UN Flag

A demonstration of the power of Matplotlib combined with cartopy’s Azimuthal Equidistant projection to reproduce the UN flag.

../_images/sphx_glr_un_flag_001.png
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from matplotlib.patches import PathPatch
import matplotlib.path
import matplotlib.ticker
from matplotlib.transforms import BboxTransform, Bbox
import numpy as np


# When drawing the flag, we can either use white filled land, or be a little
# more fancy and use the Natural Earth shaded relief imagery.
filled_land = True


def olive_path():
    """
    Return a Matplotlib path representing a single olive branch from the
    UN Flag. The path coordinates were extracted from the SVG at
    https://commons.wikimedia.org/wiki/File:Flag_of_the_United_Nations.svg.

    """
    olives_verts = np.array(
        [[0,   2,   6,   9,  30,  55,  79,  94, 104, 117, 134, 157, 177,
          188, 199, 207, 191, 167, 149, 129, 109,  87,  53,  22,   0, 663,
          245, 223, 187, 158, 154, 150, 146, 149, 154, 158, 181, 184, 197,
          181, 167, 153, 142, 129, 116, 119, 123, 127, 151, 178, 203, 220,
          237, 245, 663, 280, 267, 232, 209, 205, 201, 196, 196, 201, 207,
          211, 224, 219, 230, 220, 212, 207, 198, 195, 176, 197, 220, 239,
          259, 277, 280, 663, 295, 293, 264, 250, 247, 244, 240, 240, 243,
          244, 249, 251, 250, 248, 242, 245, 233, 236, 230, 228, 224, 222,
          234, 249, 262, 275, 285, 291, 295, 296, 295, 663, 294, 293, 292,
          289, 294, 277, 271, 269, 268, 265, 264, 264, 264, 272, 260, 248,
          245, 243, 242, 240, 243, 245, 247, 252, 256, 259, 258, 257, 258,
          267, 285, 290, 294, 297, 294, 663, 285, 285, 277, 266, 265, 265,
          265, 277, 266, 268, 269, 269, 269, 268, 268, 267, 267, 264, 248,
          235, 232, 229, 228, 229, 232, 236, 246, 266, 269, 271, 285, 285,
          663, 252, 245, 238, 230, 246, 245, 250, 252, 255, 256, 256, 253,
          249, 242, 231, 214, 208, 208, 227, 244, 252, 258, 262, 262, 261,
          262, 264, 265, 252, 663, 185, 197, 206, 215, 223, 233, 242, 237,
          237, 230, 220, 202, 185, 663],
         [8,   5,   3,   0,  22,  46,  46,  46,  35,  27,  16,  10,  18,
          22,  28,  38,  27,  26,  33,  41,  52,  52,  52,  30,   8, 595,
          77,  52,  61,  54,  53,  52,  53,  55,  55,  57,  65,  90, 106,
          96,  81,  68,  58,  54,  51,  50,  51,  50,  44,  34,  43,  48,
          61,  77, 595, 135, 104, 102,  83,  79,  76,  74,  74,  79,  84,
          90, 109, 135, 156, 145, 133, 121, 100,  77,  62,  69,  67,  80,
          92, 113, 135, 595, 198, 171, 156, 134, 129, 124, 120, 123, 126,
          129, 138, 149, 161, 175, 188, 202, 177, 144, 116, 110, 105,  99,
          108, 116, 126, 136, 147, 162, 173, 186, 198, 595, 249, 255, 261,
          267, 241, 222, 200, 192, 183, 175, 175, 175, 175, 199, 221, 240,
          245, 250, 256, 245, 233, 222, 207, 194, 180, 172, 162, 153, 154,
          171, 184, 202, 216, 233, 249, 595, 276, 296, 312, 327, 327, 327,
          327, 308, 284, 262, 240, 240, 239, 239, 242, 244, 247, 265, 277,
          290, 293, 296, 300, 291, 282, 274, 253, 236, 213, 235, 252, 276,
          595, 342, 349, 355, 357, 346, 326, 309, 303, 297, 291, 290, 297,
          304, 310, 321, 327, 343, 321, 305, 292, 286, 278, 270, 276, 281,
          287, 306, 328, 342, 595, 379, 369, 355, 343, 333, 326, 318, 328,
          340, 349, 366, 373, 379, 595]]).T
    olives_codes = np.array([1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 2, 4,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4,
                             4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
                             4, 4, 79], dtype=np.uint8)

    return matplotlib.path.Path(olives_verts, olives_codes)


def main():
    blue = '#4b92db'

    # We're drawing a flag with a 3:5 aspect ratio.
    fig = plt.figure(figsize=[7.5, 4.5], facecolor=blue)
    # Put a blue background on the figure.
    blue_background = PathPatch(matplotlib.path.Path.unit_rectangle(),
                                transform=fig.transFigure, color=blue,
                                zorder=-1)
    fig.patches.append(blue_background)

    # Set up the Azimuthal Equidistant and Plate Carree projections
    # for later use.
    az_eq = ccrs.AzimuthalEquidistant(central_latitude=90)
    pc = ccrs.PlateCarree()

    # Pick a suitable location for the map (which is in an Azimuthal
    # Equidistant projection).
    ax = fig.add_axes([0.25, 0.24, 0.5, 0.54], projection=az_eq)

    # The background patch is not needed in this example.
    ax.patch.set_facecolor('none')
    # The Axes frame produces the outer meridian line.
    for spine in ax.spines.values():
        spine.update({'edgecolor': 'white', 'linewidth': 2})

    # We want the map to go down to -60 degrees latitude.
    ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree())

    # Importantly, we want the axes to be circular at the -60 latitude
    # rather than cartopy's default behaviour of zooming in and becoming
    # square.
    _, patch_radius = az_eq.transform_point(0, -60, pc)
    circular_path = matplotlib.path.Path.circle(0, patch_radius)
    ax.set_boundary(circular_path)

    if filled_land:
        ax.add_feature(
            cfeature.LAND, facecolor='white', edgecolor='none')
    else:
        ax.stock_img()

    gl = ax.gridlines(crs=pc, linewidth=2, color='white', linestyle='-')
    # Meridians every 45 degrees, and 4 parallels.
    gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(-180, 181, 45))
    parallels = np.arange(-30, 70, 30)
    gl.ylocator = matplotlib.ticker.FixedLocator(parallels)

    # Now add the olive branches around the axes. We do this in normalised
    # figure coordinates
    olive_leaf = olive_path()

    olives_bbox = Bbox.null()
    olives_bbox.update_from_path(olive_leaf)

    # The first olive branch goes from left to right.
    olive1_axes_bbox = Bbox([[0.45, 0.15], [0.725, 0.75]])
    olive1_trans = BboxTransform(olives_bbox, olive1_axes_bbox)

    # THe second olive branch goes from right to left (mirroring the first).
    olive2_axes_bbox = Bbox([[0.55, 0.15], [0.275, 0.75]])
    olive2_trans = BboxTransform(olives_bbox, olive2_axes_bbox)

    olive1 = PathPatch(olive_leaf, facecolor='white', edgecolor='none',
                       transform=olive1_trans + fig.transFigure)
    olive2 = PathPatch(olive_leaf, facecolor='white', edgecolor='none',
                       transform=olive2_trans + fig.transFigure)

    fig.patches.append(olive1)
    fig.patches.append(olive2)

    plt.show()


if __name__ == '__main__':
    main()

Total running time of the script: ( 0 minutes 0.011 seconds)

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