{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SupplyChain class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pandas_datareader\\compat\\__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from climada.hazard import TCTracks, TropCyclone, Centroids\n",
    "from climada.entity import LitPop\n",
    "from climada.entity import ImpactFuncSet, IFTropCyclone\n",
    "from climada.engine import SupplyChain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to use the `SupplyChain` class of CLIMADA. This class allows assessing indirect impacts via Input-Ouput modeling. Before diving into this class, it is highly recommended the user first familiarizes herself with the `Exposures`, `Hazard` and `Impact` classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load Multi-Regional Input-Output Tables (MRIOT) data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, one needs to load Input Output data. `SupplyChain` has a function to download and read multi-regional input-output tables (MRIOT) from the 2016 release of WIOD project (www.wiod.org). Yearly WIOT tables are available for the period 2000-2014. A table is automatically downloaded the first time it is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:07:55,814 - climada.util.files_handler - INFO - Downloading http://www.wiod.org/protected3/data16/wiot_ROW/WIOT2012_Nov16_ROW.xlsb to file C:\\Users\\aleciu\\climada\\data\\WIOD\\WIOT2012_Nov16_ROW.xlsb\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "60.6kKB [00:10, 5.78kKB/s]                                                                                             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:08:06,307 - climada.engine.supplychain - INFO - Downloading WIOD table for year 2012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "supplychain = SupplyChain()\n",
    "supplychain.read_wiod16(year=2012)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now look at what data are now loaded into `SupplyChain`, i.e. modelled countries, sectors and IO data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AUS', 'AUT', 'BEL', 'BGR', 'BRA', 'CAN', 'CHE', 'CHN', 'CYP',\n",
       "       'CZE', 'DEU', 'DNK', 'ESP', 'EST', 'FIN', 'FRA', 'GBR', 'GRC',\n",
       "       'HRV', 'HUN', 'IDN', 'IND', 'IRL', 'ITA', 'JPN', 'KOR', 'LTU',\n",
       "       'LUX', 'LVA', 'MEX', 'MLT', 'NLD', 'NOR', 'POL', 'PRT', 'ROU',\n",
       "       'RUS', 'SVK', 'SVN', 'SWE', 'TUR', 'TWN', 'USA', 'ROW'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.mriot_reg_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 43 countries plus 1. The additional \"country\" refers to all countries not explicitly modeled which are aggregated into a Rest of World (ROW) \"country\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Crop and animal production, hunting and related service activities',\n",
       "       'Forestry and logging', 'Fishing and aquaculture',\n",
       "       'Mining and quarrying',\n",
       "       'Manufacture of food products, beverages and tobacco products',\n",
       "       'Manufacture of textiles, wearing apparel and leather products',\n",
       "       'Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials',\n",
       "       'Manufacture of paper and paper products',\n",
       "       'Printing and reproduction of recorded media',\n",
       "       'Manufacture of coke and refined petroleum products ',\n",
       "       'Manufacture of chemicals and chemical products ',\n",
       "       'Manufacture of basic pharmaceutical products and pharmaceutical preparations',\n",
       "       'Manufacture of rubber and plastic products',\n",
       "       'Manufacture of other non-metallic mineral products',\n",
       "       'Manufacture of basic metals',\n",
       "       'Manufacture of fabricated metal products, except machinery and equipment',\n",
       "       'Manufacture of computer, electronic and optical products',\n",
       "       'Manufacture of electrical equipment',\n",
       "       'Manufacture of machinery and equipment n.e.c.',\n",
       "       'Manufacture of motor vehicles, trailers and semi-trailers',\n",
       "       'Manufacture of other transport equipment',\n",
       "       'Manufacture of furniture; other manufacturing',\n",
       "       'Repair and installation of machinery and equipment',\n",
       "       'Electricity, gas, steam and air conditioning supply',\n",
       "       'Water collection, treatment and supply',\n",
       "       'Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services ',\n",
       "       'Construction',\n",
       "       'Wholesale and retail trade and repair of motor vehicles and motorcycles',\n",
       "       'Wholesale trade, except of motor vehicles and motorcycles',\n",
       "       'Retail trade, except of motor vehicles and motorcycles',\n",
       "       'Land transport and transport via pipelines', 'Water transport',\n",
       "       'Air transport',\n",
       "       'Warehousing and support activities for transportation',\n",
       "       'Postal and courier activities',\n",
       "       'Accommodation and food service activities',\n",
       "       'Publishing activities',\n",
       "       'Motion picture, video and television programme production, sound recording and music publishing activities; programming and broadcasting activities',\n",
       "       'Telecommunications',\n",
       "       'Computer programming, consultancy and related activities; information service activities',\n",
       "       'Financial service activities, except insurance and pension funding',\n",
       "       'Insurance, reinsurance and pension funding, except compulsory social security',\n",
       "       'Activities auxiliary to financial services and insurance activities',\n",
       "       'Real estate activities',\n",
       "       'Legal and accounting activities; activities of head offices; management consultancy activities',\n",
       "       'Architectural and engineering activities; technical testing and analysis',\n",
       "       'Scientific research and development',\n",
       "       'Advertising and market research',\n",
       "       'Other professional, scientific and technical activities; veterinary activities',\n",
       "       'Administrative and support service activities',\n",
       "       'Public administration and defence; compulsory social security',\n",
       "       'Education', 'Human health and social work activities',\n",
       "       'Other service activities',\n",
       "       'Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use',\n",
       "       'Activities of extraterritorial organizations and bodies'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(56,)\n"
     ]
    }
   ],
   "source": [
    "print(supplychain.sectors.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 56 economic sectors. These sectors can also be grouped into higher-level sectors. For instance, in the aftermath we will model the service sector which will include the following sectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Construction',\n",
       "       'Wholesale and retail trade and repair of motor vehicles and motorcycles',\n",
       "       'Wholesale trade, except of motor vehicles and motorcycles',\n",
       "       'Retail trade, except of motor vehicles and motorcycles',\n",
       "       'Land transport and transport via pipelines', 'Water transport',\n",
       "       'Air transport',\n",
       "       'Warehousing and support activities for transportation',\n",
       "       'Postal and courier activities',\n",
       "       'Accommodation and food service activities',\n",
       "       'Publishing activities',\n",
       "       'Motion picture, video and television programme production, sound recording and music publishing activities; programming and broadcasting activities',\n",
       "       'Telecommunications',\n",
       "       'Computer programming, consultancy and related activities; information service activities',\n",
       "       'Financial service activities, except insurance and pension funding',\n",
       "       'Insurance, reinsurance and pension funding, except compulsory social security',\n",
       "       'Activities auxiliary to financial services and insurance activities',\n",
       "       'Real estate activities',\n",
       "       'Legal and accounting activities; activities of head offices; management consultancy activities',\n",
       "       'Architectural and engineering activities; technical testing and analysis',\n",
       "       'Scientific research and development',\n",
       "       'Advertising and market research',\n",
       "       'Other professional, scientific and technical activities; veterinary activities',\n",
       "       'Administrative and support service activities',\n",
       "       'Public administration and defence; compulsory social security',\n",
       "       'Education', 'Human health and social work activities',\n",
       "       'Other service activities',\n",
       "       'Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use',\n",
       "       'Activities of extraterritorial organizations and bodies'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.sectors[range(26,56)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weather to aggregate sectors into main sectors and how to do it is up to the user, according to the application of interest and data availability. Default settings are available in CLIMADA based on the built-in datasets. These will be introduced below when calculating direct damages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11105.733356382272, 315.7113177373571, 179.43254266338693, ...,\n",
       "        9.093853356314124, 0, 1.1873518978687656e-06],\n",
       "       [116.88308162207898, 139.3046230501366, 0.4165797269551787, ...,\n",
       "        0.016109951596559337, 0, 2.9150840971500206e-08],\n",
       "       [22.556627754337466, 0.011392711240065655, 23.191690635397794,\n",
       "        ..., 0.02463511351049634, 0, 2.9671358991110717e-09],\n",
       "       ...,\n",
       "       [2.0888621906340483, 0.06898124909560921, 0.18736619171021462,\n",
       "        ..., 15914.85428702459, 0, 0.7881946937807305],\n",
       "       [0.041425098917944464, 4.0179492086967524e-05,\n",
       "        0.00019545212518185459, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.mriot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2464, 2464)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.mriot_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MRIO table is a squared matrix with columns (and rows) equal to the number of economic sectors times the number of modeled countries, i.e. 56x44 = 2464. Each column (row) reports input (output) data of *all* sectors of *a given* country untill all countries are reported. The total production from all subsectors of all countries is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71514.7394, 2525.2804, 3080.4692, ..., 409216.80039034015,\n",
       "       21108.22611227322, 33.03248952629331], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.total_prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2464,)\n"
     ]
    }
   ],
   "source": [
    "print(supplychain.total_prod.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following dict allows accessing mriot data of single countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AUS': range(0, 56),\n",
       " 'AUT': range(56, 112),\n",
       " 'BEL': range(112, 168),\n",
       " 'BGR': range(168, 224),\n",
       " 'BRA': range(224, 280),\n",
       " 'CAN': range(280, 336),\n",
       " 'CHE': range(336, 392),\n",
       " 'CHN': range(392, 448),\n",
       " 'CYP': range(448, 504),\n",
       " 'CZE': range(504, 560),\n",
       " 'DEU': range(560, 616),\n",
       " 'DNK': range(616, 672),\n",
       " 'ESP': range(672, 728),\n",
       " 'EST': range(728, 784),\n",
       " 'FIN': range(784, 840),\n",
       " 'FRA': range(840, 896),\n",
       " 'GBR': range(896, 952),\n",
       " 'GRC': range(952, 1008),\n",
       " 'HRV': range(1008, 1064),\n",
       " 'HUN': range(1064, 1120),\n",
       " 'IDN': range(1120, 1176),\n",
       " 'IND': range(1176, 1232),\n",
       " 'IRL': range(1232, 1288),\n",
       " 'ITA': range(1288, 1344),\n",
       " 'JPN': range(1344, 1400),\n",
       " 'KOR': range(1400, 1456),\n",
       " 'LTU': range(1456, 1512),\n",
       " 'LUX': range(1512, 1568),\n",
       " 'LVA': range(1568, 1624),\n",
       " 'MEX': range(1624, 1680),\n",
       " 'MLT': range(1680, 1736),\n",
       " 'NLD': range(1736, 1792),\n",
       " 'NOR': range(1792, 1848),\n",
       " 'POL': range(1848, 1904),\n",
       " 'PRT': range(1904, 1960),\n",
       " 'ROU': range(1960, 2016),\n",
       " 'RUS': range(2016, 2072),\n",
       " 'SVK': range(2072, 2128),\n",
       " 'SVN': range(2128, 2184),\n",
       " 'SWE': range(2184, 2240),\n",
       " 'TUR': range(2240, 2296),\n",
       " 'TWN': range(2296, 2352),\n",
       " 'USA': range(2352, 2408),\n",
       " 'ROW': range(2408, 2464)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.reg_pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, focusing on Switzerland, to find output data from all swiss sectors one can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16796253031842162, 0.004777144111849153, 0.002736270353709005,\n",
       "        ..., 0.12834852430129112, 0, 1.6758007628524304e-08],\n",
       "       [8.51026746906875e-05, 7.127402684742127e-05,\n",
       "        1.9350907062673556e-06, ..., 0.0007570977491247835, 0,\n",
       "        1.3699629047508064e-09],\n",
       "       [4.7138344808091295e-05, 9.671160838557614e-09,\n",
       "        6.697562625578982e-05, ..., 0.0009232593169242273, 0,\n",
       "        1.1120045630264473e-10],\n",
       "       ...,\n",
       "       [0.008508978608710327, 3.1972146323171236e-05,\n",
       "        0.00046072292226048554, ..., 0.029862336991154915, 0,\n",
       "        1.4789538839516966e-06],\n",
       "       [9.500104969801383e-07, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.mriot_data[supplychain.reg_pos['CHE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, one can find the total production of all Swiss sectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10884.279940160777, 674.088481002245, 41.224100570050936,\n",
       "       2054.5309263989934, 39841.6239844894, 3648.168376381703,\n",
       "       8480.201098809475, 3688.826352338922, 4255.674165800708,\n",
       "       3501.317455453859, 18363.278595872554, 78379.69147195964,\n",
       "       8150.894879859878, 7447.266269845102, 5718.605069058962,\n",
       "       20135.29803849402, 66467.12373541784, 22646.288496050656,\n",
       "       31162.492702290187, 2178.324505028241, 5869.153355999175,\n",
       "       10800.277292026229, 4680.307868675113, 48856.01258517912,\n",
       "       5780.830493280612, 0, 77613.75291956161, 14161.940349695808,\n",
       "       125376.47352416613, 42389.765628680085, 41523.925997007325,\n",
       "       1039.0976694128697, 13234.059687011602, 17225.389030894996,\n",
       "       8050.766299432082, 24044.706531639047, 10080.003387207098, 0,\n",
       "       17138.91756562977, 27213.104595857796, 63105.9204496511,\n",
       "       45928.766683415735, 0, 65051.40098246406, 60745.78454637557, 0,\n",
       "       19266.754769461568, 0, 9057.661417570102, 31139.843888469703,\n",
       "       63363.23886998102, 41714.31400559072, 69213.80028365219,\n",
       "       24057.595454974882, 2126.7054909496537, 0], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.total_prod[supplychain.reg_pos['CHE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Define Hazard, Exposure and Vulnerability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now define hazard, exposure and vulnerability. This is handled via the related CLIMADA classes. In this tutorial we use `LitPop` for exposure and `TropCyclone` for hazard. We will focus on the impact of tropical cyclones affecting the Philippines, Taiwan, Vietnam and Japan in 2012 and 2013. Japan and Taiwan are modeled explicitely by the MRIO table, while the Philippines and Vietnam are modeled as Rest of World, they are thus aggregated into a single country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:12:50,466 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:12:50,470 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:12:50,472 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:12:50,473 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:12:50,476 - climada.entity.exposures.base - INFO - crs set to default value: EPSG:4326\n",
      "2021-03-30 15:12:50,478 - climada.entity.exposures.litpop - WARNING - Not one of the legacy resoultions selected. Consider                        adjusting it to 120 arc-sec.\n",
      "2021-03-30 15:13:01,499 - climada.entity.exposures.litpop - INFO - Generating LitPop data at a resolution of 150.0 arcsec.\n",
      "2021-03-30 15:13:01,509 - climada.entity.exposures.litpop - WARNING - Not one of the legacy resoultions selected. Consider                        adjusting it to 120 arc-sec.\n",
      "2021-03-30 15:13:10,914 - climada.entity.exposures.gpw_import - INFO - Reference year: 2016. Using nearest available year for GWP population data: 2015\n",
      "2021-03-30 15:13:10,926 - climada.entity.exposures.gpw_import - INFO - GPW Version v4.11\n",
      "2021-03-30 15:15:15,158 - climada.util.finance - INFO - GDP JPN 2014: 4.850e+12.\n",
      "2021-03-30 15:15:16,230 - climada.util.finance - INFO - GDP JPN 2016: 4.923e+12.\n",
      "2021-03-30 15:15:17,557 - climada.util.finance - INFO - GDP VNM 2014: 1.862e+11.\n",
      "2021-03-30 15:15:18,072 - climada.util.finance - INFO - GDP VNM 2016: 2.053e+11.\n",
      "2021-03-30 15:15:18,137 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2021-03-30 15:15:18,137 - climada.util.finance - WARNING - GDP data for TWN is not provided by World Bank.                        Instead, IMF data is returned here.\n",
      "2021-03-30 15:15:19,290 - climada.util.finance - INFO - GDP PHL 2014: 2.975e+11.\n",
      "2021-03-30 15:15:19,816 - climada.util.finance - INFO - GDP PHL 2016: 3.186e+11.\n",
      "2021-03-30 15:15:37,139 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:15:37,140 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:15:37,144 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:15:37,146 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:15:37,158 - climada.entity.exposures.base - INFO - crs set to default value: EPSG:4326\n",
      "2021-03-30 15:15:39,035 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:15:39,038 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:15:39,039 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:15:39,040 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:15:39,043 - climada.entity.exposures.base - INFO - crs set to default value: EPSG:4326\n",
      "2021-03-30 15:15:43,530 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:15:43,530 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:15:43,534 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:15:43,536 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:15:43,549 - climada.entity.exposures.base - INFO - crs set to default value: EPSG:4326\n",
      "2021-03-30 15:15:47,440 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:15:47,441 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:15:47,442 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:15:47,442 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:15:47,458 - climada.entity.exposures.base - INFO - crs set to default value: EPSG:4326\n",
      "2021-03-30 15:15:47,491 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:15:47,524 - climada.entity.exposures.litpop - INFO - Creating the LitPop exposure took 177 s\n",
      "2021-03-30 15:15:47,525 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2021-03-30 15:15:47,525 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:15:47,527 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:15:47,528 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:15:47,529 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2021-03-30 15:15:47,530 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\geopandas\\geodataframe.py:91: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  super(GeoDataFrame, self).__setattr__(attr, val)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:15:48,062 - climada.util.coordinates - INFO - Setting geometry points.\n"
     ]
    }
   ],
   "source": [
    "countries = ['PHL', 'TWN', 'VNM', 'JPN']\n",
    "exp_lp = LitPop()\n",
    "exp_lp.set_country(countries, res_km=5.)\n",
    "exp_lp.set_geometry_points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Documents\\GitHub\\climada_python\\climada\\util\\plot.py:332: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x2793110d248>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_lp.plot_hexbin(pop_name=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:16:36,963 - climada.hazard.tc_tracks - INFO - Progress: 10%\n",
      "2021-03-30 15:16:37,228 - climada.hazard.tc_tracks - INFO - Progress: 21%\n",
      "2021-03-30 15:16:37,487 - climada.hazard.tc_tracks - INFO - Progress: 32%\n",
      "2021-03-30 15:16:37,807 - climada.hazard.tc_tracks - INFO - Progress: 43%\n",
      "2021-03-30 15:16:38,131 - climada.hazard.tc_tracks - INFO - Progress: 53%\n",
      "2021-03-30 15:16:38,400 - climada.hazard.tc_tracks - INFO - Progress: 64%\n",
      "2021-03-30 15:16:38,670 - climada.hazard.tc_tracks - INFO - Progress: 75%\n",
      "2021-03-30 15:16:39,023 - climada.hazard.tc_tracks - INFO - Progress: 86%\n",
      "2021-03-30 15:16:39,286 - climada.hazard.tc_tracks - INFO - Progress: 96%\n",
      "2021-03-30 15:16:39,359 - climada.hazard.tc_tracks - INFO - Progress: 100%\n"
     ]
    }
   ],
   "source": [
    "tc_tracks=TCTracks()\n",
    "tc_tracks.read_ibtracs_netcdf(year_range=(2012,2013), basin='WP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Documents\\GitHub\\climada_python\\climada\\util\\plot.py:332: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x27930df2e48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc_tracks.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "centr=Centroids()\n",
    "centr.set_lat_lon(exp_lp.gdf.latitude.values, exp_lp.gdf.longitude.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:17:10,550 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:17:31,724 - climada.util.coordinates - INFO - dist_to_coast: UTM 32648 (1/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:17:56,428 - climada.util.coordinates - INFO - dist_to_coast: UTM 32649 (2/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:18:03,957 - climada.util.coordinates - INFO - dist_to_coast: UTM 32650 (3/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:18:09,317 - climada.util.coordinates - INFO - dist_to_coast: UTM 32651 (4/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:18:26,605 - climada.util.coordinates - INFO - dist_to_coast: UTM 32652 (5/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:18:37,587 - climada.util.coordinates - INFO - dist_to_coast: UTM 32653 (6/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:18:48,669 - climada.util.coordinates - INFO - dist_to_coast: UTM 32654 (7/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:19:04,284 - climada.util.coordinates - INFO - dist_to_coast: UTM 32655 (8/8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "C:\\Users\\aleciu\\Anaconda3\\envs\\climada_env\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:19:05,660 - climada.hazard.trop_cyclone - INFO - Mapping 65 tracks to 53910 coastal centroids.\n",
      "2021-03-30 15:19:07,515 - climada.hazard.trop_cyclone - INFO - Progress: 10%\n",
      "2021-03-30 15:19:08,911 - climada.hazard.trop_cyclone - INFO - Progress: 21%\n",
      "2021-03-30 15:19:12,086 - climada.hazard.trop_cyclone - INFO - Progress: 32%\n",
      "2021-03-30 15:19:14,754 - climada.hazard.trop_cyclone - INFO - Progress: 43%\n",
      "2021-03-30 15:19:15,861 - climada.hazard.trop_cyclone - INFO - Progress: 53%\n",
      "2021-03-30 15:19:16,999 - climada.hazard.trop_cyclone - INFO - Progress: 64%\n",
      "2021-03-30 15:19:17,981 - climada.hazard.trop_cyclone - INFO - Progress: 75%\n",
      "2021-03-30 15:19:19,975 - climada.hazard.trop_cyclone - INFO - Progress: 86%\n",
      "2021-03-30 15:19:21,797 - climada.hazard.trop_cyclone - INFO - Progress: 96%\n",
      "2021-03-30 15:19:21,894 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n"
     ]
    }
   ],
   "source": [
    "tc_cyclone = TropCyclone()\n",
    "tc_cyclone.set_from_tracks(tracks=tc_tracks, centroids=centr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleciu\\Documents\\GitHub\\climada_python\\climada\\util\\plot.py:332: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x279297a49c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc_cyclone.plot_intensity(event=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:19:34,416 - climada.entity.impact_funcs.base - WARNING - For intensity = 0, mdd != 0 or paa != 0. Consider shifting the origin of the intensity scale. In impact.calc the impact is always null at intensity = 0.\n"
     ]
    }
   ],
   "source": [
    "impf_tc= IFTropCyclone()\n",
    "impf_tc.set_emanuel_usa()\n",
    "\n",
    "# add the impact function to an Impact function set\n",
    "impf_set = ImpactFuncSet()\n",
    "impf_set.append(impf_tc)\n",
    "impf_set.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:19:34,449 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:19:34,451 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:19:34,453 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:19:34,453 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "[haz_type] = impf_set.get_hazard_types()\n",
    "[haz_id] = impf_set.get_ids()[haz_type]\n",
    "\n",
    "# Exposures: rename column and assign id\n",
    "exp_lp.gdf.rename(columns={\"if_\": \"if_\" + haz_type}, inplace=True)\n",
    "exp_lp.gdf['if_' + haz_type] = haz_id\n",
    "exp_lp.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculate direct, indirect and total impact per sector and country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now calculate direct, indirect and total impacts. For the **direct impact**, `SupplyChain` requires as inputs `Hazard`, `Exposures` and `ImpactFuncSet`. In addition, one may want to specify `selected_subsec`, which allows the user to either define her own aggregation of sectors by providing a list with the positions of the sectors to aggregate or to use built-in sectors aggregations passing a string being either `service`, `manufacturing`, `agriculture` or `mining`.\n",
    "\n",
    "For this tutorial, we will model the service sector, as this sector's exposure can reasonably be modelled via nighlights and population data, i.e. via `LitPop`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Direct impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-30 15:19:34,510 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:19:34,510 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:19:34,513 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:19:34,515 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:19:34,517 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:19:34,518 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:19:34,520 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:19:34,523 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2021-03-30 15:19:34,528 - climada.entity.exposures.base - INFO - Matching 13993 exposures with 53910 centroids.\n",
      "2021-03-30 15:19:34,610 - climada.engine.impact - INFO - Calculating damage for 13784 assets (>0) and 65 events.\n",
      "2021-03-30 15:19:37,499 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:19:37,499 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:19:37,503 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:19:37,503 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:19:37,508 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:19:37,510 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:19:37,510 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:19:37,512 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2021-03-30 15:19:37,518 - climada.entity.exposures.base - INFO - Matching 1852 exposures with 53910 centroids.\n",
      "2021-03-30 15:19:37,557 - climada.engine.impact - INFO - Calculating damage for 1849 assets (>0) and 65 events.\n",
      "2021-03-30 15:19:37,948 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:19:37,948 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:19:37,952 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:19:37,952 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:19:37,958 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:19:37,959 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:19:37,959 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:19:37,959 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2021-03-30 15:19:37,968 - climada.entity.exposures.base - INFO - Matching 16090 exposures with 53910 centroids.\n",
      "2021-03-30 15:19:38,093 - climada.engine.impact - INFO - Calculating damage for 16028 assets (>0) and 65 events.\n",
      "2021-03-30 15:19:38,142 - climada.entity.exposures.base - INFO - meta set to default value {}\n",
      "2021-03-30 15:19:38,143 - climada.entity.exposures.base - INFO - tag set to default value  File: \n",
      " Description: \n",
      "2021-03-30 15:19:38,145 - climada.entity.exposures.base - INFO - ref_year set to default value 2018\n",
      "2021-03-30 15:19:38,147 - climada.entity.exposures.base - INFO - value_unit set to default value USD\n",
      "2021-03-30 15:19:38,149 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2021-03-30 15:19:38,151 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2021-03-30 15:19:38,154 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2021-03-30 15:19:38,155 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2021-03-30 15:19:38,162 - climada.entity.exposures.base - INFO - Matching 21975 exposures with 53910 centroids.\n",
      "2021-03-30 15:19:38,256 - climada.engine.impact - INFO - Calculating damage for 21832 assets (>0) and 65 events.\n"
     ]
    }
   ],
   "source": [
    "supplychain.calc_sector_direct_impact(tc_cyclone, exp_lp, impf_set, selected_subsec='service')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what new attributes the class has got now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.        ,   0.        ,   0.        , ...,  58.55209732,\n",
       "          3.42504004,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        , ..., 470.93561697,\n",
       "         27.54766205,   0.        ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2464)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_impact.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All impact matrixes (also those below) provide impacts aggregated over years. They have a number of rows equal to the years being modeled (2 years this time, i.e. 2012-2013) and columns equal to the number of countries times the number of sectors.\n",
    ", i.e. 2464 (see also above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   0.        ,   0.        , ..., 264.74385715,\n",
       "        15.48635104,   0.        ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_aai_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2464,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_aai_agg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The annual aggregated impact (aai) matrixes provide yearly average impact. They are row vectors with columns equal to the number of countries times the number of sectors, i.e. 2464."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Info for a given country can be accessed as done below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_aai_agg[supplychain.reg_pos['CHE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with e.g. CHE, we obviously get zeros, as we are modeling direct impacts in east Asia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          0.        ,    0.        ,  965.36489868,  878.15393066,\n",
       "       2594.28601074,  630.82998657, 1124.37762451,  433.43130493,\n",
       "        108.08090973,  427.46786499,  153.76459122,  995.37823486,\n",
       "        274.75501251,  420.40155029,  816.675354  ,  809.9671936 ,\n",
       "       2277.27154541,  234.72254181,    0.        ,  373.06349945,\n",
       "          0.        ,    0.        ,   93.46557999,  717.25080872,\n",
       "       2699.35137939,  565.01576233,  329.95692444,   58.4449482 ,\n",
       "        256.04863739,  370.85556793,  395.10922241,    0.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.direct_aai_agg[supplychain.reg_pos['JPN']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for e.g. Japan we instead have direct damages. In order to get all positions of countries undergoing direct damages, one can access the following list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ROW', 'TWN', 'ROW', 'JPN']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.reg_dir_imp #note we have two ROW for PHL and VNM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pos = [y for cntry in np.unique(supplychain.reg_dir_imp) for y in supplychain.reg_pos[cntry]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59692.46862970211 59692.46862970211\n"
     ]
    }
   ],
   "source": [
    "print(supplychain.direct_impact.sum(), supplychain.direct_impact[:, all_pos].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29846.234314851055 29846.234314851055\n"
     ]
    }
   ],
   "source": [
    "print(supplychain.direct_aai_agg.sum(), supplychain.direct_aai_agg[all_pos].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i.e., the matrix has non-zero values only at positions corresponding to the modelled countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Indirect impact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the **indirect impact**, one can choose the IO modeling approach between Leontief, Ghosh and EEIOA. References are provided below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0msupplychain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalc_indirect_impact\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio_approach\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ghosh'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Calculate indirect impacts according to the specified input-output\n",
       "appraoch. This function needs to be run after calc_sector_direct_impact.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "io_approach : str\n",
       "    The adopted input-output modeling approach. Possible approaches\n",
       "    are 'leontief', 'ghosh' and 'eeioa'. Default is 'gosh'.\n",
       "\n",
       "References\n",
       "----------\n",
       "[1] W. W. Leontief, Output, employment, consumption, and investment,\n",
       "The Quarterly Journal of Economics 58, 1944.\n",
       "[2] Ghosh, A., Input-Output Approach in an Allocation System,\n",
       "Economica, New Series, 25, no. 97: 58-64. doi:10.2307/2550694, 1958.\n",
       "[3] Kitzes, J., An Introduction to Environmentally-Extended Input-Output\n",
       "Analysis, Resources, 2, 489-503; doi:10.3390/resources2040489, 2013.\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\aleciu\\documents\\github\\climada_python\\climada\\engine\\supplychain.py\n",
       "\u001b[1;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "supplychain.calc_indirect_impact?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate indirect impacts according to the Ghosh method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:05<00:00,  2.96s/it]\n"
     ]
    }
   ],
   "source": [
    "supplychain.calc_indirect_impact(io_approach='ghosh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class now has the indirect impact matrix and vector, with structure equal to those introduced for the direct impact:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.5153655e-01, 3.1892043e-02, 4.7066946e-02, ..., 6.7286278e+01,\n",
       "        3.4250400e+00, 4.2803735e-03],\n",
       "       [2.1948884e+00, 9.4982922e-02, 1.2662964e-01, ..., 3.7942178e+02,\n",
       "        2.7547663e+01, 2.1249333e-02]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.indirect_impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2464)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.indirect_impact.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4732125e+00, 6.3437484e-02, 8.6848289e-02, ..., 2.2335403e+02,\n",
       "       1.5486351e+01, 1.2764853e-02], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.indirect_aai_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now check damages for e.g. Switzerland:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.68220580e-01, 9.25031211e-03, 2.74418970e-04, 4.44624610e-02,\n",
       "       6.71167612e-01, 1.16732985e-01, 1.12433434e-01, 8.17027241e-02,\n",
       "       6.89174980e-02, 1.35378033e-01, 5.81692576e-01, 2.70589256e+00,\n",
       "       1.96198016e-01, 1.54878110e-01, 2.06973076e-01, 4.29021597e-01,\n",
       "       1.89467812e+00, 7.21273184e-01, 7.28916526e-01, 6.06170967e-02,\n",
       "       1.80502594e-01, 3.23182851e-01, 1.12153739e-01, 1.45547521e+00,\n",
       "       1.51824072e-01, 0.00000000e+00, 1.18941975e+00, 2.22104624e-01,\n",
       "       2.23805451e+00, 4.34148878e-01, 1.17412543e+00, 2.86801495e-02,\n",
       "       4.84273553e-01, 4.27179277e-01, 1.50104269e-01, 3.29186380e-01,\n",
       "       2.08623365e-01, 0.00000000e+00, 2.89973140e-01, 1.03506613e+00,\n",
       "       7.41907895e-01, 7.98931241e-01, 0.00000000e+00, 4.80817735e-01,\n",
       "       5.68698585e-01, 0.00000000e+00, 3.72119516e-01, 0.00000000e+00,\n",
       "       2.05897242e-01, 4.71446127e-01, 6.04355514e-01, 2.38919139e-01,\n",
       "       7.35464454e-01, 2.93911517e-01, 0.00000000e+00, 0.00000000e+00],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.indirect_aai_agg[supplychain.reg_pos['CHE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are non-zero values, as CH undergoes indirect impacts due to events happening in east Asia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize coefficients, inverse matrix and risk matrix of the selected IO approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coefficients': array([[1.55292928e-01, 4.41463292e-03, 2.50902888e-03, ...,\n",
       "         1.27160543e-04, 0.00000000e+00, 1.66028979e-11],\n",
       "        [4.62851897e-02, 5.51640205e-02, 1.64963756e-04, ...,\n",
       "         6.37947051e-06, 0.00000000e+00, 1.15436055e-11],\n",
       "        [7.32246507e-03, 3.69836880e-06, 7.52862263e-03, ...,\n",
       "         7.99719510e-06, 0.00000000e+00, 9.63209113e-13],\n",
       "        ...,\n",
       "        [5.10453674e-06, 1.68568960e-07, 4.57865355e-07, ...,\n",
       "         3.88910100e-02, 0.00000000e+00, 1.92610537e-06],\n",
       "        [1.96250971e-06, 1.90349914e-09, 9.25952381e-09, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]], dtype=float32),\n",
       " 'inverse': array([[1.1956462e+00, 5.6268987e-03, 3.4478118e-03, ..., 1.8672845e-03,\n",
       "         0.0000000e+00, 1.3596379e-07],\n",
       "        [7.2838165e-02, 1.0588253e+00, 7.4798276e-04, ..., 1.1149000e-03,\n",
       "         0.0000000e+00, 1.1273375e-07],\n",
       "        [1.4281658e-02, 1.1088801e-04, 1.0078237e+00, ..., 5.9475366e-04,\n",
       "         0.0000000e+00, 6.5748580e-08],\n",
       "        ...,\n",
       "        [9.0543799e-05, 5.0534527e-06, 5.3664330e-06, ..., 1.0438221e+00,\n",
       "         0.0000000e+00, 2.5101926e-06],\n",
       "        [4.3887998e-05, 1.0840827e-06, 2.1887270e-06, ..., 5.4405088e-04,\n",
       "         1.0000000e+00, 4.1392159e-08],\n",
       "        [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "         0.0000000e+00, 1.0000000e+00]], dtype=float32),\n",
       " 'risk_structure': array([[[0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         ...,\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00]],\n",
       " \n",
       "        [[0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         ...,\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00]],\n",
       " \n",
       "        [[0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         ...,\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[2.6939712e-03, 2.1667661e-02],\n",
       "         [1.5035659e-04, 1.2093208e-03],\n",
       "         [1.5966877e-04, 1.2842189e-03],\n",
       "         ...,\n",
       "         [3.1057085e+01, 2.4979272e+02],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [7.4686359e-05, 6.0070382e-04]],\n",
       " \n",
       "        [[1.5031816e-04, 1.2090118e-03],\n",
       "         [3.7130264e-06, 2.9863942e-05],\n",
       "         [7.4964773e-06, 6.0294311e-05],\n",
       "         ...,\n",
       "         [1.8633960e-03, 1.4987330e-02],\n",
       "         [3.4250400e+00, 2.7547663e+01],\n",
       "         [1.4176980e-07, 1.1402572e-06]],\n",
       " \n",
       "        [[0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         ...,\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00],\n",
       "         [0.0000000e+00, 0.0000000e+00]]], dtype=float32),\n",
       " 'io_approach': 'ghosh'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.io_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Total impact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's calculate **total impacts**, as the sum of both direct and indirect. Therefore, the impact matrixes have the same structure as the direct and indirect matrixes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "supplychain.calc_total_impact()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.51536548e-01, 3.18920426e-02, 4.70669456e-02, ...,\n",
       "        1.25838375e+02, 6.85008004e+00, 4.28037345e-03],\n",
       "       [2.19488835e+00, 9.49829221e-02, 1.26629636e-01, ...,\n",
       "        8.50357400e+02, 5.50953248e+01, 2.12493334e-02]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.total_impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.47321245e+00, 6.34374823e-02, 8.68482906e-02, ...,\n",
       "       4.88097888e+02, 3.09727024e+01, 1.27648534e-02])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplychain.total_aai_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, one can for example visualize total annual average impacts to all Japanese (direct plus indirect) and Swiss (only direct) subsector after TC in East Asia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imp = pd.DataFrame(data=np.vstack([supplychain.total_aai_agg[supplychain.reg_pos['CHE']],\n",
    "                                      supplychain.total_aai_agg[supplychain.reg_pos['JPN']]]), \n",
    "                      columns=supplychain.sectors,\n",
    "                      index=['CHE', 'JPN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop and animal production, hunting and related service activities</th>\n",
       "      <th>Forestry and logging</th>\n",
       "      <th>Fishing and aquaculture</th>\n",
       "      <th>Mining and quarrying</th>\n",
       "      <th>Manufacture of food products, beverages and tobacco products</th>\n",
       "      <th>Manufacture of textiles, wearing apparel and leather products</th>\n",
       "      <th>Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials</th>\n",
       "      <th>Manufacture of paper and paper products</th>\n",
       "      <th>Printing and reproduction of recorded media</th>\n",
       "      <th>Manufacture of coke and refined petroleum products</th>\n",
       "      <th>...</th>\n",
       "      <th>Scientific research and development</th>\n",
       "      <th>Advertising and market research</th>\n",
       "      <th>Other professional, scientific and technical activities; veterinary activities</th>\n",
       "      <th>Administrative and support service activities</th>\n",
       "      <th>Public administration and defence; compulsory social security</th>\n",
       "      <th>Education</th>\n",
       "      <th>Human health and social work activities</th>\n",
       "      <th>Other service activities</th>\n",
       "      <th>Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use</th>\n",
       "      <th>Activities of extraterritorial organizations and bodies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHE</th>\n",
       "      <td>0.168221</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.044462</td>\n",
       "      <td>0.671168</td>\n",
       "      <td>0.116733</td>\n",
       "      <td>0.112433</td>\n",
       "      <td>0.081703</td>\n",
       "      <td>0.068917</td>\n",
       "      <td>0.135378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.205897</td>\n",
       "      <td>0.471446</td>\n",
       "      <td>0.604356</td>\n",
       "      <td>0.238919</td>\n",
       "      <td>0.735464</td>\n",
       "      <td>0.293912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN</th>\n",
       "      <td>94.852016</td>\n",
       "      <td>8.924538</td>\n",
       "      <td>15.782462</td>\n",
       "      <td>39.295603</td>\n",
       "      <td>423.523209</td>\n",
       "      <td>64.728909</td>\n",
       "      <td>23.186090</td>\n",
       "      <td>119.893147</td>\n",
       "      <td>62.801826</td>\n",
       "      <td>53.429245</td>\n",
       "      <td>...</td>\n",
       "      <td>187.867998</td>\n",
       "      <td>1149.998154</td>\n",
       "      <td>5108.261841</td>\n",
       "      <td>1049.191330</td>\n",
       "      <td>1184.859177</td>\n",
       "      <td>226.233683</td>\n",
       "      <td>1048.544487</td>\n",
       "      <td>865.901283</td>\n",
       "      <td>594.775421</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Crop and animal production, hunting and related service activities  \\\n",
       "CHE                                           0.168221                    \n",
       "JPN                                          94.852016                    \n",
       "\n",
       "     Forestry and logging  Fishing and aquaculture  Mining and quarrying  \\\n",
       "CHE              0.009250                 0.000274              0.044462   \n",
       "JPN              8.924538                15.782462             39.295603   \n",
       "\n",
       "     Manufacture of food products, beverages and tobacco products  \\\n",
       "CHE                                           0.671168              \n",
       "JPN                                         423.523209              \n",
       "\n",
       "     Manufacture of textiles, wearing apparel and leather products  \\\n",
       "CHE                                           0.116733               \n",
       "JPN                                          64.728909               \n",
       "\n",
       "     Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials  \\\n",
       "CHE                                           0.112433                                                                                 \n",
       "JPN                                          23.186090                                                                                 \n",
       "\n",
       "     Manufacture of paper and paper products  \\\n",
       "CHE                                 0.081703   \n",
       "JPN                               119.893147   \n",
       "\n",
       "     Printing and reproduction of recorded media  \\\n",
       "CHE                                     0.068917   \n",
       "JPN                                    62.801826   \n",
       "\n",
       "     Manufacture of coke and refined petroleum products   ...  \\\n",
       "CHE                                           0.135378    ...   \n",
       "JPN                                          53.429245    ...   \n",
       "\n",
       "     Scientific research and development  Advertising and market research  \\\n",
       "CHE                             0.372120                         0.000000   \n",
       "JPN                           187.867998                      1149.998154   \n",
       "\n",
       "     Other professional, scientific and technical activities; veterinary activities  \\\n",
       "CHE                                           0.205897                                \n",
       "JPN                                        5108.261841                                \n",
       "\n",
       "     Administrative and support service activities  \\\n",
       "CHE                                       0.471446   \n",
       "JPN                                    1049.191330   \n",
       "\n",
       "     Public administration and defence; compulsory social security  \\\n",
       "CHE                                           0.604356               \n",
       "JPN                                        1184.859177               \n",
       "\n",
       "      Education  Human health and social work activities  \\\n",
       "CHE    0.238919                                 0.735464   \n",
       "JPN  226.233683                              1048.544487   \n",
       "\n",
       "     Other service activities  \\\n",
       "CHE                  0.293912   \n",
       "JPN                865.901283   \n",
       "\n",
       "     Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use  \\\n",
       "CHE                                           0.000000                                                                            \n",
       "JPN                                         594.775421                                                                            \n",
       "\n",
       "     Activities of extraterritorial organizations and bodies  \n",
       "CHE                                                0.0        \n",
       "JPN                                                0.0        \n",
       "\n",
       "[2 rows x 56 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imp #in M USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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