main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon\n",
    "import lxml.etree as ET\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def makePolygon(coordinates):\n",
    "    split = coordinates[:-1].split(\" \")\n",
    "    df = pd.DataFrame({\"lat\": [float(x.split(\",\")[0]) for x in split], \"long\": [x.split(\",\")[1] for x in split]})\n",
    "    # gdf = gpd.GeoDataFrame(df,geometry=gpd.points_from_xy(df.lat,df.long))\n",
    "\n",
    "    polygon_geom = Polygon(zip(df['lat'], df['long']))\n",
    "    polygon = gpd.GeoDataFrame(index=[0], geometry=[polygon_geom])\n",
    "    return polygon\n",
    "\n",
    "def makePoint(coordinates):\n",
    "    lat,long = (float(x) for x in coordinates.split(\",\"))\n",
    "    polygon_geom = Point(lat,long)\n",
    "    point = gpd.GeoDataFrame(index=[0], geometry=[polygon_geom])\n",
    "    return point\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<POLYGON ((-13165358.698 3983548.084, -13165367.41 3983543.004, -13165353.64...>, <POLYGON ((-13174631.661 3988114.442, -13174655.748 3988007.037, -13174597.4...>, <POLYGON ((-13163160.513 3978957.931, -13163161.779 3978956.55, -13163160.37...>, <POLYGON ((-13162035.201 3976815.52, -13162093.786 3976741.025, -13162031.48...>, <POLYGON ((-13181891.065 3988713.354, -13181873.589 3988580.072, -13181800.2...>, <POLYGON ((-13186624.719 4006953.974, -13186699.268 4006874.556, -13186554.7...>, <POLYGON ((-13149703.164 3995881.951, -13149833.974 3995779.004, -13149692.3...>, <POLYGON ((-13180226.801 3995733.193, -13180239.526 3995720.682, -13180226.9...>, <POLYGON ((-13189552.297 3995144.22, -13189625.445 3995091.947, -13189530.8 ...>]\n"
     ]
    }
   ],
   "source": [
    "file = \"data/poly10.txt\"\n",
    "cnt = 10\n",
    "polygons = []\n",
    "with open(file,'r') as f:\n",
    "    for line in f:\n",
    "        cnt -= 1\n",
    "        if cnt == 0:\n",
    "            break\n",
    "        poly = '<' + line.split(\":<\")[1]\n",
    "        poly = poly.replace('srsName','crsName')\n",
    "        root = ET.fromstring(poly)\n",
    "        coordinates = root.findall(\".//{http://www.opengis.net/gml}coordinates\")\n",
    "        maxi = -1e15\n",
    "        point = None\n",
    "        for index,c in enumerate(coordinates):\n",
    "            p = makePolygon(c.text)\n",
    "            polygons.append(p['geometry'][0])\n",
    "            # p.to_file(f\"polygons/p_{cnt}_{index}.shp\")\n",
    "            if(line.split(\":<\")[0] == \"POLYGON:9:118523\"):\n",
    "                for i in p.geometry[0].exterior.coords:\n",
    "                    if(i[0] > maxi):\n",
    "                        maxi = i[0]\n",
    "                        point = i\n",
    "                print(point)\n",
    "                break\n",
    "\n",
    "print(polygons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<POINT (-13148438.178 3986007.337)>, <POINT (-13174008.657 4009260.508)>, <POINT (-13172687.596 3982107.396)>, <POINT (-13153240.87 4005783.647)>, <POINT (-13161794.861 3993727.197)>, <POINT (-13157693.632 3995813.539)>, <POINT (-13181920.578 3975637.6)>, <POINT (-13161974.641 4008094.96)>, <POINT (-13154990.513 3993088.308)>, <POINT (-13157379.018 3991554.604)>, <POINT (-13155117.322 3989249.268)>, <POINT (-13152078.721 4010028.541)>, <POINT (-13167512.504 3987519.605)>, <POINT (-13165170.55 3983082.901)>, <POINT (-13160791.625 3995332.34)>, <POINT (-13172406.69 3991809.344)>, <POINT (-13154120.507 4003274.759)>, <POINT (-13175995.867 3985032.872)>, <POINT (-13167340.75 3979801.404)>, <POINT (-13164531.689 4012040.181)>, <POINT (-13158539.561 3991898.57)>, <POINT (-13150640.482 4013260.553)>, <POINT (-13153847.627 4006573.454)>, <POINT (-13166881.669 4002555.078)>, <POINT (-13172045.525 3989258.317)>, <POINT (-13174040.761 3994301.452)>, <POINT (-13165764.465 4002562.844)>, <POINT (-13151192.663 3980718.577)>, <POINT (-13175363.427 3990836.964)>, <POINT (-13162875.146 3998861.612)>]\n"
     ]
    }
   ],
   "source": [
    "point_file=\"data/points500.txt\"\n",
    "count = 31\n",
    "points = []\n",
    "with open(point_file,'r') as f:\n",
    "    for line in f:\n",
    "        count -= 1\n",
    "        if count == 0:\n",
    "            break\n",
    "        point = '<' + line.split(\":<\")[1]\n",
    "        point = point.replace('srsName','crsName')\n",
    "        root = ET.fromstring(point)\n",
    "        coordinates = root.findall(\".//{http://www.opengis.net/gml}coordinates\")\n",
    "        for c in coordinates:\n",
    "            p = makePoint(c.text)\n",
    "            points.append(p['geometry'][0])\n",
    "            p.to_file(f\"points/point_{count}.shp\")\n",
    "print(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
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    "point_string = '<gml:Point srsName=\"EPSG:54004\" xmlns:gml=\"http://www.opengis.net/gml\"><gml:coordinates decimal=\".\" cs=\",\" ts=\" \">-1.31726875962544E7,3982107.39594684 </gml:coordinates></gml:Point>'\n",
    "\n",
    "root = ET.fromstring(outside_string)\n",
    "coordinates = root.find(\".//{http://www.opengis.net/gml}coordinates\").text\n",
    "outside_poly = makePolygon(coordinates)\n",
    "\n",
    "root = ET.fromstring(inside_string)\n",
    "coordinates = root.find(\".//{http://www.opengis.net/gml}coordinates\").text\n",
    "inside_poly = makePolygon(coordinates)\n",
    "\n",
    "root = ET.fromstring(point_string)\n",
    "coordinates = root.find(\".//{http://www.opengis.net/gml}coordinates\").text\n",
    "point = makePoint(coordinates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "ax = outside_poly.plot(color='red')\n",
    "inside_poly.plot(ax=ax, color='blue')\n",
    "point.plot(ax=ax,color='green')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "dtype: bool\n",
      "0    True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(outside_poly.contains(point))\n",
    "print(inside_poly.contains(point))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = [-13146587.9594042562,4006770.1723186821]\n",
    "p2 = [-13144304.9497918542, 4008803.9450738342]\n",
    "p3 = [-13142655.0017968640, 4006281.6680784738]\n",
    "p4 = [-13140484.7883724, 4005787.71055533]\n",
    "p5 = [-13134484.7883724, 4005787.71055533]\n",
    "from shapely.geometry import LineString\n",
    "\n",
    "a = LineString([p5,p4])\n",
    "gdf = gpd.GeoDataFrame({'geometry': [a]})\n",
    "gdf.to_file(\"lines/5.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-13148438.178342 3986007.33707501\n",
      "-13174008.6574142 4009260.50838627\n",
      "-13172687.5962544 3982107.39594684\n",
      "-13153240.8697033 4005783.646948\n",
      "-13161794.8611016 3993727.19691904\n",
      "-13157693.632349 3995813.53906687\n",
      "-13181920.5777751 3975637.59993071\n",
      "-13161974.6409922 4008094.96017429\n",
      "-13154990.5132806 3993088.30761502\n",
      "-13157379.0175406 3991554.60372936\n",
      "-13155117.3223106 3989249.26757539\n",
      "-13152078.7211252 4010028.54120767\n",
      "-13167512.5036914 3987519.60504839\n",
      "-13165170.5495818 3983082.90105785\n",
      "-13160791.6251053 3995332.33972004\n",
      "-13172406.6901755 3991809.34448241\n",
      "-13154120.5070248 4003274.75880012\n",
      "-13175995.8672756 3985032.87170314\n",
      "-13167340.7496889 3979801.40414987\n",
      "-13164531.6888994 4012040.18116396\n",
      "-13158539.560941 3991898.56976161\n",
      "-13150640.482001 4013260.55297853\n",
      "-13153847.6268338 4006573.45405983\n",
      "-13166881.668897 4002555.07784158\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rtree\n",
    "from shapely.geometry import Point, Polygon, LineString\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "for poly in polygons:\n",
    "    plt.plot(*poly.exterior.xy)\n",
    "\n",
    "# Plot the points\n",
    "for point in points:\n",
    "    print(point.x, point.y)\n",
    "    plt.plot(point.x, point.y, 'ro')\n",
    "\n",
    "# Set the axis limits and show the plot\n",
    "# plt.xlim([-0.5, 15])\n",
    "# plt.ylim([-0.5, 15])\n",
    "# plt.show()\n",
    "\n",
    "# Create an R-tree index using the MBRs of the polygons\n",
    "idx = rtree.index.Index()\n",
    "for i, polygon in enumerate(polygons):\n",
    "    idx.insert(i, polygon.bounds)\n",
    "\n",
    "# Define a function for ray casting algorithm\n",
    "def ray_casting(point, poly):\n",
    "    n = len(poly)\n",
    "    inside = False\n",
    "    p1x, p1y = poly[0]\n",
    "    for i in range(n+1):\n",
    "        p2x, p2y = poly[i % n]\n",
    "        if point.y > min(p1y, p2y):\n",
    "            if point.y <= max(p1y, p2y):\n",
    "                if point.x <= max(p1x, p2x):\n",
    "                    if p1y != p2y:\n",
    "                        xinters = (point.y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x\n",
    "                    if p1x == p2x or point.x <= xinters:\n",
    "                        inside = not inside\n",
    "        p1x, p1y = p2x, p2y\n",
    "    return inside\n",
    "\n",
    "# Perform a range query for each point\n",
    "for point in points:\n",
    "    bbox = point.bounds\n",
    "    intersected_polygons = [polygons[i] for i in idx.intersection(bbox)]\n",
    "    for polygon in intersected_polygons:\n",
    "        if ray_casting(point, list(polygon.exterior.coords)):\n",
    "            print(f\"Point {point.coords[0]} is inside Polygon {polygons.index(polygon)}\")\n",
    "\n",
    "# Print a message for any points that are not inside any polygons\n",
    "for point in points:\n",
    "    bbox = point.bounds\n",
    "    intersected_polygons = [polygons[i] for i in idx.intersection(bbox)]\n",
    "    if not intersected_polygons:\n",
    "        print(f\"Point {point.coords[0]} is not inside any polygons\")\n"
   ]
  }
 ],
 "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}