpMj3ukuq8eGUWSbElgq1bjrn92AMhO0GQAihpSDVHjZm4C7bVwhow3SVgSEKnqlxjwOavqO5ZTQFV9Grg8+v0Ay/GBVJZWmqDi+BpleEwzwfSc2TYe1bpOJoMKTILGAziKt6RwJNs6Y+6K6Zv4/L4/xsw/ybHeAQC6xTzDZrzquPEqod2KiMKWQB1MezPpY/PXB8YgSqc6F7+CMELZot9jt9I46AWptgjDozMcP7oH2BzuF8MZ0i3DhqepxCj6MAMoqi3TojHTA6D4Y40lQl++71eZnNjMi676UesbKbPgwKtB1dh37QFaqYLpQVd77Nh3JxtXv4gMQysfYXp8M4iwduVlIa+Z0c3c++SH+fLW3+XF5/5rJtprnATL1aNGiE9CCcSqy0UkMl6lFoB4aa37TM+Z8BtDOZxZCZfrv2qovH4bN3iHPhYPblK9gzoXa4lwmomwfZhS1aMi8sPAH6jq/yUiX2+KeKrGm0YCdQ0H9P2m/7fWvuG+e/G1JE+trucZK7/j29nz27+L9npop0tnx06GzjmLbGaqKpuK9WsTKkG1FUdD0qEQTaDCf+vxNV9tqcWNAZOP0DDwSLuFGR2mt/cAbJnou98YIs5MhdEWmZkTcrV9QB0gKxfmee6n38/ELS9lxfe/OYz98RInXqBY4bCVEvWOHufQnd9g+tarAMinx8mnxxm9+CymbnxByL69YSV7/+DTbP+532fzL3wfrRVVPY0oJeKGT0WcFMrmqBUQCkDMS6jS86RN/TAQIxiVpBlC3AgHuYjVBc+7ogJG9WjL8UP0rT7e1MOySdWnIgT1hrcgws0oajcn1FygsEBBBav2Cmoan4j7r9j9z5Rq9/Oi2n9LiqLagV212v7BheFsHIBVrY1cPfEqlJLdnSeZaq2unhGBPEeBLgvk7XG3v1iWWIOVmeMMCUE6FPMJLDBwGWeOK4X9SOI3JoUEECPqn3NqNQ9gauDwxOw+sla1R43nX5FnqNsA1XTKBBSpJ/vGlmZafVweqLlConmWTOTqAODO575Guz2BasG2p29n85YbyLMJ8GpEUcpeF5NnqCplZ4GDex6kuzDLyMFZHn36y6yauYiR9lTw2ZGY5gOrVlzIxiNXse/IVu5+6sNcedZbWDl+ThXBk65j1VdhpTgigpam2ialr0P2t4ON58n8QukIp2XbqkkL49UYtm1FFfUbBougcVki4GnL6vI19c48OCxl2nqKQu78h3wX8POnujCNoQkQLRandq0RUPnzQQseU/veo+faGyw3Zfp1r6a9fh0yMsShT3ySoS1npemXgpYlutDBDA9Xi6I6CCK61jSJRZIWcf5y1IPAcBKn5yf2CBTVkzw+T3HoKGbM7sGXCDx8Ck3gTaNz/3/AOwkGH64w3tePKhz6y0/TPnczx+9+kHzdasavvxozOmTjIVD00KwEYxARisNHOXHfo5TH5+k8upu9936DyZuusqp3ZzmmEagBmHn9dcze9Rid3Yd4+mf/F5t//u2MbKkcKRu3QC9VrACIbjsAACAASURBVGNHVYwdbYIEyUu1nNwoSLLCOOsl6d5gxzVGKEWfuSABJKWN5dtSbbvHr7XU6j2655baywzOvPHmtAZEdp8rQShRrIQlSEUEO2EIlUommqjDN1r7AsVNiH6SCp6oYxLuANg7ka3kaG8/T8zdxcFiN+PZCla3K0mLliXbD93Nowc+B8CrrvlP6f5kecUZWhQMCUH9EkznqUCUDxU50EombPs0LtZCWLnuck7M7rfpealMSNN+pFJIKikKmRGBrKog3urkiac+je54ls3rvoPhoanwnK/D/gOP0OkcY/3Gazlx4iBf/cqvkGVtRsfXMDq5lsP7t3Fibj+t9ijdhTmMyZhZeSGjQzOsmdyEMTm9suPKUgNDERDesOqF7Dr4DQDufurDtPMxLtnwatZNX2olM2VSetcPxK62tRosBoKiMLFJ1f/c72BV4nw7WR8qfilduj4rkfm/A7uxRLJJhUbzPBYH+/hpOUD9F+DTwJdU9S4RORfYeorL1B8aJhCgmpz+KWnUgFOyYGv4YLOJCczEOPPbnuTIp2/HjI7Sm51l7JoXhjhaFBy67RMc+9yXyKan2PSL/5FYAhTzJ6uHoqLVstX4vjopisRSGo0m0XCjaqZa3c3YKEOXnE/nub2wem3feiIUIpIKpSqwqJANnX/P//wgZnyC8m1vwYyOUJXSPnv8a/dTzh1n+jtfy/xDT3D4tk9jRodpbVpHa91Kjt/1ILrQgcxQnljAtFuMXnMx+cwEoy84H3noMcq5ebKJdFPlWIojxjB545Xs+9PPUhyZ46kf/y3am1ax/p2vZ+LKs6u2EKWISdGB/O3GsqjOIhEoCpWPx/4KGA2U0AyYBKzkPb4fg6/ac0v0+TNxvDmtAZF0rRNEJUdwut54dS5hbkwmpioBST4o0ysrFRmku6XEIeIP2Xj2fEVrPdvnH+SC4WuY6xwmI+d4cSTEeeTg53lm9gEArjn3e2n0OePLvoTC0AORsH/WEoOztULz/7V5MgeMya03baoyBP6SIwh7rpKoBw/RoBRUZ/WlHqxceTF7DzzFl+98P1vOvpmzz3oFJh8KllvnX/Jm7vnqbzC94ly2rHoVRdFlXmc5Mbef47N7WD38QmbWXsSR/dsoe11mjzzH3JFdHDqwFTEbybNhjh3fxfjoWsSRuBPiNnaFNT2+mcvPeQvP7Pkah+d20OnNcaJ3rOI2eVBUH01qDkDrDV22TeD9aAKM/EuI4rvtVsS4VaUqzl22A5vGqs8cmFRHxK6s2Bz3KC7bMkJR90J8CoOIvA34e1X9S+Av/XVVfRL4jlNWsKZQf5f+8oDvbRApOtwT+tKsq8zUgSGtPwt2kTM0xNHb/4Gxa65i7u77aG1YR2/fAbgEVEv2/PrvsrD1SWR4mDX/7oeIOUN9VkPQD5QGhcUAYDIWSZJYP8ASJM/RXlGpp0qpzPad9CkBbn3SLBlYlrHrrqb7mS+z8yd+gRXf81bGb7gOyU1YDK961w+w73/+HiMvuISp130bxdxxyrk5us/tobt7H1Nv+DZam9fS2f4c5bE5Ojv3MP/Idoqjc0yedw4YobPrICPjGy1A6auglRZNvuJKEOHg33yJzrMH6Ozcj/Z6feW1Q4a3aBNXtQiNSGRuTx0Y+YVSFd2nGf7Hc0Rf5gTAE9RjWr834NlFwpk23pzWgIi5E8jYiBV7qIFCrV8aVdQYTEfRlqmAUaEJGdUUpeXuYAGGWShSzlDmVvBe0pCZaouOGocIVc4buZrt8w+ytn0OW+fvJjM5zy08wYVj1zE7v59nZh9gorWal2z+XszEBKoe+XspBIhxOuUC1E/qKlayE4E8D0ykwEp/aoOq96DsVWamS2Ue70CRrTdRfQQxGUqZ0iFqK8nAyyoFMe6jLAnAqGxn0eCqlJnBdJWp1eey4Zx3snvPL/P09n9g994HuODStzAxs5ljh3fwxMN/y1nn3sz06gvolV1mZ5/l8JHtHD34NMcObQdg19N3MDK+mrHJ9axYezFbzruFkdY0k8MdtPwiD229jYe23satL/2vlcqsFkQM62YuY6g1wQPb/oJOb47Hn7udfUcf56wV17B28iLnTj9asXkSeOlXb9HA7ZvHVKo0FSjbYn1ZgQVAuZPUgZUGDhmk6wioCuQG0ymtY7ZSKVvut897SDCdIuovppIcmeWNVafZiu1s4C+dGf1ngb8D7tS+fWBOfRjEIUokOVqLE4GD+vOx599kAeSuaabVc/Fk5tMWZfUPfT+73vdrzLzljczdfR/tzRuZu+s+eMXLOPHAQyxsfZLRa17Iqh98GxJ1kCYT6pMKA8FQJRWq2mDwLCpgjSd67pnSce6IJELeZ5IPXuof8iRd6EZtPn7dNay88CJ2vue/cPDP/oa5u+5j+q2vo332Rk488BAH//yjrHzHd9Nav5Zibp6FJ3fSefJpFrZuZ+HJHUgrR4ZatDasoX3WOsauu4Kht72WfGacFftnOfy5r7DzPb/D8IVnsem//YgFEm5MiLf0kLzF9M1XgcL+v/g8vX1HeOY/f4iJF1/Iqjdfx/gVW8BXI2oqzzGKSdaJ6swBrkRalPQ7P2D3L7iXHQJZi77XeGRhvvGROJxp483pDYiKAuYXkKJlwYoHQ5kgUlrfMV2PjpvhceakIdZySDHdArNg0buKJT1bS6IMnEdqiqJSnxVugiqVlhni+unvYk6PMJpNcai7myEZZffs43RGDwJw3sQ1ZJIHIOQ3CfWTorgJV1QCR0S8xKVUTNTHTQ+yjtI6rhXHwA96XjXm1H+mG0m+vLlmvHkpVgpxYPeDrL/ghnQfMC+NigGREct1cYRgD4xU3EazuSTPFC1b5mxihGte9bM8/NU/5OiBJ3n0G39Gr7fA0Mg0Wy55NeNTm3jw/j/h0N5HGZ1cz8TKLaw5+0Wce8130h6dSuqEQta13JuhiUnWb7iWZ3Z8kTWrLnOTTIOYzQMdEWYmzuaSs9/AwaNPsu/IVg7NbufQ7HZWjp/LRetuZqK9pqq/b4OiQMmsJMdJJBMHoPG5VP6jfPB8r6KFkxCJq4+dDAoxQYonpVJkHvTgfrt2rfk1oqjbufUHRU6rAUpV3wu81+1JdivwQ8Bvi8gjwKeAT6vqnlNZxhA8eHHnQP883yQVagBSCcipSYTq6vGBeQNDmzay/ifeTXfvPgDmvnYP7fPP5fgDD3PwI38DwNSrvg0xTtqoUZkbwNAyrKijMmnfPBvSrZvZD+hz5cI8848+wcS3vcyp4e24JEX8bH/6sSWr1TRr2s7RM/mqFWz8bz/Pnt/4XRa2Pc3+3/8wxeGjtM/ayKp/832YsSH2vPe36DzzLO0tmxg672wmbr2eVeefRTYxWnuHVT7tzZOMXHUpRz/9VUZfcgVl4aS7aj1SxxIjwQKDqZtegFBy4vFnOfqVhzh25+Mcu/Nxpm+6gnXfdyOt1TNuIwH7ospSLLdZcBbVHutIRZxWv0BzQ5QCfYtAO8Ek7JBIghTRPQlqsQEgKO77U0PDLBbOxPHm9AZEvZ7b38qBFhE0U0Stbx+L0m1PqlTOA7561UCmprTqN3EWThiDlpHTvLBCLwMYUi0RMcyWh7j/6O1cNnEjDx37AgbD/fOfY1O5ifVD5zOerwz5xXnjQVGp1STpJC/BvNt1eCmrBYD0cOVOJTl14OAlFSogPY3ao/pfao8Tc/tZc86Lq7hxMcVO+JWaTpxjR7eqE6yUpBVJSmrNXGZgRka55BU/wjMPfYr92+/h7Mtey8j4Kg489yBPfOOjrD/npVx07duRvEWZS0UjiAcEzweIJpbRidVc/7Kfp52NOBWSR3BRAUSsCW1pAe+aFZeyZsWlXAw89synOHJsB6snz+eupz7ES857B9M6nL4r34ccv6dseQDU4BXdnSfPegGjj+/AkLqBzftG0tKBWm+RpwR+W+g+DlB7ftOS6xzln+Ie/18suD3J/sYdiMilwGuBPwZefQqL1h/qIGc58RmICfrVZU760686608TYPae+zn2hS8y9ZpXcuRTt9PZvoPDf/tJiqPHmHrjqzBjY42gwv6PkkwATH8+J9Vtosl6sdDbexDJc4YvvMAtzHASIW3MsI8MHrJZJDOFfMU0637qXRy67eMsbHuKFd/zVrKJcY79wxfp7HiWyVd/G2t/4p1IngU1JeK+Qe/bR3xuNlEthZHLzmfzb76HfGos4T6p41cRNbMAYgzTt17NzCuvZv2/ewM7f/EjmJE2rdVTPPEf/pCLfvtdyNAwhmqs9ukhg8BLreoWfblzicpO9ZJV+jFTvdnisX8xgLRoImfeeHNaAyLt9uzqR9zSSsSCodxLVbCTjTFBnbNoKC2p1avM1MtiY9VIHcjEwQgrhjYB8NCxLzBmpmgzxAm1+wodL47ypb1/yi2j7yYnRdepnxs3cQbTMBfHq7sisCOlYorqty2XixP207LxfCcXJWwDEQ8yRfcEraFxMsn78vFgiKqIwXoscYrtNpFt5EA5aUqZg7RanH31G1mx6XL2PXknh/Y8xvjMJq5+zc/QymskxQhYSOG/zWh0EJe2CO32mONIWZA48LU7g0SAB5/8a3YdeIDMDPHii97BRGs1T+z+R050DgPrF+XnqHO5r04lFkChsV7E++O7wS6z24H4XaZFCX6cxPtzMoJG79BW2aYZyN1GLYhWZ5G2VDiZAe15DCJyJbCFasx5SlVPGzCkQkSOdxdrC4bGZ3yQ/ntLcoZk8TQAJl5yLce+8EVm77ybbOUMrbVr4Ogc0so5+qnPceTjf89ZH/jvLGObpkXDAIwy+P5yhtvjJ2itXUPwDectwgbN1gPSrEjdA4JCNjbGqu//Ho5//SHm7rmf8vhxhi88n9U//H1I228bEpOu3XdaVtZeUY6AtTg145b6EHg/LkOJvtWqkPb3Y2/7JcpOFzHCJX/6U2ivYN9ffIniyCysnQhzlTgCrLiBW/EqMtLGDsOyBvDUB2qTfpS+rEagRYWBEuD1TYCi0zF8s+PN6Q2InB8EUYV2G7QDJkN6Bm23kKJnnTIau2mqN9P3E7tXISW/IweM4tVhfssEkfRjbVDDtc0wV02/hvsOf4q58ghzwJX5DWwcPYuv53dzpLeXnUcfYMvEDY7TpJYA7J0Qu4HXc03K3F3vVZMmWFWZCpWvJM9F8RjOc1U8COq5zzxzKi5f5sgyr5ifJc+GyeZLsq6SLZTVVhVuAkegzEwAS/k8FKPWj1BrrnRlts4kvejb7x8npUunVbX3xJpzmFx1TgLAkm/IYd2w2spcOu6eba8KqOGAm4rBiHObkKx+ox+u7iPtKUBYM3Mxk+01lGVBr5xnxfiWEC/eesRvVKvO3UPggziwp86nURH5mpPCvUtn5VNmQtGuPAebnjqXCLh99LDevXG8I7V9INRBXRur2E1mtdkvST2cTiJsH0Tk94ErgYeoWlqBvx740PMdGqR96hcGfRIYqv4q6Xfr72tWHfa3U3t74BvH9/08ysiDqfbGDcy89U0c+puPAVAcOMTK//OnWDiwn7k77+P43Q9w4hsPM3rl5dWqv2HFn0iM62WvAx338Xkrs7BgSwpXeyaO4H6Xx05gRoaRntjJvy65Wk5Xrecd3xIq5OkqMnb55YxdfnnK/yuoHLQqFnSYqizqOIMiOA4hUduJ3VYlw6nu7BxRum4cpgiDBTUqjFy2hbl7HmfND74ahkfo7NhHe90MrbUr3DM2XmZKvGfssjSYzP4uXXkDkFH7XQdplgeIMcqJAH3SaX37+bYKlwK0o7qatvKRzrcchwj4p403pzUgolTo9exEomr3BjN29LG/cyflKZFOZRUWdNTe8iiWAKj1O+SDqM0jOGP0z9UshqQ04dra4XN51cofZt/8M0xlKxlimMn2NFdM30I+O8Lhhd199Qiel0vsxOe4J1m32pHYTpwuvlObZR3FdByA06pulRWa++1J1PU9zqK6Hzu0g4mJjRYwOZ9DUqi1nlKbVtG2Th+tFEvojlXSoDLPwrYnnk8D1eBd5vZ5XXBjlHNcaHoagZzKL099pZiKcW1cSzSMxlIHVhAo8oxsHqRbEZObwvkbbuLgsadZN2MdOBqTMTa0kju2/QGvW/MugprNB5F+r+KtWF1mAY/nCwHhS7LSIKVsuQnRAcHgj0hAjR2QTc8+UOZSSQLB5g3V4NeyDbLUYkyB8jSy+ojCS1T10lNdiMVCGe+8UJ/v64BB0jixJKgu/Un2JpTqep+KzJN0ayt+NTB56w2M33AN81ufZPiicxiamGJ08xpGr7yM3Udn6T63B668vHquDop8uet1afhdD4lKffGofREWnt5Oe9OmcM8UDdGb8u9fh6bnEbgLEvG4Im7sIr6ceKi3RixBdakSwK8FNThOpxsXRFDnE9hKaVxCbrWTWDoDG9/zvWz7ofcx9qILUYXW2hk6uw+x8wOfYPW/t8ZOxlgOkYiSGRApKRxPyThjm7L0nciBHw/miEkFVKgpbp8mkOr6RHAI6W6koKh6aGp4KQ7RmTfenN6ACCy3p7BWUVIn0YpHzYPExX6QqUysY55G4nfIcYWSYEwlUQpSCvvfZDlrR85B1TpwFLd1x8GFnXTVefisW0d4B4bO4ixUI4oTSNils6ByVnGhzB4AeYAVnnX1UwdYsrSjqkCvN0+7NYpxG74G83ytvma/2vVtFiQlEKRVWVfCABC4P9H/ZHKB5PnQFokkrjaROJVSwr9w/61H64ozZtz+Y3v2PMiuvfdy5fnfRZal2wQU2mOhc5Si6KAi9IoF1k5fyv5j22yGMbk8+H+yZaz2n5PEb1RY/ScvkLB684DJq0oDqV5wInIoUUs2rW64SkWDue83LHNCOg1XbMAdInKpqj58qgsyMDQMIXUenv3RHKcRIBn74pNvxMdNAFEzgTnmu5iREUZfcJlVoUr13MLj28jGx/qe65NqNYGMpjol8aQWLQVtjerEqCJ6YoHW9IwFJyp9fTNsoeGzqrdBPLEneTSUu35P0ygJeAjDXZVBX7xI1R2n7dVahz72RXq797HqR95MVjOs6B2ZpVzoUhzvoCoUJzpM3XgF2gl8AJunVJZkBlAnMSrx+dhM/XDprWI9GEpUaEnlScdXX986KArtEIFnH2c54rszcLw5vQGRulmxKCwwceRqMr/0NhWYSRSlabB9390vSui5pUrpvFFrZGofg6Kykjr5PdNCXnkORRH0vziu0wvXvpE7nvswhRYYKmSQSm5qX7orm/htHbyaDMX0SkyvNiPGfCJf7jiU/Rp3ARbmD9NuT7g93ZwKLqThD/sxqCf7SoQ33SBit6qoAZZBKz0hnezrUbx7AZ9O3Cxa/Rc3Klg1VjToZraAnd4c+w9v5dCx7ayaviDJo9s9zonOYabGNrLr8IM8uuPv6BYnmBzdEN6bzcipyryE0NXdSoSgcKrAsuZQM5TFV1OpVDB+6RkDWLfaDKt2vwI0kkyaQWUzgMDeFJZDMzoF4Y+wg9RuYAG/LFC98tQWqwqL+QWLtTKDJuM+YOS4IH1WZdH95DnS52MwNEilJsCaf/9vOHTbJxrLLHFhFwMRg0LfkFVNyElGA0Lv0GFGLrwwGluWn38jqGsqX0040hgnyrb/+0iRYvJ6woRvx3zVCowsPLGD43c9xPSbbsBsWJlwirp7j6LzHYY2r+HAX3+JfR/6DCjMvPpqVwabZgA8vl3dbz80lKZaSKkf/1wbxqAISNRpScWJgU56L7ZmCyA3enR5KrMlo5yK8E2PN6c1ICq7PcvzyTIQgxaF9WuRZfa/3x089k61GCkaYN5Ap2MBjgdaPp5P0z2rXpXm0yqqVYa0W1aF5+9lGWSGYTNJoT2MceQgLx3plQGYWXJutGIJgEgjKYrN13RKzELRP5goqZooPo+lSFHoHD/C1PB66/umV9ptOgAK6wvcSoCErONNQ0kGc3GWUV4FBrUBH8iGIPcqM59EbCVXB3NYPo0aK7atJGT23HQ0bMIrpVXvQfQBZ4IpYcXUeZy97mWsGju32vIFoFAyWgy1Jth76BGe3vtVXnTxO2jlI7Rb4+hQRjmch3YrhzPrXdxJhcq2ocwlqMDKTIJkKJDRo8nUj5+B/xUBoVgdaMc3Cy7Llj1P+BzRu3a7ESwLEC0v0vMefh+3YSvLdjH5/IZmI4G0MZuatul9BTCbh/VFPwBqAlTRN1d5YNc+0O1XCIqQzUy4McSWsFJ/RAsMl+5AgBGDmhQfVH0yBlRNIKi2mAEojh4ln56u7idtVItc+xnfTlRiDc8ti/ju6xOr0pK2lQgN+bI6K1GqecVbhE688qUMX7CZfO1qtISSMgzp2eQY0so59MmvcvSL3+D83/1xUGVozaRNXjSZskAxRglu6dSasxelhHMFyqK6D6RfkhKZ58cdJkaEMeqx9wIAkgh4uWuTIw0b8vY18NJRTkH4pseb0xoQoSVaGqxpmHqY59QQ7mV6fzp1y4Xk7brgWXNeQuQAjzoJkRRlJX0oteIVlWWSrhbWi6KIWFAkYv0kmQzjni8orDWX8zukEQAyPYJTRvHm415HXCuzlGXgyCRbVTSBnqbzqOizJ/ZyztD1FUepVPxmtn5ClsKCITtgxIOHB0QauC+2fM7jqquecbykZOxzPlL6+E2u6nZA0yquL1/hwRCVKwAl7I3mpUaiJWOjq7nw7FdX+9G5uosqB45uQ7Vkdn4fW9a/nInxdVX2ImjmmepUW604tZway43ylnWapWUPZfLAV+Nj+aOFZmk3qxqvegdLpyaJs8nTKDyjqh871YVYMjSQmvtCDAxIz/tUYqKpiiuKn1yLgVD8bD3dhmBGhikXOlF50l3Q4xX/oHT62Gk1AL8kj6gBDGlZ0t27l9a6tQOB0HK0Lf38oAHAaECidTVfLDFa1JzfZREArRtr/NgyfPF5jFx6LsFIJEKNRz9/P0Pnrmf27sdZ/f2vpLVqyq6NjVOG+bFS7ObVWcJf9NOOAiacl6V3M+Ms0extUEm41R4YBVCU1LthPqwazgK/UMUUiA9ooTNuvFnSVlNENovI50TkERF5SER+LLr3bhF5zF1/X3T9/SJyt4jc6H5vEREVkXdHcT4gIu9YsoRaoqUFIVoUUBRot4d2u2ing3a6dkfobhftdi3Y6RXumt0pOhy9nrvfs2Ttwsaj24OQTi/JC3euReHS6bh0bd70eg6QGcgMkmUMZWPs2H93NTF7KYE/Sg0SGssRijaaLfy9EukW1f/46Nn918RLuEpHFC/UPn+i64jTVTztdTixcIix9gp3TSOv3QTukelZr9f2UHv0/HULhqSwBODgJds9Jy5/0yM5YmBTPyoyuD2MS9sTvgNIdOAmeBn3kpkGyRiQ8K5WTJxDpzfHviOPMzK0wnfAyrGjtywLXCFPoJaK/+GI1IuK6dPxJfnfNH570FnlIf2Hy7vJ51F/gtXqcrnH8xQeFZEPi8jbROTb/dEU8ZSNN24REKgu0RETov1G0/E7CVJCfwjWyilKK4lvItTs4oVnjQNRId0onj+PQjY9SXHoMCcefbwGpiogkJR/sVc+qD/E3+syMX7vwAGy8XHM0NIbhC4agl4+Ovz1mJxV5yhp+rzUy+5fQcxvKmvftwNj6kGZQtUpQJ0Ep4pn8xq75mLmH9vBwvY9tNbMNFbLS+AzU1pitZRk4s5NSWYUY0p3nyD9EaNBwiSuUuLfeVQ8GycFWv2Hf6nut4nTY2lAdAaMN/WwHAlRD/hJVb3XeYC8R0RuB9YCbwauVNUFEVkDICIXu+deAfwh8AX3ey/wYyLyO6raWX7dwHnQwkuMxFh/Qh68J1GT/Z8iaYG/78ASYIGQl/i4+BJzSlTR2DljUQSARgfnlr6wPBQvqTJCqQWPP3c7Z6+9zoF2k3ZOcBIoe5o4hIQw+aMKvQLp9ew1R/QNRwy4ov+y0EFpJ2nvOvwA40OryMsMDYDLEoFMz1gKizMvLZ15aZ3P4r8fU0Qqs+jDUbFpeJcAVaMPHkWzBaWMzde7SkwsD0CodFZxmWKMVfEF4rjW2iBSO853j3H3kx9i/cyVHDm+kzyrDdARabySY/t2jq4B+YJStKM8a53Pm1V7f1J9E4hWbdg4sTSMF0uCoL4HTjL+8xNGsLr8V0XXlGYz2FMy3vRzgOjr/0nJ++JXDZ88Z2r368/5YKr7KtXvPpc97tMPop+WHcL3/voHOfu331+Lq6E/WAGGpFlH96zwYRmdJxIcpFIukr537KtfY2jL2YsmlaiIB9wP5fTntXwGLk78vajejWo6qS7XpUiUlTQmvPJQDlcQBW8CLwILT+9i13v/hOk3vpwjt9+FGRtsqZVJzXQfyHDSH8dLtUoLtSDcSaM8sdtuDO1I14awAIx9FfXNO31NFDdo1U5SCke7S3OIzoDxJglLAiJV3QXscufHnBvsjcCPAO9VtSZVqrrXPeK87vRNGfuALwM/AHxwqXwBJG9ZQqlYwOH5Q2TVLCFOMoMq2u2lKq/YKq0srQSpcBIhqDhCDuRIlllRpUi/RRvYfcgi01F1nrTFiJUm9ayS97pNb+eL2z/IoUPbWDlxrvWTVBvZ4k08LT9JKxAEwQJO5kDmFqwCuap0WjAHnJL05xequKp05g4zzChy8CjSamGGlPxQBzWGbDhHs6xyTJeZyEmjlVTYgVoqv0bxhxAAkZDnBa3Z2saGi2hxRZVsIY7r3Qto5a4AyEYzsvke2Zzbvy43wQQ/SNi6BeIAbFn22HHwXh7b9VnOX/sKzl17PZ996H8w1pqxkjgR68OqLDHd0hGptbICw9dXKArBdJSyJWFbvXqwqjUJbWG8arGvvoS2SzhVAwaWarPdZYbnbxW27KCqP3gScU/ZeLO0iiCKF6/K6W92q15133N9tV0HRxAkSrE0oDYDR6fVKl5VWftz/wd7fvm36B7cT756ZcRDjMB7dKLRfd/3KhCn1TgQCRAG1bO6kdanmJsjNK27sQAAIABJREFUn5lZsk2X5DWVtXi1+F6yPCgkQFcjD85egkZ1Xzy4iQl7pVSYKgKqdQyhnR4H/vxTHPuHu1nzzjczdtX5HP3sPeRTo26d7CQ7ROd+rPAcL6msxkoVMkokszzDeBNVzyMqvTpdBVMqJrdWapK80OqZphDUpS7NCiQLUyMjzQ8lCX9rjzf1cFIcIhHZAlwFfA14P3CDiPwiMA/8lKrepaoPicgo8CXgP9SSeC/wd85x0tL5tfIUnOS5vWaiL9YqZ610J8vwW2yoKdOJ2JhkjzJ1FmZe6mPTE0vtN9j7jYWynCYJwKus+EYOtIzlU7xow3dw99Mf4aYLf8x6V47L7EOkCmp0AVAW0MssCbzOZfLpOeeSQcoFkc+NqrMO6wimFJhfsGq++RxOLCDG7Q+XewdHTpXkpVFgwYcxqcSkvr+Qu555EngkwVpswOoLHgTVAKL0rLoQVfs/d6DISXKsWq4MbfTws3/Hs4e/zoXrbubsFdeAKkbySuIH1heJU8F50/eg2sMOFmXPkBW2rmVhKDMLjPpAin/Wr/CHa/WurT6bCObe3cA/KZxGKzYR+Y/Ab6rqwQH3bwZGVbXfTIrnebypSzuWjOMm1AYJTny/DzyFxUOalpX+avq8B0fEw4Yk6QnC8AVbmP7O1/Lce/47mz/4XrtI81KGupQkqUdKwg6TofH9UuLLJxXymZkaKS4SwySdvia96QNx6SLipCRE9H12URr1csSR6+jWPaLukRjIqpXW7PyZX6e35yBrf/y7mHjppZZeUZbJmtyuJ1NVVgyG7GavDuCIUqhgUErxa35vlu8lRA7EOIBlPLcIiL3aawwEw8XaBV/BqC0kW8abP4PGGzgJQCQi48BtwI+r6lERyYEZ4CXAtcBfiMi5asO7m9JQ1adE5E7g7cvJc2bzFIGfI2LBUGbC7z7YW7MaS7Y6KBWKHpOrRqvy9JzKzPNYMiftwSRgwj+vXkqjimQGMleePGdyxVAE0IQJuYA9ZhNbj9/GWUMvYsPUlWTGWqWl0iEqsFP65ZoHSBmTM23odiuAFhOT1a0EirKygAPK7gKm5VRDTpU33xshL9cxNWxJ4BMTmQVbxkBbKqmbV8d5U2/jJHDhuovTJCICJsYypKh1qzKqMyTSMun0oFVjKituza+hTScnWtAtq/YBMFkF0MZdfZxX7uumXsuzhzdx5MRz7Cu/xkVrb2Hzps1Mr2gxMjQcuB6TU62EsF7fMLZsGbx0THNBW4bSWZvVGXie94EIU0M52Xh0L56Pwmq33+IuxGkARksuxnQ5kZ7X8A3g4yIyD9yLldoMAxcALwQ+A/xS04PP93izxluMxpNdE0iK0cMAMORVXivzVgSIalKYWl51qZAEFZq77q2jVFgprVpFYeX1L2bvV7+B/ucPMPnWVzHygksRkyfZ1XltMU/Gx1mZ5Um6diXA4qGhnYZGR8nGxhh37boyrzvtqiGLJM/qvFF6Gv1emeeDJ+W+dxZJesL19D36hc7KLE+/byGS4vl3Vf1e8XP/luNf/TrzX3iYoWNdpl99LQur17BKhzBiVWGiygxtSwHAfvvGS6X9uAeOb0PYJ8w2gVCI3wXN8nHK4HVGmDEtgl+iqI1iP0VLYZf4WVVYYZaAB2fQeOPDsgCRiLSwg9OHVNXr4XYCf60WddwpIiWwyhVisfBLwF8B/7hUvof3zltVmVeLtcpgzZUEdSCoV1S709fBEATu0KFds1CUKScIkCxLVHR9oSjc3lMlZnQUdMG6AMhzJM85vPdE8Gszr8fZuXOnbaidO4GPMjO8iUvWvJKx9ozbT8yV00uDvFWbqpXiqKKdLod3HrX1CtVt4DUVRX95wdYF2KaPIpKxMjsQuE+H9xy35R9qO9cGfrUWgSKTQZ5Vvx0BuUq/dt4tOHIg1oHRD1zj4p3ooLF5ZwB+VEAFoFdwZO+Jqr18ujGQ89fc/RmuYDy7kH/c+jsMnTiLZ3Y8xebheRbax209REDh0P75qs6+yMGG1qsMrVSoGM7t1iW59O1lFpOkTU85ONetrtfLSMNE5aMNmoCWYbV2EoZt/+JBVT8KfFRELgBeDqwHjgJ/Cvyoqp5oeu5UjDd7u92++XlRQCTaeD9cc5PnnrJTgaEaEKqrb2wCbpLVSpIQBDxaTVp7tJuUdX73Lvb48eb/tgKx0eteyNQbbyVfuRLJswpc1InK3uGfK8aeXpW2RPeXFVzUXQ88wNjVVzPZ9Wkpe7sNOuQ41L6HBAzVv5GoKfd10rYIyfW9s4ZrNSvAmPO1p+ym71tt/EBijgCSrB5D3vISil3n8egvfJAN56/muX17mZAFskiCY1AOyDwGJTPVdYDYOqx0JOQAitRZurprHgj53znK/rKDtzBUScFQExrqIzkn3UI42mtu1zSNJaM8b+GbHW/isCQgErsD3f8CHlHVX41u/S1wM/B5EbkQaAP7l1HoR0XkYeANwJ2L5j06Uk1UThKDl1hUCdr/RWkZvZmpPEx7Ho2fgNqt/5e8Nw+37Crr/D/v2vucc+e6Nc9JVaUyEhISEgIEwigBRERAAtKooLa02GJrD7RtK79HW9FWRGlbRUWg2wEFAZlkhpAEgSRF5pDKUJWq1DzcqrrjOWevt/9Yw157n3Nv3QxSl/ze5zn3nrP32nuvvfbaa33X+37f90UGBzHD3QRMaK8pKtGQVCQEPPQTseS55zRl0GpCsxvB1ADLWDO4nUMz97Nu+Hwy0+SRU3dw08N/RUMGeOqql7CqtbkM3tjtOp5T4YFRt+uAz7Sgs3WAYSMwmxcIJWWPcoi9+gCjOo7iAZT3shNA5xLOaY9uldJM6bUnUi+TanyaBTKR9Ls0Jco8IjMJeS8zlWPieDRo4dR0ApgSwnwwm4btEJ9jA3jKsufz7Yf/ljUDW2nN5kh3DnzYBJkxmOl2vIcQCTuOI1mG+G22kZHNFUhh0K5gkmYLEa3DRCneWy/VAoWcSeVBZbukBNMUKFVJ2YsYfZagG6yq7gR2LqbsGRtv6oBmwUqeJrJ03AAh/YP4AgHYVFLFnEZKhXJ60XC829Y6bwvNLRtp73qEsZc9j7kHHmb6m99h+pvfId+0juWveyUD52/DiKlqfJI6K1LVoCT31M/KFU8RQJYHMCdv+gazDzzIwDnnJIBG5ldRJNvrIKja/6X3GCs+UFeyPX2PasTykAM3KL+qN5JUogJ2tdJOaVmplW1tXMXyH3kue/7bX7Dy1VfPc8O9EgGTXw1ZUjBUXiAApfK7K5vbnBbdClAKZdw5qtdbyOMrxD1acZrUHe4C39/jTV0WoyG6Gh/kSES+47f9Ci740ftF5E6gDfyELiodNwD/A9hx2lKNvNTWiECeeROO/x01LH6SzjM3oQVOUWGrZGSrDrw0Gm6iKkxV4xAkXC9IurIP5XMPhPLMaVGyzAG2pPxT176Uncdu4OxllzPcXMFMZ4Jjs3vo6Cy3Hv4nmmaQq1a9huF8vARD3u0/aLDUFv1Bz2LAkJeHfATznAYxtlPQMFGU2qc+YAhw+xcCiqnM5TAz26OxUT2d3h2nCTTJsw1iFdo5zM4RUqX0BQfhOF/PkAF8nTmLNavejGCQTsffj7jn1nVtHuorRQ1wA6jjKkkm0AWjihZlWzhzmp9nBE/WpoyhpK4dRMSBorTK6WCt1W2VuE3+HKdtwyW0YnuMcsbGm9OCocc69gcUJAnw9fO0BPVPnwcXCbIBRIVq9CkrxrDqbW/k5GevZ+zl12Bag+z7tXfTPXCE7t4DHH73+8g3rGXdL7+VbDCx5QYAFMAD1Xbo8cAKxyTVdhjE/7XKxD9/DgATSLnzaHl67iFqsKrX7geE6u9KD6+oXlkP6jQBTKeV+cr1MXmmsvyVVzP+sitpDGTzDqn9JBrERMlQDI4vhNcsWcTnmC2BUgBImVoaUmCpapEMVYAUpF9IuFLUr/n/fzHeVGQxXmY3MH/X+DeLuYiq7gIuTn7fxiJiIEWNUDBZ5E5LUfF2Aqf2VHWeUpkjBYdYO3S6TnuEv2KeOaBVWGKW+/km137eXOG/MSUoCsAor5ryGgxx0ZofiKDgGWtfw1z7FN+duJF9M/eiarnp8N/x4tU/5XlANoIh23bqBy3mAT6LABgAbZ3jFBMADDMWjw2xnVgEplI6pSkRmLBHsNplRbauWtAIOtNAAyAKXK4Y1qC3ne3cLKblViLBNBVDH6S8o3aD2ZkJ7pi5nguaV7p7qbdBAqjqnn3xm+IAT5aBFNBuQFC5B5BUlBonATRzYIauOPBd4InnZSoR55lmfBZtLb1fKl5kXlPQ722qT0IBSNU5YwtJurp+gkREBnDmphZuvPiIqv66iKwAPgxsAXYBr1PV44/3emd0vAlSAyDVfX3ASFBCRA1DvULVcwYMNK+E57hQP4nn8wUFGqtXsPLHX0Uwi234zV+me3SCYx/6GLN33YdOz3D4Tz7Eul/6uZ461UFRrIpUq1HXgillhHVBae/dgwy04NQpGqtW94ChBSfQtN8nF5y69x7y0TFaGzb2BUB9tatpsT6gM3Uki/dfO0F77z6Of+yzrHrLdWSj1XxxPSJVoJo1G5yefNXnNP4cRlxDpDNKAEMx2ognWAPkWHKxEQwVFe2S9miKTNxXva4mx7lAkgvIk2C8qcvSjlQdNEIGB4RyN1GqMZ5fkkB+D5jiRGWNc6/OBGknvKKgyck0utzPO9HMB4iswuCAM8/4Ompu0EAODlqrUFY1aqpa2TCXLH8xFw4/i/smv8me2bt9QEUbveAqnm/YRYOfuhTaZTffpUmLFZzF2ZyX7LXoImzE8db9/1M6wbf4IgAv7r62p5yda2FnZghxo4DTarLsXGIyE1MBX/H6c02Y63DU7ufG2X/iRbym13QHjh8VgdE8c2CWIZnno3W7EHgOxoAJLvkmHi/qAVrRQRqZ1xbZCNDFgGYkXhkSk+dWgE06+UBV25YO6EmwyrIB5gHu1bv/11BhzwEvVNVJz+25QUQ+C7wa+JKqvktE3gG8A/gvfWslsmI+z48lK6EZ68+M+SdfN8n6iVeqx1QmZA0nKRN3Vi/c/zlXIhAvIGrF+6IY8lUrWPMffori1DSH/uAvsLOzvRqb05xyXjJ4rZrdkyc5cf3XyUZGGTzvPFrbtsbD4qcWtqIeVqKuFTr5rW9y+KMfBmD7b7+7t2nmA1phWqgAn2SRNR9oTfZJljN7170c/IP3seHX/gPzSQqEThf3ZzESOEV5DZCEyNZBbMIxamVdpyHy2iBjTSzjlOblcWUf6lfXcA1lqpg+TU2ffOPNkgZE2sx7ARE4EwVU3LJdCoowaFB6NYmgLX+bqmgrR5uJt1cwgQVS9jzcIXe8dydvOu8yHWiUJrxGjjaT5iy05Ll4c0nMz1AUNGSE7dlV7J97gK8e/2uev9w5wgggjRztZFFL9FhlH7s4zmFmmOIRHuRCufxxnQ/gIHsAWMPGeUoEknrwyHuMfuT141Rp0OAKeSE365eZZpJhRnsPK4pyydhvcCqMm3I8SNNOy3Go/HOX3OegKzxnSi10nRlNgkYxExeeoVDEOHOiZoIUmY9yLZg5SzbjUq6EZK42mnpdVSo51+IN+H1pv/Yi84WC6HP8EyXeLDXpfzb8R3FBEp/vt38Q+CrzDFDAN73566+Azz4KU9eZlzqwSaSHjFvnnlTOUZuIo4dS/bh+YMpNYhrzcAk9nBybpHBQyszofhWfDw2z8t+8lgO/9YccfO+fs/bn/22shyQeqmKlL6nfvVL9G0J8hOcTn/sydnKauYceQmdmaeRD0HHXNwim7etUVEGlhKGiriUFTn7zGwCsvPYVZcDTuvQBprEdwjAQcabboPFVFLeg8clRQ64zjNBYu4YVb3wtx/76Iy7GXTMvTzrPixbc8B9PB7cqERQFInb8npixAiCyCA0KBk2n3BYI174jFdb05SSl5yrvwf1f0VpMHKLHdIvzn+4MjzdLGxAlGiEMMeZMMKfEgQGPzGv37QiyScZ5Vbfqz93EpuEFSd3Ce9ln5XeRaMYL2ihtODOdbWTue9QOWafJ8nFuNK9NaIWl1RjlBevezA2H/obD3T2szjfH5aKIeB3/4jX9qRRasJ/dnM15nGKC7Al61Fu5kHFWMcaK+QslkcUfi2gf05paFx5hXFbxIl678KI2jWhel8KiRfocut4zMXirWQeOAtlexBFg1UaOmGBQa5EcUHHfLb5fCmSCKSym6/lOIk5zELI34ieCekRvkn4I1cFmURoinvABCkBEMuAWYDvwx6r6TRFZ64Mooqr7Q+ToeeQ84MXAW4D3isiHgQ+o6n1PfG2fYFmooy200F7M8T3Sp9/X3KiTmbxSXMNvlQogAOIqvrV5E5t+953s/ZXfpHPgEI11ayKYma8KKWG6v1ecGz/t5BRTd9zB+p98CxOtQYYvuCgCGFE3DEtRtkelWcq1a4z2HmTjj/80s3seZnDLOT33HKrSYzJLh+z6bfkxX9DKtpTblZ5n9OqrGLn6SmeC9zsDTXFes+Y8202Pna7PoR5Q9Ts2BUnhfwA/uViapogAqZtoiKyKo5gsAIgq+ySAsoXrGu/1CZYzOd4saUAUVt4OFIX/YacHDiGmYhwMfKfznCJJELWquoB+gR8SDapJstgQIDGcNMTesVrxctMsc8AqE2zDZ0f3EbNdeZDCm/UKl3LC0eS8+LKZtNg+dhW7pu5gzfKt5b0XDsiZRuMxgYsHuYsmA6xhE2tl86M6diHJJGcV609f8InSDEUpTYeLmmPUVqKKuwMN1M2EHZ/KJfCOwGmDgtmtKLwXi7i+0fSE/My46nhvNVSg47RFYsWlGfEaIDVh4A591oPmYv426uddFjlz894z1WX24mSViNyc/H6fqr6vclrVAniaiIwDHxORi3kU4ldoXwC+ICIvwLnB/pyI3Aa8Q1W/8Wgr/T2RfgAglTDxyQJl+h1Wf0Qyz/f0mFQjpPSaKlIwlPYDTT5ANjzMshc9n1Nf/Torr3tNBAPpeSpmqxRghP8ild9i4fCHP8zIUy+ltX4z61/3JrejKPeXnDp3j/OZHStgQiEbGmH4/ItiPeZr5goIrJeRqtLYv02E2D/q71VIvAfVaYvUaAyqmwKvFD31gJjQPMn29L/pqaAT65Za/W/QSwBGgWQdwFFDLbmxUdMT9gMOHFni7GNrmj7HSwpRssOCTaLH27zyJBxvljQgipGIA3k1uEYnL1PMPq7uhQsrpWhqS1fVhbrtQUMUbcpSmibEg6oiRVq4yTHxcguaoZgd3ThgFEm0XV/BQhPtlS1Bkc289sCydugc7jj+RW6e+AyXr/oh5xqb+e7czDHNZpVnswiZYZp1nNWXZ/N4pKsd7uFWLuIKMslOf8BSkj4gSQuX0FeMOAANfG7iLxEMVy17JeOynujW32pV4kE54rX6eCSCdPHcNCmT95oExCtl0tnAMapJzxgUwTnVvjyPPIZI10dU9YrFFFTVCRH5KvBS4KCIrPertfW43GH96ySyEkeIfhNwEPj3wD/hgqX9A7B1vmPPuKQE4vnAykIAZ77tHmP3JWnPq0koQVFPV0jBUABLNXAULjV82aXse9fvg8Kq172mdgPSA4R6+lRN7SIWuseOs+KF15YOARVTbwKIXLzZWBfTJYn9U7kdANrHjjBxw1dZ84r+nMFYTym/VxRYtWqnRfHjvxDa04OkyrmlbOzo3q8E7wi3Zq49DH+RhfhEpROuctO1v8vAhnEu+f3rGFw9Mu8xKRgCaJou1jdeVtMQpdqjYGSwsR9UNUUuQrYDQyWggsnuacP2POnGmyUNiFyU4BAbw2uJIGpgqLsl+04K/oVKdX5+QrGtDNvMPOE1dHScJsmRj4j5zBJ3bKDUFgVQlBkXpC8z2NxgG4aQdkLFOGKt8cTJTGI2e6x1L3dhwVrybIQNIxeyb/JupNF0mpBul8W4qveTts4ywWG2cSGqynEOs5zVTwg4ysixFBzjIKvZ8LjP968mYhzButaGUsu+LYMDmBH/7K1FvaegYjmpxxgf2IJnqHqvwkYJtoHojZgGrKzFGjJdNwkYXM40Fcdb6Jn06mbbdNG5WJPZEywishro+MFpEKeK/h3cAPMTuPQYPwF8YoHTfAP4P8CrVHVvsv1mEfnTf52aP0qxAaAQqSZ1qWgI+m0Pn3mOjxOl4jKli/rI+H4W76cp6qutCd9r5rG0THJc3YzU3LCexvr1zNy3s1cL5EFLuK8ejYvU9wtz+/bRPXmCxvgKdK7D3N69DG7eGsu4FDm4uFxQ9WxV3EJinnYUGaZ7+Aj2wf0MrNngFsChPuLWH41pdw3NoGiVgEj9a6vpM0lfUfEgIQEvMRWQ+Haz7kRq1Hs2S8BR7lTBUdmbHkMKFqPqtvlzGzEYo3TImTM5IkqeWbpHHV1mdt8EUwemMCvGY66zwtokPpHS1KJqLvMapRT8BMDUTagWTU/SCmDHqjOpBQ1SGtso/T3ePI1n3b+CnOnx5rERVL5HoqYEQwiOsOpJq86URvJxZcMHIxGkhP9Om0MEVxpj67gXQbNkWya98WggmvHKOlRHvnCeeI2QcyvzpHB/DQ3n9wEPz1/1PIYa4xyc3rmoYIYLyVEOsoyVjMgyjrCfW7ke+xhcQPuJiHCpPJvVsvTBUPRWSz5iDNJslp9GwycNdq74p4qjNMSBprtPXs/+qfu8edR9wjNMgRAe5FRCNaRdRzUmxBVVQgTuSn+tSx0MLfbW9dF9FiHrga+IyO3At4Ev+FxA7wJ+QER2Aj/gf88nv6qqv5EOTiLyowCq+juLvrl/RelruoH51UILnmyBfTWAEUz94aB+WoX5PMsqgKUOhtKOpb3HrXnTG7Ezs8w9vLevNioFUZW+EvqOD4goFqZuv41llz+DvDXE0c9/hj1/8d7yGK8tCtohKRb+mI77iP80ZZBtr3orw2MbyObAtMsypg1ZG/LZ8mM6ybkKf00b6pz0fZvUrQDpClKUHwcMaybI5Hu0RoTAkFZcrsPCkd+tlUiEVyuO1GzFOxQbVN22E7ftYeCsVQDc95sfZ2LHbgprKKyhq4ZCTSREd9VtS4nTaQBHoGIyq/COkt9GSm/FNCJ2Ssbu023m7YNPpvFmSWuIIhAKmpnwjgdSdSblagWtgBP1fCNNtESZ1/xo5tF84c1mIgjB/9ObtQKhNj1nmAgDaHIXjnWLtuNQ39TNM5khBQvWECIXi7W0ZIxzVzyHR07eybrmVireb49SWgxylAMA3MZN7t6/38xbfeSw7uM2bmI5q3m6PK9/oRQMZZk3P6T6e3FxgzLfHj5Mg+D0+N84/jEAhrJxposJbjvyGe45/jWee87PkDdHe59HAEOm9ht6JkaxGvtvpb+q9nWKqxKs9fQjVBign0BR1dtxCVbr248CL1rkad4B/H1t23/Fqa+XjgQAwPw4qEdL5LU+fQtWzi2VfX21TeFciXZI+wAdrVewT5kFuTYqNNevZ+w5VzN5yy20Nm0qneASIBPrVL+lQLL25bKBQSbvvB0tLBM3fc0FZAxgyGtZAiiqmMhIyoRrec1L2JR13DHBXb/iti+QzUE2A6ZQl9rCgM19Gf9axhyDJM9WSlBU3iBxLBcVTn7leo7+0ycYfd5zWPnaV9VefTf3xDHcnzMdahzFUAnJVQsMKsZzeAy202b37zlFR3PNMtqHTnDvO/6OZVdsY/t/fw0M+DnCuPnCxIfk6h01PrUOmIKisCsQutOyttYBTxfdukeehOPNkgZEqYksrqiNxF4dQxAFPlB6bDCxZVKS6YpSw1Sxj6vnFlnrCXQOFDkejymJ1kE7FMBWCoCoDXB56FShc3rfa4iAzpFkvTfaXJcVA5u569DnOTTzEKtZn4CiR6fdGWAQi+WYHmIZKzmXSx7V8UtVCgoMGRvYMm+ZFAxFEydZaV41njAdPAaDls6701+9+vW0shGa+RBdKTjU3sXtBz/Nl+57D0/Z9EOsX/6UmKQXUrCeaIoSvpurVAl8xIPkCOwrlfckTutHPKU6Ki0cXtZf4/RFvlciIi8DXg5sFJE/SnaN4Vh2S0f6IdIU7PTbf7rz9TtPeq5AzJWyzHxeRgtfK/3M7zYfr+2/D553Hoc+8EFGLn0arbMSvmEfMFTXNkXtj0Jz9Vpm9+xibv8+mqvXsvlNby21LzVtDFSBSDAnh3PH8+LATzYHpqsYD4Qc2BF3jIXMKo1p9YAIMELRANuonVc8MEpMo64yaSOV/4NGCxFGLrnUL6AFTTLAu/sJi3Z/eN1KL+5ZR18b4zxOrYU8b3DO772FwbNX0xjKKSanOfzpW9n/oa9xyw//T879/36U8advgabBiuMcGlEXn0iomMkKNRVSdvo91RSlmiUgaofSvGluO5xon55D9GQbb5Y0IKq42nsJfMNyRaMVt/kATsS4aMEqEEJ7ilXn/WM9GLFacjMsSFGU5/JBHFXcGySq3vSVxXAAUdsUJrdkgrNBaxQ6jBhENLwf3katjnTttVGGFl1tc+vRT5LRYG1zC5dzlZ/cF+dppqo8yD0MMcKtXM8WLmBcVj6ux7BUZJ1sZh3zeMyFgI71zc0mMb+ZqoszFCKMB25QlkXgMdpYE7VIOYYNY09hbHgj39j1Ae7a+0nu2vtJANYuu5BLz3p19DBzZlqNvynUeZFJ2Qmim33gEdU1QGnfi769LGKpltzvEhqggH3AzcArcW60QU4B80e6OxOiOCAqSYyawPMq/c3dXz8AxfnDA5G+s0M/cFHrphqvDRWlcuQKETkqQb0hRVUrJCmRmlo/SEBQqpHJh4YpTp5k3x/9EWZklJFLLmHFtdcigzXuSHJ84AQFcGPn5jj+tS/RXLuBPX/6btb98BtoDC9zvKEUDFXq6oBQ3N7tU29/nXxOK3V213UmZ7E+jqpVVydxMZUk8+UCULHl8UFzpF4bZNMZMC5w3THjz3ke49dcg2agBS70uRGtAAAgAElEQVRifeAQJSA28ISC5k6MXwir9X3EgVzXFIL1HMLCCq3tm0GUbqGY4WHWvO65DF+8hfv/8wfZ+eulQmPTjz2LrW+5GlWNIMgazxlSpdDS1T6CH0ozWBCDA1Rd6xfooog33wXFWyg/1jh9HKIn23izpAFRAEPzmvFTUJRoi8APIGGFEDqvf5nChBMBSfhdJJMRlCDHlKa1quku1LP8RBEqpG5VfH28NgIX2C+uFFXZc/h2mmaItp2hoEMjG+DO2ZtYZbez3Cz3OcgWBkUPcjdHOUgHlxB2M9tP08pPAkmiW/cDRSngiRqhyvFhBtQIhtLtI62VvPiC/4gVy8nZAzx0+CYOnriH+/Z/ifM3vJjoqeITebq+Zcv+RgJ0PECKtZwPFNGn38/3HqTyxFDFnhDxKTNuE5G/VtWlpRGqSVhoQRmjJk6ibnOyuEmO6fdMKpql9CKnqURlliUBQOX3VOPSwx9K7qWHP5SAirBt4stfJl++nO7x49jJU0je4NDf/g36gz9Ec00SWiMFKzWQc+DjH2bu4H7s7AySZYxdcElVI5SCIRznBzwgivdVB3ChTAKGVMv79NvCuJ9qq8p9AWhWr18xS2oCfBNJzYeq4r2Bk+et/vAKGAjPoBzPU1J1SHFi1cckU7DWYIz12pxy3TZ04WYu/dSvIu05pu/Zw96//DJ7/+YbDG4aZ91LnoLR4FMk8dIp8EkDO6ZapFQcj6h03dcA2KLGaTGDDU+68WZJA6IKZ2g+Se3A9cGnwinym5VIcI1gyPrJK12dQxKYz5STWY1I2xO9NfwMhPCwzXOK1LjVnPM4AvHaBLKMY9N7uGjTy+jMTnHv4S9xYPYhLm8+m1vnvs41+asQY3tA0SP6IA9xL7NM02SANrOMMk6HOc7nMlqyiIzF389SB0PzputIzJXznkt6vwewDBhjGB/cwNM2v4abd/0Nu478C41skK1rnl2OsclKvRLs0wPuHs1QKjUwnkZbD7sXkkdBXPyeiIj8vaq+DtghVTuQm09Ul44tN0yUHv0Impjkk6ElKZNygVyZdLakOh6d5tpKwMr+4JTHkYAhej5VvlAEQwsAofB/9sEHWfeWn2Ly1ls5ccPXmdn5XdY//Qp2fewf2fizb+sDsuDY5z/LxDe+3jcMyKbXvoUsa/XWMTmHFOX/+I7Ur+PraIoSDNVBj9Z+RwBUA06moNIGYXe6/nEX9udNNfsJMHJ8ocAV8iEaa1rB0AdUvdnbB2LVtD+F27XO/GatsxxggqU9EJ4V02oyctk2LnjPWXznuvew83c/i+QZa194gQNFKZm6Rq6O2+cBNkYUK+rBVVlmselhwv0+2cabJQ2IAhjqvwpzYKbHHTmYzMK2VGOQkuusdaaqsCpP0myEVbqmPOQEAMWXJpKppVx1xAzo4Tj/PZfSpVXAkrjoG8FYpdkYZrozwdZVV7Fx8Hw+v/sP2TN3N+ua2zhS7KPJAKOyDDC07Qw381WmOcVWLuQh7qGNG6ROMcEyVrBZznmMLX/mZEKPcjs3McQo53IJbWYZZozlITFtTXrAUKrdqZftB5Zqps6e8KyBx2VwBEIDIsJlZ72Wr373j9h58Cucmj3IhRteSrMxVA4tlgi0S7Nsjf640OrKaM89LGqYWuzK7nsjb/f/X3FGa7EISbUI7p8k4LSqHUiOqswIFVAUTqRl0X7X1OQc5eWkBEJA5AWFiT6Y8hKQVNEK2eo1wphTBx7ZyAjdw0dY+bIfZPx5L2DXO/87k9/ZwfDTrmDytu/QWr+R5qrVALQPHOCRP/9jiukpxq98DhPfvqFyLyuf8QJGt1w4b58Omq0AhkwSpb1CbhZKDlQNDKVaIonbtUyiHFzfAxgKnm3h+LpWSOHkvTs48MV/ZGDDZta+5FXMHtnPwJZtSGtFBFnxuOB34wGO88OpPu+gOdLQ9sEBCE+7SPpIDISojlMUQFGQMOxInnPxn/w0t//k/+a+3/oUkzsPsvXNVyMt48sJnSJzqYG8dK0hP02QIOO1Qkb75/g+tYg4RE+28ea0gEhENgMfAtbhuvv7VPUPReSdwM8Ah33RX1HVz/hj/ifwAuCXVfVrIrIFeAj4BVV9ry/zv4CbVfUDC1YgEFFr22qVjC9ODMgetnXBhCSbRchCrrVBI1k+WJBu4SavwnfPLOSqCt3GVMFSusqx6ibVmnZLk5dQggObio9/5M6xedPV3HrXXzEyvI4GhqeueTnDI5b7D9/DgeIh5tQl21uXb+GA3QXA+TyNzbKdjbqVG/hMrNL5vUT97wuZZZoWQ2xgCzfzFYYYYYZpnqMvolkHRSHWkAdCzn3eP5gAjBt5FfT4MAfRNBY+0TPMP/P54tb7fpNLi2dt+yl27PkIB07czYETd3PFljewcnSbO5e1ZWTpBGj3k14uUQLmUyL1YsaeJbRiC6H2gSPAjKpaETkPuAD4bL9jzth4Ywm+GiXHOeEURdxSKgBDjaOJjbA7cMq8HT2CqWiLKS8kjlDo4tzEdqMCdlz9Eq2PpZJyw4GeVDNJr4YjBUx++/hzX8iRf/oY2eg4tNusfvXrGFPL4W9/i2Jqku5xlx9z9NLLOXXbrUizyfrrfoJl513Kisuv5sE/Kz2YVz/rJVXStA1t4ce7DBdvSIlJjzXzWheT9n83QZuuxtAm6u8pkKrFOtOb6SqZKvmMjfw80zHYhlC0hG5LKAbKNtH0wfn/7WOHGdp8Dq21G3jwT95Fa91G2h8/zPjPvA02bHYgCKcZSl13QiyxEEIjWsySfhI1jH7hbNVALlg1Lj6daskrDSYs8YF8EbLkd7Z6ORe8+8fZ+asfZt8/fJuDn76Ni//gjYxsWxXjEM10GmQ+XZERpa1ZaSrz//q51/cTkcVxiL7fx5u6LEZD1MUNNLeKyChwi4h8we/7A1X9vbSwiFzgv14DfAD4mv99CHi7iPyZqi4qa2m5KguwXuP2hY4hHGJBCluqZdPVxDz2B7HWrexDpGoRKDxn3+JeXmsRa6KNuMIXyMr4MvUVfrlNkUx8zi5/X0YYGVvPxee+jrsf+DhNM8BQY5xhcw6rhs7msvGXMNM9xZ6pu2hqk7M5n6FimKxPKKltXMQo44tp4iUnq9nAfdzGKOM8j1eSkXM3NzPJCVakgKimFZJIkhYKKSjo0rFzYDNG8uUlbyjlD1VMoDUgJLX/fWS4uYKrt/0MD0/czL37v8D9B69n5chWv1dLUsAipC/Jug6oFiGPIXLs90KuB54rIsuBL+GIj9cBb+xT9oyNN6m1q1z3J4An3ZkcU86C8aBoTkj5KBEo1W37fchIFRBTAzh1knMKdFIwJH3Kx2OAkYsuxs5Mc+BD76exfAX58hXItu0su+Iqxi6/itm9u5m88zby0WWsvOYHaKxYhZgMumBnS+3Bxpf/GFmjVdYpkK6T+pjC8YZSXpAqaE6cmMUDociTEijysmzQHhmvYTIdt/4xHY2LD5mx2BBPyBpss7QwpKbPAFqWX3wl93/w91j3sh9lxTOeixkaZNdf/RGdo0dg/eaqhk3w6XpIOESCzs5gOx26k6cwwwPkq5Yn84LjEongPdD8AtmW44t6UBTOK+pJ2FpqjAoLg+dv5pK/+0Ue+csvcegTN/PIR77N9v/4g6goHZvRFRfryODNYZJEnq5xitLEr49HngTjTUVOC4g86gpJ1U6JyD0wb6pzcJbToARNW/wwcCMuyuSfn+66fSrSawsPdt15JgvTLpwHQuAH+fNU+EKppshFzfIRpL02KHBP1HmDuRci879dj43rhnC3fcBQ5VaMszNHDyQNsZEMK1Zt5znjv4yZ6yCdguXLm0x0pmB2jpF8gAuzq9F2B52ZQUXRokBtwbf4MgDncSlnybmLbtalJplkbNCzOchetvsUNst0pQdE3sMsNX0F7Y4Ix+1hHpjZwUT3EBkZuXFeex07y1WrX8t4c12pGYpSgqp+bvC95f0uvzIUEc4av4J7938BmxKpFwli4gqTWl+u9Fd1I/HpzpdOgktLRFWnReSngPeq6u+KyI5+Bc/UeFOZLKEKgPqAonqZtN2r3mfpRRJgFLQiyfkq0pN+ozxxHWRVAFDlew0oxQqWX8cufwbLLntG3D7eatCe64DC4OatDG7eWhl3xYJtt9n1QefVvPk1P82yLdVcYwEQRXOexWnqk3hC6X2EVB4K0KEM5CjODBaiU6sJcYmc5l8KRToW07YxL6BmxoGhrjMzFk1D0ZLKNVPs2RhdzvBZ5zJ5/12MX/oMFBjYsJn2wf3IRZe5TiW+zh7YhGc3ddsdnPjaV2nv2480cmeCPHoMaTVZ/463k68eJ1A+StDjNTNxrtA4zjiPZkoXfu8lHaYgq2CynDWvfiaHPnEzptV0QR5FXdBH8d9FXT4BLUM41DPdz8c5elTyJBhv6vKoIlV7VfRlwDf9pp8XkdtF5P0ejaGqdwFDwA3An9RO8S7gl30229NLbYUVVK3ud8Ih6jnOuTybtsV0LNIu/ItTYDpFlTuUTjzdwn+6PvaQjek1YiwizzdK3ffjSjAhUtfzVNX7nvocV5qLSxCbiUsQmxu0YdBG5j55hjZyFzMnELyzEHXZxVS6kc/SZpYLefr3NRgKsoyVnOJ4ZZtgKLSIYCglUIsxqCrfmvwUFsv5I89k7cA21g2ei6Jk0qCZDccgjBWtkPEjTo0wXzGrgfsewjckfU5UKQrH3RpprSr7ZaElZyx8+kgPKT+VwvrkskUJjE4n+ig/3xsREXkWboX2ab9tMeb6LXwvx5sgYWJPQUYlanFZJrZh4WcyFZfct1/79gE3fbVAVvo8q6qHWQUMUQNDVirnrHulpffW67FWgpkKadZvL6amuO933gHA2W/8eUbP8WDIgxiJmqASBLk4Qv6dkJLo7Cru/tmQkiOoXmpt7JxRSjBkuuq+22Ssb1tMp8C0C/KZgnzGks8o2axzy688h0QGN5zN3IG9cZ8xGerH/Aqoi95zQvf4cQ594ANko2Msf+m1jDz96Qycfx5qLWZ4CDMwEJ9jBMEhqrXvJyGKtfuADRGuw8drcoo00rUV5o456kS+aoxChY41TkNUmBjleq7IsYiLdK1uf9AKBTCkyaefnOwsIn/mk2i8WXQhf4UR4KPAL6rqSRH5E+A3cLf5G8DvA28BUNV/3+8cqvqQiHwL+LHFXHPFWBkArwQu8WzlCiNRiaKU6tPI4/AvWKGMjeTQacRzVIDRHC7LvM3dPuNTOuAnzZC6wWRoyyV6dYHCDGOjOcG0p0KcPHvaIN5QuSECqxAnSRXpNhBr3XlFkHbTgbVOF2yBzgxRzM1yR/vrrGIF2+S5rI0xek6vxxxbO38Swccjj/+8hiP6MFvYxnJxZr8BLmBy7UGOm32cP+RXsz64ouRZ5AVdM/MjTHaPMNk5zpqBdeSmxVNG/i3DzRUErV0InBjiUI2tbLm4RImWJqkKpS67jmjVAWa13LL7n9i0aRMXrXsm40POq2905UAv2HExP131fX9Oy0jUBmnp4VgBQgp7Fm69JarCfjsuUuzHVPUuEdkGfGWhA77X482aZiO+kxXv0PBbqts15QNBz3c1sDLLIdUGkBwTzifaRz1UnkzSn+q+rMzy2mTr9kuobB3ILDQRpYAKWNHIe8Bc6FPF9BQPf/h9bNq0iQ0vfwOD689yQCE0nQdGJolTFK4/PpijRhL6Amgm2HKId0NuBDtEzlC498a00pguMIX1MYiUZdbSaAh2tOUWlJ53ZHPH+ywGhO6AUDQEzZP4cP5jRZk9cYCRcy5iWasBBsaueCbm3luZvOPbjF/1nLIfmLIv2OWrGLnuOjqHj9B9eA/NjRsxw0MM/cavk48vc8/XaHktf38raXiqhda4U4okDyPwk9yw5cpKAXZ2lmO//3k2bdrEWVdezkDHxYwao0nhNViZsQgeOCYcJXeZoCmqdgMXDiBU0+cyawwt0HGqfWOJyaMeb4IsChCJSAM3OP21qv4jgKoeTPb/OfCpRVb2t4CP4Ox8C8rE0bnyR50/1MezLLg2x8StAegEb5+iALVMHPL274oHkMLcXKkRAm+g9pNhlkGz4XOQZdiB3OUn80leMXB8ohMHuhBlu68qHdxLErQPhV9BdcsM6FJ4oqCFieNzSLuLtEtAZKen+fzhP6fFEJvNuTRZxnFO+gstrpce33tyUeUerTyu84rhO/ZbXGauYcKccpvyBquGzuPGPe9nbHwbQ41lvqxAoxE1PEPZOQyZc1kz4H4rQnsa2jOdEvAYE3li0rVghONH26WutO56n4ColMcWNIUnp/fx8J5dWNtl+8gQx6ddn1URJg4nK6z0+EQzGQOJhn4YNJHUrrdIDtFSFFW9nuR9V9UHgV+Yr/yZGG8Oz3Z64tGEya8EL8k7bLS3bKwgcRI/1OmU2+I+rZ6vzyCRgpK6NggLh9v+vFGD0UdbVPterXBS3dr+Q7Od6nYLdmqKnb//38nHxln3wh9mcnw9U1OdXq0VTjMUCdbJevTItBsfg7ZGDdE5JQVwplMCojTadHNSGd4zg3QtUhRIp8Asb3J8ylIMWZdku+FI1bbhgFHRFYqOI1hr7jVRSdsXtssDO77N9mdcS2fG9QEdX8P45c/hnvf+DtsueBoy0PIApzxODfD0Z0QX/o7fNmsU7Xbi/VUa1zvVHLLtCmCSAJxCvCNxY4TJXKOKeCuEKKe+fRdH2pNQWIZXD2Ns2xO+hcO0EVygxsAZyjxTyMQOQUxom3KMHJYttUUWoZAUrX7/yKMdb1JZjNpagL8E7lHVdyfb1yes7h8B7lxkZe8VkbtxrnHfWvDaYWWcmijiysNrY1KObABDSbBFoAp8Cu0FQvXv4ECRMW5Fn0mychdCZKwY6kA8ECs0Tnz9THlCusqUuPJP3XljuhL1GdEzQbPMxSDK3TkePPIt5uZOIRieNfJKmt28fAkXY1ZZwjKhR8jIGRSnaZJmE8lz8uYgmwYvYnf7bi4cusYVTgGRN4fFKOLGUE+rAeBcYF3IgzLJbrI/AUQ9aTlyU/Ytn95lqn2cdncKgMm5w7SGkyi/6QRb8XTzRMvU5JtqglLuUp93YEFZgo/fe3r8R2ALyZijqi/sU/bMjDcpcAhdwU/kKC76MMk73I8EnRCrVahGSA7nDV/8yYNWJ3hgl4XT31ICnwA8kt89x6Tb+91nvd61/ek5VS2H//kTSNbADAxyzs/+VzJpVOvR7/xS/pTwOwCRQMvMeuvvtjuOZRzba2WyyVmvLe+gqwdizko1DgjZpsHmgs1xwKgcUmtVVU7efwemNUg2MFSpQ2PZcgbP3saJ27yWKLl+0IapVj0MHaooYxWF1B9xt1KG75CyL0XCeJaUk/LwEP9IjGV29yG6hyYAmDs6RXPNMgToYCiM4zSm3mpWsjLdRzg3YfoptUWFTxobfqs6891p5ft8vKnLYjREVwNvAu4Qke/4bb8CvEFEnoZrkl3Azz6KOv8P4LQkJ+kmZgPwyTHThxRfNyLJOaTCSAjTKTgST46uAKE6PyNJ9RCv7xF8cI+UToHiTC3R3BU4TuKuU0k+63+7l8CPjGHgDXeTclTUkQnVGDQ3zM7OcMO972XZwHqOTe/m3PFnc+2Gn4N2B52bi4AIQPJG5Xc97UdMbDpPedNsYtuLc8x5vCJZhvpwBh1tczNf5nxzOcPGaYHMyLB7No2cs8eexo2H/paty5/BQGPMAaChFtK1ru2McTnpskSzk1WTrUrHWdHdKOo5Sd7zrJKkNTZGCayCyt8WysOH/gUjWWVAaDXHEtBDZTB04N1rp6Aa/KOftjNqJtMB9TSjz0IT4ZmVfwD+FPgL+oc8SeXMjTcpeIGIWwDvYVqOOJUko/6guJjxYCiYkOLkt8C16s9NaqTqCnAIfBaIYKnO9alc8HQgKLmHIO3Dh3joj9/Fssuu4sSOb7LpR9/C2ue/IpKmK+eugTQ8fpPkuwMt/vo1UnU/C7/tw9er/D41GVMaaQ4kYKhoiM9l5vKaubGh9xozB/ew9zP/h82v/Eny5kDPs1j+zOdx4ON/w7KnXYm0Wr1E+gI/pvvI5gVJOBZH6q7kPlP/XAu/qMaTngPNIgCpoM32z9HOtTn5z18nWzZSep4CMjKCLVxgR0VcbDvRqOnJMuuiUBuL1Sy65IfnZijLFsH7LXigqTDRTiw0/eTJMd5UZDFeZjdQztmpfKbPtvnOsQu4OPl9G4sgdDsNURg5PKAJHcI4sOA4N9YBoU5RW3EnHcDi4gsV1q0uVB1hNYAhayOZOkzQLq6NcS9epjWCLW5y6+ImLQ2AjZJ2EnNXuduQjgN4koCh6FmUdiwtCdsnT+zm2NETnJjcS2HbDA2s4Motb0Q6XVfnRo7kGSbVgHS61RQW9YCERjADg5ihbnKf1fxF2VBiPy4K583W7lSB1uOVkJk+yyi0y+3db7LebGPLyGVEDUmj4Tg+AwMMjK3h7OIZ3Hbsc1yx9Q2YrIE2MuyAicAnm26jBe754LFs0CYinhBvS7AbeGSh/UKXMYAx2IY/UdBGSoYV5b69nwdgoLmMVmOE0cH1DA2sLp+nKcMAQAm2gnaKsAKuaTIr0vOGLGLFtjRt+l1VrROe+8qZGm9i3kMSHJtoFgTc0CrVYyqmsLTtxU1+Adio0aqmJLpdJ+cIw0WqeUm3+cpIJlWSd79jUoy9mElLHeCefuA+Zqzh6PUu0sHAuk1s+G/XleesA6D5RKrAMgazhaqmRmvDkxI15j2muDC0NzJY5Ty47GDDhdkYyLAZTjPUcP/V/yYBY6E+7emT7P3c37LmWdcydt4lleuH8Xro7G0MnrWNg5/5R9b+8HVIZnp6phT49xziQjjRhsXnL0QOoYvTVA3wK7H/uHO58m6eKE7OMfGRLwGQr1+NGRpg+LmXozJA0fFlMkPXGkwww4nSLYzjEFkhM84TLcQ9gipSSMnVjnANo/ki4hB9n483dVnSkarrE0VltVD4HQlPSFLNjt9dT59Qeoz5STF6kRVo8C7zgCi8G3FiswqmNMeFyVbjyxuDtNPz5ngRq0ycephbdvwZT7/sZ1k2dlbcDmC1YOLEbg4dup1HDt7Mpk2bmJsc4bLtr+ecNc8t4yQFT6g8d5qrpq+zWiTP0UTDI/XcXXkOraazjQPSaJQBDftpIbrd2CbAEwaKxIirC3C4+wjH7UEuG74WaTVdgSyD4GGXGbSZsXXDNUw+fIyv7/wzLtv+eoaHNzpNkY//ZOacxk4L3LMiaHj6RDWPN+TBNiTapHLC0sSkpkYx5Dz3iv/Ew4/cyCOHdjA2vIGVy85BcuP6BV5DlbZ70BiZkOMo1QbWZ73QQIsAQGnxPqdYIvJJEfk54GNAXHaq6rEzV6WalGuUnnk+1Qz0MzFVnDoSxU7g12iYGCWkfEjO3e8RVzQ+UtkOlB5P4Zp1YJSeSmDiWzdy6FMfZcsv/grNFasq+22nzfT93+XUXbdz6vZb2LRpEzPnXMSmN/x0ee4aMFm01Bo0AgWtFkkpAxLKp0AxOYdtZqhpucWnEccZ6qojTWcBZEgPCKvL8bu+ydyxg4yde2nvzgRYrv3BV7P3b/6Ch//8PWx441vIlzlHj0pbJPVVkQT4agVkq2+E0BdQ3FiRBmL1xwatj4qSjY2z/jd+iVNfvIHpHXeTr1/N4MXnV+LgOUWhoVCNoCia3dShLRHxJOsQ/PHxyZNxvFnSgKgn91P6X8RpYAJJNU29AeW2lIMRftui1AoVLiq1Fo6wrInWSMTZpcV68KG2XGoEPpH4/6o+tHsKiohvQrs9xc77P4lay/iyLQDcsuPPaDVHufLSn6OVDyFiuPHb/5N2ZzK2wVO2vhrbXulUpQaiCsqE3i7QbETAIoXXgjUSQlzFc0qQPHefAISazb6xdlw7KhiDdMtceU8IIEqWhbPZLLdPfQ2AHac+x0vG3oYxuatTs+nAUCND8xwaGU89/3XsP3oHt+z8vzz7Wf8Zk2UeEAnazKHddWY0vyQrM9LLwisa79URTJepeTZwFFwGe2gOLuPcrS9DTM6uR65nzfLzS+Cs6qLjJERtawvu3v0pVJULz3oZedYKlySMXD39vDZbLmrsWZoD1E/4//8p2abAtjNQl74S+UL0AUZBi5G0bepxlh5LubnUpsRDpco1qlwkkVogxVC/WL6uoekDVtpHDnHkq19A1DJw1lYAdr3nt2it3cCmn3ob2YBb/d//G+8or9FosPHHfpqJgVpE+PR6i+hfFYAIZYP6ibsPzqm0Y2i7elmAznBexpYTKAYzbGGduSwnmi2dR1iinYnXU6b37eLgTS5w8a6P/gnnvfWdbozoI6Y1wKY3v41j13+B/X/3QTb/7NuTStbaJwVxARwFYEStDT1pzB0nsW5RRCrnbKxbx4o3vYZicpqZW+50HdIGOoCiarDieKdWBTs9xaH//VGaG1ay6sdeDM3MgSwBEamazxJJ4xUtKqfZk2y8WdKAiK516uEgARRYAHWTdEi8qmXyTPCTS7coJyhVaHdAm5E/pLOziBgHgqKGqHDEZLWQGaddEeMBhwcZNU1DmlNNrA+6VnvBdj/8NQ4euh2AQ0fuZHhwDVMzhxDJueveD7N51RWsXfNUnvO0X+Tw8e+yamQbeWOIZcMtjrfnCEEAQ4dWYxwXBhNNZzG6dp6DWmLurlYTOt7TKstgaNCBqMFByAx2ZLAKePIc6XbLQJWNHObmoqYpyzKKU6di+Trn6LQcJF8vq5bD7OH2k1+p7N4/u5ON45c4/k4rd8+4kaEN62I0ZcLq9Zdw9/0fc1i0adxAKFC0MjIA2/WAGEeGJqxo6kvoKsjWAGr6EbI9IFU/qKkRNqy5nF2PXM+h4/ewacOzSt4bgjayaOI9OvkQ+4/dwfjIZq6/4z089ZzXsmrFeSVJ268II9AO1/OPeFGipeZgKYmqbj3TdViM9PB60p1dKmYXAW8CTY71yCclVChYTPAAACAASURBVIdJ3gTamlcfxLxYidZHTVmH1ISX1i1Ovra2ner+h9/3hzGa9Kk7djC4dTszD90PxrD7T/+Aja//SVrrNrD9v/02p+6+nbGLL8NkOc1mA2Y7C5Oza1qe+fZVgIiUGiKtlU/bXZL/4R0Lsd6KJhTNjHxWY/vMLcvoaOHiuGVQNARTVM1WQWynzeG7buDAjaWDYnfqFDP7dzO0cUulYvHZKYgRxi69ghM3/0tPW1faPX1XPZgLwMzhFo3aPRWQrjgqhu8zpls6CalQcpLCEGRg9DnPZOaWO5m99yEGLzqPyDkK4EgBo5z86m3M3reH4vgkD/27d7PhV3+Swa2riVGwbUaelYNFcNmHML0JJ9qniUP0JBxvljQgEvWmj+Q3BSUgia70GkFO7P/BHJZG+A08obk5ryFStGiXWiEPhtS75zsA5baT545r4699+/5PsXHl01gxvq28ngI4UGRFOXlqLxMndjE9fYT9B26O93HZRW+mmJvk5OQ+VoxuYWRgFa1sDJkryDGsH7nQT75JkD/K34jQXTXstge+0WwXM9dx99zpuhxeUGpiBlox2KDmGTo8gM55zUrm4ipFsbjJ3HpuVlFAnrm6dIdgapq82UQTonZWuBWnGRlBTgnZYM3+HMnEgVEp7Gnfy91TN3D5ildw+8QX6Fqn3bzj6OfZuObp3nzpR/8iaPzcE548tZeBgXGaHUFt1wEiIxUvQ+kUjvTYSUBPAqBjwMTAIRIBH78Erx10HISgjpfYPmHCGFi2hiuf/vPYbtuVa7o+YhsuTlUY4JavOpdz2i9i9yM3MDiwnIMT97By1fklYIeSMBlcka2NHo1uA6eXJbhiE5Eh4JeAs1T134rIucD5qrpY1/l/dZEi0WzEjdX/aRmgqimQ3u8hUKEQFQLRnBImvBRwVMjKlJON7bTZ93cfZM21r6S5em0lCjT4idtapnbeQ/vIIaZ3P1im1hDDhjf+NIiSjy5jxTUvwjRbNMaXg4JpthxpuAYGKlohUyrDUSL5JJ0MNW2nfn3QUAEL6YGxTf2rHjGVfw+sSDSRmQ50B1PNLXQHJAIPNVBk5YNMYwft/9ZnOfKdr3H2D72F3Z98fzzHwa9/mq2vf1sJNEMFkgl/ZteDtNZt9Jwhf23pvddKmxhKnlnQGFkpzyFEj7OSg+TPE7Q53eq9DJ5/Pmt/6d9hRoacr79fJGujBEVqYeS5V9A9dpLJr99KNjzI1K07aW5e64c5F9eo06051yQ3o8Do91kusyCPZ7xZ0oCoAnD878q+fiYxoJLqIN1fuEjU2ik5MRpMTH1i92jCmxFwx4gLYHV08iH2n7iTlzzt18oJTJ2GavcjN7Bz1z/HYxuNIRqNYc7d/oOsXXkxmRqkW7Bu/CKnUQB68l6pcdFSSbRR1r1UdqhRUR1LoWTGTebS7sJAA/GxRIK3UupCrlkGzRzbVEdGTtNWBHMPlCEGDD5BgnXX0EHIsvLZBHMlIAk3qXo/6YgrYAwbBp7C3u79HGw/xFhrLbPdU0x3jrNt9dWx7mK96cuDM4wDiA3TotuZwc5Mk+UDkVQtgUsWIs2G4B/++hp6vDdRRY2RCIWx7Dl8MxvXXYUJLvkJGHJER28QtZ4cD4yObXCXiH2N8tiA/8g5+6xrWLXiAv5lxx8xMzfBecXLyfNW1DhV4il7T8NKuy0ipMISten/FXAL8Gz/ey/OE2TJAKKK2aPc1CMVM1AoH7Qe/neqLerhxkDpeFG7ft/rAHQLpu+7h0eOHmHr2/9rdWWucOCTf8+JW0vtRTY8QnPVGta89EcY2nZu1OyObL9w4Zubp+/YJBZQ3WwmqX+CL9PX0pJoiCp8p7q2Sao4U0NiFl/Q1mas4L0WIv+nYCXsD79XXXYNJx64ncm9OxnasJW5YwcpZqdZ+fTn9bZB0BD5e8wHhumenHCLrYRE3Zd4VtsW7zNoBL27fjE1zeQdtzF61TN95P2k7aTU2ESTW+HuceCcbR5MQeAbufGnNNFJa4Dl172cwUvO4+Bv/yXTdzzAspdfjeQmKr8rVTbzPbiF5ck23ix5QLTgtrrrfND01PeF4zwY0na7ApaiRqhfFVJQVLglnxjDU9e+DGm2Iqm6PADnli+GtasvZf2GKxgfP9tNzJq45gfNBHgeU/3KnvtSULlnO9ykO5zTHTLeBVYx3v3TNAyZj5Vj0lEqBUR+oreNHG3aOJiUUn4X66OpZn5iLkwJfPKsnKDTZLitFgzWwZ2tPjfjRtjcDHDlih/njt0fY85Os33988lNi1XDWzk+uZfR1mryrOXNkR7o+Mcxko0zNrSBw0fvYcP4xR7oSdSauXhTNV6Zv6c0P11oH0SYaU/w3d2fZWRkPeMrtpXZtsMqLBMPykpQVGm2OLiLj7LbO8AMj64lzwfodme4acd7eO5V/8WTLInAqHwK/hmGuFc9Z+sjS3OAOkdVrxORNwCo6ozMR9o4U5JMhFHTkeybb66ocEekWjYlP6eTXQqOFlOnrDXI2h95PQMbzurRDrmxxCKNJmOXXMHyK6+muXpdlROTALVHO+dpRiU4YtCOViZTrb5KfSUBjpK0Ux2EhrpWcKcpT9ITSyhGp04AV9hXARjQHFvOOa9/O7s/+X7EZGx86RswrSZDm7Yzues+hs8+l8D5jOEFPMga2XoBhz73ceYe2cPghrMq3sQ9beav18MtC+f1C9vZXbs58tGPMHzhxeRjo1WgXAdHkGiL/XgRydPiNUmOhJ2CqdZ5zoIxe8f9HHrv37Pm7W/ARaIuidXheTwmdPMkG2+WNiBKRJt5ZWIz0+1ywosmr2QCTLQWAfzo3BzMGRe3J2iRFryohcAxUos2m75jGla3znapPDoFosbzbRTJYMuG53D22df4FzQdBaV3NAymv24tXEIXN3EXXWf6aWQUo026QzndocwBIq8dMl0wuWAagjYM+WSHYmSgfFmjy3iY2A3FYEZ3Lk9Wbr39JYA307V+VeraVpp56R0VzJa+KXWkhc6mo1INmKYcHSDLhEvP/zEOn7yfBx75EqJwYOBu9h2/nSs3v4GVI1vcYSGsQqegW8xxaOoBZudOkHVxWjHrNYapmTQNtBkAYu5il4RgjpobpzHLhKGB1Vx5yVsZGdvgopAbn2POiNdA4bQ+hboVXU3rFVPGqPrzVsk/om6l99yrf5V9+77Nd3d+gpOT+xgb3Ujgn7mZwlc5DITU+sx8orAUbfpAW0QGCXOFyDkk3h9LQUzNHKZVa0LFVFJurH0v1x9OGpQ5tPyrn07Qfc/T7zErjD/Vp6wJHl+Jhmj9K17P+le8vucYwvUCgOoHQNLyCcgSpYwdJGURCWuidC2QmJnm66Wa1CUAhnjvySIg8qpSQCEpKKpK0YAiUUj3jRgetgvko2Nse8MvMHHvLez/ysdoLluJaX6dU/ffyXlvfSeN0bFKm0kB3amTTD70XWx7DqNZNINWQUt5jRh/SZJmCucLZlSB4e0XsOmtv0BjaAy6Hosl5wtmsyhei1RdfPnvGUgRxla/WDOKSM5Zf/E/OPr+jzB10w46R6bIl494bZL664Co7SGW2+I05MUn4XiztAFREhTPNjNssxylclVMu+PAUEy8aqtagYoGSL0J6lFKiMgGpVkrJNy03qTjtT3O000cD4XwQmtM8VHhAoXzB81Nz3U9+CjK/9l0J4IJzSTGKjJ+0DVdl/05mMCihkLKN1PFJ5ENaUfSVY6prsBcCH7F5qbkb1kgBxfTx9czaJAs7t4bNRf+sC9IJMYTeU1rxrazemQbR459l6nZIzSyQXLTrJ5Dlf0n7+GBozfRykfYNH4Ja0fPc8/ZE+MroDg1o4Zkrarl/fokuc5s6II6jo1vjh5lrp3Ec4pw7Wo1riLrucpE3DOYN2FrHPiFdWsu5bs7P8G3d/xvnv/sXyeXBiUPzZerg8lFgqIlKO8E/hnYLCJ/jQu++OYzWqM+Esm9/p0IoEgFsrk+TZsAg3R+Sif0+YjafR9TbWPfBbtyWjf4+SIzV2xR8103OW/kNCWVriQ51dqxC63B69qUpF2itT4tojVAMY/UtUD1a/bbJ8aw/KIrGbvgMk7e+x3s7DSn7r+TLHfhPtJ2PXbrjRz5ly8xtPFs1r7ghxhYu7GsqFafZ1jr1jWBKVBKk+kaMQxu2lJti0rzVL0Sw9AV+1I4t+LoDZGv5MCO2qAxyln2Qy9i6qYdHPqDD7Hu1/5dGVNJwvkNWutQ38deZu/kMY43SxoQxfgv4tImdEZddd2L2KJ57FR18ksJ1lAjS4cZOKgyHgU4UotaE88pUHqwee8vup7jEtXLWgEvboVThhDol9ojpmqI92MjGFKxUAjZdMcNArnE3Geh35qOxXQstmF8H6+SgeNAmbnJ3jbSUSK8uCUoMoJ70ToO6GnQgpjyBY2kbg+ObCOLXl2xkCUmMizDKNi4X7yJyyisGTsXhrdjizY37/kwT133g6wZ2Q5Wac9Nctu+T7Bp7BLOXfN89p64jYeP7+Cs0Uudl1wIuFkHxWlAzdjWHmyHPpaAHk15Q/7ZabTxS1y59vMCUqUSPDRKYiIVhdw02X72D3D/7i9w786Pc9E5ryIjUUvUwOS8MZRqshRt+qr6eRG5BXgmbgh+u6oeOcPVqkodsUCZ9woHjqSoHzQ/HtDwR6v7+lymv7Wiz3OMQKUfGKlVYlGmsXmAULqvohkLEzr+f+IxNZ+ESTvl9FQCWlJ262i6Cfehp7mPVFsC1AFRjwmtViYzOcsvugKAmUOPsPOvfpuzXv0zDK7bBED72BEOfOGjrLv2RxnddgHHb/sGJ+/ewbILL+tp+nrVK0Ao/U3SzimgqZVLtUwRGYbxI1wr5QxZnIez4E1r1SjY+ZrVDD/rMqa+sYMTH/8yy171QrcgTJFcMj5WnsUC8mQbb5Y4IMpKIrAPzV40/UOzOY2hQeTkZEUzpEVRclu6XQeEaqTpx5rvS4vCc4ZC1FlxGgZxJjVnEik7VQAuYUKNYChMjimPKOYw0zJeUma89sN64GXRLuRTHTQzjtNkytQUZQoLR/fVzJt8gtdFEqPDGsrtaX1jXb0DRKEY61zNxWtH4j0qEcwEcOSAlinvEyqmK/XgMapng5kpmj8dQNq+4mpG8pXsnriFdneS784+wK6HHwJg36m72Xvy9ljv5WYNY9mKGGncNWNJuBd80qTQ1plxbRFAUDYfCPKfLPQ/MJ0aGBI/KaiSBEKoaokCoCFMZG4A27TuKnY/ciMHD9/BxIldXHLu61g2sqnUnqXguaY96isJzlxKIiJfUtUXAZ/us21pSLo8T34HLVHRdFqiOiiqAJ3k+Ygk2oC0fLhOvwmyXq5ev/R7/TmnE/9CIKJfH6qfW6vb4z0ngGxeDVU/UJJO7n3KxLI1YDCvekhq32Weey6HqQWvC7DppW/g0De/yPHb/4VTD45ycv/97H14DwDHd9zIgc/9Qyw7uu0iTKtVPTe1vkACXJL7St3uo3nNl+kxudH7PZ5bqXCr8LGrBDy30QMcgRDkcdmrrmXqGzs4+c9fZ/rWu1n99jfRWL2iXGdV2lWZmD292/2TbbxZ2oCo4d3EDZVgXN0BQWxGa7RF1unCyVMu5Ua3WwFGGgMtlk/Ndjo8Ku1QrIx1HKQs8zl0BOl4t3zNq+AGByq0YUpAEgYa7+4fNSOxYlqCoaDZUuu0Hl4rpYBkjoeSn2qXebpSTZAnUEeyoQHbNJXVkooj/NoaTyK0tWZgjXMPdXZpr6VK3pj4InueUZjoi1ZGMeA5RgEIWK0AATIhpLCQyPexZdwjDwBWNDfynelPcHR6Fy+8+DrGV13Aic4hRpqrODi9k9VD53Dv4S9z8OS9jLYuo6IN9Bo6adQyNud55A65/xmaFZ7z47NkB3OZAdsw7L7zM1jtctYVP0zRqg6+8b5UwFs0A8hy7RMADRVNGgoNaXLJuddx+84PM9s+ye07/4HnPOXnMdFE+6h6aI9V4kyLiAwAQ8AqEVlOWb0xYMMZq1gfSScvAc+Nczts5hbdQbtRAUVS+RclxCIy3XKiq4MWqR+4GGAEJbG6dr1+wKBvuX4gzV+/7s4PJAC9zzH9gFg/YJmOPz03lJzTllhoXmCXbg+LlnmK9j6YZFvtoNGzL+CBv3432cAQl77qjchlMPXwTlor1zG1+z4GN27lwJc+xqmdd7Hsossr4CWavSS5pFCaA8P9B81aqJvUjlPY93cfYOic81j2jGeXZSQpJJToKPyOHCIPgHzjOY2RK94YX8HKN1/H8Y98is6e/Rz7P59kzS/8BGJ8EMZYcWf2H20k6Zvmadon23izpAFRnKCNYNqWxskuppthOoZ8qiA7MeOS/IX8YyHidCBRL+A99ljEaZa8ySyYtLI+4CZ8t/icZ34yVPXq7lJbEo8z4rQlQeuE5y6lLvG+TYDSuy3GsUnKJK6mlQjUlcFAewdLArHX/ZIiTOK9ZctrSSXh4HzSE7PDEFNYxCuKOLKyBw3NbJCLl7+YO49/kfsmbmTvkb20zDBzdoqRfCWn5g6zdehSdk5/mxE7wrrmthIMgQ+qWXtlrVKxTEmiGQrfg4bI/5dGg/13fJl8eJQNF74wmgFKMETP6rm8n9JFPwVDABTKyuGzedZFb+W2B/6eE1N72XH/3/K0rdeRex989QC3r4m1b0MvrthiRUQ2Ax8C1uGezPtU9Q9FZAXwYWALLtnq61T1eO3wnwV+ETcY3ULZ+04Cf/zE1vQJkOT9kMJrA/3zNB0cp65uNptHkxH4ItU4NGFnf0CxoLrhUchCZox5eUlU+27/g+ffvyB4ScHCae4nDmnznatWNlZ5MeDpNPsG125i9VUv5vA3v8ihGz7Loek27WOHAGguX037+GFWPeMFPPKp/0tr1VrHJ6pVKCWtV7RDQaOjZRvEewz7/Xc7O8uhT36EbHiEkadcEpU9MXCnlGNLxcSWnjA1ryUao5GrrqC17WwOvOt/MXvbPRz74D+y/E0/gsn8yVOUfgY4RGd6vFlsDNwzIqnWA4HGyTkaJzs0TnVpnuzAqSlQdeaRYC4LWoInGAy5CtkycGNRuOv2I/Im4laJjtsTU41AuepKva7C5D1fGo1wSOTHlOCqnJiV4CYOYUWYABpN+n34nXiuBM+yEgy5fVGz1e8TrpN41MVkpul9SVi1+G1psLaem3RlNzbPZdvw02mIU1HP2SkMGR07y/mDV3Fw9kEAbpv9GpOd4+VA4LV4PW3Zr219/4pgKCtBks1hw6UvYdnGC9hz66cptNsLJmMb+msnk10Md6BU+oZYZ2Jtd6b+H3vvHW/JWd55ft+36qSbQ9/bWd3qVncrJxSwhISEMMFEm2CwDfauzdhjjwfPh5kdh/Xu7M4wZm0PHq/xzMCsccKJAZtgAx6DhEACoQDKqRU6x9vdN997zqmqZ/9437fqrTp17j0tlLrt5/M5955Kb6o67/ur35M4cOxeZhYO8AM7foZABTx75BukrJk9L/d8rCBuEe7104NEwIdE5AKMTv4XlFIXAr8MfE1EdgBfs9s5EZHftVFj/7WIbBORc+3nMhH5WE+1v9jirbK6Dapt/uu28UTzX9j9l3VW+OR/X97YW0YkZQ08g1tnOF1sUx4F2MNlLEiXPnVTj60KhlaoM8cMlT2i3w+N4A805d2gWL/3kR4+fjkT176OkYuvRVfrKRhSOkCFIWte+YPMPPY9APb+5X8lbi7lG+W++/e7eP/956L4nNj/G9/3AYK+fo7+zV+k9mLpc+Ofm5gOOGbNf36Ul/zXJZMlgejENLNf+TrJ0jIb/sO/pbX3IIt3PWAVEsqep/LP30q35yybb1YFREqpzUqp25RSjymlHlFKfbBw/F8rpUQptcbb91tKqXuVUq+221vtOb/onfMxpdRPrVS3D4ZQCtWKCGeWqcy2CWYWSVkal5DV/k9BywshDhS1o4KbfxkYEnQrRrVNhnXdilNQVAwKCOTZoCI7lEZJzuostS3xxqs4EXUYY6bbHgskGRBKma1YVp6Uu0yCXb2tyo6VnauNwfrGynm0xXhNhqrGltrFnFM5n+/N/T3r1RYq1Ngc7uKJ5t2IDx60RvnjWLDrcSKuPW4S1ZAENjeSTQlw3us/wBX/7KNQDa0K1N2D/PgaQOr6aMfGnwgEXEqRdnuRrz/wW+yfupeRvk08uO9zHJt9kmeO3UEaiLJHY2q//NP6rFacyGER+a79Pgc8BmwE3gb8sT3tj4G3r1DG7ymlrlNK/ZhS6v3uU3buSznfFJ9AlVgg1DL/fVDTAXqkO1gqLmgrAaTieX4KEP9jBrakD2UgqigFIFRqTF0sp6wYH1B0DN4q26tJCbjqAEElQKwD7BQ/q4iuhAzvuozEpq3Q1TrrX/sOqkPj7P/cJ5n4gdehK1UGzruI49/8Sv5if0wLz0gRCKnifrxrdcD2X/73nPdrv5EfEg84556lRBmVvf98uv0WGCmB1oEjHPy1D7P06JPUtm/l2O99ktbeg8x97U4LrshCxoliZrkHT/UzfL4pSi8qM4fYvquUGgTuU0r9g4g8aumtHwT2uZOVUufbrzcCfwTcbrePAR9USn1cRFZIdJVJXLPBBxOxb+wa3WwTzi+lGdgdS2NUJUZl1lOMoe9HxCWDNUYjxsssNlGndQJKETSd6s5ckqbBKC5wTn0WGGSe/mZTl5JsIVfiMqU7zzPJvAkSlY+dIlb9JYIKnPulK5uMAXI/KnNRaljtnl4fGAGFNyr7I/ImI99LzXpz5n4MYm3Q0+sL45ATy/j1qUF2Vl/BAQ6wRe3k3GgXc8k0AE/E32WAEXbpy7kz+jvm5BTDwaQBQ2FoXetVljLEibeZeo05DzOFDUhn7Kyc+gwFhx/7Bqd238tFb/wlglhlC1kKDs39SUGYU5Va1WP6DCQJVamybfJVHJ15jLVD5zPet5U4btKM5jN7KqtWLBrAl0pxcXueRSm1FbgC+A6wVkQOg5nElFKTK1z3p8B24H7S0JoIhhovyksz3wjZ78OBG/fbKElV0YtBcWqHBJ0xZkqq71pWkcmw7fLdu8UrRHn1rCjFSotgqBdRXb772/6Lk3j/i9Xbn0xX/F/SLz81hytjtfYWAWDRuH1gyy7C9gIHDvwZG1//HoZ3XEqlf5i5px/m0N//JUM7L2PdTW9l98f/A5PXv5GgVs+V58pS5O85itQGzfW1yGalz4aYvh39/KdJmsusf9f7szb64+quF1IboiI6d56xtYlJBq69iub+gwxeezWVLZuJZ2fteuIql7S9w8X0S0U5O+abnKwKiGwjXEPmlFIOsT0K/A7wvwGf9y5xwdb9WwdwHLgTk4n2v69WL2AWAiS12cnsbbSJdhyY/FoSJOaZsOkkJIm6l/l8ifjMVJBtJwnEGuVmT8ckFN/2C2okt5CK1gZgWQ8ytDIBIFMVk84WSF8tBRYkWfBiXfhzJkauKq3QCeh2BnpImRXbJAsMig98qv9W2TX5CaagTspR/+IBCLHRpKEzbYkYY/LIJtyNIvoYBOCZ5BHGgkmG1ThrZD1THOby8AaUaPrVEEvJPMMy4TVYZTGIfEkwgcss4+aDFmUNykEIsMbViSAB1PpHWDx5kFN7HmBiw+U5OyJf5Yhl1la0/RFhx9pXs2Ptq1PwnMt47+JaKfsG2Iuc/gS1Ril1r7f9CRH5RPEkpdQA8Fngl0Rk9jQDTV8FXCiyOtX1ks43fgHFN9piy8sQTbEFBbVDzgC3WN4KXL0IJiBityH33nV8r67Tehaej4XNL6Os/l7qKANRxWN0Ao2i6mulOnMAyGtnBjAV9TXrADj4P/+SvvVbGdh6PiqsIlGLDW98D0GlRjgwRDRzinB8fdqGIntXxC4pS1Toon88BUYJhANDnLjvLsaufw319ZtK76+CnDotTSarJAOZCpQKmXjve9P9KGDdOvM98RBc2pDOsesczB7OycvLar4pymnZEPmITSn1VuCgiDzgnyMij2Asve8A/muhiI8AH1JKFf2bSiU1IvPVG4Eyb/020jDafnc2I0rbvDAvvHmUWFulNB9aYlRjJiZOksYmUi6NhB89u0xlpL3oyWFg/9ucWoGCwCYMdclHLWvm1DypN17kVGD2eyxo2w4T8Tkxn1hMMEfvuPbO0W3x4iKRm8izH74FfI4BSQcnv7hnNHLGkhkQkt1c55GWJuGNIrCegv3084OV97JZ7+Dh+DskkjDFYTap82jQB5IQEBIlLW88C2DIMjY5cJ1g70/WR5WAjgXdtp9ICKwdydjGSwB46s5PmVxpnnrRjZOKsxxzmbrRA8OOOSzYneXBkGQA24WOiAvAsUSeg05/SkSu8j5lk1MFMzn9mYj8td19VCm13h5fj2FkusnDGCPJ05IXe77pUHGcbnvTZzxboHJqajeXld2LuMv9cWDHL6vY5kIbin3p1seyMsrUZytKt3OeCxgiAwOlH3vcP6dUNeZLl3LSuui8RhQ0Nm7l4g99lMbkZg7f+lmihVkDhl7/HsKwhkogqNRIlpbzz4w/tsXnyd1D9wz4x0vUpiQwdt3NABz41Cfyz2VJWf71ptyCHZF07s+1O/H/F1FmuZxN8w2chpeZj9gwtPavAa8rO1dEfrHL/meVUncDP9ZLnWPD1XSBD1oJuqENyBCzoNOq2IXTqszabWjHSBIjsVOk5mVo7UBvHV5FlLau99okSh0ar2XAzIKZHOixbEEWbLIA2IrqI7s4Do3WDBvmGKJ0YtCZ2z3kVSqKXH4ylQhJoDP4m8BwX2jsmexxd50p22OeVFZGavdT9nZsZXiwkoEhy3hkhtq2PpvQVkkC7dAU4NiUKLb3VEM7MGlA4oTBiToSJ1wh13BfchvDqp938b/iY/rNshnRMNIYgErFGFYH3qApBdVKCqIl1AyOVC3wVCSBJqnpLAijc+l14yAgIdz8o7/M7js/xYCeo9E/no6BSuycGwnD80T57QAAIABJREFUAyG66QcSBd2O87nWovzPLwNpFhS2dKH9wG5WlOebwrY5gP4AeExEPuod+gKGffmI/f/5ksudrAEetb/91DBBRN66Qr0v6nwz0WfDM/iLGSXf0wbmj5WN+2ij+/RatB3xU1N0GCn7buUKRmth+n3V2D7F9y7pfgxgrFrS5tWeqdXWTYGxWv63sJp0bafK7xurh6vX36VNxXr8/aP1ECXQ/9q3cPAfPs2GiTVs+N9/L3dd+9xtNFrTjPVVesN7CkarIY7h6bjnhXspGqhV6H/fP2Pqti+xJtDoMMiPgRuHMCvXP5b9lxyIzKbNwn7XOQ2V9up2uGfLfOOkJ0BURGxKqUuAc4EHLJW1CfiuUuoaETmySnH/EfgM8I3V6j11qmVZDMtmNCOzmMaCiiKT0b3dThdRWVo2bE0UrWhLdOrAbC/dXlGMF5POFt0wZGaqaeLchAG4+DcORATaU3PpTpuQbkbGlYBTM+0MjOjseBqDSOev7wj45lRVZNtBs8bsSfus+A91+qPw8p95wCtz6fdcwT1Vj44SZk55JhsCKk7sm4xjrayaqh1n8YccK7K4BO020mqb+2gZoqTdz6kDxm7oiBxlihPUVKa7v1X+msQC4DVDO6ESWhuiID+e1aq5PxYQUQmYnomQQJN4KU0cqBRNOsYZJT7Ghgvez9LyAM1l29ckzwrpZsjMkWV7H0z/HFOoIssmtkomHMcGRZF5tiGfdmQl6XGxOU25Hngf8JBS6n6771cxE9OnlVI/jbHpedcKZfy706nwpZhvphbaz5nVgDwDkC6aCZxYaJezLgVWpoP1sDjY/y9eGceb9tkoAAR/X2672IYuQEoJHF9ul7f3dIBHydhNLZaMsVdvB0BbqW8eaEjHonhOoR0dxuiFthSPTy21iRqjHDp6nOHFJhqTP1KimId+/98A0L/pPLZvuSIPaLo9N/Y+TC21c2Al7WOxb27f5vOpv30tJ1qJyRpQdr57JorXFp6nnLrMb4OWHLgWBRONapeOeP08C+YbX1YFRGWITUQeAia9c/YAV/USHltEHldKPQq8Gbi7l0amjIXWNshUnC0S2gX6MUCDOMZEjxYye6oXXpRT3zn2x3f5ztn86Lwruj2e9tMXFwsnUJ3n+W8RniGzL3mdthQmB0/F5S+0Dmg5WyD740ibJhYM2cJc0MHUBqljYGyZWqzxsilM20SttCNcqITUbib1FixXEQ0xxhH2UZU6pzhGjQbjrGOQUY6yH4kiG4MoydrkjKolMYOWiBdwTjzQmKASY1yttJAECpcjyH+BagTD0DYzQmqH6NSVcYJuB+h2nAFHywqpJFOtUvSE9CKsG7WhOx6n0cdXEgV54Ps8iIjcQfelsKdI0yJye6/1vWTzTXGRPF3WQeh4Wy5VcaUHs7o6Fqhiu1YCO2XluusK9eT2i3esW5nFF6XTGZtivavsX0l9lduvCsdU5zUd9QHONqfsfnT1shMI6/0EjX5mHr8fkoSZJx9gaOuF9K8/l751W2lOH09JFikrr9gPv45iu9z9t9/945XB0RSAlJ2ftyEqlJ0DOsa+SGwqpfQ8PLsjN6yrTDhnw3xTlF4YolLEJiJfeq6VAh8GvrfaSRm1ZxcVl25BtLGXCbRZODzDWaU1Eicm+uaLhYfcgqdUZt9kM52LUibhqTaRmV3+LOnm9ZRjerKko6mkACM7PxcDyLXDea8pEz8lVWM58BNLljbDA0SiMAG/PA8yZQFBejz3487iH5lZIQ+8UkDl/cKVS8QaxSawZhhm4EAkb5dVIpvZzgN8q2P/cQ4xyqQBU5ZdUS7jvANXnt1OCuYSjKcepq+iJQVF2hpf+2DVqQNzYycWEMUOEFnA53kIOld65cBOVDD+j7022jGYjo7ywPyt3Dj8nt7W6Of/je05i1JqjhWWRREZKjn2ks03pSxKD+O5otv6ihd2lu+YklLGpOx6uoAJV/ZK7ejWxxf6GVqlXysxQrnjJUCydNxXuJfFe1fGIk1cdTMHvvyp9LS5Zx8BYOHws4xfdJ2dM7wmltXjNaN4jo9h/eOpcbUDP0lhm+w7kL24Cen64ABPupECSHNiCirtszJ3733M33MP637x53p4AMv7+lLJc5xvctKLl9lKiM2ds3WV43uAi73tB+jFoNst9EoypkQMoFBuZXZMTKIyV2etIOYFBUWSCKWmmpYdSr3CgsC2PcjACHQAoK72Of4+X42VmzSUd53K9jmw4jNBzq5Hksyg2WuXsi4JDowqMESbK98zaXHX+m77KThK22Dbi5jyEmUMz1t5LzJn7CxLS6haLYs+XiITagNXyA18j2+m+0IqRLTZrHaQtCMzvFobtijwbpQLJqm98UgSTCYyDwBivM2S0D5LSpnEuaEDOFk/0/4nRh1oAE8A7TgzFVPKqnpjz2g8A4FADhQao+qYSlJhbbjFqBF78LToOaL1iyAiMvgcrnlp5ptuC+kK0g30nLbbehdQdFpyuuc7WYkhKqvDgaznUF8ZaCmyNqsyQuT3Z2FC7HaxyT4Y6gZwSwCt387xS68nabc58s0vpMeCWh9xc5GJy1+dMjQuEnWHFOpPAVDxvtv6/XAK6baYpSW37YEZFbs5Kf8SK379flMSct5o7mA4Mkrj/PNRiWJ2afU4RGf6fFOUl3XqDgmVyW0T2/VLK3CZ1BOQUKMkSBcQFQQGqIih/bCg5YUM0ogoSDSSMg8JqBCphoYJsuyQhCpbOAvqJSllh7IxcNnjUyZIUR6XJn1DkOxtJ120k8zWJ0ogsjY8ZOek4EwbcOAS62JVlcZLqziDeT8K69WmI8liGXngTcU2UOVSC5abxkam1UZaLZJ2RGrv1SoLG5Nnix7irtz2erZyhL0MMWqAcCLIctM8N0ECgbX3cmDE9bsdoVpGxSaBjSnlifbBLRA4JzbH+HhN89Vhqg5q0UaydahIqywBbRQhzVbWDs8DMU0/kgh9UmeXvtzYVK22Qq/GCPyTPG/yvBqTllIEhWOr7etWrnS2tVQtV/Z9hTJ7AkVdAEix/g6gowr7S4BQelxl/81O71TJkyNFdVIHUPFYIZ9ZUUIODAGMnX81p564j+rAaFpPR1oXV7EPUqxHIcU2u/P8LnrvtkDGEFHos/LKteeLyn9Pwza467V/nkpfdPu3bqfv3O0Qw4hNYNtVzsL55mUNiIBURZN6/ijLYqTeWipzvXfskN1vFkZrT/QCpPGAIGOKnGeQp46RFWYMY6js7cjZFJE+aD16P5bXUcAu+YNS/t/ZvCRJGhSQQBBtWJa0X2VvtrEYVVEUp8DCvYWotk1DEVlvQKvW6gBDPcpV6jXEOmFaTXEyOUpF1bhEv4oGg/mwC9bo3aXykOVlY2wdxyaOVattjPMDyxAVvWzSZ8x+9+MFOaDtAaIkijg+/zRBfQ20atl99e+vsxFydlNkIOhYtJ+xYB2hhHkgLwm9hNV4vnX6/yR0Zxa6SPoy4y9M/sq8Cpgo+813/RmvNDf0AEa6XrfSeb3ORx10TXmbcmX6YKgMHPnndgOQJYzb6UrKrNjv29/zQZTA9BP3016YRYUVtr35A+ggzIMTv17nxu7KsqDIBdVUKl9HWqXb57dbFQCe7XvSbjH3zKMMb9uGqg3lx9H7CFl9DpwlkjD/yIMMXHQJquI5nyhTuQnWuLKcbfPNyxoQpQu6DTIogYmfIAG5CL7K5Fowi7ny7XU0L6RhtSSC0snKs0yCVRV514WZl1mZIbXJhuz9RHpQlfjXltO2JTNFEQy579bzK1P7hWkgw25tSQ3FnfeYs51RCmxsptSbLIqhZdihpJQNWkWUZjAYQ1VCxqrnsM3tTxKjgrP9VRYop3nNggCWlk3aFeeF1mrnGCmV1FL7L9cv5cBQLAaIQ6b+ytllJRybf4L7j/8tWyvbaJ0I2NX/Smphf+d4taOsrVal2ZJl7m99HYXmB2vvyYFESXqb1V/IyLH/KKXLT6+rzUpx4fYWvBwo6lJet309YTIfIHjsRrfyVy3Ll+f4UtZRZg9Aq9QLCu/Y99GmMrbMHyPfJsff7lu/BYDG+i2ZR2FSmDpLXkDTed8BInudglyUbb8r6WNigYzPyOXaB5y895scvePvCHacz2zfKGtf8zZ0pZoHP7bfuXENoLlvD0c+/ScMXHQZ69/zkwUApcqi1qw6nme6vKwBkbMfcpSjYRu86JsWDInGLn5BZmTt2RK90CJp8ELxAhlK/tfizrW2UEmoOwuys57LkA4ZM5YroxeA5OyHvHKx+NCouApgyAEesB5QiQEzWiGh16YSUFR0yfcBEUoZFVk7gsQaSltmSJ4jGFJBgKqEaciDnI1QS2eqLF0AQ87Lr9WyDFEErQYsLGXXJ2JiFfmAzrGPUWxmEhcwcXEJKmFmo5UYY2qAuu5nT/MhgiTgwsYPlPfFhYawcioxHuRCwtFoHxOyIX9+L/r6s2yCekmluPiuwLj4dj8qna+8feRBUTcX857vX9kUUPbO08NU0dOithqYKTu/F0kHxl7mwgz427By3WXsTLH+1dpTaIfPrOTpG/LjLPl7X6b+Sz3A3PckuxZNzgbKryZlddy2+AdMmUlknEfqY2vZ+53baazbwugl15hjQXa9zxC5z+LTJqjZ/CMP0Dx4kPqGjWkDlMDcQo+5zM4ieXkDIqdO0KAwb+gSmHxfEmqk4lghw6eoig3+hb1PUfTieJtJQpoVz/8kBkggQhxqAy4CReK70kP6UKWTpZAZ0Fnw1JPxmhcPqNvx0ja6a1q2D+02/3Pv/8uaxlau3PROY3SckBpcG9fMwuurJypyXlQm35ssLKaxoYjj3lVkueCVFgjVahYIBea/F2gRpVDVal4d5YCyA8kWQEmrRby4SDytiE5kcal0o4EKQ1NOGBgPOAeIwNg+OdVoq4007aThjKBbirV6C31JHwD724/Tbi9yHhdTV30rdneCdezkcp7kfh6I7+BKbmSse8qeThHOOgr7RZXVFvyy13i36V4GCmAoXeyk8PHKy7FN/s9KvGrEYwxWaldPVFK+juJ2V9B2uqBoxYqzr4kSHv/Ih5i4+U2MX39LBoi6sES5JvlOHv7YumpOZ8EuALGyev174tgXcWNWxqq5TwLa2le6lEmmHpUFgA1IDabBA0PF/2Tn9K/bQmvnFYbtBg79/V/SOnGUiatvIWj05ftln0en+Z941euJZ2aY/t5d7Ptv/4lz//kvU52YTIHTSP/qNkRn23yzuqfXSyi5UOQuBYJzjNKZS7qf2sKlv1AVt6iF6Go1+1QqqLBSWGyfo0iSveH77JDbpw174jM6fmTp9BMqa4DtfhwqCwqo3I9Trfgx3lJiAwSK5wJu3MBVlGRu985t3IEh93ELfxBwyfjrmVraw9GFp8w+dw881/HsY4Jm6uUImhEst4waKorSsiWOs3HpRZRGV0KC/j6C/j50Xx3d14fu70P1NVB9DahVMzBkc9vlWBT/3liglxpxd2GnpNVCosiwSFFs+tFsGdWa8xDzgn8axitK3eSHGeNS/Uq2cxFXcRMAR9jHHXyJliyv2u1z1HlczLVoAh7hHpLTsK1SZAtar59/kucoqvzjFrdu+8s+PjASf/EqllFownO1LXzOZfR6bpFRKV5f6K9SirFrb2LmwXtYOrQ3B0R68vzuds5pskQ5IKtWvkeuH+L+F58FV5/9lMY/SrDzM52pW3yA5ZXh/26VwNA5F7DlDe9jww1v55w3vh+AqXtv46lP/TbSbOZTw8SgI/s9Mt83vP7dTLzq9ahKlSN/++n0XJdeZiU5G+eblzVDlA6gy8qextaxhr8KazejcgwAEho7mMS+VkVRFuRPB6mKRaLnB96Kc+Ve7bzUiysfRwhAnIpLG280o4ZTVmXW+2ufCw6Yqt+cTY/fVWv0nYKhtO2ShjBYP3whkY45NPswk+MXZHNbbP/4PG8UGxuhVozqIwNDTpLEgsUex9tFAA9DA3wAVa+j+hIDgpTKAG0SWzDksTh+zri0jca7ayUwBBiQ4x9PA4Aq45HWqGcAz7JQvleYRO3UGHpEreEWeQcPcRfHOMg3+FuGZYxRJhlilDEmCVWlow3DjKHRNFlikXkGWDV8hteBM2DWeTnKSgvvagtut+NFhsG+med+yt7i2eF+7gEj/5JV7Wh6AS7FusrK7lamv+CvVkdx6uoCktbe8laS1jKLe56ivnlLeX3dmvZ9PvJdvdk8N/rU1d3evxUdVrx2lTJHeC/7do4WgSR13iAdu5zK0HvefINtV8fIeZcz/C8uZfdf/DbLJ47wyO//CkPbLqY+sZH+defQt3k7ulLNgJotq2/dVsJGP0v7n0GWW+iqjVDdy3R9ls03L29AFHnhxN3AexS02CCNJgu8oELPliRWENoAjVrZfFqSBk1U2MXv+/U+k8QsziKIOBbGLZCYXNyKDPhobGBG27G0f8qzMRCjIvSP9zoJgXH/dgbOsaCX2vlo177qzLd5csdsWzePXcGm8BX545g3CEmS9F7oZtuwJO22yRzTbGUGw2Tj3Ev4AxUagJDaCIWhsdOp16Ah5rsXAZzIV43pLFp5WrkYVshX160iOVBU9A7zgmRKs5kBM9u/Yh+VUlwkV7OOc5jiMIfYwwwn0+M75FK2qJ25axqqnwnZwGH2sp+nuIArV21zWt/ZNT+99OIxAx3xc7zv3a7Nqczsvm73KPv9kwNDOWB1GsxJrg/FOnt/x1oZ8FFeTmpX40BRoYwyELLuTe9Ox6vseC8gxNV9WlIErR5DlGNJlJ3ui9eVMVErtcExPtaNPgU2NoAu2nu+ivGE7L4i0NKpnZJm+9t/noU9T3HiybuZfeZhZp95OD1v61s/wNC5F+Ti5w5s2klQ76M9e4rp793F+FU3gvRmQ3S2zTcva0DkDINTnbbO/6pUbBB1GqcnCSHUqMgavgbaZiS3ACWKTNLPahXi2LwAuEXs+wFG7lprR0IcGI+qQCxQMm1PKgqpuJxZKpvs0nIsw6OVoVItY1SMOZF7CMseyARUEmfpIaIY1XK+4ZZxSipZYEAXITkNbJmxLSoy4Ep8lsoGbDQu9BEsLJrI0HGC1CPimVkkKuQX6kWUVXXaHGTKAaFKiPQ3SEY9WyAsUxjHmeovSVBVy7i0WkgcIUtLaZDH04lHJXFsri1Kq5WCIBPWwcU06v78BCpkko1MspEL5BXERJzgCAd5ljblk86FXMU0UxzkGSZkPWtMoudVGk15LJR/kucmBfBRZGZyzErJtUX1So5tKKtKvI9/fZEp6MZo9NCXXINP4yXLL6PY/rTt9r+v0lnRe84vz/W1YLtTHL+uoGg1ANJNugEwZ67gB991776uzIyI77h/OZd7vx2JyRzgu98DqfosU9MpxE53qRORO9fVUVgHVGyAURAMUN96GeNbL0NEaDcXmTn4GMcfup1kdo6gmT1Pzhxj+4/8Ao/8t1/l6Nc+x9A5F1Adn2Ck3oMN0Vk237ysbYh0bA3Qyh6sjpMxKS7cgunUKtpb4K0BdrpPm1hFSpf8ansVpcnZI6WeWTqLrq0VSVWTVDVxxUQ/jquQVA1Iiqsq++Hl3AoKVfmTTSLodoKOEpv81n5cwEWRzBXeSdEDzg8mWRQRm1MsSUFl+onFRJputg0bZMFQ6t3V69AFAcHwMLpWN596zRhNV6uoWtUkYq3YIJdam3qjxASWTBJUbLLHK2fXE1vQu0Laj5dalFKEqsJatZkr1Y2cpy7pet4reDUA93NnTzGIgPyi2svnn6RcuoAgH5B0DGORzSkrs+xYgZVIPyXskA+SupXZzQ7GldnxIlbY7gBy0L1PJX0vetjl+lkAdisZTIO36Eth+3TEHz+djUspaPXb0+23UrbdS/3F3YlkhtaRoGOyT+TZFjn7H98GqOzjGCeF5/mrqfQNML7zai74kX/N+M5rcoDNAbOgWmf7u/8lALv/8Dd6t/k5y+ablzUgUu3Es3UhP6hl652NF+OywKfGtl6wRrRGKR84We8l33W75wZ2AiHTjsy4W7QmqZhI1UnogaGKIq5AXDFkTVzJQFHuzTOKWF48mbdREmzm9MxwWkc2sWiUBUBMU3N0A0WO0SoamNuIy7lPFGcAycXuabftJzJBFk/XcBqM+rJeM59azTA8tSpUzH+phgbopm22QCjyEqRGFqQ5z7YoQpza8gWUXmMDPVepqz6u5jUAnGC1pO5GzjYjxxdNSgBGx0LpSRkjVFpm2YJfxvJ47Ei6SBeMessW7g5DYL/OEuNuY16QlVlsVxJFtOemO9vWqxTrW2E8S5mgkmfSZ5y+bymMXbGdHUDOZ326gaPnIK4ObY2bdRt0283lGSgyQCnzTlNuv5vz3fyfSGoM7frpkoCLcsBHMrd/Nwd4htz9a7ey8TUmiXzzxLGe4xCdTfPNy1plphLJPKgKCTbLL7Agx+U8cyAl/Z4ZyCqMV5KAzdUlPNegRUp7YMi6afseZD4YSqqKJDQAyFxsJicdCUo8VZlVUR07/AB33/6n9PVNcOFlP05//1qDYt1DZoMdqkSM95cFCDmom+Z6wwCWNHdYkj8nDVohnodWkgEm18dW24CkdhtJ7XNs6IF4dRudjvGrWSM+pWCg37QvDI3buzOgd5/Ye2XUGFCUevclXvRo8SKHJ9nn+RR/bF4gmbX2RvPMsoZV1GZifjP/JM9RCiyJv0+8w1I4t+z6VY9JyT7/PG/B7mZP063sXFwbN/0VHos0IWlBtXPiO19j91f/lsEdF7P2NW+jOjKeL9tvi2T7cnGYSvq3oprM74crq9Cv01aTFdqYu0zTEeLAb2NXVsqbPlZd4EvK7rZ8uTk/CUCLQgKx05tKwYxXUnY/JStTt4xGRQQT1kXZ7qd9MmWarJIqU9fZ85yt0vLUYQBaJ44xN/6Pb755mQMi7JNHlmA0jfeRLZL5bO02431Ibn+2qOssu3olNAEddZwt9l2Mf1vSZJ4ZpplCE9DHIAvMscAMA8kwo+rVqErFMBtWzRPXApJqQFLTRHWVskJJRRHXyE16y4cPsvuv/jNIwmVv/VUG9SgomNh4OeddOMehvd/i3m//51ybdm59I7VwgIee+h+MDmxh3fCFnDN2pU0u6jNK/i8H+xqUZPtd3x2oyQEiDyhawCGtdupmTpzkPKuSdvv07YdcPKEwRKoVaBh3elEqs/+SzN0/x3KlQMy1294/zxNMEnl+2RwfWD3fIMvKlBxmD4/TZJkG/cT0CDTPrvnpRZUyFQ9kQ1o6tF0WVfe9m/FzbrcHLADQ0JqfpnniKAv7nqIyMEQwNEzz2CGax49Qu+pa5JxdZu4q1tuNjSnUP/f0Yxz88/8OwPkf+gg6NC8l49fcxEQrYequrzH38Q/nrtn0zp9m+fA+pu78BwZ2XMTwpVcztPPS0n53MGN27l4J5JWyRWVA0r+kDPB4ADD9Xii7GAyxo/wiGPKXkmKdyk6TJcCztIpi+zDAImxlScxd8oUO8cfZUz/qmhA0zfVaizHDcMyQSJr81QAzL7ixZZGOP3wns/sfZ/7gbqpDY8jyMsOr5TIrjMvZIKsCIqXUZuBPgHUYHPkJEfldpdS/B95m9x0DfkpEDtlrfgu4GfiQiNyulNoKPAv8SxH5PXvOx4B7ReSPulaeFLKBKTFMB5jYPaLJAhMpa+yb2EBZKktSagrzbIesUWycIEpSZikN4qg0SEIkEft4kmmmmOUU/QwxzBhCi1Mcp1+GGNWTHJdD3DX/RaamZmhUhmjTtm7gGtGKdnsBFYaMb3sFk5fdZIBYAs25k8ztf4KDt/6PtJWbrnwT9cYIasnQm+2FGZ569PO4J29y4hKWl0+hRLGwcIynT3wNgFPze2m3l3js4Jc5Z+hyto29kmMLT7Fx4CKUCtBKW9bEU30lBf7X9zpTKm8TJJLZ5vhgqJuxcglzoqvVXFoMFQSo8TFTrtbG+LsdQaIRLcY8yzFg7QTVaudBm+9l5kCQD9bAgKHnw5vwRZYpDjPNiXT7AE+zVXateI2itwn55Swv1XzTjXGRkn1ue0VPrzJQ4BWYY23sVNaen+XEvV9n8cAztKZPUJtYR2PjVpanDhPteYLa5AYGzjuf2cce4NnP/Cnh4DDhwBBJc9nWZSqM5mcJGn2MXvMqhi6/xjDiAs2jh1l8djfHvvK5tDnr3/pjxhXbSuvkMY5/40vp9vAlV9GcOoau1zl53zdpHTeq2/ndj9A6eZyDn/0j1r727fRv3cnSob0MXXo1CkHpwmq+EuPVDTRK+XpbaqxdwgblWBJ/rH0mqAxgOfbFP+4zQiX//d9etxhCuTpKJFhObCw6A4h0MYCv1y+AuFZA7P7xQntTRsmd7pgnSzicevI+Fo7tAaA1e5KTj92DXHF9eUNdP1boy5kqvTBEEWai+a5SahC4Tyn1D8BvicivAyil/iXwfwA/p5Q63153I/BHwO12+xjwQaXUx0Wkp5wNKkoK6i9IecII63Vm+E9l80wJ2kRW1jrLM5baD3n2RC7PFeQXdKURiTgoT7OXJxlmnE2cxzhrCVTxR65RWrFJ7WB4aJwj7WkW9QKVvmGS/pqxF6ooqFdoV4Wn7/40zeUZEolYmj1G8+QR+tada8tSXP4T/4EqdaafeYjju+9CiaJRmwOENeMXEEfLDA1sZLhvI33VER7Y/ZcAvObif0OFCrc/8TEAhqprOTb3FI+e+CrPnrqbpWiWKyfeypraFjQW6IhVLxVjwzug4di1ggu7FMBQCjRSAJS3ycoxM1oZWyFnrxWGSBgYlaPzeGu3IQnM268DZSLGZmm5mW8jZMEfLfCRdjsXA+lMBEMAu7iCBv3s5iEA2rS4jc+tfJFTnZ7Z8pLNNyvaCZUBoJUA0Wp1eIu6xDHH7/oqJ+//FsMXXMHEjW+kb+t52e+kACImajegb56mPTdDe/okweCgbaxZ9VRYIV5e5PBnPkXr1Ami2WmaU0eJTp2z4ni9AAAgAElEQVQkHBkDoLZ+E1t++oNoAk7e+y3mnnwYEEZbCwD0bzsfpTW1yU3U126kMjLOgc98EoDzf+W3SVotnvxPv0o4MERtYh3TD36Hk3ffzom7biWJ2mx483vp23Jeh8NKNzBUptYrZXroPKdDVefVUxb2wN/uKNMHMGXTRlFVJl69HhDqUKmVgC+/ft0SgmaMRDYki53j4pruBEUWMCWJ6sp0rTYPaOvi77zXdr3p59n77b/mxON3ATB/YDe3/eYvrFjGWTLf5GRVQCQih4HD9vucUuoxYKOIPOqd1k92uwOyFKP+nTwO3An8JPDfe2mcbkUmPYfNbK9aYlzslQU/YUCOQxKb1gONIoHY0oYOONmcGIlOQCCSJq1ogQBFO1lkKZljOj7GUdlLQMiFXMWomli5kRYUhWGNgeo4A42NSL1Ce7SBVDRxRZPUNK0GIAnHH7+TyctuZv35b6C+ZgOVWj9xEnH4G5/jqX/4JOdc+kM8c+9nOHfHGzh26AEqlQH6+iY4eeop+hrjzM8dpho2ODX9NPXKMK1onnZrnqoe5qZz/3kKHkSEJGlzYN7EoPju8S+wtr6dWtDHhY3rrJG0BUQ283vOCDlniG1mBqeK8u1yxBplu4lPaZWm1jDFSApIUqDUqJtyw8LjJ57xtmXYWFw057WU6ddy04RNsPVJ2wEiy1a12p1lnoGilGILu9go23iaR5hjmmmmVr/wDJ+fXrL5pvxlO7dgd6grpHBeyYIuyuSbUjogXpwnaTXRYYV4aYHW9EkWDz3LzKPfpTaxji0/+rPU1m7IMUvdbG10fz+1/n5q6zZ0ggsNSbNFvDjPyW9+lYk3vp3hq3+A2uR6dK1O0lzm8Kf/hMOf+wtGLruGY1//O9b/0I9y/PYvU1k3SWVukcX9z1Bbs47m1GF0GDL/zBOpN2jSbhHU61zwax9NB61vy3ZUEDC3+xGi2Wn2/fl/pbFpK0MXXsHoNTd0VaN1qNq63R4pnFdybmqnU7wvFM4vA0He92LYgMyzt/y8DhapCzvkH/O/B20hWI7Ry+00MbmzA9X1CkktyOW+lACkqtI2FYP8KgEVkQdLkjFuCgoqNJBKwJZXvZv1V76OQ/d8idb8KeYPP82qcobPN0U5LRsiS0VfAXzHbn8YeD8wg6GsEZFHlFJ9wB3AvykU8RHgy0qpT/ZUYRybh0Pr1IPMxdMxwMjSi6FCEmUIpFhIpM2J6Sep6QY6VpyYe4YT88/Sjha5auC1fGPf/yCRBK00Nd1HnLQJCGmofgbUCOfrVzDG2pUXU987zQUEtEEfXduSQCEVZXTBoWZ02xUkkrDu6tdBLUx/FIEKOefaH+bwPV9h9zf+mI3bb2TjulewfuwStDrIxEjIYGUCLdq6mFvj6Vh45tgdPHrgy1w8dgt1PWAe8jhioX0K4piN9Z1sHNmFINw//fccXX6aC6rXmmzrrbZhbYpJcd2bqQNIiWePE8cmvYVjjlSWXFVphapUULVaLoBhyjKFofk06kitYmyEWlGmpvPPb7cNI9VsweIS0ohJZmYNg+dHwXZtOp1I2GeQhKrCLi5Pt78qn1nx/LOJwn4x55t02MoW5eKiXVjc4+Yy8888Tm3NOuLFeRb2PsnSwX0sjwyyuHYrR79mVN4qrBI0+pB2m6DeoDI8Sn3tZja/82eob9iUlum7hZe2yb7f5dpUOE+FAcNXX0d1zSSjP3Bj7nJdr7Ph3T/JsS9/jsOf/3PWv+FdDJ1/Kf1btjNw8jDq+kGq4xOlNkqHvvAXHPvqF1hz4xsIBwZRSpHEEa2po6hKlTXX3WKYoUqF3R/9dZonjjF67Q0dLFepDVFaWcm2Xck7vONKQJZfRhE8dU1H4YMZIeeJBZ1Ax2ejcnGBiuWIZWty2+Z70BLChZjKfEQw30SdnM2nBQLCRsPMl40q8WDdhG7Rzh2x0HcRVJIZTJf1VTtvM8sQSWDGVGuFiFCrD3Puje9FtGJssM5XP/rzXQbM6/tZJD0DIqXUAPBZ4JdEZBZARH4N+DWl1K8A/wL4P+3+XywrQ0SeVUrdDfxYL3WOjlaNy7XGqLtMQ8z/QJFUgkznqhQqijl54gn2H/8OYVDjxMJRANatuYiNG6/nqWO3Mxs+wQ2XvZO1jW3QbFvvrBiJrYdUnEDiZSF36UKSQgTiIDR6cuvePzTZB43EGAXXQ6L+qjGorhi1WVxTjL3qzdZYTuV+uMqWt/66t6KvfgtB07wxqCRkeOBCZqdb+VxksRh7GxGuHLmR3Se+yf6lb6AI2DVyPfOtKfYs3AfWJq5/+CJONg9w08TbWGzP0Kc0jCxyZPFB+oJhRAnn1C9CKW3jNOm075IkmQrK2h4lbZtE194TFVhf3kAztG4A6jVTjutinJiUF/0NpBISD1aNyZdNcqgXW1kATWcc7RKnRiHECUPjNSQa8Ch4G4wzjuy9em5gaGjtwHO67sUrNwsCCcDeFU4VTj/swctUXuz5ZqLPS6FSVJ35LI1PnMYRs08+yML3vk2t3kfrvltRQcjaC69AT76C6v4naT36Ha7+6X9FbXI9KgxQXSKd+HkLy9RJvoxVwg5PsmJ7lVRY+873etv5c1Slwrp3/URu8ac+wti6NQy0olxZubrf8k6O3/Yllv/uUyRDw0zc9Cbmn3yIhQfuwfK+jJ97LksH93L9r/8WSwf30RcI9cN74JEHCQeH0Y0GI5dfky/fY3ZWDDxbuA9j1fwS5vriEqX641nMNF8EQGksH2C0Fuaz0uMNs2tT8bgPihIfIEkaXXqkL8zc3fuEsF8IBmLCpRA9GFg7TS+OWr1mXh6roc1woEkqiqSiifq0tQdSjPSF2XgqtSrb5lzw3XqUBGT2VgpEK0YanWmFcnIWzTdOegJESqkKZnL6MxH565JT/hz4O+wEtYr8R+AzwDdWO3H60HzqKi9hpjoDwxBJNUQCTStZAq3Zs/frnDr1NNs338LYwDncuvvDDNQnuHDNtahY2D4wzkBViGYGmVmOYbmdxq0hSTKDXC83VdeUDGElSy+hFKq/n+nZBGkkSCUiakJcD4hririqiVsGBCUBJKH5nr45AEELwmUhaCVGn7wUpbGFpo8vGXsq98NKkjT4InHCZHI1kyri0NyjfP3QZ4niJoEKGKqsJUqafOXAp2izTEiViBY11WDD4nqePfBM2p+wfxilFDXVIAhrBug5A2pnXF3wKMsYMjsGQQC1KjPHl8mp3BoN4v4Gsa4Qh5okEXRL0C1DC4cLLqq2Yb5UO4bpWWRxicxbrI9TB2bw1XPPl8H0qQOzq5/0EpVrnq/eXfvPhuzTL8V8c3yp3ckEpQ3KM0PR/CyqXufg5z+FtFtM3PhGVKOPAx//DUYuu5aBK24gAQa2n091doG58XXMxVimtMwBwQNEHt7vphpCwbGiaris3f4lRUDkGJCk8MwoOL7c7u7WrysEt7yNPhFOfutW9v/h7xM3lwkHBqmNryWJWhz82P+DxBG6WiNpNenfcSGjS7McOHAgLWbH9ouIZqapjK8xXsFF0FZUR63ALh1rZmORLvIKJBDftMqwJ3EJgxNbt/XI25fA1EI7V30HQOqynRtb5+4uGeA6Mdu2QYfN+eFiTGUhJpy1QDTKykpiIUkEaUcmzpwIEpggv1F/SBIY8KJj4dRCRFLBEgR0iv8SbvODOgPuHHuXxqlaHR6cDfONL714mSngD4DHROSj3v4dIrLbbr4VeLyXCkXkcaXUo8CbgbtXPHlh0aRuUE6vqpCqZSVixf6Dd3F4+hGm5/bhnsobr/lVqkEDlSS8+tIPEQZmcRcUdYYZaNSYnl8ydkZ+MEabDDZNByGCqMToclceIAMIwsA89Slosd/tYube6FIKFXL0aRpkK8qy1ZvM9UkWhDAbRA+wZbPIhr7z2VDfiTRbHIv2cnx5LxVdYWNlKxPhZgQhkhYPN+9kSI1zWbiJh6I7SYh5fOkuppKD1FU/1wdvImz0ZYEWJbMhKvcoM6EOCKwxdBjmAFG0boTWeI12QxPXFDoSgkDQNhaTiipplO1gUSAymeYlisxEEASe63ycn/XPQjVZmaTP6moRDc7wZIsv2Xyj6FxEPIZGlHD8jr9n7okHaZ2aMgt+vcHOD/5fqCAEBef9wq8TDg6li0plZIxafTAto2PxKANd/vcCQ7SaeiKnzvMBhdOulFwvTv3mqZhS+6guDJVpjGL8+lsYv+4WJImZeeg+lvY9QzAwyMZ3vYr+7TtJmssk7Tb7/vS/MHj1K9lw/es49FdGe3n8H77I9D13MnjxFaz/kR8384anGutqmyIek1E8R0zbTRYAgcC6lycqYzMUSKIyUBR3MjvpuAmpTZIDNDmbIhcKpmS7OM/n1G2JoCKy+EM1TRwHaMfI2JdegKReIQlte5WNIRTFaBF0RVtbWpsOJBbDzIeSpXxK71f+uwQKvFQhOsruvVO59ZLL7Eyfb4rSC0N0PfA+4CGl1P12368CP62U2oUhDvcCP3ca9X4Y+N5qJ0m7bR4Mu9hic5Ddf/SLJMScWNhDvTrEeRtuor9vHfX6EDVdx1noV/uGEaVMzCkRo36rBIZ+tAH+UjVNZFy/szQQSXkuKyvK81YzNjhioyiLUdSKiwrqzSwqPzGmOW1iE0MiB4pc1OnIsEGqnff26og47QcnRJjUm5msrrcgpoUst1FAFc3FcjWLcpJ7I/PSPMoEO5NLaLLAnEyzO/oem+a2U6fPuOuvIo4lUhUbTNG610t/ndZ4HzPbarSGIQlNRNbKokK3zRtSuCwklYqJvtpMCOaWYLlJsrREYsffsFJVD5it2qSzU4Ie7sWZPz+9JPNNGQBBQdJqsu+vP4kCmiePkbRbbHjrjxM3lxg4d5fJu2evCcdGgTQySGqfkdbh3r+8KaFYt6s31xadXeYW/ZVAUhpwrwAuyuLfpOUWWJeVVHJpPfaYImDksmsYueyaXLuDRj9BAza/9wPIY99NwdDIVdcxdt0tTN/7beYe/h6NzdvoP+98qsNjZkH3+lQ6Nn6/PbAhGqIBIakIVBMTuiM29qUGEKmMEbP/K/PKpMOIMs8r8JgzHyxJVq/PMvnbaVYFPMbIHtORELSFcNlGl25lHU0CRWuk1mGobUCTfRkMlM13pk3corm2MRkJNKovJGgmqEQhsSKqk3spLRMHtIw9kdhlS7kINgwPrB6H6CyYb3LSi5fZHZT/JL5Usq9bGXuAi73tB+glbUirjQQJiRYOtZ9m//JjzLaPAbBrzc3smLyJwf61xhPNBvIjltQAGywQKnswAgWhRiKvcx12QoHJIZVI551XOnMN911LLUjR7cS0PVToQNDO4w3MJBIbwKQjA5p0yg5h2BIXcDLJyjRtdP/zar2cFDK6F4MSVlWdtep8RtQG9sqTPM0j3MttnMsF9DHIszzGt/gKADfJ2whViS7ZGVO7T6raMjZYSIJabBI2KlSWqoYZ0hC0IVwUgpYx7GscbxE1AnQkhIsRam4RmZ0jWVzsrPMslJVSxnSoI4MAFlYoy3srPVPlJZtvPIYmbi5z6oFvMf3QPbROGDvEjW97H/WN55jIzWWqtRJVTu54oa6O7yVsTBGk5aIrW6DTzR191X2ONSppW09xgnywVcJI+XVVxyaYuOXNcP3rOfLlzzL93btYeOpxNvzI+yDQHP6bP+PYlz+LCkJ2/tvfMM96aeO8erw+5eLtxKACkFgZZigGRGXgJFKpS72OlGFqbH6w3Dg5tsgDPEUvM4p1+2CoCJwgp55SqvPGSaDSaT4Frhqj6hOBSAzIE8w6l55Mtg559kPpXGBBZoeBeQHoFZ+9FeNspWNyZs83RXlZR6p+ZPFOTiXHaMoiI5W17By4hv7qGlRYoV4dMnFsYjFpPUisLloZEKGzG6WALKJ1tl+0RgVi1GeQGW47CQILSkpuesoQqXIkbkFRYFN3OANiR0mqWCxDZIGQpU9NslabxDSN0uwxQLbsjqjSXuTu08nqvkXt5BzZwQKzPMZ9zHCSKnXO5woe53t8nc9zpdzIKBOZobTS6IrNSO/c9dOUGUkGyEQITsxTG6sBRt8dtIXKgrGTUrEQTi+j21VDAy+2kFMzxHNzPbf/jBfPJqrjUKORPmMqCPKee93kH4cG8XmXqLnIoa/8Fc2pI0TzswycdxEb3vSjBH2DBI0GQaOvKxBKt1cDPysc63Drd9sq+y7+MXe9B4pSt2p/uiou3G4fWZnK3+e3a7W2F68pgKIyWffGd7D2DT/M0v49HP7iX9E+eZz6uk0MXHApU7d9iad+59+x6b0/Q339Odl8U1aeX5dnwKzb5q0zsW0yoEalAEdF2fc0h5j3/pgDOCUAyK97RTDkMUtAqi0wainj0VUWdDEFQonYF21l1jhRKOXYIlBBVp8okxYqS1yr0j4ZW6AMOIlWpfdGnF1mt+e4m5xl883LGhAdifdwQXg145VN1MI+kABFFQiNIbRWqEBbFkdBARRl7JAHJDyJpMXDe/+GpdY0I42NjNY2ELWXqaoq860T9Ksh2tEiAZqQkJYsIgghFcb0ZmqBCTIYScSSzHNqaQodVRkMN6bMjmon6FATkpBUVPpDCRdiwx7Fxl4onG+TpPZRCarZtsg+MWW5mEEOALXb2bbrmwNQp5npXSnFAMO8Ql7NEgvcx+2Ms45X8Gru43a+a+1Rr5KbGFFr0HVDpaoggDAkWpynpVqEcRVp95NMz6Aa9VQP3vfUSThvzADDllDfN0MyWENFCfrEDCoeMCrBpWWif0xgyImyQUKDIG+wPjxowKUNhyBhLyqz5/eNzbqsvxk4JiIX231jwF8BW4E9wLtF5NTzWvGLLE9/8rdAEja+5Seor9tEUG/kUiMU35ZdPizfCLqr2Ouj6WkO/82fIe02jS3bqG3YRNJsoqtV2tMnqa6ZJFqYJ+gfQNVC4rk5M98MDFLbvp2w1gAMg9WcmiKZX0A3GlQn1uVfygqsTVe1hj0vhzdUSd8o307PL9ShsrU6X1dKWGj6ztnG1p/+JdqnTrLvTz7G+A/cTDgwyJEv/hV7P/m76GqNLf/LB6muXZcfysT0P1laQtcboMNM1SXGOFqUckG70U2TG8ywQoqgmfVPRRkYSkGhp+ZK3dc9UOOrKlObU/ubK4tL5K4zL7t2bAIQUSQV82KcdU5lL9/ahG3RsQFGIkDVzJ8uT0hS1ehWYo2jMwYKZTx4Hbvk+pCEplFloCgJPDBmx262Bxuis22+eVkDolfzVlSsjKt3sGTi2yRJZq8SaIiM2sowPSZGm+/yvdINm5p9inayxAVb3sTM3H6Ozj1FqCostk7RCIeYjY5ToUqUNImkSU01UGhassyjC/fQp4doyhJtabKtbwfHT84QJy3aB5eZHLuIjeuvoX94HUEzRiWaYJlUJ6yihNCCIWcnpJfaKcvjvMpUMzApKxIBcUlUJYsb5EQkM0T2PMFOR7QK6GeIUZlgNw9yIVfxKn6II+zjKR7mXr7ONnUxO4KrOCVT7Fl+iAsr1/J0+372J08CsGl+EzuWriVYNhO9qtdQI8qAokCbxLDzC+hDLaTdJhGB4ycQSUhavQUUPhtFaQ2V0IDNMMxs5hp1S4Mro+ZdSfIE6PMlfwR8DJNOw8kvA18TkY8opX7Zbv/b573mF1E2vuXH6d9yXrrglbnad/U88ySndnDeOvb8mQfvoTIyytCV17K05xnmH3kAXavTOnmC6sQkywf3EQwMEs3PQRKbCNRaE02fYvnTf0Z1YpL2qVM0105yZG6eoK+faHYGpQMGLr6M4WuuozI8YiunOzNEYZ8H+Dq83ew5HYCwsKbmQFWxXjwGywNqQa1BsG4jldE1HPvqF5l87VtobNjCzEP3cvJbt/Lsx3+TDe/4SQYvuozZB+9jce/TrH3dj7D3k79La8qoMls7dtH/wz/rgQLjsasj28AEVMtjiJzdpv3uj4trY85+qGAHlKnCLAgqMZz21WWpHam7PiY1zu4wfIb8b1wpYrdt64srKje2ScVmBNAZqFFtY4Jh/E/EzB/asGGiDUB0ns4oSHT+v7u/g6vZEJ2F883LGhCpoioqjcMTI9pY2+Ni57iZJ7Fv0+5B6khNYb4ePvkQTx+6nQ1jlzDSv4mR/k1sHbsaFQtPHLmVZ099hzds+aXMLT9OEGerkxj0PZNMUasO06gMM7phhOl6Cwk1C+1THJp/jPse+QO2bn0NGzdfh24nOA8DxKrIEk815tgdWJmG1Mroj7sBnuT02KEy2cnl7OZBHuM+LlGvZCvns1628jDf4ZnkYc5LruRE+wDH2nu5QF/NTrmUjWxhllMMM2C1lSZcQWq07uJrtNpI00Scljg+/USwL3fxUph0uMsXbL1MbrfAGOZ6/7HMG2FggJAms5FbUZ7/UPoi8g0bINGXtwE32e9/DHydMxwQ+WCoqKoC8sf8//4xX3yVV5Iw/e1vMn33nUy+8e30bd1O49zt6XkH/r+PMffAvWz/v3+zE2jZ1S9ZXqZ5+BCVsTWsHx+n2oqsU6vQPHCA+fvvY/9/+SiTb3sn/Rdcks6dORuj01GFFPuSdrb7aa6ubnZNYAFGAXBtfMf7OfKlzzB1+/9k8qY3Ur/5zYxceg17/uh3OfTZP+b8Cz7KzP33sPjsk6x/7TvY9lMfojV1lIX9TzO+ZoJWXKjfBzLOgNoDRM6g2vUnZYe8z0rGwulLdgkD18ES0X08XMyg8kq67Chj33CqMJfL3n9eVWYPpDIQtBKQ713OvvnmZQ2IysTFxFEAFatOsLlfUk+yQKGwOWA8hkhEmFk8xCN77+TU3D7O3/R6xoe2Zw+l1ggJifu1VCpk0ZuTVAVEYlio0WCTXcBMpGqxiWP7+ifYNraBteuv4KHH/5I9e2/jhlf+inkOY7HAyFsc/XUyIc9qpXnYLIS3LlYdKSq62KE8F6mpOhfIldzJlzkhRxljkpqq8wpeTSwGHG6vXMp2fbEBNbQZUMMMMMyoGuKUyuLvNJMlpub2mliLssRAOMqaeAIlCXPJSfbK49TpYzPnUVU9ZFd+GYtvIK20KrENytuoqWoVQhvLKgyyVCZa2bdHlTkI9Kgye5HcYNfaFBuIyGGl1OSLUekLKTnwo/P/ITtWyhb5x/0y45iF3Y8z/Z07iJeX2PCen6J+ztYOcJVEkQHDPhhSkjtP9dWpb9tmNn0WRxT1zZupb97MwOVXcOgPP0H9nm+z8ad+NmtHsam+aqRkHEpVY8V+FoGPx/zk3Mz9RnR5NKtjE6x/84+y5w9+h+ELr6C6Zi21sUl2/asPk7RbqBi2vPfnMsZGQ2NiI401GxmuVXIxpFonjrN4ZD/x7AwA9cmN9G/dgRLF4rO7mX7wbhprNjB22fXoSrXD3idnpF3o14rigTB/3AyTZgeqAEacPWlaRA+efa6ujl2hvd7aS6X7LRBKGU8bt6h473PsXQno796Ws2u+OaMAkfgqlbBi3qodu6IUVComarJSBqRUMkPUqdmnefLIraxbP0Go+3nlBR8gDOuIzyYBiObcda9i45orTNTppJJ3zXfZ4l0dYWjAUDUwLv2hAUZS0fRV1nDRzndx/8N/TNCKs/gSsdChyou7PFiKzN06iY1abHkZeYG9sAIVcrFcy4N8m4SEK+UGRtQaAhWSNJfT2ECrxQF6YuluDsfPpNtDepwHkmnAdO1cLuApHuZZHuMWeUcnK3gmiDWMzpJxahPJPPS887xxSoFQX8NL+WKZIR9E2UCkUgmQSkBSWQUQFSblHmWNUupeb/sTIvKJ0y7lTBdHMudASXa4a0yXEhERZh+8j9ZjD3Jidpa+nRcyduNrjJqfrHxX5uR7fgxVCUhChyiyOkpBiwYJPJbCfq1t2cLkO9/DzLfuyLezyAaU9CkHbApxifwLiwAoVZ0VVHPuOUyvd+1U+frc8crIGGt/8G08+4e/g65UOff9v0R1ZJxQVXOxt1JVlqunltWtBPb/+SdoT59Iz6+tWUd75iQioCsV1l77Og59/W84fs+tXPSBf5/vl2R9Sp+BVdb7ooqteCwdEm0KE+1seex+b+05nWcsu8j8SwJlbVStvVRq7JQHRUWVaIftl//MrDYXn4XzzRkDiDpsYpRGRVH2G3Mu8KK9xKJGX/rs1F3sO3Efuza+nl3bL2P6RJNUrebsM2wZSoRqY4hqPGiBlqTMjwKrkjOqulwOM6UzMBRqWkuzTM/s4fDx+xloTGY2Qc5dshfVVoeNUGSMqdsRScG1/oWQMTXJTbyNY3KQh7mbK+VG+pRJSdGLjZLEMefJRWxhB8fVIZ6Wh1hM5hhlgo1qG1oUJznGtbyWh7mbx/guF8iVZxYo8sGQ0hmgsSxPJvaZDEOohKhKBaynnnuGXA68rGzDCjkwlPTCEJ0+hT0lIled5jVHlVLr7dvaekxm+TNbvMWim81QN8nZDYlw7AufpnnoAJve/eNUJtZl5ZewUCihsnainIEqAhrJrnWGwe7c6PgUy/v2M/ONr1Pfun1lFUgXZojigljWV8pZodwJJSqkDhsib0F2dQ5fchUjF17F1Le/xoHP/wlb3vEBKvWBXLlFN/tctxLY+sM/i0oSTjxwByfuv4N4aZGhbRczsvMKJBEWDuxmx3s+xLOf/zjH772NyStu7mhrr5K64ru2+W7uJeI8wDpSa5zm89ZZcPZMICq7RzYenw+E0npUemnal3xbVW/mQWfZfHPGAKIO8d64ja2KmIXF2RlZF/qF5ZM8e/zbXLfrZ6nVh2z8h+wjVh1lHhidubBrbPx3yZgdCSBWtJIlHjv+NRbb02htojKPLvVz6NAhlApQOqDVmmd4YBODfWvZtu4G01A/plABD+UYIz/mkEBHEEZZnZl5PmVSbaQly9zDbeySyxlhnLrqs03N2r3AHDUJiMQky1Uk1KhRUzUG2MW5ahdNWeIwe9kvu2myxCLz7GO3vX6WUxxnWRYZZoztXMSomvLW7ZkAACAASURBVOjaLl2ro4eynGHSjoinp5+/jhdsgHS1WmiAeYZclO5M9apMPreG92pr3WhVpWJYxRQQWdWuzsCQA+gmTH9gvUeKAKtLk18cCvsLmCzyH7H/P/9iVPpCSoebu5NuwKC4324vPvkYS3uf5Zyf/1fUBweYtartIitUVInlj+X/t44d5eTnv0gyt2CSJ4+Ps2/3bohjdK2GJAnJcpP6li30X3wpozfc3B30rCSuHWW4W8jhsjIvM//TbTVNs7N71/vsDAJrrrqZZGmJZ/7kd9jwg++msWYDlf5BczxOjJVMnLB86iiteABRNVSlBgL14TUogY03/ggbbvhh2jMnOfHwtzh+320sHtuPRG1OPPxtJI44/K0vcvKRu2gvzDCwYTsbrnsL9dF15Q0H4prrtxmJYFmy9sPqjErOnseO5UrA6DRYIvHvWzr+bj6huzrOZ/rKWMFV5Gybb85cQOQkjo01fRCYGBNaG+CiBRHhiWO3sm38OurhgFlolEoTxjowZCzuzRMhjsXRGNCSYOIcOTCjFEdnn+bY4jNsHXkF44PbSJSwYesmNgw2SbSQkNBXGyVQYcYIOZAlKgM87iF1ZftoOwVF/z97bx5vx1HeeX+ruvvs5+77vVotyZItS5Z3vC8sBkMAg2MgC1kgJBNgkjAkIZnknUne5GUmezJkIYQknwAxBEjMjjEY73iXZVteJFuStW930d3O0t01f1R1d3Wfc3WvCWChl/p8pHO7u7q7qrv6qV89y+8xJsFGkx+QvbZtGRNnUFGd7OAJtvMwBVWiQJEaczRpIHGQSEYZYxc7AcipAhLJLNP0MMBmLiMviqxkPStZj6+aHGAPVTo5wSQ7eJx5ZhhhFT30s43vsEVdTofoXrBd4bLB+G+dA+2lASIhnQXJEbN5xGRPV5LoFhJgHYGZ6B8gCgVESYPWpyfupLewnIHiKshpU6/KOfG5MSDKvF+lSPkQLWk19r0Pg/1XtENjnxBiHzp/2IeBzwghfh54Ebjpe3rTl6MsAHCiYjsML1RfBQHHvvZF+l77BmQ+nzo3C3iyGqAWjZC1ZJ/+zgM09u2n8+oryS0bo6NcYfjV1yGkQ1irgXTIdffqsWzMJW1HwclUIKZxqbxWmccRzbEiTIOiFKCJr2edaO8zwCplSouuEUV/IRm6/PWUBldw+O4v0Zg8hlfpJNfZx9yh3chcgbBRxy1W8AcH2L3jaYTj4hRKSNejPn6Y3s1XMHL5G8l39DJy6RsAaExPcmL3U5T7l3P86Qc4/tR91KeOMnj+q3FLFXbe+jec+eMfIFfsaAvqwpyO5IraK0KBU1cJiFzqGnUB4LOUKMaWYrRtyvAQAey8/eOMbXkdRQPustdpMZXamj7r2PRcbfG+nGby5ocaEKV8ioQE3yWU8OT83Yz7hyi6VQICVlQ365xknqMnH0drhsKcNIBIJipopVGvCBSEUg98A2qklBCGDPVtphbOsvvod5gLplk7dDXFfBelvG6PEsJEk0Uh8hkkLURCHBkds1NvRBOfCqHhwHwN6g3DBG0lW/0Bly7Rx4VcQ13VmOI4EklAQJkOBIKyqNItOlgpzsFXTRrU2c7DwDTjHOFuvkSfGmaYFfSKQVzhsZw1gE4fMqyWcRdf4gC76GGAFaxlPy/gqHV0Kp0Tqq7m8cghhZZMfkee2ZE8zZLOkdbLeth76KVpimxyxIgTyNL66JQkrgYzpaKO/vIiyWhrGon3qd48odIapdmpWerBC/QNbEmZxSJArjcyERtmDCrjA7ekaA4zqXwvi1Lq7Qscuu57e6eXt7T4D7HABGVtA/j1eQ79y8dpTk4gCwW8vn5KG86KV+Wxg7ZAL9Kymqj4n2q9p7lP53VXo5pNJr5+O5WLLsD9sdfj5lzLdCTAgBTrvyV0unVDSZX4JyXNMpcVKQAT8dyK7GXCzHlZjVNozjHgKuXUbJEsdq08h+4V51CfPEZt/BAEIf0bL8ctVnALZXLVbnorHtXZBn5jjqA2x85/+0sAjj9+N1PPbaVz9Tn0briY0sAy8qUu+s+6DKGgfMUyetacz45b/4rDj9zG+pv+G7UVZzHx7KN0Lt+AKo4A0JiewCt1IszCKMyBXwTl6l93VuDNEQOjhZ+v2SVPPsbifcLIh+y4W6AooVNxAMwe38fEwafJ9w9h8yS1nrSwZkiEio7CEsLuTzN580MLiHSyz2S1LqQgVCHbgwc44u9hc+U6ZL5AV2lUT55hiAo0OIkGWgyGHJFC+Erp6wlff/V60AjtRBwInHyBM0auYbR3C/vHt3Lfzo+xnnMR9WEGujfgODls05iwU2/Yg9MaTHV/hoMntjNS2UBOFGMH6shvSPm+BkNR+PrLWPKiwACjJ63jCg8Xj7VqEy+wnQ2ch0ByhP1s5yGWqbUsZ20qV1pOFDhfXcXzPMVTPIjJVc1+drGesxhR67mbLwNwjXpzSs42KxDkBKXRKkW0x85LBUUxI7SJHBRCxhodHAmFvAY0jhP79sT+ZwIivg+AoOARlPV737z5nRCENIVCysS0lnJsVPr8iBoiWkULpfR4DFg02Y1ApaMXf1SWXhYCQ20mrOh4UJvn0Kf+meb4cQZu0nK8uGKV9l0014uvkwVDFj9RynyWuZcSIDur9Nx8I9WrLmf6zns4/Dcf5XilTOXiCyluOFOPqYhpeClYSEHj8GFqO3ZQuegizRdmawgs00vs84MeXxrFJBqil+R3Y65l+wAJQ6ooou0sQFJQqPZRrPS1Xi8Kp0fg5cp4+TLLrrmZiWceYvkr34E/N83k84/z/Jc+ythlb6J7zXmpXGnlwRWsfs3PceDBL/Ps5/8cFej55MB3voB79Y8RjpzLU5/6fZx8mU0/9XvYAyDMKQKTJFa3XeA00g+jLY7JagutimkNomgZkyfVFlng++yf+F0ItaVCCMcE4meaoVT8PrKO8SLzu1A5HeXNDy0gOhDqyKURsRKApu+zM3yKOXWCK4pvJi8rCK8IeLGUiPKDtctvps0WZsModiSglEHYzVBzHwEKiVABhWIXZwxfzcrBy1CFQzz+zF3sOPAtXrHhPXie9rFBKp1PJyoxHX3SJpRi/9QT7By/j8PTz3HR0E2Iph9z96hmGgx9N6SLL1fpFD1s4fJ4exln0KeGeJpH2MOzlFQFjzybuVRTGYh+LjCUE7PqBI9yF3VqzHCCQ7mDrJLnsqu2leOVKUY6x6h3edQ7BI0qICAoSsJSDqdcwjEmzlTJ+OHIrk6cujFj1bSKWET+QI6jAVEcSehqIGQAUZiznKCFHkPRuAoKDkHBiYXMiYndbH/yFi657NfNhKnrRVT8ALIZ6PxLaDYRqULwVRxttqTV2MtoVv2hLwuAEoDxO2+nMLac4tp1AAS1OQ7f8gmcUomhD/xmyr9MLXCN+HhqklNtJ8e4nqkD4A3303PzjfT7IXPfeZCp277J5FdvY/gD79caTRaZxKzIo6k77mD2sceo799P/9tujg/FubYiU0rbTi2hxCDKGpKhTqAaX1+1+bUBEaSzzy9wHxtgda3aSNdKncbO6cgzdO51dIys48W7PsP++79IvqOXQvcQy6+4CYGkc8VGOldsBAEzh3ax49a/AmDm4PP4skTXmRcw+ezDzEztw+tZps1mOQg9hWgKQpPAVznoKK+TPAvQWpwgp+tJP6MVjIFResEUHWvr22WuH2kkhQLhCI7vfJjxZx5k7Rt+mZSfU3yKSIGidm1d0js/zeTNKQ2IaiZVRo05GtQpUmaWaerMs5MnAAiUT44CL7Cd2fAEFzqvIqfylobGmKBC66sJNGYWgUISEiL1fGMGqpB6FRFiBpgffZ2GBl0qPfrCECQ4eHR3reX8Nct4as8XeGrPF+isLGP/0UeoFAc5NrWD/s51uDJPM5inr2MtJbeDnsLyWOZ0Foap5vqZqO+nEcySD1ztQGi0RCczk82pGU4wTpEKnaLn+/IuvpelKMqcx5XU1TwTHOVJHmQPz7JCnZmKMCuLDpaptezkCfoYZmvj/vjYtplvUV5zNnMjDrV+/TpkQ79T4YdQLSMqpRbuHuWmh7waLBLkqsh6E3HEAM0o8ksK7fzsalNrWPQ0WHZlnGVaRypGAknETLOhJwnyySQlK1VqtUlePHgfY6uvTD8Q4zwvAj0bRKBIj10QPoZba5HyfVBh//+lhCqkOT6BcCT1gwfAdXBKReqHD+FPjDP+7W8A0P/mm3CKRQ59ShPpLv/1/47M59pfNANoWo+r9pqobFEiMbELhSzmqFx+CeXLLubwn/8145/7D2ShwOzjT+AN9FF7dielLZshCBCuS371KnKjw+SXL4svmV+1HH9ykpkHH6Lv7TcZzYlINBgnaXb9wH7qBw9QXLaSfHf/ghNnRMIYm8LCzL6oTqQhhRQ4SvpP+3uINnWjQ9b1Sv3LOPPGX6MxPc7Ezsc4+NBX6BhdR/eqzfHjRUFlcBU9ay5gfOfDeKUOdn3rlvh6O/7tzzjjg78HxQpBUdv84v6EEHrpcPqFip/Xin9pGKvtvui2iPRYaKdRirpma5RS3FQg3RwzB59n9ugeygMrWn22rL/bPjdgerHUHaehvFn0FQohlqFptIfQ3f+oUuovhBB/BLwBaADPAz+rlJo05/wRcA3wAaXUnYZ5chfwfqXUX5k6/wd4WCn1Twvd+wFuR+JQZx6ACp3MMIXE4SKuZZ45xjlMgyP0Msj5XEUuzBM2GkgV6lQfLiS+PCE0dZoMlGa4FoGAUCGUJMxZXC9K6YV5Q8WqWSW1elahwZCIHGFl9HUL1i+/gZ0HvsXeIw8hhGCgdwMrRi5jYmoXM/NH6awuY3JuL7tn9jLWfS6rei7m+InneXHyUYKwgSM8wnoNVXe1JqjpoBqNloz1dtnF0xxkDwDnq6tOGpl1KpW8KDLEcqqqmyf4Dnt4jm7VT4VO+hmhKrrIUQCgRIWy6GRWTeHkCoRBwP6hSXKjVpLGBgR5yfSqCkJVQClCL4ogNJobLxEeALLoUd/tkD8mcIpFEy5vwuCNVghHO+IrR6LyxsTqCAJPmmOaByTOJ+RAsyxohAkYE5U+cqUuRFcH9Q5rv9JtdxphQsZIiGwqM07bmF1PUn7YVdgvl7w58Kl/oHZwH0JKnQ7DccgNDlM/sI9c/yDLf/U3mH9xN7XdLxDWanS/6nq6rrgCWSigrFkkxT0jdHZyfYD0L1kTSev5KSdukrqauVwv6vp/4aeZ/MJXOXH3veRXLKN0wWY6b3gVsw9vRTWbONUKtV3PM/nlr9Lz9rdQ2nQ2Mw8+xvQDDyGkRBYLhIGPzBlNukUgmPURikDS4c/eQuPgfgBW/epvk+voTUWXpYBUaJobgQDLvyjrMxT3vw3IaQ0LJ56QY5CFdV72fAT5ai/D576S6uBqdn/7k+y779+pjp5JobOfnjXnk6t045U7cHJFKoOrcctP4c+eID+6jPqBfcw2j1Aoas2/CHQCVSXBr7S/Z/y+rOJ7EDbTGjjb7N6iLczsW8gRW4l0PrLS2CqcfJGAgNDNPHOFSethFAAkc5xdp7O4OFHuD7u8yZalaIh8tKB5VAhRBR4RQnwD+AbwIaWUL4T4X8CHgN8QQqw3512Jzktyp9k+AvxXIcTfKaWWlLTqMl6LK7wFj3fQwyBjrQdUqH2MVJh2KAv0lybCUGcMDjW4ESHg6+zzylEEOaFX6SaRXpK3BiM00DbbsHXV7jgeZy57DeuWX59ysu2qJqszQpiYfJ7te77EXP04+ya2xodWlbew48R3WC/Px1UuhNpEdjJAtIoNTDPJDFM8wp2cp66k54eIPLgsqlysXkmNOcY5wjiHeYHtrFEbGWYlm3gFL7KDWaXZZ51ckeHXvQVv/RqdMNGstqQP870yJVQjBtcIBIXRtmO0OnNajR16EsdzDQWDBjpICbGPmdRgWWowFDoijuxQ0b74XiJRjccCy+Wct/0OYHFwKpPw0TdZsB2BaIbaiT/mqzJCJ1RL4K5aGmg6xcvLIm9C32f1B35XE47ak45lSvIGB+m46GJzTCU+xtZkvJAvUFsnWhbe11qJ9ORqZIvTWaH3p26i96duSqGG/Oqx1MWn736AE7d/m9mHHmXu0W0AuL095NesZur2b9H56muREZFohMSiNke3NpcfeNNbOfiJfySYPsGuP/sDVr33Q1pTtECJ02dE/kI2AMoAoqwfS6r/JM8gdXyBv9tquBRUhlZz1k0fojEzwfS+Z5jc8yQHHv4Kyy97K0PnXEOu1MmRJ+/Enz0RnzPy3vfhjg2ZaDhjQXBBOK1tT3c+8xzsMZXiHGsPhtoez44lkfyL9rmVDs7+hT+I2xUByNhCJqx3utCzXLScFvImVRYFRIYyO6LNnhZCPA2MKqVus6p9B3ir+VtnWG39hI8C96J5BP5+SY2TJ0GoNg9PNmdUdDyaRIJQp97wgzh3mAgiNXQIgTG+BspMrDoTsT3ZESo9SZpVjZISIbTDK6FACRlHBGFpJCIV6LZnbmFqei+Xb/lVpHTorq5ktGczk7N7WT9wHXvGH2ben2J1aQtbJ77OnfXP0i9HWdtcz2R4Aj9sUGeeOjVA4aKdlvV4DihTZQYNGJ7nSXq4dimP+OUvQoLSqVAq1SEqcoQxFbKj9jCHmvt4PnyKy6s34bkO+9gHgNvRzcGvfJrgM9OUVq+DIGTs+nfglroIc8QfufRJ8X5EwMgGSEpYGiTHen9tcocJ6/uPow8t/b8IRbKpaCvEICPbY2GWrhBHOiqVTuh7sqL4oRdQL5e88Xp6WukXFgIxC5m62oGhhVb3S/BGbjtBRvtk6/l2/QP//X/jdncx+Gu/AArKl5xL8/Bh/KPj9P7s2zj+j7eglKL7La/n2Mc/xYk77qZ8/rmU169nJvAJZ+cJpk7gnziBEBKnWETkCzpat+njdfUQTGvAML19G/lLr2vVDqUaRyw7ibofbbcDMUscxnGXFwJSba4llMKRDvmefvI9/XSvv4h99/47h564gwOPfI2z3vZbzJzYA/u0vHE6Oznw1x+BMKS4fj1CSgbf9hMor5ACFG1ZmxcAwO3GT4tG0B7RtkzJnKqyfXwJz7NdBNpCWrqWchrIm2x5ST5ERhW9BXggc+jngE8DKKWeEkKUgHuAD2bqfRj4qhDi40u5XwsRHmjtTxBg54XK8sXETseBidRyHB2tZbhcRKBQBMZZ1UFEYCjU/hvat0iv8oWCUEntYG1MFzGNUNOwTwtl/EqktSJMIo4Qgr7u9TT9eZ0gVjgIIVg9eLkGZ35Il6PZrD2R44LK9TTmpzkY7GKeGQ6rvTi45ClQoROBwKdBkyY6a5vEIwGPa9m8lMf78hch9Yo8sqVLCSODSOBM3sCZwJ7jD/HY5B28cuxn2X5wO/P1ceYOvMDQ295JeWw1s1sfZ/qZx5l6/CGGt7wqFrDSVzhNTOi6iEFQZEKL3o3IqeTDjrmfBIjQrKAUhEL7jTUDwrxLqHSeMtkkpmxQUiBywtxL4RQUTl0ZZlp9WTvEFUWaoC71XMyvDYRCtbREuKeRBvsHKW9OPPIAQ2+8uT3QYQlanHbHs6v+dvXMmIhMay0RQbF3cfRPGe20dazN9UsXnYt/+Fis6JH5HD0/rvl4CAVOpYI70I/X18Pwb7wff/wEcw89in/kGLMv7sEplnE6OiiMLUMFIeH8PGpuHqSDdFxyvX3U9u4GoHPj+elJtB0wygCWVHLV6DxBy/cQTdjx4iQLe7P3Osn83DL5KwgKQD7P2PVvAwX7v/05dt/1r5zzyjeyZ9ezBHOzzD39JMPvfy9eRw+zWx/nxIPfYX77M1TPOjfdpyBz/3ZjYpEZNyUfbG2Y9WyEJU8iTY9jNOXZZ3hyJ/uFt4WC6Zkl8BCdRvIGXgIgEkJUgM8Bv6KUOmHt/220mvuT0T6l1PvaXUMptUsI8SDwjqXcs3tNRg0bhBAGNPx5nmjcQ4fsZW3xAp0YMzoe+wuphEnY01FEgQwplpsoX2cWVzlHM1o70MRnVh2lVBg2YdXC+JsYgORjABNEnDFxolalqHR52qRitA6pEOkQunpfwdn+KwzjtdIOtL6CUGutuqqrTIi9r32G6g79bKFjsMxq52xUYPTOJy1XcVwdYRfb6aeXiuhYsGbHYGXBYy+9JJ3tGCy1Pd6a6FQX4STninKZxvpeaj3JsFzF6wnum2K67xAXvOO/cPDZO6m9uIvirhfoUHmWrb2AF17YxqqVm6kEDqKpcHyMD06oQ2GN6VQJk+8n1rtDRydMSYnTmcMJ22gko/BXKfU4c4Qh7DTIy+iflRSEQqDQ/zqkxJVuEn4d+S3ZqzxpALoHoghOzkE2Q/2v5iMcX58QOdY3FpuVOW1s+j9oebPl3e+lVIhMRtY1BDTHj3Po3z5Fx3nn03HJpaaOSsxllhbSLl1+QBAGyELemsV0vWBuhmB6Bm9wwLDnR9ewZyXrmtZ9ehxXE8hi1bfVBUow9JYb4s3UH0ov8ti8MT0h9vcirn81vY5LVzNItDchCJVoxrVm1Bz78Xcy+diDTH/j8/Tc8A7ccrU1agwggO6C5k2KnatjYKTS7VtkiKdoLhR0F522Wo7khIX3K1fQLGsG6qh0vvlGXvzMx8irSTb/0i8zeecdNA4dpPzsC5RXOgyddS4HHn+UZas34AovcayG1N/tigC6XRc3D7hJLucELKYbG1kXRJSxwAaDIv0sugquiQI6Sf8z2qho7iJ6p9a7E6Giq1rknoW7Y/p8esibqCwJEAkhPLRw+qRS6vPW/ncCrweuU2rJurM/BD4L3LVYxamjNUuQKJ3hPQho+jWO1I5xkMP0dZ+NjEZWYLRH2aYIAa7D14/+HWNjYxzYf5CCW6VaHKK7vJyejtUcmdnJzv23A3DVtb8PnkeYl9pXxDGgKDCAyPytB5IJ45eC8almKurIlm3S+Ibof9qxW/ihjgTxw4R8sdmERpNwfj7u98T+E4kP0SIpOyQFKmqAb/JFzuXkLM8T+04seOwllYzJMnvdhZigY0JEc77sENSGm8znYL5Xv3flgLzkevZuu4MDTz/KzLNPAtDdMcqOTyc5AVXxMWaPf5E1Z1xPxetFNgJkI0DUm1r755txESX/RQsUd7DArBlnYr6hQXV80TBJySEEynOTRL+RRslxUK4DriTMe9rJ2pHIIGRqxo/TbiiBIQTFACwRj5EIaMt6iGwEODUfOd9ETM1oMNRsohrNNBFpu6L4bnILnXLl5ZA306MrmKk3MxOO/rMZBBw4coTxJ59k6LwLAaPRyda1toPZWfb/zUfZt28fIpfD7ekmv2wZhdWrKKxby+TXb2PmwYfxhgcZ/dAHk+tkrhtrhWzAJBVHlBUBZGsF2plIlEhEosJEMRKDp3iyNb4xR5p+4mQbaheCGBDZvkAhiHVbmDs2yYMf+xNW3vxfyHV0J4AoNM03AOrYbNMsLElPxirV1HRp0dSlAZFQimMzzUU0Q+33h46gIfTnHBTMTlegXvkGdt9zO8crVaYefgiAjsExTnz9L+Jz5+/4Js3JcYavfiNesaqfkxUxl41+i9ogyzA+q9sr/aROOnWT1XcLiNqAKCFvTM45PmNnwV34edj3EUrF7zS5j5nn5ML+u3E7TwN5Y5elRJkJ4B+Ap5VSf2rtvx74DeAqpdSSU68rpZ4RQmxHC7YHT1rZzg8FMfDwZIlX5N+MiEjz4jq+nmCNQ7Vup55sn599FInDmurFrB8bohbOMhUeZby2n93HHqBU6OOsNTcSihAZSggUyldJSjMnEipKT45m4ETLfRU52QqsCTeqo4/HpplQJFFDIXpQmTQOQhjflQhkmL+1814IOO0drC2gNOSuQPiCx7ibATXKMtaeVFv0nyoirf2JAY4KrT7I5Fi2uC5RShRKRYKCxM9DkNNgSDkgyl0M3PAW9n70z+PT8oVO1t3wXo49fhfL1r2Sx7/1p3R2r+aJR/6Ryza+D6cZIhq+Tnni+wnJpZ3IFxCFADE5v3D/orYJaUBVmDZlOVrbiOsgldK+R65ENlyc+cBwE5FiRNf0DfodilDFoEiThEqU66C8UF/Xt2JzF016q1oXAz9k5eWSN9rs3v6Y19XNyg/97hI7ACoMGb/1C3QXCgz+ws+TP2MV/vFx6nteZH7n84x/+auUN22k96YbkR3VTIPNLBhpFLNgyJhI0v5J9szbrhMKgUgPjewwsYGJreGJJ2zrHHuSDqHvwmuQwmHXv/w5nRvOo++Cq/HKncl9Ym0Qcah5CgzZzY8eQbt2LjT87Tae5HjLbtunMAafkB8eouemt7Pnw78PQK5/iHzvIKM3/Rwzzz1Fx5pz2Pu5j1Fevpb9X/4Uq9/0nphjKfXs7LWrAR+Op03pyb72bcy+iySgR8RmUCWTsSB8vUhva9q1tYd2kxytzUaqJMdcCCJG2Ke/vMmWpWiILgN+CnhCCBGFQ/0W8JdAHviG4Y75jlLqF5d43z8AHlu0VpTDKZqUXNeol3WIckSYF5PtCUGU+0tYPka757ZxoLGTywfewVBplMm5GpVcH5XcMCPuuTRUgzue/GPO3fxOcF0TPWQNhmj8SgCdliP0hOaHQZPopTQBMbI3k54RHDHybttXqZN9AqgQEWh0LjxPp46AGI0LU8cuNlmjcBwGwmVUwy4O8SIPcwcV1UkfwwwwQklkhPBSS0YTZKe7iP4WjvYLMoRNSb3o3Awo0nUVoqeLsK+TydWu8fXRz82p60tJx2H0+rcz+9Q2yp2j7H/wS1RKg5x1/k8iaj7rz76J55/7Ms3mHA88+bdsGX4zJcopxm/l+zFAjtkyDRP4gsUXCeiu1SFvWMiDwGiTmtqU5rpayLsONCWi5iFnG3HIPo5AOom/EUJoH7TQRDEGinpfAb/iEOYkTk7iBiZdTM2kEnEdmF3kHf3wq7BfPnljF5WenGOH18XmCQXHP/t5mseOM/jeX+SYMefnxkbIjM73KwAAIABJREFUjY1QvewSart2c+wTt9D7trcufJ0IAMXbBgyl9qukbnz/aJ+wAMwCDY40RSFpTZE9oSthmc4SIJQFMr3nXUll2VomnnyIHf/0vygNraBj9dl0nrGJXL7DOi8BAwt2fSGNkQKBIdVdQMOUKguBDCDwdOBM9C++vvl8vHyBsbf+DLWjB3FyRY5841ZWvOlddL36ZlS9weAVr+fI/beh/Aa7v/Bxll99M45XNv1sBXvRs9MUG2ZfCjCpk/YjMccpS8tM/O6dptH02KdZG0Kl7xe65vU7oJQBWUZTLUKQCKZqi/AQwekgb1JlKVFm99BeBHxlqTdRSu0GNlrbj7NoIgKS1U4EjHIe4OmJKAJENmjyIMkIn5g7jp7Yy5n9V1PK9yX1hYiTakrpUS708szzX2T5mmvJF3pSyfLidhvnXL3SUeBpvxHQ/DdBXiQRqyEQmHpYgiD6OwJIsabITNCei5AyNjOJYgFZCWIwpKLoueRh6kFpJx0FpJCUgi5Wqw6W+WuY5BhHOchD3MHl6nWLPvoFX4kFbmJA5LoxUy6epzV3Fku0sDK568ZZfkO93QQ9VRo9eWq9rmZ8ddGOo4Cc06Aop6Dq9NNzxrXaL2jFZex58N85XrqPanGQZmOWoY4N7D3+CNO1w9y1629ZUziPQW8VFacrZvxWGUCm5l3CmQVQhlKt5r56IiSyjOGiWEAY0C4KPvKYMfk6Uo9Fz03Gqh9ogKUUFPKEXWWUpx35/bxEdTt4FRdvpoAz10RO17W269hJXo7ih16F/bLKm4W0EQYE2aBIKBIfokyZf/oZBn/p3TiFAgStYNvt7kLV60x85Wt0XHUFTqWcZqWO/85ohiJzmlQI25/Q0gCo2EEtjegiTqEE+JjfUFg+MFouavOJSACCBYZi3h+V/hUKCj3DjFz5Ywyedy0z+3cyteMJxrfdz4abPqivmRZR6fYr65fkWWcfryItS6P4wqU6D4eeTvET5rQWOsylgYtsoiONBZSGVlIeWIkIobF3Hy/e+o8UugfIdw8S1OboWnk2Ezsf48SuJ3ly15Msv/BNdI9tpFDsSmmJIu2ODMCTIbkTgXnemcew2Kcb8XVED8UswJUjkGVSqUOyYEg2icGycgwliK0hy2MCinT/naaiq7PASctpIG+y5ZRmqm5bhOaGiQn07ASZ0cuRCnA0w7AQdJZHOVLbzUB1nZmUrIg0IXCcHBdufDfP7r+NrQ//PRde++uGj0a05w2J8gaRDLzQiVSQJALJCKcEFJEMSqVQgY+jRKva0WjDAK0V8LwY+AgN6WPgp6PWDB9SdO0wjJmNVaDzivUxTB/DTKpj3MF/sF6dxfNqJ6OsZo3YyEspWY2PiJidAaLs8fZKNToWm6uShxr0VWl0F6h3O9S7ZKtt3GzLhlY1uzWF0wgZHbyAXm+U6aMv0KhNUy10sePQt1PtPFB/jhdr2xlz1nCGs7Htx6sd2BdeCal2lA7xwYxEC1Ws/VGzEjUzr3OiRc/H8xLwaiJ3ZLkEpaImfowFnB5jjapESRfXlbhSIucWm9NVa5t+VL67kgVBpkSTVttpwJyTW76M+e1Pw9iofiWZ1+b0dDH0wf/K+C2f5eg/fYLB970nufACypwYDFmASWRmUGMYs0BRcjEVhtqXzom00JnrRwDI0mzY2qBsjrGU+Syz7RYrdJ1xLl2rNrPtIx/gsb/9NdaffwXPPHI3q67+CXpWn9e+kzawifBc3EDz0+bBLyXKLKoTRpoh12iGVOa8qO+G1ywCgUOX3kD3yk3MHd5L2KhRqPax7+7Ppe4xvmsrBx6/jeWbbmBwxUWarzckdooWvorlWEojtERM0RJxJxShYcbXfpJWXQski1AhfX2/0DGUMpl8e7F7gjT/lFgC8/bpJ29ObUBkT6pAlIAz1vIYB9l4gMj06kG5EqRkxeBl3PvMXzPZdS5d7ookKs12rnVLDI9cxMGDj6DCQBPy2ZNzBrTEAzEaeIasDyzZZkgcVerLhkZjlvue/EuawTzXr//NVkAkBcgIELlJe0OV1I1Mg0qzZSvjOwU6xFbV6oYe1jTQDNyz1IU8FHyTLjGIw2528wzH1SEa1OhmgAJFPPIsE2vjjPJRiYGQ9R7irPDRfSJtSAoQmeeSt8I5hADPpdGVp1F1aFRknItHRQIYjI+V3u80FE49RDZCnHpI1e2ls7MDUfQRTZ+xsXUE01M8Ovk1xsODFFSJdWzm2WAr9wd72chFVERnqk8qXCw33NLzxol2NBF2CcPY1KZ8n3C+hsjlND9WEGiHe6EZcEMUflGgpBOH7jvRmFuoKNJO4T8qSy7ZibbFPGb/HVVYYEnf/drrOfhXH8G/9BVQzLW9gdvTSWnLJiZu/UqLVihVPzpm/RNCxzJmwVMEhgQKZaG45pHjHPjN/01u5RhDv/3+BOG1AUCR02S8P3IUhpQMy5rMss3R5wtWXPcT7PnmJ6kMrwHuZte3P8n+R76GAKrDa3ALFfLVHvrWXJyk7cmCooz8TPd5gbZhAVEDapU0QMjR/6I+R1p/SDQ6ItSmrUjbL0JBuW85lZ7l8XkDqy9GBIqtn/kfhH6DYqWP1ZvfxM6H/pXxPVtZt/kmCrlOS0OkjOwyMiWzQFtUQxR1J8JABccaF4k6zQbgdnoQ4SudtNyY9USsLTRrbJN6UQozvSyGDk5DeXNqA6IgMD5CUk+0ERgyfkNKyoQE0Zgi9OpIgRA0wjqeU8LLl1g7eh3bD32NFcvekwZE1uCZmz8KwMyJg1R79cDPysG4qETdODuxH2e+ycED+9n9yL8zvO4qVp/9+kx9ReDX2b37DnYfuBuAzWNvTj4Kd4FX4TjJsVSkmRNribRvkjUwPTcxxQUBsuIQzmo/1G5GeTU/TXe+Qj7XxY7mVnx8znEuZ0IdpqHqHFb7mRZTjDpnMKemGZNrEFFGbSHSZiTzLmITnzH3KRWiVMhUcBQUdHeugnxeUx1Y50apLiLHRm8a/HJCQ+/Uwa0rpFA4DR1Or/+FmtW5ocEQjQai6eM0FaNiFTOMM84RnuNxzuNKDrKHR7iTpmpwEdedNPruuy1yZCgee6KvhBB1k/LDjNkIJPqaJNQtl/APHsZBD0OvmjdO1dp0pqSDXxD6qDBCfLFymjk5ngpFZLQ8LeHxgEIRzs3jlEvkhgapXnoxJ755B7z+NeYirddtHjlKOD1D89hRvIH+DKJIJj6RmfVrO15kbnaS+vP7mPz8t+h71xupXntRy/XDuRrj/3Irs/dp96nut78pATK231CYTIxpJ940YIqb8BIm7u5159Nzxvn0lD3OeusABx76CrlyF33rLmb60PME9XkObfsWtYnDdI5twJ+foXfVea3PYKF7Wm0OA5/pIy/gFaoUe4f199LG5mZjTmHSb8QaKKMVikxHxIBIL8iUiAJiQCoHAQyvv4rDz93L0d0P48gcW676Ffbt+DaP3vln+M15Lr36d8i7JQ1IwshvkIR4NWpPqE7+/VpuB0oI5gfyut8u1LslzYgYFvALAnc+CtgwUdJN8GZ1dl0n5yBClfg0Kj0MQgdCw4822VyCD9FpJm9ObUAEJn2CTqkQaSJiIOQ4ac4fa8D4QZ07nvhjlg9czNoVr2a4fwt7jz7MvS98jNXdb6WQ64wnryiiY3DoXA4d3sqObZ/l3Cver8P5Rfq6UVFCMT99jKe+/Tc050+weu3Z7N7xFACDqwy9v7Xyqs2Nc99DfxKff+nad9Ph9ulqrg0SMvcpeKhi0EJBlA7TVK3moFxOa5BCBb6P9NIhlKKjSLF7iHP8V0JT61qraN6n5WzkxfrTPNG4n4aaZyY/S9XpwafJWOksciLN1+PjM+0fphHOMzEfMFGb5kDtWeaDGQqyzGwwyai7hQ1jbyHIO/hl3V8lIchFucH0CgUgd0IRuvojzc2EOA1wlcKdDXDnfB1OX/M1GJqdS9EVBDMzDKhhBOezjfsY5wh38UX6GGYjF/MMj/Ig32SLupxeMdTyXr+b4g70Ew73M70q4mFR5DpzTA80EwJIW+tl3p07102uvxtxdAJ/z15yKtRO/QWPsJwHCjhFiV+UzPc4yObibjCnm4B6OcrJSBgXSr3R2L+fA3/yZ/S8+Y10XHU5na95JbWPfJQDv/0dRv7HryNLhZZZvfOGa5jb+gQTn/si/b/8M6nExlH4vRBoFwChqL+wl0P/868JxsaYkD6NFw8BUL7grJY21Z5+nsMf/pjekJLRP/ot3GqHnvUCYWmFsk7T6e0IPKU4ajLgCSBt37KKwfNKQr5nkDNe+TOmg1DsGUYo6Fm9haPP3M+O2/8BFfrUpo/h5oqoMGRg/aVIx4uvLRT49TnmJg/SmJ0kyDU5dGyC47u30pw7Qb7ay/zkQUYufD2DW66NU/ckzzVpt0IDIKmS9yp900dHA6IolD4CSTIKjFGa/FUEMLr6Skr5Xp576JMceuE+xvc/Qd/gOZyz+ad57OG/48F7/pgt5/wsncVhZNPFqfkaDJlFbAyMTDJn3c/kYWYZ8/2+MvP9OWbGZNyXUgFqPUk9oYh9WvU9tObZLwq82ZD8VEAQEcm6ikbF+IW6GmD5BeiqLp7L7HSTN6c2ILJ9hMxvDIai1ApOJDVIgSPHLVIu9PHikQeYb04x0L2Bufo4HfQx40+QL/ekX6YQCCUZGtrCM0/9G0GzhnQqsW9OVhDOTOxj27f+EoDhs69lzUWvprp+kkKxW68cmkk4tQgUeafC+pU3UCkM0F1ahmz4li24ZSmYrESko4GgQ6q9LcOwhYVVEefBcmT6eKjAy0Eup7U5mUvlRY61uYtYoy6kzjy7559gWk0QKJ97jt9CX24ZXbkhJA7T/jEOzO+g5Hbiyhwr1CpmwknWdVxKd36UOWee+/f9C/uPPcbAqoup9KymWZZGA6IdHG0NERIIiENTtVYoRPraDi4bgQZCvtEMNX1Us4mq1TVPFeikuoywWV3K49xHkwbHOMQ4h03qE3iKh9ioLqab/xwdQQSG6oMl5vscI6AU9Yqk5tmS2FLLR4qiogRK5DA5iMcntWO2KiOlxKnlzNjXk6O/iEUu1n3/qPxgSmSuAHKjIwCM//utNA4fJr98jGLTJzgxrX3FSoX4nFiWFHIUt5zN9G13oVDphVck1sy7n33oCY79n08B0PG6y3EuWUcwOY031IuIaECs4o0O0v2TP0Zh7Uq80VF904wfUAvgMaayhQBQi5nsJCWyKEbfdOJ+IMgSEBY6+lh24RsYu+AGalNHOfrMvfjz09RnJzi8/S46R9dT7tW5IGeO7mFi7xMUOweRbo7yqrU0ZqdYfcnNlHtGOX7oKXbd+UkOPPQlOlafRW5gKHP/5J3ZWi8ZfZuGoFBGWqLIvGSH1RsNT0TW6+AxOLwZtXGeHU9+nkZ9moljz3JwryZYF0Ly+JP/zAUbfpbe6qhO2hzJZgOCUvxmkNYcxX8Igt4KjS6PeqekUY3aB35eB8SmzJ9W/yJtGOh5zptpIpQkdBSBkNrPKBA6i1WIDlBaxEJ/OsqbUxsQWX5C2rySgCEMGEqZzbDQtIC1q17LtmdvYa4+wbGpHaxZ+WpWrFiN3+zTpJ7mIwj8OvsPPMiLB+9FSJctl76PnFeOTWaRc6VdytVhzr7qlygPLNfEazmXYtlNkVvFqlGlkMJl2eBFhoxRxX2w/Z/iPptfpZQmCnMzUVrtijUww7yLrPtJglBXxo7dcd2cC4U8+IFemdp+NMYJWgAFkWd98cr4/nP+FMfm93CieYQwDCh7XVwycBNltwuAro4SAzXD61PIUy30sbn7ZzhRP8yT2z7JaufNVHs36xQajmaJjVNp2IEzitg0JhsKkTdgqGkAUV2byVRdR1+pRrMltUW/GOFadSOP8G1KVFnDOdzNl7iU66kxxxM8wIgz1j5FTJsil40S7juYbHd1EA71aTDU79HogMjTvlESNESyEs9OKEJpMCibDrJZwPOrBAcO4ijtRySEwKnlE2GOXJyoXJF+jz8q359ig5pol5T0vvXNHP/8rTQPHiKszTPylh/Dzzu4Pd0xsFECgtkZpr95HzN33I870MvIH/46wkmrWGIwJAEUpU1rGPqdX6CwbhlVt0CNBk6bbOQ6v6LA6azQ8apLIQQVsxCL9Fi0HKkjButUNvqFSlY7ZO1OPRbbJUhYmppA+zm1OHYLSbFrkOWX3Ki3FcyNH2T6wHPMHN0NQKlrhLFzX0uu2IFQ0Fv1KK1q6kWrI+hatYnVnsfcxH523PoRVr3xXRRHVuhbZTmHstouG/SYSKuoA4lvkYqPJ2Hq+ndk9CKGBs7l/rs/zPDwhXRXV/DI1r/j8vM+wJGj23js2U8yMPA+I1/D9PccuX2cpISdJeo9BWpdDo1OQVBM+hB41uUsQBRriEKt9QpCrS0KChJ3PkTlpSELFuDo/oeC2HfzpOU0lDenNiByI7d3oyWKNEOG1yX+jbVH1rlC0Nu/nvNL72Hrto+T71mLzBXAcajPz+J5JWZrRzh8ZBsHDj1MV+dKztr0E1S6l7Vw5bRzJJLSpbP/DG2nDnX0k2wmH4hsGlZqP/oNESZvGWGYaLggBeiitie/+kMJSidhDY3VuHr0h3njzxMqDaqEh5xvpjgjlOegisas5sjEOS7qe8yzgwGlElRIyelmea5LmzEb6Wva7aGQR+U8yLn09JxJR/EsqmPr2H7vxzh73XqkKNi+nVod3QRvVmvjnGYSkSH9EKcRIBvGZ6ih2Ztp+rFmaKE8X1JIKqqTA+zhIHsoUaUkKpSosE5tZrt6lJG+S6jketudnNpsjnRhv4W5dX2gYL7fpdYrUup4/QJIh2mHxOA6cvKM+KsiEBrMzBC5PjjlQmoF35Lnql05zVZsP+jSNhmvyOy3HJ0TQj9F9crLcHp7OPapz5Bfe0a86Alr84iCR333XmYfeIy5B7dSPH8jgx/6JbwhbaZumQtjxmr9K0sFiutXJJqjl/KasxxDVkezQCk6JIzMixeEWGLQ/m6z7bDARrxLZY4JM5bN9UWokq7a11NQ7hqm3DUcb9uLTb1Pp1EKDQGqdFw6V2+iw9lEvm+Yfbd/mjU/9UFiXqbosUYmwggsRoDHZuHO+hDVVfJsVBoMYbIYuNKjXBpg1wvf4AUV0N+zHle4jPacyywzbN/3JXqHX0feqxifIhX3IabkQN9POdEiX3fVr3rM9TnUegT1XpJciIpYyx47ojvmGpnnaedxlM0wppKRPiipCIXQ020AMzM/8iE6tYoQhvTOMUzQro7+cmUKBMWhzq6lcQEQUC6Mcc6Wn2H8+HMcOroNP9zJzh3bUEqRL3TQ338O5215N6XKoMlzRcwcrVAx107Ld6+AQOH6gNJOv+5caNJ76DxakblMBCGyngEPSpmBK1Krg2wetKDo0ugW+KVM6LpdlIpXdwTK2I8d87HqtihjNhOh0izcOY+wqEP2Kec1UDMfZAzaApWocm1zXk6HkYucp0FRhsBLdVZRBZcw7xHmJH7RIchLCp3LKXYPM3P4BTpWnqWFjQ9eE2MSA6emTGJW3W5vxkcEIY6jcGZqiOk5rRGq11G1OuHc4qTF69hMkTI15llN4m8xLJbT7C9w7+Ofodq9jM7+NYyecaUh/7T6GwlxKZgfGIwFVLMiqPUIgojLBGItchTNEq1AldKYMnbUNwBJuaDc9P2CmRmYmcEF3HIJp1KkGRQI5RLCPk4zXpAfWLEXPdavzWCMUKntlr+B0sYN9P3kj1Pf8yKzDz/K8RdewD9yDBWGeEMDlC44h+H/91dwuzsTwGPf09oW2RB7e5jEoVRLAMn2pZXtP0RiRjOTPqA1KTZ4sLZjQRiBdGvcRxNwBJpSgCn6DgTG/G+1QViP375HdGr0DBSxhgaItVmhkyRsjvJ/IaBzzWYO3PsFGhPHyPf0G40GqQg6GeVlizREvr6+9JQ5ltYISbPw1CYoO7clSF87TG/a8A5e3Hc3UklWDV6uTWRKsWr5NZwIH+fB+/6EyugaOldspG/thWmwHQFR49dqm/iaJaj3QuipFnkTRdDZUbp2IGQUUh82dF2nrpBRtBsOTk7fOEpwHuSgo7yYD9HpJ29ObUAUhJBLCBRxjQOuAUBhNJEI4v1R6gxbTlR6V1DpXQFAb2eO/mUnkNLFIfH5gPTqREVfr0kS27ISSkUKgCjokHAbhIgwTJzn/EgDE91AtYCbrIN4kHf0v1AZf5PomH2S3mGrdENH4DRC7SDpK6QQKEelAVKUJsI8t3ilEyqEDA0Q0m0XzUQtqlyJ8px49SsgrSnK51AIDYbymnXZ5v7oWrWJI9vvoWOVBib5E4rQ0SsUp6E5hmQz8hfS0ViiGUBT6F8LDJ2MP8gujnBZyfq2x4a6zuLiK9aw+9j97N72ZXY/9WWGz3sN1dEzKfWP6fQZ2QkQ/bdfIma5tcO0o9V1i4bIHkOCRH2/QFGzc5rWoJiLAeNJi2IRCoEflZdSUv4mURGZY5HGyBxTAoob11PctJ4+1yMIGzovYdFDuI7lJN1ytxbwE99StKvPwoOnnYbGrpvRCMUAIdM3ERKbmiKwZMRNrHBJtcDcNyZUXGCuTBEuGm1RuwStsQ+SfX0SAAPRYpjke7MixpCCzjXncHzr3YxccyMI2w+IRENkm8JC61+gUnJVBNbCM7WtiFIwiVCRd0qsXf5qPQdYztICwcgZl1If3cC+bV/hxTv+lX33/wd9F19D5YyzyPUPaW29GUepvgjwSyotb8wz1s9JgTR0C9Z7tQFrNLcIQxgsghARyDhPp/Ix82rySE9aTkN5c2oDoih0XIg4fNmexDVTpzC2YRGr/yJTR4udX+nVuFMoahK9yMxkCwhrNRKbuQ0oSl3LcppGgWyESD80pjGlJ+9Iw2JrhqI/HWvbscCQAUlB3okBRRBIzSwKC49S62OWPoSesQ2bj0pINEBSSgszVxB6xo9JCpRnwF2odFdDoQW3Ya+NzXyO0OY2KRPslvOg3tDEg56LUiJpe163PTDat761F7LvwS8Q+j5Sugb8aHOZ9BVOLQJE5lnWA+1A3TBmPRO2GquWF0ocu9TiCI4cfoL9T3wdgHzfMHOzRzh+z2ME89Oseddv4ZTKyWRh367dZJksYNMq7Ki6VadlfLpps2gwM4OT8xB1bSaU9R9FmX2/ykKTL0QAQaVX8hYYSjvsqsxxEOUiSJVykm5BC4KEaNH+266S1SQsVlqAEC0Lu1jOZYGejXZskGP3P2qGDYDstp2knS0s1MK6lkrqxNeJb5a0OzL92ASDWH8LBd0bLmbPV/85MYtFGiKjHYuTdKcAkgYIoadD1zFh9imn8uxvu+II7b8lRAyClRAceeouJp/SSWOr557HzOE9jG+9D1EosOxXfs24irQColanLZE0IapvtFcxOWc0pMLkVwZavmaL9I0myWiJxFKwzmkmb05tQOR5pLQomWisdmDIzjafLQrNsRAl2NRmq5jb1VxzaU2LIg0wWiLpqxgMYXyGYnbpQKVNee3mNdOmROWbZhL9bkqUe025pvMYgONZJkehNXBCGY6N6N4ykmdSC/1CToM7ETmyG81daMgZizqKJtHgYf0Sky56Kkexa4iD932RsQtej1PXhIzaRKbwppuair6pNVNyZl6/q6YGZaqvywCEhqYLWMzZ3C5t6ja6Cwi/J95effP7ePojvxVv7/j7P8QtVSgMjOCUKgjXxS1X6dx8EV6xkvZba1kyZ95HFtOm3rPAGU3TAPh79hKMT+Bg8LO/GJW+au/T9aOyaLEnVFsbmJhLTcVon1TxeS+ptKu/EBjK1I2VyovNQSpTp932S2lfu+tH1ZcCDNrdJtsmsy/lb9TmmnEkaua8uKpKtkUIpa4h/Llpjj16F33nXIG0U5JEAMjajgGS0ST5hUT73shJi9tHa+3duTCeC4RvwuDNduJvpOeIRqeLVxU4/Ym86b/hRnb+zgf0xhTs+8s/J5idoXTmekTOQ3geblcXlUsuRuZzJwfD9v4MwIx8iiJQ5hcc/IJDGC1Y84L8ZBB/CKEnmKvXFn6BcFrKm1MbEGVKNHnrj8L87SRAKHFSNSdkJ0ClzTMRx40EE4klFhxk8cfWNsO8sRsrfW0R6ElcBAqaAREJF2BYnjM+TpAykWX9oiJwl73nYkIr5r4JBUoaTZgrINDHZIDW3uRMNJntO4A2Rcb7hCAoeyhXxBokvToTmrAaCLrKyfmlPCrwdeZ2V7N3h4ZoUChNrlgqD3Bk+90MjV6Ikx/S2rWm1gjJuq/BZDPQYfW1uk7Mms/D9DzB0BiNDk9ns28a36bUar7Nw5HW/szh5nKXQv8GNp31p/FkOPiK13L4/q8CENbnadTnaUwcpTi8nI61m6gfP8Lzf/uH9F50Db0XXpWOUltIWKXaqOtFQDEoOsytqMCKim5uQ1E4Mgd7dP1gfALGJ5CdS6AIOM1WbD+w0g4MSWjVACWn2BqMVu2QSu2PtUOZeya+QSqz39wjqxQIzbhRIl0n0qooYViqtUyLfXqiBrdRMryUNUX65CVUiT5P06RsOHjsshC1PxMFJoPMtUSasyy6ThZgRY9TBhInV+TA3f9B3xkXIWU+A4BojRxTIPNaS+QXBIEn4+sHeWHdU9EsOqZfKt03eyFr+tKsCuSgoKPrMjquuCyWA9XNFzD9+MMANA4eAGD6oQcprllHce06ajufZ+K22+i54fVUL7hAa8UXeGcJoEwmihZ5U4Cp1R4IHZ0G0LUrIDfV1JYJT3MW9XYssgCD007enNqAaKE8KQY8xI50kVbIgKU4gWcLmNCAI3S0z4zWWghtPvsumhf7HpmM5RowYAgR06YdAqXzWoGlj0r607K9BOtI0i/7QxDxhhZGEUDUH4gwTYxMZfq7UWk7vkhApXK02Ut5EoytWTZD/cyVIMwnQ0gJCHIOygligRC/I6krhI6iUO6l2ruSjvIw7qSODtPJsDrGAAAgAElEQVQh9RHzdKC1P74JrW80oS5QtTqiESBCl3qXh/RV+0lKWIKA5Li9yo9KmfQqVwkYvPBVDFx4HSeef5I9X/6nuO78wRdZ8cafxy1XaVx6PQdu/yzPfeR/Ul27kZ7zLqc4vKz967Hul4248fMCP59Id6epKB5p7ywUTp1ou9+602ln0/9BlRaNkAEwC/kRtWiGFvLzyRYBYIEg6zeukvk763uWgJ9MHxZa2J1kwfeSS5vrLBamb/u72GCo3Xm2Y3Qko1M+MdK6FhFozdTJXLPUO4Y3tgHPyVs+Q8Q+oHGkmVLGx0jFUWYiAOESB06kQY6wxoqIo9LiPov0drNiPQNFHEQ8fOM7GHrjzRy7/UtM3Hdn3O75nc8x8s53I1yH2qEDHPuPzzLx1a9Q3nwunZddrtnNF3jWkVxPim5rmINGLl03P4l2Vaj7ZmGvQZHTWDzu/nSTN6c2IIpK9ssXxKayONw+8oUR1uqhBRCZYwY06ZWWNpst6i3fRr2b2m/8nYRSxhlZxfv0CeYeEm22auf60qa99ocOCdo/WRGRLTgaqzFINCshLC1a2wvo/8K81Boe86vwkY6DIw3tezSRW5N8mJP4hcQGnvh1mUchYHDNKzi44y5UrU6Qd3UkmTGRibqvQ/4bTZ2hvtHUxItNB1WvIxs+TsPDDxX1TtkKeuIJLQHFLb4fJHUrZrVom7N0MyWdZ2xiw8//LhPPbQUUvZsuReTyoCBX7mLlm95FY3aKqacfY8+n/5blN72b0sjKlmdZP34Yt9SBUyimJlJNuZ9+b8Xxk7yXxYri+xL1IYS4HvgL9Kj9mFLqw9/zm5zipa0TNbSCI6uesusu4Chta4ZekqYmC5KyxxRLBkJ2NNKS7/mfKZZGJ6Udir5b8+xCFeAIJ8aQ8TNNlB+J8/FJ2je0+Vp23fGJNM9QaIEe2/0h1hYZp+nQBJwoHXkV3xdaAE+LFjEjeyLNVOygHvkYChDSof81b6Tr4suZeuQBvJ5eqlsuQAgHQigMjjD2i++nfvwoM48+zL6/+DOW/7ffwOnqTBpknk39wAG8/n6k56U0RqEHjY5ksIgAcpMgG+DUA2TNj+kHwpxEtmcySb3H77W8ebllzakNiCLmzlB/OMIPQTqoUGmGalOEMvlXoP1gJDMYzQcZq1mtUM52JVo92EU2wngVI3ztrZ8ctE82Szw7Ea0jjKkuQCjtQ4OrT4rNXb4O5Y9C0CMgF5V2vgtRf2Sgow1ih28/0oaZvvsWh4bZjtssaZvPdHZ8L1vv1Mzcl177u+TcUuq4jvgD5UmUcaAWpi0y0M6FkVo6l6uSK3QyX5+g1DGoyQklSEcQeA5yto7w9NBUI33Io5PInhxy3tG8Spk+Z4WkBn3Gb0uldlsnWu8/UyfWNivwyl0MbLk6LQhV8psrd9J/wdU0po5z4Mv/SqF/hPLKdeQ7K+y69w4aU8cJa/OowEc4DoX+EZbf+C7ccrUVZLc+9pdevsfZp4UQDvAR4FXAPuAhIcQXlFLbv6c3erlLi3YoC4KifwsIigzYbjm2QNGmr4WFTypKyG5rxllam8cMAApFcu7JQFFK22HfNH3drDannXOx/S213Ma+RhbIZbdNmyZ2Psbub/0LTr7E5pv/H2REOWEtvoi6F4ENA2hUFP2Klvfl3mU0ZiYIw0DnZIzQjIW04u44Wl4HOUEYMXdb7Y9vn6wv07IkotawFlh2s1PPMkyeQWS6zHX20nfd65L+Wc9Xpz/pJ/+q11J7cTcH//EfyA0OUVy7jqLjsP+eu2gcPqT706gjXJfiGWsY+umfQzjpqT7lr7VAmZ5exIcIvqfy5lSQNac2IFJKMyk70rCtaqpNISX4CuGiwZEx/YSxVsKcnzXEK6ycPSoBBKFVJ1PslUwUYg+kOIZiRujA+hil0AM+5kiSLe0RgQLfRwihzVh5B+U4sSCWfohTC3DnAoRBfOnV6AKSVhliSAvBx4Rj0SrIRFJEYDMCQWnNim67aCqqxWHyxW7q8xPs2H4rZ296e3y7oGhScQjwc0L7JlmaMaeRPH+nqdXSnlekMX+CYtcQzQ4X2ZA6tLweEJS9VBtEdQC/w8MvVEkRrEVcG3G/iYGdBjYqLXRb/I2IVej2I83a/m3hG4ElATG7tgJ6NlwEvk9zepKDt/0bxU3n0bPlMsYfvYfRH38bTrGM4+bY//VPs/PjH6Zz/fkMX/tmRKpB6ba81KLU90WFfRGwUyn1AoAQ4hbgjcBpBYiUYwMgFTvvprWLmUgzonNstGDVi7VA0X5oRd9WG5JPpq04UvbNbQBk7Ys0QyoEEVrH210valOkus2Cn0he2KAgbP+3vbDIgohsadGuW9+oMPKqe/lGdgNBfY5DT9zB6KZXJd1sA1jjS5l8Y5HbhDQ+om6uiD9zAtHZHbdBhCCkQEUugPG3L2iWJYFJlmo/A1vWRItMZfUj8b0kLTes47bmPqojMv3Rjbf+Tskx6LrkSuZ27WD6sUeYeewRuq66hq5XXMHEPd9m9D3vhUYTkfPY//d/w57/7/fouvJaui+/Ov0OLNOhbmjyskSg6Og4OQ/R90HevOyy5tQGRKARaMTzEJmlAj1CtY05swKKB5AlaSKSxfiaKvmII6bQaM62T4vAQvwbqVYjdaqyyAvNr80v5ETRXJDKdp4tQYgIAvBNxJYnCK1IOuFr1ubU5B+vbmhDCRDGAiElkJRKAby4Xyl5bmV0Rn8YwgCTi6/4IDPTBykUezgxtZf9e+9n+dpXkqv0x7nJkmSKpv2RWjiK5jA2+t6hjTz32C2c6byDjsE12k/JUYS5DPAzv80Oj0YzjPua7pdVV6VOS80XQqVGQWqOSj0/69zoePTslXXABmSloWUUh25GhSHNmUlGhgY4Nu/Tfeb5qQuOXPcWmrPT7PvKJ5h6+lG6NpyftNmeRL5LZ8X/y96bx2lyFHfe36h6nu657xndFyAQCJBAnAKEAAvwInPZ3IcwZo1tjGEX82J2vRjW9i4YbIzXi8HG+1o25lguYwM2YECgAyFuISGBBEhCGmkOjebSHN3PU7F/5FGRWfU8/fRopOkZVcwnp5+qysqrMiN/ERkZqQd/yew44Ofm+mbg0Qc7k0NNDTCUCB7mfv5OEoe6YyZgSNP07DttZck/4ShwNAkYMvFGagQEtHDLQ+3qVFqBUnx9ri7XBoxGvRNYWtHj4S9/B3tu38ji1Uez/eZruOPGKzn+kb9Eb9GSpj2g5dth/JjxtPqkh/LjL76f+z7lFSxac3T8dokRN8Qlu6ovztWJbY98nrEANit/gl8M3gxCuVVQBQ13cNwaQV5FBNMWOyuw7AGns+y001n31POp7tzN+g3r0ZlZlp95lovUd2DmmJe+kuGdu9h44d+w5MRTWHTCSTGRHOy6sioabGIn4EEHmd8ccl4j+RLEQiEZp0fuqKN7L92oqie3PRCRfwPWzTO9RYDVjf+1qv61SfN5wNNU9VX++mXAo1T1tfPMZ8FSx2s66mgk3WP8ZiHwmgWrIVLN5bGOOupoHKnq0++GZG8G7Pa544GNd0M+h4w6XtNRR/Onu4HfHHJeM5/N3R111NG9j74JnCoip4jIFPBC4J8PcZk66qijI48OOa9ZsBqijjrq6NCTqg5E5LeBz+O2wv4fVb36EBero446OsJoIfCaBWtD1FFHHXXUUUcddXRP0WG9ZCYi/0lErhaRq0TkwyKySET+UESuFJHvicgXRORYE/+dIvItEXmiv/6UiDzbPP+RiPy+SXeHiFzs032niFzr0/6UiKw6SOk+z9+rROQRWf3uSrprROSLInKd/7v6ANJ9gG/H7SLyMxHZKSKvF5EzROTrIvIDEfkXEVkxz7T/3Kcb0t7j0z1TRC73978lIo86SOl+1Ny/QUS+dzDS9c9f6+NcLSJ/Mt82NtefEJHn0tGCJen4TcdvJku34zeHKR22gEhEjgN+B3iEqj4Yp2J7IfBOVX2oqp4JfAZ4i49/mn/1HOA1/vdlwNn++VpgN3BuSBe4E7jDp/tF4MGq+lDgx8CbD1K6VwHPBb6W1e+upvt7wJdU9VTgS/56Puk+VlV/5NtxD/A4//dTwAeA31PVh/jrN84z7VNV9UyT9m6fzp8Ab/P33+Kv73K6qvoCc/8TwCcPRroi8iScn4yHqurpwLvm28bU9Fgfp6MFSB2/6fjNpOl2/ObwpcMWEHnqAYtFpAcsATaqqj3waSm1O4gS782I2qXDpfhO4/9+Bljr030AsBfXRhtV9QuqGg6ZuhxnAX8w0r1GVX/UUre7lC5u4Fzo414IBGlh0nTXi6NTfLoPBn6iqjf6vAJD/SLwy3chbYDrfLoKBOlvJfUOg7uaLgAiIsDzgQ8fpHR/E3i7qu4HUNXNdyHdvap6Gx0tZOr4TcdvJkkX6PjN4UiHrVG1qt4iIu8CbsINoC+o6hcAROSPgZcDO4An+fhXi8gS4BK8hAF8G3iwOIv2s4GvAvfBOYe63Me5PKRr6JXAR++GdG397lK6InKUqt7q07pVRDYcQLoPBB6GG1wvpB7YVwHPBD4NPA+/VfIA095j0n098Hn/XQsf/2CkG+gJwCZVve4gpXt/4Am+v+0DfldVv3kX2rijBUodv+n4zTzSDdTxm8ONVPWwDMBq4MvAeqAP/BPw0izOm3Eq0XHpXAo8BviKT/MNwDXA3wK/lacL/FeculUOcroX4dTxc9V7onSB7dl7d8wz3d8CXoU7W+Y1wFbgKB/3NOALuEH3B8DtB5j2X+FUuSHdvwB+2f9+PvDvByNdE/+vgDccQBuPKu9VvsyCczv/s3H9Yo42/o1DPaa6MLZPdPym4zcdvznCw+G8ZPYLwM9UdYuqzuLWac/O4nyIWr06ii7Drb0uV9U7cOrHEng4cLFNV0QuAM4HXqK+xx2MdOdJk6a7SUSO8eU+BtjcntzIdC/36Zzt0/+Oqm4CUNVrVfWpqnoWTnr5yQGm/TTgypAucIEvP8DHcIP+YKSLX+Z4Ll7SPkjp3gx8Uh1dgVNbj/PcOq6NO4ltYVPHbzp+0/GbI5wOZ0B0E/AYEVni12qfAlwjIqeaOM8Erp0jnUuBVwPf99eXACf6cLVJ9+nAm4BnquqeCco3UboTpHOg6f4zbsDj/356nuleiZMuTsQNoKgODupwESmA3wfed4BpH4czmAy0EXii//1k4LqDlC64Ce1aVb15jjTnk+4/+XIiIvcHpnCS7XzTDd+uo4VLHb8Zn27Hb1Lq+M3hSIdaRXVXAvA2HAO6CvgHYBpn1X8V7uP/C3DcHGlswBmjvcrcuwGnqrTpXo9bO/+eD+87SOk+B4f89wObgM8fpHTX4nZ7XOf/rjmAdC/CGTHeDqw091+H2/nyY+DtzK3Ob0v7a8BMlu7jcWrx7wPfAM46GOn6+3/HhGrieZR3Cvigb/fvAE8+wDYe+827sDBCx2/GptvxmzRux28Ow9A5Zuyoo4466qijju71dDgvmXXUUUcdddRRRx0dFOoAUUcdddRRRx11dK+nDhB11FFHHXXUUUf3euoAUUcdddRRRx11dK+nDhB11FFHHXXUUUf3euoAUUcdddRRRx11dK+nDhB11FFHHXXUUUf3euoAUUcdddRRRx11dK+nDhB1BICIvERERp6GPce7bxWRDx7sMnXUUUdHBnX8paPDgTpA5ElEbhCRvSKyW0RuE5G/E5FlE757rohMcmbNgiVV/UdVferdnY9njLtN2CMiKiJn+edvFZHZLM597u5yddTR3Ukdf7nH+MuUiHzct7eKyLnZcxGRd4jI7T78iT+bLjw/WUS+4vnStSLyC3d3mTtaONQBopR+SVWXAWcCDwPefE9k6k9GPmR0T+bvGeOyEIDfAn6KO5sn0EdtHFX96T1VvjY61N+noyOGOv5yz9AlwEuB21qe/TrwbOAM4KHA+bgDUAN9GPgu7my2/wp8XETW362lnYMO9fe7N1EHiFpIVW8DPo9jXACIyLSIvEtEbhKRTSLyPhFZLCJLgX8FjjUajWO9BPhH5v1EyvMSzJtE5ErgThHp+Xu/KyJXisgOEfmoiCxqK6OIvEJELhWR/+XjXisiTzHPV4rI34rIrSJyi4j8kYiU2bvvFpFtwFv9vUvM+2eLyDd92t8UkbPNs1NE5KsisktEvgisuwvNfQHw93oAh+qJyGdF5LXZvStF5Nn+92ki8kUR2SYiPxKR55t4zxCR74rIThH5uYi81Tw72UuXvyYiNwFfFpFFIvJBL1Vu921y1AHXuqN7LXX85e7jL6o6o6p/rqqXAMOWKBcAf6qqN6vqLcCfAq/w+d4feDjwB6q6V1U/AfwA+OWW9jlanBZprbl3lohsEZG+v36liFwjIneIyOdF5CQT9z2e7+wUkW+LyBPMs7eK03J9UER2Aq8QkUeJyLd8/E0i8meTtklHk1MHiFpIRI4HfhF34nSgdwD3xzGx+wHHAW9R1Tt93I1Go7FxwqxeBDwDWKWqA3/v+cDTgVNwEswrxrz/aJx2ZR3wB8AnRWSNf3YhMPBlfRjwVOBVLe9uAP44q/8a4LPAX+AkpT8DPmsG/4dwp0SvA/4Qx2Ts+1eKyIvnqrxnEOcAf589+iUPYq4Wkd8ck8SFOEkwpHcG7rt8zk8kX/Rl3YBr6/eKyOk++p3Ay4FVuG/wmwFIGXoi8EDgab6OK4ETcG3yG8DeuerYUUc5dfzlnuEvI+h03On2gb7v74VnP1XVXSOeR/Kg9iJcewZ6KfARVZ31vOS/AM8F1gMX47RPgb6J+9ZrcPX9WAZOnwV8HMef/hF4D/AeVV0B3Bf4v5NXuaOJKRx7f28PwA3AbmAXoMCXcIwEQHAT6H1N/McCP/O/zwVuztL7O+CPzHUSx+f3ypYyvNRc/wnwvhHlfQWwERBz7wrgZcBRwH5gsXn2IuAr5t2bWtK7xP9+GXBF9vzrPs6JOEa41Dz7EPDBA2jz/wZclN17EHAsUAJnA7cCLxrx/jSwDTjVX78LeK///QLg4iz++3HSX1tafw682/8+2feB+5jnrwQuAx56qPtqFw6/0PGXQ8JfbgbOze4NgdPM9an+e4gv1+VZ/D8G/m5E+i8ALvW/S9wS3aP89b8Cv2biFsAe4KQRad0BnOF/vxX4Wvb8a8DbgHWHui8fyaHTEKX0bFVdjmMup1GratcDS4Bv++WS7cC/+ft3hX7ecs+ue+8Bxhle3qJ+tHi6EQcmTgL6wK2mvO/HSWvj8g50rE/L0o04qfVY4A51kqt9diD0cpykGUlVf6iqG1V1qKqX4SSjX2l7WVX34ySll4pIgWPK/+AfnwQ8OtTft8FLgKMBROTR4ownt4jIDpzGJ1fN2zb6B9wyx0dEZKM4Y8z+Ada7o3sndfzF0T3FX9poN7DCXK8Advt65s/C812006eBB4nb9HEesENVr/DPTgLeY9pnGw50HQcgIm/wy2k7/POVpPwnb79fw2kQr/VLjOdPXuWOJqUOELWQqn4VJ4G9y9/ailseOV1VV/mwUp2BJDgJI6c7cUwu0NFtWd3Foh4nUu+QwElXG3GDaT9OmgjlXaGqVvU7Lu+NuAFt6UTgFpzGZrVfkrLP5kUi8jgc8/v4HFGD9DaKLsQBnacAe1T16/7+z4GvmvqvUrfcEJbgPgT8M3CCqq4E3teST2wjVZ1V1bep6oNwmqvzcYCuo47mRR1/ufv5yxi6GmdQHegMfy88u4+ILB/xPCFV3YcTyF6C0y79g3n8c+DVGf9ZrKqXeXuhN+GW21ar6ipgByn/SdpPVa9T1RfhQOc7cMbeto06OgjUAaLR9OfAeSJypqpWwN8A7xaRDQAicpyIPM3H3QSsFZGV5v3vAf9BRNaIyNHA6++GMm4AfkdE+iLyPJy9y+dU9VbgC8CfisgKESlE5L4i8sQJ0/0ccH8RebE4Y8wX4JayPqOqNwLfAt4mbovr44FfOoCyXwB8QtP1ekTkWSKyWhw9CvgdnCTWSh4AVTjjSMuQPuPr8DLfPn0ReaSIPNA/Xw5sU9V9Pp+xNgki8iQReYg4w9GdwCztRpsddTQJdfzlbuIv4gzUgz3OlLgNEQFs/D3wn337Hgu8AQdOUdUf49r1D/w7z8HZWX1iTHZ/j1vqeyZgfSW9D3hzsFkUZ4T+PP9sOW5ZcAvQE5G30NRM5XV6qYis931lu7/d8Z+DTB0gGkGqugXX2f+bv/UmnBHk5eIs//8deICPey3OYO6nXkV6LG5y/j5u3f4LwEfvhmJ+A7cGvhW31v0rqnq7f/ZyYAr4IW59+uPAMZMk6tM4H8csbgf+P+B8Vd3qo7wYZzS5DWdsmRhFe2Pol4xK3zOr55Mtl3l6Ia6dd/l036GqbfEs/T3wEAxD8kDrqT69jbilgnfg7I7Abff/7yKyC3gLcxspHo1rw53ANcBXSRlgRx1NTB1/ufv4C/AjnMbtONwy915qjdT7gX/B7R67Cmfc/X7z7guBR/g6vd3XecuYulyKE8i+o6o3mPufwvGbj/jveRXOOB5fpn8FfoxbDtzH+CVGcIbwV4vIbpwZwQu9hqqjg0iSLhF3dLiQiLwCeJWqPv5Ql+VQk4i8HPj1ri066ujgUMdfJicR+TLwIVX9wKEuS0d3jTqHTx0d1iQiS3Danvce6rJ01FFH9y4SkUfifBc961CXpaO7Tt2SWUeHLXkbiy04G4sPHeLidNRRR/ciEpELcUubr89tITs6PKlbMuuoo4466qijju711GmIOuqoo4466qijez11gKijjjrqqKOOOrrX04I1qp5es1Zn7th2qIvRUUcLjW5U1ZPbHjztSUv19m3zc03y7Sv3f15Vn34wCna40tL163XP1q1zR+yoo3sf3av4zYIFRDN3bOOU97wLFUBABWZuu5Wt/+cfWXLWQ1j1S+f5Z+r8e3p/xhLuAaig6p+pIApHlVPcVs2AiebiujgozquEClKBVAIV7vcw3AMZunfEp79hqs+WvbPxOvoZNeUXe9//3XPD9dz44ffywNe9nbI3lTaCwvrFLt1IPi0t6msEtHT3wf8uiH5Pw+/YlgVsmO6xaXYWijQuojEdF1fdKT2+nW2bi21///foss9m2Y8UdeNqJfVfDdehrf03qlqcUYe0cd9t03DGtN8Y59X2w4ZyZunV6fbZzH7EPyvKiqLwv0Upigrxv8uyoqoEVaFSYTgsqHyfqSpBK/dRVGEDU9w2OzAfIYS63Fpl5fZ9NLaFaB2/cu/f+Jo35h5+I23dNuQbnz9+dLu0UP+Yn0x8kviRSnu2buWk974TSnVjoVCkUPb9+Cdsfd9HWfOip7PiiQ9x31cUEUVVEFEKHzeQVhL7wgaZin1L4+A039+PBa3EffNK0MBzZsX9VXctA8d3UGFDv8+WfSmvafAyT8l9YPs3L2PTZz/OA96SHZbui7Vhus+mwWxdVMO/Au/QnqbPQv0tf8joqH6fTcPZhFdooYhvb8S1Y9FTpEjHoMQAvXJIWbjrUpSjiz47yzspi4pC1I1HcONTC/bN9hlUBYNhwbAq4phVz3cCbwi/w7fawDSb2e++kUrjuyVNZ/hLKCcQ+4WIIkBRVGyQabYVexFRpnxdyqKiLCqmywGFKD2p6BUuAAwqV+Z9wx6DqmCori6zwzKWZ8XsMjazP9Y9lDH0O1VJXF/H+74dQn8Oda2GwlRZ8uMXvelexW8WLCACqEr8YHF/+ycdxfr//Eo2v+tvKNcuZ/m5j2p3dh7uBdDiGYtDJQ7YJPHt+wY8iU3P5BOYQcRhJl7jnInAsELkEN//XrTheBBh7603sfTE+8XXRD3zKWqwk/CZvN6VyyMAJTuXxnzDTzsx5+BNhTB0NFRWNb5t/3e3BSn9c21n/CKB4ah717RFWuEM5JhKS0gnqXQWN6Rj2icBQzEv81O0WZZw378fgFEh6gCkOQRQPEBCoKKqmWecQYjtHRmp6WNJVWz9LdBUHCidY/+Dosxq57z2QEj7ivQrN3l5nrP0jJPov/ll3PrHFzK9YQlLH3JKnJxVJfb1cF35e1oVfpKBqhphlRAmWv8bbU60jVf8cIi8KQdDlr+Ya0tL7/8g+CwMdu2gt9w4vg5DuGgCoSgwtQCdSBYz5P3Y1DFGJox3378lTMwax4rggZdhZKrCsAIRgcKNtwpBfPsXPr9C/GGdBlAVog3XzrbN3W/xAEZrvo2aM3yE5DATU8cEDJl786HClzMAo0qFqWJI5ZnWoCiYGZYUvkSVCoNh4drFgh8DgNQAwJxCHyyK0K8DfytYvWi6+YJ99wjkNwvbhqh0E1CQ3KRU+mtXsO7Xn88d//dzDLZv95JFhRRVlDTCyNNk4jezaUWUuGOocFKanagCRcCgjTnb3SdDQdkzn0bOyFDoTS1izcMez95NP28FXZZBRYDU8tVax90cYzGUJWGgts1ImjO2oWRxUmbnbzUYTUvhRrXZJDSCKY191sivWbCc2dUM1V67eKWXZK0Em/SzvF0ywB20AjWAMuXNcFV7+ZtUzfNfR46k5/iHlC4UvYqyrFhy3w0c9erz2fiuj1AwoFdWMfR7Q0qvUQyCRg56I9jxmiAb6rFmvn8E9pl8kPOGnCYAQwD95atYer/T2Lcxc4xsgY/hMxYMJZpp6vvxUlv4kBUQsmfSaK/2Mitmko+hvheAKBA1RDkF7c8o0pb3rFAU383TaEkzB0Ojhm3V8qRADSiq6t+o0xp57VFZVDUQR9x0ZtqnBueSaC1jyAB90JBHPtb6MdvqcGTxm4UNiEbQ9H1OYNFp92HPt9yZe/HjeoRvJfPGMtWokNO4vjDPSao1juF/0+uPZu+mmzM1tJkMx32ljJ8eMDUm71xbM+J+Xpgg+XpmpzbdNqaUAJm5B2ECWNqAj2RxczAkoTJjwFAm5dXPm+9EwGTLkLyUvdAGuMeRKFqo7w/jX1SUoc4vdDQ3LX/0A+mtWsadV/403qvipEMyObsJx3Ti14AAACAASURBVAMgMynVAMkOVqMhavsbKB8jZEA5JJUnnfMpz0umNhzNvttudnzFh2RZPSfJlsja8iatmtVkJd02CqI1j24KV6mmYxRVKgwrYVgVVLglMqdBcktMYbwWpkFr4aU5tseBorw92uK18SYxz3Mqsg9dTMgYAkiak2K/pIWft39s8UvAhQ/jkz/y+M1hCYgAVj33qez4l69wx4c+x3DXnTUYCoMsSt5kgw9SqaVFQs9phLSeC3WJsNeSXM4HA4NZfuqD2X3DtQz27k7zGSMJ2udt8W0ZcvW6mHYYqSXK3kvybqMYLwVFMS2pAUeQRsJDaTDYZkb2zlhQ1BanLaURTH2OZIGacY1kGJOA5YYk3ZaOmolMXZgjyVmqeYWOxlOYJDf86i9yy7s/xeaPXcLs3qFfDvPSeFXUUrgBR01eQzpOYMR3D6EG+CM1hTnrMsJUA3f5e8sf/DB2fP+bVNUwXd3NQRX1ewkYallCyxWcjSrZSTnhKZIKTeF2Wxoj+IIFQQOvwgpjNIKiVhAzjk+k+TY0TAbp2fvJ7/iuNgFTRjk4ao1jtEVAAuCKLO8EDIVbVkidIx8plF3DPWPjHYn85rACRBHpKkwddzTHvOW3qfbNsPH33s2W//1hhjvuzIwT6/gRAFTGMDoPoxQUFnCIJgbNCRgJfK+o/+ZMrAF4BHrLlrP8vqez44ffSSS2mI9lpHk+pY9fkqq48ypomo4FRfFelcezhR73YSwSo7E0IAVuSVO8EWVgLpbxmzZK0zY/LT+yzCwDhA0j6ixIWOYKxpyjGFqLNJkz2LkYXVL2Nu2QBbihzBlJKPMEFn8VOq/Q0QgyoEaBpWfejxPe9qvsuW4j17/mL9n43s8wmBm6iTguSdjlMMeHGAqahXDfLtuPm6RyzXHgQXG85wCINH669AWLjj+B3rLl3PmzH9U2iiG0CVV5XoXXWraAp5zPJDwn58dh84ABjulymEl3hEak0oJZb2A88EbGOQUwE4NvH5tmbXxsAG2ShjbTMbwhF9KafMRvzBizxDaOesUwLqfNRQEkBsAebNrst6i195ICeE/93pA1c9gQwZHHbw4rQBTJd9bemlWsfcVzOOYtr6Hat5893/xB8qFd3Doky2et6ZoQaEQHzLUyDS3NKMkuA0YhrD7rbLZ966vOMDdM7HnWmSQ3FoDNRaY98mVFe93QHNky2UGUgyY70VtJqkVd7Z6NL+soyt+bBJyEcllQZt8btTRmn8/LWNJ/0KQtw9+28k5ahzwbOOJU2AuBgoHq9IkbOPaNL+T4338Ze390M3t/eBO1NlTSMRInm/webknNgIAcVTQ0MvY+I4ZDS/zWuvg4Kx/9OO649Kuj34uASkdqhSbNM0nWCmFZcq6Ak6eVaz/a7IfScVuDo0nSbQdGzfi5IXV9XxMwZPnfpHzK2RFVjbqMLnsNKmuQSd1PY8TJ8h+bF0cev1nYgCgMwCBdeM1P2K4apLDeujUMb9+OLF2STtoJKEon7JHaIENtA92qoUculY35XdcrDYuPPxkp++zdeGMq3eVSWlu6WZqxjCPqZyfmwKBaweKETKvZz+uGa5WgfJRES3QXqGZI4Ub2cWP6tfGgfTcxIhSiqttS1cIc0zKMQ23uWeukM6bdm+mMziJQNc/QkScxQNeDllzzU6mgFJTr1zBzy1bK1SvrOIlmwfElBcN7MvBjpfSkHKMGbTO08R2kmV2StQ/LHnwG+2/5OYNdO+t0ijxobWfky9YQxgxIauMhyRK9r3cCinLgaNvMUMNY2DyPO6Wk3lVm7Wzal8smm5zbtMOp8JS/0J52dNEgUBZqNmfUZc6p0iKGg0VJ24X+l2nj5kNHGr9Z2IAoo9ovkCSSllaC9HrM3rIZHWo6wbd86Lal4LHjI5+4w7LZHMxJ8/uQppPRmkc+ga2XfrHJ+NqAkfUzJO2/x1HCqHJQNAFYnI+CZDyNQl0TUCb9NdJJ2iIHQuabjlCDQ8pALDPOVfrtNGZyGxW1DXRlIG5kbqrMzDN05CmMNft9h265oaqEwWxJNfTLYWUPhkP237zNG1Fbw2nMREMcVwmfybTX8TOM+sYRsJtxLwqFWTqTlEckPMRe+/SKqSlWnPUotl38JbMcpjEEMFQv0dU7bBMgJIbf2iI3eEjqZkJM+8S2gCbQaemidhyO2lUWcw2gCE0Ak4R2PUDKbSIbgla8nwKy3A1AWVTJFvoKiQb7lamfA0ZpgW3dRi37j6IcVFo7zlC23YM5bIiOQH6z8AFRGH0BCA3FOUgcFO6vX5Nf+Yxf4M6Lv8m2Cz8OgyqCJqddcnZDLr0sXRPa+lFDZW2FPCNVJaDIrLe33bfv2bDiYY9m/+bb2HPbz1MfRCZUZfMeJs225bm0QiS2C6JEGyoqMT6bQpDs71zfymfSNq+bsiUMRMzzUUxqlCSdzzYZmKwrSrQbCn5mooRmdlUINVO2Eulw6Nbh3a6WmlGlTMWow2P50rprVuekvo06mjZrjdBsoiNNYrvHyGuGbKiGBcPZgmrWgZ7hbOmuhz3W/Oqz2PJXH2fbJ79a2wdVRcqrcsEs8qPAkwxvaxtbvh+lAMTwhajF0cSGMOcvkPEan96qJz6ZXd/5FrN7dqJlDa6qHmgf54CxzJ0wEsFYY6zmso3m9ctAUXQ86Z5Fx61RY5Rqg2r7otEa23Tbeg06ykIppWrYAo2ixpMM/DSEp8BLPNiyYKjw7jmc88iKUpR+UdErh86ZJDW4G1QFA3W+hmaqOgzU3R/4HXWxWJlQ2DD8HlW5AMYz/0S2RVf0F49sn5DMkcZvFjYgsozEXkc/QvWAWnLG6Rz739/IYPNWNv3p+9l10aVopSmiielalGND6pAxkXLsBGeZUyZ5tQGoRFtk08io6PdY85gncvtlX25olxogq81XyBzU0IYZUNT4S31tqz+KJgX/NWBoB0UHRAkAakkvZxQQQUtgXvE7+edWC1RvsW76QXFxx5Qr+dsiRudlDfnndZmofYThPENHNeW+bpJJIyybDR04Wv6kx3DM//xP7P7at9n0rgu584qroi8zbRk/TQHMxMmf2wcNfpLxnRwsNQSCdhaIQLlyBcvOPJOdl17cIshlHuuzfBJtSBuYyzQ/DU10Fje515KeHZeTUtiRZQWVAFpi2c39OaltHLbcsxoguwmjwHusNvejBshohgIoCh6qR4U00wkZcF78A3zPv33E8ZvDBxCNDPVoLaamOep1r2b5OY9l18XfYOfnv1ynw4g+M59vlIEUy4SaDCr7bYFMBnKs5LbyEY9h743XM7NtSzqhZuDorlICimhpm7syTg6U5pJuJvl+cwCjptalPZsUGDnQEzzCzuUfZS5KwU4GtE2ZE03TJOkCsyrzCh15auMtuTTjgUzQZPRWreSYt76WxWc9mDs+/BnuvPz7caksneBHtPNcfCnyjlpwcElpygvikro6bZEtbosmutY+KyvPPZedl3+d4f59dXWLJhhqaGDHtmULGEraeIRJg2/zhqBxF3hRbk+ULHONey9kndclPDTtYHevtRtWt+eRO5MMWqIYkAiMBlXtTiD3Q5Q6jQ3loPHcarPs30k0Zq3l58jjN4cXIIJUimphYtLrsfSRD2PDa1/Frq9eyswNN2WJtnyU2ME1ZmGzagU8lgkJ9ZZVG8ennauxrUO0mum5UExPs/KRZ3P7ZV9OgVC2NJcAq5Yw73a2fw8GzSutbHCPKb/afgApc4rvpZ0mX993N9sLmKjozXXwNxOWy4aVO08oltmXJTnfzdYtTiZpH0vKkqsn50kKR5zEds9R3pEk7UbDcO0H2LAAhWLxIpaf80g2vP4VbPvgpxlu35nxpwNrY7VAKICUhF9ouiRvl8N6mtoD+Wst00ABvQ3rWHz/+7Pz65fVnv4tz8nK0dZUtoq5oNW436ioDRLfy0GRPYrC3s+pzftzoNxVRuAL6TKaG8M6Ig+7VJYAEGrAMZfWqc1GMQAgC5IGxrdSAEZt9RmVhyT8JMvb1H8U7ZzdO/ohRya/WeCAqJ7hw5q7KKlxdb5I6QdXb/kKlj7mUez8ysVUe/fZRA+8OJHJZEzIMxJr45P4BjKARgsfJ7cRMs9XP/Ycdl97Ffu3b6UaYTeU2xAlAIvJ+HBDTZ3fy5urLdHWjGqJp3VZKV82C6+MAi25hN5S7iZypjHoc2ktd2SWVCv48IAIgBTqAyL97qKBty0aK2E1JpOszkJzsmoAq8lonIp9TrX7vZ0sH8n9Bymt18FeqH/csUyfdl92fekymJltZzP5mvWoserjqO8XlOrOsrN9xPOd8Dzygx5RQKt63h4o2AWF0AftK1XfgaWVT/8Fdlz0VYYz+6LmSHshX43HJ8XfVls0F3l+bZfk3aAKWqIaaNbP/Tiv0uXLNq1s4AVhyQnSMeB8/1TxANVw3I41bragKKQ5zi+RBUDp4bPNbfZtv9vrUYOi2apk/7DnwqDn7Im8TdG+YS/Wy9pLtVEN+mp+i+AP0a3cETVlRVFW7j4pH5vLhihv6yOB3yxsQIThH/l8F+9nahETZ+W5j0dnZ/n5W/6QvT+8dqL8JvpmdqksqKxb7keNUb7rIwNIuXanWLqUVY95PLdf9IX4ftvyWtQY5btMDKO12q5RVWm1Z6C+f2CNdJDI52XnkZHlyNo4eTTXclxMk6SP2R1EgUHnNkVVVcRDPHN7hbQMtpxmaSOTzBsedCds7iNRYrvHyPT9aGjqJ260Bj6JAbRifArBqmc/jf3X38jNb/wjZjbeNj6/Vjki6+RWm2iXq3yfSTXGGjU5iV1hopHWuIPMDp2pY49m8QNOZedXL0k0T1bLlGim5uo2OY8mu86rmIRamAqVT/0NpVqUnPJ7ufF12NEVxyi5xsaCoRZhLpR7DAgZtY1+HMXNG17zHH4PtD7dPmqM/G8LXqzrkVQITMsanbyK2UjSUt5JltCORH6zoAHRKPVra5wWKpcvZ8OrXsHSRzyMrR/+GFo57qXVIJXWjETWnglNkJGrrMUwrqLemdHwHWSZlQVMWVj1uHPYc921zGzb2gqCGmrtMX1NZXw7pZF9lUeCoUnTkBYm55lMomMnLbuVZmyS2WvjC+kfG+mvkZi9HAMA0yWzuk5Be2SD+Po0wUyYUDQFRbYOORBqSOBzN7wizGo5r9DRGDJgvO1+Tv2jN3D07/4G/ROOZduH/8lFrQaoDpPv3CawtGkNawCi6bZ48ywCo5CeBTERaGeepbOgAiufcR47L7qY4d59kYclwtZc3qkbbTfH9Zh2bFP2jqPK8Bf3t10bYQ2cG/5/DmCubtMMjaM255Hp1vqmRiVfThsaQ+vc79I4jRXmrys7Sd0TljWaHab1OQL5zQSHASx8Eh2vtFj5tPPY8/0fcNMf/BGDFSvYuP0OymXLqPbuY+3zn8uSh5zekmh6qbbTaLiO02C9TBYGcqlRmhSa5VNhbI8rlixm1RPOZfvXL0affn5jAk1BjsS08nKaV+4a1VVtpcgMWsCGqvlAXuKza/Uxg/SGTzi7MQZwtEk0+b2QNwHQhGJpfRhkAEj1dVofEY2gKAdz8T0viVE5xuEScR9NQp8Z9WHC/aTNW9omoyCxdXQQKO92o3hMy7he9bxnsOl//m9ue/f7uenqa+itW+PjCute/RKmTjq+TtSkIy19KQxoVY1b0aPgFPpHpdhZrCk4mPoE0BQiiRPs+kevZ8kZp3PnZVfAUx6TArW8XNVkfWyUQOuHT10u9ePD8wox/d46DmzVWFhtjl+OqkQoqbUuFeI0Q0HoQBGRGjB4XiD+fWsL5P5OVF0gtetRz+Rd/nWDVOqu27ah58tpFcS4FQ4Eoa4+7lm9nb8olKoCERevKjy/QikKqHyGubuAZhncezv27x9b1yOR38ypIRKRE0TkKyJyjYhcLSKv8/fPEJGvi8gPRORfRGSFeeedIvItEXmivz5ZRFREXmvi/KWIvGLOEo6bBCw/sbNbeOwlHBGodt/Jql88j2Pf/Lsc/Tu/yaqnn4cOB2z54IfRalhLQ/kyVnBSFqSswq7hU6/jm/frHWVNQ0YtlSr49+hpakNUgvbc2n7VU5Y/4XHsv20je2+4vpFvYkRpHLSlzGvO1p2TrBZ/ImAVuF3keKRnmynxDDGMFJNoUmz5YwjxTaHawJAHItHfkKfUFsEy2noXWZsHXHs/bsOP3omNQz5SZlYYUBTLYpcuRP0Zbz7kmgJLyoSOPNyJ3/MJC40OKb8JyzM5X2n57W6EtP3vsCQ1sx+GQ1Y+52mc8IG3s/bVL2bls89jsOV2tn/yX6FQpMy+fxgThda2OoVCWUHp7D2kp0ivcjZDvcrY+ZDymMYSe4t9WqnQc0GnKnSqYsWzn8Kd372S2W2312UMoRfKbKWDrDkiuGmCoTBUU5vElt+Rj0++k7Oqai1K0J4ETQpQ2xDFvxptiZIdVgaUNpeixjO/HDQ1eEbQ8kC8HrVj1ZVfkmW0wbBk6M9ssxqjYEQuQFlU9Ern38jurmsukYVCt9dN1dlGLi2XzNHyhz+/yWmSEg6AN6jqA4HHAK8RkQcBHwB+T1UfAnwKeCOAiJzm3zsHeI1JZzPwOhGZOliFB8YDJv+hy9UrOf7tb2P5Ex4LpdA/9miWPOJM1r/q5ejefey78aYR0p8ZzY1gHJaFuIl3V5pb7e1OMQNm4jJbUH+HHWeLplj1lKew8a/fh8ZlF/++LUveJCPu3y00F0rKGWM+CNvK2qZ+zuO1vpcNbqP9CWVo8347pz+hGLHNF1GWptbpNQzHGVNnGA2GDHgbWzxglnJeYQHSwuA3E07GgepzsmDRaSdz/HvewuL7nURRCIvuewLLzn44a17yLPb98DoGm7ZkL6djO370hkBABErijZs1ArE8rgFYEbCRpBF2ngXQ01+3kqWPfAhb3/+PRmghIJ0DaMO6eo32agOaOUDK32sbHmbstgk0ud+h3Ag58VwdyjPi09ut9RYw2fjjDIjdMvvk/So4grXLZyGdPP3U51Iol6mbvR7Ds5Ndb/cOfpPQnIBIVW9V1e/437uAa4DjgAcAX/PRvgj8sv9dUu/3si26BfgScMHEpZt0DCYDKcy4/tozhnJ5hnZFWXTqfVh5/tPYddHFKROxGoixobmGHw2rc5sfw7BGOVm0NklBa7T4AacydcwxbPvcZxqOGDUrS3IPk998wdEB8L4DodTYL9MOkU4y8VFDhT8+j5H2QVFLlMarQU4eX+q/PowDKOrLGkFRnJjsZJcjMxkZcs3FqLoe7hLbIeM3tj8EQBuv62hWY5qQ4RtSCOWKJQ1+suwpj2Hp2Q9n92XfbvIZQuk19pc4gUXv6mnfEQ9sUrsh6mB3oJY2TuBRAVgR7y895yyGO3ax62tXxHtiQVVS54latrWtRz/LEs2HCHMLMFZzEig5tkPqHVqQgQcDIhLhLS9Wxi/aKNUCpUt4uT1Rq6aIOm6bd+6gEXPAz5+RhpoddToeBBneZ7t623lxbXQk8Juc5lVCETkZeBjwDeAq4Jn+0fOAEwBU9WpgCXAJ8FdZEm8H3iAiE0HFBj6pWs4FCmSvg8TeGMDZiwK9tavZ890rqfbuHQ18aLkXt6C6dLVXA5K4jGYBj9laH94fuXMsSH+lQk/Y8KoL2HXFFQx2bo9SnQU7tWF1PXDVlnu+NJ93GgzMAJAWyW9OLUwilZJ8L8m/R5a2S98M5DbJM4kcJsEa4OSuAnKmkG75z+Jl8YtCkaKK5S7KqnV50IKw1mDcScxFgYlOGhYy3aP8RiU5bkNim4t5lsVPCkvKLxobJxQphXLlMnZ+9iJzH9PfPRjKl1sNKEqW1uI9jHbZbJkPvGKqqrfTh+vEtYPPo1R6yxax/reex87PXoQO9tdbsnMg39J1Ig9KbpommhAINcD/mIm5oh7z4zQauUPDcJRHUVSpP6Gi/gbpcn5dnVHb//NQmWWvqipGgqLcsDrnkfk4batnUTje4uql9HtDCg+KwrEhYbkzbBCJmm11x9RYEwCthO379jXyabb/kcNvYB5G1SKyDPgE8HpV3SkirwT+QkTeAvwzMBPiqupr29JQ1Z+JyBXAiyfJc0Nvqn0SiO2qzXtixk/RfHltr5/AwA2PewxLvv5tVt20kcUPOa0RP9dGRAVUNmjXFv26SDri9Pj5kM9rTa+HHruBZc86n9mLvsqa5z+XAsOcQ37exkTA+PhIy2kZ0pqp+tNbrVLQTtklwXAd658BMssg1xb92tZFHCiUwhwkJw7YFKKu6IXUBuZqBqmtI7BWei3StE1zRGOHcsZlrJopAKzVXh1Pa6kwxNVQDtu/QntKHS9UN0RdK/1oyKniJbBK3KQV6loV3mIyaICE3HpXQoI+z5vaa+lbQpjRI2KfxD3ObzYw7Y3tqcFK9t2TtM2YEDDb0et+uLboNXjQ+uc+nduu+hmrN25l6sRjTH1T5CCBR/m+bZdl11AiRS8dL4kgYphUqEs/ltxd98ylWR5bV/aQhzyAFY8+k+LL32LVM5/oAHklqHjBAfHnHvr8iuysyOxa8HzXFC3yDrObrTY5qG3uBH/+GOrZiZ9W1YGV1epWRF0yFf3hkJ4qhTqg05OKXlEhlWu8/VVJJU5bUSHMSslQBJXC+XyrXP1W01xpDa4hJVRspKBlOsuQGnAqrKJP2NhRA7HmQlqCB/1TUferEKXw9Q/Lgqt12hlV+9QGVeH8T4nfqj8sqEQcv62cZsd9mzonHabIb830Eq4ZUcVQriOF3wSaqDYi0scxp39U1U8CqOq1wFP98/sDz5gwz/8BfJxa/T2SNrc5OJOW32GSywdb2/KKwKbhbJLk4Pnnc/V7/prpk09k3X98KcXUVJJuU+vSMmkBm6rZWpK3wAhS5grjhB5TXiflbapm0cc+jFvf8Rds//rXWfboR9QHI4Z8hnW+6TNSx2iGNu+frctiAE4EREHDVeFtnUK5awba1s6bqxnTTjjmVWgcfPZ4wspnHJspB0Wh7QrYVM2MBETQAooMkGhIdRHUKJt0tpbKbYQRUqo9UqDe0qoJQBJRNrPfvCN1XRUjjdUSWQSyWRlj/nMAbAWqhe1JYyI6FPxm8+xM3cZ2zMZCkfY9re13JI4J+9f16s06Q0KLYOb5T+bKP/7fLHnE6az9tecgkoL18G4E2v6/8FRE2aKz2fJRNjDCpe+syaYDKxREQcBPtii3F/sYPvuR3Pi77+Wo+x/NovufEvNy/bRArXPcQSaADTNA5Pv55tnZBq+htIKXSyTuNFNFxIEZt1NKEQ0HqNb8ZIvud0tGVEzLgFKUXuEOTp0uBxEYAeyj54yUpWAoBQPc36E4Lc4gnDgmNL6dBUTSMhjjt/C/raCEOi0OAtvYj+A0OdEVAE27Set0MlAp2Rb7wv3tVRXbi71xGW0owZ8RDLVgtnLAr1IHjIZxWaFJMV/ttz6P8Tgy+I2lSXaZCfC3wDWq+mfm/gb/twB+H3jfJBl6xvZD4Py5I9NcImsNkn5cO/nPMYkgytQJx3Ds297I7K2bmN20yWRIDRLixO/vGz8fVl2druObnWlRrZ2t+ZdZyO0BAtOdKll3wQu445OfYbBjR2LSkhh4h3K2NWdL/8+1Q/e0VjPZzeFDsqyEKVMDDM1BAXjky3gRbGVecJXaJiDpT2mG1glaa30yYBTqJKb/iOlf8d0cDIWOl0/UY2ioMq8wCYnIDX531/dE5Fv+3hoR+aKIXOf/rp4osbnzOjT8RiXxdD+y/ZM+ROwzab/KwLmkYcmZD+DYt7+O3Zd8B52ZafSH/BiJsGRWlFp7WfZLIIXdCWZ2b2LiiNkxZuMXvcqlWYaltypOsuXKpWz4tWew5X2foprZn9UhF4bm6Jh5W7T9hjn7d/QzNCZOAA+qEp0w2uUyCyZKv5xdt7VxVAiJzVHU0sWy1Pkky+z2mXHkGgGlEh0vWpugfDkpugeBuh5ZzeNOMpReUe+k60nVqF+R1LVpU5Qvz0eHtHPQ4c5vcpoE3j0OeBnwZF/A74nIfwBeJCI/Bq4FNgL//zzy/WPg+LkiSYNBpeMvUdPiJRHqe2IkcBsvIXW+Lzb/r//DYOs2yiXe+NoCIUgAkgiOqQjJttnGOr41bOxXqbGjWeenULRfJQBJp6raJX8Bg727kOk+y594Nts+9HFUqshQnKSaARohA0l13NZ+mTCquTjT6KgS28q2Vx0p3wGBiR+8qKYTRPjwo8pWT1ZBgk2YVAIm2ia4FBTFNfQ2oG1zNeUZtVxXFl4n5OtSlLU9SD4xtO5sGTUJjyBFGFLMK8yDnqSqZ6rqI/z17wFfUtVTccbLvzefxMbQoeE3ivFELU17RWtD5ONK7CPU7hdCX6wKEm2nebeameGWN/4ZDCtkesr3iXpsYCfmAFAMWJFC6fUqyrJKQU/hj2Lop0Co6FX1s7Ki3x9Slu79sjekPzXwv91W9KnekOGWO5g+9Tim73Msd3z0i2n/zPvqXPxkXLPPBYo0ixhAkdbGxLZsFjRUCL2iSo646Pl2DMChXw7r50W9HT9qfbOqWAPksV6xc35SSQRCARQNh+5sxOiZWmsDapdX3SesBinfOQc0AN9UOaiNqz1vHXX4a2xXJemzO2bG2xAdIfwmoTmXzFT1EkZ37/dMkomq3gA82Fx/n0nAWCZ8qLh7CNG2IoCiMPlHUCQaolLbgWjKnDzz2f+TG5n52U2+bFXcNZYvmSUHdwoOvFDHlV6A2X4QFJoOaG9PIBpATNjl4VeJQ3pB3Voq4pnensu/wx0f+SzLn3YuMzdvZPell7P88Y+t1/JFoRCH8AWC08iAAyTUI3T8HDD554nmyfxtfhufWHgeGpv873jpsCjUMACFqnDtZCeXqNfPuGX8jqGwmt63ZdXsOlCw4fEGJFI4uyFBG8m0Oc5rA0NhF4uIU5NbJhodpHnHcBo+UF6vNiA/h4SlwOw9t6b/LOBc//tC4CLgTXc10UPGpAR/EgAAIABJREFUb4Zz2P153hI/gXe86sCuOGGokkTT2ZaWFMqdl38f3eeWY6SaRXr9BFhbdw0BUBdFXbiSikIrRISyJDGSDcuxRZk6rSoCYAoTf2Zm3u+5/PuUFFKx7aP/zvaLfsDaXzmHOz53BUse/gAWn35qNGJ244TYJ1Vqvhb8q1rWELXYvi3zrpwIc15IjVF8Yup5jtrMQv1M+1UqiErc5G2NqQuUqXII4I7CQOiVjoeq4reGOxAlnp80NlZUEsva+MRax7ees8M3qCq3ROf4fQX+CA4KtxxmNUUW3LUdCWIPqy2lYlE5qA+DrQr6ftkQoKgKKlGCQ0r8N6nbu+Y3obwreuPPMjsS+E1OC3oBsJbAvNQW5sSW0Lxf99i5tLrl8mU+Q2C6X2ceGZNnUsn20yzzED/6Cal3gITtsXaJLRpOJu9htsgS4yNK/4SjKdeuYu93f0C5agXbP/1vzNy2iWDrY5lN23TS+lxanrfRXBqjuUhIBmGiMTI7aWrJLHsnKWsLJ1L7LAu2P0QNErUkZiX7fAntQOvq61kkqviqBqXjyIIhO0HnoK711fmprydVYftSfEFEvi0iv+7vHaWqtwL4vxsmTWwhkgVDiWbIglJ7L+lX9d90udV8M9Ofpe+n6kIopntxTLT5j4nbxE1wjgVrR3tFOLC0rOpltSxYMNQrh41Qls6pX1kovbJi2QOPZfH9juGOz32D6VOOZstffZLhrt3NpZYWISdt2Lr5RsYZRX4cBqAHJBO3u05fsYDC7t6yGqKeVHGZyW5Tt6AjnxjbNH2jyhvKlfosq4/+CQJS2IWWx0/zbWZjtUOlNxovjfYrGJLbA2ChXXgjtK3Nf5xgkFT3yOM3CxoQNdfuXYM2mBK0giJp67wtH7t/1DrW//avQq+Hzgzay5JoiwwwSoCGyTjz7xHW+KOH2rhchgdQVUuogdXUKccyvH07g823s+Lp57D6eeez5X1/R7VvrylbXUYN5TFlbGVeLdJaI84klMe17dQWPVPxhmWkBBT5dMY5Eos0ahCbfmONoVvBRp7GPHBgmy+TcXEnJrNsN8lrFcW8ArDOe3kO4ddbkn2cqj4c+EWco8Rz5lGDw4MC2InL7KkgZuPFv4Yv1cJbcxLLJ7llZ5/Bulc/BxB0kPIba0hd96W0bwUAFZbRygCKwpgqU96RaIa8N+OpcpiEfjF0npylol9ULLnfUey9/laqPfvZcMHTWH7OmWz684+gQ797Q0jGaMNOyPKVjNe03U/eHfmN6ocHIrAEEBRCAEPB5qbI2tvSSD8+LXyn4ZojCGMeoKXL+u1AqI1S04P20+5H/Y7G2+N4aVKXEZJ1Rkcav1nQe+aiDREQVNBqZpO4hKUBAFBrUgOjQuvVHXtydQAt6kT66VNOhtkB/dUrfZLhJTJgEZhBygCsAa1D2TUYaNiMUN+XEDVnKp6KoqIYKr3lU8hUH52Zpbd+FTqrDDZv5dZ3/AXHve1N/swsl6ZTaQdpVUkk1SANCI2jPkIbJurrefCdUXWoDYvr33X9UtV0JYKIl6Yo0GFLRnNIaUmZjXSZgh0rmYHE710vOyKjv12oV+68zUYdGsZ3QGTesxqMkdFVDuQAxa1mnX5EurrR/90sIp8CHgVsEpFjVPVWETkG5xn6sCW3Y8r/VjLhwS8RhXsRZJsPooFF+fthWSX0s9CnAKSkt34tvfWroei78RgTltQpo9CQ8AXvtkKh3nrtcxen4bF9E0iOrAg7oAKggnp5ZnFVspOK5Setie8WU8KSBxzHHZ/6Glvf/zHW/caL6voU+IGN2y5fueXDABCjwbn1wdb6ATT5PVZoSAAiCZCxFOoWTogP2qHKD9RClIG3jwiapWElaASd7pvE8Wu/f1aeJiiShMe4H25vXIUH0CVux57WNmNl5qYhGlZLqumxxuGFKMNK2DfstR78apfww/uhvdp5kxEQx9CRyG8WuIbI/bGaH6nINERGissUJS6NdOu7mM5pEX61cze9o9YjZYoR00GWTY45iA6TqAFQdndHEnqVd3pmdngUWhs7+iDgtEVlwdpXPhuArX/7cTa93fmgG2zeyp3fuTLdFm9BTQbo0vumKWxc00T27/yoBThmFJ6VRb0EIIQ2hHEGy23gJnk2bkAbTWN7usLYZbOMAYthVDEZDc7YDhAMpdnNyZzwUQ6251gRWSoiy8Nv3Nb3q3C+gC7w0S4APn1AlVsopClfkew61xRZjZBdMkuBQMpj7JJEtWc//WPXk2uURmkXE/uieK9+FjRFvbKKoV+kWqCp3iDuPgqaEacRGtIvXQigqr98mmNecDYAN7/9o9zy9g8DsPvSHzB721afsRGCAn/xE3quoU6ELP9uaNCGxqjl2+T9P0QtsjbLd2LZM84G/nyzsCU/CeTauCYQbWRuy9coszGQTvpAeCYRCFtNUTDMtvWwtkO5QXUh6rbZaxHrGO7bOLHoI/hpw23DqHpl1T7S+M2CBkSRIZnf4kFRc4msBdUa8NMOiuowvPNOiunpJO90OcyUy2g5cjV2iF/v6jC7P0woS79mHYCAAVJi1v2tbcHU8evonXAU1Y5dLD7zgax81nkAbP2bf6iZDkTj7RQchcarAVCr+rqlvpPQRMtAdmA23g/bRNPB3xrZkkVtLaGhHcrBkF3maAEvuc1CjqLyMirG9T3MxVPG1Ks1uznpbtj1cRRwiYh8H7gC+Kyq/hvOC/R5InIdcJ6/PmypbUdrFMJGgKKEGqDIf8LYf8T0NWG4aw/FotT5X+4UNDekbdcWQDDeD3ZAvcIte/VKt4tqUX82+uMJYyyk2yucMe5UBAl1+ktOXsuKR92P2dvuYPUvPpIVT3k4ALdf+M+twlaroFVkz1t2WM6H8mUjS1HbZe5bT9A1OCoopLYlyrUt+bEercVsKbcFNvVN+yzlNWr5RFhSMwfVjqt7KOOgKpKQt4etR16nkTQPxnWk8ZsFvWQ28qMIccOYUM9j4R7hfttLPl33vE5o/09uZPq+J9HYPZUlkVyOkOSiUy7PMAK4aYtrC2wNKV05vfq8chqk6ROPZrBpGwyGrP/tl7Dxv7ybcv0ahlu2sf+Gm5g+6UTwXpHFe0RW3ACos/Gt1gZ6JD+wtj3OyHYJdfbLhVZTNoqsWrduE4nt6IqqrtzNjzoZZe+MBRmxocTVQ10/EbTeJTgCsCV+Ssz6mcXWB1LeSeuszH0g43xJVX8KnNFy/3bgKQc1s0NJZhKwwzKupFb43af1DsTIQ5K+6X+YyU/wfQniYNx/3U1M3+9ExysOEDYH7UBINmwnz4FP6bfilrgxZcFQCJXXSvSKikKVSpTlpx3DDe/9d6aOW8OGFz+J637zfwGw76rrGW7fQblypeM3Yaz7Xq5i+rtS84X4zAhsNJ/PRbkWLdXW1vHscRiV1FoXt6QERbAd8uBCNfAiqfMwvCjypEnJgKHWxxr6mkRBqhSNoCrYflmtUCivrV+so/+2E1FrkdKbO/bPve3+SOM3CxoQSbpzNF3Dl5oHhbGoRbpdOp8/rQfn+j03oIc7dlEsWZIxNRPPliMwxTBhRyDg8wkDxzPZYSWU3hdIm/pVIDrOCgNAVRhWgmjYcgtMF0zf73hmb97M7su+g/R7DG7ZRLlmJTO3bmT6lBNRUWSYreMHnh0YfaWoaFRv27K0Lrdh7mfxJx0PlmHNZXBciBJOtVBV7yAstItJLHwcXwjrzGwcWQBd31Bs0s2ChfKlBt/hXsP/CGErf5pn3TnHwKSWdp5kzlTu0W2wRxRJdmxBm31iomUsNRWsKitEuXGbax+tjd1w+y6K0+5Tb+EWZ8LkfM44Y1XxAoqIeNsfw2s8WTuZYSVUUlJ5Y6ie8bFTV9RtOw+GxMG4uEIoKo2G1VSw5LgVFFMls5u2s/vyq1l0n6PZ88ObHN/ZspXemhXoEKTUhMdAqLPPc+gEz2Q5zTWT907t2zfuqsUIVyT1tUtN4/hIGzBom7ynyiEMoSrcs1KrCFKD1nrot8ZPAorss2DHGQHgCA20SD1nWQ/XbUtlYWkvxA2apAq3bKYezFowmG7ft79x9lKxXmCR6cqpBbXt/h6hBb1kllPSx7XlHpDYE5kBmop86X3ds5fdF3+dpWed2Ugr/k06y6gC1sF2WpS45TKL5nd/VN6OpoqH9AUDO2usLQLLH+/A8/7rbqRY5pxITt/vZJY97pFRHW2XyqzKuj4EdnTIwVCy7DaOJtAGJW0ygqzzsKg1swBEYGKVScyYpE4HSm3q+pFr8pBuu63s7pL29O+6sCUM5xk68pTzDA9ioL6O9yHhH00eZOLE5dsaGM3cvIl91/6UxWc8MAJom1++jNJ2yHDbclFUamrqBdkunfSKiqliGP/GnUqYJaPIf2Ddk09HpvrsufZmRAQGQ1b9h8ey5EGnuLEZdtEGh7QB2MRDqqmNqdt4Dc37o5xAznkw9AFSmydraApHyWVLWZLy2TSM4DSOclsyWz4gAUOW8iVBNdeqEg3HR+U/r12v6ZtHHL9Z2IDIMKlA0gJypCWeTSOJb5mUp12XXM7ih5xO/6gNaRz/TmIgSc205qIUFHnnad5xpEjtP6T0xtS1LRH1roMIClxY9qgHUu3dz51f/17csrv6hc+AsjAoK1/60sSH0lgGZd5raoQM6MkkjTQeBsRpIhkHCldtUlu6zdjkm+XRNr4mYprhu0h6PTK6AWVWw9UAeFkyo07A1qoY7Rb/LvAMBXdW0TxCR47yzRo5KGoDS6mAJiQ8yMSvgZHrAzu/cAkrnvp4isWLcAOPBPjEv4znM5YvQA2Kwonpw6qIkyE4bUgChsSd+RVC4QURa0uz4cmnobNDtn/tKoa79gCw7oVP9m5BqHdhet5V85mUV0RcWNS/2+KkFRz/zSbhwVZTYv/mZDVpoR3iM2kHIrmfIci+SaKZm6xOLo1m3nkZmv6KaiA0MEbMA21u7Mg1TwdKRyK/WdAlFE2ZVP7MMqAknqa7znLpLpfy7vjnz7LkQQ9sMLXIyCx5UGQn/CDV2GA1RbU6sk4iAiGvFUocr/nT4S0oCBqT3oolLH/yWTCo96NLITVDiUwm1e7kDEhtsFtiYzwz+O4mYN8m/QZm7qtr2mHywZtKWiMiSdYJRtHI9ycphzSZZqhfyLYFYB4ohW2w8wkdORL1oKhqB0XJ3JZ/Ks1+WwEqSUuo9s1w58XfZOmjH2Z4jSTv5c5Bk2UY/7NAkxB4RSG1J2YLlKYKC4YGzqg4GFL7CuRaIoCl9z2Kpacdi84a/xeRx+Xgx2upEj5if6vXGqUCmhV4RglAky5TjaPUaWOYpOt7SVtIbYc1KUXe33Lf/ciuW9+3guZoENZ23y2b1uekBQ1ROOQ1z3uuus1pQ3QE8puFvQBofHeIEifyZLDYiUXjH6B5/IJA6tsIYXD77QBMnZgddRQYGbilDu9jo1VbgZOUClKjJzsxF6U5Jwfi7zI6CKsjVyoMYxpuAg0OvapKWPWMc9j1+W8wc9OtLu9+j7iUlGyLCMdQ+LTEFKrQ+qTpUWSBVGxAYhvUIMswt6DWnQDAKFBVzmKi8oOlSjRo7vupt6Vy/LSK3qaTzhHfa6mGuZeUyJS7dVluQtCT/3a22C3gx8bLmya0pUrd2cV/v3lgpUm2tnbUJBlajWHNRyI/qRlLcjP4OQuAR0PfqfB+ZqQeF8Dsz24BoFixPAxIwPSJSpHCjfOi8LY9IdugKaAWpgLVHo+Fnne0GDRBQNxN1iuGLC5nXRl83nuH6anmg6pgdli6E9Krgg0vPofdb/4gs7fvdBE05W11I/rqFFYA9HyzoG7AbF7MBbn5Uqo5ceUIBsqQaoX2DXvReBxgpiqTZcPQpkWhzqaqUNzHdPY5QjgDODWGt7Y/wb+PE4SD7ZGraPBtFOJFoXeCeuZAKGj/whzRBnxUmwfHhvzU8z3ndyl0+vBbWblomrnoSOM3CxoQiSbYpaZ6vmgYWiekqWPG9BloNeT2j32SFec8nqmjj3L3aIs8In1frgR1B2ATsIl/ZsFQoHoQ1tKd7dSN8nqm21u7iqlTT2LmuhsBqPbPUC5a0vIScVL1PxNOPxIMhcF0MDQXbXn4dg7GhJU3WrRq3AAQ48BVU4H4iXwj+2fSlllW/jyOMFpiG0uxr9TXkE4U6W9J4o1MVppNrvWcOleRWrfrdjQZST10/Q0yIWsMWZ4U3kjAsFDt2cO2j3ya1S98FsXixcQdaFLHdUDap2Am2aSc1GMlapN9/ypEG2CodtTntpr3pD7jalCVcSu6M8yVeMZX0DIsPf0UiuWLGe7Yk9W5fbzl++bCodcax3CWhtGyT0pt7Dhvq7iMlMWrEOzNYFwefleJZj7ddeYMrufuD7URNjG2BdbWNjTEH7VDLNQhGtCbmgdtUKN+mj6fePfZPOhI5DcLHt7F76jZtaVx9xpr/XUaMz+/hb3XXMvKX3hSe+ZeamssnWkTrbuJtZ7Qgy+h6AStTJfFrDQRdjM0svcDoU1Fuu43XhB/D4KjtFBMq+2AWGG7RBaXzoqWIMTz1urKmZBXPP61Gpe5NSxRoxLqGoGRkZyygDSZybxItNmJopZLk3pMnE/O40f1lTm5aMvfeTEyOeiO0u41ZJpZPDhJ+Ua6WWNcGgnPMmHPldcwe9tmtwGi5b18p6J93G5r50GRd644XQ6YLgcs8iE3Fga31Xy6GNCXpgv45jJLOIFdOPZ1z4nxZm/f1Sh7XSjXAIkJgTnLMR5dZM94DGBo0r7eEm2Uj53cyDiEgbevyZcJ7Xlg1tg82dyRF6cVGFoeRgtP89Ey7ZAFRiPBDrWvM2sbZbVBw6pgWBWN9+OWfjtXZfx1AvgfK3mk8Zs5SygiJ4jIV0TkGhG5WkRe5++fKSKXi8j3/JkkjzLvvNPfe6K/PllEVERea+L8pYi8YmzmOWPKgA2kY6iVkWXP7HWxdAnl8uWUS5dndgMyZ58YtxwkeFf53lC6dqWv2SBpMiyXdtYMiR2Ku+6vW82iM08DYN+Pfpq+Y9fnC5yK2hwUi6iX2vx10RJGASBfwcZS2sh2IvkguaNEC4pC/CJvK59Hm33WvCgBi6Y98met747Iy9QjsYfKo2v9HZPy5JFjeZr5zVVbhcN+Tf9Q8ZucPwCGH7Q/G/tBgvRh4pZLlzJ94vEUU1ORx9QOQdvB0Kg88iM3FvVqILSsP9Oc5FHvmVrpmyM7AiWOC1sm3WUPvz/0XH/Zd/3NbRWuBQqhwW/sjjQbwllrrQbIbbnMc8i31TH8htSAPAb/0Xvl0AAZXzQDJGJxR/EGw6eCCUAh9TEdVjtkNeTQ9LgNKRiyf4MBfqCaF41vr0a5Jf29Y3YuP0SHP7/JaRLINgDeoKoPBB6DO2ztQcCfAG9T1TOBt/hrROQ0/945wGtMOpuB14lI6p51DMX5KmNM1vAxxMv7T8LIPHOK97wdUW/Vaoa7djHcuXPSIiUTZ7J2TTq5l4XS7w2Z7g/c396ARf06LO7P+jDDot6sc59fuJOn+94GoCj8ydb+sMZ4arU/DmSdP8pjx6e/lG59LRTtVy701Pn66Cn0feipA0nhXjkihK2zMWi9g21CUtP+1ki01Y5mDBVSe/uObeCPPIkMtkyZcGrUGYJlyBgA6Bl3UcWDeGuGFuripbAqNXxtgqH0BOm0k1BvwQ87zWw+EWxSA9oAXucYrUpTEp4rLEA6NPxGW3iKvQ8JD2ncM+lEHlWBVBL9n/XWr2P/T29EB01tsKVkohLfDVoAjPVD05OKJb1ZlvRmmSoGrOjvY9XUnhhWT+1hzdSdrOnfyeJihiXFDEvKWaaLAYvLWaa8t+q+1zZFzXZR0SuH9MqKk/7wAgC2/O1nPC9yY6XsV5RTLhT9YQzSr5BeFgpFymzMJuMRw2Mn09BaNwPOlkaihiTstItaCu/R2QKinJxhultedLy4irw57giWjEdkvxNh1/JuE8LKQa+sahMBD6AqpFGPKqmnJHWHVBsWnjnNu/dR5ZfwgqAe6lBEzV19MDCirJweb0N0hPCbhOYERKp6q6p+x//eBVwDHIcb+it8tJXARv+7JEKOpCtvAb5EfR7J3JQBngT4tE2meRyacW2Bhtt3UK5YQblsObf+xXvZ++PrRpdlvtqI8JqX0IIRZDgzKIIeM3DC+UJBIikLh/ziMpvvvEHi6q1ZwfT9TgBg7zXXG4mLOniGo2WtFYpLY2V2nYVcyouTdWgPAxZaqfEdxvvhGUVFUbmz3hLVrgUsgWm2ACCy38l1G2Cqv1uiJm+AOpLfto5t7dCqVregnfBdAcncJiQvjKcDOH16QdEh4zdtwpXlG1a4su/M9UnMe4MttzN96n0Y7tzNpne+L26KSKhF2GomOfrA4KnCLZtNF4MIdlyYYXE5S1+GLhRDeuK33Itz1Jic8UV9+GuYpJc/yG08Gd6xi8EdO+vJPTuWqOhV8fzGWriw2iDib0nuJ1Js8jXn3KCh5twy6o0o+dJSG1lQFJbLSqkiKKz5sdY7gguNZhL58pMlq/0pjLasdrmSrh64utRgN26oyYCQ9TMV+kTaHs1rqzUa6e8t551z0OHOb3KaVwlF5GTgYcA3gNcD7xSRnwPvAt4MoKpXA0uAS4C/ypJ4O/AGEZlcd5ZJbKM0Q23jJWqYQjq+k4T39v3sZ0yffDIUBft+8lM2/eX72XLhh9Bsxk5AgClUrSUYX4UAdkKI2iDjOr9nJvyeH4zW4HqULc36178UgM3v+AA6HLgv2jMSmFFLx/X7qF2BqN5OlstcCGCpCSjmh2hGqW7HOZsLz8LBr6WQSFmRkZq2iBqgHPzUqaaAJ2/bQhtMLfcjRMKc6nqlu83a69eOvkxIAFwK+iZhUqowWxXzCguZ7nF+k/XRZH7O7yfvSVMx2fLO/utvYPqUk5CiYP+Pf8pt//3P2fEvXx5ZhrallEmk7AB0+lLRlyFLPCjqy5DpYsCiogZG08WgNsT2QCD3fWMFkVPe9R8B+NmvvwukaoIhD4RKc88dcE086DoZd4kmVpt9PAFFTQEst7HMwYROAIrs/V5RMVUOKUVZVA5agVENiqrosyhZQgvBAKXAs+zZle12oxmQy8BdrmmxbkryOuYgyl43bTWJAlmsy72M38A8dpmJyDLgE8DrVXWniPwR8J9U9RMi8nzgb4FfAFDV17aloao/E5ErgBdPkuf66X4ySSRSszTvBZsttXFs/gWs7bkqz26/g5mvfY2jzjuP8paNHH+833Z/22bkA3/Pupe9kNnNm+itXUW5fk2UcMJkFXeT+UG8hr4vkiIofa2Qyu/qqBwIKvwMWggUlWE4ngsG9+uDSpjVHgMtWMkUQ4SBFKgWVOKOhKgKPxDWrGHR089l31XX0/+/X2Tty56JiKKF1GCxUNR2RoW1RQ+Ghvu2MIv41DoRNINFwrXZUrtW+khuyBVBSPir5hOqDy4NUaX09yOAUWE1bmvwQAQtJKprFUGruvzpUlxU7yRlscxrrfRr3yg55U1i+UQb4/Y6CgXW0o8V1rwB2ijoN3x93e5XSbVICjeOTsG3ycJnOpPQPc1vNozgNa1/i1qDFwWGZu6sLXvxu+69/icMrrqWNS95HjNXXlvzm29dTbl5O2te+Rz2X3cjUycfQ2/lkvqYBh96qoi6BbLV9OlVXmDysvfSoseUDpnWAUvp116oqepjOsRN9H0ZMIXQq3rMVD2kmmKmKtk/7DNVLWKfDpjV0h0YSsF+egw9l1p/6snMnnA8KCz60g9Y/dSzsjYPzNg4llRYrf0ICIbDIo4ldx3eIRnH9cQchJ7aW7OgiMIaek6Gq4L2xWt81AORoSZarn4xBGoePK0DZ5bg2yjwlGU6TaHKHu07A2wKtBBmtHRHZAgMpfDDUmjVDGPABlAIrGYqugMYuZIRgZTR5KhSZlJlXBoDVlaL3LwQBLUcfIyQYYdCnFfcnBLskYT+oGl4n9XuiOE3gSYCRCLSxzGnf1TVT/rbFwCv878/Bnxgwjz/B/Bx4GtzRdy6d7bh3j1lTO5RzrQsg0o03P7A082zA37y1j8AoLzvfdn93e9y+803s+TMh/L/2HvvcDuu8uz796yZXU7TkY56s7or2LKNsS3jgis2LS81QPJSQnupoQQCfIQEEuM0CEkg8L0foYTEtDiBGINDDG40Y+MKtmxsC0lW7zo6Ze+Ztb4/Vpk1s/cpAmILXVnXNefsmT0ze82amWfdz/205soV7L32m2z/9Odob9mG1GssuvLd9mXDTf7GfRY3obsXdSfjQYDVJQ9hrokYauThBW8kGfUkLzv2Ycjcg5mJokVK2yRoLexWLesrkBS5JrLcKb0G8hecxeZv3QibNzN+8cnUlywojaMxFLk/vIyRnB20o506x98nEsSNW0nwx+s6BkmG7aZFcPOT+LCIpiUGRqYDHPnJIGgzyrBPjZLlzp4uToNyAMmWRqn47lSvqYv2I8qw07SKHsZKmz9P3N/ourvhm1hmbTftSl+69Kl0cPxZXP4lioliGsTcb0J6/KnaEyFvdo61OxSuWMEyCWX5k5hivetF2AN3uGzyj37kbwFQixey97bbObh5M/3nnUW6YA77vnwtWw7sob15O/VjFrDg3S8nQQdlySZRLPw+UnL2qbGCdRZNO2k5p+o2Oh23rLQ7dl79ILlRtN3FNVQbbZR1dNUJI3mD0bzOuEqp65QRNRb8bDKjGE1qwYfFGGHGuy7hZ2/+DJv/YTOnXLScpLdRAjBlPxa7Xtc5+xkPsqiUdNLJJp9rLY6EisuRxEyLH2KFZreMlZiOWNFMk+KF9kxY7N+TOjcFH3bvQ9QT0RxKRshS63PU0gnjWUqWRL5JYUxs//Iu1ebL5jRQRrNXjYbf0pGyWQ3I8PIwqSjO4RFThbzRotjBeBAxWjojzCZ8ThNbtT6Mv7KJK+c2+qY8/GiQN3GbTpSZYLWx+40xH4m+2gKc7z5fCEzigFP/9IVRAAAgAElEQVQ0Y8wDwM+AZ01n/4qVqrs/UekHooUyfR2fq7liJfXFi1GNBjPOWceyD/8Z9Xnz2HPN1zCtFvmBgwxc+DRMu01r05YOoDUhxVvpU5xYMdcqvHxea/OtGkFQ/EyR0FHEm94i+74ypDN7mfnsdQAc+NYPXFr9YlEd9LWlsD29HWz91cU7LYspTGkdEWimc2yiMSo+V3xy/OYKM1NKROlNnDF9r0xw8rS+RZEZzRXI9SUFOnwWIhOUuG2e8SuH3Xdv3SLIJo3kmAaAmXBf158C5FdYtwlO8Zvu5PiEyZuKnOnoVzdt3lQenWipMnsoxcDT1qFEMfT857L4qvejDw6z78vXApDvH2bweRcz/vON5AcPdf5+NLHG/6vNhpMnZDqhbZR1CJacWhRqryth0LGWXwIKLpdRTeXUXUh/Pc2YedxcZpx8DAAHbrqXmtu35pZ6klP3ySETTS3NSaMAk25LkljnbW92S9z/smknGo8JxiAGQ9PJaxRC8CNHa++ykLrs3r7kiff7rCV5AKJeHscpVuIs115mJdH3sU9RNTed71Ms/2LzWbe8P+W8RGUlbrqt7DZAV/NktR0N8qbapsN3nQP8LnChC3m9S0SuAF4D/LWI3I3Vwl57GL/7Z8CSKffqAmQmax37mcp30Zwy8JQzaT32GBs/9CH0oRFUo8nAurNAKQafcQntx7aC1vSufRIjd95X+aEuv00ZIMV2axsxoEovjHfeS6XQTKoPTKgthHe69jZo55CXFPbt2c8/FxLFgRtuJ02dNpl0Lkmiw3Gd9v8IOKkKUPI+SGEgO29ISVjHLMoEYKhj7CgLOW0kFL5VjmlLHOgptleiJRyAi32kqhFjwcmz69L9/vqJrZwrpvv1TBsIVcB7uZ6KBCDUjfns3sROhoexHIHtCZM3U+DNkvzw613NHl2A0eCFF3Dw1u+z49Ofx7Qz0sEZ9J9/NrWF8+l96lram7aieprUVy5h9N6fF30qmVwiZiRiHyCe2C3roxFqLgljrbLkpki8mBODITvJe7nkQVHTMdoBEKicNW+6AIBH/+4/Q2RsGi31NKOe5jTTLICjepLT32jZ9YmAkZeRDkB4xfNwSvd4MHQ4yQi9/E0dEErRNJO2czq3IM+PRwyEvLnN99nL54LNqoA0yuVWJqqT5lusIIa+egeDSS6v7EvUuXSMmSr64hVh6eLjVG5HhbwptSlNZsaYW5lYFJ8+wfbqOTYAT4rW72a6Dt3G/rqYSejpbvu59cIuW+wnBgbWnsbOr1xNfuAAv3j/+znmg3/C/htvZuDcdcx65mUMXnERow+s58B3bmHe21/d8XPeh2iihGJFoUZr9kmSPLLl2xdLG5sVFkMpM6ynhxOlSYx96dA6pPHX2PT8wYHQQH2wyazLn8rea3/I8A/upf/sJ4e+JIkO1Kyf0L1/gd9WGlpj/wTHYVGFKcrbwEyZTen2UpeSj00ChqrHhOoisXZo7LoxgNJujLQbLwMuu65Spnjp44fG/6+AM4n6aXczhEy7FWBtx8mV0xBTMp91CBlTHu8pW5f9vAvRdAGWMZD/Bmhhk7UnXN5A+b57mRLdawyhjE/XW1NVxICZF13I/v/6DiN33cO2PXuZ/7bXcvCGWxm44Gz6LzobXvdCDt74Q9pbd9E8cYU9Nnqn4hpmvsW1t4L8kOL9to7VOTXJULboDUARgk4cfq6pAYlzyLayyPrOpEqjjCmiuJSmtmaIwZMXs/+ex2g/tpOeJbMnnNiNEWqSU1fWSRmKCbuW5LSy1IKz6J3xrgFKbAh6cFGYBDwcFmhy+1VBUwwGtUvcmKocNPSnLYazIouDMYJ4ee3kkt9e9Km4R64iRnEvjUGLrcud605ZoY1ED23hPlDaZxItaTLA1O0721dXMUuEA1PlIToK5E21HdGlO0qgJgI34WO0HlalzBSZWIipQmtTKmHlh/+KR97zTgA2/tEHaKxYzsA5ZwEw+tMH2HP1vzL3DS+nvnRRxBxENK430YhjbTzqL4VTEjQBKCdU00hw5ItzR3hGqdoSMRhsxFVuFNqAMca5NQkLX/o09n7jhzz2V1/l+C8dS9q0t9eCK8JnY4SaSWlI1vUlK1O1zm9JV/LtVF6EUAJHTOE/BGE8SjcKv58LqY+2x5EX3gkyUQbyIqzWGCERTT3x5U4ErU3JJu+dA3Xux7GTgZoIrHTzQ+oWxjqh8I3YMz/eE7aKJtf5fdddJ2xHm5Pj49VifOsVKr8tsJ3xPnaLlSfdngODnVlcPUbV6GHZX1zFL971h7Q2bmLLhz4CxjB4xYUAHPz29xi++ccsfO8rqc0esLIFgizx70GiNDVjTVg+MtUXbQWc0mVZjthZWJHTBmrACPUgc9omYVynYWJNIjZKSU5KTou0ZNL3yynvOI+bX341t73is1x245sRkQ7A5o/py1PGkxbaCPUkD3IPrE8luBpqrn5arhV5BIxi8zkU719dklLW7SRiXeL/Xjb7wre+byEjtVvqytZ7y/OCddNGUVcZGqEfgoltzCm1Xon1fY/ZGd+HEO1G8Z7HeYNimUtlH/9Ga+PiP8TXuDNhHpmqTSaDuslEUYaZ06hldrTJm6PraqBDmw7UdfRdsPqIYsHLXwlAMjiIajToOfEERh/6Obs+ezWzX/XbNFYvD6YLezBhPWgkEShKkyKRWTDpBBBVAUUVyjs2r8XhkhCzLZRAVuJpWWVIB5oMXXYaAAdvuceZ1mIq1+6fJkUW7SQpkoP5Jbbpp0lEB5fs+7F5rUjmFXx1iJght5Rs5268YrNXHN7r+yrSKSRSpUO/LF1dmA9Tn3TMmyYrWcK7mZ6qYfXTesYqgKlrm4rRnAoMxbtPwESWT1mUJJju8j+ts3UdZi8zKjIk3Mcui0T7iQGV1ph1xeUAqEadxurl1JctZviW29h/3c3MfetLqS2cU3peVfQuhGSJkneYs5pJm2ZSBEkkjgH2kWh2myZHCnbIMURt48CHEXJTYaEcQ5KKDqZ7/9tDq2Yx9KT5AAzftzkAM+9DFH+uOX+kmfUxetM2zSQL/8OSZqH8SM0dF453JrUgV4N8KMubicCQTzJZzUzdsU0M2tiIXv9++NxOdZWFBJbevyr0z/kVpe5/7Ec0WYvD5qtyrvT4mU7fSpicIZpubbhu4fnxuE543FEob45shghKZol4m1fCwy2rmssoHyemPHf59d7jT2LossvpP+N0kjmzaA/vZ+enP8/cV/0OPSesKc93fsJX5QnWv3w+cZe1LcdMh/1fLdERgyCg8DmKGKNcS0d1aHAMkztv4lkIgYUvOYc93/oJO750M3OesbbDRu2FnUIHzSq2t1dtz7lR7kUp6GBtivKNni4OVZbDNZft/nF0mXLarP8+ZlvisbLfub5QXKtPcKkw6EQQbalbJTYjq6extdPSbdTEL0/tlvyGiDRVty7hgSTsNw2lLTr/JMdFpr4pz8Ovdp3/0yjfAxOxQV3ur1Sezc5zeWBkgsltxnnnorMWM55xIaqnzvjmTez79/9k3jteQX3x/OI9gCJ3TQyQMKTKVCblLPgiQu5YkiJoIxENBtrU0KbIeJw7hshvAzCxgzUFi6LFM9nOEdsNyHG/s5Yf/OH1bLruZ8w/dUFXhgiBmsodCBpH5bWwTzhfJAeziFXJlAomNBFX/Nqx5ABiTJCFVb8cHy0WO4p3658fu2AqRMhQoThuw7FG2giJGMajc/ixtExSTq4VKjGB4Z8w9xHdk2t25jArGCBxk5tGitD9w2gTsUQlhog4aeNUgOjokzdHPiDyLeaqu92DqpCaaF9T2VcpZl10MUZAa8O2v/k4PSeeQM9JJ1AqiRwxHHFNLb8tEQonZ+ks1hqbyro5+8XmoDibs8GCos7oLKsFAsFfRiM05g6g+hq0dx5AmYxECWmiwwuqxEVpZYV2NJHJLNcKMdqCDvEvsDVRBW0C+3J7gFDkFIr7apyvTjF+cdSY1sV+SfAPKvtH+LPGER5KbDoDJQallQWPypoSwaAMZCQYY5AKNf1LN/fsxBS+9y06HBA06fnjdpiC72ijsI+UVlKoKrJlwlvkZY19YCxLVG8wdMUV6NSgx0bZduXfMfMFl9NY7pNxlxngGBSFhK3GhMgnGwlls1KDvf/+c4J9T8Z0LTAfORLYoUzbaLRYc8+c0aADOBjnz1NRnJactQgENl63nnXvP698jP+McYCoHfrmW00049pOQ5mxUbgjWY1MWZAmeWJN5M48FXIF+fcvL36jJFdkYgYo7mMARQ78+LEwRkJfG44hAhhxYC5WIDOjbI45LJjLtAJFkLkF8zLBY1KV+SWltNDwY5BkU450P5/vXx67D0wi92KFNDjui+FgNjrxD7h2tMmbIxsQyRT/qxpcxAyF596zRkSWr/jBNLD3pu/Qd/IpbLrqSgD6zzyjdEBnsVX3cFu1JJwuOFJT9hvx4Gg8T8MLVFd5sEV723nMEMUhsTp41xYtDiv1jsheiA2euoK9tz7AyO3rmfO01RY4JHn43trSLcU+WSSGrwMUv/zGVG3foKN8HMpoUvIS7RozUN1Akfdv8pm5fYt9r+o6L7RhVckbonzCNTvGcR6URBW+RblWZLkKbFrQ3MP9my7H7DtIJ+iOzidiAoNk3KRYepgmwzoTMJ2Tdss9W//TDr91TeoqZQuqB0Xi/ILi+9ft0RETASl3Lt1uc+CWm+l5yils+eMPA1BftiQoV97pPwZCUEy86HIAhv1OlSamRpLR1oq9WS/jpmYjyxDaOmVcpxzKGjbZqwvNz3QSztXOE1qqc1pQwRep7LOSNFJmLJvJgQ37aG3cwdxVA9ExxQANkJKkYyjRJVAUA7hxnTJKjbQeOXVHjFami3WwPkcNVaNHWVNhXG4kZn8mAkU+T1y87pmXHtOmpfJQAiVB0zbOX0lBj7LgblynQZlt5WmQ6S2dMJrV0HmZ3jdIh/yc2JexvL3kQ+TYqlgxS0QHZlxr66ytnRzWXZy2fasqxH59Rq2n6/5x/442eXNkAyIIk8GUc1WF6o6PFdNFsEXre677Bnuu+0ZYn3HBuZU+mA7fk1IiMooU8tqAiAT2xjtEWsbDZ6KOhJwuig7681Y1iW4PshYbpdDNTjzn3NXsvfUBHvnY9Sw4d2WJrQqCwhTaU7y9gwLVoNJCKyv/d9m1xaYnMkZItRU6xpRBT2wSi1mpmtP8fB+6aSpKrB9EKDYZUd8eGMXCN3NmvlhwGv+77n4GQVIZu4miL6YCS4El6nasB0VV2tI9iCGybgJgdTjtaKOwH882IRiahJH2rapXRIp9sT+Aztl77XXsvfa68FXzhFWAjkCRO6eb+HzQhAfyAQg5YIQUJqy6ykgltz50xjCep4wlNTTCmK6RRU7UuQMdJbM9nZOcwqBNYlmUylgoMSx/+jHc85l93PvZe7jsT88umevAmrnreU6eZPSoVmCE7DV0Tqi+L7kRWh5wGIVWQsuBNx8iXzd5UIaAwmk6kg+eXbNms2LfEEHm+gi27Ildt2CoJrnNBu7AVE1pai47eKpyUp3T0mmItlLaAiB73yxAy6qgKGKMpqOEVQM4vByZ6tgQNTbJftONypuoHW3y5sgGRNMc6wBwqhp1hTEy1QnHCbVZF13K3hv+E4AlH3gvqtEo0+PRMcFnxIgzxXTvp3Y0b5x7SBvr3+LBT+5CxX1UBRBARqH9dU/GpegERR5szDtvDQ9/NKW9b4Txx3YzY9lgBxOUGB1s5KXvIoGLsgIli+zhXiuIwZHXGo0Rq2WZcqRYNY+K96/y9XykCyDyzRe7VY7+92AodsSuXoPkNr2+9yVKRIOyAaVKCVqrDlZxWuxQdZLzrdszUH0Gq8dXzhWnG/hlm+HoE1CPVzMVhWeiFjM+k4HWbqcSA6qnSc8JxzF6/3oAlnzkA537uefaT3xeHtgqtoVc8M1/9qazpmqTOzNOIoYRXQ8RZSVWyDNNVXlTZSawpqpMJwFUxC4AJ73oeO793L089M1HueR9p9I3UD4+QVOXDC1tmqpdPrf7rRwFSeTH48x7NbHpRtoOCKUqL/W9lucBBEHhDxTLh0Je6NJnsCa7uFnly/poGcdceV8kbcRG+7nzjOvUukdgaHuWzrEyWln5b30YO6OGf5n31M8N3fyHfH4jZ7Sw7mseSFV+61cFQaE/HH3y5ojmu/xYVzW30jb/VVUb66adTfAczLrkMqRu80s89hcfZfTBIjFacYwJUUVWUyufzGtxvnnHXz+Jx0uidDBHeTDkczp4tqhbMq148WAkdxEiJeYlTVnwDJuHaMMXfhQ0JL8ojE3aprovPnKi6SIqfCRLnJzNZ671kWi1cHweRXqVo2MSl/jNV46OAWNc+DY4QYpznhbnSOrMfsFBUkw5SqUSrWJDlQsn92rF6vDsCCUGq9g+teDoAFLRMxr7mZVnyBgJdfmN6qbDkF+xU/50lv9prk3BDnUMlZlkqexXlU3zX/t7YXXLn3yE9radHd3x73fMJhT3Lf5sRXgaFWttRMVbU8k5lNnSHKN5jVFdo+VqmLVy+99naZ5o8Wb92Hxlf9O+7zPm1ll06lwAHrj2UWqS01RtelWLXtWiobIA1tomoanaYampouBsj9u3J2nTk7RcUdoWPapNXzpOXzpOM2nTm7ZCVF0jklPe0TwsIadQHiLlUpdYsuZMd8qlKfCpCmpS7Juggx+WwjCrdoiBZIz+ZIzeZJxZtUP0J2P0peO2eK4qy6fURZlNJ9qs2rql+Yg/V3Md+bUiormQXzFLfzj9ONCejg/R0SVvjniGKNbcphRSv+zPKFjyzj9g05V/hmm1OPiDHzoam66qXpEVuRzVEDJAd5nBtBFazkFQI7Td51w7M5r3baFA3VZLVGjpxK1K6aC5xb/hgdnSV57Hlv+4m23fvp/xt1+I7inf6pZOGMvT8Dtd+xxpjjET49musD1KSJZi67hB5wsZnwcKm3/1/NCp8TXECr1Y8/MCzp8rHrtUNFo57VNZkCm5dXpULm9Rlivrru1YNusP5YCMcQVWVRQF55igak4UpAyegtN9iQEy+JpwFlgb8AV43bl9eswIg5fNMlM8793MHf/Tpte6JdEtyZcYJFVela7sc7ReUuwEUIr5b/8/bP/IPyC1lJHb7qRxzEXhuRHKk1iiNPU0DwC/QYKkLgxclQtwBl8gZxprG8cIubplLZ0wktUtCIp8kfz71xPJBd+UGFIXYJLhHYi1+2wZi3XvOZuvvOBrfOeqO1n74tUdLEae1xnNGygxtEk6TPQ+SaQ3ofmksXY/GypasCOaHpWTG6GeZPSmLXdM2T8oCexQca6a5I7xqSq0njGy3zfzJm0HcBIHmBIMNdUOaQzaJqXhGC8fsVtXFmR6f9G6ymnpxDq3U0NlLrO1FLnkCL6M3j+qiLKLW3cXhHLixzipLWITPtrvOinNiXKr+f9T+hAdhfLmyL6aSFsLwqS6/mtqtaEhFr7ljZDnjNxxF/mBA+V+4JC4KtB3KRJEFZP7ZLkh4jxDHgzl2pvNxEVw2Qc712Jzu3VB2dW8FLG5RRsh7a0z/zKbrHfj1+4rtDy3tPLic6DN3ZKZTm2w+j9uHqAkLu+RT9/vWbKa07g83ezT/ydRHhUfQhwzWd4hsp7YUGK/n4oEnaLwD/BLVVMsUu0XDJV3XPWCg+ieFs6txfa4lTSt+PsOs1mUh4mCKSoxRtX/dAc+08opYv57NDYRSUTkThG51q0Pici3ReQh93/WtE50hDcj5YVuCxye3JngmOaalQz97vPJd+1l/9duwOjulcVDeQgV+dBF70eVKYIolB3FuE6t31Ce0nJRZS2/5Hax8iDpkBFxfa/OZ6eQG22dMGPZTOadPA+AR27b44BYEoBZyxQO3X5bAdhsWYe2SabMfBzYbbEsT00sMOxJ2jSSzDJKSYuepO22t4JjtK3ppgNz1pB2WAp2LQufa5KXwJByrFEBrOz2hmO7/PGp5C5vUTlfUS3KT+dlUFJhq5UULgaxPDocZsezRPYchQl2shplcQ6iabejUN4c0YBoUiA0yYTS0SbapwKqmqtWsPAdbwFAj452MlDht93ERvGwVX10zCQPgK6AIa0Vea5stFa0GGOjo6o1aCZ7sDxAGt91iAXPPg0EHv7kTWR54atkax0VWWG7CUFPlU/1EPsEZ0kETvwSAJATBnHCtiq13UF3q6h+ULXGUikyxOdcsb/n6fF6tMSgyPsthWzisUCaQFhABGJ+iYmxW300K7AqQEoK+0r3mmqTC0Vv0/9voLDfCtwfrf8hcIMxZg1wg1v/jW7G1b3rthj162Oj4wSv/eedyexXvsCu6xykovmrok5WML075iNVeUmJsWyP6uqkPO6in1o6ZSxPGctS2rlljEIZD2eqn/BZofN5iXMa7dk8wimvPgWAf3/9DQHw+KWlPWOlSkAoLNORNe6608AUW7kRwI8DJT0uvL83KUBKWKQdTHTV76wJz/5PJac/GQvmxxgMeTNagrZgSLKuoMgzeLEMK9WzjN7nqpLdLc1K1cfSy/rqflDNyTTxPPWrtKNR3hzRJjOjKnR0PJ7+c4WennaLjonZpvqypTTWrGLLB/+KJR/7AMlAb3GIY4g8sxAyLUcmI79froW0Cw0fKOIIDGW5fTU6Hm4sLamNil4Eb96ZuHSENsKGT9/Mrv8qitLu+clGBk9bYX/bRbZpx89rR+l2a948FpvJ4vHw+9gNBmVsiH+8fSKTXPguelGr+3mTmPdPmihVvafH6yqzTpeuv6lKSokvc6VBK2w9NIUvFEvsaO1G3j5aRV0zUQbjk384ITOhRiUgFLWu/HmKEHz/M8bvPCHekW7Pf5dmkOAA/+tqIrIEeCa2QOrb3ebnAhe4z58DbgTe/Wv94ce7ddOaq+Me7lnxf6L5petj0WVb39mnsOfqr/OLV/4xK6/+E5AiXUXwf4uZIaVJtKZdOU9uhNz9wEheD9vi9VZumaGxrNZpahdDjo3Q9Ck/7LtWNuF7cISJIlMR/vP/fJND24bDOO3dNk7fvL7Qj5ouinsqY0icdjeuUxoqI0ecc7LL/+PcBEbzWpRXqTCH+fFsJhlaZZ1RYnQ6SwMdrE+1+e2pr7sS3bNaiECzyt9AMsaYrpGLA3MVeT+u0pKsyowi1UVCXG2SUM+s6gjta1ZOp3nXifh+KDE2RYM7NwryvDiflSll+VWdSqfyIToa5c0RDYi8dlZt/h5KfAe7ASMp7+8/e23Q/jfl/ZQw73UvZ9M7/4jR+9bTv25tJzOAR/PV/+VJfbRdI1MJbW01mnaekOVW4LQzGzWR5y6HT0iiFf+GtalrEVuHjQgIKcBlbzUCWgq7vIhh6RsuY3zXMAfv2gDAw5+4kZM/tTqcO8sVGUnQHLR0ahvxtcaAqArGYjCjDeElKWkkkebScVwEmGIfgPi8lubWHefwjoxhXTRJYkJIceYBldMmRUzQhtuSUJecOjlaW0d3ezYTrtMEhs7Sk5IUv2XTCpW1PKJ74BlEt3c4j3i2T4NHW8aI9SnyJy5OdViM1K+ceLKz/Q3wLmAg2jbfGLPV/p7ZKiLzft0/+ng3XZnMSoqYX3c+YG4uwePaYqfov2eWnIwxClAmkj/OLFurMe8PXsX2D30SvWcftQUziyrpE70rFRSmMMFRejSvMbM2anP65DXG8lrIi5NpRTt3rHBsYqeoQdY2CS2TlnwjQ2JEkVI/fEuV5rxPPY9b3vQ1hjftB+Ann7ufp7z9bHt+I9R1jdG8Riq6wyw2njWj69S0dBoUn9xIiOby1+r3A2hqFzHnQZKKrkmKsiUe9HUDQ1WTGEBdta2/YeTP6IFQEuSQpleN0zYpWiwD732KCj+jJPgUKTH0mjZ9qmUZM+WK2noXCpFSjiJlyox1eW6I8rhFssZes3ObSIoaaplWkLh+G5c7Du8z5M5JIa9EYLA+uQ+R7dPRJW+mhHcislREvisi94vIT0XkrW77l0TkLrdsEJG7omP+UkRuF5Hz3fpyETEi8uZon78XkVdM9tueqg6mMjrBTdFRujJIsZZXNb/F38fnUr09DFz0NMYffHTSsenWfMJEL9DaWpHlSQkMeV8hrQv2Qft1U85W7Zkjo+2DHdZNORIl+BwZIcsVNJqsufKlnPqN9zDrnGPJR1rkmQnmOk93+rT4ky1F/+2io2Pi4zNH2fs+xWa32FdpwrGrgKHYP0iiaJDSUtHyVORf4M8Tm+PqLvIvjgKMTWg+As0Lh5BEUhlXs00Xk5lQWmIwFB7DcI4o8qP0mcA24cOUYgBeAuNT090dZpQpFmCOe1f98tqi7/IsYIcx5o4pf/jX1J4weVMZ66CIVeQFVPwXJ/hfyBoTPk8kr3qOPYbeU49l+LbCStDNtNFtW4iicpP0WG6BhwdDLWeuavl3ONQtK0xlJlq3Po4SzGc+CCQ2p8UJZb3/UW1ogIuvfhnP/s7rGDx2Nnsf3ldkwtbeP8k6dIcot7BEfk0OyI3mNef3lJJp6wPVdikDvNktM+56KEqQtF1RWF+nbaoWnKUlCw7VXvaEKDO3xGAIihxK/tgE7fyPyqa7uBZaLfIrCr6VwefShIK8nh2MGfRY1ngw1A00x75JASRJYapLomCg2KQm0W9M17z2my5vqm06DFEGvMMY8xMRGQDuEJFvG2Ne7HcQkb8G9rvPx7vN5wGfBW5y6zuAt4rIp4wxrWn1rvI8m+r2SIvr0KbpQnl3CKSJbBQw8PRz2HbV3zPrRc9A9Ten5dTmMy37fXNtIzLaWjlwlATQ47M7ezDko5pCF8QBH+XT/UUZj6WcoyRGtYGFEA1OK1rxvhfwwO9/lt3fe5BZ557ghk5CqL/9vc6EkBBrj8XgSaS2mBgEOMGqA9PhchRVBFM3Dbf4TkdO0oXwSaRInjadpkRTw+Zm8an1C00JMm3C/UowLlJOkaId0HPalWMFrMkMjC60qqcfCHAAACAASURBVG69CRpW9NkPnYRxdByUFNFrBgkRbYXKJkwHBPnmQfJhtl3GmKdM8N05wHNE5AqgCcwQkS8A20VkodPWFmLf7V9Xe0LkjYkzpFcFjaOiw1sQy5r41ehQwkxJCQv7RFq43z54xTp2f+4bzH7WGahUwvdVuROzpv4dqYmtNYaGlkkYzesBXHjHae87GKf56NZyFDoRlHF1w/zvO7OyL91RjRb1pStUqjj/Uy/g+ud/nh0P7GHmsTYcP/cFPiP2GMpJJkvnk8JUb/fXzkyniohSMeQut5Jdt+883lwmirbxuYWsA3puVFmuODAUs0ng2CDJS/2KwVBuVGk9rg2ZOLlSVf7aKrG+lA54+SoAmVYoBBKCkmnFQjHGcf4hz8qUypX4WSK+P2LsPcPKbGXstlBqKfS9GP/D8TE6SuRNqU3JEBljthpjfuI+H8Q6Oy3234udHV8EXO022fxhnUasnViHqJdPt3MdYz0BC+T3na7dftLzugeiNm82vaedyP7/+G7YLd+7H4x96GOq0D+QVeQdO8eNZ2mpfESeK8v6aMFohfEj5haj3bxosN8bb7ahYGC0cucsM0vaSGCccm0Zo/kvOZdtX/1hmdlxWqF1riyv+yV3GmBYDxpmmZXy59S6rE3Gob3xNqC0XjUJ+LDZRGxUiXferi5VoRNH2/jcInVX7yn11am9o7VjhOL6c0qZoK2pSEMTVZgQvCaVJLrEAClVTHTe3yzMqVJEI/ocTSrRYVKMI9CK46rCaSphNTXbV10ma8aY9xhjlhhjlgO/DXzHGPM7wNcp3uOXA1+bsEciS0TknSLyNRH5sYjcLCKfEJFninTmk3jC5E2FHbKLKYCSn4y6MUiU2WwEax4jPmf53hXiw97r3pNXkszoZf937nTXaRjfsR9jumvqSkzIp2MZojwoE8NZw7JDzmdopF23729unZfbWVIK3mi10xDEYbTQzqL3PHq/vX9Rt2i0UlVzVWfVS0/nwX+5K3yfx2yxUYERGsvTUsTbWJaWto9FDuGZToryGG5pOdbIO5VbBskloNQqMEV5NNV5GaFclJoSQ10yGwDi8jcF01wka2JWDVzYvDtvItanyDpWt+lV4/SHdetoPSMdI0WHYrwhCs1HoJXYojgarfCR9CxV1eezFPUsRZBLyO/mZJq3XKQV2VVLi2SbVlZpDmYjU740v+nyptoOy4dIRJYDpwI/ijafC2w3xjzkLuqnItIL3Ar8QeUUVwHfFJF/nN4PVsBLWTaFfejcpZMpKgm68noQYImxENHZ+mc+71K2vO+jDFzyVOoLZrHxjX8BwDF/+QaaK+ahtaG9bS/b/u83qT/jLPTTjrGvR0WAebOS9mYvB1Yw4oCP8yGqXJe332sjiHMADpEort92k82aDYTJOtdW+/D+Pv2nriL7h28xfP9m+o5fYoWdFEVj40zZ3ZrfbpNAeidMp8kGnxuC8AyXISYwRp6Kjp00wZUwEWVD6k2R30Q7YJSXKdeO5n0NwLFIkcBrqMxF3wipsULTCw2thKbOaEo7+BUVpoI8mDdjoKl8zSu3Htdes+U7iuv2LFFVG3PknisunBfm0egeCJEDd/EL3W9O6T4dtsb2y7SrgC+LyO8BG4EXdttJRD6DBTPXAn+O1eyawLHAM4D3icgfGmNunuD45TxO8ibkIYpZWq+Q+M9S2UVFx8Syyn/28iXIm0K+SGIg0UhiUIlBJTDvFZfy2IevZs7TT0DVEu55+SfoP24Bx7/rGTRWzqSdC8OP7GX9Z77HwhcuZtEZC6lFmZ1980Co5TK2Z3mREV9r67eYly+VVpYgQFsUmajCuVcV1dW9QqBVOf9ZHNHklwWXnsBDn/sxwztGaM7tD+BFYUqOuN2ij+LvA4PimA27noTvNLa8h+9HqvJS/qG2McE/yMosVQqdJ4HEmJJ/FEDdJHRMLt2aUa5YrgVTvapFzeQ2hYBW9KpW8IEclTq12hhZMkaWJKRZPUTqZUbRUklJgcyCPLD9qOb8iYN4UqNDCZOSg7U7V/ADi9wkEin8h/zYxrJ5VqMx9eUfRfIGDgMQiUg/8K/A7xtjoiQ9vIRCWwPAGPNmujRjzKMichvw0un85rxGrctJoj5NMT9UTWZGYKiWOmFlyoBIDCZ1mrnTDmVoNv0veibDH/0iM171XE7787ex4+++gv7Y12nNm0m2Yx8Ag8Ds/lnkumkd3Iy/XkI2ap/a3fsCeXOZnfqcGtDxbBmGqFlziqOLQlilcTSpE8bB7GSwvi6RtUUZyPYdYH59gN6f7WLOsWuYZer4RIHTakYKqp/o3FLYjkQMg6buSTQ7lGIQXdFgtL0Wb1nz2pFgC1NW7eMKQyNvMrJ3nAObDqAzjUoUvQt76ZvTBwJ5KyepJyR+fAKF7CMA7aRQz2uBOtdGaJp6oK81FphpY4/WCFpFtdu0vUfGCFoK82TRis9D1DvAkEhhHo0nUM/8aXdzQ04Qf7roHm2Y7Bbx35dK3xhzIza6A2PMbuCiaRz218aY+7psvw+4RkTqwDHdDny85c28upM18ftgClDkXbuKm+b66V+7wDA55UA5WeOdqhMgMQUTlWoLjJRGlI2OUmtW0Hjqyex7+9Uc+8EXsu59L2bj577Htg/8F/sXDTK6xTosz58/n7mz5tPXHiCRnJr7TcuWJGRZI/gLZXlCUycW2GtFbiA3Sff3XmCmqePs9davzlhTkjgWXKDkFgC4idgzvPadP7R9lPmDc1H3H2Jg5gL6dDOKoCqbguLW6RMYyRzKk71g6NHNoPxB4fgsLvmh/+yPTVwQRiq5lZ9JG5Ec0Gic/yIaac9g7/Zxdm0ewxhIa8LcpT30D9XAGNotTa2RIJ69FoNxQKsOKJOgdY1E16hjE2M2dYrKmigxtHRCj07JTRIcqds4/0wnbzyrVYCkeCSisTMwqOsdLDt0Ak5/jjaJm3fsdu9XioDPez1Ld5l/o3Y0yRvfpgWIRKSGFU7/bIy5JtqeAs8DTp/OeVy7EvgqMCFK8217uwguDYIJOoTRRC0IKSgxQjvabUuHJzjtzUV/4DQ444qTKoM+6yQ2f+YrbP7gx1n1pQ/B6y5j8/s/Td/cJoc2b2Zg3Yks/N0LaC+dz14Zwee5gaJavKd8vSnLaIXOJZi/bJhYlwsQ+2e7bltmSNmsygghS7bFUVH+ErCZl2PwJIZWNsq20f3sveOnmN86DYOwi3F7jBfikz3c7qs4CiFucTTZLhkvtlNkhfX1dgIwcjfSpsu3gqWZZEHTi/e57SM/4sfX/IhubeaqWex/dB8LzljEWe98KkPLZ4Tfjv0FtFGMqlokYCwoOpSMBP+Gdp6gEylpUtoxOCTQ1kkAQtVcUyUfMDS7GC/dA8ABLKJj7DRjhCLS0JsUO+Ltp3rgC5+wI6F1E04uqdpSY8w9zrfn5132edzlzfa85WRMNH66AoiC/InpZ985ghwByzgZBTvyViFbwCliBjHaAQyDQpO63DbyvLVsvv777L3yn1n7Dy+nt72fB//iW8xb1cOOzZtZ+eK1rP7tszALWhyU4cCIgM03NJbX2CeGtrj8PiSM6TT4DWmtyHXe8QwGGSDCTtWy/dIWFKUmL5QbcYyLu56QVyfyYVFiGNbDPLZtC3L3g3DOIrQR9isbxu0LMk/VOiNPu/zP4EBSmHZiuRH7IXrWqCjXYVmbPGmVkizWJEfI+dtX3MX9dz3StV/zV/Swa/MYp142lxe+eyVD89JgyrI1F22kl8mbYBIEZR8IXUMDeW0YYxLajiHyyrItXFswRlU3gyqzFremabJfjYax8U721dp3/ny+jmZIA6O7VEroljcmbkeJvInblIDI2ew/DdxvjPlI5euLgQeMMZsPo9MPiMjPgGcBt026r4poawduRNv/OMtIbEqNSAzbPAPiMEfMBoXvJXJ8rK6LIemtc8yn3semt/4lY/dvoOeEpQy94HzM2DiNJXM4dO+jNBbPtpEZosKDWNDUBVVdih7Lo8lOu/X42TLYVBye6TEFgg/5cBTFBWq33Q+EPwn2oU3nzGT137+JB1/5V2SjbXSzWTARpuxkN9m6qZzbt6JCtSJDBWc/7VgRK5CKF8iG8Vqh1c4LCnwss+GpTQrBV1cZ89fO4ynHnMm80xaS1hUPX/sQ2+/cxp71u9n38F5q/XW2/ugx/u2F/8YFH76AVRcvc/lSVBCAoOlJ2kVxSzG2HpsrwogqKHrPIiVKhzIr2tjijnHKfVVhifznxJSdwIO9vjLmsQM3iSmYQwOJC5v15tCpir8ayn05UpqI3Ag8Bytv7gJ2ishNxpi3d9n3iZM3Uph/A4tH0EusKVNTETLufgZfIzplCVhPJ7cuqqLQiPVHU2KoLR7ixI//Huvf/QVGNuxiwSUnsve2R5l98gLIc0Z3DFOf2cchrVFJG20UieQccpNrSyeMZrUQDZo5H6GA5bSQZyoAdWuqlZCBXyshbyckaY5WVi4ZA2miMQKJgsxIyBivjYS8Xgpn4gf618znjE+9jHve9zVWvO7p+Ppr/v1KnfnGt5jRCJ87bJQunNxXBDBi0wkEE7lhLK+5Ej/WCVubGnWVBfN7O687UGTNZG2T0J+MU5Mc7czzDQynXDqXU3+7l0XH9ZOQc/MXNvPYQ4fYeM8Btj86ikqF26/dwe3X7uDdX1rL6rX9QdbkRoJP0YiukxsVfJOypEVbtWlrDakFsSlCphNqKrclPyLzvgdGmVakie4wm/kxFG3CuBRgMApoEQeGRMIYtnLLqBsjqMQEVwdx9+DQFD5ER4O8qbbpMETnAL8L3BuFur7XGHMd1vHp6gmPnLj9GXDnlHtJsCrZVQeC/Geidd9KylsANv6zwYgpm8n898qqgD5fSBBYypDO6GXOK57J1j//J2jW0XttArLVn3orB77/UwsWnPOyNkLiEHieq7Atdp62TIC7OB0xBhWfEZNbjc0LLLQUybuCABaMGF/Mndj3KAyjsxWrnjrNVYsY+dlGOO2kkvCZrJig9iyWa2qC8/vjjItY89EMIoRolW1X38KMJy9lxsnHOArbAiOdu4g0l516JKuFUHmd11hwwRr6awtcfxSnvGnICWDDgV/spz3SZnDZIF97wVe48T03MrLrqZz44hOdUPCDU0Sf+VxFPizfm/JausjnBDqAIuMEv0acCcHeY5Hi+mPgKBXAWB1Tv58/tzYWOFrqnVKStmCWmQIQQac/xhHSBo0xB0Tk1cBnjDEfEJF7Jtj3CZE3JjGIe7+805BnjHxwg2AV/fKtLYBQzEgb5xgf5xwyibFKTuLkS1I41yuxtRBrKqe5ai6Lnn0Kd7/5n6jN6GF8x0FaW/dy/GvO4uF/uSOAiZGsHiIyM6MYy2wm6iy3JrIQ1Qoun5b3XXSmW38FbpsBG9UqkGeJzeKuDKBoG7GgyJnSMq1cGRxBJXkpOEIZ+7b1LJuDbueMbd0PC3pL/inVhH66Im/KEWb+BSCYm3zLjCpqMlKOTjPGcO9Hb2HNC57E4PJBwCpXsVN1T+LyBok1ozVVGy2Kp7xkLnl9b/id531olgVN2rDlgWFUmtA/K+GPzruVP3/xXbz+4ydy+sVDtE1qHZ6NZYp6VcuVJHFzAzkt1bZRx3liGS1jldtM23Xv85iB/U4sq5bpQuGuBqOYaMx0mDiL8VRiwnPjyzMFUComgFogKG2z6lP5EB0V8qbUphNldqsxRowxJxtj1rrlOvfdK4wxn5zGOTYYY54Urd9tjFHGmM9OeqCYQuOKBU7l81RLoLEr20pgKDhAuigfFZ0D6DvrSejRFjOe9mT6zzyBoWedSevRrfSsWBBdpxU4WV5Ef3nfkwCG3DqaAgA5wevkZmlxfHvhdO07bryQIwjtwPhE/SnfB2ht2Y3RxdvSEQHWZYlLh5SrbpcjzHLtI1OKSLY4R1J7eJytX7iZB/+fLzK6a5hcC63MhQS7SDaf18RHofgIlt3rd/Pl8z7Nwd3tot8IRhQDy2cxdOI8kr4Gz/6iLYVw21/fxm0fvS0UN9SRFunzFyXizXeFH0Cxz5ToY9J9pqM5lco0SLlsg0Tf++cwgOBJWvVeTbU8Ti114bIvwjo8TtieMHnj5IAJIKZQooLM8JFkleAMpPw5NtP74+PgDbDAyCtcAiHa0TM3c59+AnosY+lzT2bWqUtYfMmxHPj5TgbXzAldzrQqR2mF6FAVwFCui2jWIpCjYCEDGIoZbO2CPXwgSJTfLM6BZn1fyvnQ/ARpjJANt2jtPoSo4l2ISwVNJG+qucuq5YTi7Xm8HrEprTxh3yP7eOSa+7jlHdcxPqZLkWk+LUGmE0bzGiMu91FbJ4zpGj+9cTcfOOsGRvPUBnU4sxaSsOiEQRas6WfG3Abv+cZZAHzyjT/jhn/a4gI8bPSZZ8OVM4cWaRLyENHmWWSfKDKRspm/bP6b+oWNfYa6OayHc0r5nN63K/6t6fzeb7q86Tjwv6c/v55mwXGZnjbhE4U2162VSY3uwAoqwMqEMGgivxwA1bRoWdKEuS+7kDQx7P3O3fQcu8id3/oD6Mxw6L4NHFq/haHnnR+F1hdCx3fCa54eDJUdOiVoqaLFCmvtosL852A6M2EwxIDPVSQUE7O/lsbyBeTDk6dkD12IWR8Tm9E6Bz03QmvLbsaTXsz8nrDNR6SJGB75wBcBGLroFNa//XPMvujJNJfPo++YIZpLZnPo/k2Mb9hOY3YvrV0HMWMtdt3yEK29I8yfNRfd1oH69dkzqi9tY7DBWe87l/Vfuo9Hrn+EkW3DnP2us+ib3QjHevMZxuYdCgIAmwgyc6G6Xst0FknilPrK2Ci7Ei096ViWzZAx+AlMUDXvkNfeptm8j8gR2D4IXA/caoz5sYisBB56gvtUboFhNY4tkWCtDsEEzqm+eovsl5TvlVe0DMF85tMpiGOjlU/TEQEh35oLLaPRt3yIuU9ZQv+cJvf97S0suXCl9e9xz+z4qGbzrY/QHs+Zc8kpRYhzXpQGCuZ6D36ChhhdhL8u/70CtME4dlWMQKLJcuWcuJ15TAhmMs8ca7Fsb9JTp2fxLFr7RmCe389mYIbubpMQ7YcJ+8SMkV8/+PBOegbnoudEICCKFrvp5VczY/UcGrP7+M6rvsqSZxzPjCUDzFg5RN/iQbbd8ghmeISkpshGxhnbcYhNN29k/GCLod65jB1sh3D+RPIQVVt0VDF/ZR8Xv/oYNt53kC9/+BF2bhzjt96ylJ7+hLqiBIrqkpFR+CopMaTkNkN15TWPg1Wq26vyJgaTHeZG6FD2tHG55SqX48c6cfe2G8tduvyjUN4c2YAopqcNYCLzAUTUT3RQCQQ5OtHvFoXBGh9m7+dWVdDZ5SKLhPW+049l77/dSmvDVg7d+TC9Jx3D0CVrw37tbXt44DUft+tpwszfusDmG8oFnXm1E2v60nQCoY7rKTQ5D3gMTjAr7DncIeJPrUF53xPcxBsBmvqi2WT7R6zWV30jfDeiF87E6xVwVW07r/k+4+t3cPCkhcz935eimnUEQjK3oec9jZErv4ga7GfZe17E3pvu5dAN97F16x5a2/YhqWLmmWvIhkfpWTJEfmicmWeuJmkols5fwL5rRtj2k+0sffoKlwyuEv4rFjDNP2Mxd37ix4zvHWPDdzey4bsbWXHxMi7+8Pko0UFwKCkKL6JAGUchO3+izCWiU8aOc7UeeWyadIE5pVYV4gXwMaVj/Hk83R1vi+/QdKDRkURhi8hLgP80xnwF+Irfbox5BHj+E9axLs3UbOGn8D5GecD8e2qS7pNUaNHQhxQeWDO8JKYwxSfWPKZcHcQ0KTKze+Wh1lOjPref9X9/E+29I+RjGf3LZtK7wBZQVRj237+Nm177rwDMPO0YBi48nSz3eYYidihXBdAJssR3NFrEXYRnjpzupol8jpQ1LSdKk2vrU+RN6kYKPzmNgFI0F81kbN84mctvBuX3SCoTdezYG7MX3dq9H7iWraqf5PJVLHre6ai0MHcrDMe+7RIe/Oi3OemFp9OY28/276xn511bObRhD2M7D1Kf1cvQSQtAa3rn9pKPtVl26SoSBcuWLmTv3+xi84OjLDq2n1xUKPtRdD4nNwknXjifm76wGZ3DDZ/fwg2f38Lz3rGc57xuoeuLPaYmOYnKyMTWZmuqNokpnL7bfoISsMqeBaPaFCbC2Mm6GxsUzGZBxpXHbiLGqANwUiUjurejTd4c0YAIFU0yhqIGlFsPmlvlvpW0NqLP3tExdoD0eYe8qcwzQ1I+XgRmXHQah+54kNn/6xwO3fkwohT7bv4psy8+GdpZAEOL3vgs+s47rbvZZFKBGvNflOh6/1miz7ZfZdahg2SgYBms426Cye1k6xOMTdU68uFUL8utz3vNs1FfuJnN136X/df/mAVvexED5zwp7D6+dR+1hUPMes460r4G81cuRtC0t+/h0H0bGd+0AzGGbHiMPbesR7cy2mvm05w3wJ6tOQce2snw1oNs/t4mFq07phSFpoIqrulb0M/zv/k7PPTVn3L/v9zL8JZh5p40l+FtwwwubCJVddwfLwRwhC60TQ1MZa2qRu34FvtNxN93+F2ZwjxZPUd45KeBiB5HWno6bRnwFRc1dgPwTeA2Y46wXuIAiwe1Lhs5wbRkIrnjAHCVJYrNm2AVK4X1nQuyx7JDYZEimWtc2sWDonnrVrL5a/ew9o8u464PXk/PvAE2ffvnrDp+JeO7hgMYOv3Dz6Lv9GNpGQKgntxkGzNZXpC47TGT5fruE4jGqTPi/vpmmYciB5odB2VLBmlVFEGNWhX0xGa3HDom9Pj3nvzxV7Dnyht4+P/exJZ/vYPVf/gcBk9Z6nyqjDUxrl3K0PknkNQUM05ZhhjNoV/sZv/dmxjddgBpZwxv2M2uu7egEkXrhDn0zunh5w9tZvxQm0d+vIfWqOaYkwdtegFfoBbjSmtolq+dyVV3XMjXr3qA+2/ZzY4NowwtrLN/V4tZc+wUm4gJLJCVW0Vm69zlC6yJJg8MUIKPhPVj240Ziv/Hn6sO1aUINboDKT/+vpkOQd/ZjrA3+VeWN0c0ILLhqX4lMjERCSRTVnhCEzpMZt7RseyDVEmv75YALiKNfuCpxzNy4VoaK6yZ7NC9G0hm2YrOw3c9CsDcF6xj6NLTaGVJyMdz2C0SribqdwBDKtoHL7giweRNa258YnCnxzN0rqNistELMMFjUxWuMZ4oATCVMud/X86eeX3s+sdr2fbRL3PgxjtpHruUsQc30dq8k8XvfRmtXcPs/dbtjD64mdH1mxCl6D1hKc3l80jqCf2nrKB3xVzq8waoJ3b859LAnDWfn/3x1wG44qY3l23dUoTpem3nmEtW8cAXbSTmbR+7nds+djvNmQ2e88mLmLdmpu0yPmmb9TPKpLsdvVt46eFEWBQmOA+G4nH396Kyfpi/ZZAjisI2xlwFXOVKcFwMvAr4pIjcD3wLuN4Ys/2J7GNo/h1zrKtx701gjDy74ryP/fehxQqUuBxnic3BheoEGgLBZOZrWJX8OcRw3BvOJR8eY975q4HraR9qsf/nu0mVZsO1PwPg1Pc8nfnrVnCwrTopzKmaB0F+NQJnsbxRlX572egVklhu5EYsa+TW9XhGnkGmxSZ9zFUpmWk5aal7f5lMJkWD3uhh9R89n4Of7mHHv9/G/e/6F2auO5b+4xax9/sPYrKcE698Afsf2sW+2x/l4M8e4+D9W0ln9DB40iJ6l86i3pMw95yVDK6aTd+cpssenTOUN+n5UsqNf/4TBhb08vpvXREuyrPLuXcOs4nWWPeSJdzxDfs4/3/vfBCAlU/u4/c/vprZCxuOKXIlQgS0A0IeKNrIOMcQuev2bJBvMTvk1+3/7gCp27BVQZBn5jqz/k8uc45GeXNEAyJvMbKfi6iPAIJioUUnACr/L4MMlMEnZ0T5nD1lgeDBUKEdCf1nncTDr/0os555Jnu/8SPMaJu7n/WnLFmyBIChS08rP0hVerrb9so1l3bxwikCcyUWq9IK7dALV4I92GjN/utvY/EHXx0xElXk2L3F5bWMiVmn8n5ahBmXnoXq7WHH33+F9vZ91BbNpffU45j76uew/xvf4+Ct9zDwtCczcPZJzH3F5dTnzUApe27rWFrcj1zbSBaNYsYZa1hw+ZNZ8twn2zpAGNIQPeY1IhXMYo3BJs/90vMZ2X6AQ5sPsP6aB9lw40a+/vobWH3xMZz/7qcgvhYUQiK5dczMXVSG6BCmOllR2vI4TbxfDIq6fRcfX/1vV6bx+9Pq5ePbXAmOf3MLInIicDnweeCyJ7BroalU2xBzJ3TEBHLIvh+iEZ8qI74nFWYoKOUOyJM41rnEDFEUEa6wLiUgXq8xcOx8fvimrzK0djF77nqMnnl9XPeSr7B5s808sOiCVb/6PY/YoMKP0vs3mVC2BjpZom6JFb0JbXz7Pvbf+QuWvOWZaF/2I08wzkxkwVBxfLWyu8jUGC8jZcHvXYKa0cu2z99INjzO+K5h5v7WGfStnM+jn7qRA3f9gjkXncS8y9ey+g+uoO6UWCW2/Ikf75Z2UWpayEzKkmedxK4H97HuLacwrlO0UqSSg9EhR5gHRUo081f08cHvrmPvYyPsfPQQ139yI4/cO8x7nn0fz3nNAp71usUgJhSC9Ux0rmz4f01p2hraOnXJGcvsUDcwVMw1k4OZ2JRWPdZUzuu37WuNM1U72uTNEQ2ITGoie4Gb2I0p3QWJd6nOM1VwoXw2aruY1AQn5ZjO9p+L2lQmgDPV34MeHaexdDYAM889gT3fvhuAZe95Ptv++UYWv20a5kqJqK0I3JWuw4MxH/VGpKlFZr8ip0hxAg9aCsAi5IfGkZ4GjTUryPMC3RfO3hTXO0EzYVy7T/xG7Ivce9apLF65jF3/8GUO/fgB0jkz2fPlGxg47zQWf/j/UJszMwBNbUDn7rMuQvWVGHJl2S5b7fIT2gAAIABJREFUUqDO8t+/gkRpxjMblUMCKTrkNsEQbO5KDGkC2bjh4PYRDm4b5rTXPJm1LzueL73oWlZdsIjjnjKDhsqCw2RbJ5BkkKegCCG+1Qy6vh2uDb3b/lXTmR/n4PMx3TalqeSJayJyMrCcQuY8aow5IsAQQFrPyTMbRuajQYPJzDrSRCb8mOHwH9y76XZHDJJom4DRh9Z7QASuVp71H/J19MSBcJHCp0NqCQfu385JbzmXPXc9xtAJc+BhCxPOeO/5PPSFn7D61edM+Hz6Plrx4otD++1OvsWyJLG+eIEtosiR5MGQZ3g6EvmJK+fjTje6e4yeNQtRs2eTZTZJpDHQzpJJ5c1k8qfa2ijapMz6X+fTPHkNWz5yDe19oxx6ZAfjm3Yx5zlP5djfu4z6zB7LzGJoWRceJ19USCzpxz0VzaiukQz0ceafXISIZjTPgFYwfdqi3fZR1qJRblCTmuLAvmHGxw3bHh3l9R9Zw/FP6eNtF9zFOc+cydy5bepqnBxlM/YrG4HbVG3GdUoc+B0rYlW/oW5mrm7vfm6EvDKmJZM+hWJcBUQz632TD/5RKG+OaECEd2L0g6594kG7WsobAl0BRbw9hNLHPkMR0CgERdGFgj62wqFnja0zue2T1wGw59t303vsAhpDQzz8YWvTn3XFmdRXHxOZsiLg4/vvGBDb/0JIde13lxailow/Xfww+w9SupZ8eBzV1xOcsf2EW514TdyfeGxN8X9CoRXdj9rcIRa8/3W0H9tOtmc/jaXzSGfPtFXjnV+GUsX5DOICfZxZScBHXuVGWeFlXAIxp11lzoToQVEpdN5pqze//yb2/XwPtf4aT37Zk0h6hEM7Rpm5YmYIhbU1iWziRZ2LzQeipWCJnBZZNZ1NN8psOq1rxvD4+Z5G66x/9sQ3V0vsZOCnFNYUA1wz4UGPc/Pg2kdJWX3FsXnGAQlc7FlF3thmIvnhZYvzcRNXryxmXQILSokd8ut+WXTp8az/2xv56d/eAsBjN/2Ck849if0c4MdX3gTA4uecArOGwjMfGG3fR3H9FkqUbpzWoUPm+UPdZ+8fZD+Xxy6uhxW3bHiMpK8ZotDiDO8B8FeHEUIenOk0641je9ZYsYQVH3sj449sIT80Ss+qhTRmuYhXbRl+IzYauOi3Y2Hc2GUepBgbxu+BMDolVXmx7loi1ulZGWcKFM3HXnoHAAtW9XLGM+exc8MhegcSZsxOsRUErOKWiCYx2iWFLGos1kRb072TLRPJlw5g02XCqALlXzeAOdrkzRENiEwlnBwPHsBO5DE95FuVQYzAkbfrGy+MfDhsbH6SYlsVDIkYVArHffG97PvGD9jxxZsZPHMNy994GfMHZpFsOIsdX/0BB773U+astiVT7MQeCyMJAirC7B397+L32zk+QaCY4gyeSfOTa3T6bMde0sF+itD/GAxVfrA6rib+393s40FW+Tuhtngh9SULXJ+xEtUVltW5IC45YXxP/H0ORWoRcmxB2kQRQFFiLBCyUWLdc2ic/aELuemt13H6m59K2ldH5zlJI2Hnw/tZOGc+QABFvmyIB0M+oRlAW7tnwo/7BOavX0cr4SC38hvoVO3bWcaYE5/oTkzWaqkrsoubOI1YeeFKXoiKwFF0/+0HOtY9w6xcHTDPAHlmKPbBCaYybwqnAEWNgToXf/3VbPjCbTxyzX2sfu5xnPem8zhYH2Xr/fu5+xO3sfOHG5h9+ZDrgwt/FwssjHhlg/B93Kp+khP9982+34ZuUZXV9db2A6Qz+0OwQDVPUZA/E4meabxaVSAgKqGxemnoe64LWWDcq1sqTq0toEmUtglu3a/nrpRPSMegsEVko3UoTFGxT9EbvnAGn/idH/OavzuJtkmQmmXGNjzU5vjj7HFKbCFrD4p8fiKrfAmpWF8mnU89CJOZ8336gip4Cs/aJPJrOqLkaJM3RzQgEscQhaROMTiCcmJDKIGfeD3ccxWZoLwgUGVAJPH/WNOCoNUlzRoLXnwOi357ncvWavvSt2Iu7d0HyTftKl+HF0oV5iUuVWVMRViFa+gEfaUcEVJZd+cOvTYEZqe1cTv15YvD9wW6l+k9/UXnugsrf49KL1nEeogHb86nKfymIniv+gKq/ie0/Zy7SSo3CpxfEbgIDaUZ3d9C2i165/QVuTisnZTB5bMYWDpINp5bp88kYfUzVvD9j9zJk75oJVTITYTPWmuZIZspW4K/gW+5Voc5Zt1b7JvVMV6He65f8rjHof1ARE40xvzsie7IRM0DolxbfzXjJxGVBwUjz+zEJqYsX0KwA37dgSHHPgrWgTpxmZ+V2LpX4dj4uHjB/k9nNln71nU85W1nkoomyRNUqph94jz2rd9Jz6KZzL68fB4RiRS5Cs0oEvob+0iGNA8RwPP+iCJVH7jJlQFjhNENO2isWlRmhqJcTj7J42TnmUoxtHxPPIiUPBG8/PDywmaGj+cEZWWnViFtABpaWjGS1ehN20W5Cx9yr2B4+yg9PUL/YIIiL4GSZafOZnB+g+F9OTmKwUV9DC1scP3nd7D6z5aRAomreaaMq6mGipI3lu9/lYWO/Ymq26AzMs8DIP/ZjqsD3o7ZjJlvD1z3tybPV3c0ypsjGxDVbDSUeAajFNsc0XUVQFSy61fXnV3fa3FSoa9jLS+2m0u0HvoXaXP+J5rL57L/++s7ryXY3d2GinO+dHxwKEZV9jKe+SnYobDuzt3V98RAfnAEaTZdMkeH+DxgOtzmx166HFzKuFYGgcYJZ+MENoLj4MVtI2LobEJK67ypXGFVIVdCogrzVWI0j11zLxs++z3WffolDK2ZbYWDE7yHth1i+x1bOf0Pnsb4OPz839ez/ms/pzmrafOjmITEWGYoVTmZTlCiSUWoOxCktClS5yunhWcJvqRcnD/IX+t0coDEETfgBZbxI2dPFRiJyW9Jybx8ZLXPYYXUNmAcz5cac/IT262iDdTHGVOadsiybpPOhRDwXCH1rHi3KvJCos9gAVCdnIbKrG+Q8xVSEVsU58xJVDEZpqKLulQVtjNuSjQzVsyitX802ub9fDRKLMCDysSl8qDs+WM8yEkoCrWWmGansMWms6oPUbVlB0dprGg4X8VCZQsZ9rEMnD9/tzbVM6+lcD62B8RsVyE//GliH0X7u/Ze5FK8r7koMmyW/JGsFu4JQD3JaOuEm668mV/ctIk3fOdZzJxdA3wma82jd+xh//Zx5qycwYEDhps/t5HN60cYmF0nMwmZrlF3jtWJD+gwmoYqipkDtJKsPJ55Z9qCrmMS7VNN1Bgzkna7daH1ql4c8TpQ75n8h45CeXNkAyL3MFvgE2kkTiCVQsshYogovogElz2fqzItIKrIrxELCH+KmNquAqMQoVUBSItedRG7v3E7ZrwF9YallsVKE0ms9lEyf8SCoOPZMoQaHia+OBPGIPYlivvRrenhEdJ5s6MfrtJSXfo0VauMQfd9oq5H2wJTFIM7secMMM+zWVJoLmJsZldFoXn2HGOvK2mkZbu7QGN2H32LBhjdPcbtV91M2kxJGglnvLlcNL1qr0+VjTpLxaKeUKVbQ4bzGbD5/GxfYkVV6TJrRwcGLvaNJ1IKUCSmSK4ZA97JmpleIfHHu/0jrj4ZFbh8pLREadIkR8SQ4RJkKhPMPcU7XwYK/vVJogru3jRWI6HhkvAlqtNxGooJyycI9QAoAHHHMukJcoad9p6nc+vv/0dh4seDbNtfz4xU0zzE+/s+A9YA5ZWuyPzdzXTm20SALT8wQjqrD6VslksvxiRClCb87gQTa1AwJlHAptEiDFtivapNG1uYtWCj7b3RYn2KlE5QyjBj2SCwCZLEOkiTh7xu848dJEmFg7tbfPqNd7FwVS+1puIZr1lS5Gpy0iB30XdA8CXKRYVs36nKg+OzV8a0seVJ4vQ64dmpMEFQ9iOqPne2D/Z58NZA774wLbeNI/Jt/uXlzRENiFSirQbhJvzCGc69TLG7FHRqbbH27Rkhwdn1I5BE5wvvhVocWSFSOC0aM4HtVhtEKaReC5NZ8I2BwvYe36YJAEN8LeVXegJQ5Mamo+aVW21v3UXP2uOcOht9KZXJttqPLv2aspXQQcGauIsK/Q/eVOKZre6CyhgPihwwcnZ8D5BmnrmKs7/8enrmNdCUKeZ963fTHm7x2K2/oDm7l/P/9AKSxOZ+Iev8LY1QcxphFiVqDEkb7U42e214PI3zEHd9ghIgiu9Rt9YNFCnnWxUiBic8OpzlSKWwNxpjvv5Ed2Kq5kOwfStSU0CupgeI/OSTKEPNaOpJHr6vMkEQAaLIX01hqDt2wE+KmavqbplIVRzbzqj1N8K6iClYBzGoLoDIt25O/InxjBEd33WY9SvnqbbWlt00l8y2stYZt/yz7d+NmL2fqI9VxbO8AzjqOepn9K6Zgk33iCw2BXYDfKVoTwc+ALRy9dSM4qSXr+WMV51AfYZCGwdaRMAoHrplJzMWNPn2//sop1w6n+e+bbkFuZLBOGgUCXlguHOsn2SNAhC1JSkHdqic1AG0DFUoZ77pwgwLdCSSLcmX6NnTAQyZwn/c/Z86w9DRJ2+OaEBkgYQuT66m8D+pvtAdTo5x2DzOjk8UQooHLOVJODwwSpeAUBKdL1GaVpbiixv6ljZTVLOGPngINaMfg6CUsRM4VNieCVoFhBQMweSgqDQG8Tg6h9D25u3UF88vtMNCZeqUdN1AWvSzk/e/eq5uAq07KCouyUeiRMwRfpLy7FBRZZ60TjIrJdOZ1eiIUv8nirydc3DjfpZfsuL/5+684yS5qnv/PbequmdmZ1ebV7valVZZMpIIQgQBFsnk6AA2mGfCs8HYGPzwMzaPZ8Am2wSDnwNgTPQz2WBAmGSMiJIQEkISIIHSKm3S5pnuqnvP++OGulXTs7viYbTofqY/PV1dVV3x1O/8zjm/E5pNLr4TpbS5QnllyVQpzDdlshSFesCCMx0jX4hvb9A9vAc/aP0copwpWvwYThg/mQP9Xz2+LyL/DPwbnsIGQFWPmCqzCEYaFx8OmU4XUJia+bpaENpZAIikDUkMXENlPOtU9kJik347gh8fMvONP6NwaCWOWg1NkIGIbOb0miXMb9tP6CaWtsNGwbwYGplgc1rWK2MQsv3PL6aDAfpJ0+2BEc2eAwyPXk7sq5hAEC1IMaYLSn7iJN0J25DMWs9xnLQvmuZpHV0bOsAbbVkZIz7vZ7BkSFVC4xxFoVj8dMSh4kOEt16znwf+xqbEIKVjowaDw3nX3AMlYo6WS41fY5J1tDeNKxJicSGdIp7X0nRDq5NokX4VYz5NQ0FKg/EMlgp7mwOHPu53MXtzSEAkIpvwgkZH44/z21X1r8N3LwB+H+9nf1pV/zhM/0vgIcCLVfU/RWQzcC3wB6r6tjDP3wAXH6wDdQIq/Ys9+7ionkVkgDrrUkqxicbOmypOon2jEetT3dGQmCoLnWVXoFrHNc/6S07/+MszDz/ffoWiu/0LWj8Eb9QbE4VcQVfDDvaOVb7ffSBjb9+N1jXlmmXh+1AanDyoPotD/5/Du/gXA3tpeo+Kki4oat8DKApVPSrh5XOqg2Ew3nN3mrpGRxn8xOoYx57rd2HnGrZefBOnP/UXguGZvJlDs5Ay8gCnwKlhqmx8x21p2SBrQpdt2z6somGD9hwv9lCCPrgP15e23mr/wTX5GHNElsEC03jD9IhsmjKhDPbOtDcQQlcBVPRF7JYMRv5c0CapxvPWz/kpxbHEGmw5n75vk2QXNiUuxSXgY8QxNA2V8Y9MI0qjoYu9FgzcVFpucNQU9b4RF/3393G/dz0Dp8KIMiQP++2vCrvg2suvybxsvjTWVz1l117MQ+rnWS6WWwkwd+3NTB+7mqpSnDoK5yhxWGeQeE8Huxi5z5TUDa050oM7LxLs42JMRV6JC7kZWmiPYojRaMhPLGhzEZ2hMV47yIiCKxlqeI6EarRKLKrK9hv2c/uWA+y+VXxTabL1QLILlVhM9uAwxoOqIoDgVqzRMBL/qG4CKG7UMM7sTZGxkZNK9fPrLQ+tIf68N5Et0hYwrjiMHKKfd3vTH4fDEDV4Q3NJkMT+toh8HlgHPBE4S1VHIrIWQEROC8v9IvBu4D/D563AC0XkH1R1fBi/OzE/Jp+eg4m8Yovev3nYq8CXvQId9qc/ompyorh7dLenUb0gV8wnKVAssPKhZ7L9099G5g5QLJn2F1oufig57gg3apb86PctelVhP+JxCK8O6yB0DJJM4Dp3f+oChqcciykyAGm668wPmvYNhtINrR0OW9Ef/WUmnbMA5nIg3M/zyg0ykB40/THaeYAffujbbPnC1Tz8Pb/GN1/2OWZWT3eSWY20Bqi/Dt9/qK0s8SDFYPBNJFuhtFYDyTpDAZRoCru57Dpd9NCkY+G95EK8J+nUe2+SPbgOPo48A6Wqz7oDs98p9maSsm9eIh7tTb9KBxZ63p7pcVTagqvI+uSgyP9OyB8xljJ0QK/EJ2PH/wtxzGfxjCIk/+PAGcPspqPYd+0OhqbG0j4k+yChX5WkKhS9fS+tY2BsYkpUBbEm7XMEQPkxyNuOxLH1o99g6d02UoaqrlI8IIrHcRIgi8c6vrsQDZBFGPAIoPy90/39DnjL/pdsHW10oE2JSInvqh3Bxn5yezx/rS1x7Lp+D19959XccuUu/uiz5/HXT7qA5auLFJVo7VQQ48T5+zwFp5oUerMIS4t55l1FKW3TWqeG2nhQZKjSOR2qTXln8bhOSq7u/5+mkdszQVy3qnbx8XNvbzrjkIBIVW8Bbgn/7w19QY4Bfht4naqOwndbwyIFKXW4c7S2AV8Dfgt4x+FuYP+mzpWXF4yeBxP/TzdySHSNoa9YMj/pAolhsZwVGhS5QVPGtsBButEjsNr8e4+g2XOAW975WTa98PGIKQhBnok3P0BqCRMxTnj3N5PDOc8aEZW6tX+zt+s1hVvw8Jw65Rjsjts7mj8S60976t8LKOXIIuXIc7GH82JA6RBgqAvustl6YChOm3hTS2u8dlx8PVe95cssO+4oHvw3T2DZpmWMbp9jZvVUYG8CCMrAUJEbjWDErGnZv0HVsL8Z+vMfQhdNqEQqjWNsCwrxx7fEP+Dyh85iXuykkIOqIOo6D6a+IN7E8RPg1P+qISIvA/5WVXcu8v1DgRlV/VScdmfZm0ledQ6A8pwMn3g7wevufS7EMQiMY5sw7RJQ9gchACZjKULIrBBlaJoUOokP3wiKyriOYDMe/6Ff5fzf+gQ/fu+FnPqsc9I25u/9aYW0+xeFEE14SFdiO+rF3fw2XXD/9RPKAZadth47qhM7VVpvSwdFsJvaHtd+wndkQ0XbsOXEVICQl4T2bYbmM7V2g97zoAeGom2PrFORgZ4Ow5fC2i0QvuJT1/OVt3yXU89by3PedQ4FUA0Lpqck5Q8V4lpdqmDX/XocBc6z2+Ecd5giV/pWH+HclVrgAmBu1NufylimiqY9x0wWdFy8YrEVgYzyCoPiMPKRf87tTX/coRyiQEXfE/gW8JfAg0Tk1cA88EeqepGqXiEiM8BXgf/ZW8XrgPODkuQhxySPOGcuDpbgl1/8EC/+oA4qPYao5/HEkfe5iVUg0eMbhDyTxnkBr5jMGI3L8S94JN95ylsYrlvGigefSbVhdcf7ifuQLloXp3QjYoIiqqnyISV19piahRUjrUclRhFnMdODELMPhg31yzuTMUO946DdcFYKtveP/cFujJ6BWsCLZ9/lTMmCXKdsP3PvbtLYc/U2DmzZxZK1M8xv28fyY2cZzA6w+0cUK6qOMYtgKNLX8cFgRGmkAGMxgSkamiawRH75UlwyStA+WEv11wvp9E5+4PY93vyYSXwYqA8rFIvsa3elh57lZzguB/5NROaBS/AgZQo4GbgH8AXgNYst/LO0N5o9SOLIQWyt0gk9xUTffi5G/tDM84VKY1M4rAOIwmhzhtr8kco0FHiWKIKfedeyTTh8/ooK9//Tczn/WZ9kOFux6eEnIsuXLQB5eRuI+L6gslIsg8LLCzRqUBUfEiJjhHoJvZ0QTBhiGwYzlVd+R/z+4POpclaoMJpCP+DbWMTr3imoDezcpHMWbdwEO5Jvbz9SsBgYilEBEaXQhdIHU2UbTo/nuwi/ffWXbmLf9hH7d46Z3zlmzXFTzO9vEJwHNmhyskppkg6RVUnnvUBxYihEqdSX5Rt11OFaoPFs0lAbai1CpKKgVsNSHTEfwHfL9ExOi55YtSjhegjLGqeM3d6Jy/dOwpE0/r/sDdwBQCQis8BHgRep6h4RKYEVwP2Ac4APicgJ6scLJq1DVa8VkQuBpx3Ob67Rqc7nDkDSNjSUqFAyma7s4gfviZRYVmgVWB8SM2BSHCrMG5bxMd5ws7gAkJxnjqw1LNUCi9CIYZqKEhvjW8y72jd8veB6mhv3c9TDz2TpWZsxw9JvZc+D06waJMrcA6ykTA/qmFvkVBb4wx1AxMJUoKliinrDBlabQWe9qgKm56Wl4y0hThwnhG10HVTTuSlWScWCIZP+D8aqUwkYc8a69LaIsooKrw0UQZD/vlT/IBEFo47SWkpVVj/1QZz1yLPZ/Z3rueHdP+CUM49nw9HrWSHLWFrPUITwxYyd8d47ruPFmwBAZpORMIxdwSzeu23wPZQseFMnQi2FZ3NEWGIKhlKmh1DONkA3pyBNm3TcwvUUdY4OOvTIiumr6ieAT4jIycADgPXAHuD9wO+o6qLKbz9re7O0me3eA5rdm/jTYGxrT8rCYpzDiM8PEs1DIjBQy6wOqcRSGseQOgGk+GCELCnWxYelv54r1/hlgw2aVsNIK2otwXq7mIc4LNNs3LiR7R+9AX4wz4lPOJWVp69Gyq4MhZL1xwqOjc0uxKVSsSTo4URAFPPlEghYLAGPljWZGy5heu0yjjH+HlimBVPGa/u4otujS4v2HrHpWg/skAg2A1BdpwFWqtcJ6tu7vvCk37bINregKAKRyERXWFBluQ6YctOB1fFsTWXLtlrQlgzCs6Q0jl9/3SOx2/Zy5ee2cNE/7uJX//cpbNy4kenxMirTAt1yPEshYwSlkqYbtscxE+yLQ5hzAxoKRlrRqGFOC2otUPxxGmNogu0xOqAKITcAq0XH3tjes3OxoaGS0RnhqPIwepndRexNHIcFiESkwhunD2SZ2luAj6nP9LxQRBywGo/KDjZeA3wE+MqhfndrFvpfoNsjiW1O4CeyHqqSQiE5nVqqByy3M/Ix3CwcBpEObq+WQWSIWBgycyKMxd/gjRqWmoLbzVy6YZtlLnWkZssWrv7qpQAsvffxrH30vTjq/qempEKnPlnY72eXRSoUdsp82qboXbnASkH3ps+PV2QgXN3wg7/+Z5Y98G5g5sLhc2zXMUnjJ7uwNXsQJIiZGaQIlNKJ6I3bDpayoXmYL9Ldeaihqw3VhsEcO2TUUfsVUaqQ5xXP47Bo2oqdNY4Vj1rH5edfxL//xb9x89VbOHf5vTkwqJO3Pmgso8GucE69Z444HOAkeG1ahHyeAqsFjRbBSIs3PMEb80yRZ4vGxjI2c52H1mIe+6FGXO6wcoiOLI8NAFW9Grj6cOe/M+zNNkadSzlXkWpbHfjPhXHeOcI7R06FqaLpsI6lcWBhvjxAGZKkY1jMiKPqsSxlEOqLeUOVaRJTVOCotWReS0auQuyAfaWvAIrXnq4ZJXuz5aYtXPrZSwDY9NDNnPLrZ7LmzKMTKzB2bU4KkMrKASrjOFDta4+DSmJwcrY8Z4T67Od49zwX/tOXOOtFD2DFYAMAw8ay38wlNjWuMwc4jXpRzCSMiWDFJFs2qThBVNnOKNm6frVwP98wrxQ2BMHMCXmiA+fYX+1NhTWlOGw8hwG4GtMELTuLEcvs5hH3eeYS3vTEy/jAq7axv95FMb0dcL4VhzQU0lBN35pCkxEIFYE5dAEU1lpitGReB4gWzLuKJfikeqsmNIet/GcEJw6p9mXh+SKAyskskZ1ge4pOHpGBaoImSX/cBexNPg6nykyAfwSuUtU3ZV/9K/BQ4MsicgowALZPWEV/Y78vIlcCjwMuPPi8/Qnd/5Vu7JjsxoltaLrrC94+EtxxDyos4WYPMv1xpBsvUC55R/V4Y7cXUPs7DsEMSk7640dzzRvO57jffjDLzz2Vy571D+y9+Fr2Xnwtw40r2fDMh7Di3FMT89JK6LfrErqhoShGmCurivhWAXEUxqWO0k6F7R/4IgDjm7a3BkGj3ojinOnKBkVjGQQxkfBw0Ox7iQxSflKkFxLLTgx0gVD4bIoooeA3yoS2KpEhipWARdivvPVBTnHHXK+2fDlWCCoPetVDuOp9l/LINz6EqdmiZYGkpbGjamwRgFKkwmMOh1PDFDUH3IDaueBZe/raYXwzyEBfOzVMu5opU3cMTLxeOtdWNiZNy787PEB05HhsP8m4s+zNwnBmm7sF7a0QQ9eR4XPOA+LSuXTtxRJtq4axLXHGpjzA5KjRpLwNINAXgDpqCgp1RN1iFx6EtSupXUGZAWsXmhIvOXqWez37DC551/d4yBsezmDZgH9/7me48UvXceOXrmPlqas4+4/OZdUZRxNLXGM4zxhNoK9wPu8pZ7BymQAj2vk+zpNf5xe+5gsANLfvZ6rwe1E5n+MCcKCp2gqubEhmRFNaQKqWa0NtefPXwsXihxYAddMHenYyy4ESUarCdoBQPIcVltlqROOKJIUwLJoFie8evNoEXqemHC985xn8x3u38MfvOCUB2tirzITPMYRYBFsTbdBAfI+1Shy4EIKlYlDY0Lne25exFkxJ7a8LLWiKEaY84K+HoL4fz41fpnusXe/pmFe7RXs2U4445Pg5tzf9cTgM0QMIqo8icmmY9lK8GuS7ROR7wBj4LT1kXXAarwa+c8i58rCQ9uhSFub89HNsnJhUUdZ+H7wOUfKeWLGPTf+IlyuzAAAgAElEQVRGb38sS7yUNpk2n6cPptb90hksu9sxDNetQMVw6p//Cj9+2+eot+1ltGUn177mY4yf8zBWP/F+ycMxAWxE2yDaNhfND0wEQCkW3mFZFC1CQmTj2PuNqwAYblrVVtgFIS9/THSCv0CoapHQITrqyy5Ulm4ZI81e+Ynqna/8c1h3CnH29isZLxRwNFtvZ2rdMi+IKG0lSJF5csmTFR8Gm1pZce6Lzg4euuskUadE0lQB5JIXn6rPAELOmA35EAAzZsxuOx3YIx8yGwZjMrA104UHRFGALWnI0H2g5ddMfg770w95c2nGmv78jjvF3vTX5JCk5eMBu6Q+WA5h3LSl7TloyNkTq8I4nPNxxjTnFUpFmlfxJRQBmKjDaPDqxKYHn9e0Mem6sdm1dK/n3pOTHncSSzcdhVXhgX/2QC56y0WM9ozY+YMdfPH5n+bcVz2M1Q84Oe2nvxfa/a4KG5jx9kJqJM+lcUwX3RYT+XV9YM+YrZfcDMCq45cyVdQ4NQxNw1RRe+V3UyQZkUFhOwxUHsIXlZDfqOk8xMrLOCTcz5IxRH0AlOYNDlT+fRFCXslehP8rHGjN3NY9zGxYQmUsy6s56lBN2gVEPtdrSmoqsWw4bsBzXr6JShoKPFteSBBmxCZmaBDAVUELigAqHJbQIsiVKV/V4tWzazxLNK9VAkdltZsdZj4BphkzYredCddHq4bdhtS6gKjoGQ6rhoIdHHTcNexNZxxOldlXmRQX8eM3D+dHVPU64Izs82UsJHAmLCcppJKHafJyfP+QzqpwIiAyijOKYgKLELxsBGsNThQbemfFh2/Mro8PWuv8fKmCxICzoeFnxyvytEgnLh6M1NT6lWGbYcV9T+Ls+57E/mu3suV9X4fCcNM7vsDKx94HTJGMrZG25LFSm27SfETwFN8juImGfBiO2fy2Xaj1ocKlp22gDJUDMTbuXDQc7XrjcM6ghQJBJj6Ap+ue8QqGmzew4c+ftwADeeXvnvjZooCINulbuiGylOwYGKESi960jSt+5+0c+5zz2PTr9/HVNkWW+B4MWqv6a5NXV3XE7rLqEXFo8PCiF9cCova4RwZpifFeUzQoQ1OnB1I0ULUWlK6iLPcn78z3YovaRSY9yFzPUMXRD0mEK3/hTdK9Kn7qHpuITOHDTUO8vfiIqr5cRFYCHwQ2A9cBT1HV2xdZx8rFKj/6486yN/0HhFPxlZ14e2KCPfBCfRrYnyIL58Cg9GzBsGw6TJFTn6AMbQLyOITYUghGTQrj5tecQ6gpWyZAC0QNtbbXEoRrxMDSTUf5ZUU59fEncurjT+Tmb9/C5e+7krldI775yi/zuM+dnPbRCAkAFaIsoaQq5zuAKI7K+NDPlKlTmXZ0BuJ1vusHW1l7+nJuvmQ7R28esizoME1pRVOO2N8MfTVUhsLKMqjCqwmJwiaF8Zq5hq8+7q1sePK9OP75DwvhtBYEVmIZiO2ENXO72A3HdyvHPNtlk/ZUPB9GlCkqtnzjBj7/4i/x+Lc+mBMftI6h1EyX2hFOzO3GlHhWuJKGgVimxIOheC8PxKLSMC1j/9tEQBRylPJtBZaKBTNK3I23GYSQmmFeS8ZaUFOwr17CzGBnYpFqLZkxo8D2mASm43m32e2Q27n4PcBsuWLBNdAdP//2pj8Orc59BIx+WebEyiLpJeTG/2kNFrSJq7mGUb8kerHSxPhdNHDxxq2DUFaMf/dftS06n4fHHs2J/+uXmT3jWIolU+y99LrEXsXtqV2RwntxmlMvkmadj8HXtqAJ7zYmw2lbqrrn+7dwxR++FylLpo5by8rH3xfrDI012PBqrPHAJ4Yanf+clG57Y/envwaNZXTNjb3jT+cxlqrD0vlowVDqkxPPU/9ULnKPDY9Zyexp67nhH/8Tu2d/97xk+Ru5F36wkdiiQwCNWPIaE+wN2lLkUjNjxkwZ/z5M7w0zxZgZM2amGDNbzDNd1AxNw3QxZib8H1/TpmZgmvSK00tj07RcQXvRoXfwdegxAh6qqnfHV2o8SkTuB/wJ8EVVPRn4Yvi82PiWiHxYRB4jstjZvXNHvK/iKwKdliVqp/m2GC7olGnoZN86UilfjL7T5Mfh5o7lY+RK/7IlY+eB0ciV1GrS9MVeq+6xiQe/8ZGsPH0NM+tm2X75bR6o0RYLjF3pH6bqw74RgNVaMGcHzNkBI1syZyvmQ+4KtMyCU+GKz9/CJ//n19n5oz2c8vgTWHnG0Wm7a1cyZwfsa4apBUn+imAolzUxonzvZR8HYOvnr0jT4nEuTNDzMdq+x+lZXmFkigtpw2L9V14V6Oe1bLr/egD+7Q++jFib9rdVD7fJcYoOUxwmsxntchk7nUL1sbw+Ltc+lAciDESYEqESmBJlxigz4pgxlqVmzBIzZomMmTUjlpo5lpp5lpk5lpo5lpgRM2bUeff/j9P/+ecpqTv2bCBdJnDiuIvZmyO6dUf/AC7UJOqqGsfwi0IKwXSXjeEoSfRsH1wtVsYNJK8PAmixRWJknDOtSjIkddfc+PVDaisedW/mt+3lulf8Mye/638wXDXrv3MtWFPxaL7fF8s6SZ2cczAQ2TSnwo4LfsDy885g+ycuDOtt48pNAHURPC04Vp4QWnA8qvWrAVj7+08JB4yFF/oijJCfFv/RCUCpH//PdEFUMUY4/XW/zs3/3CUR8tLnDjA6yB1YSZsw388bykcEQ7lcQx5vTwrZGkIdeD0RI2OkmE9sUK3FAq/amBhqMGkd/XXGG9SHag/DohxuEOkwRwhLxSzbKrwUL5L44DD9PcCXgZcssppTgIcDzwbeJiIfBN6tqj/86W7tTz4maURFO5F/H9vGEK6d2Ng35rH1Q+gp16jncOXrPtjw3r2/fkaupHEFaqukXBztSwxbHWyc8bz7Mff6r/D1F/4rj/zM72CmfLWkc0UK+fkikcK3oQhjzlbptyIob0zBUOqOeN/3P30d933Bvfjq6y5k7y0HaAJosioYVzKWonMc4oiAMQGhLHy06pzj2HXpjZz56if5+9m4VJYPPv8ppgzkzBB0i2RykJX/Rl9BPDJ2lSizU5ZnfvZJfPcDV3WWa/N+XJscHQBPBEfx98HbjgrPZFlpQ2TxFUd8ekTt3PazJB2swjeGo1ZljKMSpVbPJBkzSsd3vw4SK2TVUHUYIpP+LzJbFvMi4zmLHR0OOu5i9uaIBkT5g3qSUmk+T56YK70HeXc9JANV9KYdauQhsQiGauuTl8e28CJ+YaSYbcZE5dvjh3D0Mx7G6NY93P7v32btbzyY1LIDcFr4zsdFS03GCjPrBGe0034kxsgB9l65hR2fv4yT/vJZzJ53b8zAJJDWNAXOmEQ/p5Bk3M9cqr/H+iw5+zRO+JdXdbanc05EO+DtYGAo/74ts+9Wh0A3fFTNVGz+7QeHeVwHDKX5c1q8d8fGJOpInxeiqZrM9AxdCl1ErzCBIz+9kganJniLeHVbdT7+bmpKM0p0dSWWEZqqO2pXpodOvEasmGSgLIYyf5AKKXdr0aGE7N+f7hCRAvg2cBLwf1T1WyKyLogooqq3ROXoiZvljdzngc+LyEPwZbDPF5HLgD9R1W/81Df6Do7FRDPjyO2Hw7d4KEQx6kFREfvnactC585TnxVyag4rAaMOVYyNK2hcwZytfNVRACkRXOV5afO2TAnM8fPAWMxUwb1f/gi+9JyPcPOXf8Rxjzw5VcmWEsJzWlC7It1DjSs4YL1UR6MmqSE7dVAQlNkdP/jcFm69fAfnvfKBzJ64htkNSz3LHRyv0hWMpVwADOdtmRyFeD/kgOj4p92b459277Ccg5DD44Ix6TMzsbgirr9vH/Icwy4jFZmhIH1gLMY4lq8b8pAX3z2E3W1HLDOxPJlMRzxuRfYewZCXcYkh+S4zVKAJCBUIhgCEwnVj0v76aUOBkdgEjJYUjrEZ4xTGGGao2esG3vkSH17Lw2Q+omCSTXOd7yTYokPULNwF7c0RDYhiPhCQGIT4f38+/y4Z8xBobsJy8QEek4OlC7JiBUMUXIvT85sq7x0TQ2SN9QjcOmGs3VLSVnV1YdgvHyue9CBuevUHWHLu3RlkAo5ilFoKxq7wOVCuDYepE5pMWNIWJoCjgsI4tn7qO6x76oOojl6NNgXqhLput6WxvoTc/5Cm4xd7pSUQKbQ5PmHYPfspli3Bd44Ohz+CygycLRjZce0rywo9YJV9R1htToP3maDk7XUMXUtRR0MVmaGYDFmKTQnTEQxd/53bKYxy3JnLGJSu9QqDd+d/1/lkx9CPyBvANr9ImyFa7mWsRUp8HJkqlcw640trbQLZZkEpbJ4M6dd7aONzOCRSb6wWkYuzz29X1bfnM6iqBe4hIsuBj4vIGdyBISKr8Pk/zwBuA14AfBJPiX8YOP4Ob/VPedieYV+MaI+hMyPiq1PDg9A6X6jhwrWnqjSu7ZSOCQ96bUMzjUZlYssgrxIV37tspFFDyLDPDhi7krEtkaZi34S2ChFsNM50xA6Bzudjn3oO137kEqZOP47BuqM8qAlSAgOGuKJKNm5sCw40Vbq/BsYyU1Y+rOv8/bOsnOei9/2Qh7/8PgyWVCw/YwONK9jXSMqjMk3FvPaTsU2nOCW/hyNjZRXqvfOUy2b8YQy2Ou5NqS6xVKZnrxfYiYkMkU3zlKF9SiV+P2scpWnbp+RCmZU0KZcqskMxd6gKwotTUqdy+gGWKbGMcEybzN4AF3xlxNo1hjNOryhEEhCqIosTLkafTi8UIRIxi28Ra9WxRypWFxW1Omoc+51jqphPOUfzvRwiaEvv24Rr05lemTULrrH+uKvZmyMcEEl4wGr4y3qbZQZrUujLl2e282m2vlTGGZB4nMPL2INk7FEc8SZzGdixrs3bUeeReBw5EFpU9DCMatM61Fquf+FbOeEDr4CiDGEiQmcNv70LNICy/bNOcOrBkHMF4x37mL3/UWHbfIJoAmquFT6D1vi3QowRPPp2IxJK4QHcgXlufMEbOfp//y7D49Z1lgPQwocPEzjNhRfD+YsilCKgDRRl8OrEJ207aROrmzCfxRvnAXQePGPb9hVrnGEQ1ISNaEpkrcThxJeyHmBAIerVX7UAV1Fb34CxMg0Vljc/zWu4PPttZ3HPh6/y3pw6aimSAUTBSR08RUksUHueWw8WgQKTumH78xa8swh4xAVNo74Xt5B1POi44wZqu6re+3BmVNVdIvJl4FHAbSKyPnhr6/G9wxYb3wDeBzxJVbdk0y8Wkb+/w1v8XzBcljMXhT+hZYWTqF+cX73tcAJGY5+zUJgJ4X7Jqwlbxw4NCcThWs/P+WJhtFy2wap0tIPy9ffzISeNZffczJ4//zSXvvhDnP3e5/pq1Oi80SaAT2K24vSx848Oa4S52rBv+zzT65elsN7YFQnoNOrzLOfxsgHDou3uHm0oQO0E67racLt/sJXvvfyT3POtT2NqzZKUwxXH2Pmk4gjqIpDqJ1PH926IzCezJ2256JyIY96W3p7ShjdrV2Ql9h5gtLlAgWkRw1gKBmIZa8lAmlRiPy8WS8XIDQJgsrja8d+e7nODP/2F1Zx2agnqLe8gVriqB0XDAAM9S2So8mdgCtNJAE3gYthEY3jOYsN3Uc8IFgKhOL04nLDJXczeHOGAKLwnd60N7cSS74XLZKxQWEcCUuH7CIpSZVlcVH1GUh/ELPiJHvOjgSbPvcyUJM0EOr53EYkYVj3jUWz9u3/Fzo0pZsvOOiJwi0zMpFyqNvRlOHDl9Yy37mbmzJMY79jHTW/9BEf/0W8iZeW32YbO8bZlzcKWtNuXQFeoMIv2fDjDmhc9E6RC66LDLsV1aGMQ63OcqPKVhd8KbF/AeR5IxjCZf/KEfVFMAjcFtSm8DkyYV6Oho5uTEStYoqfpxPgWAqE6xzNEvoO0sRVzdpjAzIF9lmq6oJ6zXPB/b+K0h67z4RGUWgtmi3lQGEgTcgYMNXmuUcakaQ6KGqx4ViiCoqgUHI/zqMcI9UtkDycZ9yfw2A6+PpE1QB2M0zQ+Nv96vMf1W/j2GL8FfOIgq3mZqn6ot95fU9UPq+rrf7pb/JON1jnQwJRKBxi1jtRCJjM6UhEMxfeOjlEeksa3dokMkTH++l2smWYdRD992CyrFOqDoni9ZKKtk4bMzrDx6fdn6xeuoJ53FMOW2c5Db5G9iSPeX01sXRNA0eWfuoHZ9bMsOXYlt1yxk8v/7/c56389Mq3LM0Gh+iljqvr7ECVR8lzI4YnHsPn5D6eWAca2Ibd4XK0zjGyJVb/Psb3GpFQLzy512aSkEB6S4Q1KQ0FpB+weLWVldaBNhBaltkXSHopOUGSfLYYlZoRRb3OMOOa18uX1gTFydkjlhl4R24y56irLuvUFt91iOf/8EZtO8c5ZgVLHHKTALHqWUag1ViD6oBd42QYbQo4Vvu2r96g9KPKl/K3jOo8sCoRaJfNDO2B3NXtzRAMiddIyDALpCRoBTQ8UpTyiBER6N0bvJokyJhFwOPW5AZEliqNfkugQX52VGY8FaqpOkjc4CVz1dU+WPOAeLP3edez6xAWsetqjwnXbMmMTgRXdsGDU7hhv30exbAaGQ3Z++KvMXXYNKl77Q1VQa3zmXs42AXn+Uvx5VJJmSJw2fcJJfnodWJ320KIYZOQBEUY95z3o5UoIgSXyFJezPg9KRXANmNLrQ6lRmkYoCkeDYWwNds889pZtLDtjk2eKAhCKZbfQlu8CoaFkE+QSbGJkGvF5EoOsYgYHX377ldRzltnVQ67++g7++G6f50WfOo/Vm2exIV/I9xwSxlImWtziDX6Fp8v75aixdxE0CRRZDLEvStQW8ZvRM1Da/XzQcRhhtTs41gPvCXF9A3xIVT8lIt/At894DnAD8GsHWcefAB/qTftTPH19RAx1sWfWwjA7gHVtfpuvxiSopPrrL0KZDijK2IxoTxJ7GXNhiMUNJuSj2RAI8ee6DqxhR+aj9w50gNeoKRkUNtm+yJ60+2I45ukPZPflN7HtS1ey7lFn+X2SbkVcX4E6rj9uu9/Jkl23zDF7zDLGWvH1V3+V3dfs5LQ/fUxbGas+Ubt2BY3tAqCxLZLOmiqMbdz21kYuvc9peO2nzA6H4zqyJfPOYZ34ij+6WkS5k1oYxRWt42RQXJDtcCrJkXIIla3Y7yrGt43RvQdYe/oKnApD01BLQaG+D9lR5VxaVzw+XpDVpP6HB/AOVy0FOA+IpqSmUMdbXruN226xGAN/88a9fOxDc7zz4+tZubrIJECUKXHMq0v5RS7kKnjGWnAozltfAKak8OAvgSK6kZVwKKO2UX5MY2htzh5OL7O7lr05ogFRDN/EZOmUdasRLqSasvR/23tUAuDpzhPLPEW0Q5PHME1QwwIXvXXXMWJWzcRS3MTmBJCRQlR9kEE3PyH/bvbB92b7P/4rK576CExhOiCozw7085J8OMob8mLlUcz98CZG+yw7P3YB1bHrUVeG/CCBsG1q/faJI13YEo57fg4I+xEdZrEZkBKCVpE/RcYYygMFErCHloobGrTSFjVFIkoIoKnwbFI4z30gbBtPQ9/y7s+x59+/weCYVZz6d8+nLFyqsvP3DxS2TaiWcM4GxjIIgnP9Uboh42IYmm9arv7adjbfby2nPmw9W7+/i+989Hre8rj/5GF/fHfu87TjcUwlynwYej4V6lLyZGSFSiqMluTl/z63wDdvjCBsPvSnciE8loOgfsz/kAxRl4z7qQxV/S6+wWp/+g7gYQdbVkQeDTwGOEZE3pp9tQw4jBKWn91wtmUpYzGBZgSeIdxzEoAReFCEn1YYfF87BfAPZxdE9GIScP4gNqJ+gQJwvqw+jtiwdeRKxkGdOrJDrmdrYmFEnenzqMKcKzsFCraXU6SUrHr4mez65tWseeTdfZoAvoouD8dFJsWpdPKQUvm6OIoVS7n5/B+zY4dj9zU72fTr5zAKbI6qLzwZu4J5KbET8pusaxkqVeFA0HdK25rZuZRTKN7GTjUDDljxfoXAeOjTBqLeWn5MRHwPuiTQiDIobSjfd2k/fTrENF9+0fvZfeWtbH7C6Zz70g0MTMPIlYkFNmiqwDOiXkpD6iC54cvY80TmSizTbobSzjBlag6MhEsvnOexv7Gc406u+OFl83zhE3t5zL1v5OVv38j9H740SXvMi2OJ1CkRu1Kvah1Dag7FhlcMdc1IQY2jQjkQQJFVCbIKXsMoB0H9ENqwOIQO0V3Q3hzhgMjD2ghrWvo6PlG7qsl5jpFmy8fclQWrpzV4seS7LbMlCLHhae0QQ056QiGROmmV9MFQ6MeTcmsy6nZio0JgePwG6htv48b/8SaWP/mhLLn36bhlQw/ICu1cfAvDcK3cgBNPu25/z/msfu4vM3P/s1s2yAXjYQRx7bRUGpMbn/h78bCH+cVmB0/80Y+nR/DfSzBQ6Ri47Lzlby4AKg0gyPQAZPZEWv6rD2P5w+7B1LGrU1hAxCtpE9nA5Hn7U5e82QnRCCPqm7Sm5FZ4+gcflcQbjSgPfck9ee9v/gdffMNlfPENl7H5vmu42y8dzd0evIqjN1S+vULYd5Mxlj4fw3TOmQmsUARNUc8ojthMGIIHmHr1BaN6GMLMR5hy7M3AxcAT8FUjcewF/vBO2aJFhjoQ0zLLxrQ2Bfp2Xzthd+jlFB3GUyJP/oWYU2RSGMoiCQyNg85ZrAgbW0Mj3b5f8T3aowiE3CK2rzDK7Nknc92bPs2Vf/he1v/qfVlxzmaaKf97C9XxuyMCJGcEs2wJ+2/ey1XvvoS7v/IxzJ57N2rbAjSrrVryuC4CWJzciiY6qcn09MFQ5rg59bmRLjhoYrwzpdKG4nJxTRFN3QtiEnxtSexQFxAZ7vbKJ6J797HyhOWMgw6RD6n58+fD8EU4346RLRPABXCmF9IUQ6kFqiU4KCrHx75/Zijj9wDtd/7M8pSzr+aVv+NTXx70qFke+LBpHvxLU0ytbJ97dQjje0wtNOoZoonnKp5zPNME3jmLwxIq/CTO5ytlD+c6vqvZmyMaEPnnnL/Y1WgXFEHLFkXjlIOibESvLQ+x5cZssXh7incbUNWeAWpvnFT9lQm6qctuZm1/L69A64/Rj24BwG69nZ3v/ww7/umTTD/h4bgnnwsIxujE5dI+Koy2bOfW172bweYN7P3ixVSbNnj2xHm2DfXAxoORAIisdC7szuGIQCoSPC4AozBj9KJV/M0hMXyWLxN/K7J8ncMdKtUkYqwAimKYLjFRQjEzjVm2AS0U53ycPuUiASKCMVG1NnhDgTmiAJNp/uf6Mo0rEvD14TCHCQaHQcHT/uWR3HTRbXz+zy/mum9t47pvbePTr4KXfPaBrDt2mhrP3vhkaxMumZhYnhnF8PMu88aKDoPkw3hODQW0ulaBeu+sa7HxU/bY/n9GUIi+TEQ+oKpHFCPUH5r17fPsgwdFEWin2y4+WLXVMoOuDYk5RZMe+JO0cSBLVDYNJgsxjQM7NLYtKHIYGlsksKHaFk1EkKDBUZlULee3W9l98Y8B2P/9m7j2bZ/lR/Njzn7u41nyhBNwKocERdYZdlxyI1e84ctMrVvKdR++jLPOPjFV38YcKi926asrvf3Merjl5yAYgdZGtt/5CFEXHDkXWe522XgsEyjJfyc6rc6DotRWJ4KwDBCpChx1FMOVS7DSMHbtsfPvbbUpAi6G3XugKI6oddZogYZ5a7U+MVtbLaOp5RUf/eFZfP0zu/jbl23hgs/u44LP7uO1wJe/ewwrl2egiFYTy0HKIbL4HEmyaTEBOxZ21NIEQCVUwDxVYJpaxkgOx5jcxezNEQ2IsJIqnDT21DKLg6IFQ/NTGqjwnBZKq4jaRb3FtQVFTgIgijRw0y2FtzaEuGKnZmtaAKRhW9L6J4OafV+5hOVPfRR7v/gt7PbbWf28p1BfdQPb3vEJ1j73yVjVAIraZdz8iNG1t2Kmp2EwYPvbP+qp4+tuptq4jqUPuT9aB9TiQtirES935fzxMGPpbJI0oEUOZsL0CKgiwyWx7D4cP0cCWREUaQzJoahpjVYnRcO13p86QUqXsYB+5hQmsAYJVWONNQt6ukUtH6th21RCm4V2/4y05c9NSFjF+VBFraG0VRWMTQZ1432O5r9/+rEMpWb7Nbt5+698idc/6qu88usPYenyEgTmXZUMm39YlSEbpAU0rfhijLe0ejSxb5oLhqoFSzG59efLQInIh1T1KcB3pOt1CKCqetadtGkLhiaQTwDukpL2/ROPFMbtVKlmjHJetXowTfFJ+ljQrd5qAis0dmVihpqOAn7bpsc56TJE2X0B3kbln2sVysKy8/OXsfHFT+Kmv/0MdvcBTnjFU9n56SvZdmAnx/76ORNB0Xj3HPuv20G1ahY3clz1+s/S7BvR7Bux9qGnsfTsExnVJomPqkJji1YI1npmJqUCWINJ4S2wdZEaPgOoFaRoZTrUgRTBdXIZeAn2xjfQBZvbj+y8uqikmRLP4/ZkjxIJVbsm2o4inBNfxWpEISS5TxV1aO/kiE1z+6Ao5TiqC+rfPsw21oIiZIxBdJT89p37uJWc9/ijGEjDZRfs4SW/dRPP+bWtvO+T65idDuuNO6heqLG1Fixgi6bEUMfSa1WWmoYDTlPuo1Xf3qkg9DETh3W7FlyjC8ZdzN4c2YAo5raY4KUZQV3rxbVP1wwUBVYpKlhn9AEOl0q+Y5QngiEjbf5RPlRh3ARE74rWE3MRALU3pAs0cQJA2Y2aPh9kNNt3MX3mqaz/8xey57NfYfvff4iZM3+BuZtuwjXSGukA3podu7j1De9jfMOtE9e35vefidamGxoL79KEl5MQ4uqCFRq6nzusUXvYEfx5CQSQicBIQ+hMPMCiFCRZuZb2VqNQiydWBHA1+y++knLtSqrjNuDFnKUFS0bDcfbLurDOeC5VBWukzROIOifBuMVcgRI3TK4AACAASURBVPiq1bctcEGYbmCC4B6aKtJ86S1gLDUFK05awS+/8T587MUX8ldP/Dq/+977sua4qXT9+WTpIh4c7wWKS0yPSQnWpARagzJjxjj1vasMgpPAJEmI5x3C+EjOyB0Z44Xh/XF36lYczrAmWAwNQN/nAInxzEFkiyKSnyQQG0ERQFn4RPtYjl+ZbuuIrpBoBl5UmLdVC4Scb60xsmUCQ1PWT1vAUAd749vy9HYvfI7bXbuS0dY9lMdu5MS3Pp8dH72AH7/ig5xwnzPZcdH1rPnlc1MrjEHRIDj233A7l/zBB6n3zE88hBue92gOjAfUTZEY9MheNVpSq/UOY86eA67J9NvC9reOqw+Ddexyvm8ZvazOAypnYh5YX/leoHColTZndO8c+y67hunj1zG9aVUAO3RyReN5KY3rFGwMTNtDzbfYsWAgqdOHirm8f2KlJc5VSaIlijpWoVS/vSZcut/v/qBl/Lc/HPHeN2/neU/fxl+9Yw2rVhrwLWAxgRAbaxR39M/DqGnks5OFYUi+trSgqFZh7MvXWlHZwJQNi6UTz3M6nndBe3NkA6LoFSuAf6BqeOootIwP/kO/qizxQwEvRZYmhdfET4sVI6KCdT5Bsp8LEHuKRQpYoXPDpyTn7D0CIQjPwkMAoqlfOJl9F3yb6bv/Aiue/AjMcMhw5Fh+vzO45dXvxMxMMX3mSSy5/1ns/sR/sOf8rwGw8W0v5dbXvIPmlm1pXSuf+RSqlau88Yg5QtomRiN0wJCEwqgc/OQ5RPH77vnJvg/zSHx+h5CXCefGqaLlwv0P9gcJrUj2f+s73P7RTyHTUwxPOg63Zy+DYzew5lceA8No9DWdNxPPr3/r6DFpCkt5oFQYl8KfsWmvJWi6GHwuAm3Sa2UsJbZlAkJorUA5+eGbuN8ztvPN9/2YNzz6KzzhZb/AA556DFXh9YWMGpyWSUnX5xqYrPeTB0O1limZOrJRJmgS+W13iUXK9WoWHf8FyrE/6YjKssB2YE5VnYicApwGnH/nbdnC0eqW+esrhurR4F6ptiH8xHB0tcz6OkUaqrZixVn03WNeWxkKNhoMg14yRqzQynuhxZemUFTLCrVsCUysFk2ESQxRKzNnncSuL3yHdc98BGv/+2OhKJg6aiXL1wy48kXvYbB6KavOO52VZ2/iR286n51fvwaAs9/721z6vPdgD4zTT5z618/GTS/BNYa6KZJ9jADHEfok1sbn+mQhrnD0wo7TNTTB6eowPf39sq2TG51UMeojzMkBDmA3hBPjOdv2ka+x+yvfBetYdv/TmL/uNpadczIrnnyeLwoJxznPJYWuLl1Hwyj/bLx6fQzBz7uKypVYV2GNZ4wKdZ3l48iZI4Bf+721/PCyA3zzSwd4xD238Pq/X8MjHjWgMO0zp8bbWYsyCDY3WYyw+jp87wi5Q+K30f9mNPTBBvbjfpPGXczeHNLCisgmEfkPEblKRK4QkReG6a8QkZtE5NLweky2zF+KyMUicl74vFlEVERekM3zNyLyzMPb0/YlEWRkYCOfJwcjExOPtVcZBp0YfMdzoa1Kyys7cpo2r9xqfz9niTwYiqBj0ZfC0gfdH7t3P1vf8m62/e0/c+Ciy5m/6hp2vvtjTJ1+Is3229n5nk9y4/NexfwPrmfF0x7Lse9+LcXSZWx41Ys7uzp9+qneUNjABNns963fXrGEnKD2NfEaiIxP75V/HxOpxXlGyFgwDZgxmBqKsWDGktbVB4z+XhQGG49BhgOOfsnvYYZDZu59d+Yu/wG7P/GlFOJTFx4AwTuOuVHqWo85Nq21LoYaJDXZbbStFrQhFBFzNMbWV/Y06vVNmtBhvHFtLymrQqMFD3nxWTzxNWcD8MlXXcnHX/OD1BBzpBWjUNI/1pJxAD7xexsqPSIY8qL+eY+hbhuAvJ3BwUb/HB3q9TMaXwGmROQYfGPGZwHvnrj9d6a9Sc6AdK7RjqRGz66otnkqffuRWKPI5GgLfKNgYV9gEWjL7HMQFF42AaHMjmW/lfIXQ36NWlnwvwv/L3/yQ9h/0VXc9KaPsOX1H+LAFTew98IfcvM7P8dRv3gme797PT967b9y0a++jXp/zQkveSJnf/pPkNWrOePtz03bW605iuKY9dRNQd0UIdHZ4GxIJbDt7xPuW28vg92MDlvaRrJXuxxOkk0j3zdHu37r151+M56/VOgS2PxwXAfHb6Bau4LjXvNs3Niy4hFns+3j32T7Zy8LRTSSGnTXrkiFNdFmzIdmu2PrmWb/KkIT3rJtyhteYxfsg6s44AbMBzux3w054IbUWoZX4TvZh/+tVLz87zfxuy/z6tEved423vtPc8yrb/C7VwfMx753CGNVxuq1jGqUWv1rrEqthP54gtPQNiSAH99CxKvwV4cBiH7e7U1/HA5D1AAvVtVLRGQp8G0R+Xz47s2q+lf5zCJyWvj3F8NG/Gf4vBV4oYj8g6qOOYzhnePMPfCysMFh8OGzlFOUUYXdarOeXlH08tCQ6EvyGKwTyiJIw2dME5AZpdwj6xmiHIxF5JwDJtpdSWVQcSiYwYB1L/od9n3lm5jZGcp1q1lzzDFQKVKVLH/8w713ai1SFsTwlyrs+2qbVL/h5X9CObvMgyEFqcMOhm0zY+8ZSBNCV008d9BzVBI7FL2xnKRI7I5m8xV0qswQD4g0sEZOJYg1htWHGyWSgYNjNlKtW8vo+z9m1dN/BQTqW7YSqXG1xpfqh2XEmU6OwfiGbcx994e43fux+w8gIqz6pTNZcsoGygqwgT0SzxL5PA1fVeOs+ORrFGfb/JDS2CReZkK+kROHlSGnPXYzG++5mv/z6H/nuu/sDk1cBcGw304xU4yosBQ49uswaIs4LK3eRyyxn8QAmYwZKhZDrL3zdQQOUdUDQUPkbar6BhH5ziLz3jn2JoFzjSsGtGWa1bNEBHsSmZ9OlZl0GxZHhqcI6/FMg7/xxxYGhV1wv3XAUGAv2xJ76YCs5KBFW7RY/mKwm+2++tBRsfwo1r/ieez98iVURy+nWrucYzYcTTWAaigsf+z9vFp8rQyGfvk6XII3ffBbaXWb/+q5WBl4oBVU8VtnUFLllzoPjiQ6ktAKuIbtSvpl6YAIWrQirGIlyXx4yZBYLettOY2gVdAxCwnEKWqAzzFK+aICs/c7ne3v/wL13hFH/96TMEbZ/dUrPasd8oh8jqN3sgalzVTyYf7qm9n17etwcyPGtx9g6qgBxz/2VFaeuJyxK5kqgpq9KI0rmKHwfQzjNRIEHH3OIkn92jOPuT1ocGXJE5+9ms2nTvGSZ9zIFZeNqYNcR62+X5mvY1XmgSlxKaVgHqgCM1Srb+URGfLYZNZGxKJQSI3V2w92x6R5j8BxR+xNZxwSEAUaKjZV2ysiVwHHHGSRmE4WHqFpbAO+hleZfMfhbBzEB6u0mjUBxEzMKTLaobw7cX7NHMDA+GCcN3gBrxgTAVDbMT4vkW+yUtLcAEWGIokYBkMk/elhO1LoKnu+qQBOMDJk2YN/Mew8lGWFsTXENkCinqR37bqaW25j5z95HapNr30lxZIlHgRFkOJoP4dpphbMOAMzEZRE0CNhubCdCeDEdJYAsExoBRbnMwLFqPUGVEBrP7/xnTJwQ8FVWYWaZudPYebuZzJ35Q9Zcs49QWB4/HE03/2BN6bxXEoEsxpykxzb/+GjzF3xI2bucQrF0mmqDevY84WL2PW5b7Pxj3+N5Q84naLweUVRv2heKuaoiE0ax65ICtdGlKYwGOvZmYHxyLERTT2PnArluqMAGC4fstdOAbCkGVIXc+yzUz5HwHgJ/whuIvuT9zCrQ6uAOGIVSKpoOZTxief7yBsiIvcHng48J0ybaHvuLHsjLmQdRlsiIWwWnQRRCKXaPucwRIXV2xnTOzl54nQrs+ATjEWUouyeqNitPqk7Z0nUTcZm2sB0WGs6YMhZk9khfLg4d86y0IY3kX7rZGoZyx59XmZTBygj6rHfZxHFFMrcKJ4g2H/ZNez4twuRQcVx7/hT7GBIM08vZaDrHKoxXsHeAY10GWabnbYI4LKzKdEwZecqzWsEqVPQ0lcjq/GzxzJ8o96RFk0CnDEPU4wwc87p7L/sxwxP3IhTZXjiMYy37kGs6UgYGKPY2oe9tW744f/+F+od+1h+r82US4dMH7+Wa9/1NX78wct44Lt+g9WnrEiNc03oj7anGTKSGUpjGZky5RdVYhmaqtM8dkpqKil8m6GsVcjKzX5vl60dsNdNhWNR4tww9FprmJImgXSIyteCDdeXpe292bJDkZH2wGxgDp5DdFewN/1xh3KIRGQzXjTpW8ADgN8Xkf+Gr/1/sarerqpXiMgM8FXgf/ZW8TrgfBF51x353UjwdMtYJ+QUxe+DiqeGZaNRi0xRW1HQ0kbtZ03JkbmGRe6RMQkMJZq9jeOTv8dtiWCo93DLLXlbXpqhuLgfEUWEC7G+dRs3v9I7zRv/4s88GGoCaIyhrl7lVwqTRS8tXz/t7+VgKbJByUg5Hw5DW0Ak1udfmZoOm5T/rrG+pFmsQKFB/yWsO5Tmm8GQZuftLfga1TTbdzK+6kcMTz8xJNmHFRtBrT9H+75+GeWq5Yyvv5XRj7ZgZqcZHr+eDb/5S8yccypNyFmMCZUi0IgwbkoK47BiUPUKv42YJOefJ8FGA2eNhNwAxw0X3AjAmtNWJuG2sXqKPDaSjdUtsfojKspGgOQNVVtxFr+7o81dj1CP7YV4pdiPB/twAvAfh1roZ2pv4r0abUlKugnOlbbTNZsePQnVbj5RVHho5TbahOtJeQqTcoUal5WuZzbI5raNFogkMNIJ+Un3moj2r3OzRzvnHQ3VsF+BSom5LKrCgcuu5tbXvheAY//+T5Fy6As+OtvRtYVivYK+WPEVrkYXXqfZtd3xY7NLftLDVwpJ9k6NemCrhN+QjrimRk2Q7BCJGmQ4ZLx1N876TgWutsxdcyvcsIOpjWva3ooh99CK4PbsZ+93b2T6uDXs/u6NzF2/nWJ2yMr7nsDGJ9+DqePXMW+9cGUTkqudGqZcxZytKAMTGIs26k4rkGAzjEmNZCspiN3pP/cx34V+zeYlzOsgHLMKq1Vgn/0e++axGhinAM6JbHR+YFumOs7jP3cFNCeOu5C9gTsAiERkFvgo8CJV3SMifwf8Bf6Q/AXwRuDZAKr6gknrUNVrReRC4GmH85vriqqDFDxz0V7Q6cIOD8f0kCT7PjtjgrIq2+X88WLUN78zqp71kLZhYwyJueB1JUn+cLepCqukCjdlAB/Ru4w/nyULTgJECzYojJWmbK1Cvm8K89duYef7/4WNGzdy9At/j2JmBg8UwzxRR8W0OkOiQAkrq3AccqCUjmW2AQoS22/lxz2s3tTtYRaBlUWJGeL15YIUj4aiq7i8G/j85Hi/RZVqjdt8+ZWsf8QjmDED1ChrH3AuS4/ZxI8+9VnWnn4qEkplc5CLgTV/9yrcrl3UN21l6vTNIFCtmElgRlQR24IhAyw1FU4NxvnHVVK/Dh5+BEORbZwuGiRML8TR7Btx+Rt/xMaNG7nv085lMPIltUWzhCJ6hzGxWhQjlkIUSaxPKJ4NIZi8F1qtkYrz11xxGMbnZxinP+yhql/Bx/Xj5x8Df3CwZX7W9mZdMejYF6H937Oy7U3rJ3VL543TxKiIeG/7KIZJoVoCkyl49mhoy9SpvbB+PUprW+qgMxQrW0e29DlyKizXYUq6d4Qka/wF7SuyuoBoAY6W7K1D1eDtWHSE4j/Bkdj/re8x/ugX2bhxI+tf9fuYqsycyvZH8t6IEtdrKu/IGPE1Avl1qp3FFx8S8h6z5VbjFbk1NnoI56xVvo9nTVtbAREVQtPQfOda1vzeU6jcFGJg1a8+lumLr+bGT3yb1b/7JCJ7JprZjqUz/OL7X0qzfQ/1tl0sPWMTxsBw2RBjHKb2zE9pNJ3/ShwDN0XjTFD5tog4CgET/o8l/CoWaxofMheHxaFSc9uP9/OND9/Exo0buc8j7sZoLjhqo1VMhQqxRiw1AVjhmey82KgvCVHgT3Gj7bOxVkNhVh76lNxF7E0chwWIRKTCG6cPqOrHwo/cln3/DuBTh7m9rwE+km/wYmNr3YBkomjRacsu9kSNpnl0oX5dkSGQEm7TOl0gfl5/sxjn0bQJHozNYvFKKAcFT1nXeRm0oGq4rQlxregN5cyQy8BQnHaYY2tTdwGLddRbbuHmv3wzS897EMsf9UvsKAYwblKCcyoZU8HEEvoApGLy89aR396UIxQATDI6keGxPUAUWaPGh8eGuxU78MaqnFG21w2uIoXe7CBse3i+OwUtw+dotCIoQrnu0stYe4+zmKnrdAxk8yZu2buXAxddysw9zvTskkm76LWLBgbWroR1K9lnnN/WceMBTumSOkNRevAjojTWsKsaew/QKK4RBoWX8zfic4mGRZO0hUrnmCnrpGq77Xtb2LJlC2KEH964kzV3W82SYkyjht1mRCmW6cLvR+MKSmOZLUa+t5H6h16/qafBsw377BSltFpIh9XL7AgcodLjj4DNZDZHVR+6yPw/c3tzmx2Da69Dn0LUAqL0UIzfe3oBE0Ix8X6On0UVFcOOYj7gdU1h2sL48GshjqmiSecb2mTfObyDZcXQYJh3LoXqnRpuc3Unn9HFHmEKCQVkLM0CexP3EVrdz4C4b2u83SXek/MN89//MVv/+j0sf9pjWXre/dgqCk3jWalGOmLqUrd5fqj4cH0l3NY0mGCbYt5QspOJWgv3dLAd0RZpyE0s5rxz5yq/T0UlbG8aH4I3hHC+dp3nIvsc9zPsu5sfc+O112GbMYOm9o5J4Vh7r5O57p8+yPyPz2Fq85pkO0SgCE6TTg+RY1dTnbCCUXhOSNMwLNv+aMYpU2WN4MN1q9UykpE/586zzzPF2DNJalhixpTGpnD5smI+sETeXlx+0XXctv1m6nnH9eOTOGZpCMW7IfPVHEvMiEoadrohhSirir24XA0fS1Z7hsEh4tjnZrBqGISE0j1umqHMHeyWOWLHHbU3+TgkIBIRAf4RuEpV35RNX5+VuT0Z+N7hbKyqfl9ErsRrBVx48JkB8hJ5/E0SlVhj3D6LC3dKTuO0RoIGhba6QUIo/4y/AKgmJVUIFGIWF+9oaOSGJ+pk5OGwHPj0jdEiYKjrNcU7VlKMvd6xA20abnrtG6jWr+fo5z2X6VNO8cvZbB1RgLFjGGkp5who+tvqwiHNPufb1Z/fNAEsWRju9gBEhtl8cTeCB5cID/Hrjt5je94cW//lfVRr1rDkbmcFet3/sFjhqAf/Inu+eAEzdz8znFOSRhW1SdeEiAet8WHmEzqzc+5M8uadEebHVfLiysIxH66nwmgQKBv4vKLC+uqwkJhdGscopOuqU+bmYd5WNK5A7BS1GVFTMGcHKbGyDIJupbEMQ05SR8BNfSWaw1A7w0jLthv1oUpc83N8ZI0PA38PvJODaxbeefYmcyDohM1aT0KRJCcRQzIuLBeZA9/fzAshWuNzhqLcg5eI0FR1hoH5pkzihymBGslaBLUValFvyE1IXD7kiLsS9zU8oMMmeTPXeMdOwz6Mr78VjHDrn72ZmXPvxbqXPp/hicd5e1jH9dDan9z+xT6J0fZYME1rj0zMG4rb1Lsq+tdxrI6NDl859vskRfsd2jtvMfc0ABlfSaLJObUHDrDtb9/D1FmnUa1djzb+eeG0QM2A2Yfdn92f/hrVb/9Ksh1RjLfNG5PURii1S4mfAztUO8/cFMYxpmD3OOQVFpZSXWpwWxpH47xafcxjHLsy5SsOi4ade0vqeX9w9o8G3FYvoRLLrBvgQtUaxGbSDqvLGIhlaTFHgfO99dBUoDFWXwk77yoshgNuGKYXaLH64NfUXcDe9MfhMEQPAJ4BXC4il4ZpLwV+Q0Tugb+krwOeO3nxiePVwKGzvjMGpgOKCPd3DjpCki2QwFHSEpFwo4QEbbT9LqRSeuMWZfujt5NyiCIgogVU0TgmAJaDoQyIhHVDD/DcwbHvku+w7b3vB2DVrzyZ2Xvcy4fIspL4+LvGtvY9gaHstyexVP1pC7Z10vc5sIrsVXyPwOdgQ1tvUIAD11zN/u99j2P/9H8hIbEoHlvUJ1vvOv9z7L/wUp9wjbbANBD1UdG8TbQPP9U+1/yxEbxOTMiZcJ4yAkiGTNXnC0ThztSuoa658PFvplw6xZLjV6XdmT5ujS/fF0PtCuZtlYwbIXyWe8IxodIFhVtD6LodFGtjwnXb3PUwGKL/j2vsv3A0qvp3hznvnWNv4vVMupIWPFxT3lC8B6SXV5TFfhQPvKOWjQa2ySsWa2ohEfVtgEzd2RdwxCRq1xNfjEnLHecs3Qcc+hrIPZxJITUVdn30M+z57JcZnHAsK572JGYfeA6mrNrHS27bes5fJ+mZ8DlWa1k64GyiE7jYZucSIZrtZv670cnVHMqGH7QEUOQXO/DNS7E7d3P0y/4gtApqE+oBlj7kftz80jcz/8MbGZ58bLvBIbfKZiE0z+bFpuEk5yrmH0VGzIaGv4VxYLu6RVGfKr4GxvqmuLfs5l9/+UMs3bCEqaM8YJlZNYWsmOWA9XpphVaoVklTyFeu+bxIZ2oq1yR7U4gLBRyxt6KX+6hDyMw3gC0pf35ziO6IvemMw6ky+yqTH22fOdwfUdXrgDOyz5dxOBpI+UWfm514Q8WbMb/6E+2gkXhoF/h/7L13nCRHef//frpnNoe73b3d2wvS6e6Us5BQQICQABNkJINBgA2IjA2IaDDYgPEPbPiCAWPAmGQkkoSFQBKSjACDhAQIBURQRuny7t7thc0z012/Pyp0dU3PzB4GdDpTr1fvTndXV1dVV33q8zz11FO+0Z8ZTP1l+cqsInFjlieFofRKDgs8qma3hsBoqMhWR5hrtgxhSGbnWLj/ATqPOkJrrmy9WEJnylQZG2fzRV9l59AgfaefTt8THk/vqafSNjKSOT+08U3ZHFEBl5fCaTrv2qLIUHjPI1sqgqQsjgzZqbBmfcXm0xK3he3jjH3lQkZf8jLKff15squfIIpihl7yF4x98jOICJ2POZas+kz5DWDpMUO/wLkT8EibO0+1RG6X4tpNVZVrRzhCtKBKtJdrxLEwfM6JjF9+CwuTswycuo6etctIunqYT7R90UISU4ljSpZkid7xPFXiDLL19cxg264wswa0lgxZw+ui/Z/qwr4JUFeKyF8D3wTseiWUUpNhxEcSbywuFJKixE/BYIfRNCMKiXGLMvQ7TRtU2tdL6PTVbi4KWbpWE2S3m7FkKPHJkP3tkSFll52brGXSkM5bbfsuahOTdBy6zlzLipHNOSvm73mArV+7ij3HHkr7mtX0/skZ9D75cZT7+3OY5p5178vqz9cOO1zySIwE8XNp4T1XEHzMC2eP3ViRZugvgll1hhN2lAHHubvuZddV32PkDa8k7uxyfV45/BCizh4Gz/8zJj75VYZe9Tw6jlib4QY4x4925aHz3G+aRc2YXMSRcrMOCZkfKbs+MvWELftfa4u0rREDSxh93AFsvXEDncu6GT5qiCOfewizSQelKKGsEpJqN+XStNszLVaZHzNSsN7xy1IjVvp31as/vYAjE7xShJlkuvhD+OFRjjdh2Mc9VQfaB3RLy5yPqtzAlQ14hsXb6RTQDRezn5b1P4QBDq+Rg9Uw4FaQ5fx5WIKUW7klxpERdaAAWQdP04RkeoZ0dpbxz/4nHYcezNJzzqZ9VbaqeOonN7Hja//lzletWkXPSSfSsXYtHWvXmikxAw4hCfJISk6VGZIeJ7l517x8+tdyzwcEy25zVO0Sh6u1TkHNk58eK8qXBxxpohj/xiWk8/Psuv46ug45PCM6RrKWBEiEjhWrGHn5S9h+yaUsbNrM0nOfmc+8+G1EctfdlFqkyJbta8LhtIpKf0YNcPneLpEiSdqIopShlz6TgXNO5eF/vpTdv95Cz/HrmKm0GwBMaVdtTNHujLJLklIZ300UQd/yLiJJmZOythcQvYw/dLzoL8W2UyfNgsC+qsJ+ifnvrwJTwNpHIC+NgyMTAbEG3W4sIbDYYSUP21YF03D06kkl2o+NMtNDcZQ6UqRQhvjG7rsmaea4sVqLtb2QnS5LPM2QL5z5pCgoQ1qrwtw8C/c9xI7PXkzf059Iz+knUh4ddvnd+Y3/Zs9VP3BVsGrVKrqOPYL2A1fTdfzR+qK18wmJD9k1h4MuDwFmRKYPp43x0U+vaKAVu+q08LuZz5CaTmzGDWfO4JFAVa0x8dkvoebnmfvl3bSNrsgww3w/MXXdecxRLH0+bP/0JfT/2ZPpfeJjs9eKtSnzTDbIxqfI2BolTuOst+WpxLEjPzVTeB9rrKF6yeybFkvKke97Nus3THDz31xOMlel1j/AZLXLLekfSMpUKr2aIBmbo50P7WHJQInegQ5m03Yis6S/TWq0i0+HdLAaIus8tjNublS9P+LNPk+IfNyxl9x1O01lGlMW3Txkp8jE3LXgochU4YpMS+Te4Ute+j25cyU5QmLj1O0HBg4k5h94kK0f/4S+19FOxxGHM3/nXWz9fx9l6Z89i87DDqFtdDmVLdpMouvE4+k79RRWHnY445VqZvhsC9qMDBWBlXee++3HD+/591X+GTCkp+zdF1AlvcLMJ0T+8nvlPS9okjhx1WXMP/wgAHO/uZfqtjHaRkYysw4vH0pBx9p1jL7hdWx413tZ8rSnELW3kan+PTAUMtal8tzM+j3ybcr0lEbkFumEFSOptgtIlfEls3SApU9/LFs+fjkLuxeopnpPsiSNqBGzICWSNCWOUvZs3s3Nr/0vEDjkRY9h/bOPoL2j5JbxVyXObIpMcDtPK6GWxnXu/etC0bfcB4JS6qBHOg8tg8MUsj6P5wAjzQQllNUqG62DedZOv9utPjTBNgRJ4Zbgp0rbNEbo64nKbkZ3sgAAIABJREFUvOFb54v+7vWZl2W8VWQZ9uT8oKHbwJ7v3cjOS65wxWtbu5o911zHnmuuY8nzz6bnlGOJ+3upPLABgP5nnUXnY45m9IADGa9ki0O0IIg7LyQzwbRZXgtE5jgxlZy7jzpms4j2m1OSOgEY3/xLf4FUuanxTKCGdGaW7V+7BDWv92PbdcU19D7uVKLurkw4xgjTZvzoOv5oSkNLmfi3L9Jz+smZsOZW9WXE2LpYAIMlkdmwOTKEV4z3buOg0xKhcJso0PHtfnLVVNG+epjlTzmch75yM7N7anpKHr2P2kJSZj7Vy/lrEvPQbVu4/E030NZZ4kkXHMFJ5yynFOvl/FWJ9SbYVktt3u17za+qEuVWizj2Q7zZpwlR3Tyx5TYExMhTXWdaJc8Dqk4NUR4pImvwoDzoM8Cm8qDjT7flQCAkJNRLSypJmPjyV136UXs76bRWR3YccjDJ1JS1xmTwOecycO6fInFc3NgaNcIAqBqSoVYNuBEZCoIonHPGjGWQTZlZ7ZAvnQVhfvNGtl35dSo7xmlfuZqFzdqfz64bfsjwc84zBMh6Cg7LGCFRhJRKubJqz+Ym8659ZHkQM4BYnyXYvYDIvqebugzyrY2zdSLWEV/3accQfeFapu/aTLUWZ4aURNSSGBVpJ3sLc4o0SeldN8Sm7/+GynSFI19xEpFEzp9RGHznjUA2jdgs7IMAZfwEvRk4QCn1KhE5GDhUKbXYlWK//1BYb6ZNSH4aTcf3frt2gdY+mvtuSw8suQHSiMg4hLVKnQiVI0N2SsX5PLNHgcbaTuf7djvpfDVHhqS7i6inG4DePzmd2vh21y9H3v5KVC1BohLOf5otX47YNJgOg7rFF4WCmTkPNdcuj43a7d60Z4X3hbwNvk36s3ffxfbLLiOtVCgPD1MdHwdg+qe30H/mE0D5XrNx31gAlSiizg4HK4CzWcKI027ccBCkILXbN2uCpB3di76eZpqhos2Cre8jMUbZSgkj557IQ1+5mR337mTkiQdr7bOKWEhL2r+RxNSihJkZ/fqlB/Vz4xfuQ+KIY85epbVHUY1UIsqSF8DKkgQ+iloIYLae9rHwv8GbfZoQ5VSzYoEGJxFk01wmimHrDqtUBmhOo5TiVqkZ3pS3DYDMt4ZHhixY6AFVseE1b6O8cpQV73iLy4vJJulCjbl77mHXtd+lsnET0t6OWtBTmX1nPJHBc5+FihTVsXFKAwNIW/4zSLwIYzbxOyKZFqSIwPw2jbbZM161uqiB5NaMCNmHkvk5FrZt5sDX/g0Pf/JD7taeW25i+NnneeiWeai2JLmycSPlkREia3zt1b+OmqPOWb5SMSvNDPj5wI737THTark8CxLr5ft2CXQUl1n3xXeYJfwpkuqpkRoRVcmcr7WvWc7JF72C+/71u0xcfx/tQz1UEqM6j0SXIwglyTzNWn8zLcM+CFDAfwK3AqeZ803olSD7ECHywMQ2skDLKJ6zP6VUpmW2SWgVI3b6xZIarVUU52iUNEIkM5FPXXuyBw7rlBKqO2fY8JoP0Pvkkxl40bnGQNmzbZxbYPbHtzH1w59Q3eq8EwAw8KLn0PPExyIRVDZvpbxyGIn9JfrobYCaTX34OEy9VqhI6Ao15HW4FD4bXG8UROW6a56k4d1zuJGRotrkTmqTk4xe8DqnrQeY+9Ud9D/pCd63Nh8gRdc1iuqDmyivHM3n0Re2jFAlfttx39F+08zQPlHisCYkQ5ARotS4cUgMUYt6+znt2rchophPaoYQpUzV2qnGZbd8f/i0NTznawP88F0/YvLBKbbeP8OhaYmqxJRVDLGeGrPBkiHrrTpBmH302hD91nizTxOinBMubxWZ1QY5Mm75i17XmhnR+i3XgJi29RGrGNIdyEl3WVpADnRQmW8hUULH4Yei5hc8O5zs/th/fJb5++93yfSdeiq9jz2Z8vAyxEiTglAeGXZxbD905QvBw7vnCJ8Yz6xk9eATlb0FmzBeI5sibxZK/7fILuQcMIbqbffT1Hv32kMYPvvP2XThp1l62hl0rlvP2Le+zgF/9cYM6KwWyuxZZHV5HavXkOzeTWXjVtpWrsjKb/2bOMLsVawtg3XToERv/2JsPJJde9jyrg8z+p43UhoaqM+3YAzvMe1FSEQDmkSQRNqHVVViqlJiPi0TR6lbiVLu7Oegdzybies/yPiP7mfDjZsZOnmNM6Isie6O9rzN+CeymqLFaIj20Tn9dUqp80TkBQBKqTmRkG0+siFfb+L+KYM1OWJk245H/C12WG/IzqGrRCRK+zdLxOxAbkmu1TSaKTK7e32aRm5PMJVGxL29lEYGSedrmR1jkmlsNr3+3bmy9D/zqfScehKlwSVGCFAoFG0rR/N+NdwXkMxGqtkciE9yTDJiiQMBCfIxRJFNlbXAlkYa6SyCR4q8tBw8ptmnst/DYkPfyadSnZxk4itfY8lTn0ppaJBd136X5a95VcaBFbgpSXBmF51HHs6uy79DumeWqLcbOw0qiHasFgl2FWJmt2qKl3orRUVPfyslzP9mE1s+8GUO/MSbkbZ23P5zZCQpcn6xFFVjexRFWiirxNoTfhyldBKxe6HD4EhKW5xQGuzg9I+dzaVP+k9u/vxdrD5rPcsO6acsKXNpm9vNXm8bUqMc1dzCjkhS2uIWW3ew/+HNvk2I/EGYrP9a1i1m8PM7g9Ma2cYaaozSTIrDNl4JNEbmxW4FmSNF2THympfr52265h2iYNnzzkPVEtqGM8ITlst2Pp80qEZ41IBY+ImEz/5WZGhv2b7BDV/Dap2jFea34H1LTzyN8sAQE1dfxvzmDYw+70VIqcyGT36E5c/9C8ojIw4ENbDqk7jURuehh7HwwEO0j67INESBllD/807cu80duzkkIHGZ8rIhWEiy/ZWC7+MGQAFlPEMq0TYLEmsfVta/UYJeLm2n0QAiiVj18jPZ9Pn/4dfvvIxTr3kzbWVjOBuZFSakbgkuQGRBstUHajWYPHKhIiKdWLldZB3e6o99IYRkwG8/uakUHZusVfmSlS+1KKcZkEhcO1Bo7/dJanyjkeYGwvzu9eJsj1b885uQqKSFNK8/kAqj73orUVsbpYGlAaB4goDSdk1ihQy87ProGvZXRT2RUfnroTYofy3/26VBg2vN2nATLPFJkY0ajhkiEYPPPJu20VEmr76K9tWrGXnZ+dR27mLsc19gxZtfj5hpMTF1azNWWrqU8sgyKhu30nn4+rygRfCyBiFNhUTMdjxKoK2T0tI+0gpEcV4DbPfR01sb6c1qa+YVUZStcI4jRZwK1ahENdUG+rVIbw5bkhSJItafdwy/ueSXXPbCKzn/Z+dTk2xT11KU6C1B9N4yVNGbSEeI1iQ1C/sh3uzThMipce1A5AVrX6JvB8TI/Hf+gTDaJCelWBCzsQ2sWaJC0LHtM95vkbwtkXhxywODdZJOiJfu3Z6KtZlwliWUr4O8GuZ/Gfw8tspGEUmTgnstriuB7vWH0PW6tzH1y1sZu+xiqnt2QZKQzM9Rpj5PM7/+NTt/8H2qk5MsedKTPOKTvSskmxSkg53GsKuAOroZfccb9b1Q8rGaAW9Jv9uexZJCmwlLhtAefFUipJZkRymDf3YavSet467XfJZNX7+F1S84JZMIRRknftpzursGi9MQ7ZsA9Q/AfwOrReQraF9DL31EcxQGZ0+og0SeIOVUDo1JUTiFprzGZ6fbtUZBYW2KQNXF84lQThMTl/TScI+Q2Ptty0c0JnltNsyuxkv7TCAc+JFcAkFxoQ7vcvcbkKFmr6i71mqAbUE6iqbT6mBWQe8Jj6H7mGPY85Mfs/VTn0YtLKCqVVRaPCk9dd2Pmbrxp6Rz87StWpnhjU2zUZ48vLfCta5+/Y1Ly4dZ8T6960ya1CcidhYkjTSOKNyijgwTUlJjp1hB78novJ5HQkmEw177BIaOGuGn7/ou9/9gEwc+8UAASpKQpEJZEkoS53wiRSqiGrU23djf8GafJkThTIcbdOw19zH8UdBLwLvvHDu6e7aV2udtb8rmnPPSlj3EIz8+ScqOIqJS936vjHWaobqB28RNs2Rz4OSpoxs20EZkRzKg+K1CQIbqrhe9r4BMSRTRd9xJ9Bx3IipJ2PT5TzB7z120j65A2tpc9C2f/w/m7ruX5ee/gs41ByFd7fn0fQS0r1B26DIR/LJ6vk3y6fgVba9FEGeO2NzqIjtdAnrliXlFitnUMtJv156m9cqS8sph2ob72fif19N33Bp6Dxt1ziDthrI6uYwUPVqnzJRS14rIrcAp6C/+BqXU9kc4W7lg+09mg9KoDdnOiMOLOmHKwyG3KMDEcdtlWBsRJcTmv9UO2fuWIOkLeHgj2W+T15CguOz5wWqJbHphJ3U2CMG1ViTFz6N/nda41og85bpdDrfrs10Yr+ieJ7hE5TJLnvhE+p7weECx4d3vYfYXv6L7lBORyC5iUGx6+3uIOjoYPP8FtK1eSdRergfMItW+/XZek9F2ixFpmt/2qaj8OlkzHvkaaKMxsvaNSkGsFBVKZkrWEBuTYCoaS4ZOXQPAj97+PZZ86wUsHe3UgpcxI4lSvQItQrlNZqeq840r1OZ+P8ObfXtzJG+Q1z4oFCrODvDuKdz0R3ZO5szLm/rKzWcr774xuMYcYq47n0Ne2jkyknuf9+xiJB5VMCUXxvH+i9nKQx/aK7UtY0MQ8UlLAwKjCuI0BBipP5R3NLpXl48igihCVCoxcs7zmNvwEDuuvRpVqzFz953s+M7VzN17D0vOOIuFTRt58D3v5IG/eQu1PVMeEaWwXp2H20TyR02Iig5Tt/bcbRtQNXszVSPzOwJzrmrmSPT/pBqTeP9rSUy1po9aUmLl67QPpbvecQnj/3M3C9WYhaREJSkxVyszXysxWyszn5SYT0pU0kV6jt2b4w8QROT7SqkdSqmrlFLfVkptF5Hv/2HevrggNXHb0NijLo5PSnwcsMvKFZmPMqsF8kiLW06f+tNi4X/JHC1akmYwxS3scJhGHmv8UCCUZffy6RdXSOOy102b2fdBjpg10xTV5ZUsvZBbNNUqtQo5TFJ11yWKkDhm+OUvZc/1NzhN0Mwvfs3k179JOjNL31POZPrGn7Hxje9k49vfi6rV6t9jCabCbPFkDoMFqhpBLfutqhGqEqMqEaoSQdGxkMVNKzFpJSapxNQWStQWYqqVEtVqiYVqiUQJ89UyC7WYhVqJ+VqZ+VqZhaTEfK1ErdzJkW98IgA/uOAaHr5pnLmkzFxSZqbWzkytnemknT21DmZq7cylbUi0pHX97md4s89riJzq13Vws6mntQOyHRO/voMll/ZeDqCy9LJ0PIlPvPjBB5XwA5skcoDh578oNJF0sjjFEXLaoBCYGoUCDVGRYNMsvrse/K4jX955jmgF580kuvblKxj6k7PZevGFzD54P6sPPIDE7Ao+/fNbqe3aSWnpAHF3N8nkJKXe3nzeDEAJTT6Cwq3YaRaqk5NEnZ3EXZ0gygmIzo5AifZbgh4QRQGSrSKxhhtKUuwWa6TQfex6lpx5DNO3P8iGT3+XuQ3bWfXiJ+jpOFGeWlzpLQBaIcofEHQWE0SkA+gChkRkKdkX6gNWPGIZKwq2vZiPq5QWPvK2QzZu0DkkaA+5NDHESNW7cfC0RCpooz5B8rUIeY2L5LAml71WZW0VxwuNtOF7pVVeLClqkMemWLo3IUzDnHeuW0f/WU9i93XXsft7/0P5+ONI57SGZPrHN1HZsJH2tWtQxrluaaCvQTkCjY+b/sR8r2zxTV2BzDPVbeOUBpci5XJm2yYGW5xvNyFNUpSKiOKUVCKSSLB7wST+8ukIKknMij89hi3fv5eFyVlu/NvvcMJbTmftM9br6Xll7B1R2g1IqvdSbBr2Q7zZ5zVE4h9Wwg+0L/XGfWRgEZIHAhBpoBKu09qYxttwHr2VarlpOaUOXHLCTA4Eg/wVBY98FJKVBqAQkpe6a0W/w7S953xtUdF5q9A+qufrK9u2kC4sUNmyiVWvfRP9jz+Dgac+g/KyYRY2bWTiW5eRLswXg6q/nYrCk6w9rVGLY/tXvsaGd/49qpq41W4SahStF97U+KSxq4GsRiAl0w6YVSdJKoy84hn0HHsQtd2zbP3ajez+9SbtwC3Vm8gmaURN2X2tFuGpOuwzLY7fc3g1evnrYea/PS4HPvl7f/teBF/T62thQhyoq7cC4SjbXb5YS+RPheVsiPzng5DzEeQiU9/mW4WQ3Phl2Mv09oqgFAlPze4XYUmTdxdpxPOYo3JpF8XvPOJQKhs2gQiVDZuI2tsYft2r6TrhWJb86dNRKqXy0EYmL/4maZpoIuynk5tR0CsBldPiGSJkbRbt4p4UjR0J7t6W936IjW95NypR5p7oldGJTVOf299pYva6S73tXszWLz6OJFLi6Hc9nZ41A9Rmq9z8z9ex8+EpamlMLY2ppjFVFVEznqprLYyq90e82ecJkQOiFMTtrq4bR2RV2x45Ee93jhSFgJcjSv6gWaAVakRYQiLWotP65fLzlYFwE/ukogQXCUhFgFKXxyJACn83Iz1evGZEKAdGTQgUAhJFrH7NBaAU1Ylxqjsn2fTJj7Ljym+y6/r/oXPdekb+8sVUNm1i7KILUSqY0LbtpsHBYg4Fy857PgAP/c3boJa66/4UbDaVYkiR+7Y+Kcp2KlcGuKSjg5GXPY3y8qUsPfNoHnjfN5i6d5sbNN2u54YYtQq/a4ASkdUi8gMRuUtE7hCRN5jrAyLyXRG5z/xfGj6rlPpX4zX2rUqptUqpg8xxrFLqE3UvewRDDj/8qagkG8zCKdmQRBRrUuq1P+QfC7RB3s2w/xP8hmLM8fukl0fbTjPjajt4B9f99zcKzYSqor7f4Jk6B67ec0WYUIQfOaJj91HMpduADAV4Fnd1sfxNryeZ3Iman2f+N/cz/on/YNe3vs3u73yf3iedxtI/fyZzt9/B7iu/65VDV5QVfFSKdovgm3BYAmRxwSNP4hEmSWHkta9GVWtsfd+/ZN/IfDubljPUN1iSGFKUpBG1RB/VNKbqYUeSRpSG+jn4tWcQd5Y54OmHcf0FV7Jn2yw1peNU0pKetk9L7Ky0Xpi1v+HNvj1l5pbH63YRGTZdNC4oy9aDzoGSzJAR5ewDlAgSZ9Mfdq8z/ax5IPWIkE9UPA1VHQjWFSLMaHbNTzvDQsFOVzlwpkVjMq/PxSnA4Bxv8+oqqwNzLlk+ReFJvAVpB6DkwMh7RxEBcnmS4us2xH19rHz16+Gqb5LOzuprvX10HX4Eu67/gXNiubBxI9O33UbPSSfm9mjz/UQVVYbEki2xLwoCbUuGWPXGtzLz61/kbUvsztkRiNIrhlSkjJ8jQWmrRf1baYNpiezu16Ckws7Lb2D3926lvKyf7pMOJ1lI2PLVGznoXc/TXmq9jyqLsar+3UthNeAtSqnbRKQXuFVEvgucD3xfKfUBEflb4G+BtxdmSal/E5HTgDV4mKOUuuh3ntvfMoi/eattkzXjxNNOUygxUxbKrUpTYs91W3ArEdH3UYD1dRUXT5sBnnaoQVtsMaAUakzMVEuunJYUuT6pXD4lNeXzErNTVZIDLhw+6H95iTCUDx3PCvHFpu/jeUE5i7Q57p5HfHJEyCQeErI6whi8rGPtgQz85XPhhz+hNjYBcUxpeJCOdWvYcdGllAb6AZi+7ia6jjuM9nUHOg/i4pHLImFWf44Mb8S751dc1/pDWP6aV1MZHyNK7G3JL+KIMtzWGwlr/0ZRnLpp3igy7j+M24/afIWNX/kxE9+9g4ETVjNw6jp23b+D+75xB0f/1SmUPAvpSlqiv9xgWjD3AVpH2cvwiOJN6x3nGzA27/5bRUSJyJB37UMicouIPNGcrzFxXu/F+YSInN/03V6Dco0nJTN6DO8H56HxtGOp9npAeDKxhHzH9BvuIhtAK9VwUVl97VbRNFlh+lJwrVHwSUtARHISWRC3TkMUgEqdxFUHOtSVu5F2qe45oH3FCtpWrCTu7iHu72f5i19CeWiIdGaG/tMfj7S1MfyiFzF55ZWks3N5MPK+aU5DFJ771/3DkN/25SsYeMrTqe3aQ21iR9COyBne+1pNiqbQlL6WLCTs+Nr3UQtVFjaMM3n1zcz9ZgtTP7vXberpa4iSRWiI6rWbLY5WySm1VSl1m/k9BdwFrATOAS400S4Ezm2Uhoh8CfgwcDpwkjlObBD3kcEb71vnscJbpOG3KfxrErQ3/V+THDHL5TNp3j7r+xzKZaVZR/Ykj0KC5PqVKux3fr7FtlfPF5cfx39dXRZCkgEZaDXAmSJtUZ322pAa122bbAFUp/VxhyokQ+6/FydMTwl0HrKe0vJhpL2N8sgyhl/7UpLpGajV6DnjFOKlfSx9wZ+y/QuXktbSnFG9E5j9qbJgkY1f7/7UmY9DXQcfQv/pj6eydZxk15SHRZLTLqk0377SJNLaIpU5+9T/hfkdc2z5r5uROGLXndt4+LJfsufucbb/fLPeaNZoidzRyoaIrEyPVrwJw2I0RIWMTSl1p4isBp4CbPAyc5j5+QTgi8B15nwceIOI/IdSqrKYzGH6dChxWI2Kbc/K3BSVr3P7rH3O7k8mCrf3ECrvp0hLPT5TMGkFxCoHhnhxvN9NCUqTxuGXs2UjCu7bdzaUJr0K1ZJffTouDe9WWJQiqS2Uvgql1iAvxrdhcT7Nf2lvp/u4E0h+dhP9j3s87atW07ZqFXP33cvk1Vex9KlPpevQQykvG6aycTOd69cXaPZMcsG76nCxqB5sBSjYfcP17L7hOg78u/dQ6u8zy2DJpDU8UpSathWJ9nEiaG1Bil7q2tbJ6o+9iblf30/vaUdR7m83xda7ovvOVa3xbdNgQfP3FERkDXA8cBMwopTaChrERKTYC6kOJwJHKLWo3dgeEbxxmhDTVuw+fQLZBp6Qa0dW0yEQ2P2I1uYpcN6M7bp8hfF2rjdjsYbYRaTIGVQvptbCwvjJFbIaXJnq8CbAscbvyeK6ustUSi7NcBqtro16WNnI/KCwKD5psvcaEKG80Kbq03JpKkpDA3Qcuh51y230Pvl0SiODDL70z9n67g3s+vpVDL7qBXSffBw7L/k2ya5pykv6XX/3ibFfTjdWxJIJTw1wx39m+8VfZ+Ghh1jzoQ9Ce6xdrxiv2JLqscoS75w9Wiqk2r4a0MJUeXSQIz5+PvMPjTH65EMptcXakz4ptTTVXrBNplIlrQWw/QNvcqElBWzC2AA+CryN/OeMySaV/CY3AXwfeMle5dCTZgo1Ql683PWAvIRaA19b5Ji8fca3N8JrzMHgqrzO5c4L8l94vkhG7V4RvL/VfKwveRURkaYaoDANyX4XSW3htcLzovcUpe3ly0qKCHQdeijLX/YK9tzyM7Zf/W0QYe6+ewFY8uQng9LeZGuTO3C7ayvJ1VdhW2jyLXyNkb229PFnALDj21d4mc0qLGtDHikyxtdWmnOaohRKw0P0nXUyUXenM7ZWRJ6DPrtcO2quObCfVu3dgV6RcYt3vKowXZEe4BvAG5VSe1pmJB9+DSxfTMRHDG8suHttItd2/EHMwxAbt9E0iSVF4H9PMsNq+9+Vv0U+nRam2K+Z/16/bLnD2bKYfuJrK3LxF0HGAizJtFP2oLCPu/5tPds3ixdiip0yirxnIrRbFh9XQnuiKKu3Qo4YZWn3Pv4kBl/1QiYvupSZn90GQLJ7iqinm55TjgOgNLiUZGIyX1e+Rgi/7dj7BO3I+x4hHgGDZ58NwJ4bf1xPoDy8sWk4m6Ja7PDEaogSFdGxfgWDTzkOVWqjprSdUaJiakprlWpGM50qYU91rsXHf/TjTRj2yobIZ2wi8ixgs1LqF74kq5S6w+w2ewPwN0ESHwCuEZEvLOp9plE56cE2dPcyc1151yXDDGX++PP2luzoPW6yDgugomzfm7yERQaYNs0GQOFrrnLPkzloq1vmbeM7KcYWwEeDxiSwWfAJRl2+/PHcv2/z4WWjLo/+87k6xIFUoVq94P2hNJh6dgBu2XMpouuIwxl58UvY9rnPMnj22fSfcQa9J55ERAyJkM4vQCXJ2fm0+maO/DYIthy2jZV6++k9/kSmfn4LQ+c8m7inS4/EZgpXGbf6KPSqEAG7qaeAM7aWCFLjpFFLzZGpO72/lb/c2+6FVldRhRluHSUI25VSTdXJIlJGg9NXlFKXmctjIjJqpLVRtEamURgC7hSRn+G50FdKPavFe9fwB8IbMbaJzg4xQmv/vIHa7aXnsEayrX4UZNt4mHuWCAvY/a6UaSepipw9km8nprz+Hoa6Pixm76xmBfPbtumEPrzUXRDJ+Wxztz3C1whHgteYetCRVaT0PmpKUDE0xDHvnYUa5RyuKNJYodI88SKIk8tTEGyfy4iW6XulNrpPPpZ0eprdV3yPrsccRc+TTmXJs87SGl4lqPkFvfLU7nEZ2galEtSh7vcWnwrHkABzO9YcRNzXx+S3Lqfv8Y+DcmRmMgRlQEnjiil8BCoBJYqkFmsnjmZ/xTTS+6AlojeXjY1GKI5S5yEfjJd8FN2l7uJKK8jvXoR9Fm9gLwiRz9jQau2/A55aFFcp9foG1x80mXzhYt65rKOcf97fRLSu4fsdPHivd32gVCpIw+s0EjxjHw0YvLttIg6WSvm4YRohwQriFsZXJl0fhFT9c2GarYIoGGgLPn2DPOXue/krqmslsLStVGf0ngOx8HrwzuwbK0fUEFMPAurwwyitXMkypRh59rPdvHply1YWpnbTtuFBRk4/o65cjdTTA+VSvu4aAadXhmXPfT4TPT0MlErE5bZc+7GEcDAO25kH2t6GjfblYlQJeg9jrQ6PPTbX0geRKdfvWoUtmn18HrhLKfUR79YVaO3LB8z/y5sk8w+/xXv/oHgz3F7OGeSGxN5pKyPTLj3tJd73dd86gsFsbUvbAAAgAElEQVTI4JfoAcoJYPZ7FvgmcvEjo1G0HolN4iIm3TIeHkk9gfAuNJ2a8XBTlG63IdbhS6FNhMG64MXL4aO7L3Vxi9LOlSvK/MshMFguZd8CzKCu3P1cKMKg2APzKOubQ3EJEcXgsUcycf1tjHZ0M/qS5+mUU2HhngdI4za6frORJYcdmZtWd/VlzQG8uhsqlTwsL8iQT45tGd/2Nia/8x2Gym16ys220TgjMQNSwrYULXylun0pEKUopXpqLFZGwFKGCKGIlaKNKiVrjC2KWFJ6paM+f7nK23/wxoZFEaKQsYnI0cBBgJXWVgG3ichjlVLbWiT3T8ClwPWt3jsxW60nPD5p8TuCLyFAceM3jWeiUi183knljdIokpq8e+Mm3UJCBLl7uTRzEfLPisryG0obTdMpSNOPpwQmFqrF8VsBXhHh9OpsvFKtI3cNNUSi8hqzKJgK8OwDxmtVRMHYwgJqYpz20RWwkKDm5nnwQ/+kkxwbJ17I6qtoMPCvicrXQ5FUmitrpDMlT3sWkxGoharXHpUbPAHGk2puypFI5aRQsXtKmXrQW3wYEItSYiPe67G26KMXhL2X2FqFxwEvAn4lIreba+9EA9PXReTlaJue5zbMklLX7c0LHwm8mZir5klQlH1HJaDijCQhKpvu8Uku5L81MJZUdXylshVqEXqwMm1APAHC7cfqDLBFG80mhhCZqZGxmocJDdQfoRAVXi98zEs7p+nw7i+GEPn4pX/o/ouQ01rl+2c9QarT9ARCsYp1P3NxU1WHPUXJuzR9MpvqF9vvMpZUUYPdbB4fQ+b2IG0dpPMV1Mw8mz/4cQDau9qpVM8y5MACAVk95X5rx4zjlVq+jEE+bd5cfjs64dxzGU8Ss7rRTPulOExRCYypiubPqdKrW237QlGKEyLzW4ASCaIgjlLKktIW1ShHibMjikShSmVahv0Ab/zQkhAVMTal1K+AYS/OQ8CJi9kvRCl1t4jcCZwN/Kzpu1XGkp2Gwu8opj2Hmgblx8+93PsvQfzgdxivrhxipfoGEQrKkstDeD24LfYkGAf3mgzVZcR7LAStgjjNQl4qI28bIAHwFoEROq6v/cgt2Yf6b6CgY+06tn/9v6jt3EkyPUXc20fvCSfRsepAdv/0hnri0+Dc/Q7aQ65sdYX24pqpMNtGbfvJgDDzmJ5rl6mZ5vClWQSU0oNgpCCNUKkirVSYv/Mhek44uEGGsmCx/XcZlFI30LhFnNU0PyJTFLdO0UmrunW9jyTe5B+kvl0EeOTwifz31YWjruRuLzMfyyS7Xmc/JPVp5LTiAS4ov12Gj7m2L/lrouowr1CDsRftKidk+tcMPjgCqEx/t/k1Lxav3uvIjWRpuQbvTS8XkaFQQ+vfc1Nktu5yzyskKtN20GrGP3YhlQc3oipVOo85nK4Tj6NtdDmVDZsL8aSODBHECUMdzhXsvxkEUUE7UzYZXfHKe7/eRy17mb+5cOIcxUZU9kwzt3GSwWNXtLQh2h/wJgyLccxoGduZInK7OZ6xiOeahfejpbyWIWxY9aw7f809g3dNBWnh3S+63jRDRRlsnHdRNHYOaL2T+mVqtgw8MPosLHOrvBccVtp1h5AnN+ERB0cEqXeelvRec/Y/UVYI5faj854tZUf4Ln9Ast9p8E+eiZTLJNNTACRTe5i67WYmrriU7vWHZnHx6tAtha2vO01S8vfCpfm5Nmjjkv9mToL33T0Ejtew3mpT42nWHKRkDhwTIU20s7WZn9/P5vd/mSSl9SqzsF0s5vg9BqVUr1Kqr+DobQJOjwzeFPXVEGc8dx3+M4785vqmePUs7tz6rNEDVubJupAMuZfgRp86Dbj3XvHyqH8H7c61Pe8I9vZDSfFy8BCDWrUhX1gKjaAD7AjxJIcHdYfSR2yxRGNPWlKkMeQwTdD4U9KHKitUWwrtKbRlh5QVUkqRUpojV6C/y+D5z6O6eRvKaOrnfnkXs7fczq4r/5vOIw93de8vnc9+e3UajGM+TmXtxj8a9/cQ7/2250iSdQCbGIewyhpZazceypwnyuyzmMZs+vad3PUv3ydVQle8SBuiRzfe5EJLDVELxmbjrGlx/yHgKO/8FyyGjCn95kKtj/+b+jiL1vYUpUPj92DvmfR9lXDDMoS/w4ZR1FDCsjaIG07/NJzyKcrfYjRBQZxQ25OfLlPa0M+7VujU0ZfyjCPD4vyZZclBGePubnqOPZ75B+5311e+6JVs/fpF9Bx5bMM6LySNwXX305fOve+svHM74+Wum5tiRTXlpeNJ79b4Vi/X9xqTSUSZNFQqdB57OKs/egGoiJarSBVI+ntGnd9zeKTwxknbobYE79uG+OIetvELpPoiPFJeq2mES6AN7rUqurUGKdfmhZbt3g9FgzL5QbZh/KCfFP83iZrp8BxmmvI1rAcv8yEZzOy17IdS9XXkG0oLiI3r5c0ZtQvGYDqfl9LQAJ3HHMHMj29x10YueA1jH/80XUcf5abD6rJtr3n1WfQtCnHbv+eV2RXc3lBew/Cuq1Q7Fc2Rb6PRdtp5pR3GpmhiJEoY/tMTWHbW4YY8Fecpy8ejH2/CsE97qgYKAaXuNxR2qqbTHyrrjy6dXG8tCH7nz13PX1RCgQ8Krzz1j9Rnz76rAbNu9HxTUuSlbY/CNPw8FKRRNwUWSq5+2uKV3+KDb7xqQSoI7lP44OeF6dtuyZ1vufiLtA0uo2P5KpslL7EA3JsMtzlibcsTDgBeumEZlb2ggkETsJuF+qpuQ420jYnCjXwK48smjikvH0E5x1nNw+9ahf1/KmTjYp4EeffF/PexoohM1Un+4BqW8u+LTkuCD+eTobq2GPY9PDci9n0+zuxFmyjUWvj/wxDkp06D5ZONgr4sfqX7yTYQ4BwJss9ENg+qjghagUtFgf2W+5A4z/EuPUVeSDFh5qe35bIy/u+fo+uYoyh1dbn3hjMVLh9hKMCTZqTIPRMSz8J4vvSmI2ZbsihXL8r9F9PW9O+oq4O4p41UJaj/g3iz7xOiMPgdsNHgVnS94HyR5j86yWCAtu+pl3iC1xR0jsU0ItmLuC1Do3pqUHeFWiA/vpUwiiRCoX6VWUGcIsnPSVWistuePw8/rHrNBQAkO3dR27OHtv4Bos4uxBgohNw2dx5+kwCg8tJ8voyFGkR7rqC6fYKJyy+ltH496klPQkpxrg7c80rMcnwDv0ZLtuXvP8bS855O59EHa7AyA2iepTUJ+xlAPeLBJ0HBN289iBXfzOxUxNgPhczZ+2+ZmZXegsG78J0Bdmg3I03y6b83EF6gwXuK8loUwo4Y5DvMV+ioNcMYr9whljjNU309qlh5WiHvty2XI7febyOY+DY4B/77ByARqlvHgIi4s4u4p6d1fyv6TovsyoVpBWH+/gfZdfm36TztZNQTHuOcuToh0/uWzobNhOpMhQfe8CnW/uMLKR9YtzUYU4vwQ7S/4c1ibIj2ndCs8hvcy9kFNiEFDe85qYR6rYr/rN9h/f82b/8LghPOOTdLoyHwtXgmZzMUY+x8vKOU/a+b3w9sAIiy54gU2Of9uf9glU5O+gtUWEVExpYzXrKEjtUHEPX2QBw1JX4+8codnk1Xw73jCuovlMhFQW33buZ+cx/VyUkefPvbmL37nkx6NJmw7/R3vpYU5u74DdWNWxn/8BdIZxZym8JqW6PW3bXZZraFG9z+MeSD9y3rtCvePYJvnx3Blhr+Pahvn/6gbO6L0WhIpHJTPBL2kwLhwoawz4hvy1Z02H5g8+rb1O1FaNZX3blvTxSR4UspwApj+0M5Nf/NeWzixVkcsbZCZaVtg9r1YW2DpJQSlVOikiKK9SGxym2pojyHrv6m2+4/UB5ZTtvwMHFPz+Kx3MOfRvjclLDWkcDsxZWNm1h44GHm77yXja96O7Vdu7N4uekx8x6VOQjdec3NVCf2cM9ffRpSDQaRiy/0lLpaF20/w5tHByFqNhgFv32gqtNghL+bJWqT8QbsllqWkBQ1S74OSBscexH2RuNVnzn/UMzcfRe12emcJ9mcZ1kzN587zCgSktCwDjMPsoYc+XHcg7SUTBuWN0dU89fq0vcOOxi02lDXHxDDAaCydYv+v2UzADu+eVkGUnUkS7z3C6XBAXdr/KNfRFWTnDfjll6MC4hzq+OPYREh/Nbu+m/R4QrqPEeGBLNBbBYxm9IJ/5Nr1zlNq32XN5jn8lBA5OxR1EYK20pB38opMovKGsaNtN3hzK0/R6mkHlM84cnhhbUJstNd4sWPs98SpUSlFLEEKMoXTAz5zGnGgvooHFvCAjUqZ6gZaxUKxppWTay6RXudSBe0/8Edn7+EdH7e4bgj1XjtzOBJ11EHuXQ2fOZ7+P4QtNeiFi/fD/Hm0UGIwtCgkeYqvNFAWHCtkRZJBY2z7npR+B189ULi1CIU2vssFq9dfENoItj2hc/x8D+8xxGPPDHKE5lAoVNPfvyVa1HQOzyVekaMihDGz28eaAq/U6P68ON7RMiRnICM5jpyg2/h319yyukc9O73s/zFL6dj7Tqq27ez4X3/yK7rrit+ziNF5aFBlv/9BZQPWMHCfQ+x86tXQSjBtgqFg12T448BCNpuYQTvfyui4Mf3QjNo8EmPJUWOHNl8GcJUl88iPPLzuZhv3oAcNcx3K41GyziWuACRYvvnv8rmd30gjxnWUWKktTlaa+YfhsxEWXoSWwKk/0tsSUFKZAiUPfB/2wyb8quw3K36StHYY8vZrI5oQnrCZwvGpoHn/hmrP/I+lv752ZRGh1m4+zdset17mLnpF6aMWVsCckJV5yErWfPevyDu7mD8ilvZ9q3b9NZBKluG3zLsZ3izbxOicKBrBAYmNCUqDeI21CLVDbAqP9AH+coTAlWf/iJCS4lsUYkU5T07D8lDOEdvzw/64AfpWL+ezZ/+dy/N/JYSdRKNUCfJuUJFqrBQjhTY8qf5SmtaDwV1WycML4IY+884LdFv2YEliog7OmkbHGLFy17NkjO164zJK65g5zXfIZmZzSRP+x5v37P21atYet6zKC1fxvSNt5HsmdP3FzFlZrnk/iSx/UFDUX/eW+GiUfAaWaGmz5AdOzi7LUHEDPw5hk79b7JrRZiT++6hO49GbaHVYO5hnBOa6jA7xAuV5ducSySs/PC7kHKJnd+6Rsd1Wh7qNDuIMpognCYoIz5WG2TqMKh3f8rICRsOrHV8tzLWHq0EkSZjQr7yw+uhENn4gxR9UyUg7TFRZzttB65k5O1/Tc9ZpwGw/VNfY8/3fkq6kN/X2LchUkroOnY9y196Fl3rl7P5kp8wX42Zr5Wo1Epsn2u+J/L+iDePGqPqcGAr1OSQxfGvNXo234BV/T5c9ou7uMpG1T4egtfaPlX34QXsFF4d6W/SSOoklGYEoFEd1CWaPae8MubqKwKiEsPnv5jq2Ji2BQqerQc68ztu0eoL7BKKdr/O/S8KorICSr563DeQ4Nx/hZjbDQYPu4+VbRP+6zDY1orsSqnE4NOeQfvoCsa+8iV2XXstu669FoCop4clZ51J7+mnIO1tBpyBCNpXribZPYWaX6By3xY6jzjEFLM1ouxvy2D/YMH3Fl0kUNDkvCjYvu7PIdlzUVmDNYTHEiCfFEURKFGoNEIiZTwpm1VBkTKekRV6X6ugjftttag/FWBUTlOaK0TQ1usGcvu7IK6NH+k+VSTkKoF4SQ/LLngppIn2B+Ti5LHH13ogEMUxkUqLNWoKEk+IsPZC7nPYc0eGcPuUiWN6Xn00YppeBlW+2vLV7AmHmfF2dq+h8Jarew+r7bYjRkiNezsZ/MtnUR5aws5LrmbywiuYvPAKAMqrhlly9uPoO+M4JDZIovTq1u5j17H5U1dDqtizaYa2YW1gPdjeU1/eMGv7Gd7s0xqiuqkY7zpQ3HCC+A2JVINGpue1cRqNhu+3LtPt9h+tQHIxIFr0WCNAg2JQwstTgzyEUkmhJCwQ93TTsf6gXJpuk0b7jDeVll0vEA2slNekxbWUIFRQrgbScK6MhS9qEsfiqQHCvdIUFaYn9BxzPOs++BHWvPu9LDnzLLqPO450eprJy69g26c+k70rBUmEqNTO8CvPB2D8458lna1CAtRaNKJQsl3M8ccANMaaZvGbR1jcvcLpG0uUxCNKof8cKMSyxeS/UGJvlF8lrcmQNMbKOk1JVP+MDeWRIdpWDWdTYJJpfdzhGURHsd6UNI5TZyMkkd4cORLz30yTqSBjjpB4ZMgWQIr6SIALxZVKA4zNDlUXP/gQBc810g75Arsl0wB9T38CB170z6z8l7fS+5RT6Tx6PdVN40x8+pvs+Nr3XPmV+S8Dgyx76dMB2Piei6jVtFPYtNUijv0Qbx41GqIiDQg0AYCCuE3jkG9krYBFaxh8ih/c8wSAIg3F/zY005j51/XNgmzmOlN2LdSOZapwL26OQKn6um7QicVmJhKjKZK96ySmMos0beBdbxIvjNP0dQrnfNE90qxe/fMCEht39zL4tGfoNP7yRVR3TpLWqnqVmf0e5n1d69Yz8JxzmPzG5Ux85kKW//UrF1VXj4aVHPtkaCJANLvWcLCqu6ifdZpGsRdNst6AFg7gIgolkmmJ7PMhwEjQnxZBeELNZ921XGTwsWIx2OO0yT4B8ON45yI4P0Fu2bghhoCZPks9w3OIRG9QCh65BOfIMjUEJ01xdeovVPAzqvw+u4i+VohDfl2G9WKuZRiksjrK1QkNrnt4HGV16tqTyscvjwwwdP7Zrq1Vt4wTdZY930N62w6A3qecwvwDY0z94FZ2XHYDS885gyRe3KrW/Sns24TIOt0yoZmmKKfhKYovBQ0Mci3aAYHf0psQI92+zV/P2aADKD8Dkr/lnwfZyOVdNcq3PV8MSfTrUXC2Pe4dUQDUBY4Si9+fxSvMb1HYWxKUe3YRxLKI6HjkNEeUfHJnz/0pvSYDWzhtZmdB7G9RECV6W4FGZSkvMavKEpNHNyLqf32nnc7cnfcwd9fdpLuniXt6WxR+/wOoP1TI+c7y24/k23bD4NrSIhu3At/XTS4d8qRILFlwwocmT0qMU0YfUGwaIelvJQDUkRh73Tzk7VfotLyh4Fg3kHsYajAohzVokuP84zhVaZaeJUb2fmTP7X88wiR54+EcAbIZM69Q3m9feMmmORcXQo21zorodhP0RXEZ9saBIEIjcpTT5AfaKJt3LbwZJmj+Z17vI8qjIwAkNVCpEJVSVwiVwsALn8H0TXew8+Lv0ve0J7ArbW5DBPsf3uz7U2bhvla2cxWQIRfC3wXtO5OKQpalDzGO8uw0Ud3hETB/l3PVIG++5iUHvt61IrX9wsQ2Nn/lc8xtfjirE1MH2TRW/nduoHdTVSrnwt6lYfb/0f6DcE7OlAT1Kvn8+p3STaM1OsiwLqtjWfTYUQjkvnrbz18uj/ly1LkQEK/+rA8ldCePErRfIrPfXN0Umsri+pJ1zueGIUUSltvsLxVVvaMS/K4IcS1m4ElPBWD2tl8RtcInhR0JFn/8MQBk+2oFe28V9t+Q/BSNn43GVIMtyu4VpuwKwqj+UUMItKFwmvdN5HtfbkLYmgpUwTNKYPbB+9n2lQupTIxl/sK8PceIfRy0133/Yn7fB8wzPhZZ/2QS63MxfoXslHu4Oiq3SsxMg8VRShyletosUpTilNhMlSkFaRrpFVOpJp0qjVB2v0Af4NwKTltZDb5b+G39I6xvUxe4cpG1J1ufZUjLGebk0jZ14ddpGuP8uIU4XReM1seuTE2TCJVGpNVYH5UYlUQkCyV9VPQ1St0MvOBZAMz9agO9tPBDtB/izb6tIWoCNA01Qo2IUbNrUDc90igdlaYQiYknJHv2ELV3Qmc5e9jky2mPID9w+xKS10YqkxPMPXg/fcedhMQxCCTzc8zceycz995JeekgS059PG0rV1Ob2kXHqtWU+pcw+9ADdKw5EIlipBznFFyF6uwCkG+kvlXevVxl4d8vrtNcdCt5/Y77RE5tbZIvmhKz8fR3wX2nXF0o9CDjSDGZROulKWTxsema9+fKbZ83xtLuHV76WeR8ftNqlerEOFO33KzzPzevN99sEX7XU7P/V4JrF3ifoYBkLwpjmr5EZS9Q4gYJO17k5A5RqDRFosgsREypTe5BLVnm9VnJHtjLbz+3ZRPViTF6jj3BeThOZqaZ+dUvmHvgN5SHhuh97GMpjy6nOrmDrsMPgVKJhY0baV93kMbBUpTPdFgvdlonAoXKdrq398KtNDwyZMvoG0wLEEWa/IBxPRSl7tlaEuc0Q9nKMtw1XzOUrShbnICWU/oXCWNe+ZUXMdMcKbdIRTcF3fBCTYvfzqwQ6ztazBFy/zmFWV0HbgpVoYlfk/Kl07Mku2eYvuFWnc70/028eXQQIq/fQ9Yoc4N3AVP301jMwJ1PANNhUqpbtzF72y+obNnGwn0PIOUypeEhqlvHUNUaUXcXfec8i9lymVL/EtK5Oa0F0HptktlZoiimY/Ua4rb2fFmA2vQUD33k/aiqVgF0rTvETad0HXAQB17wDjZ/+TNUJ3cwcfW33LNLnnAmtT27mb5dN+L2g9Yy8tzzkO4O4i5vhUAAVDntj+lYjacXQ5QO4nhz9i6KrXPbGevqth58/jcdK0eK/PNghLODnrvlkddcmQwpspoex50SLdXZ/BaSIpcJEy8gRY4QpQVl9r7B5H9fxa6fXK9Poojazl1aW9WyMhYR54+hPoRk2guFUnjYZ1oFn2XZxiL5+0oJqlJl9qEtzPz0DhY2TjB7x0OUlvYSD/RReXgMpRSlo49g6qi1lJb2E/X1o6bntR2aihCEZNc0UUcnnQeuQaL6OdvKxBgPf+yD7rz7qGOQSDfsnmOOZcXr38CWT/4bCw8/zMLDD7t4A897NlM3/pTqZu18tOf0k+k764nE/b1EXR119WOLm7nfQDMYXxghT4D8VWKhPyatHdJkyPkTMivMICBAXp1aQSxnSA2tBTSfZDrAsN9LCslRiKNW+HFY6RMdS/jSQGjGu59bwKJymJtvP5JvUx7RU4YQ1eGNdz7xiS+xcN8D+iSKqO3Yrb3otwr7Gd60JEQishq4CFiOVvJ+Rin1ryLy/wHnmGvjwPlKqS3mmQ8BTwLeopS6TkTWAA8CFyil/s3E+QRwi1Lqi43encZmICmSQMDb2C9g1EVkiIwINFIt+4OjShOmb7+dyW9cjrS30X3CMXSffAID552DUim18e2UR0aI+/qZv+d+Kg9sYM/991PdvoO425ARBSiFxDEqTUmmpljxkldS6u+nMjHBzK9/wdQvf05t104Ahs89j76jjkNKZRZ2ThCVymz6zrd4+Fe/dPkcPu8v6Vh5ALXZKbZf+U0qmzcx9Jznks7OMHnN1Wz4f/9MaWCAgWc8k/Evf4mR888nTWr0Hnd8Vk5/ZZgP7L5E4oewvvZmIHD1kEcPuyGlX/9102p78wqfBBURjQKwEXsvADF7CDjnrQqIqmTTnaLP05L3vDddKkoTKBVnpMg9r7z7nsQc2/SArrWHZIQoTdl90420LxttWgei1KN+GewjhTeWtCq/rUj+vr3mpqa987AvZUJHg+8Rkq5qjd3X/IRd3/gBpcE++h53JEv+5ERGX38uydQs1ckZyquXQ3cPPb/YyPj1NzO7cw+17TuJ+nqNFlNBqpBSCTU3D1HM6CtfiaiI6tg4U7fezJ5bb4ZEM+vRF7+crkMOQ8WKhe1jSLnExi9fyJZNm1y+Rt/weqSnnbRSY9uHPgbA0KtfQnXjZnZf/T2mb7iJrsceR9uqFey67GqWXfAyRCI6jzwkRwIyr9BWhQGgiEpp3rg51xczmyHbHy0ZAkN80ggViTtPU21i7U+NqRBTLM7YvNThhcr+O+mOwKZV5XDFFcGXzkTnw92KzTSjVQUKSM1s+IzU2xwpzB6HVig19mJiyVj2cl3VYtqdQpJIa6Ks8FmTXP6lJua+Pu95zGMyQpSm7Lz0Cjb3LaFZ2B/wJgyL0RDV0EBzm4j0AreKyHeBDyml3gUgIhcA7wZeIyKHmeeeAHwRuM6cjwNvEJH/UEq1ttYC3aBi6qUpe88bvCBPihqSJO/5umkidGOdu+suJq/8NtJWZvi1L6f9oNVeo9Txy8sGsKrIzsPXs/SII6hUqzppBST55ZuiYNtFX2Tz5z5FOj9HedkwHasOYOT5L2LqlpuYuesOSv19SEc7Wy++iLmH7ieZnmLVqlX0n3o60t7GwpYt7Prh90jm5ykPDlLZrIGr95STkUiIl/RTHl1O24oVVLeNATB24YWgFJPfupxlL30JbcuGiPt6NHhbl+6ekWTT6QC/nkJAt33T/jYkyBkpFmiLQs3ObxUCUJKC37m8e23JDmyujWEGxgSklidA/nYevq1YZAi7ioPB1H73NHjetzny9/cxcWOjBepfcwQ97/ggW6/4GlN33A7A+LcvbV0dj358ekTwxtrJQANJ3YZAQi8iQ7npIgqueb9VkjBz0y/Y9fWraVs9wgEf+ivaRpcSSTbwtw100756mMTYwXQffzDLjlxj7JC0nYjU9G+M6wZSxeYPfoRNH/4waaVC2/AIHWvXsuIVr2bPz25i7r57iAeXosrCpo9/jHRmltqunRpvnnIWaaVCdWyMia9djKrVKA1pjXW8tJ/uE45EHX84UV83ncccTrxsCfN33gfAxKcuhFpCefUogy97LuWRQaLudq3pSC05SinUCuFxJVt+c1/Q/9NAok2Bmlkebj0soyBNJZsqUx5BchKLvp7xIWuAbEKE23w5p1oOje9DYSvU3nj2pogy9lQZmVIljCZGIUoym0tjB2RXGCKGSBlm6Fbk2bqIgJpZrWrUnWLddDhS5GXTthlTjr6TT6H76GPY+olPUd26FYCtX7iIVmE/wJtcaEmIlFJbga3m95SI3AWsVErd6UXrJmsWMdlaHR9KJoAbgZcAn8CMU7EAACAASURBVF1U7kKA8fMl+evNyFGqUipjY8Td3aglS9h14/WgFOn8HLXpKaK2NtKFBWo7J5l/+GFKAwP0n3UmXaeegFiNs98Pxes84fts/5F8BaRKMXv3XahajdVv+Vvah4Zd3jsPXMPcQw+y9UtfoGvdwcxv3sCat7yT+c0biX70fRa2baF95Wp6jjue9uXLibq6qExuJyo/jYlvfIPpn99Gz4mPoeekE10dtK0c5YB//Adm7rqbuTvuIJmaYtsn/x2ShDUf/3C+TOG0WNNv4YO8x4J8wlGkmi2Swnyg8d/TqJM1urcXzzSc5rA4aYiJ+283fPVIjDZwxGnbfK1P9iJTxCabxbprljyZ1Wb2u8RRmVXnvpjaM57HwtgWFrZtZuzabzYogJfWozg8UnijIpU156JG4n3AnDbZ/43+nVSrJLsmSfqWkKga0zfehLSVSXbvIZ2fJ2ork8zOUNu+k4X7H6Z93QEMvuI59Jx4MHGcIuSXj0eGbDmCYLUT5hDvt8OjhQU9sImw5n3vJ2pvNzegY/06pm//OVs/+2naVx9A1NbGije/ielbbyW69TYWNm6iffVKek4+ifLK5UhbidrkDnjGmez4wsXMP/gw7esPoPfJp7kydx51CCs+8Dbm776fmZ/cRtTbw7b3fpx4oJ9VH/1bTXCsIbXK6krCqi4gR5YM6ZV3+eipEhSSmyZLc4bT3vds1TcMrvvf2D3oVMp+PoPM+5c8QmTPnQDma3ZUkFYoPIoYB7Fmak28gnhOJkGcMXVO0+5n19aDt+gD7/FSexer3/JWkplpKlu2UBqfYOM3L2tUW1mZ96OwVzZERhV9PHCTOX8/8GJgN1pljVLqDhHpAm4A/iZI4gPANSLyhcW8T+VUlH5GvDh+AzQsevruO9h143WgFHFvL/MPP4iqVEjn50lWrWJaIgMEZdpHV5BWFigPDdF1zFEMrVhB3N+HxLZ96ZGrjjhYicI2Zgta5hHXvm3WImH1O/4OQSj19NV17M41B7H6tW9k+te/ZOApT0Pa2+lct57hww+nXK06CcN21NKyAZTA0HnPY+xzn2dh00Y61q+j6+ijqI6Ps/3iS1l4QKtA13zkg0gcM3PX3czf9xtdTxGkNeMRNk2Rkmdr0JA0NFHxqOA6Qcds0HFUpBZlvFf0yobBJ6YhWQsxzBvM7DSWf0Q15cBDxVrSlFj7NUnb0EOxfZ/y0rGrzXIvq//t2xO5KZuAXJdKHZRWraV75drmhEiBJPsPQv1B8SbCm1ppUIcFAphSKVM/vYnpm25G2tq0JveBB0lnZolWrWLz9gna166hNDRA3NtD2+gIabVCacUyuk85jrY1K4l7u7R3ZmV2XydPhjQZMAs5TJu25EhrD4yfIjvFIyCdHax69zuJOroyMmTLoKDn+OMpLx9h9p576H3siUgk9D72RIYfdwpRUs18jxnSXx4dBIElzzub7Z/9Kl0nHUPXsYfRceha5u+9n50XX0Xloc1IucQBn30fCExff4u2p7TTXCrReU4hiovVcdLg3J8m84NSehNSax/kNEOpR4ZU8JwVksx/pyVy5ET/z7yBm+sh+IRCnU+i3PfJfrsVczbvqR1PvMHCCkoeadSvkIy02ykz+35AlOQ3pfbtowqFL/Efz+oFKHX0UjroUJYdeXxzQrSf4Q3sBSESkR7gG8AblVJ7AJRSfwf8nYi8A3gd8B5z/fVFaSilHhSRnwEvXMw7l3WWs/cbO4wssfy5KKjs2sGO//42HdUqh5xxFpPfvRp27mD0TW8l7u9j879/iuEl/Sx/2jMpDw7ktDoIwRJIhSpngKBMY87HQBurpTAYZavMxDJ3hU7YGtAuG8w3Tsi3yBXLkeXL9WVTtoFyKdvk0BXWy+vB6xl94wXMb9jA7C23Unp4A+U4ZlmlAqtWAdD5818y9YPrGHjcKVRm5ui4/ZdE2ydJb70daWtDVSoMv/mvKPV2597hClnQaYqCEhiUsluxklMzWxU0Ou+WCPiVKYnZgsAugfWWuw+WdFNtWHc2jYCAFWlm/HIMlkp5jCtBVIG4YqbMTH7iqiItidYO2Sk2lWGS0xgZAFtaLiG17D2FRM5/r5XarBQp+XMb7qJF2E/w6Q+NN8vay9kHMgaozg1DEmCDMZCvbNzE5FXX0N/dxfDppzH1wx8Rd3Qw+A/vJOpoQ33xEuKhAYbOfwFxT2e+LfiGxgISJUSx8bCMMg4H0W3MCF9JpJeTL5UyaWzsRYj0AIl252DzhhJkdMT0Mcnn3eLe2gPhoANzWozBuJThno+Lpm6GT38sC6PDVDZtZfbyH9J26EOUZucp18ThTfu3f8TsT3/B8NNOZ/bOh1lyyBpqV/6U2tiYy8aqD70WohKRr3mD3Dlox4v2m4DWCNkgoliSdJAQmeX2QpJIVj4fw0IiZbfsiE0hk7yb3QEPz60mJ4fBtkNbQuLZuvq3XBTz7ECUjWlaoLbfXyDOxg0nZ3q2apLgBDMnLaHTHqKUFTcGqZGZP0XemGTelxtKLTbZ723OS7VFrOLYT/DGhkURIhEpo8HpK0qpIsr4VeAqDEC1CP8EXApc3yrimLHJcSH4PipNUEmNPbf8DJUk7Ln5p/SdfBr9p53OdCTsufOXlFesYEdvN6QJ5Ve/mnK5zHi1CrVqxuDBNH4jhdiBLVHuumbpHoiZjuL8VyQwllSdb5E6tp47Jz8YhoO8KEjMQBspxk26DfM6MggjgyRHHMqe225HVRaYmpvTkmaasuWb36Ln5Mewe3wbC1u3UP35zzn4qWexY2kvc7/SQ+zcHXdQ3biFruOPpn3t6uIPEpKhgs4gkakHey8FykpDesn2cnFcUXy1r+h6lCQDd19aGq9UmxMikyf/mToSEhoutsH4fDXnO0iUBpS4loGQKPQ3iS3A4FTPFvTSctZ2ogR2zFXzBrj2nQ2IkSNAAUA1KmpR2B/m9B8JvNlGJWfPBeTxJgFVq6FqNfZ8/zqini52Xfs/DJ33HDqPPpLpWoUdv/oVPaedBD1aIzPymhdBUmU7QFrJTbnnNCHKbEOh9FYUgqJE6mmHrFfhiFT0aDae1PRUEYJSkbMnkhRtQOtpaRs5z3P5sWOr6CXhY2nV4Zwy+RObmALWj8L6UZKjDmL7z+8kjVOm9uxCujpQswtE1/2Ynscfz6777qWyczub/vUiDnrscexs62fmJ3cAkN59H1M/uI3B559JeZlnvOu1c1sXbjoM6pwt1lTMuKpoTLFkwmpPbCE9cuTsGyMy7VEq5Jaom4jjaTWvLEwzkgHUGUE74uVrzd00lXJ5GKtVcmOEHR8i+918oc5ih+/fbN4KnJndWlQWJireeOmlU4iHRatcg7Csu4UfIvYPvPHDYlaZCfB54C6l1Ee86wcrpe4zp88C7l7MC5VSd4vIncDZwM+axm3gNlKlKbVdu9j6n5+hOjHurpcGB+k//fFaQyGKoT9/bl5SgHzn8O8FA0/TAciXPMT+8NIOG4lVZausf+bKE8b9/9l773g7qnr/+73WzG6n95aT3gghhCSQUCSh96YCgoiIoqJX8frTq+jV6+NF7F6KHWyogIiAglKkd0IooYSEEEjPySk5ve69Z9bzx7Q1s2efc7iPj0B+rhfhzJ5ZM7NmzazP+nzL+n71e3gTapF26sUoLaHicMeuX3HkKnIdnY6zdWMtwldRQ/9DjzPy/Dpy3Xv8c7PbdzHw94fo//tDNH7+E6TnzhynA7RGFyEl6MBjeGQoeFJ/kJoK5QKBUGrizNJ6KdD56scI+juOuGmgEw2m6JvHtAnRdxrXrqdfQ3raL/dd6Q7T4zYzRPC0fR4J8wS7yRKid/iqj7cMbwxVGCbC0wzk8+Q6Omm74seo0VESLc3kdrVRdvBySvbfzyHA6SQNH7+gEG8g/JJDZnecG4ri79dbdh614gnP38XFHyEESjOZeR+/imuPfxG3BfrSblebETX16Nosr5PMuioqjnXwpvLkleR2dyHTCZLNVW62eade13V3ktvayVjHbv/WY69vZ+CRtQw8spbpV32a5JR6/7m8pnlEUCdDBTGFIGQe8zRv3uo0Fe043UaulGMCl7pvktZZQo077px4Px45cd9RMWlHuYBki0AI9LAuxN00Kcgrmknd01465nXht09IEXY9iMGo2H3es0Rjorn3nai80/EmWiajIToMOB94SQix1t33ZeAjQoj5ON22Fbj4Tdz3cuD5CWtpKkpl24xu28rYtu3s+etfQEpKF+xL43kfxCgpwaio1IJ+6Z794clIHw9FSUaIQEWRSPsrvMDokaI5rhT4sUD4k4/5/vX2hHxKPDt6lODFFCElyeZGp64XKdYtFUcdSu2yxQw8sZq+v93naIWmtlDyuYtpv/Jahp59gb4776d81SGUHLAwfI+47z8q0XgDSTr3FqYTaTYgklq2Z1v4jpbKBuGDfHyZlA/ReCVCWHUy5GmDvGexDXd+KQYmLmnxjsscvpO1cLVLoVW9UYI/Dhny2lYg3Y5XohP6O7O8NXjjYY03yeYtRjdsYvS1N+i/5wGMygrKDjmI0qUHYDbWIzMZh4R458eM04IxGyJG+O816jcD3uTqTvKulsh3rvZ4j3tNz4/I8y0JJnGvHYUDStdWhRKuasvCC4mQe82Y8SkSJqnpjUFUbRE8V8OHT6RqRx+DTz7HnpsepPrdh5NsqaPp8+9j9/dvou+B58i+0UbdeUeTmdvq94megNS7tZ+Sw9cUBYTIa3M0p5nf3/75yu1Xd5+L4kq3E3pjVWnnF3QiwTtVMd2s9LlB+QTb1xzp29p78c/y3AY8QU0jQyhHC+1riNzVsQXt05qo749aKuI00v3DYzEPHbn+Ox9vQmUyq8weI/5zuHOyN1FKbQH2036/wCTShljCYvT118l1ddH/2OOgFIm6OkQiwfTLLkMmXHtsGMuC++qaFa94/ixQSDB8AqICSUl3LvSlOx1oNFIk3S/KC8bnfjBxzF9pY8+7d6xkKYPIprHaoZDqXQT79DpSFVzbrK2m8qSjKDlwfwYfW8PgE2uwevuoOuUYhp55gdzO3Yy+spG6j36A0mWLwydHJ3KfJIhAAoJgyashwJSx5/hg5qqQHXOZ124KpHah3yvSnpBmRW9nDJkRCmdq9WJdadqhUF3/xvjv0msb4JsyQ6aJOAASDkmK1RSJmHaryP5xSKJ+GfEOCI8/XnnL8CZlY49mGXt1M9md7Qzc9zhGVSXCNDGbG2j++ueQBN+qe+XgfM/EpH+W3verjccwydCz26sgNYVQoYldCoWNR4yEf56QNkpJV8PhTeo46+60SVdFP+ZImA2l+RBhOGaqqOZbD5gY7mwQRpCMVXqECNAdotPT6qiftorSpXMZePxlem55GJFMUHfOkey57THUWI5tl77B1G9/nPScKQHh0bAkPq6QKBhvnhYpbJb0+j/QuCkFSOFLGk7OL/d3sefVrxeN/hzaFpqwpJEfA8jJkOADHh4FxK+Y4OWb9SPhPKLbIdITaVuBgBdXR0BVWnPGjyl7A95Ey9s6UvWW//gCZl0d6RnTqTn9VDL7zHcAwZtMvf8Vm2R0KQgCFqIRoVxHJ/m+PlLTpiIyqZBEpJMhoZOKKJmJzqSuqOCbx0KkyL1AlAxp7SzQAOlAG6exUpFjUSIYAWr9HomGOqrfcyJKKYaeeo7Bx9eQ293J1KsvY+eXv03Xtb9n4IHHqH7PKaRmTdcuUHgxERmEAvwoqU4C0+A9+OPI1qQmT/UdlZKiJUKGCohOMWKjHY8Ci16iFo7CCu7rE8E79oTWYtor3+co5lqB5izmr3ZsEpxoUmruf5XCMvDwarp/fRvJmVNITmuh/lMfcOKP6ZoIpfBNu5FSoAECX6ABHxLIbt+Jnc2RmtmCTBk+2Yiaijzn4qjWwY9PJAhriVzWLaTSggGq4OPU2xTBEz8nGrhxd5TfLr9RhAmGn3jWJUBSw8fA9ynSbqUomdtCydwWlGXT/efHGVi9HrOukumXf4RNH/oO2y/9OSVL51N/4YkkGmsDIuR2si4gKc3/wCeELvwqjyx5jzmOWjnUx17/RVUmenEP6fHWQtfT8UyrL2yBQPMv1d0DNOITK8hFfYuKtMlvg55DMXLOuGY0vfpkuM5ehjdva0JUfcYplK1YjqE5d/lLUv2Xqn0ocRqW6G8NDLK72th91U9INDWQ3dlGorEeO5vFKC0h29ZOorkBe2QEmUohyzJYfQNg2YhMivSCWWQWzcPqHyK3q5MUSTq2bkOYBiWLFpLZdwEylQ7a5U30ymuXCLWxYN73xpLEcd6Lan1i6nrbylShRy8gUx7I6ziJoPTQZZQdsoy2b1xFdst2pnzjUrp+cxMja19m9/d+RGrebOovOh+jzF2N5l5u5OX1dN/yFxKNjZQfsRLmzXZ8L9ybCNszYYYlbKFwgpJ5wc9i2lyUBEQkHP954+L+RI57jtAOaFGYvDVyD62TQt+aEvjZuv36MSCim+Xi3rNvFp0MAI1XFP9wm767ZP0UoEMptZ+7rwa4CZgBbAHOVkr1/ENv/E8ugw8/Te2HTqX8iAPBTDjjVetLT4J3zC3uTv1lRqO7oxEW9wLDz7/Cnt/cilldSa69k0RLA2p0DJFKkO/qJTmlDqtvELOqFJk0yfcMglIkqkooWzyT0v2mM9Y+gBhQdGzZTr5/GFmSoeSghaQWzEMYQV6ZwL+IEIGb0DdI11oRIRI6OfcIm6H81XHePinDv5USbs4xZ4BYtkCYgvqz3kXNGYfx2gXfxc7mmHnN59n1rRsYfu5Vtj73KuVHLKXuI6chzIR7HefefXc/ycBDz5BoqafyxCNRsyIR3GMkE5/AaVorEIHzn1dsGZzrS3SFxCWk2Q6RGA3H7PBxoQB9xVeUjET3xWjBi2GEL4y6dXyMG+fcguPR601EdvZCvHlbE6KKY44AtDlInzC9fUVE52JOrP5x22b4pXVkFi2k7oPnYGdzZHduR6ZTDD2zFrOpntJ3LcMoK8EeG0GNjGFUl4FpYPcPMPryJnr+cCdGdSWJpnrMac2U1FeiRsYYfOpZ9lx/C2UrllF1+skYiWRBuwRFciH5E6Q2MmJMXoUPRJD8L+QkGHcPFfyLIUelhyyj82e/o+6jH6Dh4gtQlqLn5tsZeOgxdnzh/2H6j79Hz5/vpP/eB2n5yhdQuTz5zi7ynV309PZhfOZTiETCeTcueISkMH2w2/p2AOKxEk20oW69AhWw/qh6ADLl2N0Dic3thiK294lKyGHV60532496rK1S0+MShfzV9O3oI+p4PGG7FOO/+P9V+Q3wI5x0Gl65FLhfKfVtIcSl7u8v/qNv/M8srZd/jCAKq8Ncla35ugEK6U9UUQfa2ACD2iylLIvh59dRceyhVJ52JGp0hOz2NmRpioG/P0XZkrmULJ5JoroU1dePsPMkasqQpiDX0UvfmjfY9Yt7STZVU73PPFKzm8mUlpLbM0Dv7Y+Q+8mfKD/+XVSeeiRCGoEzsYp8a1Co7dZIghMnpxBvQvkJPU2Qn2ne8mMmgZN8NXyuk+LBIyOGDJbPC0NSeeRitn7xWlr/64NM/c4nsHMWbd/+HQMPPcfIus1Mverz7PzKT8i+sZNpv/ga9vAoue27yW3fzZ7dfRiXfaLgfSrfJyH8DJ5Plt8uET5HeX3iYYpOiiJkSIQ0QBrJ8YhS3EpjKQK8iQhQIRIUg2fjKLlC946umNWvF3te5PpeEyb0IdoL8eZtTYhIhAeWM9mEPzyf+WsDv9iHo4QzUeX6eun8xXWosTFqzjsLpEJmTNLzZgAw8MRqhp54hrqLztKS6+mq6npK9puFUsf7A6RMpBjKO8s0y1auwO4fouem29n9vauoOu0EZzWK1hAVbag+MeolGpzSA1jNR8k7X/ntJCyxRlTNoQ7RL+serzjycBLNTez57c1M+foXEdKg5swzKD9kBb1/uwdhCUoPWIxRVk6ivp5UfQNlV/4Ae2yMBiHp8iQ6/XnyEfBw72kNDSPTaYSUWjuCul6kaGEEklVI4tK3KbJfBxcNNIpKUhT/hqKaHH9+iCoqJ0PSNW2lXj+Uv8+9yUQEf8J2/y+LUuoRN0CiXk4HjnC3rwMe4h1OiNIZG6WCvFrRNBC25SU7cyfVuLEUfUduIKHstjY6f/h7ZHkplacd6ZiZylJk9p2BkIqOV95goL2b+vOOcpKXTq/FMIJl93J6LdUHzcZSElsJaq0MeTvrpvIQVJ10CGNtfXRdcyvt6zZR9d7jSM+foTUkzKh1QuRrg7zn0QIHeg9pGHaojkcqpJtsNZmw/Izz0q1k44QJ8OdLrcNsFSRhtZWg7sKTMVvq6bj2bzT/14fBMGn60oUMP/cqIy+/7jzje47BHnK09VVnHEXVGUdhDY3QaGboRCNzGsFTdthtzMbGHhxBlmWCWGnawFIKlOWuZPMCBHlTkB2EWPG0hcUIkP+4XuRozT/Rd37WiHacVjpWUx2Hc/o5OqZF/RljcKHgfO+v27zK0vF9iAra+Q8obzXevK0Jke9M7O9w7fchRq4KJozA5zGMUKObXqf75Q20PbmaimOPpOLYVU5ALPAJiRKQnNpEZvGCmC+GsDoZN1q1Rz788OoKo7yU2g+fw8ADT9D58+uY9uPvBvdS2pfnFV0rFC0R4qRcwqN08uDWE5JCjZKtOX77fRchQ3rnKcjMn0tyagud1/6OzMIFlC8/kGRzMw0XfQiA1NRW0q2tIfIikymkmXBiPLllYM2z7LnhZsg7iXRSc2ZResBihGEwtm07Q88+h8rmqDrheKpPOK5g8PtBGtWbI0OxElJ04L8ZIhRXokKo+zv6LXrVovNlHBkKDoav62ufxiuKf1bk2EY3xQZKqTYhRMNEJ7zdi+kmkdMJkWU7yUIRoJS2/D3ivOrUJ/SClLIZWfca7X+7j+zmndScfyplh7qLE1ys8Ryny5bvE2BDpEgCLYZAORzLFXqkUFju0utEQzVNX7qQrl/+he7f/pmWb/67Zu4K443ug+QX/1hQ39foGLbfJ8pth8DRBJmGjSFtDKFCQRUd85AdJkWR/vX+oaDiyAPp+/szdF7zFzILZ1Fy8GJKlu5D5oAFAJQsnu+f57erNIMhkwg76/fpnhvvZuiJl8h39QKQOWAeJcsWgBCMPLeBkXWvo8ZyNPz7uZQu388nuH77vICNOhlSDhkSmpksWIQRMYlpAnsUe5zzRdhvUu8vtPqMfzxspqPwnpHz9RJ7Lb2+izcTYuFeiDdva0IkIoRI2fgkoIAYRYvCn0WsgUG6fvsH8p1d1L3ndJpXHYxZW+N/987NgtgbZatWUH7k8uC4C1xRIPHAQeEipaehke4qBykw66sxG+sdwItOgHFEJ3pI0xD5GiDPNKaE6+hJ0C7PxKa0C7sgFyJF0fvo5MrdV/eh99N//yMMPr4aq7Ob6tNOcs6JOgNGrqmTO7uv38m+7RIiYZoMv/gyZk01Mp1h2lf/i96HHqT37nsoW7aMZE2t1p7CNk1IhvSYHTEkqKC9b3Y8x9TXcKQ4+4n7TCep9fHf76TqvmmAqhNCPKP9vkYpdc2bvcg7vSRdQmQTTNjCkljC/d4NZ5/tmn8LVhe5/5QS5Nra6frZzRgNjZSvXEbqY2diVpf71T1TjZDO8vT6C05wIlTL8HJ13yfH03go16osgvxmUihnn1QIIUm21GEPDAfpMiD04XgYMa6TsU+IHLOYadj+MnjLTY0hhMJ0TWYJafsaInA0QL5uRtp+8lUhlL/tdZtPSAyDpi99iP57nqT75gewxyzKVy3TKobJlNce7/m8PrUHhhHJIBo0tmL4uQ0YlWUkZ7TQcMm5tH3r13RceSNTf/YVjNKS0LL+qDTjLQop0ApFSI1+PB6fhC+ohU1pWsdHsSpaYkiWfh39vtHzYq8b9w0LbXsyDtN7Gd68rQlR2FHRnc6V5tioEyOv6N64ypHUOn7yS9L7zKHhkxdSmkoz6GkwdK2Mq3WJLosNaYU00mH19JFr7wM3pcTIgM3Q0ADCkAjDZPTVLYxueJ18WydV7z4x/Cze+AtJaBHyp9Xx60nNHOaBpk4chHKc9lxC5EhfcbNz5JZFiIdMpKg64VjKD15B2w+uBimoPvF4nLWjhPq92CCuPPpIqo46EpW3GXz2eUZeeYWR1zYhpIFFP1u/+lWqjj6aRH09Oy7/Ji2fvoRcRyepxkaSLVND1/ellggAhEiRZwKLRLn2j8cA0HjtD5W4OlEtkbdP++sTpuKvInaFmvc7xh3izbVx/NKllDrwTZ7TLoRodqW1ZpzM8u/okjStUFoIyw4mcVtIpHQmcyGC4IAqNEjdMTc8Qvu3f0Xl6UdQf+zhKJXzr+NsuDfwSZFrFnPJkO+HI5SvHRJCMbKzl7GeYUgmKFEl9O/qwMpbiHQaO28ztHYzo69tJ7e7m4ZPnxWYj/x7BR+GTrqiRaogs7yQytH+uOY7Wwmkmz4EHOdpwyVDHiGylcDwyIqrHfLj/XgaIcLkxoklJDEry6k+6zhKDtqP3d/+DSpvUXbEcr/P9eJriiLPVv+x9zjEcSzLwIPPMLruDUY3Oslos9s76L31AWo+eCpjr25h11d+RP0nziXX3k1q5hQSzQ0+FiopHDO/INBS61ohH180jZBmpgodQ8MqHZfGI0Ax+wq0QpHrxpGrichQCE8j9x0YmciHKL6dE5S3Nd68vQmRruLFDUCm3Dzz7qRXGAdC26EEg4+uQSRNqt5zIr7copMeCPveFCFDQjs+8tImdv/PjSRbG1C5PNiKkn3mMtTVhcpbYNkkp7ZQdcYxJFqaMaoq0BM2OhteOPwJukDYCN0b12uPuzzWd/oMabB0Z1BvwGqh7fUBpPtkEezTB1+ivIIpn/sMHb/+PW1X/YT0zBmULNgXo6SEfF8f9vAwCMnIxo0kamvpyecwqqowysuQqRRCQaK2joolB1Kx5EDsgUFGCLcB2wAAIABJREFUt24h19nFyIYNDKxZQ6K2jlxnJ7t+eDVmZRUIQXraDJrOfD/CMBwfIj2NhlusdNBuYUNiKPosWl/GgERINfzmB7d7IefcEN9xiawAxvq6SJRWIY3wcBuPhE2KoMWe9799iDdVbsfJIv9t9+9f/hk3/f+z2Er4k7pSAmlYvknKkoFmw3OWt20ZfGbuGFJK0HPHg5QsnkPlsSsc4uF/bO4fDdP0pfZAKFWHrh1qv3Mt23/1MOnWGuxsnnxjC735ITAMrKExMAzS81qpO/84UtMaMcpL8POOaCTLu2/ctlcMZZEQeb+dpmGRdPvC8/3BdaJOGJZrLnN8r/JK+n5B3l8bR6umbImF9M1uejLWqGtDakYLLf/1MdqvuJ7hZ9aTmjuN1PyZGJUVZLfuQpgG9tAIo69uIdnQQDc5zNpKjLISZHkZ1uAwqdlTKT/6XZQfdTj5rh6yW3cysm4T2de3M/jQsySmNJLb2c7uy36KUVPpkK/DDqL63Sc6WGKIwpAgfuohJz6dyMogmGuEBMUJayjAJOxUrZVYHJqgTpyAl+1sJ1ldj9RDb41HjBSx01BFZjI+RHsX3ry9CRGFAzgwT4mwA6D3MQL5nn7GtuzAqCin7y/3UPdvH3JMVppZyz8thgQV2tqD40IohCFQYzlkJknD/zkHo7qSWpXCsrNBILHQQI9+1cTvjyuy8IsVbgC3kJTht5Vwjp0IGSpwcIlIKwXLSN19RlkFTZ/4OMMvvszw+vW0X3cdRiaDPTZGoqEBmUhSMn8fktVVsGMH2a1bsQYGGXlto28uqzvjPZTutz9mWQWlCxYh9oGqw1YhhGSsfTeDa5+j5+H7EKbJlAs/QfstN9D39FNUrTgs7CiodZ0yVLCiywZ7VCDzxbUtsV3+Zsd0Ma1QlBQ5XcfrN/+Q8un7MPWYc4tfcjJtmKiOAv7BNn0hxI04Do11QogdOPnDvg38UQjxEWAbcNY/9KZvQclb0idEwWooG5Bg29hSILTliHkLX0s0tquT7O5eMCT9DzzLtO9/SouaH5SwxqbIsZh90hDkB0cpmVbLtIuOpKG0hnKV881XuilLX11VbDUVoMU50rBQCUylSLjmQ09jZUjXwdu9viEdE1pCWpjSdomPc1udDFnKc6x2jvteDhqB1Is+tyaa62i57BMMPbWOoWfWMXD/ajCc0PFmTSVGZRnp/eaRSJehtm9j9JXN2INDjLzwqn+N2o+eTWb/fTGqa8hU15BZvIjq952BkJLR1zYz/MSzDD72NMlprdSc+x7af/BT0nPnkFkwzxnEPnFxU7jYOFH/JY6TtVR+/jjfZEaEDGm4ExW+immrJ6M5KsAz7Xqv/+I7NB37XmoPOGziexUr0TmgWJ29DG/e9oTILzpJURHNkHdcQXZnO23/eQVGXTUykaDi5CNJzW51nCK9Zd0RzVCBc7NHQIpob0oXzWTWry6l8xd/Zft//IiKI5ZQfcxKaCj3o8YWOElHZsrxbPj6hOup1KP1vZA+oRD1QgWER+c9GhkKqWtD/8KD2d9G+y0NyhYvpmz/xTF1nPPLkglqFyzyD3T99Q76HnmI8gOXM7plC91330l6+iya3/cBRCqNtCRKQrq+ieTKo+lb/Ti5PV10/PmPCGEw8OJzDK5/mfITT0HVNNF2y++p2PcAyvddHPSToXxuZye08PbjlElphfRjIrJ/HDNWdPf0kz+ENTYSXGOy9/RuPZm24mgj/tESm1KqGIs7+h96o7e45GwD0w77wgiXDDgzoOX7FznHHO3GwPOb2fK135OcUoswDRo/eTqJunJsy61tBfWdjeg4LnxfUfLSeML+VC+bzqYr72XtRdey+APHkj+oFbO+GlvhLrGXOCFhnOtNRitUSMIUJg7J8cx1UULkmfIMaWNG/YaEQglF1tOmuYKY7fpe2V4SWu3YeMsnZSpJ2eFLKTt8aVjjHTSXUpGk+qCFfr/u/s41jG14g/ITVjG85mW6f/cXSg/cn5rz3oOQBkJJsCAzaxZmeRWDjz3NyNp19JWVIwyDwSefpe/vD1NzzlnY6TSdv/4tVSedQHrm9CAsiOFmD5Bu2iEvsW4UO3VipLW5gCxBIZ5CwXiPPRa9H9D67gtJllYW4sU4BCu6T0TbHVP2Rrx5exMib1VDNOiZAJXLAiCTQfqO7M4O2r97LeXHHkL1eacFpjVPMpFB7J8oGfJWWUZLOCpsIGmZ5SmaPnMmua4++u55ms5r/0z7yAAVJxxK2bsOmPDRokA4/Nx6un5zB7XnnUjpikWh8PoyZrQIocI8TgQSWPBQhAiP0LZDxTvm9mOU5Ew8yLV6kXvUHXsSZfsuIt06HSElKpuj667b2fbjK6g+/Cgyza2YFZUYpWUYiRRzL72ckbbtDK5/ieEtrzO6bTMAnbePkLnwMwxtXM/guheY+W9fIllX73BAJVCmY1a0k/iESOQj3VaE6BVsxxXv+HikRiNJSvtdMmUm9ugIvRufp2rekgluNMH9J6z3jwWo/1tKLm+QkA57iSVFo6MYyZSfJDmPovfFLWz//p9pvvBoas84TNPSKJCuP44XdDQ6dqOCmHYMguXr3u9UQwULv/lehjbvwXpkO+sv+TWZmQ00nnUI5UtmI4TtefZhuKu79CIjeNNx+xo6//I0M/7jNCoWTHHvBQkVmMi8e5vC0QgZLkEypY0pbMcc5o5zKRRKKV+p4oUI8BzRPSwPfIcCUjTZxMWhomGUHw0fqP/Uh8jt6iA1cxpCgT00Stcvb2D3d3/qhxMxqyuRmQyJ6lpm/PD7jGzYyOjGTdhDwwyveQ6A/kceR55wLKOvvsbuV19j+rcvR5Sk3HlDgGk7xMgWbrBFRVS7XiCUqQCb9GcoRoJizVsx1wv3B1TMXUSup5vBzRson7FPcHyyGOeWvkn5EO1dePP2JkTgkBlbhAiEsizavvpjsG1af/BZAIbWvELHFddTefoqqs86HlBu3Ag0Ri7CucHQNCsQ0g6NB1r+uVKRbKig/vxjqMsnyb+0nvYf30airpzMwplkt7cjM2lG1r1BZtFsRDqJPThCoqG64FpWXz8yadL5s1soXTob040BYQobg0BVr0d/td3sxkFuHhd4lAhMdh4Z8pevE2g4rJgloxCO9hwlQLqN3O0uvy5O4EOZ15/MpGTKzCBfmEzQeNJ7Gdz4CgNrn6HrnjuwR4aZ9ZmvkKiqAQWZxqlkGqciFAxsfJmdN/0KZdts/PrnSDY2k21vo+OBO2j6wIVYaYVKuO9Jcyy0TZw8ZROQISsJlmuOkzkmBxrjAXhUc+T+HtnTxvZ7/0Bpy0wSZVV6E4KqKqjvz6FvBm8U/3AV9v8tRSk9dk4Yb+zhUZ5+7w+pWzmf+f95KgC7b3uaLT97kNmfP4maoxeTty0Md/K3bIkTxFFhiMAvySvjaYe9e9sIpOsjaAMpl6xVzqqmasYUMufux54nXmfTN29l/598mFRjJcNvdGBkkvSt30nV8tnYOQsUJGvLCu5pd3VjpEy2fOtWlt/4cQwX8zK2pMwcczRBrjDm+Q+NWqa/XydDeVtiKYllO/9ytuH4DUX8iXRndE9ThHL9iNxtPRCms08XtmJM/8LN8u6eZ8gMRut0cORlDLOEhos+zNAzzzG0Zi0jL60DYPp3L0cm047AMmc+JbPnU338iQw8+RR7brmFsY2b2PHAQySntpLdvoP+xx6n4oSj3FyY7gD1VrR6QpAR8AOh44SGOUo69VAu3mhlMoLauFoijWgNbXmV3Y/8lX0+/nWkaXrTWuxlRXTDfbyKkgl8iPZCvHlbE6LuG/4KSjG2aSv20AipOdMYemItKEWitZFcWxfbP/sDjMoyxl7fAUD1GUcE/jWSYOAB4CVFVNqAGp/06CWwzzsr3vTTpCkoPWAODR87ld3/cyP24Iizv7wEs6acrl//DXtkDJlJYdZVkmiopu6C40m21AGQmdtCfyqBsiwM0wl6Bo6kKCk0mSkAA2xL0HfvGrpveYjUrCk0/NvZYKZCmhqf7CiCcPJCI0O2CA2uSa2WiA7MuPMLGhycXz5nX8rn7IuyLHbccC07b/oNNStWUjJtNonKgDAOvORIbFPOvJAdV15Gtr2N9KzZDK1/mb61qyk98iCnoo1vz1eC8EI47d5+sz0QS4DKTWxii3uW2O+mCD4kK2pQVo6OZ+5nyhHvnfy9xjHNxVbfyyS2f1YZeX0XXQ8/jzQE3Y9uIN1cRbK2jD0PrwegdG4jXQ9tYOiNDsyyNAOv7CLVVEHjMQvc8OdgKYFS0icNQh+3uiDyJoq3astG+ORKYmOmDOqPmE+2e5B1/+d35LqHADArMqTqy3njyrtQlo1RkiRVX0HprDpmXnwUqeoMSimq5jcwuG4n+f5h0qbla5ASwiJtOBKN9xymsMm7jnpZ2+DVa59k+20vMOXkhcz52OEolxw5hCgcdNHXWntEyBfWwuTH3w75OBImQUSxSjjj3NIEvxgBSAhJ+YEHUX7gQVjDI7T98Ed0/PK3lK1YTsmcuRglpW5dSd+DD5FobKL+vWex46oryG7fgdnYQM9f7yQ5bzrp+TPddnhtcTVeBrg2y0BrHRFqhEuIvG39r97mgu1J1IveJ1FRgz02Qv9rL1C1z7IJP7sQpAXT3IRlb8ObCQmREGIqThjtJpxp5xql1FVCiO8Bp+Jw8deBC5VSve453wOOBD6nlHrYjTy5GbhEKfVDt86PgGeUUr8pem/DwKgsQ5aVoMayJJrrSExpwKyrpOyQRSAgv7sLa2AYs7aSREOVe6ZDeJSFu+KKQC/rmcaiUpqnHZpk8SQ534dHOQBSsXw+6ekfZ2xrO8mmGlLTnBhS+d5B8nv6STTVkOvspe/B5+m59REaPnIiu3/0ZwZXr0eWpEhNa2Bk3WYqls9zbPjYGJGAEJ7EhiVBQr5nAKu7n+Hufjp+8ifqLj7Hyf+jHIlF1/R4bQ1rzopti/B+wttxKt84KWe8c6QymHrux+hb+zSDG16i/Z7bQCmqlx1G/eEn0Hj0GWS7u9j5p18DkJkzj5FNG2n48IUkW1uci3gkz3YlMC2Kt942FdM2FeP8WqwUcJMoMRrn80mUVzHj1I+QrmsuBJx/JKa8wwHqrcKb7f99PfWnL0cmDWqP2x8pBamWatJTaymb30TdwTMhn2dkezf5oTFKWipJ1ZYBbgRkJVCW1EzrDiHyTF/RuT5axomO4ZMLiXD9efA1N9Pfu4S6ZVMZ3tZNxYJmUvVOvKORXb0oy8YsSzO6Z4jtv3+SzrvW0nzyIl760q0MbOyg+oBWR+Dc1knlzGqkUKSwyBqWf30pHP8h0/URIg9DW7vJD2XZ+sfnEZk0Lecdhq0cMmQpie0To+g/UFbgQ+Rp/9037wpskWz2UZITEd7A0xAF/RXVZuuYYyYzTLnk3+l/7FGG1jxD1x9uQpgmNcedSMWKQ2j+0EXs+uXP6bzlZgASzc3YY6PUffh8ElOawtf0yJyr8UF4ZMh5oR4xCp5Ff6nx5i69FCVB2u9iGvCyaXNpPel8yqbPY6ISvY1PiiZT3uF4Ey2T0RDlcYDmOSFEOfCsEOJe4F7gS0qpvBDiO8CXgC8KIVyjJStx8pI87P7uAD4jhPi5Uio7mcZVn3ViQGAANDOW9zc5pcH/CnQHY8eJWuInOdRXmPlfUUSJ6GthJ/eSvY/HAUAb6RKXdHMl6eZKt5azL9+xB5EwSZQnMcsaMSsOZfNnf0L/Q2sBSDRUkevopfH9q2i75k7yO3ZTfdRijJp6XzIEUGNZQCGTSXcyl9SefQRKQe8tDzK8Zh1dxi3UX3xOMHitsNrZJz1+Th6CfTFEKEpoitm2Pft4XEyMokTJBikk1YsPpnrxwSjbZnTXNrb89mp6nn2c2oNWMe2cT5B/4BbYsYORTRsR6TT9jz1Kat4cSpfsD9k8ySktDgAllAO0BfcV8fePPMebEeB9gjTR5+JWLJ+9MPzpFTsvajbz6k74WXpo/I4ubwnelB04j/qzDnd9hpTrMxMkKbVsMEyT0ln1ACGtrYHCEg4ZMqSNUhJDKqTSCJEKAglGV1dNttgI37FZ93OqmFVDxawap13C0fb0te0h3VJNpjZNqiaDPGcZa79wK5t/9TipOkcj0rduF/M/eggvXnY300/ehxknziNRkiJt5PxntEayJBIK5eVjNGHR105m3Xf+zu57N7DluiehrIz6Uw70zWghMgT+ePS1Q0IjQpH8haH0GN5x95hPiNCwSega7UICFCeIGUaC6pVHw8qjUXmL4VdeZvf119H78INUrzyKqZ/8LNbNvwcg19aGSKcZfu558t17KFm6P1gWiQZH0FWuJt/DzVDEcm/pnYtDYWsF4fEcJTjR4zG/dUz1pzV3n0RSNX9J/ILmuM9Pfwfuz4l9iPYKvAmVCQmRGzLbC5s9IIRYD0xRSv1dq/YUcKa7bRB4c+hd3wk8jhNH4NrJNM5PCqqZtbx4y8Ff0O3J3lchBEjTRtkSJTxQCmt1vJcZ8hmIsfsWc370eZoA01Utx9VTStD1+/sYWreNfW/6ImYmSaKxlPk//STZjj7SU+vY+q2bMSsyVB08FyMh6V+9kY2f+imjc2bRPtqPPZIlt2cAlc2BECjbxihJO1JXLo8aCwzS6XmzUDkZqHXtiK+QG+5W5PGJUchMFuf0V0QiKdhvUdSZOV6KC/YpCSQk6ekzmPel7zC44WU67v8rorSE5oUHwIvPIVIpZDpNor6e/M7d7LrrXrBtGi+4gNL9FhWAY0F+ocg9SaAR7vD4Hg83gMKcY/oxvb/9B4y5f1wp0tcTTqOKd7xN/63Cm3zfsBt4EZRyIzN7QRNxSJLlYom3okp3Ok4aeQxpk7MMJArbsEnaFimR91dZOc/k/NWDQAYmJjcwvec7JBzHbDvmzXuRtfWi5xF78Su3Y+dsTnr409gIShbVs/LX52INj1E+tZK7T/kl00/dl33P3Y+qphQ7Ht3GK9c8zayFs9kz2kO2f4yRziEnwa1tgxCYJUmUrbCyFvZogDeJudMZySb8VCeWJcM+jTbYSCzL0KI9h/2BCsz3kf1eKcgUj0DkAtLkC3RQfBx58C8Aw6B00WJm/tflDL38Al133o5ZVU3VvvvBptecvm5uQiRTZLfsoPfPd4Jh0PK5z5BqmRJcN4qVMW0pEMD0uWYi03iE8PiX0J8t2lcx257Xhwh3UdAGTxhjEnGI9gK8iZY35UPkqqKXAKsjhz4M3ASglFonhCgBHgP+I1Lv28BdQohfTeqGPlvV3qJyyJAaGUFkUo4vjEd4vLrubykVtuPJ665mcL4qL7q1R2nisjuP3w/K/ytcKdLTXsWvmFDM+MrZjGxpxywx8T7DRFUJyeoSlIJZ//1+95pQceBsypfNpvHDx1PZY6G6OpCZFEZ1JcJNTGiN5bEGsyhlgGGS7xtj7PXt9N3+ILmdHY6crYTvVwMBGRI2ftJUlJN9OUR6IryuWBTUOKDxtER63WIapbjBa7lfpDASlC9eQmrqVLZecwUzP/cVKpcfRt/Tj2ONjZGZO4+yRfujchadf7gRq7sPkY86iRc+S/SefrwYXRMZqRcHVLGERy9F4ibF1Q8tQvr/iC97k03/n4k3I69ux7alO7al61NjY0iwUNiDWRJlSXLu6i2ljX1bCAwcB2rbzUlm2RJD2O4/yGH4GKRUEM3Zz/ouPNN+mBwhghxhnhkLFZCf6Ooxp3FwxE0fYqxjwHeMBihvKUMKRzt0+v0f9ffPPG4WU4+Zy+iwRaLDoLO3HbM8TaquDFmSwpQ22RGb4QGLvEiQlWmGdw8y9EYHO665j/5XdlE1dapjNnQT4Xpkx9MKKSEdc1jU38cjDTqR0MxiUfNXSFhzx6+/YMTdH9Wc+PX1v7jTietrKEsylC8/GLOmlo6bb2TapV8hs2EdIxs3MrZ5C3Xnnk2yoRGVt9h15dVYPQOIxvhrx2nZ9WdRwr13VAMMYSyIea9+2+PwySeJhffUj+kKyqj8/2bL3oQ38CYIkRCiDLgF+HelVL+2/z9xpt/rvX1KqU/HXUMptVkI8TTw/sncs1GmfO2QY9d0tvPdvXR8z8G4lssvQZiBp1o4EaKDKrYb+n3gwWcwB/OU1WSQVRUkWhowKkpBCUZf28LA/WsoO/IgMvNnEPIz0hRQnrOkdEekFAqpoJqE6/NT+IHYCChLw35VBQCmS4oQlvJUUlDZkMKsq8Jftmq7AGOCXS5cm7sAU6Jqm7AXLqL7138ied/jVB59pJZDTQSSgYA6M+FohnRQ8V9UkQl6HCLj7atJmIhC4TVUJyS52MEAVRLshLtCzCslzZSe9h7sR+5m9spVdA10Yw8MIB58CPvZ50hU11LV1UnTcSdjDA5hVlQVAuI4z1BrmogkCAOkGa4bq272fmvgHGcBqU6b45KgiUoxqXbCspcA1D8bb1b8/MsYJNz36pAd05nRGdmwje1X3wnAvj//mNMOXJOadAQsU5Ma8hjYts3Y3a+Q7eoiM6OeVE05qWkNiJIMCuh7cgMDT79GzakrSM1o0hrtLGKSwnYXVNgYtkO2TGEjlaJKJUlY7tL46DPjxgSqziCq65B28D2EYhN52CUUhrKxlCSfkKQbysi45n5bCay8g61JKUiXOyvIspZBxZRaVPMMWuYuYOfVfyGZqaH0UCc+mR5ryCM8tSRRiCAWnNOIQDPhir2Ao/PzBpUuzLnnOBO+c7zWMJ2o0u7DFxO+YvcLB2uUoYL7LdyX8u2HwL13su95H6D9WjfN1vU3QV0dQikagOaZM2FkDLOszL1eIYmLE8Y8vEERr0kv8gwFgqb2bODgTQFeFROyhPavSBWAqrQRszfahr0Db7wyKUIkhEjggNP1Sqlbtf0XAKcARys16Z75JvAn4JGJKrZb2RAp8TU5VRnU587DHhmlXVhgWb4ToyPxK38VmOdrlNvTz5af3URrayv985vIdfYy9sZOZCZNeuEsUjNa2PPoanh0NdN/898IwyyIfyRd5bXjX+DF5nDBU1p0i9FY/6PxltzG+RN4UmReSSwl6CSLpaQvcdkeKfIlLoHKu2BjmNhnns7OH19DancHNaeehJEoCSQP14wmLUHnWC7k8+M3LQpCcaASs+0N2s6R3ITSTOheGiHKKxyy532ZAliyDLOulhd/ejX20CAynWbqZz5Px59vZfiRh0hPn8mO734DDIO6VcdRe/ixYS2RTooi95dp6Bp2801F/J90Scs77l1DKCe8gF0EM4SCPUO5gn1xJY5QxfX9hANMKSeGwDu8vBV405m0Ea6rkRNwDhLScrYX1FPyldOQEtrdOt7qK0PYfuBC3a+oe30b3bc9zo4dO2g4fhEjbX0MvbabZH0F5QfMxKgsZff9q5FPrWXfG74YiucD7mIKF2cMqRyTnHCCISaUxaA5FMp7BoXCld7OuP2+w7S7iixvG1hK0iXGgrQbOKvHbAQ5ZWApi1HLdPbZArsmg33Riay/7FeUt3VQfvxhCDOJJ4BhOyYzJSUd+RxkZZB8WglkXoT9cOwIudHGrn7cOyYT0JnNhcZ3UY00kXElHAHMWQqvVTr6GIZeeoENV15BvqeH0v33p/aU09j2w6uxBvopXXwAO778ZUQqRcPpZ1K+ZFm8RiZmn7Ch0zU3+mk/KPKsEMrJGGcy0+vruFuUPGlYG/odg0F9uQmwZC/BG71MZpWZAH4JrFdK/Y+2/wTgi8AqpdTwZG+olNoghHgFB9ienvgETalnu7O6EJjV1VBNoGr0JA3byzYvQDo5dnJdvbR/73qqT38X0847iU6ZBwVWXpHd0cXwi5vovvFupl/3dYRhEERF1PvB+6fnG4r+DhIyBs9L7Ezo+QVEs0gH54nAJq8kVl76JMiRwiRYDtgI211p4W6b6VKaL76YPbfcyravfYPyZQdSceAKks1TkDgmMokzoXvxgSbSqhQlQ9pvjxD5Jri4+jrZCDrDieHhdr2+8ssb2KUz51K14lC6H/g7IpFk+5XfY9pF/4447t0Iw+CNK/+bkqmz6HroHsqmzqOkeXqgxYmCjvdMgCHByFIcyIqAqXddWQQPpLuUP5YEaaClNAWe5c4jui+Tr4WaDCHyKr6Dy1uFN34QV/CXTOfyBlLaGFKQbK7DVoKcK9UnTIu8HfjKGNLGwhnPg5vaefXyOzjoYycy64ipCOEIN/kcDG7qoOvB9fQ++gqLbv9PPHN+NLK0HgjWwxW9eNodb1v/6/k4QbzGGnDa48YV8oIsZi0DaRuM2AlytuGQHhWk4LBtQd6S5C3DMY25eCSr62n88sXs+dWt9N7+MBXHr6Rk2WKSjQ14qz+FASLrCHV63CAUSG2VWCheGuExGEca9ByH0frjCnKeucwnHiJUt3zJQaTXvshgTw+jm7ew68c/YtonnJh3+b5ehl5YS7plKl133UGmeRrJ2vqCexTgjQJhRjRDxXBXw9zQohf9JcYRriK4619bM9Up6U6vIoYQCSgvmziX2Tsdb6JlMhqiw4DzgZeEEGvdfV8GrgZSwL0OhvGUUuriSd73cuD5yTbSkY4FyiU7oTftESHNvwil3J/OW+7+w/2kF82m7vxjkEL650hDkprWgFGRofv6e7AHhjCrK4FgRUiBTlr7HeQDwjej6RmrgViHyGLfkEeCvECLvhTmaoJ8QoSjEfL9g2zhBFn0NDt5MFIZGs49D3tomO577qb9ht9hZ7NULDmI+qNPCiZ1zdelqJQSJ5kQP/hCOcf0AUlhXe+AEvjL5ZUkaBvhv7Wrjqd6+SrMdIbdd9xM35onqD7gEJK11cz51FfZfc+tYNtsue4qqvZfQfNR70VKzXTl3jsEPHogSRV55mgfRPYLuzgYCK3tcc/tnS8AJURIYguFAvA+98nizjsfoN4SvIkGY0Q536WT2b2Q9dpehGIh3MOu/5ESbLrqPqaddwiNR8xjjxxxLwaGKSif34R6M8krAAAgAElEQVQ1mqf70fWo0SwykxpXg6wXP1iiiiFDkcGm+xjFaY5s138pb0vywiE+WdsgkTcYU44DuU6ELNdZ2jejuc7SysUes6qahs9+hHxnN31/vo+OK65FmAkqjl5JxWGHOs7PNoi8CIWACweBFQXjsxhJKMCbcer413H3+USA4BqIcD1pQdPZH8A+/WyMVJodv/oJgy+upXTeAtLNU5lxyaW03XI91kA/W676FvXHnkL1IUcgpJy4vXE4WwR39WMielx/xui9YsiQp2UWytFsKwjhb7RMajHkOx9vQmUyq8weo5AWANw52ZsopbYA+2m/XyBODRMpIvJSlcDRiMhIcxROBGrlrDxz//PfqD00Smb/uf4twyvKBGZVOTXnHMPOS39E1emrqDzpXb6pzalP6DwR2efZ8nUy5Gl+xlNnO30R7NdD3SscsLGV46RoW+4o9h2hhZtDB59AeCplfaWVkS6l4dT3gq3IdXay/dofkqysoXrBQkbauzBLykhV1xeXVBh/Yvfejb9/HEkmVNfdbye0AakFLVNaHQ/0pBLIZAaRh9qDVtH+99vYct3V2Lks0kygq+Z6X1xN38vPUL1wOU0Hn4hZUhYGFvevtAhrtIqBMSDzynfC1ElbQREuWVKR5w09kwqAWCqUw6oD4dglhkoGRG5CUqQUWMUcuN4Z5a3EmwLzteffZhee6kSycBZsIG3nm3W1DNbQGOnZTb6mxv/rNqJi4RQqV8xlw8U/o+WiY6h8134FhEx3hi5WCpyt/Qcp4mwdU/KuKT7vR5k2ydu2H21aJ0IqTjhTzm/PHzFRVUfdB89BWTbZLTtov/qnmGWVWLNmM7x1G+nGFoyKcm0citA4KUpu0I4TrhOr4SamHhR+WR4WaIYIT9NtWNKPZl131Il03vtXuu6/C2VZCMPw00cBdN77V3pWP0bFfkupW3kcMpGMJ2euEArjEKPItoenxYSzgufRStTc5ghgkb4Q2j+9TDRi9gK8iZa3daTqqCbB0/WpmOXO3gtXnvOwdnLZyqX03HwfFYctRCVdDYx7TTdbGtWnH05m4Wx2fuVnlB+9HJlKxtwgIEXesltdpa0vxdXrKg0YvTK8uYPBl7c78TtUxFwGDglyCZGfhsOTyDQTma+S1mIKCSscJdXRgAhStQ2Uzp5H++1/JPFcKzt27CBV30TzSe8j199L6dTZDnGI8SEaz/QT1QRFiWy0jr7ta4cMR2oReWfb6w2Z9yYl55gHmOmqBqaf9XGwbFQuhzCT9L28htGOnex5/lEAavY7GCEkm266ktYjzqZ86rwCCc1PNeIDkQq1UW+/tMICkcwXSlY+uYshTN7qRh+cbPfZXTKk/1MCnxSF/k1U9jKJ7Z9Vot2mT5C2Z07TMceSQf4uLbQHQM3R+7P9d08w/eszsaR0YxM5s5GlBNIUzPi34+ncp5W26x6g8l37FdUMRU1j+j+IIUOAKS3syIfZ/swOcv1jtB4129/n4dFY3nR8hCyDjCXJ2oYfXNFbRl/gt0iAR75w5n/XAqEM0tOnI1JpOn7xa5KtrbTv2EH5gSsoO2AJKpsjM2s2ppkOB1K1Iv0cJRXRcWn7gcIL6oTqecPaDK5fdHWndw07EEZKWmcy44OfxlYWStlIJD1PPUx+eJDupx4CoHrpoYy272TbdT+m+aSzSDe2FrTFI1txzxZHeETkt/7Xq++ZvULHRYCV+j1VDM74mKVfA+gb/Vcus7dv8dgtGlLph72YRb6GKBhVmWULGXhwDb13PknTGUc5CiX363SChzl/c20doBRGiWNcji6jFzJMejztUCDJ2Qy8sgszbVI62wncZbuSn0d48kNj9D63hc3fvBWzsoTak53UE3nLifDqmchsz2cIibKk7zQtXK2QF501pBXSJnup2Z31Sbjl1HMZnLOImfsvIffEI7TfextbrruK0pn70Pa3m1C2hZkpZfp7P066tjE4v8g7gQhZiqqw3ePegIsuK/U0fxAM2pAK29MOycAvJ6zlkSBTYEHtPsthHghbMtT2Bj0bnmXayrMob5rLjgduonzqPlRP35+yhukYZsoBipzCyAYkKB6gVOFzes/uZTO3YbTGdXyXYKUE+Sgwa0uNhQKZUxhjDlGyFdiGS9Zd06GVdAHcvf6E2KPw0wf8q/zvi56ix9nhCjcRIp+3JNL1H9Kl6ZrTDqb7/hfpfX47YnktQgkShuWm4XBWdAFkd3Rhj+UwjYA5+8EeRZBl3pB2LGEStk37Mzsob62gorXM3++FDLCVZLRvjE23vcIr1zxN88qZtBw1h6xlkLUN8m7esdFcwjePZfIJxiwCU70tsfOBqR4bfyGHL4jp5noXj7xvfNoXLmXk1ddoXbKU/j/cQP8zqxlc+yzpaTNpv/G3gCBRVc3UD38Ko6R04hhoEVIgIz5EBUJ0tHgkRwJWTF2PQBiecBm+roHn7Ah1y49y3mNnB9bwID3PPMaU0z5Idk872268huoDDqWkdRZlU2cjpOETvqIaacL3KtASRfoiVxJcxzbBSsWc62K3Nz+4MTeDfnbx2BYEfpxuQyb0IdoL8eadQ4jc4k+g0ZlVK0oJyOfovfU+Kk8+HKO8hNoLTmXX135G7pAl0JQBnDhFXhA0BSSbnNQfvbc+SM2ZRxWAkEeGDKmQ0kYIGN3eRdtvHmCwsoYN9zjhUqace7BPiDzpLZez2fXH1ez8rbPYRWaS7PvLfwPAshx1tdJAyFdJC10j5EphvjYokMp8MuSRI21C1zUSSJOKBQcgpEntssMpmzGf/EA/ZVPnoJSNyuXpfXE1b9xwFZmmaQxt38Q+F/0XibKKcSUvf9sG8haDOzaR7d/DwPbXAEXTYSeTrKkPtB9EzvPJHYEEQwB2IWdtTXKLqp2lBXULDqb/jZews6Nsue93zDvtEuac+HE6X3mcTXf/nJkr30/NzKWOX4PXL6G+UmFiFOcTFfn0cqWCXCZol5UEW9cQxZAt23AmXmMscHL3fIhsIyCGHlBNShjbyyS2f1bxA9V53NgnRX6NcH1XiPLwI9czwJ47n6XhzEMxUgmmf/I4dt74JA3zjiJd5eCNKZ0UH9hgS0HprDraeofpffAFao9aFGqDToa81WUAfevb2XL9evrlILsefgOAFV87KkSIALL9Y6z77Qu8ev0LANQfOIUVlx3nHHOXzltKkrMM11wmyFsGeVsUaoRs6WujlS+MCX+8B1rXQEvt/TVSJZTvtxgpJA3veR9VBx6KEoJMcyvKslD5PHvuv4vNV3+LdNMUxtp3MfuSryJNR0MfR4KiZMIeyzK09TVyvXsY3PwqZlkFDStPwiwrL3zPCj9GUsH0IQgJZXr4kPE03TUHrmTXHdeTHx5g200/Zcb7PsmMsz/JnjUPsu2JnzH7A58l0zg1wDFdQxS51rjaMK3NVhLsVFDPToCtO5fbkWvYAZ7IHCFiGATIdHHJJff/8iF6u5YI+dG1CoXFBTIFfXc8Qt8dj9DyjU+Snj2F9Jwp7PrW71EfOoKyJXMBh9zYCpRSlC6YRsvnzmbXD/5I1bFLMWsqwlcWQVh/lc2x+bI/MrB2MwDT3n80ALMuOZamU5YUtCrX2eeTodn//T4qls1xHBQtN++PLpFZAQgpEdjovVVkPhnSNEJCk8oKAgJGPni9pKobSFc1gAKpJJhJapccTuX8pfS+sgaVz/Ha776LkS7Bzo7RcODRVM1biplx8zjlsvRseIbB7a+RG+qj28zT3jOATKYpbZxOpraF3U/fRd/rL7LfZ36AECL07nR1uZ93TdcQ6YRIf97oM2nPnqlsZOG5X+HV265kuHMbG2+/GoDqWUuQZpLNj9xAzxsvMGfVBUgr4YKHChNH9Gs7neb4EAVaA//dlkpyJQKrJGiTnXAF0Cix0omqDL5XY8y9vhBgOBO0T4hck+LE0KMiLOxf5c0UP7pykWPh1aOB5tdWYOdt2m98lJ4HXmL2t8+nfPF02n/6GKvP/AkH/vYiKlrLg2Sp7urX5qPnMbrtEDZ//680HLXAcch1rx0lQ3b/EI9fdANjnUO0trZSuspJCn34FSfTvKIVz87taax7NnT4ZOjIX55J9T7OKqisZTCWN91VZoZLhGSgoVaOv6LnMO05TYdSavi+i2FzvS7U6Hikj99081QfooU0kKZBw3FnULPiCHqfewpraJDXf3g50kyglKJh1UmUz1mIkUwDYI0M0bP2KUbatpHt6WIgk2BXVzfJymoyjVNJN7TQtfp+xrrbmXneJYFwpQszmnakYFBpBCJkEo+SE21f+dR57POJr/PyFZ9HWRabb/ghCEnN/ocA8Prvr6D2gMNpOuKMWF+gYmSrmHbMSgX/vLrKdEhSCMO0c5VOxHBwVRGQoigGq/jF1oWdtZfhzTuDEHnF+2BcfqQQFMzwXj0zQdmRBzH0xFraLvsFiaYa8h09UFsfDoSGI43ZecXAmg303vM0pUvnkqgqLbisR4akVGR3djCwdjMNpy5jyvmH01JeRckFBxY4VXvb6ZZqlt/1RQdw8JbUO389x0WlSWVYbmh7oZnDdJDRTFO6liM6YIUKJJ0CSUOXJrT+FQoSmVLqlx5B/dIjyA/0YuWy2NkxOtbcx+6n7iZRUo4wTLJ9eyhrneuQpGQJra0tlHb3k6qsR5pJxga62f30XQBsv+t3TDnufchUKhSMMUTYLJf/um1yIyT4z+iDVOS5/efTSNO8Ey+m65UnaF/3CKmKelKZSux8limLT2Coaxtbn7yZhmPOQ1gqbK7TTWRanyopEFqo+nxGoqSjHcpnYKxGYQw7k4aVcnLvRieF0HsxvHY7yOw8mxP52CN/yjvP1RSNWxR7HUC9JcXzVYxVZWraJNw5VYFZU0XpoumMbNzFxkt+QaKqhDpKAEjVucKDS3RMaZMbteh8+DV6Hn+NlncvxUwAOESJCBkypU3Xul2MdQ6xz8WHsvTdKxgtG2XxJw8O+Q9526a0mLJiCu974iIUhp+pfjRvkrUNhnNJpFDkbIcI5fJG4DSthCuQuVog10Smm8IKTWQR/ImmCoqQIp2IeD6CybIqGlaeQMOhx5Pt6wZlk+vrYc+TD7D77j+RqKwGpcj191AxbzGV+yzFTJcwpaWZVHc/qdomhJQM7dpM1+r7Gdm5hd0P/IXGI091iGb8q4zFTGLGK7g4GgkJ4n8iChZ89Ot0Pvcg3S88TsWsheT6ewCYdsoFdKy+j641D9C46oRCgTWyHW+2J8CVJFhpsNLKJ6W5jONbq9y6Sga+kcL7ncMniGbeeW8K7X1o/5SYhA/RXog3b39C5M+cGvnxqa1bimiLqs8+HqunH6tvgLoLTiE1rZaWiio6xahfx87m6X1wLV23PYlZWUrVEftTfdxSd7IKe9AnTMt3oE7ObWLF3ZcGTtYEAdqCdmnb0gYkQimwIetqhizblcj0lRu2pxVytQh512SGphny022IAGiKaFD8phSZmP0+VcQuTU+UVDkpvxTMPOFCyFuM9nWiLIt0ea0vvaEgVZKgxC53VLFAsrKG/S/5Hywry877b2bnvX9k6inna+83+OvFRRJaW/zfee24FXmeELkLND2GSNG875H0bn2ZTHkdtdOX0L7+UabMXQWzbdY9+FPatj6FmLqcsMOz8LWQev9EHaitlCBXBrlyyJUpJ7BkqUOu8qbyNURRqc3XEHmAKkAJQbrHxja9fHtBvCjvdU0osAF7mwr7n1lCBChKhqKCl4tB+uqwKZ86he3fv41EdSkt5xzCjFnTaM3kQNrYysGS/OAYu+54ga1/Wkv5vAZmfXA5davmYUjHucNzdDalE5nadDFl2qppTHv8EwCk8xJb5mMdq6VwAjoiLaSQ5JXj6+SRoTHLRCnHbOZphiwrEMhsJVF5N82GR27yMdrpKMkpRoKi9aLjQIXHPEqQqqhFKEiX11Nx5jzsbJZsbxcAyap6DIc9ApDKJMgIL1o0lLbMZL/P/Q/5kSG23f4buh77O/WHnxASpv3XGcU/bb90CYOwCU8vkUVVehwymSql5ZBT6HnxSdLVTaRrmxntaqNq5mJK62ew6U9X0ztzGlbz3MJvKVI8EqPXsRMuEUqBnbFdfHKAShlgpTQMswV2IjhXKGdlpOefKCwn/pr+npQK90tl+l9xiN5ZRajgA48hRUZ5CY3/5zzaf/A72i7/FclpjST2nU9X2sKsKGFscxuDT2+gZP4Upn7mVEoXTtPOtkM2fSFwo1M7+zx/Iv+vio8IG7QVJzGk9wPwM0KrYNutGLLJe+BcoAzz12gTGlQFmog48qBirqd1ayyZ8ralQaa6KdhvR86NXEsBhplkyrFn8+qvLmdsTwepWsfHytcIRQE0QiKkB06abdzREKnQs8b9bd5nFa89eh2dm1bTut9xGDIBAhYc9mH2bLqV/rbt1C4+nFRlHdJjcvr35E1+MrzfSgZkCC/8v/dKJE5yUPfd6v2n9Pclg+t676rgndkEmrRxi9rrIsf+04s7HqMlDl+iZrNUcw2zvnEer3/hN2z80o2oww6gyx6kbHY9ZlLQv24nPc9uo+HQmRz0/dMpm13vEqCYlWIaGYJwXCGPLHnRpsFxwg7OtfBy3poEJMuLPeRpqD3Msb3l87Yb39+dHEOLNaLaaR0jomTI1nAgRJoCbIrTmhaMXZxzpJkkXdcSCGv6J67dS+/B/7e9cw+246rO/G9193ncl6QrybLlh9DDkm1syZbAMggiYYFBIeHpkLgCDlUpZgiQDCQZKqGGgXHIJJRhKEJVAiGExMUUTCY42MRTMTIOjngZy4BlJGxibOFHBDLYkvW8957TveaPvXf37j59ru6Vrq0raX9Vp87p7t2rd+/T++tvrf1K2kNc8Oo389BNN7LwyquJWq08Tb8ITL4P+o+WrV6osi0KZ1/5SvZ841YALth8HZJBc3Auz3v92/jFt2/lyLmPMPfy9SRz5hG5d4GUbWhNRFhjExVK25p3NVB3gm1Wd3zs+riVyhZPEHmc3sNNU8bpxzezWxBNqVdXPVnlq9onMef80VvR8XE6ux9jYCIi/e79ZIeOMLD8HBZfv4nWWSN2WHxxfnUIPZgms+J3ll/HTJZWPxIkt6eQiZq+Qe721JFP4XGRWQHm2ulrQr35C5NJBE7lpVolm+p8H/1qQj9PqnSdfiFgf59AFDcYvmAlB3c/QGv+osK+61NAneAprpEvSJvfn/aIv95vZcH5qxl905+TdidoJkN5s1izNcLSa65nx7f+H7tv/Ws6B/Zx/uveysiqNcUacE4MCUzMhcYh8v8kbUJ3RPOlBxAbnYokn1dJbR4M2UhPmft9FCQlX1fOzFMk+bWnRFYKqqcXQc025FzTRyDFgy1WfeI/EY0fZeiRA3Se3MOhXY/TGGqy4KplrH7vZgbnNktCqNrslankosftM99GBDWylFZsZhMthFJWspFgRFHXzo2UIfn0Hu6T5tN6RLaPkLmxfGoPF/kpiaHKoA2VSoSntx76wr7UZDbZOZBzQv675iWe23BOlZAL2sbwPFqjizjy+MOMLH9+Xo9KPOkEGhW7lm/6DGqucU6Ln4vWbGLBRetBxETP7TUGFixm0euuZ+edX2b3p28kGx9nyTv+gPbi840JV8/diNzKBJKaGDGE5Rt15a+YfZFRUsUwfM3Lrei+Qe2zW7q/qaqi05BvZrcgmg7yaJH2/OEiQjTYpLF6BcPS4pwrl5r1x+KMyHphbl0yB0c0pWH3OTFpLojc+kVJ1DtfSNcTP6n3ZDtyylGJDpUEinvg7e+6B7fHe/EJxRcsPjHViBzxz+9DUnWeUnXunlpYslq4diOP3vb3LFy3yZxr2+XdNVyfgtK2bTcyokhL+chJq0YI+V5oLA3ipJFHlNyxpDnIwnWb6KZjdO67hyduvYmoPcDgilUMX7qGkTVr878lHbJtmPbi3eGMfO0lZzQtWFcTs20ICauKyf9H5wmWBKoNgRdEVrw/pkRUp9kw2FmDmlGt/RwgiSOS4TYjq+eRJgs5Z8tqmnE37w8USVrb98ffl5QETrFWWhKlViylpXNi7+FIopTxrJihvToprB8dUttnCMWOaqUkhvDFjzfXmf+M1jUFmzKjJIRc2lJEuFJ/JcupwtiqiBI/PVREkrPjRfYXrNvIL767jZFlz6916kpdBPxjvnhzaf0+Q0x+PG4O5NGy/LgakTa6fiPpxBiHf/gDHvvkx4iGhhm65BKG161jYNWqnG+IzEl5oCfRcru55QtV0Mims06ZRhTc4xJ7s4SXbOQFUS6bA0enMA/RacY3s1sQVQmnrCGMV5B759D3jdGPuKR3raBqk5j77dL7cGKoIKuyWnbbmQodLPn4zWTQE9Z0qr8atenxjvzzvFv0v3vPqVR8X+jgHa98V0ksR/UaGHFUfZG7SikpDCw4j6wzTnf/Ppojo6Xhre5a5WubKFCUKpKp6WPjygnMiKzKQl/mHj3ydnkQs9/Pc7cN+77/PQ7cZ5a5Ovft7+TIgz/k0I4dHNq1g87EQUY2vBhiQbtKOuDdf1ZhF5UiHO0OecNaRbFhbZPfDCnx28SIkCWSL26bHAGJLecopWHAfXGatek/V3BcYJrB6hIUXFGFP0TfNaO5JXvUE1L+nGU+V7g+QNUojz9TtTtuFnxNadn1ZnJB5OUrtTyT2uYxoJgFvyc65OUTKZq/8n5C5W0nhnr6AfnfWZkX8uaytDdtLho8XsrFkCdO/O3qQIqSMPGatQUYOX8le776ReiY2aV7hE+NGBI1TfT+rPRAbRNWiQ/j8qumSg9ZAprA09u3cfiHPwDgeX/yJ/zi5ps58qMHOXjvds55x3+mddGFRuhEgsRSvjcfeSTI5sdrRhMFYrWOlP0vYzXRazFD67MBihGsEcTjXhmkMHcg9CGaXeh933jHtA9x1eyr8e5qL1cRQ1UhVG3nz5Dc6e+3NEcpfU3/BNU+z5T3oPfNb03lzvf3O7cqkphcDKH0ehVVe35ea4RWvvAuEBExePZSHvy7D3HJb72f9uD8stgqXd9rErMem7YoOv/h/tpCHFbbx/3mvDyU7N1v2oLhF12FthMQob10BXv++q/yc5665RaeuuUWa8ycHI8MM/LSlzBn00uIBgbqn7m6ovKI2nji5N5g1oCsIWaNIVsrmwe0EE1qCHXyC+hpN5X+c45JnSctbftQK3BjUaoB08m4wRdDpagRBf/Ur0UWkURpLoQiLxTR0cRLJ6W+Q/2mFahFpSjqIiyl/TXo4ZSqc1XhipIt/1jFjp+mxFPuuL3NRnuEKGmw66/ex6Vv/1MkLlYgkKp9796c6NK4ON5TdFK6VE/RKOWDadPU83mbX07jnLNJRucRtQc5vGNHnmbv33wW7brVXwVESEbnMeflmxh+6XokSabENznX5OpIcq5ByB27XAyNeeXh99ec9CKnH9/MbkHkQau1xd/2X3QniGpnabcPDEn5i7XWrTXUI5pqyawIV0+KmsNT0HW98Mmk7hI+0fQTZ3iVv86eFN/5yAc/zx7hNAbMlAZR1DCVrxQy117vz/9thYEviPCuU5qCXitEZp8Tn5CzFiQyzOiGTcaewvIP/BmHH9zJ3v/7+Uo5Khf8jw+SHT7M/jvvZM+NH2fu1RsZXHs58Uh5Yrx+ZegLTLNenlgxZA/bGpkcckuuuBesHFMgA+hpNgz2OYM4vVtfyFE0hcKH0qizKjKVnjrtiyG/47T5NrNNVzlFK8MdfTGUeWEEt3zHVJy1HrgI/DFfisV3P4eqKnJK0Wz1iqRPEfdz8FyEKRdZUDgZXpq4NUg6fpRI45qINiW+yfOhZpLXLDKfEodQ/FYnMmq40F+wGSBrGu5qLVxEa+PmfKDE0g/cwMEd3+epW28pxBAQtVuc/6EPMvH44+y//asc+vY9jFy9kcHLLyVyI8CO8dcWTpiJOCFGBKVxOY/JEUrNlW5G7WPhdOObWS2INPLfHlr68/t2DjsB9Iwc84RQT9rKPueFOTv+Pv/Yc4p+AsfTkX6zEh7B+On8yEbucDgIKIJ4YS61i8WJd65POnOXPJ+xp/fSbI8g3cKwqBZrl9m0rs+A81giu9ZZ1qAQR17FzZrlfkhpCyK3BqOYYatRsSZjqYlO7XXiVps5l7+Q4dVrefi/vzdPO7zuhSQDQ8jwHM5+81s4+tBDHLjnO+y77V8YXH0pC37tWqJ2s9aTrEPWMPnPEnP9dECJx4TksCHjZEzptsUuCqr1/2UJyukWwn4uIaW2zsmRjzCrOcN3dsxyHb1zGvmdoSPRnj5DsWip32E+nL4Gx1pA2vVZ7Hfc3Ef9sR4NVxUTtSfVRICq4qgqQvrY6xEsfcSLz035gsiY+jx60QsYf3qvmQjSm8esarOar7xZEHpnj7fCorSdUPAZhov8bY0rTp4dPRoPjjBvw0aGL72cR//shjwrcza8lDhuMrBsBe3fWcrh+3/AoXvu5el//BIjG65i3ut+xc6x5Ht9xfX8+1Ir+LNE89Fq8ZiNeI+ZKT6cc6qZyfeBw8fqQ3T68c2sFkSlPkJQqT30uhYlz5vC6/MiSS707T7OM8v7ElX25/YwROf2maaMIj+ZCt0sKo0acSRk2uztJGhZTJoWXpzkebTbkeYTMroart69TLH1rx5aKooeguoJV/vh3qOH+fn9X2fOkksYPut5vaZtc5J67Zgm2mO3vZXs5z5vNY9vu5mJA/tpDc3LxZBr8vKFmCYFyTlPzImjzBJM3rZvj2d2AUe195E1vXtx23j2nOhy+1yWJWbln36M7uGDEEfEQ0Pmv0nNjQysWMnAhSvJxsb46Wf+hkPbv8fgJReTzB8FlLHdjxEPD/HM1/6NeHCQeO5cmosX016+LL/H7qC3TIjtZxF1bZ6sQAQpptefDMqzEsIWkS3AX2DGcn9GVT884xc5yYjt7NFuXcPJUGo6mySNaDHAIucUjx+SKK1EhspD6F1/oZIwQhHJ6OekKtEAAA3lSURBVKiZx6whKalILqC6Wcx4ljCemXmHxtJGPiFksSC15Tssv1rucx3/VRyHiuUfKbjH1Q+dPhdVRUztMYuJZ57m6V3fYXTVC2jPW1QfbfLhhIabuMur72ddsoGdn7uBJelvFs5aNS+VCJA77LjB3as/9UZp25VJXKSv2/b7TDqbjjIbI3O58M8/xsQz+4iHBpF2M5/3SCRmeM0VDK+5gvTQQf7jYx+nvXIlrfPPI54zgqZdxnf/hGigzTNfvYvGorOIBgdoL19G89zF5nqRQrMQjlkDonEpRKXT5HZW/GPOQ/Qs8M3J5ppZLYikGqauC1tWxFBfW04MlbbL5HbM/HieXl3UKNUIVSXzBJSZ86Mshpyn1uuZmdohdnRB/XwCxb3mhPRsBJ98h0MhHT/K3nu3svferSzdfD3zV6zNs1xKX8mL2LWe/KhTRERjcIT06CF0eJ7lEsvQmZZsqZjok8ZeHxrf6/Qu6YrC/2fybXvQP97jxVaP2/OSwRH8Pj95vqzYjlptBlddxFNf/CJPmZ65pBdfzJN79pAeOMDgZZei4+Okhw6zf+sdzN24keG1a0nmzevxuEvNCd7+/B4mgQI6w6M+RCQG/hK4BngC2C4iX1bVH87ohU4yokjJst7nx6H6mPcr5dzRoixA6obXl65fEUO+49UvMtTNYjIRGpLRBVIbCfLF0IRtG8msMxdZMRSJolGGZpF5jiPLbc6BzBWTuzF/nxVJfoEU/lv/QqQScZpE3Ewc3MeT2+/gye13sPLadzN0dq8TVj3HFli59UDNSFJU0c4E0mwV+XDn+Gt3eUKmFN326mWebSlvl3jIiSWK8/ymfHeuE5YuCq4Czbmjxk4GuUOshcMWD43QXraMJz/1mdx+dvFF/HzPHtIDB5nzso10frYXaTTYf9vtzH/9axhcfRnxwGBPmTnOcX+7y7vPRZMV+UzyzWzgmlktiKrttQZ9RJLbrPkX89XpAWoIyhHElLJUE/720dWoIDMvOpSqWTAxvwt7TOyTl9utIyTxapDfnjSVPkgz8bwqtOYsZPmW3+aR2z9L58gz+f78OtVLe6HUfGJByPsJNYdG2ffIDgYWnGfWB1NHIpLbdEShCFlsRmD5ROLnz7/dOlFUyq6Wd/pzo/R4wdVJzNzIEnH3ZEbhjL5sM0OXraa7fx8/++zfMvqKa4iXXMDY7t20ly/P16k6uGQJh77/fZ7Zto3zf/8PiP0FKN1oEF8MudEuUyAo00N/xtv01wM/VtVHAETk/wCvA04vQSRavBj95l/nvNjfOb/U1L0qhzhecZMpFh2oJ/8jS6PLKhXYH4kG0PWGPmUa0dHILN6aJUykcU9zmeO+TOxs6IBEGaqRcUBVkQhyV81yktr78aPVpZd5hQty4TNd/rHph89dwdlXvpK927cycXAfQ4smEUQVJ0JrjjWHR9n/4x3Mf/76Ul0vO19e/iMTQcmbtyAfHJJfp8I7FKYMKrwC9DTRV4WR4x7XfO87XuqJvYXXXsvolldxZOdO9v3L7Sx4wxuIFowy9uhPGLhwhc2gsG/eKIfuuZcDd21j8XveTdRwnRU9XuvnfD33fHPSuWZWCyKJlFZcZHEi65S2x7NueTsttkWUCe3SSmJETFNWh4xWlDAYJ4goKRkDUYyICZl3tEPbnm/W+unQipJakTWRdWhGxRTypNCKGrkIOtpNSaRhJ12MSNPUOw5H0ox2lORRpKPdjHYjNsNgI2EsS2k1EloS0xZlYjw1ebFzg0xkXdqNJG/n7mRdWkmCqDnmfpPBxESxLRmMT3RpJDHthnn6JzrmuPNCXHpXISYmujSThPaFV7DoXZ+g0+my5zu38dMd/8rQoiWsfM3v0m6b5TuajRiRDq2Gd36nSzMq/qdO2mXZi17Dj+/6PI9tO8iqa96Sk8N4t0urmeQEMt7t0mwmJAMxjUjN8UZC4h1vNZK87o6nJu/V8/NnxKbPm8VSGHB5s9dvesfV7kdgLOvSFpNWpXKtRkK06Fxa5y5hwf/6BKONhMNjR5m/8uLStRddtYFFV23gic/dxMFv3c3iLa8unqlul3ackDSg0TAvqmYjsavdC8Nz2j3PYRUzHSECzgMe97afAK6a6YucbAw3I8bSLk0xddq9FNxkmuNZl0bUyJ/psbRLK0ryCG9HM1pRgyjKrHDIaEYJA3FMHAmRGG5JRIklQ2WMdtRArEhKMXzgBFDKOA0p+KWjhosgphkljInSlMQKngbjWUKqEd0s5khHiaVJIzJCqKMpTWmSIkREdCSj2WiQZUI3ijicKe3YLCHU0hhJO7TjRj5B7DgpLWnkL/OJ8dTwqh2K3xnv0o6S/CU6oWbbvXAnJrq0GjGtpjIx0aWdeGkr3DM+YThcgKUv+VXOvXILrSTh/i/cyJGfP8GCi6/iwlf8JuL1U2w3ylzVSrx3RsfU5xUvu46Hv/YFumMHWbL+VWY6DKnwgxT80GjH0OnQbHvcYrkJJj9XK9vu+Fi3SyuKGbDvkzHLHzm/pF56im3HZflxURgYQQdHmLN5MedsvoZ2JAyqMmD5ZrxrbC/+5V9Gt2xh90c+SnfXA8xdt84c75hyjmIhjkEpeFEjOKpdXr32Yu46Rr2ZYb456VwjOks7Rcl02rICAs4cPKqqS+sOiMjtwMJp2msDY972p1X1057NNwGvUtW32e3rgfWq+nvTvM6sReCagIC+eM74ZjZwzayNEOm0JswICAhQ1S3PgtkngAu87fOBPc/CdU4aAtcEBEwfzwLfnHSumdIC2gEBAWcstgMrRWSZiDSB64Avn+Q8BQQEnH446VwzayNEAQEBJx+q2hWR3wW+gulS/llV3XWSsxUQEHCaYTZwzaztQxQQEBAQEBAQ8FzhlG4yE5HfF5FdIrJTRL4gIm0R+ZCI3C8i94nIVhE510v/ERG5V0Q22e0vicjrveM/EpH3e3afEZGvW7sfEZEHre0vici8GbL7JrsvE5EXVu7vROzOF5E7ROQh+z16HHYvsuW4X0R2i8gBEXmPiFwuIt8WkR+IyD+LyJxp2v64tetsH7F2rxCRu+3+e0Vk/QzZ/Qdv/09E5L6ZsGuP/55Ns0tEbpxuGXvbN4vIGwmYtZDAN4FvpmY38M0pilNWEInIecB/AV6oqpdhQmzXAR9R1TWqegVwG/ABm96Nf94IvMv+/hawwR5fABwCXubsAoeBfdbuHcBlqroG+HfgfTNkdyfwRmBb5f5O1O4fA3eq6krgTrs9HbsvVtUf2XI8ArzEfn8J+Azwx6q62m6/d5q2V6rqFZ7tQ9bOjcANdv8H7PYJ21XV3/D23wz800zYFZGrMfNkrFHVS4GPTreMKfBimyZgFiLwTeCbqdoNfHPq4pQVRBYJMCAiCTAI7FHVA97xIcpT6tlZe/K5s76JfWjs923AAmv3IuAopoz2qOpWVXUr792N6QE/E3YfUNUf1dzbCdnFVJybbNqbAOctTNXuWWKwzNq9DHhYVR+113KEegdw7QnYBnjI2lXAeX9zKUYYnKhdAEREgF8HvjBDdt8BfFhVxwFU9ckTsHtUVX9GwGxG4JvAN1OxCwS+ORVxynaqVtX/EJGPAo9hKtBWVd0KICL/E/gt4Bngapt+l4gMAt/AehjAd4HLxPRo3wD8G7AcMznU3TbN3c6uh98G/uFZsOvf3wnZFZGzVfWn1tZPRWTRcdi9BFiLqVzXUVTsncBrgVuBN2GHSh6n7SOe3fcAX7H/a2TTz4Rdh18C9qrqQzNkdxXwS/Z5GwP+q6puP4EyDpilCHwT+GYadh0C35xqUNVT8gOMAv8KnAU0gFuAt1TSvA8TEp3MzjeBFwFfszb/EHgA+FvgnVW7wH/DhFtlhu3ehQnHH+u+p2QX2F85b9807b4TeBtmbZl3Ab8AzrZpLwa2YirdB4GnjtP2JzGhXGf3E8C19vevA1+dCbte+k8Cf3gcZdwvvzttngUz7fzuyZ6LY5Tx75zsOhU+kz4TgW8C3wS+Oc0/p3KT2SuA3ar6c1XtYNppN1TSfJ4ivNoP38K0vY6o6j5M+DEG1gFf9+2KyFuBXwXerPaJmwm708RU7e4VkcU234uBJ+vN9bV7t7Wzwdr/nqruBVDVB1X1lar6Aoz38vBx2n4VcL+zC7zV5h/gHzGVfibsYps53oj1tGfI7hPAP6nBPZiw9WQzt05WxsFjm90IfBP4JvDNaY5TWRA9BrxIRAZtW+3LgQdEZKWX5rXAg8ew803g7cAOu/0NYIn97PLsbgH+CHitqh6ZQv6mZHcKdo7X7pcxFR77fes07d6P8S6WYCpQHg524XARiYD3A586TtvnYTpMOuwBNtnfm4GHZsgumBfag6r6xDFsTsfuLTafiMgqoInxbKdr1/13AbMXgW8mtxv4pozAN6ciTnaI6kQ+wA0YAtoJfA5oYXr178T8+f8MnHcMG4swndHe5u37CSZU6dv9Mabt/D77+dQM2X0DRvmPA3uBr8yQ3QWY0R4P2e/5x2H3LkwnxqeAud7+d2NGvvw78GGOHc6vs70NmKjYfSkmLL4D+A7wgpmwa/f/PVMME08jv03gf9ty/x6w+TjLeNL/PHxmxyfwzaR2A9+U0wa+OQU/YWLGgICAgICAgDMep3KTWUBAQEBAQEDAjCAIooCAgICAgIAzHkEQBQQEBAQEBJzxCIIoICAgICAg4IxHEEQBAQEBAQEBZzyCIAoICAgICAg44xEEUUBAQEBAQMAZjyCIAgICAgICAs54/H/grJgHlaHJ1wAAAABJRU5ErkJggg==\n", 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CF8FiSdsBxOclGabBOedcncwCv5ktAp6WtHucNB74M3A9MDFOmwhcl1UanHPO9ZX1IG3/BFwiaSPgCeBkwpfNlZJOAZ4Cjs84Dc455xIyDfxm9gAwrsGs8Vlu1znnXHP+z13nnKsYD/zOOVcxHvidc5nym9GUjwd+55yrGA/8zjlXMR74nXOuYjzwO+dcxXjgd85lyi/ulo8HfuecqxgP/M45VzEe+Ctq2TJ4/fWiU+GcK4IH/gp69VUYORJOPbXolDjniuCBv4JWrQrPV15ZbDqcc8XwwO+cy5T36ikfD/z9mDsXli8vOhXO9bbjj4ef/rToVFSHB/5+7LYbvP3tRafCufasWQPbbw/XXlt0StK5+mr4zGeKTkV1eOBP4dlni06Bc+1ZuDA8zjij6JS4MvLA75xzFeOB3zmXKb+4Wz4e+J1zrmIyvdm6pHnASmAt8LqZjZM0ArgCGA3MA04ws+ezTIdzzrl18qjxv9/M9jWzcfH9WcB0MxsDTI/vnXPO5aSIpp5jgKnx9VTg2ALS4JxzlZV14DfgN5LulTQpTtvWzBYCxOdtGn1Q0iRJMyXNXLp0acbJdM656si0jR84yMyelbQNME3So2k/aGZTgCkA48aN834BznUp79VTPpnW+M3s2fi8BLgW2B9YLGk7gPi8JMs0OOecW19mgV/SZpK2qL0GjgAeAa4HJsbFJgLXZZUG55xzfWXZ1LMtcK2k2nYuNbNbJd0DXCnpFOAp4PgM0+Cc62FPPw2rV8Nb31p0SrpLZoHfzJ4A9mkwfRkwPqvtuv55m6vrFTvtFJ69TLfH/7nrnMuUB+Xy8cBfQaH1zbl8rFhRdApcPQ/8zrlMnXxy0Slw9TzwO+cy9dRTRafA1fPA75xzFdOyV4+kHYETgfcC2wOrCH3xbwJuMbM3Mk+hc66r+TWl8mka+CVdCOwA3Ah8k/AP22HAbsAHgS9LOsvM7swjoc657uS9esqnVY3/O2b2SIPpjwDXSNoI2CmbZLm81E5Kr5U5Vx1N2/gbBX1JwyXtHeevMbPHs0ycy0ayBnbAATDEr/S4DHmlonz6PeUlzZC0Zbxz1oPAhZLOzz5pLg933110CpxzeUtT13uTma0APgxcaGbvBA7LNlkuS14Dc67a0gT+oXH45BMIF3qdcyXnF1RdK2kC/1eB24DHzeweSbsAc7NNlsuSBwXnqq1Vd86TgN+Y2VXAVbXpcdTNj+SQNufcAHlznmulVXfOtwBXSdoQmA7cAvzJzOuL3c6DgnPV1qo753lmdigwgdCb55PAfZIulfQPkrbNK5HOOec6p98bsZjZSsL9cq8FkDQWOBL4BfCBTFPnnOt6/guzfFLdgSv+aWt0YvknzcyDfpeqcmPdM8/AI4/AB7z05qbK5a2s+g38ki4A9gZmAbVB2Qy4JsN0OZeJceNg0SIPRq7a0tT4321mYwe6AUkbADOBZ8zsKEk7A5cDI4D7gI+b2ZqBrt+1r8o/vRctKjoFzhUvTT/+P8R2/YE6HZideP9N4LtmNgZ4HjhlEOt2zjnXpjSBfyoh+M+R9JCkhyU9lGblcTz/vwV+Ft8LOBS4OrHuY9tPthsMb+ZwrtrSNPVcAHwceJh1bfxp/Sfwr8AW8f3WwAtm9np8v4Aw5r9zzrmcpAn8T5nZ9e2uWNJRwBIzu1fSIbXJDRZtWP+UNAmYBLDTTj7sv3POdUqawP+opEuBG4DVtYlm1l+vnoOAD0maQLhz15aEXwBbSRoaa/07As82+rCZTQGmAIwbN84bJ5xzrkPStPFvQgj4RwBHx8dR/X3IzL5oZjua2WjCfXv/x8w+BtwOHBcXmwhcN4B0u0HwNn7XzPz5cNttRafCZS3NP3dP7vA2zwQul3QOcD/w8w6v3zk3QHvuCatWdbZyUOXuw2XVanTOs4EfmdnyJvMPBTY1s37H6DezGcCM+PoJYP+BJNY5l61Vq4pOgctDqxr/w8ANkl4l/NFqKaGtfgywL/DfwLmZp9A551xHNQ38ZnYdcJ2kMYQLtdsBK4BfApPMzOsGzrl++TWl8knTxj8Xv+NWT/ET0eXJ2/jLJ02vHuecGzAP/OXjgd855yqm38AvaUQeCXHOdV4ZatvetFg+aWr8d0u6StKEOMia63J+IlaHH2vXSJrAvxth6ISPA49LOlfSbtkmyznnXFb6DfwWTDOzk4B/JAyz8CdJd0g6IPMUOufa5jV910qaNv6tJZ0uaSbwBeCfgJHAvwCXZpw+51wO7rgDVq7MZt3eQFw+aUbn/ANwMXCsmS1ITJ8p6SfZJMtlyWuDva+dYLt4MRxyCBx9dGbJcSWTpo3/bDP7WjLoSzoewMy+mVnKnHO5eOWV8Pzww8Wmw+UnTeA/q8G0L3Y6Ic653mAGs2at/96VS6vROY8EJgA7SPp+YtaWwOuNP+Wcq7qf/QwmTYJp0+Cww7yNv4xa1fifBWYCrwL3Jh7XAx/IPmkuK14Dc1m6//7w/NhjxabDNddqdM4HgQclXZK4ObrrIV4Tc0mdqhDUytUbb3Rmfa7zWjX1XGlmJwD3S0oWCRG69++deepcprzm77LgFYrya9Wd8/T43O/9dZ3rNmYeoOp1Kj9q6/GKxfreeAOGlGRYzKbJMLOF8eVzwNNmNh/YGNiH0P7vnHN9eODv63e/gw02gLvuKjolQZrvnzuBYZJ2AKYDJwMXZZkoly0/IV2W8gr83VSOp00Lz9OnF5uOmjSBX2b2CvBh4Adm9nfA2H4/JA2T9CdJD0qaJekrcfrOku6WNFfSFZI2GtwuONe+bgoa3cYv7vZVy4vSN/UkKA7G9jHgpjgtzVAPq4FDzWwfws3ZPyjp3cA3ge+a2RjgeeCU9pPtnEvrtdfgwgvzC8T11wqyupbSTV/etbSW5bpSmsB/OuGfutea2SxJuwC39/ehOKrnS/HthvFhwKHA1XH6VODYtlPtnEtFgvPPh09+EqZOzW+bsC7YdVOAzkrXBX4zu9PMPlQbl8fMnjCz09KsXNIGkh4AlgDTgL8ALyT+F7AA2KHJZydJmilp5tKlS9NszqX0xBPh+cUXi02Hg2OOyTYYmIVB2ACWL89uO0ne1NNXLS/KEvj7bbKJN135AjA6ubyZHdrfZ81sLbCvpK2Aa4E9Gy3W5LNTCDeAYdy4cV5n6KDJk8NzlWtiZdn366/Pb1t5BR1v6umrbDX+NG31VwE/AX4GrB3IRszsBUkzgHcDW0kaGmv9O+JdQ0vB+7X3lmRQzDtAenfOvmp50U0Xd183sx+b2Z/M7N7ao78PSRoVa/pI2gQ4DJhNuD5wXFxsInDdANPuOuinPy06BS4LyS/z/r7YOz1kgwf+dcrW1JMm8N8g6bOStpM0ovZI8bntgNslPQTcA0wzsxuBM4HPS3oc2Br4+YBT7wak0Ql50UW5J6NQVQlKRexnL/fjX7NmYJ/rxqaeifH5/ySmGbBLqw+Z2UPAfg2mPwHsnzaBbnAuuwx23RX27yfH/ULc+latCif5m95UdEoGL22A9CEbWps/H0aPDsNOn9JmJ/SuC/xmtnMeCXHZ+OhHw3N/J2GvnaSDtffe8Pjj3ZsvjdLtTT2DM3dueL7ssoEH/q5p45e0qaSzJU2J78dI8oHbekyvnaSD9fjjRaegc3r14m7e+7VRHGNgIM093fjP3QuBNcCB8f0C4JzMUuQKUbWmnip+0fVX458/vzPbqQW3XitTtcC/enX7ny1bU0+awL+rmX0LeA3AzFYRxuR3XapR0KtiIHTZyKsff9423jg8D6TG342Bf03sjmkAknYljMPjekiRgf8734F77ilu+73Om3o6YzA1/rJ150zTq2cycCvwZkmXAAcRhmZ2PaTIwP+FL+Sfhl7/hTOQi7ud0qtj9QzmC61sNf40vXp+I+lewr9uBZxuZs9lnjKXq15rj3WBVFzg7ZWAXzOY4N2NvXqmm9kyM7vJzG40s+ckleR2Aq5Teu0kdYFZ/rXNWnDLersDLbNmcOaZ8Nhj+WwPytfU0zTwxxupjABGShqe+NfuaGD7vBLoOq9RAa5ajd+/6LJT9n788+fDt74FEya097leqvG3aur5NHAGIcjfy7qePCuAH2acLpezsp6kbmCKPJ71wzKXrWzV0rO2zSEnOxH4y1Ljbxr4zex7wPck/ZOZ/SDHNLkClO3kdJ3RziBtnbLhhuH5tdey3U7eZbaXmnrSXNz9gaQD6Tse/y8yTJfLkDf1VEeyjT8vm2wSnletCs9lCXZFej3eeqr2pVi0NDdiuRjYFXiAdePxG+CBv4dUredHlX7h5N3MUB/4e8Vgykwt8G+wQWfSMlhp+vGPA8aaVelUqR4PwL2lyH78w4aF56wDf1FNPQPJx9r1hKFpIm4O0lxjfgT4m6wT4opVVAD2JqZsFdHMUqvV9tqxrVqNfyTwZ0l/IjFUg5l9KLNUudwVdZIWtd2q/NIocj/L1pNlsAazP7XAX5Yaf9ohG1wPKdPF3aoE4CINdIiBTgXsrI5xN/Xq6bqLu2Z2Rx4JccXypp7e0uhm62Wreb///TB2LPywS/4V1Ikaf1n+wNXqn7srJa1o8FgpaUWeiXTZKyrw33hjMdutiiL68acdlnnGDPjRjzJPTscM5hxp989ig3XppfDCC83nt/oD1xZZJMi5pBNOKGa73sTUWieberKS9TFcuzbU1Gvj8A9Gnv9i/uMf4WMfg6OPbr5MZj88JL1Z0u2SZkuaJen0OH2EpGmS5sbn4VmlwTVWpqBXG+PcZaNMx7rbHHnkuq6pUN4ms3q1+2zfcEPzZbJscXod+Bcz25MwpPOpksYCZwHTzWwMMD2+dxVV+7OP66xGbfxFyXP7y5aFwHzZZYNf17Rp678vOh/TWrSo/2UyC/xmttDM7ouvVwKzgR2AY4CpcbGpwLFZpcGVX1EXu7rlJO6EomuqeQ7LXBtq+fvfz257g9mfspS7XE67OJTzfsDdwLZmthDClwOwTZPPTJI0U9LMpUuX5pHMlvK+OFMVZenl0KsGenG3EwGqLEGuU3ppfzI/7SRtDvwKOMPMUvcGMrMpZjbOzMaNGjUquwSm5N0OszGYwP/aa31/jrv1FRGs8tpmNw3ZULbbUGYa+CVtSAj6l5jZNXHyYknbxfnbAUuyTIPrqyyFDwYX+P/93+GII+Cuu9r/bJnyIAu9vn9F6KU8zbJXj4CfA7PN7PzErOuBifH1ROC6rNKQtYsuCheTekFRhXow7aW19twlXnVoaTA3B89re92gl/Yryxr/QcDHgUMlPRAfE4DzgMMlzQUOj+9TOfRQOPvsbBLbrsceg5NPhhNPLDol3e3VV4tOQW9L3my97N0Q29UqEP/xj9lt7/774aqr1p+3ZAmMGBHmpVlH0TIbMsjMfsu62zXWGz+Qdd5+e3icc87A0zVQ9QdsdRyuLk3XKdfcK68UnYLeliy3eQf+sgS5LJxwwvr7d9tt8PzzcP75cPHFxaUL0uW796looZcLblnU/l144IHpP7NiRRjjpVa7yrIpoxfKQH/70Gi+N/X01Yk8ySNv0nzBl2SQ0PLrpcJcpn3Zddfw/Pa3p//MXXfB7NnZpKfeG2+UZwz1dhR5I5a8tttNo3OWjdf4U+qlg14mncjXLANatx/3MrTrd3se1vTKfoAHflewTvwE9qae5tLcbL2qTT3tprNb/tTmbfwd1C2FOY1e2pes+R/3sjfQ8uhNPQPngb+FMg101as6UeP3pp6+2rkRS6f3sf6YlqG5qRO65eJuGh74HVCeAlk23Z4vRY7V02t6KU888KfUSwe9TIqqCaXdnjf1lJefk42l+YL3wO+6XpYBoNuDy0Av7g52m72oWy7upuGBv4VebeMv076Ure2zXlnT1Z/BpjvP/e6WPO6WdKbhgT+lXjroZVJUP/6qNPVUdayesm2vbBUcD/yuFMrab7wsJ2on5N2rp+za/SLslv3yfvwd1C0HvduUPV/Lnr7+lCH9ef7S8EpAOh74W+jVNv5Git6/vPvx93pTz2DLbiebNbK6uFympp5u+ye4B/4KKkvhg/L3lChTXmWll/YxTSWgW75oBsq7c3ZQtxz0blXW/C1rutLq5Yu7eWtVFtLmbVnKkwd+V6iyj87ZrU09Ncn8zeufu/WfzYW9FSMAAA1ySURBVGNY5nbSm0XwLUtAB7+4O2hVauMvio/OmY2iA2E7Orn9sjb7FZ3H9Tzwp1S2AzcYvbQvWeuFvCpqH/Lsu57HF5039TjXIWX7Y0u9bm/qGWgzSzfUbhttp6iRWstUfgu9uCvpAklLJD2SmDZC0jRJc+Pz8Ky232llOrBu8Hq9qScp72GZ6+VxUbnoGn+e6+iELGv8FwEfrJt2FjDdzMYA0+P70irLQeplZa/xlzVd/WmUr3n16mk3z7oljzvR1FMWmQV+M7sTWF43+Rhganw9FTg2q+13WrcUzjQa7Usv7V8ndXtTTxrdWh4adb5Ik+4savzdkF9Jebfxb2tmCwHi8zbNFpQ0SdJMSTOXLl2aWwKb6bYD2y2KvvDY37xuP+5V6cdftgvIzT5blnSW9uKumU0xs3FmNm7UqFFFJ8dlxJt6ek+RxzTLf+4Odrt5KeM/dxdL2g4gPi/JefsD5gGgOpLHutubeswGNlZOnn3rO9H0UnRNutviQ96B/3pgYnw9Ebgu5+23pZd+8peV1/izkXdXx1bKVBsG79UD2XbnvAz4A7C7pAWSTgHOAw6XNBc4PL53OStL4StSFdv481JEP/48KhDd0qsnTR4MzW7jdlKTWeOz2maWuj0AlFXZa/zd3tST1E4//k4ejzIFRRh4mRtMU0/ZynlpL+66fJWlQJZBL9X4i0h//TbzbH8v6p+73cYDfwu9FADKqqiaUK839XTTPuTd5l7kWD15KGOvnq5V9pOnHWXalzKlpZFub+pJBgEfsiG7bXTbECAe+F0plOWEgO6qLacxkH3olj8rNdv2YJfpdR74Uyqi3TIrZfpZWvZ8LHv6+lPEWD15KVM//v7y1i/udpFWBassB3Agypj2srbxd2tTTzv5WXR5KNOxb7V8t/yBq6uHbHDZ6bZCmsc6ilh3Xopq6slTHmWgF8pCjQf+lJrV+Hvl53PRhbro7ScV0caf1XaS5bNMedwJA/13cpG9espyDDzwD1Av1gKK0In8G8iXb9rtdmtTT1JWY+X0t748z40st+lNPRXTa707yqgTJ2xWgSuLdafZZifXZzawPC4iaJd9e93a06kRD/wp9VJTT1kKXzeowvWDsqSjXXl9QacJ2mWKA17jz1DZvsHbUcZCOph87HRTTy/06qkp4liX/ZzwXj0e+FMr04EdrG7fl6yDWd41yay3M5BtdFsZ6Zb0liWdHvhbSPMNX6bac72yFLJWvP05G43a+PtbtiidbHPP4p+73tRTYc3a+Is+aVzn5dXUU6Y26iy2l6eifzGl3X5Z4oUH/pTKcsDa0SzNZdqXor5Ay3Ci5rHPA62JlqmMtKOoGn+ZeI0/Q93Q1NNMozQXVajLeDJVrRtv1v34szpHynSNJO1YPWXhgb+FVgWrG2oB3VDjrylTP/6kbm3qaVQ+y3jcobPpamdd7f7btluaerzG30FlPWm6XRl/aRTRqydLA9lOUcF4sNvwph4P/JnqhqaebqrxD0Yv3G4vq+0kb7aeV3no9jwbyPZqZbBbmnwKCfySPihpjqTHJZ1VRBra1Y1NPd2gE/mYZeDKq6mnl+V5B64sa/yd2n7WSlnjl7QB8EPgSGAscJKksXmno8rK/CulbMpwIrty6KWyIMt5byQdAEw2sw/E918EMLNvNPvMJpuMs112mcmf/xzej83pa2LtWpgzJ7zedVfYeON18155BebNyzc97TKD2bPD62Qaa/lYr4j9WLAAVqyAzTeHnXZK95kVK8LnarbZBkaOTPfZ2r6PGQMbbth4meRx32mnkLZ2pSmrb7wBjz4aXu+xBwzpYDVs1Sp48sn1p22/PWy1Vbpld9sNhg4d2LaXLYPFi8PrsWPXL2+NyuEee6zLh3bK4Jo18Pjj66f35Zdh/vzW60rub6vtJdM3ZAgsXw6LFjXel4UL4fnnm6+ztq7ttoPhw1vv12Ctf37rXjMbV7/MAA/toOwAPJ14vwD4X/ULSZoETALYdNO9GDsW5s6F0aPzDVC1ALDffn3nzZsH48dnfyAH48UX4U1vWj/PdtsNfv1rOPTQcAI8+SQcfnhYLm9jx8JTT6UP+jVXXw3vfz/cfju8733pP7fppjBzJuyzT+vlasd9//3bS1fNokWhotBfWa0FvL32Gth2WnnySTj44FA+f/1rOPDA1ssm7b334LZ9ww1w1FHh1+Uee8A118BBB4XAVzNvXvjS3muvEFS32SYs245a4E+mt7/AD2F/DzmkdYVhyBBYsmT9Y3PDDeELZ/fd11//nnvCr34FRx+9fgWxZo894OabQx5kbeed4aabYMKEsM1Gigj8jRoa+vzsMLMpwBSAcePG2VVXZZ0s55zrLc2adYu4uLsAeHPi/Y7AswWkwznnKqmIwH8PMEbSzpI2Ak4Eri8gHc45V0m5N/WY2euSPgfcBmwAXGBms/JOh3POVVURbfyY2c1Ak8sOzjnnsuT/3HXOuYrxwO+ccxXjgd855yrGA79zzlVM7kM2DISklcCcotNRMiOB54pORAl5vjTm+dJXFfLkLWY2qn5iIb16BmBOo/EmqkzSTM+TvjxfGvN86avKeeJNPc45VzEe+J1zrmK6JfBPKToBJeR50pjnS2OeL31VNk+64uKuc865zumWGr9zzrkO8cDvnHMVU+rA3403Ze8kSfMkPSzpAUkz47QRkqZJmhufh8fpkvT9mFcPSXpHsanvHEkXSFoi6ZHEtLbzQdLEuPxcSROL2JdOaZInkyU9E8vLA5ImJOZ9MebJHEkfSEzvmXNM0psl3S5ptqRZkk6P0ytdVhoys1I+CEM2/wXYBdgIeBAYW3S6cs6DecDIumnfAs6Kr88CvhlfTwBuIdzh7N3A3UWnv4P58D7gHcAjA80HYATwRHweHl8PL3rfOpwnk4EvNFh2bDx/NgZ2jufVBr12jgHbAe+Ir7cAHov7Xumy0uhR5hr//sDjZvaEma0BLgeOKThNZXAMMDW+ngocm5j+Cwv+CGwlabtGK+g2ZnYnsLxucrv58AFgmpktN7PngWnAB7NPfTaa5EkzxwCXm9lqM3sSeJxwfvXUOWZmC83svvh6JTCbcI/vSpeVRsoc+BvdlH2HgtJSFAN+I+neePN5gG3NbCGEgg5sE6dXLb/azYeq5M/nYrPFBbUmDSqYJ5JGA/sBd+NlpY8yB/5UN2XvcQeZ2TuAI4FTJb2vxbKeX0GzfKhC/vwY2BXYF1gIfCdOr1SeSNoc+BVwhpmtaLVog2k9my9JZQ78lb8pu5k9G5+XANcSfpovrjXhxOclcfGq5Ve7+dDz+WNmi81srZm9Afw/QnmBCuWJpA0JQf8SM7smTvayUqfMgb/SN2WXtJmkLWqvgSOARwh5UOtlMBG4Lr6+HviH2FPh3cCLtZ+3PardfLgNOELS8NgEckSc1jPqrun8HaG8QMiTEyVtLGlnYAzwJ3rsHJMk4OfAbDM7PzHLy0q9oq8ut3oQrro/Ruh58OWi05Pzvu9C6GXxIDCrtv/A1sB0YG58HhGnC/hhzKuHgXFF70MH8+IyQtPFa4Ta2CkDyQfgk4QLm48DJxe9XxnkycVxnx8iBLXtEst/OebJHODIxPSeOceA9xCaZB4CHoiPCVUvK40ePmSDc85VTJmbepxzzmXAA79zzlWMB37nnKsYD/zOOVcxHvidc65iPPC7riRpbWIUygc6PbKkpEMkHdjJdabY5ujkaJvOZWVo0QlwboBWmdm+Ga7/EOAl4PcZbqOjJA01s9eLTocrP6/xu54h6UhJVybeHyLphvj6CEl/kHSfpKvieC61ex58JU5/WNIecYCvzwD/HH9NvLduO5PjIGgzJD0h6bQ4fb0au6QvSJocX8+Q9F1Jd8bx4t8l6Zo43vs5idUPlTQ1DrR2taRN4+ffKemOOGDfbYkhCGZIOlfSHcDpHc9U15M88LtutUldU8/fE4bPfXcc4gLg74ErJI0EzgYOszDo3Uzg84l1PRen/5gwnv084CfAd81sXzO7q8H29yAM37s/8B9xjJj+rDGz98V1XwecCuwFfELS1nGZ3YEpZrY3sAL4bFz3D4DjzOydwAXA1xPr3crMDjaz7+BcCt7U47pVw6YeSbcCR0u6Gvhb4F+Bgwk35PhdGM6FjYA/JD5WG8zrXuDDKbd/k5mtBlZLWgJsm+IztXFwHgZmWRxLSdIThEHBXgCeNrPfxeV+CZwG3Er4gpgW078BYbiGmitSptk5wAO/6z1XEGrSy4F7zGxlHLxrmpmd1OQzq+PzWtKfE6sTr2ufe531f0UPa/KZN+o+/0Ziu/VjqNSGCZ5lZgc0ScvLKdPsHOBNPa73zCDckvBTrKsJ/xE4SNJbASRtKmm3ftazknD7vnYsBraRtLWkjYGj2vw8wE6SagH+JOC3hIHVRtWmS9pQ0tsGsG7nAA/8rnvVt/GfB2Bma4EbCTevuTFOWwp8ArhM0kOEL4I9+ln/DcDfNbq424yZvQZ8lXDXpxuBR9vfLWYDE2M6RwA/tnBbxOOAb0p6kDDqZK5dTV1v8dE5nXOuYrzG75xzFeOB3znnKsYDv3POVYwHfuecqxgP/M45VzEe+J1zrmI88DvnXMX8fwCk3TN9RjJKAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", 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/lxFxWe2VWN84+KvzHr+lpEzw3yzpa8DlZF09ABSGYLAJxsFfnYPfUlIm+B9LFvhHFdp8OOcE5uCvzsFvKSnzy91/6Ech1j8O/uoc/JaSbqNzfiA/61anx58v6UX1lGV1cvBX5+C3lHTb478JuFzSn4AbgU3AVGA/YC7wXeC02iu0cefgr87BbynpGPwRcSlwqaT9yEbanAncB5wDLIyIB/pToo03B391Dn5LSZk+/lupcMYtG34O/uoc/JaSMkM2WGIc/NU5+C0lDv4GagWXg788B7+lZMzg73Zkj01M3uOvzsFvKSmzx/9jSRdKOqbKOXdteDn4q3PwW0rKBP/+wGKygdpuk3SapP3rLcvq5OCvzsFvKRkz+CNzVUScALwBWADcIOl7kp5Ze4U27hz81Y2MwHbbPXLbbCIrc+rFXYDXku3xbwDeDlxG9iOuC4G96yzQxp+Dv7qREZg8GR56yMFvE1+ZQdquB74KHBcR6wrtSyWdWU9ZVqdW4DvAynPwW0rK9PF/ICL+pRj6kl4BEBEfr60yq433+KtrBX/rttlEVib4Txml7Z/HmknS2ZI2SlpVaFsk6VeSlueXY6oUa+PDwV+dg99S0rGrR9J84BjgiZL+b+GhHYEtJZb9ZeA/gK+0tX8mIj5ZsU4bRw7+6hz8lpJuffx3A0uBlwDLCu2bgXeNteCIuE7S7EdTnI2/CP9ytxcOfktJt9E5VwArJJ0bEWX28Mt6m6TXk72pvCcifjvaRJIWAgsBZs2aNY6rb7ZiaDn4y3PwW0q6nYjlgvzmTyWtLFxukrSyx/WdAexLdijoeuBTnSaMiMURMS8i5s2YMaPH1Vm7Ytg7+Mtz8FtKunX1nJxfj9tZtiJiQ+u2pC8AV4zXsq0cB39vRkZgypRHbptNZB33+CNifX7zHuCuiLgTeAxwMFn/f2WSZhbuvhRY1Wlaq4eDvzfe47eUlPkB13XAcyTtDFxN1jf/KuDEbjNJOg84HNhV0jrgw8DhkuYCAawF3tRz5dYTB39vHPyWkjLBr4j4o6STgM9FxCck/XSsmfKxfdqdVblCG1cO/t44+C0lZX7ApXwwthOBb+RtZd4wbAg5+Hvj4LeUlAn+k8l+qXtJRKyWtA9wTb1lWV0c/L1x8FtKypxs/Tqyfv7W/TuAd9RZlNXHwd8bB7+lpMywzPsD7wVmF6ePiOfXV5bVxcHfGwe/paRMX/2FwJnAFwFHxQTn4O+Ng99SUib4t0TEGbVXYn3h4O+Ng99SUubL3cslvUXSTEnTW5faK7NaeKye3jj4LSVl9vgX5Nf/VGgLYJ/xL8fq5j3+3jj4LSVljurxOXUT4uDvjYPfUjJmV4+kaZI+IGlxfn8/SeM2cJv1l4O/Nw5+S0mZPv4vAQ8Bz8rvrwP+tbaKrFYO/t44+C0lZYJ/34j4BPAwQEQ8AKjWqqw2Dv7eOPgtJWWC/yFJjyX7QhdJ+wIP1lqV1cbBX11Edu3gt1SUOapnEfBtYE9J5wKHAf9QZ1FWHwd/da2gd/BbKsoc1XOlpGXAM8i6eE6OiHtqr8xq4eCvzsFvqSlzVM/VEXFvRHwjIq6IiHskXd2P4mz8Ofirc/Bbajru8UuaCkwjO4PWzjzyhe6OwB59qM1q4OCvzsFvqenW1fMm4J1kIb+MR4L/PuDzNddlNSmGvQOsHAe/paZj8EfEZ4HPSnp7RHyujzVZjbzHX52D31JT5svdz0l6Fn85Hv9XaqzLauLgr87Bb6kpcyKWrwL7Ast5ZDz+ABz8E1Ar7CdNcvCX5eC31JQ5jn8eMCei9TMWm8haYT9lioO/LAe/pabML3dXAX9VdyHWHw7+6hz8lpoye/y7Aj+TdAOFoRoi4iXdZpJ0NvAiYGNEHJi3TQfOJ/u+YC3wyoj4bU+VW08c/NU5+C01ZYds6MWXgf9g2+8CTgGujojTJZ2S339fj8u3Hjj4q3PwW2rKHNXzvV4WHBHXSZrd1nwscHh+ewlwLQ7+vnLwV9cK+kmTtr1vNlF1++XuZvIROdsfAiIiduxhfbtHxHqyBayXtFuX9S8EFgLMmjWrh1XZaBz81RWDX3Lw28TX7QdcO/SzkFHWvxhYDDBv3jwfUTROHPzVtYJ+u+2yi7ebTXRljuoZTxskzQTIrzf2ef2N5+Cvrj34vcdvE12/g/8yYEF+ewFwaZ/X33it0HLwl+fgt9TUFvySzgOuB54saZ2kk4DTgRdIuhV4QX7f+sh7/NU5+C01ZQ7n7ElEnNDhoSPqWqeNzcFfnYPfUtPvrh4bMAd/dQ5+S42Dv2Ec/NU5+C01Dv6GcfBX5+C31Dj4G8bBX52D31Lj4G+YVthvv72DvywHv6XGwd8wW7dmww5MnuzgL8vBb6lx8DfM1q3ZmDM+A1d5Dn5LjYO/YRz81Tn4LTUO/obZutWDjVXl4LfUOPgbxnv81Tn4LTUO/oZx8Ffn4LfUOPgbxsFfnYPfUuPgbxgHf3UOfkuNg79hisHvACvHwW+pcfA3TDH4wSFWhoPfUuPgb5j24Hd3z9gc/JYaB3/DOPirc/Bbahz8DePgr87Bb6lx8DeMg786B7+lxsHfMA7+6hz8lhoHf8M4+Ktz8FtqHPwNMzLi4K/KwW+pmTyIlUpaC2wGtgJbImLeIOpoIu/xV+fgt9QMJPhzfxcR9wxw/Y3k4K/OwW+pcVdPwzj4q3PwW2oGFfwBXClpmaSFo00gaaGkpZKWbtq0qc/lpcvBX52D31IzqOA/LCKeBswH3irpue0TRMTiiJgXEfNmzJjR/woT5eCvzsFvqRlI8EfE3fn1RuAS4NBB1NFEDv7qHPyWmr4Hv6THSdqhdRs4CljV7zqaysFfnYPfUjOIo3p2By6R1Fr/1yLi2wOoo5Ec/NU5+C01fQ/+iLgDOLjf67WMg786B7+lxodzNoyDvzoHv6XGwd8wDv7qHPyWGgd/wzj4q3PwW2oc/A3j4K/OwW+pcfA3jIO/Oge/pcbB3zBbtz4SYK371p2D31Lj4G8Y7/FX5+C31Dj4G8bBX52D31Lj4G8YB391Dn5LjYO/YRz81Tn4LTUO/oZpD36H2Nha26i13bzNbKJz8DeM9/ir8x6/pcbB3zAO/upaQS85+C0NDv6GGRlx8Fc1MpKFvoPfUuHgbxjv8Vc3MvLID94c/JYCB3/DOPirc/Bbahz8DePgr87Bb6lx8DdIRHZx8Ffj4LfUOPgbpBXyDv5qHPyWGgd/gzj4e+Pgt9Q4+BvEwd8bB7+lxsHfIA7+3jj4LTUO/gZx8PfGwW+pGUjwSzpa0i2SbpN0yiBqaCIHf28c/Jaavge/pEnA54H5wBzgBElz+l1HEzn4e+Pgt9RMHsA6DwVui4g7ACT9F3As8LNOM6xeDQcc0KfqErZlS3ZdDP7PfhbOO29wNU0Ed98NU6dmt1vB79ejDbNp0+AnP+n8+CCC/4nAXYX764C/bZ9I0kJgIcC0aQcyx58JxsW8eXDUUVmAffCDsGbNoCsafnPmwGGHZbdf9jK45RZ/UrLh1tpR6UQR0Z9KWiuUXgG8MCLekN9/HXBoRLy90zzz5s2LpUuX9qtEM7MkSFoWEfPa2wfx5e46YM/C/ScBdw+gDjOzRhpE8P8E2E/S3pKmAK8GLhtAHWZmjdT3Pv6I2CLpbcB3gEnA2RGxut91mJk11SC+3CUivgl8cxDrNjNrOv9y18ysYRz8ZmYN4+A3M2sYB7+ZWcP0/QdcvZC0Gbhl0HV0sCtwz6CLGMWw1gWurVfDWtuw1gWuba+ImNHeOJCjenpwy2i/PhsGkpYOY23DWhe4tl4Na23DWhe4tk7c1WNm1jAOfjOzhpkowb940AV0May1DWtd4Np6Nay1DWtd4NpGNSG+3DUzs/EzUfb4zcxsnDj4zcwaZqiDf5hOyi5pT0nXSFojabWkk/P2RZJ+JWl5fjlmQPWtlXRTXsPSvG26pKsk3Zpf7zyAup5c2DbLJd0n6Z2D2m6Szpa0UdKqQlvH7STpn/PX3y2SXtjnuv5d0s2SVkq6RNJOeftsSQ8Utt2ZddXVpbaOz1+/tlmX2s4v1LVW0vK8vW/brUteDPy1BkBEDOWFbMjm24F9gCnACmDOAOuZCTwtv70D8HOyk8UvAt47BNtrLbBrW9sngFPy26cAHx+C5/TXwF6D2m7Ac4GnAavG2k7587sCeAywd/56nNTHuo4CJue3P16oa3ZxugFts1Gfv35us061tT3+KeBD/d5uXfJi4K+1iBjqPf4/n5Q9Ih4CWidlH4iIWB8RN+a3NwNryM4fPMyOBZbkt5cAxw2wFoAjgNsj4s5BFRAR1wG/aWvutJ2OBf4rIh6MiF8At5G9LvtSV0RcGRFb8rv/TXa2ur7rsM066ds2G6s2SQJeCZxX1/o76ZIXA3+twXB39Yx2UvahCFpJs4FDgB/nTW/LP46fPYjulFwAV0palp+oHmD3iFgP2QsR2G1AtbW8mm3/CYdhu0Hn7TRMr8F/BL5VuL+3pJ9K+p6k5wyoptGev2HaZs8BNkTErYW2vm+3trwYitfaMAe/Rmkb+LGnkh4PXAS8MyLuA84A9gXmAuvJPloOwmER8TRgPvBWSc8dUB2jUnaazZcAF+ZNw7LduhmK16CkU4EtwLl503pgVkQcArwb+JqkHftcVqfnbyi2We4Ett3R6Pt2GyUvOk46Sltt222Yg3/oTsouaXuyJ/HciLgYICI2RMTWiBgBvkCNH8+6iYi78+uNwCV5HRskzcxrnwlsHERtufnAjRGxAYZnu+U6baeBvwYlLQBeBJwYeWdw3h1wb357GVl/8P79rKvL8zfwbQYgaTLwMuD8Vlu/t9toecGQvNaGOfiH6qTseX/hWcCaiPh0oX1mYbKXAqva5+1DbY+TtEPrNtmXgqvItteCfLIFwKX9rq1gm72vYdhuBZ2202XAqyU9RtLewH7ADf0qStLRwPuAl0TEHwvtMyRNym/vk9d1R7/qytfb6fkb6DYrOBK4OSLWtRr6ud065QXD8lrrxzfcj+Kb8WPIvg2/HTh1wLU8m+yj10pgeX45BvgqcFPefhkwcwC17UN2RMAKYHVrWwG7AFcDt+bX0we07aYB9wJPKLQNZLuRvfmsBx4m28s6qdt2Ak7NX3+3APP7XNdtZP2+rdfbmfm0L8+f5xXAjcCLB7DNOj5//dpmnWrL278MvLlt2r5tty55MfDXWkR4yAYzs6YZ5q4eMzOrgYPfzKxhHPxmZg3j4DczaxgHv5lZwzj4bUKStFXbjvo5rqO3Sjpc0rPGc5kl1jm7OMqkWV0mD7oAsx49EBFza1z+4cD9wI9qXMe4kjQ5HhnUzawj7/FbMiTNl3RB4f7hki7Pbx8l6XpJN0q6MB9DpXUeg4/k7TdJeko+qNabgXflnyae07aeRfnAZNdKukPSO/L2bfbYJb1X0qL89rWSPiPpunyM9r+RdHE+Lvu/FhY/WdKSfPCzr0uals//9HxgsWWSvlP42f+1kk6T9D3g5HHfqJYkB79NVI9t6+p5FXAV8Ix82AqAVwHnS9oV+ABwZGQD2S0lG6Sr5Z68/QyyMebXAmcCn4mIuRHx/VHW/xTghWRj1Hw4H5dlLA9FxHPzZV8KvBU4EPh7Sbvk0zwZWBwRBwH3AW/Jl/054PiIeDpwNvCxwnJ3iojnRcQwDnRnQ8hdPTZRjdrVI+nbwIslfR34n8D/Bp5HdqKLH2ZDqDAFuL4wW2sArWVkA3uV8Y2IeBB4UNJGYPcS87TGmroJWB358LyS7iAboOt3wF0R8cN8unOAdwDfJnuDuCqvfxLZMAUt52NWgYPfUnM+2Z70b4CfRMTmfMCsqyLihA7zPJhfb6X8/8SDhdut+baw7afoqR3mGWmbf6Sw3vYxVIJsyN7VEfHMDrX8oWTNZoC7eiw915Kdiu+NPLIn/N/AYZL+GkDSNEljDce7meyUeVVsAHaTtIukx5ANp1zVLEmtgD8B+AHZoF0zWu2Stpd0QA/LNgMc/DZxtffxnw4QEVuBK8jG/78ib9sE/D1wnqSVZG8ETxlj+ZcDLx3ty91OIuJh4KNkZ1q6Ari5+p/FGmBBXud04IzITj16PPBxSSvIRnrs66GmlhaPzmlm1jDe4zczaxgHv5lZwzj4zbgYlMAAAAAeSURBVMwaxsFvZtYwDn4zs4Zx8JuZNYyD38ysYf4/eeyMua9eQJcAAAAASUVORK5CYII=\n", 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" ] @@ -644,15 +644,13 @@ "source": [ "# 1. intensities of the largest event (defined as greater sum of intensities):\n", "# all events:\n", - "haz_tc_fl.plot_intensity(event=-1) # 1985260N13336_gen3 is a synthetic event\n", - "# only historical events:\n", - "haz_tc_fl.select(orig=True).plot_intensity(-1) # largest historical event: 1992230N11325 hurricane ANDREW\n", + "haz_tc_fl.plot_intensity(event=-1) # largest historical event: 1992230N11325 hurricane ANDREW\n", "\n", "# 2. maximum intensities at each centroid:\n", "haz_tc_fl.plot_intensity(event=0)\n", "\n", - "# 3. intensities of hurricane 2010236N12341_gen3:\n", - "haz_tc_fl.plot_intensity(event='2010236N12341_gen3', cmap='BuGn') # setting color map\n", + "# 3. intensities of hurricane 1998295N12284:\n", + "haz_tc_fl.plot_intensity(event='1998295N12284', cmap='BuGn') # setting color map\n", "\n", "# 4. tropical cyclone intensities maps for the return periods [10, 50, 75, 100]\n", "_, res = haz_tc_fl.plot_rp_intensity([10, 50, 75, 100])\n", @@ -671,7 +669,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -699,7 +697,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Write a hazard" + "## Write a hazard" ] }, { @@ -718,8 +716,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:39,316 - climada.hazard.base - INFO - Writting results/haz_tc_fl.h5\n", - "2019-10-29 21:57:40,229 - climada.hazard.base - INFO - Reading results/haz_tc_fl.h5\n" + "2020-09-16 09:44:10,656 - climada.hazard.base - INFO - Writing results/haz_tc_fl.h5\n", + "2020-09-16 09:44:10,697 - climada.hazard.base - INFO - Reading results/haz_tc_fl.h5\n" ] } ], @@ -747,7 +745,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:40,264 - climada.util.coordinates - INFO - Writting results/haz_ven.tif\n" + "2020-09-16 09:44:10,714 - climada.util.coordinates - INFO - Writting results/haz_ven.tif\n" ] } ], @@ -771,7 +769,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2019-10-29 21:57:40,332 - climada.util.save - INFO - Written file /Users/aznarsig/Documents/Python/climada_python/doc/tutorial/results/tutorial_haz_tc_fl.p\n" + "2020-09-16 09:44:10,763 - climada.util.save - INFO - Written file /home/tovogt/code/climada_python/doc/tutorial/results/tutorial_haz_tc_fl.p\n" ] } ], diff --git a/doc/tutorial/climada_hazard_RiverFlood.ipynb b/doc/tutorial/climada_hazard_RiverFlood.ipynb new file mode 100644 index 0000000000..3ff19142ea --- /dev/null +++ b/doc/tutorial/climada_hazard_RiverFlood.ipynb @@ -0,0 +1,1537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hazard:RiverFlood" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A river flood hazard is generated by the class RiverFlood() that extracts flood data simulated within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, https://www.isimip.org/). \n", + "The method set_from_nc() generates a data set with flood depth in m and the flooded fraction in each centroid. The data derived from global hydrological models driven by various climate forcings. A link to the ISIMIP data repository will be provided soon. In this tutorial we show how flood depth and fractions can be transated into socio-economic impacts.\n", + "\n", + "Besides, all other general Hazard Attributes, the class RiverFlood() has further Attributes related to the flooded area and flood volume:\n", + "\n", + "- fla_ann_av (float) average flooded area per year\n", + "- fla_ev_av (float) average flooded area per event\n", + "- fla_event (1d array(n_events)) total flooded area for every event\n", + "- fla_annual (1d array (n_years)) total flooded area for every year\n", + "\n", + "- fv_annual (1d array (n_years)) total flood volume for every year (area*depth)\n", + "\n", + "Only set if save_centr = True in set_flooded_area():\n", + "- fla_ev_centr (2d array(n_events x n_centroids)) flooded area in every centroid for every event\n", + "- fla_ann_centr (2d array(n_years x n_centroids)) flooded area in every centroid for every year\n", + "\n", + "- fv_ann_centr (2d array(n_years x n_centroids)) flooded area in every centroid for every year " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating a RiverFlood Hazard\n", + "\n", + "A river flood is generated with the method set_from_nc(). There are different options for choosing centroids.\n", + "You can set centroids for:\n", + "- countries\n", + "- regions\n", + "- global hazards\n", + "- with random coordinates\n", + "- with random shape files (under development)\n", + "\n", + "Countries or regions can either be set with corresponding ISIMIPNatID centroids (ISINatIDGrid = True) or with Natural Earth Multipolygons (default).\n", + "It is obligatory to set paths for flood depth and flood fraction, here we present example files from floods for the year 2000.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting floods for countries with Natural Earth Multipolygons:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 12:10:15,184 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 12:10:15,213 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n" + ] + }, + { + "data": { + "text/plain": [ + "['2000']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.path.append('/home/insauer/Climada/climada_python')\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from climada.hazard.river_flood import RiverFlood\n", + "from climada.hazard.centroids import Centroids\n", + "from climada.util.constants import HAZ_DEMO_FLDDPH, HAZ_DEMO_FLDFRC\n", + "\n", + "\n", + "years = [2000]\n", + "# generating RiverFlood hazard from netCDF file\n", + "# uses centroids from Natural Earth Multipolygon for Germany, Austria and Switzerland\n", + "rf = RiverFlood()\n", + "rf.set_from_nc(countries = ['DEU','AUT','CHE'], years=years, dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC)\n", + "rf.event_name " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resolution:\n" + ] + }, + { + "data": { + "text/plain": [ + "0.04166666666666666" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Note: Points outside the selected countries are masked in further analysis.\n", + "# plot centroids:\n", + "rf.centroids.plot()\n", + "# get resolution\n", + "print('resolution:')\n", + "rf.centroids.meta['transform'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting intensity (Flood depth in m)\n", + "rf.plot_intensity(event=0, smooth = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting flood with ISIMIP Grid:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 12:13:44,079 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 12:13:44,544 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/Climada/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# generating RiverFlood hazard from netCDF file, using the ISIMIP NatIDGrid (according to ISIMIP standards) with a resolution of 150as (aprox 5km)\n", + "# setting centroids for a region\n", + "rf_SSA = RiverFlood()\n", + "rf_SSA.set_from_nc(reg = ['SWA'], years=years, dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC, ISINatIDGrid=True)\n", + "rf_SSA.centroids.plot()\n", + "rf_SSA.plot_intensity(event=0, smooth = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting flood with random points as coordinates:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2019-09-13 10:12:22,712 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2019-09-13 10:12:23,370 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/anaconda3/envs/climada_env/lib/python3.7/site-packages/matplotlib/tight_layout.py:176: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " warnings.warn('Tight layout not applied. The left and right margins '\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rand_centroids = Centroids()\n", + "lat = np.arange(47, 56, 0.2)\n", + "lon = np.arange(5, 15, 0.2)\n", + "lon, lat = np.meshgrid(lon, lat)\n", + "rand_centroids.set_lat_lon(lat.flatten(), lon.flatten())\n", + "rf_rand = RiverFlood()\n", + "rf_rand.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC,\n", + " centroids=rand_centroids, ISINatIDGrid=False)\n", + "rf_rand.centroids.plot()\n", + "rf_rand.plot_intensity(event = 0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 12:14:55,218 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 12:14:55,866 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/Climada/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# setting random poits using raster\n", + "min_lat, max_lat, min_lon, max_lon = 45. , 49., 9., 14.\n", + "cent = Centroids()\n", + "cent.set_raster_from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.1)\n", + "rf_rast = RiverFlood()\n", + "rf_rast.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC,\n", + " centroids=cent, ISINatIDGrid=False)\n", + "rf_rast.plot_intensity(event=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating Flooded Area\n", + "The fraction indicates the flooded part of a grid cell. It is possible to calculate the flooded area for each grid cell and for the whole area under consideration \n", + "\n", + "As ISIMIP simulations currently provide yearly data with the maximum event, event and yearly flooded area are the same." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 15:46:07,847 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 15:46:07,890 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/Climada/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 15:46:08,205 - climada.hazard.centroids.centr - INFO - Setting geometry points.\n", + "Total flooded area for year 2000 in Germany:\n", + "2437074832.0380197 m2\n", + "Total flooded area at first event in Germany:\n", + "2437074832.0380197 m2\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#setting river flood\n", + "rf_DEU = RiverFlood()\n", + "rf_DEU.set_from_nc(countries = ['DEU'], years=years, dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC)\n", + "rf_DEU.plot_fraction(event=0, smooth = False)\n", + "# calculating flooded area\n", + "rf_DEU.set_flooded_area()\n", + "print(\"Total flooded area for year \" + str(years[0]) + \" in Germany:\")\n", + "print(str(rf_DEU.fla_annual[0]) + \" m2\")\n", + "\n", + "print(\"Total flooded area at first event in Germany:\")\n", + "print(str(rf_DEU.fla_event[0]) + \" m2\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2019-09-13 10:12:42,648 - climada.hazard.centroids.centr - INFO - Setting geometry points.\n", + "2019-09-13 10:12:42,649 - climada.hazard.centroids.centr - DEBUG - Setting area_pixel 41548 points.\n", + "affected area in each affected centroid and each event:\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 584715.81718072, 5053615.42987189, 584715.81718072,\n", + " 772660.21477247, 4635961.19139216, 4844788.31063191,\n", + " 877073.72577108, 62648.1284788 , 793542.93642074,\n", + " 710012.09844898, 104512.31458989, 7942935.27615562,\n", + " 12980428.99755675, 8862643.59587924, 11015597.79960522,\n", + " 8151960.31900815, 8026545.7605029 , 8110155.46617279,\n", + " 3428003.77254628, 2550100.30565756, 5748176.99827292,\n", + " 10743865.08816197, 10116791.51695982, 167219.69117698,\n", + " 167377.62651458, 2426975.61490712, 7866748.5314332 ,\n", + " 12448711.57332617, 10691246.31833798, 3807841.25590698,\n", + " 795043.75943462, 9958969.25866094, 10858623.66475107,\n", + " 10586635.08712338, 9938046.70065339, 9414992.10340605,\n", + " 4100752.0018361 , 20922.20331432, 4020851.5658597 ,\n", + " 8628077.07461064, 12271973.68427272, 11036399.71240725,\n", + " 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2273638.55111259, 7027610.14485272,\n", + " 2296604.5581947 , 8727097.23558423, 206694.41130696,\n", + " 620083.26065688, 6384560.66322575, 987540.01563454,\n", + " 1377962.77769441, 4409481.05973228, 5833375.63770317,\n", + " 160762.31693474, 2664061.31317241, 2916687.81885159,\n", + " 183728.36412085, 2503299.04970965, 3031518.06815012,\n", + " 1056438.14382486, 2939653.82593355, 6476425.11933014,\n", + " 4478379.08097884, 1377962.77769435, 826777.64522785,\n", + " 2687027.32025439, 5029564.10650142, 45932.09103021,\n", + " 160891.41056458, 10848678.76457667, 436705.28018168,\n", + " 2206510.95693692, 9354686.58283404, 1333100.28954359,\n", + " 3585580.03744784, 1103255.47846846, 436705.28018168,\n", + " 2183526.29387863, 3585580.03744784, 344767.32364264,\n", + " 4527944.15886665, 2987983.50724635, 2022634.93682888,\n", + " 3884378.30254858, 22984.48746242, 137906.93480856,\n", + " 3930347.62866496, 4665851.28097723, 1195193.38149251,\n", + " 551627.73923423, 344767.32364266, 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2099962.43658919, 23076.50876076,\n", + " 5561438.76077762, 1476896.56068868, 2076885.75152954,\n", + " 11953632.16603328, 369224.14017217, 1823044.15012417,\n", + " 8007548.46442689, 276918.12527756, 346423.14080041,\n", + " 5658244.43590979, 3210187.66745817, 3210187.66745801,\n", + " 323328.25040786, 1662831.03282448, 4087792.9646553 ,\n", + " 484992.37561179, 3810654.49503253, 4572785.39403904,\n", + " 14388107.73464195, 577371.88341003, 1570451.57879826,\n", + " 4318741.76103709, 3048523.59602618, 415707.75820612,\n", + " 5450390.90632412, 6674418.96791818, 3325662.06564879,\n", + " 2632815.89159201, 11131730.17885479, 14226443.44812145,\n", + " 8175585.9293106 , 2218870.16200852, 3258965.45627431,\n", + " 901415.93604765, 184905.83338037, 3582550.55369718,\n", + " 254245.52594315, 138679.38512554, 2704247.80814282,\n", + " 346698.44936016, 13428786.49400053, 1479246.66704298,\n", + " 3490097.79172456, 1433020.28605656, 1317454.11833161,\n", + " 3883022.46062701, 8806140.64603732, 7350007.4923756 ,\n", + " 6818402.8194786 , 9846235.50978522, 8783027.02502618,\n", + " 4691985.84996017, 12481144.39222511, 13729258.40093017,\n", + " 12527370.77321098, 10239161.03972273, 138679.38512554,\n", + " 670815.55181263, 3053367.36157104, 3539130.3900809 ,\n", + " 300710.42442097, 208184.13584087, 2775788.54968814,\n", + " 1734867.78969761, 69394.71643507, 3770446.13845983,\n", + " 532026.14587118, 3284683.10995014, 1919920.36685789,\n", + " 2752657.01793615, 2382551.86361565, 693947.13742203,\n", + " 1387894.27484407, 1434157.44606281, 3354077.70520576,\n", + " 12699232.90565248, 1272236.40065454, 2081841.30455133,\n", + " 2428814.92711973, 7656550.36653829, 6800681.96827855,\n", + " 717078.72303141, 23149.8991283 , 3403035.29818859,\n", + " 3310435.64103781, 347248.49871516, 4375330.94367087,\n", + " 902846.06431938, 4954078.74696339, 810246.51496873,\n", + " 578747.47989191, 4143831.90859385, 4467930.81642166,\n", + " 8171914.94645002, 2500189.23386921, 5486526.3982794 ,\n", + " 925996.03250709, 46299.7982566 , 208349.09383908,\n", + " 3266718.48119539, 2919195.18801548, 417027.87629611,\n", + " 671878.24814055, 4957998.37854157, 4934829.77060926,\n", + " 1876625.52424655, 3336223.01036871, 810887.57620102,\n", + " 8386894.52601658, 46336.43032497, 4401960.85052897,\n", + " 3730082.87210176, 46373.03711108, 231865.2024258 ,\n", + " 2921501.41560192, 1159325.93115107, 3918521.55659529,\n", + " 69559.56072774, 347797.79014239, 1669429.3494952 ,\n", + " 2156346.28808562, 6469038.64831591, 12798958.41810963,\n", + " 1808548.44395811, 3895335.0768415 , 69559.56072774,\n", + " 1669429.3494952 , 1738988.89672661, 2133159.80833214,\n", + " 23186.51855554, 23204.80929843, 116024.05493398,\n", + " 835373.14149679, 6195684.08774463, 69614.43296039,\n", + " 3202263.72708018, 5105058.14695586, 46409.61859685,\n", + " 3619950.24380083, 3411107.09349641, 139228.86592078,\n", + " 3643155.0142667 , 7843225.38416032, 348072.151295 ,\n", + " 46409.61859685, 9142695.12358882, 696144.30258996,\n", + " 348346.32255875, 1254046.78283975, 6618580.07454613,\n", + " 1045038.91360584, 441238.6788458 , 464461.78143516,\n", + " 9266012.36390235, 2090077.82721168, 1394481.21519663,\n", + " 2788962.43039327, 5229304.71932662, 7111854.17585799,\n", + " 418344.35373641, 1301515.74312973, 2672755.64442255,\n", + " 2091721.71456907, 3114341.25794985, 3230548.04392058,\n", + " 1882549.67298325, 5024075.05294284, 4748680.79119586,\n", + " 116480.37319707, 4286477.62517144, 163200.01148239,\n", + " 536228.64017519])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#calculate flooded area\n", + "rf_DEU.set_flooded_area(save_centr = True)\n", + "print(\"affected area in each affected centroid and each event:\")\n", + "rf_DEU.fla_ev_centr.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generating ISIMIP Exposure\n", + "The exposed assets are calculated by means of national GDP converted to total national wealth as a proxy for asset distribution, downscaled by means of data from population distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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valuelatitudelongitudeif_RFregion_id
03.556720e+0945.8539168.9373643.011.0
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" + ], + "text/plain": [ + " value latitude longitude if_RF region_id\n", + "0 3.556720e+09 45.853916 8.937364 3.0 11.0\n", + "1 2.400900e+09 45.853916 8.979031 3.0 11.0\n", + "2 2.250146e+08 45.853916 9.020698 3.0 11.0\n", + "3 2.939545e+08 45.895583 7.104034 3.0 11.0\n", + "4 3.476220e+08 45.895583 7.145701 3.0 11.0\n", + "5 1.224757e+08 45.895583 7.187367 3.0 11.0\n", + "6 2.287360e+07 45.895583 7.229034 3.0 11.0\n", + "7 3.556720e+09 45.895583 8.937364 3.0 11.0\n", + "8 2.400900e+09 45.895583 8.979031 3.0 11.0\n", + "9 2.250146e+08 45.895583 9.020698 3.0 11.0\n", + "10 1.794207e+08 45.895583 9.062364 3.0 11.0\n", + "11 4.880104e+07 45.937249 7.062367 3.0 11.0\n", + "12 2.939545e+08 45.937249 7.104034 3.0 11.0\n", + "13 3.476220e+08 45.937249 7.145701 3.0 11.0\n", + "14 1.224757e+08 45.937249 7.187367 3.0 11.0\n", + "15 2.287360e+07 45.937249 7.229034 3.0 11.0\n", + "16 1.839505e+07 45.937249 7.270700 3.0 11.0\n", + "17 5.233581e+06 45.937249 7.312367 3.0 11.0\n", + "18 1.293465e+07 45.937249 7.354034 3.0 11.0\n", + "19 1.168229e+07 45.937249 7.395700 3.0 11.0\n", + "20 5.846366e+06 45.937249 7.437367 3.0 11.0\n", + "21 3.556720e+09 45.937249 8.937364 3.0 11.0\n", + "22 2.400900e+09 45.937249 8.979031 3.0 11.0\n", + "23 2.250146e+08 45.937249 9.020698 3.0 11.0\n", + "24 4.880104e+07 45.978916 7.062367 3.0 11.0\n", + "25 2.939545e+08 45.978916 7.104034 3.0 11.0\n", + "26 3.476220e+08 45.978916 7.145701 3.0 11.0\n", + "27 1.224757e+08 45.978916 7.187367 3.0 11.0\n", + "28 2.287360e+07 45.978916 7.229034 3.0 11.0\n", + "29 1.839505e+07 45.978916 7.270700 3.0 11.0\n", + "... ... ... ... ... ...\n", + "2730 2.581580e+08 47.645580 8.979031 3.0 11.0\n", + "2731 2.610993e+08 47.645580 9.020698 3.0 11.0\n", + "2732 3.352810e+08 47.645580 9.062364 3.0 11.0\n", + "2733 4.556854e+08 47.645580 9.104031 3.0 11.0\n", + "2734 2.038779e+09 47.645580 9.145697 3.0 11.0\n", + "2735 3.144292e+09 47.645580 9.187364 3.0 11.0\n", + "2736 1.268677e+09 47.645580 9.229031 3.0 11.0\n", + "2737 1.174246e+07 47.645580 9.270697 3.0 11.0\n", + "2738 6.597677e+08 47.687246 8.437365 3.0 11.0\n", + "2739 8.684369e+08 47.687246 8.479032 3.0 11.0\n", + "2740 2.973245e+08 47.687246 8.520698 3.0 11.0\n", + "2741 1.012704e+09 47.687246 8.562365 3.0 11.0\n", + "2742 2.280473e+09 47.687246 8.604032 3.0 11.0\n", + "2743 2.067377e+09 47.687246 8.645698 3.0 11.0\n", + "2744 4.591594e+08 47.687246 8.687365 3.0 11.0\n", + "2745 6.352261e+08 47.687246 8.729031 3.0 11.0\n", + "2746 3.265897e+08 47.687246 8.812365 3.0 11.0\n", + "2747 6.205473e+08 47.687246 8.854031 3.0 11.0\n", + "2748 2.561842e+08 47.687246 9.020698 3.0 11.0\n", + "2749 5.782308e+08 47.728913 8.479032 3.0 11.0\n", + "2750 2.768114e+08 47.728913 8.520698 3.0 11.0\n", + "2751 3.711769e+08 47.728913 8.562365 3.0 11.0\n", + "2752 1.095256e+09 47.728913 8.604032 3.0 11.0\n", + "2753 2.452124e+09 47.728913 8.645698 3.0 11.0\n", + "2754 8.220082e+08 47.728913 8.687365 3.0 11.0\n", + "2755 1.935802e+08 47.770580 8.520698 3.0 11.0\n", + "2756 1.665060e+08 47.770580 8.562365 3.0 11.0\n", + "2757 2.254221e+08 47.770580 8.604032 3.0 11.0\n", + "2758 4.107628e+08 47.770580 8.645698 3.0 11.0\n", + "2759 6.403091e+08 47.770580 8.687365 3.0 11.0\n", + "\n", + "[2760 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set exposure for damage calculation\n", + "from climada.entity.exposures.gdp_asset import GDP2Asset\n", + "from climada.util.constants import DEMO_GDP2ASSET\n", + "gdpa = GDP2Asset()\n", + "gdpa.set_countries(countries = ['CHE'], ref_year = 2000, path=DEMO_GDP2ASSET)\n", + "gdpa" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/Climada/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import colors\n", + "norm=colors.LogNorm(vmin=1.0e2, vmax=1.0e10)\n", + "gdpa.plot_scatter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting JRC damage functions" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# import impact function set for RiverFlood using JRC damage functions () for 6 regions\n", + "from climada.entity.impact_funcs.river_flood import IFRiverFlood,flood_imp_func_set\n", + "if_set = flood_imp_func_set()\n", + "if_AFR = if_set.get_func(fun_id=1)\n", + "if_AFR[0].plot()\n", + "if_EUR = if_set.get_func(fun_id=3)\n", + "if_EUR[0].plot()\n", + "if_OCE = if_set.get_func(fun_id=6)\n", + "if_OCE[0].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deriving flood impact" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-06-24 13:11:29,593 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/flddph_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 13:11:29,610 - climada.util.coordinates - INFO - Reading /home/insauer/Climada/climada_python/data/demo/fldfrc_WaterGAP2_miroc5_historical_flopros_gev_picontrol_2000_0.1.nc\n", + "2020-06-24 13:11:32,428 - climada.entity.exposures.base - INFO - Matching 2760 exposures with 5390 centroids.\n", + "2020-06-24 13:11:32,430 - climada.engine.impact - INFO - Calculating damage for 2577 assets (>0) and 1 events.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/insauer/Climada/climada_python/climada/util/plot.py:311: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n", + "/home/insauer/anaconda3/envs/climada_env/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:704: UserWarning: Attempting to set identical left == right == 8.020699199999967 results in singular transformations; automatically expanding.\n", + " self.set_xlim([x1, x2])\n", + "/home/insauer/anaconda3/envs/climada_env/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:705: UserWarning: Attempting to set identical bottom == top == 47.478913399999996 results in singular transformations; automatically expanding.\n", + " self.set_ylim([y1, y2])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.engine import Impact\n", + "\n", + "rf_CHE = RiverFlood()\n", + "rf_CHE.set_from_nc(dph_path=HAZ_DEMO_FLDDPH, frc_path=HAZ_DEMO_FLDFRC,\n", + " countries = ['CHE']) \n", + "gdpa = GDP2Asset()\n", + "gdpa.set_countries(countries=['CHE'], ref_year=2000, path = DEMO_GDP2ASSET)\n", + "if_set = flood_imp_func_set()\n", + "imp=Impact()\n", + "imp.calc(gdpa, if_set,rf_CHE,save_mat=True)\n", + "rf_CHE.plot_intensity(0)\n", + "imp.plot_scatter_eai_exposure()" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/tutorial/climada_hazard_drought.py b/doc/tutorial/climada_hazard_drought.py index 9509016da8..ffa96f75f5 100644 --- a/doc/tutorial/climada_hazard_drought.py +++ b/doc/tutorial/climada_hazard_drought.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # coding: utf-8 -from climada.hazard.drought import Drought +from climada.hazard.drought import Drought from climada.entity.impact_funcs.drought import IFDrought # from climada.entity import Entity @@ -10,13 +10,13 @@ from climada.entity.exposures.spam_agrar import SpamAgrar import numpy as np -""" Set Area to be analysed""" +"""Set Area to be analysed""" """Set method for defining intensity between 1 (default): min 2:sum-threshold 3:sum""" intensity_definition = 3 -""" Initialize default threshold (default: -1)""" +"""Initialize default threshold (default: -1)""" threshold = -1.5 #Threshold and intensity_definition to be defined only if not defalut values are used @@ -44,13 +44,13 @@ #d.set_file_name(spei_file_name) #d.set_file_url(spei_file_url) -"""Set path if the data are not in 'climada_python\data\system' """ +"""Set path if the data are not in 'climada_python\data\system'""" #d.set_file_path(file_path_spei) """Setup the hazard""" new_haz = d.setup() -"""Plot intensity of one year event""" +"""Plot intensity of one year event""" # new_haz.plot_intensity_drought(event='2003') """Initialize Impact function""" @@ -67,7 +67,7 @@ exposure_agrar.init_spam_agrar(country='CHE') """If intensity def is not default, exposure has to be adapted""" -"""In case of sum-thr: 'if_DR_sumthr', in case of sum:'if_DR_sum' """ +"""In case of sum-thr: 'if_DR_sumthr', in case of sum:'if_DR_sum'""" #exposure_agrar['if_DR_sumthr'] = np.ones(exposure_agrar.shape[0]) exposure_agrar['if_DR_sum'] = np.ones(exposure_agrar.shape[0]) diff --git a/doc/tutorial/climada_hazard_emulator.ipynb b/doc/tutorial/climada_hazard_emulator.ipynb new file mode 100644 index 0000000000..37f5174f51 --- /dev/null +++ b/doc/tutorial/climada_hazard_emulator.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hazard Emulator\n", + "===============\n", + "\n", + "Given a database of hazard events, the subpackage `climada.hazard.emulator` provides tools to sample time series of events according to a climate scenario in a specific georegion.\n", + "\n", + "The given event database is supposed to be divided into a (smaller) set of observed hazard events and a (much larger) set of simulated hazard events. The database of observed events is used to statistically fit the frequency and intensity of events in a fixed georegion to (observed) climate indices. Then, given a hypothetical (future) time series of these climate indices (a \"climate scenario\"), a \"hazard emulator\" can draw random samples from the larger database of simulated hazard events that mimic the expected occurrence of events under the given climate scenario in the specified georegion.\n", + "\n", + "The concept and algorithm as applied to tropical cyclones is originally due to Tobias Geiger (unpublished as of now) and has been generalized within this package by Thomas Vogt.\n", + "\n", + "This notebook illustrates the functionality through the example of tropical cyclones in the Pacific Ocean under the RCP 2.6 climate scenario according to the MIROC5 global circulation model (GCM).\n", + "\n", + "### About the input data used for this notebook\n", + "\n", + "For historical reasons, this example loads tropical cyclone windfields that have been\n", + "precomputed with the old MATLAB version of CLIMADA. However, the computation can be done with\n", + "current Python-based versions of CLIMADA, as well. Since windfield computation is quite time-consuming,\n", + "the windfield computation is not part of this notebook, but precomputed windfields are used.\n", + "\n", + "The example is based on simulated TC tracks provided by Kerry Emanuel for ISIMIP (version 2b).\n", + "The tracks and precomputed windfields are placed in the following directories:\n", + "```\n", + "$CLIMADA_DIR/data/emulator/tracks/*.mat\n", + "$CLIMADA_DIR/data/emulator/windfields/*.mat\n", + "```\n", + "\n", + "Precomputed windfields for the IBTrACS TCs are in\n", + "```\n", + "$CLIMADA_DIR/data/emulator/windfields/GLB_0360as_hazard_1950-2015.mat\n", + "```\n", + "\n", + "The climate index time series for the different GCMs and RCPs should be available in\n", + "```\n", + "$CLIMADA_DIR/data/emulator/climate_index/*.csv\n", + "```\n", + "\n", + "Accordingly, we define an input data directory as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:01,780 - climada - DEBUG - Loading default config file: /home/tovogt/code/climada_python/climada/conf/defaults.conf\n" + ] + } + ], + "source": [ + "import os\n", + "from climada.util.constants import DATA_DIR\n", + "EMULATOR_DATA_DIR = os.path.join(DATA_DIR, \"emulator\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the input data\n", + "First, we choose the georegion of interest: a TC ocean basin (Eastern North Pacific). Only hazard intensities observable within this region will be loaded:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.hazard.emulator.geo import TCRegion\n", + "reg = TCRegion(tc_basin=\"EP\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the database of observed events which is made up of IBTrACS storms within a known reliable time period:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:04,652 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/GLB_0360as_hazard_1950-2015.mat\n", + "2020-08-07 09:52:06,129 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/GLB_0360as_hazard_1950-2015.mat\n" + ] + } + ], + "source": [ + "import datetime as dt\n", + "import numpy as np\n", + "import shapely\n", + "from climada.hazard import TropCyclone, TCTracks\n", + "from climada.hazard.base import DEF_VAR_MAT\n", + "from climada.hazard.emulator.const import TC_BASIN_NORM_PERIOD\n", + "\n", + "def _ibtracs_id2meta(id_int):\n", + " \"\"\"Derive storm meta data from ibtracs storm ID (int)\"\"\"\n", + " id_str = str(int(id_int))\n", + " hemisphere = 'N' if id_str[7] == '0' else 'S'\n", + " id_str = id_str[:7] + hemisphere + id_str[8:]\n", + " year = int(id_str[:4])\n", + " days = int(id_str[4:7])\n", + " date = dt.datetime(year, 1, 1) + dt.timedelta(days - 1)\n", + " return (id_str, year, date.month, date.day, hemisphere)\n", + "\n", + "def ibtracs_windfields(region, period=None):\n", + " \"\"\"Load subset of precomputed windfields for ibtracs TCs (1950-2015)\n", + "\n", + " Parameters\n", + " ----------\n", + " region : TCRegion object\n", + " The geographical region to consider.\n", + " period : pair of ints (minyear, maxyear)\n", + " First and last year to consider.\n", + "\n", + " Returns\n", + " -------\n", + " windfields : climada.hazard.TropCyclone object\n", + " \"\"\"\n", + " var_names = DEF_VAR_MAT\n", + " var_names['var_name']['even_id'] = \"ID_no\"\n", + "\n", + " fname = 'GLB_0360as_hazard_1950-2015.mat'\n", + " path = os.path.join(EMULATOR_DATA_DIR, \"windfields\", fname)\n", + " windfields = TropCyclone()\n", + " windfields.read_mat(path, var_names=var_names)\n", + " ibtracs_meta = [_ibtracs_id2meta(i) for i in windfields.event_id]\n", + " dates = [dt.date(*m[1:4]).toordinal() for m in ibtracs_meta]\n", + " windfields.date = np.array(dates, dtype=np.int64)\n", + " windfields.event_name = [m[0] for m in ibtracs_meta]\n", + " windfields.event_id = np.arange(len(ibtracs_meta))\n", + "\n", + " # identify centroids in specified region\n", + " lat, lon = windfields.centroids.lat, windfields.centroids.lon\n", + " windfields.centroids.region_id \\\n", + " = shapely.vectorized.contains(region.shape, lon, lat)\n", + "\n", + " # select windfields in specified period and region\n", + " if period is not None:\n", + " period = [f\"{period[0]}-01-01\", f\"{period[0]}-12-31\"]\n", + " windfields = windfields.select(date=period, reg_id=1)\n", + "\n", + " return windfields\n", + "\n", + "def precompute_ibtracs_windfields():\n", + " \"\"\"This is how you would precompute the IBTrACS windfields in climada_python\"\"\"\n", + " tracks = TCTracks()\n", + " tracks.read_ibtracs_netcdf(year_range=(1950, 2019), estimate_missing=True)\n", + " tracks.equal_timestep(time_step_h=1)\n", + " fname = 'GLB_0360as_hazard_1950-2019.hdf5'\n", + " path = os.path.join(EMULATOR_DATA_DIR, \"windfields\", fname)\n", + " windfields = TropCyclone()\n", + " windfields.set_from_tracks(tracks)\n", + " windfields.write_hdf5(path)\n", + "\n", + "norm_period = TC_BASIN_NORM_PERIOD[reg.tc_basin[:2]]\n", + "windfields_obs = ibtracs_windfields(reg, period=norm_period)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a database of simulated TC events we use the TC tracks provided by Kerry Emanuel for ISIMIP2b:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:09,031 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_20thcal_N_0360as.mat\n", + "2020-08-07 09:52:09,802 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_20thcal_N_0360as.mat\n", + "2020-08-07 09:52:13,037 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat\n", + "2020-08-07 09:52:14,463 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat\n", + "2020-08-07 09:52:20,945 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat\n", + "2020-08-07 09:52:22,863 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat\n", + "2020-08-07 09:52:29,945 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat\n", + "2020-08-07 09:52:31,705 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat\n", + "2020-08-07 09:52:41,342 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_20thcal_N_0360as.mat\n", + "2020-08-07 09:52:42,160 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_20thcal_N_0360as.mat\n", + "2020-08-07 09:52:45,797 - climada.hazard.base - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat\n", + "2020-08-07 09:52:47,438 - climada.hazard.centroids.centr - INFO - Reading /home/tovogt/code/climada_python/data/emulator/windfields/Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "def emanuel_meta():\n", + " meta_path = os.path.join(EMULATOR_DATA_DIR, \"emanuel_fnames.csv\")\n", + " if os.path.exists(meta_path):\n", + " return pd.read_csv(meta_path)\n", + "\n", + " pattern = \"(temp_|Trial(?P[0-9])_GB_dk)\" \\\n", + " \"(?P[0-9a-z]+)_?\" \\\n", + " \"((?PpiControl|20th|rcp[0-9]{2})cal)(|_full)_\" \\\n", + " \"(?PN|S)_0360as\\.mat\"\n", + " prog = re.compile(pattern)\n", + " df = []\n", + " for path in glob.glob(os.path.join(EMULATOR_DATA_DIR, \"windfields\", \"*.mat\")):\n", + " fname = os.path.basename(path)\n", + " m = prog.match(fname)\n", + " try:\n", + " haz = h5py.File(path, \"r\")['hazard']\n", + " except OSError:\n", + " continue\n", + " is_rcp85 = \"rcp85\" if m.group(\"trial\") is None else m.group(\"rcp\")\n", + " df.append({\n", + " \"basename\": fname[:-13],\n", + " \"windfield_fname\": fname,\n", + " \"minyear\": int(haz['yyyy'][0,0]),\n", + " \"maxyear\": int(haz['yyyy'][-1,0]),\n", + " \"gcm\": gcm_trans_inv(m.group(\"gcm\"), is_rcp85),\n", + " \"rcp\": m.group(\"rcp\"),\n", + " \"hemisphere\": m.group(\"hemisphere\"),\n", + " \"trial\": 0 if is_rcp85 == \"rcp85\" else int(m.group(\"trial\")),\n", + " \"tracks_per_year\": 600 if is_rcp85 == \"rcp85\" else 300,\n", + " })\n", + " cols = [\"basename\", \"windfield_fname\", \"minyear\", \"maxyear\",\n", + " \"gcm\", \"rcp\", \"hemisphere\", \"trial\", \"tracks_per_year\"]\n", + " df = pd.DataFrame(df, columns=cols)\n", + " df = df.sort_values(by=[\"gcm\", \"rcp\", \"minyear\", \"hemisphere\"])\n", + " df.to_csv(meta_path, index=None)\n", + " return df\n", + "\n", + "def emanuel_windfields(region, gcm=None, rcp=None, period=None, trial=None):\n", + " \"\"\" Load pre-calculated windfields for simulated storm tracks\n", + "\n", + " Parameters\n", + " ----------\n", + " region : TCRegion object\n", + " The geographical region to consider. This is not optional since\n", + " windfields are separated by hemisphere.\n", + " gcm : list of str, optional\n", + " Name of GCMs, such as \"MPI-ESM-MR\".\n", + " rcp : list of str, optional\n", + " Name of RCPs, such as \"rcp26\". The historical data (\"20th\") doesn't need\n", + " to be selected explicitly.\n", + " period : pair of ints (minyear, maxyear), optional\n", + " First and last year to consider.\n", + " trial : list of int, optional\n", + " Trials to include in the selection. By default, 2 and 3 are excluded\n", + " and 0 is only used for rcp85.\n", + "\n", + " Returns\n", + " -------\n", + " windfields : climada.hazard.TropCyclone object\n", + " \"\"\"\n", + " meta = emanuel_meta()\n", + " meta = meta[meta['hemisphere'] == region.hemisphere]\n", + "\n", + " if trial is None:\n", + " trial = [1, 4]\n", + " if rcp is not None and \"rcp85\" in rcp:\n", + " trial.append(0)\n", + " meta = meta[meta['trial'].isin(trial)]\n", + "\n", + " if gcm is not None:\n", + " meta = meta[meta['gcm'].isin(gcm)]\n", + "\n", + " if rcp is not None:\n", + " meta = meta[(meta['rcp'] == '20th') | meta['rcp'].isin(rcp)]\n", + "\n", + " # intersection with specified period\n", + " if period is not None:\n", + " meta = meta[(period[0] <= meta['maxyear']) & (meta['minyear'] <= period[1])]\n", + "\n", + " if meta.shape[0] == 0:\n", + " raise Exception(\"Given gcm/rcp/period matches no trials!\")\n", + "\n", + " hazards = []\n", + " for idx, row in meta.iterrows():\n", + " fname = row['windfield_fname']\n", + " path = os.path.join(EMULATOR_DATA_DIR, \"windfields\", fname)\n", + " haz = TropCyclone()\n", + " haz.read_mat(path)\n", + " haz.event_name = [f\"{fname}-{n}\" for n in haz.event_name]\n", + " # some datasets include centroids beyond 60° that are irrelevant for TC hazards\n", + " cutidx = 901186 if region.hemisphere == 'N' else 325229\n", + " haz.centroids.region_id = np.zeros_like(haz.centroids.lat)\n", + " haz.centroids.region_id[:cutidx] = 1\n", + " haz = haz.select(reg_id=1)\n", + " hazards.append(haz)\n", + " windfields = TropCyclone()\n", + " windfields.concatenate(hazards)\n", + "\n", + " # identify centroids in specified region\n", + " lat, lon = windfields.centroids.lat, windfields.centroids.lon\n", + " windfields.centroids.region_id \\\n", + " = shapely.vectorized.contains(region.shape, lon, lat)\n", + "\n", + " # select windfields in specified period and region\n", + " if period is not None:\n", + " period = (f\"{period[0]}-01-01\", f\"{period[1]}-12-31\")\n", + " windfields = windfields.select(date=period, reg_id=1)\n", + "\n", + " return windfields\n", + "\n", + "# one database for sampling, and one for the statistical calibration (bias correction) according to the chosen climate scenario:\n", + "windfields_pool = emanuel_windfields(reg, gcm=[\"MIROC5\"], period=(1950, 2100))\n", + "windfields_rcp = emanuel_windfields(reg, gcm=[\"MIROC5\"], rcp=[\"rcp26\"], period=(1950, 2015))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract events that affect the region of interest\n", + "From the Hazard objects, we extract those events that actually \"affect\" the georegion of interest and store for each the maximum intensity observed within the region:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:55,232 - climada.hazard.emulator.stats - INFO - Condensing 5688 hazards to 316 max events ...\n", + "2020-08-07 09:52:56,148 - climada.hazard.emulator.stats - INFO - Condensing 58401 hazards to 15474 max events ...\n", + "2020-08-07 09:52:56,908 - climada.hazard.emulator.stats - INFO - Condensing 10908 hazards to 2921 max events ...\n" + ] + } + ], + "source": [ + "from climada.hazard.emulator.stats import haz_max_events\n", + "\n", + "# for this example, we regard regions as `affected` if they face at least 34 knots wind speeds\n", + "KNOTS_2_MS = 0.514444\n", + "MIN_WIND_KT = 34\n", + "MIN_WIND_MS = MIN_WIND_KT * KNOTS_2_MS\n", + "\n", + "tc_events_obs = haz_max_events(windfields_obs, min_thresh=MIN_WIND_MS)\n", + "tc_events_pool = haz_max_events(windfields_pool, min_thresh=MIN_WIND_MS)\n", + "tc_events_rcp = haz_max_events(windfields_rcp, min_thresh=MIN_WIND_MS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the simulated TC tracks in ISIMIP we can extract a time series of expected global annual TC frequencies under the given RCP:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "\n", + "def emanuel_frequency_normalization(gcm, rcp, period):\n", + " \"\"\" Frequency normalization factors for given GCM and RCP, in 1950-2100\n", + "\n", + " Parameters\n", + " ----------\n", + " gcm : str\n", + " Name of GCM, such as \"MPI-ESM-MR\".\n", + " rcp : str\n", + " Name of RCP, such as \"rcp26\".\n", + " period : pair of ints (minyear, maxyear)\n", + " First and last year to consider.\n", + "\n", + " Returns\n", + " -------\n", + " freq_norm : DataFrame { year, freq }\n", + " Information about the relative surplus of simulated events, i.e.,\n", + " if `freq_norm` specifies the value 0.2 in some year, then it is\n", + " assumed that the number of events simulated for that year is 5 times as\n", + " large as it is estimated to be.\n", + " \"\"\"\n", + " meta = emanuel_meta()\n", + " meta = meta[meta['hemisphere'] == 'N']\n", + " meta = meta[(meta['trial'] != 2) & (meta['trial'] != 3)]\n", + " if rcp != \"rcp85\":\n", + " meta = meta[meta['trial'] != 0]\n", + " meta = meta[(meta['gcm'] == gcm)]\n", + " meta = meta[(meta['rcp'] == '20th') | (meta['rcp'] == rcp)]\n", + " freq = []\n", + " for idx, row in meta.iterrows():\n", + " path = os.path.join(EMULATOR_DATA_DIR, \"tracks\", f\"{row['basename']}.mat\")\n", + " tracks = scipy.io.loadmat(path, variable_names=['yearstore', 'freqyear'])\n", + " freq.append(pd.DataFrame({\n", + " 'year': np.unique(tracks['yearstore'].ravel()),\n", + " 'freq': tracks['freqyear'].ravel() / row['tracks_per_year'],\n", + " }))\n", + " freq = pd.concat(freq, ignore_index=True)\n", + " freq = freq[(period[0] <= freq['year']) & (freq['year'] <= period[1])]\n", + " freq = freq.sort_values(by=[\"year\"]).reset_index(drop=True)\n", + " return freq\n", + "\n", + "freq = emanuel_frequency_normalization(\"MIROC5\", \"rcp26\", (1950, 2015))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize and calibrate the hazard emulator\n", + "We have all data that is required to set up a hazard emulator:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:57,405 - climada.hazard.emulator.random - INFO - Results of intensity normalization by subsampling:\n", + "2020-08-07 09:52:57,405 - climada.hazard.emulator.random - INFO - - drop 66% of entries satisfying 'intensity > 37.89053267580431'\n", + "2020-08-07 09:52:57,406 - climada.hazard.emulator.random - INFO - - mean intensity of simulated events before dropping is 37.8905\n", + "2020-08-07 09:52:57,406 - climada.hazard.emulator.random - INFO - - mean intensity of simulated events after dropping is 33.1730\n", + "2020-08-07 09:52:57,406 - climada.hazard.emulator.random - INFO - - mean intensity of observed events is 32.5577\n" + ] + } + ], + "source": [ + "from climada.hazard.emulator.emulator import EventPool, HazardEmulator\n", + "em = HazardEmulator(tc_events_rcp, tc_events_obs, reg, freq, pool=EventPool(tc_events_pool))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We calibrate the emulator, i.e., we determine a statistical connection between climate indices (GMT and ENSO in this example) and `tc_events_rcp`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def climate_index(gcm, rcp, index, running_mean=21):\n", + " \"\"\" Load time series of a climate index (e.g. GMT) for a given GCM/RCP\n", + "\n", + " The time period is 1861-2100 (1861-2299 for rcp26)\n", + "\n", + " The data is concatenated from historical and future datasets, applying a\n", + " 21-year running mean in the case of GMT-based indices.\n", + "\n", + " CAUTION: For the running mean, the data is *extended* at the edges by\n", + " repeating the edge values; thereby any trend present in the data will\n", + " become attenuated at the edges!\n", + "\n", + " GMT data is relative to piControl mean over 500 year reference period.\n", + "\n", + " Parameters\n", + " ----------\n", + " gcm : str\n", + " Name of GCM, such as \"MPI-ESM-MR\".\n", + " rcp : str\n", + " Name of RCP, such as \"rcp26\".\n", + " index : str\n", + " Name of index, one of [\"gmt\", \"gmtTR\", \"esoi\", \"nao\", \"nino34\", \"pdo\"],\n", + "\n", + " GMT : Global mean (surface) temperature\n", + " GMT TR : GMT in the tropics, between -30 and +30 degrees latitude\n", + " ESOI : El Nino southern oscillation index\n", + " NAO : North Atlantic Oscillation\n", + " NINO34 : Nino 3.4 sea surface temperature index\n", + " PDO : Pacific decadal oscillation\n", + " running_mean : int\n", + " For GMT data, the running mean period. Defaults to 21.\n", + "\n", + " Returns\n", + " -------\n", + " ci : DataFrame { year, month, `index` }\n", + " Monthly data of given climate index.\n", + " \"\"\"\n", + " index_path = os.path.join(EMULATOR_DATA_DIR, \"climate_index\")\n", + " base_min, base_max, avg_interval = ({\n", + " 'gmt': (1971, 2000, ''),\n", + " 'gmtTR': (1971, 2000, ''),\n", + " 'esoi': (1950, 1979, '_3m'),\n", + " 'nao': (1950, 1979, '_3m'),\n", + " 'nino34': (1950, 1979, '_3m'),\n", + " 'pdo': (1971, 2000, ''),\n", + " })[index]\n", + "\n", + " allmin = 1861\n", + " allmax = 2299 if rcp == 'rcp26' else 2100\n", + "\n", + " ci = pd.DataFrame()\n", + " periods = [('historical', allmin, 2005), (rcp, 2006, allmax)]\n", + " for pname, minyear, maxyear in periods:\n", + " fname = f\"{index}-index_monthly_{gcm}-{pname}_{minyear}-{maxyear}\" \\\n", + " f\"_base-{base_min}-{base_max}{avg_interval}.csv\"\n", + " path = os.path.join(index_path, fname)\n", + " if index == 'pdo':\n", + " tmp = pd.read_csv(path, delim_whitespace=True, skiprows=1, header=None)\n", + " cols = ['time', 'pdo']\n", + " tmp.columns = cols\n", + " else:\n", + " tmp = pd.read_csv(path)\n", + " if 'Unnamed: 0' in tmp.columns:\n", + " del tmp['Unnamed: 0']\n", + " ci = ci.append(tmp).reset_index(drop=True)\n", + " year_month_day = ci['time'].str.split(\"-\", expand=True)\n", + " ci['year'] = year_month_day[0].astype(int)\n", + " ci['month'] = year_month_day[1].astype(int)\n", + " ci = ci.drop(labels=['time'], axis=1)\n", + " if ci['year'].max() == 2099:\n", + " ci2100 = ci[ci['year'] == 2099]\n", + " ci2100['year'] = 2100\n", + " ci = ci.append(ci2100)\n", + "\n", + " if index in ['gmt', 'gmtTR']:\n", + " # define GMT change wrt piControl mean and apply running mean\n", + " minyear, maxyear = ({\n", + " 'GFDL-ESM2M': (1, 500),\n", + " 'IPSL-CM5A-LR': (1800, 2299),\n", + " 'MIROC5': (2000, 2499),\n", + " 'HadGEM2-ES': (1900, 2399),\n", + " })[gcm]\n", + " fname = f\"{index}-index_monthly_{gcm}-piControl_{minyear}-{maxyear}\" \\\n", + " f\"_base-{minyear}-{maxyear}.csv\"\n", + " path = os.path.join(index_path, fname)\n", + " ci[index] = ci[index] - pd.read_csv(path)['gmt'].mean()\n", + "\n", + " N = running_mean\n", + " halfwin = N // 2\n", + " padded_data = np.pad(ci[index].to_numpy(), halfwin, mode='edge')\n", + " ci[index] = np.convolve(padded_data, np.ones(N)/N, mode='valid')\n", + "\n", + " return ci\n", + "\n", + "ci = [climate_index(\"MIROC5\", \"rcp26\", ci) for ci in [\"gmt\", \"esoi\"]]\n", + "em.calibrate_statistics(ci)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the emulator is calibrated, we use GMT and ENSO time series to predict TC statistics under the chosen climate scenario:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:57,505 - climada.hazard.emulator.emulator - INFO - Predicting TCs with new climate index dataset...\n" + ] + } + ], + "source": [ + "em.predict_statistics(ci)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Draw samples according to climate scenario\n", + "The emulator can now be used to sample hypothetical events within an arbitrary time period covered by the climate index time series used above:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 09:52:57,527 - climada.hazard.emulator.emulator - INFO - Drawing 100 realizations for period (2020, 2050)\n", + "2020 ... 2050 ... 2050\n" + ] + } + ], + "source": [ + "draws = em.draw_realizations(100, (2020, 2050))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned object `draws` is a `DataFrame` with each row corresponding to a storm event from the hazard pool `windfields_pool` (see above): The column `real_id` assigns one of 100 realizations to each of the events while the columns `id` and `name` are the unique ID and name used in `windfields_pool` to identify this hazard event. The column `year` indicates the year in which the event would occur under the hypothetical climate scenario and will usually differ from the date associated with the event in `windfields_pool`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id name year real_id\n", + "0 29344 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-6076 2020 0\n", + "1 28893 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-5272 2020 0\n", + "2 50512 Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat-14879 2020 0\n", + "3 210 Trial1_GB_dkmiroc_20thcal_N_0360as.mat-328 2020 0\n", + "4 30111 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-7503 2020 0\n", + "5 27807 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-3499 2020 0\n", + "6 25513 Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat-27994 2020 0\n", + "7 8970 Trial1_GB_dkmiroc_20thcal_N_0360as.mat-16451 2020 1\n", + "8 41882 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-28187 2020 1\n", + "9 33033 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-12694 2020 1\n", + "10 13170 Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat-7212 2020 1\n", + "11 37879 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-21292 2020 1\n", + "12 1252 Trial1_GB_dkmiroc_20thcal_N_0360as.mat-2161 2020 1\n", + "13 20287 Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat-19475 2020 2\n", + "14 30980 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-9074 2020 2\n", + "15 32587 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-11892 2020 2\n", + "16 56887 Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat-25691 2020 2\n", + "17 42985 Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat-1651 2020 2\n", + "18 54592 Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat-21784 2020 2\n", + "19 2511 Trial1_GB_dkmiroc_20thcal_N_0360as.mat-4481 2020 3\n", + "20 4082 Trial1_GB_dkmiroc_20thcal_N_0360as.mat-7410 2020 3\n", + "21 9685 Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat-960 2020 3\n", + "22 50617 Trial1_GB_dkmiroc_rcp85cal_N_0360as.mat-15050 2020 3\n", + "23 20518 Trial1_GB_dkmiroc_rcp26cal_N_0360as.mat-19822 2020 3\n", + "24 41643 Trial1_GB_dkmiroc_rcp60cal_N_0360as.mat-27806 2020 3\n" + ] + } + ], + "source": [ + "print(draws[:25])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/tutorial/climada_hazard_entity_Crop.ipynb b/doc/tutorial/climada_hazard_entity_Crop.ipynb new file mode 100644 index 0000000000..cec501d63d --- /dev/null +++ b/doc/tutorial/climada_hazard_entity_Crop.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crop production risk based on ISIMIP and FAO data\n", + "\n", + "\n", + "## Summary\n", + "\n", + "This tutorial gives an overview of the modules in CLIMADA used to compute climate related risks to crop production on a 0.5° grid. The risk calculation is based on yearly crop yield as simulated by global gridded crop models (GGCMs) that are forced with climate variables such as temperature and water availability provided from climate model output or re-analysis data.\n", + "\n", + "The hazard *relative_cropyield* is not a typical natural hazard but rather an aggregation of climatic impacts on the crop yield at each location. *relative_cropyield* intensi is equal to the change in crop_yield in a given year from the long-term averageIt is based on the crop yield simulated by GGCMs. In the CLIMADA framework, we are setting relative_cropyield as “hazard” because it describes the year-by-year climatic influence on agriculture, each year representing one event. This hazard can be applied on any exposure that represents a (mean) amount of crop produced at any location.\n", + "\n", + "Here, we use the exposure *crop_production* (in tonnes or USD per year) that distributes national crop production as extracted from FAO statistics proportional to a gridded distribution of crop production that is based on mean crop yield in tonnes per hectare multiplied with the hectares of harvest area per grid cell.\n", + "\n", + "### Example calculation:\n", + "\n", + "At a certain grid cell, the area fraction for non-irrigated rice is 5% and the area of the grid cell is 250,000 ha with an average historical crop yield of 6 tonnes per ha and year.\n", + "\n", + "The exposure (*crop_production*) value is the product of area fraction, area, and average yield = $0.05 * 250,000 ha * 6 t/(ha*y) = 75,000 t/y$.\n", + "\n", + "The hazard intensity at this grid cell for non-irrigated rice in a certain year is -20% (*relative_cropyield*), caused by climate variables such as temperature and water availability during that year.\n", + "\n", + "For this specific year, the impact as computed with Impact.calc is the product of hazard and exposure: $75,000 t/y * (-0.20) * 1 y = -15,000 t$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "ISIMIP", + "FAO", + "Crop", + "Agriculture" + ] + }, + "source": [ + "## Data sources\n", + "\n", + "The two classes *RelativeCropyield(Hazard)* and *CropProduction(Exposure)* can be combined to calculate climate impacts on crop production based on simulations data from global gridded crop models (GGCMs) within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, https://www.isimip.org/) as well as statistics from the Food and Agriculture Organization of the United Nations (FAO, http://www.fao.org/faostat/en/#home).\n", + "\n", + "\n", + "From the ISIMIP project, a variety of model runs with yearly crop yield data on a spatial resolution of 0.5° x 0.5° are available. Each run is based on one GGCM forced by a climate model output or re-analysis data. Runs are available for different crop types, model combinations, historical climate and future climate scenarios, and other model parameters. The hazard is generated by the class *RelativeCropyield(Hazard)* that extracts crop yield data simulated by GGCMs. The GGCM runs provided by ISIMIP are forced with the output from climate models (e.g. in ISIMIP2b, ISIMIP3b) or re-analyis data (ISIMIP2a, ISIMIP3a). The driving climate variables for crop yield are temperature, water availability, CO2 concentrations, and nitrogen availability. Additionally, land use data required for *CropProduction(Exposure)* is available from the ISIMIP input data (e.g. histsoc_landuse-15crops_annual_1861_2005.nc for ISIMIP2).\n", + "\n", + "\n", + "The required ISIMIP data sets are available from https://esg.pik-potsdam.de/search/isimip/ (choose *Variable = yield* for crop yield and *landuse-15crops* for land use data).\n", + "\n", + "\n", + "In this tutorial we show how a *RelativeCropyield* and a *CropProduction* instance can be initiated and translated into socio-economic impacts in the form of (yearly) crop production losses / gains in tonnes or USD." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Hazard", + "relativeCropyield" + ] + }, + "source": [ + "## RelativeCropyield Hazard\n", + "\n", + "Hazard intensity in the class *RelativeCropyield* is defined as yearly crop yield relative to a historical mean simulated with the same model combination. Each model year represents one event in the hazard instance.\n", + "\n", + "The method *set_from_single_run()* generates a *Hazard* instance from one model run, with intensity 'Yearly Yield'.\n", + "This requires multiple input parameters to specify the model run:\n", + "\n", + " input_dir (string): path to input data directory\n", + " bbox (list of four floats): bounding box:\n", + " [lon min, lat min, lon max, lat max],\n", + " yearrange (int tuple): year range for hazard set, f.i. (1976, 2005)\n", + " ag_model (str): abbrev. agricultural model (only when input_dir is selected)\n", + " f.i. 'gepic' etc.\n", + " cl_model (str): abbrev. climate model (only when input_dir is selected)\n", + " f.i. 'gfdl-esm2m' etc.\n", + " scenario (str): climate change scenario (only when input_dir is selected)\n", + " f.i. 'historical' or 'rcp60'\n", + " soc (str): socio-economic trajectory (only when input_dir is selected)\n", + " f.i. '2005soc' or 'histsoc'\n", + " co2 (str): CO2 forcing scenario (only when input_dir is selected)\n", + " f.i. 'co2' or '2005co2'\n", + " crop (str): crop type, e.g. 'whe', 'mai', 'soy' or 'ric'\n", + " irr (str): irrigation type, e.g. 'noirr' or 'irr'\n", + "\n", + "In addition to the general attributes of the *Hazard()* class, the class *RelativeCropyield()* has further attributes related to the crop type and intensity definition:\n", + " \n", + " crop (str): crop type, e.g. 'whe', 'mai', 'soy', or 'ric';\n", + " intensity_def (str): intensity unit definition, either \n", + " 'Relative Yield' (unitless), 'Yearly Yield' [t/(y*ha)], or 'Percentile' [t/(y*ha)]\n", + "\n", + "To convert intensity to 'Relative Yield', the methods *calc_mean()* and *set_rel_yield_to_int()* can be applied as shown below. Attention: This is required for impact calculations in combination with *CropProduction(Exposure)*.\n", + "\n", + "To initiate one or more hazard files from a variety of model runs, a more convenient function is available:\n", + "*climada.hazard.relative_cropyield.generate_full_hazard_set()*, setting intensity to 'Relative Yield' for you.\n", + "The function *generate_full_hazard_set()* extracts all model specifications directly from the filenames. Thus, it only requires the following inputs:\n", + "\n", + " input_dir (str): path to input data directory\n", + " output_dir (str): path to output data directory (hazard sets are saved there in HDF5-format)\n", + " return_data (boolean): set to True if you want the function to return a list containing the haz. sets \n", + " \n", + "\n", + "Below two examples for initiating an hazard instance and setting intensity to relative yield:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Hazard", + "singleFile", + "RelativeCropyield" + ] + }, + "source": [ + "### Initiate a single hazard instance and set intensity to relative yield manually (demo data sample):\n", + "- using *set_from_single_run()*\n", + "- using demo data (cropped to France and Germany, 2001-2005)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-09-10 10:38:46,631 - climada - DEBUG - Loading default config file: /Users/eberenzs/Documents/Projects/climada_python/climada/conf/defaults.conf\n", + "2020-09-10 10:38:48,692 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/demo/lpjml_ipsl-cm5a-lr_ewembi_historical_2005soc_co2_yield-whe-noirr_annual_FR_DE_DEMO_1861_2005.nc\n", + "\n", + "Before calling set_rel_yield_to_int(), intensity is 'Yearly Yield' with unit 't / y / ha'.\n", + "\n", + "After calling set_rel_yield_to_int(), intensity is 'Relative Yield' with unit ''.\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/eberenzs/Documents/Projects/climada_python/climada/util/plot.py:314: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "data": { + "text/plain": [ + "'The map shows relative crop yield in the model year 2003. Positive (negative) values correspond to a relative yield surplus (deficit)'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "from climada.hazard.relative_cropyield import RelativeCropyield\n", + "from climada.util.constants import DATA_DIR\n", + "\n", + "INPUT_DIR = os.path.join(DATA_DIR, 'demo')\n", + "FN_STR_DEMO = 'annual_FR_DE_DEMO'\n", + "\n", + "yearrange_haz = (2001, 2005) # yearrange for hazard (demo data only available from 2001 to 2005)\n", + "yearrange_hist_mean = (2001, 2005) # yearrange for reference historical mean (demo data only available from 2001 to 2005)\n", + "haz = RelativeCropyield()\n", + "haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=yearrange_haz, ag_model='lpjml',\n", + " cl_model='ipsl-cm5a-lr', scenario='historical', soc='2005soc',\n", + " co2='co2', crop='whe', irr='noirr', fn_str_var=FN_STR_DEMO)\n", + "\n", + "print(\"\\nBefore calling set_rel_yield_to_int(), intensity is '%s' with unit '%s'.\\n\" %(haz.intensity_def, haz.units))\n", + "\n", + "hist_mean = haz.calc_mean(yearrange_hist_mean) # requires reference year range as input\n", + "\"\"\"compute historical mean yield per grid cell for reference (base line)\"\"\"\n", + "haz.set_rel_yield_to_int(hist_mean)\n", + "\"\"\"set intensity to relative yield by dividing yield/hist_mean\"\"\"\n", + "\n", + "print(\"After calling set_rel_yield_to_int(), intensity is '%s' with unit '%s'.\\n\" %(haz.intensity_def, haz.units))\n", + "\n", + "haz.plot_intensity_cp(event=3)\n", + "\"\"\"The map shows relative crop yield in the model year 2003. Positive (negative) values correspond to a relative yield surplus (deficit)\"\"\"\n", + "# please note that the run used here is not based on historical re-analysis data but on simulated climate,\n", + "# i.e. the climate variables of year \"2003\" do not correspond to the actual year 2003.\n", + "# (only the forcing of the climate models is historical) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Hazard", + "mutlipleFiles", + "RelativeCropyield" + ] + }, + "source": [ + "### Initiate a relative yield hazard set from multiple input files:\n", + "\n", + "- using *generate_full_hazard_set()*, which creates hazard sets from all NetCDF-files found in the input directory\n", + "- in this example, I used the output from GGCM GEPIC forced with GFDL-ESM2M output for rice, both irrigated and non-irrigated. You can however also run the script with other data sets of the same format.\n", + "\n", + "Requires data download from https://esg.pik-potsdam.de/search/isimip/:\n", + "- gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-firr_global_annual_1861_2005.nc\n", + "- gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-noirr_global_annual_1861_2005.nc\n", + "- gepic_gfdl-esm2m_ewembi_rcp60_2005soc_co2_yield-ric-firr_global_annual_2006_2099.nc\n", + "- gepic_gfdl-esm2m_ewembi_rcp60_2005soc_co2_yield-ric-noirr_global_annual_2006_2099.nc\n", + "\n", + "These files contain historical and RCP6.0 future simulations for _rice_ yield, both for fully irrigated (\"firr\") and no irrigation (\"noirr\"). Forcing climate model: _gfdl-esm2m_. Crop model: _gepic_.\n", + "\n", + "Please note: when calling *init_full_hazard_set()*. the historical mean (hist_mean) is averaged over all model combinations for each crop and irrigation type. Like this, a cross-model exposure can be initiated using this model average of hist_mean as input." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-09-10 10:38:55,529 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Input/Hazard_tutorial/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-firr_global_annual_1861_2005.nc\n", + "2020-09-10 10:39:32,529 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Input/Hazard_tutorial/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-firr_global_annual_1861_2005.nc\n", + "2020-09-10 10:40:09,494 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Input/Hazard_tutorial/gepic_gfdl-esm2m_ewembi_rcp60_2005soc_co2_yield-ric-firr_global_annual_2006_2099.nc\n", + "2020-09-10 10:40:46,880 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Input/Hazard_tutorial/gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-noirr_global_annual_1861_2005.nc\n", + "2020-09-10 10:41:22,372 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Input/Hazard_tutorial/gepic_gfdl-esm2m_ewembi_rcp60_2005soc_co2_yield-ric-noirr_global_annual_2006_2099.nc\n", + "2020-09-10 10:41:59,176 - climada.hazard.base - INFO - Writing /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output/Hazard/haz_gepic_gfdl-esm2m_historical_2005soc_co2_ric-firr_1976-2005.hdf5\n", + "2020-09-10 10:41:59,479 - climada.hazard.base - INFO - Writing /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output/Hazard/haz_gepic_gfdl-esm2m_rcp60_2005soc_co2_ric-firr_2006-2099.hdf5\n", + "2020-09-10 10:41:59,660 - climada.hazard.base - INFO - Writing /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output/Hazard/haz_gepic_gfdl-esm2m_historical_2005soc_co2_ric-noirr_1976-2005.hdf5\n", + "2020-09-10 10:41:59,930 - climada.hazard.base - INFO - Writing /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output/Hazard/haz_gepic_gfdl-esm2m_rcp60_2005soc_co2_ric-noirr_2006-2099.hdf5\n", + "\n", + "Computed and saved the following files: \n", + "\n", + "['haz_gepic_gfdl-esm2m_historical_2005soc_co2_ric-firr_1976-2005.hdf5', 'haz_gepic_gfdl-esm2m_rcp60_2005soc_co2_ric-firr_2006-2099.hdf5', 'haz_gepic_gfdl-esm2m_historical_2005soc_co2_ric-noirr_1976-2005.hdf5', 'haz_gepic_gfdl-esm2m_rcp60_2005soc_co2_ric-noirr_2006-2099.hdf5', 'hist_mean_ric-firr_1976-2005.hdf5', 'hist_mean_ric-noirr_1976-2005.hdf5']\n", + "\n", + "Intensity of the hazard sets is 'Relative Yield' with unit ''.\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pathlib import Path\n", + "from climada.util.constants import DATA_DIR\n", + "from climada.hazard.relative_cropyield import generate_full_hazard_set\n", + "\n", + "\n", + "data_path = Path(DATA_DIR) / \"ISIMIP_crop\" # set path of working data directory \n", + "input_haz_dir = data_path / \"Input\" / \"Hazard_tutorial\" # set path where you place hazard input data\n", + "# (Place crop yield data (.nc) from ISIMIP in input_haz_dir)\n", + "\n", + "output_dir = data_path / \"Output\" # set output directory\n", + "path_hist_mean = output_dir / \"Hist_mean\" # set output directory for hist_mean\n", + "\n", + "filelist_haz, hazards_list = generate_full_hazard_set(input_dir=input_haz_dir, output_dir=output_dir,\n", + " isimip_run='ISIMIP2b', return_data=True)\n", + "\n", + "print(\"\\nComputed and saved the following files: \\n\")\n", + "print(filelist_haz)\n", + "\n", + "print(\"\\nIntensity of the hazard sets is '%s' with unit '%s'.\\n\" %(hazards_list[0].intensity_def, hazards_list[0].units))\n", + "\n", + "hazards_list[3].plot_intensity_cp(event=37)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Exposure", + "CropProduction" + ] + }, + "source": [ + "## CropProduction Exposure\n", + "\n", + "\n", + "The *CropProduction* exposure data represents the mean crop production per grid cell.\n", + "For creating an exposure instance, the following main input data are combined:\n", + "- harvest area fraction (from landuse data): fraction of grid cell area where a crop is grown with / without irrigation, unitless;\n", + "- total grid cell area [$ha$]: computed from grid;\n", + "- historical mean yield (hist_mean): simulated crop yield with / without irrigation per grid cell, usually averaged over several model combinations and years, can be initiated with function *generate_full_hazard_set* [$t / (ha * y)$];\n", + "- crop production price from FAO when unit is USD $[USD / t]$.\n", + "\n", + "Crop production = fraction * area * hist_mean * price\n", + "\n", + "$[USD / y = ha * t / (ha * y) * USD / t]$\n", + "\n", + "Unit definitions:\n", + "\n", + "- $USD$: US dollars\n", + "- $y$: year\n", + "- $ha$: hectar, $1 ha = 10000 m^2$\n", + "- $t$: tonnnes, $1 t = 1000 kg$\n", + "\n", + "The method *set_from_single_run()* generates a *Exposure* instance for one crop type and irrigation parameter, with unit 't/year' or 'USD/year'.\n", + "This requires multiple input parameters:\n", + "\n", + " input_dir (string): path to input data directory\n", + " filename (string): name of the landuse-file to use,\n", + " e.g. \"histsoc_landuse-15crops_annual_1861_2005.nc\"\n", + " hist_mean (array): historic mean crop yield per centroid (from hazard)\n", + " bbox (list of four floats): bounding box:\n", + " [lon min, lat min, lon max, lat max]\n", + " yearrange (int tuple): year range for exposure set\n", + " f.i. (1990, 2010)\n", + " scenario (string): climate change and socio economic scenario\n", + " f.i. 'histsoc' or 'rcp60soc'\n", + " cl_model (string): abbrev. climate model (only when landuse data\n", + " is future projection)\n", + " f.i. 'gfdl-esm2m' etc.\n", + " crop (string): crop type\n", + " f.i. 'mai', 'ric', 'whe', 'soy'\n", + " irr (string): irrigation type\n", + " f.i 'firr' (full irrigation), 'noirr' (no irrigation) or 'combined'= firr+noirr\n", + " unit (string): unit of the exposure (per year)\n", + " f.i 'USD' or 't'\n", + " fn_str_var (string): FileName STRing depending on VARiable and\n", + " ISIMIP simuation round\n", + "\n", + "\n", + "In addition to the general attributes of the *Exposures()* class, the class *CropProduction()* has one further attribute related:\n", + " \n", + " crop (str): crop type, e.g. 'whe', 'mai', 'soy', or 'ric'\n", + "\n", + "\n", + "Below two examples for initiating an Exposures instance:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Exposure", + "CropProduction", + "singleFile" + ] + }, + "source": [ + "### Initiating a single exposure instance (demo data sample):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 14:06:04,706 - climada.util.coordinates - INFO - Setting region_id 1092 points.\n", + "2020-08-03 14:06:05,944 - climada.entity.exposures.base - INFO - Setting if_ to default impact functions ids 1.\n", + "2020-08-03 14:06:05,945 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-03 14:06:05,946 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-03 14:06:05,947 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-03 14:06:05,949 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-03 14:06:05,949 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-08-03 14:06:05,954 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2020-08-03 14:06:06,037 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/xarray/core/nanops.py:140: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis=axis, dtype=dtype)\n", + "/Users/eberenzs/Documents/Projects/climada_python/climada/util/plot.py:312: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/contextily/tile.py:199: FutureWarning: The url format using 'tileX', 'tileY', 'tileZ' as placeholders is deprecated. Please use '{x}', '{y}', '{z}' instead.\n", + " FutureWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 14:06:19,673 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2020-08-03 14:06:19,809 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n", + "2020-08-03 14:06:19,810 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-03 14:06:19,810 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-03 14:06:19,811 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-03 14:06:19,811 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-03 14:06:19,849 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/eberenzs/Documents/Projects/climada_python/climada/entity/exposures/crop_production.py:442: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " (fao['year'] >= yearrange[0]) &\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 14:06:33,543 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import colors\n", + "\n", + "from climada.entity.exposures.crop_production import CropProduction\n", + "from climada.util.constants import DATA_DIR\n", + "\n", + "INPUT_DIR = os.path.join(DATA_DIR, 'demo')\n", + "FILENAME = 'histsoc_landuse-15crops_annual_FR_DE_DEMO_2001_2005.nc'\n", + "FILENAME_MEAN = 'hist_mean_mai-firr_1976-2005_DE_FR.hdf5'\n", + "\n", + "exp = CropProduction()\n", + "exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME, hist_mean=FILENAME_MEAN,\n", + " bbox=[-5, 42, 16, 55], yearrange=(2001, 2005),\n", + " scenario='flexible', unit='t', irr='firr')\n", + "\"\"\"compute maize crop production...\"\"\"\n", + "norm=colors.LogNorm(vmin=1e2, vmax=3e5)\n", + "exp.plot_basemap(norm=norm, pop_name=False) # warning: slow to plot basemap\n", + "# exp.plot_scatter(norm=norm, s=50) # faster\n", + "\n", + "exp.set_to_usd(INPUT_DIR)\n", + "\"\"\"compute USD value (with prices from FAO)...\"\"\"\n", + "norm=colors.LogNorm(vmin=1e2, vmax=5e7)\n", + "exp.plot_basemap(norm=norm, pop_name=False) # warning: slow to plot basemap\n", + "# exp.plot_scatter(norm=norm, s=50) # faster" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Exposure", + "CropProduction", + "multipleFiles" + ] + }, + "source": [ + "### Initiating an exposure set from several model runs:\n", + "Requires data download from https://esg.pik-potsdam.de/search/isimip/:\n", + "- gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-firr_global_annual_1861_2005.nc\n", + "- gepic_gfdl-esm2m_ewembi_historical_2005soc_co2_yield-ric-noirr_global_annual_1861_2005.nc\n", + "- histsoc_landuse-15crops_annual_1861_2005.nc\n", + "\n", + "Requires data download from http://www.fao.org/faostat/en/#data/QC:\n", + "- Countries: all; Items: \"Rice, paddy\"; Elements: \"Production Quantity\", Years: 2008 to 2018.\n", + "- save as FAOSTAT_data_production_quantity.csv in your local input data directory (*input_exp_dir*)\n", + "\n", + "Please note: when calling *generate_full_hazard_set()*. the historical mean (hist_mean) required here is averaged over all model combinations for each crop and irrigation type. This means that the exposure per crop type and irrigation type represents an average over all model ciombinations used.\n", + "\n", + "#### Normalization:\n", + "It is possible to normalize the crop production per country with FAO data using *normalize_with_fao_cp()* and *normalize_several_exp()* for multiple exposure pairs.\n", + "The normalization follows three steps:\n", + "1. Total crop production per crop type (full irrigation + no irrigation) is summed for each country (model crop production).\n", + "2. An average crop production per crop type and country is extracted from FAO statistics (reported crop production).\n", + "3. Crop production is normalized by multiplying the values of each grid cell with the ratio of reported over model crop production.\n", + "\n", + "As a result of normalization, crop production summed over a country and both irrigation types is equal to the avaerage reported crop production.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "tags": [ + "Exposure", + "CropProduction", + "multipleFiles" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 15:23:25,850 - climada.util.coordinates - INFO - Setting region_id 244800 points.\n", + "2020-08-03 15:24:07,277 - climada.entity.exposures.base - INFO - Setting if_ to default impact functions ids 1.\n", + "2020-08-03 15:24:07,279 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-03 15:24:07,279 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-03 15:24:07,280 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-03 15:24:07,282 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-03 15:24:07,283 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-08-03 15:24:07,295 - climada.entity.exposures.base - INFO - Writting /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output_tutorial/Exposure/crop_production_ric-noirr_1976-2005.hdf5\n", + "2020-08-03 15:24:07,400 - climada.util.coordinates - INFO - Setting region_id 244800 points.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/xarray/core/nanops.py:140: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis=axis, dtype=dtype)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 15:24:48,471 - climada.entity.exposures.base - INFO - Setting if_ to default impact functions ids 1.\n", + "2020-08-03 15:24:48,473 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-03 15:24:48,473 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-03 15:24:48,474 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-03 15:24:48,475 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-03 15:24:48,476 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-08-03 15:24:48,495 - climada.entity.exposures.base - INFO - Writting /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output_tutorial/Exposure/crop_production_ric-firr_1976-2005.hdf5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/xarray/core/nanops.py:140: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis=axis, dtype=dtype)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Exposure files created:\n", + "\n", + "['crop_production_ric-noirr_1976-2005.hdf5', 'crop_production_ric-firr_1976-2005.hdf5']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/eberenzs/Documents/Projects/climada_python/climada/util/plot.py:312: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n", + " fig.tight_layout()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-03 15:24:50,133 - climada.entity.exposures.base - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output_tutorial/Exposure/crop_production_ric-firr_1976-2005.hdf5\n", + "2020-08-03 15:24:50,149 - climada.entity.exposures.base - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/ISIMIP_crop/Output_tutorial/Exposure/crop_production_ric-noirr_1976-2005.hdf5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'FAO statistics (used for normalization) [t/y]')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/matplotlib/colors.py:1110: RuntimeWarning: invalid value encountered in less_equal\n", + " mask |= resdat <= 0\n" + ] + }, + { + "data": { + "image/png": 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TqlUrSpcuTbVq1R7rb/+k97WMwCOztOGRW+pRSiEidlcjr1tCyYGhj7tFGt5TOSIidTOm9rTjkqKklBoJDEZbCv4U2g7sxYEf0HarPoq2qWSCcdXe1Wj7OvUXkVtKqUFoi7Q9KyInjWWeRlsQ8Kqd+jyKUhrIbA8Ug8HA7du3+eeffzh27Bi5cuXi7t27TJkyxSJf5cqVOX/e2VqYlkydOpWpU6cCULBgQcLCwqhUqRJxcXFcu+bqtnLwww8/kD9/fpYsWYK3tzfly5fnyJEjbN2qrXvXpEkTcufOzbZt2/Dz8yM5OZk7d7Tt4ooXL05oaCiJiYku12dOcHAwpUuXTjmji+TPn5+wsDAWLlxImTJlaN++velhyYMHDxARsmTJQs6cOfHyevQR98zW1zIriYmJREZGIiLkyJHDI7M04OlrqcejKLmXFIfelFJ+aCsLVxWRh0qpdWi7RLcHPhORH5RSC4HX0DZS7A8sAoKN531gLOoG2saWvd1+FR4ynKSkJDZt2kS3bt3IkiULycmOdj+xxd/f36GiNGTIEH799VcqVKjA2rVrKVy4MICNshUTE0ONGjVS1eY+ffpQsmRJbtyw3PezYcOG7N+/H39/f91adPOm5ZZxI0aMoEiRIsyfP58cOXKwf/+/O2xUq1aNCxcu8Mcff9CoUSMePnxIUFAQtWvXtijD/GMgLi6OHDlypKr95kRGRpIlSxanebJnz05c3L97pm7cuJHOnTunuc7/ZaKjo9m9ezdt27bFfFvD69ev07dvX/bssd1ubtiwYXTr1o3u3bsTFRUFQLt27Rg7dixVqlShSJEij6395sTGxrJ27Vp8fHx47rnn8PPzIy4ujrCwMNavX8+vv/7KtGnTqFfvUXbA8ZBpUHh2vkslKVqUjIrSfrRNCKPQttX4Am35+GIikqSUagRMFZE2Sqm30JbBDwYGiMhYo0WpPtqeO91F5ILHouR+MuLLKywsjEKFCrm1zKxZs9q11Gzfvp0XXnjBJj4mJgY/Pz/u37/vUvm5c+cmOjoaf39/AgIC8Pb25u+//+b9998nNDSUPXv2UKpUKZKSkhgxYgRz584le3ZtS6ywsDBOnDiBv7/tBvIigoik2WJz7949smXLRr58tjuNJCYmMnnyZHLnzk29evUIDQ2lX79+dkqxpU6dOpw+fZr4+HhAsz4VKVKEH374gVq1aqWprenZ186dO8ft27cZN24cly5domzZsixZsoQZM2bg7+/PpUuXmDhxIr6+1nsR22IwGFBKWSgzrrJr1y5atGiRYr69e/cCmgXSGeZKeZ48eXjw4IFF+qM+81auXMnAgQMBOHz4MHXq1HHpvD///FPvz9WqVePMGW0XmMKFC3PvnvUOJv/K9HHhsSilHqcWJT8lB96wl5L+eE9+Mi1Krg69jUDbZ+sh2l5TI4D9IlLemF4K2Coi1ZVS+dH25smONhx3w6go1UXbrPN5ERnoUZTcT3o/UG7fvk2JEiUAaN68Obt27bL7wHz48CHZs2fn0qVL9OnTh3v37vEozvk9evSgUaNGDBo0iJCQEKpUqeIwb65cufjwww8ZM2aM3fRly5YxYMAA3frypD6E//77b4YPH86nn37Kc889R2BgIK1bt3aYf+XKlZQqVYrnnnvOLS+5tMht0KBBrFmzhoQEbfuvXLly0bRpU5o2bcqkSZPInTs3Pj4+hIWFparcV199laVLl1rEVa5cmQsXtO29ihcvzq1btwA4e/Ys3bt359w5u3u1WuCqouQq5orSmDFjKFSoEIUKFaJIkSLUqlWLkiVTtz9ubGwsixYtomHDhtSsWZO4uDgOHjxIrly5aNy4cYoWRnPS8sEzcOBAVqxYkapz0sKTeo9mJB5Fyb24YlEqAPyMNmQWibYD/c/AFCtF6VcRecZBGYPQFKV3gTNom4puwomiZDJNe3CdmJgYcuXKlWI+k99K1qxZbYZ7bt++zfPPP29zzuuvv85TTz3FoUOHCAgIYPDgwbz44ovMnz+f7du3O61v0aJF1K9fn6lTp7Jx40aXr2f27NmMHTvWpbz+/v689dZbLF68GF9fX1avXm2T588//6Ro0aIWca7KLLMTERFhYdHw9vbm2WefZdmyZXh7u39ya1rkVrVqVafplSpV0pUbR1SuXJmYmBiuX78OgJ+fH2PHjmXv3r0kJCRQuXJl6tati4+PDyEhIZQqVYqiRYvqMhARwsPDKViwYKra/vDhQy5cuECuXLnw9fVl8+bN7Nixg7p16zJ06FBiY2PJmTMnWbJkIS4uzmIoecuWLVy+fJnBgwfTrFmzVNXrjP379/Pqq68CmmP/qFGj0lzW9evXadOmjd20okWL4uvr65JymT17diZOnEjnzp3d1u/+K/fo4yRv3rzOFaVhj7tFGt6T/ruKUk+0XaRfMx4PABoBPbEz9OagjEFAXREZrpQaAtQGmuKxKLkVe19eycnJXLp0iejoaOrWtd8/TRYgeLShtEaNGrFv3740nWuO6bffsGED3bp1s5tn48aNdOnSxW5a1apVuXbtmj4r7o8//rCr/MF/42v1+vXrdh3D69Spw+HDh9OlztTKLSEhAR8fnxTzTZkyhVq1avHiiy/apPn7+9OmTRsaNmyIn58f+/bt4+WXX2bkyJF89tlnANStW5dXX32VYcMy6E3ghCehrx09epR33nmH2bNnc+XKFQICArh586aFD54rVKlShXPnztGpUydmz57t1AqcEk+C3DIbKVqUPIpSqnBFUWqANmOtHtrQ2wrgMJq/0c9mztwnReRrB2UM4l9FKRtwFsgDNPAoSu7j1KlTXLt2jVOnTjF+/HgAXn75Zb777juH59SvX58DBw7ox/7+/vz555/Url2bf/75h+zZsxMaGmpzXq1atTh27JjT9pQuXVofcvPx8dF9ZFJi9OjR1KtXj9jYWP2L2Zzz589Trlw5smbN6lJ5zvrSk/4QduYTtXTpUrvycwdpkVt4eDhvv/02x48f5+zZsynm3759O61atbKJL1u2LJcvXwY0BSxbtmypakdGkRn7WkJCAkeOHKFu3bpkzZqVbdu2UbBgQYKDg0lOTqZnz56A5ryeO3duAHbu3EnLli0BzRk9ISGBo0ePAtpHV4ECBbh9+7Zex7hx4/j444/T3MbMKLfMToqK0puPu0Ua3hOfTEUpRa9TETkA/IS2BMAp4znfAmOBUUqpS0BBYKnDQizLSwAWABkzxeM/RGxsLMePH6ddu3YopWjfvj2dOnXSlSSA7777Tvd9yJ07Nx06dKBTp040a9aMypUrc/DgQZRSut+IaVbZ0aNHadCgAYGBgaxZs4auXbtSuHBhOnfuTNmyZTl27JjNV2bOnDkBKFGiBGvWrOGpp57S01xVkgDmzp1Lnz59LF7yBoNBd5auVKkS3t7e/P7776mSl+n8/wrOlKSqVavatcpkJL6+vnz//fecOXPG6e/Qv39/DAYDL7zwAiNGjLBI8/f3Z/369UyePJmLFy8+MUqSMy5dusScOXPo2LEjzZo1o3z58iil+OGHH9K1XhHBx8eHxo0b89tvvwHakE2pUqXo2LEjly9f5qWXXkIpRZ48eWjbti1r1qyhdOnSnDt3ju+++46tW7eyY8cOOnTowLvvvktcXJyFkgQwc+ZM3n33XWbMmMGYMWNQSvH888/z3nvvER0dna7XmB7s2bOHevXq8ffffzvNd/r0ab799tvH1CoP6YlnwcknFHsOufamupuTN29efVpyq1at7PoW9e7dm+bNm/PWW2+5r7FpwHzmW9OmTdm9e7fDvFu3buXVV1/V1zhKDSVLlqRu3bp8+umnlCtXLs3tzQgMBoNDh90PPviAmTNnplvd7vzK37Fjhz6bcciQISxatMhp/l9++YUuXbqwe/dumjZt6pY2pBdRUVFMmjSJixcvcurUKaf3J2gzEiMjIwFtvTDrZTBcJTY2li5dunDv3j3q1avH4sWL7eYLCgqiePHiZMuWjfDwcO7cucPzzz9v14rsjMWLFzN48GBiYmKIj49nx44d9OrVS08fNGgQhQsXJiIigiVLllicu2PHDruzSE24s68lJCSwd+9e6tSpQ5s2bfSPve7du1OlShVmzJiBn58fvXr1omLFitSuXZtq1aqRK1cuRASlFF9++SVvv/02O3fu5LnnngNg3759fP311xbWexEhPj5ed2t4nKRkUTqYQY/3LBOeTIuSR1F6Qpk7dy5jxozhmWee4dSpU0DKitLj4qWXXmLNmjWpOqdy5cpUqFCBTZssV7XfsGGDy5YREWHfvn389ddfXLp0iaVLl+Ln50eBAgXo1q0bnTt3JkeOHMTGxvLHH3/w008/cffuXV1mn3zyCUOHDn2izPzOZrC1atUq1VY3V/EMhzhm//79NGrUyCKuTJkyJCcn270/mzRpwtNPP03ZsmWpVKkS1apVo3z58o/kwGw+5b9Hjx6sW7dO7ysBAQEMHTqUEiVKcPz4cafl1KlThyNHjljEzZgxg4kTJ/L999/Tt2/fNLVv9+7dNG/eXD9+XMPjQ4cOtbDy+Pj4MHnyZCZMmOD0PG9vb5KSkvTjli1bUq5cOZYsWcLZs2cpUaIE+/fvd+gQ/7hxqiiVzEBFaTyXgD/xbGHy6HgUJce89957JCcnkyNHDmJiYggNDcXX15c6depw//59ihQpwp49e9i5c6e+HoqrmHyK3nnnHRYsWEDRokXZvn07xYsXp0CBAnZnsZQqVUqfgZQrVy6XthVxRKVKlWjQoAErV67U49K7Hzx48ICwsDCLbU0eR73uokGDBhw8eNBhenpdR3oqSpcvX2bIkCEsX76cUqVKpUsd6cXRo0ct1i+qUaMGJ0+eBP79kGnQoAFLly6lQoUKbh063LRpE507d+batWv4+vqSJ08e3nrrLYYNG8aJEyfo37+/TX9o3bo1w4cP57nnniNv3rxua4s1IsKMGTOYPHmyHuft7U3RokU5efIkvr6+HDhwgCJFitjci+7sa0ePHmX//v089dRTdOjQwWKLJNPK6SEhIXr+p59+mjFjxpCYmGgzDGxi9erVvPTSS25pn7vIxIqSx6LkLjyKkmNMX4WdOnWiRYsWFCpUiKtXrzJlyhT9Qdy6dWtOnTpFvnz5aNeuHd27dycwMJCYmBiyZctGyZIlqVKlCg0bNiRr1qzcuHGDZcuWISIcPXqUfPnyUbx4caZMmaL7HVWoUIFLly7ZtKdAgQIMGjRIn3UE2KzMXaNGDX214s2bNwP/LvpYvXp1AIoUKUJ4eDgFChTg7NmzNGvWjBo1apCYmEjBggVp0aIFNWvWdLs8TQ9h0xYTJl8qPz+/TGGdA+0ls3btWho0aIC/vz9Xr151mr9hw4asWrUqXYcS01NRCgkJYejQoXz11Vf6ul2ZnUuXLlGhQgWneXbu3Enz5s1dXsdKRDh48CBHjhzhhRdeoGLFinpaQkIC/fv3J0eOHHz88cf4+fkRGxvLsGHD6NWrF4cPHyYiIoKFCxfqfdq0pY2JatWqcfr06TRcrWttDw4OZtasWSxcuNBp3lmzZhEfH8+ZM2fw8fGhTp06hIaGkjdvXsaMGUN0dLTe14KCgihfvrzF+TVr1qR+/foUK1aMDz/80Kl8P/zwQ6ZOncqqVavo168fDx484OrVq2zYsEEf5qxRowbPPvssAwYMsDtbNjExUX+Wmp6PmY0UFaXhj7tFGlnGeRQlt+FRlOxz+vRpnnlGW6rq7t27FlseiAhBQUH6kNKxY8cIDw+3OL9mzZq6s+gff/zB5s2b8fX1tcln4tixY3h5edkoKOXKlSMoKMgmf+XKlalZsyatW7fm9u3bTJw4EYDJkyczbdo0AObPn8++fftsHFX9/f3p1asXn3/+ub4GTalSpbh58yYGg0HPFxsb+0hbfVhj/cKfNm2ahV9IZuiHX3zxBe+8845LeZOTk92yl1tKeIbe/mXhwoVOlyMwLb+RGpkFBwfTvn17C6vwkCFDmD59OqNGjeL777/X4wcMGMC4ceMYOXKk7pQNMHHiRLy9vTEYDHh5eeHr62vRjw4cOED9+vVTc6kuYfIhc8TAgQNp2LAhs2bN0vdm/OCDDyhYsCCxsbHcu3ePL7/8Us9fsmRJWsq/M68AACAASURBVLRowapVq1yqv2PHjjZD+ObcvHmTfPny6bP4AMqXL2/3mXby5EkKFixIsWLFHst95S48ipKbMc0EykxBa5YHc/766y9B25RYjh07ZpF2/fp1AaRkyZJ6nnfffVcCAwMlNDRUjwOkS5cuFsfm4fvvv5dFixbpxyIiX331lU2+xMRE6dChg+TIkcNhWR07dpQlS5ZIcnKyHmcwGEREpHTp0jb5ExISREQclmceKleuLIcPH3aLXKOioiyOjx49alPfqFGjJDY2Vm//4+Sbb76RnDlzpiiTM2fOPNZ2Wcvtf5Hk5GR577339N9g/fr1smLFCv341q1bFvlTIzNTGU2bNpWhQ4dKsWLFBJBnn31WJkyYoKfXq1fPoh94eXnp/z/99NPSoEEDm+fFqVOn5Pr1626RgT0mTZqkt2HYsGFy9+5du/fO999/r9/P1iQmJsrJkyelcePGUrJkSfn000+lYsWKAsiECRMs8iYnJ0u1atUs5GAiPj5eRo8eLbt373bYXut7qXXr1tK8eXPJly+f3XutcePGUqNGDXn77bflwYMHjyCp9MMoA7vv1zp+SPKsjAnAYUftyswhwxtgt1EgefPmFUA+/vhjERFZsmSJXLlyxaVOkhb27NkjgLzyyisye/ZsSUxMTLe60sKXX36p36j58+eXb775RlavXi3ly5eXokWLCiB169a1e2OvWLFCLyc+Pl7efPNNOXXqlNy7d08MBoPFQ1ZE5ODBgwLIt99+KyIiDx8+lFdffdUlJQaQWrVqyd27d0VELBS1adOmybx582Tt2rUW+cePH6+3r1mzZnbLfPnllx3Wp5SSwMDANMnV3svL2bUlJyenqZ604ufnZ7cdlSpVsjgeNmzYI9Vz584d+f33313On1GKUlhYmJw+fVrWrFkj8+bNk8TERPnrr7/k9OnTj70t586ds/ldVq9eLYCMHj1aYmJiLPKnJLO7d+/KkCFDJEuWLAJYnP/KK6+keN8VLlxY9u3bJy+99JJN2pIlS9JFBo8DV/rarVu3pFChQvr1RkREyIMHDyxk4OheMoWRI0dalGl9PiA7duyQTz75xCKuT58+snfvXv286OhoOXfunBw+fFhOnDgh586dk7CwMLfLxR4XL16UVq1apawozc6Y4FGU3KwomUKXLl1ERGTjxo0SEhLiWm9xwMOHD2XMmDEyb948mxsgpZto+PDhMnr0aKlXr57069dPtm3bJvHx8ZKUlPRIbbJHYmKi3Lt3T6ZPny6VKlWSU6dOWaSfPHlSmjVrJu3bt5fBgwfL4sWLJSQkRG7fvi1KKZu2V6xYUapVqyYNGjQQf39/mTJlisUXnsFgkNu3b1vUkStXLl0p++yzzyQ4OFiqV68u/v7+MmbMGGnZsqXs3bvXop6EhAS5du2afjxv3jwRESlQoIBT2YqIBAUFWSinO3fuFEDKlSsn0dHRulzsXZ91aNeunURERLgka3sP4YsXL4qPj4/D8h+XZenGjRtOr3Pu3Lk2ckwLy5cvt/gNXSG9FaWwsDC5ceOGREVFyY8//pjib24dTIp6erNjx44U2/Lpp5/Ktm3bJDIy0mlZpvyDBw+2+R22bNliUeaaNWukcePGDu8nEbGwNr355pvpcv2Pg9T0NetnEiDnzp2TH374QYYMGSLjx4+XkydPyuzZs/X0GTNmyJdffin37t2zKc9gMMj9+/dt7vn79+9LixYt7Mo/e/bsAkjZsmWlevXqUrFiRcmTJ48UL15cevfuLYCUL19e+vbta/Nsf1R69Ohh3g88itJ/XVFyt0XnxRdftNupX3nlFTl8+LAkJyfLgwcPZO/evRIVFSXHjx+XsLAw/esOtC82QHx9fVN8OBYsWFD69esna9eulWnTpsm+ffv0thgMBtm1a5csWrRI6tWrJ7t3706xvK+//trieMqUKfLWW2/J5MmTZd26dRIbGytRUVHy8OFDad68eYrlnT171kZGDx8+1E3JBoPBYhjOFJ5//nkRERkxYoSULFlSateuLYAUK1ZMFi9eLGfOnLHIP2fOHImOjpZVq1Y5bItJKUspzJw506K91pYp6/DFF1+k2C8cPYSdlWuycqY3JiunK2HQoEFpric+Pl6Cg4NTdU56KEqJiYkyceJEl6/ZldC8eXOJjo52m3IbExMjV69etSnvzp07Mn/+fP0laQrt2rXT/y9ZsqQ0bdpUpk6dKnPnzpVly5bJX3/9pX8ItG3bVr/H4uLiLMo3WX5Nw0FHjhyxSY+IiLBo1/vvv6/XvXz5crdcf0aQlr4WHBwsu3fvTpcPWXuEhoZKUFCQ3Lhxw67V2WAwyM8//yyAPPPMMzJmzBjp27evfP755/Lll1/KTz/9JDt37pQjR47IoUOHdKtQgQIFpFevXgKaFblJkyb6bzp48GD9/wULFsi6deskODhYrly5krKiNCdjwpOqKGVaZ+7du3fTpEmTNO90fu3aNY4cOUJCQgJ37txh5MiRgOaEau7E9yiYZHfhwgWuXbtG7ty5yZs3Lz///DMLFy7k7t27Nue88sorLF++3KXyy5Yty9GjR9m5cyfvvfeezayzHj16UKVKFQ4cOMDu3bt55plnyJ07N7Vq1WLu3LmAtlp2gQIFuHnzJs899xw7d+60aH9ISAg//vgj0dHRjBs3zqL8/v372zhQNmzYMNX7PoG24vfQoUMZNGiQvjibaV+4fPnycf/+/VSVd+/ePQoVKqRfw/r163nzTdt1+a2d3q1x5GCbUr97XPfN/fv32b9/P9mzZ6d06dI8/fTTNnlu3bpF8eLFH0t7TLjLmfu9997j008/dSnvgAEDqFChAh06dKBt27aEhITQs2dPatasqU8cSIn27dvTpEkTevXqZTN7yoRpvRzr5TDWr19P9+7d9eN169bRs2dP7t+/z6ZNm3j55Zd5/fXX8fPzY/z48frq8f379yc0NFSflfrOO++glCI8PJwLFy44XN6hRYsW/Pnnn/qxUspmRilAz549GTlypM3aTaCtiB8VFaWvuP8k8l+ZOBATE2Pz7unSpQsFChTg1q1bPHz4kOvXr3P16lW8vLz0SSwFChQgIiKC4cOH07VrV7766iv27t3Lhx9+yObNm9m8eTNt2rRh27ZtFmWLM2du+ysdpDtZ3vc4c7vVomQeXn/9dXn48KEdPd4+5l/iDRs2lGHDhsnq1asfu38J/DsE8Mcff8j48eOlUaNGFlanDz/8UD9etmyZLFy40OHXb3h4uCQlJUlYWJjNl1JMTIycOXNGNm7cKOPGjbMYrzcP1g6KzoaYvL29LY7NncVTCiIip0+ftol/+umnLb50TXlNX9OABAYGyv79+/XjU6dO6T5r9uoxYc/CVLp0afm///s/h8O2zr5WY2Ji7NZpGg52N67K1rxfZRSPalEaM2bMI1uLhg8frpdnijtw4IBFPT169JDx48fLtm3bJCAgQL7++mvp27evFCxYUAApUaKELFq0yGKo680339TLK1WqlHTs2FF2796tOx+D5s+XNWvWVLXXdP/cuXPHoo1bt26V3r17S9euXZ32b2uZ5c+f3+L44MGDj/SbZFb+axMHTL9bSn51hw4dknHjxum/7x9//JFi2eHh4frogziyKJVEkj/JmMATalHK8AbYbRRIVFSU3YeNI9OmOXfv3hVAXnjhBQFtqOzkyZNy//59iY+Pd3quO3ncLzTrB4q5o3ZqQvXq1QWQQoUK2ShLzkLevHklICDAQon7+uuvpWvXrnbbsnLlSj3foUOH9Pjg4GBJTk6W4cOHy/r162Xo0KECSLdu3WzKKFKkiMU1x8XF6Q619sL8+fMlPDzcoczM+fPPPx2W406c/U4NGzaUCRMmyKFDhzJk1p0jHuXlFRsb67QflStXTlatWiUDBgyQa9euSXx8vKxatUrvizExMTazjSZOnCjZsmUTQMLCwiQxMVFOnz7t0OcqPj5eunfvLkWKFNHrnTp1qohoQ4D2lJxOnTpZxL311lvy4YcfyrvvviuzZs0SQNq2bSvbt2+XpKQkCQ0NlcTERImLi5Off/7ZYvjdGatXr5aWLVta3EdBQUF6vf379xdAYmNjRcRSwV69enVafpJMzX9NUbp9+3aK/mqPikdR+h9RlCIiIvSH3M2bN6V48eJ2H6ovvPCCjSOyid27d8srr7xi4zcASJMmTXTfgPQioxUlEZGkpCS5du2aHDt2TA4cOCCBgYHy+++/S0hIiERERFhMb7anACQlJcmRI0fkt99+k9OnT0tUVJRcuHBBLl26JPPnz5fu3bu7/JAODg7W81o7Qi5dulT/3/qFsmnTJgGkUaNGMmTIEAkKCpI1a9Y4bLOpnrJly+pO4fZCfHy804ews5d5WgkJCdFf8pcvX5bAwEC9zEaNGqVKGQoJCZHvvvvOZnaViEhkZKQYDAaZPn26bNu2TUJDQ93qr/GoLy97Mi1QoIDTc2bOnCmALF682CbNZP0rWbKkJCYm2sxMKliwoOzbt8+urEznenl5yccff2zRt6zDyJEjZceOHWm65tTIzOTPYh2qVq0qgMUsqzNnzkiPHj3E19dXrl69mqa2ZWb+a4rS4yBFRenTjAlPqqKUaX2UTP/Hx8djMBjInj27xerQzzzzDC+++CLTp0/Xzztz5gwbNmxw2V/hzJkzVK1a1c2t/xelVIo+Mu4krWP55vtCpbY/iAj169fnwoUL3L9/38K3p3Xr1vrGu6NHj6Znz540aNDAof/PsGHDmDRpUor+NqbzJ02aZLMSb/fu3Vm/fr1LbY+MjMTLy8uhzB4+fGh35d2QkJBU+XwYDAbWr19PYGAg33zzjd08w4YN4+uvv3a5TD8/P27duuVyfhPx8fFu2TYjrX3NkUyrVKnC0aNH7W4gGhERQYUKFYiKiiIxMZGyZcuyfft2i5XHDQYD9evX5+jRo9y5c4ciRYqwbt06evfubVGWj48PSimqVq2Kn58foaGhuq+cPaKiovjkk0/Ily8f7du3p0qVKqm+ZhOpkdnBgwdp0KCBTfzcuXPp3bs3fn5+aW7Hk8Z/xUfpceJ0wclSSg6OfNwt0sgy+sn0Ucr0S40WLlyYfPnyoZRiy5Yt3Lp1i4iICE6dOmWhJI0dO5bLly9bKElt2rQhMTGRw4cPExAQwJw5c2jWrBkDBw4kOTk5XZWkJ4mWLVuaW/NShVKKQ4cOERUVZaMAmTsuzp07l4YNGzp1kv7yyy9tlKRTp06hlLIIJkwvPXOWLVvmUrujo6PJly+f0zyOVgBPzW7ggwcPJkuWLPTs2dOukhQUFISIpEpJ2rt3r1Ml6dlnnwXA19eXadOm6ZsmgyazjGLXrl0WzuitW7cGNIfu06dP25Xr3bt38fX1JSwsjMTERACuXLlC+fLluXjxop7Py8uL0qVLIyK6c3SvXr0QETZu3Kjna9SoEX379qVevXq0atWKDh06AJqjeGBgIAaDgdOnTzN69GiOHTtGnjx5mDZtGqNHj34kJSm11K9f3+KLNioqiri4OEaNGvU/pSR5cIyI8ODBA15//XUqVKhAYGAgoH0AenAvmdaiFBUVRWRkJD4+PuTPn9/hA956FtuJEydYunQpX3zxhdM6xo4dy6xZs9zabmuUUty+fZtixYqlaz0mMsOX1/Hjx6lYsaJuNRDRNsM07eUUEBCg5w0ODmbjxo18/fXXnDt3DoBBgwYxffp0SpYsCaQ8+ywhIYGsWbNaxCUnJ1O7dm19M1JrzPu8KzKzZ725f/9+ipuIhoSEULRoUf04JibGLXtDiQh169bl6NGjFvH2ZnSGh4dTrlw5i4enO+751Pa1mTNnMn78eJt4b29v4uPjHW4PYfr9GzZsyK5du3jllVdYvXo1oG0vYdrSB2DEiBEsWLCAJk2asGfPHj3eYDCQJUsWp+3LnTs3Dx48cPl60kJmuD+fRDxys8WRZdYcpxalUenSrBTJMspjUXIrefLkoVSpUhQpUoRs2bIhIiQnJ1u8aMuWLUuePHnw9fWlbdu2HDhwgJo1a7JgwQJu3brFr7/+CqBvFFmgQAHy588PoL+IPbiXWrVqkStXLt364+XlxeTJk/noo48ICAigX79+et4dO3ZQsmRJXUkCWLFiBaVKlWLgwIHEx8dTq1Ytp/Vly5ZNH+IEePnll/H29ubkyZNs2bLFIu+iRYvSpCRcvnzZJs6VcsyVpMuXL7ttA02TFc9kYTGRJ08eOnbsyJw5c3T5FyxYUFeS3nzzTbcoSa5y48YNvR3mSpL50F/Lli1tlCSDwcCECRMslOT9+/fj4+PD6tWrWbJkCSJioSSBto8gaBa3uLg4Pd7Ly4vt27dTunRpi/zt2rXj9OnTTJs2jZ9++unRL9iDh8dEjhw5uHjxIomJiRw5coSdO3fqG4y7hMqg4GaUUi2UUruVUguVUi3cX4OxnsxqUXKlXSVKlOD27dsWcSdOnKBGjRrmZdmcN2TIECZPnpzuJuz/RYvSzZs3mTRpks1aUXFxcWTLlo1Lly6xcOFCfvvtN86ePUuZMmVo27YtFy9eJDAwkBYtWpA7d25mz55NQkKChaK0bNkyBg4cyIwZM+jQoQMnTpzgtddec7lt9nZLd1Vm586dsxiqTUpKcmilsLfDeXrdZwcOHMDb25uff/6ZmTNn2qQvXryYwYMHu71eZ3KztqSZkz9/fiIjI1m7di29evWySbe+X63X2IqKinL6ew0ZMoTly5eTlJSkr7Vljoi2q314eDg1a9Z8rBudZob780nkvyy3mJgYTpw4QUREBHXr1qVIkSKpXjtQRKhWrRoFCxa0sKQ6tSiNfrR2p5UsI1O2KCmllgEdgRARqW4W3xaYD2QBlojILKXUc8AHwF1ghohcslfmI+OKxzeQH/gJOA+cAxoBvsB24KLxbwFjXi9gJfA3UM0Y1wJt1kYnszI3Ay0c1OfAl98+Dx8+lM8//1zu37+vx12/ft1i37AtW7Y8tv12TAAOZ+SlB5lhdohpRVlTaN26td2ZXAaDQW7duiV37tyxyP/jjz/K8ePH9eOmTZumOJXcNBMopWBvi4LUyCwiIsKivMuXL9vNt3DhQovrf1Iwn+1lvZGqNfbkdv78ebty79Chg57HtEny2rVrbc7/7bff9HP27NkjIv/OjnN1huqmTZskb968kjVr1se6FIgrZIb780nkSZabwWCQoKAg2b9/v6xcuVLWr18vR48e1dOt7xXzfTmdlfnnn39Khw4dbM4330lCHM16K4UYPs+YgAuz3oDmQG3gtFlcFiAIeBrIBpwAqgJexvSiwPcplZ3W4Orn1HzgNxGpDNREU5Y+AHaISAVgh/EYoDVwAOgKmOutN4AJLtaXKrJnz86IESMsfEZKlSrF7t27AejXrx9NmjTB19c3TeU/ePCASZMm6cMIP/74I2XKlKF3796MHz/eZgjkf5UtW7bos9xee+01RIRt27bZ/UJSSlG8eHGKFi3Kjh072Lp1K/Hx8fTo0YPz588D2sxGk2UiLCyMtWvXMnv2bKZOnUpwcDCgWW/Onj1Lr169CAgI4OTJk6xfv96uP4y1dSG15M+fn/j4eP346aefZtq0afpQ17vvvgvAG2+8oeexXi03M2M+NGU9rOUKppmT5ty8eZPNmzfrx15eXiileP311/WVh020bdsW0IbemjRpAqBbjPv27eu07gsXLtCkSRM6d+5Mq1atiIyMdMvsPg8e0srBgwfx8vKiQoUKdO7cmQEDBtCtWzdq165N//79LZ6LTZs25e7duwwcONBheYmJiVSoUAEvLy9atmypuxbs27ePpKQkRIS4uDiuX7+ecuMy8dCbiPwFhFtF1wcuichlEUkAfgC6iIjpIRIBpN9MFRe0u7zAFYzDdGbxF4Dixv+LAxeM/3cAhgGFgeXyr0VpM7ANaCWPaFH69ddfpX79+haauTnXr1+3Wf0ZtFWawfVNTW/dumV3tVxHYd26dfq5pkUEv/rqK/n444/lxIkTLtX5KGTkl1dYWJguB3csjBgZGWmxweWVK1fs5rPe187Hx0fGjx8vf/31l4hov0Pr1q2lcOHCds9Pi8ySkpJc6g89evRIddkZzcmTJ+Wnn36yiTdfFLN48eJ25Wa9UjQgN2/etMlnWh3eGtM5+fLl01d1N7//TAuXxsbGOlzZe9u2be4RRDrwJFtGMpInTW5BQUH65rRDhgzRn4f2FjI1hYsXL8qtW7ekcuXKMnjwYOnTp4++l9ucOXOkUqVKFvlDQ0OdtoGULErzMyYAV4HDZmGIvTYCT2FpUeqBNtxmOn4Z+BLoBiwC1jrSJ9wRXFGUngUOAiuAY8ASIBcQaZUvwvjXG03b2wc8I5aKUjNglzyionTw4EEB201SRVJe9Rdc23DXlY1q7QVvb2/JkSOH3bRNmzaJiMioUaOkbt26snDhwhTbkRoy8oFiWj3bHW0YPXq0PPfccxIbG2uxy7cjrLcuMW3QKyKyefNmizRrHqW9S5culdWrVzvcyPW/hPkQ6dGjR+3K7auvvrK4/u3bt+tppm2FpkyZIkopqVevns354eHh8tJLL6XpvjMPmXUD2CfthZ9ZyAi5JScny8OHD+XOnTsSEREh4eHhcvv2bUlOTpaQkBBp3ry5DBkyREaOHCkfffSRPsz7999/C2i7FOzfv9+m3IMHD0qzZs2kSJEiUqtWLaf9febMmRbvkkmTJrm8lVeKitKCjAm4uOCkHUWppx1F6QtXynJHSNGZWylVF9gPNBGRA0qp+UAU8LaI5DfLFyEiBRyU0QIYIyIdlVK7gInAWOBTEdlpJ79ERUU5bZczLl++zJ49ezh16hQzZsxgy5YtJCcnExcXZ+HwWqJECX3ad/ny5Wnfvj1vvPEGcXFx1K5dG8DuZoNjx47lwIEDDBw4kHz58lGqVCnu3bvHhQsXqFevHtmzZ2fHjh2cPXuWn376iZiYGIdtrV69Ov/3f//ncM2e1BATE0OuXLkeuZy0MHbsWDZt2sTMmTOpVKkSlStXTnNZjRs3JmfOnEyfPl131h4zZgyvvvqq3fwGg4HNmzfzwQfa6O/cuXNp164dFy9epEuXLjb5z549q//vTplduXKFCRMmkCVLFj766CObWVZPGgkJCWTJkkUfhpswYQI3btxg7NixduWWmJhIzZo19WOTnGvVqmUxZJk1a1YOHjxoseTHvXv3OHHiBO+8844et3nzZv755x+qVq3Kiy++aDGTzUTp0qX1Ydjx48eTJ08eWrRokeIaWRlBRt6fTzKPU27R0dHMmjXLpUVry5UrR1BQkMP0n376yWICSFhYGEOHDrVYD6tr1656+rx588ifPz/ly5e3cBO4efMmPj4+qXIdyJs3L+LImbu0kkPvuVyUW/F6x7XlAZRSTwGbxejMrZRqBEwVkTbG43EAImI7gyU9cEGzKwZcNTtuBmzBwdCbgzJaGC8aNB+m33CjM7erXLhwwemX6FtvvSXJyckyatQom7QNGzakeVPdhIQEef31153W7Q6n74z8Yj137pzbric6Olri4+Olbt26enk7d+50eo615cg6nDhxwuI4Li5ORDxf+SZMwwPOZGiOI7nVrl3b4fnmm8haY523T58++qahpj0bzUP//v3ln3/+0be3ceRYn5nw9LW08bjkNm/ePClYsKAMHTpUbt68abHlz+HDhyUyMlJu3rwpkZGRcvz4cUlISJCFCxdK1qxZpUCBAtKvXz+ZNm2afPfdd3L27FkxGAwSHx8vJ0+elD59+ghoWzclJyfLgwcPLPqzr6+vXL9+3W3XQkoWpS8yJqBN/voWs4ld9gK2FiVv4DJQln+duas5K8OdwbVMsBuoZPx/KvCJMXxgjPsAmOPkfF1RMh4fAK67S1E6fPiwzSaZjjAYDLrCExcXJ/PmzdM7a+7cuR0+5N2xT5bBYHC6a/q1a9ceqfyMfhAfPHhQbty4Ib169ZKqVavKsWPHZOvWrXZnm6XEggUL5NChQzJ//nx9p/d//vnH6TnmG+sC8vvvv+uzp0TEZpxfJONl9jhJSkrSFURrli1bJhMmTEhxWMt0HziSm8FgsFFaAwICpEGDBg6VLpF/FaWtW7fq/wcGBoqIyLBhwwS0GY7mewuCtj8baL6AmZ3/pb7mTtJbbtu3b5fGjRtLnjx5UnzGpAbrza537typf5DcuHFDj0+PvpuiovRlxgRcm/W2BrgNJKJNAnvNGN8e+Adt9tuElMpxZ3BVUXoWzfHqJBAAFAAKos12u2j86+vkfGtFqbOxk7hFUapTp4707dvX7oaXjrh27ZrdF4H1Zq3g/h25Bw4c6PBFtGLFCpd8qOyRWR7EjRs3FkBKlSol/v7+kiNHDilevLj06dPHrnOvNQaDQYoWLSpZs2aVbt26yYkTJ6RQoULy5ptvPnLbnn/+eV3Wa9euzTQyexwkJyc7dbS31y9z5cqlb0ZbtmxZ/XxX5Pbw4UOb8ho1aiSA+Pv7W7TFlG5qQ40aNUREZNWqVQLIiBEjpFSpUgJI9erV5fz587Jjxw4ZMmSIAA6d9TMT/0t9zZ2kl9zi4+P1fvfJJ5+4dTkJ0zIYgMONipcsWSKAfPfdd26r18STrChlxuDS8gAiclxE6opIDRF5UUQiRCRMRJ4XkQrGv9bT+czP3ykiHc2OfxERJXb8k9LCX3/9RZ48eUhISHCaLzw8nAEDBqCUokyZMhZp+fPnZ/ny5QwbNowXX3yRrFmz8sUXXyAivPTSS+5ops6KFSsc+tsMGjSIrFmz2izY+CSxYcMGbt68SXBwMDt27ODevXv8/PPP5MiRAz8/P0qXLs0vv/xCdHS03fOvXLnC3bt3SUxMZP369axYsYKYmBi3LNxpvm1N796907Sx7JOKaWq+I5YuXcrs2bMt4urXr8+4ceMArW+mZjE86y1WAH0D2sDAQK5du2axf1/RokVZvnw5ISEhnDhxAoBPP/0UgG+++Uaf9nz66CT/rQAAIABJREFU9GkqV67Mq6++SmRkJEop7t27R968eXnmmWd45513SEpKcrmdHv63EBE2bNig+8idPn2aMWPGuHU5iR9++AHQfO+s3zUA58+f1xeCfexLiGTU0gDabZ5PKfWtUqpTul+nO8loTc1eIJUWJWc8ePBAVq5c6XQ4YcGCBRIfHy9nz56VxYsXS0hIiNvqd8a2bdss/DasQ2p5Er5Yt27dKoMGDdKn9RctWlS/3qxZs4qINo3WNNW8TZs2Aoifn1+aLW3WmM+MLFmypG7Ju3HjhlvKfxJp27at03vE5GPx+eefi4hrfS04ODjFoTxAunXrJrt375bExES5e/euHDt2TAwGg/z999+ilBJAmjVrZjFEsWXLFv386tWry6hRo6RChQpSpkwZAaRly5bpJqu0kpLMkpOTZc+ePdK6dWv92nLnzi25cuVy2bXgv4g7n2u7d++WZs2aSZ48eaR06dJSuXJlt5VtzqRJk2TChAkO06dMmSLNmzeXHTt26JYskx9lQEDAI/vc4cyiVBoxfJUxgSfUopThDbDbqEdUlJKSkuTUqVNSq1YtyZs3r82D2eTzAMjLL7/8SHU9KtZO0OYhMjIyVWU9CYqSifj4eBk+fLhs2bJF3n//fX3dkYiICImJidFl0Lp1awkICHB7/aY6TIqSKcyYMcPtdWVmTP5fKYXXXntN3n77bQHkjTfecKlv3r17N8VyZ82aJT179tSPCxQo4DT/xo0bZdeuXRZDddaY0h7nqviuEBUVJREREfLHH3/I2rVr5f3335cGDRpIsWLF9DYrpcTb21sKFSokn332mfTr189CaVrzwyq5eu2qJCcny5w5cyRr1qxSp06djL60dMWdz7WcOXPa9KkuXbrI+++/r69NNGXKFGnYsKEkJCSkuZ758+fLK6+8YhO/f/9+adWqlXh5eUmBAgUs0kwfcIUKFRJAxo0bl+b6U1SUvs6Y8KQqSk/0Xm/WBAcHM3LkSIupnTly5GDdunXUrVuXN954g8WLF1O4cGF3NveRcTacUbduXQ4dOuRSOU/ynkjXrl3jqaee4uDBg5QtW5auXbvq+xalRx/duXMnLVu2pGTJkty4ccMm/e7duxQpUsTt9WY2nPW9Z555Bl9fX3bt2gVoyzAMHjyYZcuW4e/vz44dOyzyGwwGZs6cSWBgIH///bfd6fzO6NixI7/88guHDx8mICCAc+fOkTdvXmJjYwkNDeXYsWP6Br8AderU4fDhwzbl7Nu3j8aNGwPw/vvv2wwnZhQPHjyw2D3Ay8uLQoUKUaZMGRo0aMCYMWPsDtNUrFiRixcvOi37888/Z8SIEW5vc2bg9u3b/PXXX/z555/cu3ePnTt38tZbb/Hmm2+mejheRLh06RIVK1a0mz5p0iSmT58OaP1x06ZNaWrz1atXqVOnDqGhofo9lpCQgI+PD3PmzKFOnTrUrFmTggUL2j0/PDycqKgonnrqqTTVr5RCHC0PUEbJoQ/spaQ/Xm9yCfgT2CQiaRNuRpDRmpq9gBOLkvXss3379knXrl2lXLlyNl8KKU0pzyyUKFFCb3OvXr1srqNXr14SHBycYjlPkkXJGnPHysKFC0uNGjXSPATpCqGhobpF6bPPPhMRkezZs+t1li5dOl3qzWyY9zPrOJPzNGirZYuI3Lx5U5db2bJl5dChQ/p57dq1E8BmwdXJkyfLBx98YDHjVET0ZThcnRV5+fJlgZRnCZkvFluoUKHUiCNdMZ9AkhorkEES5JbMkMvx70uJUnmkYOGc0rVfNTn/YJzcffirDB06VLy8vGTo0KHp2PqMYf369VKyZElRSkmJEiWkcuXK4ufnpw/JArJq1apUl3vixAnp1auXVKtWTfbs2WPhfG0KRYsWfaS258+fXw4cOCAilvfZL7/8kuYyTRMrGjdu7NRVgJQsSt9kTMBjUXIfSim5desWq1at4sSJE3z//fcULVqUu3fv6nlatGhBu3btiIuL49KlS4SHhxMTE8Mvv/zyRFpVsmfPri/KFxsbS758+Sz2kPPx8UnxC/1JtigBVKlShfPnz9OhQwf8/Pz49ttvgfSxKJmwllmOHDl0OWfGe8OdnD9/nipVqthNmzp1KlOnTgW0Be+Sk5MpUaIEWbJk4cyZM3z66aesWLGCESNG8Pnnn7Nu3Tp69+7Nxx9/zMGDBwkICKB8+fJOLSFHjx6lTp06dO7cmY0bN6bY3vLlyxMUFESPHj3w9vYmT548NGjQgE6dOpE3b168vb3x9vamcePGutO4iS5durAhYD0POUMy98lGKXywtd64giDEcZ49e3dSodyz3LiUTNOmTZ2eExcXR5MmTTh69Chdu3Zl/Pjx1K2b4rp7ACRyj3t8jWBvsoo3xRjNll920rVrVz766CN94dX0IDw8nI4dO1K6dGn69u3LCy+8QM6cOdOtvhkzZrBo0SJq165t00eCgoIoX748AMnJyXh5ubp1qX32799Po0aNKFu2LFeuXAEe7Rng7+/PoUOHuH37tv6MSUxMxNvbO81lBgYG8vzzzwMQERFBREQEW7dupVmzZnTv3p1p06ZR5P/ZO+/wKKruj3/uzPZk0zsbCBBqAGmCdKQrFpSmFLFS7OIrKhYE2yu8KmIvYAERpSgKKk1Eeu8BaYGQBNKzm+1l5vfHJhvyAoKKCu+P7/PkebIzs3fu3L1z5zvnfM85CQl07979ty1Kp5fC/FsgjT6/hJMXGy5aovTwww8zderU0DZZlgkEAqHPn376KQMGDPhLb9K/G4sXL+a6665DURSEEKSmplZzC23YsIFGjRpVM9+fikudKBUWFvLWW28xadKkatv/aqJ0xx13MH/+/NP2XYz3xoVEYWHhae7F+vXr065dO06ePHlaNM7OnTtp2LAhHo+Hxo0bk5OTw2uvvcbPP//Mt99+e1r7hw4dom7dumc9f6UrwmQy/Wb2+krUrl2bo0ePYjAYqFWrFqWlpRQUFJzx2JYtW2Kpmczoe2+n/4234XK5GD6mDS+/0yN0jEwMsdyGlt/nin/z4/sY/9BH2MurE5ekpCQee+wx7r//fj777DMyMzMpKysjKSmJx8ffR5NGV5GTk4NOp+W44zMUTS4y0URzIxrO7IJRUfFygiLeBZTT9gv0xHE3OlJ48803eeihh+jTpw/Hjx/n448/Jj09nRUrVtCzZ8+zrhv/Da/Xe8YIMLvdTlRUFBqNhtjYWAoKCggEAhgMBqZOncrIkSPPq/3fg6SkJLRaLVOnTqV///4AuDmIlSWouMneF03bxnfTrFmzUKTkH0WDBg04cOBAtW1/dA1wuVzMnz+f4cOHExYWRosWLVi5cuWfIkmnIhAIIMsyECRf33zzDaNGjUIIQUlJSWXfLxOlC4SLliitXr06VEFcVdU//bZwqSEQCBAXF1dNk1GJGTNmnDFU+1InShCsuN22bdvQ58zMzLNaPc6FXbt2sXPnTiRJwmQyYbPZkGWZqKgovF4vBw4c4O233z6jRqlu3bocOnToD1/HpYKSkhKmTp3KfffdR2JiYmj72rVrf9NKYrFYyMvLQ1FOf3gDREdHhxbs/8aNN97I0aNH2bVr12n7+vXrx/Tp04mJiTltn81mIzIykvj4+BBBcnOQH3+eTnGRjdxDeiR/LFu3bWXGghE42QYIFny2iace+Ilym5cmLRJp0jKJ6FgjT73SDdCTxKPIVC+RoeKnnNU42QVI7Nus5d0pK9ibuZvMvb+SkhrBoxM7ISRB7fQYdv2cyoSnXwROf6k7dcwq51p2YDzBJU0gYSSRsUhUL2PkIpMSvgHOTiIFBpIYh1RROL1Lly788ssvpx0nSRJfffVViGz8N9auXcsLL7zAzz//jNvtJiwsjPj4eObMmRO6H7ds2cKVV17JzJkzGTZsWGhbnz59sNvtvPbaa+h0On744Qfsdjvp6ence++9ZGRknLX/fr+fTz75hPT0dLp27Vpt37hx45gyZQrTpk3jgQceCI5JYDXl/s9QZCMB2QRCpm/rWezcmo3D4fjDL84ulwuTycTixYvp168f8+fP54YbbvjDWsVRo0aFLOKzZs1iyJAhvyu1xh9FIBBgwoQJvPjii79NlJ76y7tyRkijLhOlC4Y/Kub+X0Ol4PhsKCwsrFb/558kSpU5kcLDwy9Ie6qq4nA4/lB7qqoyffp07rnnHiBYW+xUN+apsFgsTJ48mZ9//jm0sFVCkiRee+21/1mR7Nmwe/dumjVrFvrctm1bWrVqxaJFi+jcuTODBg1iypQprF69mgceeICGDRty3333ndbOme5hv9+PVqsF4N5772XKlCmUl5eTnp5eLa9WkyZNeGL8vzCYfDjsCnVqNaZ3794oisJHH33E0KFDKWcjNqpbsrSkEkY7ypgHKHBKH0YNWsDan44RCKiUW4Nu7th4E1e2bcqc2e+i0+cQkPRIci0c/pmElfyAXxOO31FGUt3d+PzBtlLTolifdW+180pE4j52E++89QFhYWF06tSJ7t27U85adh6cw8tPLiO9XhppDbX0vD6d6JiqB7pATzQDMVL1QmBjJeUsP8OvoyOYkMaLRBixDEeHJTjeBPCRT2FBCbFRtVmy+nUkUw6t29XmmpYfs3P7r6TVScVaZuOW4T25/a4+vPfGMmbP+gaPx4PFYmHIkCGkpKSQm5vL7Nmzyc3NBUCj0RAeHk5ZWRmvvvoqY8eODfVoz549DBkyhP379wOQlpaG0WgkNzeX4uJiTCYTGzduJC0tjfDwcIqKili7di3z589n9uzZSJIUmhc+n49u3bpht9vZuHEj8fHxHD58GLPZjMv5IVLBFHQnj4OkggLOyLpYIxpSw7KAeul16NK1G2NG3UWL+lpUtRDVn4fwFaGYIlD19dHorkSI09fIShKelZVF06ZNWbZsGRMmTKBFixbVcq+dC5Xzc9SoUUAwJ9wfFWT/GZxTzP3PEaVLUsx9mShd5FAUhUaNGp1mEq5EvXr1Qv8nJCSc1RXxV6NSi/Ldd98RHR1N27ZtL5iZ+fdg2LBhfP7550Awais/Px+Hw3Gaa+e5555jwoQJlJeX89ZbbzF+/Nlt0UuWLKFXr15/ab8vJpSVlWE0GsnLy2Po0KFs3LiRmJgYioqKQsdYLBY2btxISkoKEJyn7733Hi+++GK1JJ4tWrQ4LfHkoEGDmDt3LrGxsQwbNozPPvuM+Ph4GjRowJEjR9i7d+9Z+3aqFiWXZ4HTLTcggRqo2CcQATcCBVVoUSUdCIncbCsL52Qy/fWN5J908sakSO68vykuY238mggUYUDnLiKybB1z1qUwYtAqvlt5HRvWlzNkTBsio7SnnTOKGwmj+styCXNwsRsAV7kBo/l0naFARwy3YiAYiaWikMczZx2DoKvNgqCqDwpOCvmAAFZARaBHwUFQPwyoAd6YuJr3pm4NkcRKxMQZ+WTOs1zTfTQaoqraVBR27NhBZmYmNpuNDRs20L9//zMWmj4btm3bRrdu3bBarUCVtU2j0RAZGcmoUaN48cUXOXjwIMOGDWPz5s00bNgQv9/PhAkTGDhwIC5PGX7NR+gKv8d4bDeyx4EwacCsQy11QqHKxM8V3lkBhRW11DMs8MNTYKklUCP0qBFGlLBI/GEJaGLeRqut/jv17NmT5cuXU1paSnR09druW7duDRVJPxeWLVvG4MGDufXWW3n77bfPe5wuNH6TKKX9g0Rp5GWL0gXDZaJ0Og4ePEhGRsZplpHevXuHQmTNZjPl5eX/RPdwOBwsW7YstCACJCcnEx8fT3p6OkII1q1bR5cuXXjllVeoWbPmBTv3Cy+8wDPPVD1YNBoNw4cPZ926dfz666906NCBtWvXMm7cOMaMGcP+/ftp3bo1cXFxrFu3LuTiXbNmDR06dGDdunXs2rWLMWPGhNr8/zYfN27cyFVXXXXa9meffZaJEyee03r5+eefM2zYMOLi4jh+/DgGgyG0r6CggJSUlJB7KiwsDKfTGRrjf/3rX2zZ8zWb1x7H8V86oDFjxtCoUSNSUlJ44/1/sXrZ0dC+oaOa88p711RZkQIehOJAlYwgDBBy36sgJLzF2fRs/QWHjwY4fKgDNaJsKJKBcmMGfm0MkQWr8JmTGXLLVn5YfILJzyczekxthEaP25CKR2+p1rcIrsFMdXeljZ8oZxXgPwtREmiIJ4H7EARfLBScnODFs45tDMOqWZ8ASphbQcjORByBgBtFhVaW9ynMd1CrbiTHDlvR6WUG3dGMYSNb0qRFItEMwcTZXWV/BidPnmTVqlU0a9bsNHf6nj17aNq0KSNGjODOO+9k3rx53HzzzXTuehVHy94gwfEVhoIcRL4N4fUgtBL4AqhlBH9OIyABdnhjCTwyB7q2lvn+85qMf/4ES9d5yDyi8uLDeh59pA661J0IESSa5eXltGrVikOHDnHgwAHq1auHLMsUFBTQo0cPxo0bxy233HLO6ztw4ADLly9HURTuu+++v8XVdjaciyhtefrv7lEQ4p7LROmC4TJROjv+++Z76623Qm6Pi0WjZLPZuOOOO9i2bRtmsxm73Y4kSXTt2pUff/yR3Nxc6tWrx9NPP81tt932p8/ndDoJC6vSmLRt25Y333yTNm3aVDvO5XJhMBhCY+hwOHjzzTfPK0po4sSJPPvss3+6r5cSzrTQVwYanGuuJSbGUlBw1qpGADw4sgtvvLcShOD6669n0aJFoX1Gk4bGVyTywPj2ZB0sYeLYYM6muLg4SktLURSFiCgD1lIXAOYIHeU2L8NHN+fld3pzeH8hetmNpV5NKmsnnIpvv8rk3luCUVR6vcTxopsxlR1E1irYI5rg08SgSCY0gWLK809Sr+b3AEREatmy9zriogR5VjNlLhOpaZHIskQCD6Clel4fFR+FfISfArx5xSQfmItGsaIajdhrtkZNGk2k6IMQeoSQUCuiyM9uUZJJ5DFkqruk85QXEYFCdL48PNp4kCNASBQVOpg/cy+DRmRgs3rpkP4eP267gyYtkiktcXLnDfPYtjGPgF/hvbk3cd2ARsQxBj2Ws5z/r8H3339P3759Qxan2NhYiouLadykLj8tTifavx/t4VyI0KE6vEGiVOYFNxAAVQB2EGGAWdD9SZWVQUMeQkDzOrD9MDSpJ7FrYQRq+iYkbT1cLhc9e/YkLS2Nd999F7/fH9LH2e32auvKb2HBggXs3r2bdu3aXRTW58tE6cLiMlG6xDBt2rRqmplTxYYXC1E6FyprKy1fvpzo6GimTp3K0KFDf3c7x48f5+6772bp0qWhbWFhYaHQ8ePHj5Obm4vFUrXo9+nThx9//JGIiAhsNhtjxozh7rvvxu12c+LECUaPHl3NxVSJ7OxsUlNT/9gFX+K47bbbmDlzJgArV66ka9euvznXMjLqkJmZhV4LberJHCuG7PzTLR0tG0vUqJHG9n3ekMjZ7/djkxfiZCsAe3bk06fFdOo2iGHj/jeIZRiKouD3+dDYnsF+7APi2ls51dA6Zlxb3p28EYBv1t5G6/YWPB4/77yyngWz9pB1sLRaPzZu7UG99DA8RgsBTQSyrxSh+gjIZlQ5KLAOeBz4bXnUTZx31nE624NVRcFnW4xz9SPIbdMImKLQ5Bxk4du7aFwLGjVJIhAZg7tGOu7kxmjkGih4UPhvoimjIQE/JwAQig8UJ1LAB0KDKskokhGEjMZThKk4kwHDd7F0ZZWVWasV/LrlCqIsNXFF1keVg9FtQ3t/wS/LsjjmfwKdlEgiD5/1Ov8q2O12duzYwXPPPceRI0fIzs4mEAjwyhvXc+dtejSeEvymGIy5+zEc349k9aMWAV5AD8QDJgFRRhSPwpEjAVzhEVhaWBh0+yF+Wuvg6CI9NdNNUHsnQpfKgAED8Pv9zJ07F61We1rKjHM9h7Kysli+fDkjR47k559/pkuXLn/lEJ03zkmUzu7Z/Ush7r5MlC4YLhOls8PtdmM0Bhdvr9cbEsbCpUOUKpGVlUW9evUIBAL06NGDJUuWnFd0Y15eHoMHD2bNmjWhnCErVqzA4/FQVlbG448/zrRp00K6pFatWrFjx44ztmWxWNi9ezdRUUFtxqFDh0IWL0mS6N69O1OnTqVnz54X7sIvQZw4caKaHslut2M2m1FRsCvfI0reQg24cYbXJin8k7O2s+njcJZvdjH+nSBxEoRUNCFEx4ZRWlylKdMbZPaWPkqSYWBIA6TaPkEtewpBgJtGWVn44/llAR/9r7aEGRVefT6Y7X7jyvo0axaBKgQg4ZfCKA9rQljBToz2I6h6DcLhYutuhfa3VNf/rdkygARLP5Z8fYwHxjxFeXn5acEHbrebLVu20C7sPxRE6TAmBpDtVh67bQXvLwlev3WhjJrWEmPpUXzREVjrXo1iigNReS8IIrkeD0dwswcAye8A/CjChAg4Q9oryWNHbz2CTxuNw+YgPX0xn74STq+R17J+fQl96hxCdzIPNSDjaNyG8pjmoDGwaN4BRg/6miOeceh14aT8hkbqr8Ljjz/O5MmTkSSJtu2aEx7tx+O28sq42lwZtw3JKMCvghKAEj9qlidoRYoGYgCzAcoCeBKSEYlGZFsRwu4l1xVJrR451EgNI2uBETkqAqnuId6YNo3Jkyeze/duYmJi8PoPsWbVWLr3qNIYB7xFSNrqqRu8Xi8nT54MZVGv1NQ1btz47xqqc+KcROkfMo6Luy5NMfffr7a9jD+FSq3Ht99+W40kXYqoXbs2Xq+XYcOGMWfOHK666io2bdp01uMVReHpp5/m5ZdfBsBkMtGkSRO++OILAL788ksAOnXqxOTJk0N5Rn4LvXv3JjIyMvQ5PT09lMQO+E1h8f8nJCcns2DBAm6++WaMRiMFBTm4fGtw+77GYN+MtiQX2eci3JPJiS0xJLcuQRbwwWhYtF3w7jcDMLjzsae0Yvwdr4faPdPrUCVJ6t63Lj99fxiPO8D2DYVc3z4jGPQFKO6P8WiScEfVJTFtM3A09P35P99K285pPDTiO+bP3IMQkN4ojoOZRbz3n42h424dEEXTpmYQIuicU/14dfGEn9iGEmFG1ZiRi4uQSktpf0uVyWr+4m6sW32S96eVMvOzYaHtlURp/fr1jB07ln379mG1WhFCYDLKPPxYRx5+NAa9tYhrW8EXv4AlHtzNriW2aBWqqqAJCGKOLUMJj8AVkY4rsgEIqYIkHUQKOND4rSjIoCqYyzYjRACvKRmPIZWj63YzbPg67H6J/KJg6oYajVLRyCo9mtrQHc5FjYtCiY9FMUaAJhh9t2ZFcPyubf0xcfExxEX/wqNP3E671jee5ub7sygoKGDgwIFYLBZ69erFk08+yYkTJxBC8OC4G3n8hZoIoaCoGiJ+XYU/LxdxvBwUNSjBMgqQQYQD0QISjKCRoNhDICYcbbiDsiP53Pigh+2HwO0NWtQ2bL4Om95PuG8gQwYMYM+ePaxcuZKYmBj8SjazPryBic8FI/e+fDeOq9JdiJ2pqNoERIOfsHnjQmtFq1atANi+fftFRZAuAVhVVb3wCbf+YlwmSpcYiouLAejbt+8/3JMLA0mSmD17Nv369WP48OFnPW7Lli307duXsrIy9Ho9CQkJJCYmYrPZ6Nq1Ky+99FLooXS+2L9/P1qt9h8VXV7s8FFEkToLNZDL1t3Bmm/DRrTB6n4JEwdRZDPlpgx0chJm+3ZkFKIigpYQoxGyiiTyvOFMeTePMf9qiUk6u6VYCEF8fDwJCfHs2bOX3Gwbm7If5MrUaQy8+mO02pk47MfRBAopNdRi9Y4AyxYfJrVxfSqJ0ujbw7mqYzIq8MoH17Jnez6/7inkYGYRqWmRWNIi2bEhG5cbNm1zsmp1OV27VBJllYAUhiY8jAj7DkR+Kbj8/LRbA/h4YYyGJ2+XsUcUMfD6TBQlEwhGCZaVlZGYlEAAG0uW/siGDRuYNPFeBl/9Mxb1MI99HMEXM4/wn3//wsfvNOaWK8H6DvjqxuMvPwIlbiThQxg1qJJCwB/A6DyA1p8LGg3lYSVIPh9m61ZkvHj18djDm2BN6IbkK0JWHOB3sumgm8MnITZOx0P3J+JVJOr2vg63LOEOq40jOoMo6xoc2nRcxvoEqapg0rSemCN1ZO4oxFpWzvGjW+h05XJSUiMYNvR2Xnn5jQs2pwYPHsymTZs4cOAAc+fOpVmzZqxatYr42oU41S+QvU6iDv6A1l2MiDFSmlqXYstANK5ywjevQuNzQZgGNc8HGhURcIJRBgnknFIo0TBzmcravTD4aih3wLhBEGU9TI4hnXY9nuHgwYNYrVYiIiLwBY7gPT6CQRnHWXelyto9cP/4YmokCIb20+Ky5fLsu1WJU6+//no+/fTT06LjLilcXvJ+Fy673i4xTJgwgUmTJp3Rd36pud5ORWWW5lOzOXft2jWUFqGgoIDGjRvz008/VcsdVYk2bdpUKx589913061bN4YMGRLa1qhRI/bs2RNy761evZohQ4awb9++C5b/6VKEx+OhR48eoSLEdevWxWQy0aFjG957dzoAg7pL7MlSaVFH5eNHw7GrFkzmUvwJyTj8Wr6ac4TJH5aSV6hwdF0sqW2LQ+0/8mxHXp+0hpuHNuTtT3szeugPLPzyALfedQXH9hWzbl1Vws+nnnqKTp060adPHwA+/34obTvHkx5elaVfoxFodRJuZ4CIMAmrI2g5ufeRJrzxpJuC2NuIkgZQqs7C5fSw8pvttG9rIjE1Bo8umcii9dhL8/nym3Jeeb0QVYWUZC0Db4qhbpMadG6YR4JkQ5Q6Ka3dHbs+htrxswkEIHdlGMlRRlaWfEmPHj24dcgg3p/1MAHKcLAFFR+vT1rFJ29u48j+KxFldtQwI/rCQziLtYx58RhzV6hkpEmUWRX0RplSp0q/NgrvT00AvYyiCCSvD+HzYktsgyO+ZVCRXPF02/BLNo2bJxARURVJeCrapr2FJVHwy/QIhOJnkxaUAAAgAElEQVQnYI7AF1UDBQ2GkoMIFTzGJOzxrVC04SCd2fKauSufIb3mUJTv4NixYxckUtXtdtOgQQOys7MpLi4mJiYmVBKmNPAN4aXLMFqPIPAieT0EAjoKwtsRZ92E9sBJ1PwAgZrxBFKikKJVZJzIZWVQ5gEkiJDBr3CiDGpc6+XRYTLj+ytEh6nskzPI6B60EFfqyfxKLsqhjsjeYn7ZHOC28W7GjK7B4P6xtGi/i/LyqoSqGo2GFStW0Llz5z89Dn81ftP1VluoWyb83T0KQtxxWaN0wXCZKJ0dlWUc/teI0vTp0xk5ciRWqxWDwcCMGTMYNWoUTZs2DbnHxo8ff0YNU2XETPfu3Vm6dGm1Yyrrx51JPwKX9phdKJxqURs9ehDh4SmUldnxqMeZOT1YxqRlA4ltvwYfGrGREJ8QzDJ967VGtmab8flVdm8vrNbu6zP60LlDBK0afIWlVgQ5x2w8/NSVPD6xPUWFTq7vNI/Jr4+mUdoQmjZt+pt9jAwDqwM2LG/EkZIojm86Qs+4ItKu6UDMFcFM1L8evJqUGlcjGwaB6iQQKENbci+Sy4XqcuGKzQCtgpCNoGjR+7M4mR+g23VHOHI0mFtISAJVUamRKCEbw2nVzsJTr3Tm61k7eXH8eo6U3kG41kt4oCXfLo3lloG3o9PJTPv8Bq69uQGSJPH6pDV8/OZmdhU8zEdvbObdKRv4z7/rcE2nSJTEmrz3fg6rlhwl3ViOq7AEd3wsX31dTEqSzP5trQk7uhucXrw1kvEZYnl+/C7m/6KSlGxkzwEfNlvQDRgda8DrCRAIqOh1EuFaH+3rq8xdFxwzZZMBd3J9bAntUSUteushfPp4FK0ZrfMEYUU7KLP0JGA8c/mUSnRu8D45R8tp2LAhMTEx1KhRA7PZjNVqJT09nU6dOp2m4Qtgp5BPKS3Zy4ENu1AlHdt3enn7P7vwuFUeeOABXnrpJRTFSanzSfTOjWj9RWgLisDqJBBuxBdXE685BWVnPhF794HVixIfhhphQM7QIJn8IAmQNeDzIvJtlJcGiLzu9LUxzACK0NB/wCDeefu90D3vKh2P4cTbvPC2m2ffCaaimPlhXfr3j+OVV3OZ+GKQxJv08MlncxgwcNAlYYG+TJQuLM6bKAkhZGALkKuq6nVCiNrAHIIyum3AcFVVvUKIcGA2EA4MU1U1TwhxOzADaK6q6q6K9vYA16mqevQM57pMlM6CJ598koULF5K55WOwLwM5CqKGgRx1yT70K3OoxMbG4vf7sVqt6HQ62rdvz5dffnnOEgKnLly/d95cqmN2waCqdGhTl3VbgkVA42Nlml8Zx6wFN6FqIvDLKiCj8RfiL81nzmcHef6FgzTPsLBqfXaomQ7dUvlq6UASt77PcZ8OU91WRHASY/Ke0DEJySYKTjjp2qsm7825lo1rinnk9uUMHTYMvcnO8dxjfDnzp9/sbuBYKgFfAM32fJ6ZHeCtJWB1Qs0UiSPLo7FHNcUe1wI0BtYuP8y+b5dSZFM5cFzBaJIZdFtjrr6pFYpsRFYCxDKGInke+/YeZkDXWXy+5BYOZhYwcexPREYZyDpUiizD/Q/XZ80aK4oq8cWyWwk365HVeA5nHaRj+ruh/oVH6OhzUwNWLDpE3QYxbFmXG9pnsVhYsmsgkdFGUFU0ziJiTxxDFkcY8kg2c77zsn6GjrbxXlStzDGHTK8HvRzKBZMB6tU3s3NXOU++1IX0RnH8vOQIyZYIDJoAypY1bDsC31fk9pz1UQMGtLGjsxdRkn4t7vhGofxSQvUiVB9CVRCKH782EsTZ9Xxet46xt+5ARaWg6CgFBSdxOX2Eh0VRUmyjuLiYxMREWrZsSd26dYmLi2Hxshls35SLz6sgywIV0OtlWrez8M6X/agTezv4SvHaJ6N1FaA/mYVUZkOgBvMjIVAwQqSOcm1tzFmZBJqlIiku5GNF4PIhTIBR5t9fQpMEH09Oh73Z1fv+7jiZ2Utlkmp35LVpn1aLgAXw5g1iwwfz6TKxynJUNwHybILkZB1vv1Ybi5RLRvsPEXGDf3NuXkw4J1F67m/uUAXE7f/7RGks0BqIqCBKXwELVFWdI4R4D9ipquq7QojRwHEgGxiqquoTFURpErBeVdXBFe1dJkp/AJVlTfwf6yDHG1SZmWWkIduxa9IuyYd+UVERTZs2xel0cuedd/Loo4+etqCdiszMTK655hrKysqw2WzV9l0mSr8PysHBHCraSIP2x4Cgh0dV4YXnGnL7E31AV1F/TFUwHd2Eed9GZI+TY24Lc7aVsHipi8yjKh991Ze2PeqRsGchmsR++Lzf4sNIWFoVUcp33cFJq5Hb+s5j/95i2ndszaOPPM23S17mg2nr6NyjJs9P603jhlcTL1W5TNt2TKbgZDlZh+z8FmrVMnLDkBaER+hY9t1Bsg6W0m9QHWonudm7s5Av5gWToWY0T6Tn9elc0TqZogIXCUkGdm/J4z8T14faqlEzgoKTdnzeqgeoViPw+VW69kxl5veDELIeBFjESwBIkkBRqs+/e8a24dHnOlKU7+SWqxdRUHgCS61I3vqgLd1qZCIkPyBxeHsJVw53YDJKGMO0CFnLsaPVrzctPZovlt9Kas1INI4CIo+uIMeTzFtf2dmzu5wVPxwlMtpAmw7JzPyqE7KkEnZ0I4bSbE62HImiCwc1gKS4qiQqqooqJBS5Mq2BxH8X341hCEYyKOJTPARd4ZLfgcZXTpj2LvZu1/LVV1/x7bffcujQIRRVoXHTeEaPTKff4AZIMUnBiVX5QqMqCL8NvfsEOucJcHrRZWcjl9iQyr3B0nYeQAb0YE9LY09OHpEGyDD6UH0qWEF1wG2z4fMqjztNa8LubLi9K8x4XECUAXHFFjCeWXCtbOhP5g8LuHU67KngtLsmQVY5RHVrRJs2ZnT+YUgJD5x50l2kuIiJ0iUZ9XZeREkIYQE+BV4ExgLXA4VAkqqqfiFEO+A5VVV7CyHuA44QJEq3qar6eAVRagN0BvqrqvrrZaL0x1BZ/sE/GdCDUAhKFwxgv+kQEQlnr9b+vwKTyYTL5Qp9vuaaa0hMTCQtLY0JE36fTfn/M1EKuHbgzRqO5ug+dH0DLJqs4d4pfgptYF1pxla/B+6YhsGosICXsBPbMB3eiquknIwHU8jJyeGzKeHc0thOfvu7QBLU0E9AKEX4TvTj+IEy6l5TlandfTQNa/IgdKILX31ynPvvewCfz8eoR9vQf3hTejUP6qEaNo1n/+7Cs3X7nBhyT3OSLWbuHXcVev0p8SqqwrbNJ1mzLIsvpu8gJlpDUrKBwrwyGmdE0L5XQ+JrxDH8+m9wu/zIGkGgor7bNdcmElAEjz59Jb07Btf3ia93466Hr+LROxfx5cdVBX5vuaMxWr8NVWPi2Wl9MIWFgRB88cFhFi3YxI5NJ5AkyDrcATWgRZ+1D539BMv2CJ77SGXjjurZyP8bazYOZNA1cyl1CXy+YP9iIyXuf7A+dzx9IwgJoXrR+YsRfi8xuxZQ1Ggg3shUhOJDqJ5qWl4VUKRojKIpEfTEymK8ZCEwEsm1GGmAjyIKCEYrGq17Mbqz8Rli8WnjkKUMzIaxyJhQVZWioqHErvgKoQRAAx5LTYqvGhLMjK6qCJ8Ds303kupDKC70RceRSkuDUW1lAXCpqIVAGfgCcMuamnyzNGgq+vL1MAbGuZnzQ4Chs4N9r5MIMx6AD5bApgNwKB/8XxJMJ9BxBSL67PUyldkZUJhJnYlwrCK91swhMKQT+Lo2RE4ehSby788p9WdxTqI08e/uURBixKVpUTrfqLepwDig8okSC5Spquqv+JwD1Kj4/3PgC8AAnBrGpACTgfHAiD/R5//XGDlyJHPnzmVX7WY00x5A6Hz4wmJQAuEENj0A133/T3fxL0FJSQmxsdW1FF988cV5lRa4lKCqKp9++in9+vUL5Xb6q+B2voVpXyYf79EBAezJV5BduJVmDXVIaoCond8gAYHIMALR8cgl5VDuZOAEhYY1YN9rsPyEgq4vyPIMAgEVVf0Q/AbkslLS6lZ35+hUB+HObExR1zLyHrjj9jt5bfoNPDHmR95/tSotxP7dhfS5qT4/fh20Xiz4vCYrN0i8+ebRs16LXg+de6Xz6ozriIk7SwV5IdOyTQotW0XwyL/SkISEzpWL0X4YVejxmQR+rY9Hn+vIi4//HCJJAL06KHzzbTmbPvsZCD7zHaVlALicVakDDu3uQqJSgGJOxRcZi6rmoS0qRpV0DOluZNQ1CeTlRNKoww6699rJZ7OvokbN2viUNDqke5gasZun3/SxYvOZXxR796tPx7ZzCTNJREVoiDYpLPw4gRoNE1AVCVdJJu7YJqhCF0y9UCHUVvQmImyb0PlLUIQGp7E+Pm0wA7VMBMniudA5Yrn1tPP6ORk81mtDozrxmhKxhzcP5npSnTjUFzGIZsjOQsx7VqG6VVRn8PwaKR/htqEaw0EVqKgIaxmyrQhPeALOWj3wpZtB6JADDvTWbIy/buejT4q5bwa0bKaSkiDIK1AZ/IiDUx1gn4yE2zpD++dhw6/QrRlsmQyyDMT3hd8gSaqqQtl+/PogSXrmWog2wfDZMKQ5aMU9SJcgSbqMC49zEiUhxHVAgaqqW4UQXSs3n+FQFUBV1TLgmrM0Nxt4qkLf9Jv4p2qWXexo27Ytt9yUwayFZUy4I5FAuBnJ50bVaSiNi8Vv3YJBavBPdzMIbzbKkedQpFwIjwRzH+Tw2xDi92Wl2LBhA3feeWc1d9ywYcPo27fvn54n/10s95+G3W5n79699OrVC1mWyczMZOnSpVgsFq6++urTyOKfgSvfij8pnQkPO7FYwOMQWCwWbmgHdo8mmMBPA5i0CB/glXH7DGSWwn8GJ9JoLFzbQYPF4qd9h/Y8/PDD2MptCPSo/kbgd2KxBF06kWZBuTuKgM1EQC7H5XLh4wQduzRh8YZ0Rg34BoCBI5rS/dq61LH42LPZCcCDjys89HQ7LBZ/qO9XdYhnw9qg1enlV5rQ8ZoM/HIECIGrYkpIAReS4iKApiLDtgSqQKgCvc+KJuDA7dNSZuiJ35AMCFBUbh8N38w6jrW0qnjsq+8C6DmcCxZLJHOGQIO6HspzfTwz+Sa2rQ2eNPynw/jMemxtG+OLigO/hM4XRnj+dtQyE4EIK9HhsSxcNoznntjMtd130rWjiRdH+AjHx4SPYvj1RDQWC8RHCWwugcdT5Qrbu8WJxWJh+qetaSWO4a1hwZFwBbmSBm3xCcJzd6Er24jXmIxDG41QfASiuiGKi/GqMj6C5AhHAR5TCqpkJEIaTLn47fvITzguDGhdNiSfFpu5DdhPCaxQFHze1ZiK9qEKA+6kGgiPirBDWaNueH2x4NdAwI+hPIcyTSM8lhRUOQy8AnxgyN9J2NHNvP81TFtkBCyMHJnMveOv47qOH1J5+9eMF4QLlcwCePp7uLGbTI4jgMUCM58MJqAsD0RDg+nw3+uDqsDx2aglG1BdHghPQTFCq8bQqhncPwssFrDpQLgSkP5Xn0MXvx79osL5PLE6ADcIIa4laCWKIGhhihJCaCqsShYg7zfaAKDCTfcq8Pi5jv3/6g45H4wcpqVb/2xemxX8fPCnWtRK8yB8u4iwXw8xMxDGs3HVvwk5cwgsuhU8ICKAKBDhS/E3WYUmaf7vihw5U+2kyqSTFwIX01wzm81MmTIl9Hnw4MEcO3aMfv36kZ6eTnZ2NikpKdWSYv5RyJFhOC0tycmZA0CyK5+cHB9R4RLmJDOBiBiQNGhcRYAKyYJPFtrIyfFyxJVMvTperhrQmbEfJhMWrkOIRZSzCD2NUZr3wqf1MfCOXzi6J4tPJnkxG9yo0VezcPlypkyZTHh4DtnHCvj1QJWr6dZR/UixRKD35jL+CSP33n8QgMdGf1mt7zqDLlT2ZNILTlYOaYmWKmIj+0rR+QoQPh8mx2FkfxmOqOYgwKuJQqO14zLVRRV6tBD8bkhD48OvWsnNLWL8y52Y/dFubh6YitZbyr9fzSEhRtD67lqowk3UoQ9IKXFxZys/kxYqHBSpPPV2AYcmfEVpiZtr+9XlhSmtMDXMgDKBYi5HI2Q6pllZtiyVaS/t5s33c5j1hUqdGoLXn9DT70E3lgTB6kUt6DIgi61bSslonsB3G0bw9exMHr1zMb2757B5dXuS6zXBKBT0J/YTXbwEa/0OmIqPE1Z6AuH3obPlYW12D2HKakQ17ZEes6kWGv0IJHHuemYq4XiJAv1JIuwF+MynjBeAqiL5FHxRjfE1bFv1O9jy0Wr0yCY/4Ef2WTEp+QTQEVW0F0PZMYRORRE6NPnF4Fc4kCeTkxMgtZaZ+yddx8p5q8jJyaFNSx0zvu6HT5PAnNEfsXRbMBt7z8fgRC4EVHjsLZj1IIhYJ0SfHgSirh2IunseIgqQYdDbIPvAVwL+PKiYUkRogcRGSBfR2nAZ/xx+V3qACovSvyrE3HOB+aeIuXepqvrOWb53O9BaVdX7hRA6IJOgG6/tZY3S74f9aBeatF/NsRPBMTrwSyzpdTSUu5IwG/NByIiEVaD5Z/RKatE6lM86gB2ICBIloQkWrhTJEnQ5jNCmnXd7//nPf3jsscdCn0tKSi5YsreLXaOkqioOh4Pw8HD8fj9arZZu3bqxYsWKP92217kUqzSb/OxCmjb4ngEdYN5a+OhFI3cOqhBxCxF6Ho551sl7n7u4bkBDXp9+K0azGxCnvZ3ayz3kZdvweAI0apbAT4sP8eDwb3h9Ymuuv/Vr+g8YwNq1awFo09rEpi1O2l0VzvuzryUmLR2tqE2U0pp9Oa/RvNbMs/ZfowF/hZFp8eY7uKJ1cuWgofXmY7AfJ9y6q6KPCkInoyAhUPHokymJ6QmShNvtZ8HM3Xg8AaZP3YROr+H4sTJcDj9Lt99J4ysSkXzF6L35zPvyBOMf20JxqcKeHW2pE2FHdtsoEinUaLQRISDZEkHd+jH4fF62bjgZivx659P+3NDfiN55krCcrWhKy0EreOsHDY/+24Xfr6LVVumOKmEyyTidAV55qwvPP7kWh92PqsKA4Y2Y+mlQkxS34hN8TWsTUHU4YpqHyp/Ibhux2z7Hk9EGrSg7pVUjctgEZH3v854vCn4K1bcwOFdgN2WEIuVEwI3WV4hPCkdFA5KEIPjDqOiQ/VbM9kz8Qo9QFNymGvi0cRgLfyUyfzMitwxRoVtX60XjMIUx95MC7poSJNAWiyVEigHyfA+ycPxcxkw5EdrWzAIP94CnvoH37oIGtYHOa4lJTCc+QoaAD5zZqL+0RY0Q1B6iopHhaAGYjbBoFOzNh1eWwN6nQB8lECO8COnSzMn8mxqlOkLdMunv7lEQYvj/tkbpTHgcmCOEeAHYDkw/ny9VpBCYBly4VK//z6BNeoKsH7chXRFcXerV1QYfaJJUUTxLAe/Wv50oqaob1fYA6rY5QUesoeKl01VBkqKAcgVcR+B3EKXWravuq1GjRl3aGXF/J4QQofxPGo2Gw4cP88MPP1yYtg31ER4Vhz2osZkX5C7c/ZSLA8dAr4Of1vvwKAaysn0gySzeNILGzZPYu6WIn5fvpPCknc1rczi0rxivN4DRpMXnDeD3K6ed756xG+j14wgee+wxnhsv8+GMPXw1P1j49Z3X06gZ78QhdMQyBFkykZiSwFHfE6Rp/33G/vurPHH0vfJjAJ57vQfPPbKcuvWjeOhWP/cP0/HJfA/PvekiO7eqT0ZDHm7PVqLjTJSVuJAE+Cs0SekNY1ECwf/vvGEOmw/fQUATjUsY6T9IS902TZn+xhY6ddvB1a1U1ECAr386DkDPG+rxwdyb0WiDJUaUgI/srDLG3PIdzz+xgYnjCpk4LppbbsvAXcfM4YNWHnphLWlpJqIiwemEAweDLkcBvP1MDI6CEh57Hx6/f1W16583cx8ZLVK45+E2CDWATxeHTxuLKuuDIirAb4zGYWmFIdABNCsIVpDVgVwTSdftPGdKEBIaEsRDeE198Hlew6OPQ6h+NN4CDIWH8Se2QtWZACmow1A8gIqiMePVxyKEhEuXhOyyEXPoR3QHjiHKfKAHNUYCrwr7yzDZSxlRA0pvgGUHodxQZekB0PlLuPOqAPdMAk1FzbJdOTBigB6XRWL6ej8zumtxZY5B2bIbu6pSUAhpiUA5FO1Xya6oe92zGUwbAQ2M8P5auOEK0BtAJHe8ZEnSZVx4XE44eYnCVziO+cvXcOuQ9Xw2LZLhA0wVFqWTQdl87Cww/L6F8M9CsT2Luu0NxCEblBKsy6UBpIqwc1FRwPLGkwh94vm3qyjV6rZdyLlxsVuU/kqoKBT6nkX2HSLyxGrEySIC0bE07J1PVo5CcoJg0utdMUTHA1A/I44lCw+yeN5+HKV62naLpkbNCJJqmKmfEUed+rEs++4gGc0TSU2LxOnw0ijyNT5dPIj6jRNpV/tNZs+eTbNmzTDKL3H40DZ6Xb+fubPT6dsnloC+LSbTv5EIWrPs6kasLGTn1hOM7D+f3Oxy+txUnzBNLDnZCgkJCRQW57Bh3fbTrm3uol6Mv38FB48Gi++2bKJh254qZvX8ODPprTNYu12idfsarPzhCJ+9u5U5y4dwdVtB5NGfGTfFzhufWjEaZX5e2YJaTTNQZT0KMjpfGSWHd7J+vRWtx0bL2gG8PkHcjaNQNRUZs9XKkNQgls7czai7FiFLkDVXpvmdCvnFKjXTzKw7PAZUEAE7YY59GOxZSDY7mrJSrhgaYPcxePgGGDNURtFpqZcqUecGL9l5fvoPb0LfNloG3GjEntQ6mG37FBjzdhMd+SKqzo/q3woiBknfFyH0f2r+uDiEzfcqhvxMHAnNUbVhVe64gJvIE2sw7tmK8Co4G16BtXZHCChEHl2L1p6HVrKj+gIEwmKwprRDf+AApp37EKUKVNQ41kyssigtWdGN9nUO4Eq5hug1eYh1i+k+A1ZlwTPXwcR+8O8lMH4uKHMr0hw4ofYjEGGACf3godmQWwZt0uCL+6F2hXfu4c9g2nL4elSQLImm9yI6vv2nxuefxDktSs//3T0KQgz7/2dRuox/ENr4yfS8dgwL397FjfdZSYiTad8WVEVFSMmg7/r3dsi9G5H1BsLgQU2SwKEEU7JoCWpo/SBUIKLh7yJJAG+/fekuWBczBBJx2qcoEx9gTfITSI3Bp4mmyw1LyJuxG6sDxj+6gcKTwVQMRpOGxBQznXvW5r7HelGj9ulZ0vv2bxj631xRYsPj8lMzLShCnzJlCrNmzeLAke50bHucd6YG6NMjCSHMhBmeQ1SQJIBw0RZVheatlrDx6EN8+NpuJv5rYfVrEILIqAi2ZT9LuFmPSe2AVpUo8U8nzLQ8dNy2PX7uHWHgnU/dWDPjMUdoKYxrQqcBQRL4w4JfMZi0dG4fRljWKqTycqaMNbJghZfjOS6UmAZBAiIkzMVb0bvyMCcaqTUgGolIUAPIxYXkowaJfGXJkZDuSaVTvyvR3fcjDruf5BuDBO6D95twSy8NNtUPqEh+D5riAmTFieRxgSLRpqnK7mMKr98lQUBl/V43G/bB4vFw5ViYP3MP82fC0eMNGD1JAcUPldaQgBetmoIIaxqkbNoWf3S6nAYj6diEEZ0jH7vGGLpWEfASlr0dzfbjBJxmMMs4YluCxozOmY3GX462rAQlLopAXBSa8mJid36HyA+6FFUJ0MCsrcHz3NgA3rgLAgW78bdoQLh2LFLbCFy/1GBVME8qzy8K/lVCLVTwGuDpr+FY0GjJgFOEIZuOwqiPIMwEC7dVbU+pLPtn+O0kt5fxhxEphPiASyyP0mWidAkjMvJlOg2wcdPCufQZUkLLK8IYe08Et45ajSROf4j9ZVBV2NMNUWariH3UoCYKAk0SUSPNCIcXeW8OaokPqcPvuzc+/PBDHnzwwdDnzMzMC9v3/+eQMBIlj6BEsxOfNoasA4UsnPMr6w7chc+nsHZlNkaTnlkf7mLdymzen3sTGc2TcJUbCL32nwVD+3wBQOfe9Vj6eQQQ1Jc1btyYxo0boyp9uWf0OkCLrOtKMKl/dZhFW8wExcETxqpk1JzHmjVrOHnyJGvXrmXbtm3VM7dX8JNw+WoOH3+XyuSJMyabycySATdNepRwYN9YDCQiBwQfvb2bRV8dolnLZGS/C39sCgZ7LsJazPEcPxqNoE6iAEcJBttRpIAdyWlDyjqOCNOCTkYxGPDE1kIROlBE8CXhlASLek8u8z7NxmH389GbtbA6BEMGxmIMM2LI34d0Yino9QiXG8ngw6WvQ5ndxoKvDjF9kULNeFByFb7eDAM+rj5GDWppqNe0N5MmLyYq5U6GPAB+NahHCvPUIDzlQf4qhMsDUDQVejlVRefJxXgyE112FlpNCcQIQCVh2wzUGBOKzoB0xAr5AaTDxQi1GMqDL1Kqp3rbeRUBZwnhgAB5fyHagbkIoUU1qWzO5ax49BuYuvTM+xJioKAElu+v2iYJ+GA4XJlWsYTVG3LmL/+v4J+LerOqqjryHzv7H8RlonQJQ0MUUQmzeOaV+1iyugdIcQy7fzvD7g8nOzub1NTUv6cj/kIoDTr9VY0W1Q/+rumosgY54EBE+PG3rYvml1/BfH7RWk6nk7Cwqmgcv99fzf12GecHt9uNwXDm4qmVUJQcMvd7mPzCQhbP288r7/cm0RIsPDy4dgKoAW64tRmp0ktsXZ9LRvOk8zr3qiVZPDqxM6nhw/lg2wwA8vPzcTqdmEwmhJSIRn/TeV+LEIKBAwcycODAcx5rlm/CanXhC5SiFRBQotDqdAA0btodQ8RkXp4wgddffx2Xy8XgwYN55o0a6LzbMW9bhShTg5YNQKtRicpfg2pzoZgikE0KwmTAmxaLz5SMEAEckQ0IaKODUQsCggSt6mnk0aewZkMwL1SPO64hXJRiLPgVw5EtqEJFCasonycAACAASURBVDfhjmpIINKAJmBF+BVmL/iFJ97yIgn4/B7AB3Uq5HmPdIHbB8o0ukKHJlaPCLOi/VHDimW/8MhDi1DwIJAQJu15j+8fgUm0o9RyG7ry7eiOH8W8cS1CA8ggjIAJMMigk8HvR7L7wCMBgSDP9hOUTQVOb3tcRxjUBLrOhx6pcFUtUOZdhzx4CUIIMurX4bXrj/Cv76BLA/h2LNz0Kiw/cHaSBEGSBLDuMbiqKczbBPvy4I52oKoSovtMRGS9CzxSFxkupwf4XbhMlC5xCAQtmrfnSFYWmzdvZsSIESQlJVGzZk0GDx5MQUEB+fn5rF+/noiIiL+mE2oFgZEEqge8derBST9KfBj+8Fi07hNIwo8aYfztdgCfz4eu4oEG4HK5zvmgPxf8fj9FRUX8H3vnHR1Vtbbx3z7Tkkx6DyShQ2jSmxCkiBSBS4kVFEHBgnwWEAVRQASlCCjiBRFBAQVUBKSLCoigFEFCryEJ6Qkpk8nUs78/TkiIhC7eC9dnrVnJnLLP3ntOec5bnjc8/Noe8HcKHnjgAXbu3MlPP/1EjRqXv/Hv3JFAr17f0G9IY36If4padTV3VGn6t/b7evuamP3uTh5/tgma484PFQvlPuWAu9tHM2P8zwwf4s+UKVM4c+YM3377LWazmXbt2mEymejcuTM9e/akWrVLEw/sdjtz5sxh5cqVZGRk0KJFC2ZMeAHv7X1wO87jjA7G7ReEKLTi4Q5AZ66H3PEVZGdoFh1/BV2TrojQpjw3trQ8Se/efQgLCyMjIwOdTkd+fj5eXl6kuodj/m03IleCL7gCg3nrdS/enJhImwfTmT/CTe27bIg8kHodigFMGYdxmCuhM1hwe/uA3kXpU6j4b7F1NzRMI/5mNQOXMRBwofqY0SlOPPTpGM4XIuw2VL0BaT3PG5PTAdj7ngcNjDakC+4qlvWd9TP8Xyc3SmIR6nkbGfI3hFBZu3YtWVlZBAcHX+HM+OsgEAQYX8K1uhpKQgLSBFIPwlkcouUhUSNMOOtVQ/FV0eenQY4TWeTWYpFAi2X0RStbYqPYpAMoEOgDzSpC6w/B8SYoaZtQf5uA0uINgp/9hYgTzQkwJ/F6T1i3TyNJ5cGgh4fvhhH/gg1ndIyo577ws3B/Yz2nZStymz5GQEw3hHfF8hv5B/+z+CeY+w5CQUEBhw8fRlVV7r77bjw8PIiKiuLECU2LJjIykh49evDKK69QpUqp5ueWLVtYvnw5QggaN27Mk08+eX0HlhJ+qQzWRNyJQHrxcgO47q6Mq14UHkVJiIRaKK02XLGp9u3bs2XLFuCvkwG4oNlU3jl1Jwdzv//++xw7dozZs2dfVrfK6j5FbKsu9IyrzVOv1CurjQNYLA4e77aU33emlGSyOd1FWAvtePsYyWQObvKLn21uypImI7HVFnIuOYVDhw7RpUsXTp06xdSpU6lbty6FhYV89dVXrF69mgMHDlxC5uLi4sjPz+fJJ58kLCyM9u01leW2Lf3oGleZjg+0IdojF8+isxjzknCqoZiOHCcrIQ9dqCCothGdxQFFEqVXabs6nY6qVatisVhITU0lLCyM++67jzdmBVNt+wcowk1unQ4UVtJcfrt+PsugHsuwWFWOnehCuJKM0zcCXBLP03vR2wuwRcVQFBiD0yMEt8Gn3CKzpzb/xj2dfiDZNRyduwBjUSbGgmQUeyF2cyU8zh/FERDNxx8nM+fjM5w+p5L7ucDXJsFWXNPWCW+th/E/gL8HZL0OIgQ+2QVPfwp+fn6cTUxA53sEB+cwEI43LRG36p1YStTtdyFPHYFMN0JXzHMcIIyAP+CnID1MOCKj0J/KQDlwkUyBAe2UkcUfTxCBoIb74DT54dU5uSSYu14o7H8WCDWhe67U7StVNwLJzxM8GLbQzR8Jpc17GuCJNjDxcfA3A94KssgHEf0iNB6jxU3q9MycOZMXXnjhuvTd/ptx1WDuiX93jzSIR2/PYO5/iNIdhIsf+jVq1ODkyZNs2rSpjGBjYGAgOTk5eHl5oRSnEFssFoQQREVFkZiYyH333cfatWvR6y9/c5W4cWQuRex/A/35FIR3VWTOGSi0aeb04qoO0gb2R5piEvkoEftA73nZm5GUsqRPf+Xvr6raA/5C2xfjTiZKV4OF3Xyz5j1eeWoNOxOex8Oku8Qk36nBJxw5kMHd7aO5P642VWsF0qXjo4iCljh8VuPgFKBgoiZ+9MJNBg7OoicET2oj0GM2mzGbzeTl5XH06NEyJB1gxIgRbNy4kaZNm9KqVSv69OlDcHAw9erVY9asWSUEyZbyC1uWP8IT72aQnm4nOMhAfoGbJi0jaNW+Ci2b+fNg9zVcCaMHhfLekjzsdjuNGjWidevWOJ1Ovv76a3LzchncGWa8Hkx2iydKyn+Y8k5RoART1Xc2yz+tzL0PNwenHb/En7QHrdUJXnowG0Cvw+JTB4f/k0hhws4f2oGlm8wkO40qzcRgUFizrTuNmoTgVhW8bKdRCvKY9d4RXp9ZWGJQaV7Pg+1j9Cg5Fs2N5aEZp/pMhZV7YNswuDsa8AUZ5o2hlwWdAmvf0dOptcDtE8T56t0RXi0IZhCCvz5uUSY/i+uHxbjCgzAUnEck50M+msvNB4jWIcLNUOSEAhsyU8I5oAjNUGkG/EAYBdImtXuHjwIROkhwMnUFzD5QqqNU9DYYjKB75dL7g/zpHlzp26g5DBKK0/9/fBPaVQbhBQTUhjbfgtellQsuWDrLE7e9HXFVojTp7+6RBvHI7UmU/saI33/wdyI+Ph6Axx7Tyu0dOnQIgLfffpv09HRmzJjBtGnTmDZtGnPmzCEtLY2zZ8+ydetWNm3aRGBg4GXbljjJyR6O69QkbPWqYG3bDHugDYkNt40SkgRwNA+i6u9FX+k4itGMoigIIRBCEBERwYkTJzhy5AigxdNc3Pe/CoqilEuS7nRIJC63k5ycnEvWFbKPPFaSnWkhMMQLD4+yJElVVca99D1HDmRQrVYgy3/sz4DnmhDbsQqF/EYOy4tJEoCKnaNkMgMDIfjSAS/ql1gxKleuTGZmJk2bNr2EJAFMmTKFmTNn0qpVKzZt2kTt2rV58803ycrKwmQqTWH38A4itqMPNcMd1KphIvFkM47FN6JbRz2piTmMGfEzACMntOWuJuW7WQe8UoOlX74HwOOPP86sWbOYM2cOWVlZfPD+Byz4HmrF5XAuKb90J6EnJ0+zlD04KIFAr+Vs+Xob+BhBkaiBHkibE5ldhDxfhDntD0z5GwnmQfSEXPgx8PbzZfqn3XA61ZLyKIoQ2HQh2MxVeX1mIc1iBB88A+r38OvrNvR2C7JhAJaubTnf/UF+qfc4K/dAvepG7m5vgiDNeCUSLUy7HwI8ocurLsK6OmncM42NUxdizPoKp5qElOW7SG8YUsVWsA1L7w7Y69dCGkEiwEsTmVV8QClyIxLzEVlFCLcEvVLqWlPRCJUTZGIxgToPZKiIP5wIFYb2LHvIZzei+dHKgQi5B7sLJhSXf/Q0Qru6IHRG6FMA9x0ulyQBtG7dmpYtW978nNwOEP/Bz22KfyxKdxAKCgrw9vYusdiMHTuWt97SJFirVq1K06ZN2bhxI+fPn7+iiTklJYWKFSsybtw4bDYb776rCf6NGDGCqVOnks9PcGQMSkUPFOlAIHnm+RN8vjgDgFaVYWfC9fV94cKFDBgwAB8fHxYuXEjfvn2vd/g3hDvVomRhJ3lsICUph5wUPzq1GIOCh3aO+HiQwjgA5s3Yxeplh/nu1yc4uD+duVN3kp5q4fAfGVgLnQwb3ZqnRzTHy8tYpv2iAo9iZe6y8KAuQZTNGGrcuDH79u0jMzPzmmJn9u3bx4IFC+jevXvZN3x7MnJnFSr1g3r1vPlmeV3NH5WSgUfeWaSiID29sQdUBIeKufpBAPLPNcO34m4Axr/blHGj9uLh4cG+ffuoVavsgzMrK4u2LatyPMHCuJmdGPh8UyjIpEb4AoqsrjLbdrvXm8XftMeQn0ZehQ4lsUgAOkcuIYbxSEVPBrOQ0kZRvpEa/uMRAgYMjsHlluTlqZxPyyUlIYvTyZIzX/oSVWRFFLo0K1Ig4Av2wEpk1n+IGj7TcTrc5CU0w12oYMjLwXjujBYkbQVc4DMcrKVVYagSJpj1aiAd23mjevljDH0ZvX9/EAqqzMXtjkeqRbhEHqoAo64lRhGFxWIhKSmJ48ePExwcTOvWrcuM32XbRFHyGxhT0sFiwRURilfmCfDXQ74Tke3WLEQexW5DK6hFaBanQjSLkg7tAepCI07+musNo0BW8uP4Ucl9b/iUWJQq+kHSLBCPlfN8cBaQ+3UVnv8om8Qs6BcLT3fyh/t+A7+aVzznHA4HOp3ujkkYuaJFqdp/0KL08O1pUfqHKN1BKCgooGfPnvTt25fnn38el8vF1KlTGT16dJntYmNj2bZt2xXbGj58ONOnTwegQYMG5ObmkpiYyOTJk3nylcqo1lV4ORNZtTqTRx8vzbMd3EZwXw3JoC+g4E/pvgMGDGDhwoWA5lqLjY3l7rvvpm3btvTo0YPXXnsNP19fRo0ejcuWi87kx63GnUiUbJxk2qxh3NO5Eh6eBnz8vAjxrc8rg35k+fLl5FhOkK7O4t9Tf2XutF9Z8fNjzH3vN5bO/4OQMDPhFX2w213M/rIXMfXK15O5HFHSE0IYZSuup6WlERERcc1E6bIo+A25J5YHZnqw/6CT+L2NMSQeQ/X2wxpQDYQBc+oe7IGVyQtoTs3AuVgLnWz/tR1tWm4BtBCs1159gLcnLr2ilfHV17oydepGTZdMFD/oi2HLbsTcOWd54fUcflxekZjecQjcIBTciqdm4pESg+pNqG40bvIoZA9Z+Xvp03EGf+xJIzjEA08vPU6XpFKok0oVFAZ319HBWKjVR/RHS7VxAy442ro/PbutJykhj9OpvZC+1cCZi/+prZgSTqIUuBAOOHgC7poAr3SAd7vCN/vg0aXgUqF9DCwYDVEV0Kw6gYHYgivg8vSjyFQJc9YRDK58bAY/Znwayfi3V5eZkw0L61OnaXN8qvoiXYnozp7E49xhiAzF7h2C0ZKF1OvQWy0oaTlaUVyzgrSpYAc1D0ij1OJsQCNKLrTYJB8QZm25FAL0EuGGfDUS/4c0onT0VahRF5TyiBLgtuXww5KRdGzsj67i/RDa/rpOsTsFVyVKf12pzOuCeOgfovSXQQghrVYrv/zyCz///DNjxozBYLi1aa53AgoKCkhJSUFVVWrXrk1hYSFt27bl999LFdU8PT0pKtIEBNPS0ggLu7z4Y1ZWFgsWLCipsxYVFUVmZia7j3xBRPi3zBi/lUmTtdINVSqb2LOjIX4/7kNkOUBq9+INGXWo/8x6NmzYwODBgy9rybqw/Ph8hZpPqrwxSOGtoY2gwVbQXb1o543iTiRKeWzk0bgXyMks4tdtiVSpEcCzr8QycshqHn/8cWrUqsTqdQtwuyUzP+9B1RqBROvf4fXJHRg2/BEcnP5TixcIxYUSIIKiAk88fayXHNuLZgTQq8yyC8rqSUlJRF4oAX8jcGYjf43i6bl6Nv3s4MRafyi0Y63YkMKwekidGUW1ojgLUVxWzqQaaVHr8zJN5Obfj6/3Vwhx9QxMh5pHYsrvnD6ezdGjh3hh6DgAPptTmbg+IXR78DRut8L7n3UmMsq72KMkcOn8NOuShAribUSxzyEvP4ci85e0qjWGhFO5DBtRj3FvVqXIsypGSwamrLOYf9uJcKkagfCAlz8Hux02HzaQXqDw7fbHiKkbAqqKp+04XpYTGM6nIjIsiPN28s5B9ZGQXQiRvjCnJ9xbDSZshYlbtHGZ9HBvLUjNA6fUXmgSssDDAJ0bwOZ4KLRDx8aCma8aMXsJqvYoJcXrljeks188zqgo3H7+mhyAVJAuUF0KppOn0afkgK/AFRqA2+SDPjsbpagImemGTEDViBHeaIrkqjZeGagDI4gidwmhGvNtJJMWa0TJPR0ICUTpn136Q9kzIe07EpOzqH3fOKzWIpYuXcpDDz10PWdXCf7OrMFbhX+I0l+L/1qiJKWmcJuVlUVISMh/uku3Bcp76A8dOpSPPtIkaUePHs2kSZN4/vnn+fDDDwHIsP6E22MPAh2+ohuelO/DB03bKCgoCLfbTeL54UR4ay65g/saUaWKGUWNxmQYD39MhoI0iOoBdZ5CXIP45QWi5DoVgr5aJooC7rNhqAVtyAufd8vqu92JRCk5dyPPDnmJmPohCAHfLIpn2Kj7SDtamd27dxMQEEDnh8x4+zvwC/Bk/64UXh+6kfFTH+eNEQso4iAuspHYUfDESBQ2TmFhG6CgwxdDQXdsPkvRRHA06IkghCEolHXT5efn4+fnx65du2jWrNnNDe78Bp4e2J2NvyscWxVIfu3O+CZuw5iRgjQIVB9vQEG6VYoCatD6/uMcPFAao/XpZy0Z+PjOy7d/FVw4T6dMakH3h4J59KFd7N+TyYg3mjBsZCNMHnoSEl0smX+IlrFVebTLYgCkmkpOwov4OHaAouPuR86z9/d8DuW8QFTer3iKVBTFCZlW+C0PgFfWwvS1oNdBRIDCR2seo1GLiqC68Tq1C5+Tv2CNrIOqeGNOiEdGe6LLtkCSlSWbVEathpSLQq1iK0OeDQ6mawKLF8rxKQIejYUVuwXWIu15sHUcxFYDvARLDut57A2Nteh04HaDe4WCKFSRJnBGh6ErzMXtG4hQ3BiKzoPDqREdgwBPHVhcyAw0cUkbCBMaSRJALpr1zKzFNhFohDQHWGBvAvSerwVzJ34EFW0gen6NqFbsmreehS1NkC4rk5Y7WPGrSrtug3hn2kdlZEauBampqdjtdl566SXmzp1bVsj0NsNViVL55RNvOcSD/xClvwz/uN5uDJd76C9ZsoT+/fuXWfb0008zd+5cAOITHiYiTEEVRgz67vjrLh8ftH37dmJjY+ncuTMbN24EwJLXBUXXDA+v1xDC64b6/v6bD/HihOUAzBjvy0tj83mklwdfrtTeZD09PbFaL7Vg3CzuRKKk4iCTf+MmF6mF1+LjeIR/z1zF9OnTOXPmDIOffooli74AQFEEqqpdb61atWLFihXlak6p2IrJkw+WgkK8fcy4yEbFgo4AdPiVWE8uxrFjx4iJiWHv3r00btz4psf37NND2LBhLfsONsYndTeKzo3UCdx4oz+ViDQquCIiKajQFJsSSKTnXEZOiKVZm2geve9LTpw4TaVKlW7o2BfXHfxmwzO0bOvmj9+zeLTnOuw2J1Wq+3P4ImKWnp5OSIgXztT2pNEYXbg/Wzee4am+KygqcvP9jr7cE70PvTUX3G7krnzIlOCp4POyiiIgbYEXuX2eBoOmJ6az52AozMAr6xBuvRcO3yg8U49iSD+HzmrVDH8OkBY4kQbvrYWTGfBTcex9iA8EecPR1PLH+EhrWDwYhB3+9TF8txeiIgz4eah8PcJNzFBwrQLh54laIQiXyQf9qVSUwgKE4oZAE5iNCIMCBXbkORuyOAsNR7FyggmkF5RUebkgPukjNDOXGzLyIHyoJmvy9ZBkmtcGWVgb8fihUsv0nn4c3vElry+RrNwFHz8Hgwc8AM2XX/dvW1hYiMlkol69erz77rv06tXr6jv9l+K/mCidBH7iNithctulAu3YsaNEFygxMRGn03mVPf5Bv379OH/+fElQNsCnn37Kqp/6ABDb6BvcwojDEISVHeS5V162rdZtWtG2w10lJAnA7LseT/NbN0ySAF4Y3LXk/8PHNHJUaJW0bKxluBQVFVFQUHDD7f8vQcFIKM/hTy/86UYow5j34VqWLFnCjz/+yNtvv11CknQ6XYnFb/HixaSmphIZGcmwYcNKZBVK2/UoJkPa9gIFAyGYqIIe/3JJEsDrr78OQJ06df6iAepAMaKTRSguJ+gkbk9/RHImmY0fIrXlUPIiWoDqQtHpGT+jPZu+O0nz2CgaNq9A9erVb5h0K4qC261lj/XtMoeKXvNo0DiEHYceZtRbzYmpG0x0lVK3Te3atZnz0Th2HjFwJtuL3Dwng/qswNffgwkT6yNteahFVpKSnbT9Vx7JyRJRAagGi2dVwGKDd7ZAxJ7ZhMTPx+fkJsxnd+OdvBtDZjIy34Vn8kF0ZhsyJgx3jQBkmB5XRV/ye3akau8I5n7syw+LPMlaDK89AA1jdCRllx2X2QC/DIPjk2DJEM2y46go+G4vPNTFzNmP3Bx4z83g4nppaoA/9grRSKcT07Fj6ApzwShwRYSienujYkLNcyNznMhCNMOjC01cMhDQgyjUPtIKskir8SazJeQBhdBglHasMH9oXg1QfaD/ASZNmsSZM8VF3oqSUYRk10nt65MdAds5pJQsW7bsun5ns9mM1Wpl6dKl9OzZ8+o73M74z2W95Ukph9xOJAluc4vS2bNnCQwMLGMR2L59O02aNMHT8+oxCHca/mwdcbvdl2RxXBwj9OobDZg84Y+S78myOOhbSoLEM3gQXWZfKVUymYOTJBwON92bf0ZYRR/Wr9uCiZuIPQFwZJG4viKVepW6ct540YsJM600adKEvXu1Cpl5eXl/qcL4nWhRKg933XUXkyZNonv37oSHh5Oens727dtL1pvNZho2bEhKSgp169YlN1cTBfzuu+/o3r37Je1dz7zl5uYSFhbGqFGjGDdu3E2P5dlnn2XDhg0c2lcfz/O7QLixGULJCe2KyZ0F0okq9OhVGxKJTQni/rarGPR/zdiy4RTfLjnE0888zZx/z7nhPgQEBJTMUZZlFYrpOAJ/3hkTz5TJHwDwxBNPkJ6eTnz8XpyuAgz6oJLsrSshfyUYGtVD9fGlQ4e97DlgJ2trBL6ubITVDd4KKBI8jUiHC3zNCCGRZwshw6mFR/kriBC9ZtkpdINDBacbaXGhpgE5EPG25um7AA892FwQGQCJu4N47k0Lc76wk/YlhHnB++vgxbnQqZnCxvlm0AtwSUi1IK0Cqddr6f7oUAqKwKpZtSikVFBSj6avpBYvF4BH8fJikUqpgyW/wOMfa/2aNDCS0Qu0eXO5XGzZsoU2bdqwd+9e/M5/Q111DtJl1SxTOi+o+TrUGs3Zs2eJjo6+LhHJ1atX071799teTuSqFqXJf3ePNIgHbk/X2219NlSqVOmSm3WLFi2u2zd9p0JRlJLA7fIwecIfrFvXmOoxWmX3SFGaM5rNXKyULUCbq87F8/wSTEVnMerdZGcWMuH9jhTw0w31T0o77qwpqEcrIE9X5+GxmnXQoMDmJT6MfaULAHv37uWJJ54ANOXhsWPH4nK5LtfsP7gIUkpef/11kpKSuPvuu2nXrh3p6ekoikLr1q1LPg0bNgQgKSmJ3Nxc+vbtS7Vq1ejRowfdunUr0bi6Efj7+3Pvvfcyfvx4zGYzq1evvvpO1wAPn7moegMugxfSy5sAyzYUVz5uYUJVPHEYw7F5VAWjNyPGx/LCY6t5eFAD/u/1u5k7Zy6fffbZDR87ISGh5P9eXabSLGYOAYbBJSQJICMjg2XLlpGYmMihUw/zY/wgfj7xDKPeacfHX/Vi1/HHOJE/jGPbo3B/b2JjcbKgby/wrX6IqOq7OXBcxa3CsnVGXGf0SJuK1CmoRiNO6YlU9Yi0XMi1IsL0yHq+uJtEII2eyMMO5DEraq4bmWpHJrtQE4EcQAcX1A7OfwCrh0N3TYic9Hx4fkw+C5bbaVQJgl0Ct07PO19p620OSZI1CmeKAdUukJ4eCBOIXAfimANxuggsgBMt0NtISXA63oAKsgCkU+CIicTWtQn2exoiIz3BG2q9WkqSVg6FzQchIshYMu8//PADdevWZefOnfyWVhuiByIUAwgDRD0GNV8FtOfD9Spt+/v7k5+ff/UNL4M//viD48cvU0flvwn/6ChdF25rolQeDAZDGSvK/v37yzxUL5jN/xcghLjEsuZyucrIBUx8+xRbDg9hzNQOGI06vpi+Ef+k7/FN2U6ecx4OUgBwqhkY8xdj9a6P3bMS+QWS9NRCoqsG4OT8dfdNqlk4UtuhpE1EuKwUFahMe8WTzyd70KIhZCXaWLdpN62aae68Ll3uKVEKX7x4MVlZWVdq/h8U45133mHSpEns2bOH2NhYduzYQePGjS/7MtGgQQNA07U6efIkW7du5ddffyUoKIivv/76hvuxdu1akpKS8PHxIS4ujri4ONas0VS0ly1bRmxs7HUTKEUXgRo4ikL/BthNUdhMlXCawvBypeJtP4W56AhGVyYIPe27arXkFs35nZfGxvJwv3/xxBNP8Msvv9zQePz8/Epcwdu3b8dmsxEaGlpybbVr1w5vb2+Cg4MJCanI3h33o0ehahUfnh/ZnO59GtOw2iyq+3xA9Zbx4B3AvbVL23c6JQEh3tRtGMqjA+sRFxeBEiUQQQrC24AiVAy2PBSTCmHeuCuEoirecNAGW9LhdKEWPJ2ualmoF257xVYbYYBPB2mLkoCe78HXxVPh6wUfLXXicMGygUCGxGGqTJPGWkLFz39I5s06jT4jF8UpESYBblWTkdLBmOXw6XcgHZQSJFnsYssEmacFcyvekt/XJ2NdvBdUia1ZfZQH4WSa1o/vX4Pes+F4KjzafxDDhw8nMjKStm3bMnbsWIYPH86gJ5+CBh9CT7v2aTiH8srHXCsaNmx4U7UlIyIiqFjxn1pxdxpua9fbtSAzMxMfH5+Sk3/NmjUlrgQpZekbhysbrHtA5w1eLW/qYvtP4XrcITk5OQQFBdH9/ljmrGoGiokTP2yjfadf+Pes2vQaHIsinaAEEWx4B5d7H3braPJ9GgM6ThzNon2deSSro9CJUML/pJ1zMTZs2EDlypWJiYkpWWYrGIHp7DymL7Qz4j17ufv5eoGXJ2z5AKrXD2fMgg68O+OLkvWTJ09mxIgRN2Umv5Ndb1JKmjVrRnR0NCtWrEBRFKpW2ivVBgAAIABJREFUrUpqairDhw8vESP9M/R6PZs3b6Zdu3aAFsA8cOBAFi1aRNu2bVmzZg1Syuuaty1btvDII4+QlZVF69atSU9P59ixYxgMBhwOBzExMRw7dozIyEjef/99evfufdm2Lrjezpw5g8XSD6fiAKHgUswY3Lno3XmoqorVXBe3zrv4WpZ0bvQph/anszfl/7gr4g0MIqRknm4UJ06coG7dugD4+voipaSgoACHQ3MhFxUVMXDgQFasWMHKVd/SsWttJCpGostkB0q3A5ZXQzqS+cMCjZ8vPUa/gXWZ29/BsQOnqF9LolclzoqhuALCcZmDEBThdToecaRQiwfyQHNrOQE3CG80S44eZA5a8Vk3HE2DetOhfiWIP3vp2NI+hlBfLrIG6cDPA2PzQlwucG1SyEdHj5edHDsLuVZw/ek9dHQXmBBXTI70WlsCwAt+OAydpl163DoVIX46uGzgMwhCIyKx2WwsWbLkjikxcqtxRddbdSH3TPm7e6RB9L1DXW9CiCghxE9CiCNCiENCiBeKlwcKIb4XQpwo/htQvFwRQnwuhNghhKhbvKydEEIKIXpc1O4aIUS7WzSuEoSEhJR5Q7hAkubMmYOiKCxevJgvFk7BfbghJA2ChDg43Q1Ux+WavCMQGBhIVlYWq1ZvIVgMBgTGvOM8PbQGk947h+IuQBUmnIqbVHUUDty4FTPana70+ks4nYug/CBuJ5lks5wi4xaOJfzExo0bWb9+vRZv5DzBz3vdlyVJAPlWSMuGmH7w2VdZhItlrPiwY8n6V199FZPJxJODBoL7n6D+P+Pzzz/n2LFjTJw4kfHjxyOEwGw206lTp8uSJNDilfbv31/yXVEUPvvsM3bt2sWRI0cIDg5m5crLB/xfjKysLNq2bUuHDh2oX78+mZmZbNmyhSNHjrBlyxY6dOiAEIIjR46QkpJCvXr1iIuLIzIykuXLy89cys7OJiEhgdDQUKpU+Yaalb6jZvQqakd9SY1Ka6lUeRt+/luoHTafBmGzaBA6kwah75N8Vku7b1LhA1SnD/Pnz7+O2SwfNWrUIC0tja+++ornn3+eli1b4nQ6WbduHaBlay5dupTKlSvzybz5mKiKB9UvkVAQOiM8eBIRM5KGoRV4qVdpmY73Bhjx7niCJi+pGLtJHF6B5ER14nzFzhQEtSA/oC3p9R/D3qgqop4nSowHSnUjwqBZj5CADmQqmtvNAsnnNZIE5ZMkgNAgQNGIlvDRoUaE4vSLoF0D7fpv/5JK48ed/HIQsgo0khQWpPDNEEHCcK2NGZtB9xToh0PDqeA2wLeHQRl0KUkKCTFw5nNPDs3UyJSh1jDSszRr9fnz57FYLDf8O/2Df3AzuKpFSQgRAURIKX8XQvgAe4FewBNAjpTyXSHEa0CAlPJVIUQXoBrwNfCOlHJQMSFaBJyTUrYsbncNME1KuaWcY95yeYB169Zx8uRJmjdvTqtWrQjwhZjKCovf9aRqlFmLdAwafEv78FfjZqwjRZyk/4P3cuCghZNHsvn8i2Z06x6J3lmAIp3YPSvgNISh6gzaq6EQJTFNGfYn8dR1x1tXmk7rIod0OY3tP5xm9NDvKSxwkJ5aeqM7fvg58uM/p1U/C0/01DNvhYsKwVAhFNq3MjJ1fvlEtVML2LBuHY8+NoD1WzPJLwQ/T1j/JrSqbYL6M6DKM2XI3K2as/92XC4+49577+X777+/7H6VK1fmvvvu4+OPP75knaqqDBs2jO+++46qVauyZcuWy7bzyiuvMGPGDCpUqMBXX31FixYtLtnGarViNpv57LPPePzxxwEttmfw4MGsWbOGsLAwpk6dSr9+/Ur2eeqpp/jiiy8YOXIkTudvuDiLRODS+Wg+JSTJpzKIrBYCf9LwUvDlvfHrGDduHF26dKFly5ZYrda/LPnjgqU2Pj6eevXqAVrB1QEDBrB161YaNWp0Te04HA7q1I8mqqqZqR3SaDayNOrauccXl08QuAWyyIk0eKFzFqIvzEEpsiPsKqoC5ILwV5BOkOfcWukQJ2xPgHYLLj3my71h+rfa//Ofg0EdQOpBNfkjDXpcbhOuQH9E0mG6jZVsK6ck40v3w9v3gIcn6IZdfZytqsFHj0C9aCOu2OoY9x9G6DyQTeehVNEkTQ4cOEBqair79u3jtddeu6b5+1/HVS1KU//uHmkQfW5Pi9J1u96EEKuAD4s/7aSUqcVkaouUspYQ4n4gGo0oTZFSDiwmSiPQROunSSm//08TpYux6N1gPlySw66DkqZ1FTbN9SKgyjCo8B8qiHODuNmH/jk5iv2/naV7qy9ZvqwhnTsHIxFYPKuDwQ9UF14F+3HrfbCbQlnwwT5mvbOX+FV+BIT74qrwKp6ezyCRpLreQrpzaVPvM0wmHQtX9yYq2peP3y3i7bFzOPDHTqqHjsWYoYn/SVVF6L0R+kCwJJF6Np8KfS7f1/AgqBoOq8dCptGXyoEOTHY7uCRC5weNvobge2/5nP03Q1EUpJRMnDiRRo0albgom7VohL+//yVWjQto1aoVZrOZzZs3X7bttWvX0r17dypXrsyiRYto06ZNyTqHw0Hz5s05fPgwU6ZM4cUXL++WBWjatCnHjh27RP4hKyuLIUOGsGrVKkJCQpgyZQrdunXj0Ucf5fjx4yQkJCClgwL7m1h1Ftx631KCLC+kWV0Q6hHoCCKM/yMivCJhYWHs37+/ZE7S09P/EoHBkSNHMm/ePM6f1ywh0dHRJCUlERcXx6effnpd55rdfZ7nR3Tm8w93E+QLqcXyTM59odgCwvGQOShFVtiXi0xUNTOMGfAAEQJCgkwBCrSpkBKcNvAsNia6xgKBIKKK3WJ6eGkuvL+qeP1yEMKENEiEj0AqApHpYuvvLu6fAUV2CA+EPq0hJMLI+I+1lxudAonvQcWXtHZqhMAvUyB0oPZ94D0w/0k0F6AepE2P2nsWutDBiHJCHvKz08hPP83QkW+x5Muv8P4br1eLxUJaWhrVqlW77sDw/ySuSpTKcXn+HRC9/36iJISIBB4GYoEKQBFwEFgLrJdSqlfYXWvjegiJEKIysA2oByRKKf0vWndeShkghNADi4FKwBApZfxFRGky8LaU8p6rEaWbyTy4biT/H27Lbyz41sb0RZoLZ8+Pk/EK73GVHf+7UFhYiNl8beU+EhMTiYqKKnPxp8sZ5GXk0Kv9N7w9MYZOHQOx+tS9bBtSSj6YtJP8rDzefyYPDEaUqptxnJmAkvsTjugqxD12hKHPRdPl7kKsDhMtO55CUQTx8QcBFek+C84chKEmQu/LzJkz6dzuLj6Y9jKj+ruwCC/iXtIsUb7egnyLdr52aqXw/c6y53f9KjCwK3RpIcAtIOYT8L9yRfDrmbPbEXXq1CE8PJwff/wRiZtc1paUKNETSgAPoFC2PNDw4cM5evQoa9euvWy7hYWF7Nu3jzFjxpCRkcG//vUvnn32WTZv3synn36Ky+VixYoVVKhQ4ap9nDVrFgsXLiyRgPgz8vPzefPNN9m8eXOJtlPz5s3L1A3MZDZSlJMJWcyVQOBHDzyoyuTJkxFCMHLkSLKzs+nevTt5eXls2rTphkqsZGVlMXfuXL7//nsyMjLo378/o0ePxuFw0LBhQ1q1asVrr71GhQoVrvtcK2AHjqxVBCaup25/bXyHPtHhDvRFenigChMy3YU+KQthtyEkWuq9H1pckQWNkNig3kVWhJGt4bEGQI270LUaDa48ODoc3IXUebr0mXDwHUABV2ggSfpw+g48jOOiaRYClg2HupVh1ykYOKNs/38eCX4V4K4/ceVD7wGFAhnzDCLmaYT+UtIuXXbkpscpLMogIzWdrcfhsXv8MXRbAZ6XCqLeCrjdbn799VdiYmIICgq65ceTUmKxWG765a04Xu5/nigJIRYAFYE1wB4gA+3KqAm0B5oAr0kpr1j8VH+llX86oDfwDfCilDL/cuxaSulCY2/lrftZCIEQIvZqx/tb3/JrTIHTPWhzVwovJ+dRt1YwnqH335aWhmvpc1paGgsXLuStt94q43LIly68Pey8964XD/XfTESkN+v3hBMc5n3Ztga9VIvGER/w9TI9O5eGEOmxkJ82fsbr8+ycOKnZ5pduvhebwYMWVeZxLtlCj+4h+Ph4IoQB8C/T3ptvvkn2Cy/wwpilVKuWwsH8dXywpICatYMIDDDgc3AJ23bkM26Og+TkUqL080yIfRHW/wyj+8OoR+H4j8+gb/QttWrVYvbs2XTs2JFq1arh7V12PLfj73wtkFKSnJzME/3j8LH/Rr4xHp13Gp7Khcs+ESufEM6IEhFJgLCwMFauXKnNi3Sguk5g4zR5yj5UijC4VPRqHF27dqVr167Uq1eP2bNnM3v2bHx9fWnYsCHr16/Hy+vaBEgPHTqEoiiX/R18fLR4ovz8fCpUqEDfvn0vSe0vwE5ptdXy4eArgniD6tWrM3r0aIYNG0blypU5ePAgtWrVYvLkySXk63JITk7mnXfe4fDhwyQmJpKamkpRURHBwcF0796dMWPGUK1aNTIyMhg+fDjJycnMmTOHwMDAG7Je+tAZu091DIXfkpyspYOZzV5QlAIWA8LuhgI7QgHVjSbW6EKrp3aBJJph/Vm4IOFUMBY8AXS+KL1XIHQmbUVES0hbRsLmDPQx4wCQyeBr1v55ct5xTifY+PApeLIxjF6hxSBt3A0ta8CabdoxPE2atWnXOKikhzU/a8vnPglPzwd/L/BxAWHtEa3KFuy+GOrG56BoPSeyInlgSjLeRng8Ohmv9feiG3CZwKpbgAYNGvwl5OVaYLVa+fDDD5k06RZ7M24f49jN4j0p5cFylh8EVgghjPAnwcBycE3pQkJ7on0DLJFSrihenF7scrsQx5RxTd2GicDr17jt3wN9CNTYSbO+xzh6aA/xRzLw9bv1lev/Drjdbnbu3Fkms8fPz4/+/ftfEpfhL3ojhYke3QI4kTWAVu0q0b3FZ4x98Xt+WHuy3PZDw7158oVmWAtdNH8gk6Dar/Dgq3mcOKnp7jRqHs6UCftpWfNTziVplqGatbxwubaU256UkpkzZ9KsWTMsgfk0q/Ixfe75knqhHzJu5C+cNTXn/qE2dseXtSbFXvTGOmkx3PcKNHniNA0aNMDDw4Phw4ezZs0aOnbsSEJCQolY4J0McX4f05/yI2ffTDh6Pz6HXqXCnvfxO7sZnU1zDankYf9TEdw6depQUFCAtG3GnVwDZ2pf3Oen4ZH/G8Fnl2MqPIA9fwbWAq0Ezvbt23nttdcwmUzk5eWxdevWayZJoNXYupYipL6+vgQHB19CdAE8rlCjsBSSXFbTNa4yqamphIWFsW2b9iL5xRdf8Nlnn3HwYHn3VA2jRo0qySB0OBy0bdu2JHbqyJEjLFiwgGrVqrF69Wp69epFXl4ee/fuJTAw8Br6dnmYqIbwLtUOEC4XTq8I7OZw3HkCmQ2qDYQXmmbRBa+jNmQW/QI9PoRxPcE1GTz1QJ04lJfSS0kSgDEQop9FV3ko84rdZC9/ixbjZIHVW7Vr+pl2YCiCif/Sthm/BJT7NZedQCNJAM3HgctDoUVxtvzTxbHzBy4UCGj+3hXHLVJWgAK934eEXDiYAXN3AOcSr3MGbw6FhYXs2LHjbzmWl5fXrSdJ/0O4QJKEEN1FOUVHpZQOKWX5D7eLcC1ZbwKYDxyRUk6/aNVqYEDx/wOAVdfY8U1AANDgWrb/2yAEGMKoVafJbeWLvhp0Oh0tWrQoMyZPT88SvZyLYaYR3vp+OPSBhBgzeP+z+3l/UQ98/EwM6L6cx7ot48DeSwtEjZ9xL8lyNOv2DGRz/FMkqaNIVkcRGmFm3640vpz/B4mn80q3Hx2B6j4HUPJgLQ8qBaz4+bGS7+mpFsKiAsjcZua5fia+GwPTirVgFo8G2yY4sxS2vg+j+0H1aDP9+vVj8ODBPP/883Tu3JmMjAzuuece2rZte0Pz+Z/GuXPnePfdd7lQNPqywpupG2BzU6z5eRj8dKC6EBYVityYT+3FP34twqEFCLspGxvUsGFD7HYbmSwjI7wXFt/6eGUexNOaSEFEKzxIx5N0nKyiyPkr/v7+NGzY8Iavm8zMzHJry10PAujFtbwmF7GfDz56C0WnbfvJJ58A0KRJE1588UXq16+Pv79/SYZgUFAQd911Fy1btmTy5MnMnz+f1NRUfvnlFxYsWMC8efPw9/enU6dOxMfHM2TIEHr37o3RaGTu3Ll/SW07ALdXNF9/oBFEd75LKxliUxHZNk3Lz1OLW1d8QQkDEQQ2FeKWwcCVsHkAvNldmyLx6CZ0D36FMJSvFyQPDGZQGwjzhoW7AStM21HKveau0dr59SKjToAXvBEHFYI0i9IFzP9BEmQE+xRYMQTUxRAZBA6/ZqgBd11xzKrNSZFWrYqcYnL1+SEursP8tyAgIKAk5uwf3LZ4GDghhJgihKh91a3/hGuxKLUGHgM6CCH2F3+6Ae8CnYQQJ4BOxd+vFRPhZmtelMUTTzxBYuLf+6Zxu+B6dIZ8RDN8vL/DU/8AHg4bLdpEMWJcLHvPDaN6TBDdmi4gUkzinVE/kZNVnIkjBGu/OUrHevMozLcjpAtUJwEBWuzLPe1Kg2TjHqvDN1v8eOypCRw6uhRfX1/q169fbl88qEXzNlVJlqNJlqP56MsH8JA1CA40M3t0AN0f9uflHuBeB4/eA0ajoFK4QtuGerq3q8CJk2dZvHgxH3/8MbNmzaJp06aMHDmS/Px8UlJSSmoG3k74/fffGTt2LP7+/syaNYuHHnqo/A33PYN0S8Ysh49WuDF0UAl+wM3izQq2yNpYKzXAdD6hOOj5IutcxnpquIYiJcQvWk/Q3m/wTDpIXvg92H0r4W/Zg6qYKDJVQlWMOOxaLYSTJ09eUi7nWsdz5MgRmjdvfv2TcREUPPGnDyXRBFJF58rFYE9H78wBWSrwM+nVnwgO8WLtltksWrSohGzOnj0b0Mh7kyZNmDZtGkOGDKFatWocO3aMhx9+mIEDB5Y5rpSSDh06sH//fmJjY1mzZg3Tpk1jxYoVRERE3NSYysCVTJ++mpt64wk9uoR8PI8koAs3ImobEf5okRcGwATbM8B7Aqw8CsnDoV0VQIL417coVTtd/jhSQvYaZCakW7Svvb6C1z4v3WToUkjPg7YhcPxdUBdB9scw/ikdYx4GhxMWf1QdgGeXSPQvgksH/4oBadWTHfYUP4o3iI8vJ23uQjfykiDdyew92ndz8S0ks4Biv+Hfh/z8/HKtmLc1/seUuaWU/YFGwClggRBipxBiSHEm/1VxxwhO2my2m1JUvRNwKzK4nGRRyG4K2Q3YKbI6+WNPKnH3LAag9l2hhISZ2fb9mZJ95q/oxc+rd7Fwoabq7WEC20VySaERZjJSC8scR1XVEouE2+3m0KFD+Af4kl60lgo10wEw0xI/uiKK4sF+HIyVwX4Mcn4Htwd41wadDvTe4N9Rq/t0EfLy8vD29iYuLo6VK1fy73//m379+t12MUp2u525c+ei0+mYNGkSSUlJl5Bh664mJEQ0pm70J3TsEELNSBtfrrCQa5G8PjkWl0vBWuikcjV/ls0/Se0arahoToXzm8ANk7+GJ/oEMW16Q9xefnjkJoAZVC9f8n2aU2Txxk0uuVl5JO/vyOAnh1KpchgbdvdCFRKDiCFQPIzCpdekxIWDcySeTaZezQ60b9+eDRs2XHHMqqrSs2dP1q5dy3PPPVdCai6ZG5LJkrNRnLnoVCtOfRDojGh3acnuHSn0brOI31P/j9Bw7xKJi5EjRzJq1CiGDh3KF198cc0ilF988QVPPfUUixYtokePHlcsn3Qz16c7rR1K3iGUGE2R3jUXRLRRIw0GifAwQI4DMiXdJrrZcAhCfGDZQ9C2IqAD5cltiApXCQ+VLuQ6D9QDbvR/Ch+qGQnHi+Ocdr0ETcLQQgy9AQ+BKzqKdGMAlRv9QaA3hPvBwYtK27n/DdghpecxzqZk0rRpU0wmE+VB3bcAuWoQ+vEQGRnJgPYpTFykMvsZeLarQLT8GkJ6X7MMyM0gIyMDm81GdPRVQ1n+a3DFrLcaQu6ZXt6aWw/R8z8rDyCECAb6Ay8CR4DqwAdSyllX2u+ag7n/2/G/TpJuFQwE409XvGlBOh/g6QUt20aTLEeTe76IzavSyM7OZObnPQgN82LvjgT+1WYpAMOHhTFxhB/nQ9sQ6b0At1syZkQYNauCX2hlvt9t5sPJu4DSVHbQ3IXR0dF4eXnhbXmEALTSCSXV6T3v0j4AXk0hoB/XArfbjaIoTJ8+nZUrV94eNZnKweLFi5k3bx4HDx6kffv2l5AkO4mcq9qMe+9awP1d/Fm2tA5uYeLdWYLWsb8x8dWfS7YVQrMaHD2YhqcuH6nTIxU9QcEwpEMRurzzuHU+WELu4uzu3QwcfpDjh7dRs1YVDh88VdzKfDw9jcz4OAZJEUJKXPIPshUrIeK5kmNJJBb1B2ThDPTufNq3+Z2qNYJYt+Hq4pVffvkla9eupU+fPrz88suX3U7FqgWgG3xQi8+bUgjeee0nqtQI4JtF8YRX8CEwxJOczCKmTJnCq6++esVMvwvYv38/y5cvZ+PGjezfv5+33nqLvn37XnW/m4Fiuhtc8cS1gK9/g0N6A/VVhxa8bQIsTsiGlLOw4RAMbQvvx6FpJ6lGlKd/R4RePoO1BEKPCG2G3fgra/rB4ytgwgPw7ADByI8k04qJT5NoIFxBROpBCki1YziRSKSaSGwMbD0Cu0dDlVEQ5gPpBfDNDujbBHyKTnD33d0u66rNycnBkl1ERV9oVRuSCmDiIs3qWasKmh9kzwNQoQ00+KFY2+DWYfny5aSnpzNhwoRbepw/46/KgLvTIYQwo2Xjj5VSrrnMNj2AQWgaj4uA5lLKDCGEFxphuiJRumMsSv/g1msCqdixchgHCSh44EENTFRFxUoRh7HJk9jlfk58v5IOXU/Qpa2RwSOaM6D/bgryNJPStJf09O8kiBvtYvv+sr/xunXr6Nq16y3r/8W4cJPOz8+/7W5E69evZ8eOHbjdbt58881LXhLy3cs58OVTxD6mxR6d3VGJhMIQYlrVw23ypWbgHCwFDn4+/jSVqgWgd1sJzPgJIRyAwGUReGScRkiJNaw2uxxN6dJ0IQ67m6o1A3A6VKTLh/EftqBzjyoIqSKFDvLPcuSXeIJ9ndSsH4YUOnT6Fjg8Y3HZF+NWzDiMmg9l/aKtDB7wK7sP9yK6Viz+4gGMIuqyY/b09MTX15f09PQrzo2FveTJryiJKvjTw7hTg084cqD8vJOgYD/mzplPXFwcdrv9EuvQ6tWrGTNmDPHx8QwaNIj+/fvTqlWra35Ju5nrU6pZcLop7EtCeVAjDe6vTeBwgCI1g1kGJKdApXe0oG3hAbLp9P9n77zDq6i2Nv6bU9MbCSmkUEOXLh2kS7HQOyiC2FBpAjYEFbBfil4UQVCRKiBS5EqTLl16AqEmJCG957RZ3x8TEiItEQL46fs850lmZrfZs2fvNWuv9S50dZ5Ccf6z0HgL2JL5/t2HaeSIolzedp6ursLpRD2V+2vblEc/hGpNncis2hxHjpE3X9rAlz85cNyAkeaR6rD1OLg7QdqnINXHoGtxc8ZDu92ONSsNp1W+vLsavt4bTHR0dKE0Yx+HDwaA8vA0CBtX9HsrJkSEH3/8EaPRyBNPPFFi9dwIKSkpLF26lOHDhxc77201Sp/d6ErJQ3ns9holRVHmAV2AKyJS45rzjwLT0YLqfC0i0/LOT0YL2nP8FoLSt3l5rqMBUBSljYhsumWbHkSB5F9B6a/hfpMniqjEydt4p67ndHZNZo1bzub9Os5EZtwwfaC/jjObfRnyfiiVK3bizTffJC5Oc4H+/fff6dGjx23qs4IIiu7G6vtbYeDAgYSFhVGnTh3q1q1LuXLlil3GgwprTE+MO5ZTZZyRyAuFXebfmFgNVw9nXh99gAWru9O5peAdsxklLQdVMZLrWx6nS6fRuSkkOsxUfTSVpFRBUWDy9PYMf+EJjp3Yz6BOv5KUHMe5pAEYHRms35jJwK4buPrahgSbadvYQG6WhZlTQ1mwtRQLv7+AQ3QEhzixduV5AKZ90YoBzzUCFFxojLfS5br7iY6OJiQkhK+//ppnnnnmlveey2mSZC5a7I3r14n6Zabj4qKjQZMyjH2vOT6lPThz4gpDnlxJ9EXNK9Pdw40fl6+gbdu2rFixgo0bN3L48GHOnTtHcHAwc+fOvaEzxO1wp++nnPkciRrDwqMuDBqbjLrOCJ46JN2CJACX4XIShH4EW4dB83BQ+mxGCW5VrHpat27N6dOn2bp1K25ubvj7+4M1AcuBOjg10ZwwqpRVOHVe6NDERKd2Rl6ZlMX4/jrKOaus+B12RUKTirD5FPR7GBZovLI4vgUl1wNlWNotWpB3vz+58NOmHEasvF5QAvh5HHTp0gia7S7W/RUHH3/8MRs3bmTlypV3jb39XuBvLii1QGMA+/aqoKRoTKSRaPbQ0cA+oC8agaQvmoVe4s0EpTvFX48m+i/+xZ+gKDp8lVfIcK1NiFcCM98tzd6DbdhxehjDnihMarh7ezNO7WmC1acWS37Yz6FD+0lLSyMuLg5/f/8bhrsgZz/EvICcfRzHqTByLzfgnfcb0KFLGKfiP+bLhS+y+dBkTkavQCX3pu1MSI1gydIllK3kQUZmxt92C+5mMKYloOqd8A+5ntzwg/dOsGZpBACDH/8Rp6Nref0TKwt3eqLihMvFI0iAOxafUAaPSicpVfDyUPgjqjejusXjmjqfeiEX6dQ9nJxsO5UCFjHhrQtMHLeP0oFuxDhGE3GiMRlpFpavy2LtNgd+tc8y5tV9uJhteHnq+d8azemibAUPxr+whaFPLkZRc8hmN1biCrXXQTZvvfc8AC6Bx8hgF+ot3J5MhHLttHZN6120AAAgAElEQVQlriBszrQJW4i7nMX0hd34ZH5XAkL8MJlMVKsdzMZjwylXSdO6ZKRn0q1bV3Q6HT169CA8PJyJEydy/vx59u/fX0hIEhFSU1OZM2cOzs7OfPHFF8V8WkWH4hKOeLrQs5Omwfr9PHDSgpJMPoVUYN4jN4jm1o/j5rEUb4YtW7YQHR2Nqqp4eHhoJ01+WAPGYsiz1z91XpOIN+yy8sokzd5wQhuVoU1g7QBIeQPWDgSjHmzXyuopIEoRY7aZ/alRDnzy7KhNf9ph+/ZXOLc/ErFlXZ/3LiEsLIwhQ4b8rYSkIuEBNubO0/ok/+n0w8AZETkrIlZgMfAEGmlkI6AfMOxGFAB3A/9vbJT+xYMBo+JHKfPn5CjLyS21AefEvYSHNGbGO2WY/U4iTQdns+eIypUdu2jYTEG1axPu/n27URQFX19fTCYTISF/2oZJ+Ai59A5Z3pXApGI3hrDugAeT314PwLMDZrJ940WMRgVnZ3BydaZWvQAGDa1F/aYNOXfhLA/V8wUMzJ6zg9Cy7jw6KA01M5AQ9/9fEckV50aIyx882qEU23cU5ouyO+CPIwULlVuzqwtpNl2S22JMiscp5zKvvRvF+l0O/EvpOHbiceyeHmQo9VHEhs0UxNjJTvgHw7tjN/PVZ5pr0pqtXdDZMyinO0/SoVKg06HmWthzQMUYVolKDaqAw4aCoOqdWLkyhmH9tvLL6nM4WaKx6dyxOEVhQqMKyOUsSczl6ZFlmP8lDO/3X06mupDOOvwZiYHrmZJ1mHHncSKjv6dBqGbw/cm8zoSU9WLWtN089WJd6jTwR4vpQb7Wyc3dTNd+Nfh0kma/Vat+EM0aduPZZ5+lfPnyheqwWq1s3ryZadOm0bFjR8aPH0/Hjh3Jzc0lOfnP8/vdQ7pzbVLT9ZRx1ySPxi/YUHebkVgLJAEqTNmhpW0YiiY8BTQuVh3XBp7V6/WFBITNm3/CXuA8SJM6eho29uKzL5IAqDgCYsdo10b9AjN+1/5vVBZ+yPNew47G11QEKOWGUqnjm1wlS7fawcNFC5YNsOwgLDuYjPrQC9B6wc0LugP07NmzRMq9EVauXEmrVq3w8vK6feI7xf3zQPNVFGX/Ncdficj1QSWvRxng0jXH0UBDEXkJQFGUp9A0SrcNR/JX8K+g9C/uOnQ44WoaAKYB4GHHbDmJrVwnOPkUO+a5sP33HFq96ECvh0qh2hu77nMHpVxiKVWqxvUF2lMg8k3EAvN+vMArU64ni9y+UdNS2GyCzQbfvq/w2Q/x6K3R/LwwmsU/XOD82Qw2/NaWqmGZdHysDE82XcCCVc+Q434SF6qVaJ/cU4S8gf78Al5+KZg33ooqdOmZriZmfFCZmIA2LPz6KGEhJiqWiqdF64OULfcbUTFPEnEIvl2TitGosPvSKxyKTGPUo6txczdxJiKJpCvZlClTsB3S76mK/DD/DGF+mXhkHkZ8SqFKNjpUdC4uNGmYQZa3J+bY3zWvRFVQ7TpmflBgK+SdvhMBstxMZLgKViWGXI4A4BfoioenmfQ0C3a7isGgI57pBPE2yg2mMA+lERX9XQBNUDKZ9LzQZzW+/i5M+6whqmpBVZxBl2fJnicsLZn/R34Z6ekZvD/tTU4eO8+GDRs4cuQImzZtYvfu3WRnZxMSEsK5c+eoVasWqqpis9lu6fF2p8jMzGTy+x8Q4tOc57y3cna2jvLPqaxdY6FTNbS4bnrYq+2MoauoQ66AYi4ecW52tiaFTJ06tbCAaImmQaV9hdK+96yelm0NvP6UN34Pp5CQBb+cgd/OFQhJx1+A5XkK2/CrLCG13ipUTmpqKiaTiZycHHx8fFBSD0PiNtoM+QEAD2dY/jr0mFIgJBVC/Lpi3eODCl9fX6Kjozl06BBNmzYt0fF0H5H4F73ebiTa5dvniMj8G2ZSlK+A9cBGEbmxDUgR8O/W2z8csbGxWK0lyOCmGFCcamLy7IWu5kF0FiuPNNW2DhwOOHVOG+u9RmegxrYl92IdHBEhOM5UQk2egapmYU/4CJuzD/2n2vOFpFlvuGI0Frw7s98JJHuTN1n7tIXhyRFZ/LY7i3699jH9swgO7r1CcmIODar/TP/ee5n1yVFK++kxOxuw8P9r6w2DO0qLMxhy/XmqC1QrW3Bp4kA92VlXOLLoO5rVymZQi1PUq23Ax9eJtDQbvm7LqNf8KGnpDux2ISvLwauDf+bYoXgcedq/a80HY60v8PGsJgAsW3Qeh8EDq9kXm8ELu2JGVRRsngGYrHHolRz0koXd3ZfvN+s5eiSV3t2157X5TE1iS/cj3dWbdNbnC0mqqtK47BdkZGiarwnPr+fY4XiaVZpF3Xq18mO/AVy+fJmuXbuyefNmenYejbOzM35+fowYsJrEKxm07RiK3eCBqnMucPe7xo6pz5DagHYqJ9tGnVoNqVmzJt27d+e3335j4MCB1K5dG39/f86e1djMZ8yYgaIoJb6oXblyhdjYWMw+7Th0IpSy1U281N+Nx95HM2M1QKoCa09CWCkgW0Upo0LiymLVc1Ujdp1nYe5B+JN35SPOVpRT8fiUcmZ6L+1cl4Xw0TUk1tW/gC92wzd94PexeSfDC7xURYSYmBh27tzJhQsX4NIy2NoU68HX2LzrBABt6oDXTeQ9vQ6y3RPhQKfCA/NviObNm1OjRg2cnZ0xGAp/AFy5cqWQtu8fiGjg2m2GYOByEfLNQyO3XqcoyiZFUcYpilJsA8N/BaV/OIxG418iCvxLcK2C4l4XFJg1oWBheeUpM6fPC6XrxpN1+jjWxEQMlc7gEvQqY99uQUzyVnK8A1m8UVsUrWdCqVjRhM1WMDEeyggmpeVTpNR9lkOxL7M9cjjnLK9RqWopYi5oAZa/W9erUHNSUrWQ6tkcIIuD96AD7iH0rpj95zF3ohdHFxnZmedkFNotB7868fQYnkS7Nrv5/Otk0t0a8OoL2naXt7eBIH8dvqX0iEBNvxmc+CMBgPWbHuGPmOepXruAQDTqUCSvPKM5jAx+OhQFFUVUyLaji0lGic1ESc3A6EhHZ9ahMwrm9DOMHhOJCCz5UTPq/eLLaNCZNCklT3aJu5xBmGEaaSm5/PjbAD6c04nFc/9g1FM/k3gli2NHI/Dz8yMoKIjatWtTrly5/DA1W7Zs4ddff80nofX2NvHxvCdA0YOSZ+j9J2PvkW83Y/2BpxGgYYsQpn74DhcuXCAzM5M1a9bQv39/QkJCcHFxyaey+HPMuZJC+fLlWbBgAc2aNaNM5eexefjSq7Fm97frPCgu8JRGbcaFJKj0GnAW1CXdENVx03L/jCpVqnDkyJHreKTEWAYXV1OhBeOJT4ELoEQ356V+rVjWEyrdYFftwih4vAa4m4BGb6NzLtgyTU9PJyIiAm9vbwICAlAODcdmycHvae3jbUBLPRMGVyMuBV67wS6YQ4VWY4HMX+DSf4p8n6Bp6YKCgvLv9eLFi0XmzypJNGrUqBDlR1xcHPHx8cUiDr4t7p+NkqeiKF/luesXB/uASoqilMuLz9YHLTrILSEie0TkHRFpDvQCLgKjFUU5pCjKPEVRet2mCOBfQekfD19f33snKAEEvwkovNjXzB+LzTSvq2P6fE1bkJQGvm1thHTWDLEtFuHT9w9StsZuQmtr2yIL51YiqUxHvtlWmNi9aetyiM4JFAN+AW6Uq1QKo8nAlhPD85m9W3WsyCV1AmMma+FL9my7xLrP1uP/w38wf98Kx4n75ApSQlAUV5Qa+1FcnGlcCQ6sD8u/Ztnqin8pHa+8kcbEl36i3cCWAHR5LIgzBypxLqIRsz8J4q1XPRg+SPPUUh0WjLYEHmmrMU47O+v56MMzLFsSw7Bhwfj5mVAVE2Rk4XThJHolGx05GHOSUVLTUSwWFAW++MFCeoaDiW9VRFGgY9dwZnxX4Hq9Z9tFgpUp1C8zExFY+EtvHm4Wyr4dlxCBE39c4f3PO7Bu/1M0b1ORmjVrcvHiRXr06EFaWhonTpwgLi6Opk2bEhkZicFgoG6DokV+r1k3kDc+aMUPcw4zbMjLfPTRR1y+rH245ubmsnLlSpo1a8bChQupUKEC/fr1uyvPqigwGAxUr16dkAaDsXiXo5mbFlajxyzoPxvWHClIezYZPtkJ5ID8Vjz3+cuXL7NhwwbS09MLThpdcUrJuJbDnagkwB04tgSl5Ty6VoP5XbVrzgYIcIXjL4JBB16+Huie2ICuyaRCdXl6etKxY0fq169PUGAgYkvH1BfSc2BER3i4AszfJNQI1zGyg6ZBAijtUVBGZDRapN246cW6T71ez+DBg/OPAwMDixyGR1VVfv75ZxYtWlSsOouKyMhIcnO1edDX1xdvb+9ixU98gJEmIs+KyM83S6AoyiJgN1BZUZRoRVGeERE78BKwAY33aKmIHC9OxSKSJCKLRGSQiNRB25uvVJS8/9ID/D/C/aYHKDJiJkHSJ6CqOBQFQ+2iqZRX/9qWJs38ceicsBlL06/DIg7sjuF4ymgMBl2B6v02k92urRfo1WohoLH+RkdHE7e8LN6Shd6jDfr2JTP53Tc4UpHN5Uhu1BFfD+3eomJ7UbvyCjLSNV6cRo192bM7kVdf8ueTMSqK3QF6A2IysfeMO41bnuTQ2T4ElXEjIT6TR5vsID3jCgFBLpyJSCM5vTM55rKAoNhz8bm4AaMtHVQV1LznYtCjejmjD44nJNjI7otj+W1DFAM6LeHlt5pxdH8syYnZRBxPJCfLxpGEV/HxLVgcTp9MoFP9b8jJttOgaTArdwwCwJsBuHDj8E1XF75pH7diwKhGtx0bV3E+Ko2v38tg7dr1JCQk4O/vT2pqKk5OTkRERNCgQQO+/PLLYvF+3en7mZCQwKpVqxg2bBiZKa/gunoGi3ZB/zxT2E97w6glhfOoS0ESQBmaiWK63gvyRti+fTt9+vRh0qRJDB06FACxn4Md1dC10hbv1zvDlLWQOz/PG82jLmp2ec7tWk6lGRAxAi6mQWVfCOr5GfpGr968wmsw+akQJi6IZsFLMKglfLvdQPWmD1PPaw/zVqs8cw0h++N1YXWeIlj+cAXVCWon3rTsiIgInJycCAsLu2maosJqtZKSkkJycjJVqxY7dNgtERsby5YtW+jTpw9ZWVm4u7uTmZmJq6trkQW5W9IDhCuyf8ZdbXKRoXS8v8zcfxX/apT+xb1HmYlQ+RD4v4Y+14ac8OCH9w14/OmD6dTlwlrR+nVdUdIT0WWlYcq+RM1KKlmZNvo1nc5LfZbx2//OFqn6Jo+E8fMe7UuybVNfyvpCQI/zGDMT0MUuRt3+NA6yyJTfyc79CnvGbMRy86jyDzz0XigtIlEdzozopy2WFQKXkpFup0NnTTO3Z7e2wHz8ugnFateEG5sNJSuLMW8kUDrAmaBgd3SqBY9ALY/BqENv0KOqQo45DBQ9iurAN2YdBsnUfMNNBtDr2LTHTqfnsug1XNtq2xL5CgCNW4chAtMn72Dzuiiys2x0G1CdnWefLyQkAVSq6sfprNf46OtO7NsZzdhhGot2Ct9jLeQQU4Bq1TQj/VMnixerq2wFT977JpiTV6YTdfYMOTk5WCwWVqxYwcyZM2nQoME9I0e9iqNHj7Jpk7bN6eQxGtHr6dcYzn8EEVPg1U5al5fzgw153me7j4DiB0T/r8j1NG3aFA8PDxYv+gE5/xOy6zU4tRHxqMC7L2rjZ0oegXmzqTrinVyRzD/QGfdRrkFH7BOhgg+0qmgmaNjqIgtJAPsTq+HlptCmpp4rWV4MGruI8DA9qr2wkATw4QBwymMdcSTZbulNZ7PZaNWqFePHjy9yW26Fo0ePYjQaqVq1KvPmzbsl55vdbuf3338vctmBgYH069ePESNGMGmSpoGLjIxk3LhxLF68+I7bDvwdt97uK/71evsX9wfmChD4NvgMgXMv0LfzNlrUzSXygsqxKOGJlka8XfxZsPIxBnf9mW/ecMf31E4sriFkVCiHKTueKSM82PSrM0dO2jBGRPHT0tM82bcawWW9SE+18O7MdugU3Q39JQZ01D69H2nozsl9cD4RKr4GB94BU/qvpOSMRqeoOOfGYLx0CDU3A8WjKbrw9UXWSjxQMPvhbBzCB2/vpsvwEF55YS+njqdSt74Pzevk8OZ7mot3ZrYRF1cf9NZsdDZNe7D390RGvFYPvSMLi6kMBtG0UMmJuSQnamkCjZ/zWI9KzJgajOiMpJWuj6p3winzLF/NPM7IaVdpCBy0ahWMk5MJFBgzdB1mJz27zr6Af2DRtC19n6lNZoaVSSM3ciEqhaWbB5DAbPwZi4HCrtXHjx/no48+4rXXXqN+Gx1P9i1CGI9rkMsx+g1+HYvFwqFDhyhTpgzdu3fnyJEjt898l9GtWzfS0tJYvHgxBn0o9rZLUTb0INQ3j+rACi+0hmX7wNdNc+rzddXOY08qcj06nY46tatz+fBq2LxFWyUsoNjg9VoKoYNgcF6Q3P0RKoE9svj1R39am6+gL/8MDCi6F5qdVFJZjZ14zkc6+HmtJtC99ltvRo8eTengupw9uYvjO/egU2wMagKBfloYl4nLIDePo0mXaYdqT920noyMDJ588kkmT55c5LbdDqmpqfj4+PDoo49Sv/7NlSQ7d+7kkUcewWq1YjQab5ruzxgyZAienp6oqkpERAQLFy7EarXSp0+fu9H8+4U0EXn2XleqKIpZRCy3O3cz/KM0SmfPnuXjjz++3834R8NqtRIREVFwwhwMVVbDQ8mUab6DVj0WMOL1FYS2i8Tdcwa9O2qROTftzUGyBH1SIqVXf4X39rW4JMZydL6Z1PUGLp2oR4/+lfllVSRffbqXBV8c4Pjh+GuEJAUwY5KKmC2xpKXkMnxoAI90DeaDEX5sfA3OJoD3i+DaJYZgly8Jcp5DaNhGvthfg7i6zxFXsSppic8+EAaffwWuuiao7nWp18CX7Qcep3W7IKZOOkJqqoMRQzWjD6+KZxEnN+zuvpy7YmTal7kgsGxhJIoIWdk2Pn1XY0JWdFAh3IsOj5UDwOFQKVdpCy/N8iPLqzo5HpVYdbQ8I6fl8tpTRiIOTWPo091YsWEhKGC12lnx3TG6DahRZCHpKqrU9KNWg0B2bbnIpNEbAUjhxxumHTt2LA0b12XC879wPiqlWPX0f3QRh/ZeYuOhN6lduzZz5syhXbt21/N83QMEBmq2YVOmaIF8DQHdUHpfQWk6F6XJlxD+AuM6weVUaPsRTO0J4YFoQz+4Q7HqWjjcjS3jbZQfDzUmQIwdMn1BPISOTaBRlcLp23WP55ibN3K+6NvWFs4Rz0dYiGD7lsM0rjyBRo000s8ffviBbj06cOnSeb5alUv/KTZqV4APesOYLprmbEkeIffiV0BxqQilr7fFys3NRUTw8fHhiy++wNfXt1j9cDPUrVs3nz4hKCiIhx7SYk9mZBT2QL906RLlypVDRG4oJN2IcRw0b0B3d3eWLVtG06ZN6du3L++99x42m+2G6YuN+6dRul+4EX17kSnd/1E2SiJCSkoK3t7epKen4+lZPI6RBx1/Bxul1NRU4uPjqVy58m3TCkLG2XZ4VtjEkCdMzBmnoJj1kGFDMOJw98IWHIIuNwcy0kmp0h2dPozxQ/azZPGyfE6Ya5Fofwv39LW0feICO3YkU7V6eX492ou5r/3IxI9PU7csTPjsYRwuZejTYSVh5b24cFajJDiVNgo3dxOeaifc9LeJwv6gQoSsjInkms6DCNEXs6hdZSWVK5mIOK15Go0e4ccnMxOuy1q1ug8njyfTom0YzzzfhDoPmykd4AoYcFLqk60eIchUEMOrfpMy7N+lEftI0mLw6Q2AnUTi0Qznyxqn8US/6kxfUKCJz8y00qzif0lLzmHUpOa8OK4xqcm5PP34Ms5GJpOSlHNd25Zu6U9Wmh5ffU+6dCkcBiU3N5fOT7Ri8//2YDLpicwaq9m03QQzp+5kybwjZGdaSUrMZvWugdSqX4a4Ix15tG039u7d+5dC3tzp+1mhQgXOnj1LcHAwly5dv9UoySdhxUPYsKPTgUGfZ7b30CiUup8Uqy75sR7YDvLIJNh2CkxGsNqgUVWF7W/oSU+0U+pVCPCFlweY8XZR+eQHlROzG2PsUBB0WVVVYmJirhMsBTuxTMFizWLGezv5z7s7AbgQ+zh2nSsV/DWBy8nJhK9vaaZMmcKAAQPg8kbY2h708NlaCA+CAB8I6boJ//DW191HZGQknp6eWhiWe4Bff/2VVq1a5bv3iwgickOPNRFhxYoVtG7dGm/vwrH40tPT2bBhA6GhoYSGhuYLyUXFbW2UZhWruLsGpcO9tVFSFCUAjazyezT27qt94gHMFpEqN8tbqJx/kqB0FQ6Hg3PnznH8+HHq1q17X74OSwJ/B0HpVnA4HOTm5uLqqtlBONRkci+1xa3sIQBse7zQpWagszhAAdErqG5uWKrXRJ+cjFJpM0ZK06Xz46xfvz6fY+edd95h2rRp1KlTC6s9iksX0khIsGMy6ShdOog33y/P+xP/wJ6eQcRUIePp50hINlI7YAa/X3iRtFQL7Wt9DcDhuJfx9wsiQFdygTjvBRySiEg6eiWQ6Mu/Mf3DIXwyIxaAF4b50rKpK5UrmqhRsSu/7q/I0RPxqGTTb0B9nHx3kJHpgot7OjqccVGa46rrhJDLK2P7M+uTVTes89p3OoY3AOjS8BuOHozngk2zHcnOttK16XdERSTRfVANFn55OM+TX0FVhaAQd9w9zXTrX4Mn+1Vn6y9RTB6zmayMAi6wTz75hCpVqhAQEMChQ4cYO3Y0KSmFY4s1axPGvNU9MZkMjH5mLVvWnSG0gjdRpxLJyrDRuUdl3NzN9HqqJg2aBpOelkv3FssY9fI7+QbOxcWdvp+TJ09m4sSJhIaGapxDN4BEb4EtvcCRDHonqP8BStWXil2XbH8BLv2X2AyoMwGGtoWXhvoQ1CGZx+rpWTnEgeFFTVCJ2OWHqCq68kmcPfAT5eo+nl9OVlYWTZo04e2336Z79+4IKhZOYyWWDDYx4/1tfPjmdXFK8zH85Xo83e/zQiGN5Oxi2D8U1ZGFXeeJU580OnbsyLp1DwbxZEZGBna7/Trhp7hYt24dp06dup7Tqgi4raD0+Y2ulDyU9pwBtgA/38rz7a7VpyiDgaeA+mgUA1f7JB1YICIrilTOP1FQuoqPPvoIb2/vvzzxPWj4uwtKubm55OTk5E8wVut+LmV8TkXf+QDEZQzG5+w6DDnpKHEW0IPodeTWro2eapgCvwPg9OnThIeHs3z5crp3786OHTto3rxAA1StmguTptSibbWL1Gyt8OYrWXy7wsGu3ems/e8z1BpeGhSF8k4fEFrOi7mretCyypcAvP95B55+riWBugn3tnNKGKrtMGlxw7BYc/DxNgBO6JyHY3B++obpbzbWbCSw9JeRGM1CvcbBdG36HUcPxubncXPTjKpjmAJkcexwPI/WmUuTVqHs+e0Sap6HXO2Hg1jz+1NkZ1n43+IdHDuUSN/+ITxUy5NccygOfUHdqqqya1MyTauPY8yo11mypMD1S6/X4cgLaR8t2jbuvp2XePrxZaQm58UDVKBCuA9REcnUb1KGaf/tQM0qevRqNqIYSLN60r/zCqo+FMC8mXuL7HlU1D4rKurVq8fBgwfp2bMnS5cu/cvlFAViz4ZlPmC2cD4TnnxXwdPTTP9GFkbPFR59CPx94Yt1cGoOVGjihbF6KvGXIykdWImVK1dy7Ngxxo8fj8lk4rEuHVm1fC7p6jQU9SK5Nj1rN9sYOWQDXXpVZcvPx4iNs9OjvYHl/9Ns4DIPeWIv1xOd7tPr+82eyaxZszh/KYHtO3YwZsyYQuFGrsbgc3V1vecM1/v27cNqtdK0adO7XrbNZsu3eboVHmBB6b54vSmK0l1Ebrw3XwT8o425x44de/tE/+KewcnJCScnp/zjo5dXUr/cfACWbu6H3TWIKzWG4Hfie0we8ZCpTag4VPR+BcLu/Planqs8MM2aNaNmzZpcuHCBOd9OoH2rLXjmnOC7pZmAO8MHOtP7CR3e4ek89uI31P8+iNiYDKwWB2kpOfRqtZAyYR60e6wSJ49cwVmpdy+6455CZ6yNp/90HDkzEclFZ+6K3mlgscsx4kfPR6eQyjqEHF58dQDOVGfwoKfYtm0bly5dIjc3Fyd3A0HVY/DxdeHjuZ0Y88w6TGY9S7f058km3/LIo5r9h4c5h/59A1D6Xt06EcyWaHJcauNCY0yUwajzpVc7XxR0LF68mCVLlvD888/T7+mmVKy7n9jLGdQNncvMqTsZMaEpDZqGcDzhfXZsPcCB3TF07BbOkCeWU7FKKVbtHIzenobenoJerCQnZfBUv3WUCfVj8vT2f1lIuhto3LgxBw8ezOfXKUkoBhekWyysC6D8YCsgVA/N5eR5uDILqoyDS3sh2B+qDIMre/0o5Z3Ga+MnMuvzr+jWrRvdu3cnOSmRMz89zsy5qzn7XRBlw93x6pRFVpZKrdoezPyuMy07hlN2zmGC/RW+/awMy6tr2rKLcQqVZQ2Z1iRsnvHoEjNQLLno1GTISSPwsgPVGszv2/8g225m7Nix1KxZk0GDBmGz2bh48SJBQUH4+fmVeH9diwYNGpRY2ZGRkcyZM+e2gtJt8Tf0R/krUBRlALDwZkKSoigVgEAR2XGrckpUUFIU5VFgOqAHvhaRaYqiVAe+BiKBp0sqiF1xMGLECGbNmkVOTk6hhfpf/HVkZGSgKEq+BqG4EFR6PzobgFr1A2jSqmzeFR1JlZ4kMPkrRAeqkzN6j6fRm1vSq1cv4uLi2L5ds5EYOnQoTz+taUSys7NJT0/n6P4sHu/0JpLTny7tnHnjQ/h0diZRFwVF0bQTe3dGYzJpJJwJ8dnodArfrevFpQtpjB/+CyE+W5gy5f9XIF0AnakJOlOTOy7HRFLOE0EAACAASURBVChe1iGMHDmSzz//nCtXtJhunTt3BsDFxeWG9mOPdg1n7/ZLODkbGDWxmdYmuf591AFBvH3LNsyf/w2T/2NFHA6C/aFSZU/WLj3JiAnaV77okmjauixNW5cFIDPDSq+naoIIDr0Hdp0b3/1nE59OO0rPPqG8+WELDLrQv9oldwXh4eGAtp11L6CYvaFTHAvGlWbwB3YiYuDiFZi5DiKnQsXxEB0PTiYo/fBpHmkdwIJvF/HVnPnk5uZitVpxj5+FP6upHgqVhoGLOYNsC9SrpqdNIwV3cw5dG2lhVlbNr4z5eBQvPgqf/wK/rEilcj8TVv803A7vQvEwgE6HJFlJy4TpP0HfZpeQQ8Po/Z6FNWvW0KhRIwYNGoTJZKJWrWJHqnjgER4ezqlTp1i0aBF9+/a93835O6AUcFhRlAPAASABcAIqAi2BROC2nBEl5vWmKIoejfmyI1AN6KsoSjVgFPA4sB8o0mpz4sSJkmomAKNHjwa4r1+L/99w/Phx9u3bR07O9Ya3t4PNZqPzY22IitDiTrXqWKHgoqKgmtwRuwECe6FrEoXJ42UAli1bxvbt2zEYDDg7O6OqKu3bt0en0xEVFcWIESN49913cTI0AZ0H3t46Hq5jZPQ76XwxL4NqVSszfPhwxkxqy9nccfmM3hcdE2jZoQIDnq2Lf4APU6dOuyt9dDukpKT8beM7XbvNNG6cZs9Vs2ZNQFvoAwK0kClms5mXJ7SmRp0A3vigA4d+v4x/oBs6nR4nHsJb9ywK13oL6dHrbk6m265dOxRFYc73nQEHChB/OZuoM+mUCXNH78jALesobplH0TkKPJQKnCMVLFYHz/f5iSWLo1mzoSXvf1AbvUFPKeX+umW//PLL9O7dm3fffffeVWr2ZtC7CcQdqI7dARm5GsXWlQzI+RZe66Vgz4uQsnVzHKBpVMxmM+7u7lzaP4fYFHg2z3g4O88Z+8AJBx/OTqNbh/UcPhhB95Y66uojUFxs9G+lpRk1B05cMlKnfST6vlD7FZVek618tAG8n4PtkdqX/h97t7BmzRpAI+W8ijlz5hSKBXivBMyoqChiY2NLpGyDwcDhw4fvzP7pfnm83QceJRGZDtQFFgF+QJu84xhgoIh0F5HTtyunJOkBHgbOiMhZEbECi4En0LRLAqgUQQEoImzbto34+PgSa2jZsmUREcxmc4nV8U9Dw4YNOXnyJCNHjuTcuXNFzygqu7Z8y/o1Wxn6cn0efTKc/7y7kzDDVCo4fcBbIzZgzbKitMpCqbsExaxtyfz000/5RdhsNhITE+nQoQObN2/G3d2ds2fPMmNGHh2tokfnvRJ0QXwzw5v9v4YSfW4Zx46fYvbs2Ux7ezmKcr0m7OcFCvFxmvC2fPnyv945RURWVlbJBiwuIVy4cIGePXvyzjvvAAVboR4eHgwbNgzBwe5Dq6hZKxyLxcInkzdw9GAsD4d+RvwFEwH+IfjxHKXojUFfGSfjMMAE6NApZXExX28fZuUS+y9MZOPGjcxb+jIdugTlX+vZ+X94eBqZu6gNRlsSVqMvqs4AooJq0f4CCNhsDp7ruRKbVWXZ5v6EV/FA0OGlfwU999dL9ptvvmH79u35QuY9g9GL0rV2MXmApmWtHgxN3ofsSpWY0t/EjD+x4iRcOIIkaTxTnm4m3PK67c0hepo8pNCyjsLgzjqy1iucng/WrQaWTHXG5lcaCfDk3Z+0ZalTXag5qEC4OXJGZfluGKeZIjKqMwxrBeXDCjzCoqKiiIyM5LHHHrtuzUhISLgn1B4WiwWHo+jx9YqLhg0bMmvWLBITb85C/gDjtiFM7jZExCEiv+bFfBsuIq+KyJcicrGoZZTk1lsZKESXGw00RNuKWwucBmbfLPO1fBTVqlXDbrdfx1FxJ7h8+TKKohTb7fJBxr36YioKoqOjmTp1KgBeXl60adOGRo0a3TqTPQt7VB8++PAiDeuGMOnVMKyuQex/oTnLvj3Frq0X+HVVPL+uWsr27SMoVaogfpfFYiEkJIT//ve/+eNk2bJlhYovPH4CwGkz2fZUwh/2BEW55roOd15CxY7gQEFh9U/reP/N1wkO1lipR44cyVtvvZWv8Tl06NBdF7Sv0lfczXF/t3CrsWaz2ShdujQWi4UDBw7k99mSJUtw8zBxLmMWqms6VotdCyGT+j9KmTVj+6xMK3379sWS4YmFq/fdGEUaAQ5QDGRlAhT0iZ10UmQBy+dsonz5EB5pDRlZXuhVQadasVo8mPBOc2y5rthwA3Q4lADEZuJqgNyAgCDMBm8er/8jpQPdmf7DEygq5Foa4WLsjdXuiZU7ew53+n7Wr1+f0qVLo9fr78OYUHj1+SHsjVrP4UvQoomJ/3yew5i+fvR4XGFLlLD7GsV/ws9dcOq8DaXSBLj4NsHBKvP/B8cXm1AUECvkePnjVdmVlBQLxtMX0YkD9EaOXnSnbyc9Mz8LpXabc/j7+/NKb4XZKwQPJ/Bxhzf7Qd1KkJGqsPLyAIKDNevkoUOH8tZbb3H48GEOHz7MsWPHsFgsNG3alLZt23L06FF2797NuHHj7m6Q2Wtw1Yv6rz6jxMRE3N3dMZvN2O326ziYNmzYwObNm1m6dCkDBxbfjhD4x9go3TVc5Xm42z+gJ5pd0tXjgcDMIuaVkkZqaqpkZ2eXeD33Eunp6fe7CflQNfclAaRVq1bSsGHD22RwiHqgghxY5S2A/Pw6Yt3kKelH60l8cn+JsY+SOd+PzC8TkAMHDhQqIiwsTBo0aFCsdhalz7Zt21ao3h4Dahc6vvb35bwPxKFaxWY7LLbMeeLI2Sqi2ovVpr8DbtZvSUlJcunSpfzjnj17CiAjR47UrstyiZbXJVpel93nXxBADsa+KrmOGClfvrwYDAY5c+ZMkduhiioJji8lKbOb1K3jdtPn4uNjlJ+3dJaE9J6SEtNWEq50l2j7uPy2VK7hm5/2TM5rEi1vSJYcubNO+hPu9P1MSkqS9u3bi91+n8ZT9kX5aXTeO9BJe08/exZRv0HsXxX09RtPIJ8NQZK+N0lmxtuSktlDmjbV0r/4BKJuVEQ94iGO075i2+Mj9m+NYp+vE8dcJb+MNyeUlVVzawggwcHB1z3PyFlIzA+IxK2VPVtn5Z9///3388fc1d/3338vLi4u4uHhIQEBAQLIiBEj5MiRI7J7925RVfW6W7VarbfsitjYWPn444/F4XDc9W6+cuWK5ObmiohIRkaGZGRkXJcmOTlZypUrJ19++aU8/PDDcu7cuULX89bQG66v9cIR2XR/fsD+m7XrQf6VpKDUGNhwzfEEYEIR8143MP7F7fEgCUoiIhaLRU6cOCHbtm2TU6dO3Tpx+l6xn/KXje9qk1uF0kj8j06iHvES9bCHnPy1orh7mKV169aSlJSUPwmOHDlSAgMD5a233pLGjRvLI488Uqw2FqXPnn/++ZsuwMnyo+w7uLPQuTHjqsjFqMaSnthcEtN7SLR1pESrEyVa3pFE+VbsklasNj6IuFm/xcbGyu7du/OPrxWYRURWbX9FAoPdBRD/IE2wicwaI737dRKz2Szx8fHFakeG7JGUjM6Sdr6CAGIyaYttt+4BEhPbTuKiG8rxA7WlWVN3URSk4UM6Ob/VQ+wnfSXrXC2JsY6UaHk9v43zfuonSbJMcqTowlpR8aC9n38FtqSD+X3VpBLSsjKifoGo3yL2H5CawQXvweB2SPbxKhKT+KS0aOUrVcJ0Uj0MkVPeosYGi/VkiDi+RBxfK+KYjkzvo+X7dYaf2Da5yfiuSK8GyLJxBYKS2aj9NekL6nminbO4uyHOTjqx22zXvaNjxozJ/79hw4aFroWEhEidOnVk4cKFcujQIVFVVVRVlf/+97+FBKiYmJj8PImJiXLkyBGpV6+e7N+//749i5UrV8qAAQNkwoQJcuzYsULXbisobVHuy+9fQel6YccAnAXKoRkY/AFUL2LeoowTmTt3rixdurRIaf8J+FtPxMmbxLHJJAenFExiiyYo8sf3ZqlbTS86HVKliqdYbTkiIvLLL78IIIqiFJr4XnvttWJVW5w+O5u4WLoO0L5y6z1cWpKSe8iV7FfytCRv3VSYatu5gjw9or5EZoyUeOtwibc+Jza1eALBg4bi9Fu3bt0EEG9vb3FyMhbW9Pi5SLS8Lt6lXGTEq0OK3Y4rjtliuxIuvbu6Fir3h1XtJCmrhyRl95GkrF6SmdxIdm+uLBXKm0SnIP0eN4l61lfUM77y/MhaAkjztmUlVTYWuw1Fxd/6/bwG9pj14uGiCS0eZmTreET9DlG/RrxdCo/9qiHa302/PSIrJmr/+/ro5IUhXrL3HeT7fkjqJMQxDelRU7ueuQhJ+sYsgHzxrCLtGhYISiteR+aNQGaPCZWfviojLR7Wy5svOsn2xZrQXadWRWnQoIEMGjRItm/fft272KpVK2nQoIGULl1a1q9fLw6HQ5YuXSpNmjTJE7RNMnXqVElJSRGbzZavWUpISNA+4CpUkFWrVomIyOOPP55fbvv27eX48eMioj3nG2mpSgJxcXGyePFiWbx4caHzD7CgdBr4CnjsZu0riR/gD8wF1ucdVwOeKXL+Em5cJzQagCjgjWLkK9IgOXr0qMycOTNfTXm3YLPZ7mp59wp/64k4+gtRFyNZnyNmgzb5pC/T/gaVQlbPdpGMmCaiyvUTkO2ar8gtW7YUq9ri9FmabJVo9U05s6+a2Pa7iCPCSxynfcR2voLE2l6SRi1CBJDdv4TKih8qXLONUEYCy7iKh7teflzXTuJyhkhyZk9xOOIKlZ+bmyuZmZnFav/9QnHH2urVq/P7w2TW5295Ravj5WzuWDGZ9TLuvTZil+Ldf5JjntiuVJRz0Y8KIB6e2gK7+OcOkpjTV5Jy+kpaekfJSa4nucl1xXG5jHw9TROqViwIlH0bQwWQHzf3lgSZK6rc+bZWUlLSDRfKv/X7+SdE/m+yADK+E+LviWydoG3BxXyERH6EBHkjfu5I/UraM9//R3vJ+lmR8oE3/ph4pWnB/45VyJX/lRdAqpS5fuvt8ExE1upFIn2kYW2DAKLXXV/muHHjblhX7dq1b7id1bVrV/H09JTOnTuLi4uLBAUFybhx4yQt7cYa4NTUVBk1alR+uS1atJCJEydKixYt5Ouvvy7pR1AIfzYjua2gtFV3X373S6MErAd6AX9IgSLnaFHzl2hQXBFZJyLhIlJBRN6/0/IyMjLYvVuLY3fmzBk8PT0ZPHjwXTWitdvtTJw48Wrn/ot7hZOTkFx4cXMpLHk8ku0nacPzzE9mujxkxyk2GkUt7E0yderUQoaOzZo1K7EmutEAj9SDhDkl4PAKINcUjE08UdISKX3me9b+XA9rTEVqNCpH68eqkJzVg4Tsvjw/uQdHIrvy/mdN6Nd1M2t+SSPbpSKxzCCdrdjQeIbi4+NL1LvzfqLLY114d3pHAELCPGhU7nPaVPucWn6fUdP3MwwGhaGvNsRGXLHKdVe6oCpmnP3LYTLpSU+z0KpDKK3ahyB5vio6NRObwYccUxmyDOXo3y2ELm1d6f5ULA3aao4vnR7KpRRPo6C/o/vMysqicuXKTJo06Y7KedBRqWEf7N8oTOkFU3tB789h5ykIDIaK4ZCcCf/tBnuehtwvPaicmYwS3ogz37qgRgfwy7IQot6Dy29Al6owfSeU94GqpUEcOnyzz/LjKDilhQrE5ZopfvgsQATspZk71Y2eHU280N+EXymFV1/qzYwZMxg4cCCbNm1iwYIF17V9wIABpKRcHxzZzc2NgIAA1qxZQ0JCAv379+eDDz6gSpUqfPXVV4U8UDMzM0lMTOSTTz6hX79+AGzbto2tW7fSrl07Bg8efFf7+3ZwdnYueuL7Sw9wv+ArIkvRvO0RETtQZNfEvxUz98GDB9m0aRONGzcmKSmJo0eP0r1797tah16v57333vuXU+leIzOZ3OqVOPB2FG5mMBlgzzGVTm1KIXihplxGL9FwuA7UOQKKQnh4OKdPnyYgIICwsDA6d+6cH4yyJKDDBXPWeTID6uCUfRGDMRed0Y7iZCbHFIrVXJorbl0pPCsoIHYsTgH06G/EJ9CTFweuZWjXHwmv5ss7/zlHi3bl8aYfoaHVSqzt9x8Onn65LplZOXzw+tZCV8qWc2PHwc4YzNnocSlWqUbFD7vXPBR1DmZnPaUDXViwtjc2nQ5QMFnjyXYOx653Q2fLxmh14HzlD1ZPtDGzuo5XpmvUAAZJQrmDmTw6Ohp/f38yMjJITEy86x5Vc+fOpWHDhtSoUeOulvuX4VEJfeMvkT3PEh4Awx+B5h9A3+bwTiMw6sALwAb6y+noL+9HMbuhig52xNHWQ4da3xv94RSWDwCLHUatgc8Hgq72AjgxkNACp1bGdIHJedzKv0dAerbg4f8J1V3GsvTzDPYezuKlEcMIbzCNLVu20LJlSwwGA5s2bWLTpk1ERUVRpUoVdu7cya5duxgzZgxAfpijq7j6gezi4sKHH37IlClTGDFiBMOHDyc9PR13d3fCwsLYunUrq1evxmw2c/jw4fz8o0ePZtiwYfj7+zNs2LASfgh3gn/c+palKEopNO0fiqI0AtJunaUAfytBqWXLlrRs2RLQuCSuDZR4NxATE0NkZCStWrW6q+X+iyLAJRibbxnqBZ0mPhUufwrbzkGLGkno4pNQ3ECMIDHHUMptZvcpF06fPs3Ro0fv6eKR7lUdp/TzZPg3wmBLxG4shaqYses9QJ8XU0oERWyIkqfpyvvrMAfQsmMA+y5VZer4Lagi9Gu/iGh5nRQW4cI9JBK8x1AwoMeHF8c1yheUjEYFbx8zOTl2alZYiaeXC2ejPi122QbDw3iqflSr/hPpmaDTF2gYrUY/EAfoDDhMZszKRewevujSY9DprtEa63zv6P4iIiLy6UbmzZuXzwh/N2CxWNi1axfOzs4PjqAEUHkYqBZObh+BQ4WID6DyOFikEePz0R74/g9oWxE6VwV3SyZXssDVBOYMlR+/TaFzXXD3MmJU3PjqvW4oj/wHxWBCTgzkXAF3JL0bwzdb4VISNKgEC/bVYUTPTuDdHuzRLJ7xMZXCwwhvAC1atLga64ylS5fSpk0bunfvzrPPaoRPIkJ0dDQhISH06NGD7du331QTbTAYGDNmDLNnz2bs2LF4eXmRmpp6w7Q2mw2DwYDNZuPZZ5+lTZs2lC9f/m72+A0xc+ZMBg8ejIuLS9E/FP9xchKjgNVABUVRdqKRT/Yoaua/laBU0ihTpgxBQUG3T/gv7j7qfI0+aRyNysP8PfDKIpj5LFDahDibwJqLkutA3ATifmTw4I14eHjc84VDcCLHuwqm7GhynUKxG301Lh7Vjkfq71hNpXHonRCdEbuhFIpYMOVcRHXoUNBhc/HH2dmFyTPak5NtY/6sA0SeSCC8mh8qKrqS3Q2/r/BlMIm6eXh4OZGZbmH4iGrkZNtwctJx/mwWa1dfIiY6huDgYFJTU5k1axbOzs75zPm3ghF3Sge4cmJjLHGXMwgI0ljBFbsd55TjGp+SexB2j9I4MuPAcZGrpPHVww3o3F7/y/eVmZlJixYt8reA69Wrh4jcNa30okWLeO6550o0hthfhVL1JYZOexg5/ikSuYSot6Hn13DwMmyPglpBMH85eJhhUmvYmwyLfodXmkN6FiRlgpubDWclBf3OeXQ3qpC9ERxGejSzkVYTqk+EtGy4+BWkKy5kB09k3+WKkLsPrGdBceXTD9/Chg8XLlwgISGBihUrcvHiRdq0acM333yTz0l29ZkEBwfnP6PmzZtzJmciW7ev5tL5NHKJwomCaAAVKlRARJg4cSKTJ0+mZ8+ehTjapk6dyvjxBVEwVq5cScuWLalQoQIdOnRg3rx5JbquNG7cmPj4eEQkP8zNbfEP2zERkYOKorQEKqOJiREiYitqfuVBtMVRFEUexHY96LjT6OT3G+qR4cier0iwQKf/wKBenjz1UgNw2HBJjsBgSUPRqczfUJchb+2+5ZdgUVHcPktM7Yd7yi4cHkHYje7kOJXDbvACBEW1InpnrtqNKrYsdIqKYslCjCYcpmv2EhRNIApWpuDmbuJU+hgCebdEBSW73X7Xtib/6lgThJMXfqZ62SeIzBiNi6sJVCvOdpVSTp8REhLC+fPnadmyJTt2aHEqFUWhe/fu1xGIXgtVHESmjaJ1zW8oVdqFXw48g9vlPXgk7gWzQYu7oarkeFQiw68e7hf2/B975x0dVfE24GfuluymNwKhhVASCL1JkxoQBGkKiF2kqSiICqgg/CygUj8UUUGKoIjSpdkA6b1J6DUYIL23LXfn+2OTJSEJBEggaJ5zcpK9OzN3dnL33nfeijHyOEo7G+1aVmXLzvN3uhQcOHAANzc3goODHceGDBnC559/nqt25J2uWVxcHKqqUqlSJZKTk0tsBQHblZ2w8mHeWw3T/gKrDV5oCE3Kw+vr7U+nh6tC22pw+hosC7P38zBARTc4GQvnP4GAwKwBJeAMHT6qiL8xgv8NDSGo734wLcSaOhOrMPDHHwns3R1L4xAFs64z52IaUaZMGTIzMxkxYgQuLi6kpqYSHh5Or169mDlzJm3atAHs/49aIcFcibjG1HndmDNtL6kpZvZdfg1fXuXj97/m448/ZtasWbzyyiv06dOHVatW8ffffzvK8RTEU089xdKlSx2vnZ2d+eSTTxg+fHiRfg+z2bx5MwaDgeDgYIQQ+Pj4IKXMVxpqUlPIA/Puj45EPGw9B2wB1sp7mJ1bCPF8fsellIsK1b8kCiSFEZRiYmJwd3cvsTcNgBUrVtCgQQOqVat268ZFwIMuKAHYltVBWI+z8ZqBbm9lcnRrbapWc0ETE4cuKRpNWgpbzhgJHZmRx78gJ5mcIpH1SEwYqY0H3RD5KFBvZ82kGokluj2KKQVV6BFoUPUupLkHYzGUQ6JDarKux3yuX6Ga8Ty+FkPKZYRWYHMxoq0XT6VAD3ZfGEYF7jreoUD279/Pnj17eP3114tkvDu91sxcIYbZDOixjFahAQwa8VC21wDjBl5i4YIfHG179uzJqFGj6Nu3Lx4eHpw8eTLXWGkcIIXNqFhIiM3k07Hr2LUlnItnE1i0ugcdHquOEApIGzpzPF7X/kSTkUxihXbYhBGfo8sRbe3+nEV9H8xPo3S3388ZM2YwcuTIu51asSGlxLY0FK5uQejgz8Nw4AqsOwVnYsHHE85cg1ZVwMcAv5zK3f+ZpmDRwC8HIdMCvm7QtSlonCuSmRKBrxt8YS/phpPeLnhlZvlX16wq8PEUPPP8G3yzYBOKonD48GFeeukl5s2bl+98R40axdSpU3MdqxrkTfO2lVky90ie9l999RWvvPKK47OCXYgNCwtzuIRkEx8fz/Llyxk6dCgAiqI46s717t2b6dOnU7ZsWWw2Gy4uLoVd4gLJjs4aP3483bt3p3nz5jcRlBR5YP59EpRaWQ5KKZvc8/MK8UWOlwbsNd8OSSkLZX57YAWljRs3otfrad68+V1faGfOnKFixYo4O9+eI+mtsFgsdOnSBR8fH37++eciHTs//g2Ckky7AusrYqtThn5jrKz9LZGI2OewaQ04pV/BPXIPypUE2v0vhO17j5OZmYleryc1NRVXV3t9NjMRxPAtkK1Z1eFMfbzoned8hV0zKS2oMa0QaeFgloj4FIRVxeZsID6kJ2ZDxeyGKDITRVqQaFA1rg41t/eBH5AaF0yeVdBkJBGzcx/Vh1i4eKk7uoC6lOfju3IovhU2m63InIzv9FqLZi4WLlFRTAIgQl43eTkRxNFNFdFoNAQEBBAQEICiKLRr145jx44RFxfnaJvBSeL5iez/cY8WCzm05yot2lXm7Mk4YqPSqBTowbB3WvDskEZgU9Go6fidXUS6T31M+jJ4H1uN0lbF29s719g3snv3bkJCQhzmmzvl3/D9vBVSNSN/HYw8vwiSsw7a7D9KMOw/AGuOQlwSXE6D87FwJuZmI9rNZBEREbmOaTU4ivECNGts4JHmkradXqV+q/c4c+YMO3fuZNSoUQWOu2jRIpau+Jq4hAhatKvMzI924uyqw9PbyNXL9slfuHCBwMBA3nvvPT755BN27txJly5d8PPz47XXXiMsLIxDhw5RpUoVVq1aRXp6uiMCzWw206FDB3bu3AmAt7c3SUlJjjpwUVFRODk53fV1lZOEhASmTJnCJ598Uioo3WweQngAi6WUPQrT/oF1iHj00UcJDQ3lzz//vO3d4I0h2EajMZeKvKjQ6XRUqFABLy+vPDuXUvJHuFRAlGmHVDSMfs6IxSKJj0hAk5GIRetJgn8oqTUa8WY/b6S0FzLu0qULbm5urFq1CoAMTmGzmXjrpXWEeE2jbc0vyOD4Xc0r3bKBRLdgrDpPRGoa0tWAxb8s6QF1UdQMNJYEuxZJCGyKEavGHRs2dNZY+3GbitmjEol1u5Ie2IBz7vWpPsRC31aCcupZBIZiFZKAYqttdTvYyHD8/fKo3LX/rEQTGhpKu3btCAwMdMzXy8sLk8mUq20GR7kuCMOhPVd5pGcNlm15liORI5g8tys6nYZ3hv5KRTGJ1jXnoGqcsDp5YdU643r5IMJ/KEFBQcTHx9/0HtKyZUtGjx7tSE1SSsEIjR6l23cog+Pg0WngUQE0wh78mQJNA+Gjx+DLnrD2GTj5AaRPgHMjYfdoGPEoTOwPm/8H1XOU4Zw+BLZ/7Ur66WrUq35dSOrTQWHbfBf+WhPIhyPcCO3YDV9fX1q2bHlTIQng+eefZ+2ajazc9jyjPmzLgl/60KRlRTw8nageVIk1a9YQGGi3A370kT3QolWrVjz77LOcP3+ekSNHMm/ePCIjI3nkkUcAcl2ner2eHTt2kJyczLVr1/Dw8MhVLPfHH3/MIySpqsr58+dzRdLdDl5eXrdOTSGwb97ux0/JIR2oUdjGje3K3AAAIABJREFU9//OeZf07NmzQKfJgiqvv/7661y6dMnxulKlSsX2EFm0aBEdO3bk9OnTJCUVOhrxv02r1UhVUqumH51DfZn56Q5sLh5o1UTconZgtF6iW7eLqKazVK5cmePH7UKQwWDg6NGjfPbBEqoZJ7NqyXGSE02cPx2PQHeLkxaMSV7Gav4W96jd6BMjkR5upFRoQXK5lqS7hWDReaNqXLJuBCKr0KqC1Lhj0XjYo66EQmq1VkitnriYNIa/sB6ABVOCsQknvHmyKFauxGOkNgDfrevHup9PkplpdbynpUy+fSZNmkRaWhq//fYbq1atIjw8HIGBnKE77p5Oue4DTw9qwLbTL+PmYTeFXjybwKULqViMQTh7zcKp/kWo8SVz584Fbi5ERkZG8uWXX1KnVg34+yPY0hOOTgA1s8A+69ev5/LlQhcn/1eRmJjIZzPnoDQaieb1CDRjbWgmSJTBNnj0K0TZQISnAXR6ZLIeva+eKj5aHvKDGY/Du72gXR1Y/T70yXJBjEqAFZtMvPlxHH+fsx/7aaKOZZ/qadlIj86cDG5PgHOHPPPJ7zmQLRhr8MCHlwANnboHseS3p3F2caNtm0706HFd2aDRaLDZbHTt2pWvvvoq11jXrl3j0KFDrF+/Hk9PzzzncnNzo1y5cpw/f54lS5YwfPhwAN544w2Sk5Md7U6ePEndunXZvXv3XWmZcuaVK5D/WB4lIcRaIcQvWT/rgNPAmkL3f1BNb9mEhYURFBSEXq/PdVxKyeHDh2nUqFGePrGxsXh7exdaODp16hTVq1e/Kwe8w4cPU79+/WLd1f+bVPu2s12xRP7N8UwnGj9ygbA/GlCpgg48DOi0yQjVTIYxEFw6oqcGrvruufo/0qMms37syXdf7mPi6C2kyaM4Uy/PeQqzZqnmmSjJSzDGn0WqksSyrXFNOIYqnJFShxBmdOlRxFXuitWtUt6dk5TY7xISk1mlmmEyAOeSRlDu3Ba0IZNxMnS+m+W659zptaZiJpIPkFLSsupsZv3Yi8bNKwBQlrfQ4p1vvxs3Q05Oev46/QoXTkVSsYon7Wp9w3ufteeVUS3sDbKWvIbrFBRFkJZixresM02aV6V9y+dYtWoVx44dIz093fHQzP69cuVKAgICaNy4seN8MdHROG3virv1IOiwm5OcasFjYQ7H/JxYLBaklLnuS/+m7+fNsNlsrFu3LpegcSukzQZRRyHpDGRcgLg/wPQXQpEkqBWJuBzB7vgeLN10iIgLERz70YBeBwgFtVwPtL7vgcHuYL13716aN8+trXzuueeYP38+Wq2WNWvW0KtXLyIiIqhQwX7t2TBh4RoKTuiFPULNarWi0eSfgDQzMxOj0UhAQABvvPEG7du3Z+TIkYSHh9O2bVvmz5+f77qMHTuWxx57jK+//prvv/+eEydOUKtWLQB+//13pk+fTtu2bRk1atRdPW+y0iPkb3qrpcgDC/X5vVXsiOam++WjlNOJzAqESykjCmqfp/+DLigVZRhuQezfv58GDRoUTlK/j/yrbsSmK5j21MFcqwFPdt/B7uNWzp7ogeruikvycUzulch0DYasnDnmzAwu7TxEh07bWfXzPLr3fZI09nH16hVqVXgOHx8fli9fTrt27XKdpjBrlpY5EU3iUpxSrmLReKJJS8Si8ya+Ri+QEikECC2KmorefBWLzifLN0kLQuN4kEop2bjyNEP6rOR08lu4GqBsYhM0ZfoXyxIWJ3d6rSXxO6lsxWJRCdR/xo5zr1ClmhegpQIFmwzWrVtHYGAgbdu2JSUlJZeWQFEENpvEyaClU/fqfP3z4473anpMpUw5Vy6eic81nsFgoF27dgwYMICtW7cye/Zsh6DUoUMHtFot9evXZ8qUKQCc2f8L+n09ORsNj3wI+/8PmlQDQuZB1ZcK9dkf5O/nmTNncHZ2pmLFivfupJd/gsuzSbH54FbjGfB/AilVIq8uxNu6AQ1GNN7DEW4P5er29ttvM23atFsOf/DgwTwb6TFjxjB5sn0j8/HHHzN27NhCT7dfv34sW7aMl19+OY/WKZuaNWty+vTpAk29ERERDB8+nHHjxlGnTh0sFkuhfHDT09MxGAyOjfgtBaXv7pOg1Oz+CEp3ywNhesvMzCzQbHUvMmg3bdq0xAtJ/zqcKqCr8X/oL19kxiArQoE96zbjcXUXFqdymJ0DQNE5zFx6owtBoQ8jBHz1xTg0uOBOe2qWf5b58+cTFxdH+/bt0Wq1PP98vpGiBU9F0wapdQIk0mpG1XuQEtDa7nehdbInmpQWFGlGCAWNLQ2dJQGHrtmmgpRUUj5hSJ+VLFj1NJ5Gf3zFKw+kkHQ3mDiDlJJH6n9LtWBvAqraTRUuPFRgn4iICL755htGjRpFXFwcL7zwAlFyEWt2Pc/kuV2pXtOedsGUaWXdsuuhVMnJmaQmm1EU+//h0LXXiZDvIaUkIyODjRs30q9fP65evZrrfHPnzqVevXr8+eefjvtLUPJbVA6EQVnPv6ZvAEYBZ0YU1dKUaGJjY/n111/v7UkrPwkPb4WG34G/PbpVCA1RMY1RyyxGG/B9HiEJ7NqjbBo0aED//v1JSUnJlesIYPv27Xn6ZgtJvXr1Yty4cbc13Z9//hkpZYFCEtitEzn9lG7E2dmZTz/9lEaNGmEymQp0H7mRP//8kzNnztzGbP8btjchRIoQIjmfnxQhRPKtR7DzQAhKu3btYv/+/Y7XNpuNRo0a0blzZ8LCwu7jzEopTpTyL6CxCKr7G3nzeQPdh6WCkwa9LgkbAqGmgZrDyTfrodb9xbpkcD2UfMCAAUgpuXbtGvXq1WPx4sVotVpCQmqSnHqLkBtAq2uNxvlFbDoDGr0GnXMGPvG/4564A6wZWU7cGoTMJEPvj1lfAYve77o2SdGw7LtjADzxXB2e6jUZP+1b6LX3Jm1ESULgzDfT9mI22/jj78FZgogGd0IL7LN69WrWrVtHbGws9evX57kX+mDhFI1bVOTpQQ3YHDaYvs/nzWvzzZS9AKzf/yIA7736GwLXPO1uzIlTrVo1pk6dypIlS5gwYQLsag7p59B0g8sxMPhR+3WWmCGQTml3uBIPFi1btmTQoEH3exqAXfjJjlD+5ZdfEEI4MnFLKWnQoAFgN50dPnyYH3/8EVdXVz755BNHiD5Aw4YN84ydbQb77LPPim3+N3O/OHbsGB9++CGvvvoqqqri5eVVqDF79OhBzZo1Cz+J+ycneQgh5gghcvtKFBNSSjcppXs+P25SSvfCjlOiBaUFCxaQmppKhw4d6Nixo+O4oij069ePgwcP8v777zNjxgymTp3KkSNHOHDgQLHPy2q97oD68ccfU69evewEX47j0dHRBebvKKXwaKp+DdU9SY61O85e01UjzaUmbslHMUadwy18J7qUq6BaiI+MR0qIT0gj3raQdHLn3SlXrhyHDh3i8NH9vD2+KydPnmbazIHEWMeSKS/ddB564yugumJx9SXVvRbxPu1J9mgGmqxoSaFg0VcAjWuWpkuDUNNR1HSEzUR0ZCoAMxY+hobbKGD5L0IlDQtX2PrbBfq9WBe93u7/4ctAFG4ederu7s6+ffs4cuQIDVrlSOMh7Q++GQsf40DEa+y++Aq7/gpnaN+VzP7MHqVWz2cGAQFGfv/lDNPf+5kMeTTX2PXr1wfIXShVWgn23c7YvktRLcdI1l33VfnqZUnNihAWZrMH3qWduNMlKeUuEELQs2dPx+vKlSsze/ZsAI4cOZKvf9GePXscf9+oYQK7kLxw4UJHtJsQgkWLCpWTsEho0qQJjRs3Zt26dcWXI/B+CUl2QSlJSjnkXiabzIkQwk8IUTn7p7D9SrSg1KZNG4d9Njk5Oddu4J133iE2Npa5c+fy2GOP8cQTT1CnTh2Hc15xMnLkSNzc3GjTpg2HDh3CYDDk2ZX6+fndtomnlLyIsp1Bo+WTodC1axlq19xE3N/70V6NxCjDMWhjUDChS/+Hj0ZvAWDAiJaAJNE6H5N1L6qa27RSpd4VRr9tF1YO7I/DrLERxzekyWMFT8R0lgwXT9INgaQ618SkL49UDGAzo9jSQTWDtAI2sFlBWpEaIzbFgBR6Oj5WHa1WQa/4o6Fwu8R/GybOYbFY2P7nJbr1ub77FeiwYSWRDVzlM64xlVT2IbHlGcNKPBlkmRgcTvKAEPj5u/LmgPX0a/8Dfx+4xmvvtWT57z2oGqAjPDyDJs3KMnf2CVKss7HkEIyzd+3x8Vl+TOYU5KYKyH0jSFe82BvtjsejdnPJ2/2dSMzQkmmBlbuBTAkX/r01+koqOZ3k9+3bx9atW4mIiOC1114DYOvWrfn2+/777wF72ZFffvklz/vnz58nJSUFnU7H5s2bCQ4O5oUXXsglYBUnixYt4s0330RVVVq3bl18J/qPpQcQQvQQQpwFLgJbgUvAxsL2L9GCUrVq1Rw+AlFRUddvZDnw9fWlRo0aBAYGotVq8ff3z9PmdlFV1bEzyVbn5uSDDz7gu+++Y/z48SQnJ3Px4kUiIiIcZsDPP/8ck8nE9u3befvtt4mOjr7rOf2n8eyNomj4/qcWtOlYiYYPn2DE1DhSouNIN1TBLWoPHtf24WKOQaMRZKYkIKQZvfUq6aaJxFnHEW0ZTaYtHACTPItGpuHr50xasoVffj7Fzi2XSeRnTITnOb1EkmL+mgyPmpgN5UBo0Vri0VjicTJH4pxxFreMUzhZosCWnmVy05LtPwVQNdgHVbWRmp5EVirqEktaWhpLly69qS/FnaGw7LsjlK/kTrXg6+VcYviSKKaQxk4kydhIIIk1XOV/RPEFifIPTOYMYlhMFNOwkFVuJMcNeOfmS1Q3TmH3X/aQ/KpB3vR5oS7NO4bQqa89ei0xUcXV3QmJIEX9EbB/11999VXAfr/BZoIdFTBnRDNrkwbPkD206hcJwLRRZfn0SfjyZyuXosCcncYp/Zpj3e7VA/W/iqqqDBw4EIvFvvibN2+madOm1Ktnj2j94osv+PrrrwvMQD99+nSGDRvGu+++S7169RxaxOz8R3q9nurVqwPQvn17Tp2y+7wdPHiwWD9XNt26dQPsKQdOnz59R2N8+eWXt2hxn4Sk+5tH6SOgOXBGShmIPTP3zsJ2LrGC0o1ObDVq1MDX9+4qfBcWKSXt2rUjJiaGXbt28eabbzJ06FBSUlI4e/Yszs7OtGnTho4dO/Lpp5+SlpZG3759adKkCR4eHri4uKAoCo0aNWLMmDF4e+cf8lxK4VB8J2Or8igaSwaLloeyfU0gF86nUblVNBNHbYPYqzglRNCvZTqqKvlp8SUMpggUNZN0Y22kcMIQsxtT1PPEpL+AVUaTaqzNwBFNSUrK5NX+q3kydAlgI5b5eZJTJslVpDulYNGXQQoFm9Bj1XqB0GPRepJqDCHFtS4SAejy3hSE4J9LSfiUccbJmIGF3AlPSxpOTk54enoWeSoLA0H8tuYsOr3C7M92M/f/9jnes5GaTw8VK5G4e0lMmRYWL/gp33F/nHeE5x79CT9/VzYfH0yX3kEcOxRJq6pfUc9vJh272X3BzpyIZeDwJli1XlhkCqdOncLNzY3Tp087NjOZYW+QFJ/Jsogg3phh9z8a2EvL6e81jGwbhWIyEZ811Y8eAVTA51HA7ogbEBBQJGtVSl6OHj2KVqvNFXrfvn17ADw9PZFS8tprrzF06NACg3ycnJyYNWsW33zzDfXr1+fhhx/Gx8cHg8HAlStXOHr0KM2aNcvVp1+/fkyePLnIy9zkR+XKlRkzZgxSSlJTU4mMjCxUv40bNzo+d7ZW7abcP9Pb/cIipYwDFCGEIqXcAjQobOcSmx6gUaNGbN68uUjTu98OcXFxmM1m/P392b59O2vXrqVZs2ZMmzaNuXPnUrt2bUfbtm3bMnHiRKSUvP/++1SoUIFBgwbx119/MWDAAKpUqcK7777LuHHjiqSuT0E8yOHHhcGasZMU2wykquKWcJRTJ9J464NY/thrI3qDjiqPW8g0Qcw/oTgb4ojz7oL92ynRmBPAZkE1+GWNZv/WZqQa2bfrOM90+YmXhjfhw5mPoOCGP3b/BRsZxFjeRlUMOJmv4WS6bBeQNG6kOwfbNUc3IKQVvSUapMSk9wNFz6olx1n1QxiL1j+JN09hpM69WbRi4k6vtdBObdn85zbHayeDFkWT+w6qKIKnBzWgRogvter74VfWhaF9V3Jk3zXKlnclNdmEVqcwY2F3pJQM6r2Cfi/WY8L/dcTd/bqv04YVpxjSZyUXMt9iyritfD3tAP+o77Jr62X6tb9eUy4uLs6xmYn4wZNIBUZNS6FpiMJnQwGLCmYJVgWRbqPem5CSARc+AzyMiN7JoNw6582//ftZXGSvm1ardWg5a9SocZtRXnmRUrJ8+XKeffZZ/Pz8qF27NmXKlGHx4sW52oWFhVG3bl1OnTqVq/BxcbJixQr69OnDoUOH8nU6v5GQkJA8tRALTA8QopEHfij6ShSFQTRKv195lP4EegGfAL5ANNBUStmyMP0LldFKCFEJWASUw55qbY6UcqYQwhv4CaiC3ebXT0qZIIRQgIVAdWCwlPK4EMIZmAvUw/6USgS6SCnz20rSrVs31q1bxzPPPJPr+LZt26hatWqx5/Pw8bluGmjdujXBwcGcPn0anU5HWFhYLkHp//7v/wgMDMTDw4Nhw4Zx4cIF2rZtS9++fXF2dmbMmDGMHTsWZ2dnPvroI86dO8eCBQtKREmJBwmtsRUesj4W9QRW8zhqV97Lb5/rePVTC0H9LKRnJUrWXj1NZnAwDhUzAtXJx1FiJBdS0rZjZTqG+jH/8wPM6nwGJ5EGlfZCyE/YFBNSaNGbLmNTnEnysKcKznbUtilGe3oAAKmi2ExorAmYtGVAmu2ClJR8M3UPXXrbb7L/VR8lgH59n0LVRLB4Qz+OHYpiz7bLeHrlvmnv2HSJJXOPkJFhwabm3shFXb1+u3ip53IAej0VwrT5j+U5V3Ade6Zvm01h7OROjJ3cgV2bz9Kv43KCa/uyeedPlPfIncm5QnkTFVxspKfYCK2vATcjUqPFqnFGTXVBczkegyaGyc9jTzxZ7f1CCUml3B2ZmZmoqkr//v3p3bs3vXr1uusxhRD07duXTp068cUXX5CSksKYMWPytKtTpw5arYbx/+vOVwtaolPq4qp7FSGKLyijTh37RupmNQhz0qdPH0eZlewIwJtSssqJ3At6ApnASOAZwAP4sLCdC/uktgJvSSlrYbfzDRNChADvAJuklDWATVmvAR4B9gK9gbeyjo0AoqSUdaWUdYCB5CzWdAMXL16katWquY6lpKSwcOHCPI56+/btu+3Q1bNnz7JlyxYyMwsuQ5ATPz8/WrduzdatW3nyydzlJho2bIinp6fjizdmzBiSkpJo2rQpJpOJc+fOMWXKFIQQtGzZEkVRckfYlFJoFOGKk/YhDD5rsVZ4CyTMHq2lRV1B/Rr27/+IycmkG2vl7Zzj5pCZaWXDilN8PW0vMybuweCWpekLqoC5cnnUixvg5EA0uGNTPDDpK2DWlwObDWwqUuOCTetqf0hKCWomWms8NqHBovPPygztBAhMZpVTx2IY/p5986JQtMWXHySe6v80keHww5wj1G/iz9A3m/HkgPo8M6AzLwx4kScH1OeL73tyOuVtLlvfpV6Tcri46pn4ZWd+2vQ0IfX92Bs+jM69gjAYtRyLe4NZS/J/aP5v5B8AaHUaEIKZH++hX0e7cLUpbCieHvmYxA2ukKzSopUza8/5kVa+LqpWizYxFn1GOFddtJyJVWgRgN3sVvGRPENIbMgS7of2oDF9+nQAZs6cSb9+/fJUYrgbPD09ef/995k8eXKuDXI2Vts5atZyZ9WKc8ybe4o0cZlo9TPSKb7UNMHBwVy6dIlOnTrl63R+I2PHjqVtW3vy6eyyPAVyv8xu90E2E0LMEkK0lFKmSSlVKaVVSvmdlPLzLFNcoSiUoCSlvCalPJT1dwpwEqiAXUr7LqvZd9hVWwAaHDWjHcvjD1zJMeZpKWXuSpc5aNy4cS7VqqqqDBgwAC8vL/r27ZurbWBgIC+9VLjsuNlUr16dOnXq8N133zF06NAiTyvg6urKO++8Q0xMDIqiUKaMfXcbGhrKggUL8v1CllJ4hHBC7zkeUW0JIGhWW0GvFZT3hZ/WpyG1bnbBSEqwZSJsZnvNNWBo35VUN05m2NOr+eOXs3z56W7Wrb5IvQZeSDdnpK8X1qplsdp+xfxPexQ1ERQjQs1AUbOctaWKUE3oTVdxMv2DIlWsGm9c0s9T7sJ8yoQvwzn1DFpzHBuXh2G15ozgyhvN9V/B3d2d7xf/xOcfHkFJ6YwbnfBlEH4Mw4c++DIcHRUAPaCgKIKmD1fkhVcfokuHwRw+cpAqlVvw5Y+9UK02Otb9lsP7r+Z7rpp17WZWKSUVxSSmjN/Gq6ObE6G+jQYPR925nAjfjshK7rSoY2PjrzEomcko8ekIm0RRbHw26xoPh9hwqWoEdAif65mdJVbiWcpVJnCVCYQnLGfZ8mUFrsWGDRty5YcrJTdTp07lgw8+YNSoUcycOZPjx4/j5+d3645FjMUWxupfOxJU05PRw/fir/+ciSPnYU7/H2lyx12NnZyczFNPPcXatXmj5QMCAmjUqBHHjt0kGjcLJycnBg0ahE6no3v3wqQo+o9ISnAWmCaEuCSE+EwIUWi/pJzcts5YCFEFaIhdY1RWSnkN7MKUECL7Kv4N+B54HhiSdWw+8LsQog927dN3UsqzBZ0nNDSUlJQUUlJSHMcGDhxIamoqJpMpV5Xm7PD8nG0Lg8FgoHPnzpjNZoxG4233vxUNGzYkODgYk8mERqMhPDy8WB2709L+G8nvcuHUCaofpEzViVSKvECKLQGdi5nMVF+kTEGjpqFqsvOKCf65mMjfe1P56ofn6Ni9BqY0J3TGDELrfItW0ZMZraBYzEjVFYu+ChmamqjJWmw6A0iDXVMkTYAWpEBjc0dIGyoKSCs2iztpTqGYXSsj0ZIcY+bTdzbQrXcTTGlGEFrS0SK49bV24sQJLl++TJcuXYpzBe+Iu7nWgoOD6dLlUZbM2c+QIUMwA2ayx3PGyHOOTFP+Pgdxd3fHPcVeCd4EGOmBgW5s3hPMqwM/5ZXH/2Th2r4EVMtt0oyP1FCxYkXM6UaHqX7k2C6QWhuj6EAqmdi18dexJZ0B9yos2JhEo4Y60mP0mG1+CKuKsNk4Hi0Z1kuQrkio2AORet0UmMxWMrjI4b0xhDQox6IvF7Hk28N0DO2IVqvNs2Y1atTA09OzyO87DyLnz58nPDycJUuWkJ6ezpEjRwDo3LkzzZs3Z+zYsfdtrUxWI076sqzf8jSbVh9nwv/OsWo1jBihYvD4Hpuhfq72Vqv1tuq07dy5kx07dhAWFkZgYCBff/01L7/8Mt26dWPevHlcvHixUJ+7Z8+ehTbVldwwrqJFSjkTmCmECAD6AwuEEAbgR2CplLJQjm635cwthHDFnoNgopRypRAiUUrpmeP9BCllgQ4YWf0fAToCTwMtpJQn82knN2zYQMeOHR2lQyZOnMjnn3/OsWPHimRXcfXqVQ4fPuwIxywOUlNT0Wq1nDp1igMHDhASEkJCQgLDhg1j8uTJdOvWrUidu//LzqKjR49m1apVvP3227z88svsPfY9FYL3EJ8o8C5zfU0WfXWQD9/axLn00QBkpBgwumYQ4j0dq8XGldNNSC/XHCmcMCadwskcSYZLICbn8iAVhLSgsSYgFQM2RYtGNWOIOYs+PRLVyYV033pYdR6gFSjA2OFbOHYkmhV/PYOi1eFBD1xpVsCnyE1GRgbJycmULVu2OJbsrrjba+3AgQN0796do0eP3vT73Lx5czw8PPjtt9/yff904lRCfEZTp1E5pi/oRs069rHS080EuUyl+5O1+GppbyqKSQBEp26mjEt7R//Y2FjS09OpXLkyMnk3MulxVq5Ioe/INA5taUrV6kYMGeHoEqOJiTRTroeK+U+B1ga0jkYYrkfiRjGDkydP0T5kDu9MascLwxoTH+FHy5DRRbJm/1YyMzOZNGkSs2fPxt3dndatW9OrVy969eqFEOK+r5uUmSSZxyHVcPRqPOfOpdOg0R6eebos/fpXokf3/TnaShYsWHBbFo6rV6/myv+nKAo2m43Zs2fzyiuvFNgvKSkJd3f3fCP8blrrrbZGHlhafEFFN0PUS7nvtd6EEA2xK27qSSnzr3p8A4UWe4UQOmAF8IOUcmXW4SghhH+WNskfuyd5gWQ5bq8EVgohbEBXII+gBPDoo486/lZVldGjR+Pq6orRWDQOdH5+fg6b7pEjRxzO2PlhtVpJTEy87fQE//zzD66urri6urJp0yYGDRpEUlISQ4YMISMjI1eG71LujurVq+Pr68vQoUN59913mTXlV7Zu/4XLF5PxKeOMqtpISTKhqhIfvxt8hIRAVSWZGVYq1z5ExYBzrNrQEa2vDxnu1eyZtqUNjTUBvfkqmfpKdtMeYNW4klr+ITTmZFxjD1Lm0goSy7YmwzuEPdsv8cuyM3wwvS0axQZ43rSm2Y0YjcYiu95LGk2aNKF///4MGDCA9evX59umRo0anDt3rsAq9PGswtUzgfenhTJp9GY61v2WilU8CArxZd+Of/D0NvDp1/b7yNGrH1O//Dg2rY2if47yei4uLkRFRZGQkICneQMiMY2+I9OoU1UQGOKGLj0aoZVIL1d+XJ1K/0dM6LQSadPlEpIAFNyoXtOHRRueJKS+H27uRsqF5DXvlXKdOXPmMHToUFxdXZk3bx79+vW731PKgxAGPPQfk5Y5Cckuqld3JijIyA9Loli1OoaEBLPDZ0oIcdtuIOXLlycsLMzhwG2z2XjppZdyRbHZbDYSEhJyuWyYzWYyMzPv7B5x/3y5PYQQc4C19zI7d5b80gW7VinQbBjNAAAgAElEQVQUu8Lng8L2L5QCTthF1nnASSnl9Bxv/QK8kPX3C8Cam4zRSgjhlfW3HgiBfLL75UNycjImk4kRI0YU2c5Cq9Xi6mqv+xQUFHTTNASpqamOPCs5TX63olatWlSqVInq1auzZMkSADw8PBg0aBBPPPFEoQselnJrNmzYQIsWLQB7XarFi77n8sVk3pzwMI8/V4fE+EweDq1Cg4f8iYtOZ+tv53P1n720FzVq+5KZYeHk3zGMevMw1qzQfgCEgqr3JcO1HlLvZY9mE1q7v5KiRTV4s/xgJZQ6yXiXWU8FzRR6t1vGCy/Xo9sTNQBBWYYh7nMykZLEpEmT2LBhA1euXMn3/UuXLvHaa6/x00958ydlco4M7H6Fm9efw2Kx4eKmJ7C6F6fDYkhNNrNrfyf8OYdT2j9ULWO/No4fz50jy2g04uXlZU/uZ7sCCEIbCR5/LhjFasbsUg6RbEJEp7BsTQYDummQTjqEX+88c/KkO4owEPpoTfzL+6Dgihvt7m6R/uVklwfZtWtXiRSSshHCgIvhHYTwQ6Llp6X2kP30dBuPP/74XY9fu3btPCWwPv/8c8drk8lERkZGrj5lypS5843U/Us4eU9LmAghOgkh5gMR2N2ANgDVpJRPSilXF3acwmqUWgHPAceEEEeyjr0HfAr8LIQYCFwG+hbQH6Aa8FWW0KUA67FrqG5JYQsD3inZBRYLwtPTE09Pu4Xx2LFj1KlTB0VRMJlMhRbccqpH/fz8iI+P59ChQ3Tu3DlXu4yMDDQaTZFGdfwXWLNmDbNmzQLs2Xl37NhB5cqVeWtCKFJYaNMxkNadAtFqFXq0WMgzXX6ibedAXh/ThebtPHi8UQR9NpTF7NqYrt0P8sfac4weuoFPv+piT+OQU70tVTRqGlLosGmMfDb2L76YtCvXfBo2LUvHblV5Y1wzhBC48ySa/3C0W34YDPa0ANnm9RvRaDS0a9fO0S4bGxbisOdByjap9R9Yn6nfZpnRpSRA9ymrNpp44VV7Vm6Tza61ys+MWb58ecqXL4+MXgpxGQzq78Yb08MZOTwAj7BNYLESFQ+7jkOL6haE1gCV3s4zjo6ylGUEmZxFoMFArVvWsPuv4+TkxMyZM/OUgCqJCOGMs8t8VOsuatfOQFUb4ewcyPr16zl9+nSR5FjKFpZylusCu0Bf3Clx/qW8BywB3pZS5i3tUUgKG/W2Q0oppJT1pJQNsn42SCnjpJShUsoaWb8LnIiUclFW/7pSytpSytGyJGa7vAVNmjTBYDBw8uTJ29Iu3Yi3t3ceIQnsfgzZSdXCw8P5559/cr3/1Vdf3fE5/+3UrFmT8+fPU6dOHZKTkxk3bhw+4kWQCu27VEOrtV/uv+x+kQ8/78TR/dcY+cJaejaaxdlTVzB5VMVsLM+Q12qh08GSuUf5c/35XEKSYk3DI34HPjG/o7mwgSHd5zmEpGZtKhGV+iKWUxXZsOd13hrXESdRAx8xEhflvprlSyRCCLy9vQvMPiylzGOelti4xhQyM9N57Wn7hvDrZb2vC0n2gWnVIYBvZ+4HIZDA0UP2W1NwrYLrYArn9iAE1StqUG1gvLgLYbEiJHz/J9SsBG56wKoF9/z/nxo8cKEJzjQsFZJuQWJiIps3b36gamIKYUCr64BO3w1F8efQoUOA/d5z8OBBwsMLZSS5JcWaY+8/lB5AStleSjn3boQkuIOot1LsZFccL2r8/PzYsmWLoxzKjY56lStXRkpZYIr+/yq+vr7o9XomTJhAenq6w9ERwEc8RxzfY098Y+el15vy0muNObVxHwNf3k6j9uHUqx3D7/v7061nFSpV8aBj87XXZSRpwzn+GJr4aLQxEfR4M5Zf99rTgK3b/CgN2jcEmwXXK7tQqm6lnFKVUm5N3759WblypaNWV07MZjNPPfUUTz75JFarlYfbNMXZM42Pv+zIsgV/s2bpCd77rD2P9cmbM6t+0/IcPxrNsKdXs37ZKfwruvHpV51p16FpwZNx6YIoX5WGmafRaIwEdE5lxlBYuxt+3AI/jctKj+X9ZMFjlFJonnnmGYQQzJ07l1GjRt3v6dwRISEhqKpKaGgoTZrYheeSv/93mMFKKSQltoRJSZzXvcJisRRojrgZRRUdYrPZ7mvWcFVVuXjxoqM45a1ITU3Fzc2NiIgIvvzyS6ZPn54nkaiZq6SwlcwcSeI8oraSnuGJW/pZ3OudwapCRnwTJPD04CusXmEvdnrVMhwsFsocWERc3b7ozHH4lLWb2KN/1mBo3oqUit3w4QmcRLWiWYQSTlFda2+++SYxMTF5ykaA3SRntVpZvHgxW3ds5Ns5S3BzdyIlya7JffG1xnz8RV6t7EejNvHN1L2O13NWPM6jvYJQUPAX/8Pu11kAthS48jZhF//m3Q+PEvlPBs+GQsva0CQYhMYJ2sSCxvW2P+v9jt4qaXh7ezsS72ZmZuLk5JRvuwdh3RITE/Hy8qJXr16sWrXqfk/n5lFvdbTywPL7s56iVuJ9j3q7E0o1SiWQOxGS7gQpJYcOHaJRo0YODVV2eOvAgQPvyRwKmteuXbsIDAxEo7l19ObZs/Z0XOXLl+fKlSuYTKY8wp6e8vjwFFYSiGU+KvEYMi+TJAPYveUSVhXGDvdCd+YUB8+58vuGGJycNJhMKjaNCyhWopoOwDX6GK4n7MVctQr4lNdhvXIR10pvIShUpGkpOejUqRNdu3Zl9qJhJIvfAQt6AvGmH9n/vpdfHUhaipnX32vJmIntCL+YgJubE96++ft8zfu//bh5XBeouva2l7PxYdDNhSQAxQ0qfUPtSpIffl2G4cIYdFcug02CSw1ouPWOhKRS8vLhhx/y+uuvAzwQPpkWoolhHjKrgLMWf7zpg45yDh/WG6tJlFhKFUq3RalGqYiIjIwkOTmZoKCg+zaH2915xcTEMH/+fEaMGJHHYfZBYuzYsaxevZpjx45hMBhwdXUlOjq6wKRvKsnEswZb+u8Qm8zzz/7K79vNaLIezGoOP0qdXuFiyki7tjo1GY8T23CKi0DX05480LYa8K2IaPVP3hP9iymqXb6qqhiNRn7e8gwNm5VDUa77Z1Qzfka9xv54+hjp/XRtejwZUqgxWwd9zcWzdpcE/4pu7P/nddx5BDfa3vV874YHQTNyrxk/fjwbNmy4aWWEkrBuZiKJYRYgQUoUWyZgQ6LBXemLq2iOEAIXFxdSU/MtX3pPuaVGaYV7fm8VO6JmQqlG6b+MwWB44DJjlylTJt8ikA8aBw8epG/fvnTs2BEpJZGRkQUKSWau2W94UoKhAsKazq9TtMhEK2s22/jzGMzeaG8b5A8npthQVk+zF0C1ADYQWQJVzfKACkKWRrPdKRqNhs+/fZWhfecRfS0VJ4OWk0lvcuxgJKZMlYFvNM3XByknNpuNH789SsPm5QmpV5btZ14mNdXMmMEbHIVxtdz70hel3JouXbowbdo0zp07V2hT+73ExBXi+AFJkv2AlChqKgIbAnvZmhR1KUZNPV588UUWLlx4P6dbeEo1SrdFiU5kvmDBAi5evHi/p1EoPD09qVbtv+GfUpKIjY3lt99+o23btmzZsoVZs2blUeNLJDbSsWEhhqyCkdKKc9oJpNEZs+IG7lp69NSy7hBoFFj/Pzj1o0A08kRWckVK+1dFWOB9e2Q6Xw3OOoFPYWorlVIQTz/fg34v2sPDTZlWqjpNpmfLRfhXdKNlu4Cb9o2NTqWy5lPGDN1I54bz2b/zH6xWG33aLGbN0hN0fSIY0GCg5D2ES7FrD4cMGcLw4cPv91TykMpBYpl9XUjKwiY0DjnDHsxlxcIVXnzxxSJLZbN8+XKmTp1aJGPl4T8U9VZUlGiNUsOGDUuTMpZyU7Ij27JV9zf6VqkkE80cbCRcPyglevM1pNTieuQv9LYoVCuUfxFiEuHcDwrVPG3YVC0y2oISmwYqjFgIlbxg8S6I/RK8vQGhgerj782H/ZfiSgvemdSRTevP0/eFunTqXp3UFDO1G5S9ZVBB+xC74DtywsPM+GAHB3ZF8P03hwk/n8jfMW/g7euMG6EI7o3fXym3R/PmzQkKCipxZXokKknkk49QCFCcQLXkOqzBlb/++ounnnqqSM6frR0vNkqj3m6LEq1RatCgQZEk8Srl38vGjXY72R9//AGAajNjJQ4bJlRSiWR6biEJQAjM2rJ47lyLkIBBQ/+ZWmIS7W83HGLj10MgIi0oF9MQZkhLhS82wuglML43+LhhN8N1PA66+2Pv/7egYKQMr/H6ew+zYnEYAdW8qdvIv1CRl32et2uiZnywg/aPVuWxvrVYsTiM79b3w9vXGSdq4kbr4v4IpdwFHh4eaLXa+249kKgk8iuRTCOabwDbTdpe/62hDFrKsnnzZho3blwkc/H09CzeRMv3LzP3A0mJFpRKKeVWKIpChw4d+OOPP+jZuzNx+mlEM4trTCKSGdgdi/JBaLC4+2D2rsC1tm9w8LIrgdW92LblYVLS4NH3oet0UN1BGsEth6Lq+TZZ+XQazAC3UkG+KBCoPNY3GL2Thq+n7il0vwnTO/J/33Vn4dq+vD81lOe7/sS4KR146OFKgBM+PIsovc2VaDQaDa+++iqTJk26q3FsNhubNm1izpw5zJw5k7FjxxIWFnbrjlnEs4wM9TcUy2mwHAN5g6AkJUgVgRGEK2BAL+rhoxkNCLZt20ZISMEBB2vXrn0AciyVkh8l2vRWSim3orK/yoH921AEXD6/A9Q6SE12PpabFB3WaIlt+RIZKQaMWhOXzify9KD6POR9FctqFyb9Ipm4KB2fZ2FyjsTB0XPsaQGoOhRqjSjOj/afwkw0iiKYtaQnHULmMHjkQ+h0hUu30Of5uoQdjqRP2+95ZXQLhr7VDHs6gGdLa+s9ILRu3ZrevXszZ86cQifTTUlJYc6cOcTFxREVFcWmTZvw8vKiUaNGODs7o9Vqc5VGmTp1Km+99ZbjdXJyMhqNBhcXFyQWzKbtuCSGYUi6iLBmYvaqhIIZbXoipJvI8AlCa7SBoiXDWBtP3RQ0wp4qYvDgwRiNRh56KP+i11JKYmNjS06y4BIwhQeJUkGplAcXNZbYE4NJTrESVEXhaFgaPlEbiS3fq3D9s9TBCfH2YpMPNXYjuWxNLNX9GG1YxZgO6RiehZe/AZ0WTEuz+nWNAmNpFFVRkp2D6pH68+y5q2yF23lLKfn+m8N88s4WJs/tymN966GnEu6E4kSVYpxxKUVJYfKlZZOamsqIESNYvXo17du3p06dOtSvX5+RI0dSu3btXILI+PHjuXDhAk2aNOHtt9+mZcuWjuLZHh4eeHl5ER1/jij5BW7pZ9GakkmsEIrFyQsUDSIzFc9/fkfRaNGkpiMSMhHlDLikHSTd9QfcdEMZP3483377LcuXLy/QXCyEYMCAAXe3SEXGg20Gux+UCkqllCjMZjMXLlwgODiYCRMm0LJlS7p06ZJ/42vvsXNXInWqCSa/rqHrGzYiL1xDV9aUQ6t0axbNPghA6+71MfvZ/Y3iQp/CZ9cK4AIAp2fY2wqvJqVCUjGgpxwAoye2ZcKIPzh/Op6QetfXed+Of9j2x0VqNyiLr58zf/16gVNhMZw9EUtaipmVO56jZh0/fOiPgZr362OUcgeoqkqPHj0A8mhbtm/fzqFDhwgPDyc8PJxy5cpx4MABXFxc2Ldv3y0jjb28vGjcuDFxcXH4+PjQsmVL4uPj6dbNXhswISGBOJagN19CmjJJqPgIxtRzeP69ASl0aFIT0cSlgQ2svimk1m+FxpqJnjgU8yGOHD/CRx99xGeffcYTTzxRPAtUSoEIIWoBIwBfYJOUsliKoZYKSqWUKLKzkgsheOWVV26eCDNqCRt2qrz5tIYjZ+z+BN4GlRThwk3NbjdQrrw9mV3jil/y4muNqdfYn3IV3Hj6EbuQdHwaBJbBrq5uvvJOPlYpt0CLLxq8GDi8KRNG/MEj9b+laauK7N8ZkatdUIgvMVFpOBm09H6mNgNea0yrDlUQQuBELZwo9Rl70MjWwjRv3pwDBw7QuHFj9uzZQ8uWLQEYPnw4Pj4+HD58mCtXrlC3bl0mTpx4W5Fy3t7ejlxN3t7eud7bt/coreqmkVrmIfTmaxiuniC5UnO0lgyc3c6iVgsAKyiXYnA9uI3UZqHYLKloRHnHJm706NFFtBo3Z9u2bbRt25YffviBp59++s4GEYBScjVKQoj5wGNAtJSyTo7jXYCZgAb4Vkr5qZTyJPCyEEKB7NwvxTCnkuhc9iBm5i4JlIQMtvcMm439i420e9lMxHon/LuYCKosOLKuNuaaK8ngLOnsvOUwGSkG0Kbw9kub+P2Xv/Ep40JEeBIVAzyICE/ij7HQMbtea/ttUKY0ggqK51qzEkcU00mIz2DDilNsWH6KNo9UpV6TctRuUBZ3j4KFZg+ewIWGJdon6T/1/bxNYmNjee655/j1118dx5o1a8bnn39OrVq1imzdsjVWBoMhVz3I8RMbM+SdjrilHiJdVw2njAhcE48SV7knqsYVIS24pJ3EeOpv0mo2RS+TMVlm4eNbi6CgIE6fPl0k87sVcXFx+Pr6Ol4X9Jy8aWbuejp5YG0xRtTdBFEl5paZuYUQbYBUYFG2oCSE0ABngE5ABLAfeEpKeUII0QN4B5glpVxSHPMu1SiV8mCiKEyYo/JSDw0CickCIVUFisdLWIknnb3Y02lbEbjgRitU0khjR56h/Ixd+enHoTSs34qqQV7suTQMrBZcT+/FPWwboIF2m0uFpGJGiw/ePAfei3lmcEOeGdwwn1bOCDRIzAi0GAjCg0fR4HLP51tK0eHr68vGjRtRVZXk5GRSU1OpVKkSYBcwi4psp+oyZcrkOv7h2IO88lZTrEKP1BtwvvA3ccF2IQmhIIUTqa510dTIAKHF6DyX0cPtCSGPHz9eZPO7FT4+PrleV61alc2bN1OlSpXbG6gE+yhJKbcJIarccPgh4JyU8gKAEGIp0BM4IaX8BfhFCLEeKBWUSiklm8GDB7Nxl8rVjUaC+9idsT8YVhlb2RdI4v8A1dFWkomR2mjxwYVmpLGbNPY62qTyFwYG8sqrgxn+8sekJWvxdQnBxb0xouUbULY96D3vw6csHuLi4jCbzfj7+9/vqeTBSE38GU8m57AQCYBAhxMB6KlUGur/L0ej0eDl5VWsOYSyhbLk5GRatGiBlJKAgACSop7Ay38bkIJEQdW4XK9XBIDEqnXHzTgDRVOB9evX8+677xZYLqm4yBk5d/HiRQIDA28/7cD9k5N8hRA5C/vNkVLOKUS/CkDOgpoRQDMhRDvgcf6/vTOPq6paG/93AQKHUUBEBGcURYVy1sTUq5VjOXRNrfSmvT8b9JZeh1K7vnab82pz3dTSqzmVlWXmlL4qivOY4ACi4MAk8zys3x/7cAQZBDxwzoH1/Xz2h7PXXmfvZz+ss/az13rW84Ad8JvRpLyLKv2H9cNfx4DrUsrhQohWwHrAHTgBPCOlzBVCOKFZdk7A01LKG0IIL2AF0AztVT9KSjnUiPeiqEesWLGCNm3a4N1nJQkp/en+QHP8h58jzypDP+Jwx1AS2FBACjZ40AB3commpCGVRzohXL2URb9+/fBz+qfmN9HMBDdWC1y5coX8/HyzNJQArLDDgY5AR1OLYhGkpqaSl5dXarRBUT7FF4gUFGh9wcZVh5g3byHJ4gty3FsBcCMmlcS4TDp30RYbJCd68fuh/Vy5coWrV69WGDepJsnJycHO7s6ClaVLl3Lz5k02bNjAmTNnKnEGk1lKCdVMiluWwFJKuRfYe18SVYKqmsJ/B8KAolDE7wFLpZTrhRBfAlOAL4Cnga+Aa8AMtPnDxcBOKeVHAEKIQBSKapCYmIivry/z588Hl374+7cnK98arB2xpgGlf1MFhqSoUkpSUtJxaKh9TkrM4tCBSHb+eIitP5zmwoULlYoIbcl062ZxybsVFZCYmEhOTo4ylKqJtbU1YWFhPProo7zxxhu4u7vz3IvBdPA7wJTJmp+jRyMdT00KYtN/I+nTJw5HR0fGjRvHU089ZRKZi+ezbNOmDTNnzqzaCcx35q08Yij56uoL3Kiti1f6iSCE8AWGAcv1+wIYCHyvr7IKKApgY40W/72QO/8Sb7SbBUBKWRmzV6EohZOTE1ZWVmzevJn8/HzCwsIMOd+ssMWDSQjsARsEDXBnPNZogeHef/992rnNppPHv+no9m+eHbKBL94PpU2LBzl37lwJR0mFwhJwd3fHx8fH1GJYNO3bt+fq1av89ttvLFmyhBuRjix8/QpjxmqTHokJWXy2JJS1a9fy448/smbNGtavX1/r026gveA988wzmlyJiVy+fJmPPvoIAGdnZ1xdXSs+gcCUKUxchRD/EUJUNZP4UaCtEKKVEMIWeArYUsVzVJuq/JeXAXOAouUHHkCylLJoHXYM2jwiwFpgHWAPPKMv+wzYIIR4GdgFfCOlrDWLUFF3sLOz4+rVq6SkpBiWEO/fv//OcVrgzXwKSccKR0MwQ8BgCH2wbD4PDbFHZ+tGE5d+2NO2dm/CTEhJScHW1hadTmdqURTV5J4PRkWlGTJkCACTJ09m2rRpREVFkZOTw61bt4iLi2PVqlUMHjyY3NxcQygTUxAfH4+dnR2bN29m8uTJ/P3vf2fEiBF88sknlTuB6UaUUqSU/1NRBSHEOqA/mj9TDPBPKeUKve2wHW0gZqWUsta86CtlKAkhimIaHNc7T0E5c4YAUspkYEiJA1JuF0K0Bh7THzsphOgkpYwv65rGXOlQX8jIyDC1CLXCzZs38fX1ZceOHUycOJH27duXc+8CyKRfv35kZWWxefNmDhw4gK+vLz2CRuJr14qMjAzyhCN51M/2dvLkSWxtbUukeqgM9aWtGROls+phKr0NHDiQr776ig4dOvD222/j7OzM9evX8fX1ZdeuXfTt27dW5cnPzycw8I7HyujRo/niiy84duwYvr6+rFq1Chsbm0o8O807MreUcnw55b9Rgw7bFVGpOEpCiHfQRoby0UaJXIAfgUeBJlLKfCFEb2CRlPLRSl1YiF/RRpV+KOOYiqNUDepLnJaQkJASndQjjzzC9u3by61ftELExcUFBwcHmjdvzuHDh4H6ozNjo/RWdZTOqocp9da7d29CQ0NxcXEhNTUVR0dHMjIyiIiIoHXr1rUqy8yZM1m6dGmpci8vL4OxVESFcZSCbOWx303jYiCa3rwM7AF+kVL+YhIhqkGlfJSklK9JKX2llC3R5gb/kFJORLvhsfpqk4CfyzuHEGKgEMJB/9kZaIPm7K1QVIkPPvigxP6VK1cqrN+hQwdAWx0UERFhMJIUCoWiIlasWAHA7NmzkVLyww/ae318fJkTITVGdHQ0S5cuZcuWLURGahkDOnXqxIIFCzh+/HgJI6lSCBNt+qk3SzKS4P7jKM0F1gsh/gWcRFv+Xx5dgU+FEPloBtpyKeXR+7y+oh5ydxTc7t27V1g/KirK8Dk7OxsHB4eaEEuhUNQxipb/L1y4kF9++YUjR44AVHmq+n544403ePPNN5k/fz59+/ZlyJAhtGzZkhMnTlTfT8qMp97MkSqvg5ZS7pVSDtd/jpRS9pBS+kkpn5RS5lTwvQ+klAFSykApZScp5ZLKXC88PBwhRPXz2ijqFDt27Chh+ACsX7++3Prx8fFkZWXx5JNPcuPGjVJ5nhQKhaIiduzYAWAwkvr06VMrL1v//e9/GTFiBG+++SYvv/wyEydOxN3dncOHD7N9+/b7MJJACmuTbJaK2QeMadxYi39TWFhoYkkU5kB0dHSJHE2gtY0vviidNFpKaYhzsm7dOrMNsKhQKMyXwYMHU1hYSGxsLIWFhYSE3DuH5P0QExODEIJnn32Wfv36kZyczKRJkwgICKBTJy1HbKtWre7jCqaad7uv8AAmxexTmLi7u1NYWFj1EO2KOsm5c+fKLG/atGmJ/aIs20VYW1vu24xCoTAtQgjDS3tNUjzH3ZEjRwxuBZ988gn9+/dnz549RrmONN0YyT3DA5gjZj+iBFojrevRkhX3pqCggGXLlpUoa9JESy0wYsSdF5Ts7GwefvhhPv74Y3JycpSRrSjF/Y5QX758matXr3Lu3DmSk5ONJJWivvPQQw8ZPv/yyy9kZmbywgsvsHr1ahYvXmy8C5ku4KRFoqwPhcVwdxTcI0eOkJ6ejouLSwlDukGDBnTo0IHdu3ezevVqsrKyaltUhRlTUFDAK6+8wrZt26p9Djs7O1xcXLhy5QoRERFGlE5Rn9m1a5fB/+nNN9/E0dGRL7/8krCwMIKDg410FYH26DfFZplYruSKesexY8dK7H/zzTekp6czZsyYEuXW1tYcP36cRo0a8fzzz/Ptt9/WopQKc0cIQdeuXe8r512zZs1wc3NjxIgRdO3a1YjSKeozbm5u7Nq1y7B//fp1CgsLad++vVGvIxEm2VA+SgpFzfI//1NyavuLL77AysqKlStXlqp7+/Ztw2o4Ly+vWpFPYRlYWVkxadIkU4tRIUuXLiUzM1NL/KyoN5w6dYr4+PgadReQYMoVaMpHSaGoSU6cOFGqbNSoUaXKLl26hK+vL5MmTSIqKorRo0fXhngKhdGYOXMmCxYsQAjBu+++a2pxFLVEt27dGDlyZLW/n52dXWpVcGkECCvTbBaKGlFSWAx9+/blwIEDhn0HBwe+//77EnU8PT1JSEgA4LPPPqtV+RSKmuD8+fOmFkFhIZw+fbpS9aQJs+JaIpZr4inqHXf7lGRmZpYICxAXF2cwks6cOVPhuRITE8nIyFAr4hRmSVFIlMLCQlavXm1qcRS1QGJiIkII/vKXv1T7HD169KBHjx6VqKmcuauC5UquqHcsWrSoVNnNmze5dOkScXFxzJo1C9DewO+VYmDGjBk4OTnRsWNHhBCGTaEwB4raomqT9Qc7Ow7elbIAACAASURBVDugdC7LqlC5fsxEoQGEcuZWKGocFxeXMsvbtWtXYj8oKIjc3NwKz7Vu3bpSZT179qy+cAqFQnEfODk5lRjhLiwsZMGCBSxcuBCdTmfUa0mUM3dVUCNKCotBCMHXX399z3p5eXnExcVVWCc3N5fPP/+cxx57jMWLF5OSkkJoaKixRFUoagQhBM8//7ypxVDUAllZWURERBAbG2vcEwuBFFYm2SwVYY4+GkIIaY5ymTtpaWk4OzubWowap1u3bhw/fvye9c6dO0fHjh0rrFPbOgsICCAsLAzQVqgUDbdbGvWlrRkTY+hsxowZBAUFMWXKFCNJZf6otlZ1hBBIKcucg+v6oKM8+EeH2hYJAHv348ellNUPYGYiLNfEU9Rbjh07VmZYgCKsrKxo1qwZnTp1QghBq1atEELw//7f/6tFKcumyEgCsLe3rxPJnsPDw/n5559NLUa94OOPP65XRlJd4eTJk7z//vumFsOAxMokm6ViuZIr6jWbN2/m1q1bDBo0qNSxwsJCoqOjDftFubjy8/NrTb7ykFJy+/ZtQ7LLujBy6u/vz4ABA0wthkJhtjRt2hQ/Pz9Ti3EHleutSqiptzpEfR6iTkxMpGvXrly9etVQ5ufnx7lz5yqc3rIEnSUlJZGYmGhWHa0l6M3cUDqrHkpvVafiqTcnGbKn4lXBNYXOLfQysAf4RUr5i0mEqAZq1ZuiTuDh4UFUVJRhv7CwsE4s+c/IyCA8PJy4uDgOHTrEM888Y2qRFAqFBaNSmFSdSk+9CSEaCiG+F0KECyHChBC9hRDuQoidQohL+r9u+rpWQojVQoiDQoiO+jIHIcRaIcRZIcQ5IcQBIYRTTd2Yon5jZWVl8UYSgKOjIz169KBfv36kp6dz+/ZtU4ukUCgsGtMkxLXkaOBV8VH6CPhdStkeCALCgHnAbillW2C3fh/gEeAwMAqYpS/7OxArpewspewETAHy7v8WFIq6jbW1NW5ubvzf//0fe/fuNbU4CoXC0lG53qpEpSQXQrgA/YAVAFLKXCllMvA4sEpfbRXwhP6zNVCo34rMSG/getE5pZQXpJQ593sDCkVdp7CwkE2bNuHi4sLDDz9sanEUCoXFI0y0WSaVNfFaA/HAN0KIk0KI5UIIR8BLSnkTQP+3sb7+duBhYAvwb33ZSmCuEOKQEOJfQoi2RrsLhcLE1OSKOiklR44cYf78+Xh4eNTYdRQKRX1AqPAAVaSyztw2QBdgupTysBDiI+5Ms5VCSpkPPHVX2SkhRGu0ablBwFEhRG8pZVhZ50hLS6ukaIoiMjIyTC2CxVGWzjIyMli7di19+vShefPm5aZOKSIxMZHg4GAA3NzcCAkJMbqcb7zxBmA+vwvV1qqO0ln1UHozMgKLXqpvCiprKMUAMVLKw/r979EMpVghhLeU8qYQwhuoMG+ElDId2AxsFkIUAkPRfJ1KoZaDVg+lt6pja2vLiRMncHV1JSAggLVr1/KPf/yDoUOH0qtXLxYuXFjh952dnQkNDWXNmjUEBgbWm/9BfblPY6J0Vj2U3oyLCXO9WSSVjqMkhNgPTJVSXhBCLAIc9YcSpZTvCiHmAe5SyjnlfP8h4LyUMkkIYQv8Dnwupfy+jLoqjlI1UPFGKoeUkhUrVvD888/Trl07Ll68WOKY4t6otlZ1lM6qh9Jb1akojlKXLq7y//b1rm2RAHBx3l7n4yhNB9bqjZxI4G9oPk4bhRBTgGvAkxV8vw3whdDWbFsBW4EfqiW1QlFNnn32Wfbu3UtCQgIAmZmZPP3007Rt25bt27fj7e3NrVu3aNasGeHh4Tg4OJhYYoVCoTA2Jpt6s8g4SpU2lKSUp4Cyktn9pZLfXw2sruz1FApj8+eff9KyZUuio6Np1KgRWVlZAKxZs6bM+gUFBbUpnkKhUNQSlutYbQpUZG5FveCvf/0rmzZtMuwXjSiVRVJSEg0bNqwNsRSKOkFOTg4NGjTAyko9gM0diUAqZ+4qoVq1os6Sn59PYGAgH374YQkjqYipU6cihGDZsmXk5uYSFhZGWlqaMpIUVSYjI4Po6GgSExMteiQyJSWFbdu2VaruyJEj8fHxMSSntra2prCw8J7fk1KSnp7O+fPnuXjxovILNAlWJtosEzWipKhz5Obmsn79eiZNmgTA7Nmzy6y3fPlybt26haenJ1ZWVrRv3742xVRYIDk5Odja2nLo0CFWr17NgQMHuHjxoiF6enZ2NklJSQgh+Oqrr5g4caLZ+LnduHEDOzs7dDpdKZmklFy+fJkdO3awfv16XFxc8Pb2JiwsDA8PD3r37o2fnx+LFy9m69atdOvWjV9+0Xxxvb29cXJywtramsaNGxMYGEj79u1p1aoVfn5+tG7dmsDAQIQQvPrqqyxbtgydToePjw+pqanExcXx4osvMm/ePJo1a3bP+0hJSSE6Oprs7GxWrlxJaGgoO3fuxMPDw7BQY/HixXTt2hWA5ORk4uLiOH/+vOG67dq1o2fPnnh5eREUFISvry8+Pj61mvborbfeYsCAAfTu3buW0y0JU+Z6s0gqveqtNlGr3qqHWh0Ce/fuZcCAAZWqu379eoYOHVrvdVYd6lNbCw8P5+LFi7i5udGvX78Sx3755RcGDRqEnZ2d4WE3Z84cPvjgA0MdR0dHNmzYQIsWLfD29q71oKEJCQns3LmTOXPmEBMTA8CDDz5IdHQ0BQUF6HQ6EhMT8fDwoGXLliQmJnLhwgUA2rRpQ0REBJ6enuTk5FBYWEh6errh3AEBARw5cgRHR0dycnJISEjg3LlznD17lvPnz/PDDz+QmpqKj48P169riRl8fX25du0aQghu3rxJ06ZNDeebMGECqampREVFce7cOUP91NRUUlNT8fDwIDEx0VB/2LBhbN26FdBGiPft28fFixf5xz/+wYMPPoiNjQ0uLi7k5uby+OOP8+CDDzJkyBCWL1/OU089xccff0yjRo0MU/E6nQ4vLy8efPBBfvzxRwAuXLhAu3btjP5/mTJlCitXrmTu3LmMHz+eFi1aGG00u6JVbw92cZN7DlSujzQ2bo4/HpdSluXrbNYoQ6kOUZ8eXmWRkZGBk1P5eZbPnj1Lp06dSpTVd51Vl7qqt7S0NI4cOUJcXBwLFy4kIiKiwvrR0dH4+vqWKFu9ejWTJk3iySefJC0tjd9//x3QHvhFhkqLFi1ITEwkPz+fVq1a4e3tjZubGw0aNMDT0xNnZ2dOnDjBW2+9RZcuXSole1ZWFsePHyc8PJxLly5x9uxZIiMjuXDhAkIIxowZQ3R0NMOGDeOBBx6gY8eO2NjYUFBQgKOjI56eniVGNqSUZY50XLp0CT8/v0qNguTn55OWlsapU6cQQtCzZ090Op3heK9evTh8WAvP5+zsTPfu3fH39+f7778nPj7eoLcBAwYQGBjIM888g7u7O9bW1kgpsba2Zu/evXz55ZcEBASQmJjIpEmTKq2z4nLm5+cTFRVFWloaly5dYuLEiaXqXb16lebNm1fp3BVx9epVPD09GT16NFZWVvz2229GOe89DaWQSq3BMjpuDj8oQ8lYKEOpepjjwysiIoLGjRvXmFypqamcPXuW2NhYunfvTnh4OHl5eQwbNqzc7+Tm5nLx4kUuXbrEX/7yF7PTmSVgjm3tbm7evMmmTZsIDw9n+PDhDB06tFQdKSW///47+/fvZ/v27Zw4cQKAUaNGER4ezmOPPYa/vz/x8fFs2LCByMhIYmJicHNzq5IsSUlJWFlZYWdnR1paGrdv36ZRo0bY2dkRERFBfHw8t2/fJisri6SkJFJSUli0aBGgPcStrSueKlm0aBH/+7//S/fu3QkICKBt27YEBATg6+uLjY0N/v7+ZjMFWFVM1damTZvGlStXmDt3LqtWrWL16tKLtu3s7MjJyeGtt97C19fXMN0/Z84cZs+eTaNGjSp1rezsbGxtbY3mDF+xoeRuQkPpe2UoGQtlKFUPc3x4nTp1Cjc3N1q0aGGU88XGxuLo6IiTkxNLlizhH//4R4nj//73v3nnnXcMb6O5ubn87W9/Y+3atSXqff7556SkpPDSSy+Znc4sAWO2NSkl165dw9nZGXd3d6OcE2DGjBl88sknhv2ePXuycOFCmjZtio2NDS1btmTMmDHs3LmTadOm0atXL3r37k3btm1rxGeksjrLyspi1KhRbN++HSgZBLWgoIDY2Fi8vb3Zt28f77zzDn/88Qd5eXmAlkC5dv1dah5z6dfi4uKIiYkhNTWVrKwskpOTSU5O5pVXXqFr1664urpy4sQJ4uLuJKho1aoVV65cQQjB4MGDeeaZZxg7diz29vY1Kuu9DKU/QgbX6PXLw91hozKUjIUylKqHuXQoNcX06dP59NNPAc1gmjJlCr/++muZdR955BFmz57NoEGDDGVxcXF4e3tTWFiIt7c3y5cvJzg4uE7rrDqEhYXh5uZGkyZNkFLy448/4uLiUkKXISEhPP7445w9e5br169jb2/Prl276NGjByEhIbRp04bdu3cTGBjIp59+ypAhQ9izZw+vv/46x44d4+2332bJkiU4OTmxb98+1q5di4eHBwcPHmTr1q1s27aNb7/9lqZNm7J161Zyc3MZNmwY58+fx9PTE3d3d1544QXs7e354osvEEJw6dIlDh48SEJCAikpKezevZuDBw+SnJzMTz/9xOTJkw0jAHdTG/1NZX6fn3zyCTNmzDDsF031bNy4kXfeeYdTp06V+s66desYMGAAXl5eRpfZHLDEfi0nJ4ebN2+SkJDAsmXLCA4O5vjx43z99deANj2bkpJC48aNadasGS1atMDd3R07OzuA+zZ2KzSUurrLP0Ieua/zVxd33QZlKBkLZShVD0vsUCrLrl27GDZsGLm5uYay119/ncjISNavX28o69ixI3/++SegRd0u7g8BsHnzZsaMGQOAvb09x44do2PHjrVwB+ZDfn4+sbGxuLm58dBDD/HEE0/w/PPP89prrxESEkJERAQ2NjZcvXqVkydPMnz4cABWrFjBkCFDaNKkCTt27ODDDz8kOjqaCxcu4OrqSmpqKjY2NobRjeL4+fkRHR1Ns2bNuHz5MqAtL3d0dDRMhZ49exYHBwcyMzMB6NSpk8Gh925Gjx7NwYMHSUtLo7CwEGdnZ/Lz8+nXrx/NmjWjYcOG+Pv706dPH1q1alXiu9nZ2VhbW/PZZ5/RoEEDXnrpJWOqt1wq8/v84IMPmDPnThYoKSURERH4+fkBMG/ePGbPnk18fDz29vZGG6k1Z+pSv3b8+HHmzp3L7t2771n3xRdfZNGiRbi4uGBlZUWDBg0qfZ2KDKUHunrIP0Ieq7zQRsRD951FpjBRhlIdoqY6lKioKCIjI7l69Spbt27F39+fy5cvk5aWxqBBg7C1tWXChAlGnTa5m+3btzN27NgSK27uRghBeno6jo5aGsKZM2eyZMmSEnWysrJK+GokJCTU+iokU5KXl8cLL7zAihUryjzu5eVFbGwsADY2NuTn55dZr3PnzjRr1oyOHTvSqlUr3N3d8fT0JCAgABcXF3Q6Xam34szMTMNy85YtW5Yro5SSn3/+mWvXrmFvb0/fvn1JT08nOTmZDh06EBoaSkhICDdv3sTFxQUfHx8mTZpEy5YtzXraqTK/zxMnThiWtS9duhQ7OztefPFFwPiOxJZCXTKUinjzzTd54403OHLkCLa2tvj5+WFvb09cXBwff/wx7777bqnvXLt2rVLhE+DehtLug6X99WqDRvZr1IiSsVCGUvUwdodSUFDAoUOHGD9+vGG1TkUMGDCAt99+m9u3bzNo0CA++ugjHB0dmTBhAsuXLycgIID+/ftX26k0Li6u0tMLffr04eDBgxQUFJRwkPzxxx8ZPXq0YX/Hjh0MHmya+fqaRkrJ+++/z+jRo2nVqhU7duwgMjKSV1991WAA7d+/nz59+hiCbj711FMljJi8vDxDrKkbN24QHh7OjRs3GDNmTJUdmus7lfl9Fhl6d08RLl++nClTptSofOZKXTSUykNKSWFhIVZWVnh7exteWgDOnDlD586dK3WeextK5S92qUka2f9XGUrGQhlKd4iJiWHgwIGMGjWKHj168MQTTxgi4BYtjy3CWB3KkiVLmDdvHm+99RZz586tsG7z5s0NbzoJCQlYW1vTsmVLWrduzZYtW0rV1+l0DB48mIiICIKCgpgzZw6Ojo60adMGIQSffvop06dPB2D8+PGGB7KUsoSPDGhLrK9evVpCltzcXPLy8khNTWX79u1lxlQqPurQsWPHcqd3LIm8vDxycnJKhEeQUjJy5EgiIiK4du0aGRkZAIwdO5bOnTvTt29fBg4cWK3r1aeHl7G4l8569OjB0aNHAXBycmLy5Mm89NJL9T4Qal1va1JK3njjDdatW1duOIp9+/YRHBxc6XNWbCg1krsPDa+esPdJI7tVylAyFvXRUDp8+DD5+fkEBQUZHnY5OTn885//5L333qvUOXx9fZk8eTJBQUGMHz+e/Px8vvvuOyZMmABoARaHDx/OunXr8PDwICgoiNatW5c6T2xsLL/++ivjx483TGPdzfTp02ncuDGenp5ER0fj6elp8B/y9vbGysqKHTt20KBBA3x9fenatStnzpwhKyuLo0ePGuLE2NjYcPHiRQA6dOhAWFgYAAsWLODkyZPs3bvX8IC/myZNmpCWlsb48eNp27atYRqmc+fOtGjRotxpmOLlGzZs4K9//Wul9GuO5OfnM2zYMHbs2AHccUouKCjgwIED9O/f31DX39+fU6dOGWXFTV1/eNUE99LZxo0bef3111m4cCFjx44t97dX36jLbS0zM5Pk5GR8fHxKHRs0aBDJyckcO3aMvLw8bGwqn0jjXobSrkMjqi/0feBp960ylIxFXTOUrl+/TlZWFunp6axZs4YVK1aQnJyMu7s7hYWFtG7d2hC/BeDdd98lOzubY8eOGVZ19evXj3379hnqNGjQoITTbKNGjbC3t6/UFFlxipx6izsKJicnEx8fj5WVFadPn+bPP//kjTfeMByfNWsWL7/8coV+JlWhyBD6888/ady4cSkjJz09nbCwMJo0aULTpk0NcWWq0nHcHQG4iNTUVIvuhHNzcxk1apQhUF1hYSGXLl3C39+/VN1jx44Z/F/ul7r88KoplM6qR13UW35+Pp9++imvvvpqifIPPviAWbNm1eiqN81Qevy+zl9dPO1WKkPJWFiSobRx40ZmzJjBnDlzmDVrFpMnT2bnzp34+voaIs7a2tri6+uLTqczrMhat24d2dnZzJ8/n6lTp+Lo6EhGRobBWVmn0xmClTk4OGBnZ4eDgwNSSuLj44mLi8PHx4fo6GhsbW3p3r079vb2tG7dGnd3d5KTk3F0dERKSUhICCNHjmTChAl4e3uXcHB2c3MjNTXVYCxlZ2eTnp6Om5sbOp0OR0dHMjMziYqKKnXvGRkZZh/ELjExkZMnT5bwQxoxYgRz586ld+/eZGRkWEwnXFhYSExMDDdu3ODWrVukp6cTFxdHTk4OYWFhJCYmEhsby/HjxwFt2rasN1VjUBcfXjWN0ln1qEt6k1KWCippZWXFtGnTeOGFFwgICDBK0MmKDSVPuTN01H1fozo0tv1aGUrGwhwNpaysLKKionjuuecIDQ0tt977779PdnY2V65c4ZtvvmHAgAHs3r27VlbjVLZDKUpNUPzvrVu3yMzMxN7e3jB1Vpzw8HCSkpJwc3PD0dERV1dXXFxcaupWSsg6c+ZM2rRpQ/PmzcnOzqZdu3bk5eUZZHFxceHQoUMMHjyYp59+mjVr1gCan4eVlRWBgYF4eXnRqVMnJkyYUCJvk7l2wjdu3GDnzp2cOXOGqKgoYmJiCAsLw9nZmaZNm+Lt7Y2joyPOzs7k5eVx+/Zt+vfvj06n49ChQ8yYMcNoo0dlYa56M2eUzqpHXdPbgQMHyMjI4LHHtCX6NfGsu7ehNLqsQzVOY9v/KEPJWNSmoXT69GlefvllmjVrxrp165g0aRLZ2dmEh4fj6enJuHHjcHJy4uWXXy6RjPFugoKCygwGV5vUtQ4FtKmloiBsd+Pt7U1eXh4ZGRlkZWUB8OGHH9KgQQP69u1LmzZtcHFxqdBINQedXbt2jQMHDhAWFkZERAQnTpzgxo0bNG/enNGjR9O5c2e8vb1p3bp1mdOHpsAc9GZpKJ1Vj7qqt6J+6cMPP2TWrFmA9jyaNGkSU6dOpUuXLri7u+Pv71/lF+2KDaXGcsfhMfcpffXwavBl3TaUhBCvAlMBCZwF/gZ4A+sBd+AE8IyUMlcI4QR8BzgBT0spbwghvIAVQDOgARAlpSwzmENtGko+Pj7cuHHDsD916lR27txZYjVVZXjiiScM2aZNhaV2KP/5z3/o378/DRs2xNfXl7y8PIQQdOnShaCgIBo1asQ333xjSEtSnPXr1zNu3LhS5VJKCgoK7unHVB2dbdmyhccff5znnnuOpKQk8vLycHZ2pm3btgQGBjJ48OB7jrZJKfnvf/9ryA31+OOP07lzZ1xdXRk8eDDt2rUrFSzTnLDUtmZKlM6qR13V26pVq5g/fz7Xr18H4L333itzlfHx48ernOT33obS2GpIfP94Nfii7hpKQggf4AAQIKXMEkJsBH4DhgKbpZTrhRBfAqellF8IIaYB0cA1YKKUcp4Q4ivgvJTyI/05A6WUZ8q5nlENpUuXLvHZZ5/h4OBAQEAAffv2NTgi79+/n6VLl7Jnzx6Sk5NLfO/nn39mxIgRFBQUkJ+fz+HDh9m/fz8jR44kMDDQaPIZC0vsUPLy8rC1tb1nvW7dutGwYUNDAlyAt99+m7lz55KdnU1oaChhYWGEhoYapt5Ac6SvaBSmqjobN24cGzduRKfTMW/ePDp06ICdnR1//vknr7/+uqHevXJuvfrqqyxbtoyBAwfywgsvMHbsnY5LSklKSgqurq5mG0DREtuaqVE6qx51TW/FF5ZMnjyZb7/9tsx6p0+fplWrVtW694oMpaCujeWOw6ZZ6dukwWd13lAKBYKAVOAn4BNgLdBESpkvhOgNLJJSPiqEeAmIRDOUnpVSzhVCbAFWSSl/qMT1jGYoZWdnG97MbW1tDUvYfXx86NChA6+//joDBgxASsm//vUv3nnnHcM0TvHIxP3792fs2LE4ODjg6emJk5MTDz/8sFk9yCy1Q/nuu+94+umnK5yrDwoKwtvbm6FDhxriLIEWsbtorv9u7O3t6dWrF1u2bCmll5ycHH7//Xfs7OzK/f7dnDt3js6dOxMcHFxiBSJoxnhx3ydXV9dShncRu3btKhXk0sHBAZ1OR3JyMgUFBYbytWvX4unpiU6nw97eHp1Oh52dHVJKmjZtioODg0naoKW2NVOidFY96oreUlNT8fDwKDPa/Zw5c7C3tycrK4tbt24xa9YsgoKCqn2tCg2lbo3ljsOlR+FrgyY2n9ZdQwlACPF34C0gC9gB/B0IlVL66Y83A7ZJKTsJIRoC6wB7tOm4GCHEo8AG4CSwC/hGSnmjjEsZfURp7dq1PP300wD07t2byMhIw6iETqcz5JZKT08v8YMMCQnB3t6e1157zRCn5m5u3rxJkyZNjCbr/VAXOpRNmzbx008/ceLECcLDw0sd37t3Lw8//LBh/+jRoyxatIi0tDT279/PtGnT+PLLL0t977vvvjPkIps6daqh3NfXl5iYmDKNtMuXLzN79mxCQkKIj49Hp9NhZWXFiRMnaN68OQkJCSQkJBAfH2/4/N5773H9+nUCAwM5ffp0mfeYl5fH559/jqenJ1lZWcTExNC2bVsmTpxYHZVx69atWk+IWhfaWm2jdFY9LF1vUkqWLFnC7NmzS5RnZWUZJaZZWVRsKHnJ7YfH18h174W3zUd111ASQrgBPwDjgGRgk37/n3cZSr9JKcuNsS6EcAceA4YAjwCdpJSlHE+EEDI1NbXqd1MO2dnZhoehu7s7DRs2LBHRGjSH2qNHjyKEICkpicjISFq0aEFBQQF5eXllPnxffvllQx4mcyAjI6POBKlLSEjgjz/+4ObNm+Tm5tKwYUOWLl1qON69e3diY2NJSEigoKCAnJwcmjdvzsMPP8yaNWt48803WblyJZGRkeVeIzg4mIsXL6LT6QxxiIqQUpZKluvn54etrS3nz59HCIGnpycNGzbEzc3NsLm4uJCamsrMmTOxs7Nj//793Lhxg7i4OFatWkXLli1p3LgxNjY27N27F9CM9aJRzAceeMCwKKBXr16MHDnSEDPKxsYGBwcHrKyssLW1Naz6M0XW+LrU1moLpbPqYel6S09PJzg4uERKGoDz58/X2DVdXFwqNpSOVO+l7H7xtl5apw2lJ4HHpJRT9PvPAr2BJylj6q1SFxbiV7RRpVJTccYeURoxYoQhcGNxEhMTmT59On5+foSFhbFp06YSx/39/Rk7diy2trbY2dlha2tLQkICiYmJREdH8/nnn5tV9m5Lf/O6F7du3WLx4sUMGTIEnU7HtWvX6Ny5M+3btyc2Npb169cjhGDBggW0b9+e1NTUEo76Reh0Or766iuys7Pp3bs3nTp1KvN6p06dIioqCiklOTk5JCUlkZubi42NDc7OzuTk5LBv3z4effRRAgMDDX5r586dY9GiRfzwg9a0fX19ee6553B1dSUzM5P9+/czcOBAHnjgAR566KESaUcshbre1moCpbPqUVf0lpycXGZ+xMOHD9OjRw+jXqviEaUm8ncTGUpNrf9dpw2lnsBKoDva1Nu3wDGgH/BDMWfuM1LKz8s5x0C0qbpMIYQzcATNf+loGXXLNZT27t1LkyZNaNiwIVevXsXNzQ0/P78Kg3RlZ2ezbds2PDw8cHNzIzMzE51OR/PmzenatSuRkZFcuHCBdu3akZWVRdOmTQ3+JS1atODixYuVcjg2NXWlQzEW4eHhdOjQ4Z51/P39kVISGRnJqgQanQAACmlJREFU8ePH2bVrF6tWrTL4s7m6uuLq6oqPjw8uLi7Y29vj6OhITk6OwRgCePLJJw3GtpeXF0uWLGHChAlm5cdmLFRbqzpKZ9Wjrumta9euJTIxgBb4NykpicmTJ9OrVy+GDBlCs2bNqt133NNQOvpMtc57vzS1+tCohpIQ4glgGNAY+ExKWbaPzH1SqRwQUsrDQojv0UIA5KP5Gf0H2AqsF0L8S1+2ooLTdAU+FULkA1bA8rKMpIrIy8srM8lpcXr27Imnp6dh1dH27dsJCAjgzJk7C+wWLFhA586dDTnKIiMjS6V8GDNmDIWFhXTu3NkokVIVtU/79u05c+YMGRkZXLp0iS5dujBv3rwSo4shISF8/fXX/PbbbyQnJ9OjRw+6dOnCW2+9xahRo2jduvU9O6t58+aRnJxMcHAw48ePp2PHjiUcuxUKhaKI6dOns3fvXtzc3Lhy5QqBgYEMHz6c559/nm3btpVaBdeiRQv69u3LtWvX+Oqrr2jcuDHu7u739QImMd+XNyHESmA4ECel7FSs/DHgI8AazX54V0r5E/CT3j3oQzT/aePLZK4BJ/fs2UNqairt2rXj+vXrBAQEkJWVhU6nw8bGhqSkJLZv386MGTMMGewrg42NDUOHDsXW1pbU1FSys7PZt28fbdq0Yc2aNbRo0YImTZpY5ChAXXvzqik2btzIuHHjsLa2Ztq0aTRq1IihQ4fSrVs3ZRRXEtXWqo7SWfWob3o7ePAgDz30EADu7u7cvn27zHpFo1DBwcE0bdqULVu24O/vz8iRI1m8eHEFI0rectvRSTV3AxXgY/XePUeUhBD9gHRgdZGhJISwBi4Cg4EY4CgwXkp5Xn98CbBWSnmi7LPeH2ZrKAUHB7N//37atGlDRESE4ZiXlxdZWVlkZ2eTl5eHvb29YbOzsyszJ1lVSU5OxtXV9b7PU9vUtw7FGCidVQ+lt6qjdFY9lN5KkpaWxtmzZ/Hw8GDDhg0EBgaSkpLChg0bOHbsGK6urly+fLlCQ+m3Y8/VttgA+Iq3KzX1JoRoCfxazFAq4QMthHhNX/Vd/bZTSrmrRoTGjA0lU8ugUCgUCoWFclVK2bKsA0KI34FGtSuOAXsgu9j+f6SU/7m7UhmG0li0BWVT9fvPAD3RRpkmoY0wnZJSll6ebgQq5aNU25RnCSsUCoVCoag+UsrKRdg1L8qyCaSU8mPg45q+uHLIUCgUCoVCYc7EoOWJLcIXKDNgdU2gDCWFQqFQKBTmzFGgrRCilRDCFngK2FJbF1eGkkKhUCgUCrNACLEOOAT4CyFihBBTpJT5wMvAdiAM2Cil/LO2ZKoRQ0kIsVIIESeEOFes7AEhRKgQ4pQQ4pgQooe+XAghPhZCXBZCnBFCdCn2nVeFECeEEOP0+0uFEK8UO75dCLG82P4SIcTMmrin2qAcvX0ghAjX6+ZHfR69omOv6fV2QZ9Lr6j8Kb3eXtHv/10IsazY8a+EELuK7U8XQtT4PG9tIIRoKIT4Xq+zMCFEbyGEuxBipxDikv6vm76ulRBitRDioBCio77spBDiAf1nGyFEhhDi6WLnP168jdYXhBCP6dvZZSHEPH1ZRyHEISHEKiFEvX3pMkKb6y+ESNH3jUXbINPelfGpqf5NXxYlhDhbTH91oj+Dmnue6ssK7mp382r37kojpRwvpfSWUjaQUvpKKVfoy3+TUraTUraRUr5V20IZfUOL2N0FOFesbAcwRP95KLC32OdtaM5avYDD+nIn4Ds0h/Of9WVPolmSoBl5x4FDxa5xCOhZE/dUG1s5ensEsNF/fg94T/85ADgN2AGtgAjAWn/sJ7SgXOv1euwOHCl2zsNoQ5lF9dcB40x9/0bS4Spgqv6zLdAQeB+Ypy+bV0yHjwEvAV7ASn3ZZ8CL+s9d0YKsfq7fdwSSivRWXzZ9W4oAWut1elrf/lYAnsB0tBUpJpfVQttcf7QVPia/lxrWU430b/qyKKCRqe+xFvV2389TfXm6qe/PErYaeQuUUu4D7o6SJQEX/WdX7jhiPY4WWEpKKUOBhkIIb+54uRcPFRAC9NF/7gicA9KEEG5CCDugA1qEcIukLL1JKXdIbdgRIBTNiQ00va2XUuZIKa8Al4GihEHFdSfQdNJOCKETQrgCmcApoCiBcR/gYA3cUq0ihHBB61SK3kBypZTJaLpapa+2CnhC/9kaKNRvRTor3sb6AF8CD+j3ewAnpJQFNXgb5kgP4LKUMlJKmYv2gHocTX+SkvqrVxipzdULarB/q9PU4PNUUUlqc7j8FeADIUQ0WqjxooBRPkB0sXoxgI+UMg04i5ZTbgOAlPIGkC+EaI72EDuENjrSG+iGlmsutxbuxVQ8h/a2AOXoTf95M5rejkkp0/Qd0Sm0kaVeaDoLBfoIIZqixdMqfi5LpTUQD3yjn0JbLoRwBLyklDcB9H8b6+tvBx5Gcwr8t77sICUNpX1AjtDyE/ZBM6TqG+W1tY/Q0hj1poZSB1gAxmhzAMF3TYG0qcV7MBeq1b8Vq7OnmP5erXFpTct9P0/16O5qd+NQlKI24yi9ALwqpfxBCPFXtDewQZQTHwFASvkO8M5dx4re+PugdTQ++s8p1IFRkfIQQsxHy7O3tqiojGpFelvFnbfZIor0pkMzMC8Br6N18nVFbzZoQ9TTpZaf8CO0aY8y0RuQT91VFiWEsBVCNAHaAxfQpil7ounvk5oS3owpL4bJSTS91Gfuu83p2S+lHF5DMpo9RujfAAZIKRNqRkKzw1jP0ywp5QOlv6IoTm2OKE1CexMA2MSdYdSqxkcoeuPvjDb1For2Rltn3/aFEJPQkgROlFIWDZ1WV2+90QylMDQ/gLqktxggRkp5WL//PdpDLFY//Iz+b9w9znMIGAvc1Os7FHgIrc2G1oTgZo5JY5iYOcZqc/UWI/Vv9Q1jPU8VlaA2DaUbaEPOAAPRRjRAG4J+Vu+t3wtIKRqyLocQtB/VbSllgZTyNprzZJEBUKcQWsbkucBIKWVmsUNbgKeEEHZCiFZAW+BIBac6iDbt5imljNN3SPFoc9p1YkRJSnkLiBZC+OuL/gKcR9NVURbIScDP9zhVCPAqd9rTIeBZ4Jbe/6S+YdIYJuaMEdtcvcSI/Vt9w1jPU0UlqJGpN6HFQegPNBJCxAD/BJ4HPhJC2KDlevkfffXf0Dz1L6M5Gf/tHqc/i5an5ru7ypwsfdi1HL29hrbyY6cQAiBUSjlNSvmnEGIjWqecD7xUkZOxlDJJCBEPFI89cQhtpOR0TdyPiZgOrNU/0CPR2pMVsFEIMQW4hrZ6siJCgKXoDSUp5U2hZa+uEwZlVZFS5gshimKYWKOt1qq1GCYWgDHaXLAQ4lSx/X9JKb+vEWlNRE32b3r2CCGK6pyRUj5bE/dR29Tw81R3V7v7XUpp8hAB5oZZJsVVKBQKhUKhMAfqbZA4hUKhUCgUinuhDCWFQqFQKBSKclCGkkKhUCgUCkU5KENJoVAoFAqFohyUoaRQKBQKhUJRDspQUigUCoVCoSgHZSgpFAqFQqFQlIMylBQKhUKhUCjK4f8Dn2ny4DbJtfUAAAAASUVORK5CYII=\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "from iso3166 import countries as iso_cntry\n", + "\n", + "from climada.util.constants import DATA_DIR\n", + "from climada.hazard.relative_cropyield import generate_full_hazard_set\n", + "from climada.entity.exposures.crop_production import init_full_exposure_set, normalize_several_exp\n", + "\n", + "data_path = Path(DATA_DIR) / 'ISIMIP_crop' # set path of working data directory \n", + "input_haz_dir = data_path / \"Input\" / \"Hazard_tutorial\" # set path where you place hazard input data\n", + "# (Place crop yield data (.nc) from ISIMIP in input_haz_dir)\n", + "input_exp_dir = data_path / \"Input\" / \"Exposure\" # save FAO data and histsoc_landuse-15crops_annual_1861_2005.nc here.\n", + "\n", + "output_dir = data_path / \"Output_tutorial\" # set output directory\n", + "path_hist_mean = output_dir / 'Hist_mean' # set output directory for hist_mean\n", + "\n", + "# # only required if hazard set has not yet been initiated above:\n", + "#---------------------------------------------------------------\n", + "# generate_full_hazard_set(input_dir=input_haz_dir, output_dir=output_dir)\n", + "# \"\"\"compute historical mean yield for all runs available in input_haz directory\"\"\"\n", + "#---------------------------------------------------------------\n", + "\n", + "filelist_exp, exposures = init_full_exposure_set(input_dir=input_exp_dir, hist_mean_dir=path_hist_mean, \\\n", + " output_dir=output_dir, return_data=True)\n", + "\"\"\"create exposures for all hist_mean files available in path_hist_mean directory\"\"\"\n", + "print(\"\\nExposure files created:\\n\")\n", + "print(filelist_exp)\n", + "\n", + "norm=colors.LogNorm(vmin=1e2, vmax=3e5)\n", + "exposures[0].plot_scatter(norm=norm, s=20, pop_name=False)\n", + "exposures[1].plot_scatter(norm=norm, s=20, pop_name=False)\n", + "\"\"\"For each crop type, an exposure with full irrigation crop production and one with no irrigation is created.\"\"\"\n", + "\n", + "crop_list, countries_list, ratio_list, exp_firr_norm, exp_noirr_norm, fao_cp_list, exp_tot_cp_list = \\\n", + " normalize_several_exp(input_dir=input_exp_dir, output_dir=output_dir,\n", + " yearrange=(2008, 2018),\n", + " unit='t', returns='all')\n", + "\"\"\"normalize crop production per country using FAO data\"\"\"\n", + "\n", + "exp_noirr_norm[0].plot_scatter(norm=norm, s=20, pop_name=False)\n", + "exp_firr_norm[0].plot_scatter(norm=norm, s=20, pop_name=False)\n", + "\n", + "fig_scatter = plt.figure(facecolor='w', figsize=(7, 7))\n", + "ax_s = fig_scatter.add_subplot(1,1,1)\n", + "ax_s.scatter(exp_tot_cp_list, fao_cp_list)\n", + "ax_s.plot([0,2e8], [0,2e8], alpha=.5)\n", + "index_max_fao = np.where(fao_cp_list[0]==np.nanmax(fao_cp_list[0]))[0][0]\n", + "index_max_isimip = np.where(exp_tot_cp_list[0]==np.nanmax(exp_tot_cp_list[0]))[0][0]\n", + "# print(ratio_list[0])\n", + "\n", + "ax_s.text(exp_tot_cp_list[0][index_max_fao], fao_cp_list[0][index_max_fao],\n", + " iso_cntry.get(countries_list[0][index_max_fao]).name)\n", + "ax_s.text(exp_tot_cp_list[0][index_max_isimip], fao_cp_list[0][index_max_isimip],\n", + " iso_cntry.get(countries_list[0][index_max_isimip]).name)\n", + "ax_s.set_title('Rice: total crop production (CP) per country')\n", + "ax_s.set_xlabel('CP before normalization [t/y]')\n", + "ax_s.set_ylabel('FAO statistics (used for normalization) [t/y]')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Impact", + "Crop" + ] + }, + "source": [ + "## Impact: Deviation in yearly crop production \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "Impact", + "singleFiles", + "end-to-end", + "demo" + ] + }, + "source": [ + "### Computing impact from single file (end-to-end; demo data):\n", + "\n", + "Relative crop yield and historical crop production is combined to calculate the deviation of crop production from the historical mean production.\n", + "\n", + "The impact function (*IFRelativeCropyield*) corresponds to a simple multiplication of hazard intensity (relative yield) with exposure (baseline production). As a result, the impact represents the deviation of yearly production from the exposure value.\n", + "\n", + "There are positive and negative impact values. Positive values represent a crop production surplus. Negative values represent a deficit." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "Impact" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 11:12:44,461 - climada - DEBUG - Loading default config file: /Users/eberenzs/Documents/Projects/climada_python/climada/conf/defaults.conf\n", + "2020-08-07 11:13:00,294 - climada.util.coordinates - INFO - Reading /Users/eberenzs/Documents/Projects/climada_python/data/demo/lpjml_ipsl-cm5a-lr_ewembi_historical_2005soc_co2_yield-whe-noirr_annual_FR_DE_DEMO_1861_2005.nc\n", + "2020-08-07 11:13:01,155 - climada.util.coordinates - INFO - Setting region_id 1092 points.\n", + "2020-08-07 11:13:02,594 - climada.entity.exposures.base - INFO - Setting if_ to default impact functions ids 1.\n", + "2020-08-07 11:13:02,596 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-07 11:13:02,596 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-07 11:13:02,597 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-07 11:13:02,599 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-07 11:13:02,601 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-08-07 11:13:02,707 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n", + "2020-08-07 11:13:02,708 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2020-08-07 11:13:02,708 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2020-08-07 11:13:02,709 - climada.entity.exposures.base - INFO - cover not set.\n", + "2020-08-07 11:13:02,709 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2020-08-07 11:13:02,711 - climada.entity.exposures.base - INFO - geometry not set.\n", + "2020-08-07 11:13:02,712 - climada.entity.exposures.base - INFO - Matching 1092 exposures with 1092 centroids.\n", + "2020-08-07 11:13:02,731 - climada.entity.impact_funcs.base - WARNING - For intensity = 0, mdd != 0 or paa != 0. Consider shifting the origin of the intensity scale. In impact.calc the impact is always null at intensity = 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/xarray/core/nanops.py:140: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis=axis, dtype=dtype)\n", + "/Users/eberenzs/Documents/Projects/climada_python/climada/entity/exposures/crop_production.py:446: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " (fao['year'] >= yearrange[0]) &\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/opt/anaconda3/envs/climada_env/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-08-07 11:13:02,788 - climada.engine.impact - INFO - Exposures matching centroids found in centr_RC\n", + "2020-08-07 11:13:02,791 - climada.engine.impact - INFO - Calculating damage for 204 assets (>0) and 5 events.\n", + "2020-08-07 11:13:02,793 - climada.engine.impact - INFO - Missing exposures impact functions for hazard if_RC. Using impact functions in if_.\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$\\\\Delta$ Crop Production [USD / y]')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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z5rFhwwZEhMuXLyMiDBw4kPXr17N161Yee+wxAC5evMidd95J+/bt3YhvjElCs2ZtZdCgIixcmI5ChXrRo0dh2rWr4nas6/JaT0FEcuL59B8E5AeyAHEt7h29Il9cvYJrVutT1RBVDVbV4ABvfMfbSyZPnkyLFi3YvXs3u3btYu/evQQFBbF48WImTJjAu+++y65du9i1axf79+9n37597N692+3YxpibtHv3ScqV60TduiVZsWIiI0bAjh3PpeiCAN4dPnoU2KmqR1T1EjAVqAzkcIaTAAKB/c7P4UBBAGd/duC4F/MlqwkTJtCgQYN/bWvUqBHfffcdoaGh1+xr0KBBnGcoGWNStsjIKF544WuCgoqxdu1wSpV6mdWrH+OVVyBdOrfTJcybax/tAe4Xkcx4ho9qACuA+cDTQCjQEpjhtJ/p3F7m7J+nqXRd7zNnzlyzLa6L7HTs2PG6xxgyZEhSRjLGJIO//oJatRpz4sQUbr21Cl98MYdmzcq7HeuGeK2noKp/4pkwXgWsdx4rBOgBdBWRbXjmDEY5dxkF5Ha2dwV6eiubMcYkpY0bD9Oy5XkqVQLVF2jb9ltOnlyU6goCeHmVVFV9B3jnqs07gIpxtD0PPOPNPMYYk5TOn4/k2Wc/Y9q0d/Dz6063br14++0nyJbN7WQ3z5bONsaYmzBs2AJ69nyNCxc2kDv344wd25A6ddxO9d/ZMhfGGHMD9u6F0qX70KVLdS5fPkPPntM4fPhn6tQp4Xa0JGE9BWOMSYSTJ8/z0UcXGDIkO5GRdahWTZgy5Q1y5brF7WhJynoKxhiTgN69fyQgoDR9+3ajVi3YvPk+5s/vneYKAlhR8AoRoXnz5jG3IyMjCQgIoG7dugCMHj2agIAAypcvT9GiRalZsyZLly6Nad+qVSuCgoIoV64c99xzD7/99luyPwdjDPz661by5n2C999/Ej+/DPTr15gpU6BwYbeTeY8VBS/IkiULGzZs4Ny5cwD8+uuvFChQ4F9tmjRpwurVq9m6dSs9e/akYcOGhIWFxewfNGgQa9asYdiwYbzyyivJmt8YX3fmDNSvP5HHHy/NkSOLqFfvI06cWEfPno+5Hc3rrCh4Se3atZk1axZwZQXU66levTpt2rQhJCTkmn0PPPAA+/bt81pOY8wVUVHKqFGnKFECZsyoTJEizVm7dgszZnQlc+YMbsdLFml+orlatWrXbGvcuDHt2rUjIiKCOnGcQ9aqVStatWrF0aNHefrpp/+1L65vJseladOmvPfee9StW5d169bRunVrFi1adN32FSpU4Isvvrhm+88//0z9+vXjuIcxJilNnryOl156jVOnMlC+/K98/31BKlf+yu1YyS7NFwW3lC1bll27djFhwoQ4C8/Vrl7R44033qB79+4cPnyYP/74w1sxjfF5O3ee4KmnerN+/eeI5OS55z7km2+UDBlS5cr9/1maLwrxfbLPnDlzvPvz5MmT6J5BXOrVq0e3bt1YsGABx44di7ft6tWrKVmyZMztQYMG0bBhQz755BNatmzJypUrbzqHMeZaly/D22//Rf/+T6B6nNKlX2X69Pe4665cbkdzlc0peFHr1q3p3bs3ZcqUibfdwoULCQkJ4eWXX/7Xdj8/Pzp16kRUVBRz5szxZlRjfMr8+WepVAn69StFrlzVmDhxFevXf+rzBQF8oKfgpsDAQDp16hTnvokTJ7J48WIiIiIICgpiypQp/+opRBMR3nrrLQYOHEjNmjW9HdmYNG39+oM0aNCT7dv/JF++tYwfn5VmzSaROq/x6B2SSlenBiA4OFhXrFjxr21hYWFxvrmmVb72fI25GRERl3j22U+ZMeNd4ByVKnVl5sx3yJs37X35LDFEZKWqBse1z3oKxpg0LTQ0nFatanLhwt/kyVOLb7/9mFq1irkdK8WyOQVjTJq0ZcsFnn4amjXLR7p0pejVawaHDs22gpCANNlTUFXEBwYJU/PQnzHecuLEORo1GsSCBV+RKdNq3n8/N926TcLf3+1kqUOa6yn4+/tz7NixNP+GqaocO3YMf/tNNwbwfBu5V68Z5M1bivnz36FAgftZtOgSb72FFYQbkOZ6CoGBgYSHh3PkyBG3o3idv78/gYGBbscwxnXr1p3nkUfqc+zYHDJlupv+/X/j9dcfcTtWqpTmikKGDBkICgpyO4YxJhmcOBFJv37pGTbMH8hH/fpDGT++vc+sU+QNaW74yBiT9kVFKa++Oo6AgCIMGrSZ55+HvXu/Ydq0zlYQ/iMrCsaYVCU0dDU5c1Zl5MjmZMoUwLhxF/n6a7jtNreTpQ1pbvjIGJM2HT2qPPJIZ9av/xSRXLRo8SWjRrUmfXr7bJuU7NU0xqRokZHKyJFQvLiwYUMmypZtz/btWxgz5iUrCF5gr6gxJsUaMWIJ2bLdy6uvzqdMGVizZgBr135CUFBOt6OlWVYUjDEpzpo1B7jrrha0a/cgFy8eoWfPC8yfD2XLpv0vpbrN5hSMMSnGxYvQrNlIpk7tDlygcuVeTJvWi7x5s7gdzWdYT8EYkyLMmaOUKaNMnXqJvHkfYu7cjSxZ8oEVhGRmRcEY46rff99J/vwNqFXrG6KihB9+6MChQz9So0YRt6P5JBs+Msa44tixCBo1GsjChQMAP+rVe4zvv4dMmWzewE3WUzDGJCtVePvtOdx2W0kWLuxDoUL1+fPPzcyY0Y5MmdxOZ7xaFEQkh4hMFpFNIhImIg+ISC4R+VVEtjp/53Taioh8IiLbRGSdiFTwZjZjTPL7+2/l8cehb99LZMiQnaFDF7B79wQqVrSFHVMKb/cUPgZ+VtUSwD1AGNAT+E1ViwK/ObcBagNFnT9tgBFezmaMSSbh4acJDn6dMmXeZ8UK+OSTupw8uZrOnR92O5q5iteKgohkAx4CRgGo6kVVPQk8BYxxmo0B6js/PwWMVY8/gBwiks9b+Ywx3hcZGUWbNmO4445irFw5lBIljrBlC7z2GmTKlM7teCYO3uwp3AkcAb4RkdUi8pWIZAFuU9UDAM7feZ32BYC9se4f7mwzxqRCkyZtJGfOB/nyy1bcckthRo/+k40bhxMQ4HYyEx9vFoX0QAVghKqWB85yZagoLnGdcnDN5dNEpI2IrBCRFb5wIR1jUpujR6FtW2jc+DIREbt54YWvOXlyKS1b3ud2NJMI3iwK4UC4qv7p3J6Mp0gcih4Wcv4+HKt9wVj3DwT2X31QVQ1R1WBVDQ6wjxzGpBjnz0fSpMnnBAZ2YNQo6Ny5LAcP7uTrr1+whetSEa/9S6nqQWCviBR3NtUA/gZmAi2dbS2BGc7PM4EWzllI9wOnooeZjDEp26efLiJnzmC+/749mTNvYvnyCwwdCgEBGd2OZm6Qt7+89howXkQyAjuAF/AUou9F5EVgD/CM03Y2UAfYBkQ4bY0xKdiaNYeoX78ru3d/R7p0BenadRKDBjXCz8++gJZaebUoqOoaIDiOXTXiaKtAe2/mMcYkjQsXYNgw6NNHOXfuV6pWfZupU3uSJ09mt6OZ/8gG+owxN6Rv358JCGhJz57KY4/dzoYNu/j99/esIKQRtvaRMSZRFizYQbNmXTh4cCYZMhRj3LgDPPdcfsCKQVpiPQVjTLyOHTtH1apvU716KQ4e/I3atQdw/Ph6pyCYtMaKgjEmTqowaRLccw8sXjyewoUbsWLFZmbP7k7WrHZWUVplRcEYc40ZMzaSP38LGjc+R548tzB79mp27hzPvffaIgNpnRUFY0yMPXtOUaFCF+rXv4dDh36ke/cNrFwJtWtndzuaSSY20WyMiVm4bvTonqgeoWTJNkyb1pfixfO4Hc0kM+spGOPjli+HKlXgm2++JGvWIowbt4K//x5pBcFHWVEwxkeFhR2hVKn2VKx4iN27/fjssx84eXIxzz1n17fyZfEOH4mIP1AXqArkB84BG4BZqrrR+/GMMUnt/PlInn/+c6ZM6Q2cpW7dhxk/vjHZsuV2O5pJAa7bUxCRd4ElwAPAn8AXwPdAJNDfuZRm2eQIaYxJGh9/vJCcOSswZUoncuWqyA8/rOOHHxqTLZvbyUxKEV9PYbmqvnudfUNEJC9QKOkjGWOSWng4dOsGEyeGkC7dabp3n0q/fvVt4TpzjesWBVWddfU2Zzgpo6qeVtXDXLkWgjEmBTp9+gLPPDOE33+vC5She/fhdO/uT+7ctjSFiVuiT0kVkZeA5oCfiCxS1V7ei2WM+a/efXcWH37YmUuXtlGy5CVmzy5D4cK53I5lUrj45hSevGrTo6r6sKpWBZ7wbixjzM367bdt3HZbXfr0qYtIevr1+4W//+5N4cJuJzOpQXynpN4jIjNE5B7n9joRGS8i4wA788iYFObsWejVCx5/fDSHD/9O3bqDOXZsLT17PuZ2NJOKxDen0FdEbgfeExGA3kBWILOqrkumfMaYBERFKV26TOS77wI4erQGzZq9Sffu7SlXLp/b0UwqlNCcwlmgM1AUCAGWA4O8HcoYkzhTpqzjxRc7curUQnLkeIYlS2pQuXIWIIvb0UwqFd+cQl9gFvAbUF1V6wFrgVki0jyZ8hlj4rBz5wnuuacjTz9dntOn1/PssyM5dGgClSu7ncykdvHNKdRV1YeAykALAFWdCdQE7BQGY1wQFQVffQVly05n3brPKF36FbZu3cr48W3JmDGd2/FMGhDf8NEGEfkWuAVYGL1RVSOBj70dzBjzb6NG/Um/fuFs396IKlVa0qVLJRo1KuV2LJPGxDfR/LyIlAEuqeqmZMxkjIllw4ZDNGjQk23bRpM+/d2MHduA55/3Q8QKgkl68c0pPKiq669XEEQkm4iU9l40Y3xbRMQlGjQYRpkyxdi2bTwVK3Zn9+5lNG/uh9jqFMZL4hs+aiQiA4GfgZXAEcAfKAJUB+4AXvd6QmN80Lx58OKLf7BrVxfy5KnF2LHDqF27uNuxjA+Ib/ioi4jkBJ4GngHy4Vk6Owz4QlUXJ09EY3zHsmV76Nx5EX/99RxBQVUZNOhPuna9zxauM8km3u8pqOoJ4EvnjzHGS06ePE+jRoOYN68fkIFevery9tvZ8fev6HY042PsymvGuCgqSvnf/2YQEFCKefN6U6BAHRYvXscHH2TH39/tdMYXWVEwxiVbtsAjj+zlww+fwc/Pn4ED5xIePpkqVe5wO5rxYQkWBRHJlJhtxpjE2b//H+rW/ZbSpWH16kJ06PAbJ06s5Y03argdzZhE9RSWJXKbMSYeUVFK+/bfUbBgCWbNakGdOhvZsgWGD69K5swZ3I5nDBDPRLOzQmoB4BYRKQ9En/6QDbDLNhlzA77/fi0vv/wap08vInPmYD7+eAovvXS327GMuUZ8Zx/VBFoBgcBHXCkKp4FEX3VNRNIBK4B9qlpXRIKAUDzrJ60CmqvqRWdIaixwL3AMaKKqu27o2RiTwhw/Dm++eZ6QkMcQUVq0+JJRo1qTPr1N55mU6bq/mao6RlWrA61U9RFVre78eUpVp97AY3TC892GaAOAoapaFDgBvOhsfxE4oapFgKFOO2NSpYsXL/PSS6EULXqZr77yp2HDKWzfvoUxY16ygmBStMT8dt4rIjmib4hITmdZ7QSJSCCeS3d+5dwW4BFgstNkDFDf+fkp5zbO/hpOe2NSlZCQZeTIUZFRo5px++0zWb0apkypSlBQTrejGZOgxBSF2qp6MvqG84W2Ook8/jCgOxDl3M4NnHRWWgUIxzNvgfP3XucxIoFTTvt/EZE2IrJCRFYcOXIkkTGM8b516w5y110tadu2MhcuHOS11yawfn19ypZ1O5kxiZeYopAu9imoInILkOApqSJSFzisqitjb46jqSZi35UNqiGqGqyqwQEBAQnFMMbrLl2CwYOV8uWfZMeOUB544E327dvMJ580teUpTKqT0OU4AcYBv4nIN3jepFtzZZgnPlWAeiJSB89Cetnw9BxyiEh6pzcQCOx32ocDBYFwEUkPZAeO38iTMSa5DRz4G6NGVWLLlqxUrvwZ776bk8ceK+p2LGNuWoI9BVUdCHwAlATuBt53tiV0vzdVNVBVCwNNgXmq+hwwH88iewAtgRnOzzOd2zj756nqNT0FY1KCxYt3UaBAQ3r0eJSjR4fzww+wZOdB6CoAABVtSURBVElFKwgm1UtMTwFV/Qn4KYkeswcQ6kxWrwZGOdtHAd+KyDY8PYSmSfR4xiSZ48fP0ajRQBYs6A/48eijfZk0qQs5ciR4V2NShQSLgojcDwzH01PICKQDzqpqtsQ+iKouABY4P+8Arln6UVXP41mi25gURxWmT4fmzdty9uy3FCzYmEmTBlOpUkG3oxmTpBIz0fwp0AzYiud6zS/hKRLG+ITZszdRrdo+GjaEfPne5KOP5rFnz0QrCCZNSuzw0TYRSaeql4FvRGSpl3MZ47rw8NM0aPAeK1Z8TIYMz/Pxx9/Qrl1J0qcv6XY0Y7wmMUUhQkQyAmucy3MeALJ4N5Yx7rl8OYp27cbx1Vc9iIo6RLFirZk69UPutqWKjA9IzPBRc6ddB+AsntNGG3kzlDFuWbUK7rxzACEhLbnlloJ8880fbN78FXffndftaMYkiwR7Cqq62+kpFAamAptV9aK3gxmTnLZuPUbv3ieYOLEIuXK9yAsv3E5ISEtbp8j4nMRcZOcJYDvwCZ5J520iUtvbwYxJDhcvXqZZsxEUL16M0NAX6NgRtm3Ly9dfv2AFwfikxMwpfARUV9VtACJyFzCLpPvegjGu+PzzxXTr9hrnzq0hR47qfP31JzRo4HYqY9yVmKJwOLogOHYAh72Uxxiv278fmjWbzu+/NyBdukA6d57IRx89Y+sUGUPiisJGEZkNfI9n7aNngOUi0hDgBq+tYIxrzpy5SJ8+Oxg5sgQXLtSievX+hIZ2IG9eO5nOmGiJGTT1Bw4BDwPVgCN4rpr2JFDXa8mMSUIffDCHXLnKMHhwTR566AJhYf7Mm9fDCoIxV0nM2UcvJEcQY7xh4cIdNG3alYMHZ5AhQ1H69BlJ794JrvxujM9KzNpHQcBreE5JjWmvqvW8F8uY/yYiArp0WU9IyH1AemrV6s/EiZ3Jls0KgjHxScycwnQ8K5j+wJUrqBmTIkVFKZ9/vp1Bg4qwZ09pypTpxddfv0hwcIGE72yMSVRROK+qn3g9iTH/0cyZf/PCCx05fvxPSpbcysKFt/PQQ73djmVMqpKYovCxiLwD/AJciN6oqqu8lsqYG7BnzykaNOjDqlXDEbmVZ57pz9ixefD3dzuZMalPYopCGTzrHz3CleEjdW4b45qoKBg58gSvvVaKqKhDlCjxMtOnf0Dx4nncjmZMqpWYotAAuNPWOzIpyezZ4bz/fiB//JGTQoU68sEHj/P88/e6HcuYVC8x31NYC9jFBk2KEBZ2hJIl2/DEE0Fs2bKGMWNg5843rSAYk0QS01O4DdgkIsv595yCnZJqks3585E0bz6SKVPeRvUM997bkalTgyhUyO1kxqQtiSkK73g9hTHxWLAgijp1qnDu3F/kyvUoY8Z8Qt26dvUzY7whMd9oXpgcQYy52rp1R+jXLw+hoX7kzNmc9u27M2BAQ1u4zhgvum5REJF/8JxldM0uQFU1m9dSGZ92+vQFGjcewpw5fUmfPpTevZ+kR48OZM7sdjJj0r7rFgVVvTU5gxgD0KfPbD74oBOXLm0jX776hIaW5qGH3E5ljO9IzJyCMV63fTtUr/4Se/eOImPG4nz44RzefPNxt2MZ43OsKBhXHT58liFDMjJ0aAZEavDEEyUIDe1I1qwZ3Y5mjE+yi9AaV0RFKZ07f0/+/CUYMOBzGjeGHTua8eOP3awgGOMiKwom2U2btoHcuWvw8cdNyJgxD59/Hsy330L+/G4nM8bY8JFJNidPQr16Q1m06A1EstOs2QhGj36ZjBnTuR3NGOOwnoLxusjIKEaOPE+xYrBoUQVKlXqZzZu38N13r1hBMCaFsaJgvOqbb/4iR44HePXVNylWDFatepiNG0dQtGhut6MZY+LgtaIgIgVFZL6IhInIRhHp5GzPJSK/ishW5++cznYRkU9EZJuIrBORCt7KZrxv48bDFCv2Iq1bV+LcuT28+up9LFoE5cu7ncwYEx9v9hQigddVtSRwP9BeREoBPYHfVLUo8JtzG6A2UNT50wYY4cVsxksiI+Hll3+gdOlibN06luDgbuzevZnPP38WsdUpjEnxvFYUVPVA9NXZVPUfIAwoADwFjHGajQHqOz8/BYxVjz+AHCKSz1v5TNL75ZeLlCsHX31Vkjx5qjJr1nqWLx9EYKCtiGJMapEscwoiUhgoD/wJ3KaqB8BTOIC8TrMCwN5Ydwt3tpkU7o8/9lKwYBNq1nyGs2dh2rQiHD78A3XqlHA7mjHmBnm9KIhIVmAK0FlVT8fXNI5t1yzIJyJtRGSFiKw4cuRIUsU0N+HkyfM89tgHPPBACcLDZ1KtWgU2bIiifn1sqMiYVMqrRUFEMuApCONVdaqz+VD0sJDz92FnezhQMNbdA4H9Vx9TVUNUNVhVgwMCArwX3lyXKnzyyVoCAu5m7ty3yJ+/FosWhTF//jtkyWIntBmTmnnz7CMBRgFhqjok1q6ZQEvn55bAjFjbWzhnId0PnIoeZjIpR1jYZZ54Ajp1KkiGDAUYMOBX9u2bwoMPFnY7mjEmCXjzG81VgObAehFZ42zrBfQHvheRF4E9wDPOvtlAHWAbEAG84MVs5gYdPHiG+vX78tdf88mSZSkffZSL1177nQwZ3E5mjElKXisKqrqYuOcJAGrE0V6B9t7KY25OVJTSqVMon3/+BlFR+7jrrpb8/HMERYrY5TaMSYtsANhc17x5B8mVqxqffvos/v6388UXS9m2bbQVBGPSMCsK5hrHjikdOsCjj+bm7Fl4/vkQTpz4kzZtHnA7mjHGy2yVVBPj4sXLtG49itDQT4mKWkK7drfSp88Ccue280uN8RXWUzAAhIQsI2fOSowf35YsWXIwa9ZxPv0UKwjG+BgrCj5u167zFCnSirZtK3P+/AHatRvPiRMLqV37DrejGWNcYEXBR128qAwZAmXKZGLHjmNUqtSDffs289lnz+LnZ70DY3yVFQUfNHDgXLJnv4/XX99N1apCWNhM/vijP7ffntXtaMYYl1lR8CFLluymQIFG9OjxGJGRJxg06CCzZkHx4tYzMMZ42NlHPiAiQnnyyQ+ZN68vINSo8T6TJ3cjRw5/t6MZY1IY6ymkYaowfTrcfbcwb94eAgOfZOnSTcyd+5YVBGNMnKwopFE//bSZvHnr0KDBn2TJAr/88hl7937PAw8UcjuaMSYFs6KQxuzf/w8VK3anTp0yHD26hJYtd7N6NTz2mI0UGmMSZkUhjVCF1177noIFi7N8+SCKFn2e9eu3MHp0Y1vJ1BiTaKn74+PmzVCtmtspXPfPGdi2FS6c3kMJvzMML1aeR/LtgA5N3I5mjEllUndR8HEHIy7RdeMuikZk5/EMeXmraCAF8hUknV0L0xhzk1J3USheHBYscDtFsrt48TItW37JxIn/Q/UU99/fjs6z/0fOnG4nM8akCvF8cLQ5hVQmJORPcuS4j9DQV8mWrSyTJ69m2TIrCMaYpGFFIZU4cACaN4e2bXdz8eIROnYM5fjxeTRqVMbtaMaYNCR1Dx/5gDNnLtKkycfMnZsJ6Mibbz5Dly51CQjI7HY0Y0waZD2FFOyDD+aQO3dZZs/uTu7cS9mwQfnwQ7GCYIzxGisKKdDixbvJn78Bb71VC9XLvPPOj+zfH0rRonZWkTHGu2z4KAWJiIABA6B//4NcvDiXxx//kEmTupItWya3oxljfIQVhRQgKkrp0WMaISHrOH36XZo0qcRbb+2ldOkcbkczxvgYKwou++GHMFq16sjx43Px9y/HnDk9efxxf8AKgjEm+dmcgkvCw08THPw69eqV5cSJFTz99HBOnFjuFARjjHGHFYVkFhUFY8ZA+fInWLnyC4oXb8XGjVuYNKkD/v7WcTPGuMuKQjIaP34VgYFv0KqVctddd/DzzzvYtOlLSpYMcDuaMcYAVhSSxebNRylZsi3PPx/MwYNjGTx4H0uXQs2aed2OZowx/2JFwYvOn4+kcePPKFmyGJs2jaJ8+c7s2rWF118PxM9eeWNMCmRvTV6yaBEEB59j0qS+5MhRnunT17Jq1RAKFcrudjRjjLkuKwpJbMWKfZQs+QYPPXSJ06dvZcSIvzh6dC5PPXW329GMMSZBKaooiEgtEdksIttEpKfbeW7E6dMXqF17APfdV5xNm4bTsuVywsLglVcK4udny1MYY1KHFFMURCQd8BlQGygFNBORUu6mSpz33/+ZPHnK8PPPPbn99keZP/9vRo+uTJYsbiczxpgbk2KKAlAR2KaqO1T1IhAKPOVypnht3w5PPhlF7949AeG9937iwIHpVKt2p9vRjDHmpqSkb0sVAPbGuh0OVLq6kYi0AdoAFCpUKHmSXeXo0Qiefvojli3rQIYMOenRYwb/+18+br01oyt5jDEmqaSknkJcA+96zQbVEFUNVtXggIDk/dJXVJTSteskbr+9BAsX9qZChZls3gz9+99hBcEYkyakpKIQDhSMdTsQ2O9SlmvMmLGR3LlrMHRoYzJkyMXw4b+zbFlLChRwO5kxxiSdlFQUlgNFRSRIRDICTYGZLmfi5Eno3Bnq13+bU6fW0KTJZ5w8uZIOHaq6Hc0YY5JciplTUNVIEekAzAHSAV+r6ka38kRGRtGmzRhmzHiQEyeK8txzw3n77UwUL57HrUjGGON1KaYoAKjqbGC22znGjFlO+/avcfbsn+TP353lywdw7702TmSMSftS0vCR6zZuPEzx4i/RqlUlzp3bRZs2Y9mzpz/33ut2MmOMSR5WFIDISPjkE6hQYTBbtozh3nu7snv3Fr74ojnp0tm3kY0xvsPni8KwYQsoXvwPOnWCypV78cMP61ixYjCBgdncjmaMMckuRc0pJKfly8Np1Kgbe/dO5JZb6jJ16g/Ur58DEbs2sjHGd/lcT+H06QvUrNmPihWLs3fvDB5++F327v2eBg1AbKTIGOPjfKoo/PgjFCnyLb/80ov8+WuxaFEYCxa8Q+7ct7gdzRhjUgSfKApz526jUqW5PPkk5MrVisGDF7Bv3xQefLCw29GMMSZFSdNzCocOnaVBgw9YtuwjRAoxYMBmOndOT8aMD7sdzRhjUqQ02VOIilI6dgwlf/7iLFvWjzvvbMKqVb/TvbsfGW3dOmOMua40VxTWrYMKFRYxfHgzMmXKy8iRS9i+fSzlyuVzO5oxxqR4aaYo7Nx5gnr1fqJ8edizpyrt2s3k5MnltG1b2e1oxhiTaqT6OYXIyChefPFrvv32TVTP0br1XgYNykmuXE+6Hc0YY1KdVF0Ujh49S/bslYiIWEG2bA8SEjKcJk1yuh3LGGNSrVRdFHbv3oSfXz5efXUcn376LH5+9u0zY4z5L1J1UciR4y42blxN/vy3uh3FGGPShFQ90XzXXTmsIBhjTBJK1UXBGGNM0rKiYIwxJoYVBWOMMTGsKBhjjIlhRcEYY0wMKwrGGGNiWFEwxhgTw4qCMcaYGKKqbme4aSJyBNjtdg4gD3DU7RAphL0WHvY6XGGvxRUp5bW4Q1UD4tqRqotCSiEiK1Q12O0cKYG9Fh72Olxhr8UVqeG1sOEjY4wxMawoGGOMiWFFIWmEuB0gBbHXwsNehyvstbgixb8WNqdgjDEmhvUUjDHGxLCiYIwxJoYVhf9ARGqJyGYR2SYiPd3O4xYRKSgi80UkTEQ2ikgntzO5TUTSichqEfnR7SxuEpEcIjJZRDY5vx8PuJ3JLSLSxfn/sUFEJoiIv9uZ4mJF4SaJSDrgM6A2UApoJiKl3E3lmkjgdVUtCdwPtPfh1yJaJyDM7RApwMfAz6paArgHH31NRKQA0BEIVtXSQDqgqbup4mZF4eZVBLap6g5VvQiEAk+5nMkVqnpAVVc5P/+D5z9+AXdTuUdEAoEngK/czuImEckGPASMAlDVi6p60t1UrkoP3CIi6YHMwH6X88TJisLNKwDsjXU7HB9+I4wmIoWB8sCf7iZx1TCgOxDldhCX3QkcAb5xhtK+EpEsbodyg6ruAwYDe4ADwClV/cXdVHGzonDzJI5tPn1+r4hkBaYAnVX1tNt53CAidYHDqrrS7SwpQHqgAjBCVcsDZwGfnHsTkZx4RhKCgPxAFhF53t1UcbOicPPCgYKxbgeSQruDyUFEMuApCONVdarbeVxUBagnIrvwDCk+IiLj3I3kmnAgXFWje42T8RQJX/QosFNVj6jqJWAqUNnlTHGyonDzlgNFRSRIRDLimTSa6XImV4iI4Bk3DlPVIW7ncZOqvqmqgapaGM/vxDxVTZGfCL1NVQ8Ce0WkuLOpBvC3i5HctAe4X0QyO/9fapBCJ93Tux0gtVLVSBHpAMzBcybB16q60eVYbqkCNAfWi8gaZ1svVZ3tYiaTMrwGjHc+OO0AXnA5jytU9U8RmQyswnO23mpS6JIXtsyFMcaYGDZ8ZIwxJoYVBWOMMTGsKBhjjIlhRcEYY0wMKwrGGGNiWFEwPkVEziSiTWcRyZzEj5vfOSURESknInVu4hj1RaS383MHEfHJ0zuNd9kpqcaniMgZVc2aQJtdeFazPOqlDK2c43e4wfstBeqp6lGnaC1xlo8wJslYT8H4JBGpJiILYq31P148OuJZm2a+iMx32j4uIstEZJWITHLWeEJEdolIH2f7ehEp4Wx/WETWOH9Wi8itIlLYWUc/I/Ae0MTZ30REtopIgHNfP+f6HHmuylsMuBBdqFQ1AtglIhWT6zUzvsGKgvFl5YHOeK6HcSdQRVU/wbOGVXVVre68Ob8FPKqqFYAVQNdYxzjqbB8BdHO2dQPaq2o5oCpwLrqxs8x6b2CiqpZT1YnAOOA5p8mjwNo4eilV8HwbNrYVzvGNSTJWFIwv+0tVw1U1ClgDFI6jzf14isYSZwmPlsAdsfZHL/63Mtb9lwBDnF5HDlWNTCDH10AL5+fWwDdxtMmHZxnq2A7j6dUYk2Rs7SPjyy7E+vkycf9/EOBXVW2WwDFi7q+q/UVkFlAH+ENEHgXOXy+Equ4VkUMi8ghQiSu9htjOAdmv2uZPrF6IMUnBegrGXOsf4Fbn5z+AKiJSBMBZ5bJYfHcWkbtUdb2qDsAzxFMinuNH+wrPMNL3qno5jsOGAUWu2lYM2JDQkzHmRlhRMOZaIcBPIjJfVY8ArYAJIrIOT5G4+k3+ap2dSeW1eD7J/3TV/vlAqeiJZmfbTCArcQ8dAfwOlHeWXY5WBZib2CdlTGLYKanGpAAiEgwMVdXrThyLyMfAD6o6V0TKA11VtXmyhTQ+wXoKxrhMRHriuWrdmwk0/RDPBd8B8gBvezOX8U3WUzDGGBPDegrGGGNiWFEwxhgTw4qCMcaYGFYUjDHGxLCiYIwxJsb/Aa9KlgbZ32aJAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import colors\n", + "from climada.entity.exposures.crop_production import CropProduction\n", + "from climada.hazard.relative_cropyield import RelativeCropyield\n", + "from climada.util.constants import DATA_DIR\n", + "from climada.entity import ImpactFuncSet, IFRelativeCropyield\n", + "from climada.engine import Impact\n", + "\n", + "INPUT_DIR = os.path.join(DATA_DIR, 'demo')\n", + "FN_STR_DEMO = 'annual_FR_DE_DEMO'\n", + "FILENAME_LU = 'histsoc_landuse-15crops_annual_FR_DE_DEMO_2001_2005.nc'\n", + "FILENAME_MEAN = 'hist_mean_mai-firr_1976-2005_DE_FR.hdf5'\n", + "\n", + "yearrange_haz = (2001, 2005) # yearrange for hazard (demo data only available from 2001 to 2005)\n", + "yearrange_hist_mean = (2001, 2005) # yearrange for reference historical mean (demo data only available from 2001 to 2005)\n", + "haz = RelativeCropyield()\n", + "haz.set_from_single_run(input_dir=INPUT_DIR, yearrange=yearrange_haz, ag_model='lpjml',\n", + " cl_model='ipsl-cm5a-lr', scenario='historical', soc='2005soc',\n", + " co2='co2', crop='whe', irr='noirr', fn_str_var=FN_STR_DEMO)\n", + "hist_mean = haz.calc_mean(yearrange_hist_mean) # requires reference year range as input\n", + "\"\"\"compute historical mean yield per grid cell for reference (base line)\"\"\"\n", + "haz.set_rel_yield_to_int(hist_mean)\n", + "\n", + "exp = CropProduction()\n", + "exp.set_from_single_run(input_dir=INPUT_DIR, filename=FILENAME_LU, hist_mean=FILENAME_MEAN,\n", + " bbox=[-5, 42, 16, 55], yearrange=(2001, 2005),\n", + " scenario='flexible', unit='t', irr='firr')\n", + "exp.set_to_usd(INPUT_DIR) # convert exposure from t/y to USD/y using FAO statistics\n", + "exp.assign_centroids(haz, threshold=20) # assign exposure points to centroids\n", + "\"\"\"Init hazard and exposure\"\"\"\n", + "\n", + "if_cp = ImpactFuncSet()\n", + "if_def = IFRelativeCropyield()\n", + "if_def.set_relativeyield()\n", + "if_cp.append(if_def)\n", + "if_cp.check()\n", + "if_def.plot()\n", + "\"\"\"Import impact function\"\"\"\n", + "\n", + "impact_demo= Impact()\n", + "impact_demo.calc(exp, if_cp, haz) \n", + "\"\"\"Calculate impact\"\"\"\n", + "\n", + "fig_imp_demo = plt.figure(facecolor='w')\n", + "ax_imp_demo = fig_imp_demo.add_subplot(1,1,1)\n", + "ax_imp_demo.plot(impact_demo.event_id, impact_demo.at_event, 'g', lw=3)\n", + "ax_imp_demo.hlines(0, xmin=1, xmax=5, alpha=.85, ls=':')\n", + "ax_imp_demo.set_xticks(impact_demo.event_id)\n", + "ax_imp_demo.set_xticklabels(impact_demo.event_name)\n", + "ax_imp_demo.set_title('Impact: Maize production deviation (demo data)')\n", + "ax_imp_demo.set_xlabel('model year')\n", + "ax_imp_demo.set_ylabel('$\\Delta$ Crop Production [%s]' %(exp.value_unit))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements/env_climada.yml b/requirements/env_climada.yml index 7c46790fb4..c05587c94e 100644 --- a/requirements/env_climada.yml +++ b/requirements/env_climada.yml @@ -2,55 +2,43 @@ name: climada_env channels: - defaults dependencies: - - blas=1.0 - - cartopy=0.17.0 - - curl=7.65.3 - - cython=0.29.7 - - dask=1.2.2 - - dill=0.2.9 - - fiona=1.8.4 - - gdal=2.3.3 - - geos=3.7.1 - - geopandas=0.4.1 - - h5py=2.9.0 - - kealib=1.4.7 - - libtiff=4.0 - - matplotlib=3.1.0 - - mkl=2019.3 - - mkl_fft=1.0.12 - - mkl_random=1.0.2 - - netcdf4=1.4.2 - - numba=0.43.1 - - numpy=1.16.3 - - pandas=0.24.2 - - pandas-datareader=0.7.0 - - pip=19.1 - - proj4=5.2.0 - - pysocks=1.6.8 - - pyshp=2.1.0 - - pytables=3.5.1 - - python=3.7.3 - - rasterio=1.0.21 - - requests=2.21.0 - - scikit-learn=0.20.3 - - scipy=1.2.1 - - setuptools=41.0.1 - - shapely=1.6.4 - - tabulate=0.8.3 - - tqdm=4.31.1 - - xarray=0.12.1 + - bottleneck=1.3.2 + - cartopy=0.18.0 + - conda-forge::cfgrib=0.9.7.7 + - cython=0.29.21 + - dask=2.25.0 + - fiona=1.8.13.post1 + - gdal=3.0.4 + - geopandas=0.6.1 + - h5py=2.10.0 + - haversine=2.3.0 + - nbconvert=5.6.1 + - nbformat=5.0.7 + - netcdf4=1.5.4 + - numba=0.51.2 + - numpy=1.19.1 + - matplotlib=3.2.2 + - matplotlib-base=3.2.2 + - pandas=1.0.5 + - pandas-datareader=0.8.1 + - pillow=7.2.0 + - pint=0.15 + - pip + - conda-forge::proj=7.0.0 + - pytables=3.6.1 + - rasterio=1.1.5 + - scikit-learn=0.23.2 + - statsmodels=0.11.1 + - tabulate=0.8.7 + - tqdm=4.48.2 + - xarray=0.13.0 - xlrd=1.2.0 - - xlsxwriter=1.1.7 + - xlsxwriter=1.3.3 - pip: - - contextily==1.0rc2 - - elevation==1.0.6 - - haversine==2.1.1 - - iso3166==1.0 - - multiprocess==0.70.7 - - nbconvert==5.5.0 - - nbformat==4.4 + - contextily==1.0.0 + - iso3166==1.0.1 + - jupyter - overpy==0.4 - - pathos==0.2.3 - - pint==0.9 - - ppft==1.6.4.9 + - pathos==0.2.6 + - pybufrkit==0.2.17 - xmlrunner==1.7.7 diff --git a/requirements/env_developer.yml b/requirements/env_developer.yml index 0ba0d8c8f3..15abdb508f 100644 --- a/requirements/env_developer.yml +++ b/requirements/env_developer.yml @@ -2,8 +2,9 @@ name: climada_env channels: - defaults dependencies: + - astroid=2.3.3 - mccabe=0.6.1 - - pylint=2.3.1 + - pylint=2.4.4 - sphinx=2.0.1 - pip: - coverage==4.5.3 diff --git a/requirements/env_docs.yml b/requirements/env_docs.yml index e8c42e4522..9d9fc9f0f6 100644 --- a/requirements/env_docs.yml +++ b/requirements/env_docs.yml @@ -44,9 +44,12 @@ dependencies: - ipykernel=5.1.0 - ipython=7.3.0 - jupyter_client=5.2.4 + - mock + - pandoc + - sphinx + - sphinx_rtd_theme - pip: - contextily==1.0rc2 - - elevation==1.0.6 - haversine==2.1.1 - iso3166==1.0 - multiprocess==0.70.7 @@ -54,8 +57,10 @@ dependencies: - nbformat==4.4 - overpy==0.4 - pathos==0.2.3 + - pillow==6.2.2 - pint==0.9 - ppft==1.6.4.9 - xmlrunner==1.7.7 - nbsphinx==0.4.2 - - sphinx + - recommonmark + - readthedocs-sphinx-ext diff --git a/setup.py b/setup.py index 194ed6c710..6a2fc0a22c 100644 --- a/setup.py +++ b/setup.py @@ -54,51 +54,46 @@ def package_files(directory): packages=find_packages(where='.'), install_requires=[ - 'cartopy==0.17.0', # conda! - 'cloudpickle', # install_test - 'contextily==1.0rc2', - 'dask==1.2.2', - 'descartes', - #'earthengine_api==0.1.210', # ee, conda! - 'elevation==1.0.6', - 'fiona==1.8.4', - 'fsspec>=0.3.6', # < dask - 'gdal==2.3.3', # conda! - 'geopandas==0.4.1', - 'h5py==2.9.0', - 'haversine==2.1.1', - 'iso3166==1.0', - #'kealib==1.4.7', < fiona - 'matplotlib==3.1', # - 'mercantile', - #'mpl_toolkits', matplotlib - 'netCDF4==1.4.2', # conda! - 'numba==0.43.1', # conda! - 'numpy==1.16.3', # conda+ - 'overpy==0.4', - 'pandas==0.24.2', - 'pandas_datareader==0.7.0', - 'pathos==0.2.3', - 'pillow==7.0', # PIL - 'pint==0.9', - #'pylab', matplotlib - 'pyproj==1.9.6', # - 'pyshp', # shapefile - 'rasterio==1.0.21', - 'requests==2.21.0', # - 'rtree==0.8.3', # < geopandas.overlay - 'scikit-learn==0.20.3', # sklearn - 'scipy==1.2.1', # conda+ - 'shapely==1.6.4', # - 'six==1.13.0', # - 'tables', # < pandas (climada.entity.measures.test.test_base.TestApply) - 'tabulate==0.8.3', - 'toolz', # < dask - 'tqdm==4.31.1', - 'xarray==0.12.1', - 'xlrd', # < pandas - 'xlsxwriter==1.1.7', - 'xmlrunner==1.7.7', # ci tests + 'bottleneck>=1.3.2', + 'cfgrib>=0.9.7.7', + 'cython>=0.28.2', + 'dask==2.25.0', + 'fsspec>=0.3.3', # Needed for dask dataframes + 'fiona>=1.8.13.post1', + 'gdal==3.0.4', + 'geopandas>=0.5.0', + 'rtree==0.8.3', # Optional geopandas dependency + 'h5py>=2.10.0,<3.*', + 'haversine>=2.3.0', + 'nbconvert>=5.6.1', + 'nbformat>=4.4', + 'netcdf4>=1.5.4', + 'numba>=0.51.2', + 'numpy>=1.18.1', + 'matplotlib>=3.1.2', + # Pandas can be a bit of a problem: + #'pandas==1.0.5', + #'pandas-datareader==0.8.1', + 'pandas>=0.23.4', + 'pandas_datareader>=0.7.0', + 'pillow>=7.0.0', + 'pint>=0.15', + 'pyproj>=2.6.0', + 'tables>=3.6.1', + 'rasterio>=1.1.5', + 'scikit-learn>=0.23.2', + 'statsmodels>=0.11.1', + 'tabulate>=0.8.7', + 'tqdm>=4.48.2', + 'xarray==0.13.0', + 'xlrd>=1.1.0', + 'xlsxwriter>=1.1.7', + 'contextily>=1.0.0', + 'iso3166>=1.0.1', + 'overpy>=0.4', + 'pathos>=0.2.6', + 'pybufrkit>=0.2.17', + 'xmlrunner>=1.7.7', ], package_data={'': extra_files}, diff --git a/test_data_api.py b/test_data_api.py index bea64de052..9cb1f539ef 100644 --- a/test_data_api.py +++ b/test_data_api.py @@ -33,12 +33,13 @@ from climada.entity.exposures.nightlight import NOAA_SITE, NASA_SITE, BM_FILENAMES from climada.hazard.tc_tracks import IBTRACS_URL, IBTRACS_FILE +from climada.hazard.tc_tracks_forecast import TCForecast from climada.util.finance import WORLD_BANK_WEALTH_ACC, WORLD_BANK_INC_GRP from climada.util.files_handler import download_file, download_ftp from climada.util.constants import SOURCE_DIR class TestDataAvail(unittest.TestCase): - """Test availability of data used through APIs """ + """Test availability of data used through APIs""" def test_noaa_nl_pass(self): """Test NOAA nightlights used in BlackMarble.""" @@ -52,31 +53,37 @@ def test_nasa_nl_pass(self): os.remove(file_down) def test_wb_wealth_pass(self): - """ Test world bank's wealth data """ + """Test world bank's wealth data""" file_down = download_file(WORLD_BANK_WEALTH_ACC) os.remove(file_down) def test_wb_lev_hist_pass(self): - """ Test world bank's historical income group levels data """ + """Test world bank's historical income group levels data""" file_down = download_file(WORLD_BANK_INC_GRP) os.remove(file_down) # TODO: FILE_GWP_WEALTH2GDP_FACTORS def test_wb_api_pass(self): - """ Test World Bank API """ + """Test World Bank API""" wb.download(indicator='NY.GDP.MKTP.CD', country='CHE', start=1960, end=2030) def test_ne_api_pass(self): - """ Test Natural Earth API """ + """Test Natural Earth API""" url = 'http://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_countries.zip' file_down = download_file(url) os.remove(file_down) def test_ibtracs_pass(self): - download_ftp(os.path.join(IBTRACS_URL, IBTRACS_FILE), IBTRACS_FILE) + download_ftp("/".join([IBTRACS_URL, IBTRACS_FILE]), IBTRACS_FILE) os.remove(IBTRACS_FILE) + def test_ecmwf_tc_bufr(self): + """Test availability ECMWF essentials TC forecast.""" + fcast = TCForecast.fetch_bufr_ftp() + [f.close() for f in fcast] + # Execute Tests -TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDataAvail) -xmlrunner.XMLTestRunner(output=os.path.join(SOURCE_DIR, '../tests_xml')).run(TESTS) +if __name__ == '__main__': + TESTS = unittest.TestLoader().loadTestsFromTestCase(TestDataAvail) + xmlrunner.XMLTestRunner(output=os.path.join(SOURCE_DIR, '../tests_xml')).run(TESTS) diff --git a/test_notebooks.py b/test_notebooks.py new file mode 100644 index 0000000000..530428a57b --- /dev/null +++ b/test_notebooks.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python +# coding: utf-8 + +import os +import sys +import unittest +import nbformat + +from climada.util.constants import SOURCE_DIR + + +NOTEBOOK_DIR = os.path.abspath('doc/tutorial') +'''The path to the notebook directories.''' + +BOUND_TO_FAIL = '# Note: execution of this cell will fail' +'''Cells containing this line will not be executed in the test''' + + +class NotebookTest(unittest.TestCase): + '''Generic TestCase for testing the executability of notebooks + + Attributes + ---------- + wd : str + Absolute Path to the working directory, i.e., the directory of the notebook. + notebook : str + File name of the notebook. + + ''' + + def __init__(self, methodName, wd=None, notebook=None): + super(NotebookTest, self).__init__(methodName) + self.wd = wd + self.notebook = notebook + + def test_notebook(self): + '''Extracts code cells from the notebook and executes them one by one, using `exec`. + Magic lines and help/? calls are eliminated. + Cells containing `BOUND_TO_FAIL` are elided. + Cells doing multiprocessing are elided.''' + + # cd to the notebook directory + os.chdir(self.wd) + print(f'start testing {self.notebook}') + + # read the notebook into a string + with open(self.notebook, encoding='utf8') as nb: + content = nb.read() + + # parse the string with nbformat.reads + cells = nbformat.reads(content, 4)['cells'] + + namespace = dict() + for i, c in enumerate(cells): + + # skip markdown cells + if c['cell_type'] != 'code': continue + + # skip deliberately failing cells + if BOUND_TO_FAIL in c['source']: continue + + # skip multiprocessing cells + if any([ tabu in c['source'].split() for tabu in [ + 'pathos.pools', + 'mulitprocessing', + ]]): + print('\n'.join([ + f'\nskip multiprocessing cell {i} in {self.notebook}', + '+'+'-'*68+'+', + c['source'] + ])) + continue + + # remove non python lines and help calls which require user input + python_code = "\n".join([ln for ln in c['source'].split("\n") + if not ln.startswith('%') + and not ln.startswith('help(') + and not ln.startswith('ask_ok(') + and not ln.strip().endswith('?') + ]) + + # execute the python code + try: + exec(python_code, namespace) + + # report failures + except Exception as e: + failure = "\n".join([ + f"notebook {self.notebook} cell {i} failed with {e.__class__}", + f"{e}", + '+'+'-'*68+'+', + c['source'] + ]) + print(f'failed {self.notebook}') + self.fail(failure) + + print(f'succeeded {self.notebook}') + + +def main(): + # list notebooks in the NOTEBOOK_DIR + notebooks = [(NOTEBOOK_DIR, f) + for f in sorted(os.listdir(NOTEBOOK_DIR)) + if os.path.splitext(f)[1] == ('.ipynb')] + + # build a test suite with a test for each notebook + suite = unittest.TestSuite() + for (jd,nb) in notebooks: + suite.addTest(NotebookTest('test_notebook', jd, nb)) + + # run the tests depending on the first input argument: None or 'report'. + # write xml reports for 'report' + if sys.argv[1:]: + arg = sys.argv[1] + if arg == 'report': + import xmlrunner + output = os.path.join(SOURCE_DIR, '../tests_xml') + xmlrunner.XMLTestRunner(output=output).run(suite) + else: + jd, nb = os.path.split(arg) + unittest.TextTestRunner(verbosity=2).run(NotebookTest('test_notebook', jd, nb)) + # with no argument just run the test + else: + unittest.TextTestRunner(verbosity=2).run(suite) + + +if __name__ == '__main__': + sys.path.append(os.getcwd()) + main() diff --git a/tests_install.py b/tests_install.py index 3ce7e8f581..3f5bbcef54 100644 --- a/tests_install.py +++ b/tests_install.py @@ -9,7 +9,7 @@ from climada.util.constants import SOURCE_DIR def find_install_tests(): - """ select unit tests.""" + """select unit tests.""" suite = unittest.TestLoader().discover(start_dir='climada.engine.test', pattern='test_cost_benefit.py') suite.addTest(unittest.TestLoader().discover(start_dir='climada.engine.test', @@ -17,7 +17,7 @@ def find_install_tests(): return suite def main(): - """ parse input argument: None or 'report'. Execute accordingly.""" + """parse input argument: None or 'report'. Execute accordingly.""" if sys.argv[1:]: import xmlrunner arg = sys.argv[1] diff --git a/tests_runner.py b/tests_runner.py index 07e81ba890..bcc6f06835 100755 --- a/tests_runner.py +++ b/tests_runner.py @@ -6,7 +6,7 @@ from climada.util.constants import SOURCE_DIR def find_unit_tests(): - """ select unit tests.""" + """select unit tests.""" suite = unittest.TestLoader().discover('climada.entity.exposures.test') suite.addTest(unittest.TestLoader().discover('climada.entity.disc_rates.test')) suite.addTest(unittest.TestLoader().discover('climada.entity.impact_funcs.test')) @@ -14,26 +14,30 @@ def find_unit_tests(): suite.addTest(unittest.TestLoader().discover('climada.entity.test')) suite.addTest(unittest.TestLoader().discover('climada.hazard.test')) suite.addTest(unittest.TestLoader().discover('climada.hazard.centroids.test')) + suite.addTest(unittest.TestLoader().discover('climada.hazard.emulator.test')) suite.addTest(unittest.TestLoader().discover('climada.engine.test')) suite.addTest(unittest.TestLoader().discover('climada.util.test')) return suite def find_integ_tests(): - """ select integration tests.""" + """select integration tests.""" suite = unittest.TestLoader().discover('climada.test') return suite def main(): - """ parse input argument: None, 'unit' or 'integ'. Execute accordingly.""" + """parse input argument: None, 'unit' or 'integ'. Execute accordingly.""" if sys.argv[1:]: import xmlrunner arg = sys.argv[1] + output = os.path.join(SOURCE_DIR, '../tests_xml') if arg == 'unit': - output = os.path.join(SOURCE_DIR, '../tests_xml') xmlrunner.XMLTestRunner(output=output).run(find_unit_tests()) elif arg == 'integ': - output = os.path.join(SOURCE_DIR, '../tests_xml') xmlrunner.XMLTestRunner(output=output).run(find_integ_tests()) + else: + xmlrunner.XMLTestRunner(output=output).run( + unittest.TestLoader().discover(arg) + ) else: # execute without xml reports unittest.TextTestRunner(verbosity=2).run(find_unit_tests())