Loop_Over_Cities.ipynb

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    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "sys.path.append('/home/users/kp_epcc/aq_stripes/visualisation_code/')\n",
    "    \n",
    "from plotting_routines_cities import (plot_absolute_bar_plot,\n",
    "                                      plot_aq_stripes_withline,\n",
    "                                      plot_aq_stripes_withline_withindicativecolourbar,\n",
    "                                      plot_summary_statistics)\n",
    "\n",
    "\n",
    "import settings\n",
    "\n",
    "from settings import OUTPUT_DATA_DIR\n"
   ]
  },
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   "id": "17920772-7b25-4739-aea9-65efe10a1255",
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   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from matplotlib.colors import ListedColormap\n",
    "import os\n",
    "import pandas as pd \n",
    "import textwrap\n",
    "\n",
    "\n",
    "def create_custom_cmap():\n",
    "\n",
    "    colors = [\n",
    "        '#a4ffff',  # 1 Blue\n",
    "        '#b0dae9',  # 2 Blue\n",
    "        '#b0ceed',  # 3 Blue\n",
    "        '#F9E047',  # 4 Yellow\n",
    "        '#f2c84b',  # 5 Yellow\n",
    "        '#f1a63f',  # 6 Orange      \n",
    "        '#E98725',  # 7 Orange\n",
    "        '#af4553',  # 8 (Red)\n",
    "        '#863b47',  # 9 (Dark Red)\n",
    "        '#673a3d',  # 10 (Darker Red)\n",
    "        '#462f30',  # 11 (Very Dark Brown)\n",
    "        '#252424',  # 12 (Almost Black)\n",
    "    ]\n",
    "\n",
    "    num_colors = len(colors)\n",
    "    custom_cmap = ListedColormap(colors)    \n",
    "    return custom_cmap\n",
    "\n"
   ]
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   "source": [
    "from collections import Counter\n",
    "\n",
    "# Define the path to cities data file\n",
    "cities_file_path = os.path.join('List_of_Cities_Continent.txt')\n",
    "\n",
    "# Create an empty dictionary to store city data\n",
    "cities_continent = {}\n",
    "\n",
    "# Load the cities dictionary\n",
    "with open(cities_file_path, 'r') as file:\n",
    "    file_content = file.read()\n",
    "    exec(file_content)  # Executes the cities_continent dictionary assignment\n",
    "\n",
    "# Count the number of unique entries in the dictionary\n",
    "unique_entries_count = len(cities_continent)\n",
    "\n",
    "print(f\"Number of unique entries in cities_continent: {unique_entries_count}\")\n",
    "\n",
    "# Check for duplicate city entries\n",
    "city_names = list(cities_continent.keys())\n",
    "city_counts = Counter(city_names)\n",
    "duplicates = {city: count for city, count in city_counts.items() if count > 1}\n",
    "if duplicates:\n",
    "    print(\"Duplicate entries found:\")\n",
    "    for city, count in duplicates.items():\n",
    "        print(f\"{city} appears {count} times\")\n",
    "else:\n",
    "    print(\"No duplicate entries found.\")"
   ]
  },
  {
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   "cell_type": "code",
   "execution_count": null,
   "id": "2cf18bb9-fb4c-4c30-95fd-773ad3050385",
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   "source": [
    "%reload_ext autoreload\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read in CMIP (model) and VAND (satellitte) data\n",
    "dir_path = \"/home/users/kp_epcc/aq_stripes/input_data/\"\n",
    "\n",
    "ds_VAND = xr.open_dataset(dir_path + \"vand_annual_mean_output.nc\")\n",
    "ds_CMIP = xr.open_dataset(dir_path + \"cmip_annual_mean_output.nc\")\n",
    "\n",
    "# Set the time coordinates to just the year value\n",
    "ds_CMIP['time'] = ds_CMIP['time'].dt.year\n",
    "ds_VAND['time'] = ds_VAND['time'].dt.year\n",
    "\n",
    "# Make a long time axis from start of CMIP to end of VAND\n",
    "years = np.arange(ds_CMIP['time'].min(), ds_VAND['time'].max() + 1)\n",
    "\n",
    "# Define the time period used to average for the weighting\n",
    "time_period = slice(2000, 2002)  \n",
    "time_period_list = [2000, 2001, 2002]  # Only used for plotting\n",
    "\n",
    "#  Call colour scheme\n",
    "custom_cmap = create_custom_cmap()\n",
    "\n",
    "# Dictionary to hold combined data for each city\n",
    "city_combined_data = {}\n",
    "\n",
    "# Create a DataFrame to store the city and corresponding data_ratio\n",
    "df_data_ratio = pd.DataFrame(columns=['City', 'Data_Ratio'])\n",
    "df_format1 = pd.DataFrame({'Year': years})\n",
    "\n",
    "count=0\n",
    "for city, info in cities_continent.items():\n",
    "\n",
    "    count += 1\n",
    "    print(f\"Processing {city}\", f\"Total cities processed: {count}\")    \n",
    "    \n",
    "    latitude = info['lat']\n",
    "    longitude = info['lon']\n",
    "    continent = info['continent'].replace(' ', '_')\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"******************************************************\")\n",
    "    print(\"latitude\",latitude)\n",
    "    print(\"longitude\", longitude)\n",
    "    print(\"City = \",city)\n",
    "    print(\"Continent = \",continent)\n",
    "    print(\"\")   \n",
    "    \n",
    "    # Extract the value at the correct lat / lon of interest\n",
    "    \n",
    "    # Use linear interpolation for CMIP model data and nearest for VAND satellitte data\n",
    "    data_CMIP = ds_CMIP['PM25_CMIP'].interp(latitude=latitude, longitude=longitude, method='linear')    \n",
    "    data_VAND = ds_VAND['PM25_VAND'].sel(latitude=latitude, longitude=longitude, method='nearest')\n",
    "    \n",
    "    # Calculate the three year average value (time_period) for the lat / lon of interest\n",
    "    data_CMIP_mean = data_CMIP.sel(time=time_period).mean(dim='time')\n",
    "    data_VAND_mean = data_VAND.sel(time=time_period).mean(dim='time')   \n",
    "   \n",
    "    # Calculate the ratio of the averaged CMIP data to the averaged VAND data for the lat / lon of interest \n",
    "    data_ratio = data_VAND_mean / data_CMIP_mean\n",
    "\n",
    "    if data_ratio < 2:\n",
    "        uncertainty_classification = \"Low Uncertainty\"\n",
    "    elif data_ratio < 4:\n",
    "        uncertainty_classification = \"Moderate Uncertainty\"\n",
    "    else:\n",
    "        uncertainty_classification = \"High Uncertainty\"\n",
    "\n",
    "    print(uncertainty_classification)\n",
    "\n",
    "    \n",
    "    print(\"Calculated data ratio (VAND/CMIP):\")\n",
    "    print(data_ratio)  \n",
    "\n",
    "    # Weight the CMIP data by the rato of VAND / CMIP\n",
    "    CMIP_data_weighted = data_CMIP * data_ratio\n",
    "\n",
    "    print(\"data_VAND_mean\",data_VAND_mean, \"data_CMIP_mean\",data_CMIP_mean,\" data_ratio = \",data_ratio)\n",
    "    print(\"CMIP_data_weighted size and dimensions before concatenation:\", CMIP_data_weighted.size, CMIP_data_weighted.dims)\n",
    "    print(\"CMIP_data_weighted time range:\", CMIP_data_weighted.time.min().values, \"to\", CMIP_data_weighted.time.max().values)\n",
    "    print(\"data_VAND size and dimensions before concatenation:\", data_VAND.size, data_VAND.dims)\n",
    "    print(\"data_VAND time range:\", data_VAND.time.min().values, \"to\", data_VAND.time.max().values)\n",
    "\n",
    "    # Concatenate data_weighted and data_VAND at the year 2000\n",
    "    data_combined = xr.concat([CMIP_data_weighted.sel(time=slice(None, 2000)), data_VAND.sel(time=slice(2001, None))], dim='time')\n",
    "    \n",
    "    # Print the size and dimensions of the array after concatenation\n",
    "    print(\"data_combined size and dimensions after concatenation:\", data_combined.size, data_combined.dims)\n",
    "    print(\"data_combined time range:\", data_combined.time.min().values, \"to\", data_combined.time.max().values)\n",
    "   \n",
    "    # Store the combined data in the dictionary\n",
    "    city_combined_data[city] = data_combined\n",
    "\n",
    "    # Calculate the maximum value of your data\n",
    "    max_value = max(data_combined.max(), data_CMIP.max(), data_VAND.max())\n",
    "\n",
    "    # Code to create plots to check VAND, CMIMP and combined values   \n",
    "    plt.ylim(0, max_value * 1.2)  \n",
    "    plt.plot(years, data_combined, label=f\"{city} Combined\", color=\"darkgrey\", linewidth=4)\n",
    "    plt.plot(data_CMIP['time'], data_CMIP, label=f\"{city} Model\", linewidth=1.5, linestyle='-.', color=\"blue\")\n",
    "    plt.plot(data_VAND['time'], data_VAND, label=f\"{city} Satellite\", linewidth=1.5, linestyle='--', color=\"red\")\n",
    "    plt.scatter(time_period_list, [data_CMIP_mean] * len(time_period_list), color='darkblue', label=f\"{city} Model 3-year Mean\", zorder=5)\n",
    "    plt.scatter(time_period_list, [data_VAND_mean] * len(time_period_list), color='darkred', label=f\"{city} Satellite 3-year Mean\", zorder=5)\n",
    "    extra_info = f\"{uncertainty_classification}, Data Ratio = {data_ratio:.1f}\"\n",
    "    plt.plot([], [], label=extra_info, color='none')  # Invisible plot for the extra legend entry\n",
    "\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Annual mean PM2.5 (ug m-3)')\n",
    "    plt.title('PM2.5 Timeseries')\n",
    "    plt.legend(fontsize=8)\n",
    "    plt.grid(True)\n",
    "    plot_title = city    \n",
    "    plt.savefig(OUTPUT_DATA_DIR + plot_title + '_'+continent+ '_CMIP_VAND_May.png', dpi=300, bbox_inches='tight', pad_inches=0.05)\n",
    "    plt.show()   \n",
    "    plt.close()\n",
    "    \n",
    "    # Stripes plotting routines\n",
    "    plot_absolute_bar_plot(data_combined.values, data_combined['time'].values, custom_cmap, plot_title, continent)   \n",
    "    plot_aq_stripes_withline(data_combined.values, data_combined['time'].values, custom_cmap, plot_title, continent)\n",
    "    plot_aq_stripes_withline_withindicativecolourbar(data_combined.values, data_combined['time'].values, custom_cmap, plot_title, continent, uncertainty_classification, data_ratio)   \n",
    "    plot_summary_statistics(data_combined.values, data_combined['time'].values, custom_cmap, plot_title, continent)\n",
    "\n",
    "    # Append the city and its data_ratio to the DataFrame\n",
    "    df_data_ratio = pd.concat([df_data_ratio, pd.DataFrame({'City': [city], 'Data_Ratio': [data_ratio.values]})], ignore_index=True)\n",
    "\n",
    "    # Add the city's data to the DataFrame in wide format\n",
    "    df_format1[city] = data_combined.sel(time=years).values\n",
    "\n",
    "# Write cities PM2.5 data and sat to model data ratio to file\n",
    "df_data_ratio.to_excel(OUTPUT_DATA_DIR + \"Cities_Data_Ratios.xlsx\", index=False)\n",
    "df_format1.to_excel(OUTPUT_DATA_DIR + \"Cities_Wide_Format_Data.xlsx\", index=False)  \n",
    "\n",
    "# Count the number of files containing the word \"CMIP\"\n",
    "cmip_file_count = sum(1 for file_name in os.listdir(OUTPUT_DATA_DIR) if \"CMIP\" in file_name)\n",
    "\n",
    "# Compare and print results\n",
    "if cmip_file_count == unique_entries_count:\n",
    "    print(f\"The number of files with 'CMIP' ({cmip_file_count}) matches the unique city count ({unique_entries_count}).\")\n",
    "else:\n",
    "    print(f\"Mismatch: {cmip_file_count} files with 'CMIP', but {unique_entries_count} unique city entries.\")\n"
   ]
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