{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"unsequa-a-module-for-uncertainty-and-sensitivity-analysis\"></a>\n",
    "# Unsequa - a module for uncertainty and sensitivity analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a tutorial for the unsequa module in CLIMADA. A detailled description can be found in [Kropf et al. (2022)](https://doi.org/10.5194/gmd-15-7177-2022)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"uncertainty-and-sensitivity-analysis\"></a>\n",
    "## Uncertainty and sensitivity analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before doing an uncertainty quantification in CLIMADA, it is imperative that you get first comfortable with the different notions of uncertainty in the modelling world (see e.g.[Pianosi (2016)](https://www.sciencedirect.com/science/article/pii/S1364815216300287) or [Douglas-Smith(2020)](https://doi.org/10.1007/978-1-4899-7547-8_5) for a review). In particular, note that the uncertaintity values will only be as good as the input from the user. In addition, not all uncertainties can be numerically quantified, and even worse, some unkonwns are unknown. This means that sometimes, quantifying uncertainty can lead to false confidence in the output!. For a more philosophical discussion about the types of uncertainties in climate research see [Knüsel (2020)](https://www.research-collection.ethz.ch/handle/20.500.11850/399735) and [Otth (2022)](https://gmd.copernicus.org/articles/15/7177/2022).\n",
    "\n",
    "In this module, it is possible to perform global uncertainty analysis, as well as a sensitivity analysis. The word global is meant as opposition to the 'one-factor-at-a-time' (OAT) strategy. The OAT strategy, which consists in analyzing the effect of varying one model input factor at a time while keeping all other fixed, is popular among modellers, but has major shortcomings [Saltelli (2010)](https://www.sciencedirect.com/science/article/abs/pii/S1364815210001180), [Saltelli(2019)](http://www.sciencedirect.com/science/article/pii/S1364815218302822) and should not be used.\n",
    "\n",
    "A rough schemata of how to perform uncertainty and sensitivity analysis (taken from [Kropf et al. (2022)](https://doi.org/10.5194/gmd-15-7177-2022)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T15:10:09.719716Z",
     "start_time": "2021-03-18T15:10:09.709628Z"
    }
   },
   "source": [
    "<img src=\"./img/UncertaintySensitivity.jpg\" alt=\"Uncertainty and Sensitivity analysis\" width=\"1000\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Kropf, C.M. et al. Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geoscientific Model Development, 15, 7177–7201 (2022)](https://doi.org/10.5194/gmd-15-7177-2022).\n",
    "2. [Pianosi, F. et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software 79, 214–232 (2016)](https://www.sciencedirect.com/science/article/pii/S1364815216300287).\n",
    "3.[Douglas-Smith, D., Iwanaga, T., Croke, B. F. W. & Jakeman, A. J. Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques. Environmental Modelling & Software 124, 104588 (2020)](https://doi.org/10.1007/978-1-4899-7547-8_5)\n",
    "4. [Knüsel, B. Epistemological Issues in Data-Driven Modeling in Climate Research. (ETH Zurich, 2020)](https://www.research-collection.ethz.ch/handle/20.500.11850/399735)\n",
    "5. [Saltelli, A. et al. Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software 114, 29–39 (2019)](http://www.sciencedirect.com/science/article/pii/S1364815218302822)\n",
    "6. [Saltelli, A. & Annoni, P. How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 25, 1508–1517 (2010)](https://www.sciencedirect.com/science/article/abs/pii/S1364815210001180)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"unsequa-module-structure\"></a>\n",
    "## Unsequa Module Structure\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unsequa module contains several key classes. \n",
    "\n",
    "The model input parameters and their distribution are specified as\n",
    "- `InputVar`: defines input uncertainty variables\n",
    "\n",
    "The input parameter sampling, Monte-Carlo uncertainty distribution calculation and the sensitivity index computation are done in\n",
    "- `CalcImpact`: compute uncertainties for outputs of `climada.engine.impact.calc` (child class of `Calc`)\n",
    "- `CalcDeltaImpact`: compute uncertainties for outputs of `climada.engine.impact.calc` (child class of `Calc`)\n",
    "- `CalcCostBenefit`: compute uncertainties for outputs of `climada.engine.cost_benefit.calc` (child class of `Calc`)\n",
    "\n",
    "The results are stored in\n",
    "- `UncOutput`: store the uncertainty and sensitivity analysis results. Contains also several plotting methods. This is a class which only stores data.\n",
    "- `UncImpactOutput`: subclass with dataframes specifically for `climada.engine.impact.calc` uncertainty and sensitivity analysis results.\n",
    "- `UncCostBenefitOutput`: subclass with dataframes specifically for `climada.engine.cost_benefit.calc` uncertainty and sensitivity analysis results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"inputvar\"></a>\n",
    "## InputVar\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The InputVar class is used to define uncertainty variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Attribute | Type | Description |\n",
    "| --- | --- | --- |\n",
    "| func | function | Model variable defined as a function of the uncertainty input parameters |\n",
    "| distr_dict | dict | Dictionary of the probability density distributions of the uncertainty input parameters |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An **input uncertainty parameter** is a numerical input value that has a certain probability density distribution in your model, such as the total exposure asset value, the slope of the vulnerability function, the exponents of the litpop exposure,  the value of the discount rate, the cost of an adaptation measure, ... \n",
    "\n",
    "The probability densitity distributions (values of `distr_dict`) of the input uncertainty parameters (keyword arguments of the `func` and keys of the `distr_dict`) can be any of the ones defined in [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Several helper methods exist to make generic InputVar for Exposures, ImpactFuncSet, Hazard, Entity (including DiscRates and Measures)**. These are described in details in the tutorial [Helper methods for InputVar](climada_engine_unsequa_helper.ipynb). These are a good bases for your own computations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"example-custom-continuous-uncertainty-parameter\"></a>\n",
    "### Example - custom continuous uncertainty parameter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we assume that the GDP value used to scale the exposure has a relative error of +-10%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.604222Z",
     "start_time": "2023-08-03T11:57:41.913838Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # Ignore warnings for making the tutorial's pdf.\n",
    "\n",
    "# Define the base exposure\n",
    "from climada.util.constants import EXP_DEMO_H5\n",
    "from climada.entity import Exposures\n",
    "\n",
    "exp_base = Exposures.from_hdf5(EXP_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.614953Z",
     "start_time": "2023-08-03T11:57:46.610164Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the function that returns an exposure with scaled total assed value\n",
    "# Here x_exp is the input uncertainty parameter and exp_func the inputvar.func.\n",
    "def exp_func(x_exp, exp_base=exp_base):\n",
    "    exp = exp_base.copy()\n",
    "    exp.gdf[\"value\"] *= x_exp\n",
    "    return exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.645171Z",
     "start_time": "2023-08-03T11:57:46.619390Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the Uncertainty Variable with +-10% total asset value\n",
    "# The probability density distribution of the input uncertainty parameter x_exp is sp.stats.uniform(0.9, 0.2)\n",
    "from climada.engine.unsequa import InputVar\n",
    "import scipy as sp\n",
    "\n",
    "exp_distr = {\n",
    "    \"x_exp\": sp.stats.uniform(0.9, 0.2),\n",
    "}\n",
    "exp_iv = InputVar(exp_func, exp_distr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.659207Z",
     "start_time": "2023-08-03T11:57:46.650628Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x_exp']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncertainty parameters\n",
    "exp_iv.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.676106Z",
     "start_time": "2023-08-03T11:57:46.662971Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base value is 657053294559.9105, and the value for x_exp=0.95 is 624200629831.9148\n"
     ]
    }
   ],
   "source": [
    "# Evaluate for a given value of the uncertainty parameters\n",
    "exp095 = exp_iv.func(x_exp=0.95)\n",
    "print(\n",
    "    f\"Base value is {exp_base.gdf['value'].sum()}, and the value for x_exp=0.95 is {exp095.gdf['value'].sum()}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.896851Z",
     "start_time": "2023-08-03T11:57:46.678120Z"
    }
   },
   "outputs": [
    {
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      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defined distribution\n",
    "exp_iv.plot(figsize=(5, 3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<a id=\"example-custom-categorical-uncertainty-parameter\"></a>\n",
    "### Example - custom categorical uncertainty parameter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Suppose we want to test different exponents (m=1,2 ; n=1,2) for the LitPop exposure for the country Switzerland."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:46.904751Z",
     "start_time": "2023-08-03T11:57:46.900228Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from climada.entity import LitPop\n",
    "\n",
    "m_min, m_max = (1, 2)\n",
    "n_min, n_max = (1, 2)\n",
    "\n",
    "\n",
    "# Define the function\n",
    "# Note that this here works, but might be slow because the method LitPop is called everytime the the function\n",
    "# is evaluated, and LitPop is relatively slow.\n",
    "def litpop_cat(m, n):\n",
    "    exp = Litpop.from_countries(\"CHE\", res_arcsec=150, exponent=[m, n])\n",
    "    return exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:57.533389Z",
     "start_time": "2023-08-03T11:57:46.907556Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# A faster method would be to first create a dictionnary with all the exposures. This however\n",
    "# requires more memory and precomputation time (here ~3-4mins)\n",
    "exp = LitPop()\n",
    "litpop_dict = {}\n",
    "for m in range(m_min, m_max + 1):\n",
    "    for n in range(n_min, n_max + 1):\n",
    "        exp_mn = LitPop.from_countries(\"CHE\", res_arcsec=150, exponents=[m, n])\n",
    "        litpop_dict[(m, n)] = exp_mn\n",
    "\n",
    "\n",
    "def litpop_cat(m, n, litpop_dict=litpop_dict):\n",
    "    return litpop_dict[(m, n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:57.541937Z",
     "start_time": "2023-08-03T11:57:57.535710Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Define the distribution dictionnary\n",
    "import scipy as sp\n",
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "distr_dict = {\n",
    "    \"m\": sp.stats.randint(low=m_min, high=m_max + 1),\n",
    "    \"n\": sp.stats.randint(low=n_min, high=n_max + 1),\n",
    "}\n",
    "\n",
    "cat_iv = InputVar(\n",
    "    litpop_cat, distr_dict\n",
    ")  # One can use either of the above definitions of litpop_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:57:57.557449Z",
     "start_time": "2023-08-03T11:57:57.552753Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['m', 'n']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncertainty parameters\n",
    "cat_iv.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:02.034735Z",
     "start_time": "2023-08-03T11:57:57.560037Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_iv.evaluate(m=1, n=2).plot_raster();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:02.296648Z",
     "start_time": "2023-08-03T11:58:02.036611Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_iv.plot(figsize=(10, 3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "<a id=\"uncoutput\"></a>\n",
    "## UncOutput\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The `UncOutput` class is used to store data from sampling, uncertainty and sensitivity analysis. An UncOutput object can be saved and loaded from .hdf5. The classes `UncImpactOuput` and `UncCostBenefitOutput` are extensions of `UncOutput` specific for `CalcImpact` and `CalcCostBenefit`, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-24T09:13:12.855902Z",
     "start_time": "2021-08-24T09:13:12.848615Z"
    },
    "hidden": true
   },
   "source": [
    "**Data attributes**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "| Attribute | Type | Description |\n",
    "| --- | --- | --- |\n",
    "| samples_df | pandas.dataframe | Each row represents a sample obtained from the input parameters (one per column) distributions |\n",
    "| | | |\n",
    "| *UncImpactOutput* | |  |\n",
    "| | | |\n",
    "| aai_agg_unc_df | pandas.dataframe | Uncertainty data for `aai_agg`|\n",
    "| freq_curve_unc_df | pandas.dataframe | Uncertainty data for `freq_curve`. One return period per column.|\n",
    "| eai_exp_unc_df | pandas.dataframe | Uncertainty data for `eai_exp`. One exposure point per column.|\n",
    "| at_event_unc_df | pandas.dataframe | Uncertainty data for `at_event`. One event per column.|\n",
    "| | | |\n",
    "| *UncCostBenefitOutput* | |  |\n",
    "| | | |\n",
    "| imp_meas_present_unc_df | pandas.dataframe | Uncertainty data for `imp_meas_present`. One measure per column.|\n",
    "| imp_meas_future_unc_df| pandas.dataframe | Uncertainty data for `imp_meas_present`. One measure per column |\n",
    "| tot_climate_risk_unc_df | pandas.dataframe | Uncertainty data for `tot_climate_risk`. One measure per column.|\n",
    "| benefit_unc_df | pandas.dataframe | Uncertainty data for `benefit`. One measure per column.|\n",
    "| cost_ben_ratio_unc_df | pandas.dataframe | Uncertainty data for `cost_ben_ratio`. One measure per column.|\n",
    "| cost_benefit_kwargs | dictionary | Keyword arguments for `climada.engine.cost_benefit.calc`. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-24T09:13:25.319589Z",
     "start_time": "2021-08-24T09:13:25.315834Z"
    },
    "hidden": true
   },
   "source": [
    "**Metadata and input data attributes**\n",
    "\n",
    "These attributes are used for book-keeping and characterize the sample, uncertainty and sensitivity data. These attributes are set by the methods from classes `CalcImpact` and `CalcCostBenefit` used to generate sample, uncertainty and sensitivity data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "| Attribute | Type | Description |\n",
    "| --- | --- | --- |\n",
    "| sampling_method | str | The sampling method as defined in [SALib](https://salib.readthedocs.io/en/latest/api.html). Possible choices: 'saltelli', 'fast_sampler', 'latin', 'morris', 'dgsm', 'ff', 'finite_diff'|\n",
    "| sampling_kwargs | dict | Keyword arguments for the sampling_method. |\n",
    "| n_samples | int | Effective number of samples (number of rows of samples_df)|\n",
    "| param_labels | list(str) | Name of all the uncertainty input parameters|\n",
    "| problem_sa | dict | The description of the uncertainty variables and their distribution as used in [SALib](https://salib.readthedocs.io/en/latest/basics.html). |\n",
    "| sensitivity_method | str | Sensitivity analysis method from [SALib.analyse](https://salib.readthedocs.io/en/latest/api.html) Possible choices: 'sobol', 'fast', 'rbd_fast', 'morris', 'dgsm', 'ff', 'pawn', 'rhdm', 'rsa', 'discrepancy', 'hdmr'. Note that in Salib, sampling methods and sensitivity analysis methods should be used in specific pairs.|\n",
    "| sensitivity_kwargs | dict | Keyword arguments for sensitivity_method. |\n",
    "| unit | str | Unit of the exposures value |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<a id=\"example-from-file\"></a>\n",
    "### Example from file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here we show an example loaded from file. In the sections below this class is extensively used and further examples can be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:02.373485Z",
     "start_time": "2023-08-03T11:58:02.298723Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Download the test file from the API\n",
    "# Requires internet connection\n",
    "from climada.util.constants import TEST_UNC_OUTPUT_IMPACT\n",
    "from climada.util.api_client import Client\n",
    "\n",
    "apiclient = Client()\n",
    "ds = apiclient.get_dataset_info(name=TEST_UNC_OUTPUT_IMPACT, status=\"test_dataset\")\n",
    "_target_dir, [filename] = apiclient.download_dataset(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:02.490565Z",
     "start_time": "2023-08-03T11:58:02.375912Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# If you produced your own data, you do not need the API. Just replace 'filename' with the path to your file.\n",
    "from climada.engine.unsequa import UncOutput\n",
    "\n",
    "unc_imp = UncOutput.from_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.060914Z",
     "start_time": "2023-08-03T11:58:02.492871Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unc_imp.plot_uncertainty(metric_list=[\"aai_agg\"], figsize=(12, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.124126Z",
     "start_time": "2023-08-03T11:58:03.063366Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Download the test file from the API\n",
    "# Requires internet connection\n",
    "from climada.util.constants import TEST_UNC_OUTPUT_COSTBEN\n",
    "from climada.util.api_client import Client\n",
    "\n",
    "apiclient = Client()\n",
    "ds = apiclient.get_dataset_info(name=TEST_UNC_OUTPUT_COSTBEN, status=\"test_dataset\")\n",
    "_target_dir, [filename] = apiclient.download_dataset(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.218940Z",
     "start_time": "2023-08-03T11:58:03.126055Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# If you produced your own data, you do not need the API. Just replace 'filename' with the path to your file.\n",
    "from climada.engine.unsequa import UncOutput\n",
    "\n",
    "unc_cb = UncOutput.from_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.249395Z",
     "start_time": "2023-08-03T11:58:03.220866Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mangroves Benef</th>\n",
       "      <th>Beach nourishment Benef</th>\n",
       "      <th>Seawall Benef</th>\n",
       "      <th>Building code Benef</th>\n",
       "      <th>Mangroves CostBen</th>\n",
       "      <th>Beach nourishment CostBen</th>\n",
       "      <th>Seawall CostBen</th>\n",
       "      <th>Building code CostBen</th>\n",
       "      <th>no measure - risk - future</th>\n",
       "      <th>no measure - risk_transf - future</th>\n",
       "      <th>...</th>\n",
       "      <th>Beach nourishment - cost_ins - future</th>\n",
       "      <th>Seawall - risk - future</th>\n",
       "      <th>Seawall - risk_transf - future</th>\n",
       "      <th>Seawall - cost_meas - future</th>\n",
       "      <th>Seawall - cost_ins - future</th>\n",
       "      <th>Building code - risk - future</th>\n",
       "      <th>Building code - risk_transf - future</th>\n",
       "      <th>Building code - cost_meas - future</th>\n",
       "      <th>Building code - cost_ins - future</th>\n",
       "      <th>tot_climate_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2.375510e+08</td>\n",
       "      <td>1.932608e+08</td>\n",
       "      <td>234557.682554</td>\n",
       "      <td>1.584398e+08</td>\n",
       "      <td>6.347120</td>\n",
       "      <td>10.277239</td>\n",
       "      <td>4.350910e+04</td>\n",
       "      <td>66.742129</td>\n",
       "      <td>6.337592e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6.335246e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.020539e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.753194e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057461e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.337592e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>9.272772e+07</td>\n",
       "      <td>7.643803e+07</td>\n",
       "      <td>9554.257314</td>\n",
       "      <td>5.501366e+07</td>\n",
       "      <td>16.260133</td>\n",
       "      <td>25.984286</td>\n",
       "      <td>1.068151e+06</td>\n",
       "      <td>192.217876</td>\n",
       "      <td>2.200547e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.200451e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.020539e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.650410e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057461e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>2.200547e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.464219e+08</td>\n",
       "      <td>1.179927e+08</td>\n",
       "      <td>192531.748810</td>\n",
       "      <td>8.979471e+07</td>\n",
       "      <td>10.297402</td>\n",
       "      <td>16.833137</td>\n",
       "      <td>5.300629e+04</td>\n",
       "      <td>117.764285</td>\n",
       "      <td>3.591788e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3.589863e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.020539e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>2.693841e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057461e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>3.591788e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9.376369e+07</td>\n",
       "      <td>7.722882e+07</td>\n",
       "      <td>10681.112247</td>\n",
       "      <td>5.555413e+07</td>\n",
       "      <td>12.965484</td>\n",
       "      <td>20.736269</td>\n",
       "      <td>7.703765e+05</td>\n",
       "      <td>153.475031</td>\n",
       "      <td>2.222165e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.222058e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>8.228478e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.666624e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>8.526172e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>2.222165e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9.376369e+07</td>\n",
       "      <td>7.722882e+07</td>\n",
       "      <td>10681.112247</td>\n",
       "      <td>5.555413e+07</td>\n",
       "      <td>16.080478</td>\n",
       "      <td>25.718218</td>\n",
       "      <td>9.554617e+05</td>\n",
       "      <td>190.347852</td>\n",
       "      <td>2.222165e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.222058e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.020539e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.666624e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057461e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>2.222165e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mangroves Benef  Beach nourishment Benef  Seawall Benef  \\\n",
       "35     2.375510e+08             1.932608e+08  234557.682554   \n",
       "36     9.272772e+07             7.643803e+07    9554.257314   \n",
       "37     1.464219e+08             1.179927e+08  192531.748810   \n",
       "38     9.376369e+07             7.722882e+07   10681.112247   \n",
       "39     9.376369e+07             7.722882e+07   10681.112247   \n",
       "\n",
       "    Building code Benef  Mangroves CostBen  Beach nourishment CostBen  \\\n",
       "35         1.584398e+08           6.347120                  10.277239   \n",
       "36         5.501366e+07          16.260133                  25.984286   \n",
       "37         8.979471e+07          10.297402                  16.833137   \n",
       "38         5.555413e+07          12.965484                  20.736269   \n",
       "39         5.555413e+07          16.080478                  25.718218   \n",
       "\n",
       "    Seawall CostBen  Building code CostBen  no measure - risk - future  \\\n",
       "35     4.350910e+04              66.742129                6.337592e+08   \n",
       "36     1.068151e+06             192.217876                2.200547e+08   \n",
       "37     5.300629e+04             117.764285                3.591788e+08   \n",
       "38     7.703765e+05             153.475031                2.222165e+08   \n",
       "39     9.554617e+05             190.347852                2.222165e+08   \n",
       "\n",
       "    no measure - risk_transf - future  ...  \\\n",
       "35                                0.0  ...   \n",
       "36                                0.0  ...   \n",
       "37                                0.0  ...   \n",
       "38                                0.0  ...   \n",
       "39                                0.0  ...   \n",
       "\n",
       "    Beach nourishment - cost_ins - future  Seawall - risk - future  \\\n",
       "35                                      1             6.335246e+08   \n",
       "36                                      1             2.200451e+08   \n",
       "37                                      1             3.589863e+08   \n",
       "38                                      1             2.222058e+08   \n",
       "39                                      1             2.222058e+08   \n",
       "\n",
       "    Seawall - risk_transf - future  Seawall - cost_meas - future  \\\n",
       "35                               0                  1.020539e+10   \n",
       "36                               0                  1.020539e+10   \n",
       "37                               0                  1.020539e+10   \n",
       "38                               0                  8.228478e+09   \n",
       "39                               0                  1.020539e+10   \n",
       "\n",
       "    Seawall - cost_ins - future  Building code - risk - future  \\\n",
       "35                            1                   4.753194e+08   \n",
       "36                            1                   1.650410e+08   \n",
       "37                            1                   2.693841e+08   \n",
       "38                            1                   1.666624e+08   \n",
       "39                            1                   1.666624e+08   \n",
       "\n",
       "    Building code - risk_transf - future  Building code - cost_meas - future  \\\n",
       "35                                     0                        1.057461e+10   \n",
       "36                                     0                        1.057461e+10   \n",
       "37                                     0                        1.057461e+10   \n",
       "38                                     0                        8.526172e+09   \n",
       "39                                     0                        1.057461e+10   \n",
       "\n",
       "    Building code - cost_ins - future  tot_climate_risk  \n",
       "35                                  1      6.337592e+08  \n",
       "36                                  1      2.200547e+08  \n",
       "37                                  1      3.591788e+08  \n",
       "38                                  1      2.222165e+08  \n",
       "39                                  1      2.222165e+08  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unc_cb.get_uncertainty().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"calcimpact\"></a>\n",
    "## CalcImpact\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"set-the-inputvars\"></a>\n",
    "### Set the InputVars\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we model the impact function for tropical cyclones on the parametric function suggested in Emanuel (2015) with 4 parameters. The exposures total value varies between 80% and 120%. For that hazard, we assume to have no good error estimate and thus do not define an InputVar for the hazard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.313925Z",
     "start_time": "2023-08-03T11:58:03.252329Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the input variable functions\n",
    "import numpy as np\n",
    "\n",
    "from climada.entity import ImpactFunc, ImpactFuncSet, Exposures\n",
    "from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "\n",
    "def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
    "\n",
    "    def xhi(v, v_half, vmin):\n",
    "        return max([(v - vmin), 0]) / (v_half - vmin)\n",
    "\n",
    "    def sigmoid_func(v, G, v_half, vmin, k):\n",
    "        return G * xhi(v, v_half, vmin) ** k / (1 + xhi(v, v_half, vmin) ** k)\n",
    "\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    import numpy as np\n",
    "    from climada.entity import ImpactFunc, ImpactFuncSet\n",
    "\n",
    "    intensity_unit = \"m/s\"\n",
    "    intensity = np.linspace(0, 150, num=100)\n",
    "    mdd = np.repeat(1, len(intensity))\n",
    "    paa = np.array([sigmoid_func(v, G, v_half, vmin, k) for v in intensity])\n",
    "    imp_fun = ImpactFunc(\"TC\", _id, intensity, mdd, paa, intensity_unit)\n",
    "    imp_fun.check()\n",
    "    impf_set = ImpactFuncSet([imp_fun])\n",
    "    return impf_set\n",
    "\n",
    "\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n",
    "exp_base = Exposures.from_hdf5(EXP_DEMO_H5)\n",
    "# It is a good idea to assign the centroids to the base exposures in order to avoid repeating this\n",
    "# potentially costly operation for each sample.\n",
    "exp_base.assign_centroids(haz)\n",
    "\n",
    "\n",
    "def exp_base_func(x_exp, exp_base):\n",
    "    exp = exp_base.copy()\n",
    "    exp.gdf[\"value\"] *= x_exp\n",
    "    return exp\n",
    "\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "exp_func = partial(exp_base_func, exp_base=exp_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.583185Z",
     "start_time": "2023-08-03T11:58:03.315873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of the parametrized impact function\n",
    "impf_func(G=0.8, v_half=80, vmin=30, k=5).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.591398Z",
     "start_time": "2023-08-03T11:58:03.585492Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the InputVars\n",
    "\n",
    "import scipy as sp\n",
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "exp_distr = {\n",
    "    \"x_exp\": sp.stats.beta(10, 1.1)\n",
    "}  # This is not really a reasonable distribution but is used\n",
    "# here to show that you can use any scipy distribution.\n",
    "\n",
    "exp_iv = InputVar(exp_func, exp_distr)\n",
    "\n",
    "impf_distr = {\n",
    "    \"G\": sp.stats.truncnorm(0.5, 1.5),\n",
    "    \"v_half\": sp.stats.uniform(35, 65),\n",
    "    \"vmin\": sp.stats.uniform(0, 15),\n",
    "    \"k\": sp.stats.uniform(1, 4),\n",
    "}\n",
    "impf_iv = InputVar(impf_func, impf_distr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.793861Z",
     "start_time": "2023-08-03T11:58:03.593678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = exp_iv.plot(figsize=(6, 4))\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xticks(fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"compute-uncertainty-and-sensitivity-using-default-methods\"></a>\n",
    "### Compute uncertainty and sensitivity using default methods\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define the UncImpact object with our uncertainty variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:03.808020Z",
     "start_time": "2023-08-03T11:58:03.799037Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import CalcImpact\n",
    "\n",
    "calc_imp = CalcImpact(exp_iv, impf_iv, haz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we generate samples for the uncertainty parameters using the default methods. Note that depending on the chosen Salib method, the effective number of samples differs from the input variable N. For the default 'saltelli', with `calc_second_order=True`, the effective number is N(2D+2), with D the number of uncertainty parameters. See [SAlib](https://salib.readthedocs.io/en/latest/api.html) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:05.118435Z",
     "start_time": "2023-08-03T11:58:03.810704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_exp</th>\n",
       "      <th>G</th>\n",
       "      <th>v_half</th>\n",
       "      <th>vmin</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>0.876684</td>\n",
       "      <td>1.242977</td>\n",
       "      <td>53.662109</td>\n",
       "      <td>2.080078</td>\n",
       "      <td>4.539062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>0.876684</td>\n",
       "      <td>0.790617</td>\n",
       "      <td>44.013672</td>\n",
       "      <td>2.080078</td>\n",
       "      <td>4.539062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>0.876684</td>\n",
       "      <td>0.790617</td>\n",
       "      <td>53.662109</td>\n",
       "      <td>13.681641</td>\n",
       "      <td>4.539062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>0.876684</td>\n",
       "      <td>0.790617</td>\n",
       "      <td>53.662109</td>\n",
       "      <td>2.080078</td>\n",
       "      <td>3.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>0.876684</td>\n",
       "      <td>0.790617</td>\n",
       "      <td>53.662109</td>\n",
       "      <td>2.080078</td>\n",
       "      <td>4.539062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x_exp         G     v_half       vmin         k\n",
       "1531  0.876684  1.242977  53.662109   2.080078  4.539062\n",
       "1532  0.876684  0.790617  44.013672   2.080078  4.539062\n",
       "1533  0.876684  0.790617  53.662109  13.681641  4.539062\n",
       "1534  0.876684  0.790617  53.662109   2.080078  3.960938\n",
       "1535  0.876684  0.790617  53.662109   2.080078  4.539062"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp = calc_imp.make_sample(N=2**7, sampling_kwargs={\"skip_values\": 2**8})\n",
    "output_imp.get_samples_df().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting samples can be visualized in plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:06.593742Z",
     "start_time": "2023-08-03T11:58:05.121207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp.plot_sample(figsize=(15, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the value of the impact metrics for all the samples. In this example, we additionaly chose to restrict the return periods 50, 100, and 250 years. By default, `eai_exp` and `at_event` are not stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:19.586495Z",
     "start_time": "2023-08-03T11:58:06.596075Z"
    }
   },
   "outputs": [],
   "source": [
    "output_imp = calc_imp.uncertainty(output_imp, rp=[50, 100, 250])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-24T17:47:52.949464Z",
     "start_time": "2021-03-24T17:47:52.945123Z"
    }
   },
   "source": [
    "The distributions of metrics ouputs are stored as dictionaries of pandas dataframe. The metrics are directly taken from the output of `climada.impact.calc`. For each metric, on dataframe is made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:19.593106Z",
     "start_time": "2023-08-03T11:58:19.588476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aai_agg', 'freq_curve']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the computed uncertainty metrics attribute\n",
    "output_imp.uncertainty_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:19.608246Z",
     "start_time": "2023-08-03T11:58:19.602600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aai_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>2.905571e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>3.755172e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1.063119e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>2.248718e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>1.848139e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           aai_agg\n",
       "1531  2.905571e+09\n",
       "1532  3.755172e+09\n",
       "1533  1.063119e+09\n",
       "1534  2.248718e+09\n",
       "1535  1.848139e+09"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One uncertainty dataframe\n",
    "output_imp.get_unc_df(\"aai_agg\").tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the uncertainty is in general done via the method `get_uncertainty()`. If none are specified, all metrics are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:19.618733Z",
     "start_time": "2023-08-03T11:58:19.609952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aai_agg</th>\n",
       "      <th>rp50</th>\n",
       "      <th>rp100</th>\n",
       "      <th>rp250</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>2.905571e+09</td>\n",
       "      <td>8.324391e+10</td>\n",
       "      <td>1.162643e+11</td>\n",
       "      <td>1.510689e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>3.755172e+09</td>\n",
       "      <td>1.096005e+11</td>\n",
       "      <td>1.460838e+11</td>\n",
       "      <td>1.809413e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1.063119e+09</td>\n",
       "      <td>2.892734e+10</td>\n",
       "      <td>4.720869e+10</td>\n",
       "      <td>6.807561e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>2.248718e+09</td>\n",
       "      <td>6.468855e+10</td>\n",
       "      <td>8.653474e+10</td>\n",
       "      <td>1.085266e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>1.848139e+09</td>\n",
       "      <td>5.294874e+10</td>\n",
       "      <td>7.395191e+10</td>\n",
       "      <td>9.609003e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           aai_agg          rp50         rp100         rp250\n",
       "1531  2.905571e+09  8.324391e+10  1.162643e+11  1.510689e+11\n",
       "1532  3.755172e+09  1.096005e+11  1.460838e+11  1.809413e+11\n",
       "1533  1.063119e+09  2.892734e+10  4.720869e+10  6.807561e+10\n",
       "1534  2.248718e+09  6.468855e+10  8.653474e+10  1.085266e+11\n",
       "1535  1.848139e+09  5.294874e+10  7.395191e+10  9.609003e+10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp.get_uncertainty().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions of the one-dimensioanl metrics (`eai_exp` and `at_event` are never shown with this method) can be vizualised with plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.031168Z",
     "start_time": "2023-08-03T11:58:19.620870Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp.plot_uncertainty(figsize=(12, 12));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.449828Z",
     "start_time": "2023-08-03T11:58:21.033584Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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6+OOPtW7dOnXv3l0vvviisrOzdeqpp0py9Qx6+OGH9dBDDykoKEhnn322cnNztWPHDs2YMUO9e/dWaGiovvrqK/Xt21chISF1ehc1xmq1Kjs7W5LrguaVV15RcXGxrrjiihYd94MPPqhp06Zp2LBhmjx5ssrKyvT666/rwIEDuueeeyS5ejD96le/0m233aZRo0YpMjJSGzdu1LPPPqurrrrKo76ioiJlZ2fLbrcrNTVV7733nt58800tXLjQowcXAABoPyZNmqThw4drwYIFeuWVVzRv3jzdd9996tatmy677DJZrVZt3LhReXl5mjNnTr3XNhdeeKHmzJmjpUuXatCgQXrppZeUn5/v07Y3dA338MMPa8KECbrnnnt0++23Kzw83D3p9v/93/81eR933323Xn75Zc2aNUv33nuv9uzZo8cff1xz5syR2WxWRESEZsyYod/97ncebTGbT74PxcyZM3X55ZfL4XBo2rRp7uWRkZF68MEH9dvf/lZOp1PnnHOOCgsLtW7dOkVERGjatGkaPHiw3nnnHX399ddKSkrSu+++qw0bNtAbHWgGAigAPvP000/r/vvv1759+zR69Gh99tln7v+MPfroo0pNTdWUKVMUFhamO+64Q1OnTvUYuvboo4/KYrHoscceU2ZmpuLj43XXXXdJkiwWi/7yl7/oySef1GOPPaZzzz1Xq1atalb7vvrqK/ecTpGRkRo2bJj+85//uG97fLKuvfZaGYah559/Xo888ohCQkI0ZswYrV27Vv3795fkugve+PHj9dJLL+nAgQOy2+1KTEzU7bffrj/84Q8e9T322GN67LHHFBQUpLi4OE2YMEErVqzQBRdc0KJ2AgAA35ozZ45uvfVWPfzww5o5c6bCwsL03HPP6aGHHlJ4eLhGjhyp2bNnS6r/2ua2227Tli1bdMstt8hisei3v/2tz68BGrqGGzVqlFavXq1HHnlE5557rgzD0KBBg3Tdddc1ax99+vTRF198od/97ncaPXq0evTooRkzZuiPf/yju8xzzz2n4uJiXXnllYqMjNQDDzzgdZqDprr44osVHx+v4cOHKyEhwWPdn/70J/Xu3VsLFy7UwYMHFR0drTPOOMN9XXbXXXcpJSVF1113nUwmk2644Qbdfffd+vLLL0+6PUBXYzJae3AtgC5v1apVuuCCC5SXl6fo6Gja0obmzZunTz/9VCkpKf5uCgAA6GA6w3VTQ9dCpaWlSkhI0FtvvaWrr766Tdt16NAhJSUlKTk5Waeffnqb7htoL5gDCkCX0LdvX91www3+bobPpKWlKSIiQgsWLPB3UwAAAPxq27ZtioiI0KuvvirJNcl4ZmamHn30UUVFRenKK69s0/ZcdtllGj58eJvuE2iPGIIHoFMbP368e3LziIgIP7fGdxISEtz/6au6WwsAAEBXc9999+mmm26S5JrAXXL9oy4pKUl9+/bV22+/LYulbT8Gv/nmm+6b3NS8UyDQ1TAEDwAAAAAAAD7FEDwAAAAAAAD4FAEUAAAAAAAAfIoACgAAAAAAAD5FAAUAAAAAAACfIoACAAAAAACATxFAAQAAAAAAwKcIoAAAAAAAAOBTBFAAAAAAAADwKQIoAAAAAAAA+NT/B9+FiT1bcPYaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1430x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specific plot for the return period distributions\n",
    "output_imp.plot_rp_uncertainty(figsize=(14.3, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that a distribution of the impact metrics has been computed for each sample, we can also compute the sensitivity indices for each metrics to each uncertainty parameter. Note that the chosen method for the sensitivity analysis should correpond to its sampling partner as defined in the [SAlib](https://salib.readthedocs.io/en/latest/api.html) package.\n",
    "\n",
    "The sensitivity indices dictionnaries outputs from the SAlib methods are stored in the same structure of nested dictionnaries as the metrics distributions. Note that depending on the chosen sensitivity analysis method the returned indices dictionnary will return specific types of sensitivity indices with specific names. Please get familiar with [SAlib](https://salib.readthedocs.io/en/latest/api.html) for more information.\n",
    "\n",
    "Note that in our case, several of the second order sensitivity indices are negative. For the default method `sobol`, this indicates that the algorithm has not converged and cannot give realiable values for these sensitivity indices. If this happens, please use a larger number of samples. Here we will focus on the first-order indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.532951Z",
     "start_time": "2023-08-03T11:58:21.452246Z"
    }
   },
   "outputs": [],
   "source": [
    "output_imp = calc_imp.sensitivity(output_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the uncertainty case, the data is stored in dataframe attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.539626Z",
     "start_time": "2023-08-03T11:58:21.535288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aai_agg', 'freq_curve']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp.sensitivity_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.549400Z",
     "start_time": "2023-08-03T11:58:21.542078Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>si</th>\n",
       "      <th>param</th>\n",
       "      <th>param2</th>\n",
       "      <th>aai_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>S2_conf</td>\n",
       "      <td>k</td>\n",
       "      <td>x_exp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>S2_conf</td>\n",
       "      <td>k</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>S2_conf</td>\n",
       "      <td>k</td>\n",
       "      <td>v_half</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>S2_conf</td>\n",
       "      <td>k</td>\n",
       "      <td>vmin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>S2_conf</td>\n",
       "      <td>k</td>\n",
       "      <td>k</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         si param  param2  aai_agg\n",
       "65  S2_conf     k   x_exp      NaN\n",
       "66  S2_conf     k       G      NaN\n",
       "67  S2_conf     k  v_half      NaN\n",
       "68  S2_conf     k    vmin      NaN\n",
       "69  S2_conf     k       k      NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp.get_sens_df(\"aai_agg\").tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain the sensitivity interms of a particular sensitivity index, use the method `get_sensisitivity()`. If none is specified, the value of the index for all metrics is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.564807Z",
     "start_time": "2023-08-03T11:58:21.551832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>si</th>\n",
       "      <th>param</th>\n",
       "      <th>param2</th>\n",
       "      <th>aai_agg</th>\n",
       "      <th>rp50</th>\n",
       "      <th>rp100</th>\n",
       "      <th>rp250</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>x_exp</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>0.001150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S1</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>0.073408</td>\n",
       "      <td>0.075781</td>\n",
       "      <td>0.084662</td>\n",
       "      <td>0.093718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S1</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.514220</td>\n",
       "      <td>0.553640</td>\n",
       "      <td>0.596659</td>\n",
       "      <td>0.619366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S1</td>\n",
       "      <td>vmin</td>\n",
       "      <td>None</td>\n",
       "      <td>0.012642</td>\n",
       "      <td>0.014407</td>\n",
       "      <td>0.012068</td>\n",
       "      <td>0.010065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S1</td>\n",
       "      <td>k</td>\n",
       "      <td>None</td>\n",
       "      <td>0.213491</td>\n",
       "      <td>0.189862</td>\n",
       "      <td>0.134867</td>\n",
       "      <td>0.095861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   si   param param2   aai_agg      rp50     rp100     rp250\n",
       "0  S1   x_exp   None  0.001040  0.000993  0.000930  0.001150\n",
       "1  S1       G   None  0.073408  0.075781  0.084662  0.093718\n",
       "2  S1  v_half   None  0.514220  0.553640  0.596659  0.619366\n",
       "3  S1    vmin   None  0.012642  0.014407  0.012068  0.010065\n",
       "4  S1       k   None  0.213491  0.189862  0.134867  0.095861"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp.get_sensitivity(\"S1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, it is useful to simply know what is the largest sensitivity index for each metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:21.579391Z",
     "start_time": "2023-08-03T11:58:21.566526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>param</th>\n",
       "      <th>param2</th>\n",
       "      <th>si</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aai_agg</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.514220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rp50</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.553640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rp100</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.596659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rp250</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.619366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    metric   param param2        si\n",
       "0  aai_agg  v_half   None  0.514220\n",
       "1     rp50  v_half   None  0.553640\n",
       "2    rp100  v_half   None  0.596659\n",
       "3    rp250  v_half   None  0.619366"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp.get_largest_si(salib_si=\"S1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value of the sensitivity indices can be plotted for each metric that is one-dimensional (`eai_exp` and `at_event` are not shown in this plot). \n",
    "\n",
    "We see that both the errors in `freq_curve` and in `aai_agg` are mostly determined by `x_exp` and `v_half`. Finally, we see small differences in the sensitivity of the different return periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:22.457213Z",
     "start_time": "2023-08-03T11:58:21.581245Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Default for 'sobol' is to plot 'S1' sensitivity index.\n",
    "output_imp.plot_sensitivity(figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that since we have quite a few measures, the imp_meas_fut and imp_meas_pres plots are too crowded. We can select only the other metrics easily. In addition, instead of showing first order sensitivity 'S1', we can plot the total sensitivity 'ST'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:23.209868Z",
     "start_time": "2023-08-03T11:58:22.459782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp.plot_sensitivity(salib_si=\"ST\", figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also vizualise the second-order sensitivity indices in the form of a correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:24.428272Z",
     "start_time": "2023-08-03T11:58:23.212055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp.plot_sensitivity_second_order(figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"a-few-non-default-parameters\"></a>\n",
    "### A few non-default parameters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall use the same uncertainty variables as in the previous section but show a few possibilities to use non-default method arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:25.951160Z",
     "start_time": "2023-08-03T11:58:24.430789Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sampling method \"latin\" hypercube instead of `saltelli`.\n",
    "from climada.engine.unsequa import CalcImpact\n",
    "\n",
    "calc_imp2 = CalcImpact(exp_iv, impf_iv, haz)\n",
    "output_imp2 = calc_imp2.make_sample(N=1000, sampling_method=\"latin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:28.073212Z",
     "start_time": "2023-08-03T11:58:25.954868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp2.plot_sample(figsize=(15, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:38.966759Z",
     "start_time": "2023-08-03T11:58:28.076017Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time passed with pool: 2.8349649906158447\n"
     ]
    }
   ],
   "source": [
    "# Compute also the distribution of the metric `eai_exp`\n",
    "# To speed-up the comutations, we can use more than one process\n",
    "# Note that for large dataset a single process might be more efficient\n",
    "import time\n",
    "\n",
    "calc_imp2 = CalcImpact(exp_iv, impf_iv, haz)\n",
    "output_imp2 = calc_imp2.make_sample(N=1000, sampling_method=\"latin\")\n",
    "\n",
    "start = time.time()\n",
    "output_imp2 = calc_imp2.uncertainty(\n",
    "    output_imp2, rp=[50, 100, 250], calc_eai_exp=True, calc_at_event=True, processes=4\n",
    ")\n",
    "end = time.time()\n",
    "time_passed = end - start\n",
    "print(f\"Time passed with pool: {time_passed}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:38.980434Z",
     "start_time": "2023-08-03T11:58:38.971826Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import CalcImpact\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:53.470000Z",
     "start_time": "2023-08-03T11:58:38.984769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time passed without pool: 8.287853956222534\n"
     ]
    }
   ],
   "source": [
    "calc_imp2 = CalcImpact(exp_iv, impf_iv, haz)\n",
    "output_imp2 = calc_imp2.make_sample(N=1000, sampling_method=\"latin\")\n",
    "\n",
    "start2 = time.time()\n",
    "output_imp2 = calc_imp2.uncertainty(\n",
    "    output_imp2, rp=[50, 100, 250], calc_eai_exp=True, calc_at_event=True\n",
    ")\n",
    "end2 = time.time()\n",
    "time_passed_nopool = end2 - start2\n",
    "print(f\"Time passed without pool: {time_passed_nopool}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:53.489875Z",
     "start_time": "2023-08-03T11:58:53.473025Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add the original value of the impacts (without uncertainty) to the uncertainty plot\n",
    "from climada.engine import ImpactCalc\n",
    "\n",
    "imp = ImpactCalc(exp_base, impf_func(), haz).impact(assign_centroids=False)\n",
    "aai_agg_o = imp.aai_agg\n",
    "freq_curve_o = imp.calc_freq_curve([50, 100, 250]).impact\n",
    "orig_list = [aai_agg_o] + list(freq_curve_o) + [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T11:58:55.128642Z",
     "start_time": "2023-08-03T11:58:53.492920Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the aai_agg and freq_curve uncertainty only\n",
    "# use logarithmic x-scale\n",
    "output_imp2.plot_uncertainty(\n",
    "    metric_list=[\"aai_agg\", \"freq_curve\"],\n",
    "    orig_list=orig_list,\n",
    "    log=True,\n",
    "    figsize=(12, 8),\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:06.079414Z",
     "start_time": "2023-08-03T11:58:55.131655Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use the method 'rbd_fast' which is recommend in pair with 'latin'. In addition, change one of the kwargs\n",
    "# (M=15) of the salib sampling method.\n",
    "output_imp2 = calc_imp2.sensitivity(\n",
    "    output_imp2, sensitivity_method=\"rbd_fast\", sensitivity_kwargs={\"M\": 15}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we computed the distribution and sensitivity indices for the total impact at each exposure point, we can plot a map of the largest sensitivity index in each exposure location. For every location, the most sensitive parameter is `v_half`, meaning that the average annual impact at each location is most sensitivity to the ucnertainty in the impact function slope scaling parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.112044Z",
     "start_time": "2023-08-03T12:00:06.099744Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_imp2.plot_sensitivity_map();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.133901Z",
     "start_time": "2023-08-03T12:00:12.115402Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>param</th>\n",
       "      <th>param2</th>\n",
       "      <th>si</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.471587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.471587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.471587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.467530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>v_half</td>\n",
       "      <td>None</td>\n",
       "      <td>0.471587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   metric   param param2        si\n",
       "45     45  v_half   None  0.471587\n",
       "46     46  v_half   None  0.471587\n",
       "47     47  v_half   None  0.471587\n",
       "48     48  v_half   None  0.467530\n",
       "49     49  v_half   None  0.471587"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_imp2.get_largest_si(salib_si=\"S1\", metric_list=[\"eai_exp\"]).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"calcdeltaimpact\"></a>\n",
    "## CalcDeltaImpact\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-23T14:32:01.173184Z",
     "start_time": "2021-09-23T14:32:01.169411Z"
    }
   },
   "source": [
    "The main goal of this class is to perform an uncertainty and sensitivity analysis of the \"delta\" impact between a reference state and future (or any other \"to be compared\") state.\n",
    "\n",
    "Classical example: risk increase in the future with climate change and socio economic development. In this case, the uncertainty and sensitivity analysis in performed on the estimated risk (delta) increase in the future relative to the present-day baseline.\n",
    "\n",
    "The uncertainty and sensitivity analysis for CalcDeltaImpact is completely analogous to the Impact case. It is slightly more complex as there are more input variables.\n",
    "\n",
    "Note, the logic of this class works with any comparison between an initial (reference and final (altered) risk or impact state and is not limited to the scope of climate change and socio-economic development in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"set-the-input-vars\"></a>\n",
    "### Set the Input Vars\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll work through an analogous example as in CalcImpact next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from climada.entity import ImpactFunc, ImpactFuncSet, Exposures\n",
    "from climada.util.constants import EXP_DEMO_H5, HAZ_DEMO_H5\n",
    "from climada.hazard import Centroids, TCTracks, Hazard, TropCyclone\n",
    "\n",
    "\n",
    "def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
    "\n",
    "    def xhi(v, v_half, vmin):\n",
    "        return max([(v - vmin), 0]) / (v_half - vmin)\n",
    "\n",
    "    def sigmoid_func(v, G, v_half, vmin, k):\n",
    "        return G * xhi(v, v_half, vmin) ** k / (1 + xhi(v, v_half, vmin) ** k)\n",
    "\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    intensity_unit = \"m/s\"\n",
    "    intensity = np.linspace(0, 150, num=100)\n",
    "    mdd = np.repeat(1, len(intensity))\n",
    "    paa = np.array([sigmoid_func(v, G, v_half, vmin, k) for v in intensity])\n",
    "    imp_fun = ImpactFunc(\"TC\", _id, intensity, mdd, paa, intensity_unit)\n",
    "    imp_fun.check()\n",
    "    impf_set = ImpactFuncSet([imp_fun])\n",
    "    return impf_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the hazard set and apply climate change factors to it. This yields a hazard representation in 2050 under 4 RCP scenarios. For a full documentation of this function please refer to the [TropCyclone](climada_hazard_TropCyclone.ipynb) tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load historical hazard set\n",
    "haz = TropCyclone.from_hdf5(HAZ_DEMO_H5)\n",
    "haz.basin = [\"NA\"] * haz.size\n",
    "\n",
    "# apply climate change factors\n",
    "haz_26 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"2.6\")\n",
    "haz_45 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"4.5\")\n",
    "haz_60 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"6.0\")\n",
    "haz_85 = haz.apply_climate_scenario_knu(target_year=2050, scenario=\"8.5\")\n",
    "\n",
    "# pack future hazard sets into dictionary - we want to sample from this dictionary later\n",
    "haz_fut_list = [haz_26, haz_45, haz_60, haz_85]\n",
    "tc_haz_fut_dict = {}\n",
    "for r, rcp in enumerate([\"26\", \"45\", \"60\", \"85\"]):\n",
    "    tc_haz_fut_dict[rcp] = haz_fut_list[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_base = Exposures.from_hdf5(EXP_DEMO_H5)\n",
    "# It is a good idea to assign the centroids to the base exposures in order to avoid repeating this\n",
    "# potentially costly operation for each sample.\n",
    "exp_base.assign_centroids(haz)\n",
    "\n",
    "\n",
    "def exp_base_func(x_exp, exp_base):\n",
    "    exp = exp_base.copy()\n",
    "    exp.data[\"value\"] *= x_exp\n",
    "    return exp\n",
    "\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "exp_func = partial(exp_base_func, exp_base=exp_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy as sp\n",
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "exp_distr = {\n",
    "    \"x_exp\": sp.stats.beta(10, 1.1)\n",
    "}  # This is not really a reasonable distribution but is used\n",
    "# here to show that you can use any scipy distribution.\n",
    "\n",
    "exp_iv = InputVar(exp_func, exp_distr)\n",
    "\n",
    "impf_distr = {\n",
    "    \"G\": sp.stats.truncnorm(0.5, 1.5),\n",
    "    \"v_half\": sp.stats.uniform(35, 65),\n",
    "    \"vmin\": sp.stats.uniform(0, 15),\n",
    "    \"k\": sp.stats.uniform(1, 4),\n",
    "}\n",
    "impf_iv = InputVar(impf_func, impf_distr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define the function for the future hazard representation. It's a simple function that allows us to draw from the hazard dictionary of hazard sets under different RCP scenarios. Note, we do not investigate other hazard related uncertainties in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcp_key = {0: \"26\", 1: \"45\", 2: \"60\", 3: \"85\"}\n",
    "\n",
    "\n",
    "# future\n",
    "def haz_fut_func(rcp_scenario):\n",
    "    haz_fut = tc_haz_fut_dict[rcp_key[rcp_scenario]]\n",
    "    return haz_fut\n",
    "\n",
    "\n",
    "haz_fut_distr = {\"rcp_scenario\": sp.stats.randint(0, 4)}\n",
    "\n",
    "haz_fut_iv = InputVar(haz_fut_func, haz_fut_distr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"compute-uncertainty-and-sensitivity\"></a>\n",
    "### Compute uncertainty and sensitivity\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast to CalcImpact, we define InputVars for initial and final states of exposure, impact function, hazard. This class requires 6 input variables. For the sake of simplicity, we did not define varying input variables for the initial and future exposure and vulernability in the example. Hence, the exp_iv and impf_iv are passed to CalcDeltaImpact twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-25 15:36:53,385 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter x_exp is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n",
      "2024-01-25 15:36:53,389 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter G is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n",
      "2024-01-25 15:36:53,390 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter v_half is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n",
      "2024-01-25 15:36:53,393 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter vmin is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n",
      "2024-01-25 15:36:53,394 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter k is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from climada.engine.unsequa import CalcDeltaImpact\n",
    "\n",
    "calc_imp = CalcDeltaImpact(exp_iv, impf_iv, haz, exp_iv, impf_iv, haz_fut_iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_imp = calc_imp.make_sample(N=2**7)\n",
    "output_imp.get_samples_df().tail()\n",
    "\n",
    "output_imp = calc_imp.uncertainty(output_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting functionalities work analogous to CalcImpact. By setting `calc_delta=True`, the axis labels are adjusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No data to plot for 'rp5'.\n",
      "No data to plot for 'rp10'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<Axes: xlabel='aai_agg change [%]', ylabel='density of samples'>,\n",
       "        <Axes: xlabel='rp5', ylabel='density of samples'>],\n",
       "       [<Axes: xlabel='rp10', ylabel='density of samples'>,\n",
       "        <Axes: xlabel='rp20 change [%]', ylabel='density of samples'>],\n",
       "       [<Axes: xlabel='rp50 change [%]', ylabel='density of samples'>,\n",
       "        <Axes: xlabel='rp100 change [%]', ylabel='density of samples'>],\n",
       "       [<Axes: xlabel='rp250 change [%]', ylabel='density of samples'>,\n",
       "        <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x2000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.engine.unsequa import UncOutput\n",
    "\n",
    "output_imp.plot_uncertainty(calc_delta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping plot for 'rp5': insufficient data.\n",
      "Skipping plot for 'rp10': insufficient data.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='Impact change [%]', ylabel='Return period [years]'>,\n",
       "       <Axes: xlabel='Return period [year]', ylabel='Impact change [%]'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.engine.unsequa import UncOutput\n",
    "\n",
    "output_imp.plot_rp_uncertainty(calc_delta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute sensitivity\n",
    "output_imp = calc_imp.sensitivity(output_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-25 15:37:27,753 - climada.engine.unsequa.unc_output - WARNING - All-NaN columns encountered: ['rp5', 'rp10']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='Input parameter', ylabel='S1'>,\n",
       "       <Axes: xlabel='Input parameter', ylabel='S1'>], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot sensitivity\n",
    "output_imp.plot_sensitivity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the functionalities that apply to CalcImpact also work for the CalcDeltaImpact class. Hence, refer to the sections above for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"calccostbenefit\"></a>\n",
    "## CalcCostBenefit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-23T14:32:01.173184Z",
     "start_time": "2021-09-23T14:32:01.169411Z"
    }
   },
   "source": [
    "The uncertainty and sensitivity analysis for CostBenefit is completely analogous to the Impact case. It is slightly more complex as there are more input variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"set-the-input-vars\"></a>\n",
    "### Set the Input Vars\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.180767Z",
     "start_time": "2023-08-03T12:00:12.138310Z"
    }
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "from climada.util.constants import ENT_DEMO_TODAY, ENT_DEMO_FUTURE, HAZ_DEMO_H5\n",
    "from climada.entity import Entity\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "\n",
    "# Entity today has an uncertainty in the total asset value\n",
    "def ent_today_func(x_ent):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import Entity\n",
    "    from climada.util.constants import ENT_DEMO_TODAY\n",
    "\n",
    "    entity = Entity.from_excel(ENT_DEMO_TODAY)\n",
    "    entity.exposures.ref_year = 2018\n",
    "    entity.exposures.data[\"value\"] *= x_ent\n",
    "    return entity\n",
    "\n",
    "\n",
    "# Entity in the future has a +- 10% uncertainty in the cost of all the adapatation measures\n",
    "def ent_fut_func(m_fut_cost):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import Entity\n",
    "    from climada.util.constants import ENT_DEMO_FUTURE\n",
    "\n",
    "    entity = Entity.from_excel(ENT_DEMO_FUTURE)\n",
    "    entity.exposures.ref_year = 2040\n",
    "    for meas in entity.measures.get_measure(\"TC\"):\n",
    "        meas.cost *= m_fut_cost\n",
    "    return entity\n",
    "\n",
    "\n",
    "haz_base = Hazard.from_hdf5(HAZ_DEMO_H5)\n",
    "\n",
    "\n",
    "# The hazard intensity in the future is also uncertainty by a multiplicative factor\n",
    "def haz_fut(x_haz_fut, haz_base):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    import copy\n",
    "    from climada.hazard import Hazard\n",
    "    from climada.util.constants import HAZ_DEMO_H5\n",
    "\n",
    "    haz = copy.deepcopy(haz_base)\n",
    "    haz.intensity = haz.intensity.multiply(x_haz_fut)\n",
    "    return haz\n",
    "\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "haz_fut_func = partial(haz_fut, haz_base=haz_base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that costs for measures are changed as desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.789398Z",
     "start_time": "2023-08-03T12:00:12.184147Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The cost for m_fut_cost=1 are [1311768360.8515418, 1728000000.0, 8878779433.630093, 9200000000.0]\n",
      "The cost for m_fut_cost=0.5 are [655884180.4257709, 864000000.0, 4439389716.815046, 4600000000.0]\n"
     ]
    }
   ],
   "source": [
    "costs_1 = [meas.cost for meas in ent_fut_func(1).measures.get_measure(\"TC\")]\n",
    "costs_05 = [meas.cost for meas in ent_fut_func(0.5).measures.get_measure(\"TC\")]\n",
    "print(\n",
    "    f\"\\nThe cost for m_fut_cost=1 are {costs_1}\\n\"\n",
    "    f\"The cost for m_fut_cost=0.5 are {costs_05}\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the InputVars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.802267Z",
     "start_time": "2023-08-03T12:00:12.793743Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy as sp\n",
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "haz_today = haz_base\n",
    "\n",
    "haz_fut_distr = {\n",
    "    \"x_haz_fut\": sp.stats.uniform(1, 3),\n",
    "}\n",
    "haz_fut_iv = InputVar(haz_fut_func, haz_fut_distr)\n",
    "\n",
    "ent_today_distr = {\"x_ent\": sp.stats.uniform(0.7, 1)}\n",
    "ent_today_iv = InputVar(ent_today_func, ent_today_distr)\n",
    "\n",
    "ent_fut_distr = {\"m_fut_cost\": sp.stats.norm(1, 0.1)}\n",
    "ent_fut_iv = InputVar(ent_fut_func, ent_fut_distr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:12.959984Z",
     "start_time": "2023-08-03T12:00:12.804842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>value</th>\n",
       "      <th>deductible</th>\n",
       "      <th>cover</th>\n",
       "      <th>impf_TC</th>\n",
       "      <th>Value_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.933899</td>\n",
       "      <td>-80.128799</td>\n",
       "      <td>1.671301e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.392750e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>5.139301e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.957203</td>\n",
       "      <td>-80.098284</td>\n",
       "      <td>1.511528e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.259606e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.647994e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.783846</td>\n",
       "      <td>-80.748947</td>\n",
       "      <td>1.511528e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.259606e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.647994e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.645524</td>\n",
       "      <td>-80.550704</td>\n",
       "      <td>1.511528e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.259606e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.647994e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.897796</td>\n",
       "      <td>-80.596929</td>\n",
       "      <td>1.511528e+10</td>\n",
       "      <td>0</td>\n",
       "      <td>1.259606e+10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.647994e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    latitude  longitude         value  deductible         cover  impf_TC  \\\n",
       "0  26.933899 -80.128799  1.671301e+10           0  1.392750e+10        1   \n",
       "1  26.957203 -80.098284  1.511528e+10           0  1.259606e+10        1   \n",
       "2  26.783846 -80.748947  1.511528e+10           0  1.259606e+10        1   \n",
       "3  26.645524 -80.550704  1.511528e+10           0  1.259606e+10        1   \n",
       "4  26.897796 -80.596929  1.511528e+10           0  1.259606e+10        1   \n",
       "\n",
       "     Value_2010  \n",
       "0  5.139301e+09  \n",
       "1  4.647994e+09  \n",
       "2  4.647994e+09  \n",
       "3  4.647994e+09  \n",
       "4  4.647994e+09  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ent_avg = ent_today_iv.evaluate()\n",
    "ent_avg.exposures.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"compute-cost-benefit-uncertainty-and-sensitivity-using-default-methods\"></a>\n",
    "### Compute cost benefit uncertainty and sensitivity using default methods\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For examples of how to use non-defaults please see the [impact example](#compute-uncertainty-and-sensitivity-using-default-methods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:13.085529Z",
     "start_time": "2023-08-03T12:00:12.963156Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import CalcCostBenefit\n",
    "\n",
    "unc_cb = CalcCostBenefit(\n",
    "    haz_input_var=haz_today,\n",
    "    ent_input_var=ent_today_iv,\n",
    "    haz_fut_input_var=haz_fut_iv,\n",
    "    ent_fut_input_var=ent_fut_iv,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:13.121927Z",
     "start_time": "2023-08-03T12:00:13.087709Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_ent</th>\n",
       "      <th>x_haz_fut</th>\n",
       "      <th>m_fut_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.35625</td>\n",
       "      <td>2.96875</td>\n",
       "      <td>0.813727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.04375</td>\n",
       "      <td>2.96875</td>\n",
       "      <td>0.813727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.35625</td>\n",
       "      <td>2.03125</td>\n",
       "      <td>0.813727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.35625</td>\n",
       "      <td>2.96875</td>\n",
       "      <td>0.899001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.04375</td>\n",
       "      <td>2.03125</td>\n",
       "      <td>0.899001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x_ent  x_haz_fut  m_fut_cost\n",
       "45  1.35625    2.96875    0.813727\n",
       "46  1.04375    2.96875    0.813727\n",
       "47  1.35625    2.03125    0.813727\n",
       "48  1.35625    2.96875    0.899001\n",
       "49  1.04375    2.03125    0.899001"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_cb = unc_cb.make_sample(N=10, sampling_kwargs={\"calc_second_order\": False})\n",
    "output_cb.get_samples_df().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For longer computations, it is possible to use a pool for parallel computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:33.857265Z",
     "start_time": "2023-08-03T12:00:13.124046Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           13.8606        10.6498\n",
      "Beach nourishment          1.71445           10.7904         6.29377\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.4038         3.22132\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           13.8606        10.6498\n",
      "Beach nourishment          1.71445           10.7904         6.29377\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.4038         3.22132\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           14.0781        10.817\n",
      "Beach nourishment          1.71445           10.968          6.39739\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.5124         3.23322\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   118.05    (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3155  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148          14.1012        10.8347\n",
      "Beach nourishment          1.71445          10.9632         6.39461\n",
      "Seawall                    8.80916           0.0376243      0.00427104\n",
      "Building code              9.12786          13.3845         1.46633\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   53.5379   (USD bn)\n",
      "Average annual risk:   6.15933  (USD bn)\n",
      "Residual risk:        15.0513   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612           13.8606         8.90716\n",
      "Beach nourishment          2.04988           10.7904         5.2639\n",
      "Seawall                   10.5327             0.175596       0.0166716\n",
      "Building code             10.9137            29.4038         2.69421\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612          14.3188         9.20163\n",
      "Beach nourishment          2.04988          11.1409         5.4349\n",
      "Seawall                   10.5327            0.0376243      0.00357216\n",
      "Building code             10.9137           13.4931         1.23634\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   53.9724   (USD bn)\n",
      "Average annual risk:   6.15933  (USD bn)\n",
      "Residual risk:        14.982    (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             6.59956        4.24104\n",
      "Beach nourishment          2.04988             5.16368        2.51902\n",
      "Seawall                   10.5327              3.55475        0.337498\n",
      "Building code             10.9137             48.016          4.3996\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   192.064   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.73    (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             6.43034        4.1323\n",
      "Beach nourishment          2.04988             5.02552        2.45161\n",
      "Seawall                   10.5327              3.55475        0.337498\n",
      "Building code             10.9137             47.9315         4.39186\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   191.726   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.784   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             7.59067         4.87796\n",
      "Beach nourishment          2.04988             5.96389         2.90939\n",
      "Seawall                   10.5327              1.31269         0.12463\n",
      "Building code             10.9137             43.2513          3.96302\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   173.005   (USD bn)\n",
      "Average annual risk:   20.0179  (USD bn)\n",
      "Residual risk:        114.887   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148             6.59956        5.0708\n",
      "Beach nourishment          1.71445             5.16368        3.01186\n",
      "Seawall                    8.80916             3.55475        0.403528\n",
      "Building code              9.12786            48.016          5.26038\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   192.064   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.73    (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148             7.42146        5.70231\n",
      "Beach nourishment          1.71445             5.82573        3.39801\n",
      "Seawall                    8.80916             1.31269        0.149014\n",
      "Building code              9.12786            43.1668         4.72913\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   172.667   (USD bn)\n",
      "Average annual risk:   20.0179  (USD bn)\n",
      "Residual risk:        114.941   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5404         8.71168\n",
      "Beach nourishment          1.59383           8.59532        5.39285\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.53759        0.88827\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1504   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45852  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5646         8.73166\n",
      "Beach nourishment          1.59383           8.61505        5.40524\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.54966        0.889693\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1986   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45082  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992           12.8863        10.6505\n",
      "Beach nourishment          1.59383            9.98362        6.2639\n",
      "Seawall                    8.18941            0.257712       0.0314689\n",
      "Building code              8.48569           33.7244         3.97427\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   134.898   (USD bn)\n",
      "Average annual risk:   15.5605  (USD bn)\n",
      "Residual risk:         78.0457  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5404         8.71168\n",
      "Beach nourishment          1.59383           8.59532        5.39285\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.53759        0.88827\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1504   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45852  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992           12.9104        10.6705\n",
      "Beach nourishment          1.59383           10.0034         6.27629\n",
      "Seawall                    8.18941            0.257712       0.0314689\n",
      "Building code              8.48569           33.7365         3.97569\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   134.946   (USD bn)\n",
      "Average annual risk:   15.5605  (USD bn)\n",
      "Residual risk:         78.038   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.44955        6.08873\n",
      "Beach nourishment          1.82807            6.4567         3.53197\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.407          4.25438\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.628   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.419   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.47373        6.10615\n",
      "Beach nourishment          1.82807            6.47644        3.54276\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.4191         4.25562\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.676   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.411   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774         1.39186         1.00297\n",
      "Beach nourishment          1.82807         1.13491         0.62082\n",
      "Seawall                    9.39298         0.000227424     2.42121e-05\n",
      "Building code              9.7328          0.76062         0.0781501\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:    3.04248   (USD bn)\n",
      "Average annual risk:   0.260244  (USD bn)\n",
      "Residual risk:        -0.245137  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.44955        6.08873\n",
      "Beach nourishment          1.82807            6.4567         3.53197\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.407          4.25438\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.628   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.419   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774         1.41604         1.02039\n",
      "Beach nourishment          1.82807         1.15464         0.631618\n",
      "Seawall                    9.39298         0.000227424     2.42121e-05\n",
      "Building code              9.7328          0.77269         0.0793903\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:    3.09076   (USD bn)\n",
      "Average annual risk:   0.260244  (USD bn)\n",
      "Residual risk:        -0.252837  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.77518        1.92153\n",
      "Beach nourishment          1.90253          2.24443        1.17971\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.68273        0.166127\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.73092    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0259328  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.70266        1.87132\n",
      "Beach nourishment          1.90253          2.18521        1.14859\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.64652        0.162552\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.58607    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0490347  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          5.43516        3.76329\n",
      "Beach nourishment          1.90253          4.37024        2.29707\n",
      "Seawall                    9.77553          0.00764219     0.000781768\n",
      "Building code             10.1292           3.61055        0.35645\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.4422   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0186   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.77518        1.92153\n",
      "Beach nourishment          1.90253          2.24443        1.17971\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.68273        0.166127\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.73092    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0259328  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          5.36264        3.71308\n",
      "Beach nourishment          1.90253          4.31103        2.26595\n",
      "Seawall                    9.77553          0.00764219     0.000781768\n",
      "Building code             10.1292           3.57434        0.352875\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.2973   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0417   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4834         8.32677\n",
      "Beach nourishment          1.65849            8.23954        4.96809\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8908         4.17793\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.563   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5342  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4109         8.26917\n",
      "Beach nourishment          1.65849            8.18032        4.93239\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8546         4.17383\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.418   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5573  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259              8.79133        6.98278\n",
      "Beach nourishment          1.65849            6.82168        4.11319\n",
      "Seawall                    8.52163            0.621468       0.0729283\n",
      "Building code              8.82993           39.3223         4.45329\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.289   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.732   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4834         8.32677\n",
      "Beach nourishment          1.65849            8.23954        4.96809\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8908         4.17793\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.563   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5342  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259              8.71881        6.92518\n",
      "Beach nourishment          1.65849            6.76247        4.07748\n",
      "Seawall                    8.52163            0.621468       0.0729283\n",
      "Building code              8.82993           39.286          4.44919\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.144   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.755   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288          14.575         10.8535\n",
      "Beach nourishment          1.76899          11.5436         6.52553\n",
      "Seawall                    9.08939           0.0620626      0.00682803\n",
      "Building code              9.41823          19.0306         2.02062\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   76.1225   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9112   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288          14.3091        10.6555\n",
      "Beach nourishment          1.76899          11.3265         6.40279\n",
      "Seawall                    9.08939           0.0620626      0.00682803\n",
      "Building code              9.41823          18.8979         2.00652\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   75.5914   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9959   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288             7.41063        5.51844\n",
      "Beach nourishment          1.76899             5.87997        3.32392\n",
      "Seawall                    9.08939             2.58858        0.284791\n",
      "Building code              9.41823            46.6706         4.95535\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   186.682   (USD bn)\n",
      "Average annual risk:   21.5984  (USD bn)\n",
      "Residual risk:        124.133   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888          14.575         12.7977\n",
      "Beach nourishment          1.50025          11.5436         7.69445\n",
      "Seawall                    7.70855           0.0620626      0.00805114\n",
      "Building code              7.98743          19.0306         2.38257\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   76.1225   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9112   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.14472        6.27348\n",
      "Beach nourishment          1.50025             5.66285        3.77461\n",
      "Seawall                    7.70855             2.58858        0.335806\n",
      "Building code              7.98743            46.5378         5.82638\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   186.151   (USD bn)\n",
      "Average annual risk:   21.5984  (USD bn)\n",
      "Residual risk:        124.217   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.63243        6.70172\n",
      "Beach nourishment          1.50025             5.87772        3.91783\n",
      "Seawall                    7.70855             1.82863        0.237221\n",
      "Building code              7.98743            44.9771         5.63099\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.909  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.593  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.7533         6.80785\n",
      "Beach nourishment          1.50025             5.97641        3.98361\n",
      "Seawall                    7.70855             1.82863        0.237221\n",
      "Building code              7.98743            45.0375         5.63855\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   180.15   (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.554  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888           14.9037        13.0863\n",
      "Beach nourishment          1.50025           11.752          7.83335\n",
      "Seawall                    7.70855            0.108627       0.0140917\n",
      "Building code              7.98743           24.3925         3.05386\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   97.5699  (USD bn)\n",
      "Average annual risk:  11.2725  (USD bn)\n",
      "Residual risk:        46.4132  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288             7.63243        5.68361\n",
      "Beach nourishment          1.76899             5.87772        3.32264\n",
      "Seawall                    9.08939             1.82863        0.201183\n",
      "Building code              9.41823            44.9771         4.77554\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.909  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.593  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288           15.0245        11.1883\n",
      "Beach nourishment          1.76899           11.8507         6.69911\n",
      "Seawall                    9.08939            0.108627       0.0119509\n",
      "Building code              9.41823           24.4528         2.59633\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   97.8114  (USD bn)\n",
      "Average annual risk:  11.2725  (USD bn)\n",
      "Residual risk:        46.3747  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205          5.31429        4.01972\n",
      "Beach nourishment          1.74155          4.27155        2.45273\n",
      "Seawall                    8.9484           0.00764219     0.000854029\n",
      "Building code              9.27214          3.5502         0.382889\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.2008   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0571   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205          5.5802         4.22086\n",
      "Beach nourishment          1.74155          4.48867        2.5774\n",
      "Seawall                    8.9484           0.00764219     0.000854029\n",
      "Building code              9.27214          3.68297        0.397208\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   14.7319    (USD bn)\n",
      "Average annual risk:   1.57569   (USD bn)\n",
      "Residual risk:         0.972395  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205             7.60825        5.75487\n",
      "Beach nourishment          1.74155             5.85798        3.36366\n",
      "Seawall                    8.9484              1.82863        0.204353\n",
      "Building code              9.27214            44.9651         4.84948\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.86   (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.6    (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.36453          5.31429        3.89458\n",
      "Beach nourishment          1.79751          4.27155        2.37637\n",
      "Seawall                    9.23593          0.00764219     0.000827442\n",
      "Building code              9.57007          3.5502         0.370969\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.2008   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0571   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.36453             7.87416        5.77059\n",
      "Beach nourishment          1.79751             6.0751         3.37973\n",
      "Seawall                    9.23593             1.82863        0.197991\n",
      "Building code              9.57007            45.0978         4.71238\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   180.391  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.516  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742            8.67047        8.12282\n",
      "Beach nourishment          1.40612            6.72299        4.78124\n",
      "Seawall                    7.2249             0.621468       0.0860176\n",
      "Building code              7.48629           39.2619         5.24451\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.048   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.771   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742            8.5496         8.00959\n",
      "Beach nourishment          1.40612            6.6243         4.71105\n",
      "Seawall                    7.2249             0.621468       0.0860176\n",
      "Building code              7.48629           39.2016         5.23645\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   156.806   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.809   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742          14.5509         13.6318\n",
      "Beach nourishment          1.40612          11.5238          8.19549\n",
      "Seawall                    7.2249            0.0620626       0.0085901\n",
      "Building code              7.48629          19.0186          2.54045\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   76.0742   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9189   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.17928            8.67047        7.35233\n",
      "Beach nourishment          1.55347            6.72299        4.32772\n",
      "Seawall                    7.98203            0.621468       0.0778584\n",
      "Building code              8.27081           39.2619         4.74705\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.048   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.771   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.17928          14.43          12.2363\n",
      "Beach nourishment          1.55347          11.4252         7.35459\n",
      "Seawall                    7.98203           0.0620626      0.00777529\n",
      "Building code              8.27081          18.9582         2.29218\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   75.8328   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9574   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           13.8606        10.6498\n",
      "Beach nourishment          1.71445           10.7904         6.29377\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.4038         3.22132\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           13.8606        10.6498\n",
      "Beach nourishment          1.71445           10.7904         6.29377\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.4038         3.22132\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5404         8.71168\n",
      "Beach nourishment          1.59383           8.59532        5.39285\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.53759        0.88827Net Present Values\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1504   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45852  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4109         8.26917\n",
      "Beach nourishment          1.65849            8.18032        4.93239\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8546         4.17383\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.418   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5573  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288           15.0245        11.1883\n",
      "Beach nourishment          1.76899           11.8507         6.69911\n",
      "Seawall                    9.08939            0.108627       0.0119509\n",
      "Building code              9.41823           24.4528         2.59633\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   97.8114  (USD bn)\n",
      "Average annual risk:  11.2725  (USD bn)\n",
      "Residual risk:        46.3747  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148           14.0781        10.817\n",
      "Beach nourishment          1.71445           10.968          6.39739\n",
      "Seawall                    8.80916            0.175596       0.0199334\n",
      "Building code              9.12786           29.5124         3.23322\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   118.05    (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3155  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992           12.9104        10.6705\n",
      "Beach nourishment          1.59383           10.0034         6.27629\n",
      "Seawall                    8.18941            0.257712       0.0314689\n",
      "Building code              8.48569           33.7365         3.97569\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   134.946   (USD bn)\n",
      "Average annual risk:   15.5605  (USD bn)\n",
      "Residual risk:         78.038   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259              8.79133        6.98278\n",
      "Beach nourishment          1.65849            6.82168        4.11319\n",
      "Seawall                    8.52163            0.621468       0.0729283\n",
      "Building code              8.82993           39.3223         4.45329\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205          5.31429        4.01972\n",
      "Beach nourishment          1.74155          4.27155        2.45273\n",
      "Seawall                    8.9484           0.00764219     0.000854029\n",
      "Building code              9.27214          3.5502         0.382889--------------------  --------  --------\n",
      "Total climate risk:   157.289   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.732   (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "\n",
      "Net Present Values--------------------  --------  --------\n",
      "Total climate risk:   14.2008   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0571   (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148          14.1012        10.8347\n",
      "Beach nourishment          1.71445          10.9632         6.39461\n",
      "Seawall                    8.80916           0.0376243      0.00427104\n",
      "Building code              9.12786          13.3845         1.46633\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   53.5379   (USD bn)\n",
      "Average annual risk:   6.15933  (USD bn)\n",
      "Residual risk:        15.0513   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.44955        6.08873\n",
      "Beach nourishment          1.82807            6.4567         3.53197\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.407          4.25438\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4834         8.32677\n",
      "Beach nourishment          1.65849            8.23954        4.96809\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8908         4.17793\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.628   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.419   (USD bn)\n",
      "--------------------  --------  --------\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.563   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5342  (USD bn)\n",
      "--------------------  --------  --------Net Present Values\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205          5.5802         4.22086\n",
      "Beach nourishment          1.74155          4.48867        2.5774\n",
      "Seawall                    8.9484           0.00764219     0.000854029\n",
      "Building code              9.27214          3.68297        0.397208\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   14.7319    (USD bn)\n",
      "Average annual risk:   1.57569   (USD bn)\n",
      "Residual risk:         0.972395  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612           13.8606         8.90716\n",
      "Beach nourishment          2.04988           10.7904         5.2639\n",
      "Seawall                   10.5327             0.175596       0.0166716\n",
      "Building code             10.9137            29.4038         2.69421\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   117.615   (USD bn)\n",
      "Average annual risk:   13.6166  (USD bn)\n",
      "Residual risk:         63.3848  (USD bn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.47373        6.10615\n",
      "Beach nourishment          1.82807            6.47644        3.54276\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.4191         4.25562Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259              8.71881        6.92518\n",
      "Beach nourishment          1.65849            6.76247        4.07748\n",
      "Seawall                    8.52163            0.621468       0.0729283\n",
      "Building code              8.82993           39.286          4.44919\n",
      "\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.676   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.411   (USD bn)\n",
      "--------------------  --------  ----------------------------  --------  --------\n",
      "Total climate risk:   157.144   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.755   (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Net Present ValuesNet Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.32205             7.60825        5.75487\n",
      "Beach nourishment          1.74155             5.85798        3.36366\n",
      "Seawall                    8.9484              1.82863        0.204353\n",
      "Building code              9.27214            44.9651         4.84948\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.86   (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.6    (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612          14.3188         9.20163\n",
      "Beach nourishment          2.04988          11.1409         5.4349\n",
      "Seawall                   10.5327            0.0376243      0.00357216\n",
      "Building code             10.9137           13.4931         1.23634\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   53.9724   (USD bn)\n",
      "Average annual risk:   6.15933  (USD bn)\n",
      "Residual risk:        14.982    (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774         1.39186         1.00297\n",
      "Beach nourishment          1.82807         1.13491         0.62082\n",
      "Seawall                    9.39298         0.000227424     2.42121e-05\n",
      "Building code              9.7328          0.76062         0.0781501Net Present Values\n",
      "\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:    3.04248   (USD bn)\n",
      "Average annual risk:   0.260244  (USD bn)\n",
      "Residual risk:        -0.245137  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288          14.575         10.8535\n",
      "Beach nourishment          1.76899          11.5436         6.52553\n",
      "Seawall                    9.08939           0.0620626      0.00682803\n",
      "Building code              9.41823          19.0306         2.02062\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   76.1225   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9112   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.36453          5.31429        3.89458\n",
      "Beach nourishment          1.79751          4.27155        2.37637\n",
      "Seawall                    9.23593          0.00764219     0.000827442\n",
      "Building code              9.57007          3.5502         0.370969\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.2008   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0571   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774            8.44955        6.08873\n",
      "Beach nourishment          1.82807            6.4567         3.53197\n",
      "Seawall                    9.39298            0.895618       0.0953497\n",
      "Building code              9.7328            41.407          4.25438\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   165.628   (USD bn)\n",
      "Average annual risk:   19.1818  (USD bn)\n",
      "Residual risk:        108.419   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             6.59956        4.24104\n",
      "Beach nourishment          2.04988             5.16368        2.51902\n",
      "Seawall                   10.5327              3.55475        0.337498\n",
      "Building code             10.9137             48.016          4.3996\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   192.064   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.73    (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288          14.3091        10.6555\n",
      "Beach nourishment          1.76899          11.3265         6.40279\n",
      "Seawall                    9.08939           0.0620626      0.00682803\n",
      "Building code              9.41823          18.8979         2.00652\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   75.5914   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9959   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.36453             7.87416        5.77059\n",
      "Beach nourishment          1.79751             6.0751         3.37973\n",
      "Seawall                    9.23593             1.82863        0.197991\n",
      "Building code              9.57007            45.0978         4.71238\n",
      "Net Present Values\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   180.391  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.516  (USD bn)\n",
      "--------------------  -------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.38774         1.41604         1.02039\n",
      "Beach nourishment          1.82807         1.15464         0.631618\n",
      "Seawall                    9.39298         0.000227424     2.42121e-05\n",
      "Building code              9.7328          0.77269         0.0793903\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:    3.09076   (USD bn)\n",
      "Average annual risk:   0.260244  (USD bn)\n",
      "Residual risk:        -0.252837  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             6.43034        4.1323\n",
      "Beach nourishment          2.04988             5.02552        2.45161\n",
      "Seawall                   10.5327              3.55475        0.337498\n",
      "Building code             10.9137             47.9315         4.39186\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288             7.41063        5.51844\n",
      "Beach nourishment          1.76899             5.87997        3.32392\n",
      "Seawall                    9.08939             2.58858        0.284791\n",
      "Building code              9.41823            46.6706         4.95535\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   191.726   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.784   (USD bn)\n",
      "--------------------  --------  ----------------------------  --------  --------\n",
      "Total climate risk:   186.682   (USD bn)\n",
      "Average annual risk:   21.5984  (USD bn)\n",
      "Residual risk:        124.133   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742            8.67047        8.12282\n",
      "Beach nourishment          1.40612            6.72299        4.78124\n",
      "Seawall                    7.2249             0.621468       0.0860176\n",
      "Building code              7.48629           39.2619         5.24451\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.048   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.771   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.77518        1.92153\n",
      "Beach nourishment          1.90253          2.24443        1.17971\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.68273        0.166127\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.73092    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0259328  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.55612             7.59067         4.87796\n",
      "Beach nourishment          2.04988             5.96389         2.90939\n",
      "Seawall                   10.5327              1.31269         0.12463\n",
      "Building code             10.9137             43.2513          3.96302Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888          14.575         12.7977\n",
      "Beach nourishment          1.50025          11.5436         7.69445\n",
      "Seawall                    7.70855           0.0620626      0.00805114\n",
      "Building code              7.98743          19.0306         2.38257\n",
      "\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   173.005   (USD bn)\n",
      "Average annual risk:   20.0179  (USD bn)\n",
      "Residual risk:        114.887   (USD bn)\n",
      "--------------------  --------  ----------------------------  --------  --------\n",
      "Total climate risk:   76.1225   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9112   (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Net Present ValuesNet Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742            8.5496         8.00959\n",
      "Beach nourishment          1.40612            6.6243         4.71105\n",
      "Seawall                    7.2249             0.621468       0.0860176\n",
      "Building code              7.48629           39.2016         5.23645\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   156.806   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.809   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.70266        1.87132\n",
      "Beach nourishment          1.90253          2.18521        1.14859\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.64652        0.162552\n",
      "\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.58607    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0490347  (USD bn)\n",
      "--------------------  ---------  --------Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148             6.59956        5.0708\n",
      "Beach nourishment          1.71445             5.16368        3.01186\n",
      "Seawall                    8.80916             3.55475        0.403528\n",
      "Building code              9.12786            48.016          5.26038\n",
      "\n",
      "Net Present Values\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.14472        6.27348\n",
      "Beach nourishment          1.50025             5.66285        3.77461\n",
      "Seawall                    7.70855             2.58858        0.335806\n",
      "Building code              7.98743            46.5378         5.82638\n",
      "\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   192.064   (USD bn)\n",
      "Average annual risk:   22.2359  (USD bn)\n",
      "Residual risk:        128.73    (USD bn)\n",
      "--------------------  --------  --------\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   186.151   (USD bn)\n",
      "Average annual risk:   21.5984  (USD bn)\n",
      "Residual risk:        124.217   (USD bn)\n",
      "--------------------  --------  --------Net Present Values\n",
      "Net Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.06742          14.5509         13.6318\n",
      "Beach nourishment          1.40612          11.5238          8.19549\n",
      "Seawall                    7.2249            0.0620626       0.0085901\n",
      "Building code              7.48629          19.0186          2.54045\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   76.0742   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9189   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.63243        6.70172\n",
      "Beach nourishment          1.50025             5.87772        3.91783\n",
      "Seawall                    7.70855             1.82863        0.237221\n",
      "Building code              7.98743            44.9771         5.63099Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.30148             7.42146        5.70231\n",
      "Beach nourishment          1.71445             5.82573        3.39801\n",
      "Seawall                    8.80916             1.31269        0.149014\n",
      "Building code              9.12786            43.1668         4.72913\n",
      "\n",
      "\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          5.43516        3.76329\n",
      "Beach nourishment          1.90253          4.37024        2.29707\n",
      "Seawall                    9.77553          0.00764219     0.000781768\n",
      "Building code             10.1292           3.61055        0.35645\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.909  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.593  (USD bn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------  -------  --------\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   172.667   (USD bn)\n",
      "Average annual risk:   20.0179  (USD bn)\n",
      "Residual risk:        114.941   (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.4422   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0186   (USD bn)\n",
      "--------------------  --------  --------Net Present Values\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.17928            8.67047        7.35233\n",
      "Beach nourishment          1.55347            6.72299        4.32772\n",
      "Seawall                    7.98203            0.621468       0.0778584\n",
      "Building code              8.27081           39.2619         4.74705\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   157.048   (USD bn)\n",
      "Average annual risk:   18.1551  (USD bn)\n",
      "Residual risk:        101.771   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888             7.7533         6.80785\n",
      "Beach nourishment          1.50025             5.97641        3.98361\n",
      "Seawall                    7.70855             1.82863        0.237221\n",
      "Building code              7.98743            45.0375         5.63855\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   180.15   (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.554  (USD bn)\n",
      "--------------------  -------  --------\n",
      "\n",
      "Net Present ValuesMeasure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5404         8.71168\n",
      "Beach nourishment          1.59383           8.59532        5.39285\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.53759        0.88827\n",
      "\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1504   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45852  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          2.77518        1.92153\n",
      "Beach nourishment          1.90253          2.24443        1.17971\n",
      "Seawall                    9.77553          0.00264385     0.000270456\n",
      "Building code             10.1292           1.68273        0.166127Net Present Values\n",
      "\n",
      "\n",
      "--------------------  ---------  --------\n",
      "Total climate risk:   6.73092    (USD bn)\n",
      "Average annual risk:  0.678263   (USD bn)\n",
      "Residual risk:        0.0259328  (USD bn)\n",
      "--------------------  ---------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.17928          14.43          12.2363\n",
      "Beach nourishment          1.55347          11.4252         7.35459\n",
      "Seawall                    7.98203           0.0620626      0.00777529\n",
      "Building code              8.27081          18.9582         2.29218\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   75.8328   (USD bn)\n",
      "Average annual risk:   8.73152  (USD bn)\n",
      "Residual risk:        30.9574   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.13888           14.9037        13.0863\n",
      "Beach nourishment          1.50025           11.752          7.83335\n",
      "Seawall                    7.70855            0.108627       0.0140917\n",
      "Building code              7.98743           24.3925         3.05386\n",
      "\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   97.5699  (USD bn)\n",
      "Average annual risk:  11.2725  (USD bn)\n",
      "Residual risk:        46.4132  (USD bn)\n",
      "--------------------  -------  --------Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992          10.5646         8.73166\n",
      "Beach nourishment          1.59383           8.61505        5.40524\n",
      "Seawall                    8.18941           0.0184996      0.00225897\n",
      "Building code              8.48569           7.54966        0.889693\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   30.1986   (USD bn)\n",
      "Average annual risk:   3.37008  (USD bn)\n",
      "Residual risk:         3.45082  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.44426          5.36264        3.71308\n",
      "Beach nourishment          1.90253          4.31103        2.26595\n",
      "Seawall                    9.77553          0.00764219     0.000781768\n",
      "Building code             10.1292           3.57434        0.352875\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   14.2973   (USD bn)\n",
      "Average annual risk:   1.57569  (USD bn)\n",
      "Residual risk:         1.0417   (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n",
      "\n",
      "Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.34288             7.63243        5.68361\n",
      "Beach nourishment          1.76899             5.87772        3.32264\n",
      "Seawall                    9.08939             1.82863        0.201183\n",
      "Building code              9.41823            44.9771         4.77554\n",
      "\n",
      "\n",
      "--------------------  -------  --------\n",
      "Total climate risk:   179.909  (USD bn)\n",
      "Average annual risk:   20.855  (USD bn)\n",
      "Residual risk:        119.593  (USD bn)\n",
      "--------------------  -------  --------Measure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.20992           12.8863        10.6505\n",
      "Beach nourishment          1.59383            9.98362        6.2639\n",
      "Seawall                    8.18941            0.257712       0.0314689\n",
      "Building code              8.48569           33.7244         3.97427\n",
      "\n",
      "Net Present Values\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   134.898   (USD bn)\n",
      "Average annual risk:   15.5605  (USD bn)\n",
      "Residual risk:         78.0457  (USD bn)\n",
      "--------------------  --------  --------\n",
      "\n",
      "Net Present ValuesMeasure              Cost (USD bn)    Benefit (USD bn)    Benefit/Cost\n",
      "-----------------  ---------------  ------------------  --------------\n",
      "Mangroves                  1.259             10.4834         8.32677\n",
      "Beach nourishment          1.65849            8.23954        4.96809\n",
      "Seawall                    8.52163            0.415328       0.0487381\n",
      "Building code              8.82993           36.8908         4.17793\n",
      "\n",
      "\n",
      "--------------------  --------  --------\n",
      "Total climate risk:   147.563   (USD bn)\n",
      "Average annual risk:   17.0232  (USD bn)\n",
      "Residual risk:         91.5342  (USD bn)\n",
      "--------------------  --------  --------\n",
      "Net Present Values\n"
     ]
    }
   ],
   "source": [
    "# without pool\n",
    "output_cb = unc_cb.uncertainty(output_cb)\n",
    "\n",
    "# with pool\n",
    "output_cb = unc_cb.uncertainty(output_cb, processes=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of `CostBenefit.calc` is rather complex in its structure. The metrics dictionary inherits this complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:33.867733Z",
     "start_time": "2023-08-03T12:00:33.861208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imp_meas_present',\n",
       " 'imp_meas_future',\n",
       " 'tot_climate_risk',\n",
       " 'benefit',\n",
       " 'cost_ben_ratio']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top level metrics keys\n",
    "macro_metrics = output_cb.uncertainty_metrics\n",
    "macro_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:33.887774Z",
     "start_time": "2023-08-03T12:00:33.870471Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mangroves Benef</th>\n",
       "      <th>Beach nourishment Benef</th>\n",
       "      <th>Seawall Benef</th>\n",
       "      <th>Building code Benef</th>\n",
       "      <th>Mangroves CostBen</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8.670468e+09</td>\n",
       "      <td>6.722992e+09</td>\n",
       "      <td>6.214684e+08</td>\n",
       "      <td>3.926190e+10</td>\n",
       "      <td>0.123110</td>\n",
       "      <td>0.209151</td>\n",
       "      <td>11.625533</td>\n",
       "      <td>0.190676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>8.549601e+09</td>\n",
       "      <td>6.624301e+09</td>\n",
       "      <td>6.214684e+08</td>\n",
       "      <td>3.920155e+10</td>\n",
       "      <td>0.124850</td>\n",
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       "      <td>11.625533</td>\n",
       "      <td>0.190969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.455086e+10</td>\n",
       "      <td>1.152385e+10</td>\n",
       "      <td>6.206260e+07</td>\n",
       "      <td>1.901856e+10</td>\n",
       "      <td>0.073358</td>\n",
       "      <td>0.122018</td>\n",
       "      <td>116.413127</td>\n",
       "      <td>0.393631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8.670468e+09</td>\n",
       "      <td>6.722992e+09</td>\n",
       "      <td>6.214684e+08</td>\n",
       "      <td>3.926190e+10</td>\n",
       "      <td>0.136011</td>\n",
       "      <td>0.231069</td>\n",
       "      <td>12.843826</td>\n",
       "      <td>0.210657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.443000e+10</td>\n",
       "      <td>1.142516e+10</td>\n",
       "      <td>6.206260e+07</td>\n",
       "      <td>1.895821e+10</td>\n",
       "      <td>0.081724</td>\n",
       "      <td>0.135970</td>\n",
       "      <td>128.612593</td>\n",
       "      <td>0.436265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mangroves Benef  Beach nourishment Benef  Seawall Benef  \\\n",
       "45     8.670468e+09             6.722992e+09   6.214684e+08   \n",
       "46     8.549601e+09             6.624301e+09   6.214684e+08   \n",
       "47     1.455086e+10             1.152385e+10   6.206260e+07   \n",
       "48     8.670468e+09             6.722992e+09   6.214684e+08   \n",
       "49     1.443000e+10             1.142516e+10   6.206260e+07   \n",
       "\n",
       "    Building code Benef  Mangroves CostBen  Beach nourishment CostBen  \\\n",
       "45         3.926190e+10           0.123110                   0.209151   \n",
       "46         3.920155e+10           0.124850                   0.212267   \n",
       "47         1.901856e+10           0.073358                   0.122018   \n",
       "48         3.926190e+10           0.136011                   0.231069   \n",
       "49         1.895821e+10           0.081724                   0.135970   \n",
       "\n",
       "    Seawall CostBen  Building code CostBen  \n",
       "45        11.625533               0.190676  \n",
       "46        11.625533               0.190969  \n",
       "47       116.413127               0.393631  \n",
       "48        12.843826               0.210657  \n",
       "49       128.612593               0.436265  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The benefits and cost_ben_ratio are available for each measure\n",
    "output_cb.get_uncertainty(metric_list=[\"benefit\", \"cost_ben_ratio\"]).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:33.913186Z",
     "start_time": "2023-08-03T12:00:33.890497Z"
    }
   },
   "outputs": [
    {
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       "      <td>1.040893e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>8.878779e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>7.806698e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>9.200000e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>8.010560e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.999849e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.311768e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>4.735703e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.728000e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>8.010560e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>8.878779e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>6.007920e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>9.200000e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.040893e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.197409e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.311768e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>6.153578e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.728000e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.040893e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>8.878779e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>7.806698e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>9.200000e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.040893e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.197409e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.311768e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>6.153578e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.728000e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.040893e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>8.878779e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>7.806698e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>9.200000e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>8.010560e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.999849e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.311768e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>4.735703e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>1.728000e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>8.010560e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>8.878779e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>6.007920e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>9.200000e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    no measure - risk - present  no measure - risk_transf - present  \\\n",
       "45                 1.040893e+08                                 0.0   \n",
       "46                 8.010560e+07                                 0.0   \n",
       "47                 1.040893e+08                                 0.0   \n",
       "48                 1.040893e+08                                 0.0   \n",
       "49                 8.010560e+07                                 0.0   \n",
       "\n",
       "    no measure - cost_meas - present  no measure - cost_ins - present  \\\n",
       "45                                 0                                0   \n",
       "46                                 0                                0   \n",
       "47                                 0                                0   \n",
       "48                                 0                                0   \n",
       "49                                 0                                0   \n",
       "\n",
       "    Mangroves - risk - present  Mangroves - risk_transf - present  \\\n",
       "45                5.197409e+07                                  0   \n",
       "46                3.999849e+07                                  0   \n",
       "47                5.197409e+07                                  0   \n",
       "48                5.197409e+07                                  0   \n",
       "49                3.999849e+07                                  0   \n",
       "\n",
       "    Mangroves - cost_meas - present  Mangroves - cost_ins - present  \\\n",
       "45                     1.311768e+09                               1   \n",
       "46                     1.311768e+09                               1   \n",
       "47                     1.311768e+09                               1   \n",
       "48                     1.311768e+09                               1   \n",
       "49                     1.311768e+09                               1   \n",
       "\n",
       "    Beach nourishment - risk - present  \\\n",
       "45                        6.153578e+07   \n",
       "46                        4.735703e+07   \n",
       "47                        6.153578e+07   \n",
       "48                        6.153578e+07   \n",
       "49                        4.735703e+07   \n",
       "\n",
       "    Beach nourishment - risk_transf - present  \\\n",
       "45                                          0   \n",
       "46                                          0   \n",
       "47                                          0   \n",
       "48                                          0   \n",
       "49                                          0   \n",
       "\n",
       "    Beach nourishment - cost_meas - present  \\\n",
       "45                             1.728000e+09   \n",
       "46                             1.728000e+09   \n",
       "47                             1.728000e+09   \n",
       "48                             1.728000e+09   \n",
       "49                             1.728000e+09   \n",
       "\n",
       "    Beach nourishment - cost_ins - present  Seawall - risk - present  \\\n",
       "45                                       1              1.040893e+08   \n",
       "46                                       1              8.010560e+07   \n",
       "47                                       1              1.040893e+08   \n",
       "48                                       1              1.040893e+08   \n",
       "49                                       1              8.010560e+07   \n",
       "\n",
       "    Seawall - risk_transf - present  Seawall - cost_meas - present  \\\n",
       "45                                0                   8.878779e+09   \n",
       "46                                0                   8.878779e+09   \n",
       "47                                0                   8.878779e+09   \n",
       "48                                0                   8.878779e+09   \n",
       "49                                0                   8.878779e+09   \n",
       "\n",
       "    Seawall - cost_ins - present  Building code - risk - present  \\\n",
       "45                             1                    7.806698e+07   \n",
       "46                             1                    6.007920e+07   \n",
       "47                             1                    7.806698e+07   \n",
       "48                             1                    7.806698e+07   \n",
       "49                             1                    6.007920e+07   \n",
       "\n",
       "    Building code - risk_transf - present  \\\n",
       "45                                      0   \n",
       "46                                      0   \n",
       "47                                      0   \n",
       "48                                      0   \n",
       "49                                      0   \n",
       "\n",
       "    Building code - cost_meas - present  Building code - cost_ins - present  \n",
       "45                         9.200000e+09                                   1  \n",
       "46                         9.200000e+09                                   1  \n",
       "47                         9.200000e+09                                   1  \n",
       "48                         9.200000e+09                                   1  \n",
       "49                         9.200000e+09                                   1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The impact_meas_present and impact_meas_future provide values of the cost_meas, risk_transf, risk,\n",
    "# and cost_ins for each measure\n",
    "output_cb.get_uncertainty(metric_list=[\"imp_meas_present\"]).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the distributions for the top metrics or our choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:35.497893Z",
     "start_time": "2023-08-03T12:00:33.916462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tot_climate_risk and benefit\n",
    "output_cb.plot_uncertainty(metric_list=[\"benefit\"], figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogously to the impact example, now that we have a metric distribution, we can compute the sensitivity indices. Since we used the default sampling method, we can use the default sensitivity analysis method. However, since we used `calc_second_order = False` for the sampling, we need to specify the same for the sensitivity analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:35.632065Z",
     "start_time": "2023-08-03T12:00:35.500390Z"
    }
   },
   "outputs": [],
   "source": [
    "output_cb = unc_cb.sensitivity(\n",
    "    output_cb, sensitivity_kwargs={\"calc_second_order\": False}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensitivity indices can be plotted. For the default method 'sobol', by default the 'S1' sensitivity index is plotted.\n",
    "\n",
    "Note that since we have quite a few measures, the plot must be adjusted a bit or dropped. Also see that for many metrics, the sensitivity to certain uncertainty parameters appears to be 0. However, this result is to be treated with care. Indeed, we used for demonstration purposes a rather too low number of samples, which is indicated by large confidence intervals (vertical black lines) for most sensitivity indices. For a more robust result the analysis should be repeated with more samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-03T12:00:36.659813Z",
     "start_time": "2023-08-03T12:00:35.635106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot only certain metrics\n",
    "axes = output_cb.plot_sensitivity(\n",
    "    metric_list=[\"cost_ben_ratio\", \"tot_climate_risk\", \"benefit\"], figsize=(12, 8)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"advanced-examples\"></a>\n",
    "## Advanced examples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"coupled-variables\"></a>\n",
    "### Coupled variables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we show how you can define correlated input variables. Suppose your exposures and hazards are conditioned on the same Shared Socio-economic Pathway (SSP). Then, you want that only exposures and hazard belonging to the same SSP are present in each sample.\n",
    "\n",
    "In order to achieve this, you must simply define an uncertainty parameter that shares the same name and the same distribution for both the exposures and the hazard uncertainty variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"many-scenarios-of-hazards-and-exposures\"></a>\n",
    "#### Many scenarios of hazards and exposures\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we look at the case where many scenarios are tested in the uncertainty analysis. For instance, suppose you have data for different Shared Socio-economic Pathways (SSP) and different Climate Change projections. From the SSPs, you have a number of Exposures, saved to files. From the climate projections, you have a number of Hazards, saved to file.\n",
    "\n",
    "The task is to sample from the SSPs and the Climate change scenarios for the uncertainty and sensitivity analysis efficiently.\n",
    "\n",
    "For demonstration purposes, we will use below as exposures files the litpop for three countries, and for tha hazard files the winter storms for the same three countries. Instead of having SSPs, we now want to only combine exposures and hazards of the same countries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.util.api_client import Client\n",
    "\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_litpop(iso):\n",
    "    return client.get_litpop(country=iso)\n",
    "\n",
    "\n",
    "def get_ws(iso):\n",
    "    properties = {\n",
    "        \"country_iso3alpha\": iso,\n",
    "    }\n",
    "    return client.get_hazard(\"storm_europe\", properties=properties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define list of exposures and/or of hazard files\n",
    "\n",
    "exp_list = [get_litpop(iso) for iso in [\"CHE\", \"DEU\", \"ITA\"]]\n",
    "haz_list = [get_ws(iso) for iso in [\"CHE\", \"DEU\", \"ITA\"]]\n",
    "for exp, haz in zip(exp_list, haz_list):\n",
    "    exp.gdf[\"impf_WS\"] = 1\n",
    "    exp.assign_centroids(haz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the input variable\n",
    "from climada.entity import ImpactFuncSet, Exposures\n",
    "from climada.entity.impact_funcs.storm_europe import ImpfStormEurope\n",
    "from climada.hazard import Hazard\n",
    "from climada.engine.unsequa import InputVar\n",
    "import scipy as sp\n",
    "import copy\n",
    "\n",
    "\n",
    "def exp_func(cnt, x_exp, exp_list=exp_list):\n",
    "    exp = exp_list[int(cnt)].copy()\n",
    "    exp.data[\"value\"] *= x_exp\n",
    "    return exp\n",
    "\n",
    "\n",
    "exp_distr = {\n",
    "    \"x_exp\": sp.stats.uniform(0.9, 0.2),\n",
    "    \"cnt\": sp.stats.randint(\n",
    "        low=0, high=len(exp_list)\n",
    "    ),  # use the same parameter name accross input variables\n",
    "}\n",
    "exp_iv = InputVar(exp_func, exp_distr)\n",
    "\n",
    "\n",
    "def haz_func(cnt, i_haz, haz_list=haz_list):\n",
    "    haz = copy.deepcopy(\n",
    "        haz_list[int(cnt)]\n",
    "    )  # use the same parameter name accross input variables\n",
    "    haz.intensity *= i_haz\n",
    "    return haz\n",
    "\n",
    "\n",
    "haz_distr = {\n",
    "    \"i_haz\": sp.stats.norm(1, 0.2),\n",
    "    \"cnt\": sp.stats.randint(low=0, high=len(haz_list)),\n",
    "}\n",
    "haz_iv = InputVar(haz_func, haz_distr)\n",
    "\n",
    "impf = ImpfStormEurope.from_schwierz()\n",
    "impf_set = ImpactFuncSet()\n",
    "impf_set.append(impf)\n",
    "impf_iv = InputVar.impfset([impf_set], bounds_mdd=[0.9, 1.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-25 15:38:30,713 - climada.engine.unsequa.calc_base - WARNING - \n",
      "\n",
      "The input parameter cnt is shared among at least 2 input variables. Their uncertainty is thus computed with the same samples for this input paramter.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from climada.engine.unsequa import CalcImpact\n",
    "\n",
    "calc_imp = CalcImpact(exp_iv, impf_iv, haz_iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_imp = calc_imp.make_sample(N=2**2, sampling_kwargs={\"skip_values\": 2**3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_exp</th>\n",
       "      <th>cnt</th>\n",
       "      <th>MDD</th>\n",
       "      <th>i_haz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.9875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0375</td>\n",
       "      <td>1.097755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.0625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0375</td>\n",
       "      <td>1.097755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.0625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>1.097755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.0625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0375</td>\n",
       "      <td>1.097755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0375</td>\n",
       "      <td>1.097755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x_exp  cnt     MDD     i_haz\n",
       "35  0.9875  0.0  1.0375  1.097755\n",
       "36  1.0625  1.0  1.0375  1.097755\n",
       "37  1.0625  0.0  0.9375  1.097755\n",
       "38  1.0625  0.0  1.0375  1.097755\n",
       "39  1.0625  0.0  1.0375  1.097755"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we can see, there is only a single input parameter \"cnt\" to select the country for both the exposures and the hazard\n",
    "output_imp.samples_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_imp = calc_imp.uncertainty(output_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_imp.aai_agg_unc_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"input-variable-repeated-loading-of-files-made-efficient\"></a>\n",
    "### Input variable: Repeated loading of files made efficient\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading Hazards or Exposures from file is a rather lengthy operation. Thus, we want to minimize the reading operations, ideally reading each file only once. Simultaneously, Hazard and Exposures can be large in memory, and thus we would like to have at most one of each loaded at a time. Thus, we do not want to use the list capacity from the helper method InputVar.exposures and InputVar.hazard.\n",
    "\n",
    "For demonstration purposes, we will use below as exposures files the litpop for three countries, and for the hazard files the winter storms for the same three countries. Note that this does not make a lot of sense for an uncertainty analysis. For your use case, please replace the set of exposures and/or hazard files with meaningful sets, for instance sets of exposures for different resolutions or hazards for different model runs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.util.api_client import Client\n",
    "\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_litpop_path(iso):\n",
    "    properties = {\n",
    "        \"country_iso3alpha\": iso,\n",
    "        \"res_arcsec\": \"150\",\n",
    "        \"exponents\": \"(1,1)\",\n",
    "        \"fin_mode\": \"pc\",\n",
    "    }\n",
    "    litpop_datasets = client.list_dataset_infos(\n",
    "        data_type=\"litpop\", properties=properties\n",
    "    )\n",
    "    ds = litpop_datasets[0]\n",
    "    download_dir, ds_files = client.download_dataset(ds)\n",
    "    return ds_files[0]\n",
    "\n",
    "\n",
    "def get_ws_path(iso):\n",
    "    properties = {\n",
    "        \"country_iso3alpha\": iso,\n",
    "    }\n",
    "    hazard_datasets = client.list_dataset_infos(\n",
    "        data_type=\"storm_europe\", properties=properties\n",
    "    )\n",
    "    ds = hazard_datasets[0]\n",
    "    download_dir, ds_files = client.download_dataset(ds)\n",
    "    return ds_files[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define list of exposures and/or of hazard files\n",
    "\n",
    "f_exp_list = [get_litpop_path(iso) for iso in [\"CHE\", \"DEU\", \"ITA\"]]\n",
    "f_haz_list = [get_ws_path(iso) for iso in [\"CHE\", \"DEU\", \"ITA\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the input variable for the loading files\n",
    "# The trick is to not reload a file if it is already in memory. This is done using a global variable.\n",
    "from climada.entity import ImpactFunc, ImpactFuncSet, Exposures\n",
    "from climada.hazard import Hazard\n",
    "from climada.engine.unsequa import InputVar\n",
    "import scipy as sp\n",
    "import copy\n",
    "\n",
    "\n",
    "def exp_func(f_exp, x_exp, filename_list=f_exp_list):\n",
    "    filename = filename_list[int(f_exp)]\n",
    "    global exp_base\n",
    "    if (\n",
    "        \"exp_base\" in globals()\n",
    "        and isinstance(exp_base, Exposures)\n",
    "        and exp_base.description == str(filename)\n",
    "    ):\n",
    "        pass  # if correct file is already loaded in memory, we do not need to reload it\n",
    "    else:\n",
    "        exp_base = Exposures.from_hdf5(filename)\n",
    "        exp_base.description = str(filename)\n",
    "\n",
    "    exp = exp_base.copy()\n",
    "    exp.data[\"value\"] *= x_exp\n",
    "    return exp\n",
    "\n",
    "\n",
    "exp_distr = {\n",
    "    \"x_exp\": sp.stats.uniform(0.9, 0.2),\n",
    "    \"f_exp\": sp.stats.randint(low=0, high=len(f_exp_list)),\n",
    "}\n",
    "exp_iv = InputVar(exp_func, exp_distr)\n",
    "\n",
    "\n",
    "def haz_func(f_haz, i_haz, filename_list=f_haz_list):\n",
    "    filename = filename_list[int(f_haz)]\n",
    "    global haz_base\n",
    "    if (\n",
    "        \"haz_base\" in globals()\n",
    "        and isinstance(haz_base, Hazard)\n",
    "        and hasattr(haz_base, \"description\")\n",
    "        and haz_base.description == str(filename)\n",
    "    ):\n",
    "        pass\n",
    "    else:\n",
    "        haz_base = Hazard.from_hdf5(filename)\n",
    "        setattr(haz_base, \"description\", str(filename))\n",
    "\n",
    "    haz = copy.deepcopy(haz_base)\n",
    "    haz.intensity *= i_haz\n",
    "    return haz\n",
    "\n",
    "\n",
    "haz_distr = {\n",
    "    \"i_haz\": sp.stats.norm(1, 0.2),\n",
    "    \"f_haz\": sp.stats.randint(low=0, high=len(f_haz_list)),\n",
    "}\n",
    "haz_iv = InputVar(haz_func, haz_distr)\n",
    "\n",
    "\n",
    "def impf_func(G=1, v_half=84.7, vmin=25.7, k=3, _id=1):\n",
    "\n",
    "    def xhi(v, v_half, vmin):\n",
    "        return max([(v - vmin), 0]) / (v_half - vmin)\n",
    "\n",
    "    def sigmoid_func(v, G, v_half, vmin, k):\n",
    "        return G * xhi(v, v_half, vmin) ** k / (1 + xhi(v, v_half, vmin) ** k)\n",
    "\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    import numpy as np\n",
    "    from climada.entity import ImpactFunc, ImpactFuncSet\n",
    "\n",
    "    imp_fun = ImpactFunc()\n",
    "    imp_fun.haz_type = \"WS\"\n",
    "    imp_fun.id = _id\n",
    "    imp_fun.intensity_unit = \"m/s\"\n",
    "    imp_fun.intensity = np.linspace(0, 150, num=100)\n",
    "    imp_fun.mdd = np.repeat(1, len(imp_fun.intensity))\n",
    "    imp_fun.paa = np.array(\n",
    "        [sigmoid_func(v, G, v_half, vmin, k) for v in imp_fun.intensity]\n",
    "    )\n",
    "    imp_fun.check()\n",
    "    impf_set = ImpactFuncSet()\n",
    "    impf_set.append(imp_fun)\n",
    "    return impf_set\n",
    "\n",
    "\n",
    "impf_distr = {\n",
    "    \"G\": sp.stats.truncnorm(0.5, 1.5),\n",
    "    \"v_half\": sp.stats.uniform(35, 65),\n",
    "    \"vmin\": sp.stats.uniform(0, 15),\n",
    "    \"k\": sp.stats.uniform(1, 4),\n",
    "}\n",
    "impf_iv = InputVar(impf_func, impf_distr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import CalcImpact\n",
    "\n",
    "calc_imp = CalcImpact(exp_iv, impf_iv, haz_iv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the samples have been generated, it is crucial to oder the samples in order to minimize the number of times files have to be loaded. In this case, loading the hazards take more time than loading the exposures. We thus sort first by hazards (which then each have to be loaded one single time), and then by exposures (which have to be each loaded once for each hazard).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordering of the samples by hazard first and exposures second\n",
    "output_imp = calc_imp.make_sample(N=2**2, sampling_kwargs={\"skip_values\": 2**3})\n",
    "output_imp.order_samples(by_parameters=[\"f_haz\", \"f_exp\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify how the samples are ordered. In the graph below, it is confirmed that the hazard are ordered, and thus the hazards will be loaded once each. The exposures on the other changes at most once per hazard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "e = output_imp.samples_df[\"f_exp\"].values\n",
    "h = output_imp.samples_df[\"f_haz\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that due to the very small number of samples chosen here for illustrative purposes, not all combinations of hazard and exposures are part of the samples. This is due to the nature of the Sobol sequence (default sampling method).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(e, label=\"exposures\")\n",
    "plt.plot(h, label=\"hazards\")\n",
    "plt.xlabel(\"samples\")\n",
    "plt.ylabel(\"file number\")\n",
    "plt.title(\"Order of exposures and hazards files in samples\")\n",
    "plt.legend(loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_imp = calc_imp.uncertainty(output_imp)"
   ]
  }
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