diff --git a/.gitignore b/.gitignore index 60e40c3..364f175 100644 --- a/.gitignore +++ b/.gitignore @@ -3,9 +3,13 @@ _targets #All model output */out/* +#Except modeling results from manuscript +!03_model/out/Run_Manuscript_Results/* -#All model inputs -*/in/* +#Certain model inputs +02_munge/in/* +03_model/in/COAWST_model_runs/raw/* +03_model/out/ #R files # History files @@ -67,3 +71,4 @@ __pycache__/ #dask dask-worker-space/ +.Rproj.user diff --git a/01_fetch/in/953860.zip b/01_fetch/in/953860.zip new file mode 100644 index 0000000..10c92b3 Binary files /dev/null and b/01_fetch/in/953860.zip differ diff --git a/01_fetch/in/river_mile_coords.csv b/01_fetch/in/river_mile_coords.csv deleted file mode 100644 index 56672ef..0000000 --- a/01_fetch/in/river_mile_coords.csv +++ /dev/null @@ -1,1371 +0,0 @@ -,dist_mile,model_i,model_j,model_lat,model_lon -0,-6.864468077786821,69,1,38.709598541117266,-74.89854692992532 -1,-6.73695053186866,69,2,38.71190500922644,-74.90100044817035 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@@ -1,52 +0,0 @@ -#choose where you want to write your data outputs: local or S3 -write_location: 'local' -#set name of AWS profile storing credentials for S3 -aws_profile: 'dev' -#set AWS bucket to read/write to -s3_bucket: 'drb-estuary-salinity' -#set up AWS client -s3_client: utils.prep_write_location(write_location, aws_profile) - -# select COAWST model run -# options: tide, tide_subtidal, tide_subtidal_bulk -coawst_run_name: '2016_tide_subtidal_bulk' - -# define COAWST model run source catalog -# for the runs specified above, includes base url for files and -# number of files needed to pull to get the complete time -# series for the run -coawst_run_catalog: { - '2019_tide': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07C/testcoawst/tide/model_output/ocean_deb_his_run07C_tsvar_testcoawst_tide_{file_num}.nc', - num_files: 13 - }, - '2019_tide_subtidal': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07C/testcoawst/tide_subtidal/model_output/ocean_deb_his_run07C_tsvar_testcoawst_{file_num}.nc', - num_files: 13 - }, - '2019_tide_subtidal_bulk': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07C/testcoawst/bulk/obcfac/model_output/ocean_deb_his_run07C_tsvar_testcoawst_bulk_{file_num}.nc', - num_files: 13 - }, - '2018_bulk': { - url: 'https://geoport.whoi.edu/thredds/catalog/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07F/2018/obcfac/ocean_deb_his_run07F_{file_num}.nc', - num_files: 13 - }, - '2016_tide': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07E/run07E_tide/model_output/ocean_deb_his_run07E_tide_{file_num}.nc', - num_files: 13 - }, - '2016_tide_subtidal': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07E/run07E_tide_subtidal/model_output/ocean_deb_his_run07E_subtidal_{file_num}.nc', - num_files: 13 - }, - # the 2016 bulk model is not performing as expected - # we probably shouldn't use this data without checking in with Salme to see if it - # has been fixed - '2016_tide_subtidal_bulk': { - url: 'https://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07E/run07E_bulk/model_output/ocean_deb_his_run07E_bulk_{file_num}.nc', - num_files: 13 - }, -} - -river_mile_coords_filepath: './01_fetch/in/river_mile_coords.csv' \ No newline at end of file diff --git a/01_fetch/params_config_fetch_usgs_nwis.yaml b/01_fetch/params_config_fetch_usgs_nwis.yaml index 595788e..1c58203 100644 --- a/01_fetch/params_config_fetch_usgs_nwis.yaml +++ b/01_fetch/params_config_fetch_usgs_nwis.yaml @@ -8,4 +8,4 @@ s3_bucket: 'drb-estuary-salinity' # start and end dates for data fetch # should be in the format 'YYYY-MM-DD' start_dt: '2000-01-01' -end_dt: '2019-12-31' +end_dt: '2021-12-31' diff --git a/01_fetch/src/fetch_coawst_model.py b/01_fetch/src/fetch_coawst_model.py index 3a2f7a2..09477ef 100644 --- a/01_fetch/src/fetch_coawst_model.py +++ b/01_fetch/src/fetch_coawst_model.py @@ -8,6 +8,8 @@ import os from dask.distributed import Client import yaml +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) import utils #client = Client() diff --git a/01_fetch/src/fetch_gridmet.R b/01_fetch/src/fetch_gridmet.R deleted file mode 100644 index 31825f8..0000000 --- a/01_fetch/src/fetch_gridmet.R +++ /dev/null @@ -1,50 +0,0 @@ -#===================================================================================# -# NOTES: this script is for querying gridMET for daily meteorological data for -# specific sites -#-----------------------------------------------------------------------------------# -# Galen Gorski # -# ggorski@usgs.gov # -# 02-24-2022 # -#-----------------------------------------------------------------------------------# -#===================================================================================# - -#===================================================================================# -#####INSTALL PACKAGES##### -# install.packages('tidyverse') -library(tidyverse) -#remotes::install_github("mikejohnson51/climateR") -library(climateR) -# install.packages('sf') -library(sf) -# install.packages('dplyr') -library(dplyr) -##### -#===================================================================================# - - -#===================================================================================# -lat = c( - 38.7816, - 39.9538, - 39.50083) - -long = c(-75.119, - -75.137, - -75.56861) - -site = c('lewes', - 'benfranklin', - 'reedyisland') - -df <- data.frame(site = site, lat = lat, long = long) -sites_sf <- st_as_sf(df, coords = c("long","lat"), crs = 4326, agr = "constant") - -for(i in 1:3){ - ts = getGridMET(sites_sf[i,], param = c('prcp','tmax','tmin','wind_dir','wind_vel'), startDate = "2000-12-01", endDate = "2019-12-31") - - ts %>% - mutate(wspdir = -1*wind_vel*cos(wind_dir*(pi/180))) %>% - mutate(datetime = date) %>% - dplyr::select(datetime, prcp, tmax, tmin, wspdir) %>% - write_csv(paste0('01_fetch/out/gridmet_',site[i],'.csv')) -} diff --git a/01_fetch/src/fetch_noaa_nerrs.py b/01_fetch/src/fetch_noaa_nerrs.py index 28bdf3d..bd8bd2f 100644 --- a/01_fetch/src/fetch_noaa_nerrs.py +++ b/01_fetch/src/fetch_noaa_nerrs.py @@ -1,9 +1,12 @@ import os -import utils import yaml import glob from zipfile import ZipFile import shutil +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) +import utils + # import config with open("01_fetch/params_config_fetch_noaa_nerrs.yaml", 'r') as stream: diff --git a/01_fetch/src/fetch_noaa_nos.py b/01_fetch/src/fetch_noaa_nos.py index 8213c26..8c2c736 100644 --- a/01_fetch/src/fetch_noaa_nos.py +++ b/01_fetch/src/fetch_noaa_nos.py @@ -4,8 +4,10 @@ import datetime from dateutil.relativedelta import relativedelta import requests, json -import utils import yaml +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) +import utils # import config with open("01_fetch/params_config_fetch_noaa_nos.yaml", 'r') as stream: diff --git a/01_fetch/src/fetch_usgs_nwis.py b/01_fetch/src/fetch_usgs_nwis.py index bacc213..d0bec51 100644 --- a/01_fetch/src/fetch_usgs_nwis.py +++ b/01_fetch/src/fetch_usgs_nwis.py @@ -3,6 +3,8 @@ import yaml import datetime import pandas as pd +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) import utils # import config @@ -48,11 +50,6 @@ def process_site_info_to_csv(raw_site_info_txt, site_info_outfile_csv): print('uploading to s3') s3_client.upload_file(site_info_outfile_csv, s3_bucket, local_to_s3_pathname(site_info_outfile_csv)) - # get minimum date to pull for this site - start_dt = site_info_df_subset[['begin_date']].min().values[0] - - return start_dt - def fetch_params(outfile): '''get table of all possible USGS site parameters''' params_url = 'https://help.waterdata.usgs.gov/code/parameter_cd_query?fmt=rdb&group_cd=%' @@ -101,8 +98,9 @@ def fetch_single_site_data(site_num): site_info_outfile_txt = os.path.join('.', '01_fetch', 'out', 'metadata', f'usgs_nwis_site_info_{site_num}.txt') fetch_site_info(site_num, site_info_outfile_txt) site_info_outfile_csv = os.path.join('.', '01_fetch', 'out', 'metadata', f'usgs_nwis_site_info_{site_num}.csv') - start_dt = process_site_info_to_csv(site_info_outfile_txt, site_info_outfile_csv) - end_dt = datetime.datetime.today().strftime("%Y-%m-%d") + process_site_info_to_csv(site_info_outfile_txt, site_info_outfile_csv) + start_dt = config['start_dt'] + end_dt = config['end_dt'] # start and end dates for data fetch data_outfile_txt = os.path.join('.', '01_fetch', 'out', f'usgs_nwis_{site_num}.txt') diff --git a/utils.py b/01_fetch/src/utils.py similarity index 98% rename from utils.py rename to 01_fetch/src/utils.py index 188b413..87d2f05 100644 --- a/utils.py +++ b/01_fetch/src/utils.py @@ -35,8 +35,8 @@ def process_to_timestep(df, cols, agg_level, prop_obs_required): must have datetimes in a column named 'datetime' ''' # get proportion of measurements available for timestep - expected_measurements = df.resample(agg_level, on='datetime').count().mode()[cols].loc[0] - observed_measurements = df.resample(agg_level, on='datetime').count()[cols].loc[:] + expected_measurements = df.resample(agg_level, on='datetime').count().mode().loc[0] + observed_measurements = df.resample(agg_level, on='datetime').count().loc[:] prop_df = observed_measurements / expected_measurements # calculate averages for timestep df = df.resample(agg_level, on='datetime').mean() @@ -84,4 +84,4 @@ def get_datafile_list(read_location, s3_client=None, s3_bucket=None): prefix = os.path.join('01_fetch', 'out') file_prefix='usgs_nwis_0' raw_datafiles = [os.path.join(prefix, f) for f in os.listdir(prefix) if f.startswith(file_prefix)] - return raw_datafiles \ No newline at end of file + return raw_datafiles diff --git a/02_munge/params_config_munge_coawst_model.yaml b/02_munge/params_config_munge_coawst_model.yaml deleted file mode 100644 index 99e4e33..0000000 --- a/02_munge/params_config_munge_coawst_model.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# choose where you want to read your data inputs from: local or S3 -read_location: 'S3' -# choose where you want to write your data outputs: local or S3 -write_location: 'local' -# set name of AWS profile storing credentials for S3 -aws_profile: 'dev' -# set AWS bucket to read/write to -s3_bucket: 'drb-estuary-salinity' - -#location of new netcdf with subsetted data -netcdf_path: f'./01_munge/out/coawst_salt_only_2019.nc' diff --git a/02_munge/src/fill_discharge_prms.py b/02_munge/src/fill_discharge_prms.py index 8eb13c3..3351649 100644 --- a/02_munge/src/fill_discharge_prms.py +++ b/02_munge/src/fill_discharge_prms.py @@ -28,8 +28,8 @@ def download_unzip_sb(sb_url, prms_predictions, destination): os.makedirs(destination, exist_ok=True) sb = sciencebasepy.SbSession() sb.download_file(sb_url, prms_predictions+'.zip', destination) - with zipfile.ZipFile(os.path.join(destination,prms_predictions+'.zip'), 'r') as zip_ref: - zip_ref.extractall(destination) + with zipfile.ZipFile(os.path.join(destination,prms_predictions+'.zip'), 'r') as zip_ref: + zip_ref.extractall(destination) diff --git a/02_munge/src/munge_noaa_nerrs.py b/02_munge/src/munge_noaa_nerrs.py index 97b08cc..341ed0a 100644 --- a/02_munge/src/munge_noaa_nerrs.py +++ b/02_munge/src/munge_noaa_nerrs.py @@ -2,6 +2,8 @@ import pandas as pd import numpy as np import yaml +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) import utils # import config diff --git a/02_munge/src/munge_noaa_nos.py b/02_munge/src/munge_noaa_nos.py index 624e31b..8383e32 100644 --- a/02_munge/src/munge_noaa_nos.py +++ b/02_munge/src/munge_noaa_nos.py @@ -3,8 +3,10 @@ import pandas as pd import boto3 import yaml -import utils from scipy import signal +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) +import utils # import config with open("02_munge/params_config_munge_noaa_nos.yaml", 'r') as stream: diff --git a/02_munge/src/munge_usgs_nwis.py b/02_munge/src/munge_usgs_nwis.py index bc08501..e5bad60 100644 --- a/02_munge/src/munge_usgs_nwis.py +++ b/02_munge/src/munge_usgs_nwis.py @@ -3,6 +3,8 @@ import pandas as pd import re import yaml +import sys +sys.path.insert(0, os.path.join('01_fetch', 'src')) import utils # import config diff --git a/03b_model/hyperparameter_config.yaml b/03_model/hyperparameter_config.yaml similarity index 96% rename from 03b_model/hyperparameter_config.yaml rename to 03_model/hyperparameter_config.yaml index e5bad6b..389fb5e 100644 --- a/03b_model/hyperparameter_config.yaml +++ b/03_model/hyperparameter_config.yaml @@ -1,5 +1,5 @@ -out_dir: '03b_model/out/HP_Tune_02' -run_id: 'Run_29' +out_dir: 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b/03_model/model_config.yaml @@ -1,17 +1,17 @@ -out_dir: '03b_model/out' +out_dir: '03_model\out' -run_id: 'Run_52' +run_id: 'Test_Run' -inputs: ['discharge_01463500', +inputs: ['discharge_01463500', #'specific_conductance_01463500', - 'discharge_01474500', - #'specific_conductance_01474500', + 'discharge_01474500', + #'specific_conductance_01474500', 'wl_range_8557380','wl_max_8557380','wl_obs_pred_8557380','wl_filtered_8557380', - 'air_pressure_delsjmet', + 'air_pressure_delsjmet', 'temperature_delsjmet', #'precipitation_delsjmet', 'wind_direction_delsjmet', - 'wind_speed_delsjmet']#,'wind_speed_direction_delsjmet'] + 'wind_speed_delsjmet']#,'wind_speed_direction_delsjmet'] #the sinks to be analyzed, this will likely be the salt front location @@ -22,13 +22,13 @@ replicates: 10 #window_size and antecedant_variables must be in a list #as they are iterated over include_antecedant_data: False -window_size: [60] -antecedant_variables: ['discharge'] +#window_size: [60] +#antecedant_variables: ['discharge'] #include lagged input only for discharge -include_lagged_input: True +#include_lagged_input: True #list of the lags for Delaware and Schuylkill -lag: [7,8] +#lag: [7,8] #set the seed for reproducibility for a single model run @@ -55,8 +55,3 @@ learn_rate: 0.003 recur_dropout: 0.3 dropout: 0.1 n_epochs: 500 - - - - - diff --git a/03_model/model_config_Run_Manuscript_Results.yaml b/03_model/model_config_Run_Manuscript_Results.yaml new file mode 100644 index 0000000..e807767 --- /dev/null +++ b/03_model/model_config_Run_Manuscript_Results.yaml @@ -0,0 +1,57 @@ +out_dir: '03_model\out' + +run_id: 'Test_Run' + +inputs: ['discharge_01463500', + #'specific_conductance_01463500', + 'discharge_01474500', + #'specific_conductance_01474500', + 'wl_range_8557380','wl_max_8557380','wl_obs_pred_8557380','wl_filtered_8557380', + 'air_pressure_delsjmet', + 'temperature_delsjmet', + #'precipitation_delsjmet', + 'wind_direction_delsjmet', + 'wind_speed_delsjmet']#,'wind_speed_direction_delsjmet'] + + +#the sinks to be analyzed, this will likely be the salt front location +target: 'saltfront7_weekly' +replicates: 10 + +#explicitly including summary antecedant data +#window_size and antecedant_variables must be in a list +#as they are iterated over +include_antecedant_data: False +#window_size: [60] +#antecedant_variables: ['discharge'] + +#include lagged input only for discharge +#include_lagged_input: True +#list of the lags for Delaware and Schuylkill +#lag: [7,8] + + +#set the seed for reproducibility for a single model run +#if replicates are run using the run_replicates function +#then the seed is set within the run_replicates function +#as the iterator within each replicate loop +#this ensures that each replicate is unique, but reproducible +seed_set: True +seed: 24 + +#select the split dates +train_start_date: '2001-01-01' +train_end_date: '2015-12-31' +val_start_date: '2016-01-01' +val_end_date: '2021-06-30' +test_start_date: '2016-01-01' +test_end_date: '2021-06-30' + +#model parameters +seq_len: 365 +offset: 1 +hidden_units: 20 +learn_rate: 0.003 +recur_dropout: 0.3 +dropout: 0.1 +n_epochs: 500 diff --git a/03_model/out/Run_Manuscript_Results/00/ModelResults.csv b/03_model/out/Run_Manuscript_Results/00/ModelResults.csv new file mode 100644 index 0000000..f92837f --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/00/ModelResults.csv @@ -0,0 +1,7301 @@ +,saltfront_obs,saltfront_pred,train/val +2001-01-01,,70.12,Training +2001-01-02,,70.83697,Training +2001-01-03,,71.433136,Training +2001-01-04,,72.024284,Training +2001-01-05,,72.69188,Training +2001-01-06,,73.44646,Training +2001-01-07,,73.90104,Training +2001-01-08,,73.92027,Training +2001-01-09,,73.778465,Training +2001-01-10,,73.9856,Training +2001-01-11,,74.104904,Training +2001-01-12,,74.07067,Training +2001-01-13,,74.68457,Training +2001-01-14,,75.60236,Training +2001-01-15,,76.564095,Training +2001-01-16,,77.51146,Training 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b/03_model/out/Run_Manuscript_Results/00/ModelResultsTimeSeries.png new file mode 100644 index 0000000..beb0b6c Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/00/ModelResultsTimeSeries.png differ diff --git a/03_model/out/Run_Manuscript_Results/00/losses.png b/03_model/out/Run_Manuscript_Results/00/losses.png new file mode 100644 index 0000000..666f07d Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/00/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/00/model_param_output.txt b/03_model/out/Run_Manuscript_Results/00/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/00/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 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+2018-09-22,61.915714,64.72353,Validation +2018-09-23,62.00857,64.89041,Validation +2018-09-24,62.27429,65.16038,Validation +2018-09-25,62.88,65.24345,Validation +2018-09-26,62.84857,64.91631,Validation +2018-09-27,61.87857,64.70849,Validation +2018-09-28,60.675716,63.887375,Validation +2018-09-29,58.49286,63.525726,Validation +2018-09-30,55.568573,63.16187,Validation +2018-10-01,,62.582912,Validation +2018-10-02,,62.15348,Validation +2018-10-03,,61.52439,Validation +2018-10-04,,61.14176,Validation +2018-10-05,,61.43837,Validation +2018-10-06,,61.31988,Validation +2018-10-07,,61.367836,Validation +2018-10-08,,61.57404,Validation +2018-10-09,,61.902313,Validation +2018-10-10,,61.91363,Validation +2018-10-11,,61.74276,Validation +2018-10-12,,61.119022,Validation +2018-10-13,,60.740246,Validation +2018-10-14,,60.42461,Validation +2018-10-15,,60.1504,Validation +2018-10-16,,59.471973,Validation +2018-10-17,,59.03045,Validation +2018-10-18,,59.48232,Validation +2018-10-19,,60.24774,Validation +2018-10-20,,61.165535,Validation +2018-10-21,57.471428,61.98362,Validation +2018-10-22,61.140003,62.458084,Validation +2018-10-23,63.515717,63.671787,Validation +2018-10-24,65.05286,64.17229,Validation +2018-10-25,66.17286,64.88943,Validation +2018-10-26,66.32,65.68852,Validation +2018-10-27,66.774284,65.90462,Validation +2018-10-28,67.22,67.00491,Validation +2018-10-29,67.200005,66.9976,Validation +2018-10-30,67.1,66.809006,Validation +2018-10-31,67.18429,66.29818,Validation +2018-11-01,67.05429,66.06283,Validation +2018-11-02,66.88143,65.94952,Validation +2018-11-03,66.19,65.33468,Validation +2018-11-04,64.82286,63.69307,Validation +2018-11-05,63.427143,63.2787,Validation +2018-11-06,61.884285,61.908978,Validation +2018-11-07,58.905716,60.967716,Validation +2018-11-08,55.394287,60.181957,Validation +2018-11-09,,59.590214,Validation +2018-11-10,,59.19474,Validation +2018-11-11,,57.973988,Validation +2018-11-12,,57.55054,Validation +2018-11-13,,56.87728,Validation +2018-11-14,,56.341248,Validation +2018-11-15,,58.146343,Validation +2018-11-16,,57.490707,Validation +2018-11-17,,56.564926,Validation +2018-11-18,,56.98557,Validation +2018-11-19,57.702858,56.90423,Validation +2018-11-20,61.517143,57.520775,Validation +2018-11-21,63.79286,57.7403,Validation +2018-11-22,63.42286,58.062157,Validation +2018-11-23,62.358574,58.355522,Validation +2018-11-24,61.924286,58.602386,Validation +2018-11-25,61.12286,58.48871,Validation +2018-11-26,59.397144,57.75615,Validation +2018-11-27,56.144287,57.91921,Validation +2018-11-28,,57.868114,Validation +2018-11-29,,57.519375,Validation +2018-11-30,,57.399364,Validation +2018-12-01,,57.108105,Validation +2018-12-02,,57.293396,Validation +2018-12-03,,57.540047,Validation +2018-12-04,,57.477184,Validation +2018-12-05,,57.588398,Validation +2018-12-06,,57.9084,Validation +2018-12-07,,57.938454,Validation +2018-12-08,,57.95856,Validation +2018-12-09,,58.385895,Validation +2018-12-10,,58.25531,Validation +2018-12-11,,58.2816,Validation +2018-12-12,,58.588413,Validation +2018-12-13,,59.192368,Validation +2018-12-14,,59.76452,Validation +2018-12-15,,60.62522,Validation +2018-12-16,,61.131927,Validation +2018-12-17,,62.811794,Validation +2018-12-18,,63.635803,Validation +2018-12-19,,63.750034,Validation +2018-12-20,,63.829247,Validation +2018-12-21,,63.84335,Validation +2018-12-22,,63.431427,Validation +2018-12-23,,62.67859,Validation +2018-12-24,,61.867867,Validation +2018-12-25,,61.09452,Validation +2018-12-26,,60.197556,Validation +2018-12-27,,59.705887,Validation +2018-12-28,,71.205086,Validation +2018-12-29,,69.95618,Validation +2018-12-30,,68.42508,Validation +2018-12-31,,67.48342,Validation +2019-01-01,,66.81107,Validation +2019-01-02,,65.31004,Validation +2019-01-03,,64.06563,Validation +2019-01-04,,63.475845,Validation +2019-01-05,,63.26496,Validation +2019-01-06,,62.84645,Validation +2019-01-07,,62.10199,Validation +2019-01-08,,61.068924,Validation +2019-01-09,,60.910362,Validation +2019-01-10,,60.098656,Validation +2019-01-11,,59.348675,Validation +2019-01-12,,59.291973,Validation +2019-01-13,,60.555317,Validation +2019-01-14,,60.9616,Validation +2019-01-15,57.268356,62.824074,Validation +2019-01-16,62.422215,64.46746,Validation +2019-01-17,66.64393,65.2637,Validation +2019-01-18,67.344,65.85486,Validation +2019-01-19,67.672714,66.38106,Validation +2019-01-20,68.133575,66.583694,Validation +2019-01-21,67.737,68.447716,Validation +2019-01-22,67.19886,69.11469,Validation +2019-01-23,67.082146,67.87083,Validation +2019-01-24,67.12186,67.53111,Validation +2019-01-25,66.28928,66.18063,Validation +2019-01-26,64.25329,63.225143,Validation +2019-01-27,61.576145,61.84276,Validation +2019-01-28,58.747,60.54236,Validation +2019-01-29,56.46486,59.888306,Validation +2019-01-30,,59.61467,Validation +2019-01-31,,59.447926,Validation +2019-02-01,,59.28053,Validation +2019-02-02,,59.12143,Validation 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0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/01/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/01/weights.pt b/03_model/out/Run_Manuscript_Results/01/weights.pt new file mode 100644 index 0000000..a687b76 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/01/weights.pt differ diff --git 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mode 100644 index 0000000..c03b445 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/02/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/02/model_param_output.txt b/03_model/out/Run_Manuscript_Results/02/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/02/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 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+2018-10-07,,61.553593,Validation +2018-10-08,,62.236115,Validation +2018-10-09,,62.58498,Validation +2018-10-10,,62.261147,Validation +2018-10-11,,61.861485,Validation +2018-10-12,,61.1108,Validation +2018-10-13,,60.6316,Validation +2018-10-14,,60.034534,Validation +2018-10-15,,59.592403,Validation +2018-10-16,,59.925217,Validation +2018-10-17,,60.070385,Validation +2018-10-18,,60.796345,Validation +2018-10-19,,61.499466,Validation +2018-10-20,,62.32869,Validation +2018-10-21,57.471428,63.15557,Validation +2018-10-22,61.140003,64.16947,Validation +2018-10-23,63.515717,64.93632,Validation +2018-10-24,65.05286,65.299225,Validation +2018-10-25,66.17286,65.993546,Validation +2018-10-26,66.32,66.31221,Validation +2018-10-27,66.774284,66.78904,Validation +2018-10-28,67.22,67.26132,Validation +2018-10-29,67.200005,67.049385,Validation +2018-10-30,67.1,66.80222,Validation +2018-10-31,67.18429,66.35747,Validation +2018-11-01,67.05429,65.96919,Validation +2018-11-02,66.88143,65.83061,Validation +2018-11-03,66.19,65.51839,Validation +2018-11-04,64.82286,63.958427,Validation +2018-11-05,63.427143,62.133144,Validation +2018-11-06,61.884285,60.340405,Validation +2018-11-07,58.905716,59.19185,Validation +2018-11-08,55.394287,58.62135,Validation +2018-11-09,,58.525738,Validation +2018-11-10,,58.36177,Validation +2018-11-11,,57.938164,Validation +2018-11-12,,58.01219,Validation +2018-11-13,,58.362934,Validation +2018-11-14,,58.4217,Validation +2018-11-15,,58.75011,Validation +2018-11-16,,58.925854,Validation +2018-11-17,,58.434814,Validation +2018-11-18,,58.657856,Validation +2018-11-19,57.702858,58.45935,Validation +2018-11-20,61.517143,58.775257,Validation +2018-11-21,63.79286,58.78406,Validation +2018-11-22,63.42286,58.585598,Validation +2018-11-23,62.358574,59.090176,Validation +2018-11-24,61.924286,58.51363,Validation +2018-11-25,61.12286,58.794426,Validation +2018-11-26,59.397144,58.316475,Validation +2018-11-27,56.144287,58.29522,Validation +2018-11-28,,58.193897,Validation +2018-11-29,,57.636185,Validation +2018-11-30,,57.8328,Validation +2018-12-01,,57.518913,Validation +2018-12-02,,57.60153,Validation +2018-12-03,,57.866653,Validation +2018-12-04,,57.690777,Validation +2018-12-05,,58.005234,Validation +2018-12-06,,58.16496,Validation +2018-12-07,,58.122856,Validation +2018-12-08,,58.484886,Validation +2018-12-09,,58.205265,Validation +2018-12-10,,57.917076,Validation +2018-12-11,,57.91874,Validation +2018-12-12,,58.383446,Validation +2018-12-13,,59.25055,Validation +2018-12-14,,59.997284,Validation +2018-12-15,,60.48126,Validation +2018-12-16,,61.435715,Validation +2018-12-17,,62.480103,Validation +2018-12-18,,63.192883,Validation +2018-12-19,,64.033325,Validation +2018-12-20,,64.47831,Validation +2018-12-21,,64.44235,Validation +2018-12-22,,63.2465,Validation +2018-12-23,,61.781433,Validation +2018-12-24,,60.677513,Validation +2018-12-25,,60.001434,Validation +2018-12-26,,59.743652,Validation +2018-12-27,,59.249153,Validation +2018-12-28,,69.39364,Validation +2018-12-29,,68.41724,Validation +2018-12-30,,67.021935,Validation +2018-12-31,,66.03228,Validation +2019-01-01,,65.54993,Validation +2019-01-02,,63.80735,Validation +2019-01-03,,62.401447,Validation +2019-01-04,,61.184845,Validation +2019-01-05,,61.024292,Validation +2019-01-06,,60.017426,Validation +2019-01-07,,59.307076,Validation +2019-01-08,,58.418953,Validation +2019-01-09,,58.710686,Validation +2019-01-10,,58.6884,Validation +2019-01-11,,58.41241,Validation +2019-01-12,,58.717323,Validation +2019-01-13,,59.762016,Validation +2019-01-14,,60.684998,Validation +2019-01-15,57.268356,62.334938,Validation +2019-01-16,62.422215,63.937637,Validation +2019-01-17,66.64393,64.94262,Validation +2019-01-18,67.344,66.10256,Validation +2019-01-19,67.672714,66.97781,Validation +2019-01-20,68.133575,66.88526,Validation +2019-01-21,67.737,67.76538,Validation +2019-01-22,67.19886,68.03259,Validation +2019-01-23,67.082146,67.01674,Validation +2019-01-24,67.12186,66.998535,Validation +2019-01-25,66.28928,66.45599,Validation +2019-01-26,64.25329,63.418156,Validation +2019-01-27,61.576145,61.44993,Validation +2019-01-28,58.747,60.136383,Validation +2019-01-29,56.46486,59.88369,Validation +2019-01-30,,60.20405,Validation +2019-01-31,,60.266766,Validation +2019-02-01,,60.190247,Validation +2019-02-02,,59.88777,Validation +2019-02-03,,58.986073,Validation +2019-02-04,55.507217,59.032406,Validation +2019-02-05,57.542503,58.850372,Validation +2019-02-06,60.541073,58.89596,Validation +2019-02-07,61.365356,58.856068,Validation +2019-02-08,61.86636,59.009983,Validation +2019-02-09,60.770073,58.627724,Validation +2019-02-10,59.611286,58.076565,Validation +2019-02-11,58.03143,57.70269,Validation +2019-02-12,57.47843,58.04592,Validation +2019-02-13,57.574142,58.742935,Validation +2019-02-14,56.657646,58.913486,Validation +2019-02-15,56.463646,60.68406,Validation +2019-02-16,57.52279,61.456413,Validation 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a/03_model/out/Run_Manuscript_Results/03/losses.png b/03_model/out/Run_Manuscript_Results/03/losses.png new file mode 100644 index 0000000..43ac39b Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/03/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/03/model_param_output.txt b/03_model/out/Run_Manuscript_Results/03/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/03/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/03/weights.pt b/03_model/out/Run_Manuscript_Results/03/weights.pt new file mode 100644 index 0000000..1edb099 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/03/weights.pt differ diff --git a/03_model/out/Run_Manuscript_Results/04/ModelResults.csv b/03_model/out/Run_Manuscript_Results/04/ModelResults.csv new file mode 100644 index 0000000..e6baee6 --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/04/ModelResults.csv @@ -0,0 +1,7301 @@ +,saltfront_obs,saltfront_pred,train/val +2001-01-01,,70.25026,Training +2001-01-02,,71.09038,Training +2001-01-03,,71.64161,Training +2001-01-04,,72.284904,Training +2001-01-05,,73.14763,Training +2001-01-06,,74.02501,Training +2001-01-07,,74.70575,Training +2001-01-08,,74.840706,Training +2001-01-09,,74.47813,Training 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--git a/03_model/out/Run_Manuscript_Results/04/losses.png b/03_model/out/Run_Manuscript_Results/04/losses.png new file mode 100644 index 0000000..e6509c0 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/04/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/04/model_param_output.txt b/03_model/out/Run_Manuscript_Results/04/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/04/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/04/weights.pt b/03_model/out/Run_Manuscript_Results/04/weights.pt new file mode 100644 index 0000000..01275a6 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/04/weights.pt differ diff --git a/03_model/out/Run_Manuscript_Results/05/ModelResults.csv b/03_model/out/Run_Manuscript_Results/05/ModelResults.csv new file mode 100644 index 0000000..8da96df --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/05/ModelResults.csv @@ -0,0 +1,7301 @@ +,saltfront_obs,saltfront_pred,train/val +2001-01-01,,69.797714,Training +2001-01-02,,70.545166,Training +2001-01-03,,71.52035,Training +2001-01-04,,72.79378,Training +2001-01-05,,74.4014,Training +2001-01-06,,75.33299,Training +2001-01-07,,75.67007,Training +2001-01-08,,75.378555,Training +2001-01-09,,75.23181,Training 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+2019-03-11,,66.024536,Validation +2019-03-12,,65.37707,Validation +2019-03-13,,64.97504,Validation +2019-03-14,,64.825775,Validation +2019-03-15,,65.287155,Validation +2019-03-16,,64.82753,Validation +2019-03-17,,64.607994,Validation +2019-03-18,63.1505,64.96532,Validation +2019-03-19,62.701645,64.990204,Validation +2019-03-20,62.007072,65.40893,Validation +2019-03-21,61.633144,65.5919,Validation +2019-03-22,61.296143,65.19706,Validation +2019-03-23,60.684143,65.38504,Validation +2019-03-24,58.844215,63.51177,Validation +2019-03-25,56.80486,62.58471,Validation +2019-03-26,54.96486,61.366264,Validation +2019-03-27,,60.7838,Validation +2019-03-28,,59.945507,Validation +2019-03-29,,59.255333,Validation +2019-03-30,,59.220184,Validation +2019-03-31,,59.42111,Validation +2019-04-01,,59.704292,Validation +2019-04-02,54.730858,61.047108,Validation +2019-04-03,56.773716,61.02026,Validation +2019-04-04,58.363716,61.74243,Validation +2019-04-05,60.17943,62.39582,Validation 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b/03_model/out/Run_Manuscript_Results/05/ModelResultsTimeSeries.png new file mode 100644 index 0000000..79e49bd Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/05/ModelResultsTimeSeries.png differ diff --git a/03_model/out/Run_Manuscript_Results/05/losses.png b/03_model/out/Run_Manuscript_Results/05/losses.png new file mode 100644 index 0000000..587bd47 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/05/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/05/model_param_output.txt b/03_model/out/Run_Manuscript_Results/05/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/05/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 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+2018-08-05,62.36,65.46761,Validation +2018-08-06,60.99286,64.9705,Validation +2018-08-07,59.694286,64.433754,Validation +2018-08-08,58.587143,63.750866,Validation +2018-08-09,57.755714,63.23172,Validation +2018-08-10,57.31,62.405582,Validation +2018-08-11,57.157143,61.910763,Validation +2018-08-12,57.43,60.777267,Validation +2018-08-13,57.885715,59.67871,Validation +2018-08-14,57.592857,59.40101,Validation +2018-08-15,55.538574,59.00004,Validation +2018-08-16,,58.56542,Validation +2018-08-17,,58.366005,Validation +2018-08-18,,58.007523,Validation +2018-08-19,,58.098507,Validation +2018-08-20,,57.854855,Validation +2018-08-21,,57.908707,Validation +2018-08-22,,58.045197,Validation +2018-08-23,,57.849686,Validation +2018-08-24,56.31286,58.054077,Validation +2018-08-25,59.52143,58.409378,Validation +2018-08-26,61.65143,58.871468,Validation +2018-08-27,62.187145,59.252777,Validation +2018-08-28,62.57286,59.752144,Validation +2018-08-29,63.227142,60.623787,Validation +2018-08-30,64.167145,61.420933,Validation +2018-08-31,64.454285,61.926273,Validation +2018-09-01,64.68429,62.532314,Validation +2018-09-02,64.788574,63.328293,Validation +2018-09-03,65.08714,63.935333,Validation +2018-09-04,65.26572,64.346,Validation +2018-09-05,65.41857,64.76217,Validation +2018-09-06,65.63286,65.18528,Validation +2018-09-07,65.83714,65.49309,Validation +2018-09-08,66.03143,65.64256,Validation +2018-09-09,66.32714,65.77711,Validation +2018-09-10,66.58714,66.2217,Validation +2018-09-11,66.68143,66.52331,Validation +2018-09-12,66.49715,66.34375,Validation +2018-09-13,66.05,66.17012,Validation +2018-09-14,65.45143,65.9659,Validation +2018-09-15,64.65143,65.958176,Validation +2018-09-16,63.670002,65.83664,Validation +2018-09-17,62.707146,65.467545,Validation +2018-09-18,61.835716,65.15118,Validation +2018-09-19,61.458572,65.09953,Validation +2018-09-20,61.66857,64.90841,Validation +2018-09-21,61.91143,65.065346,Validation +2018-09-22,61.915714,65.34607,Validation +2018-09-23,62.00857,65.369156,Validation +2018-09-24,62.27429,65.32789,Validation +2018-09-25,62.88,65.40518,Validation +2018-09-26,62.84857,65.39956,Validation +2018-09-27,61.87857,64.922455,Validation +2018-09-28,60.675716,64.311676,Validation +2018-09-29,58.49286,63.38075,Validation +2018-09-30,55.568573,62.94478,Validation +2018-10-01,,62.667316,Validation +2018-10-02,,62.425167,Validation +2018-10-03,,61.983276,Validation +2018-10-04,,61.675034,Validation +2018-10-05,,61.952984,Validation +2018-10-06,,61.376606,Validation +2018-10-07,,61.231567,Validation +2018-10-08,,61.19403,Validation +2018-10-09,,61.270905,Validation +2018-10-10,,61.074387,Validation +2018-10-11,,60.762928,Validation +2018-10-12,,59.862854,Validation +2018-10-13,,59.178047,Validation +2018-10-14,,58.603264,Validation +2018-10-15,,58.675606,Validation +2018-10-16,,58.60526,Validation +2018-10-17,,58.673252,Validation +2018-10-18,,59.52943,Validation +2018-10-19,,60.27424,Validation +2018-10-20,,61.162407,Validation +2018-10-21,57.471428,61.66115,Validation +2018-10-22,61.140003,62.35739,Validation +2018-10-23,63.515717,63.211704,Validation +2018-10-24,65.05286,63.93219,Validation +2018-10-25,66.17286,64.54269,Validation +2018-10-26,66.32,64.52148,Validation +2018-10-27,66.774284,64.7123,Validation +2018-10-28,67.22,65.18661,Validation +2018-10-29,67.200005,65.06604,Validation +2018-10-30,67.1,64.69517,Validation +2018-10-31,67.18429,64.47911,Validation +2018-11-01,67.05429,64.39993,Validation +2018-11-02,66.88143,64.35158,Validation +2018-11-03,66.19,63.020065,Validation +2018-11-04,64.82286,60.846912,Validation +2018-11-05,63.427143,60.061356,Validation +2018-11-06,61.884285,58.876198,Validation +2018-11-07,58.905716,58.488663,Validation +2018-11-08,55.394287,58.30776,Validation +2018-11-09,,58.314606,Validation +2018-11-10,,58.25842,Validation +2018-11-11,,57.738293,Validation +2018-11-12,,57.513844,Validation +2018-11-13,,57.03576,Validation 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b/03_model/out/Run_Manuscript_Results/06/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/06/weights.pt b/03_model/out/Run_Manuscript_Results/06/weights.pt new file mode 100644 index 0000000..7e64564 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/06/weights.pt differ diff --git 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index 0000000..f45ec76 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/07/ModelResultsTimeSeries.png differ diff --git a/03_model/out/Run_Manuscript_Results/07/losses.png b/03_model/out/Run_Manuscript_Results/07/losses.png new file mode 100644 index 0000000..dd74757 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/07/losses.png differ diff --git a/03_model/out/Run_Manuscript_Results/07/model_param_output.txt b/03_model/out/Run_Manuscript_Results/07/model_param_output.txt new file mode 100644 index 0000000..acac42a --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/07/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning 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+2018-10-24,65.05286,64.333084,Validation +2018-10-25,66.17286,65.146545,Validation +2018-10-26,66.32,65.5099,Validation +2018-10-27,66.774284,66.013306,Validation +2018-10-28,67.22,66.34624,Validation +2018-10-29,67.200005,66.185745,Validation +2018-10-30,67.1,66.02475,Validation +2018-10-31,67.18429,65.852974,Validation +2018-11-01,67.05429,65.49308,Validation +2018-11-02,66.88143,65.169,Validation +2018-11-03,66.19,64.04547,Validation +2018-11-04,64.82286,62.224384,Validation +2018-11-05,63.427143,60.869198,Validation +2018-11-06,61.884285,59.6871,Validation +2018-11-07,58.905716,58.879288,Validation +2018-11-08,55.394287,58.387432,Validation +2018-11-09,,58.550632,Validation +2018-11-10,,58.107,Validation +2018-11-11,,57.21227,Validation +2018-11-12,,57.187737,Validation +2018-11-13,,56.47608,Validation +2018-11-14,,56.24551,Validation +2018-11-15,,57.589607,Validation +2018-11-16,,57.577934,Validation +2018-11-17,,56.919487,Validation +2018-11-18,,57.291985,Validation +2018-11-19,57.702858,57.11905,Validation +2018-11-20,61.517143,58.085155,Validation +2018-11-21,63.79286,58.484913,Validation +2018-11-22,63.42286,58.841515,Validation +2018-11-23,62.358574,59.42165,Validation +2018-11-24,61.924286,59.31955,Validation +2018-11-25,61.12286,59.300907,Validation +2018-11-26,59.397144,58.52882,Validation +2018-11-27,56.144287,58.1476,Validation +2018-11-28,,57.75309,Validation +2018-11-29,,57.44175,Validation +2018-11-30,,57.689747,Validation +2018-12-01,,57.480217,Validation +2018-12-02,,57.65819,Validation +2018-12-03,,57.805622,Validation +2018-12-04,,57.5649,Validation +2018-12-05,,57.973816,Validation +2018-12-06,,58.213036,Validation +2018-12-07,,58.262047,Validation +2018-12-08,,58.537613,Validation +2018-12-09,,59.04542,Validation +2018-12-10,,59.328945,Validation +2018-12-11,,59.232204,Validation +2018-12-12,,59.384277,Validation +2018-12-13,,60.056076,Validation +2018-12-14,,60.20582,Validation +2018-12-15,,60.78058,Validation +2018-12-16,,61.10977,Validation +2018-12-17,,61.31515,Validation +2018-12-18,,61.590782,Validation +2018-12-19,,61.436363,Validation +2018-12-20,,61.79621,Validation +2018-12-21,,61.41964,Validation +2018-12-22,,60.528885,Validation +2018-12-23,,59.478107,Validation +2018-12-24,,58.953217,Validation +2018-12-25,,58.690254,Validation +2018-12-26,,58.44908,Validation +2018-12-27,,58.339542,Validation +2018-12-28,,68.07233,Validation +2018-12-29,,66.87821,Validation +2018-12-30,,65.61624,Validation +2018-12-31,,65.04297,Validation +2019-01-01,,64.34583,Validation +2019-01-02,,62.80646,Validation +2019-01-03,,61.345196,Validation +2019-01-04,,60.56841,Validation +2019-01-05,,60.45787,Validation +2019-01-06,,60.07166,Validation +2019-01-07,,59.374275,Validation +2019-01-08,,58.559093,Validation +2019-01-09,,59.024395,Validation +2019-01-10,,58.872623,Validation +2019-01-11,,58.750683,Validation +2019-01-12,,58.679634,Validation +2019-01-13,,59.9383,Validation +2019-01-14,,60.03866,Validation +2019-01-15,57.268356,60.156853,Validation +2019-01-16,62.422215,61.078728,Validation +2019-01-17,66.64393,62.20146,Validation +2019-01-18,67.344,63.40863,Validation +2019-01-19,67.672714,64.36089,Validation +2019-01-20,68.133575,64.311165,Validation +2019-01-21,67.737,65.83808,Validation +2019-01-22,67.19886,67.12178,Validation +2019-01-23,67.082146,66.58855,Validation +2019-01-24,67.12186,65.923515,Validation +2019-01-25,66.28928,65.407845,Validation +2019-01-26,64.25329,62.795013,Validation +2019-01-27,61.576145,61.16392,Validation +2019-01-28,58.747,60.05439,Validation +2019-01-29,56.46486,59.645813,Validation +2019-01-30,,60.180183,Validation +2019-01-31,,60.95092,Validation +2019-02-01,,61.207657,Validation +2019-02-02,,61.38036,Validation +2019-02-03,,60.57894,Validation +2019-02-04,55.507217,60.59091,Validation +2019-02-05,57.542503,60.147907,Validation +2019-02-06,60.541073,60.396645,Validation +2019-02-07,61.365356,60.246014,Validation +2019-02-08,61.86636,60.40018,Validation +2019-02-09,60.770073,60.185516,Validation +2019-02-10,59.611286,58.644005,Validation +2019-02-11,58.03143,57.99031,Validation +2019-02-12,57.47843,59.065834,Validation +2019-02-13,57.574142,59.058876,Validation +2019-02-14,56.657646,58.714752,Validation +2019-02-15,56.463646,60.411636,Validation +2019-02-16,57.52279,60.52922,Validation +2019-02-17,59.1325,61.152107,Validation +2019-02-18,60.94136,61.89406,Validation +2019-02-19,61.044075,63.513336,Validation +2019-02-20,60.467003,63.831577,Validation +2019-02-21,61.107788,63.986866,Validation +2019-02-22,60.67236,63.87167,Validation +2019-02-23,,63.062637,Validation +2019-02-24,,62.59986,Validation +2019-02-25,,63.12807,Validation +2019-02-26,,62.219334,Validation +2019-02-27,,61.733345,Validation +2019-02-28,,61.471687,Validation +2019-03-01,,61.505985,Validation +2019-03-02,,61.967415,Validation +2019-03-03,,62.188362,Validation +2019-03-04,,63.29431,Validation +2019-03-05,,64.0411,Validation 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'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/08/weights.pt b/03_model/out/Run_Manuscript_Results/08/weights.pt new file mode 100644 index 0000000..8b15f75 Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/08/weights.pt differ diff --git a/03_model/out/Run_Manuscript_Results/09/ModelResults.csv b/03_model/out/Run_Manuscript_Results/09/ModelResults.csv new file mode 100644 index 0000000..41aa058 --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/09/ModelResults.csv 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b/03_model/out/Run_Manuscript_Results/09/model_param_output.txt @@ -0,0 +1,16 @@ +Date: Oct-28-2022 +Feature List: ['discharge_01463500', 'discharge_01474500', 'wl_range_8557380', 'wl_max_8557380', 'wl_obs_pred_8557380', 'wl_filtered_8557380', 'air_pressure_delsjmet', 'temperature_delsjmet', 'wind_direction_delsjmet', 'wind_speed_delsjmet'] +Target: saltfront7_weekly +Include antecedant variable: False +Epochs: 500 +Learning rate: 0.003000 +Sequence Length: 365 +Cells: 20 +Recurrent Dropout: 0.300000 +Dropout: 0.100000 +Train date start: 2001-01-01 +Train date end: 2015-12-31 +Validation date start: 2016-01-01 +Validation date end: 2021-06-30 +Test date start: 2016-01-01 +Test date end: 2021-06-30 diff --git a/03_model/out/Run_Manuscript_Results/09/weights.pt b/03_model/out/Run_Manuscript_Results/09/weights.pt new file mode 100644 index 0000000..15b582a Binary files /dev/null and b/03_model/out/Run_Manuscript_Results/09/weights.pt differ diff --git a/03_model/out/Run_Manuscript_Results/inputs.zarr/.zattrs b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zattrs new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zattrs @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/03_model/out/Run_Manuscript_Results/inputs.zarr/.zgroup b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zgroup new file mode 100644 index 0000000..3b7daf2 --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zgroup @@ -0,0 +1,3 @@ +{ + "zarr_format": 2 +} \ No newline at end of file diff --git a/03_model/out/Run_Manuscript_Results/inputs.zarr/.zmetadata b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zmetadata new file mode 100644 index 0000000..5166644 --- /dev/null +++ b/03_model/out/Run_Manuscript_Results/inputs.zarr/.zmetadata @@ -0,0 +1,286 @@ +{ + "metadata": { + ".zattrs": {}, + ".zgroup": { + "zarr_format": 2 + }, + "air_pressure_delsjmet/.zarray": { + "chunks": [ + 7486 + ], + "compressor": { + "blocksize": 0, + "clevel": 5, + "cname": "lz4", + "id": "blosc", + "shuffle": 1 + }, + "dtype": "0 or len(newFiles)>0 else 'clean' + #if the repo is dirty, make a zipped archive of the code directory + if config['gitStatus']=='dirty': + shutil.make_archive(os.path.join(os.path.dirname(outFile),"river_dl"),"zip",os.path.join(code_dir,"river_dl")) + config['gitModified']=modifiedFiles + config['gitNew']=newFiles + config['gitBranch']=branch + config['gitCommit'] = commit + #and the file info for the input files + config['input_file_info']={config[x]:{'file_size':os.stat(config[x]).st_size,'file_date':time.strftime("%m/%d/%Y %I:%M:%S %p",time.localtime(os.stat(config[x]).st_ctime))} for x in config.keys() if "file" in x and x!="input_file_info"} + with open(outFile,'w') as f: + yaml.dump(config, f, default_flow_style=False) + + #add the log entry + saveRunLog(config,code_dir,os.path.join(code_dir,"runLog.csv")) + +def scale(dataset, std=None, mean=None): + """ + scale the data so it has a standard deviation of 1 and a mean of zero + :param dataset: [xr dataset] input or output data + :param std: [xr dataset] standard deviation if scaling test data with dims + :param mean: [xr dataset] mean if scaling test data with dims + :return: scaled data with original dims + """ + if not isinstance(std, xr.Dataset) or not isinstance(mean, xr.Dataset): + std = dataset.std(skipna=True) + mean = dataset.mean(skipna=True) + # adding small number in case there is a std of zero + scaled = (dataset - mean) / (std + 1e-10) + check_if_finite(std) + check_if_finite(mean) + return scaled, std, mean + + +def sel_partition_data(dataset, time_idx_name, start_dates, end_dates): + """ + select the data from a date range or a set of date ranges + :param dataset: [xr dataset] input or output data with date dimension + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param start_dates: [str or list] fmt: "YYYY-MM-DD"; date(s) to start period + (can have multiple discontinuos periods) + :param end_dates: [str or list] fmt: "YYYY-MM-DD"; date(s) to end period + (can have multiple discontinuos periods) + :return: dataset of just those dates + """ + # if it just one date range + if isinstance(start_dates, str): + if isinstance(end_dates, str): + return dataset.sel({time_idx_name: slice(start_dates, end_dates)}) + else: + raise ValueError("start_dates is str but not end_date") + # if it's a list of date ranges + elif isinstance(start_dates, list) or isinstance(start_dates, tuple): + if len(start_dates) == len(end_dates): + data_list = [] + for i in range(len(start_dates)): + date_slice = slice(start_dates[i], end_dates[i]) + data_list.append(dataset.sel({time_idx_name: date_slice})) + return xr.concat(data_list, dim=time_idx_name) + else: + raise ValueError("start_dates and end_dates must have same length") + else: + raise ValueError("start_dates must be either str, list, or tuple") + + +def separate_trn_tst( + dataset, + time_idx_name, + train_start_date, + train_end_date, + val_start_date=None, + val_end_date=None, + test_start_date=None, + test_end_date=None, +): + """ + separate the train data from the test data according to the start and end + dates. This assumes your training data is in one continuous block. Be aware, + if your train/test/val partitions are discontinuous (composed of multiple + periods), depending on your sequence length and how the data line up, you + could end up with sequences starting in one period and ending in another. + The breaking up of sequences would happen in the `convert_batch_reshape` + function + :param dataset: [xr dataset] input or output data with dims + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param train_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + train period (can have multiple discontinuous periods) + :param train_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end train + period (can have multiple discontinuous periods) + :param val_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + validation period (can have multiple discontinuous periods) + :param val_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end + validation period (can have multiple discontinuous periods) + :param test_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + test period (can have multiple discontinuous periods) + :param test_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end test + period (can have multiple discontinuous periods) + :return: [tuple] separated data + """ + train = sel_partition_data( + dataset, time_idx_name, train_start_date, train_end_date + ) + + + if val_start_date and val_end_date: + val = sel_partition_data( + dataset, time_idx_name, val_start_date, val_end_date + ) + + elif val_start_date and not val_end_date: + raise ValueError("With a val_start_date a val_end_date must be given") + elif val_end_date and not val_start_date: + raise ValueError("With a val_end_date a val_start_date must be given") + else: + val = None + + if test_start_date and test_end_date: + test = sel_partition_data( + dataset, time_idx_name, test_start_date, test_end_date + ) + elif test_start_date and not test_end_date: + raise ValueError("With a test_start_date a test_end_date must be given") + elif test_end_date and not test_start_date: + raise ValueError("With a test_end_date a test_start_date must be given") + else: + test = None + + return train, val, test + + +def split_into_batches(data_array, seq_len=365, offset=1.0): + """ + split training data into batches with size of batch_size + :param data_array: [numpy array] array of training data with dims [nseg, + ndates, nfeat] + :param seq_len: [int] length of sequences (e.g., 365) + :param offset: [float] How to offset the batches. Values < 1 are taken as fractions, (e.g., 0.5 means that + the first batch will be 0-365 and the second will be 182-547), values > 1 are used as a constant number of + observations to offset by. + :return: [numpy array] batched data with dims [nbatches, nseg, seq_len + (batch_size), nfeat] + """ + if offset>1: + period = int(offset) + else: + period = int(offset*seq_len) + num_batches = data_array.shape[1]//period + combined=[] + for i in range(num_batches+1): + idx = int(period*i) + batch = data_array[:,idx:idx+seq_len,...] + combined.append(batch) + combined = [b for b in combined if b.shape[1]==seq_len] + combined = np.asarray(combined) + return combined + + +def read_obs(obs_file, y_vars, x_data): + """ + read and format multiple observation files. we read in the pretrain data to + make sure we have the same indexing. + :param x_data: [xr.Dataset] xarray dataset used to match spatial and + temporal domain + :param y_vars: [list of str] which variables_to_log to prepare data for + :param obs_file: [list] filenames of observation file + :return: [xr dataset] the observations in the same time + """ + ds = xr.open_zarr(obs_file, consolidated=False) + obs = xr.merge([x_data, ds], join="left") + obs = obs[y_vars] + return obs + + +def join_catch_properties(x_data_ts, catch_props): + """ + append the catchment properties to the x time series data + :param x_data_ts: [xr dataset] timeseries x-data + :param catch_props: [xr dataset] catchment properties data + :return: [xr dataset] the merged datasets + """ + # broadcast the catchment properties on the ts data so that there is a value + # for each date + _, ds_catch = xr.broadcast(x_data_ts, catch_props) + return xr.merge([x_data_ts, ds_catch], join="left") + + +def prep_catch_props(x_data_ts, catch_prop_file, catch_prop_vars, spatial_idx_name, replace_nan_with_mean=True): + """ + read catch property file and join with ts data + :param x_data_ts: [xr dataset] timeseries x-data + :param catch_prop_file: [str] the feather file of catchment attributes + :param catch_prop_vars: [list of str] the catchment attributes to use, if None, all attributes will be kept + :param spatial_idx_name: [str] name of column that is used for spatial + index (e.g., 'seg_id_nat') + :param replace_nan_with_mean: [bool] if true, any nan will be replaced with + the mean of that variable + :return: [xr dataset] merged datasets + """ + df_catch_props = pd.read_feather(catch_prop_file) + + #keep only the requested variables + if catch_prop_vars: + catch_prop_vars.append(spatial_idx_name) + df_catch_props = df_catch_props[catch_prop_vars] + + # replace nans with column means + if replace_nan_with_mean: + df_catch_props = df_catch_props.apply( + lambda x: x.fillna(x.mean()), axis=0 + ) + ds_catch_props = df_catch_props.set_index(spatial_idx_name).to_xarray() + + return join_catch_properties(x_data_ts, ds_catch_props) + + +def reshape_for_training(data): + """ + reshape the data for training + :param data: training data (either x or y_dataset or mask) dims: [nbatch, nseg, + len_seq, nfeat/nout] + :return: reshaped data [nbatch * nseg, len_seq, nfeat/nout] + """ + n_batch, n_seg, seq_len, n_feat = data.shape + return np.reshape(data, [n_batch * n_seg, seq_len, n_feat]) + + +def reduce_training_data_random( + data_file, + train_start_date="1980-10-01", + train_end_date="2004-09-30", + reduce_amount=0, + out_file=None, + segs=None, +): + """ + artificially reduce the amount of training data in the training dataset + :param train_start_date: [str] date (fmt YYYY-MM-DD) for when training data + starts + :param train_end_date: [str] date (fmt YYYY-MM-DD) for when training data + ends + :param data_file: [str] path to the observations data file + :param reduce_amount: [float] fraction to reduce the training data by. + For example, if 0.9, a random 90% of the training data will be set to nan + :param out_file: [str] file to which the reduced dataset will be written + :param segs: [array-like] segments to reduce data of + :return: [xarray dataset] updated weights (nan where reduced) + """ + # read in an convert to dataframe + ds = xr.open_zarr(data_file) + df = ds.to_dataframe() + idx = pd.IndexSlice + df_trn = df.loc[idx[train_start_date:train_end_date, :], :] + if segs: + df_trn = df_trn.loc[idx[:, segs], :] + non_null = df_trn.dropna() + reduce_idx = non_null.sample(frac=reduce_amount).index + df.loc[reduce_idx] = np.nan + reduced_ds = df.to_xarray() + if out_file: + reduced_ds.to_zarr(out_file) + return reduced_ds + + +def filter_reduce_dates(df, start_date, end_date, reduce_between=False): + df_filt = df.copy() + df_filt = df_filt.reset_index() + if reduce_between: + df_filt = df_filt[ + (df_filt["date"] > start_date) & (df_filt["date"] < end_date) + ] + else: + df_filt = df_filt[ + (df_filt["date"] < start_date) | (df_filt["date"] > end_date) + ] + return df_filt.set_index(df.index.names) + + +def reduce_training_data_continuous( + data_file, + reduce_start="1980-10-01", + reduce_end="2004-09-30", + train_start=None, + train_end=None, + segs=None, + reduce_between=True, + out_file=None, +): + """ + reduce the amount of data in the training dataset by replacing a section + or the inverse of that section with nan + :param data_file: [str] path to the observations data file + :param reduce_start: [str] date (fmt YYYY-MM-DD) for when reduction data + starts + :param reduce_end: [str] date (fmt YYYY-MM-DD) for when reduction data + ends + :param train_start: [str] data (fmt YYYY-MM-DD) the start of the training + data. If left None (default) the reduction will be done for all of the + dates. This is only relevant if reduce_between == False, because in that + case, the inverse of the range (reduce_start, reduce_end) is used and it may + be necessary to limit making the nans to the training time period + :param train_end: [str] data (fmt YYYY-MM-DD) the end of the training + data. If left None (default) the reduction will be done for all of the + dates. This is only relevant if reduce_between == False, because in that + case, the inverse of the range (reduce_start, reduce_end) is used and it may + be necessary to limit making the nans to the training time period + :param segs: [list-like] segment id's for which the data should be reduced + :param reduce_between: [bool] if True the data *in* the range (reduce_start, + reduce_end) will be made nan. if False, the data *outside* of that range + will be made nan + :param out_file: [str] file to which the reduced dataset will be written + :return: [xarray dataset] updated weights (nan where reduced) + """ + # read in an convert to dataframe + ds = xr.open_zarr(data_file) + df = ds.to_dataframe() + idx = pd.IndexSlice + df_red = df.copy() + df_red = df_red.loc[idx[train_start:train_end, :]] + if segs: + df_red = df_red.loc[idx[:, segs], :] + df_red = filter_reduce_dates( + df_red, reduce_start, reduce_end, reduce_between + ) + df.loc[df_red.index] = np.nan + reduced_ds = df.to_xarray() + if out_file: + reduced_ds.to_zarr(out_file) + return reduced_ds + + +def convert_batch_reshape( + dataset, + spatial_idx_name="seg_id_nat", + time_idx_name="date", + seq_len=365, + offset=1.0 +): + """ + convert xarray dataset into numpy array, swap the axes, batch the array and + reshape for training + :param dataset: [xr dataset] data to be batched + :param spatial_idx_name: [str] name of column that is used for spatial + index (e.g., 'seg_id_nat') + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param seq_len: [int] length of sequences (e.g., 365) + :param offset: [float] 0-1, how to offset the batches (e.g., 0.5 means that + the first batch will be 0-365 and the second will be 182-547) + :return: [numpy array] batched and reshaped dataset + """ + # If there is no dataset (like if a test or validation set is not supplied) + # just return None + if not dataset: + return None + + # convert xr.dataset to numpy array + dataset = dataset.transpose(spatial_idx_name, time_idx_name) + + arr = dataset.to_array().values + + # if the dataset is empty, just return it as is + if dataset[time_idx_name].size == 0: + return arr + + # before [nfeat, nseg, ndates]; after [nseg, ndates, nfeat] + # this is the order that the split into batches expects + arr = np.moveaxis(arr, 0, -1) + + # batch the data + # after [nbatch, nseg, seq_len, nfeat] + batched = split_into_batches(arr, seq_len=seq_len, offset=offset) + + # reshape data + # after [nbatch * nseg, seq_len, nfeat] + reshaped = reshape_for_training(batched) + return reshaped + + +def coord_as_reshaped_array( + dataset, + coord_name, + spatial_idx_name="seg_id_nat", + time_idx_name="date", + seq_len=365, + offset=1.0, +): + """ + convert an xarray coordinate to an xarray data array and reshape that array + :param dataset: + :param coord_name: [str] the name of the coordinate to convert/reshape + :param spatial_idx_name: [str] name of column that is used for spatial + index (e.g., 'seg_id_nat') + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param seq_len: [int] length of sequences (e.g., 365) + :param offset: [float] 0-1, how to offset the batches (e.g., 0.5 means that + the first batch will be 0-365 and the second will be 182-547) + :return: + """ + # If there is no dataset (like if a test or validation set is not supplied) + # just return None + if not dataset: + return None + + # I need one variable name. It can be any in the dataset, but I'll use the + # first + first_var = next(iter(dataset.data_vars.keys())) + coord_array = xr.broadcast(dataset[coord_name], dataset[first_var])[0] + new_var_name = coord_name + "1" + dataset[new_var_name] = coord_array + reshaped_np_arr = convert_batch_reshape( + dataset[[new_var_name]], + spatial_idx_name, + time_idx_name, + seq_len=seq_len, + offset=offset, + ) + return reshaped_np_arr + + +def check_if_finite(xarr): + assert np.isfinite(xarr.to_array().values).all() + + +def log_variables(y_dataset, variables_to_log): + """ + take the log of given variables + :param variables_to_log: [list of str] variables to take the log of + :param y_dataset: [xr dataset] the y data + :return: [xr dataset] the data logged + """ + for v in variables_to_log: + y_dataset[v].load() + y_dataset[v].loc[:, :] = y_dataset[v] + 1e-6 + y_dataset[v].loc[:, :] = xr.ufuncs.log(y_dataset[v]) + return y_dataset + + +def prep_y_data( + y_data_file, + y_vars, + x_data, + train_start_date, + train_end_date, + val_start_date=None, + val_end_date=None, + test_start_date=None, + test_end_date=None, + val_sites=None, + test_sites=None, + spatial_idx_name="seg_id_nat", + time_idx_name="date", + seq_len=365, + log_vars=None, + normalize_y=True, + y_type="obs", + y_std=None, + y_mean=None, + trn_offset = 1.0, + tst_val_offset = 1.0 +): + """ + prepare y_dataset data + + :param y_data_file: [str] temperature observations file + :param y_vars: [str or list of str] target variable(s) + :param x_data: [xr.Dataset] xarray dataset used to match spatial and + temporal domain + :param spatial_idx_name: [str] name of column that is used for spatial + index (e.g., 'seg_id_nat') + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param train_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + train period (can have multiple discontinuous periods) + :param train_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end train + period (can have multiple discontinuous periods) + :param val_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + validation period (can have multiple discontinuous periods) + :param val_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end + validation period (can have multiple discontinuous periods) + :param test_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + test period (can have multiple discontinuous periods) + :param test_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end test + period (can have multiple discontinuous periods) + :param val_sites: [list of site_ids] sites to retain for validation. These + sites will be witheld from training + :param test_sites: [list of site_ids] sites to retain for testing. These + sites will be witheld from training and validation + :param seq_len: [int] length of sequences (e.g., 365) + :param log_vars: [list-like] which variables_to_log (if any) to take log of + :param normalize_y: [bool] whether or not to normalize the y_dataset values + :param y_type: [str] "obs" if observations or "pre" if pretraining + :param y_std: [array-like] standard deviations of y_dataset variables_to_log + :param y_mean: [array-like] means of y_dataset variables_to_log + :param trn_offset: [str] value for the training offset + :param tst_val_offset: [str] value for the testing and validation offset + :returns: training and testing data along with the means and standard + deviations of the training input and output data + """ + # I assume that if `y_vars` is a string only one variable has been passed + # so I put that in a list which is what the rest of the functions expect + if isinstance(y_vars, str): + y_vars = [y_vars] + + # when specifying mean and std, they get passed as an np.ndarray where we need xr.Datasets + if isinstance(y_mean, np.ndarray): + y_mean = xr.Dataset(dict(zip(y_vars,y_mean))) + y_std = xr.Dataset(dict(zip(y_vars,y_std))) + + y_data = read_obs(y_data_file, y_vars, x_data) + + y_trn, y_val, y_tst = separate_trn_tst( + y_data, + time_idx_name, + train_start_date, + train_end_date, + val_start_date, + val_end_date, + test_start_date, + test_end_date, + ) + + + # replace validation sites' (and test sites') data with np.nan + if val_sites: + y_trn = y_trn.where(~y_trn[spatial_idx_name].isin(val_sites)) + + if test_sites: + y_trn = y_trn.where(~y_trn[spatial_idx_name].isin(test_sites)) + y_val = y_val.where(~y_val[spatial_idx_name].isin(test_sites)) + + + if log_vars: + y_trn = log_variables(y_trn, log_vars) + + # scale y_dataset training data and get the mean and std + # scale the validation partition to benchmark epoch performance + if normalize_y: + # check if mean and std are already calculated/exist + if not isinstance(y_std, xr.Dataset) or not isinstance(y_mean, xr.Dataset): + y_trn, y_std, y_mean = scale(y_trn) + else: + y_trn, _, _ = scale(y_trn,y_std,y_mean) + + if y_val: + y_val, _, _ = scale(y_val, y_std, y_mean) + + if y_tst: + y_tst, _, _ = scale(y_tst, y_std, y_mean) + else: + _, y_std, y_mean = scale(y_trn) + y_std = y_std/y_std + y_mean = y_mean * 0 + + if y_type == 'obs': + data = { + "y_obs_trn": convert_batch_reshape( + y_trn, spatial_idx_name, time_idx_name, offset=trn_offset, seq_len=seq_len + ), + "y_obs_val": convert_batch_reshape( + y_val, spatial_idx_name, time_idx_name, offset=tst_val_offset, seq_len=seq_len + ), + "y_obs_tst": convert_batch_reshape( + y_tst, spatial_idx_name, time_idx_name, offset=tst_val_offset, seq_len=seq_len + ), + "y_std": y_std.to_array().values, + "y_mean": y_mean.to_array().values, + "y_obs_vars": y_vars, + } + elif y_type == 'pre': + if normalize_y: + if not isinstance(y_std, xr.Dataset) or not isinstance(y_mean, xr.Dataset): + y_trn, y_std, y_mean = scale(y_trn) + else: + y_data, _, _ = scale(y_data, y_std, y_mean) + + data = { + "y_pre_full": convert_batch_reshape( + y_data, spatial_idx_name, time_idx_name, offset=trn_offset, seq_len=seq_len + ), + "y_pre_trn": convert_batch_reshape( + y_trn, spatial_idx_name, time_idx_name, offset=trn_offset, seq_len=seq_len + ), + "y_pre_vars": y_vars, + } + return data + + +def prep_all_data( + x_data_file, + y_data_file, + x_vars, + train_start_date, + train_end_date, + val_start_date=None, + val_end_date=None, + test_start_date=None, + test_end_date=None, + val_sites=None, + test_sites=None, + y_vars_finetune=None, + y_vars_pretrain=None, + spatial_idx_name="seg_id_nat", + time_idx_name="date", + seq_len=365, + pretrain_file=None, + distfile=None, + dist_idx_name="rowcolnames", + dist_type="updown", + catch_prop_file=None, + catch_prop_vars=None, + log_y_vars=False, + out_file=None, + segs=None, + normalize_y=True, + trn_offset = 1.0, + tst_val_offset = 1.0 +): + """ + prepare input and output data for DL model training read in and process + data into training and testing datasets. the training and testing data are + scaled to have a std of 1 and a mean of zero + :param x_data_file: [str] path to Zarr file with x data. Data should have + a spatial coordinate and a time coordinate that are specified in the + `spatial_idx_name` and `time_idx_name` arguments + :param y_data_file: [str] observations Zarr file. Data should have a spatial + coordinate and a time coordinate that are specified in the + spatial_idx_name` and `time_idx_name` arguments + :param train_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + train period (can have multiple discontinuous periods) + :param train_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end train + period (can have multiple discontinuous periods) + :param val_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + validation period (can have multiple discontinuous periods) + :param val_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end + validation period (can have multiple discontinuous periods) + :param test_start_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to start + test period (can have multiple discontinuous periods) + :param test_end_date: [str or list] fmt: "YYYY-MM-DD"; date(s) to end test + period (can have multiple discontinuous periods) + :param val_sites: [list of site_ids] sites to retain for validation. These + sites will be witheld from training + :param test_sites: [list of site_ids] sites to retain for testing. These + sites will be witheld from training and validation + :param spatial_idx_name: [str] name of column that is used for spatial + index (e.g., 'seg_id_nat') + :param time_idx_name: [str] name of column that is used for temporal index + (usually 'time') + :param x_vars: [list] variables_to_log that should be used as input. If None, all + of the variables_to_log will be used + :param y_vars_finetune: [str or list of str] finetune target variable(s) + :param y_vars_pretrain: [str or list of str] pretrain target variable(s) + :param seq_len: [int] length of sequences (e.g., 365) + :param pretrain_file: [str] Zarr file with the pretraining data. Should have + a spatial coordinate and a time coordinate that are specified in the + `spatial_idx_name` and `time_idx_name` arguments + :param distfile: [str] path to the distance matrix .npz file + :param dist_idx_name: [str] name of index to sort dist_matrix by. This is + the name of an array in the distance matrix .npz file + :param dist_type: [str] type of distance matrix ("upstream", "downstream" or + "updown") + :param catch_prop_file: [str] the path to the catchment properties file. If + left unfilled, the catchment properties will not be included as predictors + :param catch_prop_vars: [list of str] list of catchment properties to use. If + left unfilled and a catchment property file is supplied all variables will be used. + :param log_y_vars: [bool] whether or not to take the log of discharge in + training + :param segs: [list-like] which segments to prepare the data for + :param normalize_y: [bool] whether or not to normalize the y_dataset values + :param out_file: [str] file to where the values will be written + :param trn_offset: [str] value for the training offset + :param tst_val_offset: [str] value for the testing and validation offset + :returns: training and testing data along with the means and standard + deviations of the training input and output data + "x_trn": x training data + "x_val": x validation data + "x_tst": x test data + "x_std": x standard deviations + "x_mean": x means + "x_cols": x column names + "ids_trn": segment ids of the training data + "times_trn": dates of the training data + "ids_val": segment ids of the validation data + "times_val": dates of the validation data + "ids_tst": segment ids of the test data + "times_tst": dates of the test data + 'y_pre_trn': y_dataset pretrain data for train set + 'y_obs_trn': y_dataset observations for train set + "y_pre_wgts": y_dataset weights for pretrain data + "y_obs_wgts": weights for y_dataset observations + "y_obs_val": y_dataset observations for validation set + "y_obs_tst": y_dataset observations for train set + "y_std": standard deviations of y_dataset data + "y_mean": means of y_dataset data + "y_obs_vars": y_dataset observation variable names + 'y_pre_val': y_dataset pretrain data for validation data + 'y_pre_tst': y_dataset pretrain data for test data + 'y_pre_vars': y_dataset pretrain data variable names + "dist_matrix": prepared adjacency matrix + """ + if pretrain_file and not y_vars_pretrain: + raise ValueError("included pretrain file but no pretrain vars") + + x_data = xr.open_zarr(x_data_file,consolidated=False) + x_data = x_data.sortby([spatial_idx_name, time_idx_name]) + + if segs: + x_data = x_data.sel({spatial_idx_name: segs}) + + x_data = x_data[x_vars] + + if catch_prop_file: + x_data = prep_catch_props(x_data, catch_prop_file, catch_prop_vars, spatial_idx_name) + #update the list of x_vars + x_vars = list(x_data.data_vars) + # make sure we don't have any weird or missing input values + check_if_finite(x_data) + x_trn, x_val, x_tst = separate_trn_tst( + x_data, + time_idx_name, + train_start_date, + train_end_date, + val_start_date, + val_end_date, + test_start_date, + test_end_date, + ) + + x_trn_scl, x_std, x_mean = scale(x_trn) + + x_scl, _, _ = scale(x_data,std=x_std,mean=x_mean) + + if x_val: + x_val_scl, _, _ = scale(x_val, std=x_std, mean=x_mean) + else: + x_val_scl = None + + if x_tst: + x_tst_scl, _, _ = scale(x_tst, std=x_std, mean=x_mean) + else: + x_tst_scl = None + + # read, filter observations for finetuning + + x_data_dict = { + "x_pre_full": convert_batch_reshape( + x_scl,spatial_idx_name, time_idx_name, seq_len=seq_len, + offset=trn_offset, + ), + "x_trn": convert_batch_reshape( + x_trn_scl, spatial_idx_name, time_idx_name, seq_len=seq_len, + offset=trn_offset, + ), + "x_val": convert_batch_reshape( + x_val_scl, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + "x_tst": convert_batch_reshape( + x_tst_scl, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + "x_std": x_std.to_array().values, + "x_mean": x_mean.to_array().values, + "x_vars": np.array(x_vars), + "ids_trn": coord_as_reshaped_array( + x_trn, + spatial_idx_name, + spatial_idx_name, + time_idx_name, + offset=trn_offset, + seq_len=seq_len, + ), + "times_trn": coord_as_reshaped_array( + x_trn, + time_idx_name, + spatial_idx_name, + time_idx_name, + offset=trn_offset, + seq_len=seq_len, + ), + "ids_val": coord_as_reshaped_array( + x_val, + spatial_idx_name, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + "times_val": coord_as_reshaped_array( + x_val, + time_idx_name, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + "ids_tst": coord_as_reshaped_array( + x_tst, + spatial_idx_name, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + "times_tst": coord_as_reshaped_array( + x_tst, + time_idx_name, + spatial_idx_name, + time_idx_name, + offset=tst_val_offset, + seq_len=seq_len, + ), + } + if distfile: + x_data_dict["dist_matrix"] = prep_adj_matrix( + infile=distfile, + dist_type=dist_type, + dist_idx_name=dist_idx_name, + segs=segs, + ) + + y_obs_data = {} + y_pre_data = {} + if y_data_file: + y_obs_data = prep_y_data( + y_data_file=y_data_file, + y_vars=y_vars_finetune, + x_data=x_data, + train_start_date=train_start_date, + train_end_date=train_end_date, + val_start_date=val_start_date, + val_end_date=val_end_date, + test_start_date=test_start_date, + test_end_date=test_end_date, + val_sites=val_sites, + test_sites=test_sites, + spatial_idx_name=spatial_idx_name, + time_idx_name=time_idx_name, + seq_len=seq_len, + log_vars=log_y_vars, + normalize_y=normalize_y, + y_type="obs", + trn_offset = trn_offset, + tst_val_offset = tst_val_offset + ) + # if there is a y_data_file and a pretrain file, use the observation + # mean and standard deviation to do the scaling/centering + if pretrain_file: + y_pre_data = prep_y_data( + y_data_file=pretrain_file, + y_vars=y_vars_pretrain, + x_data=x_data, + train_start_date=train_start_date, + train_end_date=train_end_date, + val_start_date=val_start_date, + val_end_date=val_end_date, + test_start_date=test_start_date, + test_end_date=test_end_date, + spatial_idx_name=spatial_idx_name, + time_idx_name=time_idx_name, + seq_len=seq_len, + log_vars=log_y_vars, + normalize_y=normalize_y, + y_type="pre", + y_std=y_obs_data["y_std"], + y_mean=y_obs_data["y_mean"], + trn_offset = trn_offset, + tst_val_offset = tst_val_offset + ) + # if there is no observation file, use the pretrain mean and standard dev + # to do the scaling/centering + elif pretrain_file and not y_obs_data: + y_pre_data = prep_y_data( + y_data_file=pretrain_file, + y_vars=y_vars_pretrain, + x_data=x_data, + train_start_date=train_start_date, + train_end_date=train_end_date, + val_start_date=val_start_date, + val_end_date=val_end_date, + test_start_date=test_start_date, + test_end_date=test_end_date, + spatial_idx_name=spatial_idx_name, + time_idx_name=time_idx_name, + seq_len=seq_len, + log_vars=log_y_vars, + normalize_y=normalize_y, + y_type="pre", + trn_offset = trn_offset, + tst_val_offset = tst_val_offset + ) + else: + raise Warning("No y_dataset data was provided") + + all_data = {**x_data_dict, **y_obs_data, **y_pre_data} + if out_file: + np.savez_compressed(out_file, **all_data) + return all_data + + +def sort_dist_matrix(mat, row_col_names, segs=None): + """ + sort the distance matrix by id + :return: + """ + if segs is not None: + row_col_names = row_col_names.astype(type(segs[0])) + df = pd.DataFrame(mat, columns=row_col_names, index=row_col_names) + if segs: + df = df[segs] + df = df.loc[segs] + df = df.sort_index(axis=0) + df = df.sort_index(axis=1) + return df + + +def prep_adj_matrix(infile, dist_type, dist_idx_name, segs=None, out_file=None): + """ + process adj matrix. + **The resulting matrix is sorted by id ** + :param infile: [str] path to the distance matrix .npz file + :param dist_type: [str] type of distance matrix ("upstream", "downstream" or + "updown") + :param dist_idx_name: [str] name of index to sort dist_matrix by. This is + the name of an array in the distance matrix .npz file + :param segs: [list-like] which segments to prepare the data for + :param out_file: [str] path to save the .npz file to + :return: [numpy array] processed adjacency matrix + """ + adj_matrices = np.load(infile) + adj = adj_matrices[dist_type] + adj = sort_dist_matrix(adj, adj_matrices[dist_idx_name], segs=segs) + adj = np.where(np.isinf(adj), 0, adj) + adj = -adj + mean_adj = np.mean(adj[adj != 0]) + std_adj = np.std(adj[adj != 0]) + adj[adj != 0] = adj[adj != 0] - mean_adj + adj[adj != 0] = adj[adj != 0] / std_adj + adj[adj != 0] = 1 / (1 + np.exp(-adj[adj != 0])) + + I = np.eye(adj.shape[0]) + A_hat = adj.copy() + I + D = np.sum(A_hat, axis=1) + D_inv = D ** -1.0 + D_inv = np.diag(D_inv) + A_hat = np.matmul(D_inv, A_hat) + if out_file: + np.savez_compressed(out_file, dist_matrix=A_hat) + return A_hat + diff --git a/03b_model/src/run_model.py b/03_model/src/run_model.py similarity index 86% rename from 03b_model/src/run_model.py rename to 03_model/src/run_model.py index 29543cc..bbc7315 100644 --- a/03b_model/src/run_model.py +++ b/03_model/src/run_model.py @@ -15,16 +15,17 @@ import pandas as pd import pickle import random -from river_dl.preproc_utils import separate_trn_tst, scale, split_into_batches import shutil import torch import yaml +import sys +sys.path.insert(0, os.path.join('03_model', 'src')) +from river_dl.preproc_utils import separate_trn_tst, scale, split_into_batches + -#set seed for reprodubility -#random.seed(10) -with open("03b_model/model_config.yaml", 'r') as stream: +with open("03_model/model_config.yaml", 'r') as stream: config = yaml.safe_load(stream) inputs = config['inputs'] @@ -124,51 +125,9 @@ def select_inputs_targets(inputs, target, train_start_date, test_end_date, out_d #inputs_df = inputs_df.join(nerrs_input) inputs_df = inputs_df[train_start_date:test_end_date] - - if inc_ante: - - win_sizes = config['window_size'] - ante_var = config['antecedant_variables'] - - #if discharge is in the antecedant variable list make a combined - #discharge variable adding the discharge from the Delaware at Trenton (01474500) - #to the discharge from the Schuylkill at Philadelphia (01463500) - if 'discharge' in ante_var: - inputs_df['discharge'] = inputs_df['discharge_01463500']+inputs_df['discharge_01474500'] - - for win_size in win_sizes: - for j,col in enumerate(ante_var): - inputs_df[col+'_'+str(win_size).rjust(3,'0')+'_mean'] = float('nan') - col_values = [] - for i in range(len(inputs_df)-win_size): - temp_win = inputs_df[col].iloc[i:win_size+i].copy() - win_mean = np.mean(temp_win) - col_values.append(win_mean) - - print('Calculating '+inputs_df.columns[-1]+' '+str(win_size)) - inputs_df.iloc[win_size:len(inputs_df), -1] = col_values - #if discharge is in the antecedant variable list - if 'discharge' in ante_var: - #remove the combined discharge from the list of inputs - #this way we have the discharge at Trenton and from the Schuylkill as separate predictors - #and the antecedant discharge of the two combined as another predictor - inputs_df = inputs_df.drop(columns = 'discharge') - - inputs_df = inputs_df.dropna() - - #include lagged inputs, drop the first `lag` number of days from the record - if config['include_lagged_input']: - inputs_df['discharge_01463500'+'_lag'+str(config['lag'][0])] = inputs_df['discharge_01463500'].shift(config['lag'][0]) - inputs_df['discharge_01474500'+'_lag'+str(config['lag'][1])] = inputs_df['discharge_01474500'].shift(config['lag'][1]) - - inputs_df.drop(inputs_df.index[0:max(config['lag'])], inplace = True) - - #inputs_df = inputs_df.drop(columns = 'discharge_01463500') - #inputs_df = inputs_df.drop(columns = 'discharge_01474500') - - + #read in the salt front record - target_df = pd.read_csv(os.path.join('03a_it_analysis', 'in', 'saltfront_updated.csv'), parse_dates = True, index_col = 'datetime') + target_df = pd.read_csv(os.path.join('03_model', 'in', 'saltfront_updated.csv'), parse_dates = True, index_col = 'datetime') target_df = target_df[target].to_frame() target_df.index = pd.to_datetime(target_df.index.date) target_df = target_df[str(inputs_df.index[0]):test_end_date] @@ -176,31 +135,20 @@ def select_inputs_targets(inputs, target, train_start_date, test_end_date, out_d #set everything below 54 to nan target_df_c = target_df.copy() - #set river mile < 54 to NA as the salt front is not tracked below river mile 54 per DRBC - # if train_high: - # mask_high = target_df_c['saltfront_daily'] < 78 - # target_df_c.loc[mask_high,'saltfront_daily'] = np.nan - # else: - # mask = target_df_c['saltfront_daily'] < 54 - # target_df_c.loc[mask,'saltfront_daily'] = np.nan - + mask = target_df_c[target] < 54 target_df_c.loc[mask,target] = np.nan - - #if lagged inputs are included then remove the nans - if config['include_lagged_input']: - target_df_c.drop(target_df_c.index[0:max(config['lag'])], inplace = True) - + inputs_xarray = inputs_df.to_xarray() target_xarray = target_df_c.to_xarray() - if os.path.exists(os.path.join(out_dir,'inputs.zarr')): - shutil.rmtree(os.path.join(out_dir,'inputs.zarr')) - if os.path.exists(os.path.join(out_dir, 'target.zarr')): - shutil.rmtree(os.path.join(out_dir, 'target.zarr')) + if os.path.exists(os.path.join(out_dir, run_id, 'inputs.zarr')): + shutil.rmtree(os.path.join(out_dir, run_id, 'inputs.zarr')) + if os.path.exists(os.path.join(out_dir, run_id, 'target.zarr')): + shutil.rmtree(os.path.join(out_dir, run_id, 'target.zarr')) - inputs_xarray.to_zarr(os.path.join(out_dir,'inputs.zarr')) - target_xarray.to_zarr(os.path.join(out_dir,'target.zarr')) + inputs_xarray.to_zarr(os.path.join(out_dir, run_id,'inputs.zarr')) + target_xarray.to_zarr(os.path.join(out_dir, run_id,'target.zarr')) return inputs_xarray, target_xarray @@ -317,7 +265,7 @@ def prep_input_target_data(inputs_xarray, target_xarray, 'test_features':test_features, 'test_targets':test_targets, 'means_stds':means_stds} - with open(os.path.join(out_dir,'prepped_model_io_data'), 'wb') as handle: + with open(os.path.join(out_dir, run_id, 'prepped_model_io_data'), 'wb') as handle: pickle.dump(prepped_model_io_data, handle) def write_model_params(out_dir, run_id, inputs, n_epochs, @@ -374,20 +322,12 @@ def write_model_params(out_dir, run_id, inputs, n_epochs, #write out model paramters - with open("03b_model/model_config.yaml", 'r') as stream: + with open("03_model/model_config.yaml", 'r') as stream: config = yaml.safe_load(stream) dir = os.path.join(out_dir, run_id) os.makedirs(dir, exist_ok=True) - # if os.path.exists(dir): - # overwrite = input('Directory for this run id already exists, would you like to continue? (yes or no)') - # if overwrite == 'yes': - # os.makedirs(dir, exist_ok=True) - # else: - # exit('Nothing written to file') - # else: - # os.mkdir(dir) - + inputs_log = inputs.copy() if inc_ante: @@ -395,10 +335,7 @@ def write_model_params(out_dir, run_id, inputs, n_epochs, for w in config['window_size']: inputs_log.append(var+'_'+str(w).rjust(3,'0')+'_mean') - if config['include_lagged_input']: - inputs_log.append('discharge_01463500'+'_lag'+str(config['lag'][0])) - inputs_log.append('discharge_01474500'+'_lag'+str(config['lag'][1])) - + f = open(os.path.join(dir,"model_param_output.txt"),"w+") f.write("Date: %s\r\n" % date.today().strftime("%b-%d-%Y")) f.write("Feature List: %s\r\n" % inputs_log) @@ -684,7 +621,7 @@ def test_hyperparameters(): specified within the hyperparameter_config.yaml file ''' - with open("03b_model/hyperparameter_config.yaml", 'r') as stream: + with open("03_model/hyperparameter_config.yaml", 'r') as stream: hp_config = yaml.safe_load(stream) out_dir = hp_config['out_dir'] @@ -692,8 +629,7 @@ def test_hyperparameters(): #hp tuning but they will be read from the hp_config file inputs = hp_config['inputs'] target = hp_config['target'] - inc_ante = hp_config['include_antecedant_data'] - + train_start_date = hp_config['train_start_date'] train_end_date = hp_config['train_end_date'] @@ -738,8 +674,8 @@ def test_hyperparameters(): val_start_date, val_end_date, test_start_date, test_end_date, seq_len, offset, out_dir) - if os.path.exists(os.path.join(out_dir,'prepped_model_io_data')): - prepped_model_io_data_file = os.path.join(out_dir,'prepped_model_io_data') + if os.path.exists(os.path.join(out_dir, run_id, 'prepped_model_io_data')): + prepped_model_io_data_file = os.path.join(out_dir, run_id,'prepped_model_io_data') for i in range(hp_config['replicates']): now = datetime.now() @@ -789,8 +725,8 @@ def main(): val_start_date, val_end_date, test_start_date, test_end_date, seq_len, offset, out_dir) - if os.path.exists(os.path.join(out_dir,'prepped_model_io_data')): - prepped_model_io_data_file = os.path.join(out_dir,'prepped_model_io_data') + if os.path.exists(os.path.join(out_dir, run_id,'prepped_model_io_data')): + prepped_model_io_data_file = os.path.join(out_dir, run_id,'prepped_model_io_data') train_model(prepped_model_io_data_file, inputs, seq_len, diff --git a/03a_it_analysis/in/saltfront.csv b/03a_it_analysis/in/saltfront.csv deleted file mode 100644 index bc4de54..0000000 --- a/03a_it_analysis/in/saltfront.csv +++ /dev/null @@ -1,7597 +0,0 @@ -datetime,saltfront_daily,saltfront7_weekly -2000-01-01T00:00:00Z,NA,NA -2000-01-02T00:00:00Z,NA,NA -2000-01-03T00:00:00Z,NA,NA -2000-01-04T00:00:00Z,NA,NA -2000-01-05T00:00:00Z,NA,NA -2000-01-06T00:00:00Z,NA,NA -2000-01-07T00:00:00Z,NA,NA -2000-01-08T00:00:00Z,NA,NA -2000-01-09T00:00:00Z,NA,NA -2000-01-10T00:00:00Z,NA,NA -2000-01-11T00:00:00Z,NA,NA -2000-01-12T00:00:00Z,NA,NA -2000-01-13T00:00:00Z,NA,NA -2000-01-14T00:00:00Z,NA,NA 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-2000-09-17T00:00:00Z,69.52,69.61714286 -2000-09-18T00:00:00Z,69.08,69.56571429 -2000-09-19T00:00:00Z,69.44,69.45857143 -2000-09-20T00:00:00Z,69,69.30714286 -2000-09-21T00:00:00Z,68.75,69.14285714 -2000-09-22T00:00:00Z,68.74,69.08428571 -2000-09-23T00:00:00Z,69.06,69.03 -2000-09-24T00:00:00Z,69.14,69.04142857 -2000-09-25T00:00:00Z,69.16,69.02 -2000-09-26T00:00:00Z,69.29,69.08285714 -2000-09-27T00:00:00Z,69.44,69.12571429 -2000-09-28T00:00:00Z,69.05,69.18428571 -2000-09-29T00:00:00Z,69.15,69.22571429 -2000-09-30T00:00:00Z,69.35,69.24428571 -2000-10-01T00:00:00Z,69.27,69.27857143 -2000-10-02T00:00:00Z,69.4,69.31571429 -2000-10-03T00:00:00Z,69.55,69.29857143 -2000-10-04T00:00:00Z,69.32,69.29857143 -2000-10-05T00:00:00Z,69.05,69.35 -2000-10-06T00:00:00Z,69.51,69.38571429 -2000-10-07T00:00:00Z,69.6,69.49857143 -2000-10-08T00:00:00Z,70.06,69.61571429 -2000-10-09T00:00:00Z,70.22,69.76142857 -2000-10-10T00:00:00Z,70.57,69.87714286 -2000-10-11T00:00:00Z,70.13,70.01285714 -2000-10-12T00:00:00Z,70,70.10571429 -2000-10-13T00:00:00Z,70.16,70.20142857 -2000-10-14T00:00:00Z,70.27,70.25142857 -2000-10-15T00:00:00Z,70.41,70.31714286 -2000-10-16T00:00:00Z,70.68,70.43428571 -2000-10-17T00:00:00Z,71.39,70.64428571 -2000-10-18T00:00:00Z,71.6,70.81428571 -2000-10-19T00:00:00Z,71.19,70.97285714 -2000-10-20T00:00:00Z,71.27,71.1 -2000-10-21T00:00:00Z,71.16,71.16857143 -2000-10-22T00:00:00Z,70.89,71.22285714 -2000-10-23T00:00:00Z,71.06,71.19857143 -2000-10-24T00:00:00Z,71.22,71.12285714 -2000-10-25T00:00:00Z,71.07,71.13714286 -2000-10-26T00:00:00Z,71.29,71.20714286 -2000-10-27T00:00:00Z,71.76,71.32142857 -2000-10-28T00:00:00Z,71.96,71.44142857 -2000-10-29T00:00:00Z,71.73,71.58142857 -2000-10-30T00:00:00Z,72.04,71.77571429 -2000-10-31T00:00:00Z,72.58,72.17714286 -2000-11-01T00:00:00Z,73.88,72.67142857 -2000-11-02T00:00:00Z,74.75,73.17571429 -2000-11-03T00:00:00Z,75.29,73.63571429 -2000-11-04T00:00:00Z,75.18,74.23571429 -2000-11-05T00:00:00Z,75.93,74.90857143 -2000-11-06T00:00:00Z,76.75,75.6 -2000-11-07T00:00:00Z,77.42,76.19714286 -2000-11-08T00:00:00Z,78.06,76.74857143 -2000-11-09T00:00:00Z,78.61,77.30714286 -2000-11-10T00:00:00Z,79.2,77.53 -2000-11-11T00:00:00Z,76.74,77.92571429 -2000-11-12T00:00:00Z,78.7,78.29285714 -2000-11-13T00:00:00Z,79.32,78.56714286 -2000-11-14T00:00:00Z,79.34,78.47 -2000-11-15T00:00:00Z,77.38,78.23285714 -2000-11-16T00:00:00Z,76.95,77.78285714 -2000-11-17T00:00:00Z,76.05,77.64285714 -2000-11-18T00:00:00Z,75.76,77.29714286 -2000-11-19T00:00:00Z,76.28,76.89857143 -2000-11-20T00:00:00Z,76.53,76.37857143 -2000-11-21T00:00:00Z,75.7,75.98 -2000-11-22T00:00:00Z,74.59,75.64142857 -2000-11-23T00:00:00Z,74.58,75.77428571 -2000-11-24T00:00:00Z,76.98,75.99571429 -2000-11-25T00:00:00Z,77.31,76.39142857 -2000-11-26T00:00:00Z,79.05,76.45 -2000-11-27T00:00:00Z,76.94,76.39571429 -2000-11-28T00:00:00Z,75.32,76.40142857 -2000-11-29T00:00:00Z,74.63,76.29714286 -2000-11-30T00:00:00Z,73.85,75.84571429 -2000-12-01T00:00:00Z,73.82,75.38857143 -2000-12-02T00:00:00Z,74.11,74.64142857 -2000-12-03T00:00:00Z,73.82,NA -2000-12-04T00:00:00Z,NA,NA -2000-12-05T00:00:00Z,NA,NA -2000-12-06T00:00:00Z,NA,NA -2000-12-07T00:00:00Z,NA,NA -2000-12-08T00:00:00Z,NA,NA -2000-12-09T00:00:00Z,NA,NA -2000-12-10T00:00:00Z,NA,NA -2000-12-11T00:00:00Z,NA,NA -2000-12-12T00:00:00Z,NA,NA -2000-12-13T00:00:00Z,NA,NA -2000-12-14T00:00:00Z,NA,NA -2000-12-15T00:00:00Z,NA,NA -2000-12-16T00:00:00Z,NA,NA -2000-12-17T00:00:00Z,NA,NA -2000-12-18T00:00:00Z,NA,NA -2000-12-19T00:00:00Z,NA,NA -2000-12-20T00:00:00Z,NA,NA -2000-12-21T00:00:00Z,NA,NA -2000-12-22T00:00:00Z,NA,NA -2000-12-23T00:00:00Z,NA,NA -2000-12-24T00:00:00Z,NA,NA -2000-12-25T00:00:00Z,NA,NA -2000-12-26T00:00:00Z,NA,NA -2000-12-27T00:00:00Z,NA,NA -2000-12-28T00:00:00Z,NA,NA -2000-12-29T00:00:00Z,NA,NA -2000-12-30T00:00:00Z,NA,NA -2000-12-31T00:00:00Z,NA,NA -2001-01-01T00:00:00Z,NA,NA -2001-01-02T00:00:00Z,NA,NA -2001-01-03T00:00:00Z,NA,NA -2001-01-04T00:00:00Z,NA,NA -2001-01-05T00:00:00Z,NA,NA -2001-01-06T00:00:00Z,NA,NA -2001-01-07T00:00:00Z,NA,NA -2001-01-08T00:00:00Z,NA,NA -2001-01-09T00:00:00Z,NA,NA -2001-01-10T00:00:00Z,NA,NA -2001-01-11T00:00:00Z,NA,NA -2001-01-12T00:00:00Z,NA,NA -2001-01-13T00:00:00Z,NA,NA -2001-01-14T00:00:00Z,NA,NA -2001-01-15T00:00:00Z,NA,NA -2001-01-16T00:00:00Z,NA,NA -2001-01-17T00:00:00Z,NA,NA -2001-01-18T00:00:00Z,NA,NA -2001-01-19T00:00:00Z,NA,NA -2001-01-20T00:00:00Z,NA,NA -2001-01-21T00:00:00Z,NA,NA -2001-01-22T00:00:00Z,NA,NA -2001-01-23T00:00:00Z,NA,NA -2001-01-24T00:00:00Z,NA,NA -2001-01-25T00:00:00Z,NA,NA -2001-01-26T00:00:00Z,NA,NA -2001-01-27T00:00:00Z,NA,NA -2001-01-28T00:00:00Z,NA,NA -2001-01-29T00:00:00Z,NA,NA -2001-01-30T00:00:00Z,NA,NA -2001-01-31T00:00:00Z,NA,NA -2001-02-01T00:00:00Z,NA,NA -2001-02-02T00:00:00Z,NA,NA -2001-02-03T00:00:00Z,NA,NA -2001-02-04T00:00:00Z,NA,NA -2001-02-05T00:00:00Z,NA,NA -2001-02-06T00:00:00Z,NA,NA -2001-02-07T00:00:00Z,NA,NA -2001-02-08T00:00:00Z,NA,NA -2001-02-09T00:00:00Z,NA,NA -2001-02-10T00:00:00Z,NA,NA -2001-02-11T00:00:00Z,NA,NA -2001-02-12T00:00:00Z,NA,NA -2001-02-13T00:00:00Z,NA,NA -2001-02-14T00:00:00Z,NA,NA -2001-02-15T00:00:00Z,NA,NA -2001-02-16T00:00:00Z,NA,NA -2001-02-17T00:00:00Z,NA,NA -2001-02-18T00:00:00Z,NA,NA -2001-02-19T00:00:00Z,NA,NA -2001-02-20T00:00:00Z,NA,NA -2001-02-21T00:00:00Z,NA,NA -2001-02-22T00:00:00Z,NA,NA -2001-02-23T00:00:00Z,NA,NA -2001-02-24T00:00:00Z,NA,NA -2001-02-25T00:00:00Z,NA,NA -2001-02-26T00:00:00Z,NA,NA -2001-02-27T00:00:00Z,NA,NA -2001-02-28T00:00:00Z,NA,NA -2001-03-01T00:00:00Z,NA,NA -2001-03-02T00:00:00Z,NA,NA -2001-03-03T00:00:00Z,NA,NA -2001-03-04T00:00:00Z,NA,NA -2001-03-05T00:00:00Z,NA,NA -2001-03-06T00:00:00Z,NA,NA -2001-03-07T00:00:00Z,NA,NA -2001-03-08T00:00:00Z,NA,NA -2001-03-09T00:00:00Z,NA,NA -2001-03-10T00:00:00Z,NA,NA -2001-03-11T00:00:00Z,NA,NA -2001-03-12T00:00:00Z,NA,NA -2001-03-13T00:00:00Z,NA,NA -2001-03-14T00:00:00Z,NA,NA -2001-03-15T00:00:00Z,NA,NA -2001-03-16T00:00:00Z,NA,NA -2001-03-17T00:00:00Z,NA,NA -2001-03-18T00:00:00Z,NA,NA -2001-03-19T00:00:00Z,NA,NA -2001-03-20T00:00:00Z,NA,NA -2001-03-21T00:00:00Z,NA,NA -2001-03-22T00:00:00Z,NA,NA -2001-03-23T00:00:00Z,NA,NA -2001-03-24T00:00:00Z,NA,NA -2001-03-25T00:00:00Z,NA,NA -2001-03-26T00:00:00Z,NA,NA -2001-03-27T00:00:00Z,NA,64.84 -2001-03-28T00:00:00Z,64.84,64.55 -2001-03-29T00:00:00Z,64.26,64.67 -2001-03-30T00:00:00Z,64.91,63.7375 -2001-03-31T00:00:00Z,60.94,62.79 -2001-04-01T00:00:00Z,59,61.79 -2001-04-02T00:00:00Z,56.79,60.65285714 -2001-04-03T00:00:00Z,53.83,58.81142857 -2001-04-04T00:00:00Z,51.95,56.61142857 -2001-04-05T00:00:00Z,48.86,53.80285714 -2001-04-06T00:00:00Z,45.25,51.50142857 -2001-04-07T00:00:00Z,44.83,49.80285714 -2001-04-08T00:00:00Z,47.11,48.28 -2001-04-09T00:00:00Z,46.13,46.48857143 -2001-04-10T00:00:00Z,41.29,44.65714286 -2001-04-11T00:00:00Z,39.13,42.95142857 -2001-04-12T00:00:00Z,36.92,41.58428571 -2001-04-13T00:00:00Z,35.68,40.22142857 -2001-04-14T00:00:00Z,35.29,38.92142857 -2001-04-15T00:00:00Z,38.01,39.99857143 -2001-04-16T00:00:00Z,53.67,42.34285714 -2001-04-17T00:00:00Z,57.7,45.89142857 -2001-04-18T00:00:00Z,63.97,50.12571429 -2001-04-19T00:00:00Z,66.56,54.50714286 -2001-04-20T00:00:00Z,66.35,58.89285714 -2001-04-21T00:00:00Z,65.99,62.85571429 -2001-04-22T00:00:00Z,65.75,64.41571429 -2001-04-23T00:00:00Z,64.59,65.44428571 -2001-04-24T00:00:00Z,64.9,65.50428571 -2001-04-25T00:00:00Z,64.39,65.30285714 -2001-04-26T00:00:00Z,65.15,65.23285714 -2001-04-27T00:00:00Z,65.86,65.11142857 -2001-04-28T00:00:00Z,65.14,65.01571429 -2001-04-29T00:00:00Z,65.08,65.16142857 -2001-04-30T00:00:00Z,65.61,65.14428571 -2001-05-01T00:00:00Z,64.78,65.24571429 -2001-05-02T00:00:00Z,65.1,65.19142857 -2001-05-03T00:00:00Z,64.77,65.03428571 -2001-05-04T00:00:00Z,64.76,64.99428571 -2001-05-05T00:00:00Z,64.86,65.13285714 -2001-05-06T00:00:00Z,66.05,65.27428571 -2001-05-07T00:00:00Z,66.6,65.44428571 -2001-05-08T00:00:00Z,65.97,65.56285714 -2001-05-09T00:00:00Z,65.93,65.74857143 -2001-05-10T00:00:00Z,66.07,65.96428571 -2001-05-11T00:00:00Z,66.27,66.19 -2001-05-12T00:00:00Z,66.44,66.27285714 -2001-05-13T00:00:00Z,66.63,66.46571429 -2001-05-14T00:00:00Z,67.95,66.83 -2001-05-15T00:00:00Z,68.52,67.29714286 -2001-05-16T00:00:00Z,69.2,67.81571429 -2001-05-17T00:00:00Z,69.7,68.30857143 -2001-05-18T00:00:00Z,69.72,68.76142857 -2001-05-19T00:00:00Z,69.61,69.20571429 -2001-05-20T00:00:00Z,69.74,69.48285714 -2001-05-21T00:00:00Z,69.89,69.66428571 -2001-05-22T00:00:00Z,69.79,69.71714286 -2001-05-23T00:00:00Z,69.57,69.73714286 -2001-05-24T00:00:00Z,69.84,69.75714286 -2001-05-25T00:00:00Z,69.86,69.77428571 -2001-05-26T00:00:00Z,69.73,69.63571429 -2001-05-27T00:00:00Z,68.77,69.46 -2001-05-28T00:00:00Z,68.66,69.26142857 -2001-05-29T00:00:00Z,68.4,69.07 -2001-05-30T00:00:00Z,68.23,68.80714286 -2001-05-31T00:00:00Z,68,68.55 -2001-06-01T00:00:00Z,68.06,68.26428571 -2001-06-02T00:00:00Z,67.73,68.07142857 -2001-06-03T00:00:00Z,67.42,67.81428571 -2001-06-04T00:00:00Z,66.86,67.57428571 -2001-06-05T00:00:00Z,66.72,67.31142857 -2001-06-06T00:00:00Z,66.39,67.11571429 -2001-06-07T00:00:00Z,66.63,66.93428571 -2001-06-08T00:00:00Z,66.79,66.82 -2001-06-09T00:00:00Z,66.93,66.78571429 -2001-06-10T00:00:00Z,67.18,66.86571429 -2001-06-11T00:00:00Z,67.42,66.92714286 -2001-06-12T00:00:00Z,67.15,67.09428571 -2001-06-13T00:00:00Z,67.56,67.25857143 -2001-06-14T00:00:00Z,67.78,67.41142857 -2001-06-15T00:00:00Z,67.86,67.58 -2001-06-16T00:00:00Z,68.11,67.46857143 -2001-06-17T00:00:00Z,66.4,67.28142857 -2001-06-18T00:00:00Z,66.11,67.14 -2001-06-19T00:00:00Z,66.16,66.86285714 -2001-06-20T00:00:00Z,65.62,66.51285714 -2001-06-21T00:00:00Z,65.33,66.26714286 -2001-06-22T00:00:00Z,66.14,66.01857143 -2001-06-23T00:00:00Z,66.37,65.84285714 -2001-06-24T00:00:00Z,65.17,65.70857143 -2001-06-25T00:00:00Z,65.17,65.55714286 -2001-06-26T00:00:00Z,65.1,65.43571429 -2001-06-27T00:00:00Z,64.77,65.33428571 -2001-06-28T00:00:00Z,64.62,65.25142857 -2001-06-29T00:00:00Z,65.56,65.20714286 -2001-06-30T00:00:00Z,66.06,65.27285714 -2001-07-01T00:00:00Z,65.63,65.30857143 -2001-07-02T00:00:00Z,65.42,65.54142857 -2001-07-03T00:00:00Z,66.73,65.79428571 -2001-07-04T00:00:00Z,66.54,66.08428571 -2001-07-05T00:00:00Z,66.65,66.27714286 -2001-07-06T00:00:00Z,66.91,66.46 -2001-07-07T00:00:00Z,67.34,66.7 -2001-07-08T00:00:00Z,67.31,66.93285714 -2001-07-09T00:00:00Z,67.05,67.03857143 -2001-07-10T00:00:00Z,67.47,67.16571429 -2001-07-11T00:00:00Z,67.43,67.36285714 -2001-07-12T00:00:00Z,68.03,67.61571429 -2001-07-13T00:00:00Z,68.68,67.88571429 -2001-07-14T00:00:00Z,69.23,68.21714286 -2001-07-15T00:00:00Z,69.63,68.60428571 -2001-07-16T00:00:00Z,69.76,68.93571429 -2001-07-17T00:00:00Z,69.79,69.26285714 -2001-07-18T00:00:00Z,69.72,69.53857143 -2001-07-19T00:00:00Z,69.96,69.78857143 -2001-07-20T00:00:00Z,70.43,69.94714286 -2001-07-21T00:00:00Z,70.34,70.04857143 -2001-07-22T00:00:00Z,70.34,70.13142857 -2001-07-23T00:00:00Z,70.34,70.23285714 -2001-07-24T00:00:00Z,70.5,70.30714286 -2001-07-25T00:00:00Z,70.24,70.32857143 -2001-07-26T00:00:00Z,70.11,70.38142857 -2001-07-27T00:00:00Z,70.8,70.45714286 -2001-07-28T00:00:00Z,70.87,70.54285714 -2001-07-29T00:00:00Z,70.94,70.66714286 -2001-07-30T00:00:00Z,71.21,70.82714286 -2001-07-31T00:00:00Z,71.62,71.02142857 -2001-08-01T00:00:00Z,71.6,71.26428571 -2001-08-02T00:00:00Z,71.81,71.42571429 -2001-08-03T00:00:00Z,71.93,71.56142857 -2001-08-04T00:00:00Z,71.82,71.73142857 -2001-08-05T00:00:00Z,72.13,71.85 -2001-08-06T00:00:00Z,72.04,71.87857143 -2001-08-07T00:00:00Z,71.82,71.91428571 -2001-08-08T00:00:00Z,71.85,71.99714286 -2001-08-09T00:00:00Z,72.39,72.07428571 -2001-08-10T00:00:00Z,72.47,72.08285714 -2001-08-11T00:00:00Z,71.88,72.06857143 -2001-08-12T00:00:00Z,72.03,71.96142857 -2001-08-13T00:00:00Z,71.29,71.84428571 -2001-08-14T00:00:00Z,71,71.77 -2001-08-15T00:00:00Z,71.33,71.64 -2001-08-16T00:00:00Z,71.48,71.48142857 -2001-08-17T00:00:00Z,71.36,71.35 -2001-08-18T00:00:00Z,70.96,71.25571429 -2001-08-19T00:00:00Z,71.37,71.33428571 -2001-08-20T00:00:00Z,71.84,71.47857143 -2001-08-21T00:00:00Z,72.01,71.65714286 -2001-08-22T00:00:00Z,72.58,71.89857143 -2001-08-23T00:00:00Z,73.17,72.21857143 -2001-08-24T00:00:00Z,73.6,72.71285714 -2001-08-25T00:00:00Z,74.42,73.16714286 -2001-08-26T00:00:00Z,74.55,73.48142857 -2001-08-27T00:00:00Z,74.04,73.72714286 -2001-08-28T00:00:00Z,73.73,73.94857143 -2001-08-29T00:00:00Z,74.13,74.11714286 -2001-08-30T00:00:00Z,74.35,74.26857143 -2001-08-31T00:00:00Z,74.66,74.28428571 -2001-09-01T00:00:00Z,74.53,74.34 -2001-09-02T00:00:00Z,74.94,74.61285714 -2001-09-03T00:00:00Z,75.95,74.90571429 -2001-09-04T00:00:00Z,75.78,75.10428571 -2001-09-05T00:00:00Z,75.52,75.34857143 -2001-09-06T00:00:00Z,76.06,75.60571429 -2001-09-07T00:00:00Z,76.46,75.88142857 -2001-09-08T00:00:00Z,76.46,76.09714286 -2001-09-09T00:00:00Z,76.45,76.17428571 -2001-09-10T00:00:00Z,76.49,76.26 -2001-09-11T00:00:00Z,76.38,76.57 -2001-09-12T00:00:00Z,77.69,76.84571429 -2001-09-13T00:00:00Z,77.99,77.01142857 -2001-09-14T00:00:00Z,77.62,77.39 -2001-09-15T00:00:00Z,79.11,77.82571429 -2001-09-16T00:00:00Z,79.5,78.28 -2001-09-17T00:00:00Z,79.67,78.77428571 -2001-09-18T00:00:00Z,79.84,79.16 -2001-09-19T00:00:00Z,80.39,79.60428571 -2001-09-20T00:00:00Z,81.1,80.02857143 -2001-09-21T00:00:00Z,80.59,80.15571429 -2001-09-22T00:00:00Z,80,80.11857143 -2001-09-23T00:00:00Z,79.24,80.11142857 -2001-09-24T00:00:00Z,79.62,79.99857143 -2001-09-25T00:00:00Z,79.05,79.62857143 -2001-09-26T00:00:00Z,77.8,79.03428571 -2001-09-27T00:00:00Z,76.94,78.38142857 -2001-09-28T00:00:00Z,76.02,77.85714286 -2001-09-29T00:00:00Z,76.33,77.81571429 -2001-09-30T00:00:00Z,78.95,77.77142857 -2001-10-01T00:00:00Z,79.31,77.72571429 -2001-10-02T00:00:00Z,78.73,77.72571429 -2001-10-03T00:00:00Z,77.8,77.77714286 -2001-10-04T00:00:00Z,77.3,77.94571429 -2001-10-05T00:00:00Z,77.2,78.01571429 -2001-10-06T00:00:00Z,76.82,77.58285714 -2001-10-07T00:00:00Z,75.92,77.06714286 -2001-10-08T00:00:00Z,75.7,76.88857143 -2001-10-09T00:00:00Z,77.48,76.85428571 -2001-10-10T00:00:00Z,77.56,76.91571429 -2001-10-11T00:00:00Z,77.73,77.11714286 -2001-10-12T00:00:00Z,78.61,77.50571429 -2001-10-13T00:00:00Z,79.54,78.23428571 -2001-10-14T00:00:00Z,81.02,79.2 -2001-10-15T00:00:00Z,82.46,79.77285714 -2001-10-16T00:00:00Z,81.49,80.29428571 -2001-10-17T00:00:00Z,81.21,80.5 -2001-10-18T00:00:00Z,79.17,80.81857143 -2001-10-19T00:00:00Z,80.84,81.03714286 -2001-10-20T00:00:00Z,81.07,81.12285714 -2001-10-21T00:00:00Z,81.62,80.98714286 -2001-10-22T00:00:00Z,81.51,81.10428571 -2001-10-23T00:00:00Z,82.31,81.40142857 -2001-10-24T00:00:00Z,83.29,81.93428571 -2001-10-25T00:00:00Z,82.9,82.12571429 -2001-10-26T00:00:00Z,82.18,82.13857143 -2001-10-27T00:00:00Z,81.16,82.15857143 -2001-10-28T00:00:00Z,81.76,82.40428571 -2001-10-29T00:00:00Z,83.23,82.44571429 -2001-10-30T00:00:00Z,82.6,82.48571429 -2001-10-31T00:00:00Z,83.57,82.54142857 -2001-11-01T00:00:00Z,83.29,82.69142857 -2001-11-02T00:00:00Z,83.23,82.98714286 -2001-11-03T00:00:00Z,83.23,83.25714286 -2001-11-04T00:00:00Z,83.65,83.26571429 -2001-11-05T00:00:00Z,83.29,83.39 -2001-11-06T00:00:00Z,83.47,83.41714286 -2001-11-07T00:00:00Z,83.76,83.55142857 -2001-11-08T00:00:00Z,84.23,83.66428571 -2001-11-09T00:00:00Z,84.02,83.82714286 -2001-11-10T00:00:00Z,84.37,83.83857143 -2001-11-11T00:00:00Z,83.73,84.02857143 -2001-11-12T00:00:00Z,84.62,84.20142857 -2001-11-13T00:00:00Z,84.68,84.34285714 -2001-11-14T00:00:00Z,84.75,84.43 -2001-11-15T00:00:00Z,84.84,84.63 -2001-11-16T00:00:00Z,85.42,84.83857143 -2001-11-17T00:00:00Z,85.83,85.20285714 -2001-11-18T00:00:00Z,86.28,85.38857143 -2001-11-19T00:00:00Z,85.92,85.54 -2001-11-20T00:00:00Z,85.74,85.71 -2001-11-21T00:00:00Z,85.94,85.91 -2001-11-22T00:00:00Z,86.24,86.03142857 -2001-11-23T00:00:00Z,86.27,86.14 -2001-11-24T00:00:00Z,86.59,86.20428571 -2001-11-25T00:00:00Z,86.73,86.27 -2001-11-26T00:00:00Z,86.38,86.35428571 -2001-11-27T00:00:00Z,86.33,86.39 -2001-11-28T00:00:00Z,86.19,86.40285714 -2001-11-29T00:00:00Z,86.33,86.43 -2001-11-30T00:00:00Z,86.46,86.37285714 -2001-12-01T00:00:00Z,86.19,86.27285714 -2001-12-02T00:00:00Z,86.03,86.21142857 -2001-12-03T00:00:00Z,85.95,86.13857143 -2001-12-04T00:00:00Z,85.82,86.06142857 -2001-12-05T00:00:00Z,85.65,85.96285714 -2001-12-06T00:00:00Z,85.64,85.82428571 -2001-12-07T00:00:00Z,85.49,85.76428571 -2001-12-08T00:00:00Z,85.77,85.72 -2001-12-09T00:00:00Z,85.72,85.68714286 -2001-12-10T00:00:00Z,85.72,85.61714286 -2001-12-11T00:00:00Z,85.33,85.58142857 -2001-12-12T00:00:00Z,85.4,85.62428571 -2001-12-13T00:00:00Z,85.94,85.62714286 -2001-12-14T00:00:00Z,85.51,85.49571429 -2001-12-15T00:00:00Z,84.85,85.39142857 -2001-12-16T00:00:00Z,84.99,85.38 -2001-12-17T00:00:00Z,85.64,85.35285714 -2001-12-18T00:00:00Z,85.14,85.25 -2001-12-19T00:00:00Z,84.68,84.97428571 -2001-12-20T00:00:00Z,84.01,84.39285714 -2001-12-21T00:00:00Z,81.44,83.96714286 -2001-12-22T00:00:00Z,81.87,83.61142857 -2001-12-23T00:00:00Z,82.5,83.01142857 -2001-12-24T00:00:00Z,81.44,82.31857143 -2001-12-25T00:00:00Z,80.29,81.66 -2001-12-26T00:00:00Z,80.07,81.13142857 -2001-12-27T00:00:00Z,80.31,80.85 -2001-12-28T00:00:00Z,79.47,80.39571429 -2001-12-29T00:00:00Z,78.69,79.77714286 -2001-12-30T00:00:00Z,78.17,79.23 -2001-12-31T00:00:00Z,77.61,78.85285714 -2002-01-01T00:00:00Z,77.65,78.53142857 -2002-01-02T00:00:00Z,77.82,78.20142857 -2002-01-03T00:00:00Z,78,78.03428571 -2002-01-04T00:00:00Z,78.3,77.99428571 -2002-01-05T00:00:00Z,78.41,78.03571429 -2002-01-06T00:00:00Z,78.46,78.17714286 -2002-01-07T00:00:00Z,78.6,78.32714286 -2002-01-08T00:00:00Z,78.7,78.43428571 -2002-01-09T00:00:00Z,78.57,78.39428571 -2002-01-10T00:00:00Z,77.72,78.46571429 -2002-01-11T00:00:00Z,78.8,78.43571429 -2002-01-12T00:00:00Z,78.2,78.36571429 -2002-01-13T00:00:00Z,77.97,78.07 -2002-01-14T00:00:00Z,76.53,77.79428571 -2002-01-15T00:00:00Z,76.77,77.60857143 -2002-01-16T00:00:00Z,77.27,77.59857143 -2002-01-17T00:00:00Z,77.65,77.42285714 -2002-01-18T00:00:00Z,77.57,77.31571429 -2002-01-19T00:00:00Z,77.45,77.38428571 -2002-01-20T00:00:00Z,78.45,77.64714286 -2002-01-21T00:00:00Z,78.37,77.66857143 -2002-01-22T00:00:00Z,76.92,77.62428571 -2002-01-23T00:00:00Z,76.96,77.66857143 -2002-01-24T00:00:00Z,77.96,77.67571429 -2002-01-25T00:00:00Z,77.62,77.62428571 -2002-01-26T00:00:00Z,77.09,77.31285714 -2002-01-27T00:00:00Z,76.27,77.08571429 -2002-01-28T00:00:00Z,76.78,77.12714286 -2002-01-29T00:00:00Z,77.21,77.20571429 -2002-01-30T00:00:00Z,77.51,77.21714286 -2002-01-31T00:00:00Z,78.04,77.35285714 -2002-02-01T00:00:00Z,78.57,77.19285714 -2002-02-02T00:00:00Z,75.97,77.12142857 -2002-02-03T00:00:00Z,75.77,76.91 -2002-02-04T00:00:00Z,75.3,76.44571429 -2002-02-05T00:00:00Z,73.96,76 -2002-02-06T00:00:00Z,74.39,75.42428571 -2002-02-07T00:00:00Z,74.01,74.80285714 -2002-02-08T00:00:00Z,74.22,74.51 -2002-02-09T00:00:00Z,73.92,74.24285714 -2002-02-10T00:00:00Z,73.9,73.95142857 -2002-02-11T00:00:00Z,73.26,73.92142857 -2002-02-12T00:00:00Z,73.75,73.74428571 -2002-02-13T00:00:00Z,73.15,73.56857143 -2002-02-14T00:00:00Z,72.78,73.33714286 -2002-02-15T00:00:00Z,72.6,73.09142857 -2002-02-16T00:00:00Z,72.2,72.86285714 -2002-02-17T00:00:00Z,72.3,72.68 -2002-02-18T00:00:00Z,71.98,72.59571429 -2002-02-19T00:00:00Z,73.16,72.57571429 -2002-02-20T00:00:00Z,73.01,72.54714286 -2002-02-21T00:00:00Z,72.58,72.55428571 -2002-02-22T00:00:00Z,72.65,72.56285714 -2002-02-23T00:00:00Z,72.26,72.58142857 -2002-02-24T00:00:00Z,72.43,72.69428571 -2002-02-25T00:00:00Z,72.77,72.66428571 -2002-02-26T00:00:00Z,72.95,72.62285714 -2002-02-27T00:00:00Z,72.72,72.53714286 -2002-02-28T00:00:00Z,71.98,72.46714286 -2002-03-01T00:00:00Z,72.16,72.50428571 -2002-03-02T00:00:00Z,72.52,72.73571429 -2002-03-03T00:00:00Z,74.05,72.56428571 -2002-03-04T00:00:00Z,71.57,72.28571429 -2002-03-05T00:00:00Z,71,72.03857143 -2002-03-06T00:00:00Z,70.99,71.80428571 -2002-03-07T00:00:00Z,70.34,71.64571429 -2002-03-08T00:00:00Z,71.05,71.46428571 -2002-03-09T00:00:00Z,71.25,70.98285714 -2002-03-10T00:00:00Z,70.68,70.67571429 -2002-03-11T00:00:00Z,69.42,70.57571429 -2002-03-12T00:00:00Z,70.3,70.53571429 -2002-03-13T00:00:00Z,70.71,70.62285714 -2002-03-14T00:00:00Z,70.95,70.57 -2002-03-15T00:00:00Z,70.68,70.44428571 -2002-03-16T00:00:00Z,70.37,70.43 -2002-03-17T00:00:00Z,70.58,70.65571429 -2002-03-18T00:00:00Z,71,70.74428571 -2002-03-19T00:00:00Z,70.92,70.82 -2002-03-20T00:00:00Z,71.24,70.72857143 -2002-03-21T00:00:00Z,70.31,70.55428571 -2002-03-22T00:00:00Z,69.46,70.51714286 -2002-03-23T00:00:00Z,70.11,70.34 -2002-03-24T00:00:00Z,69.34,70.15714286 -2002-03-25T00:00:00Z,69.72,70.05857143 -2002-03-26T00:00:00Z,70.23,69.87428571 -2002-03-27T00:00:00Z,69.95,69.73714286 -2002-03-28T00:00:00Z,69.35,69.68 -2002-03-29T00:00:00Z,69.06,69.45714286 -2002-03-30T00:00:00Z,68.55,69.26 -2002-03-31T00:00:00Z,67.96,68.95857143 -2002-04-01T00:00:00Z,67.61,68.48428571 -2002-04-02T00:00:00Z,66.91,67.98142857 -2002-04-03T00:00:00Z,66.43,67.38714286 -2002-04-04T00:00:00Z,65.19,66.95 -2002-04-05T00:00:00Z,66,66.74 -2002-04-06T00:00:00Z,67.08,66.72428571 -2002-04-07T00:00:00Z,67.85,66.81714286 -2002-04-08T00:00:00Z,68.26,66.94428571 -2002-04-09T00:00:00Z,67.8,66.96714286 -2002-04-10T00:00:00Z,66.59,67.23142857 -2002-04-11T00:00:00Z,67.04,67.42714286 -2002-04-12T00:00:00Z,67.37,67.46 -2002-04-13T00:00:00Z,67.31,67.32857143 -2002-04-14T00:00:00Z,66.93,67.13 -2002-04-15T00:00:00Z,66.87,66.99285714 -2002-04-16T00:00:00Z,66.84,66.98285714 -2002-04-17T00:00:00Z,66.52,66.86714286 -2002-04-18T00:00:00Z,66.23,66.75571429 -2002-04-19T00:00:00Z,66.59,66.60714286 -2002-04-20T00:00:00Z,66.27,66.50714286 -2002-04-21T00:00:00Z,66.23,66.44571429 -2002-04-22T00:00:00Z,66.44,66.41714286 -2002-04-23T00:00:00Z,66.64,66.46 -2002-04-24T00:00:00Z,66.82,66.56714286 -2002-04-25T00:00:00Z,66.98,66.64571429 -2002-04-26T00:00:00Z,67.14,66.79142857 -2002-04-27T00:00:00Z,67.29,66.95714286 -2002-04-28T00:00:00Z,67.39,67.03 -2002-04-29T00:00:00Z,66.95,67.01142857 -2002-04-30T00:00:00Z,66.51,66.82748152 -2002-05-01T00:00:00Z,65.54307658,66.59669409 -2002-05-02T00:00:00Z,65.37204282,66.19968972 -2002-05-03T00:00:00Z,64.35745537,65.65734616 -2002-05-04T00:00:00Z,63.50446673,65.07888294 -2002-05-05T00:00:00Z,63.35717309,64.71559496 -2002-05-06T00:00:00Z,64.40347216,64.28030965 -2002-05-07T00:00:00Z,63.4749149,63.9707136 -2002-05-08T00:00:00Z,63.38067013,63.8558058 -2002-05-09T00:00:00Z,64.55708554,63.77320512 -2002-05-10T00:00:00Z,63.78560031,63.67758392 -2002-05-11T00:00:00Z,62.86494335,63.64349374 -2002-05-12T00:00:00Z,63.12881232,63.50601826 -2002-05-13T00:00:00Z,63.45720865,63.5305398 -2002-05-14T00:00:00Z,63.64119376,62.79933055 -2002-05-15T00:00:00Z,58.40995293,61.62453907 -2002-05-16T00:00:00Z,56.40956558,60.35006864 -2002-05-17T00:00:00Z,54.81136787,59.12206939 -2002-05-18T00:00:00Z,54.1109983,58.9718407 -2002-05-19T00:00:00Z,62.13598698,58.64998783 -2002-05-20T00:00:00Z,61.32333174,58.27789446 -2002-05-21T00:00:00Z,61.03,58.69121823 -2002-05-22T00:00:00Z,61.14,59.46456209 -2002-05-23T00:00:00Z,61.75,60.42149471 -2002-05-24T00:00:00Z,61.56,61.47745873 -2002-05-25T00:00:00Z,61.62,61.52881224 -2002-05-26T00:00:00Z,62.41,61.64428571 -2002-05-27T00:00:00Z,62,61.77285714 -2002-05-28T00:00:00Z,61.93,61.86 -2002-05-29T00:00:00Z,61.75,61.84714286 -2002-05-30T00:00:00Z,61.66,61.82142857 -2002-05-31T00:00:00Z,61.38,61.62714286 -2002-06-01T00:00:00Z,60.26,61.40142857 -2002-06-02T00:00:00Z,60.83,61.50428571 -2002-06-03T00:00:00Z,62.72,61.96857143 -2002-06-04T00:00:00Z,65.18,62.40571429 -2002-06-05T00:00:00Z,64.81,62.70142857 -2002-06-06T00:00:00Z,63.73,63.12142857 -2002-06-07T00:00:00Z,64.32,63.93 -2002-06-08T00:00:00Z,65.92,64.43 -2002-06-09T00:00:00Z,64.33,64.36142857 -2002-06-10T00:00:00Z,62.24,63.97714286 -2002-06-11T00:00:00Z,62.49,63.6 -2002-06-12T00:00:00Z,62.17,63.39571429 -2002-06-13T00:00:00Z,62.3,63.28 -2002-06-14T00:00:00Z,63.51,62.94 -2002-06-15T00:00:00Z,63.54,62.75857143 -2002-06-16T00:00:00Z,63.06,62.76142857 -2002-06-17T00:00:00Z,62.26,62.63142857 -2002-06-18T00:00:00Z,61.58,62.46 -2002-06-19T00:00:00Z,60.97,62.21 -2002-06-20T00:00:00Z,60.55,61.74571429 -2002-06-21T00:00:00Z,60.26,61.20857143 -2002-06-22T00:00:00Z,59.78,60.77428571 -2002-06-23T00:00:00Z,60.02,60.47857143 -2002-06-24T00:00:00Z,60.19,60.35285714 -2002-06-25T00:00:00Z,60.7,60.49285714 -2002-06-26T00:00:00Z,61.95,60.67714286 -2002-06-27T00:00:00Z,61.84,60.70714286 -2002-06-28T00:00:00Z,60.47,60.96 -2002-06-29T00:00:00Z,61.55,61.38428571 -2002-06-30T00:00:00Z,62.99,61.82714286 -2002-07-01T00:00:00Z,63.29,62.13571429 -2002-07-02T00:00:00Z,62.86,62.36571429 -2002-07-03T00:00:00Z,63.56,62.84285714 -2002-07-04T00:00:00Z,65.18,63.68428571 -2002-07-05T00:00:00Z,66.36,64.47428571 -2002-07-06T00:00:00Z,67.08,65.16 -2002-07-07T00:00:00Z,67.79,65.86428571 -2002-07-08T00:00:00Z,68.22,66.65857143 -2002-07-09T00:00:00Z,68.42,67.29428571 -2002-07-10T00:00:00Z,68.01,67.79285714 -2002-07-11T00:00:00Z,68.67,68.16285714 -2002-07-12T00:00:00Z,68.95,68.43428571 -2002-07-13T00:00:00Z,68.98,68.61428571 -2002-07-14T00:00:00Z,69.05,68.77 -2002-07-15T00:00:00Z,69.31,68.90285714 -2002-07-16T00:00:00Z,69.35,69.17857143 -2002-07-17T00:00:00Z,69.94,69.41142857 -2002-07-18T00:00:00Z,70.3,69.61714286 -2002-07-19T00:00:00Z,70.39,69.84714286 -2002-07-20T00:00:00Z,70.59,70.17285714 -2002-07-21T00:00:00Z,71.33,70.49 -2002-07-22T00:00:00Z,71.53,70.75714286 -2002-07-23T00:00:00Z,71.22,70.87857143 -2002-07-24T00:00:00Z,70.79,71.19857143 -2002-07-25T00:00:00Z,72.54,71.56714286 -2002-07-26T00:00:00Z,72.97,71.97285714 -2002-07-27T00:00:00Z,73.43,72.27714286 -2002-07-28T00:00:00Z,73.46,72.62857143 -2002-07-29T00:00:00Z,73.99,73.08714286 -2002-07-30T00:00:00Z,74.43,73.67714286 -2002-07-31T00:00:00Z,74.92,74.10857143 -2002-08-01T00:00:00Z,75.56,74.59142857 -2002-08-02T00:00:00Z,76.35,74.98428571 -2002-08-03T00:00:00Z,76.18,75.31285714 -2002-08-04T00:00:00Z,75.76,75.61571429 -2002-08-05T00:00:00Z,76.11,75.82714286 -2002-08-06T00:00:00Z,75.91,76.21714286 -2002-08-07T00:00:00Z,77.65,76.73714286 -2002-08-08T00:00:00Z,79.2,77.25428571 -2002-08-09T00:00:00Z,79.97,77.91428571 -2002-08-10T00:00:00Z,80.8,78.72428571 -2002-08-11T00:00:00Z,81.43,79.59857143 -2002-08-12T00:00:00Z,82.23,80.53571429 -2002-08-13T00:00:00Z,82.47,81.28571429 -2002-08-14T00:00:00Z,82.9,81.85428571 -2002-08-15T00:00:00Z,83.18,82.32714286 -2002-08-16T00:00:00Z,83.28,82.74714286 -2002-08-17T00:00:00Z,83.74,83.13571429 -2002-08-18T00:00:00Z,84.15,83.46285714 -2002-08-19T00:00:00Z,84.52,83.80571429 -2002-08-20T00:00:00Z,84.87,84.14285714 -2002-08-21T00:00:00Z,85.26,84.46142857 -2002-08-22T00:00:00Z,85.41,84.75 -2002-08-23T00:00:00Z,85.3,85.05285714 -2002-08-24T00:00:00Z,85.86,85.28 -2002-08-25T00:00:00Z,85.74,85.44 -2002-08-26T00:00:00Z,85.64,85.53857143 -2002-08-27T00:00:00Z,85.56,85.60857143 -2002-08-28T00:00:00Z,85.75,85.63571429 -2002-08-29T00:00:00Z,85.6,85.59714286 -2002-08-30T00:00:00Z,85.03,85.50285714 -2002-08-31T00:00:00Z,85.2,85.51142857 -2002-09-01T00:00:00Z,85.8,85.36714286 -2002-09-02T00:00:00Z,84.63,85.24714286 -2002-09-03T00:00:00Z,84.72,85.10428571 -2002-09-04T00:00:00Z,84.75,84.99285714 -2002-09-05T00:00:00Z,84.82,84.97857143 -2002-09-06T00:00:00Z,84.93,85.00571429 -2002-09-07T00:00:00Z,85.39,84.98142857 -2002-09-08T00:00:00Z,85.63,85.19142857 -2002-09-09T00:00:00Z,86.1,85.46142857 -2002-09-10T00:00:00Z,86.61,85.78428571 -2002-09-11T00:00:00Z,87.01,86.07428571 -2002-09-12T00:00:00Z,86.85,86.42142857 -2002-09-13T00:00:00Z,87.36,86.69714286 -2002-09-14T00:00:00Z,87.32,86.96285714 -2002-09-15T00:00:00Z,87.49,87.15571429 -2002-09-16T00:00:00Z,87.45,87.30285714 -2002-09-17T00:00:00Z,87.64,87.43142857 -2002-09-18T00:00:00Z,87.91,87.61142857 -2002-09-19T00:00:00Z,88.11,87.74 -2002-09-20T00:00:00Z,88.26,87.90285714 -2002-09-21T00:00:00Z,88.46,88.04571429 -2002-09-22T00:00:00Z,88.49,88.22857143 -2002-09-23T00:00:00Z,88.73,88.41142857 -2002-09-24T00:00:00Z,88.92,88.56142857 -2002-09-25T00:00:00Z,88.96,88.78857143 -2002-09-26T00:00:00Z,89.7,88.95428571 -2002-09-27T00:00:00Z,89.42,88.87 -2002-09-28T00:00:00Z,87.87,88.70285714 -2002-09-29T00:00:00Z,87.32,88.48571429 -2002-09-30T00:00:00Z,87.21,88.18571429 -2002-10-01T00:00:00Z,86.82,87.80857143 -2002-10-02T00:00:00Z,86.32,87.32142857 -2002-10-03T00:00:00Z,86.29,86.97 -2002-10-04T00:00:00Z,86.96,86.92571429 -2002-10-05T00:00:00Z,87.56,86.87142857 -2002-10-06T00:00:00Z,86.94,86.97142857 -2002-10-07T00:00:00Z,87.91,87.07571429 -2002-10-08T00:00:00Z,87.55,87.32714286 -2002-10-09T00:00:00Z,88.08,87.60285714 -2002-10-10T00:00:00Z,88.22,87.71428571 -2002-10-11T00:00:00Z,87.74,87.48 -2002-10-12T00:00:00Z,85.92,87.08142857 -2002-10-13T00:00:00Z,84.15,85.88142857 -2002-10-14T00:00:00Z,79.51,84.50142857 -2002-10-15T00:00:00Z,77.89,82.91428571 -2002-10-16T00:00:00Z,76.97,80.92571429 -2002-10-17T00:00:00Z,74.3,78.65857143 -2002-10-18T00:00:00Z,71.87,76.50857143 -2002-10-19T00:00:00Z,70.87,74.41285714 -2002-10-20T00:00:00Z,69.48,72.90285714 -2002-10-21T00:00:00Z,68.94,71.59142857 -2002-10-22T00:00:00Z,68.71,70.34571429 -2002-10-23T00:00:00Z,68.25,69.44857143 -2002-10-24T00:00:00Z,68.02,68.93714286 -2002-10-25T00:00:00Z,68.29,68.59142857 -2002-10-26T00:00:00Z,68.45,68.39142857 -2002-10-27T00:00:00Z,68.08,68.22142857 -2002-10-28T00:00:00Z,67.75,68.12142857 -2002-10-29T00:00:00Z,68.01,68.14428571 -2002-10-30T00:00:00Z,68.41,68.23857143 -2002-10-31T00:00:00Z,68.68,68.29714286 -2002-11-01T00:00:00Z,68.7,68.26 -2002-11-02T00:00:00Z,68.19,68.25714286 -2002-11-03T00:00:00Z,68.06,68.36285714 -2002-11-04T00:00:00Z,68.49,68.42714286 -2002-11-05T00:00:00Z,68.46,68.45714286 -2002-11-06T00:00:00Z,68.62,68.37857143 -2002-11-07T00:00:00Z,68.13,68.34428571 -2002-11-08T00:00:00Z,68.46,68.30285714 -2002-11-09T00:00:00Z,67.9,68.33142857 -2002-11-10T00:00:00Z,68.26,68.28714286 -2002-11-11T00:00:00Z,68.18,68.19571429 -2002-11-12T00:00:00Z,67.82,68.18142857 -2002-11-13T00:00:00Z,68.52,68.28142857 -2002-11-14T00:00:00Z,68.83,68.29857143 -2002-11-15T00:00:00Z,68.58,68.38428571 -2002-11-16T00:00:00Z,68.5,68.51714286 -2002-11-17T00:00:00Z,69.19,68.51857143 -2002-11-18T00:00:00Z,68.19,68.23714286 -2002-11-19T00:00:00Z,65.85,67.74571429 -2002-11-20T00:00:00Z,65.08,67.18142857 -2002-11-21T00:00:00Z,64.88,66.71142857 -2002-11-22T00:00:00Z,65.29,65.88571429 -2002-11-23T00:00:00Z,62.72,64.66285714 -2002-11-24T00:00:00Z,60.63,63.63285714 -2002-11-25T00:00:00Z,60.98,63.18142857 -2002-11-26T00:00:00Z,62.69,63.01857143 -2002-11-27T00:00:00Z,63.94,63.00857143 -2002-11-28T00:00:00Z,64.81,62.93142857 -2002-11-29T00:00:00Z,64.75,63.07571429 -2002-11-30T00:00:00Z,63.73,63.37715634 -2002-12-01T00:00:00Z,62.7389437,NA -2002-12-02T00:00:00Z,NA,NA -2002-12-03T00:00:00Z,NA,NA -2002-12-04T00:00:00Z,NA,NA -2002-12-05T00:00:00Z,NA,NA -2002-12-06T00:00:00Z,NA,NA -2002-12-07T00:00:00Z,NA,NA -2002-12-08T00:00:00Z,NA,NA -2002-12-09T00:00:00Z,NA,NA -2002-12-10T00:00:00Z,NA,NA -2002-12-11T00:00:00Z,NA,NA -2002-12-12T00:00:00Z,NA,NA -2002-12-13T00:00:00Z,NA,NA -2002-12-14T00:00:00Z,NA,NA -2002-12-15T00:00:00Z,NA,NA -2002-12-16T00:00:00Z,NA,NA -2002-12-17T00:00:00Z,NA,NA -2002-12-18T00:00:00Z,NA,NA -2002-12-19T00:00:00Z,NA,NA -2002-12-20T00:00:00Z,NA,NA -2002-12-21T00:00:00Z,NA,NA -2002-12-22T00:00:00Z,NA,NA -2002-12-23T00:00:00Z,NA,NA -2002-12-24T00:00:00Z,NA,NA -2002-12-25T00:00:00Z,NA,NA -2002-12-26T00:00:00Z,NA,NA -2002-12-27T00:00:00Z,NA,NA -2002-12-28T00:00:00Z,NA,NA -2002-12-29T00:00:00Z,NA,NA -2002-12-30T00:00:00Z,NA,NA -2002-12-31T00:00:00Z,NA,NA -2003-01-01T00:00:00Z,NA,NA -2003-01-02T00:00:00Z,NA,NA -2003-01-03T00:00:00Z,NA,NA -2003-01-04T00:00:00Z,NA,NA -2003-01-05T00:00:00Z,NA,NA -2003-01-06T00:00:00Z,NA,52.58 -2003-01-07T00:00:00Z,52.58,NA -2003-01-08T00:00:00Z,NA,NA -2003-01-09T00:00:00Z,NA,NA -2003-01-10T00:00:00Z,NA,NA -2003-01-11T00:00:00Z,NA,NA -2003-01-12T00:00:00Z,NA,NA -2003-01-13T00:00:00Z,NA,NA -2003-01-14T00:00:00Z,NA,NA -2003-01-15T00:00:00Z,NA,NA -2003-01-16T00:00:00Z,NA,NA -2003-01-17T00:00:00Z,NA,NA -2003-01-18T00:00:00Z,NA,NA -2003-01-19T00:00:00Z,NA,NA -2003-01-20T00:00:00Z,NA,NA -2003-01-21T00:00:00Z,NA,NA -2003-01-22T00:00:00Z,NA,NA -2003-01-23T00:00:00Z,NA,NA -2003-01-24T00:00:00Z,NA,NA -2003-01-25T00:00:00Z,NA,NA -2003-01-26T00:00:00Z,NA,NA -2003-01-27T00:00:00Z,NA,NA -2003-01-28T00:00:00Z,NA,NA -2003-01-29T00:00:00Z,NA,NA -2003-01-30T00:00:00Z,NA,NA -2003-01-31T00:00:00Z,NA,NA -2003-02-01T00:00:00Z,NA,NA -2003-02-02T00:00:00Z,NA,NA -2003-02-03T00:00:00Z,NA,NA -2003-02-04T00:00:00Z,NA,NA -2003-02-05T00:00:00Z,NA,NA -2003-02-06T00:00:00Z,NA,NA -2003-02-07T00:00:00Z,NA,NA -2003-02-08T00:00:00Z,NA,NA -2003-02-09T00:00:00Z,NA,NA -2003-02-10T00:00:00Z,NA,NA -2003-02-11T00:00:00Z,NA,NA -2003-02-12T00:00:00Z,NA,NA -2003-02-13T00:00:00Z,NA,NA -2003-02-14T00:00:00Z,NA,NA -2003-02-15T00:00:00Z,NA,NA -2003-02-16T00:00:00Z,NA,NA -2003-02-17T00:00:00Z,NA,NA -2003-02-18T00:00:00Z,NA,NA -2003-02-19T00:00:00Z,NA,NA -2003-02-20T00:00:00Z,NA,NA -2003-02-21T00:00:00Z,NA,NA -2003-02-22T00:00:00Z,NA,NA -2003-02-23T00:00:00Z,NA,NA -2003-02-24T00:00:00Z,NA,NA -2003-02-25T00:00:00Z,NA,NA -2003-02-26T00:00:00Z,NA,NA -2003-02-27T00:00:00Z,NA,NA -2003-02-28T00:00:00Z,NA,NA -2003-03-01T00:00:00Z,NA,NA -2003-03-02T00:00:00Z,NA,NA -2003-03-03T00:00:00Z,NA,NA -2003-03-04T00:00:00Z,NA,NA -2003-03-05T00:00:00Z,NA,NA -2003-03-06T00:00:00Z,NA,NA -2003-03-07T00:00:00Z,NA,NA -2003-03-08T00:00:00Z,NA,NA -2003-03-09T00:00:00Z,NA,NA -2003-03-10T00:00:00Z,NA,NA -2003-03-11T00:00:00Z,NA,NA -2003-03-12T00:00:00Z,NA,NA -2003-03-13T00:00:00Z,NA,NA -2003-03-14T00:00:00Z,NA,NA -2003-03-15T00:00:00Z,NA,NA -2003-03-16T00:00:00Z,NA,NA -2003-03-17T00:00:00Z,NA,NA -2003-03-18T00:00:00Z,NA,NA -2003-03-19T00:00:00Z,NA,NA -2003-03-20T00:00:00Z,NA,NA -2003-03-21T00:00:00Z,NA,NA -2003-03-22T00:00:00Z,NA,52.58 -2003-03-23T00:00:00Z,52.58,49.195 -2003-03-24T00:00:00Z,45.81,46.84333333 -2003-03-25T00:00:00Z,42.14,45.4825 -2003-03-26T00:00:00Z,41.4,44.874 -2003-03-27T00:00:00Z,42.44,44.145 -2003-03-28T00:00:00Z,40.5,43.39 -2003-03-29T00:00:00Z,38.86,41.28714286 -2003-03-30T00:00:00Z,37.86,40.31428571 -2003-03-31T00:00:00Z,39,40.82714286 -2003-04-01T00:00:00Z,45.73,40.76428571 -2003-04-02T00:00:00Z,40.96,40.43571429 -2003-04-03T00:00:00Z,40.14,41.34857143 -2003-04-04T00:00:00Z,46.89,43.16857143 -2003-04-05T00:00:00Z,51.6,44.17571429 -2003-04-06T00:00:00Z,44.91,44.32142857 -2003-04-07T00:00:00Z,40.02,45.61285714 -2003-04-08T00:00:00Z,54.77,48.42142857 -2003-04-09T00:00:00Z,60.62,51.74857143 -2003-04-10T00:00:00Z,63.43,54.46428571 -2003-04-11T00:00:00Z,65.9,56.72857143 -2003-04-12T00:00:00Z,67.45,59.86857143 -2003-04-13T00:00:00Z,66.89,63.56142857 -2003-04-14T00:00:00Z,65.87,64.95428571 -2003-04-15T00:00:00Z,64.52,65.34428571 -2003-04-16T00:00:00Z,63.35,65.47285714 -2003-04-17T00:00:00Z,64.33,65.54 -2003-04-18T00:00:00Z,66.37,65.29142857 -2003-04-19T00:00:00Z,65.71,65.05571429 -2003-04-20T00:00:00Z,65.24,64.95714286 -2003-04-21T00:00:00Z,65.18,65.03571429 -2003-04-22T00:00:00Z,65.07,65.08142857 -2003-04-23T00:00:00Z,63.67,65.00714286 -2003-04-24T00:00:00Z,63.81,64.85285714 -2003-04-25T00:00:00Z,65.29,64.91714286 -2003-04-26T00:00:00Z,66.16,65.03857143 -2003-04-27T00:00:00Z,66.09,65.09857143 -2003-04-28T00:00:00Z,65.6,65.13428571 -2003-04-29T00:00:00Z,65.32,65.39285714 -2003-04-30T00:00:00Z,65.48,65.72857143 -2003-05-01T00:00:00Z,66.16,65.80142857 -2003-05-02T00:00:00Z,65.8,65.76142857 -2003-05-03T00:00:00Z,65.88,65.81714286 -2003-05-04T00:00:00Z,66.48,65.91857143 -2003-05-05T00:00:00Z,66.31,65.99142857 -2003-05-06T00:00:00Z,65.83,65.93428571 -2003-05-07T00:00:00Z,65.08,65.78285714 -2003-05-08T00:00:00Z,65.1,65.80714286 -2003-05-09T00:00:00Z,65.97,65.75857143 -2003-05-10T00:00:00Z,65.54,65.73857143 -2003-05-11T00:00:00Z,66.34,65.85285714 -2003-05-12T00:00:00Z,67.11,65.90857143 -2003-05-13T00:00:00Z,66.22,66.06142857 -2003-05-14T00:00:00Z,66.15,66.23142857 -2003-05-15T00:00:00Z,66.29,66.42428571 -2003-05-16T00:00:00Z,67.32,66.79714286 -2003-05-17T00:00:00Z,68.15,67 -2003-05-18T00:00:00Z,67.76,67.03571429 -2003-05-19T00:00:00Z,67.36,67.15571429 -2003-05-20T00:00:00Z,67.06,67.22428571 -2003-05-21T00:00:00Z,66.63,67.28714286 -2003-05-22T00:00:00Z,66.73,67.30714286 -2003-05-23T00:00:00Z,67.46,67.25857143 -2003-05-24T00:00:00Z,67.81,67.3 -2003-05-25T00:00:00Z,68.05,67.30571429 -2003-05-26T00:00:00Z,67.4,67.29428571 -2003-05-27T00:00:00Z,66.98,67.33 -2003-05-28T00:00:00Z,66.88,67.31428571 -2003-05-29T00:00:00Z,66.62,67.18857143 -2003-05-30T00:00:00Z,66.58,67.04142857 -2003-05-31T00:00:00Z,66.78,66.78571429 -2003-06-01T00:00:00Z,66.26,66.58857143 -2003-06-02T00:00:00Z,66.02,66.33714286 -2003-06-03T00:00:00Z,65.22,65.81428571 -2003-06-04T00:00:00Z,63.22,64.92857143 -2003-06-05T00:00:00Z,60.42,63.44571429 -2003-06-06T00:00:00Z,56.2,61.98857143 -2003-06-07T00:00:00Z,56.58,59.77571429 -2003-06-08T00:00:00Z,50.77,57.81571429 -2003-06-09T00:00:00Z,52.3,55.21857143 -2003-06-10T00:00:00Z,47.04,52.66285714 -2003-06-11T00:00:00Z,45.33,50.59857143 -2003-06-12T00:00:00Z,45.97,49.17 -2003-06-13T00:00:00Z,46.2,47.80714286 -2003-06-14T00:00:00Z,47.04,46.54428571 -2003-06-15T00:00:00Z,41.93,44.94 -2003-06-16T00:00:00Z,41.07,43.90142857 -2003-06-17T00:00:00Z,39.77,42.83428571 -2003-06-18T00:00:00Z,37.86,41.58714286 -2003-06-19T00:00:00Z,37.24,40.33 -2003-06-20T00:00:00Z,37.4,38.70714286 -2003-06-21T00:00:00Z,35.68,37.46571429 -2003-06-22T00:00:00Z,33.24,36.91857143 -2003-06-23T00:00:00Z,37.24,38.34714286 -2003-06-24T00:00:00Z,49.77,41.11919473 -2003-06-25T00:00:00Z,57.26159752,44.40858126 -2003-06-26T00:00:00Z,60.26228182,47.90795218 -2003-06-27T00:00:00Z,61.89392725,51.67411328 -2003-06-28T00:00:00Z,62.04158877,55.88247721 -2003-06-29T00:00:00Z,62.69738291,59.48591275 -2003-06-30T00:00:00Z,62.4627866,61.18734132 -2003-07-01T00:00:00Z,61.68,61.92100373 -2003-07-02T00:00:00Z,62.4,62.2116172 -2003-07-03T00:00:00Z,62.3,62.29224628 -2003-07-04T00:00:00Z,62.46,62.30751375 -2003-07-05T00:00:00Z,62.15,62.19629268 -2003-07-06T00:00:00Z,61.92,62.20714286 -2003-07-07T00:00:00Z,62.54,62.41714286 -2003-07-08T00:00:00Z,63.15,62.62571429 -2003-07-09T00:00:00Z,63.86,62.93714286 -2003-07-10T00:00:00Z,64.48,63.41714286 -2003-07-11T00:00:00Z,65.82,63.87714286 -2003-07-12T00:00:00Z,65.37,64.34714286 -2003-07-13T00:00:00Z,65.21,64.78571429 -2003-07-14T00:00:00Z,65.61,65.14571429 -2003-07-15T00:00:00Z,65.67,65.49571429 -2003-07-16T00:00:00Z,66.31,65.63142857 -2003-07-17T00:00:00Z,65.43,65.68714286 -2003-07-18T00:00:00Z,66.21,65.8 -2003-07-19T00:00:00Z,66.16,66.04857143 -2003-07-20T00:00:00Z,66.95,66.30857143 -2003-07-21T00:00:00Z,67.43,66.47142857 -2003-07-22T00:00:00Z,66.81,66.42857143 -2003-07-23T00:00:00Z,66.01,66.43142857 -2003-07-24T00:00:00Z,65.45,66.2 -2003-07-25T00:00:00Z,64.59,66.13428571 -2003-07-26T00:00:00Z,65.7,65.89571429 -2003-07-27T00:00:00Z,65.28,65.55285714 -2003-07-28T00:00:00Z,65.03,65.62714286 -2003-07-29T00:00:00Z,67.33,65.85285714 -2003-07-30T00:00:00Z,67.59,66.15285714 -2003-07-31T00:00:00Z,67.55,66.64142857 -2003-08-01T00:00:00Z,68.01,66.91428571 -2003-08-02T00:00:00Z,67.61,67.21 -2003-08-03T00:00:00Z,67.35,67.51 -2003-08-04T00:00:00Z,67.13,67.41857143 -2003-08-05T00:00:00Z,66.69,67.13714286 -2003-08-06T00:00:00Z,65.62,66.72714286 -2003-08-07T00:00:00Z,64.68,66.12 -2003-08-08T00:00:00Z,63.76,65.45428571 -2003-08-09T00:00:00Z,62.95,64.67285714 -2003-08-10T00:00:00Z,61.88,63.68571429 -2003-08-11T00:00:00Z,60.22,62.55 -2003-08-12T00:00:00Z,58.74,61.35714286 -2003-08-13T00:00:00Z,57.27,59.95285714 -2003-08-14T00:00:00Z,54.85,58.53428571 -2003-08-15T00:00:00Z,53.83,57.75857143 -2003-08-16T00:00:00Z,57.52,57.18142857 -2003-08-17T00:00:00Z,57.84,57.29428571 -2003-08-18T00:00:00Z,61.01,57.93 -2003-08-19T00:00:00Z,63.19,58.83 -2003-08-20T00:00:00Z,63.57,60.19142857 -2003-08-21T00:00:00Z,64.38,61.92714286 -2003-08-22T00:00:00Z,65.98,63.21285714 -2003-08-23T00:00:00Z,66.52,64.62571429 -2003-08-24T00:00:00Z,67.73,65.64142857 -2003-08-25T00:00:00Z,68.12,66.32857143 -2003-08-26T00:00:00Z,68,67.00142857 -2003-08-27T00:00:00Z,68.28,67.59 -2003-08-28T00:00:00Z,68.5,67.94571429 -2003-08-29T00:00:00Z,68.47,68.21714286 -2003-08-30T00:00:00Z,68.42,68.31428571 -2003-08-31T00:00:00Z,68.41,68.38 -2003-09-01T00:00:00Z,68.58,68.44285714 -2003-09-02T00:00:00Z,68.44,68.51428571 -2003-09-03T00:00:00Z,68.78,68.52571429 -2003-09-04T00:00:00Z,68.58,68.45571429 -2003-09-05T00:00:00Z,67.98,68.32428571 -2003-09-06T00:00:00Z,67.5,68.13857143 -2003-09-07T00:00:00Z,67.11,67.86571429 -2003-09-08T00:00:00Z,66.67,67.70428571 -2003-09-09T00:00:00Z,67.31,67.53285714 -2003-09-10T00:00:00Z,67.58,67.30428571 -2003-09-11T00:00:00Z,66.98,67.19571429 -2003-09-12T00:00:00Z,67.22,67.24142857 -2003-09-13T00:00:00Z,67.82,67.23142857 -2003-09-14T00:00:00Z,67.04,67.20857143 -2003-09-15T00:00:00Z,66.51,66.93714286 -2003-09-16T00:00:00Z,65.41,66.54714286 -2003-09-17T00:00:00Z,64.85,66.37142857 -2003-09-18T00:00:00Z,65.75,66.33857143 -2003-09-19T00:00:00Z,66.99,65.88571429 -2003-09-20T00:00:00Z,64.65,65.58571429 -2003-09-21T00:00:00Z,64.94,65.53571429 -2003-09-22T00:00:00Z,66.16,65.62714286 -2003-09-23T00:00:00Z,66.05,65.56857143 -2003-09-24T00:00:00Z,64.44,65.07857143 -2003-09-25T00:00:00Z,62.32,64.16285714 -2003-09-26T00:00:00Z,60.58,63.46285714 -2003-09-27T00:00:00Z,59.75,62.63285714 -2003-09-28T00:00:00Z,59.13,61.43714286 -2003-09-29T00:00:00Z,57.79,59.85714286 -2003-09-30T00:00:00Z,54.99,58.34142857 -2003-10-01T00:00:00Z,53.83,57.10571429 -2003-10-02T00:00:00Z,53.67,56.94 -2003-10-03T00:00:00Z,59.42,57.19285714 -2003-10-04T00:00:00Z,61.52,57.41 -2003-10-05T00:00:00Z,60.65,57.88 -2003-10-06T00:00:00Z,61.08,58.86857143 -2003-10-07T00:00:00Z,61.91,59.97571429 -2003-10-08T00:00:00Z,61.58,61.05857143 -2003-10-09T00:00:00Z,61.25,61.41142857 -2003-10-10T00:00:00Z,61.89,61.82285714 -2003-10-11T00:00:00Z,64.4,62.49142857 -2003-10-12T00:00:00Z,65.33,62.97 -2003-10-13T00:00:00Z,64.43,63.44142857 -2003-10-14T00:00:00Z,65.21,63.91428571 -2003-10-15T00:00:00Z,64.89,63.91 -2003-10-16T00:00:00Z,61.22,63.93571429 -2003-10-17T00:00:00Z,62.07,64.12714286 -2003-10-18T00:00:00Z,65.74,64.36857143 -2003-10-19T00:00:00Z,67.02,64.78714286 -2003-10-20T00:00:00Z,67.36,65.10857143 -2003-10-21T00:00:00Z,67.46,65.39857143 -2003-10-22T00:00:00Z,66.92,66.32 -2003-10-23T00:00:00Z,67.67,67.08714286 -2003-10-24T00:00:00Z,67.44,67.29857143 -2003-10-25T00:00:00Z,67.22,67.33 -2003-10-26T00:00:00Z,67.24,67.30571429 -2003-10-27T00:00:00Z,67.19,67.08714286 -2003-10-28T00:00:00Z,65.93,66.63142857 -2003-10-29T00:00:00Z,63.73,65.12142857 -2003-10-30T00:00:00Z,57.1,62.76571429 -2003-10-31T00:00:00Z,50.95,59.73 -2003-11-01T00:00:00Z,45.97,56.24428571 -2003-11-02T00:00:00Z,42.84,52.51285714 -2003-11-03T00:00:00Z,41.07,49.1 -2003-11-04T00:00:00Z,42.04,46.97571429 -2003-11-05T00:00:00Z,48.86,45.23428571 -2003-11-06T00:00:00Z,44.91,43.85428571 -2003-11-07T00:00:00Z,41.29,42.83857143 -2003-11-08T00:00:00Z,38.86,43.59142857 -2003-11-09T00:00:00Z,48.11,44.87142857 -2003-11-10T00:00:00Z,50.03,46.97857143 -2003-11-11T00:00:00Z,56.79,48.16571429 -2003-11-12T00:00:00Z,57.17,49.79714286 -2003-11-13T00:00:00Z,56.33,52.11142857 -2003-11-14T00:00:00Z,57.49,55.77571429 -2003-11-15T00:00:00Z,64.51,58.45 -2003-11-16T00:00:00Z,66.83,60.97 -2003-11-17T00:00:00Z,67.67,62.57285714 -2003-11-18T00:00:00Z,68.01,64.19857143 -2003-11-19T00:00:00Z,68.55,65.80285714 -2003-11-20T00:00:00Z,67.56,67.08857143 -2003-11-21T00:00:00Z,66.49,67.14428571 -2003-11-22T00:00:00Z,64.9,66.65 -2003-11-23T00:00:00Z,63.37,65.98285714 -2003-11-24T00:00:00Z,63,65.07571429 -2003-11-25T00:00:00Z,61.66,63.97285714 -2003-11-26T00:00:00Z,60.83,62.88 -2003-11-27T00:00:00Z,59.91,61.92857143 -2003-11-28T00:00:00Z,59.83,60.26285714 -2003-11-29T00:00:00Z,53.24,56.89142857 -2003-11-30T00:00:00Z,39.77,53.09285714 -2003-12-01T00:00:00Z,36.41,50.05285714 -2003-12-02T00:00:00Z,40.38,NA -2003-12-03T00:00:00Z,NA,NA -2003-12-04T00:00:00Z,NA,NA -2003-12-05T00:00:00Z,NA,NA -2003-12-06T00:00:00Z,NA,NA -2003-12-07T00:00:00Z,NA,NA -2003-12-08T00:00:00Z,NA,NA -2003-12-09T00:00:00Z,NA,NA -2003-12-10T00:00:00Z,NA,NA -2003-12-11T00:00:00Z,NA,NA -2003-12-12T00:00:00Z,NA,NA -2003-12-13T00:00:00Z,NA,NA -2003-12-14T00:00:00Z,NA,50.77 -2003-12-15T00:00:00Z,50.77,50.77 -2003-12-16T00:00:00Z,50.77,NA -2003-12-17T00:00:00Z,NA,51.68333333 -2003-12-18T00:00:00Z,53.51,50.8075 -2003-12-19T00:00:00Z,48.18,NA -2003-12-20T00:00:00Z,NA,NA -2003-12-21T00:00:00Z,NA,48.8 -2003-12-22T00:00:00Z,42.74,48.205 -2003-12-23T00:00:00Z,48.39,49.18 -2003-12-24T00:00:00Z,53.08,46.966 -2003-12-25T00:00:00Z,42.44,44.778 -2003-12-26T00:00:00Z,37.24,43.16166667 -2003-12-27T00:00:00Z,35.08,41.85142857 -2003-12-28T00:00:00Z,33.99,40.53142857 -2003-12-29T00:00:00Z,33.5,38.36714286 -2003-12-30T00:00:00Z,33.24,35.70428571 -2003-12-31T00:00:00Z,34.44,34.68285714 -2004-01-01T00:00:00Z,35.29,36.92142857 -2004-01-02T00:00:00Z,52.91,NA -2004-01-03T00:00:00Z,NA,NA -2004-01-04T00:00:00Z,NA,NA -2004-01-05T00:00:00Z,NA,NA -2004-01-06T00:00:00Z,NA,46.77333333 -2004-01-07T00:00:00Z,52.12,51.45 -2004-01-08T00:00:00Z,49.32,NA -2004-01-09T00:00:00Z,NA,NA -2004-01-10T00:00:00Z,NA,NA -2004-01-11T00:00:00Z,NA,NA -2004-01-12T00:00:00Z,NA,NA -2004-01-13T00:00:00Z,NA,NA -2004-01-14T00:00:00Z,NA,NA -2004-01-15T00:00:00Z,NA,NA -2004-01-16T00:00:00Z,NA,NA -2004-01-17T00:00:00Z,NA,NA -2004-01-18T00:00:00Z,NA,NA -2004-01-19T00:00:00Z,NA,NA -2004-01-20T00:00:00Z,NA,NA -2004-01-21T00:00:00Z,NA,NA -2004-01-22T00:00:00Z,NA,NA -2004-01-23T00:00:00Z,NA,NA -2004-01-24T00:00:00Z,NA,NA -2004-01-25T00:00:00Z,NA,NA -2004-01-26T00:00:00Z,NA,NA -2004-01-27T00:00:00Z,NA,NA -2004-01-28T00:00:00Z,NA,NA -2004-01-29T00:00:00Z,NA,NA -2004-01-30T00:00:00Z,NA,NA -2004-01-31T00:00:00Z,NA,NA -2004-02-01T00:00:00Z,NA,NA -2004-02-02T00:00:00Z,NA,NA -2004-02-03T00:00:00Z,NA,NA -2004-02-04T00:00:00Z,NA,NA -2004-02-05T00:00:00Z,NA,NA -2004-02-06T00:00:00Z,NA,NA -2004-02-07T00:00:00Z,NA,NA -2004-02-08T00:00:00Z,NA,NA -2004-02-09T00:00:00Z,NA,NA -2004-02-10T00:00:00Z,NA,NA -2004-02-11T00:00:00Z,NA,NA -2004-02-12T00:00:00Z,NA,NA -2004-02-13T00:00:00Z,NA,NA -2004-02-14T00:00:00Z,NA,NA -2004-02-15T00:00:00Z,NA,NA -2004-02-16T00:00:00Z,NA,NA -2004-02-17T00:00:00Z,NA,NA -2004-02-18T00:00:00Z,NA,NA -2004-02-19T00:00:00Z,NA,NA -2004-02-20T00:00:00Z,NA,NA -2004-02-21T00:00:00Z,NA,NA -2004-02-22T00:00:00Z,NA,NA -2004-02-23T00:00:00Z,NA,NA -2004-02-24T00:00:00Z,NA,NA -2004-02-25T00:00:00Z,NA,NA -2004-02-26T00:00:00Z,NA,NA -2004-02-27T00:00:00Z,NA,NA -2004-02-28T00:00:00Z,NA,NA -2004-02-29T00:00:00Z,NA,NA -2004-03-01T00:00:00Z,NA,NA -2004-03-02T00:00:00Z,NA,NA -2004-03-03T00:00:00Z,NA,NA -2004-03-04T00:00:00Z,NA,NA -2004-03-05T00:00:00Z,NA,NA -2004-03-06T00:00:00Z,NA,NA -2004-03-07T00:00:00Z,NA,NA -2004-03-08T00:00:00Z,NA,NA -2004-03-09T00:00:00Z,NA,NA -2004-03-10T00:00:00Z,NA,NA -2004-03-11T00:00:00Z,NA,NA -2004-03-12T00:00:00Z,NA,NA -2004-03-13T00:00:00Z,NA,NA -2004-03-14T00:00:00Z,NA,NA -2004-03-15T00:00:00Z,NA,NA -2004-03-16T00:00:00Z,NA,NA -2004-03-17T00:00:00Z,NA,NA -2004-03-18T00:00:00Z,NA,NA -2004-03-19T00:00:00Z,NA,NA -2004-03-20T00:00:00Z,NA,NA -2004-03-21T00:00:00Z,NA,NA -2004-03-22T00:00:00Z,NA,NA -2004-03-23T00:00:00Z,NA,NA -2004-03-24T00:00:00Z,NA,NA -2004-03-25T00:00:00Z,NA,NA -2004-03-26T00:00:00Z,NA,NA -2004-03-27T00:00:00Z,NA,NA -2004-03-28T00:00:00Z,NA,NA -2004-03-29T00:00:00Z,NA,67.98 -2004-03-30T00:00:00Z,67.98,68.41 -2004-03-31T00:00:00Z,68.84,68.77333333 -2004-04-01T00:00:00Z,69.5,68.895 -2004-04-02T00:00:00Z,69.26,68.91 -2004-04-03T00:00:00Z,68.97,68.815 -2004-04-04T00:00:00Z,68.34,68.42428571 -2004-04-05T00:00:00Z,66.08,68.25285714 -2004-04-06T00:00:00Z,66.78,68.16571429 -2004-04-07T00:00:00Z,68.23,67.98 -2004-04-08T00:00:00Z,68.2,67.83 -2004-04-09T00:00:00Z,68.21,67.61142857 -2004-04-10T00:00:00Z,67.44,67.42142857 -2004-04-11T00:00:00Z,67.01,67.62571429 -2004-04-12T00:00:00Z,67.51,67.80571429 -2004-04-13T00:00:00Z,68.04,67.56142857 -2004-04-14T00:00:00Z,66.52,67.06571429 -2004-04-15T00:00:00Z,64.73,66.55 -2004-04-16T00:00:00Z,64.6,65.99857143 -2004-04-17T00:00:00Z,63.58,65.14142857 -2004-04-18T00:00:00Z,61.01,64.24571429 -2004-04-19T00:00:00Z,61.24,63.03714286 -2004-04-20T00:00:00Z,59.58,62.31571429 -2004-04-21T00:00:00Z,61.47,61.84571429 -2004-04-22T00:00:00Z,61.44,61.23 -2004-04-23T00:00:00Z,60.29,60.90571429 -2004-04-24T00:00:00Z,61.31,60.97571429 -2004-04-25T00:00:00Z,61.5,61.09 -2004-04-26T00:00:00Z,62.04,61.41142857 -2004-04-27T00:00:00Z,61.83,61.50142857 -2004-04-28T00:00:00Z,62.1,62.22285714 -2004-04-29T00:00:00Z,66.49,63.1 -2004-04-30T00:00:00Z,66.43,63.77571429 -2004-05-01T00:00:00Z,66.04,64.37 -2004-05-02T00:00:00Z,65.66,64.70142857 -2004-05-03T00:00:00Z,64.36,65.01285714 -2004-05-04T00:00:00Z,64.01,65.27857143 -2004-05-05T00:00:00Z,63.96,64.86714286 -2004-05-06T00:00:00Z,63.61,64.39285714 -2004-05-07T00:00:00Z,63.11,63.89571429 -2004-05-08T00:00:00Z,62.56,63.38428571 -2004-05-09T00:00:00Z,62.08,62.96428571 -2004-05-10T00:00:00Z,61.42,62.48857143 -2004-05-11T00:00:00Z,60.68,61.91571429 -2004-05-12T00:00:00Z,59.95,61.36857143 -2004-05-13T00:00:00Z,59.78,60.95428571 -2004-05-14T00:00:00Z,60.21,60.50285714 -2004-05-15T00:00:00Z,59.4,59.90142857 -2004-05-16T00:00:00Z,57.87,59.47428571 -2004-05-17T00:00:00Z,58.43,59.27857143 -2004-05-18T00:00:00Z,59.31,59.28285714 -2004-05-19T00:00:00Z,59.98,59.39428571 -2004-05-20T00:00:00Z,60.56,59.51714286 -2004-05-21T00:00:00Z,61.07,59.82142857 -2004-05-22T00:00:00Z,61.53,60.40142857 -2004-05-23T00:00:00Z,61.93,61.11857143 -2004-05-24T00:00:00Z,63.45,61.90142857 -2004-05-25T00:00:00Z,64.79,62.81857143 -2004-05-26T00:00:00Z,66.4,63.73285714 -2004-05-27T00:00:00Z,66.96,64.60857143 -2004-05-28T00:00:00Z,67.2,65.39428571 -2004-05-29T00:00:00Z,67.03,66.21 -2004-05-30T00:00:00Z,67.64,66.81142857 -2004-05-31T00:00:00Z,67.66,67.29142857 -2004-06-01T00:00:00Z,68.15,67.53 -2004-06-02T00:00:00Z,68.07,67.64571429 -2004-06-03T00:00:00Z,67.77,67.72 -2004-06-04T00:00:00Z,67.72,67.87857143 -2004-06-05T00:00:00Z,68.14,67.96 -2004-06-06T00:00:00Z,68.21,68.04428571 -2004-06-07T00:00:00Z,68.25,68.01571429 -2004-06-08T00:00:00Z,67.95,67.96428571 -2004-06-09T00:00:00Z,67.71,67.95428571 -2004-06-10T00:00:00Z,67.7,68.04142857 -2004-06-11T00:00:00Z,68.33,68.11571429 -2004-06-12T00:00:00Z,68.66,68.18285714 -2004-06-13T00:00:00Z,68.68,68.21714286 -2004-06-14T00:00:00Z,68.49,68.16285714 -2004-06-15T00:00:00Z,67.57,68.07857143 -2004-06-16T00:00:00Z,67.12,68.02571429 -2004-06-17T00:00:00Z,67.33,67.92857143 -2004-06-18T00:00:00Z,67.65,67.81428571 -2004-06-19T00:00:00Z,67.86,67.74857143 -2004-06-20T00:00:00Z,68.22,67.78285714 -2004-06-21T00:00:00Z,68.73,67.91714286 -2004-06-22T00:00:00Z,68.51,68.06428571 -2004-06-23T00:00:00Z,68.15,68.25714286 -2004-06-24T00:00:00Z,68.68,68.41571429 -2004-06-25T00:00:00Z,68.76,68.53571429 -2004-06-26T00:00:00Z,68.7,68.63 -2004-06-27T00:00:00Z,68.88,68.65142857 -2004-06-28T00:00:00Z,68.88,68.72 -2004-06-29T00:00:00Z,68.99,68.86571429 -2004-06-30T00:00:00Z,69.17,68.93428571 -2004-07-01T00:00:00Z,69.16,69.01285714 -2004-07-02T00:00:00Z,69.31,69.13285714 -2004-07-03T00:00:00Z,69.54,69.29428571 -2004-07-04T00:00:00Z,70.01,69.47428571 -2004-07-05T00:00:00Z,70.14,69.61142857 -2004-07-06T00:00:00Z,69.95,69.73857143 -2004-07-07T00:00:00Z,70.06,69.88428571 -2004-07-08T00:00:00Z,70.18,69.98285714 -2004-07-09T00:00:00Z,70,70.04571429 -2004-07-10T00:00:00Z,69.98,70.08714286 -2004-07-11T00:00:00Z,70.3,70.01142857 -2004-07-12T00:00:00Z,69.61,69.85428571 -2004-07-13T00:00:00Z,68.85,69.6 -2004-07-14T00:00:00Z,68.28,69.19857143 -2004-07-15T00:00:00Z,67.37,68.71285714 -2004-07-16T00:00:00Z,66.6,68.24285714 -2004-07-17T00:00:00Z,66.69,67.72 -2004-07-18T00:00:00Z,66.64,67.30714286 -2004-07-19T00:00:00Z,66.72,66.99428571 -2004-07-20T00:00:00Z,66.66,66.79285714 -2004-07-21T00:00:00Z,66.87,66.73285714 -2004-07-22T00:00:00Z,66.95,66.81285714 -2004-07-23T00:00:00Z,67.16,66.79714286 -2004-07-24T00:00:00Z,66.58,66.88285714 -2004-07-25T00:00:00Z,67.24,67.04285714 -2004-07-26T00:00:00Z,67.84,67.20714286 -2004-07-27T00:00:00Z,67.81,67.14571429 -2004-07-28T00:00:00Z,66.44,66.77142857 -2004-07-29T00:00:00Z,64.33,66.21285714 -2004-07-30T00:00:00Z,63.25,65.67285714 -2004-07-31T00:00:00Z,62.8,64.87 -2004-08-01T00:00:00Z,61.62,63.73714286 -2004-08-02T00:00:00Z,59.91,62.56285714 -2004-08-03T00:00:00Z,59.59,61.55142857 -2004-08-04T00:00:00Z,59.36,60.82714286 -2004-08-05T00:00:00Z,59.26,60.53571429 -2004-08-06T00:00:00Z,61.21,60.64857143 -2004-08-07T00:00:00Z,63.59,61.06 -2004-08-08T00:00:00Z,64.5,61.90142857 -2004-08-09T00:00:00Z,65.8,63.02571429 -2004-08-10T00:00:00Z,67.46,64.22 -2004-08-11T00:00:00Z,67.72,65.41857143 -2004-08-12T00:00:00Z,67.65,66.31571429 -2004-08-13T00:00:00Z,67.49,66.66428571 -2004-08-14T00:00:00Z,66.03,66.64428571 -2004-08-15T00:00:00Z,64.36,66.23571429 -2004-08-16T00:00:00Z,62.94,65.50285714 -2004-08-17T00:00:00Z,62.33,64.69571429 -2004-08-18T00:00:00Z,62.07,63.74285714 -2004-08-19T00:00:00Z,60.98,62.61714286 -2004-08-20T00:00:00Z,59.61,61.65714286 -2004-08-21T00:00:00Z,59.31,60.81714286 -2004-08-22T00:00:00Z,58.48,60.22285714 -2004-08-23T00:00:00Z,58.78,59.46571429 -2004-08-24T00:00:00Z,57.03,58.93571429 -2004-08-25T00:00:00Z,58.36,58.64285714 -2004-08-26T00:00:00Z,58.93,58.51857143 -2004-08-27T00:00:00Z,58.74,58.37571429 -2004-08-28T00:00:00Z,58.31,58.50428571 -2004-08-29T00:00:00Z,59.38,58.58428571 -2004-08-30T00:00:00Z,59.34,58.93571429 -2004-08-31T00:00:00Z,59.49,59.12142857 -2004-09-01T00:00:00Z,59.66,59.17 -2004-09-02T00:00:00Z,59.27,59.33285714 -2004-09-03T00:00:00Z,59.88,59.47571429 -2004-09-04T00:00:00Z,59.31,59.75285714 -2004-09-05T00:00:00Z,61.32,60.38285714 -2004-09-06T00:00:00Z,63.75,60.95 -2004-09-07T00:00:00Z,63.46,61.67142857 -2004-09-08T00:00:00Z,64.71,62.62285714 -2004-09-09T00:00:00Z,65.93,63.06428571 -2004-09-10T00:00:00Z,62.97,63.65285714 -2004-09-11T00:00:00Z,63.43,64.13 -2004-09-12T00:00:00Z,64.66,64.18428571 -2004-09-13T00:00:00Z,64.13,64.28142857 -2004-09-14T00:00:00Z,64.14,64.35857143 -2004-09-15T00:00:00Z,65.25,64.13428571 -2004-09-16T00:00:00Z,64.36,64.42571429 -2004-09-17T00:00:00Z,65.01,64.67857143 -2004-09-18T00:00:00Z,65.2,64.16142857 -2004-09-19T00:00:00Z,61.04,60.62714286 -2004-09-20T00:00:00Z,39.39,57.21571429 -2004-09-21T00:00:00Z,40.26,54.63428571 -2004-09-22T00:00:00Z,47.18,52.77857143 -2004-09-23T00:00:00Z,51.37,51.85857143 -2004-09-24T00:00:00Z,58.57,51.01285714 -2004-09-25T00:00:00Z,59.28,50.55571429 -2004-09-26T00:00:00Z,57.84,53.32571429 -2004-09-27T00:00:00Z,58.78,56.08571429 -2004-09-28T00:00:00Z,59.58,56.37285714 -2004-09-29T00:00:00Z,49.19,54.30857143 -2004-09-30T00:00:00Z,36.92,50.43571429 -2004-10-01T00:00:00Z,31.46,46.46142857 -2004-10-02T00:00:00Z,31.46,42.78571429 -2004-10-03T00:00:00Z,32.11,39.63857143 -2004-10-04T00:00:00Z,36.75,37.71714286 -2004-10-05T00:00:00Z,46.13,39.19714286 -2004-10-06T00:00:00Z,59.55,42.85714286 -2004-10-07T00:00:00Z,62.54,47.59571429 -2004-10-08T00:00:00Z,64.63,52.50714286 -2004-10-09T00:00:00Z,65.84,57.32571429 -2004-10-10T00:00:00Z,65.84,61.54857143 -2004-10-11T00:00:00Z,66.31,64.52428571 -2004-10-12T00:00:00Z,66.96,65.61428571 -2004-10-13T00:00:00Z,67.18,66.27714286 -2004-10-14T00:00:00Z,67.18,66.71714286 -2004-10-15T00:00:00Z,67.71,66.94857143 -2004-10-16T00:00:00Z,67.46,67.02428571 -2004-10-17T00:00:00Z,66.37,67.00285714 -2004-10-18T00:00:00Z,66.16,66.92285714 -2004-10-19T00:00:00Z,66.4,66.98142857 -2004-10-20T00:00:00Z,67.59,67.06285714 -2004-10-21T00:00:00Z,67.75,67.04857143 -2004-10-22T00:00:00Z,67.61,67.12142857 -2004-10-23T00:00:00Z,67.97,67.38428571 -2004-10-24T00:00:00Z,68.21,67.67571429 -2004-10-25T00:00:00Z,68.2,67.87571429 -2004-10-26T00:00:00Z,67.8,67.87285714 -2004-10-27T00:00:00Z,67.57,67.85142857 -2004-10-28T00:00:00Z,67.6,67.86714286 -2004-10-29T00:00:00Z,67.72,67.77714286 -2004-10-30T00:00:00Z,67.34,67.67142857 -2004-10-31T00:00:00Z,67.47,67.47 -2004-11-01T00:00:00Z,66.79,67.40571429 -2004-11-02T00:00:00Z,67.35,67.41142857 -2004-11-03T00:00:00Z,67.61,67.54142857 -2004-11-04T00:00:00Z,68.51,67.56 -2004-11-05T00:00:00Z,67.85,67.63428571 -2004-11-06T00:00:00Z,67.86,67.68857143 -2004-11-07T00:00:00Z,67.85,67.93571429 -2004-11-08T00:00:00Z,68.52,68.14571429 -2004-11-09T00:00:00Z,68.82,68.37 -2004-11-10T00:00:00Z,69.18,68.46571429 -2004-11-11T00:00:00Z,69.18,68.60428571 -2004-11-12T00:00:00Z,68.82,68.79285714 -2004-11-13T00:00:00Z,69.18,68.97571429 -2004-11-14T00:00:00Z,69.13,69.04428571 -2004-11-15T00:00:00Z,69,69.08857143 -2004-11-16T00:00:00Z,69.13,69.09285714 -2004-11-17T00:00:00Z,69.21,69.09142857 -2004-11-18T00:00:00Z,69.17,69.11 -2004-11-19T00:00:00Z,68.95,69.09285714 -2004-11-20T00:00:00Z,69.06,69.06571429 -2004-11-21T00:00:00Z,68.94,69.07428571 -2004-11-22T00:00:00Z,69.06,69.07428571 -2004-11-23T00:00:00Z,69.13,69.10571429 -2004-11-24T00:00:00Z,69.43,69.14285714 -2004-11-25T00:00:00Z,69.43,69.06 -2004-11-26T00:00:00Z,68.37,68.99714286 -2004-11-27T00:00:00Z,68.62,68.95 -2004-11-28T00:00:00Z,68.61,68.45571429 -2004-11-29T00:00:00Z,65.6,67.65428571 -2004-11-30T00:00:00Z,63.52,66.50428571 -2004-12-01T00:00:00Z,61.38,64.16857143 -2004-12-02T00:00:00Z,53.08,61.51142857 -2004-12-03T00:00:00Z,49.77,60.27571429 -2004-12-04T00:00:00Z,59.97,59.18 -2004-12-05T00:00:00Z,60.94,58.65285714 -2004-12-06T00:00:00Z,61.91,58.70571429 -2004-12-07T00:00:00Z,63.89,58.89 -2004-12-08T00:00:00Z,62.67,59.99714286 -2004-12-09T00:00:00Z,60.83,61.77714286 -2004-12-10T00:00:00Z,62.23,62.01 -2004-12-11T00:00:00Z,61.6,61.51428571 -2004-12-12T00:00:00Z,57.47,60.11571429 -2004-12-13T00:00:00Z,52.12,57.6 -2004-12-14T00:00:00Z,46.28,54.78142857 -2004-12-15T00:00:00Z,42.94,52.85285714 -2004-12-16T00:00:00Z,47.33,49.43428571 -2004-12-17T00:00:00Z,38.3,46.75428571 -2004-12-18T00:00:00Z,42.84,46.08 -2004-12-19T00:00:00Z,52.75,45.58571429 -2004-12-20T00:00:00Z,48.66,NA -2004-12-21T00:00:00Z,NA,NA -2004-12-22T00:00:00Z,NA,NA -2004-12-23T00:00:00Z,NA,NA -2004-12-24T00:00:00Z,NA,NA -2004-12-25T00:00:00Z,NA,NA -2004-12-26T00:00:00Z,NA,NA -2004-12-27T00:00:00Z,NA,NA -2004-12-28T00:00:00Z,NA,NA -2004-12-29T00:00:00Z,NA,NA -2004-12-30T00:00:00Z,NA,NA -2004-12-31T00:00:00Z,NA,NA -2005-01-01T00:00:00Z,NA,NA -2005-01-02T00:00:00Z,NA,NA -2005-01-03T00:00:00Z,NA,NA -2005-01-04T00:00:00Z,NA,NA -2005-01-05T00:00:00Z,NA,NA -2005-01-06T00:00:00Z,NA,NA -2005-01-07T00:00:00Z,NA,NA -2005-01-08T00:00:00Z,NA,NA -2005-01-09T00:00:00Z,NA,NA -2005-01-10T00:00:00Z,NA,NA -2005-01-11T00:00:00Z,NA,NA -2005-01-12T00:00:00Z,NA,NA -2005-01-13T00:00:00Z,NA,NA -2005-01-14T00:00:00Z,NA,44.14 -2005-01-15T00:00:00Z,44.14,39.91 -2005-01-16T00:00:00Z,35.68,37.85666667 -2005-01-17T00:00:00Z,33.75,41.04 -2005-01-18T00:00:00Z,50.59,NA -2005-01-19T00:00:00Z,NA,NA -2005-01-20T00:00:00Z,NA,NA -2005-01-21T00:00:00Z,NA,NA -2005-01-22T00:00:00Z,NA,NA -2005-01-23T00:00:00Z,NA,NA -2005-01-24T00:00:00Z,NA,NA -2005-01-25T00:00:00Z,NA,NA -2005-01-26T00:00:00Z,NA,NA -2005-01-27T00:00:00Z,NA,NA -2005-01-28T00:00:00Z,NA,NA -2005-01-29T00:00:00Z,NA,NA -2005-01-30T00:00:00Z,NA,NA -2005-01-31T00:00:00Z,NA,NA -2005-02-01T00:00:00Z,NA,NA -2005-02-02T00:00:00Z,NA,NA -2005-02-03T00:00:00Z,NA,NA -2005-02-04T00:00:00Z,NA,NA -2005-02-05T00:00:00Z,NA,NA -2005-02-06T00:00:00Z,NA,NA -2005-02-07T00:00:00Z,NA,NA -2005-02-08T00:00:00Z,NA,NA -2005-02-09T00:00:00Z,NA,NA -2005-02-10T00:00:00Z,NA,NA -2005-02-11T00:00:00Z,NA,NA -2005-02-12T00:00:00Z,NA,NA -2005-02-13T00:00:00Z,NA,NA -2005-02-14T00:00:00Z,NA,NA -2005-02-15T00:00:00Z,NA,NA -2005-02-16T00:00:00Z,NA,NA -2005-02-17T00:00:00Z,NA,NA -2005-02-18T00:00:00Z,NA,NA -2005-02-19T00:00:00Z,NA,NA -2005-02-20T00:00:00Z,NA,NA -2005-02-21T00:00:00Z,NA,NA -2005-02-22T00:00:00Z,NA,NA -2005-02-23T00:00:00Z,NA,NA -2005-02-24T00:00:00Z,NA,NA -2005-02-25T00:00:00Z,NA,NA -2005-02-26T00:00:00Z,NA,NA -2005-02-27T00:00:00Z,NA,NA -2005-02-28T00:00:00Z,NA,NA -2005-03-01T00:00:00Z,NA,NA -2005-03-02T00:00:00Z,NA,NA -2005-03-03T00:00:00Z,NA,NA -2005-03-04T00:00:00Z,NA,NA -2005-03-05T00:00:00Z,NA,NA -2005-03-06T00:00:00Z,NA,NA -2005-03-07T00:00:00Z,NA,NA -2005-03-08T00:00:00Z,NA,NA -2005-03-09T00:00:00Z,NA,NA -2005-03-10T00:00:00Z,NA,NA -2005-03-11T00:00:00Z,NA,NA -2005-03-12T00:00:00Z,NA,NA -2005-03-13T00:00:00Z,NA,NA -2005-03-14T00:00:00Z,NA,NA -2005-03-15T00:00:00Z,NA,NA -2005-03-16T00:00:00Z,NA,NA -2005-03-17T00:00:00Z,NA,NA -2005-03-18T00:00:00Z,NA,NA -2005-03-19T00:00:00Z,NA,NA -2005-03-20T00:00:00Z,NA,NA -2005-03-21T00:00:00Z,NA,NA -2005-03-22T00:00:00Z,NA,NA -2005-03-23T00:00:00Z,NA,NA -2005-03-24T00:00:00Z,NA,NA -2005-03-25T00:00:00Z,NA,NA -2005-03-26T00:00:00Z,NA,NA -2005-03-27T00:00:00Z,NA,NA -2005-03-28T00:00:00Z,NA,NA -2005-03-29T00:00:00Z,NA,64.97 -2005-03-30T00:00:00Z,64.97,63.17 -2005-03-31T00:00:00Z,61.37,58.51 -2005-04-01T00:00:00Z,49.19,56.39 -2005-04-02T00:00:00Z,50.03,52.858 -2005-04-03T00:00:00Z,38.73,51.08833333 -2005-04-04T00:00:00Z,42.24,49.34142857 -2005-04-05T00:00:00Z,38.86,NA -2005-04-06T00:00:00Z,NA,NA -2005-04-07T00:00:00Z,NA,NA -2005-04-08T00:00:00Z,NA,NA -2005-04-09T00:00:00Z,NA,NA -2005-04-10T00:00:00Z,NA,NA -2005-04-11T00:00:00Z,NA,NA -2005-04-12T00:00:00Z,NA,NA -2005-04-13T00:00:00Z,NA,NA -2005-04-14T00:00:00Z,NA,31.46 -2005-04-15T00:00:00Z,31.46,37.2 -2005-04-16T00:00:00Z,42.94,42.84666667 -2005-04-17T00:00:00Z,54.14,46.7275 -2005-04-18T00:00:00Z,58.37,49.84 -2005-04-19T00:00:00Z,62.29,52.06666667 -2005-04-20T00:00:00Z,63.2,53.63857143 -2005-04-21T00:00:00Z,63.07,58.37714286 -2005-04-22T00:00:00Z,64.63,61.51428571 -2005-04-23T00:00:00Z,64.9,63.05142857 -2005-04-24T00:00:00Z,64.9,63.90428571 -2005-04-25T00:00:00Z,64.34,64.04571429 -2005-04-26T00:00:00Z,63.28,64.01571429 -2005-04-27T00:00:00Z,62.99,63.92142857 -2005-04-28T00:00:00Z,62.41,63.62571429 -2005-04-29T00:00:00Z,62.56,63.20142857 -2005-04-30T00:00:00Z,61.93,62.48714286 -2005-05-01T00:00:00Z,59.9,61.94 -2005-05-02T00:00:00Z,60.51,61.54428571 -2005-05-03T00:00:00Z,60.51,61.16571429 -2005-05-04T00:00:00Z,60.34,60.85714286 -2005-05-05T00:00:00Z,60.25,60.58857143 -2005-05-06T00:00:00Z,60.68,60.92714286 -2005-05-07T00:00:00Z,64.3,61.73 -2005-05-08T00:00:00Z,65.52,62.46142857 -2005-05-09T00:00:00Z,65.63,63.24714286 -2005-05-10T00:00:00Z,66.01,63.96857143 -2005-05-11T00:00:00Z,65.39,64.56571429 -2005-05-12T00:00:00Z,64.43,65.22 -2005-05-13T00:00:00Z,65.26,65.38714286 -2005-05-14T00:00:00Z,65.47,65.34142857 -2005-05-15T00:00:00Z,65.2,65.52 -2005-05-16T00:00:00Z,66.88,65.73714286 -2005-05-17T00:00:00Z,67.53,66.16142857 -2005-05-18T00:00:00Z,68.36,66.77714286 -2005-05-19T00:00:00Z,68.74,67.31 -2005-05-20T00:00:00Z,68.99,67.86142857 -2005-05-21T00:00:00Z,69.33,68.41428571 -2005-05-22T00:00:00Z,69.07,68.72285714 -2005-05-23T00:00:00Z,69.04,68.95142857 -2005-05-24T00:00:00Z,69.13,69.09428571 -2005-05-25T00:00:00Z,69.36,69.17857143 -2005-05-26T00:00:00Z,69.33,69.22714286 -2005-05-27T00:00:00Z,69.33,69.19428571 -2005-05-28T00:00:00Z,69.1,69.16428571 -2005-05-29T00:00:00Z,68.86,69.18714286 -2005-05-30T00:00:00Z,69.2,69.20428571 -2005-05-31T00:00:00Z,69.25,69.24142857 -2005-06-01T00:00:00Z,69.62,69.26571429 -2005-06-02T00:00:00Z,69.5,69.29 -2005-06-03T00:00:00Z,69.5,69.35857143 -2005-06-04T00:00:00Z,69.58,69.46428571 -2005-06-05T00:00:00Z,69.6,69.51571429 -2005-06-06T00:00:00Z,69.56,69.56428571 -2005-06-07T00:00:00Z,69.59,69.51 -2005-06-08T00:00:00Z,69.24,69.49857143 -2005-06-09T00:00:00Z,69.42,69.50428571 -2005-06-10T00:00:00Z,69.54,69.47571429 -2005-06-11T00:00:00Z,69.38,69.41142857 -2005-06-12T00:00:00Z,69.15,69.38 -2005-06-13T00:00:00Z,69.34,69.37714286 -2005-06-14T00:00:00Z,69.57,69.51 -2005-06-15T00:00:00Z,70.17,69.70857143 -2005-06-16T00:00:00Z,70.81,69.91857143 -2005-06-17T00:00:00Z,71.01,70.16428571 -2005-06-18T00:00:00Z,71.1,70.45571429 -2005-06-19T00:00:00Z,71.19,70.75285714 -2005-06-20T00:00:00Z,71.42,71.01285714 -2005-06-21T00:00:00Z,71.39,71.16714286 -2005-06-22T00:00:00Z,71.25,71.26428571 -2005-06-23T00:00:00Z,71.49,71.37142857 -2005-06-24T00:00:00Z,71.76,71.43 -2005-06-25T00:00:00Z,71.51,71.46 -2005-06-26T00:00:00Z,71.4,71.50428571 -2005-06-27T00:00:00Z,71.73,71.57285714 -2005-06-28T00:00:00Z,71.87,71.64 -2005-06-29T00:00:00Z,71.72,71.66142857 -2005-06-30T00:00:00Z,71.64,71.66285714 -2005-07-01T00:00:00Z,71.77,71.67142857 -2005-07-02T00:00:00Z,71.57,71.67428571 -2005-07-03T00:00:00Z,71.42,71.65142857 -2005-07-04T00:00:00Z,71.57,71.60428571 -2005-07-05T00:00:00Z,71.54,71.55428571 -2005-07-06T00:00:00Z,71.37,71.53714286 -2005-07-07T00:00:00Z,71.52,71.52571429 -2005-07-08T00:00:00Z,71.69,71.42285714 -2005-07-09T00:00:00Z,70.85,71.26714286 -2005-07-10T00:00:00Z,70.33,71.11 -2005-07-11T00:00:00Z,70.47,70.98428571 -2005-07-12T00:00:00Z,70.66,70.94285714 -2005-07-13T00:00:00Z,71.08,70.85285714 -2005-07-14T00:00:00Z,70.89,70.73 -2005-07-15T00:00:00Z,70.83,70.72714286 -2005-07-16T00:00:00Z,70.83,70.76714286 -2005-07-17T00:00:00Z,70.61,70.72571429 -2005-07-18T00:00:00Z,70.18,70.64285714 -2005-07-19T00:00:00Z,70.08,70.49571429 -2005-07-20T00:00:00Z,70.05,70.41714286 -2005-07-21T00:00:00Z,70.34,70.36 -2005-07-22T00:00:00Z,70.43,70.32285714 -2005-07-23T00:00:00Z,70.57,70.36142857 -2005-07-24T00:00:00Z,70.88,70.46714286 -2005-07-25T00:00:00Z,70.92,70.52857143 -2005-07-26T00:00:00Z,70.51,70.61857143 -2005-07-27T00:00:00Z,70.68,70.66857143 -2005-07-28T00:00:00Z,70.69,70.76142857 -2005-07-29T00:00:00Z,71.08,70.81714286 -2005-07-30T00:00:00Z,70.96,70.83857143 -2005-07-31T00:00:00Z,71.03,70.89714286 -2005-08-01T00:00:00Z,71.33,71.03571429 -2005-08-02T00:00:00Z,71.48,71.16714286 -2005-08-03T00:00:00Z,71.6,71.41714286 -2005-08-04T00:00:00Z,72.44,71.65285714 -2005-08-05T00:00:00Z,72.73,71.89142857 -2005-08-06T00:00:00Z,72.63,72.22571429 -2005-08-07T00:00:00Z,73.37,72.57714286 -2005-08-08T00:00:00Z,73.79,72.83285714 -2005-08-09T00:00:00Z,73.27,73.05285714 -2005-08-10T00:00:00Z,73.14,73.18857143 -2005-08-11T00:00:00Z,73.39,73.28285714 -2005-08-12T00:00:00Z,73.39,73.40285714 -2005-08-13T00:00:00Z,73.47,73.42428571 -2005-08-14T00:00:00Z,73.52,73.34571429 -2005-08-15T00:00:00Z,73.24,73.45428571 -2005-08-16T00:00:00Z,74.03,73.63714286 -2005-08-17T00:00:00Z,74.42,73.82142857 -2005-08-18T00:00:00Z,74.68,74.17571429 -2005-08-19T00:00:00Z,75.87,74.59 -2005-08-20T00:00:00Z,76.37,75.05857143 -2005-08-21T00:00:00Z,76.8,75.61 -2005-08-22T00:00:00Z,77.1,76.10285714 -2005-08-23T00:00:00Z,77.48,76.59857143 -2005-08-24T00:00:00Z,77.89,77.11714286 -2005-08-25T00:00:00Z,78.31,77.50428571 -2005-08-26T00:00:00Z,78.58,77.84428571 -2005-08-27T00:00:00Z,78.75,78.14 -2005-08-28T00:00:00Z,78.87,78.43857143 -2005-08-29T00:00:00Z,79.19,78.73571429 -2005-08-30T00:00:00Z,79.56,79.02571429 -2005-08-31T00:00:00Z,79.92,78.57534716 -2005-09-01T00:00:00Z,75.1578377,78.41290236 -2005-09-02T00:00:00Z,77.44446836,78.24692184 -2005-09-03T00:00:00Z,80.32383262,78.17874206 -2005-09-04T00:00:00Z,78.13514786,78.17844987 -2005-09-05T00:00:00Z,78.96577662,78.20111073 -2005-09-06T00:00:00Z,79.51105367,78.23774566 -2005-09-07T00:00:00Z,79.97773729,78.975342 -2005-09-08T00:00:00Z,80.12417319,79.6377868 -2005-09-09T00:00:00Z,82.08,80.39662446 -2005-09-10T00:00:00Z,82.9,81.0719471 -2005-09-11T00:00:00Z,83.12,81.56652501 -2005-09-12T00:00:00Z,82.65,81.93957843 -2005-09-13T00:00:00Z,82.33,82.47580065 -2005-09-14T00:00:00Z,83.93,82.99714286 -2005-09-15T00:00:00Z,83.97,83.27857143 -2005-09-16T00:00:00Z,84.05,83.49285714 -2005-09-17T00:00:00Z,84.4,83.66285714 -2005-09-18T00:00:00Z,84.31,83.89857143 -2005-09-19T00:00:00Z,84.3,84.21857143 -2005-09-20T00:00:00Z,84.57,84.29 -2005-09-21T00:00:00Z,84.43,84.41571429 -2005-09-22T00:00:00Z,84.85,84.55 -2005-09-23T00:00:00Z,84.99,84.70571429 -2005-09-24T00:00:00Z,85.49,84.87714286 -2005-09-25T00:00:00Z,85.51,85.05571429 -2005-09-26T00:00:00Z,85.55,85.08857143 -2005-09-27T00:00:00Z,84.8,85.23714286 -2005-09-28T00:00:00Z,85.47,85.38571429 -2005-09-29T00:00:00Z,85.89,85.42 -2005-09-30T00:00:00Z,85.23,85.43142857 -2005-10-01T00:00:00Z,85.57,85.44 -2005-10-02T00:00:00Z,85.57,85.42689911 -2005-10-03T00:00:00Z,85.40637154,85.53139045 -2005-10-04T00:00:00Z,85.48320565,85.55336974 -2005-10-05T00:00:00Z,85.58012732,85.53585511 -2005-10-06T00:00:00Z,85.73021583,85.68478311 -2005-10-07T00:00:00Z,86.25169956,85.70040129 -2005-10-08T00:00:00Z,85.63820244,83.1729337 -2005-10-09T00:00:00Z,67.90881217,80.51650476 -2005-10-10T00:00:00Z,66.89502741,77.68177656 -2005-10-11T00:00:00Z,65.70204368,75.10408299 -2005-10-12T00:00:00Z,67.58,72.56731191 -2005-10-13T00:00:00Z,68.01,69.89124105 -2005-10-14T00:00:00Z,67.54,67.03562287 -2005-10-15T00:00:00Z,65.69,66.56451903 -2005-10-16T00:00:00Z,64.58,65.86833457 -2005-10-17T00:00:00Z,61.99,65.60428571 -2005-10-18T00:00:00Z,63.84,65.08714286 -2005-10-19T00:00:00Z,63.96,64.38714286 -2005-10-20T00:00:00Z,63.11,63.77428571 -2005-10-21T00:00:00Z,63.25,63.64857143 -2005-10-22T00:00:00Z,64.81,63.62285714 -2005-10-23T00:00:00Z,64.4,64.00571429 -2005-10-24T00:00:00Z,64.67,64.45714286 -2005-10-25T00:00:00Z,67,64.86857143 -2005-10-26T00:00:00Z,66.84,65.47714286 -2005-10-27T00:00:00Z,67.37,66.11714286 -2005-10-28T00:00:00Z,67.73,66.56285714 -2005-10-29T00:00:00Z,67.93,66.99428571 -2005-10-30T00:00:00Z,67.42,67.29571429 -2005-10-31T00:00:00Z,66.78,67.22428571 -2005-11-01T00:00:00Z,66.5,67.12285714 -2005-11-02T00:00:00Z,66.13,66.95714286 -2005-11-03T00:00:00Z,66.21,66.57285714 -2005-11-04T00:00:00Z,65.04,66.23857143 -2005-11-05T00:00:00Z,65.59,66.04285714 -2005-11-06T00:00:00Z,66.05,65.87142857 -2005-11-07T00:00:00Z,65.58,65.71571429 -2005-11-08T00:00:00Z,65.41,65.7 -2005-11-09T00:00:00Z,66.02,65.64571429 -2005-11-10T00:00:00Z,65.83,65.75428571 -2005-11-11T00:00:00Z,65.8,65.83714286 -2005-11-12T00:00:00Z,66.17,65.86142857 -2005-11-13T00:00:00Z,66.22,65.89285714 -2005-11-14T00:00:00Z,65.8,65.97857143 -2005-11-15T00:00:00Z,66.01,66.06428571 -2005-11-16T00:00:00Z,66.62,65.94428571 -2005-11-17T00:00:00Z,64.99,65.83428571 -2005-11-18T00:00:00Z,65.03,65.79857143 -2005-11-19T00:00:00Z,65.92,65.69714286 -2005-11-20T00:00:00Z,65.51,65.50714286 -2005-11-21T00:00:00Z,64.47,65.56 -2005-11-22T00:00:00Z,66.38,65.62 -2005-11-23T00:00:00Z,67.04,66.09285714 -2005-11-24T00:00:00Z,68.3,66.34285714 -2005-11-25T00:00:00Z,66.78,66.58428571 -2005-11-26T00:00:00Z,67.61,66.90142857 -2005-11-27T00:00:00Z,67.73,67.41142857 -2005-11-28T00:00:00Z,68.04,67.68857143 -2005-11-29T00:00:00Z,68.32,67.70285714 -2005-11-30T00:00:00Z,67.14,67.41428571 -2005-12-01T00:00:00Z,66.28,67.10428571 -2005-12-02T00:00:00Z,64.61,65.69857143 -2005-12-03T00:00:00Z,57.77,64.43571429 -2005-12-04T00:00:00Z,58.89,63.06285714 -2005-12-05T00:00:00Z,58.43,61.88857143 -2005-12-06T00:00:00Z,60.1,60.25142857 -2005-12-07T00:00:00Z,55.68,59.11571429 -2005-12-08T00:00:00Z,58.33,58.86571429 -2005-12-09T00:00:00Z,62.86,59.60571429 -2005-12-10T00:00:00Z,62.95,60.47 -2005-12-11T00:00:00Z,64.94,61.39285714 -2005-12-12T00:00:00Z,64.89,NA -2005-12-13T00:00:00Z,NA,NA -2005-12-14T00:00:00Z,NA,NA -2005-12-15T00:00:00Z,NA,NA -2005-12-16T00:00:00Z,NA,NA -2005-12-17T00:00:00Z,NA,NA -2005-12-18T00:00:00Z,NA,NA -2005-12-19T00:00:00Z,NA,NA -2005-12-20T00:00:00Z,NA,NA -2005-12-21T00:00:00Z,NA,NA -2005-12-22T00:00:00Z,NA,NA -2005-12-23T00:00:00Z,NA,NA -2005-12-24T00:00:00Z,NA,NA -2005-12-25T00:00:00Z,NA,NA -2005-12-26T00:00:00Z,NA,NA -2005-12-27T00:00:00Z,NA,NA -2005-12-28T00:00:00Z,NA,NA -2005-12-29T00:00:00Z,NA,NA -2005-12-30T00:00:00Z,NA,NA -2005-12-31T00:00:00Z,NA,NA -2006-01-01T00:00:00Z,NA,NA -2006-01-02T00:00:00Z,NA,NA -2006-01-03T00:00:00Z,NA,NA -2006-01-04T00:00:00Z,NA,NA -2006-01-05T00:00:00Z,NA,NA -2006-01-06T00:00:00Z,NA,NA -2006-01-07T00:00:00Z,NA,52.75 -2006-01-08T00:00:00Z,52.75,NA -2006-01-09T00:00:00Z,NA,NA -2006-01-10T00:00:00Z,NA,NA -2006-01-11T00:00:00Z,NA,NA -2006-01-12T00:00:00Z,NA,NA -2006-01-13T00:00:00Z,NA,NA -2006-01-14T00:00:00Z,NA,48.18 -2006-01-15T00:00:00Z,48.18,NA -2006-01-16T00:00:00Z,NA,NA -2006-01-17T00:00:00Z,NA,NA -2006-01-18T00:00:00Z,NA,46.795 -2006-01-19T00:00:00Z,45.41,43.81666667 -2006-01-20T00:00:00Z,37.86,41.83 -2006-01-21T00:00:00Z,35.87,38.555 -2006-01-22T00:00:00Z,35.08,38.774 -2006-01-23T00:00:00Z,39.65,41.075 -2006-01-24T00:00:00Z,52.58,NA -2006-01-25T00:00:00Z,NA,NA -2006-01-26T00:00:00Z,NA,NA -2006-01-27T00:00:00Z,NA,NA -2006-01-28T00:00:00Z,NA,NA -2006-01-29T00:00:00Z,NA,NA -2006-01-30T00:00:00Z,NA,NA -2006-01-31T00:00:00Z,NA,NA -2006-02-01T00:00:00Z,NA,NA -2006-02-02T00:00:00Z,NA,NA -2006-02-03T00:00:00Z,NA,NA -2006-02-04T00:00:00Z,NA,NA -2006-02-05T00:00:00Z,NA,NA -2006-02-06T00:00:00Z,NA,49.12 -2006-02-07T00:00:00Z,49.12,NA -2006-02-08T00:00:00Z,NA,NA -2006-02-09T00:00:00Z,NA,NA -2006-02-10T00:00:00Z,NA,NA -2006-02-11T00:00:00Z,NA,NA -2006-02-12T00:00:00Z,NA,NA -2006-02-13T00:00:00Z,NA,NA -2006-02-14T00:00:00Z,NA,NA -2006-02-15T00:00:00Z,NA,NA -2006-02-16T00:00:00Z,NA,NA -2006-02-17T00:00:00Z,NA,NA -2006-02-18T00:00:00Z,NA,NA -2006-02-19T00:00:00Z,NA,NA -2006-02-20T00:00:00Z,NA,NA -2006-02-21T00:00:00Z,NA,NA -2006-02-22T00:00:00Z,NA,NA -2006-02-23T00:00:00Z,NA,NA -2006-02-24T00:00:00Z,NA,NA -2006-02-25T00:00:00Z,NA,NA -2006-02-26T00:00:00Z,NA,NA -2006-02-27T00:00:00Z,NA,NA -2006-02-28T00:00:00Z,NA,NA -2006-03-01T00:00:00Z,NA,NA -2006-03-02T00:00:00Z,NA,NA -2006-03-03T00:00:00Z,NA,NA -2006-03-04T00:00:00Z,NA,NA -2006-03-05T00:00:00Z,NA,NA -2006-03-06T00:00:00Z,NA,NA -2006-03-07T00:00:00Z,NA,NA -2006-03-08T00:00:00Z,NA,NA -2006-03-09T00:00:00Z,NA,NA -2006-03-10T00:00:00Z,NA,NA -2006-03-11T00:00:00Z,NA,NA -2006-03-12T00:00:00Z,NA,NA -2006-03-13T00:00:00Z,NA,NA -2006-03-14T00:00:00Z,NA,NA -2006-03-15T00:00:00Z,NA,NA -2006-03-16T00:00:00Z,NA,NA -2006-03-17T00:00:00Z,NA,NA -2006-03-18T00:00:00Z,NA,NA -2006-03-19T00:00:00Z,NA,NA -2006-03-20T00:00:00Z,NA,NA -2006-03-21T00:00:00Z,NA,NA -2006-03-22T00:00:00Z,NA,NA -2006-03-23T00:00:00Z,NA,NA -2006-03-24T00:00:00Z,NA,NA -2006-03-25T00:00:00Z,NA,NA -2006-03-26T00:00:00Z,NA,NA -2006-03-27T00:00:00Z,NA,NA -2006-03-28T00:00:00Z,NA,NA -2006-03-29T00:00:00Z,NA,NA -2006-03-30T00:00:00Z,NA,71.32 -2006-03-31T00:00:00Z,71.32,71.295 -2006-04-01T00:00:00Z,71.27,71.2 -2006-04-02T00:00:00Z,71.01,71.2075 -2006-04-03T00:00:00Z,71.23,71.186 -2006-04-04T00:00:00Z,71.1,71.15166667 -2006-04-05T00:00:00Z,70.98,71.19285714 -2006-04-06T00:00:00Z,71.44,71.24 -2006-04-07T00:00:00Z,71.65,71.27428571 -2006-04-08T00:00:00Z,71.51,71.38 -2006-04-09T00:00:00Z,71.75,71.43857143 -2006-04-10T00:00:00Z,71.64,71.48857143 -2006-04-11T00:00:00Z,71.45,71.54 -2006-04-12T00:00:00Z,71.34,71.50142857 -2006-04-13T00:00:00Z,71.17,71.41857143 -2006-04-14T00:00:00Z,71.07,71.40142857 -2006-04-15T00:00:00Z,71.39,71.37 -2006-04-16T00:00:00Z,71.53,71.35142857 -2006-04-17T00:00:00Z,71.51,71.38571429 -2006-04-18T00:00:00Z,71.69,71.44571429 -2006-04-19T00:00:00Z,71.76,71.53 -2006-04-20T00:00:00Z,71.76,71.66142857 -2006-04-21T00:00:00Z,71.99,71.78285714 -2006-04-22T00:00:00Z,72.24,71.74857143 -2006-04-23T00:00:00Z,71.29,71.63428571 -2006-04-24T00:00:00Z,70.71,71.41428571 -2006-04-25T00:00:00Z,70.15,71.09142857 -2006-04-26T00:00:00Z,69.5,70.67571429 -2006-04-27T00:00:00Z,68.85,70.12142857 -2006-04-28T00:00:00Z,68.11,69.55428571 -2006-04-29T00:00:00Z,68.27,69.1 -2006-04-30T00:00:00Z,68.11,68.78428571 -2006-05-01T00:00:00Z,68.5,68.55714286 -2006-05-02T00:00:00Z,68.56,68.31857143 -2006-05-03T00:00:00Z,67.83,68.21285714 -2006-05-04T00:00:00Z,68.11,68.22714286 -2006-05-05T00:00:00Z,68.21,68.23571429 -2006-05-06T00:00:00Z,68.33,68.31714286 -2006-05-07T00:00:00Z,68.68,68.35142857 -2006-05-08T00:00:00Z,68.74,68.41428571 -2006-05-09T00:00:00Z,69,68.61857143 -2006-05-10T00:00:00Z,69.26,68.77857143 -2006-05-11T00:00:00Z,69.23,68.96857143 -2006-05-12T00:00:00Z,69.54,69.10571429 -2006-05-13T00:00:00Z,69.29,69.18571429 -2006-05-14T00:00:00Z,69.24,69.24 -2006-05-15T00:00:00Z,69.12,69.22857143 -2006-05-16T00:00:00Z,68.92,69.15 -2006-05-17T00:00:00Z,68.71,69.07142857 -2006-05-18T00:00:00Z,68.68,68.94571429 -2006-05-19T00:00:00Z,68.66,68.86 -2006-05-20T00:00:00Z,68.69,68.76571429 -2006-05-21T00:00:00Z,68.58,68.63142857 -2006-05-22T00:00:00Z,68.18,68.58 -2006-05-23T00:00:00Z,68.56,68.62285714 -2006-05-24T00:00:00Z,69.01,68.66142857 -2006-05-25T00:00:00Z,68.95,68.69857143 -2006-05-26T00:00:00Z,68.92,68.73714286 -2006-05-27T00:00:00Z,68.96,68.77571429 -2006-05-28T00:00:00Z,68.85,68.87571429 -2006-05-29T00:00:00Z,68.88,68.91857143 -2006-05-30T00:00:00Z,68.86,68.87571429 -2006-05-31T00:00:00Z,68.71,68.81285714 -2006-06-01T00:00:00Z,68.51,68.71714286 -2006-06-02T00:00:00Z,68.25,68.48428571 -2006-06-03T00:00:00Z,67.33,68.34285714 -2006-06-04T00:00:00Z,67.86,68.27 -2006-06-05T00:00:00Z,68.37,68.19142857 -2006-06-06T00:00:00Z,68.31,68.21571429 -2006-06-07T00:00:00Z,68.88,68.28285714 -2006-06-08T00:00:00Z,68.98,68.34714286 -2006-06-09T00:00:00Z,68.7,68.47571429 -2006-06-10T00:00:00Z,68.23,68.53142857 -2006-06-11T00:00:00Z,68.25,68.55 -2006-06-12T00:00:00Z,68.5,68.56 -2006-06-13T00:00:00Z,68.38,68.49 -2006-06-14T00:00:00Z,68.39,68.38428571 -2006-06-15T00:00:00Z,68.24,68.30142857 -2006-06-16T00:00:00Z,68.12,68.27285714 -2006-06-17T00:00:00Z,68.03,68.25 -2006-06-18T00:00:00Z,68.09,68.16 -2006-06-19T00:00:00Z,67.87,68.08 -2006-06-20T00:00:00Z,67.82,68.00285714 -2006-06-21T00:00:00Z,67.85,67.98714286 -2006-06-22T00:00:00Z,68.13,67.98285714 -2006-06-23T00:00:00Z,68.09,67.97571429 -2006-06-24T00:00:00Z,67.98,67.93571429 -2006-06-25T00:00:00Z,67.81,67.95285714 -2006-06-26T00:00:00Z,67.99,67.80142857 -2006-06-27T00:00:00Z,66.76,66.98714286 -2006-06-28T00:00:00Z,62.15,63.62142857 -2006-06-29T00:00:00Z,44.57,59.21428571 -2006-06-30T00:00:00Z,37.24,54.54428571 -2006-07-01T00:00:00Z,35.29,49.95428571 -2006-07-02T00:00:00Z,35.68,45.49142857 -2006-07-03T00:00:00Z,36.75,41.86857143 -2006-07-04T00:00:00Z,41.4,40.14571429 -2006-07-05T00:00:00Z,50.09,41.95714286 -2006-07-06T00:00:00Z,57.25,45.15857143 -2006-07-07T00:00:00Z,59.65,48.84285714 -2006-07-08T00:00:00Z,61.08,52.64285714 -2006-07-09T00:00:00Z,62.28,56.15285714 -2006-07-10T00:00:00Z,61.32,58.94142857 -2006-07-11T00:00:00Z,60.92,60.44 -2006-07-12T00:00:00Z,60.58,60.92142857 -2006-07-13T00:00:00Z,60.62,61.11285714 -2006-07-14T00:00:00Z,60.99,61.20571429 -2006-07-15T00:00:00Z,61.73,61.07714286 -2006-07-16T00:00:00Z,61.38,61.15285714 -2006-07-17T00:00:00Z,61.85,61.32142857 -2006-07-18T00:00:00Z,62.1,61.57571429 -2006-07-19T00:00:00Z,62.36,61.99285714 -2006-07-20T00:00:00Z,63.54,62.50285714 -2006-07-21T00:00:00Z,64.56,62.82714286 -2006-07-22T00:00:00Z,64,63.05714286 -2006-07-23T00:00:00Z,62.99,63.38714286 -2006-07-24T00:00:00Z,64.16,63.80571429 -2006-07-25T00:00:00Z,65.03,64.14857143 -2006-07-26T00:00:00Z,64.76,64.38571429 -2006-07-27T00:00:00Z,65.2,64.36285714 -2006-07-28T00:00:00Z,64.4,64.31571429 -2006-07-29T00:00:00Z,63.67,64.54285714 -2006-07-30T00:00:00Z,64.58,64.84285714 -2006-07-31T00:00:00Z,66.26,65.12857143 -2006-08-01T00:00:00Z,67.03,65.48 -2006-08-02T00:00:00Z,67.22,65.89285714 -2006-08-03T00:00:00Z,68.09,66.52285714 -2006-08-04T00:00:00Z,68.81,67.31857143 -2006-08-05T00:00:00Z,69.24,68.04857143 -2006-08-06T00:00:00Z,69.69,68.55857143 -2006-08-07T00:00:00Z,69.83,68.92428571 -2006-08-08T00:00:00Z,69.59,69.29142857 -2006-08-09T00:00:00Z,69.79,69.58285714 -2006-08-10T00:00:00Z,70.13,69.77857143 -2006-08-11T00:00:00Z,70.18,69.97428571 -2006-08-12T00:00:00Z,70.61,70.11714286 -2006-08-13T00:00:00Z,70.69,70.30428571 -2006-08-14T00:00:00Z,71.14,70.46142857 -2006-08-15T00:00:00Z,70.69,70.63 -2006-08-16T00:00:00Z,70.97,70.84714286 -2006-08-17T00:00:00Z,71.65,71.07285714 -2006-08-18T00:00:00Z,71.76,71.32571429 -2006-08-19T00:00:00Z,72.38,71.65 -2006-08-20T00:00:00Z,72.96,71.90428571 -2006-08-21T00:00:00Z,72.92,72.34142857 -2006-08-22T00:00:00Z,73.75,72.80285714 -2006-08-23T00:00:00Z,74.2,73.32571429 -2006-08-24T00:00:00Z,75.31,73.88714286 -2006-08-25T00:00:00Z,75.69,74.41714286 -2006-08-26T00:00:00Z,76.09,74.88428571 -2006-08-27T00:00:00Z,76.23,74.99142857 -2006-08-28T00:00:00Z,73.67,74.81857143 -2006-08-29T00:00:00Z,72.54,74.46714286 -2006-08-30T00:00:00Z,71.74,73.90714286 -2006-08-31T00:00:00Z,71.39,73.29857143 -2006-09-01T00:00:00Z,71.43,72.70571429 -2006-09-02T00:00:00Z,71.94,71.84142857 -2006-09-03T00:00:00Z,70.18,71.21142857 -2006-09-04T00:00:00Z,69.26,70.64428571 -2006-09-05T00:00:00Z,68.57,70.19 -2006-09-06T00:00:00Z,68.56,69.77714286 -2006-09-07T00:00:00Z,68.5,69.39714286 -2006-09-08T00:00:00Z,68.77,68.98285714 -2006-09-09T00:00:00Z,69.04,68.78428571 -2006-09-10T00:00:00Z,68.79,68.78428571 -2006-09-11T00:00:00Z,69.26,68.90714286 -2006-09-12T00:00:00Z,69.43,69.01571429 -2006-09-13T00:00:00Z,69.32,69.10857143 -2006-09-14T00:00:00Z,69.15,69.10428571 -2006-09-15T00:00:00Z,68.74,69.10428571 -2006-09-16T00:00:00Z,69.04,69.10714286 -2006-09-17T00:00:00Z,68.81,68.96428571 -2006-09-18T00:00:00Z,68.26,68.76285714 -2006-09-19T00:00:00Z,68.02,68.53571429 -2006-09-20T00:00:00Z,67.73,68.33714286 -2006-09-21T00:00:00Z,67.76,68.28571429 -2006-09-22T00:00:00Z,68.38,68.20428571 -2006-09-23T00:00:00Z,68.47,68.06 -2006-09-24T00:00:00Z,67.8,68.00428571 -2006-09-25T00:00:00Z,67.87,68.03 -2006-09-26T00:00:00Z,68.2,68.16 -2006-09-27T00:00:00Z,68.64,68.33285714 -2006-09-28T00:00:00Z,68.97,68.29857143 -2006-09-29T00:00:00Z,68.14,68.27857143 -2006-09-30T00:00:00Z,68.33,68.34285714 -2006-10-01T00:00:00Z,68.25,68.34571429 -2006-10-02T00:00:00Z,67.89,68.30571429 -2006-10-03T00:00:00Z,67.92,68.15 -2006-10-04T00:00:00Z,67.55,67.96428571 -2006-10-05T00:00:00Z,67.67,67.99857143 -2006-10-06T00:00:00Z,68.38,68.11 -2006-10-07T00:00:00Z,69.11,68.20142857 -2006-10-08T00:00:00Z,68.89,68.28571429 -2006-10-09T00:00:00Z,68.48,68.34142857 -2006-10-10T00:00:00Z,68.31,68.49 -2006-10-11T00:00:00Z,68.59,68.63857143 -2006-10-12T00:00:00Z,68.71,68.59428571 -2006-10-13T00:00:00Z,68.07,68.48714286 -2006-10-14T00:00:00Z,68.36,68.39714286 -2006-10-15T00:00:00Z,68.26,68.38285714 -2006-10-16T00:00:00Z,68.38,68.48 -2006-10-17T00:00:00Z,68.99,68.49142857 -2006-10-18T00:00:00Z,68.67,68.41 -2006-10-19T00:00:00Z,68.14,68.41285714 -2006-10-20T00:00:00Z,68.09,68.21428571 -2006-10-21T00:00:00Z,66.97,68.02714286 -2006-10-22T00:00:00Z,66.95,67.79428571 -2006-10-23T00:00:00Z,66.75,67.45142857 -2006-10-24T00:00:00Z,66.59,67.16428571 -2006-10-25T00:00:00Z,66.66,67.11857143 -2006-10-26T00:00:00Z,67.82,67.17428571 -2006-10-27T00:00:00Z,68.48,67.43571429 -2006-10-28T00:00:00Z,68.8,66.99285714 -2006-10-29T00:00:00Z,63.85,65.66 -2006-10-30T00:00:00Z,57.42,64.97428571 -2006-10-31T00:00:00Z,61.79,64.41428571 -2006-11-01T00:00:00Z,62.74,63.70142857 -2006-11-02T00:00:00Z,62.83,62.85428571 -2006-11-03T00:00:00Z,62.55,61.94285714 -2006-11-04T00:00:00Z,62.42,61.74571429 -2006-11-05T00:00:00Z,62.47,62.49142857 -2006-11-06T00:00:00Z,62.64,62.58142857 -2006-11-07T00:00:00Z,62.42,62.64142857 -2006-11-08T00:00:00Z,63.16,62.45142857 -2006-11-09T00:00:00Z,61.5,61.79857143 -2006-11-10T00:00:00Z,57.98,61 -2006-11-11T00:00:00Z,56.83,60.31857143 -2006-11-12T00:00:00Z,57.7,60.52857143 -2006-11-13T00:00:00Z,64.11,61.00285714 -2006-11-14T00:00:00Z,65.74,61.28142857 -2006-11-15T00:00:00Z,65.11,61.85857143 -2006-11-16T00:00:00Z,65.54,62.54 -2006-11-17T00:00:00Z,62.75,62.88428571 -2006-11-18T00:00:00Z,59.24,62.76428571 -2006-11-19T00:00:00Z,56.86,61.46142857 -2006-11-20T00:00:00Z,54.99,59.55714286 -2006-11-21T00:00:00Z,52.41,59.00571429 -2006-11-22T00:00:00Z,61.25,58.94571429 -2006-11-23T00:00:00Z,65.12,59.3 -2006-11-24T00:00:00Z,65.23,60.03714286 -2006-11-25T00:00:00Z,64.4,61.04285714 -2006-11-26T00:00:00Z,63.9,62.08 -2006-11-27T00:00:00Z,62.25,63.46428571 -2006-11-28T00:00:00Z,62.1,63.75285714 -2006-11-29T00:00:00Z,63.27,63.36 -2006-11-30T00:00:00Z,62.37,NA -2006-12-01T00:00:00Z,NA,NA -2006-12-02T00:00:00Z,NA,NA -2006-12-03T00:00:00Z,NA,NA -2006-12-04T00:00:00Z,NA,NA -2006-12-05T00:00:00Z,NA,NA -2006-12-06T00:00:00Z,NA,NA -2006-12-07T00:00:00Z,NA,NA -2006-12-08T00:00:00Z,NA,NA -2006-12-09T00:00:00Z,NA,NA -2006-12-10T00:00:00Z,NA,NA -2006-12-11T00:00:00Z,NA,NA -2006-12-12T00:00:00Z,NA,NA -2006-12-13T00:00:00Z,NA,NA -2006-12-14T00:00:00Z,NA,NA -2006-12-15T00:00:00Z,NA,NA -2006-12-16T00:00:00Z,NA,NA -2006-12-17T00:00:00Z,NA,NA -2006-12-18T00:00:00Z,NA,NA -2006-12-19T00:00:00Z,NA,NA -2006-12-20T00:00:00Z,NA,NA -2006-12-21T00:00:00Z,NA,NA -2006-12-22T00:00:00Z,NA,NA -2006-12-23T00:00:00Z,NA,NA -2006-12-24T00:00:00Z,NA,NA -2006-12-25T00:00:00Z,NA,NA -2006-12-26T00:00:00Z,NA,NA -2006-12-27T00:00:00Z,NA,NA -2006-12-28T00:00:00Z,NA,NA -2006-12-29T00:00:00Z,NA,NA -2006-12-30T00:00:00Z,NA,NA -2006-12-31T00:00:00Z,NA,NA -2007-01-01T00:00:00Z,NA,NA -2007-01-02T00:00:00Z,NA,NA -2007-01-03T00:00:00Z,NA,NA -2007-01-04T00:00:00Z,NA,NA -2007-01-05T00:00:00Z,NA,NA -2007-01-06T00:00:00Z,NA,NA -2007-01-07T00:00:00Z,NA,NA -2007-01-08T00:00:00Z,NA,45.41 -2007-01-09T00:00:00Z,45.41,46 -2007-01-10T00:00:00Z,46.59,NA -2007-01-11T00:00:00Z,NA,NA -2007-01-12T00:00:00Z,NA,NA -2007-01-13T00:00:00Z,NA,NA -2007-01-14T00:00:00Z,NA,NA -2007-01-15T00:00:00Z,NA,NA -2007-01-16T00:00:00Z,NA,NA -2007-01-17T00:00:00Z,NA,NA -2007-01-18T00:00:00Z,NA,NA -2007-01-19T00:00:00Z,NA,NA -2007-01-20T00:00:00Z,NA,NA -2007-01-21T00:00:00Z,NA,NA -2007-01-22T00:00:00Z,NA,NA -2007-01-23T00:00:00Z,NA,NA -2007-01-24T00:00:00Z,NA,NA -2007-01-25T00:00:00Z,NA,NA -2007-01-26T00:00:00Z,NA,NA -2007-01-27T00:00:00Z,NA,NA -2007-01-28T00:00:00Z,NA,NA -2007-01-29T00:00:00Z,NA,NA -2007-01-30T00:00:00Z,NA,NA -2007-01-31T00:00:00Z,NA,NA -2007-02-01T00:00:00Z,NA,NA -2007-02-02T00:00:00Z,NA,NA -2007-02-03T00:00:00Z,NA,NA -2007-02-04T00:00:00Z,NA,NA -2007-02-05T00:00:00Z,NA,NA -2007-02-06T00:00:00Z,NA,NA -2007-02-07T00:00:00Z,NA,NA -2007-02-08T00:00:00Z,NA,NA -2007-02-09T00:00:00Z,NA,NA -2007-02-10T00:00:00Z,NA,NA -2007-02-11T00:00:00Z,NA,NA -2007-02-12T00:00:00Z,NA,NA -2007-02-13T00:00:00Z,NA,NA -2007-02-14T00:00:00Z,NA,NA -2007-02-15T00:00:00Z,NA,NA -2007-02-16T00:00:00Z,NA,NA -2007-02-17T00:00:00Z,NA,NA -2007-02-18T00:00:00Z,NA,NA -2007-02-19T00:00:00Z,NA,NA -2007-02-20T00:00:00Z,NA,NA -2007-02-21T00:00:00Z,NA,NA -2007-02-22T00:00:00Z,NA,NA -2007-02-23T00:00:00Z,NA,NA -2007-02-24T00:00:00Z,NA,NA -2007-02-25T00:00:00Z,NA,NA -2007-02-26T00:00:00Z,NA,NA -2007-02-27T00:00:00Z,NA,NA -2007-02-28T00:00:00Z,NA,NA -2007-03-01T00:00:00Z,NA,NA -2007-03-02T00:00:00Z,NA,NA -2007-03-03T00:00:00Z,NA,NA -2007-03-04T00:00:00Z,NA,NA -2007-03-05T00:00:00Z,NA,NA -2007-03-06T00:00:00Z,NA,NA -2007-03-07T00:00:00Z,NA,NA -2007-03-08T00:00:00Z,NA,NA -2007-03-09T00:00:00Z,NA,NA -2007-03-10T00:00:00Z,NA,NA -2007-03-11T00:00:00Z,NA,NA -2007-03-12T00:00:00Z,NA,NA -2007-03-13T00:00:00Z,NA,NA -2007-03-14T00:00:00Z,NA,NA -2007-03-15T00:00:00Z,NA,NA -2007-03-16T00:00:00Z,NA,NA -2007-03-17T00:00:00Z,NA,NA -2007-03-18T00:00:00Z,NA,NA -2007-03-19T00:00:00Z,NA,NA -2007-03-20T00:00:00Z,NA,NA -2007-03-21T00:00:00Z,NA,NA -2007-03-22T00:00:00Z,NA,NA -2007-03-23T00:00:00Z,NA,NA -2007-03-24T00:00:00Z,NA,NA -2007-03-25T00:00:00Z,NA,NA -2007-03-26T00:00:00Z,NA,50.15 -2007-03-27T00:00:00Z,50.15,50.4 -2007-03-28T00:00:00Z,50.65,50.06333333 -2007-03-29T00:00:00Z,49.39,NA -2007-03-30T00:00:00Z,NA,51.995 -2007-03-31T00:00:00Z,57.79,53.112 -2007-04-01T00:00:00Z,57.58,53.83666667 -2007-04-02T00:00:00Z,57.46,55.095 -2007-04-03T00:00:00Z,57.7,56.49666667 -2007-04-04T00:00:00Z,59.06,57.70666667 -2007-04-05T00:00:00Z,56.65,56.74142857 -2007-04-06T00:00:00Z,50.95,56.86 -2007-04-07T00:00:00Z,58.62,57.43428571 -2007-04-08T00:00:00Z,61.6,58.15571429 -2007-04-09T00:00:00Z,62.51,59.3 -2007-04-10T00:00:00Z,65.71,60.44571429 -2007-04-11T00:00:00Z,67.08,62.04428571 -2007-04-12T00:00:00Z,67.84,64.39 -2007-04-13T00:00:00Z,67.37,65.51857143 -2007-04-14T00:00:00Z,66.52,66.21428571 -2007-04-15T00:00:00Z,66.47,65.66857143 -2007-04-16T00:00:00Z,58.69,63.12428571 -2007-04-17T00:00:00Z,47.9,59.29285714 -2007-04-18T00:00:00Z,40.26,55.15285714 -2007-04-19T00:00:00Z,38.86,50.07 -2007-04-20T00:00:00Z,31.79,44.95714286 -2007-04-21T00:00:00Z,30.73,39.90571429 -2007-04-22T00:00:00Z,31.11,36.01571429 -2007-04-23T00:00:00Z,31.46,33.84428571 -2007-04-24T00:00:00Z,32.7,32.80285714 -2007-04-25T00:00:00Z,32.97,33.37142857 -2007-04-26T00:00:00Z,42.84,33.81285714 -2007-04-27T00:00:00Z,34.88,34.62428571 -2007-04-28T00:00:00Z,36.41,35.52285714 -2007-04-29T00:00:00Z,37.4,37.13428571 -2007-04-30T00:00:00Z,42.74,39.32571429 -2007-05-01T00:00:00Z,48.04,42.10285714 -2007-05-02T00:00:00Z,52.41,43.58857143 -2007-05-03T00:00:00Z,53.24,45.85857143 -2007-05-04T00:00:00Z,50.77,47.94428571 -2007-05-05T00:00:00Z,51.01,50.45714286 -2007-05-06T00:00:00Z,54.99,53.07857143 -2007-05-07T00:00:00Z,61.09,54.71142857 -2007-05-08T00:00:00Z,59.47,55.73 -2007-05-09T00:00:00Z,59.54,56.86142857 -2007-05-10T00:00:00Z,61.16,58.59571429 -2007-05-11T00:00:00Z,62.91,60.42285714 -2007-05-12T00:00:00Z,63.8,61.75142857 -2007-05-13T00:00:00Z,64.29,62.28428571 -2007-05-14T00:00:00Z,64.82,63.00571429 -2007-05-15T00:00:00Z,64.52,63.56571429 -2007-05-16T00:00:00Z,63.46,63.89 -2007-05-17T00:00:00Z,63.43,64.3 -2007-05-18T00:00:00Z,65.78,64.67428571 -2007-05-19T00:00:00Z,66.42,64.92285714 -2007-05-20T00:00:00Z,66.03,65.07857143 -2007-05-21T00:00:00Z,65.91,65.40142857 -2007-05-22T00:00:00Z,66.78,65.88428571 -2007-05-23T00:00:00Z,66.84,66.33285714 -2007-05-24T00:00:00Z,66.57,66.39857143 -2007-05-25T00:00:00Z,66.24,66.43714286 -2007-05-26T00:00:00Z,66.69,66.63285714 -2007-05-27T00:00:00Z,67.4,66.88285714 -2007-05-28T00:00:00Z,67.66,67.01714286 -2007-05-29T00:00:00Z,67.72,67.22857143 -2007-05-30T00:00:00Z,68.32,67.48857143 -2007-05-31T00:00:00Z,68.39,67.80428571 -2007-06-01T00:00:00Z,68.45,68.05428571 -2007-06-02T00:00:00Z,68.44,68.21142857 -2007-06-03T00:00:00Z,68.5,68.45142857 -2007-06-04T00:00:00Z,69.34,68.67142857 -2007-06-05T00:00:00Z,69.26,68.75 -2007-06-06T00:00:00Z,68.87,68.85428571 -2007-06-07T00:00:00Z,69.12,68.94857143 -2007-06-08T00:00:00Z,69.11,69.08428571 -2007-06-09T00:00:00Z,69.39,69.31428571 -2007-06-10T00:00:00Z,70.11,69.42142857 -2007-06-11T00:00:00Z,70.09,69.53 -2007-06-12T00:00:00Z,70.02,69.73142857 -2007-06-13T00:00:00Z,70.28,69.95285714 -2007-06-14T00:00:00Z,70.67,70.17 -2007-06-15T00:00:00Z,70.63,70.34714286 -2007-06-16T00:00:00Z,70.63,70.40142857 -2007-06-17T00:00:00Z,70.49,70.45428571 -2007-06-18T00:00:00Z,70.46,70.54142857 -2007-06-19T00:00:00Z,70.63,70.57857143 -2007-06-20T00:00:00Z,70.54,70.62714286 -2007-06-21T00:00:00Z,71.01,70.65285714 -2007-06-22T00:00:00Z,70.81,70.76142857 -2007-06-23T00:00:00Z,71.39,70.90285714 -2007-06-24T00:00:00Z,71.48,71.06285714 -2007-06-25T00:00:00Z,71.58,71.16714286 -2007-06-26T00:00:00Z,71.36,71.27714286 -2007-06-27T00:00:00Z,71.31,71.27714286 -2007-06-28T00:00:00Z,71.01,71.32285714 -2007-06-29T00:00:00Z,71.13,71.31285714 -2007-06-30T00:00:00Z,71.32,71.32714286 -2007-07-01T00:00:00Z,71.58,71.33714286 -2007-07-02T00:00:00Z,71.65,71.38428571 -2007-07-03T00:00:00Z,71.69,71.47142857 -2007-07-04T00:00:00Z,71.92,71.54857143 -2007-07-05T00:00:00Z,71.55,71.55428571 -2007-07-06T00:00:00Z,71.17,71.54142857 -2007-07-07T00:00:00Z,71.23,71.51571429 -2007-07-08T00:00:00Z,71.4,71.48571429 -2007-07-09T00:00:00Z,71.44,71.47142857 -2007-07-10T00:00:00Z,71.59,71.44142857 -2007-07-11T00:00:00Z,71.71,71.47714286 -2007-07-12T00:00:00Z,71.8,71.61142857 -2007-07-13T00:00:00Z,72.11,71.76571429 -2007-07-14T00:00:00Z,72.31,71.95142857 -2007-07-15T00:00:00Z,72.7,72.11857143 -2007-07-16T00:00:00Z,72.61,72.35142857 -2007-07-17T00:00:00Z,73.22,72.63142857 -2007-07-18T00:00:00Z,73.67,72.92 -2007-07-19T00:00:00Z,73.82,73.1 -2007-07-20T00:00:00Z,73.37,73.25714286 -2007-07-21T00:00:00Z,73.41,73.44142857 -2007-07-22T00:00:00Z,73.99,73.68714286 -2007-07-23T00:00:00Z,74.33,73.89285714 -2007-07-24T00:00:00Z,74.66,74.04571429 -2007-07-25T00:00:00Z,74.74,74.13285714 -2007-07-26T00:00:00Z,74.43,74.31428571 -2007-07-27T00:00:00Z,74.64,74.52285714 -2007-07-28T00:00:00Z,74.87,74.63571429 -2007-07-29T00:00:00Z,74.78,74.71285714 -2007-07-30T00:00:00Z,74.87,74.73428571 -2007-07-31T00:00:00Z,74.81,74.79428571 -2007-08-01T00:00:00Z,75.16,74.91142857 -2007-08-02T00:00:00Z,75.25,74.97428571 -2007-08-03T00:00:00Z,75.08,74.96714286 -2007-08-04T00:00:00Z,74.82,75.13571429 -2007-08-05T00:00:00Z,75.96,75.36142857 -2007-08-06T00:00:00Z,76.45,75.61 -2007-08-07T00:00:00Z,76.55,75.84571429 -2007-08-08T00:00:00Z,76.81,76.08285714 -2007-08-09T00:00:00Z,76.91,76.35142857 -2007-08-10T00:00:00Z,76.96,76.75 -2007-08-11T00:00:00Z,77.61,76.89857143 -2007-08-12T00:00:00Z,77,76.94857143 -2007-08-13T00:00:00Z,76.8,76.95857143 -2007-08-14T00:00:00Z,76.62,76.92571429 -2007-08-15T00:00:00Z,76.58,76.83142857 -2007-08-16T00:00:00Z,76.25,76.72285714 -2007-08-17T00:00:00Z,76.2,76.44857143 -2007-08-18T00:00:00Z,75.69,76.32142857 -2007-08-19T00:00:00Z,76.11,76.15428571 -2007-08-20T00:00:00Z,75.63,76.01428571 -2007-08-21T00:00:00Z,75.64,75.69 -2007-08-22T00:00:00Z,74.31,75.32571429 -2007-08-23T00:00:00Z,73.7,74.95714286 -2007-08-24T00:00:00Z,73.62,74.62857143 -2007-08-25T00:00:00Z,73.39,74.2 -2007-08-26T00:00:00Z,73.11,73.82571429 -2007-08-27T00:00:00Z,73.01,73.49571429 -2007-08-28T00:00:00Z,73.33,73.40428571 -2007-08-29T00:00:00Z,73.67,73.43714286 -2007-08-30T00:00:00Z,73.93,73.5 -2007-08-31T00:00:00Z,74.06,73.66428571 -2007-09-01T00:00:00Z,74.54,73.99 -2007-09-02T00:00:00Z,75.39,74.37714286 -2007-09-03T00:00:00Z,75.72,74.60285714 -2007-09-04T00:00:00Z,74.91,74.97142857 -2007-09-05T00:00:00Z,76.25,75.38428571 -2007-09-06T00:00:00Z,76.82,75.83285714 -2007-09-07T00:00:00Z,77.2,76.22857143 -2007-09-08T00:00:00Z,77.31,76.58142857 -2007-09-09T00:00:00Z,77.86,77.02571429 -2007-09-10T00:00:00Z,78.83,77.71714286 -2007-09-11T00:00:00Z,79.75,78.12142857 -2007-09-12T00:00:00Z,79.08,78.46714286 -2007-09-13T00:00:00Z,79.24,78.85285714 -2007-09-14T00:00:00Z,79.9,79.09714286 -2007-09-15T00:00:00Z,79.02,79.25142857 -2007-09-16T00:00:00Z,78.94,79.34285714 -2007-09-17T00:00:00Z,79.47,79.36428571 -2007-09-18T00:00:00Z,79.9,79.50142857 -2007-09-19T00:00:00Z,80.04,79.53571429 -2007-09-20T00:00:00Z,79.48,79.45857143 -2007-09-21T00:00:00Z,79.36,79.57714286 -2007-09-22T00:00:00Z,79.85,79.74857143 -2007-09-23T00:00:00Z,80.14,79.92714286 -2007-09-24T00:00:00Z,80.72,80.12857143 -2007-09-25T00:00:00Z,81.31,80.32571429 -2007-09-26T00:00:00Z,81.42,80.70142857 -2007-09-27T00:00:00Z,82.11,81.27285714 -2007-09-28T00:00:00Z,83.36,81.75 -2007-09-29T00:00:00Z,83.19,82.28428571 -2007-09-30T00:00:00Z,83.88,82.80571429 -2007-10-01T00:00:00Z,84.37,83.24 -2007-10-02T00:00:00Z,84.35,83.68857143 -2007-10-03T00:00:00Z,84.56,84.07 -2007-10-04T00:00:00Z,84.78,84.28428571 -2007-10-05T00:00:00Z,84.86,84.50857143 -2007-10-06T00:00:00Z,84.76,84.69142857 -2007-10-07T00:00:00Z,85.16,84.88285714 -2007-10-08T00:00:00Z,85.71,85.11285714 -2007-10-09T00:00:00Z,85.96,85.34 -2007-10-10T00:00:00Z,86.15,85.57428571 -2007-10-11T00:00:00Z,86.42,85.55714286 -2007-10-12T00:00:00Z,84.74,85.49142857 -2007-10-13T00:00:00Z,84.3,85.22285714 -2007-10-14T00:00:00Z,83.28,84.76142857 -2007-10-15T00:00:00Z,82.48,84.15285714 -2007-10-16T00:00:00Z,81.7,83.54142857 -2007-10-17T00:00:00Z,81.87,82.92571429 -2007-10-18T00:00:00Z,82.11,82.61857143 -2007-10-19T00:00:00Z,82.59,82.23571429 -2007-10-20T00:00:00Z,81.62,81.83571429 -2007-10-21T00:00:00Z,80.48,81.54428571 -2007-10-22T00:00:00Z,80.44,81.41571429 -2007-10-23T00:00:00Z,80.8,81.13 -2007-10-24T00:00:00Z,79.87,80.87714286 -2007-10-25T00:00:00Z,80.34,80.86285714 -2007-10-26T00:00:00Z,82.49,80.51142857 -2007-10-27T00:00:00Z,79.16,79.55428571 -2007-10-28T00:00:00Z,73.78,78.43714286 -2007-10-29T00:00:00Z,72.62,77.05571429 -2007-10-30T00:00:00Z,71.13,75.68571429 -2007-10-31T00:00:00Z,70.28,74.18571429 -2007-11-01T00:00:00Z,69.84,72.34714286 -2007-11-02T00:00:00Z,69.62,71.11857143 -2007-11-03T00:00:00Z,70.56,70.63714286 -2007-11-04T00:00:00Z,70.41,70.25428571 -2007-11-05T00:00:00Z,69.94,70.12571429 -2007-11-06T00:00:00Z,70.23,70.04428571 -2007-11-07T00:00:00Z,69.71,70.05142857 -2007-11-08T00:00:00Z,69.89,70.10428571 -2007-11-09T00:00:00Z,69.99,70.06 -2007-11-10T00:00:00Z,70.25,70.04571429 -2007-11-11T00:00:00Z,70.31,70.10714286 -2007-11-12T00:00:00Z,70.37,70.07285714 -2007-11-13T00:00:00Z,69.99,70.15571429 -2007-11-14T00:00:00Z,70.29,70.16857143 -2007-11-15T00:00:00Z,69.98,70.13 -2007-11-16T00:00:00Z,69.72,70.06142857 -2007-11-17T00:00:00Z,69.77,69.98428571 -2007-11-18T00:00:00Z,69.77,69.95428571 -2007-11-19T00:00:00Z,70.16,69.98571429 -2007-11-20T00:00:00Z,70.21,69.94428571 -2007-11-21T00:00:00Z,70,69.95428571 -2007-11-22T00:00:00Z,70.05,69.94142857 -2007-11-23T00:00:00Z,69.63,69.92428571 -2007-11-24T00:00:00Z,69.65,69.89571429 -2007-11-25T00:00:00Z,69.57,69.78571429 -2007-11-26T00:00:00Z,69.39,69.59857143 -2007-11-27T00:00:00Z,68.9,69.29285714 -2007-11-28T00:00:00Z,67.86,68.98428571 -2007-11-29T00:00:00Z,67.89,68.62714286 -2007-11-30T00:00:00Z,67.13,68.10285714 -2007-12-01T00:00:00Z,65.98,67.71714286 -2007-12-02T00:00:00Z,66.87,67.35142857 -2007-12-03T00:00:00Z,66.83,66.84142857 -2007-12-04T00:00:00Z,65.33,66.70571429 -2007-12-05T00:00:00Z,66.91,66.71571429 -2007-12-06T00:00:00Z,67.96,66.86571429 -2007-12-07T00:00:00Z,68.18,67.16714286 -2007-12-08T00:00:00Z,68.09,67.35571429 -2007-12-09T00:00:00Z,68.19,67.60285714 -2007-12-10T00:00:00Z,68.56,68.1 -2007-12-11T00:00:00Z,68.81,68.34 -2007-12-12T00:00:00Z,68.59,68.43571429 -2007-12-13T00:00:00Z,68.63,68.58142857 -2007-12-14T00:00:00Z,69.2,68.68285714 -2007-12-15T00:00:00Z,68.8,68.97428571 -2007-12-16T00:00:00Z,70.23,68.86857143 -2007-12-17T00:00:00Z,67.82,68.74 -2007-12-18T00:00:00Z,67.91,NA -2007-12-19T00:00:00Z,NA,NA -2007-12-20T00:00:00Z,NA,NA -2007-12-21T00:00:00Z,NA,NA -2007-12-22T00:00:00Z,NA,NA -2007-12-23T00:00:00Z,NA,NA -2007-12-24T00:00:00Z,NA,NA -2007-12-25T00:00:00Z,NA,NA -2007-12-26T00:00:00Z,NA,NA -2007-12-27T00:00:00Z,NA,NA -2007-12-28T00:00:00Z,NA,NA -2007-12-29T00:00:00Z,NA,NA -2007-12-30T00:00:00Z,NA,NA -2007-12-31T00:00:00Z,NA,NA -2008-01-01T00:00:00Z,NA,NA -2008-01-02T00:00:00Z,NA,NA -2008-01-03T00:00:00Z,NA,NA -2008-01-04T00:00:00Z,NA,NA -2008-01-05T00:00:00Z,NA,NA -2008-01-06T00:00:00Z,NA,NA -2008-01-07T00:00:00Z,NA,NA -2008-01-08T00:00:00Z,NA,NA -2008-01-09T00:00:00Z,NA,NA -2008-01-10T00:00:00Z,NA,NA -2008-01-11T00:00:00Z,NA,NA -2008-01-12T00:00:00Z,NA,NA -2008-01-13T00:00:00Z,NA,NA -2008-01-14T00:00:00Z,NA,NA -2008-01-15T00:00:00Z,NA,NA -2008-01-16T00:00:00Z,NA,NA -2008-01-17T00:00:00Z,NA,NA -2008-01-18T00:00:00Z,NA,NA -2008-01-19T00:00:00Z,NA,NA -2008-01-20T00:00:00Z,NA,NA -2008-01-21T00:00:00Z,NA,NA -2008-01-22T00:00:00Z,NA,NA -2008-01-23T00:00:00Z,NA,NA -2008-01-24T00:00:00Z,NA,NA -2008-01-25T00:00:00Z,NA,NA -2008-01-26T00:00:00Z,NA,NA -2008-01-27T00:00:00Z,NA,NA -2008-01-28T00:00:00Z,NA,NA -2008-01-29T00:00:00Z,NA,NA -2008-01-30T00:00:00Z,NA,NA -2008-01-31T00:00:00Z,NA,NA -2008-02-01T00:00:00Z,NA,NA -2008-02-02T00:00:00Z,NA,NA -2008-02-03T00:00:00Z,NA,NA -2008-02-04T00:00:00Z,NA,NA -2008-02-05T00:00:00Z,NA,NA -2008-02-06T00:00:00Z,NA,NA -2008-02-07T00:00:00Z,NA,NA -2008-02-08T00:00:00Z,NA,NA -2008-02-09T00:00:00Z,NA,NA -2008-02-10T00:00:00Z,NA,NA -2008-02-11T00:00:00Z,NA,NA -2008-02-12T00:00:00Z,NA,NA -2008-02-13T00:00:00Z,NA,NA -2008-02-14T00:00:00Z,NA,NA -2008-02-15T00:00:00Z,NA,51.6 -2008-02-16T00:00:00Z,51.6,NA -2008-02-17T00:00:00Z,NA,NA -2008-02-18T00:00:00Z,NA,50.23 -2008-02-19T00:00:00Z,48.86,47.02666667 -2008-02-20T00:00:00Z,40.62,44.62 -2008-02-21T00:00:00Z,37.4,43.012 -2008-02-22T00:00:00Z,36.58,40.042 -2008-02-23T00:00:00Z,36.75,39.34666667 -2008-02-24T00:00:00Z,35.87,38.85 -2008-02-25T00:00:00Z,35.87,37.38285714 -2008-02-26T00:00:00Z,38.59,36.98857143 -2008-02-27T00:00:00Z,37.86,38.50857143 -2008-02-28T00:00:00Z,48.04,NA -2008-02-29T00:00:00Z,NA,NA -2008-03-01T00:00:00Z,NA,NA -2008-03-02T00:00:00Z,NA,NA -2008-03-03T00:00:00Z,NA,NA -2008-03-04T00:00:00Z,NA,NA -2008-03-05T00:00:00Z,NA,NA -2008-03-06T00:00:00Z,NA,NA -2008-03-07T00:00:00Z,NA,47.76 -2008-03-08T00:00:00Z,47.76,42.81 -2008-03-09T00:00:00Z,37.86,39.87 -2008-03-10T00:00:00Z,33.99,38.005 -2008-03-11T00:00:00Z,32.41,36.762 -2008-03-12T00:00:00Z,31.79,35.75666667 -2008-03-13T00:00:00Z,30.73,35.03857143 -2008-03-14T00:00:00Z,30.73,32.60571429 -2008-03-15T00:00:00Z,30.73,32.05285714 -2008-03-16T00:00:00Z,33.99,32.62714286 -2008-03-17T00:00:00Z,38.01,NA -2008-03-18T00:00:00Z,NA,NA -2008-03-19T00:00:00Z,NA,37.128 -2008-03-20T00:00:00Z,52.18,38.584 -2008-03-21T00:00:00Z,38.01,39.086 -2008-03-22T00:00:00Z,33.24,38.77 -2008-03-23T00:00:00Z,32.41,37.708 -2008-03-24T00:00:00Z,32.7,36.87333333 -2008-03-25T00:00:00Z,32.7,36.49428571 -2008-03-26T00:00:00Z,34.22,33.82571429 -2008-03-27T00:00:00Z,33.5,33.82571429 -2008-03-28T00:00:00Z,38.01,35.78571429 -2008-03-29T00:00:00Z,46.96,NA -2008-03-30T00:00:00Z,NA,NA -2008-03-31T00:00:00Z,NA,43.18 -2008-04-01T00:00:00Z,63.21,47.882 -2008-04-02T00:00:00Z,57.73,53.596 -2008-04-03T00:00:00Z,62.07,58.268 -2008-04-04T00:00:00Z,61.37,60.97 -2008-04-05T00:00:00Z,60.47,60.97 -2008-04-06T00:00:00Z,60.97,61.16285714 -2008-04-07T00:00:00Z,62.32,60.99714286 -2008-04-08T00:00:00Z,62.05,61.38285714 -2008-04-09T00:00:00Z,60.43,60.95428571 -2008-04-10T00:00:00Z,59.07,60.34714286 -2008-04-11T00:00:00Z,57.12,59.75142857 -2008-04-12T00:00:00Z,56.3,58.96428571 -2008-04-13T00:00:00Z,55.46,58.34142857 -2008-04-14T00:00:00Z,57.96,57.96285714 -2008-04-15T00:00:00Z,59.4,58.2 -2008-04-16T00:00:00Z,62.09,58.60714286 -2008-04-17T00:00:00Z,61.92,59.52857143 -2008-04-18T00:00:00Z,63.57,60.60714286 -2008-04-19T00:00:00Z,63.85,61.77142857 -2008-04-20T00:00:00Z,63.61,62.53 -2008-04-21T00:00:00Z,63.27,63.09285714 -2008-04-22T00:00:00Z,63.34,63.27285714 -2008-04-23T00:00:00Z,63.35,63.44857143 -2008-04-24T00:00:00Z,63.15,63.46428571 -2008-04-25T00:00:00Z,63.68,63.67285714 -2008-04-26T00:00:00Z,65.31,64.10714286 -2008-04-27T00:00:00Z,66.65,64.67428571 -2008-04-28T00:00:00Z,67.24,65.11 -2008-04-29T00:00:00Z,66.39,65.69285714 -2008-04-30T00:00:00Z,67.43,66.34571429 -2008-05-01T00:00:00Z,67.72,66.92142857 -2008-05-02T00:00:00Z,67.71,67.43857143 -2008-05-03T00:00:00Z,68.93,67.72571429 -2008-05-04T00:00:00Z,68.66,67.89571429 -2008-05-05T00:00:00Z,68.43,68.18285714 -2008-05-06T00:00:00Z,68.4,68.31142857 -2008-05-07T00:00:00Z,68.33,68.43714286 -2008-05-08T00:00:00Z,68.6,68.49571429 -2008-05-09T00:00:00Z,68.12,68.43857143 -2008-05-10T00:00:00Z,68.53,68.44 -2008-05-11T00:00:00Z,68.67,68.61285714 -2008-05-12T00:00:00Z,69.64,68.83428571 -2008-05-13T00:00:00Z,69.95,69.01142857 -2008-05-14T00:00:00Z,69.57,69.13428571 -2008-05-15T00:00:00Z,69.46,69.26857143 -2008-05-16T00:00:00Z,69.06,69.32857143 -2008-05-17T00:00:00Z,68.95,69.36 -2008-05-18T00:00:00Z,68.89,69.19571429 -2008-05-19T00:00:00Z,68.49,68.94857143 -2008-05-20T00:00:00Z,68.22,68.80714286 -2008-05-21T00:00:00Z,68.58,68.61 -2008-05-22T00:00:00Z,68.08,68.34857143 -2008-05-23T00:00:00Z,67.23,68.19857143 -2008-05-24T00:00:00Z,67.9,68.11714286 -2008-05-25T00:00:00Z,68.32,68.13714286 -2008-05-26T00:00:00Z,68.63,68.07571429 -2008-05-27T00:00:00Z,67.79,67.97285714 -2008-05-28T00:00:00Z,67.86,68.09571429 -2008-05-29T00:00:00Z,68.94,68.29 -2008-05-30T00:00:00Z,68.59,68.41142857 -2008-05-31T00:00:00Z,68.75,68.42571429 -2008-06-01T00:00:00Z,68.42,68.41428571 -2008-06-02T00:00:00Z,68.55,68.53857143 -2008-06-03T00:00:00Z,68.66,68.69428571 -2008-06-04T00:00:00Z,68.95,68.63571429 -2008-06-05T00:00:00Z,68.53,68.53285714 -2008-06-06T00:00:00Z,67.87,68.38428571 -2008-06-07T00:00:00Z,67.71,68.29 -2008-06-08T00:00:00Z,67.76,68.17857143 -2008-06-09T00:00:00Z,67.77,68.1 -2008-06-10T00:00:00Z,68.11,68.01571429 -2008-06-11T00:00:00Z,68.36,68.02 -2008-06-12T00:00:00Z,68.56,68.19285714 -2008-06-13T00:00:00Z,69.08,68.34857143 -2008-06-14T00:00:00Z,68.8,68.51285714 -2008-06-15T00:00:00Z,68.91,68.81571429 -2008-06-16T00:00:00Z,69.89,69.03285714 -2008-06-17T00:00:00Z,69.63,69.30142857 -2008-06-18T00:00:00Z,70.24,69.55142857 -2008-06-19T00:00:00Z,70.31,69.76714286 -2008-06-20T00:00:00Z,70.59,70.01857143 -2008-06-21T00:00:00Z,70.56,70.24 -2008-06-22T00:00:00Z,70.46,70.33428571 -2008-06-23T00:00:00Z,70.55,70.43857143 -2008-06-24T00:00:00Z,70.36,70.49 -2008-06-25T00:00:00Z,70.6,70.54857143 -2008-06-26T00:00:00Z,70.72,70.59 -2008-06-27T00:00:00Z,70.88,70.66142857 -2008-06-28T00:00:00Z,71.06,70.75571429 -2008-06-29T00:00:00Z,71.12,70.83142857 -2008-06-30T00:00:00Z,71.08,70.93714286 -2008-07-01T00:00:00Z,71.1,71.07285714 -2008-07-02T00:00:00Z,71.55,71.23714286 -2008-07-03T00:00:00Z,71.87,71.35714286 -2008-07-04T00:00:00Z,71.72,71.53857143 -2008-07-05T00:00:00Z,72.33,71.74571429 -2008-07-06T00:00:00Z,72.57,71.94285714 -2008-07-07T00:00:00Z,72.46,72.11142857 -2008-07-08T00:00:00Z,72.28,72.19714286 -2008-07-09T00:00:00Z,72.15,72.22428571 -2008-07-10T00:00:00Z,72.06,72.36571429 -2008-07-11T00:00:00Z,72.71,72.47857143 -2008-07-12T00:00:00Z,73.12,72.58142857 -2008-07-13T00:00:00Z,73.29,72.68285714 -2008-07-14T00:00:00Z,73.17,72.84571429 -2008-07-15T00:00:00Z,73.42,73.06428571 -2008-07-16T00:00:00Z,73.68,73.36142857 -2008-07-17T00:00:00Z,74.14,73.56571429 -2008-07-18T00:00:00Z,74.14,73.74 -2008-07-19T00:00:00Z,74.34,73.91714286 -2008-07-20T00:00:00Z,74.53,74.16142857 -2008-07-21T00:00:00Z,74.88,74.47 -2008-07-22T00:00:00Z,75.58,74.75285714 -2008-07-23T00:00:00Z,75.66,74.77 -2008-07-24T00:00:00Z,74.26,74.63857143 -2008-07-25T00:00:00Z,73.22,74.39142857 -2008-07-26T00:00:00Z,72.61,74.02428571 -2008-07-27T00:00:00Z,71.96,73.54857143 -2008-07-28T00:00:00Z,71.55,72.93428571 -2008-07-29T00:00:00Z,71.28,72.28142857 -2008-07-30T00:00:00Z,71.09,71.85714286 -2008-07-31T00:00:00Z,71.29,71.63142857 -2008-08-01T00:00:00Z,71.64,71.55285714 -2008-08-02T00:00:00Z,72.06,71.51428571 -2008-08-03T00:00:00Z,71.69,71.52571429 -2008-08-04T00:00:00Z,71.63,71.59571429 -2008-08-05T00:00:00Z,71.77,71.73142857 -2008-08-06T00:00:00Z,72.04,71.88285714 -2008-08-07T00:00:00Z,72.35,71.97857143 -2008-08-08T00:00:00Z,72.31,72.02714286 -2008-08-09T00:00:00Z,72.4,72.22142857 -2008-08-10T00:00:00Z,73.05,72.36714286 -2008-08-11T00:00:00Z,72.65,72.47857143 -2008-08-12T00:00:00Z,72.55,72.61 -2008-08-13T00:00:00Z,72.96,72.73142857 -2008-08-14T00:00:00Z,73.2,72.85285714 -2008-08-15T00:00:00Z,73.16,72.95714286 -2008-08-16T00:00:00Z,73.13,73.03714286 -2008-08-17T00:00:00Z,73.61,73.21 -2008-08-18T00:00:00Z,73.86,73.38857143 -2008-08-19T00:00:00Z,73.8,73.56857143 -2008-08-20T00:00:00Z,74.22,73.81142857 -2008-08-21T00:00:00Z,74.9,74.19142857 -2008-08-22T00:00:00Z,75.82,74.66285714 -2008-08-23T00:00:00Z,76.43,75.19285714 -2008-08-24T00:00:00Z,77.32,75.73571429 -2008-08-25T00:00:00Z,77.66,76.39714286 -2008-08-26T00:00:00Z,78.43,77.14857143 -2008-08-27T00:00:00Z,79.48,77.97428571 -2008-08-28T00:00:00Z,80.68,78.84 -2008-08-29T00:00:00Z,81.88,79.74857143 -2008-08-30T00:00:00Z,82.79,80.54714286 -2008-08-31T00:00:00Z,82.91,81.34142857 -2008-09-01T00:00:00Z,83.22,82.09857143 -2008-09-02T00:00:00Z,83.73,82.80285714 -2008-09-03T00:00:00Z,84.41,83.36142857 -2008-09-04T00:00:00Z,84.59,83.74142857 -2008-09-05T00:00:00Z,84.54,84.05857143 -2008-09-06T00:00:00Z,85.01,83.85857143 -2008-09-07T00:00:00Z,81.51,83.43857143 -2008-09-08T00:00:00Z,80.28,82.96142857 -2008-09-09T00:00:00Z,80.39,82.25857143 -2008-09-10T00:00:00Z,79.49,81.58857143 -2008-09-11T00:00:00Z,79.9,80.87142857 -2008-09-12T00:00:00Z,79.52,79.96285714 -2008-09-13T00:00:00Z,78.65,79.55142857 -2008-09-14T00:00:00Z,78.63,79.32571429 -2008-09-15T00:00:00Z,78.7,79 -2008-09-16T00:00:00Z,78.11,78.92285714 -2008-09-17T00:00:00Z,78.95,78.90857143 -2008-09-18T00:00:00Z,79.8,79.10285714 -2008-09-19T00:00:00Z,80.88,79.48 -2008-09-20T00:00:00Z,81.29,79.80571429 -2008-09-21T00:00:00Z,80.91,80.13142857 -2008-09-22T00:00:00Z,80.98,80.72285714 -2008-09-23T00:00:00Z,82.25,81.28857143 -2008-09-24T00:00:00Z,82.91,81.83571429 -2008-09-25T00:00:00Z,83.63,82.33714286 -2008-09-26T00:00:00Z,84.39,82.70857143 -2008-09-27T00:00:00Z,83.89,83.12714286 -2008-09-28T00:00:00Z,83.84,83.44857143 -2008-09-29T00:00:00Z,83.23,83.34857143 -2008-09-30T00:00:00Z,81.55,83.09857143 -2008-10-01T00:00:00Z,81.16,82.54571429 -2008-10-02T00:00:00Z,79.76,81.73714286 -2008-10-03T00:00:00Z,78.73,80.92285714 -2008-10-04T00:00:00Z,78.19,80.05714286 -2008-10-05T00:00:00Z,77.78,79.32 -2008-10-06T00:00:00Z,78.07,78.83571429 -2008-10-07T00:00:00Z,78.16,78.43714286 -2008-10-08T00:00:00Z,78.37,78.21 -2008-10-09T00:00:00Z,78.17,78.11285714 -2008-10-10T00:00:00Z,78.05,78.19428571 -2008-10-11T00:00:00Z,78.76,78.47428571 -2008-10-12T00:00:00Z,79.74,78.78 -2008-10-13T00:00:00Z,80.21,79.25 -2008-10-14T00:00:00Z,81.45,79.84 -2008-10-15T00:00:00Z,82.5,80.59428571 -2008-10-16T00:00:00Z,83.45,81.40285714 -2008-10-17T00:00:00Z,83.71,82.22142857 -2008-10-18T00:00:00Z,84.49,83.01571429 -2008-10-19T00:00:00Z,85.3,83.86857143 -2008-10-20T00:00:00Z,86.18,84.46571429 -2008-10-21T00:00:00Z,85.63,84.73142857 -2008-10-22T00:00:00Z,84.36,85.08285714 -2008-10-23T00:00:00Z,85.91,85.42428571 -2008-10-24T00:00:00Z,86.1,85.68714286 -2008-10-25T00:00:00Z,86.33,85.80428571 -2008-10-26T00:00:00Z,86.12,85.68285714 -2008-10-27T00:00:00Z,85.33,85.35142857 -2008-10-28T00:00:00Z,83.31,84.28428571 -2008-10-29T00:00:00Z,76.89,82.6 -2008-10-30T00:00:00Z,74.12,80.83857143 -2008-10-31T00:00:00Z,73.77,78.83142857 -2008-11-01T00:00:00Z,72.28,76.82714286 -2008-11-02T00:00:00Z,72.09,74.92285714 -2008-11-03T00:00:00Z,72,73.25857143 -2008-11-04T00:00:00Z,71.66,72.47571429 -2008-11-05T00:00:00Z,71.41,72.15142857 -2008-11-06T00:00:00Z,71.85,71.84285714 -2008-11-07T00:00:00Z,71.61,71.74857143 -2008-11-08T00:00:00Z,71.62,71.64142857 -2008-11-09T00:00:00Z,71.34,NA -2008-11-10T00:00:00Z,NA,NA -2008-11-11T00:00:00Z,NA,NA -2008-11-12T00:00:00Z,NA,NA -2008-11-13T00:00:00Z,NA,NA -2008-11-14T00:00:00Z,NA,NA -2008-11-15T00:00:00Z,NA,NA -2008-11-16T00:00:00Z,NA,NA -2008-11-17T00:00:00Z,NA,NA -2008-11-18T00:00:00Z,NA,NA -2008-11-19T00:00:00Z,NA,NA -2008-11-20T00:00:00Z,NA,70.47 -2008-11-21T00:00:00Z,70.47,70.46 -2008-11-22T00:00:00Z,70.45,70.45666667 -2008-11-23T00:00:00Z,70.45,70.445 -2008-11-24T00:00:00Z,70.41,70.454 -2008-11-25T00:00:00Z,70.49,70.44666667 -2008-11-26T00:00:00Z,70.41,70.44714286 -2008-11-27T00:00:00Z,70.45,70.43285714 -2008-11-28T00:00:00Z,70.37,70.40428571 -2008-11-29T00:00:00Z,70.25,70.39 -2008-11-30T00:00:00Z,70.35,70.44571429 -2008-12-01T00:00:00Z,70.8,70.32142857 -2008-12-02T00:00:00Z,69.62,70.20571429 -2008-12-03T00:00:00Z,69.6,70.05857143 -2008-12-04T00:00:00Z,69.42,69.90714286 -2008-12-05T00:00:00Z,69.31,69.86857143 -2008-12-06T00:00:00Z,69.98,69.8 -2008-12-07T00:00:00Z,69.87,69.58714286 -2008-12-08T00:00:00Z,69.31,69.62571429 -2008-12-09T00:00:00Z,69.89,69.65142857 -2008-12-10T00:00:00Z,69.78,69.66571429 -2008-12-11T00:00:00Z,69.52,69.56 -2008-12-12T00:00:00Z,68.57,68.79 -2008-12-13T00:00:00Z,64.59,67.39142857 -2008-12-14T00:00:00Z,60.08,65.69142857 -2008-12-15T00:00:00Z,57.41,63.64857143 -2008-12-16T00:00:00Z,55.59,61.68714286 -2008-12-17T00:00:00Z,56.05,NA -2008-12-18T00:00:00Z,NA,57.77166667 -2008-12-19T00:00:00Z,52.91,NA -2008-12-20T00:00:00Z,NA,NA -2008-12-21T00:00:00Z,NA,53.95 -2008-12-22T00:00:00Z,51.25,NA -2008-12-23T00:00:00Z,NA,NA -2008-12-24T00:00:00Z,NA,NA -2008-12-25T00:00:00Z,NA,NA -2008-12-26T00:00:00Z,NA,NA -2008-12-27T00:00:00Z,NA,NA -2008-12-28T00:00:00Z,NA,NA -2008-12-29T00:00:00Z,NA,50.52 -2008-12-30T00:00:00Z,50.52,49.21 -2008-12-31T00:00:00Z,47.9,50.39 -2009-01-01T00:00:00Z,52.75,NA -2009-01-02T00:00:00Z,NA,NA -2009-01-03T00:00:00Z,NA,NA -2009-01-04T00:00:00Z,NA,NA -2009-01-05T00:00:00Z,NA,NA -2009-01-06T00:00:00Z,NA,NA -2009-01-07T00:00:00Z,NA,NA -2009-01-08T00:00:00Z,NA,NA -2009-01-09T00:00:00Z,NA,NA -2009-01-10T00:00:00Z,NA,NA -2009-01-11T00:00:00Z,NA,NA -2009-01-12T00:00:00Z,NA,NA -2009-01-13T00:00:00Z,NA,NA -2009-01-14T00:00:00Z,NA,NA -2009-01-15T00:00:00Z,NA,NA -2009-01-16T00:00:00Z,NA,NA -2009-01-17T00:00:00Z,NA,NA -2009-01-18T00:00:00Z,NA,NA -2009-01-19T00:00:00Z,NA,NA -2009-01-20T00:00:00Z,NA,NA -2009-01-21T00:00:00Z,NA,NA -2009-01-22T00:00:00Z,NA,NA -2009-01-23T00:00:00Z,NA,NA -2009-01-24T00:00:00Z,NA,NA -2009-01-25T00:00:00Z,NA,NA -2009-01-26T00:00:00Z,NA,NA -2009-01-27T00:00:00Z,NA,NA -2009-01-28T00:00:00Z,NA,NA -2009-01-29T00:00:00Z,NA,NA -2009-01-30T00:00:00Z,NA,NA -2009-01-31T00:00:00Z,NA,NA -2009-02-01T00:00:00Z,NA,NA -2009-02-02T00:00:00Z,NA,NA -2009-02-03T00:00:00Z,NA,NA -2009-02-04T00:00:00Z,NA,NA -2009-02-05T00:00:00Z,NA,NA -2009-02-06T00:00:00Z,NA,NA -2009-02-07T00:00:00Z,NA,NA -2009-02-08T00:00:00Z,NA,NA -2009-02-09T00:00:00Z,NA,NA -2009-02-10T00:00:00Z,NA,NA -2009-02-11T00:00:00Z,NA,NA -2009-02-12T00:00:00Z,NA,NA -2009-02-13T00:00:00Z,NA,NA -2009-02-14T00:00:00Z,NA,NA -2009-02-15T00:00:00Z,NA,NA -2009-02-16T00:00:00Z,NA,NA -2009-02-17T00:00:00Z,NA,NA -2009-02-18T00:00:00Z,NA,NA -2009-02-19T00:00:00Z,NA,NA -2009-02-20T00:00:00Z,NA,NA -2009-02-21T00:00:00Z,NA,NA -2009-02-22T00:00:00Z,NA,NA -2009-02-23T00:00:00Z,NA,NA -2009-02-24T00:00:00Z,NA,NA -2009-02-25T00:00:00Z,NA,NA -2009-02-26T00:00:00Z,NA,NA -2009-02-27T00:00:00Z,NA,NA 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-2009-04-04T00:00:00Z,68.63,68.5975 -2009-04-05T00:00:00Z,67.84,68.508 -2009-04-06T00:00:00Z,68.15,68.435 -2009-04-07T00:00:00Z,68.07,68.22142857 -2009-04-08T00:00:00Z,66.94,67.88857143 -2009-04-09T00:00:00Z,66.46,67.53285714 -2009-04-10T00:00:00Z,66.64,67.18142857 -2009-04-11T00:00:00Z,66.17,67.00142857 -2009-04-12T00:00:00Z,66.58,66.80571429 -2009-04-13T00:00:00Z,66.78,66.49 -2009-04-14T00:00:00Z,65.86,66.56 -2009-04-15T00:00:00Z,67.43,66.90428571 -2009-04-16T00:00:00Z,68.87,67.20714286 -2009-04-17T00:00:00Z,68.76,67.55 -2009-04-18T00:00:00Z,68.57,67.89714286 -2009-04-19T00:00:00Z,69.01,68.24571429 -2009-04-20T00:00:00Z,69.22,68.70285714 -2009-04-21T00:00:00Z,69.06,68.89857143 -2009-04-22T00:00:00Z,68.8,68.81857143 -2009-04-23T00:00:00Z,68.31,68.65714286 -2009-04-24T00:00:00Z,67.63,68.53571429 -2009-04-25T00:00:00Z,67.72,68.35285714 -2009-04-26T00:00:00Z,67.73,68.11142857 -2009-04-27T00:00:00Z,67.53,67.91714286 -2009-04-28T00:00:00Z,67.7,67.76285714 -2009-04-29T00:00:00Z,67.72,67.80142857 -2009-04-30T00:00:00Z,68.58,67.91428571 -2009-05-01T00:00:00Z,68.42,67.97714286 -2009-05-02T00:00:00Z,68.16,68.06142857 -2009-05-03T00:00:00Z,68.32,68.21142857 -2009-05-04T00:00:00Z,68.58,68.33571429 -2009-05-05T00:00:00Z,68.57,68.46285714 -2009-05-06T00:00:00Z,68.61,68.33714286 -2009-05-07T00:00:00Z,67.7,68.05714286 -2009-05-08T00:00:00Z,66.46,67.71142857 -2009-05-09T00:00:00Z,65.74,67.27285714 -2009-05-10T00:00:00Z,65.25,66.69571429 -2009-05-11T00:00:00Z,64.54,66.17714286 -2009-05-12T00:00:00Z,64.94,65.77285714 -2009-05-13T00:00:00Z,65.78,65.67 -2009-05-14T00:00:00Z,66.98,65.39 -2009-05-15T00:00:00Z,64.5,65.28285714 -2009-05-16T00:00:00Z,64.99,65.22857143 -2009-05-17T00:00:00Z,64.87,65.53571429 -2009-05-18T00:00:00Z,66.69,65.90142857 -2009-05-19T00:00:00Z,67.5,66.12285714 -2009-05-20T00:00:00Z,67.33,66.11142857 -2009-05-21T00:00:00Z,66.9,66.38428571 -2009-05-22T00:00:00Z,66.41,66.58857143 -2009-05-23T00:00:00Z,66.42,66.92142857 -2009-05-24T00:00:00Z,67.2,66.88571429 -2009-05-25T00:00:00Z,66.44,66.84 -2009-05-26T00:00:00Z,67.18,66.89714286 -2009-05-27T00:00:00Z,67.73,66.99571429 -2009-05-28T00:00:00Z,67.59,67.23571429 -2009-05-29T00:00:00Z,68.09,67.43857143 -2009-05-30T00:00:00Z,67.84,67.44142857 -2009-05-31T00:00:00Z,67.22,67.52 -2009-06-01T00:00:00Z,66.99,67.41571429 -2009-06-02T00:00:00Z,66.45,67.19142857 -2009-06-03T00:00:00Z,66.16,67.03 -2009-06-04T00:00:00Z,66.46,66.85285714 -2009-06-05T00:00:00Z,66.85,66.77142857 -2009-06-06T00:00:00Z,67.27,66.70571429 -2009-06-07T00:00:00Z,66.76,66.65714286 -2009-06-08T00:00:00Z,66.65,66.69714286 -2009-06-09T00:00:00Z,66.73,66.80285714 -2009-06-10T00:00:00Z,66.9,66.88571429 -2009-06-11T00:00:00Z,67.04,66.87142857 -2009-06-12T00:00:00Z,66.75,66.71 -2009-06-13T00:00:00Z,66.14,66.60857143 -2009-06-14T00:00:00Z,66.05,66.51571429 -2009-06-15T00:00:00Z,66,66.28142857 -2009-06-16T00:00:00Z,65.09,65.92285714 -2009-06-17T00:00:00Z,64.39,65.45571429 -2009-06-18T00:00:00Z,63.77,64.84571429 -2009-06-19T00:00:00Z,62.48,64.16285714 -2009-06-20T00:00:00Z,61.36,63.34285714 -2009-06-21T00:00:00Z,60.31,62.55571429 -2009-06-22T00:00:00Z,60.49,61.90428571 -2009-06-23T00:00:00Z,60.53,61.07285714 -2009-06-24T00:00:00Z,58.57,60.07571429 -2009-06-25T00:00:00Z,56.79,59.21285714 -2009-06-26T00:00:00Z,56.44,58.33142857 -2009-06-27T00:00:00Z,55.19,57.97142857 -2009-06-28T00:00:00Z,57.79,57.79428571 -2009-06-29T00:00:00Z,59.25,57.83428571 -2009-06-30T00:00:00Z,60.81,58.23285714 -2009-07-01T00:00:00Z,61.36,59.1 -2009-07-02T00:00:00Z,62.86,59.99142857 -2009-07-03T00:00:00Z,62.68,61.16571429 -2009-07-04T00:00:00Z,63.41,62.21 -2009-07-05T00:00:00Z,65.1,63.09 -2009-07-06T00:00:00Z,65.41,63.91285714 -2009-07-07T00:00:00Z,66.57,64.64714286 -2009-07-08T00:00:00Z,66.5,65.28142857 -2009-07-09T00:00:00Z,67.3,65.98 -2009-07-10T00:00:00Z,67.57,66.54428571 -2009-07-11T00:00:00Z,67.36,66.71428571 -2009-07-12T00:00:00Z,66.29,66.86142857 -2009-07-13T00:00:00Z,66.44,66.86285714 -2009-07-14T00:00:00Z,66.58,66.89571429 -2009-07-15T00:00:00Z,66.73,66.79285714 -2009-07-16T00:00:00Z,66.58,66.69571429 -2009-07-17T00:00:00Z,66.89,66.74142857 -2009-07-18T00:00:00Z,67.68,66.95571429 -2009-07-19T00:00:00Z,67.79,67.14 -2009-07-20T00:00:00Z,67.73,67.35 -2009-07-21T00:00:00Z,68.05,67.56 -2009-07-22T00:00:00Z,68.2,67.82142857 -2009-07-23T00:00:00Z,68.41,68.08714286 -2009-07-24T00:00:00Z,68.75,68.2 -2009-07-25T00:00:00Z,68.47,68.29428571 -2009-07-26T00:00:00Z,68.45,68.34285714 -2009-07-27T00:00:00Z,68.07,68.32571429 -2009-07-28T00:00:00Z,67.93,68.29571429 -2009-07-29T00:00:00Z,67.99,68.21571429 -2009-07-30T00:00:00Z,67.85,68.09 -2009-07-31T00:00:00Z,67.87,67.90428571 -2009-08-01T00:00:00Z,67.17,67.68428571 -2009-08-02T00:00:00Z,66.91,67.18 -2009-08-03T00:00:00Z,64.54,66.56142857 -2009-08-04T00:00:00Z,63.6,65.84 -2009-08-05T00:00:00Z,62.94,65.16285714 -2009-08-06T00:00:00Z,63.11,64.64428571 -2009-08-07T00:00:00Z,64.24,64.22571429 -2009-08-08T00:00:00Z,64.24,63.87857143 -2009-08-09T00:00:00Z,64.48,63.79285714 -2009-08-10T00:00:00Z,63.94,63.87714286 -2009-08-11T00:00:00Z,64.19,64.1 -2009-08-12T00:00:00Z,64.5,64.42428571 -2009-08-13T00:00:00Z,65.38,64.48 -2009-08-14T00:00:00Z,64.63,64.43857143 -2009-08-15T00:00:00Z,63.95,64.39 -2009-08-16T00:00:00Z,64.14,64.50142857 -2009-08-17T00:00:00Z,64.72,64.59285714 -2009-08-18T00:00:00Z,64.83,64.63285714 -2009-08-19T00:00:00Z,64.78,64.69 -2009-08-20T00:00:00Z,65.78,64.90285714 -2009-08-21T00:00:00Z,66.12,65.04714286 -2009-08-22T00:00:00Z,64.96,65.08571429 -2009-08-23T00:00:00Z,64.41,64.86857143 -2009-08-24T00:00:00Z,63.2,64.51571429 -2009-08-25T00:00:00Z,62.36,64.20571429 -2009-08-26T00:00:00Z,62.61,63.79571429 -2009-08-27T00:00:00Z,62.91,63.55 -2009-08-28T00:00:00Z,64.4,63.57857143 -2009-08-29T00:00:00Z,65.16,63.59428571 -2009-08-30T00:00:00Z,64.52,63.86714286 -2009-08-31T00:00:00Z,65.11,64.54142857 -2009-09-01T00:00:00Z,67.08,65.31571429 -2009-09-02T00:00:00Z,68.03,66.03428571 -2009-09-03T00:00:00Z,67.94,66.63571429 -2009-09-04T00:00:00Z,68.61,67.07714286 -2009-09-05T00:00:00Z,68.25,67.63 -2009-09-06T00:00:00Z,68.39,68.18428571 -2009-09-07T00:00:00Z,68.99,68.45285714 -2009-09-08T00:00:00Z,68.96,68.57571429 -2009-09-09T00:00:00Z,68.89,68.77142857 -2009-09-10T00:00:00Z,69.31,68.87571429 -2009-09-11T00:00:00Z,69.34,69.01428571 -2009-09-12T00:00:00Z,69.22,69.10857143 -2009-09-13T00:00:00Z,69.05,69.20142857 -2009-09-14T00:00:00Z,69.64,69.27285714 -2009-09-15T00:00:00Z,69.46,69.34571429 -2009-09-16T00:00:00Z,69.4,69.43571429 -2009-09-17T00:00:00Z,69.94,69.51428571 -2009-09-18T00:00:00Z,69.89,69.56428571 -2009-09-19T00:00:00Z,69.57,69.68 -2009-09-20T00:00:00Z,69.86,69.69142857 -2009-09-21T00:00:00Z,69.72,69.73714286 -2009-09-22T00:00:00Z,69.78,69.81142857 -2009-09-23T00:00:00Z,69.92,69.80285714 -2009-09-24T00:00:00Z,69.88,69.79857143 -2009-09-25T00:00:00Z,69.86,69.96142857 -2009-09-26T00:00:00Z,70.71,70.03857143 -2009-09-27T00:00:00Z,70.4,70.05 -2009-09-28T00:00:00Z,69.8,70.05571429 -2009-09-29T00:00:00Z,69.82,70.06428571 -2009-09-30T00:00:00Z,69.98,70.15285714 -2009-10-01T00:00:00Z,70.5,70.25714286 -2009-10-02T00:00:00Z,70.59,70.26571429 -2009-10-03T00:00:00Z,70.77,70.28857143 -2009-10-04T00:00:00Z,70.56,70.42 -2009-10-05T00:00:00Z,70.72,70.57714286 -2009-10-06T00:00:00Z,70.92,70.66857143 -2009-10-07T00:00:00Z,70.62,70.58142857 -2009-10-08T00:00:00Z,69.89,70.58 -2009-10-09T00:00:00Z,70.58,70.56142857 -2009-10-10T00:00:00Z,70.64,70.64285714 -2009-10-11T00:00:00Z,71.13,70.67 -2009-10-12T00:00:00Z,70.91,70.70142857 -2009-10-13T00:00:00Z,71.14,70.81285714 -2009-10-14T00:00:00Z,71.4,71.11 -2009-10-15T00:00:00Z,71.97,71.58285714 -2009-10-16T00:00:00Z,73.89,72.06714286 -2009-10-17T00:00:00Z,74.03,72.33285714 -2009-10-18T00:00:00Z,72.99,72.56714286 -2009-10-19T00:00:00Z,72.55,72.71857143 -2009-10-20T00:00:00Z,72.2,72.81 -2009-10-21T00:00:00Z,72.04,72.81428571 -2009-10-22T00:00:00Z,72,72.53285714 -2009-10-23T00:00:00Z,71.92,72.29428571 -2009-10-24T00:00:00Z,72.36,71.97285714 -2009-10-25T00:00:00Z,70.74,71.72571429 -2009-10-26T00:00:00Z,70.82,71.55 -2009-10-27T00:00:00Z,70.97,71.32 -2009-10-28T00:00:00Z,70.43,71.00714286 -2009-10-29T00:00:00Z,69.81,70.64 -2009-10-30T00:00:00Z,69.35,70.01857143 -2009-10-31T00:00:00Z,68.01,69.43142857 -2009-11-01T00:00:00Z,66.63,68.87428571 -2009-11-02T00:00:00Z,66.92,68.39571429 -2009-11-03T00:00:00Z,67.62,67.78 -2009-11-04T00:00:00Z,66.12,67.20857143 -2009-11-05T00:00:00Z,65.81,66.59142857 -2009-11-06T00:00:00Z,65.03,66.42285714 -2009-11-07T00:00:00Z,66.83,66.22571429 -2009-11-08T00:00:00Z,65.25,65.96857143 -2009-11-09T00:00:00Z,65.12,65.54857143 -2009-11-10T00:00:00Z,64.68,65.53714286 -2009-11-11T00:00:00Z,66.04,65.87142857 -2009-11-12T00:00:00Z,68.15,66.44857143 -2009-11-13T00:00:00Z,69.07,66.78571429 -2009-11-14T00:00:00Z,69.19,67.26285714 -2009-11-15T00:00:00Z,68.59,67.74 -2009-11-16T00:00:00Z,68.46,68.27428571 -2009-11-17T00:00:00Z,68.42,68.64857143 -2009-11-18T00:00:00Z,68.66,68.73 -2009-11-19T00:00:00Z,68.72,68.69 -2009-11-20T00:00:00Z,68.79,68.59142857 -2009-11-21T00:00:00Z,68.5,68.59571429 -2009-11-22T00:00:00Z,68.62,68.72 -2009-11-23T00:00:00Z,69.33,68.90857143 -2009-11-24T00:00:00Z,69.74,69.05571429 -2009-11-25T00:00:00Z,69.69,69.21428571 -2009-11-26T00:00:00Z,69.83,69.37857143 -2009-11-27T00:00:00Z,69.94,69.52285714 -2009-11-28T00:00:00Z,69.51,69.66571429 -2009-11-29T00:00:00Z,69.62,69.72714286 -2009-11-30T00:00:00Z,69.76,69.73142857 -2009-12-01T00:00:00Z,69.77,69.77 -2009-12-02T00:00:00Z,69.96,69.78142857 -2009-12-03T00:00:00Z,69.91,69.68714286 -2009-12-04T00:00:00Z,69.28,69.62571429 -2009-12-05T00:00:00Z,69.08,69.43714286 -2009-12-06T00:00:00Z,68.3,69.17285714 -2009-12-07T00:00:00Z,67.91,68.71142857 -2009-12-08T00:00:00Z,66.54,68.39 -2009-12-09T00:00:00Z,67.71,67.57571429 -2009-12-10T00:00:00Z,64.21,66.37571429 -2009-12-11T00:00:00Z,60.88,64.83285714 -2009-12-12T00:00:00Z,58.28,NA -2009-12-13T00:00:00Z,NA,NA -2009-12-14T00:00:00Z,NA,NA -2009-12-15T00:00:00Z,NA,NA -2009-12-16T00:00:00Z,NA,NA -2009-12-17T00:00:00Z,NA,NA -2009-12-18T00:00:00Z,NA,NA -2009-12-19T00:00:00Z,NA,NA -2009-12-20T00:00:00Z,NA,NA -2009-12-21T00:00:00Z,NA,NA -2009-12-22T00:00:00Z,NA,NA -2009-12-23T00:00:00Z,NA,NA -2009-12-24T00:00:00Z,NA,NA -2009-12-25T00:00:00Z,NA,NA -2009-12-26T00:00:00Z,NA,NA -2009-12-27T00:00:00Z,NA,NA -2009-12-28T00:00:00Z,NA,NA -2009-12-29T00:00:00Z,NA,NA -2009-12-30T00:00:00Z,NA,NA -2009-12-31T00:00:00Z,NA,NA -2010-01-01T00:00:00Z,NA,NA -2010-01-02T00:00:00Z,NA,NA -2010-01-03T00:00:00Z,NA,NA -2010-01-04T00:00:00Z,NA,NA -2010-01-05T00:00:00Z,NA,NA -2010-01-06T00:00:00Z,NA,NA -2010-01-07T00:00:00Z,NA,NA -2010-01-08T00:00:00Z,NA,NA -2010-01-09T00:00:00Z,NA,NA -2010-01-10T00:00:00Z,NA,NA -2010-01-11T00:00:00Z,NA,NA -2010-01-12T00:00:00Z,NA,NA -2010-01-13T00:00:00Z,NA,NA -2010-01-14T00:00:00Z,NA,NA -2010-01-15T00:00:00Z,NA,NA -2010-01-16T00:00:00Z,NA,NA -2010-01-17T00:00:00Z,NA,NA -2010-01-18T00:00:00Z,NA,NA -2010-01-19T00:00:00Z,NA,NA -2010-01-20T00:00:00Z,NA,NA -2010-01-21T00:00:00Z,NA,NA -2010-01-22T00:00:00Z,NA,NA -2010-01-23T00:00:00Z,NA,NA -2010-01-24T00:00:00Z,NA,NA -2010-01-25T00:00:00Z,NA,NA -2010-01-26T00:00:00Z,NA,NA -2010-01-27T00:00:00Z,NA,NA -2010-01-28T00:00:00Z,NA,NA -2010-01-29T00:00:00Z,NA,NA -2010-01-30T00:00:00Z,NA,NA -2010-01-31T00:00:00Z,NA,NA -2010-02-01T00:00:00Z,NA,NA -2010-02-02T00:00:00Z,NA,NA -2010-02-03T00:00:00Z,NA,NA -2010-02-04T00:00:00Z,NA,NA -2010-02-05T00:00:00Z,NA,NA -2010-02-06T00:00:00Z,NA,NA -2010-02-07T00:00:00Z,NA,NA -2010-02-08T00:00:00Z,NA,NA -2010-02-09T00:00:00Z,NA,NA -2010-02-10T00:00:00Z,NA,NA -2010-02-11T00:00:00Z,NA,NA -2010-02-12T00:00:00Z,NA,NA -2010-02-13T00:00:00Z,NA,NA -2010-02-14T00:00:00Z,NA,NA -2010-02-15T00:00:00Z,NA,NA -2010-02-16T00:00:00Z,NA,NA -2010-02-17T00:00:00Z,NA,NA -2010-02-18T00:00:00Z,NA,NA -2010-02-19T00:00:00Z,NA,NA -2010-02-20T00:00:00Z,NA,NA -2010-02-21T00:00:00Z,NA,NA -2010-02-22T00:00:00Z,NA,NA -2010-02-23T00:00:00Z,NA,NA -2010-02-24T00:00:00Z,NA,NA -2010-02-25T00:00:00Z,NA,NA -2010-02-26T00:00:00Z,NA,NA -2010-02-27T00:00:00Z,NA,NA -2010-02-28T00:00:00Z,NA,NA -2010-03-01T00:00:00Z,NA,NA -2010-03-02T00:00:00Z,NA,NA -2010-03-03T00:00:00Z,NA,NA -2010-03-04T00:00:00Z,NA,NA -2010-03-05T00:00:00Z,NA,NA -2010-03-06T00:00:00Z,NA,NA -2010-03-07T00:00:00Z,NA,NA -2010-03-08T00:00:00Z,NA,NA -2010-03-09T00:00:00Z,NA,NA -2010-03-10T00:00:00Z,NA,NA -2010-03-11T00:00:00Z,NA,NA -2010-03-12T00:00:00Z,NA,NA -2010-03-13T00:00:00Z,NA,NA -2010-03-14T00:00:00Z,NA,NA -2010-03-15T00:00:00Z,NA,NA -2010-03-16T00:00:00Z,NA,NA -2010-03-17T00:00:00Z,NA,NA -2010-03-18T00:00:00Z,NA,46.96 -2010-03-19T00:00:00Z,46.96,47.675 -2010-03-20T00:00:00Z,48.39,47.48666667 -2010-03-21T00:00:00Z,47.11,47.625 -2010-03-22T00:00:00Z,48.04,48.042 -2010-03-23T00:00:00Z,49.71,48.38333333 -2010-03-24T00:00:00Z,50.09,NA -2010-03-25T00:00:00Z,NA,49.45666667 -2010-03-26T00:00:00Z,53.4,NA -2010-03-27T00:00:00Z,NA,NA -2010-03-28T00:00:00Z,NA,NA -2010-03-29T00:00:00Z,NA,49.15 -2010-03-30T00:00:00Z,43.96,44.92 -2010-03-31T00:00:00Z,37.4,42.6575 -2010-04-01T00:00:00Z,35.87,37.805 -2010-04-02T00:00:00Z,33.99,NA -2010-04-03T00:00:00Z,NA,NA -2010-04-04T00:00:00Z,NA,NA -2010-04-05T00:00:00Z,NA,NA -2010-04-06T00:00:00Z,NA,NA -2010-04-07T00:00:00Z,NA,NA -2010-04-08T00:00:00Z,NA,NA -2010-04-09T00:00:00Z,NA,NA -2010-04-10T00:00:00Z,NA,NA -2010-04-11T00:00:00Z,NA,NA -2010-04-12T00:00:00Z,NA,NA -2010-04-13T00:00:00Z,NA,NA -2010-04-14T00:00:00Z,NA,NA -2010-04-15T00:00:00Z,NA,64.78 -2010-04-16T00:00:00Z,64.78,64.945 -2010-04-17T00:00:00Z,65.11,64.57333333 -2010-04-18T00:00:00Z,63.83,64.4875 -2010-04-19T00:00:00Z,64.23,64.556 -2010-04-20T00:00:00Z,64.83,64.62 -2010-04-21T00:00:00Z,64.94,64.74142857 -2010-04-22T00:00:00Z,65.47,64.87285714 -2010-04-23T00:00:00Z,65.7,65.08 -2010-04-24T00:00:00Z,66.56,65.45142857 -2010-04-25T00:00:00Z,66.43,65.83571429 -2010-04-26T00:00:00Z,66.92,66.17 -2010-04-27T00:00:00Z,67.17,66.24285714 -2010-04-28T00:00:00Z,65.45,66.21428571 -2010-04-29T00:00:00Z,65.27,66.16714286 -2010-04-30T00:00:00Z,65.37,65.94571429 -2010-05-01T00:00:00Z,65.01,65.68571429 -2010-05-02T00:00:00Z,64.61,65.37428571 -2010-05-03T00:00:00Z,64.74,64.82428571 -2010-05-04T00:00:00Z,63.32,64.57857143 -2010-05-05T00:00:00Z,63.73,64.46571429 -2010-05-06T00:00:00Z,64.48,64.39285714 -2010-05-07T00:00:00Z,64.86,64.55285714 -2010-05-08T00:00:00Z,66.13,64.50714286 -2010-05-09T00:00:00Z,64.29,64.92571429 -2010-05-10T00:00:00Z,67.67,65.76571429 -2010-05-11T00:00:00Z,69.2,66.60428571 -2010-05-12T00:00:00Z,69.6,67.24571429 -2010-05-13T00:00:00Z,68.97,67.76142857 -2010-05-14T00:00:00Z,68.47,68.03142857 -2010-05-15T00:00:00Z,68.02,68.55571429 -2010-05-16T00:00:00Z,67.96,68.61428571 -2010-05-17T00:00:00Z,68.08,68.44285714 -2010-05-18T00:00:00Z,68,68.29428571 -2010-05-19T00:00:00Z,68.56,68.22142857 -2010-05-20T00:00:00Z,68.46,68.14857143 -2010-05-21T00:00:00Z,67.96,68.17285714 -2010-05-22T00:00:00Z,68.19,68.20571429 -2010-05-23T00:00:00Z,68.19,68.13857143 -2010-05-24T00:00:00Z,67.61,68.09 -2010-05-25T00:00:00Z,67.66,68.01142857 -2010-05-26T00:00:00Z,68.01,68.03571429 -2010-05-27T00:00:00Z,68.63,68.21571429 -2010-05-28T00:00:00Z,69.22,68.32142857 -2010-05-29T00:00:00Z,68.93,68.41142857 -2010-05-30T00:00:00Z,68.82,68.6 -2010-05-31T00:00:00Z,68.93,68.81428571 -2010-06-01T00:00:00Z,69.16,68.92714286 -2010-06-02T00:00:00Z,68.8,69.05857143 -2010-06-03T00:00:00Z,69.55,69.06428571 -2010-06-04T00:00:00Z,69.26,69.18571429 -2010-06-05T00:00:00Z,69.78,69.35571429 -2010-06-06T00:00:00Z,70.01,69.50142857 -2010-06-07T00:00:00Z,69.95,69.66142857 -2010-06-08T00:00:00Z,70.28,69.92285714 -2010-06-09T00:00:00Z,70.63,70.08857143 -2010-06-10T00:00:00Z,70.71,70.29571429 -2010-06-11T00:00:00Z,70.71,70.44714286 -2010-06-12T00:00:00Z,70.84,70.59714286 -2010-06-13T00:00:00Z,71.06,70.77571429 -2010-06-14T00:00:00Z,71.2,70.87714286 -2010-06-15T00:00:00Z,70.99,70.92571429 -2010-06-16T00:00:00Z,70.97,70.90571429 -2010-06-17T00:00:00Z,70.57,70.91285714 -2010-06-18T00:00:00Z,70.76,70.87 -2010-06-19T00:00:00Z,70.54,70.80428571 -2010-06-20T00:00:00Z,70.6,70.71714286 -2010-06-21T00:00:00Z,70.59,70.69142857 -2010-06-22T00:00:00Z,70.81,70.67428571 -2010-06-23T00:00:00Z,70.85,70.69714286 -2010-06-24T00:00:00Z,70.73,70.66714286 -2010-06-25T00:00:00Z,70.55,70.77285714 -2010-06-26T00:00:00Z,71.28,70.90714286 -2010-06-27T00:00:00Z,71.54,71.05571429 -2010-06-28T00:00:00Z,71.63,71.14020899 -2010-06-29T00:00:00Z,71.40637589,71.2232089 -2010-06-30T00:00:00Z,71.43583559,71.38289837 -2010-07-01T00:00:00Z,71.85165133,71.5782986 -2010-07-02T00:00:00Z,71.92147055,71.80015284 -2010-07-03T00:00:00Z,72.83497526,71.99831244 -2010-07-04T00:00:00Z,72.92897832,72.15704362 -2010-07-05T00:00:00Z,72.74325162,72.50786086 -2010-07-06T00:00:00Z,73.85805876,72.97828792 -2010-07-07T00:00:00Z,74.72444647,73.60168354 -2010-07-08T00:00:00Z,76.21226741,74.22135729 -2010-07-09T00:00:00Z,76.25621157,74.64329374 -2010-07-10T00:00:00Z,75.78703796,74.97267492 -2010-07-11T00:00:00Z,75.23319429,75.36171449 -2010-07-12T00:00:00Z,75.46482557,75.48554379 -2010-07-13T00:00:00Z,74.72444647,75.16497396 -2010-07-14T00:00:00Z,72.48,74.56474602 -2010-07-15T00:00:00Z,72.01,73.97967204 -2010-07-16T00:00:00Z,72.16,73.39730992 -2010-07-17T00:00:00Z,71.71,72.87834057 -2010-07-18T00:00:00Z,71.6,72.35485554 -2010-07-19T00:00:00Z,71.8,71.89571429 -2010-07-20T00:00:00Z,71.51,71.80285714 -2010-07-21T00:00:00Z,71.83,71.75285714 -2010-07-22T00:00:00Z,71.66,71.70142857 -2010-07-23T00:00:00Z,71.8,71.71857143 -2010-07-24T00:00:00Z,71.83,71.82714286 -2010-07-25T00:00:00Z,72.36,71.87 -2010-07-26T00:00:00Z,72.1,71.98714286 -2010-07-27T00:00:00Z,72.33,72.07714286 -2010-07-28T00:00:00Z,72.46,72.19714286 -2010-07-29T00:00:00Z,72.5,72.33428571 -2010-07-30T00:00:00Z,72.76,72.55142857 -2010-07-31T00:00:00Z,73.35,72.71428571 -2010-08-01T00:00:00Z,73.5,72.91571429 -2010-08-02T00:00:00Z,73.51,73.12142857 -2010-08-03T00:00:00Z,73.77,73.26571429 -2010-08-04T00:00:00Z,73.47,73.41428571 -2010-08-05T00:00:00Z,73.54,73.54 -2010-08-06T00:00:00Z,73.64,73.70571429 -2010-08-07T00:00:00Z,74.51,74.00285714 -2010-08-08T00:00:00Z,75.58,74.35285714 -2010-08-09T00:00:00Z,75.96,74.71285714 -2010-08-10T00:00:00Z,76.29,75.24428571 -2010-08-11T00:00:00Z,77.19,75.91857143 -2010-08-12T00:00:00Z,78.26,76.68714286 -2010-08-13T00:00:00Z,79.02,77.36428571 -2010-08-14T00:00:00Z,79.25,77.86 -2010-08-15T00:00:00Z,79.05,78.31857143 -2010-08-16T00:00:00Z,79.17,78.67142857 -2010-08-17T00:00:00Z,78.76,78.90857143 -2010-08-18T00:00:00Z,78.85,78.99285714 -2010-08-19T00:00:00Z,78.85,79.02714286 -2010-08-20T00:00:00Z,79.26,79.04285714 -2010-08-21T00:00:00Z,79.36,79.16 -2010-08-22T00:00:00Z,79.87,79.20571429 -2010-08-23T00:00:00Z,79.49,79.46428571 -2010-08-24T00:00:00Z,80.57,79.73142857 -2010-08-25T00:00:00Z,80.72,79.85142857 -2010-08-26T00:00:00Z,79.69,79.80428571 -2010-08-27T00:00:00Z,78.93,79.67571429 -2010-08-28T00:00:00Z,78.46,79.42285714 -2010-08-29T00:00:00Z,78.1,79.21857143 -2010-08-30T00:00:00Z,78.06,78.88571429 -2010-08-31T00:00:00Z,78.24,78.63142857 -2010-09-01T00:00:00Z,78.94,78.64285714 -2010-09-02T00:00:00Z,79.77,78.84 -2010-09-03T00:00:00Z,80.31,79.26 -2010-09-04T00:00:00Z,81.4,79.54 -2010-09-05T00:00:00Z,80.06,79.94285714 -2010-09-06T00:00:00Z,80.88,80.56285714 -2010-09-07T00:00:00Z,82.58,81.08 -2010-09-08T00:00:00Z,82.56,81.47857143 -2010-09-09T00:00:00Z,82.56,81.93714286 -2010-09-10T00:00:00Z,83.52,82.28714286 -2010-09-11T00:00:00Z,83.85,82.91714286 -2010-09-12T00:00:00Z,84.47,83.45714286 -2010-09-13T00:00:00Z,84.66,83.73285714 -2010-09-14T00:00:00Z,84.51,84.02 -2010-09-15T00:00:00Z,84.57,84.3 -2010-09-16T00:00:00Z,84.52,84.35714286 -2010-09-17T00:00:00Z,83.92,84.39857143 -2010-09-18T00:00:00Z,84.14,84.42714286 -2010-09-19T00:00:00Z,84.67,84.45714286 -2010-09-20T00:00:00Z,84.87,84.58428571 -2010-09-21T00:00:00Z,85.4,84.68714286 -2010-09-22T00:00:00Z,85.29,84.72857143 -2010-09-23T00:00:00Z,84.81,84.92857143 -2010-09-24T00:00:00Z,85.32,85.10285714 -2010-09-25T00:00:00Z,85.36,85.18714286 -2010-09-26T00:00:00Z,85.26,85.37571429 -2010-09-27T00:00:00Z,86.19,85.52142857 -2010-09-28T00:00:00Z,86.42,85.56857143 -2010-09-29T00:00:00Z,85.62,85.76428571 -2010-09-30T00:00:00Z,86.18,84.74142857 -2010-10-01T00:00:00Z,78.16,82.48714286 -2010-10-02T00:00:00Z,69.58,79.81857143 -2010-10-03T00:00:00Z,66.58,77.10285714 -2010-10-04T00:00:00Z,67.18,74.24857143 -2010-10-05T00:00:00Z,66.44,71.42571429 -2010-10-06T00:00:00Z,65.86,68.40714286 -2010-10-07T00:00:00Z,65.05,66.42142857 -2010-10-08T00:00:00Z,64.26,65.55714286 -2010-10-09T00:00:00Z,63.53,65.24428571 -2010-10-10T00:00:00Z,64.39,64.76285714 -2010-10-11T00:00:00Z,63.81,64.40857143 -2010-10-12T00:00:00Z,63.96,64.17857143 -2010-10-13T00:00:00Z,64.25,64.13 -2010-10-14T00:00:00Z,64.71,64.23571429 -2010-10-15T00:00:00Z,65,64.42142857 -2010-10-16T00:00:00Z,64.83,64.85285714 -2010-10-17T00:00:00Z,67.41,65.38571429 -2010-10-18T00:00:00Z,67.54,65.90428571 -2010-10-19T00:00:00Z,67.59,66.4 -2010-10-20T00:00:00Z,67.72,66.86857143 -2010-10-21T00:00:00Z,67.99,67.23142857 -2010-10-22T00:00:00Z,67.54,67.58714286 -2010-10-23T00:00:00Z,67.32,67.54285714 -2010-10-24T00:00:00Z,67.1,67.58285714 -2010-10-25T00:00:00Z,67.82,67.59285714 -2010-10-26T00:00:00Z,67.66,67.57428571 -2010-10-27T00:00:00Z,67.59,67.48714286 -2010-10-28T00:00:00Z,67.38,67.44428571 -2010-10-29T00:00:00Z,67.24,67.53142857 -2010-10-30T00:00:00Z,67.93,67.51428571 -2010-10-31T00:00:00Z,66.98,67.50285714 -2010-11-01T00:00:00Z,67.74,67.60571429 -2010-11-02T00:00:00Z,68.38,67.77428571 -2010-11-03T00:00:00Z,68.77,68.00571429 -2010-11-04T00:00:00Z,69,68.27 -2010-11-05T00:00:00Z,69.09,68.37142857 -2010-11-06T00:00:00Z,68.64,68.58142857 -2010-11-07T00:00:00Z,68.45,68.68142857 -2010-11-08T00:00:00Z,68.44,68.76428571 -2010-11-09T00:00:00Z,68.96,68.87714286 -2010-11-10T00:00:00Z,69.56,69.04285714 -2010-11-11T00:00:00Z,70.16,69.22285714 -2010-11-12T00:00:00Z,70.35,69.46428571 -2010-11-13T00:00:00Z,70.33,69.73857143 -2010-11-14T00:00:00Z,70.37,70.02285714 -2010-11-15T00:00:00Z,70.43,70.23 -2010-11-16T00:00:00Z,70.41,70.37142857 -2010-11-17T00:00:00Z,70.55,70.27142857 -2010-11-18T00:00:00Z,69.46,70.14285714 -2010-11-19T00:00:00Z,69.45,70.04714286 -2010-11-20T00:00:00Z,69.66,69.88 -2010-11-21T00:00:00Z,69.2,69.75 -2010-11-22T00:00:00Z,69.52,69.60285714 -2010-11-23T00:00:00Z,69.38,69.38285714 -2010-11-24T00:00:00Z,69.01,69.34142857 -2010-11-25T00:00:00Z,69.17,69.37142857 -2010-11-26T00:00:00Z,69.66,69.25571429 -2010-11-27T00:00:00Z,68.85,69.16142857 -2010-11-28T00:00:00Z,68.54,69.05714286 -2010-11-29T00:00:00Z,68.79,69 -2010-11-30T00:00:00Z,68.98,69.01428571 -2010-12-01T00:00:00Z,69.11,NA -2010-12-02T00:00:00Z,NA,NA -2010-12-03T00:00:00Z,NA,NA -2010-12-04T00:00:00Z,NA,NA -2010-12-05T00:00:00Z,NA,NA -2010-12-06T00:00:00Z,NA,NA -2010-12-07T00:00:00Z,NA,NA -2010-12-08T00:00:00Z,NA,NA -2010-12-09T00:00:00Z,NA,NA -2010-12-10T00:00:00Z,NA,NA -2010-12-11T00:00:00Z,NA,NA -2010-12-12T00:00:00Z,NA,NA -2010-12-13T00:00:00Z,NA,NA -2010-12-14T00:00:00Z,NA,NA -2010-12-15T00:00:00Z,NA,NA -2010-12-16T00:00:00Z,NA,NA -2010-12-17T00:00:00Z,NA,NA -2010-12-18T00:00:00Z,NA,NA -2010-12-19T00:00:00Z,NA,NA -2010-12-20T00:00:00Z,NA,NA -2010-12-21T00:00:00Z,NA,NA -2010-12-22T00:00:00Z,NA,NA -2010-12-23T00:00:00Z,NA,NA -2010-12-24T00:00:00Z,NA,NA -2010-12-25T00:00:00Z,NA,NA -2010-12-26T00:00:00Z,NA,NA -2010-12-27T00:00:00Z,NA,NA -2010-12-28T00:00:00Z,NA,NA -2010-12-29T00:00:00Z,NA,NA -2010-12-30T00:00:00Z,NA,NA -2010-12-31T00:00:00Z,NA,NA -2011-01-01T00:00:00Z,NA,NA -2011-01-02T00:00:00Z,NA,NA -2011-01-03T00:00:00Z,NA,NA -2011-01-04T00:00:00Z,NA,NA -2011-01-05T00:00:00Z,NA,NA -2011-01-06T00:00:00Z,NA,NA -2011-01-07T00:00:00Z,NA,NA -2011-01-08T00:00:00Z,NA,NA -2011-01-09T00:00:00Z,NA,NA -2011-01-10T00:00:00Z,NA,NA -2011-01-11T00:00:00Z,NA,NA -2011-01-12T00:00:00Z,NA,NA -2011-01-13T00:00:00Z,NA,NA -2011-01-14T00:00:00Z,NA,NA -2011-01-15T00:00:00Z,NA,NA -2011-01-16T00:00:00Z,NA,NA -2011-01-17T00:00:00Z,NA,NA -2011-01-18T00:00:00Z,NA,NA -2011-01-19T00:00:00Z,NA,NA -2011-01-20T00:00:00Z,NA,NA -2011-01-21T00:00:00Z,NA,NA -2011-01-22T00:00:00Z,NA,NA -2011-01-23T00:00:00Z,NA,NA -2011-01-24T00:00:00Z,NA,NA -2011-01-25T00:00:00Z,NA,NA -2011-01-26T00:00:00Z,NA,NA -2011-01-27T00:00:00Z,NA,NA -2011-01-28T00:00:00Z,NA,NA -2011-01-29T00:00:00Z,NA,NA -2011-01-30T00:00:00Z,NA,NA -2011-01-31T00:00:00Z,NA,NA -2011-02-01T00:00:00Z,NA,NA -2011-02-02T00:00:00Z,NA,NA -2011-02-03T00:00:00Z,NA,NA -2011-02-04T00:00:00Z,NA,NA -2011-02-05T00:00:00Z,NA,NA -2011-02-06T00:00:00Z,NA,NA -2011-02-07T00:00:00Z,NA,NA -2011-02-08T00:00:00Z,NA,NA -2011-02-09T00:00:00Z,NA,NA -2011-02-10T00:00:00Z,NA,NA -2011-02-11T00:00:00Z,NA,NA -2011-02-12T00:00:00Z,NA,NA -2011-02-13T00:00:00Z,NA,NA -2011-02-14T00:00:00Z,NA,NA -2011-02-15T00:00:00Z,NA,NA -2011-02-16T00:00:00Z,NA,NA -2011-02-17T00:00:00Z,NA,NA -2011-02-18T00:00:00Z,NA,NA -2011-02-19T00:00:00Z,NA,NA -2011-02-20T00:00:00Z,NA,NA -2011-02-21T00:00:00Z,NA,NA -2011-02-22T00:00:00Z,NA,NA -2011-02-23T00:00:00Z,NA,NA -2011-02-24T00:00:00Z,NA,NA -2011-02-25T00:00:00Z,NA,NA -2011-02-26T00:00:00Z,NA,NA -2011-02-27T00:00:00Z,NA,NA -2011-02-28T00:00:00Z,NA,NA -2011-03-01T00:00:00Z,NA,NA -2011-03-02T00:00:00Z,NA,NA -2011-03-03T00:00:00Z,NA,NA -2011-03-04T00:00:00Z,NA,NA -2011-03-05T00:00:00Z,NA,NA -2011-03-06T00:00:00Z,NA,NA -2011-03-07T00:00:00Z,NA,47.76 -2011-03-08T00:00:00Z,47.76,44.525 -2011-03-09T00:00:00Z,41.29,43.37333333 -2011-03-10T00:00:00Z,41.07,42.245 -2011-03-11T00:00:00Z,38.86,40.97 -2011-03-12T00:00:00Z,35.87,40.15 -2011-03-13T00:00:00Z,36.05,39.39714286 -2011-03-14T00:00:00Z,34.88,39.68428571 -2011-03-15T00:00:00Z,49.77,41.18285714 -2011-03-16T00:00:00Z,51.78,40.70285714 -2011-03-17T00:00:00Z,37.71,40.24857143 -2011-03-18T00:00:00Z,35.68,39.91 -2011-03-19T00:00:00Z,33.5,39.54571429 -2011-03-20T00:00:00Z,33.5,39.51428571 -2011-03-21T00:00:00Z,34.66,37.15285714 -2011-03-22T00:00:00Z,33.24,34.57714286 -2011-03-23T00:00:00Z,33.75,34.17285714 -2011-03-24T00:00:00Z,34.88,33.70571429 -2011-03-25T00:00:00Z,32.41,33.46142857 -2011-03-26T00:00:00Z,31.79,34.01857143 -2011-03-27T00:00:00Z,37.4,NA -2011-03-28T00:00:00Z,NA,38.84939193 -2011-03-29T00:00:00Z,62.86523818,44.15904411 -2011-03-30T00:00:00Z,65.60872755,49.46612288 -2011-03-31T00:00:00Z,66.72567505,55.36699469 -2011-04-01T00:00:00Z,67.82158516,61.19704466 -2011-04-02T00:00:00Z,66.77362672,65.821324 -2011-04-03T00:00:00Z,65.14580934,65.87235141 -2011-04-04T00:00:00Z,66.18042472,66.24287261 -2011-04-05T00:00:00Z,65.46,65.96459932 -2011-04-06T00:00:00Z,63.66,65.36567466 -2011-04-07T00:00:00Z,62.53,64.78492738 -2011-04-08T00:00:00Z,63.75,64.23917027 -2011-04-09T00:00:00Z,62.95,63.73693084 -2011-04-10T00:00:00Z,61.63,62.83714286 -2011-04-11T00:00:00Z,59.88,61.58 -2011-04-12T00:00:00Z,56.66,61.07 -2011-04-13T00:00:00Z,60.09,60.55714286 -2011-04-14T00:00:00Z,58.94,59.50714286 -2011-04-15T00:00:00Z,56.4,59.22428571 -2011-04-16T00:00:00Z,60.97,58.88285714 -2011-04-17T00:00:00Z,59.24,56.97285714 -2011-04-18T00:00:00Z,46.51,54.73 -2011-04-19T00:00:00Z,40.96,51.71714286 -2011-04-20T00:00:00Z,39,48.64 -2011-04-21T00:00:00Z,37.4,45.78428571 -2011-04-22T00:00:00Z,36.41,42.27571429 -2011-04-23T00:00:00Z,36.41,38.96285714 -2011-04-24T00:00:00Z,36.05,37.46857143 -2011-04-25T00:00:00Z,36.05,36.68571429 -2011-04-26T00:00:00Z,35.48,36.18285714 -2011-04-27T00:00:00Z,35.48,35.99 -2011-04-28T00:00:00Z,36.05,35.91285714 -2011-04-29T00:00:00Z,35.87,35.63142857 -2011-04-30T00:00:00Z,34.44,35.43285714 -2011-05-01T00:00:00Z,34.66,35.35142857 -2011-05-02T00:00:00Z,35.48,35.29428571 -2011-05-03T00:00:00Z,35.08,35.23714286 -2011-05-04T00:00:00Z,35.08,35.21142857 -2011-05-05T00:00:00Z,35.87,36.26142857 -2011-05-06T00:00:00Z,43.22,37.66 -2011-05-07T00:00:00Z,44.23,39.73571429 -2011-05-08T00:00:00Z,49.19,41.89428571 -2011-05-09T00:00:00Z,50.59,45.24857143 -2011-05-10T00:00:00Z,58.56,49.00571429 -2011-05-11T00:00:00Z,61.38,52.66428571 -2011-05-12T00:00:00Z,61.48,55.08142857 -2011-05-13T00:00:00Z,60.14,57.27285714 -2011-05-14T00:00:00Z,59.57,58.77714286 -2011-05-15T00:00:00Z,59.72,60.02142857 -2011-05-16T00:00:00Z,59.3,60.19285714 -2011-05-17T00:00:00Z,59.76,59.92285714 -2011-05-18T00:00:00Z,59.49,59.53 -2011-05-19T00:00:00Z,58.73,58.88285714 -2011-05-20T00:00:00Z,55.61,57.62571429 -2011-05-21T00:00:00Z,50.77,55.95714286 -2011-05-22T00:00:00Z,48.04,54.14142857 -2011-05-23T00:00:00Z,46.59,51.47142857 -2011-05-24T00:00:00Z,41.07,49.12 -2011-05-25T00:00:00Z,43.03,47.24 -2011-05-26T00:00:00Z,45.57,45.79428571 -2011-05-27T00:00:00Z,45.49,45.50285714 -2011-05-28T00:00:00Z,48.73,45.28428571 -2011-05-29T00:00:00Z,46.51,45.06857143 -2011-05-30T00:00:00Z,45.08,46.10428571 -2011-05-31T00:00:00Z,48.32,47.67 -2011-06-01T00:00:00Z,53.99,48.46428571 -2011-06-02T00:00:00Z,51.13,49.54857143 -2011-06-03T00:00:00Z,53.08,51.23 -2011-06-04T00:00:00Z,60.5,53.54428571 -2011-06-05T00:00:00Z,62.71,56.21571429 -2011-06-06T00:00:00Z,63.78,58.47142857 -2011-06-07T00:00:00Z,64.11,59.87 -2011-06-08T00:00:00Z,63.78,61.75142857 -2011-06-09T00:00:00Z,64.3,63.43142857 -2011-06-10T00:00:00Z,64.84,64.15285714 -2011-06-11T00:00:00Z,65.55,64.65428571 -2011-06-12T00:00:00Z,66.22,65.04857143 -2011-06-13T00:00:00Z,66.54,65.43428571 -2011-06-14T00:00:00Z,66.81,65.93285714 -2011-06-15T00:00:00Z,67.27,66.40428571 -2011-06-16T00:00:00Z,67.6,66.79 -2011-06-17T00:00:00Z,67.54,67.02 -2011-06-18T00:00:00Z,67.16,67.20285714 -2011-06-19T00:00:00Z,67.5,67.36 -2011-06-20T00:00:00Z,67.64,67.44714286 -2011-06-21T00:00:00Z,67.42,67.41285714 -2011-06-22T00:00:00Z,67.03,67.41428571 -2011-06-23T00:00:00Z,67.61,67.51714286 -2011-06-24T00:00:00Z,68.26,67.68285714 -2011-06-25T00:00:00Z,68.32,67.76142857 -2011-06-26T00:00:00Z,68.05,67.77857143 -2011-06-27T00:00:00Z,67.76,67.82571429 -2011-06-28T00:00:00Z,67.75,67.92857143 -2011-06-29T00:00:00Z,67.75,67.95571429 -2011-06-30T00:00:00Z,67.8,67.88285714 -2011-07-01T00:00:00Z,67.75,67.78 -2011-07-02T00:00:00Z,67.6,67.72714286 -2011-07-03T00:00:00Z,67.68,67.69428571 -2011-07-04T00:00:00Z,67.53,67.63285714 -2011-07-05T00:00:00Z,67.32,67.50428571 -2011-07-06T00:00:00Z,66.85,67.29142857 -2011-07-07T00:00:00Z,66.31,67.04142857 -2011-07-08T00:00:00Z,66,66.76 -2011-07-09T00:00:00Z,65.63,66.44285714 -2011-07-10T00:00:00Z,65.46,66.16857143 -2011-07-11T00:00:00Z,65.61,65.89285714 -2011-07-12T00:00:00Z,65.39,65.69 -2011-07-13T00:00:00Z,65.43,65.68285714 -2011-07-14T00:00:00Z,66.26,65.82142857 -2011-07-15T00:00:00Z,66.97,66 -2011-07-16T00:00:00Z,66.88,66.18 -2011-07-17T00:00:00Z,66.72,66.30142857 -2011-07-18T00:00:00Z,66.46,66.42285714 -2011-07-19T00:00:00Z,66.24,66.68142857 -2011-07-20T00:00:00Z,67.24,66.84571429 -2011-07-21T00:00:00Z,67.41,66.82 -2011-07-22T00:00:00Z,66.79,66.86142857 -2011-07-23T00:00:00Z,67.17,67.01285714 -2011-07-24T00:00:00Z,67.78,67.29142857 -2011-07-25T00:00:00Z,68.41,67.55857143 -2011-07-26T00:00:00Z,68.11,67.70857143 -2011-07-27T00:00:00Z,68.29,67.87285714 -2011-07-28T00:00:00Z,68.56,68.17285714 -2011-07-29T00:00:00Z,68.89,68.42285714 -2011-07-30T00:00:00Z,68.92,68.54285714 -2011-07-31T00:00:00Z,68.62,68.60285714 -2011-08-01T00:00:00Z,68.83,68.71857143 -2011-08-02T00:00:00Z,68.92,68.84714286 -2011-08-03T00:00:00Z,69.19,68.94142857 -2011-08-04T00:00:00Z,69.22,68.97428571 -2011-08-05T00:00:00Z,69.12,69.01714286 -2011-08-06T00:00:00Z,69.22,69.08 -2011-08-07T00:00:00Z,69.06,69.10428571 -2011-08-08T00:00:00Z,69,69.10714286 -2011-08-09T00:00:00Z,68.94,69.02857143 -2011-08-10T00:00:00Z,68.64,68.94285714 -2011-08-11T00:00:00Z,68.62,68.91857143 -2011-08-12T00:00:00Z,68.95,68.88714286 -2011-08-13T00:00:00Z,69,68.80142857 -2011-08-14T00:00:00Z,68.46,68.47714286 -2011-08-15T00:00:00Z,66.73,68.03714286 -2011-08-16T00:00:00Z,65.86,67.54571429 -2011-08-17T00:00:00Z,65.2,66.93857143 -2011-08-18T00:00:00Z,64.37,66.12142857 -2011-08-19T00:00:00Z,63.23,65.23571429 -2011-08-20T00:00:00Z,62.8,64.56285714 -2011-08-21T00:00:00Z,63.75,63.96 -2011-08-22T00:00:00Z,62.51,63.78571429 -2011-08-23T00:00:00Z,64.64,63.88571429 -2011-08-24T00:00:00Z,65.9,64.12857143 -2011-08-25T00:00:00Z,66.07,64.35142857 -2011-08-26T00:00:00Z,64.79,64.67285714 -2011-08-27T00:00:00Z,65.05,64.58714286 -2011-08-28T00:00:00Z,63.15,61.50857143 -2011-08-29T00:00:00Z,40.96,56.94571429 -2011-08-30T00:00:00Z,32.7,51.92142857 -2011-08-31T00:00:00Z,30.73,46.87285714 -2011-09-01T00:00:00Z,30.73,42.00714286 -2011-09-02T00:00:00Z,30.73,37.10428571 -2011-09-03T00:00:00Z,30.73,32.47285714 -2011-09-04T00:00:00Z,30.73,31.11571429 -2011-09-05T00:00:00Z,31.46,31.48571429 -2011-09-06T00:00:00Z,35.29,31.91714286 -2011-09-07T00:00:00Z,33.75,31.91714286 -2011-09-08T00:00:00Z,30.73,31.91714286 -2011-09-09T00:00:00Z,30.73,31.91714286 -2011-09-10T00:00:00Z,30.73,31.91714286 -2011-09-11T00:00:00Z,30.73,31.81285714 -2011-09-12T00:00:00Z,30.73,31.16142857 -2011-09-13T00:00:00Z,30.73,30.73 -2011-09-14T00:00:00Z,30.73,30.73 -2011-09-15T00:00:00Z,30.73,30.73 -2011-09-16T00:00:00Z,30.73,30.73 -2011-09-17T00:00:00Z,30.73,31.19571429 -2011-09-18T00:00:00Z,33.99,32.78142857 -2011-09-19T00:00:00Z,41.83,34.82 -2011-09-20T00:00:00Z,45,37.41 -2011-09-21T00:00:00Z,48.86,40.93285714 -2011-09-22T00:00:00Z,55.39,44.62142857 -2011-09-23T00:00:00Z,56.55,47.55285714 -2011-09-24T00:00:00Z,51.25,48.91142857 -2011-09-25T00:00:00Z,43.5,49.02714286 -2011-09-26T00:00:00Z,42.64,48.55857143 -2011-09-27T00:00:00Z,41.72,47.75285714 -2011-09-28T00:00:00Z,43.22,45.69142857 -2011-09-29T00:00:00Z,40.96,42.62428571 -2011-09-30T00:00:00Z,35.08,40.15857143 -2011-10-01T00:00:00Z,33.99,38.69285714 -2011-10-02T00:00:00Z,33.24,37.35 -2011-10-03T00:00:00Z,33.24,36.06142857 -2011-10-04T00:00:00Z,32.7,36.11571429 -2011-10-05T00:00:00Z,43.6,38.28857143 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-2014-03-20T00:00:00Z,NA,NA -2014-03-21T00:00:00Z,NA,NA -2014-03-22T00:00:00Z,NA,NA -2014-03-23T00:00:00Z,NA,NA -2014-03-24T00:00:00Z,NA,NA -2014-03-25T00:00:00Z,NA,NA -2014-03-26T00:00:00Z,NA,69.6305486 -2014-03-27T00:00:00Z,69.63993543,69.26401528 -2014-03-28T00:00:00Z,68.90664172,69.03601019 -2014-03-29T00:00:00Z,68.58,69.11700764 -2014-03-30T00:00:00Z,69.36,68.50360611 -2014-03-31T00:00:00Z,66.05,67.86467176 -2014-04-01T00:00:00Z,64.67,67.05971865 -2014-04-02T00:00:00Z,62.23,65.48964028 -2014-04-03T00:00:00Z,58.64,63.68285714 -2014-04-04T00:00:00Z,56.25,61.94571429 -2014-04-05T00:00:00Z,56.42,59.50857143 -2014-04-06T00:00:00Z,52.3,58.80571429 -2014-04-07T00:00:00Z,61.13,58.90428571 -2014-04-08T00:00:00Z,65.36,59.29714286 -2014-04-09T00:00:00Z,64.98,60.35142857 -2014-04-10T00:00:00Z,66.02,61.7 -2014-04-11T00:00:00Z,65.69,62.8 -2014-04-12T00:00:00Z,64.12,64.56142857 -2014-04-13T00:00:00Z,64.63,65.00285714 -2014-04-14T00:00:00Z,64.22,64.70857143 -2014-04-15T00:00:00Z,63.3,63.56285714 -2014-04-16T00:00:00Z,56.96,62.65714286 -2014-04-17T00:00:00Z,59.68,61.49857143 -2014-04-18T00:00:00Z,57.58,59.85 -2014-04-19T00:00:00Z,52.58,57.46 -2014-04-20T00:00:00Z,47.9,55.20714286 -2014-04-21T00:00:00Z,48.45,53.35571429 -2014-04-22T00:00:00Z,50.34,52.10142857 -2014-04-23T00:00:00Z,48.18,51.94714286 -2014-04-24T00:00:00Z,58.6,52.68571429 -2014-04-25T00:00:00Z,62.75,54.30142857 -2014-04-26T00:00:00Z,63.89,56.44857143 -2014-04-27T00:00:00Z,62.93,58.49285714 -2014-04-28T00:00:00Z,62.76,60.43714286 -2014-04-29T00:00:00Z,63.95,62.91285714 -2014-04-30T00:00:00Z,65.51,62.79428571 -2014-05-01T00:00:00Z,57.77,60.19714286 -2014-05-02T00:00:00Z,44.57,56.73428571 -2014-05-03T00:00:00Z,39.65,53.11 -2014-05-04T00:00:00Z,37.56,49.51 -2014-05-05T00:00:00Z,37.56,48.83 -2014-05-06T00:00:00Z,59.19,48.49142857 -2014-05-07T00:00:00Z,63.14,49.47 -2014-05-08T00:00:00Z,64.62,52.46428571 -2014-05-09T00:00:00Z,65.53,56.23428571 -2014-05-10T00:00:00Z,66.04,60.12571429 -2014-05-11T00:00:00Z,64.8,63.97428571 -2014-05-12T00:00:00Z,64.5,64.86428571 -2014-05-13T00:00:00Z,65.42,65.32571429 -2014-05-14T00:00:00Z,66.37,65.58285714 -2014-05-15T00:00:00Z,66.42,65.74714286 -2014-05-16T00:00:00Z,66.68,65.43428571 -2014-05-17T00:00:00Z,63.85,65.01 -2014-05-18T00:00:00Z,61.83,64.18857143 -2014-05-19T00:00:00Z,58.75,62.93428571 -2014-05-20T00:00:00Z,56.64,61.45571429 -2014-05-21T00:00:00Z,56.02,60.20714286 -2014-05-22T00:00:00Z,57.68,58.72857143 -2014-05-23T00:00:00Z,56.33,57.76714286 -2014-05-24T00:00:00Z,57.12,57.38142857 -2014-05-25T00:00:00Z,59.13,57.42142857 -2014-05-26T00:00:00Z,59.03,57.76142857 -2014-05-27T00:00:00Z,59.02,58.51142857 -2014-05-28T00:00:00Z,61.27,59.22857143 -2014-05-29T00:00:00Z,62.7,60.02857143 -2014-05-30T00:00:00Z,61.93,60.59142857 -2014-05-31T00:00:00Z,61.06,61.12142857 -2014-06-01T00:00:00Z,62.84,61.66571429 -2014-06-02T00:00:00Z,62.84,62.19 -2014-06-03T00:00:00Z,62.69,62.56857143 -2014-06-04T00:00:00Z,63.92,62.96571429 -2014-06-05T00:00:00Z,65.48,63.62142857 -2014-06-06T00:00:00Z,66.52,64.54 -2014-06-07T00:00:00Z,67.49,65.32857143 -2014-06-08T00:00:00Z,68.36,66.18 -2014-06-09T00:00:00Z,68.8,66.99714286 -2014-06-10T00:00:00Z,68.41,67.63714286 -2014-06-11T00:00:00Z,68.4,68.12428571 -2014-06-12T00:00:00Z,68.89,68.46428571 -2014-06-13T00:00:00Z,68.9,68.6 -2014-06-14T00:00:00Z,68.44,68.53285714 -2014-06-15T00:00:00Z,67.89,68.34714286 -2014-06-16T00:00:00Z,67.5,68.19285714 -2014-06-17T00:00:00Z,67.33,68.00714286 -2014-06-18T00:00:00Z,67.1,67.73857143 -2014-06-19T00:00:00Z,67.01,67.45714286 -2014-06-20T00:00:00Z,66.93,67.3 -2014-06-21T00:00:00Z,67.34,67.27142857 -2014-06-22T00:00:00Z,67.69,67.31571429 -2014-06-23T00:00:00Z,67.81,67.35142857 -2014-06-24T00:00:00Z,67.58,67.38571429 -2014-06-25T00:00:00Z,67.34,67.40714286 -2014-06-26T00:00:00Z,67.16,67.51285714 -2014-06-27T00:00:00Z,67.67,67.59 -2014-06-28T00:00:00Z,67.88,67.56571429 -2014-06-29T00:00:00Z,67.52,67.49428571 -2014-06-30T00:00:00Z,67.31,67.46285714 -2014-07-01T00:00:00Z,67.36,67.45 -2014-07-02T00:00:00Z,67.25,67.42714286 -2014-07-03T00:00:00Z,67,67.25714286 -2014-07-04T00:00:00Z,66.48,67.35714286 -2014-07-05T00:00:00Z,68.58,67.53 -2014-07-06T00:00:00Z,68.73,67.72857143 -2014-07-07T00:00:00Z,68.7,67.96714286 -2014-07-08T00:00:00Z,69.03,68.21 -2014-07-09T00:00:00Z,68.95,68.49714286 -2014-07-10T00:00:00Z,69.01,68.85571429 -2014-07-11T00:00:00Z,68.99,68.92571429 -2014-07-12T00:00:00Z,69.07,69.00714286 -2014-07-13T00:00:00Z,69.3,69.03857143 -2014-07-14T00:00:00Z,68.92,68.98714286 -2014-07-15T00:00:00Z,68.67,68.87 -2014-07-16T00:00:00Z,68.13,68.70285714 -2014-07-17T00:00:00Z,67.84,68.49428571 -2014-07-18T00:00:00Z,67.53,68.22857143 -2014-07-19T00:00:00Z,67.21,67.99571429 -2014-07-20T00:00:00Z,67.67,67.88142857 -2014-07-21T00:00:00Z,68.12,67.76428571 -2014-07-22T00:00:00Z,67.85,67.72142857 -2014-07-23T00:00:00Z,67.83,67.68714286 -2014-07-24T00:00:00Z,67.6,67.77857143 -2014-07-25T00:00:00Z,68.17,67.97142857 -2014-07-26T00:00:00Z,68.56,68.11428571 -2014-07-27T00:00:00Z,68.67,68.18285714 -2014-07-28T00:00:00Z,68.6,68.24285714 -2014-07-29T00:00:00Z,68.27,68.36857143 -2014-07-30T00:00:00Z,68.71,68.55857143 -2014-07-31T00:00:00Z,68.93,68.64857143 -2014-08-01T00:00:00Z,68.8,68.69285714 -2014-08-02T00:00:00Z,68.87,68.77428571 -2014-08-03T00:00:00Z,69.24,68.88285714 -2014-08-04T00:00:00Z,69.36,69.07142857 -2014-08-05T00:00:00Z,69.59,69.26142857 -2014-08-06T00:00:00Z,70.04,69.45142857 -2014-08-07T00:00:00Z,70.26,69.66 -2014-08-08T00:00:00Z,70.26,69.87428571 -2014-08-09T00:00:00Z,70.37,70.05571429 -2014-08-10T00:00:00Z,70.51,70.25285714 -2014-08-11T00:00:00Z,70.74,70.46142857 -2014-08-12T00:00:00Z,71.05,70.60857143 -2014-08-13T00:00:00Z,71.07,70.69571429 -2014-08-14T00:00:00Z,70.87,70.76857143 -2014-08-15T00:00:00Z,70.77,70.83571429 -2014-08-16T00:00:00Z,70.84,70.88428571 -2014-08-17T00:00:00Z,70.85,70.92285714 -2014-08-18T00:00:00Z,71.01,70.97285714 -2014-08-19T00:00:00Z,71.4,71.04428571 -2014-08-20T00:00:00Z,71.57,71.15714286 -2014-08-21T00:00:00Z,71.66,71.29428571 -2014-08-22T00:00:00Z,71.73,71.44571429 -2014-08-23T00:00:00Z,71.9,71.61714286 -2014-08-24T00:00:00Z,72.05,71.77428571 -2014-08-25T00:00:00Z,72.11,71.85714286 -2014-08-26T00:00:00Z,71.98,71.92428571 -2014-08-27T00:00:00Z,72.04,71.97142857 -2014-08-28T00:00:00Z,71.99,72.04142857 -2014-08-29T00:00:00Z,72.22,72.12714286 -2014-08-30T00:00:00Z,72.5,72.24285714 -2014-08-31T00:00:00Z,72.86,72.22428571 -2014-09-01T00:00:00Z,71.98,72.27571429 -2014-09-02T00:00:00Z,72.34,72.31428571 -2014-09-03T00:00:00Z,72.31,72.38285714 -2014-09-04T00:00:00Z,72.47,72.45714286 -2014-09-05T00:00:00Z,72.74,72.50571429 -2014-09-06T00:00:00Z,72.84,72.47714286 -2014-09-07T00:00:00Z,72.66,72.72571429 -2014-09-08T00:00:00Z,73.72,72.99714286 -2014-09-09T00:00:00Z,74.24,73.42428571 -2014-09-10T00:00:00Z,75.3,73.92285714 -2014-09-11T00:00:00Z,75.96,74.33285714 -2014-09-12T00:00:00Z,75.61,74.84142857 -2014-09-13T00:00:00Z,76.4,75.36 -2014-09-14T00:00:00Z,76.29,75.74 -2014-09-15T00:00:00Z,76.38,76.03714286 -2014-09-16T00:00:00Z,76.32,76.24428571 -2014-09-17T00:00:00Z,76.75,76.35428571 -2014-09-18T00:00:00Z,76.73,76.51285714 -2014-09-19T00:00:00Z,76.72,76.67857143 -2014-09-20T00:00:00Z,77.56,76.91428571 -2014-09-21T00:00:00Z,77.94,77.15714286 -2014-09-22T00:00:00Z,78.08,77.23285714 -2014-09-23T00:00:00Z,76.85,77.41285714 -2014-09-24T00:00:00Z,78.01,77.70142857 -2014-09-25T00:00:00Z,78.75,78.09571429 -2014-09-26T00:00:00Z,79.48,78.37857143 -2014-09-27T00:00:00Z,79.54,78.58857143 -2014-09-28T00:00:00Z,79.41,78.79857143 -2014-09-29T00:00:00Z,79.55,79.24142857 -2014-09-30T00:00:00Z,79.95,79.76571429 -2014-10-01T00:00:00Z,81.68,80.34428571 -2014-10-02T00:00:00Z,82.8,80.98428571 -2014-10-03T00:00:00Z,83.96,81.82285714 -2014-10-04T00:00:00Z,85.41,82.38 -2014-10-05T00:00:00Z,83.31,82.93142857 -2014-10-06T00:00:00Z,83.41,83.52857143 -2014-10-07T00:00:00Z,84.13,83.87857143 -2014-10-08T00:00:00Z,84.13,84.00714286 -2014-10-09T00:00:00Z,83.7,83.93 -2014-10-10T00:00:00Z,83.42,83.75714286 -2014-10-11T00:00:00Z,84.2,83.93857143 -2014-10-12T00:00:00Z,84.58,84.04857143 -2014-10-13T00:00:00Z,84.18,84.03714286 -2014-10-14T00:00:00Z,84.05,84.04857143 -2014-10-15T00:00:00Z,84.21,83.98857143 -2014-10-16T00:00:00Z,83.28,83.86857143 -2014-10-17T00:00:00Z,82.58,83.40428571 -2014-10-18T00:00:00Z,80.95,82.52 -2014-10-19T00:00:00Z,78.39,81.67 -2014-10-20T00:00:00Z,78.23,80.78285714 -2014-10-21T00:00:00Z,77.84,79.92 -2014-10-22T00:00:00Z,78.17,79.11285714 -2014-10-23T00:00:00Z,77.63,78.45428571 -2014-10-24T00:00:00Z,77.97,78.1 -2014-10-25T00:00:00Z,78.47,77.97428571 -2014-10-26T00:00:00Z,77.51,77.83857143 -2014-10-27T00:00:00Z,77.28,77.79285714 -2014-10-28T00:00:00Z,77.52,77.68 -2014-10-29T00:00:00Z,77.38,77.68142857 -2014-10-30T00:00:00Z,77.64,77.69 -2014-10-31T00:00:00Z,78.03,77.68857143 -2014-11-01T00:00:00Z,78.46,77.72714286 -2014-11-02T00:00:00Z,77.78,77.73714286 -2014-11-03T00:00:00Z,77.35,77.67 -2014-11-04T00:00:00Z,77.05,77.80428571 -2014-11-05T00:00:00Z,78.32,78.07 -2014-11-06T00:00:00Z,79.5,78.28428571 -2014-11-07T00:00:00Z,79.53,78.34285714 -2014-11-08T00:00:00Z,78.87,78.55285714 -2014-11-09T00:00:00Z,79.25,78.97857143 -2014-11-10T00:00:00Z,80.33,79.69142857 -2014-11-11T00:00:00Z,82.04,80.34857143 -2014-11-12T00:00:00Z,82.92,80.77857143 -2014-11-13T00:00:00Z,82.51,81.06857143 -2014-11-14T00:00:00Z,81.56,81.42428571 -2014-11-15T00:00:00Z,81.36,81.79285714 -2014-11-16T00:00:00Z,81.83,82.01857143 -2014-11-17T00:00:00Z,81.91,81.52571429 -2014-11-18T00:00:00Z,78.59,80.64571429 -2014-11-19T00:00:00Z,76.76,79.94 -2014-11-20T00:00:00Z,77.57,79.28571429 -2014-11-21T00:00:00Z,76.98,78.87714286 -2014-11-22T00:00:00Z,78.5,78.26571429 -2014-11-23T00:00:00Z,77.55,77.91857143 -2014-11-24T00:00:00Z,79.48,78.07285714 -2014-11-25T00:00:00Z,79.67,78.43857143 -2014-11-26T00:00:00Z,79.32,78.66714286 -2014-11-27T00:00:00Z,79.17,78.63142857 -2014-11-28T00:00:00Z,76.73,78.36428571 -2014-11-29T00:00:00Z,76.63,78.15428571 -2014-11-30T00:00:00Z,76.08,77.53857143 -2014-12-01T00:00:00Z,75.17,76.90285714 -2014-12-02T00:00:00Z,75.22,76.50428571 -2014-12-03T00:00:00Z,76.53,75.87571429 -2014-12-04T00:00:00Z,74.77,75.53571429 -2014-12-05T00:00:00Z,74.35,75.25285714 -2014-12-06T00:00:00Z,74.65,74.87428571 -2014-12-07T00:00:00Z,73.43,74.64428571 -2014-12-08T00:00:00Z,73.56,74.39285714 -2014-12-09T00:00:00Z,73.46,73.82 -2014-12-10T00:00:00Z,72.52,73.39571429 -2014-12-11T00:00:00Z,71.8,72.96285714 -2014-12-12T00:00:00Z,71.32,72.49142857 -2014-12-13T00:00:00Z,71.35,72.21714286 -2014-12-14T00:00:00Z,71.51,71.95285714 -2014-12-15T00:00:00Z,71.71,71.68428571 -2014-12-16T00:00:00Z,71.58,71.55 -2014-12-17T00:00:00Z,71.58,71.45428571 -2014-12-18T00:00:00Z,71.13,71.39428571 -2014-12-19T00:00:00Z,70.9,71.33857143 -2014-12-20T00:00:00Z,70.96,71.27285714 -2014-12-21T00:00:00Z,71.05,71.16571429 -2014-12-22T00:00:00Z,70.96,71.10142857 -2014-12-23T00:00:00Z,71.13,71.05 -2014-12-24T00:00:00Z,71.22,71.01428571 -2014-12-25T00:00:00Z,70.88,70.91142857 -2014-12-26T00:00:00Z,70.18,70.80571429 -2014-12-27T00:00:00Z,70.22,70.67285714 -2014-12-28T00:00:00Z,70.12,70.51571429 -2014-12-29T00:00:00Z,69.86,70.33 -2014-12-30T00:00:00Z,69.83,70.12 -2014-12-31T00:00:00Z,69.75,69.90285714 -2015-01-01T00:00:00Z,69.36,69.70571429 -2015-01-02T00:00:00Z,68.8,69.53714286 -2015-01-03T00:00:00Z,69.04,69.49428571 -2015-01-04T00:00:00Z,69.82,69.25 -2015-01-05T00:00:00Z,68.15,68.88 -2015-01-06T00:00:00Z,67.24,68.63285714 -2015-01-07T00:00:00Z,68.02,68.4 -2015-01-08T00:00:00Z,67.73,68.48 -2015-01-09T00:00:00Z,69.36,68.30571429 -2015-01-10T00:00:00Z,67.82,68.23857143 -2015-01-11T00:00:00Z,69.35,68.58285714 -2015-01-12T00:00:00Z,70.56,69.47 -2015-01-13T00:00:00Z,73.45,70.05857143 -2015-01-14T00:00:00Z,72.14,70.68714286 -2015-01-15T00:00:00Z,72.13,71.02428571 -2015-01-16T00:00:00Z,71.72,71.51714286 -2015-01-17T00:00:00Z,71.27,72.2 -2015-01-18T00:00:00Z,74.13,72.45714286 -2015-01-19T00:00:00Z,72.36,72.12285714 -2015-01-20T00:00:00Z,71.11,71.94714286 -2015-01-21T00:00:00Z,70.91,71.79714286 -2015-01-22T00:00:00Z,71.08,71.69142857 -2015-01-23T00:00:00Z,70.98,72.09857143 -2015-01-24T00:00:00Z,74.12,72.05 -2015-01-25T00:00:00Z,73.79,72.12428571 -2015-01-26T00:00:00Z,72.88,72.47857143 -2015-01-27T00:00:00Z,73.59,72.83285714 -2015-01-28T00:00:00Z,73.39,73.12285714 -2015-01-29T00:00:00Z,73.11,73.42428571 -2015-01-30T00:00:00Z,73.09,73.22571429 -2015-01-31T00:00:00Z,72.73,73.11857143 -2015-02-01T00:00:00Z,73.04,73.66857143 -2015-02-02T00:00:00Z,76.73,74.09 -2015-02-03T00:00:00Z,76.54,74.27428571 -2015-02-04T00:00:00Z,74.68,74.41571429 -2015-02-05T00:00:00Z,74.1,74.60857143 -2015-02-06T00:00:00Z,74.44,74.83714286 -2015-02-07T00:00:00Z,74.33,75.04428571 -2015-02-08T00:00:00Z,74.49,74.77857143 -2015-02-09T00:00:00Z,74.87,74.60285714 -2015-02-10T00:00:00Z,75.31,74.75 -2015-02-11T00:00:00Z,75.71,75.02142857 -2015-02-12T00:00:00Z,76,75.24428571 -2015-02-13T00:00:00Z,76,75.53285714 -2015-02-14T00:00:00Z,76.35,75.74714286 -2015-02-15T00:00:00Z,75.99,76.02142857 -2015-02-16T00:00:00Z,76.79,76.26571429 -2015-02-17T00:00:00Z,77.02,76.53142857 -2015-02-18T00:00:00Z,77.57,76.76142857 -2015-02-19T00:00:00Z,77.61,76.97714286 -2015-02-20T00:00:00Z,77.51,77.16857143 -2015-02-21T00:00:00Z,77.69,77.59714286 -2015-02-22T00:00:00Z,78.99,78 -2015-02-23T00:00:00Z,79.61,78.32857143 -2015-02-24T00:00:00Z,79.32,78.62571429 -2015-02-25T00:00:00Z,79.65,78.88428571 -2015-02-26T00:00:00Z,79.42,79.14714286 -2015-02-27T00:00:00Z,79.35,79.31142857 -2015-02-28T00:00:00Z,78.84,79.32 -2015-03-01T00:00:00Z,79.05,79.59428571 -2015-03-02T00:00:00Z,81.53,79.91142857 -2015-03-03T00:00:00Z,81.54,80.28285714 -2015-03-04T00:00:00Z,82.25,80.40428571 -2015-03-05T00:00:00Z,80.27,80.41428571 -2015-03-06T00:00:00Z,79.42,80.46857143 -2015-03-07T00:00:00Z,79.22,80.53571429 -2015-03-08T00:00:00Z,79.52,80.33571429 -2015-03-09T00:00:00Z,80.13,79.99 -2015-03-10T00:00:00Z,79.12,79.38 -2015-03-11T00:00:00Z,77.98,78.99857143 -2015-03-12T00:00:00Z,77.6,78.70714286 -2015-03-13T00:00:00Z,77.38,78.27285714 -2015-03-14T00:00:00Z,76.18,77.47571429 -2015-03-15T00:00:00Z,73.94,76.48714286 -2015-03-16T00:00:00Z,73.21,75.68428571 -2015-03-17T00:00:00Z,73.5,74.75714286 -2015-03-18T00:00:00Z,71.49,73.68714286 -2015-03-19T00:00:00Z,70.11,72.66142857 -2015-03-20T00:00:00Z,70.2,71.99142857 -2015-03-21T00:00:00Z,71.49,71.59714286 -2015-03-22T00:00:00Z,71.18,70.95 -2015-03-23T00:00:00Z,68.68,70.01142857 -2015-03-24T00:00:00Z,66.93,69.46 -2015-03-25T00:00:00Z,67.63,69.14142857 -2015-03-26T00:00:00Z,67.88,68.87285714 -2015-03-27T00:00:00Z,68.32,68.56 -2015-03-28T00:00:00Z,69.3,68.54714286 -2015-03-29T00:00:00Z,71.09,69.01 -2015-03-30T00:00:00Z,71.92,69.68 -2015-03-31T00:00:00Z,71.62,70.28 -2015-04-01T00:00:00Z,71.83,70.80571429 -2015-04-02T00:00:00Z,71.56,71.15 -2015-04-03T00:00:00Z,70.73,71.22 -2015-04-04T00:00:00Z,69.79,71.06142857 -2015-04-05T00:00:00Z,69.98,70.54142857 -2015-04-06T00:00:00Z,68.28,69.86571429 -2015-04-07T00:00:00Z,66.89,69.13285714 -2015-04-08T00:00:00Z,66.7,68.60571429 -2015-04-09T00:00:00Z,67.87,68.14142857 -2015-04-10T00:00:00Z,67.48,67.36 -2015-04-11T00:00:00Z,64.32,66.20714286 -2015-04-12T00:00:00Z,61.91,65.22857143 -2015-04-13T00:00:00Z,61.43,64.33142857 -2015-04-14T00:00:00Z,60.61,63.41714286 -2015-04-15T00:00:00Z,60.3,62.29142857 -2015-04-16T00:00:00Z,59.99,61.34857143 -2015-04-17T00:00:00Z,60.88,60.77714286 -2015-04-18T00:00:00Z,60.32,60.63 -2015-04-19T00:00:00Z,60.88,60.84571429 -2015-04-20T00:00:00Z,62.94,60.97714286 -2015-04-21T00:00:00Z,61.53,60.95571429 -2015-04-22T00:00:00Z,60.15,60.25714286 -2015-04-23T00:00:00Z,55.1,59.04714286 -2015-04-24T00:00:00Z,52.41,59.22571429 -2015-04-25T00:00:00Z,61.57,59.62428571 -2015-04-26T00:00:00Z,63.67,60.00285714 -2015-04-27T00:00:00Z,65.59,60.78571429 -2015-04-28T00:00:00Z,67.01,62.02857143 -2015-04-29T00:00:00Z,68.85,64.02571429 -2015-04-30T00:00:00Z,69.08,66.45857143 -2015-05-01T00:00:00Z,69.44,67.65571429 -2015-05-02T00:00:00Z,69.95,68.49857143 -2015-05-03T00:00:00Z,69.57,69.01857143 -2015-05-04T00:00:00Z,69.23,69.31 -2015-05-05T00:00:00Z,69.05,69.29714286 -2015-05-06T00:00:00Z,68.76,69.27142857 -2015-05-07T00:00:00Z,68.9,69.18428571 -2015-05-08T00:00:00Z,68.83,69.01142857 -2015-05-09T00:00:00Z,68.74,68.91714286 -2015-05-10T00:00:00Z,68.91,68.85142857 -2015-05-11T00:00:00Z,68.77,68.84714286 -2015-05-12T00:00:00Z,69.02,68.84571429 -2015-05-13T00:00:00Z,68.75,68.91857143 -2015-05-14T00:00:00Z,69.41,69.05571429 -2015-05-15T00:00:00Z,69.79,69.20285714 -2015-05-16T00:00:00Z,69.77,69.33571429 -2015-05-17T00:00:00Z,69.84,69.48285714 -2015-05-18T00:00:00Z,69.8,69.66 -2015-05-19T00:00:00Z,70.26,69.87714286 -2015-05-20T00:00:00Z,70.27,69.98428571 -2015-05-21T00:00:00Z,70.16,70.07285714 -2015-05-22T00:00:00Z,70.41,70.17714286 -2015-05-23T00:00:00Z,70.5,70.28428571 -2015-05-24T00:00:00Z,70.59,70.36428571 -2015-05-25T00:00:00Z,70.36,70.41571429 -2015-05-26T00:00:00Z,70.62,70.47 -2015-05-27T00:00:00Z,70.65,70.49428571 -2015-05-28T00:00:00Z,70.33,70.48142857 -2015-05-29T00:00:00Z,70.32,70.49714286 -2015-05-30T00:00:00Z,70.61,70.52428571 -2015-05-31T00:00:00Z,70.78,70.59142857 -2015-06-01T00:00:00Z,70.83,70.55428571 -2015-06-02T00:00:00Z,70.36,70.57142857 -2015-06-03T00:00:00Z,70.77,70.61142857 -2015-06-04T00:00:00Z,70.61,70.66857143 -2015-06-05T00:00:00Z,70.72,70.68571429 -2015-06-06T00:00:00Z,70.73,70.67714286 -2015-06-07T00:00:00Z,70.72,70.64857143 -2015-06-08T00:00:00Z,70.63,70.61571429 -2015-06-09T00:00:00Z,70.13,70.45 -2015-06-10T00:00:00Z,69.61,70.32285714 -2015-06-11T00:00:00Z,69.72,70.15857143 -2015-06-12T00:00:00Z,69.57,69.96714286 -2015-06-13T00:00:00Z,69.39,69.80285714 -2015-06-14T00:00:00Z,69.57,69.75571429 -2015-06-15T00:00:00Z,70.3,69.75571429 -2015-06-16T00:00:00Z,70.13,69.79714286 -2015-06-17T00:00:00Z,69.9,69.83428571 -2015-06-18T00:00:00Z,69.98,69.87142857 -2015-06-19T00:00:00Z,69.83,69.87714286 -2015-06-20T00:00:00Z,69.43,69.87714286 -2015-06-21T00:00:00Z,69.57,69.70428571 -2015-06-22T00:00:00Z,69.09,69.58285714 -2015-06-23T00:00:00Z,69.28,69.47 -2015-06-24T00:00:00Z,69.11,69.4 -2015-06-25T00:00:00Z,69.49,69.41714286 -2015-06-26T00:00:00Z,69.95,69.51428571 -2015-06-27T00:00:00Z,70.11,69.48428571 -2015-06-28T00:00:00Z,69.36,69.41857143 -2015-06-29T00:00:00Z,68.63,69.25 -2015-06-30T00:00:00Z,68.1,69.06428571 -2015-07-01T00:00:00Z,67.81,68.57714286 -2015-07-02T00:00:00Z,66.08,67.90285714 -2015-07-03T00:00:00Z,65.23,67.07571429 -2015-07-04T00:00:00Z,64.32,66.22 -2015-07-05T00:00:00Z,63.37,65.36428571 -2015-07-06T00:00:00Z,62.64,64.45857143 -2015-07-07T00:00:00Z,61.76,63.43857143 -2015-07-08T00:00:00Z,60.67,62.74714286 -2015-07-09T00:00:00Z,61.24,62.17 -2015-07-10T00:00:00Z,61.19,61.84714286 -2015-07-11T00:00:00Z,62.06,61.74857143 -2015-07-12T00:00:00Z,62.68,61.81857143 -2015-07-13T00:00:00Z,63.13,62.10285714 -2015-07-14T00:00:00Z,63.75,62.57 -2015-07-15T00:00:00Z,63.94,63.02142857 -2015-07-16T00:00:00Z,64.4,63.59714286 -2015-07-17T00:00:00Z,65.22,63.92428571 -2015-07-18T00:00:00Z,64.35,64.08285714 -2015-07-19T00:00:00Z,63.79,64.24714286 -2015-07-20T00:00:00Z,64.28,64.49 -2015-07-21T00:00:00Z,65.45,64.83 -2015-07-22T00:00:00Z,66.32,65.22285714 -2015-07-23T00:00:00Z,67.15,65.59 -2015-07-24T00:00:00Z,67.79,66.21857143 -2015-07-25T00:00:00Z,68.75,67.00857143 -2015-07-26T00:00:00Z,69.32,67.67142857 -2015-07-27T00:00:00Z,68.92,68.16 -2015-07-28T00:00:00Z,68.87,68.49571429 -2015-07-29T00:00:00Z,68.67,68.71285714 -2015-07-30T00:00:00Z,68.67,68.79285714 -2015-07-31T00:00:00Z,68.35,68.81142857 -2015-08-01T00:00:00Z,68.88,68.75142857 -2015-08-02T00:00:00Z,68.9,68.80714286 -2015-08-03T00:00:00Z,69.31,68.82857143 -2015-08-04T00:00:00Z,69.02,68.90571429 -2015-08-05T00:00:00Z,69.21,68.98714286 -2015-08-06T00:00:00Z,69.24,69.19571429 -2015-08-07T00:00:00Z,69.81,69.37857143 -2015-08-08T00:00:00Z,70.16,69.58571429 -2015-08-09T00:00:00Z,70.35,69.74857143 -2015-08-10T00:00:00Z,70.45,69.94 -2015-08-11T00:00:00Z,70.36,70.03428571 -2015-08-12T00:00:00Z,69.87,70.14857143 -2015-08-13T00:00:00Z,70.04,70.17 -2015-08-14T00:00:00Z,69.96,70.17285714 -2015-08-15T00:00:00Z,70.18,70.15428571 -2015-08-16T00:00:00Z,70.22,70.13857143 -2015-08-17T00:00:00Z,70.34,70.15285714 -2015-08-18T00:00:00Z,70.46,70.24857143 -2015-08-19T00:00:00Z,70.54,70.36 -2015-08-20T00:00:00Z,70.82,70.45857143 -2015-08-21T00:00:00Z,70.65,70.51428571 -2015-08-22T00:00:00Z,70.57,70.61714286 -2015-08-23T00:00:00Z,70.94,70.73285714 -2015-08-24T00:00:00Z,71.15,70.86285714 -2015-08-25T00:00:00Z,71.37,70.98428571 -2015-08-26T00:00:00Z,71.39,71.07142857 -2015-08-27T00:00:00Z,71.43,71.21285714 -2015-08-28T00:00:00Z,71.64,71.39285714 -2015-08-29T00:00:00Z,71.83,71.56571429 -2015-08-30T00:00:00Z,72.15,71.71571429 -2015-08-31T00:00:00Z,72.2,71.85571429 -2015-09-01T00:00:00Z,72.35,72.07 -2015-09-02T00:00:00Z,72.89,72.32 -2015-09-03T00:00:00Z,73.18,72.57 -2015-09-04T00:00:00Z,73.39,72.88714286 -2015-09-05T00:00:00Z,74.05,73.13 -2015-09-06T00:00:00Z,73.85,73.37285714 -2015-09-07T00:00:00Z,73.9,73.63428571 -2015-09-08T00:00:00Z,74.18,73.88 -2015-09-09T00:00:00Z,74.61,74.11142857 -2015-09-10T00:00:00Z,74.8,74.08571429 -2015-09-11T00:00:00Z,73.21,74.07571429 -2015-09-12T00:00:00Z,73.98,74.02857143 -2015-09-13T00:00:00Z,73.52,73.89428571 -2015-09-14T00:00:00Z,72.96,73.68142857 -2015-09-15T00:00:00Z,72.69,73.41285714 -2015-09-16T00:00:00Z,72.73,73.14428571 -2015-09-17T00:00:00Z,72.92,73.14857143 -2015-09-18T00:00:00Z,73.24,73.04714286 -2015-09-19T00:00:00Z,73.27,73.00857143 -2015-09-20T00:00:00Z,73.25,73.15142857 -2015-09-21T00:00:00Z,73.96,73.38142857 -2015-09-22T00:00:00Z,74.3,73.63857143 -2015-09-23T00:00:00Z,74.53,73.85428571 -2015-09-24T00:00:00Z,74.43,74.09 -2015-09-25T00:00:00Z,74.89,74.38142857 -2015-09-26T00:00:00Z,75.31,74.82857143 -2015-09-27T00:00:00Z,76.38,75.26 -2015-09-28T00:00:00Z,76.98,75.7 -2015-09-29T00:00:00Z,77.38,76.09428571 -2015-09-30T00:00:00Z,77.29,76.39428571 -2015-10-01T00:00:00Z,76.53,76.57857143 -2015-10-02T00:00:00Z,76.18,76.57571429 -2015-10-03T00:00:00Z,75.29,76.33142857 -2015-10-04T00:00:00Z,74.67,75.96571429 -2015-10-05T00:00:00Z,74.42,75.50428571 -2015-10-06T00:00:00Z,74.15,75.00571429 -2015-10-07T00:00:00Z,73.8,74.61142857 -2015-10-08T00:00:00Z,73.77,74.33142857 -2015-10-09T00:00:00Z,74.22,74.07 -2015-10-10T00:00:00Z,73.46,73.95714286 -2015-10-11T00:00:00Z,73.88,73.89571429 -2015-10-12T00:00:00Z,73.99,74.01857143 -2015-10-13T00:00:00Z,75.01,74.13571429 -2015-10-14T00:00:00Z,74.62,74.26857143 -2015-10-15T00:00:00Z,74.7,74.35571429 -2015-10-16T00:00:00Z,74.83,74.55714286 -2015-10-17T00:00:00Z,74.87,74.70571429 -2015-10-18T00:00:00Z,74.92,75.00428571 -2015-10-19T00:00:00Z,76.08,75.12 -2015-10-20T00:00:00Z,75.82,75.22142857 -2015-10-21T00:00:00Z,75.33,75.54142857 -2015-10-22T00:00:00Z,76.94,75.92428571 -2015-10-23T00:00:00Z,77.51,76.45285714 -2015-10-24T00:00:00Z,78.57,77.33 -2015-10-25T00:00:00Z,81.06,77.86571429 -2015-10-26T00:00:00Z,79.83,78.66428571 -2015-10-27T00:00:00Z,81.41,79.86857143 -2015-10-28T00:00:00Z,83.76,80.84142857 -2015-10-29T00:00:00Z,83.75,81.07571429 -2015-10-30T00:00:00Z,79.15,80.72571429 -2015-10-31T00:00:00Z,76.12,79.83571429 -2015-11-01T00:00:00Z,74.83,78.98857143 -2015-11-02T00:00:00Z,73.9,77.86428571 -2015-11-03T00:00:00Z,73.54,76.35571429 -2015-11-04T00:00:00Z,73.2,74.83285714 -2015-11-05T00:00:00Z,73.09,73.95 -2015-11-06T00:00:00Z,72.97,73.42714286 -2015-11-07T00:00:00Z,72.46,73.04571429 -2015-11-08T00:00:00Z,72.16,72.81142857 -2015-11-09T00:00:00Z,72.26,72.69428571 -2015-11-10T00:00:00Z,72.72,72.63285714 -2015-11-11T00:00:00Z,72.77,72.67142857 -2015-11-12T00:00:00Z,73.36,72.63714286 -2015-11-13T00:00:00Z,72.73,72.50285714 -2015-11-14T00:00:00Z,71.52,72.42 -2015-11-15T00:00:00Z,71.58,72.29571429 -2015-11-16T00:00:00Z,71.39,72.16142857 -2015-11-17T00:00:00Z,71.78,72.01571429 -2015-11-18T00:00:00Z,71.75,71.78857143 -2015-11-19T00:00:00Z,71.77,71.53714286 -2015-11-20T00:00:00Z,70.97,71.44428571 -2015-11-21T00:00:00Z,70.87,71.33 -2015-11-22T00:00:00Z,70.78,71.19285714 -2015-11-23T00:00:00Z,70.43,70.99142857 -2015-11-24T00:00:00Z,70.37,70.76714286 -2015-11-25T00:00:00Z,70.18,70.54428571 -2015-11-26T00:00:00Z,70.21,70.41571429 -2015-11-27T00:00:00Z,70.07,70.30428571 -2015-11-28T00:00:00Z,70.09,70.24285714 -2015-11-29T00:00:00Z,70.35,70.29857143 -2015-11-30T00:00:00Z,70.82,NA -2015-12-01T00:00:00Z,NA,NA -2015-12-02T00:00:00Z,NA,NA -2015-12-03T00:00:00Z,NA,NA -2015-12-04T00:00:00Z,NA,NA -2015-12-05T00:00:00Z,NA,NA -2015-12-06T00:00:00Z,NA,NA -2015-12-07T00:00:00Z,NA,NA -2015-12-08T00:00:00Z,NA,NA -2015-12-09T00:00:00Z,NA,NA -2015-12-10T00:00:00Z,NA,NA -2015-12-11T00:00:00Z,NA,NA -2015-12-12T00:00:00Z,NA,NA -2015-12-13T00:00:00Z,NA,NA -2015-12-14T00:00:00Z,NA,NA -2015-12-15T00:00:00Z,NA,NA -2015-12-16T00:00:00Z,NA,NA -2015-12-17T00:00:00Z,NA,NA -2015-12-18T00:00:00Z,NA,NA -2015-12-19T00:00:00Z,NA,NA -2015-12-20T00:00:00Z,NA,NA -2015-12-21T00:00:00Z,NA,NA -2015-12-22T00:00:00Z,NA,NA -2015-12-23T00:00:00Z,NA,NA -2015-12-24T00:00:00Z,NA,NA -2015-12-25T00:00:00Z,NA,NA -2015-12-26T00:00:00Z,NA,NA -2015-12-27T00:00:00Z,NA,NA -2015-12-28T00:00:00Z,NA,NA -2015-12-29T00:00:00Z,NA,NA -2015-12-30T00:00:00Z,NA,NA -2015-12-31T00:00:00Z,NA,NA -2016-01-01T00:00:00Z,NA,NA -2016-01-02T00:00:00Z,NA,NA -2016-01-03T00:00:00Z,NA,NA -2016-01-04T00:00:00Z,NA,NA -2016-01-05T00:00:00Z,NA,NA -2016-01-06T00:00:00Z,NA,NA -2016-01-07T00:00:00Z,NA,NA -2016-01-08T00:00:00Z,NA,NA -2016-01-09T00:00:00Z,NA,NA -2016-01-10T00:00:00Z,NA,NA -2016-01-11T00:00:00Z,NA,NA -2016-01-12T00:00:00Z,NA,NA -2016-01-13T00:00:00Z,NA,NA -2016-01-14T00:00:00Z,NA,NA -2016-01-15T00:00:00Z,NA,NA -2016-01-16T00:00:00Z,NA,NA -2016-01-17T00:00:00Z,NA,NA -2016-01-18T00:00:00Z,NA,NA -2016-01-19T00:00:00Z,NA,NA -2016-01-20T00:00:00Z,NA,NA -2016-01-21T00:00:00Z,NA,NA -2016-01-22T00:00:00Z,NA,NA -2016-01-23T00:00:00Z,NA,NA -2016-01-24T00:00:00Z,NA,NA -2016-01-25T00:00:00Z,NA,NA -2016-01-26T00:00:00Z,NA,NA -2016-01-27T00:00:00Z,NA,NA -2016-01-28T00:00:00Z,NA,NA -2016-01-29T00:00:00Z,NA,NA -2016-01-30T00:00:00Z,NA,NA -2016-01-31T00:00:00Z,NA,NA -2016-02-01T00:00:00Z,NA,NA -2016-02-02T00:00:00Z,NA,NA -2016-02-03T00:00:00Z,NA,NA -2016-02-04T00:00:00Z,NA,NA -2016-02-05T00:00:00Z,NA,NA -2016-02-06T00:00:00Z,NA,NA -2016-02-07T00:00:00Z,NA,NA -2016-02-08T00:00:00Z,NA,NA -2016-02-09T00:00:00Z,NA,NA -2016-02-10T00:00:00Z,NA,NA -2016-02-11T00:00:00Z,NA,NA -2016-02-12T00:00:00Z,NA,NA -2016-02-13T00:00:00Z,NA,NA -2016-02-14T00:00:00Z,NA,NA -2016-02-15T00:00:00Z,NA,NA -2016-02-16T00:00:00Z,NA,NA -2016-02-17T00:00:00Z,NA,NA -2016-02-18T00:00:00Z,NA,NA -2016-02-19T00:00:00Z,NA,NA -2016-02-20T00:00:00Z,NA,NA -2016-02-21T00:00:00Z,NA,NA -2016-02-22T00:00:00Z,NA,NA -2016-02-23T00:00:00Z,NA,NA -2016-02-24T00:00:00Z,NA,NA -2016-02-25T00:00:00Z,NA,52.91 -2016-02-26T00:00:00Z,52.91,48.87 -2016-02-27T00:00:00Z,44.83,46.92333333 -2016-02-28T00:00:00Z,43.03,45.7025 -2016-02-29T00:00:00Z,42.04,46.666 -2016-03-01T00:00:00Z,50.52,NA -2016-03-02T00:00:00Z,NA,NA -2016-03-03T00:00:00Z,NA,NA -2016-03-04T00:00:00Z,NA,NA -2016-03-05T00:00:00Z,NA,NA -2016-03-06T00:00:00Z,NA,NA -2016-03-07T00:00:00Z,NA,NA -2016-03-08T00:00:00Z,NA,NA -2016-03-09T00:00:00Z,NA,NA -2016-03-10T00:00:00Z,NA,NA -2016-03-11T00:00:00Z,NA,NA -2016-03-12T00:00:00Z,NA,NA -2016-03-13T00:00:00Z,NA,NA -2016-03-14T00:00:00Z,NA,NA -2016-03-15T00:00:00Z,NA,NA -2016-03-16T00:00:00Z,NA,NA -2016-03-17T00:00:00Z,NA,NA -2016-03-18T00:00:00Z,NA,NA -2016-03-19T00:00:00Z,NA,NA -2016-03-20T00:00:00Z,NA,NA -2016-03-21T00:00:00Z,NA,NA -2016-03-22T00:00:00Z,NA,NA -2016-03-23T00:00:00Z,NA,NA -2016-03-24T00:00:00Z,NA,NA -2016-03-25T00:00:00Z,NA,NA -2016-03-26T00:00:00Z,NA,NA -2016-03-27T00:00:00Z,NA,NA -2016-03-28T00:00:00Z,NA,67.08712357 -2016-03-29T00:00:00Z,67.09131409,67.55756927 -2016-03-30T00:00:00Z,68.03501178,68.08171285 -2016-03-31T00:00:00Z,69.13,68.11128464 -2016-04-01T00:00:00Z,68.2,68.10702771 -2016-04-02T00:00:00Z,68.09,67.79918976 -2016-04-03T00:00:00Z,66.26,67.88644836 -2016-04-04T00:00:00Z,68.41,68.14685928 -2016-04-05T00:00:00Z,68.91,68.46 -2016-04-06T00:00:00Z,70.22,68.62285714 -2016-04-07T00:00:00Z,70.27,68.84285714 -2016-04-08T00:00:00Z,69.74,69.03142857 -2016-04-09T00:00:00Z,69.41,69.46571429 -2016-04-10T00:00:00Z,69.3,69.61142857 -2016-04-11T00:00:00Z,69.43,69.55857143 -2016-04-12T00:00:00Z,68.54,69.27714286 -2016-04-13T00:00:00Z,68.25,68.99571429 -2016-04-14T00:00:00Z,68.3,68.80714286 -2016-04-15T00:00:00Z,68.42,68.70857143 -2016-04-16T00:00:00Z,68.72,68.69285714 -2016-04-17T00:00:00Z,69.19,68.6 -2016-04-18T00:00:00Z,68.78,68.59 -2016-04-19T00:00:00Z,68.47,68.67714286 -2016-04-20T00:00:00Z,68.86,68.78142857 -2016-04-21T00:00:00Z,69.03,68.80428571 -2016-04-22T00:00:00Z,68.58,68.82142857 -2016-04-23T00:00:00Z,68.84,68.84857143 -2016-04-24T00:00:00Z,69.38,68.97857143 -2016-04-25T00:00:00Z,69.69,69.12857143 -2016-04-26T00:00:00Z,69.52,69.28142857 -2016-04-27T00:00:00Z,69.93,69.39285714 -2016-04-28T00:00:00Z,69.81,69.57571429 -2016-04-29T00:00:00Z,69.86,69.74857143 -2016-04-30T00:00:00Z,70.05,69.82428571 -2016-05-01T00:00:00Z,69.91,69.85428571 -2016-05-02T00:00:00Z,69.9,69.90142857 -2016-05-03T00:00:00Z,69.85,69.89285714 -2016-05-04T00:00:00Z,69.87,69.94428571 -2016-05-05T00:00:00Z,70.17,69.97 -2016-05-06T00:00:00Z,70.04,69.84571429 -2016-05-07T00:00:00Z,69.18,69.67714286 -2016-05-08T00:00:00Z,68.73,69.43142857 -2016-05-09T00:00:00Z,68.18,69.03714286 -2016-05-10T00:00:00Z,67.09,68.59857143 -2016-05-11T00:00:00Z,66.8,68.10571429 -2016-05-12T00:00:00Z,66.72,67.63714286 -2016-05-13T00:00:00Z,66.76,67.33142857 -2016-05-14T00:00:00Z,67.04,67.04428571 -2016-05-15T00:00:00Z,66.72,66.93428571 -2016-05-16T00:00:00Z,67.41,66.99571429 -2016-05-17T00:00:00Z,67.52,67.23857143 -2016-05-18T00:00:00Z,68.5,67.53142857 -2016-05-19T00:00:00Z,68.77,67.80428571 -2016-05-20T00:00:00Z,68.67,68.03 -2016-05-21T00:00:00Z,68.62,68.34 -2016-05-22T00:00:00Z,68.89,68.60857143 -2016-05-23T00:00:00Z,69.29,68.86428571 -2016-05-24T00:00:00Z,69.31,68.93571429 -2016-05-25T00:00:00Z,69,68.94428571 -2016-05-26T00:00:00Z,68.83,68.97714286 -2016-05-27T00:00:00Z,68.9,69.01285714 -2016-05-28T00:00:00Z,68.87,68.99285714 -2016-05-29T00:00:00Z,68.75,68.87571429 -2016-05-30T00:00:00Z,68.47,68.70571429 -2016-05-31T00:00:00Z,68.12,68.57285714 -2016-06-01T00:00:00Z,68.07,68.5 -2016-06-02T00:00:00Z,68.32,68.45714286 -2016-06-03T00:00:00Z,68.6,68.43428571 -2016-06-04T00:00:00Z,68.71,68.44714286 -2016-06-05T00:00:00Z,68.84,68.50428571 -2016-06-06T00:00:00Z,68.87,68.60285714 -2016-06-07T00:00:00Z,68.81,68.65 -2016-06-08T00:00:00Z,68.4,68.6 -2016-06-09T00:00:00Z,67.97,68.59285714 -2016-06-10T00:00:00Z,68.55,68.67285714 -2016-06-11T00:00:00Z,69.27,68.75428571 -2016-06-12T00:00:00Z,69.41,68.92428571 -2016-06-13T00:00:00Z,70.06,69.18285714 -2016-06-14T00:00:00Z,70.62,69.52285714 -2016-06-15T00:00:00Z,70.78,69.95 -2016-06-16T00:00:00Z,70.96,70.30714286 -2016-06-17T00:00:00Z,71.05,70.56142857 -2016-06-18T00:00:00Z,71.05,70.77428571 -2016-06-19T00:00:00Z,70.9,70.90142857 -2016-06-20T00:00:00Z,70.95,70.96428571 -2016-06-21T00:00:00Z,71.06,71.01571429 -2016-06-22T00:00:00Z,71.14,71.06285714 -2016-06-23T00:00:00Z,71.29,71.11142857 -2016-06-24T00:00:00Z,71.39,71.18857143 -2016-06-25T00:00:00Z,71.59,71.31428571 -2016-06-26T00:00:00Z,71.78,71.44 -2016-06-27T00:00:00Z,71.83,71.55428571 -2016-06-28T00:00:00Z,71.86,71.65571429 -2016-06-29T00:00:00Z,71.85,71.76142857 -2016-06-30T00:00:00Z,72.03,71.89142857 -2016-07-01T00:00:00Z,72.3,71.99428571 -2016-07-02T00:00:00Z,72.31,72.10285714 -2016-07-03T00:00:00Z,72.54,72.22428571 -2016-07-04T00:00:00Z,72.68,72.36571429 -2016-07-05T00:00:00Z,72.85,72.57571429 -2016-07-06T00:00:00Z,73.32,72.85 -2016-07-07T00:00:00Z,73.95,73.13714286 -2016-07-08T00:00:00Z,74.31,73.46 -2016-07-09T00:00:00Z,74.57,73.69428571 -2016-07-10T00:00:00Z,74.18,73.88285714 -2016-07-11T00:00:00Z,74,74.08285714 -2016-07-12T00:00:00Z,74.25,74.14428571 -2016-07-13T00:00:00Z,73.75,74.08714286 -2016-07-14T00:00:00Z,73.55,73.94285714 -2016-07-15T00:00:00Z,73.3,73.73857143 -2016-07-16T00:00:00Z,73.14,73.60142857 -2016-07-17T00:00:00Z,73.22,73.49 -2016-07-18T00:00:00Z,73.22,73.32 -2016-07-19T00:00:00Z,73.06,73.25571429 -2016-07-20T00:00:00Z,73.3,73.25428571 -2016-07-21T00:00:00Z,73.54,73.33428571 -2016-07-22T00:00:00Z,73.86,73.44571429 -2016-07-23T00:00:00Z,73.92,73.58714286 -2016-07-24T00:00:00Z,74.21,73.78571429 -2016-07-25T00:00:00Z,74.61,73.84714286 -2016-07-26T00:00:00Z,73.49,73.87142857 -2016-07-27T00:00:00Z,73.47,73.85285714 -2016-07-28T00:00:00Z,73.41,73.79428571 -2016-07-29T00:00:00Z,73.45,73.73285714 -2016-07-30T00:00:00Z,73.49,73.56857143 -2016-07-31T00:00:00Z,73.06,73.27714286 -2016-08-01T00:00:00Z,72.57,73.15714286 -2016-08-02T00:00:00Z,72.65,73.04 -2016-08-03T00:00:00Z,72.65,72.89857143 -2016-08-04T00:00:00Z,72.42,72.75714286 -2016-08-05T00:00:00Z,72.46,72.59714286 -2016-08-06T00:00:00Z,72.37,72.48285714 -2016-08-07T00:00:00Z,72.26,72.42857143 -2016-08-08T00:00:00Z,72.19,72.36714286 -2016-08-09T00:00:00Z,72.22,72.33142857 -2016-08-10T00:00:00Z,72.4,72.28571429 -2016-08-11T00:00:00Z,72.1,72.23142857 -2016-08-12T00:00:00Z,72.08,72.18857143 -2016-08-13T00:00:00Z,72.07,72.17857143 -2016-08-14T00:00:00Z,72.19,72.13285714 -2016-08-15T00:00:00Z,71.87,72.10714286 -2016-08-16T00:00:00Z,72.04,72.04428571 -2016-08-17T00:00:00Z,71.96,72.01285714 -2016-08-18T00:00:00Z,71.88,72.02857143 -2016-08-19T00:00:00Z,72.19,72.10285714 -2016-08-20T00:00:00Z,72.59,72.21571429 -2016-08-21T00:00:00Z,72.98,72.31142857 -2016-08-22T00:00:00Z,72.54,72.37857143 -2016-08-23T00:00:00Z,72.51,72.47428571 -2016-08-24T00:00:00Z,72.63,72.59714286 -2016-08-25T00:00:00Z,72.74,72.64714286 -2016-08-26T00:00:00Z,72.54,72.66714286 -2016-08-27T00:00:00Z,72.73,72.69428571 -2016-08-28T00:00:00Z,73.17,72.82428571 -2016-08-29T00:00:00Z,73.45,72.97 -2016-08-30T00:00:00Z,73.53,73.17571429 -2016-08-31T00:00:00Z,74.07,73.50428571 -2016-09-01T00:00:00Z,75.04,73.81285714 -2016-09-02T00:00:00Z,74.7,74.25714286 -2016-09-03T00:00:00Z,75.84,74.79142857 -2016-09-04T00:00:00Z,76.91,75.44285714 -2016-09-05T00:00:00Z,78.01,76.25714286 -2016-09-06T00:00:00Z,79.23,76.95142857 -2016-09-07T00:00:00Z,78.93,77.50571429 -2016-09-08T00:00:00Z,78.92,78.10285714 -2016-09-09T00:00:00Z,78.88,78.56857143 -2016-09-10T00:00:00Z,79.1,78.82857143 -2016-09-11T00:00:00Z,78.73,78.90714286 -2016-09-12T00:00:00Z,78.56,78.91857143 -2016-09-13T00:00:00Z,79.31,78.98857143 -2016-09-14T00:00:00Z,79.42,79.12142857 -2016-09-15T00:00:00Z,79.85,79.51285714 -2016-09-16T00:00:00Z,81.62,79.91285714 -2016-09-17T00:00:00Z,81.9,80.51714286 -2016-09-18T00:00:00Z,82.96,81.09285714 -2016-09-19T00:00:00Z,82.59,81.50285714 -2016-09-20T00:00:00Z,82.18,81.93714286 -2016-09-21T00:00:00Z,82.46,82.36428571 -2016-09-22T00:00:00Z,82.84,82.66571429 -2016-09-23T00:00:00Z,83.73,82.97428571 -2016-09-24T00:00:00Z,84.06,83.14428571 -2016-09-25T00:00:00Z,84.15,83.4 -2016-09-26T00:00:00Z,84.38,83.77142857 -2016-09-27T00:00:00Z,84.78,84.13285714 -2016-09-28T00:00:00Z,84.99,84.76 -2016-09-29T00:00:00Z,87.23,85.24285714 -2016-09-30T00:00:00Z,87.11,85.58142857 -2016-10-01T00:00:00Z,86.43,85.76 -2016-10-02T00:00:00Z,85.4,85.84142857 -2016-10-03T00:00:00Z,84.95,85.83428571 -2016-10-04T00:00:00Z,84.73,85.86 -2016-10-05T00:00:00Z,85.17,85.44428571 -2016-10-06T00:00:00Z,84.32,84.98428571 -2016-10-07T00:00:00Z,83.89,84.60142857 -2016-10-08T00:00:00Z,83.75,84.20571429 -2016-10-09T00:00:00Z,82.63,83.89714286 -2016-10-10T00:00:00Z,82.79,83.67857143 -2016-10-11T00:00:00Z,83.2,83.42857143 -2016-10-12T00:00:00Z,83.42,83.37571429 -2016-10-13T00:00:00Z,83.95,83.37571429 -2016-10-14T00:00:00Z,83.89,83.54142857 -2016-10-15T00:00:00Z,84.91,83.96571429 -2016-10-16T00:00:00Z,85.6,84.39 -2016-10-17T00:00:00Z,85.76,84.81428571 -2016-10-18T00:00:00Z,86.17,85.28285714 -2016-10-19T00:00:00Z,86.7,85.7 -2016-10-20T00:00:00Z,86.87,86.27142857 -2016-10-21T00:00:00Z,87.89,86.59285714 -2016-10-22T00:00:00Z,87.16,86.63714286 -2016-10-23T00:00:00Z,85.91,86.66142857 -2016-10-24T00:00:00Z,85.93,86.71571429 -2016-10-25T00:00:00Z,86.55,86.78285714 -2016-10-26T00:00:00Z,87.17,86.88285714 -2016-10-27T00:00:00Z,87.57,86.86428571 -2016-10-28T00:00:00Z,87.76,86.90857143 -2016-10-29T00:00:00Z,87.47,87.17428571 -2016-10-30T00:00:00Z,87.77,87.46142857 -2016-10-31T00:00:00Z,87.94,87.67714286 -2016-11-01T00:00:00Z,88.06,87.77428571 -2016-11-02T00:00:00Z,87.85,87.78571429 -2016-11-03T00:00:00Z,87.65,87.77142857 -2016-11-04T00:00:00Z,87.66,87.82571429 -2016-11-05T00:00:00Z,87.85,87.75571429 -2016-11-06T00:00:00Z,87.28,87.78714286 -2016-11-07T00:00:00Z,88.16,87.84714286 -2016-11-08T00:00:00Z,88.48,87.92142857 -2016-11-09T00:00:00Z,88.37,87.97428571 -2016-11-10T00:00:00Z,88.02,88.03 -2016-11-11T00:00:00Z,88.05,88.00142857 -2016-11-12T00:00:00Z,87.65,88.09428571 -2016-11-13T00:00:00Z,87.93,88.05857143 -2016-11-14T00:00:00Z,87.91,88.13857143 -2016-11-15T00:00:00Z,89.04,88.30428571 -2016-11-16T00:00:00Z,89.53,88.49285714 -2016-11-17T00:00:00Z,89.34,88.70857143 -2016-11-18T00:00:00Z,89.56,89.15 -2016-11-19T00:00:00Z,90.74,89.32285714 -2016-11-20T00:00:00Z,89.14,89.31571429 -2016-11-21T00:00:00Z,87.86,89.15285714 -2016-11-22T00:00:00Z,87.9,89.03857143 -2016-11-23T00:00:00Z,88.73,89.03571429 -2016-11-24T00:00:00Z,89.32,89.06571429 -2016-11-25T00:00:00Z,89.77,88.93571429 -2016-11-26T00:00:00Z,89.83,89.04571429 -2016-11-27T00:00:00Z,89.91,89.36714286 -2016-11-28T00:00:00Z,90.11,89.67 -2016-11-29T00:00:00Z,90.02,89.75 -2016-11-30T00:00:00Z,89.29,89.60857143 -2016-12-01T00:00:00Z,88.33,89.17 -2016-12-02T00:00:00Z,86.7,88.39714286 -2016-12-03T00:00:00Z,84.42,87.57285714 -2016-12-04T00:00:00Z,84.14,86.69571429 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-2017-03-31T00:00:00Z,70.3,70.68 -2017-04-01T00:00:00Z,68.18,69.78166667 -2017-04-02T00:00:00Z,65.29,68.65857143 -2017-04-03T00:00:00Z,61.92,67.01428571 -2017-04-04T00:00:00Z,60.6,65.40714286 -2017-04-05T00:00:00Z,60.54,63.90857143 -2017-04-06T00:00:00Z,60.53,61.70285714 -2017-04-07T00:00:00Z,54.86,59.87571429 -2017-04-08T00:00:00Z,55.39,58.06 -2017-04-09T00:00:00Z,52.58,56.17571429 -2017-04-10T00:00:00Z,48.73,54.29 -2017-04-11T00:00:00Z,47.4,52.37142857 -2017-04-12T00:00:00Z,47.11,50.25714286 -2017-04-13T00:00:00Z,45.73,48.78714286 -2017-04-14T00:00:00Z,44.57,47.16714286 -2017-04-15T00:00:00Z,44.05,45.79 -2017-04-16T00:00:00Z,42.94,44.97571429 -2017-04-17T00:00:00Z,43.03,46.46714286 -2017-04-18T00:00:00Z,57.84,48.44857143 -2017-04-19T00:00:00Z,60.98,50.38857143 -2017-04-20T00:00:00Z,59.31,52.78571429 -2017-04-21T00:00:00Z,61.35,55.45857143 -2017-04-22T00:00:00Z,62.76,58.28142857 -2017-04-23T00:00:00Z,62.7,61.06714286 -2017-04-24T00:00:00Z,62.53,61.84142857 -2017-04-25T00:00:00Z,63.26,62.32571429 -2017-04-26T00:00:00Z,64.37,63.03285714 -2017-04-27T00:00:00Z,64.26,63.27857143 -2017-04-28T00:00:00Z,63.07,63.24714286 -2017-04-29T00:00:00Z,62.54,63.15571429 -2017-04-30T00:00:00Z,62.06,63.24857143 -2017-05-01T00:00:00Z,63.18,63.17285714 -2017-05-02T00:00:00Z,62.73,62.76428571 -2017-05-03T00:00:00Z,61.51,62.56571429 -2017-05-04T00:00:00Z,62.87,62.69 -2017-05-05T00:00:00Z,63.94,62.69 -2017-05-06T00:00:00Z,62.54,62.40857143 -2017-05-07T00:00:00Z,60.09,61.82 -2017-05-08T00:00:00Z,59.06,61.28 -2017-05-09T00:00:00Z,58.95,61.18571429 -2017-05-10T00:00:00Z,60.85,60.93 -2017-05-11T00:00:00Z,61.08,60.58857143 -2017-05-12T00:00:00Z,61.55,60.52 -2017-05-13T00:00:00Z,62.06,60.84 -2017-05-14T00:00:00Z,62.33,60.79428571 -2017-05-15T00:00:00Z,58.74,61.49714286 -2017-05-16T00:00:00Z,63.87,62.00428571 -2017-05-17T00:00:00Z,64.4,62.55571429 -2017-05-18T00:00:00Z,64.94,63.09285714 -2017-05-19T00:00:00Z,65.31,63.66714286 -2017-05-20T00:00:00Z,66.08,64.32857143 -2017-05-21T00:00:00Z,66.96,65.46857143 -2017-05-22T00:00:00Z,66.72,65.89857143 -2017-05-23T00:00:00Z,66.88,66.29857143 -2017-05-24T00:00:00Z,67.2,66.69428571 -2017-05-25T00:00:00Z,67.71,66.97285714 -2017-05-26T00:00:00Z,67.26,67.04857143 -2017-05-27T00:00:00Z,66.61,67.02571429 -2017-05-28T00:00:00Z,66.8,67.07 -2017-05-29T00:00:00Z,67.03,67.10428571 -2017-05-30T00:00:00Z,67.12,67.01857143 -2017-05-31T00:00:00Z,66.6,66.80142857 -2017-06-01T00:00:00Z,66.19,66.68 -2017-06-02T00:00:00Z,66.41,66.64142857 -2017-06-03T00:00:00Z,66.34,66.64285714 -2017-06-04T00:00:00Z,66.81,66.65 -2017-06-05T00:00:00Z,67.08,66.75857143 -2017-06-06T00:00:00Z,67.88,67.00714286 -2017-06-07T00:00:00Z,68.34,67.19857143 -2017-06-08T00:00:00Z,67.53,67.30428571 -2017-06-09T00:00:00Z,67.15,67.30857143 -2017-06-10T00:00:00Z,66.37,67.10428571 -2017-06-11T00:00:00Z,65.38,66.87 -2017-06-12T00:00:00Z,65.44,66.59714286 -2017-06-13T00:00:00Z,65.97,66.32142857 -2017-06-14T00:00:00Z,66.41,66.31857143 -2017-06-15T00:00:00Z,67.51,66.37428571 -2017-06-16T00:00:00Z,67.54,66.58285714 -2017-06-17T00:00:00Z,67.83,66.86285714 -2017-06-18T00:00:00Z,67.34,67.02142857 -2017-06-19T00:00:00Z,66.55,67.02142857 -2017-06-20T00:00:00Z,65.97,66.90428571 -2017-06-21T00:00:00Z,65.59,66.6 -2017-06-22T00:00:00Z,65.38,66.39142857 -2017-06-23T00:00:00Z,66.08,65.97571429 -2017-06-24T00:00:00Z,64.92,65.52142857 -2017-06-25T00:00:00Z,64.16,65.26714286 -2017-06-26T00:00:00Z,64.77,65.09285714 -2017-06-27T00:00:00Z,64.75,64.99285714 -2017-06-28T00:00:00Z,64.89,64.97857143 -2017-06-29T00:00:00Z,65.28,64.76714286 -2017-06-30T00:00:00Z,64.6,64.72 -2017-07-01T00:00:00Z,64.59,64.80142857 -2017-07-02T00:00:00Z,64.73,64.88142857 -2017-07-03T00:00:00Z,65.33,65.09142857 -2017-07-04T00:00:00Z,66.22,65.38428571 -2017-07-05T00:00:00Z,66.94,65.67285714 -2017-07-06T00:00:00Z,67.3,66.13428571 -2017-07-07T00:00:00Z,67.83,66.63428571 -2017-07-08T00:00:00Z,68.09,67.10142857 -2017-07-09T00:00:00Z,68,67.52285714 -2017-07-10T00:00:00Z,68.28,67.86 -2017-07-11T00:00:00Z,68.58,68.11 -2017-07-12T00:00:00Z,68.69,68.35857143 -2017-07-13T00:00:00Z,69.04,68.57142857 -2017-07-14T00:00:00Z,69.32,68.76571429 -2017-07-15T00:00:00Z,69.45,68.98 -2017-07-16T00:00:00Z,69.5,69.16857143 -2017-07-17T00:00:00Z,69.6,69.29714286 -2017-07-18T00:00:00Z,69.48,69.40428571 -2017-07-19T00:00:00Z,69.44,69.46 -2017-07-20T00:00:00Z,69.43,69.47 -2017-07-21T00:00:00Z,69.39,69.50142857 -2017-07-22T00:00:00Z,69.67,69.57 -2017-07-23T00:00:00Z,69.98,69.58571429 -2017-07-24T00:00:00Z,69.71,69.56571429 -2017-07-25T00:00:00Z,69.34,69.50285714 -2017-07-26T00:00:00Z,69,69.38285714 -2017-07-27T00:00:00Z,68.59,69.17285714 -2017-07-28T00:00:00Z,67.92,68.97714286 -2017-07-29T00:00:00Z,68.3,68.94428571 -2017-07-30T00:00:00Z,69.75,68.88571429 -2017-07-31T00:00:00Z,69.3,68.86428571 -2017-08-01T00:00:00Z,69.19,68.94142857 -2017-08-02T00:00:00Z,69.54,69.10571429 -2017-08-03T00:00:00Z,69.74,69.39142857 -2017-08-04T00:00:00Z,69.92,69.59142857 -2017-08-05T00:00:00Z,69.7,69.50285714 -2017-08-06T00:00:00Z,69.13,69.48142857 -2017-08-07T00:00:00Z,69.15,69.45714286 -2017-08-08T00:00:00Z,69.02,69.39571429 -2017-08-09T00:00:00Z,69.11,69.32714286 -2017-08-10T00:00:00Z,69.26,69.24142857 -2017-08-11T00:00:00Z,69.32,69.19714286 -2017-08-12T00:00:00Z,69.39,69.24714286 -2017-08-13T00:00:00Z,69.48,69.24571429 -2017-08-14T00:00:00Z,69.14,69.28 -2017-08-15T00:00:00Z,69.26,69.31571429 -2017-08-16T00:00:00Z,69.36,69.29428571 -2017-08-17T00:00:00Z,69.11,69.26857143 -2017-08-18T00:00:00Z,69.14,69.18 -2017-08-19T00:00:00Z,68.77,69.03714286 -2017-08-20T00:00:00Z,68.48,68.91714286 -2017-08-21T00:00:00Z,68.3,68.80857143 -2017-08-22T00:00:00Z,68.5,68.59571429 -2017-08-23T00:00:00Z,67.87,68.44 -2017-08-24T00:00:00Z,68.02,68.28714286 -2017-08-25T00:00:00Z,68.07,68.18714286 -2017-08-26T00:00:00Z,68.07,68.16428571 -2017-08-27T00:00:00Z,68.32,68.23571429 -2017-08-28T00:00:00Z,68.8,68.32 -2017-08-29T00:00:00Z,69.09,68.65428571 -2017-08-30T00:00:00Z,70.21,68.95571429 -2017-08-31T00:00:00Z,70.13,69.26 -2017-09-01T00:00:00Z,70.2,69.62857143 -2017-09-02T00:00:00Z,70.65,69.99428571 -2017-09-03T00:00:00Z,70.88,70.28571429 -2017-09-04T00:00:00Z,70.84,70.49 -2017-09-05T00:00:00Z,70.52,70.50428571 -2017-09-06T00:00:00Z,70.31,70.59 -2017-09-07T00:00:00Z,70.73,70.62857143 -2017-09-08T00:00:00Z,70.47,70.60571429 -2017-09-09T00:00:00Z,70.49,70.59285714 -2017-09-10T00:00:00Z,70.79,70.58857143 -2017-09-11T00:00:00Z,70.81,70.62714286 -2017-09-12T00:00:00Z,70.79,70.72714286 -2017-09-13T00:00:00Z,71.01,70.76714286 -2017-09-14T00:00:00Z,71.01,70.82571429 -2017-09-15T00:00:00Z,70.88,70.88428571 -2017-09-16T00:00:00Z,70.9,70.90142857 -2017-09-17T00:00:00Z,70.91,70.94428571 -2017-09-18T00:00:00Z,71.11,71.05571429 -2017-09-19T00:00:00Z,71.57,71.20571429 -2017-09-20T00:00:00Z,72.06,71.33714286 -2017-09-21T00:00:00Z,71.93,71.51285714 -2017-09-22T00:00:00Z,72.11,71.69285714 -2017-09-23T00:00:00Z,72.16,71.84428571 -2017-09-24T00:00:00Z,71.97,72.01 -2017-09-25T00:00:00Z,72.27,72.13571429 -2017-09-26T00:00:00Z,72.45,72.21714286 -2017-09-27T00:00:00Z,72.63,72.35857143 -2017-09-28T00:00:00Z,72.92,72.53714286 -2017-09-29T00:00:00Z,73.36,72.73 -2017-09-30T00:00:00Z,73.51,73.01285714 -2017-10-01T00:00:00Z,73.95,73.25285714 -2017-10-02T00:00:00Z,73.95,73.49714286 -2017-10-03T00:00:00Z,74.16,73.76857143 -2017-10-04T00:00:00Z,74.53,73.97714286 -2017-10-05T00:00:00Z,74.38,74.14857143 -2017-10-06T00:00:00Z,74.56,74.52285714 -2017-10-07T00:00:00Z,76.13,75.00428571 -2017-10-08T00:00:00Z,77.32,75.49714286 -2017-10-09T00:00:00Z,77.4,75.89 -2017-10-10T00:00:00Z,76.91,76.23571429 -2017-10-11T00:00:00Z,76.95,76.80285714 -2017-10-12T00:00:00Z,78.35,77.35142857 -2017-10-13T00:00:00Z,78.4,77.57714286 -2017-10-14T00:00:00Z,77.71,77.61571429 -2017-10-15T00:00:00Z,77.59,77.55 -2017-10-16T00:00:00Z,76.94,77.58571429 -2017-10-17T00:00:00Z,77.16,77.72857143 -2017-10-18T00:00:00Z,77.95,77.65428571 -2017-10-19T00:00:00Z,77.83,77.58285714 -2017-10-20T00:00:00Z,77.9,77.66428571 -2017-10-21T00:00:00Z,78.28,77.85285714 -2017-10-22T00:00:00Z,78.91,78.23714286 -2017-10-23T00:00:00Z,79.63,78.80857143 -2017-10-24T00:00:00Z,81.16,79.09 -2017-10-25T00:00:00Z,79.92,79.30142857 -2017-10-26T00:00:00Z,79.31,79.44571429 -2017-10-27T00:00:00Z,78.91,79.60857143 -2017-10-28T00:00:00Z,79.42,79.8 -2017-10-29T00:00:00Z,80.25,79.34 -2017-10-30T00:00:00Z,76.41,78.25571429 -2017-10-31T00:00:00Z,73.57,77.26857143 -2017-11-01T00:00:00Z,73.01,76.27714286 -2017-11-02T00:00:00Z,72.37,75.25142857 -2017-11-03T00:00:00Z,71.73,74.10142857 -2017-11-04T00:00:00Z,71.37,72.85428571 -2017-11-05T00:00:00Z,71.52,72.13428571 -2017-11-06T00:00:00Z,71.37,71.75714286 -2017-11-07T00:00:00Z,70.93,71.52857143 -2017-11-08T00:00:00Z,71.41,71.42428571 -2017-11-09T00:00:00Z,71.64,71.31571429 -2017-11-10T00:00:00Z,70.97,71.27285714 -2017-11-11T00:00:00Z,71.07,71.21714286 -2017-11-12T00:00:00Z,71.13,71.19 -2017-11-13T00:00:00Z,71.18,71.26571429 -2017-11-14T00:00:00Z,71.46,71.30285714 -2017-11-15T00:00:00Z,71.67,71.35571429 -2017-11-16T00:00:00Z,72.01,71.40571429 -2017-11-17T00:00:00Z,71.32,71.48285714 -2017-11-18T00:00:00Z,71.61,71.47142857 -2017-11-19T00:00:00Z,71.05,71.35857143 -2017-11-20T00:00:00Z,70.39,71.21714286 -2017-11-21T00:00:00Z,70.47,71.02428571 -2017-11-22T00:00:00Z,70.32,70.86 -2017-11-23T00:00:00Z,70.86,70.83857143 -2017-11-24T00:00:00Z,71.17,70.83285714 -2017-11-25T00:00:00Z,71.57,70.89142857 -2017-11-26T00:00:00Z,71.46,71.03857143 -2017-11-27T00:00:00Z,71.42,71.18142857 -2017-11-28T00:00:00Z,71.47,71.40142857 -2017-11-29T00:00:00Z,71.86,71.51571429 -2017-11-30T00:00:00Z,71.66,71.62714286 -2017-12-01T00:00:00Z,71.95,71.68857143 -2017-12-02T00:00:00Z,72,71.78 -2017-12-03T00:00:00Z,72.1,72.33557969 -2017-12-04T00:00:00Z,75.30947085,NA -2017-12-05T00:00:00Z,NA,NA -2017-12-06T00:00:00Z,NA,NA -2017-12-07T00:00:00Z,NA,NA -2017-12-08T00:00:00Z,NA,NA -2017-12-09T00:00:00Z,NA,NA -2017-12-10T00:00:00Z,NA,NA -2017-12-11T00:00:00Z,NA,NA -2017-12-12T00:00:00Z,NA,NA -2017-12-13T00:00:00Z,NA,NA -2017-12-14T00:00:00Z,NA,NA -2017-12-15T00:00:00Z,NA,NA -2017-12-16T00:00:00Z,NA,NA -2017-12-17T00:00:00Z,NA,NA -2017-12-18T00:00:00Z,NA,NA -2017-12-19T00:00:00Z,NA,NA -2017-12-20T00:00:00Z,NA,NA -2017-12-21T00:00:00Z,NA,NA -2017-12-22T00:00:00Z,NA,NA -2017-12-23T00:00:00Z,NA,NA -2017-12-24T00:00:00Z,NA,NA -2017-12-25T00:00:00Z,NA,NA -2017-12-26T00:00:00Z,NA,NA -2017-12-27T00:00:00Z,NA,NA -2017-12-28T00:00:00Z,NA,NA -2017-12-29T00:00:00Z,NA,NA -2017-12-30T00:00:00Z,NA,NA -2017-12-31T00:00:00Z,NA,NA -2018-01-01T00:00:00Z,NA,NA -2018-01-02T00:00:00Z,NA,NA -2018-01-03T00:00:00Z,NA,74.74 -2018-01-04T00:00:00Z,74.74,74.84 -2018-01-05T00:00:00Z,74.94,74.56 -2018-01-06T00:00:00Z,74,74.545 -2018-01-07T00:00:00Z,74.5,74.708 -2018-01-08T00:00:00Z,75.36,74.845 -2018-01-09T00:00:00Z,75.53,74.98 -2018-01-10T00:00:00Z,75.79,75.15571429 -2018-01-11T00:00:00Z,75.97,75.38142857 -2018-01-12T00:00:00Z,76.52,75.79857143 -2018-01-13T00:00:00Z,76.92,75.99714286 -2018-01-14T00:00:00Z,75.89,76.08714286 -2018-01-15T00:00:00Z,75.99,76.12857143 -2018-01-16T00:00:00Z,75.82,76.03285714 -2018-01-17T00:00:00Z,75.12,75.80428571 -2018-01-18T00:00:00Z,74.37,75.47428571 -2018-01-19T00:00:00Z,74.21,75.04428571 -2018-01-20T00:00:00Z,73.91,74.70285714 -2018-01-21T00:00:00Z,73.5,74.34428571 -2018-01-22T00:00:00Z,73.48,74.01428571 -2018-01-23T00:00:00Z,73.51,73.68571429 -2018-01-24T00:00:00Z,72.82,73.31 -2018-01-25T00:00:00Z,71.74,72.83428571 -2018-01-26T00:00:00Z,70.88,72.29285714 -2018-01-27T00:00:00Z,70.12,71.62857143 -2018-01-28T00:00:00Z,68.85,71.03714286 -2018-01-29T00:00:00Z,69.34,70.56714286 -2018-01-30T00:00:00Z,70.22,70.10285714 -2018-01-31T00:00:00Z,69.57,69.71714286 -2018-02-01T00:00:00Z,69.04,69.34857143 -2018-02-02T00:00:00Z,68.3,69.08 -2018-02-03T00:00:00Z,68.24,68.96857143 -2018-02-04T00:00:00Z,68.07,68.80285714 -2018-02-05T00:00:00Z,68.18,68.44428571 -2018-02-06T00:00:00Z,67.71,68.35285714 -2018-02-07T00:00:00Z,68.93,68.46285714 -2018-02-08T00:00:00Z,69.81,68.73142857 -2018-02-09T00:00:00Z,70.18,69.07857143 -2018-02-10T00:00:00Z,70.67,69.49714286 -2018-02-11T00:00:00Z,71,69.53571429 -2018-02-12T00:00:00Z,68.45,69.5 -2018-02-13T00:00:00Z,67.46,69.21571429 -2018-02-14T00:00:00Z,66.94,68.59857143 -2018-02-15T00:00:00Z,65.49,68.03 -2018-02-16T00:00:00Z,66.2,67.18285714 -2018-02-17T00:00:00Z,64.74,65.96 -2018-02-18T00:00:00Z,62.44,64.40428571 -2018-02-19T00:00:00Z,57.56,62.65428571 -2018-02-20T00:00:00Z,55.21,60.10857143 -2018-02-21T00:00:00Z,49.12,57.56571429 -2018-02-22T00:00:00Z,47.69,55.87285714 -2018-02-23T00:00:00Z,54.35,53.64142857 -2018-02-24T00:00:00Z,49.12,51.73 -2018-02-25T00:00:00Z,49.06,50.59857143 -2018-02-26T00:00:00Z,49.64,48.53 -2018-02-27T00:00:00Z,40.73,47.06428571 -2018-02-28T00:00:00Z,38.86,45.80285714 -2018-03-01T00:00:00Z,38.86,43.53142857 -2018-03-02T00:00:00Z,38.45,45.08285714 -2018-03-03T00:00:00Z,59.98,47.77428571 -2018-03-04T00:00:00Z,67.9,50.42428571 -2018-03-05T00:00:00Z,68.19,54.47571429 -2018-03-06T00:00:00Z,69.09,58.83428571 -2018-03-07T00:00:00Z,69.37,63.16285714 -2018-03-08T00:00:00Z,69.16,67.29285714 -2018-03-09T00:00:00Z,67.36,68.27571429 -2018-03-10T00:00:00Z,66.86,68.38714286 -2018-03-11T00:00:00Z,68.68,68.67571429 -2018-03-12T00:00:00Z,70.21,69.06857143 -2018-03-13T00:00:00Z,71.84,69.46285714 -2018-03-14T00:00:00Z,72.13,69.72571429 -2018-03-15T00:00:00Z,71,70.20428571 -2018-03-16T00:00:00Z,70.71,70.74428571 -2018-03-17T00:00:00Z,70.64,70.98428571 -2018-03-18T00:00:00Z,70.36,71.05285714 -2018-03-19T00:00:00Z,70.69,70.90428571 -2018-03-20T00:00:00Z,70.8,70.89714286 -2018-03-21T00:00:00Z,72.08,71.16142857 -2018-03-22T00:00:00Z,72.85,71.51714286 -2018-03-23T00:00:00Z,73.2,71.87571429 -2018-03-24T00:00:00Z,73.15,72.26285714 -2018-03-25T00:00:00Z,73.07,72.64142857 -2018-03-26T00:00:00Z,73.34,72.98142857 -2018-03-27T00:00:00Z,73.18,73.12142857 -2018-03-28T00:00:00Z,73.06,73.14 -2018-03-29T00:00:00Z,72.98,73.02285714 -2018-03-30T00:00:00Z,72.38,72.82142857 -2018-03-31T00:00:00Z,71.74,72.59571429 -2018-04-01T00:00:00Z,71.49,72.25285714 -2018-04-02T00:00:00Z,70.94,71.97285714 -2018-04-03T00:00:00Z,71.22,71.64857143 -2018-04-04T00:00:00Z,70.79,70.88428571 -2018-04-05T00:00:00Z,67.63,70.21285714 -2018-04-06T00:00:00Z,67.68,69.45714286 -2018-04-07T00:00:00Z,66.45,69.06571429 -2018-04-08T00:00:00Z,68.75,68.88 -2018-04-09T00:00:00Z,69.64,68.66428571 -2018-04-10T00:00:00Z,69.71,68.54857143 -2018-04-11T00:00:00Z,69.98,68.90857143 -2018-04-12T00:00:00Z,70.15,69.11 -2018-04-13T00:00:00Z,69.09,69.36428571 -2018-04-14T00:00:00Z,68.23,69.39 -2018-04-15T00:00:00Z,68.93,69.38428571 -2018-04-16T00:00:00Z,69.6,69.10714286 -2018-04-17T00:00:00Z,67.77,68.43857143 -2018-04-18T00:00:00Z,65.3,67.55285714 -2018-04-19T00:00:00Z,63.95,66.51 -2018-04-20T00:00:00Z,61.79,65.55714286 -2018-04-21T00:00:00Z,61.56,64.37142857 -2018-04-22T00:00:00Z,60.63,63.07571429 -2018-04-23T00:00:00Z,60.53,62.06285714 -2018-04-24T00:00:00Z,60.68,61.68285714 -2018-04-25T00:00:00Z,62.64,61.35 -2018-04-26T00:00:00Z,61.62,61.16285714 -2018-04-27T00:00:00Z,60.48,60.92571429 -2018-04-28T00:00:00Z,59.9,60.39714286 -2018-04-29T00:00:00Z,56.93,59.70857143 -2018-04-30T00:00:00Z,55.71,59.38857143 -2018-05-01T00:00:00Z,58.44,58.81571429 -2018-05-02T00:00:00Z,58.63,58.07714286 -2018-05-03T00:00:00Z,56.45,57.08142857 -2018-05-04T00:00:00Z,53.51,56.42 -2018-05-05T00:00:00Z,55.27,56.82285714 -2018-05-06T00:00:00Z,59.75,57.85714286 -2018-05-07T00:00:00Z,62.95,58.73857143 -2018-05-08T00:00:00Z,64.61,59.69428571 -2018-05-09T00:00:00Z,65.32,61.02714286 -2018-05-10T00:00:00Z,65.78,62.76857143 -2018-05-11T00:00:00Z,65.7,64.33857143 -2018-05-12T00:00:00Z,66.26,65.30714286 -2018-05-13T00:00:00Z,66.53,65.69285714 -2018-05-14T00:00:00Z,65.65,65.75285714 -2018-05-15T00:00:00Z,65.03,65.59428571 -2018-05-16T00:00:00Z,64.21,65.29 -2018-05-17T00:00:00Z,63.65,64.66571429 -2018-05-18T00:00:00Z,61.33,63.94714286 -2018-05-19T00:00:00Z,61.23,62.44428571 -2018-05-20T00:00:00Z,56.01,60.12142857 -2018-05-21T00:00:00Z,49.39,57.54 -2018-05-22T00:00:00Z,46.96,54.78285714 -2018-05-23T00:00:00Z,44.91,51.95714286 -2018-05-24T00:00:00Z,43.87,49.23 -2018-05-25T00:00:00Z,42.24,46.50285714 -2018-05-26T00:00:00Z,42.14,44.95285714 -2018-05-27T00:00:00Z,45.16,44.57428571 -2018-05-28T00:00:00Z,46.74,44.37571429 -2018-05-29T00:00:00Z,45.57,44.34 -2018-05-30T00:00:00Z,44.66,44.69571429 -2018-05-31T00:00:00Z,46.36,45.57428571 -2018-06-01T00:00:00Z,48.39,46.73714286 -2018-06-02T00:00:00Z,50.28,48.72571429 -2018-06-03T00:00:00Z,59.08,51.05714286 -2018-06-04T00:00:00Z,63.06,53.78571429 -2018-06-05T00:00:00Z,64.67,56.87142857 -2018-06-06T00:00:00Z,66.26,59.94285714 -2018-06-07T00:00:00Z,67.86,62.70857143 -2018-06-08T00:00:00Z,67.75,65.15571429 -2018-06-09T00:00:00Z,67.41,66.35857143 -2018-06-10T00:00:00Z,67.5,67.01571429 -2018-06-11T00:00:00Z,67.66,67.34571429 -2018-06-12T00:00:00Z,66.98,67.37428571 -2018-06-13T00:00:00Z,66.46,67.05571429 -2018-06-14T00:00:00Z,65.63,66.85285714 -2018-06-15T00:00:00Z,66.33,66.81 -2018-06-16T00:00:00Z,67.11,66.70285714 -2018-06-17T00:00:00Z,66.75,66.60142857 -2018-06-18T00:00:00Z,66.95,66.55 -2018-06-19T00:00:00Z,66.62,66.65857143 -2018-06-20T00:00:00Z,67.22,66.96571429 -2018-06-21T00:00:00Z,67.78,67.24142857 -2018-06-22T00:00:00Z,68.26,67.43571429 -2018-06-23T00:00:00Z,68.47,67.71142857 -2018-06-24T00:00:00Z,68.68,67.89857143 -2018-06-25T00:00:00Z,68.26,68.21857143 -2018-06-26T00:00:00Z,68.86,68.49857143 -2018-06-27T00:00:00Z,69.18,68.63714286 -2018-06-28T00:00:00Z,68.75,68.61714286 -2018-06-29T00:00:00Z,68.12,68.59 -2018-06-30T00:00:00Z,68.28,68.57285714 -2018-07-01T00:00:00Z,68.56,68.66285714 -2018-07-02T00:00:00Z,68.89,68.68285714 -2018-07-03T00:00:00Z,69,68.64428571 -2018-07-04T00:00:00Z,68.91,68.68571429 -2018-07-05T00:00:00Z,69.04,68.76857143 -2018-07-06T00:00:00Z,68.7,68.88714286 -2018-07-07T00:00:00Z,69.11,69.09428571 -2018-07-08T00:00:00Z,70.01,69.24714286 -2018-07-09T00:00:00Z,69.96,69.36 -2018-07-10T00:00:00Z,69.79,69.48571429 -2018-07-11T00:00:00Z,69.79,69.68428571 -2018-07-12T00:00:00Z,70.43,69.93428571 -2018-07-13T00:00:00Z,70.45,70.16285714 -2018-07-14T00:00:00Z,70.71,70.26571429 -2018-07-15T00:00:00Z,70.73,70.37285714 -2018-07-16T00:00:00Z,70.71,70.51 -2018-07-17T00:00:00Z,70.75,70.61142857 -2018-07-18T00:00:00Z,70.5,70.64285714 -2018-07-19T00:00:00Z,70.65,70.71428571 -2018-07-20T00:00:00Z,70.95,70.77142857 -2018-07-21T00:00:00Z,71.11,70.90714286 -2018-07-22T00:00:00Z,71.68,70.98 -2018-07-23T00:00:00Z,71.22,70.98571429 -2018-07-24T00:00:00Z,70.79,70.92428571 -2018-07-25T00:00:00Z,70.07,70.59428571 -2018-07-26T00:00:00Z,68.34,69.95 -2018-07-27T00:00:00Z,66.44,69.08142857 -2018-07-28T00:00:00Z,65.03,68.06 -2018-07-29T00:00:00Z,64.53,67.08 -2018-07-30T00:00:00Z,64.36,66.04571429 -2018-07-31T00:00:00Z,63.55,65.24571429 -2018-08-01T00:00:00Z,64.47,64.52857143 -2018-08-02T00:00:00Z,63.32,64.00571429 -2018-08-03T00:00:00Z,62.78,63.45714286 -2018-08-04T00:00:00Z,61.19,62.36 -2018-08-05T00:00:00Z,56.85,60.99285714 -2018-08-06T00:00:00Z,54.79,59.69428571 -2018-08-07T00:00:00Z,54.46,58.58714286 -2018-08-08T00:00:00Z,56.72,57.75571429 -2018-08-09T00:00:00Z,57.5,57.31 -2018-08-10T00:00:00Z,59.66,57.15714286 -2018-08-11T00:00:00Z,60.12,57.43 -2018-08-12T00:00:00Z,58.76,57.88571429 -2018-08-13T00:00:00Z,57.98,57.59285714 -2018-08-14T00:00:00Z,52.41,55.53857143 -2018-08-15T00:00:00Z,42.34,52.93285714 -2018-08-16T00:00:00Z,39.26,50.03714286 -2018-08-17T00:00:00Z,39.39,47.31571429 -2018-08-18T00:00:00Z,41.07,46.02285714 -2018-08-19T00:00:00Z,49.71,46.30857143 -2018-08-20T00:00:00Z,59.98,47.67 -2018-08-21T00:00:00Z,61.94,50.23714286 -2018-08-22T00:00:00Z,60.31,52.95285714 -2018-08-23T00:00:00Z,58.27,56.31285714 -2018-08-24T00:00:00Z,62.91,59.52142857 -2018-08-25T00:00:00Z,63.53,61.65142857 -2018-08-26T00:00:00Z,64.62,62.18714286 -2018-08-27T00:00:00Z,63.73,62.57285714 -2018-08-28T00:00:00Z,64.64,63.22714286 -2018-08-29T00:00:00Z,64.89,64.16714286 -2018-08-30T00:00:00Z,64.85,64.45428571 -2018-08-31T00:00:00Z,64.92,64.68428571 -2018-09-01T00:00:00Z,65.14,64.78857143 -2018-09-02T00:00:00Z,65.35,65.08714286 -2018-09-03T00:00:00Z,65.82,65.26571429 -2018-09-04T00:00:00Z,65.89,65.41857143 -2018-09-05T00:00:00Z,65.96,65.63285714 -2018-09-06T00:00:00Z,66.35,65.83714286 -2018-09-07T00:00:00Z,66.35,66.03142857 -2018-09-08T00:00:00Z,66.5,66.32714286 -2018-09-09T00:00:00Z,67.42,66.58714286 -2018-09-10T00:00:00Z,67.64,66.68142857 -2018-09-11T00:00:00Z,66.55,66.49714286 -2018-09-12T00:00:00Z,64.67,66.05 -2018-09-13T00:00:00Z,63.22,65.45142857 -2018-09-14T00:00:00Z,62.16,64.65142857 -2018-09-15T00:00:00Z,60.9,63.67 -2018-09-16T00:00:00Z,60.55,62.70714286 -2018-09-17T00:00:00Z,60.9,61.83571429 -2018-09-18T00:00:00Z,60.45,61.45857143 -2018-09-19T00:00:00Z,62.03,61.66857143 -2018-09-20T00:00:00Z,64.69,61.91142857 -2018-09-21T00:00:00Z,63.86,61.91571429 -2018-09-22T00:00:00Z,60.93,62.00857143 -2018-09-23T00:00:00Z,61.2,62.27428571 -2018-09-24T00:00:00Z,62.76,62.88 -2018-09-25T00:00:00Z,64.69,62.84857143 -2018-09-26T00:00:00Z,61.81,61.87857143 -2018-09-27T00:00:00Z,57.9,60.67571429 -2018-09-28T00:00:00Z,55.44,58.49285714 -2018-09-29T00:00:00Z,45.65,55.56857143 -2018-09-30T00:00:00Z,40.73,52.62285714 -2018-10-01T00:00:00Z,42.14,49.83285714 -2018-10-02T00:00:00Z,45.16,47.59285714 -2018-10-03T00:00:00Z,46.13,46.31142857 -2018-10-04T00:00:00Z,48.93,45.62714286 -2018-10-05T00:00:00Z,50.65,46.28857143 -2018-10-06T00:00:00Z,50.28,47.09285714 -2018-10-07T00:00:00Z,46.36,47.20714286 -2018-10-08T00:00:00Z,42.94,47.45428571 -2018-10-09T00:00:00Z,46.89,46.97 -2018-10-10T00:00:00Z,42.74,46.34714286 -2018-10-11T00:00:00Z,44.57,45.05714286 -2018-10-12T00:00:00Z,41.62,43.67714286 -2018-10-13T00:00:00Z,40.62,42.80571429 -2018-10-14T00:00:00Z,40.26,42.61714286 -2018-10-15T00:00:00Z,41.62,43.11 -2018-10-16T00:00:00Z,50.34,44.91714286 -2018-10-17T00:00:00Z,55.39,47.00142857 -2018-10-18T00:00:00Z,59.16,50.47428571 -2018-10-19T00:00:00Z,65.93,53.97857143 -2018-10-20T00:00:00Z,65.15,57.47142857 -2018-10-21T00:00:00Z,64.71,61.14 -2018-10-22T00:00:00Z,67.3,63.51571429 -2018-10-23T00:00:00Z,66.97,65.05285714 -2018-10-24T00:00:00Z,66.15,66.17285714 -2018-10-25T00:00:00Z,67,66.32 -2018-10-26T00:00:00Z,66.96,66.77428571 -2018-10-27T00:00:00Z,68.33,67.22 -2018-10-28T00:00:00Z,67.83,67.2 -2018-10-29T00:00:00Z,67.16,67.1 -2018-10-30T00:00:00Z,66.27,67.18428571 -2018-10-31T00:00:00Z,66.74,67.05428571 -2018-11-01T00:00:00Z,66.09,66.88142857 -2018-11-02T00:00:00Z,65.75,66.19 -2018-11-03T00:00:00Z,63.49,64.82285714 -2018-11-04T00:00:00Z,58.26,63.42714286 -2018-11-05T00:00:00Z,57.39,61.88428571 -2018-11-06T00:00:00Z,55.47,58.90571429 -2018-11-07T00:00:00Z,45.89,55.39428571 -2018-11-08T00:00:00Z,41.51,51.85285714 -2018-11-09T00:00:00Z,40.96,48.23428571 -2018-11-10T00:00:00Z,38.16,45.20857143 -2018-11-11T00:00:00Z,37.08,42.33 -2018-11-12T00:00:00Z,37.24,39.68 -2018-11-13T00:00:00Z,36.92,39.71428571 -2018-11-14T00:00:00Z,46.13,42.37285714 -2018-11-15T00:00:00Z,60.12,46.1 -2018-11-16T00:00:00Z,67.05,49.91 -2018-11-17T00:00:00Z,64.83,53.78571429 -2018-11-18T00:00:00Z,64.21,57.70285714 -2018-11-19T00:00:00Z,64.66,61.51714286 -2018-11-20T00:00:00Z,63.62,63.79285714 -2018-11-21T00:00:00Z,62.06,63.42285714 -2018-11-22T00:00:00Z,57.53,62.35857143 -2018-11-23T00:00:00Z,59.6,61.92428571 -2018-11-24T00:00:00Z,61.79,61.12285714 -2018-11-25T00:00:00Z,58.6,59.39714286 -2018-11-26T00:00:00Z,52.58,56.14428571 -2018-11-27T00:00:00Z,40.85,53.03 -2018-11-28T00:00:00Z,40.26,51.28714286 -2018-11-29T00:00:00Z,45.33,50.60714286 -2018-11-30T00:00:00Z,54.84,49.40857143 -2018-12-01T00:00:00Z,53.4,48.80285714 -2018-12-02T00:00:00Z,54.36,47.92571429 -2018-12-03T00:00:00Z,46.44,47.49857143 -2018-12-04T00:00:00Z,37.86,48.15142857 -2018-12-05T00:00:00Z,44.83,48.10428571 -2018-12-06T00:00:00Z,45,45.31142857 -2018-12-07T00:00:00Z,35.29,43.89714286 -2018-12-08T00:00:00Z,43.5,42.09142857 -2018-12-09T00:00:00Z,41.72,42.51285714 -2018-12-10T00:00:00Z,49.39,NA -2018-12-11T00:00:00Z,NA,NA -2018-12-12T00:00:00Z,NA,NA -2018-12-13T00:00:00Z,NA,NA -2018-12-14T00:00:00Z,NA,NA -2018-12-15T00:00:00Z,NA,NA -2018-12-16T00:00:00Z,NA,NA -2018-12-17T00:00:00Z,NA,NA -2018-12-18T00:00:00Z,NA,NA -2018-12-19T00:00:00Z,NA,NA -2018-12-20T00:00:00Z,NA,66.29851054 -2018-12-21T00:00:00Z,66.30068451,64.17081913 -2018-12-22T00:00:00Z,62.04158877,60.49230827 -2018-12-23T00:00:00Z,53.1394694,57.6017312 -2018-12-24T00:00:00Z,48.93,54.76338496 -2018-12-25T00:00:00Z,43.41,52.42448747 -2018-12-26T00:00:00Z,40.73,50.36527497 -2018-12-27T00:00:00Z,38.01,46.23691632 -2018-12-28T00:00:00Z,37.4,42.47075522 -2018-12-29T00:00:00Z,35.68,39.76857143 -2018-12-30T00:00:00Z,34.22,37.6 -2018-12-31T00:00:00Z,33.75,36.18428571 -2019-01-01T00:00:00Z,33.5,34.99571429 -2019-01-02T00:00:00Z,32.41,34.15285714 -2019-01-03T00:00:00Z,32.11,33.44 -2019-01-04T00:00:00Z,32.41,33.29428571 -2019-01-05T00:00:00Z,34.66,33.22714286 -2019-01-06T00:00:00Z,33.75,32.94714286 -2019-01-07T00:00:00Z,31.79,32.74857143 -2019-01-08T00:00:00Z,32.11,32.70571429 -2019-01-09T00:00:00Z,32.11,33.54857143 -2019-01-10T00:00:00Z,38.01,37.96096105 -2019-01-11T00:00:00Z,63.29570888,42.40906095 -2019-01-12T00:00:00Z,65.79784496,46.94236839 -2019-01-13T00:00:00Z,65.4837646,52.05990725 -2019-01-14T00:00:00Z,67.61851071,57.22577021 -2019-01-15T00:00:00Z,68.27874549,62.37643939 -2019-01-16T00:00:00Z,68.17208427,66.57813866 -2019-01-17T00:00:00Z,67.42705921,67.26845344 -2019-01-18T00:00:00Z,68.13622645,67.62695283 -2019-01-19T00:00:00Z,68.3139982,68.12511817 -2019-01-20T00:00:00Z,68.97957884,67.70516045 -2019-01-21T00:00:00Z,64.67269054,67.1082454 -2019-01-22T00:00:00Z,64.09186315,66.99221216 -2019-01-23T00:00:00Z,67.35740982,67.04734222 -2019-01-24T00:00:00Z,67.81413699,66.26200308 -2019-01-25T00:00:00Z,62.63036789,64.21683237 -2019-01-26T00:00:00Z,53.99,61.52821673 -2019-01-27T00:00:00Z,50.15,58.72920702 -2019-01-28T00:00:00Z,45.08,56.50454482 -2019-01-29T00:00:00Z,48.52,52.73419459 -2019-01-30T00:00:00Z,40.96,51.07926683 -2019-01-31T00:00:00Z,56.22189811,50.69273622 -2019-02-01T00:00:00Z,59.92218548,51.84599069 -2019-02-02T00:00:00Z,62.06125838,53.29668201 -2019-02-03T00:00:00Z,60.30144736,55.76867093 -2019-02-04T00:00:00Z,62.38261834,57.82119185 -2019-02-05T00:00:00Z,62.88653871,60.87180164 -2019-02-06T00:00:00Z,62.31292399,61.77415352 -2019-02-07T00:00:00Z,62.53854738,62.20032134 -2019-02-08T00:00:00Z,62.90791511,60.80920404 -2019-02-09T00:00:00Z,52.33075692,59.54994129 -2019-02-10T00:00:00Z,51.49,57.87366666 -2019-02-11T00:00:00Z,50.65,57.42697872 -2019-02-12T00:00:00Z,59.75709067,57.51885281 -2019-02-13T00:00:00Z,62.95629685,56.46512303 -2019-02-14T00:00:00Z,55.16398345,56.27120757 -2019-02-15T00:00:00Z,61.54958043,57.68764396 -2019-02-16T00:00:00Z,62.23862303,59.33048102 -2019-02-17T00:00:00Z,62.98877676,61.12981099 -2019-02-18T00:00:00Z,63.24428093,61.20659808 -2019-02-19T00:00:00Z,60.29494332,60.68565251 -2019-02-20T00:00:00Z,59.30690302,61.53377031 -2019-02-21T00:00:00Z,61.09792917,61.12097279 -2019-02-22T00:00:00Z,58.65823482,NA -2019-02-23T00:00:00Z,NA,NA -2019-02-24T00:00:00Z,NA,NA -2019-02-25T00:00:00Z,NA,NA -2019-02-26T00:00:00Z,NA,NA -2019-02-27T00:00:00Z,NA,NA -2019-02-28T00:00:00Z,NA,NA -2019-03-01T00:00:00Z,NA,NA -2019-03-02T00:00:00Z,NA,NA -2019-03-03T00:00:00Z,NA,NA -2019-03-04T00:00:00Z,NA,NA -2019-03-05T00:00:00Z,NA,NA -2019-03-06T00:00:00Z,NA,NA -2019-03-07T00:00:00Z,NA,NA -2019-03-08T00:00:00Z,NA,NA -2019-03-09T00:00:00Z,NA,NA -2019-03-10T00:00:00Z,NA,NA -2019-03-11T00:00:00Z,NA,63.93582652 -2019-03-12T00:00:00Z,63.93498494,64.49555418 -2019-03-13T00:00:00Z,65.05530323,64.63003545 -2019-03-14T00:00:00Z,64.89884199,64.54270687 -2019-03-15T00:00:00Z,64.28005149,63.6305504 -2019-03-16T00:00:00Z,59.97830023,63.36352332 -2019-03-17T00:00:00Z,62.02683672,63.200906 -2019-03-18T00:00:00Z,62.22380021,62.85740299 -2019-03-19T00:00:00Z,61.52925376,62.30173401 -2019-03-20T00:00:00Z,61.16311541,61.98664455 -2019-03-21T00:00:00Z,62.69220546,61.67190195 -2019-03-22T00:00:00Z,62.07601187,60.69775503 -2019-03-23T00:00:00Z,53.16702408,58.83512819 -2019-03-24T00:00:00Z,48.99,56.73867075 -2019-03-25T00:00:00Z,47.55,54.8613414 -2019-03-26T00:00:00Z,48.39,53.42768441 -2019-03-27T00:00:00Z,51.13,51.82720272 -2019-03-28T00:00:00Z,51.49,49.92041374 -2019-03-29T00:00:00Z,48.73,50.01782023 -2019-03-30T00:00:00Z,53.84752432,51.1998721 -2019-03-31T00:00:00Z,57.26159752,52.32685417 -2019-04-01T00:00:00Z,55.43871577,54.51399702 -2019-04-02T00:00:00Z,63.7,56.55685417 -2019-04-03T00:00:00Z,65.43,58.14685417 -2019-04-04T00:00:00Z,62.62,59.96256845 -2019-04-05T00:00:00Z,61.44,61.25760537 -2019-04-06T00:00:00Z,62.91,62.15269635 -2019-04-07T00:00:00Z,63.53,63.32857143 -2019-04-08T00:00:00Z,63.67,63.34 -2019-04-09T00:00:00Z,63.78,63.21285714 -2019-04-10T00:00:00Z,64.54,63.36571429 -2019-04-11T00:00:00Z,63.69,63.61714286 -2019-04-12T00:00:00Z,63.2,63.43714286 -2019-04-13T00:00:00Z,61.65,63.07142857 -2019-04-14T00:00:00Z,60.97,62.58285714 -2019-04-15T00:00:00Z,60.25,61.00714286 -2019-04-16T00:00:00Z,52.75,58.50714286 -2019-04-17T00:00:00Z,47.04,56.32142857 -2019-04-18T00:00:00Z,48.39,53.92714286 -2019-04-19T00:00:00Z,46.44,51.34857143 -2019-04-20T00:00:00Z,43.6,48.47428571 -2019-04-21T00:00:00Z,40.85,45.23285714 -2019-04-22T00:00:00Z,37.56,42.92285714 -2019-04-23T00:00:00Z,36.58,41.32714286 -2019-04-24T00:00:00Z,35.87,39.42571429 -2019-04-25T00:00:00Z,35.08,38.49142857 -2019-04-26T00:00:00Z,39.9,37.81428571 -2019-04-27T00:00:00Z,38.86,40.47 -2019-04-28T00:00:00Z,59.44,44.19285714 -2019-04-29T00:00:00Z,63.62,48.16857143 -2019-04-30T00:00:00Z,64.41,52.15857143 -2019-05-01T00:00:00Z,63.8,56.21857143 -2019-05-02T00:00:00Z,63.5,59.64285714 -2019-05-03T00:00:00Z,63.87,63.17428571 -2019-05-04T00:00:00Z,63.58,63.76571429 -2019-05-05T00:00:00Z,63.58,63.73857143 -2019-05-06T00:00:00Z,63.43,63.30714286 -2019-05-07T00:00:00Z,61.39,62.44428571 -2019-05-08T00:00:00Z,57.76,61.34571429 -2019-05-09T00:00:00Z,55.81,60.12571429 -2019-05-10T00:00:00Z,55.33,58.47285714 -2019-05-11T00:00:00Z,52.01,57.21428571 -2019-05-12T00:00:00Z,54.77,56.52285714 -2019-05-13T00:00:00Z,58.59,55.15 -2019-05-14T00:00:00Z,51.78,53.26571429 -2019-05-15T00:00:00Z,44.57,51.04428571 -2019-05-16T00:00:00Z,40.26,48.67285714 -2019-05-17T00:00:00Z,38.73,46.65142857 -2019-05-18T00:00:00Z,37.86,44.17 -2019-05-19T00:00:00Z,37.4,41.12 -2019-05-20T00:00:00Z,37.24,39.04285714 -2019-05-21T00:00:00Z,37.24,37.92571429 -2019-05-22T00:00:00Z,36.75,37.4 -2019-05-23T00:00:00Z,36.58,37.11714286 -2019-05-24T00:00:00Z,36.75,39.08 -2019-05-25T00:00:00Z,51.6,41.14285714 -2019-05-26T00:00:00Z,51.84,43.62714286 -2019-05-27T00:00:00Z,54.63,47.08857143 -2019-05-28T00:00:00Z,61.47,50.76714286 -2019-05-29T00:00:00Z,62.5,54.67571429 -2019-05-30T00:00:00Z,63.94,58.35857143 -2019-05-31T00:00:00Z,62.53,59.67142857 -2019-06-01T00:00:00Z,60.79,60.99857143 -2019-06-02T00:00:00Z,61.13,61.74428571 -2019-06-03T00:00:00Z,59.85,61.52 -2019-06-04T00:00:00Z,59.9,61.29 -2019-06-05T00:00:00Z,60.89,60.69714286 -2019-06-06T00:00:00Z,59.79,60.47 -2019-06-07T00:00:00Z,60.94,60.58428571 -2019-06-08T00:00:00Z,61.59,60.74285714 -2019-06-09T00:00:00Z,62.24,61.19285714 -2019-06-10T00:00:00Z,63,61.62285714 -2019-06-11T00:00:00Z,62.91,62.02142857 -2019-06-12T00:00:00Z,63.68,62.59857143 -2019-06-13T00:00:00Z,63.83,62.82428571 -2019-06-14T00:00:00Z,62.52,62.82142857 -2019-06-15T00:00:00Z,61.57,62.63142857 -2019-06-16T00:00:00Z,60.91,62.40571429 -2019-06-17T00:00:00Z,61.42,62.39714286 -2019-06-18T00:00:00Z,62.85,62.31714286 -2019-06-19T00:00:00Z,63.12,62.04428571 -2019-06-20T00:00:00Z,61.92,61.19142857 -2019-06-21T00:00:00Z,56.55,60.78714286 -2019-06-22T00:00:00Z,58.74,60.91142857 -2019-06-23T00:00:00Z,61.78,61.28 -2019-06-24T00:00:00Z,64,61.73142857 -2019-06-25T00:00:00Z,66.01,62.21571429 -2019-06-26T00:00:00Z,66.51,62.91 -2019-06-27T00:00:00Z,66.78,64.44142857 -2019-06-28T00:00:00Z,67.27,65.70285714 -2019-06-29T00:00:00Z,67.57,66.55 -2019-06-30T00:00:00Z,67.71,67.14714286 -2019-07-01T00:00:00Z,68.18,67.5 -2019-07-02T00:00:00Z,68.48,67.74 -2019-07-03T00:00:00Z,68.19,67.93428571 -2019-07-04T00:00:00Z,68.14,68.09142857 -2019-07-05T00:00:00Z,68.37,68.2 -2019-07-06T00:00:00Z,68.33,68.20142857 -2019-07-07T00:00:00Z,67.72,68.20428571 -2019-07-08T00:00:00Z,68.2,68.16142857 -2019-07-09T00:00:00Z,68.18,68.11857143 -2019-07-10T00:00:00Z,67.89,68.10571429 -2019-07-11T00:00:00Z,68.05,67.89571429 -2019-07-12T00:00:00Z,66.9,67.60285714 -2019-07-13T00:00:00Z,66.28,67.34285714 -2019-07-14T00:00:00Z,65.9,67.05 -2019-07-15T00:00:00Z,66.15,66.86 -2019-07-16T00:00:00Z,66.85,66.74714286 -2019-07-17T00:00:00Z,67.1,66.63142857 -2019-07-18T00:00:00Z,67.24,66.67 -2019-07-19T00:00:00Z,67.17,66.83428571 -2019-07-20T00:00:00Z,67.43,67.09428571 -2019-07-21T00:00:00Z,67.72,67.43 -2019-07-22T00:00:00Z,68.5,67.58428571 -2019-07-23T00:00:00Z,67.93,67.77 -2019-07-24T00:00:00Z,68.4,67.99428571 -2019-07-25T00:00:00Z,68.81,68.22142857 -2019-07-26T00:00:00Z,68.76,68.45571429 -2019-07-27T00:00:00Z,69.07,68.67857143 -2019-07-28T00:00:00Z,69.28,68.74714286 -2019-07-29T00:00:00Z,68.98,68.92142857 -2019-07-30T00:00:00Z,69.15,69.01428571 -2019-07-31T00:00:00Z,69.05,69.07 -2019-08-01T00:00:00Z,69.2,69.15857143 -2019-08-02T00:00:00Z,69.38,69.22142857 -2019-08-03T00:00:00Z,69.51,69.25857143 -2019-08-04T00:00:00Z,69.54,69.33428571 -2019-08-05T00:00:00Z,69.51,69.41714286 -2019-08-06T00:00:00Z,69.73,69.50857143 -2019-08-07T00:00:00Z,69.69,69.53857143 -2019-08-08T00:00:00Z,69.41,69.53714286 -2019-08-09T00:00:00Z,69.37,69.55 -2019-08-10T00:00:00Z,69.6,69.57714286 -2019-08-11T00:00:00Z,69.73,69.61571429 -2019-08-12T00:00:00Z,69.78,69.57714286 -2019-08-13T00:00:00Z,69.46,69.56 -2019-08-14T00:00:00Z,69.57,69.64714286 -2019-08-15T00:00:00Z,70.02,69.77428571 -2019-08-16T00:00:00Z,70.26,69.86285714 -2019-08-17T00:00:00Z,70.22,69.95 -2019-08-18T00:00:00Z,70.34,70.02571429 -2019-08-19T00:00:00Z,70.31,70.13428571 -2019-08-20T00:00:00Z,70.22,70.29428571 -2019-08-21T00:00:00Z,70.69,70.37285714 -2019-08-22T00:00:00Z,70.57,70.43285714 -2019-08-23T00:00:00Z,70.68,70.56285714 -2019-08-24T00:00:00Z,71.13,70.75428571 -2019-08-25T00:00:00Z,71.68,71.00428571 -2019-08-26T00:00:00Z,72.06,71.25142857 -2019-08-27T00:00:00Z,71.95,71.42857143 -2019-08-28T00:00:00Z,71.93,71.61285714 -2019-08-29T00:00:00Z,71.86,71.79142857 -2019-08-30T00:00:00Z,71.93,71.90714286 -2019-08-31T00:00:00Z,71.94,71.97571429 -2019-09-01T00:00:00Z,72.16,72.03 -2019-09-02T00:00:00Z,72.44,72.07857143 -2019-09-03T00:00:00Z,72.29,72.16142857 -2019-09-04T00:00:00Z,72.51,72.23571429 -2019-09-05T00:00:00Z,72.38,72.34857143 -2019-09-06T00:00:00Z,72.72,72.54 -2019-09-07T00:00:00Z,73.28,72.69285714 -2019-09-08T00:00:00Z,73.23,72.8 -2019-09-09T00:00:00Z,73.19,72.97571429 -2019-09-10T00:00:00Z,73.52,73.16285714 -2019-09-11T00:00:00Z,73.82,73.34142857 -2019-09-12T00:00:00Z,73.63,73.61142857 -2019-09-13T00:00:00Z,74.61,73.82571429 -2019-09-14T00:00:00Z,74.78,73.97857143 -2019-09-15T00:00:00Z,74.3,74.14571429 -2019-09-16T00:00:00Z,74.36,74.34857143 -2019-09-17T00:00:00Z,74.94,74.57857143 -2019-09-18T00:00:00Z,75.43,74.96428571 -2019-09-19T00:00:00Z,76.33,75.14857143 -2019-09-20T00:00:00Z,75.9,75.29 -2019-09-21T00:00:00Z,75.77,75.63428571 -2019-09-22T00:00:00Z,76.71,75.98 -2019-09-23T00:00:00Z,76.78,76.19285714 -2019-09-24T00:00:00Z,76.43,76.45 -2019-09-25T00:00:00Z,77.23,76.70857143 -2019-09-26T00:00:00Z,78.14,77.03857143 -2019-09-27T00:00:00Z,78.21,77.49 -2019-09-28T00:00:00Z,78.93,77.86 -2019-09-29T00:00:00Z,79.3,78.31 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'wspdir' - - 'wl_range' - - 'wl_max' - - 'wl_obs_pred' - - 'wl_filtered' - - 'conductivity' - - 'air_pressure' - #the sinks to be analyzed, this will likely be the salt front location - snks: - - 'saltfront_daily' - - 'saltfront7_weekly' - preprocess_steps: - mutual_information: - sources: - prcp: - - remove_seasonal_signal - - normalize - tmax: - - remove_seasonal_signal - - normalize - tmin: - - remove_seasonal_signal - - normalize - wspdir: - - remove_seasonal_signal - - normalize - discharge: - - remove_seasonal_signal - - normalize - specific_conductance: - - remove_seasonal_signal - - normalize - wl_range: - - remove_seasonal_signal - - normalize - wl_max: - - remove_seasonal_signal - - normalize - wl_obs_pred: - - remove_seasonal_signal - - normalize - wl_filtered: - - remove_seasonal_signal - - normalize - conductivity: - - remove_seasonal_signal - - normalize - air_pressure: - - remove_seasonal_signal - - normalize - #air_temperature: - # - remove_seasonal_signal - # - normalize - sinks: - saltfront: - - remove_seasonal_signal - - normalize - saltfront7: - - remove_seasonal_signal - - normalize - - correlation: - sources: - prcp: - - none - tmax: - - none - tmin: - - none - wspdir: - - none - discharge: - - none - specific_conductance: - - none - wl_range: - - none - wl_max: - - none - wl_obs_pred: - - none - wl_filtered: - - none - conductivity: - - none - air_pressure: - - none - #air_temperature: - # - none - sinks: - saltfront: - - none - saltfront7: - - none - #date range of interest - date_start: '2000-01-01' - date_end: '2019-12-31' - #number of lags to analyze - n_lags: 0 - #where to write the sinks and lagged sources as binary pickle files - out_dir: '03a_it_analysis/out/' - #should preprocessing plots be saved - plot: True - - \ No newline at end of file diff --git a/03a_it_analysis/make_heatmap_matrix_config.yaml b/03a_it_analysis/make_heatmap_matrix_config.yaml deleted file mode 100644 index 6aaa1d4..0000000 --- a/03a_it_analysis/make_heatmap_matrix_config.yaml +++ /dev/null @@ -1,7 +0,0 @@ -make_heatmap_matrix.py: - #lagged sources from file - srcs_lagged_pickle: '03a_it_analysis/out/srcs_proc_lagged' - #sinks from file - snks_list_pickle: '03a_it_analysis/out/snks_proc' - #directory to save the correlation matrix - out_dir: '03a_it_analysis/out/' diff --git a/03a_it_analysis/params_config_it_analysis_data_prep.yaml b/03a_it_analysis/params_config_it_analysis_data_prep.yaml deleted file mode 100644 index 9b88031..0000000 --- a/03a_it_analysis/params_config_it_analysis_data_prep.yaml +++ /dev/null @@ -1,113 +0,0 @@ -it_analysis_data_prep.py: - #the sources to be analyzed - srcs: - - 'discharge' - - 'specific_conductance' - - 'prcp' - - 'tmax' - - 'tmin' - - 'wspdir' - - 'wl_range' - - 'wl_max' - - 'wl_obs_pred' - - 'wl_filtered' - - 'conductivity' - - 'air_pressure' - #the sinks to be analyzed, this will likely be the salt front location - snks: - - 'saltfront_daily' - - 'saltfront7_weekly' - preprocess_steps: - mutual_information: - sources: - prcp: - - remove_seasonal_signal - - normalize - tmax: - - remove_seasonal_signal - - normalize - tmin: - - remove_seasonal_signal - - normalize - wspdir: - - remove_seasonal_signal - - normalize - discharge: - - remove_seasonal_signal - - normalize - specific_conductance: - - remove_seasonal_signal - - normalize - wl_range: - - remove_seasonal_signal - - normalize - wl_max: - - remove_seasonal_signal - - normalize - wl_obs_pred: - - remove_seasonal_signal - - normalize - wl_filtered: - - remove_seasonal_signal - - normalize - conductivity: - - remove_seasonal_signal - - normalize - air_pressure: - - remove_seasonal_signal - - normalize - #air_temperature: - # - remove_seasonal_signal - # - normalize - sinks: - saltfront: - - remove_seasonal_signal - - normalize - saltfront7: - - remove_seasonal_signal - - normalize - - correlation: - sources: - prcp: - - none - tmax: - - none - tmin: - - none - wspdir: - - none - discharge: - - none - specific_conductance: - - none - wl_range: - - none - wl_max: - - none - wl_obs_pred: - - none - wl_filtered: - - none - conductivity: - - none - air_pressure: - - none - #air_temperature: - # - none - sinks: - saltfront: - - none - saltfront7: - - none - #date range of interest - date_start: '2000-01-01' - date_end: '2019-12-31' - #number of lags to analyze - n_lags: 0 - #where to write the sinks and lagged sources as binary pickle files - out_dir: '03a_it_analysis/out/' - #should preprocessing plots be saved - plot: True - - \ No newline at end of file diff --git a/03a_it_analysis/plot_heatmap_config.yaml b/03a_it_analysis/plot_heatmap_config.yaml deleted file mode 100644 index 82eb88d..0000000 --- a/03a_it_analysis/plot_heatmap_config.yaml +++ /dev/null @@ -1,12 +0,0 @@ -plot_heatmap.py: - #matrix to plot - mi_matrix: '03a_it_analysis/out/mi_matrix' - corr_matrix: '03a_it_analysis/out/corr_matrix' - #mask threshold mutual information - mask_threshold_mi: 0.01 - #mask threshold correlation - mask_threshold_corr: 0.05 - #save location - save_location: '03a_it_analysis/out/' - #save? true/false - save: True diff --git a/03a_it_analysis/snakefile_func_perf.smk b/03a_it_analysis/snakefile_func_perf.smk deleted file mode 100644 index ef9f554..0000000 --- a/03a_it_analysis/snakefile_func_perf.smk +++ /dev/null @@ -1,43 +0,0 @@ -import pickle -import os -import sys -sys.path.insert(0, '03a_it_analysis\\src') -sys.path.append('methods_exploration') -import estuary_salinity_functional_performance_func as esfp - -run_id = 'Run_51' -sources = ['discharge_01474500','discharge_01463500'] -sinks = ['saltfront_obs', 'ml_pred','coawst_pred'] -years = ['2001','2002','2003','2004','2005','2006','2007','2008','2009','2010', - '2011','2012','2013','2014','2015', '2016','2017', '2018','2019','2020'] - - -rule create_cleaned_io_file: - output: - os.path.join('03b_model/out',run_id,'ML_COAWST_Results_Input_Cleaned.csv') - run: - esfp.create_cleaned_io_file(run_id) - -rule calc_it_metrics_for_pairs: - input: - os.path.join('03b_model/out',run_id,'ML_COAWST_Results_Input_Cleaned.csv') - output: - os.path.join('03a_it_analysis/out',run_id+'_it_df.csv') - run: - esfp.calc_it_metrics_for_pairs(data_loc = input[0], - sources = sources, - sinks = sinks, - years = years, - write_loc = output[0]) - -rule calc_functional_performance: - input: - os.path.join('03a_it_analysis/out/',run_id+'_it_df.csv') - output: - os.path.join('03a_it_analysis/out', run_id+'_functional_performance_df.csv') - run: - esfp.calc_functional_performance(it_df_loc = input[0], - sources = sources, - sinks = sinks, - years = years, - write_loc = output[0]) \ No newline at end of file diff --git a/03a_it_analysis/src/it_analysis_data_prep.py b/03a_it_analysis/src/it_analysis_data_prep.py deleted file mode 100644 index e59fa81..0000000 --- a/03a_it_analysis/src/it_analysis_data_prep.py +++ /dev/null @@ -1,336 +0,0 @@ -# -*- coding: utf-8 -*- -#Created on Wed Dec 22 20:58:51 2021 -#@author: ggorski - - -import datetime -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import os -import pickle -import yaml -import utils - -#import config -with open("03a_it_analysis/params_config_it_analysis_data_prep.yaml", 'r') as stream: - config = yaml.safe_load(stream)['it_analysis_data_prep.py'] - -### -def select_sources(srcs, date_start, date_end): - '''select the variables you are interested in examining, - srcs must be a list using the exact variable names, - it will return a list of dataframes, each dataframe corresponding to a site - with the requested variables as columns''' - - print('Looking for sources: ', srcs) - - srcs_list = list() - srcs_list_historical = list() - - date_start_pd = pd.to_datetime(date_start) - date_end_pd = pd.to_datetime(date_end) - - - files_to_read = [] - for top,dirs,files in os.walk('02_munge/out/'): - if top == '02_munge/out/D' or top == '02_munge/out/daily_summaries': - files_to_read.extend([os.path.join(top,files) for files in files]) - - - - for file in files_to_read: - #read each file - data = pd.read_csv(file, parse_dates = True, index_col='datetime') - col_list = set(data.columns) - srcs_set = set(srcs) - #if the columns of the dataframe contain any of the entries in srcs - if len(col_list.intersection(srcs_set)) > 1: - #select the columns that contain the entries in srcs - matches = list(col_list.intersection(srcs_set)) - #subset those columns - data_col_select = data.loc[:,matches] - #this relies on a specific file naming structure, it appends the site name to the column header - data_col_select = data_col_select.add_suffix('_'+str(file.split('_')[-1].split('.')[0])) - - #make a copy to store for historical data - data_col_select_historical = data_col_select[:date_end_pd.date()].copy() - #store all the data in _historical for preprocessing - srcs_list_historical.append(data_col_select_historical) - #subset only the date range of interest - data_col_select = data_col_select[date_start_pd.date():date_end_pd.date()] - print(str(file)+' : Sources Found') - print(matches) - srcs_list.append(data_col_select) - else: - print(str(file)+' : No Data') - continue - return srcs_list, srcs_list_historical - -### -def select_sinks(snks, date_start, date_end): - '''This is a filler function, for now it is hard coded and very specific to - the salt front location spreadsheet we have, but in the future it should look - like the select_sources function from above''' - - print('Looking for sinks: ', snks) - - snks_list = list() - snks_list_historical = list() - - date_start_pd = pd.to_datetime(date_start) - date_end_pd = pd.to_datetime(date_end) - - #read in the salt front record - sf_loc = pd.read_csv(os.path.join('03a_it_analysis', 'in', 'saltfront.csv'), parse_dates = True, index_col = 'datetime') - sf_loc.index = sf_loc.index.date - sf_loc.index = pd.to_datetime(sf_loc.index) - #make a copy for historical - sf_loc_historical = sf_loc[:date_end_pd.date()].copy() - #save it to a list of _historical - snks_list_historical.append(sf_loc_historical) - snks_list_historical.append(sf_loc_historical) - #trim sf_loc to the dates of interest - sf_loc = sf_loc[date_start_pd.date():date_end_pd.date()] - - #we'll make snks_list a list of df here they are both 2019, but these could be different model runs - snks_list.append(sf_loc) - snks_list.append(sf_loc) - - #add a suffix to one of the snks_list entries so we can tell the difference - #snks_list[0] = snks_list[0].add_suffix('_Model_A') - #snks_list_historical[0] = snks_list_historical[0].add_suffix('_Model_A') - #noise = np.random.gamma(8, 2, len(sf_loc)) - #snks_list[0].iloc[:,0] = snks_list[0].iloc[:,0].add(noise) - #snks_list[0].iloc[:,1] = snks_list[0].iloc[:,1].add(noise) - return snks_list, snks_list_historical - -### -def save_sources_sinks(srcs_list, snks_list, out_dir): - #create out directory if it doesn't already exist - os.makedirs(out_dir, exist_ok = True) - #write snks_list to file - snks_file = open(out_dir+'snks_proc', "wb") - pickle.dump(snks_list, snks_file) - snks_file.close() - #write srcs_list_lagged to file - srcs_file = open(out_dir+'srcs_proc_lagged', "wb") - pickle.dump(srcs_list, srcs_file) - srcs_file.close() - print('sinks and sources saved to file') - - -### -def lag_sources(n_lags, srcs_list): - '''Takes in the list of sources called srcs_list in which each list item is a - dataframe of site variables, and creates lagged time series for all variables - according to the n_lag variable. Column headers are updted with the n_lag. n_lag refers - to the number of time steps to lag, and it is agnostic of the time resolution of the data. - length of data > n_lag >=0''' - srcs_list_metrics = dict.fromkeys(srcs_list.keys()) - for key in srcs_list: - site_list = srcs_list[key] - #lag the source variables - for s in range(len(site_list)): - for col_name in list(site_list[s].columns): - for lag in range(1, n_lags+1): - #create the lagged time series and name the columns - site_list[s][col_name+'_lag_'+str(lag)] = site_list[s][col_name].shift(lag) - #sort the varaibles for ease of plotting - site_list[s] = site_list[s].sort_index(axis=1) - srcs_list_metrics[key] = site_list - return srcs_list_metrics -### -class pre_proc_func: - def __init__(self): - pass - - def standardize(self, sr): - standardized = list() - for i in range(len(sr)): - #standardize - value_z = (sr[i] - np.nanmean(sr))/np.nanstd(sr, ddof = 1) - standardized.append(value_z) - return standardized - - def normalize(self, sr): - normalized = list() - for i in range(len(sr)): - #normalize - value_z = (sr[i]-np.nanmin(sr))/(np.nanmax(sr)-np.nanmin(sr)) - normalized.append(value_z) - return normalized - - def remove_seasonal_signal(self, sr, sr_historical): - #calculate doy for sr - sr_doy = sr.index.strftime('%j') - #convert sr_historical to df - sr_historical_df = sr_historical.to_frame().copy() - #calculate doy - sr_historical_df['doy'] = list(sr_historical.index.strftime('%j')) - #calculate the doy means - doy_means = sr_historical_df.groupby('doy').mean() - #convert the index (doy) to int64 - doy_means.index = doy_means.index.astype('int64') - - seasonal_removed = list() - for i in range(len(sr)): - doy = int(sr_doy[i]) - doy_mean = doy_means.loc[doy] - value = sr.iloc[i]-doy_mean[0] - seasonal_removed.append(value) - return seasonal_removed - -### -def apply_preprocessing_functions(var_list, var_list_historical, source_sink, out_dir, plot): - '''Apply preprocessing functions to each variable. The preprocessing functions - and the order that they are applied in are stored in the it_analysis_preprocess_config.yaml - file. For each step in the process it saves a histogram of that data to the out_dir. - The available preprocessing functions are of the class pre_proc_func and can be custom - designed within the it_analysis_preprocess.py file. This function (apply_preprocessing_functions) has - inputs and outputs that are structurally identical. Input is a list of dataframes of 'raw' data - and output is a list of dataframes of processed data''' - - #import preprocessing steps from config - preprocess_config = config['preprocess_steps'] - - #get the functions of class pre_proc_func - ppf = globals()['pre_proc_func']() - - #make an ouptut directory for the histograms - os.makedirs(out_dir+'preprocess_plots', exist_ok=True) - - #make empty dictionary to store processed data, one entry for each metric - processed_dict = dict.fromkeys(preprocess_config.keys()) - - #for each metric - for n_metric, metric in enumerate(preprocess_config.keys()): - #print(n_metric, metric) - - #select the steps for the sources and sinks - if source_sink == 'sources': - pre_process_steps = preprocess_config[metric]['sources'] - else: - pre_process_steps = preprocess_config[metric]['sinks'] - - #make new empty list of processed source data - data_proc_list = [] - #for each entry in the list, likely each site - for site_num, site_data in enumerate(var_list): - #print(site_num, site_data) - #create a new data frame with the same structure as the raw data - var_proc_df = pd.DataFrame().reindex_like(var_list[site_num]) - #an empty list to be populated with source/sink variables from that site - var_at_site = [] - #for each variable in the config file - for i in list(pre_process_steps.keys()): - #for each column name - for j in var_proc_df.columns: - #check if the config file name is contained within the column name - if(i in j): - #if it is then put it in var_at_site - var_at_site.append(i) - #for each preprocessing key - for pp_key in var_at_site: - print(pp_key) - cols = list(var_proc_df.columns) - #ucn is the unique column name - #find the ucn that contains the pp_key, should only be one per var_list[site_num] - ucn = [string for string in cols if pp_key in string][0] - - if plot: - #if the preprocess step is set to 'none' create a histogram of the raw data - fig, (ax1, ax2) = plt.subplots(2) - ax1.plot(var_list[site_num].index, var_list[site_num][ucn]) - ax1.set_ylabel(ucn) - ax2.hist(var_list[site_num][ucn]) - ax2.set_ylabel('Count') - plt.suptitle(ucn) - ax1.set_title('raw') - fig.savefig(out_dir+'preprocess_plots/' +ucn + '_raw.png', bbox_inches = 'tight') - #plt.show() - plt.close() - - if pre_process_steps[pp_key][0] == 'none': - #store the variable's data - raw_data = var_list[site_num][ucn].copy() - var_proc_df[ucn] = raw_data - print(ucn+'-- nothing to do here') - else: - #if there are preprocessing steps to do - #store the data in temp data - temp_data = var_list[site_num][ucn].copy() - temp_data_historical = var_list_historical[site_num][ucn].copy() - - #for each preprocessing step for that variable - fname = os.path.join(out_dir, 'preprocess_plots', ucn) - for count,value in enumerate(pre_process_steps[pp_key]): - #apply the function - func = getattr(ppf,value) - - if value == 'remove_seasonal_signal': - #print('got the right function') - temp_data = func(temp_data, temp_data_historical) - else: - temp_data = func(temp_data) - - if plot: - #create histogram - fig, (ax1, ax2) = plt.subplots(2) - ax1.plot(var_list[site_num].index, temp_data) - ax1.set_ylabel(ucn) - ax2.hist(temp_data) - ax2.set_ylabel('Count') - #ax1.set_title(ucn + '_' + value + '_step ' + str(count+1)+ '/'+ str(len(pre_process_steps[pp_key]))) - plt.suptitle(ucn) - if count==0: - pltsubt = value - else: - pltsubt = pltsubt+', '+ value - ax1.set_title(pltsubt) - fname = fname+'_'+ value - fig.savefig(fname+'.png', bbox_inches = 'tight') - #plt.show() - plt.close() - - #store the variable's data - var_proc_df[ucn] = temp_data - print(ucn+"-- apply: "+value) - data_proc_list.append(var_proc_df) - processed_dict[metric] = data_proc_list - return(processed_dict) - -### -def main(): - #select the sources - srcs = config['srcs'] - #select the sinks - snks = config['snks'] - #start date for analysis - date_start = config['date_start'] - #end date for analysis - date_end = config['date_end'] - #should preprocessing plots be saved - plot = config['plot'] - - #number of lag days to consider for sources - n_lags = config['n_lags'] - #generate the sources data - srcs_list, srcs_list_historical = select_sources(srcs, date_start, date_end) - #generate the sinks data - snks_list, snks_list_historical = select_sinks(snks, date_start, date_end) - #output file - out_dir = config['out_dir'] - #process the sources - srcs_proc = apply_preprocessing_functions(srcs_list, srcs_list_historical, 'sources', out_dir, plot) - #process the sinks - snks_proc = apply_preprocessing_functions(snks_list, snks_list_historical, 'sinks', out_dir, plot) - - #lag the sources - srcs_list_lagged = lag_sources(n_lags, srcs_proc) - out_dir = config['out_dir'] - save_sources_sinks(srcs_list_lagged, snks_proc, out_dir) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/03a_it_analysis/src/make_heatmap_matrix.py b/03a_it_analysis/src/make_heatmap_matrix.py deleted file mode 100644 index 1339861..0000000 --- a/03a_it_analysis/src/make_heatmap_matrix.py +++ /dev/null @@ -1,114 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Dec 23 15:40:35 2021 - -@author: ggorski -""" -import numpy as np -import pandas as pd -import pickle -import TEpython_ParallelNAN2 -#import it_functions -import yaml - - -### -def create_correlation_matrix(srcs_lagged_pickle, snks_list_pickle, out_dir): - '''Takes in the lagged sources and calculates the correlation between them and the - sinks. Pearson correlation is used''' - with open(srcs_lagged_pickle, 'rb') as src: - srcs_list_lagged = pickle.load(src) - with open(snks_list_pickle, 'rb') as snk: - snks_list = pickle.load(snk) - - #select data preprocessed for correlation - srcs_list_lagged_corr = srcs_list_lagged['correlation'] - snks_list_corr = snks_list['correlation'] - - corrs = pd.DataFrame() - for sc in range(len(srcs_list_lagged_corr)): - col_snks = pd.DataFrame() - for sk in range(len(snks_list_corr)): - sc_sk_corr = srcs_list_lagged_corr[sc].apply(lambda s: snks_list_corr[sk].corrwith(s)) - col_snks = col_snks.append(sc_sk_corr) - corrs[list(col_snks.columns)] = col_snks - - #save correlation matrix - corr_matrix_save = open(out_dir+'corr_matrix', "wb") - pickle.dump(corrs, corr_matrix_save) - corr_matrix_save.close() - - -### -def create_mutual_information_matrix(srcs_lagged_pickle, snks_list_pickle ,out_dir): - '''Takes in the lagged sources and calculates the mutual information between them and the - sinks. mutual information is used''' - bins = [11,11,11] - with open(srcs_lagged_pickle, 'rb') as src: - srcs_list_lagged = pickle.load(src) - with open(snks_list_pickle, 'rb') as snk: - snks_list = pickle.load(snk) - - #select data preprocessed for mutual information - srcs_list_lagged_mi = srcs_list_lagged['mutual_information'] - snks_list_mi = snks_list['mutual_information'] - - a_list = [] - - for sc in range(len(srcs_list_lagged_mi)): - a = srcs_list_lagged_mi[sc] - - dfs = pd.DataFrame() - for sk in range(len(snks_list_mi)): - - b = snks_list_mi[sk] - MI_array = np.zeros((a.shape[1], b.shape[1])) - - - for i,src_name in enumerate(a): - - for j,snk_name in enumerate(b): - temp_src = a[src_name] - temp_snk = b[snk_name] - paired = temp_src.to_frame().join(temp_snk).to_numpy() - MI, n = TEpython_ParallelNAN2.mutinfo_new(paired, nbins = bins) - #MI = it_functions.calcMI(paired, nbins = bins[0]) - MI_array[i,j] = MI - #print(str(i)+' | '+str(j)+' | '+src_name+' | '+snk_name+'| MI = '+str(MI)) - mat = pd.DataFrame(MI_array.T, columns = a.columns) - mat = mat.set_index(b.columns) - - dfs = dfs.append(mat) - a_list.append(dfs) - - mi_mat = pd.concat(a_list, axis = 1) - - #save mutual information matrix - mi_matrix_save = open(out_dir+'mi_matrix', "wb") - pickle.dump(mi_mat, mi_matrix_save) - mi_matrix_save.close() - - -### -def main(): - # import config - with open("03a_it_analysis/make_heatmap_matrix_config.yaml", 'r') as stream: - config = yaml.safe_load(stream)['make_heatmap_matrix.py'] - - #select the sources - srcs_lagged_pickle = config['srcs_lagged_pickle'] - #select the sinks - snks_list_pickle = config['snks_list_pickle'] - #select the out_directory - out_dir = config['out_dir'] - - #create correlation matrix - create_correlation_matrix(srcs_lagged_pickle, snks_list_pickle, out_dir) - #create mutual information matrix - create_mutual_information_matrix(srcs_lagged_pickle, snks_list_pickle, out_dir) - - - - -if __name__ == '__main__': - main() diff --git a/03a_it_analysis/src/plot_heatmap.py b/03a_it_analysis/src/plot_heatmap.py deleted file mode 100644 index 5352115..0000000 --- a/03a_it_analysis/src/plot_heatmap.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Dec 23 16:20:06 2021 - -@author: ggorski -""" -import matplotlib.pyplot as plt -import numpy as np -import pickle -import seaborn as sns -import yaml - -def plot_heatmap(matrix, mask_threshold, metric, date_start, date_end, save_location, vmin_val, vmax_val, save=False): - '''Takes in a correlation matrix and plots a heat map of correlations, - mask_threshold is a threshold where if abs(correlation) < threshold those - squares of the heatmap are masked out to highlight stronger correlation. save is - true/false whether it will save the plot to file, default false''' - - with open(matrix, 'rb') as mat: - matrix_plot = pickle.load(mat) - - mask = abs(matrix_plot) < mask_threshold - plt.figure(figsize = (5,10)) - cbar_kws = {"shrink":0.85, - 'extendfrac':0.1, - 'label': metric - } - heatmap = sns.heatmap(matrix_plot.transpose(), vmin = vmin_val, vmax = vmax_val, cbar = True, cmap='coolwarm', - annot = True, cbar_kws = cbar_kws, linewidth = 1, mask = mask.transpose()) - plt.xticks(rotation=45,rotation_mode='anchor',ha = 'right') - plt.yticks(rotation = 'horizontal') - heatmap.set_title('|'+metric+'| > '+str(mask_threshold)+'\n '+date_start+' - '+date_end, fontdict={'fontsize':14}, pad=12) - if save: - plt.savefig(save_location, bbox_inches = 'tight') - else: - plt.show() - -def main(): - # import config from data prep step - with open("03a_it_analysis/it_analysis_data_prep_config.yaml", 'r') as stream: - config_data_prep = yaml.safe_load(stream)['it_analysis_data_prep.py'] - - #import config file - with open("03a_it_analysis/plot_heatmap_config.yaml", 'r') as stream: - config = yaml.safe_load(stream)['plot_heatmap.py'] - - date_start = config_data_prep['date_start'] - date_end = config_data_prep['date_end'] - save = config['save'] - - #make and save mutual information heatmap - save_location = config['save_location']+'mutual_information.png' - mi_matrix = config['mi_matrix'] - mask_threshold_mi = config['mask_threshold_mi'] - plot_heatmap(mi_matrix, mask_threshold_mi, 'mutual_information', date_start, date_end, save_location, 0,1,save) - - #make and save correlation heat map - save_location = config['save_location']+'correlation.png' - corr_matrix = config['corr_matrix'] - mask_threshold_corr = config['mask_threshold_corr'] - plot_heatmap(corr_matrix, mask_threshold_corr, 'correlation', date_start, date_end, save_location, -1,1,save) - - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/03b_model/src/river-dl b/03b_model/src/river-dl deleted file mode 160000 index b25a45e..0000000 --- a/03b_model/src/river-dl +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b25a45ee6fbd2eb5f9b08de939c5f7ab2878fa7b diff --git a/04_analysis/src/XAI_functions.py b/04_analysis/src/XAI_functions.py new file mode 100644 index 0000000..4c2026e --- /dev/null +++ b/04_analysis/src/XAI_functions.py @@ -0,0 +1,599 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Aug 31 17:53:10 2022 + +@author: ggorski +""" +import os +import sys +sys.path.insert(0, os.path.join('03_model', 'src')) +from LSTMDA_torch import LSTMDA, rmse_masked +from matplotlib import pyplot as plt, dates +import numpy as np +import pandas as pd +import pickle +import seaborn as sns +import torch +import yaml + +#%%Expected gradients +def expected_gradients_lstm(x_data_in, model, n_samples, temporal_focus=None): + ''' + A general function for generating expected gradients from a pytorch model + Parameters + ---------- + x_data_in : torch.Tensor + x variables prepared for model + model : pytorch model + pre trained pytorch model + n_samples : int + number of samples to draw for calculating expcted gradient + temporal_focus : None (default) or int + If the expected gradient should be calculated with respect to a single + day of year then that day of year should be input, otherwise none for every day. + The default is None. + + Returns + ------- + numpy.ndarray of the same shape as x_data_in + + ''' + device = 'cpu' + n_series = x_data_in.shape[0] + #num_vars = x_set.shape[2] + #seq_len = x_set.shape[1] + + for k in range(n_samples): + # SAMPLE A SERIES FROM OUR DATA + rand_seq = np.random.choice(n_series) # rand_time may be more accurate + baseline_x = x_data_in[rand_seq].to(device) + + # SAMPLE A RANDOM SCALE ALONG THE DIFFERENCE + scale = np.random.uniform() + + # SAME IG CALCULATION + x_diff = x_data_in - baseline_x + curr_x = baseline_x + scale*x_diff + if curr_x.requires_grad == False: + curr_x.requires_grad = True + model.zero_grad() + y,_ = model(curr_x) + + # GET GRADIENT + if temporal_focus == None: + gradients = torch.autograd.grad(y[:, :, :], curr_x, torch.ones_like(y[:, :, :])) + else: + gradients = torch.autograd.grad(y[:, temporal_focus, :], curr_x, torch.ones_like(y[:,temporal_focus, :])) + + if k == 0: + expected_gradients = x_diff*gradients[0] * 1/n_samples + else: + expected_gradients = expected_gradients + ((x_diff*gradients[0]) * 1/n_samples) + + return(expected_gradients.detach().cpu().numpy()) + +def calc_EGs_for_model(config_loc, n_samples, write_loc): + ''' + Uses the parameters from the model config file to prepare data + and models for expected_gradients_lstm function. Then read in the + raw x variables and the model results and merge them into a csv + and write that csv to a directory. + + Parameters + ---------- + config_loc : str + location of the model config + n_samples : int + number of samples to draw for calculating expcted gradient + write_loc : str + location to write the final csv to + + Returns + ------- + nothing + + ''' + + + with open(config_loc, 'r') as stream: + config = yaml.safe_load(stream) + + out_dir = config['out_dir'] + run_id = config['run_id'] + train_start_date = config['train_start_date'] + hidden_units = config['hidden_units'] + recur_dropout = config['recur_dropout'] + dropout = config['dropout'] + replicates = config['replicates'] + + prepped_model_io_data_file = os.path.join(out_dir,'prepped_model_io_data') + + with open(prepped_model_io_data_file, 'rb') as f: + prepped_model_io_data = pickle.load(f) + + x_data_in = prepped_model_io_data['trainval_features'] + + n_batch, seq_len, n_feat = prepped_model_io_data['train_features'].shape + model = LSTMDA(n_feat, hidden_units, recur_dropout, dropout) + + egs_all_reps_list = list() + reps = range(replicates) + for rep in reps: + #load the model weights + model.load_state_dict(torch.load(os.path.join(out_dir,run_id, '0'+str(rep),'weights.pt'))) + print('Calculating expected gradients for model in ',os.path.join(out_dir, run_id, '0'+str(rep))) + + egs_np_rep = expected_gradients_lstm(x_data_in, model, n_samples, temporal_focus=None) + + egs_all_reps_list.append(egs_np_rep) + + #stack the resulting list into a numpy array + egs_all_reps_np = np.stack(egs_all_reps_list, axis = 0) + #take the mean of all reps + egs_np = np.mean(egs_all_reps_np, axis = 0) + + dates = pd.date_range(start = train_start_date, periods = x_data_in.shape[0]*x_data_in.shape[1], freq = 'D') + EGs_overall_full = np.resize(egs_np, (egs_np.shape[0]*egs_np.shape[1], x_data_in.shape[2])) + + #read in model results and raw x_variables + model_results = pd.read_csv(os.path.join('03_model/out',run_id,'ML_COAWST_Results_Input_Cleaned.csv'), parse_dates = True, index_col = 0) + wl = pd.read_csv('02_munge/out/daily_summaries/noaa_nos_8557380.csv', index_col='datetime', parse_dates=True) + met = pd.read_csv('02_munge/out/D/noaa_nerrs_delsjmet.csv', index_col = 'datetime', parse_dates = True) + t_q = pd.read_csv('02_munge/out/D/usgs_nwis_01463500.csv', index_col = 'datetime', parse_dates = True) + s_q = pd.read_csv('02_munge/out/D/usgs_nwis_01474500.csv', index_col = 'datetime', parse_dates = True) + + #join them together + x_var_df = wl.drop(columns = 'air_pressure').join(met.drop(columns = ['precipitation', 'wind_speed_direction']), ) + x_var_df = x_var_df.join(t_q.add_suffix('_01463500')['discharge_01463500']) + x_var_df = x_var_df.join(s_q.add_suffix('_01474500')['discharge_01474500']) + + mod_x_var = model_results.join(x_var_df.add_suffix('_obs')) + #add the expected gradients + eg_df = pd.DataFrame(EGs_overall_full) + eg_df = eg_df.set_index(dates) + eg_df.columns = ['Discharge_Trenton','Discharge_Schuylkill','Water_Level_Range','Water_Level_Max','Water_Level_Resid', + 'Water_Level_Filtered','Air_Pressure','Temperature','Wind_Direction','Wind_Speed'] + eg_vars_df = eg_df.join(mod_x_var) + + eg_vars_df.to_csv(write_loc) + +def plot_EGs_single_year(eg_df_loc, year, save_loc, ext): + ''' + save a plot of the expected gradients plotted in time series + with the observed and modeled salt front and discharge + + Parameters + ---------- + eg_df_loc : str + location of the expected gradients csv + year : str + year to plot, plotting a single calendar year at a time + save_loc : str + where should the plot be saved as a pdf + ext : str + what extension to the save the plot as usually pdf or jpg + + Returns + ------- + None. + + ''' + egs_df = pd.read_csv(eg_df_loc, parse_dates = True, index_col = 0) + + eg_ann = egs_df.loc[year] + labels = ['Trenton','Schuylkill','Range','Max','Residual','Filtered','Air pressure','Temperature','Wind direction','Wind speed'] + + #colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + colors = sns.color_palette('hls',12) + sns.set(font_scale=1) + sns.set_style('ticks', {'axes.linewidth': 0.5}) + plt.rcParams['xtick.major.size'] = 3 + plt.rcParams['xtick.major.width'] = 1 + plt.rcParams['ytick.major.size'] = 3 + plt.rcParams['ytick.major.width'] = 1 + + plt.rcParams['xtick.bottom'] = True + plt.rcParams['ytick.left'] = True + plt.rcParams['xtick.top'] = True + plt.rcParams['ytick.right'] = True + plt.rcParams['xtick.direction'] = 'in' + plt.rcParams['ytick.direction'] = 'in' + plt.rcParams['legend.frameon'] = False + + fig, ax = plt.subplots(4,1,figsize=(8,10),constrained_layout=True) + + #Discharge and salt front record + ax[0].plot(eg_ann.index, eg_ann.saltfront_obs, label = 'Salt Front Observed', color = 'lightgray', marker = 'o', markeredgecolor = 'black', alpha = 0.4, markersize = 6) + ax[0].plot(eg_ann.index, eg_ann.ml_pred, label = 'Salt Front Predicted', color = 'red') + ax[0].legend(loc='upper left', bbox_to_anchor=(1.1, 0.75)) + ax[0].set_title('Salt Front Location') + ax[0].set_ylabel('Salt front location (RM)') + ax[0].set_ylim([20,95]) + ax01=ax[0].twinx() + ax01.plot(eg_ann.index, eg_ann.discharge_01463500_obs, color = 'blue', label = 'Trenton Discharge') + ax01.plot(eg_ann.index, eg_ann.discharge_01474500_obs, color = 'green', label = 'Schuylkill Discharge') + ax01.tick_params(axis='y', color='blue', labelcolor='blue') + ax01.set_ylim([0,100000]) + ax01.legend(loc = 'lower left', bbox_to_anchor=(1.1, 0.15)) + + + #Discharge + for i in range(0,2): + ax[1].plot(eg_ann.index,eg_ann.iloc[:, i], label = labels[i], color = colors[i]) + ax[1].plot(eg_ann.index,np.repeat(0,len(eg_ann.index)),'--',alpha=.3) + ax[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), title = 'Discharge') + ax[1].set_ylim(-3,3) + ax[1].tick_params(direction="in") + + #Water level + for i in range(2,6): + ax[2].plot(eg_ann.index,eg_ann.iloc[:, i], label = labels[i], color = colors[i]) + ax[2].plot(eg_ann.index,np.repeat(0,len(eg_ann.index)),'--',alpha=.3) + ax[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), title = 'Water level') + ax[2].set_ylim(-3,3) + ax[2].set_ylabel('Expected Gradients') + + #Meteorological + for i in range(6,10): + ax[3].plot(eg_ann.index,eg_ann.iloc[:, i], label = labels[i], color = colors[i]) + ax[3].plot(eg_ann.index,np.repeat(0,len(eg_ann.index)),'--',alpha=.3) + ax[3].legend(loc='center left', bbox_to_anchor=(1, 0.5), title = 'Meteorological') + ax[3].set_ylim(-3,3) + + #remove x axis labels for all except bottom row + plt.setp(ax[0].get_xticklabels(), visible=False) + plt.setp(ax[1].get_xticklabels(), visible=False) + plt.setp(ax[2].get_xticklabels(), visible=False) + + plt.savefig(save_loc+'_'+year+'.'+ext) + +def calc_Egs_for_all_days(config_loc, rep, n_samples, write_loc): + + ''' + Uses the parameters from the model config file to prepare data + and models for expected_gradients_lstm function with temporal + focus for each day of the year (doy). Saves the expected gradients + merged with model results and raw input variables in a dataframe + as a pickle file in write_loc + + Parameters + ---------- + config_loc : str + location of the model config + rep : int + replicate number to evalute EGs for (0-4 for salt front model) + n_samples : int + number of samples to draw for calculating expcted gradient + write_loc : str + location to write the final csv to + + Returns + ------- + nothing + + ''' + + doy = range(365) + doy_names = [str(x) for x in doy] + + with open(config_loc, 'r') as stream: + config = yaml.safe_load(stream) + + out_dir = config['out_dir'] + run_id = config['run_id'] + train_start_date = config['train_start_date'] + hidden_units = config['hidden_units'] + recur_dropout = config['recur_dropout'] + dropout = config['dropout'] + + prepped_model_io_data_file = os.path.join(out_dir,'prepped_model_io_data') + + with open(prepped_model_io_data_file, 'rb') as f: + prepped_model_io_data = pickle.load(f) + + x_data_in = prepped_model_io_data['trainval_features'] + + n_batch, seq_len, n_feat = prepped_model_io_data['train_features'].shape + model = LSTMDA(n_feat, hidden_units, recur_dropout, dropout) + + model.load_state_dict(torch.load(os.path.join(out_dir,run_id, '0'+str(rep),'weights.pt'))) # ensure that dropout layers are active + + EGs_all_days = {} + for i in range(len(doy)): + print('Calculating expected gradients for '+str(doy[i])) + EGs_all_days[doy_names[i]] = expected_gradients_lstm(x_data_in, model, n_samples, temporal_focus=doy[i]) + + eg_df_list = {} + EGs_temp_full = {} + #read in model results and raw x_variables + model_results = pd.read_csv(os.path.join('03_model/out',run_id,'ML_COAWST_Results_Input_Cleaned.csv'), parse_dates = True, index_col = 0) + t_q = pd.read_csv('02_munge/out/D/usgs_nwis_01463500.csv', index_col = 'datetime', parse_dates = True) + s_q = pd.read_csv('02_munge/out/D/usgs_nwis_01474500.csv', index_col = 'datetime', parse_dates = True) + + for i, day in enumerate(EGs_all_days.keys()): + dates = pd.date_range(start = train_start_date, periods = x_data_in.shape[0]*x_data_in.shape[1], freq = 'D') + EGs_temp_full[day] = np.resize(EGs_all_days[day], (EGs_all_days[day].shape[0]*EGs_all_days[day].shape[1], x_data_in.shape[2])) + + eg_temp_df = pd.DataFrame(EGs_temp_full[day]) + eg_temp_df = eg_temp_df.set_index(dates) + + eg_temp_df.columns = ['Discharge_Trenton','Discharge_Schuylkill','Water_Level_Range','Water_Level_Max','Water_Level_Resid', + 'Water_Level_Filtered','Air_Pressure','Temperature','Wind_Direction','Wind_Speed'] + eg_temp_df['Salt_Front_Location'] = model_results['saltfront_obs'] + eg_temp_df['Salt_Front_Location_Preds'] = model_results['ml_pred'] + eg_temp_df['Trenton_Discharge'] = t_q.add_suffix('_01463500').discharge_01463500 + eg_temp_df['Schuylkill_Discharge'] = s_q.add_sffix('_01474500').discharge_01474500 + + eg_df_list[day] = eg_temp_df.copy() + + with open(os.path.join(write_loc), 'wb') as handle: + pickle.dump(eg_df_list, handle) + + + + + +def EGs_lookback_df(eg_all_days_loc, t): + ''' + Calculates the number of days it takes to accumulate t of today's gradient + for each day of the year, where t is a threshold + Parameters + ---------- + eg_all_days_loc : str + location of the dataframe of all EGs for all days, created + by the calc_Egs_for_all_days function + t : float + between 0-1, this function calculates the amount of time it takes to accumulate the + t fraction of the total gradient for each day (usually set to 0.9 or 0.99) + + Returns + ------- + thresh_df : pandas dataframe with row for each doy and columns for each input variable, + the number of days to accumulate gradient for each input variable are the values + + ''' + with open(eg_all_days_loc,'rb') as f: + eg_df_dict = pickle.load(f) + + doy = range(365) + doy_names = [str(x) for x in range(365)] + all_days_thresh = {} + for i, doy in enumerate(doy_names): + doy_avg = np.abs(eg_df_dict[doy]).groupby([eg_df_dict[doy].index.month, eg_df_dict[doy].index.day]).mean() + doy_avg['date'] = pd.date_range(start = '2000-01-01', end = '2000-12-31', freq = 'D') + doy_avg = doy_avg.set_index('date') + #doy_avg['doy'] = doy_avg.index.strftime('%j') + doy_avg = doy_avg.drop(columns = ['Trenton_Discharge','Schuylkill_Discharge', 'Salt_Front_Location', 'Salt_Front_Location_Preds']) + doy_cumsum = doy_avg.cumsum() + thresh = doy_cumsum.iloc[i,:]*(1-t) + thresh_dict = {} + for j in range(len(thresh)): + thresh_dict[doy_cumsum.columns[j]] = int(doy_cumsum.index[i].strftime('%j'))- int(doy_cumsum[doy_cumsum.iloc[:,j] >= thresh[j]].index[0].strftime('%j')) + + all_days_thresh[doy] = thresh_dict + + thresh_df = pd.DataFrame(data = all_days_thresh).transpose() + thresh_df['date'] = pd.date_range(start = '2000-01-01', end = '2000-12-30', freq = 'D') + thresh_df = thresh_df.set_index('date') + + return thresh_df + + + +def plot_EGs_lookback(eg_all_days_loc, t, save_loc): + ''' + Take the lookback csv created in EGs_lookback_df and + generate and save a plot + + Parameters + ---------- + eg_all_days_loc : str + location of the dataframe of all EGs for all days, created + by the calc_Egs_for_all_days function + t : float + between 0-1, this function calculates the amount of time it takes to accumulate the + t fraction of the total gradient for each day (usually set to 0.9 or 0.99) + save_loc : save location for the plot + + Returns + ------- + None. + + ''' + + thresh_df = EGs_lookback_df(eg_all_days_loc, t) + + thresh_df = thresh_df[thresh_df.index > '2000-03-01'] + t_pct = t*100 + sns.set(font_scale=0.95) + sns.set_style('ticks', {'axes.linewidth': 0.5}) + plt.rcParams['xtick.major.size'] = 3 + plt.rcParams['xtick.major.width'] = 1 + plt.rcParams['ytick.major.size'] = 3 + plt.rcParams['ytick.major.width'] = 1 + + plt.rcParams['xtick.bottom'] = True + plt.rcParams['ytick.left'] = True + plt.rcParams['xtick.top'] = True + plt.rcParams['ytick.right'] = True + plt.rcParams['legend.fontsize'] = 10 + plt.rcParams['legend.handlelength'] = 2 + dateform = dates.DateFormatter('%b-%d') + + colors = sns.color_palette('hls',12) + #sns.set_style('white') + fig= plt.figure(figsize=(10,12))#,constrained_layout=True, sharey = True) + fig.suptitle(f"How many days does it take to accumulate {t_pct}% of the expected gradient for each day?") + + #Discharge + ax0 = plt.subplot(311) + for i in range(0,2): + ax0.plot(thresh_df.iloc[:, i], label = thresh_df.columns[i], color = colors[i]) + ax0.set_title('Discharge') + ax0.legend() + ax0.xaxis.set_major_formatter(dateform) + ax0.xaxis.set_major_locator(dates.MonthLocator(interval = 1)) + ax0.tick_params(direction='in') + + #Water level + ax1 = plt.subplot(312, sharey = ax0) + for i in range(2,6): + ax1.plot(thresh_df.iloc[:, i], label = thresh_df.columns[i], color = colors[i]) + ax1.set_title('Water Level') + ax1.legend() + ax1.set_ylabel(f'Number of days to accumulate\n{t_pct}% of expected gradient') + ax1.xaxis.set_major_formatter(dateform) + ax1.xaxis.set_major_locator(dates.MonthLocator(interval = 1)) + ax1.tick_params(direction="in") + + #Meteorological + ax2 = plt.subplot(313, sharey = ax0) + for i in range(6,10): + ax2.plot(thresh_df.iloc[:, i], label = thresh_df.columns[i], color = colors[i]) + ax2.set_title('Meteorological') + ax2.legend() + ax2.xaxis.set_major_formatter(dateform) + ax2.xaxis.set_major_locator(dates.MonthLocator(interval = 1)) + ax2.tick_params(direction="in") + plt.savefig(save_loc) + +def calc_permutation_feature_importance(model, x_data_in, x_vars, y_obs): + ''' + Calculate permutation feature importance for all variables in x_vars. + Feature importance is calculated as the difference in RMSE when the variable + values are replaced with a random distribution within the range of the variable values + Parameters + ---------- + model : pytorch model + pre trained pytorch model + x_data_in : torch.Tensor + x variables prepared for model + x_vars : list + list of names of x_variables to make calculations for + y_obs : torch.tensor + y observations in model ready form + + Returns + ------- + np.array + dimensions len(x_vars) with each entry a difference in RMSE + + ''' + original_y_hat, _ = model(x_data_in) + original_y_hat = original_y_hat.detach() + rmse_original = rmse_masked(y_obs,original_y_hat) + fi_ls=[] + for var in range(len(x_vars)): + x_hypothesis = x_data_in.detach().clone() + var_range = torch.quantile(x_hypothesis[:,:,var].flatten(),torch.tensor([.1,.9])) + #Make random distribution within the range of the target variable + x_hypothesis[:, :, var] = (var_range[0]-var_range[1])*torch.rand_like(x_hypothesis[:, :, var])+var_range[1] + y_hypothesis,_ = model(x_hypothesis) + y_hypothesis = y_hypothesis.detach() + rmse_hypothesis = rmse_masked(y_obs,y_hypothesis) + delta_rmse = rmse_hypothesis-rmse_original + fi_ls.append(delta_rmse) + return np.array(fi_ls) + +def calc_feature_importance_reps(config_loc, reps, write_loc): + ''' + A wrapper function which uses the parameters from the model + config file to prepare data and models for + calc_permutation_feature_importance function. Then make the calculations + and take the mean and standard deviation of the replicates and + collect them in a csv and save it to write_loc + + Parameters + ---------- + config_loc : str + location of the model config + reps : list + list of strings for estuary salinity ['00','01','02','03','04'] + write_loc : str + location to write the final csv to + + Returns + ------- + nothing + + ''' + + with open(config_loc, 'r') as stream: + config = yaml.safe_load(stream) + + out_dir = config['out_dir'] + run_id = config['run_id'] + hidden_units = config['hidden_units'] + recur_dropout = config['recur_dropout'] + dropout = config['dropout'] + x_vars = config['inputs'] + + prepped_model_io_data_file = os.path.join(out_dir,'prepped_model_io_data') + + with open(prepped_model_io_data_file, 'rb') as f: + prepped_model_io_data = pickle.load(f) + + x_data_in = prepped_model_io_data['trainval_features'] + + n_batch, seq_len, n_feat = prepped_model_io_data['train_features'].shape + + y_obs = prepped_model_io_data['trainval_targets'] + + replicate_feat_imp = np.empty([10,len(reps)]) + + for i,rep in enumerate(reps): + print('Calculating feature importance for replicate ', rep) + model = LSTMDA(n_feat, hidden_units, recur_dropout, dropout) + model.load_state_dict(torch.load(os.path.join(out_dir,run_id, rep,'weights.pt'))) # ensure that dropout layers are active + + y_pred, _ = model(prepped_model_io_data['trainval_features']) + + temp_feat_imp = calc_permutation_feature_importance(model, x_data_in, x_vars, y_obs) + + replicate_feat_imp[:,i] = temp_feat_imp + + mean_feat_imp = np.mean(replicate_feat_imp, axis = 1) + std_feat_imp = np.std(replicate_feat_imp, axis = 1) + + replicate_feat_imp_df = pd.DataFrame(replicate_feat_imp) + replicate_feat_imp_df['mean_feat_imp'] = mean_feat_imp + replicate_feat_imp_df['std_feat_imp'] = std_feat_imp + replicate_feat_imp_df.index = ['Trenton discharge','Schuylkill discharge','Water level range','Water level max','Water level residual','Water level filtered','Air pressure','Air temperature','Wind direction','Wind speed'] + + replicate_feat_imp_df.to_csv(write_loc) + +def plot_feature_importance(feat_imp_loc, save_loc): + ''' + read in the feature importance csv and save a barplot + + Parameters + ---------- + feat_imp_loc : str + location of the feature importance csv + save_loc : str + save location for the barplot + + Returns + ------- + None. + + ''' + + feat_imp = pd.read_csv(feat_imp_loc, index_col = 0) + + y_pos = np.arange(feat_imp.shape[0]) + ordered_mean_imp = feat_imp.sort_values(by = 'mean_feat_imp').mean_feat_imp + y_labels = feat_imp.sort_values(by = 'mean_feat_imp').index + + fig, ax = plt.subplots() + ax.barh(y_pos, ordered_mean_imp, align = 'center', alpha = 0.5, ecolor = 'black', capsize = 10) + ax.set_xlabel('∆ RMSE') + ax.set_ylabel('Variable') + ax.set_title('Relative feature importance') + ax.set_yticks(y_pos) + ax.set_yticklabels(y_labels) + plt.tight_layout() + plt.savefig(save_loc) + + + diff --git a/03a_it_analysis/src/estuary_salinity_functional_performance_func.py b/04_analysis/src/estuary_salinity_functional_performance_func.py similarity index 74% rename from 03a_it_analysis/src/estuary_salinity_functional_performance_func.py rename to 04_analysis/src/estuary_salinity_functional_performance_func.py index 5d2c00e..c6076a4 100644 --- a/03a_it_analysis/src/estuary_salinity_functional_performance_func.py +++ b/04_analysis/src/estuary_salinity_functional_performance_func.py @@ -27,7 +27,7 @@ def find_reps(run_id): ''' reps = list() - rootdir = os.path.join('03b_model/out',run_id) + rootdir = os.path.join('03_model/out',run_id) for file in os.listdir(rootdir): d = os.path.join(rootdir, file) #replicates have to be in a directory with a two character name(e.g., "02") @@ -38,7 +38,7 @@ def find_reps(run_id): def create_cleaned_io_file(run_id): ''' creates a cleaned csv with aggregate results from 5 replicates and merges - with input data and outputs from COAWST model, saves csv to 03b_model/out + with input data and outputs from COAWST model, saves csv to 03_model/out directory Parameters @@ -52,9 +52,9 @@ def create_cleaned_io_file(run_id): ''' reps = find_reps(run_id) - ml_sf = pd.read_csv(os.path.join('03b_model/out',run_id,reps[0],'ModelResults.csv'), parse_dates = True, index_col = 'Unnamed: 0') + ml_sf = pd.read_csv(os.path.join('03_model/out',run_id,reps[0],'ModelResults.csv'), parse_dates = True, index_col = 'Unnamed: 0') for i,rep in enumerate(reps): - temp = pd.read_csv(os.path.join('03b_model/out',run_id,rep,'ModelResults.csv'), parse_dates = True, index_col = 'Unnamed: 0') + temp = pd.read_csv(os.path.join('03_model/out',run_id,rep,'ModelResults.csv'), parse_dates = True, index_col = 'Unnamed: 0') ml_sf['saltfront_pred_'+rep] = temp['saltfront_pred'].copy() ml_sf_mean = ml_sf.filter(regex='saltfront_pred_').mean(axis = 1) @@ -63,25 +63,25 @@ def create_cleaned_io_file(run_id): ml_sf = ml_sf[['saltfront_obs','ml_pred','train/val']] # read in observations - inputs = xr.open_zarr(os.path.join('03b_model/out/inputs.zarr'), consolidated=False) + inputs = xr.open_zarr(os.path.join(f'03_model/out/{run_id}/inputs.zarr'), consolidated=False) inputs_df = inputs.to_dataframe() #join ml and inputs ml_sf_inputs = ml_sf.join(inputs_df) ## read in COAWST model output - coawst_2016 = pd.read_csv('03b_model/in/COAWST_model_runs/processed/COAWST_2016_7day.csv', parse_dates = True, index_col = 'date') - coawst_2018 = pd.read_csv('03b_model/in/COAWST_model_runs/processed/COAWST_2018_7day.csv', parse_dates = True, index_col = 'date') - coawst_2019 = pd.read_csv('03b_model/in/COAWST_model_runs/processed/COAWST_2019_7day.csv', parse_dates = True, index_col = 'date') + coawst_2016 = pd.read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2016_7day.csv', parse_dates = True, index_col = 'date') + coawst_2018 = pd.read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2018_7day.csv', parse_dates = True, index_col = 'date') + coawst_2019 = pd.read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2019_7day.csv', parse_dates = True, index_col = 'date') coawst_3_years = pd.concat([coawst_2016, coawst_2018, coawst_2019]) ## fp_data = ml_sf_inputs.join(coawst_3_years, how = 'outer') - fp_data.to_csv(os.path.join('03b_model\\out',run_id,'ML_COAWST_Results_Input_Cleaned.csv')) + fp_data.to_csv(os.path.join('03_model\\out',run_id,'ML_COAWST_Results_Input_Cleaned.csv')) -def calc_it_metrics_for_pairs(data_loc, sources, sinks, years, write_loc): +def calc_it_metrics_for_pairs(data_loc, sources, sinks, years, run_id): ''' Generate pairs of sources and sinks from the list for each year (or range of years) listed, then calculate the transfer entropy (TE), mutual information (MI), and correlation for those pairs at @@ -98,8 +98,8 @@ def calc_it_metrics_for_pairs(data_loc, sources, sinks, years, write_loc): list of column names to use as sinks ['saltfront_obs','ml_pred','coawst_pred'] years : list list of years to analyze, years can either be a single year ('2019') or a span ('2018:2020') - write_loc : str - location to write the results + run_id : str + the model run id Returns ------- @@ -197,10 +197,11 @@ def calc_it_metrics_for_pairs(data_loc, sources, sinks, years, write_loc): temp_df['lag'] = range(0,len(temp_df)) #get TE from t = 3 it_df = pd.concat([it_df,temp_df]) + - it_df.to_csv(write_loc) + it_df.to_csv(os.path.join('04_analysis/out',run_id+'_it_df.csv')) -def calc_functional_performance(it_df_loc, sources, sinks, years, write_loc): +def calc_functional_performance(it_df_loc, sources, sinks, years, run_id): ''' Using the calculations of transfer entropy at various time lags, this function calculates the functional performance for each source/sink/year pairing by @@ -217,8 +218,8 @@ def calc_functional_performance(it_df_loc, sources, sinks, years, write_loc): list of column names to use as sinks ['saltfront_obs','ml_pred','coawst_pred']. Must be the same as found in the it_df dataframe years : list list of years to analyze, years can either be a single year ('2019') or a span ('2018:2020'). Must be the same as found in the it_df dataframe - write_loc : str - location to write the resulting csv to file + run_id : str + the model run id Returns ------- None. @@ -243,8 +244,51 @@ def calc_functional_performance(it_df_loc, sources, sinks, years, write_loc): func_perf_df = func_perf_df.rename(columns={'TE':'Func_Perf'}) - func_perf_df.to_csv(write_loc) + func_perf_df.to_csv(os.path.join('04_analysis/out',run_id+'_functional_performance_df.csv')) + + +def calc_functional_performance_wrapper(run_id, sources, sinks, years, it_df_loc): + ''' + + + Parameters + ---------- + run_id : str + the run number to aggregate. + data_loc : str + location of cleaned data. + sources : list + list of column names to use as sources. Must be the same as found in the it_df dataframe + sinks : list + list of column names to use as sinks ['saltfront_obs','ml_pred','coawst_pred']. Must be the same as found in the it_df dataframe + years : list + list of years to analyze, years can either be a single year ('2019') or a span ('2018:2020'). Must be the same as found in the it_df dataframe + it_df_loc : str + location of the it_df dataframe created by calc_it_metrics_for_pairs + + Returns + ------- + None. + + ''' + print('----------------------------------------') + print('Prepping data...') + print('----------------------------------------') + + create_cleaned_io_file(run_id) + print('----------------------------------------') + print('Calculating pair-wise it metrics...') + print('----------------------------------------') + data_loc = os.path.join('03_model/out/',run_id,'ML_COAWST_Results_Input_Cleaned.csv') + calc_it_metrics_for_pairs(data_loc, sources, sinks, years, run_id) + + print('----------------------------------------') + print('Calculating functional performance...') + print('----------------------------------------') + calc_functional_performance(it_df_loc, sources, sinks, years, run_id) + + diff --git a/methods_exploration/it_functions.py b/04_analysis/src/it_functions.py similarity index 100% rename from methods_exploration/it_functions.py rename to 04_analysis/src/it_functions.py diff --git a/04_analysis/src/results.R b/04_analysis/src/results.R new file mode 100644 index 0000000..32727ab --- /dev/null +++ b/04_analysis/src/results.R @@ -0,0 +1,496 @@ +#===================================================================================# +# NOTES: script for making results figures for manuscript +#-----------------------------------------------------------------------------------# +# Galen Gorski # +# ggorski@usgs.gov # +# 08-10-2022 # +#-----------------------------------------------------------------------------------# +#===================================================================================# + +#===================================================================================# +#####INSTALL PACKAGES##### +pkgLoad <- function( packages = "favourites" ) { + + if( length( packages ) == 1L && packages == "favourites" ) { + packages <- c( "cowplot", "ggplot2", "gridExtra", "hexbin", "hydroGOF", + "lubridate", "ModelMetrics", "scales", "tidyverse", + "patchwork", "viridis" + ) + } + + packagecheck <- match( packages, utils::installed.packages()[,1] ) + + packagestoinstall <- packages[ is.na( packagecheck ) ] + + if( length( packagestoinstall ) > 0L ) { + utils::install.packages( packagestoinstall, + repos = "http://cran.csiro.au" + ) + } else { + print( "All requested packages already installed" ) + } + + for( package in packages ) { + suppressPackageStartupMessages( + library( package, character.only = TRUE, quietly = TRUE ) + ) + } + +} + +pkgLoad() + + +# install.packages('cowplot') +library(cowplot) +# install.packages('ggplot2') +library(ggplot2) +# install.packages('gridExtra') +library(gridExtra) +# install.packages('hexbin') +library(hexbin) +# install.packages('hydroGOF') +library(hydroGOF) +# intstall.packages('lubridate') +library(lubridate) +# install.packages('ModelMetrics') +library(ModelMetrics) +#install.packages("scales") +library(scales) +# install.packages('tidyverse') +library(tidyverse) +# install.packages('patchwork') +library(patchwork) +#install.packages("viridis") +library(viridis) + +#load plotting functions +source('04_analysis/src/results_functions.R') +dir.create('04_analysis/fig', showWarnings = FALSE) +##### +#===================================================================================# + +#===================================================================================# + +#run id +run <- 'Run_Manuscript_Results' +#number and ids of reps +reps <- c( '00', '01', '02', '03', '04', '05', '06', '07', '08', '09') +#aggregate results from replicates +model_results_df <- aggr_reps(run, reps) +#subset results into training and validation +train_set <- model_results_df[model_results_df$train_val == 'Training',] +val_set <- model_results_df[model_results_df$train_val == 'Validation',] +#caculate rmse by interval for validation +model_results_in_val <- rmse_by_int(model_results_df %>% filter(train_val == 'Validation'),"saltfront_obs","saltfront_pred_mean", "2016-2020") + +#read in COAWST results +c_16 <- read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2016_7day.csv', show_col_types = FALSE) +c_18 <- read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2018_7day.csv', show_col_types = FALSE) +c_19 <- read_csv('03_model/in/COAWST_model_runs/processed/COAWST_2019_7day.csv', show_col_types = FALSE) + +c_all <- rbind(c_16, c_18, c_19) + + +####-----------Figure 2---------------#### +print("Creating Figure 2...") + +trenton <- read_csv('02_munge/out/D/usgs_nwis_01463500.csv', show_col_types = FALSE) %>% + dplyr::select(datetime, discharge) %>% + rename_with(.fn = ~paste0(.,'_del'), .cols = everything()) + +schuylkill <- read_csv('02_munge/out/D/usgs_nwis_01474500.csv', show_col_types = FALSE) %>% + dplyr::select(datetime, discharge) %>% + rename_with(.fn = ~paste0(.,'_sch'), .cols = everything()) + +sf_q <- model_results_df %>% + dplyr::select(date, saltfront_obs, saltfront_pred_mean) %>% + left_join(trenton, by = c('date' = 'datetime_del')) %>% + left_join(schuylkill, by = c('date' = 'datetime_sch')) %>% + left_join(c_all, by = c('date' = 'date')) + +sf_del_corr <- lagged_correlation(sf_q, 'discharge_del', seq(0,50)) +sf_sch_corr <- lagged_correlation(sf_q, 'discharge_sch', seq(0,50)) + +ts_flow <- sf_q %>% + rename('Delaware' = 'discharge_del', 'Schuylkill' = 'discharge_sch') %>% + pivot_longer(cols = c(Delaware, Schuylkill)) %>% + dplyr::select(date, name, value) %>% + rename('Site' = 'name', 'Discharge' = 'value') %>% + mutate(year = as.factor(year(date)), month = as.factor(month(date))) + +ts_flow %>% + filter(Site == 'Schuylkill') %>% + group_by(year) %>% + summarise(sum_q = sum(Discharge)) %>% + arrange(sum_q) + + +ts_flow_plot_l <- ts_flow %>% + ggplot(aes(x = month, y = Discharge, fill = Site))+ + geom_boxplot()+ + scale_fill_manual(values = c('#c1121f','#669bbc'))+ + theme_bw()+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + legend.text = element_text(size=14, color = 'black'), + legend.key.size = unit(1, 'cm'), #change legend key size + legend.key.height = unit(1, 'cm'), #change legend key height + legend.key.width = unit(1, 'cm'), + legend.title = element_blank(), + axis.text.x=element_blank(), + panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ + scale_y_log10()+ + ylab('Discharge (cfs)')+ + xlab('') + +flow_leg <- get_legend(ts_flow_plot_l) +ts_flow_plot <- ts_flow_plot_l + theme(legend.position = 'none') + +sf_obs <- model_results_df %>% + mutate(site = 'salt_front') %>% + dplyr::select(date, saltfront_obs, site) %>% + filter(year(date) > 2000 & year(date) < 2021) %>% + mutate(month = as.factor(month(date))) %>% + ggplot(aes(x = month, y = saltfront_obs, fill = site))+ + geom_boxplot(fill = '#57cc99')+ + theme_bw()+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ + scale_y_log10()+ + ylab('Salfront location (RM)')+ + scale_x_discrete(name = '', breaks = seq(1,12), labels = c('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec')) + +corr_plot_df <- rbind(sf_sch_corr %>% + mutate(site = 'Schuylkill'), + sf_del_corr %>% + mutate(site = 'Delaware')) + +corr_plot <- corr_plot_df %>% + ggplot(aes(x = lags, y = corr_list, color = site))+ + geom_line(size = 1.5)+ + scale_color_manual(values = c('#c1121f','#669bbc'))+ + theme_bw()+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + panel.grid.major = element_blank(), panel.grid.minor = element_blank(), + legend.position = 'none')+ + labs(x = 'Lag (days)', y = 'Pearson r')+ + geom_vline(xintercept = 7, linetype = 2, color = '#c1121f', size = 1)+ + geom_vline(xintercept = 8, linetype = 2, color = '#669bbc', size = 1) + + + + +#pdf('04_analysis/fig/fig_2.pdf', height = 6, width = 10) +fig_2 <- plot_grid(ts_flow_plot,flow_leg, sf_obs, corr_plot, ncol = 2, align = 'v', axis = 'rl', rel_widths = c(2,1), + labels = c('a)','','b)','c)')) +ggsave('04_analysis/fig/fig_2.pdf', fig_2, height = 6, width = 10) +#dev.off() + +####-----------FIGURE 3---------------#### +#validation time series +print("Creating Figure 3...") +val_ts <-model_results_df %>% + filter(train_val == 'Validation') %>% + ggplot()+ + geom_point(aes(x = date, y = saltfront_obs), fill = 'gray30', shape = 21, + show.legend = FALSE, size = 2.3, color = 'black', alpha = 0.3)+ + theme_classic()+ + ylim(54,90)+ + xlab('')+ + ylab('7-day average\nsalt front location\n(River mile)')+ + geom_line(data = model_results_df %>% filter(train_val == 'Validation'), + aes(x = date, y = saltfront_pred_mean), size = 0.9, + color = '#00b4d8')+ + theme(axis.text = element_text(size = 12), + axis.title = element_text(size = 12)) + +#training time series +trn_ts <-model_results_df %>% + filter(train_val == 'Training') %>% + ggplot()+ + geom_point(aes(x = date, y = saltfront_obs), fill = 'gray30', shape = 21, + show.legend = FALSE, size = 2.3, color = 'black', alpha = 0.3)+ + theme_classic()+ + ylim(54,90)+ + xlab('')+ + ylab('7-day average\nsalt front location\n(River mile)')+ + geom_line(data = model_results_df %>% filter(train_val == 'Training'), + aes(x = date, y = saltfront_pred_mean), size = 0.7, + color = 'indianred2')+ + theme(axis.text = element_text(size = 12), + axis.title = element_text(size = 12)) + +#validation cross plot +val_xplot <- val_set %>% + ggplot(aes(x = saltfront_obs, y = saltfront_pred_mean))+ + geom_abline(slope = 1, intercept = 0, linetype = 2)+ + geom_hex(bins = 50)+ + scale_fill_gradient(low = "#00b4d8",high = "midnightblue")+ + theme_classic() + + theme(legend.position = 'none')+ + ylim(55,90)+ + xlim(55,90)+ + ylab('Predicted Salt Front\n(River mile)')+ + xlab('Observed Salt Front\n(River mile)')+ + ggtitle('Testing')+ + theme(plot.title = element_text(color = "#00b4d8"))+ + theme(axis.text = element_text(size = 12), + axis.title = element_text(size = 12)) + +#training cross plot +train_xplot <- train_set %>% + ggplot(aes(x = saltfront_obs, y = saltfront_pred_mean))+ + geom_abline(slope = 1, intercept = 0, linetype = 2)+ + geom_hex(bins = 50)+ + scale_fill_gradient(low = "indianred2",high = "orangered4")+ + theme_classic() + + theme(legend.position = 'none')+ + ylim(55,90)+ + xlim(55,90)+ + ylab('Predicted Salt Front\n(River mile)')+ + xlab('Observed Salt Front\n(River mile)')+ + ggtitle('Training')+ + theme(plot.title = element_text(color = "indianred2"))+ + theme(axis.text = element_text(size = 12), + axis.title = element_text(size = 12)) + +#validation rmse by interval +val_int <- model_results_in_val %>% + mutate(interval = factor(interval, + levels = c("< 58", "58-68", "68-70", + "70-78", "78-82","> 82","All River Miles"))) %>% + ggplot(aes(x = interval, y = rmse, fill = interval))+ + geom_bar(stat = 'identity', color = 'black', position = position_dodge())+ + geom_errorbar(aes(ymin = rmse-std_rmse , ymax = rmse+std_rmse ), width = .2)+ + labs(title="RMSE by river mile interval", x="River mile interval", y = "RMSE (River mile)")+ + theme_classic() + + scale_fill_manual(values = c('#00b4d8','#00b4d8','#00b4d8','#00b4d8','#00b4d8','#00b4d8','#0049b6'))+ + theme(axis.text = element_text(size = 12), + axis.title = element_text(size = 12), + legend.position = 'None') + +lay <- rbind(c(1,1,2,2,2,2,2,2), c(3,3,4,4,4,4,5,5)) +#pdf('04_analysis/fig/fig_3.pdf', height = 5, width = 10) +fig_3 <- grid.arrange(train_xplot, trn_ts, val_xplot, val_ts,val_int, layout_matrix = lay) +ggsave('04_analysis/fig/fig_3.pdf', fig_3, height = 5, width = 10) +dev.off() +####---------------------------------#### + + +####-----------FIGURE 4---------------#### +print("Creating Figure 4...") + + +#Bar plots comparing ML and COAWST +#run <- 'Run_00' +reps <- c( '00', '01', '02', '03', '04', '05', '06', '07', '08', '09') +#aggregate results from replicates +model_results_df <- aggr_reps(run, reps) +#subset results into training and validation +train_set <- model_results_df[model_results_df$train_val == 'Training',] +val_set <- model_results_df[model_results_df$train_val == 'Validation',] + +#merge coawst with observations +ml_c <- merge(model_results_df[,c('date','saltfront_obs')], c_all, by = 'date', all.x = TRUE, all.y = TRUE) %>% + as_tibble() + +#calculate rmse by interval for all coawst years +model_results_in_val_coawst <- rmse_by_int(ml_c,"saltfront_obs","coawst_pred","coawst") + + +dummy_df <- rbind( + tibble(interval = c("< 58", "58-68", "68-70", "70-78", "78-82","> 82","All River Miles"), + std_rmse = rep(NA,7), + rmse = rep(NA,7), + nse = rep(NA,7), + r = rep(NA,7), + nobs_yr = rep(NA,7), + nobs = rep(NA,7), + run = rep('coawst_2017',7)), + tibble(interval = c("< 58", "58-68", "68-70", "70-78", "78-82","> 82","All River Miles"), + std_rmse = rep(NA,7), + rmse = rep(NA,7), + nse = rep(NA,7), + r = rep(NA,7), + nobs_yr = rep(NA,7), + nobs = rep(NA,7), + run = rep('coawst_2020',7))) + +rmse_year <- rbind( + rmse_by_int(model_results_df %>% filter(year(date) == '2016'),"saltfront_obs","saltfront_pred_mean","ml_2016"), + rmse_by_int(model_results_df %>% filter(year(date) == '2017'),"saltfront_obs","saltfront_pred_mean","ml_2017"), + rmse_by_int(model_results_df %>% filter(year(date) == '2018'),"saltfront_obs","saltfront_pred_mean","ml_2018"), + rmse_by_int(model_results_df %>% filter(year(date) == '2019'),"saltfront_obs","saltfront_pred_mean","ml_2019"), + rmse_by_int(model_results_df %>% filter(year(date) == '2020'),"saltfront_obs","saltfront_pred_mean","ml_2020"), + + rmse_by_int(ml_c %>% filter(year(date) == '2016'),"saltfront_obs","coawst_pred","coawst_2016"), + rmse_by_int(ml_c %>% filter(year(date) == '2018'),"saltfront_obs","coawst_pred","coawst_2018"), + rmse_by_int(ml_c %>% filter(year(date) == '2019'),"saltfront_obs","coawst_pred","coawst_2019"), + dummy_df) + +rmse_year$model <- str_split_fixed(rmse_year$run, '_', 2)[,1] +rmse_year$year <- str_split_fixed(rmse_year$run, '_', 2)[,2] + +rmse_plot <- rmse_year %>% + filter(interval == 'All River Miles') %>% + mutate(model = factor(model, levels = c('ml','coawst'))) %>% + ggplot(aes(x = year, y = as.numeric(rmse), fill = model))+ + geom_bar(stat = 'identity', color = 'black', position = position_dodge())+ + geom_errorbar(aes(ymin = rmse-std_rmse, ymax = rmse+std_rmse), width = .2, position = position_dodge(0.9))+ + theme_bw() + + scale_fill_manual(values = c('#00b4d8','#feb24c'))+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5), + panel.grid.major = element_blank(), panel.grid.minor = element_blank(), + strip.background =element_rect(fill="white"), + legend.position = 'none')+ + ylab('RMSE (River mile)')+ + xlab('Year') + +comp_by_int <- rbind(model_results_in_val, + model_results_in_val_coawst) %>% + mutate(run = factor(run, + levels = c('coawst','2016-2020'))) %>% + mutate(interval = factor(interval, + levels = c("< 58", "58-68", "68-70", + "70-78", "78-82","> 82","All River Miles"))) %>% + mutate(run = factor(run, levels = c('2016-2020','coawst'))) %>% + ggplot(aes(x = interval, y = rmse, fill = run))+ + geom_bar(stat = 'identity', color = 'black', position = position_dodge())+ + geom_errorbar(aes(ymin = rmse-std_rmse, ymax = rmse+std_rmse), width = .2, position = position_dodge(0.9))+ + labs(x="River mile interval", y = "RMSE (River mile)")+ + theme_bw() + + scale_fill_manual(values = c('#00b4d8','#feb24c'))+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5), + panel.grid.major = element_blank(), panel.grid.minor = element_blank(), + strip.background =element_rect(fill="white"), + legend.position = 'none') + + +#pdf('04_analysis/fig/fig_4.pdf', height = 3, width = 10) +fig_4 <- plot_grid(rmse_plot, comp_by_int, nrow = 1, align = 'l', labels = c('a)','b)')) +ggsave('04_analysis/fig/fig_4.pdf', fig_4, height = 3, width = 10) +#dev.off() +####---------------------------------#### + + +####-----------FIGURE 5---------------#### +print("Creating Figure 5...") + +#merge coawst with observations +ml_c <- merge(model_results_df[,c('date','saltfront_obs','saltfront_pred_mean')], c_all, by = 'date', all.x = TRUE, all.y = TRUE) %>% + as_tibble() %>% + rename('ml_pred' = 'saltfront_pred_mean') + +#Time series comparison of ML ML Lagged and COAWST +comp_years_ml <- ml_c %>% + mutate(year = year(date)) %>% + filter(year %in% c('2016','2018','2019')) + + +ts_comp <- ggplot(data = comp_years_ml)+ + geom_point(aes(x = date, y = saltfront_obs), fill = 'gray30', shape = 21, + show.legend = FALSE, size = 2.3, color = 'black', alpha = 0.3)+ + theme_bw()+ + xlab('')+ + ylab('7-day average\nsalt front location\n(River mile)')+ + geom_line(data = comp_years_ml, + aes(x = date, y = ml_pred), size = 1.2, + color = '#00b4d8')+ + geom_line(data = comp_years_ml, + aes(x = date, y = coawst_pred), size = 1.2, + color = '#feb24c')+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5), + panel.grid.major = element_blank(), panel.grid.minor = element_blank(), + strip.background =element_rect(fill="white"))+ + facet_wrap(~year, ncol = 1, scales = 'free_x')#+ + + +int <- rmse_year %>% + filter(year %in% c('2016','2018','2019')) %>% + filter(!grepl('mll', model)) %>% + mutate(model = factor(model, levels = c('ml','coawst'))) %>% + mutate(interval = factor(interval, + levels = c("< 58", "58-68", "68-70", + "70-78", "78-82","> 82","All River Miles"))) %>% + filter(interval != 'All River Miles') %>% + ggplot(aes(x = interval, y = rmse, fill = model))+ + geom_bar(stat = 'identity', color = 'black', position = position_dodge())+ + theme_bw() + + scale_fill_manual(values = c('#00b4d8','#feb24c'))+ + facet_wrap(~year, ncol = 1, scales='free_x')+ + scale_x_discrete(limits=c("< 58", "58-68", "68-70", + "70-78", "78-82","> 82"))+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5), + panel.grid.major = element_blank(), panel.grid.minor = element_blank(), + strip.background =element_rect(fill="white"), + legend.position = 'none')+ + ylab('RMSE (River mile)')+ + xlab('River mile interval') + + +##pdf('04_analysis/fig/fig_5.pdf', height = 8.64, width = 10.51) +fig_5 <- plot_grid(ts_comp, int, ncol = 2) +ggsave('04_analysis/fig/fig_5.pdf', fig_5, height = 8.64, width = 10.51) +#dev.off() +####---------------------------------#### + + +####-----------FIGURE 6---------------#### +print("Creating Figure 6...") +#Functional performance +fp_ann <- read_csv(paste0('04_analysis/out/',run,'_functional_performance_df.csv'), show_col_types = FALSE) %>% + separate(source, c('source', 'site')) %>% + mutate(site = replace(site, site == '01463500', 'Schuylkill')) %>% + mutate(site = replace(site, site == '01474500', 'Delaware')) %>% + mutate(train_set = if_else(year %in% c('2016','2017','2018','2019','2020'), 'Testing','Training')) %>% + filter(train_set == 'Testing') %>% + filter((lag == 7 & site == 'Delaware')|(lag == 8 & site == 'Schuylkill')) + +shape <- c(24,23,22,21,20) + +fp <- + fp_ann %>% + filter(year %in% c('2016','2018','2019')) %>% + mutate(year = as.character(year)) %>% + left_join(rmse_year %>% filter(interval == 'All River Miles'), by = c('year', 'model')) %>% + ggplot(aes(x = Func_Perf, y = rmse, fill = model, shape = year)) + + geom_point(size = 8)+ + scale_fill_manual(values = c('#feb24c','#00b4d8','#80ed99'))+ + scale_shape_manual(values = shape)+ + theme_bw()+ + theme(axis.text = element_text(size = 14, color = 'black'), + axis.title = element_text(size = 14), + strip.text.x = element_text(size = 14), + strip.background =element_rect(fill="white"), + panel.grid.major = element_blank(), panel.grid.minor = element_blank()#, + #legend.position = 'none' + )+ + ylab('RMSE (River Mile)')+ + xlab('Functional performance')+ + facet_wrap(~site)+ + xlim(-0.1, 0.1)+ + geom_vline(xintercept=0, + linetype=2, colour="black") + +#pdf('04_analysis/fig/fig_6.pdf', height = 5, width = 10) +ggsave('04_analysis/fig/fig_6.pdf',fp, height = 5, width = 10) +#dev.off() +####---------------------------------#### + diff --git a/04_analysis/src/results_functions.R b/04_analysis/src/results_functions.R new file mode 100644 index 0000000..9caf3dc --- /dev/null +++ b/04_analysis/src/results_functions.R @@ -0,0 +1,214 @@ +#===================================================================================# +# NOTES: functions for plotting the results figures for the manuscript +#-----------------------------------------------------------------------------------# +# Galen Gorski # +# ggorski@usgs.gov # +# 10-28-2022 # +#-----------------------------------------------------------------------------------# +#===================================================================================# + +#===================================================================================# +#####INSTALL PACKAGES##### +# install.packages('tidyverse') +library(tidyverse) +# install.packages('docstring') +library(docstring) +# install.packages('roxygen2') +library(roxygen2) +##### +#===================================================================================# + + +#===================================================================================# + +#read in the model results for each replicate and cbind them in a data frame +aggr_reps <- function(run, reps){ + #' @describeIn + #' Join replicate model runs into a data frame and take the mean + #' + #' @param run the directory where the run can be found e.g. "Run_00" + #' @param reps list of replicates e.g. c('00','01','02) + #' + #' @returns tibble with observed salt front, each replicate predictions, and mean predictions + #' as columns + #' + model_results_df <- read_csv(paste0('03_model/out/', run,'/',reps[1],'/ModelResults.csv'), lazy = FALSE, show_col_types = FALSE) + colnames(model_results_df)[c(1,3)] <- c('date','saltfront_pred_00') + model_results_df <- model_results_df[,c('date','saltfront_obs','saltfront_pred_00')] + for(i in 2:length(reps)){ + temp_data <- read_csv(paste0('03_model/out/', run,'/',reps[i],'/ModelResults.csv'), lazy = FALSE, show_col_types = FALSE) + colnames(temp_data)[3] <- paste('saltfront_pred',reps[i], sep = '_') + model_results_df <- cbind(model_results_df, temp_data[,3]) + } + + model_results_df$saltfront_pred_mean <- rowMeans(model_results_df[,c(3:ncol(model_results_df))]) + model_results_df <- model_results_df %>% + as_tibble() %>% + mutate(train_val = temp_data$`train/val`) + + return(model_results_df) +} + + +rmse_by_int <- function(data, sf_obs, sf_pred, model_run){ + #' @description calculate performance statisticcs by river mile + #' + #' @param data dataframe with predictions, output from aggr_reps + #' @sf_obs sring, column name of salt front observations + #' @sf_pred string, column name of salt front predictions + #' @model_run string, name of model run + #' + #' @returns dataframe with performance metrics by river mile interval + data$interval <- NA + if(TRUE %in% is.na(data[sf_obs])){ + data[is.na(data[sf_obs]),][sf_obs] <- -1 + } + + data[data[sf_obs] <58 & data[sf_obs] >=0,]$interval <- '< 58' + data[data[sf_obs] <68 & data[sf_obs] >=58,]$interval <- '58-68' + data[data[sf_obs] <70 & data[sf_obs] >=68,]$interval <- '68-70' + data[data[sf_obs] <78 & data[sf_obs] >=70,]$interval <- '70-78' + data[data[sf_obs] <82 & data[sf_obs] >=78,]$interval <- '78-82' + data[data[sf_obs] >=82,]$interval <- '> 82' + + if(TRUE %in% (data[sf_obs] < 0)){ + data[data[sf_obs] < 0,][sf_obs] <- NA + } + colnames(data)[1] <- 'date' + n_years <- length(unique(year(data$date))) + + dat <- data[,c('date',sf_obs, sf_pred, 'interval')] + names(dat) <- c('date', 'obs', 'pred', 'int') + full_rmse <- round(hydroGOF::rmse(dat$pred, dat$obs), digits = 2) + + rmse_df_mean <- dat %>% + group_by(int) %>% + filter(!is.na(obs)) %>% + summarise(rmse = round(hydroGOF::rmse(pred,obs), digits = 2), + nse = round(hydroGOF::NSE(pred,obs), digits = 2), + r = round(hydroGOF::rPearson(pred,obs), digits = 2), + nobs_yr =round(n()/n_years,0), nobs = n()) %>% + mutate(run = model_run) + rmse_df_mean <- rbind(rmse_df_mean, c('All River Miles', full_rmse, + round(hydroGOF::NSE(dat$pred,dat$obs), digits = 2), + round(hydroGOF::rPearson(dat$pred,dat$obs), digits = 2), + round(nrow(dat[!is.na(dat$obs),])/n_years,0), + nrow(dat[!is.na(dat$obs),]), + model_run)) + #if else statement for adding min and max rmses from replicates for river mile interval + if(!grepl('coawst',model_run)){ + + std_rmse_all_reps <- data %>% + filter(!is.na(saltfront_obs)) %>% + summarise(across(contains('saltfront_pred_0'), hydroGOF::rmse, obs = saltfront_obs)) %>% + rowwise() %>% + mutate(std_rmse = sd(c_across(saltfront_pred_00:saltfront_pred_09)))%>% + dplyr::select(std_rmse) + + + rmse_df <- data %>% + group_by(interval)%>% + filter(!is.na(saltfront_obs)) %>% + summarise(across(contains('saltfront_pred_0'), hydroGOF::rmse, obs = saltfront_obs)) %>% + rowwise() %>% + mutate(std_rmse = sd(c_across(saltfront_pred_00:saltfront_pred_09)))%>% + dplyr::select(interval, std_rmse) %>% + add_row(interval = 'All River Miles', std_rmse = std_rmse_all_reps$std_rmse) %>% + full_join(rmse_df_mean, by = c('interval' = 'int')) + + }else{ + + rmse_df <- rmse_df_mean %>% + mutate(std_rmse = NA) %>% + relocate(std_rmse, .after = int) %>% + rename('interval' = 'int') + } + + + rmse_df <- transform(rmse_df, rmse = as.numeric(rmse)) + + return(rmse_df) +} + +#calculate the lagged correlation between salt front and discharge +lagged_correlation <- function(sf_q, lag_col, lags){ + #' @description generic function to calculate the lagged correlation between observed and modeled + #' salt front and another variable + #' + #' @param sf_q dataframe with observed and modeled saltfront and discharge + #' @param lag_col string, column to be lagged + #' @param lags list of integers, lags to be investigated + #' + #' @returns dataframe of correlation coefficients and lags + corr_list <- vector() + corr_list_pred <- vector() + corr_list_coawst <- vector() + + for(lag in lags){ + sf_q[paste0(lag_col, '_', lag)] <- c(rep(NA,lag),(sf_q%>%pull(lag_col))[lag:nrow(sf_q)-lag]) + + #obs + corr <- hydroGOF::rPearson(sf_q[paste0(lag_col, '_', lag)], sf_q$saltfront_obs) + corr_list <- c(corr_list, corr) + #ml pred + corr_pred <- hydroGOF::rPearson(sf_q[paste0(lag_col, '_', lag)], sf_q$saltfront_pred_mean) + corr_list_pred <- c(corr_list_pred, corr_pred) + #coawst pred + corr_coawst <- hydroGOF::rPearson(sf_q[paste0(lag_col, '_', lag)], sf_q$coawst_pred) + corr_list_coawst <- c(corr_list_coawst, corr_coawst) + } + corr_df <- data.frame(corr_list, corr_list_pred, corr_list_coawst, lags) + return(corr_df) +} + +#calculate annual lagged correlation +ann_lagged_correlation <- function(sf_q, lag_col, lags){ + #' @description a function for calculating the annual lagged correlation and merge it with the cumulative discharge + #' + #' @param sf_q dataframe with observed and modeled saltfront and discharge + #' @param lag_col string, column to be lagged + #' @param lags list of integers, lags to be investigated + #' + #' @returns dataframe of correlation coefficients and lags + cum_flow <- sf_q %>% + rename('discharge' = lag_col) %>% + #filter(month(date) %in% c(7,8,9,10)) %>% + mutate(year = year(date)) %>% + group_by(year) %>% + summarise(cum_flow = sum(discharge)) + + years <- unique(year(sf_q$date)) + corr_df_all_years <- data.frame() + for(y in years){ + + corr_list <- vector() + corr_list_pred <- vector() + corr_list_coawst <- vector() + + for(lag in lags){ + sf_q_year <- sf_q %>% + filter(year(date) == y) + sf_q_year[paste0(lag_col, '_', lag)] <- c(rep(NA,lag),(sf_q_year%>%pull(lag_col))[lag:nrow(sf_q_year)-lag]) + + #obs + corr <- hydroGOF::rPearson(sf_q_year[paste0(lag_col, '_', lag)], sf_q_year$saltfront_obs) + corr_list <- c(corr_list, corr) + #ml pred + corr_pred <- hydroGOF::rPearson(sf_q_year[paste0(lag_col, '_', lag)], sf_q_year$saltfront_pred_mean) + corr_list_pred <- c(corr_list_pred, corr_pred) + #coawst pred + corr_coawst <- hydroGOF::rPearson(sf_q_year[paste0(lag_col, '_', lag)], sf_q_year$coawst_pred) + corr_list_coawst <- c(corr_list_coawst, corr_coawst) + } + + corr_df_ann <- data.frame(corr_list, corr_list_pred, corr_list_coawst, lags) %>% + arrange(corr_list) %>% + dplyr::summarise(max_corr = first(corr_list), max_lag = first(lags)) %>% + mutate(year = y) + corr_df_all_years <- rbind(corr_df_all_years, corr_df_ann) + } + corr_df_all_years <- corr_df_all_years %>% + left_join(cum_flow, by = 'year') + + return(corr_df_all_years) +} diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..11ba4c9 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,9 @@ +Code of Conduct +=============== + +All contributions to - and interactions surrounding - this project will abide by +the [USGS Code of Scientific Conduct][1]. + + + +[1]: https://www.usgs.gov/office-of-science-quality-and-integrity/fundamental-science-practices diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..cab8e8e --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,18 @@ +Contributing +============ + +Contributions are welcome from the community. 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For more information, see the official USGS copyright policy at +https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits + +Additionally, we waive copyright and related rights in the work +worldwide through the CC0 1.0 Universal public domain dedication. + + +CC0 1.0 Universal Summary +------------------------- + +This is a human-readable summary of the +[Legal Code (read the full text)][1]. + + +### No Copyright + +The person who associated a work with this deed has dedicated the work to +the public domain by waiving all of his or her rights to the work worldwide +under copyright law, including all related and neighboring rights, to the +extent allowed by law. + +You can copy, modify, distribute and perform the work, even for commercial +purposes, all without asking permission. + + +### Other Information + +In no way are the patent or trademark rights of any person affected by CC0, +nor are the rights that other persons may have in the work or in how the +work is used, such as publicity or privacy rights. + +Unless expressly stated otherwise, the person who associated a work with +this deed makes no warranties about the work, and disclaims liability for +all uses of the work, to the fullest extent permitted by applicable law. +When using or citing the work, you should not imply endorsement by the +author or the affirmer. + + + +[1]: https://creativecommons.org/publicdomain/zero/1.0/legalcode diff --git a/README.md b/README.md index f1bb7c9..cb52f4e 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,32 @@ # drb-estuary-salinity-ml -Code repo for Delaware River Basin information theory code and machine learning models that predict estuary salinity +This model archive contains code used for deep learning and hydrodynamic modeling aimed at understanding and simulating salinity dynamics in the Delaware Bay. The steps for reproducing the results from the manuscript are detailed below. -This information is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The information has not received final approval by the U.S. Geological Survey (USGS) and is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the information. +You will need to have [Anaconda](https://docs.anaconda.com/anaconda/install/index.html), [R](https://cran.r-project.org/), and [mamba](https://mamba.readthedocs.io/en/latest/installation.html) installed on your computer to run this pipeline. -Steps for running the model: +To retrain a new model run all steps 1-6 below. -1) clone this repo using `git clone git@github.com:USGS-R/drb-estuary-salinity-ml.git --recurse-submodules`, the `--recurse-submodules` command initiates and updates the `river-dl` submodule housed in `03b_model/src/` directory -1) add the file `953860.zip` into the `01_fetch/in folder`, the file can be found on S3 in drb_estuary_salinity/01_fetch/in -2) from within the github cloned directory create the environment using `conda env create -f environment.yaml` or `conda env update --file environment.yaml –prune` if you have already created the environment and just need to update it -3) run `snakemake -s Snakefile_fetch_munge -j` (-j runs the job on the available number of cpus cores, use -j 2 for fewer) -4) you might have to rerun the same command if there is an error that pops up, this is because snakemake doesn't run rules in order and some directories need to be created -5) now open the file `03b_model/model_config.yaml` and change n_epochs to a small number say 5, and change the run_id to whatever you want to name the test run, say Test_Run -6) run `snakemake -s Snakefile_b_ml_model_baseline -j`, you should see the training progress in the command window and you should have model results written to `03b_model/out/Test_Run/` +If you want to reproduce the analysis of the results from the manuscript, run steps 1-4 to prepare the environment and input datasets, then use steps 7-8 to analyze the results contained in `03_model\out\Run_Manuscript_Results`. + +If you would like to analyze the results of your own model run that you produced by running steps 1-6, you may need to manually reconfigure some of the scripts used in steps 7-8 of the pipeline, as there are elements that are hard-coded for the manuscript run. + +## Prepare your environment + +1) Download the model archive and unzip its contents. +2) Navigate into the root directory of the archive and use [mamba](https://mamba.readthedocs.io/en/latest/installation.html) to create the environment using `mamba env create -f environment.yaml`. +3) Activate your conda environment with `conda activate drb_estuary_salinity`. + +## To train a new model: + +4) Run `snakemake -s Snakefile_fetch_munge -j` (-j runs the job on the available number of cpus cores, use -j 2 for fewer) to fetch and munge the data inputs used in the model. You might have to rerun the same command if an error that pops up, this is because snakemake doesn't run rules in the necessary order sometimes. +5) Open the file `03_model/model_config.yaml` and adjust modeling parameters. Change the run_id to whatever you want to name your run of the model (e.g. Test_Run). +6) Run `snakemake -s Snakefile_run_ml_model run_replicates -j` to train your model. Your model results will be written to `03_model/out/{run_id}/` where `{run_id}` is the run_id specified in `03_model/model_config.yaml`. + +## To analyze model output and produce figures for the modeling run used in the associated manuscript (contained in 03_model\out\Run_Manuscript_Results): + +7) To calculate functional performance for model output: + - run `snakemake -s Snakefile_model_analysis calc_functional_performance_wrapper -j` and the results should be written to two csv files starting with `04_analysis/out/{run_id}_` where `{run_id}` is the run_id specified in `03_model/model_config.yaml`. +8) To reproduce manuscript figures: + - check that the correct filepath for your RScript.exe is inserted in `rule generate_manuscript_figures` in `Snakefile_model_analysis` + - run `snakemake -s Snakefile_model_analysis generate_manuscript_figures -j` + - run `snakemake -s Snakefile_model_analysis generate_expected_gradient_figure -j`; note that this job may fail due to filesystem latency, but if you check in the `04_analysis/fig` folder you will see the output `fig_7_2019.pdf` has been generated diff --git a/Snakefile_a_it_analysis b/Snakefile_a_it_analysis deleted file mode 100644 index d349364..0000000 --- a/Snakefile_a_it_analysis +++ /dev/null @@ -1,53 +0,0 @@ -import importlib - -configfile: "01_fetch/wildcards_fetch_config.yaml" - -usgs_nwis_sites = config["fetch_usgs_nwis.py"]["sites"] -noaa_nos_sites = config["fetch_noaa_nos.py"]["sites"] -noaa_nos_products = config["fetch_noaa_nos.py"]["products"] - -wildcard_constraints: - noaa_nos_sites="\d+" - -include: "Snakefile_fetch_munge" - -rule all_a_it_analysis: - input: - "03a_it_analysis/out/srcs_proc_lagged", - "03a_it_analysis/out/snks_proc", - "03a_it_analysis/out/correlation.png", - "03a_it_analysis/out/mutual_information.png", - expand("02_munge/out/D/usgs_nwis_{usgs_nwis_site}.csv", usgs_nwis_site = usgs_nwis_sites), - expand("02_munge/out/daily_summaries/noaa_nos_{noaa_nos_site}.csv", noaa_nos_site = noaa_nos_sites) - -rule it_analysis_data_prep: - input: - "03a_it_analysis/params_config_it_analysis_data_prep.yaml", - expand("02_munge/out/D/usgs_nwis_{usgs_nwis_site}.csv", usgs_nwis_site = usgs_nwis_sites), - expand("02_munge/out/daily_summaries/noaa_nos_{noaa_nos_site}.csv", noaa_nos_site = noaa_nos_sites) - output: - "03a_it_analysis/out/srcs_proc_lagged", - "03a_it_analysis/out/snks_proc" - shell: - "python -m 03a_it_analysis.src.it_analysis_data_prep" - -rule make_heatmap_matrix: - input: - "03a_it_analysis/out/srcs_proc_lagged", - "03a_it_analysis/out/snks_proc" - output: - "03a_it_analysis/out/corr_matrix", - "03a_it_analysis/out/mi_matrix" - shell: - "python -m 03a_it_analysis.src.make_heatmap_matrix" - -rule plot_heatmap: - input: - "03a_it_analysis/out/corr_matrix", - "03a_it_analysis/out/mi_matrix" - output: - "03a_it_analysis/out/correlation.png", - "03a_it_analysis/out/mutual_information.png" - shell: - "python -m 03a_it_analysis.src.plot_heatmap" - diff --git a/Snakefile_fetch_munge b/Snakefile_fetch_munge index 0cfd243..f16627a 100644 --- a/Snakefile_fetch_munge +++ b/Snakefile_fetch_munge @@ -32,7 +32,7 @@ rule fetch_usgs_nwis: "01_fetch/out/usgs_nwis_{usgs_nwis_sites}.txt" run: fetch = importlib.import_module('01_fetch.src.fetch_usgs_nwis') - fetch.fetch_single_site_data({wildcards.usgs_nwis_sites}) + fetch.fetch_all_sites_data() fetch.fetch_param_file() rule fetch_noaa_nos: diff --git a/Snakefile_model_analysis b/Snakefile_model_analysis new file mode 100644 index 0000000..ae91ab5 --- /dev/null +++ b/Snakefile_model_analysis @@ -0,0 +1,121 @@ +import pickle +import os +import sys +sys.path.insert(0, os.path.join('04_analysis', 'src')) +import estuary_salinity_functional_performance_func as esfp +import XAI_functions + +run_id = 'Run_Manuscript_Results' +sources = ['discharge_01474500','discharge_01463500'] +sinks = ['saltfront_obs', 'ml_pred','coawst_pred'] +years = ['2001','2002','2003','2004','2005','2006','2007','2008','2009','2010', + '2011','2012','2013','2014','2015', '2016','2017', '2018','2019','2020'] + + +rule all_model_analysis: + input: + os.path.join('03_model','out',run_id,'ML_COAWST_Results_Input_Cleaned.csv'), + os.path.join('04_analysis','out',run_id+'_it_df.csv'), + os.path.join('04_analysis','out', run_id+'_functional_performance_df.csv') + +########################################################################################### +#SUMMARIZE MODEL RESULTS AND PREPARE DATA FOR CALCULATIONS +########################################################################################### +rule create_cleaned_io_file: + output: + os.path.join('03_model','out',run_id,'ML_COAWST_Results_Input_Cleaned.csv') + run: + esfp.create_cleaned_io_file(run_id) +##---------------------------------------------------------------------------------------## + +########################################################################################### +#CALCULATE MUTUAL INFORMATION AND TRANSFER ENTROPY BY PAIRS +########################################################################################### +rule calc_it_metrics_for_pairs: + input: + os.path.join('03_model','out',run_id,'ML_COAWST_Results_Input_Cleaned.csv') + output: + os.path.join('04_analysis','out',run_id+'_it_df.csv') + run: + esfp.calc_it_metrics_for_pairs(data_loc = input[0], + sources = sources, + sinks = sinks, + years = years, + run_id = run_id) +##---------------------------------------------------------------------------------------## + +########################################################################################### +#CALCULATE FUNCTIONAL PERFORMANCE BASED ON TRANSFER ENTROPY CALCS +########################################################################################### +rule calc_functional_performance: + input: + os.path.join('04_analysis','out',run_id+'_it_df.csv') + output: + os.path.join('04_analysis', 'out', run_id+'_functional_performance_df.csv') + run: + esfp.calc_functional_performance(it_df_loc = input[0], + sources = sources, + sinks = sinks, + years = years, + write_loc = output[0]) +##---------------------------------------------------------------------------------------## + +########################################################################################### +#WRAPPER FUNCTION FOR CALCULATING FUNCTIONAL PERFORMANCE +########################################################################################### +rule calc_functional_performance_wrapper: + output: + os.path.join('04_analysis/out',run_id+'_it_df.csv'), + os.path.join('04_analysis/out', run_id+'_functional_performance_df.csv') + run: + esfp.calc_functional_performance_wrapper(run_id = run_id, sources = sources, sinks = sinks, years = years, it_df_loc = output[0]) +##---------------------------------------------------------------------------------------## + +########################################################################################### +#GENERATE MANUSCRIPT FIGURES 2-6 (RUN ID PARAMETER MUST BE UPDATED IN THE results.R script) +########################################################################################### +rule generate_manuscript_figures: + input: + script = '04_analysis/src/results.R' + output: + '04_analysis/fig/fig_2.pdf', + '04_analysis/fig/fig_3.pdf', + '04_analysis/fig/fig_4.pdf', + '04_analysis/fig/fig_5.pdf', + '04_analysis/fig/fig_6.pdf' + shell: + r""" + "C:\Users\yourname\AppData\Local\R\R-41~1.1\bin\x64\Rscript.exe" 04_analysis\src\results.R + """ + +##---------------------------------------------------------------------------------------## + +########################################################################################### +#CALCULATE EXPECTED GRADIENTS FOR MODEL RESULTS +########################################################################################### +rule calc_expected_gradients: + input: + '03_model/model_config_Run_Manuscript_Results.yaml', + os.path.join('03_model/out',run_id,'ML_COAWST_Results_Input_Cleaned.csv') + output: + os.path.join('04_analysis/out/',run_id+'_expected_gradients_w_variables.csv') + run: + XAI_functions.calc_EGs_for_model(config_loc = input[0], n_samples = 2, write_loc = output[0]) + +##---------------------------------------------------------------------------------------## + +########################################################################################### +#PLOT EXPECTED GRADIENTS FOR A SINGLE YEAR (FIGURE 7 IN MANUSCRIPT) +########################################################################################### +rule generate_expected_gradient_figure: + input: + os.path.join('04_analysis/out/',run_id+'_expected_gradients_w_variables.csv') + output: + '04_analysis/fig/fig_7' + run: + XAI_functions.plot_EGs_single_year(eg_df_loc = input[0], + year = '2019', + save_loc = output[0], + ext = 'pdf') +##---------------------------------------------------------------------------------------## + diff --git a/Snakefile_b_ml_model_baseline b/Snakefile_run_ml_model similarity index 54% rename from Snakefile_b_ml_model_baseline rename to Snakefile_run_ml_model index de59faf..be74c81 100644 --- a/Snakefile_b_ml_model_baseline +++ b/Snakefile_run_ml_model @@ -1,13 +1,13 @@ import importlib import os import sys +sys.path.insert(0, os.path.join(os.getcwd(), '03_model', 'src')) from river_dl.preproc_utils import prep_all_data -sys.path.append('03b_model/src') import run_model configfile: "01_fetch/wildcards_fetch_config.yaml" -configfile: "03b_model/model_config.yaml" +configfile: "03_model/model_config.yaml" usgs_nwis_sites = config["fetch_usgs_nwis.py"]["sites"] noaa_nos_sites = config["fetch_noaa_nos.py"]["sites"] @@ -23,19 +23,20 @@ include: "Snakefile_fetch_munge" ########################################################################################### #RULE ALL ########################################################################################### -rule all_b_ml_model_baseline: +rule all_ml_model: input: expand("02_munge/out/D/usgs_nwis_{usgs_nwis_site}.csv", usgs_nwis_site = usgs_nwis_sites), expand("02_munge/out/daily_summaries/noaa_nos_{noaa_nos_site}.csv", noaa_nos_site = noaa_nos_sites), expand("02_munge/out/D/noaa_nerrs_{noaa_nerr_site}.csv", noaa_nerr_site = noaa_nerr_site), - "03b_model/out/inputs.zarr", - "03b_model/out/target.zarr", - "03b_model/out/prepped_model_io_data", + os.path.join("03_model", "out", config['run_id'], "inputs.zarr"), + os.path.join("03_model", "out", config['run_id'], "target.zarr"), + os.path.join("03_model", "out", config['run_id'], "prepped_model_io_data"), os.path.join(config['out_dir'], config['run_id'], 'model_param_output.txt'), os.path.join(config['out_dir'], config['run_id'], 'losses.png'), os.path.join(config['out_dir'], config['run_id'], 'weights.pt'), os.path.join(config['out_dir'],config['run_id'],"ModelResults.csv"), - os.path.join(config['out_dir'],config['run_id'],"ModelResultsTimeSeries.png") + os.path.join(config['out_dir'],config['run_id'],"ModelResultsTimeSeries.png"), + "03_model/hyperparameter_config.yaml" ##---------------------------------------------------------------------------------------## ########################################################################################### @@ -43,14 +44,14 @@ rule all_b_ml_model_baseline: ########################################################################################### rule prepare_inputs_targets: input: - "03b_model/model_config.yaml", + "03_model/model_config.yaml", expand("02_munge/out/D/usgs_nwis_{usgs_nwis_site}.csv", usgs_nwis_site = usgs_nwis_sites), expand("02_munge/out/daily_summaries/noaa_nos_{noaa_nos_site}.csv", noaa_nos_site = noaa_nos_sites), expand("02_munge/out/D/noaa_nerrs_{noaa_nerr_site}.csv", noaa_nerr_site = noaa_nerr_site) output: - directory("03b_model/out/inputs.zarr"), - directory("03b_model/out/target.zarr"), - "03b_model/out/prepped_model_io_data" + directory(os.path.join("03_model", "out", config['run_id'], "inputs.zarr")), + directory(os.path.join("03_model", "out", config['run_id'], "target.zarr")), + os.path.join("03_model", "out", config['run_id'], "prepped_model_io_data"), run: inputs_xarray, target_xarray = run_model.select_inputs_targets(inputs = config['inputs'], target = config['target'], @@ -71,76 +72,29 @@ rule prepare_inputs_targets: ##---------------------------------------------------------------------------------------## ########################################################################################### -#TRAIN THE MODEL +#HYPERPARAMETER TESTING ########################################################################################### -rule train_model: +rule hyperparameter_testing: input: - "03b_model/model_config.yaml", - os.path.join(config['out_dir'],'prepped_model_io_data') - output: - os.path.join(config['out_dir'], config['run_id'], 'model_param_output.txt'), - os.path.join(config['out_dir'], config['run_id'], 'losses.png'), - os.path.join(config['out_dir'], config['run_id'], 'weights.pt') + "03_model/hyperparameter_config.yaml" run: - run_model.train_model(prepped_model_io_data_file = input[1], - inputs = config['inputs'], - seq_len = config['seq_len'], - hidden_units = config['hidden_units'], - recur_dropout = config['recur_dropout'], - dropout = config['dropout'], - n_epochs = config['n_epochs'], - learn_rate = config['learn_rate'], - out_dir = config['out_dir'], - run_id = config['run_id'], - train_start_date = config['train_start_date'], - train_end_date = config['train_end_date'], - val_start_date = config['val_start_date'], - val_end_date = config['val_end_date'], - test_start_date = config['test_start_date'], - test_end_date = config['test_end_date'], - inc_ante = config['include_antecedant_data']) -##---------------------------------------------------------------------------------------## + run_model.test_hyperparameters() + -########################################################################################### -#MAKE PLOT AND SAVE MODEL PREDICTIONS -########################################################################################### -rule make_plot_save_predictions: - input: - "03b_model/model_config.yaml", - os.path.join(config['out_dir'],'prepped_model_io_data') - output: - os.path.join(config['out_dir'],config['run_id'],"ModelResults.csv"), - os.path.join(config['out_dir'],config['run_id'],"ModelResultsTimeSeries.png") - run: - predictions = run_model.make_predictions(prepped_model_io_data_file = input[1], - hidden_units = config['hidden_units'], - recur_dropout = config['recur_dropout'], - dropout = config['dropout'], - n_epochs = config['n_epochs'], - learn_rate = config['learn_rate'], - out_dir = config['out_dir'], - run_id = config['run_id'], - train_start_date = config['train_start_date'], - train_end_date = config['train_end_date'], - val_start_date = config['val_start_date'], - val_end_date = config['val_end_date'], - test_start_date = config['test_start_date'], - test_end_date = config['test_end_date']) - run_model.plot_save_predictions(predictions, config['out_dir'], config['run_id']) -##---------------------------------------------------------------------------------------## +##---------------------------------------------------------------------------------------## ########################################################################################### #RUN REPLICATES ########################################################################################### rule run_replicates: input: - "03b_model/model_config.yaml", - #os.path.join(config['out_dir'],'prepped_model_io_data') + "03_model/model_config.yaml", + os.path.join(config['out_dir'], config['run_id'], 'prepped_model_io_data') output: expand(os.path.join(config['out_dir'], config['run_id'], '{rep}/model_param_output.txt'), rep = replicates), expand(os.path.join(config['out_dir'], config['run_id'], '{rep}/losses.png'), rep = replicates), expand(os.path.join(config['out_dir'], config['run_id'], '{rep}/weights.pt'), rep = replicates) run: - run_model.run_replicates(config['replicates'], os.path.join(config['out_dir'],'prepped_model_io_data')) + run_model.run_replicates(config['replicates'], os.path.join(config['out_dir'], config['run_id'],'prepped_model_io_data')) ##---------------------------------------------------------------------------------------## diff --git a/TEpython_ParallelNAN2.py b/TEpython_ParallelNAN2.py deleted file mode 100644 index 1a5eb1d..0000000 --- a/TEpython_ParallelNAN2.py +++ /dev/null @@ -1,679 +0,0 @@ -#!/usr/bin/env python -# Edom Moges (ESDL) - -# ## This module contains the TE code translation from Matlab -# -# (MatLab version written by Laurel L. and modified by Dino B. Translation to Python by Edom M.) -# -# The following functions are included in this module: -# -# 1. Mutual information -# -# 1. mutinfo_new(M, nbins) - Calculates mutual information I(x,y). -# -# -# 2. Tranfer entropy -# -# 1. transen_new(M, lag, nbins) - Calculates transfer information - TE(x,y) x to y. x source M[:,0] and y the sink M[:,1]. -# -# -# 3. Intermediate functions -# -# 1. LagData_new - shifts a matrix so that it is rearranged to be ready for TE calculation as in Knutt et al., 2005 -# 2. jointentropy_new(M, nbins) - Calculates the joint entropy H(x,y) -# 3. jointentropy3_new(M, nbins) - Calculates the joint entropy for three variables H(x,y,z) -# 4. shuffle( M ) - shuffles the entries of the matrix M in time while keeping NaNs (blank data values) NaNs. So that, Monte Carlo is possible -# 5. transenshuffle_new(M, lag, nbins) - Calculates the transfer entropy for a shuffled time series that has already been lined up with LagData -# -# -# 4. Monte Carlo analysis of mutual information and transfer entropy -# -# 1. mutinfo_crit_new( M, nbins, alpha, numiter) - Finds critical values of mutual information statistics that needs to be exceeded for statistical significance -# 2. transen_crit_new( M, lag, alpha, numiter, nbins) - Finds the critical value of the transfer entropy statistic that needs to be exceeded for statistical signficance -# -# -# 5. All in one code -# RunNewTE2VarsPar(DataMatrix, LabelCell, shift, SinkNodes=None, SourceNodes=None, - # maxLag=100, minSamples=200, numShuffles = 500, sigLevel=0.05, numBins=[11,11,11],ncores=4) - runs all together in #parallel mode. - - -#get_ipython().run_line_magic('matplotlib', 'inline') -import pandas as pd -import numpy as np -import datetime as dt -import matplotlib.pyplot as plt -import copy -import os -from joblib import Parallel, delayed -from tqdm import tqdm -np.random.seed(50) - - -# In[2]: - - -def checkMakeDir2(dirName): # - result = dirName - result2 = dirName*2 - return result, result2 - - -# ### Mutual information - -# In[8]: - -def computeEntropy(M,nbin): - N, binEdges1d=np.histogram(M[np.isfinite(M)],bins=nbin) #Which bin the data column is in - p2 = N/sum(N) - - # Shanon entropy - p2gt0 = p2[p2>0] # py - log2p2gt0 = np.log2(p2gt0) - H = (-sum(p2gt0*log2p2gt0)) - return H - - - -def mutinfo_new(M2, nbins): - # Calculates mutual information with the sink variable's H used as a normalization - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[1]) # Source Variable - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[1]) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins[1]+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins[1]**2 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - p = N/sum(N); # Vector of probabilities - - # 1D probability/histogram - N1, binEdges1d1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[0]) # Which bin the first data column is in - N2, binEdges1d2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[0]) #Which bin the second data column is in - - p1 = N1/sum(N1) - p2 = N2/sum(N2) - - - # Shanon entropy - pgt0 = p[p>0] # px,y - p1gt0 = p1[p1>0] # px - p2gt0 = p2[p2>0] # py - - - log2p2gt0 = np.log2(p2gt0) - #Shannon entropy of the sink variable. Used to normalize mutual informaiton in the next line. - Hy = (-sum(p2gt0*log2p2gt0)) - # Mutual information, in bits. Joint entropy is scaled to the number of bins in a single dimension. - I = ( (-sum(p1gt0*np.log2(p1gt0)) - sum(p2gt0*log2p2gt0) ) + (sum(pgt0*np.log2(pgt0)))*np.log2(nbins[0])/np.log2(nbins[1]))/Hy - # double integral in the last component is done as a 1D. - - #return nbins_2, jointentcat,p , sum(N), I, Hy - return I, sum(N) - - - -def mutinfo_newRel(M2, nbins): # MI is computed relative to max value of Hx or Hy than sink based MI.mutinfo_new - # Calculates mutual information with the max H used as a normalization. - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[1]) # Source Variable - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[1]) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins[1]+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins[1]**2 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - p = N/sum(N); # Vector of probabilities - - # 1D probability/histogram - N1, binEdges1d1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[0]) # Which bin the first data column is in - N2, binEdges1d2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[0]) #Which bin the second data column is in - - p1 = N1/sum(N1) - p2 = N2/sum(N2) - - - # Shanon entropy - pgt0 = p[p>0] # px,y - p1gt0 = p1[p1>0] # px - p2gt0 = p2[p2>0] # py - - - log2p2gt0 = np.log2(p2gt0) - log2p1gt0 = np.log2(p1gt0) - #Shannon entropy of the sink variable. Used to normalize mutual informaiton in the next line. - Hy = (-sum(p2gt0*log2p2gt0)) - Hx = (-sum(p1gt0*log2p1gt0)) - maxH = np.max(np.array([Hx,Hy])) - - # Mutual information, in bits. Joint entropy is scaled to the number of bins in a single dimension. - I = ( (-sum(p1gt0*np.log2(p1gt0)) - sum(p2gt0*log2p2gt0) ) + (sum(pgt0*np.log2(pgt0)))*np.log2(nbins[0])/np.log2(nbins[1]))/maxH - - # double integral in the last component is done as a 1D. - - #return nbins_2, jointentcat,p , sum(N), I, Hy - return I, sum(N) - -# ## Intermediate functions - -# In[10]: - - -def LagData_new( M_unlagged, shift ): - # LagData Shifts two time-series so that a matrix is generated that allows easy computation of Knutt et al 2005 based TE computation - # M_unlagged is a matrix [X Y..n], where X and Y are column vectors of the - # variables to be compared. shift is a row vector that says how much each - # variable in M_unlagged is to be shifted by. - - nR,nC = np.shape(M_unlagged) - maxShift = max(shift) - minShift = min(shift) - newlength = nR - maxShift + minShift - M_lagged = np.nan*np.ones([newlength, nC]) #[source_lagged(1:n-lag), sink_unlagged(lag:n), sink_lagged(1:n-lag)] - - #@@@@@@@@@@@@@@######## Dino's verson uses shift of [0, 0, -lag ] for the shuffle case of transfer entropy (transenshuffle_new) - for ii in range(np.shape(M_lagged)[1]): - M_lagged[:,ii] = M_unlagged[(shift[ii]-minShift):(np.shape(M_unlagged)[0]-maxShift+shift[ii]), ii] - - return M_lagged - -# Alternatively -# lag = np.abs(shift[0]) -# M_lagged[:,0] = M_unlagged[0:(nR-lag), 0] -# M_lagged[:,1] = M_unlagged[lag:(nR),1] -# M_lagged[:,2] = M_unlagged[0:(nR-lag),2] -# return M_lagged - - -# In[12]: - - -def jointentropy_new(M2, nbins): - # Calculates the joint entropy H(x,y) - # M is two dimensional column matrix for which joint entropy is to be computed - # H is the normalized joint entropy - # nvalidpoints is the number of rows (samples) used to calculate the joint entropy - - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins) # Source Variable [ ] - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins**2 - N = np.bincount(jointentcat[jointentcat > 0])[1:] # Number of datapoints within each joint entropy bin. @@@ - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - p = N/sum(N); # Vector of probabilities - - pgt0 = p[p>0] # p(x,y) - H = -sum(pgt0*np.log2(pgt0)) - nvalidpoints = sum(N) - - return H, nvalidpoints - - -# In[14]: - - -def jointentropy3_new(M2, nbins): - # Calculates the joint entropy for three variables H(x,y,z) - # M is a three-column matrix that contains the input vectors of data. - # nvalidpoints is the number of rows (samples) used to calculate the joint entropy - - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins) # Source Variable [ ] - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts3, binEdges3=np.histogram(M[:,2][np.isfinite(M[:,2])],bins=nbins) # Source Variable [ ] - binEdges3[0] = binEdges3[0]-ths - binEdges3[len(binEdges3)-1]=binEdges3[len(binEdges3)-1]+ths - col3cat = np.digitize(M[:,2], binEdges3, right=False) - - col3cat[col3cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - # Assign 0 to NAN values - - col1cat[col2cat==0.] = 0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col1cat[col3cat==0.] = 0. # See comment above. @@@ - - col2cat[col1cat==0.] = 0. # @@@ - col3cat[col1cat==0.] = 0. # @@@ - - # This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - jointentcat = (col1cat-1)*nbins**2 + (col2cat-1)*nbins + col3cat - - #print(np.asarray((jointentcat,col1cat,col2cat, col3cat)).T) - - nbins_3 = nbins**3 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - sumN = sum(N) - - - p = N/sumN # Vector of probabilities - pgt0 = p[p>0] - H = -sum(pgt0*np.log2(pgt0)) - nvalidpoints = sumN - - - return H, nvalidpoints - - -# In[16]: - - -def shuffle( M ): - # shuffles the entries of the matrix M in time while keeping NaNs (blank data values) NaNs. - # M is the matrix where the columns are individual variables and the rows are entries in time - - Mss = np.ones(np.shape(M))*np.nan # Initialize - - for n in range(np.shape(M)[1]): # Columns are shuffled separately - notnans = np.argwhere(~np.isnan(M[:,n])) - R = np.random.rand(np.shape(notnans)[0],1) #np.random.rand(5,1) - I = np.argsort(R,axis=0) - #print(notnans[:,0]) - #print(notnans[I,0]) - #print('a',M[notnans[:,0],n]) - Mss[notnans[:,0],n] = M[notnans[I[:],0],n].reshape(np.shape(M[notnans[I[:],0],n])[0],) - - return Mss - - - -# ## Transfer entropy - - - - -# In[20]: - - -def transen_new2(M, shift, nbins): # with shift as an input different lags btween source and sink are possible - # shift [-lag of source, 0, - lag of sink] # lag of sink usually being 1 - # Calculates transfer information - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - # lag is the time lag of interest. - # M4 is the lagged subset of data transfer entropy was run on. - - MQ = LagData_new(np.column_stack((M, M[:,1])), shift) # source, sink, sink is input then - M4 = MQ[~np.isnan(MQ).any(axis=1)] # clears the nans at all columns simultaneously - - # M4 becomes [source_lagged(1:n-lag), sink_unlagged(lag:n), sink_lagged(1:n-lag)] => H(Xt-T, Yt, Yt-T) - - M4[np.argwhere(np.isnan(np.sum(M4,axis=1))), :] = np.nan # Reset rows with any NaN entry to NaN. - M4short = M4[np.argwhere(~np.isnan(np.sum(M4,axis=1))),:] # Time series without NaN that will be passed on for shuffling. - - M1 = M4[:,(0,2)] # [source_lagged(1:n-lag), sink_lagged(1:n-lag)] =>H(Xt-T,Yt-T) - M2 = M4[:,(1,2)] # [sink_unlagged(lag:n), sink_lagged(1:n-lag)] =>H(Yt,Yt-T) - - #@@@@@@@@@@@@@@######## Dino uses M4[:,1] to be predicted - M3 = M4[:,2] # [sink_unlagged(lag:n)] to be predicted is used with DINO. BUT, need CORRECTION =>H(Yt) should be corrected to H(Yt-T) M[:,2] - - - # Knutt et al indicates lagged being used H(Yt-T). Thus, M4[:,2] - # Now calculate the joint and marginal entropy components: - T1, n_valid_pairs1 = jointentropy_new(M1,nbins[1]) - T2, n_valid_pairs2 = jointentropy_new(M2,nbins[1]) - - # Entropy for the single predictor - n3, valueatn = np.histogram(M3[np.isfinite(M3)], nbins[0]) # results in count [n3] and the corresponding value - n3gt0 = n3[n3>0] - sumn3gt0 = sum(n3gt0) - T3 = -sum((n3gt0/sumn3gt0)*(np.log2(n3gt0/sumn3gt0))) # Nonnormalized Shannon entropy of variable Y - - # Three variable entropy - T4, n_valid_pairs4 = jointentropy3_new(M4,nbins[2]) - - Tn = T3 # This is the Shannon entropy of Y, used to normalize the value of transfer entropy obtained below. - - log2nbins1 = np.log2(nbins[0]) - log2nbins2 = np.log2(nbins[1]) - log2nbins3 = np.log2(nbins[2]) - log2nbins1_2 = log2nbins1/log2nbins2 - log2nbins1_3 = log2nbins1/log2nbins3 - T1 = T1*log2nbins1_2 - T2 = T2*log2nbins1_2 - T4 = T4*log2nbins1_3 - - T = (T1+T2-T3-T4)/Tn # Knuth formulation of transfer entropy - - N = min([n_valid_pairs1, n_valid_pairs2, n_valid_pairs4]) # Number of valid matched pairs used in the calculation - - return T, N, M4short - - - -# In[22]: - - - - -# In[24]: - - -def transenshuffle_new2(M, shift, nbins): - - # Calculates the transfer entropy for a shuffled time series that has already been lined up with LagData - - # Calculates the transfer entropy of X>Y, the amount by which knowledge - # of variable X at a time lag reduces the uncertainty in variable Y. M = - # [X Y], and lag is the time lag of interest. nbins is the number of bins - # used to discretize the probability distributions. - - - Minput = shuffle(M[:,(0,1)]) - T, N,c = transen_new2(Minput, shift, nbins)# use it but not understood why [0 0 -lag] is used instead of [-lag 0 -lag] - - - return T - - -# ## Critical values of Mutual information and Transfer entropy - -# In[30]: - - - - -def mutualshuffle_new2(M,nbins): - Mss = shuffle(M) - MIss = mutinfo_new(Mss,nbins) - return MIss - - -# In[ ]: - - -def mutinfo_crit_newPar( M, nbins, alpha, numiter,ncores): - - MIss = Parallel(n_jobs=ncores)(delayed(mutualshuffle_new2)(M, nbins) for ii in range(numiter)) - MIss = np.sort(MIss) - MIcrit = MIss[round((1-alpha)*numiter)] # develop a histogram and peak the 95% quantile significance level with alpha = 0.05 - - return MIcrit - - -# In[44]: - - -def transen_crit_new2( M, shift, alpha, numiter, nbins,ncores): - - # Finds the critical value of the transfer entropy statistic - # that needs to be exceeded for statistical signficance. - # M = matrix of unshifted variables, e.g., [X Y] for calculating the X>Y transfer entropy. - # lag = time lag. - # alpha = significance level. - # numiter = number of Monte Carlo shufflings to perform. - # nbins = number of bins to use to discretize the probability distributions. - - # Serial -# Tss = np.ones([numiter])*np.nan # Initializing shuffled transfer entropy table -# #print(Tss) - -# for ii in range(numiter): -# Tss[ii] = transenshuffle_new2(M, shift, nbins) # Calculates TE for each Monte Carlo Shuffling - # parallel - Tss = Parallel(n_jobs=ncores)(delayed(transenshuffle_new2)(M, shift, nbins) for ii in range(numiter)) - ## print(Tss) - - Tss = np.sort(Tss) - Tcrit = Tss[round((1-alpha)*numiter)] # develop a histogram and peaks the 1-aplpha (95%) quantile significance level with alpha (= 0.05) - - return Tcrit - - - - -# ## Parallel TE & I calculater - - -# number of monteCarlo shuffle - kills the time - going from 100 to 1000 very time consuming. Parallel!! -# maxLag also takes a lot of time. Number of lag considered. 3*365 -# number of source variables -- 20 -def RunNewTE2VarsPar(DataMatrix, LabelCell, shift, SinkNodes=None, SourceNodes=None, - maxLag=100, minSamples=1000, numShuffles = 500, sigLevel=0.05, numBins=[11,11,11],ncores=4): - # computes TE assumes a data matrix with time in first columns and vars on others - - # Inputs - # DataMatrix - data matrix with time in the first column - # LabelCell - variable name of each data matrix entry - # shift - shift for sink variable (-1,0,-1) for self optimality - # Source_nodes - array of column indices for source variables [2] - # Sink_nodes - array of column of indices for sink variales [3:end] - # resultsDir - directory for results ./Results/ - # maxLag - maximum lag (3*365) 3 years - # minSamples - minimum number of valid samples for TE (suggestion 200) - # numShuffles - number of MonteCarlo shuffle iterations (suggestion 500) - # sigLevel - significance level (suggested 0.05) - # numBins - number of bins to use in 1, 2, and 3 dimensions default [11,11,11] - # ncores - pc cores to use - # Outputs - # Imat - mutual information - # Icritmat - significance threshold - # Tfirstmat - first T > Tcrit - # Tbiggestmat - Tmax for T > Tcrit - # Tcube_store - all T for all sink, source, lag combinations - # Tcritcube_store - all Tcrits for all sink, source, lag combinations - - if DataMatrix.size == 0: - return 'no dataMatrix' - - if LabelCell.size == 0: - return 'no variable names' - - if SourceNodes is None: - SourceNodes = np.arange(2,np.shape(DataMatrix)[1]) - - if SinkNodes is None: - SinkNodes = np.array([1]) - - nSources = len(SourceNodes) - nSinks = len(SinkNodes) - - # Start clock - # print('Beginning 2-variable analysis (serial) ...') - # Tot = tic - # print(SourceNodes,SinkNodes) - # ========================================= - ## Shrink input matrices to include only variables that are used - # now the order is time, sinks, sources - - #@@@@@@@@@@@@@@@@@@@@@ - # from Pd to np.array - dataMat = np.column_stack((DataMatrix[:,0], DataMatrix[:,SinkNodes], DataMatrix[:,SourceNodes])) # date, sink, sources - labCell = np.r_[np.array([LabelCell[0]]), np.array(LabelCell[SinkNodes]), np.array(LabelCell[SourceNodes])] - #np.r_[np.array(LabelCell[0]), np.array(LabelCell[1]), np.array(LabelCell[[2,3,4]])] - #Or labCell = np.column_stack((LabelCell[:,0], LabelCell[:,SinkNodes], LabelCell[:,SourceNodes])) - - - del DataMatrix # or set it to empty DataMatrix = [] - del LabelCell - - # ============================================= - # Initialize output matrices - # mutual information between sources and sinks - # the sink is daily mean Q, and all pairwise interactions are evaluated - - Imat = np.ones([nSinks,nSources])*np.nan # row value = # sink vars, col values = # source vars; - - # significance threshold - - Icritmat = copy.deepcopy(Imat) - - # first T > Tcrit - Tfirstmat = copy.deepcopy(Imat) - - - # Tmax for T > Tcrit - Tbiggestmat = copy.deepcopy(Imat) - - # All T for all sink, source, lag combinations - Tcube_store = np.ones([nSinks,nSources,maxLag])*np.nan - - # All Tcrits for all sink, source, lag combinations - Tcritcube_store = copy.deepcopy(Tcube_store) - - - # ============================================= - # LOOP OVER ALL PAIRS OF SOURCE AND SINK VARIABLES TO CALCULATE MI and TE - - for mySinkIter in range(nSinks): # loop over Sink nodes (information receivers) [ 0] - - mySinkNum = SinkNodes[mySinkIter] - mySinkInd = 1 + mySinkIter # exclude time - - - # extract sub-matrices for the ease of computation - Ivec = Imat[mySinkIter,:] - Icritvec = Icritmat[mySinkIter,:] - Tfirstvec = Tfirstmat[mySinkIter,:] - Tbiggestvec = Tbiggestmat[mySinkIter,:] - Tmat_store = np.reshape(Tcube_store[mySinkIter,:,:],[nSources,maxLag]) - Tcritmat_store = np.reshape(Tcritcube_store[mySinkIter,:,:], [nSources,maxLag]) - sinkName = labCell[mySinkInd] # Text name of the Sink variable - MmySink = dataMat[:,mySinkInd].astype(float) # Select the sink variable to run - - #print('sink node = ', mySinkIter) - - for mySourceIter in range(nSources): # Loop over the source nodes - #print(mySourceIter) - - mySourceNum = SourceNodes[mySourceIter] - mySourceInd = 1 + nSinks + mySourceIter - Mmysource = dataMat[:,mySourceInd].astype(float) # Select source variables - sourceName = labCell[mySourceInd] # Name of the source variable - #print('Source node ', mySourceNum-1, sourceName, ':=>', 'Sink node ', mySinkNum, sinkName) - #print('Lag ', 'Sink', 'Source') - - M = np.column_stack((Mmysource, MmySink)) # Source followed by Sink - M = M.astype(float) - #print(M.shape) - # MUTUAL INFORMATION - - II,validN = mutinfo_new(M,numBins) # computes mutual information - #print(II,validN,M[500,:]) - if validN >= minSamples: # enough length to compute MI - I = II - Icrit, nvd = mutinfo_crit_newPar(M=M, alpha=sigLevel, nbins=numBins,numiter = numShuffles,ncores=ncores) - else: - I = np.nan - Icrit = np.nan - - # print(M.shape, II, I,validN) - - Ivec[mySourceIter] = I # save it in a matrix - Icritvec[mySourceIter] = Icrit - - - # TRANSFER ENTROPY - T = np.ones([maxLag])*np.nan # intialize the TE vector over the range of lags examined - Tcrit = copy.deepcopy(T) # Initialize the vector of the critical TE - - #for lag in tqdm(range(maxLag)): #[0 to 364] in a year i.e., no lag day - for lag in range(maxLag): #[0 to 364] in a year i.e., no lag day - t, N, Mshort = transen_new2(M=M, shift=[-lag,shift[1],shift[2]], nbins=numBins) # Computes TE for at a given lag of 'lag' - #print(Mshort, type(Mshort),Mshort.shape) - Mshort = Mshort.reshape(Mshort.shape[0],Mshort.shape[2]) - if N >= minSamples: # enough length to compute TE - T[lag] = t # save TE computed - Tcrit[lag] = transen_crit_new2(M=M, shift=[-lag,shift[1],shift[2]], alpha= sigLevel,nbins=numBins,numiter=numShuffles,ncores=ncores) # TE critical - #print(lag, mySinkIter, mySourceIter) - - # Save the first and biggest value of T over the significance threshold - - TgTcrit = np.argwhere(T >= Tcrit) # np.argwhere(np.array([5,6,9,18]) > np.array([3,9,2,9])) - - if any(TgTcrit): - Tfirstvec[mySourceIter] = T[TgTcrit[0,0]] - Tbiggestvec[mySourceIter] = max(T[TgTcrit[:,0]]) # @@@@@ Should be T-Tcrit biggest!!!!!! - - #print(Tcrit.shape, T.shape, Tcritcube_store.shape) - Tmat_store[mySourceIter,:] = T - Tcritmat_store[mySourceIter,:] = Tcrit - - - # replace column vectors from source iterations into matrices - Imat[mySinkIter, :] = Ivec - Icritmat[mySinkIter, :] = Icritvec - Tfirstmat[mySinkIter,:] = Tfirstvec - Tbiggestmat[mySinkIter,:] = Tbiggestvec - Tcube_store[mySinkIter,:,:] = Tmat_store - Tcritcube_store[mySinkIter,:,:] = Tcritmat_store - - # save results (modify to save just relevant variables) - # save([resultsDir 'TE_analysis_workspace.mat'], '-v7.3'); - - # Stop clock - #print('Finished 2-variable analysis (Parallel)!'); - - - return Imat, Icritmat, Tfirstmat, Tbiggestmat, Tcube_store, Tcritcube_store # | sink | source | lag | - diff --git a/code.json b/code.json new file mode 100644 index 0000000..7926cc0 --- /dev/null +++ b/code.json @@ -0,0 +1,47 @@ +[ + { + "name": "drb-estuary-salinity-ml", + "organization": "U.S. Geological Survey", + "description": "drb-estuary-salinity-ml", + "version": "main", + "status": "Development", + "permissions": { + "usageType": "openSource", + "licenses": [ + { + "name": "Public Domain, CC0-1.0", + "URL": "https://raw.githubusercontent.com/USGS-R/drb-estuary-salinity-ml/main/LICENSE.md" + } + ] + }, + "homepageURL": "https://github.com/USGS-R/drb-estuary-salinity-ml/", + "downloadURL": "https://github.com/USGS-R/drb-estuary-salinity-ml/archive/refs/heads/main.zip", + "disclaimerURL": "https://raw.githubusercontent.com/USGS-R/drb-estuary-salinity-ml/main/DISCLAIMER.md", + "repositoryURL": "https://github.com/USGS-R/drb-estuary-salinity-ml.git", + "vcs": "git", + "laborHours": -1, + "tags": [ + "R", + "USGS", + "machine learning", + "estuary", + "salinity", + "explainable AI", + "information theory", + "usg-artificial-intelligence", + "research" + ], + "languages": [ + "R", + "Jupyter Notebook", + "python" + ], + "contact": { + "name": "Galen Gorski", + "email": "ggorski@usgs.gov" + }, + "date": { + "metadataLastUpdated": "2023-04-19" + } + } +] \ No newline at end of file diff --git a/data_exploration/data_exploration_config.yaml b/data_exploration/data_exploration_config.yaml deleted file mode 100644 index a9e0d55..0000000 --- a/data_exploration/data_exploration_config.yaml +++ /dev/null @@ -1,14 +0,0 @@ -gap_analysis.py: - source: 'usgs_nwis' - # choose where you want to write your data outputs: local or S3 - usgs_nwis_site_colors: {'01411390': '#1f77b4', - '01463500': '#aec7e8', - '01464040': '#ff7f0e', - '014670261': '#ffbb78', - '01467059': '#2ca02c', - '01467200': '#98df8a', - '01474500': '#d62728', - '01474703': '#ff9896', - '01477050': '#9467bd', - '01482695': '#c5b0d5', - '01482800': '#8c564b'} \ No newline at end of file diff --git a/data_exploration/src/gap_analysis.py b/data_exploration/src/gap_analysis.py deleted file mode 100644 index 31266d9..0000000 --- a/data_exploration/src/gap_analysis.py +++ /dev/null @@ -1,126 +0,0 @@ -import os -import yaml -import utils -import pandas as pd -import datetime as dt -import matplotlib.pyplot as plt - -def compile_data(nwis_var_names, source): - # select munged files we want to fetch data for - files_to_process = [f for f in os.listdir(os.path.join('.', '02_munge', 'out')) if f.startswith(f'{source}_')] - - # create dataframe per variable - var_dfs = {} - for var in nwis_var_names: - var_dfs[var] = pd.DataFrame() - for f in files_to_process: - print(f'processing {f}') - # get site id - site_id = os.path.splitext(f)[0].split('_')[-1] - # read in data per site and append it to the appropriate dataframe - site_data_csv = os.path.join('.', '02_munge', 'out', f) - site_data_df = pd.read_csv(site_data_csv, index_col='datetime') - # make index into datetime type - site_data_df.index = pd.to_datetime(site_data_df.index) - for var in site_data_df.columns: - var_dfs[var][site_id] = site_data_df[var] - return var_dfs - -def add_year_start_end(df, year): - date_ny = f'{year+1}-01-01' - df[date_ny] = np.nan - if f'{year}-01-01' not in df.index: - df[f'{year}-01-01'] = np.nan - return df.sort_index() - -def gap_analysis_calc(source, var_dfs): - # make output directory if it doesn't exist - os.makedirs(os.path.join('data_exploration', 'out', 'gap_analysis_csvs'), exist_ok = True) - # define metric names that we will calculate - metrics = ['p_coverage', 'n_gaps', 'gap_median_days', 'gap_max_days'] - gap_template_df = pd.DataFrame(columns=metrics) - metric_dfs = {} - for var, df in var_dfs.items(): - print(f'calculating metrics for {var}') - df.dropna(axis=0, how='all', inplace=True) - if df.empty: - continue - metric_dfs[var] = {} - # get list of years available for this variable - # # include all years with any measurements at any of our sites - years = df.index.year.unique() - for site in df.columns: - var_site_gap_df = gap_template_df.copy() - for year in years: - year_df = df[df.index.year==year][site] - # year_df.dropna(inplace=True) - if year == datetime.datetime.today().year: - elapsed_days = datetime.datetime.today().timetuple().tm_yday - else: - elapsed_days = (datetime.datetime(year+1, 1, 1, 0, 0) - datetime.datetime(year, 1, 1, 0, 0)).days - var_site_gap_df.loc[year, 'p_coverage'] = year_df.count()/elapsed_days - # drop all nan values so that we can calculate gaps using indices - delta_data = year_df.dropna() - # add back first day of year and first day of subsequent year to capture - # gaps at start/end of year - delta_data = add_year_start_end(delta_data, year) - # calculate the length of all data gaps - deltas = delta_data.index.to_series().diff()[1:] - gaps = deltas[deltas > dt.timedelta(days=1)] - # populate metrics related to gaps - var_site_gap_df.loc[year, 'n_gaps'] = len(gaps) - var_site_gap_df.loc[year, 'gap_median_days'] = gaps.median().days if pd.notna(gaps.median().days) else 0 - var_site_gap_df.loc[year, 'gap_max_days'] = gaps.max().days if pd.notna(gaps.max().days) else 0 - var_site_gap_df.to_csv(os.path.join('data_exploration', 'out', 'gap_analysis_csvs', f'{source}_{var}_{site}_gap_analysis.csv')) - metric_dfs[var][site]= var_site_gap_df - return metric_dfs, metrics - -def plot_gap_analysis(source, metric_dfs, metrics, site_colors): - # make output directory if it doesn't exist - os.makedirs(os.path.join('data_exploration', 'out', 'gap_analysis_plots'), exist_ok = True) - for var, data_by_site in metric_dfs.items(): - print(f'plotting metrics for {var}') - plot_df = pd.DataFrame() - fig, axs = plt.subplots(4, sharex=True, figsize=(8,8)) - i=0 - for metric in metrics: - for site, df in data_by_site.items(): - #df.plot.line(y='p_coverage', label=site) - plot_df[site] = df[metric] - #years_to_keep - lines = plot_df.plot.line(ylabel=f'{metric}', ax=axs[i], legend=False, color=[site_colors.get(x) for x in plot_df.columns]) - i+=1 - handles, labels = axs[0].get_legend_handles_labels() - fig.legend(handles, labels, bbox_to_anchor=(1.15,0.9), loc='upper right') - fig.suptitle(var) - save_path = os.path.join('data_exploration', 'out', 'gap_analysis_plots', f'{source}_{var}_gap_analysis_plot.png') - fig.savefig(save_path, bbox_inches = 'tight') - -def main(): - # import config - with open("02_munge/munge_config.yaml", 'r') as stream: - config = yaml.safe_load(stream)['munge_usgs.py'] - # read in list of variables of interest - var_codes = config['params_to_process'] - # change to machine+human-readable names - var_names = [utils.usgs_nwis_param_code_to_name(code) for code in var_codes] - - # import site:color mapping from config - with open("data_exploration/data_exploration_config.yaml", 'r') as stream: - config = yaml.safe_load(stream)['gap_analysis.py'] - # read in data source we want to do gap analysis for - source = config['source'] - # make output directory if it doesn't exist - os.makedirs(os.path.join('data_exploration', 'out'), exist_ok = True) - # fetch site data and compile into nested dictionary of dataframes - var_dfs = compile_data(var_names, source) - - # calculate gap metrics per site variable combo - metric_dfs, metrics = gap_analysis_calc(source, var_dfs) - - # plot gap metrics per variable - site_colors = config[f'{source}_site_colors'] - plot_gap_analysis(source, metric_dfs, metrics, site_colors) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/environment.yaml b/environment.yaml index 7b8639d..518f99c 100644 --- a/environment.yaml +++ b/environment.yaml @@ -1,43 +1,42 @@ name: drb_estuary_salinity channels: - - defaults + - conda-forge - bioconda - pytorch - plotly - - conda-forge - pypi + - defaults dependencies: - - numpy - - pandas - - python=3.8 - - joblib - - matplotlib - - yaml - - boto3 - - tqdm - - snakemake-minimal - - ca-certificates - - certifi - - openssl - - xarray - - torchvision - - pytorch + - boto3=1.26.110 + - ca-certificates=2022.12.7 + - certifi=2022.12.7 - cudatoolkit=10.2 - - torchaudio - - ipympl - - plotly=5.5.0 - - spyder - - scipy - - zarr - - lz4 - - seaborn - - dask - - scikit-learn + - dask=2023.3.2 + - ipympl=0.9.3 + - joblib=1.2.0 + - lz4=4.3.2 + - matplotlib=3.7.1 + - numpy=1.24.2 + - openssl=3.1.0 + - pandas=1.4.2 - pip=21.2.2 + - plotly=5.5.0 + - python=3.8 + - pytorch=2.0.0 + - scikit-learn=1.2.2 + - scipy=1.10.1 + - seaborn=0.12.2 + - snakemake-minimal=7.25.0 + - spyder=5.4.3 + - torchaudio=2.0.0 + - torchvision=0.15.0 + - tqdm=4.65.0 + - xarray=2023.1.0 + - yaml=0.2.5 + - zarr=2.13.3 - pip: - - pypi_install - - sciencebasepy - - scikit-learn + - pypi_install=0.0.5 + - sciencebasepy=2.0.12 diff --git a/methods_exploration/COAWST_Model_Output/Collab_COAWST_Model.ipynb b/methods_exploration/COAWST_Model_Output/Collab_COAWST_Model.ipynb deleted file mode 100644 index 0578aaf..0000000 --- a/methods_exploration/COAWST_Model_Output/Collab_COAWST_Model.ipynb +++ /dev/null @@ -1,13433 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 210, - "id": "866364a2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\envs\\estuary\\lib\\site-packages\\distributed\\node.py:160: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 63172 instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import xarray as xr\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "import numpy as np\n", - "from datetime import datetime\n", - "import xarray.ufuncs as xu\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import matplotlib.dates as mdates\n", - "# import plotly.express as px\n", - "# import plotly.io as pio\n", - "# pio.renderers.default='notebook'\n", - "import cmocean.cm as cm\n", - "import dask.array as da\n", - "import scipy.io\n", - "from scipy import signal\n", - "import os\n", - "from dask.distributed import Client\n", - "client = Client()\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a87918c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "Dimensions:          (boundary: 4, eta_psi: 1369, eta_rho: 1370, eta_u: 1370, eta_v: 1369, ocean_time: 720, s_rho: 16, s_w: 17, tracer: 5, xi_psi: 183, xi_rho: 184, xi_u: 183, xi_v: 184)\n",
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-       "  * ocean_time       (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019...\n",
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-       "    LtracerSponge    (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Akt_bak          (tracer) float64 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Akv_bak          float64 ...\n",
-       "    Akk_bak          float64 ...\n",
-       "    Akp_bak          float64 ...\n",
-       "    rdrg             float64 ...\n",
-       "    rdrg2            float64 ...\n",
-       "    Zob              float64 ...\n",
-       "    Zos              float64 ...\n",
-       "    gls_p            float64 ...\n",
-       "    gls_m            float64 ...\n",
-       "    gls_n            float64 ...\n",
-       "    gls_cmu0         float64 ...\n",
-       "    gls_c1           float64 ...\n",
-       "    gls_c2           float64 ...\n",
-       "    gls_c3m          float64 ...\n",
-       "    gls_c3p          float64 ...\n",
-       "    gls_sigk         float64 ...\n",
-       "    gls_sigp         float64 ...\n",
-       "    gls_Kmin         float64 ...\n",
-       "    gls_Pmin         float64 ...\n",
-       "    Charnok_alpha    float64 ...\n",
-       "    Zos_hsig_alpha   float64 ...\n",
-       "    sz_alpha         float64 ...\n",
-       "    CrgBan_cw        float64 ...\n",
-       "    Znudg            float64 ...\n",
-       "    M2nudg           float64 ...\n",
-       "    M3nudg           float64 ...\n",
-       "    Tnudg            (tracer) float64 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Tnudg_SSS        float64 ...\n",
-       "    FSobc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    FSobc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M2obc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M2obc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    Tobc_in          (boundary, tracer) float64 dask.array<chunksize=(4, 5), meta=np.ndarray>\n",
-       "    Tobc_out         (boundary, tracer) float64 dask.array<chunksize=(4, 5), meta=np.ndarray>\n",
-       "    M3obc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M3obc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    rho0             float64 ...\n",
-       "    R0               float64 ...\n",
-       "    Tcoef            float64 ...\n",
-       "    Scoef            float64 ...\n",
-       "    gamma2           float64 ...\n",
-       "    LuvSrc           int32 ...\n",
-       "    LwSrc            int32 ...\n",
-       "    LtracerSrc       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    LsshCLM          int32 ...\n",
-       "    Lm2CLM           int32 ...\n",
-       "    Lm3CLM           int32 ...\n",
-       "    LtracerCLM       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    LnudgeM2CLM      int32 ...\n",
-       "    LnudgeM3CLM      int32 ...\n",
-       "    LnudgeTCLM       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    spherical        int32 ...\n",
-       "    xl               float64 ...\n",
-       "    el               float64 ...\n",
-       "    Vtransform       int32 ...\n",
-       "    Vstretching      int32 ...\n",
-       "    theta_s          float64 ...\n",
-       "    theta_b          float64 ...\n",
-       "    Tcline           float64 ...\n",
-       "    hc               float64 ...\n",
-       "    grid             int32 ...\n",
-       "    Cs_r             (s_rho) float64 dask.array<chunksize=(16,), meta=np.ndarray>\n",
-       "    Cs_w             (s_w) float64 dask.array<chunksize=(17,), meta=np.ndarray>\n",
-       "    h                (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    f                (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    pm               (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    pn               (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    angle            (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    mask_rho         (eta_rho, xi_rho) float64 dask.array<chunksize=(1370, 184), meta=np.ndarray>\n",
-       "    mask_u           (eta_u, xi_u) float64 dask.array<chunksize=(1370, 183), meta=np.ndarray>\n",
-       "    mask_v           (eta_v, xi_v) float64 dask.array<chunksize=(1369, 184), meta=np.ndarray>\n",
-       "    mask_psi         (eta_psi, xi_psi) float64 dask.array<chunksize=(1369, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_psi  (ocean_time, eta_psi, xi_psi) float32 dask.array<chunksize=(1, 1369, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_rho  (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    wetdry_mask_u    (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_v    (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>\n",
-       "    zeta             (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    ubar             (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    vbar             (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>\n",
-       "    ubar_eastward    (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    vbar_northward   (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    u                (ocean_time, s_rho, eta_u, xi_u) float32 dask.array<chunksize=(1, 16, 1370, 183), meta=np.ndarray>\n",
-       "    v                (ocean_time, s_rho, eta_v, xi_v) float32 dask.array<chunksize=(1, 16, 1369, 184), meta=np.ndarray>\n",
-       "    u_eastward       (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 16, 1370, 184), meta=np.ndarray>\n",
-       "    v_northward      (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 16, 1370, 184), meta=np.ndarray>\n",
-       "    temp             (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 16, 1370, 184), meta=np.ndarray>\n",
-       "    salt             (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 16, 1370, 184), meta=np.ndarray>\n",
-       "    AKr              (ocean_time, s_w, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 17, 1370, 184), meta=np.ndarray>\n",
-       "    shflux           (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    ssflux           (ocean_time, eta_rho, xi_rho) float32 dask.array<chunksize=(1, 1370, 184), meta=np.ndarray>\n",
-       "    bustr            (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    bvstr            (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    file:                            ocean_deb_his_run07C_tsvar_00006.nc\n",
-       "    format:                          netCDF-3 64bit offset file\n",
-       "    Conventions:                     CF-1.4, SGRID-0.3\n",
-       "    type:                            ROMS/TOMS history file\n",
-       "    title:                           Delaware Bay Salinity Test\n",
-       "    var_info:                        varinfo.dat\n",
-       "    rst_file:                        ocean_deb_rst_run07C_tsvar.nc\n",
-       "    his_base:                        ocean_deb_his_run07C_tsvar\n",
-       "    grd_file:                        /vortexfs1/scratch/scook/proj/input/dela...\n",
-       "    ini_file:                        /vortexfs1/scratch/scook/proj/DE_Bay/run...\n",
-       "    tide_file:                       /vortexfs1/scratch/scook/proj/DE_Bay/run...\n",
-       "    bry_file_01:                     /vortexfs1/scratch/scook/proj/DE_Bay/run...\n",
-       "    script_file:                     \n",
-       "    NLM_TADV:                        \\nADVECTION:   HORIZONTAL   VERTICAL    ...\n",
-       "    NLM_LBC:                         \\nEDGE:    WEST   SOUTH  EAST   NORTH  \\...\n",
-       "    svn_url:                         \n",
-       "    svn_rev:                         \n",
-       "    code_dir:                        /vortexfs1/home/scook/src/COAWST_v_3_6\n",
-       "    header_dir:                      /vortexfs1/scratch/scook/proj/DE_Bay/run...\n",
-       "    header_file:                     de_bay_test.h\n",
-       "    os:                              Linux\n",
-       "    cpu:                             x86_64\n",
-       "    compiler_system:                 ifort\n",
-       "    compiler_command:                /vortexfs1/apps/intel/compilers_and_libr...\n",
-       "    compiler_flags:                  -fc=ifort -fp-model precise -heap-arrays...\n",
-       "    tiling:                          015x012\n",
-       "    history:                         ROMS/TOMS, Version 3.8, Thursday - March...\n",
-       "    ana_file:                        ROMS/Functionals/ana_btflux.h, /vortexfs...\n",
-       "    CPP_options:                     DE_BAY_TEST, ADD_FSOBC, ADD_M2OBC, ANA_B...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  ocean_time\n",
-       "    EXTRA_DIMENSION.N:               16
" - ], - "text/plain": [ - "\n", - "Dimensions: (boundary: 4, eta_psi: 1369, eta_rho: 1370, eta_u: 1370, eta_v: 1369, ocean_time: 720, s_rho: 16, s_w: 17, tracer: 5, xi_psi: 183, xi_rho: 184, xi_u: 183, xi_v: 184)\n", - "Coordinates:\n", - " * s_rho (s_rho) float64 -0.9688 -0.9062 ... -0.09375 -0.03125\n", - " * s_w (s_w) float64 -1.0 -0.9375 -0.875 ... -0.125 -0.0625 0.0\n", - " lon_rho (eta_rho, xi_rho) float64 dask.array\n", - " lat_rho (eta_rho, xi_rho) float64 dask.array\n", - " lon_u (eta_u, xi_u) float64 dask.array\n", - " lat_u (eta_u, xi_u) float64 dask.array\n", - " lon_v (eta_v, xi_v) float64 dask.array\n", - " lat_v (eta_v, xi_v) float64 dask.array\n", - " lon_psi (eta_psi, xi_psi) float64 dask.array\n", - " lat_psi (eta_psi, xi_psi) float64 dask.array\n", - " * ocean_time (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019...\n", - "Dimensions without coordinates: boundary, eta_psi, eta_rho, eta_u, eta_v, tracer, xi_psi, xi_rho, xi_u, xi_v\n", - "Data variables:\n", - " ntimes int32 ...\n", - " ndtfast int32 ...\n", - " dt float64 ...\n", - " dtfast float64 ...\n", - " dstart datetime64[ns] ...\n", - " nHIS int32 ...\n", - " ndefHIS int32 ...\n", - " nRST int32 ...\n", - " Falpha float64 ...\n", - " Fbeta float64 ...\n", - " Fgamma float64 ...\n", - " nl_tnu2 (tracer) float64 dask.array\n", - " nl_visc2 float64 ...\n", - " LuvSponge int32 ...\n", - " LtracerSponge (tracer) int32 dask.array\n", - " Akt_bak (tracer) float64 dask.array\n", - " Akv_bak float64 ...\n", - " Akk_bak float64 ...\n", - " Akp_bak float64 ...\n", - " rdrg float64 ...\n", - " rdrg2 float64 ...\n", - " Zob float64 ...\n", - " Zos float64 ...\n", - " gls_p float64 ...\n", - " gls_m float64 ...\n", - " gls_n float64 ...\n", - " gls_cmu0 float64 ...\n", - " gls_c1 float64 ...\n", - " gls_c2 float64 ...\n", - " gls_c3m float64 ...\n", - " gls_c3p float64 ...\n", - " gls_sigk float64 ...\n", - " gls_sigp float64 ...\n", - " gls_Kmin float64 ...\n", - " gls_Pmin float64 ...\n", - " Charnok_alpha float64 ...\n", - " Zos_hsig_alpha float64 ...\n", - " sz_alpha float64 ...\n", - " CrgBan_cw float64 ...\n", - " Znudg float64 ...\n", - " M2nudg float64 ...\n", - " M3nudg float64 ...\n", - " Tnudg (tracer) float64 dask.array\n", - " Tnudg_SSS float64 ...\n", - " FSobc_in (boundary) float64 dask.array\n", - " FSobc_out (boundary) float64 dask.array\n", - " M2obc_in (boundary) float64 dask.array\n", - " M2obc_out (boundary) float64 dask.array\n", - " Tobc_in (boundary, tracer) float64 dask.array\n", - " Tobc_out (boundary, tracer) float64 dask.array\n", - " M3obc_in (boundary) float64 dask.array\n", - " M3obc_out (boundary) float64 dask.array\n", - " rho0 float64 ...\n", - " R0 float64 ...\n", - " Tcoef float64 ...\n", - " Scoef float64 ...\n", - " gamma2 float64 ...\n", - " LuvSrc int32 ...\n", - " LwSrc int32 ...\n", - " LtracerSrc (tracer) int32 dask.array\n", - " LsshCLM int32 ...\n", - " Lm2CLM int32 ...\n", - " Lm3CLM int32 ...\n", - " LtracerCLM (tracer) int32 dask.array\n", - " LnudgeM2CLM int32 ...\n", - " LnudgeM3CLM int32 ...\n", - " LnudgeTCLM (tracer) int32 dask.array\n", - " spherical int32 ...\n", - " xl float64 ...\n", - " el float64 ...\n", - " Vtransform int32 ...\n", - " Vstretching int32 ...\n", - " theta_s float64 ...\n", - " theta_b float64 ...\n", - " Tcline float64 ...\n", - " hc float64 ...\n", - " grid int32 ...\n", - " Cs_r (s_rho) float64 dask.array\n", - " Cs_w (s_w) float64 dask.array\n", - " h (eta_rho, xi_rho) float64 dask.array\n", - " f (eta_rho, xi_rho) float64 dask.array\n", - " pm (eta_rho, xi_rho) float64 dask.array\n", - " pn (eta_rho, xi_rho) float64 dask.array\n", - " angle (eta_rho, xi_rho) float64 dask.array\n", - " mask_rho (eta_rho, xi_rho) float64 dask.array\n", - " mask_u (eta_u, xi_u) float64 dask.array\n", - " mask_v (eta_v, xi_v) float64 dask.array\n", - " mask_psi (eta_psi, xi_psi) float64 dask.array\n", - " wetdry_mask_psi (ocean_time, eta_psi, xi_psi) float32 dask.array\n", - " wetdry_mask_rho (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " wetdry_mask_u (ocean_time, eta_u, xi_u) float32 dask.array\n", - " wetdry_mask_v (ocean_time, eta_v, xi_v) float32 dask.array\n", - " zeta (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " ubar (ocean_time, eta_u, xi_u) float32 dask.array\n", - " vbar (ocean_time, eta_v, xi_v) float32 dask.array\n", - " ubar_eastward (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " vbar_northward (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " u (ocean_time, s_rho, eta_u, xi_u) float32 dask.array\n", - " v (ocean_time, s_rho, eta_v, xi_v) float32 dask.array\n", - " u_eastward (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array\n", - " v_northward (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array\n", - " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array\n", - " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 dask.array\n", - " AKr (ocean_time, s_w, eta_rho, xi_rho) float32 dask.array\n", - " shflux (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " ssflux (ocean_time, eta_rho, xi_rho) float32 dask.array\n", - " bustr (ocean_time, eta_u, xi_u) float32 dask.array\n", - " bvstr (ocean_time, eta_v, xi_v) float32 dask.array\n", - "Attributes:\n", - " file: ocean_deb_his_run07C_tsvar_00006.nc\n", - " format: netCDF-3 64bit offset file\n", - " Conventions: CF-1.4, SGRID-0.3\n", - " type: ROMS/TOMS history file\n", - " title: Delaware Bay Salinity Test\n", - " var_info: varinfo.dat\n", - " rst_file: ocean_deb_rst_run07C_tsvar.nc\n", - " his_base: ocean_deb_his_run07C_tsvar\n", - " grd_file: /vortexfs1/scratch/scook/proj/input/dela...\n", - " ini_file: /vortexfs1/scratch/scook/proj/DE_Bay/run...\n", - " tide_file: /vortexfs1/scratch/scook/proj/DE_Bay/run...\n", - " bry_file_01: /vortexfs1/scratch/scook/proj/DE_Bay/run...\n", - " script_file: \n", - " NLM_TADV: \\nADVECTION: HORIZONTAL VERTICAL ...\n", - " NLM_LBC: \\nEDGE: WEST SOUTH EAST NORTH \\...\n", - " svn_url: \n", - " svn_rev: \n", - " code_dir: /vortexfs1/home/scook/src/COAWST_v_3_6\n", - " header_dir: /vortexfs1/scratch/scook/proj/DE_Bay/run...\n", - " header_file: de_bay_test.h\n", - " os: Linux\n", - " cpu: x86_64\n", - " compiler_system: ifort\n", - " compiler_command: /vortexfs1/apps/intel/compilers_and_libr...\n", - " compiler_flags: -fc=ifort -fp-model precise -heap-arrays...\n", - " tiling: 015x012\n", - " history: ROMS/TOMS, Version 3.8, Thursday - March...\n", - " ana_file: ROMS/Functionals/ana_btflux.h, /vortexfs...\n", - " CPP_options: DE_BAY_TEST, ADD_FSOBC, ADD_M2OBC, ANA_B...\n", - " DODS_EXTRA.Unlimited_Dimension: ocean_time\n", - " EXTRA_DIMENSION.N: 16" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = 'http://geoport.whoi.edu/thredds/dodsC/vortexfs1/usgs/users/scook/proj/DE_Bay/run07/run07C/ocean_deb_his_run07C_tsvar_00006.nc'\n", - "ds = xr.open_dataset(url, chunks={'ocean_time':1})\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "90f9da6e", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "source": [ - "### import locally (decrease computation time)" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "f50b83b1", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true, - "source_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:     (s_rho: 16, points: 1370, ocean_time: 720)\n",
-       "Coordinates:\n",
-       "  * s_rho       (s_rho) float64 -0.9688 -0.9062 -0.8438 ... -0.09375 -0.03125\n",
-       "    lon_rho     (points) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    lat_rho     (points) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "  * ocean_time  (ocean_time) datetime64[ns] 2019-09-28T01:00:00 ... 2019-10-28\n",
-       "Dimensions without coordinates: points\n",
-       "Data variables:\n",
-       "    salt        (ocean_time, s_rho, points) float32 dask.array<chunksize=(1, 16, 1370), meta=np.ndarray>
" - ], - "text/plain": [ - "\n", - "Dimensions: (s_rho: 16, points: 1370, ocean_time: 720)\n", - "Coordinates:\n", - " * s_rho (s_rho) float64 -0.9688 -0.9062 -0.8438 ... -0.09375 -0.03125\n", - " lon_rho (points) float64 dask.array\n", - " lat_rho (points) float64 dask.array\n", - " * ocean_time (ocean_time) datetime64[ns] 2019-09-28T01:00:00 ... 2019-10-28\n", - "Dimensions without coordinates: points\n", - "Data variables:\n", - " salt (ocean_time, s_rho, points) float32 dask.array" - ] - }, - "execution_count": 209, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path = 'salt.nc'\n", - "ds = xr.open_dataset(path, chunks={'ocean_time':1})\n", - "# dist_mile = xr.DataArray(dist_mile,dims=['points'])\n", - "# ds = xr.Dataset(ds, coords={'dist_mile': dist_mile})\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "2743b01e-6e7a-4d8c-93ab-874b22f7db22", - "metadata": {}, - "source": [ - "### define river mile coors" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b4bb6d94", - "metadata": {}, - "outputs": [], - "source": [ - "river_mile_coords = pd.read_csv('river_mile_coords.csv', index_col=0 )\n", - "target_x = np.array(river_mile_coords.iloc[:,[1]].values).squeeze()\n", - "target_x = xr.DataArray(target_x-1, dims=[\"dist_miles\"]) \n", - "target_y = np.array(river_mile_coords.iloc[:,[2]].values).squeeze()\n", - "target_y = xr.DataArray(target_y-1,dims=[\"dist_miles\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "74d70ecc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ -6.86446808, -6.73695053, -6.60944252, ..., 134.16375535,\n", - " 134.32597435, 134.48806645])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dist_mile = np.array(river_mile_coords.iloc[:,[0]].values)\n", - "dist_mile = dist_mile.squeeze()\n", - "dist_mile = xr.DataArray(dist_mile,dims=['points']) #change to points\n", - "dist_mile = dist_mile.values\n", - "dist_mile" - ] - }, - { - "cell_type": "markdown", - "id": "b92d03b8-f20c-49c2-b2cc-de791ac27e43", - "metadata": {}, - "source": [ - "### select bottom values" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b6760531", - "metadata": {}, - "outputs": [], - "source": [ - "ds = ds.isel(s_rho=0)" - ] - }, - { - "cell_type": "markdown", - "id": "08ebc97d-40b2-4b69-a484-d82b4ce13a45", - "metadata": {}, - "source": [ - "### subset river miles" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d0353ef1-8df4-452b-b302-da548fc71a66", - "metadata": {}, - "outputs": [], - "source": [ - "ds = ds.isel(xi_rho=target_x, eta_rho=target_y)" - ] - }, - { - "cell_type": "markdown", - "id": "ddbe2c0e-ddbd-4c65-ba39-0aa8dd19cf9e", - "metadata": {}, - "source": [ - "### assign river miles as new coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "af2660c5", - "metadata": {}, - "outputs": [], - "source": [ - "dist_mile = xr.DataArray(dist_mile,dims=['points'])" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "17bf4965", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:          (boundary: 4, dist_miles: 1370, eta_psi: 1369, eta_u: 1370, eta_v: 1369, ocean_time: 720, points: 1370, s_rho: 16, s_w: 17, tracer: 5, xi_psi: 183, xi_u: 183, xi_v: 184)\n",
-       "Coordinates:\n",
-       "  * s_rho            (s_rho) float64 -0.9688 -0.9062 ... -0.09375 -0.03125\n",
-       "  * s_w              (s_w) float64 -1.0 -0.9375 -0.875 ... -0.125 -0.0625 0.0\n",
-       "    lon_rho          (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    lat_rho          (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    lon_u            (eta_u, xi_u) float64 dask.array<chunksize=(1370, 183), meta=np.ndarray>\n",
-       "    lat_u            (eta_u, xi_u) float64 dask.array<chunksize=(1370, 183), meta=np.ndarray>\n",
-       "    lon_v            (eta_v, xi_v) float64 dask.array<chunksize=(1369, 184), meta=np.ndarray>\n",
-       "    lat_v            (eta_v, xi_v) float64 dask.array<chunksize=(1369, 184), meta=np.ndarray>\n",
-       "    lon_psi          (eta_psi, xi_psi) float64 dask.array<chunksize=(1369, 183), meta=np.ndarray>\n",
-       "    lat_psi          (eta_psi, xi_psi) float64 dask.array<chunksize=(1369, 183), meta=np.ndarray>\n",
-       "  * ocean_time       (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019...\n",
-       "    dist_mile        (points) float64 -6.864 -6.737 -6.609 ... 134.2 134.3 134.5\n",
-       "Dimensions without coordinates: boundary, dist_miles, eta_psi, eta_u, eta_v, points, tracer, xi_psi, xi_u, xi_v\n",
-       "Data variables:\n",
-       "    ntimes           int32 ...\n",
-       "    ndtfast          int32 ...\n",
-       "    dt               float64 ...\n",
-       "    dtfast           float64 ...\n",
-       "    dstart           datetime64[ns] ...\n",
-       "    nHIS             int32 ...\n",
-       "    ndefHIS          int32 ...\n",
-       "    nRST             int32 ...\n",
-       "    Falpha           float64 ...\n",
-       "    Fbeta            float64 ...\n",
-       "    Fgamma           float64 ...\n",
-       "    nl_tnu2          (tracer) float64 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    nl_visc2         float64 ...\n",
-       "    LuvSponge        int32 ...\n",
-       "    LtracerSponge    (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Akt_bak          (tracer) float64 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Akv_bak          float64 ...\n",
-       "    Akk_bak          float64 ...\n",
-       "    Akp_bak          float64 ...\n",
-       "    rdrg             float64 ...\n",
-       "    rdrg2            float64 ...\n",
-       "    Zob              float64 ...\n",
-       "    Zos              float64 ...\n",
-       "    gls_p            float64 ...\n",
-       "    gls_m            float64 ...\n",
-       "    gls_n            float64 ...\n",
-       "    gls_cmu0         float64 ...\n",
-       "    gls_c1           float64 ...\n",
-       "    gls_c2           float64 ...\n",
-       "    gls_c3m          float64 ...\n",
-       "    gls_c3p          float64 ...\n",
-       "    gls_sigk         float64 ...\n",
-       "    gls_sigp         float64 ...\n",
-       "    gls_Kmin         float64 ...\n",
-       "    gls_Pmin         float64 ...\n",
-       "    Charnok_alpha    float64 ...\n",
-       "    Zos_hsig_alpha   float64 ...\n",
-       "    sz_alpha         float64 ...\n",
-       "    CrgBan_cw        float64 ...\n",
-       "    Znudg            float64 ...\n",
-       "    M2nudg           float64 ...\n",
-       "    M3nudg           float64 ...\n",
-       "    Tnudg            (tracer) float64 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    Tnudg_SSS        float64 ...\n",
-       "    FSobc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    FSobc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M2obc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M2obc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    Tobc_in          (boundary, tracer) float64 dask.array<chunksize=(4, 5), meta=np.ndarray>\n",
-       "    Tobc_out         (boundary, tracer) float64 dask.array<chunksize=(4, 5), meta=np.ndarray>\n",
-       "    M3obc_in         (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    M3obc_out        (boundary) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
-       "    rho0             float64 ...\n",
-       "    R0               float64 ...\n",
-       "    Tcoef            float64 ...\n",
-       "    Scoef            float64 ...\n",
-       "    gamma2           float64 ...\n",
-       "    LuvSrc           int32 ...\n",
-       "    LwSrc            int32 ...\n",
-       "    LtracerSrc       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    LsshCLM          int32 ...\n",
-       "    Lm2CLM           int32 ...\n",
-       "    Lm3CLM           int32 ...\n",
-       "    LtracerCLM       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    LnudgeM2CLM      int32 ...\n",
-       "    LnudgeM3CLM      int32 ...\n",
-       "    LnudgeTCLM       (tracer) int32 dask.array<chunksize=(5,), meta=np.ndarray>\n",
-       "    spherical        int32 ...\n",
-       "    xl               float64 ...\n",
-       "    el               float64 ...\n",
-       "    Vtransform       int32 ...\n",
-       "    Vstretching      int32 ...\n",
-       "    theta_s          float64 ...\n",
-       "    theta_b          float64 ...\n",
-       "    Tcline           float64 ...\n",
-       "    hc               float64 ...\n",
-       "    grid             int32 ...\n",
-       "    Cs_r             (s_rho) float64 dask.array<chunksize=(16,), meta=np.ndarray>\n",
-       "    Cs_w             (s_w) float64 dask.array<chunksize=(17,), meta=np.ndarray>\n",
-       "    h                (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    f                (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    pm               (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    pn               (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    angle            (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    mask_rho         (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    mask_u           (eta_u, xi_u) float64 dask.array<chunksize=(1370, 183), meta=np.ndarray>\n",
-       "    mask_v           (eta_v, xi_v) float64 dask.array<chunksize=(1369, 184), meta=np.ndarray>\n",
-       "    mask_psi         (eta_psi, xi_psi) float64 dask.array<chunksize=(1369, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_psi  (ocean_time, eta_psi, xi_psi) float32 dask.array<chunksize=(1, 1369, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_rho  (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    wetdry_mask_u    (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    wetdry_mask_v    (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>\n",
-       "    zeta             (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    ubar             (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    vbar             (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>\n",
-       "    ubar_eastward    (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    vbar_northward   (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    u                (ocean_time, s_rho, eta_u, xi_u) float32 dask.array<chunksize=(1, 16, 1370, 183), meta=np.ndarray>\n",
-       "    v                (ocean_time, s_rho, eta_v, xi_v) float32 dask.array<chunksize=(1, 16, 1369, 184), meta=np.ndarray>\n",
-       "    u_eastward       (ocean_time, s_rho, dist_miles) float32 dask.array<chunksize=(1, 16, 1370), meta=np.ndarray>\n",
-       "    v_northward      (ocean_time, s_rho, dist_miles) float32 dask.array<chunksize=(1, 16, 1370), meta=np.ndarray>\n",
-       "    temp             (ocean_time, s_rho, dist_miles) float32 dask.array<chunksize=(1, 16, 1370), meta=np.ndarray>\n",
-       "    salt             (ocean_time, s_rho, dist_miles) float32 dask.array<chunksize=(1, 16, 1370), meta=np.ndarray>\n",
-       "    AKr              (ocean_time, s_w, dist_miles) float32 dask.array<chunksize=(1, 17, 1370), meta=np.ndarray>\n",
-       "    shflux           (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    ssflux           (ocean_time, dist_miles) float32 dask.array<chunksize=(1, 1370), meta=np.ndarray>\n",
-       "    bustr            (ocean_time, eta_u, xi_u) float32 dask.array<chunksize=(1, 1370, 183), meta=np.ndarray>\n",
-       "    bvstr            (ocean_time, eta_v, xi_v) float32 dask.array<chunksize=(1, 1369, 184), meta=np.ndarray>
" - ], - "text/plain": [ - "\n", - "Dimensions: (boundary: 4, dist_miles: 1370, eta_psi: 1369, eta_u: 1370, eta_v: 1369, ocean_time: 720, points: 1370, s_rho: 16, s_w: 17, tracer: 5, xi_psi: 183, xi_u: 183, xi_v: 184)\n", - "Coordinates:\n", - " * s_rho (s_rho) float64 -0.9688 -0.9062 ... -0.09375 -0.03125\n", - " * s_w (s_w) float64 -1.0 -0.9375 -0.875 ... -0.125 -0.0625 0.0\n", - " lon_rho (dist_miles) float64 dask.array\n", - " lat_rho (dist_miles) float64 dask.array\n", - " lon_u (eta_u, xi_u) float64 dask.array\n", - " lat_u (eta_u, xi_u) float64 dask.array\n", - " lon_v (eta_v, xi_v) float64 dask.array\n", - " lat_v (eta_v, xi_v) float64 dask.array\n", - " lon_psi (eta_psi, xi_psi) float64 dask.array\n", - " lat_psi (eta_psi, xi_psi) float64 dask.array\n", - " * ocean_time (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019...\n", - " dist_mile (points) float64 -6.864 -6.737 -6.609 ... 134.2 134.3 134.5\n", - "Dimensions without coordinates: boundary, dist_miles, eta_psi, eta_u, eta_v, points, tracer, xi_psi, xi_u, xi_v\n", - "Data variables:\n", - " ntimes int32 ...\n", - " ndtfast int32 ...\n", - " dt float64 ...\n", - " dtfast float64 ...\n", - " dstart datetime64[ns] ...\n", - " nHIS int32 ...\n", - " ndefHIS int32 ...\n", - " nRST int32 ...\n", - " Falpha float64 ...\n", - " Fbeta float64 ...\n", - " Fgamma float64 ...\n", - " nl_tnu2 (tracer) float64 dask.array\n", - " nl_visc2 float64 ...\n", - " LuvSponge int32 ...\n", - " LtracerSponge (tracer) int32 dask.array\n", - " Akt_bak (tracer) float64 dask.array\n", - " Akv_bak float64 ...\n", - " Akk_bak float64 ...\n", - " Akp_bak float64 ...\n", - " rdrg float64 ...\n", - " rdrg2 float64 ...\n", - " Zob float64 ...\n", - " Zos float64 ...\n", - " gls_p float64 ...\n", - " gls_m float64 ...\n", - " gls_n float64 ...\n", - " gls_cmu0 float64 ...\n", - " gls_c1 float64 ...\n", - " gls_c2 float64 ...\n", - " gls_c3m float64 ...\n", - " gls_c3p float64 ...\n", - " gls_sigk float64 ...\n", - " gls_sigp float64 ...\n", - " gls_Kmin float64 ...\n", - " gls_Pmin float64 ...\n", - " Charnok_alpha float64 ...\n", - " Zos_hsig_alpha float64 ...\n", - " sz_alpha float64 ...\n", - " CrgBan_cw float64 ...\n", - " Znudg float64 ...\n", - " M2nudg float64 ...\n", - " M3nudg float64 ...\n", - " Tnudg (tracer) float64 dask.array\n", - " Tnudg_SSS float64 ...\n", - " FSobc_in (boundary) float64 dask.array\n", - " FSobc_out (boundary) float64 dask.array\n", - " M2obc_in (boundary) float64 dask.array\n", - " M2obc_out (boundary) float64 dask.array\n", - " Tobc_in (boundary, tracer) float64 dask.array\n", - " Tobc_out (boundary, tracer) float64 dask.array\n", - " M3obc_in (boundary) float64 dask.array\n", - " M3obc_out (boundary) float64 dask.array\n", - " rho0 float64 ...\n", - " R0 float64 ...\n", - " Tcoef float64 ...\n", - " Scoef float64 ...\n", - " gamma2 float64 ...\n", - " LuvSrc int32 ...\n", - " LwSrc int32 ...\n", - " LtracerSrc (tracer) int32 dask.array\n", - " LsshCLM int32 ...\n", - " Lm2CLM int32 ...\n", - " Lm3CLM int32 ...\n", - " LtracerCLM (tracer) int32 dask.array\n", - " LnudgeM2CLM int32 ...\n", - " LnudgeM3CLM int32 ...\n", - " LnudgeTCLM (tracer) int32 dask.array\n", - " spherical int32 ...\n", - " xl float64 ...\n", - " el float64 ...\n", - " Vtransform int32 ...\n", - " Vstretching int32 ...\n", - " theta_s float64 ...\n", - " theta_b float64 ...\n", - " Tcline float64 ...\n", - " hc float64 ...\n", - " grid int32 ...\n", - " Cs_r (s_rho) float64 dask.array\n", - " Cs_w (s_w) float64 dask.array\n", - " h (dist_miles) float64 dask.array\n", - " f (dist_miles) float64 dask.array\n", - " pm (dist_miles) float64 dask.array\n", - " pn (dist_miles) float64 dask.array\n", - " angle (dist_miles) float64 dask.array\n", - " mask_rho (dist_miles) float64 dask.array\n", - " mask_u (eta_u, xi_u) float64 dask.array\n", - " mask_v (eta_v, xi_v) float64 dask.array\n", - " mask_psi (eta_psi, xi_psi) float64 dask.array\n", - " wetdry_mask_psi (ocean_time, eta_psi, xi_psi) float32 dask.array\n", - " wetdry_mask_rho (ocean_time, dist_miles) float32 dask.array\n", - " wetdry_mask_u (ocean_time, eta_u, xi_u) float32 dask.array\n", - " wetdry_mask_v (ocean_time, eta_v, xi_v) float32 dask.array\n", - " zeta (ocean_time, dist_miles) float32 dask.array\n", - " ubar (ocean_time, eta_u, xi_u) float32 dask.array\n", - " vbar (ocean_time, eta_v, xi_v) float32 dask.array\n", - " ubar_eastward (ocean_time, dist_miles) float32 dask.array\n", - " vbar_northward (ocean_time, dist_miles) float32 dask.array\n", - " u (ocean_time, s_rho, eta_u, xi_u) float32 dask.array\n", - " v (ocean_time, s_rho, eta_v, xi_v) float32 dask.array\n", - " u_eastward (ocean_time, s_rho, dist_miles) float32 dask.array\n", - " v_northward (ocean_time, s_rho, dist_miles) float32 dask.array\n", - " temp (ocean_time, s_rho, dist_miles) float32 dask.array\n", - " salt (ocean_time, s_rho, dist_miles) float32 dask.array\n", - " AKr (ocean_time, s_w, dist_miles) float32 dask.array\n", - " shflux (ocean_time, dist_miles) float32 dask.array\n", - " ssflux (ocean_time, dist_miles) float32 dask.array\n", - " bustr (ocean_time, eta_u, xi_u) float32 dask.array\n", - " bvstr (ocean_time, eta_v, xi_v) float32 dask.array" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = xr.Dataset(ds, coords={'dist_mile': dist_mile})\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f7a4417a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray 'salt' (ocean_time: 720, dist_miles: 1370)>\n",
-       "dask.array<getitem, shape=(720, 1370), dtype=float32, chunksize=(1, 1370), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "    s_rho       float64 -0.9688\n",
-       "    lon_rho     (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "    lat_rho     (dist_miles) float64 dask.array<chunksize=(1370,), meta=np.ndarray>\n",
-       "  * ocean_time  (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019-06-30\n",
-       "Dimensions without coordinates: dist_miles\n",
-       "Attributes:\n",
-       "    long_name:  salinity\n",
-       "    time:       ocean_time\n",
-       "    grid:       grid\n",
-       "    location:   face\n",
-       "    field:      salinity, scalar, series
" - ], - "text/plain": [ - "\n", - "dask.array\n", - "Coordinates:\n", - " s_rho float64 -0.9688\n", - " lon_rho (dist_miles) float64 dask.array\n", - " lat_rho (dist_miles) float64 dask.array\n", - " * ocean_time (ocean_time) datetime64[ns] 2019-05-31T01:00:00 ... 2019-06-30\n", - "Dimensions without coordinates: dist_miles\n", - "Attributes:\n", - " long_name: salinity\n", - " time: ocean_time\n", - " grid: grid\n", - " location: face\n", - " field: salinity, scalar, series" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "salt" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "13ac3378", - "metadata": {}, - "outputs": [], - "source": [ - "saltfront = salt.where(salt < 0.52).where(salt > 0.51)\n", - "salfront = saltfront.where(saltfront.max('ocean_time'))" - ] - }, - { - "cell_type": "markdown", - "id": "c189c773-2fb0-49cc-b972-3a9bdafbd47f", - "metadata": {}, - "source": [ - "### subset salt variable" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c20d4910-aa37-47bc-ac3b-5e997cb9db53", - "metadata": {}, - "outputs": [], - "source": [ - "salfront_location = saltfront.salt" - ] - }, - { - "cell_type": "markdown", - "id": "550f4461-8ac1-44c7-8ed3-b956dddcfe0b", - "metadata": {}, - "source": [ - "### convert to DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c95e4db4", - "metadata": {}, - "outputs": [], - "source": [ - "# saltfront_location= saltfront.to_dataframe()\n", - "saltfront_location= saltfront_location.to_dataframe()\n", - "saltfront_location" - ] - }, - { - "cell_type": "markdown", - "id": "9e94d450", - "metadata": {}, - "source": [ - "### Datastructure" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "id": "094a8742", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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s_rholon_rholat_rhodist_milesalt
ocean_time
2019-09-28 02:00:00-0.96875-75.40903439.80793979.1852400.514313
2019-09-28 02:00:00-0.96875-75.40817939.80851979.2512980.512174
2019-09-28 03:00:00-0.96875-75.38682339.82043180.7947890.516436
2019-09-28 04:00:00-0.96875-75.34702739.84224783.6479220.519163
2019-09-28 04:00:00-0.96875-75.34587839.84270283.7217120.514050
..................
2019-10-27 19:00:00-0.96875-75.33345939.84774984.4830100.517190
2019-10-27 20:00:00-0.96875-75.37160939.82804681.8589090.512234
2019-10-27 21:00:00-0.96875-75.39980439.80985079.7101480.517799
2019-10-27 23:00:00-0.96875-75.44417439.79092476.7635740.512841
2019-10-28 00:00:00-0.96875-75.45628939.78354675.8724650.513719
\n", - "

2226 rows × 5 columns

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" - ], - "text/plain": [ - " s_rho lon_rho lat_rho dist_mile salt\n", - "ocean_time \n", - "2019-09-28 02:00:00 -0.96875 -75.409034 39.807939 79.185240 0.514313\n", - "2019-09-28 02:00:00 -0.96875 -75.408179 39.808519 79.251298 0.512174\n", - "2019-09-28 03:00:00 -0.96875 -75.386823 39.820431 80.794789 0.516436\n", - "2019-09-28 04:00:00 -0.96875 -75.347027 39.842247 83.647922 0.519163\n", - "2019-09-28 04:00:00 -0.96875 -75.345878 39.842702 83.721712 0.514050\n", - "... ... ... ... ... ...\n", - "2019-10-27 19:00:00 -0.96875 -75.333459 39.847749 84.483010 0.517190\n", - "2019-10-27 20:00:00 -0.96875 -75.371609 39.828046 81.858909 0.512234\n", - "2019-10-27 21:00:00 -0.96875 -75.399804 39.809850 79.710148 0.517799\n", - "2019-10-27 23:00:00 -0.96875 -75.444174 39.790924 76.763574 0.512841\n", - "2019-10-28 00:00:00 -0.96875 -75.456289 39.783546 75.872465 0.513719\n", - "\n", - "[2226 rows x 5 columns]" - ] - }, - "execution_count": 220, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = saltfront_location[saltfront_location['salt'].notna()]\n", - "df = df.droplevel(level=1)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "f1e0f523", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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s_rholon_rholat_rhodist_milesalt
ocean_time
2019-09-28-0.96875-75.38352139.82058880.9529960.514530
2019-09-29-0.96875-75.37534439.82445181.5121870.515184
2019-09-30-0.96875-75.35966739.82704582.4129030.515213
2019-10-01-0.96875-75.34577439.83159883.2830820.514956
2019-10-02-0.96875-75.35728339.82990282.6287740.514394
2019-10-03-0.96875-75.35438839.83043482.8008180.515585
2019-10-04-0.96875-75.29937639.84450286.1002510.514963
2019-10-05-0.96875-75.31924939.84008384.9397710.514766
2019-10-06-0.96875-75.32302839.84014484.7514510.515470
2019-10-07-0.96875-75.31588039.84035285.1180460.515512
2019-10-08-0.96875-75.33035339.83885784.3527850.514952
2019-10-09-0.96875-75.27448239.84886287.4891130.515306
2019-10-10-0.96875-75.29219239.84943686.6072800.515290
2019-10-11-0.96875-75.25874139.85895888.5849690.515097
2019-10-12-0.96875-75.27340639.85297687.6917810.515678
2019-10-13-0.96875-75.27584939.85303587.5527250.513272
2019-10-14-0.96875-75.29353439.84987686.5728950.514882
2019-10-15-0.96875-75.29963739.84931686.2440260.515288
2019-10-16-0.96875-75.30191139.84737486.0517960.515355
2019-10-17-0.96875-75.29861139.84891786.2923040.515077
2019-10-18-0.96875-75.34292739.83430283.5236250.515065
2019-10-19-0.96875-75.31179039.84165785.3768630.515145
2019-10-20-0.96875-75.32922739.83818284.3636550.515301
2019-10-21-0.96875-75.34745139.83373983.2775180.515003
2019-10-22-0.96875-75.34736439.83291383.2573200.515298
2019-10-23-0.96875-75.38061939.82057581.1019160.514116
2019-10-24-0.96875-75.38533439.81984780.8374500.514733
2019-10-25-0.96875-75.39176839.81772280.4355100.514442
2019-10-26-0.96875-75.39986739.81284179.8374160.514215
2019-10-27-0.96875-75.38793039.81885480.6612810.514909
2019-10-28-0.96875-75.45628939.78354675.8724650.513719
\n", - "
" - ], - "text/plain": [ - " s_rho lon_rho lat_rho dist_mile salt\n", - "ocean_time \n", - "2019-09-28 -0.96875 -75.383521 39.820588 80.952996 0.514530\n", - "2019-09-29 -0.96875 -75.375344 39.824451 81.512187 0.515184\n", - "2019-09-30 -0.96875 -75.359667 39.827045 82.412903 0.515213\n", - "2019-10-01 -0.96875 -75.345774 39.831598 83.283082 0.514956\n", - "2019-10-02 -0.96875 -75.357283 39.829902 82.628774 0.514394\n", - "2019-10-03 -0.96875 -75.354388 39.830434 82.800818 0.515585\n", - "2019-10-04 -0.96875 -75.299376 39.844502 86.100251 0.514963\n", - "2019-10-05 -0.96875 -75.319249 39.840083 84.939771 0.514766\n", - "2019-10-06 -0.96875 -75.323028 39.840144 84.751451 0.515470\n", - "2019-10-07 -0.96875 -75.315880 39.840352 85.118046 0.515512\n", - "2019-10-08 -0.96875 -75.330353 39.838857 84.352785 0.514952\n", - "2019-10-09 -0.96875 -75.274482 39.848862 87.489113 0.515306\n", - "2019-10-10 -0.96875 -75.292192 39.849436 86.607280 0.515290\n", - "2019-10-11 -0.96875 -75.258741 39.858958 88.584969 0.515097\n", - "2019-10-12 -0.96875 -75.273406 39.852976 87.691781 0.515678\n", - "2019-10-13 -0.96875 -75.275849 39.853035 87.552725 0.513272\n", - "2019-10-14 -0.96875 -75.293534 39.849876 86.572895 0.514882\n", - "2019-10-15 -0.96875 -75.299637 39.849316 86.244026 0.515288\n", - "2019-10-16 -0.96875 -75.301911 39.847374 86.051796 0.515355\n", - "2019-10-17 -0.96875 -75.298611 39.848917 86.292304 0.515077\n", - "2019-10-18 -0.96875 -75.342927 39.834302 83.523625 0.515065\n", - "2019-10-19 -0.96875 -75.311790 39.841657 85.376863 0.515145\n", - "2019-10-20 -0.96875 -75.329227 39.838182 84.363655 0.515301\n", - "2019-10-21 -0.96875 -75.347451 39.833739 83.277518 0.515003\n", - "2019-10-22 -0.96875 -75.347364 39.832913 83.257320 0.515298\n", - "2019-10-23 -0.96875 -75.380619 39.820575 81.101916 0.514116\n", - "2019-10-24 -0.96875 -75.385334 39.819847 80.837450 0.514733\n", - "2019-10-25 -0.96875 -75.391768 39.817722 80.435510 0.514442\n", - "2019-10-26 -0.96875 -75.399867 39.812841 79.837416 0.514215\n", - "2019-10-27 -0.96875 -75.387930 39.818854 80.661281 0.514909\n", - "2019-10-28 -0.96875 -75.456289 39.783546 75.872465 0.513719" - ] - }, - "execution_count": 221, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df.resample('1D').mean()\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "id": "5c6a44b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 222, - "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": [ - "df['dist_mile'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "02bb909c-b5f6-4c2f-a760-2c73ae4e149b", - "metadata": {}, - "source": [ - "### Load csv" - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "id": "014b6219-de1d-4093-bfd3-2251afe5d839", - "metadata": {}, - "outputs": [], - "source": [ - "jan = pd.read_csv('salt_jan.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "jan['date'] = pd.to_datetime(jan.index)\n", - "\n", - "feb = pd.read_csv('salt_feb.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "feb['date'] = pd.to_datetime(feb.index)\n", - "\n", - "mar = pd.read_csv('salt_mar.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "mar['date'] = pd.to_datetime(mar.index)\n", - "\n", - "apr = pd.read_csv('salt_apr.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "apr['date'] = pd.to_datetime(apr.index)\n", - "\n", - "\n", - "may = pd.read_csv('salt_may.csv', index_col=0 )\n", - "# may['date'] = may['ocean_time']\n", - "may['date'] = pd.to_datetime(may.index)\n", - "\n", - "jun = pd.read_csv('salt_jun.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "jun['date'] = pd.to_datetime(jun.index)\n", - "\n", - "jul = pd.read_csv('salt_jul.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "jul['date'] = pd.to_datetime(jul.index)\n", - "\n", - "aug = pd.read_csv('salt_aug.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "aug['date'] = pd.to_datetime(aug.index)\n", - "\n", - "sep = pd.read_csv('salt_sep.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "sep['date'] = pd.to_datetime(sep.index)\n", - "\n", - "octb = pd.read_csv('salt_oct.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "octb['date'] = pd.to_datetime(octb.index)\n", - "\n", - "nov = pd.read_csv('salt_nov.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "nov['date'] = pd.to_datetime(nov.index)\n", - "\n", - "dec = pd.read_csv('salt_dec.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "dec['date'] = pd.to_datetime(dec.index)\n", - "\n", - "jan_2020 = pd.read_csv('salt_jan_2020.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "jan_2020['date'] = pd.to_datetime(jan_2020.index)\n", - "\n", - "feb_2020 = pd.read_csv('salt_feb_2020.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "feb_2020['date'] = pd.to_datetime(feb_2020.index)\n", - "\n", - "mar_2020 = pd.read_csv('salt_mar_2020.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "mar_2020['date'] = pd.to_datetime(mar_2020.index)\n", - "\n", - "mar_end_2020 = pd.read_csv('salt_mar_end_2020.csv', index_col=0 )\n", - "# apr['date'] = apr['ocean_time']\n", - "mar_end_2020['date'] = pd.to_datetime(mar_end_2020.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "e3a3544d-bb2e-4134-9e48-ef07d15ba7e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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s_rholon_rholat_rhodist_milesaltdateyear
ocean_time
2019-04-01-0.96875-75.55687939.50565554.5041440.5162002019-04-012019
2019-04-01-0.96875-75.55354939.50846554.6727480.5116992019-04-012019
2019-04-01-0.96875-75.55280839.57661659.4508760.5140512019-04-012019
2019-04-02-0.96875-75.54214939.54483757.1620450.5128932019-04-022019
2019-04-02-0.96875-75.55941139.58635260.1520010.5184232019-04-022019
........................
2019-04-30-0.96875-75.56347539.47731452.5279500.5122322019-04-302019
2019-04-30-0.96875-75.56318539.48231952.8843650.5155162019-04-302019
2019-04-30-0.96875-75.54479439.56284058.4400560.5150102019-04-302019
2019-04-30-0.96875-75.55082139.57423159.2846370.5167252019-04-302019
2019-04-30-0.96875-75.55477039.57903559.6202700.5117392019-04-302019
\n", - "

215 rows × 7 columns

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" - ], - "text/plain": [ - " s_rho lon_rho lat_rho dist_mile salt date \\\n", - "ocean_time \n", - "2019-04-01 -0.96875 -75.556879 39.505655 54.504144 0.516200 2019-04-01 \n", - "2019-04-01 -0.96875 -75.553549 39.508465 54.672748 0.511699 2019-04-01 \n", - "2019-04-01 -0.96875 -75.552808 39.576616 59.450876 0.514051 2019-04-01 \n", - "2019-04-02 -0.96875 -75.542149 39.544837 57.162045 0.512893 2019-04-02 \n", - "2019-04-02 -0.96875 -75.559411 39.586352 60.152001 0.518423 2019-04-02 \n", - "... ... ... ... ... ... ... \n", - "2019-04-30 -0.96875 -75.563475 39.477314 52.527950 0.512232 2019-04-30 \n", - "2019-04-30 -0.96875 -75.563185 39.482319 52.884365 0.515516 2019-04-30 \n", - "2019-04-30 -0.96875 -75.544794 39.562840 58.440056 0.515010 2019-04-30 \n", - "2019-04-30 -0.96875 -75.550821 39.574231 59.284637 0.516725 2019-04-30 \n", - "2019-04-30 -0.96875 -75.554770 39.579035 59.620270 0.511739 2019-04-30 \n", - "\n", - " year \n", - "ocean_time \n", - "2019-04-01 2019 \n", - "2019-04-01 2019 \n", - "2019-04-01 2019 \n", - "2019-04-02 2019 \n", - "2019-04-02 2019 \n", - "... ... \n", - "2019-04-30 2019 \n", - "2019-04-30 2019 \n", - "2019-04-30 2019 \n", - "2019-04-30 2019 \n", - "2019-04-30 2019 \n", - "\n", - "[215 rows x 7 columns]" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "6a3f8501-33ec-4fad-9a93-7ca11dc5b290", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ocean_times_rholon_rholat_rhodist_milesaltdate
02019-01-01-0.96875-75.55120039.51814455.3460380.5103392019-01-01
12019-01-01-0.96875-75.54118339.54717157.3205210.5179882019-01-01
22019-01-01-0.96875-75.54031039.55172757.6458110.5135152019-01-01
32019-01-01-0.96875-75.54039139.55396357.8192500.5140652019-01-01
42019-01-01-0.96875-75.54691039.56510958.5950960.5128402019-01-01
........................
1252019-01-29-0.96875-75.56352139.48399253.0055740.5147202019-01-29
1262019-01-30-0.96875-75.53283339.43107248.7188540.5163662019-01-30
1272019-01-30-0.96875-75.55496839.45020450.5222620.5134992019-01-30
1282019-01-30-0.96875-75.55537139.45155150.6108630.5160362019-01-30
1292019-01-30-0.96875-75.56352139.48399253.0055740.5162582019-01-30
\n", - "

130 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " ocean_time s_rho lon_rho lat_rho dist_mile salt date\n", - "0 2019-01-01 -0.96875 -75.551200 39.518144 55.346038 0.510339 2019-01-01\n", - "1 2019-01-01 -0.96875 -75.541183 39.547171 57.320521 0.517988 2019-01-01\n", - "2 2019-01-01 -0.96875 -75.540310 39.551727 57.645811 0.513515 2019-01-01\n", - "3 2019-01-01 -0.96875 -75.540391 39.553963 57.819250 0.514065 2019-01-01\n", - "4 2019-01-01 -0.96875 -75.546910 39.565109 58.595096 0.512840 2019-01-01\n", - ".. ... ... ... ... ... ... ...\n", - "125 2019-01-29 -0.96875 -75.563521 39.483992 53.005574 0.514720 2019-01-29\n", - "126 2019-01-30 -0.96875 -75.532833 39.431072 48.718854 0.516366 2019-01-30\n", - "127 2019-01-30 -0.96875 -75.554968 39.450204 50.522262 0.513499 2019-01-30\n", - "128 2019-01-30 -0.96875 -75.555371 39.451551 50.610863 0.516036 2019-01-30\n", - "129 2019-01-30 -0.96875 -75.563521 39.483992 53.005574 0.516258 2019-01-30\n", - "\n", - "[130 rows x 7 columns]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test = pd.read_csv('salt_jan.csv', index_col=0 )\n", - "test['date'] = pd.to_datetime(test.index)\n", - "test = test.reset_index('ocean_time')\n", - "test" - ] - }, - { - "cell_type": "markdown", - "id": "5a473390", - "metadata": {}, - "source": [ - "## Set up data for polar plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "3f139cbf", - "metadata": {}, - "outputs": [], - "source": [ - "# Add datetime column\n", - "df['date'] = pd.to_datetime(df.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "7d9a6c6b", - "metadata": {}, - "outputs": [], - "source": [ - "# Prep dummy data - from 2018-01-01 to 2019-09-27 (ends before start of data in df - October 2019)\n", - "df_mult_years = pd.DataFrame({'date':pd.date_range(start='2018-01-01', end='2019-09-27')})\n", - "df_mult_years.index = df_mult_years.date\n", - "df_mult_years['dist_mile'] = np.random.randint(70,92,df_mult_years.shape[0]) #np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "6eee2641", - "metadata": {}, - "outputs": [], - "source": [ - "# Prep dummy data - from 2019-10-29 to 2019-12-31 (picks up after end of data in df - October 2019)\n", - "df_mult_years_end = pd.DataFrame({'date':pd.date_range(start='2019-10-29', end='2019-12-31')})\n", - "df_mult_years_end.index = df_mult_years_end.date\n", - "df_mult_years_end['dist_mile'] = np.random.randint(70,92,df_mult_years_end.shape[0]) # np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "id": "a12d8892", - "metadata": {}, - "outputs": [], - "source": [ - "# Concatenate the data into a single continous dataframe (2 years of data)\n", - "# df_plot = pd.concat([df_mult_years, df[['date','dist_mile']], df_mult_years_end])\n", - "# df_plot = pd.concat([df_mult_years, apr[['date','dist_mile']],may[['date','dist_mile']]])\n", - "# df_plot = pd.concat( [jan[['date','dist_mile']], feb[['date','dist_mile']], mar[['date','dist_mile']], \n", - "# apr[['date','dist_mile']], may[['date','dist_mile']], jun[['date','dist_mile']],\n", - "# jul[['date','dist_mile']], aug[['date','dist_mile']], sep[['date','dist_mile']],\n", - "# octb[['date','dist_mile']], nov[['date','dist_mile']], dec[['date','dist_mile']],\n", - "# jan_2020[['date','dist_mile']], feb_2020[['date','dist_mile']], mar_2020[['date','dist_mile']],\n", - "# mar_end_2020[['date','dist_mile']]])\n", - "df_plot = pd.concat( [jan[['date','dist_mile']], feb[['date','dist_mile']], mar[['date','dist_mile']], \n", - " apr[['date','dist_mile']], may[['date','dist_mile']], jun[['date','dist_mile']],\n", - " jul[['date','dist_mile']], aug[['date','dist_mile']], sep[['date','dist_mile']],\n", - " octb[['date','dist_mile']], nov[['date','dist_mile']], dec[['date','dist_mile']]])\n", - "# df_plot = pd.concat([df_mult_years, apr[[apr.index, 'dist_mile']],may[[may.index,'dist_mile']]])\n", - "# df_plot = df\n", - "df_plot['year'] = df_plot.date.dt.year # Add column for year" - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "id": "09d212cb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2019], dtype=int64)" - ] - }, - "execution_count": 340, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get list of years represented in the dataset\n", - "years_list = df_plot.year.unique()\n", - "years_list" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "id": "64258154-c0d5-4f5b-8470-c331f90512ac", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "outputs": [], - "source": [ - "df_salt = pd.concat( [jan, feb, mar, \n", - " apr, may, jun,\n", - " jul, aug, sep,\n", - " octb, nov, dec,\n", - " jan_2020, feb_2020, mar_2020,\n", - " mar_end_2020])\n", - "year_df_idx = pd.date_range(f'01-01-{years_list[0]}',f'12-31-{years_list[-1]}')\n", - "df_salt.index = pd.DatetimeIndex(df_salt.index)\n", - "df_salt.date = pd.DatetimeIndex(df_salt.date)\n", - "# df_plot = df_plot.reindex(year_df_idx, fill_value=np.nan)\n", - "df_salt['date'] = pd.to_datetime(df_salt.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "id": "e593f0c0", - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the data covers complete years for all years in the dataset\n", - "year_df_idx = pd.date_range(f'01-01-{years_list[0]}',f'12-31-{years_list[-1]}')\n", - "df_plot.index = pd.DatetimeIndex(df_plot.index)\n", - "df_plot.date = pd.DatetimeIndex(df_plot.date)\n", - "# df_plot = df_plot.reindex(year_df_idx, fill_value=np.nan)\n", - "df_plot['date'] = pd.to_datetime(df_plot.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "id": "b578d36b", - "metadata": {}, - "outputs": [], - "source": [ - "# Add time-related columns\n", - "df_plot['year'] = df_plot.date.dt.year # Add column for year\n", - "df_plot['mdate'] = mdates.date2num(df_plot.index.to_pydatetime()) # Convert dates to matplotlib dates\n", - "df_plot['tnorm'] = np.nan # for now, set normalized time column to NaN\n", - "df_plot['doy'] = df_plot.date.dt.dayofyear # compute doy #option 2\n", - "df_plot['doy_norm'] = np.nan # for now, set normalized doy column to NaN" - ] - }, - { - "cell_type": "code", - "execution_count": 343, - "id": "079e9305", - "metadata": {}, - "outputs": [], - "source": [ - "# For each year in the dataset\n", - "for year in years_list:\n", - " year_df = df_plot.loc[df_plot['year']==year]\n", - " # normalize dates for that year to circle (2pi radians)\n", - " df_plot.loc[df_plot['year']==year, 'tnorm'] = (year_df['mdate']-year_df['mdate'].min())/(year_df['mdate'].max()-year_df['mdate'].min())*2.*np.pi\n", - " # normalize DOY for that year to circle (2pi radians)\n", - " df_plot.loc[df_plot['year']==year, 'doy_norm'] = (year_df['doy']-year_df['doy'].min())/(year_df['doy'].max()-year_df['doy'].min())*2.*np.pi" - ] - }, - { - "cell_type": "code", - "execution_count": 344, - "id": "c45cc934", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['01/01', '02/15', '04/01', '05/16', '06/30', '08/14', '09/28', '11/12']" - ] - }, - "execution_count": 344, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Use an arbitrary year to get a set of dates for labeling at 8 places around polar chart\n", - "# dates_df = pd.DataFrame({'date':pd.date_range(start='2019-01-01', end='2019-12-31')})\n", - "dates_df = pd.DataFrame({'date':pd.date_range(start='2019-01-01', end='2019-12-26')})\n", - "date_labels = []\n", - "for i in range(0,8):\n", - " date_labels.append(dates_df.iloc[round((dates_df.shape[0]/8)*i)].date.strftime(\"%m/%d\"))\n", - "date_labels" - ] - }, - { - "cell_type": "code", - "execution_count": 345, - "id": "c43d02b1", - "metadata": {}, - "outputs": [], - "source": [ - "# Get series representing Wilmington river mile\n", - "rm_Wilmington = df_plot.copy()\n", - "rm_Wilmington['dist_mile'] = 70" - ] - }, - { - "cell_type": "code", - "execution_count": 346, - "id": "e9cc3c22", - "metadata": {}, - "outputs": [], - "source": [ - "# Get series representing south Philadelphia river mile\n", - "rm_Philadelphia = df_plot.copy()\n", - "rm_Philadelphia['dist_mile'] = 91" - ] - }, - { - "cell_type": "code", - "execution_count": 347, - "id": "4df2c214", - "metadata": {}, - "outputs": [], - "source": [ - "# Get series representing north Philadelphia river mile\n", - "rm_Philadelphia_max = df_plot.copy()\n", - "rm_Philadelphia_max['dist_mile'] = 110" - ] - }, - { - "cell_type": "code", - "execution_count": 348, - "id": "a2db91e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[38.77466698064672, 93.58496888557525]" - ] - }, - "execution_count": 348, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Set y bound (y axis limits) to be used in the plot\n", - "# y_bounds = [df.dist_mile.min()-8, df.dist_mile.max()+5]\n", - "y_bounds = [df_plot.dist_mile.min()-8, df_plot.dist_mile.max()+5]\n", - "y_bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 352, - "id": "04b632a1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\TTHOMP~1\\AppData\\Local\\Temp\\1/ipykernel_23920/4109311998.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax1.set_xticklabels(date_labels)\n" - ] - }, - { - "data": { - "image/png": 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irFYro0ePZvz48YwbN4729naOHz/O0aNH+6R3nWmanDp1yvz444+bgsHgTp/P91+klMd7vRBF6WMqmClDiohb5na7/0d+fv6wpUuXZhcVFfV6Hfn5+UyaNImysjKsVivHjx/nxIkTVFaqHW66SwWz7hNCMHz4cCZPnszEiRNpbW3l2LFjHD16lPb29l6tRUrJhQsX+OijjxpaW1tP+Hy+/yCl3NOrRShKH1LBTBkSOq6wfNjlcv3tyJEjc+64447M3NzcXq3B4/FQXl5OeXk5drudY8eOcfz4cS5fvtyrdQxWKpglhxCCkpISJk+ezIQJE6ivr+fo0aMcP3681zdmr66u5qOPPmqsq6ur9Pl8/xH4SF3JqQx2Kpgpg5oQwmqz2Z61Wq1/PWHChIxFixalpaen99rjW61WJkyYwNSpUykqKuLkyZMcOnSIS5fUEppkU8Es+XRdZ8yYMUyaNIlx48ZRWVnJwYMHOXXq1NduxH5+1HkujbgEAkZcGsGYC2OIWCPsnbmXoDOIM+hk1r5Z2KJd20O2vr6eLVu2NFdUVNQHg8G/MgzjDSll/92rSlF6QAUzZVASQmgWi2WN3W7/+6lTp2YsWLDA05t7V44aNYry8nImTJhARUUFhw8f5tSpUwN6y6P+TgWz1Lr6IWPatGnk5+dz5MgRDhw4QF3dFy+kbPO2sW/GPhZ+uhDN1Nh12y7Kj5RzacQlbFEbpWdLOTP2DFFrlIknJiZUQ0tLC1u3bm09ffp0fSAQ+DdSyg/UCJoy2Khgpgw6mqYtc7lcPx4/fnzunXfeme7xeHrlcd1uN9OmTWPmzJmEQiEOHTrE0aNHVY+xXqKCWe/JyMhg+vTpTJ06FZ/Px8GDBzly5AjhcJjLhZepy61j2uFpAJwqPYVmalSMqGDBjgU4wg5C9hCfzf+MpVuWduvxm5qaeP/995urqqou+ny+70kpP0/i01OUPmXp6wIUJVmEELM9Hs9Px48fP+ruu+/O7K0NxUtKSpg5cyZjx47lxIkTvP7662rdmDKotbS0sGXLFj755BNGjx7N9OnTWbp0KadPn2bbsW1syN5AxBpBMzXq8urIaM0gbA/jCDsAcIQdRGzd3zozKyuLNWvWZF65ciXz3Xff3eT1eg/5fL4/UZumK4OBGjFTBjwhxDiPx/OTrKysqStWrMguKChI+WM6HI7O0THTNNm3bx+HDh3q04adQ50aMetbTqeTqVOnMmvWLHzSx2/rf8vWy1uxt9rRTZ2K4gpWbF7Ref9N92zi3vfuTcpjX7x4kXfffbcxEAh85PP5/lRKqT4ZKQOWCmbKgCWEKPJ4PP/L5XItXbFiRXZJSUnKHzMvL4+5c+dSVlbG6dOn2bt3r2px0U+oYNZ/jB07ljlz5lBUVMQHlz5g27Ft7M3fm7SpzBuRUnLy5Enz/fffb4pEIhs6GtX2fkM2RekhFcyUAUcIkeF2u//WZrM9fvfdd2dNmDBBS3Vj2DFjxjBv3jzy8vL4/PPP2bt3L8FgMKWPqSRGBbP+I2wLY4/YcRY5GXXvKO7Luo8jTUf48MSHxHbEODP2DBFrhEknJiX9sU3T5ODBg9FPPvmkORqN/jgYDP6DlFL9sioDhgpmyoAhhNDtdvuPbDbbf1myZEnmtGnTrKncOknXdcrLy5k7dy5SSnbu3MnRo0e/tk2A0ndUMOs/Pp3/KRFbfI3ZpOOTGNY2jEnTJzHt9mmECPH6udcJvhvEEkndMudoNMru3buDu3btagmFQn8ei8VeU1dwKgOBCmbKgCCEmOd2u18qLy8vWLJkiddm61r/o+5wuVzMmjWL2bNnU1NTw86dO7lw4ULKHk9JDhXMBobS0lIWLFiA1+tl586dHDx4MKVtZAKBAJs3b245d+7ceb/f/5SU8kTKHkxRkkAFM6VfE0Lkud3un2ZmZi5evXp1Vk5OTsoey+PxMH/+fKZNm8aJEyfYtWsX9fX1KXs8JblUMBtYiouLWbBgAcOGDWP37t18/vnnKb14pqqqijfffLMxGAz+zu/3/ycpZe/uNaUoXaSCmdIvXZ22tNvtf7F8+fLMsrIyS6rWkaWlpbFgwQKmTJnCoUOH2LFjR6/vD6j0nApmA1Nubi4LFiygtLSUAwcOsGvXLnw+X0oeq+MK6vDWrVubw+Hwn0ej0VfV9KbS36hgpvQ7101bFi5ZssSTqmnLjIwMbr/9diZOnMj+/fvZuXOnagY7gKlgNrClp6czb948ysvLOXToEJ9++mnKfh/V9KbSn6lgpvQbV6cts7KyFq9atSpl05aZmZksXLiQ8ePHs2/fPnbu3KmusBwEVDAbHDweD7fffjtTpkzhwIEDfPbZZyn7/ayurubNN99sDAQCanpT6TdUMFP6nBBCdzgc/85ms/2X5cuXZ5WVlempmLb0er0sXryYsrIy9uzZw+7duwmFQkl/HKVvqGA2uHi9XhYtWsTEiRPZu3cvO3fuTMnvq5reVPobFcyUPiWEKHO73b8vLy8vTtW0pcPh4Pbbb2f69Ons37+fzz77TAWyQUgFs8EpPT2dxYsXM378eHbv3s2uXbuIRLq/ndPNBAIBNm3a1Hr+/Pnjfr//USllddIfRFG6QAUzpU8IIXSn0/nXLpfr3z7yyCPZhYWFSX8Mi8XCbbfdxrx58zh58iRbt25Vi/oHMRXMBresrCwWLVrEmDFj2L59O3v37sU0zaQ/zvnz580//vGPjaFQ6K/D4fDzavRM6W0qmCm9Tggxwe12vzF9+vQRd9xxh9tiSW6TSU3TmD59OosWLaKyspItW7bQ2NiY1MdQ+h8VzIaGvLw8li1bRmZmJh988AGnTp1K+mOEw2Hefffd1rNnzx7z+/2PqdEzpTepYKb0mo5Rsr9yuVw/StUoWWlpKXfffTdtbW189NFHXL6s9jIeKlQwG1rGjBnDsmXLCAaDfPDBByn5XT9//rz55ptvNobD4b8Mh8O/VKNnSm9QwUzpFR2jZK9PmzZt5JIlSzzJHiXLycnhnnvuISMjg/fee4+zZ88m9fxK/6eC2dAjhGDatGnccccdXLx4kY8//pjW1takPkY4HGbjxo2t586dO9oxeqY+7SkppYKZklJXR8mcTuePHnnkkeyioqKknt/hcLB48WKmTJnC9u3b+fzzz1Oy7kTp/1QwG7qsVivz589nzpw57N+/n23bthGNRpP6GOfOnTP/+Mc/qtEzJeVUMFNSRggx3u12/z4Vo2RCCGbMmMEdd9zBqVOn+PjjjwkEAkk7vzLwqGCmeDwe7rrrLkpKSnjvvfc4cSK5fWNDoRDvvvtu67lz5474/f7H1eiZkgoqmClJJ4QQTqfzPzqdzv+UilGyESNGcO+99xIOh9m0aRO1tbVJPb8yMKlgplw1YsQI7rvvPtra2ti0aRNNTU1JPf/V0bNQKPSn4XD4laSeXBnyVDBTkkoIkeV2u9+YMGHC9HvvvTctmaNkTqeTZcuWMXr0aN5//32OHz+etHMrA58KZsr1NE1jzpw5LFy4kH379rFt2zZisVjSzh8Khfj973/ffPny5ff8fv+3pJRq+xAlKVQwU5JG07QFbrd7w3333ZdXVlaW1NX95eXlLFu2jKNHj7Jly5aUNJhUBjYVzJQb8Xg83H333RQXF7N58+aktteQUrJ3797wli1bqgKBwP1qz00lGVQwU3pMCKE5nc6/SUtL+zdPPPFEVkZGRtLOnZWVxX333YfT6eTtt9+mpqYmaedWBhcVzJSvU1JSwooVK2hqamLjxo1JbTZ95coVfvvb3zYEAoG/DIVCzyftxMqQpIKZ0iMdG4+/NWXKlInLli3z6rqelPNqmsaCBQuYO3cu27dvZ/fu3aifVeXrqGCm3IqmaSxcuJDZs2fz8ccfs3///qSdOxwO88c//rHl0qVL2/x+/5NSSl/STq4MKSqYKd2m6/qdLpfrlVWrVuWVlpZqyTrv8OHDuf/++2lubmbTpk1J70ukDE4qmCldlZuby6pVq4hEIrz99ts0Nzcn7dwHDhyIfPjhhzV+v3+VlPJQ0k6sDBkqmCkJE0LoLpfr7zMzM7/5+OOPZ6WlpSXlvLqus2TJEsrLy9m8ebNa3K8kRAUzJRFCCObOncvtt9+e9FH5+vp6XnvttUa/3//fQqHQ/6t6nimJUMFMSYgQYpjb7X57xowZpUuWLPFoWnIGygoLC1m9ejUNDQ1s3LhR9SRTEqaCmdIdWVlZ3H///ei6zltvvUVDQ0NSzhuNRnnnnXdaz549u8fv9z8qpVRD/0qXqGCmdJmu60vdbvcrDz30UO6oUaOSksg0TWPx4sXMmDGD9957j6NHjybjtMoQpIKZ0hMzZ85kyZIl7Nixgx07diTtvEePHo1u2rSp1u/3r1RTm0pXqGCm3FJHw9g/S09P/8snn3wyy+v1JuW8eXl5PPjgg7S2tvLOO+/g86m1skr3qWCm9FR6ejoPPvggpmny5ptv0tbWlpTzNjQ08MorrzT4/f5/Ew6HNyTlpMqgpYKZ8rWEEDa32/1SSUnJvQ8++GB6MhrGCiE6r7j88MMPOXjwYM8LVYY8FcyUZLj+9WnTpk0cO3YsKecNBoO8+uqrzQ0NDc8HAoG/kFKqTX2VG1LBTLkpIUSu2+3+YP78+ePmz5/vFEL0+Jxer5eHHnoIKWVSP5EqigpmSjIVFhby0EMPcfnyZd59913C4XCPz2maJps3b24/duzYbr/fv1pK6U9Cqcogo4KZckNCiKkej2fj6tWrC8eOHZuU9WTjx49n5cqV7N69m88++0z1JVOSSgUzJdksFgt33303paWlvPnmm1y6dCkp5+1oqXHJ7/cvk1Im56TKoKGCmfIVNpvtcY/H8y9PPvlkdk5OTo/Pp+t654vbG2+8QVVVVRKqVJQvUsFMSZWxY8fywAMPcPDgQbZs2ZKUD5WVlZVs2LCh1ufzPWKa5qdJKFMZJFQwUzoJITSXy/V3OTk531m7dm2mw+Ho8TlzcnJ4+OGHaWxs5O23307KdICi3IgKZkoquVwuHnroIXRd5/e//31SLlZqa2vjN7/5TVN7e/v/HQwG/yUJZSqDgApmCgBCCLfL5frjlClT5txzzz3eZPQnmz59OkuXLk361ieKciMqmCmpJoRg4cKFzJo1iz/84Q9cuHChx+eMRqO8/vrrLVVVVW/6/f7vSiljSShVGcBUMFMQQoxwu90fLlu2bMS0adPsPT2fxWJh5cqVFBQU8PrrryetYaOifB0VzJTeUlJSwkMPPcS+ffvYtm1bj6c2pZRs27bNv3v37hOBQOAeKWVTkkpVBiAVzHrZerH+V8BKoG6dXDe543uPAn8DlAFz1sl1e790zD5gHrAOeBrIXCfXea778z8DvgPEgHrgW+vkui4tKBVClHs8ns2PP/54YXFxcU+fHpmZmTz22GPU1tbyzjvvEIupD39K71DBTOlNHo+n8wrzN954A7+/5xdYnjx5Mvb2229X+v3+xVLKyq4cs16sXw78b0AHfrFOrvu79WL9/wDuByLAOeCb6+S6luuOudV7yrPA/wCqO771f9bJdb/o6fNTuiZpG08rXfYisPxL3zsKPARs+/Kd14v1JUD1OrkuArwNzLnBOQ8As9bJdeXA68A/dKUQXdfvSE9P//DZZ59NSigbO3Ys3/rWt9i/fz9vvvmmCmWKogxaPp+P3/zmN1RVVfHcc88xcuTIHp9zwoQJljVr1pR4PJ5dQohJt7r/erFeB/4FuBeYCDyxXqyfCHwATO54TzgN/MV1x5Rw6/cUgN+uk+umddxUKOtFKpj1snVy3Tag6UvfO7FOrjt1k0PuBTZ33G/XOrmu5gbn3LJOrru6ueQuYPit6rDb7Y9mZma+/p3vfCc3GVdeLl68mPvvv58NGzbw+eef9/h8iqIo/Z2Uki1btvDWW2/xyCOPMHv27B6fs7i4WDzzzDNFaWlpWzRNm3+Lu88Bzq6T6853BK3XgFXr5Lr318l1Vz8Zf/k94ZbvKUrfUsGs/1tOxy9RF30b2PR1d3A6nT/Kycn52Xe/+93snm6v5HA4eOKJJxg1ahQ///nPqazs0ui7oijKoHHu3Dl++ctfMnPmzM4N0XsiNzeXb3/727mZmZlv2Wy21V9z12HA9S+6VR3fu963+OJ7QlffUx5eL9YfXi/Wv75erO/5lIrSZSqY9WPrxXobMHydXHe+i/d/CphFfG3AVwghhNvt/vthw4at/+Y3v9njdhh5eXl897vfpampiV//+tdJWWOhKIoyELW0tPDLX/4Sp9PJ008/jdvt7tH50tPT+e53v5udnZ39S6fT+b2b3O1G27F0LhxfL9b/FfG1x690/P+uvqe8DZR0TIV+CLyU8BNQuk0Fs/5tIdClxoPrxfq7gL8CHlgn132lWZgQQne5XL8pLS393pNPPplhtVp7VNi4ceN4+umn2bJlC++99x6mqbZ9UxRlaItGo2zYsIFz587x3e9+l8LCwh6dz+l08q1vfSursLDw79xu938VX90Xrwq4fjRrOHAZYL1Y/wzxC82eXCfXXQ1rXXpPWSfXNV73PvI8MLMnz0NJjApm/dtybjEtCbBerJ8O/Ix4KKv78p8LIRwul2vTzJkzV61evTq9pz3K5s6dy3333cerr77K0aNHe3QuRVGUwWbbtm1s2rSJJ598kilTpvToXDabjaeeeipj9OjRP3K73b8QQlz/Av45ULperB/VMRq2Bnir40rN/0z8PSFw3f27+p5yfaJ8ADjRoyehJES1y+hl68X6V4E7gByglvjlyk3APwO5QAtwcJ1cd896sf5zYNE6uS7Ycew/AGuBIuKfin6xTq77m/Vi/YfAFODqIs6KdXLdAwBCiHSXy/XxHXfcMXHOnDk9mrvUNI0VK1YwbNgwXn31VbUBudKvqHYZSn+Tm5vLmjVrOHHiBB9++GGPziWl5IMPPmg/dOjQdr/f/6CUMgKwXqxfAfwT8XYZv1on1/339WL9WcAONHYcvmudXPf9BN5T/h/igSxG/P3pT9bJdSd79ASULlPBrJ9aL9YPB55fJ9fd291zCCEKXC7XtpUrV5ZMnDixR3OXDoeDRx99lGg0yhtvvEEkEunJ6RQl6VQw65lzo89RMaICJKS1pzHt4DQM3WDvzL0EnUGcQSez9s3CFrX1dakDitPp5LHHHsPv9yeljdDOnTuD27dvPxIIBJZKKbu8L1Qy3lOU3qGC2SAlhBjmdrs/feSRR0aMGjWqR3OXWVlZrF27llOnTvHhhx8mZQNfRUk2Fcy6L+gI8tmCz1iyZQm6qbN35l7yavNo97Zji9ooPVvKmbFniFqjTDwxsa/LHXB0XWfVqlVkZGTw6quvEgwGe3S+w4cPRzdv3nwiEAgslFKqqYtBRq0xG4SEEMVut3vH448/PrKnoWzEiBF885vfZMeOHXzwwQcqlCnKIGUKE0M3Or86wg6uFFyhuDK+try4spiaAtXyqjsMw+CNN97g4sWLfPvb3yYzM7NH5ysvL7fed999ZS6Xa4cQIj1JZSr9hKWvC1CSSwgx0u12b1+zZs3w4uLiG11K3WUTJkxg5cqV/P73v0/KZr2KovRPzpCTsefG8sFdH6AbOrn1ueTV5xG2h3GE40tTHWEHEZtawtATH3/8Ma2trXzzm99kw4YNVFVVdftckyZNsmqaNv7tt9/eKYRYIKVsTmKpSh9SwWwQEUKMcrvd29euXTts2LAv9xhMzMyZM1m0aBEvv/wyV65cSVKFiqL0RxFrhCsFV7jro7uwRq3snbWXymGqWXQq7Nu3j9bWVtasWcPGjRs5caL7FzyWlZVZNE0r/eMf/7hTCDFfbX4+OKipzEFCCDE6IyNj15NPPtnjUHbHHXcwb948XnjhBRXKFGUIaMhpwBVwYY/Y0aRGYU0hzVnN2MN2QvYQACF7CFtELfxPhrNnz/Lyyy+zfPlybrvtth6da/z48ZbVq1ePzcjI2C+EyE5SiUofUsFsEBBClHg8nm0/+tGP8mbMmNGT87By5UpKS0t54YUXaGlpSV6RiqL0W86gk+bMZmJ6DImkPqceT7uHgisFVBbHR84qiyspuFLQx5UOHleuXOFXv/oVs2fPZvHixT0619KlS/Wnnnqq2O12fyaE6NkCNqXPqWA2wAkhRrjd7u1PPPHEME3TsFqtFBQk/uJpsVh49NFHycjI4KWXXlLbKynKEJLZkknh5UK2LdrGJ4s/AQEjK0ZSeraU+px6PlryEfU59ZSeLe3rUgeV1tZWXnjhBSZMmMDy5cu7dY7i4mKi0Sg5OTnaAw88MKbjgoCM5Faq9CbVLmMAE0IMd7vdO5544onhw4cP71zof/UXtavTkHa7nSeeeIK2tjbefPNNtb2SMiCpdhnKQGW321m7di1NTU289dZbXb76/Uav9SdOnIi9/fbbpwOBwHwpZWuqalZSR42YDVBCiCK32/3p448//oVQBlBZWdnlkTOHw8E3vvENamtreeONN1QoUxRF6WXhcJiXX34Zj8fDo48+iq7rtzzmZh/Ay8rKLCtXrix1uVzbhRDeVNWspI4KZgOQECLH7XZ/+thjj40YMWLEDVtidCWcuVwunnnmGS5dusSmTbfcPk1RFEVJkWg0ymuvvYaUkieeeAKr9eabtdxqVmTixInWFStWTHC5XNuEEM5U1aykhgpmA4wQwu1yubauXr16xMiRI7+2T9nXhTO3280zzzzD6dOn+eCDD1JWr6IoitI1hmHw+uuv09bWxje+8Q3sdvtX7tPVpSqTJ0+23nnnnWVut/tdIYRqjTWAqGA2gAghrC6X6/177rlnbGlp6a3HurlxOPN6vTz77LMcPXqULVu2pKxeRVEUJTFSSt566y0uX778lXCW6PrhWbNm2WfPnj3b7Xa/LIToUcNxpfeoYDZACCE0l8v1+wULFkybOnVqQs2Erg9n6enpPPvssxw4cIDt27enqlxFURSlBzZv3kxlZWVnOEs0lF21ePFi9/jx4+91uVz/mKJSlSRTwWyAcLvdP54yZcodCxYscHXn+MrKSrKysvj2t7/N7t272bFjR7JLVBRFUZLovffeo6qqim9/+9sIIbrV8LujP2Xa8OHDv+Vyuf48BWUqSaaC2QDgdrv/r5EjRz6+fPnybl9hk56ezrJlyzh8+DAVFRXJLE9RFEVJkWPHjnHlyhWWLVt2wzVnXaFpGo899lhGdnb2X9nt9ieSXKKSZCqY9XNOp/Pbubm5f/rwww9ndHeJgNfr5emnn2bnzp18+OGH3W5CqyiKovSeq9OXb7zxBpcvX+app57qdjizWCw89dRTmenp6f+s6/rSJJeqJJEKZv2Y1Wq9Ly0t7R+efPLJzK70tbkRt9vN008/zb59+9izZw+QWJ8zRVEUpfd9eU3Zpk2bqKmp4cknn8Rm696epQ6Hg2eeeSY7LS3tNSHE9GTWqySPCmb9lBDiNq/X+9Kzzz6b1ZNfwm984xscPXr0K2vKVDhTFEXpn2620P/dd9/lypUrPPnkk1gs3euA4fF4ePrpp3M8Hs8mIcSYZNSrJJcKZv2QEGK81+t965lnnsl2ubq11h+73c5TTz3F2bNn2bp16w3vo8KZoihK/3Krqy/fffddmpqaeOyxx9C07r2FZ2Vl8eSTT+a73e4tQoj8ntSrJJ8KZv2MECLX7XZ/+NRTT+VlZGR06xxWq5W1a9dSXV3Nhx9++LX3VeFMURSlf+hqS4y33noLwzB48MEH6e7a48LCQh5++OFhHeFM7Q7Qj6hg1o8IIWwul+v9Bx98sCg/v3sfYnRdZ82aNTQ0NHR5myUVzhRFUfpWIn3KpJS8/vrruN1uVqxY0e3HHD16tLZkyZLRbrf7d6oBbf+hglk/IYQQbrf7pQULFowbO3Zst/9dVq9eTTAY5J133knoOBXOFEVR+kZ3mscahsFrr71GUVERS5Ys6fZjz5o1yz5+/PiFLpfrb7p9EiWpVDDrJ5xO55+WlJTcO3/+/O4tKgPuuecePB4Pf/jDH5BSJny8CmeKoii9q7sd/QEikQivvPIKEydOZN68ed2u4b777kvLysr6tzabbVW3T6IkjQpm/YCu60vT09P/6sEHH0zv7mjy/PnzGTVqFK+99hqGYXS7FhXOFEVRekdPQtlVgUCA3/zmN8yZM4dp06Z16xy6rrN27dpMt9v9CyHExG4XoySFCmZ9TAgx2uPx/OuTTz6Z1d3Ln8vLy5k9ezavvPIK4XC4xzWpcKYoipJayQhlV7W1tfHyyy+zdOlSxozpXgcMl8vF2rVrc9xu9yYhRFaPi1K6TQWzPiSESHO73R+sWbMmz+vt3m5LY8aMYdmyZbzyyiu0t7cnrTYVzhRFUVIjmaHsqsbGRjZs2MCDDz5IXl5et86Rl5fHAw88UOR2u98TQliTVpySEBXM+ogQQnO73W8vX768uKioqFvnKCws5MEHH2TDhg00NDQkuUIVzhRFUZItFaHsqsrKSjZt2sTatWvp7of98ePHW+bMmVPmdrufT3J5ShepYNZHXC7X/5o6der0KVOmdOtTSXp6Ok888QTvvPMOlZWVyS6vkwpniqIoyZHKUHbVsWPH2Lt3L0888US3t25atGiRe/jw4aucTucPk1ye0gUqmPUBh8PxdH5+/tN33XVXtz7S2Gw2nnjiCXbs2MHJkyeTXd5XqHCmKIrSM70Ryq769NNPqamp4eGHH+5WA1ohBI888kiG1+tdr2nawhSUqHwNFcx6mRBiutvt/p9r1qzJ6O52Gg8++CDV1dXs2rUrydXdnApniqIo3dOboeyqjRs3omka9957b7eOt1qtfOMb38jyeDyvCyGKk1ye8jVUMOtFQgiv2+3+49q1a7Ptdnu3zrF06VIcDgcbN25McnW3psKZoihKYvoilAGYpsnrr7/OiBEjut3jLC0tjUceeSTX5XJtFEJ0r22AkjAVzHpJR2f/3959990FOTk53TrH1KlTmThxIhs2bMA0zSRX2DUqnCmKonRNX4Wyq8LhMP/6r//KvHnzut1GY+TIkWL27Nmj3W73Pya5POUmVDDrJQ6H4/ujRo2aP3Xq1G4t9i8uLmbZsmW8+uqrBIPBZJeXEBXOFEVRvl5fh7Kr2traeP3111m9ejWZmZndOsfixYvdmZmZT1sslnuSXJ5yAyqY9QIhxGSXy/XfHnjggfTuHJ+ens6jjz7KH/7wh5S0xegOFc4URVFurL+EsqsqKirYunUra9as6daVmpqm8fjjj2e6XK6XhBDqRT/FVDBLsfgMpvutNWvWZHfnF8JisbBmzRo+++wzzp07l4IKu0+FM0VRlC/qb6Hsqr1791JZWcnq1au7dbzX62X16tW5brf7bSGEntzqlOupYJZibrf7pTvvvLOou52YV65cSW1tLbt3705yZcmhwpmiKEpcfw1lV23atAmPx8OiRYu6dfyYMWO0adOmTXC5XP8tyaUp11HBLIXsdvuzxcXFS2fMmNGtSzBnzZpFQUEB77zzTrJLSyoVzhRFGer6eygDMAyDDRs2MHPmTMaNG9etc9x5552e9PT072ma1r10p9ySCmYpIoQY53K5/seDDz6Y0Z0Gf8OGDeOOO+5gw4YNxGKxFFSYXCqcKYoyVA2EUHaVz+djw4YNPPDAA3SnQ4Cu66xZsybL7Xa/JoToXosB5WupYJYCQgiH2+1+5/HHH8/pTr8yl8vFI488wttvv01TU1MKKkwNFc4URRlqBlIou6q6upqPP/6YRx99FIsl8fZk6enp3H///Xlut/tNIYTKEUmm/kJTwO12/3zhwoXFhYWFCR8rhOChhx7i6NGjnDp1KgXVpZYKZ4qiDBUDMZRdtX//fmpqalixYkW3jh8/frw+ceLEKU6n8y+TXNqQp4JZklmt1tX5+fkrb7vtNkd3jl+yZAlCCD7++ONkl9ZrVDhTFGWwG8ih7KqNGzdSXFxMeXl5t46/55570rxe778XQsxIcmlDmpBS9nUNg4YQIsvj8Rz7kz/5kwK3253w8WPHjmXlypX8/Oc/JxAIpKDC3jUYXriUvieEwOFw4HA4sFqtWCyWzq8WiwVN07BYLMyaNYtdu3ZhmmbnzTAMotEokUiEcDhMOBwmEomgXveUnhhMr215eXk8/fTTvPjii93qk1lfX8+LL754zu/3T5RSRlJQ4pCjglkSeTyed1asWHHPpEmTEp6093g8PPfcc/zud7+jsrIyFeX1icH0AqYkl81mIzMzk7S0NLxeb+dXr9eL2+3G6XTidDqx2WyEQiHC4TDRaJRoNEosFuv8apomVquV0tJSjh8/jqZpaJqGrutomobVasVut2O327HZbNhsNqLRKH6//ws3n89HW1sbLS0ttLS00NraimEYff3XpPQzg/E1bfr06cydO5fnn3++Wxebbd++PbBz586f+f3+P0tBeUOOCmZJYrVaHxw1atQvn3zyyW7tefHUU09RWVnJ1q1bk11anxuML2T9SaNVcNhrocWqkRMxKW+PkR7rH7/XQggyMzPJz88nJyeHrKyszpvNZusMQO3t7bS3t9PW1kZ7ezt+v59gMEgwGCQUCnXpscaNG8fp06e7dF+73Y7b7e68eTwe3G436enppKenk5GRQVpaGsFgkObmZhoaGmhoaKC+vp6GhgZaW1vVqNsQNJhfy1avXo1pmrz11lsJH2uaJj/72c8aa2tr75FS7ktBeUOKCmZJIITI7pjCzO/OFOb8+fMZP348L7744qB9sR/ML2h9qcau8UGOjZgAhEBIiS7hvrow2dHe/VnSdZ2CggKGDRtGQUEBeXl55Obm4vf7qauro76+nqamJhobG2lqasLn8yX18RMJZl0hhMDj8ZCVlUVubi45OTmdN6fTSW1tLVeuXKGmpoaamhrq6uowTTNpj6/0L4P9NcxqtfLcc8+xfft2Dh8+nPDxDQ0NvPDCC+c7pjTDKShxyFDBLAk8Hs/G++677+6JEycmPIVZWFjIk08+yfPPP09ra2sqyus3BvsLW1/4fYGdVqsGEojawBoBJIVhk3vrU7vcIy0tjZEjRzJ8+HCGDRtGbm4ujY2NVFdXc+XKFWpra6mrqyMS6Z1lJ8kOZl/HbrdTUFBAYWFh59fMzEyuXLlCZWUlFRUVVFZWpnytqM/tY+/MvZ3/P+AKMP7UeIqritk7cy9BZxBn0MmsfbOwRRPfEk6JGyqvXVfXm/3yl7+kubk54eM//fTT4I4dO37u9/v/ffKrGzpUMOshm8328KhRo55fu3ZtwlOYNpuN5557jo8//pjjx4+norx+Z6i8wPWGGPCb4Q6kENCQi2jKQ3paoaAaHZNnqrs2BdhVaWlplJSUdN6sViuXLl2iqqqK6upqampq+rQZcm8GsxuxWq0UFRUxYsQIiouLKS4uxu/3c+nSJc6dO8eFCxcIBoMpe3yJ5P1l77Pw04VcKLmALWqj9GwpZ8aeIWqNMvHExJQ99mA21F6zbrvtNiZNmsQLL7yQ8AxOx5RmU8eU5t5bH6HciApmPdDTKcxVq1YhpezWnP5ANtRe6FLluMvCriwrmBqcH4eQ8X2FpdOPI/cSs30RgpogP2KQFzVJdP8JXdcZOXIkpaWljB07FqfTycWLFztv3bmCK5X6Oph9mRCC3NxcRo0axejRoxkxYgRNTU2cP3+ec+fOUVFRkdSpz7rcOk6NO8XCzxby0ZKPWLBjAY6wg5A9xGfzP2PplqVJe6yhYqi+Vn3jG9/g4sWLbN++PeFj1ZRmzyXe8lfp5Ha7f7NixYrs7oSySZMmMXz4cH7+85+noLL+rbKykuLiYgoKCobcC16yNFg19qZZ41OYLVkIqSPtQTAsiKCbcM0YduZdwrTG0KWVgojB0pbwLRsXulwuxk2YQMn48YwbOZL6ujrOnjnDG2+8QU1NTW88tUFDSkldXR11dXXs3r0bTdMYPnw4o0eP5q677iIzM5OzZ89y6tQpzp492+Mp3+qiaoZXDwcgbA/jCMdbKTrCDiI21cUgUUM1lAG8+eabfO973+Ps2bMJ/97n5OQwb968wh07dvwD8O9SU+HgpoJZN9lstkdGjx49rzvryjweD/feey+vvPIK0Wg0FeX1eyqcJcYAzrh0LjgtWE2JKcAUgCGgJTt+p5xasIaheiREHRhXRkFBJTFbmCs2nTNOC+ODX51q9Hg8TJgwgYkTJ1JQWMiWiou8dP40n37yPs2hEIt9IYqjqm1ET5mmSUVFBRUVFXzyySd4vV7GjRvHtGnTeOCBB6isrOTYsWOcOHGCcDixgQZTmNQW1FJ2oixF1Q8tQzmUAbS3t7N582YefPBBfv7znye8RGH+/PnOo0ePPiWE+I2a0kycCmbdIITI8Xg8/2fVqlUZ3Tl+5cqV7N27d8iPQKhw1jUmsCnHTrNFI6bFJySFlEghEAEPGBakLQR2PxYJZsFFZH0xhNxweSQUXSRmi3D6umBmt9uZOHEi5eXl5Ofnc+bMGT7ds5u/b7yC37wuhGmCT7wOHm4O4FLLHpKqvb2dffv2sW/fPmw2G6WlpUyaNIl77rmHixcvcvToUU6fPt2lD2+1ebWkt6bjiMRHyexhOyF7qHMq0xZRC/+7aqiHsquOHj3KuHHjuOuuu9i8eXNCx2qaxiOPPJL1wgsvbBBClKkpzcSoYNYNHo/nxRUrVmS7XK6Ej506dSrp6els2LAhBZUNPCqc3dpFp34tlMV0QCK1jrVJvjQAhKcVhEBKia6ZxAoqoa4IAmlQXwRFF9E1jdLSUsrLyxk7diwXLlxg9+7dnDlzBsMwOG23EHbb4+dpzQHDAhl1mBgccViZFIriuUU48wtBRAjSTVPt95aASCTCsWPHOHbsGHa7nQkTJjB16lRWrlzJyZMnOXDgABUVFTc9vnpYNcOqh3X+/4IrBVQWV1J6tpTK4koKrqjt0bpChbIvevfdd/n+97/P6dOnOX/+fELH5uTkMH/+/ILPPvvs74A/TU2Fg5MKZgnSNG3xyJEjuzWF6fV6WbZsGb/5zW9Uv6PrqHD29SrsejyUmQIqSkEKcPrA0woBb/xOrnaElMxoj3LcZSWmScitgSonw91ZPFFeytryMfiamzl8+DDvvvvuV64QjApB50+lLwukBhEnZlYlp+1wxm4lN2awxB/C9qV8FhKCT5wOGnS98yKDkkiUXNOkOBbDqUbbuiwcDnPo0CEOHTqE2+2mvLyclStXomkaBw4c4NChQ1/oARfTY9Tn1jP18NTO75WeLWXvzL1UFFd0tstQvp4KZV8VCoX44x//yOrVq/nJT37S5WbPV82dO9e5f//+bwghfiylPJOiMgcddVVmAoQQVrfbfeo73/nOqMzMxBv8r127lurq6kHZ3T8ZBtILY1TAyTSdC24Nq4SyNoOR/ltf+SiBK3aN804dDRgTiJEXkV/484AmsErZGX52p1k54bYgpQYXJsCXH8UaguLzCBOeqg3QZNX4MMvJ4pGjWTN+JtPyC/nDgSquHPiYWFP1TWtr1jXeSXdiCIGsLoWOqzzRYoisKoQthCYlhTGDu3whJPGWHQDvuF20a1q8dUdH3QKJJuPVzguFGJ3iVhr97arMZBs+fDjTp0+nrKyM8+fPs2fPnq8dRVO6biC99vSFFStWoOs6b7/9dsLHVlRU8Nvf/naX3++fL1Xg6BI1YpYAp9P5H2bMmJHXnVA2bdo0PB5Pty4/HioGysiZAWwsstJmFRgda74a7IJau8FtTV9cJB/W4vd3dQxF7cywctatxzv1A6fdOpPbY8xsi1Fl1/gsw0ZYE0hgWMhgYUuE8YEYp1wWDE2CLQwRB3ibIWKHsAvSmkEKBBKrw86q6TP42zlzqPf72HT4AH/78jbOB7LIFmmstF9GEzd+bcw0TMaEo5y3WYkKGQ9Y1iBEnciGEZBVjenwU2PROWa1ctRuIywEnWcTAhlMQ0ZcCGsQbH5MPR7GdjocFPr9auSsB6qqqqiqquK9995j6tSp3H///cRiMfbs2cORI0f6tIfcQKZC2a19+OGH/OAHP6CkpISLFy8mdOyIESMYOXLkhNOnTz8IvJGSAgcZNWLWRUKIwvT09EM//OEPc61Wa0LHpqWl8dxzz/HrX/+aurq6FFU4ePT3F8qzHo2dOZb49GLECpoJFgPdlDxUGcFjgF+Hrdk26u3xlVbemGRKa5QdmbZ4mLv6aydANyV3NkTYkmnvDHoAmpTkRkxWNIY559DZkWHDqC9C+jIhqwbSW+LnETDc4+X7k2bw8PjJnD17lp07d3ZeXBKRGm+GJ+GXdmZYqplqvflFJxKosups8U3CMG2IvHNIXxYEMkGPIPLOo5kSTQqM60bupATpz4VA1hfOJ+xtiLQaLEhmhcOMT+FVyIN9xOxGRo8ezZw5cxg+fDh79+5lz549Kd9tYDDp7681/UlpaSnLly/nJz/5ScIfAnw+Hz/5yU8u+/3+Uiml+gG9BbU+t4vcbvfzK1asyE40lEH8Ksw9e/aoUNZFlZWVWK1WCgr654LlSpd2bc3X2XFwegLU5YEpqHVotFgEfyxwUGfXMIXAFIJWq8anWTYMQfy4S2PgQikEXEjgoNcab38hgaZc8HswhaDBqtGqC8aEDJ64EmRCrB0AS9iBxZSMz87h/9x5H28//DSFwSg//elPv9JzzCZMbrdeBOBgrJAm03nT5yaA4qiBW8aH+IQEkV4LegQMG4S8SER8utPUMH05mK1FyKaSjlAmwdUEtnbARIbTIBq/SEZ9BEy+8+fP89prr/GrX/0Kj8fDD3/4Q5YvX056enpfl9bvqVCWmDNnzlBdXc2SJUsSPtbj8XD77bdnuVyu/56C0gYdFcy6QNO0Rbm5ufPGjx+f8N/XxIkTSU9P59NPP01FaYNWfw5n7phEXD/SLDWoz8c4N56jMps38+2ENZAIaMmE+jxkyI6UAhDQno6IOBAxG1SVQGMeISHi67MCHmjOhSsjoDEPIcFniY9MWYDRph+Amen5vLr0fl5d8QhN1Zf5p3/6Jz59/wPa2tpuWHOR3s54vQ4Tje2REiJSxB/zuvu0aBoXLRaaNQ2t4zIAqwk6EuFpit/Jl0XnFQKhNAhkQ9gLhh2EgcioQvPUo2dcRrjjx5iB+NT/cDXVljJNTU288847/PjHPyYWi/Hcc8+xevVqsrKybn3wEKRCWfds3ryZ8vJyioqKEj52zpw5DqfT+ZQQYmwKShtU1FTmLVxd8P/tb397VKIvcna7nR/84Ae8/vrrVFZWpqjCwa0/voC2WgV/HGaNTzueHwNBF2gGmB2L5R0ByKmD5izwp3UeJ+2B+PRjSxYi4kC62iHgQSAQtgAyvzoeynwZ1x7M6WOR5RyXnBp+TWNRZi73zVnBpKJ09u/cyoF9e7s8rRCVGn/omNLU3PVonkacUnJbMMRJq426jisqDcBoHomMOclIO0e2JUCzrtPSMA5TWkhLv4DPEUEG0pHtBWD1o3kakHoEoZnQsejfNHXMxjGAxhT3SWaZvq8vsIeG4lTmzdjtdubMmcNtt93GmTNn2Lp1Ky0tLX1dVr/QH19TBpIpU6Ywf/58nn/++YS7C6gLAbpGjZjdgsPh+PMZM2bkdeeT55133snZs2dVKOuB/jhylh6VLK6LYTMkmiO+XMKeUYc3qxL0KIRc8ZEwf1o8sKW1IDUDEXYh6orioUyPQmElDL+IrkWQERdUjb4W5HIvgxaDoIdtgTL0zGGsX7mK//TwI2w9e5nF/2ML//3ASarp+gujVZg4vPEpTtOfgxFz4Nc0tjid1Oo6hhDEEEiujaS1aVaqNCuL/SGm6PGpeLsvE13K+No6AM1AWINomolVSkZFY9wbCFAeC5Jji4+ahULZPf57v5lLmoW3LG6ahMZGi5saoafssQaKcDjM9u3b+ed//mdaWlr47ne/y8qVK0lLS7v1wYOYCmU9d+TIEdrb21mwYEHCx169EMBisaxOfmWDhwpmX0MIUWi32/9s0aJFCW+GWVRURFlZGR988EEqShtS+jqcRQWcc2uc9Oqc8ui8WWRjW44VVxRGGPFRIKvfhchogdFnIasehAlOP5Scg8JqKDmFzK9GOuNTkWQ2gJAIewBb0Tlwt8XbU0gt3v7C2wJF58nPkvz/HpjJ7x59jKNVrdzx61/x6/3HCcdMqnHzvtfJEXvX1j22aYI2ZwhczYBAthYgzfg0qSkEMmLHaB6B0TIcjI5zSoEBHLHZKLPUoWFSb6Qzx2fikfGROt3UyI8ZlESjLAwGWRgKkWOaTI9EWCRqAMk5I5ugTP5F4Gc1Kzt0J22ajkTQrOl8YnGpcNYhHA6zdetW/vmf/5lAIMD3vvc97rrrLux2e1+X1utUKEuejRs3MnfuXLrToWDFihUZdrv9/wghEu/QPkSoYPY1PB5Ptxb8CyFYuXIlH374YcIN+ZQb66twVmsX/HaEnR05VnZnWdiRbaHZFl/832LVuJgd3xzaF3PTpgkQBuTWwdiTUHwRrFGQkgzDQHhaYNgl5JjjkNEMErKiEl2YkFsNOZfje11mNODUbfz53NvY/G+X0ma0c+f/fys/2VxPqKoEoh2L96PxnmMHXTZCt2qgBviFhiYlwlMfX8wfcyCbRmAa8cBkBrIh5oSoG66GKBHf+qlJ13CKGGP1RkDQEM5hYSgeMrNicE8wyKJQiGLD+EKXtXQtzAitBRONY7E8krkviwQO6nYMITBjDnbXWpGGFUMIDuhDL3h8nVAoxMcff8xPfvITnE4nP/zhD5kzZw6aNjTeAlQoS67W1lZ27NjB8uXLEz72ugsB/lsKShsUhsZvZTcIIRbm5OR0a8H/nDlzCAaDHD58OBWlDVm9Hc4M4MN8G1FNENMEJiLeO6zdC01Z0JoOMWs8jBmW+FWLxPexRJNoSHRTsrQhwr0NYVyGxGLKzhYZVgkLm8OMDcbQkeBtheHnWTVtOB89/k3GeNNZ+dpL/N3+zbQ6KuJhyrRArCN0RONfNQlXLLceIcowTQwhEJpEZFZdC2fNI5FhF0Q8gER4LyPctQhPLViCHcEyPm05yVILwFkjh6ubLkX4+sce13HMESOP3+tpvG1xU5+EEa0YEO6IgTKaxvYaG2Y0PlXXqkbMbsjn8/H222/zm9/8htLSUn7wgx8wbty4vi4rpVQoS42dO3eSlZXVrZ+fjgsBnlEXAtyYajB7A0II4Xa7n7///vsTXljm9XpZtGgRv/rVr1JR2pDXm01oax0a8urwT20+NObwla771ws7wRZFNySjAzGchqTUb+A14iu2Hq4Lcd6pU2fVSDckpYEYDhPSYlFq7DqFhYX89e1LsWs6/+H9tyk8fo6Yx46w6EhPKzhbIWaDkAfCbrBfW0z/5S2SbsQpJaWRKGdtVgxLFLIqkM1FEHNhthZ3nKgdzdH2heepS5gSjY8MZmghhmstVJkZnDfivx5R+fUh6KjVRMSCSNNJLJZOm62Vj3UX98V8eHrQRMPScYtIMKMeADRr/O/EI9WWZ1+nrq6OV155hTFjxrB8+XJmzJjB5s2bB90FAiqUpY5pmmzatImVK1dy/vz5hHqb6brOypUrs3//+9//BFiWuioHJjVidgMWi+WR0tLSguzsxBcs33333Xz++ec0NjamoDIFem/kzBQdnSEk8SssEWCNgNsH6U3xES6nP/49axhs8WlrC7CgOcqMtlhnKAOwSBgXMLi9NcoUXzyUAbjtdv557p28tPxB9hw4wP/81S8Ye+QsaYZkSVuYnJiJLiUWIRHWMMLbhMipRHibgXg7i4LYF3ccuJnbQmFmhcJ4DRMbUfS0SoT9WosNzdEW30VAys4p2KWhIFnXXX01uWME7Gow+7oRsyY0WjUdzR6v1YhkImV8NPK0ZutSzTcjgElGGC1mB2nBazVBC6NLyVQjmZOmg9e5c+f4yU9+QmVlJd/97ndZtGgRuj44RhtVKEu98+fPU1NTw/z58xM+dtSoUWRmZk4XQsxMQWkDmgpmXyKEsNjt9n9cunRpwh0ai4uLKS4uVj3LekFvhLNGi4g3hA05460wrGEYexpGXISiy1BUCSMuwOgz8UX/9gi6KRnr7/onx7KyMn7wgx+gaxo//ZcfU79nHyVhozPqOKVkZWuQVc0BlrUFmeWPBw+rKbFKicM0WdYe6vIvsgAmRKI85PNzZyAUD3uemvjUpaMZrD6ElIyLRHnK7+OBYIAC44uhr0BrJ1v4iXVUGUPHvMnAl09oaIBm8YGIgmnDjKYjhaBV9PzlZ5IZIbtjzV1pegwnJnOMECOk6pnWVaZp8tlnn/Gzn/2MgoICfvCDHzBq1Ki+LqtHVCjrPe+99x633XYbGRkZCR+7YsWKbI/H87PkVzWwqanML7Hb7d+bOnVqltfrTfjY5cuX8+GHH6o963pJKqc1YwIOZ1hACvB3XDzkCgDxPSQF8REwYQqkkBgivtYrM2oyrfXW//7p6emsWLGCjIyMLvW5Szcl6aakIGZSGolRZ9GxSMiPGd3+dJVvGtilJCZAOFs6v69JGB+L3vS8QsBkyxW2Rsd0fu+EcDCe0FdeUDKlgdlxjMVRTyxYhBHKwaK1kymMqztKdZkJXMLCeWyEEZhAUyz+GWp6epTyy76Ezqdc09bWxoYNGygtLWXVqlWcO3eO999/n3B4YI0+qlDWu9ra2ti1axf33HMPv/3tbxM6trCwkGHDho3WNO0u0zQ/TFGJA44aMbuOEMJltVr/etGiRZ5Ej506dSqGYXD06NFUlKbcRKpGzlot4tobfKjjKkhHEKTAG5E8WRHmycowT1SHWNQYYVZLlGX1Ee6rjWC9xbKp6dOn89xzz1FVVcXPfvazhPvc2WV826TCHoQyiAeiu0MB0mV8qtTacbs9HCLjVmu0LO0IcW3fy0PCxSbdzZd3wvQiGS6j6FIiLD6E7gN0ouF8Tko7bwgPe7FTG7/84WtJ4FOcfI6TWmGhRei0SAeGtAMGWW75lcdXEnfmzBl+/OMfYxjGgLs4QIWyvrFjxw7y8vIYOzbxtfz33HNPpsvl+hchkjCEPkiov4jrOJ3O/zR37twMh8OR0HE2m40777yTzZs3p6gy5eukIpw5DYl5NZnZOkYMgvGRM68Rv6JSEP8FGhE0mdRuUBA2v3a0xuv1snbtWmbPns2LL77I9u3bE+6cnWxeKVkVDLAyGGBZKMDjAR8jja8f8TOAvboTzdZ83fd0fGicEl9dNzbfCDHFDOPGxGarBUxMw0s05iGMxmlh4xNcvIWHrxvvOi8tXMZCTAik1DANJ0Ys3kdJ0/0g4Aw9W7emxEUiEd59913eeOMN7r77bh566CESfV3sbSqU9R3DMNi8eTN33303QiQ2Zt1xZWe+xWJ5PEXlDTgqmHUQQmRaLJYf3HbbbQm/+tx+++2cP3+ey5cvp6I0pQuSHc5cJhQFTTRTQlpL/JvtaWgxKG/t2kL7602ZMoXvfe97VFdX84tf/IL6+vqk1Jks6dIkxzS79ILQ3HEvzdpKPKYBMr5Ze6X4as8/jfhasPtiPjQRQ7c1ABCL5mEaLqTUMIQggGA7X+05KSXslXb24MQETNNOJDiKaLgYM5YRfwzdBwhq1OqMpLp06RI//elPCQQCfP/736ekpKSvS7ohFcr63pkzZ/D5fMyYMSPhY5cuXZput9v/XogbvIAMQSqYdXC73X+7ZMmSjESbyWZkZDBz5kw+/vjjFFWmdFWyw9nihijDgyaaNRLf/1JqlNa6KQx3fZTLbrfz0EMPcfvtt/Pyyy+zdevWPh8l6ykrdKwbk+iOuo4pynDHn918QjJI/EIA3dKC0EIgrUTDw4kEx2DE0pBC0Ib2lVGzWnTOY0MiME070dBwQEeIMELzo1laOoKZxJXAFlVK18RiMTZv3szbb7/Ngw8+yLJly/rVlZsqlPUfH3zwAYsXL8ZmS2zk2uPxMG3atCy73f79FJU2oKhgBgghhtlstsemTZuWcFpftmwZu3btor29PRWlKQlKZjizSljaEGVNVZhyGR/l8QUyutRlH+Lbcj333HOEw2Gef/75QfPGkYaJBxOkRLe2Y3Vejoc0KRkvIzc9zo0ZX+wvwGqvQrc0IkQY0DCi8SlJAcSuC2ZSwnFsGBKk1ImG46FM03xYHZewOaqx2uq4OnsygZs/vtIz586d46c//SkZGRl85zvfIScnp69LUqGsn6mpqeHChQvdap+xcOFCt9Vq/Uu1VZMKZgB4PJ7/dc8992Qluj3JsGHDGDZsGDt37kxRZUp3JHvkzG7CMEszIKk20nktx83b2Xba9JsntPnz5/PEE0/wwQcfsHHjxkF1pa4A7jACuJFYOi4Y0DpC2fCvaVNhASbJeLsPIUwstkasjgrAREo7UuroSNI6Rr2khG2mkzppQUpBLJoPWBBaAIutJr7VupSdFy64kWSpEbOUCgaD/O53v2PPnj08++yzTJkypc9qUaGsf/r444+ZPXs2Hk9i19A5HA7mz5+f4XQ6/yJFpQ0YQz6YCSHGu93uJd3Zemnp0qVs3bp1UL3pDhbJCmcSuGTTeD/PAg4/oCED6TRYNTZmO/jyv7zD4WDt2rWMHz+e559/npMnT/bo8fsrD5JVho8lRoB5ZpDVho/pZviWrSomEWG2DOHBAOIBTWjB+B/GnMwj2PmiVI2FeuKhzDDTMU0PYGC1XkEg0SUslz7uxs/DtGPvwS4CSmIOHDjAr3/9axYtWsTKlSuxWHp3bZ8KZf1Xa2srBw4cYMmSJQkfO2fOHIfVav2eECLhXXcGkyEfzDwez7/ce++9OYleSTJ69Gi8Xi8HDx5MTWFKj/U0nMWAd7PsfJJpjy9x93R0yPengRDEBFQ4rq21KSgo4LnnnqOhoYGXXnqJtra2G5120BBAHgbFMoazi6FIAKOJ8oD085hs5zYZIkuLLwMoMm0Uce3CiirTQoz4FZixWC4AVksdGvH2G/MJkiUkmXTtogUluerq6nj++eex2Wx8+9vfJjMzs1ceV4Wy/m/79u2MHz+e3NzchI6zWCzceeedGW63+7+nqLQBYUi/ngkhJqSlpU3tzpVGS5cuZcuWLUipPqX3Zz0JZyfcFhqtGqYm4huGt3e88UTim4cbAnwd05nl5eU89dRTfPTRR7z//vsDfoF/b7AAY4gyX28CoMH0cv2vkwWJQCKlC9AQIogm2tGkZI4IUizUSHVfi0QivPHGG+zbt49vfetbjBkz5tYH9YAKZQNDOBxm+/btLFuW+DaY5eXlVovF8vBQHjUb0sHM4/H83dKlSxNewVpWVgbA8ePHk16TknzdDWdnnRYMTUDUBtWj45uUWyKQXwXEN/fOM+Dee+9l0aJFvPTSSxw7diwVT2FQyxYB7MTwSTvt0t75/dFafPcBw4yvBdY1P5oAC4IRX5lEVvrS3r17+d3vfseqVauYN29eSh5DhbKB5fPPPyc3N5fi4uKEjtM0jYULF6a7XK7/nKLS+r0hG8yEECMcDseC0aNHJ3ocd955p2qPMcB0J5xFr85u+7wgtfgas+HnwRFEk5Jizcp/eGQNmZmZPP/88/2uN9lAIQQUaPFp3xrz2lZobiSFMorZEcxswocVySItwNdcd9HvxCRcMK0cNOycN63EBukge0VFBb/4xS+YPHkyq1evTuq6MxXKBh7TNNm2bRt33HFHwsdOmzbNpuv6s0IId/Ir6/+GbDDzeDz/9c4778xKdG3Z1KlT8fl8nDt3LkWVKamSSDhr1QUBTYuv/g91vDakNYMwEFJyl+5mw4NP0dDQwKuvvjrg9hPsb4r0+Dqzy2YaAPWmzsawh5qoE4kNMMgwAzwg2skViTf47StBKdhoetgnHZzEzn7p4B3Tg18OoGSZgLa2Nl544QU0TeOZZ57B7e75+6oKZQPXoUOHyMzMZMSIEQkdZ7FYuO2229IcDscPU1RavzYkg5kQItdisdw3YcKEhJ6/russXryYjz76KFWlKSnW1XB22mmJL2eXXNsr0x4ABM94C/ifa55hz549bN68Wa0zTILCqyNmhhfDhB0RJzEEEeJv7DpBfFKn0hxYjcH3mQ5CiM7ebDEEIQT7zP69vVFPxGIx3njjDc6ePcu3vvUtsrOzu30uFcoGtp6Mms2ePdthsVj+vRA32OdtkBuSwczlcv3V4sWLMxLtWzZ9+nTq6uqoqqpKUWVKb7hROJPAKaeFDblOXsp3cdppic+xSS1+Q4IwuX/UeP5s1cP84Q9/YO/evX32HAabNBHGJSKEsVJpujE6gozRsUWTjh8DwQVjYAWzyx2XMBimh2BkLIbp5urWUYM9z2/dupXt27fz7LPPJjxiAiqUDRaHDh0iPT094e287HY706ZN89pstm+mprL+a8gFMyFEmq7ra6dMmZLQAghN01iwYAHbtm1LVWlKL/pyODvmsrDHa8Ova5hCENG1eIdTTca3Y0LwzPh5/OX8JTz/ysucP3++b5/AICMEFHWMmtVft85MEg9iGgNzqlgQ/zGKxnIADSktnd8fCg4ePMgf/vAHHnvsMSZNmtTl41QoGzyklGzdurVbo2bz5893W63WvxJC9J89wHrBkAtmTqfzT+fPn5+W6MLU8vJyGhsbqa6uTlFlSm+7Gs7yCwo46LER0wQYOrSnx78KEX9XdbfxH+4ez7PTpvAXr/0aX416s0iFq9OZLdJDx0QysuMlSmCiIxmtR/usvu4oJopppnWskwNNhBBIhhEjweWtA9b58+f59a9/zd13382sWbNueX8VygafI0eO4PF4GDVqVELHuVwuysrK0nVdfyRFpfVLQyqYCSEcuq7/ycyZM+23vvcXjuP2229n+/btqSpN6SOVlZXoNhvLvB3rYNoyoaEIKsdCQwGWqI1/WnEbt5dm8/CPd/J5W5h9ThvhIfKm2puuXgBwxfQyVQ91hLP4S5SOQb4WY+QAC2blIkzMuLrGysQqQngwmaWF+rSu3lZXV8cLL7zAvHnzWLBgwU3vp0LZ4NSTUbOFCxemORyOvxWJXqk3gA2pYGaz2b47c+ZMr82W2FrCSZMm4fP5uHTpUooqU/pSZUUFmbqFez1Z4G4DYYLUsPiz+J8LHyHfkcPaF7bR5I/ij2RwzGllY7pLddJKMr9hosswMXT2hLJwxky0jpeohTY/C6xBtAH20lxh5mB2TMemCT8L9CD3an7sYpAvMLuBlpYWXnjhBcrLy1m6dOlX/lyFssHt6NGjuFyuhNeadaxPyxZC3J2ayvqfIRPMhBAWq9X6n+fOnZvwzvULFy5Uo2WDmA4cr64iS7Nwb5YHci9j1QX/Z+0MvA4rz75wkECgY+o74MUUgoAQnLf37v6Ag1nIFHwadKHJ+L6ZEeHCJzUMGX+JytMiA27qz5CCQ7HCzv8/Sm+nSMQGXLhMJp/Px4svvsioUaO47777Or+vQtngJ6Vkx44dzJ8/P+FjlyxZkul2u/8hBWX1S0MmmFksljWTJ09OczqdCR03YcIEotHokOtbJpFELBJDGxqf7Kf6o5yuqiRbs3B/gYOfPzsFTcD3frOXiKUlPooGEHEhTUFME9RYhtR61JS6GLUiAV0GADC0+DZMCIGOMSDDzGkjh4C8Njqfp/n6sJr+IxgM8utf/5q8vDzuu+8+FcqGkMOHD1NQUEB+fn5Cx+Xk5FBQUDBMCJGabSX6mSETzBwOx1/Pnz/fe+t7ftFQHC2rTzfYOiPMlpkhPpod4uDYCLEuBrSoJql3S9rt8fu32GHbSHijDDaNhcq0VFbefQKYHIjhOFfBf160lGItwn97dQsRQ4JhhYLzkFYHafUgJJqUeMyhEVp7Q1AKTAQWGQApMXBiEl8KqjPw9h2NScHhaOEXvper+fuomv4nEonwyiuvUFxczG233aZC2RBhGAa7d+/u1qjZ4sWLs71e739NQVn9zpAIZkKIOfn5+Tnp6ekJHTdq1CgsFgunTp1KUWV9TyKpyYvyeXmA3dMCnBoZ5sD4CCG7RGpgalCXbXBwXOSW5zqVI3lrMnw6Ct4fD5vGSd4fC9VeCFsELU7BzmI43U+3ptU0jUcffZTW1la2b9vGQ0U+PCKMjDqhNQe8TYi0RoSI/+KMCw+shej9WZ5uoCMRmFikD4QgosU3jbcxcDr9X3XKyCWADdERKjNEEPsA2rGgN+Tn57Nx40bS0tJYsWJFX5ej9JK9e/dSWlpKou/Hw4cPx263TxNCFKWotH5jSAQzr9f7fy9atCjh9tNz585l165dqSip3zg2LsTJMWHa0kx8HpNLRTEMKQg1ZNJ2tgTfpeGE2tJo9EgC9i+OXBhCcibX5KPxBu9NMDhSJDE0iFrA0KDFJohFbJjtXozGTEyfi5gQHC6gcwwkJuBgtuCN0TpvjNY5mCOI9cG0lRCChx56CIA33niDiooK0h06jxW2xEdsAhlovnQsUuI0TJa2hfCqEbOkKbTESNPiLTGssgWAmPAA4BAD5zILKSFsahyJxvvj5XdMX6ppzC+6On1ZWVnJyy+/TEFBAcuXL+/rspReEA6HOXDgQMKb3Xd0R8hwuVx/lqLS+o1Bv3pZCJGXlZU1J9HO09nZ2RQVFbFhw4YUVdY3JJL6nBiVw8KEbZKQVSA1MMI2/FfyCDRkEWnJiA+VXU+YvG/xM0z6ydN85Grt7Bwfoc0Bpg6GKZCmhlGfSawxGzPgQEZsHV3zr6MZxDw+9pk+imU7+0qCtDjB6FhEdCxL47Jbcu8ls1ebcD7wwAM4HA5effVVTDMeGysrKykuLua+3Gbeqs+G1nzmh1sZJfxDpkFob9EELHb5ORuxcSlmEpVhYiI+lRk0oTWmkW7pv1OaMQkHQw4qolZCWiZh3UaG8GPvuHY3VwWzTl9eU3Z1WvOZZ55h8eLFbN26tY8rVFJt165d/Mmf/Albt24lGAx2+bhJkyZZPvzww6eEEH8ppbz1NM4ANeiDmdPp/NH8+fMzEm2BMnfuXPbt24dhDK7ph0vFYSqKI5g6mJKObWEEdQemEG2/ugRPYvW2Y8tqRkatRFrTiPldNEe9NHNtmZ52Moie5kP3thPzuYk1ZIPxpR8paxRhCyNsUWTAiQw7MNrSOUo6RwHOmOAOINw+RHobhjtAiw0uuwXD/L0zInXvvfeSkZHBK6+88pV/78rKSqYVF1MTbufzNi97QqMpcJzAJdQ0ZrJZBEywRyjUY2wOtBKz5gEQNiWftLuZ7wmQa+2fv4+fBVw0GjoGGhEtPlcfjTZTZ4k/h7zr1pdFLVEOTj1Ie1o7SJh2aBoen4e9M/cSdAZxBp3M2jcLW3TwbRF4s4X+4XCYl19+mW9961sEg0H27NnTRxUqvaG9vZ2TJ08ye/bshHbTsVqtTJkyxb1nz57HgJdTV2HfGtTBTAhh8Xg83yovL09ogz2n08mkSZP4l3/5l1SV1idiuuwMZUbUQqTdjTW9HSHAXVhLi88NUkNYYjgLr+AadiW+nsqAwss2Mi9lUmt6qDM91Eo3ZsiJGXISrcvtfAzN7ceSV4+e1g72cEf/9muhWIQsZNd68DR4uaR5CEZd4PMgfR5kbQGktyILLtPoCDCsF9ZK33777YwYMYIXXniBWOzGU2aVlZXcPbyY2oiNipCdLZExLLedQh+Cvah6w+GgA91oBUsOCA0hTQwEBwIO7k7vfwvo2wyNJkPHRBDRMpDCgmYGETJMEBt2YqSLaw1lj0w+Ql59HrP3zcYUJoZucLr0NLkNuZSeLeXM2DOcHXuWiScm9uGzSr5bXX0ZCAT4zW9+wze/+U1CoRCHDx/u5QqV3rRjxw6eeeYZPvvss4QGQObOnes5cuTIX6GC2cCkadoDZWVl7kQbys6cOZOTJ0/i9/e/N4Ge8LsNrmaJxpPj8NcUoNtDuIfV4BlWgzO7iaZTpYSbMmk7XUrocgG5JeeY0B6kuE5D6G0M1+Pb5hwpkJzwOIn5vBjtboQ1ip7bgO4KdmxlBLoJw5qgOi2+5gxglD/KjPZmdFszeWmCXdlWYiEPst2LbMyC1nTM1jSqHI2MltV4tNSNTE2bNo0ZM2bwq1/9ikjk5qPiUWBr7RWWFlvYcN5CneFhT7SYebaKlNU2lDXFdDQkVrOdqJ7euYDeb2oYEvSbDH5LoDpsISoFudYYbr13grPPjLfBjQHRjgsW7EYjMRFvmZir+Tp7sEUtUZqym5h+cDoAmtTQYhpXCq6wYEe8I35xZTGfzf9sUAWzrrbEaG1t5eWXX+bpp58mEAhw9uzZXqpQ6W0NDQ3U1tYyadKkhEJ4eno6ubm5OUKIaVLKg6mrsO8M6mDmdrv/4rbbbkuoQYOmacyePZt//dd/TVVZfcYW0ZAdbxAWR/wTvBF20HZ+FG0XSnBlN5A1rJJhRgPH20YS8HmpPjoNl15PnrX6C4uwRzfBuXw/etq18CpN0EzwBgTOGIyvg1y/wKyGkAVsBliue68c0S7Zm2dgpLUi0tuQ+bXIK/nIhhxqQzm8QRZlllrKLVeSfkVbaWkpS5cu5cUXX8Tnu/n6n1ZNY5PLiSEEO9saWDbSwvsX0jhp5JEd8zPO0pjUuhSwCokhBY5oI0iwGa1A/Eqlm12t1BTVaY1p7G93IomHtDGOCJM9ydv83JRwMmjjQthGTAryrTGmuEKk6QYG8YsVpLCgyTC6DBDW4iPJ4ViYqA2sAgKuALawjYPTDtKa1kpGSwaTj00mbA/jCDsAcIQdRGyDZ/lMon3KGhoa2LBhA2vWrOHll19WrTQGsc8//5zbb7894dHRBQsW5NTX1/9n4InUVNa3Bu1VmUKI0R6PpyQnJyeh4yZNmtSZ5AcbZ0jD264jTPAUXnt+rtx6hJQEGnKpOVLOybYRlOoNlOr1aJicMXL5fWgyJ2K5XL0Q0R0RzL2gYYuBxQDdAG8E7jqtseyM4PYLglx/PAVqgCv2xVAGYJVwT4VBZhg0U6LrUbJzq1jmOUqJ3oSBxtFYIa+HpnAkmk9MJmfJfWFhIatWreK1116jsfHrg9V2h4OwEMSEwJTwgf8KcwrjYXRndCT1ZsIbSSi3MNYeQUeiEcMVq0WXETQkI2037v5vStjZ5kQiiCEwiPdEOx+yURdJXhPg3T4np0N2wlLDQHA5amFLmxsr8atKY1r88n+r0YoGGCLezDpghNnhj/+cSCFpTW+l5GIJd2y7A93QOTt28I4Kdbd5bFVVFRs3bmTNmjV4vQm3n1QGiNOnT+PxeCgsLLz1na8zevRodF1fKoQYlD8cgzaYud3uP1uwYEHCHbNuu+02du/enYqS+oXJJ1xktujY3H6snvhIkauohmHzd1FYeBG3COOTdg7FijhnZJMpglgwiGBhV3Qkb4cnUmvE2xgUtAnuO6yx6LTGnac07j6ukRZKLDxlROD+iwYPn4vf7r9oMNwIs8R2npX24xRobUSwsDdWzO/DUzgdy6YnXSq8Xi+PP/4477zzDtXV1V973zDQrMe7z8tgGrJ+LEZ7DnuilxmXGcJE4+PIWIJyUA8897pSR4SRtngYsyDRkBRaY0xx3Xj0qzEaX98FELJm0e4oRiIwgIuh5Cygbzc0aqMWTASmsBDTnIAgJgUXwlbK7CYxzQ3SxGa2xjuyCTtIiSbDNBs6bYaGI+TAEXKQ2RKf8iyqKaIlvQV72E7IHh/FDtlD2CIDf+F/Tzv6nzhxgt27d7N27VoSXY6iDAxSSvbu3cvs2bMTOk7TNGbOnOmx2WxPp6i0PjUog5kQwiaEeHTChAkJPb+CggLcbjdnzpxJVWl9xucyODQpwI7ZPtpckpih4SyoA8B/JR9cUWwTLrE07yDLbKcp1pqRCBqlmxjXRh2apIt3IxPYFikhKC1oCDKDgrSQQPSgiYTTiN+ul6sFWG47zd2202SJAAFp47PoKP4YnkSFkd5xRWnXWSwW1qxZw969ezl58uQt7y+Aqw8hw26QOgSyMRtHcSnWxHBHhIC08UlkdI/CovJFQsA0d5h7030s8AZYnu7jNk/wpmvLTICOK4xDthwM3UlMcxAPTsmpqc2IryOTCHz2Efhtw5FomAiaYjpnzPjIvEe2IZDx9WVCoMkwAolAEjAFjrADZ9CJzx3/UFSfU4+33UvBlQIqiysBqCyupOBKQXIK7yPJ2mZp586dVFVV8cgjj5DolfXKwLB//37KyspIdLvEGTNmOG0225+KQfiDMSiDmaZpD02ePNlpsSQ2kjFz5kz279+PTPQdv58L2k32lwdoyjAwdQhZJaGWdCIt8eV3oYZ4711Dg8bMGMP1Nu6yn+MR+xGmWi7j5KvrXc4ZOfw+NJljsbyUhhIhYJjexgP24yyynscjwrRIJx9FSnk3Mr5z9K4rVq9eTX19PZ9++mmX7m+B+MUSUsZDGYAWA9NKuL2I1nB8UfoVM43Po8OpFzrVwkLyVjUNbXZNkm0xcNxiO7Bsq4EEmiLXXf2LREdS7EjOxSNuzUQCYUsWUlx9XYmP5nl1yZlYPJiN0BrQkMS0+M+l1YwHMBNBuh7/eZlydAr7Zuxjy+IttKW3UXq2lNKzpdTn1PPRko+oz6mn9GxpUuruC8ne+/Ldd99F13WWLl2alPMp/UswGOTkyZNMnz49oeM6pkAzgMSG2waAQTkH43a7/+OcOXMSmnu2Wq1MmjSJn/zkJ6kqq9cFHQY1BRHqsmMYOkgpaK8qor1yGFG/u+NeEld+PRDfp9t6Xdt9jxZhhnaZaZYaKsx0TsVyuWxe20YjioU90RGcjuUwz1qBze3jaK6gxQFZQZhcL8lIUkoRAsZYmijRmzll5HIwWkid6eXdyARGaM3MtFYTcETY77HRZtFIi5nM9EUojMTfDBcvXkxaWhovvfRSlx9zp80eD51CxLvoAiK9GmI2NF8u7dLRed/jRgFnpInV2oaBYJoRZqI5eBZw92cWAdM9QS4Hrn3O1IBsa4wMzeCMz4YECh0xvDdpUuuLaVQH41OVhY4oGdYv3i/DYuK2CJos8SlITUYQSDTAanURjlnJFAHKra3URt1EO4KZxWyPB0RrFGdHwExvS2fx9sVfqWH+rsT3D+xvUrEhuZSS119/ne9+97vU1NRw7NixpJ1b6R/27NnDY489xs6dOxMaGJk3b172lStX/hx4PHXV9b5BF8yEEAV5eXnFWVmJLS+bMmUKly5dor29PUWV9YypSUxNose6NmXYlBnl+MQAUkAMQcTnoeFoGZGOJrKaLYK7qAb3sBosznh6EkBh/Vd/JDQhKdFbKNFbaDPtnDJyORPLJky8PVyLdLEpMgHd04jmqEbYY7TbJFVpgrsuSHK63tj5lnQhmWipY6zewNFYAcdi+VSYmVSEMxDWFqTWAFqMepvOB5kO7mwOc8eIUcyYMYOf//znXe6XU6FbuGC1IoTADLkh5gAkaFGEI0SR1ozHl81JIw/Z8e9hhAqRehShhzmk28mWBvmyfzZEHWyKHTEOh64FswnuCI5ojE8aPJ3T0afa7YzzhBnv/WJgPu+3cqzd0Xk15zm/jRJXhMlpYdpjGkfa7TRELPic+SDij6GbYTJ0g+nuILuM+OjWeEs9Tl0y0aWzPaqjyzBeEWaMNUKWbtAc1ciwmDe8gGEwSEUouyoYDPLb3/6Wb3zjG4P24qyhrKamBp/PR2lpKadPn+7ycSUlJQBLhBC2wbQTwKCbyrTb7c/MnDkzI9HjZsyYwb59+1JQUc8Yuknl5BZOLKnl1OI6Ti9owJf59cNQEsmp8cF4d38ELRdGcnnXLCLtXnRHkNwpRxl++w7Sx1zAbg2jx0CPwZTTdhyRr/+RSNPCzLZW8ZjjMIus58nXrgVZoymb6NFJGFfiYcXQBPsKU/MuZBMmM6yXedhxhAl6fK2c9GVC1RhoygVDxxCC6qJcVq1axeuvv55QX7pjNiumiG8zJdvj632Eux5NM7BIySgzwlxbJYscJ9D0AACmFFhDw5ESDOCUphYs94aIITjXbqPCf20tpFMzOd7uwOxY4WV23E777LRFr/2MBw3BsevuJzuu6rwYsHElrLO1yU1dxEJEcxC2XhstnuJo4850P0KzccX0YsFgjB6/wveKmRG/j7WB6Y4QpwN2dra5+LTVzeZmD82xQfeym9JQdlVtbS3vvvsujz/+eMLrkZT+b//+/QlPZ2qaRllZmV0IcW+KyuoTg+4Vwmq1fmfy5MkJdfq/uuj/3LlzqSqr2yqmttCWF0Jq8W0noy6DS9ObCblv3KW+OTPKvlltRK0mpqFRs38qLWfHgNTwDKtm2LzPcRfUY0FS0Kgx7aSDqScdLN7jIq+p6wOoFiEZY2lihf0Uq+xH0fLq4j0zTB2jcjhmU3zKpzHFr58uEWOurQKGnQN3W/wvqTUHKsZibx7OP9z7IJ9++imVlZUJnTd8dVjDsELHVZfCGgQp8ZgmJR27BDi1MHZXJbrzMogoMVNjSXYWCHHtHErKtEc1Pqp1c6zdSX342stZbfjaS0C7u4h2z/DOEbGakOW6+1k6x5/b3MNp84xAEg/WJ9vjU9kSQdBxbXcLADfxYeBTsfj3R+tN2ISJKQUVRvxnv5AW9rQ5icp4G48YgpCp8VmrC2MQLWPtjVB21fHjxzlx4gSrV69O+WMpvevYsWOMHDkSt9t96ztfZ/r06Wkej+ffpaisPjGogpkQYkJWVlZ6ov+wM2fO5MCBA/1u0X/YGSOQEUHq0N6YScXRidScGUN99TBOZdpoMR1ErtskvCE7wolJfoIuiWno1OybRrAxG80WoWDGAXImnkLTDTQDCmqtTDztJKtVJ7tVR+9Bj7BMLYRzeCXWqUfQSy6h5TSieeKjU7ZemMkTgEMPQ141FF0AVzsg+K9LF3ChJsb/tbWVc7EsjI7n6BOCvQ4bH7odHLbbuFGHj2GxGJqUCGsY4WgBwGwrQpg6K4KBzutUc6SBFKDZfFi8F8BzjnyHzvKcDEaYaj/NVDvY7CAqBRFLvH3FVc2RePiSCKJWDzGL6wt/fpXo+B9TaMQsLgzd0Xm/sIxfdRnTncQsLoRpXN1cFosRJiYFZ434hTPjLfF1mjWmlzAW0kWQlqjRGQaD1hzClviIm5SCmsjgWEXSm6Hsqo8++gin08n8+QN/TZ5yTSQS4dSpU5SXlyd0XEFBAbquTxFCpN/63gPD4Hh16OByuZ6bM2dOdiLH9OdF/2FPfFTGNDTO7Z1JNHxtsfkFYH/Hf1swcIkoRnsQ7WgIiyNMoCmTcGs6uj1E0awD2DwBNAPGnLWTX2tD60Friy8TwLhGyckcAyO3EXLjUzq6ISlr6J2wO9UfZZ/HRswegvwqVo2azKzRaaz+f7fTLj3URz18Ho0y3NrAxQw/Uo9hCsEVi+S43crK9gCe64L55GiUixYrEUB66pAxO8SceFoL0K2tne/xVmCmEWKf7sAAECa/r67jiaJcJuZlU6e6lqeMIaElqgOCmOWLQ7NBQ8MGmNr1L3Hx1YBFzmujzQWOGIfbIGq99mEufkVn/OKB6rCViDV+9bI15idiS0OYMbJtES4aWUSwkC385Gjx6eyLHaNlJXozkVh8+jRiySBsy0Izw9hjrZhAxBz4o6l9EcoATNPsvBigsrIy4dFwpf86ePAgK1asYOfOnV0+RgjB9OnT3du3b38MeD511fWeQTNiJuIeHzduXELPqaysjMrKyj5f9B+zx4g5YsiOpcpRu8HliS1IDRoqiomGHTg8PvLHnCOrqJpMdwteEULHIIZOm3Tgb82kvaaQ5gslhFvTsTiCDJuzD5vHj2ZAXq2VgiSHsqvK62BUS7yDv9WQaKZkbDNMbEj6Q91QWSBGuS+C1ZSUeNJYt3ARP37nd6wSB5hvvUimCBDEyploIdH6McRaipARBzHiU477nPYvnM8pJfcHAkyKRMg1o4xwXcJGlBbDy4HYsC/cd5wZ5a5YgFFmlEIzxgwjRPTSeexWKwUFA7sf1UBi02TniJYmYHJaCKldW3emIZngDX/hyky7JpmeHuwYUbt2v3GeMGXeMEIIItb4FZa6GV/b6ZQhvFaTU0Z8GnNCx2iZKeGSkQHEg1mezQChE7TFW2lYjHgDWQHkWAf2RSF9Fcquamtr46233uLhhx9W680GkUuXLmGxWCgqKkrouKlTpzodDscPU1RWrxtMI2bzRowYYU+0Q3R5eTkHDhxIUUm3FnVFqZ3WQNQTn/bSQxbyD+ZQV+LHtElMU1BzdgwAwyacJKvwClpMULozB6tDR0qIoBOQVvZPiBI2bMTCdqSpkTa8GosjjCUmmLbfizOUuhyuAbddlkyrBb8VPBGw3bgzQUoIYGogxtSQyTfvf4LdW7eRdrEaBIy3NDBOb6Da9PCBKIKwF0JpyFAaWMLgbKPK3gqEMIEzFitnLFZMYFQsxrJgECtQYzvPe5FxHI4VkqP5Gam3dD5+rjTI/dIVn5WVlRQXF1NQUKD2+0sBXUC+I0ZtyIIr2Mi3pjv434cMIvY0smwmo9xRglaD3R3/LEtyfHitXx3BHe6MIbk2YnZnjg93x/5ho9MtNJo6ekeoAhhp89FkOqkzPVgxGKU3AXDF9BLGSpoIkSmCSAuEHdeu5LQYfnQkw+1R0m7StmMg6OtQdtWZM2c4duwYDzzwAL/97W/7tBYleQ4ePMj06dO5fPlyl4/JyMjA7XYXCiGGSSm/fkuXAWDQjJh5PJ5/M2vWrISmMT0eD0VFRZw6dSpVZX2BaTUIFvoI5fmRmkRqkstza4l4o0g93sM07DA5PibG6bZhnN4zi8Mf3EUk6MLhaSezsAaEZMyeLKzhjr5aAuzCIFMLMbE9QEZhDVljLpJdeh6rM4xuQsl5R0pD2fXsBmSFejeUXe/OO+4gHAyy50vbagkBhboPS+ZlRN55cDfGm8XG7Mj2XCINY3g/XMq75LHX4qBZ02nVdI5Ybbxvd2EChXo7syxVAGyPjKLVtN+ggi+qrKzEqkbOUmZqRgi3xUQXEl3EG74C5HdsIxET1y4CcH95s9brRDo+o2aI4Bfud4V4250Z9jryXfHfuWwt2Lnof4zeiFXEf9ivn8YUAs6Z2QQ7+pkhJQVaGzO9QaZ7roW8gaa/hLKrPv74YzIyMpg2bVpfl6IkyaFDh5g4cSKJNoifNWtWhsPh+GaKyupVg2LETAhh83g8d40aNSqh4yZPnszJkyeJxW58hWMy+Ue20DaxEdEZWASOs1nEgNbGHBqvFNDakIOvJQMpvxiirPYQo6YeQdcAE2yBG/+zFdTYMDVJ5cgwhi7RDUHxRTsFV4ZG24YRI0Ywbdo0fvrTn97wz3VgRDRGhRXMtAaktwHCbgikQ9hNtZkOwXQIGQhbO5qjFcMSok3TqNItjDBiTLLUUi/dXDSy+DgylpX2EwhhonHzTzlq5Cx17LpkSZ6fxoiO0yIpcES5eLUpMBCQ14LZzSbwo9f9vhVqbZ3/HZIWqsw0BPErkE+H42EsTYTZbRQD1xb9x6cx48HMFW3jUszBHoo7z1Wgt7PY5evx8+1L/S2UARiGwRtvvMEzzzzDxYsXaWlp6euSlB5qa2ujpqaGCRMmcPTo0S4fN3nyZOvWrVu/Bfxt6qrrHYMimAH3TJgwwa5piY0KlZeX8/7776eopGui3jBtZY2gy/ioWMhB/ZVC6pqKaDxZgBG7vruHxJ3eQpq7DVdeI57sZuxuf/x9xoC0OsdNG8wKBMOqHRRV2zuDWU/2rxxIrFYrq1atYuPGjQQCgZveb14whF9z0aTH9z407T6Gay3MEVH2yFwuxrKRhhMZzsAIZyAsAUxnIzUiyghiCAG3Wy/SbDppkU42RMcibDXoAkqJME2GbxjQVDhLHSEgx25g0yQOzQSDzk3NA/LWH0qazGvryzziWo/K80YmEo3hWgtWTNqkA4GkSTqJYiFX85Glxdtm1JpeQljRjAjn2qDNPYKI7drL63CtNVlPt0/0x1B21dVt1h588EFefPHFfnd1vZK4gwcPUl5enlAwczqd5OTkeIUQk6WUXT+wHxoUwczr9f5oxowZCV0qm5ubi9vt5tKlS6kqq1OguA00iZSw/9NF1F/54sJGl7eNrIIrZObVkZbVhFUY5BzJotEbIuiIgBF/k7EHdApP3PppCgQWY2gEsquWLl1KZWXlLaelbRJW+AI0aRrtukamYZBmShAw2tJAjdtH1LBjhtOR4XRkzIVsd3FRyyFXv0yJ3kxMSDLtlbSGxhAx0uLrj6wtnJE2IkIwV954qkqFs9S7OpVpdrRG8V8/YnaTX4lGeS2YucS1FifnYvGVEWP0RppNBxKBwwxxOJQLOozX6zvvezISn/K0RXyELB7CtjSQZuf6smJ94Aaz/hzKrtq1axfjxo1j/vz5fPbZZ31djtJDp06d4r777sPpdBIMdn3rmDlz5mTV19d/D/i3qasu9QZ8MBNCeDMyMqYWFhYmdFx5eTlHjhzplU9X0mKCBqah09IYf7EXwmD85EPkpDdhyWpHdqxrEYbA6rfgqXXhveIm6I0S9kax+S04W61DZgQsESNHjqSsrCyhlidZpkmW+cWFcMOMGDqSmB5Gd9cjnY3IcAZmMJOA6WSrOYa9sTCGpRnT0obFXkssXIQRyUXTwqAHuSitTCeMnRv/XKlwllqa6AhmXxkxu/nveeN1I2bOjmDWatppkPHF/UWihS0tBeCCqCkwOnqaGf52SI9PY1bJDBBgi/pp98Rfi2wRHxF7Gk7CpIuBua5sIISyq/74xz/y3HPPcfLkSRobG/u6HKUHotEo586do6ysjP3799/6gA6lpaUa8LAQ4kdyAA+dDvjF/0KIuydNmuQUCXZZnzJlCocPH05RVV/kuOJBxAS6bjDj9m3olihS6jQ35TLyiIfMs2lYfRYsfgvpF7wU7cpHdHzid7ZbybjswtVqU6HsBqxWKw888ADvvPMOoVDP3vx04J5QgHRpokuJRRh47A0sdx5hvvUiXhHCL+2EogVEgqORhgMwAYER83aeI3CLfyd1QUDqdI6YITAlBDv2c/26f5EbBbNzHY1j82ULWyrdtBJvyWBY4hd8OEKtnG+xEYwJ6kwPMWFFM6LELA6kZkGPhdA7Ggzn0jYg98ccSKEMoLW1la1bt3L//ff3dSlKEhw7doxJkyYldIzVamXYsGFWILG9nfqZAR/MvF7vs2VlZZ5EjhkxYgShUIi6urqk12Pao0QK24hlBTp7ktnrXNganYiYIDOnkdkLt2CxRKitLubT9vF4z2dQvL2IEduKyDqTgWYM+H+WXnPHHXdQVVXFmTNnknK+dCl5IBTg/pCflSE/D4X8FEqD8ZYGHrIfJc1WhdBCgAUjlgVoCC2AxRpvmWACHm59SaoKZ6lxNZhJBEGsnRvM30xMClrktT5Y9W2SbZUujoXjvcf8zX4MBDGL4wvHOYKtCAH1QUvn1Zj2aDshewYALn8D0Y7eaLmyjYFmoIWyqz7//HM0TWPmzJl9XYrSQ2fOnKGoqCjhLZrKy8uzXS7XmhSV1SsGdAIQQuimac5JtBldWVkZx48fT2otEkmwrI62ZecITK/BN7eCtrvOYbgiCASZnxeScSAfR7WHQn+Mu9KPYyPGJTOTTyKjiQrw5YTw5YQwtQE7Atur8vPzmTp1Ku+9917Sz+2VkjT5xbd1TUCGpQ2rowKrowpNb8Niq8XqqEJoMXQpGUeErm7UqsJZ8l0/YvbFhf/x74djglMNNnZWOjlSa6c67O4Mb0KanGuy0oSHmG5DM6JYIgEkELVdG1VzBFuxGPGLBISQnVdj2nUwdSt6LIw1GoqHOWlS0RijMaQzUAzUUAYgpeTtt99myZIleL3evi5H6YFYLMbp06eZOHFiQseNHTtWaJr2cIrK6hUDOpgB80pKSiyJXo1ZVlbGiRMnklpIrMBHeHQT6BKsJlgl0hnFf1slEolA4Kj1kHmggIwjeRQGYtxjP42NGBVmJhu9w6ma1MLl8ibOLKnBlzMw16T0FiEEK1eu5OOPP/7aqzCTrUxGsCDR9ABWxxV0aysCiSYlU2WIaTKc0PlUOEuu64OZ/0utMgJRwfZLbi4222gJWahus7K/KePasUYMUwpCzvgWTI5gG6Jjs0vNiE9Lettq8LRfCyyaw0EAG24RRtriI2/OQBMRmxuEwBoNICWcah0YLWsGcii7qr6+ns8//5x77723r0tReqg705kOhwOv1+sVQgxPUVkpN6CDmdvtXjtlypSsRI4pLCwkGo1SX19/6zsnIDyqCSySWNTCZ58s5ZP37+WzbXey+8gcdsrhHIgWcSyWx9lYFpVGOnWGGysGC11n0C1RmmsLOHFgFjFNIC2S6mlNxHpjB/ABaubMmZimmdDC0GQoIsZUGUKXEquU6FKSh8GDsp0JRLu1ClCFs+TpXPwvvzhiJoDTDXaiJh0bk1swEYSt15oEa2YMCYSc8ZEWR6ANEGhAZsNFcupOd4Q1iSYks3KDVJnx0TINSat0ohlR/j/2/jvGsizP78Q+57rnbXifJtJXljddXdO+e6Z7OD0ih6REagRpsUstIECCIKwkLCCTSkh/CJC0Wsfd0WIFkhJXZndFLofktJueaVPdXS6rsrLSu/A+3ovnzTXn6I8bkZG2Ml5kRsaNiPsBAt0Rec97v/devXO/52cj7Qp2xM+usNp1AKpO8Lfa/SDKNnj//ffp6+vjyJEju21KyDNw584dent7O/Z+vvzyyxnTNP/mzli18+zpqkwhxB912lT21KlTXL9+/bnbokw/r6iw2svyYmeh1Q1KS/3c/vR1jr/1CaCo9jfJTXeUPncgiMfjfPOb3+Sf/JN/sivPfxKHceVQQiOGIvElFX9bJazWfD6I9fy+hz1mAKsNf+B5I5GlkusjUV7FsTZzxzTPw44kUJqO4bQxXBuEH+jUlUQpX/jlIy5v9rXQBfxV2xdmtXURGG8WUULHtpKgFJG2P4M3EfARTPtJlIHfePbHP/4xP/jBD/izP/szPC885O5FPM/j+vXrnD59mg8fmubyZZw8edL67W9/+z8A/uOds27nCP4x7gkIIY7l8/lop7MxdyK/DMCYT4EEx/FvBt29i7z79Z/z5lu/4W1zileNeU7pSxzRCwxpZXpEjbRoYek295fyN8pplAIlwPuSETIHmW9/+9t88cUXz93r2QkG0I18LqJsg9Bz9ux8WY6ZuZ7mVcn1AVDPdOPe7zHzXFpR/yAUaVZ9QQa81NWiJ+bQH3N4vafFO/0tDA1WVOLecyg0IricNlewY2kQAsuuoSkPTShOZm2Cyn4TZRvcunWLQqHAV77yld02JeQZuH79OidOnOhoTS6XwzCMMSHEnvRs7FmPWSQS+TuvvPJKrpM1PT09mKbJwsLC8zfI0VACHNsXZvFEjb6BeczJDEnjyTMVWymbybdWcZWB65hY0RZC+P3MkqtPn8V40Ojr6+PEiRP8w3/4D3fblB0h9Jw9G/dXZdblpjBTStAbd5iubP5Nd208Y/N3zfNoJLMAxNsVoobk9b4m2ahkNP3o2LaNaswNThnLnIq0+KLl56hFWmXihuSlXIvuaDA9NvtVlG3wk5/8hH/wD/4Bly5dolqt7rY5Idvg7t27/Mmf/AnRaLSjlkgnT56MfvDBB98D/vnOWbcz7FmPmWmaf3r8+PGOhOVOJP1v0Dpe9Ku31j1mpun4v/fXv3RdtGqRWYphai7RRANNlwhXkFqMEq3sjYThF8kf/MEf8Mtf/vKZe5YFmdBztn02hJmnBCX3fmEGMwWThLEpkHTXeWCtZ1oo3cBw2nyjr8S3Rutko48PQSr1oDDT8ThlLLOqEjRFlBg2f6t3ke8M1u8NVA8a+12UAaytrXHhwgW+9a1v7bYpIdvEdV0mJycZHx/vaN2ZM2dSqVRqTw4135PCTAiRj0ajvZ0mBJ48eXJHhJnSJCrqopRgZXm963ekBQhUxLvXz+xJ9F/JMvR5ntRilNRilKFLOQaudOQMPBCcPHmSeDzOhQsXdtuUHScUZ9tjY0NrewJbPDiDVilB2dvsWfZwMbcd8VtidHllUhH5pU1hV1WCutr0aB/XV4kKl5ue3//sqFFAD/DuehBE2Qbvv/8+x44do6+vb7dNCdkmN2/e5Pjx4x2tGRwcREr5jhBi7/SqWSfAW8eT0TTtD8+cOdNR17l0Ok06nWZmZua52uKl2lR+/zYAC3MjLC8MYZg2o4fv+LY2nj5GSSBIrkYZ+ryLoc+7SK7Ewi7/D6FpGt/97nf56U9/emCGFIfirHM2qjKrnoXSNN+1dR+2tSnMDOvB/VrqvgP+1eTTx/nc7y0TKM4YSzhKY8Lzi8SP66vbewFAqa3x8UqUv16Ic7EQoe48373gIIkyANu2+dWvfsV3v/vd3TYlZJvcvHmT8fFxOmmNpWkaY2NjOvDOzlm2M+xJYZZMJv/NU6dOxZ9+5Sbj4+PcuXPnud7UFYr6W7Moy8NzdS5/9iYAp85eJBprgSuIXel5bs93kHnttdcol8vcvXt3t015oYTirDPERiNZzfeWbYQrlfCDnO3Y5rbRWm8FbMnNkGaCNgPGl6cfPBzGPKwXSWk2k14OF51erUpG66yf3QZLTZ3fLsdZbBrUHJ2ZusmvFhNU7eezVR80UbbBhQsXyGazYfuMPUqtVqNYLDI6OtrRurNnz3YlEok/3SGzdow9J8yEEIZS6qVO3dIbwux5IhMOMuaAgGuXX6XZSJDJFTh89CZ4kLwwRGQx/Vyf8yBiGAZf//rX+fnPf77bpuwKoTjbOto9YeaHGQ3XQfNcEAKp6TTjm99HF52kaGPcN2njkLH21LmWBRWndl8Y86zhi5xb62HM7XrLlIIvilE8JVAIXN0EBK6Cq6Vnzzc9qKIMQErJz3/+c773ve/ttikh22Q74cwjR44ghPgbO2TSjrHnhBnw2sjIiNbJ0HJN0zh8+DC3b99+bkY4g2Ua702CIZmZPMSdm6cQQvLamx+i6wqjYWIt7clK3cDx9ttvMzc3x/z8/G6bsmuE4mxriIc84rrroK33sFKmwcOqKy8atO8rTh/T1p76HPd7y4a0MnmtSVlGWJIpDDwO6U9/jMfhKGh5viirZEYo5Y9gm3FAUGw/WwH9QRZlG1y/fh3P8zh16tRumxKyDW7evNlx2wzLskgmk3EhRO8OmbUj7DlhFo1Gv3/8+PGOMuNHRkYoFovPbXSPM1im9fo8KuZSKub59ON3AXj59Y/JdRXAFVgzmefyXAedSCTCV7/6Vf7qr/5qt03ZdUJx9uU4Lnwx8aBnaSDawpLrrS4Sj2Y/RNw23vo2GMOmV+ssjPmy4bfe2fCWHdaLmGJ7zWQN4Ydcy5lhfzanUujro6AsffspGKEo2+QXv/gF3/zmN3fbjJBtsLi4iK7rdHV1dbTu2LFjceDrO2PVzrDnhJllWX986NChjuweHx9/Jm+ZitnIwRIqV0ehaJ9eBkNRq6b48P1vIj2DscO3ODx+E1yBXokSudvRpKiQJ/D2229z+/ZtVle3n0y9nwjF2eOpNjSatoZ8SBMVCxJl+8KsZD2aVlBY3VxglGqUG1++tRRVjKrypwX0iBp9Wg2p4Lbr3yyOPUPSv4tGMzeEuz4wPdoqoUsHXSiOprbXoDYUZQ9y+/ZtbNvueP5iSDC4e/dux3mCR48eTaRSqT/ZIZN2hD3VYFYIYaTT6dFsNtvRuqNHj/KjH/2o4+dTKOQrs6hDRZDrIZCmyWqjixufnWF+dhQQ5LtWePXNDxFCEf9kGHMpGVZVPgdM0+Sdd97hH/2jf7TbpgSKsAnto0yvGhw7ziNVmLrroa+HMu1Y7JF1mr3ZODZSrXJpLcLXTjWfmGc26W0euM6aiwgBM16GJhYZ0Xyix82TsFrX8aTAMhR3iiZrLb8q1NKhN+VxN3WIuu4XmwvpkaqvIlAcStqMJZ3HPu6XEYqyx/OLX/yCP/iDP+Dq1asHpsJ7vzAxMcGpU6f4+OOPt7xmaGgIpdR7O2jWc2dPCTPg9eHh4Y68Zclkkmw2y+zsbMdPpkbXUGNF0BVKU8zMjnH16llWVn1vhaZ5jB66y9lXPsU0JKJpYC111lst5Mm8+eabTExMUCg8vX3BQSMUZw/Stn0lpXkPusx010F3H+3aD6C5zr2cNM11iTQbuJqg1hKkYo/esD25GcbMiCajWgmAW64fxjymrz5W0BUbGp/OxlD4utEDf6r6+uGtKXUuWodw1KZwTNYK6LbH4azNydyD3rKGK7hRilBo60R1yXjapj/+YBPbUJQ9mTt37tBsNjl9+jRXrlzZbXNCOuDu3bv84Ac/QAixZVFtmibJZDImhOhVSi3vsInPhT0lzKLR6PdPnDjRUYzw6NGj3L17d1snI3l0BQxFoxnjL3/+A8pl/6lNs82RozcZP3mdWKzpX+wKrKtha4znhWEYvPvuu/zTf/pPd9uUwBKKs0260r4wMdptNNdFGv7WprvuE4WZ1Wph2Da6bZMol57o4663BNdmIiy7MSqH/DDmKc33ljWUwYzMIlCMG48eIDwJn87GcNc97mr9SRTQSmRQQCOZw71/mLrrEKuVUAimyha5qEdfwn99DVfwy4UErgIQND2NTws6p7w2h1O+V+1+UeYpcKXA0tRTq00PEu+//z7f+ta3QmG2x6jX61QqFQYGBjoqBjt+/Hh8cXHxG8B/tXPWPT/2VI6ZaZo/HBsb68jmw4cPMzExsb0ntPzN8PKVVymX88TjNd5843f87R/+f3nTmCUuXVAgGgaRz/uxZsJu/c+L1157jbm5OZaX98QBZ9cIc858hrpchABNgdVs3vu75rjo9uPDgFa7he65DEzeIb3miypTVySjm4c4x4WPb8Uo1TWaST9HTXcc1u42UAruuF0oBCNaiZh4VACu1nXfUwasdQ+y1j2EElBLd1POD1DJD+BaUTTX2QzDCkE5248UGp4STJY3Cxpuli1c5c8CdYzovWuulSJ4alOUzS0s8lkpyl8spvjpcpKfLidZaO2pc/iOcuvWLXRdD/ua7UEmJiY6/tyOHDmSSKVSf2uHTHru7BlhJoQwhBBjneaXjY2NMTk52fHzeceXIGbTbkW4fdvvnfLtb/6EUyevYAqJeaeb5M+OkfwXp0j+9Hgoyp4jQgjeffddfvOb3+y2KXuCUJyBoYMufAlk2puhP02C8QRhlhcNdM1fo2sKQ1O8fKj9gGdpvmggpS+EGilfmCXXirRtwVpdu1eNecx4fNK/JwUoqOZ6aSXStOMpWrEU9Uw3KEWkUSFWKxFp1u618pC6QTue9icXAI63aVChbaAQ1JJ9lHNjNOL+8wtgaHj0nqfs01KM2aaJRCARtKTGhbUYRXvPTafZMX7729/y3nt7KvUoBD+cefjw4Y7W7LU8sz0jzPDzyzpyxmcyGQzD6DhHSWUbqJOLoMGNW6fwPJPBgRly2TVwBdqF0XvJ/WGS//PnxIkT1Ov1beUFHlRCcbaBIFUsIqQksVYC/Cazj+PNgTKvH2kx3u9wYsjma6cbZOIP5qjVWhpSCRACNxJBeB7J0hpSwo1SmrKKEcNmWCs/9jm6Eh6OblBPb2ZgVPL+PN1UaYXc6jyp0gqtxIMVo5rroHsumlD0JTY9cVFdUU0N0I5lQSksu4YA/vCVXlzHZnFxkZYnWGwZSASOEaMZyaPwc9tu1Z69Ue1+4YsvvqCrq4uBgYHdNiWkA6amphgeHsYwtu4Bvj/PbAdNe27sGWG2nfyysbExpqamtnStyDTQRlcR+RpyxE/4n5sf5vqNlwA4ffoSSBBXBtCWwm7+O8lXvvIVPvjgg902Y89x0MWZofuzMg3HYejmTfLrOSiafDS/1JAuSWGTTUgO9zkM5V2MxziT0jGJJhRCKXpmpumdnkKTEqVgRvdbZMTKZTzv0bXg9x8rDt53upcSpWnE6mXi1SICqKe7UNp9T64UXSuTaEIRNSSHMr4H0FEaxfQwdtTff+L1VSJOgx++2oPr2Kwu+3mGLekLyXqsl2pyhGasG0+PAIK6t2e2/B1HSsmHH37Iu+++u9umhHSAbfsHkJGRkY7WHT9+PA58Y2eser7smW+paZod9y87dOjQ04WZJjG+dg3j21fQX5/A+Po1KlmXn//1H/BXf/192u0ofX3z9PctgNQQdpinsZMMDAyQzWa5evXqbpuyJznI4sw0FPGoRNMUmpT4PfR9UdY1N3dvAgBA0n1yS4z7Gci7GLof7ow26ljtFgBSbIY2I4USN+fMx66/4vbhiPv2DE0joxp8PTKBuT5w3dMfVIQ9pRl6ozYn8m1+b7iBqUNb6fzEPs4q/nNGW2VSrQI/fLUHz7FR5U3vck3EKSYP0Y5kAbDsCrrXRqDImU9QkAeUzz77jPHxcRKJxG6bEtIBk5OTHYcz91I/sz0hzIQQphBiJJPprJv+Vjxm+plZRHcVYUja0uS3n32Vf/GzP2F+YQTTtHnj9Q/5zrd+7G/imkKE3rId5Z133uGjjz4K+ws9AwdVnAkBb55sc2LEpivt0t+14QVTJEtlkmubo5IGrC/v8L+BocPbx1v0ZLx7Ig+gkU6jdB2r0cCwHZZLjx7Y1mSUj90HT/VRHH4/eovuuHvv0bLFRcy2X7AQa1R4NV7k7cEmh7MOhgYNZfKj9glWpD/irVvU+DuZ2/xbX8nTbTTQqrMYGrhK8KE9ws+cE0jdD1kaToNEYxGBQhdwPLm94er7lVarxdWrV3njjTd225SQDpienmZ4eLijNXspz2yvuH9e6bR/WSqVIhqNPrWqTzu8gjAUhbUu/uVP/iZtO4oQkhPHrnD29OfEEg2/pMoViC+GQo/ZDhKLxThx4gQ//vGPd9uUPc9BbaWhazDY7THY7XuGak2XS7ciOC545qZXq8/cmjADiFqKVw63+dl3fkaraoES1L/4m7AG8cYqrf/eX6MydX4ro7x54U0sx8JTgl/ZD1aOCSTfsu6Q1Pyct6NdNncLFtV4GicSQ3guA/V5BoY3c8qq0uIn9vF70wbi2HzbukP3wBh3Ch6/uFojG4nR36VzkTHK9/dCU02yrRkcoeiyPE6n2ySM8MDzMB999BF/+qd/yvvvv498eHRESCCZnZ1lcHAQTdO2/JkZhkEikYgIIXqUUis7bOIzsSdUhq7rbx86dCjbyZot55fp/odabyRo2/7m19+7wFde/S3iw3Fkf8VP+J/NI6rRL3ukkGfk1Vdf5fr167Rard02ZV9wUMXZ/SRjinfPtqg1BX+NzoYc69K2Nzd34J99g2IhgT1+CITCOPsJ+kQfQ1eOE/veFW6P3+bEldP8pj5E0XhwNudXzBn69dq93492OURjGj9Xfj7yKW+ON4dr6OtH0DUZ5Sft4zTxvV86ku9EbtPXN8RcSfLLa36vsxmznxsy/8CA9oRo84fRmyTinU8MOGgsLy9TLBY5ceIE165d221zQrZAu92mXC7T19fHwsLClteNjY1ZS0tLrwM/2Tnrnp09EcpMJBLfHRwc7EhEblWYyYUMSsLo0DTf+dpPMA2bhaUh/tm//u9QWOpD/2II/dpgKMpeAK+//jqffvrpbpuxrzioYc37EQJScUVL8z1mJi4psb2Q3viQjR1PgKZhNZpw6g7RL8Y4MewwMjPCXO8Cf3krzx2974F1x/UVTuibh3Sl/J+b+hCeZjCklXk7vXJPlK3IBH/RPnlPlAH8njnJK6NdLFYkv7y2hmNEWOseo5Hq2nxQwMLle9YtEiIUZVvl448/5q233tptM0I6YOPg2Qmjo6PZaDQa+HDmnhBmUspX+/r6nn7hfQwNDW2p3YJ3aQxsA+VqjB++zZ98/7+iK7dCtZHmX7dPcsXtfXj8XsgOMDY2hpSSmZmZ3TZl3xGKM5AKGsoXOV3a1hL/H0EJPnjnAyqv+X63eKWCSrSgEKPVgkgrSkPAcv/wA96rSLPB0cYcQkDTFnx6J8JfXorzL+/2MSnzGHh81Zy6t2TeS/Hj9nHs+wIaLxsLfONwgnrL5f3rRWrJLta6x3DNKLprY7XrIAQaku9Yt8lpode5E27cuEFvby+d9skM2T1mZmY6zjMbGBjAsqxv7oxFz4/ACzMhRMQ0zYRpPr7q6XEYhkFXV9fWwjeNCM6PX8G7PIw3nSc5l+BvyDuc0peQaHzkjPJz5yjlM6tU/+ga1T++SuPdKWTcfvpjh2yZN954gwsXLuy2GfuWgy7Ompio9Z6DXWLr+WUbtG1I/GffxvyPfh9n8RgAbr6NUgIl4fJti0pDo37le7jWppdLdxy6Z2ZYWNHxPPjoVpRCTUdqOmu9fv+s9Moy1+5qXJmxuN7M8TP7GC6blZo5u8x3Bg1abYerCxVWug7RSHWDEMRqa8SrRexoEpTitDP9QLg0ZGt4nsfly5d59dVXd9uUkC0yMzPD6OhoR2vy+TxSyvEdMum5EXhhBpwdGBjoyM7+/n5WV1fxntRc6GEcA3lrAO/DY8jrw+i2wVesGb5l3cbEZcbL8eOrv0eh1AUaeD116t+YQIWl58+FSCTCsWPHuHTp0m6bsq85yOKsLjfF0nbyyy5dj9BejdNOJJC678mqRcZo3/gKMtWi3Rbc1uK0p18HKYnWamieR/fsDLrnIRUslgx/EgCCUk8f0jCwmg1ipTVKDZ1bXje/U0eQ923Lpt3ih4MeMwXJn12K8lP35D0vWW5lmkirRjXnf57JygqF1RYLtT2ROhw4PvvsM1555ZXdNiNkixSLRQzDIJ3eeqcEIQSJREIXQnTtoGnPTOCFma7rb42NjWU7WTM4ONjRgNMncUgv8Uc9n5LLr9JoJPnFX36fWzdO+u+aLrFH1576GCFP59SpU0xMTIRJ/y+AgyrO6mr7wqzRFNQ9D2m5VLse3M9bd9/CfW0Sqetc0Q4BkF1ZpndmmqGbN4i0Wmiaoj/vUmsJPCloJpI0MlmQktziAgJBNZtnrW/QD4Gu505onsufDLSYrcN/s5alkulGKRh0VuhansCym1Szmyke8UYJqQS3imF3/+2wtLREs9nsuD9WyO6xnXDm6OioBQS6P0rghdl2Ev+HhoaYm5vb1vMpXeKdWMT93jXc714jdmKWd975NZbVRimNzz99G8cxwFDIbCgkngcvv/xy6C17gRxEcbYhzHQ80qKz763jgkq1aP7bP0eeugNAzJpESInnZXAOlVj6po4no+RVmcxaASH8wKmuKdJxyUCXRyqmwNRY6/NDmJnCCoZjU853U+7xBZbZat4TZ9/vanC5avBXjRyuFcGw23QvTjHuzqOtd0GLtDbDlu2I3yS15QZ+Ww8sFy9eDL1me4i5uTmGhoY6WrMXCgAC7/OWUr7a6Q1kcHBwWwOwlVB4X78FqZYvvKTg2vQJLn3wGp5rYhgOZ1/9FNN0/RYa5bBS81lJp9P09fVx69at3TblQHGQWmkoBSXHAgF50UTrMPE/mVDoxSSJ//j7xABp3EAoxewp/9+dn/wxdn8vmvT4TmwK/bTDQlHHdgRdaUl3xkMI6M24/FoNIg2DSKNOcq1IpauHapc/0DxRLlFP+02030o1+LQaYdGL4DfILZAqraIpScryEJ7/wjbCnrrTJtLyc+eSVphisV2uXLnCt771LQzDwHXdpy8I2VUWFxc7Hqm1XgAQ6NFMgRZmQggrm82mOkn8j0QipNNpVlY67x+n+iuQbIOhWFnt4YMPvkap7I/nHBqe5JU3PiEeb4AEpMCaynb8HCEPcvbsWa5du7b1fMCQ58ZBEGe1Klz7wmBhLAJ58JabVLOCVHrrpda6BscOOdycNP15uY7DWn8fStcwmy0qPX5482RzlmTChggcGXj0pn5bddOIp9ClR9fSHJWeXqq5LlCKzMoy1XwXaBq9hsOVepSG0hGeR25lnmizDiiiluTifAylFM14mlYyC1KSXZ1DSImmw8musDBpu9Trdebn5xkfH+f69eu7bU7IU1hcXOzY879eAHBsh0x6LgTd5/3SwMBAR+fbwcFBFhYWtjXSR+VrYPoNZ3/+V9+/J8pefukCXzvzIfFIEyToqwkSvzyMcAKta/cEp0+f5osvvthtMw4s+zms6bpw6TODVkvgrFdKGuUWX3ym43bY4mug1+PVU2168h5K16iuizE3YqF0nfhamTPxJx8GyzLCR47fc+n3IpNkxjP3RFnX/CztWAxp+PvJimvQUDoxp0H//F3irRq6pogaipbU8JTA1S3KXYMApEvLmI6NkIozXU3ysfCQ8yxcuXKFM2fO7LYZIVugVqshpeyoAEDTNBKJhCGEyO+gac9EoIWZpmlvjY2NdTQg81kS/0XTAtfXgadPfYGu+6feS5ff4Ne//g6N/+ZNkn9+ivhvx9DqkW09R8gmmUyGTCbD9PT0bptyoNmv4qywIu71IHQt3+seqTdQClaWO9/6MinFSL9Lua8Htd4JVmkauuPQMzPHyor+2HVSCX5pH8FD54heYFGmuOb1IZSkZ3YazfVopTZvLApBplLk76au8e5IlZP9bV4ZbnFyoM3GKbWa673XK60VT91bO7WFxP+N5rYAKy2dz4sRblVM7Icm2zgSXLm5puaJR655XtSUoKw0ZAB6Rl67do3x8XE6idSE7B4LCwsd710jIyMW8PrOWPTsBNrlk0wmvzs0NNTRt6O3t5e7d+9u6/nEbA7OLACKl89e5Nj4Da5eO8vNm6eYa3QzRzcDWoVXjAX6ter2mlSG3OPUqVPcuHEjHFgeAPZjWNNuC+S680gohe44WM02UoG9zWhf2TapdD9Ymdk9NYfmShrNx28In7kDFFSChGjjKcFd2XtPlEVqDWZPnLx3rZCS/PIC7+SW0YUiE1NkYr4aWq1vCj+z3aQdS4IQ2NEErXiKWKNKzdZwJRiP0Z1tT3CpEGG5aSABTQPJps3Xy1He6m4QMxSflmNU14sIkoZHAw2p/DHuvZbLm6km5nM41teU4DdunCqaXyyB4h29yYC2e16/ZrPJ7Owsx44d4+rVq7tmR8jWWFpaYmBggJs3b255zejoaOby5cvvAX+5c5Ztn0B7zLbT8b+3t/epg8ufhLAN9F+PQ833nMWsFm8cvcTfyV7gZWMeE5cFmebH9gl+ZJ9gzkuHUwGegZMnT4az6QLEfvOcpbMKbV3LDF++zfClWwjl/y2d2d4Xdybeh9I2t83USpF4pYamKTKPyVtb8pJ84fpVmAKYknl0JL0z00RrDard3fceT3ccBqbv8m7XMn3ZR4VJPu6hCQUKUqUiA5M3iNYqANiWP7xcE2B7jwpEqeA3i3GWm4bfaFcIPB68TgEfrcZ5v5ig4mooBBJBWeo4SsNb/33JNviwEnvkOTpFKfhrN0EZ/7FdBG00fuPFqandPfVev36dEydO7KoNIVtjYWGBTnXCwMCACHIBQGA9ZkIIkU6n05a19Z48mqbR1dW1rcT/e89biqP/9BTEbVAC0bQwgDfMeV4ylrjm9nLF7WNJpvipnaJb1HjVXGBYK2Nn2qwdreDEXaKlCNm7acxmYN/iXSWRSNDb28vExMRumxJyH/vJc5bOKNJpRaUMhu0nlWmaIpVSZHOdC7OGMrkjeu79brRt8nOLgMIwoL/XQykorGksr2hIXePK4GHUumu9piL++CV5h5mmgxSC/uE0pfWxnQN375COuPSkH+8t0gSc7mtzaTYKCOxIlFYy7Vd0Vkr3rokaj762laZO2xNINFrRFI4ZQ2oGjpXwJwjUV0k0C0hN4SqBo8dwjARS00GIe1MTIk4JQ7YoOAYNTxDXt38yXVY6bSVwieHJJKAwtQJSwB3P4hVje/NMnwc3b97k29/+NkKI0KMfcBYXF/ne977X0Zr1AoAjO2TSMxNk1dCTTCY7WpDP56lUKs9c5iwQ0Hg0hywiPF41FzhtLHHd7eWy28eqSvKX9jGyZo2h0St09TQROjhJh9pAnaHf9WPVw1yFhzl+/Dh37twJqzEDyH4RZ0LAmVc8FuY0lhZ8r1TfgGRgSG4rDeGS0493X5BhYH4WS/Po7vY4POai63DtpsFKQUcpwcrYEA2xuY+Y68PFu6gzQ4zff6+PQq3C2uQCVrOJhiKb/PLvQ2k9nKmAUtd6x/9yAcOxEbrieHf7se1AVt0IlXgPzVgWpT2aC+caUdpWinYkgWMmUNrjbw0ChdFuoQloSo243vn3VypYlkm+8LqoeVkUG/ujxKSIAursrsesWq1SKpUYHR1lampqV20J+XKKxSKxWIxoNLrlJuW6rqNpWlwIIVQAlXeQhdnx3t7ejux7ljBmJ1hC8rK5yCljmRtuD5fdPkpOktLH75BIlxk9eZ2eoTnQFcUTJfo/7Xn6gx4wxsfHuXHjxm6bEfIE9os40zQYGpEMjTxb1npdmVz3eu/9flJf5t3TxQeuWStr90RZPZum1pW792+6dPmD2C16NL/X2N/6bi8Lqw6fXCziSzeFrsNY35MPlaW6xnJJR3hQ7u7BjUTRHYdkqQAoLE1xazXCQsXkWE+bXEyyLBNc9fqYMnMo0xc7htMk0vZDoPWkHwJyIkmcyOZBWPNsTLeOp5m4pv933WsRtQuAL67SHYgyqWBRppj0ckx7OZo8eFjVRA1TKyKEQkfRv4s5ZhvcvHmT48ePh8JsD7C8vExPTw8zMzNbXpPJZFSlUhkEtteNfgcJrDATQpzo7+/feg0sL06Y3SPVYrT/GgPqBle1bqZvnKReyXDto3fQ3/0tXQOLtPLhdICH0TSNw4cP8xd/8Re7bUrIl7BfxNnz4JIzcC+clxIt3jRnH7mmUNT8ikdgdWTw3t8116X/9gT5V3xRNjIygm23KRVXSMZMbAfyKcmRQYeo9fjD+90lk4llE6mgmUxRz/qtNrLLcwilQEDb8715xabOrwp9qK4sJeFPAxAoEnYZq7aG5rYRAprR+wrelcJwGlhuHdOuo0mbVqyLdsQXl6ZdId5euiecjsXbT03+95Rg4Z4Yy9Lm4ciBIi0quNoaCL8aQ0MRRzImOuxnsgPcvHmTv/23/zY/+9nPdtuUkKdQKBTo7u7uSJj19vZaMzMzxwmF2dZJJpNvdnd3d+wxu3z58k6Z9ADlY0VqR8sgFFKBNpnDdX1zY8kqqZw/R1OzH19Cf5AZHh5mbW2Ner2+26aEPIVQnEFNPugt+z1zElM86oHT78u3slotWmYSzXEZuD1BpNXCtgXHjw3jOA5LS4v056E//3TPUMsR66JM4JjW5kinlSUirSZKB4RAahr1VI5GMnevJ1oEhxPGKieNZayIw1UnwrRnAoJIu4rUDAy3jeE00IT0HwdBPTmAbfn5a7HWKrF2EU2DhCk5HmszFHm8Z89VgnmZZtLLMeNlsR9zi9GQHNULnDUWSYs2k8rktmfiIhjRHE5oNnoAKt4XFhaIRCJkMhnK5fJumxPyJayurtLd3d3Rmv7+/rSmaSeBv94Zq7ZPYIWZEOJsV1dnA+BflMfMSbWpHy2DrnBsk6ufvsHS7CgAfaNTHHv1IrrhIVxB5m7qKY928BgfH+f27du7bUbIFjno4uyiu+n9Oq0v0a/XHntdX49kasavvuy7M00tnyVWqWLaDpoOx8Z9Udbpe7hW81tJtKMxiv1DKE0nVi2TLK+h64q2plNPd1FP5+7ljxl2i0R1jT8emscQ64JRwCvdbbJVjyulKChJouGHJoVQSOG3w6imhnDNuP84XpOIXQUESireS9eJPHTWdJXGrEwztS7GHB5/GNXxOKGvcsZYJKltesQOC4fD2u57yB7HxMQER44c4bPPPtttU0K+hEKhwMjISEdrurq6tEQi8Rbwn+6MVdsnsMJMSjmayWy9t6yu62QyGYrF4tMvfkaa/XXU+mb38S++Ra2SRTccjr/6Kb3DcwhPgAfpqSTp2c4KGA4CR48e5Sc/+clumxHSAQdRnLkOVKXFLbmZI/qG+eSoRzymGB12mZ410KQkvervRZqm+OEP+nHd9rbeO02DUr6bcq4HhMBqNsgtLaA0gRrIshzpvSfIrGadZKWA1WoQNeSmKLuP0aSLpM2NsoUrBYaAbNRjxfZvB+q+ygjXiNOIdZFsLKGAX64mea+rjqULZrwMU16OWZnBfYIYA7BwOWksc8ZYJir21vzJiYkJDh8+HAqzgLMdj1lXVxdCiECOeAikMBNC6JlMJqppW2+zlsvlKJfLSLlDranvZ32vUwraLb+fz2tffZ981yrJmzkihRhm3UR3A90mbleIRCJ0dXUxO/tojk5IsDko4qzdhJuXoLoGk6+Pw7rT+w+taxiPCWHez5Exj1RCceuuge0ITBN++P0+THN7oqyuTC5EDlO2/HTbVHGV1FqBWjZPLdeF1H1BFGnVSZZWsdpNAHShOPqEmZlCwOGUw6Gkg6vAEFB1NX61bOAJDd2z8Yz1vmieTbTlp2VIdEp6gj9vDGGbiXsD1AG6RB0PQVVF71WuRnE4Yyxx0ljBErufzL8d7t69y7e//e3dNiPkKRSLRTKZDJqmbVkDpFIppJRDO2zatgikMANGcrnOGg3lcjnW1tZ2yp4HiC0mqY6XEZoi37vM0uwI9WqaXM8Kmi2IlsNxTU9idHSU+fn5FyOgQ547+12cKQmXPgC7DXYsSjvlh/SShRJdfXW+xDF0j55uSU+3jVIwOjqC49gdv1eNluDjlS4ms8NIw8DwHHLLiziWxeKho0jd37r7tCqv6vM06i0mbAsl/F5mR7psRrJf7p0SAtYLNUkZkkw6xh1t0G+VoRTRdpGIXcUx4jTNHhwjzv19Rnq1KiNamTY6k16emvL3vYRo85KxyHG98FQhG3TK5TK2bb/4wrKQjpBSUi6XyefzrK6ubmmNpmmYpmkJIUylVKBi6UEVZsf7+vo6UjcvUpiZNYv0zSyVk2vke5dYmh2hsNTH0NE7lF9aI7oWxwx7lz2WsbGxsPx8j7OfxdnaKngOoASTb7907++D1+6w7MLgoa0/li/KOs8pa9qCHxUP3Rv9FKnXiTYbrPX24+r+vtItarxhzjGwMRquG450OdiuwDLUY3uZAXgS7pYsZqv+4wwmHXqzgo/cUeYNP3VEKElS1mmYKVrR+/J81ys3I06VV+NFWlqKK24frfVqywQtjmlLvGyuomuBaw21bSYnJxkbGwuFWcDZCGduVZgB5HI5tba2dgi4tWOGbYNAxtoMwzjd19fXUauMfD7/QvLLNjBqEZQryPf5m25xpRcpNZRQ1IcrL8yOvcbY2BiTk5O7bUbIM7Lfxjdt0G5yb5C2vj4toP/aBLjQamz9cUZGtifKyjLCv2qfopLz22Hoto1jWX7fMt0kR53vWTf5o8h1BvUH5/VqAqLmk0WZUvDRQow7JYumq9FwNb5w+/kX7TPMy818XiU0qnoKT7cQysOyqyQai2Qrd0k2FvA0nQ/UcS64w7QwSdIg0Z4j0pxkptbip+UEVS+Qt5ZtsXEQCQk2Gy0zOqG/vz8GHN8Zi7ZPID1m8Xj8za6uro4KpnO53Asd7+OZHkoTaLoEFJ5roKQGhocX2dvu+53CNE16e3uZmwtc25iQbbAfPWeJtB+tU8DIxRtITSdaa6DpilR2a4+xHVGmFNz2uvjAGcU11uOlQuCtj6Qzm03yq8u83lOkP7a9fK1CU6fS1tfbbkSpZPtxzUcDE3nRYFgv06+V+XRZIAEpDJqRPO1IBsT6FAVR5ZBY4mbNQyLu5Za5En5TifMH2dq2JiwEjZmZGb7xjcCOVQxZp1gsMjg4+PQL76O3tzdpWdZLwL/eGau2RyCFGXAqn893tCCXy70wj5nUJLVDFdAUC5OHAEH3wPy9Fhmx5fgLsWOvMTAwwPLy8jOPzAoJDvtNnKWykMxAtaSwGv6sRiEUVgS6tjAneTuizFYav3PGuOs92h5It21yS0vEqlV0TZEY3v6hr9zW8da9gaXcINLwQ5BCeuRljZOxNYb0Mon7mrte0tOUzG5akew9QWY6NRLtIl/PrXK5GcHWUrh6Ak+LYro1LK9MW8Kaq5M3ty4iG0ow7Zl4wIDmkteCccAtFApYlkUqlaJare62OSFPoFwuc/LkyY7WdHV1iWg0+sYOmbRtAinMpJTdnc7JzGazLyzHrDpewknZKGBuwp+DOnT4LigwKxaxpVCYPY6hoSHm5+d324yQ58x+EmdCwJk3YfYOLM0plISuARg7Bo8ZMfkA2xFlKzLOL+0jVFX0wX+QknShQHp1FU0phFCk45JUfPu5W1FDogvwFCSrq7iGRaRdJ+o0ONPdZsTYFGS20rni9rGc7EMK/4WbdpVY2z/82kaM33hHWdBTKGMzbCmUh+WVEULhdGDqlGvwsRdDARK45kU4pNm8YbQD4XWbmZlheHiYa9eu7bYpIU+gXC7TSYstgHQ6DTC6IwY9A4EUZpqmRUQH38Z0Ok2r1doxT4w0PFTEQ2saCKlRH67hSoOJK6do1pNEYg26+hdBQfcnvQgVgJ0kgAwODnLrVqByLEOeE/tJnGk6jB73f7ZKp6JMKbjs9nHBHUI9lOo7pJU5ywxzDUVZaWgC+vMux0aerXCsP+FybVXhKYg1N/Jg/Zy0gaT/2I7SuOb28oXb73ftF37LDMNt+rlnieF7PdMaEu7NGlcKy10j6hTWfxXkja15y2wFH3sxvPsGl3vApLQYVS69AWi1MTs7y8jISCjMAsx2hFkymURK2fv0K18sgRNmQohIPp/vKHN0p7xlSijqLy9iD1f8pBMF1rVeJibGmbh9Asf28zMOnbjun+oUQCjKnsTQ0BC/+MUvdtuMkB1iP4mzTuhUlDWVwa/tw8zJB28icWHzjjnNmFZCCBg46Qs44Ll4jXQNvjLU4LPFGFVnc4v1FHyxEsPMp/jCHbhXZbmB1C1s3Xri40ZknWh7GZQDKHTgpXjrqbM0AVrK4KqbwlYZHBVFEkXgERXTeEIx5Zn0BmCg+cLCAl/96ld324yQL8G2bTzPIxaL0Ww2t7RG13WEENGnX/liCZwwA/pTqVRH/vp0Ok2l8vwrIetnl7CHKqArpBTMTB7hxuTLNJv+YOBs1wrjZ78g213ww5hVC90JZ2M+jlgsRiwWo1Ao7LYpITvIQRNnnYqyOS/Nr+3DNO8TPwLJGWOJV42FR2ZwPu8wXtJS9CRc6mULqfwRTI14hkuJLqT7+BY/Ji7CbmC4TVwjhm2mQAiEdDgq53k3scK0ZjJvm0Q0yZGoQ9djcsscpVGQcVZkglWVYFUm7vU+exCPe128A8Li4iIDAwO7bUbIUyiXy2Sz2S0LMwBd1w0hhK6U2v0TwDpBFGYDmUymI3WTTCap1R4/v267KE1ij/jzMCulDB//9uvUqv4JN50qcezYNXLDc2AphCtACrou9jzlUQ8uB+VGHXJwxFknokwqwQV3kMvugzf3fq3Cu+Y0Wa21U2Y+wvS6KHMNi1J+CM940Btm4NGvVRnQqgzqFUpNly8aUUBQiuZBCCLtNeLNVYRpY6TgSMzhSGwz1OopQVHFWJWJez8l5T/G49Boo1FDF3U0Wgjh9/I9pAej72e9Xsd13XCgecDZCGcuLCxseU0ikVClUqkbWNo5yzojiMJsMJvNxjpZsBPVMsrcPLnOz47dE2WgyOUL9F7JkiraOLkWRsMkPp9EC0cwPZGwc/bBYr+Ls05EWVVa/MI+wqraLGiKYfOWOcsRvfhCk9uVAnfdGeWYUV+UKYVpN4m063y9u0C3VkcpxVzDZMHWcNFAgWtE7uXDRdplBApHaUgFFRX1PWHrP0UVe2Bk08NoSPq1KiN6iRGtTFlJPnD9bV/hy7dxzaYnAGHMDRYXF+nv7w+FWYDZTp5ZJpPR5ubmBgmF2ZPRdX0ok8l0LMye9+Yv2jrC1VC6x7FTlzFMh9nJw5RLeaamjjIFJKeOcGS2wLheQNPaz/X59xu9vb1hReYBY7+Ks05E2V03x2+dMZz1rVagOKUv85o5h7UL44qEgExEUm7rRJsVDKeN4dkIpchGXPr0Jg1X8OvFBJ4SeEogBDTiPbRiufUmbx6eEcXW0rTMCP9FK/qlQ8w3iOAwrJcZ1csMauUHXn8K+COtxow08JRgQHPJBKRdxgYbwuzGjRu7bUrIE9imMIsAA0BgJtUHTpjFYrFjqVSqozPkTnjMBIL45V7qryyiG5LxE9cYP3aNSinL8m9eZaLZS01FuOQOcskdpFvUOWoUOKwXiYnN6lCpKSo9bVxTkixaRBuBe8tfCL29vVy8eHG3zQh5wew3cfYkUWa3YXkGmnVI5yEzoPGJHOGWt5ne0KtVedecJq9tPf9lJzjT3eLD+TieAtNtAwpdwJke/3B5qRjFlgIQ2GacWqoPeX/yv9Cpx7c28SEjmozoZUa1Ej1a7YlTCQCiQnEsIKHLx7GyssLx44FrEh9yH9VqteNpJNlsNo4vzAJD4FSCpmmHUqlUR2t2qvFfZC6D1jZoHl9Fxh2MtRgjN3IcUou8FVlkUaa44+WZ9PJ+MquT4CNnhCGtzFG9QHdulek3CiD8Ck+A3HyMoespxAGr3uzp6WFlZWW3zQjZBfaLOHuSKKuV4fIHICUoKZitRFmIH6Ed9x3/URzeNGcZ1wuB6MmVjUreG65zt2RRaeukIx5HszYJS6EUrLR0QGBbCSqZ4UfWC+mt72mPeskEij6tyqheZkQrkd5HkYTV1dWwMjPg1Ot1EolER2tSqZQej8fHd8ikbRE4YQYMddpcdic7MpurCczVRz9oIWBArzKgV/mKmmbay3LX62JWppmVWWZlFq0wSu7yIvnhOVLd/qZcGmiRKlhkVgJXobtjpNNpbNum1XpxCc4hwWKvi7MvC1/evAie61c4loZ6WB0fRukaKMVJY4XXzTkiAejFdT9JS/Fy7+NF00bnH6EkmmujSRfDs1GaBrqBrVkosXnrsHAZ0suMamWG9PIjr9VT4CiBJZ48x3MvUCgU6Op6dDpDSHDYpjDDMIwjO2TStgicMPM8r7uTN9ayLJRS2La9g1Z9OYZQHDHWOGKs0VIGd708t8hRdFIUZkYozIwQSdQ5+bX3MSyX4lDzQAmzFz1gPiSY7FVx9mWizGlDq+7//9JQDysn/CbikUqdoYkp3v1KB5PPXyCehJqtEdEVUXOzNYUQ0BdzWWoaaJ5DrLmGHUnSimYe6N2heTaWU6NblflOrogmHm1voRRcbkW407ZQ4IdLIy2ORoMbrvwybNum2WyGlZkBptFoEI93Nnln3RH0qGt4FwmcMNM0zdK0rVc3plKp594q41mICpfTxjKj2VmuHXMoLA6yPDlGu56gvpYj07eC1ILVo2enCYVZyAZ7TZw9LdFf3LdVmS0bq9ogO7dCZn6VaCyY3/OJNZNbqxZCgFSQj3m8OtDC0GBVJVDpLKVoDsd46PCoJLrnoHstou01DM+mjaLmCtLmo6/1SsviTtu619FfKviiFcXSFCPW3pyXu7q6Snd3dyjMAkq9Xt+WMJNSdpaYtsMESpgJIcxcLteRTdFotKNmci+KeNkkliwxePImditCYXqUdiMOyv+3g0Qul3thc0xDgs9eEWdbqb40TD/Zv1xQJAtlkgX/hq1pir6RF2Xp1lmu6dxc9fuYoUAKwZxMMlcZpBVNbTa+fdwuLDQ8I4JnRFCaQao2hxBQczXS6+2FPCVoKpOaMrnmpHE1c33WpiDiFfGQXGtF9qwwW1tbI5fL7bYZIU9ASonjOESj0S2nzpimCdBRJ4idJlDCDMjGYrGOaqRjsVggc5c0JUgvRFgbbmHFfOHYbvhDelcHW/TfSR6YAoBcLsf169d324yQABF0cdZJS4xjr8Ll34Hd3vQapfMwdHQHDdwmd9d8USaFRiXfTyuahA4iFAAohZAujWgXUjf4lacjGjqeMGjfP87pofOnrlqYskZT7t1+j6VSqeN2DCEvlo08s050ga7rgRrZEzRhloxEHjei48kE1WMG0Ex7KEDT/WRYe12YKR1aSZdY7WB4znK5XBjKDHmEoIqzTscsRaLw+jehvAqtJiQz/k8Qabv+YdAzTFrx9L2/C+mR0dqktTa6dCjUFLgO7WiadjT94IMIgR3Z+gvUZAvLK2NIP+UkqwerEKITyuUyx44d220zQr6EjXBmJ+P/hBCaEEJTSgWieV7QhFkqEol05EYKmsdMCcXy0SprIw3auqCy3MP89RMAJHIlNmqehDoY3jLYuVmmIXufoImzTkXZBkJAdg9MZOuKuzTLJqbTpmtxAgDddbCEx3eO1tEELDQMPm9GcZWgHVlvXaQUmnTRlQvSRVMumvIQSuLpFo6RQGqbB80IDr2UKNl1hNoszNJRvBTbuy00ttPANOTF0mg0Oq7MtCxLAglgZ9o7dEjghFk0Gu3IpdhJLPlFMHu2RK2nhdKhttzF3Y/fQEmd7rEpeo5MggLD1ojUA+U53TE0TSMWi1Gv13fblJCAEhRxtl1Rtpc4mndYrJq4UmE6vkDSheJkd/teK4vuqMuG2yBVXSRZW0YoiSEUp7oc2pE4nzVy2EYCT49uVmsqRdSr8dX4EsNaGV0oFgyDqy2LhtTI6JKXom3yxt72mGWz2d02I+RLaDabRKOddT2wLAv8ARShMHsMye0Is6BUZdox954oqxby3P7oLZTU6RqdZvjMFXRPICQc/jx7YPLLEokE9bo/ey8k5Enstjg7CKIMIGYqfm+swd01k9WGTsxQHMk7dMU3xZKpwZlcm8trEVzNxDXjuGYMz4rx11oEPGAj40QpdLeF5VSJOBVyus1YavMQNmC6DJh7M9H/cVQqFRKJBEKIcE8LKLZtbwitLbOeQtVZA9UdJGjCLBWNRjt6R2OxGKurqztlT0e0Ey5CCpSumL91FOkZRBM1Dr/yOVbDoO92ivRqBE0eDFEGO9v8N2R/sRPiTKH45dd/SawV452P3sE2bT554xOasSaxZow3L7zJ0f6j+1qUKQVLFZ25kokABnMOp3rsR6YQeEpQVHGWvCRLVpJyT/LBZH7AwKNHq9Nut3HsFobbRqz713QURxK710/yRaCUotVqEYvFaDSC2aPuoGPbNtvIVdfwPWaBIGjCLNmpMAtSKDNSN1DrPcr6j05QXumhVU8yfeksYyevMHWyRqLcZuh2glg9aG/9zhAKs5BOeN7i7O6Ru6SqKdx1r82t8Vv0rPZw7PYxbo3fYum1JUanR/e1KPt8JsJK1fBbZADFus5y2uXkkMuKTLAkkyzJJCsygffQMPIYDr1alT69Rq9Wo0s00YSiZQh+Z8epo93rhzYatxnZo81jO2EjhykUZsFkm8JMJ/SYPR5d17ORSKSjUGYkEgmMMLOaBvGiRSNvk+lZ5fg7H3Pzw7dYnjqEqzSGz16h0mVTyzqc/ChHtLn/88ySyWS4gYV0xPMSZ81ok6XeJY7dOsbdo3cBWOxf5L3fvgfAO947/PP0P2dkMYANx54TUwWD5YqBQiA1jXY8QTsaZzEa40IrAg+lVGREkz7NF2F9Wo2UaD92vmdUV3yzq07Z1Wh6GlnTI6YfjNDeRjuGcPZvMLFtm07nbUejUYPQY/Z4LMvq6jQ2bJomjhOcU9rI53mWjlcoDTXI9qxw7O2PuPXR2xSnR0EJhl++jNQUS2N1xq6nn/6Ae5wgtzMJCS7PQ5xdPnOZ09dO4xqbOU7tSJtoO+rnlDUdGvr+OjQoBaW6RrmuI4Ti1lIEhMDTDVaGDyGNzS1fKEm3Xn9AiEXF1vPBhICsKcmagegw8MLYTnf5kBfHdnLM1iN1ocfscei6nuv0DTUMA9cNTnKpJgUD1zP0X0/TSDnYr5c5/PYFJj56g+LMCNLTGXnlC+qZ4Ni8kwQp1Byyt3gWcbbYu0jEjpAtZ1ntejAH9YFE/1eep8W7i5Tw2d0olYaGJ33hpBS0kzHK3X1Iw8Bot4jVq5jtOhGnRcL0ODHYvte5P+TpbKcdQ8iLo91ub1eYhR6zx6FpWq7T2HDQhNkGAoHVMlBCkeoucOTtT5j45A1K84O4tsWhVz7jt99Yw7QFw1NR+ucj+7JSMxaLUSqVdtuMkD3KdsVZMV9ksW+Rpd4lpCZxTZcLr10g6SUpU6ayWKEVaWHZnW3gQWZ6xaBc1+519q9nMtSyOdz1PVVzHfJLM+jSuxfBrNsaH03H+MbROub+z6x4LrRarY5zmEJeHNvxmFmWJSzL6tohkzomULMxhBCZve4xux/T0ciuWAgPkt1Fjr77IUakTW21m9sfv41rmzgRxdTRJvMj+9OrFHrMQp6VmZkZTNOkv3/rc4ZPXz/N7//l7/O9n3+PNz59g+7Vbv7m6t9kdG2UC5EL/uOOzNC/GKjZxc/EfNFEKoFrGMwdP0Gpr/+eKAMQSlHqGaTYO8xa9yD1ZBYQSAULlUCd0QPNdm78IS+ObQozDMMIzBDUQAkzpVR6PwkzgLFrafILUYQHiVSF8Xc+wIw3aFUyTH/wNk4jitRhdqyFFPsveTZIxRkhe5ftiLP7iUajOI7D4OeDrHSv8PNv/ZyV7hWO3d4/43XkxvYhBEI+2sTVMy3sWIJ2PEkrkaaa60EBUgmaTqBuBYFmO1V/IS8O13U3BpNvmUgkgq7r+R0yqWOCdkyKd/qGBl2YaVIwdiPFyM0kUld8+k6Z0a98zOwnr9GupJn77BVGv/IRQlO0I5JYa/vxBIUKXDg0aMUZIXuX7YY1X4u/xks3X2JxcRELi69+8NUdtHL3iJiKlq3QHYf06iq1bA7v4YOuUpjtJlarQaxRXd8tFInI47vxtz2BhgrDnPdh23bHN/6QF4eUEvG4UuIvwTRNhBBhjtkT0DWts5Nb0IXZBpoSaK4g2tBxMzYjb19g6jfv0K6kWb01Ts+JW1x4o8rYZJSRuc7GSUzlJFeGFE0LojacmRccKgbjBLxXPp+QvUGn4uygdPQHsAwFCBCCck8vD/e50B2HRLmIZdcxHPuBI9xc2WQ4synOSm2Ni6tRGo6GAvJRj1e7W8SM/efV75R2ux16zAKMUopOdYQQAiFEYI4fwbh7byI6VbqGYeB5e2f22uhEDM0D3XQZeOUyCMnaxCFqK3mkLpg61KaQ37qHaTon+WzUF2UALQsujigm88GosgqFWcjzZqthzYMkygBySQ9NKIRS9E5PkSwWsJoNv1wT8EyTSncfq4NHWBo5TqFvlHK+j2qmmwUtz0Q7RUVGaLgav1uIU3N0JAKFoNDS+d1inHAKETiOE3rMAoyUclvCjADpoS15zM6L898H/gNAB/7zc+rc//G8OP8K8Gf4vT8mgT89p85V7ltzAfgW8P8GjuJPWPuX59S5f3f930eBfwJk1x/331UppXUizDRNQym1p2aWZUsmx68kmTjWQGXLdI3fpXBrnMXPXyaSroJQLHmQaWsIQEMhhEIAAuX/ft//n3Il7jQgFEL3MPuXwXS5Mqg4VNzd1wqhMAvZGZ7mOTtoogxgMO8ytWxiOxBpNbHaTZRQKCFwIlHsaAw7FsOOxPx8s2gcO7rZj+sXEvDnmiO6XXTpoHku0VaFSLtG04XVlk4m4iHwZ2oeRJRSHYfKQl4c2wllbgizx2mdjWvOi/P/c+D/BPScU+dW7/v7BeBd4Czwj4EY8BfA//ScOqfOi/NfB/594GXg751T5/7rp9nz1K/WeXFeB/4h8APgNPD3z4vzp4H/HPh3z6lzZ4F/Dvwv7ltzCJgDXOD/fE6dOwm8Brx3Xpz/wfpl/2vgvzynzr0G/D3gPwE6EmbAnhJlG+SLJicuJ8ERZA5PEcsXka5Js5inWeiiUupiRuaYljkmZZ4Jr4u7Xhd3vG5ueT3c9Hq44fVyzeujsTyAPT+APTdIe3oEd9Wv+G2Zfs7ZbrPXPJohe4cnec4OoigDMHR453iTwS4Xy5DEDEnEVOhCYbVbJMtr5Fbm6Z2/Q+/MLXJLM6QLiyTKBWL1Mj2iSkK0QSmUbuCaMexoilqyBwAJfLYW4yfzKX48n+K3K3Fa3sETKNvxyIS8OLbtMVMYPF7rcF6cHwG+B0zfv25D65xT52zgPwX+beDY+s/31y+bBv4N4P+1VXu24jF7G7h9Tp27u27I/wf4bwEngF+tX/Mz4CfA/2b99x8APz6nzjWAvwY4p87Z58X5T4Hh9WsUsNH6PgPMA9ltKt09R7yhAQIhYODNz2iVsihPAwXRus7hiTi20lmRCVZkkjUVW/eVPYowHLRkHSNVw+zxhXzMJhCFAEIIpAxGWDVk/3G/5wwOrijbwDLh1IjNqfUpU66E6VWThZKOrkFfxuVuyUJ4LnqrDoAuFEe6bI6uz7n85VyckhfBMWNUs4NI3aScGcR0anhOHW19aPlqW+f95QTf6a89dmyTUrDm6kggZ3jou78dPRdCYRZstptjFnNjeR6vda4C/1fgfwn8i4eW/gD48XlxfgBIn1Pnfre+9v8B/E3gR+fUucn1v235RrgVYTYEzNz3+yzwDnAZ+ON1Q/8ucP/Aue8D/7P7H+S8OJ8FfojvJgT43wE/PS/O/0+ABPBdpdR/0YnQ2ovesg00JRiasZg+ZCMERDNl2pUU7XKaWjnNqpulKmOPrDPw6NYadGt1ukUdN1vl8pE28r5PUvfgpfng7IJ7VTyH7A02xFk0GqVSqRxYUfY4DA2O9Doc6d3MWx3IetwpmKzWDSK64lCXTX9q06udMiVVx0FruzTtBq4Vx46msKMp6kphuC0Mt4nhtlGyzWKrxUDswXSFNVfjt5U4nhJ+M1sFb6aaDFp7P61hOzf+kBfHdkOZutTjwLX7/jwLvHNenP9jfK/Y5+fF+YeXbmidofXr71871KntG2xFmD3uFSrg3wT+w/Pi/P8W+HPABjgvzlvA8IbqXP+bgZ9r9h/e9/e/D/zjc+rc/+W8OP8u8P8UCHH48OGOxl1omsbx48e3fH2QOGRJ2nVfXP6jz1Ms1B78OHShGIhKhuPevZ+eiEQTAPH1nx6+5yiagNRAkxBzwOoR0POCX9Bj0HWdI0eOhIPMQ3YUy7IQQtDV1UU6vf9n0D4rL3/Jvx0Zh7qrrSf6S8p2ndt1gzs1nemGjmvGcM3NQ+NPUGQ9RW/Eozcq6Y1KUobi70cUkfvq3ASKtL6xfz3IvRZsz+G17TTd3d3E4/E9e9/Z70SjUSzL6ujzicViRCKRKI+23IwA/yvg9x/+h/u1znlx/nFTA7btOdqKMJvlQW/YMDB/Tp27zrqx58X548DfWP/3rwHvP/QY/xlw65w69+/f97d/i/UY7Dl17nfnxfmopjT37t27HZci37x5s6Prg8LMcJvJsRZKh2X79wCDeO8K8XyBI9U2JwsuOgoaQAPK+D97Cdd1mZiYoFzea5aH7BVGRkaoVCqk02mazeaBDmU+L26VLG6VLMS6w0sKkJogg4ZjxnGNKJ5u4ekW0rBYszXWbI0b1QcfR0gHXdnoXhNTNjkRKfNyfPPuV5OCD1sx1qSv4HKax9vRFiktuOkPrVaL48eP79n7zn4nkUjgum5Hn8/i4iINp1HkUa0zjR/O3PCWDQOfnhfn3wbOsKl1ZtlM09q4bn67r2Erwuxj4Nh5cf4wfkL/3wP+u+fF+d5z6tzyeXFew0/k/7P1678P/Ghj8Xlx/v+An0P2Dx563GngO8A/Pi/OnwKiUpPFvRye7JSeFZPJMX+TiubK1Jd6SfSukB5cwC0a6MW9Pyj3IH2eIS+e+3PK0un0Mw0+D9nkWNZmJOmw2tIxNUUu6vGrpSRNT2I5dSynjoaiK+LyTneLiopQUlFKMsa0E2dNxpCahdJMXExcPUEbuKCGudNq0aPV6BJ1rrUVLva9fNii1PnrRpy/kagFNidN1/WwoCnAbOfzUUrRMlpF4MzDWuecOve/37juvDg/Cbx5Tp1bPS/O/zusa51z6tzCeXG+el6c/wrwIfDfB/6j7b6GpwbKz6lzLvA/xk/uv4ZfSXkFv2LhJnAdXxn+o/Ul3wR+uf4ihvHdgKfxVebF8+L8hkD7d4D/4Xlx/nP8MOe/AciDdCOPtjVyRR2lIJItAdAsZpFAOePRiO39L3+YjxGyUzwp0f9ZxzeF+EQNxXDSpS/uYWnwtd46YwkbS5NEdcl4qs142uZO3aTa8hgWJca1JWgsk25NkWncJtmYINaax3LW0L0mQihKKsYtr4cP3EOU9cPUtWM0tWEckQQEHoJZN7h9wnRdDwuaAsx2hZkS6kla50l8k3Wts87/CL9bxW3gDuui7bw4/9Z5cX4WPxf//3ZenP+yxwS22MfsnDr3F/h9Oe7/23/AZiI/6wYMA6vn1Lnm+jWzPCFt4Jw6dxV47/6//XuZf69jYbaR6LdXBZ0UAgVEcyUAavMD6JZD19gEK3kPV3hoUtC/qhOz957AcRwHwwjagImQvc7Tqi9Dz9nzJ6IrXs61eTnXRir4aC3G7WIECWgCvqhEyEQ8PECi4+lRpDCRwkAJDVNINFwaPDQmSmh4xJGYmKqGC9RVQN1lhB6zoLMd4byuH+TjtM79nFPnDsGjWmf93z4BXnrMmo95MMz5VJ7rHXNdiP3gqRc+GdWpwNpo9rdXhVmiobOW9Yika2SPTFC6e4jy5BiNlS7qp25gpOsIBXdGHE7fMRlcDe5J8nGEXbJDnjdbbYkRirOdY6phsto28PAPlo6wcI0oa1oM1/LDmA9z/zyTCA4RHBrKQygXgYuh/PYdBpALcI6ZpmmhMAsw2/l8NoTZVq9/DlrnSwmaK8PrVOm6rothGNi2vUMm7SxD8xbzAzaegtz4BLHuAquXT+HUkyxdeJ3UoWlSh6YRuuLqUYeekoHpBvc0+TChMAt5nnTapywUZzvDVMPCQyCFTiU1itQe+o4riS5b/vQA5aArh3eTFdKaTUI4gGTOMfnCjtBS/tgnhD/pJKlJ+vXgttUwTTOcZhJgth3KVCowajtosbGG42x9TiTs/Rt/1NZ49VKCVNVvLhtLVzjy8gUSI7OgBNWJMYqXTwIgFKxmA/PfzpZwHAfLevT0HBLSKdttHhvmnD1/NuMT4gmNr/2BcqDQlEu/VqdH1EiLNjUJ/7qa5LNWlLYUCAW6UkSQjJs234zVH9uwNihEIhHa7fZumxHyBLYjzBzHQSlVffqVL4ZAecyEEJVOPV97XZgBpOo6b3yeRAqFUHD9sE1t/C6xngKrn52ltdKNdDWEJgMxZqkT9sPnE7L7PGtH/9Bz9nwZjTlcq2qgXLKVCTw9gqtHUEYEzChNInh6FE+PYuNnQt9pSwzVwpAtEG001ULDuTcT+LTZZjzS2cF8N4hGo7Rajza8CgkG2xFm7XYbz/MCMF3aJ1DCTClVPojCbANtPeF1YMVgrtcjkitjJus41RRONYWWLVNKSAZXn/JAAaLVahGNRnfbjJA9zPMasxSKs+fH4YTNQtug5Oh4ShHxmkRlk3dTDfKWR1NqzLlJJt0kSzKJK6JIzcIVcVxtc3C6UA5xdxrwmHSsPSHMIpEIzWbz6ReG7ArbSf63bRvXddd2yKSOCVQoU0q51qmLeD8Jsw2yNR3L9mfNmekKAM2VPJ6C2T6Jq216zRQKV1OB9aQ1m03i8fjTLwwJeQzPe/ZlGNZ8PmgC3ss3eDvX4HjC5ky6xfd6q+Qt31MR0yRHzQqNdpWYs0jCnsHwao95JD8UqvCLA7xgbmMPEIYyg41hGB3nANq2rWzbLuyQSR0TKI+Z53lrB9ljdj9SaCgkVqZCY26I+swIzeVeEr3LrOhrDEib+azk0oikZfmjmI4uC07PaYEYXr5Bo9HoaMRWSMgGOzWQPPScPR+EwB/DFHl82Mhb/wFw9DSuntz8RyWJyAKGLCE0BSgaQuPPGynetJqMmMFNrg9DmcEmEol0/Pm0Wi0bCEyOWaA8ZrZtF0Jh5pNqCFCCaG+B1JFJ9GgT2Y5QnRnhx5WX+WfOSX4d7aGuGSgBng53ehVfjASrzLzZbBKLPTqMPSTky9gpUbZB6DnbeXTAEr4LzPJKRNwCQq6HKoVGW++haQxjiwxK6EgELoKP7RgVGahb0wMkEgnq9fpumxHyBKLRaMddGtaF2eNcurtC0DxmpXa77eF/p7fEfhVm4zMmhXQbqSuSh2ZIjM3graURd/tZq3RRdpMwkYTJMcx8ieiRKYi2mexWnJ5TGDIYXrNGoxGGMkM6YqdF2QY77TmzWzB5U1BcBk2D3mEYParQvmR38zw/hWE/9GQWAs7GWnzWiOEBUbdAhAJKRElH4yyoHFLEsEUMW/ViUCGilvCAO47Ja5FghguTySS1WmDu4SEPsU2PmUuAPGZB+/rX1pXrll0s+7UdQ6au8eZ1i2tjDrWEwnLgcKXBIWcaLzrLnw+nqRd6cNcyOIU8WrRF7Mg0AG0DjIC0davX62EoM2TLvChRtsFOiTPPhYu/FdgOsF7UMzsB5QK88u6jiVSOAzduGBTXfE9RLKY4ccIlnXpy0pUnYbWk43iCbNIjGQtegtZoxMUQTa62IjQ8jZTu8VKsQI+5zE17jk+cHlyRwRMJXDJYqoDApRFwj1kozILLdnIAW62WR+gxeyLVToVZu93et1V/+YrOe188eLxWKK4ectC6V4l3l6h8/Bp4BmbXZkFJNECFTZVKhXQ6vdtmhOwBXrQo22AnxNnyPNiOAAXNTBKpa8TWqlQrUFhSdPVtXru0JLh500B5AuUvod7QuPi5yeuv2iSTjz5+tSH49GYUpXwPG5j05V1OjTmB6wE2aLkMWo/mjNkITKoYqkadcRAaCgNwiIhNkeko8BBEULv+2jRNC6syA040GqVSqXS0ptVqSUKP2ROprSvXLdNoNA5UDtNKWjHTLREatOcGwDMwsiWMTBXdgxMLAj1Ac+ZqtRqJRGJPj80K2Xl2S5Rt8LzFWbnoKyypacy+cowNRZFcLuKulXivt4TwJBc/M2jUBRsjhZvJOPV8lnilQrTW4JPPIxw95DIytLktKgWf347geg9+z5fWDLrSkr78o1to2xXMlQ1ajiAf9+hNeWi7vE2o+/YpgUSh0dRG0VSDtlimJVt8ImMsKP82FUfylt6iV9u9JtthflnwsSyr41Dmuoct9Jg9gep2hFlPT89O2RM4Zro9PA2QGu15P3E5OjoLSjFSEBxfDFYIQEpJvV4nmUxSrQbmQBISIHZblG3wPMWZYSgQAqEUmuch15PGar15auSZbkmS9SpWvEKiXUEoRSsRZ2l8FGkaVPq7AYiXKlSrVfRqkcGUH56pNsU9UVbJ56nm8xiOg9lqcanZ4mWvSlZrEhH+VrrW0PhkJoYCpBLMlRXxguSdsSbGLm4XfYbLLdfCA2JqGocsDhmkiHNTHuJOexBdK6GLCkJIauj8yovzB6JOSuxOkVMmk+nYGxPyYolGox2HMteLBQJzgwqcMGu32x25VQ5c1d/m4Roh/O5lXiVNJFthugdMT3ImYC0zKpUKmUwmFGYhjxAUUbbB8xJnA2OwOOd/VYc/v83S8VHaqc0iGA+NciIDRzKsKIWQEqU/WhXQyKZpZNP8hCHSrRZDWpmsrCKFn0TqGQaeZeFZFu1Eghowu55fGsMmK1rUag5a3MZwbAynDUpStzUmiybj3buX99CjefTqLkuegcLFUqtEWSWhJamSp66ieLIX6MIQZUytgBSKa57JMd0hicR8wdtcLpdjbS0wfUhDHsN2csxs29aAwLhCgybMap2+oQet6m94VWcx6+LpkuTJW1Qvn6I5OYKRrKLnKtztVWSaipFisIRZmGcW8jBBE2UbPA9xlkhBtltRWoVIvcnIZzeo9WQpHBrEiUXuXSc86eeV6TpWo4lC4MQ3c2a7ZuZpJhO0MwkqWpSKFwW9D45JIo0GsXqd7OIirWSS1kPJaE0smsqCFP7POrpjY9otml6TmFcmrzWIiRffN0wIOGI4LEljvcUsGCjeMRdpssyv3W7aKo9UcVyVx/OSmMYSdzSYxkICp1Sbl7BfWO5ZNpulXC6/mCcL2RaxWKzjUKZSSiqlAtNrKmjCrNRsNjtyrh80j1lvWTBQ1JjPS8x8idjYDM2pUWrXj5N683MwXW73SkaKwQlprq2tkc/nd9uMkAARVFG2wfMQZ6dfU0zcgKVZkBJ6G2t8hSKLZjcXnUFamChdI1qtk1lcJVEsIwAJeJbuz5BUilyhwPHjDmRjzMsMs16aVZGgnUzSXhdjuuMQbzdwIhbOQ9u6bvtCzDMtHNPCW/9pkeantl+FEMemS2us/9Tp0hrE2XohQWM9tBrXtx7waEjB7+wY3n3efRvBL9txvhuto2t1ojSQKkLb60MRxXaH0dUayigghOIaERIojvBiPH/ZbJa5ubkX8lwh22M7VbNep8M1d5hACTOllJNOpzs6uh0kj5lC0Ygoji8Ieks6F8YUXsMXpco1UbYFposTqE8VCoUCIyMju21GSEAIuijb4FnFmabB0VOKIycVSvm/A+RYYVwv8FG5n1tGH61UglYyTnJ1jdzsIrr00JCAQGmKRFLSm5cIUadPr/OaOU9LGUy109xpZynqKRzTpMHj+zl6loXSBD1zU2jSxTEjuLEIyYyJY0UpyDgNLBrSYkZm762L4Nwn1hp0iQYp0X5ArJVdjY8qMRqe/+ISuuTtdJO08eXOB6n8XmV+z3+w6UbiewqbwE/bEimgve5LE0KuFw8JPC+PlAksawZPSK5hvVBhduXKlRfyXCGdo2ka0WiURqOx5TWO44D/n11gCNgtHKSUtpQSTduax+egeMyqMcmnxx1alvLPl5UY5Yun8Jpx0Dzix++iJxoICf2l4IQxAYrFIq+++upumxESAPaKKNvgeXjOhOARz5MpJO9l5zleWeHj2gDLqW5qPXnq3VlSq0Uyiyvo64f4WkOjbUN0MwJKVLiciBY5ES2iFKypGHNehrt2hqJIPvKEStNQ65mnltNiKFbn1WQLTfhVnhUVoSDjFFTC/18Zp43JvMwwLzObduPSpTXpEnUyosG1Ckjp3MtprXoavyrF+X5XDeMx25Cj4LNWlFnXRAIKARq4Igtic8+vAF82/lepCFJG0fUGrReYT5vP58McswATj8dpNpsddQCo1WpomhaoDSlwwkzX9dV6vT6aSqWefjF+NYWmadsaXLpX8DTFh6dt3xMmoLXcRfn6MZRnoMWaJE7eRE820SRYLpwIWGVmoVCgq6trt80I2WX2mijbYCcnBPSkHf4wPU1VLvJxe4gp8lR6uqnmc2SWVsksr+ApxcSswbFDLsZjpgYIAXnRJK81GWqvcGE6RiOSpB1PIDWNaKNOpFFDkxKhKYZyDmcG7AfWZ0SbjNbmCL7oUArqyqKg4veEWkHGaWKxKFMsbiStxQEl0WUbXdoI5WErl09bMYatFjHhEBMuEVyEgN824xQ8f/wSrNcySTD0Ei55wCPCAgJFTkjOmu179vyOGO31KYJCeAjRAhQ9vJgolGmaJBIJSqXSC3m+kM7ZzlSG9etnd8SgbRI4YQbM1Wq117cqzMB/Y5PJ5L79wizlfLe+Amq3D1GfGQYg2r3C2cwE7ZaiIaGnKjiyrGF5wfKY1Wo1TNPcVrVMyP5gr4qyDXZ6fFNKs3mlNYU3U6TQ108znaI06Ic5u6dnWCgIFosG3VmPU0ecxwo0gFzCI6JJVKNKrLF5g1KApikSEcmJvqePBRECksImic2YXrr394Yy74m0KSdJScWQmoWnx/D0zcjFFeDKfU8jkERwcZCgeQj8Q7QUFpLIfd4yHRDoNGgpyZC++Rq+ohr8jti6DBMIFDrwCi9mT+nu7qZQKIT9GAPMdoRZtVrFdd27O2TStgicMJNSTlarVQYGBra8ZqPqb78Ks7alcKVO6coJ2sU8CEnq6CTx4XnMOZ2XJoM/K3RlZYWenh5mZwN1MAl5Aex1UbbBTouzeExhNVr03Z2imUywMjZCK5Vk8dhReiansdptVks6X9wSvHby8eJKCHjzUJPPpqM0bQ2BL8pySZehnPvMjWXjwiGulxnRywwonQ/LcRx0PD2CFCZKGKDp5CxQwqCpDJrKxMaghfVAu58H7FYuQthotNHX033i4kEBNCJcoqrBVSyqaHTjcYY2KfFihFJPTw8rKysv5LlCtsc2hZnXaDRu75BJ2yJwwqzZbN6qVqt+lucW2W/tGGxTcmfUZqXbRZOC+GKcwmencRsJhOGQfek6kVwZ3YNcLVhhyyexvLxMX19fKMwOGPtFlG2wk+IsFlXks5JiSSNWqzNw6w4rh0ax4zEWjx4mP79AolSmXNVotATx6OMFSTyieO9Yk3pb4ElBMip3pMt/r+mRMTxKLmheE2iiocgZHl+LNB5Ic/OUoCBNftFI+wIOHYVAUzY6bYTwkPrmAh3FGfNRT1iP8PjGLuVph8Is+GxnMkOpVGoACztj0fYI3F3d87y5crnc0TdvPwkzT1N89EqThT4Xx4RyI80XE6/gNhIY8QZdb35OJFdG8yDVEPSUAvcRPpalpSX6+vqefmHIvmG/ibINZmZmME2T/v7+5/7YZ447DA146LrCcB367k6QWCuhdJ3CyDDLh0ZxIyZt++lKKxFRpGM7I8rA9879XrbByXibhO6R1D1Oxtu8l208UuygC0WvbjNq1ImoGqaqYKkyBg0sXA6bNjoKDYWF5HWzxZAerJzhnp4eVldXd9uMkC9hOx6zcrncJmDCLHAeM2C+VCo18dNKt0SlUiGXy+2gSS+OxR4Xx1QoDWrzfRSvngClEc0XOZu/RUm6yKYgVRd0lzRcHcxAdWB5PIuLi5w+fXq3zQh5QexXUbbB4zxnSj1afdkpmgbjYy7jYy63pw1mlgy6ZueI1OuUBvpppVLMxsfpNefIqOVdn3epCziRsDmReHreGsDb0SbXbYs7joWnBL26y8vRFklN8apq4yKwAjCs/HEMDAzw4x//eLfNCPkSUqlUx1GZcrksgfmdsWh7BFGYLZTL5Y6kRqVSYWxsbKfseaGUUx5SByXFPVEGYMYbGPUYw3aDa4c8GqZiKeNx+ZDHa7cN+ktPyAYOCMvLy/T29u62GSEvgP0uyjbYEGepRD9/9eMV2i2BFVH0D0sGRxXGM+6uI/0u8ysGylOk1krEqzXWBvupZzJckKNMtbt4z5okrwWqBdOXogk4HbE5HXlUyOnCD2EGkUQigWma+zaPeb+QzWY7/ozq9boAAhWjDmIcbLFarXZ0XtpPocxEU0PzQGiK7PE7GDF/063ODvO7hZf51crLVKeHse0IngFSh8/GXewOOm7vBs1mk3a7vW88myGP5yCIslYTVhYE5SJ88uEsdtvilTf8ML3dFkzf0fnglwZ3bmk8SwFfxIK3XmrT3+VhmYq0afN72l2+Y94iLmxWVYI/b5/mE2cIRwqWKzqfz0S4NBuhUA/2QW2vMTAwwMJCoKJdIY9hO8LM8zw3SOOYIIAeM6VUO5PJdPQm7SdhNrBkMjFig4L06BypkTnsUpr2XB+15V7cZpz65Bj1yTGiA4ukT9xB4LfUGFkN9mY8OzvL8PBw2KBxn7LfRZlScOeaYGlerIfaBFIKbl+Z42t/MEz2vUMsXlwiWm+iJCzOaVgWjIxtf8+PRRSnjzrwQGf7MgP6ZS44Q1zzevnCHeBGO09mdRGz2QQUSxWD0bzDif6thRhDvpzBwcFQmAUc0zSxLKuj5H/P81BKBa6HUxA9Zkgp7U4798bj8S1PCwgylit444sYybqGkKApGFB1/rA+x6tHL5B56QpmtgSAU/F7vSlA7oGXPjc3x/Dw8G6bEbID7HdRBr6XbGlOoKRAev4Pyhds/+pik49FlpnXTjD9ynHaiSjSg7npnflimkLyFWuGvxG5Tko1sfUIKwNjrPX0IzUdqQTTRZPGFooEQp7OwMAA8/OBSkMKeYhcLtext2y96//Szli0fQJ5O9c0baWTygqlFOVymWw2u3NGvUBSdZ13Lsb52ocJvv5BgteuxlBCsNjrEOlaQ1vPz4j2FPwFgj1RnTk7O8vQ0NBumxHynDkIogxgbkqglMAzdGbPjlMa7Ln3b/FC+d7/b6fizJ49RiObYqeHkfRqdU5XbpMqLoOSNFNZVoYO4Zo6ngafzUWptIK/NwSd4eHhcHh5wMlmsx1HYyqVCsD0jhj0DAT1G3utWCx2tKBYLO67sT+mJzCkf+K9PubgGiA9jfaK/zqjfUugFMdmdeK2oBZR3OiXXBvwKMeCl3O2sLBAb28vxrNmRYcEhoMiygDs9ahgOxmnmU2xcnSY4rCfW2Y4LsOf3bh3rTJ05k8eoT288zmVliZJlwr0zk5itFt4pkWxbwSp6VRtjQ+mY5SaQd3qg08+n8fzPMrl8tMvDtk1tpNfVigUVKvVurAzFm2fQH5bG43GJ4VCoSNlsR+F2f2sZD2EBm4p62f8C0l9ahR7pYvBRYuJbo+fn/G4Nii5Pqj45UmPy0PB6qPhui4rKysMDg7utikhz4GDJMoATAsQArO1mbdVODzI2lAPShdE2i16b01tLtAE08NjXHQGnqkI4GkMZv05lIZjk1+eQXdsXCvKWvcQCoFUghsrkac/UMhjGR0dZXo6cE6VkIfYTihzeXm5Ztv25Z2xaPsEUpi5rnt1aWmp0smaQqFAPp/fKZN2HW19YzcSDfRIC5RGa7GP0tVT/JeN1/j12mnqM8M4tSQK8HS426tYiwfLczY1NcWhQ4d224yQZ+SgiTKAfLcCFEbbJlaq3vv76pFhSoPdIATJYpnM/PID6z5zh/hFYwzvvhoA24G7MwafXrW4dtek1th+LljcUpwZbCOEQpcu+eUZNM/FjiUo9o7iaTrlMJy5bUJhtjfIZDIdC7PFxcUmcHNHDHoGgvptvbm0tNRRpUSxWNzXwmx4WUfzwEw06fnqx3S/9SmpIxMkkyVA4dVStKeHqX3+ErWLLwHgaTCXC1QVMBMTE6Ew2+McRFEGMDCi0HR/VlzfrWk0d9MjvXpomMXxEVbHBsgsrhIrPXiunNR6+Gerx6m2NFpt+PBSlKkFg1JVZ3FF55MrEQrPkCc6mHX51vE6mgaG5/jizHWwo3EK/YdQEWvbj33QCYXZ3iCfz3ecY7a2tiaAyR0x6BkIqjCbWX/DtkyhUNjXoczxGZNcVUP3wJAQjdcZ6Jnjj8VNXj9ygcSp6+hp/2agnM0crqDVZE1NTTE0NISuB7u1R8jjOaiiDCAShZde94hEFRHHpmfywWTweleOSn8P06+fpntqHqPlny2TwsNCUkul+VeNk9yYj+G4rBcSGEihIaXg85sWf/VpjAs3IlTqnW/NpgFjOQdNKEynTffSFGa7iWeYLPYc5no7z6WVCL+YifPhYozVZvgdfBqpVIpYLMby8vLTLw7ZNYQQ5PP5jkZmSSlxHMdWSjlPv/rFEsgsbKWUl0qlWlLKLbfAKJVKJJNJdF3H84KVW/U80JXgrWsRKnFJNSGJNwXZmsalMY+ZbomhSqi7hwCIjPo3DF3CcDFY2tu2bZaXlxkeHmZqaurpC0ICw0EWZRuks/Dm73m0WyDEMr8gw7zKPHLd0rExeiZmWTx+iJquc8JqMWcb1GJxLunH6C1PITWNxWNHAcjNL5IoFNGkolTTuXBT480TLVIdpiIc67bxJMyWTXTp0LM8hdvfy7KZ53fyCHFWSbgF6i6stXRe6moxnArWTMogcfToUe7evbvbZoQ8hVwuR7Vaxe2gDLparaJpWiBLbYN1174PTdOmO6mCUUpRKpX2dTgTIN3QGFoxyNV01hKK2W6J1MFe6EW2o2jxBlbfMpqE4wuCTDNoPjOYnJzk8OHDu21GSAeEomwTISAa8z1ovxeZRFs/CGr33RTseIzVsSG6ZvympDfaEd6JN+gSNq5lsXj0MJ5p3rt+bbCf+VPHKXd3I4VASrg7b9IpmoDTfTbfHq/z7qEG3x2v8Yepuwy15kEpGsluytkhpNDwlOBqMYIMVhpqoDhy5EgozPYAPT09rKx0NlWpUCiglLqyQyY9E4EVZkqpLwqFQkdr9ntl5sMs5iSe5je4bM2MABA7NIUuFMcW4eRiMEMVd+7cYXx8fLfNCNkioSh7MgnhcKo5s/6boPf2FINXb2M2WzjxKOW+bpKrRRCCX5RjfC9VY8xqIg2DldFh0kubNxNpGJT7+lg4epRGMkW1sf3t2dAgaSl0zReSerVMdm0WIT3saJJSbhgpBJ4S1J3gHd6CwpEjR7hz585umxHyFHp6ejoKYwIUCgVZr9c/3iGTnonACrNarfbJ6upqRz725eVlenp6nn7hPkGTINZPu8JYf6vsCELA3X5JJRrMo/D09DRdXV0kEondNiXkKYSi7Om8ll0lWa4gDZ1qd55IvcnQzbu+OItFaSfjmM0Wnmny06LJn45WOGnVQdOo9HYTL5URcrNIx7MsVkdHWRoepeJZzBQMfnsrxq9vxrm5aFFvC9odiqmIrrDsBvnCFJrn4FoxytkhHA1+sZTg/aU4VSewt4NdYWBggEajsdGENCTAbMdjtri4WJFSXt8hk56JwH4TlVI3FhcXO/pGLC0t0dfXt1MmBY7hoo7APxHHj/j5Wo2pETzX7/r98biLInjiTErJ3bt3Q69ZwAlF2dYwTfj93ASG59LMpmgO5Dgy0OaHyeskvCZONIqyDFCKopHiX05K/u5Yja9lyiAEjWwG027DhjhTCuF5VGJp/ln7JT5uDVJtGzRtjclVk9/civPLW3F+cydGdYttMI5mbXSh0D2HTHEGIV2cSIJqehCJoGhrvL8Ux95/6bnbZnx8PPSW7RG2I8yWl5dtAtgqAwIszICby8vLHXnMDpowS7YEZ6Z0UGDmixjpMsoxac0MgRA0LWgEtK/krVu3OHbs2G6bEfIEQlHWGZmIw3tRv6VCYXiArhGdjOXyw8QNsqKJqxusTz7nC3eA305W+FZfix/2FNGQ2NHYZgW1EIyoNUYpooRGOd/L8sghmvEkUmgo/NFQtbbGR5Mx3C2IqYGEy7FsG10oTGmTLq+HNSNp6oleQOABM/XO89r2KydOnODGjRtPvzBkVxFC0N3d3XEos1wuCyCQA1CDLMw6mpcJfjJfJpPBNA/O5nJ4RSfeeNBr1l7oR3kangaryWD1Mdvg1q1bHDlyZF8Mnt9vhKJsexzWi4xpazjovO8cQimICZfvR26QFY0Hrv3r1mH+7z8qM4Dk+6kiJi7qvu/CtNGN2W7RMz+FYbdxrQjFgWEWDh9nZWiMarZrfVg5LFa2Vlx/NOvwvdEa0YjCkG2SNf/zbcdy9yYEVJxg5qW+aFKpFLlcLuxftgfIZDI0Gg1s2376xet4noeUsqHUTs7k2D6BvSsqpZSUstLJmy2lZHV19UDlmQGMFXQ0T2Ck6+ipKso1aC93g4BLY5KmGbz/9ur1Oqurq2Gz2YARirLtIwS8a00RwWFBpvndWh9XJ0xWl+G7+k2SsnnvWqkbrAyO8KP3VxiyBN+Nlojx4F53JzKIY1r0zkyQXl3GbPnrnWiMar6HpeGjlNNd1NbFVNMVTJZNpsomLffxOWi6Bg1PAwRS89dpnu03PBSQs8JYJvjeslu3biFlMA+2IZv09fV13GeuWCyiaVpgy20DK8wANE27uLS01NGagxbOBDiypBF1AAXRofVT8Jw/n08JmOwO5mZ77do1zpw5s9tmhKwTirJnJyZc3sD3styMDDFTiXN71uTiFZOeyUnMVuvetXYsRmFgkJ/8ZonhpOBv54qkVPOBxyt199NIpkiWi/TMT9E/cZP8/AxWs47Sdar5Xj5NnuQ31X5+MZ3geiHCtUKEX0wnuFU0mayYzNYM3Pv0xYZk0z0HlELqFpX0MFLoDCcC12tzVzh58iTXrwcyLzzkIQYGBlhYWOhozcLCgrJt+5c7ZNIzE2hhVqvV/nJubq6jnWJpaYn+/v6dMimQ6EpwZEkgJFjdBYTVxmvEaU0PowTcHFRc7w+eOLt69SonTpxAiLBcf7cJRdnzozzRIF4uozSNwtAgUgkcF7ympHd66gFx1khncHSDn/xmiUzE4I8yFaxmffPBhKDUN0QzkQRAQxJt1elanKF7cZqo3aQtTG4aw6z0HKEezeApPyx5ay3C1UKELwpRfjaT5KPlKO8vxUjoHkIpTLdJuuIXArhmnFp2jDXiL/rtChzRaJTh4eEw8X+PsB1hNj09XW61Wr/ZIZOemUALMynlx1NTU1vvMosvzHp7e3fKpMDSU9XRAKEpkifuAIrm1DB2KY0UcHNAsZQOllu+XC5TKpXCcOYuE4qy54ftQK2hkV9YRHNc2okEzVQKEAhA9zz6pibJLi0RL5VIr66guy5KKT65NEc6bvDDrjqx6oPb3lr/MI1kxo+XCkVEl5xMrPF3klc4bU+iO208w6KSG2C19wiVTB/NaAoXHU/5/cqWGgZrbYO6qwEKDUXEbZIvTxJxGziayY/aJ7npHpxekI/j1KlT3LlzB8cJvYd7gcHBQebnO8vhn5mZsYFPd8aiZyfQwgy4vLCw0FGC1EH0mAGkm4LBovAHnefKREfmAEH9xjGkq+PpcLc3eLlmV69eDcOZu0goyp4vq2U/b0v3PAzHzxm7/1unCYUmJelige6FeTIrKwj8jv0jvS6fXZknlzT4o742uaU5rOZm0UA15wsmARzqcjjW62Dq0C0rdK9MkF5bQHMdpGHSTGSp5Ico9I1T7DpEJd1PM5bDMaLI9WBm1nQ5k23x1XyV/3biGofFCh4av3EO85PGIdryYHqyX3rpJb744ovdNiNkC6RSKYQQHfWak1JSr9ddpVRxB017JgItzJRStuu61U5OLo1Gg2azeaAmAGzw2qTO+IIGEqJjM34hgG3hrPrvRd0KnjC7cuUKJ0+eDKszd4FQlD1/PO8+MbMeovdMA1AkY5LTYzZRS+LLNYXQFJapOHu4TSqmONxr87OPlsgkDb53xKRnfpqe6bvEy2uk1/w+TZoG0fsKevoSDpqAWLNC9/Jd8itTJCsrmO06oPCMCO1Yhnq6j3JujHJmFNuMU3F0DicduqMeC22daqVIvLkASjIvuvmvG6eoeAenwh0gmUwyMDDArVu3dtuUkC2wHW/ZeuJ/oD/gwN8NNU272OmNY25ujqGhoR2yKLgIBMeWNIQUIARWn7+ROwV/fmgtCuVYsMRZuVxmZWWF48eP77YpB4pQlO0MXRkPbV2bZVb8vkrl3l48XefosENf3uO9l1p85/UmX3u5ybunWnztpSY9GT/NIG4p3jnS4OLVObJJnfdezmG6bbLFJWL1KqDQhKL3vsHjSUtxNGujCYVAYTpNErUC2eIs3cu3yRamSFaWiDRL6/lkMSqZUcqpYYoyhqfgYi2Gh8ByqySb8wjpYOtx/rx9miUviatgzjaYsY197Ul76aWXuH79Op4XvJzckEfZZuI/QU78hz0gzOr1+l/Oz8931Gh2dnaW4eHhnTIp0BhScGpO+E1nu9YAhbuWQbk6SsAnhzzmMypQEwE+//xzXnnlld0248AQirKdIx5VDPe5aJoiVq0QrdWQuk57tIf8QzmelgHxiOLh2pdERHF2pI1TmeBov+Bbr+X8EKhQpCKSdw410R/auY/lbd4bbjCesxnP2uTiLrquEEhMt0msuUaqukS+eJd4fQUhPVpWkn/RPs1ftY8ghd8LrRnpoRYfZqN20xEmP7JP8P+rjvBxI8anjRh/UUlyt70/PWlnz57l8uXLu21GyBbZZuJ/KciJ/7AHhJnneR9PTU2VOllzkIUZQL6uoXugWQ56ugpKo3rpNLJtUY7Dx4ckf3lK4mjBEGdXr17l0KFDxGKx3TZl3xOKsp1nfNjhlWNtBrtdjjdnEUqyEs+zqjqveFycn+Zon+Dvfz3F18YbfPVok0Tk8d/blCU5nrc53mXzlf4Wb/U2OZyyOZaxycc8NE1hCo9ks8CZ1g1O6UtoKGZVnmL8MI1ID0L54lFpm01rFYKG2UfN6MdBQyL4vBml4gX+9tERfX19JBIJJiYmdtuUkC2ynVDm9PS0DVzYGYueD1trGb27fLGwsNBROeHi4iJdXV2YpnkgK2uSLVDre2b82F3qV04g6wmqn50lfuomKl2jFoHPhyXJpoYUMFiBbGt3QhS2bXPz5k3Onj3LRx99tCs2HARCUfbiyKUkuZQEHKSzzGW3nw/sUf4ocv0RD9nTmJmZYWRkhEMjfVv+7ISA7phHd2wzJFd3BQ1XI2VKoroCGpyRS3zqDHHXy9O2cqAkKMXjjHT0DFKYxJ1ZFDBlm5yNtTt7MQHmtdde4+LFiwS0GXzIQ2QyGYCOEv+VUtTrdU8pVdgpu54HgT/yKKXajuPUOxFYnuexvLzMwMDADloWXCKe4NCKQPdAj7dIvnoZI1tCuSb1y6do3jyCU08w2QVf9MPlfvjLY3BxYPc2pIsXL/Laa6/t2vPvd0JRtnu8aswTw2ZVJbntba8oaWZmBtM0n6niPGEoeqLeuijzSWk234hM8PvmNSJuDYT2gCjTZJuYvYDhVUBJ5PpZXgHuPtIvuq5z9uxZPvvss902JWSLjI6Odjwyaz3x//YOmfTcCLwwg+1NAJibmzvQ4cyXZzTOzApibRCGR/zMdayheVACZ6WH+qWXqF86g13MI5XA0+B2F6zGd2e3nZiYwDTNA/2Z7RShKNsZHBcKVY1qU/BlThZTSN4yZwH4xBmmrbY3j/J5iLMnMWQ2+PvJ63xVu0WGzQa3Uovg6EniziKp9m2S9qTfjw0YNDtK/Q00p06dYmFhgXK5o7aZIbvIdoTZeuL/L3bGoufHnhBm2y0AOIiVmRsIBEdXdL7/hUG+IkAKoodmSL7xOdbgAuguspbEvn2U1qUzKAWeBtPZ3bP5woULvPnmm7tnwD4kFGU7w8Siya+vxLk0EeXjmzE+vBGl7Tw5RnlEL9KnVWlhctEZ3Pbz7qQ4EwJORMr8ceQacXsOTfp92Fw9hasl/AbWgI6i33TpNfZP5eLrr78eesv2GNsRZnsh8R/2iDDzPO+jycnJUidrZmdnGRkZ2SGL9hZvTWlEPDA80CJt4mPTJN/4DOvwBKBQrRh4/ile7GJ44uLFixw/fjwsAnhOhKJsZ1gt60wsm76nWfrjj2otjYt3I09cIwS8Y04jUFzzelmT0W0//06KM/Cb3UZkjaQ9ScxZwvQq6NIfI6WjeCfR5J14s+NcuaDS09NDd3c3165d221TQrZILBYjk8l0vLdNTU0FPvEf9ogwAz6fnZ3tqACgVCohpTyQjWYfJmELfnBZ47VpwZkFwWtT4v/f3p3HR3Wl+f3/nHtrU1VpFwIEAsS+GzAYg22829jGO+2lbfd09/T0MlsySSbJJJlRlGSSSTLJJL9MTy+223Z3Y+Md4wWwMYvNDsbsOwKtSGiX6tZ67z2/P6oksA2GElVSSTrv10ttIereekRLt7517jnPwRYSx/DmC0lMi//zjmnvvzpDoRDHjx9nzpw5/VfEIKFCWfpUNzmwbYGtaTSPLCHoj2+5ZIQ1gpFLpxUpQYaijLKakQh2xMZ86+3PK0lnONMEjHHF0JG4rA68sQY0LHQk0zwRRjrNQRPKABYuXMgXX3yBbWfWlnXK5ZWWllJbW5vUQg3TNDEMIyKlbE5jaSkxIIKZlDImpaxJ9v7/mTNnKCsrS1NVA4suBWPaNKY2aEghEFIgbZFYvilBSDQJZu+mv6TM7t27WbBggdrY/BqoUJZeUTP+s2k6nIRycmkdMRJL1xECYtY3f27DpuDzSi9f1nqwzrWiWSYNdg7rzxezvTqLqjYHVi8yQTrD2XXeMMUOEw2JI7Gv5mhXjMmeaMqfqz95PB6mT5/Onj17+rsUJQm9uY1ZV1eHECLjb2PCAAlmALFYbPXZs2eTunydPXtWbZB9Ce1ZgA52R078C474O2AhoLP3d1hSor6+nq6uLqZNm9a/hQxQKpSl37BcC01InNEIeiyG1HU6C4oA8Hu+eYk6WO8mGItvJI4lyW6P78hR7x1Ja8TBsWY3u2qzsHsxgpaucKYLWJwd4p6cAIv8QZbmBrjeFx5UI2UQn1t24sQJDMO48oOVjNGbYHb69Gmjq6vrnTSVlFIDJpiFw+G1x48fT2rT0TNnzqhg9jUxTeIyQYQdRCrjo4nOkgaEAE1Cdga0Jdq2bRuLFy/u7zIGHBXK+saYYTFcTokuJN7O+Ch+ID+f0tF8oyO/aUNrUAcE4Sw/TSPHoZsxnJEQtsOJkVOILQVdEY3GQOat1vTqkmFOC0+GNKNOJU3TWLBgATt37uzvUpQkOBwOhg8fTm1tbVLHnThxIghk9FZM3QZMMAP21tbWJnV16OzsJBKJMGzYsHTVNGBIJAdGW3w0x+LUCIvQmTKIOdFyOuPBzIasGAzv6u9K4cSJE3g8HsaMGdPfpQwYKpT1HacDbpwSomxEjOFme/yLQlDnH/6Nx148BcZ0ODFdHtqGjSbLiDfFNLILsDQdSwqaAr3v953uBQGD0cyZM2lra0t6Sx+lf40ZM4aGhgZM8+obNcRiMQKBQEhKeT6NpaXMgAlmUkpTSlnV3t6e1HFnz55V88yA4yNszg6T2BqEzo/Aas8Dh4l7wmk0CaM74I6T8TYb/U1KyY4dO1i0aFF/lzIgqFDW9xw6lA2PcfPYdvJECIBKq5Bm+6vbLjl18LttQOINdCAsE6lpdOUWoceiIDTC3mwEErfj2kalVDhLzk033cSWLVv6uwwlSRMmTOD06dNJHTOQ5pfBAApmALFY7P2qqqqk5pmp25lxp0ZILB0sI4vQmfhIlHfSafzuCMsPwKIqgfsSE5f7y759+xg9erQa7bwCFcr6lxDxHmXdvoh9s3fi7JERHBo4sPB3xHeCkbqO5XQBEPbmIDUo9F17w1YVzq7OlClTME2TysrK/i5FSdL48eOTDmaVlZVGV1fXu2kqKeUGVDDrzTwztQAgfhszlpi+EmvPBamh+wO4itqIOSBwUfslS0hCDolN/84pMU2T7du3s2TJkn6tI5OpUJYZxusXtt2rt3Ops3K+8vfZHpsl4w0mFUWZrJ3HbX91ZWPM7cV0Otl93ktN57VvX6zC2ZXdfPPNarRsAPL7/eTm5ia9cflAml8GAyyYEZ9nltSIWSAQIBAIUFLS+27bA51AkBO/24JrWAtoFlbAjxnwIoHNUyxims2hYsmqGfDhNHh3JhwZJpH9GNB2795NWVkZRUVF/VZDplKhLHNka1GGiUDPn/fERn2jR5nLAWWFMWaPjLDAXfeNc4SycrGl4FCrh0gKRq5VOLu8cePG4fF4OHbsWH+XoiRp/PjxnDlzJqn+ZQNtfhkMsGAmpTSB6mTnmZ08eZJJkyalpaaBYnaNhrBBc8Zwj4zvOxo6VUbkXDHhrmx2ljg4Oizex8zS4v89Mjy+f2Z/icVi7NixQ42afY0KZZlnvOPCQH6r9FFpFVz2sRP0FrISnfS7Rdx+IL7l0flgapoJqnB2aXfccQebN29O6sVdyQxDYX4ZDLBgBhCNRpPuZ6aCGQzr0hjbJJCAa9S5+KhZVzbh0+PpOjKDkyfnEfhyLuGD04hUjsMOu7B02D8SWr+2Q5IE6n2wdaRg60jBOS9pG1fbtWsX48ePVzs4JKhQlplGyravLMHcHh5NIHrpy6smYHQs8f+flOixCA4zPX1qVDj7qokTJ+J2uzl8+HB/l6L0wvjx45OeFziQ+pd1G3DBLBwOrz1x4kRbMsdUV1dTWFiIz+dLV1kDQkmHQLdAc8Xwzz6MZ1w1zuLz6P4u0E2wHNiGH+v8MCJHpmJHXJgafDoBjlw0B3/HCMHmURqVuYLKXMGmUo09w9OzcCAajbJ9+3Zuv/32tJx/IFGhLDPZEr6sc+GKBHu+FtNdfNJWctmO/jM9rTijIRACT7iLnM74KLYEhnuvfRHAxVQ4u+COO+5g48aNarRsABo+fDiRSIRe3DELAp+lpag0GXDBDPiypqbGTuYXy7ZtKisrmThxYhrLynzFnYLscHxbTN0fxD26Hv/ESkZPOkzRzL1kXf8l7ulH0fwBZNRN5MgU7KAH03RyIN9Jk+6kxumi0uMmZrqRUTdSapia4GSeoN2Vnrp37txJaWnpkJ4nqEJZ5jpvOIhZAk8o3puse+Ss01dEjXHpX4o8j81kGZ/AHPTlg6ahCcnswjCuNGyLpsIZTJ06FSmlmls2QE2cODHp0bJoNEogEAgOpPllMACDmZTSFEIcamxsTOo4dTszvghgyQmdSY2CrAh4ojCxMf61688JHLqJnhPAPfUEms9ARjxEDs4i/OV1hPZfxweB61jfOZvo0ZlYh2fEP45ORUqwBdRkp2fUzDRNNm/ezF133ZWW82c6FcoymxHTsCS4QwGQ8SEy3Yxi6w5OyG82ne12Q14bI0QHUtNxFeZx+2iDUdmpHS272FAOZ0KIntEyZWCaPHkyx48fT+qYyspKpJQfpqmktBlwwQwgEAj85ujRo8ErP/KCkydPMn78eDRtQH7LKeOwBTPqde475OD+gw5m1us4bEFJl+CWSsiKgtAtXFOPo+W1gzMGzijCGcWtRXHpUXBGwBUBzYKIB9mRgwRO5gl6sRfzVfnyyy/Jzs5mwoQJaXqGzKRCWebLdlnoAjRp4w4bIASOWHzO2DnHMAzpvOyxN7ji28rU64WY2uUflypDNZxdf/31dHV1cerUqf4uRekFn89HcXExZ8+eTeq4gwcPthiGsSI9VaXPgEwptm1/dPjw4aR2nTUMg7a2NkpLS9NV1oA33BDcVinQbIFw2LinnCJr3n6y5h3AN+cAj3sP8Kj3AO4Zh3HMPII2Mh4WZEsRCEFYh6qc9IyaSSn59NNPueuuuxCDbSfly1ChbGAY5rXIctgIJJ5g/Ham5XDiDXdiC419scvfgi/UQozXW7DR2Gf2za36oRbO3G43t956Kx9//HF/l6L00pQpUzh16hSWZV31MbZtU1VVZQEDbjPUARnMpJSt4XD4fFdXchs7njx5ksmTJ6epqsEhJwJz6uPz0BxW/EO3YVE1uC3wmjCxTYKUiIJWEHZ8xCzixNIFhW0rLAAActBJREFUp3LTV9uxY8eIRCLMmzcvfU+SIVQoGziEgEWjg4zOieGLdCFsC9Pp4Xp/EwLJSauIdttz2ePnOuoR2JyyCr/1cak0lMLZLbfcwsmTJ0l2+ouSOaZMmZL03MD6+no0Tdsppbz6NJchBmQwA4jFYitOnDiR1ISMY8eOMXXq1HSVNGhMaoWHjsK8ephfBw8fgdGdF/5+lCHRbRBOE5HfDgjsxvhcmgafoD6Ni1/XrFnDbbfdhsfTNy9g/UGFsoHHqcOs4gj3TggwxRnfCaANH5P1JiTikls1dcvRIkzRm6/4uFQbCuEsLy+PuXPnsmHDhv4uRekll8vF2LFjk74Nffjw4a6urq6X0lRWWg3YYBaJRN7av39/Um0zGhoaEEIM6gtRqngsGN8G49rB9bWJYyMNcEhASrThiWX+LYXImAOEYM/w9P1YNTY2cuzYMW677ba0PUd/UqFs4IpacLrTiWXER/JPmwVc5zyHA4tqO59Gy3/ZYy9+3Hm779r6DPZwds8997Bjxw4CgcCVH6xkpAkTJlBTU0Mkklyvv2PHjoWllJ+kqay0GrDBTEp5srW1NRyNRq/84IscOXKE6dOnp6mqoUED7q6OpzWRFUbkdoDUkG15AHSkqW1Gtw0bNjBz5kyKi4vT+0R9TIWygSsQE2yo93Osw01rl4luRQnh4nwsixmO+JuXPebob2zV1M0rYkx3xFf0b4+M5nTQSVNUv+zjU2mwhrNJkyZRXFzMtm3b+rsU5RpMnTo16dWYbW1tmKZZJaUckIl8wAYzACnlB2fOnEnqGBXMUiM/As7uO/fdLx7O+J1lT5rv6IdCITZv3sx9992X3ifqQyqUpZctIRgTmClaNiwlRKwL59vf6iEmwZbxhSmucPze/77wMGY5GvAQ47ztp9rOu+w5p2oNaNKklWy+DBWwo93LhlYf0XQtdb7IYAtnDoeD++67j48++iipCeNKZtE0jUmTJiUdzI4dOxYNh8OvpKmstBvQwcwwjFcPHjzYeuVHXpCYEMjw4ZfvL6RcWbsbLF0ipUQa8VsvwhtEk5KZzel/m79nzx6cTidz585N+3Olmwpl6XW6w8knVX4+q/PxSbWfQ81u7Gv4ET0f0ll/zsf6eh/r6vzsafbQGtEBQcTlp6VoEiLxbqVDz0EA1znjzWS/iI3ClhC2BNUhJ/VhR0+4O2E4cUfil7OgexgmgoClsb9LLQhI1i233EJdXV3SDUmVzFJWVkZLSwvJLvQ7cOBARywWW5WeqtJvQAczYPvZs2dN207uLeXRo0fVqNk1OjBMIAUQdYHlAEcs3tsMyaT29AczKSXvv/8+d95554DeakuFsvSq7XJwss2NKQWWFNhSUBNwcqy1d/fbO6Iau1uyCFsaNgIbQWPI0fP3psMNQiOUlY9uRpCaTrWVxxS9mWwRpkNmsTU4gk+a/BzodPNlRxZrm7JpiujURpy4Yx0IO4ale4g5/EgE9RFnn9zShMERzgoLC5k/f75qjzEIzJw5k0OHDiV1TDgcpqurq0tKWZumstJuQAczKaWladqu+vr6pI5TtzOvXUsWCE0gW+ObiwtfEE0DBxBMf59MIL4Q4Msvv+Tee+/tmydMMRXK0u9UhyseyIQg4C/C0hzYUlDd5erVqNnpxHESCGYVEHN4sBEgIP7V+Eml5sDS478Ip60CdCGZ56gDoJIRWGhYUusJjLvavdg28V5o0fiappgjvligr3d1HMjhTAjBww8/zObNm5MeZVEyi8PhYMqUKUlvOH/q1Clp2/bbaSqrTwzoYAbQ1dX18tGjR5Oa4FdXV4fD4Rh0k8f7UnYM7C4/9rkRgEQf0YgQIAVkpW9XmW/YvHkzo0aNGnD7oKpQ1jciZvwSF/HkEPQXEsgeBsTnnF1ug/FvE4hpSAQxp5egbxidOaOwhUAXYOuCr+x9IeLPXWfnEpIOyvQ2vHYQW3MQdufT5S3B8BQj46mOPIcFSBxWCABT8yCRFDtN+rqn8kANZzfccAO2bbNr167+LkW5RpMmTeLcuXNJr6g9cOBASzAYXJmmsvrEgA9mUsqPDx8+HEpmU3OI386cMWNGmqoa/KY0aFiV4wCBNrIBLTuAbkvK2sHZB5OVu5mmyQcffMCyZcsGTG8zFcr6Tq47PvHb1uK3G2MuLxJw6RJHL65+bt0GAaYjfitUag5CWQWYicuPuMT4lkRwxipACBhpxVdohtyFxJx+Iu48At4SLCmwRDzLaTIM0sbWXdi6htTB6uthMwZeOMvPz2fJkiWsXr26v0tRUmDmzJkcPHgwqWNisRh1dXUx4Mv0VNU3BkMw67Isa/+5c+eSOu7AgQPMnj07TVUNPhLJyULJe9Mkr8+SbLHHImMudG8A54h6dFsysQ0WnOv7V5AzZ85w/Phxli5d2ufPnSwVyvrWtIIIupDYiT1ybd2BdDiYXhD51lGozphGfchBV+zCJdKS0BxzghDY+oU5aqGsAqSugxDIRDBzxII9G5oDnDbjt/ynuDtwmgEufvKY00eHbzRttgtLABrodrxnk6ln0WQ62Gv0z5uOgRTOHnzwQbZs2UJra1LrwZQM5Ha7GT9+PEePHk3quJMnT9rA2zLZkZoMM+CDGUBXV9f/t3fv3o5kjmloaCASiTB27Nh0lTWoHBsG+0dC2AVmUxHhjgLQLK7LrmTxOckjxyXzG2S//UCtX7+e0aNHZ/TODiqU9b1ct82ikUHczgtBqKTISYn/0vfbTRu2NWfx+Xkf+9qy2Hzex+bzPna1ZrGj1dszi8zS4sFM2CYIjaC3CKmB1HQgHqyywi09522WPjpsN8UukzFWA1+fzW86vBie0djEV3fqdhgAS4vPYauLOon100vNQAhnN9xwAw6Hgx07dvR3KUoKTJ06lTNnziTdVHbXrl2thmH8Kk1l9ZlBEcyAdceOHYskuzpz//79XHfddWkqafCwkRwZDpYOdshDtGoMAK7xZzk2NsKeUbB6Gpwo6L8aY7EYq1at4oEHHsjIVZoqlPWfXLdNzkUDTgH9ws9HzIagKXpy0uEON60RBxYCU8ZXXXbENM6FnDRHHVgAAmRixCsr1ApSEnblYWnOnlummjRxR9tw2hdeWE5bhQgBt+S2M0pc2LRESAvNjmLpHgxPKbZw9AQzWyQCIBCz+3ii2UUyOZwVFxezZMkS3n33XQb4QImSMGvWrKRXYwaDQZqbm7uklMkdmIEGRTCTUkaB9ck2mz148CBTp07F6eyjZYQDVMQRn9QPYDYXgB0fFbDa8rAMHzFdYGmCfSPjqzX7S21tLfv27ePBBx/svyIuQYWy/hdF7/m8wcomZsGu1izWNmSz4byftY1+ag0HNUEXNgJTd9OZPYqYw0N85lj8AyFASERiX2TNNvFEOkAIQp5hPcFMyPiE/aLY+Z7nPW0VImX8FIvdtfSs4BQ6WdFzaHYEW3MTcI/BFvHz2JqOFKAJSZbWv6EjE8OZrus89thjrF+/nra2pHboUzKUz+dj1KhRnDhxIqnjDh8+HDNN8zdpKqtPDYpgBhAIBH6+Z8+elis/8ivHUFtbm9G3vzKBywKReE1wDj+PXtgCSKyWQiKHpxM9PgFpxycvn+zHUTOAjRs34vf7WbhwYf8WkqBCWWaIygu9xoK42N6RQ2PYgY3AQhC1NfZ1ZtHdIz7q9BFz+ujyj8LU3XTkjqU9dzxhVx6Si1ZfCsEYuwGkTczpx3TER+M0O4YEnFaAkVp8F4CAdLMzlE9d1IFXRJmgXQhttubCH6lBt4JIzUHEFV89KtFBSLQLubJfZVo4u+uuu2hubmbfvn39XYqSItdddx1Hjx7FNJNb3r9nz572cDg8IDct/7pBE8yA7dXV1ZFk985UtzOvTJeCqedBt0C4TNyTKvHMPYBj5DnQLOyOPOyOHBCCQJr3ybwS27Z5++23ueWWWxg5cmS/1qJCWf+zJdSEnHRa8WCWQxCARjsHG0FM9xD0FCLR4isfRXyhi+jpR6bTkVOKrbuQQifoLabTX9pzfrdLoDtM3NH4hHMpdJB2/FakkIx0mczWanrmlJ22CtkTyGJ9h48Wo7PnPGHnMISw8UVrcZoXvi5FfFGBKQXnzcxIZ5kSziZMmMC0adP44IMP+rUOJbXmzp3L3r17kzqmvb0dwzDOSSnr0lRWnxo0wUzGvX7ixImkJpodP36ckpISsrOz01XaoDD9PMxsALcJSNBcURyldThGxpf/2+25gKTVCxc1Qk+ZDodgZ56DT4tcHPXrxL5luk1bWxtr1qzh8ccfx+Xqn6SoQln/kxJ2tGWxv8ODJeKhJhaOB7OoMwsEhLzDCGcV0uUvIb50RcSvivKi/RUT/ciywk1odgzLkUXUmQNA0HZwLurEE2uLLwQAHFYwvuhSwERfhJMhgctqjz+vno2JhmFrgI3Tiq9ZksKBBISQeMwGXNZXb8vZQKedGcEM+j+c5eTk8Mgjj/DOO+8QDof7pQYl9caMGYOUktra5Jr279+/PxQOh/8xTWX1uUETzACCweCvd+3aldTtTNM0OXLkiGqdcQUCwdRmwSOHBU8cgAlNIh7Q8toBsNpzkRJsAScKr3y+qAYtbghd9FojgRaXoNEjMC8KXjUejfdGuDma7aDaq7M7z8l7I9xEviWcHT58mKqqKpYtW9ar7/daqFCWGc5HHbTGHJgIutcLO2MGEF8FKQWYujv+Z6eXgH9kfJxMgEPE39/pZgjdiuCOtJEVaSO7qwZhx3raXcTbcAgEkqxoE0iJKxYPWwLQBLSaOu5YS3xemtAxNW/PmFxWYg6auCgIakg8VhNZsTqyzIbE1yBHy6zNuPsrnGmaxvLly9mxYwfV1dV9+txKevVmtExKyZdffmmYpvlGmsrqc4MqmEkpj7W2tnYYhpHUcfv27RsUm2H3FYGgIAy6jG9cjjMKMRfW+WGgwdFiOJ1/6WMl8EWRxpsTHHxc6uDt8Q4+G6HT4oS3xzhZM8rB+hEOVo5zUunTsIHPC11YmkBKDUwdSxMEHIItBU6+7aVqzZo1DBs2jBtuuCEN/wqXpkJZZrAl1AQdmFIghQZCIKSFbkcQtoWtOYk5vCA0hG0hbIuY00/UnQMIHIk5ZJq0yA1U4Qs3AaDLGNlGHcKO/+RJ0f3OQuIyu8g1TuKyDASSYpdJ93sHDRtP7DxCmmjywtwZgSQnfAp/5AwC0AGnFm/K4ZQGDhlCQ5Kl2RQ7MiuYQf+Es7vvvptgMMjWrVv77DmV9HO73UydOpUDBw4kdVxDQwOWZR2UUibVMiuTDapgBhCLxV44dOhQUhPNamtricVijB8/Pl1lDToFofiggaaBc2wNAGZ1KXbIAwJ2j4Y3p8EHk6Ay78J+f8fzNI7la1iaIKYLbE1Qky1YM8ZJwAFm4usxqbPFl8M2MZxo4yg4MwFOToPTU6AjDykE1V6d90a6iV5m5Mw0TV5//XVuueUWxowZk/Z/ExXKMkNtyMHaZj91MScIiZVoBqvZMQTxW40ARlY8TDisEFmRePAKu/OQSHIcsfjJhIaGRBcSDYkmJLodxReqQ7fC8WaxCVpicyUdiVNI5vjD6AKKHBYCicvqIidciS4jaEhyNAsdicBGw0ZHUuQwucNvMNLRPc4nGeWMcZvP6PNtma5WX4azadOmMWXKFFatWpX251L61qxZszh9+jTBYDCp47788svOQCDwf9NUVr9Iw2yg/hWJRF754osv/uXChQuHJXPcnj17mD9/PpWVlekqbVDJC0NxABr84Chsw25vxmouInp8Eo6Sc2gFrdgOm5hDsLtEYjhhynmN/W4/sbYsCLuRETfIxKRrKei5j2Q6IOrGAk5+5VkTs7MbSuKf53bQpcP+HAcLOi69gqe9vZ1Vq1bx+OOP88ILL6RtY2MVyjJDa0zny64srO6xKgGmFr9dqVvxnmLecDOdehYy0dpCt8K4Yl2E3MOwdA9S91CWFeSsBV4HTMuO4BSSEk+MMyEXR4NuHDJMdqg68RSSfIdFqTtGh6WRq9uUemI4E297r/eF2NTpw5RgER8V8+o2S7KDNJsOzkac2AjGuKKMdploAhb7Qn35z3bNampqKC0tZcSIEWn7HSguLuaBBx5gxYoVal7ZIDR37lw+/fTTpI6xbZujR49GpJRr0lRWvxh0wUxK2ZCTk1PT2to6rKDg6ns3HDhwgDvvvJPs7Oy0vXgPNjdXwedjoSEbnOOqsQ0vMuQldmYcVI9GK2xBaJJoKIsvQh72RN1w9ipPLmxwh8l2BLFdYQxfBDwRaCuE5uHQMAo8IWx3lDM+ndmdJoZDkG1KnF9r93T69Gn27NnDd77zHV555RUsK7W3hFQoyxwnDRfxrcAh7CrCYRlYiXlkDisCSHQ7Ro5RQ6evFKk5cFhhhJC4Yu1E3IX4srNxivhIWFjqFLpN8h3xW5sTvVFaTJ2maKJfmQCXkNyQEyJLv3SfMa8uWZoXoD7mwLA0cnWL4U4LIaDEZVLiSq4tQKZKZzjLysriqaeeYu3atSS7/Z6S+UaMGIHX6016YOTs2bMAGxO9TAcNMRg7Jeu6/tQNN9zw66VLlya11PL+++8nGAyyadOmNFU2+HS6YN1ksDTivcxa87Eai7ED/m88VsNGd0UwvWHwhBGeCGh2vEmaBNuW8RnTejyUOaVkUZPF8JBkTbGLgCPR4LOqDMJeGFUF/gAOW2JL0GR89drMgMm8zgvze7p95zvfIRqN8t5776Xs+1ehLHNMnjyZX+2oo8PUiek+At5RCGkhbBNbdzM+VskwApwMuuM3EIWO6cjCaQXQkAxzw3HXZJASf6SagGccQprkh08zwRNlpvdCF/8OU6MtpuPVbYYlQpYSl+rfCU3TeO6556itrU16REUZGB544AG6urr47LPPkjpuxYoVLSdPnrxfSrkrTaX1i0E3xwzAtu13Dh06FEq2Qd3u3buZO3cumjYo/1nSIicK087Hs5QQEkdhK67px3DNOIw+sgF9VB2OiadxzzzId3x7eSDrMK4xVejDGxF5HYjsThy+LsbanTi9BsIXRHjCOKQkNyoZF7DxW5LHz0Vw2omdCs3EQK8ripAyfotICGJafAeCw34HJ7zfbC2watUqiouLufnmm1PyvatQlnmGOeNzs7rnlUmhYydGzFqiFg4nTPJH0IREExYuKxCfyyVguCuM0wqA0Ig54u/pJBoWgtNhF+3mhetCrsNmXFaMYpcKZV+X6jlnS5cuJRKJsGHDhpScT8ksHo+HGTNm8MUXXyR1XCAQ4Ny5c+3A7rQU1o8GZQKRUkallG8eO3YsqZ5mTU1NtLW1MWXKlHSVNijNPA93nYKpTVDaHr8LKXzheJ+zUQ248toYG43gtSE/CsvOmozvsMmNSMYEJPdWW9zWaHFro8kow6I4ZDO/xeK+erNnI50zPi2+E5QtwHQBEiwNGchBthfB+ZHQWAKmjqkJDmR/8y59LBbjtddeY/78+UyfPv2avmcVyjLTRG8Up5A9m4x3i7e4kBwNupnsjTIrO4xXs3AISbHT5JY8g2MRd09fsaiemzhQi29aDtRHB93Mj7RJVTi78cYbGTt2LO+8847aB3OQmjdvHidOnCDZbgp79+4NRaPRf5CD8Adj0F5pDMP431u3bn1y5syZRckc170I4OjRo+kqbVDKD0N+IqNU58CeEkk0karGtsOC+guPzYnBTY028NXcXBqUlAYvPf/rRLaOpQuIdr/gCqie0P1ZD+mMQkEzIf3SwxiBQIDXXnuN5557jo6ODurqkm8UrUJZ5vLoktsLDN4MdU/4D2HpWeiJzcQFEJIaZd4YZd5Yz3GGFe+u75BBNDuKnQh2uh3sOW5QvotNo2udczZt2jQWLVrEiy++SLI7uigDgxCCBQsW8OabbyZ1nG3bfPHFF4FoNPrbNJXWrwbttUZKWRkIBM42NzcnddzRo0cpLi6mqCipPKdcZEwnPHoMHj4Oy4/AjXXxnmfXoudwVwwcMdAscIfB34nMa0ZmJzqlh7MAiSW4bAPaxsZGVq9ezZNPPkkyC0RAhbKBwK3JnmDljdbjjjbjicWvAzbgEd8cSHcK2b3mlyyzEYfVhTdajy9aS6KJP6Pdg2OSfl/q7chZaWkpDzzwAK+++iqdnZ1XPkAZkKZMmUJXVxf19fVXfvBFKisrsSzrUynloFypN2iDGYBhGP9t586dSf1WW5bFnj17uPHGG9NV1pAQf4EDR4oGmScGbBy2jE9mm3QcphyFCafQR1fBsAYoSATwcBZIgSbglO/yW9icOHGCjRs38uyzz+L3f3OhwqWoUDYwNFhZSKEh7BiatPCYregyio5ktPtCG4uLuTQodphoSBx2CF/sHE47gJDxHv2zvGH8elIzI5SEZMNZYWEhTzzxBO+88w6NjY1prk7pTwsXLmTnzp1JH7d169bmQCDw39NQUkYY1MHMtu3VR48eNZIdBt+9ezfTp0/H5/OlqTIlWRMDFsPDiXCGRJcShy2Z2mHF56E5o/HNpy0noGFrgvZLvQJf5Msvv2Tv3r08++yzeDyeb32sCmWZz5awvsvHlmB8fphOFE2z441hkYxxR5nnu3z/q/m+EAWOeNNXR2LTpGKHyb25ASZ4Ypc9Trmyqw1nOTk5PPvss6xfv171lBzkhg8fTkFBQdLThjo6OmhqamqWUu5LT2X9b1AHMymlaVnWbw4cOJDUVTUYDHL48GEWLFiQrtKUJGnA3Y0x7myMMbvd4vo2k+U1EcYG7fjW06YLgQBHDKHHA1xh9MojHFu2bOHMmTM89dRTOByXnnKpQtnAELA12m2NmIjPLxMyitQE87NDPFjQxVx/BO1bVlC6NFiSHeTOHINF/iD35wa4OSeE91rvwyvAlcOZz+fje9/7Hjt27GD//v19XJ3S1xYuXMiePXuw7eRGonfs2BEIhUJ/m6ayMsKgDmYAoVDo/9u2bVtHsgs3duzYwfz58y/7Yq30PQGUhG2ubzeZ0WmRZUNMB1MjMbcM8IRASpw2TLjMQoKvW7duHR0dHTzxxBPo+ldvf6pQNjB0WVpi31SBnQhmmoxgITgbc3KZtSCX5E/0JnNrKpCl2uXCmcfj4bnnnuPAgQO9urWlDCxer5epU6cm3SIjFotx8OBBw7KsQbNh+aUM+mAmpTwfjUZ3VFdXJ3VcS0sLNTU1zJkzJz2FKdfMFLCp2Bn/Ke4JZkGEgAUd0W/sAPBtVq1aRSwW4zvf+U5PHzsVygaOiBQ9q3OlcAKgyfhAedge9Je5AeXr4czlcvHMM89w+vTppBuMKgPTjTfeyOHDh5PeF/Pw4cOmbdu/H2yd/r9uSFyxAoHAf/rss89akj1u+/bt3HjjjQjVQTIjNXgSP76C+E4AAL4QUocab3I/2lJK3n77baSULF++nDFjxqhQNoDk6VbPyl2ZuKwJLDQkJU41PyzTdIez0tJSnnnmGRobG/nkk0/6uyylD7hcLq6//nq2bt2a1HFSSrZs2dIeDAb/d5pKyxhDIphJKXc3NjY2d3R0JHVcdXU1oVBINZzNULL7f2wBYU/8D1mhC3+XJNu2eeutt/D5fCxZskStCBtAHAK8QqIjkSJ+WdOlhUdIJroH9ZvrAauxsZEHHniAQCDABx980N/lKH1kwYIFnDp1ivb29qSOq62tJRKJ7JNSJtdbYwAaEsEMIBwO/5dt27Yl3fNk+/btLF68OB0lKddoZNjGFiRCmQB3fO9NISW1WTqvl7r5Mk/n6zPNGlwan+c72ZzvpMajfSXElZSU8LuN66lzwMJnnuLzAi9dyUxQUvqNW5Pc7AsiEpe1Ka4Qd2UHcKn/+zKO2+3mueeeo7q6mh07dqRs+yYlszkcDhYuXJj0aBnA5s2bW7q6uv4mDWVlnCETzEzTXHno0KHOUCiU1HFHjx7F6/VSVlaWpsqU3jqaoyE1G0KJ+WVZQZDxRqGmLgg6BAdzHawf7uw5Zk+Og4+LXJz06pz2OdhY4GJNkYtmh6C0tJRTms1vjBZ+tG41UsB/ePhxPi7OJvhty/kyhAV0Ibjcjbswgk40BnM3rkLdwhbxBRwzPGEVyjJQ90T/uro6Pvroo5Tvralkrjlz5lBfX8/58+eTOq65uZmGhoY6KeX2NJWWUYZMMJNSmrFY7L/v2LEjqdmGUko+++wzlixZkq7SlF4wdNhXoCM1AaHE/DLdhKAXunKgLQ8ibixNcN6j0eISdOqCw9kOTE2A1MAWmJqg0a2TPWEcB1ySlaE2LCGIGm7+5L0NtIXD/PqBxzmb4+3X7/dKjgonb2nZfKT5eUvLZqfw9ASwKLCRLFbhZy0+3sFP1SDdjS2W2F3VifWtrTGU/uH3+/n+979PVVUVa9eu7fm6CmeDnxCCxYsXs2XLlqSP3bhxY5thGP86DWVlpCETzACi0ejzX3zxRVeyDWcPHjxITk4OY8eOTVNlSrLqLp7cH040h20phqoJUDsWzo2Gs2Xx8CWgJkuj1q0hg15oKIHKKVA5BdFczGNZI2mImrx73sRqHgXVk6FxLFZjKf9i3SdUdrTx108+TVZWVv98s1dwVjjYLzyYQmAKgS0EZ4STLxJtIz7DSyMO7MTfR4XGDrJo5vI7IwxUMZkIZuLqWqUofSc/P58f/vCHHDx48JIT/VU4G9xmzpxJR0cHtbW1SR3X0dHB2bNnW6SUH6eptIwzpIKZlDJsWdYvvvjii0iSx/H5559z6623pqs0JUm6vGjz8pxOcEbjPcy8AcjuiP/ZckBnLsSc7DNHsq9tOrJ+PKIrHyF1hNShbRirDuXz6ZFixPnRCCMX5IXAIoXk32/+mCM11fzwhz8kNze3X77fb3NIuLGEQNo6ZqQIabuwhOC0cNGBoAUdWwhsy4sZLUTK+G3Po7iueO6BJpoIm65vzCxU+tOIESP4wQ9+wJYtW751fpEKZ4PXzTff3KvRss8//7wzHA7/B5lsM9IBbEgFM4BQKPQP27Zt6zTN5DYkPnDgAHl5eYwZMyZNlSnJGB20eybtixENiCnHEBNPIsoqYXQVDEvMYWgYCaemIJtHELHdoMeQ+U0w9iT3TO6gKDuCLQUgkVkBZOE55KiT0H0jULPQgJ3rPmH37t388Ic/zLgXjRACKTXM8GjsWAFWNB+Ir0ztQkMDLDObWGQUllmIlC4QAmMA//rbEhpsnbOWky554fuIJkK1S42YZYxx48bx7LPPsmbNGvbu3XvFx6twNvjMmDGDaDTK6dOnkzrOMAyOHj3aYVnWW2kqLSMN3CtzL0kpOy3LevXgwYNJJTPbttWoWQZx23Bbo4nDlj0fui0pNWxAQE47OGJg6yAk5LQzrPg0d/sP4ixo5Lv5+WieEE3DTyHHHofxJ2BUNeS1gm4BGggrvspTSPyWZNeuXaxdu5Znn32W8ePH9/O/wAUF0sYMjUTa8VuXUsbnjzmQFGIRNbMxoyOIjzHaCBFFk5LhJPfmJFMEpODDmJ+tppcvLDdrYz52mvHb2RdGzAbm9zbYzJkzh8cff5w333wzqT0RVTgbPIQQ3HbbbWzcuDHpY7dv327EYrG/lVIOqXdaQy6YAQSDwb/dvHlze7J7dO3fv5+CggJKS0vTVJmSjNKg5ImzMRY3WdzYbLG8Osa8VgtdStAkjDkDo2pg8lH0kmrGaJ2Mjtr8lT6MbCPCxq5mNBtwWiAshJSAQCSCDZoNiS2fPs9zIomv0n399dd59NFHmTdvXj9+93FSghkZgbR99IzySQcSSUQI3mUk0dhIum/8anoQDYkTyVQGZn+vrTEvQQQmAhMNG0GN7SQiRc8cMzVi1v/uuOMObrnlFl5++WWqqqqSPl6Fs8Fh9uzZBAKBpDelj0Qi7Nu3rysajb6UptIy1pAMZlLKJtM0Pzx27FhSyUyNmmUel4TxAZuJXTZZFuTHJMPDNrotwROB3HbQLHQJk7ssSktLMWMxorXnWN4YZl5XjBERi/Ehi+KIFY8vugnCBssJXTlIITib5eCgPx7YampqeOmll1i0aBFLly5Ny84QNlCr6xxzOmnWtMs2zN1tjqbeKgAsdE+876K0HUihYVs5REIlgEAkQptHCzCBKPdhkNWrNrz9y5CCrvi29Zi2n7A1Gik1LAQRRM+ImVPNMes3DoeDxx9/nLFjx/Liiy/S0pL0pis9VDgb2DRN49Zbb+3VaNnu3bvDpmn+n8G+/dKlDMlgBhAIBP5mw4YNrcnOJ9y3bx/5+fmMGzcuPYUp1+zO8yZTOy1cVuL2Zsjmwfook0Z9de9Ll4TZAZP7myPc1hZlumHhsCXoNhQltmJqHgkxJ5YQ7Mt29twga21t5YUXXqCwsJBnnnkGj8eTsvoDQvCOz8dnWVl84XazzutlfVbWN6LGodhwDpsjENg4vPUIV5B4pNMxuyZgheK3LzVXc08wW6o1cQMRvIlQZgOnhZP1mpcNmpdq4cjouGZdWPKBKQuw8WLJbCA+p07NMetfOTk5/OAHP0BKyW9/+9uk90K8FBXOBq65c+fS0tJCsntVm6bJzp07O8Ph8D+mqbSMNmSDmZSyOhwOb012eNW2bTZu3Mhdd92VpsqUa+WQcEObxTPVUb5XFeWuRpMZI6+8IfnYsEVJxAIkMrsd6euMz1E7XwLEN01/a7iHSo+OSXyo/dVXX6WpqYk//MM/pLCwMCX1f+7xEEq0trASH+d1nQ0eD6/7fPzO5+ctbQS7zfgt9emuGpyO+ObtuvccQg9CYlRJc7cgnAY2DvwiQkQzqdEc8QUDwAbNy27NQ6Pm4JzmYLuWxS7NnZLvIx2ysXEikVJgE6/Txo2GxIVUqzL70dixY/nRj37EoUOHeOedd7Cs1P1/oMLZwONwOFiyZEmvRsv27dsXNU3zJSmlkYbSMt6QDWYAgUDg33766afNyR536NAhhBBMnz49HWUpKVZaeuVQBvFfhjvaovisRC+OYeeQegwR9kFbEQhBUBd8nu/i9RFZtDgEUkrWrVvH1q1b+cEPfpDUz0RICDo18ZURqjDQouvYCOxAIVbLOKQZb39xzuEgIjTsmI8uYxQA0521zNeb8CARUqI5DRz+Whz+M+i+GnRPC5jx5rgxR5ANLi/bHFm86/LzuSOLZqH3tNqQUsMUgjPCRWeGXhqEgIWOEAIX3fPmbOnGj41HSNXHrJ/ccMMNLF++nFWrVrF9e3qas6twNrDMnz+furo66uuT29oyMWWoIxQK/Y80lZbxMvPq20eklMc6Ozv3nz17Nulj169fzx133IGmDel/wox3taGsmwBubouiIcFhwfD4RUW0Fseb1kY9WJogosH6QndPqNq3bx+///3vufPOO1m6dCmapmEBNS6dM26d8EXT0EJCsDbbw5t5Xlbnenkjz0utMx4oZCIk2e2jkcEisNzIUF7P39nRLOzOxLwxTysBXzsacG/MoNQ246sBpEToMTRHKL6gIRHMLGcQUwhiiSa0NZoj3pDWchMzyjCDo3tqbBCZ23x2hGYx5Stt/V3c5TAQqD5mfc3lcvHoo48yd+5cXnzxxaQneCdLhbOBweVycdNNN7Fp06akjz1w4EDMNM23pZStqa9sYBjyqSIQCPzJmjVrmpOda3bmzBna29szYmWecmnJhrJuJVGbG9qj8VDjNZCFjUhhI4wcRM0EaCsEIYhogvX5Tn4/IosVI7J4OdrKsnd+R9fwQh780Q/ZWDaczTlutmS7eb3Iy6Gs+Pytj7M9NDr0nk78IU1jvd/Dx34356QPu20cxHzxdh2AjPixw37s1jHIjjEgdYS7E+Fvol2PB5EsJEusEN+NdbHYCpFvW3ilzTjLwrayAAmOEFYkDzMwCim1eAiUOmZoFKAhbQ9SagjAndEzzaBLXtgiy0YnmLitGU2sqFVzzNJv+PDh/NEf/RGmafLiiy/S3t7eJ8+rwlnmu+mmmzh9+nTSe2KapsmGDRvaDcP492kqbUAY8sFMSnncMIyNx48fT3pv5/Xr17NkyRKcTueVH6z0qd6Gsm5TgxZZdnz0ifwWGHsKmdMW/8uuePd/U0Ctx0FME0Q1wTm3g3o7yh+te5d11ad5Z/lz3DFhEqYWH6H6wu/ikMdBl65hoyEDecj2YqTlQCKoixaxOTQZ23aCI4goOAtaDGwnsnMUmFnxth7eFkR2AwJJvv3VAKIB422TB0yDx2IBxsScSDSEHsaO5GOHipGmDxnzIaXADI4CeWHfTGm7sAUMl73vAxZEcEI4OSmchEjPhpXNtg8Ab6LlR4sdD2pqxKxvXH/99Tz33HN89tlnvP/++yTbsPtaqXCWuXJycpg/fz4bNmxI+thdu3aFY7HYr4byaBkwSHcyTlIgEPiLdevW3T558uSiZG5NNjQ0cPbsWRYtWsRnn32WxgqVZFxrKIN4wFnaHOHjQjdBHWw9BoWN0JkPMVd8CaAAaemIoD/eWiMxyCRtwS/WV7PzcIT/88jd3DpsFv9590eEZJg9fhcikAudw+LHAARzwRWCiB+ArKwWIv4mEALp6YBgEehRhK8NPB1oidFdXcLsy+z72iUEB50uzoYLEjW5kJELe31Ky40dzQbbAyKK0GJIy4e0XEhHiF2OLJaYoUufG8FBzU2T5sAnbWbaEUYk+j8eF072Ck9PHPtCwA0yxPhrCHpfF5Ma7dKDhs14RyuHzBG0JkbQ1Byz9PJ4PCxbtoyCggJ+85vf0Nraf6+fNTU1lJaWMmLEiGv6XVdS64477mDPnj10dnYmdVw4HGbbtm3toVDov6WptAFjyI+YAUgp66LR6Bv79u2LJXvshg0bWLhwIV6v98oPVtIuFaGsW54p+U5jmPuawvEv6BZSMxFSg9ZhUFsGZ6fA+dHQMhxaEx/txdCVz94TUe7/v1twCzcfPP59ZvjGQ8N4ZFtJPJQ5IuAOxPfmjPjjvdPy64nln8cnZHzzgewWRNGZ+EdWGzo2SEmeZXFnOETRJZoktwuN970+TjudRMx42Ivv/ykRzvjF0o7kIU0/CAvdV4twxEOYlC5sTVCrOWgR4hstaLsQrHH4OaM5CQiNRs3BJt3Lbs3NWt3LHt2DfdFqUksIdomslI6cxUfHBPkihEOL133MzsZAEJFqxCxdJkyYwM9+9jO6urp48cUX+zWUdVMjZ5mlpKSE8ePH93ZPzEAsFvuvUspr77EywKkRswTDMP7Dxo0bvzNr1qxhydyabG9vZ//+/dx55528//77aaxQuZJUhrJuAnACupBYQsQ3R484EG3DAJDYkGWAK5I4IjFsppvgMAloJv/y3QAPTR/Pbx9fxsvbzvKLz49i+psgqxMhQIZ9EMoGfxvCGcFjSRaFImz0JnqXOeLz3RzAMsMg25bfGnP2uuMbEknbCVZ36wuJll0HxJCxHOLvySS6vxbhiIEdr797WydbwDqXHwmMsWIsMsM4gP26O35uBNJyI7QoloATWnz/TQDbzMKKFiKEie6O/39RIxxMlkm/77mkpsRtTKcW4pgW//eOSQ8RwoRQfcxSzel0cvfddzNp0iTeffdderNYKp3UyFnmuOeee9i4cSOxWHK/64FAgH379rVFIpFfpqm0AUWNmCVIKdtM0/ynnTt3hpM9dvPmzUyaNImRI0emozTlKqQjlHXTEt0zEEBeG9IVRua0IUdWw/jjUFINRY3xW52F56HgPOS2gbcTXEEYXsXqY6dY9vNNXD/Ry6o/X8TNo3SyZHz0S3gMRH4DwhnBISUzwlFGmRb3B4KUxUwKTYsp0RgPdRnkXCGUAZzXHfGQZHY3vbXRcuoQ7kA8WCZCi+ZvQLjiP+5CTwQzyx1f2Em8mastBDW6k63O+G3Q88KRWB1agGmMxYokerclVpOaoRGYoVKk5cU2c+K3UAE7BSNmMeCEcHJU5gDQqsewNROwQDroimpAfGRQdf5PjdLSUn7yk5/gdDr55S9/mXGhrJsaOet/06ZNw+12s2/fvqSP/fTTTzsikci/kTJF794GOBXMLhIKhf7Hjh072sPh5LJZJBJhw4YN3HfffWmqTPk26QxlALmmxGslFgLktsO40zCiHt3XSa5lImS8h1hezMIV70+L17KZFUhcY3QbhldxLvsg31/3Or87sJd/evRJfj57PlNj8b09nbZEl5Jp4RjTIvH5WAW2zZJQmGVGkIXhCP6rXDns7n6cM4TwtKPl1iLc8T6NQoCWXYeWXYvm7kSXEl0SX2Qg4gFHSh1pubBCRUjTg5VordGChkfa2DEfVqQo/hx2fHRZSjCDo7BjOYANWjzo2WY2tgDrW3KZhCuuAY0AH+l+9moejMREf1sPgwCReK7KrvhomcDuHrxTesnlcnH//fezfPly1q9fz3vvvUckErnygf1IhbP+o+s6d911Fx9//DHJdjhobW3lxIkTjaZpvp6m8gYckew/4mDn8Xj+5Prrr/+7e+65x5/ssT/60Y/YtWsXBw4cSEdpyiWkO5R1a3MI1hS7sUTPVuGMCVnc1hrDJj6YFr852L0pUtxJt872HHfP1x0SyiIm9+Fm2bJl5Obm8u5HH3Ksrg6/ZZOK9b0nHE52u93xW68JupRoUmJJgez+upR4pGSYZVKruTADo5GmD/QQWJ74dyUs9JyzCC1+AxPbhdkxju73dMLRhTOrHiuWhxUeDiKG01eLtByYoVLQIjh9VehCcrMZovSiRQBtQmOn7qFZ6OjABDvKPCtyyfkVOzU3p4ULSzowjQmAjdN/CgFY0TysaDG5LpuOqIYuYnzPsz8F/5JD0+TJk7n//vs5deoU69evJ9k3qv2tr64JygWLFi1i3LhxvPbaa0kf+9prr7WeOHHiSdu216ehtAFJBbOvEUI4fD7f6Z/+9KdjsrOzkzp21KhRPPHEE/z85z8nepnVckrq9PUF2AJqsjRCmmB41KYgdnW/O12aoNLjICZgTMRimHnhxt706dO59957e14EQ6FLr4RMhgS+dLk46nShEQ+Eo02T+ZEIX7jcVOvx6FNimSyMRXjf4yMmBJYxDJlYxQky0arDhXAYaP5aQMPqHAu2C0QMpBNND+DOqidkjAfpwJFVj+YMYNtgBiYAOg7/WYQexSlt7jMNcqTEQPC+0x/fezQRFDUpyZU295pGTzgLIfjckcV5oQMCK5KHHSlG6EGc3lqQEmnrxIJldMdhtwjzmOcwngzvxZZpsrOzuffeexkxYgTvv/8+VVVV/V1Sr6lw1neys7P56U9/2qsFIefOnWPFihVfdHV1zU9TeQOSCmaX4HQ6vzNjxoxfP/roo3nJHvvwww8TCAT49NNP01CZ0m0wXXhdLhd33HEHM2bM4NNPP+3VHI1LiQKdmoZPSrIu+j3v/qw7HL6e5SMqNKTpxuoqQTiDaFktgMTqGBfvcyas+IftAj2MI6sFMzCKEVoHOVqYE+ZwND2IO6sGS4svDjBDw5GxXIQeQvfVgrBxCLgvalCpOTmmubCFwI75QZhojjAg0YFbzBCjbZMPHT7ahZaY15aLFRoOgO45h+7sAinRbIhFh2GZ8VAp9CAeXw13mwaFMun2hEOOrussWrSIRYsWsXv3brZs2dLnfcnSYTBdIzLZ8uXLaWlp6dWemC+++GJLTU3NvVLKL9JQ2oCl5phdgmmab506daqxpaUl6WM//fRT5s2bR0FBwZUfrPTKYLvgRqNR1q5dy4oVK7j++uv50Y9+xOjRo6984BW4gCLb/koog3ggu3gK1jjTRJMS4YjgyD+D7m9EaCZCs9B99aBF4+02bBcIEz27Di2xgCAg3Zw0hwGS2x1nudsycBJfLaF7mkHEkFYWVnBEvKEt8KXDTauI73xgm16sYAmWUYoVLkBKgZUYJasWOh1Ciz8umtMTyjT3eTRXF0JKiqTFWBFFd7fRs+uosDGFYJuehfLtJk6cyM9+9jNGjRrF888/z6ZNmwZFKAM156wvjB8/npKSEj7//POkjz1z5gxtbW1fqlD2TSqYXYKUUhqG8Ufvv/9+0o16AoEAW7duZenSpekobcgbbKHsYg0NDbz44ovs3LmT5cuX8/jjj5Obm5v2550bi5Br2zgSixgcUqLHG2Ig3KF4WMurRPOdQ8+pQegmloiHoID0INEYrzczRgtSiM1cK4IuJUKzcPjqAAtpZsfbdAjBeU2nUFpoUmJHu78/gR0pwgqWIGV8scBmlxdLB9v0XQhlnvPonnYEEj82t9ghzulOpMNicl5iJaaIj5J1CY1wmnYeGOiGDRvG008/zb333suaNWt4/fXX+2xLpb6kwln66LrO/fffz5o1a5IO85Zl8cEHH7QEAoGfpam8AU0Fs8uQUn7e1NS0rTdbNe3YsYOcnBxmzJiRjtKGrMEcyi528OBBfv7zn9Pc3MyPf/xj7rjjDtxu95UP7CUX8EAkyK2REHNjEW6Khnk8FGC4HV8xipQILYrm6UQ4ooDkK007hEVn9oX3MJPsGDdaIUCCHkXLagZI9E8Dj5RMsaJoto6M+QGJntUAwkKafqzQCGwp4i04zCxsYyQg0DzN6O52NCmZYMV40DLwInsuYotHRBF6CM3Z1VOLpuaZfUV2djYPPfQQ3/ve96isrOQXv/gFp0+f7u+y0kqFs/S46aabOH/+PCdPnkz62F27doVDodArUspTaShtwFPB7FsYhvHjjz76qCXZZnm2bfPBBx9w77334vF4rnyAckVDJZR1i8VibN68mV/+8pf4/X7+7M/+jJtvvjlt+7IKoMS2mGHGGGOZuIG7IyEeCBssjEYoToQ0vfu2qLgQeIS3lU6HpO2i7czKbJMJdgxdSISrC7CRZhbS1ohpENUEE8LxlZ/CYaC5OnH4auOPi+VgB0dgGcOxAvEN1oW7HeFpRUiJG8k8+8JY2CQrii4lhR6Jw1+D5jR6bnO60vKvNfC43W7uvPNOfvrTnxIIBPjHf/xHdu7ciX2JnSMGIxXOUis/P5+FCxeybt26pI8NBAJs2bKlNRgM/nUaShsUVDD7FlLKc5FI5H9/9tlngWSPra2t5ejRo9x9993pKG1IGWqh7GJdXV2sXr2al156ieHDh/Nnf/Zn3HDDDei6fuWDUyBXSiZbMe6NhPhuKECRtJCaiO9skBgRE1ltaIDxteZhN5hhRlsxhGYhnAYgsGM5BDXBGo+XKiveC22UoxkNiXCE0Xz1gETGcpDRXJA6whlA855HCMlEO8oDMeMrgWuaHWW4NBHEg6NDSnzE23MMdW63m1tvvZU///M/x+fz8ctf/pINGzZkfE+ydFDhLHWWLl3Ktm3b6OjoSPrYjz76qD0Sifyp2nrp8lQwu4JwOPz3e/fuberNvnCffvopEyZMYOzYsWmobGgYyqHsYi0tLbz99tusWLGCCRMm8Gd/9mcsXLgQh6Nvd1UbYcXnhgndRMurRsurRgiJRbwh7sV0YJw00TSJ8MT36JTRbNAEVsxLl/TgJcodNDPPjPfK0pxBNF89wt2K5m1Ey65B89cjBPik5AYr8o02GDpwhxkiR9rMt8IsMYM8FAvgHcK3MS8OZLm5ubzwwgusXr2arq6uKx88iKlwdu2mTZtGfn4+27dvT/rY6upqqqqqjpmmuSr1lQ0eKphdgZTSDAaD31+9enXSySwajbJmzRqWLVvWZyMcg4kKZd/U2NjIa6+9xuuvv86YMWP4Z//sn3HzzTendQ7axabEorgSiwSEMxyf4C8lE2MxvJdovRPQBFIAbiPebsPMQsY8mJE8ACY5mtEFTLFj5GGjCYnmNtB9zWieDjRnqGckbGHs27dCdxCf31YirSF7YfP5fNxxxx3fCGRtbW39XVrGUOGs97KysrjvvvtYvXp10rfBLcvivffeazEM4zmp+nR9K9XH7Cr5/f4PHnroofumTJmS9DX/iSee4Pz582zatCkNlQ1OKpRdnaKiIm6++WYmTZrE3r172bVrV9pHRYJCcMDpotbhwIlkWjTGJDN2ydDUoOlsyMrCFALbKEIGC8ERAtMNCG7xHmaijI+WRRBsc3mo1xxIwEl8Z4IC22KGGaXgCj3JJk+ezIkTJ1L+/Q4EhYWFLF68mGnTpnHo0CG2b9+uwtgVqGtM8h599FGCwWCv5pZt3749/Pnnn//SMIy/SENpg4oKZldJCDEyNzd3/5/+6Z8OS3YCdnZ2Nj/5yU94+eWXaW5uTlOFg4e6YCYvLy+PG2+8kdmzZ3P69Gl27NhBXV1df5eFBNZmeWnRNCypY7eOj/dEA3AFcObVsjwQwHPRZShGfNNzd5K3IodiMCsrK2PhwoWMGjWKPXv2sGvXrpTsHjFUqGvN1Zs8eTL33nsvv/jFL5JujxEIBPjFL35RbxjGJDW37MpUMEtCVlbWXy1YsODf33nnnb5kj73++uuZM2cOv/nNb5Le5HUoURfKa+N2u5kzZw433HADwWCQXbt2cfTo0X5tGmoCG7KyaND1xKhZfNK/yK3D4e5iQTjClCRXPl/KUAlmbreb6667jgULFmBZFrt372b//v2DpjFsX1PXnCvzeDz87Gc/45133unVVl1vvPFG+4kTJ/4wFou9k4byBh0VzJIghHD6fL7jf/iHf1jWm87+zz77LGfPnmXLli1pqG7gUxfI1BFCMHnyZObPn09JSQkHDx5k7969nD9/vl/q2edysd/tQkod2TIOhEQUnkFHMjcSYWZUBbMrKSkpYd68eUyfPp1Tp06xZ88eqqur+7usQUFde77dQw89hGmafPTRR0kfW11dzeuvv77DMIzFam7Z1VHBLEmapi0ZO3bsu9///veTTmY5OTn8+Mc/5ne/+x2NjY3pKG/AUhfG9MnNzWXu3LnMnTuXzs5OvvzyS44cOUI4HO6zGpp0jXVeL5YQSBmfjSZEfFL/A0aQ/BT00xqMwczv9zN79mzmzJmDruvs27ePvXv3YhhGf5c26Khr0KVNmDCBZcuW8Ytf/IJoNJrUsZZl8U//9E8tLS0tN6pmsldPBbNeyM7O/uD++++/d/r06Un3Kpg9ezaLFy/m+eefx7KsdJQ34KgLYt8QQjBp0iRmz57NhAkTOHPmDAcPHuTEiRNp/1mUwBaPh2qnAzPR78whJeOjMRalqKfWYAlmLpeLKVOmMGvWLEaPHs3Ro0fZt28fNTU1/V3aoKeuRV/lcrn42c9+xvvvv09lZWXSx2/dujW0devWX6kJ/8lRwawXhBBFfr//0B//8R8P93q9SR//xBNP0NLSwqeffpqG6gYWdSHsH263m2nTpjFr1ixGjhzJ8ePHOXbsGKdPn07bXCUJ1Dp0TjudCGBCLMYo00rZbpYDOZh1h7Hp06czbtw4zp49y5EjR/p9fuBQpK5JFzz88MPd+1omfWxzczMvvfTSGcMwpkkph15H42ugglkvuVyu5ePHj3/+6aefzkv2WK/Xy09/+lPeeOMNamtr01DdwKAugJnB7/czffp0pk6dysiRIzlz5gzHjh3jxIkTfXq781oNtGCWnZ3N5MmTmTRpEmPHjqWqqoojR45w/PjxIdmZP5OoaxNMnz6dO+64g1/96lf0ZlvCX//61y0NDQ1LpZR70lTioKWC2TXw+/0fPfDAA3f35pbm1KlTueuuu3r1Qz8YqAtfZsrKymLy5MlMnTqVsrIyGhsbOX36NKdOneLcuXMZvaI404OZpmmUlJQwadIkJk2aRG5uLqdOneLEiROcOnVKhbEMM5SvUd3zoV999VXq6+uTPn7Lli2hbdu2PW8Yxj9LQ3mDngpm10AIUej3+w//7Gc/G+7zJd1Bg0ceeYRoNNqrlS4D2VC+4A0kDoeDMWPGMHHiRCZMmIDf7+f06dNUVVVRVVWVcT35MjGYjRgxgrKyMsaNG8eYMWNoa2ujsrKS48ePU1tbm9FBVxma1yohBM899xyVlZW96iCQuIVZaRjGdHULs3dUMLtGLpfr8bKysue/+93v5id7rNvt5ic/+Qnr1q3j+PHj6Sgv4wzFC91gkZ2dzYQJExgzZgxjx47F4/FQVVVFdXU1tbW1NDQ09Ot8qP4OZi6Xi1GjRlFaWsro0aMZPXo0hmFw5swZzpw5w9mzZ1Xz1wFoqF2zFi9ezOTJk3nllVeSfuNg2za/+tWvWhsbG++RUn6RphIHPRXMUsDv93/4wAMP3NObW5qjRo3iqaee4vnnn6ezszMd5WWMoXaBG+yys7N7QtqoUaMYNmwYLS0t1NfXU19fT0NDA01NTUkvse+tvgxmWVlZDB8+nBEjRjB8+HBGjhxJfn4+586do7a2ltraWmpqalRbi0FiqFy7Ro4cyTPPPMPzzz9PR0dH0scnbmH+2jCMf5766oYOFcxS4FpvaS5evJgpU6bw8ssvD9pbG0PlwjaU6brO8OHDKSkpoaSkhOHDh1NUVEQwGKSpqYmmpibOnz9PW1sbbW1tKd/TMx3BLCcnh8LCQoqKiigsLKSgoIDi4mI8Hg8NDQ00NjbS0NDQ83myGzsrA8dgv4Y5HA5+8pOfsHnzZg4dOpT08eoWZuqoYJYiTqfz0bKyshefeeaZpG9pAjzzzDPU19ezcePGVJfW7wb7BU25PCEEeXl5FBcXM2zYMIqKisjPzyc/Px+Px0N7ezvt7e10dnYSCATo6urq+a9hGITD4aueFJ9MMMvKysLr9fZ8+P1+cnJyyMnJITc3l9zcXLKzswmHwzQ3N9Pa2kpLSwvNzc00NzerDcKHqMF8LXvwwQdxOBy8++67SR+buIXZ0tjYeK+6hXntVDBLIb/f/8H9999/74wZM5K+pen1evnJT37CqlWrOHPmTDrK6xeD+UKmXBun00leXh75+flkZ2eTnZ2N3+//yofb7cblchGNRntCWiwWw7IsLMvCNE0sy8K2bXJycjAMAyEEQgg0TcPpdOJ0OnG5XLhcrp7PI5EIwWDwKx+dnZ10dHTQ0dFBZ2cnnZ2dQ3LFtPLtBuM17brrruPmm2/m+eef79XUg88//zy4ffv2XxmG8S/SUN6Qo4JZCgkhChK3NEf05pZmWVkZjzzyCL/+9a8HxdyUwXgBU/qeEAKXy4XH48Hj8eBwOHA4HOi6jq7rOBwOhBCMGDGC+vp6pJTYto2UklgsRjQaJRqN9nweiUQG7ZQBpW8MpmtbcXEx3/ve93jllVdoampK+vimpiZefvnl04lbmH0zoXSQU8EsxZxO5yNjxoz5zXPPPZcvRPI9zW+//XZKSkpYsWJFGqrrO4PpwqUMDP29KlMZWgbDNc7lcvFHf/RHfP755xw4cCDp403T5Ne//nXL+fPn75FS7k1DiUOS1t8FDDaxWGxVY2PjBzt37uxVy/RNmzbhdDq59dZbU11anxkMFyxFUZRvU1NTg9PpZMSIEf1dSq89+OCDVFVV9SqUAaxbt66zq6vr/6hQlloqmKWBYRg//vzzz6vPnTuX9LFSSt58803mzp3LpEmT0lBdeqlQpijKUDGQw9mCBQsoLCxkzZo1vTr++PHj1pEjRw6GQqH/muLShjwVzNJAShk2DOPB119/vbk326wYhsFbb73Fww8/TEFBQRoqTA8VyhRFGWoGYjgbNWoUt956K2+++SaWZSV9fEdHB++///55wzAekVKqHjEppoJZmkgpTwSDwb9855132nszj6+2tpaNGzfy5JNP4nQ601BhaqlQpijKUDWQwpnX62X58uV88MEHvWr7YlkWK1eubDUM40kpZWbtyzZIqGCWRpFI5OXa2tpP9+7d26tme1988QV1dXU89NBDqS4tpVQoUxRlqBsI4UzTNL7zne9w8OBBjh071qtzbNiwIdDR0fEr27Y/T3F5SoIKZmlmGMYfbNiwof78+fO9Ov7DDz8kPz+fRYsWpbiy1FChTFEUJS7Tw9n9999PJBJhw4YNvTr+9OnT9r59+44Fg8G/TnFpykVUMEszKaVhGMaDK1eubOlN4z7LsnjjjTdYtGgRZWVlaaiw91QoUxRF+apMDWfz58+ntLSUd955p1fHd3V18e677zYZhvGglDL5iWnKVVPBrA9IKQ8Hg8G/Xr16dfK7wgKdnZ28/fbbPPbYYxmzGECFMkVRlEvLtHA2btw4br31VlauXNmrzv62bfP666+3BoPB56SU6qKfZiqY9ZFwOPzLM2fObN2/f3+v9nipqqpi48aNfPe738Xj8aS6vKSoUKYoivLtMiWc5eXl8fjjj/P222/3eo/XzZs3G21tbb+zLOuTFJenXIIKZn1Exu9pPvXxxx83NDf3biHL3r17OX78OE8++SSa1j//16lQpiiKcnX6O5y5XC6efvppPvvsM86ePdurc1RVVcndu3dXGobxr1JbnXI5Kpj1ISlll2EYD7/66qstvelvBrB+/XpCoRDLli1LcXVXpkKZoihKcvornAkheOyxx6ipqWH37t29OkdnZydvvfVWUzAYfEBKaaa4ROUyVDDrY1LKLw3D+IuVK1e22XbyffmklLz77rsMHz6cm266KQ0VXpoKZYqiKL3TH+Hsvvvuw+Fw8NFHH/Xq+Fgsxu9+97vWQCDwuJSyJsXlKd9CBbN+EA6Hf9fY2Pi79evXd/Xm+FgsxsqVK1mwYAHTpk1LdXnfoEKZoijKtenLcLZ48WJKS0t544036O0AwFtvvdXe1dX1N7Ztb0lDicq3UMGsnwSDwb/Yv3//lwcPHuzVYoCuri5WrlzJAw88QElJSarL66FCmaIoSmr0RTibOXMmCxYs4NVXX+3VCkyAzz77zKitrV0VCoV+nuLylKugglk/kVLahmEsW7t2bU19fX2vztHQ0MDq1at56qmn0tJGQ4UyRVGU1EpnOBs3bhxLly7l1VdfpaurVzdkOH78uLlr164jhmH8UYrLU66SCmb9KLEY4O6VK1ee7+0v0YkTJ9iwYQPPPvssfr8/ZbWpUKYoipIe6Qhnw4YN4/HHH+ett96iqampV+c4f/48q1evrjcMY6ma7N9/VDDrZ1LKykAg8PSKFStaTbN3vwf79u3jiy++4Nlnn01JjzMVyhRFUdIrleHM7/fz3e9+l3Xr1vW6LUYwGOTVV19tNgzjPill6zUXpfSaCmYZwLKsDR0dHX/77rvvdkgpe3WOrVu3UllZydNPP43D4eh1LSqUKYqi9I1UhDOPx8Ozzz7Lnj17OHToUK/OYVkWr776apthGD+SUh7pdTFKSqhgliFCodA/nD17ds22bduCvT3Hxx9/TFtbG8uXL0cIkfTxKpQpiqL0rWsJZy6Xi2eeeYbTp0+zdevWXtfw4Ycfdra2tv6/aDT6Xq9PoqSMCmYZIrEzwB9s3br1xKlTp5Jf35ywevVqNE3jwQcfTOo4FcoURVH6R2/CmcPh4KmnnqKxsZFPPun9Tkm7d++OHD9+/PNgMPgfe30SJaVUMMsgUspoMBi85913361vbGzs1Tls2+bNN9+kqKiIu++++6qOUaFMURSlfyUTzjRN4zvf+Q6BQIAPP/yw189ZWVlpb9y4sdIwjO/I3s6jUVJOBbMMI6VsMgzjrt///vfn29vbe3WOWCzGq6++yvjx47n99tu/9bEqlCmKomSGqwlnQggeffRRpJSsWrWK3uap+vp63n777bpgMHiblDLU25qV1FPBLANJKY93dXU99Morr7QYhtGrc4TDYX73u98xdepUlixZcsnHqFCmKIqSWa4UzpYtW4bX6+XNN9/sVVd/gNbWVl599dVGwzBul1Kev5Z6ldRTwSxDSSl3dnV1fe+VV15p7W335mAwyG9/+1tmzpzJzTff/JW/U6FMURQlM10unN17770MGzaMlStXYllWr84dCAR45ZVXmgOBwH1SytOpqFdJLRXMMlgsFvuoo6PjL1esWNHW219CwzD47W9/y5w5c1i0aBGgQpmiKEqm+3o4u+eeexg7diyvvvoqsVivdvIjHA7z8ssvt3Z1dT0lpfwylfUqqaOCWYYLh8O/aWpq+t9vv/12r3ucJd4hMX/+fO69914VyhRFUQaA7nD22GOPMXbsWH77298SDod7dS7TNPn973/f1tnZ+aeWZX2a4lKVFFLBbAAwDOO/VFVVrVy7dm3v9m0ivun5J598wvTp0xk9enQqy1MURVHSZObMmYwYMYJ169b1OpTZts3rr7/e3tLS8l8ikchrKS5RSTEVzAYIwzD++NChQxu3bNnSqwa0paWltLe389JLL3HTTTexcOHCVJeoKIqipND999/PyJEjefHFF5FS9qoJrZSSDz74oLOuru43wWDwf6ehTCXFVDAbIKSUtmEYy7dt27Zv//79kWSOvXhOWXt7Oy+//DI33HDDNxYEKIqiKJlh2bJlDB8+nN///vdEIpFe7xCwadOmwLFjx9YEg8F/laZSlRRTwWwAkVLGgsHgPevWrTt98uTJq1oNcKmJ/h0dHbz00kvMmjWLO+64I231KoqiKMl78MEHKSoqYsWKFVy8Kj/ZcLZnz57I7t27dweDwWdVA9mBQwWzAUZKaQSDwVtXrVpVXVVV9a2/aN+2+jIQCPDyyy8zceJEli5dmrZ6FUVRlKujaRqPPvooBQUF3whl3a42nB06dCi6YcOGo8Fg8AEppZmumpXUU8FsAJJSNhuGcfMbb7xRXV1dfclwdjUtMUKhEK+88gojR47koYce6tXG54qiKMq1czgcPPnkk7jdblasWPGtLTGuFM4OHz4c++ijj44Hg8Elqqv/wKOC2QAlpaw3DOOm119/vaa2tvYr4SyZPmWRSITf//735Obm8thjj6Fp6kdCURSlL3k8Hp577jmCwSBvvPEGpnnlAa7LhbOjR4+aH3744clgMHizlLLXK/mV/qNehQcwKWWdYRg3v/baa7V1dXVA75rHdu+t6XQ6efLJJ3E6nekqWVEURbmI3+/n+9//PnV1dbz33ntJbbP09XB2/Phx8/333z8VDAZvklJ2pqtmJb1UMBvgpJQ1hmEsee211+ps2+5181jLsnjjjTcIBoP8wR/8AV6vNw3VKoqiKN3y8/P54Q9/yKFDh/j44497dY7ucNbc3GyvXr36dDAYXCylbE9tpUpfEmqhxuAghBifl5e3/YknniguKSm5pnPdfvvtzJgxgxUrVtDW1paiChUlvSZPnsyJEyf6uwxFuSrDhw/nmWeeYdOmTezdu/eaznXixAnro48+qmlvb58vpWxJUYlKP1EjZoOElLKyvb194YoVK2pra2uv6VwbN25k+/bt/OAHP+BaQ56iKIryVWVlZTz33HOsWbPmmkPZsWPHzPfee+9ke3v79SqUDQ4qmA0iUsqziTlnNTU1Ndc0FPrFF1/wwQcf8N3vfpdJkyalqkRFUZQhbe7cuTz22GO8+eabHD169JrOdeTIEXP16tUnDcNYLKVsTVGJSj9TwWyQkVJWGYaxeOXKlTVX6nN2JSdOnOC1117joYceYu7cuakqUVEUZUi68847uemmm3j55Zepqqq6pnMdPnw49sEHHxwPBoOLpJRqzskgouaYDVJCiBKfz7d1+fLlY8rKyq4pgBcUFPDMM89w+PBhNmzYkKoSFSWl1BwzJVM5HA4eeeQRsrOzWblyJaHQtbUWO3DgQGzt2rVHg8HgLWr15eCjgtkgJoQY4fV6Ny9btqxs+vTp19QDw+v18sQTTxAMBnn33Xe/tfmhovQHFcyUTOT1enn66adpbW1l9erVWNZV7aZ3Wdu3bw99/vnnB4PB4J1SykCKylQyiApm/aBCVCwF/i+gAy+Uy/K/qxAVc4BfAh7ABP64XJbvuuiYL4BFQDnwPSC/XJb7L3Hu5cCbwIJyWb5HCJHr9Xo33HbbbdNvuOEGz7XUrWkay5YtY+TIkaxcuZKOjo5rOZ2ipJQKZkqmKSoq4rvf/S4HDhxg06ZN13QuKSUff/xx4MCBA58ZhvGolDIKl309+Y/AHwFNicP/Xbks/6j7XFfzelIhKp4A/iMggf3lsvy71/QNKFdNzTHrYxWiQgd+DtwHTAeerhAV04H/AVSUy/I5wN8k/tx9zDigrlyWR4H3gRsuc+5s4M+Bnd1fk1J2BIPBmzZt2vT5p59+GriWIG7bNqtXr2bfvn384R/+IaWlpb0+l6IoymA2adIkvv/977Np06ZrDmWWZfH22293HDhw4A3DMB68KJRd7vUE4B/KZfmcxMfFoWwcV3g9qRAVk4C/Am4ql+UzgH9+Td+AkhQVzPreDcCpcllemfjFWAk8TPxdSU7iMblA/UXH3AesBSiX5TvKZfm5y5z7PxMPdOGLvyilDAeDwfv27t27atWqVR3JdJa+lJ07d/Lee+/x5JNPMmfOnGs6l6IoymBzyy23sGzZMlauXMmBAweu6VzRaJTf//737ZWVlf/XMIwfSSkvvoBf7vXk21zN68kfAT8vl+Vticedv6ZvQkmKCmZ9bxRQc9GfaxNf++fA/6wQFTXA3xN/t9JtKYlfpMupEBVzgdJyWf7Bpf5eSmkZhvG9kydP/nLFihXt1zpH7PTp07z00kvcfPPN3HPPPWoDdEVRhjyXy8UTTzzBpEmTeP7557nWnpLBYJDf/OY3refOnfu3hmGUy2/e8rjc6wnAn1aIigMVouI3FaIi/6LHXPH1BJgMTK4QFVsrRMWOxO1SpY+oYNb3LpVgJPAz4C/KZXkp8BfAiwAVosIFjC6X5ZWXO2GFqNCAfwD+5bc9sZRSGobxb+vr6//mpZdear3WlUEtLS288MILFBcX8+yzz6ptnBRFGbIKCwv50Y9+hGEYvPLKKwQC1zYvv6OjgxdeeKGlpaXlh6FQ6FeXedjlXk9+AUwA5gDngP8FV/d6kuAAJgG3AU8DL1SIirxkvweld1Qw63u1wMWTs0YTv235B8A7ia+9yYX7/rcAW65wzmxgJrCpQlScBW4EVleIivmXenAwGPx/zc3NP33hhRdau7q6evVNdAuHw6xYsYK6ujp+/OMfM3r06Gs6n6IoykAzadIkfvCDH7Bjxw4+/PDDa1552dTUxIsvvtjU1tb2UDQafe9bHnrJ15NyWd5YLsutclluA8+T3OtJ93nfK5flsXJZfgY4TjyoKX3A0d8FDEG7gUkVoqIMqAOeAr5LfGXMrcAm4A7gZOLxS4E133bCclneARR1/7lCVGwC/lW5LN9zuWMikcibmqY1vvDCC28999xzw4qKii730CuSUrJhwwZqamp48skn2bJlCzt37rzygYqiKAPckiVLmDdvHq+99hp1dXXXfL6amhr5+uuvnwsEAndLKY9c4eGXfD2pEBUjL5o79ihwKPH5FV9PElYRHyl7uUJUFBG/tXmlUTYlRdSIWR8rl+Um8KfAOuAo8Ea5LD9MfLLl/6oQFfuB/wr8OHHIbcDm7uMrRMX/qBAVtYC3QlTUJpZF94pt2591dHTc+fLLL5+rqam58gFXcPLkSV588UVmz57N8uXLcblc13xORVGUTOT1ennmmWcYP348zz//fEpC2bFjx8yVK1eeDQQCN15FKPu215P/USEqDlaIigPA7cSnx8DVv56sA1oqRMURYCPwl+WyXO3D2UdUH7MMViEqRgPPl8vy+9L5PEKIMT6fb/1dd901du7cudecpnRd57777mPs2LG88cYbNDU1XfkgRblGqo+Z0lfGjRvHo48+yr59+9i0aRPX+joqpeSzzz4zdu3adcQwjKXp2Peyr15PlGungpkCgBDC5/P5Vs2YMWPh0qVLszXt2gdTr7vuOu6++24++eQT9u/fn4IqFeXyVDBT0k0IwZIlS7j++utZtWoVlZXXfncvFovx1ltvtdfW1r5rGMaPpZRmCkpVBjAVzJQeQgjN6/X+t6Kioj96+umn87Oysq75nMXFxSxfvpyGhgY+/PBDIpFICipVlG9SwUxJJ7/fz2OPPYaUknfeeQfDMK75nB0dHfz+979v7erq+ptQKPTzFJSpDAIqmCnf4Ha7n/D5fD9/5plniq5lUUA3h8PBPffcw8SJE3n33XdJxXw2Rfk6FcyUdJkwYQIPP/wwe/bs4fPPP7/mW5cANTU1vPHGG42BQGC5bdtXs1JSGSJUMFMuSQhxnd/v//CRRx4ZOXHixJQsEpk8eTIPPvgge/bs4bPPPkvJxU1RuqlgpqSaruvceeedzJgxg3feeYeqqqqUnPfLL7+Mrl+/vsowjLuklNUpOakyaKhgplyWEGKY1+v9+Kabbpq8ePFibyq6+/v9fh555BGcTifvvvsu7e3t116ooqCCmZJaI0aM4NFHH6WpqYkPP/yQa23IDfH9hteuXdt1+PDhHYmNyK/9fqgy6KhgpnwrIYTL5/O9Mm7cuPseffTRXIcjNa3vFi1axE033cS6des4ePBgSs6pDG0qmCmpIITg5ptvZuHChSm9PoVCIV577bW25ubmXweDwb+6xPZKigKoYKZcBSGEyMrK+oucnJx//+yzzxZkZ2en5Lzd70hbWlr48MMPUzKZVhm6VDBTrlVBQQGPPPIIsViM9957j87OzpSct7m5mRUrVjQbhvHHkUjkzZScVBm0VDBTrpqu63f6fL4Vjz322LCysrKUzDvTdZ1bb72VuXPnsm7dOg4dOnTlgxTlElQwU67F/Pnzue2229i8eTO7d+9O2XkPHToUW7NmTaNhGA9IKQ+k7MTKoKWCmZIUIUSJz+f7YN68eZNuv/12fyr6nQGUlJTw8MMPq9EzpddUMFN6Iy8vj2XLluHxeHj33XdpaUlNg/tYLMYHH3zQcerUqV2GYXxHStmRkhMrg54KZkrShBC61+v9u7y8vB8+9dRTBTk5OSk578WjZx9//LGae6YkRQUzJRlCCBYuXMgtt9zCtm3b2L59O7Ztp+TcTU1NrFy5ssUwjP8UDof/n5pPpiRDBTOl13Rdv8Pr9a54+OGHiydNmpSyfVdHjhzJww8/TFtbGx9++CGBQCBVp1YGMRXMlKs1fPhwHnzwQaLRKB988AGtranbASnRCuOcYRgPqVuXSm+oYKZcEyFEsc/nWz1r1qzpd999d7au6yk5r67r3HLLLcyfP5/PPvuM3bt3q75nyrdSwUy5EofDwZIlS5g3bx7r169n3759KTt3JBLhvffe66iqqtpkGMazUkr1jlLpFRXMlGuW2MqpPDs7+0+ffvrpgry8vJSdu6ioiAceeACXy8WHH35IfX19ys6tDC4qmCnfZty4cSxbtoyGhgbWrFmT0nmsDQ0NvP76683BYPDfhcPh51N2YmVIUsFMSRlN0xZ7vd43ly1bVjxt2rTUNDxLmD17NnfffTdHjx7l008/VXtuKt+ggplyKT6fj7vuuouysjI++uijlP6MSCnZvXt3ePPmzXWGYTwopTyaspMrQ5YKZkpKCSEKfD7fO1OnTp1733335aSqIS2Ax+PhzjvvZMqUKXzyySdqcYDyFSqYKRfTNI0bbriBW265hS+//JLPPvuMaDSasvOHw2Hefvvt9vr6+rWGYfxQSnntWwMoCiqYKWmQaEj7l1lZWX+5fPnyopKSkpSef9SoUSxbtoxgMMjatWtpampK6fmVgUkFM6VbWVkZS5cupbOzk7Vr16asBUa3U6dO2atXr24Jh8N/EYlEVqT05MqQp4KZkjZCiCk+n++t6667btwdd9zhT+XomRCCBQsWsGTJEo4cOcKmTZsIBoMpO78y8KhgpuTk5HDPPfdQUlLCunXrOH78eErPHw6H+fDDDzsqKysPGIbxpJTyXEqfQFFQwUxJMyGEnpWV9e+ysrL+2fLlywtTPXqWlZXFrbfeysyZM9myZQu7du1KWS8iZWBRwWzocjgcLFq0iBtvvJGdO3eybds2TNNM6XOcPn3afu+991rC4fBfRaPR36jeZEq6qGCm9AkhxFSfz/fWnDlzxt5+++0pHT0DKCws5J577qGwsJBPPvkk5e+UlcyngtnQI4Rg9uzZ3H777dTV1fHxxx/T0ZHaBvuRSIQPP/yw4/Tp04cMw3hCSqmWhitppYKZ0mfSPXoGMH78eO655x6CwSDr1q2jsbEx5c+hZCYVzIaWiRMnctdddxEOh1m/fj21tbUpf47EKFlzJBL5d5FIRI2SKX1CBTOlzyVGz96eM2fOmHSMngkhmDdvHrfddhuVlZVs2rSJtra2lD6HknlUMBsaRo4cyV133UVOTg7r169Py+h4JBLho48+6jh16tShxFyyupQ/iaJchgpmSr9IjJ79e6/X++fLly8vHDlyZMqfw+VysXDhQm688UaOHj3K5s2b6erqSvnzKJlBBbPBLS8vjzvuuINx48axadMmvvzyy7TsBlJZWWmvWrWqJTFK9qIaJVP6mgpmSr8SQkzz+Xxvz549u/T222/3u1yulD+Hx+PhpptuYt68eezfv58tW7aoFZyDkApmg1NOTg4333wzM2bMYOfOnWzfvp1YLJby5wkGg6xZs6ajsrLycGIumRolU/qFCmZKvxNC6B6P589dLtdf3XvvvQXTp0/XhRApfx6/388tt9zCzJkz2b17N9u3b1c7CAwiKpgNLhcHsr1797J9+/a0vKGybZsvvvgisnnz5rZIJPIvYrHYSjVKpvQnFcyUjCGEGOb3+3+Zn59/28MPP1xQVFSUlufJzc3l1ltvZfLkyezevZudO3cSDofT8lxK31HBbHDoq0AGUFtby3vvvdcSDAbfMAzjX6uNx5VMoIKZknGEEIt8Pt8rs2fPHpmu25sABQUF3HzzzUyZMoUvv/yS7du3p3RjY6VvqWA2sPVlIDMMg7Vr13ZUVlaeMgzjObXHpZJJVDBTMlLi9uafOZ3Of7d06dL86dOnO9JxexPiLwg33XQTs2bN4tChQ2zdujXlvZCU9FPBbGAqLCxk8eLFTJ06Ne2B7KLblq2RSORfqtuWSiZSwUzJaEKIYT6f7xf5+fm3P/LII2m7vQng8/m48cYbmTdvHidOnGDLli0p32NPSR8VzAaWMWPGsHjxYkaNGsXu3bvZvXs3oVD69gGvra1l1apVLaFQSN22VDKaCmbKgHDR7c0Rt99+e3a6bm9CfBXnggULWLhwITU1NezcuZOzZ8+m7fmU1FDBLPMJIZg6dSqLFy8mKyuL7du3s3///pRvn3SxxG3L9srKytPqtqUyEKhgpgwYF93e/Kvbbrstb86cOS5d19P2fE6nk9mzZ7Nw4UIsy2Lnzp0cPHgQy7LS9pxK76lglrkcDgdz5szhxhtvJBgMsm3bNo4fP56WPmTdYrEYO3bsCO3cubMtHA7/K9M01W1LZUBQwUwZcIQQeT6f7z+5XK6n77nnnoKpU6dq6Zp/1m38+PHceOONjBw5kr1797J7924CAXUnJJOoYJZ58vPzmT9/Ptdddx01NTVs27aNmpqatD6nZVns27cvunnz5vZYLPbzUCj0P6WU6btHqigppoKZMmAJIUr8fv//8nq9d91///1F48aNS/tzFhYWsnDhQmbOnMnJkyfZtWsXdXWqD2UmUMEsMwghmDRpEgsWLGDkyJHs27ePPXv20N7entbnlVJy7Ngx++OPP26NRqMrDcP4ayllep9UUdJABTNlwBNCTPb7/f9UUFAw5/777y8cMWJE2p/T4/Ewb948rr/+eqLRKHv37uXAgQOqYW0/UsGsf3m93p7fiUAgwO7duzl8+HCf3Po/e/YsH330UXMwGPw0EAj8CyllfdqfVFHSRAUzZdAQQsz3+/2/GjVqVNm9996bX1BQ0CfPW1ZWxrx585g4cSLHjh1j7969ab9do3yTCmb9o6ysjDlz5jBp0iSOHTvG7t27OXfuXJ88d0NDAx999FFLa2vrvkAg8MdSSvUDoAx4Kpgpg46maXd7vd6fT548ufjOO+/M9fv9ffK8Xq+X6667jnnz5iGlZO/evezfvz+tLQCUC1Qw6zv5+fnMmTOH2bNnEwqF2L9/P/v37++zHTRaW1tZt25dW11d3ZlAIPATKeWePnliRekDKpgpg5IQQnM4HE+63e7/ft111+XfdNNNfp/P12fPP2bMGObNm8eUKVM4e/YsBw8e5MSJE2ltCzDUqWCWXi6XixkzZnDddddRVFTEwYMH2bdvH42NjX1WQ3t7O5s3b+44ceLE+WAw+Ce2bX/SZ0+uKH1EBTNlUBNCOJ1O5x+4XK6/njJlSu6tt96am5ub22fP73K5mDZtGrNmzaKkpIRjx45x8OBBzp49m9ZWAUORCmapp2kaZWVlzJo1iylTpnDmzBn279/PyZMnsW27z+poampiw4YNbdXV1U3hcPjfWZb1rpSy7wpQlD6kgpkyJAghdF3XH/N4PH87duzYottvvz1/2LBhfVqD3+9n5syZzJo1i+zsbA4dOsTBgwf7bD7OYKeCWWoIISgrK2PGjBlMnTqVlpYWDh8+zMGDB9O2VdLl1NXVsX79+pampqbqQCDwl8AG1YtMGexUMFOGFBF3l8/n+5/FxcWj77zzzsJRo0b1eR2FhYXMmjWLWbNmAXDs2DGOHj1KbW1tn9cyWKhg1ntCCMaNG9cTxtra2jhy5AhHjhzp831jpZScOXOGTz/9tLmjo+NIIBD4V1LK3X1ahKL0IxXMlCFLCLHQ7/f/fW5u7tS77rqraNy4caS7Ue2ljBgxgmnTpjF16lSysrI4fvw4x44d48yZM316u2igU8EsOQ6Hg3HjxjFlyhSmTp1KR0cHR44c4fDhw30exiC+wfjx48ftDRs2tIZCoe2BQODfqO2TlKFIBTNlyBNCzPD7/X+XlZV145133lkwefJkTdO0fqmloKCAqVOnMnXqVIqKijh58iTHjh3j9OnTRKPRfqlpoFDB7Mqys7OZNGkSkydPZty4cZw7d46TJ09y5MiRtDeAvRzLsjhw4ID52WeftcdisTWBQOCvpZRV/VKMomQAFcwUJUEIMc7v9/9nh8OxdNGiRTlz5sxxud3ufqvH7/czdepUpkyZQmlpKQ0NDZw+fZpTp06peWmXoILZpZWUlDB58mQmT55Mbm4up06d4uTJk5w6darP2ltcimEY7NmzJ7hnzx7DsqzXgsHg30opz/dbQYqSIVQwU5SvEUIUeDyeP9F1/adTpkzxLV68OLeoqKhfa3I4HIwdO5aJEycyYcIEvF4vp0+f7vkwDKNf68sEKpjFFRQUUFZWRllZGePGjcMwDE6ePMmJEyeoqanp99XA9fX1bNmypbWqqqozFov9r2g0+pKUUv0AK0qCCmaKchlCCF3TtGVer/dv8vLyxtxyyy2FkyZNEv11m/NiOTk5PSGtrKyMjo4OqqqqqKqqorq6ekgGtaEazLKzs3uCWFlZGUIIzpw50/PR2dnZ3yVimiZHjhyxtmzZ0hoKhY50dXX9R2CzWmGpKN+kgpmiXAUhxDS/3/9XQoil8+bN882fP9+bnZ3d32UB8RV1o0aNYsyYMYwdO5bS0lIMw6C6uronrPXHZO6+NlSCWVFREaNHj6a0tJQxY8bg9Xo5c+YMZ8+epbKyktbW1v4usUdrays7d+7sOnz4cNC27TeCweDfSymr+7suRclkKpgpShKEED6n0/mM2+3+l8XFxQU33XRTUVlZGZkwitZNCEFxcTFjx47tCWuWZVFdXU19fT319fWcO3eOWCzW36Wm1GAMZm63m1GjRlFaWsqoUaMYPXo04XCYmpoa6urqqK6upqGhob/L/ArLsjh+/Li9devW1o6OjtpgMPjfbNteJaVUq1cU5SqoYKYovSSEmOf3+/+Nruu3z5492ztnzhxfYWFhf5d1SQUFBZSWllJSUkJJSQnFxcW0t7f3hLT6+noaGhoG9JZRAz2YeTweRowYwYgRIxg+fDglJSXk5eVx7tw5amtrqampoba2NiNvU0spaWxsZO/evZ1HjhwJSylXGYbx91LKk/1dm6IMNCqYKco1EkL4dV1/1Ov1/rnH4xl7/fXX586aNcvVl3tzJkvTNIqLi3uC2siRIxk2bBitra00NTXR1NTE+fPnaWpqorW1td8njF+NgRLMhBDk5eX1BLDuMObxeGhsbKShoaHno7GxMaN72bW3t7N///7Qvn37AqZpHg4EAv9HSrlWShnp79oUZaBSwUxRUkgIMdztdj/ndDp/nJ+fn3fDDTcUTpkyRXO5XP1d2hXpus6wYcMoLi5m2LBhPZ/7/X5aW1t7glpzczNtbW20tbURiWTO62+mBbOsrCyKioooLCz8ykd+fj6GYXwlhDU2NtLW1tbfJV+VUCjE4cOHzd27d7cFg8FzoVDoH03TfENKOfgnMipKH1DBTFHSRAgx2ev1/lgI8VRpaaln/vz5hWVlZei63t+lJcXpdFJUVNQT2AoKCsjPzyc/Px/btntCWnt7e8/nHR0dBAKBPg1ufR3MvF4vubm55ObmkpOT0/PfvLw8CgsLEULQ3NxMS0vLVz5aW1sH3Py+WCzGyZMn5e7du1uampq6TNN8KRwOvySlVHuIKUqKqWCmKGkm4vs8LfT7/X8K3D1lyhT33Llzc0tKSjJq0UBveL1e8vLyeoJa90dOTg5+vx9N0+jq6iIQCHzlo6urC8MwiEQihMPhno9r2d3gWoOZrut4vd5v/fD5fD0BLBqN0tHRQWdnJ52dnXR0dPR8tLS09PmG36lmWRZVVVXs3bu39ezZsxEp5buGYfxSSnmwv2tTlMFMBTNF6UNCCCdwT3Z29s+klAvGjRvnmDVrVkFZWRkD4XZnspxOJ9nZ2fj9fvx+/1c+93q9eDwePB4Pbrcbj8eD0+n8SliLRqNYloVpml/5b/eHaZrYto2maRQUFNDe3o4QoudD07Se/zqdTlwuFy6Xq+fzi/8rhCAYDH7rh2EYPUFsoI16XY1wOMypU6fkgQMHWurq6kxN0zZ3dXX9AvhcSpm5k90UZRBRwUxR+okQQgdu9Pl8zwghlhUUFHiuu+66/MmTJzsypUdaXxNCfCWsud1udF1H13UcDsclP9c0DSklRUVFnD9/Hikltm0jpez5sG2baDRKLBb7yn8v/tyyrP7+9vtFW1sbx44dix44cKCjq6ur07Kst0Oh0Epgn2oAqyh9TwUzRckQQohJbrd7ucvl+q7b7R4+Y8YM37Rp07zDhw8nfjdU+TaZNvk/U9m2TV1dHUeOHOk6duxY2DTNqnA4/EosFlul5owpSv9TwUxRMpAQokDTtPt9Pt8PgFkTJ050TJs2LX/MmDF4PJ7+Li8jqWB2eYZhcPbsWY4cOdJaVVVlCiF2BgKBl6SUn0gpA/1dn6IoF6hgpigZTgjhApb4/f7HgLvcbnfehAkTXBMnTsxVQe0CFcwuMAyDqqoqTp482XbmzBnTsqwmy7I+CgaDq4AdUsqhed9WUQYAFcwUZYARQuQAN/n9/oeIB7VcFdSGdjC7OIidPXvWNE2zybKsNcFg8ANgp5Qy1N81KopydVQwU5QB7mtB7U632503ceJE18SJE3NHjx5NVlZWf5fYJ4ZSMAsEAtTU1PSMiF0UxD4kPiKmgpiiDFAqmCnKIJMIajf7/f6HhBC3CCGKioqKGDt2bPbo0aOzRo4cidfr7e8yU26wBrOurq7u/TKNs2fPGokdAs5ZlrUhMSK2XQUxRRk8VDBTlEFOCKEBk4Dr/X7/HUKIG4UQwwoLCxk3blz2qFGjskpKSgZ8WBsMwayzs5Nz585RU1NjVFdXG21tbRKot217q2EYm4AvgCrVxkJRBi8VzBRlCEqEtQlcCGuLgOLCwkJGjx7tLS4u9nfv7ThQ5qwNpGAWDAa7t2iSjY2NXbW1teH29nYphKizLGuLYRibiYewahXCFGVoUcFMURSgJ6yNB2a43e5Zbrf7einlFCDf4/FoxcXF2ogRI/xFRUWewsJCCgoKcDgc/Vz1BZkWzKLRKK2trbS0tHD+/PlgY2Oj0dTURCQSMYUQzUKIo6FQaE8sFjsCHABqVQhTFEUFM0VRrigxb20SMNnr9c51OBxzbNueqGmaLzs7m8LCQj0vL8+Tm5vrzc7OFt3bL/l8vj7bD7Qvg5llWV/Z97Orq8vu6OgItre3h5ubm+1gMCht2+7Udf1kNBrdGwqF9gMngFNSyoG9iaaiKGmlgpmiKL2W2KC9GBgHjNR1fXRWVtZETdPGAaNt2y4SQrgdDofu9/tlTk6OlpeX58nLy/P6/X7N7Xb37F/pcrno/rOu60nX0ttgZppmz/ZMkUik5/NwOEwgELDb29uNjo6OSEdHh20YhmZZVkxKGdE0rRGosyzrTCgUOmnbdj1wDjgjpWxJuhBFURRUMFMUpQ8IIRzAcGAkUKJpWonH4ynTdb1A07Q8IEdKmSOl9Nu27RNCOIUQmhBCF0LoTqdTulwu2b13ZmKTctG9SbkQQhQUFGS3trZ2ybju/TKllBLTNLtDlzBNEymllfg7C4gKIQwhRJcQoktK2SGlbDNNszUUClUC3YHrHNCkmrMqipJOKpgpipLREqNyHsAPZAMOQPvah574rwXYl/iIAV2Jj6iay6UoSqZSwUxRFEVRFCVD9M2sXEVRFEVRFOWKVDBTFEVRFEXJECqYKYqiKIqiZAgVzBRFURRFUTKECmaKoiiKoigZQgUzRVEURVGUDKGCmaIoiqIoSoZQwUxRFEVRFCVDOPq7AEVRlEupEBVLgf9LvKv/C+Wy/O8SX/8z4E8BE/iwXJb/64uO+QJYBKwmvv2TA/gc+JNyWW5ViAo38FvgeqAFeLJclp/ts29KURTlCtSImaIoGadCVOjAz4H7gOnA0xWiYnqFqLgdeBiYXS7LZwB/f9Ex44C6clkeBZ4ol+XXATOBYcB3Eg/7Q6CtXJZPBP4B+O999C0piqJcFRXMFEXJRDcAp8pleWUiaK0kHsh+BvxduSyPAJTL8vMXHXMfsDbx9c7E1xyAC+jee+5h4JXE528Bd1aICpHOb0RRFCUZKpgpipKJRgE1F/25NvG1ycAtFaJiZ4Wo2FwhKhZc9JilJIIZQIWoWAecJ75x+VtfP2+5LDeBDqAwXd+EoihKslQwUxQlE11qFEsSHwHLB24E/hJ4o0JUiApR4QJGl8vyyu4Hl8vye4nPM3MDd1zhvIqiKBlBBTNFUTJRLVB60Z9HA/WJr79TLstluSzfBdhAEXALsOXrJymX5WHiCwEe/vp5K0SFA8gFWtP0PSiKoiRNrcpUFCUT7QYmVYiKMqAOeAr4LtBGfPRrU4WomEx8/lgz8duYawAqRIUfyC6X5ecS4et+4iszIR7S/gDYDiwHNpTLcjVipihKxlAjZoqiZJzE/K8/BdYBR4E3ymX5YeA3wPgKUXGI+IKAP0gEq9uAzYnDfcDqClFxANhPfJ7ZLxN/9yJQWCEqTgH/Avi3ffMdKYqiXB0hpXqzqCjKwFUhKkYDz5fL8vv6uxZFUZRrpYKZoiiKoihKhlC3MhVFURRFUTKECmaKoiiKoigZQgUzRVEURVGUDKGCmaIoiqIoSoZQwUxRFEVRFCVDqGCmKIqiKIqSIf5/GBUJqwHa4pYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot w/ normalized date\n", - "import matplotlib.colors\n", - "import matplotlib.cm\n", - "\n", - "fig = plt.figure(figsize = (10, 10))\n", - "ax1 = fig.add_subplot(111, projection='polar')\n", - "ax1.set_theta_direction(-1)\n", - "ax1.set_theta_zero_location(\"N\")\n", - "# colors= matplotlib.cm.viridis\n", - "# cmap= matplotlib.colors.ListedColormap(colors)\n", - "# norm = matplotlib.colors.Normalize(vmin=37, vmax=100) \n", - "# cmap=cmap=plt.cm.get_cmap('cmo.haline')\n", - "for year in years_list:\n", - " ax1.plot(df_plot.loc[df_plot['year']==year].tnorm,df_plot.loc[df_plot['year']==year].dist_mile, linewidth=2)\n", - " ax1.scatter(df_plot.loc[df_plot['year']==year].doy_norm, df_plot.loc[df_plot['year']==year].dist_mile, c=df_plot.loc[df_plot['year']==year].dist_mile, vmin=37, vmax=100)\n", - "# ax1.plot(df_plot['tnorm'],df_plot['dist_mile'], linewidth=2)\n", - "ax1.plot(rm_Wilmington['tnorm'],rm_Wilmington['dist_mile'] , linewidth=1, color='white')\n", - "ax1.plot(rm_Philadelphia['tnorm'],rm_Philadelphia['dist_mile'] , linewidth=1, color='white')\n", - "# ax1.plot(rm_Philadelphia_max['tnorm'],rm_Philadelphia_max['dist_mile'] , linewidth=1, color='white')\n", - "# ax1.fill_between(rm_Philadelphia_max['tnorm'],rm_Philadelphia['dist_mile'],rm_Philadelphia_max['dist_mile'], color='white', alpha=0.4)\n", - "# ax1.set_rorigin(-5)\n", - "ax1.tick_params(axis='y', colors='limegreen')\n", - "ax1.tick_params(axis='x', colors='purple')\n", - "ax1.set_ylim(y_bounds)\n", - "ax1.set_xticklabels(date_labels)\n", - "# ax1.set_yticklabels([])\n", - "ax1.set_facecolor('grey')\n", - "ax1.grid(False, axis='y')\n", - "# ax2 = fig.add_subplot(232, sharey=ax1)\n", - "# ax2.set_ylim(y_bounds)\n", - "# ax2.plot([0,0],y_bounds)\n", - "# ax2.set_yticks(y_bounds)\n", - "# ax2.set_yticklabels([round(y_bounds[0],0), round(y_bounds[1],0)])\n", - "# ax2.axis('off')\n", - "plt.show()\n", - "# ax.plot(df.index, df.salt) #doesn't seem to a registering the dates\n", - "# ax.set_rmin(0.5)\n", - "# ax.set_rmax(0.52)\n", - "plt.savefig('polar_plot_saltfront.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "id": "5a3bc3a1-24a9-458f-a8ed-a59977508c66", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "45.37331847094633" - ] - }, - "execution_count": 283, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_plot.dist_mile.min()" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "a998eeeb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Plot w/ normalized doy\n", - "\n", - "fig = plt.figure(figsize = (10, 10))\n", - "ax1 = fig.add_subplot(111, projection='polar')\n", - "ax1.set_theta_direction(-1)\n", - "ax1.set_theta_zero_location(\"N\")\n", - "for year in years_list:\n", - " ax1.plot(df_plot.loc[df_plot['year']==year].doy_norm,df_plot.loc[df_plot['year']==year].dist_mile, linewidth=2)\n", - "# ax1.plot(df_plot['doy_norm'],df_plot['dist_mile'], linewidth=2)\n", - "ax1.plot(rm_Wilmington['tnorm'],rm_Wilmington['dist_mile'] , linewidth=1, color='white')\n", - "ax1.plot(rm_Philadelphia['tnorm'],rm_Philadelphia['dist_mile'] , linewidth=1, color='white')\n", - "ax1.set_ylim(y_bounds)\n", - "ax1.set_xticklabels(date_labels)\n", - "# ax1.set_yticklabels([])\n", - "ax1.set_facecolor('grey')\n", - "ax1.grid(False)\n", - "# ax2 = fig.add_subplot(232, sharey=ax1)\n", - "# ax2.set_ylim(y_bounds)\n", - "# ax2.plot([0,0],y_bounds)\n", - "# ax2.set_yticks(y_bounds)\n", - "# ax2.set_yticklabels([round(y_bounds[0],0), round(y_bounds[1],0)])\n", - "# ax2.axis('off')\n", - "plt.show()\n", - "# ax.plot(df.index, df.salt) #doesn't seem to a registering the dates\n", - "# ax.set_rmin(0.5)\n", - "# ax.set_rmax(0.52)" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "id": "3559f993-5169-41be-bcd5-07566778932b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 184, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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datedist_mileyearmdatetnormdoydoy_norm
ocean_time
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........................
2020-03-272020-03-2750.921016202018348.06.283185876.283185
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dist_milemodel_latmodel_longeometry
0-6.86446838.709599-74.898547POINT (-74.89855 38.70960)
1-6.73695138.711905-74.901000POINT (-74.90100 38.71191)
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4-6.08514938.720744-74.905398POINT (-74.90540 38.72074)
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"execution_count": 143, - "id": "2bed1007", - "metadata": {}, - "outputs": [], - "source": [ - "rm_coords_gdf.to_file('river_miles.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 343, - "id": "bd742bd9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 343, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "rm_coords_gdf.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 344, - "id": "0edd64a8", - "metadata": {}, - "outputs": [], - "source": [ - "coast = gpd.read_file('../../../../../ArcGIS/Projects/DRB/Data/Simplified_coastline_and_buffers/Coastline_DRB.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 366, - "id": "6a8a855a", - "metadata": {}, - "outputs": [], - "source": [ - "drb_estuary = gpd.read_file('../../../../../ArcGIS/Projects/DRB/Data/Simplified_coastline_and_buffers/DRB_ocean_estuary.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "id": "922959f9", - "metadata": {}, - "outputs": [], - "source": [ - "drb_bounds = gpd.read_file('../../../../../ArcGIS/Projects/DRB/Data/Simplified_DRB_boundaries/DRB_bound.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 389, - "id": "5c333d45", - "metadata": {}, - "outputs": [], - "source": [ - "segments = gpd.read_file('../../../../../ArcGIS/Projects/DRB/Data/Simplified_Network_Shapefiles/seg_subset_simp_smo.shp')\n", - "segments = segments.to_crs(coast.crs)" - ] - }, - { - "cell_type": "code", - "execution_count": 385, - "id": "c0b282be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Name: USA_Contiguous_Albers_Equal_Area_Conic\n", - "Axis Info [cartesian]:\n", - "- [east]: Easting (metre)\n", - "- [north]: Northing (metre)\n", - "Area of Use:\n", - "- undefined\n", - "Coordinate Operation:\n", - "- name: unnamed\n", - "- method: Albers Equal Area\n", - "Datum: North American Datum 1983\n", - "- Ellipsoid: GRS 1980\n", - "- Prime Meridian: Greenwich" - ] - }, - "execution_count": 385, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coast.crs" - ] - }, - { - "cell_type": "code", - "execution_count": 360, - "id": "5828cd46", - "metadata": {}, - "outputs": [], - "source": [ - "rm_coords_gdf =rm_coords_gdf.to_crs(coast.crs)" - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "id": "8b436dbf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-180000.0, 50000.0)" - ] - }, - "execution_count": 395, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize = (5, 5))\n", - "ax = fig.add_subplot()\n", - "coast.plot(ax=ax, zorder=0)\n", - "segments.plot(ax=ax, linewidth=0.5, zorder=0)\n", - "drb_estuary.plot(ax=ax, zorder=0)\n", - "drb_bounds.plot(ax=ax, color='black', linewidth=0.5, zorder=0)\n", - "rm_coords_gdf.plot(ax=ax, color='red', markersize=0.1, zorder=1)\n", - "ax.set_ylim(-250000,210000)\n", - "ax.set_xlim(-180000,50000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15b51e35", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/methods_exploration/COAWST_Model_Output/river_mile_coords.csv b/methods_exploration/COAWST_Model_Output/river_mile_coords.csv deleted file mode 100644 index 56672ef..0000000 --- a/methods_exploration/COAWST_Model_Output/river_mile_coords.csv +++ /dev/null @@ -1,1371 +0,0 @@ -,dist_mile,model_i,model_j,model_lat,model_lon -0,-6.864468077786821,69,1,38.709598541117266,-74.89854692992532 -1,-6.73695053186866,69,2,38.71190500922644,-74.90100044817035 -2,-6.609442517401746,69,3,38.7142114242525,-74.9034541123693 -3,-6.212643840710634,70,4,38.7184376960278,-74.90294376870048 -4,-6.085149298388187,70,5,38.72074405712185,-74.9053976974323 -5,-5.957664290884426,70,6,38.723050365108286,-74.90785177215378 -6,-5.830188819218872,70,7,38.7253566199794,-74.9103059928749 -7,-5.433402100328129,71,8,38.729582867561504,-74.90979594935746 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-2019-08-25,-0.96875,-75.47152266968226,39.765298463857604,74.4674533440309,0.5151742 -2019-08-26,-0.96875,-75.45243094807437,39.779653680133066,75.94631509366347,0.51671827 -2019-08-27,-0.96875,-75.44680990444299,39.784669553442065,76.41351233700468,0.51513547 -2019-08-28,-0.96875,-75.44575465789599,39.784608583701385,76.467371668288,0.51405376 diff --git a/methods_exploration/COAWST_Model_Output/salt_dec.csv b/methods_exploration/COAWST_Model_Output/salt_dec.csv deleted file mode 100644 index e2b9b56..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_dec.csv +++ /dev/null @@ -1,31 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-11-27,-0.96875,-75.50401129544258,39.72131864516537,70.99291950601257,0.5158909 -2019-11-28,-0.96875,-75.51358326082212,39.7021164299162,69.68491762292201,0.5152474 -2019-11-29,-0.96875,-75.52025571231648,39.69696738733451,69.16184318810346,0.51348174 -2019-11-30,-0.96875,-75.50101097909148,39.72191809097672,71.15211624053558,0.51435 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-2020-02-20,-0.96875,-75.55670449282687,39.58148734184725,59.7929897575911,0.51784647 -2020-02-21,-0.96875,-75.56344414709491,39.47121322985658,52.108282295012685,0.51930964 -2020-02-21,-0.96875,-75.56377739385314,39.472626577356486,52.205532730768965,0.5109331 -2020-02-21,-0.96875,-75.5641089667183,39.47406234915377,52.30346751928067,0.51942426 -2020-02-21,-0.96875,-75.55833606397199,39.498754373308785,54.02989215368692,0.5127824 -2020-02-21,-0.96875,-75.54758671306891,39.528056396460165,56.017743651463405,0.51050764 -2020-02-21,-0.96875,-75.5468902376685,39.53285062675551,56.34976537100757,0.51764935 -2020-02-21,-0.96875,-75.54518457332304,39.54002065540174,56.83996200470469,0.51259977 -2020-02-21,-0.96875,-75.5447937848836,39.562840340150665,58.44005593469792,0.5171146 -2020-02-21,-0.96875,-75.55280828835795,39.57661585798313,59.45087585903198,0.51252943 -2020-02-22,-0.96875,-75.56242807884175,39.467062522238116,51.81735734392735,0.51387346 -2020-02-22,-0.96875,-75.56347542669609,39.47731404584592,52.52794983111889,0.5105499 -2020-02-22,-0.96875,-75.56014818259959,39.494219056982175,53.722923813142586,0.5147275 -2020-02-22,-0.96875,-75.54823375606975,39.52325073310537,55.67508981524701,0.514478 -2020-02-22,-0.96875,-75.54152724400281,39.55616545438693,57.95805533630733,0.517248 -2020-02-22,-0.96875,-75.54988125650517,39.571937139302605,59.11038129813908,0.51118326 -2020-02-22,-0.96875,-75.55280828835795,39.57661585798313,59.45087585903198,0.51702106 -2020-02-23,-0.96875,-75.55991213784704,39.45755725365286,51.12194673681742,0.51136434 -2020-02-23,-0.96875,-75.56065451170278,39.460266699046144,51.32240201452099,0.5196669 -2020-02-23,-0.96875,-75.56071931627203,39.49012017521256,53.43769264150382,0.5134768 -2020-02-23,-0.96875,-75.540310167641,39.551726849174806,57.64581073313797,0.51213086 -2020-02-24,-0.96875,-75.55953365887385,39.456202447538104,51.02154541656383,0.5144555 -2020-02-24,-0.96875,-75.56065451170278,39.460266699046144,51.32240201452099,0.5116439 -2020-02-24,-0.96875,-75.56377739385314,39.472626577356486,52.205532730768965,0.5188222 -2020-02-24,-0.96875,-75.5641089667183,39.47406234915377,52.30346751928067,0.5121009 -2020-02-24,-0.96875,-75.56164478292999,39.488028196056334,53.2890283263766,0.51291126 -2020-02-24,-0.96875,-75.55266705990525,39.510904329459024,54.838169876355856,0.5157881 -2020-02-24,-0.96875,-75.54501227629483,39.53770369233143,56.67875609538825,0.51365066 -2020-02-24,-0.96875,-75.54518457332304,39.54002065540174,56.83996200470469,0.518509 -2020-02-24,-0.96875,-75.54372872550329,39.56060468734208,58.27272330966954,0.5175626 diff --git a/methods_exploration/COAWST_Model_Output/salt_jan.csv b/methods_exploration/COAWST_Model_Output/salt_jan.csv deleted file mode 100644 index ef7ab87..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_jan.csv +++ /dev/null @@ -1,31 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-01-01,-0.96875,-75.54399878947552,39.547222704770654,57.345343287703756,0.5137494 -2019-01-02,-0.96875,-75.54999284992157,39.569699662156744,58.92593188694961,0.51486117 -2019-01-03,-0.96875,-75.56161082183591,39.47596290283312,52.43781184841355,0.5141355 -2019-01-04,-0.96875,-75.5615148990824,39.46517145288857,51.66301558507331,0.5154065 -2019-01-05,-0.96875,-75.5490809661033,39.553289502335346,57.77905939946185,0.5131895 -2019-01-06,-0.96875,-75.55112583642088,39.49626881266907,53.78051417349636,0.5149688 -2019-01-07,-0.96875,-75.55558851612241,39.47718338987189,52.4354288067599,0.5123873 -2019-01-08,-0.96875,-75.5610184469298,39.461621877478535,51.422493392436365,0.51139367 -2019-01-09,-0.96875,-75.5528747000756,39.5118491684065,54.90761434570327,0.516751 -2019-01-10,-0.96875,-75.52932704369148,39.42942059099923,48.51441329507831,0.5141857 -2019-01-11,-0.96875,-75.54208351301196,39.55727251850135,58.03232412737616,0.5185684 -2019-01-12,-0.96875,-75.55292217444442,39.507063809171385,54.56999151426305,0.51687926 -2019-01-13,-0.96875,-75.56020236518079,39.47833022033864,52.59199753059094,0.5154023 -2019-01-14,-0.96875,-75.55265201154363,39.51896085121689,55.396628260945675,0.51551986 -2019-01-15,-0.96875,-75.54510165790647,39.55959148209514,58.201258991300406,0.5156374 -2019-01-16,-0.96875,-75.5592404622734,39.57720605675291,59.53934304421048,0.5150243 -2019-01-17,-0.96875,-75.55275655057781,39.52648025768161,55.93887428810087,0.5119908 -2019-01-18,-0.96875,-75.54975949176568,39.53201753368606,56.30536826059116,0.51371646 -2019-01-19,-0.96875,-75.55571051571941,39.591628120317104,60.736410380537116,0.51522136 -2019-01-20,-0.96875,-75.55468535924,39.62674240804706,63.531318295574,0.5135441 -2019-01-21,-0.96875,-75.5618034671646,39.584860102428024,60.15528375240046,0.51571095 -2019-01-22,-0.96875,-75.56379552916282,39.47688558248769,52.503799641279805,0.5162893 -2019-01-23,-0.96875,-75.56019496008486,39.54378452616786,57.243760862495186,0.5152633 -2019-01-24,-0.96875,-75.55763138363547,39.57080247810261,59.106280938554946,0.5145189 -2019-01-25,-0.96875,-75.5538964274182,39.535057784845854,56.54857812110812,0.5134598 -2019-01-26,-0.96875,-75.55564496549809,39.47248042088519,52.12067981687585,0.513095 -2019-01-27,-0.96875,-75.54436937789434,39.45488515147303,50.62519971901484,0.51512027 -2019-01-28,-0.96875,-75.55107069706033,39.46014597667349,51.12463395184967,0.5141538 -2019-01-29,-0.96875,-75.54929546175657,39.45860736617466,50.95936314633859,0.51484704 -2019-01-30,-0.96875,-75.55167334920472,39.45420491454041,50.71438820856397,0.51553977 diff --git a/methods_exploration/COAWST_Model_Output/salt_jan_2020.csv b/methods_exploration/COAWST_Model_Output/salt_jan_2020.csv deleted file mode 100644 index 9194987..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_jan_2020.csv +++ /dev/null @@ -1,32 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-12-27,-0.96875,-75.54856821371261,39.64900832425515,65.41233834466162,0.51551056 -2019-12-28,-0.96875,-75.54003528878772,39.646917079089526,65.36438796966584,0.5140308 -2019-12-29,-0.96875,-75.55191967465748,39.62476944115961,63.42213058345114,0.5161093 -2019-12-30,-0.96875,-75.54367681059063,39.635738447829105,64.34513488771422,0.51506084 -2019-12-31,-0.96875,-75.53175934275664,39.673202273751514,67.40142687842823,0.51464295 -2020-01-01,-0.96875,-75.55241577164651,39.636664688757925,64.38903740957798,0.51499414 -2020-01-02,-0.96875,-75.55760217373195,39.62522305121189,63.400838679984886,0.5146993 -2020-01-03,-0.96875,-75.55740085887963,39.625048626283764,63.36968933227119,0.5157842 -2020-01-04,-0.96875,-75.5447937848836,39.562840340150665,58.44005593469792,0.5142423 -2020-01-05,-0.96875,-75.57899368340007,39.62365384361046,63.027274670158505,0.5179103 -2020-01-06,-0.96875,-75.5786213027969,39.6247401496518,63.11319317426184,0.5114349 -2020-01-07,-0.96875,-75.55112104653993,39.64999285631515,65.49961261514517,0.51448876 -2020-01-08,-0.96875,-75.57194950841523,39.609772979933176,61.95682029836934,0.51249886 -2020-01-09,-0.96875,-75.55073343766907,39.613153091297626,62.46529660278122,0.51445025 -2020-01-10,-0.96875,-75.55213602163512,39.634144818181476,64.16706214871296,0.51331246 -2020-01-11,-0.96875,-75.54649056370556,39.61357593217077,62.56975242704228,0.5142636 -2020-01-12,-0.96875,-75.54569581906732,39.64218043942294,64.91300952147972,0.5167458 -2020-01-13,-0.96875,-75.5511634523704,39.6283046738031,63.73487155676807,0.5142766 -2020-01-14,-0.96875,-75.554611518471,39.6217388075141,63.161035636684026,0.5155833 -2020-01-15,-0.96875,-75.5524567832991,39.6004368732493,61.47721967292472,0.51526713 -2020-01-16,-0.96875,-75.56038763258644,39.59392109491938,60.95990275155612,0.51363224 -2020-01-17,-0.96875,-75.55804485670548,39.58071556981988,59.885735928702786,0.51539683 -2020-01-18,-0.96875,-75.55679413771709,39.56621034729146,58.782066011220266,0.5132302 -2020-01-19,-0.96875,-75.55212310289203,39.53609212467229,56.59938118684902,0.5135415 -2020-01-20,-0.96875,-75.55082137484473,39.574231101847616,59.28463693121821,0.51356226 -2020-01-21,-0.96875,-75.56041001470824,39.53483760617484,56.55775764079276,0.5134019 -2020-01-22,-0.96875,-75.553458046178,39.554545905105826,57.91240756681706,0.51773846 -2020-01-23,-0.96875,-75.55758965864659,39.51512624620625,55.15858267040441,0.5158585 -2020-01-24,-0.96875,-75.55169229545847,39.5243225669794,55.78056185280595,0.5129776 -2020-01-25,-0.96875,-75.55617888922298,39.517740478441944,55.3315770607568,0.5173017 -2020-01-26,-0.96875,-75.56277004863482,39.46843508885267,51.91448438901112,0.5199588 diff --git a/methods_exploration/COAWST_Model_Output/salt_jul.csv b/methods_exploration/COAWST_Model_Output/salt_jul.csv deleted file mode 100644 index 9ca450f..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_jul.csv +++ /dev/null @@ -1,31 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-06-30,-0.96875,-75.53994928974666,39.64137077018539,64.92864118449579,0.5157119 -2019-07-01,-0.96875,-75.55548025113451,39.618058282723915,62.804139364896564,0.51528406 -2019-07-02,-0.96875,-75.54478520847313,39.64836187696484,65.39040034502814,0.51484895 -2019-07-03,-0.96875,-75.54487702518571,39.65577549836269,65.91268787278585,0.513867 -2019-07-04,-0.96875,-75.53924696969777,39.66246500907325,66.53776125973646,0.51606816 -2019-07-05,-0.96875,-75.53811321134444,39.66487699523749,66.65125860343124,0.5148056 -2019-07-06,-0.96875,-75.5464810406376,39.659930148118754,66.20501863102508,0.51553404 -2019-07-07,-0.96875,-75.54741992515055,39.65621728612749,65.92597547539212,0.51464593 -2019-07-08,-0.96875,-75.5308437914434,39.681803711333835,67.96744118825164,0.5133144 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mode 100644 index f531030..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_jun.csv +++ /dev/null @@ -1,31 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-05-31,-0.96875,-75.55640160666431,39.58388025735799,60.224002394665874,0.5158491 -2019-06-01,-0.96875,-75.55227625105852,39.571094355251674,59.18000570318471,0.51362574 -2019-06-02,-0.96875,-75.54815089545271,39.55830845314536,58.13600901170355,0.51140237 -2019-06-03,-0.96875,-75.5595563578696,39.5803422023457,59.79043148942939,0.5143838 -2019-06-04,-0.96875,-75.56314575515572,39.47621377669465,52.46002916286507,0.5131521 -2019-06-05,-0.96875,-75.56294874254216,39.513027504429225,55.06283631798657,0.51563275 -2019-06-06,-0.96875,-75.55280828835795,39.57661585798313,59.45087585903198,0.5199387 -2019-06-07,-0.96875,-75.56400031493504,39.536005461673255,56.659393550116675,0.5158438 -2019-06-08,-0.96875,-75.55043802841634,39.549849364551676,57.55787687361834,0.5161287 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file mode 100644 index a55019f..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_mar.csv +++ /dev/null @@ -1,31 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-03-02,-0.96875,-75.56083781959329,39.53974503094497,56.92484307230295,0.51304185 -2019-03-03,-0.96875,-75.55524089056213,39.53460168409258,56.51677090680933,0.5150597 -2019-03-04,-0.96875,-75.55337056189433,39.556255777489724,58.03640768676063,0.51316625 -2019-03-05,-0.96875,-75.55119041597862,39.566290279813735,58.72878920425877,0.5150117 -2019-03-06,-0.96875,-75.55344052484422,39.54548182658065,57.267872203475825,0.51304317 -2019-03-07,-0.96875,-75.55775845123043,39.55149210857394,57.723536321857594,0.5153038 -2019-03-08,-0.96875,-75.55469988539315,39.51917353993502,55.435390314402945,0.51663154 -2019-03-09,-0.96875,-75.56018511063111,39.55390526262275,57.89238169873868,0.5129109 -2019-03-10,-0.96875,-75.579343190944,39.62376344692387,63.034566439069714,0.5166329 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b/methods_exploration/COAWST_Model_Output/salt_mar_2020.csv deleted file mode 100644 index e506edf..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_mar_2020.csv +++ /dev/null @@ -1,174 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2020-02-25,-0.96875,-75.56065451170278,39.460266699046144,51.32240201452099,0.51432806 -2020-02-25,-0.96875,-75.5641089667183,39.47406234915377,52.30346751928067,0.5119182 -2020-02-25,-0.96875,-75.56413901113912,39.48041963683643,52.750014670128905,0.51022685 -2020-02-25,-0.96875,-75.55266705990525,39.510904329459024,54.838169876355856,0.5185866 -2020-02-25,-0.96875,-75.54118271286877,39.54717145539737,57.320520744560696,0.519445 -2020-02-25,-0.96875,-75.54039141371236,39.55396275711659,57.81925031963709,0.51123124 -2020-02-26,-0.96875,-75.56173216025294,39.4643359531311,51.621508789874966,0.5146284 -2020-02-26,-0.96875,-75.56380673991737,39.47884444252606,52.63735523545286,0.5113794 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a/methods_exploration/COAWST_Model_Output/salt_mar_end_2020.csv b/methods_exploration/COAWST_Model_Output/salt_mar_end_2020.csv deleted file mode 100644 index 40ee7a1..0000000 --- a/methods_exploration/COAWST_Model_Output/salt_mar_end_2020.csv +++ /dev/null @@ -1,15 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2020-03-26,-0.96875,-75.56173216025294,39.4643359531311,51.621508789874966,0.5185386 -2020-03-26,-0.96875,-75.56347542669609,39.47731404584592,52.52794983111889,0.5108462 -2020-03-26,-0.96875,-75.55833606397199,39.498754373308785,54.02989215368692,0.5125886 -2020-03-26,-0.96875,-75.55743803262969,39.50108502540483,54.187632164968974,0.5104096 -2020-03-26,-0.96875,-75.54912663399548,39.52077636363599,55.50726534974365,0.5183303 -2020-03-26,-0.96875,-75.54595432933559,39.535286030627844,56.515033912846405,0.51472175 -2020-03-26,-0.96875,-75.54021046589138,39.54948330203355,57.47658609565121,0.5127795 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-ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-09-28,-0.96875,-75.38352097110898,39.820588348090105,80.95299641581812,0.5145304 -2019-09-29,-0.96875,-75.37534406422719,39.824450951509576,81.51218710337427,0.5151844 -2019-09-30,-0.96875,-75.35966737259926,39.827044983866,82.41290315159186,0.5152131 -2019-10-01,-0.96875,-75.34577390907819,39.83159836308757,83.28308211417209,0.5149555 -2019-10-02,-0.96875,-75.35728334243423,39.82990222495285,82.62877427615457,0.514394 -2019-10-03,-0.96875,-75.35438806116326,39.83043425790809,82.80081834580642,0.51558495 -2019-10-04,-0.96875,-75.29937623532622,39.84450217842033,86.1002514474045,0.51496345 -2019-10-05,-0.96875,-75.31924939329768,39.84008258552354,84.93977081817236,0.51476634 -2019-10-06,-0.96875,-75.32302842323983,39.840143774670544,84.7514506992334,0.51547 -2019-10-07,-0.96875,-75.31588008226487,39.84035211795665,85.11804646782753,0.5155119 -2019-10-08,-0.96875,-75.33035285727715,39.83885722702464,84.35278451265324,0.5149524 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+++ /dev/null @@ -1,32 +0,0 @@ -ocean_time,s_rho,lon_rho,lat_rho,dist_mile,salt -2019-08-29,-0.96875,-75.44822601553813,39.78327182739353,76.3096998322176,0.5154508 -2019-08-30,-0.96875,-75.43992478078528,39.78595234477617,76.81510331536424,0.51529735 -2019-08-31,-0.96875,-75.45875665843887,39.775477807131885,75.49583958056445,0.5134751 -2019-09-01,-0.96875,-75.46459897219832,39.769692625189876,74.98334019091098,0.5149505 -2019-09-02,-0.96875,-75.46463895168571,39.76927135445522,74.98326348032653,0.5150995 -2019-09-03,-0.96875,-75.46549006862404,39.769800938033704,74.94532379078663,0.51573145 -2019-09-04,-0.96875,-75.44614274083058,39.78399387924052,76.43280932064471,0.5152496 -2019-09-05,-0.96875,-75.44336699352586,39.78745607102638,76.69173480343757,0.51550657 -2019-09-06,-0.96875,-75.4271038563419,39.796161049994986,77.82968780961046,0.5151512 -2019-09-07,-0.96875,-75.4268213327413,39.7976389547076,77.8970111042759,0.51505506 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1d1a374..0000000 --- a/methods_exploration/Example TE Workflow.ipynb +++ /dev/null @@ -1,909 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e4d034e7", - "metadata": {}, - "source": [ - "## Example workflow for using information theory to examine model outputs" - ] - }, - { - "cell_type": "markdown", - "id": "e68ad833", - "metadata": {}, - "source": [ - "#### Galen Gorski\n", - "##### 11/5/2021" - ] - }, - { - "cell_type": "markdown", - "id": "2130be6b", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "cef76ef3", - "metadata": {}, - "source": [ - "This notebook is an example to demonstrate a potential workflow for applying information theory to investigate model outputs. This notebook reads in observed and modeled in-stream nitrate data and prepares it for analysis using information theory codes. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3ff00211", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['C:\\\\Users\\\\ggorski\\\\OneDrive - DOI\\\\USGS_ML\\\\Estuary Salinity\\\\github\\\\drb-estuary-salinity-ml\\\\methods_exploration', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\python37.zip', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\DLLs', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity', '', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib\\\\site-packages', 'C:\\\\Users\\\\ggorski\\\\OneDrive - DOI\\\\USGS_ML\\\\Estuary Salinity\\\\github\\\\drb-estuary-salinity-ml', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib\\\\site-packages\\\\win32', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib\\\\site-packages\\\\Pythonwin', 'C:\\\\Users\\\\ggorski\\\\Miniconda3\\\\envs\\\\drb_estuary_salinity\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\ggorski\\\\.ipython']\n" - ] - } - ], - "source": [ - "import sys\n", - "print(sys.path)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c820d10d", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import pandas as pd\n", - "import numpy as np\n", - "import datetime as dt\n", - "import matplotlib.pyplot as plt\n", - "import copy\n", - "\n", - "\n", - "# Local modules\n", - "import TEpython_ParallelNAN2" - ] - }, - { - "cell_type": "markdown", - "id": "d8139de4", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "0a82c07e", - "metadata": {}, - "source": [ - "### Read in the modeled data" - ] - }, - { - "cell_type": "markdown", - "id": "b8173a1c", - "metadata": {}, - "source": [ - ">This example usese two model: both models use discharge, precipitation, and temperature \n", - "to predict in-stream nitrate. Model A does not include watershed attributes (eg landuse,\n", - "soils, climate) while Model B does include watershed attributes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "20f117b3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " datetime julianday nitrateobs nitratemdlapred nitratemdlbpred \\\n", - "0 20141003 276 2.76 2.797727 2.743747 \n", - "1 20141004 277 2.79 2.924915 2.671877 \n", - "2 20141005 278 2.90 2.844627 2.764798 \n", - "3 20141006 279 2.93 2.939729 2.863276 \n", - "4 20141007 280 NaN NaN NaN \n", - "... ... ... ... ... ... \n", - "1091 20170928 271 2.69 2.834144 2.563795 \n", - "1092 20170929 272 2.79 2.828825 2.683540 \n", - "1093 20170930 273 2.90 2.722842 2.697483 \n", - "1094 20171001 274 2.96 2.770724 2.761296 \n", - "1095 20171002 275 2.99 2.780663 2.792421 \n", - "\n", - " discharge prcp tmax tmin \n", - "0 261 160 200 111 \n", - "1 246 8 128 22 \n", - "2 250 18 67 17 \n", - "3 247 0 83 0 \n", - "4 247 0 128 28 \n", - "... ... ... ... ... \n", - "1091 261 0 167 67 \n", - "1092 252 0 211 83 \n", - "1093 250 0 211 28 \n", - "1094 248 0 183 33 \n", - "1095 252 8 206 100 \n", - "\n", - "[1096 rows x 9 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv('C:/Users/ggorski/InfoTheoryScratch/TE_Example/modeled_nitrate_data/prepped_data/it_example_data.csv')\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f0a85cb7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "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": [ - "#plot the data\n", - "plt.plot(data.nitrateobs,'-', c='gray', label = 'Observed')\n", - "plt.plot(data.nitratemdlapred,'-',c='blue', lw = 0.8, label = 'Model A')\n", - "plt.plot(data.nitratemdlbpred,'-',c='red', lw = 0.8, label = 'Model B')\n", - "plt.ylabel('Nitrate')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "c51d4f99", - "metadata": {}, - "source": [ - ">Model B performs better than Model A, our goal is to see if we can detect why that is the case by comparing information transfer from the common sources (discharge, precip, tmax and tmin) to the targets (Observed Nitrate, Model A Nitrate, and Model B Nitrate)" - ] - }, - { - "cell_type": "markdown", - "id": "a377ea0f", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "93a5fa42", - "metadata": {}, - "source": [ - "### Preprocess the data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fccc7acf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Make initial plots of the data\n", - "plt.subplot(2,2,1)\n", - "plt.plot(data.nitrateobs)\n", - "plt.ylabel('Nitrate')\n", - "\n", - "plt.subplot(2,2,2)\n", - "plt.plot(data.discharge)\n", - "plt.ylabel('Discharge')\n", - "\n", - "plt.subplot(2,2,3)\n", - "plt.plot(data.prcp)\n", - "plt.ylabel('Precipitation')\n", - "\n", - "plt.subplot(2,2,4)\n", - "plt.plot(data.tmin, label = 'tmin')\n", - "plt.plot(data.tmax, label = 'tmax')\n", - "plt.ylabel('Temperature')\n", - "plt.legend()\n", - "\n", - "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.5,wspace=0.35)" - ] - }, - { - "cell_type": "markdown", - "id": "a12de930", - "metadata": {}, - "source": [ - ">Remove the seasonal component of the signal and standardize each time series. This step will likely need to be revisited\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6e3f68de", - "metadata": {}, - "outputs": [], - "source": [ - "#function for removing seasonal signal\n", - "def remove_seasonal_signal(raw_data,j):\n", - " seasonal_removed = list()\n", - " for i in range(len(raw_data)):\n", - " doy = raw_data.loc[i,'julianday']\n", - " doy_mean = np.nanmean(raw_data[raw_data['julianday'] == doy].iloc[:,j])\n", - " value = raw_data.iloc[i,j]-doy_mean\n", - " seasonal_removed.append(value)\n", - " seasonal_removed_np = np.array(seasonal_removed)\n", - " return seasonal_removed_np\n", - "\n", - "#function for standardizing the data\n", - "def standardize_data(sr):\n", - " standardized = list()\n", - " for i in range(len(sr)):\n", - " value_z = (sr[i] - np.nanmean(sr))/np.nanstd(sr, ddof = 1)\n", - " #value_z = (sr[i]-np.nanmin(sr))/(np.nanmax(sr)-np.nanmin(sr))\n", - " standardized.append(value_z)\n", - " return standardized\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9d6571ab", - "metadata": {}, - "source": [ - ">The goal is to convert each variable to approximately normal distributions. View how this changes the distribution of data for variables (change the j value to view)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "338c2aca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#j = 2 -> nitrate observed; j = 3 -> nitrate modeled model A; j = 4 -> nitrate modeled model B\n", - "#j = 5 -> discharge; j = 6 -> precip; j = 7 -> tmax; j = 8 -> tmin\n", - "j = 8\n", - "#data\n", - "name = data.columns.values[j]\n", - "seasonal_removed_data = remove_seasonal_signal(data,j)\n", - "standarized_data = standardize_data(sr = seasonal_removed_data)\n", - "\n", - "plt.subplot(3,1,1)\n", - "plt.hist(data.iloc[:,j])\n", - "plt.title(name + ' Raw')\n", - "\n", - "plt.subplot(3,1,2)\n", - "plt.hist(seasonal_removed_data)\n", - "plt.title(name + ' Seasonal Signal Removed')\n", - "\n", - "plt.subplot(3,1,3)\n", - "plt.hist(standarized_data)\n", - "plt.title(name + ' Standardized')\n", - "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.5,wspace=0.35)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "e04b4782", - "metadata": {}, - "source": [ - ">Create the matrix for calculating information theory metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a7e81789", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":6: RuntimeWarning: Mean of empty slice\n", - " doy_mean = np.nanmean(raw_data[raw_data['julianday'] == doy].iloc[:,j])\n", - ":6: RuntimeWarning: Mean of empty slice\n", - " doy_mean = np.nanmean(raw_data[raw_data['julianday'] == doy].iloc[:,j])\n", - ":6: RuntimeWarning: Mean of empty slice\n", - " doy_mean = np.nanmean(raw_data[raw_data['julianday'] == doy].iloc[:,j])\n" - ] - } - ], - "source": [ - "#targets\n", - "#remove seasonal signal\n", - "ssn_rmv_nobs, ssn_rmv_mdla, ssn_rmv_mdlb = remove_seasonal_signal(data,2), remove_seasonal_signal(data,3), remove_seasonal_signal(data,4)\n", - "#standardize\n", - "std_nobs, std_mdla, std_mdlb = standardize_data(ssn_rmv_nobs), standardize_data(ssn_rmv_mdla), standardize_data(ssn_rmv_mdlb)\n", - "\n", - "#sources\n", - "#remove seasonal signal -- discharge and precip\n", - "ssn_rmv_discharge, ssn_rmv_precip = remove_seasonal_signal(data,5), remove_seasonal_signal(data,6)\n", - "#remove seasonal signal -- tmin and tmax\n", - "ssn_rmv_tmin, ssn_rmv_tmax = remove_seasonal_signal(data,7), remove_seasonal_signal(data,8)\n", - "#standardize -- discharge and precip\n", - "std_discharge, std_precip = standardize_data(ssn_rmv_discharge), standardize_data(ssn_rmv_precip)\n", - "#standardize -- tmin and tmax\n", - "std_tmin, std_tmax = standardize_data(ssn_rmv_tmin), standardize_data(ssn_rmv_tmax)\n", - "\n", - "# create data frame\n", - "d = {'nobs': std_nobs, 'nmdla': std_mdla, 'nmdlb': std_mdlb, \n", - " 'discharge': std_discharge, 'precip': std_precip, 'tmin': std_tmin, 'tmax': std_tmax}\n", - "\n", - "d_small = {'discharge': std_discharge, 'precip': std_precip}\n", - "\n", - "data_prepped = pd.DataFrame(data = d)\n" - ] - }, - { - "cell_type": "markdown", - "id": "01947cfa", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "eebdf6ef", - "metadata": {}, - "source": [ - "### Calculate Mutual Information" - ] - }, - { - "cell_type": "markdown", - "id": "369513de", - "metadata": {}, - "source": [ - ">*mutual information* - the amount of information obtained by one variable when observing another. Here mutual information between source and sink variables is normalized by the shannon entropy of the sink variables, so that the output can be conceptualized as the fraction of entropy in the sink variable that can be explained by the source variable. __[More info](https://github.com/pdirmeyer/l-a-cheat-sheets/blob/main/Coupling_metrics_V30_MI.pdf)__\n", - "\n", - ">Additionally, the critical mutual information threshold is calculated by shuffling the time series to destroy temporal relationships and calculating the mutual information 1000 times (numiter variable)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "e4b267f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.0720835 , 0.03434555, 0.07722762]),\n", - " array([0.02650792, 0.02350049, 0.03521877]))" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#choose a source variable and view the mututal information between it and the model runs\n", - "source = 'tmax'\n", - "sinks = ['nobs','nmdla','nmdlb']\n", - "MI_mat = np.empty([0,1])\n", - "MI_crit_mat = np.empty([0,1])\n", - "bins = [11,11,11]\n", - "for snk in sinks:\n", - " paired_for_calcs = data_prepped[[source,snk]].to_numpy()\n", - " #print(paired_for_calcs)\n", - " MI, n = TEpython_ParallelNAN2.mutinfo_new(paired_for_calcs, nbins = bins)\n", - " MIcrit = TEpython_ParallelNAN2.mutinfo_crit_newPar(paired_for_calcs, nbins = bins, alpha = 0.05, numiter = 1000 ,ncores = 8)\n", - " MI_mat = np.append(MI_mat,MI)\n", - " MI_crit_mat = np.append(MI_crit_mat, MIcrit[0])\n", - "MI_mat, MI_crit_mat" - ] - }, - { - "cell_type": "markdown", - "id": "d98e0c36", - "metadata": {}, - "source": [ - "### Plot mutual information results" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "d9860070", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_axes([0,0,1,1])\n", - "nitrate = ['Observed','Model A','Model B']\n", - "mi = MI_mat\n", - "barlist = ax.bar(nitrate,mi)\n", - "barlist[0].set_color('gray')\n", - "barlist[1].set_color('blue')\n", - "barlist[2].set_color('red')\n", - "plt.plot([-.45, .45], [MI_crit_mat[0], MI_crit_mat[0]], '-o', c = 'black')\n", - "plt.plot([0.55,1.45], [MI_crit_mat[1], MI_crit_mat[1]], '-o', c = 'black')\n", - "plt.plot([1.55,2.45], [MI_crit_mat[2], MI_crit_mat[2]], '-o', c = 'black')\n", - "plt.title('Mutual Information Between '+source+' and nitrate')\n", - "plt.ylabel('Mutual information (bits/bits)')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ab7e5823", - "metadata": {}, - "source": [ - ">Choose 'tmax' as the source variable and view how both the observations (gray) and model B (red) show similar mutual information between tmax and nitrate (about 0.07) compared to model A (blue), which shows lower shared information between tmax and modeled nitrate. Critical mutual information threshold is shown with black lines.\n", - "\n", - ">Model A shows significantly less coupling between tmax and nitrate, which could be one of the reasons that the results are worse. The same is true for discharge, and precipitation to a lesser extent." - ] - }, - { - "cell_type": "markdown", - "id": "5eb101df", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "0e7bf17f", - "metadata": {}, - "source": [ - "### Calculate Transfer Entropy" - ] - }, - { - "cell_type": "markdown", - "id": "a5abd40b", - "metadata": {}, - "source": [ - ">*transfer entropy* - the reduction of uncertainty in the sink variable (Y) due to knowledge of the source variable (X) independent of reduction in uncertainty in the sink variable due to knowledge of its own past. The calculation is done over a range of time lags from 0 to max_lag. Here mutual information between source and sink variables is normalized by the shannon entropy of the sink variables, so that the output can be conceptualized as the fraction of entropy in the sink variable that can be explained by the source variable at a certain time lag, in excess of the sink variable's own history. __[More info](https://github.com/pdirmeyer/l-a-cheat-sheets/blob/main/Coupling_metrics_V30_TE.pdf)__\n", - "\n", - ">Additionally, the critical transfer entropy threshold is calculated at each time lag by shuffling the time series to destroy temporal relationships and calculating the transfer entropy 1000 times (numiter variable)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "17bb0917", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tmax --> nobs\n", - "tmax --> nmdla\n", - "tmax --> nmdlb\n" - ] - } - ], - "source": [ - "source = 'tmax'\n", - "sinks = ['nobs','nmdla','nmdlb']\n", - "\n", - "max_lag = 180\n", - "\n", - "te_list = []\n", - "te_crit_list = []\n", - "\n", - "for k in range(len(sinks)):\n", - " paired_for_calcs = data_prepped[[source,sinks[k]]].to_numpy()\n", - " te_list.append([])\n", - " te_crit_list.append([])\n", - " print(source, '-->', sinks[k])\n", - " for i in range(max_lag):\n", - " T, N, M4short = TEpython_ParallelNAN2.transen_new2(paired_for_calcs, shift = [i,0,i], nbins = bins)\n", - " Tcrit = TEpython_ParallelNAN2.transen_crit_new2(paired_for_calcs, shift = [i,0,i], alpha = 0.5, numiter = 1000, nbins = bins, ncores = 8)\n", - " te_list[k].append(T)\n", - " te_crit_list[k].append(Tcrit)\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "398fbc5b", - "metadata": {}, - "source": [ - "### Plot Results" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "6b1e0844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot the data\n", - "#observed\n", - "plt.plot(te_list[0],'-', c='gray', label = 'Observed')\n", - "plt.plot(te_crit_list[0], '--', c='gray', lw = 0.5, label = 'Obs Crit')\n", - "#model A\n", - "plt.plot(te_list[1],'-', c='blue', label = 'Model A')\n", - "plt.plot(te_crit_list[1], '--', c='blue', lw = 0.5, label = 'Model A Crit')\n", - "#model B\n", - "plt.plot(te_list[2],'-', c='red', label = 'Model B')\n", - "plt.plot(te_crit_list[2], '--', c='red', lw = 0.5, label = 'Model B Crit')\n", - "plt.title('Transfer Entropy -- source: '+source)\n", - "plt.ylabel('TE sink to source')\n", - "plt.xlabel('Time Lag (days)')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "6f59dd06", - "metadata": {}, - "source": [ - "> Choose 'tmax' as the source variable and view how Model A (red) and the Observations (gray) show good agreement across a range of time lags, while Model B (blue) deviates particularly after ~10 days. " - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "6150063f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot the data zoomed in \n", - "#observed\n", - "plt.plot(te_list[0],'-', c='gray', label = 'Observed')\n", - "plt.plot(te_crit_list[0], '--', c='gray', lw = 0.5, label = 'Obs Crit')\n", - "#model A\n", - "plt.plot(te_list[1],'-', c='blue', label = 'Model A')\n", - "plt.plot(te_crit_list[1], '--', c='blue', lw = 0.5, label = 'Model A Crit')\n", - "#model B\n", - "plt.plot(te_list[2],'-', c='red', label = 'Model B')\n", - "plt.plot(te_crit_list[2], '--', c='red', lw = 0.5, label = 'Model B Crit')\n", - "plt.title('Transfer Entropy -- source: '+source)\n", - "plt.ylabel('TE sink to source')\n", - "plt.xlabel('Time Lag (days)')\n", - "plt.xlim([0,25])\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "220d2dff", - "metadata": {}, - "source": [ - ">Zoom in on time lags from 0 to 10 days. Model B and Observed show good agreement with the peak of TE at a lag of 3 days (above the TE critical threshold), while Model A misses this completely." - ] - }, - { - "cell_type": "markdown", - "id": "d80e30b4", - "metadata": {}, - "source": [ - "#### Concluding thoughts" - ] - }, - { - "cell_type": "markdown", - "id": "fc9b8517", - "metadata": {}, - "source": [ - ">- The analysis of mutual information between inputs and modeled outputs can tell us how subsystems are coupled within models and how that coupling has changed due to modeling decisions\n", - ">- The comparison of mutual information of modeled results to observations tells us if the models are overdetermined, or if they are underutilizing the information present in the input time series \n", - ">- The analysis of transfer entropy allows us to examine the one-way transfer of information from input variables to output, the comparison with observed data allows us to determine how faithfully the models are representing the driving processes\n", - ">- TE analysis also allows us to observe the critical time lags associated with these processes, and how modeling decisions have affected the timing of drivers within the model\n", - ">- For example, maximum temperature should signficantly reduce variance in nitrate at the 3 day time lag. In Model A this is not captured, however in Model B this dynamic is well captured\n", - ">- This analysis and interpretation has been fairly qualitative, but I can imagine an analysis of the TE and MI results that gets really quantitative by looking at the time lags where T > Tcrit and comparing those between model runs. Also there has been some interesting work done by __[Ruddell et al.](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR023692)__ looking at MI and TE together and what it tells you about a model\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "934de9a0", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/methods_exploration/IT_output_plots/TE_discharge_180_days.png b/methods_exploration/IT_output_plots/TE_discharge_180_days.png deleted file mode 100644 index d26f47a..0000000 Binary files a/methods_exploration/IT_output_plots/TE_discharge_180_days.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/TE_precip_180_days.png b/methods_exploration/IT_output_plots/TE_precip_180_days.png deleted file mode 100644 index c45185b..0000000 Binary files a/methods_exploration/IT_output_plots/TE_precip_180_days.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/TE_tmax_180_days.png b/methods_exploration/IT_output_plots/TE_tmax_180_days.png deleted file mode 100644 index 1aece35..0000000 Binary files a/methods_exploration/IT_output_plots/TE_tmax_180_days.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/TE_tmin_180_days.png b/methods_exploration/IT_output_plots/TE_tmin_180_days.png deleted file mode 100644 index fb1d378..0000000 Binary files a/methods_exploration/IT_output_plots/TE_tmin_180_days.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/discharge_MI.png b/methods_exploration/IT_output_plots/discharge_MI.png deleted file mode 100644 index 80b83c1..0000000 Binary files a/methods_exploration/IT_output_plots/discharge_MI.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/discharge_TE.png b/methods_exploration/IT_output_plots/discharge_TE.png deleted file mode 100644 index c5523c3..0000000 Binary files a/methods_exploration/IT_output_plots/discharge_TE.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/precip_MI.png b/methods_exploration/IT_output_plots/precip_MI.png deleted file mode 100644 index a492b43..0000000 Binary files a/methods_exploration/IT_output_plots/precip_MI.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/precip_TE.png b/methods_exploration/IT_output_plots/precip_TE.png deleted file mode 100644 index 779c868..0000000 Binary files a/methods_exploration/IT_output_plots/precip_TE.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/tmax_MI.png b/methods_exploration/IT_output_plots/tmax_MI.png deleted file mode 100644 index 439d7cb..0000000 Binary files a/methods_exploration/IT_output_plots/tmax_MI.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/tmax_TE.png b/methods_exploration/IT_output_plots/tmax_TE.png deleted file mode 100644 index e07853d..0000000 Binary files a/methods_exploration/IT_output_plots/tmax_TE.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/tmin_MI.png b/methods_exploration/IT_output_plots/tmin_MI.png deleted file mode 100644 index 9078ab5..0000000 Binary files a/methods_exploration/IT_output_plots/tmin_MI.png and /dev/null differ diff --git a/methods_exploration/IT_output_plots/tmin_TE.png b/methods_exploration/IT_output_plots/tmin_TE.png deleted file mode 100644 index 17fa646..0000000 Binary files a/methods_exploration/IT_output_plots/tmin_TE.png and /dev/null differ diff --git a/methods_exploration/Information Theory Toy Example.ipynb b/methods_exploration/Information Theory Toy Example.ipynb deleted file mode 100644 index ca9c33b..0000000 --- a/methods_exploration/Information Theory Toy Example.ipynb +++ /dev/null @@ -1,339 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "cc161951", - "metadata": {}, - "outputs": [], - "source": [ - "import ipywidgets as widgets\n", - "from ipywidgets import HBox, interact, interactive, IntSlider, Label \n", - "from IPython.display import display\n", - "import it_functions\n", - "import it_functions_examples as ife\n", - "import math\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "%matplotlib widget\n", - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = [12, 7]" - ] - }, - { - "cell_type": "markdown", - "id": "705cc46c", - "metadata": {}, - "source": [ - "# Application of information theory to synthetic data\n", - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "id": "34465d5d", - "metadata": {}, - "source": [ - "This notebook is a demonstration of the calculation and visualization of mutual information and transfer entropy using synthetic data with known time lags of interaction. " - ] - }, - { - "cell_type": "markdown", - "id": "615897bd", - "metadata": {}, - "source": [ - "## Definitions" - ] - }, - { - "cell_type": "markdown", - "id": "32982535", - "metadata": {}, - "source": [ - ">*mutual information* - the amount of information obtained by one variable when observing another. Here mutual information between source (x) and sink (y) is normalized by the shannon entropy of the sink variables, so that the output can be conceptualized as the fraction of uncertainty in the sink variable that can be explained by the source variable. Mutual information is as symmetric metric ($MI_{xy} == MI_{yx}$) and is similar to a non-linear correlation __[More info](https://github.com/pdirmeyer/l-a-cheat-sheets/blob/main/Coupling_metrics_V30_MI.pdf)__\n", - "\n", - ">*transfer entropy* - the reduction of uncertainty in the sink variable (y) due to knowledge of the source variable (x) independent of reduction in uncertainty in the sink variable due to knowledge of its own past. The output can be conceptualized as the fraction of entropy in the sink variable that can be explained by the source variable at a certain time lag, in excess of the sink variable's own history. Transfer entropy is an asymmetrical metric ($TE_{x->y} \\neq TE_{y->x}$) __[More info](https://github.com/pdirmeyer/l-a-cheat-sheets/blob/main/Coupling_metrics_V30_TE.pdf)__\n", - "\n", - ">For both metrics, significance is determined by shuffling the time series to destroy temporal relationships and calculating mutual information and transfer entropy. This is done 1000 times and we use the 99th percentile of those metrics calculated on shuffled values as a significance threshold for each metric" - ] - }, - { - "cell_type": "markdown", - "id": "ed550ef2", - "metadata": {}, - "source": [ - "## Logistic mapping with known time lag" - ] - }, - { - "cell_type": "markdown", - "id": "0e7ea511", - "metadata": {}, - "source": [ - "> Using a choatic logistic mapping with a known time lag, we can see how effective mutual information and transfer entropy are at identifying that time lag. The logistic map is defined as:\n", - "\n", - "> $y_t = ax_{(t-lag)} [1-x_{(t-lag)}]+z_te$\n", - "\n", - "> with lag = 5, a = 4 as the growth rate, z is drawn from a random guassian distribution, and e = 0.2 is a noise factor" - ] - }, - { - "cell_type": "markdown", - "id": "a64033c3", - "metadata": {}, - "source": [ - "### Mutual information" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c21f53ed", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d6ccfb0049e648969a12b056f911b0e4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntText(value=500, description='Number of data points:'), FloatText(value=0.2, descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def f(ndata, e):\n", - " ife.gen_plot_logistic_it_mi(ndata, e)\n", - "interactive_plot = interactive(f,\n", - " ndata = widgets.IntText(value=500,description='Number of data points:',disabled=False),\n", - " e = widgets.FloatText(value = 0.2, description = 'e:'))\n", - " \n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "32c8b90b", - "metadata": {}, - "source": [ - "> The plot above shows the mutual information (blue), critical mutual information (blue dashed), and the pearson correlation coefficient (red) all calculated across a range of time lags. A couple of things to note:\n", - "> - Mutual information shows a significant value only at the correct time lag (5), at all other time lags the mutual information is below the significance value\n", - "> - Pearson correlation coefficient does show a peak there, but it's difficult to identify this as the most important time lag\n", - "> - By adjusting the two interactive inputs at the top of the plot (number of data points and random noise), you can see how those affect the mutual information calculations. Fewer data points and more noise makes the singal less clear" - ] - }, - { - "cell_type": "markdown", - "id": "3820b2e0", - "metadata": {}, - "source": [ - "### Transfer entropy" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9102d9f5", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "628e6780d1c74eba8c98d776b9cff1e9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntText(value=500, description='Number of data points:'), FloatText(value=0.2, descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def f(ndata, e):\n", - " ife.gen_plot_logistic_it_te(ndata, e)\n", - "interactive_plot = interactive(f,\n", - " ndata = widgets.IntText(value=500,description='Number of data points:',disabled=False),\n", - " e = widgets.FloatText(value = 0.2, description = 'Random noise:'))\n", - " \n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "d2390edb", - "metadata": {}, - "source": [ - "> The plot above shows the transfer entropy from source to sink (blue) and from sink to source (orange), as well as the respective critical values for transfer entropy in both directions (dotted lines). A couple things to note:\n", - "> - With number of data = 500 and random noise = 0.2, the known critical time lag (5) should be the only significant transfer entropy value \n", - "> - No value for $TE_{y->x}$ should rise above the critical value, unless the number of data is very low or the noise is very high\n", - "> - Similar to mutual information, by adjusting the two interactive inputs at the top of the plot (number of data points and random noise), you can see how those affect the mutual information calculations. Fewer data points and more noise makes the singal less clear" - ] - }, - { - "cell_type": "markdown", - "id": "ec6f6878", - "metadata": {}, - "source": [ - "## Periodic signal with variable coupling coefficients" - ] - }, - { - "cell_type": "markdown", - "id": "097256a3", - "metadata": {}, - "source": [ - "> Here we use a different relationship between x and y with coupling at multiple different time lags. The coupling also takes on multiple different functional forms:\n", - "\n", - "> $y_t = cc_1 e^{x_{t-1}}+cc_2{x_{x-2}}^2+cc_3x_{x-3}+cc_4cos(x_{x-4})+z_te$\n", - "\n", - "> where $cc_{1,2,3,4}$ are coupling coefficients and z is drawn from a random guassian distribution, and e = 0.2 is a noise factor" - ] - }, - { - "cell_type": "markdown", - "id": "8bba5a77", - "metadata": {}, - "source": [ - "### Mutual information" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "86c3d535", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fd3d968997df4aafac118c84c327cb7e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntText(value=1000, description='Number of data points:'), FloatText(value=2.0, descript…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def f(ndata, cc1, cc2, cc3, cc4, e):\n", - " ife.gen_plot_periodic_it_mi(ndata, cc1, cc2, cc3, cc4, e)\n", - "interactive_plot = interactive(f,\n", - " ndata = widgets.IntText(value=500,description='Number of data points:',disabled=False),\n", - " cc1 = widgets.FloatText(value = 2, description = '$cc_1$: '),\n", - " cc2 = widgets.FloatText(value = 7, description = \"$cc_2$ :\"),\n", - " cc3 = widgets.FloatText(value = 0.5, description = \"$cc_3 :$\"),\n", - " cc4 = widgets.FloatText(value = 5, description = \"$cc_4$ :\"), \n", - " e = widgets.FloatText(value = 0.2, description = 'e :'))\n", - " \n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "d8d019ca", - "metadata": {}, - "source": [ - "> The plot above shows the mutual information (blue), critical mutual information (blue dashed), and the pearson correlation coefficient (red) all calculated across a range of time lags. A couple of things to note:\n", - "> - With default values mutual information shows a significant value only at time lags of 1, 2, and 4, but not 3\n", - "> - Pearson correlation coefficient does show high values at 1, 2, and 3\n", - "> - By adjusting the six interactive inputs at the top of the plot (number of data points, coupling coefficients, and random noise), you can see how those affect the mutual information calculations. Fewer data points, weaker coupling, and more noise makes the singal less clear" - ] - }, - { - "cell_type": "markdown", - "id": "a4476c0e", - "metadata": {}, - "source": [ - "### Transfer entropy" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ebde5528", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "98ad8b663b9d4fcc886d6f97eaf30a84", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntText(value=500, description='Number of data points:'), FloatText(value=2.0, descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def f(ndata, cc1, cc2, cc3, cc4, e):\n", - " ife.gen_plot_periodic_it_te(ndata, cc1, cc2, cc3, cc4, e)\n", - "interactive_plot = interactive(f,\n", - " ndata = widgets.IntText(value=500,description='Number of data points:',disabled=False),\n", - " cc1 = widgets.FloatText(value = 2, description = '$cc_1$: '),\n", - " cc2 = widgets.FloatText(value = 7, description = \"$cc_2$ :\"),\n", - " cc3 = widgets.FloatText(value = 0.5, description = \"$cc_3 :$\"),\n", - " cc4 = widgets.FloatText(value = 5, description = \"$cc_4$ :\"), \n", - " e = widgets.FloatText(value = 0.2, description = 'e :'))\n", - " \n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "6859c6a4", - "metadata": {}, - "source": [ - "> The plot above shows the transfer entropy from source to sink (blue) and from sink to source (orange), as well as the respective critical values for transfer entropy in both directions (dotted lines). A couple things to note:\n", - "> - With number of data = 500 and random noise = 0.2, the cofficients set to their default values, then a time lag of 2 should be the only significant transfer entropy value \n", - "> - No value for $TE_{y->x}$ should rise above the critical value, unless the number of data is very low or the noise is very high\n", - "> - Similar to mutual information, by adjusting the six interactive inputs at the top of the plot (number of data points, coupling coefficients, and random noise), you can see how those affect the mutual information calculations. Fewer data points, weaker coupling, and more noise makes the singal less clear" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4659e33c", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/methods_exploration/Salt_front_drivers_exploration.ipynb b/methods_exploration/Salt_front_drivers_exploration.ipynb deleted file mode 100644 index 503ead9..0000000 --- a/methods_exploration/Salt_front_drivers_exploration.ipynb +++ /dev/null @@ -1,171809 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8b9d8641", - "metadata": {}, - "outputs": [], - "source": [ - "import ipywidgets as widgets\n", - "from ipywidgets import HBox, interact, interactive, IntSlider, Label \n", - "from IPython.display import display\n", - "%matplotlib widget\n", - "%matplotlib inline\n", - "import math\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "import pandas as pd\n", - "import plotly.express as px\n", - "import sys\n", - "#add methods_exploration/functions to python path\n", - "sys.path.append('functions')\n", - "import data_exploration_functions as dexf\n", - "import utils\n", - "#sys.path\n", - "plt.rcParams['figure.figsize'] = [12, 5]" - ] - }, - { - "cell_type": "markdown", - "id": "900decbe", - "metadata": {}, - "source": [ - "# Correlation and mutual information between sources and sinks: Salt front drivers\n", - "***\n", - "All data in this notebook are observational, although the hope is that this analysis will be applied to compare model output from COAWST and machine learning model" - ] - }, - { - "cell_type": "markdown", - "id": "0b305b39", - "metadata": {}, - "source": [ - "## 1) Input sources and sinks" - ] - }, - { - "cell_type": "markdown", - "id": "22b0ed59", - "metadata": {}, - "source": [ - ">Sources should be a csv with the first column named 'datetime' with dates in the format YYYY-MM-DD. Other column titles should be of of the form 'site_variable' for example 'Trenton_discharge'. \n", - "\n", - ">Sinks should be a csv with the first column named 'datetime' with dates in the format YYYY-MM-DD, one additional column will be the sink variable(s), for example 'Daily_salt_front' and '7_day_avg_salt_front'\n", - "\n", - ">datetime column must be continuous, missing data in other columns are OK and will skipped in analysis\n", - "\n", - ">sources and sinks must have the same time step although they do not need to perfectly overlap in time, the analyses will be run on their overlapping period\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a6d8c213", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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}, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Sinks" - }, - "width": 800, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "datetime" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "River Mile" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sources_long = sources.stack().reset_index()\n", - "sources_long.columns = ['datetime','name','value']\n", - "sources_long[['site','var']] = sources_long['name'].str.split('_',expand = True)\n", - "\n", - "sinks_long = sinks.stack().reset_index()\n", - "sinks_long.columns = ['datetime','sink_name','value']\n", - "\n", - "fig = px.line(sources_long, x='datetime', y='value', color='site', facet_row = 'var', height=600, width=800,\n", - " title=\"Sources\")\n", - "fig.update_yaxes(matches=None,title_text='')\n", - "#fig.for_each_yaxis(lambda yaxis: yaxis.update(title = var))\n", - "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "fig.show()\n", - "\n", - "fig = px.line(sinks_long, x='datetime', y='value', color='sink_name', height=500, width=800,\n", - " title=\"Sinks\")\n", - "fig.update_yaxes(title_text='River Mile')\n", - "#fig.for_each_yaxis(lambda yaxis: yaxis.update(title = var))\n", - "#fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "c9ee3c8b", - "metadata": {}, - "source": [ - "## 3) Make interactive heat map for correlation" - ] - }, - { - "cell_type": "markdown", - "id": "2e433a45", - "metadata": {}, - "source": [ - ">Running the following cell will generate: \n", - "> 1. a heat map of lagged correlations between sources and sinks \n", - "> 2. a plot of the correlations between sources and sinks across the time lags considered\n", - "\n", - "> Both plots are reactive to the inputs in the upper left where you can select: \n", - "> 1. the start and end date for the analysis (start_date and end_date)\n", - "> 2. the number of lags to consider (n_lags)\n", - "> 3. the threshold level you'd like to mask out in the heat map (mask_threshold) " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a323f645", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "43a260571a7247b5b6c90e9f19e79de5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(DatePicker(value=Timestamp('2001-01-01 00:00:00'), description='start_date'), DatePicker…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def f(start_date, end_date, n_lags, mask_threshold):\n", - " dexf.generate_correlation_heatmap(sources, sinks, start_date, end_date, n_lags, mask_threshold)\n", - " dexf.generate_correlation_timeseries(sources, sinks, start_date, end_date, n_lags, mask_threshold)\n", - "interactive_plot = interactive(f,\n", - " start_date = widgets.DatePicker(value = pd.to_datetime('2001-01-01')),\n", - " end_date = widgets.DatePicker(value = pd.to_datetime('2020-01-01')),\n", - " n_lags = widgets.IntSlider(min = 0,max = 9,step = 1, value = 4, continuous_update=False),\n", - " mask_threshold = widgets.FloatSlider(min = 0,max = 1,step = 0.01, value = 0.5, continuous_update=False))\n", - "interactive_plot" - ] - }, - { - "cell_type": "markdown", - "id": "2c2ddd69", - "metadata": {}, - "source": [ - "## 4) Make interactive heat map for mutual information\n" - ] - }, - { - "cell_type": "markdown", - "id": "3a3df938", - "metadata": {}, - "source": [ - ">Running the following cell will generate: \n", - "> 1. a heat map of lagged mutual information between sources and sinks \n", - "> 2. a plot of the mutual information between sources and sinks across the time lags considered\n", - "\n", - ">*mutual information* - the amount of information obtained by one variable when observing another. Here mutual information between source and sink variables is normalized by the uncertainty of the sink variables, so that the output can be conceptualized as the fraction of uncertainty in the sink variable that can be explained by the source variable. __[More info](https://github.com/pdirmeyer/l-a-cheat-sheets/blob/main/Coupling_metrics_V30_MI.pdf)__\n", - "\n", - ">mutual information differs from correlation in a few key ways:\n", - " > - it compares the probability distributions of the variables so it makes no assumption about the functional form of the relationship\n", - " > - it considers the entire distrubtion of the variables, as such it is more sensitive to outliers and small sample size\n", - "\n", - "> Both plots are reactive to the inputs in the upper left where you can select: \n", - "> 1. the start and end date for the analysis (start_date and end_date)\n", - "> 2. the number of lags to consider (n_lags)\n", - "> 3. the threshold level you'd like to mask out in the heat map (mask_threshold) " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4529a43c", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2c3f443c01ef40dbb0befee86f0296b0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(DatePicker(value=Timestamp('2001-01-01 00:00:00'), description='start_date'), DatePicker…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def mi(start_date, end_date, n_lags, mask_threshold):\n", - " dexf.generate_mutual_information_heatmap(sources, sinks, start_date, end_date, n_lags, mask_threshold)\n", - " dexf.generate_mutual_information_timeseries(sources, sinks, start_date, end_date, n_lags, mask_threshold)\n", - "interactive_plot = interactive(mi,\n", - " start_date = widgets.DatePicker(value = pd.to_datetime('2001-01-01')),\n", - " end_date = widgets.DatePicker(value = pd.to_datetime('2020-01-01')),\n", - " n_lags = widgets.IntSlider(min = 0,max = 9,step = 1, value = 4, continuous_update=False),\n", - " mask_threshold = widgets.FloatSlider(min = 0,max = 1,step = 0.01, value = 0.0, continuous_update=False))\n", - "interactive_plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "448d38bd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/methods_exploration/data/saltfront.csv b/methods_exploration/data/saltfront.csv deleted file mode 100644 index 70bec76..0000000 --- a/methods_exploration/data/saltfront.csv +++ /dev/null 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-2017-12-06,3757.916667,253.7083333,1088.333333, -2017-12-07,3828.958333,267.75,1124.375, -2017-12-08,3883.020833,269.2916667,1082.916667, -2017-12-09,4047.8125,262.7916667,1043.416667, -2017-12-10,4159.479167,257.6666667,1050, -2017-12-11,4074.0625,260.3333333,1027.958333, -2017-12-12,3886.145833,261.9583333,1019.625, -2017-12-13,3781.5625,276.9166667,983.1666667, -2017-12-14,3801.979167,282.125,983.75, -2017-12-15,3622.916667,282.6666667,928, -2017-12-16,3323.333333,288.9583333,874.5625, -2017-12-17,3046.979167,292.5,921.5208333, -2017-12-18,3108.125,294.9166667,946.625, -2017-12-19,3588.4375,297.5263158,960, -2017-12-20,3897.916667,292.625,960.1666667, -2017-12-21,4110.625,270.4166667,922.0208333, -2017-12-22,4089.6875,264.375,893.6875, -2017-12-23,4036.666667,254.9583333,1070.5, -2017-12-24,5110.3125,254.8333333,2599.583333, -2017-12-25,5596.458333,274.1666667,2237.708333, -2017-12-26,7025.208333,286.25,1954.375, -2017-12-27,6394.895833,257.8333333,1433.125, -2017-12-28,5645.368421,267.6363636,1097.5, -2017-12-29,,274.25,931.0625, -2017-12-30,,278.3333333,942.5208333, -2017-12-31,,279.0833333,888.1041667, -2018-01-01,,277.7083333,795.5833333, -2018-01-02,,292,841.5416667, -2018-01-03,,306,867.4166667, -2018-01-04,,303.5833333,894, -2018-01-05,,291.7083333,798.8541667, -2018-01-06,,285.4166667,720.4375, -2018-01-07,,293.0416667,760.1875, -2018-01-08,,301.375,820.8125, -2018-01-09,,297.25,827.2291667, -2018-01-10,,276.25,828.8333333, -2018-01-11,,261.0833333,844.1666667, -2018-01-12,,270.125,2086.3125, -2018-01-13,,281.4166667,7242.083333, -2018-01-14,,263.5833333,9334.791667, -2018-01-15,,181.7083333,5379.583333, -2018-01-16,,178,3716.875, -2018-01-17,,181.4166667,3093.75, -2018-01-18,,199.375,2639.791667, -2018-01-19,,201.8,2162.5, -2018-01-20,,201.375,1915.833333, -2018-01-21,,203.9583333,1843.333333, -2018-01-22,,206.2916667,1771.666667, -2018-01-23,12627.14286,210.5833333,2311.041667, -2018-01-24,25064.58333,221.7,10773.95833, 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-2018-02-15,18794.79167,216.0416667,6532.291667, -2018-02-16,21271.875,228.7083333,9049.166667, -2018-02-17,25915.625,215.2916667,9311.25, -2018-02-18,27863.54167,194.375,7649.166667, -2018-02-19,23888.54167,194.8333333,7302.291667, -2018-02-20,21710.41667,199.2916667,7025, -2018-02-21,23670.83333,199.2083333,6697.708333, -2018-02-22,28934.375,180,6175.625, -2018-02-23,31450,171.0416667,6470.833333, -2018-02-24,30596.875,166.8333333,6894.375, -2018-02-25,34288.54167,169.3333333,10211.875, -2018-02-26,50890.625,171.1363636,12091.66667, -2018-02-27,47454.16667,152.4285714,9504.791667, -2018-02-28,37142.70833,,7455.833333, -2018-03-01,30954.16667,157,6143.125, -2018-03-02,40259.375,161.9583333,17672.29167, -2018-03-03,44505.20833,187.375,15402.08333, -2018-03-04,37296.61017,182.9583333,11087.91667, -2018-03-05,30688.23529,186.2916667,8157.708333, -2018-03-06,27393.33333,193.625,6518.333333, -2018-03-07,25877.77778,192.0833333,6272.916667, -2018-03-08,25725,208.4583333,7214.166667, 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-2018-03-31,15356.25,219.125,2958.125, -2018-04-01,20348.95833,191.9583333,2752.708333, -2018-04-02,18789.58333,171.9166667,2721.666667, -2018-04-03,19407.29167,183.5,3623.125, -2018-04-04,22945.83333,199.5833333,4495.625, -2018-04-05,25964.58333,186.4583333,4016.25, -2018-04-06,26765.625,169.5833333,3581.666667, -2018-04-07,24111.45833,163.1666667,3361.041667, -2018-04-08,20683.33333,165.9166667,3090.416667, -2018-04-09,18932.29167,171.375,2852.083333, -2018-04-10,17386.45833,173.4166667,2670.625, -2018-04-11,16378.125,178.125,2522.708333, -2018-04-12,15262.5,181.25,2327.916667, -2018-04-13,13739.58333,187.0833333,2207.083333, -2018-04-14,12809.375,194.125,2116.458333, -2018-04-15,12046.875,196.9166667,1981.666667, -2018-04-16,23062.5,183.5,12877.95918, -2018-04-17,30028.125,199.4166667,10396.25, -2018-04-18,33712.5,175.5,6298.125, -2018-04-19,31187.5,157.1666667,4973.125, -2018-04-20,26962.5,157.4166667,4447.083333, -2018-04-21,22422.91667,159.8333333,3886.458333, -2018-04-22,18818.75,168.375,3498.333333, -2018-04-23,17406.25,175.0833333,3247.916667, -2018-04-24,16262.5,177.7083333,3001.666667, -2018-04-25,16546.875,180.5833333,3337.708333, -2018-04-26,20051.04167,185.2173913,5644.166667, -2018-04-27,21225,177.3333333,5603.333333, -2018-04-28,20763.54167,167.9583333,4866.25, -2018-04-29,19553.125,167.2083333,4797.916667, -2018-04-30,18827.08333,169.3333333,4428.958333, -2018-05-01,17586.45833,170.0416667,3811.25, -2018-05-02,16892.70833,172.8333333,3519.375, -2018-05-03,16485.41667,170.625,3300.416667, -2018-05-04,15976.04167,171.1666667,3068.958333, -2018-05-05,15240.625,175.7083333,2936.875, -2018-05-06,13527.08333,179.9166667,2828.75, -2018-05-07,12892.70833,188.5,2993.541667, -2018-05-08,13130.20833,194,2789.791667, -2018-05-09,13215.625,187.2083333,2510.416667, -2018-05-10,12204.16667,187.7083333,2262.291667, -2018-05-11,11944.79167,192.375,2159.791667, -2018-05-12,12788.54167,193.2083333,2465.208333, -2018-05-13,13502.08333,195.2083333,4188.125, -2018-05-14,14270.83333,196.5833333,6088.333333, -2018-05-15,14818.88889,200.4583333,4945, -2018-05-16,16930.20833,192.0416667,5513.125, -2018-05-17,29045.83333,178.625,12221.25, -2018-05-18,29976.04167,166.375,9325.833333, -2018-05-19,26704.5977,164.375,10175.83333, -2018-05-20,33546.875,167.5416667,13904.16667, -2018-05-21,38045.83333,152.5833333,10724.58333, -2018-05-22,33118.75,149.75,8412.5, -2018-05-23,32854.16667,157,8493.333333, -2018-05-24,36064.58333,152.8333333,7330.208333, -2018-05-25,30091.66667,148.7916667,6090, -2018-05-26,23958.33333,156.5,5237.291667, -2018-05-27,28323.95833,155.0416667,11092.2449, -2018-05-28,19608.98876,178.6666667,6634.375, -2018-05-29,17289.77273,185.4166667,4563.75, -2018-05-30,14945.83333,190.8333333,3943.333333, -2018-05-31,13105.20833,200.4347826,3627.291667, -2018-06-01,12087.5,207.4583333,3389.375, -2018-06-02,11465.625,214.0833333,3174.166667, -2018-06-03,11258.33333,218,3823.333333, -2018-06-04,12165.625,215.5833333,3585.833333, -2018-06-05,11038.54167,212,3216.875, -2018-06-06,10229.89583,221.7083333,2844.166667, -2018-06-07,9430,224.7083333,2485.833333, -2018-06-08,8847.916667,226.5416667,2253.125, -2018-06-09,8251.25,229.6666667,2116.458333, -2018-06-10,7693.020833,231.625,2109.583333, -2018-06-11,8271.666667,237.2083333,17371.04167, -2018-06-12,7812.44186,248.8333333,9040.625, -2018-06-13,6985.444444,251.3478261,6166.25, -2018-06-14,6649.54023,253.0833333,4155.208333, -2018-06-15,6559.67033,258.625,3397.916667, -2018-06-16,6250.11236,257.2916667,2966.666667, -2018-06-17,5892.111111,259.2083333,2596.25, -2018-06-18,5510.9375,259.3333333,2467.5, -2018-06-19,5332.1875,263.0833333,2687.5, -2018-06-20,5565.104167,268.4583333,2559.166667, -2018-06-21,5374.166667,269.7916667,2276.25, -2018-06-22,5108.571429,270.375,2083.958333, -2018-06-23,4805.376344,274.9583333,1970.833333, -2018-06-24,4665.604396,279,2061.875, -2018-06-25,4835.625,282.8235294,2523.75, -2018-06-26,4468.958333,284.7083333,1944.166667, -2018-06-27,4354.044944,280.25,1836.25, -2018-06-28,5815,253.1304348,5333.333333, -2018-06-29,7527.8125,270.8333333,7095.625, -2018-06-30,6502.083333,261.3181818,4015.625, -2018-07-01,7239.6875,245.2173913,2854.791667, -2018-07-02,6348.4375,231,2404.375, -2018-07-03,5777.708333,234.875,2088.125, -2018-07-04,5805.9375,240.0869565,2181.041667, -2018-07-05,5967.395833,244.9166667,2018.125, -2018-07-06,6418.541667,244.4583333,5489.583333, -2018-07-07,5763.645833,250.875,4015, -2018-07-08,4909.479167,254.3333333,2415.833333, -2018-07-09,4513.854167,258.0416667,1918.75, -2018-07-10,4273.645833,262.875,1732.916667, -2018-07-11,3714.270833,266.9166667,1618.125, -2018-07-12,3533.975904,270.3043478,1526.041667, -2018-07-13,3452.790698,277.875,1441.041667, -2018-07-14,3370.45977,282.7222222,1278.75, -2018-07-15,3446.629213,282.9565217,1610.416667, -2018-07-16,4653.75,281.173913,2898.75, -2018-07-17,4736.875,272,2377.708333, -2018-07-18,5458.229167,262.5652174,5419.583333, -2018-07-19,5722.708333,264.9130435,2383.333333, -2018-07-20,4938.958333,259.8636364,1925, -2018-07-21,4177.395833,252.9166667,1906.25, -2018-07-22,4839.6875,254.5416667,4978.333333, -2018-07-23,8197.916667,268.2380952,8277.916667, -2018-07-24,12859.375,238.4166667,9747.916667, -2018-07-25,26710.41667,191.4583333,11024.375, -2018-07-26,30778.125,169.125,11239.58333, -2018-07-27,33763.54167,155.9583333,9638.125, -2018-07-28,30669.79167,133.4583333,8372.5, -2018-07-29,23001.04167,139.0416667,6413.75, -2018-07-30,18981.25,139.6086957,4678.333333, -2018-07-31,14693.75,145.3333333,4193.333333, -2018-08-01,11045.83333,162.2173913,3658.541667, -2018-08-02,12251.04167,182.5263158,4287.291667, -2018-08-03,16057.29167,177.5833333,6536.458333, -2018-08-04,34910.41667,163.4782609,17952.70833, -2018-08-05,51835.41667,164.25,19812.5, -2018-08-06,35951.04167,150.5833333,12056.25, -2018-08-07,27413.54167,158.9565217,8819.375, -2018-08-08,22225,162.9545455,8401.041667, -2018-08-09,18526.04167,171.1666667,5777.083333, -2018-08-10,21196.875,176.1666667,4362.708333, -2018-08-11,23677.08333,144.9166667,5350.416667, -2018-08-12,24658.33333,151.2916667,9130.625, -2018-08-13,22479.16667,187.75,19898.33333, -2018-08-14,37700,165.25,31736.73469, -2018-08-15,33277.08333,185.5416667,20164.58333, -2018-08-16,36531.25,154.1666667,11892.5, -2018-08-17,31105.20833,150.125,8477.083333, -2018-08-18,26303.125,140.125,7455.208333, -2018-08-19,25508.33333,151.0833333,7002.083333, -2018-08-20,29287.5,139.6428571,6756.25, -2018-08-21,25090.625,132.0526316,5661.458333, -2018-08-22,29208.33333,148.0833333,17693.95833, -2018-08-23,26267.70833,163.4583333,11058.54167, -2018-08-24,21358.33333,175.25,7940.833333, -2018-08-25,17320.83333,180.8333333,5558.958333, -2018-08-26,15076.04167,189.125,4752.916667, -2018-08-27,13069.79167,192.2916667,4335.625, -2018-08-28,11655.20833,201.4166667,4172.916667, -2018-08-29,10942.70833,207.7916667,3914.583333, -2018-08-30,10568.64583,211.625,3467.708333, -2018-08-31,10276.97917,213.7083333,3263.958333, -2018-09-01,10765.3125,215.6666667,7535.833333, -2018-09-02,11034.375,217.3333333,8092.708333, -2018-09-03,10578.125,215.8333333,6176.25, -2018-09-04,9755.520833,204.75,4610.625, -2018-09-05,9189.166667,210.625,3962.5, -2018-09-06,8916.770833,216.75,4052.708333, -2018-09-07,9779.479167,221.1666667,4672.5, -2018-09-08,10335.9375,204.2916667,10631.04167, -2018-09-09,10117.08333,202.8333333,7217.291667, -2018-09-10,11664.54545,203.1666667,13566.66667, -2018-09-11,19669.79167,198.8695652,12333.33333, -2018-09-12,30040.625,162.5416667,11343.75, -2018-09-13,28554.16667,139.9166667,11302.91667, -2018-09-14,22318.75,144.4166667,7718.541667, -2018-09-15,19331.25,153.2083333,6124.583333, -2018-09-16,17632.29167,158.375,5217.708333, -2018-09-17,15941.66667,163.875,4719.166667, -2018-09-18,18487.5,168.9166667,9118.75, -2018-09-19,29454.16667,163.5,7292.916667, -2018-09-20,28313.54167,142.5833333,5923.125, -2018-09-21,23462.5,143.8333333,5050.416667, -2018-09-22,19757.29167,148.25,4573.541667, -2018-09-23,16742.70833,154.9166667,4304.166667, -2018-09-24,16964.58333,161.826087,3995.625, -2018-09-25,17404.16667,160,6276.041667, -2018-09-26,29286.45833,166.0416667,10402.70833, -2018-09-27,41908.33333,138.2083333,7251.25, -2018-09-28,52209.375,126.625,19100.20833, -2018-09-29,48867.70833,134.2083333,11085.83333, -2018-09-30,41431.25,128.7083333,7906.041667, -2018-10-01,32576.04167,138.0416667,6442.916667,318.8 -2018-10-02,27377.08333,147.6666667,5480.833333,332.7083333 -2018-10-03,28231.25,156.9411765,5622.291667,348.2083333 -2018-10-04,39043.75,145.6428571,5254.791667,351.4583333 -2018-10-05,32576.04167,131.2173913,4470.625,353.625 -2018-10-06,30000,142.6666667,4183.333333,367.1666667 -2018-10-07,25296.875,145.9130435,3955.416667,380.5625 -2018-10-08,22275,156.0833333,3707.291667,392.2083333 -2018-10-09,18850,162.7083333,4215,398.3125 -2018-10-10,17329.16667,174.5,4135.416667,399.3541667 -2018-10-11,16691.66667,179.875,4019.791667,385.0416667 -2018-10-12,32753.125,174.75,7450.625,381.7291667 -2018-10-13,38917.70833,137.5833333,7024.791667,322.375 -2018-10-14,31840.625,135.125,5761.25,309.0416667 -2018-10-15,26916.66667,142.9166667,4939.375,314.1458333 -2018-10-16,23298.95833,151.7083333,4608.75,331.0625 -2018-10-17,21182.29167,159.0952381,4215,345.2916667 -2018-10-18,19447.91667,160.875,3815.416667,359.1666667 -2018-10-19,17389.58333,166.8333333,3620.833333,368 -2018-10-20,15728.125,172.4583333,3451.458333,380.25 -2018-10-21,14732.29167,179.5,3453.541667,389.1666667 -2018-10-22,14123.95833,183.6956522,3341.666667,399.125 -2018-10-23,13759.375,185,3161.875,400.8333333 -2018-10-24,13046.875,185.8333333,3008.125,404.4583333 -2018-10-25,12147.91667,187.625,2932.5,413.0208333 -2018-10-26,11826.04167,192.3333333,2858.125,420.2916667 -2018-10-27,12795.83333,195,4126.041667,424.0416667 -2018-10-28,14450,199.5833333,4645.416667,418.6666667 -2018-10-29,17935.41667,193,3697.5,390.4583333 -2018-10-30,18838.54167,162.5652174,3275.208333,384.9583333 -2018-10-31,18333.33333,154.7916667,3053.541667,390.6041667 -2018-11-01,16969.79167,152.7083333,2827.291667,407.25 -2018-11-02,15451.04167,158.2380952,2706.875,437.7083333 -2018-11-03,44356.25,153.3043478,22956.12245,295.5 -2018-11-04,50354,142.8181818,12390.2,221.46 -2018-11-05,40796.875,130.6521739,8656.25,278.8333333 -2018-11-06,37287.5,138.3478261,10079.79167,302.4375 -2018-11-07,44533.33333,140.5833333,13854.16667,264.6041667 -2018-11-08,41650,133.4166667,10527.91667,252.9791667 -2018-11-09,34981.25,138.0909091,8491.041667,279.6875 -2018-11-10,38096.875,148.8333333,13627.08333,248.2291667 -2018-11-11,38944.79167,145.125,8659.375,245.2083333 -2018-11-12,34265.625,138.5416667,6981.458333,273.3333333 -2018-11-13,33962.5,145.6086957,10599.16667,281.7083333 -2018-11-14,37120.83333,151.2916667,10111.66667,246.4166667 -2018-11-15,35682.29167,140.3333333,7335,279.875 -2018-11-16,33770.83333,147.6818182,10837.70833,402.2291667 -2018-11-17,32176.04167,172.826087,11372.5,394.625 -2018-11-18,30590.625,167.7916667,9819.791667,349.2916667 -2018-11-19,28288.54167,164.125,8072.291667,328.5833333 -2018-11-20,27382.29167,166,7693.333333,332.9166667 -2018-11-21,26995.83333,167.625,7161.875,333.4166667 -2018-11-22,26700,160.1666667,6289.375,334.7083333 -2018-11-23,23659.375,161.1304348,5545.833333,340.75 -2018-11-24,21551.04167,163.9583333,5443.333333,342.2978723 -2018-11-25,47420.83333,152.9166667,25345.83333,231.2916667 -2018-11-26,45047.91667,149.875,14602.08333,237.5208333 -2018-11-27,54588.54167,141.25,16064.58333,260.4791667 -2018-11-28,59283.33333,123.6666667,11877.08333,263.1666667 -2018-11-29,48755.20833,121.5416667,8955.833333,281.4166667 -2018-11-30,40450,130.2916667,7356.041667,289.5833333 -2018-12-01,34890.625,137.4166667,6385,302.6875 -2018-12-02,34630.20833,145.3333333,7923.333333,312.9791667 -2018-12-03,35788.54167,150.125,8598.125,288.5625 -2018-12-04,33426.04167,145.8333333,6871.041667,289.0833333 -2018-12-05,30736.45833,146.5416667,5983.333333,313.0208333 -2018-12-06,27364.58333,149.1666667,5262.708333,328.5 -2018-12-07,23769.79167,155.7916667,4852.045455,339.8666667 -2018-12-08,21362.5,161.5,4529.166667,346.1666667 -2018-12-09,19158.33333,167.0416667,4223.541667,356.0208333 -2018-12-10,17319.79167,175.5416667,4024.583333,363.5957447 -2018-12-11,16677.08333,181,3716.666667,372.3125 -2018-12-12,16197.91667,180.6666667,3463.333333,378.625 -2018-12-13,15512.5,181.75,3417.291667,387.9166667 -2018-12-14,14931.25,183.75,3354.583333,395.6041667 -2018-12-15,14686.45833,187.5833333,3460.833333,401.1041667 -2018-12-16,17960.41667,195.9166667,7935.833333,386.125 -2018-12-17,26733.33333,183.5416667,8417.916667,278.7083333 -2018-12-18,28209.375,162.6521739,5801.666667,293.4166667 -2018-12-19,25257.29167,156.4,4902.083333,330.5 -2018-12-20,21709.375,159.3181818,4387.083333,343.2083333 -2018-12-21,32912.5,158.7826087,17381.25,285.9791667 -2018-12-22,53138.54167,144.375,15643.75,222.2708333 -2018-12-23,56473.95833,122.2857143,11252.70833,239.9791667 -2018-12-24,44353.125,118.6666667,8320,264.8333333 -2018-12-25,36253.125,128.8333333,6968.125,280.2083333 -2018-12-26,31413.54167,136.5,5973.958333,289.9791667 -2018-12-27,27246.875,142.875,5307.916667,303.0833333 -2018-12-28,29031.25,147.8333333,9989.375,305.25 -2018-12-29,33836.45833,153.2916667,12795.83333,232.2916667 -2018-12-30,29706.25,155.6666667,8719.583333,261.4375 -2018-12-31,26323.95833,160.125,7408.541667,278.6041667 -2019-01-01,35687.5,153.6666667,15471.66667,253.625 -2019-01-02,36248.95833,148.0416667,10570.20833,237.1458333 -2019-01-03,33458.33333,143,8418.333333,270.6666667 -2019-01-04,29132.29167,145.375,7257.291667,289.8541667 -2019-01-05,29780.20833,149.4583333,7302.291667,307.75 -2019-01-06,35703.125,149.75,7818.125,301.7916667 -2019-01-07,32804.16667,146.2083333,5979.583333,297.875 -2019-01-08,28420.83333,150.7916667,5317.291667,315.375 -2019-01-09,26540.625,158.5333333,5671.875,336.7916667 -2019-01-10,25190.625,165.7083333,5202.5,349.5208333 -2019-01-11,24088.54167,158.7083333,4548.333333,351.8541667 -2019-01-12,20938.54167,162.5833333,4076.666667,364.0833333 -2019-01-13,18391.66667,173.375,3852.708333,391.6041667 -2019-01-14,17442.70833,181.3333333,3786.041667,405.8958333 -2019-01-15,16229.16667,182.2916667,3641.875,409.4791667 -2019-01-16,15368.75,183.7916667,3501.666667,404.1458333 -2019-01-17,14676.04167,188.75,3337.708333,403.1041667 -2019-01-18,13929.16667,191.5,3213.333333,420.8958333 -2019-01-19,13456.25,196.0833333,3169.166667,434.0833333 -2019-01-20,19697.91667,198.8333333,12319.58333,474.125 -2019-01-21,19828.125,283.4583333,10046.66667,387.8958333 -2019-01-22,15831.25,241.9166667,6024.166667,369.5 -2019-01-23,14550,234.1666667,5389.166667,390.7291667 -2019-01-24,25126.04167,212.9166667,13687.7551,397.5 -2019-01-25,63520.83333,161.25,21633.33333,257.6875 -2019-01-26,58841.66667,137.4583333,11896.875,262.2708333 -2019-01-27,41343.75,141.0833333,8608.958333,279.1458333 -2019-01-28,33991.66667,143.4117647,7258.958333,301.1041667 -2019-01-29,28664.58333,146.2380952,6225.625,313.3958333 -2019-01-30,24460.41667,157.25,5520.208333,345.8333333 -2019-01-31,21618.75,177.2916667,4630.416667,385.4583333 -2019-02-01,19928.26087,189.7083333,3824.166667,390.2291667 -2019-02-02,,205.0833333,3823.958333,413.6041667 -2019-02-03,,206.875,4077.708333,420.1875 -2019-02-04,,205.7083333,4057.916667,409.1666667 -2019-02-05,,202.625,4043.333333,400.6875 -2019-02-06,17468.75,198.6666667,4229.375,399.4375 -2019-02-07,21538.54167,190.8333333,5498.333333,436.3333333 -2019-02-08,25315.625,192.5416667,5441.041667,468.8333333 -2019-02-09,32783.33333,180.625,5920,452.9375 -2019-02-10,33234.375,146.2083333,4902.708333,418.1875 -2019-02-11,26500,145.7916667,4553.541667,424.75 -2019-02-12,23747.91667,150.4583333,4773.125,484.75 -2019-02-13,23144.79167,173.3333333,6658.125,826.5208333 -2019-02-14,21500,207.6666667,6346.041667,778.5 -2019-02-15,20187.5,201.375,5462.340426,650.875 -2019-02-16,20022.91667,195.2916667,6371.666667,554.75 -2019-02-17,18458.33333,198.7916667,5053.958333,469.6666667 -2019-02-18,16269.79167,201.1666667,4701.875,455.8541667 -2019-02-19,16073.95833,215.5,4490.208333,456.6666667 -2019-02-20,15683.33333,208.1666667,4271.041667,464.1875 -2019-02-21,16487.5,215.0833333,5413.333333,564.25 -2019-02-22,19561.45833,288.7083333,7711.041667,639.8125 -2019-02-23,17819.79167,262.125,5980.625,529.7291667 -2019-02-24,18336.45833,238.125,7269.375,477.5833333 -2019-02-25,20468.75,239.625,8149.791667,456.3541667 -2019-02-26,19130.20833,230.7916667,6279.583333,411.375 -2019-02-27,18638.54167,215.5416667,5493.541667,414.0416667 -2019-02-28,17082.29167,211.2608696,4952.5,418.2291667 -2019-03-01,16084.375,212.5833333,4664.375,430.625 -2019-03-02,15327.08333,216.8333333,4994.791667,481.3958333 -2019-03-03,14228.125,240.125,5019.166667,626.4166667 -2019-03-04,14480.20833,253.4166667,5625,663.9583333 -2019-03-05,14103.125,264.7272727,5131.25,696.2291667 -2019-03-06,13800,261.0416667,4159.375,641.3125 -2019-03-07,12784.375,243.4166667,3634.791667,604.6041667 -2019-03-08,11714.58333,236.8695652,3439.375,574.2291667 -2019-03-09,11106.25,243.4166667,3399.347826,546.7083333 -2019-03-10,14920.65217,245.7826087,9528.913043,540.2608696 -2019-03-11,16992.70833,256.7083333,11047.70833,385.875 -2019-03-12,16042.70833,268.0416667,8484.166667,383.1666667 -2019-03-13,14848.78049,264.3125,6775.625,388.5208333 -2019-03-14,13889.58333,246.25,5901.25,399.4375 -2019-03-15,13038.54167,244.0416667,5371.666667,406.6875 -2019-03-16,17928.125,241.9583333,7570.833333,411.6458333 -2019-03-17,22016.66667,223,5730.625,372.6458333 -2019-03-18,19938.54167,195.0666667,5047.083333,384.5 -2019-03-19,17261.45833,193.8333333,4710.625,390.3541667 -2019-03-20,15621.875,196.3913043,4332.916667,394.0416667 -2019-03-21,14803.125,201.75,4842.916667,403.0416667 -2019-03-22,25554.16667,205.375,21183.87755,326.7708333 -2019-03-23,34019.79167,193.0434783,16337.5,263.4791667 -2019-03-24,32504.16667,167.3333333,10690.41667,283.6041667 -2019-03-25,25365.625,170.15,8136.25,300.5 -2019-03-26,22644.79167,178.4583333,6861.25,317.6875 -2019-03-27,21013.54167,180.7916667,5777.083333,330.6666667 -2019-03-28,19030.20833,181.125,4948.333333,344 -2019-03-29,17136.45833,184.5833333,4524.375,355.7291667 -2019-03-30,15403.125,191.7083333,4319.375,362.375 -2019-03-31,14806.25,196.8333333,3992.083333,372.625 -2019-04-01,16268.75,194.1666667,3691.25,378.9375 -2019-04-02,18027.08333,178.8333333,3217.708333,382.7083333 -2019-04-03,16977.08333,171.2916667,2956.25,390.2708333 -2019-04-04,14992.70833,172,2863.125,400.6875 -2019-04-05,13806.25,178.5833333,2761.25,410 -2019-04-06,13831.25,184.375,2820,421.0833333 -2019-04-07,13582.29167,187.0833333,2811.041667,426.7708333 -2019-04-08,13184.375,191.625,2760.416667,428.3333333 -2019-04-09,13354.16667,192.75,2808.333333,425.7111111 -2019-04-10,14796.875,188.125,2884.166667,425.1875 -2019-04-11,14954.16667,180.0416667,2553.617021,432.2340426 -2019-04-12,14061.45833,174.5,2456.595745,435.4583333 -2019-04-13,18493.75,180.875,3665.744681,449.3333333 -2019-04-14,26248.95833,177.5833333,9784.166667,372.7291667 -2019-04-15,29501.04167,164.0416667,10127.02128,263.8958333 -2019-04-16,49981.25,147.5416667,12639.58333,246.5208333 -2019-04-17,48556.25,101.6363636,8551.276596,242.0625 -2019-04-18,37411.45833,114.6666667,6196.666667,272.5208333 -2019-04-19,30960.41667,128.5833333,5528.958333,293.8541667 -2019-04-20,31486.45833,139.9583333,9344.166667,318.2708333 -2019-04-21,44809.375,144.0416667,9418.958333,243.5416667 -2019-04-22,45845.45455,124.3333333,5443.617021,285.4375 -2019-04-23,37366.66667,125.5833333,4516.041667,307.6875 -2019-04-24,30856.25,135,4041.875,324.9583333 -2019-04-25,26417.70833,142.4166667,3660.638298,340.6666667 -2019-04-26,24054.16667,148.9166667,3873.125,351.9583333 -2019-04-27,28144.79167,159.75,7917.083333,331.1458333 -2019-04-28,32133.33333,149.5416667,4699.787234,292.3333333 -2019-04-29,29116.66667,136.4583333,3673.404255,326.8541667 -2019-04-30,26243.47826,140.95,3303.829787,346.375 -2019-05-01,23738.54167,146.5217391,3155,357.0227273 -2019-05-02,22092.70833,150.0833333,3016.666667,372.8541667 -2019-05-03,20142.70833,154.875,2868.085106,389.1458333 -2019-05-04,19692.70833,159.3333333,3381.25,404.8958333 -2019-05-05,26839.58333,165.9166667,6375.531915,390.8333333 -2019-05-06,39170.83333,165.0833333,12333.125,266.3541667 -2019-05-07,33421.875,164.75,6820.416667,284.2708333 -2019-05-08,31406.25,163.875,6573.958333,325.5833333 -2019-05-09,26452.08333,167,4950.638298,305.0416667 -2019-05-10,21816.66667,173.8333333,4136.458333,325.8125 -2019-05-11,20232.29167,181.9583333,4390.625,351.75 -2019-05-12,24127.08333,181.0833333,6130.851064,345.7291667 -2019-05-13,38157.29167,171.375,14053.19149,271.4375 -2019-05-14,49990.625,155.625,17210.41667,240.2916667 -2019-05-15,52207.29167,137.5416667,12337.5,252.9166667 -2019-05-16,43516.66667,134.75,8675.208333,271.7708333 -2019-05-17,35202.08333,140.875,7042.291667,277.8958333 -2019-05-18,29736.45833,149.8333333,6345,298.4166667 -2019-05-19,26485.41667,157.5416667,5347.291667,311.4583333 -2019-05-20,29342.70833,162.375,7275.833333,321.4791667 -2019-05-21,26717.70833,171.75,10368.54167,269.5833333 -2019-05-22,23522.91667,175.7083333,6491.666667,284.5625 -2019-05-23,20564.58333,179.1666667,5278.085106,299.7291667 -2019-05-24,19691.66667,187.5833333,5011.489362,323.4166667 -2019-05-25,18697.91667,190.0416667,4438.085106,339.0625 -2019-05-26,16259.375,190.5,4037.659574,346.4791667 -2019-05-27,14309.375,202.5,4165.744681,359.8541667 -2019-05-28,13800,209.4166667,3851.489362,369.6666667 -2019-05-29,15529.16667,208,5044.680851,365.2708333 -2019-05-30,26333.33333,189.5833333,12349.375,283.3541667 -2019-05-31,36523.95833,166.2083333,11948.125,257.0416667 -2019-06-01,29997.91667,160.25,7390.434783,273.5869565 -2019-06-02,24046.875,170.25,5895.531915,310.1458333 -2019-06-03,20333.33333,180.4166667,5992.553191,324.6458333 -2019-06-04,17183.33333,191.4583333,4844.042553,319 -2019-06-05,15252.08333,198.9583333,4236.382979,332.75 -2019-06-06,13775,206.2916667,4718.085106,345.0208333 -2019-06-07,12586.45833,217.8333333,3657.87234,343.2173913 -2019-06-08,12076.04167,218.5833333,3317.87234,373.3958333 -2019-06-09,11264.58333,220.4166667,3047.87234,389.2916667 -2019-06-10,10102.08333,227.5,3127.021277,399.5 -2019-06-11,11648.95833,231.875,5310,376.0416667 -2019-06-12,14034.375,229.7083333,4116.666667,376.7291667 -2019-06-13,15407.29167,194.5,4022.083333,366.9166667 -2019-06-14,14095.83333,192.5416667,3858.333333,369.3125 -2019-06-15,13075,201.1666667,3285,403.5625 -2019-06-16,11836.45833,201.25,2891.666667,411.5208333 -2019-06-17,10454.16667,209.875,3175.208333,423.9791667 -2019-06-18,12582.29167,220.2083333,2914.375,431.2708333 -2019-06-19,21898.94737,196.3333333,3764.166667,428.9583333 -2019-06-20,24479.16667,179.7083333,17949.16667,313.2916667 -2019-06-21,24475,170.875,16835.41667,224.212766 -2019-06-22,26514.58333,170.1666667,10516.875,306.2708333 -2019-06-23,23416.66667,154.125,6330.833333,326.6666667 -2019-06-24,18740.625,163.5416667,4803.333333,346.3333333 -2019-06-25,17300,170.7083333,4607.5,356.8125 -2019-06-26,16775,174.0416667,5268.333333,365.9583333 -2019-06-27,14348.95833,184.5416667,3998.541667,338.5625 -2019-06-28,12732.29167,194.8333333,3454.791667,373.1875 -2019-06-29,11969.79167,204.25,3386.875,401.8958333 -2019-06-30,10868.4375,212,3198.333333,419.5416667 -2019-07-01,9596.666667,213.952381,2876.875,411.2916667 -2019-07-02,8788.333333,221,2663.75,433.2291667 -2019-07-03,8498.333333,227.4583333,2880.416667,435.75 -2019-07-04,8187.5,230.3333333,3121.875,449.3333333 -2019-07-05,7653.958333,238.1666667,3294.791667,433.2291667 -2019-07-06,7397.8125,237,3181.875,425.625 -2019-07-07,10183.02083,227.4583333,4812.5,408.25 -2019-07-08,9674.0625,230.8333333,3361.25,387.4375 -2019-07-09,10861.35417,227.9583333,5413.125,369.6666667 -2019-07-10,9540.416667,227.875,4011.875,388.3191489 -2019-07-11,9560.9375,224.9583333,3943.125,388.4375 -2019-07-12,20063.73626,190.8333333,30145.10204,186.9166667 -2019-07-13,11567.70833,205.5833333,6340.625,251 -2019-07-14,8636.979167,235.4166667,3723.958333,336.0625 -2019-07-15,7414.375,257.6428571,2968.75,370.3125 -2019-07-16,6723.541667,270.9583333,2613.75,398.2916667 -2019-07-17,6337.291667,274.5833333,2515.833333,412.2291667 -2019-07-18,7915.520833,276.7142857,5355.833333,416.2083333 -2019-07-19,10161.97917,261.3478261,7111.041667,297.3829787 -2019-07-20,9388.229167,249.9583333,3427.708333,288.5208333 -2019-07-21,8495.520833,244.1666667,2639.583333,346.5833333 -2019-07-22,9106.5625,250.1666667,2600.208333,394.9583333 -2019-07-23,18682.29167,226.875,9366.875,382.9791667 -2019-07-24,19126.04167,197.5833333,6948.75,264.0625 -2019-07-25,14940.625,214.4583333,4123.541667,311.4583333 -2019-07-26,11606.25,222.2083333,3100,351.0416667 -2019-07-27,9378.125,238.6666667,2671.666667,373.2291667 -2019-07-28,8489.270833,254.9583333,2402.916667,393.75 -2019-07-29,7550.104167,263.875,2223.333333,411.4791667 -2019-07-30,7124.479167,269.0526316,2015.833333,423.6041667 -2019-07-31,6985,269.1363636,2083.125,434.3333333 -2019-08-01,7664.583333,270.8333333,2041.666667,443.3958333 -2019-08-02,7332.1875,260.75,1880,438.7916667 -2019-08-03,6425.9375,260.5,1935,459.4791667 -2019-08-04,6316.041667,273.8333333,2098.333333,477.9166667 -2019-08-05,6119.583333,282.0416667,2090.833333,476.5625 -2019-08-06,5884.895833,284.25,2045,472.1458333 -2019-08-07,6526.354167,274.5,2817.291667,445.5208333 -2019-08-08,8866.770833,257.25,2537.708333,337.3333333 -2019-08-09,8626.041667,257.7916667,2205,438.7916667 -2019-08-10,8073.4375,238.75,1833.75,478.1458333 -2019-08-11,7393.645833,237.25,1535.833333,458.9791667 -2019-08-12,6513.229167,242.4166667,1390,457.5833333 -2019-08-13,5835,254.5833333,1323.75,475 -2019-08-14,5393.333333,261.1666667,1643.541667,490.0416667 -2019-08-15,5342.916667,266.0416667,2981.458333,458.125 -2019-08-16,5080.833333,279.6666667,1732.708333,408.5208333 -2019-08-17,4994.166667,283.3333333,1593.125,457.7291667 -2019-08-18,5053.645833,286.5,1467.916667,474.5 -2019-08-19,6581.666667,280.4166667,1831.041667,494.1666667 -2019-08-20,6474.270833,263.1666667,1994.166667,499.9166667 -2019-08-21,5913.229167,254.2083333,1639.583333,497.625 -2019-08-22,5806.666667,254.5416667,2324.791667,475.5 -2019-08-23,6301.875,261.4166667,1869.375,452.75 -2019-08-24,6290.208333,265.2916667,2009.166667,462.4166667 -2019-08-25,5567.604167,261.2916667,1528.125,453.1458333 -2019-08-26,5161.354167,281.2916667,1273.541667,447.6666667 -2019-08-27,4667.8125,284.0833333,1191.875,473.6041667 -2019-08-28,4215.208333,277.3913043,1214.375,475.625 -2019-08-29,3972.291667,280.5,1191.875,482.5833333 -2019-08-30,3842.291667,297.375,1143.75,502.1041667 -2019-08-31,3694.270833,309.0833333,1072.104167,520.875 -2019-09-01,3746.458333,312.5,1019.625,536.9583333 -2019-09-02,4134.791667,314.0416667,1389.791667,546.0208333 -2019-09-03,4621.979167,313.4583333,2275.416667,546.625 -2019-09-04,4385,287.25,1622.916667,496.5208333 -2019-09-05,3894.375,274.9583333,1334.583333,504.8125 -2019-09-06,3905.833333,286.8333333,1133.75,491.9166667 -2019-09-07,3798.645833,293.9583333,1113.958333,459.1666667 -2019-09-08,3548.75,291.6666667,1029.979167,472.8958333 -2019-09-09,3529.583333,298.7083333,980.5416667,503.6875 -2019-09-10,3502.916667,302.7083333,965.9791667,528.375 -2019-09-11,3371.5625,303.3333333,946.2291667,556.3333333 -2019-09-12,3469.270833,305.5625,942.25,575.0625 -2019-09-13,3909.166667,294.0833333,1271.875,575.9583333 -2019-09-14,3843.958333,298.9583333,1180,580.7708333 -2019-09-15,3735,301.8333333,1007.395833,567.7708333 -2019-09-16,3605.416667,291.3333333,922.4583333,555.9583333 -2019-09-17,3425.208333,292.2941176,833.6875,571.75 -2019-09-18,3394.895833,293.5,799.5,571.6041667 -2019-09-19,3187.708333,292.7916667,745.8958333,565.9375 -2019-09-20,3113.4375,295.1666667,733.8125,562.0208333 -2019-09-21,3028.4375,300.125,717.8541667,569.8333333 -2019-09-22,2954.375,302.0833333,685.0833333,581.0625 -2019-09-23,2995.3125,305.25,686.7291667,588.9583333 -2019-09-24,3013.4375,314.2916667,676.4791667,599.3333333 -2019-09-25,3111.5625,316.875,681.6041667,610.4166667 -2019-09-26,3094.375,303.1666667,654.4583333,621.1666667 -2019-09-27,3204.270833,291.2916667,655.7291667,630.3541667 -2019-09-28,3170.3125,287.125,674.75,636.9166667 -2019-09-29,3348.020833,288.9583333,837.125,634.4583333 -2019-09-30,3267.916667,285.5416667,707.6666667,624.3125 -2019-10-01,3280.470588,284.6666667,744.3958333,638.1666667 -2019-10-02,3200.833333,282.75,701.8125,644.4166667 -2019-10-03,3051.071429,278.1304348,700.875,639.125 -2019-10-04,3231.458333,281.1666667,789.8333333,626.1458333 -2019-10-05,3168.020833,288.1363636,748.3125,620.7916667 -2019-10-06,2931.5625,298.6086957,699.3958333,620.8125 -2019-10-07,2990.3125,305.25,728.0833333,624.0833333 -2019-10-08,3480,302.4166667,863.7291667,620.0416667 -2019-10-09,3728.020833,294.5833333,1089.395833,634.7708333 -2019-10-10,3277.291667,291.2916667,912.5208333,624.1041667 -2019-10-11,3259.146341,283.0416667,758.7291667,610.4375 -2019-10-12,3210.571429,291.25,690.2083333,598.7291667 -2019-10-13,3593.571429,286.3333333,668.9791667,588.2916667 -2019-10-14,4357.916667,281.875,684.5625,585.9375 -2019-10-15,3424.895833,301.9166667,643.2916667,571.8125 -2019-10-16,3097.916667,250.8095238,1034.541667,566.4375 -2019-10-17,6567.604167,252.0416667,3816.25,544.1666667 -2019-10-18,7925.104167,247.625,2682.083333,487.8863636 -2019-10-19,12018.33333,217.5416667,1707.708333,384.125 -2019-10-20,9258.854167,175.375,1356.041667,358.125 -2019-10-21,7399.166667,181.875,1625.833333,405.2708333 -2019-10-22,6330.9375,198.9583333,1476.458333,437.9375 -2019-10-23,6391.666667,209.0416667,2643.125,454.75 -2019-10-24,6970.208333,228.125,2353.75,440.2083333 -2019-10-25,6694.895833,231.4583333,1750.833333,428.1875 -2019-10-26,6845,216.1666667,1512.708333,401.4166667 -2019-10-27,6480.243902,208.4166667,2989.583333,415.5625 -2019-10-28,13746.97917,217.375,7510.833333,353.5833333 -2019-10-29,12803.125,197.75,4280.625,286.6041667 -2019-10-30,12464.58333,179.625,3158.958333,309.5625 -2019-10-31,11232.29167,178.2916667,3307.5,358.8333333 -2019-11-01,25812.5,186.2727273,15358.97959,309.4375 -2019-11-02,39898.95833,146.8181818,13568.125,220 -2019-11-03,33781,109.28,7094,253.16 -2019-11-04,23345.83333,115.9583333,4935.416667,282.1458333 -2019-11-05,18547.91667,130.375,3674.375,297.6875 -2019-11-06,15148.95833,143.6666667,3032.5,315.0625 -2019-11-07,12409.375,158.7142857,2666.666667,331.0625 -2019-11-08,11551.04167,174.4583333,2513.125,348.6041667 -2019-11-09,10845.83333,182.0416667,2540.625,362.4791667 -2019-11-10,9785.520833,187.125,2203.333333,375.0625 -2019-11-11,8938.229167,193.2173913,2048.333333,382.5416667 -2019-11-12,8216.25,201.375,1987.5,385.8333333 -2019-11-13,7712.395833,208.0833333,1944.166667,398.9375 -2019-11-14,7615.9375,216.125,1840.208333,407.0416667 -2019-11-15,7306.458333,213.9166667,1703.75,413.7708333 -2019-11-16,6585.3125,213.5416667,1573.541667,424.0833333 -2019-11-17,6512.395833,224.3333333,1530.208333,432.2291667 -2019-11-18,5921.5625,225,1476.666667,445.6458333 -2019-11-19,5685.833333,232.75,1446.666667,452.4791667 -2019-11-20,5720.9375,241.2083333,1406.875,452.6041667 -2019-11-21,6144.270833,241.125,1369.375,455.8958333 -2019-11-22,6276.25,230.5416667,1341.666667,461.7083333 -2019-11-23,6084.791667,224.8333333,1340.208333,470.1666667 -2019-11-24,7936.5625,223.4583333,3847.291667,468.2916667 -2019-11-25,10749.16667,230.0769231,5105.833333,375.9166667 -2019-11-26,12895.83333,210.2608696,3285.208333,309.2916667 -2019-11-27,12311.45833,179.1176471,2657.5,325.5208333 -2019-11-28,10739.58333,181,2299.166667,346.6875 -2019-11-29,10135.41667,182.7058824,1997.5,370.3541667 -2019-11-30,9714.0625,185.4705882,1844.583333,390.4583333 -2019-12-01,9405.208333,190.5625,1933.958333,408.9791667 -2019-12-02,10604.0625,198.0666667,3960.208333,412.6666667 -2019-12-03,11132.29167,235,3364.791667,383.5833333 -2019-12-04,10496.25,242.7083333,2620.625,358.9375 -2019-12-05,9761.979167,235.25,2280.416667,374.3541667 -2019-12-06,8978.958333,226.7083333,2097.5,393.4583333 -2019-12-07,8466.979167,230.375,1924.375,410.8541667 -2019-12-08,8083.020833,241.0416667,1776.041667,424.7083333 -2019-12-09,8402.395833,232.5833333,2250,434.3125 -2019-12-10,12887.5,230.3333333,6040.208333,399.3098592 -2019-12-11,14687.5,244.75,5304.375,323.5538462 -2019-12-12,21722.91667,215.0833333,4312.5,332.8958333 -2019-12-13,19794.79167,186.5416667,3380.625,342.7291667 -2019-12-14,19935.41667,187.125,4887.916667,367.5625 -2019-12-15,19690.625,200.4166667,5021.666667,353.9583333 -2019-12-16,22676.04167,185.4583333,4166.875,331.6875 -2019-12-17,23922.91667,171.375,7592.083333,334.5833333 -2019-12-18,21807.29167,192.9583333,7685,298.1458333 -2019-12-19,18333.33333,194.9583333,4731.458333,297.0208333 -2019-12-20,15751.04167,191.9230769,3775.208333,325.1666667 -2019-12-21,13627.08333,,3205.416667,346.5833333 -2019-12-22,12172.91667,,2887.291667,363.5957447 -2019-12-23,10777.08333,206.0909091,2714.375,376.1041667 -2019-12-24,10443.75,,2581.25,383.0833333 -2019-12-25,10468.75,,2429.166667,384.7708333 -2019-12-26,9880.208333,,2285.833333,394.6041667 -2019-12-27,9489.270833,204.4444444,2153.333333,402.3333333 -2019-12-28,9235.520833,206.6111111,2053.333333,405.375 -2019-12-29,9019.375,211.4,2003.333333,406.0833333 -2019-12-30,10905.10417,212,2943.125,406.7916667 -2019-12-31,16931.25,202.0952381,5891.458333,390.8958333 -2020-01-01,20259.375,178.6086957,4099.375,330.2291667 -2020-01-02,17663.54167,167.4166667,3257.916667,332.3333333 -2020-01-03,15827.08333,168.1304348,2871.875,353.6666667 -2020-01-04,15117.70833,172.25,3017.5,368.0416667 -2020-01-05,14933.33333,177.5416667,3290.833333,377.125 -2020-01-06,14690.625,179.5,2849.375,378.875 -2020-01-07,14023.95833,174.1666667,2602.083333,369.25 -2020-01-08,13700,178.7916667,2551.25,371.8125 -2020-01-09,12878.125,181.0416667,2417.083333,384.3958333 -2020-01-10,11902.08333,182.8333333,2258.333333,402.1041667 -2020-01-11,11167.70833,185.625,2107.5,413.1875 -2020-01-12,11355.20833,189.0416667,2373.541667,417.5416667 -2020-01-13,11890.625,197.7916667,3170.625,421.5 -2020-01-14,14727.08333,196.4166667,2711.458333,408.9791667 -2020-01-15,14281.25,168.9583333,2549.583333,407.5416667 -2020-01-16,12936.45833,171.1666667,2547.5,403.3125 -2020-01-17,12022.91667,176.7916667,2350,413.3958333 -2020-01-18,11685.41667,176.9583333,2123.333333,417.6666667 -2020-01-19,10709.0625,177.0833333,2256.25,448.2708333 -2020-01-20,9812.708333,197.625,2504.166667,571.1041667 -2020-01-21,9472.291667,219.5833333,2170.833333,572.5833333 -2020-01-22,9465.416667,209.7083333,1936.666667,570.4375 -2020-01-23,9080.208333,205.4761905,1851.666667,573.5833333 -2020-01-24,9429.583333,203.375,1832.083333,535 -2020-01-25,12980.9375,199,11256.73469,480.1041667 -2020-01-26,22320.83333,213.3333333,17904.16667,237.5416667 -2020-01-27,20911.45833,202.875,9114.375,284.2708333 -2020-01-28,20798.95833,179.2083333,6513.541667,300.5833333 -2020-01-29,17408.33333,170.125,5265.625,309.9791667 -2020-01-30,15160.41667,174.2916667,4250,324.6458333 -2020-01-31,13983.33333,191.625,3664.166667,334.4375 -2020-02-01,13162.5,180,3396.25,345.125 -2020-02-02,12618.75,182.4583333,3207.083333,359.2916667 -2020-02-03,11961.45833,186.4166667,3046.875,364.3541667 -2020-02-04,11915.625,196.7083333,2846.666667,374.125 -2020-02-05,12408.33333,190.7083333,2653.541667,375.3125 -2020-02-06,13221.875,188.3333333,3154.166667,388.9375 -2020-02-07,15058.33333,188.875,4585.416667,405.0416667 -2020-02-08,18069.79167,187.9166667,6005.416667,374.5 -2020-02-09,22662.5,166.4583333,4468.958333,342.1458333 -2020-02-10,19245.83333,161.2916667,3863.125,342.5 -2020-02-11,20323.95833,171.1666667,6158.333333,363.0625 -2020-02-12,19759.375,166.4583333,5755.625,335.5833333 -2020-02-13,20036.45833,164.6666667,5195.208333,336.5416667 -2020-02-14,21403.125,174.0833333,6016.458333,351.125 -2020-02-15,20865.625,171.25,4850.208333,335.0625 -2020-02-16,17406.25,172.2916667,4122.5,346.4166667 -2020-02-17,15484.375,186.9166667,3891.25,357.4166667 -2020-02-18,14971.875,187.3529412,3645.208333,360.25 -2020-02-19,14471.875,185.4166667,3425.208333,366.75 -2020-02-20,13800,203.1904762,3250.625,377.5416667 -2020-02-21,13719.79167,186.1666667,2924.791667,388.8333333 -2020-02-22,12441.66667,184.2083333,2677.291667,402.3541667 -2020-02-23,11310.41667,190.375,2552.916667,413.5208333 -2020-02-24,10301.25,200.5416667,2466.875,416.8125 -2020-02-25,10108.02083,205.0416667,2418.541667,426.3958333 -2020-02-26,10696.875,204.875,2403.958333,428.0625 -2020-02-27,12037.5,198.2083333,3856.041667,422.1666667 -2020-02-28,14962.36559,194.625,3736.875,404.25 -2020-02-29,18471.875,166.25,2862.291667,377.9791667 -2020-03-01,15277.08333,157,2482.291667,380.6875 -2020-03-02,13094.79167,168.375,2290.416667,411.9791667 -2020-03-03,12317.70833,176.875,2231.041667,421.0416667 -2020-03-04,13243.75,175.7916667,3078.333333,421.4166667 -2020-03-05,13790.625,181.25,3065.416667,440.2083333 -2020-03-06,14920.83333,170.3333333,2711.875,412.3958333 -2020-03-07,14297.91667,162.7916667,2936.666667,419.5 -2020-03-08,13168.47826,164.2608696,2758.478261,426.4565217 -2020-03-09,11832.29167,171.047619,2432.291667,440.3958333 -2020-03-10,11222.91667,178.4166667,2300.416667,421.5 -2020-03-11,11101.04167,177.7916667,2269.791667,404.4166667 -2020-03-12,10589.58333,179.5416667,2377.083333,400.125 -2020-03-13,10275,185.1666667,2525,406.75 -2020-03-14,10729.16667,188.875,2546.666667,413.8541667 -2020-03-15,11143.75,189.0833333,2327.5,412.6458333 -2020-03-16,10882.29167,179.8333333,2088.333333,409.5416667 -2020-03-17,10172.91667,179.375,2002.5,409.2083333 -2020-03-18,9690.520833,181.0416667,1940,405.9375 -2020-03-19,11589.375,180.9166667,4341.458333,400.4375 -2020-03-20,12781.25,188.5416667,4550,348.8541667 -2020-03-21,13853.125,184.875,3436.041667,327.25 -2020-03-22,13545.83333,170.2608696,2930,337.0625 -2020-03-23,13198.95833,168.9166667,3135,357.6666667 -2020-03-24,15593.75,172.0416667,4995.625,356.6666667 -2020-03-25,15201.04167,175.7083333,3956.875,330.7291667 -2020-03-26,15296.875,173.4583333,3452.083333,334.5434783 -2020-03-27,14508.33333,169.25,3229.791667,345.7291667 -2020-03-28,13990.625,176.0833333,4138.125,355.9166667 -2020-03-29,16397.91667,175.625,10518.95833,289.25 -2020-03-30,16088.54167,187.5416667,7418.541667,245.8958333 -2020-03-31,18015.625,167.0416667,6080.625,284.0625 -2020-04-01,18505.20833,159.9583333,5180,296.7083333 -2020-04-02,17212.5,158.5,4413.333333,306.125 -2020-04-03,15704.16667,160.7083333,3870.625,315.125 -2020-04-04,14562.96296,163.2083333,3497.083333,326.625 -2020-04-05,12898.66667,167.9166667,3217.916667,334.7291667 -2020-04-06,12029.16667,174.5833333,3026.041667,343.1458333 -2020-04-07,11526.66667,178.3333333,2802.083333,344 -2020-04-08,12144.79167,,3369.791667,348.2083333 -2020-04-09,11837.68116,,3631.25,349.9791667 -2020-04-10,11610.41667,,3012.083333,346.5833333 -2020-04-11,12243.75,,2625.208333,340.6666667 -2020-04-12,12329.16667,,2387.291667,350.4375 -2020-04-13,19383.33333,147.75,11235.41667,321.5319149 -2020-04-14,33616.66667,159.5416667,15575,191.3333333 -2020-04-15,38808.33333,129.375,9763.333333,239.8333333 -2020-04-16,30287.5,127.6666667,6903.125,252.4583333 -2020-04-17,24315.625,123.9583333,5436.458333,264.8125 -2020-04-18,20963.54167,133.875,4684.583333,279.4166667 -2020-04-19,19418.75,143.7916667,4467.291667,296.9583333 -2020-04-20,18279.16667,148.2083333,4007.916667,303.6875 -2020-04-21,17572.91667,149,3634.375,304.1458333 -2020-04-22,16887.5,,3422.708333,319.1875 -2020-04-23,16071.875,,3071.458333,330.8541667 -2020-04-24,16476.04167,,4487.083333,335.3333333 -2020-04-25,16718.75,,5173.541667,323.7083333 -2020-04-26,15744.79167,,4191.666667,308.3541667 -2020-04-27,17657.29167,170.125,6672.083333,304.3333333 -2020-04-28,23136.45833,153.5,5223.541667,275.25 -2020-04-29,23310.41667,135.7083333,4479.166667,295.25 -2020-04-30,21033.33333,137.9166667,4196.666667,313.8958333 -2020-05-01,28948.95833,148,12032.65306,279.7291667 -2020-05-02,46844.79167,125.625,11055.41667,229.3333333 -2020-05-03,47898.95833,102.8333333,7727.708333,223.25 -2020-05-04,36844.79167,106.4583333,6302.083333,246.3958333 -2020-05-05,30312.5,118.875,5460.625,263 -2020-05-06,24834.66667,128.9166667,4635.208333,281.8958333 -2020-05-07,21493.9759,138.125,4181.875,304.0833333 -2020-05-08,18658.75,145.75,3906.875,312.4583333 -2020-05-09,19336.45833,152.7916667,5777.5,318.375 -2020-05-10,17576.04167,157.2083333,4604.166667,305.5625 -2020-05-11,15550,161.2916667,3688.125,297.2083333 -2020-05-12,14498.95833,166.2631579,3372.916667,317.3913043 -2020-05-13,14072.91667,167.7727273,3118.958333,337.1041667 -2020-05-14,13214.58333,166.9444444,2860.416667,352.5625 -2020-05-15,11792.70833,171.125,2723.958333,365.9583333 -2020-05-16,11500,178.0416667,2592.291667,374.6875 -2020-05-17,11897.91667,178,2493.333333,381.9375 -2020-05-18,13685.41667,171.8333333,2388.75,382.3125 -2020-05-19,12702.08333,160.25,2271.041667,390.6875 -2020-05-20,12372.91667,160.9166667,2165.833333,391.8541667 -2020-05-21,11070.83333,159.9166667,2069.166667,396.1666667 -2020-05-22,10035.72917,162.5,2194.166667,405.7083333 -2020-05-23,9698.125,173,3216.458333,412.0833333 -2020-05-24,9763.854167,183.6666667,3150.416667,392.3125 -2020-05-25,9736.744186,190.3333333,2435.833333,367.3958333 -2020-05-26,9296.770833,183.9166667,2175,366.5208333 -2020-05-27,8846.5625,190.3333333,2016.666667,384.0454545 -2020-05-28,8524.270833,187.2083333,1958.333333,398.6666667 -2020-05-29,7963.333333,188.375,1977.5,402.5 -2020-05-30,7925.833333,195.4166667,2480.416667, -2020-05-31,8519.791667,199.375,2584.375, -2020-06-01,8426.875,197.875,2165.208333, -2020-06-02,7677.916667,186.625,1935, -2020-06-03,7196.041667,190.5,2126.25,396.7 -2020-06-04,7407.093023,198.6190476,2869.583333,402.5833333 -2020-06-05,9208.023256,202.4166667,4358.125,390.5833333 -2020-06-06,11197.91667,197.7916667,5827.083333,327.7916667 -2020-06-07,9324.166667,182.3333333,3602.083333,313.6041667 -2020-06-08,7602.771084,183.4583333,2744.583333,342.0208333 -2020-06-09,6851.27907,197.5,2450.208333,356.5 -2020-06-10,6459.583333,204.5416667,2273.75,360.1875 -2020-06-11,6201.458333,207.375,2431.875,371.8958333 -2020-06-12,11804.6875,214.1666667,3624.166667,388.375 -2020-06-13,19305.20833,136.2916667,3464.583333,387.125 -2020-06-14,14998.95833,125.125,2807.291667,347.3958333 -2020-06-15,9757.395833,132.75,2434.583333,318.625 -2020-06-16,8374.895833,158.0416667,2222.5,327.1276596 -2020-06-17,7951.770833,170.3333333,2010,348.375 -2020-06-18,7188.4375,174.2916667,1971.666667,372.0625 -2020-06-19,6679.101124,181.625,1904.166667,387.625 -2020-06-20,6602.083333,188.9583333,1966.666667,404.2291667 -2020-06-21,6516.458333,197.9583333,2123.333333,429.6458333 -2020-06-22,5778.333333,202.3333333,1858.958333,433.6875 -2020-06-23,5648.854167,205.4166667,1682.916667,432.7916667 -2020-06-24,5386.941176,214.5,1735,436.6666667 -2020-06-25,6206.506024,217.3684211,1836.666667,425.9166667 -2020-06-26,5545.243902,216.5416667,1763.125,440.2291667 -2020-06-27,5031.041667,216.8333333,1622.916667,453.9375 -2020-06-28,5258.125,221.0416667,1437.708333,456.9791667 -2020-06-29,5173.958333,230.9166667,1632.083333,462.8333333 -2020-06-30,5376.145833,229.2916667,1456.875,465.0625 -2020-07-01,5267.395833,222.5,1302.083333,476.0625 -2020-07-02,5229.0625,216.4166667,1248.125,480.4772727 -2020-07-03,4997.1875,222.9583333,1165.625,479.9166667 -2020-07-04,5165.104167,226.9166667,1130,482.0416667 -2020-07-05,4826.25,228,1126.666667,493.8125 -2020-07-06,4609.066667,229.5416667,2565.416667,504.0625 -2020-07-07,4767.1875,226.9166667,2145.416667,421.8958333 -2020-07-08,4557.395833,228.7083333,1346.041667,347.6875 -2020-07-09,4895.9375,239.3333333,2282.083333,349.1666667 -2020-07-10,7750.520833,226.125,6752.5,416.375 -2020-07-11,16042.70833,176.6666667,9878.77551,246 -2020-07-12,15964.58333,193.75,4459.583333,263.1041667 -2020-07-13,16075,161.8333333,3718.333333,289.8541667 -2020-07-14,13532.29167,156.9583333,3231.458333,329.5625 -2020-07-15,10007.08333,156.5217391,2352.708333,322.1666667 -2020-07-16,8100,165.4166667,1823.958333,338.3488372 -2020-07-17,6843.4375,178.9166667,1600.416667,378.0208333 -2020-07-18,5947.708333,192.4166667,1436.458333,403.8958333 -2020-07-19,5482.1875,207.7916667,1330.208333,426.1041667 -2020-07-20,5201.097561,214.9583333,1233.333333,446.125 -2020-07-21,5198.795181,219.7083333,1120.854167,460.5416667 -2020-07-22,5429.058824,225.375,1186.5,470.7916667 -2020-07-23,6098.390805,220.0416667,1867.708333,479.7083333 -2020-07-24,5895.416667,219.2916667,2220.833333,473.5 -2020-07-25,5428.020833,227.4583333,1614.791667,435.2708333 -2020-07-26,4759.0625,235.75,1336.458333,429.1041667 -2020-07-27,4645.833333,234.9166667,1170.833333,440.625 -2020-07-28,4635.416667,242.7083333,1053.479167,452.4166667 -2020-07-29,4252.934783,239.6666667,957.3541667,468.0625 -2020-07-30,3929.08046,232.1666667,910.7708333,482.4782609 -2020-07-31,4362.857143,237.5833333,1012.166667,495.8125 -2020-08-01,4218.452381,235.7083333,1356.666667,512.7291667 -2020-08-02,3942.409639,256.2083333,1067.916667,524.1041667 -2020-08-03,7762.1875,256,6072.291667,477.4166667 -2020-08-04,16322.36842,208.9583333,35024.08163,219.125 -2020-08-05,64704.30108,189.0666667,42972.91667,164.2708333 -2020-08-06,44747.91667,146.0416667,11007.29167,221.7083333 -2020-08-07,27219.79167,149.2083333,5522.5,248.6666667 -2020-08-08,23843.75,151.4166667,6840.416667,284.2916667 -2020-08-09,16883.33333,165.4285714,6180.833333,270.4791667 -2020-08-10,13207.29167,170.1875,4347.291667,279.1875 -2020-08-11,10699.16667,183.8333333,3587.708333,309.2291667 -2020-08-12,9537.291667,192.75,3308.75,326.7083333 -2020-08-13,9051.041667,197.9166667,3190.851064,351.9302326 -2020-08-14,9782.708333,205.7083333,2546.458333,350.4166667 -2020-08-15,9245.368421,187.8333333,2225.833333,381.9583333 -2020-08-16,7994.942529,195.5416667,2119.166667,386.75 -2020-08-17,7308.192771,205.8333333,2076.666667,408.5833333 -2020-08-18,6924.096386,217.5833333,1903.541667,418.6041667 -2020-08-19,7134.823529,216.9166667,1771.25,428.4583333 -2020-08-20,6845.348837,215.3636364,1652.916667,437.5 -2020-08-21,6017.954545,216.9166667,1551.666667,442.6458333 -2020-08-22,5535.208333,227.0833333,1454.166667,451.5416667 -2020-08-23,5289.6875,234.0416667,1438.75,467.5833333 -2020-08-24,5201.875,239.375,1549.791667,474.5625 -2020-08-25,5596.666667,244.125,1371.666667,487.7916667 -2020-08-26,5209.479167,234.5,1279.791667,486.375 -2020-08-27,4856.666667,234.25,1231.666667,481.1111111 -2020-08-28,4763.645833,234.375,1464.791667,488.1041667 -2020-08-29,4762.708333,238.25,2088.125,482.3125 -2020-08-30,6336.741573,246.2916667,1850.208333,471 -2020-08-31,6490.208333,239.2173913,1577.5,479.25 -2020-09-01,5499.270833,216.5882353,1379.791667,485.8125 -2020-09-02,5100.689655,214.8695652,1287.083333,468.9574468 -2020-09-03,5339.39759,215.5,1330.208333,464.9375 -2020-09-04,10621.04167,217.875,3027.916667,481.6458333 -2020-09-05,8616.875,198.4166667,2753.958333,498.787234 -2020-09-06,7566.770833,200,1944.583333,433.8541667 -2020-09-07,6524.375,208.125,1611.666667,387.5208333 -2020-09-08,5666.666667,206.9583333,1359.166667,396.2083333 -2020-09-09,4940.9375,214.8333333,1253.541667,411.875 -2020-09-10,5038.488372,218.2916667,1278.541667,433.6458333 -2020-09-11,5738.333333,226.125,1812.708333,450.0833333 -2020-09-12,5203.229167,241.5833333,1661.458333,461.9166667 -2020-09-13,4562.340426,237.1666667,1355.833333,477.75 -2020-09-14,4438.636364,236.1818182,1151.458333,496.7777778 -2020-09-15,4164.791667,244.2608696,1057.208333,471.5833333 -2020-09-16,3931.37931,246.875,1028.6875,457.5625 -2020-09-17,3748.255814,250.375,984.0416667,474.125 -2020-09-18,3723.72093,252.0416667,973.0416667,491.1041667 -2020-09-19,3707.977528,257.4166667,927.625,497.8125 -2020-09-20,3528.313253,258.2916667,920.5208333,510.1875 -2020-09-21,3238.390805,261.4583333,865.6875,528.8541667 -2020-09-22,3072.25,269.3333333,863.6875,543.1875 -2020-09-23,3066.428571,274.125,865.1875,551.9583333 -2020-09-24,3057.5,278.0434783,857.4791667,565.4583333 -2020-09-25,3056.770833,277.7,887.1875,576.8125 -2020-09-26,3182.916667,278.375,904.5833333,577.7291667 -2020-09-27,3131.979167,275.9166667,986.75,569.8541667 -2020-09-28,3123.020833,274.125,1175.625,570.9555556 -2020-09-29,3358.020833,271.625,1104.416667,575.4791667 -2020-09-30,5501.666667,259.5,2396.666667,558.2291667 -2020-10-01,7300.208333,273.0869565,3173.541667,511.125 -2020-10-02,6722.604167,239.4166667,1971.458333,461.9375 -2020-10-03,7070.104167,211.5833333,1372.916667,375.8541667 -2020-10-04,5381.979167,196.25,1164.375,361.6666667 -2020-10-05,4470.625,207.1666667,1070.895833,392.9583333 -2020-10-06,3906.770833,222.1666667,1004.958333,417.375 -2020-10-07,3533.541667,238.3043478,993.9166667,451.0833333 -2020-10-08,3223.333333,249,922.2708333,475.6041667 -2020-10-09,3132.8125,257.875,881.0416667,494.9791667 -2020-10-10,3067.083333,263.0416667,840.3958333,511.375 -2020-10-11,3148.854167,263.2916667,835.9583333,519.0416667 -2020-10-12,4341.041667,261.6666667,1442.583333,531.0625 -2020-10-13,4813.958333,270.6666667,1718.333333,540.4166667 -2020-10-14,4062.150538,240.4583333,1375.416667,536.5217391 -2020-10-15,3690.116279,233.2380952,1169.375,529.6666667 -2020-10-16,4075.274725,235.4583333,1322.5,547.8125 -2020-10-17,4114.166667,252,1286.666667,494.6458333 -2020-10-18,3843.222222,255.25,1181.875,492.6041667 -2020-10-19,3342.386364,251.4583333,1139.375,493.7083333 -2020-10-20,3480.104167,258.9583333,1088.958333,513.7291667 -2020-10-21,3711.413043,264.0416667,1096.25,533.625 -2020-10-22,3668.043478,258.875,1078.333333,551.4791667 -2020-10-23,3576.979167,261.5,1058.208333,537.893617 -2020-10-24,3473.645833,258.2916667,1070.333333,531.7708333 -2020-10-25,3412.5,257.6666667,1048.041667,533 -2020-10-26,3400,258.4166667,1086.666667,542.0416667 -2020-10-27,3431.25,257.875,1096.25,550.1818182 -2020-10-28,3476.5625,255.8695652,1048.666667,554.0208333 -2020-10-29,4586.041667,252.5833333,2407.5,546.8333333 -2020-10-30,10530.52083,240.375,4947.291667,456.5 -2020-10-31,15301.04167,220.4583333,4799.375,391.6875 -2020-11-01,15391,171.4166667,3465,366.48 -2020-11-02,13616.66667,165.1904762,3320,366.3541667 -2020-11-03,11492.70833,174.4347826,3057.708333,367.1041667 -2020-11-04,10256.5625,173.4166667,2430.625,362.8333333 -2020-11-05,9156.458333,175.5,2049.375,366.2083333 -2020-11-06,7836.770833,183.3333333,1728.125,374.9791667 -2020-11-07,7138.958333,191.375,1592.916667,394.0833333 -2020-11-08,6615.729167,200,1480.208333,415.2708333 -2020-11-09,6183.229167,203.6666667,1415.416667,427.25 -2020-11-10,5793.958333,207.9583333,1336.25,441.4772727 -2020-11-11,5577.5,211.25,1453.333333,448.7083333 -2020-11-12,7338.125,213.7083333,3878.085106,437.0638298 -2020-11-13,7694.270833,227.3478261,3733.958333,403.6041667 -2020-11-14,7705.833333,226.625,2793.541667,360.6666667 -2020-11-15,7488.522727,215.875,2221.25,361.625 -2020-11-16,7945.833333,208.2857143,3196.666667,372.2083333 -2020-11-17,8097.708333,223.0833333,2631.666667,378.1458333 -2020-11-18,8539.583333,218.125,2108.541667,348.125 -2020-11-19,8441.235955,201.625,1777.083333,361.5625 -2020-11-20,7743.229167,191.8333333,1642.5,383.0833333 -2020-11-21,7106.875,194.5217391,1516.458333,395.1666667 -2020-11-22,6543.854167,200.5416667,1419.166667,412.9166667 -2020-11-23,6549.791667,203.6666667,1834.583333,425.4772727 -2020-11-24,6900.520833,209.75,1826.25,439.5208333 -2020-11-25,7101.5625,212.5217391,1597.916667,421.1666667 -2020-11-26,8605.416667,201.9583333,1774.791667,432.25 -2020-11-27,8737.708333,185.1666667,2004.791667,420.0833333 -2020-11-28,8542.604167,,1759.166667,430.4166667 -2020-11-29,8959.0625,,1566.875,431.2291667 -2020-11-30,13741.875,166.5555556,7329.583333,402.6458333 -2020-12-01,35622.91667,177.3043478,19271.25,170.3541667 -2020-12-02,33356.25,161.2916667,6321.041667,227.9166667 -2020-12-03,25663.54167,133.4166667,4529.791667,281.3958333 -2020-12-04,20307.29167,135.5,3723.75,307.7291667 -2020-12-05,20554.16667,142.3333333,6937.5,318.5 -2020-12-06,18093.75,154.1666667,5795.416667,258.1875 -2020-12-07,14858.33333,165,3710.833333,266.5625 -2020-12-08,13261.45833,174.9166667,3157.5,296.5 -2020-12-09,12614.70588,179.7083333,2876.875,320.6458333 -2020-12-10,,185.1666667,2696.25,341.75 -2020-12-11,,192.8333333,2431.458333,359.5 -2020-12-12,,199.0416667,2124.166667,372.1666667 -2020-12-13,,199.25,2020.833333,385.4583333 -2020-12-14,8796,199.875,2358.541667,392.1458333 -2020-12-15,,208.625,3287.916667,388.1458333 -2020-12-16,,224.875,2567.083333,374.8958333 -2020-12-17,,222.0416667,2508.043478,389.5434783 -2020-12-18,,214.8333333,2323.125,453.5833333 -2020-12-19,,217.375,2184.166667,475.6458333 -2020-12-20,,219.3333333,2014.166667,496.6458333 -2020-12-21,,212.9166667,2016.666667,500.5833333 -2020-12-22,,219.4166667,2624.583333,508.4583333 -2020-12-23,,230.3333333,4013.125,559.4583333 -2020-12-24,,229.7916667,3637.916667,527.9791667 -2020-12-25,,213.125,35089.79592,263.5 -2020-12-26,,136.6666667,21077.08333,213.1041667 -2020-12-27,,111.7083333,11953.95833,228.8333333 -2020-12-28,,124.2916667,8669.375,260.8541667 -2020-12-29,,130,7046.458333,281.4375 -2020-12-30,,126.5416667,5446.666667,296.3958333 -2020-12-31,,134.0833333,4612.916667,311.4583333 diff --git a/methods_exploration/functions/TEpython_ParallelNAN2.py b/methods_exploration/functions/TEpython_ParallelNAN2.py deleted file mode 100644 index 1a5eb1d..0000000 --- a/methods_exploration/functions/TEpython_ParallelNAN2.py +++ /dev/null @@ -1,679 +0,0 @@ -#!/usr/bin/env python -# Edom Moges (ESDL) - -# ## This module contains the TE code translation from Matlab -# -# (MatLab version written by Laurel L. and modified by Dino B. Translation to Python by Edom M.) -# -# The following functions are included in this module: -# -# 1. Mutual information -# -# 1. mutinfo_new(M, nbins) - Calculates mutual information I(x,y). -# -# -# 2. Tranfer entropy -# -# 1. transen_new(M, lag, nbins) - Calculates transfer information - TE(x,y) x to y. x source M[:,0] and y the sink M[:,1]. -# -# -# 3. Intermediate functions -# -# 1. LagData_new - shifts a matrix so that it is rearranged to be ready for TE calculation as in Knutt et al., 2005 -# 2. jointentropy_new(M, nbins) - Calculates the joint entropy H(x,y) -# 3. jointentropy3_new(M, nbins) - Calculates the joint entropy for three variables H(x,y,z) -# 4. shuffle( M ) - shuffles the entries of the matrix M in time while keeping NaNs (blank data values) NaNs. So that, Monte Carlo is possible -# 5. transenshuffle_new(M, lag, nbins) - Calculates the transfer entropy for a shuffled time series that has already been lined up with LagData -# -# -# 4. Monte Carlo analysis of mutual information and transfer entropy -# -# 1. mutinfo_crit_new( M, nbins, alpha, numiter) - Finds critical values of mutual information statistics that needs to be exceeded for statistical significance -# 2. transen_crit_new( M, lag, alpha, numiter, nbins) - Finds the critical value of the transfer entropy statistic that needs to be exceeded for statistical signficance -# -# -# 5. All in one code -# RunNewTE2VarsPar(DataMatrix, LabelCell, shift, SinkNodes=None, SourceNodes=None, - # maxLag=100, minSamples=200, numShuffles = 500, sigLevel=0.05, numBins=[11,11,11],ncores=4) - runs all together in #parallel mode. - - -#get_ipython().run_line_magic('matplotlib', 'inline') -import pandas as pd -import numpy as np -import datetime as dt -import matplotlib.pyplot as plt -import copy -import os -from joblib import Parallel, delayed -from tqdm import tqdm -np.random.seed(50) - - -# In[2]: - - -def checkMakeDir2(dirName): # - result = dirName - result2 = dirName*2 - return result, result2 - - -# ### Mutual information - -# In[8]: - -def computeEntropy(M,nbin): - N, binEdges1d=np.histogram(M[np.isfinite(M)],bins=nbin) #Which bin the data column is in - p2 = N/sum(N) - - # Shanon entropy - p2gt0 = p2[p2>0] # py - log2p2gt0 = np.log2(p2gt0) - H = (-sum(p2gt0*log2p2gt0)) - return H - - - -def mutinfo_new(M2, nbins): - # Calculates mutual information with the sink variable's H used as a normalization - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[1]) # Source Variable - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[1]) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins[1]+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins[1]**2 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - p = N/sum(N); # Vector of probabilities - - # 1D probability/histogram - N1, binEdges1d1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[0]) # Which bin the first data column is in - N2, binEdges1d2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[0]) #Which bin the second data column is in - - p1 = N1/sum(N1) - p2 = N2/sum(N2) - - - # Shanon entropy - pgt0 = p[p>0] # px,y - p1gt0 = p1[p1>0] # px - p2gt0 = p2[p2>0] # py - - - log2p2gt0 = np.log2(p2gt0) - #Shannon entropy of the sink variable. Used to normalize mutual informaiton in the next line. - Hy = (-sum(p2gt0*log2p2gt0)) - # Mutual information, in bits. Joint entropy is scaled to the number of bins in a single dimension. - I = ( (-sum(p1gt0*np.log2(p1gt0)) - sum(p2gt0*log2p2gt0) ) + (sum(pgt0*np.log2(pgt0)))*np.log2(nbins[0])/np.log2(nbins[1]))/Hy - # double integral in the last component is done as a 1D. - - #return nbins_2, jointentcat,p , sum(N), I, Hy - return I, sum(N) - - - -def mutinfo_newRel(M2, nbins): # MI is computed relative to max value of Hx or Hy than sink based MI.mutinfo_new - # Calculates mutual information with the max H used as a normalization. - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[1]) # Source Variable - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[1]) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins[1]+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins[1]+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins[1]**2 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - p = N/sum(N); # Vector of probabilities - - # 1D probability/histogram - N1, binEdges1d1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins[0]) # Which bin the first data column is in - N2, binEdges1d2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins[0]) #Which bin the second data column is in - - p1 = N1/sum(N1) - p2 = N2/sum(N2) - - - # Shanon entropy - pgt0 = p[p>0] # px,y - p1gt0 = p1[p1>0] # px - p2gt0 = p2[p2>0] # py - - - log2p2gt0 = np.log2(p2gt0) - log2p1gt0 = np.log2(p1gt0) - #Shannon entropy of the sink variable. Used to normalize mutual informaiton in the next line. - Hy = (-sum(p2gt0*log2p2gt0)) - Hx = (-sum(p1gt0*log2p1gt0)) - maxH = np.max(np.array([Hx,Hy])) - - # Mutual information, in bits. Joint entropy is scaled to the number of bins in a single dimension. - I = ( (-sum(p1gt0*np.log2(p1gt0)) - sum(p2gt0*log2p2gt0) ) + (sum(pgt0*np.log2(pgt0)))*np.log2(nbins[0])/np.log2(nbins[1]))/maxH - - # double integral in the last component is done as a 1D. - - #return nbins_2, jointentcat,p , sum(N), I, Hy - return I, sum(N) - -# ## Intermediate functions - -# In[10]: - - -def LagData_new( M_unlagged, shift ): - # LagData Shifts two time-series so that a matrix is generated that allows easy computation of Knutt et al 2005 based TE computation - # M_unlagged is a matrix [X Y..n], where X and Y are column vectors of the - # variables to be compared. shift is a row vector that says how much each - # variable in M_unlagged is to be shifted by. - - nR,nC = np.shape(M_unlagged) - maxShift = max(shift) - minShift = min(shift) - newlength = nR - maxShift + minShift - M_lagged = np.nan*np.ones([newlength, nC]) #[source_lagged(1:n-lag), sink_unlagged(lag:n), sink_lagged(1:n-lag)] - - #@@@@@@@@@@@@@@######## Dino's verson uses shift of [0, 0, -lag ] for the shuffle case of transfer entropy (transenshuffle_new) - for ii in range(np.shape(M_lagged)[1]): - M_lagged[:,ii] = M_unlagged[(shift[ii]-minShift):(np.shape(M_unlagged)[0]-maxShift+shift[ii]), ii] - - return M_lagged - -# Alternatively -# lag = np.abs(shift[0]) -# M_lagged[:,0] = M_unlagged[0:(nR-lag), 0] -# M_lagged[:,1] = M_unlagged[lag:(nR),1] -# M_lagged[:,2] = M_unlagged[0:(nR-lag),2] -# return M_lagged - - -# In[12]: - - -def jointentropy_new(M2, nbins): - # Calculates the joint entropy H(x,y) - # M is two dimensional column matrix for which joint entropy is to be computed - # H is the normalized joint entropy - # nvalidpoints is the number of rows (samples) used to calculate the joint entropy - - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins) # Source Variable [ ] - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - col1cat[col2cat==0.]=0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col2cat[col1cat==0.]=0. # See comment above. @@@ - - #print(col1cat) - # convert 1D histogram to a 2D histogram - jointentcat = (col1cat-1)*nbins+col2cat #This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - nbins_2 = nbins**2 - N = np.bincount(jointentcat[jointentcat > 0])[1:] # Number of datapoints within each joint entropy bin. @@@ - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - p = N/sum(N); # Vector of probabilities - - pgt0 = p[p>0] # p(x,y) - H = -sum(pgt0*np.log2(pgt0)) - nvalidpoints = sum(N) - - return H, nvalidpoints - - -# In[14]: - - -def jointentropy3_new(M2, nbins): - # Calculates the joint entropy for three variables H(x,y,z) - # M is a three-column matrix that contains the input vectors of data. - # nvalidpoints is the number of rows (samples) used to calculate the joint entropy - - ths = 10e-4 - M = M2[~np.isnan(M2).any(axis=1)] # clears the nans at both columns - counts1, binEdges1=np.histogram(M[:,0][np.isfinite(M[:,0])],bins=nbins) # Source Variable [ ] - binEdges1[0] = binEdges1[0]-ths - binEdges1[len(binEdges1)-1]=binEdges1[len(binEdges1)-1]+ths - col1cat = np.digitize(M[:,0], binEdges1, right=False) - - col1cat[col1cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts2, binEdges2=np.histogram(M[:,1][np.isfinite(M[:,1])],bins=nbins) # Sink Variable - binEdges2[0] = binEdges2[0]-ths - binEdges2[len(binEdges2)-1]=binEdges2[len(binEdges2)-1]+ths - col2cat = np.digitize(M[:,1], binEdges2, right=False) # which bin (ID) is the data located - - col2cat[col2cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - counts3, binEdges3=np.histogram(M[:,2][np.isfinite(M[:,2])],bins=nbins) # Source Variable [ ] - binEdges3[0] = binEdges3[0]-ths - binEdges3[len(binEdges3)-1]=binEdges3[len(binEdges3)-1]+ths - col3cat = np.digitize(M[:,2], binEdges3, right=False) - - col3cat[col3cat==nbins+1] = 0. # change the bin index of nan to zero. np assigns it to nbins[1]+1 @@@ - - # Assign 0 to NAN values - - col1cat[col2cat==0.] = 0. # If there is an NaN for any row, assign the other column in that row to the NaN bin too @@@ - col1cat[col3cat==0.] = 0. # See comment above. @@@ - - col2cat[col1cat==0.] = 0. # @@@ - col3cat[col1cat==0.] = 0. # @@@ - - # This classifies the joint entropy bin into a number between 1 and nbins^2. 0 is assigned to rows with misisng data. - jointentcat = (col1cat-1)*nbins**2 + (col2cat-1)*nbins + col3cat - - #print(np.asarray((jointentcat,col1cat,col2cat, col3cat)).T) - - nbins_3 = nbins**3 - #N = np.bincount(jointentcat)[1:] # Number of datapoints within each joint entropy bin. - N = np.bincount(jointentcat[jointentcat>0])[1:] # Number of datapoints within each joint entropy bin. @@@ - sumN = sum(N) - - - p = N/sumN # Vector of probabilities - pgt0 = p[p>0] - H = -sum(pgt0*np.log2(pgt0)) - nvalidpoints = sumN - - - return H, nvalidpoints - - -# In[16]: - - -def shuffle( M ): - # shuffles the entries of the matrix M in time while keeping NaNs (blank data values) NaNs. - # M is the matrix where the columns are individual variables and the rows are entries in time - - Mss = np.ones(np.shape(M))*np.nan # Initialize - - for n in range(np.shape(M)[1]): # Columns are shuffled separately - notnans = np.argwhere(~np.isnan(M[:,n])) - R = np.random.rand(np.shape(notnans)[0],1) #np.random.rand(5,1) - I = np.argsort(R,axis=0) - #print(notnans[:,0]) - #print(notnans[I,0]) - #print('a',M[notnans[:,0],n]) - Mss[notnans[:,0],n] = M[notnans[I[:],0],n].reshape(np.shape(M[notnans[I[:],0],n])[0],) - - return Mss - - - -# ## Transfer entropy - - - - -# In[20]: - - -def transen_new2(M, shift, nbins): # with shift as an input different lags btween source and sink are possible - # shift [-lag of source, 0, - lag of sink] # lag of sink usually being 1 - # Calculates transfer information - # M is an array with two columns [ source, sink] - # nbins list of number of bins in 1D, 2D and 3D, with three elements - # lag is the time lag of interest. - # M4 is the lagged subset of data transfer entropy was run on. - - MQ = LagData_new(np.column_stack((M, M[:,1])), shift) # source, sink, sink is input then - M4 = MQ[~np.isnan(MQ).any(axis=1)] # clears the nans at all columns simultaneously - - # M4 becomes [source_lagged(1:n-lag), sink_unlagged(lag:n), sink_lagged(1:n-lag)] => H(Xt-T, Yt, Yt-T) - - M4[np.argwhere(np.isnan(np.sum(M4,axis=1))), :] = np.nan # Reset rows with any NaN entry to NaN. - M4short = M4[np.argwhere(~np.isnan(np.sum(M4,axis=1))),:] # Time series without NaN that will be passed on for shuffling. - - M1 = M4[:,(0,2)] # [source_lagged(1:n-lag), sink_lagged(1:n-lag)] =>H(Xt-T,Yt-T) - M2 = M4[:,(1,2)] # [sink_unlagged(lag:n), sink_lagged(1:n-lag)] =>H(Yt,Yt-T) - - #@@@@@@@@@@@@@@######## Dino uses M4[:,1] to be predicted - M3 = M4[:,2] # [sink_unlagged(lag:n)] to be predicted is used with DINO. BUT, need CORRECTION =>H(Yt) should be corrected to H(Yt-T) M[:,2] - - - # Knutt et al indicates lagged being used H(Yt-T). Thus, M4[:,2] - # Now calculate the joint and marginal entropy components: - T1, n_valid_pairs1 = jointentropy_new(M1,nbins[1]) - T2, n_valid_pairs2 = jointentropy_new(M2,nbins[1]) - - # Entropy for the single predictor - n3, valueatn = np.histogram(M3[np.isfinite(M3)], nbins[0]) # results in count [n3] and the corresponding value - n3gt0 = n3[n3>0] - sumn3gt0 = sum(n3gt0) - T3 = -sum((n3gt0/sumn3gt0)*(np.log2(n3gt0/sumn3gt0))) # Nonnormalized Shannon entropy of variable Y - - # Three variable entropy - T4, n_valid_pairs4 = jointentropy3_new(M4,nbins[2]) - - Tn = T3 # This is the Shannon entropy of Y, used to normalize the value of transfer entropy obtained below. - - log2nbins1 = np.log2(nbins[0]) - log2nbins2 = np.log2(nbins[1]) - log2nbins3 = np.log2(nbins[2]) - log2nbins1_2 = log2nbins1/log2nbins2 - log2nbins1_3 = log2nbins1/log2nbins3 - T1 = T1*log2nbins1_2 - T2 = T2*log2nbins1_2 - T4 = T4*log2nbins1_3 - - T = (T1+T2-T3-T4)/Tn # Knuth formulation of transfer entropy - - N = min([n_valid_pairs1, n_valid_pairs2, n_valid_pairs4]) # Number of valid matched pairs used in the calculation - - return T, N, M4short - - - -# In[22]: - - - - -# In[24]: - - -def transenshuffle_new2(M, shift, nbins): - - # Calculates the transfer entropy for a shuffled time series that has already been lined up with LagData - - # Calculates the transfer entropy of X>Y, the amount by which knowledge - # of variable X at a time lag reduces the uncertainty in variable Y. M = - # [X Y], and lag is the time lag of interest. nbins is the number of bins - # used to discretize the probability distributions. - - - Minput = shuffle(M[:,(0,1)]) - T, N,c = transen_new2(Minput, shift, nbins)# use it but not understood why [0 0 -lag] is used instead of [-lag 0 -lag] - - - return T - - -# ## Critical values of Mutual information and Transfer entropy - -# In[30]: - - - - -def mutualshuffle_new2(M,nbins): - Mss = shuffle(M) - MIss = mutinfo_new(Mss,nbins) - return MIss - - -# In[ ]: - - -def mutinfo_crit_newPar( M, nbins, alpha, numiter,ncores): - - MIss = Parallel(n_jobs=ncores)(delayed(mutualshuffle_new2)(M, nbins) for ii in range(numiter)) - MIss = np.sort(MIss) - MIcrit = MIss[round((1-alpha)*numiter)] # develop a histogram and peak the 95% quantile significance level with alpha = 0.05 - - return MIcrit - - -# In[44]: - - -def transen_crit_new2( M, shift, alpha, numiter, nbins,ncores): - - # Finds the critical value of the transfer entropy statistic - # that needs to be exceeded for statistical signficance. - # M = matrix of unshifted variables, e.g., [X Y] for calculating the X>Y transfer entropy. - # lag = time lag. - # alpha = significance level. - # numiter = number of Monte Carlo shufflings to perform. - # nbins = number of bins to use to discretize the probability distributions. - - # Serial -# Tss = np.ones([numiter])*np.nan # Initializing shuffled transfer entropy table -# #print(Tss) - -# for ii in range(numiter): -# Tss[ii] = transenshuffle_new2(M, shift, nbins) # Calculates TE for each Monte Carlo Shuffling - # parallel - Tss = Parallel(n_jobs=ncores)(delayed(transenshuffle_new2)(M, shift, nbins) for ii in range(numiter)) - ## print(Tss) - - Tss = np.sort(Tss) - Tcrit = Tss[round((1-alpha)*numiter)] # develop a histogram and peaks the 1-aplpha (95%) quantile significance level with alpha (= 0.05) - - return Tcrit - - - - -# ## Parallel TE & I calculater - - -# number of monteCarlo shuffle - kills the time - going from 100 to 1000 very time consuming. Parallel!! -# maxLag also takes a lot of time. Number of lag considered. 3*365 -# number of source variables -- 20 -def RunNewTE2VarsPar(DataMatrix, LabelCell, shift, SinkNodes=None, SourceNodes=None, - maxLag=100, minSamples=1000, numShuffles = 500, sigLevel=0.05, numBins=[11,11,11],ncores=4): - # computes TE assumes a data matrix with time in first columns and vars on others - - # Inputs - # DataMatrix - data matrix with time in the first column - # LabelCell - variable name of each data matrix entry - # shift - shift for sink variable (-1,0,-1) for self optimality - # Source_nodes - array of column indices for source variables [2] - # Sink_nodes - array of column of indices for sink variales [3:end] - # resultsDir - directory for results ./Results/ - # maxLag - maximum lag (3*365) 3 years - # minSamples - minimum number of valid samples for TE (suggestion 200) - # numShuffles - number of MonteCarlo shuffle iterations (suggestion 500) - # sigLevel - significance level (suggested 0.05) - # numBins - number of bins to use in 1, 2, and 3 dimensions default [11,11,11] - # ncores - pc cores to use - # Outputs - # Imat - mutual information - # Icritmat - significance threshold - # Tfirstmat - first T > Tcrit - # Tbiggestmat - Tmax for T > Tcrit - # Tcube_store - all T for all sink, source, lag combinations - # Tcritcube_store - all Tcrits for all sink, source, lag combinations - - if DataMatrix.size == 0: - return 'no dataMatrix' - - if LabelCell.size == 0: - return 'no variable names' - - if SourceNodes is None: - SourceNodes = np.arange(2,np.shape(DataMatrix)[1]) - - if SinkNodes is None: - SinkNodes = np.array([1]) - - nSources = len(SourceNodes) - nSinks = len(SinkNodes) - - # Start clock - # print('Beginning 2-variable analysis (serial) ...') - # Tot = tic - # print(SourceNodes,SinkNodes) - # ========================================= - ## Shrink input matrices to include only variables that are used - # now the order is time, sinks, sources - - #@@@@@@@@@@@@@@@@@@@@@ - # from Pd to np.array - dataMat = np.column_stack((DataMatrix[:,0], DataMatrix[:,SinkNodes], DataMatrix[:,SourceNodes])) # date, sink, sources - labCell = np.r_[np.array([LabelCell[0]]), np.array(LabelCell[SinkNodes]), np.array(LabelCell[SourceNodes])] - #np.r_[np.array(LabelCell[0]), np.array(LabelCell[1]), np.array(LabelCell[[2,3,4]])] - #Or labCell = np.column_stack((LabelCell[:,0], LabelCell[:,SinkNodes], LabelCell[:,SourceNodes])) - - - del DataMatrix # or set it to empty DataMatrix = [] - del LabelCell - - # ============================================= - # Initialize output matrices - # mutual information between sources and sinks - # the sink is daily mean Q, and all pairwise interactions are evaluated - - Imat = np.ones([nSinks,nSources])*np.nan # row value = # sink vars, col values = # source vars; - - # significance threshold - - Icritmat = copy.deepcopy(Imat) - - # first T > Tcrit - Tfirstmat = copy.deepcopy(Imat) - - - # Tmax for T > Tcrit - Tbiggestmat = copy.deepcopy(Imat) - - # All T for all sink, source, lag combinations - Tcube_store = np.ones([nSinks,nSources,maxLag])*np.nan - - # All Tcrits for all sink, source, lag combinations - Tcritcube_store = copy.deepcopy(Tcube_store) - - - # ============================================= - # LOOP OVER ALL PAIRS OF SOURCE AND SINK VARIABLES TO CALCULATE MI and TE - - for mySinkIter in range(nSinks): # loop over Sink nodes (information receivers) [ 0] - - mySinkNum = SinkNodes[mySinkIter] - mySinkInd = 1 + mySinkIter # exclude time - - - # extract sub-matrices for the ease of computation - Ivec = Imat[mySinkIter,:] - Icritvec = Icritmat[mySinkIter,:] - Tfirstvec = Tfirstmat[mySinkIter,:] - Tbiggestvec = Tbiggestmat[mySinkIter,:] - Tmat_store = np.reshape(Tcube_store[mySinkIter,:,:],[nSources,maxLag]) - Tcritmat_store = np.reshape(Tcritcube_store[mySinkIter,:,:], [nSources,maxLag]) - sinkName = labCell[mySinkInd] # Text name of the Sink variable - MmySink = dataMat[:,mySinkInd].astype(float) # Select the sink variable to run - - #print('sink node = ', mySinkIter) - - for mySourceIter in range(nSources): # Loop over the source nodes - #print(mySourceIter) - - mySourceNum = SourceNodes[mySourceIter] - mySourceInd = 1 + nSinks + mySourceIter - Mmysource = dataMat[:,mySourceInd].astype(float) # Select source variables - sourceName = labCell[mySourceInd] # Name of the source variable - #print('Source node ', mySourceNum-1, sourceName, ':=>', 'Sink node ', mySinkNum, sinkName) - #print('Lag ', 'Sink', 'Source') - - M = np.column_stack((Mmysource, MmySink)) # Source followed by Sink - M = M.astype(float) - #print(M.shape) - # MUTUAL INFORMATION - - II,validN = mutinfo_new(M,numBins) # computes mutual information - #print(II,validN,M[500,:]) - if validN >= minSamples: # enough length to compute MI - I = II - Icrit, nvd = mutinfo_crit_newPar(M=M, alpha=sigLevel, nbins=numBins,numiter = numShuffles,ncores=ncores) - else: - I = np.nan - Icrit = np.nan - - # print(M.shape, II, I,validN) - - Ivec[mySourceIter] = I # save it in a matrix - Icritvec[mySourceIter] = Icrit - - - # TRANSFER ENTROPY - T = np.ones([maxLag])*np.nan # intialize the TE vector over the range of lags examined - Tcrit = copy.deepcopy(T) # Initialize the vector of the critical TE - - #for lag in tqdm(range(maxLag)): #[0 to 364] in a year i.e., no lag day - for lag in range(maxLag): #[0 to 364] in a year i.e., no lag day - t, N, Mshort = transen_new2(M=M, shift=[-lag,shift[1],shift[2]], nbins=numBins) # Computes TE for at a given lag of 'lag' - #print(Mshort, type(Mshort),Mshort.shape) - Mshort = Mshort.reshape(Mshort.shape[0],Mshort.shape[2]) - if N >= minSamples: # enough length to compute TE - T[lag] = t # save TE computed - Tcrit[lag] = transen_crit_new2(M=M, shift=[-lag,shift[1],shift[2]], alpha= sigLevel,nbins=numBins,numiter=numShuffles,ncores=ncores) # TE critical - #print(lag, mySinkIter, mySourceIter) - - # Save the first and biggest value of T over the significance threshold - - TgTcrit = np.argwhere(T >= Tcrit) # np.argwhere(np.array([5,6,9,18]) > np.array([3,9,2,9])) - - if any(TgTcrit): - Tfirstvec[mySourceIter] = T[TgTcrit[0,0]] - Tbiggestvec[mySourceIter] = max(T[TgTcrit[:,0]]) # @@@@@ Should be T-Tcrit biggest!!!!!! - - #print(Tcrit.shape, T.shape, Tcritcube_store.shape) - Tmat_store[mySourceIter,:] = T - Tcritmat_store[mySourceIter,:] = Tcrit - - - # replace column vectors from source iterations into matrices - Imat[mySinkIter, :] = Ivec - Icritmat[mySinkIter, :] = Icritvec - Tfirstmat[mySinkIter,:] = Tfirstvec - Tbiggestmat[mySinkIter,:] = Tbiggestvec - Tcube_store[mySinkIter,:,:] = Tmat_store - Tcritcube_store[mySinkIter,:,:] = Tcritmat_store - - # save results (modify to save just relevant variables) - # save([resultsDir 'TE_analysis_workspace.mat'], '-v7.3'); - - # Stop clock - #print('Finished 2-variable analysis (Parallel)!'); - - - return Imat, Icritmat, Tfirstmat, Tbiggestmat, Tcube_store, Tcritcube_store # | sink | source | lag | - diff --git a/methods_exploration/functions/data_exploration_functions.py b/methods_exploration/functions/data_exploration_functions.py deleted file mode 100644 index 553e98f..0000000 --- a/methods_exploration/functions/data_exploration_functions.py +++ /dev/null @@ -1,235 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Jan 25 10:01:14 2022 - -@author: ggorski -""" -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import plotly.express as px -import seaborn as sns -import TEpython_ParallelNAN2 - -def lag_sources_df(n_lags, srcs_df): - n_lags_int = int(n_lags) - '''Takes in the list of sources called srcs_list in which each list item is a - dataframe of site variables, and creates lagged time series for all variables - according to the n_lag variable. Column headers are updted with the n_lag. n_lag refers - to the number of time steps to lag, and it is agnostic of the time resolution of the data. - length of data > n_lag >=0''' - lagged_df = pd.DataFrame() - for col_name in list(srcs_df.columns): - for lag in range(0, int(n_lags_int+1)): - #create the lagged time series and name the columns - lagged_df[col_name+'_lag_'+str(lag)] = srcs_df[col_name].shift(lag) - #sort the varaibles for ease of plotting - lagged_df = lagged_df.sort_index(axis=1) - return lagged_df - -def create_correlation_matrix(sources_lagged, sinks, start_date, end_date): - start_date_str = start_date.strftime('%Y-%m-%d') - end_date_str = end_date.strftime('%Y-%m-%d') - srcs_snks = sinks.join(sources_lagged, how = 'outer') - srcs_snks.index = pd.to_datetime(srcs_snks.index) - srcs_snks_clipped = srcs_snks[start_date_str:end_date_str] - snks_cols = list(sinks.columns) - correlation_matrix = srcs_snks_clipped.corr().filter(snks_cols).drop(snks_cols).transpose() - return(correlation_matrix) - -def generate_correlation_heatmap(sources, sinks, start_date, end_date, n_lags, mask_threshold): - #lag sources - sources_lagged = lag_sources_df(n_lags, sources) - #create correlation matrix - correlation_matrix = create_correlation_matrix(sources_lagged, sinks, start_date, end_date) - #plot heat map - mask = abs(correlation_matrix) < mask_threshold - plt.figure(figsize = (5,10)) - cbar_kws = {"shrink":0.85, - 'extendfrac':0.1, - 'label': 'correlation' - } - heatmap = sns.heatmap(correlation_matrix.transpose(), vmin = -1, vmax = 1, cbar = True, cmap='coolwarm', - annot = True, cbar_kws = cbar_kws, linewidth = 1, mask = mask.transpose()) - plt.xticks(rotation=45,rotation_mode='anchor',ha = 'right') - plt.yticks(rotation = 'horizontal') - heatmap.set_title('|'+'Correlation'+'| > '+str(mask_threshold)+'\n '+start_date.strftime('%Y-%m-%d')+' - '+end_date.strftime('%Y-%m-%d'), fontdict={'fontsize':14}, pad=12) - plt.show() - -def generate_correlation_timeseries(sources, sinks, start_date, end_date, n_lags, mask_threshold): - #lag sources - sources_lagged = lag_sources_df(n_lags, sources) - #create correlation matrix - correlation_matrix = create_correlation_matrix(sources_lagged, sinks, start_date, end_date) - - - corr_long = correlation_matrix.stack().reset_index() - corr_long.columns = ['sink','source','correlation'] - corr_long[['site','var','none','lag']] = corr_long['source'].str.split('_',expand = True) - - fig = px.line(corr_long, x='lag', y='correlation', color='site', facet_row = 'var', facet_col = 'sink', - title="Lagged Correlation") - fig.update_yaxes(matches=None) - fig.show() - - -def create_mutual_information_matrix(sources_lagged, sinks, start_date, end_date): - '''Takes in the lagged sources and calculates the mutual information between them and the - sinks. mutual information is used''' - start_date_str = start_date.strftime('%Y-%m-%d') - end_date_str = end_date.strftime('%Y-%m-%d') - - - sources_lagged.index = pd.to_datetime(sources_lagged.index) - sinks.index = pd.to_datetime(sinks.index) - - sources_clipped = sources_lagged[start_date_str:end_date_str] - sinks_clipped = sinks[start_date_str:end_date_str] - - bins = [11,11,11] - dfs = pd.DataFrame() - MI_array = np.zeros((sources_clipped.shape[1], sinks_clipped.shape[1])) - #MI_crit_array = np.zeros((sources_clipped.shape[1], sinks_clipped.shape[1])) - for i,src_name in enumerate(sources_clipped): - #print(src_name) - for j,snk_name in enumerate(sinks_clipped): - temp_src = sources_clipped[src_name] - temp_snk = sinks_clipped[snk_name] - paired = temp_src.to_frame().join(temp_snk).to_numpy() - MI, n = TEpython_ParallelNAN2.mutinfo_new(paired, nbins = bins) - #MIcrit = TEpython_ParallelNAN2.mutinfo_crit_newPar(paired, nbins = bins, alpha = 0.05, numiter = 1000 ,ncores = 8) - - MI_array[i,j] = MI - #MI_crit_array[i,j] = MIcrit[1] - #print(str(i)+' | '+str(j)+' | '+src_name+' | '+snk_name+'| MI = '+str(MI)) - mat = pd.DataFrame(MI_array.T, columns = sources_clipped.columns) - mat = mat.set_index(sinks_clipped.columns) - - dfs = dfs.append(mat) - - return(dfs) - -#%% -### - -def standardize(sr): - standardized = list() - for i in range(len(sr)): - #standardize - value_z = (sr[i] - np.nanmean(sr))/np.nanstd(sr, ddof = 1) - standardized.append(value_z) - return standardized - -def normalize(sr): - normalized = list() - for i in range(len(sr)): - #normalize - value_z = (sr[i]-np.nanmin(sr))/(np.nanmax(sr)-np.nanmin(sr)) - normalized.append(value_z) - return normalized - -def remove_seasonal_signal(sr, sr_historical): - #calculate doy for sr - sr_doy = sr.index.strftime('%j') - #convert sr_historical to df - sr_historical_df = sr_historical.to_frame().copy() - #calculate doy - sr_historical_df['doy'] = list(sr_historical.index.strftime('%j')) - #calculate the doy means - doy_means = sr_historical_df.groupby('doy').mean() - #convert the index (doy) to int64 - doy_means.index = doy_means.index.astype('int64') - - seasonal_removed = list() - for i in range(len(sr)): - doy = int(sr_doy[i]) - doy_mean = doy_means.loc[doy] - value = sr.iloc[i]-doy_mean[0] - seasonal_removed.append(value) - return seasonal_removed - -#%% -def preprocess_data_for_it(sources, sinks): - sources_proc_df = pd.DataFrame().reindex_like(sources) - sinks_proc_df = pd.DataFrame().reindex_like(sinks) - - for src in sources: - temp_data = remove_seasonal_signal(sources[src], sources[src]) - temp_data = normalize(temp_data) - sources_proc_df[src] = temp_data - - for snk in sinks: - temp_data = remove_seasonal_signal(sinks[snk], sinks[snk]) - temp_data = normalize(temp_data) - sinks_proc_df[snk] = temp_data - - return sources_proc_df, sinks_proc_df - -#%% -def generate_mutual_information_heatmap(sources, sinks, start_date, end_date, n_lags, mask_threshold): - #preprocess data - sources_proc, sinks_proc = preprocess_data_for_it(sources, sinks) - - #lag sources - sources_lagged_proc = lag_sources_df(n_lags, sources_proc) - #create correlation matrix - mi_matrix = create_mutual_information_matrix(sources_lagged_proc, sinks_proc, start_date, end_date) - #plot heat map - mask = abs(mi_matrix) < mask_threshold - plt.figure(figsize = (5,10)) - cbar_kws = {"shrink":0.85, - 'extendfrac':0.1, - 'label': 'mutual information' - } - heatmap = sns.heatmap(mi_matrix.transpose(), vmin = 0, vmax = 1, cbar = True, cmap='coolwarm', - annot = True, cbar_kws = cbar_kws, linewidth = 1, mask = mask.transpose()) - plt.xticks(rotation=45,rotation_mode='anchor',ha = 'right') - plt.yticks(rotation = 'horizontal') - heatmap.set_title('|'+'Mutual Information'+'| > '+str(mask_threshold)+'\n '+start_date.strftime('%Y-%m-%d')+' - '+end_date.strftime('%Y-%m-%d'), fontdict={'fontsize':14}, pad=12) - plt.show() - -def generate_mutual_information_timeseries(sources, sinks, start_date, end_date, n_lags, mask_threshold): - sources_proc, sinks_proc = preprocess_data_for_it(sources, sinks) - - #lag sources - sources_lagged_proc = lag_sources_df(n_lags, sources_proc) - #create correlation matrix - mi_matrix = create_mutual_information_matrix(sources_lagged_proc, sinks_proc, start_date, end_date) - - - mi_long = mi_matrix.stack().reset_index() - mi_long.columns = ['sink','source','mutual_information'] - mi_long[['site','var','none','lag']] = mi_long['source'].str.split('_',expand = True) - - fig = px.line(mi_long, x='lag', y='mutual_information', color='site', facet_row = 'var', facet_col = 'sink', - title="Lagged Mutual Information") - fig.update_yaxes(matches=None) - fig.show() - -#%% -# import plotly.express as px -# import plotly.io as pio -# pio.renderers.default='browser' -# start_date = pd.to_datetime('2015-01-01') -# end_date = pd.to_datetime('2019-01-01') - -# # generate_correlation_timeseries(sources, sinks, start_date, end_date, n_lags) -# # data.columns.to_series().str.contains(srcs[0]).any(): - - -# corr_long = correlation_matrix.stack().reset_index() -# corr_long.columns = ['sink','source','correlation'] -# corr_long[['site','var','none','lag']] = corr_long['source'].str.split('_',expand = True) - -# fig = px.line(corr_long, x='lag', y='correlation', color='site', facet_row = 'var', facet_col = 'sink', height=600, width=800, -# title="Lagged Correlation") -# fig.update_yaxes(matches=None) -# fig.show() - - #preprocess data -# sources_proc, sinks_proc = preprocess_data_for_it(sources, sinks) - -# #lag sources -# sources_lagged_proc = lag_sources_df(n_lags, sources_proc) -# #create correlation matrix -# mi_matrix = create_mutual_information_matrix(sources_lagged_proc, sinks_proc, start_date, end_date) diff --git a/methods_exploration/it_functions_examples.py b/methods_exploration/it_functions_examples.py deleted file mode 100644 index 600977a..0000000 --- a/methods_exploration/it_functions_examples.py +++ /dev/null @@ -1,284 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Mar 24 10:17:34 2022 - -@author: ggorski -""" -import it_functions -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats.stats import pearsonr - - -#%% -# ndata = 365 - -# n_lags = 25 - -# #choatic logistic mapping -# #growth rate -# a = 4 -# #coupling lag -# lag = 5 -# #noise (0,1) -# e = 0.5 -# #type of link -# map_type = 'logistic' - -# # -# cca = 1 -# cc2 = 5 -# cc3 = 1 -# cc4 = 4 -def generate_logistic_data(ndata, growth, lag, e): - x = np.random.rand(ndata) - y = np.zeros(ndata) - z = np.random.randn(ndata) - - for i in np.arange(lag,ndata): - y[i] = growth*x[i-lag]*(1-x[i-lag])+e*z[i] - - # Normalize - x = (x - np.min(x))/(np.max(x) - np.min(x)) - y = (y - np.min(y))/(np.max(y) - np.min(y)) - - M = np.stack([x,y], axis = 1) - M = M[np.s_[lag:]] - - Mswap = np.stack([y,x], axis = 1) - Mswap = Mswap[np.s_[lag:]] - - return M, Mswap - -def generate_periodic_data(ndata, cc1, cc2, cc3, cc4, e): - x = np.random.rand(ndata) - y = np.zeros(ndata) - z = np.random.randn(ndata) - - for i in np.arange(4,ndata): - y[i] = cc1*np.exp(x[i-1]) + cc2*x[i-2]**2 + cc3*x[i-3] + cc4*np.cos(x[i-4]) + e*z[i] - - # Normalize - x = (x - np.min(x))/(np.max(x) - np.min(x)) - y = (y - np.min(y))/(np.max(y) - np.min(y)) - - M = np.stack([x,y], axis = 1) - M = M[np.s_[4:]] - - Mswap = np.stack([y,x], axis = 1) - Mswap = Mswap[np.s_[4:]] - - return M, Mswap - -def generate_independent_data(ndata): - x = np.random.rand(ndata) - y = np.random.rand(ndata) - - # Normalize | - x = (x - np.min(x))/(np.max(x) - np.min(x)) - y = (y - np.min(y))/(np.max(y) - np.min(y)) - - M = np.stack([x,y], axis = 1) - - Mswap = np.stack([y,x], axis = 1) - - return M, Mswap - - -def generate_data(ndata, map_type, log_growth, log_lag, cc1, cc2, cc3, cc4, e): - - if map_type == 'logistic': - lag = log_lag - else: - lag = 4 - - x = np.random.rand(ndata) - y = np.zeros(ndata) - z = np.random.randn(ndata) - - for i in np.arange(lag,ndata): - if map_type == 'logistic': - y[i] = log_growth*x[i-lag]*(1-x[i-lag])+e*z[i] - elif map_type == 'periodic': - y[i] = cc1*np.exp(x[i-1]) + cc2*x[i-2]**2 + cc3*x[i-3] + cc4*np.cos(x[i-4]) + e*z[i] - elif map_type == 'independent': - y[i] = np.random.rand(1) - # Normalize | - x = (x - np.min(x))/(np.max(x) - np.min(x)) - y = (y - np.min(y))/(np.max(y) - np.min(y)) - - M = np.stack([x,y], axis = 1) - M = M[np.s_[lag:]] - - Mswap = np.stack([y,x], axis = 1) - Mswap = Mswap[np.s_[lag:]] - - return M, Mswap -#%% -#plt.plot(M[:,0], label = 'Source x') -#plt.plot(M[:,1], label = 'Sink y') -#plt.legend() -#%% -# plt.hist(M[:,0]) -# plt.hist(M[:,1]) -# plt.ylabel('Frequency') -# plt.xlabel('Value') -# plt.legend(('Source x','Sink y' ),loc='upper right') - -#%% - -def calc_it_metrics(M, Mswap, n_lags, calc_swap = True, nbins = 11, alpha = 0.01): - MI = [] - MIcrit = [] - corr = [] - TE = [] - TEcrit = [] - TEswap = [] - TEcritswap = [] - for i in range(0,n_lags): - #lag data - M_lagged = it_functions.lag_data(M,shift = i) - #remove any rows where there is an nan value - M_short = M_lagged[~np.isnan(M_lagged).any(axis=1)] - MItemp = it_functions.calcMI(M_short[:,(0,1)], nbins = nbins) - MI.append(MItemp) - MIcrittemp = it_functions.calcMI_crit(M_short[:,(0,1)], nbins = nbins, ncores = 8, alpha = 0.01) - MIcrit.append(MIcrittemp) - - corrtemp = pearsonr(M_short[:,0], M_short[:,1])[0] - corr.append(corrtemp) - - TEtemp = it_functions.calcTE(M, shift = i, nbins = nbins) - TE.append(TEtemp) - TEcrittemp = it_functions.calcTE_crit(M, shift = i, nbins = nbins, ncores = 8, alpha = 0.01) - TEcrit.append(TEcrittemp) - - if calc_swap: - TEtempswap = it_functions.calcTE(Mswap, shift = i) - TEswap.append(TEtempswap) - TEcrittempswap = it_functions.calcTE_crit(Mswap, shift = i, nbins = nbins, ncores = 8, alpha = 0.01) - TEcritswap.append(TEcrittempswap) - - it_metrics = {'MI':MI, 'MIcrit':MIcrit, - 'TE':TE, 'TEcrit':TEcrit, - 'TEswap':TEswap, 'TEcritswap':TEcritswap, - 'corr':corr} - - return it_metrics - -#%% -def plot_te(n_lags, it_metrics, plot_swap = True): - plt.plot(range(0,n_lags),it_metrics['TE'], color='#0077b6', marker='o', linewidth=2, markersize=5, label = 'TE x -> y') - plt.plot(range(0,n_lags),it_metrics['TEcrit'], color = '#00b4d8', linewidth=1, linestyle='dashed', label = 'TE critical x -> y') - if plot_swap: - plt.plot(range(0,n_lags),it_metrics['TEswap'], color='#ff5400', marker='o', linewidth=2, markersize=5, label = 'TE y -> x') - plt.plot(range(0,n_lags),it_metrics['TEcritswap'], color = '#ff9e00', linewidth=1, linestyle='dashed', label = 'TE critical y -> x') - plt.xlabel('Time lag') - plt.ylabel('TE as a fraction of H(y)') - plt.title('Transfer entropy') - plt.legend() - plt.show() -#%% -def plot_mi_corr(n_lags, it_metrics): - fig, ax1 = plt.subplots() - - ax2 = ax1.twinx() - mi_p = ax1.plot(range(0,n_lags),it_metrics['MI'], color='dodgerblue', marker='o', linewidth=2, markersize=5, label = 'MI') - micrit_p = ax1.plot(range(0,n_lags),it_metrics['MIcrit'], color = 'lightskyblue', linewidth=2, linestyle='dashed', label = 'MI critical') - corr_p = ax2.plot(range(0,n_lags),it_metrics['corr'], color='red', marker='o', linewidth=2, markersize=5, label = 'Pearson correlation') - ax1.set_xlabel('Time lag') - ax1.set_ylabel('MI as a fraction of H(y)', color='dodgerblue') - ax2.set_ylabel('Pearson correlation', color='red') - # Solution for having two legends - leg = mi_p +micrit_p + corr_p - labs = [l.get_label() for l in leg] - ax1.legend(leg, labs, loc=0) - plt.show() - -#%% - -def gen_plot_it_metrics(ndata, map_type, log_growth, log_lag, cc1, cc2, cc3, cc4, e, n_lags_plot): - M, Mswap = generate_data(ndata, map_type, log_growth, log_lag, cc1, cc2, cc3, cc4, e) - it_metrics = calc_it_metrics(M,Mswap,n_lags_plot) - plot_te(n_lags_plot, it_metrics) - plot_mi_corr(n_lags_plot, it_metrics) - -#%% -def gen_plot_logistic_it_te(ndata, e): - growth = 4 - lag = 5 - n_lags_plot = 10 - calc_swap = True - M, Mswap = generate_logistic_data(ndata, growth, lag, e) - it_dict = calc_it_metrics(M,Mswap, n_lags_plot ,calc_swap) - plt.plot(range(0,n_lags_plot),it_dict['TE'], color='#0077b6', marker='o', linewidth=2, markersize=5, label = 'TE x -> y') - plt.plot(range(0,n_lags_plot),it_dict['TEcrit'], color = '#00b4d8', linewidth=1, linestyle='dashed', label = 'TE critical x -> y') - if calc_swap: - plt.plot(range(0,n_lags_plot),it_dict['TEswap'], color='#ff5400', marker='o', linewidth=2, markersize=5, label = 'TE y -> x') - plt.plot(range(0,n_lags_plot),it_dict['TEcritswap'], color = '#ff9e00', linewidth=1, linestyle='dashed', label = 'TE critical y -> x') - plt.xlabel('Time lag') - plt.ylabel('TE as a fraction of H(y)') - plt.title('Transfer entropy | number of data points = '+str(ndata) + ' | noise = '+ str(e)) - plt.legend() - plt.show() -#%% -def gen_plot_logistic_it_mi(ndata, e): - growth = 4 - lag = 5 - n_lags_plot = 10 - calc_swap = False - M, Mswap = generate_logistic_data(ndata, growth, lag, e) - it_dict = calc_it_metrics(M,Mswap, n_lags_plot ,calc_swap) - fig, ax1 = plt.subplots() - - ax2 = ax1.twinx() - mi_p = ax1.plot(range(0,n_lags_plot),it_dict['MI'], color='dodgerblue', marker='o', linewidth=2, markersize=5, label = 'MI') - micrit_p = ax1.plot(range(0,n_lags_plot),it_dict['MIcrit'], color = 'lightskyblue', linewidth=2, linestyle='dashed', label = 'MI critical') - corr_p = ax2.plot(range(0,n_lags_plot),it_dict['corr'], color='red', marker='o', linewidth=2, markersize=5, label = 'Pearson correlation') - ax1.set_xlabel('Time lag') - ax1.set_ylabel('MI as a fraction of H(y)', color='dodgerblue') - ax2.set_ylabel('Pearson correlation', color='red') - fig.suptitle('Mutual information | number of data points = '+str(ndata) + ' | noise = '+ str(e)) - # Solution for having two legends - leg = mi_p +micrit_p + corr_p - labs = [l.get_label() for l in leg] - ax1.legend(leg, labs, loc=0) - plt.show() -#%% -def gen_plot_periodic_it_te(ndata, cc1, cc2, cc3, cc4, e): - n_lags_plot = 10 - calc_swap = True - M, Mswap = generate_periodic_data(ndata, cc1, cc2, cc3, cc4, e) - it_dict = calc_it_metrics(M,Mswap, n_lags_plot ,calc_swap) - plt.plot(range(0,n_lags_plot),it_dict['TE'], color='#0077b6', marker='o', linewidth=2, markersize=5, label = 'TE x -> y') - plt.plot(range(0,n_lags_plot),it_dict['TEcrit'], color = '#00b4d8', linewidth=1, linestyle='dashed', label = 'TE critical x -> y') - if calc_swap: - plt.plot(range(0,n_lags_plot),it_dict['TEswap'], color='#ff5400', marker='o', linewidth=2, markersize=5, label = 'TE y -> x') - plt.plot(range(0,n_lags_plot),it_dict['TEcritswap'], color = '#ff9e00', linewidth=1, linestyle='dashed', label = 'TE critical y -> x') - plt.xlabel('Time lag') - plt.ylabel('TE as a fraction of H(y)') - plt.title('Transfer entropy | number of data points = '+str(ndata) + ' | noise = '+ str(e)) - plt.legend() - plt.show() - -#%% -def gen_plot_periodic_it_mi(ndata, cc1, cc2, cc3, cc4, e): - n_lags_plot = 10 - calc_swap = False - M, Mswap = generate_periodic_data(ndata, cc1, cc2, cc3, cc4, e) - it_dict = calc_it_metrics(M,Mswap, n_lags_plot ,calc_swap) - fig, ax1 = plt.subplots() - - ax2 = ax1.twinx() - mi_p = ax1.plot(range(0,n_lags_plot),it_dict['MI'], color='dodgerblue', marker='o', linewidth=2, markersize=5, label = 'MI') - micrit_p = ax1.plot(range(0,n_lags_plot),it_dict['MIcrit'], color = 'lightskyblue', linewidth=2, linestyle='dashed', label = 'MI critical') - corr_p = ax2.plot(range(0,n_lags_plot),it_dict['corr'], color='red', marker='o', linewidth=2, markersize=5, label = 'Pearson correlation') - ax1.set_xlabel('Time lag') - ax1.set_ylabel('MI as a fraction of H(y)', color='dodgerblue') - ax2.set_ylabel('Pearson correlation', color='red') - fig.suptitle('Mutual information | number of data points = '+str(ndata) + ' | noise = '+ str(e)) - # Solution for having two legends - leg = mi_p +micrit_p + corr_p - labs = [l.get_label() for l in leg] - ax1.legend(leg, labs, loc=0) - plt.show() diff --git a/methods_exploration/modeled_nitrate_data/it_example_data.csv b/methods_exploration/modeled_nitrate_data/it_example_data.csv deleted file mode 100644 index b51a3a7..0000000 --- a/methods_exploration/modeled_nitrate_data/it_example_data.csv +++ /dev/null @@ -1,1097 +0,0 @@ -datetime,julianday,nitrateobs,nitratemdlapred,nitratemdlbpred,discharge,prcp,tmax,tmin -20141003,276,2.76,2.797727,2.7437468,261,160,200,111 -20141004,277,2.79,2.9249146,2.6718771,246,8,128,22 -20141005,278,2.9,2.8446267,2.7647977,250,18,67,17 -20141006,279,2.93,2.9397287,2.863276,247,0,83,0 -20141007,280,,,,247,0,128,28 -20141008,281,,,,243,0,156,22 -20141009,282,,,,242,0,128,6 -20141010,283,,,,237,0,128,-17 -20141011,284,,,,238,0,128,-6 -20141012,285,,,,239,0,117,-17 -20141013,286,,,,244,23,156,-11 -20141014,287,,,,337,419,144,89 -20141015,288,,,,376,66,122,83 -20141016,289,,,,364,13,111,78 -20141017,290,2.3,3.585421,2.2900038,321,5,150,50 -20141018,291,,,,301,25,122,28 -20141019,292,,,,292,0,150,-28 -20141020,293,,,,278,8,139,-17 -20141021,294,,,,270,0,117,33 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-dask -certifi -xarray -torchvision -torch -torchaudio -ipympl -plotly -seaborn -utils -netCDF4 -h5netcdf -scipy -pydap -zarr -fsspec -cftime -rasterio -cfgrib -pooch \ No newline at end of file