hexbuilder_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shapely.geometry as  shp\n",
    "from shapely.geometry import Polygon, shape, mapping, GeometryCollection\n",
    "import geojson\n",
    "import math\n",
    "import geopandas as gdp\n",
    "import descartes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample parcel\n",
    "parcel=Polygon([[-96.8209655,32.9657427],[-96.8205803,32.9657453],[-96.8203764,32.9655743],[-96.8202387,32.9655751],[-96.8201779,32.9656267],[-96.8190891,32.965643],[-96.8190795,32.9665821],[-96.8190844,32.966667],[-96.8190967,32.9667513],[-96.8191163,32.9668347],[-96.8191431,32.9669166],[-96.8191769,32.9669967],[-96.8198674,32.9670017],[-96.8201743,32.9672649],[-96.8211773,32.9672722],[-96.8211686,32.9671324],[-96.8211512,32.9669933],[-96.8211252,32.9668551],[-96.8210907,32.9667182],[-96.8210864,32.9667023],[-96.8210474,32.9665445],[-96.8210159,32.9663855],[-96.8209919,32.9662255],[-96.8209755,32.9660649],[-96.8209667,32.9659039],[-96.8209655,32.9657427]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parcel GeoJson\n",
    "parcelGeoJson = { \"geometry\": { \"coordinates\": [ [ [ -96.8209655, 32.9657427 ], [ -96.8205803, 32.9657453 ], [ -96.8203764, 32.9655743 ], [ -96.8202387, 32.9655751 ], [ -96.8201779, 32.9656267 ], [ -96.8190891, 32.965643 ], [ -96.8190795, 32.9665821 ], [ -96.8190844, 32.966667 ], [ -96.8190967, 32.9667513 ], [ -96.8191163, 32.9668347 ], [ -96.8191431, 32.9669166 ], [ -96.8191769, 32.9669967 ], [ -96.8198674, 32.9670017 ], [ -96.8201743, 32.9672649 ], [ -96.8211773, 32.9672722 ], [ -96.8211686, 32.9671324 ], [ -96.8211512, 32.9669933 ], [ -96.8211252, 32.9668551 ], [ -96.8210907, 32.9667182 ], [ -96.8210864, 32.9667023 ], [ -96.8210474, 32.9665445 ], [ -96.8210159, 32.9663855 ], [ -96.8209919, 32.9662255 ], [ -96.8209755, 32.9660649 ], [ -96.8209667, 32.9659039 ], [ -96.8209655, 32.9657427 ] ] ], \"type\": \"Polygon\" }, \"properties\": { \"address\": \"15950 DALLAS PKWY STE 600\", \"address2\": \"blank\", \"address_source\": \"county;accuzip\", \"agval\": -9999, \"block\": \"blank\", \"book\": \"blank\", \"call-date\": \"03/04/2020 - 12:39:44\", \"careof\": \"blank\", \"census_block\": \"blank\", \"census_blockgroup\": \"blank\", \"census_tract\": \"blank\", \"city\": \"dallas\", \"county\": \"dallas\", \"geoid\": \"48113\", \"gisacre\": -9999, \"improvval\": 1340.0, \"landval\": 0.0, \"lbcs_activity\": -9999, \"lbcs_function\": 2000.0, \"legaldesc\": \"blank\", \"ll_stable_id\": \"blank\", \"ll_updated_at\": \"2019-07-19 10:40:07.520000-04:00\", \"ll_uuid\": \"3edd18fe-31f9-4513-841c-bac8ea034a7d\", \"location_name\": \"blank\", \"lot\": \"blank\", \"mail_addno\": \"blank\", \"mail_addpref\": \"blank\", \"mail_address2\": \"blank\", \"mail_addstr\": \"blank\", \"mail_addstsuf\": \"blank\", \"mail_addsttyp\": \"blank\", \"mail_city\": \"DALLAS\", \"mail_state2\": \"TX\", \"mail_unit\": \"blank\", \"mail_urbanization\": \"blank\", \"mail_zip\": \"75266\", \"mailadd\": \"JEFF MANKOFF PRES PO BOX 660675 90691\", \"multistruct\": -9999, \"neighborhood\": \"N/A\", \"numstories\": -9999, \"numunits\": -9999, \"ogc_fid\": 65438, \"original_address\": \"15950 DALLAS PKWY; 600\", \"owner\": \"GRAND PASS MGMT\", \"owner2\": \"blank\", \"owner3\": \"blank\", \"owner4\": \"blank\", \"ownfrst\": \"blank\", \"ownlast\": \"blank\", \"owntype\": \"blank\", \"page\": \"blank\", \"parcelnumb\": \"99972800000074200\", \"parval\": 1340.0, \"parvaltype\": \"assessed\", \"path\": \"/us/tx/dallas/dallas/65438\", \"plat\": \"blank\", \"recrdareano\": -9999, \"recrdareatx\": \"blank\", \"reviseddate\": \"blank\", \"saddno\": \"15950\", \"saddpref\": \"blank\", \"saddstr\": \"DALLAS\", \"saddstsuf\": \"blank\", \"saddsttyp\": \"PKWY\", \"saledate\": \"blank\", \"saleprice\": -9999, \"scity\": \"DALLAS\", \"source\": \"openparcel-02\", \"sourceagent\": \"blank\", \"sourcedate\": \"blank\", \"sourceref\": \"blank\", \"sourceurl\": \"blank\", \"sqft\": -9999, \"state2\": \"tx\", \"struct\": -9999, \"structno\": -9999, \"structstyle\": \"blank\", \"subdivision\": \"blank\", \"subowntype\": \"blank\", \"subsurfown\": \"blank\", \"sunit\": \"blank\", \"szip\": \"75248-6685\", \"table_name\": \"tx_dallas\", \"taxamt\": -9999, \"urbanization\": \"blank\", \"usecode\": \"3\", \"usedesc\": \"BPP\", \"yearbuilt\": -9999, \"zoning\": \"31\", \"zoning_description\": \"COMMERCIAL BPP\" }, \"type\": \"Feature\" }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-96.8211773, 32.9655743, -96.8190795, 32.9672722]\n",
      "[-96.82217671070545, 32.96457431687606, -96.8180795522462, 32.9682721735152]\n"
     ]
    }
   ],
   "source": [
    "# parcel bounds\n",
    "parcel_bounds = list(parcel.bounds)\n",
    "print(parcel_bounds)\n",
    "\n",
    "# buffered - parcel bounds\n",
    "parcel_bounds_buffer = list(parcel.buffer(0.001).bounds) #Approx 100m buffer\n",
    "print(parcel_bounds_buffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import json\n",
    "\n",
    "class Hexbuild:\n",
    "    \"\"\"\n",
    "    Purpose to build hexgrid to cover the extend to an area\n",
    "    Inputs:\n",
    "        boundaries (as list = xmin, ymin, xmax, ymax) in decimal degrees\n",
    "        radius (the hexagon radius) numeric\n",
    "        units (\"kilometers\") string\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, boundaries, radius, units):\n",
    "        \n",
    "        self.boundaries = boundaries\n",
    "        self.radius = radius\n",
    "        self.units = units\n",
    "        \n",
    "        self.isBoundaries = self._validateBoundaries()\n",
    "        self.isRadiusValue = self._validateRadius()\n",
    "        self.isUnitsValue = self._validateUnits()\n",
    "        \n",
    "        self.rEarth = 6371.01 # Earth's average radius in km\n",
    "        self.rEquator = 6378.137 # Earth's radius at equator in km\n",
    "        self.rPolar = 6356.752 # Earth's radius at poles in km\n",
    "        \n",
    "        self.epsilon = 0.000001 # threshold for floating-point equality\n",
    "        self.pi = 3.14159265358979323846264338327950288419716939937510\n",
    "        self.center = [] # Stores center point of hexegon\n",
    "        self._hexgridjson = {} # Stores Hexagon GeoJson\n",
    "        \n",
    "        self._hexgrid = self._buildHexGrid() # Executes build hexagon Json\n",
    "        \n",
    "        \n",
    "        \n",
    "    # Build Hexagon \n",
    "    def _buildHexGrid(self):\n",
    "        \n",
    "        # Validate inputs into buildHex function\n",
    "        if self.isBoundaries == True and self.isRadiusValue == True and self.isUnitsValue == True:\n",
    "            \n",
    "            # Determine Grid Size\n",
    "            x_min = self.boundaries[0]\n",
    "            y_min = self.boundaries[1]\n",
    "            x_max = self.boundaries[2]\n",
    "            y_max = self.boundaries[3]\n",
    "            \n",
    "            # Calculate radius at latitude\n",
    "            self.rEarth = math.sqrt(((6378.137**2 * math.cos(y_min))**2 + (6356.752**2 * math.sin(y_min))**2) /\n",
    "                          ((6378.137 * math.cos(y_min))**2 + (6356.752 * math.sin(y_min))**2))\n",
    "            \n",
    "            # Calculate circumference of earth at latitude\n",
    "            C = 2 * self.pi * self.rEarth\n",
    "            \n",
    "            # Calculate number of kilometers in degree of longitude\n",
    "            M = C / 360\n",
    "             \n",
    "            # Unit Conversion\n",
    "            if self.units == \"kilometers\":\n",
    "                self.calcradius = float(self.radius) * 2 / M\n",
    "            \n",
    "            # Calculate number of Hexagons required\n",
    "            x_width = abs(x_min) - abs(x_max) + (self.calcradius*2) \n",
    "            y_width = y_max - y_min  + (self.calcradius*2)\n",
    "            x_cell_count = int(round(x_width/self.calcradius, 0))\n",
    "            y_cell_count = int(round(y_width/self.calcradius*1.5, 0))\n",
    "                \n",
    "            \n",
    "            # Variable to hold feature collection\n",
    "            feature_Collection = []\n",
    "                        \n",
    "            # Generate hexagon\n",
    "            self.center.append(self.boundaries[0]) # Position of first Hexagon(X)\n",
    "            self.center.append(self.boundaries[3]) # Position of first Hexagon(Y)\n",
    "            \n",
    "            # Set required variables\n",
    "            direction = \"right\"\n",
    "            count = 1\n",
    "            x_count = 1\n",
    "            \n",
    "            # Loop to create Hexagons and store in GeoJson format\n",
    "            for y in range(y_cell_count):\n",
    "            \n",
    "                for x in range(x_cell_count):\n",
    "\n",
    "                    hexagon = {\n",
    "                        \"type\": \"Feature\",\n",
    "                        \"geometry\": {\n",
    "                            \"type\": \"Polygon\",\n",
    "                            \"coordinates\": self._drawHexagon()\n",
    "                        },\n",
    "                        \"properties\": {\n",
    "                            \"longitude\": self.center[0],\n",
    "                            \"latitude\": self.center[1],\n",
    "                            \"call-date\": str(time.strftime(\"%m/%d/%Y - %H:%M:%S\")),\n",
    "                            \"counter\": count\n",
    "                        }\n",
    "                    }\n",
    "                    feature_Collection.append(hexagon)\n",
    "                    \n",
    "                    # Increment counters\n",
    "                    count += 1\n",
    "                    x_count += 1\n",
    "                    \n",
    "                    # Move centroid\n",
    "                    if x_count <= x_cell_count:\n",
    "                        offsetDist = self.radius*math.sqrt(3)/2*4\n",
    "                        if direction == \"right\":\n",
    "                            self.center[0] = self._pointRadialDistance(self.center[0], self.center[1], 210, offsetDist)[0]\n",
    "                        elif direction == \"left\":\n",
    "                            self.center[0] = self._pointRadialDistance(self.center[0], self.center[1], 30, offsetDist)[0]\n",
    "                \n",
    "                # Reset inner loop counter\n",
    "                x_count = 1\n",
    "                \n",
    "                # Establish next starting row direction and center point\n",
    "                offsetShift = (self.radius*math.sqrt(3)/2)*2\n",
    "                if direction == \"right\":\n",
    "                    direction = \"left\"\n",
    "                    new_center = self._pointRadialDistance(self.center[0], self.center[1], 150, offsetShift)\n",
    "                \n",
    "                elif direction == \"left\":\n",
    "                    direction = \"right\"\n",
    "                    new_center = self._pointRadialDistance(self.center[0], self.center[1], 210, offsetShift)\n",
    "                \n",
    "                self.center[0] = new_center[0]\n",
    "                self.center[1] = new_center[1]\n",
    "                \n",
    "            # Collect features collection\n",
    "            feature_Collection_Frame = { \"type\": \"FeatureCollection\", \"features\": feature_Collection}\n",
    "            self._hexgridjson = feature_Collection_Frame\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        else:\n",
    "            self.error = \"Build Hex inputs not correct\"\n",
    "            self._reportError()\n",
    "            \n",
    "            return False \n",
    "        \n",
    "    # Draw Hexagon \n",
    "    def _drawHexagon(self):\n",
    "       \n",
    "        # Need to generate H1 through H6 in counterclockwise direction terminating back at H1\n",
    "        center_pt = [self.center[0], self.center[1]]\n",
    "        #print(center_pt)\n",
    "        h1 = self._pointRadialDistance(self.center[0],self.center[1],60,self.radius)\n",
    "        h2 = self._pointRadialDistance(self.center[0],self.center[1],120,self.radius)\n",
    "        h3 = self._pointRadialDistance(self.center[0],self.center[1],180,self.radius)\n",
    "        h4 = self._pointRadialDistance(self.center[0],self.center[1],240,self.radius)\n",
    "        h5 = self._pointRadialDistance(self.center[0],self.center[1],300,self.radius)\n",
    "        h6 = self._pointRadialDistance(self.center[0],self.center[1],360,self.radius)\n",
    "        \n",
    "        return [[h1,h2,h3,h4,h5,h6,h1]] # Return hegagon geometry (closed polygon)\n",
    "     \n",
    "    \"\"\"\n",
    "    Section required to calculate offsets and to generate hexagon geometry\n",
    "    \"\"\"\n",
    "    \n",
    "    def _deg2rad(self, angle):\n",
    "        return angle*self.pi/180\n",
    "\n",
    "\n",
    "    def _rad2deg(self, angle):\n",
    "        return angle*180/self.pi\n",
    "\n",
    "\n",
    "    def _pointRadialDistance(self,lon1, lat1, bearing, distance):\n",
    "        \"\"\"\n",
    "        Return final coordinates (lat2,lon2) [in degrees] given initial coordinates\n",
    "        (lat1,lon1) [in degrees] and a bearing [in degrees] and distance [in km]\n",
    "        \"\"\"\n",
    "        rlat1 = self._deg2rad(lat1)\n",
    "        rlon1 = self._deg2rad(lon1)\n",
    "        rbearing = self._deg2rad(bearing)\n",
    "        rdistance = distance / self.rEarth # normalize linear distance to radian angle\n",
    "\n",
    "        rlat = math.asin( math.sin(rlat1) * math.cos(rdistance) + \n",
    "                         math.cos(rlat1) * math.sin(rdistance) * math.cos(rbearing) )\n",
    "\n",
    "        if math.cos(rlat) == 0 or abs(math.cos(rlat)) < self.epsilon: # Endpoint a pole\n",
    "            rlon=rlon1\n",
    "        else:\n",
    "            rlon = ( (rlon1 - math.asin( math.sin(rbearing)* math.sin(rdistance) \n",
    "                                        / math.cos(rlat) ) + self.pi ) % (2 * self.pi) ) - self.pi\n",
    "\n",
    "        lat = self._rad2deg(rlat)\n",
    "        lon = self._rad2deg(rlon)\n",
    "        return [lon, lat]\n",
    "    \n",
    "    \"\"\"\n",
    "    Validation Section\n",
    "    \"\"\"\n",
    "    # Validate Boundaries input\n",
    "    def _validateBoundaries(self):\n",
    "        if (type(self.boundaries) == list):\n",
    "            if (len(self.boundaries) == 4):\n",
    "                for bound in self.boundaries:\n",
    "                    if isinstance(bound, (int, float)) == False:\n",
    "                        self.error = \"Boundaries contains non-numbers\"\n",
    "                        self._reportError()\n",
    "                        return False\n",
    "                return True\n",
    "            else:\n",
    "                self.error = \"Boundaries requires 4 variables\"\n",
    "                self._reportError()\n",
    "                return False\n",
    "        else:\n",
    "            self.error = \"Boundaries is not a list\"\n",
    "            self._reportError()\n",
    "            return False\n",
    "    \n",
    "    # Validate that radius is number\n",
    "    def _validateRadius(self):\n",
    "        if isinstance(self.radius, (int, float)) == False:\n",
    "            self.error = \"Radius contains non-numbers\"\n",
    "            self._reportError()\n",
    "            return False\n",
    "        return True\n",
    "    \n",
    "    # Validate that Radius Units is specified correctly \n",
    "    def _validateUnits(self):\n",
    "        if self.units in [\"kilometers\"]:\n",
    "            return True\n",
    "        self.error = \"Radius Units must be kilometers\"\n",
    "        self._reportError()\n",
    "        return False\n",
    "            \n",
    "    # Issue Error Message\n",
    "    def _reportError(self):\n",
    "        print(self.error)\n",
    "    \n",
    "    \"\"\"\n",
    "    Output Section\n",
    "    \"\"\"\n",
    "    # Return GeoJson Results on request as Text\n",
    "    def sendGeoJsonText(self):\n",
    "        return json.dumps(self._hexgridjson)\n",
    "    \n",
    "    # Return GeoJson Results on request as Dictionary\n",
    "    def sendGeoJsonDictionary(self):\n",
    "        return self._hexgridjson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hexgrid = Hexbuild(parcel_bounds, 0.015, \"kilometers\")\n",
    "geojson = hexgrid.sendGeoJsonDictionary()\n",
    "# print(geojson)\n",
    "\n",
    "# geofile = open(r\"c:\\temp\\testhex.json\", \"w\")\n",
    "# geofile.write(hexgrid.sendGeoJsonText())\n",
    "# geofile.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Working with GeoPandas\n",
    "ParcelDF = gdp.read_file(json.dumps(parcelGeoJson))\n",
    "HexDF = gdp.read_file(hexgrid.sendGeoJsonText())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intersect Clip\n",
    "IntHexDF = gdp.overlay(HexDF, ParcelDF, how='intersection')\n",
    "# IntHexDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22862f5a108>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ParcelDF.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22862e37548>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "HexDF.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22862ecf7c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "IntHexDF.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "IntHexDF.to_file(r\"c:\\temp\\testhex.json\", driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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