Prominent British Mountains

metadata

In a former life I considered myself a mountaineer; recently I stumbled across The Database of British and Irish Hills (DoBIH) and it got me thinking about how many of them I have visited. In the UK a “mountain” is anything with an elevation over 600m. Below is a map of all of the mountains in the British Isles.

Download:
  1. 512 px × 384 px (0.2 Mpx; 93.5 KiB)
  2. 1,024 px × 768 px (0.8 Mpx; 308.5 KiB)
  3. 2,048 px × 1,536 px (3.1 Mpx; 950.9 KiB)
  4. 2,880 px × 2,160 px (6.2 Mpx; 1.2 MiB)

As you can see, the vast majority of them are in Scotland.

Below is a map of all of the mountains over 900m in the British Isles.

Download:
  1. 512 px × 384 px (0.2 Mpx; 84.7 KiB)
  2. 1,024 px × 768 px (0.8 Mpx; 279.5 KiB)
  3. 2,048 px × 1,536 px (3.1 Mpx; 860.4 KiB)
  4. 2,880 px × 2,160 px (6.2 Mpx; 1.1 MiB)

Once again, the vast majority are in Scotland. Furthermore, the only ones in England are in The Lake District.

Recently, I have become interested in the idea of the prominence of mountains as a metric for is it worth climbing? Wikipedia has a list of all of the mountains in the British Isles with a prominence over 600m. Using The Database of British and Irish Hills (DoBIH) I wrote a Python script to create a map of these prominent mountains, which is shown below.

Download:
  1. 512 px × 384 px (0.2 Mpx; 86.4 KiB)
  2. 1,024 px × 768 px (0.8 Mpx; 280.9 KiB)
  3. 2,048 px × 1,536 px (3.1 Mpx; 857.9 KiB)
  4. 2,880 px × 2,160 px (6.2 Mpx; 1.1 MiB)

As you can see, adding a prominence filter on top of the elevation filter really starts to curtail the list of mountains in the British Isles: there are only 7 in Wales; and there are only 4 in England.

Finally, to be really strict, I created a map and table (shown below) of all of the mountains in the British Isles with a prominence over 900m.

Download:
  1. 512 px × 384 px (0.2 Mpx; 81.5 KiB)
  2. 1,024 px × 768 px (0.8 Mpx; 269.9 KiB)
  3. 2,048 px × 1,536 px (3.1 Mpx; 830.9 KiB)
  4. 2,880 px × 2,160 px (6.2 Mpx; 1.1 MiB)
Mountain Elevation [m] Prominence [m]
Ben More 1,174.0 986.0
Ben Lawers 1,214.0 915.0
Ben Nevis 1,344.5 1,344.5
Ben Macdui 1,309.0 950.0
Carn Eighe 1,182.8 1,147.0
Liathach - Spidean a’ Choire Leith 1,054.8 957.0
Sgurr Mor 1,108.9 914.0
Sgurr Alasdair 992.0 992.0
Ben More 966.0 966.0
Snowdon - Yr Wyddfa 1,085.0 1,039.0
Scafell Pike 978.1 912.0
Carrauntoohil 1,038.6 1,038.6
Brandon Mountain 951.7 927.0

This seems like a decent “bucket list” doesn’t it?

Below you will find the four Python scripts that I wrote to generate these four maps, enjoy!

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#!/usr/bin/env python3

# Use the proper idiom in the main module ...
# NOTE: See https://docs.python.org/3.12/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
    # NOTE: "The Database of British and Irish Hills" can be visited here:
    #         * http://www.hills-database.co.uk/
    #       ... and the links to download the database can be found here:
    #         * http://www.hills-database.co.uk/downloads.html

    # Import standard modules ...
    import csv
    import json
    import os
    import zipfile

    # Import special modules ...
    try:
        import cartopy
        cartopy.config.update(
            {
                "cache_dir" : os.path.expanduser("~/.local/share/cartopy_cache"),
            }
        )
    except:
        raise Exception("\"cartopy\" is not installed; run \"pip install --user Cartopy\"") from None
    try:
        import matplotlib
        matplotlib.rcParams.update(
            {
                       "backend" : "Agg",                                       # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
                    "figure.dpi" : 300,
                "figure.figsize" : (9.6, 7.2),                                  # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes
                     "font.size" : 8,
            }
        )
        import matplotlib.pyplot
    except:
        raise Exception("\"matplotlib\" is not installed; run \"pip install --user matplotlib\"") from None
    try:
        import numpy
    except:
        raise Exception("\"numpy\" is not installed; run \"pip install --user numpy\"") from None

    # Import my modules ...
    try:
        import pyguymer3
        import pyguymer3.geo
        import pyguymer3.image
    except:
        raise Exception("\"pyguymer3\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH") from None

    # **************************************************************************

    # Start session ...
    with pyguymer3.start_session() as sess:
        # Check if the database is missing ...
        if not os.path.exists("hillcsv.zip"):
            # Download the database ...
            pyguymer3.download_file(sess, "http://www.hills-database.co.uk/hillcsv.zip", "hillcsv.zip")

    # **************************************************************************

    # Initialize counter ...
    n = 0                                                                       # [#]

    # Initialize lists ...
    x = []                                                                      # [°]
    y = []                                                                      # [°]
    z = []                                                                      # [m]
    xRaw = []                                                                   # [°]
    yRaw = []                                                                   # [°]

    # Open output file ...
    with open("british-hills-E600.txt", "wt", encoding = "utf-8") as fObjOut:
        # Open input file ...
        with zipfile.ZipFile("hillcsv.zip", "r") as fObjIn:
            # Loop over members ...
            for fname in fObjIn.namelist():
                # Skip those that are not CSVs ...
                if not fname.endswith(".csv"):
                    continue

                # Read CSV dataset as a list of strings ...
                rows = fObjIn.read(fname).decode("ascii").splitlines()

                # Loop over rows ...
                for row in csv.DictReader(rows):
                    # Append to lists ...
                    xRaw.append(float(row["Longitude"]))                        # [°]
                    yRaw.append(float(row["Latitude"]))                         # [°]

                    # Skip this row if the elevation is too small ...
                    if float(row["Metres"]) < 600.0:
                        continue

                    # Increment counter ...
                    n += 1                                                      # [#]

                    # Append to lists ...
                    x.append(float(row["Longitude"]))                           # [°]
                    y.append(float(row["Latitude"]))                            # [°]
                    z.append(float(row["Metres"]))                              # [m]

                    # Write summary ...
                    fObjOut.write(f'"{row["Name"]}" is {float(row["Metres"]):,.1f}m ASL with a prominence of {float(row["Drop"]):,.1f}m at ({float(row["Latitude"]):f}°,{float(row["Longitude"]):f}°).\n')

    # Print summary ...
    print(f"There are {n:,d} E600 mountains in The British Isles.")

    # **************************************************************************

    # Load tile metadata ...
    with open("OrdnanceSurveyBackgroundImages/miniscale.json", "rt", encoding = "utf-8") as fObj:
        meta = json.load(fObj)

    # Convert lists to arrays and find the sorted keys ...
    x = numpy.array(x)                                                          # [°]
    y = numpy.array(y)                                                          # [°]
    z = numpy.array(z)                                                          # [m]
    k = numpy.argsort(z)
    xRaw = numpy.array(xRaw)                                                    # [°]
    yRaw = numpy.array(yRaw)                                                    # [°]

    # Find middle of points and the furthest distance ...
    midLon, midLat, maxDist = pyguymer3.geo.find_middle_of_locs(
        xRaw,
        yRaw,
        pad = 12.0 * 1852.0,
    )                                                                           # [°], [°], [m]

    # Create figure ...
    fg = matplotlib.pyplot.figure()

    # Create axis ...
    ax = pyguymer3.geo.add_axis(
        fg,
        dist = maxDist,
         lat = midLat,
         lon = midLon,
    )

    # Add background image ...
    ax.imshow(
        matplotlib.pyplot.imread(f'OrdnanceSurveyBackgroundImages/{meta["MiniScale_(mono)_R22"]["greyscale"]}'),
                 cmap = "gray",
               extent = meta["MiniScale_(mono)_R22"]["extent"],
        interpolation = "bicubic",
               origin = "upper",
            transform = cartopy.crs.OSGB(),
                 vmax = 1.0,
                 vmin = 0.0,
    )

    # Plot data (layering them correctly) ...
    # NOTE: As of 5/Dec/2023, the default "zorder" of the coastlines is 1.5, the
    #       default "zorder" of the gridlines is 2.0 and the default "zorder" of
    #       the scattered points is 1.0.
    sc = ax.scatter(
        x[k],
        y[k],
                 c = z[k],
              cmap = matplotlib.pyplot.cm.rainbow,
        edgecolors = "black",
         linewidth = 0.1,
                 s = 10.0,
         transform = cartopy.crs.PlateCarree(),
              vmin = 600.0,
            zorder = 5.0,
    )

    # Add colour bar ...
    cb = fg.colorbar(sc, ax = ax, orientation = "vertical")

    # Configure axis ...
    ax.set_title("E600 Mountains In The British Isles")

    # Configure colour bar ...
    cb.set_label("Elevation [m]")

    # Configure figure ...
    fg.tight_layout()

    # Save figure ...
    fg.savefig("british-hills-E600.png")
    matplotlib.pyplot.close(fg)

    # Optimize PNG ...
    pyguymer3.image.optimize_image("british-hills-E600.png", strip = True)
You may also download “british-hills-E600.py” directly or view “british-hills-E600.py” on GitHub Gist (you may need to manually checkout the “main” branch).
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#!/usr/bin/env python3

# Use the proper idiom in the main module ...
# NOTE: See https://docs.python.org/3.12/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
    # NOTE: "The Database of British and Irish Hills" can be visited here:
    #         * http://www.hills-database.co.uk/
    #       ... and the links to download the database can be found here:
    #         * http://www.hills-database.co.uk/downloads.html

    # Import standard modules ...
    import csv
    import json
    import os
    import zipfile

    # Import special modules ...
    try:
        import cartopy
        cartopy.config.update(
            {
                "cache_dir" : os.path.expanduser("~/.local/share/cartopy_cache"),
            }
        )
    except:
        raise Exception("\"cartopy\" is not installed; run \"pip install --user Cartopy\"") from None
    try:
        import matplotlib
        matplotlib.rcParams.update(
            {
                       "backend" : "Agg",                                       # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
                    "figure.dpi" : 300,
                "figure.figsize" : (9.6, 7.2),                                  # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes
                     "font.size" : 8,
            }
        )
        import matplotlib.pyplot
    except:
        raise Exception("\"matplotlib\" is not installed; run \"pip install --user matplotlib\"") from None
    try:
        import numpy
    except:
        raise Exception("\"numpy\" is not installed; run \"pip install --user numpy\"") from None

    # Import my modules ...
    try:
        import pyguymer3
        import pyguymer3.geo
        import pyguymer3.image
    except:
        raise Exception("\"pyguymer3\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH") from None

    # **************************************************************************

    # Start session ...
    with pyguymer3.start_session() as sess:
        # Check if the database is missing ...
        if not os.path.exists("hillcsv.zip"):
            # Download the database ...
            pyguymer3.download_file(sess, "http://www.hills-database.co.uk/hillcsv.zip", "hillcsv.zip")

    # **************************************************************************

    # Initialize counter ...
    n = 0                                                                       # [#]

    # Initialize lists ...
    x = []                                                                      # [°]
    y = []                                                                      # [°]
    z = []                                                                      # [m]
    xRaw = []                                                                   # [°]
    yRaw = []                                                                   # [°]

    # Open output file ...
    with open("british-hills-E900.txt", "wt", encoding = "utf-8") as fObjOut:
        # Open input file ...
        with zipfile.ZipFile("hillcsv.zip", "r") as fObjIn:
            # Loop over members ...
            for fname in fObjIn.namelist():
                # Skip those that are not CSVs ...
                if not fname.endswith(".csv"):
                    continue

                # Read CSV dataset as a list of strings ...
                rows = fObjIn.read(fname).decode("ascii").splitlines()

                # Loop over rows ...
                for row in csv.DictReader(rows):
                    # Append to lists ...
                    xRaw.append(float(row["Longitude"]))                        # [°]
                    yRaw.append(float(row["Latitude"]))                         # [°]

                    # Skip this row if the elevation is too small ...
                    if float(row["Metres"]) < 900.0:
                        continue

                    # Increment counter ...
                    n += 1                                                      # [#]

                    # Append to lists ...
                    x.append(float(row["Longitude"]))                           # [°]
                    y.append(float(row["Latitude"]))                            # [°]
                    z.append(float(row["Metres"]))                              # [m]

                    # Write summary ...
                    fObjOut.write(f'"{row["Name"]}" is {float(row["Metres"]):,.1f}m ASL with a prominence of {float(row["Drop"]):,.1f}m at ({float(row["Latitude"]):f}°,{float(row["Longitude"]):f}°).\n')

    # Print summary ...
    print(f"There are {n:,d} E900 mountains in The British Isles.")

    # **************************************************************************

    # Load tile metadata ...
    with open("OrdnanceSurveyBackgroundImages/miniscale.json", "rt", encoding = "utf-8") as fObj:
        meta = json.load(fObj)

    # Convert lists to arrays and find the sorted keys ...
    x = numpy.array(x)                                                          # [°]
    y = numpy.array(y)                                                          # [°]
    z = numpy.array(z)                                                          # [m]
    k = numpy.argsort(z)
    xRaw = numpy.array(xRaw)                                                    # [°]
    yRaw = numpy.array(yRaw)                                                    # [°]

    # Find middle of points and the furthest distance ...
    midLon, midLat, maxDist = pyguymer3.geo.find_middle_of_locs(
        xRaw,
        yRaw,
        pad = 12.0 * 1852.0,
    )                                                                           # [°], [°], [m]

    # Create figure ...
    fg = matplotlib.pyplot.figure()

    # Create axis ...
    ax = pyguymer3.geo.add_axis(
        fg,
        dist = maxDist,
         lat = midLat,
         lon = midLon,
    )

    # Add background image ...
    ax.imshow(
        matplotlib.pyplot.imread(f'OrdnanceSurveyBackgroundImages/{meta["MiniScale_(mono)_R22"]["greyscale"]}'),
                 cmap = "gray",
               extent = meta["MiniScale_(mono)_R22"]["extent"],
        interpolation = "bicubic",
               origin = "upper",
            transform = cartopy.crs.OSGB(),
                 vmax = 1.0,
                 vmin = 0.0,
    )

    # Plot data (layering them correctly) ...
    # NOTE: As of 5/Dec/2023, the default "zorder" of the coastlines is 1.5, the
    #       default "zorder" of the gridlines is 2.0 and the default "zorder" of
    #       the scattered points is 1.0.
    sc = ax.scatter(
        x[k],
        y[k],
                 c = z[k],
              cmap = matplotlib.pyplot.cm.rainbow,
        edgecolors = "black",
         linewidth = 0.1,
                 s = 10.0,
         transform = cartopy.crs.PlateCarree(),
              vmin = 900.0,
            zorder = 5.0,
    )

    # Add colour bar ...
    cb = fg.colorbar(sc, ax = ax, orientation = "vertical")

    # Configure axis ...
    ax.set_title("E900 Mountains In The British Isles")

    # Configure colour bar ...
    cb.set_label("Elevation [m]")

    # Configure figure ...
    fg.tight_layout()

    # Save figure ...
    fg.savefig("british-hills-E900.png")
    matplotlib.pyplot.close(fg)

    # Optimize PNG ...
    pyguymer3.image.optimize_image("british-hills-E900.png", strip = True)
You may also download “british-hills-E900.py” directly or view “british-hills-E900.py” on GitHub Gist (you may need to manually checkout the “main” branch).
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#!/usr/bin/env python3

# Use the proper idiom in the main module ...
# NOTE: See https://docs.python.org/3.12/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
    # NOTE: "The Database of British and Irish Hills" can be visited here:
    #         * http://www.hills-database.co.uk/
    #       ... and the links to download the database can be found here:
    #         * http://www.hills-database.co.uk/downloads.html
    # NOTE: https://en.wikipedia.org/wiki/List_of_P600_mountains_in_the_British_Isles

    # Import standard modules ...
    import csv
    import json
    import os
    import zipfile

    # Import special modules ...
    try:
        import cartopy
        cartopy.config.update(
            {
                "cache_dir" : os.path.expanduser("~/.local/share/cartopy_cache"),
            }
        )
    except:
        raise Exception("\"cartopy\" is not installed; run \"pip install --user Cartopy\"") from None
    try:
        import matplotlib
        matplotlib.rcParams.update(
            {
                       "backend" : "Agg",                                       # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
                    "figure.dpi" : 300,
                "figure.figsize" : (9.6, 7.2),                                  # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes
                     "font.size" : 8,
            }
        )
        import matplotlib.pyplot
    except:
        raise Exception("\"matplotlib\" is not installed; run \"pip install --user matplotlib\"") from None
    try:
        import numpy
    except:
        raise Exception("\"numpy\" is not installed; run \"pip install --user numpy\"") from None

    # Import my modules ...
    try:
        import pyguymer3
        import pyguymer3.geo
        import pyguymer3.image
    except:
        raise Exception("\"pyguymer3\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH") from None

    # **************************************************************************

    # Start session ...
    with pyguymer3.start_session() as sess:
        # Check if the database is missing ...
        if not os.path.exists("hillcsv.zip"):
            # Download the database ...
            pyguymer3.download_file(sess, "http://www.hills-database.co.uk/hillcsv.zip", "hillcsv.zip")

    # **************************************************************************

    # Initialize counter ...
    n = 0                                                                       # [#]

    # Initialize lists ...
    x = []                                                                      # [°]
    y = []                                                                      # [°]
    z = []                                                                      # [m]
    xRaw = []                                                                   # [°]
    yRaw = []                                                                   # [°]

    # Open output file ...
    with open("british-hills-P600.txt", "wt", encoding = "utf-8") as fObjOut:
        # Open input file ...
        with zipfile.ZipFile("hillcsv.zip", "r") as fObjIn:
            # Loop over members ...
            for fname in fObjIn.namelist():
                # Skip those that are not CSVs ...
                if not fname.endswith(".csv"):
                    continue

                # Read CSV dataset as a list of strings ...
                rows = fObjIn.read(fname).decode("ascii").splitlines()

                # Loop over rows ...
                for row in csv.DictReader(rows):
                    # Append to lists ...
                    xRaw.append(float(row["Longitude"]))                        # [°]
                    yRaw.append(float(row["Latitude"]))                         # [°]

                    # Skip this row if the prominence is too small ...
                    if float(row["Drop"]) < 600.0:
                        continue

                    # Increment counter ...
                    n += 1                                                      # [#]

                    # Append to lists ...
                    x.append(float(row["Longitude"]))                           # [°]
                    y.append(float(row["Latitude"]))                            # [°]
                    z.append(float(row["Drop"]))                                # [m]

                    # Write summary ...
                    fObjOut.write(f'"{row["Name"]}" is {float(row["Metres"]):,.1f}m ASL with a prominence of {float(row["Drop"]):,.1f}m at ({float(row["Latitude"]):f}°,{float(row["Longitude"]):f}°).\n')

    # Print summary ...
    print(f"There are {n:,d} P600 mountains in The British Isles.")

    # **************************************************************************

    # Load tile metadata ...
    with open("OrdnanceSurveyBackgroundImages/miniscale.json", "rt", encoding = "utf-8") as fObj:
        meta = json.load(fObj)

    # Convert lists to arrays and find the sorted keys ...
    x = numpy.array(x)                                                          # [°]
    y = numpy.array(y)                                                          # [°]
    z = numpy.array(z)                                                          # [m]
    k = numpy.argsort(z)
    xRaw = numpy.array(xRaw)                                                    # [°]
    yRaw = numpy.array(yRaw)                                                    # [°]

    # Find middle of points and the furthest distance ...
    midLon, midLat, maxDist = pyguymer3.geo.find_middle_of_locs(
        xRaw,
        yRaw,
        pad = 12.0 * 1852.0,
    )                                                                           # [°], [°], [m]

    # Create figure ...
    fg = matplotlib.pyplot.figure()

    # Create axis ...
    ax = pyguymer3.geo.add_axis(
        fg,
        dist = maxDist,
         lat = midLat,
         lon = midLon,
    )

    # Add background image ...
    ax.imshow(
        matplotlib.pyplot.imread(f'OrdnanceSurveyBackgroundImages/{meta["MiniScale_(mono)_R22"]["greyscale"]}'),
                 cmap = "gray",
               extent = meta["MiniScale_(mono)_R22"]["extent"],
        interpolation = "bicubic",
               origin = "upper",
            transform = cartopy.crs.OSGB(),
                 vmax = 1.0,
                 vmin = 0.0,
    )

    # Plot data (layering them correctly) ...
    # NOTE: As of 5/Dec/2023, the default "zorder" of the coastlines is 1.5, the
    #       default "zorder" of the gridlines is 2.0 and the default "zorder" of
    #       the scattered points is 1.0.
    sc = ax.scatter(
        x[k],
        y[k],
                 c = z[k],
              cmap = matplotlib.pyplot.cm.rainbow,
        edgecolors = "black",
         linewidth = 0.1,
                 s = 10.0,
         transform = cartopy.crs.PlateCarree(),
              vmin = 600.0,
            zorder = 5.0,
    )

    # Add colour bar ...
    cb = fg.colorbar(sc, ax = ax, orientation = "vertical")

    # Configure axis ...
    ax.set_title("P600 Mountains In The British Isles")

    # Configure colour bar ...
    cb.set_label("Prominence [m]")

    # Configure figure ...
    fg.tight_layout()

    # Save figure ...
    fg.savefig("british-hills-P600.png")
    matplotlib.pyplot.close(fg)

    # Optimize PNG ...
    pyguymer3.image.optimize_image("british-hills-P600.png", strip = True)
You may also download “british-hills-P600.py” directly or view “british-hills-P600.py” on GitHub Gist (you may need to manually checkout the “main” branch).
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#!/usr/bin/env python3

# Use the proper idiom in the main module ...
# NOTE: See https://docs.python.org/3.12/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
    # NOTE: "The Database of British and Irish Hills" can be visited here:
    #         * http://www.hills-database.co.uk/
    #       ... and the links to download the database can be found here:
    #         * http://www.hills-database.co.uk/downloads.html

    # Import standard modules ...
    import csv
    import json
    import os
    import zipfile

    # Import special modules ...
    try:
        import cartopy
        cartopy.config.update(
            {
                "cache_dir" : os.path.expanduser("~/.local/share/cartopy_cache"),
            }
        )
    except:
        raise Exception("\"cartopy\" is not installed; run \"pip install --user Cartopy\"") from None
    try:
        import matplotlib
        matplotlib.rcParams.update(
            {
                       "backend" : "Agg",                                       # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
                    "figure.dpi" : 300,
                "figure.figsize" : (9.6, 7.2),                                  # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes
                     "font.size" : 8,
            }
        )
        import matplotlib.pyplot
    except:
        raise Exception("\"matplotlib\" is not installed; run \"pip install --user matplotlib\"") from None
    try:
        import numpy
    except:
        raise Exception("\"numpy\" is not installed; run \"pip install --user numpy\"") from None

    # Import my modules ...
    try:
        import pyguymer3
        import pyguymer3.geo
        import pyguymer3.image
    except:
        raise Exception("\"pyguymer3\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH") from None

    # **************************************************************************

    # Start session ...
    with pyguymer3.start_session() as sess:
        # Check if the database is missing ...
        if not os.path.exists("hillcsv.zip"):
            # Download the database ...
            pyguymer3.download_file(sess, "http://www.hills-database.co.uk/hillcsv.zip", "hillcsv.zip")

    # **************************************************************************

    # Initialize counter ...
    n = 0                                                                       # [#]

    # Initialize lists ...
    x = []                                                                      # [°]
    y = []                                                                      # [°]
    z = []                                                                      # [m]
    xRaw = []                                                                   # [°]
    yRaw = []                                                                   # [°]

    # Open output file ...
    with open("british-hills-P900.txt", "wt", encoding = "utf-8") as fObjOut:
        # Open input file ...
        with zipfile.ZipFile("hillcsv.zip", "r") as fObjIn:
            # Loop over members ...
            for fname in fObjIn.namelist():
                # Skip those that are not CSVs ...
                if not fname.endswith(".csv"):
                    continue

                # Read CSV dataset as a list of strings ...
                rows = fObjIn.read(fname).decode("ascii").splitlines()

                # Loop over rows ...
                for row in csv.DictReader(rows):
                    # Append to lists ...
                    xRaw.append(float(row["Longitude"]))                        # [°]
                    yRaw.append(float(row["Latitude"]))                         # [°]

                    # Skip this row if the prominence is too small ...
                    if float(row["Drop"]) < 900.0:
                        continue

                    # Increment counter ...
                    n += 1                                                      # [#]

                    # Append to lists ...
                    x.append(float(row["Longitude"]))                           # [°]
                    y.append(float(row["Latitude"]))                            # [°]
                    z.append(float(row["Drop"]))                                # [m]

                    # Write summary ...
                    fObjOut.write(f'"{row["Name"]}" is {float(row["Metres"]):,.1f}m ASL with a prominence of {float(row["Drop"]):,.1f}m at ({float(row["Latitude"]):f}°,{float(row["Longitude"]):f}°).\n')

    # Print summary ...
    print(f"There are {n:,d} P900 mountains in The British Isles.")

    # **************************************************************************

    # Load tile metadata ...
    with open("OrdnanceSurveyBackgroundImages/miniscale.json", "rt", encoding = "utf-8") as fObj:
        meta = json.load(fObj)

    # Convert lists to arrays and find the sorted keys ...
    x = numpy.array(x)                                                          # [°]
    y = numpy.array(y)                                                          # [°]
    z = numpy.array(z)                                                          # [m]
    k = numpy.argsort(z)
    xRaw = numpy.array(xRaw)                                                    # [°]
    yRaw = numpy.array(yRaw)                                                    # [°]

    # Find middle of points and the furthest distance ...
    midLon, midLat, maxDist = pyguymer3.geo.find_middle_of_locs(
        xRaw,
        yRaw,
        pad = 12.0 * 1852.0,
    )                                                                           # [°], [°], [m]

    # Create figure ...
    fg = matplotlib.pyplot.figure()

    # Create axis ...
    ax = pyguymer3.geo.add_axis(
        fg,
        dist = maxDist,
         lat = midLat,
         lon = midLon,
    )

    # Add background image ...
    ax.imshow(
        matplotlib.pyplot.imread(f'OrdnanceSurveyBackgroundImages/{meta["MiniScale_(mono)_R22"]["greyscale"]}'),
                 cmap = "gray",
               extent = meta["MiniScale_(mono)_R22"]["extent"],
        interpolation = "bicubic",
               origin = "upper",
            transform = cartopy.crs.OSGB(),
                 vmax = 1.0,
                 vmin = 0.0,
    )

    # Plot data (layering them correctly) ...
    # NOTE: As of 5/Dec/2023, the default "zorder" of the coastlines is 1.5, the
    #       default "zorder" of the gridlines is 2.0 and the default "zorder" of
    #       the scattered points is 1.0.
    sc = ax.scatter(
        x[k],
        y[k],
                 c = z[k],
              cmap = matplotlib.pyplot.cm.rainbow,
        edgecolors = "black",
         linewidth = 0.1,
                 s = 10.0,
         transform = cartopy.crs.PlateCarree(),
              vmin = 900.0,
            zorder = 5.0,
    )

    # Add colour bar ...
    cb = fg.colorbar(sc, ax = ax, orientation = "vertical")

    # Configure axis ...
    ax.set_title("P900 Mountains In The British Isles")

    # Configure colour bar ...
    cb.set_label("Prominence [m]")

    # Configure figure ...
    fg.tight_layout()

    # Save figure ...
    fg.savefig("british-hills-P900.png")
    matplotlib.pyplot.close(fg)

    # Optimize PNG ...
    pyguymer3.image.optimize_image("british-hills-P900.png", strip = True)
You may also download “british-hills-P900.py” directly or view “british-hills-P900.py” on GitHub Gist (you may need to manually checkout the “main” branch).