03 Create OSM labels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get OSM solar PV labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import itertools\n",
    "import collections\n",
    "import re\n",
    "import json\n",
    "import requests\n",
    "from shutil import copyfile\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import colour\n",
    "\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "# https://matplotlib.org/3.1.0/tutorials/colors/colormap-manipulation.html\n",
    "\n",
    "from shapely.geometry import Polygon, Point\n",
    "import geopandas as gpd\n",
    "import pyproj\n",
    "import folium\n",
    "from folium import Rectangle\n",
    "from folium import Polygon as f_Polygon\n",
    "import affine\n",
    "\n",
    "from tqdm import tqdm # Progress bar stuff cos progress bars are good\n",
    "tqdm.pandas() # now can use progress_apply instead of apply\n",
    "\n",
    "import pickle # Pickle stuff, cos pickling is good\n",
    "\n",
    "# Styles\n",
    "# pd.set_option('display.float_format', lambda x: '%.2f' % x) # pandas\n",
    "# np.set_printoptions(precision=5, suppress=True) # numpy\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# seaborn plotting style\n",
    "sns.set(style='ticks', context='poster')\n",
    "\n",
    "import rasterio\n",
    "from rasterio.crs import CRS\n",
    "from rasterio.plot import show\n",
    "from rasterio.warp import transform_bounds\n",
    "from rasterio.features import geometry_mask\n",
    "\n",
    "from shapely.ops import transform\n",
    "from functools import partial\n",
    "import pyproj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Utilities\n",
    "def bng(df):\n",
    "    # Takes dataframe or geodataframe and converts it to a geodataframe with CRS in British National Grid\n",
    "    if type(df) != gpd.GeoDataFrame:\n",
    "        df = gpd.GeoDataFrame(df, crs={'init':'epsg:27700'})\n",
    "    elif type(df) == gpd.GeoDataFrame:\n",
    "        df = df.to_crs(crs={'init':'epsg:27700'})\n",
    "    return df\n",
    "\n",
    "def wgs(df):\n",
    "    # Takes dataframe or geodataframe and converts it to a geodataframe with CRS in WGS84\n",
    "    if type(df) != gpd.GeoDataFrame:\n",
    "        df = gpd.GeoDataFrame(df, crs={'init':'epsg:4326'})\n",
    "    elif type(df) == gpd.GeoDataFrame:\n",
    "        df = df.to_crs(crs={'init':'epsg:4326'})\n",
    "    return df\n",
    "\n",
    "# Mapping outlines\n",
    "gb_outline = gpd.read_file('../../Data/shapefiles/Download_UK+boundaries_1183810/gb-outlines_2856975/greatbritain.shp')\n",
    "towns = bng(gpd.read_file('../../Data/shapefiles/Major_Towns_and_Cities_December_2015_Boundaries_V2.geojson'))\n",
    "\n",
    "# Raise error if CRS is not BNG\n",
    "def check_bng(df):\n",
    "    # Check if GeoDataFrame has CRS of BNG\n",
    "    assert (df.crs == {'init': 'epsg:27700'}), 'CRS is not BNG'\n",
    "    print(f'CRS of GeoDataFrame is BNG / epsg:27700')\n",
    "    \n",
    "def plot_uk(df):\n",
    "    # Check a GeoDataFrame is in the UK somewhere. Expects dataframe in CRS BNG\n",
    "    check_bng(df)\n",
    "    fig, ax = plt.subplots(1, figsize=(8, 8))\n",
    "    gb_outline.plot(ax=ax, color='white', linewidth=0.1, edgecolor='0.6')\n",
    "    df.plot(ax=ax, linewidth=0.1, color='red', edgecolor='red')\n",
    "\n",
    "def plot_tile_and_label(tiles, labels):\n",
    "    # Expects dataframe of tiles, dataframe of labels, both in CRS BNG\n",
    "    check_bng(tiles)\n",
    "    check_bng(labels)\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.axis('off')\n",
    "    tiles.plot(ax=ax, color='blue')\n",
    "    labels.plot(ax=ax, color='red', linewidth=0.8, edgecolor='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define OSM Queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # https://wiki.openstreetmap.org/wiki/Overpass_API/Overpass_API_by_Example\n",
    "\n",
    "solar_query = \"\"\"\n",
    "    [out:json];\n",
    "    area[\"name\"=\"United Kingdom\"]->.searchArea;\n",
    "    (\n",
    "    node(area.searchArea)[\"generator:source\"=\"solar\"]['generator:output:electricity'='yes'];\n",
    "    way(area.searchArea)[\"generator:source\"=\"solar\"]['generator:output:electricity'='yes'];\n",
    "    relation(area.searchArea)[\"generator:source\"=\"solar\"]['generator:output:electricity'='yes'];    \n",
    "    );\n",
    "    out body;\n",
    "    >;\n",
    "    out skel qt;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "overpass_url = \"http://overpass-api.de/api/interpreter\"\n",
    "\n",
    "# query all solar panels saved as polygon\n",
    "osm_pv = requests.get(overpass_url, params={'data': solar_query})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(osm_pv.json()['elements'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>members</th>\n",
       "      <th>nodes</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</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>node</th>\n",
       "      <td>95873</td>\n",
       "      <td>95873</td>\n",
       "      <td>95873</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relation</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>way</th>\n",
       "      <td>9687</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9687</td>\n",
       "      <td>9408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             id    lat    lon  members  nodes   tags\n",
       "type                                                \n",
       "node      95873  95873  95873        0      0  31763\n",
       "relation     62      0      0       62      0     62\n",
       "way        9687      0      0        0   9687   9408"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('type').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = df[df['type']=='node'].id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "way_nodes = df[df['type']=='way'].nodes.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "unpacked_way_nodes = list(itertools.chain.from_iterable(way_nodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9687, 74404, 64110)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(way_nodes), len(unpacked_way_nodes), len(set(unpacked_way_nodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_in_ways = list(set(nodes).intersection(set(unpacked_way_nodes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_not_in_ways = list(set(nodes).difference(set(unpacked_way_nodes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64110, 31762)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nodes_in_ways), len(nodes_not_in_ways)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>members</th>\n",
       "      <th>nodes</th>\n",
       "      <th>tags</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>432708661</td>\n",
       "      <td>57.15</td>\n",
       "      <td>-6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'generator:method': 'photovoltaic', 'generato...</td>\n",
       "      <td>node</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "          id   lat   lon members nodes  \\\n",
       "0  432708661 57.15 -6.21     NaN   NaN   \n",
       "\n",
       "                                                tags  type  \n",
       "0  {'generator:method': 'photovoltaic', 'generato...  node  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6357939628"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_not_in_ways[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>members</th>\n",
       "      <th>nodes</th>\n",
       "      <th>tags</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>432708661</td>\n",
       "      <td>57.15</td>\n",
       "      <td>-6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'generator:method': 'photovoltaic', 'generato...</td>\n",
       "      <td>node</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          id   lat   lon members nodes  \\\n",
       "0  432708661 57.15 -6.21     NaN   NaN   \n",
       "\n",
       "                                                tags  type  \n",
       "0  {'generator:method': 'photovoltaic', 'generato...  node  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_not_in_ways = df[df['id'].isin(nodes_not_in_ways)]\n",
    "nodes_not_in_ways.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Pickle\n",
    "# with open(os.path.join('data/OSM_solar_20190808.pkl'), 'wb') as f:\n",
    "#     pickle.dump(osm_queried_panels, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note this is polygons and points. And that multi-polygons here are turned into separate IDs... that might be troublesome.  \n",
    "Try to get capacity out.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try working with direct export\n",
    "It works much better!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = gpd.read_file('data/OSM_solar_PV_export_20190808.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>generator:type</th>\n",
       "      <th>building</th>\n",
       "      <th>notional_area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bing</th>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>22</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bing Imagery</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bing;survey</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DECC - RESTATS</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digital Globe Premium Imagery</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digital Globe Stadard Imagery</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digital Globe Standard</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digital Globe Standard Imagery</th>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe Premium Imagery</th>\n",
       "      <td>377</td>\n",
       "      <td>377</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe Standard Imagery</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe Vivid Imagery</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe-Premium</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DigitalGlobe-Standard</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRI Imagery</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRI Wolrd Imagery</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRI World</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRI World Imagery</th>\n",
       "      <td>409</td>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESRI imagery</th>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esri World Imagery</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EsriWorldImagery</th>\n",
       "      <td>364</td>\n",
       "      <td>364</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mapillary;survey</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maxar Imagery</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS OpenData StreetView</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS StreetView</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS_OpenData_StreetView</th>\n",
       "      <td>51</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS_OpenData_Streetview</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentinal</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Grand Tour s01e01 00:37:33</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>View from footpaths on each site;DigitalGlobe</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwick DC, bing</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks Aerial Imagery OGL v2</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks Aerial Imagery OGLv2</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC 2013 Aerial Imagery</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC Aerial Imagery</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC Aerial Imagery 2013</th>\n",
       "      <td>1291</td>\n",
       "      <td>1290</td>\n",
       "      <td>1285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC Aerial Imagery 2013 OGL v2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC Aerial Imagery 2013 OGLv2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC Aerial Imagery OGLv2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CC aerial imagery 2013</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwicks CCAerial Imagery 2013</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwickshire Aerial Imagery 2013</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warwickshire_CC_ Aerial_Imagery_2013</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auto_os_street_view</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bing</th>\n",
       "      <td>1209</td>\n",
       "      <td>1169</td>\n",
       "      <td>1174</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bing, local knowledge</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bing;Lichfield_district_council</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bing;local_knowledge</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bing;website</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cities_revealed_aerial_imagery</th>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>digitalglobe</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>digitalglobe premium</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>digitalglobe standard</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>local_knowledge</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observation &amp; OS OpenData StreetView</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey</th>\n",
       "      <td>185</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey (2013)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey (May 2013)</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey (view from train)</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey;Bing</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey;digitalglobe</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survey;estimation</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual survey,bing</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual survey; bing</th>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual survey;Maxar Imagery</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual survey;bing</th>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual survey;estimate</th>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  generator:type  building  \\\n",
       "source                                                                          \n",
       "Bing                                            168             168        22   \n",
       "Bing Imagery                                     45              45         0   \n",
       "Bing;survey                                      49              49         0   \n",
       "DECC - RESTATS                                    1               0         0   \n",
       "Digital Globe Premium Imagery                    33              33         0   \n",
       "Digital Globe Stadard Imagery                    12              12         0   \n",
       "Digital Globe Standard                           25              25        24   \n",
       "Digital Globe Standard Imagery                  101             101         0   \n",
       "DigitalGlobe                                      1               1         1   \n",
       "DigitalGlobe Premium Imagery                    377             377         0   \n",
       "DigitalGlobe Standard Imagery                    31              31         0   \n",
       "DigitalGlobe Vivid Imagery                       14              14         0   \n",
       "DigitalGlobe-Premium                             27              27         0   \n",
       "DigitalGlobe-Standard                             1               1         0   \n",
       "ESRI Imagery                                      7               7         7   \n",
       "ESRI Wolrd Imagery                                1               1         0   \n",
       "ESRI World                                        1               1         0   \n",
       "ESRI World Imagery                              409             409         0   \n",
       "ESRI imagery                                    116             116         0   \n",
       "Esri World Imagery                                2               2         0   \n",
       "EsriWorldImagery                                364             364         0   \n",
       "Mapillary;survey                                  1               1         0   \n",
       "Maxar Imagery                                    21              21         1   \n",
       "OS OpenData StreetView                            2               2         2   \n",
       "OS StreetView                                     1               1         1   \n",
       "OS_OpenData_StreetView                           51              45        51   \n",
       "OS_OpenData_Streetview                            1               0         1   \n",
       "Sentinal                                          3               3         0   \n",
       "The Grand Tour s01e01 00:37:33                    7               0         0   \n",
       "View from footpaths on each site;DigitalGlobe     1               1         0   \n",
       "Warwick DC, bing                                  1               1         1   \n",
       "Warwicks Aerial Imagery OGL v2                   64              64        64   \n",
       "Warwicks Aerial Imagery OGLv2                    20              20        20   \n",
       "Warwicks CC 2013 Aerial Imagery                   5               5         5   \n",
       "Warwicks CC Aerial Imagery                        4               4         4   \n",
       "Warwicks CC Aerial Imagery 2013                1291            1290      1285   \n",
       "Warwicks CC Aerial Imagery 2013 OGL v2            1               1         1   \n",
       "Warwicks CC Aerial Imagery 2013 OGLv2             1               1         1   \n",
       "Warwicks CC Aerial Imagery OGLv2                  3               3         3   \n",
       "Warwicks CC aerial imagery 2013                  11              11        11   \n",
       "Warwicks CCAerial Imagery 2013                    4               4         4   \n",
       "Warwickshire Aerial Imagery 2013                  1               1         1   \n",
       "Warwickshire_CC_ Aerial_Imagery_2013              3               3         3   \n",
       "auto_os_street_view                              12              12        12   \n",
       "bing                                           1209            1169      1174   \n",
       "bing, local knowledge                             6               6         6   \n",
       "bing;Lichfield_district_council                   1               0         1   \n",
       "bing;local_knowledge                              1               1         1   \n",
       "bing;website                                      1               1         1   \n",
       "cities_revealed_aerial_imagery                   34              33        34   \n",
       "digitalglobe                                     65              65         0   \n",
       "digitalglobe premium                             65              65         0   \n",
       "digitalglobe standard                            14              14         0   \n",
       "local_knowledge                                   1               1         0   \n",
       "observation & OS OpenData StreetView              1               1         1   \n",
       "survey                                          185             183         1   \n",
       "survey (2013)                                     1               1         0   \n",
       "survey (May 2013)                                 2               2         0   \n",
       "survey (view from train)                          2               2         0   \n",
       "survey;Bing                                       1               1         1   \n",
       "survey;digitalglobe                               1               1         0   \n",
       "survey;estimation                                 1               1         1   \n",
       "visual survey,bing                                1               1         1   \n",
       "visual survey; bing                              46              37        46   \n",
       "visual survey;Maxar Imagery                       1               1         0   \n",
       "visual survey;bing                               18               9        18   \n",
       "visual survey;estimate                           75              74        74   \n",
       "\n",
       "                                               notional_area  \n",
       "source                                                        \n",
       "Bing                                                     130  \n",
       "Bing Imagery                                               0  \n",
       "Bing;survey                                                0  \n",
       "DECC - RESTATS                                             0  \n",
       "Digital Globe Premium Imagery                              0  \n",
       "Digital Globe Stadard Imagery                              0  \n",
       "Digital Globe Standard                                     0  \n",
       "Digital Globe Standard Imagery                            82  \n",
       "DigitalGlobe                                               0  \n",
       "DigitalGlobe Premium Imagery                               0  \n",
       "DigitalGlobe Standard Imagery                              0  \n",
       "DigitalGlobe Vivid Imagery                                 0  \n",
       "DigitalGlobe-Premium                                      27  \n",
       "DigitalGlobe-Standard                                      1  \n",
       "ESRI Imagery                                               0  \n",
       "ESRI Wolrd Imagery                                         0  \n",
       "ESRI World                                                 0  \n",
       "ESRI World Imagery                                        15  \n",
       "ESRI imagery                                               0  \n",
       "Esri World Imagery                                         0  \n",
       "EsriWorldImagery                                         324  \n",
       "Mapillary;survey                                           0  \n",
       "Maxar Imagery                                              0  \n",
       "OS OpenData StreetView                                     0  \n",
       "OS StreetView                                              0  \n",
       "OS_OpenData_StreetView                                     0  \n",
       "OS_OpenData_Streetview                                     0  \n",
       "Sentinal                                                   0  \n",
       "The Grand Tour s01e01 00:37:33                             0  \n",
       "View from footpaths on each site;DigitalGlobe              0  \n",
       "Warwick DC, bing                                           0  \n",
       "Warwicks Aerial Imagery OGL v2                             0  \n",
       "Warwicks Aerial Imagery OGLv2                              0  \n",
       "Warwicks CC 2013 Aerial Imagery                            0  \n",
       "Warwicks CC Aerial Imagery                                 0  \n",
       "Warwicks CC Aerial Imagery 2013                            0  \n",
       "Warwicks CC Aerial Imagery 2013 OGL v2                     0  \n",
       "Warwicks CC Aerial Imagery 2013 OGLv2                      0  \n",
       "Warwicks CC Aerial Imagery OGLv2                           0  \n",
       "Warwicks CC aerial imagery 2013                            0  \n",
       "Warwicks CCAerial Imagery 2013                             0  \n",
       "Warwickshire Aerial Imagery 2013                           0  \n",
       "Warwickshire_CC_ Aerial_Imagery_2013                       0  \n",
       "auto_os_street_view                                        0  \n",
       "bing                                                       0  \n",
       "bing, local knowledge                                      0  \n",
       "bing;Lichfield_district_council                            0  \n",
       "bing;local_knowledge                                       0  \n",
       "bing;website                                               0  \n",
       "cities_revealed_aerial_imagery                             0  \n",
       "digitalglobe                                               0  \n",
       "digitalglobe premium                                       0  \n",
       "digitalglobe standard                                      0  \n",
       "local_knowledge                                            0  \n",
       "observation & OS OpenData StreetView                       0  \n",
       "survey                                                    68  \n",
       "survey (2013)                                              1  \n",
       "survey (May 2013)                                          2  \n",
       "survey (view from train)                                   0  \n",
       "survey;Bing                                                0  \n",
       "survey;digitalglobe                                        0  \n",
       "survey;estimation                                          0  \n",
       "visual survey,bing                                         0  \n",
       "visual survey; bing                                        0  \n",
       "visual survey;Maxar Imagery                                0  \n",
       "visual survey;bing                                         0  \n",
       "visual survey;estimate                                     0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('source')[['id', 'generator:type', 'building', 'notional_area']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['osm_type'] = [x[0] for x in df['id'].str.split('/')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "osm_type\n",
       "node        31763\n",
       "relation       62\n",
       "way          9408\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('osm_type')['id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a31978da0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['@relations'].notna()].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"1.07597 51.0658009 0.0003482999999999681 0.00017070000000529717\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,102.13177250000001)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"6.965999999999361e-06\" opacity=\"0.6\" d=\"M 1.0759903,51.0659115 L 1.0760088,51.0658856 L 1.0759829,51.0658808 L 1.0760012,51.0658531 L 1.0760285,51.065858 L 1.0760472,51.0658318 L 1.0760186,51.0658267 L 1.0760272,51.0658138 L 1.0763054,51.0658669 L 1.076239,51.0659587 L 1.0762147,51.0659541 L 1.0762334,51.0659274 L 1.0762039,51.0659218 L 1.0761856,51.0659486 L 1.076156,51.065943 L 1.0761759,51.0659167 L 1.0761469,51.0659113 L 1.0761277,51.0659376 L 1.0761005,51.0659324 L 1.0761193,51.0659062 L 1.0760898,51.0659005 L 1.0760712,51.0659269 L 1.0760444,51.0659218 L 1.0760639,51.0658959 L 1.0760336,51.0658902 L 1.0760142,51.0659161 L 1.0759903,51.0659115 z M 1.0762237,51.065893 L 1.0762535,51.0658985 L 1.0762712,51.0658732 L 1.0762411,51.0658676 L 1.0762237,51.065893 z M 1.0761678,51.0658829 L 1.0761974,51.0658883 L 1.0762165,51.0658631 L 1.0761863,51.0658575 L 1.0761678,51.0658829 z M 1.0761095,51.0658724 L 1.0761394,51.0658779 L 1.0761578,51.0658522 L 1.0761276,51.0658467 L 1.0761095,51.0658724 z M 1.0760545,51.0658624 L 1.0760847,51.065868 L 1.076104,51.0658423 L 1.0760738,51.0658367 L 1.0760545,51.0658624 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x1a4c14da58>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate shapes of relations from OSM\n",
    "r = df[df['osm_type']=='relation']\n",
    "i = np.random.randint(0, len(r))\n",
    "# r.iloc[i].geometry\n",
    "r.iloc[i].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting geometries loses the green area vs grey area\n",
    "fig, axes = plt.subplots(1, len(r[:10]), figsize=(12,12))\n",
    "for i, ax in enumerate(axes):\n",
    "    r.iloc[[i], :].plot(ax=ax)\n",
    "    ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-1.401210796 50.926441904 7.279199999987718e-05 4.80920000001106e-05\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,101.8529319)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"1.4558399999975435e-06\" opacity=\"0.6\" d=\"M -1.4012081,50.9264585 L -1.4011934,50.9264446 L -1.4011407,50.9264668 L -1.4011624,50.9264873 L -1.4011795,50.9264801 L -1.4011724,50.9264735 L -1.4012081,50.9264585 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x1a31872240>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect Ways. Why are there green areas and grey lines? What do these represent exactly?\n",
    "i = np.random.randint(0, len(df[df['osm_type'] == 'way']))\n",
    "df[df['osm_type'] == 'way'].iloc[i].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRS of GeoDataFrame is BNG / epsg:27700\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_uk(bng(df[df['osm_type'] == 'node']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to generate labels from the PV points?\n",
    "Ways have already been used to create labels.  \n",
    "I want to add in PV points.  \n",
    "Is it reasonable to just create a buffer zone and hope that will be good enough?  \n",
    "PV arrays vary a lot in size, so buffer zones around central point lat/long seems like it will be inaccurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label the tiles asap, this is good enough!\n",
    "With lat & lon for 16k points, this is close enough to label image chips in tiles.  \n",
    "Need to only use tiles that are recent enough.  \n",
    "And need to filter points to ensure they are not overlapping - but it's ok if the same image chip is picked twice, we just remove duplicates at some point.  \n",
    "\n",
    "Assume CRS is wgs84\n",
    "\n",
    "Data © OpenStreetMap contributors, ODbL (Terms)  \n",
    "Data mining by Overpass API  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "osm_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>roof:shape</th>\n",
       "      <th>source:name</th>\n",
       "      <th>start_date</th>\n",
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       "      <th>plant:source</th>\n",
       "      <th>repd:id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>leisure</th>\n",
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       "      <th>roof:orientation</th>\n",
       "      <th>generator:solar:modules</th>\n",
       "      <th>notional_area</th>\n",
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       "      <th>floating</th>\n",
       "      <th>source:geometry</th>\n",
       "      <th>generator:output:hot_water</th>\n",
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       "      <th>pv_module_array</th>\n",
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       "      <th>generator:output:biogas</th>\n",
       "      <th>orientation</th>\n",
       "      <th>highway</th>\n",
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       "      <th>roof:colour</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>generator:solar_modules</th>\n",
       "      <th>geneator:solar:modules</th>\n",
       "      <th>@relations</th>\n",
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       "      <th>generator:modules</th>\n",
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       "      <td>relation/8244429</td>\n",
       "      <td>relation/8244429</td>\n",
       "      <td>photovoltaic</td>\n",
       "      <td>yes</td>\n",
       "      <td>solar</td>\n",
       "      <td>generator</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>(POLYGON ((-2.880001 54.0362956, -2.8796828 54...</td>\n",
       "      <td>relation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id               @id generator:method  \\\n",
       "0  relation/8244429  relation/8244429     photovoltaic   \n",
       "\n",
       "  generator:output:electricity generator:source      power          type  \\\n",
       "0                          yes            solar  generator  multipolygon   \n",
       "\n",
       "  generator:type generator:location generator:orientation source location  \\\n",
       "0           None               None                  None   None     None   \n",
       "\n",
       "  building  name tourism amenity  note building:levels addr:city  \\\n",
       "0     None  None    None    None  None            None      None   \n",
       "\n",
       "  addr:housenumber addr:postcode addr:street  shop addr:place addr:housename  \\\n",
       "0             None          None        None  None       None           None   \n",
       "\n",
       "  ref:edubase operator fhrs:id wheelchair fixme addr:country wikidata  \\\n",
       "0        None     None    None       None  None         None     None   \n",
       "\n",
       "  description generator:place source:addr:postcode source:postcode  \\\n",
       "0        None            None                 None            None   \n",
       "\n",
       "  roof:levels roof:shape source:name start_date plant:method plant:source  \\\n",
       "0        None       None        None       None         None         None   \n",
       "\n",
       "  repd:id frequency leisure sport opening_hours barrier   url layer source:1  \\\n",
       "0    None      None    None  None          None    None  None  None     None   \n",
       "\n",
       "  source:2 generator:plant level roof:material roof:orientation  \\\n",
       "0     None            None  None          None             None   \n",
       "\n",
       "  generator:solar:modules notional_area notional_capacity floating  \\\n",
       "0                    None          None              None     None   \n",
       "\n",
       "  source:geometry generator:output:hot_water latest_start_date  \\\n",
       "0            None                       None              None   \n",
       "\n",
       "  pv_module_array height generator:output:biogas orientation highway  \\\n",
       "0            None   None                    None        None    None   \n",
       "\n",
       "  building:material direction roof:colour manufacturer  \\\n",
       "0              None      None        None         None   \n",
       "\n",
       "  generator:solar_modules geneator:solar:modules @relations  \\\n",
       "0                    None                   None       None   \n",
       "\n",
       "  earliest_start_date generator:modules  \\\n",
       "0                None              None   \n",
       "\n",
       "                                            geometry  osm_type  \n",
       "0  (POLYGON ((-2.880001 54.0362956, -2.8796828 54...  relation  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osm_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "# osm_df['geometry'] = list(zip(osm_df['lon'], osm_df['lat']))\n",
    "# osm_df['geometry'] = osm_df['geometry'].apply(lambda x: Point(x))\n",
    "osm_df['point'] = osm_df['geometry'].centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "# osm_df = gpd.GeoDataFrame(osm_df, crs={'init': 'epsg:4326'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>@id</th>\n",
       "      <th>generator:method</th>\n",
       "      <th>generator:output:electricity</th>\n",
       "      <th>generator:source</th>\n",
       "      <th>power</th>\n",
       "      <th>type</th>\n",
       "      <th>generator:type</th>\n",
       "      <th>generator:location</th>\n",
       "      <th>generator:orientation</th>\n",
       "      <th>source</th>\n",
       "      <th>location</th>\n",
       "      <th>building</th>\n",
       "      <th>name</th>\n",
       "      <th>tourism</th>\n",
       "      <th>amenity</th>\n",
       "      <th>note</th>\n",
       "      <th>building:levels</th>\n",
       "      <th>addr:city</th>\n",
       "      <th>addr:housenumber</th>\n",
       "      <th>addr:postcode</th>\n",
       "      <th>addr:street</th>\n",
       "      <th>shop</th>\n",
       "      <th>addr:place</th>\n",
       "      <th>addr:housename</th>\n",
       "      <th>ref:edubase</th>\n",
       "      <th>operator</th>\n",
       "      <th>fhrs:id</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>fixme</th>\n",
       "      <th>addr:country</th>\n",
       "      <th>wikidata</th>\n",
       "      <th>description</th>\n",
       "      <th>generator:place</th>\n",
       "      <th>source:addr:postcode</th>\n",
       "      <th>source:postcode</th>\n",
       "      <th>roof:levels</th>\n",
       "      <th>roof:shape</th>\n",
       "      <th>source:name</th>\n",
       "      <th>start_date</th>\n",
       "      <th>plant:method</th>\n",
       "      <th>plant:source</th>\n",
       "      <th>repd:id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>leisure</th>\n",
       "      <th>sport</th>\n",
       "      <th>opening_hours</th>\n",
       "      <th>barrier</th>\n",
       "      <th>url</th>\n",
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       "      <th>source:2</th>\n",
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       "      <th>level</th>\n",
       "      <th>roof:material</th>\n",
       "      <th>roof:orientation</th>\n",
       "      <th>generator:solar:modules</th>\n",
       "      <th>notional_area</th>\n",
       "      <th>notional_capacity</th>\n",
       "      <th>floating</th>\n",
       "      <th>source:geometry</th>\n",
       "      <th>generator:output:hot_water</th>\n",
       "      <th>latest_start_date</th>\n",
       "      <th>pv_module_array</th>\n",
       "      <th>height</th>\n",
       "      <th>generator:output:biogas</th>\n",
       "      <th>orientation</th>\n",
       "      <th>highway</th>\n",
       "      <th>building:material</th>\n",
       "      <th>direction</th>\n",
       "      <th>roof:colour</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>generator:solar_modules</th>\n",
       "      <th>geneator:solar:modules</th>\n",
       "      <th>@relations</th>\n",
       "      <th>earliest_start_date</th>\n",
       "      <th>generator:modules</th>\n",
       "      <th>geometry</th>\n",
       "      <th>osm_type</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>relation/8244429</td>\n",
       "      <td>relation/8244429</td>\n",
       "      <td>photovoltaic</td>\n",
       "      <td>yes</td>\n",
       "      <td>solar</td>\n",
       "      <td>generator</td>\n",
       "      <td>multipolygon</td>\n",
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       "      <td>(POLYGON ((-2.880001 54.0362956, -2.8796828 54...</td>\n",
       "      <td>relation</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id               @id generator:method  \\\n",
       "0  relation/8244429  relation/8244429     photovoltaic   \n",
       "\n",
       "  generator:output:electricity generator:source      power          type  \\\n",
       "0                          yes            solar  generator  multipolygon   \n",
       "\n",
       "  generator:type generator:location generator:orientation source location  \\\n",
       "0           None               None                  None   None     None   \n",
       "\n",
       "  building  name tourism amenity  note building:levels addr:city  \\\n",
       "0     None  None    None    None  None            None      None   \n",
       "\n",
       "  addr:housenumber addr:postcode addr:street  shop addr:place addr:housename  \\\n",
       "0             None          None        None  None       None           None   \n",
       "\n",
       "  ref:edubase operator fhrs:id wheelchair fixme addr:country wikidata  \\\n",
       "0        None     None    None       None  None         None     None   \n",
       "\n",
       "  description generator:place source:addr:postcode source:postcode  \\\n",
       "0        None            None                 None            None   \n",
       "\n",
       "  roof:levels roof:shape source:name start_date plant:method plant:source  \\\n",
       "0        None       None        None       None         None         None   \n",
       "\n",
       "  repd:id frequency leisure sport opening_hours barrier   url layer source:1  \\\n",
       "0    None      None    None  None          None    None  None  None     None   \n",
       "\n",
       "  source:2 generator:plant level roof:material roof:orientation  \\\n",
       "0     None            None  None          None             None   \n",
       "\n",
       "  generator:solar:modules notional_area notional_capacity floating  \\\n",
       "0                    None          None              None     None   \n",
       "\n",
       "  source:geometry generator:output:hot_water latest_start_date  \\\n",
       "0            None                       None              None   \n",
       "\n",
       "  pv_module_array height generator:output:biogas orientation highway  \\\n",
       "0            None   None                    None        None    None   \n",
       "\n",
       "  building:material direction roof:colour manufacturer  \\\n",
       "0              None      None        None         None   \n",
       "\n",
       "  generator:solar_modules geneator:solar:modules @relations  \\\n",
       "0                    None                   None       None   \n",
       "\n",
       "  earliest_start_date generator:modules  \\\n",
       "0                None              None   \n",
       "\n",
       "                                            geometry  osm_type  \\\n",
       "0  (POLYGON ((-2.880001 54.0362956, -2.8796828 54...  relation   \n",
       "\n",
       "                                          point  \n",
       "0  POINT (-2.878820741122532 54.03901327067169)  "
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osm_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "osm_df = osm_df.to_crs({'init':'epsg:27700'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# osm_df.to_pickle('data/osm_v1.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-26769.15000000001, 687992.1500000001, -53559.5, 1279189.5)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "# Grab UK outline # crs epsg:27700\n",
    "gb_outline = gpd.read_file('../../Data/shapefiles/Download_UK+boundaries_1183810/gb-outlines_2856975/greatbritain.shp')\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(16, 16))\n",
    "gb_outline.plot(ax=ax, color='white', linewidth=0.8, edgecolor='0.6')\n",
    "osm_df.plot(ax=ax, markersize=1, color='red', alpha=0.4)\n",
    "\n",
    "ax.set_title(\"OpenStreetMap PV Array Locations\", fontdict={'fontsize': '25', 'fontweight' : '3'})\n",
    "ax.axis('off')\n",
    "# plt.savefig('charts/osm_pv_locations_v1.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import tagging information:\n",
    "# https://wiki.openstreetmap.org/wiki/Tag:power%3Dgenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41233"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(osm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}