FigureS1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplemantary Figure 1 - Light Change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Import Necessary Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy \n",
    "import cartopy.io.shapereader as shpreader \n",
    "import cartopy.io.shapereader as Reader \n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import proplot\n",
    "import rioxarray\n",
    "import xarray as xr \n",
    "\n",
    "from cartopy.feature import ShapelyFeature \n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes \n",
    "from netCDF4 import Dataset \n",
    "from shapely.geometry import mapping\n",
    "from wrf import xy_to_ll \n",
    "\n",
    "# warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Open Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset path names (1990 and 2013)\n",
    "years = ['F101992.v4b',\n",
    "         'F182013.v4c']\n",
    "\n",
    "appending = '_web.stable_lights.avg_vis.tif'\n",
    "\n",
    "# list of 2 images\n",
    "ims = []\n",
    "for y in years:\n",
    "    \n",
    "    file = fr'{y}{appending}'\n",
    "    im = xr.open_rasterio(file)\n",
    "    ims.append(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 1, y: 16801, x: 43201)&gt;\n",
       "[725820001 values with dtype=uint8]\n",
       "Coordinates:\n",
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       "  * y        (y) float64 75.0 74.99 74.98 74.98 ... -64.97 -64.98 -64.99 -65.0\n",
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       "Attributes:\n",
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       "    TIFFTAG_XRESOLUTION:  1\n",
       "    TIFFTAG_YRESOLUTION:  1\n",
       "    _FillValue:           255</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 16801</li><li><span class='xr-has-index'>x</span>: 43201</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3701e5e7-434e-4cea-b6c7-5e8536da8384' class='xr-array-in' type='checkbox' checked><label for='section-3701e5e7-434e-4cea-b6c7-5e8536da8384' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[725820001 values with dtype=uint8]</pre></div></div></li><li class='xr-section-item'><input id='section-b45436c1-b813-49a3-b860-7630f0cb26af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b45436c1-b813-49a3-b860-7630f0cb26af' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-b2cd5396-accc-486c-8bf8-fb7f58c175e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2cd5396-accc-486c-8bf8-fb7f58c175e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4bdb489-e248-4879-ae00-6710e6807305' class='xr-var-data-in' type='checkbox'><label for='data-c4bdb489-e248-4879-ae00-6710e6807305' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>75.0 74.99 74.98 ... -64.99 -65.0</div><input id='attrs-439fda24-286f-468b-a519-050f8992d289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-439fda24-286f-468b-a519-050f8992d289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58d7b920-b2b5-4675-b889-54b8180649cb' class='xr-var-data-in' type='checkbox'><label for='data-58d7b920-b2b5-4675-b889-54b8180649cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 75.      ,  74.991667,  74.983333, ..., -64.983333, -64.991666,\n",
       "       -64.999999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-795026ce-97f4-4bf9-9ac5-68bc1b7d6c8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-795026ce-97f4-4bf9-9ac5-68bc1b7d6c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b367fa34-ad7a-49b8-aacd-a3a40daeaea0' class='xr-var-data-in' type='checkbox'><label for='data-b367fa34-ad7a-49b8-aacd-a3a40daeaea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180.      , -179.991667, -179.983333, ...,  179.983332,  179.991665,\n",
       "        179.999999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9ccf503-e8bd-4e07-9166-a36e271b2575' class='xr-section-summary-in' type='checkbox'  ><label for='section-b9ccf503-e8bd-4e07-9166-a36e271b2575' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(0.0083333333, 0.0, -180.00416666665, 0.0, -0.0083333333, 75.00416666665)</dd><dt><span>crs :</span></dt><dd>+init=epsg:4326</dd><dt><span>res :</span></dt><dd>(0.0083333333, 0.0083333333)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan,)</dd><dt><span>scales :</span></dt><dd>(1.0,)</dd><dt><span>offsets :</span></dt><dd>(0.0,)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd><dt><span>_FillValue :</span></dt><dd>255</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 16801, x: 43201)>\n",
       "[725820001 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * band     (band) int64 1\n",
       "  * y        (y) float64 75.0 74.99 74.98 74.98 ... -64.97 -64.98 -64.99 -65.0\n",
       "  * x        (x) float64 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0 180.0\n",
       "Attributes:\n",
       "    transform:            (0.0083333333, 0.0, -180.00416666665, 0.0, -0.00833...\n",
       "    crs:                  +init=epsg:4326\n",
       "    res:                  (0.0083333333, 0.0083333333)\n",
       "    is_tiled:             0\n",
       "    nodatavals:           (nan,)\n",
       "    scales:               (1.0,)\n",
       "    offsets:              (0.0,)\n",
       "    AREA_OR_POINT:        Area\n",
       "    TIFFTAG_XRESOLUTION:  1\n",
       "    TIFFTAG_YRESOLUTION:  1\n",
       "    _FillValue:           255"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write nodata information\n",
    "ims[0].rio.write_nodata(255, inplace=True)\n",
    "ims[1].rio.write_nodata(255, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Define Shapefile Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shapefile path names for:\n",
    "\n",
    "# Turkey - country\n",
    "turkey_shape = r'turkey_provinces.shp'\n",
    "\n",
    "# Ankara - province\n",
    "ankara_shape = r'ankara_shape.shp'\n",
    "\n",
    "# Ankara - districts\n",
    "ankara_shape_district = r'ankara_shape_districts.shp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Plot Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_to_city(data, shapefile, x_dims, y_dims):\n",
    "    \"\"\"\n",
    "    Clips xarray Dataset into given shapefile\n",
    "    \n",
    "    Parameters:\n",
    "        data: xr.Dataset -- xarray dataset to be clipped\n",
    "        shapefile: gpd.DataDrame -- shapefile data to clip\n",
    "        x_dims: str -- x dimension name\n",
    "        y_dims: str -- y dimension name\n",
    "        \n",
    "    Returns:\n",
    "        xr.Dataset -- clipped dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    # set spatial dim names\n",
    "    data= data.rio.set_spatial_dims(x_dim=x_dims, y_dim=y_dims)\n",
    "    \n",
    "    # clip data into shapefile\n",
    "    clipped = data.rio.clip(shapefile.geometry.apply(mapping), shapefile.crs, all_touched=True, invert=False)\n",
    "    \n",
    "    return clipped\n",
    "\n",
    "def plot_turkey(province, turkey_shape, ankara_shape,\n",
    "                ankara_shape_district, lon_lat_limits):\n",
    "    \"\"\"\n",
    "    Create light plots\n",
    "    \n",
    "    Parameters:\n",
    "        province: str -- province name\n",
    "        turkey_shape: str -- turkey shape path\n",
    "        ankara_shape: str -- ankara shape path\n",
    "        ankara_shape_district: str -- ankara districts shape path\n",
    "        lon_lat_limits: dict -- dict containing ankara's lon/lat limits\n",
    "        \n",
    "    Returns:\n",
    "        plt.figure\n",
    "    \"\"\"\n",
    "    \n",
    "    # lon and lat information to clip the data into approximate turkey borders\n",
    "    lat_tur = slice(51.9, 30.9)\n",
    "    lon_tur = slice(25.7, 41.7)\n",
    "    \n",
    "    # x and y dimension names\n",
    "    x_dim = 'x'\n",
    "    y_dim = 'y'\n",
    "    \n",
    "    # open ankara gpd dataframe\n",
    "    shp_ankara = gpd.read_file(ankara_shape)\n",
    "    \n",
    "    # projection\n",
    "    cart_proj = cartopy.crs.Mercator()\n",
    "\n",
    "    # Create Figure\n",
    "    fig_array = [[1,1,2,2]]\n",
    "    fig, axs = proplot.subplots(fig_array, \n",
    "                                aspect=30, axwidth=15,\n",
    "                                proj=cart_proj, hratios=(1),\n",
    "                                includepanels=True, wspace=3\n",
    "                                )\n",
    "    \n",
    "    # format figure\n",
    "    for i in range(2):\n",
    "        \n",
    "        axs[i].format(lonlim=(lon_lat_limits[province][0],\n",
    "                              lon_lat_limits[province][1]),\n",
    "                      latlim=(lon_lat_limits[province][2],\n",
    "                              lon_lat_limits[province][3]),\n",
    "                      labels=False, longrid=False, latgrid = False,\n",
    "                      nsteps=100, labelsize=15, abc='(a)',\n",
    "                      abcloc='ul', abcsize = 40)\n",
    "\n",
    "        \n",
    "    # add shapefiles to figure\n",
    "    # Turkey - country\n",
    "    shape_feature_turkey = ShapelyFeature(shpreader.Reader(turkey_shape).geometries(),\n",
    "                                          cartopy.crs.PlateCarree(),\n",
    "                                          facecolor='none', edgecolor = 'black',\n",
    "                                          linewidth = 0.5, zorder = 0.3)\n",
    "    \n",
    "    # Ankara - province\n",
    "    shape_feature_ankara = ShapelyFeature(shpreader.Reader(ankara_shape).geometries(),\n",
    "                                          cartopy.crs.PlateCarree(),\n",
    "                                          facecolor='none', edgecolor = 'black',\n",
    "                                          linewidth = 6.2, zorder = 0.3)\n",
    "    \n",
    "    # Ankara - districts\n",
    "    shape_feature_ankara_dist = ShapelyFeature(shpreader.Reader(ankara_shape_district).geometries(),\n",
    "                                               cartopy.crs.PlateCarree(),\n",
    "                                               facecolor='none', edgecolor = 'white',\n",
    "                                               linewidth = 0.5, zorder = 0.3)\n",
    "\n",
    "    # add shapefiles as features\n",
    "    for i in range(2):\n",
    "\n",
    "        axs[i].add_feature(shape_feature_turkey)\n",
    "        axs[i].add_feature(shape_feature_ankara)\n",
    "        axs[i].add_feature(shape_feature_ankara_dist)\n",
    "        \n",
    "    \n",
    "    # cmap\n",
    "    cmap = proplot.Colormap('Solar')\n",
    "    \n",
    "    # clip and plot data\n",
    "    for i in range(2):\n",
    "        \n",
    "        # nearest turkey borders\n",
    "        data_spec = ims[i].squeeze().sel(y=lat_tur, x=lon_tur)\n",
    "        \n",
    "        # reproject\n",
    "        data_spec = data_spec.rio.reproject('EPSG:4326')\n",
    "        \n",
    "        # clip into turkey shapefile\n",
    "        dt_clipped = clip_to_city(data_spec, shp_ankara, x_dim, y_dim)\n",
    "        \n",
    "        # nodata to np.nan\n",
    "        dt_clipped = xr.where(dt_clipped==255, np.nan, dt_clipped)\n",
    "        \n",
    "        # write nodata\n",
    "        dt_clipped = dt_clipped.rio.write_nodata(np.nan)\n",
    "        \n",
    "        # plot mesh\n",
    "        mesh = dt_clipped.plot.pcolormesh(ax = axs[i], \n",
    "                             alpha=1, add_colorbar = False,\n",
    "                             cmap=cmap, transform=cartopy.crs.PlateCarree(),\n",
    "                             zorder = 0.1, add_labels=False\n",
    "                             )\n",
    "        # title\n",
    "        mesh.title=''\n",
    "    \n",
    "    # save figure\n",
    "    plt.savefig(r'FigureS1.jpeg',\n",
    "                bbox_inches='tight', optimize=True,\n",
    "                progressive=True, dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# province name\n",
    "province = 'Ankara'\n",
    "\n",
    "# longitude latitude extent for Ankara\n",
    "lon_lat_limits = {\n",
    "                  'Ankara':[30.7, 34.1, 38.6, 40.9]\n",
    "                 }\n",
    "\n",
    "# figure\n",
    "fig = plot_turkey(province, turkey_shape, ankara_shape, ankara_shape_district, lon_lat_limits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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