{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c1b65cb-7982-4700-8b19-510424f02e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61bdecc6-0495-422c-9e32-610c0381ca4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load menu\n",
    "with open(\"mnt/city-directories/01-user-input/menu.yml\", 'r') as f:\n",
    "    menu = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b70b249-9d76-4391-92c2-1ddd7b639980",
   "metadata": {},
   "outputs": [],
   "source": [
    "if menu['all_stats']:\n",
    "    import os\n",
    "    import glob\n",
    "    import math\n",
    "    import geopandas as gpd\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from io import StringIO\n",
    "    import requests\n",
    "    from sklearn.preprocessing import MinMaxScaler\n",
    "    from shapely.geometry import shape\n",
    "    from shapely.ops import unary_union\n",
    "    import pint\n",
    "    import folium\n",
    "    from pathlib import Path\n",
    "    import matplotlib.pyplot as plt\n",
    "    import requests\n",
    "    import re\n",
    "    import rasterio\n",
    "    from rasterio.mask import mask\n",
    "    from shapely.geometry import Point\n",
    "    from fiona.crs import from_epsg\n",
    "    from nbconvert import MarkdownExporter\n",
    "    import nbformat\n",
    "    import base64\n",
    "    import pickle\n",
    "    import plotly.graph_objects as go\n",
    "    import osmnx as ox\n",
    "    from shapely.geometry import box\n",
    "    from matplotlib.lines import Line2D\n",
    "    from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset\n",
    "    import contextily as cx\n",
    "    import matplotlib.patheffects as pe\n",
    "    from sklearn.preprocessing import robust_scale\n",
    "    from sklearn.cluster import AgglomerativeClustering\n",
    "    from rasterio.plot import show\n",
    "    import plotly.express as px\n",
    "    from shapely.geometry import shape\n",
    "    from scipy.stats import linregress\n",
    "    from rasterio.warp import reproject, Resampling\n",
    "    from shapely.ops import transform\n",
    "    from functools import partial\n",
    "    import pyproj\n",
    "    import warnings\n",
    "    import plotly.offline as pyo\n",
    "    #from pysal import lib\n",
    "    #from pysal.explore import esda\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd6b142",
   "metadata": {},
   "source": [
    "##Text begins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f9f5a53-588d-4a6c-b788-a68208b8c2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged pluvial data saved as mnt/city-directories/02-process-output/goris_merged_pluvial_data.tif\n",
      "Merged pluvial data saved as mnt/city-directories/02-process-output/goris_merged_pluvial_data_utm.tif\n",
      "Merged fluvial data saved as mnt/city-directories/02-process-output/goris_merged_fluvial_data.tif\n",
      "Merged fluvial data saved as mnt/city-directories/02-process-output/goris_merged_fluvial_data_utm.tif\n",
      "city name: goris\n",
      "country name:armenia\n"
     ]
    }
   ],
   "source": [
    "# SET UP ##############################################\n",
    "# load city inputs files, to be updated for each city scan\n",
    "with open(\"city_inputs.yml\", 'r') as f:\n",
    "    city_inputs = yaml.safe_load(f)\n",
    "\n",
    "city = city_inputs['city_name'].replace(' ', '_').lower()\n",
    "country = city_inputs['country_name'].replace(' ', '_').lower()\n",
    "\n",
    "# load global inputs, such as data sources that generally remain the same across scans\n",
    "\n",
    "with open(\"global_inputs.yml\", 'r') as f:\n",
    "    global_inputs = yaml.safe_load(f)\n",
    "# run scan assembly and toolbox\n",
    "%run 'scan_assembly.ipynb'\n",
    "%run 'toolbox.ipynb'\n",
    "\n",
    "# transform the input shp to correct prj (epsg 4326)\n",
    "lowest_admin_file = gpd.read_file(city_inputs['lowest_admin_shp']).to_crs(epsg = 4326) \n",
    "lowest_admin_country = lowest_admin_file.geometry\n",
    "aoi_file = gpd.read_file(city_inputs['AOI_path']).to_crs(epsg = 4326)\n",
    "features = aoi_file.geometry\n",
    "# Define output folder ---------\n",
    "output_folder = Path('mnt/city-directories/02-process-output')\n",
    "render_folder = Path('mnt/city-directories/03-render-output')\n",
    "if not os.path.exists(output_folder):\n",
    "    os.mkdir(output_folder)\n",
    "print(f\"city name: {city}\")\n",
    "print(f\"country name:{country}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3cac1a2",
   "metadata": {},
   "source": [
    "##City Subdistricts available?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93b5b0a7-6fc0-417b-a530-d93fd849bc38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city subdistricts are larger than goris: Not available\n"
     ]
    }
   ],
   "source": [
    "#Intersect subdistricts to AOI - Is this necessary? We need ward level files\n",
    "clip_extent = features.geometry.total_bounds\n",
    "clip_box = box(*clip_extent)\n",
    "lower_admin_level = lowest_admin_country.cx[clip_box.bounds[0]:clip_box.bounds[2], clip_box.bounds[1]:clip_box.bounds[3]]\n",
    "#plot check\n",
    "fig, ax = plt.subplots()\n",
    "lower_admin_level.plot(ax=ax)\n",
    "plt.title('Subdistricts')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.show()\n",
    "\n",
    "print(f\"city subdistricts are larger than {city}: Not available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5834e3d7-bb81-44e9-b173-069d3e78581e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standard Units\n",
    "def enumerate_items(source):\n",
    "    print(\"/n\")\n",
    "    for ele in enumerate(source): \n",
    "        print(ele)\n",
    "\n",
    "def list_df_columns(df):\n",
    "    field_list = list(df)\n",
    "    enumerate_items(field_list)\n",
    "    return field_list\n",
    "\n",
    "def percentage_formatter(x, pos):\n",
    "    return f'{x * 100 :,.0f}'\n",
    "\n",
    "def millions_formatter(x, pos):\n",
    "    return f'{x / 1000000 :,.0f}'\n",
    "\n",
    "\n",
    "def hundred_thousand_formatter(x, pos):\n",
    "    return f'{x / 100000 :,.0f}'\n",
    "\n",
    "def billions_formatter(x, pos):\n",
    "    return f'{x / 1000000000 :,.0f}'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfc14d5",
   "metadata": {},
   "source": [
    "##Lowest admin level available?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba0fcbb7-b91e-4b34-80a0-bdc28a3fafa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not available\n",
      "aspect must be finite and positive \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ipshitakarmakar/mambaforge/envs/geo/lib/python3.11/site-packages/geopandas/geodataframe.py:1538: SettingWithCopyWarning:\n",
      "\n",
      "\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",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_lowest_admin_level():\n",
    "    try:\n",
    "        # Reset index of features DataFrame\n",
    "        vector = features.reset_index()\n",
    "        \n",
    "        # Set the CRS to EPSG 4326 (WGS84)\n",
    "        crs = 4326\n",
    "        \n",
    "        # Define OSM admin layers tags\n",
    "        tags = {\"boundary\": \"administrative\"}\n",
    "        \n",
    "        # Get the bounding box of the features\n",
    "        minx, miny, maxx, maxy = vector.to_crs(epsg=4326).total_bounds\n",
    "        \n",
    "        # Retrieve OSM geometries within the bounding box with the specified tags\n",
    "        all_admin_layers = ox.geometries.geometries_from_bbox(miny, maxy, minx, maxx, tags)\n",
    "        \n",
    "        # Get the lowest admin level \n",
    "        lowest_admin_level = all_admin_layers[\"admin_level\"].mode()[0]\n",
    "        warnings.filterwarnings(\"ignore\", message=\"Geometry is in a geographic CRS\")\n",
    "        # Filter OSM geometries to only include the lowest admin level\n",
    "        vector_gdf = all_admin_layers[all_admin_layers['admin_level'] == lowest_admin_level]\n",
    "        \n",
    "        # Define the name for the lowest admin level\n",
    "        lowest_admin_level_name = \"Settlement\"\n",
    "        \n",
    "        # Calculate the area of the geometries before clipping\n",
    "        vector_gdf[\"pre_clip_area\"] = vector_gdf['geometry'].area\n",
    "        \n",
    "        # Clip the OSM geometries using the features geometry\n",
    "        sub_city_gdf = gpd.clip(vector_gdf.to_crs(crs), vector.to_crs(crs))\n",
    "        \n",
    "        # Calculate the area of the clipped geometries\n",
    "        sub_city_gdf[\"post_clip_area\"] = sub_city_gdf['geometry'].area\n",
    "        \n",
    "        # Calculate the percentage of area retained after clipping\n",
    "        sub_city_gdf[\"pct_clip_area\"] = (sub_city_gdf[\"post_clip_area\"] / sub_city_gdf[\"pre_clip_area\"]) * 100\n",
    "        \n",
    "        # Filter out geometries with less than 50% area retained after clipping\n",
    "        sub_city_gdf = sub_city_gdf[sub_city_gdf['pct_clip_area'] > 50]\n",
    "        \n",
    "        # Fill English names with native names\n",
    "        sub_city_gdf['name:en'] = sub_city_gdf['name:en'].str.strip().replace('', np.nan).fillna(sub_city_gdf['name'])\n",
    "        \n",
    "        # Convert the clipped OSM geometries to the desired CRS\n",
    "        sub_city_gdf = sub_city_gdf.to_crs(crs)\n",
    "        \n",
    "    \n",
    "        # Plot your GeoDataFrame\n",
    "        ax = sub_city_gdf.plot(alpha=0.8,\n",
    "                               facecolor='none',\n",
    "                               edgecolor='black',\n",
    "                               label=f\"{city}\",\n",
    "                               missing_kwds={\"color\": \"white\", \"edgecolor\": \"black\", \"label\": \"none\"},\n",
    "                               zorder=5)\n",
    "        \n",
    "        # Show plot\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"Yes, it is available\")\n",
    "    except Exception as e:\n",
    "        print(\"Not available\")\n",
    "        print(e)\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    \n",
    "get_lowest_admin_level() \n",
    "\n",
    "      "
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   "cell_type": "markdown",
   "id": "482571d7",
   "metadata": {},
   "source": [
    "Area of the city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "801220d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Area of the city of goris is 1.4398561252041046e-09 kilometer ** 2\n"
     ]
    }
   ],
   "source": [
    "area = calculate_aoi_area(features)\n",
    "print(f\"Area of the city of {city} is {area}\")"
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  },
  {
   "cell_type": "markdown",
   "id": "3f19f953",
   "metadata": {},
   "source": [
    "Koppen Climate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c0cd019",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Köppen climate classification:  Dfb,  Cfa,  Dfa (See https://en.wikipedia.org/wiki/Köppen_climate_classification for classes)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([' Dfb', ' Cfa', ' Dfa'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "get_koeppen_classification()"
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  },
  {
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   "metadata": {},
   "source": [
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           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Population distribution in goris by sex"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "tickangle": 45,
         "title": {
          "text": "Age Bracket"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Percentage"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "under5: 9.40%\n",
      "youth (15-24): 21.01%\n",
      "working_age (15-64): 0.00%\n",
      "elderly (60+): 39.65%\n",
      "reproductive_age, percent of women (15-50): 45.97%\n",
      "sex_ratio: 0.0 males to 100 females\n"
     ]
    }
   ],
   "source": [
    "age_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e723a110",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Text option 1 - Delta and Range\n",
    "\n",
    "def find_highest_lowest_pixel_value_path(raster_path):\n",
    "    try:\n",
    "        with rasterio.open(raster_path) as src:\n",
    "            # Read the raster data\n",
    "            raster_data = src.read(1)\n",
    "\n",
    "            # Find the highest and lowest pixel values\n",
    "            highest_value = round(raster_data.max(), 2)\n",
    "            lowest_value = round(raster_data[raster_data > 0].min(), 2)  # Exclude zero values\n",
    "            \n",
    "            # Print the statement with reduced digits after the decimal point\n",
    "            print(f\"Values range from {lowest_value:.2f} units to {highest_value:.2f} units\")\n",
    "\n",
    "            return highest_value, lowest_value\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(\"Error:\", e)\n",
    "        return None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe2d7e76-68dc-44bc-879c-69df92053d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Text option 1 - Delta and Range\n",
    "def find_highest_lowest_pixel_value(raster_data):\n",
    "    try:\n",
    "        # Flatten the array, excluding NaN values\n",
    "        valid_values = raster_data[~np.isnan(raster_data)].flatten()\n",
    "        \n",
    "        # Find the highest and lowest pixel values\n",
    "        highest_value = round(np.nanmax(valid_values), 2)\n",
    "        lowest_value = round(np.nanmin(valid_values), 2)\n",
    "        \n",
    "        # Print the statement with reduced digits after the decimal point\n",
    "        print(f\"Values range from {lowest_value:.2f} units to {highest_value:.2f} units\")\n",
    "\n",
    "        return highest_value, lowest_value\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(\"Error:\", e)\n",
    "        return None, None\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6c93ce0-a06a-4f44-88cb-8b1b8e88d827",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Text Option 2 - Clustering \n",
    "def create_raster_clusters(raster_data, n_clusters=5):\n",
    "    try:\n",
    "        if isinstance(raster_data, str):\n",
    "            with rasterio.open(raster_data) as src:\n",
    "                raster_data = src.read(1)\n",
    "                transform = src.transform\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "        flat_data = raster_data.flatten().reshape(-1, 1)\n",
    "\n",
    "        clustering = AgglomerativeClustering(n_clusters=n_clusters).fit(flat_data)\n",
    "\n",
    "        cluster_labels = clustering.labels_.reshape(raster_data.shape)\n",
    "\n",
    "        return cluster_labels\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"Error:\", e)\n",
    "        return None\n",
    "\n",
    "#example usage\n",
    "raster_path = os.path.join(output_folder, city + '_population.tif')\n",
    "create_raster_clusters(raster_path)\n",
    "cluster_labels = create_raster_clusters(raster_path)\n",
    "\n",
    "\n",
    "\n",
    "def plot_cluster_labels_plotly(cluster_labels):\n",
    "    fig = px.imshow(cluster_labels, color_continuous_scale='viridis')\n",
    "    fig.update_layout(\n",
    "        xaxis_title='Column Index',\n",
    "        yaxis_title='Row Index'\n",
    "    )\n",
    "    fig.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "54ded187-d221-48ac-b528-a4921a852ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_raster_cluster_labels(raster_path, n_clusters=5):\n",
    "    try:\n",
    "        # Open the raster file\n",
    "        with rasterio.open(raster_path) as src:\n",
    "            raster_data = src.read(1)\n",
    "            transform = src.transform\n",
    "\n",
    "        # Flatten the raster data to a 1D array\n",
    "        flat_data = raster_data.flatten().reshape(-1, 1)\n",
    "\n",
    "        # Perform agglomerative clustering\n",
    "        clustering = AgglomerativeClustering(n_clusters=n_clusters).fit(flat_data)\n",
    "\n",
    "        # Reshape the cluster labels to match the original raster dimensions\n",
    "        cluster_labels = clustering.labels_.reshape(raster_data.shape)\n",
    "\n",
    "        return cluster_labels, transform\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"Error:\", e)\n",
    "        return None, None\n",
    "\n",
    "# Example usage:\n",
    "raster_path = os.path.join(output_folder, city + '_population.tif')\n",
    "cluster_labels, transform = get_raster_cluster_labels(raster_path)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d2474bc-e0fd-4862-8736-4af7428ca87f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.crs import CRS\n",
    "\n",
    "# Define the Affine transformation matrix\n",
    "affine_transform = transform\n",
    "\n",
    "# Define the EPSG code for the CRS\n",
    "epsg_code = 4326 \n",
    "\n",
    "# Create a CRS object\n",
    "crs = CRS.from_epsg(epsg_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "561c9b5a-1f09-4fdf-9bec-d9be50ada036",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cluster_boundaries(cluster_labels, transform):\n",
    "    # Get unique cluster labels\n",
    "    unique_labels = np.unique(cluster_labels)\n",
    "\n",
    "    # Initialize a list to store cluster boundary polygons\n",
    "    cluster_polygons = []\n",
    "\n",
    "    # Iterate over each unique cluster label\n",
    "    for label in unique_labels:\n",
    "        # Create a mask for pixels with the current label\n",
    "        mask = cluster_labels == label\n",
    "\n",
    "        # Get the indices of non-zero elements in the mask\n",
    "        nonzero_indices = np.argwhere(mask)\n",
    "\n",
    "        # Calculate the bounding box of the cluster\n",
    "        min_row, min_col = np.min(nonzero_indices, axis=0)\n",
    "        max_row, max_col = np.max(nonzero_indices, axis=0)\n",
    "\n",
    "        # Calculate the bounding box coordinates\n",
    "        minx, miny = transform * (min_col, min_row)\n",
    "        maxx, maxy = transform * (max_col, max_row)\n",
    "\n",
    "        # Create the bounding box polygon\n",
    "        geom = box(minx, miny, maxx, maxy)\n",
    "\n",
    "        # Transform the polygon to the desired CRS\n",
    "        geom = gpd.GeoSeries(geom, crs=crs).to_crs(crs)\n",
    "\n",
    "        # Add the polygon to the list\n",
    "        cluster_polygons.append(geom[0])  # Take the first element\n",
    "\n",
    "    # Combine all polygons into a GeoDataFrame\n",
    "    cluster_gdf = gpd.GeoDataFrame({'cluster_label': unique_labels}, geometry=cluster_polygons, crs=crs)\n",
    "\n",
    "    return cluster_gdf\n",
    "\n",
    "cluster_gdf = get_cluster_boundaries(cluster_labels, transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dfe4c998-e046-49b3-820f-1762935462b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_cluster_location(cluster_gdf, aoi_file):\n",
    "    # Get the centroid of the city\n",
    "    city_centroid = shape(aoi_file.geometry.centroid.iloc[0])\n",
    "        \n",
    "    # Get the centroid of the cluster with the highest value\n",
    "    highest_cluster = cluster_gdf[cluster_gdf['cluster_label'] == cluster_gdf['cluster_label'].max()]['geometry'].centroid.values[0]\n",
    "    # Get the centroid of the cluster with the lowest value\n",
    "    lowest_cluster = cluster_gdf[cluster_gdf['cluster_label'] == cluster_gdf['cluster_label'].min()]['geometry'].centroid.values[0]\n",
    "    \n",
    "    # Determine the location of the clusters\n",
    "    highest_location = \"north\" if highest_cluster.y > city_centroid.y else \"south\"\n",
    "    lowest_location = \"north\" if lowest_cluster.y > city_centroid.y else \"south\"\n",
    "    \n",
    "    if highest_cluster.x > city_centroid.x:\n",
    "        highest_location += \" east\"\n",
    "    elif highest_cluster.x < city_centroid.x:\n",
    "        highest_location += \" west\"\n",
    "        \n",
    "    if lowest_cluster.x > city_centroid.x:\n",
    "        lowest_location += \" east\"\n",
    "    elif lowest_cluster.x < city_centroid.x:\n",
    "        lowest_location += \" west\"\n",
    "        \n",
    "    # Check if the centroids are within a range of the city centroid \n",
    "    center_range = 0.2  \n",
    "    center_clusters = cluster_gdf[cluster_gdf['geometry'].apply(lambda geom: abs(geom.centroid.x - city_centroid.x) < center_range and abs(geom.centroid.y - city_centroid.y) < center_range)]\n",
    "    \n",
    "    if len(center_clusters) > 0:\n",
    "        if cluster_gdf['cluster_label'].max() == center_clusters['cluster_label'].max():\n",
    "            highest_location = \"center\"\n",
    "        else:\n",
    "            center_location = \"center\"\n",
    "    \n",
    "    # Print statements describing cluster locations\n",
    "    print(f\"The cluster with the highest value is located in the {highest_location} of the city.\")\n",
    "    print(f\"The cluster with the lowest value is located in the {lowest_location} of the city.\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8c3295a5-bd43-4a44-b602-83195d8056e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Text option 4 - OLS\n",
    "#Bit tricky but might be fun\n",
    "if menu['summer_lst']:  \n",
    "    summer_path = os.path.join(output_folder, city + '_summer.tiff')\n",
    "    with rasterio.open(summer_path) as src:\n",
    "        summer_data = src.read(1)\n",
    "        summer_data = np.nan_to_num(summer_data, nan=0) \n",
    "        transform = src.transform\n",
    "def pixelwise_regression(pop_path, summer_path):\n",
    "    try:\n",
    "        # Open population raster\n",
    "        with rasterio.open(pop_path) as pop_src:\n",
    "            pop_data = pop_src.read(1)\n",
    "            pop_profile = pop_src.profile\n",
    "\n",
    "        # Open summer LST raster and reproject to match population raster\n",
    "        with rasterio.open(summer_path) as summer_src:\n",
    "            summer_data = summer_src.read(1)\n",
    "            summer_profile = summer_src.profile\n",
    "\n",
    "            # Reproject summer LST raster to match population raster\n",
    "            pop_data_resampled = np.zeros_like(summer_data)\n",
    "            reproject(\n",
    "                source=pop_data,\n",
    "                destination=pop_data_resampled,\n",
    "                src_transform=pop_src.transform,\n",
    "                src_crs=pop_src.crs,\n",
    "                dst_transform=summer_src.transform,\n",
    "                dst_crs=summer_src.crs,\n",
    "                resampling=Resampling.bilinear\n",
    "            )\n",
    "\n",
    "        # Flatten the arrays\n",
    "        summer_flat = summer_data.flatten()\n",
    "        pop_flat = pop_data_resampled.flatten()\n",
    "\n",
    "        # Perform pixel-wise linear regression\n",
    "        slope, intercept, r_value, p_value, std_err = linregress(pop_flat, summer_flat)\n",
    "\n",
    "        # Check if p-value is significant\n",
    "        if np.isnan(p_value):\n",
    "            print(\"Regression is not significant. Results may not be reliable.\")\n",
    "\n",
    "        return slope, intercept, r_value, p_value, std_err\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"Error:\", e)\n",
    "        return None, None, None, None, None\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54275b76",
   "metadata": {},
   "source": [
    "Population Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbfd8158-1cc0-4888-bdc5-2890e85ec27d",
   "metadata": {},
   "outputs": [
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    {
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     "text": [
      "Cluster labels shape: (71, 80)\n",
      "The cluster with the highest value is located in the center of the city.\n",
      "The cluster with the lowest value is located in the south east of the city.\n",
      "Values range from 7.36 units to 47.96 units\n",
      "Regression is not significant. Results may not be reliable.\n"
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   ],
   "source": [
    "#Iterate\n",
    "if menu['population']:  \n",
    "    pop_path = os.path.join(output_folder, city + '_population.tif')\n",
    "    with rasterio.open(pop_path) as src:\n",
    "        pop_data = src.read(1)\n",
    "        transform = src.transform  \n",
    "\n",
    "    create_raster_clusters(pop_data, n_clusters=5)\n",
    "    plot_cluster_labels_plotly(cluster_labels)\n",
    "    if cluster_labels is not None:\n",
    "        print(\"Cluster labels shape:\", cluster_labels.shape)\n",
    "        pop_cluster_gdf = get_cluster_boundaries(cluster_labels, transform)\n",
    "        pop_cluster = describe_cluster_location(pop_cluster_gdf, aoi_file)\n",
    "        pop_cluster\n",
    "    find_highest_lowest_pixel_value_path(pop_path)\n",
    "    pixelwise_regression(pop_path, summer_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f09b4e",
   "metadata": {},
   "source": [
    "Economic Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c3c2778",
   "metadata": {},
   "outputs": [
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     "text": [
      "Cluster labels shape: (66, 74)\n",
      "The cluster with the highest value is located in the center of the city.\n",
      "The cluster with the lowest value is located in the south east of the city.\n",
      "Values range from 0.00 units to 1329.00 units\n"
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    "if menu['raster_processing']:  \n",
    "    rad_path = os.path.join(output_folder, city + '_avg_rad_sum.tiff')\n",
    "    with rasterio.open(rad_path) as src:\n",
    "        rad_data = src.read(1)\n",
    "        rad_data = np.nan_to_num(rad_data, nan=0) \n",
    "        transform = src.transform\n",
    "    rad_cluster_labels = create_raster_clusters(rad_data, n_clusters=5)\n",
    "    plot_cluster_labels_plotly(rad_cluster_labels)\n",
    "    if cluster_labels is not None:\n",
    "        print(\"Cluster labels shape:\", rad_cluster_labels.shape)\n",
    "        rad_cluster_gdf = get_cluster_boundaries(rad_cluster_labels, transform)\n",
    "        rad_cluster = describe_cluster_location(rad_cluster_gdf, aoi_file)\n",
    "        rad_cluster\n",
    "    find_highest_lowest_pixel_value(rad_data)"
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   "metadata": {},
   "source": [
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   "execution_count": 31,
   "id": "83bf5ffc",
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     "text": [
      "Cluster labels shape: (66, 74)\n",
      "The cluster with the highest value is located in the center of the city.\n",
      "The cluster with the lowest value is located in the south east of the city.\n",
      "Values range from -0.10 units to 1.76 units\n"
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   "source": [
    "if menu['raster_processing']:  \n",
    "    linfit_path = os.path.join(output_folder, city + '_linfit.tiff')\n",
    "    with rasterio.open(linfit_path) as src:\n",
    "        linfit_data = src.read(1)\n",
    "        linfit_data = np.nan_to_num(linfit_data, nan=0) \n",
    "        transform = src.transform\n",
    "    linfit_cluster_labels = create_raster_clusters(linfit_data, n_clusters=5)\n",
    "    plot_cluster_labels_plotly(linfit_cluster_labels)\n",
    "    if cluster_labels is not None:\n",
    "        print(\"Cluster labels shape:\", linfit_cluster_labels.shape)\n",
    "        linfit_cluster_gdf = get_cluster_boundaries(linfit_cluster_labels, transform)\n",
    "        linfit_cluster = describe_cluster_location(linfit_cluster_gdf, aoi_file)\n",
    "        linfit_cluster\n",
    "    find_highest_lowest_pixel_value(linfit_data)"
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           "title": {
            "standoff": 15
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        },
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        },
        "xaxis": {
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         "domain": [
          0,
          1
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         "showgrid": true,
         "ticktext": [
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          "Aug",
          "Sep",
          "Oct",
          "Nov",
          "Dec"
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         "tickvals": [
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          4,
          5,
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         "title": {
          "text": "Month"
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         "zeroline": false
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        "yaxis": {
         "anchor": "x",
         "domain": [
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   "source": [
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   "metadata": {},
   "source": [
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   "id": "758bb1be-2bfd-455f-ae86-5bf4c5a7dd6a",
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   "outputs": [
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     "text": [
      "Values range from 0.00 units to 47.84 units\n"
     ]
    }
   ],
   "source": [
    "\n",
    "if menu['summer_lst']:  \n",
    "    summer_path = os.path.join(output_folder, city + '_summer.tiff')\n",
    "    with rasterio.open(summer_path) as src:\n",
    "        summer_data = src.read(1)\n",
    "        summer_data = np.nan_to_num(summer_data, nan=0) \n",
    "        transform = src.transform\n",
    "    find_highest_lowest_pixel_value(summer_data)\n"
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   "cell_type": "markdown",
   "id": "c8a7abb9",
   "metadata": {},
   "source": [
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   "execution_count": 36,
   "id": "96f6175d",
   "metadata": {},
   "outputs": [
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     "text": [
      "Values range from -0.04 units to 0.53 units\n"
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    "    NDVI_path = os.path.join(output_folder, city + '_NDVI_Annual.tiff')\n",
    "    with rasterio.open(NDVI_path) as src:\n",
    "        NDVI_data = src.read(1)\n",
    "        NDVI_data = np.nan_to_num(NDVI_data, nan=0) \n",
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   "id": "c1b1a8c2",
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      "      legend  count   percent Percent  Elevation\n",
      "0  1160-1290   1581  0.096520     10%       1160\n",
      "1  1290-1410   5121  0.312637     31%       1290\n",
      "2  1410-1530   5101  0.311416     31%       1410\n",
      "3  1530-1640   4577  0.279426     28%       1530\n",
      "4  1640-1780   3447  0.210440     21%       1640\n"
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    {
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     "text": [
      "Cluster labels shape: (66, 74)\n",
      "The cluster with the highest value is located in the center of the city.\n",
      "The cluster with the lowest value is located in the north west of the city.\n",
      "Values range from 0.00 units to 65535.00 units\n"
     ]
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   ],
   "source": [
    "if menu['slope']:  \n",
    "    slope_path = os.path.join(output_folder, city + '_slope.tif')\n",
    "    with rasterio.open(slope_path) as src:\n",
    "        slope_data = src.read(1)\n",
    "        slope_data = np.nan_to_num(slope_data, nan=0) \n",
    "        transform = src.transform\n",
    "    slope_cluster_labels = create_raster_clusters(linfit_data, n_clusters=10)\n",
    "    plot_cluster_labels_plotly(slope_cluster_labels)\n",
    "    if cluster_labels is not None:\n",
    "        print(\"Cluster labels shape:\", slope_cluster_labels.shape)\n",
    "        slope_cluster_gdf = get_cluster_boundaries(slope_cluster_labels, transform)\n",
    "        slope_cluster = describe_cluster_location(slope_cluster_gdf, aoi_file)\n",
    "        slope_cluster\n",
    "    find_highest_lowest_pixel_value(slope_data)"
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         "text": "Slope Distribution"
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       }
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     },
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    {
     "name": "stdout",
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     "text": [
      "Highest percentage entry for Slope:\n",
      "legend        10-20\n",
      "count          6627\n",
      "percent    0.345084\n",
      "Percent         35%\n",
      "Slope          10.0\n",
      "Name: 3, dtype: object\n"
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   ],
   "source": [
    "slope_stats()"
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  },
  {
   "cell_type": "markdown",
   "id": "52b6d564",
   "metadata": {},
   "source": [
    "NDMI"
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   "execution_count": 40,
   "id": "3c9e6b2e-b35b-4b22-958a-d4d459fb1f43",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values range from -0.22 units to 0.31 units\n"
     ]
    }
   ],
   "source": [
    "if menu['ndmi']:  \n",
    "    NDMI_path = os.path.join(output_folder, city + '_NDMI_Annual.tiff')\n",
    "    with rasterio.open(NDMI_path) as src:\n",
    "        NDMI_data = src.read(1)\n",
    "        NDMI_data = np.nan_to_num(NDMI_data, nan=0) \n",
    "    find_highest_lowest_pixel_value(NDMI_data)"
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  {
   "cell_type": "markdown",
   "id": "5e4311f7",
   "metadata": {},
   "source": [
    "Flooding "
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  {
   "cell_type": "markdown",
   "id": "d1037b36",
   "metadata": {},
   "source": [
    "Pluvial and OSM"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de3c25a1",
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     "text": [
      "0 of 3 (0.00%) health are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "2 of 3 (66.67%) police are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "Fire stations do not exist\n",
      "6 of 10 (60.00%) schools are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "Statistics saved to mnt/city-directories/02-process-output/pu_osmpt.xlsx\n",
      "Total length of highways intersecting pluvial data: 0.04 meters\n",
      "Percentage of highways intersecting pluvial data: 0.0011%\n"
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    },
    {
     "data": {
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       "<Figure size 640x480 with 0 Axes>"
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   "source": [
    "get_pu_am()\n",
    "get_pu_roads()"
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   "id": "7b7a7cd1",
   "metadata": {},
   "source": [
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  {
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   "id": "2cb3943e",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Statistics by year saved to mnt/city-directories/02-process-output/goris_pu_wsf_areas_by_year.xlsx\n",
      "PNG saved to mnt/city-directories/03-render-output/pu_wsf.png\n"
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        },
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         "text": "Flooded Area from 1986 to 2015"
        },
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         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "title": {
          "text": "Area (square units)"
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2015, 152792.80 sq.m of the city’s built-up area (15.33%) was exposed to surface water flooding.\n"
     ]
    }
   ],
   "source": [
    "stats_by_year = get_pu_wsf()\n",
    "\n",
    "if stats_by_year is not None:\n",
    "    df = pd.DataFrame(stats_by_year.items(), columns=['Year', 'Area (square units)'])\n",
    "\n",
    "    excel_file = os.path.join(output_folder, f\"{city}_pu_wsf_areas_by_year.xlsx\")\n",
    "\n",
    "    df.to_excel(excel_file, index=False)\n",
    "    \n",
    "    print(f\"Statistics by year saved to {excel_file}\")\n",
    "else:\n",
    "    print(\"No statistics calculated.\")\n",
    "\n",
    "if stats_by_year is not None:\n",
    "    years = list(stats_by_year.keys())\n",
    "    areas = list(stats_by_year.values())\n",
    "\n",
    "    # Filter years for plotting\n",
    "    years_to_plot = [1986, 1995, 2005, 2015]\n",
    "    areas_to_plot = [stats_by_year.get(year, np.nan) for year in years_to_plot]\n",
    "\n",
    "    # Interpolate missing years' data for a smoother curve\n",
    "    interp_years = np.arange(min(years_to_plot), max(years_to_plot) + 1)\n",
    "    interp_areas = np.interp(interp_years, years_to_plot, areas_to_plot)\n",
    "\n",
    "    # Matplotlib plot\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(interp_years, interp_areas, marker='o', linestyle='-')\n",
    "    plt.title('Flooded Area from 1986 to 2015')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Area (square units)')\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    render_path = os.path.join(render_folder, \"pu_wsf.png\")\n",
    "    plt.savefig(render_path)\n",
    "    plt.close()\n",
    "    print(f\"PNG saved to {render_path}\")\n",
    "\n",
    "    # Plotly plot\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(x=interp_years, y=interp_areas, mode='lines+markers', name='Flooded Area'))\n",
    "    fig.update_layout(title='Flooded Area from 1986 to 2015',\n",
    "                      xaxis_title='Year',\n",
    "                      yaxis_title='Area (square units)')\n",
    "    fig.show()\n",
    "    fig.write_html(render_path.replace('.png', '.html'))\n",
    "\n",
    "    # Calculate 2015 text\n",
    "    total_built_up_area = sum(stats_by_year.values())\n",
    "    flooded_area_2015 = stats_by_year.get(2015, 0)\n",
    "    percentage_2015 = (flooded_area_2015 / total_built_up_area) * 100\n",
    "    print(f\"In 2015, {flooded_area_2015:.2f} sq.m of the city’s built-up area ({percentage_2015:.2f}%) was exposed to surface water flooding.\")\n",
    "else:\n",
    "    print(\"No areas calculated.\")"
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   "id": "21475702",
   "metadata": {},
   "source": [
    "Pluvial and Population"
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  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c5b95240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.90% of densely populated areas are located within the rainwater flood risk zone with a minimum depth of 15 cm\n",
      "Result saved to mnt/city-directories/02-process-output/pu_pop_area.csv\n"
     ]
    }
   ],
   "source": [
    "get_pu_pop_norm()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ff0548",
   "metadata": {},
   "source": [
    "Fluvial and OSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c5e5c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 of 3 (0.00%) health are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "0 of 3 (0.00%) police are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "Fire stations do not exist\n",
      "0 of 10 (0.00%) schools are located in a riverine flood risk zone with a minimum depth of 15 cm.\n",
      "Statistics saved to mnt/city-directories/02-process-output/fu_osmpt.xlsx\n",
      "Total length of highways flooded due to pluvial conditions: 0.00 meters\n",
      "Percentage of highways flooded due to pluvial conditions: 0.00%\n"
     ]
    }
   ],
   "source": [
    "get_fu_am()\n",
    "get_fu_roads()"
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  },
  {
   "cell_type": "markdown",
   "id": "c2e09c9a",
   "metadata": {},
   "source": [
    "Fluvial and WSF"
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  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "99224e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics by year saved to mnt/city-directories/02-process-output/goris_fu_wsf_areas_by_year.xlsx\n",
      "PNG saved to mnt/city-directories/03-render-output/fu_wsf.png\n"
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Flooded Area from 1986 to 2015 "
        },
        "xaxis": {
         "title": {
          "text": "Year"
         }
        },
        "yaxis": {
         "title": {
          "text": "Area (square units)"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "float division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 52\u001b[0m\n\u001b[1;32m     50\u001b[0m     total_built_up_area \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(stats_by_year\u001b[38;5;241m.\u001b[39mvalues())\n\u001b[1;32m     51\u001b[0m     flooded_area_2015 \u001b[38;5;241m=\u001b[39m stats_by_year\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;241m2015\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 52\u001b[0m     percentage_2015 \u001b[38;5;241m=\u001b[39m (\u001b[43mflooded_area_2015\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtotal_built_up_area\u001b[49m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn 2015, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mflooded_area_2015\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m sq.m of the city’s built-up area (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpercentage_2015\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%) was exposed to surface water flooding.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
     ]
    }
   ],
   "source": [
    "stats_by_year = get_fu_wsf()\n",
    "\n",
    "if stats_by_year is not None:\n",
    "    df = pd.DataFrame(stats_by_year.items(), columns=['Year', 'Area (square units)'])\n",
    "\n",
    "    excel_file = os.path.join(output_folder, f\"{city}_fu_wsf_areas_by_year.xlsx\")\n",
    "\n",
    "    df.to_excel(excel_file, index=False)\n",
    "    \n",
    "    print(f\"Statistics by year saved to {excel_file}\")\n",
    "else:\n",
    "    print(\"No statistics calculated.\")\n",
    "\n",
    "if stats_by_year is not None:\n",
    "    years = list(stats_by_year.keys())\n",
    "    areas = list(stats_by_year.values())\n",
    "\n",
    "    # Filter years for plotting\n",
    "    years_to_plot = [1986, 1995, 2005, 2015]\n",
    "    areas_to_plot = [stats_by_year.get(year, np.nan) for year in years_to_plot]\n",
    "\n",
    "    # Interpolate missing years' data for a smoother curve\n",
    "    interp_years = np.arange(min(years_to_plot), max(years_to_plot) + 1)\n",
    "    interp_areas = np.interp(interp_years, years_to_plot, areas_to_plot)\n",
    "\n",
    "    # Matplotlib plot\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(interp_years, interp_areas, marker='o', linestyle='-')\n",
    "    plt.title('Flooded Area from 1986 to 2015 ')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Area (square units)')\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    render_path = os.path.join(render_folder, \"fu_wsf.png\")\n",
    "    plt.savefig(render_path)\n",
    "    plt.close()\n",
    "    print(f\"PNG saved to {render_path}\")\n",
    "\n",
    "    # Plotly plot\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(x=interp_years, y=interp_areas, mode='lines+markers', name='Flooded Area'))\n",
    "    fig.update_layout(title='Flooded Area from 1986 to 2015 ',\n",
    "                      xaxis_title='Year',\n",
    "                      yaxis_title='Area (square units)')\n",
    "    fig.show()\n",
    "    fig.write_html(render_path.replace('.png', '.html'))\n",
    "\n",
    "    # Calculate 2015 text\n",
    "    total_built_up_area = sum(stats_by_year.values())\n",
    "    flooded_area_2015 = stats_by_year.get(2015, 0)\n",
    "    percentage_2015 = (flooded_area_2015 / total_built_up_area) * 100\n",
    "    print(f\"In 2015, {flooded_area_2015:.2f} sq.m of the city’s built-up area ({percentage_2015:.2f}%) was exposed to surface water flooding.\")\n",
    "else:\n",
    "    print(\"No areas calculated.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8874f0",
   "metadata": {},
   "source": [
    "Fluvial and Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5d135cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.72% of densely populated areas are located within the fluvial flood risk zone with a minimum depth of 10 cm\n",
      "Result saved to mnt/city-directories/02-process-output/fu_pop_area.csv\n"
     ]
    }
   ],
   "source": [
    "get_fu_pop_norm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2dd419f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_outputs_to_markdown(notebook_path, output_path):\n",
    "    # Load the notebook\n",
    "    with open(notebook_path, 'r', encoding='utf-8') as f:\n",
    "        notebook_content = nbformat.read(f, as_version=4)\n",
    "    \n",
    "    # Initialize the Markdown exporter\n",
    "    markdown_exporter = MarkdownExporter()\n",
    "    markdown_exporter.exclude_input = True  # Exclude input cells from the Markdown\n",
    "    \n",
    "    # Convert the notebook to Markdown format\n",
    "    markdown_output, resources = markdown_exporter.from_notebook_node(notebook_content)\n",
    "    \n",
    "    # Remove the folders for plots and pickles from the resources\n",
    "    resources.pop('outputs', None)\n",
    "    resources.pop('output_files', None)\n",
    "    \n",
    "    # Write the Markdown content to a file\n",
    "    with open(output_path, 'w', encoding='utf-8') as f:\n",
    "        f.write(markdown_output)\n",
    "\n",
    "# Path to the input notebook\n",
    "input_notebook_path = \"/Users/ipshitakarmakar/Documents/GitHub/city-scan-automation/Text Options- Clustering.ipynb\"\n",
    "\n",
    "# Path for the output Markdown file\n",
    "output_markdown_path = \"/Users/ipshitakarmakar/Documents/GitHub/city-scan-automation/output_notebook.md\"\n",
    "        \n",
    "# Export the outputs to Markdown\n",
    "export_outputs_to_markdown(input_notebook_path, output_markdown_path)\n"
   ]
  }
 ],
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