Heat-scan-workflow-standard_abridged.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><center> <font color='cyan'> Urban Heat Scan Workflow </font> </center></h2>\n",
    "<h3><center> <font color='cyan'>Table of Contents</font>   </center></h3>\n",
    "\n",
    "[Preface: Import dependencies and set paths](#section0)\n",
    "<br>\n",
    "[Section 1: Big picture: City map and context](#section1)\n",
    "<br>\n",
    "[Section 2: Population density and tree cover city level](#section2) \n",
    "<br>\n",
    "[Section 3: Visualize Vito heat rasters at city level](#section3) \n",
    "<br>\n",
    "[Section 4: Create the Heat Scan PPT](#section4) \n",
    "<br>\n",
    "[Section 5: Population density and tree cover Sub-city level. NA ](#section5) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section0'></a>\n",
    "<h5><center> <font color='cyan'> Preface: Import dependencies and set paths</font>   </center></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3326451490.py:40: UserWarning: The `utils.config` function is deprecated and will be removed in a future release. Instead, use the `settings` module directly to configure a global setting's value. For example, `ox.settings.log_console=True`.\n",
      "  ox.config(use_cache=True, log_console=True)\n"
     ]
    }
   ],
   "source": [
    "# Import packages\n",
    "import geopandas as gpd , os , time , glob\n",
    "from collections import defaultdict\n",
    "from itertools import combinations\n",
    "from geemap import cartoee\n",
    "import transbigdata as tbd\n",
    "import matplotlib.patheffects as pe\n",
    "from adjustText import adjust_text\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import matplotlib.patches as patches\n",
    "from mpl_toolkits.axes_grid1.inset_locator import (inset_axes, TransformedBbox,\n",
    "                                                   BboxPatch, BboxConnector)\n",
    "\n",
    "# from matplotlib_scalebar.scalebar import ScaleBar\n",
    "\n",
    "\n",
    "from rasterstats import raster_stats\n",
    "from scipy import stats\n",
    "\n",
    "from urllib.request import urlopen\n",
    "from pyproj import CRS\n",
    "from os.path import exists\n",
    "from geopandas.tools import overlay\n",
    "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
    "from osgeo import ogr\n",
    "import networkx as nx\n",
    "import osmnx as ox\n",
    "from shapely.geometry import mapping, Polygon\n",
    "import fiona\n",
    "import math\n",
    "from shapely.affinity import rotate\n",
    "from shapely.geometry import LineString, Point\n",
    "import matplotlib.image as image\n",
    "from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes , mark_inset\n",
    "from scipy.spatial import cKDTree\n",
    "\n",
    "ox.config(use_cache=True, log_console=True)\n",
    "\n",
    "import  seaborn as sn\n",
    "import geemap \n",
    "import ee \n",
    "import rasterio\n",
    "from rasterio.mask import mask\n",
    "from rasterio.features import shapes\n",
    "import osmnx as ox\n",
    "from shapely.geometry import box\n",
    "from rasterio.plot import show\n",
    "import pylab as plt\n",
    "from geopandas.tools import sjoin\n",
    "import contextily as cx\n",
    "from h3 import h3\n",
    "import h3pandas\n",
    "from rasterstats import zonal_stats\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from rasterio.crs import CRS\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.lines import Line2D\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# InteractiveShell.ast_node_interactivity = \"last_expr\" #Supress matplotlib output\n",
    "\n",
    "# Stats heavy-lifting\n",
    "from esda.moran import Moran\n",
    "from libpysal.weights import Queen, KNN\n",
    "import  libpysal\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import robust_scale\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from pysal.lib import weights\n",
    "from pysal.explore import esda\n",
    "from pysal.model import spreg\n",
    "\n",
    "from pptx import Presentation\n",
    "from pptx.util import Inches\n",
    "from pptx.util import Inches, Pt\n",
    "from pptx.enum.text import PP_ALIGN\n",
    "\n",
    "from pptx.oxml.xmlchemy import OxmlElement\n",
    "from pptx.util import Cm\n",
    "from pptx.enum.shapes import MSO_SHAPE\n",
    "from pptx.dml.color import RGBColor\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "plt.rcParams['hatch.color'] = 'white'\n",
    "plt.rcParams['hatch.linewidth'] = 4.2\n",
    "plt.rcParams['font.family'] = 'Yu Gothic'\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = 7.5 , 7.5\n",
    "width=12\n",
    "height=10\n",
    "plt.rcParams['figure.figsize'] = width , height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set controls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_map=True\n",
    "run_vito_analysis=False #\n",
    "test_maps=False\n",
    "run_tree_and_pop_analysis=False  #False True\n",
    "create_the_ppt=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_paths(base_dir):\n",
    "    data = os.path.join(base_dir, 'data') \n",
    "    output = os.path.join(base_dir, 'output') \n",
    "    shapefiles = os.path.join(output, 'shapefiles') \n",
    "    maps = os.path.join(output, 'maps') \n",
    "    rasters = os.path.join(output, 'rasters') \n",
    "    tables = os.path.join(output, 'tables') \n",
    "    \n",
    "    dirs_list= [data, output, base_dir, shapefiles , maps , rasters , tables]\n",
    "    for dir in dirs_list:\n",
    "        if not os.path.exists(dir):\n",
    "            os.mkdir(dir)\n",
    "    return data, shapefiles , maps , rasters , output ,tables\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\1473309264.py:32: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  sub_city_gdf[\"pre_clip_area\"] = sub_city_gdf['geometry'].area\n",
      "c:\\Users\\Aziz\\anaconda3\\envs\\uh\\lib\\site-packages\\geopandas\\geodataframe.py:1538: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\1473309264.py:34: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  sub_city_gdf[\"post_clip_area\"] = sub_city_gdf['geometry'].area\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\1473309264.py:44: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  sub_city_gdf_1[\"pre_clip_area\"] = sub_city_gdf_1['geometry'].area\n",
      "c:\\Users\\Aziz\\anaconda3\\envs\\uh\\lib\\site-packages\\geopandas\\geodataframe.py:1538: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\1473309264.py:46: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  sub_city_gdf_1[\"post_clip_area\"] = sub_city_gdf_1['geometry'].area\n"
     ]
    },
    {
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       "      <th>name</th>\n",
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       "      <th>ref:nuts:3</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">relation</th>\n",
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       "      <td>83113</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>boundary</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.001196</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>100.000000</td>\n",
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       "    <tr>\n",
       "      <th>10276201</th>\n",
       "      <td>Градска општина Црвени Крст</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Gradska opština Crveni Krst</td>\n",
       "      <td>29518</td>\n",
       "      <td>...</td>\n",
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       "      <td>Градска општина Пантелеј</td>\n",
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       "                                              name name:bg name:de name:el  \\\n",
       "element_type osmid                                                           \n",
       "relation     10276545     Градска општина Палилула     NaN     NaN     NaN   \n",
       "             10276511     Градска општина Медијана     NaN     NaN     NaN   \n",
       "             10276201  Градска општина Црвени Крст     NaN     NaN     NaN   \n",
       "             10276594   Градска општина Нишка Бања     NaN     NaN     NaN   \n",
       "             10276595     Градска општина Пантелеј     NaN     NaN     NaN   \n",
       "\n",
       "                      name:en name:hr name:ru                      name:sr  \\\n",
       "element_type osmid                                                           \n",
       "relation     10276545     NaN     NaN     NaN     Градска општина Палилула   \n",
       "             10276511     NaN     NaN     NaN     Градска општина Медијана   \n",
       "             10276201     NaN     NaN     NaN  Градска општина Црвени Крст   \n",
       "             10276594     NaN     NaN     NaN   Градска општина Нишка Бања   \n",
       "             10276595     NaN     NaN     NaN     Градска општина Пантелеј   \n",
       "\n",
       "                                      name:sr-Latn population  ... ref:nuts:3  \\\n",
       "element_type osmid                                             ...              \n",
       "relation     10276545     Gradska opština Palilula      69811  ...        NaN   \n",
       "             10276511     Gradska opština Medijana      83113  ...        NaN   \n",
       "             10276201  Gradska opština Crveni Krst      29518  ...        NaN   \n",
       "             10276594  Gradska opština Niška Banja      12940  ...        NaN   \n",
       "             10276595     Gradska opština Pantelej      54119  ...        NaN   \n",
       "\n",
       "                           type website ref:RS:opstina ref:RS:grad  \\\n",
       "element_type osmid                                                   \n",
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       "\n",
       "                      ref:RS:naselje short_name pre_clip_area post_clip_area  \\\n",
       "element_type osmid                                                             \n",
       "relation     10276545            NaN        NaN      0.012949       0.012823   \n",
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       "             10276594            NaN        NaN      0.016226       0.015627   \n",
       "             10276595            NaN        NaN      0.015656       0.015434   \n",
       "\n",
       "                      pct_clip_area  \n",
       "element_type osmid                   \n",
       "relation     10276545     99.022955  \n",
       "             10276511    100.000000  \n",
       "             10276201     99.305930  \n",
       "             10276594     96.309597  \n",
       "             10276595     98.582177  \n",
       "\n",
       "[5 rows x 34 columns]"
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     "execution_count": 13,
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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize ee and import shapefile and select city\n",
    "ee.Initialize()\n",
    "WGS84=4326 #uses degrees\n",
    "WGS84_meters=3857 #uses meters\n",
    "EPSG_str= 'EPSG:4326'\n",
    "crs = 4326\n",
    "city= \"Nis City\"\n",
    "base_dir = f'C:/Users/Aziz/Dropbox/CRP/UHI/{city}'\n",
    "data, shapefiles , maps , rasters , output ,tables = set_paths(base_dir)\n",
    "\n",
    "# shapefile_path=f'{shapefiles}/geoBoundaries-SRB-ADM1_simplified.shp'\n",
    "# Sub city donot exist somehow. City and municipailty are the same\n",
    "# Serbia is divided into 145 municipalities and 29 cities,[2] which form the basic units of local government. \n",
    "# Each municipality has its own assembly (elected every four years in local elections), a municipal president, \n",
    "# public service property and a budget. Municipalities usually have more than 10,000 inhabitants.[2]\n",
    "\n",
    "shapefile_path_country=f'{shapefiles}/geoBoundaries-SRB-ADM0.shp'\n",
    "gdf_country = gpd.read_file(shapefile_path_country).to_crs(WGS84)\n",
    "shapefile_path=f'{shapefiles}/geoBoundaries-SRB-ADM2.shp'\n",
    "gdf_city = gpd.read_file(shapefile_path).to_crs(WGS84)\n",
    "gdf_city=gdf_city[gdf_city[\"shapeName\"] == city]\n",
    "country= gdf_city.iloc[0]['shapeGroup']\n",
    "gdf_citties_all = gpd.read_file(shapefile_path).to_crs(WGS84)\n",
    "\n",
    "# OSM admin layers\n",
    "tags = {\"boundary\":\"administrative\" }\n",
    "minx, miny, maxx, maxy = gdf_city.to_crs(WGS84).total_bounds\n",
    "all_admin_layers =ox.geometries.geometries_from_bbox(miny,maxy,minx, maxx, tags)\n",
    "# Get the lowest \n",
    "lowest_admin_level=all_admin_layers[\"admin_level\"].mode()[0]\n",
    "sub_city_gdf = all_admin_layers[all_admin_layers['admin_level'] == lowest_admin_level]\n",
    "sub_city_gdf[\"pre_clip_area\"] = sub_city_gdf['geometry'].area\n",
    "sub_city_gdf = gpd.clip(sub_city_gdf.to_crs(crs), gdf_city.to_crs(crs))\n",
    "sub_city_gdf[\"post_clip_area\"] = sub_city_gdf['geometry'].area\n",
    "sub_city_gdf[\"pct_clip_area\"] = (sub_city_gdf[\"post_clip_area\"]/sub_city_gdf[\"pre_clip_area\"])*100\n",
    "sub_city_gdf = sub_city_gdf[sub_city_gdf['pct_clip_area'] > 50]\n",
    "\n",
    "# English name is missing at admin 7. Use names as the column   \n",
    "all_admin_layers_reset=all_admin_layers.reset_index()\n",
    "all_admin_layers_reset=pd.DataFrame(all_admin_layers_reset[\"admin_level\"])\n",
    "df_frequencies=pd.DataFrame(all_admin_layers_reset.admin_level.value_counts().T)\n",
    "second_third_frequent_admin=df_frequencies.index[2]\n",
    "sub_city_gdf_1 = all_admin_layers[all_admin_layers['admin_level'] == second_third_frequent_admin]\n",
    "sub_city_gdf_1[\"pre_clip_area\"] = sub_city_gdf_1['geometry'].area\n",
    "sub_city_gdf_1 = gpd.clip(sub_city_gdf_1.to_crs(crs), gdf_city.to_crs(crs))\n",
    "sub_city_gdf_1[\"post_clip_area\"] = sub_city_gdf_1['geometry'].area\n",
    "sub_city_gdf_1[\"pct_clip_area\"] = (sub_city_gdf_1[\"post_clip_area\"]/sub_city_gdf_1[\"pre_clip_area\"])*100\n",
    "sub_city_gdf_1 = sub_city_gdf_1[sub_city_gdf_1['pct_clip_area'] > 50]\n",
    "sub_city_gdf_1.plot()\n",
    "sub_city_gdf_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section1'></a>\n",
    "<h5><center> <font color='cyan'> Section 1: Big picture: City map</font>   </center></h5>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_city_map(country, city, vector_file,sub_city_gdf,admin_name, lowest_admin_level,basemap, basemap_label, map_output):\n",
    "        plt.rc('font', weight='bold')\n",
    "        crs=3857\n",
    "        vector_file_degrees=vector_file.copy()\n",
    "        vector_file=vector_file.to_crs(crs)\n",
    "        width=11\n",
    "        height=10\n",
    "        # fig = plt.figure(figsize=(width+5, height+5))\n",
    "        fig = plt.figure(figsize=(width, height))\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax = vector_file.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.6,  \n",
    "                                        facecolor='none',\n",
    "                                        edgecolor='blue' , \n",
    "                                        label=f\"{city}\",\n",
    "                                        linewidth=2  \n",
    "                                        )\n",
    "\n",
    "\n",
    "        sub_city_gdf=sub_city_gdf.to_crs(crs)\n",
    "        ax = sub_city_gdf.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.7,  \n",
    "                                        facecolor='none',\n",
    "                                        edgecolor='cyan' ,\n",
    "                                        label=f\"{city}\",\n",
    "                                        linewidth=.5,\n",
    "                                        missing_kwds={\"color\": \"white\", \"edgecolor\": \"magenta\", \"label\": \"none\"}\n",
    "                                        )\n",
    "        # \n",
    "        # gdf=gdf.rename(columns = {'name:en':'name_en'}, inplace = True)\n",
    "        sub_city_gdf['name_en'] =sub_city_gdf[admin_name]               \n",
    "        sub_city_gdf= sub_city_gdf.replace('nan', np.nan) #.dropna(how='all', axis=1, inplace=True)\n",
    "        label = sub_city_gdf.dropna(subset='name_en')\n",
    "        texts = []\n",
    "        for i, row in label.iterrows(): \n",
    "                # print(f\"Subcity name: {str(row['name_en'])}\")\n",
    "                texts.append(ax.annotate(text=str(row['name_en']), \n",
    "                                         xy=row.geometry.centroid.coords[0], color=\"black\", \n",
    "                                         ha='center', fontsize=7,  path_effects=[pe.withStroke(linewidth=2, \n",
    "                                                                                               foreground=\"white\")]))\n",
    "                \n",
    "        # adjust_text(texts, arrowprops=dict(arrowstyle='->', color='blue'), ax=ax)\n",
    "        # adjust_text(texts, ax=ax)\n",
    "\n",
    "        # adjust_text(texts)\n",
    "        vector_file_buffer = vector_file.to_crs(crs).buffer(2000) #for zoom out\n",
    "        minx, miny, maxx, maxy = vector_file_buffer.total_bounds\n",
    "        ax.set_xlim(minx, maxx)\n",
    "        ax.set_ylim(miny, maxy)\n",
    "    \n",
    "        #Identifying how many tiles\n",
    "        latlon_outline = vector_file.to_crs(WGS84).total_bounds\n",
    "        def_zoom = cx.tile._calculate_zoom(*latlon_outline)\n",
    "        howmany= cx.howmany(*latlon_outline, def_zoom+2, ll=True)\n",
    "        print(f'Default Zoom level {def_zoom} and +2 is {howmany}')\n",
    "\n",
    "\n",
    "        try:\n",
    "                cx.add_basemap(ax,\n",
    "                        zoom=\"auto\",\n",
    "                        # zoom=abs(def_zoom-1),\n",
    "                        # zoom=def_zoom,\n",
    "                        crs=vector_file.crs.to_string(),\n",
    "                        source= basemap,\n",
    "                        # source=cx.providers.CartoDB.PositronOnlyLabels,\n",
    "                        # source=cx.providers.CartoDB.DarkMatter,\n",
    "                        attribution=False, #No citation\n",
    "                        # zorder = 2\n",
    "                        )\n",
    "        except:\n",
    "                pass\n",
    "\n",
    "        try:\n",
    "                cx.add_basemap(ax,\n",
    "                        # zoom=\"auto\",\n",
    "                        zoom=abs(def_zoom),\n",
    "                        crs=vector_file.crs.to_string(),\n",
    "                        # source=cx.providers.OpenStreetMap.Mapnik,\n",
    "                        source=basemap_label, #cx.providers.CartoDB.PositronOnlyLabels,\n",
    "                        # source=cx.providers.CartoDB.DarkMatter,\n",
    "                        attribution=False, #No citation\n",
    "                        # zorder = 3\n",
    "                        )\n",
    "        except:\n",
    "                pass\n",
    "\n",
    "\n",
    "        try:\n",
    "                cx.add_basemap(ax,\n",
    "                        # zoom=\"auto\",\n",
    "                        zoom=abs(def_zoom-1),\n",
    "                        crs=vector_file.crs.to_string(),\n",
    "                        # source=cx.providers.OpenStreetMap.Mapnik,\n",
    "                        source=basemap_label, #cx.providers.CartoDB.PositronOnlyLabels,\n",
    "                        # source=cx.providers.CartoDB.DarkMatter,\n",
    "                        attribution=False, #No citation\n",
    "                        # zorder = 2\n",
    "                        )\n",
    "        except:\n",
    "                pass\n",
    "\n",
    "        ax2 = inset_axes(ax, \"25%\", \"35%\", loc=\"upper right\")\n",
    "        ax2 = vector_file.to_crs(crs).plot(ax=ax2, \n",
    "                                        #    alpha=0.6,\n",
    "                                        #    column= visualize_column, \n",
    "                                        facecolor='none',\n",
    "                                        edgecolor='blue' ,\n",
    "                                        label=f\"{city}\", \n",
    "                                        linewidth=1,\n",
    "                                        #    zorder = 2\n",
    "\n",
    "                                        )\n",
    "        ax2 = gdf_country.to_crs(crs).plot(ax=ax2, \n",
    "                                        #  alpha=0.6,\n",
    "                                        facecolor='none',\n",
    "                                        edgecolor='black' , \n",
    "                                        label=f\"{country}\",\n",
    "                                        linewidth=1,\n",
    "                                                # zorder = 3\n",
    "\n",
    "                                        )\n",
    "        try:\n",
    "                cx.add_basemap(ax2,\n",
    "                        zoom=\"auto\",\n",
    "                        # zoom=10,\n",
    "                        crs=vector_file.crs.to_string(),\n",
    "                        source=basemap, #cx.providers.OpenStreetMap.Mapnik,\n",
    "                        attribution=False, #No citation\n",
    "                        # zorder = 4\n",
    "                        )\n",
    "        except:\n",
    "                pass\n",
    "\n",
    "        try:\n",
    "                cx.add_basemap(ax2,\n",
    "                        # zoom=\"auto\",\n",
    "                        zoom=abs(def_zoom-6),\n",
    "                        crs=vector_file.crs.to_string(),\n",
    "                        # source=cx.providers.OpenStreetMap.Mapnik,\n",
    "                        source=basemap_label, #cx.providers.CartoDB.PositronOnlyLabels,\n",
    "                        # source=cx.providers.CartoDB.DarkMatter,\n",
    "                        attribution=False, #No citation\n",
    "                        # zorder = 4\n",
    "                        )\n",
    "        except:\n",
    "                pass\n",
    "\n",
    "        box, c1, c2 = mark_inset(ax2, ax, loc1=2, loc2=4, fc=\"none\", lw=.5, ec='r')\n",
    "        box.set_linewidth(.5)\n",
    "        plt.setp([c1,c2], linewidth=0)\n",
    "\n",
    "\n",
    "        LegendElement = [\n",
    "                        Line2D([0],[0],\n",
    "                               color='black', \n",
    "                               label=f'{country}', \n",
    "                               fillstyle=None,\n",
    "                               linestyle=\"none\", \n",
    "                               markerfacecolor=None,\n",
    "                               marker=\"s\", \n",
    "                               markersize=5.5),\n",
    "                        Line2D([0],[0], \n",
    "                               color='blue', \n",
    "                               label=f'{city}',\n",
    "                               linestyle=\"none\", \n",
    "                               markerfacecolor=None,\n",
    "                               fillstyle=None, \n",
    "                               marker=\"s\", \n",
    "                               markersize=5.5),\n",
    "                      Line2D([0],[0], \n",
    "                               color='cyan', \n",
    "                               label=f'Admin-level:{lowest_admin_level}',\n",
    "                               linestyle='none', \n",
    "                               markerfacecolor=None,\n",
    "                               fillstyle=None, \n",
    "                               marker=\"s\", \n",
    "                               markersize=5.5)\n",
    "                               ]\n",
    "        ax2.legend(handles=LegendElement,loc='upper right', prop = { \"size\": 8 },)\n",
    "        # get axis position and instert the N arrow \n",
    "        # x, y , x+dx, y+dy\n",
    "\n",
    "        minx, miny, maxx, maxy =  gdf_country.to_crs(crs).total_bounds # [113.6,22.4,114.8,22.9]\n",
    "        bounds=[minx, miny, maxx, maxy]\n",
    "        print(f\"Country bounds:{bounds}\")\n",
    "        dx=maxx-minx\n",
    "        dy=maxy-miny\n",
    "        arrow_x= maxx-abs(dx/6.5)\n",
    "        arrow_y= maxy-abs(dy/4.7)\n",
    "        arrow_sign_x=maxx-abs(dx/8.1)\n",
    "        arrow_sign_y=maxy-abs(dy/3.4)\n",
    "        head_width=abs(dy/17)\n",
    "        overhang=.1\n",
    "        head_length=1.6*head_width\n",
    "        ax2.text(x=arrow_x, y=arrow_y, s='N', fontsize=13)\n",
    "        ax2.arrow(arrow_sign_x, arrow_sign_y, dx=dx*0, dy=dy/14 , length_includes_head=True,\n",
    "                head_width=head_width, head_length=head_length, overhang=overhang, facecolor='k')\n",
    "\n",
    "        ax2.axis('off')\n",
    "        index=0\n",
    "        polygon = vector_file_degrees.reset_index().iloc[index]['geometry']\n",
    "        tags={'amenity':['college', 'dancing_school', 'driving_school', 'kindergarten', \\\n",
    "                        'language_school', 'library', 'surf_school', 'toy_library', 'research_institute', \\\n",
    "                        'training', 'music_school', 'school', 'university'],\n",
    "                        'landuse': 'education'\n",
    "                        }\n",
    "        education=ox.features.features_from_polygon(polygon, tags)\n",
    "        # tags={'leisure':['park', 'nature_reserve', 'protected_area', 'garden']}\n",
    "        tags={'leisure':['park',  'garden']}\n",
    "        park=ox.features.features_from_polygon(polygon, tags)\n",
    "        park.geometry = park['geometry'].centroid\n",
    "        tags={'landuse':['commercial', 'industrial', 'construction', 'retail']                   }\n",
    "        industry=ox.features.features_from_polygon(polygon, tags)\n",
    "        industry.geometry = industry['geometry'].centroid\n",
    "        tags={'building': 'government'}\n",
    "        government_building=ox.features.features_from_polygon(polygon, tags)\n",
    "        government_building.geometry = government_building['geometry'].centroid\n",
    "        tags = {\"building\": True}\n",
    "        building_footprint=ox.features.features_from_polygon(polygon, tags)\n",
    "        building_footprint.geometry = building_footprint['geometry'].centroid\n",
    "        tags = {'amenity':['clinic', 'doctors', 'dentist', 'health_post', 'hospital', 'nursing_home', 'pharmacy', 'veterinary']}\n",
    "        health_care=ox.features.features_from_polygon(polygon, tags)\n",
    "        health_care.geometry = health_care['geometry'].centroid\n",
    "        G = ox.graph.graph_from_polygon(polygon, network_type='drive')\n",
    "        # add travel time based on maximum speed\n",
    "        G = ox.add_edge_speeds(G) \n",
    "        G = ox.add_edge_travel_times(G) \n",
    "        G = ox.projection.project_graph(G, to_crs=3857)\n",
    "        # get edges as Geo data frame\n",
    "        _, gdf_edges = ox.graph_to_gdfs(G)\n",
    "\n",
    "        ax = education.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.6,  \n",
    "                                        facecolor='yellow',\n",
    "                                        edgecolor='yellow' , \n",
    "                                        label=\"Education\",\n",
    "                                        linewidth=3,\n",
    "                                        markersize=10,\n",
    "                                        marker=\"P\"  \n",
    "                                        )\n",
    "        \n",
    "        ax = industry.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.6,  \n",
    "                                        facecolor='red', \n",
    "                                        edgecolor='red' , \n",
    "                                        label=\"Industries\",\n",
    "                                        linewidth=3,\n",
    "                                        markersize=10,\n",
    "                                        marker=\"p\"  \n",
    "                                        )\n",
    "        \n",
    "        ax = park.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.6,  \n",
    "                                        facecolor='green',\n",
    "                                        edgecolor='green' , \n",
    "                                        label=\"Parks\",\n",
    "                                        linewidth=3,\n",
    "                                        markersize=10,\n",
    "                                        marker=\"*\"   \n",
    "                                        )\n",
    "        ax = health_care.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.6,  \n",
    "                                        facecolor='magenta',\n",
    "                                        edgecolor='magenta' , \n",
    "                                        label=\"Healthcare\",\n",
    "                                        linewidth=3,\n",
    "                                        markersize=10,\n",
    "                                        marker=\"o\"   \n",
    "                                        )\n",
    "\n",
    "        ax = gdf_edges.to_crs(crs).plot(ax=ax,\n",
    "                                        alpha=0.4,  \n",
    "                                        facecolor='none',\n",
    "                                        edgecolor='black' , \n",
    "                                        label=\"Roads\",\n",
    "                                        linewidth=.3,\n",
    "                                        linestyle='dashed',\n",
    "                                        # zorder=1\n",
    "                                        )\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "        lines = [Line2D([0], [0], linestyle=\"none\",  fillstyle=None, marker=\"P\", markersize=10, color='yellow' , label='Education'),\n",
    "                 Line2D([0], [0], linestyle=\"none\",  fillstyle=None, marker=\"p\", markersize=10, color='red' , label='Industries'),\n",
    "                 Line2D([0], [0], linestyle=\"none\",  fillstyle=None, marker=\"*\", markersize=10, color='green' , label='Parks'),\n",
    "                 Line2D([0], [0], linestyle=\"none\",  fillstyle=None, marker=\"o\", markersize=10, color='magenta' , label='Healthcare'),\n",
    "                 Line2D([0], [0], linestyle=\"dashed\",  fillstyle=None, marker=\"_\", markersize=10, color='black' , label='Roads')]\n",
    "        ax.legend(handles=lines,loc='lower right', prop = { \"size\": 8 },)\n",
    "\n",
    "        ax.axis('off')\n",
    "        ax.set_title(f'{city}', fontdict={'fontsize': '16', 'fontweight' : '6'})\n",
    "        # ax.annotate('Source: OSM basemaps',xy=(0.1, .1),  xycoords='figure fraction', horizontalalignment='left', verticalalignment='top', fontsize=6, color='#555555')\n",
    "        plt.xticks(visible=False)\n",
    "        plt.yticks(visible=False)\n",
    "        plt.tight_layout()\n",
    "        ax.figure.savefig(map_output,  bbox_inches='tight',   dpi = 400)\n",
    "        return education, park, industry, government_building, building_footprint, gdf_edges\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using zoom level 14, this will download 378 tiles\n",
      "Default Zoom level 12 and +2 is 378\n",
      "Country bounds:[2094396.9041382559, 5195832.129412373, 2561050.602912707, 5810857.733396657]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:211: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  park.geometry = park['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:214: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  industry.geometry = industry['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:217: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  government_building.geometry = government_building['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:220: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  building_footprint.geometry = building_footprint['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:223: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  health_care.geometry = health_care['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:296: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using zoom level 14, this will download 378 tiles\n",
      "Default Zoom level 12 and +2 is 378\n",
      "Country bounds:[2094396.9041382559, 5195832.129412373, 2561050.602912707, 5810857.733396657]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:211: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  park.geometry = park['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:214: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  industry.geometry = industry['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:217: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  government_building.geometry = government_building['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:220: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  building_footprint.geometry = building_footprint['geometry'].centroid\n",
      "C:\\Users\\Aziz\\AppData\\Local\\Temp\\ipykernel_2940\\3496515525.py:223: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  health_care.geometry = health_care['geometry'].centroid\n"
     ]
    }
   ],
   "source": [
    "# %%capture\n",
    "city_map=True\n",
    "if __name__ ==  '__main__': \n",
    "    if city_map:\n",
    "        # # City level map\n",
    "        vector_file=gdf_city\n",
    "        country=\"Serbia\"\n",
    "        city=\"Nis\"\n",
    "\n",
    "        try:\n",
    "                basemap= cx.providers.OpenStreetMap.Mapnik\n",
    "                basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "                basemap_name=\"Mapnik\"\n",
    "                admin_name='name:en'                \n",
    "                map_output= os.path.join(maps, f\"{city}_{lowest_admin_level}_{basemap_name}.png\")\n",
    "                create_city_map(country, city, vector_file,sub_city_gdf,admin_name, lowest_admin_level,basemap, basemap_label, map_output)\n",
    "                admin_name='name'  \n",
    "                lowest_admin_level= second_third_frequent_admin  \n",
    "                sub_city_gdf=sub_city_gdf_1         \n",
    "                map_output= os.path.join(maps, f\"{city}_{lowest_admin_level}_{basemap_name}.png\")\n",
    "                create_city_map(country, city, vector_file,sub_city_gdf,admin_name, lowest_admin_level,basemap, basemap_label, map_output)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.OpenStreetMap.Mapnik\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"Mapnik\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.OpenStreetMap.BlackAndWhite\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"BlackAndWhite\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.Stamen.Toner\n",
    "        #     basemap_label=cx.providers.Stamen.TonerLabels\n",
    "        #     basemap_name=\"Stamen\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf,lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "        # try:\n",
    "        #     basemap= source=cx.providers.CartoDB.DarkMatter\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"CartoDB\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf,lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.Esri.WorldStreetMap\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"Esri\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level,basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "        # try:\n",
    "        #     basemap= cx.providers.Esri.WorldImagery\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"Esri_WorldImagery\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.USGS.USImagery\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"USGS_USImagery\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level,basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.CartoDB.Positron\n",
    "        #     basemap_label=cx.providers.CartoDB.PositronOnlyLabels\n",
    "        #     basemap_name=\"CartoDB_Positron\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf,lowest_admin_level, basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n",
    "        # try:\n",
    "        #     basemap= cx.providers.CartoDB.Voyager\n",
    "        #     basemap_label=cx.providers.CartoDB.VoyagerOnlyLabels\n",
    "        #     basemap_name=\"CartoDB_Voyager\"\n",
    "        #     map_output= os.path.join(maps, f\"{city}_{basemap_name}.png\")\n",
    "        #     city_map= create_city_map(country, city, vector_file, sub_city_gdf, lowest_admin_level,basemap, basemap_label, map_output)\n",
    "        # except:\n",
    "        #     pass\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section2'></a>\n",
    "<h5><center> <font color='cyan'> Section 2: Population density and tree cover city level</font>   </center></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def create_fc_collection(gdf_city):\n",
    "    poly = gdf_city.geometry.unary_union\n",
    "    gdf_boundary = gpd.GeoDataFrame(geometry=[poly],crs=gdf_city.crs)\n",
    "    geom = gdf_boundary['geometry']\n",
    "    jsonDict = eval(geom.to_json())\n",
    "    for index, row in gdf_boundary.iterrows(): \n",
    "        polygon_list= []\n",
    "        for x in jsonDict['features'][index]['geometry']['coordinates']:\n",
    "            polygon_list.append(x)\n",
    "            region = ee.Geometry.Polygon(polygon_list)\n",
    "            fc_filtered = ee.FeatureCollection(region)\n",
    "    # gdf_boundary.plot()\n",
    "    return fc_filtered  , region \n",
    "\n",
    "def clipToCol(image):\n",
    "  \"\"\"clip gee collection\n",
    "  args:\n",
    "      image: Image collection\n",
    "  returns:\n",
    "    ee-collection: Clipped image collection\n",
    "   \n",
    "   \"\"\"\n",
    "  return image.clip(fc_filtered)\n",
    "\n",
    "def download_tree_cover(ee , EPSG_str, region):\n",
    "    tree_cover=ee.ImageCollection(\"projects/sat-io/open-datasets/GFCC30TC\") \\\n",
    "        .limit(1, 'system:time_start', False).first().clip(fc_filtered)\n",
    "    tree_cover_tif = os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "    geemap.ee_export_image(\n",
    "        tree_cover, filename=tree_cover_tif,  \n",
    "        crs=EPSG_str,\n",
    "        # crs_transform=crs_transform,\n",
    "        scale=30, region=region, file_per_band=False\n",
    "    )\n",
    "    return tree_cover_tif\n",
    "\n",
    "def download_tree_cover_esa_vito(ee , EPSG_str, region):\n",
    "    tree_cover=ee.ImageCollection(\"ESA/WorldCover/v100\") \\\n",
    "        .limit(1, 'system:time_start', False).first().clip(fc_filtered).eq(10) #trees only\n",
    "        # .limit(1, 'system:time_start', False).select(10).first().clip(fc_filtered) #trees only\n",
    "    tree_cover_tif_esa = os.path.join(rasters, 'tree_cover_projected_esa.tif')\n",
    "    geemap.ee_export_image(\n",
    "        tree_cover, filename=tree_cover_tif_esa,  \n",
    "        crs=EPSG_str,\n",
    "        # crs_transform=crs_transform,\n",
    "        scale=10, region=region, file_per_band=False\n",
    "    )\n",
    "    return tree_cover_tif_esa\n",
    "\n",
    "\n",
    "def dowload_pop_density(ee , EPSG_str, region):\n",
    "    #  Define WorldPop & clip using function \n",
    "    pop_density = ee.ImageCollection(\"WorldPop/GP/100m/pop\").map(clipToCol). \\\n",
    "                    filterDate('2020').select('population').mosaic()\n",
    "\n",
    "    pop_density_tif = os.path.join(rasters, 'pop_density_projected.tif')\n",
    "    geemap.ee_export_image( pop_density, \n",
    "                           filename=pop_density_tif,  \n",
    "                           crs=EPSG_str,\n",
    "                           # crs_transform=crs_transform,\n",
    "                           scale=100, \n",
    "                           region=region, \n",
    "                           file_per_band=False\n",
    "                           )\n",
    "\n",
    "    return pop_density_tif\n",
    "\n",
    "# Takes longer\n",
    "def download_google_dynamic_worldcover(ee , EPSG_str, region):\n",
    "    # dynamic_world_cover=ee.ImageCollection(\"GOOGLE/DYNAMICWORLD/V1\") \\\n",
    "    #     .limit(1, 'system:time_start', False).map(clipToCol).mosaic() #all only\n",
    "    dynamic_world_cover=ee.ImageCollection(\"GOOGLE/DYNAMICWORLD/V1\") \\\n",
    "        .limit(1, 'system:time_start', False).mosaic().clip(fc_filtered)\n",
    "    google_dynamic_world_cover_projected = os.path.join(rasters, 'google_dynamic_world_cover_projected.tif')\n",
    "    geemap.ee_export_image(\n",
    "        dynamic_world_cover, filename=google_dynamic_world_cover_projected,  \n",
    "        crs=EPSG_str,\n",
    "        # crs_transform=crs_transform,\n",
    "        scale=10, region=region, file_per_band=False\n",
    "    )\n",
    "    return google_dynamic_world_cover_projected\n",
    "\n",
    "def download_esri_dynamic_worldcover(ee , EPSG_str, region): \n",
    "    dynamic_world_cover=ee.ImageCollection(\"projects/sat-io/open-datasets/landcover/ESRI_Global-LULC_10m_TS\")\\\n",
    "        .filterDate('2022-01-01','2022-12-31').mosaic().clip(fc_filtered)\n",
    "    esri_dynamic_world_cover_projected = os.path.join(rasters, 'esri_dynamic_world_cover_projected.tif')\n",
    "    geemap.ee_export_image(\n",
    "        dynamic_world_cover, filename=esri_dynamic_world_cover_projected,  \n",
    "        crs=EPSG_str,\n",
    "        # crs_transform=crs_transform,\n",
    "        scale=10, region=region, file_per_band=False\n",
    "    )\n",
    "    return esri_dynamic_world_cover_projected\n",
    "\n",
    "def reproject_rasters(crs ,output_dir, unprojected_raster, projected_raster):\n",
    "    if not exists(projected_raster):\n",
    "        with rasterio.open(unprojected_raster) as src:\n",
    "            dst_crs = 'EPSG:' + str(crs)\n",
    "            transform, width, height = calculate_default_transform(\n",
    "                src.crs, dst_crs, src.width, src.height, *src.bounds)\n",
    "            kwargs = src.meta.copy()\n",
    "            kwargs.update({\n",
    "                'crs': dst_crs,\n",
    "                'transform': transform,\n",
    "                'width': width,\n",
    "                'height': height\n",
    "                # ,'dtype': np.float32\n",
    "            })\n",
    "\n",
    "            with rasterio.open(projected_raster, 'w', **kwargs) as dst:\n",
    "                for i in range(1, src.count + 1):\n",
    "                    reproject(\n",
    "                        source=rasterio.band(src, i),\n",
    "                        destination=rasterio.band(dst, i),\n",
    "                        src_transform=src.transform,\n",
    "                        src_crs=src.crs,\n",
    "                        dst_transform=transform,\n",
    "                        dst_crs=dst_crs,\n",
    "                        # dtype= np.float32,\n",
    "                        resampling=Resampling.nearest)\n",
    "\n",
    "def polygonize(raster_file ,output_shapefile, crs, mask_value, raster_val):\n",
    "    # mask = None\n",
    "    with rasterio.Env():\n",
    "        with rasterio.open(raster_file ) as src: #, dtype= np.float32\n",
    "            image = src.read(1) # first band\n",
    "            no_data_value=0\n",
    "            image = np.where(image<0,no_data_value,image) #replaced negative values with 0\n",
    "            results = (\n",
    "            {'properties': {raster_val : v}, 'geometry': s}\n",
    "            for i, (s, v) \n",
    "            in enumerate(\n",
    "                shapes(image, mask=mask_value, transform=src.transform)))\n",
    "\n",
    "    # The result is a generator of GeoJSON features\n",
    "    geoms = list(results)\n",
    "    # first feature\n",
    "    # print(geoms[0])\n",
    "    gpd_polygonized_raster  = gpd.GeoDataFrame.from_features(geoms)\n",
    "    crs_utm = CRS.from_user_input(crs)\n",
    "    driver= 'ESRI Shapefile'\n",
    "    gpd_polygonized_raster.to_file(output_shapefile, driver, crs=crs_utm)\n",
    "    return gpd_polygonized_raster\n",
    "\n",
    "def calcBearing (lat1, long1, lat2, long2):\n",
    "    dLon = (long2 - long1)\n",
    "    x = math.cos(math.radians(lat2)) * math.sin(math.radians(dLon))\n",
    "    y = math.cos(math.radians(lat1)) * math.sin(math.radians(lat2)) - math.sin(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.cos(math.radians(dLon))\n",
    "    bearing = math.atan2(x,y)   # use atan2 to determine the quadrant\n",
    "    bearing = math.degrees(bearing)\n",
    "    return bearing\n",
    "\n",
    "def calcNSEW(lat1, long1, lat2, long2):\n",
    "    points = [\"north\", \"north east\", \"east\", \"south east\", \"south\", \"south west\", \"west\", \"north west\"]\n",
    "    bearing = calcBearing(lat1, long1, lat2, long2)\n",
    "    bearing += 22.5\n",
    "    bearing = bearing % 360\n",
    "    bearing = int(bearing / 45) # values 0 to 7\n",
    "    NSEW = points [bearing]\n",
    "    return NSEW\n",
    "\n",
    "def ordinal(n: int):\n",
    "    if 11 <= (n % 100) <= 13:\n",
    "        suffix = 'th'\n",
    "    else:\n",
    "        suffix = ['th', 'st', 'nd', 'rd', 'th'][min(n % 10, 4)]\n",
    "    return str(n) + suffix\n",
    "\n",
    "def get_osm_tags_inside_clusters(cluster_boundaries):   \n",
    "    for index, poi in cluster_boundaries.iterrows():\n",
    "        polygon = cluster_boundaries.iloc[index]['geometry']\n",
    "        park , education, industry, building=get_services_infrastructure_inside_poly(polygon)\n",
    "    return park , education, industry, building\n",
    "\n",
    "def delete_shapefile(output_shapefile):\n",
    "    shapefile = ogr.Open(output_shapefile,1 )\n",
    "    layer=shapefile.GetLayerByIndex(0)\n",
    "    count=layer.GetFeatureCount()\n",
    "    for feature in range(count):\n",
    "        layer.DeleteFeature(feature)\n",
    "\n",
    "\n",
    "def map_clusters(maps, hexagons, legend_title,legends_format, crs ,cmap,  visualize_column, title):\n",
    "    hexagons=hexagons.to_crs(crs)\n",
    "    hexagons = hexagons.sort_values('cluster_area_deg', ascending = False).groupby('ward5wknn').head(2)\n",
    "    hexagons=hexagons.reset_index()\n",
    "    for index, poi in hexagons.iterrows():\n",
    "        try:\n",
    "            cluster_class=poi[\"ward5wknn\"]\n",
    "            polygon = hexagons[hexagons[\"ward5wknn\"]==index]\n",
    "            # polygon = hexagons.iloc[index]['geometry']  \n",
    "            map_output= f'{maps}/{city}_{visualize_column}_{index}_{cluster_class}_cluster.png'\n",
    "            ax = polygon.plot(figsize=(10, 10), \\\n",
    "                                        column= visualize_column, \\\n",
    "                                        facecolor='none',edgecolor='cyan' , \\\n",
    "                                        linewidth=1.5 , \\\n",
    "                                        alpha=0.6, categorical=True , cmap=cmap, \\\n",
    "                                        legend=True,  # Add legend \n",
    "                                        legend_kwds={'loc':'upper right', \n",
    "                                                        'bbox_to_anchor':(1, 1), \n",
    "                                                        'fmt':legends_format,\n",
    "                                                        'markerscale':1.01, \n",
    "                                                        'title_fontsize':'small', \n",
    "                                                        'fontsize':'x-small'\n",
    "                                                        # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                        } ,\n",
    "                                        aspect=1\n",
    "                                                )\n",
    "            # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "            polygon_buffer = polygon.to_crs(crs).buffer(0.016) #for zoom out\n",
    "            minx, miny, maxx, maxy = polygon_buffer.total_bounds\n",
    "            ax.set_xlim(minx, maxx)\n",
    "            ax.set_ylim(miny, maxy)\n",
    "\n",
    "            cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "            # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "            # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "            cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "\n",
    "            # plt.title(f'{title}',fontsize=16)\n",
    "            plt.xticks([])\n",
    "            plt.yticks([]) \n",
    "            leg1 = ax.get_legend()\n",
    "            # Set markers to square shape\n",
    "            for ea in leg1.legendHandles:\n",
    "                ea.set_marker('s')\n",
    "            leg1.set_title(f'{legend_title}')\n",
    "            ax.title.set_text(f'{title}')\n",
    "            plt.tight_layout()\n",
    "            # ax.figure.savefig(map_output)\n",
    "            ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "def map_clusters_land_cover(maps, hexagons, legend_title,legends_format, crs ,cmap,  visualize_column, title):\n",
    "    hexagons=hexagons.to_crs(crs)\n",
    "    hexagons=hexagons.reset_index()\n",
    "    hexagons = hexagons.sort_values('cluster_area_deg', ascending = False).groupby('ward5wknn').head(2)\n",
    "    for index, poi in hexagons.iterrows():\n",
    "        try:\n",
    "            cluster_class=poi[\"ward5wknn\"]\n",
    "            polygon = hexagons[hexagons[\"ward5wknn\"]==index]\n",
    "            # polygon = hexagons.iloc[index]['geometry']  \n",
    "            map_output= f'{maps}/{city}_{visualize_column}_{index}_{cluster_class}_cluster.png'\n",
    "            ax = polygon.plot(figsize=(10, 10), \\\n",
    "                                        column= visualize_column, \\\n",
    "                                        facecolor='none',edgecolor='cyan' , \\\n",
    "                                        linewidth=1.5 , \\\n",
    "                                        alpha=0.6, categorical=True , cmap=cmap, \\\n",
    "                                        legend=True,  # Add legend \n",
    "                                        legend_kwds={'loc':'upper right', \n",
    "                                                        'bbox_to_anchor':(1, 1), \n",
    "                                                        'fmt':legends_format,\n",
    "                                                        'markerscale':1.01, \n",
    "                                                        'title_fontsize':'small', \n",
    "                                                        'fontsize':'x-small'\n",
    "                                                        # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                        } ,\n",
    "                                        aspect=1\n",
    "                                                )\n",
    "            # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "            polygon_buffer = polygon.to_crs(crs).buffer(0.016) #for zoom out\n",
    "            minx, miny, maxx, maxy = polygon_buffer.total_bounds\n",
    "            print(\"Pre-non bounds\", minx, miny, maxx, maxy)\n",
    "            if not [x for x in (minx, miny, maxx, maxy) if x is None]:\n",
    "                print(minx, miny, maxx, maxy)\n",
    "                ax.set_xlim(minx, maxx)\n",
    "                ax.set_ylim(miny, maxy)\n",
    "\n",
    "                cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "                # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "                # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "                cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "\n",
    "                plt.title(f'{title}',fontsize=16)\n",
    "                plt.xticks([])\n",
    "                plt.yticks([]) \n",
    "                leg1 = ax.get_legend()\n",
    "                # Set markers to square shape\n",
    "                for ea in leg1.legendHandles:\n",
    "                    ea.set_marker('s')\n",
    "                leg1.set_title(f'{legend_title}')\n",
    "                ax.title.set_text(f'{title}')\n",
    "                plt.tight_layout()\n",
    "                # ax.figure.savefig(map_output)\n",
    "                ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "def map_func(maps, vector_file,  hexagons, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output, scheme, categorical):\n",
    "    if categorical==True:\n",
    "        ax = hexagons.to_crs(crs).plot(figsize=(10, 10), \\\n",
    "                                    column= visualize_column, \\\n",
    "                                    alpha=0.6, categorical=True , cmap=cmap, \\\n",
    "                                    legend=True,  # Add legend \n",
    "                                    legend_kwds={'loc':'upper right', \n",
    "                                                    'bbox_to_anchor':(1, 1), \n",
    "                                                    'fmt':legends_format,\n",
    "                                                    'markerscale':1.01, \n",
    "                                                    'title_fontsize':'small', \n",
    "                                                    'fontsize':'x-small'\n",
    "                                                    # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                    } ,\n",
    "                                    aspect=1\n",
    "                                            )\n",
    "    elif categorical==False:\n",
    "        ax = hexagons.to_crs(crs).plot(figsize=(10, 10), \\\n",
    "                                    column= visualize_column, \\\n",
    "                                    alpha=0.6, scheme=scheme, cmap=cmap, \\\n",
    "                                    legend=True,  # Add legend \n",
    "                                    legend_kwds={'loc':'upper right', \n",
    "                                                    'bbox_to_anchor':(1, 1), \n",
    "                                                    'fmt':legends_format,\n",
    "                                                    'markerscale':1.01, \n",
    "                                                    'title_fontsize':'small', \n",
    "                                                    'fontsize':'x-small'\n",
    "                                                    # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                    } ,\n",
    "                                    aspect=1\n",
    "                                            )\n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    cx.add_basemap(ax, zoom=\"auto\", crs=crs, source=cx.providers.OpenStreetMap.Mapnik)\n",
    "\n",
    "    # plt.title(f'{title}',fontsize=16)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    leg1 = ax.get_legend()\n",
    "    # Set markers to square shape\n",
    "    for ea in leg1.legendHandles:\n",
    "        ea.set_marker('s')\n",
    "    leg1.set_title(f'{legend_title}')\n",
    "    ax.title.set_text(f'{title}')\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "\n",
    "def map_land_cover(vector_file,  hexagons,title, legend_title, crs,  visualize_column, map_output):\n",
    "    class_labels = {1: \"Water , blue\", 2: \"Trees, green\", 4:\"Flooded Vegetation, magenta\", 5:\"Crops, seagreen\", 7:\"Built Area, tan\", 8:\"Bare Ground, goldenrod\", 9:\"Snow/Ice, snow\", 10:\"Clouds, ghostwhite\", 11:\"Rangeland, greenyellow\" }\n",
    "    hexagons[['class', 'color']] = hexagons['esri_dyna'].map(class_labels).str.split(', ',expand=True).values\n",
    "    color_map= dict(zip(hexagons[\"class\"], hexagons[\"color\"]))\n",
    "    ax = hexagons.to_crs(crs).plot(figsize=(10, 10), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0.6, categorical=True , color=hexagons.color, \\\n",
    "                                legend=True,  # Add legend \n",
    "                                legend_kwds={'loc':'upper right', \n",
    "                                                'bbox_to_anchor':(1, 1), \n",
    "                                                'fmt':legends_format,\n",
    "                                                'markerscale':1.01, \n",
    "                                                'title_fontsize':'small', \n",
    "                                                'fontsize':'x-small'\n",
    "                                                # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                } ,\n",
    "                                aspect=1\n",
    "                                        )\n",
    "    \n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.title(f'{title}',fontsize=16)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    handles = [Line2D([0], [0], marker='s', color='w', markerfacecolor=k, label=v, markersize=8) for v, k in color_map.items()]\n",
    "    ax.legend(title=legend_title, handles=handles, bbox_to_anchor=(1, 1),markerscale=1.01,title_fontsize='small',fontsize='x-small', loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OSM Network: Get Travel Time and Shortest path from a given point or an edge of a hotspot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def get_network_stats(polygon):\n",
    "     try:\n",
    "          # polygon = cluster_boundaries.iloc[index]['geometry'] \n",
    "          graph = ox.graph.graph_from_polygon(polygon, network_type='drive')\n",
    "          # Retrieve only edges from the graph\n",
    "          edges = ox.graph_to_gdfs(graph, nodes=False, edges=True)\n",
    "          graph_proj = ox.project_graph(graph)\n",
    "          # fig, ax = ox.plot_graph(graph_proj) \n",
    "          # Get Edges and Nodes\n",
    "          nodes_proj, edges_proj = ox.graph_to_gdfs(graph_proj, nodes=True, edges=True)\n",
    "          # print(\"Coordinate system:\", edges_proj.crs)\n",
    "          # edges_proj.head()\n",
    "          # Get Edges and Nodes\n",
    "          nodes_proj, edges_proj = ox.graph_to_gdfs(graph_proj, nodes=True, edges=True)\n",
    "          # print(\"Network Coordinate system: projected\", edges_proj.crs)\n",
    "          # edges_proj.head()\n",
    "          # Get the Convex Hull of the network\n",
    "          convex_hull = edges_proj.unary_union.convex_hull\n",
    "          # Calculate the area\n",
    "          area = convex_hull.area\n",
    "          # Calculate statistics with density information\n",
    "          stats = ox.stats.basic_stats(graph_proj, area=area) #replace area by city polygon\n",
    "          stats =pd.Series(stats) #edge_density_km(road density) - edge_length_total per sq km\n",
    "     except:\n",
    "          stats=0\n",
    "          stats =pd.Series(stats) \n",
    "          pass     \n",
    "     return stats\n",
    "\n",
    "def get_osm_network():\n",
    "    G = ox.graph_from_place(place, network_type='drive')\n",
    "    # impute speed on all edges missing data\n",
    "    G = ox.add_edge_speeds(G)\n",
    "    # calculate travel time (seconds) for all edges\n",
    "    G = ox.add_edge_travel_times(G)\n",
    "    # see mean speed/time values by road type\n",
    "    edges = ox.graph_to_gdfs(G, nodes=False)\n",
    "    edges['highway'] = edges['highway'].astype(str)\n",
    "    edges.groupby('highway')[['length', 'speed_kph', 'travel_time']].mean().round(1)\n",
    "\n",
    "    # same thing again, but this time pass in a few default speed values (km/hour)\n",
    "    # to fill in edges with missing `maxspeed` from OSM\n",
    "    hwy_speeds = {'residential': 35,\n",
    "                'secondary': 50,\n",
    "                'tertiary': 60}\n",
    "    G = ox.add_edge_speeds(G, hwy_speeds)\n",
    "    G = ox.add_edge_travel_times(G)\n",
    "    # calculate two routes by minimizing travel distance vs travel time\n",
    "    orig = list(G)[1]\n",
    "    dest = list(G)[-1]\n",
    "    route1 = nx.shortest_path(G, orig, dest, weight='length')\n",
    "    route2 = nx.shortest_path(G, orig, dest, weight='travel_time')\n",
    "\n",
    "    # compare the two routes\n",
    "    route1_length = int(sum(ox.utils_graph.get_route_edge_attributes(G, route1, 'length')))\n",
    "    route2_length = int(sum(ox.utils_graph.get_route_edge_attributes(G, route2, 'length')))\n",
    "    route1_time = int(sum(ox.utils_graph.get_route_edge_attributes(G, route1, 'travel_time')))\n",
    "    route2_time = int(sum(ox.utils_graph.get_route_edge_attributes(G, route2, 'travel_time')))\n",
    "    print('Route 1 is', route1_length, 'meters and takes', route1_time, 'seconds.')\n",
    "    print('Route 2 is', route2_length, 'meters and takes', route2_time, 'seconds.')\n",
    "\n",
    "    # pick route colors\n",
    "    c1 = 'r' #length\n",
    "    c2 = 'b' #travel_time\n",
    "    rc1 = [c1] * (len(route1) - 1)\n",
    "    rc2 = [c2] * (len(route2) - 1)\n",
    "    rc = rc1 + rc2\n",
    "    nc = [c1, c1, c2, c2]\n",
    "\n",
    "    # plot the routes\n",
    "    fig, ax = ox.plot_graph_routes(G, [route1, route2], route_color=rc, route_linewidth=6,\n",
    "                                node_size=0, bgcolor='k')\n",
    "    location_point = (33.299896, -111.831638)\n",
    "    G2 = ox.graph_from_point(location_point, dist=400, truncate_by_edge=True)\n",
    "    # impute speed on all edges missing data\n",
    "    G2 = ox.add_edge_speeds(G2)\n",
    "    # calculate travel time (seconds) for all edges\n",
    "    G2 = ox.add_edge_travel_times(G2)\n",
    "    origin = (33.301821, -111.829871)\n",
    "    destination = (33.301402, -111.833108)\n",
    "    origin_node = ox.distance.nearest_nodes(G2, origin[1], origin[0])\n",
    "    destination_node = ox.distance.nearest_nodes(G2, destination[1], destination[0])\n",
    "    route = ox.shortest_path(G2, origin_node, destination_node)\n",
    "    # compare the two routes\n",
    "    route1_length = int(sum(ox.utils_graph.get_route_edge_attributes(G2, route, 'length')))\n",
    "    route1_time = int(sum(ox.utils_graph.get_route_edge_attributes(G2, route, 'travel_time')))\n",
    "    print('Route 1 is', route1_length, 'meters and takes', route1_time, 'seconds.')\n",
    "    fig, ax = ox.plot_graph_route(G2, route, route_color=\"c\", node_size=0)\n",
    "\n",
    "def get_services_infrastructure(center_point, dist):\n",
    "\n",
    "     tags={'leisure':['park', 'nature_reserve', 'protected_area', 'garden']}\n",
    "     park= ox.features.features_from_point(center_point=center_point, tags=tags, dist=dist)\n",
    "     \n",
    "     tags={'amenity':['college', 'dancing_school', 'driving_school', 'kindergarten', \\\n",
    "                    'language_school', 'library', 'surf_school', 'toy_library', 'research_institute', \\\n",
    "                         'training', 'music_school', 'school', 'university'],\n",
    "                         'landuse': 'education'\n",
    "                         }\n",
    "     education= ox.features.features_from_point(center_point=center_point, tags=tags, dist=dist)\n",
    "     \n",
    "     tags={'landuse':['commercial', 'industrial', 'construction', 'retail']                   }\n",
    "     industry= ox.features.features_from_point(center_point=center_point, tags=tags, dist=dist)\n",
    "     \n",
    "     # tags={'building': True}\n",
    "     tags={'building': 'government'}\n",
    "     building= ox.features.features_from_point(center_point=center_point, tags=tags, dist=dist)\n",
    "     return park , education, industry, building\n",
    "\n",
    "def get_services_infrastructure_inside_poly(polygon):\n",
    "     try:\n",
    "          tags={'leisure':['park', 'nature_reserve', 'protected_area', 'garden']}\n",
    "          park=ox.features.features_from_polygon(polygon, tags)\n",
    "     except:\n",
    "          park=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'amenity':['college', 'dancing_school', 'driving_school', 'kindergarten', \\\n",
    "                         'language_school', 'library', 'surf_school', 'toy_library', 'research_institute', \\\n",
    "                              'training', 'music_school', 'school', 'university'],\n",
    "                              'landuse': 'education'\n",
    "                              }\n",
    "          education=ox.features.features_from_polygon(polygon, tags)\n",
    "     except:\n",
    "          education=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'landuse':['commercial', 'industrial', 'construction', 'retail']                   }\n",
    "          industry=ox.features.features_from_polygon(polygon, tags)\n",
    "     except:\n",
    "          industry=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'building': 'government'}\n",
    "          building=ox.features.features_from_polygon(polygon, tags)\n",
    "     except:\n",
    "          building=0\n",
    "          pass\n",
    "     # print(\"park , education, industry, building:\", park , education, industry, building)\n",
    "     return park , education, industry, building\n",
    "\n",
    "\n",
    "\n",
    "def export_services_infrastructure_inside_poly_to_shp(shapefile):\n",
    "     index=0\n",
    "     polygon = shapefile.iloc[index]['geometry']\n",
    "     # polygon = shapefile['geometry']\n",
    "     try:\n",
    "          tags={'leisure':['park', 'nature_reserve', 'protected_area', 'garden']}\n",
    "          park=ox.features.features_from_polygon(polygon, tags)\n",
    "          # print(\"nodes to be dropped\")\n",
    "          # park.drop(['nodes'], axis = 1)\n",
    "          # print(\"nodes dropped\")\n",
    "          # copy poly to new GeoDataFrame\n",
    "          points = park.copy()\n",
    "          # change the geometry\n",
    "          points.geometry = points['geometry'].centroid\n",
    "          # same crs\n",
    "          points.crs =points.crs\n",
    "          points=points.applymap(lambda x: str(x) if isinstance(x, list) else x)\n",
    "          points.to_file(f'{shapefiles}/park.shp', driver='ESRI Shapefile')\n",
    "          park=points\n",
    "          # park.to_file(f'{shapefiles}/park.shp', driver='ESRI Shapefile')\n",
    "     except:\n",
    "          # park=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'amenity':['college', 'dancing_school', 'driving_school', 'kindergarten', \\\n",
    "                         'language_school', 'library', 'surf_school', 'toy_library', 'research_institute', \\\n",
    "                              'training', 'music_school', 'school', 'university'],\n",
    "                              'landuse': 'education'\n",
    "                              }\n",
    "          education=ox.features.features_from_polygon(polygon, tags)\n",
    "          # print(\"nodes to be dropped\")\n",
    "          # education.drop(['nodes'], axis = 1)\n",
    "          # print(\"nodes dropped\")\n",
    "          points = education.copy()\n",
    "          # change the geometry\n",
    "          points.geometry = points['geometry'].centroid\n",
    "          # same crs\n",
    "          points.crs =points.crs\n",
    "          points=points.applymap(lambda x: str(x) if isinstance(x, list) else x)\n",
    "          points.to_file(f'{shapefiles}/education.shp', driver='ESRI Shapefile')\n",
    "          education=points\n",
    "\n",
    "          # education.to_file(f'{shapefiles}/education.shp', driver='ESRI Shapefile')\n",
    "\n",
    "     except:\n",
    "          # education=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'landuse':['commercial', 'industrial', 'construction', 'retail']                   }\n",
    "          industry=ox.features.features_from_polygon(polygon, tags)\n",
    "          # print(\"industry to be dropped\")\n",
    "          # industry.drop(['nodes'], axis = 1)\n",
    "          # print(\"industry nodes dropped\")\n",
    "          # industry.to_file(f'{shapefiles}/industry.shp', driver='ESRI Shapefile')\n",
    "          points = industry.copy()\n",
    "          # change the geometry\n",
    "          points.geometry = points['geometry'].centroid\n",
    "          # same crs\n",
    "          points.crs =points.crs\n",
    "          points=points.applymap(lambda x: str(x) if isinstance(x, list) else x)\n",
    "          points.to_file(f'{shapefiles}/industry.shp', driver='ESRI Shapefile')\n",
    "          industry=points\n",
    "\n",
    "     except:\n",
    "          # industry=0\n",
    "          pass\n",
    "     try:\n",
    "          tags={'building': 'government'}\n",
    "          building=ox.features.features_from_polygon(polygon, tags)\n",
    "          # print(\"building to be dropped\")\n",
    "          # building.drop(['nodes'], axis = 1)\n",
    "          # print(\"building nodes dropped\")\n",
    "          # building.to_file(f'{shapefiles}/building_government.shp', driver='ESRI Shapefile')\n",
    "          points = building.copy()\n",
    "          # change the geometry\n",
    "          points.geometry = points['geometry'].centroid\n",
    "          # same crs\n",
    "          points.crs =points.crs\n",
    "          points=points.applymap(lambda x: str(x) if isinstance(x, list) else x)\n",
    "          points.to_file(f'{shapefiles}/government_building.shp', driver='ESRI Shapefile')\n",
    "          building=points\n",
    "\n",
    "     except:\n",
    "          # building=0\n",
    "          pass\n",
    "\n",
    "     try:\n",
    "          # fprints = ox.footprints.footprints_from_polygon(polygon, footprint_type ='building')\n",
    "          \n",
    "          tags = {\"building\": True}\n",
    "          building_footprint=ox.features.features_from_polygon(polygon, tags)\n",
    "          # print(\"footprints building to be dropped\")\n",
    "          # # fprints.drop(['nodes'], axis = 1)\n",
    "          # print(\"footprints building nodes dropped\")\n",
    "          # fprints.to_file(f'{shapefiles}/building_footprint.shp', driver='ESRI Shapefile')\n",
    "          points = building_footprint.copy()\n",
    "          # change the geometry\n",
    "          points.geometry = points['geometry'].centroid\n",
    "          # same crs\n",
    "          points.crs =points.crs\n",
    "          points=points.applymap(lambda x: str(x) if isinstance(x, list) else x)\n",
    "          points.to_file(f'{shapefiles}/building_footprint.shp', driver='ESRI Shapefile')\n",
    "          building_footprint=points\n",
    "\n",
    "\n",
    "     except:\n",
    "          # fprints=0\n",
    "          pass\n",
    "     # print(\"park , education, industry, building:\", park , education, industry, building)\n",
    "\n",
    "     return park , education, industry, building, building_footprint\n",
    "\n",
    "\n",
    "def multi2single(gpdf):\n",
    "    gpdf_singlepoly = gpdf[gpdf.geometry.type == 'Polygon']\n",
    "    gpdf_multipoly = gpdf[gpdf.geometry.type == 'MultiPolygon']\n",
    "\n",
    "    for i, row in gpdf_multipoly.iterrows():\n",
    "        Series_geometries = pd.Series(row.geometry)\n",
    "        df = pd.concat([gp.GeoDataFrame(row, crs=gpdf_multipoly.crs).T]*len(Series_geometries), ignore_index=True)\n",
    "        df['geometry']  = Series_geometries\n",
    "        gpdf_singlepoly = pd.concat([gpdf_singlepoly, df])\n",
    "\n",
    "    gpdf_singlepoly.reset_index(inplace=True, drop=True)\n",
    "    return gpdf_singlepoly\n",
    "\n",
    "\n",
    "def create_ahc_knn_clusters(db  , raster_val , WGS84_meters):\n",
    "    raster_val=raster_val[0:9]\n",
    "    db =db.to_crs(WGS84_meters)\n",
    "    db['area_sqm'] = db.geometry.area\n",
    "    cluster_variables = [raster_val]\n",
    "    db_scaled = robust_scale(db[cluster_variables])\n",
    "    w = KNN.from_dataframe(db, k=4) #k for four nearest neighbors\n",
    "    # Specify cluster model with spatial constraint\n",
    "    model = AgglomerativeClustering(\n",
    "        linkage=\"ward\", connectivity=w.sparse, n_clusters=5\n",
    "    )\n",
    "    # Fit algorithm to the data\n",
    "    model.fit(db_scaled)\n",
    "    db[\"ward5wknn\"] = model.labels_\n",
    "    db['ward5wknn_sum'] = db[cluster_variables].groupby(db['ward5wknn']).transform('sum')\n",
    "    db['area_sum'] = db['area_sqm'].groupby(db['ward5wknn']).transform('sum')\n",
    "    db['area_sum'] = db['area_sum'].apply(lambda x: x/10000) #Hectares\n",
    "    visualize_var= 'ward5wknn_sum_per_meter'\n",
    "    db[visualize_var] = db['ward5wknn_sum'].div(db['area_sum']).round(2)\n",
    "    # select the columns that you with to use for the dissolve and that will be retained\n",
    "    cluster_boundaries = db[[raster_val , visualize_var,\"ward5wknn\", \"geometry\", \"area_sqm\", \"area_sum\", \"ward5wknn_sum\"]]\n",
    "    # dissolve the state boundary by region \n",
    "    cluster_boundaries = cluster_boundaries.dissolve(by=\"ward5wknn\" , aggfunc={raster_val: \"sum\", visualize_var: \"sum\" , \"area_sqm\":\"sum\" , \"area_sum\": \"sum\", \"ward5wknn_sum\": \"sum\",} , )\n",
    "    cluster_boundaries=cluster_boundaries.explode().reset_index() #convert multipolygons to single part polygons. Multis cause errors\n",
    "    cluster_boundaries[\"area_for_dupes\"]=cluster_boundaries.geometry.area\n",
    "#     cluster_boundaries=cluster_boundaries.sort_values('area_for_dupes', ascending=False).drop_duplicates([\"ward5wknn\"])\n",
    "    cluster_boundaries=multi2single(cluster_boundaries)\n",
    "    cluster_boundaries = cluster_boundaries.to_crs(crs)\n",
    "    cluster_boundaries[\"centroid\"] = cluster_boundaries[\"geometry\"].centroid\n",
    "    cluster_boundaries['lon'] = cluster_boundaries['centroid'].x\n",
    "    cluster_boundaries['lat'] = cluster_boundaries['centroid'].y\n",
    "    cluster_boundaries['cluster_area_deg'] = cluster_boundaries.geometry.area\n",
    "    return db , cluster_boundaries, visualize_var\n",
    "\n",
    "\n",
    "def create_ahc_knn_clusters_vito(db  , raster_val , WGS84_meters):\n",
    "     raster_val=raster_val[0:9]\n",
    "     db =db.to_crs(WGS84_meters)\n",
    "     db['area_sqm'] = db.geometry.area\n",
    "     cluster_variables = [raster_val]\n",
    "     db_scaled = robust_scale(db[cluster_variables])\n",
    "     w = KNN.from_dataframe(db, k=4) #k for four nearest neighbors\n",
    "     # Specify cluster model with spatial constraint\n",
    "     model = AgglomerativeClustering(\n",
    "          linkage=\"ward\", connectivity=w.sparse, n_clusters=5\n",
    "     )\n",
    "     # Fit algorithm to the data\n",
    "     model.fit(db_scaled)\n",
    "     db[\"ward5wknn\"] = model.labels_\n",
    "     db['ward5wknn_sum'] = db[cluster_variables].groupby(db['ward5wknn']).transform('sum')\n",
    "     db['area_sum'] = db['area_sqm'].groupby(db['ward5wknn']).transform('sum')\n",
    "     db['area_sum'] = db['area_sum'].apply(lambda x: x/10000) #Hectares\n",
    "     visualize_var= 'ward5wknn_sum_per_meter'\n",
    "     db[visualize_var] = db['ward5wknn_sum'].div(db['area_sum']).round(2)\n",
    "     # select the columns that you with to use for the dissolve and that will be retained\n",
    "     cluster_boundaries = db[[raster_val , visualize_var,\"ward5wknn\", \"geometry\", \"area_sqm\", \"area_sum\", \"ward5wknn_sum\"]]\n",
    "     # dissolve the state boundary by region \n",
    "     cluster_boundaries = cluster_boundaries.dissolve(by=\"ward5wknn\" , aggfunc={raster_val: \"sum\", visualize_var: \"sum\" , \"area_sqm\":\"sum\" , \"area_sum\": \"sum\", \"ward5wknn_sum\": \"sum\",} , )\n",
    "     cluster_boundaries=cluster_boundaries.explode().reset_index() #convert multipolygons to single part polygons. Multis cause errors\n",
    "     cluster_boundaries[\"area_for_dupes\"]=cluster_boundaries.geometry.area\n",
    "     # cluster_boundaries=cluster_boundaries.sort_values('area_for_dupes', ascending=False).drop_duplicates([\"ward5wknn\"])\n",
    "     cluster_boundaries=multi2single(cluster_boundaries)\n",
    "     cluster_boundaries = cluster_boundaries.to_crs(crs)\n",
    "     cluster_boundaries[\"centroid\"] = cluster_boundaries[\"geometry\"].centroid\n",
    "     cluster_boundaries['lon'] = cluster_boundaries['centroid'].x\n",
    "     cluster_boundaries['lat'] = cluster_boundaries['centroid'].y\n",
    "     cluster_boundaries['cluster_area_deg'] = cluster_boundaries.geometry.area\n",
    "     return db , cluster_boundaries, visualize_var\n",
    "\n",
    "\n",
    "def dissolve_categorical(db  , raster_val , WGS84_meters):\n",
    "     db =db.to_crs(WGS84_meters)\n",
    "     db['area_sqm'] = db.geometry.area\n",
    "     cluster_variables = [raster_val]\n",
    "     # db_scaled = robust_scale(db[cluster_variables])\n",
    "     # w = KNN.from_dataframe(db, k=4) #k for four nearest neighbors\n",
    "     # # Specify cluster model with spatial constraint\n",
    "     # model = AgglomerativeClustering(\n",
    "     #      linkage=\"ward\", connectivity=w.sparse, n_clusters=15\n",
    "     # )\n",
    "     # # Fit algorithm to the data\n",
    "     # model.fit(db_scaled)\n",
    "     # db[\"ward5wknn\"] = model.labels_\n",
    "     db[\"ward5wknn\"] = db[raster_val]\n",
    "     db['ward5wknn_sum'] = db[cluster_variables].groupby(db['ward5wknn']).transform('sum')\n",
    "     db['area_sum'] = db['area_sqm'].groupby(db['ward5wknn']).transform('sum')\n",
    "     db['area_sum'] = db['area_sum'].apply(lambda x: x/10000) #Hectares\n",
    "     visualize_var= 'ward5wknn_sum_per_meter'\n",
    "     db[visualize_var] = db['ward5wknn_sum'].div(db['area_sum']).round(2)\n",
    "     # select the columns that you with to use for the dissolve and that will be retained\n",
    "     cluster_boundaries = db[[raster_val , visualize_var,\"ward5wknn\", \"geometry\", \"area_sqm\", \"area_sum\", \"ward5wknn_sum\"]]\n",
    "     # dissolve the state boundary by region \n",
    "     cluster_boundaries = cluster_boundaries.dissolve(by=\"ward5wknn\" , aggfunc={raster_val: \"sum\", visualize_var: \"sum\" , \"area_sqm\":\"sum\" , \"area_sum\": \"sum\", \"ward5wknn_sum\": \"sum\",} , )\n",
    "     cluster_boundaries=cluster_boundaries.explode().reset_index() #convert multipolygons to single part polygons. Multis cause errors\n",
    "     cluster_boundaries[\"area_for_dupes\"]=cluster_boundaries.geometry.area\n",
    "     # cluster_boundaries=cluster_boundaries.sort_values('area_for_dupes', ascending=False).drop_duplicates([\"ward5wknn\"])\n",
    "     cluster_boundaries=multi2single(cluster_boundaries)\n",
    "     cluster_boundaries = cluster_boundaries.to_crs(crs)\n",
    "     cluster_boundaries[\"centroid\"] = cluster_boundaries[\"geometry\"].centroid\n",
    "     cluster_boundaries['lon'] = cluster_boundaries['centroid'].x\n",
    "     cluster_boundaries['lat'] = cluster_boundaries['centroid'].y\n",
    "     cluster_boundaries['cluster_area_deg'] = cluster_boundaries.geometry.area\n",
    "     return db , cluster_boundaries, visualize_var\n",
    "\n",
    "\n",
    "\n",
    "def describe_cluster_trees_pop(cluster_boundaries, category, maps):\n",
    "     category=category[0:9]\n",
    "     df = pd.DataFrame(cluster_boundaries.drop(columns='geometry'))\n",
    "     df = df.sort_values('cluster_area_deg', ascending = False).groupby('ward5wknn').head(2)\n",
    "     df = df.reset_index() \n",
    "     df['Rank'] = df['ward5wknn_sum_per_meter'].rank(method='dense', ascending=False)\n",
    "     df= df.sort_values(['Rank'], ascending=[True])\n",
    "     cent= cluster_boundaries.dissolve().centroid \n",
    "     # White house 38.8977° N, 77.0365° W. centroid of the city.\n",
    "     lat1 = float(cent.centroid.y)\n",
    "     long1= float(cent.centroid.x)\n",
    "     list_statements= []\n",
    "     stats_dict= defaultdict(list)\n",
    "     for index, row in df.iterrows():\n",
    "          lat2 = row['lat'] # cent.centroid.y  #38.8893\n",
    "          long2 =row['lon'] # cent.centroid.x  # -77.0506\n",
    "          # print(f\"lat1 {lat1} , long1 {long1}, lat2 {lat2}, long2 {long2}\")\n",
    "          points = calcNSEW(lat1, long1, lat2, long2)\n",
    "          rank= int(row['Rank'])\n",
    "          rank= ordinal(rank)\n",
    "          value= row['ward5wknn_sum_per_meter']\n",
    "          statement= f\"A cluster that is located in the {points} of the city, is ranked {rank} and has the value of {value}\"\n",
    "          list_statements.append(statement)\n",
    "          try:\n",
    "               polygon = cluster_boundaries.iloc[index]['geometry']\n",
    "               cluster_area_deg= cluster_boundaries.iloc[index]['cluster_area_deg']\n",
    "               ward5wknn= cluster_boundaries.iloc[index]['ward5wknn']\n",
    "               park , education, industry, building=get_services_infrastructure_inside_poly(polygon)\n",
    "               stats_dict[\"cluster\"].append(index)\n",
    "               stats_dict[\"ward5wknn\"].append(ward5wknn)\n",
    "               stats_dict[\"points\"].append(points)\n",
    "               stats_dict[\"cluster_area_deg\"].append(cluster_area_deg)\n",
    "               stats_dict[\"rank\"].append(rank)\n",
    "               stats_dict[\"value\"].append(value)\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               stats_dict[\"park\"].append(len(park))\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               stats_dict[\"education\"].append(len(education))\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               stats_dict[\"industry\"].append(len(industry))\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               stats_dict[\"building\"].append(len(building))\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               \n",
    "               park[\"ward5wknn\"]=ward5wknn\n",
    "               park[\"index\"]=index\n",
    "               park[\"points\"]=points\n",
    "               park[\"rank\"]=rank\n",
    "               park[\"value\"]=value\n",
    "               park.to_csv(f\"{tables}/{ward5wknn}_{category}_park_df.csv\")\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               education[\"ward5wknn\"]=ward5wknn\n",
    "               education[\"index\"]=index\n",
    "               education[\"points\"]=points\n",
    "               education[\"rank\"]=rank\n",
    "               education[\"value\"]=value\n",
    "               education.to_csv(f\"{tables}/{ward5wknn}_{category}_education_df.csv\")\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               industry[\"ward5wknn\"]=ward5wknn\n",
    "               industry[\"index\"]=index\n",
    "               industry[\"points\"]=points\n",
    "               industry[\"rank\"]=rank\n",
    "               industry[\"value\"]=value\n",
    "               industry.to_csv(f\"{tables}/{ward5wknn}_{category}_industry_df.csv\")\n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               building[\"ward5wknn\"]=ward5wknn\n",
    "               building[\"index\"]=index\n",
    "               building[\"points\"]=points\n",
    "               building[\"rank\"]=rank\n",
    "               building[\"value\"]=value\n",
    "               building.to_csv(f\"{tables}/{ward5wknn}_{category}_building_df.csv\")               \n",
    "          except:\n",
    "               pass\n",
    "\n",
    "          try:\n",
    "               stats=get_network_stats(polygon)\n",
    "               stats = stats.to_frame(0).T\n",
    "               # category=category[0:9]\n",
    "               stats.to_csv(f\"{tables}/{ward5wknn}_{category}_infra_network_stats.csv\")\n",
    "          except:\n",
    "               print(f\"no network for {ward5wknn}\")\n",
    "               pass\n",
    "     \n",
    "     try:\n",
    "          statement_df=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in stats_dict.items() ]))\n",
    "          # category=category[0:9]\n",
    "          statement_df.to_csv(f\"{tables}/{category}_amenities_stats_all.csv\")\n",
    "     except:\n",
    "          print(\"No amenities\")\n",
    "          pass\n",
    "\n",
    "     return list_statements\n",
    "\n",
    "\n",
    "def describe_db( db_var, category, tables):\n",
    "     try:\n",
    "          category=category[0:9]\n",
    "          cluster_variables= category\n",
    "          k5desc = db_var.groupby('ward5wknn')[cluster_variables].describe()\n",
    "          df1=k5desc.reset_index(0).reset_index(drop=True)\n",
    "          # category=category[0:9]\n",
    "          df1.to_csv(f\"{tables}/{category}_summary_stats_df.csv\")\n",
    "     except:\n",
    "          pass\n",
    "\n",
    "# place = 'Nis, Serbia'\n",
    "# get_osm_network_and_travel_time=get_osm_network()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def download_landsurface_temperature(city, cities_reprojected, max_value , years, month0 ,  month1):\n",
    "\n",
    "    \"\"\"\n",
    "    download_landsurface_temperature: Exports Land Surface temperature rasters for a given country in the cities shapefile\n",
    "\n",
    "    :param country: Name of the country\n",
    "    :param cities_reprojected: geopandas read cities shapefile\n",
    "    :param max_value: cap the number of cities\n",
    "    :param years: years\n",
    "    :param month0 : first hottest month\n",
    "    :param month1 : last hottest month\n",
    "\n",
    "    :return: confirms the export of rasters\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # Create a jsondictionary from the geometry of the  shapefile\n",
    "    geom = cities_reprojected['geometry']\n",
    "    jsonDict = eval(geom.to_json())\n",
    "\n",
    "    for index, row in cities_reprojected.iterrows():\n",
    "        #   print(1)\n",
    "        #   city= row['NAME_1'].lower() #instruct the column for city name in the city shapefile\n",
    "          if index  <= max_value:\n",
    "              city_number= index\n",
    "              print(1)\n",
    "              for x in jsonDict['features'][city_number]['geometry']['coordinates']:\n",
    "                  try:\n",
    "                      AOI = ee.Geometry.Polygon(x) #cast polygon as ee geometry\n",
    "                      landsat = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\")\n",
    "                      #   print(3)\n",
    "                      # Filter for hottest months in the past X years\n",
    "                      def filter_hot_month(i):\n",
    "                          return ee.Filter.date(years[i] + '-' + month0 + '-01', years[i] + '-' + month1 + '-01')\n",
    "\n",
    "                      range_list= list(map(filter_hot_month, list(range(0, 10)))) #combination of months and years\n",
    "                      rangefilter = ee.Filter.Or(range_list)\n",
    "                    #   print(4)\n",
    "                      # Define a function to scale the data and mask unwanted pixels\n",
    "                      def maskL457sr(image):\n",
    "                          # Bit 0 - Fill\n",
    "                          # Bit 1 - Dilated Cloud\n",
    "                          # Bit 2 - Cirrus (high confidence)\n",
    "                          # Bit 3 - Cloud\n",
    "                          # Bit 4 - Cloud Shadow\n",
    "                          qaMask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0)\n",
    "                          saturationMask = image.select('QA_RADSAT').eq(0)\n",
    "                          # Apply the scaling factors to the appropriate bands.\n",
    "                          thermalBand = image.select('ST_B10').multiply(0.00341802).add(149.0)\n",
    "                          # Replace the original bands with the scaled ones and apply the masks.\n",
    "                          return image.addBands(thermalBand, None, True).updateMask(qaMask).updateMask(saturationMask)\n",
    "                      #   print(5)\n",
    "                      # Apply filter and mask\n",
    "                      collectionSummer = landsat.filter(rangefilter).filterBounds(AOI).map(maskL457sr).select('ST_B10').mean().add(-273.15).clip(AOI)\n",
    "                      # collectionSummer=collectionSummer.toFloat()\n",
    "\n",
    "                      lst_tif = os.path.join(rasters, 'lst_projected.tif')\n",
    "                      geemap.ee_export_image( \n",
    "                                            # collectionSummer.toUint8(), \n",
    "                                            collectionSummer.toFloat(), \n",
    "                                            filename=lst_tif,  \n",
    "                                            crs=EPSG_str,\n",
    "                                            # crs_transform=crs_transform,\n",
    "                                            scale=30, \n",
    "                                            region=AOI\n",
    "                                            # file_per_band=False\n",
    "                                            )\n",
    "                        \n",
    "                  except Exception as e:\n",
    "                      print(f\"Error : {e} \")\n",
    "                  return  lst_tif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "if __name__ ==  '__main__': \n",
    "    if run_tree_and_pop_analysis:\n",
    "        # # City level map\n",
    "        df_filtered= gdf_city\n",
    "        title=\"City Boundary\"\n",
    "        legend_title= 'Boundary'\n",
    "        visualize_column=\"shapeName\"\n",
    "        map_output= os.path.join(maps, f\"{city}_{title}_map.png\")\n",
    "        export = map_city(maps=maps, vector_file=gdf_city, crs=crs, legend_title=legend_title,  \\\n",
    "                            visualize_column=visualize_column, title=title, map_output=map_output)\n",
    "\n",
    "        crs = 4326\n",
    "        fc_filtered  , region= create_fc_collection(gdf_city= gdf_city) \n",
    "\n",
    "        \n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        # #Args\n",
    "        cities_reprojected=gdf_city.reset_index()\n",
    "        max_value=len(cities_reprojected)\n",
    "        years =list(str(i) for i in range(2013, 2024)) #years from 2013-2023\n",
    "        month0 = '06'  # first hottest month  # update for each country\n",
    "        month1 = '09'  # end of hottest month (note that this is exclusive)  # update for each country\n",
    "        country= 'SRB' # replace with the relevent country name\n",
    "        lst_t= download_landsurface_temperature( city, cities_reprojected, max_value, years, month0, month1)\n",
    "        raster_filename= \"land_surface_temperature\"\n",
    "        unprojected_raster =lst_t  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "                                projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster #os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "                            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        cmap = \"OrRd\"\n",
    "        # cmap = \"Greens\"\n",
    "        # cmap = \"YlGn\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"LST\"\n",
    "        map_title= f\"Land Surface Temperature across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "                hexagons=newdf,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                    crs=crs, cmap=cmap , visualize_column=raster_val,  \\\n",
    "                        title=map_title, map_output=map_output, scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =create_ahc_knn_clusters(db = newdf , raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "        map_title= f\"Land Surface Temperature across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "            hexagons=db,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                crs=crs, cmap=cmap , visualize_column=visualize_var,  \\\n",
    "                    title=map_title, map_output=map_output,  scheme=scheme,  categorical=categorical)\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "\n",
    "        # legend_title= \"Surface Temperature\"\n",
    "        # map_title= f\"Land Surface Temperature across {city} cluster\"\n",
    "        # map=map_clusters(maps=maps, hexagons=cluster_boundaries, legend_title=legend_title, \\\n",
    "        #                 legends_format=legends_format, crs=crs ,cmap=cmap,  \\\n",
    "        #                     visualize_column=raster_val, title=map_title)\n",
    "                        \n",
    "        cluster_boundaries_lst= cluster_boundaries\n",
    "        db_lst = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        tree_cover_tif_esa= download_tree_cover_esa_vito(ee , EPSG_str, region)\n",
    "        # raster_filename= \"tree_cover_esa\"\n",
    "        raster_filename= \"esa_tree_cover\"\n",
    "        unprojected_raster =tree_cover_tif_esa  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "                                            projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster #os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        # cmap = \"OrRd\"\n",
    "        cmap = \"Greens\"\n",
    "        # cmap = \"YlGn\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"Tree Cover\"\n",
    "        map_title= f\"Tree Cover (ESA 10 meters) across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "                hexagons=newdf,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                    crs=crs, cmap=cmap , visualize_column=raster_val,  \\\n",
    "                        title=map_title, map_output=map_output, scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =create_ahc_knn_clusters(db = newdf, raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "        map_title= f\"Tree Cover (ESA 10 meters) across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "            hexagons=db,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                crs=crs, cmap=cmap , visualize_column=visualize_var,  \\\n",
    "                    title=map_title, map_output=map_output,  scheme=scheme,  categorical=categorical)\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "\n",
    "        db_esa = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        tree_cover_tif= download_tree_cover(ee , EPSG_str, region)\n",
    "        raster_filename= \"land_tree\"\n",
    "        unprojected_raster =tree_cover_tif  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "                                          projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster#os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        # cmap = \"OrRd\"\n",
    "        cmap = \"Greens\"\n",
    "        # cmap = \"YlGn\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"Tree Cover\"\n",
    "        map_title= f\"Tree Cover (Landsat 30 meters) across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "                hexagons=newdf,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                    crs=crs, cmap=cmap , visualize_column=raster_val,  \\\n",
    "                        title=map_title, map_output=map_output, scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =create_ahc_knn_clusters(db = newdf , raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "        map_title= f\"Tree Cover (Landsat 30 meters) across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "            hexagons=db,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                crs=crs, cmap=cmap , visualize_column=visualize_var,  \\\n",
    "                    title=map_title, map_output=map_output,  scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "\n",
    "        # legend_title= \"Tree Cover (Landsat 30 meters)\"\n",
    "        # map_title= f\"Tree Cover (Landsat 30 meters) across {city} cluster\"\n",
    "        # map=map_clusters(maps=maps, hexagons=cluster_boundaries, legend_title=legend_title, \\\n",
    "        #                 legends_format=legends_format, crs=crs ,cmap=cmap,  \\\n",
    "        #                     visualize_column=raster_val, title=map_title)\n",
    "\n",
    "        db_landsat = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        pop_density_tif= dowload_pop_density(ee , EPSG_str, region)\n",
    "        raster_filename= \"pop_density\"\n",
    "        unprojected_raster =pop_density_tif  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "            projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster #os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        cmap = \"OrRd\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"Population\"\n",
    "        map_title= f\"Population (Worldpop 100 meters) across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "                hexagons=newdf,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                    crs=crs, cmap=cmap , visualize_column=raster_val,  \\\n",
    "                        title=map_title, map_output=map_output, scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =create_ahc_knn_clusters(db = newdf , raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "\n",
    "        map_title= f\"Population (Worldpop 100 meters) \\n across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "            hexagons=db,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                crs=crs, cmap=cmap , visualize_column=visualize_var,  \\\n",
    "                    title=map_title, map_output=map_output,  scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "        # legend_title= \"Population\"\n",
    "        # map_title= f\"Population (Worldpop 100 meters) across {city} cluster\"\n",
    "        # map=map_clusters(maps=maps, hexagons=cluster_boundaries, legend_title=legend_title, \\\n",
    "        #                 legends_format=legends_format, crs=crs ,cmap=cmap,  \\\n",
    "        #                     visualize_column=raster_val, title=map_title)\n",
    "\n",
    "\n",
    "        db_pop = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "\n",
    "\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        # google_dynamic_worldcover= download_google_dynamic_worldcover(ee , EPSG_str, region)\n",
    "        esri_dynamic_worldcover= download_esri_dynamic_worldcover(ee , EPSG_str, region)\n",
    "\n",
    "        # Esri cover\n",
    "        raster_filename= \"esri_dynamic_worldcover\"\n",
    "        unprojected_raster =esri_dynamic_worldcover  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "            projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster #os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "                            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        cmap = \"OrRd\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"Land Cover\"\n",
    "        map_title= f\"Land Cover (ESRI 10 meters) across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        \n",
    "        export_the_map= map_land_cover(vector_file=gdf_city , \\\n",
    "                                        hexagons=newdf,title=map_title, legend_title=legend_title, \\\n",
    "                                            crs=crs,  visualize_column=raster_val, map_output=map_output)\n",
    "\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =dissolve_categorical(db = newdf , raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "        map_title= f\"Land Cover (ESRI 10 meters) across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "        export_the_map= map_land_cover(vector_file=gdf_city , \\\n",
    "                                    hexagons=db,title=map_title, legend_title=legend_title, \\\n",
    "                                        crs=crs,  visualize_column=raster_val, map_output=map_output)\n",
    "\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "        # legend_title= \"Land Cover\"\n",
    "        # map_title= f\"Land Cover (ESRI 10 meters) across {city} cluster\"\n",
    "        # map=map_clusters(maps=maps, hexagons=cluster_boundaries, legend_title=legend_title, \\\n",
    "        #                 legends_format=legends_format, crs=crs ,cmap=cmap,  \\\n",
    "        #                     visualize_column=raster_val, title=map_title)\n",
    "\n",
    "\n",
    "        db_lc = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "        # Put everything togther\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        # cluster_boundaries_lst=cluster_boundaries_lst.to_crs(crs)\n",
    "        # db_lc=db_lc.to_crs(crs)\n",
    "        # db_lc['geometry'] = db_lc['geometry'].centroid\n",
    "        # df = gpd.sjoin(cluster_boundaries_lst, db_lc, how='left', op='contains')\n",
    "        # db_pop= db_pop.to_crs(crs)\n",
    "        # df = gpd.sjoin(df, db_pop, how='left', op='contains')\n",
    "        # db_landsat= db_landsat.to_crs(crs)\n",
    "        # df = gpd.sjoin(df, db_landsat, how='left', op='contains')\n",
    "        # db_esa= db_esa.to_crs(crs)\n",
    "        # df = gpd.sjoin(df, db_esa, how='left', op='contains')\n",
    "\n",
    "        # # # # Place all other dbs i  heat and then do some regs and descriptive stats\n",
    "        # # cluster_boundaries_lst=cluster_boundaries_lst.to_crs(crs)\n",
    "        # # raster_filename= \"land_surface_temperature\"\n",
    "        # # db_var=db_var.to_crs(crs)\n",
    "        # # db_var['geometry'] = db_var['geometry'].centroid\n",
    "        # # df_all= overlay(cluster_boundaries_lst, db_var, how=\"intersection\" , keep_geom_type=False)\n",
    "        # # df_all=df_all.sort_values('area_for_dupes', ascending=False).drop_duplicates([\"geometry\"])\n",
    "        # # df_all\n",
    "        # #-----------------------------------------------------------#\n",
    "        # #-----------------------------------------------------------#\n",
    "        # # Group table by cluster label, keep the variables used \n",
    "        # # for clustering, and obtain their descriptive summary\n",
    "        # cluster_variables=[]\n",
    "        # k5desc = db.groupby('ward5wknn')[cluster_variables].describe()\n",
    "        # # Loop over each cluster and print a table with descriptives\n",
    "        # for cluster in k5desc.T:\n",
    "        #     print('\\n\\t---------\\n\\tCluster %i'%cluster)\n",
    "        #     print(k5desc.T[cluster])\n",
    "            \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "if __name__ ==  '__main__': \n",
    "    if test_maps:\n",
    "        # # City level map\n",
    "        df_filtered= gdf_city\n",
    "        title=\"City Boundary\"\n",
    "        legend_title= 'Boundary'\n",
    "        visualize_column=\"shapeName\"\n",
    "        map_output= os.path.join(maps, f\"{city}_{title}_map.png\")\n",
    "        export = map_city(maps=maps, vector_file=gdf_city, crs=crs, legend_title=legend_title,  \\\n",
    "                            visualize_column=visualize_column, title=title, map_output=map_output)\n",
    "\n",
    "        crs = 4326\n",
    "        fc_filtered  , region= create_fc_collection(gdf_city= gdf_city) \n",
    "\n",
    "        \n",
    "        #-----------------------------------------------------------------------------------#\n",
    "        #-----------------------------------------------------------------------------------#\n",
    "\n",
    "        pop_density_tif= dowload_pop_density(ee , EPSG_str, region)\n",
    "        raster_filename= \"pop_density\"\n",
    "        unprojected_raster =pop_density_tif  \n",
    "        projected_raster = os.path.join(rasters, f'{raster_filename}.tif')\n",
    "        reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \\\n",
    "            projected_raster=projected_raster)\n",
    "\n",
    "        output_shapefile = f'{shapefiles}/polygonized_{raster_filename}.shp'\n",
    "        mask_value=None\n",
    "        variable= raster_filename\n",
    "        tif = projected_raster #os.path.join(rasters, 'tree_cover_projected.tif')\n",
    "        raster_val= variable[0:9]\n",
    "        name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \\\n",
    "            mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "        cmap = \"OrRd\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        legend_title= \"Population\"\n",
    "        map_title= f\"Population (Worldpop 100 meters) across {city}\"\n",
    "        scheme=\"quantiles\"\n",
    "        categorical=False\n",
    "        map_output= f'{maps}/{city}_map_{raster_val}.png'\n",
    "        polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "        newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "                hexagons=newdf,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                    crs=crs, cmap=cmap , visualize_column=raster_val,  \\\n",
    "                        title=map_title, map_output=map_output, scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        db , cluster_boundaries, visualize_var =create_ahc_knn_clusters(db = newdf , raster_val=raster_val, \\\n",
    "                                                                        WGS84_meters=WGS84_meters)\n",
    "\n",
    "        map_title= f\"Population (Worldpop 100 meters) \\n across {city} clusters\"\n",
    "        legends_format= '{:,.0f}'\n",
    "        categorical=True\n",
    "        map_output= f'{maps}/{city}_map_clusters_{raster_val}.png'\n",
    "        export_the_map= map_func(maps= maps, vector_file=gdf_city , \\\n",
    "            hexagons=db,legend_title=legend_title, legends_format=legends_format,  \\\n",
    "                crs=crs, cmap=cmap , visualize_column=visualize_var,  \\\n",
    "                    title=map_title, map_output=map_output,  scheme=scheme,  categorical=categorical)\n",
    "\n",
    "        category=raster_filename\n",
    "        describe_cluster_trees= describe_cluster_trees_pop(cluster_boundaries, category, maps)\n",
    "        describe=describe_db( db_var=db, category=raster_val, tables=tables)\n",
    "        delete_shapefil= delete_shapefile(output_shapefile=output_shapefile)\n",
    "\n",
    "        # legend_title= \"Population\"\n",
    "        # map_title= f\"Population (Worldpop 100 meters) across {city} cluster\"\n",
    "        # map=map_clusters(maps=maps, hexagons=cluster_boundaries, legend_title=legend_title, \\\n",
    "        #                 legends_format=legends_format, crs=crs ,cmap=cmap,  \\\n",
    "        #                     visualize_column=raster_val, title=map_title)\n",
    "\n",
    "\n",
    "        db_pop = db[[f\"{raster_val}\",'geometry' , 'ward5wknn']]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section3'></a>\n",
    "<h5><center> <font color='cyan'> Section 3: Visualize Vito heat rasters at city level</font>   </center></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "\n",
    "def create_hexbins_and_geom_features(gdf_city, hexbin_res, WGS84):\n",
    "    poly = gdf_city.geometry.unary_union\n",
    "    gdf_boundary = gpd.GeoDataFrame(geometry=[poly],crs=gdf_city.crs)\n",
    "    # gdf_h3 = gdf_boundary.h3.polyfill(9,explode=True)\n",
    "    gdf_h3 = gdf_boundary.h3.polyfill(hexbin_res,explode=True)\n",
    "    gdf_h3 = gdf_h3.set_index('h3_polyfill').h3.h3_to_geo_boundary()\n",
    "    output_shapefile= f'{shapefiles}/h3_grid.shp'\n",
    "    gdf_h3.to_file(output_shapefile)\n",
    "    hexbins_projected = gpd.read_file(output_shapefile).to_crs(WGS84)\n",
    "\n",
    "    geom = gdf_boundary['geometry']\n",
    "    jsonDict = eval(geom.to_json())\n",
    "    for index, row in gdf_boundary.iterrows(): \n",
    "        polygon_list= []\n",
    "        for x in jsonDict['features'][index]['geometry']['coordinates']:\n",
    "            polygon_list.append(x)\n",
    "            region = ee.Geometry.Polygon(polygon_list)\n",
    "            fc_filtered = ee.FeatureCollection(region)  \n",
    "    # gdf_h3.plot()\n",
    "    return fc_filtered  , region , hexbins_projected\n",
    "\n",
    "\n",
    "def get_uh_raster_dirs(input_raster_dir):\n",
    "    raster_dirs=  [x[1] for x in os.walk(input_raster_dir)] # ['Present', 'SSP119' , 'SSP370']\n",
    "    raster_dirs = [x for x in raster_dirs if len(x)>0]\n",
    "    raster_dirs = list(np.concatenate(raster_dirs))\n",
    "    return raster_dirs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create Hex, clip rasters and get zonals**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "\n",
    "def create_hexbins_and_geom_features(gdf_city, hexbin_res, WGS84):\n",
    "    poly = gdf_city.geometry.unary_union\n",
    "    gdf_boundary = gpd.GeoDataFrame(geometry=[poly],crs=gdf_city.crs)\n",
    "    # gdf_h3 = gdf_boundary.h3.polyfill(9,explode=True)\n",
    "    gdf_h3 = gdf_boundary.h3.polyfill(hexbin_res,explode=True)\n",
    "    gdf_h3 = gdf_h3.set_index('h3_polyfill').h3.h3_to_geo_boundary()\n",
    "    output_shapefile= f'{shapefiles}/h3_grid.shp'\n",
    "    gdf_h3.to_file(output_shapefile)\n",
    "    hexbins_projected = gpd.read_file(output_shapefile).to_crs(WGS84)\n",
    "\n",
    "    geom = gdf_boundary['geometry']\n",
    "    jsonDict = eval(geom.to_json())\n",
    "    for index, row in gdf_boundary.iterrows(): \n",
    "        polygon_list= []\n",
    "        for x in jsonDict['features'][index]['geometry']['coordinates']:\n",
    "            polygon_list.append(x)\n",
    "            region = ee.Geometry.Polygon(polygon_list)\n",
    "            fc_filtered = ee.FeatureCollection(region)  \n",
    "    # gdf_h3.plot()\n",
    "    return fc_filtered  , region , hexbins_projected\n",
    "\n",
    "\n",
    "def clip_and_export_raster(input_gpdf, raster , out_raster, zonal_stat_var, hexbins_projected, statistic):\n",
    "    Vector=input_gpdf\n",
    "    with rasterio.open(raster) as src:\n",
    "        Vector=Vector.to_crs(src.crs)\n",
    "        # print(f'Gdf srs: {Vector.crs}')\n",
    "        out_image, out_transform=mask(src,Vector.geometry,crop=True)\n",
    "        out_meta=src.meta.copy() # copy the metadata of the source DEM\n",
    "        \n",
    "    out_meta.update({\n",
    "        \"driver\":\"Gtiff\",\n",
    "        \"height\":out_image.shape[1], # height starts with shape[1]\n",
    "        \"width\":out_image.shape[2], # width starts with shape[2]\n",
    "        \"transform\":out_transform\n",
    "    })\n",
    "                \n",
    "    with rasterio.open(out_raster,'w',**out_meta) as dst:\n",
    "        dst.write(out_image)\n",
    "    # add zonal stats to the hexbin gpd dataframe and replace nans with 0 \n",
    "    hexbins_projected[zonal_stat_var] = zonal_stats(hexbins_projected, out_raster ,stats=statistic)\n",
    "    hexbins_projected[zonal_stat_var] = [item[statistic] for item in hexbins_projected[zonal_stat_var]]\n",
    "    hexbins_projected[zonal_stat_var].fillna(0,inplace=True)\n",
    "    max=int(hexbins_projected.loc[hexbins_projected[zonal_stat_var].idxmax()][zonal_stat_var])\n",
    "    return hexbins_projected , max\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# %%capture\n",
    "if __name__ ==  '__main__': \n",
    "    # if run_vito_analysis:\n",
    "    input_raster_dir = data\n",
    "    fc_filtered  , region , hexbins_projected= create_hexbins_and_geom_features(gdf_city, hexbin_res=8, WGS84=WGS84)\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                statistic='mean'\n",
    "                hexbins_projected , max= clip_and_export_raster(input_gpdf=gdf_city, raster=file_name_path , \n",
    "                                                                out_raster=out_rst, zonal_stat_var=file_name , \n",
    "                                                                hexbins_projected=hexbins_projected,\n",
    "                                                                statistic=statistic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def get_zonals(input_raster, input_raster_uhi_night_p, input_shapefile):\n",
    "    categorical_stats = raster_stats(input_shapefile, input_raster, \n",
    "        stats=\"mean sum count majority minority unique\",\n",
    "        copy_properties=True,\n",
    "        nodata_value=0,\n",
    "        categorical=True)\n",
    "\n",
    "    zonal_stats_input_raster_uhi_night_p = raster_stats(input_shapefile, input_raster_uhi_night_p, \n",
    "        # stats=\"mean sum count majority minority unique\",\n",
    "        stats=\"mean\",\n",
    "        copy_properties=True,\n",
    "        nodata_value=0,\n",
    "        categorical=False)\n",
    "\n",
    "    stats = pd.DataFrame(categorical_stats)\n",
    "    stats['RegionName'] = input_shapefile['h3_polyfil']\n",
    "    stats['geometry'] = input_shapefile['geometry']\n",
    "    stats['area'] = input_shapefile.to_crs(WGS84_meters)['geometry'].area\n",
    "    class_labels = {1: \"Water , blue\", 2: \"Trees, green\", 4:\"Flooded Vegetation, magenta\", 5:\"Crops, seagreen\", 7:\"Built Area, tan\", 8:\"Bare Ground, goldenrod\", 9:\"Snow/Ice, snow\", 10:\"Clouds, ghostwhite\", 11:\"Rangeland, greenyellow\" }\n",
    "    df = stats.rename(columns=class_labels)\n",
    "\n",
    "    zonal_stats_input_raster_uhi_night_df = pd.DataFrame(zonal_stats_input_raster_uhi_night_p)\n",
    "    zonal_stats_input_raster_uhi_night_df['RegionName'] = input_shapefile['h3_polyfil']\n",
    "    merge_df=pd.merge(df, zonal_stats_input_raster_uhi_night_df, on='RegionName')\n",
    "    merge_df[\"Pixel area\"]=10*10\n",
    "    merge_df[\"Total area of buildings\"]= merge_df[[\"Built Area, tan\"]].multiply(merge_df[\"Pixel area\"], axis=\"index\")\n",
    "    merge_df[\"Building Density(%)\"]= merge_df[[\"Total area of buildings\"]].div(merge_df[\"area\"], axis=\"index\").multiply(100)\n",
    "    merge_df[\"Fraction of Area\"]= merge_df[[\"Total area of buildings\"]].div(merge_df[\"area\"], axis=\"index\")\n",
    "    return merge_df , class_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def building_density_versus_uhi(merge_df, x, y, maps):\n",
    "    x = merge_df[x]\n",
    "    y = merge_df[y]\n",
    "    xlim = x.min(), x.max()\n",
    "    ylim = y.min(), y.max()\n",
    "    # 'viridis', 'plasma', 'inferno', 'magma', 'cividis'\n",
    "    # fig, (ax0, ax1) = plt.subplots(ncols=2, sharey=True, figsize=(9, 4))\n",
    "    fig, (ax0) = plt.subplots(ncols=1, figsize=(7, 5))\n",
    "    # hb = ax0.hexbin(x, y, gridsize=30, cmap='viridis', bins='log')\n",
    "    hb = ax0.hexbin(x, y, gridsize=40, cmap='viridis')\n",
    "    ax0.set_ylabel('Building Density (%)')\n",
    "    ax0.set_xlabel('UHI effect ($^\\circ$C)')\n",
    "    cb = fig.colorbar(hb, ax=ax0)\n",
    "    map_output=f\"{maps}/building_density_versus_uhi.png\"\n",
    "    ax0.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "    map_output=f\"{maps}/building_density_versus_uhi_1.png\"\n",
    "    ax0.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "input_raster= r\"C:/Users/Aziz/Dropbox/CRP/UHI/Nis City\\data\\esri_dynamic_world_cover_projected.tif\"\n",
    "input_raster_uhi_night_p= r\"C:/Users/Aziz/Dropbox/CRP/UHI/Nis City\\data\\T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326.tif\"\n",
    "input_shapefile= hexbins_projected\n",
    "\n",
    "merge_df , class_labels= get_zonals(input_raster, input_raster_uhi_night_p, input_shapefile)\n",
    "x='mean_y'\n",
    "y=\"Building Density(%)\"\n",
    "building_density= building_density_versus_uhi(merge_df, x, y, maps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def create_regplots(merge_df, class_labels,maps):\n",
    "    x=\"UHI effect(night-time)\"\n",
    "    merge_df[x]= merge_df['mean_y']\n",
    "    for key, value in class_labels.items():\n",
    "        class_lc= value.split(', ')[0]\n",
    "        class_color= value.split(', ')[1]\n",
    "        print(f\"class_lc, class_color: {class_lc}, {class_color}\")\n",
    "        column=class_lc+', '+class_color\n",
    "        y=column\n",
    "        try:\n",
    "            fig, ax = plt.subplots()\n",
    "            filtered_df = merge_df[merge_df[[ x, y]].notnull().all(1)]\n",
    "            slope, intercept, r_value, p_value, std_err = stats.linregress(x=filtered_df[x],y=filtered_df[y])\n",
    "            print(f\"slope, intercept: {slope}, {intercept}\")\n",
    "            # use line_kws to set line label for legend\n",
    "            sn.regplot(x=x, y=y, data=merge_df, ci = 95, color=class_color, line_kws={'color': \"r\", 'label':\"Regression equation: y={0:.1f}x+{1:.1f} \\n Correlation coefficient:{2:.1f} \\n P-value:{3:.2f}\".format(slope,intercept,r_value, p_value )})\n",
    "            plt.legend(loc='best', markerscale=5)\n",
    "            plt.show()\n",
    "            plt.tight_layout()\n",
    "            map_output= os.path.join(maps, f\"{city}_regression_{y}_{x}.png\")\n",
    "            ax.figure.savefig(map_output,  bbox_inches='tight',   dpi = 400)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    y=\"Building Density(%)\"\n",
    "    try:\n",
    "        fig, ax = plt.subplots()\n",
    "        filtered_df = merge_df[merge_df[[x, y]].notnull().all(1)]\n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(x=filtered_df[x],y=filtered_df[y])\n",
    "        print(f\"slope, intercept , r_value, p_value, std_err: {slope}, {intercept}, {r_value}, {p_value}, {std_err}\")\n",
    "        # use line_kws to set line label for legend\n",
    "        class_color='cyan'\n",
    "        sn.regplot(x=x, y=y, data=merge_df, ci = 95, color=class_color, line_kws={'color':\"r\",'label':\"Regression equation:y={0:.1f}x+{1:.1f} \\n Correlation coefficient:{2:.1f} \\n P-value:{3:.2f}\".format(slope,intercept,r_value, p_value )})\n",
    "        plt.legend(loc='best', markerscale=5)\n",
    "        plt.show()\n",
    "        plt.tight_layout()\n",
    "        map_output= os.path.join(maps, f\"{city}_regression_{y}_{x}.png\")\n",
    "        ax.figure.savefig(map_output,  bbox_inches='tight',   dpi = 400)\n",
    "    except:\n",
    "        pass\n",
    "create_regplots(merge_df, class_labels,maps)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sub-city barplot and map. Create separate and then juxtapose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                sub_city_gdf_uhi=sub_city_gdf.reset_index()\n",
    "                sub_city_gdf_uhi['admin_level_index']=sub_city_gdf_uhi.index\n",
    "                zonal_stats_uhi_night_temperature= raster_stats(sub_city_gdf_uhi, \n",
    "                    projected_raster, \n",
    "                    # stats=\"mean sum count majority minority unique\",\n",
    "                    stats=\"mean\",\n",
    "                    copy_properties=True,\n",
    "                    nodata_value=0,\n",
    "                    categorical=False\n",
    "                    )\n",
    "                zonal_stats_uhi_night_temperature = pd.DataFrame(zonal_stats_uhi_night_temperature)\n",
    "                sub_city_gdf_uhi['uhi_temp_mean']=zonal_stats_uhi_night_temperature['mean']\n",
    "                sub_city_gdf_uhi['index_joined']=zonal_stats_uhi_night_temperature.index\n",
    "\n",
    "                # Admin third most frequest\n",
    "                sub_city_gdf_uhi_1=sub_city_gdf_1.reset_index()\n",
    "                sub_city_gdf_uhi_1['admin_level_index']=sub_city_gdf_uhi_1.index\n",
    "                zonal_stats_uhi_night_temperature= raster_stats(sub_city_gdf_uhi_1, \n",
    "                    projected_raster, \n",
    "                    # stats=\"mean sum count majority minority unique\",\n",
    "                    stats=\"mean\",\n",
    "                    copy_properties=True,\n",
    "                    nodata_value=0,\n",
    "                    categorical=False\n",
    "                    )\n",
    "                zonal_stats_uhi_night_temperature = pd.DataFrame(zonal_stats_uhi_night_temperature)\n",
    "                sub_city_gdf_uhi_1['uhi_temp_mean']=zonal_stats_uhi_night_temperature['mean']\n",
    "                sub_city_gdf_uhi_1['index_joined']=zonal_stats_uhi_night_temperature.index\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def sub_city_nigt_temperature(gdf, sub_city_names_column, visualize_column):\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = gdf[visualize_column].min()\n",
    "    vmax = gdf[visualize_column].max()\n",
    "    diff=vmax-vmin\n",
    "    print(f\"vmin {vmin}, vmax {vmax} , diff:{diff}\")   \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(width/2, height/2))  \n",
    "    gdp_ax= gdf.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # sub_city_names_column\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=\"UHI effect (day-time)\"\n",
    "    ax.set_title(title)\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([.9, .15, 0.02, 0.75]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='vertical' , shrink=0.8)\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_subcity_temperature_heatmap.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    # barplot\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(width/2, height/2))\n",
    "    y=sub_city_names_column\n",
    "    x=visualize_column\n",
    "    # Filter nans\n",
    "    gdf = gdf[gdf[[y, x]].notnull().all(1)]\n",
    "    # Sort the table\n",
    "    gdf = gdf.sort_values(by=[x])\n",
    "    y=gdf[y]\n",
    "    x=gdf[x]\n",
    "    ax=sn.barplot(data=gdf , orient='h',  x=x, y=y ,  palette=cmap,  ax=ax)  #order=y, orient='h',\n",
    "    # plt.yticks(rotation=90)\n",
    "    ax.set_ylabel('Admin Level',  fontsize=10,  fontweight='bold')\n",
    "    ax.set_xlabel('UHI effect(Night-time)',  fontsize=10,  fontweight='bold')\n",
    "    ax.set_title('Night-time UHI effect \"($^\\circ$C)\"' ,  fontsize=12,  fontweight='bold')\n",
    "    plt.yticks(fontsize=6) \n",
    "    plt.xticks(fontsize=7) \n",
    "    plt.setp(ax.get_yticklabels(), rotation=20, horizontalalignment='right')\n",
    "    # ax.invert_yaxis()  # labels read top-to-bottom\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_subcity_temperature_barplot.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "sub_city_nigt_temperature(gdf=sub_city_gdf_uhi , sub_city_names_column='name:en', visualize_column='uhi_temp_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def sub_city_nigt_temperature(gdf, sub_city_names_column, visualize_column):\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = gdf[visualize_column].min()\n",
    "    vmax = gdf[visualize_column].max()\n",
    "    diff=vmax-vmin\n",
    "    print(f\"vmin {vmin}, vmax {vmax} , diff:{diff}\")   \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(width/2, height/2))  \n",
    "    gdp_ax= gdf.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # sub_city_names_column\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=\"UHI effect (day-time)\"\n",
    "    ax.set_title(title)\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([.9, .15, 0.02, 0.75]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='vertical' , shrink=0.8)\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_subcity_temperature_heatmap.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    # barplot\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(width/2, height/2))\n",
    "    y=sub_city_names_column\n",
    "    x=visualize_column\n",
    "    # Filter nans\n",
    "    gdf = gdf[gdf[[y, x]].notnull().all(1)]\n",
    "    # Sort the table\n",
    "    gdf = gdf.sort_values(by=[x])\n",
    "    y=gdf[y]\n",
    "    x=gdf[x]\n",
    "    ax=sn.barplot(data=gdf , orient='h',  x=x, y=y ,  palette=cmap,  ax=ax)  #order=y, orient='h',\n",
    "    # plt.yticks(rotation=90)\n",
    "    ax.set_ylabel('Admin Level',  fontsize=10,  fontweight='bold')\n",
    "    ax.set_xlabel('UHI effect(Night-time)',  fontsize=10,  fontweight='bold')\n",
    "    ax.set_title('Night-time UHI effect \"($^\\circ$C)\"' ,  fontsize=12,  fontweight='bold')\n",
    "    plt.yticks(fontsize=7) \n",
    "    plt.xticks(fontsize=7) \n",
    "    # plt.setp(ax.get_yticklabels(), rotation=20, horizontalalignment='right')\n",
    "    plt.setp(ax.get_yticklabels(), horizontalalignment='right')\n",
    "    # ax.invert_yaxis()  # labels read top-to-bottom\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_subcity_temperature_barplot.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "\n",
    "sub_city_nigt_temperature(gdf=sub_city_gdf_uhi_1 , sub_city_names_column='name', visualize_column='uhi_temp_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_func_colorbar(vector_file,  hexagons,  crs , cmap, visualize_column, legend_title, map_output):\n",
    "    fig, ax = plt.subplots(figsize = (width, height))\n",
    "    vmin = hexagons[visualize_column].min()\n",
    "    vmax = hexagons[visualize_column].max()\n",
    "    ax_gpd= hexagons.to_crs(crs).plot(ax=ax,column = visualize_column,edgecolor='none',cmap = cmap,legend = False)\n",
    "    vector_file.to_crs(crs).plot(ax=ax_gpd,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    plt.xticks([], [])\n",
    "    plt.yticks([], [])\n",
    "    ax.set_axis_off()\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([.95, 0.1, 0.02, 0.8]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    # cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=legend_title, orientation='vertical' , shrink=0.8)\n",
    "    CB.ax.tick_params(labelsize=16) \n",
    "    plt.tight_layout()\n",
    "    # map_output= f'{maps}/{city}_{visualize_column}_map_heat_colorbar.png'\n",
    "    plt.savefig(map_output, format = 'png',bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def map_func_new(maps, vector_file,  hexagons, legend_title,legends_format, crs ,cmap,  visualize_column, title, scheme, categorical, map_output):\n",
    "    if categorical==True:\n",
    "        ax = hexagons.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                    column= visualize_column, \\\n",
    "                                    alpha=0.6, categorical=True , cmap=cmap, \\\n",
    "                                    legend=True,  # Add legend \n",
    "                                    legend_kwds={'loc':'upper right', \n",
    "                                                    'bbox_to_anchor':(1, 1), \n",
    "                                                    'fmt':legends_format,\n",
    "                                                    'markerscale':1.01, \n",
    "                                                    'title_fontsize':'small', \n",
    "                                                    'fontsize':'x-small'\n",
    "                                                    # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                    } ,\n",
    "                                    aspect=1\n",
    "                                            )\n",
    "    elif categorical==False:\n",
    "        ax = hexagons.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                    column= visualize_column, \\\n",
    "                                    alpha=0.6, scheme=scheme, cmap=cmap, \\\n",
    "                                    legend=True,  # Add legend \n",
    "                                    legend_kwds={'loc':'upper right', \n",
    "                                                    'bbox_to_anchor':(1, 1), \n",
    "                                                    'fmt':legends_format,\n",
    "                                                    'markerscale':1.01, \n",
    "                                                    'title_fontsize':'small', \n",
    "                                                    'fontsize':'x-small'\n",
    "                                                    # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                    } ,\n",
    "                                    aspect=1\n",
    "                                            )\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    leg1 = ax.get_legend()\n",
    "    # Set markers to square shape\n",
    "    for ea in leg1.legendHandles:\n",
    "        ea.set_marker('s')\n",
    "    leg1.set_title(f'{legend_title}')\n",
    "    ax.title.set_text(f'{title}')\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    # map_output= f'{maps}/{city}_{visualize_column}_map_heat.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Juxtapose day and night. Subtract sum of heatwave nights for pressent from 2050 ad show it as pct change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                # raster_filename= \"heatwave_days_present_2001_2020_30m_EPSG4326\"\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326_all=polygonized_shapefile\n",
    "                T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326_newdf=newdf\n",
    "\n",
    "            elif os.path.splitext(file_name)[-1] == extension and \"T2M_DJF_UHI_day_present_2001_2020_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                # raster_filename= \"heatwave_days_present_2001_2020_30m_EPSG4326\"\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                T2M_DJF_UHI_day_present_2001_2020_30m_EPSG4326_EPSG4326_all=polygonized_shapefile\n",
    "                T2M_DJF_UHI_day_present_2001_2020_30m_EPSG4326_EPSG4326_newdf=newdf\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def juxtapose_day_and_night(gdf, gdf_1, visualize_column):\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = gdf[visualize_column].min()\n",
    "    vmax = gdf[visualize_column].max()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(width, height))\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= gdf.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=\"UHI effect (day-time)\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([0.475, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    # cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='vertical' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    vmin = gdf_1[visualize_column].min()\n",
    "    vmax = gdf_1[visualize_column].max()\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= gdf_1.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0,legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=\"UHI effect (night-time)\"\n",
    "    ax.set_title(title)\n",
    "    fig = ax.get_figure()\n",
    "    cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    # fake up the array of the scalar mappable. Urgh...\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='vertical' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    plt.show()\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_day_and_night_juxatposed.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "\n",
    "juxtapose_day_and_night(gdf=T2M_DJF_UHI_day_present_2001_2020_30m_EPSG4326_EPSG4326_all, gdf_1=T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326_all, visualize_column=raster_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_profile(maps, vector_file,  hexagons, profile_x_var, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output, scheme):\n",
    "    #------------------------------------------------------\n",
    "    # Centroid for starting the line. use the sample function if needed. \n",
    "    cent_x = gdf_city.centroid.x.values[0]  \n",
    "    cent_y = gdf_city.centroid.y.values[0]  \n",
    "    # print(cent_x)\n",
    "    start = Point(cent_x, cent_y)\n",
    "    length = .05\n",
    "    angle = math.pi / 2\n",
    "    end = Point(start.x + length, start.y)\n",
    "    line = LineString([start, end])\n",
    "    line = rotate(line, angle, origin=start, use_radians=True)\n",
    "    # Define a LineString feature geometry with one attribute\n",
    "    schema = {\n",
    "        'geometry': 'LineString',\n",
    "        'properties': {'id': 'int'},\n",
    "        }\n",
    "    # Write a new Shapefile\n",
    "    with fiona.open(f\"{shapefiles}/Poly.shp\", mode='w', driver='ESRI Shapefile',crs='epsg:4326', schema=schema) as c:\n",
    "        ## If there are multiple geometries, put the \"for\" loop here\n",
    "        c.write({\n",
    "            'geometry': mapping(line),\n",
    "            'properties': {'id': 123},\n",
    "        })\n",
    "\n",
    "    lines_read= gpd.read_file(f\"{shapefiles}/Poly.shp\").to_crs(crs)\n",
    "    newdf = gpd.sjoin(hexagons.to_crs(crs), lines_read)\n",
    "    for ind, row in lines_read.iterrows():\n",
    "        XS_ID = row['id']\n",
    "        start_coords =  list([row.geometry][0].coords)[0]\n",
    "        end_coords = list([row.geometry][0].coords)[1]\n",
    "        lon = [start_coords[0]]\n",
    "        lat = [start_coords[1]]\n",
    "        n_points = 10\n",
    "        for i in np.arange(1, n_points+1):\n",
    "            x_dist = end_coords[0] - start_coords[0]\n",
    "            y_dist = end_coords[1] - start_coords[1]\n",
    "            point  = [(start_coords[0] + (x_dist/(n_points+1))*i), (start_coords[1] + (y_dist/(n_points+1))*i)]\n",
    "            lon.append(point[0])\n",
    "            lat.append(point[1])\n",
    "            \n",
    "        lon.append(end_coords[0])\n",
    "        lat.append(end_coords[1])\n",
    "        df = pd.DataFrame({'Latitude': lat, \n",
    "                        'Longitude': lon})\n",
    "        gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.Longitude, df.Latitude))\n",
    "        gdf.crs = {'init': 'epsg:4326'}\n",
    "        gdf_pcs = gdf.to_crs(epsg = 3857)\n",
    "        gdf_pcs['h_distance'] = 0\n",
    "        for index, row in gdf_pcs.iterrows():\n",
    "            gdf_pcs['h_distance'].loc[index] = gdf_pcs.geometry[0].distance(gdf_pcs.geometry[index])\n",
    "            \n",
    "    line_spat_join = gpd.sjoin(hexagons.to_crs(crs), gdf_pcs.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    fig, ax = plt.subplots(figsize = (3, 2))\n",
    "    # ax.plot(line_spat_join['h_distance'], line_spat_join[f'{visualize_column}'])\n",
    "    ax.plot(line_spat_join[profile_x_var], line_spat_join[f'{visualize_column}'])\n",
    "    ax.set_title(label=f\"{legend_title} profile\" , fontdict={'fontsize': 10, 'fontweight': 'medium'})\n",
    "    \n",
    "    # fig.suptitle(f\"{legend_title} profile\", fontsize=10)\n",
    "    plt.xticks(fontsize=8) # rotation=90\n",
    "    plt.yticks(fontsize=8) \n",
    "    # Hide the right and top spines\n",
    "    ax.spines[['right', 'top']].set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(f'{maps}/profile_{visualize_column}.png',  bbox_inches='tight', dpi=50) #, transparent=True                \n",
    "    # plt.show()\n",
    "    \n",
    "    #------------------------------------------------------------------------\n",
    "  \n",
    "    ax = hexagons.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0.6, scheme=scheme, cmap=cmap, \\\n",
    "                                legend=True,  # Add legend \n",
    "                                legend_kwds={'loc':'upper right', \n",
    "                                                'bbox_to_anchor':(1, 1), \n",
    "                                                'fmt':legends_format,\n",
    "                                                'markerscale':1.01, \n",
    "                                                'title_fontsize':'small', \n",
    "                                                'fontsize':'x-small'\n",
    "                                                # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                } ,\n",
    "                                aspect=1\n",
    "                                        )\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    lines_read.to_crs(crs).plot(ax=ax,facecolor='blue',edgecolor='black', linewidth=1 ,alpha=0.9)\n",
    "\n",
    "    file = f'{maps}/profile_{visualize_column}.png'\n",
    "    logo = image.imread(file)\n",
    "    imagebox = OffsetImage(logo)\n",
    "    #Container for an `OffsetBox` (here imagebox) referring to a specific position *xy*.\n",
    "    x= cent_x #21.933\n",
    "    y= cent_y #43.343\n",
    "    xy=(x+.09, y+.01)\n",
    "    ab = AnnotationBbox(imagebox, xy, frameon = False)\n",
    "    ax.add_artist(ab)\n",
    "\n",
    "    max_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()][f'{visualize_column}'].round(1)\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Longitude']\n",
    "    \n",
    "    value=f\"Max: {max_value}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude-.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "\n",
    "    \n",
    "    min_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()][f'{visualize_column}'].round(1)\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Longitude']\n",
    "    \n",
    "    value=f\"Min: {min_value}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude+.003), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")) #, shrink=0.007\n",
    "\n",
    "    for x, y in zip(lon[::2], lat[::2]):\n",
    "        # print(f'{x} -> {y}')\n",
    "        value=\"------------\"\n",
    "        ax.text(x, y, f\"{value}\", ha=\"center\", va=\"center\", rotation=90, size=2,\n",
    "                bbox=dict(boxstyle=\"rarrow,pad=0.2\", fc=\"steelblue\", ec=\"black\", lw=2))\n",
    "\n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    leg1 = ax.get_legend()\n",
    "    # Set markers to square shape\n",
    "    for ea in leg1.legendHandles:\n",
    "        ea.set_marker('s')\n",
    "    leg1.set_title(f'{legend_title}')\n",
    "    ax.title.set_text(f'{title}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# def juxtapose_ssps(data, heatwave_days_present_2001_2020_30m_EPSG4326, heatwave_days_ssp370_2021_2040_30m_EPSG4326, heatwave_days_ssp370_2041_2060_30m_EPSG4326, raster_val):\n",
    "def juxtapose_ssps(data):\n",
    "    # gdf, gdf_1, gdf_2, visualize_column\n",
    "    cities_reprojected=gdf_city.reset_index()\n",
    "    heat_raster_list= [\"heatwave_days_present_2001_2020_30m_EPSG4326\", \"heatwave_days_ssp370_2021_2040_30m_EPSG4326\", \"heatwave_days_ssp370_2041_2060_30m_EPSG4326\", \"heatwave_days_ssp370_2021_2040_30m_EPSG4326\", \"heatwave_days_ssp370_2041_2060_30m_EPSG4326\"]\n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    # heatwave_days_present_2001_2020_30m_EPSG4326=None\n",
    "    # heatwave_days_ssp370_2021_2040_30m_EPSG4326=None\n",
    "    # heatwave_days_ssp370_2041_2060_30m_EPSG4326=None\n",
    "    # raster_val=None\n",
    "\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"heatwave_days_present_2001_2020_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                heatwave_days_present_2001_2020_30m_EPSG4326=polygonized_shapefile\n",
    "            elif os.path.splitext(file_name)[-1] == extension and \"heatwave_days_ssp370_2021_2040_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                heatwave_days_ssp370_2021_2040_30m_EPSG4326=polygonized_shapefile\n",
    "            elif os.path.splitext(file_name)[-1] == extension and \"heatwave_days_ssp370_2041_2060_30m_EPSG4326\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                heatwave_days_ssp370_2041_2060_30m_EPSG4326=polygonized_shapefile\n",
    "            try:\n",
    "                visualize_column=raster_val\n",
    "                cmap = \"coolwarm\"\n",
    "                vmin = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].min()\n",
    "                vmax = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].max()\n",
    "                vsum0 = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].sum()\n",
    "\n",
    "                # Start a loop over all the variables of interest\n",
    "                f, axs = plt.subplots(nrows=1, ncols=3, figsize=(width, height))\n",
    "                # Make the axes accessible with single indexing\n",
    "                axs = axs.flatten()\n",
    "\n",
    "                i=0\n",
    "                ax = axs[i]    \n",
    "                gdp_ax= heatwave_days_present_2001_2020_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "                # Remove axis clutter\n",
    "                ax.set_axis_off()\n",
    "                # Set the axis title to the name of variable being plotted\n",
    "                title=\"Heatwave days present 2001 2020\"\n",
    "                ax.set_title(title)\n",
    "\n",
    "                vsum1 = heatwave_days_ssp370_2021_2040_30m_EPSG4326[visualize_column].sum()\n",
    "                i=1\n",
    "                ax = axs[i]    \n",
    "                gdp_ax= heatwave_days_ssp370_2021_2040_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "                # Remove axis clutter\n",
    "                ax.set_axis_off()\n",
    "                # Set the axis title to the name of variable being plotted\n",
    "                title=\"Heatwave days: SSP1 2021-2040\"\n",
    "                ax.set_title(title)\n",
    "\n",
    "                vmin = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].min()\n",
    "                vmax = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].max()\n",
    "                vsum2 = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].sum()\n",
    "                i=2\n",
    "                ax = axs[i]    \n",
    "                gdp_ax= heatwave_days_ssp370_2041_2060_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "                # Remove axis clutter\n",
    "                ax.set_axis_off()\n",
    "                # Set the axis title to the name of variable being plotted\n",
    "                title=\"Heatwave days: SSP1 2041-2060\"\n",
    "                ax.set_title(title)\n",
    "\n",
    "                fig = ax.get_figure()\n",
    "                # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "                cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "                sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "                # fake up the array of the scalar mappable. Urgh...\n",
    "                sm._A = []\n",
    "                # fig.colorbar(sm, cax=cax,  label=\"Population\")\n",
    "                # CB=fig.colorbar(sm, cax=cax,  label=\"Population\", orientation='horizontal', shrink=0.8)\n",
    "                CB=fig.colorbar(sm, cax=cax,  label=\"Heatwave days\", orientation='horizontal' , shrink=0.8)\n",
    "                # Display the figure\n",
    "                plt.show()\n",
    "\n",
    "                pct_change_heat_days_vsum1= round((vsum1 - vsum0) / abs(vsum0) * 100, 0) \n",
    "                pct_change_heat_days_vsum2= round((vsum2 - vsum0) / abs(vsum0) * 100, 0) \n",
    "            except:\n",
    "                pass\n",
    "                return pct_change_heat_days_vsum1 , pct_change_heat_days_vsum2\n",
    "                # fig = ax.get_figure()\n",
    "                # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "                # sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "                # # fake up the array of the scalar mappable. Urgh...\n",
    "                # sm._A = []\n",
    "                # CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='vertical' , shrink=0.8)\n",
    "                # Display the figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def juxtapose_ssps_max_temp_june_august(data):\n",
    "    input_raster_dir = data\n",
    "    root=input_raster_dir\n",
    "    file_name=\"WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"WBGT_JJA_daily_mean_max_ssp370_2021_2040_ssp370_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    WBGT_JJA_daily_mean_max_ssp370_2021_2040_ssp370_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    visualize_column=raster_val\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326[visualize_column].max()\n",
    "    vsum0 = WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326[visualize_column].sum()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=3, figsize=(width, height))\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= WBGT_JJA_daily_mean_max_present_2001_2020_present_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days present 2001 2020\"\n",
    "    title=\"Present\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vsum1 = WBGT_JJA_daily_mean_max_ssp370_2021_2040_ssp370_30m_EPSG4326[visualize_column].sum()\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= WBGT_JJA_daily_mean_max_ssp370_2021_2040_ssp370_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2021-2040\"\n",
    "    title=\"2040\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vmin = WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326[visualize_column].max()\n",
    "    vsum2 = WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326[visualize_column].sum()\n",
    "\n",
    "    i=2\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= WBGT_JJA_daily_mean_max_ssp370_2041_2060_ssp370_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2041-2060\"\n",
    "    title=\"2060\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    fig = f #ax.get_figure()\n",
    "    # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    # fake up the array of the scalar mappable. Urgh...\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"($^\\circ$C)\", orientation='horizontal' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    plt.show()\n",
    "    WBGT_JJA_daily_mean_max_vsum1= round((vsum1 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    WBGT_JJA_daily_mean_max_vsum2= round((vsum2 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    plt.tight_layout()\n",
    "    map_output=f\"{maps}/WBGT_JJA_daily_mean_max.png\"\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "    return WBGT_JJA_daily_mean_max_vsum1 , WBGT_JJA_daily_mean_max_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "WBGT_JJA_daily_mean_max_vsum1 , WBGT_JJA_daily_mean_max_vsum2=juxtapose_ssps_max_temp_june_august(data)\n",
    "WBGT_JJA_daily_mean_max_vsum1 , WBGT_JJA_daily_mean_max_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def juxtapose_ssps_energy_cooling_days(data):\n",
    "    input_raster_dir = data\n",
    "    root=input_raster_dir\n",
    "    file_name=\"cooling_degree_hours_present_2001_2020_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    cooling_degree_hours_present_2001_2020_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"cooling_degree_hours_ssp370_2021_2040_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    cooling_degree_hours_ssp370_2021_2040_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    visualize_column=raster_val\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = cooling_degree_hours_present_2001_2020_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = cooling_degree_hours_present_2001_2020_30m_EPSG4326[visualize_column].max()\n",
    "    vsum0 = cooling_degree_hours_present_2001_2020_30m_EPSG4326[visualize_column].sum()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=3, figsize=(width, height))\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= cooling_degree_hours_present_2001_2020_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days present 2001 2020\"\n",
    "    title=\"Present\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vsum1 = cooling_degree_hours_ssp370_2021_2040_30m_EPSG4326[visualize_column].sum()\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= cooling_degree_hours_ssp370_2021_2040_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2021-2040\"\n",
    "    title=\"2040\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vmin = cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326[visualize_column].max()\n",
    "    vsum2 = cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326[visualize_column].sum()\n",
    "\n",
    "    i=2\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2041-2060\"\n",
    "    title=\"2060\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    fig = f #ax.get_figure()\n",
    "    # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    # fake up the array of the scalar mappable. Urgh...\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"Cooling degees: Energy demand\", orientation='horizontal' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    plt.show()\n",
    "    cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum1= round((vsum1 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum2= round((vsum2 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    plt.tight_layout()\n",
    "    map_output=f\"{maps}/cooling_degree_hours_ssp370.png\"\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "    return cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum1 , cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum1 , cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum2=juxtapose_ssps_energy_cooling_days(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum1 , cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def juxtapose_ssps_heatwave_days(data):\n",
    "    cities_reprojected=gdf_city.reset_index()\n",
    "    heat_raster_list= [\"heatwave_days_present_2001_2020_30m_EPSG4326\", \"heatwave_days_ssp370_2021_2040_30m_EPSG4326\", \"heatwave_days_ssp370_2041_2060_30m_EPSG4326\", \"heatwave_days_ssp370_2021_2040_30m_EPSG4326\", \"heatwave_days_ssp370_2041_2060_30m_EPSG4326\"]\n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    root=input_raster_dir\n",
    "    file_name=\"heatwave_days_present_2001_2020_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_present_2001_2020_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"heatwave_days_ssp370_2021_2040_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2021_2040_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"heatwave_days_ssp370_2041_2060_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2041_2060_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    visualize_column=raster_val\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].max()\n",
    "    vsum0 = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].sum()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=3, figsize=(width, height))\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_present_2001_2020_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days present 2001 2020\"\n",
    "    title=\"Present\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vsum1 = heatwave_days_ssp370_2021_2040_30m_EPSG4326[visualize_column].sum()\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_ssp370_2021_2040_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2021-2040\"\n",
    "    title=\"2040\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vmin = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].max()\n",
    "    vsum2 = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].sum()\n",
    "\n",
    "    i=2\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_ssp370_2041_2060_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2041-2060\"\n",
    "    title=\"2060\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    fig = f #ax.get_figure()\n",
    "    # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    # fake up the array of the scalar mappable. Urgh...\n",
    "    sm._A = []\n",
    "    # fig.colorbar(sm, cax=cax,  label=\"Population\")\n",
    "    # CB=fig.colorbar(sm, cax=cax,  label=\"Population\", orientation='horizontal', shrink=0.8) #\"($^\\circ$C)\"\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"days\", orientation='horizontal' , shrink=0.8)\n",
    "    # CB=fig.colorbar(gdp_ax, cax=cax,  label=\"Heatwave days\", orientation='horizontal' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    plt.show()\n",
    "    pct_change_heat_days_vsum1= round((vsum1 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    pct_change_heat_days_vsum2= round((vsum2 - vsum0) / abs(vsum0) * 100, 0) \n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    map_output=f\"{maps}/ssps_heatwave_days.png\"\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    return pct_change_heat_days_vsum1 , pct_change_heat_days_vsum2\n",
    "\n",
    "pct_change_heat_days_vsum1 , pct_change_heat_days_vsum2=juxtapose_ssps_heatwave_days(data)\n",
    "pct_change_heat_days_vsum1 , pct_change_heat_days_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "pct_change_heat_days_vsum1 , pct_change_heat_days_vsum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def juxtapose_ssps_tropical_nights(data):\n",
    "    cities_reprojected=gdf_city.reset_index()\n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    root=input_raster_dir\n",
    "    file_name=\"T2M_nightover25_present_2001_2020_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_present_2001_2020_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"T2M_nightover25_ssp370_2021_2040_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2021_2040_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    file_name=\"T2M_nightover25_ssp370_2041_2060_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2041_2060_30m_EPSG4326=polygonized_shapefile\n",
    "\n",
    "    visualize_column=raster_val\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].max()\n",
    "    vsum0 = heatwave_days_present_2001_2020_30m_EPSG4326[visualize_column].sum()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=3, figsize=(width, height))\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_present_2001_2020_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days present 2001 2020\"\n",
    "    title=\"Present\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vsum1 = heatwave_days_ssp370_2021_2040_30m_EPSG4326[visualize_column].sum()\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_ssp370_2021_2040_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2021-2040\"\n",
    "    title=\"2040\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    vmin = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].min()\n",
    "    vmax = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].max()\n",
    "    vsum2 = heatwave_days_ssp370_2041_2060_30m_EPSG4326[visualize_column].sum()\n",
    "\n",
    "    i=2\n",
    "    ax = axs[i]    \n",
    "    gdp_ax= heatwave_days_ssp370_2041_2060_30m_EPSG4326.to_crs(crs).plot(ax=ax, column= visualize_column, linewidth=0, legend=False, cmap=cmap,)\n",
    "    # Remove axis clutter\n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    # title=\"Heatwave days: SSP1 2041-2060\"\n",
    "    title=\"2060\"\n",
    "    ax.set_title(title)\n",
    "\n",
    "    fig = f #ax.get_figure()\n",
    "    # cax = fig.add_axes([0.9, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.2, 0.25, 0.6, 0.02]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    # fake up the array of the scalar mappable. Urgh...\n",
    "    sm._A = []\n",
    "    # fig.colorbar(sm, cax=cax,  label=\"Population\")\n",
    "    # CB=fig.colorbar(sm, cax=cax,  label=\"Population\", orientation='horizontal', shrink=0.8) #\"($^\\circ$C)\"\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"nights\", orientation='horizontal' , shrink=0.8)\n",
    "    # CB=fig.colorbar(gdp_ax, cax=cax,  label=\"Heatwave days\", orientation='horizontal' , shrink=0.8)\n",
    "    # Display the figure\n",
    "    plt.show()\n",
    "    pct_change_T2M_nightover25_vsum1= round((vsum1 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    pct_change_T2M_nightover25_vsum2= round((vsum2 - vsum0) / abs(vsum0) * 100, 0) \n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    map_output=f\"{maps}/ssps_tropical_night_T2M_over_25.png\"\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "    return pct_change_T2M_nightover25_vsum1 , pct_change_T2M_nightover25_vsum2\n",
    "\n",
    "pct_change_T2M_nightover25_vsum1 , pct_change_T2M_nightover25_vsum2=juxtapose_ssps_tropical_nights(data)\n",
    "pct_change_T2M_nightover25_vsum1 , pct_change_T2M_nightover25_vsum2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_profile(maps, vector_file,  hexagons, profile_x_var, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output, scheme):\n",
    "    #------------------------------------------------------\n",
    "    # Centroid for starting the line. use the sample function if needed. \n",
    "    cent_x = gdf_city.centroid.x.values[0]  \n",
    "    cent_y = gdf_city.centroid.y.values[0]  \n",
    "    # print(cent_x)\n",
    "    start = Point(cent_x, cent_y)\n",
    "    length = .05\n",
    "    angle = math.pi / 2\n",
    "    end = Point(start.x + length, start.y)\n",
    "    line = LineString([start, end])\n",
    "    line = rotate(line, angle, origin=start, use_radians=True)\n",
    "    # Define a LineString feature geometry with one attribute\n",
    "    schema = {\n",
    "        'geometry': 'LineString',\n",
    "        'properties': {'id': 'int'},\n",
    "        }\n",
    "    # Write a new Shapefile\n",
    "    with fiona.open(f\"{shapefiles}/Poly.shp\", mode='w', driver='ESRI Shapefile',crs='epsg:4326', schema=schema) as c:\n",
    "        ## If there are multiple geometries, put the \"for\" loop here\n",
    "        c.write({\n",
    "            'geometry': mapping(line),\n",
    "            'properties': {'id': 123},\n",
    "        })\n",
    "\n",
    "    lines_read= gpd.read_file(f\"{shapefiles}/Poly.shp\").to_crs(crs)\n",
    "    newdf = gpd.sjoin(hexagons.to_crs(crs), lines_read)\n",
    "    for ind, row in lines_read.iterrows():\n",
    "        XS_ID = row['id']\n",
    "        start_coords =  list([row.geometry][0].coords)[0]\n",
    "        end_coords = list([row.geometry][0].coords)[1]\n",
    "        lon = [start_coords[0]]\n",
    "        lat = [start_coords[1]]\n",
    "        n_points = 10\n",
    "        for i in np.arange(1, n_points+1):\n",
    "            x_dist = end_coords[0] - start_coords[0]\n",
    "            y_dist = end_coords[1] - start_coords[1]\n",
    "            point  = [(start_coords[0] + (x_dist/(n_points+1))*i), (start_coords[1] + (y_dist/(n_points+1))*i)]\n",
    "            lon.append(point[0])\n",
    "            lat.append(point[1])\n",
    "            \n",
    "        lon.append(end_coords[0])\n",
    "        lat.append(end_coords[1])\n",
    "        df = pd.DataFrame({'Latitude': lat, \n",
    "                        'Longitude': lon})\n",
    "        gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.Longitude, df.Latitude))\n",
    "        gdf.crs = {'init': 'epsg:4326'}\n",
    "        gdf_pcs = gdf.to_crs(epsg = 3857)\n",
    "        gdf_pcs['h_distance'] = 0\n",
    "        for index, row in gdf_pcs.iterrows():\n",
    "            gdf_pcs['h_distance'].loc[index] = gdf_pcs.geometry[0].distance(gdf_pcs.geometry[index])\n",
    "            \n",
    "    line_spat_join = gpd.sjoin(hexagons.to_crs(crs), gdf_pcs.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    fig, ax = plt.subplots(figsize = (3.5, 2.5))\n",
    "    # ax.plot(line_spat_join['h_distance'], line_spat_join[f'{visualize_column}'])\n",
    "    ax.plot(line_spat_join[profile_x_var], line_spat_join[f'{visualize_column}'])\n",
    "    ax.set_title(label=f\"{legend_title} profile\" , fontdict={'fontsize': 11, 'fontweight': 'medium'})\n",
    "    \n",
    "    # fig.suptitle(f\"{legend_title} profile\", fontsize=10)\n",
    "    plt.xticks(fontsize=9) # rotation=90\n",
    "    plt.yticks(fontsize=9) \n",
    "    # Hide the right and top spines\n",
    "    ax.spines[['right', 'top']].set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(f'{maps}/profile_{visualize_column}.png',  bbox_inches='tight', dpi=50) #, transparent=True                \n",
    "    # plt.show()\n",
    "    \n",
    "    #------------------------------------------------------------------------\n",
    "  \n",
    "    ax = hexagons.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0.6, scheme=scheme, cmap=cmap, \\\n",
    "                                legend=True,  # Add legend \n",
    "                                legend_kwds={'loc':'upper right', \n",
    "                                                'bbox_to_anchor':(1, 1), \n",
    "                                                'fmt':legends_format,\n",
    "                                                'markerscale':1.01, \n",
    "                                                'title_fontsize':'small', \n",
    "                                                'fontsize':'x-small'\n",
    "                                                # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                } ,\n",
    "                                aspect=1\n",
    "                                        )\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    lines_read.to_crs(crs).plot(ax=ax,facecolor='blue',edgecolor='black', linewidth=1 ,alpha=0.9)\n",
    "\n",
    "    file = f'{maps}/profile_{visualize_column}.png'\n",
    "    logo = image.imread(file)\n",
    "    imagebox = OffsetImage(logo)\n",
    "    #Container for an `OffsetBox` (here imagebox) referring to a specific position *xy*.\n",
    "    x= cent_x #21.933\n",
    "    y= cent_y #43.343\n",
    "    xy=(x+.09, y+.01)\n",
    "    ab = AnnotationBbox(imagebox, xy, frameon = False)\n",
    "    ax.add_artist(ab)\n",
    "\n",
    "    max_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()][f'{visualize_column}'].round(1)\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Longitude']\n",
    "    \n",
    "    value=f\"Max: {max_value}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude-.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "\n",
    "    \n",
    "    min_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()][f'{visualize_column}'].round(1)\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Longitude']\n",
    "    \n",
    "    value=f\"Min: {min_value}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude+.003), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")) #, shrink=0.007\n",
    "\n",
    "    for x, y in zip(lon[::2], lat[::2]):\n",
    "        # print(f'{x} -> {y}')\n",
    "        value=\"------------\"\n",
    "        ax.text(x, y, f\"{value}\", ha=\"center\", va=\"center\", rotation=90, size=2,\n",
    "                bbox=dict(boxstyle=\"rarrow,pad=0.2\", fc=\"steelblue\", ec=\"black\", lw=2))\n",
    "\n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    leg1 = ax.get_legend()\n",
    "    # Set markers to square shape\n",
    "    for ea in leg1.legendHandles:\n",
    "        ea.set_marker('s')\n",
    "    leg1.set_title(f'{legend_title}')\n",
    "    ax.title.set_text(f'{title}')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tree cover, population density and LST profiles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"pop_density\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                # raster_filename= \"heatwave_days_present_2001_2020_30m_EPSG4326\"\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "\n",
    "                cmap = \"OrRd\"\n",
    "                legends_format= '{:,.2f}'\n",
    "                legend_title= \"Population\"\n",
    "                map_title= f\"Population Density {city}\"\n",
    "                scheme=\"quantiles\"\n",
    "                map_output= f'{maps}/{city}_map_profile_{file_name}.png'\n",
    "                export_the_map= map_profile(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,  profile_x_var='h_distance',legend_title=legend_title,\n",
    "                                            legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                            visualize_column=raster_val,title=map_title, map_output=map_output, scheme=scheme)\n",
    "\n",
    "            elif os.path.splitext(file_name)[-1] == extension and \"land_surface_temperature\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                # raster_filename= \"heatwave_days_present_2001_2020_30m_EPSG4326\"\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                \n",
    "                cmap = \"OrRd\"\n",
    "                legends_format= '{:,.0f}'\n",
    "                legend_title= \"LST\"\n",
    "                map_title= f\"Land Surface Temperature {city}\"\n",
    "                scheme=\"quantiles\"\n",
    "                map_output= f'{maps}/{city}_map_profile_{file_name}.png'\n",
    "                export_the_map= map_profile(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,  profile_x_var='h_distance',legend_title=legend_title,\n",
    "                                            legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                            visualize_column=raster_val,title=map_title, map_output=map_output, scheme=scheme)\n",
    "\n",
    "            elif os.path.splitext(file_name)[-1] == extension and \"tree_cover_landsat\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                # raster_filename= \"heatwave_days_present_2001_2020_30m_EPSG4326\"\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                \n",
    "                cmap = \"Greens\"\n",
    "                legends_format= '{:,.0f}'\n",
    "                legend_title= \"Tree cover\"\n",
    "                map_title= f\"Tree cover {city}\"\n",
    "                scheme=\"quantiles\"\n",
    "                map_output= f'{maps}/{city}_map_profile_{file_name}.png'\n",
    "                export_the_map= map_profile(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,  profile_x_var='h_distance',legend_title=legend_title,\n",
    "                                            legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                            visualize_column=raster_val,title=map_title, map_output=map_output, scheme=scheme)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "park , education, industry, building, building_footprint=export_services_infrastructure_inside_poly_to_shp(shapefile=gdf_city.reset_index())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a map with zoomin to parks, draw a crossection and another for profiles through UHI days and nights. Also export the ssps changes to as a values to the table in ppt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "\n",
    "def get_park_coordinate(park):\n",
    "    print(park.crs)\n",
    "    series = park.geometry\n",
    "    max_distance = 1\n",
    "\n",
    "    points_within = []\n",
    "    for i1,i2 in combinations(range(0,series.shape[0]-1),2):  #Iterate over all pairs/combinations of indices\n",
    "        # if 0.05<series[i1].distance(series[i2])<=max_distance:\n",
    "        if series[i1].distance(series[i2])>0:\n",
    "            points_within.append([series[i1],series[i2]])\n",
    "    geoms = [LineString(pointpair) for pointpair in points_within]\n",
    "    df2 = gpd.GeoDataFrame(geometry=gpd.GeoSeries(geoms))\n",
    "    [points_within[0], points_within[-1]]\n",
    "    return points_within\n",
    "\n",
    "def get_angle(x1,y1,x2,y2):\n",
    "    return math.degrees(math.atan2(y2-y1, x2-x1))\n",
    "\n",
    "points_within=get_park_coordinate(park)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " add min max difference too. Count trees from landsat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_profile_parks(maps, vector_file,  hexagons, park, profile_x_var, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output, scheme):\n",
    "    #------------------------------------------------------\n",
    "    # Centroid for starting the line. use the sample function if needed. \n",
    "    cent_x = gdf_city.centroid.x.values[0]  \n",
    "    cent_y = gdf_city.centroid.y.values[0]  \n",
    "    end = Point(cent_x, cent_y)\n",
    "    park_coords_all=get_park_coordinate(park)\n",
    "    start = park_coords_all[-1][0]\n",
    "    line = LineString([start, end])\n",
    "\n",
    "    lon_park = start.x\n",
    "    lat_park  = start.y\n",
    "    \n",
    "    # Define a LineString feature geometry with one attribute\n",
    "    schema = {\n",
    "        'geometry': 'LineString',\n",
    "        'properties': {'id': 'int'},\n",
    "        }\n",
    "    # Write a new Shapefile\n",
    "    with fiona.open(f\"{shapefiles}/Poly.shp\", mode='w', driver='ESRI Shapefile',crs='epsg:4326', schema=schema) as c:\n",
    "        ## If there are multiple geometries, put the \"for\" loop here\n",
    "        c.write({\n",
    "            'geometry': mapping(line),\n",
    "            'properties': {'id': 123},\n",
    "        })\n",
    "\n",
    "    lines_read= gpd.read_file(f\"{shapefiles}/Poly.shp\").to_crs(crs)\n",
    "    newdf = gpd.sjoin(hexagons.to_crs(crs), lines_read)\n",
    "    for ind, row in lines_read.iterrows():\n",
    "        XS_ID = row['id']\n",
    "        start_coords =  list([row.geometry][0].coords)[0]\n",
    "        end_coords = list([row.geometry][0].coords)[1]\n",
    "        lon = [start_coords[0]]\n",
    "        lat = [start_coords[1]]\n",
    "        n_points = 10\n",
    "        for i in np.arange(1, n_points+1):\n",
    "            x_dist = end_coords[0] - start_coords[0]\n",
    "            y_dist = end_coords[1] - start_coords[1]\n",
    "            point  = [(start_coords[0] + (x_dist/(n_points+1))*i), (start_coords[1] + (y_dist/(n_points+1))*i)]\n",
    "            lon.append(point[0])\n",
    "            lat.append(point[1])\n",
    "            \n",
    "        lon.append(end_coords[0])\n",
    "        lat.append(end_coords[1])\n",
    "        df = pd.DataFrame({'Latitude': lat, \n",
    "                        'Longitude': lon})\n",
    "        gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.Longitude, df.Latitude))\n",
    "        gdf.crs = {'init': 'epsg:4326'}\n",
    "        gdf_pcs = gdf.to_crs(epsg = 3857)\n",
    "        gdf_pcs['h_distance'] = 0\n",
    "        for index, row in gdf_pcs.iterrows():\n",
    "            gdf_pcs['h_distance'].loc[index] = gdf_pcs.geometry[0].distance(gdf_pcs.geometry[index])\n",
    "            \n",
    "    fig, ax = plt.subplots(figsize = (3.5, 2.5))\n",
    "    line_spat_join = gpd.sjoin(hexagons.to_crs(crs), gdf_pcs.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax.plot(line_spat_join[profile_x_var], line_spat_join[f'{visualize_column}'])\n",
    "    ax.set_title(label=f\"{legend_title} profile\" , fontdict={'fontsize': 10, 'fontweight': 'medium'})\n",
    "    \n",
    "    plt.xticks(fontsize=9) # rotation=90\n",
    "    plt.yticks(fontsize=9) \n",
    "    # Hide the right and top spines\n",
    "    ax.spines[['right', 'top']].set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(f'{maps}/profile_{visualize_column}.png',  bbox_inches='tight', dpi=50) #, transparent=True                \n",
    "    # plt.show()\n",
    "    \n",
    "    #-----------------------------------------------------------------------------------------------------------\n",
    "    ax=hexagons.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0.6, scheme=scheme, cmap=cmap, \\\n",
    "                                legend=True,  # Add legend \n",
    "                                legend_kwds={'loc':'upper right', \n",
    "                                                'bbox_to_anchor':(1, 1), \n",
    "                                                'fmt':legends_format,\n",
    "                                                'markerscale':1.01, \n",
    "                                                'title_fontsize':'small', \n",
    "                                                'fontsize':'x-small'\n",
    "                                                # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                                } ,\n",
    "                                aspect=1\n",
    "                                        )\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    lines_read.to_crs(crs).plot(ax=ax,facecolor='blue',edgecolor='black', linewidth=1 ) #,alpha=0.9\n",
    "    file = f'{maps}/profile_{visualize_column}.png'\n",
    "    logo = image.imread(file)\n",
    "    imagebox = OffsetImage(logo)\n",
    "    #Container for an `OffsetBox` (here imagebox) referring to a specific position *xy*.\n",
    "    x= cent_x #21.933\n",
    "    y= cent_y #43.343\n",
    "    xy=(x+.09, y+.01)\n",
    "    ab = AnnotationBbox(imagebox, xy, frameon = False)\n",
    "    ax.add_artist(ab)\n",
    "\n",
    "    max_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()][f'{visualize_column}'].round()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Longitude']\n",
    "    \n",
    "    # print(1)\n",
    "    park_value= line_spat_join.loc[(line_spat_join['Latitude'] == lat_park) & (line_spat_join['Longitude'] == lon_park)][f'{visualize_column}'].round()\n",
    "    park_value=park_value.item()\n",
    "    park_Latitude= line_spat_join.loc[line_spat_join['Latitude']== lat_park, 'Latitude']\n",
    "    park_Longitude= line_spat_join.loc[line_spat_join['Longitude']== lon_park, 'Longitude']\n",
    "    # print(2)\n",
    "\n",
    "    value=f\"Park: {int(park_value)}\"\n",
    "    xy=(park_Longitude, park_Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(park_Longitude+.06, park_Latitude+.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "    # print(3)\n",
    "\n",
    "    value=f\"Max: {int(max_value)}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude-.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "\n",
    "    min_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()][f'{visualize_column}'].round()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Longitude']\n",
    "    \n",
    "    value=f\"Min: {int(min_value)}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude-.06, Latitude+.003), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")) #, shrink=0.007\n",
    "\n",
    "\n",
    "    angle=get_angle(x1=lon_park,y1=lat_park,x2=cent_x,y2=cent_y)\n",
    "    for x, y in zip(lon[::2], lat[::2]):\n",
    "        value=\"---\"\n",
    "        ax.text(x, y, f\"{value}\", ha=\"center\", va=\"center\", rotation=angle, size=2,\n",
    "                bbox=dict(boxstyle=\"rarrow,pad=0.3\", fc=\"steelblue\", ec=\"black\", lw=1))\n",
    "    # print(4)\n",
    "\n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    leg1 = ax.get_legend()\n",
    "    # Set markers to square shape\n",
    "    for ea in leg1.legendHandles:\n",
    "        ea.set_marker('s')\n",
    "    leg1.set_title(f'{legend_title}')\n",
    "    ax.title.set_text(f'{title}')\n",
    "    # print(5)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # ax.figure.savefig(map_output)\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"land_surface_temperature\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                cmap = \"OrRd\"\n",
    "                legends_format= '{:,.0f}'\n",
    "                legend_title= \"LST\"\n",
    "                map_title= f\"Land Surface Temperature {city}\"\n",
    "                scheme=\"quantiles\"\n",
    "                map_output= f'{maps}/{city}_map_park_profile_{file_name}.png'\n",
    "                export_the_map=map_profile_parks(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,park=park,  \n",
    "                                                 profile_x_var='h_distance',legend_title=legend_title, \n",
    "                                                 legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                                 visualize_column=raster_val,title=map_title, map_output=map_output, \n",
    "                                                 scheme=scheme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_profile_parks_zoomed_in(maps, vector_file,  hexagons, park, profile_x_var, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output, scheme):\n",
    "    vmin = hexagons[visualize_column].min()\n",
    "    vmax = hexagons[visualize_column].max()\n",
    "    cent_x = gdf_city.centroid.x.values[0]  \n",
    "    cent_y = gdf_city.centroid.y.values[0]  \n",
    "    park_coords_all=get_park_coordinate(park)\n",
    "    park_coords = park_coords_all[-1][0]\n",
    "    lon_park = park_coords.x\n",
    "    lat_park  = park_coords.y\n",
    "\n",
    "    length = .025\n",
    "    angle = -math.pi / 8\n",
    "    start = Point(park_coords.x - length, park_coords.y)\n",
    "    end = Point(park_coords.x + length, park_coords.y)\n",
    "    park_point = Point(park_coords.x, park_coords.y)\n",
    "    line = LineString([start, end])\n",
    "    # for restircting line within the boundary\n",
    "    gdf_line = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[line])\n",
    "    # line_spat_join_city = gpd.sjoin(gdf_city.to_crs(crs), gdf_line.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    line_spat_join_city =overlay(gdf_line.to_crs(crs), gdf_city.to_crs(crs), how=\"intersection\")\n",
    "\n",
    "    # Extract start and end coordinates of each linestring\n",
    "    first_coord = line_spat_join_city[\"geometry\"].apply(lambda g: g.coords[0])\n",
    "    last_coord = line_spat_join_city[\"geometry\"].apply(lambda g: g.coords[-1])\n",
    "    # Add start and end as columns to the gdf\n",
    "    line_spat_join_city[\"start_coord\"] = first_coord\n",
    "    line_spat_join_city[\"end_coord\"] = last_coord\n",
    "    line_spat_join_city\n",
    "    # line_spat_join_city.end_coord\n",
    "    end=line_spat_join_city['end_coord'].iloc[0]\n",
    "    start=line_spat_join_city['start_coord'].iloc[0]\n",
    "    end_x= end[0]\n",
    "    end_y= end[1]\n",
    "    start_x= start[0]\n",
    "    start_y= start[1]\n",
    "\n",
    "    line = LineString([Point(start_x , start_y), Point(end_x , end_y)])\n",
    "    gdf_line_export = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[line])\n",
    "    gdf_line_export.to_file(f\"{shapefiles}/Poly.shp\")\n",
    "    lines_read= gpd.read_file(f\"{shapefiles}/Poly.shp\").to_crs(crs)\n",
    "    for ind, row in lines_read.iterrows():\n",
    "        # XS_ID = row['id']\n",
    "        start_coords =  list([row.geometry][0].coords)[0]\n",
    "        end_coords = list([row.geometry][0].coords)[1]\n",
    "        lon = [start_coords[0]]\n",
    "        lat = [start_coords[1]]\n",
    "        n_points = 20\n",
    "        for i in np.arange(1, n_points+1):\n",
    "            x_dist = end_coords[0] - start_coords[0]\n",
    "            y_dist = end_coords[1] - start_coords[1]\n",
    "            point  = [(start_coords[0] + (x_dist/(n_points+1))*i), (start_coords[1] + (y_dist/(n_points+1))*i)]\n",
    "            lon.append(point[0])\n",
    "            lat.append(point[1])\n",
    "        lon.append(end_coords[0])\n",
    "        lat.append(end_coords[1])\n",
    "        df = pd.DataFrame({'Latitude': lat, \n",
    "                        'Longitude': lon})\n",
    "        gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.Longitude, df.Latitude))\n",
    "        gdf.crs = {'init': 'epsg:4326'}\n",
    "        gdf_pcs = gdf.to_crs(epsg = 3857)\n",
    "        gdf_pcs['h_distance'] = 0\n",
    "        for index, row in gdf_pcs.iterrows():\n",
    "            gdf_pcs['h_distance'].loc[index] = gdf_pcs.geometry[0].distance(gdf_pcs.geometry[index])\n",
    "            \n",
    "    # fig, ax = plt.subplots(figsize = (3.8, 2.8))\n",
    "    fig, ax = plt.subplots(figsize=(width, height))\n",
    "    line_spat_join = gpd.sjoin(hexagons.to_crs(crs), gdf_pcs.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax.plot(line_spat_join[profile_x_var], line_spat_join[f'{visualize_column}'])\n",
    "    ax.set_title(label=f\"\" , fontdict={'fontsize': 11, 'fontweight': 'medium'})\n",
    "    \n",
    "    plt.xticks(fontsize=9) # rotation=90\n",
    "    plt.yticks(fontsize=9)\n",
    "    ax.set_xlabel('Distance (meters)' , fontsize=9)\n",
    "    ax.set_ylabel(f'{legend_title}', fontsize=9)   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "     \n",
    "    # Hide the right and top spines\n",
    "    ax.spines[['right', 'top']].set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(f'{maps}/profile_{visualize_column}.png',  bbox_inches='tight', dpi=300) #, transparent=True                \n",
    "    # plt.show()\n",
    "    \n",
    "    # Buffer for zoom in\n",
    "    locations = pd.DataFrame([\n",
    "        {'id': '1',\n",
    "        'Longitude': lon_park,\n",
    "        'Latitude': lat_park\n",
    "        }\n",
    "    ])\n",
    "\n",
    "    park_gpd = gpd.GeoDataFrame(locations,geometry=gpd.points_from_xy(locations.Longitude, locations.Latitude),crs=crs)\n",
    "    park_point_feature =park_gpd\n",
    "    park_gpd['geometry'] = park_gpd['geometry'].buffer(length*1.2, cap_style = 3) #Buffer\n",
    "    park_gpd_spat_join =overlay(hexagons.to_crs(crs), park_gpd.to_crs(crs), how=\"intersection\")\n",
    "\n",
    "    #-----------------------------------------------------------------------------------------------------------\n",
    "    ax=park_gpd_spat_join.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0, scheme=scheme, cmap=cmap, \\\n",
    "                                legend=False,  # Add legend \n",
    "                                # legend_kwds={'loc':'upper right', \n",
    "                                #                 'bbox_to_anchor':(1, 1), \n",
    "                                #                 'fmt':legends_format,\n",
    "                                #                 'markerscale':1.01, \n",
    "                                #                 'title_fontsize':'small', \n",
    "                                #                 'fontsize':'x-small'\n",
    "                                #                 # title_fontsizeint or {'xx-small', 'x-small', 'small'\n",
    "                                #                 } ,\n",
    "                                aspect=1\n",
    "                                        )\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    lines_read.to_crs(crs).plot(ax=ax,facecolor='blue',edgecolor='black', linewidth=3 ) #,alpha=0.9\n",
    "    # cx.add_basemap(ax, source=cx.providers.Esri.WorldImagery,  crs=crs) \n",
    "    cx.add_basemap(ax,\n",
    "                zoom=\"auto\",\n",
    "                # zoom=10,\n",
    "                crs=park_gpd_spat_join.crs.to_string(),\n",
    "                # source=cx.providers.CartoDB.Voyager,\n",
    "                source=cx.providers.OpenStreetMap.Mapnik,\n",
    "                attribution=False, #No citation\n",
    "                # attribution_size=1\n",
    "                )\n",
    "    \n",
    "    file = f'{maps}/profile_{visualize_column}.png'\n",
    "    logo = image.imread(file)\n",
    "    imagebox = OffsetImage(logo)\n",
    "    #Container for an `OffsetBox` (here imagebox) referring to a specific position *xy*.\n",
    "    x= end_x #21.933\n",
    "    y= end_y #43.343\n",
    "    xy=(x-length/2, y-length/1.5)\n",
    "    # ab = AnnotationBbox(imagebox, xy, frameon = False)\n",
    "    # ax.add_artist(ab)\n",
    "\n",
    "    max_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()][f'{visualize_column}'].item()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Longitude']\n",
    "    print(1)\n",
    "\n",
    "    points = gpd.GeoSeries([park_point])\n",
    "    point_gdf = gpd.GeoDataFrame({'geometry': points})\n",
    "    park_value_intersected_only = gpd.overlay(point_gdf,  hexagons.to_crs(crs), how='intersection')\n",
    "    park_value= park_value_intersected_only[f'{visualize_column}']\n",
    "    park_value=park_value.item()\n",
    "    print(park_value_intersected_only)\n",
    "    print(park_value)\n",
    "    value=f\"Park: {int(park_value)}\"\n",
    "    # value=f\"Park: {park_value}\"\n",
    "    xy=(lon_park, lat_park)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(lon_park, lat_park+.01), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "    print(3)\n",
    "\n",
    "    value=f\"Max: {int(max_value)}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude, Latitude+.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "\n",
    "    min_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()][f'{visualize_column}'].item()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Longitude']\n",
    "    \n",
    "    value=f\"Min: {int(min_value)}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude, Latitude+.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")) #, shrink=0.007\n",
    "\n",
    "    angle=get_angle(x1=start_x,y1=start_y,x2=end_x,y2=end_y)\n",
    "    for x, y in zip(lon[::], lat[::]):\n",
    "        value=\"-\"\n",
    "        ax.text(x, y, f\"{value}\", ha=\"center\", va=\"center\", rotation=angle, size=2,\n",
    "                bbox=dict(boxstyle=\"rarrow,pad=0.4\", fc=\"black\", ec=\"black\", lw=2))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    title=f\"\"\n",
    "    ax.title.set_text(f'{title}')\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "        for file_name in files_list:\n",
    "            if os.path.splitext(file_name)[-1] == extension and \"land_surface_temperature\" in file_name:\n",
    "                file_name_path = os.path.join(root, file_name)\n",
    "                file_name = os.path.splitext(file_name)[0]\n",
    "                # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "                out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "                print(file_name, file_name_path)\n",
    "                cities_reprojected=gdf_city.reset_index()\n",
    "                unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "                projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "                reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "                projected_raster=projected_raster)\n",
    "                output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "                mask_value=None\n",
    "                variable= file_name\n",
    "                tif = projected_raster \n",
    "                raster_val= variable[0:9]\n",
    "                raster_val= raster_val.replace(\" \", \"_\")\n",
    "                name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                                mask_value=mask_value, raster_val=raster_val)  \n",
    "                polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "                newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "                cmap = \"OrRd\"\n",
    "                legends_format= '{:,.0f}'\n",
    "                legend_title= \"LST\"\n",
    "                map_title= f\"Land Surface Temperature {city}\"\n",
    "                scheme=\"quantiles\"\n",
    "                map_output= f'{maps}/{city}_map_park_profile_zoomed_{file_name}.png'\n",
    "                export_the_map=map_profile_parks_zoomed_in(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,park=park,  \n",
    "                                                 profile_x_var='h_distance',legend_title=legend_title, \n",
    "                                                 legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                                 visualize_column=raster_val,title=map_title, map_output=map_output, \n",
    "                                                 scheme=scheme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "gpd1 = park \n",
    "gpd2 = gdf_city.centroid \n",
    "\n",
    "def ckdnearest(gdA, gdB):\n",
    "    nA = np.array(list(gdA.geometry.apply(lambda x: (x.x, x.y))))\n",
    "    nB = np.array(list(gdB.geometry.apply(lambda x: (x.x, x.y))))\n",
    "    btree = cKDTree(nB)\n",
    "    dist, idx = btree.query(nA, k=1)\n",
    "    gdB_nearest = gdB.iloc[idx].drop(columns=\"geometry\").reset_index(drop=True)\n",
    "    gdf = pd.concat(\n",
    "        [\n",
    "            gdA.reset_index(drop=True),\n",
    "            gdB_nearest,\n",
    "            pd.Series(dist, name='dist')\n",
    "        ], \n",
    "        axis=1)\n",
    "\n",
    "    return gdf\n",
    "\n",
    "nearest_df= ckdnearest(gpd1, gpd2)\n",
    "\n",
    "min_value_row= nearest_df.loc[nearest_df['dist'].idxmin()]\n",
    "min_value_row=min_value_row['geometry']\n",
    "print(min_value_row)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add rivers and water bodies profiles too. And annotate these on the profile line plot too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_profile_parks_zoomed_in_all(maps, vector_file,  hexagons, park_coords, profile_x_var, legend_title,legends_format, crs ,cmap,  visualize_column, title, map_output,profile_output, scheme):\n",
    "    vmin = hexagons[visualize_column].min()\n",
    "    vmax = hexagons[visualize_column].max()\n",
    "    vmean=hexagons[visualize_column].mean()\n",
    "    lon_park = park_coords.x\n",
    "    lat_park  = park_coords.y\n",
    "    length = 0.013\n",
    "    angle = -math.pi / 8\n",
    "    start = Point(park_coords.x - length, park_coords.y)\n",
    "    end = Point(park_coords.x + length, park_coords.y)\n",
    "    park_point = Point(park_coords.x, park_coords.y)\n",
    "    line = LineString([start, end])\n",
    "    # for restircting line within the boundary\n",
    "    gdf_line = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[line])\n",
    "    # line_spat_join_city = gpd.sjoin(gdf_city.to_crs(crs), gdf_line.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    line_spat_join_city =overlay(gdf_line.to_crs(crs), gdf_city.to_crs(crs), how=\"intersection\")\n",
    "\n",
    "    # Extract start and end coordinates of each linestring\n",
    "    first_coord = line_spat_join_city[\"geometry\"].apply(lambda g: g.coords[0])\n",
    "    last_coord = line_spat_join_city[\"geometry\"].apply(lambda g: g.coords[-1])\n",
    "    # Add start and end as columns to the gdf\n",
    "    line_spat_join_city[\"start_coord\"] = first_coord\n",
    "    line_spat_join_city[\"end_coord\"] = last_coord\n",
    "    # line_spat_join_city.end_coord\n",
    "    end=line_spat_join_city['end_coord'].iloc[0]\n",
    "    start=line_spat_join_city['start_coord'].iloc[0]\n",
    "    end_x= end[0]\n",
    "    end_y= end[1]\n",
    "    start_x= start[0]\n",
    "    start_y= start[1]\n",
    "\n",
    "    line = LineString([Point(start_x , start_y), Point(end_x , end_y)])\n",
    "    gdf_line_export = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[line])\n",
    "    gdf_line_export.to_file(f\"{shapefiles}/Poly.shp\")\n",
    "    lines_read= gpd.read_file(f\"{shapefiles}/Poly.shp\").to_crs(crs)\n",
    "    for ind, row in lines_read.iterrows():\n",
    "        # XS_ID = row['id']\n",
    "        start_coords =  list([row.geometry][0].coords)[0]\n",
    "        end_coords = list([row.geometry][0].coords)[1]\n",
    "        lon = [start_coords[0]]\n",
    "        lat = [start_coords[1]]\n",
    "        n_points = 20\n",
    "        for i in np.arange(1, n_points+1):\n",
    "            x_dist = end_coords[0] - start_coords[0]\n",
    "            y_dist = end_coords[1] - start_coords[1]\n",
    "            point  = [(start_coords[0] + (x_dist/(n_points+1))*i), (start_coords[1] + (y_dist/(n_points+1))*i)]\n",
    "            lon.append(point[0])\n",
    "            lat.append(point[1])\n",
    "        lon.append(end_coords[0])\n",
    "        lat.append(end_coords[1])\n",
    "\n",
    "        # Append Park's coords\n",
    "        lon.append(lon_park)\n",
    "        lat.append(lat_park)\n",
    "        \n",
    "        df = pd.DataFrame({'Latitude': lat, \n",
    "                        'Longitude': lon})\n",
    "        \n",
    "        # Sort by two columns after parks added\n",
    "        df = df.sort_values(['Latitude', 'Longitude'], ascending = [True, True])\n",
    "\n",
    "        gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.Longitude, df.Latitude))\n",
    "        gdf.crs = {'init': 'epsg:4326'}\n",
    "        gdf_pcs = gdf.to_crs(epsg = 3857)\n",
    "        gdf_pcs['h_distance'] = 0\n",
    "        for index, row in gdf_pcs.iterrows():\n",
    "            gdf_pcs['h_distance'].loc[index] = gdf_pcs.geometry[0].distance(gdf_pcs.geometry[index])\n",
    "            \n",
    "    fig, ax = plt.subplots(figsize=(width, height))\n",
    "    line_spat_join = gpd.sjoin(hexagons.to_crs(crs), gdf_pcs.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax.plot(line_spat_join[profile_x_var], line_spat_join[f'{visualize_column}'],  marker=\"o\")\n",
    "    ax.set_title(label=f\"\" , fontdict={'fontsize': 12, 'fontweight': 'medium'})\n",
    "    \n",
    "    plt.xticks(fontsize=10) # rotation=90\n",
    "    plt.yticks(fontsize=10)\n",
    "    ax.set_xlabel('Distance (meters)' , fontsize=10)\n",
    "    ax.set_ylabel(f'{legend_title}', fontsize=10)   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
    "\n",
    "    # Hide the right and top spines\n",
    "    ax.spines[['right', 'top']].set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(profile_output,  bbox_inches='tight', dpi=400) #, transparent=True                \n",
    "                \n",
    "    # plt.show()\n",
    "    \n",
    "    # Buffer for zoom in\n",
    "    locations = pd.DataFrame([\n",
    "        {'id': '1',\n",
    "        'Longitude': lon_park,\n",
    "        'Latitude': lat_park\n",
    "        }\n",
    "    ])\n",
    "\n",
    "    park_gpd = gpd.GeoDataFrame(locations,geometry=gpd.points_from_xy(locations.Longitude, locations.Latitude),crs=crs)\n",
    "    park_point_feature =park_gpd\n",
    "    park_gpd['geometry'] = park_gpd['geometry'].buffer(length*1.2, cap_style = 3) #Buffer\n",
    "    park_gpd_spat_join =overlay(hexagons.to_crs(crs), park_gpd.to_crs(crs), how=\"intersection\")\n",
    "\n",
    "    #-----------------------------------------------------------------------------------------------------------\n",
    "    ax=park_gpd_spat_join.to_crs(crs).plot(figsize=(width, height), \\\n",
    "                                column= visualize_column, \\\n",
    "                                alpha=0, scheme=scheme, cmap=cmap, \\\n",
    "                                legend=False,  # Add legend \n",
    "                                aspect=1\n",
    "                                        )\n",
    "    lines_read.to_crs(crs).plot(ax=ax,facecolor='crimson',edgecolor='crimson', linewidth=3 ) #,alpha=0.9\n",
    "    cx.add_basemap(ax,\n",
    "                zoom=\"auto\",\n",
    "                # zoom=10,\n",
    "                crs=park_gpd_spat_join.crs.to_string(),\n",
    "                # source=cx.providers.CartoDB.Voyager,\n",
    "                source=cx.providers.OpenStreetMap.Mapnik,\n",
    "                attribution=False, #No citation\n",
    "                # attribution_size=1\n",
    "                )\n",
    "    \n",
    "    file =profile_output # f'{maps}/profile_{visualize_column}.png'\n",
    "    logo = image.imread(file)\n",
    "    imagebox = OffsetImage(logo)\n",
    "    #Container for an `OffsetBox` (here imagebox) referring to a specific position *xy*.\n",
    "    x= end_x #21.933\n",
    "    y= end_y #43.343\n",
    "    xy=(x-length/2, y-length/1.5)\n",
    "\n",
    "    max_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()][f'{visualize_column}'].round(1) #.item()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmax()]['Longitude']\n",
    "    print(1)\n",
    "\n",
    "    park_value= line_spat_join.loc[(line_spat_join['Latitude'] == lat_park) & (line_spat_join['Longitude'] == lon_park)][f'{visualize_column}'].round(1)\n",
    "    park_value=park_value.item()\n",
    "  \n",
    "    value=f\"Park:{park_value}\"\n",
    "    xy=(lon_park, lat_park)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(lon_park, lat_park+.01), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "    # print(3)\n",
    "\n",
    "    value=f\"Max:{max_value}\"\n",
    "    xy=(Longitude, Latitude)\n",
    "    ax.annotate(f\"{value}\", xy=xy, xycoords='data', xytext=(Longitude, Latitude-.009), textcoords='data',\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "\n",
    "    min_value= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()][f'{visualize_column}'].round(1) #.item()\n",
    "    Latitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Latitude']\n",
    "    Longitude= line_spat_join.loc[line_spat_join[f'{visualize_column}'].idxmin()]['Longitude']\n",
    "    \n",
    "    angle=get_angle(x1=start_x,y1=start_y,x2=end_x,y2=end_y)\n",
    "    for x, y in zip(lon[::], lat[::]):\n",
    "        value=\"-\"\n",
    "        ax.text(x, y, f\"{value}\", ha=\"center\", va=\"center\", rotation=angle, size=1.5,\n",
    "                bbox=dict(boxstyle=\"rarrow,pad=0.4\", fc=\"crimson\", ec=\"crimson\", lw=3))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([]) \n",
    "    title=f\"\"\n",
    "    ax.title.set_text(f'{title}')\n",
    "    plt.tight_layout()\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "    return min_value, max_value, park_value, vmin, vmax, vmean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# if __name__ ==  '__main__': \n",
    "#     input_raster_dir = data\n",
    "#     extension = '.tif'\n",
    "#     for root, dirs_list, files_list in os.walk( input_raster_dir):\n",
    "#         for file_name in files_list:\n",
    "#             if os.path.splitext(file_name)[-1] == extension and \"T2M_DJF_UHI_night_present_2001_2020_30m_EPSG4326\" in file_name:\n",
    "#                 file_name_path = os.path.join(root, file_name)\n",
    "#                 file_name = os.path.splitext(file_name)[0]\n",
    "#                 # file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "#                 out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "#                 print(file_name, file_name_path)\n",
    "#                 cities_reprojected=gdf_city.reset_index()\n",
    "#                 unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "#                 projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "#                 reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "#                 projected_raster=projected_raster)\n",
    "#                 output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "#                 mask_value=None\n",
    "#                 variable= file_name\n",
    "#                 tif = projected_raster \n",
    "#                 raster_val= variable[0:9]\n",
    "#                 raster_val= raster_val.replace(\" \", \"_\")\n",
    "#                 name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "#                                 mask_value=mask_value, raster_val=raster_val)  \n",
    "#                 polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "#                 newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "#                 cmap = \"OrRd\"\n",
    "#                 legends_format= '{:,.0f}'\n",
    "#                 legend_title= \"UHI (Night-time)\"\n",
    "#                 map_title= f\"\"\n",
    "#                 scheme=\"quantiles\"\n",
    "#                 map_output= f'{maps}/{city}_map_park_profile_zoomed_uhi_night{file_name}.png'\n",
    "#                 export_the_map=map_profile_parks_zoomed_in_all(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,min_value_row=min_value_row,  \n",
    "#                                                  profile_x_var='h_distance',legend_title=legend_title, \n",
    "#                                                  legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "#                                                  visualize_column=raster_val,title=map_title, map_output=map_output, \n",
    "#                                                  scheme=scheme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "for index, poi in nearest_df.iterrows():\n",
    "    # if index==49 or index==12 : #checked it.\n",
    "    try:\n",
    "        point_park=poi['geometry']\n",
    "        print(point_park)\n",
    "        cmap = \"coolwarm\"\n",
    "        map_output= f'{maps}/{city}_park_map_{raster_val}_{index}.png'\n",
    "        profile_output= f'{maps}/{city}_park_profile_{raster_val}_{index}.png'\n",
    "        min_value, max_value, park_value, vmin, vmax, vmean=map_profile_parks_zoomed_in_all(maps=maps, vector_file=gdf_city, hexagons=polygonized_shapefile,\n",
    "                                            park_coords=point_park,  profile_x_var='h_distance', legend_title=legend_title, \n",
    "                                            legends_format=legends_format, crs=crs, cmap=cmap, \n",
    "                                            visualize_column=raster_val,title=map_title, map_output=map_output,profile_output=profile_output, scheme=scheme)\n",
    "    except:\n",
    "        print(f'Skipped {index}')\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the same for all infra type health etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def infra_density_map_and_kdeplot(maps, vector_file,  layer, amenity_type):\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    root=input_raster_dir\n",
    "    file_name=\"heatwave_days_present_2001_2020_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "    \n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_present_2001_2020_30m_EPSG4326=newdf\n",
    "    visualize_column=raster_val\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    line_spat_join_layer = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = line_spat_join_layer[visualize_column].min()\n",
    "    vmax = line_spat_join_layer[visualize_column].max()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(width+10, height),  gridspec_kw = {'wspace':.113, 'hspace':0})\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i] \n",
    "    line_spat_join_layer_ax= line_spat_join_layer.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k' , marker ='o' , \n",
    "                        column= visualize_column, \n",
    "                        alpha=0.7, scheme=scheme, cmap=cmap,\n",
    "                        legend=False\n",
    "                        )\n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k', alpha=0.2 ,  linewidth=2)\n",
    "    vector_file.to_crs(crs).buffer(.02).plot(ax=ax,facecolor='none',edgecolor='k', alpha=0.1 ,  linewidth=0)\n",
    "    \n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=f\"{city} {amenity_type}: heatwave days per year\"\n",
    "    ax.set_title(title, fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    cx.add_basemap(ax, zoom=10, crs=line_spat_join_layer.crs.to_string(), source=cx.providers.OpenStreetMap.Mapnik   ,\n",
    "                   attribution=False, #No citation attribution=False, #No citation\n",
    "                   )\n",
    "    # cx.add_basemap(ax,\n",
    "    #             # zoom=\"auto\",\n",
    "    #             zoom=10,\n",
    "    #             crs=vector_file.crs.to_string(),\n",
    "    #             source=cx.providers.CartoDB.Voyager,\n",
    "    #             attribution=False, #No citation\n",
    "    #             # attribution_size=1\n",
    "    #             )\n",
    "    \n",
    "    fig = ax.get_figure()\n",
    "    # cax = fig.add_axes([0.475, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.14, 0.64, 0.022, 0.15]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"\", orientation='vertical' )\n",
    "    field=line_spat_join_layer[visualize_column]\n",
    "    m0=int(np.floor(field.min())+2)            # colorbar min value\n",
    "    m4=int(np.ceil(field.max())-2)             # colorbar max value\n",
    "    m1=int(1*(m4-m0)/2.0 + m0)               # colorbar mid value 1\n",
    "    CB.set_ticks([m0,m1,m4])\n",
    "    CB.set_ticklabels([m0,m1,m4])\n",
    "    CB.ax.tick_params(labelsize=15) \n",
    "\n",
    "    #---------------------------------------------------------------------------------------------------------------------\n",
    "    #Add scenario kdensity plots\n",
    "    #---------------------------------------------------------------------------------------------------------------------\n",
    "    file_name=\"heatwave_days_ssp370_2021_2040_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2021_2040_30m_EPSG4326=newdf\n",
    "\n",
    "    file_name=\"heatwave_days_ssp370_2041_2060_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2041_2060_30m_EPSG4326=newdf\n",
    "\n",
    "    visualize_column=raster_val\n",
    "\n",
    "    # plot\n",
    "    fig_sns, ax_sns = plt.subplots(figsize=(10, 8))\n",
    "    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax_sns, \n",
    "                        common_norm=True, color='blue') #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax_sns, \n",
    "                        common_norm=True,  color='red') \n",
    "    label=\"2041-2060\"\n",
    "    hexagons=heatwave_days_ssp370_2041_2060_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax_sns, \n",
    "                        common_norm=True, color='green') \n",
    "    \n",
    "    \n",
    "    # get the min and max of the x-axis\n",
    "    xmin, xmax = ax_sns.get_xlim()\n",
    "    ymin, ymax = ax_sns.get_ylim()\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    "\n",
    "    ax.set_title(label=f\"{amenity_type} in {city}: Expected heatwave days per year\" , fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Days per year' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_infra_density_graph_1.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column],  ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True,  color='red',  fill=True)\n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    " \n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Days per year' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    # ax.legend(prop=dict(size=16))\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_infra_density_graph_2.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax, \n",
    "                        common_norm=True,  color='red',  fill=True) \n",
    "    label=\"2041-2060\"\n",
    "    hexagons=heatwave_days_ssp370_2041_2060_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True, color='green',  fill=True) \n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    "\n",
    "    ax.set_title(label=f\"{amenity_type} in {city}: Expected heatwave days per year\" , fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Days per year' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_infra_density_graph_3.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    layer=education\n",
    "    amenity_type=\"Schools\"\n",
    "    export_the_map=infra_density_map_and_kdeplot(maps=maps, vector_file=gdf_city, layer=layer, amenity_type=amenity_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def infra_cooling_degrees_density_map_and_kdeplot(maps, vector_file,  layer, amenity_type):\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    input_raster_dir = data\n",
    "    extension = '.tif'\n",
    "    root=input_raster_dir\n",
    "    file_name=\"cooling_degree_hours_present_2001_2020_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "    \n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_present_2001_2020_30m_EPSG4326=newdf\n",
    "    visualize_column=raster_val\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    line_spat_join_layer = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    cmap = \"coolwarm\"\n",
    "    vmin = line_spat_join_layer[visualize_column].min()\n",
    "    vmax = line_spat_join_layer[visualize_column].max()\n",
    "    # Start a loop over all the variables of interest\n",
    "    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(width+10, height),  gridspec_kw = {'wspace':.113, 'hspace':0})\n",
    "    # Make the axes accessible with single indexing\n",
    "    axs = axs.flatten()\n",
    "    i=0\n",
    "    ax = axs[i] \n",
    "    line_spat_join_layer_ax= line_spat_join_layer.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k' , marker ='o' , \n",
    "                        column= visualize_column, \n",
    "                        alpha=0.7, scheme=scheme, cmap=cmap,\n",
    "                        legend=False\n",
    "                        )\n",
    "    vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k', alpha=0.2 ,  linewidth=2)\n",
    "    vector_file.to_crs(crs).buffer(.02).plot(ax=ax,facecolor='none',edgecolor='k', alpha=0.1 ,  linewidth=0)\n",
    "    \n",
    "    ax.set_axis_off()\n",
    "    # Set the axis title to the name of variable being plotted\n",
    "    title=f\"{city} {amenity_type}: Energy needs for space coolings\"\n",
    "    ax.set_title(title, fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    # cx.add_basemap(ax, zoom=\"auto\", crs=line_spat_join_layer.crs, source=cx.providers.OpenStreetMap.Mapnik)\n",
    "    cx.add_basemap(ax,\n",
    "                # zoom=\"auto\",\n",
    "                zoom=10,\n",
    "                crs=vector_file.crs.to_string(),\n",
    "                source=cx.providers.CartoDB.Voyager,\n",
    "                attribution=False, #No citation\n",
    "                # attribution_size=1\n",
    "                )\n",
    "    \n",
    "    fig = ax.get_figure()\n",
    "    # cax = fig.add_axes([0.475, 0.3, 0.02, 0.4]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    cax = fig.add_axes([0.14, 0.64, 0.022, 0.15]) #rect : This parameter is the dimensions [left, bottom, width, height] of the new axes.\n",
    "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    CB=fig.colorbar(sm, cax=cax,  label=\"\", orientation='vertical' )\n",
    "    field=line_spat_join_layer[visualize_column]\n",
    "    m0=int(np.floor(field.min())+2)            # colorbar min value\n",
    "    m4=int(np.ceil(field.max())-2)             # colorbar max value\n",
    "    m1=int(1*(m4-m0)/2.0 + m0)               # colorbar mid value 1\n",
    "    CB.set_ticks([m0,m1,m4])\n",
    "    CB.set_ticklabels([m0,m1,m4])\n",
    "    CB.ax.tick_params(labelsize=15) \n",
    "\n",
    "    #---------------------------------------------------------------------------------------------------------------------\n",
    "    #Add scenario kdensity plots\n",
    "    #---------------------------------------------------------------------------------------------------------------------\n",
    "    file_name=\"cooling_degree_hours_ssp370_2021_2040_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2021_2040_30m_EPSG4326=newdf\n",
    "\n",
    "    file_name=\"cooling_degree_hours_ssp370_2041_2060_30m_EPSG4326.tif\"\n",
    "    file_name_path = os.path.join(root, file_name)\n",
    "    file_name = os.path.splitext(file_name)[0]\n",
    "    file_name = ' '.join(file_name.split(\"_\")[:-2])\n",
    "    out_rst = os.path.join(rasters, f'{file_name}.tif')\n",
    "    print(file_name, file_name_path)\n",
    "    unprojected_raster= file_name_path #os.path.join(data, f'{raster_filename}.tif')  \n",
    "    projected_raster = os.path.join(rasters, f'{file_name}_projected.tif')\n",
    "    reproject_rast= reproject_rasters(crs=crs, output_dir=rasters, unprojected_raster=unprojected_raster, \n",
    "    projected_raster=projected_raster)\n",
    "    output_shapefile = f'{shapefiles}/polygonized_{file_name}.shp'\n",
    "    mask_value=None\n",
    "    variable= file_name\n",
    "    tif = projected_raster \n",
    "    raster_val= variable[0:9]\n",
    "    raster_val= raster_val.replace(\" \", \"_\")\n",
    "    name = polygonize(raster_file=projected_raster ,output_shapefile=output_shapefile ,crs=EPSG_str,  \n",
    "                    mask_value=mask_value, raster_val=raster_val)  \n",
    "\n",
    "    polygonized_shapefile = gpd.read_file(output_shapefile).to_crs(crs)\n",
    "    newdf = overlay(polygonized_shapefile, gdf_city, how=\"intersection\")\n",
    "    heatwave_days_ssp370_2041_2060_30m_EPSG4326=newdf\n",
    "\n",
    "    visualize_column=raster_val\n",
    "\n",
    "    # plot\n",
    "    fig_sns, ax_sns = plt.subplots(figsize=(10, 8))\n",
    "    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax_sns, \n",
    "                        common_norm=True, color='blue') #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax_sns, \n",
    "                        common_norm=True,  color='red') \n",
    "    label=\"2041-2060\"\n",
    "    hexagons=heatwave_days_ssp370_2041_2060_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax_sns = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax_sns, \n",
    "                        common_norm=True, color='green') \n",
    "    \n",
    "    \n",
    "    # get the min and max of the x-axis\n",
    "    xmin, xmax = ax_sns.get_xlim()\n",
    "    ymin, ymax = ax_sns.get_ylim()\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    "\n",
    "    ax.set_title(label=f\"{amenity_type} in {city}:Energy needs for space coolings\" , fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Energy needs for space coolings' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_Energy_infra_density_graph_1.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column],  ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True,  color='red',  fill=True)\n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    " \n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Energy needs for space coolings' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    # ax.legend(prop=dict(size=16))\n",
    "    plt.tight_layout()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_Energy_infra_density_graph_2.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)\n",
    "\n",
    "    i=1\n",
    "    ax = axs[i]    \n",
    "    label=\"Present\"\n",
    "    hexagons=heatwave_days_present_2001_2020_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax, \n",
    "                        common_norm=True, color='blue',  fill=True) #, height=height, aspect=width/height fill=True,\n",
    "    label=\"2021-2040\"\n",
    "    hexagons=heatwave_days_ssp370_2021_2040_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], ax=ax, \n",
    "                        common_norm=True,  color='red',  fill=True) \n",
    "    label=\"2041-2060\"\n",
    "    hexagons=heatwave_days_ssp370_2041_2060_30m_EPSG4326\n",
    "    infra_spat_join_uh = gpd.sjoin(hexagons.to_crs(crs), layer.to_crs(crs), how=\"right\", predicate='contains')\n",
    "    ax = sn.kdeplot(data=infra_spat_join_uh[visualize_column], label=label, ax=ax, \n",
    "                        common_norm=True, color='green',  fill=True) \n",
    "    \n",
    "    ax.set_xlim(xmin, xmax)\n",
    "    ax.set_ylim(ymin, ymax)\n",
    "\n",
    "    ax.set_title(label=f\"{amenity_type} in {city}: Energy needs for space coolings\" , fontdict={'fontsize': 15, 'fontweight': 'medium'})\n",
    "    # sn.set_style('darkgrid', {'legend.frameon':True})\n",
    "    ax.legend(bbox_to_anchor=(.02, 1), loc='upper left', prop=dict(size=16))\n",
    "    ax.set_xlabel('Energy needs for space coolings' , fontsize=11,  fontweight='bold')\n",
    "    ax.set_ylabel(f'Density(Proportion of all {amenity_type})', fontsize=11,  fontweight='bold')   # relative to plt.rcParams['font.size'] , fontsize='medium'\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    map_output= f'{maps}/{city}_{visualize_column}_{amenity_type}_Energy_infra_density_graph_3.png'\n",
    "    ax.figure.savefig(map_output,  bbox_inches='tight' ,   dpi = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "if __name__ ==  '__main__': \n",
    "    layer=industry\n",
    "    amenity_type=\"Industries\"\n",
    "    export_the_map=infra_cooling_degrees_density_map_and_kdeplot(maps=maps, vector_file=gdf_city, layer=layer, amenity_type=amenity_type)\n",
    "    layer=building_footprint\n",
    "    amenity_type=\"Buildings\"\n",
    "    export_the_map=infra_cooling_degrees_density_map_and_kdeplot(maps=maps, vector_file=gdf_city, layer=layer, amenity_type=amenity_type)\n",
    "    layer=building\n",
    "    amenity_type=\"Government buildings\"\n",
    "    export_the_map=infra_cooling_degrees_density_map_and_kdeplot(maps=maps, vector_file=gdf_city, layer=layer, amenity_type=amenity_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='section4'></a>\n",
    "<h5><center> <font color='cyan'> Section 4: Creat the Heat Scan PPT</font>   </center></h5>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def add_city_map_slide(img_path ,tables, prs, width, height, var):\n",
    "    prs.slide_height = Inches(height)\n",
    "    prs.slide_width  = Inches(width+4)\n",
    "    blank_slide_layout = prs.slide_layouts[6]\n",
    "    slide = prs.slides.add_slide(blank_slide_layout)\n",
    "    left = Inches(4)\n",
    "    top =Inches(0)\n",
    "    width = Inches(width)\n",
    "    height = Inches(height)\n",
    "    pic = slide.shapes.add_picture(img_path, left, top, width = width, height = height)\n",
    "\n",
    "    # Add key message box.\n",
    "    # df_= points\n",
    "\n",
    "    text_width = Inches(4)\n",
    "    text_height = Inches(4)\n",
    "    left =Inches(0)\n",
    "    top =Inches(1)\n",
    "    txBox = slide.shapes.add_textbox(left, top, text_width, text_height)\n",
    "    tf = txBox.text_frame\n",
    "    tf.word_wrap = True\n",
    "    p = tf.add_paragraph()\n",
    "    run = p.add_run()\n",
    "    run.text = f'Key Message: {var}'\n",
    "    font = run.font\n",
    "    font.name = 'Yu Gothic'\n",
    "    font.size = Pt(12)\n",
    "    font.bold = True\n",
    "    run.font.color.rgb = RGBColor(0, 0, 0)\n",
    "    prs.save(f'{tables}/Heat_Scan.pptx')\n",
    "\n",
    "def add_cluster_map_slide(img_path ,tables, prs, width, height, var):\n",
    "    prs.slide_height = Inches(height)\n",
    "    prs.slide_width  = Inches(width+4)\n",
    "    blank_slide_layout = prs.slide_layouts[6]\n",
    "    slide = prs.slides.add_slide(blank_slide_layout)\n",
    "    left = Inches(4)\n",
    "    top =Inches(0)\n",
    "    width = Inches(width)\n",
    "    height = Inches(height)\n",
    "    pic = slide.shapes.add_picture(img_path, left, top, width = width, height = height)\n",
    "\n",
    "    # Add key message box.\n",
    "    text_width = Inches(4)\n",
    "    text_height = Inches(2)\n",
    "    left =Inches(0)\n",
    "    top =Inches(0)\n",
    "    txBox = slide.shapes.add_textbox(left, top, text_width, text_height)\n",
    "    tf = txBox.text_frame\n",
    "    tf.word_wrap = True\n",
    "    p = tf.add_paragraph()\n",
    "    run = p.add_run()\n",
    "    run.text = f'Key Message: For {var}, there are five clusters in the city.'\n",
    "    font = run.font\n",
    "    font.name = 'Yu Gothic'\n",
    "    font.size = Pt(13)\n",
    "    font.bold = True\n",
    "    run.font.color.rgb = RGBColor(0, 0, 0)\n",
    "    category=var[0:9]\n",
    "    summary_df=pd.read_csv(f\"{tables}/{category}_summary_stats_df.csv\")\n",
    "    # summary_df=pd.read_csv(f\"{tables}/CSDI ssp370 2021 2040_summary_stats_df.csv\")\n",
    "\n",
    "    # for cluster in summary_df[\"ward5wknn\"]:\n",
    "    # for cluster in range(0,3):\n",
    "    for index, poi in summary_df.iterrows():\n",
    "        cluster=poi[\"ward5wknn\"]\n",
    "        try:\n",
    "            # print(\"read cluster\")\n",
    "            # var1= \"CSDI ssp370 2021 2040\"\n",
    "            clusters_level=pd.read_csv(f\"{tables}/{var}_amenities_stats_all.csv\")\n",
    "            clusters_level= clusters_level.sort_values(['rank'], ascending=[True]) \n",
    "            orientation= clusters_level[clusters_level['ward5wknn']==cluster]['points'].item()\n",
    "            rank= clusters_level[clusters_level['ward5wknn']==cluster]['rank'].item()\n",
    "        except:\n",
    "            orientation=\"in the city\"\n",
    "            rank=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            # var1= \"CSDI ssp370 2021 2040\"\n",
    "            network_df=pd.read_csv(f\"{tables}/{cluster}_{var}_infra_network_stats.csv\")\n",
    "            k_avg= network_df[network_df['node_density_km']!=0]['k_avg'].item()\n",
    "            node_density_km= network_df[network_df['k_avg']!=0]['node_density_km'].item()\n",
    "        except:\n",
    "            k_avg=0\n",
    "            node_density_km=0\n",
    "            pass\n",
    "        try:\n",
    "            parks= pd.read_csv(f\"{tables}/{cluster}_{var}_park_df.csv\") \n",
    "            parks= len(parks)\n",
    "        except:\n",
    "            parks=0\n",
    "            pass\n",
    "        try:\n",
    "            education= pd.read_csv(f\"{tables}/{cluster}_{var}_education_df.csv\") \n",
    "            education= len(education)\n",
    "        except:\n",
    "            education=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            industry= pd.read_csv(f\"{tables}/{cluster}_{var}_industry_df.csv\") \n",
    "            industry= len(industry)\n",
    "        except:\n",
    "            industry=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            buildings= pd.read_csv(f\"{tables}/{cluster}_{var}_building_df.csv\") \n",
    "            buildings= len(buildings)\n",
    "        except:\n",
    "            buildings=0\n",
    "            pass\n",
    "        try:\n",
    "            road_netwrok= pd.read_csv(f\"{tables}/{cluster}_{var}_infra_network_stats.csv\") \n",
    "            road_netwrok= len(road_netwrok)\n",
    "        except:\n",
    "            road_netwrok=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            mean= poi[\"mean\"].round(2)\n",
    "        except:\n",
    "            mean=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            std= poi[\"std\"].round(2)\n",
    "        except:\n",
    "            std=0\n",
    "            pass\n",
    "        try:\n",
    "            min= poi[\"min\"].round(2)\n",
    "        except:\n",
    "            min=0\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            max= poi[\"max\"].round(2)\n",
    "        except:\n",
    "            max=0\n",
    "            pass\n",
    "\n",
    "\n",
    "        # Add key message box.\n",
    "        text_width = Inches(4)\n",
    "        text_height = Inches(4)\n",
    "        left =Inches(0)\n",
    "        top =Inches(2+cluster)\n",
    "        txBox = slide.shapes.add_textbox(left, top, text_width, text_height)\n",
    "        tf = txBox.text_frame\n",
    "        tf.word_wrap = True\n",
    "        p = tf.add_paragraph()\n",
    "        run = p.add_run()   \n",
    "        run.text = f'Cluster {int(cluster)} located in {orientation} of the city is ranked {rank}. It has infrastructure desnity of {int(node_density_km)}. Furthermore, it has a mean of {mean}, Standard Dev of {std}, min {min}, max {max}. It has {parks} parks, {education} education, {industry} industry, {buildings} buildings, {road_netwrok} road_network.'\n",
    "        font = run.font\n",
    "        font.name = 'Yu Gothic'\n",
    "        font.size = Pt(10)\n",
    "        font.bold = True\n",
    "        run.font.color.rgb = RGBColor(0, 0, 0)\n",
    "    prs.save(f'{tables}/Heat_Scan.pptx')\n",
    "\n",
    "            #    stats.to_csv(f\"{tables}/{ward5wknn}_{category}_infra_network_stats.csv\")\n",
    "\n",
    "def add_title_plus_big_picture_slide(city,tables, prs, width, height):\n",
    "    prs.slide_height = Inches(height)\n",
    "    prs.slide_width  = Inches(width)\n",
    "    blank_slide_layout = prs.slide_layouts[1]\n",
    "    slide = prs.slides.add_slide(blank_slide_layout)\n",
    "\n",
    "    shapes = slide.shapes\n",
    "    title_shape = shapes.title\n",
    "    body_shape = shapes.placeholders[1]\n",
    "    title_shape.text = f'{city} Heat Scan'\n",
    "    title_shape.text_frame.paragraphs[0].alignment = PP_ALIGN.CENTER\n",
    "    # title_shape.left = Inches(4)\n",
    "\n",
    "    tf = body_shape.text_frame\n",
    "    tf.text = 'City context: Demographic, tree canopy and Land cover for the city'\n",
    "\n",
    "    p = tf.add_paragraph()\n",
    "    p.text = 'City and Cluster level analysis'\n",
    "    p.level = 1\n",
    "\n",
    "    p = tf.add_paragraph()\n",
    "    p.text = 'Key Messages from each Vito rasters.'\n",
    "    p.level = 2\n",
    "\n",
    "    p = tf.add_paragraph()\n",
    "    p.text = 'Rank and situate clusters and recap descriptives like mean, standard dev. etc for each clusters map.'\n",
    "    p.level = 2\n",
    "\n",
    "    p = tf.add_paragraph()\n",
    "    p.text = 'Infrastructure in each cluster'\n",
    "    p.level = 2\n",
    "\n",
    "    p = tf.add_paragraph()\n",
    "    p.text = 'Amenities like educational facilities, industry, parks and government buildings'\n",
    "    p.level = 2\n",
    "\n",
    "    prs.save(f'{tables}/Heat_Scan.pptx')\n",
    "\n",
    "\n",
    "# Add descriptive stats to both city and cluster maps. Title pages too. Add CRP and Bank Logo\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "\n",
    "def replace_text(input_pptx, prs, search_str , replace_str):\n",
    "    # for slide in ppt.slides:\n",
    "    for slide in prs.slides:\n",
    "        for shape in slide.shapes:\n",
    "            if shape.has_text_frame:\n",
    "                for paragraph in shape.text_frame.paragraphs:\n",
    "                    for run in paragraph.runs:\n",
    "                        for find in search_str:\n",
    "                            # print(run.text)\n",
    "                            if(run.text.find(str(find)))!=-1:\n",
    "                                print(run.text)\n",
    "                                run.text = run.text.replace(find, replace_str)\n",
    "    prs.save(input_pptx)\n",
    "    return prs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "input_pptx= r'C:/Users/Aziz/Dropbox/CRP/UHI/Nis City/output/tables/Nis heat Scan.pptx'\n",
    "prs = Presentation(input_pptx)\n",
    "city_trim=city.split(\" \")[0]\n",
    "print(city_trim)\n",
    "search_str = ['West Athens' , 'WEST ATHENS', 'Western Athens', f'{city}']\n",
    "replace_str = f'{city_trim}'\n",
    "prs= replace_text(input_pptx, prs, search_str , replace_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def map_func_colorbar(vector_file,  hexagons,  crs , visualize_column, map_output):\n",
    "    # hexagons=hexagons.to_crs(crs)\n",
    "    # fig, ax = plt.subplots(figsize = (7,5))\n",
    "    vmin = hexagons[visualize_column].min()\n",
    "    vmax = hexagons[visualize_column].max()\n",
    "    ax = hexagons.to_crs(crs).plot(figsize=(10, 10),column = visualize_column,edgecolor='black',cmap = 'YlOrRd',legend = False)\n",
    "    # plt.xlim([6,19])\n",
    "    # plt.ylim([36,47.7])\n",
    "    aspect = 10\n",
    "    pad_fraction = 0.8\n",
    "    divider = make_axes_locatable(ax)\n",
    "    width = axes_size.AxesY(ax, aspect=1./aspect)\n",
    "    pad = axes_size.Fraction(pad_fraction, width)\n",
    "    cax = divider.append_axes(\"left\", size=width, pad=pad)\n",
    "    sm = plt.cm.ScalarMappable(cmap='YlOrRd', norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    sm._A = []\n",
    "    # cbr = fig.colorbar(sm, cax=cax,)\n",
    "    cbr = plt.colorbar(sm, cax=cax,)\n",
    "    cbr.ax.tick_params(labelsize=25) \n",
    "\n",
    "    # vector_file.to_crs(crs).plot(ax=ax,facecolor='none',edgecolor='k',alpha=0.2)\n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLite,  crs=crs , zoom=12) \n",
    "    # cx.add_basemap(ax, source=cx.providers.Stamen.TonerLabels,   crs=crs, zoom=11) # zoom=13\n",
    "    plt.xticks([], [])\n",
    "    plt.yticks([], [])\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(map_output, bbox_inches='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sectionnew'></a>\n",
    "<h5><center> <font color='cyan'> Section new: Create the biplots and cross-section profiles </font>   </center></h5>\n",
    "\n",
    "**Playground**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import osmnx as ox\n",
    "from io import BytesIO\n",
    "from urllib.request import urlopen\n",
    "from zipfile import ZipFile\n",
    "import tempfile\n",
    "import geopandas as gpd\n",
    "import networkx as nx\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import contextily as ctx\n",
    "\n",
    "# G = ox.graph_from_polygon(mission_shape, network_type='drive')\n",
    "# ox.plot_graph(G)\n",
    "# ox.plot_graph_route\n",
    "\n",
    "# origin_address = \"Pyrmont, NSW\"\n",
    "origin_address = \"Nis, SRB\"\n",
    "\n",
    "# zipurl = 'https://github.com/maxim75/data-visualization/raw/master/notebooks/data/sydney_drive.graphml.zip'\n",
    "# # download ZIP file, unzip it into temp directory and load network from file\n",
    "# with urlopen(zipurl) as zipresp:\n",
    "#     with ZipFile(BytesIO(zipresp.read())) as zfile:\n",
    "#         with tempfile.TemporaryDirectory() as tempdir:\n",
    "#             zfile.extractall(tempdir)\n",
    "#             G = ox.load_graphml(f\"{tempdir}/sydney_drive.graphml\")\n",
    "\n",
    "polygon = gdf_city.iloc[0]['geometry'] \n",
    "G = ox.graph.graph_from_polygon(polygon, network_type='drive')\n",
    "\n",
    "# add travel time based on maximum speed\n",
    "G = ox.add_edge_speeds(G) \n",
    "G = ox.add_edge_travel_times(G) \n",
    "\n",
    "# change map projection to Pseudo-Mercator\n",
    "# this projection is used by most base map tile providers \n",
    "G = ox.projection.project_graph(G, to_crs=3857)\n",
    "# get edges as Geo data frame\n",
    "_, gdf_edges = ox.graph_to_gdfs(G)\n",
    "\n",
    "fig = plt.figure(figsize=(20,16))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.set(facecolor = \"none\")\n",
    "# ax.set(facecolor = \"black\")\n",
    "ax.set_title(\"Roads in Nis with maximum speed more than 50 km/h\", fontsize=20)\n",
    "gdf_edges[gdf_edges.speed_kph > 0].plot(ax=ax, cmap=\"viridis\", column=\"speed_kph\", legend=True)\n",
    "# gdf_edges['speed_kph'].plot(ax=ax, cmap=\"viridis\", column=\"speed_kph\", legend=True)\n",
    "\n",
    "# converts string address into geographocal coordinates\n",
    "def geocode_address(address, crs=4326):\n",
    "    geocode = gpd.tools.geocode(address, provider='nominatim', user_agent=\"drive time demo\").to_crs(crs)\n",
    "    return (geocode.iloc[0].geometry.y, geocode.iloc[0].geometry.x)\n",
    "\n",
    "# find origin network node\n",
    "origin_coordinates = geocode_address(origin_address, crs=3857)\n",
    "origin_node_id = ox.distance.nearest_nodes(G, origin_coordinates[1], origin_coordinates[0])\n",
    "\n",
    "# find travel times and route to each node in network\n",
    "(travel_times, routes) = nx.single_source_dijkstra(G, origin_node_id, weight=\"travel_time\")\n",
    "\n",
    "# set travel_time in minutes as attribute to each network node\n",
    "for node_id in travel_times:\n",
    "    G.nodes.get(node_id)[\"travel_time\"] = travel_times[node_id]/60\n",
    "    \n",
    "# get nodes and edges as Geo data frame\n",
    "gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)\n",
    "\n",
    "# find longest travel time \n",
    "max_time_sec = max(travel_times.values())/60\n",
    "\n",
    "# define color scale using minimum and maximum travel times\n",
    "norm = mcolors.TwoSlopeNorm(vmin=0, vcenter=max_time_sec/3, vmax=max_time_sec)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = plt.axes()\n",
    "# hide x and y axis\n",
    "ax.set_axis_off()\n",
    "\n",
    "# add colotbat showing travel time and corresponding colour\n",
    "cb = fig.colorbar(cm.ScalarMappable(norm=norm, cmap=\"jet\"), \n",
    "    ax=ax, orientation='horizontal')\n",
    "\n",
    "# plot each node with color indicating travel time\n",
    "gdf_nodes.plot(ax=ax, \n",
    "    column=\"travel_time\", cmap=\"jet\", norm=norm, s=10, alpha=.5)\n",
    "\n",
    "# add basemap with location labels\n",
    "ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, alpha=.3)\n",
    "ctx.add_basemap(ax, source=ctx.providers.CartoDB.PositronOnlyLabels)\n",
    "\n",
    "# cx.providers.OpenStreetMap.Mapnik\n",
    "cb.set_label('Driving time in minutes', fontsize = 20)\n",
    "\n",
    "# add city name label\n",
    "# gdf_edges.to_crs(epsg=3857).apply(lambda x: ax.annotate(text=f\"{x.name}  \", fontsize=10, color=\"black\", xy=x.geometry.centroid.coords[0], ha='right'), axis=1)\n",
    "\n",
    "\n",
    "# X=[-1511254.2189787594, -4511254.2189787594,]\n",
    "# Y= [29830656.894387625, 19830656.894387625,]\n",
    "# origin_node_idv = ox.distance.nearest_nodes(G, X, Y)\n",
    "\n",
    "# # find travel times and route to each node in network\n",
    "# # (travel_times, routes) = nx.single_source_dijkstra(G, origin_node_idv, weight=\"travel_time\")\n",
    "# (travel_times, routes) = nx.multi_source_dijkstra(G, origin_node_idv, weight=\"travel_time\")\n",
    "\n",
    "# # set travel_time in minutes as attribute to each network node\n",
    "# for node_id in travel_times:\n",
    "#     G.nodes.get(node_id)[\"travel_time\"] = travel_times[node_id]/60\n",
    "    \n",
    "# # get nodes and edges as Geo data frame\n",
    "# gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)\n",
    "\n",
    "# # find longest travel time \n",
    "# max_time_sec = max(travel_times.values())/60\n",
    "\n",
    "# # define color scale using minimum and maximum travel times\n",
    "# norm = mcolors.TwoSlopeNorm(vmin=0, vcenter=max_time_sec/3, vmax=max_time_sec)\n",
    "\n",
    "# fig = plt.figure(figsize=(20,20))\n",
    "# ax = plt.axes()\n",
    "# # hide x and y axis\n",
    "# ax.set_axis_off()\n",
    "\n",
    "# # add colotbat showing travel time and corresponding colour\n",
    "# cb = fig.colorbar(cm.ScalarMappable(norm=norm, cmap=\"jet\"), \n",
    "#     ax=ax, orientation='horizontal')\n",
    "\n",
    "# # plot each node with color indicating travel time\n",
    "# gdf_nodes.plot(ax=ax, \n",
    "#     column=\"travel_time\", cmap=\"jet\", norm=norm, s=10, alpha=.5)\n",
    "\n",
    "# # add basemap with location labels\n",
    "# ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, alpha=.3)\n",
    "# ctx.add_basemap(ax, source=ctx.providers.CartoDB.PositronOnlyLabels)\n",
    "\n",
    "# # cx.providers.OpenStreetMap.Mapnik\n",
    "# cb.set_label('Driving time in minutes', fontsize = 20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import osmnx as ox\n",
    "\n",
    "# get boundary polygon and unsimplified graph\n",
    "gdf = ox.geocode_to_gdf(\"Nis, Serbia\")\n",
    "geom = gdf.iloc[0][\"geometry\"]\n",
    "G = ox.graph_from_polygon(geom, retain_all=True, truncate_by_edge=True, simplify=False, custom_filter='[\"waterway\"~\"river\"]')\n",
    "\n",
    "# plot unsimplified graph over boundary polygon\n",
    "ax = gdf.plot()\n",
    "fig, ax = ox.plot_graph(G, ax=ax, node_color=\"r\", edge_color=\"k\")\n",
    "\n",
    "# plot simplified graph over boundary polygon\n",
    "Gs = ox.simplify_graph(G)\n",
    "ax = gdf.plot()\n",
    "fig, ax = ox.plot_graph(Gs, ax=ax, node_color=\"r\", edge_color=\"k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "gdf_edges.name.describe()\n",
    "gdf_edges.highway.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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   ],
   "source": [
    "gdf_edges.highway.unique"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mFailed to start the Kernel. \n",
      "\u001b[1;31mUnable to start Kernel 'uh (Python 3.8.17)' due to a timeout waiting for the ports to get used. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  }
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