GMaps_DSet_Gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import requests\n",
    "import polyline\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import webbrowser\n",
    "import urllib.request\n",
    "from PIL import Image\n",
    "\n",
    "# Google Maps Account Key\n",
    "API_KEY = \"AIzaSyBlPHAYsu_KjwqrZj3LK_3RdCw4aDLdSPI\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HTTPS Request Parameters\n",
    "#PARAMS = {}\n",
    "#center = \"\"\n",
    "#zoom = \"\"\n",
    "\n",
    "#requests.get(\"https://maps.googleapis.com/maps/api/staticmap?center=Williamsburg,Brooklyn,NY&zoom=13&size=400x400&markers=color:blue%7Clabel:S%7C11211%7C11206%7C11222&key=AIzaSyDzhP-wAh1hJpqu2ZSuxuS-MU84SuVAWb4\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(response)\n",
    "#open('test_reg.png', 'wb').write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is just for testing my implementation\n",
    "ML_output = [[(37.874824, -122.262206),(37.874383, -122.268643), 3],[(37.874383, -122.268643),(37.874214, -122.268643), 1],[(37.874214, -122.268643),(37.872181, -122.268429), 2],[(37.874214, -122.268643),(37.878380, -122.269159), 1]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_coord_dict = {}\n",
    "num_nodes = 0\n",
    "for i in np.arange(len(ML_output)):\n",
    "   node_coord_dict.update({ML_output[i][0] : \"Node\"+str(num_nodes)})\n",
    "   node_coord_dict.update({ML_output[i][1] : \"Node\"+str(num_nodes+1)})\n",
    "   num_nodes += 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "for i in np.arange(len(ML_output)):\n",
    "    G.add_edge(node_coord_dict[ML_output[i][0]], node_coord_dict[ML_output[i][1]], weight=ML_output[i][2])\n",
    "    \n",
    "shortest_path = nx.shortest_path(G, \"Node0\", \"Node\"+str(len(ML_output)*2-1), weight='weight')\n",
    "\n",
    "inverted_dict = dict([[v,k] for k,v in node_coord_dict.items()])\n",
    "\n",
    "shortest_path_coord = []\n",
    "for i in np.arange(len(shortest_path)):\n",
    "   shortest_path_coord.append(inverted_dict[shortest_path[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "for i in np.arange(len(ML_output)):\n",
    "    G.add_edge(ML_output[i][0], ML_output[i][1], weight=ML_output[i][2])\n",
    "    \n",
    "astar_path = nx.astar_path(G, ML_output[0][0], ML_output[len(ML_output)-1][1], weight='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_path = \"\"\n",
    "for i in np.arange(len(shortest_path_coord)):\n",
    "   original_path += str(shortest_path_coord[i][0]) + \",\" + str(shortest_path_coord[i][1]) + \"|\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_path = \"\"\n",
    "for i in np.arange(len(shortest_path)):\n",
    "    original_path += str(shortest_path[i][0]) + \",\" + str(shortest_path[i][1]) + \"|\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://maps.googleapis.com/maps/api/staticmap?size=800x800&zoom=15&path=color:0x0000ff|weight:5&key=AIzaSyBlPHAYsu_KjwqrZj3LK_3RdCw4aDLdSPI\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('map.jpg', <http.client.HTTPMessage at 0xa2462dd68>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_path = original_path[:-1]\n",
    "#original_path = \"37.864424,-122.267399|37.866583,-122.261123\"\n",
    "r = requests.post(\"https://roads.googleapis.com/v1/snapToRoads?path=\" + original_path + \"&interpolate=true&key=\" + API_KEY)\n",
    "text = r.text.replace('latitude', 'x').replace('longitude', 'y')\n",
    "x_values = []\n",
    "y_values = []\n",
    "text_len = len(text)\n",
    "for i in np.arange(text_len):\n",
    "    if text[i-1] == '\"' and text[i] == 'x' and i+4 < text_len and i+4+text[i+4:].index(',') < text_len:\n",
    "        x_values += [text[i+4 : i+4+text[i+4:].index(',')]]\n",
    "    elif text[i-1] == '\"' and text[i] == 'y' and i+4 < text_len and i+4+text[i+4:].index('\\n') < text_len:\n",
    "        y_values += [text[i+4 : i+4+text[i+4:].index('\\n')]]\n",
    "\n",
    "x_y_pairs = []\n",
    "for i in np.arange(len(x_values)):\n",
    "    x_y_pairs.append((float(x_values[i]), float(y_values[i])))\n",
    "\n",
    "#polyline_points = polyline.encode(x_y_pairs, 7)\n",
    "#print(\"https://maps.googleapis.com/maps/api/staticmap?size=800x800&zoom=8&path=weight:3%7Ccolor:orange%7Cenc:\" + str(polyline_points) + \"&key=\" + API_KEY)\n",
    "\n",
    "path=\"path=color:0x0000ff|weight:5\"\n",
    "for i in np.arange(len(x_y_pairs)):\n",
    "    path += \"|\" + str(x_y_pairs[i][0]) + \",\" + str(x_y_pairs[i][1])\n",
    "\n",
    "print(\"https://maps.googleapis.com/maps/api/staticmap?size=800x800&zoom=15&\" + path + \"&key=\" + API_KEY)\n",
    "\n",
    "#webbrowser.open(\"https://maps.googleapis.com/maps/api/staticmap?size=800x800&zoom=15&\" + path + \"&key=\" + API_KEY)\n",
    "\n",
    "urllib.request.urlretrieve(\"https://maps.googleapis.com/maps/api/staticmap?size=800x800&zoom=15&\" + path + \"&key=\" + API_KEY, \"map.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Python/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G, with_labels=True, edge_color=list(nx.get_edge_attributes(G,'weight').values()), width=9.0, edge_cmap=plt.cm.Reds, font_size=9, labels=dict([[node,node] for node in list(G.nodes)]))\n",
    "plt.savefig('graph.jpg', bbox_inches='tight')\n",
    "image1 = Image.open(\"graph.jpg\")\n",
    "image1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "image2 = Image.open(\"map.jpg\")\n",
    "image2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
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