docs/notebooks/lmr_analyzer/geometry_class_example.ipynb

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   "metadata": {},
   "source": [
    "# lmr_analyzer.geometry example\n",
    "\n",
    "by Guilherme Fernandes Alves, October 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lmr_analyzer import geometry\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use(\"seaborn-dark-palette\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the geometry object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chicago = geometry(\n",
    "    name=\"chicago\",\n",
    "    shapefile=\"../../data/shapefiles/chicago/chicago.shp\",\n",
    "    graph_key=\"pri_neigh\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything went good, let's get some good statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Basic statistics for 'Kenwood' calculated! Completed: 48 of 98\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chicago.evaluate_basic_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the basic statistics we just calculated:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "chicago.stats_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Street Orientation Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Completed 98 street orientation calculation using a total CPU time of: 5.3 s'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chicago.evaluate_street_orientation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_street_orientation = pd.DataFrame.from_dict(\n",
    "    chicago.street_orientation_dict, orient=\"index\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "chicago.plot_street_orientation_polar(grid=False, savefig=True, dpi=300, figsize=(5, 5))\n",
    "plt.close(\"all\")  # close all figures, just for precaution"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving as an .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "chicago.export_basic_stats_to_csv(\"chicago_basic_stats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "chicago.export_street_orientation_to_csv(\"chicago_st_orientation.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For plotting each of the graphs we have used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "chicago.plot_graphs(grid=False, savefig=True)\n",
    "# chicago.plot_graphs(grid=True, savefig=True, figsize=(30, 30))\n",
    "plt.close(\"all\")  # close all figures, just for precaution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickle the object with cloudpickle\n",
    "import cloudpickle\n",
    "\n",
    "with open(\"chicago.pkl\", \"wb\") as f:\n",
    "    cloudpickle.dump(chicago, f)"
   ]
  }
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