MapTheWorld.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.colors import ListedColormap\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "from rasterio.plot import show\n",
    "import rasterio\n",
    "from rasterio.enums import Resampling\n",
    "# 18.4 sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLOUR = ['#1E90FF', # 0 # Dodgerblue\n",
    "          '#FFBF00', # 1 # Amber\n",
    "          '#FF6347', # 2 # Tomato\n",
    "          '#00A86B', # 3 # Jade\n",
    "          '#8A2BE2', # 4 # Blueviolet\n",
    "          '#FF6FFF', # 5 # Ultra Pink\n",
    "          '#00CCFF', # 6 # Vivid Sky Blue\n",
    "          '#00ff40', # 7 # Erin\n",
    "          '#FF004F', # 8 # Folly\n",
    "          '#0063A6', # 9 # Lapis Lazuli\n",
    "        ]\n",
    "def setMplParam(classNum):\n",
    "    # Define effective colors, line styles, and markers based on the class number\n",
    "   \n",
    "    LINE = ['-', '-.', '--', '-.', ':','--','-.','-', ':', '--']\n",
    "    MARKER = ['.','*', '^', 's', '.', 'p', 'o', 's', '.', 'd']\n",
    "    COLOUR_EFF = COLOUR[:classNum]\n",
    "    LINE_EFF = LINE[:classNum]\n",
    "    MARKER_EFF = MARKER[:classNum]\n",
    "\n",
    "    # Set the color cycle for lines including color, line style, and marker\n",
    "    plt.rcParams['axes.prop_cycle'] = (plt.cycler(color=COLOUR_EFF) +\n",
    "                                       plt.cycler(linestyle=LINE_EFF)+\n",
    "                                       plt.cycler(marker=MARKER_EFF))\n",
    "\n",
    "    # Set default line and marker sizes\n",
    "    plt.rcParams['lines.markersize'] = 3  # Example size\n",
    "    plt.rcParams['lines.linewidth'] = 2   # Example width for lines\n",
    "\n",
    "    # Set label and title sizes\n",
    "    plt.rcParams['axes.labelsize'] = 20\n",
    "    plt.rcParams['axes.titlesize'] = 20\n",
    "\n",
    "    # Set tick properties\n",
    "    plt.rcParams['xtick.direction'] = 'in'\n",
    "    plt.rcParams['xtick.labelsize'] = 20\n",
    "    plt.rcParams['ytick.direction'] = 'in'\n",
    "    plt.rcParams['ytick.labelsize'] = 20\n",
    "\n",
    "    # Set legend font size\n",
    "    plt.rcParams['legend.fontsize'] = 12\n",
    "\n",
    "    # Enable and configure grid\n",
    "    plt.rcParams['axes.grid'] = True\n",
    "    plt.rcParams['grid.alpha'] = 0.8\n",
    "    plt.rcParams['grid.linestyle'] = '--'\n",
    "    plt.rcParams['grid.linewidth'] = 1\n",
    "\n",
    "    # Set axes line width\n",
    "    plt.rcParams['axes.linewidth'] = 2\n",
    "\n",
    "    # Set tick sizes and widths\n",
    "    plt.rcParams['xtick.major.size'] = 7\n",
    "    plt.rcParams['xtick.major.width'] = 3\n",
    "    plt.rcParams['xtick.minor.size'] = 2\n",
    "    plt.rcParams['xtick.minor.width'] = 2\n",
    "\n",
    "    plt.rcParams['ytick.major.size'] = 7\n",
    "    plt.rcParams['ytick.major.width'] = 3\n",
    "    plt.rcParams['ytick.minor.size'] = 2\n",
    "    plt.rcParams['ytick.minor.width'] = 2\n",
    "\n",
    "setMplParam(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             City  Latitude  Longitude      Area\n",
      "0    Buenos Aires  -34.6037   -58.3816    203.00\n",
      "1         Chicago   41.8781   -87.6298    606.00\n",
      "2      Copenhagen   55.6761    12.5683     88.00\n",
      "3          Dublin   53.3498    -6.2603    115.00\n",
      "4         Jakarta   -6.2088   106.8456    662.00\n",
      "5          London   51.5074    -0.1278   1572.00\n",
      "6     Los Angeles   34.0522  -118.2437   1302.00\n",
      "7       Melbourne  -37.8136   144.9631   9996.00\n",
      "8     Mexico City   19.4326   -99.1332   1485.00\n",
      "9          Moscow   55.7558    37.6173   2561.00\n",
      "10         Mumbai   19.0760    72.8777    603.00\n",
      "11         Munich   48.1351    11.5820    310.70\n",
      "12  New York City   40.7128   -74.0060    783.80\n",
      "13          Paris   48.8566     2.3522    105.40\n",
      "14         Riyadh   24.7136    46.6753   1975.00\n",
      "15          Seoul   37.5665   126.9780    605.21\n",
      "16       Shanghai   31.2304   121.4737   6340.00\n",
      "17      Singapore    1.3521   103.8198    728.60\n",
      "18      Stockholm   59.3293    18.0686    188.00\n",
      "19         Sydney  -33.8688   151.2093  12367.50\n",
      "20       Tel Aviv   32.0853    34.7818     52.00\n",
      "21          Tokyo   35.6895   139.6917   2191.00\n",
      "22        Toronto   43.6510   -79.3470    630.20\n",
      "23          Wuhan   30.5928   114.3055   8494.00\n",
      "24         Zurich   47.3769     8.5417     87.88\n"
     ]
    }
   ],
   "source": [
    "def readCityLocation():\n",
    "    fileName = 'city_locations.txt'\n",
    "    data = pd.read_csv(fileName, sep=',', \n",
    "        names=['City', 'Latitude', 'Longitude', \"Area\"], \n",
    "        dtype={'City':str, 'Latitude':float, 'Longitude':float, 'Area':float})\n",
    "    return data\n",
    "\n",
    "data = readCityLocation()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEarth():\n",
    "    RESOLUTION = 180 * 100\n",
    "    latitudes = np.linspace(-90, 90, RESOLUTION)\n",
    "    longitudes = np.linspace(-180, 180, RESOLUTION * 2)\n",
    "    unitCellSize = latitudes[1] - latitudes[0]\n",
    "    # If you need a meshgrid for plotting purposes\n",
    "    lon, lat = np.meshgrid(longitudes, latitudes)\n",
    "    return lat, lon, unitCellSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = [lat, lon]\n",
    "# y = [lat, lon]\n",
    "def angularDistance(x, y):\n",
    "    return np.sqrt(np.sum((x - y)**2, axis=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSquareCorners(centre_lat, centre_lon, angularRadius):\n",
    "    ur = [centre_lat + angularRadius, centre_lon + angularRadius]\n",
    "    ul = [centre_lat + angularRadius, centre_lon - angularRadius]\n",
    "    lr = [centre_lat - angularRadius, centre_lon + angularRadius]\n",
    "    ll = [centre_lat - angularRadius, centre_lon - angularRadius]\n",
    "    return ur, ul, lr, ll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCircleAround(centre_lat, centre_lon, angularRadius):\n",
    "    EARTH_LAT, EARTH_LON, gridSize = getEarth()\n",
    "    centre_point = np.array([centre_lat, centre_lon])\n",
    "\n",
    "    # Create an array of all points\n",
    "    all_points = np.stack([EARTH_LAT.ravel(), EARTH_LON.ravel()], axis=-1)\n",
    "    # Calculate distance from the centre point to all points\n",
    "    distances = angularDistance(centre_point, all_points)\n",
    "\n",
    "    # Filter points within the specified angular radius\n",
    "    within_circle = distances < angularRadius\n",
    "\n",
    "    # Extract the latitudes and longitudes of points within the circle\n",
    "    circle_lat = EARTH_LAT.ravel()[within_circle]\n",
    "    circle_lon = EARTH_LON.ravel()[within_circle]\n",
    "\n",
    "    return circle_lat, circle_lon\n",
    "lat, lon = getCircleAround(0, 0, 1)\n",
    "# print(lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCircle(ax, centre_lat, centre_lon, angularRadius):\n",
    "    lat, lon = getCircleAround(centre_lat, centre_lon, angularRadius)\n",
    "    ax.plot(lon, lat, color = COLOUR[2], markersize=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotCities(data):\n",
    "    fig, ax = plt.subplots(figsize=(16, 8))\n",
    "    # Plot cities as blue dots\n",
    "    ax.plot(data['Longitude'], data['Latitude'], linestyle='', markersize=2, label='Cities')\n",
    "    ax.set_xlabel('Longitude')\n",
    "    ax.set_ylabel('Latitude')\n",
    "    ax.set_title('Cities and Surrounding Areas')\n",
    "    \n",
    "    angularRadius = 1  # Example angular radius\n",
    "    for _, row in data.iterrows():\n",
    "        plotCircle(ax, row['Latitude'], row['Longitude'], angularRadius)\n",
    "    ax.legend()\n",
    "plotCities(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_raster_to_image():\n",
    "    original_resolution = 15  # 15 arc seconds\n",
    "    new_resolution = 36  # 36 arc seconds\n",
    "    resampling_factor = new_resolution / original_resolution\n",
    "\n",
    "    input_file = 'average_masked_2022.tif'\n",
    "    image_output_file = 'resampled_2022.png'\n",
    "    \n",
    "    with rasterio.open(input_file) as src:\n",
    "        # Calculate new transform and dimensions\n",
    "        new_transform = src.transform * src.transform.scale(\n",
    "            (src.width / resampling_factor) / src.width,\n",
    "            (src.height / resampling_factor) / src.height\n",
    "        )\n",
    "        new_width = int(src.width / resampling_factor)\n",
    "        new_height = int(src.height / resampling_factor)\n",
    "\n",
    "        # Read and resample the first band of the data\n",
    "        data = src.read(1, out_shape=(new_height, new_width), resampling=Resampling.bilinear)\n",
    "        \n",
    "        # Normalize the data to 0-255 if necessary, handling no-data values if present\n",
    "        nodata_value = src.nodatavals[0]\n",
    "        if nodata_value is not None:\n",
    "            data[data == nodata_value] = np.nan\n",
    "        data_min, data_max = np.nanmin(data), np.nanmax(data)\n",
    "        data_normalized = (data - data_min) / (data_max - data_min) * 255\n",
    "        data_normalized = np.nan_to_num(data_normalized, nan=0).astype('uint8')\n",
    "        \n",
    "        # Save to image using Matplotlib\n",
    "        plt.imsave(image_output_file, data_normalized, cmap='gray')  # Use an appropriate colormap\n",
    "\n",
    "# Example usage\n",
    "resample_raster_to_image()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}