compute_sector_impacts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "662fffda",
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.util.api_client import Client\n",
    "from climada_petals.engine import SupplyChain\n",
    "from climada.entity import ImpfSetTropCyclone, ImpactFuncSet\n",
    "from climada.engine.impact_calc import ImpactCalc\n",
    "from climada.hazard import Hazard\n",
    "import datetime\n",
    "import numpy as np\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3fbf7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the dates we want to study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9bafb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime object\n",
    "dt_start = datetime(2012, 1, 1)\n",
    "dt_end = datetime(2017, 1, 1)\n",
    "\n",
    "# Get the proleptic Gregorian ordinal value\n",
    "ordinal_value_start = dt_start.toordinal()\n",
    "ordinal_value_end = dt_end.toordinal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de7f3462",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell provides the inputs for boario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "0f98a2d5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='climada.ethz.ch', port=443): Max retries exceeded with url: /data-api/v2/dataset/?data_type=tropical_cyclone&name=tropical_cyclone_0synth_tracks_150arcsec_genesis_WP_1980_2020&status=active&limit=100000 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x1fcad10a0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mgaierror\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connection.py:174\u001B[0m, in \u001B[0;36mHTTPConnection._new_conn\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    173\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 174\u001B[0m     conn \u001B[38;5;241m=\u001B[39m \u001B[43mconnection\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_connection\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    175\u001B[0m \u001B[43m        \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_dns_host\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mport\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mextra_kw\u001B[49m\n\u001B[1;32m    176\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    178\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m SocketTimeout:\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/util/connection.py:72\u001B[0m, in \u001B[0;36mcreate_connection\u001B[0;34m(address, timeout, source_address, socket_options)\u001B[0m\n\u001B[1;32m     68\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m six\u001B[38;5;241m.\u001B[39mraise_from(\n\u001B[1;32m     69\u001B[0m         LocationParseError(\u001B[38;5;124mu\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, label empty or too long\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m host), \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m     70\u001B[0m     )\n\u001B[0;32m---> 72\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m res \u001B[38;5;129;01min\u001B[39;00m \u001B[43msocket\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgetaddrinfo\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhost\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mport\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfamily\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msocket\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mSOCK_STREAM\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m     73\u001B[0m     af, socktype, proto, canonname, sa \u001B[38;5;241m=\u001B[39m res\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/socket.py:954\u001B[0m, in \u001B[0;36mgetaddrinfo\u001B[0;34m(host, port, family, type, proto, flags)\u001B[0m\n\u001B[1;32m    953\u001B[0m addrlist \u001B[38;5;241m=\u001B[39m []\n\u001B[0;32m--> 954\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m res \u001B[38;5;129;01min\u001B[39;00m \u001B[43m_socket\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgetaddrinfo\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhost\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mport\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfamily\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mtype\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mproto\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m    955\u001B[0m     af, socktype, proto, canonname, sa \u001B[38;5;241m=\u001B[39m res\n",
      "\u001B[0;31mgaierror\u001B[0m: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mNewConnectionError\u001B[0m                        Traceback (most recent call last)",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connectionpool.py:703\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001B[0m\n\u001B[1;32m    702\u001B[0m \u001B[38;5;66;03m# Make the request on the httplib connection object.\u001B[39;00m\n\u001B[0;32m--> 703\u001B[0m httplib_response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_make_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    704\u001B[0m \u001B[43m    \u001B[49m\u001B[43mconn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    705\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    706\u001B[0m \u001B[43m    \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    707\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout_obj\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    708\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    709\u001B[0m \u001B[43m    \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    710\u001B[0m \u001B[43m    \u001B[49m\u001B[43mchunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    711\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    713\u001B[0m \u001B[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001B[39;00m\n\u001B[1;32m    714\u001B[0m \u001B[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001B[39;00m\n\u001B[1;32m    715\u001B[0m \u001B[38;5;66;03m# it will also try to release it and we'll have a double-release\u001B[39;00m\n\u001B[1;32m    716\u001B[0m \u001B[38;5;66;03m# mess.\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connectionpool.py:386\u001B[0m, in \u001B[0;36mHTTPConnectionPool._make_request\u001B[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001B[0m\n\u001B[1;32m    385\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 386\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mconn\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    387\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (SocketTimeout, BaseSSLError) \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    388\u001B[0m     \u001B[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connectionpool.py:1042\u001B[0m, in \u001B[0;36mHTTPSConnectionPool._validate_conn\u001B[0;34m(self, conn)\u001B[0m\n\u001B[1;32m   1041\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(conn, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msock\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m):  \u001B[38;5;66;03m# AppEngine might not have  `.sock`\u001B[39;00m\n\u001B[0;32m-> 1042\u001B[0m     \u001B[43mconn\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconnect\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1044\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m conn\u001B[38;5;241m.\u001B[39mis_verified:\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connection.py:363\u001B[0m, in \u001B[0;36mHTTPSConnection.connect\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    361\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mconnect\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m    362\u001B[0m     \u001B[38;5;66;03m# Add certificate verification\u001B[39;00m\n\u001B[0;32m--> 363\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msock \u001B[38;5;241m=\u001B[39m conn \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_new_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    364\u001B[0m     hostname \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connection.py:186\u001B[0m, in \u001B[0;36mHTTPConnection._new_conn\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    185\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m SocketError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m--> 186\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m NewConnectionError(\n\u001B[1;32m    187\u001B[0m         \u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFailed to establish a new connection: \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m e\n\u001B[1;32m    188\u001B[0m     )\n\u001B[1;32m    190\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m conn\n",
      "\u001B[0;31mNewConnectionError\u001B[0m: <urllib3.connection.HTTPSConnection object at 0x1fcad10a0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mMaxRetryError\u001B[0m                             Traceback (most recent call last)",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/adapters.py:487\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m    486\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 487\u001B[0m     resp \u001B[38;5;241m=\u001B[39m \u001B[43mconn\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murlopen\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    488\u001B[0m \u001B[43m        \u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    489\u001B[0m \u001B[43m        \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    490\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    491\u001B[0m \u001B[43m        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    492\u001B[0m \u001B[43m        \u001B[49m\u001B[43mredirect\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    493\u001B[0m \u001B[43m        \u001B[49m\u001B[43massert_same_host\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    494\u001B[0m \u001B[43m        \u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    495\u001B[0m \u001B[43m        \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    496\u001B[0m \u001B[43m        \u001B[49m\u001B[43mretries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmax_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    497\u001B[0m \u001B[43m        \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    498\u001B[0m \u001B[43m        \u001B[49m\u001B[43mchunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    499\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    501\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/connectionpool.py:787\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001B[0m\n\u001B[1;32m    785\u001B[0m     e \u001B[38;5;241m=\u001B[39m ProtocolError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConnection aborted.\u001B[39m\u001B[38;5;124m\"\u001B[39m, e)\n\u001B[0;32m--> 787\u001B[0m retries \u001B[38;5;241m=\u001B[39m \u001B[43mretries\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mincrement\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    788\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merror\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43me\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_pool\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_stacktrace\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msys\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[1;32m    789\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    790\u001B[0m retries\u001B[38;5;241m.\u001B[39msleep()\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/util/retry.py:592\u001B[0m, in \u001B[0;36mRetry.increment\u001B[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001B[0m\n\u001B[1;32m    591\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m new_retry\u001B[38;5;241m.\u001B[39mis_exhausted():\n\u001B[0;32m--> 592\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m MaxRetryError(_pool, url, error \u001B[38;5;129;01mor\u001B[39;00m ResponseError(cause))\n\u001B[1;32m    594\u001B[0m log\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIncremented Retry for (url=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m): \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, url, new_retry)\n",
      "\u001B[0;31mMaxRetryError\u001B[0m: HTTPSConnectionPool(host='climada.ethz.ch', port=443): Max retries exceeded with url: /data-api/v2/dataset/?data_type=tropical_cyclone&name=tropical_cyclone_0synth_tracks_150arcsec_genesis_WP_1980_2020&status=active&limit=100000 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x1fcad10a0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mConnectionError\u001B[0m                           Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[397], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m tc_wp \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_hazard\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mtropical_cyclone\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mtropical_cyclone_0synth_tracks_150arcsec_genesis_WP_1980_2020\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      2\u001B[0m tc_na \u001B[38;5;241m=\u001B[39m client\u001B[38;5;241m.\u001B[39mget_hazard(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtropical_cyclone\u001B[39m\u001B[38;5;124m'\u001B[39m, name\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtropical_cyclone_0synth_tracks_150arcsec_genesis_NA_1980_2020\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m      3\u001B[0m assets \u001B[38;5;241m=\u001B[39m client\u001B[38;5;241m.\u001B[39mget_litpop()\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:721\u001B[0m, in \u001B[0;36mClient.get_hazard\u001B[0;34m(self, hazard_type, name, version, properties, status, dump_dir)\u001B[0m\n\u001B[1;32m    718\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m hazard_type \u001B[38;5;129;01min\u001B[39;00m HAZ_TYPES:\n\u001B[1;32m    719\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mValid hazard types are a subset of CLIMADA hazard types.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    720\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Currently these types are supported: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mHAZ_TYPES\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 721\u001B[0m dataset \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_dataset_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhazard_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mversion\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mversion\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    722\u001B[0m \u001B[43m                                \u001B[49m\u001B[43mproperties\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mproperties\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatus\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstatus\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    723\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mto_hazard(dataset, dump_dir)\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:430\u001B[0m, in \u001B[0;36mClient.get_dataset_info\u001B[0;34m(self, data_type, name, version, properties, status)\u001B[0m\n\u001B[1;32m    399\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_dataset_info\u001B[39m(\u001B[38;5;28mself\u001B[39m, data_type\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, name\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, version\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, properties\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m    400\u001B[0m                 status\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mactive\u001B[39m\u001B[38;5;124m'\u001B[39m):\n\u001B[1;32m    401\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Find the one dataset that matches the given parameters.\u001B[39;00m\n\u001B[1;32m    402\u001B[0m \n\u001B[1;32m    403\u001B[0m \u001B[38;5;124;03m    Parameters\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    428\u001B[0m \u001B[38;5;124;03m        when there is no dataset matching the search parameters\u001B[39;00m\n\u001B[1;32m    429\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 430\u001B[0m     jarr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlist_dataset_infos\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mversion\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mversion\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    431\u001B[0m \u001B[43m                             \u001B[49m\u001B[43mproperties\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mproperties\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatus\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstatus\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    432\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(jarr) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m    433\u001B[0m         shown \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m10\u001B[39m\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:393\u001B[0m, in \u001B[0;36mClient.list_dataset_infos\u001B[0;34m(self, data_type, name, version, properties, status)\u001B[0m\n\u001B[1;32m    390\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m straight_props:\n\u001B[1;32m    391\u001B[0m     params\u001B[38;5;241m.\u001B[39mupdate(straight_props)\n\u001B[0;32m--> 393\u001B[0m datasets \u001B[38;5;241m=\u001B[39m [DatasetInfo\u001B[38;5;241m.\u001B[39mfrom_json(ds) \u001B[38;5;28;01mfor\u001B[39;00m ds \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_request_200\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m]\n\u001B[1;32m    395\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m datasets \u001B[38;5;129;01mand\u001B[39;00m multi_props:\n\u001B[1;32m    396\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_filter_datasets(datasets, multi_props)\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:311\u001B[0m, in \u001B[0;36mClient._request_200\u001B[0;34m(self, url, params)\u001B[0m\n\u001B[1;32m    308\u001B[0m     params \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m()\n\u001B[1;32m    310\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39monline:\n\u001B[0;32m--> 311\u001B[0m     page \u001B[38;5;241m=\u001B[39m requests\u001B[38;5;241m.\u001B[39mget(url, params\u001B[38;5;241m=\u001B[39mparams)\n\u001B[1;32m    312\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m page\u001B[38;5;241m.\u001B[39mstatus_code \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m200\u001B[39m:\n\u001B[1;32m    313\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m Client\u001B[38;5;241m.\u001B[39mNoResult(page\u001B[38;5;241m.\u001B[39mcontent\u001B[38;5;241m.\u001B[39mdecode())\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/api.py:73\u001B[0m, in \u001B[0;36mget\u001B[0;34m(url, params, **kwargs)\u001B[0m\n\u001B[1;32m     62\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget\u001B[39m(url, params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m     63\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Sends a GET request.\u001B[39;00m\n\u001B[1;32m     64\u001B[0m \n\u001B[1;32m     65\u001B[0m \u001B[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m     70\u001B[0m \u001B[38;5;124;03m    :rtype: requests.Response\u001B[39;00m\n\u001B[1;32m     71\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m---> 73\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mget\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/api.py:59\u001B[0m, in \u001B[0;36mrequest\u001B[0;34m(method, url, **kwargs)\u001B[0m\n\u001B[1;32m     55\u001B[0m \u001B[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001B[39;00m\n\u001B[1;32m     56\u001B[0m \u001B[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001B[39;00m\n\u001B[1;32m     57\u001B[0m \u001B[38;5;66;03m# cases, and look like a memory leak in others.\u001B[39;00m\n\u001B[1;32m     58\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m sessions\u001B[38;5;241m.\u001B[39mSession() \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[0;32m---> 59\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43msession\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/sessions.py:587\u001B[0m, in \u001B[0;36mSession.request\u001B[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[0m\n\u001B[1;32m    582\u001B[0m send_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m    583\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtimeout\u001B[39m\u001B[38;5;124m\"\u001B[39m: timeout,\n\u001B[1;32m    584\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow_redirects\u001B[39m\u001B[38;5;124m\"\u001B[39m: allow_redirects,\n\u001B[1;32m    585\u001B[0m }\n\u001B[1;32m    586\u001B[0m send_kwargs\u001B[38;5;241m.\u001B[39mupdate(settings)\n\u001B[0;32m--> 587\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mprep\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43msend_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    589\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/sessions.py:701\u001B[0m, in \u001B[0;36mSession.send\u001B[0;34m(self, request, **kwargs)\u001B[0m\n\u001B[1;32m    698\u001B[0m start \u001B[38;5;241m=\u001B[39m preferred_clock()\n\u001B[1;32m    700\u001B[0m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[0;32m--> 701\u001B[0m r \u001B[38;5;241m=\u001B[39m \u001B[43madapter\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    703\u001B[0m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n\u001B[1;32m    704\u001B[0m elapsed \u001B[38;5;241m=\u001B[39m preferred_clock() \u001B[38;5;241m-\u001B[39m start\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/adapters.py:520\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m    516\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e\u001B[38;5;241m.\u001B[39mreason, _SSLError):\n\u001B[1;32m    517\u001B[0m         \u001B[38;5;66;03m# This branch is for urllib3 v1.22 and later.\u001B[39;00m\n\u001B[1;32m    518\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m SSLError(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[0;32m--> 520\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[1;32m    522\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ClosedPoolError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    523\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(e, request\u001B[38;5;241m=\u001B[39mrequest)\n",
      "\u001B[0;31mConnectionError\u001B[0m: HTTPSConnectionPool(host='climada.ethz.ch', port=443): Max retries exceeded with url: /data-api/v2/dataset/?data_type=tropical_cyclone&name=tropical_cyclone_0synth_tracks_150arcsec_genesis_WP_1980_2020&status=active&limit=100000 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x1fcad10a0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))"
     ]
    }
   ],
   "source": [
    "tc_wp = client.get_hazard('tropical_cyclone', name='tropical_cyclone_0synth_tracks_150arcsec_genesis_WP_1980_2020')\n",
    "tc_na = client.get_hazard('tropical_cyclone', name='tropical_cyclone_0synth_tracks_150arcsec_genesis_NA_1980_2020')\n",
    "assets = client.get_litpop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "id": "e2491005",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[586], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m haz_concat \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_hazard\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mtropical_cyclone\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mtropical_cyclone_0synth_tracks_150arcsec_global_1980_2020\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:723\u001B[0m, in \u001B[0;36mClient.get_hazard\u001B[0;34m(self, hazard_type, name, version, properties, status, dump_dir)\u001B[0m\n\u001B[1;32m    719\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mValid hazard types are a subset of CLIMADA hazard types.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    720\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m Currently these types are supported: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mHAZ_TYPES\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    721\u001B[0m dataset \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_dataset_info(data_type\u001B[38;5;241m=\u001B[39mhazard_type, name\u001B[38;5;241m=\u001B[39mname, version\u001B[38;5;241m=\u001B[39mversion,\n\u001B[1;32m    722\u001B[0m                                 properties\u001B[38;5;241m=\u001B[39mproperties, status\u001B[38;5;241m=\u001B[39mstatus)\n\u001B[0;32m--> 723\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_hazard\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataset\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdump_dir\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:746\u001B[0m, in \u001B[0;36mClient.to_hazard\u001B[0;34m(self, dataset, dump_dir)\u001B[0m\n\u001B[1;32m    726\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Downloads hdf5 files belonging to the given datasets reads them into Hazards and\u001B[39;00m\n\u001B[1;32m    727\u001B[0m \u001B[38;5;124;03mconcatenates them into a single climada.Hazard object.\u001B[39;00m\n\u001B[1;32m    728\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    742\u001B[0m \u001B[38;5;124;03m    The combined hazard object\u001B[39;00m\n\u001B[1;32m    743\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    744\u001B[0m target_dir \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_organize_path(dataset, dump_dir) \\\n\u001B[1;32m    745\u001B[0m              \u001B[38;5;28;01mif\u001B[39;00m dump_dir \u001B[38;5;241m==\u001B[39m SYSTEM_DIR \u001B[38;5;28;01melse\u001B[39;00m dump_dir\n\u001B[0;32m--> 746\u001B[0m hazard_list \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m    747\u001B[0m     Hazard\u001B[38;5;241m.\u001B[39mfrom_hdf5(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_download_file(target_dir, dsf))\n\u001B[1;32m    748\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m dsf \u001B[38;5;129;01min\u001B[39;00m dataset\u001B[38;5;241m.\u001B[39mfiles\n\u001B[1;32m    749\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m dsf\u001B[38;5;241m.\u001B[39mfile_format \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhdf5\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m    750\u001B[0m ]\n\u001B[1;32m    751\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m hazard_list:\n\u001B[1;32m    752\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mno hdf5 files found in dataset\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:747\u001B[0m, in \u001B[0;36m<listcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m    726\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Downloads hdf5 files belonging to the given datasets reads them into Hazards and\u001B[39;00m\n\u001B[1;32m    727\u001B[0m \u001B[38;5;124;03mconcatenates them into a single climada.Hazard object.\u001B[39;00m\n\u001B[1;32m    728\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    742\u001B[0m \u001B[38;5;124;03m    The combined hazard object\u001B[39;00m\n\u001B[1;32m    743\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    744\u001B[0m target_dir \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_organize_path(dataset, dump_dir) \\\n\u001B[1;32m    745\u001B[0m              \u001B[38;5;28;01mif\u001B[39;00m dump_dir \u001B[38;5;241m==\u001B[39m SYSTEM_DIR \u001B[38;5;28;01melse\u001B[39;00m dump_dir\n\u001B[1;32m    746\u001B[0m hazard_list \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m--> 747\u001B[0m     Hazard\u001B[38;5;241m.\u001B[39mfrom_hdf5(\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_download_file\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtarget_dir\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdsf\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m    748\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m dsf \u001B[38;5;129;01min\u001B[39;00m dataset\u001B[38;5;241m.\u001B[39mfiles\n\u001B[1;32m    749\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m dsf\u001B[38;5;241m.\u001B[39mfile_format \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhdf5\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m    750\u001B[0m ]\n\u001B[1;32m    751\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m hazard_list:\n\u001B[1;32m    752\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mno hdf5 files found in dataset\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:595\u001B[0m, in \u001B[0;36mClient._download_file\u001B[0;34m(self, local_path, fileinfo, check, retries)\u001B[0m\n\u001B[1;32m    593\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m local_path\u001B[38;5;241m.\u001B[39mis_dir():\n\u001B[1;32m    594\u001B[0m     local_path \u001B[38;5;241m/\u001B[39m\u001B[38;5;241m=\u001B[39m fileinfo\u001B[38;5;241m.\u001B[39mfile_name\n\u001B[0;32m--> 595\u001B[0m downloaded \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_tracked_download(remote_url\u001B[38;5;241m=\u001B[39mfileinfo\u001B[38;5;241m.\u001B[39murl, local_path\u001B[38;5;241m=\u001B[39mlocal_path)\n\u001B[1;32m    596\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m downloaded\u001B[38;5;241m.\u001B[39menddownload:\n\u001B[1;32m    597\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m Download\u001B[38;5;241m.\u001B[39mFailed(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDownload seems to be in progress, please try again later\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    598\u001B[0m                           \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m or remove cache entry by calling\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    599\u001B[0m                           \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m `Client.purge_cache(Path(\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mlocal_path\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m))`!\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:559\u001B[0m, in \u001B[0;36mClient._tracked_download\u001B[0;34m(self, remote_url, local_path)\u001B[0m\n\u001B[1;32m    557\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m dlf\n\u001B[1;32m    558\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 559\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_download(url\u001B[38;5;241m=\u001B[39mremote_url, path\u001B[38;5;241m=\u001B[39mlocal_path, replace\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    560\u001B[0m     dlf\u001B[38;5;241m.\u001B[39menddownload \u001B[38;5;241m=\u001B[39m datetime\u001B[38;5;241m.\u001B[39mutcnow()\n\u001B[1;32m    561\u001B[0m     dlf\u001B[38;5;241m.\u001B[39msave()\n",
      "File \u001B[0;32m~/python_projects/climada_python/climada/util/api_client.py:535\u001B[0m, in \u001B[0;36mClient._download\u001B[0;34m(self, url, path, replace)\u001B[0m\n\u001B[1;32m    533\u001B[0m     stream\u001B[38;5;241m.\u001B[39mraise_for_status()\n\u001B[1;32m    534\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mopen\u001B[39m(path, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwb\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m dump:\n\u001B[0;32m--> 535\u001B[0m         \u001B[38;5;28;01mfor\u001B[39;00m chunk \u001B[38;5;129;01min\u001B[39;00m stream\u001B[38;5;241m.\u001B[39miter_content(chunk_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mchunk_size):\n\u001B[1;32m    536\u001B[0m             dump\u001B[38;5;241m.\u001B[39mwrite(chunk)\n\u001B[1;32m    537\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m path\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/requests/models.py:816\u001B[0m, in \u001B[0;36mResponse.iter_content.<locals>.generate\u001B[0;34m()\u001B[0m\n\u001B[1;32m    814\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstream\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m    815\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 816\u001B[0m         \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw\u001B[38;5;241m.\u001B[39mstream(chunk_size, decode_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    817\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m ProtocolError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    818\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m ChunkedEncodingError(e)\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/response.py:628\u001B[0m, in \u001B[0;36mHTTPResponse.stream\u001B[0;34m(self, amt, decode_content)\u001B[0m\n\u001B[1;32m    626\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    627\u001B[0m     \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_fp_closed(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp):\n\u001B[0;32m--> 628\u001B[0m         data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mamt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdecode_content\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    630\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m data:\n\u001B[1;32m    631\u001B[0m             \u001B[38;5;28;01myield\u001B[39;00m data\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/response.py:567\u001B[0m, in \u001B[0;36mHTTPResponse.read\u001B[0;34m(self, amt, decode_content, cache_content)\u001B[0m\n\u001B[1;32m    564\u001B[0m fp_closed \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mclosed\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m    566\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_error_catcher():\n\u001B[0;32m--> 567\u001B[0m     data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fp_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m fp_closed \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    568\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m amt \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m    569\u001B[0m         flush_decoder \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/site-packages/urllib3/response.py:533\u001B[0m, in \u001B[0;36mHTTPResponse._fp_read\u001B[0;34m(self, amt)\u001B[0m\n\u001B[1;32m    530\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m buffer\u001B[38;5;241m.\u001B[39mgetvalue()\n\u001B[1;32m    531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    532\u001B[0m     \u001B[38;5;66;03m# StringIO doesn't like amt=None\u001B[39;00m\n\u001B[0;32m--> 533\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m amt \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp\u001B[38;5;241m.\u001B[39mread()\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/http/client.py:463\u001B[0m, in \u001B[0;36mHTTPResponse.read\u001B[0;34m(self, amt)\u001B[0m\n\u001B[1;32m    460\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m amt \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m    461\u001B[0m     \u001B[38;5;66;03m# Amount is given, implement using readinto\u001B[39;00m\n\u001B[1;32m    462\u001B[0m     b \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mbytearray\u001B[39m(amt)\n\u001B[0;32m--> 463\u001B[0m     n \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreadinto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    464\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mmemoryview\u001B[39m(b)[:n]\u001B[38;5;241m.\u001B[39mtobytes()\n\u001B[1;32m    465\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    466\u001B[0m     \u001B[38;5;66;03m# Amount is not given (unbounded read) so we must check self.length\u001B[39;00m\n\u001B[1;32m    467\u001B[0m     \u001B[38;5;66;03m# and self.chunked\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/http/client.py:507\u001B[0m, in \u001B[0;36mHTTPResponse.readinto\u001B[0;34m(self, b)\u001B[0m\n\u001B[1;32m    502\u001B[0m         b \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmemoryview\u001B[39m(b)[\u001B[38;5;241m0\u001B[39m:\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlength]\n\u001B[1;32m    504\u001B[0m \u001B[38;5;66;03m# we do not use _safe_read() here because this may be a .will_close\u001B[39;00m\n\u001B[1;32m    505\u001B[0m \u001B[38;5;66;03m# connection, and the user is reading more bytes than will be provided\u001B[39;00m\n\u001B[1;32m    506\u001B[0m \u001B[38;5;66;03m# (for example, reading in 1k chunks)\u001B[39;00m\n\u001B[0;32m--> 507\u001B[0m n \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreadinto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    508\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m n \u001B[38;5;129;01mand\u001B[39;00m b:\n\u001B[1;32m    509\u001B[0m     \u001B[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001B[39;00m\n\u001B[1;32m    510\u001B[0m     \u001B[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001B[39;00m\n\u001B[1;32m    511\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_close_conn()\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/socket.py:704\u001B[0m, in \u001B[0;36mSocketIO.readinto\u001B[0;34m(self, b)\u001B[0m\n\u001B[1;32m    702\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m    703\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 704\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_sock\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrecv_into\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    705\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m timeout:\n\u001B[1;32m    706\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_timeout_occurred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/ssl.py:1242\u001B[0m, in \u001B[0;36mSSLSocket.recv_into\u001B[0;34m(self, buffer, nbytes, flags)\u001B[0m\n\u001B[1;32m   1238\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m flags \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m   1239\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m   1240\u001B[0m           \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m\n\u001B[1;32m   1241\u001B[0m           \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m)\n\u001B[0;32m-> 1242\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnbytes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1243\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   1244\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001B[0;32m~/miniconda3/envs/climada_boario_env/lib/python3.9/ssl.py:1100\u001B[0m, in \u001B[0;36mSSLSocket.read\u001B[0;34m(self, len, buffer)\u001B[0m\n\u001B[1;32m   1098\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m   1099\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m buffer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1100\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_sslobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1101\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   1102\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sslobj\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;28mlen\u001B[39m)\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "haz_concat = client.get_hazard('tropical_cyclone', name='tropical_cyclone_0synth_tracks_150arcsec_global_1980_2020')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "id": "679eb68a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-25 14:31:52,592 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 5137781 coordinates.\n",
      "2023-05-25 14:31:53,006 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 7249 coordinates.\n"
     ]
    }
   ],
   "source": [
    "# This cell provides the inputs for boario\n",
    "\n",
    "\n",
    "haz_concat = Hazard.concat([tc_wp,tc_na])\n",
    "\n",
    "def get_impf_id(cnt):\n",
    "    for basin, iso_list in ImpfSetTropCyclone.get_countries_per_region()[2].items():\n",
    "        for iso in iso_list:\n",
    "            if iso == cnt:\n",
    "                return basin, ImpfSetTropCyclone.get_countries_per_region()[1][basin]\n",
    "            \n",
    "impf_TC = ImpfSetTropCyclone.from_calibrated_regional_ImpfSet()\n",
    "\n",
    "assets.gdf['impf_TC'] = 1\n",
    "\n",
    "for cnt in np.unique(assets.gdf.region_id):\n",
    "    assets.gdf.loc[assets.gdf['region_id']==cnt, 'impf_TC'] = get_impf_id(int(cnt))[1]\n",
    "\n",
    "# impact calculation, already selecting the date\n",
    "imp_calc = ImpactCalc(assets, impf_TC, haz_concat)\n",
    "direct_impact = imp_calc.impact().select(dates=(ordinal_value_start,ordinal_value_end))   \n",
    "\n",
    "\n",
    "supchain = SupplyChain.from_mriot(mriot_type='WIOD16', mriot_year=2011)\n",
    "\n",
    "impacted_secs = supchain.mriot.get_sectors().tolist()\n",
    "\n",
    "supchain.calc_secs_exp_imp_shock(assets, direct_impact, impacted_secs)\n",
    "supchain.calc_direct_production_impacts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "id": "d497e481",
   "metadata": {},
   "outputs": [],
   "source": [
    "quakes = client.get_hazard('earthquake', properties={\"spatial_coverage\":\"global\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "id": "e3e935d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "supchain_impacted = supchain.dir_prod_impt_mat.loc[:, (supchain.dir_prod_impt_mat > 0).any()]\n",
    "supchain_impacted = supchain_impacted.loc[(supchain_impacted > 0).any(axis=1), :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "id": "bf2cd0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region = supchain_impacted.groupby('region', axis=1).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "b8501bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f5/lms0b90j1kx7kk4v552qkjqw0000gp/T/ipykernel_86346/2518366494.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  production_by_country = assets.gdf.groupby(\"region_id\").sum().reset_index()\n"
     ]
    }
   ],
   "source": [
    "production_by_country = assets.gdf.groupby(\"region_id\").sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "id": "c8b28848",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pycountry\n",
    "production_by_country[\"country_iso3\"] = [pycountry.countries.lookup(format(reg_id, '03d')).alpha_3 for reg_id in production_by_country[\"region_id\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "id": "18b171e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f5/lms0b90j1kx7kk4v552qkjqw0000gp/T/ipykernel_86346/1802810957.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  dates = dates_wp.append(dates_na)\n"
     ]
    }
   ],
   "source": [
    "dates_wp = pd.DataFrame({'date':tc_wp.date,'event_id':tc_wp.event_id})\n",
    "dates_na = pd.DataFrame({'date':tc_na.date,'event_id':tc_na.event_id})\n",
    "dates = dates_wp.append(dates_na)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "id": "15c9a6ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region[\"event_id\"] = impacts_by_region.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "id": "2cd7d0c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "impacts_by_region = pd.merge(impacts_by_region, dates, on='event_id', how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "id": "c6383cbb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "impacts_by_region['year'] = [datetime.date.fromordinal(date).year for date in impacts_by_region.date]\n",
    "impacts_by_region_by_years = impacts_by_region.groupby('year').sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "id": "21821bec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "impacts_by_region_by_years = impacts_by_region_by_years.drop(columns=[\"ROW\", \"event_id\",'date'])\n",
    "impacts_by_region = impacts_by_region.drop(columns=[\"ROW\",'date', 'year'])\n",
    "\n",
    "for col in impacts_by_region_by_years.columns:\n",
    "    try:\n",
    "        pycountry.countries.lookup(col)        \n",
    "        impacts_by_region_by_years[col] = impacts_by_region_by_years[col]/production_by_country[production_by_country[\"country_iso3\"]==col].value.unique()[0]\n",
    "        impacts_by_region[col] = impacts_by_region[col]/production_by_country[production_by_country[\"country_iso3\"]==col].value.unique()[0]\n",
    "\n",
    "    except LookupError:\n",
    "        continue\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "id": "e638a445",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region = impacts_by_region.set_index(\"event_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "6146cf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if we want to get 5 years\n",
    "#rolling_mean_rel_impact_5y = impacts_by_region_by_years.rolling(window=5).mean().mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "id": "24b6a0c6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>MEX</th>\n",
       "      <th>PRT</th>\n",
       "      <th>RUS</th>\n",
       "      <th>TWN</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          CAN  CHN  FRA  JPN  KOR  MEX  PRT  RUS  TWN  USA\n",
       "event_id                                                  \n",
       "3018      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3019      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3020      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3022      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3023      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "...       ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
       "1432      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1436      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1441      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1442      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1443      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "\n",
       "[113 rows x 10 columns]"
      ]
     },
     "execution_count": 698,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impacts_by_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "id": "68d44900",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region = impacts_by_region[impacts_by_region>1e-4]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "id": "e8c86f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region = impacts_by_region.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "id": "ef150d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3032, 3033, 3049, 3059, 3095, 3096, 3121, 3132, 3147, 3151, 1390], dtype='int64', name='event_id')"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impacts_by_region.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853b4d5c",
   "metadata": {},
   "source": [
    "## calculate supchain impact with only events causing damages>1e-4\n",
    "(see sell above for events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "id": "6a8fd234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-25 14:38:07,596 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 5137781 coordinates.\n",
      "2023-05-25 14:38:07,984 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 7249 coordinates.\n"
     ]
    }
   ],
   "source": [
    "# This cell provides the inputs for boario\n",
    "\n",
    "\n",
    "haz_concat = Hazard.concat([tc_wp,tc_na])\n",
    "\n",
    "def get_impf_id(cnt):\n",
    "    for basin, iso_list in ImpfSetTropCyclone.get_countries_per_region()[2].items():\n",
    "        for iso in iso_list:\n",
    "            if iso == cnt:\n",
    "                return basin, ImpfSetTropCyclone.get_countries_per_region()[1][basin]\n",
    "            \n",
    "impf_TC = ImpfSetTropCyclone.from_calibrated_regional_ImpfSet()\n",
    "\n",
    "assets.gdf['impf_TC'] = 1\n",
    "\n",
    "for cnt in np.unique(assets.gdf.region_id):\n",
    "    assets.gdf.loc[assets.gdf['region_id']==cnt, 'impf_TC'] = get_impf_id(int(cnt))[1]\n",
    "\n",
    "# impact calculation, already selecting the date\n",
    "haz_subset = haz_concat.select(event_id=np.array(impacts_by_region.index))\n",
    "\n",
    "imp_calc = ImpactCalc(assets, impf_TC, haz_subset)\n",
    "direct_impact = imp_calc.impact().select(dates=(ordinal_value_start,ordinal_value_end))   \n",
    "\n",
    "\n",
    "supchain = SupplyChain.from_mriot(mriot_type='WIOD16', mriot_year=2011)\n",
    "\n",
    "impacted_secs = supchain.mriot.get_sectors().tolist()\n",
    "\n",
    "supchain.calc_secs_exp_imp_shock(assets, direct_impact, impacted_secs)\n",
    "supchain.calc_direct_production_impacts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "id": "5a0077c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region_by_years = impacts_by_region_by_years[impacts_by_region_by_years>1e-3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "id": "e5871486",
   "metadata": {
    "scrolled": false
   },
   "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>CAN</th>\n",
       "      <th>CHN</th>\n",
       "      <th>FRA</th>\n",
       "      <th>JPN</th>\n",
       "      <th>KOR</th>\n",
       "      <th>MEX</th>\n",
       "      <th>PRT</th>\n",
       "      <th>RUS</th>\n",
       "      <th>TWN</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001365</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAN  CHN  FRA  JPN  KOR  MEX  PRT  RUS       TWN       USA\n",
       "year                                                            \n",
       "2012  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.001996\n",
       "2013  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.001365  0.000000\n",
       "2015  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.001326  0.000000\n",
       "2016  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.001045  0.000000"
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impacts_by_region_by_years.dropna(how=\"all\").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "id": "2640f263",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "impacts_by_region_by_years_binary = copy.deepcopy(impacts_by_region_by_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "id": "e90b10c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts_by_region_by_years_binary = impacts_by_region_by_years_binary>1e-3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "392db565",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rolling_mean_ncountries_5y = impacts_by_region_by_years_binary.rolling(window=5).sum().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "e89cb44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_series(series):\n",
    "    min_value = series.min()\n",
    "    max_value = series.max()\n",
    "    normalized_series = (series - min_value) / (max_value - min_value)\n",
    "    return normalized_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "e3da9674",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_mean_ncountries_5y = normalize_series(rolling_mean_ncountries_5y)\n",
    "rolling_mean_rel_impact_5y = normalize_series(rolling_mean_rel_impact_5y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "556f1d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1998"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(rolling_mean_rel_impact_5y+rolling_mean_ncountries_5y).idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "f4045ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime object\n",
    "dt_start = datetime(2012, 1, 1)\n",
    "dt_end = datetime(2017, 1, 1)\n",
    "\n",
    "# Get the proleptic Gregorian ordinal value\n",
    "ordinal_value_start = dt_start.toordinal()\n",
    "ordinal_value_end = dt_end.toordinal()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "965a2efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "736330"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_value_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "bdcb9b3c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1998    1.937500\n",
       "2017    1.841243\n",
       "1997    1.840993\n",
       "2000    1.808039\n",
       "1994    1.759149\n",
       "2008    1.710399\n",
       "1996    1.694779\n",
       "2012    1.688950\n",
       "2011    1.684069\n",
       "2018    1.670143\n",
       "2009    1.657504\n",
       "2010    1.601042\n",
       "2016    1.600024\n",
       "1999    1.523996\n",
       "2019    1.513391\n",
       "2015    1.423282\n",
       "1990    1.335353\n",
       "1993    1.307293\n",
       "1991    1.247323\n",
       "2013    1.233385\n",
       "1992    1.218813\n",
       "2001    1.211861\n",
       "2020    1.209157\n",
       "1995    1.190694\n",
       "2014    1.190612\n",
       "2004    1.006045\n",
       "1989    1.001927\n",
       "2007    0.996839\n",
       "1987    0.986057\n",
       "1986    0.980155\n",
       "1988    0.953122\n",
       "2002    0.905339\n",
       "2003    0.875000\n",
       "1984    0.872199\n",
       "2005    0.852464\n",
       "2006    0.770690\n",
       "1985    0.725971\n",
       "1980         NaN\n",
       "1981         NaN\n",
       "1982         NaN\n",
       "1983         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(rolling_mean_rel_impact_5y+rolling_mean_ncountries_5y).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "3050b074",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2000    1.0000\n",
       "2016    1.0000\n",
       "2001    1.0000\n",
       "1998    0.9375\n",
       "1994    0.9375\n",
       "1997    0.9375\n",
       "2018    0.9375\n",
       "2017    0.9375\n",
       "2015    0.8750\n",
       "1996    0.8750\n",
       "2007    0.8750\n",
       "2004    0.8750\n",
       "2003    0.8750\n",
       "1999    0.8750\n",
       "2014    0.8750\n",
       "2013    0.8750\n",
       "1995    0.8750\n",
       "1993    0.8750\n",
       "1990    0.8750\n",
       "1989    0.8750\n",
       "2012    0.8125\n",
       "2010    0.8125\n",
       "2009    0.8125\n",
       "2008    0.8125\n",
       "1988    0.8125\n",
       "2005    0.8125\n",
       "2002    0.8125\n",
       "2019    0.8125\n",
       "1987    0.8125\n",
       "1991    0.8125\n",
       "1992    0.8125\n",
       "2006    0.7500\n",
       "2011    0.7500\n",
       "1986    0.7500\n",
       "2020    0.6875\n",
       "1985    0.6250\n",
       "1984    0.6250\n",
       "1981    0.0000\n",
       "1983    0.0000\n",
       "1982    0.0000\n",
       "1980    0.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolling_mean_ncountries_5y.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "3dcbbf55",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1998    1.000000\n",
       "2011    0.934069\n",
       "2017    0.903743\n",
       "1997    0.903493\n",
       "2008    0.897899\n",
       "2012    0.876450\n",
       "2009    0.845004\n",
       "1994    0.821649\n",
       "1996    0.819779\n",
       "2000    0.808039\n",
       "2010    0.788542\n",
       "2018    0.732643\n",
       "2019    0.700891\n",
       "1999    0.648996\n",
       "2016    0.600024\n",
       "2015    0.548282\n",
       "2020    0.521657\n",
       "1990    0.460353\n",
       "1991    0.434823\n",
       "1993    0.432293\n",
       "1992    0.406313\n",
       "2013    0.358385\n",
       "1995    0.315694\n",
       "2014    0.315612\n",
       "1984    0.247199\n",
       "1986    0.230155\n",
       "2001    0.211861\n",
       "1987    0.173557\n",
       "1988    0.140622\n",
       "2004    0.131045\n",
       "1989    0.126927\n",
       "2007    0.121839\n",
       "1985    0.100971\n",
       "2002    0.092839\n",
       "2005    0.039964\n",
       "2006    0.020690\n",
       "2003    0.000000\n",
       "1980         NaN\n",
       "1981         NaN\n",
       "1982         NaN\n",
       "1983         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolling_mean_rel_impact_5y.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "06e2f4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1998"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolling_mean_rel_impact_5y.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "994c280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolling_mean_ncountries_5y.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "f361a947",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1980    3\n",
       "1981    2\n",
       "1982    2\n",
       "1983    1\n",
       "1984    2\n",
       "1985    3\n",
       "1986    4\n",
       "1987    3\n",
       "1988    1\n",
       "1989    3\n",
       "1990    3\n",
       "1991    3\n",
       "1992    3\n",
       "1993    2\n",
       "1994    4\n",
       "1995    2\n",
       "1996    3\n",
       "1997    4\n",
       "1998    2\n",
       "1999    3\n",
       "2000    4\n",
       "2001    3\n",
       "2002    1\n",
       "2003    3\n",
       "2004    3\n",
       "2005    3\n",
       "2006    2\n",
       "2007    3\n",
       "2008    2\n",
       "2009    3\n",
       "2010    3\n",
       "2011    1\n",
       "2012    4\n",
       "2013    3\n",
       "2014    3\n",
       "2015    3\n",
       "2016    3\n",
       "2017    3\n",
       "2018    3\n",
       "2019    1\n",
       "2020    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impacts_by_region_by_years_binary.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "87f42498",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "impacts_by_region_by_years_binary.sum(axis=1).plot()\n",
    "impacts_by_region_by_years.drop([\"IND\",\"RUS\"], axis=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9127fe1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of countries affected by damages over 0.1%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc9ee7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxes: title={'center': 'Event ID 3187: 2017288N09138'}>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc_wp.plot_intensity(supchain_impacted.sum(axis=1).idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d208ad39",
   "metadata": {},
   "outputs": [],
   "source": [
    "supchain_impacted.to_csv(\"1980_2020_wp_secs_exp_imp_shock.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}