{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d357be88",
   "metadata": {},
   "source": [
    "# Helper methods for InputVar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090219e9-ed92-4c1e-8059-9da9992dd743",
   "metadata": {},
   "source": [
    "This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module [unsequa](climada_engine_unsequa.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1a119e-8506-47dd-8b2c-7476b3f1170a",
   "metadata": {},
   "source": [
    "The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e446c327-d6cf-457f-b181-fef150bdbe81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:17:40.632751Z",
     "start_time": "2022-07-07T13:17:40.629637Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # Ignore warnings for making the tutorial's pdf."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef9636c-c8a0-434a-b136-af54085141d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:22:14.943204Z",
     "start_time": "2021-08-18T12:22:14.938441Z"
    }
   },
   "source": [
    "\n",
    "## Exposures "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c92d6f3-8326-458c-9931-bce0ccb94d0b",
   "metadata": {},
   "source": [
    "The following types of uncertainties can be added:\n",
    "\n",
    "- ET: scale the total value (homogeneously)\n",
    "> The value at each exposure point is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_totvalue\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60601195-ef75-492f-b7f2-371928575249",
   "metadata": {},
   "source": [
    "\n",
    "### Example: single exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fd2b3ec-c0e8-4ae6-8439-39b4501e6196",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.066932Z",
     "start_time": "2022-07-07T13:13:28.979968Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:32,000 - climada.entity.exposures.base - INFO - Reading /Users/ckropf/climada/demo/data/exp_demo_today.h5\n"
     ]
    }
   ],
   "source": [
    "# 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": 3,
   "id": "b062b610-787c-41ab-a355-bbb6291361bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.552786Z",
     "start_time": "2022-07-07T13:13:32.068811Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "bounds_totval = [0.9, 1.1]  # +- 10% noise on the total exposures value\n",
    "bounds_noise = [0.9, 1.2]  # -10% - +20% noise each exposures point\n",
    "exp_iv = InputVar.exp([exp_base], bounds_totval, bounds_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87cc5928-3a53-4cef-acc9-097e85da398a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.566201Z",
     "start_time": "2022-07-07T13:13:32.554801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03700231587024304"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The difference in total value between the base exposure and the average input uncertainty exposure\n",
    "# due to the random noise on each exposures point (the average change in the total value is 1.0).\n",
    "avg_exp = exp_iv.evaluate()\n",
    "(sum(avg_exp.gdf[\"value\"]) - sum(exp_base.gdf[\"value\"])) / sum(exp_base.gdf[\"value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53e56665-c74f-49f3-a355-135fd0e5ed40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.776385Z",
     "start_time": "2022-07-07T13:13:32.568822Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The values for EN are seeds for the random number generator for the noise sampling and\n",
    "# thus are uniformly sampled numbers between (0, 2**32-1)\n",
    "exp_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c5bd7e-09d5-4c94-a72a-8691641bf837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:22:14.943204Z",
     "start_time": "2021-08-18T12:22:14.938441Z"
    }
   },
   "source": [
    "\n",
    "### Example: list of litpop exposures with different exponents "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "802ac379-39a0-476d-b068-36520d03a459",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.783469Z",
     "start_time": "2022-07-07T13:13:32.778810Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "\n",
    "\n",
    "def generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    "):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print(\"\\n Computing litpop for m=%d, n=%d \\n\" % (m, n))\n",
    "        litpop_kwargs[\"exponents\"] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.data[\"impf_\" + haz.haz_type] = impf_id\n",
    "        exp.data.drop(\"impf_\", axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac28063d-83aa-43f8-a75d-f91fe20e44e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:32.811623Z",
     "start_time": "2022-07-07T13:13:32.785563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:32,787 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "# Define the parameters of the LitPop instances\n",
    "tot_pop = 11.317e6\n",
    "impf_id = 1\n",
    "value_unit = \"people\"\n",
    "litpop_kwargs = {\n",
    "    \"countries\": [\"CUB\"],\n",
    "    \"res_arcsec\": 150,\n",
    "    \"reference_year\": 2020,\n",
    "    \"fin_mode\": \"norm\",\n",
    "    \"total_values\": [tot_pop],\n",
    "}\n",
    "assign_centr_kwargs = {}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0370352-db8d-4507-90e2-931108fd0854",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:39.652566Z",
     "start_time": "2022-07-07T13:13:32.813253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=0, n=0 \n",
      "\n",
      "2022-07-07 15:13:33,055 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:13:34,051 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,082 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,109 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,135 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,163 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,188 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,223 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,289 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,316 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,325 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:34,326 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,355 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,385 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,410 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,435 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,459 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,487 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,508 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,519 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:34,520 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,543 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,570 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,597 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,621 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,645 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,685 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,710 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,768 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,791 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,830 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,862 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,899 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,933 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,955 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:34,981 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,019 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,043 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,068 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,090 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,119 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,142 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:35,173 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:13:35,173 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:13:35,174 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:13:35,174 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:13:35,175 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:13:35,179 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
      "2022-07-07 15:13:35,181 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:13:35,189 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-07-07 15:13:35,416 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:13:36,256 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,284 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,309 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,335 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,367 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,392 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,424 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,494 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,519 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,529 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:36,530 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,556 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,586 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,610 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,637 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,666 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,691 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,716 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,725 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:36,726 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,754 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,786 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,818 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,843 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,872 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,909 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,936 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:36,965 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:36,989 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,013 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,052 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,087 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,123 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,156 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,177 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,201 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,241 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,263 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,288 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,311 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,343 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,367 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:37,400 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:13:37,400 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:13:37,401 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:13:37,401 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:13:37,402 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:13:37,406 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
      "2022-07-07 15:13:37,407 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:13:37,415 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=2 \n",
      "\n",
      "2022-07-07 15:13:37,637 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:13:38,561 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,589 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,615 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,638 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,665 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,689 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,720 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,784 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,808 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,820 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:38,820 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,844 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,873 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,897 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,920 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,944 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,990 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:38,999 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:13:39,000 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,022 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,047 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,074 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,097 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,123 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,161 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,187 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,217 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,242 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,265 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,304 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,334 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,370 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,402 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,423 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,448 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,487 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,510 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,532 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,554 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,580 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,603 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:13:39,633 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:13:39,634 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:13:39,634 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:13:39,635 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:13:39,635 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:13:39,639 - climada.entity.exposures.base - INFO - Matching 5524 exposures with 2500 centroids.\n",
      "2022-07-07 15:13:39,641 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:13:39,649 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 332 coordinates.\n"
     ]
    }
   ],
   "source": [
    "# Generate the LitPop list\n",
    "\n",
    "choice_mn = [[0, 0.5], [0, 1], [0, 2]]  # Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfa54889-cb6c-4b4d-afc7-81a20dd77eb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:39.658265Z",
     "start_time": "2022-07-07T13:13:39.654050Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "bounds_totval = [0.9, 1.1]  # +- 10% noise on the total exposures value\n",
    "litpop_iv = InputVar.exp(exp_list=litpop_list, bounds_totval=bounds_totval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee5cfd3d-9f71-4ccc-95e1-dc8e6ea9d8b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:39.666418Z",
     "start_time": "2022-07-07T13:13:39.660259Z"
    }
   },
   "outputs": [],
   "source": [
    "# To choose n=0.5, we have to set EL=1 (the index of 0.5 in choice_n = [0, 0.5, 1, 2])\n",
    "pop_half = litpop_iv.evaluate(ET=1, EL=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15c78c6a-d1ee-4eb0-982a-2920eda04f25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:39.681827Z",
     "start_time": "2022-07-07T13:13:39.670420Z"
    }
   },
   "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>value</th>\n",
       "      <th>geometry</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>region_id</th>\n",
       "      <th>impf_TC</th>\n",
       "      <th>centr_TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5519</th>\n",
       "      <td>92.974926</td>\n",
       "      <td>POINT (-80.52083 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.520833</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5520</th>\n",
       "      <td>131.480741</td>\n",
       "      <td>POINT (-80.47917 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.479167</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>77.695093</td>\n",
       "      <td>POINT (-80.68750 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.687500</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>43.122163</td>\n",
       "      <td>POINT (-80.89583 23.14583)</td>\n",
       "      <td>23.145833</td>\n",
       "      <td>-80.895833</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5523</th>\n",
       "      <td>106.033524</td>\n",
       "      <td>POINT (-80.85417 23.14583)</td>\n",
       "      <td>23.145833</td>\n",
       "      <td>-80.854167</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value                    geometry   latitude  longitude  region_id  \\\n",
       "5519   92.974926  POINT (-80.52083 23.18750)  23.187500 -80.520833        192   \n",
       "5520  131.480741  POINT (-80.47917 23.18750)  23.187500 -80.479167        192   \n",
       "5521   77.695093  POINT (-80.68750 23.18750)  23.187500 -80.687500        192   \n",
       "5522   43.122163  POINT (-80.89583 23.14583)  23.145833 -80.895833        192   \n",
       "5523  106.033524  POINT (-80.85417 23.14583)  23.145833 -80.854167        192   \n",
       "\n",
       "      impf_TC  centr_TC  \n",
       "5519        1       619  \n",
       "5520        1       619  \n",
       "5521        1       618  \n",
       "5522        1       617  \n",
       "5523        1       617  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_half.gdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b11722a1-0609-4fdc-9f92-e735a14f5c56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:45.556216Z",
     "start_time": "2022-07-07T13:13:39.684061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:39,690 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_half.plot_hexbin();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d6d5ea3-4733-4bff-88c8-e9fc14090497",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:45.565152Z",
     "start_time": "2022-07-07T13:13:45.558121Z"
    }
   },
   "outputs": [],
   "source": [
    "# To choose n=1, we have to set EL=2 (the index of 1 in choice_n = [0, 0.5, 1, 2])\n",
    "pop_one = litpop_iv.evaluate(ET=1, EL=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ced52885-d233-4c55-b84b-b2821847f281",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:45.578591Z",
     "start_time": "2022-07-07T13:13:45.567310Z"
    }
   },
   "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>value</th>\n",
       "      <th>geometry</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>region_id</th>\n",
       "      <th>impf_TC</th>\n",
       "      <th>centr_TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5519</th>\n",
       "      <td>0.567593</td>\n",
       "      <td>POINT (-80.52083 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.520833</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5520</th>\n",
       "      <td>1.135089</td>\n",
       "      <td>POINT (-80.47917 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.479167</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>0.396363</td>\n",
       "      <td>POINT (-80.68750 23.18750)</td>\n",
       "      <td>23.187500</td>\n",
       "      <td>-80.687500</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>0.122097</td>\n",
       "      <td>POINT (-80.89583 23.14583)</td>\n",
       "      <td>23.145833</td>\n",
       "      <td>-80.895833</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5523</th>\n",
       "      <td>0.738231</td>\n",
       "      <td>POINT (-80.85417 23.14583)</td>\n",
       "      <td>23.145833</td>\n",
       "      <td>-80.854167</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         value                    geometry   latitude  longitude  region_id  \\\n",
       "5519  0.567593  POINT (-80.52083 23.18750)  23.187500 -80.520833        192   \n",
       "5520  1.135089  POINT (-80.47917 23.18750)  23.187500 -80.479167        192   \n",
       "5521  0.396363  POINT (-80.68750 23.18750)  23.187500 -80.687500        192   \n",
       "5522  0.122097  POINT (-80.89583 23.14583)  23.145833 -80.895833        192   \n",
       "5523  0.738231  POINT (-80.85417 23.14583)  23.145833 -80.854167        192   \n",
       "\n",
       "      impf_TC  centr_TC  \n",
       "5519        1       619  \n",
       "5520        1       619  \n",
       "5521        1       618  \n",
       "5522        1       617  \n",
       "5523        1       617  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_one.gdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33d4b6b5-643e-47e3-bf62-a28a104c6bc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:50.931372Z",
     "start_time": "2022-07-07T13:13:45.580862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:45,584 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_one.plot_hexbin();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2294a2ae-b312-483e-bab7-f8c30469d6be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.141418Z",
     "start_time": "2022-07-07T13:13:50.933853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The values for EN are seeds for the random number generator for the noise sampling and\n",
    "# thus are uniformly sampled numbers between (0, 2**32-1)\n",
    "litpop_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f78643e-7343-4d1d-843b-969884aefae5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:31:26.382961Z",
     "start_time": "2021-08-18T12:31:26.379295Z"
    }
   },
   "source": [
    "## Hazard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ebdffd-6a08-4278-a83b-24a3c488b706",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-01T14:00:53.406863Z",
     "start_time": "2021-11-01T14:00:53.401328Z"
    }
   },
   "source": [
    "The following types of uncertainties can be added:\n",
    "\n",
    "- HE: sub-sampling events from the total event set\n",
    "> For each sub-sample, n_ev events are sampled with replacement. HE is the value of the seed for  the uniform random number generator.\n",
    "- HI: scale the intensity of all events (homogeneously)\n",
    "> The instensity of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_int\n",
    "- HA: scale the fraction of all events (homogeneously)\n",
    "> The fraction of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_frac\n",
    "- HF: scale the frequency of all events (homogeneously)\n",
    "> The frequency of all events is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_freq\n",
    "- HL: sample uniformly from hazard list\n",
    "> Uniformly sample one element from the provided list of hazards. For example, Hazards outputs from dynamical models for different input factors.\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21876972-5120-44ce-81f3-6bb52a32cc97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.170646Z",
     "start_time": "2022-07-07T13:13:51.143728Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:13:51,145 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "# Define the base exposure\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "haz_base = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0adedfaf-bd2b-4ec4-9d28-28c0c69d56c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.177209Z",
     "start_time": "2022-07-07T13:13:51.173049Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "bounds_freq = [0.9, 1.1]  # +- 10% noise on the frequency of all events\n",
    "bounds_int = None  # No uncertainty on the intensity\n",
    "n_ev = None\n",
    "haz_iv = InputVar.haz(\n",
    "    [haz_base], n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "187b9c74-3ab3-403d-95ec-20ab148f0b4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.188466Z",
     "start_time": "2022-07-07T13:13:51.180776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10000000000000736"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The difference in frequency for HF=1.1 is indeed 10%.\n",
    "haz_high_freq = haz_iv.evaluate(HE=n_ev, HI=None, HF=1.1)\n",
    "(sum(haz_high_freq.frequency) - sum(haz_base.frequency)) / sum(haz_base.frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "040bcec7-406e-4007-94a1-0650c3686920",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.196611Z",
     "start_time": "2022-07-07T13:13:51.190590Z"
    }
   },
   "outputs": [],
   "source": [
    "bounds_freq = [0.9, 1.1]  # +- 10% noise on the frequency of all events\n",
    "bounds_int = None  # No uncertainty on the intensity\n",
    "bounds_frac = [0.7, 1.1]  # noise on the fraction of all events\n",
    "n_ev = round(\n",
    "    0.8 * haz_base.size\n",
    ")  # sub-sample with re-draw events to obtain hazards with n=0.8*tot_number_events\n",
    "haz_iv = InputVar.haz(\n",
    "    [haz_base],\n",
    "    n_ev=n_ev,\n",
    "    bounds_freq=bounds_freq,\n",
    "    bounds_int=bounds_int,\n",
    "    bounds_frac=bounds_frac,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffbde49-fc7c-49bf-9e41-831fb5b18afc",
   "metadata": {},
   "source": [
    "Note that the HE is not a univariate distribution, but for each sample corresponds to the names of the sub-sampled events. However, to simplify the data stream, the HE is saved as the seed for the random number generator that made the sample. Hence, the value of HE is a label for the given sample. If really needed, the exact chosen events can be obtained as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cdd89a1-831d-49a6-bcdd-6d650150ecb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.204357Z",
     "start_time": "2022-07-07T13:13:51.199302Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "HE = 2618981871  # The random seed (number between 0 and 2**32)\n",
    "rng = np.random.RandomState(int(HE))  # Initialize a random state with the seed\n",
    "chosen_ev = list(\n",
    "    rng.choice(haz_base.event_name, int(n_ev))\n",
    ")  # Obtain the corresponding events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c21c99a6-0e13-4e56-bab3-36fd5af1f387",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.210906Z",
     "start_time": "2022-07-07T13:13:51.206889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1998209N11335'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first event is\n",
    "chosen_ev[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afc1d32a-63cb-4a8f-911c-a9e539b21a61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.503718Z",
     "start_time": "2022-07-07T13:13:51.214496Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The values for HE are seeds for the random number generator for the noise sampling and\n",
    "# thus are uniformly sampled numbers between (0, 2**32-1)\n",
    "haz_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3102f7ea-8a23-4c2d-9c27-e6bd8f3c63f0",
   "metadata": {},
   "source": [
    "The number of events per sub-sample is equal to n_ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f7252dd-b1ba-43c4-ba56-160faf16de43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:13:51.517317Z",
     "start_time": "2022-07-07T13:13:51.505856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of events per sample is equal to n_ev\n",
    "haz_sub = haz_iv.evaluate(HE=928165924, HI=None, HF=1.1, HA=None)\n",
    "# The number for HE is irrelevant, as all samples have the same n_Ev\n",
    "haz_sub.size - n_ev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca01a5d-bf43-4ca5-b69c-9a8a9f3b683d",
   "metadata": {},
   "source": [
    "## ImpactFuncSet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76d5c86-2f98-4f56-a1eb-90b8a5b3aa8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-01T14:01:03.864028Z",
     "start_time": "2021-11-01T14:01:03.858880Z"
    }
   },
   "source": [
    "The following types of uncertainties can be added:\n",
    "- MDD: scale the Mean Damage Degree MDD (homogeneously)\n",
    "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n",
    "- PAA: scale the Percentage of Affected Assets PAA (homogeneously)\n",
    "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
    "- IFi: shift the intensity (homogeneously)\n",
    "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n",
    "- IL: sample uniformly from impact function set list\n",
    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b44d6e2-1905-4191-a91d-f4dd80c3d4ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:17:56.457258Z",
     "start_time": "2022-07-07T13:17:53.260369Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n",
    "\n",
    "impf = ImpfTropCyclone.from_emanuel_usa()\n",
    "impf_set_base = ImpactFuncSet([impf])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26d664f-82eb-478d-9f4d-644ef45ac91d",
   "metadata": {},
   "source": [
    "It is necessary to specify the hazard type and the impact function id. For simplicity, the default uncertainty input variable only looks at the uncertainty on one single impact function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9d1c458-6af7-4103-8b8c-99edd9306e89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:17:56.937145Z",
     "start_time": "2022-07-07T13:17:56.459327Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "bounds_impfi = [-10, 10]  # -10 m/s ; +10m/s uncertainty on the intensity\n",
    "bounds_mdd = [0.7, 1.1]  # -30% - +10% uncertainty on the mdd\n",
    "bounds_paa = None  # No uncertainty in the paa\n",
    "impf_iv = InputVar.impfset(\n",
    "    impf_set_list=[impf_set_base],\n",
    "    bounds_impfi=bounds_impfi,\n",
    "    bounds_mdd=bounds_mdd,\n",
    "    bounds_paa=bounds_paa,\n",
    "    haz_id_dict={\"TC\": [1]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "376e5b42-5a5f-4a3b-8316-90bd1ac638e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:17:59.935968Z",
     "start_time": "2022-07-07T13:17:56.940333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the impact function for 50 random samples (note for the expert, these are not global)\n",
    "n = 50\n",
    "ax = impf_iv.evaluate().plot()\n",
    "inten = impf_iv.distr_dict[\"IFi\"].rvs(size=n)\n",
    "mdd = impf_iv.distr_dict[\"MDD\"].rvs(size=n)\n",
    "for i, m in zip(inten, mdd):\n",
    "    impf_iv.evaluate(IFi=i, MDD=m).plot(axis=ax)\n",
    "ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f00c4e-e2fc-4565-9ca0-ff7136326508",
   "metadata": {},
   "source": [
    "## Entity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8cb3e2-2a85-4ee4-a565-f9351768997a",
   "metadata": {},
   "source": [
    "\n",
    "The following types of uncertainties can be added:\n",
    "- DR: value of constant discount rate (homogeneously)\n",
    "> The value of the discounts in each year is sampled uniformly from a distribution with (min, max) = bounds_disc\n",
    "- CO: scale the cost (homogeneously)\n",
    "> The cost of all measures is multiplied by the same number sampled uniformly from a distribution with (min, max) = bounds_cost\n",
    "- ET: scale the total value (homogeneously)\n",
    "> The value at each exposure point is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_totval\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
    "- MDD: scale the mdd (homogeneously)\n",
    "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n",
    "- PAA: scale the paa (homogeneously)\n",
    "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
    "- IFi: shift the intensity (homogeneously)\n",
    "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n",
    "- IL: sample uniformly from impact function set list\n",
    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
    "\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d23eda3-bda4-402f-9f07-3b8646794704",
   "metadata": {},
   "source": [
    "### Example: single exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b83130a-4f80-445b-9be8-b7851560cc03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:00.296882Z",
     "start_time": "2022-07-07T13:17:59.939044Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:00,292 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:00,293 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-07-07 15:18:00,294 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-07-07 15:18:00,294 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_TODAY\n",
    "\n",
    "ent = Entity.from_excel(ENT_DEMO_TODAY)\n",
    "ent.exposures.ref_year = 2018\n",
    "ent.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "402cf2f7-8b50-450f-a878-321272b3fd64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:00.307821Z",
     "start_time": "2022-07-07T13:18:00.298504Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "ent_iv = InputVar.ent(\n",
    "    impf_set_list=[ent.impact_funcs],\n",
    "    disc_rate=ent.disc_rates,\n",
    "    exp_list=[ent.exposures],\n",
    "    meas_set=ent.measures,\n",
    "    bounds_disc=[0, 0.08],\n",
    "    bounds_cost=[0.5, 1.5],\n",
    "    bounds_totval=[0.9, 1.1],\n",
    "    bounds_noise=[0.3, 1.9],\n",
    "    bounds_mdd=[0.9, 1.05],\n",
    "    bounds_paa=None,\n",
    "    bounds_impfi=[-2, 5],\n",
    "    haz_id_dict={\"TC\": [1]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50b9cd64-1c42-42ef-9955-762615fefb02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:00.947057Z",
     "start_time": "2022-07-07T13:18:00.309713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ent_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498373e1-cc10-4b14-b938-22c9a0b11832",
   "metadata": {},
   "source": [
    "\n",
    "### Example: list of Litpop exposures with different exponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9bcf449-5d5d-4fac-a10a-e28cf1581773",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:00.952942Z",
     "start_time": "2022-07-07T13:18:00.948371Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "\n",
    "\n",
    "def generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    "):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print(\"\\n Computing litpop for m=%d, n=%d \\n\" % (m, n))\n",
    "        litpop_kwargs[\"exponents\"] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.gdf[\"impf_\" + haz.haz_type] = impf_id\n",
    "        exp.gdf.drop(\"impf_\", axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9adcdda7-5488-42cc-abfd-d098e359d0f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:00.986469Z",
     "start_time": "2022-07-07T13:18:00.954689Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:00,956 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "# Define the parameters of the LitPop instances\n",
    "impf_id = 1\n",
    "value_unit = None\n",
    "litpop_kwargs = {\n",
    "    \"countries\": [\"CUB\"],\n",
    "    \"res_arcsec\": 300,\n",
    "    \"reference_year\": 2020,\n",
    "}\n",
    "assign_centr_kwargs = {}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6afb089a-a268-40dd-ae7d-8e12e23ae925",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:09.287329Z",
     "start_time": "2022-07-07T13:18:00.988977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=1, n=0 \n",
      "\n",
      "2022-07-07 15:18:01,386 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:01,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:01,997 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,022 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,044 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,069 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,093 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,125 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,187 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,210 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,220 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,221 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,233 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,234 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,248 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,249 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,264 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,265 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,278 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,279 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,303 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,330 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,341 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,342 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,351 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,351 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,363 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,364 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,391 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,417 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,443 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,466 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,504 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,528 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,559 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,585 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,596 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,597 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,634 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,666 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,700 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,734 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,758 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,768 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,769 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,809 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,821 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,822 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,845 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,869 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,896 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,919 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:02,930 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:02,959 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:04,013 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:04,017 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:04,028 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:04,029 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:04,030 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:04,031 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:04,032 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:04,037 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:04,039 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:04,046 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-07-07 15:18:04,291 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:04,812 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,842 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,869 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,894 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,923 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,953 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:04,987 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,049 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,075 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,086 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,087 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,098 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:05,099 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,114 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,115 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,131 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,132 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,148 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,149 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,174 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,199 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,208 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,209 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,220 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,221 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,233 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,234 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,261 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,287 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,310 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,333 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,368 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,392 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,420 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,444 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,455 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,456 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,492 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,523 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,557 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,588 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,611 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,622 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,623 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,659 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,671 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,671 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,693 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,716 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,764 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:05,775 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:05,801 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:06,617 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:06,621 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:06,630 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:06,631 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:06,631 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:06,632 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:06,632 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:06,636 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:06,637 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:06,643 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
      "\n",
      " Computing litpop for m=1, n=1 \n",
      "\n",
      "2022-07-07 15:18:06,884 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:07,423 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,449 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,473 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,496 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,521 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,544 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,574 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,637 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,660 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,670 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,670 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,682 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,683 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,697 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,698 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,710 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,711 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,723 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,724 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,748 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,773 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,783 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,784 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,793 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,794 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:07,805 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:07,806 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,832 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,859 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,882 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,907 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,943 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,968 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:07,997 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,021 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,032 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:08,032 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,071 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,102 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,136 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,167 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,189 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,200 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:08,201 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,240 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,252 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:08,253 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,276 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,298 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,323 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,345 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:08,357 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:08,383 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:09,253 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:09,257 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:09,267 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:09,268 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:09,268 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:09,269 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:09,270 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:09,275 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:09,277 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:09,283 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n"
     ]
    }
   ],
   "source": [
    "# Generate the LitPop list\n",
    "\n",
    "choice_mn = [[1, 0.5], [0.5, 1], [1, 1]]  # Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3bff058-b814-4059-8893-9de07fb8b360",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:09.405351Z",
     "start_time": "2022-07-07T13:18:09.292279Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:09,400 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:09,401 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-07-07 15:18:09,402 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-07-07 15:18:09,402 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_TODAY\n",
    "\n",
    "ent = Entity.from_excel(ENT_DEMO_TODAY)\n",
    "ent.exposures.ref_year = 2020\n",
    "ent.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99939159-2a7c-4f32-8f78-8fcbe067b1b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:09.424434Z",
     "start_time": "2022-07-07T13:18:09.408541Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "ent_iv = InputVar.ent(\n",
    "    impf_set_list=[ent.impact_funcs],\n",
    "    disc_rate=ent.disc_rates,\n",
    "    exp_list=litpop_list,\n",
    "    meas_set=ent.measures,\n",
    "    bounds_disc=[0, 0.08],\n",
    "    bounds_cost=[0.5, 1.5],\n",
    "    bounds_totval=[0.9, 1.1],\n",
    "    bounds_noise=[0.3, 1.9],\n",
    "    bounds_mdd=[0.9, 1.05],\n",
    "    bounds_paa=None,\n",
    "    bounds_impfi=[-2, 5],\n",
    "    haz_id_dict={\"TC\": [1]},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d786ada6-bb2d-4542-894b-dff7ba12a377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:11.834519Z",
     "start_time": "2022-07-07T13:18:09.427602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:09,448 - climada.util.plot - WARNING - Error parsing coordinate system 'GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'. Using projection PlateCarree in plot.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ent_iv.evaluate().exposures.plot_hexbin();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25dbb8ab-bbf4-4b2e-a8f5-86d8d61bf106",
   "metadata": {},
   "source": [
    "## Entity Future"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61212965-7e5e-4df8-b0ba-4b39b14524f9",
   "metadata": {},
   "source": [
    "The following types of uncertainties can be added:\n",
    "- CO: scale the cost (homogeneously)\n",
    "> The cost of all measures is multiplied by the same number sampled uniformly from a distribution with (min, max) = bounds_cost\n",
    "- EG: scale the exposures growth (homogeneously)\n",
    "> The value at each exposure point is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_eg\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
    "- MDD: scale the mdd (homogeneously)\n",
    "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n",
    "- PAA: scale the paa (homogeneously)\n",
    "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
    "- IFi: shift the impact function intensity (homogeneously)\n",
    "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_impfi\n",
    "- IL: sample uniformly from impact function set list\n",
    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90593a9-efe5-471f-b894-6a90db970bd2",
   "metadata": {},
   "source": [
    "### Example: single exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36d59a4c-eb0a-4b11-a19b-89b968240c29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:11.940658Z",
     "start_time": "2022-07-07T13:18:11.836643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:11,936 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:11,937 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-07-07 15:18:11,938 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-07-07 15:18:11,938 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_FUTURE\n",
    "\n",
    "ent_fut = Entity.from_excel(ENT_DEMO_FUTURE)\n",
    "ent_fut.exposures.ref_year = 2040\n",
    "ent_fut.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46115ff5-ddea-4ceb-9653-578b74c07297",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:11.948329Z",
     "start_time": "2022-07-07T13:18:11.942392Z"
    }
   },
   "outputs": [],
   "source": [
    "entfut_iv = InputVar.entfut(\n",
    "    impf_set_list=[ent_fut.impact_funcs],\n",
    "    exp_list=[ent_fut.exposures],\n",
    "    meas_set=ent_fut.measures,\n",
    "    bounds_cost=[0.6, 1.2],\n",
    "    bounds_eg=[0.8, 1.5],\n",
    "    bounds_noise=None,\n",
    "    bounds_mdd=[0.7, 0.9],\n",
    "    bounds_paa=[1.3, 2],\n",
    "    haz_id_dict={\"TC\": [1]},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb14ecfc-cc9b-4dea-9f67-bc52a8a79a95",
   "metadata": {},
   "source": [
    "### Example: list of exposures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5766a082-03dc-4e8c-896a-88e6411774a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:11.954599Z",
     "start_time": "2022-07-07T13:18:11.950148Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "\n",
    "\n",
    "def generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    "):\n",
    "    # In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print(\"\\n Computing litpop for m=%d, n=%d \\n\" % (m, n))\n",
    "        litpop_kwargs[\"exponents\"] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.gdf[\"impf_\" + haz.haz_type] = impf_id\n",
    "        exp.gdf.drop(\"impf_\", axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f505ca7-1133-450b-96a0-e8133ba285ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:11.985438Z",
     "start_time": "2022-07-07T13:18:11.957060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:11,958 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "# Define the parameters of the LitPop instances\n",
    "impf_id = 1\n",
    "value_unit = None\n",
    "litpop_kwargs = {\n",
    "    \"countries\": [\"CUB\"],\n",
    "    \"res_arcsec\": 300,\n",
    "    \"reference_year\": 2040,\n",
    "}\n",
    "assign_centr_kwargs = {}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f6bc7a9-3779-4ae7-94e2-91808cad51d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:19.888504Z",
     "start_time": "2022-07-07T13:18:11.987390Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=1, n=0 \n",
      "\n",
      "2022-07-07 15:18:12,244 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:12,773 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,773 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,803 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,804 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,830 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,831 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,854 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,854 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,881 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,881 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,906 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,907 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:12,937 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:12,938 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,001 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,002 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,025 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,025 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,034 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,035 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,035 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,047 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,048 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,048 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,062 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,063 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,064 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,077 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,078 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,078 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,090 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,090 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,091 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,117 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,118 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,144 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,145 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,155 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,156 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,157 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,165 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,166 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,166 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,178 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,178 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,179 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,202 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,203 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,231 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,232 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,256 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,257 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,281 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,281 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,316 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,316 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,344 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,344 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,374 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,375 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,399 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,400 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,412 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,412 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:13,413 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,451 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,452 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,483 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,483 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,521 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,522 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,554 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,555 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,579 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,579 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,590 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,591 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,592 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,630 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,630 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,643 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,644 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,644 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,666 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,667 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,690 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,691 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,718 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,718 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,742 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:13,742 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:13,754 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:13,780 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:14,384 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:14,388 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:14,396 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:14,397 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:14,397 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:14,398 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:14,398 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:14,401 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:14,403 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:14,409 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-07-07 15:18:14,640 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:15,172 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,173 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,199 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,200 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,223 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,224 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,249 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,250 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,279 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,279 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,301 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,302 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,330 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,331 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,390 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,391 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,417 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,418 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,428 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,429 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,429 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,441 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,441 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,442 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,457 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,457 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,458 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:15,471 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,472 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,472 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,483 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,484 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,485 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,512 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,513 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,539 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,540 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,550 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,551 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,552 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,562 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,562 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,563 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,575 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,575 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,576 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,601 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,602 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,633 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,634 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,661 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,662 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,688 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,689 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,729 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,730 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,757 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,758 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,787 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,787 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,812 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,813 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,824 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:15,824 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,825 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,862 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,862 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,893 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,894 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,928 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,929 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,961 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,962 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,984 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:15,985 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:15,999 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:16,000 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,000 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,040 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,040 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,051 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:16,052 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,052 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,076 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,077 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,103 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,103 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,131 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,132 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,156 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:16,156 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:16,167 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:16,195 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:17,186 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:17,190 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:17,200 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:17,201 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:17,201 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:17,202 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:17,203 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:17,208 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:17,210 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:17,216 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n",
      "\n",
      " Computing litpop for m=1, n=1 \n",
      "\n",
      "2022-07-07 15:18:17,454 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-07-07 15:18:17,967 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:17,967 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:17,997 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:17,998 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,024 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,024 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,050 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,050 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,079 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,080 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,111 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,111 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,155 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,155 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,223 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,224 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,250 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,251 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,261 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,262 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,263 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,275 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,275 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,276 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,291 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,291 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,292 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,304 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,305 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,305 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,316 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,317 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,317 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,344 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,345 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,371 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,372 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,385 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,386 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,387 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,399 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,399 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,400 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,413 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,414 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,414 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,440 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,441 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,468 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,469 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,492 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,493 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,516 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,516 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,553 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:18,553 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,577 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,578 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,607 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,607 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,631 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,632 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,644 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,644 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,645 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,681 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,682 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,714 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,714 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,748 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,748 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,782 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,782 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,804 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,804 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,816 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,817 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,818 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,855 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,856 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,866 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:18,867 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,867 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,891 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,891 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,914 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,914 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,942 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,943 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,965 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-07-07 15:18:18,966 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-07-07 15:18:18,978 - climada.entity.exposures.litpop.litpop - INFO - No data point on destination grid within polygon.\n",
      "2022-07-07 15:18:19,006 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-07-07 15:18:19,855 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-07-07 15:18:19,859 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-07-07 15:18:19,869 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-07-07 15:18:19,870 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:19,870 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-07-07 15:18:19,871 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-07-07 15:18:19,872 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-07-07 15:18:19,875 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-07-07 15:18:19,878 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-07-07 15:18:19,884 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 78 coordinates.\n"
     ]
    }
   ],
   "source": [
    "# Generate the LitPop list\n",
    "\n",
    "choice_mn = [[1, 0.5], [0.5, 1], [1, 1]]  # Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(\n",
    "    impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7d61fe7-7496-4e89-9393-6e8ca5e9f080",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:19.993383Z",
     "start_time": "2022-07-07T13:18:19.890454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-07-07 15:18:19,989 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-07-07 15:18:19,989 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-07-07 15:18:19,990 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-07-07 15:18:19,990 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_FUTURE\n",
    "\n",
    "ent_fut = Entity.from_excel(ENT_DEMO_FUTURE)\n",
    "ent_fut.exposures.ref_year = 2040\n",
    "ent_fut.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b63f8e63-052d-4ac7-9d9b-56e070e54990",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-07T13:18:20.002393Z",
     "start_time": "2022-07-07T13:18:19.995393Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "\n",
    "entfut_iv = InputVar.entfut(\n",
    "    impf_set_list=[ent_fut.impact_funcs],\n",
    "    exp_list=litpop_list,\n",
    "    meas_set=ent_fut.measures,\n",
    "    bounds_cost=[0.6, 1.2],\n",
    "    bounds_eg=[0.8, 1.5],\n",
    "    bounds_noise=None,\n",
    "    bounds_mdd=[0.7, 0.9],\n",
    "    bounds_paa=[1.3, 2],\n",
    "    haz_id_dict={\"TC\": [1]},\n",
    ")"
   ]
  }
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