griddata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from osgeo import gdal,osr\n",
    "from os import mkdir,path\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat_min = 48.5\n",
    "lat_max = 55.5\n",
    "lon_min = 83.0\n",
    "lon_max = 95.5\n",
    "m = Basemap(projection='merc', \n",
    "            llcrnrlon=lon_min,\n",
    "            llcrnrlat=lat_min,\n",
    "            urcrnrlon=lon_max,\n",
    "            urcrnrlat=lat_max,\n",
    "            lon_0=lon_min, \n",
    "            ellps='WGS84',\n",
    "            fix_aspect=True,\n",
    "            resolution='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "block_xmin = 89\n",
    "block_xmax = 91\n",
    "block_ymin = 50.5\n",
    "block_ymax = 52.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy import random\n",
    "npixels = 100000\n",
    "lat_range = lat_max - lat_min\n",
    "lats = lat_range * random.random(npixels) + lat_min\n",
    "lon_range = lon_max - lon_min\n",
    "lons = lon_range * random.random(npixels) + lon_min\n",
    "values = np.zeros(npixels)\n",
    "for p in range(npixels):\n",
    "    if block_ymin < lats[p] < block_ymax \\\n",
    "    and block_xmin < lons[p] < block_xmax:\n",
    "        values[p] = 1.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import interpolate\n",
    "nx = ny = 1000\n",
    "mapx = np.linspace(lon_min,lon_max,nx)\n",
    "mapy = np.linspace(lat_min,lat_max,ny)\n",
    "mapgridx,mapgridy = np.meshgrid(mapx,mapy)\n",
    "mapdata = interpolate.griddata(list(zip(lons,lats)),\n",
    "                               values,(mapgridx,mapgridy),method='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f04b89f5f28>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6UGEmSIUZr9pSYXCvl8seoyEbV8xjn7/csewxclNhqjALocKMl+o1TD1LHkuFmSAVZrxq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\nctIJU0QkJ50wRURy0glTRCSn/wO3B1PlnjnnMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04b8a00470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "m.drawparallels(np.arange(lat_min, lat_max + 0.25, 2.0),\n",
    "                    labels=[True,False,True,False])\n",
    "m.drawmeridians(np.arange(lon_min, lon_max + 0.25, 2.0), \n",
    "                    labels=[True,True,False,True])\n",
    "cm = LinearSegmentedColormap.from_list('blue_red', ['b','r'])\n",
    "m.imshow(mapdata,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f04b8956f60>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8qTD9xbguVJgRUmH6U2H6U2GqMFOhkvCnwvQX47pQYUZIhelPhelPhanCTIVK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\nTBGRhHTCFBFJSCdMEZGE/hfULxGSQEdZogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f04b8d793c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "m.drawparallels(np.arange(lat_min, lat_max + 0.25, 2.0),\n",
    "                    labels=[True,False,True,False])\n",
    "m.drawmeridians(np.arange(lon_min, lon_max + 0.25, 2.0), \n",
    "                    labels=[True,True,False,True])\n",
    "m.scatter(lons,lats,c=values,latlon=True,edgecolors='none',cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}