{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plotting Data\n\nIn this example, we plot some data obtained from a Tradesman model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Imports\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os\n\nos.environ[\"USE_PYGEOS\"] = \"0\"\n\nfrom uuid import uuid4\nfrom tempfile import gettempdir\nfrom aequilibrae.project import Project\nimport branca\nimport folium\nimport geopandas as gpd\nfrom zipfile import ZipFile\nfrom urllib.request import urlretrieve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's download our project data.\nThis data is available as part of the project documentation, and presents\nLa Serena and Coquimbo metropolitan area in Chile.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "URL = \"https://github.com/AequilibraE/tradesman/releases/download/V0.1b/coquimbo.zip\"\n\nproj_fldr = os.path.join(gettempdir(), uuid4().hex)\nfile_fldr = os.path.join(gettempdir(), \"coquimbo.zip\")\n\nif not os.path.isfile(file_fldr):\n    urlretrieve(URL, file_fldr)\n\nZipFile(file_fldr).extractall(proj_fldr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Open an AequilibraE project\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "proj = Project()\nproj.open(proj_fldr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We establish a connection to identify the region we are plotting our data.\nFirst we import our subdivisions\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "with proj.db_connection as conn:\n    subdivisions = gpd.read_postgis(\n        \"SELECT division_name, level, ST_AsBinary(geometry)geom FROM political_subdivisions;\",\n        con=conn,\n        geom_col=\"geom\",\n        crs=4326,\n    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can plot our map!\nGo ahead and check it out\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "colors = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\nm = None\n\nfor lvl in range(-1, subdivisions.level.max() + 1):\n    gdf = subdivisions[subdivisions.level == lvl]\n\n    if m:\n        gdf.explore(\n            m=m,\n            name=f\"level {lvl}\",\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            location=[-29.935717, -71.260520],\n            zoom_start=11,\n            legend=False,\n            color=colors[lvl + 1],\n        )\n    else:\n        m = gdf.explore(\n            name=f\"model_area\",\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            location=[-29.935717, -71.260520],\n            zoom_start=11,\n            legend=False,\n            color=colors[lvl + 1],\n        )\n\nfolium.LayerControl().add_to(m)\n\nm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Feel free to turn on/off all the layers. If you click on the subdivisions, you can also check its name and level.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let's move on and import some information about our model's TAZs.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "with proj.db_connection as conn:\n    zones = gpd.read_postgis(\"SELECT *, ST_AsBinary(geometry) geom FROM zones;\", con=conn, geom_col=\"geom\", crs=4326)\n    zones.drop(columns=[\"geometry\"], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "And create new columns\nPopulation per square kilometer\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "zones[\"POP_DENSITY\"] = zones[\"population\"] / (zones[\"geom\"].to_crs(3857).area * 10e-6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's plot our data!\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "zones.explore(\n    \"POP_DENSITY\",\n    tiles=\"CartoDB positron\",\n    cmap=\"Greens\",\n    tooltip=False,\n    style_kwds={\"fillOpacity\": 1.0},\n    zoom_start=11,\n    location=[-29.935717, -71.260520],\n    popup=True,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Total female population per zone\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "zones[\"TOTALF_POP\"] = zones[[f\"POPF{i}\" for i in range(1, 19)]].sum(axis=1)\n# Total male population per zone\nzones[\"TOTALM_POP\"] = zones[[f\"POPM{i}\" for i in range(1, 19)]].sum(axis=1)\n# Ratio of the male population with respect to the female population\nzones[\"PP_FM\"] = zones.TOTALM_POP / zones.TOTALF_POP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "zones.explore(\n    \"PP_FM\",\n    tiles=\"CartoDB positron\",\n    cmap=\"RdPu\",\n    tooltip=False,\n    style_kwds={\"fillOpacity\": 1.0},\n    zoom_start=11,\n    location=[-29.935717, -71.260520],\n    popup=True,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In an ideal scenario, the ratio of the male population with respect to the female population would be close to 1.06. In countries such as India or China, this ratio is a bit larger, 1.12 and 1.15, respectively. This difference is responsible for creating abnormal sex ratios at birth.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, let's analyze the median age of male and female inhabitants per zone.\nTo plot this data, we shall do a little bit of math first, as our data is represented in intervals.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "interval_min = [0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]\ninterval_mean = [0.5, 3, 7.5, 12.5, 17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5, 52.5, 57.5, 62.5, 67.5, 72.5, 77.5, 82.5]\ninterval_range = [1, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n\nfor sex in [\"F\", \"M\"]:\n    list_values = zones[[f\"POP{sex}{i}\" for i in range(1, 19)]].to_numpy()\n\n    median_values = []\n\n    for idx, lst in enumerate(list_values):\n        median = lst.sum() / 2\n        counter = 0\n        for pos, element in enumerate(lst):\n            counter += element\n            if counter > median:\n                counter -= element\n                break\n\n        median_values.append(interval_min[pos - 1] + ((median - counter) * (interval_range[pos - 1] / lst[pos - 1])))\n\n    zones[f\"MEDIAN_AGE_{sex}\"] = median_values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's take a look at our data!\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig = branca.element.Figure()\n\nsubplot1 = fig.add_subplot(1, 2, 1)\nsubplot2 = fig.add_subplot(1, 2, 2)\n\nmap1 = folium.Map(location=[-29.935717, -71.260520], zoom_start=12)\nmap1 = zones.explore(\n    m=map1,\n    column=\"MEDIAN_AGE_F\",\n    linewidth=0.1,\n    cmap=\"Oranges\",\n    scheme=\"equal_interval\",\n    k=5,\n    legend=False,\n    legend_kwds={\"loc\": \"upper left\", \"fmt\": \"{:.2f}\"},\n    tiles=\"CartoDB positron\",\n)\nfolium.LayerControl().add_to(map1)\n\nmap2 = folium.Map(location=[-29.935717, -71.260520], zoom_start=12)\nmap2 = zones.explore(\n    m=map2,\n    column=\"MEDIAN_AGE_M\",\n    linewidth=0.1,\n    cmap=\"Blues\",\n    legend=False,\n    scheme=\"equal_interval\",\n    k=5,\n    legend_kwds={\"loc\": \"upper left\", \"fmt\": \"{:.2f}\"},\n    tiles=\"CartoDB positron\",\n)\nfolium.LayerControl().add_to(map2)\n\nsubplot1.add_child(map1)\nsubplot2.add_child(map2)\n\nfig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Our model also has OpenStreetMaps Building information. Let's take a look at the location of some building types.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import the data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "with proj.db_connection as conn:\n    qry = \"SELECT building, zone_id, ST_AsBinary(geometry)geom FROM osm_building WHERE geometry IS NOT NULL;\"\n    buildings = gpd.read_postgis(qry, con=conn, geom_col=\"geom\", crs=4326)\n    buildings = buildings[buildings.building.isin([\"undetermined\", \"Religious\", \"residential\", \"commercial\"])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "And plot it\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "colors = [\"#73b7b8\", \"#0077b6\", \"#f05a29\", \"#f05a29\"]\n\nm = None\n\nfor idx, bld in enumerate(buildings.building.unique()):\n    gdf = buildings[buildings.building == bld]\n\n    if m:\n        gdf.explore(\n            m=m,\n            name=bld,\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            zoom_start=15,\n            location=[-29.9541855, -71.3479664],\n            legend=False,\n            color=colors[idx],\n        )\n    else:\n        m = gdf.explore(\n            name=bld,\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            zoom_start=15,\n            location=[-29.9541855, -71.3479664],\n            legend=False,\n            color=colors[idx],\n        )\n\nfolium.LayerControl().add_to(m)\n\nm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finally, let's check out our model's network.\nAs we imported data from OpenStreetMaps, it is possible that we have several _link_type_ categories. We'll plot only five of them.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "with proj.db_connection as conn:\n    qry = \"SELECT link_type, distance, modes, ST_AsBinary(geometry) geom FROM links;\"\n    links = gpd.read_postgis(qry, con=conn, geom_col=\"geom\", crs=4326)\n    links = links[links.link_type.isin([\"motorway\", \"trunk\", \"primary\", \"secondary\", \"tertiary\"])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "colors = [\"#219EBC\", \"#ffb703\", \"#8ECAE6\", \"#023047\", \"#fb8500\"]\nm = None\n\nfor idx, tp in enumerate(links.link_type.unique()):\n    gdf = links[links.link_type == tp]\n    if m:\n        gdf.explore(\n            m=m,\n            name=tp,\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            zoom_start=11,\n            location=[-29.935717, -71.260520],\n            legend=False,\n            color=colors[idx],\n        )\n    else:\n        m = gdf.explore(\n            name=tp,\n            tiles=\"CartoDB positron\",\n            tooltip=False,\n            popup=True,\n            zoom_start=11,\n            location=[-29.935717, -71.260520],\n            legend=False,\n            color=colors[idx],\n        )\n\nfolium.LayerControl().add_to(m)\n\nm"
      ]
    }
  ],
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      "codemirror_mode": {
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