Chapter 2 - Spatial Raster Data in Python cont.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 2 - Spatial Raster Data in Python cont.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-90., -54., -18.,  18.,  54.,  90.],\n",
       "       [-90., -54., -18.,  18.,  54.,  90.],\n",
       "       [-90., -54., -18.,  18.,  54.,  90.],\n",
       "       [-90., -54., -18.,  18.,  54.,  90.],\n",
       "       [-90., -54., -18.,  18.,  54.,  90.],\n",
       "       [-90., -54., -18.,  18.,  54.,  90.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(-90, 90, 6)\n",
    "y = np.linspace(90, -90, 6)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Z1 =  np.abs(((X - 10) ** 2 + (Y - 10) ** 2) / 1 ** 2)\n",
    "Z2 =  np.abs(((X + 10) ** 2 + (Y + 10) ** 2) / 2.5 ** 2)\n",
    "Z =  (Z1 - Z2)\n",
    "\n",
    "plt.imshow(Z)\n",
    "plt.title(\"Temperature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.transform import Affine\n",
    "import rasterio as rio\n",
    "\n",
    "res = (x[-1] - x[0]) / 240.0\n",
    "transform = Affine.translation(x[0] - res / 2, y[0] - res / 2) * Affine.scale(res, res)\n",
    "\n",
    "# open in 'write' mode, unpack profile info to dst\n",
    "with rio.open(\n",
    "   \"Data/new_raster.tif\",\n",
    "   \"w\",\n",
    "   driver=\"GTiff\",         # output file type\n",
    "   height=Z.shape[0],      # shape of array\n",
    "   width=Z.shape[1],\n",
    "   count=1,                # number of bands\n",
    "   dtype=Z.dtype,          # output datatype\n",
    "   crs=\"+proj=latlong\",    # CRS\n",
    "   transform=transform,    # location and resolution of upper left cell\n",
    ") as dst:\n",
    "   # check for number of bands\n",
    "   if dst.count == 1:\n",
    "       # write single band\n",
    "       dst.write(Z, 1)\n",
    "   else:\n",
    "       # write each band individually\n",
    "       for band in range(len(Z)):\n",
    "           # write data, band # (starting from 1)\n",
    "           dst.write(Z[band], band + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rasterio.plot import show\n",
    "%matplotlib inline\n",
    "\n",
    "# Open the file:\n",
    "raster = rio.open(\"Data/new_raster.tif\")\n",
    "\n",
    "# Plot band 1\n",
    "show((raster, 1))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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",
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