.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/miscellanea/un_flag.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_miscellanea_un_flag.py: UN Flag ------- A demonstration of the power of Matplotlib combined with cartopy's Azimuthal Equidistant projection to reproduce the UN flag. .. GENERATED FROM PYTHON SOURCE LINES 9-163 .. image-sg:: /gallery/miscellanea/images/sphx_glr_un_flag_001.png :alt: un flag :srcset: /gallery/miscellanea/images/sphx_glr_un_flag_001.png :class: sphx-glr-single-img .. code-block:: default 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.744 seconds) .. _sphx_glr_download_gallery_miscellanea_un_flag.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: un_flag.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: un_flag.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_