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Remove the table of contents on top of each tutorial file
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‎doc/guide/Guide_CLIMADA_Tutorial.ipynb‎

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‎doc/tutorial/0_intro_python.ipynb‎

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"Most of the examples come from the official Python tutorial: https://docs.python.org/3/tutorial/"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Contents\n",
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"\n",
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"- [Numbers and Strings](#Numbers-and-Strings)\n",
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"- [Lists](#Lists)\n",
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"- [Tuples](#Tuples)\n",
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"- [Sets](#Sets)\n",
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"- [Dictonaries](#Dictionaries)\n",
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"- [Functions](#Functions)\n",
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"- [Objects](#Objects)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",

‎doc/tutorial/1_main_climada.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# CLIMADA overview\n",
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"\n",
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"## Contents\n",
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"\n",
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"- [Introduction](#Introduction)\n",
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" - [What is CLIMADA?](#What-is-CLIMADA)\n",
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" - [This tutorial](#This-tutorial)\n",
14-
" - [Resources beyond this tutorial](#Resources-beyond-this-tutorial)\n",
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"- [CLIMADA features](#CLIMADA-features)\n",
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" - [CLIMADA classes](#CLIMADA-classes)\n",
17-
"- [Tutorial: an example risk assessment](#Tutorial--an-example-risk-assessment)\n",
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" - [Hazard](#Hazard)\n",
19-
" - [Storm tracks](#Storm-tracks)\n",
20-
" - [Centroids](#Centroids)\n",
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" - [Hazard footprint](#Hazard-footprint)\n",
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" - [Entity](#Entity)\n",
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" - [Exposures](#Exposures)\n",
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" - [Impact functions](#Impact-functions)\n",
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" - [Adaptation measures](#Adaptation-measures)\n",
26-
" - [Discount rates](#Discount-rates)\n",
27-
" - [Engine](#Engine)\n",
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" - [Impact](#Impact)\n",
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" - [Adaptation options appraisal](#Adaptation-options-appraisal)\n",
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" "
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"# CLIMADA overview"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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],
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"outputs": [],
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"source": [
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"from climada.entity import Entity\n",
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"\n",

‎doc/tutorial/climada_engine_CostBenefit.ipynb‎

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"# END-TO-END COST BENEFIT CALCULATION"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Contents\n",
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"\n",
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"- [Introduction](#Introduction)\n",
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"- [What is a cost-benefit?](#What-is-a-cost-benefit?)\n",
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"- [CostBenefit class data structure](#CostBenefit-class-data-structure)\n",
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"- [Detailed CostBenefit calculation: LitPop + TropCyclone](#Detailed-CostBenefit-calculation:-LitPop-+-TropCyclone)\n",
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"- [Conclusion](#Conclusion)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"\n",
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"client = Client()\n",
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"future_year = 2080\n",
273-
"haz_present = client.get_hazard('tropical_cyclone', \n",
274-
" properties={'country_name': 'Haiti', \n",
260+
"haz_present = client.get_hazard('tropical_cyclone',\n",
261+
" properties={'country_name': 'Haiti',\n",
275262
" 'climate_scenario': 'historical',\n",
276263
" 'nb_synth_tracks':'10'})\n",
277-
"haz_future = client.get_hazard('tropical_cyclone', \n",
278-
" properties={'country_name': 'Haiti', \n",
264+
"haz_future = client.get_hazard('tropical_cyclone',\n",
265+
" properties={'country_name': 'Haiti',\n",
279266
" 'climate_scenario': 'rcp60',\n",
280267
" 'ref_year': str(future_year),\n",
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" 'nb_synth_tracks':'10'})\n"

‎doc/tutorial/climada_engine_unsequa.ipynb‎

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"This is a tutorial for the unsequa module in CLIMADA. A detailled description can be found in [Kropf (2021)](https://eartharxiv.org/repository/view/3123/)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents\n",
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"- [Unsequa - a module for uncertainty and sensitivity analysis](#unsequa-a-module-for-uncertainty-and-sensitivity-analysis)\n",
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" - [Uncertainty and sensitivity analysis](#uncertainty-and-sensitivity-analysis)\n",
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" - [Unsequa Module Structure](#unsequa-module-structure)\n",
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" - [InputVar](#inputvar)\n",
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" - [Example - custom continuous uncertainty parameter](#example-custom-continuous-uncertainty-parameter)\n",
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" - [Example - custom categorical uncertainty parameter](#example-custom-categorical-uncertainty-parameter)\n",
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" - [UncOutput](#uncoutput)\n",
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" - [Example from file](#example-from-file)\n",
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" - [CalcImpact](#calcimpact)\n",
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" - [Set the InputVars](#set-the-inputvars)\n",
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" - [Compute uncertainty and sensitivity using default methods](#compute-uncertainty-and-sensitivity-using-default-methods)\n",
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" - [A few non-default parameters](#a-few-non-default-parameters)\n",
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" - [CalcDeltaImpact](#calcdeltaimpact)\n",
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" - [Set the Input Vars](#set-the-input-vars)\n",
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" - [Compute uncertainty and sensitivity](#compute-uncertainty-and-sensitivity)\n",
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" - [CalcCostBenefit](#calccostbenefit)\n",
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" - [Set the Input Vars](#set-the-input-vars)\n",
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" - [Compute cost benefit uncertainty and sensitivity using default methods](#compute-cost-benefit-uncertainty-and-sensitivity-using-default-methods)\n",
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" - [Advanced examples](#advanced-examples)\n",
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" - [Coupled variables](#coupled-variables)\n",
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" - [Many scenarios of hazards and exposures](#many-scenarios-of-hazards-and-exposures)\n",
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" - [Input variable: Repeated loading of files made efficient](#input-variable-repeated-loading-of-files-made-efficient)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"warnings.filterwarnings('ignore') #Ignore warnings for making the tutorial's pdf. \n",
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"warnings.filterwarnings('ignore') #Ignore warnings for making the tutorial's pdf.\n",
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"\n",
188159
"#Define the base exposure\n",
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"from climada.util.constants import EXP_DEMO_H5\n",
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"\n",
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"# Define the function\n",
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"# Note that this here works, but might be slow because the method LitPop is called everytime the the function\n",
349-
"# is evaluated, and LitPop is relatively slow. \n",
320+
"# is evaluated, and LitPop is relatively slow.\n",
350321
"def litpop_cat(m, n):\n",
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" exp = Litpop.from_countries('CHE', res_arcsec=150, exponent=[m, n])\n",
352323
" return exp"
@@ -372,7 +343,7 @@
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" for n in range(n_min, n_max + 1):\n",
373344
" exp_mn = LitPop.from_countries('CHE', res_arcsec=150, exponents=[m, n]);\n",
374345
" litpop_dict[(m, n)] = exp_mn\n",
375-
" \n",
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"\n",
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"def litpop_cat(m, n, litpop_dict=litpop_dict):\n",
377348
" return litpop_dict[(m, n)]"
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]
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992963
"from climada.hazard import Hazard\n",
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"\n",
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"def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
995-
" \n",
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"\n",
996967
" def xhi(v, v_half, vmin):\n",
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" return max([(v - vmin), 0]) / (v_half - vmin)\n",
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"\n",
999970
" def sigmoid_func(v, G, v_half, vmin, k):\n",
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" return G * xhi(v, v_half, vmin)**k / (1 + xhi(v, v_half, vmin)**k)\n",
1001972
"\n",
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" #In-function imports needed only for parallel computing on Windows\n",
1003-
" import numpy as np \n",
1004-
" from climada.entity import ImpactFunc, ImpactFuncSet \n",
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" import numpy as np\n",
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" from climada.entity import ImpactFunc, ImpactFuncSet\n",
1005976
" intensity_unit = 'm/s'\n",
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" intensity = np.linspace(0, 150, num=100)\n",
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" mdd = np.repeat(1, len(intensity))\n",
@@ -1015,7 +986,7 @@
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"exp_base = Exposures.from_hdf5(EXP_DEMO_H5)\n",
1016987
"#It is a good idea to assign the centroids to the base exposures in order to avoid repeating this\n",
1017988
"# potentially costly operation for each sample.\n",
1018-
"exp_base.assign_centroids(haz) \n",
989+
"exp_base.assign_centroids(haz)\n",
1019990
"def exp_base_func(x_exp, exp_base):\n",
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" exp = exp_base.copy()\n",
1021992
" exp.gdf.value *= x_exp\n",
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"import scipy as sp\n",
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"from climada.engine.unsequa import InputVar\n",
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"\n",
1069-
"exp_distr = {\"x_exp\": sp.stats.beta(10, 1.1)} #This is not really a reasonable distribution but is used \n",
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"exp_distr = {\"x_exp\": sp.stats.beta(10, 1.1)} #This is not really a reasonable distribution but is used\n",
10701041
" #here to show that you can use any scipy distribution.\n",
1071-
" \n",
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"\n",
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"exp_iv = InputVar(exp_func, exp_distr)\n",
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"\n",
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"impf_distr = {\n",
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],
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"source": [
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"# Compute also the distribution of the metric `eai_exp`\n",
2131-
"# To speed-up the comutations, we can use more than one process \n",
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"# To speed-up the comutations, we can use more than one process\n",
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"# Note that for large dataset a single process might be more efficient\n",
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"import time\n",
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"\n",
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},
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"outputs": [],
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"source": [
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"# Use the method 'rbd_fast' which is recommend in pair with 'latin'. In addition, change one of the kwargs \n",
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"# Use the method 'rbd_fast' which is recommend in pair with 'latin'. In addition, change one of the kwargs\n",
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"# (M=15) of the salib sampling method.\n",
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"output_imp2 = calc_imp2.sensitivity(output_imp2, sensitivity_method='rbd_fast', sensitivity_kwargs = {'M': 15})"
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]
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"from climada.hazard import Centroids, TCTracks, Hazard, TropCyclone\n",
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"\n",
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"def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
2434-
" \n",
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"\n",
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" def xhi(v, v_half, vmin):\n",
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" return max([(v - vmin), 0]) / (v_half - vmin)\n",
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"\n",
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"exp_base = Exposures.from_hdf5(EXP_DEMO_H5)\n",
24892460
"#It is a good idea to assign the centroids to the base exposures in order to avoid repeating this\n",
24902461
"# potentially costly operation for each sample.\n",
2491-
"exp_base.assign_centroids(haz) \n",
2462+
"exp_base.assign_centroids(haz)\n",
24922463
"def exp_base_func(x_exp, exp_base):\n",
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" exp = exp_base.copy()\n",
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" exp.gdf.value *= x_exp\n",
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"import scipy as sp\n",
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"from climada.engine.unsequa import InputVar\n",
25082479
"\n",
2509-
"exp_distr = {\"x_exp\": sp.stats.beta(10, 1.1)} #This is not really a reasonable distribution but is used \n",
2480+
"exp_distr = {\"x_exp\": sp.stats.beta(10, 1.1)} #This is not really a reasonable distribution but is used\n",
25102481
" #here to show that you can use any scipy distribution.\n",
2511-
" \n",
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"\n",
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"exp_iv = InputVar(exp_func, exp_distr)\n",
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"\n",
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"impf_distr = {\n",
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],
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"source": [
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"from climada.engine.unsequa import CalcDeltaImpact\n",
2605-
"calc_imp = CalcDeltaImpact(exp_iv, impf_iv, haz, \n",
2576+
"calc_imp = CalcDeltaImpact(exp_iv, impf_iv, haz,\n",
26062577
" exp_iv, impf_iv, haz_fut_iv)"
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]
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},
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"# Entity today has an uncertainty in the total asset value\n",
28232794
"def ent_today_func(x_ent):\n",
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" #In-function imports needed only for parallel computing on Windows\n",
2825-
" from climada.entity import Entity \n",
2826-
" from climada.util.constants import ENT_DEMO_TODAY \n",
2796+
" from climada.entity import Entity\n",
2797+
" from climada.util.constants import ENT_DEMO_TODAY\n",
28272798
" entity = Entity.from_excel(ENT_DEMO_TODAY)\n",
28282799
" entity.exposures.ref_year = 2018\n",
28292800
" entity.exposures.gdf.value *= x_ent\n",
@@ -2832,10 +2803,10 @@
28322803
"# Entity in the future has a +- 10% uncertainty in the cost of all the adapatation measures\n",
28332804
"def ent_fut_func(m_fut_cost):\n",
28342805
" #In-function imports needed only for parallel computing on Windows\n",
2835-
" from climada.entity import Entity \n",
2836-
" from climada.util.constants import ENT_DEMO_FUTURE \n",
2806+
" from climada.entity import Entity\n",
2807+
" from climada.util.constants import ENT_DEMO_FUTURE\n",
28372808
" entity = Entity.from_excel(ENT_DEMO_FUTURE)\n",
2838-
" entity.exposures.ref_year = 2040 \n",
2809+
" entity.exposures.ref_year = 2040\n",
28392810
" for meas in entity.measures.get_measure('TC'):\n",
28402811
" meas.cost *= m_fut_cost\n",
28412812
" return entity\n",
@@ -2844,9 +2815,9 @@
28442815
"# The hazard intensity in the future is also uncertainty by a multiplicative factor\n",
28452816
"def haz_fut(x_haz_fut, haz_base):\n",
28462817
" #In-function imports needed only for parallel computing on Windows\n",
2847-
" import copy \n",
2848-
" from climada.hazard import Hazard \n",
2849-
" from climada.util.constants import HAZ_DEMO_H5 \n",
2818+
" import copy\n",
2819+
" from climada.hazard import Hazard\n",
2820+
" from climada.util.constants import HAZ_DEMO_H5\n",
28502821
" haz = copy.deepcopy(haz_base)\n",
28512822
" haz.intensity = haz.intensity.multiply(x_haz_fut)\n",
28522823
" return haz\n",
@@ -5100,7 +5071,7 @@
51005071
}
51015072
],
51025073
"source": [
5103-
"# The impact_meas_present and impact_meas_future provide values of the cost_meas, risk_transf, risk, \n",
5074+
"# The impact_meas_present and impact_meas_future provide values of the cost_meas, risk_transf, risk,\n",
51045075
"# and cost_ins for each measure\n",
51055076
"output_cb.get_uncertainty(metric_list=['imp_meas_present']).tail()"
51065077
]
@@ -5286,7 +5257,7 @@
52865257
"metadata": {},
52875258
"outputs": [],
52885259
"source": [
5289-
"#Define the input variable \n",
5260+
"#Define the input variable\n",
52905261
"from climada.entity import ImpactFuncSet, Exposures\n",
52915262
"from climada.entity.impact_funcs.storm_europe import ImpfStormEurope\n",
52925263
"from climada.hazard import Hazard\n",
@@ -5306,7 +5277,7 @@
53065277
"\n",
53075278
"\n",
53085279
"def haz_func(cnt, i_haz, haz_list=haz_list):\n",
5309-
" haz = copy.deepcopy(haz_list[int(cnt)]) #use the same parameter name accross input variables \n",
5280+
" haz = copy.deepcopy(haz_list[int(cnt)]) #use the same parameter name accross input variables\n",
53105281
" haz.intensity *= i_haz\n",
53115282
" return haz\n",
53125283
"\n",
@@ -5588,16 +5559,16 @@
55885559
"\n",
55895560
"\n",
55905561
"def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
5591-
" \n",
5562+
"\n",
55925563
" def xhi(v, v_half, vmin):\n",
55935564
" return max([(v - vmin), 0]) / (v_half - vmin)\n",
55945565
"\n",
55955566
" def sigmoid_func(v, G, v_half, vmin, k):\n",
55965567
" return G * xhi(v, v_half, vmin)**k / (1 + xhi(v, v_half, vmin)**k)\n",
55975568
"\n",
55985569
" #In-function imports needed only for parallel computing on Windows\n",
5599-
" import numpy as np \n",
5600-
" from climada.entity import ImpactFunc, ImpactFuncSet \n",
5570+
" import numpy as np\n",
5571+
" from climada.entity import ImpactFunc, ImpactFuncSet\n",
56015572
" imp_fun = ImpactFunc()\n",
56025573
" imp_fun.haz_type = 'WS'\n",
56035574
" imp_fun.id = _id\n",
@@ -5715,7 +5686,7 @@
57155686
"name": "python",
57165687
"nbconvert_exporter": "python",
57175688
"pygments_lexer": "ipython3",
5718-
"version": "3.9.16"
5689+
"version": "3.9.18"
57195690
},
57205691
"latex_envs": {
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"LaTeX_envs_menu_present": true,

‎doc/tutorial/climada_engine_unsequa_helper.ipynb‎

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‎doc/tutorial/climada_entity_Exposures.ipynb‎

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"# Exposures class"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Content\n",
16-
"\n",
17-
"- [Defining exposuresfrom your owndata](#Defining-exposures-from-your-own-data) \n",
18-
"- [Loading CLIMADA generated exposure files or generating new ones](#Loading-CLIMADA-generated-exposure-files-or-generating-new-ones)\n",
19-
"- [Visualize Exposures](#Visualize-Exposures)\n",
20-
"- [Write (Save) Exposures](#Write-(Save)-Exposures)\n",
21-
"- [Dask: improving performance for big exposure](#Dask---improving-performance-for-big-exposure)\n"
22-
]
23-
},
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{
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"attachments": {},
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"cell_type": "markdown",

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