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Merge pull request #872 from CLIMADA-project/feature/update_tuto_files
Remove content tables and update tutorial files
2 parents 7ce9484 + 794f063 commit 07f7281

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‎CHANGELOG.md‎

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### Changed
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- Remove content tables and make minor improvements (fix typos and readability) in
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CLIMADA tutorials. [#872](https://github.com/CLIMADA-project/climada_python/pull/872)
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- Centroids complete overhaul. Most function should be backward compatible. Internal data is stored in a geodataframe attribute. Raster are now stored as points, and the meta attribute is removed. Several methds were deprecated or removed. [#787](https://github.com/CLIMADA-project/climada_python/pull/787)
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- Improved error messages produced by `ImpactCalc.impact()` in case impact function in the exposures is not found in impf_set [#863](https://github.com/CLIMADA-project/climada_python/pull/863)
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‎doc/guide/Guide_CLIMADA_Tutorial.ipynb‎

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‎doc/guide/Guide_Introduction.ipynb‎

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" Cambridge University Press, United Kingdom and New York, NY,\n",
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" USA., 2014."
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]
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},
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{
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"cell_type": "markdown",
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"id": "503ec4a7",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {

‎doc/guide/install.rst‎

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.. code-block:: shell
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mamba env activate climada_env
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mamba activate climada_env
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jupyter-lab
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JupyterLab will open in a new window of your default browser.

‎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",
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" - [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",
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"- [Tutorial: an example risk assessment](#Tutorial--an-example-risk-assessment)\n",
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" - [Hazard](#Hazard)\n",
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" - [Storm tracks](#Storm-tracks)\n",
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" - [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",
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" - [Discount rates](#Discount-rates)\n",
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" - [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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"We can modify the benefit part of cost-benefit to reflect this. CLIMADA doesn't assume that the user will have explicit hazard and impact objects for every year in the study period, and so interpolates between the impacts at the start and the end of the period of interest. If we're evaluating between years $T_0$, usually close to the present, and $T_1$ in the future, then we can say:\n",
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"\n",
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"$$\n",
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"\\text{benefit} = \\sum_{t = T_0}^{T_1} \\alpha(t) \\bigg{(} \\text{AAI with measures}_{T_1} - \\text{AAI with measures}_{T_0} \\bigg{)} - N * \\text{AAI without measure}_{T_0}\n",
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"\\text{benefit} = \\sum_{t = T_0}^{T_1} \\alpha(t) \\bigg( \\text{AAI with measures}_{T_1} - \\text{AAI with measures}_{T_0} \\bigg) - N * \\text{AAI without measure}_{T_0}\n",
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"$$\n",
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"\n",
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"Where $\\alpha(t)$ is a function of the year $t$ describing the interpolation of hazard and exposure values between $T_0$ and $T_1$. The function returns values in the range $[0, 1]$, usually with $\\alpha(T_0) = 0$ and $\\alpha(T_0) = 1$.\n",
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"\n",
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"client = Client()\n",
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"future_year = 2080\n",
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"haz_present = client.get_hazard('tropical_cyclone', \n",
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" properties={'country_name': 'Haiti', \n",
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"haz_present = client.get_hazard('tropical_cyclone',\n",
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" properties={'country_name': 'Haiti',\n",
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" 'climate_scenario': 'historical',\n",
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" 'nb_synth_tracks':'10'})\n",
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"haz_future = client.get_hazard('tropical_cyclone', \n",
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" properties={'country_name': 'Haiti', \n",
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"haz_future = client.get_hazard('tropical_cyclone',\n",
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" properties={'country_name': 'Haiti',\n",
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" 'climate_scenario': 'rcp60',\n",
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" 'ref_year': str(future_year),\n",
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" 'nb_synth_tracks':'10'})\n"

‎doc/tutorial/climada_engine_Impact.ipynb‎

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‎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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"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",
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"#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",
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"# is evaluated, and LitPop is relatively slow. \n",
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"# is evaluated, and LitPop is relatively slow.\n",
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"def litpop_cat(m, n):\n",
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" exp = Litpop.from_countries('CHE', res_arcsec=150, exponent=[m, n])\n",
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" return exp"
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" for n in range(n_min, n_max + 1):\n",
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" exp_mn = LitPop.from_countries('CHE', res_arcsec=150, exponents=[m, n]);\n",
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" litpop_dict[(m, n)] = exp_mn\n",
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" \n",
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"\n",
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"def litpop_cat(m, n, litpop_dict=litpop_dict):\n",
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" return litpop_dict[(m, n)]"
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]
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"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",
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" \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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" 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",
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"\n",
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" #In-function imports needed only for parallel computing on Windows\n",
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" import numpy as np \n",
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" 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",
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" 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",
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"#It is a good idea to assign the centroids to the base exposures in order to avoid repeating this\n",
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"# potentially costly operation for each sample.\n",
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"exp_base.assign_centroids(haz) \n",
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"exp_base.assign_centroids(haz)\n",
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"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",
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"\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",
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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",
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" \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",
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"# 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",
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" \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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24882459
"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",
24932464
" exp = exp_base.copy()\n",
24942465
" 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",
2482+
"\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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]
26082579
},
@@ -2822,8 +2793,8 @@
28222793
"# Entity today has an uncertainty in the total asset value\n",
28232794
"def ent_today_func(x_ent):\n",
28242795
" #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
},
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"latex_envs": {
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