Whitelake by dachas for Khitrov.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем программные библиотеки\n",
    "from osgeo import gdal\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#подгрузка вероятностей, землепользования на 1770 и пустого файла для лэнд юза\n",
    "dataset = gdal.Open('Probability/lu1860.asc')\n",
    "dataset0 = gdal.Open('Probability/P0.asc')\n",
    "dataset1 = gdal.Open('Probability/P1.asc')\n",
    "dataset2 = gdal.Open('Probability/P2.asc')\n",
    "dataset3 = gdal.Open('Probability/P3.asc')\n",
    "dataset4 = gdal.Open('Probability/Empty.asc')\n",
    "dachi_id = gdal.Open('Probability/Drivers/dachi_id.asc').ReadAsArray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#determining raster size\n",
    "X=dataset0.RasterXSize\n",
    "Y=dataset0.RasterYSize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "       [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "       [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "       ...,\n",
       "       [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "       [-99999., -99999., -99999., ..., -99999., -99999., -99999.],\n",
       "       [-99999., -99999., -99999., ..., -99999., -99999., -99999.]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Матрица чисел\n",
    "dataset.ReadAsArray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "LU1860=dataset.ReadAsArray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fields = 0\n",
      "Others = 1244\n",
      "diff1 = 0.0\n",
      "success for region 869\n",
      "Fields = 0\n",
      "Others = 1591\n",
      "diff1 = 0.0\n",
      "success for region 856\n",
      "Fields = 114\n",
      "Others = 5079\n",
      "diff1 = -0.0219526285384171\n",
      "one more\n",
      "Fields = 94\n",
      "Others = 5099\n",
      "diff1 = -0.018101290198343924\n",
      "one more\n",
      "Fields = 75\n",
      "Others = 5118\n",
      "diff1 = -0.014442518775274409\n",
      "one more\n",
      "Fields = 67\n",
      "Others = 5126\n",
      "diff1 = -0.012901983439245137\n",
      "one more\n",
      "Fields = 55\n",
      "Others = 5138\n",
      "diff1 = -0.010591180435201233\n",
      "one more\n",
      "Fields = 48\n",
      "Others = 5145\n",
      "diff1 = -0.00924321201617562\n",
      "success for region 859\n",
      "Fields = 0\n",
      "Others = 1686\n",
      "diff1 = 0.0\n",
      "success for region 860\n",
      "Fields = 0\n",
      "Others = 1619\n",
      "diff1 = 0.0\n",
      "success for region 861\n",
      "Fields = 874\n",
      "Others = 20238\n",
      "diff1 = 0.1036017430845017\n",
      "one more\n",
      "Fields = 1152\n",
      "Others = 19960\n",
      "diff1 = 0.09043387646835921\n",
      "one more\n",
      "Fields = 1397\n",
      "Others = 19715\n",
      "diff1 = 0.0788291019325502\n",
      "one more\n",
      "Fields = 1611\n",
      "Others = 19501\n",
      "diff1 = 0.06869268662372109\n",
      "one more\n",
      "Fields = 1844\n",
      "Others = 19268\n",
      "diff1 = 0.05765630920803333\n",
      "one more\n",
      "Fields = 2012\n",
      "Others = 19100\n",
      "diff1 = 0.049698749526335725\n",
      "one more\n",
      "Fields = 2126\n",
      "Others = 18986\n",
      "diff1 = 0.04429897688518378\n",
      "one more\n",
      "Fields = 2226\n",
      "Others = 18886\n",
      "diff1 = 0.039562334217506614\n",
      "one more\n",
      "Fields = 2331\n",
      "Others = 18781\n",
      "diff1 = 0.034588859416445614\n",
      "one more\n",
      "Fields = 2404\n",
      "Others = 18708\n",
      "diff1 = 0.031131110269041293\n",
      "one more\n",
      "Fields = 2470\n",
      "Others = 18642\n",
      "diff1 = 0.02800492610837438\n",
      "one more\n",
      "Fields = 2519\n",
      "Others = 18593\n",
      "diff1 = 0.025683971201212574\n",
      "one more\n",
      "Fields = 2562\n",
      "Others = 18550\n",
      "diff1 = 0.023647214854111392\n",
      "one more\n",
      "Fields = 2607\n",
      "Others = 18505\n",
      "diff1 = 0.021515725653656684\n",
      "one more\n",
      "Fields = 2636\n",
      "Others = 18476\n",
      "diff1 = 0.0201420992800303\n",
      "one more\n",
      "Fields = 2681\n",
      "Others = 18431\n",
      "diff1 = 0.01801061007957558\n",
      "one more\n",
      "Fields = 2705\n",
      "Others = 18407\n",
      "diff1 = 0.01687381583933306\n",
      "one more\n",
      "Fields = 2724\n",
      "Others = 18388\n",
      "diff1 = 0.015973853732474425\n",
      "one more\n",
      "Fields = 2738\n",
      "Others = 18374\n",
      "diff1 = 0.015310723758999611\n",
      "one more\n",
      "Fields = 2755\n",
      "Others = 18357\n",
      "diff1 = 0.014505494505494487\n",
      "one more\n",
      "Fields = 2770\n",
      "Others = 18342\n",
      "diff1 = 0.013794998105342932\n",
      "one more\n",
      "Fields = 2787\n",
      "Others = 18325\n",
      "diff1 = 0.012989768851837807\n",
      "one more\n",
      "Fields = 2805\n",
      "Others = 18307\n",
      "diff1 = 0.012137173171655913\n",
      "one more\n",
      "Fields = 2823\n",
      "Others = 18289\n",
      "diff1 = 0.011284577491474046\n",
      "one more\n",
      "Fields = 2836\n",
      "Others = 18276\n",
      "diff1 = 0.010668813944676003\n",
      "one more\n",
      "Fields = 2851\n",
      "Others = 18261\n",
      "diff1 = 0.00995831754452442\n",
      "success for region 862\n",
      "Fields = 402\n",
      "Others = 10746\n",
      "diff1 = 0.09743972012917115\n",
      "one more\n",
      "Fields = 634\n",
      "Others = 10514\n",
      "diff1 = 0.07662881234302119\n",
      "one more\n",
      "Fields = 834\n",
      "Others = 10314\n",
      "diff1 = 0.05868837459634016\n",
      "one more\n",
      "Fields = 972\n",
      "Others = 10176\n",
      "diff1 = 0.04630947255113026\n",
      "one more\n",
      "Fields = 1075\n",
      "Others = 10073\n",
      "diff1 = 0.03707014711158953\n",
      "one more\n",
      "Fields = 1155\n",
      "Others = 9993\n",
      "diff1 = 0.029893972012917117\n",
      "one more\n",
      "Fields = 1212\n",
      "Others = 9936\n",
      "diff1 = 0.024780947255113026\n",
      "one more\n",
      "Fields = 1256\n",
      "Others = 9892\n",
      "diff1 = 0.020834050950843203\n",
      "one more\n",
      "Fields = 1295\n",
      "Others = 9853\n",
      "diff1 = 0.017335665590240412\n",
      "one more\n",
      "Fields = 1328\n",
      "Others = 9820\n",
      "diff1 = 0.014375493362038042\n",
      "one more\n",
      "Fields = 1352\n",
      "Others = 9796\n",
      "diff1 = 0.012222640832436321\n",
      "one more\n",
      "Fields = 1370\n",
      "Others = 9778\n",
      "diff1 = 0.01060800143523502\n",
      "one more\n",
      "Fields = 1386\n",
      "Others = 9762\n",
      "diff1 = 0.00917276641550055\n",
      "success for region 865\n",
      "Fields = 0\n",
      "Others = 10687\n",
      "diff1 = 0.0\n",
      "success for region 866\n",
      "Fields = 0\n",
      "Others = 3121\n",
      "diff1 = 0.0\n",
      "success for region 867\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 4786\n",
      "diff1 = 0.033\n",
      "one more\n",
      "Fields = 1\n",
      "Others = 4785\n",
      "diff1 = 0.032791057250313414\n",
      "one more\n",
      "Fields = 2\n",
      "Others = 4784\n",
      "diff1 = 0.03258211450062683\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 4\n",
      "Others = 4782\n",
      "diff1 = 0.03216422900125366\n",
      "one more\n",
      "Fields = 6\n",
      "Others = 4780\n",
      "diff1 = 0.031746343501880485\n",
      "one more\n",
      "Fields = 9\n",
      "Others = 4777\n",
      "diff1 = 0.03111951525282073\n",
      "one more\n",
      "Fields = 9\n",
      "Others = 4777\n",
      "diff1 = 0.03111951525282073\n",
      "one more\n",
      "Fields = 11\n",
      "Others = 4775\n",
      "diff1 = 0.030701629753447555\n",
      "one more\n",
      "Fields = 12\n",
      "Others = 4774\n",
      "diff1 = 0.03049268700376097\n",
      "one more\n",
      "Fields = 13\n",
      "Others = 4773\n",
      "diff1 = 0.030283744254074384\n",
      "one more\n",
      "Fields = 17\n",
      "Others = 4769\n",
      "diff1 = 0.029447973255328042\n",
      "one more\n",
      "Fields = 26\n",
      "Others = 4760\n",
      "diff1 = 0.027567488508148767\n",
      "one more\n",
      "Fields = 26\n",
      "Others = 4760\n",
      "diff1 = 0.027567488508148767\n",
      "one more\n",
      "Fields = 27\n",
      "Others = 4759\n",
      "diff1 = 0.027358545758462183\n",
      "one more\n",
      "Fields = 28\n",
      "Others = 4758\n",
      "diff1 = 0.027149603008775596\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 4757\n",
      "diff1 = 0.026940660259089012\n",
      "one more\n",
      "Fields = 31\n",
      "Others = 4755\n",
      "diff1 = 0.026522774759715838\n",
      "one more\n",
      "Fields = 41\n",
      "Others = 4745\n",
      "diff1 = 0.02443334726284998\n",
      "one more\n",
      "Fields = 43\n",
      "Others = 4743\n",
      "diff1 = 0.02401546176347681\n",
      "one more\n",
      "Fields = 45\n",
      "Others = 4741\n",
      "diff1 = 0.023597576264103637\n",
      "one more\n",
      "Fields = 50\n",
      "Others = 4736\n",
      "diff1 = 0.022552862515670707\n",
      "one more\n",
      "Fields = 51\n",
      "Others = 4735\n",
      "diff1 = 0.02234391976598412\n",
      "one more\n",
      "Fields = 51\n",
      "Others = 4735\n",
      "diff1 = 0.02234391976598412\n",
      "one more\n",
      "Fields = 53\n",
      "Others = 4733\n",
      "diff1 = 0.021926034266610953\n",
      "one more\n",
      "Fields = 57\n",
      "Others = 4729\n",
      "diff1 = 0.021090263267864607\n",
      "one more\n",
      "Fields = 62\n",
      "Others = 4724\n",
      "diff1 = 0.020045549519431678\n",
      "one more\n",
      "Fields = 63\n",
      "Others = 4723\n",
      "diff1 = 0.019836606769745094\n",
      "one more\n",
      "Fields = 63\n",
      "Others = 4723\n",
      "diff1 = 0.019836606769745094\n",
      "one more\n",
      "Fields = 64\n",
      "Others = 4722\n",
      "diff1 = 0.019627664020058506\n",
      "one more\n",
      "Fields = 64\n",
      "Others = 4722\n",
      "diff1 = 0.019627664020058506\n",
      "one more\n",
      "Fields = 67\n",
      "Others = 4719\n",
      "diff1 = 0.019000835770998748\n",
      "one more\n",
      "Fields = 70\n",
      "Others = 4716\n",
      "diff1 = 0.01837400752193899\n",
      "one more\n",
      "Fields = 72\n",
      "Others = 4714\n",
      "diff1 = 0.01795612202256582\n",
      "one more\n",
      "Fields = 74\n",
      "Others = 4712\n",
      "diff1 = 0.017538236523192648\n",
      "one more\n",
      "Fields = 76\n",
      "Others = 4710\n",
      "diff1 = 0.017120351023819477\n",
      "one more\n",
      "Fields = 80\n",
      "Others = 4706\n",
      "diff1 = 0.01628458002507313\n",
      "one more\n",
      "Fields = 82\n",
      "Others = 4704\n",
      "diff1 = 0.01586669452569996\n",
      "one more\n",
      "Fields = 84\n",
      "Others = 4702\n",
      "diff1 = 0.015448809026326789\n",
      "one more\n",
      "Fields = 86\n",
      "Others = 4700\n",
      "diff1 = 0.015030923526953618\n",
      "one more\n",
      "Fields = 87\n",
      "Others = 4699\n",
      "diff1 = 0.01482198077726703\n",
      "one more\n",
      "Fields = 89\n",
      "Others = 4697\n",
      "diff1 = 0.01440409527789386\n",
      "one more\n",
      "Fields = 89\n",
      "Others = 4697\n",
      "diff1 = 0.01440409527789386\n",
      "one more\n",
      "Fields = 89\n",
      "Others = 4697\n",
      "diff1 = 0.01440409527789386\n",
      "one more\n",
      "Fields = 90\n",
      "Others = 4696\n",
      "diff1 = 0.014195152528207272\n",
      "one more\n",
      "Fields = 90\n",
      "Others = 4696\n",
      "diff1 = 0.014195152528207272\n",
      "one more\n",
      "Fields = 91\n",
      "Others = 4695\n",
      "diff1 = 0.013986209778520688\n",
      "one more\n",
      "Fields = 94\n",
      "Others = 4692\n",
      "diff1 = 0.01335938152946093\n",
      "one more\n",
      "Fields = 94\n",
      "Others = 4692\n",
      "diff1 = 0.01335938152946093\n",
      "one more\n",
      "Fields = 96\n",
      "Others = 4690\n",
      "diff1 = 0.012941496030087759\n",
      "one more\n",
      "Fields = 99\n",
      "Others = 4687\n",
      "diff1 = 0.012314667781028\n",
      "one more\n",
      "Fields = 102\n",
      "Others = 4684\n",
      "diff1 = 0.011687839531968242\n",
      "one more\n",
      "Fields = 106\n",
      "Others = 4680\n",
      "diff1 = 0.0108520685332219\n",
      "one more\n",
      "Fields = 119\n",
      "Others = 4667\n",
      "diff1 = 0.008135812787296283\n",
      "success for region 868\n",
      "Fields = 0\n",
      "Others = 1237\n",
      "diff1 = 0.1054\n",
      "one more\n",
      "Fields = 0\n",
      "Others = 1237\n",
      "diff1 = 0.1054\n",
      "one more\n",
      "Fields = 2\n",
      "Others = 1235\n",
      "diff1 = 0.10378318512530314\n",
      "one more\n",
      "Fields = 21\n",
      "Others = 1216\n",
      "diff1 = 0.0884234438156831\n",
      "one more\n",
      "Fields = 28\n",
      "Others = 1209\n",
      "diff1 = 0.08276459175424414\n",
      "one more\n",
      "Fields = 28\n",
      "Others = 1209\n",
      "diff1 = 0.08276459175424414\n",
      "one more\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fields = 28\n",
      "Others = 1209\n",
      "diff1 = 0.08276459175424414\n",
      "one more\n",
      "Fields = 28\n",
      "Others = 1209\n",
      "diff1 = 0.08276459175424414\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 1208\n",
      "diff1 = 0.0819561843168957\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 1208\n",
      "diff1 = 0.0819561843168957\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 1208\n",
      "diff1 = 0.0819561843168957\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 1208\n",
      "diff1 = 0.0819561843168957\n",
      "one more\n",
      "Fields = 29\n",
      "Others = 1208\n",
      "diff1 = 0.0819561843168957\n",
      "one more\n",
      "Fields = 42\n",
      "Others = 1195\n",
      "diff1 = 0.0714468876313662\n",
      "one more\n",
      "Fields = 109\n",
      "Others = 1128\n",
      "diff1 = 0.017283589329021826\n",
      "one more\n",
      "Fields = 121\n",
      "Others = 1116\n",
      "diff1 = 0.007582700080840735\n",
      "success for region 870\n",
      "Fields = 0\n",
      "Others = 3997\n",
      "diff1 = 0.0\n",
      "success for region 872\n",
      "Fields = 0\n",
      "Others = 5931\n",
      "diff1 = 0.0\n",
      "success for region 873\n",
      "Fields = 0\n",
      "Others = 8219\n",
      "diff1 = 0.0\n",
      "success for region 874\n",
      "Fields = 105\n",
      "Others = 6282\n",
      "diff1 = 0.06156035697510569\n",
      "one more\n",
      "Fields = 153\n",
      "Others = 6234\n",
      "diff1 = 0.054045091592296854\n",
      "one more\n",
      "Fields = 205\n",
      "Others = 6182\n",
      "diff1 = 0.04590355409425395\n",
      "one more\n",
      "Fields = 246\n",
      "Others = 6141\n",
      "diff1 = 0.03948426491310474\n",
      "one more\n",
      "Fields = 279\n",
      "Others = 6108\n",
      "diff1 = 0.03431751996242367\n",
      "one more\n",
      "Fields = 317\n",
      "Others = 6070\n",
      "diff1 = 0.028367934867700013\n",
      "one more\n",
      "Fields = 355\n",
      "Others = 6032\n",
      "diff1 = 0.02241834977297636\n",
      "one more\n",
      "Fields = 385\n",
      "Others = 6002\n",
      "diff1 = 0.01772130890872084\n",
      "one more\n",
      "Fields = 401\n",
      "Others = 5986\n",
      "diff1 = 0.015216220447784562\n",
      "one more\n",
      "Fields = 429\n",
      "Others = 5958\n",
      "diff1 = 0.010832315641146076\n",
      "one more\n",
      "Fields = 439\n",
      "Others = 5948\n",
      "diff1 = 0.009266635353060909\n",
      "success for region 875\n",
      "Fields = 2377\n",
      "Others = 20018\n",
      "diff1 = 0.026860236659968756\n",
      "one more\n",
      "Fields = 2447\n",
      "Others = 19948\n",
      "diff1 = 0.0237345389595892\n",
      "one more\n",
      "Fields = 2508\n",
      "Others = 19887\n",
      "diff1 = 0.021010716677829874\n",
      "one more\n",
      "Fields = 2560\n",
      "Others = 19835\n",
      "diff1 = 0.01868876981469078\n",
      "one more\n",
      "Fields = 2590\n",
      "Others = 19805\n",
      "diff1 = 0.01734918508595669\n",
      "one more\n",
      "Fields = 2619\n",
      "Others = 19776\n",
      "diff1 = 0.01605425318151374\n",
      "one more\n",
      "Fields = 2663\n",
      "Others = 19732\n",
      "diff1 = 0.014089528912703736\n",
      "one more\n",
      "Fields = 2688\n",
      "Others = 19707\n",
      "diff1 = 0.012973208305425324\n",
      "one more\n",
      "Fields = 2706\n",
      "Others = 19689\n",
      "diff1 = 0.012169457468184874\n",
      "one more\n",
      "Fields = 2731\n",
      "Others = 19664\n",
      "diff1 = 0.011053136860906462\n",
      "one more\n",
      "Fields = 2748\n",
      "Others = 19647\n",
      "diff1 = 0.01029403884795714\n",
      "one more\n",
      "Fields = 2766\n",
      "Others = 19629\n",
      "diff1 = 0.009490288010716691\n",
      "success for region 877\n",
      "Fields = 27\n",
      "Others = 3808\n",
      "diff1 = -0.0070404172099087356\n",
      "success for region 880\n",
      "Fields = 92\n",
      "Others = 15163\n",
      "diff1 = 0.03526919042936742\n",
      "one more\n",
      "Fields = 117\n",
      "Others = 15138\n",
      "diff1 = 0.03363038348082596\n",
      "one more\n",
      "Fields = 140\n",
      "Others = 15115\n",
      "diff1 = 0.03212268108816782\n",
      "one more\n",
      "Fields = 169\n",
      "Others = 15086\n",
      "diff1 = 0.030221665027859723\n",
      "one more\n",
      "Fields = 197\n",
      "Others = 15058\n",
      "diff1 = 0.028386201245493285\n",
      "one more\n",
      "Fields = 230\n",
      "Others = 15025\n",
      "diff1 = 0.026222976073418554\n",
      "one more\n",
      "Fields = 255\n",
      "Others = 15000\n",
      "diff1 = 0.024584169124877094\n",
      "one more\n",
      "Fields = 278\n",
      "Others = 14977\n",
      "diff1 = 0.02307646673221895\n",
      "one more\n",
      "Fields = 304\n",
      "Others = 14951\n",
      "diff1 = 0.021372107505735826\n",
      "one more\n",
      "Fields = 320\n",
      "Others = 14935\n",
      "diff1 = 0.020323271058669292\n",
      "one more\n",
      "Fields = 335\n",
      "Others = 14920\n",
      "diff1 = 0.019339986889544414\n",
      "one more\n",
      "Fields = 361\n",
      "Others = 14894\n",
      "diff1 = 0.017635627663061294\n",
      "one more\n",
      "Fields = 378\n",
      "Others = 14877\n",
      "diff1 = 0.0165212389380531\n",
      "one more\n",
      "Fields = 397\n",
      "Others = 14858\n",
      "diff1 = 0.01527574565716159\n",
      "one more\n",
      "Fields = 410\n",
      "Others = 14845\n",
      "diff1 = 0.01442356604392003\n",
      "one more\n",
      "Fields = 437\n",
      "Others = 14818\n",
      "diff1 = 0.01265365453949525\n",
      "one more\n",
      "Fields = 465\n",
      "Others = 14790\n",
      "diff1 = 0.010818190757128813\n",
      "one more\n",
      "Fields = 486\n",
      "Others = 14769\n",
      "diff1 = 0.009441592920353986\n",
      "success for region 881\n",
      "Fields = 0\n",
      "Others = 186\n",
      "diff1 = 0.0\n",
      "success for region 882\n"
     ]
    }
   ],
   "source": [
    "#открытие растров\n",
    "P0=dataset0.ReadAsArray()\n",
    "P1=dataset1.ReadAsArray()\n",
    "P2=dataset2.ReadAsArray()\n",
    "P3=dataset3.ReadAsArray()\n",
    "LU=dataset4.ReadAsArray()\n",
    "#ввод E0, E1, E2, E3\n",
    "iter0=0\n",
    "iter1=0\n",
    "iter2=0\n",
    "iter3=0\n",
    "count=0\n",
    "#ввод эластичностей\n",
    "E0=0.2177815772600983\n",
    "E1=0.43029512473503995\n",
    "E2=0.24595155709342562\n",
    "E3=3.6788990825688074\n",
    "#добавление эластичностей в пикселях с опр. LU (поощряет сохранение в старых границах)\n",
    "for i in range(0, Y):\n",
    "    for j in range(0, X):\n",
    "        if P0[i][j]!=-99999:\n",
    "            if LU[i][j]==0:\n",
    "                P0[i][j]+=E0\n",
    "            if LU[i][j]==1:\n",
    "                P1[i][j]+=E1\n",
    "            if LU[i][j]==2:\n",
    "                P2[i][j]+=E2\n",
    "            if LU[i][j]==3:\n",
    "                P3[i][j]+=E3\n",
    "            else:\n",
    "                continue\n",
    "#по очереди обращаемся к каждой даче с её спросом\n",
    "dachas=[869, 856, 859, 860, 861, 862, 865, 866, 867, 868, 870, 872, 873, 874, 875, 877, 880, 881, 882]\n",
    "demands=[0, 0, 0, 0, 0, 0.145, 0.1335, 0, 0, 0.033, 0.1054, 0, 0, 0, 0.078, 0.133, 0, 0.0413, 0]\n",
    "L=len(dachas)\n",
    "for N in range(0, L):\n",
    "    breaker=0\n",
    "    while breaker==0:\n",
    "        dachasN=dachas[N]\n",
    "        for i in range(0, Y):\n",
    "            for j in range(0, X):\n",
    "                if dachi_id[i][j]==dachasN:\n",
    "                    #добавление итерационного параметрa \n",
    "                    if P0[i][j]!=-99999:\n",
    "                        P0[i][j]+=iter0\n",
    "                        P1[i][j]+=iter1\n",
    "                        P2[i][j]+=iter2\n",
    "                        P3[i][j]+=iter3\n",
    "        #первичное распределение лэнд юза\n",
    "                    if P0[i][j]!=-99999:\n",
    "                        PG=max(P0[i][j],P1[i][j],P2[i][j],P3[i][j])\n",
    "                        if P1[i][j]>=PG:\n",
    "                            LU[i][j]=1\n",
    "                        else:\n",
    "                            LU[i][j]=4\n",
    "                    else:\n",
    "                        LU[i][j]=-99999\n",
    "                    if LU1860[i][j]==5:\n",
    "                        LU[i][j]=5\n",
    "        #подсчёт доли пикселей от общего LU\n",
    "        uses=0\n",
    "        others=0\n",
    "        fields=0\n",
    "        for i in range(0, Y):\n",
    "            for j in range(0, X):\n",
    "                if dachi_id[i][j]==dachasN:\n",
    "                    if P0[i][j]!=-99999:\n",
    "                        uses+=1\n",
    "                        if LU[i][j]==1:\n",
    "                            fields+=1\n",
    "                        elif LU[i][j]==4:\n",
    "                            others+=1\n",
    "        print ('Fields =', fields)\n",
    "        print ('Others =', others)\n",
    "        #определение разницы с демандом\n",
    "        #ввод demand0, demand1, demand2, demand3\n",
    "        demand1=demands[N]\n",
    "        diff1=demand1-fields/uses\n",
    "        #выведем разницы\n",
    "        print ('diff1 =', diff1)\n",
    "        check1=0\n",
    "        if diff1>0.01 or diff1<-0.01: #число из головы (1%)\n",
    "            if diff1>0.01:\n",
    "                iter1=diff1*0.5\n",
    "            else:\n",
    "                iter1=diff1 #чтобы не получалось одинаковых \"перескоков\"\n",
    "        else:\n",
    "            check1=1\n",
    "        #находим подходящее распределение\n",
    "        if check1==1:\n",
    "            print ('success for region', dachasN)\n",
    "            breaker=1\n",
    "        else:\n",
    "            print ('one more')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fields = 75\n",
      "Others = 492\n",
      "diff1 = 0.07622486772486772\n",
      "one more\n",
      "Fields = 81\n",
      "Others = 486\n",
      "diff1 = 0.06564285714285714\n",
      "one more\n",
      "Fields = 82\n",
      "Others = 485\n",
      "diff1 = 0.06387918871252204\n",
      "one more\n",
      "Fields = 83\n",
      "Others = 484\n",
      "diff1 = 0.06211552028218695\n",
      "one more\n",
      "Fields = 87\n",
      "Others = 480\n",
      "diff1 = 0.05506084656084656\n",
      "one more\n",
      "Fields = 88\n",
      "Others = 479\n",
      "diff1 = 0.05329717813051146\n",
      "one more\n",
      "Fields = 89\n",
      "Others = 478\n",
      "diff1 = 0.051533509700176366\n",
      "one more\n",
      "Fields = 92\n",
      "Others = 475\n",
      "diff1 = 0.046242504409171076\n",
      "one more\n",
      "Fields = 94\n",
      "Others = 473\n",
      "diff1 = 0.04271516754850088\n",
      "one more\n",
      "Fields = 95\n",
      "Others = 472\n",
      "diff1 = 0.040951499118165785\n",
      "one more\n",
      "Fields = 99\n",
      "Others = 468\n",
      "diff1 = 0.0338968253968254\n",
      "one more\n",
      "Fields = 100\n",
      "Others = 467\n",
      "diff1 = 0.0321331569664903\n",
      "one more\n",
      "Fields = 101\n",
      "Others = 466\n",
      "diff1 = 0.030369488536155204\n",
      "one more\n",
      "Fields = 102\n",
      "Others = 465\n",
      "diff1 = 0.028605820105820107\n",
      "one more\n",
      "Fields = 104\n",
      "Others = 463\n",
      "diff1 = 0.025078483245149913\n",
      "one more\n",
      "Fields = 104\n",
      "Others = 463\n",
      "diff1 = 0.025078483245149913\n",
      "one more\n",
      "Fields = 105\n",
      "Others = 462\n",
      "diff1 = 0.023314814814814816\n",
      "one more\n",
      "Fields = 106\n",
      "Others = 461\n",
      "diff1 = 0.02155114638447972\n",
      "one more\n",
      "Fields = 106\n",
      "Others = 461\n",
      "diff1 = 0.02155114638447972\n",
      "one more\n",
      "Fields = 107\n",
      "Others = 460\n",
      "diff1 = 0.019787477954144622\n",
      "one more\n",
      "Fields = 107\n",
      "Others = 460\n",
      "diff1 = 0.019787477954144622\n",
      "one more\n",
      "Fields = 107\n",
      "Others = 460\n",
      "diff1 = 0.019787477954144622\n",
      "one more\n",
      "Fields = 112\n",
      "Others = 455\n",
      "diff1 = 0.010969135802469138\n",
      "one more\n",
      "Fields = 113\n",
      "Others = 454\n",
      "diff1 = 0.00920546737213404\n",
      "success for region 871\n"
     ]
    }
   ],
   "source": [
    "#Если что-то доделать надо (маленький регион, однородное поле вероятности)\n",
    "dachas=[871]\n",
    "demands=[0.2085]\n",
    "L=len(dachas)\n",
    "for N in range(0, L):\n",
    "    breaker=0\n",
    "    while breaker==0:\n",
    "        dachasN=dachas[N]\n",
    "        for i in range(0, Y):\n",
    "            for j in range(0, X):\n",
    "                if dachi_id[i][j]==dachasN:\n",
    "                    #добавление итерационного параметрa \n",
    "                    if P0[i][j]!=-99999:\n",
    "                        P0[i][j]+=iter0\n",
    "                        P1[i][j]+=iter1\n",
    "                        P2[i][j]+=iter2\n",
    "                        P3[i][j]+=iter3\n",
    "        #первичное распределение лэнд юза\n",
    "                    if P0[i][j]!=-99999:\n",
    "                        PG=max(P0[i][j],P1[i][j],P2[i][j],P3[i][j])\n",
    "                        if P1[i][j]>=PG:\n",
    "                            LU[i][j]=1\n",
    "                        else:\n",
    "                            LU[i][j]=4\n",
    "                    else:\n",
    "                        LU[i][j]=-99999\n",
    "                    if LU1860[i][j]==5:\n",
    "                        LU[i][j]=5\n",
    "        #подсчёт доли пикселей от общего LU\n",
    "        uses=0\n",
    "        others=0\n",
    "        fields=0\n",
    "        for i in range(0, Y):\n",
    "            for j in range(0, X):\n",
    "                if dachi_id[i][j]==dachasN:\n",
    "                    if P0[i][j]!=-99999:\n",
    "                        uses+=1\n",
    "                        if LU[i][j]==1:\n",
    "                            fields+=1\n",
    "                        elif LU[i][j]==4:\n",
    "                            others+=1\n",
    "        print ('Fields =', fields)\n",
    "        print ('Others =', others)\n",
    "        #определение разницы с демандом\n",
    "        #ввод demand0, demand1, demand2, demand3\n",
    "        demand1=demands[N]\n",
    "        diff1=demand1-fields/uses\n",
    "        #выведем разницы\n",
    "        print ('diff1 =', diff1)\n",
    "        check1=0\n",
    "        if diff1>0.01 or diff1<-0.01: #число из головы (1%)\n",
    "            if diff1>0.01:\n",
    "                iter1=diff1*0.02\n",
    "            else:\n",
    "                iter1=diff1*0.2 #чтобы не получалось одинаковых \"перескоков\"\n",
    "        else:\n",
    "            check1=1\n",
    "        #находим подходящее распределение\n",
    "        if check1==1:\n",
    "            print ('success for region', dachasN)\n",
    "            breaker=1\n",
    "        else:\n",
    "            print ('one more')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#импорт созданного грида (без привязки!)\n",
    "#координаты добавлены вручную\n",
    "np.savetxt('C:/Users/olegz/Desktop/CLUE/White Lake project/Predicted/Predicted_by_dacha.asc', LU, fmt='%4.1f', comments='', header='NCOLS %s \\nNROWS %s \\nXLLCORNER 569875.0000000000 \\nYLLCORNER 6120445.0000000000 \\nCELLSIZE 30.000000 \\nNODATA_VALUE -99999.0'%(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#подготовка к быстрой визуализации в Питоне\n",
    "#убираем nodata\n",
    "Dachi = gdal.Open('C:/Users/olegz/Desktop/CLUE/White Lake project/Predicted/Predicted_by_dacha.asc').ReadAsArray()\n",
    "Dachi[Dachi == -99999.] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset18 = gdal.Open('Probability/Predicted16.asc')\n",
    "General=dataset18.ReadAsArray()\n",
    "Full_time = gdal.Open('C:/Users/olegz/Desktop/CLUE/White Lake project/Predicted/Predicted_full-time_arable.asc').ReadAsArray()\n",
    "Full_time[Full_time == -99999.] = np.nan\n",
    "General[General == -99999.] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 505.5, 522.5, -0.5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C=[[0.95, 0.95, 0.65],[0.65, 0.9, 0.3],[0.3, 0.55, 0.2], [0.3, 0.55, 0.2], [0.7, 0.82, 0.8]]\n",
    "cm = mpl.colors.ListedColormap(C)\n",
    "mpl.rcParams['figure.figsize'] = (30, 30)\n",
    "\n",
    "plt.subplot(231)\n",
    "plt.imshow(General, cmap=cm)\n",
    "plt.axis('off')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.imshow(Dachi, cmap=cm)\n",
    "plt.axis('off')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.imshow(Full_time, cmap=cm)\n",
    "plt.axis('off')"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}