import matplotlib import matplotlib.pyplot as plt from matplotlib.testing.decorators import image_comparison from mpl_toolkits.axes_grid1 import host_subplot from mpl_toolkits.axes_grid1 import make_axes_locatable from mpl_toolkits.axes_grid1 import AxesGrid from mpl_toolkits.axes_grid1 import ImageGrid from mpl_toolkits.axes_grid1.inset_locator import ( zoomed_inset_axes, mark_inset, inset_axes, BboxConnectorPatch ) from mpl_toolkits.axes_grid1.anchored_artists import ( AnchoredSizeBar, AnchoredDirectionArrows) from matplotlib.colors import LogNorm from matplotlib.transforms import Bbox, TransformedBbox, \ blended_transform_factory from itertools import product import pytest import platform import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal @image_comparison(baseline_images=['divider_append_axes']) def test_divider_append_axes(): # the random data np.random.seed(0) x = np.random.randn(1000) y = np.random.randn(1000) fig, axScatter = plt.subplots() # the scatter plot: axScatter.scatter(x, y) # create new axes on the right and on the top of the current axes # The first argument of the new_vertical(new_horizontal) method is # the height (width) of the axes to be created in inches. divider = make_axes_locatable(axScatter) axHistbot = divider.append_axes("bottom", 1.2, pad=0.1, sharex=axScatter) axHistright = divider.append_axes("right", 1.2, pad=0.1, sharey=axScatter) axHistleft = divider.append_axes("left", 1.2, pad=0.1, sharey=axScatter) axHisttop = divider.append_axes("top", 1.2, pad=0.1, sharex=axScatter) # now determine nice limits by hand: binwidth = 0.25 xymax = max(np.max(np.abs(x)), np.max(np.abs(y))) lim = (int(xymax/binwidth) + 1) * binwidth bins = np.arange(-lim, lim + binwidth, binwidth) axHisttop.hist(x, bins=bins) axHistbot.hist(x, bins=bins) axHistleft.hist(y, bins=bins, orientation='horizontal') axHistright.hist(y, bins=bins, orientation='horizontal') axHistbot.invert_yaxis() axHistleft.invert_xaxis() axHisttop.xaxis.set_ticklabels(()) axHistbot.xaxis.set_ticklabels(()) axHistleft.yaxis.set_ticklabels(()) axHistright.yaxis.set_ticklabels(()) @image_comparison(baseline_images=['twin_axes_empty_and_removed'], extensions=["png"], tol=1) def test_twin_axes_empty_and_removed(): # Purely cosmetic font changes (avoid overlap) matplotlib.rcParams.update({"font.size": 8}) matplotlib.rcParams.update({"xtick.labelsize": 8}) matplotlib.rcParams.update({"ytick.labelsize": 8}) generators = [ "twinx", "twiny", "twin" ] modifiers = [ "", "host invisible", "twin removed", "twin invisible", "twin removed\nhost invisible" ] # Unmodified host subplot at the beginning for reference h = host_subplot(len(modifiers)+1, len(generators), 2) h.text(0.5, 0.5, "host_subplot", horizontalalignment="center", verticalalignment="center") # Host subplots with various modifications (twin*, visibility) applied for i, (mod, gen) in enumerate(product(modifiers, generators), len(generators)+1): h = host_subplot(len(modifiers)+1, len(generators), i) t = getattr(h, gen)() if "twin invisible" in mod: t.axis[:].set_visible(False) if "twin removed" in mod: t.remove() if "host invisible" in mod: h.axis[:].set_visible(False) h.text(0.5, 0.5, gen + ("\n" + mod if mod else ""), horizontalalignment="center", verticalalignment="center") plt.subplots_adjust(wspace=0.5, hspace=1) def test_axesgrid_colorbar_log_smoketest(): fig = plt.figure() grid = AxesGrid(fig, 111, # modified to be only subplot nrows_ncols=(1, 1), label_mode="L", cbar_location="top", cbar_mode="single", ) Z = 10000 * np.random.rand(10, 10) im = grid[0].imshow(Z, interpolation="nearest", norm=LogNorm()) grid.cbar_axes[0].colorbar(im) @image_comparison( baseline_images=['inset_locator'], style='default', extensions=['png'], remove_text=True) def test_inset_locator(): def get_demo_image(): from matplotlib.cbook import get_sample_data import numpy as np f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False) z = np.load(f) # z is a numpy array of 15x15 return z, (-3, 4, -4, 3) fig, ax = plt.subplots(figsize=[5, 4]) # prepare the demo image Z, extent = get_demo_image() Z2 = np.zeros([150, 150], dtype="d") ny, nx = Z.shape Z2[30:30 + ny, 30:30 + nx] = Z # extent = [-3, 4, -4, 3] ax.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") axins = zoomed_inset_axes(ax, zoom=6, loc='upper right') axins.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") axins.yaxis.get_major_locator().set_params(nbins=7) axins.xaxis.get_major_locator().set_params(nbins=7) # sub region of the original image x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 axins.set_xlim(x1, x2) axins.set_ylim(y1, y2) plt.xticks(visible=False) plt.yticks(visible=False) # draw a bbox of the region of the inset axes in the parent axes and # connecting lines between the bbox and the inset axes area mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") asb = AnchoredSizeBar(ax.transData, 0.5, '0.5', loc='lower center', pad=0.1, borderpad=0.5, sep=5, frameon=False) ax.add_artist(asb) @image_comparison( baseline_images=['inset_axes'], style='default', extensions=['png'], remove_text=True) def test_inset_axes(): def get_demo_image(): from matplotlib.cbook import get_sample_data import numpy as np f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False) z = np.load(f) # z is a numpy array of 15x15 return z, (-3, 4, -4, 3) fig, ax = plt.subplots(figsize=[5, 4]) # prepare the demo image Z, extent = get_demo_image() Z2 = np.zeros([150, 150], dtype="d") ny, nx = Z.shape Z2[30:30 + ny, 30:30 + nx] = Z # extent = [-3, 4, -4, 3] ax.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") # creating our inset axes with a bbox_transform parameter axins = inset_axes(ax, width=1., height=1., bbox_to_anchor=(1, 1), bbox_transform=ax.transAxes) axins.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") axins.yaxis.get_major_locator().set_params(nbins=7) axins.xaxis.get_major_locator().set_params(nbins=7) # sub region of the original image x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 axins.set_xlim(x1, x2) axins.set_ylim(y1, y2) plt.xticks(visible=False) plt.yticks(visible=False) # draw a bbox of the region of the inset axes in the parent axes and # connecting lines between the bbox and the inset axes area mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") asb = AnchoredSizeBar(ax.transData, 0.5, '0.5', loc='lower center', pad=0.1, borderpad=0.5, sep=5, frameon=False) ax.add_artist(asb) def test_inset_axes_complete(): dpi = 100 figsize = (6, 5) fig, ax = plt.subplots(figsize=figsize, dpi=dpi) fig.subplots_adjust(.1, .1, .9, .9) ins = inset_axes(ax, width=2., height=2., borderpad=0) fig.canvas.draw() assert_array_almost_equal( ins.get_position().extents, np.array(((0.9*figsize[0]-2.)/figsize[0], (0.9*figsize[1]-2.)/figsize[1], 0.9, 0.9))) ins = inset_axes(ax, width="40%", height="30%", borderpad=0) fig.canvas.draw() assert_array_almost_equal( ins.get_position().extents, np.array((.9-.8*.4, .9-.8*.3, 0.9, 0.9))) ins = inset_axes(ax, width=1., height=1.2, bbox_to_anchor=(200, 100), loc=3, borderpad=0) fig.canvas.draw() assert_array_almost_equal( ins.get_position().extents, np.array((200./dpi/figsize[0], 100./dpi/figsize[1], (200./dpi+1)/figsize[0], (100./dpi+1.2)/figsize[1]))) ins1 = inset_axes(ax, width="35%", height="60%", loc=3, borderpad=1) ins2 = inset_axes(ax, width="100%", height="100%", bbox_to_anchor=(0, 0, .35, .60), bbox_transform=ax.transAxes, loc=3, borderpad=1) fig.canvas.draw() assert_array_equal(ins1.get_position().extents, ins2.get_position().extents) with pytest.raises(ValueError): ins = inset_axes(ax, width="40%", height="30%", bbox_to_anchor=(0.4, 0.5)) with pytest.warns(UserWarning): ins = inset_axes(ax, width="40%", height="30%", bbox_transform=ax.transAxes) @image_comparison( baseline_images=['fill_facecolor'], extensions=['png'], remove_text=True, style='mpl20') def test_fill_facecolor(): fig, ax = plt.subplots(1, 5) fig.set_size_inches(5, 5) for i in range(1, 4): ax[i].yaxis.set_visible(False) ax[4].yaxis.tick_right() bbox = Bbox.from_extents(0, 0.4, 1, 0.6) # fill with blue by setting 'fc' field bbox1 = TransformedBbox(bbox, ax[0].transData) bbox2 = TransformedBbox(bbox, ax[1].transData) # set color to BboxConnectorPatch p = BboxConnectorPatch( bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3, ec="r", fc="b") p.set_clip_on(False) ax[0].add_patch(p) # set color to marked area axins = zoomed_inset_axes(ax[0], 1, loc='upper right') axins.set_xlim(0, 0.2) axins.set_ylim(0, 0.2) plt.gca().axes.get_xaxis().set_ticks([]) plt.gca().axes.get_yaxis().set_ticks([]) mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5") # fill with yellow by setting 'facecolor' field bbox3 = TransformedBbox(bbox, ax[1].transData) bbox4 = TransformedBbox(bbox, ax[2].transData) # set color to BboxConnectorPatch p = BboxConnectorPatch( bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3, ec="r", facecolor="y") p.set_clip_on(False) ax[1].add_patch(p) # set color to marked area axins = zoomed_inset_axes(ax[1], 1, loc='upper right') axins.set_xlim(0, 0.2) axins.set_ylim(0, 0.2) plt.gca().axes.get_xaxis().set_ticks([]) plt.gca().axes.get_yaxis().set_ticks([]) mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5") # fill with green by setting 'color' field bbox5 = TransformedBbox(bbox, ax[2].transData) bbox6 = TransformedBbox(bbox, ax[3].transData) # set color to BboxConnectorPatch p = BboxConnectorPatch( bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3, ec="r", color="g") p.set_clip_on(False) ax[2].add_patch(p) # set color to marked area axins = zoomed_inset_axes(ax[2], 1, loc='upper right') axins.set_xlim(0, 0.2) axins.set_ylim(0, 0.2) plt.gca().axes.get_xaxis().set_ticks([]) plt.gca().axes.get_yaxis().set_ticks([]) mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5") # fill with green but color won't show if set fill to False bbox7 = TransformedBbox(bbox, ax[3].transData) bbox8 = TransformedBbox(bbox, ax[4].transData) # BboxConnectorPatch won't show green p = BboxConnectorPatch( bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3, ec="r", fc="g", fill=False) p.set_clip_on(False) ax[3].add_patch(p) # marked area won't show green axins = zoomed_inset_axes(ax[3], 1, loc='upper right') axins.set_xlim(0, 0.2) axins.set_ylim(0, 0.2) axins.get_xaxis().set_ticks([]) axins.get_yaxis().set_ticks([]) mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False) @image_comparison(baseline_images=['zoomed_axes', 'inverted_zoomed_axes'], extensions=['png']) def test_zooming_with_inverted_axes(): fig, ax = plt.subplots() ax.plot([1, 2, 3], [1, 2, 3]) ax.axis([1, 3, 1, 3]) inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') inset_ax.axis([1.1, 1.4, 1.1, 1.4]) fig, ax = plt.subplots() ax.plot([1, 2, 3], [1, 2, 3]) ax.axis([3, 1, 3, 1]) inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') inset_ax.axis([1.4, 1.1, 1.4, 1.1]) @image_comparison(baseline_images=['anchored_direction_arrows'], tol={'aarch64': 0.02}.get(platform.machine(), 0.0), extensions=['png']) def test_anchored_direction_arrows(): fig, ax = plt.subplots() ax.imshow(np.zeros((10, 10))) simple_arrow = AnchoredDirectionArrows(ax.transAxes, 'X', 'Y') ax.add_artist(simple_arrow) @image_comparison(baseline_images=['anchored_direction_arrows_many_args'], extensions=['png']) def test_anchored_direction_arrows_many_args(): fig, ax = plt.subplots() ax.imshow(np.ones((10, 10))) direction_arrows = AnchoredDirectionArrows( ax.transAxes, 'A', 'B', loc='upper right', color='red', aspect_ratio=-0.5, pad=0.6, borderpad=2, frameon=True, alpha=0.7, sep_x=-0.06, sep_y=-0.08, back_length=0.1, head_width=9, head_length=10, tail_width=5) ax.add_artist(direction_arrows) def test_axes_locatable_position(): fig, ax = plt.subplots() divider = make_axes_locatable(ax) cax = divider.append_axes('right', size='5%', pad='2%') fig.canvas.draw() assert np.isclose(cax.get_position(original=False).width, 0.03621495327102808) @image_comparison(baseline_images=['image_grid'], extensions=['png'], remove_text=True, style='mpl20', savefig_kwarg={'bbox_inches': 'tight'}) def test_image_grid(): # test that image grid works with bbox_inches=tight. im = np.arange(100) im.shape = 10, 10 fig = plt.figure(1, (4., 4.)) grid = ImageGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.1) for i in range(4): grid[i].imshow(im) grid[i].set_title('test {0}{0}'.format(i)) def test_gettightbbox(): fig, ax = plt.subplots(figsize=(8, 6)) l, = ax.plot([1, 2, 3], [0, 1, 0]) ax_zoom = zoomed_inset_axes(ax, 4) ax_zoom.plot([1, 2, 3], [0, 1, 0]) mark_inset(ax, ax_zoom, loc1=1, loc2=3, fc="none", ec='0.3') bbox = fig.get_tightbbox(fig.canvas.get_renderer()) np.testing.assert_array_almost_equal(bbox.extents, [-18.022743, -14.118056, 7.332813, 5.4625])