Visualising accessibility by suburb.ipynb

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   "source": [
    "# Introduction\n",
    "\n",
    "## Technical details\n",
    "To do this analysis, we need to overcome some technical aspects:\n",
    "- Test a Bayesian model at the suburban level\n",
    "- Model accessibility at the meshblock level\n",
    "- Gather modeled meshblock accessibilities at the suburban level\n",
    "- Compare suburban averages in Wellington\n"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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    "slideshow": {
     "slide_type": "skip"
    },
    "tags": [
     "tech"
    ]
   },
   "outputs": [],
   "source": [
    "# Import some packages\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import osmnx as ox \n",
    "import networkx as nx\n",
    "import pandana as pa\n",
    "\n",
    "import geopandas \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import folium\n",
    "import yaml\n",
    "from importlib import reload\n",
    "from shapely.geometry import Point, Polygon, LineString\n",
    "from descartes import PolygonPatch\n",
    "import matplotlib.pyplot as plt\n",
    "import pystan\n",
    "\n",
    "import utils.data_processing as dp\n",
    "import utils.accessibility_analysis as aa\n",
    "import utils.util as ut\n",
    "import utils.stan_utils as su\n",
    "\n",
    "# Some configs\n",
    "warnings.filterwarnings('ignore')\n",
    "ox.config(log_console=True, use_cache=True)\n",
    "pd.set_option('display.max_columns', 200)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [],
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Define bounding box (W, S, E, N) for the area of Wellington we're interested in\n",
    "# Copied from http://boundingbox.klokantech.com/\n",
    "\n",
    "general_bbox = [174.57,-41.38,174.84,-41.1527] # original\n",
    "\n",
    "# general_bbox = [174.613106,-41.36238,174.89541,-41.134213] # new: making sure I don't miss any Tawa areas\n",
    "\n",
    "# Separate out the bounding box list into 4 vertices. \n",
    "south = general_bbox[1]\n",
    "west = general_bbox[0]\n",
    "north = general_bbox[3]\n",
    "east = general_bbox[2]\n",
    "\n",
    "# Set OSM bounding box\n",
    "osm_bbox = [south, west, north, east]\n",
    "\n",
    "# centroids of bounding box\n",
    "mean_lat = np.mean([north, south])\n",
    "mean_lon = np.mean([west, east])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "\n",
    "| Dataset | Description | Format | Link |\n",
    "| :-----: | :---------: | :----: | :--: |\n",
    "| WCC playground locations | jsdh | .zip| |\n",
    "| WCC suburb boundaries | ;lf | .gdb | |\n",
    "| StatsNZ 2019 meshblock boundaries | ;lf | .gdb | |\n",
    "| Wellington street network without elevation | ksfj| kjfd | lkd| \n",
    "| Wellington street network with elevation | ksfj| kjfd | lkd| "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WCC Playgrounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcc_playgrounds = geopandas.read_file('zip://./data/WCC_Playgrounds.zip')\n",
    "wcc_playgrounds['lon'] = wcc_playgrounds['geometry'].apply(lambda p: p.x)\n",
    "wcc_playgrounds['lat'] = wcc_playgrounds['geometry'].apply(lambda p: p.y)\n",
    "wcc_playgrounds = wcc_playgrounds.to_crs(epsg=4167)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f720be454e0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_playground = folium.Map(location=[mean_lat, mean_lon], zoom_start = 11)\n",
    "\n",
    "for each in wcc_playgrounds.iterrows():\n",
    "    folium.Marker(location = [each[1]['lat'],each[1]['lon']]).add_to(map_playground)\n",
    "\n",
    "map_playground"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boundary files: Suburbs and meshblocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcc_suburbs = geopandas.read_file('zip://./data/WCC_Suburbs_Boundaries.zip')\n",
    "meshblock= geopandas.read_file('./data/meshblock-higher-geographies-2019-generalised-4326.gdb')\n",
    "wlg_meshblock = meshblock.query('REGC2019_V1_00_NAME == \"Wellington Region\"')\n",
    "\n",
    "# Two different types of joining operations\n",
    "wlg_meshblock_suburbs = geopandas.sjoin(wcc_suburbs, wlg_meshblock, op='contains', how='right')\n",
    "wlg_meshblock_suburbs_ov = geopandas.overlay(wcc_suburbs, wlg_meshblock, how='identity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wlg_meshblock_suburbs.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Statistical Area Unit 2\n",
    "sa2 = geopandas.read_file('./data/area-unit-2013.gdb')\n",
    "sa2_proj = sa2.to_crs(wlg_meshblock_suburbs.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AU2013_V1_00</th>\n",
       "      <th>AU2013_V1_00_NAME</th>\n",
       "      <th>AREA_SQ_KM</th>\n",
       "      <th>LAND_AREA_SQ_KM</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>geometry</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500202</td>\n",
       "      <td>Karikari Peninsula-Maungataniwha</td>\n",
       "      <td>791.188485</td>\n",
       "      <td>784.395200</td>\n",
       "      <td>285586.495714</td>\n",
       "      <td>(POLYGON ((1636527.006899834 6150940.062399864...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500203</td>\n",
       "      <td>Taipa Bay-Mangonui</td>\n",
       "      <td>5.545054</td>\n",
       "      <td>5.545054</td>\n",
       "      <td>25711.852169</td>\n",
       "      <td>(POLYGON ((1648130.469600201 6128680.422100067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500204</td>\n",
       "      <td>Herekino</td>\n",
       "      <td>396.511344</td>\n",
       "      <td>391.067273</td>\n",
       "      <td>172051.055675</td>\n",
       "      <td>(POLYGON ((1622142.151400089 6120287.439599991...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500205</td>\n",
       "      <td>Ahipara</td>\n",
       "      <td>8.000104</td>\n",
       "      <td>8.000104</td>\n",
       "      <td>20638.880336</td>\n",
       "      <td>(POLYGON ((1615173.015799999 6110225.846300125...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500206</td>\n",
       "      <td>North Cape</td>\n",
       "      <td>690.237222</td>\n",
       "      <td>626.035844</td>\n",
       "      <td>177953.473387</td>\n",
       "      <td>(POLYGON ((1601317.232200146 6194284.575900078...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AU2013_V1_00                 AU2013_V1_00_NAME  AREA_SQ_KM  LAND_AREA_SQ_KM  \\\n",
       "0       500202  Karikari Peninsula-Maungataniwha  791.188485       784.395200   \n",
       "1       500203                Taipa Bay-Mangonui    5.545054         5.545054   \n",
       "2       500204                          Herekino  396.511344       391.067273   \n",
       "3       500205                           Ahipara    8.000104         8.000104   \n",
       "4       500206                        North Cape  690.237222       626.035844   \n",
       "\n",
       "    Shape_Length                                           geometry  \n",
       "0  285586.495714  (POLYGON ((1636527.006899834 6150940.062399864...  \n",
       "1   25711.852169  (POLYGON ((1648130.469600201 6128680.422100067...  \n",
       "2  172051.055675  (POLYGON ((1622142.151400089 6120287.439599991...  \n",
       "3   20638.880336  (POLYGON ((1615173.015799999 6110225.846300125...  \n",
       "4  177953.473387  (POLYGON ((1601317.232200146 6194284.575900078...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Residential polygons\n",
    "res = geopandas.read_file('./data/nz-residential-area-polygons-topo-150k.gdb')\n",
    "wlg_res = res.query('name == \"Wellington\"')\n",
    "wlg_res_poly_suburbs_ov = geopandas.overlay(wcc_suburbs, wlg_res, how='identity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wlg_sa2_suburbs_ov = geopandas.overlay(wcc_suburbs, sa2_proj, how='identity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "sa2_proj[sa2_proj['AU2013_V1_00_NAME'].str.contains('Karori')].plot()\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "plt.title('Karori SA2 boundaries');\n",
    "plt.ylabel('Lattitude ($^\\circ$)');\n",
    "plt.xlabel('Longitude ($^\\circ$)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "wlg_res_poly_suburbs_ov.query('suburb == \"Karori\"').plot(edgecolor='white')\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "plt.title('Karori residential polygon boundaries');\n",
    "plt.ylabel('Lattitude ($^\\circ$)');\n",
    "plt.xlabel('Longitude ($^\\circ$)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "wlg_sa2_suburbs_ov.query('suburb == \"Karori\"').plot()\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "plt.title('SA2 boundaries within Karori');\n",
    "plt.ylabel('Lattitude ($^\\circ$)');\n",
    "plt.xlabel('Longitude ($^\\circ$)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f720b5ef860>"
      ]
     },
     "execution_count": 14,
     "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": [
    "wlg_sa2_suburbs_ov[wlg_sa2_suburbs_ov['suburb'] == 'Karori']['geometry'].plot()   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up figure\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, sharex=True, sharey=True, figsize=(13,10))\n",
    "\n",
    "# Plot meshblocks contained within suburb boundary\n",
    "wlg_meshblock.plot(ax=ax1)\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "ax1.set_title('Meshblock boundaries');\n",
    "\n",
    "# Plot meshblocks contained within suburb boundary\n",
    "wlg_meshblock_suburbs.query('suburb == \"Karori\"').plot(ax=ax2)\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "ax2.set_title('Sjoin: contains');\n",
    "\n",
    "# Plot meshblocks intersecting with suburb boundary\n",
    "wlg_meshblock_suburbs_ov.query('suburb == \"Karori\"').plot(ax=ax3)\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "ax3.set_title('Overlay: identity');\n",
    "\n",
    "# Suburb boundary within meshblock limits\n",
    "wcc_suburbs.query('suburb == \"Karori\"').plot(ax=ax4)\n",
    "plt.ylim(-41.320, -41.260)\n",
    "plt.xlim(174.69, 174.76)\n",
    "ax4.set_title('Suburb boundary');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# Plot meshblocks intersecting with suburb boundary\n",
    "wlg_meshblock_suburbs_ov.query('suburb == \"Karori\"').plot()\n",
    "plt.ylim(-41.320, -41.260);\n",
    "plt.xlim(174.69, 174.76);\n",
    "plt.title('Karori suburb boundary');\n",
    "plt.ylabel('Lattitude ($^\\circ$)');\n",
    "plt.xlabel('Longitude ($^\\circ$)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suburb</th>\n",
       "      <th>postcode</th>\n",
       "      <th>MB2019_V1_00</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>Karori</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2104100</td>\n",
       "      <td>POLYGON ((174.7527410567269 -41.27200381936174...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>Karori</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2106100</td>\n",
       "      <td>POLYGON ((174.7533828776446 -41.28300381209932...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>Karori</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2105503</td>\n",
       "      <td>(POLYGON ((174.7530386517898 -41.2800340600980...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>Karori</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2149500</td>\n",
       "      <td>POLYGON ((174.7535317353109 -41.28234654562732...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Karori</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>2149400</td>\n",
       "      <td>(POLYGON ((174.754264330901 -41.27988740170357...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     suburb  postcode MB2019_V1_00  \\\n",
       "917  Karori    6012.0      2104100   \n",
       "934  Karori    6012.0      2106100   \n",
       "942  Karori    6012.0      2105503   \n",
       "944  Karori    6012.0      2149500   \n",
       "947  Karori    6012.0      2149400   \n",
       "\n",
       "                                              geometry  \n",
       "917  POLYGON ((174.7527410567269 -41.27200381936174...  \n",
       "934  POLYGON ((174.7533828776446 -41.28300381209932...  \n",
       "942  (POLYGON ((174.7530386517898 -41.2800340600980...  \n",
       "944  POLYGON ((174.7535317353109 -41.28234654562732...  \n",
       "947  (POLYGON ((174.754264330901 -41.27988740170357...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wlg_meshblock_suburbs_ov.query('suburb == \"Karori\"')[['suburb', 'postcode', 'MB2019_V1_00', 'geometry']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wellington street network: without elevation\n",
    "Getting the Wellington street network in a form suitable for accessibility analysis is trivial. The previous posts [on fuel station](https://shriv.github.io/Fuel-Stations-Analysis-Part-3/) and [playground](https://shriv.github.io/Playgrounds-vs-pubs/) acessibility cover the process in detail. Without delving into the specifics, the process basically involves calling _pandana's_ OpenStreetMap loader. And voila, we have a street network that can be consumed by _pandana_ for the accessibility analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _pandana_ network above has edge weights in the default units of metres, which means that the accessibility analyses will also be in metres. We can post-hoc convert the distance units to travel time with an average walking speed of 5 km/h or, 83 m/minute if we want travel time in minutes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wellington street network: with elevation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the API keys stored in a YAML file\n",
    "with open(\"utils/api_keys.yaml\", 'r') as stream:\n",
    "    data_loaded = yaml.load(stream)\n",
    "\n",
    "# Get Google Elevation API key\n",
    "google_elevation_api_key = data_loaded['google_elevation_api_key'][0]\n",
    "\n",
    "# Create an OSMNX walking street netwoek for the Wellington bounding box\n",
    "G = ox.graph_from_bbox(north, south, east, west, network_type='walk')\n",
    "\n",
    "# Add elevation values for the nodes in the OSMNX graph\n",
    "G = ox.add_node_elevations(G, api_key=google_elevation_api_key)\n",
    "\n",
    "# Generate an edge grade (inclination) with the elevations at the nodes\n",
    "G = ox.add_edge_grades(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessibility analysis using network with street gradients\n",
    "\n",
    "## Reproducing existing accessibility analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get nodes and edges as geodataframes (gdfs) from OSMNX network\n",
    "graph_df = ox.graph_to_gdfs(G)\n",
    "nodes_gdfs = graph_df[0]\n",
    "edges_gdfs = graph_df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting indices of Edges gdfs to match expected dataframe for Pandana\n",
    "edges_gdfs['from_idx'] = edges_gdfs['u']\n",
    "edges_gdfs['to_idx'] = edges_gdfs['v']\n",
    "edges_gdfs= edges_gdfs.set_index(['from_idx', 'to_idx'])\n",
    "edges_gdfs.index.names= ['','']\n",
    "\n",
    "# Setting indices of Nodes gdfs to match expected dataframe for Pandana\n",
    "nodes_gdfs.index.name = 'id'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandana network with travel times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Break up network\n",
    "edges_gdfs_undir_inv, edges_gdfs_undir, nodes_gdfs = dp.separate_elevation_graph_by_direction(G)\n",
    "\n",
    "# Create pandana network objects for flat and hilly terrain travel times\n",
    "network = pa.Network(nodes_gdfs['x'], nodes_gdfs['y'],\n",
    "                     edges_gdfs_undir['u'], edges_gdfs_undir['v'], \n",
    "                     edges_gdfs_undir[['time_5khr']])\n",
    "\n",
    "network_hills = pa.Network(nodes_gdfs['x'], nodes_gdfs['y'],\n",
    "                           edges_gdfs_undir['u'], edges_gdfs_undir['v'], \n",
    "                           edges_gdfs_undir[['time_tobler']])\n",
    "\n",
    "network_hills_inv = pa.Network(nodes_gdfs['x'], nodes_gdfs['y'],\n",
    "                               edges_gdfs_undir_inv['u'], edges_gdfs_undir_inv['v'], \n",
    "                               edges_gdfs_undir_inv[['time_tobler']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>grade</th>\n",
       "      <th>length</th>\n",
       "      <th>osmid</th>\n",
       "      <th>maxspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1259077823</th>\n",
       "      <th>1259072929</th>\n",
       "      <td>Truscott Avenue</td>\n",
       "      <td>0.1319</td>\n",
       "      <td>66.800</td>\n",
       "      <td>110175609</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259072943</th>\n",
       "      <td>Truscott Avenue</td>\n",
       "      <td>-0.0475</td>\n",
       "      <td>65.443</td>\n",
       "      <td>110175609</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6083853567</th>\n",
       "      <td>John Sims Drive</td>\n",
       "      <td>-0.1116</td>\n",
       "      <td>177.292</td>\n",
       "      <td>110176112</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259077824</th>\n",
       "      <th>6083853567</th>\n",
       "      <td>John Sims Drive</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>13.022</td>\n",
       "      <td>110176112</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259077827</th>\n",
       "      <th>465611807</th>\n",
       "      <td>Cambrian Street</td>\n",
       "      <td>0.0396</td>\n",
       "      <td>71.272</td>\n",
       "      <td>107284021</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name   grade   length      osmid maxspeed\n",
       "                                                                           \n",
       "1259077823 1259072929  Truscott Avenue  0.1319   66.800  110175609       50\n",
       "           1259072943  Truscott Avenue -0.0475   65.443  110175609       50\n",
       "           6083853567  John Sims Drive -0.1116  177.292  110176112       50\n",
       "1259077824 6083853567  John Sims Drive  0.1650   13.022  110176112       50\n",
       "1259077827 465611807   Cambrian Street  0.0396   71.272  107284021       50"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_gdfs_undir[['name', 'grade', 'length', 'osmid', 'maxspeed']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandana accessibility with hill-accounted travel times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate accessibility\n",
    "playground_accessibility = aa.get_accessibility(network, wcc_playgrounds, distance=60, num_pois=10)\n",
    "playground_hills_accessibility = aa.get_accessibility(network_hills, wcc_playgrounds, distance=60, num_pois=10)\n",
    "playground_hills_inv_accessibility = aa.get_accessibility(network_hills_inv, wcc_playgrounds, distance=60, num_pois=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_flat = (playground_accessibility[1] + playground_accessibility[1])\n",
    "total_hills_1 = (playground_hills_inv_accessibility[1] + playground_hills_accessibility[1])\n",
    "total_hills_2 = (playground_hills_inv_accessibility[2] + playground_hills_accessibility[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessibility by Wellington suburb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions within suburbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#analysis = 'residential polygons'\n",
    "analysis = 'suburb boundaries'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'network_hills' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-040fe281a8db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Get lat and lon coordinates for accessibility values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0morig_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetwork_hills\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf_joined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_nodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_hills_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_joined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'node_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lon'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'accessibility'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'network_hills' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# Get lat and lon coordinates for accessibility values\n",
    "orig_nodes = network_hills.nodes_df \n",
    "df_joined = pd.merge(orig_nodes.reset_index(), total_hills_1.reset_index(), how='inner')\n",
    "df_joined.columns = ['node_id', 'lon', 'lat', 'accessibility']\n",
    "# Convert lat and lon to geoseries\n",
    "df_joined_coords = dp.coords_df_to_geopandas_points(df_joined, crs={'init': 'epsg:4167'})\n",
    "\n",
    "# Extract only the residential polygons\n",
    "res_polys_only = wlg_res_poly_suburbs_ov[(~wlg_res_poly_suburbs_ov['t50_fid'].isnull())]\n",
    "\n",
    "if analysis == 'residential polygons':\n",
    "    # Get accessibility within residential polygons\n",
    "    playground_df = geopandas.sjoin(res_polys_only, df_joined_coords, op='contains').drop('index_right', axis=1)\n",
    "else:\n",
    "    # Join to suburb boundaries\n",
    "    playground_df = geopandas.sjoin(wcc_suburbs, df_joined_coords, op='contains').drop('index_right', axis=1)\n",
    "\n",
    "\n",
    "#df_sel = playground_df[playground_df['AU2013_V1_00_NAME'].isin(['Karori Park', 'Te Kainga',\n",
    "#                                          'Karori South', 'Karori East',\n",
    "#                                         'Newlands South', 'Hataitai North', \n",
    "#                                          'Strathmore Park', 'Raroa',\n",
    "#                                         'Ngaio South', 'Brooklyn'])]\n",
    "\n",
    "df_sel = playground_df[playground_df['suburb'].isin(['Karori'])]\n",
    "\n",
    "g = sns.FacetGrid(col='suburb', data=df_sel, size=4, col_wrap=5)\n",
    "g.map(plt.hist, 'accessibility', normed=True, bins=50)\n",
    "g.map(ut.vertical_average_lines, 'accessibility')\n",
    "plt.xlim(0,60);\n",
    "#plt.ylim(0, 0.0035);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising accessibility within suburb boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "karori = playground_df[playground_df['suburb'] == 'Karori']\n",
    "karori_mesh = wlg_meshblock_suburbs[wlg_meshblock_suburbs['suburb'] == \"Karori\"]\n",
    "\n",
    "fig = plt.figure(figsize=(10,9))\n",
    "#f, ax = plt.subplots()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "a0 = ax.scatter(karori['lon'], \n",
    "                karori['lat'], \n",
    "                s=10,\n",
    "                c=karori['accessibility'],\n",
    "                cmap='viridis_r')\n",
    "\n",
    "#wlg_meshblock_suburbs[wlg_meshblock_suburbs['suburb'] == 'Karori'].plot(ax=ax, alpha=0.9)    \n",
    "wcc_suburbs[wcc_suburbs['suburb'] == 'Karori']['geometry'].plot(ax=ax, color='black', alpha=0.2)   \n",
    "\n",
    "cbar1 = fig.colorbar(a0, ax=ax)\n",
    "ax.axis('off')\n",
    "cbar1.set_clim(0, 120)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "karori = playground_df[playground_df['suburb'] == 'Rongotai']\n",
    "karori_mesh = wlg_sa2_suburbs_ov[wlg_sa2_suburbs_ov['suburb'] == \"Karori\"]\n",
    "\n",
    "fig = plt.figure(figsize=(10,9))\n",
    "#f, ax = plt.subplots()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "#wlg_meshblock_suburbs[wlg_meshblock_suburbs['suburb'] == 'Karori'].plot(ax=ax, alpha=0.9)    \n",
    "a0 = ax.scatter(karori['lon'], \n",
    "                karori['lat'], \n",
    "                s=10,\n",
    "                c=karori['accessibility'],\n",
    "                cmap='viridis_r')\n",
    "wlg_sa2_suburbs_ov[wlg_sa2_suburbs_ov['suburb'] == 'Rongotai']['geometry'].plot(ax=ax, color='black', edgecolor='red', alpha=0.2)   \n",
    "\n",
    "cbar1 = fig.colorbar(a0, ax=ax)\n",
    "ax.axis('off')\n",
    "cbar1.set_clim(0, 120)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "karori = playground_df[playground_df['suburb'] == 'Rongotai']\n",
    "karori_mesh = wlg_res_poly_suburbs_ov[wlg_res_poly_suburbs_ov['suburb'] == \"Rongotai\"]\n",
    "\n",
    "fig = plt.figure(figsize=(10,9))\n",
    "#f, ax = plt.subplots()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "#wlg_meshblock_suburbs[wlg_meshblock_suburbs['suburb'] == 'Karori'].plot(ax=ax, alpha=0.9)    \n",
    "a0 = ax.scatter(karori['lon'], \n",
    "                karori['lat'], \n",
    "                s=10,\n",
    "                c=karori['accessibility'],\n",
    "                cmap='viridis_r')\n",
    "wcc_suburbs[wcc_suburbs['suburb'] == 'Rongotai']['geometry'].plot(ax=ax, color='black', alpha=0.2)   \n",
    "\n",
    "wlg_res_poly_suburbs_ov[(wlg_res_poly_suburbs_ov['suburb'] == 'Rongotai') & \n",
    "                        (~wlg_res_poly_suburbs_ov['t50_fid'].isnull())].plot(ax=ax, alpha=0.4)\n",
    "\n",
    "cbar1 = fig.colorbar(a0, ax=ax)\n",
    "ax.axis('off')\n",
    "cbar1.set_clim(0, 120)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f71d28f2b70>"
      ]
     },
     "execution_count": 30,
     "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": [
    "wlg_res_poly_suburbs_ov[(wlg_res_poly_suburbs_ov['suburb'] == 'Karori') & (~wlg_res_poly_suburbs_ov['t50_fid'].isnull())].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t50_fid</th>\n",
       "      <th>level_1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((174.7353105300001 -41.28739130899993...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((174.7266746510001 -41.28304035599996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON ((174.7208309800001 -41.28959583299996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((174.759869683805 -41.3237081287967, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((174.744012619715 -41.2297466358026, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t50_fid  level_1                                           geometry\n",
       "613      NaN        9  POLYGON ((174.7353105300001 -41.28739130899993...\n",
       "614      NaN       10  POLYGON ((174.7266746510001 -41.28304035599996...\n",
       "615      NaN       11  POLYGON ((174.7208309800001 -41.28959583299996...\n",
       "616      NaN        0  POLYGON ((174.759869683805 -41.3237081287967, ...\n",
       "617      NaN        0  POLYGON ((174.744012619715 -41.2297466358026, ..."
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstacked_poly = wlg_res_poly_suburbs_ov.set_index(['t50_fid'])['geometry'].apply(pd.Series).stack().reset_index(name='geometry')\n",
    "unstacked_poly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jville = playground_df[playground_df['suburb'] == 'Johnsonville']\n",
    "\n",
    "fig = plt.figure(figsize=(10,9))\n",
    "#f, ax = plt.subplots()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "wcc_suburbs[wcc_suburbs['suburb'] == 'Johnsonville']['geometry'].plot(ax=ax, color='black', alpha=0.2)    \n",
    "#wlg_meshblock_suburbs[wlg_meshblock_suburbs['suburb'] == 'Johnsonville'].plot(ax=ax)    \n",
    "\n",
    "a0 = ax.scatter(jville['lon'], \n",
    "                jville['lat'], \n",
    "                s=10,\n",
    "                c=jville['accessibility'],\n",
    "                cmap='viridis_r')\n",
    "\n",
    "cbar1 = fig.colorbar(a0, ax=ax)\n",
    "cbar1.set_clim(0, 120)\n",
    "ax.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stan experiments\n",
    "This section is all about writing Bayesian models with Stan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stan/univariate_normal.pkl\n",
      "stan/lower_truncated_univariate_normal.pkl\n",
      "stan/truncated_univariate_normal.pkl\n"
     ]
    }
   ],
   "source": [
    "uni_norm_model = su.load_or_generate_stan_model('stan', 'univariate_normal')\n",
    "lower_trunc_norm_model = su.load_or_generate_stan_model('stan', 'lower_truncated_univariate_normal')\n",
    "trunc_norm_model = su.load_or_generate_stan_model('stan', 'truncated_univariate_normal')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_cc3fc1beb21cbbe7b94ad66105c98210.\n",
      "4 chains, each with iter=2000; warmup=1000; thin=1; \n",
      "post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
      "\n",
      "         mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu       23.0  6.3e-3   0.36  22.26  22.75   23.0  23.24   23.7   3320    1.0\n",
      "sigma   14.28  4.4e-3   0.25   13.8  14.11  14.27  14.44  14.79   3246    1.0\n",
      "y_pred  23.03    0.23  14.14  -4.63  13.13  23.35  32.72  50.02   3844    1.0\n",
      "lp__    -5086    0.02   1.02  -5089  -5087  -5086  -5085  -5085   1690    1.0\n",
      "\n",
      "Samples were drawn using NUTS at Sat May 11 23:03:17 2019.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "# Run precompiled model\n",
    "hataitai = playground_df[playground_df['suburb'] == 'Hataitai']\n",
    "karori = playground_df[playground_df['suburb'] == 'Karori']\n",
    "karori = karori[karori['accessibility'] > 0]\n",
    "\n",
    "karori_dat = {'n': karori.shape[0],\n",
    "              'y': karori['accessibility'].values,}\n",
    "karori_fit = uni_norm_model.sampling(karori_dat, chains=4)\n",
    "print(karori_fit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = karori_fit.plot(pars=['y_pred'])\n",
    "fig.subplots_adjust(hspace=1.0, wspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30, 80)"
      ]
     },
     "execution_count": 36,
     "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": [
    "plt.hist(karori_fit['y_pred'], bins=100);\n",
    "plt.xlim(-30, 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_cc3fc1beb21cbbe7b94ad66105c98210.\n",
      "4 chains, each with iter=2000; warmup=1000; thin=1; \n",
      "post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
      "\n",
      "         mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu      16.78  4.7e-3   0.29  16.23   16.6  16.79  16.97  17.34   3669    1.0\n",
      "sigma    5.88  3.6e-3    0.2    5.5   5.74   5.88   6.02   6.28   3125    1.0\n",
      "y_pred  16.68    0.09   5.92   5.23   12.6  16.65  20.67  28.48   4000    1.0\n",
      "lp__   -963.4    0.02   0.98 -966.1 -963.8 -963.1 -962.7 -962.4   1580    1.0\n",
      "\n",
      "Samples were drawn using NUTS at Sat May 11 23:03:18 2019.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "# Run precompiled model\n",
    "hataitai = playground_df[playground_df['suburb'] == 'Hataitai']\n",
    "\n",
    "hataitai_dat = {'n': hataitai.shape[0],\n",
    "                'y': hataitai['accessibility'].values,}\n",
    "hataitai_fit = uni_norm_model.sampling(hataitai_dat, chains=4)\n",
    "print(hataitai_fit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30, 80)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suburb_name = 'Karori'\n",
    "suburb_df = playground_df[playground_df['suburb'] == suburb_name]\n",
    "suburb_df_dat = {'n': suburb_df.shape[0],\n",
    "                 'y': suburb_df['accessibility'].values,}\n",
    "\n",
    "# Run Stan model for suburb\n",
    "suburb_trunc_fit = uni_norm_model.sampling(suburb_df_dat, chains=4)\n",
    "\n",
    "# Plot model posterior predictive against raw values\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8,6))\n",
    "ax1.hist(suburb_trunc_fit['y_pred'], bins=100, density=True);\n",
    "ax1.set_title('Normal model for {:s}'.format(suburb_name));\n",
    "plt.xlim(-30,80)\n",
    "\n",
    "ax2.hist(suburb_df['accessibility'], bins=100, density=True);\n",
    "ax2.set_title('Raw accessibility for {:s}'.format(suburb_name));\n",
    "plt.xlim(-30,80)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = hataitai_fit.plot(pars=['y_pred'])\n",
    "fig.subplots_adjust(hspace=1.0, wspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Truncated Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_da01b1257b3430beee0da77218725620.\n",
      "4 chains, each with iter=2000; warmup=1000; thin=1; \n",
      "post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
      "\n",
      "         mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu      17.55    0.02   0.82  15.88  17.01  17.59  18.12  19.07   1169    1.0\n",
      "sigma   18.13    0.02   0.56  17.09  17.74  18.11  18.49  19.29   1245    1.0\n",
      "y_pred  23.08    0.22  14.14   1.44  11.93  21.65  32.65   53.1   4000    1.0\n",
      "lp__    -4946    0.03   1.08  -4949  -4946  -4945  -4945  -4945   1025    1.0\n",
      "\n",
      "Samples were drawn using NUTS at Sat May 11 23:03:31 2019.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "# Run precompiled model\n",
    "karori_trunc_dat = {'N': karori.shape[0],\n",
    "                    'L': 0,\n",
    "                    'U': 80,\n",
    "                    'y': karori['accessibility'].values,}\n",
    "karori_trunc_fit = lower_trunc_norm_model.sampling(karori_trunc_dat, chains=4)\n",
    "print(karori_trunc_fit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30, 80)"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.hist(karori_trunc_fit['y_pred'], bins=100);\n",
    "plt.xlim(-30, 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = karori_trunc_fit.plot(pars=['y_pred'])\n",
    "fig.subplots_adjust(hspace=1.0, wspace=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.00378287, 20.17230381, 31.27007723, ..., 34.42684909,\n",
       "       15.40266759, 52.49256071])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "karori_trunc_fit['y_pred']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking model performance with posterior predictive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "karori_fit = su.run_plot_suburb_stan(playground_df, lower_trunc_norm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tearo_fit = su.run_plot_suburb_stan(playground_df, lower_trunc_norm_model, 'Te Aro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makara_fit = su.run_plot_suburb_stan(playground_df, lower_trunc_norm_model, 'Makara')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suburb_fit = su.run_plot_suburb_stan(playground_df, lower_trunc_norm_model, 'Rongotai')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stan/univariate_normal_hierarchical_single_level.pkl\n"
     ]
    }
   ],
   "source": [
    "norm_hier_model = su.load_or_generate_stan_model('stan', 'univariate_normal_hierarchical_single_level')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stan/univariate_normal_hierarchical_single_level_centered.pkl\n"
     ]
    }
   ],
   "source": [
    "norm_hier_model_cent = su.load_or_generate_stan_model('stan', 'univariate_normal_hierarchical_single_level_centered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suburb</th>\n",
       "      <th>counts</th>\n",
       "      <th>cumulative</th>\n",
       "      <th>fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Karori</td>\n",
       "      <td>1619</td>\n",
       "      <td>1619</td>\n",
       "      <td>0.104210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miramar</td>\n",
       "      <td>1208</td>\n",
       "      <td>2827</td>\n",
       "      <td>0.181964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>802</td>\n",
       "      <td>3629</td>\n",
       "      <td>0.233587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Te Aro</td>\n",
       "      <td>743</td>\n",
       "      <td>4372</td>\n",
       "      <td>0.281411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Newtown</td>\n",
       "      <td>729</td>\n",
       "      <td>5101</td>\n",
       "      <td>0.328334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tawa</td>\n",
       "      <td>603</td>\n",
       "      <td>5704</td>\n",
       "      <td>0.367147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Island Bay</td>\n",
       "      <td>602</td>\n",
       "      <td>6306</td>\n",
       "      <td>0.405896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Johnsonville</td>\n",
       "      <td>550</td>\n",
       "      <td>6856</td>\n",
       "      <td>0.441298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kelburn</td>\n",
       "      <td>493</td>\n",
       "      <td>7349</td>\n",
       "      <td>0.473030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mount Victoria</td>\n",
       "      <td>442</td>\n",
       "      <td>7791</td>\n",
       "      <td>0.501480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           suburb  counts  cumulative  fraction\n",
       "0          Karori    1619        1619  0.104210\n",
       "1         Miramar    1208        2827  0.181964\n",
       "2        Brooklyn     802        3629  0.233587\n",
       "3          Te Aro     743        4372  0.281411\n",
       "4         Newtown     729        5101  0.328334\n",
       "5            Tawa     603        5704  0.367147\n",
       "6      Island Bay     602        6306  0.405896\n",
       "7    Johnsonville     550        6856  0.441298\n",
       "8         Kelburn     493        7349  0.473030\n",
       "9  Mount Victoria     442        7791  0.501480"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = playground_df['suburb'].value_counts().reset_index().rename({'suburb': 'counts',\n",
    "                                                                         'index': 'suburb'}, axis=1)\n",
    "summary_df['cumulative'] = summary_df['counts'].cumsum()\n",
    "summary_df['fraction'] = summary_df['cumulative'] / summary_df['counts'].sum()\n",
    "\n",
    "top_80 = summary_df[summary_df['fraction'] <= 0.80]\n",
    "top_80.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_80_df = playground_df[playground_df['suburb'].isin(top_80['suburb'])]\n",
    "top_80_df = su.convert_categorical_to_ID(top_80_df, 'suburb')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload(su)\n",
    "hier_summary_2 = su.train_acc_hierarchical(top_80_df,\n",
    "                                           norm_hier_model,\n",
    "                                           return_levels_stanfit = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference for Stan model: anon_model_8e1cf432d03a78f6f8fe41c8d6494cf9.\n",
       "1 chains, each with iter=1000; warmup=500; thin=1; \n",
       "post-warmup draws per chain=500, total post-warmup draws=500.\n",
       "\n",
       "              mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
       "mu_l[0]       8.81    0.11   1.62   5.49   7.84    9.0   9.94  11.16    234    1.0\n",
       "mu_l[1]      11.54    0.02   0.54  10.52  11.18  11.57  11.91  12.56    500    1.0\n",
       "mu_l[2]      11.32    0.02   0.42  10.51  11.05  11.32  11.59  12.15    500    1.0\n",
       "mu_l[3]       8.99    0.08   1.39   5.87   8.19   9.07  10.01  11.35    271    1.0\n",
       "mu_l[4]      26.19    0.02   0.35  25.52  25.95  26.19  26.45  26.83    500    1.0\n",
       "mu_l[5]      28.54    0.03   0.75  27.09   28.0  28.51  29.04  30.09    500    1.0\n",
       "mu_l[6]      17.65    0.04   0.85  15.74  17.14   17.7  18.22  19.18    500   1.01\n",
       "mu_l[7]        8.9  7.2e-3   0.16   8.59   8.79    8.9   9.01   9.22    500    1.0\n",
       "mu_l[8]      14.06    0.04   0.93  12.21  13.38  14.11  14.72  15.71    500   1.01\n",
       "mu_l[9]      16.74    0.01    0.3  16.18  16.54  16.74  16.94  17.29    500    1.0\n",
       "mu_l[10]     14.34    0.02   0.49  13.31  14.02  14.32  14.68  15.33    500    1.0\n",
       "mu_l[11]      3.26    0.09   1.47   0.42   2.28   3.26   4.33   5.93    261    1.0\n",
       "mu_l[12]     14.94    0.02   0.36  14.21  14.72  14.93  15.16   15.7    500    1.0\n",
       "mu_l[13]     12.99    0.01   0.24  12.54  12.82   13.0  13.15  13.47    500    1.0\n",
       "mu_l[14]     12.21    0.03   0.73  10.65  11.71  12.25  12.71  13.58    500    1.0\n",
       "mu_l[15]     18.35    0.07    1.2  15.82  17.58  18.42  19.17  20.54    295    1.0\n",
       "mu_l[16]     19.91    0.02   0.38  19.08  19.65  19.94  20.16  20.64    500    1.0\n",
       "mu_l[17]     11.65    0.01   0.27  11.07  11.46  11.65  11.84  12.14    500    1.0\n",
       "mu_l[18]      8.35    0.01    0.3   7.71   8.16   8.35   8.56   8.94    500    1.0\n",
       "mu_l[19]     12.69    0.02   0.42  11.87  12.43   12.7  12.97   13.5    500    1.0\n",
       "mu_l[20]      12.5    0.01   0.26  12.01  12.31   12.5  12.67  12.97    500    1.0\n",
       "mu_l[21]      2.38     0.1   1.68   0.09    0.9   2.12    3.6   5.94    299    1.0\n",
       "mu_l[22]     17.09    0.04   0.87  15.29   16.5  17.15  17.68  18.72    500    1.0\n",
       "sigma_l[0]   11.26    0.06    1.0   9.64  10.52  11.18  11.83  13.49    244    1.0\n",
       "sigma_l[1]    8.44    0.02   0.39   7.71   8.17   8.43    8.7   9.24    500    1.0\n",
       "sigma_l[2]    6.81    0.01   0.32   6.25   6.58    6.8   7.03   7.55    500    1.0\n",
       "sigma_l[3]   12.19    0.05   0.87  10.63  11.52  12.18  12.76  14.03    312    1.0\n",
       "sigma_l[4]     6.7    0.01   0.27   6.21   6.51   6.69   6.88   7.28    500    1.0\n",
       "sigma_l[5]   11.91    0.02   0.52  11.07  11.49  11.86  12.25  13.07    500    1.0\n",
       "sigma_l[6]   11.44    0.03   0.66  10.32  10.97  11.36  11.84  12.91    500   1.01\n",
       "sigma_l[7]    3.89  5.3e-3   0.12   3.68   3.81   3.89   3.97   4.14    500    1.0\n",
       "sigma_l[8]   11.43    0.03   0.71  10.16  10.95  11.41  11.88  12.86    500    1.0\n",
       "sigma_l[9]    5.96  8.8e-3    0.2   5.58   5.83   5.96    6.1   6.36    500    1.0\n",
       "sigma_l[10]   7.26    0.02   0.36   6.63    7.0   7.23   7.51   8.07    500    1.0\n",
       "sigma_l[11]  12.51    0.04    0.7  11.17   12.0  12.49   13.0  13.87    303    1.0\n",
       "sigma_l[12]   6.97    0.01   0.26   6.52   6.78   6.97   7.15   7.51    500    1.0\n",
       "sigma_l[13]   7.05  8.3e-3   0.19   6.71   6.91   7.05   7.18    7.4    500    1.0\n",
       "sigma_l[14]    9.1    0.03   0.57   8.13    8.7   9.04   9.44  10.41    500    1.0\n",
       "sigma_l[15]  14.31    0.06   0.91  12.75  13.65  14.21  14.91  16.24    259    1.0\n",
       "sigma_l[16]   9.18    0.01   0.32   8.58   8.96   9.16   9.38   9.83    500    1.0\n",
       "sigma_l[17]   5.33    0.01   0.23   4.91   5.19   5.32   5.47   5.82    500    1.0\n",
       "sigma_l[18]   5.93    0.01   0.23   5.48   5.77   5.93   6.09   6.39    500    1.0\n",
       "sigma_l[19]   7.99    0.02   0.35   7.35   7.73    8.0   8.21   8.71    500    1.0\n",
       "sigma_l[20]    5.3  9.1e-3    0.2   4.94   5.15   5.29   5.43   5.74    500    1.0\n",
       "sigma_l[21]  25.44    0.04   0.94  23.38  24.85  25.49  26.04  27.26    500    1.0\n",
       "sigma_l[22]  18.33    0.03   0.57  17.17  17.97  18.32  18.71  19.45    366    1.0\n",
       "sigma_m       6.64    0.05   1.14   4.79   5.82   6.51   7.23   9.27    500    1.0\n",
       "mu_m         13.62    0.06    1.4   10.8  12.75  13.65  14.45  16.28    500    1.0\n",
       "sigma_s       5.17    0.04   0.89   3.85   4.53   5.04    5.7   7.07    500    1.0\n",
       "mu_s          9.67    0.05   1.15   7.31   8.88   9.67  10.46  11.93    500    1.0\n",
       "lp__        -3.1e4    0.39   5.46 -3.1e4 -3.1e4 -3.1e4 -3.1e4 -3.1e4    196    1.0\n",
       "\n",
       "Samples were drawn using NUTS at Sat May 11 23:05:35 2019.\n",
       "For each parameter, n_eff is a crude measure of effective sample size,\n",
       "and Rhat is the potential scale reduction factor on split chains (at \n",
       "convergence, Rhat=1)."
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_summary_2['stanfit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload(su)\n",
    "hier_summary_cent = su.train_acc_hierarchical(top_80_df,\n",
    "                                           norm_hier_model_cent,\n",
    "                                           return_levels_stanfit = True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot average accessibility at suburban level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {},
   "outputs": [],
   "source": [
    "summ = hier_summary_2['stanfit'].summary(['mu_m'])\n",
    "summ_df = pd.DataFrame(summ['summary'],\n",
    "                       columns=(u'mean', u'se_mean', u'sd', u'hpd_2.5',u'hpd_25',\n",
    "                                u'hpd_50',u'hpd_75',u'hpd_97.5',u'n_eff',u'Rhat'),\n",
    "                       index=summ['summary_rownames']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(20.80000000000001, 0.5, '')"
      ]
     },
     "execution_count": 889,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = su.df_for_forestplot(hier_summary_2['stanfit'], top_80_df, 'mu_l', 'suburb_id')\n",
    "df_plot_sort = df_plot.sort_values('mean')\n",
    "su.custom_forestplot(df_plot_sort, 'suburb', size=8, aspect=0.8, facetby=None, \n",
    "                     avg_min=summ_df['hpd_2.5'][0], avg_max=summ_df['hpd_97.5'][0])\n",
    "plt.xlim(0, 30)\n",
    "plt.xlabel('$\\mu$', size=20)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>mean</th>\n",
       "      <th>se_mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd_2.5</th>\n",
       "      <th>hpd_25</th>\n",
       "      <th>hpd_50</th>\n",
       "      <th>hpd_75</th>\n",
       "      <th>hpd_97.5</th>\n",
       "      <th>n_eff</th>\n",
       "      <th>Rhat</th>\n",
       "      <th>ypos</th>\n",
       "      <th>suburb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mu_l[0]</td>\n",
       "      <td>8.811920</td>\n",
       "      <td>0.105971</td>\n",
       "      <td>1.621049</td>\n",
       "      <td>5.487433</td>\n",
       "      <td>7.840005</td>\n",
       "      <td>9.002371</td>\n",
       "      <td>9.935698</td>\n",
       "      <td>11.163111</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.997999</td>\n",
       "      <td>0</td>\n",
       "      <td>Churton Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mu_l[1]</td>\n",
       "      <td>11.542421</td>\n",
       "      <td>0.024170</td>\n",
       "      <td>0.540447</td>\n",
       "      <td>10.524593</td>\n",
       "      <td>11.180405</td>\n",
       "      <td>11.565696</td>\n",
       "      <td>11.911710</td>\n",
       "      <td>12.562399</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998198</td>\n",
       "      <td>1</td>\n",
       "      <td>Johnsonville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mu_l[2]</td>\n",
       "      <td>11.318424</td>\n",
       "      <td>0.018749</td>\n",
       "      <td>0.419234</td>\n",
       "      <td>10.514659</td>\n",
       "      <td>11.050510</td>\n",
       "      <td>11.322219</td>\n",
       "      <td>11.592589</td>\n",
       "      <td>12.148097</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.000345</td>\n",
       "      <td>2</td>\n",
       "      <td>Lyall Bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mu_l[3]</td>\n",
       "      <td>8.987805</td>\n",
       "      <td>0.084408</td>\n",
       "      <td>1.389527</td>\n",
       "      <td>5.872613</td>\n",
       "      <td>8.188782</td>\n",
       "      <td>9.073300</td>\n",
       "      <td>10.005487</td>\n",
       "      <td>11.353686</td>\n",
       "      <td>271.0</td>\n",
       "      <td>1.000652</td>\n",
       "      <td>3</td>\n",
       "      <td>Newlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mu_l[4]</td>\n",
       "      <td>26.189760</td>\n",
       "      <td>0.015506</td>\n",
       "      <td>0.346727</td>\n",
       "      <td>25.520780</td>\n",
       "      <td>25.953086</td>\n",
       "      <td>26.188692</td>\n",
       "      <td>26.446181</td>\n",
       "      <td>26.831352</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998131</td>\n",
       "      <td>4</td>\n",
       "      <td>Pipitea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mu_l[5]</td>\n",
       "      <td>28.540351</td>\n",
       "      <td>0.033588</td>\n",
       "      <td>0.751053</td>\n",
       "      <td>27.089057</td>\n",
       "      <td>28.002423</td>\n",
       "      <td>28.506275</td>\n",
       "      <td>29.043969</td>\n",
       "      <td>30.087154</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998647</td>\n",
       "      <td>5</td>\n",
       "      <td>Rongotai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mu_l[6]</td>\n",
       "      <td>17.650823</td>\n",
       "      <td>0.037842</td>\n",
       "      <td>0.846179</td>\n",
       "      <td>15.744027</td>\n",
       "      <td>17.137777</td>\n",
       "      <td>17.704049</td>\n",
       "      <td>18.224870</td>\n",
       "      <td>19.181802</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.010710</td>\n",
       "      <td>6</td>\n",
       "      <td>Strathmore Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mu_l[7]</td>\n",
       "      <td>8.901505</td>\n",
       "      <td>0.007154</td>\n",
       "      <td>0.159962</td>\n",
       "      <td>8.594219</td>\n",
       "      <td>8.790442</td>\n",
       "      <td>8.904644</td>\n",
       "      <td>9.008640</td>\n",
       "      <td>9.220547</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.999518</td>\n",
       "      <td>7</td>\n",
       "      <td>Te Aro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mu_l[8]</td>\n",
       "      <td>14.064208</td>\n",
       "      <td>0.041790</td>\n",
       "      <td>0.934457</td>\n",
       "      <td>12.207773</td>\n",
       "      <td>13.383473</td>\n",
       "      <td>14.108049</td>\n",
       "      <td>14.722342</td>\n",
       "      <td>15.707675</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.005541</td>\n",
       "      <td>8</td>\n",
       "      <td>Thorndon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mu_l[9]</td>\n",
       "      <td>16.736541</td>\n",
       "      <td>0.013240</td>\n",
       "      <td>0.296051</td>\n",
       "      <td>16.176170</td>\n",
       "      <td>16.544056</td>\n",
       "      <td>16.736907</td>\n",
       "      <td>16.937339</td>\n",
       "      <td>17.292489</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998068</td>\n",
       "      <td>9</td>\n",
       "      <td>Hataitai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mu_l[10]</td>\n",
       "      <td>14.339074</td>\n",
       "      <td>0.021697</td>\n",
       "      <td>0.485151</td>\n",
       "      <td>13.314758</td>\n",
       "      <td>14.023824</td>\n",
       "      <td>14.323463</td>\n",
       "      <td>14.679002</td>\n",
       "      <td>15.330341</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998954</td>\n",
       "      <td>10</td>\n",
       "      <td>Wellington Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mu_l[11]</td>\n",
       "      <td>3.260697</td>\n",
       "      <td>0.091156</td>\n",
       "      <td>1.472667</td>\n",
       "      <td>0.418071</td>\n",
       "      <td>2.281118</td>\n",
       "      <td>3.256726</td>\n",
       "      <td>4.326260</td>\n",
       "      <td>5.927055</td>\n",
       "      <td>261.0</td>\n",
       "      <td>0.998005</td>\n",
       "      <td>11</td>\n",
       "      <td>Tawa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mu_l[12]</td>\n",
       "      <td>14.940408</td>\n",
       "      <td>0.016031</td>\n",
       "      <td>0.358467</td>\n",
       "      <td>14.212089</td>\n",
       "      <td>14.721686</td>\n",
       "      <td>14.934741</td>\n",
       "      <td>15.162752</td>\n",
       "      <td>15.702823</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998751</td>\n",
       "      <td>12</td>\n",
       "      <td>Kelburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mu_l[13]</td>\n",
       "      <td>12.988906</td>\n",
       "      <td>0.010897</td>\n",
       "      <td>0.243668</td>\n",
       "      <td>12.543909</td>\n",
       "      <td>12.819579</td>\n",
       "      <td>12.998290</td>\n",
       "      <td>13.148811</td>\n",
       "      <td>13.468493</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998119</td>\n",
       "      <td>13</td>\n",
       "      <td>Miramar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mu_l[14]</td>\n",
       "      <td>12.205576</td>\n",
       "      <td>0.032729</td>\n",
       "      <td>0.731850</td>\n",
       "      <td>10.650325</td>\n",
       "      <td>11.714872</td>\n",
       "      <td>12.252250</td>\n",
       "      <td>12.708764</td>\n",
       "      <td>13.576306</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.003164</td>\n",
       "      <td>14</td>\n",
       "      <td>Seatoun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mu_l[15]</td>\n",
       "      <td>18.352974</td>\n",
       "      <td>0.069752</td>\n",
       "      <td>1.198027</td>\n",
       "      <td>15.818768</td>\n",
       "      <td>17.578358</td>\n",
       "      <td>18.417808</td>\n",
       "      <td>19.172921</td>\n",
       "      <td>20.538911</td>\n",
       "      <td>295.0</td>\n",
       "      <td>0.998377</td>\n",
       "      <td>15</td>\n",
       "      <td>Khandallah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mu_l[16]</td>\n",
       "      <td>19.912002</td>\n",
       "      <td>0.016989</td>\n",
       "      <td>0.379882</td>\n",
       "      <td>19.084610</td>\n",
       "      <td>19.653336</td>\n",
       "      <td>19.938488</td>\n",
       "      <td>20.160588</td>\n",
       "      <td>20.635919</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998599</td>\n",
       "      <td>16</td>\n",
       "      <td>Island Bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mu_l[17]</td>\n",
       "      <td>11.646210</td>\n",
       "      <td>0.012182</td>\n",
       "      <td>0.272398</td>\n",
       "      <td>11.068919</td>\n",
       "      <td>11.456731</td>\n",
       "      <td>11.650884</td>\n",
       "      <td>11.840466</td>\n",
       "      <td>12.144603</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998083</td>\n",
       "      <td>17</td>\n",
       "      <td>Kilbirnie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mu_l[18]</td>\n",
       "      <td>8.346935</td>\n",
       "      <td>0.013479</td>\n",
       "      <td>0.301395</td>\n",
       "      <td>7.705091</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>8.347011</td>\n",
       "      <td>8.555790</td>\n",
       "      <td>8.942960</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>18</td>\n",
       "      <td>Newtown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mu_l[19]</td>\n",
       "      <td>12.690921</td>\n",
       "      <td>0.018956</td>\n",
       "      <td>0.423862</td>\n",
       "      <td>11.870901</td>\n",
       "      <td>12.430559</td>\n",
       "      <td>12.699118</td>\n",
       "      <td>12.974997</td>\n",
       "      <td>13.504462</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998184</td>\n",
       "      <td>19</td>\n",
       "      <td>Mount Victoria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mu_l[20]</td>\n",
       "      <td>12.498744</td>\n",
       "      <td>0.011590</td>\n",
       "      <td>0.259154</td>\n",
       "      <td>12.006268</td>\n",
       "      <td>12.311268</td>\n",
       "      <td>12.504603</td>\n",
       "      <td>12.671649</td>\n",
       "      <td>12.970420</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.997999</td>\n",
       "      <td>20</td>\n",
       "      <td>Mount Cook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mu_l[21]</td>\n",
       "      <td>2.382064</td>\n",
       "      <td>0.097418</td>\n",
       "      <td>1.684511</td>\n",
       "      <td>0.086232</td>\n",
       "      <td>0.901471</td>\n",
       "      <td>2.119761</td>\n",
       "      <td>3.600129</td>\n",
       "      <td>5.938208</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.998304</td>\n",
       "      <td>21</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>mu_l[22]</td>\n",
       "      <td>17.091480</td>\n",
       "      <td>0.039105</td>\n",
       "      <td>0.874425</td>\n",
       "      <td>15.285500</td>\n",
       "      <td>16.504152</td>\n",
       "      <td>17.147309</td>\n",
       "      <td>17.682461</td>\n",
       "      <td>18.719674</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.998067</td>\n",
       "      <td>22</td>\n",
       "      <td>Karori</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index       mean   se_mean        sd    hpd_2.5     hpd_25     hpd_50  \\\n",
       "0    mu_l[0]   8.811920  0.105971  1.621049   5.487433   7.840005   9.002371   \n",
       "1    mu_l[1]  11.542421  0.024170  0.540447  10.524593  11.180405  11.565696   \n",
       "2    mu_l[2]  11.318424  0.018749  0.419234  10.514659  11.050510  11.322219   \n",
       "3    mu_l[3]   8.987805  0.084408  1.389527   5.872613   8.188782   9.073300   \n",
       "4    mu_l[4]  26.189760  0.015506  0.346727  25.520780  25.953086  26.188692   \n",
       "5    mu_l[5]  28.540351  0.033588  0.751053  27.089057  28.002423  28.506275   \n",
       "6    mu_l[6]  17.650823  0.037842  0.846179  15.744027  17.137777  17.704049   \n",
       "7    mu_l[7]   8.901505  0.007154  0.159962   8.594219   8.790442   8.904644   \n",
       "8    mu_l[8]  14.064208  0.041790  0.934457  12.207773  13.383473  14.108049   \n",
       "9    mu_l[9]  16.736541  0.013240  0.296051  16.176170  16.544056  16.736907   \n",
       "10  mu_l[10]  14.339074  0.021697  0.485151  13.314758  14.023824  14.323463   \n",
       "11  mu_l[11]   3.260697  0.091156  1.472667   0.418071   2.281118   3.256726   \n",
       "12  mu_l[12]  14.940408  0.016031  0.358467  14.212089  14.721686  14.934741   \n",
       "13  mu_l[13]  12.988906  0.010897  0.243668  12.543909  12.819579  12.998290   \n",
       "14  mu_l[14]  12.205576  0.032729  0.731850  10.650325  11.714872  12.252250   \n",
       "15  mu_l[15]  18.352974  0.069752  1.198027  15.818768  17.578358  18.417808   \n",
       "16  mu_l[16]  19.912002  0.016989  0.379882  19.084610  19.653336  19.938488   \n",
       "17  mu_l[17]  11.646210  0.012182  0.272398  11.068919  11.456731  11.650884   \n",
       "18  mu_l[18]   8.346935  0.013479  0.301395   7.705091   8.164537   8.347011   \n",
       "19  mu_l[19]  12.690921  0.018956  0.423862  11.870901  12.430559  12.699118   \n",
       "20  mu_l[20]  12.498744  0.011590  0.259154  12.006268  12.311268  12.504603   \n",
       "21  mu_l[21]   2.382064  0.097418  1.684511   0.086232   0.901471   2.119761   \n",
       "22  mu_l[22]  17.091480  0.039105  0.874425  15.285500  16.504152  17.147309   \n",
       "\n",
       "       hpd_75   hpd_97.5  n_eff      Rhat  ypos              suburb  \n",
       "0    9.935698  11.163111  234.0  0.997999     0        Churton Park  \n",
       "1   11.911710  12.562399  500.0  0.998198     1        Johnsonville  \n",
       "2   11.592589  12.148097  500.0  1.000345     2           Lyall Bay  \n",
       "3   10.005487  11.353686  271.0  1.000652     3            Newlands  \n",
       "4   26.446181  26.831352  500.0  0.998131     4             Pipitea  \n",
       "5   29.043969  30.087154  500.0  0.998647     5            Rongotai  \n",
       "6   18.224870  19.181802  500.0  1.010710     6     Strathmore Park  \n",
       "7    9.008640   9.220547  500.0  0.999518     7              Te Aro  \n",
       "8   14.722342  15.707675  500.0  1.005541     8            Thorndon  \n",
       "9   16.937339  17.292489  500.0  0.998068     9            Hataitai  \n",
       "10  14.679002  15.330341  500.0  0.998954    10  Wellington Central  \n",
       "11   4.326260   5.927055  261.0  0.998005    11                Tawa  \n",
       "12  15.162752  15.702823  500.0  0.998751    12             Kelburn  \n",
       "13  13.148811  13.468493  500.0  0.998119    13             Miramar  \n",
       "14  12.708764  13.576306  500.0  1.003164    14             Seatoun  \n",
       "15  19.172921  20.538911  295.0  0.998377    15          Khandallah  \n",
       "16  20.160588  20.635919  500.0  0.998599    16          Island Bay  \n",
       "17  11.840466  12.144603  500.0  0.998083    17           Kilbirnie  \n",
       "18   8.555790   8.942960  500.0  0.998118    18             Newtown  \n",
       "19  12.974997  13.504462  500.0  0.998184    19      Mount Victoria  \n",
       "20  12.671649  12.970420  500.0  0.997999    20          Mount Cook  \n",
       "21   3.600129   5.938208  299.0  0.998304    21            Brooklyn  \n",
       "22  17.682461  18.719674  500.0  0.998067    22              Karori  "
      ]
     },
     "execution_count": 890,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot accessibility variation at suburban level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {},
   "outputs": [],
   "source": [
    "summ = hier_summary_2['stanfit'].summary(['mu_s'])\n",
    "summ_df = pd.DataFrame(summ['summary'],\n",
    "                       columns=(u'mean', u'se_mean', u'sd', u'hpd_2.5',u'hpd_25',\n",
    "                                u'hpd_50',u'hpd_75',u'hpd_97.5',u'n_eff',u'Rhat'),\n",
    "                       index=summ['summary_rownames']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(20.80000000000001, 0.5, '')"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = su.df_for_forestplot(hier_summary_2['stanfit'], top_80_df, 'sigma_l', 'suburb_id')\n",
    "df_plot_sort = df_plot.sort_values('mean')\n",
    "su.custom_forestplot(df_plot_sort, 'suburb', size=8, aspect=0.8, facetby=None, \n",
    "                     avg_min=summ_df['hpd_2.5'][0], avg_max=summ_df['hpd_97.5'][0])\n",
    "plt.xlim(0,30)\n",
    "plt.xlabel('$\\sigma$', size=20)\n",
    "plt.ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot average and variation by suburb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 893,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get individual suburb distribution summaries\n",
    "df_plot_sigma = su.df_for_forestplot(hier_summary_2['stanfit'], top_80_df, 'sigma_l', 'suburb_id')\n",
    "df_plot_mu = su.df_for_forestplot(hier_summary_2['stanfit'], top_80_df, 'mu_l', 'suburb_id')\n",
    "\n",
    "# Summarise all suburb distribution\n",
    "summ_sigma_df = su.summarise_variable(hier_summary_2['stanfit'], 'mu_s')\n",
    "summ_mu_df = su.summarise_variable(hier_summary_2['stanfit'], 'mu_m')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 894,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename suburban average df\n",
    "df_plot_mu = df_plot_mu[['mean', 'hpd_2.5', \n",
    "                         'hpd_97.5', 'suburb']].rename({'mean': 'mu_mean',\n",
    "                                                        'hpd_2.5': 'mu_hpd_2.5',\n",
    "                                                        'hpd_97.5': 'mu_hpd_97.5'},\n",
    "                                                axis=1)\n",
    "\n",
    "# Rename suburban variation df\n",
    "df_plot_sigma = df_plot_sigma[['mean', 'hpd_2.5', \n",
    "                               'hpd_97.5', 'suburb']].rename({'mean': 'sigma_mean',\n",
    "                                                              'hpd_2.5': 'sigma_hpd_2.5',\n",
    "                                                              'hpd_97.5': 'sigma_hpd_97.5'}, \n",
    "                                                      axis=1)\n",
    "\n",
    "\n",
    "# Normalise\n",
    "df_plot_mu['mu_norm'] = df_plot_mu['mu_mean'] - summ_mu_df['mean'][0]\n",
    "df_plot_sigma['sigma_norm'] = df_plot_sigma['sigma_mean'] - summ_sigma_df['mean'][0]\n",
    "\n",
    "# Join normalised mean and sigma by suburb\n",
    "df_all = pd.merge(df_plot_mu, df_plot_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 895,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colorpoint(row):\n",
    "    if (row['sigma_norm'] < -2.35) & (row['mu_norm'] < -2.35):\n",
    "        return('y')\n",
    "    if (row['sigma_norm'] < -2.35) & (row['mu_norm'] > 2.26):\n",
    "        return('g')\n",
    "    if (row['sigma_norm'] > 2.26) & (row['mu_norm'] > 2.26):\n",
    "        return('r')\n",
    "    if (row['sigma_norm'] > 2.26) & (row['mu_norm'] < -2.35):\n",
    "        return('b')\n",
    "    else:\n",
    "        return('k')\n",
    "    \n",
    "def legend(row):\n",
    "    if (row['sigma_norm'] < -2.35) & (row['mu_norm'] < -2.35):\n",
    "        return('Low $\\sigma$ and $mu$')\n",
    "    if (row['sigma_norm'] < -2.35) & (row['mu_norm'] > 2.26):\n",
    "        return('Low $\\sigma$; High $\\mu$')\n",
    "    if (row['sigma_norm'] > 2.26) & (row['mu_norm'] > 2.26):\n",
    "        return('High $\\sigma$ and $\\mu$')\n",
    "    if (row['sigma_norm'] > 2.26) & (row['mu_norm'] < -2.35):\n",
    "        return('High $\\sigma$; Low $\\mu$')\n",
    "    else:\n",
    "        return('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Colour extremes into quadrants\n",
    "df_all['color'] = df_all.apply (lambda row: colorpoint(row),axis=1)\n",
    "df_all['labels'] = df_all.apply (lambda row: legend(row),axis=1)\n",
    "\n",
    "# Plot\n",
    "x = df_all['mu_norm']\n",
    "y = df_all['sigma_norm']\n",
    "c = df_all['color']\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.scatter(x, y, color=c)\n",
    "plt.xlim(-20, 20)\n",
    "plt.ylim(-20, 20)\n",
    "plt.xlabel('$\\mu_{norm}$', size=20)\n",
    "plt.ylabel('$\\sigma_{norm}$', size=20)\n",
    "\n",
    "# Label figure into quadrants\n",
    "plt.axhspan(-2.35, 2.26, color='k', alpha=0.2)\n",
    "plt.axvspan(-2.35, 2.26, color='k', alpha=0.2)\n",
    "plt.text(10, 15, 'High $\\sigma$ and $\\mu$', color='r');\n",
    "plt.text(10, -15, 'Low $\\sigma$; High $\\mu$', color='g');\n",
    "plt.text(-18, 15, 'High $\\sigma$; Low $\\mu$', color='b');\n",
    "plt.text(-18, -15, 'Low $\\sigma$; Low $\\mu$', color='y');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mu_mean</th>\n",
       "      <th>mu_hpd_2.5</th>\n",
       "      <th>mu_hpd_97.5</th>\n",
       "      <th>suburb</th>\n",
       "      <th>mu_norm</th>\n",
       "      <th>sigma_mean</th>\n",
       "      <th>sigma_hpd_2.5</th>\n",
       "      <th>sigma_hpd_97.5</th>\n",
       "      <th>sigma_norm</th>\n",
       "      <th>color</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.811920</td>\n",
       "      <td>5.487433</td>\n",
       "      <td>11.163111</td>\n",
       "      <td>Churton Park</td>\n",
       "      <td>-4.804474</td>\n",
       "      <td>11.264981</td>\n",
       "      <td>9.644012</td>\n",
       "      <td>13.492103</td>\n",
       "      <td>1.597595</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.542421</td>\n",
       "      <td>10.524593</td>\n",
       "      <td>12.562399</td>\n",
       "      <td>Johnsonville</td>\n",
       "      <td>-2.073972</td>\n",
       "      <td>8.442223</td>\n",
       "      <td>7.708558</td>\n",
       "      <td>9.243401</td>\n",
       "      <td>-1.225163</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.318424</td>\n",
       "      <td>10.514659</td>\n",
       "      <td>12.148097</td>\n",
       "      <td>Lyall Bay</td>\n",
       "      <td>-2.297969</td>\n",
       "      <td>6.811343</td>\n",
       "      <td>6.245523</td>\n",
       "      <td>7.545993</td>\n",
       "      <td>-2.856043</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.987805</td>\n",
       "      <td>5.872613</td>\n",
       "      <td>11.353686</td>\n",
       "      <td>Newlands</td>\n",
       "      <td>-4.628588</td>\n",
       "      <td>12.190420</td>\n",
       "      <td>10.626995</td>\n",
       "      <td>14.032725</td>\n",
       "      <td>2.523033</td>\n",
       "      <td>b</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.189760</td>\n",
       "      <td>25.520780</td>\n",
       "      <td>26.831352</td>\n",
       "      <td>Pipitea</td>\n",
       "      <td>12.573367</td>\n",
       "      <td>6.702840</td>\n",
       "      <td>6.214374</td>\n",
       "      <td>7.278882</td>\n",
       "      <td>-2.964546</td>\n",
       "      <td>g</td>\n",
       "      <td>Low $\\sigma$; High $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.540351</td>\n",
       "      <td>27.089057</td>\n",
       "      <td>30.087154</td>\n",
       "      <td>Rongotai</td>\n",
       "      <td>14.923957</td>\n",
       "      <td>11.910157</td>\n",
       "      <td>11.070945</td>\n",
       "      <td>13.072341</td>\n",
       "      <td>2.242771</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.650823</td>\n",
       "      <td>15.744027</td>\n",
       "      <td>19.181802</td>\n",
       "      <td>Strathmore Park</td>\n",
       "      <td>4.034430</td>\n",
       "      <td>11.443268</td>\n",
       "      <td>10.323429</td>\n",
       "      <td>12.911709</td>\n",
       "      <td>1.775881</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.901505</td>\n",
       "      <td>8.594219</td>\n",
       "      <td>9.220547</td>\n",
       "      <td>Te Aro</td>\n",
       "      <td>-4.714889</td>\n",
       "      <td>3.891348</td>\n",
       "      <td>3.676540</td>\n",
       "      <td>4.138993</td>\n",
       "      <td>-5.776039</td>\n",
       "      <td>y</td>\n",
       "      <td>Low $\\sigma$ and $mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.064208</td>\n",
       "      <td>12.207773</td>\n",
       "      <td>15.707675</td>\n",
       "      <td>Thorndon</td>\n",
       "      <td>0.447814</td>\n",
       "      <td>11.430552</td>\n",
       "      <td>10.155717</td>\n",
       "      <td>12.858341</td>\n",
       "      <td>1.763166</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.736541</td>\n",
       "      <td>16.176170</td>\n",
       "      <td>17.292489</td>\n",
       "      <td>Hataitai</td>\n",
       "      <td>3.120148</td>\n",
       "      <td>5.960217</td>\n",
       "      <td>5.578005</td>\n",
       "      <td>6.361081</td>\n",
       "      <td>-3.707169</td>\n",
       "      <td>g</td>\n",
       "      <td>Low $\\sigma$; High $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.339074</td>\n",
       "      <td>13.314758</td>\n",
       "      <td>15.330341</td>\n",
       "      <td>Wellington Central</td>\n",
       "      <td>0.722680</td>\n",
       "      <td>7.258799</td>\n",
       "      <td>6.632033</td>\n",
       "      <td>8.065508</td>\n",
       "      <td>-2.408587</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.260697</td>\n",
       "      <td>0.418071</td>\n",
       "      <td>5.927055</td>\n",
       "      <td>Tawa</td>\n",
       "      <td>-10.355696</td>\n",
       "      <td>12.507253</td>\n",
       "      <td>11.171427</td>\n",
       "      <td>13.871949</td>\n",
       "      <td>2.839867</td>\n",
       "      <td>b</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.940408</td>\n",
       "      <td>14.212089</td>\n",
       "      <td>15.702823</td>\n",
       "      <td>Kelburn</td>\n",
       "      <td>1.324015</td>\n",
       "      <td>6.973711</td>\n",
       "      <td>6.518932</td>\n",
       "      <td>7.507720</td>\n",
       "      <td>-2.693675</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.988906</td>\n",
       "      <td>12.543909</td>\n",
       "      <td>13.468493</td>\n",
       "      <td>Miramar</td>\n",
       "      <td>-0.627487</td>\n",
       "      <td>7.048739</td>\n",
       "      <td>6.705686</td>\n",
       "      <td>7.397691</td>\n",
       "      <td>-2.618647</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.205576</td>\n",
       "      <td>10.650325</td>\n",
       "      <td>13.576306</td>\n",
       "      <td>Seatoun</td>\n",
       "      <td>-1.410817</td>\n",
       "      <td>9.096852</td>\n",
       "      <td>8.128727</td>\n",
       "      <td>10.407343</td>\n",
       "      <td>-0.570534</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.352974</td>\n",
       "      <td>15.818768</td>\n",
       "      <td>20.538911</td>\n",
       "      <td>Khandallah</td>\n",
       "      <td>4.736581</td>\n",
       "      <td>14.308553</td>\n",
       "      <td>12.748447</td>\n",
       "      <td>16.242063</td>\n",
       "      <td>4.641167</td>\n",
       "      <td>r</td>\n",
       "      <td>High $\\sigma$ and $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19.912002</td>\n",
       "      <td>19.084610</td>\n",
       "      <td>20.635919</td>\n",
       "      <td>Island Bay</td>\n",
       "      <td>6.295609</td>\n",
       "      <td>9.176587</td>\n",
       "      <td>8.583310</td>\n",
       "      <td>9.833967</td>\n",
       "      <td>-0.490800</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.646210</td>\n",
       "      <td>11.068919</td>\n",
       "      <td>12.144603</td>\n",
       "      <td>Kilbirnie</td>\n",
       "      <td>-1.970183</td>\n",
       "      <td>5.333860</td>\n",
       "      <td>4.905545</td>\n",
       "      <td>5.818890</td>\n",
       "      <td>-4.333526</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.346935</td>\n",
       "      <td>7.705091</td>\n",
       "      <td>8.942960</td>\n",
       "      <td>Newtown</td>\n",
       "      <td>-5.269459</td>\n",
       "      <td>5.930688</td>\n",
       "      <td>5.484353</td>\n",
       "      <td>6.386222</td>\n",
       "      <td>-3.736698</td>\n",
       "      <td>y</td>\n",
       "      <td>Low $\\sigma$ and $mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.690921</td>\n",
       "      <td>11.870901</td>\n",
       "      <td>13.504462</td>\n",
       "      <td>Mount Victoria</td>\n",
       "      <td>-0.925472</td>\n",
       "      <td>7.992841</td>\n",
       "      <td>7.351767</td>\n",
       "      <td>8.709380</td>\n",
       "      <td>-1.674545</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.498744</td>\n",
       "      <td>12.006268</td>\n",
       "      <td>12.970420</td>\n",
       "      <td>Mount Cook</td>\n",
       "      <td>-1.117650</td>\n",
       "      <td>5.297973</td>\n",
       "      <td>4.937049</td>\n",
       "      <td>5.735892</td>\n",
       "      <td>-4.369413</td>\n",
       "      <td>k</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.382064</td>\n",
       "      <td>0.086232</td>\n",
       "      <td>5.938208</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>-11.234330</td>\n",
       "      <td>25.435810</td>\n",
       "      <td>23.379767</td>\n",
       "      <td>27.260489</td>\n",
       "      <td>15.768424</td>\n",
       "      <td>b</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.091480</td>\n",
       "      <td>15.285500</td>\n",
       "      <td>18.719674</td>\n",
       "      <td>Karori</td>\n",
       "      <td>3.475087</td>\n",
       "      <td>18.331627</td>\n",
       "      <td>17.174004</td>\n",
       "      <td>19.451761</td>\n",
       "      <td>8.664240</td>\n",
       "      <td>r</td>\n",
       "      <td>High $\\sigma$ and $\\mu$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mu_mean  mu_hpd_2.5  mu_hpd_97.5              suburb    mu_norm  \\\n",
       "0    8.811920    5.487433    11.163111        Churton Park  -4.804474   \n",
       "1   11.542421   10.524593    12.562399        Johnsonville  -2.073972   \n",
       "2   11.318424   10.514659    12.148097           Lyall Bay  -2.297969   \n",
       "3    8.987805    5.872613    11.353686            Newlands  -4.628588   \n",
       "4   26.189760   25.520780    26.831352             Pipitea  12.573367   \n",
       "5   28.540351   27.089057    30.087154            Rongotai  14.923957   \n",
       "6   17.650823   15.744027    19.181802     Strathmore Park   4.034430   \n",
       "7    8.901505    8.594219     9.220547              Te Aro  -4.714889   \n",
       "8   14.064208   12.207773    15.707675            Thorndon   0.447814   \n",
       "9   16.736541   16.176170    17.292489            Hataitai   3.120148   \n",
       "10  14.339074   13.314758    15.330341  Wellington Central   0.722680   \n",
       "11   3.260697    0.418071     5.927055                Tawa -10.355696   \n",
       "12  14.940408   14.212089    15.702823             Kelburn   1.324015   \n",
       "13  12.988906   12.543909    13.468493             Miramar  -0.627487   \n",
       "14  12.205576   10.650325    13.576306             Seatoun  -1.410817   \n",
       "15  18.352974   15.818768    20.538911          Khandallah   4.736581   \n",
       "16  19.912002   19.084610    20.635919          Island Bay   6.295609   \n",
       "17  11.646210   11.068919    12.144603           Kilbirnie  -1.970183   \n",
       "18   8.346935    7.705091     8.942960             Newtown  -5.269459   \n",
       "19  12.690921   11.870901    13.504462      Mount Victoria  -0.925472   \n",
       "20  12.498744   12.006268    12.970420          Mount Cook  -1.117650   \n",
       "21   2.382064    0.086232     5.938208            Brooklyn -11.234330   \n",
       "22  17.091480   15.285500    18.719674              Karori   3.475087   \n",
       "\n",
       "    sigma_mean  sigma_hpd_2.5  sigma_hpd_97.5  sigma_norm color  \\\n",
       "0    11.264981       9.644012       13.492103    1.597595     k   \n",
       "1     8.442223       7.708558        9.243401   -1.225163     k   \n",
       "2     6.811343       6.245523        7.545993   -2.856043     k   \n",
       "3    12.190420      10.626995       14.032725    2.523033     b   \n",
       "4     6.702840       6.214374        7.278882   -2.964546     g   \n",
       "5    11.910157      11.070945       13.072341    2.242771     k   \n",
       "6    11.443268      10.323429       12.911709    1.775881     k   \n",
       "7     3.891348       3.676540        4.138993   -5.776039     y   \n",
       "8    11.430552      10.155717       12.858341    1.763166     k   \n",
       "9     5.960217       5.578005        6.361081   -3.707169     g   \n",
       "10    7.258799       6.632033        8.065508   -2.408587     k   \n",
       "11   12.507253      11.171427       13.871949    2.839867     b   \n",
       "12    6.973711       6.518932        7.507720   -2.693675     k   \n",
       "13    7.048739       6.705686        7.397691   -2.618647     k   \n",
       "14    9.096852       8.128727       10.407343   -0.570534     k   \n",
       "15   14.308553      12.748447       16.242063    4.641167     r   \n",
       "16    9.176587       8.583310        9.833967   -0.490800     k   \n",
       "17    5.333860       4.905545        5.818890   -4.333526     k   \n",
       "18    5.930688       5.484353        6.386222   -3.736698     y   \n",
       "19    7.992841       7.351767        8.709380   -1.674545     k   \n",
       "20    5.297973       4.937049        5.735892   -4.369413     k   \n",
       "21   25.435810      23.379767       27.260489   15.768424     b   \n",
       "22   18.331627      17.174004       19.451761    8.664240     r   \n",
       "\n",
       "                      labels  \n",
       "0                             \n",
       "1                             \n",
       "2                             \n",
       "3   High $\\sigma$; Low $\\mu$  \n",
       "4   Low $\\sigma$; High $\\mu$  \n",
       "5                             \n",
       "6                             \n",
       "7      Low $\\sigma$ and $mu$  \n",
       "8                             \n",
       "9   Low $\\sigma$; High $\\mu$  \n",
       "10                            \n",
       "11  High $\\sigma$; Low $\\mu$  \n",
       "12                            \n",
       "13                            \n",
       "14                            \n",
       "15   High $\\sigma$ and $\\mu$  \n",
       "16                            \n",
       "17                            \n",
       "18     Low $\\sigma$ and $mu$  \n",
       "19                            \n",
       "20                            \n",
       "21  High $\\sigma$; Low $\\mu$  \n",
       "22   High $\\sigma$ and $\\mu$  "
      ]
     },
     "execution_count": 897,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suburb</th>\n",
       "      <th>labels</th>\n",
       "      <th>mu_norm</th>\n",
       "      <th>sigma_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Khandallah</td>\n",
       "      <td>High $\\sigma$ and $\\mu$</td>\n",
       "      <td>4.736581</td>\n",
       "      <td>4.641167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Karori</td>\n",
       "      <td>High $\\sigma$ and $\\mu$</td>\n",
       "      <td>3.475087</td>\n",
       "      <td>8.664240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Newlands</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "      <td>-4.628588</td>\n",
       "      <td>2.523033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tawa</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "      <td>-10.355696</td>\n",
       "      <td>2.839867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>High $\\sigma$; Low $\\mu$</td>\n",
       "      <td>-11.234330</td>\n",
       "      <td>15.768424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Te Aro</td>\n",
       "      <td>Low $\\sigma$ and $mu$</td>\n",
       "      <td>-4.714889</td>\n",
       "      <td>-5.776039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Newtown</td>\n",
       "      <td>Low $\\sigma$ and $mu$</td>\n",
       "      <td>-5.269459</td>\n",
       "      <td>-3.736698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pipitea</td>\n",
       "      <td>Low $\\sigma$; High $\\mu$</td>\n",
       "      <td>12.573367</td>\n",
       "      <td>-2.964546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hataitai</td>\n",
       "      <td>Low $\\sigma$; High $\\mu$</td>\n",
       "      <td>3.120148</td>\n",
       "      <td>-3.707169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        suburb                    labels    mu_norm  sigma_norm\n",
       "15  Khandallah   High $\\sigma$ and $\\mu$   4.736581    4.641167\n",
       "22      Karori   High $\\sigma$ and $\\mu$   3.475087    8.664240\n",
       "3     Newlands  High $\\sigma$; Low $\\mu$  -4.628588    2.523033\n",
       "11        Tawa  High $\\sigma$; Low $\\mu$ -10.355696    2.839867\n",
       "21    Brooklyn  High $\\sigma$; Low $\\mu$ -11.234330   15.768424\n",
       "7       Te Aro     Low $\\sigma$ and $mu$  -4.714889   -5.776039\n",
       "18     Newtown     Low $\\sigma$ and $mu$  -5.269459   -3.736698\n",
       "4      Pipitea  Low $\\sigma$; High $\\mu$  12.573367   -2.964546\n",
       "9     Hataitai  Low $\\sigma$; High $\\mu$   3.120148   -3.707169"
      ]
     },
     "execution_count": 898,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_all[df_all['color']\n",
    "        .isin(['r', 'b', 'y', 'g'])]\n",
    " .sort_values(['labels'])[['suburb', 'labels', 'mu_norm', 'sigma_norm']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 899,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "su.run_plot_suburb_stan(top_80_df, lower_trunc_norm_model, 'Newtown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 900,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sel = playground_df[playground_df['suburb'].isin(['Brooklyn'])]\n",
    "\n",
    "g = sns.FacetGrid(col='suburb', data=df_sel, size=4, col_wrap=5)\n",
    "g.map(plt.hist, 'accessibility', normed=True, bins=50)\n",
    "g.map(ut.vertical_average_lines, 'accessibility')\n",
    "plt.xlim(0,60);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 908,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 0., 4., 6., 4., 3., 2., 0., 0., 2.]),\n",
       " array([ 2.38206386,  4.99789255,  7.61372125, 10.22954994, 12.84537864,\n",
       "        15.46120733, 18.07703603, 20.69286472, 23.30869342, 25.92452211,\n",
       "        28.5403508 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 908,
     "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": [
    "df_pp = pd.merge(df_all, summary_df)\n",
    "plt.hist(df_pp['mu_mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hilliness of park environs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 902,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEKCAYAAAAPVd6lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAF6NJREFUeJzt3Xm0ZGV57/HvAw0yNUHgKAq0JxjFKCLokeu9JDhhZIjgwI0YMeLS1RFDwMSJaHJdV8mSTDdqHJKORjEaQRGISnAKNopLIQ02yKCIpBGQoXFgnn3uH+9uuvpwzqm36lSd02/7/axV69Tw1ruf/dauX+3ae9fZkZlIktq12WIXIEmaH4NckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TGLUqQR8RlEfGcEfSzJiIOHGWfv2r6jVvvGC+kiNgzIr4bEbdHxHELPf1x2dTma7GWj01VRHw8Ik4c9HmLEuSZ+ZTMXLmx9wkQETtGxBkRcWdEXBMRvz9s+4j4zYg4JyJujYirIuIl0577yYi4ISJui4grI+J1PY9NRsR/RMTPI+LGiPhARCyZ7/z1jttG9qZ8K7AyM5dm5vunPzjI69Kv7VxjGxGPiIiPds+7vQvhg3ueuzIi7omIO7rLD+YzX4Oa6TWLiKMj4rz59j1f416euv7v7sb95xFxVkTsPubp3NiF7Xajns58uGmlvw8C9wGPBl4JfDginjJo+y4Y/h34IrAjsBz4ZEQ8see57wEmM3N74DDgxIh4RvfYh4CbgccA+wDPBt4wmlncKD0OuGyOxwd5Xfq1nWtslwDXdvf9GvAXwGciYrLn+cdm5nbdZc95zteMRvGhvYl6UWZuR3ntbgL+YczT2QfYF/izMU1nOJk5kgvwDuDDPbcfCdwPbDVD2zXAgT3X3wxcAtwKnNr7HGB34HRgLfBT4ANz9FPb52OBz3V9/jdw3CzztC0lAJ7Yc9+/AicN2h7YC7gDiJ7HvgK8e5a+9gRuAH6vu30FcEjP438D/NMsz30N8IWe21cBn+m5fS2wT++4dXX+Eri7q/OtNWM5bbpbAH/ZPed+ILvLxbO0/01gJfALSrgd1t1/DvAgcE9XyxOnPa/6dalpO8jYdo9fArysu74SeF3le+Rh8zXbGPSM/du66d0LLJnrvdRz39HAef3GuXssgd/ouf1x4MSe208HvgvcDny2e/1PnDb9hy0fcyxP/ea3almbad6BQ4Are26fAPyoq/1y4CXd/W8BPjetr38A3ls5nb8GzqpZlueqo3tsX+Ci7rFTgVN6x7f2MsogPwX4w57bzwUu7Tcw3fULKOG6I+VN9frusc2Bi4G/p7whtwJ+a45+avrcDLgQ+D/AlsAewNXAC2eoc1/g7mn3vZmekKxtDzyVhwf5V4EzprX/EHAX5Q12EbBdd//rgU8A2wC7Apf2LhDT+tijW6A2o6ypXANc3/PYz4HN5hrDaWM841jOMN2/Ar5D+fDdFvga5UN4jxnabkH5gHl79zo8r1uY9+weX8ksATnI61LTdsCxfTQliJ/UU+da4BbgW8Bz+rxPHpqvijFYA6zuxnPrfu+lnvuOpgvyimnMGuRd+2uA47t+Xkr5UJwe5LO91zaorXJ+q5a1GZbdbYCTgU/0PP6/u742A14O3El5Pzymu75D124J5RvZMyqmsxvwPeB9A8zXbHWsG98/6fo4grICNHCQj3LTylMpC906+1BCuMb7M/MnmfkzSujt092/H2UA3pKZd2bmPZlZu+1vtj6fCUxk5rsy877MvBr4Z+DIGfrYjrJm0OtWYOks05yr/fcpC8tbImKLiPgdytf1bXobZ+Ybuva/TQnBe7uHzgWeAtwGXAesAs6cqYhunm5n/WaCLwPXR8STutvfzMxfzjIPM5ltLB8SEUuB44BXZea1mXkn5VvPjl090z2LMl4nda/DOZTNTq+oqGeQ16WmbdXYRsQWwKeAkzPz+93db6N8OO4KrAC+EBGPr5gHqBuD93fjefcc/ZwZEb9Yd6GsDAwyjbnqW9LVcH9mnk4J2un6Lh8Dzm9NX+uc2c3zbcALKN+mAMjMz3Z9/TIzTwV+COyXmTcA36AELMBBwC2ZeWGf6dxO+TZ7M/DO2vmarY7ueVtQvgncn5mnAf/VZ35nNJIgj4gtgcdTPqnWeRobBvtcbuy5fhdlUKCsiVyTmQ8MUdZsfT4OeOy0Bf/tlDWt6e4Atp923/aUkJzJrO0z837gxcChXW1vAj5DCY4NZOaD3QfWbsAxEbEZJYxPp6zp7kzZdPVXs9QBJZyeAxzQXV9JCfFnd7cHMdtY9joAuDozf9hz3yOnPbfXY4Frp32gXEMJxH4GeV3mbFs7tl27f6WskR677v7MPD8zb8/MezPzZMpa+SEV8wB1Y3BtRT8vzswd1l3YcN/JfMb5sZRvcr3/63qmemqWj9paavta58XdPD+C8rqcGxG7AETEH0TE6p73+V6U1xfK2vtR3fWjKK9tv+kspbynntTTT9/5mqOOmcb3mj51zGhUa+RPphR0F0BEBGWGa9fIZ3MtsGzEO3quBf67d8HPcgTBTG++K4ElEfGEnvuexuw7q+Zsn5mXZOazM3OnzHwhZU1upjWcdZZQPiB3pHyofaALjJ8CH2PuwFgX5L/dXT+X/kGes9xfY4KyyQZ4aBl4CWXNZCY/AXbvAnKdZcD1FdMa5HXp17bv2Hbz8lHKh/3Lug/l2SQQFfMAdWMwn9ekZhp3seG3wl16rt8A7NrN/zqDHBUyvfb5vOZzT6is/JxO2QfxWxHxOMo37WOBnbqwv5T1r82ZwN4RsRfwu5RvWjXTOZey+elve+6edb761DHT+C6rn+v1RhXkTwUeFRGPj4itgXdT1nzXzLPfCygze1JEbBsRW0XE/iPo87aIeFtEbB0Rm0fEXhHxzOkNu80DpwPv6qa/P3A4s3x692sfEXt387BNRLyZsp3s491jj4qIIyNiu66mF1K+mp2TmbdQdsoeExFLImIH4NXM/UF5LmU/xdaZeR3wTcpXyJ0oO69mchPlw2UYlwJPj4h9umXgPZQ38qmztD+fsq3wrd2mpucAL6Lsa5nTIK9Lv7aVY/thys6sF/Vu4oiIHSLihd1ruiQiXkn5ZvLlfvMw3zEYQL9prAZ+v1vmDqJ80K/zbUowHtvN3+GUTQK1pi9PY5vfKA6nfJu6gvLtKin7L4iI11DWhAHIzHuA04B/Ay7IzB8PMLn3Ai+IiHWbfeaar7nq+DbwAHBcN74vZbDxfcgog/zLwNmUjf43UXYgvmM+nWbmg5QB+Q3gx5TNEC8fUZ/7UN7AtwAfoRxaNpM3AFtTtot9GjgmMx9a84uIsyPi7ZXtX0X5YLoZeD7wgsxctw08gWO6efw55RP/jZn5793jL6UE8VrKGD9A2Uky23xeSdms8M3u9m2U1+Rb3RjM5D3An3dfAd88W9+zTG8V5YiV/+imswvlSJAZ114z8z7KIZYHU16DDwF/0LPtuZ9BXpc52zLH2HZrVH9IWV5ujPXHi7+Ssn3zRNbv7PxjylfwfseSj2oMRjGN4ynvh19QDs08c9pzXwq8tnv8KMo3rHXLbD8bLE9jmt8vRMQdlG3kfwm8OjMvy8zLgb+jhOVNlIz61rTnntzd32+zygYycy1l5/hfdLdnna+56ugZ36Mp7/mXU1Y6BhYbbp4ZTkScDXwkMz83784kbbQi4nzgHzPzY4tdy3xFxDLKQQi7dCs6zRrlGvkVI+pL0kYiIp4dEbt0X/1fDewNfGmx65qvbnv2nwKntB7iUHamzUtEPBJ4FOWQGkmblj0pR1dtR/lRyxHd4XvNiohtKZs5rqFsUmveSDatSJIWj/9rRZIaN5Z/xLPzzjvn5OTkOLqWpE3ShRdeeEtmTgzz3LEE+eTkJKtWrRpH15K0SYqIoX7VCW5akaTmGeSS1DiDXJIaZ5BLUuMMcklqnEEuSY2rCvKI+JOIuCwiLo2IT0fEVuMuTJJUp2+QR8SulFN4TWXmXpTzaM50WjRJ0iKo3bSyBNi6O1PPNpQzYkiSNgJ9f9mZmddHxN9STuxwN/CVzPzK9HYRsRxYDrBs2VBnKxqpyRPOeth9a046dOT9j7JPSRpGzaaVR1JOjfXrlJOFbhsRR01vl5krMnMqM6cmJob6dwGSpCHUbFo5kHKy4rXdabtOB/7XeMuSJNWqCfIfA8/qThgclHNNejYgSdpI9A3yzDyfcrbpi4Dvdc9ZMea6JEmVqv6NbWa+E3jnmGuRJA3BX3ZKUuMMcklqnEEuSY0zyCWpcQa5JDXOIJekxhnkktQ4g1ySGmeQS1LjDHJJapxBLkmNM8glqXEGuSQ1ziCXpMYZ5JLUOINckhpXc/LlPSNidc/ltoh440IUJ0nqr+8ZgjLzB8A+ABGxOXA9cMaY65IkVRp008rzgR9l5jXjKEaSNLhBg/xI4NPjKESSNJzqII+ILYHDgM/O8vjyiFgVEavWrl07qvokSX0MskZ+MHBRZt4004OZuSIzpzJzamJiYjTVSZL6GiTIX4GbVSRpo1MV5BGxDfAC4PTxliNJGlTfww8BMvMuYKcx1yJJGoK/7JSkxhnkktQ4g1ySGmeQS1LjDHJJapxBLkmNM8glqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TG1Z7qbYeIOC0ivh8RV0TE/xx3YZKkOlWnegPeB3wpM4+IiC2BbcZYkyRpAH2DPCK2Bw4AjgbIzPuA+8ZbliSpVs0a+R7AWuBjEfE04ELg+My8s7dRRCwHlgMsW7asb6eTJ5z1sPvWnHRoRTmD9zvfvkZRlySNS8028iXA04EPZ+a+wJ3ACdMbZeaKzJzKzKmJiYkRlylJmk1NkF8HXJeZ53e3T6MEuyRpI9A3yDPzRuDaiNizu+v5wOVjrUqSVK32qJU/Bj7VHbFyNfCa8ZUkSRpEVZBn5mpgasy1SJKG4C87JalxBrkkNc4gl6TGGeSS1DiDXJIaZ5BLUuMMcklqnEEuSY0zyCWpcQa5JDXOIJekxhnkktQ4g1ySGmeQS1LjDHJJapxBLkmNM8glqXFVZwiKiDXA7cCDwAOZ6dmCJGkjUXvOToDnZuYtY6tEkjQUN61IUuNqgzyBr0TEhRGxfKYGEbE8IlZFxKq1a9eOrkJJ0pxqg3z/zHw6cDDwRxFxwPQGmbkiM6cyc2piYmKkRUqSZlcV5Jn5k+7vzcAZwH7jLEqSVK9vkEfEthGxdN114HeAS8ddmCSpTs1RK48GzoiIde3/LTO/NNaqJEnV+gZ5Zl4NPG0BapEkDcHDDyWpcQa5JDXOIJekxhnkktQ4g1ySGmeQS1LjDHJJapxBLkmNM8glqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalx1UEeEZtHxHcj4ovjLEiSNJhB1siPB64YVyGSpOFUBXlE7AYcCnxkvOVIkgbV9+TLnfcCbwWWztYgIpYDywGWLVu2wWOTJ5w1ZHnrn7vmpEOH7mOQvgattbf9KGqcaxrj6l9S2/qukUfE7wI3Z+aFc7XLzBWZOZWZUxMTEyMrUJI0t5pNK/sDh0XEGuAU4HkR8cmxViVJqtY3yDPzzzJzt8ycBI4EzsnMo8ZemSSpiseRS1Ljand2ApCZK4GVY6lEkjQU18glqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TGGeSS1DiDXJIaZ5BLUuMMcklqnEEuSY0zyCWpcX2DPCK2iogLIuLiiLgsIv7vQhQmSapTc6q3e4HnZeYdEbEFcF5EnJ2Z3xlzbZKkCn2DPDMTuKO7uUV3yXEWJUmqV7WNPCI2j4jVwM3AVzPz/BnaLI+IVRGxau3ataOuU5I0i6ogz8wHM3MfYDdgv4jYa4Y2KzJzKjOnJiYmRl2nJGkWAx21kpm/AFYCB42lGknSwGqOWpmIiB2661sDBwLfH3dhkqQ6NUetPAY4OSI2pwT/ZzLzi+MtS5JUq+aolUuAfRegFknSEPxlpyQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TGGeSS1DiDXJIaZ5BLUuMMcklqnEEuSY0zyCWpcQa5JDWu5pydu0fE1yPiioi4LCKOX4jCJEl1as7Z+QDwpsy8KCKWAhdGxFcz8/Ix1yZJqtB3jTwzb8jMi7rrtwNXALuOuzBJUp2aNfKHRMQk5UTM58/w2HJgOcCyZctGUNqGJk84C4A1Jx36sPuG7au3v9q+5mpXW+P0adbO00ztB21X28dszxvmucMatta5+urX3yimubH0Md++FuM113Cqd3ZGxHbA54A3ZuZt0x/PzBWZOZWZUxMTE6OsUZI0h6ogj4gtKCH+qcw8fbwlSZIGUXPUSgAfBa7IzP83/pIkSYOoWSPfH3gV8LyIWN1dDhlzXZKkSn13dmbmeUAsQC2SpCH4y05JapxBLkmNM8glqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TGGeSS1DiDXJIaZ5BLUuMMcklqXM05O/8lIm6OiEsXoiBJ0mBq1sg/Dhw05jokSUPqG+SZ+Q3gZwtQiyRpCCPbRh4RyyNiVUSsWrt27ai6lST1MbIgz8wVmTmVmVMTExOj6laS1IdHrUhS4wxySWpczeGHnwa+DewZEddFxGvHX5YkqdaSfg0y8xULUYgkaThuWpGkxhnkktQ4g1ySGmeQS1LjDHJJapxBLkmNM8glqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWqcQS5JjTPIJalxBrkkNc4gl6TGVQV5RBwUET+IiKsi4oRxFyVJqldzzs7NgQ8CBwNPBl4REU8ed2GSpDo1a+T7AVdl5tWZeR9wCnD4eMuSJNWKzJy7QcQRwEGZ+bru9quA/5GZx05rtxxY3t3cC7h09OU2aWfglsUuYiPgOKznWKznWKy3Z2YuHeaJSyraxAz3PSz9M3MFsAIgIlZl5tQwBW1qHIvCcVjPsVjPsVgvIlYN+9yaTSvXAbv33N4N+MmwE5QkjVZNkP8X8ISI+PWI2BI4Evj8eMuSJNXqu2klMx+IiGOBLwObA/+SmZf1edqKURS3iXAsCsdhPcdiPcdivaHHou/OTknSxs1fdkpS4wxySWrc0EHe72f7EfGIiDi1e/z8iJicT6Ebs4qxOCAiLoqIB7rj8jdZFWPxpxFxeURcEhH/GRGPW4w6F0LFWLw+Ir4XEasj4rxN+RfTtf/mIyKOiIiMiE32kMSK5eLoiFjbLRerI+J1fTvNzIEvlJ2ePwL2ALYELgaePK3NG4B/7K4fCZw6zLQ29kvlWEwCewOfAI5Y7JoXeSyeC2zTXT/mV3y52L7n+mHAlxa77sUai67dUuAbwHeAqcWuexGXi6OBDwzS77Br5DU/2z8cOLm7fhrw/IiY6cdFres7Fpm5JjMvAX65GAUuoJqx+Hpm3tXd/A7ldwmbopqxuK3n5rbM8EO7TUTtv/l4N/DXwD0LWdwCG8u/PBk2yHcFru25fV1334xtMvMB4FZgpyGntzGrGYtfFYOOxWuBs8da0eKpGouI+KOI+BElwI5boNoWWt+xiIh9gd0z84sLWdgiqH2PvKzb/HhaROw+w+MbGDbIa362X/XT/k3Ar8p81qgei4g4CpgC/masFS2e2n9t8cHMfDzwNuDPx17V4phzLCJiM+DvgTctWEWLp2a5+AIwmZl7A19j/ZaNWQ0b5DU/23+oTUQsAX4N+NmQ09uY+S8M1qsai4g4EHgHcFhm3rtAtS20QZeLU4AXj7WixdNvLJZS/tHeyohYAzwL+PwmusOz73KRmT/teV/8M/CMfp0OG+Q1P9v/PPDq7voRwDnZbcnfxPgvDNbrOxbdV+h/ooT4zYtQ40KpGYsn9Nw8FPjhAta3kOYci8y8NTN3zszJzJyk7Ds5LDOH/idSG7Ga5eIxPTcPA67o2+s89r4eAlxJ2QP7ju6+d1FeAICtgM8CVwEXAHss9h7jMe6J7jcWz6R8Et8J/BS4bLFrXsSx+BpwE7C6u3x+sWtexLF4H3BZNw5fB56y2DUv1lhMa7uSTfSolcrl4j3dcnFxt1w8qV+f/kRfkhrnLzslqXEGuSQ1ziCXpMYZ5JLUOINckhpnkEtS4wxySWrc/wdSX14gcCkDagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 16\n",
    "z = ox.truncate_graph_dist(G, \n",
    "                           ox.utils.get_nearest_node(G, tuple(wcc_playgrounds.ix[i][['lat', 'lon']])), \n",
    "                           max_distance=500, \n",
    "                           weight='length')\n",
    "\n",
    "n_z, e_z = ox.graph_to_gdfs(z)\n",
    "plt.hist(e_z['grade_abs'], bins=100);\n",
    "plt.title('$\\mu$ incline {} with $\\sigma$ of {} for {}'.format(round(np.mean(e_z['grade_abs']), 4),\n",
    "                                                               round(np.std(e_z['grade_abs']), 4),\n",
    "                                                               wcc_playgrounds.ix[i]['location']))\n",
    "plt.xlim(0, 0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 903,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: anon_model_da01b1257b3430beee0da77218725620.\n",
      "4 chains, each with iter=2000; warmup=1000; thin=1; \n",
      "post-warmup draws per chain=1000, total post-warmup draws=4000.\n",
      "\n",
      "         mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu       0.01  3.3e-4   0.01 3.5e-4 4.5e-3   0.01   0.02   0.04   1102    1.0\n",
      "sigma    0.12  2.5e-4 9.0e-3    0.1   0.12   0.12   0.13   0.14   1277    1.0\n",
      "y_pred    0.1  1.2e-3   0.08 4.2e-3   0.04   0.09   0.15   0.29   4000    1.0\n",
      "lp__   269.06    0.04   1.12 265.92 268.65 269.41 269.86 270.15    992    1.0\n",
      "\n",
      "Samples were drawn using NUTS at Sat May 11 20:16:12 2019.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "# Run precompiled model\n",
    "park_trunc_dat = {'N': e_z.shape[0],\n",
    "                  'L': 0,\n",
    "                  'U': 80,\n",
    "                  'y': e_z['grade_abs'].values,}\n",
    "park_trunc_fit = lower_trunc_norm_model.sampling(park_trunc_dat, chains=4)\n",
    "print(park_trunc_fit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 904,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.5)"
      ]
     },
     "execution_count": 904,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot model posterior predictive against raw values\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8,6))\n",
    "ax1.hist(park_trunc_fit['y_pred'], bins=100, density=True);\n",
    "ax1.set_title('Model grades for {:s}'.format(wcc_playgrounds.ix[i]['location']));\n",
    "ax1.set_xlim(0, 0.5)\n",
    "\n",
    "ax2.hist(e_z['grade_abs'], bins=100, density=True);\n",
    "ax2.set_title('Raw grades for {:s}'.format(wcc_playgrounds.ix[i]['location']));\n",
    "ax2.set_xlim(0, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incorporating LINZ residential spatial filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 905,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22b2c26390>"
      ]
     },
     "execution_count": 905,
     "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": [
    "karori_residence = wlg_res_poly_suburbs_ov[(wlg_res_poly_suburbs_ov['suburb'] == 'Karori') & \n",
    "                                           (~wlg_res_poly_suburbs_ov['t50_fid'].isnull())]\n",
    "karori_residence.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 906,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get lat and lon coordinates for accessibility values\n",
    "orig_nodes = network_hills.nodes_df \n",
    "df_joined = pd.merge(orig_nodes.reset_index(), total_hills_1.reset_index(), how='inner')\n",
    "df_joined.columns = ['node_id', 'lon', 'lat', 'accessibility']\n",
    "\n",
    "# Convert lat and lon to geoseries\n",
    "df_joined_coords = dp.coords_df_to_geopandas_points(df_joined, crs={'init': 'epsg:4167'})\n",
    "\n",
    "# Extract only the residential polygons\n",
    "res_polys_only = wlg_res_poly_suburbs_ov[(~wlg_res_poly_suburbs_ov['t50_fid'].isnull())]\n",
    "\n",
    "# Get accessibility within residential polygons\n",
    "playground_res_df = geopandas.sjoin(res_polys_only, df_joined_coords, op='contains').drop('index_right', axis=1)\n",
    "\n",
    "# Get accessibility for Karori\n",
    "karori_acc = playground_df[playground_df['suburb'].isin(['Karori'])]\n",
    "\n",
    "# Plot\n",
    "g = sns.FacetGrid(col='suburb', data=karori_acc, size=4, col_wrap=5)\n",
    "g.map(plt.hist, 'accessibility', normed=True, bins=50)\n",
    "g.map(ut.vertical_average_lines, 'accessibility')\n",
    "plt.xlim(0,60);\n",
    "#plt.ylim(0, 0.0035);"
   ]
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