EX-sainshand.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "pip install geopandas\n",
    "pip install descartes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/content/data/shp/Sainshand_sum.shp']\n"
     ]
    }
   ],
   "source": [
    "files = sorted(glob('/content/data/*/*.shp'))\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sainshand = gpd.read_file(files[0], SHAPE_RESTORE_SHX='YES')\n",
    "crs = {'init': 'epsg:32649'}\n",
    "sainshand.crs = crs\n",
    "#utm_zone is 48N or code 32649\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,1)\n",
    "cmaps = ['viridis']\n",
    "sainshand_wgs = sainshand.to_crs({'init': 'epsg:4326'})\n",
    "sainshand_wgs.plot(ax=ax, cmap=cmaps[0])\n",
    "ax.grid(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POINT (115.938524861939 44.60691719930802)\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sainshand_wgs.centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import Point\n",
    "import random\n",
    "\n",
    "def list_to_dataframe(data, crs={'init':'epsg:4326'}):\n",
    "    outdf = gpd.GeoDataFrame(data, columns=['geometry'])\n",
    "    outdf.crs = crs\n",
    "    return(outdf)\n",
    " \n",
    "def check_distance(point, point_list, min_distance, \\\n",
    "                   crs={'init':'epsg:4326'}, utm={'init':'epsg:32649'}):\n",
    "    points = list_to_dataframe(point_list)\n",
    "    points = points.to_crs(utm)\n",
    "    p = list_to_dataframe([point]).to_crs(utm)\n",
    "    p = p.to_crs(utm)\n",
    "    distances = np.array([p.geometry.distance(pnt) for pnt in points.geometry])\n",
    "    m = np.min(distances)\n",
    "    if m>min_distance:\n",
    "        return(True)\n",
    "    else:\n",
    "        return(False)\n",
    "    \n",
    "def random_points_in_polygon(number, polygon, min_distance):\n",
    "    points = []\n",
    "    min_x, min_y, max_x, max_y = polygon.bounds\n",
    "    i = 0\n",
    "    while i < number:\n",
    "        candidate_point = Point(random.uniform(min_x, max_x), \\\n",
    "                                random.uniform(min_y, max_y))\n",
    "        if polygon.contains(candidate_point):\n",
    "            if i==0:\n",
    "                points.append(candidate_point)\n",
    "                i += 1\n",
    "            if i>=1:\n",
    "                if check_distance(candidate_point, points, min_distance):\n",
    "                    points.append(candidate_point)\n",
    "                    i += 1\n",
    "                \n",
    "    return gpd.GeoDataFrame(points, columns=['geometry'])  # returns list of shapely point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 300\n",
    "points = random_points_in_polygon(n, ikhnart_bounds.iloc[0].geometry, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,1)\n",
    "cmaps = ['viridis']\n",
    "ikhnart_bounds.plot(ax=ax, cmap=cmaps[0])\n",
    "ax.grid(alpha=0.5)\n",
    "points.plot(ax=ax, color='red')\n",
    "points.crs = crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "out = pd.DataFrame(np.array([np.arange(len(points)),\\\n",
    "                             points.geometry.y,\\\n",
    "                             points.geometry.x]).T,\\\n",
    "                   columns=['PlotKey','Latitude','Longitude'])\n",
    "out.PlotKey = out.PlotKey.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "out.to_csv('ikhnart_points_v1.csv',index=False)"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}