{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation and infrastructure exposure to flooding\n",
    "\n",
    "This notebook forms the basis of \"Hands-On 5\" in the CCG course.\n",
    "\n",
    "1. Extract infrastructure data from OpenStreetMap\n",
    "2. Extract flood hazard data from Aqueduct\n",
    "3. Intersect floods with roads to calculate exposure\n",
    "4. Open QGIS to look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The os and subprocess modules are built into Python\n",
    "# see https://docs.python.org/3/library/os.html\n",
    "import os\n",
    "\n",
    "# see https://docs.python.org/3/library/subprocess.html\n",
    "import subprocess\n",
    "\n",
    "# see https://docs.python.org/3/library/pathlib.html\n",
    "from pathlib import Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare infrastructure data\n",
    "\n",
    "### 1.1 Create a working folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir = Path().resolve().parent.parent\n",
    "new_folder = dir / \"ghana_tutorial\"\n",
    "if not new_folder.exists():\n",
    "    os.makedirs(new_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edit this if using a Mac (otherwise delete)\n",
    "data_folder = Path(new_folder)  # Path(\"YOUR_PATH/ghana_tutorial\")\n",
    "\n",
    "# edit this if using Windows (otherwise delete)\n",
    "# data_folder = Path(\"C:YOUR_PATH/ghana_tutorial\")\n",
    "\n",
    "# delete this line\n",
    "# data_folder = Path(\"../data\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Load Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas and GeoPandas are libraries for working with datasets\n",
    "# see https://geopandas.org/\n",
    "import geopandas as gpd\n",
    "\n",
    "gpd._compat.USE_PYGEOS = False\n",
    "# see https://pandas.pydata.org/\n",
    "import pandas as pd\n",
    "\n",
    "# This package interacts with a risk data extract service, also accessible at\n",
    "# https://global.infrastructureresilience.org/downloads\n",
    "import irv_autopkg_client\n",
    "\n",
    "# We'll use snail to intersect roads with flooding\n",
    "import snail.intersection\n",
    "import snail.io\n",
    "\n",
    "# snkit helps generate connected networks from lines and nodes\n",
    "# see https://snkit.readthedocs.io/\n",
    "import snkit\n",
    "\n",
    "# PyPROJ is a library for working with geographic projections\n",
    "# see https://pyproj4.github.io/\n",
    "from pyproj import Geod\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "import zipfile"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1.3 Download and save data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the `ghana-latest-free.shp.zip` dataset from http://download.geofabrik.de/africa/ghana.html, extract the zip folder and save the extracted folder within your new folder `ghana_tutorial`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data already exists\n"
     ]
    }
   ],
   "source": [
    "download_url = \"https://download.geofabrik.de/africa/ghana-latest-free.shp.zip\"\n",
    "file_path = os.path.join(data_folder, \"ghana-osm.zip\")\n",
    "# check if directory already exists\n",
    "if not os.path.exists(Path(os.path.splitext(file_path)[0])):\n",
    "    urlretrieve(download_url, file_path)  # note: this can take a few minutes\n",
    "    with zipfile.ZipFile(file_path, \"r\") as zip_ref:\n",
    "        zip_ref.extractall(os.path.splitext(file_path)[0])\n",
    "else:\n",
    "    print(\"data already exists\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Load the road dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "roads = gpd.read_file(\n",
    "    os.path.join(os.path.splitext(file_path)[0], \"gis_osm_roads_free_1.shp\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>osm_id</th>\n",
       "      <th>code</th>\n",
       "      <th>fclass</th>\n",
       "      <th>name</th>\n",
       "      <th>ref</th>\n",
       "      <th>oneway</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>layer</th>\n",
       "      <th>bridge</th>\n",
       "      <th>tunnel</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4790591</td>\n",
       "      <td>5121</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>Airport Road</td>\n",
       "      <td>None</td>\n",
       "      <td>B</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>LINESTRING (-0.17184 5.60847, -0.17182 5.60849...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4790592</td>\n",
       "      <td>5122</td>\n",
       "      <td>residential</td>\n",
       "      <td>Nortei Ababio Road</td>\n",
       "      <td>None</td>\n",
       "      <td>B</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>LINESTRING (-0.18282 5.61197, -0.18336 5.61198...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4790594</td>\n",
       "      <td>5115</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>Airport Road</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>LINESTRING (-0.17544 5.6055, -0.17418 5.60555,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4790596</td>\n",
       "      <td>5121</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>Airport Road</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>LINESTRING (-0.17207 5.60853, -0.17207 5.60844...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4790597</td>\n",
       "      <td>5122</td>\n",
       "      <td>residential</td>\n",
       "      <td>Volta Road</td>\n",
       "      <td>None</td>\n",
       "      <td>B</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>LINESTRING (-0.18282 5.61197, -0.1828 5.61262,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    osm_id  code        fclass                name   ref oneway  maxspeed  \\\n",
       "0  4790591  5121  unclassified        Airport Road  None      B        50   \n",
       "1  4790592  5122   residential  Nortei Ababio Road  None      B        50   \n",
       "2  4790594  5115      tertiary        Airport Road  None      F        50   \n",
       "3  4790596  5121  unclassified        Airport Road  None      F        50   \n",
       "4  4790597  5122   residential          Volta Road  None      B        50   \n",
       "\n",
       "   layer bridge tunnel                                           geometry  \n",
       "0      0      F      F  LINESTRING (-0.17184 5.60847, -0.17182 5.60849...  \n",
       "1      0      F      F  LINESTRING (-0.18282 5.61197, -0.18336 5.61198...  \n",
       "2      0      F      F  LINESTRING (-0.17544 5.6055, -0.17418 5.60555,...  \n",
       "3      0      F      F  LINESTRING (-0.17207 5.60853, -0.17207 5.60844...  \n",
       "4      0      F      F  LINESTRING (-0.18282 5.61197, -0.1828 5.61262,...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roads.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unclassified', 'residential', 'tertiary', 'tertiary_link',\n",
       "       'secondary', 'trunk', 'service', 'primary', 'motorway_link',\n",
       "       'trunk_link', 'primary_link', 'secondary_link', 'footway', 'path',\n",
       "       'track', 'motorway', 'track_grade3', 'track_grade4', 'steps',\n",
       "       'pedestrian', 'bridleway', 'cycleway', 'track_grade2', 'busway',\n",
       "       'track_grade5', 'track_grade1', 'living_street', 'unknown'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roads.fclass.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Filter and clean the data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out minor and residential roads, tracks and paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only the specified columns\n",
    "roads = roads[[\"osm_id\", \"fclass\", \"name\", \"geometry\"]]\n",
    "# Keep only the roads whose \"fclass\" is in the list\n",
    "roads = roads[\n",
    "    roads.fclass.isin(\n",
    "        [\n",
    "            \"motorway\",\n",
    "            \"motorway_link\",\n",
    "            \"trunk\",\n",
    "            \"trunk_link\",\n",
    "            \"primary\",\n",
    "            \"primary_link\",\n",
    "            \"secondary\",\n",
    "            \"secondary_link\",\n",
    "            \"tertiary\",\n",
    "            \"tertiary_link\",\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "# Rename some columns\n",
    "roads = roads.rename(\n",
    "    columns={\n",
    "        \"fclass\": \"road_type\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create topological network information - this adds information that will let us find routes over the road network.\n",
    "- add nodes at the start and end of each road segment\n",
    "- split roads at junctions, so each segment goes from junction to junction\n",
    "- add ids to each node and edge, and add `from_id` and `to_id` to each edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "road_network = snkit.Network(edges=roads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "snkit.network.Network"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(road_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with_endpoints = snkit.network.add_endpoints(road_network)\n",
    "split_edges = snkit.network.split_edges_at_nodes(with_endpoints)\n",
    "with_ids = snkit.network.add_ids(\n",
    "    split_edges, id_col=\"id\", edge_prefix=\"roade\", node_prefix=\"roadn\"\n",
    ")\n",
    "connected = snkit.network.add_topology(with_ids)\n",
    "roads = connected.edges\n",
    "road_nodes = connected.nodes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the length of each road segment in meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "geod = Geod(ellps=\"WGS84\")\n",
    "roads[\"length_m\"] = roads.geometry.apply(geod.geometry_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>osm_id</th>\n",
       "      <th>road_type</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>id</th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "      <th>length_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16244</th>\n",
       "      <td>1439354540</td>\n",
       "      <td>primary</td>\n",
       "      <td>Graphic Road</td>\n",
       "      <td>LINESTRING (-0.21592 5.5546, -0.21562 5.55452,...</td>\n",
       "      <td>roade_16244</td>\n",
       "      <td>roadn_8718</td>\n",
       "      <td>roadn_5135</td>\n",
       "      <td>188.675096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16245</th>\n",
       "      <td>1439354541</td>\n",
       "      <td>trunk</td>\n",
       "      <td>Dr. Busia Highway</td>\n",
       "      <td>LINESTRING (-0.23183 5.56271, -0.23141 5.56246...</td>\n",
       "      <td>roade_16245</td>\n",
       "      <td>roadn_11348</td>\n",
       "      <td>roadn_7945</td>\n",
       "      <td>154.835760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16246</th>\n",
       "      <td>1439354542</td>\n",
       "      <td>trunk</td>\n",
       "      <td>Winneba Road</td>\n",
       "      <td>LINESTRING (-0.38247 5.54668, -0.38234 5.54674...</td>\n",
       "      <td>roade_16246</td>\n",
       "      <td>roadn_5479</td>\n",
       "      <td>roadn_11392</td>\n",
       "      <td>97.011372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16247</th>\n",
       "      <td>1439354543</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (-0.45285 5.50639, -0.4531 5.50646,...</td>\n",
       "      <td>roade_16247</td>\n",
       "      <td>roadn_5473</td>\n",
       "      <td>roadn_12716</td>\n",
       "      <td>82.067327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16248</th>\n",
       "      <td>1441492227</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>Abeka Road</td>\n",
       "      <td>LINESTRING (-0.23592 5.59925, -0.23531 5.59905...</td>\n",
       "      <td>roade_16248</td>\n",
       "      <td>roadn_7929</td>\n",
       "      <td>roadn_7926</td>\n",
       "      <td>252.712114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           osm_id road_type               name  \\\n",
       "16244  1439354540   primary       Graphic Road   \n",
       "16245  1439354541     trunk  Dr. Busia Highway   \n",
       "16246  1439354542     trunk       Winneba Road   \n",
       "16247  1439354543  tertiary               None   \n",
       "16248  1441492227  tertiary         Abeka Road   \n",
       "\n",
       "                                                geometry           id  \\\n",
       "16244  LINESTRING (-0.21592 5.5546, -0.21562 5.55452,...  roade_16244   \n",
       "16245  LINESTRING (-0.23183 5.56271, -0.23141 5.56246...  roade_16245   \n",
       "16246  LINESTRING (-0.38247 5.54668, -0.38234 5.54674...  roade_16246   \n",
       "16247  LINESTRING (-0.45285 5.50639, -0.4531 5.50646,...  roade_16247   \n",
       "16248  LINESTRING (-0.23592 5.59925, -0.23531 5.59905...  roade_16248   \n",
       "\n",
       "           from_id        to_id    length_m  \n",
       "16244   roadn_8718   roadn_5135  188.675096  \n",
       "16245  roadn_11348   roadn_7945  154.835760  \n",
       "16246   roadn_5479  roadn_11392   97.011372  \n",
       "16247   roadn_5473  roadn_12716   82.067327  \n",
       "16248   roadn_7929   roadn_7926  252.712114  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roads.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roads.set_crs(4326, inplace=True)\n",
    "road_nodes.set_crs(4326, inplace=True)\n",
    "road_nodes.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(170.1578223077743, 0.5, 'Latitude [deg]')"
      ]
     },
     "execution_count": 16,
     "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"
    }
   ],
   "source": [
    "main_roads = roads[\n",
    "    roads[\"road_type\"].isin(\n",
    "        [\n",
    "            \"trunk\",\n",
    "            \"secondary\",\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "main_roads.plot(\n",
    "    ax=ax,\n",
    "    alpha=1,\n",
    "    linewidth=0.5,\n",
    ")\n",
    "\n",
    "ax.grid()\n",
    "ax.set_title(\"Main roads of Ghana\")\n",
    "ax.set_xlabel(\"Longitude [deg]\")\n",
    "ax.set_ylabel(\"Latitude [deg]\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Save the pre-processed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "roads.to_file(\n",
    "    data_folder / \"GHA_OSM_roads.gpkg\",\n",
    "    layer=\"edges\",\n",
    "    driver=\"GPKG\",\n",
    ")\n",
    "road_nodes.to_file(\n",
    "    data_folder / \"GHA_OSM_roads.gpkg\",\n",
    "    layer=\"nodes\",\n",
    "    driver=\"GPKG\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Prepare hazard data\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Aqueduct Global Flood Hazard Maps](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) are available to download openly.\n",
    "\n",
    "Country-level extracts are available through the [GRI Risk Viewer](https://global.infrastructureresilience.org/downloads/). This section uses that service to download an extract for Ghana."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternative: download flood hazard data from Aqueduct"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download. There are some scripts and summary of the data you may find useful at [nismod/aqueduct](https://github.com/nismod/aqueduct).\n",
    "\n",
    "There are almost 700 files in the full Aqueduct dataset, of up to around 100MB each, so we don't recommend downloading all of them unless you intend to do further analysis. If you do, we recommend clipping to the study area of interest before proceeding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_iso = \"gha\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a client to connect to the data API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = irv_autopkg_client.Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client.dataset_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client.dataset(\"wri_aqueduct.version_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.extract_download(\n",
    "    country_iso,\n",
    "    data_folder / \"flood_layer\",\n",
    "    # there may be other datasets available, but only download the following\n",
    "    dataset_filter=[\"wri_aqueduct.version_2\"],\n",
    "    overwrite=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Intersect hazard and infrastructure data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Specify your input and output path as well as the name of the intersection"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now intersect the hazard and the roads, starting with one hazard initially so we save time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_path = Path(\n",
    "    data_folder,\n",
    "    \"flood_layer\",\n",
    "    \"gha\",\n",
    "    \"wri_aqueduct_version_2\",\n",
    "    f\"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-{country_iso}.tif\",\n",
    ")\n",
    "\n",
    "output_path = Path(\n",
    "    data_folder,\n",
    "    \"results\",\n",
    "    \"inunriver_historical_000000000WATCH_1980_rp00100__roads_exposure.gpkg\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in pre-processed road edges, as created earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "roads = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Run the intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid, bands = snail.io.read_raster_metadata(flood_path)\n",
    "\n",
    "prepared = snail.intersection.prepare_linestrings(roads)\n",
    "flood_intersections = snail.intersection.split_linestrings(prepared, grid)\n",
    "flood_intersections = snail.intersection.apply_indices(\n",
    "    flood_intersections, grid\n",
    ")\n",
    "flood_data = snail.io.read_raster_band_data(flood_path)\n",
    "flood_intersections[\"inunriver__epoch_historical__rcp_baseline__rp_100\"] = (\n",
    "    snail.intersection.get_raster_values_for_splits(\n",
    "        flood_intersections, flood_data\n",
    "    )\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the exposed length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "geod = Geod(ellps=\"WGS84\")\n",
    "flood_intersections[\"flood_length_m\"] = flood_intersections.geometry.apply(\n",
    "    geod.geometry_length\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>osm_id</th>\n",
       "      <th>road_type</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>from_id</th>\n",
       "      <th>to_id</th>\n",
       "      <th>length_m</th>\n",
       "      <th>geometry</th>\n",
       "      <th>split</th>\n",
       "      <th>index_i</th>\n",
       "      <th>index_j</th>\n",
       "      <th>inunriver__epoch_historical__rcp_baseline__rp_100</th>\n",
       "      <th>flood_length_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16247</th>\n",
       "      <td>1439354543</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>None</td>\n",
       "      <td>roade_16247</td>\n",
       "      <td>roadn_5473</td>\n",
       "      <td>roadn_12716</td>\n",
       "      <td>82.067327</td>\n",
       "      <td>LINESTRING (-0.45285 5.50639, -0.4531 5.50646,...</td>\n",
       "      <td>0</td>\n",
       "      <td>337</td>\n",
       "      <td>679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.067327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16248</th>\n",
       "      <td>1441492227</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>Abeka Road</td>\n",
       "      <td>roade_16248</td>\n",
       "      <td>roadn_7929</td>\n",
       "      <td>roadn_7926</td>\n",
       "      <td>252.712114</td>\n",
       "      <td>LINESTRING (-0.23592 5.59925, -0.23531 5.59905...</td>\n",
       "      <td>0</td>\n",
       "      <td>363</td>\n",
       "      <td>668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.712114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           osm_id road_type        name           id     from_id        to_id  \\\n",
       "16247  1439354543  tertiary        None  roade_16247  roadn_5473  roadn_12716   \n",
       "16248  1441492227  tertiary  Abeka Road  roade_16248  roadn_7929   roadn_7926   \n",
       "\n",
       "         length_m                                           geometry  split  \\\n",
       "16247   82.067327  LINESTRING (-0.45285 5.50639, -0.4531 5.50646,...      0   \n",
       "16248  252.712114  LINESTRING (-0.23592 5.59925, -0.23531 5.59905...      0   \n",
       "\n",
       "       index_i  index_j  inunriver__epoch_historical__rcp_baseline__rp_100  \\\n",
       "16247      337      679                                                0.0   \n",
       "16248      363      668                                                0.0   \n",
       "\n",
       "       flood_length_m  \n",
       "16247       82.067327  \n",
       "16248      252.712114  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flood_intersections.tail(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the proportion of roads in our dataset which are exposed to >=1m flood depths in this scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(748.0933306998515)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exposed_1m = flood_intersections[\n",
    "    flood_intersections.inunriver__epoch_historical__rcp_baseline__rp_100 >= 1\n",
    "]\n",
    "exposed_length_km = exposed_1m.flood_length_m.sum() * 1e-3\n",
    "exposed_length_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(29279.892175665864)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_roads_in_dataset_length_km = roads.length_m.sum() * 1e-3\n",
    "all_roads_in_dataset_length_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.02554972969885395)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion = exposed_length_km / all_roads_in_dataset_length_km\n",
    "proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6% of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"{proportion:.1%} of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path.parent.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to file (with spatial data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_intersections.to_file(output_path, driver=\"GPKG\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to CSV (without spatial data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "flood_intersections.drop(columns=\"geometry\").to_csv(\n",
    "    output_path.parent / output_path.name.replace(\".gpkg\", \".csv\")\n",
    ")"
   ]
  }
 ],
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