doc/tutorial/climada_util_api_client.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data API\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial is separated into three main parts: the first two parts shows how to find and get data to do impact calculations and should be enough for most users. The third part provides more detailed information on how the API is built.\n",
    "\n",
    "## Contents\n",
    "\n",
    "- [Finding Datasets](#Finding-datasets)\n",
    "    - [Data types and data type groups](#Data-types-and-data-type-groups)\n",
    "    - [Datasets and Properties](#Datasets-and-Properties)\n",
    "- [Basic impact calculation](#Basic-impact-calculation)\n",
    "    - [Wrapper functions to open datasets as CLIMADA objects](#Wrapper-functions-to-open-datasets-as-CLIMADA-objects)\n",
    "    - [Calculate the impact](#Calculate-the-impact)\n",
    "- [Technical Information](#Technical-Information)\n",
    "    - [Server](#Server)\n",
    "    - [Client](#Client)\n",
    "    - [Metadata](#Metadata)\n",
    "    - [Download](#Download)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.util.api_client import Client\n",
    "client = Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data types and data type groups\n",
    "The datasets are first separated into 'data_type_groups', which represent the main classes of CLIMADA (exposures, hazard, vulnerability, ...). So far, data is available for exposures and hazard. Then, data is separated into data_types, representing the different hazards and exposures available in CLIMADA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_type</th>\n",
       "      <th>data_type_group</th>\n",
       "      <th>status</th>\n",
       "      <th>description</th>\n",
       "      <th>properties</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>crop_production</td>\n",
       "      <td>exposures</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'property': 'crop', 'mandatory': True, 'desc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>litpop</td>\n",
       "      <td>exposures</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'property': 'res_arcsec', 'mandatory': False...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>centroids</td>\n",
       "      <td>hazard</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>river_flood</td>\n",
       "      <td>hazard</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'property': 'res_arcsec', 'mandatory': False...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>storm_europe</td>\n",
       "      <td>hazard</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'property': 'country_iso3alpha', 'mandatory'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tropical_cyclone</td>\n",
       "      <td>hazard</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'property': 'res_arcsec', 'mandatory': True,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data_type data_type_group  status description  \\\n",
       "3   crop_production       exposures  active        None   \n",
       "0            litpop       exposures  active        None   \n",
       "5         centroids          hazard  active        None   \n",
       "2       river_flood          hazard  active        None   \n",
       "4      storm_europe          hazard  active        None   \n",
       "1  tropical_cyclone          hazard  active        None   \n",
       "\n",
       "                                          properties  \n",
       "3  [{'property': 'crop', 'mandatory': True, 'desc...  \n",
       "0  [{'property': 'res_arcsec', 'mandatory': False...  \n",
       "5                                                 []  \n",
       "2  [{'property': 'res_arcsec', 'mandatory': False...  \n",
       "4  [{'property': 'country_iso3alpha', 'mandatory'...  \n",
       "1  [{'property': 'res_arcsec', 'mandatory': True,...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data_types = client.list_data_type_infos()\n",
    "\n",
    "dtf = pd.DataFrame(data_types)\n",
    "dtf.sort_values(['data_type_group', 'data_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datasets and Properties\n",
    "For each data type, the single datasets can be differentiated based on properties. The following function provides a table listing the properties and possible values. This table does not provide information on properties that can be combined but the search can be refined in order to find properties to query a unique dataset. Note that a maximum of 10 property values are shown here, but many more countries are available for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "litpop_dataset_infos = client.list_dataset_infos(data_type='litpop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_properties = client.get_property_values(litpop_dataset_infos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['res_arcsec', 'exponents', 'fin_mode', 'spatial_coverage', 'country_iso3alpha', 'country_name', 'country_iso3num'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_properties.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Refining the search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'res_arcsec': ['150'],\n",
       " 'exponents': ['(0,1)', '(1,1)', '(3,0)'],\n",
       " 'fin_mode': ['pop', 'pc'],\n",
       " 'spatial_coverage': ['global']}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as datasets are usually available per country, chosing a country or global dataset reduces the options\n",
    "# here we want to see which datasets are available for litpop globally:\n",
    "client.get_property_values(litpop_dataset_infos, known_property_values = {'spatial_coverage':'global'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'res_arcsec': ['150'],\n",
       " 'exponents': ['(3,0)', '(0,1)', '(1,1)'],\n",
       " 'fin_mode': ['pc', 'pop'],\n",
       " 'spatial_coverage': ['country'],\n",
       " 'country_iso3alpha': ['CHE'],\n",
       " 'country_name': ['Switzerland'],\n",
       " 'country_iso3num': ['756']}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#and here for Switzerland:\n",
    "client.get_property_values(litpop_dataset_infos, known_property_values = {'country_name':'Switzerland'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic impact calculation\n",
    "We here show how to make a basic impact calculation with tropical cyclones for Haiti, for the year 2040, rcp4.5  and generated with 10 synthetic tracks. For more technical details on the API, see below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrapper functions to open datasets as CLIMADA objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The wrapper functions client.get_hazard() \n",
    "gets the dataset information, downloads the data and opens it as a hazard instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'res_arcsec': ['150'],\n",
       " 'climate_scenario': ['rcp26', 'rcp45', 'rcp85', 'historical', 'rcp60'],\n",
       " 'ref_year': ['2040', '2060', '2080'],\n",
       " 'nb_synth_tracks': ['50', '10'],\n",
       " 'spatial_coverage': ['country'],\n",
       " 'tracks_year_range': ['1980_2020'],\n",
       " 'country_iso3alpha': ['HTI'],\n",
       " 'country_name': ['Haiti'],\n",
       " 'country_iso3num': ['332'],\n",
       " 'resolution': ['150 arcsec']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc_dataset_infos = client.list_dataset_infos(data_type='tropical_cyclone')\n",
    "client.get_property_values(tc_dataset_infos, known_property_values = {'country_name':'Haiti'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxesSubplot:title={'center':'TC max intensity at each point'}>"
      ]
     },
     "execution_count": 9,
     "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": [
    "client = Client()\n",
    "tc_haiti = client.get_hazard('tropical_cyclone', properties={'country_name': 'Haiti', 'climate_scenario': 'rcp45', 'ref_year':'2040', 'nb_synth_tracks':'10'})\n",
    "tc_haiti.plot_intensity(0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The wrapper functions client.get_litpop_default() \n",
    "gets the default litpop, with exponents (1,1) and 'produced capital' as financial mode. If no country is given, the global dataset will be downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "litpop_default = client.get_property_values(litpop_dataset_infos, known_property_values = {'fin_mode':'pc', 'exponents':'(1,1)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "litpop = client.get_litpop_default(country='Haiti')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the default impact function for tropical cyclones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-31 22:30:21,359 - 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"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAxQ0lEQVR4nO3deZyP9f7/8cfL2HcyImOZCiWyHKdNZT2JZG0hiZYjrdoP7aVT/dLitKeNE4miKCnlSw4ia1KoFLKUJbLMjDHm9fvjc9GQZcbMZ675zDzvt9vnNp/rel/L6/0xPq95v9/X9b7M3REREcmsQmEHICIisUWJQ0REskSJQ0REskSJQ0REskSJQ0REskSJQ0REskSJQyQGmVktM3MzKxx2LFLwKHFIrjCzHRle6WaWnGG5p5mVNbMhZrY6WPdjsFzpEMcbZGbfmFmamT2YxVgeNLPdB8S0NSfqmReYWTEze93MVpnZdjNbaGbtDtimtZktM7MkM5tqZjUzlLUM1v1hZisPcvyj/uwlf1DikFzh7qX3voDVwIUZlt8FpgCnAOcDZYGzgM3AaYc45I/AXcDEowxpdMaY3L38UR4nLyoM/AI0B8oB9wFjzKwWQJCMxwXrKwLzgNEZ9t8JvAHceYjjZ/ezlxinxCF5wRVADaCLu3/n7unuvsHdB7n7xwfbwd2Hu/skYHtOBxN0AV1vZj8Ef7EPMrMTzOxLM9tmZmPMrGiwbQUz+8jMNprZluB9QoZjTQv2nxkca/LeVpSZtTCzNQece6WZtQneFzKzAWa2wsw2B+eteKT43X2nuz/o7iuDz/Ij4Gfgb8EmXYFv3f1dd08BHgQamtlJwf5fuftbwE+HOH7UPnuJDUockhe0AT5x9x05cTAzq2FmW82sRjYOcz6RL9oziPx1PRToCVQH6gM9gu0KAW8CNYkkv2Tg+QOOdRlwJVAZKArckckYbgY6E2k5HAdsAV7IakXM7FigDvBtsOoU4Ou95e6+E1gRrBc5IiUOyQuOAdbn1MHcfbW7l3f31YfZ7JIguex9TT2g/P+5+zZ3/xZYAkx295/c/Q9gEtA4ONdmdx/r7knuvh34N5Ev+ozedPfv3T0ZGAM0ymRVrgXucfc17r6LSMvgoqwMiJtZEWAkMNzdlwWrSwN/HLDpH0CZzB5XCjZdkSF5wWagai6fc4y7X36Y8t8yvE8+yHIVADMrCTxDpIVSISgvY2Zx7r4nWP41w75JRL64M6Mm8L6ZpWdYtwc4NjM7m1kh4C0gFbgxQ9EOIuNIGZVFXU+SSWpxSF7wOdDWzEqFHchRuB2oC5zu7mWBc4P1lol9dwIl9y6YWRwQn6H8F6Bd0Hra+yru7muPdGAzM+B1Ikmmm7vvzlD8LdAww7algBP4sytL5LCUOCQveIvIl+RYMzspGBQ+xszuNrP2B9vBzIqYWXEiv8OFzax48MWb28oQaYFsDQauH8jCvt8Dxc3sgqBL6V6gWIbyl4F/771U1szizaxTJo/9EnAykavXkg8oex+ob2bdgs/wfmDx3q6s4PMvDhSJLFrxvRcDBOV55bOXkChxSOiC/vs2wDLgM2Ab8BVQCZhziN1eJfKF3QO4J3jfC/YNju84wuD4pQfcx7HDzCofRfhDgBLAJmA28ElmdwzGS64HXgPWEmmBZLzK6j/ABGCymW0Pjn/6kY4bJJpriYyl/Jqhfj2D824EuhEZj9kSHLN7hkOcS+Tz/Jg/B/wnZyg/5GcvBYPpQU4iIpIVanGIiEiWKHGIiEiWKHGIiEiWKHGIiEiWxPQNgJUqVfJatWqFHYaISEyZP3/+JnePP/KWBxfTiaNWrVrMmzcv7DBERGKKma3Kzv7qqhIRkSxR4hARkSxR4hARkSxR4hARkSxR4hARkSyJWuIwszfMbIOZLcmwrqKZfRY8kvMzM6uQoWygmf1oZsvNrG204hIRkeyJZotjGJGH22Q0AJji7rWBKcEyZlaPyOycpwT7vKhpmkVE8qao3cfh7tPNrNYBqzsBLYL3w4FpwL+C9e8E02v/bGY/AqcBXx72JMuXQ4sWh91ERERyVm6PcRzr7usBgp97n39QjciDfPZaE6z7CzPra2bzzGze7t27D7aJiIhEUV65c/xgj9k86INC3H0oMBSgadOmzrRpUQxLRCQfssw82fjQcrvF8ZuZVQUIfm4I1q8BqmfYLgFYl8uxiYhIJuR24pgA9A7e9wbGZ1jf3cyKmVkiUJvIo0NFRCSPiVpXlZmNIjIQXsnM1gAPAI8DY8zsamA1cDGAu39rZmOA74A04AZ33xOt2ERE5OhF86qqHocoan2I7f8N/Dta8YiISM7QneMiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIpIlShwiIgXI5s3Zfyp31B4dKyIi4du2bRevv/4lc+Yk8/PP7Zg7Nz3bx1SLQ0QkH0lPh/feW0KHDk9SqdL5lCtXgdtua8no0fdQuDDce2+RbJ9DLQ4RkRg3Y8ZKhg+fxfbtlzFlCmza9DDwLsWK1aNhw39y4YVtuPba5iQkRLYfNCh751PiEBGJMb//nsxTT33G++9/zA8/fE5a2goA4uOb0b59TerX/zetWw+hSZPjonJ+JQ4RkRiwatVWJk8uxKefluXDD98lNbU3UIZjj23J2Wf3p0+fNrRvX4NChQBqRzUWJQ4RkTzq66/X88QT4/n00/fZvHkqMJiqVftz+eUdqV17Mjff3IKSJbM/ZpFVShwiInnIihXw3ntpPPpoK7ZtmwE4RYqcyN//fgs339yKyy6DQoXKA/8ILUYlDhGREKWnO++9t5gXXhjHkiUb+f33F4HCHHPMybRq9Q9uvLELnTqdQqFCFnao+yhxiIiEYNGi9dx991tMmTKM1NSlQCHKl2/JU0/toWvXOGrVeiXsEA9JiUNEJJfs2JHKxInGW28V4eOPh+M+kDJlmnHxxa8wcGBnTjmlctghZooSh4hIlI0evYjHHnuTxYtH4v4c1ar14Oabr+H887tw/vl1ww4vy5Q4RESi4Lff0rj++heZNOlNkpMXAUWpXr0zt99+AjfeCHFxlYBKIUd5dJQ4RERySEpKGq+/vpSpUxswfnwcaWmvULJkcS6++Hkee6wHJ5xQMewQc0QoicPMbgWuARz4BrgSKAmMBmoBK4FL3H1LGPGJiGTF0qUbuf76F5k+/RXS03dQqdJ6brqpFN26zaRZs/Jhh5fjcn2SQzOrBtwMNHX3+kAc0B0YAExx99rAlGBZRCTPmjZtFaecch316tVg2rQHqVSpCQMG/JeVK4vy9NPky6QB4XVVFQZKmNluIi2NdcBAoEVQPhyYBvwrjOBERA7F3Zk2bRfPPVec99/fDLzJSSddwVNP3Ub79ieFHV6uyPXE4e5rzexJYDWQDEx298lmdqy7rw+2WW9mB70uzcz6An0BatSokVthi0gBl5q6h3vu+YCXX36SHTvqULHicO67rwk9e66nbt0KYYeXq3I9cZhZBaATkAhsBd41s8szu7+7DwWGAjRt2tSjEaOIyF4bN+7kxhuHMW7c06Sl/UThwsfTvXsfXnsNSpUCKFhJA8J5kFMb4Gd33+juu4FxwFnAb2ZWFSD4uSGE2EREAPjtN7j/fqhR42HGjLmRYsXiueOO99i583tGjbo2SBoFUxhjHKuBM8ysJJGuqtbAPGAn0Bt4PPg5PoTYRKSAmz79Z6699jFWrLiMtLQWnHfeTXTseCH9+jXLU/NFhSmMMY45ZvYesABIAxYS6XoqDYwxs6uJJJeLczs2ESm4li/fRPfuj7Bo0YtAIc4+uxGvvdaCunUTgISww8tTQrmqyt0fAB44YPUuIq0PEZFck5QEl176HB99dC+wg7p1r2LEiAdp2rRa2KHlWWGMcYiIhC4lJY1XXknnxBPho49SqFKlBRMmfMOyZa8qaRyBEoeIFCjp6c7dd4+nXLlT6dfvbWrVgunT72D9+vFceGG9sMOLCUocIlJgvPLKLMqXP4fHHusMpHPfffHMnAnnnKNB76zQJIciku8tWwYdOtzGihXPUKhQFXr2fJnXXrua4sX1FXg09KmJSL61aNF6nn22LP/9bymKFm1B69YVefvtW6lcuQDfhJED1FUlIvlOSkoa7ds/QePGJzJ8+NNcfz2sWtWRzz+/V0kjB6jFISL5yujRi7jyyqtJTl5AlSqdGDnyMlq1Cjuq/EUtDhHJF1JSoH37oXTv3pRdu9Zy223vsnbt+7RqdULYoeU7anGISMybPj2dvn0LsXz56dSu3ZtJkwbnm6ft5UVqcYhIzFqzZhsNGtxA8+ZXs2sXTJ7ckO+/f11JI8qUOEQkJj344ERq1TqFJUteolGjcixenM4//hF2VAWDEoeIxJRlyzZRq1ZPHnqoA4ULl+XVV2excOEQypTR11lu0SctIjHBHd5+G846axerVn1G8+YPsHHjAq655oywQytwlDhEJM+bM+cX6tS5j549nTp1qvHVVz8xbdqDlClTLOzQCiQlDhHJ066/fiRnnHEKP/74NHfd9S0zZ8Lf/1467LAKNF2OKyJ50tatKTRrdgvfffcKZcuew4cfDufccxPDDktQ4hCRPOjnn6Fx40v4448POf30fzFt2iOakDAPUVeViOQp48c7TZrAnj13cffd45k9+3EljTxG/xoikiekpKTRvPk9fPVVHE2aPMp7751Nonqm8iS1OEQkdAsWrOPYY1vx1VdPUK/eVmbMcCWNPEyJQ0RC9dRTU2natDHbts3nuutG8O23L1KihJ7Il5epq0pEQpGeDvfeu5HHHutA0aI1ePfdqXTsqGd+xwIlDhHJdb/8ksS115Zk0qR4WrUaz8iRZ1Cliu7NiBXqqhKRXPXGG1+RmHgykye/w0svweeft1HSiDFKHCKSK9LTnYsvfp6rrz4bs0K88caJ9OsHpuGMmKOuKhGJupSUNBo3voFly4ZSuXIHZs/+L4mJFcIOS46SWhwiElVJSdC8+WcsWzaUM88cyNq145U0YpwSh4hEzYYN6bRuDXPntuOOO+Ywa9ajFC6sr51Yd9iuKjMrDnQAzgGOA5KBJcBEd/82+uGJSKyaPv1nzjuvK+4vM3bs6XTpclrYIUkOOWTiMLMHgQuBacAcYANQHKgDPB4kldvdfXH0wxSRWDJy5AKuuKI97qm88EIaXbqEHZHkpMO1OOa6+4OHKHvazCoDNY7mpGZWHngNqA84cBWwHBgN1AJWApe4+5ajOb6IhOexxyZz993diIuryPjxU+nQ4eSwQ5IcdsjORnefeOA6MytuZmWD8g3uPu8oz/sf4BN3PwloCCwFBgBT3L02MCVYFpEYcv/9X3L33RdQvPgJfPXVl0oa+VSmR6nM7BrgU2CimT16tCcMEs+5wOsA7p7q7luBTsDwYLPhQOejPYeI5C53ePxxGDToNBIT72f58i9o0uS4sMOSKDlk4jCzCw9Y1cbdm7v7OcAF2Tjn8cBG4E0zW2hmr5lZKeBYd18PEPysfIi4+prZPDObt3HjxmyEISI5ITV1D6ed9gADB66lR484li27jxo1yoUdlkTR4VocDc1svJk1DJYXm9lIMxsBZOeKqsJAE+Ald28M7CQL3VLuPtTdm7p70/j4+GyEISLZ9fvvySQmXsy8eQ/TsuV7jBgBRYuGHZVE2yEHx939ETOrAjxskTkB7gdKAyWzeSXVGmCNu88Jlt8jkjh+M7Oq7r7ezKoSuYpLRPKoFSt+p0mTjmzbNosuXYYwblz/sEOSXHKkMY6dwC3AC8BQoAfwfXZO6O6/Ar+YWd1gVWvgO2AC0DtY1xsYn53ziEj0zJmzhnr1zmbbtrnccss7ShoFzOHu43iEyCB2EWC0u3c0s45EBseHuftb2TjvTcBIMysK/ARcSSSJjTGzq4HVwMXZOL6IRMmyZdC5cyn27KnIkCEv0b9/87BDklx2uPs4Orh7I4v0U80Hhrj7BDP7GLghOyd190VA04MUtc7OcUUkuqZOXUWPHlWACixY8D9OPVVT2xZEh0scS8zsLaAE8MXele6eRuQ+DBEpQKZM+ZHzzmtOkSL/YMGCYdSrp6RRUB1ucPxyM2sA7Hb3ZbkYk4jkMV988RNt27bEfRdvv3079fSE1wLtcPdxnO3u3xwqaZhZWTOrH73QRCQvmDlzFa1btyQ9PYl33plC164Nwg5JQna4rqpuZvYE8AmRMY6NRCY5PBFoCdQEbo96hCISmtWrnVatupKevo233prCJZc0PPJOku8drqvqVjOrAFxE5AqnqkSmVV8KvOLuM3InRBEJw9q10Lq1UaTIqwwduoeePZuEHZLkEYd9HkcwO+2rwUtECohFi9bTtu14kpP78fnnTTjjjLAjkrxEzxwXkf0sWfIbp5/emtTU1YwdewFnnFE97JAkj1HiEJF9li7dSNOmrUlNXcWzz06ia1clDfmrI06rbmbFMrNORGLbDz9spkmTNuzatYInn/yQm246N+yQJI/KzPM4vszkOhGJUVu2QNu2X5CS8iOPPTaB229vFXZIkocdbq6qKkA1oISZNQb23iZaFiiZC7GJSC7YssU57zxj7dqujBhxFj17Vgk7JMnjDjfG0RboAyQAT/Fn4tgG3B3dsEQkN6xZs4169TqRlDSQDz44jw4dlDTkyA53H8dwYLiZdXP3sbkYk4jkgg0bdlKvXju2b/+KAQOS6dAh7IgkVmRmjONvZlZ+74KZVQimXBeRGJWSkkb9+pewfftsbrttFI891inskCSGZCZxtHP3rXsXgpsC20ctIhGJqvR0p1Gj69m48WMuu+xFnnrqorBDkhiTmcQRl/HyWzMrAehyXJEY9fDD6SxfDmeddQ8jR14bdjgSgzJzA+AIYIqZvQk4cBUwPKpRiUhUDB26m4ceKkKvXq8wbFjY0UisOmKLw92fAP4NnAycAgwK1olIDHnkkU+49tp6NGu2gtdeMwoV0oOY5OhkasoRd58ETIpyLCISJSNGzOe++y6iRInajBlTmaJFw45IYllmphw5w8zmmtkOM0s1sz1mti03ghOR7Js+/Wd6976AuLhKzJr1MccdVybskCTGZWZw/HmgB/ADkeePXwM8F82gRCRnLF++iTZtzsc9lQ8+mESjRlXDDknygcwkDtz9RyDO3fe4+5tEngAoInlYUhJcfnkh0tJq8PzzH9Khw8lhhyT5RGbGOJLMrCiwKHiU7HqgVHTDEpHsSE3dQ/fuacyfX5ExYyZz0UUaCJeck5kWR69guxuBnUB1oFs0gxKRo5ee7vztb/358MO2PPnkLiUNyXFHbHG4+6qgxVELGAcsd/fUaAcmIkfnggueYMmSF/j73+/kttt0r67kvCMmDjO7AHgZWEFkhtxEM7s2uERXRPKQ664bySefDKBGje7MmvV42OFIPpWZMY6ngJbBADlmdgIwEd3XIZKnPPHEFF5++UrKl2/JN98Mo3DhTF37IpJlmfnN2rA3aQR+AjZEKR4ROQqLFsFDD1WhdOl/sGjROMqWVReVRE9mWhzfmtnHwBgic1VdDMw1s64A7j4uivGJyBEsX76Ddu1KUaHCKXz55USqVw87IsnvMpM4igO/Ac2D5Y1AReBCIolEiUMkJBs27KRx4+akpbVk/vwnlTQkV2TmqqorcyMQEcmatLR0GjfuTXLyIh54YBANGoQdkRQUmbmqKhG4icjluPu2d/eO2TmxmcUB84C17t7BzCoCo4PzrAQuCR4aJSIH0bLlg6xbN5YLL3yKBx/Us9Uk92Smq+oD4HXgQyA9B8/dH1gKlA2WBwBT3P1xMxsQLP8rB88nkm/ceOMoZswYRJ06V/PBB7eGHY4UMJlJHCnu/mxOntTMEoALiDzn47ZgdSegRfB+ODANJQ6Rv5gzB155pRQVK7Zn/vwX9VwNyXWZSRz/MbMHgMnArr0r3X1BNs47BLgLyDi/87Huvj449nozq3ywHc2sL9AXoEaNGtkIQST2/PxzGp06FaZ69Y7MmXMhpUsraUjuy0ziaEBkvqpW/NlV5cFylplZByL3hsw3sxZZ3d/dhwJDAZo2bepHE4NILNqwYSf167dkz56+TJlyDfHxShoSjswkji7A8Tk4P1UzoKOZtSdyqW9ZMxsB/GZmVYPWRlV0k6HIPpErqK4gKWk+Dz1UjVNOCTsiKcgyc+f410D5nDqhuw909wR3rwV0B/7P3S8HJgC9g816A+Nz6pwisa5Fi/tZt24cnTo9yf33tws7HCngMtPiOBZYZmZz2X+MI1uX4x7E48AYM7saWE3kDnWRAu+GG95m5sx/U7fuNYwbd0vY4YhkKnE8EK2Tu/s0IldP4e6bgdbROpdILJo9G155ZS3lyrVg3rwXdAWV5AmZuXP8i9wIRET2t2qV07mzUbPmncyceSulS2fm7zyR6Dvkb6KZbSdy9dRfigB397IHKRORHPDrrzuoX78z6en38n//14IqVZQ0JO845OC4u5dx97IHeZVR0hCJnrS0dJo06cWOHVMZMCCFevXCjkhkf/ozRiSPad78Ptav/4DOnYdw333nhx2OyF/oEWEiech1141g1qxHOemkfzJ27M1hhyNyUEocInnE7NkwdOhkypdvwdy5z+sKKsmz1FUlkgesXg2dO0PNmsOZOnUnpUsXDTskkUNSi0MkZBs27KRhw8vYufMnPvrIqFmzdNghiRyWEodIiPbOQbV162juvHO5rqCSmKCuKpEQtWz5AOvWjaNjx6c0B5XEDLU4REJy002jmDHjEWrXvor339dT/CR2KHGIhGD27HReeOF5ypY9hwULXtIVVBJT1FUlksvWroWuXQtRvfpkJk9O0RVUEnPU4hDJRZs2JfG3v/2Lbdu2M3FiKerWPSbskESyTIlDJJekpzuNG1/Jb78NZsCAOdSvH3ZEIkdHXVUiuaR164dZs2YM7ds/wb33tgk7HJGjphaHSC647bZ3mTbtQU44oTcffnhH2OGIZIsSh0iUzZ6dypAhd1GmzFksWPCKrqCSmKeuKpEoWr8eLrqoKFWrTuPTT4tTtmyxsEMSyTa1OESi5PffkznzzBfZsiWdjz+uSf36x4YdkkiOUOIQiYL0dKdJk6tZteoG7rtvNg0bhh2RSM5R4hCJgrZtH2XVqlGcd96jDBhwVtjhiOQoJQ6RHHbDDW/z+ef3UrPmZUyaNCDscERynBKHSA4aN24zL754LeXKNWfRotd1BZXkS7qqSiSHfP019OlzDImJnzB16imUL1887JBEokItDpEcMHPmKlq0GEO5cjB9ejNq1iwfdkgiUaMWh0g2rVjxO61anU9q6q989FEbEhIqhh2SSFQpcYhkw++/J9O4cUdSU39iyJDJNGumpCH5nxKHyFFKTd1DgwaXs337LG655R36928edkgiuUKJQ+QouEPXrpNYt24cXboM4ZlnLgk7JJFck+uJw8yqA/8FqgDpwFB3/4+ZVQRGA7WAlcAl7r4lt+MTyYwnnoCJEzvQo8dM3n5bN/jFkt27d7NmzRpSUlLCDiXqihcvTkJCAkWKFMnR44bR4kgDbnf3BWZWBphvZp8BfYAp7v64mQ0ABgD/CiE+kcO64YZ3ePHFE+nRoykjRihpxJo1a9ZQpkwZatWqhVn+vc/G3dm8eTNr1qwhMTExR4+d65fjuvt6d18QvN8OLAWqAZ2A4cFmw4HOuR2byJE89thkXnyxF5UqPcybb0IhXdAec1JSUjjmmGPyddIAMDOOOeaYqLSsQv21N7NaQGNgDnCsu6+HSHIBKh9in75mNs/M5m3cuDHXYhV5++0F3H13N4oXr8f8+W9RTDOkx6z8njT2ilY9Q0scZlYaGAvc4u7bMrufuw9196bu3jQ+Pj56AYpkMH36z/TqdQFxcRWZOXMSNWqUCzskkdCEkjjMrAiRpDHS3ccFq38zs6pBeVVgQxixiRxo0ybo1Olp3HfxwQef0KTJcWGHJDHOzOjVq9e+5bS0NOLj4+nQoQMAw4YNIz4+nsaNG1O7dm3atm3LrFmz9m3fp08fEhMTadiwIXXq1OGKK65g7dq1uRZ/ricOi7SdXgeWuvvTGYomAL2D972B8bkdm8iBkpKgY0dISnqGt96aRYcOJ4cdkuQDpUqVYsmSJSQnJwPw2WefUa1atf22ufTSS1m4cCE//PADAwYMoGvXrixdunRf+eDBg/n6669Zvnw5jRs3pmXLlqSmpuZK/GFcVdUM6AV8Y2aLgnV3A48DY8zsamA1cHEIsYnss2NHKo0a3cuKFbcxdmwVunY9KeyQJIfdcgssWpSzx2zUCIYMOfJ27dq1Y+LEiVx00UWMGjWKHj168L///e+g27Zs2ZK+ffsydOhQnnnmmf3KzIxbb72V999/n0mTJtGpU6fsV+IIwriqaoa7m7uf6u6NgtfH7r7Z3Vu7e+3g5++5HZvIXlu2JHP88V1YsWIwl1/+CV27hh2R5Dfdu3fnnXfeISUlhcWLF3P66acfdvsmTZqwbNmyoy7PSbpzXOQAv/66g3r1OrFly1R69nyFt97qE3ZIEiWZaRlEy6mnnsrKlSsZNWoU7du3P+L27p6t8pykq9BFMli1ait16rRly5Zp9Ov3X0aM6Bt2SJKPdezYkTvuuIMePXoccduFCxdy8smHHmM7UnlOUotDJLBpE1x44W62b9/JHXeMYfDgbmGHJPncVVddRbly5WjQoAHTpk075HZffPEFQ4cOZerUqX8pc3eee+451q9fz/nnnx/FaP+kxCECLFmykUsvLc9PP8UzYcI8LrxQ/zUk+hISEujfv/9By0aPHs2MGTNISkoiMTGRsWPH7teiuPPOOxk0aBBJSUmcccYZTJ06laJFi+ZK3Jab/WI5rWnTpj5v3ryww5AY9+WXq2nevDXu5zB58hu0bBl2RBJNS5cuzbUunbzgYPU1s/nu3vRoj6kxDinQpkz5kXPOOYfduzfywgv/VNIQyQS1x6XAmjDhO7p0aYP7bkaOnMpllzUOOySRmKDEIQXS3LlpdOnSEXA++OALOnasF3ZIIjFDiUMKnC+/hHbtChMf/xYjR8bTuvWJYYckElM0xiEFypAh02jR4j/Ex8OcOWcqaYgcBSUOKTAeeeQTbr21HWZD+fTTZGrWDDsikdikxCEFwl13jeO++zpSosTJfP31Fxx/fImwQ5ICLC4ujkaNGlG/fn0uvvhikpKSgMj06pUqVWLgwIEH3a9hw4aZuss82pQ4JF9LTYWzzrqPwYO7Ubp0U5Yu/T/q1q0UdlhSwJUoUYJFixaxZMkSihYtyssvvwzA5MmTqVu3LmPGjPnL3FNLly4lPT2d6dOns3PnzjDC3keD45Jv/fADXHYZzJtXjXr1+vHFF09RqVLJsMOSvCTMedUD55xzDosXLwZg1KhR9O/fn5deeonZs2dz5pln7tvu7bffplevXixdupQJEyaE2vJQi0PynfR0p2/f/9KgwWhWrIBx4/rx7bcvKWlInpOWlsakSZNo0KABycnJTJkyhQ4dOtCjRw9GjRq137ajR4/m0ksvPWhZblOLQ/KVNWu2cfbZ17Fq1dtUrHghCxdeQo0aFnZYkleFNK96cnIyjRo1AiItjquvvprx48fTsmVLSpYsSbdu3Rg0aBDPPPMMcXFxzJ07l/j4eGrWrElCQgJXXXUVW7ZsoUKFCqHEr8Qh+cbrr8+mX7/LSEtbTevWg/j444EULaqkIXnP3jGOjEaNGsXMmTOpVasWAJs3b2bq1Km0adOGUaNGsWzZsn1l27ZtY+zYsVxzzTW5G3hAXVUS8/bsgdtv/55rrjkH93Refnk6n39+L0WLxoUdmkimbNu2jRkzZrB69WpWrlzJypUreeGFFxg1ahTp6em8++67LF68eF/Z+PHjQ+2uUuKQmPbzz7s47zx4+uk6NG36EitWLOLaa88KOyyRLBk3bhytWrWiWLFi+9Z16tSJCRMm8Nlnn1GtWjWqVau2r+zcc8/lu+++Y/369WGEq2nVJXbdd9+HPProdRQt+hEvvNCIK68EU8+UHIGmVc/+tOoa45CYs3VrCueeeyfffPM8JUo0YuzYErRrF3ZUIgWHuqokpnzwwbdUrXoa33zzPI0b38Kvv86mXbu6YYclUqAocUhM+Ppr6NEDunb9gF27fuXBByeyYMEzlC1b7Mg7i0iOUuKQPO3FF2dQufIFNGr0Dh99BDfddBPffLOEBx5oH3ZoIgWWxjgkz0lPdwYNmsTTTz/Gtm0zMKtEly4X8frrUKFCWaBs2CGKFGhKHJJnpKXBe+9Bv36X8scf7xIXV4Nu3Z7l5Zev1nQhInmIuqokdH/8kUKvXq9Sp84OevSA0qV7cs01w9i27Ufee+8mJQ3Jd8yMXr167VtOS0sjPj6eDh06ADBs2DDi4+Np3LgxtWvXpm3btsyaNWvf9n369CExMZFGjRrRsGFDpkyZkqvxK3FIaNat206HDoOpWDGRESP6AuMYNw5Wr+7Eq6/2pmTJImGHKBIVpUqVYsmSJSQnJwPsu8kvo0svvZSFCxfyww8/MGDAALp27crSpUv3lQ8ePJhFixYxZMgQ+vXrl6vxq6tKctXWrTB9uvPww3exYMFruG+lQoXWDBw4gttvb0Uh/SkjuaxFixZ/WXfJJZdw/fXXk5SURPv2f70Qo0+fPvTp04dNmzZx0UUX7Vc2bdq0TJ23Xbt2TJw4kYsuuohRo0bRo0cP/ve//x1025YtW9K3b1+GDh3KM888s1/ZmWeeydq1azN1zpyi/6YSVStW/M7AgR/QpMmtVKrUj4oVoVMnY8GCuVSt2ophw77i998/5847W1OokG77loKje/fuvPPOO6SkpLB48WJOP/30w27fpEkTli1b9pf1n3zyCZ07d45SlAenFofkqE2bYPp0eO65l5k9+yVSUhYHJcWpWLEV99/vtGxpnHbaVEqUUKKQ8B2uhVCyZMnDlleqVCnTLYwDnXrqqaxcuZJRo0YdtFVzoAOnh7rzzju566672LBhA7Nnzz6qGI5WnkscZnY+8B8gDnjN3R8POSQ5iPR0Z/36HSxd+hsff7yQzz//gu+/n86uXV8AFShSJIUyZSrTrNkgunRpQa9efz/gZj0lDZGOHTtyxx13MG3aNDZv3nzYbRcuXLjfnFODBw+ma9euPPvss/Tu3Zv58+dHO9x98lTiMLM44AXgH8AaYK6ZTXD378KNrGBIStrNhg3O1q1FWb78N6ZO/Yx16zawYcMGNm/ewB9//EalSg+zc+ffWL9+DLt3d8+wdymOOaYZN964ma5dK9C06S0ULXpLWFURiQlXXXUV5cqVo0GDBodtuXzxxRcMHTqUqVOn7re+UKFC9O/fn+HDh/Ppp5/Stm3bKEcckacSB3Aa8KO7/wRgZu8AnYCDJo5Fi36hePFT9ltXuPBx1Kz5GQDr1v2T5ORZ+5UXLVqb6tU/AGDNmp7s2rVov/JixRqRkDASgF9+6Uxq6g/7lZcocRbHHfcqAKtW/YO0tHX7lZcq9Q+qVBkCwM8/n0V6+h/7lZcu3Yljj30UgBUrGgJp+5WXLXsZ8fH34L6bn35q9Jc6ly//T4455hb27NnKypXN/lJesWJ/ypXry65dv/DLLy2BdNz37PtZuvQjFC16Famp37JtWzMgHdgTvHYBI4HLgO+BvZcLFiUurjLFilXm+ON3ctppUKRIE9avH8xxx1Xm9NPr0qNHE10FJZJFCQkJ9O/f/6Blo0ePZsaMGSQlJZGYmMjYsWMPOquvmXHvvffyxBNPFNjEUQ34JcPyGmC/ESMz6wv0BShSpAqVKtXb7wDFisVTb9+qGvzxx9b9ykuWrLGvPD29Ftu3p+5XXqZMrX3lu3efQFLS/l+G5cr9uX9KSm127Sq/X3nFignUDebc27mzLmlpO/Yrj4+vxoknRt5v316P9PT9E0eVKlVITIT0dNi2bf+6AVSrVpkaNWD37jh27PhreY0alUhIgN27i+N+BoUKFcKsEIUKxVGoUCFq165F9eqQnFyBhQt7ExcXh1kh4uLiKFmyJM2a1adJEyhX7m/s2bOck046loSEsgcZuK4N3PGX84vIke3YseMv61q0aLHvCq+9V20dyrBhw/Zb7tatG926dcvBCA8vTz2Pw8wuBtq6+zXBci/gNHe/6WDb63kcIpJVeh5H9p/Hkdcux10DVM+wnACsO8S2IiISgryWOOYCtc0s0cyKAt2BCSHHJCL5TF7qaYmmaNUzTyUOd08DbgQ+BZYCY9z923CjEpH8pHjx4mzevDnfJw93Z/PmzRQvXjzHj53XBsdx94+Bj8OOQ0Typ4SEBNasWcPGjRvDDiXqihcvTkJCQo4fN88lDhGRaCpSpAiJiYlhhxHT8lRXlYiI5H1KHCIikiVKHCIikiV56gbArDKz7cDysOOIokrAprCDiCLVL7bl5/rl57oB1HX3Mke7c6wPji/Pzt2PeZ2ZzVP9YpfqF7vyc90gUr/s7K+uKhERyRIlDhERyZJYTxxDww4gylS/2Kb6xa78XDfIZv1ienBcRERyX6y3OEREJJcpcYiISJbEbOIws/PNbLmZ/WhmA8KOJ7vMrLqZTTWzpWb2rZn1D9ZXNLPPzOyH4GeFsGM9WmYWZ2YLzeyjYDk/1a28mb1nZsuCf8Mz81n9bg1+L5eY2SgzKx7L9TOzN8xsg5ktybDukPUxs4HBd81yM8ud57NmwyHqNzj4/VxsZu+bWfkMZVmqX0wmDjOLA14A2gH1gB5m9tfnqMaWNOB2dz8ZOAO4IajTAGCKu9cGpgTLsao/keny98pPdfsP8Im7nwQ0JFLPfFE/M6sG3Aw0dff6QByRZ+XEcv2GAecfsO6g9Qn+H3YHTgn2eTH4DsrLhvHX+n0G1Hf3U4HvgYFwdPWLycQBnAb86O4/uXsq8A7QKeSYssXd17v7guD9diJfPNWI1Gt4sNlwoHMoAWaTmSUAFwCvZVidX+pWFjgXeB3A3VPdfSv5pH6BwkAJMysMlCTyZM6YrZ+7Twd+P2D1oerTCXjH3Xe5+8/Aj0S+g/Ksg9XP3ScHzzwCmE3kCatwFPWL1cRRDfglw/KaYF2+YGa1gMbAHOBYd18PkeQCVA4xtOwYAtwFpGdYl1/qdjywEXgz6Ip7zcxKkU/q5+5rgSeB1cB64A93n0w+qV8Gh6pPfvy+uQqYFLzPcv1iNXHYQdbli+uKzaw0MBa4xd23hR1PTjCzDsAGd58fdixRUhhoArzk7o2BncRWt81hBX39nYBE4DiglJldHm5UuSpffd+Y2T1EusZH7l11kM0OW79YTRxrgOoZlhOINJ1jmpkVIZI0Rrr7uGD1b2ZWNSivCmwIK75saAZ0NLOVRLoVW5nZCPJH3SDy+7jG3ecEy+8RSST5pX5tgJ/dfaO77wbGAWeRf+q316Hqk2++b8ysN9AB6Ol/3sSX5frFauKYC9Q2s0QzK0pkYGdCyDFli5kZkT7ype7+dIaiCUDv4H1vYHxux5Zd7j7Q3RPcvRaRf6v/c/fLyQd1A3D3X4FfzKxusKo18B35pH5EuqjOMLOSwe9payJjcPmlfnsdqj4TgO5mVszMEoHawFchxJctZnY+8C+go7snZSjKev3cPSZfQHsiVwasAO4JO54cqM/ZRJqHi4FFwas9cAyRKzx+CH5WDDvWbNazBfBR8D7f1A1oBMwL/v0+ACrks/o9BCwDlgBvAcViuX7AKCLjNbuJ/MV99eHqA9wTfNcsB9qFHf9R1u9HImMZe79fXj7a+mnKERERyZJY7aoSEZGQKHGIiEiWKHGIiEiWKHGIiEiWKHGIiEiWKHFIvmFmOzKxzS1mVjKHz3ucmb0XvG9kZu2P4hidzez+o9hvoJn1PERZAzMbltVjihyJEocUNLcQmaQvx7j7One/KFhsROT+m6y6C3jxKPY7D5h8iLi+ARLMrMZRHFfkkJQ4JN8xsxZmNi3D8zFGWsTNROZammpmU4NtzzOzL81sgZm9G8wVhpmtNLOHgvXfmNlJwfrmZrYoeC00szJmVit4TkVR4GHg0qD80uDZDvHBvoWCZx5UOiDeOsAud98ULA8zs5cs8nyWn4JzvmGR53wMy7BfWaCou280s4uDGL42s+kZDv8hkbv1RXKMEofkV42JtC7qEZm9tpm7P0tkDp6W7t4y+AK/F2jj7k2I3Pl9W4ZjbArWvwTcEay7A7jB3RsB5wDJezf2yBT/9wOj3b2Ru48GRgB7u5LaAF/vTRAZNAMWHLCuAtAKuJXIl/8zRJ6X0MDMGmU43pTg/f1AW3dvCHTMcJx5QZwiOUaJQ/Krr9x9jbunE5leodZBtjmDSGKZaWaLiMxPVDND+d6JJudn2H8m8HTQeinvfz7f4FDeAK4I3l8FvHmQbaoSmZY9ow89Mq3DN8Bv7v5NUJdvM8RyPn9OjT0TGGZm/yTyoKW9NhBpZYnkGCUOya92ZXi/h8jU5wcy4LOgddDI3eu5+9UHOca+/d39ceAaoAQwe28X1qG4+y9EZl1tBZzOn1/0GSUDxQ8Rf/oBdUnPUJfTCCajc/d+RFpP1YFFZnZMsE1xMrSKRHKCEocUNNuBMsH72UAzMzsRIJj9tc7hdjazE4K//v8fkW6gAxNHxuPv9RqRLqsx7r7nIIddCpyYlUqY2SnAsr3HC+Ka4+73A5v4c5rsOkQmJhTJMUocUtAMBSaZ2VR33wj0AUaZ2WIiieSwLQjglr2D0ET+kj+wBTEVqLd3cDxYNwEozcG7qQCmA42DKcszqx3wSYblwcEg/pLgeF8H61sCE7NwXJEj0uy4IlFmZk2BZ9z9kIPUZvYfIuMan2fymJ8BV3jwqNNDbFMM+AI4OxNjMSKZpsQhEkVmNgC4jsgT12YcZrtjgdPdPcceSGZmtYFq7j4tp44pAkocIiKSRRrjEBGRLFHiEBGRLFHiEBGRLFHiEBGRLFHiEBGRLPn/B9NKwXFnKs4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.entity.impact_funcs import ImpactFuncSet, ImpfTropCyclone\n",
    "\n",
    "imp_fun = ImpfTropCyclone.from_emanuel_usa()\n",
    "imp_fun.check()\n",
    "imp_fun.plot()\n",
    "\n",
    "imp_fun_set = ImpactFuncSet()\n",
    "imp_fun_set.append(imp_fun)\n",
    "\n",
    "litpop.impact_funcs = imp_fun_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from climada.engine import Impact\n",
    "impact = Impact()\n",
    "impact.calc(litpop, imp_fun_set, tc_haiti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting other Exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'crop': ['whe', 'soy', 'ric', 'mai'],\n",
       " 'irrigation_status': ['noirr', 'firr'],\n",
       " 'unit': ['USD', 'Tonnes'],\n",
       " 'spatial_coverage': ['global']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop_dataset_infos = client.list_dataset_infos(data_type='crop_production')\n",
    "\n",
    "client.get_property_values(crop_dataset_infos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rice_exposure = client.get_exposures(exposures_type='crop_production', properties = {'crop':'ric', 'unit': 'USD','irrigation_status': 'noirr'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technical Information\n",
    "\n",
    "For programmatical access to the CLIMADA data API there is a specific REST call wrapper class: `climada.util.client.Client`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Server\n",
    "The CLIMADA data file server is hosted on https://data.iac.ethz.ch that can be accessed via a REST API at https://climada.ethz.ch.\n",
    "For REST API details, see the [documentation](https:/climada.ethz.ch/rest/docs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mInit signature:\u001b[0m \u001b[0mClient\u001b[0m\u001b[1;33m(\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",
       "Python wrapper around REST calls to the CLIMADA data API server.\n",
       "    \n",
       "\u001b[1;31mInit docstring:\u001b[0m\n",
       "Constructor of Client.\n",
       "\n",
       "Data API host and chunk_size (for download) are configurable values.\n",
       "Default values are 'climada.ethz.ch' and 8096 respectively.\n",
       "\u001b[1;31mFile:\u001b[0m           c:\\users\\me\\polybox\\workshop\\climada_python\\climada\\util\\api_client.py\n",
       "\u001b[1;31mType:\u001b[0m           type\n",
       "\u001b[1;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Client?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8192"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client()\n",
    "client.chunk_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The url to the API server and the chunk size for the file download can be configured in 'climada.conf'. Just replace the corresponding default values:\n",
    "\n",
    "```json\n",
    "    \"data_api\": {\n",
    "        \"host\": \"https://climada.ethz.ch\",\n",
    "        \"chunk_size\": 8192,\n",
    "        \"cache_db\": \"{local_data.system}/.downloads.db\"\n",
    "    }\n",
    "```\n",
    "\n",
    "The other configuration value affecting the data_api client, `cache_db`, is the path to an SQLite database file, which is keeping track of the files that are successfully downloaded from the api server. Before the Client attempts to download any file from the server, it checks whether the file has been downloaded before and if so, whether the previously downloaded file still looks good (i.e., size and time stamp are as expected). If all of this is the case, the file is simply read from disk without submitting another request."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Metadata\n",
    "\n",
    "#### Unique Identifiers\n",
    "Any dataset can be identified with **data_type**, **name** and **version**. The combination of the three is unique in the API servers' underlying database.\n",
    "However, sometimes the name is already enough for identification.\n",
    "All datasets have a UUID, a universally unique identifier, which is part of their individual url. \n",
    "E.g., the uuid of the dataset https://climada.ethz.ch/rest/dataset/b1c76120-4e60-4d8f-99c0-7e1e7b7860ec is \"b1c76120-4e60-4d8f-99c0-7e1e7b7860ec\".\n",
    "One can retrieve their meta data by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetInfo(uuid='b1c76120-4e60-4d8f-99c0-7e1e7b7860ec', data_type=DataTypeShortInfo(data_type='litpop', data_type_group='exposures'), name='LitPop_assets_pc_150arcsec_SGS', version='v1', status='active', properties={'res_arcsec': '150', 'exponents': '(3,0)', 'fin_mode': 'pc', 'spatial_coverage': 'country', 'date_creation': '2021-09-23', 'climada_version': 'v2.2.0', 'country_iso3alpha': 'SGS', 'country_name': 'South Georgia and the South Sandwich Islands', 'country_iso3num': '239'}, files=[FileInfo(uuid='b1c76120-4e60-4d8f-99c0-7e1e7b7860ec', url='https://data.iac.ethz.ch/climada/b1c76120-4e60-4d8f-99c0-7e1e7b7860ec/LitPop_assets_pc_150arcsec_SGS.hdf5', file_name='LitPop_assets_pc_150arcsec_SGS.hdf5', file_format='hdf5', file_size=1086488, check_sum='md5:27bc1846362227350495e3d946dfad5e')], doi=None, description=\"LitPop asset value exposure per country: Gridded physical asset values by country, at a resolution of 150 arcsec. Values are total produced capital values disaggregated proportionally to the cube of nightlight intensity (Lit^3, based on NASA Earth at Night). The following values were used as parameters in the LitPop.from_countries() method:{'total_values': 'None', 'admin1_calc': 'False','reference_year': '2018', 'gpw_version': '4.11'}Reference: Eberenz et al., 2020. https://doi.org/10.5194/essd-12-817-2020\", license='Attribution 4.0 International (CC BY 4.0)', activation_date='2021-09-13 09:08:28.358559+00:00', expiration_date=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.get_dataset_info_by_uuid('b1c76120-4e60-4d8f-99c0-7e1e7b7860ec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or by filtering:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Data Set Status\n",
    "The datasets of climada.ethz.ch may have the following stati:\n",
    "- **active**: the default for real life data\n",
    "- **preliminary**: when the dataset is already uploaded but some information or file is still missing\n",
    "- **expired**: when a dataset is inactivated again\n",
    "- **test_dataset**: data sets that are used in unit or integration tests have this status in order to be taken seriously by accident\n",
    "When collecting a list of datasets with `get_datasets`, the default dataset status will be 'active'. With the argument `status=None` this filter can be turned off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DatasetInfo Objects and DataFrames\n",
    "\n",
    "As stated above `get_dataset` (or `get_dataset_by_uuid`) return a `DatasetInfo` object and `get_datasets` a list thereof."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mInit signature:\u001b[0m\n",
       "\u001b[0mDatasetInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0muuid\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdata_type\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mclimada\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataTypeShortInfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mversion\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mstatus\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mproperties\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mfiles\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdoi\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdescription\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlicense\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mactivation_date\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mexpiration_date\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m      dataset data from CLIMADA data API.\n",
       "\u001b[1;31mFile:\u001b[0m           c:\\users\\me\\polybox\\workshop\\climada_python\\climada\\util\\api_client.py\n",
       "\u001b[1;31mType:\u001b[0m           type\n",
       "\u001b[1;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.util.api_client import DatasetInfo\n",
    "DatasetInfo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where files is a list of `FileInfo` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mInit signature:\u001b[0m\n",
       "\u001b[0mFileInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0muuid\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0murl\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mfile_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mfile_format\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mfile_size\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcheck_sum\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m      file data from CLIMADA data API.\n",
       "\u001b[1;31mFile:\u001b[0m           c:\\users\\me\\polybox\\workshop\\climada_python\\climada\\util\\api_client.py\n",
       "\u001b[1;31mType:\u001b[0m           type\n",
       "\u001b[1;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from climada.util.api_client import FileInfo\n",
    "FileInfo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert into DataFrame\n",
    "There are conveinience functions to easily convert datasets into pandas DataFrames, `get_datasets` and `expand_files`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minto_datasets_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset_infos\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",
       "Convenience function providing a DataFrame of datasets with properties.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "dataset_infos : list of DatasetInfo\n",
       "     as returned by list_dataset_infos\n",
       "\n",
       "Returns\n",
       "-------\n",
       "pandas.DataFrame\n",
       "    of datasets with properties as found in query by arguments\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\me\\polybox\\workshop\\climada_python\\climada\\util\\api_client.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.into_datasets_df?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_type</th>\n",
       "      <th>data_type_group</th>\n",
       "      <th>uuid</th>\n",
       "      <th>name</th>\n",
       "      <th>version</th>\n",
       "      <th>status</th>\n",
       "      <th>doi</th>\n",
       "      <th>description</th>\n",
       "      <th>license</th>\n",
       "      <th>activation_date</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>res_arcsec</th>\n",
       "      <th>exponents</th>\n",
       "      <th>fin_mode</th>\n",
       "      <th>spatial_coverage</th>\n",
       "      <th>date_creation</th>\n",
       "      <th>climada_version</th>\n",
       "      <th>country_iso3alpha</th>\n",
       "      <th>country_name</th>\n",
       "      <th>country_iso3num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>litpop</td>\n",
       "      <td>exposures</td>\n",
       "      <td>b1c76120-4e60-4d8f-99c0-7e1e7b7860ec</td>\n",
       "      <td>LitPop_assets_pc_150arcsec_SGS</td>\n",
       "      <td>v1</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>LitPop asset value exposure per country: Gridd...</td>\n",
       "      <td>Attribution 4.0 International (CC BY 4.0)</td>\n",
       "      <td>2021-09-13 09:08:28.358559+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>(3,0)</td>\n",
       "      <td>pc</td>\n",
       "      <td>country</td>\n",
       "      <td>2021-09-23</td>\n",
       "      <td>v2.2.0</td>\n",
       "      <td>SGS</td>\n",
       "      <td>South Georgia and the South Sandwich Islands</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>litpop</td>\n",
       "      <td>exposures</td>\n",
       "      <td>3d516897-5f87-46e6-b673-9e6c00d110ec</td>\n",
       "      <td>LitPop_pop_150arcsec_SGS</td>\n",
       "      <td>v1</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>LitPop population exposure per country: Gridde...</td>\n",
       "      <td>Attribution 4.0 International (CC BY 4.0)</td>\n",
       "      <td>2021-09-13 09:09:10.634374+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>(0,1)</td>\n",
       "      <td>pop</td>\n",
       "      <td>country</td>\n",
       "      <td>2021-09-23</td>\n",
       "      <td>v2.2.0</td>\n",
       "      <td>SGS</td>\n",
       "      <td>South Georgia and the South Sandwich Islands</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>litpop</td>\n",
       "      <td>exposures</td>\n",
       "      <td>a6864a65-36a2-4701-91bc-81b1355103b5</td>\n",
       "      <td>LitPop_150arcsec_SGS</td>\n",
       "      <td>v1</td>\n",
       "      <td>active</td>\n",
       "      <td>None</td>\n",
       "      <td>LitPop asset value exposure per country: Gridd...</td>\n",
       "      <td>Attribution 4.0 International (CC BY 4.0)</td>\n",
       "      <td>2021-09-13 09:09:30.907938+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>(1,1)</td>\n",
       "      <td>pc</td>\n",
       "      <td>country</td>\n",
       "      <td>2021-09-23</td>\n",
       "      <td>v2.2.0</td>\n",
       "      <td>SGS</td>\n",
       "      <td>South Georgia and the South Sandwich Islands</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  data_type data_type_group                                  uuid  \\\n",
       "0    litpop       exposures  b1c76120-4e60-4d8f-99c0-7e1e7b7860ec   \n",
       "1    litpop       exposures  3d516897-5f87-46e6-b673-9e6c00d110ec   \n",
       "2    litpop       exposures  a6864a65-36a2-4701-91bc-81b1355103b5   \n",
       "\n",
       "                             name version  status   doi  \\\n",
       "0  LitPop_assets_pc_150arcsec_SGS      v1  active  None   \n",
       "1        LitPop_pop_150arcsec_SGS      v1  active  None   \n",
       "2            LitPop_150arcsec_SGS      v1  active  None   \n",
       "\n",
       "                                         description  \\\n",
       "0  LitPop asset value exposure per country: Gridd...   \n",
       "1  LitPop population exposure per country: Gridde...   \n",
       "2  LitPop asset value exposure per country: Gridd...   \n",
       "\n",
       "                                     license  \\\n",
       "0  Attribution 4.0 International (CC BY 4.0)   \n",
       "1  Attribution 4.0 International (CC BY 4.0)   \n",
       "2  Attribution 4.0 International (CC BY 4.0)   \n",
       "\n",
       "                    activation_date expiration_date res_arcsec exponents  \\\n",
       "0  2021-09-13 09:08:28.358559+00:00            None        150     (3,0)   \n",
       "1  2021-09-13 09:09:10.634374+00:00            None        150     (0,1)   \n",
       "2  2021-09-13 09:09:30.907938+00:00            None        150     (1,1)   \n",
       "\n",
       "  fin_mode spatial_coverage date_creation climada_version country_iso3alpha  \\\n",
       "0       pc          country    2021-09-23          v2.2.0               SGS   \n",
       "1      pop          country    2021-09-23          v2.2.0               SGS   \n",
       "2       pc          country    2021-09-23          v2.2.0               SGS   \n",
       "\n",
       "                                   country_name country_iso3num  \n",
       "0  South Georgia and the South Sandwich Islands             239  \n",
       "1  South Georgia and the South Sandwich Islands             239  \n",
       "2  South Georgia and the South Sandwich Islands             239  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from climada.util.api_client import Client\n",
    "client = Client()\n",
    "litpop_datasets = client.list_dataset_infos(data_type='litpop', properties={'country_name': 'South Georgia and the South Sandwich Islands'})\n",
    "litpop_df = client.into_datasets_df(litpop_datasets)\n",
    "litpop_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Download\n",
    "\n",
    "The wrapper functions get_exposures or get_hazard fetch the information, download the file and opens the file as a climada object. But one can also just download dataset files using the method `download_dataset` which takes a `DatasetInfo` object as argument and downloads all files of the dataset to a directory in the local file system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdownload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mtarget_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mWindowsPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'C:/Users/me/climada/data'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0morganize_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\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",
       "Download all files from a given dataset to a given directory.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "dataset : DatasetInfo\n",
       "    the dataset\n",
       "target_dir : Path, optional\n",
       "    target directory for download, by default `climada.util.constants.SYSTEM_DIR`\n",
       "organize_path: bool, optional\n",
       "    if set to True the files will end up in subdirectories of target_dir:\n",
       "    [target_dir]/[data_type_group]/[data_type]/[name]/[version]\n",
       "    by default True\n",
       "\n",
       "Returns\n",
       "-------\n",
       "download_dir : Path\n",
       "    the path to the directory containing the downloaded files,\n",
       "    will be created if organize_path is True\n",
       "downloaded_files : list of Path\n",
       "    the downloaded files themselves\n",
       "\n",
       "Raises\n",
       "------\n",
       "Exception\n",
       "    when one of the files cannot be downloaded\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\me\\polybox\\workshop\\climada_python\\climada\\util\\api_client.py\n",
       "\u001b[1;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client.download_dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Cache\n",
    "The method avoids superfluous downloads by keeping track of all downloads in a sqlite db file. The client will make sure that the same file is never downloaded to the same target twice.\n",
    "\n",
    "#### Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(WindowsPath('C:/Users/me/climada/data/exposures/litpop/LitPop_assets_pc_150arcsec_SGS/v1/LitPop_assets_pc_150arcsec_SGS.hdf5'),\n",
       " True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at an example for downloading a litpop dataset first\n",
    "ds = litpop_datasets[0]  # litpop_datasets is a list and download_dataset expects a single object as argument.\n",
    "download_dir, ds_files = client.download_dataset(ds)\n",
    "ds_files[0], ds_files[0].is_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PosixPath('/home/yuyue/climada/data/hazard/tropical_cyclone/tropical_cyclone_50synth_tracks_150arcsec_rcp26_BRA_2040/v1/tropical_cyclone_50synth_tracks_150arcsec_rcp26_BRA_2040.hdf5'),\n",
       " True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another example for downloading a hazard (tropical cyclone) dataset\n",
    "ds_tc = tc_dataset_infos[0] \n",
    "download_dir, ds_files = client.download_dataset(ds_tc)\n",
    "ds_files[0], ds_files[0].is_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Offline Mode\n",
    "\n",
    "The API Client is silently used in many methods and functions of CLIMADA, including the installation test that is run to see whether the CLIMADA installation was successful.\n",
    "Most methods of the client send GET requests to the API server assuming the latter is accessible through a working internet connection.\n",
    "If this is not the case, the functionality of CLIMADA is severely limited if not altogether lost. Often this is an unnecessary restriction, \n",
    "e.g., when a user wants to access a file through the API Client that is already downloaded and available in the local filesystem.\n",
    "\n",
    "In such cases the API Client runs in _offline mode_.\n",
    "In this mode the client falls back to previous results for the same call in case there is no internet connection or the server is not accessible.\n",
    "\n",
    "To turn this feature off and make sure that all results are current and up to date - at the cost of failing when there is no internet connection - one has to disable tha _cache_.\n",
    "This can be done programmatically, by initializing the API Client with the optional argument `cache_enabled`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(cache_enabled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or it can be done through configuration. Edit the `climada.conf` file in the working directory or in ~/climada/ and change the \"cache_enabled\" value, like this:\n",
    "\n",
    "```javascript\n",
    "...\n",
    "    \"data_api\": {\n",
    "        ...\n",
    "        \"cache_enabled\": false\n",
    "    },\n",
    "...\n",
    "```\n",
    "\n",
    "While `cache_enabled` is `true` (default), every result from the server is stored as a json file in ~/climada/data/.apicache/ by a unique name derived from the method and arguments of the call.\n",
    "If the very same call is made again later, at a time where the server is not accessible, the client just comes back to the cached result from the previous call."
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}