Plotting diff on the watershed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from osgeo import gdal\n",
    "import rasterio\n",
    "import rasterio.plot as rioplot\n",
    "import os\n",
    "from osgeo import gdal\n",
    "import shapefile\n",
    "import shapely.geometry as shp\n",
    "from rasterio import mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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273140.467589433,4229580.934460746 L 273140.467589433,4229610.934460746 L 273170.467589433,4229610.934460746 L 273170.467589433,4229640.934460746 z\" /></g></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x119cf8898>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf = shapefile.Reader(\"../scene_30m_mask.shp\")\n",
    "recs    = sf.records()\n",
    "shapes  = sf.shapes()\n",
    "Nshp    = len(shapes)\n",
    "cns     = []\n",
    "\n",
    "for nshp in  range(Nshp):\n",
    "    ptchs   = []\n",
    "    pts     = np.array(shapes[nshp].points)\n",
    "    prt     = shapes[nshp].parts\n",
    "    par     = list(prt) + [pts.shape[0]]\n",
    "    poly = [shp.Polygon(pts[par[pij]:par[pij+1]]) for pij in range(len(prt))]\n",
    "multipolygon2 = shp.MultiPolygon(poly)    \n",
    "multipolygon2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tmean_2015-2014\n",
      "tmax_2015-2014\n",
      "tmin_2015-2014\n",
      "tmean_2015-2016\n",
      "tmax_2015-2016\n",
      "tmin_2015-2016\n"
     ]
    }
   ],
   "source": [
    "datafiles = [\"tmean_2015-2014\",\"tmax_2015-2014\",\"tmin_2015-2014\",\"tmean_2015-2016\",\"tmax_2015-2016\",\"tmin_2015-2016\"]\n",
    "data_matrices = []\n",
    "for files in datafiles:\n",
    "    print(files)\n",
    "    input_prismfile = '../interimfiles/prism_diff_{}.tif'.format(files)\n",
    "    input_raster = gdal.Open(input_prismfile)\n",
    "    output_raster = 'test_epsg32611'\n",
    "    gdal.Warp(output_raster,input_raster,dstSRS='EPSG:32611')\n",
    "    prism_new_projection = rasterio.open(output_raster)\n",
    "    prism_diff_cropped = rasterio.mask.mask(prism_new_projection,multipolygon2,nodata = -9999,crop = True)\n",
    "    data_matrices.append(prism_diff_cropped[0])\n",
    "    del input_prismfile, input_raster, output_raster, prism_new_projection, prism_diff_cropped\n",
    "data_for_plotting = np.reshape(np.array(data_matrices),(6,13,13))\n",
    "data_for_plotting= np.ma.masked_values(data_for_plotting, -9999.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,14))\n",
    "plot_number=1\n",
    "for matrix in data_for_plotting:\n",
    "   ax = plt.subplot(3,3, plot_number)\n",
    "   plt.imshow(matrix, vmin = -2, vmax = 2,cmap = 'bwr')\n",
    "   plt.title(datafiles[plot_number-1])\n",
    "   plot_number = plot_number + 1\n",
    "   plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121528518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_30m = rasterio.open('../scene_30m_mask.tif')\n",
    "rioplot.show(data_30m, with_bounds=True)"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}