import numpy as np from io import BytesIO from matplotlib.testing.decorators import image_comparison import matplotlib.pyplot as plt import matplotlib.path as mpath import matplotlib.patches as mpatches from matplotlib.ticker import FuncFormatter @image_comparison(baseline_images=['bbox_inches_tight'], remove_text=True, savefig_kwarg=dict(bbox_inches='tight')) def test_bbox_inches_tight(): #: Test that a figure saved using bbox_inches='tight' is clipped correctly data = [[66386, 174296, 75131, 577908, 32015], [58230, 381139, 78045, 99308, 160454], [89135, 80552, 152558, 497981, 603535], [78415, 81858, 150656, 193263, 69638], [139361, 331509, 343164, 781380, 52269]] colLabels = rowLabels = [''] * 5 rows = len(data) ind = np.arange(len(colLabels)) + 0.3 # the x locations for the groups cellText = [] width = 0.4 # the width of the bars yoff = np.zeros(len(colLabels)) # the bottom values for stacked bar chart fig, ax = plt.subplots(1, 1) for row in range(rows): ax.bar(ind, data[row], width, bottom=yoff, align='edge', color='b') yoff = yoff + data[row] cellText.append(['']) plt.xticks([]) plt.legend([''] * 5, loc=(1.2, 0.2)) # Add a table at the bottom of the axes cellText.reverse() the_table = plt.table(cellText=cellText, rowLabels=rowLabels, colLabels=colLabels, loc='bottom') @image_comparison(baseline_images=['bbox_inches_tight_suptile_legend'], remove_text=False, savefig_kwarg={'bbox_inches': 'tight'}) def test_bbox_inches_tight_suptile_legend(): plt.plot(np.arange(10), label='a straight line') plt.legend(bbox_to_anchor=(0.9, 1), loc='upper left') plt.title('Axis title') plt.suptitle('Figure title') # put an extra long y tick on to see that the bbox is accounted for def y_formatter(y, pos): if int(y) == 4: return 'The number 4' else: return str(y) plt.gca().yaxis.set_major_formatter(FuncFormatter(y_formatter)) plt.xlabel('X axis') @image_comparison(baseline_images=['bbox_inches_tight_clipping'], remove_text=True, savefig_kwarg={'bbox_inches': 'tight'}) def test_bbox_inches_tight_clipping(): # tests bbox clipping on scatter points, and path clipping on a patch # to generate an appropriately tight bbox plt.scatter(np.arange(10), np.arange(10)) ax = plt.gca() ax.set_xlim([0, 5]) ax.set_ylim([0, 5]) # make a massive rectangle and clip it with a path patch = mpatches.Rectangle([-50, -50], 100, 100, transform=ax.transData, facecolor='blue', alpha=0.5) path = mpath.Path.unit_regular_star(5).deepcopy() path.vertices *= 0.25 patch.set_clip_path(path, transform=ax.transAxes) plt.gcf().artists.append(patch) @image_comparison(baseline_images=['bbox_inches_tight_raster'], remove_text=True, savefig_kwarg={'bbox_inches': 'tight'}) def test_bbox_inches_tight_raster(): """Test rasterization with tight_layout""" fig = plt.figure() ax = fig.add_subplot(111) ax.plot([1.0, 2.0], rasterized=True) def test_only_on_non_finite_bbox(): fig, ax = plt.subplots() ax.annotate("", xy=(0, float('nan'))) ax.set_axis_off() # we only need to test that it does not error out on save fig.savefig(BytesIO(), bbox_inches='tight', format='png')