from itertools import product import platform import matplotlib import matplotlib.pyplot as plt from matplotlib import cbook from matplotlib.cbook import MatplotlibDeprecationWarning from matplotlib.backend_bases import MouseEvent from matplotlib.colors import LogNorm from matplotlib.transforms import Bbox, TransformedBbox from matplotlib.testing.decorators import ( image_comparison, remove_ticks_and_titles) from mpl_toolkits.axes_grid1 import ( axes_size as Size, host_subplot, make_axes_locatable, AxesGrid, ImageGrid) from mpl_toolkits.axes_grid1.anchored_artists import ( AnchoredSizeBar, AnchoredDirectionArrows) from mpl_toolkits.axes_grid1.axes_divider import HBoxDivider from mpl_toolkits.axes_grid1.inset_locator import ( zoomed_inset_axes, mark_inset, inset_axes, BboxConnectorPatch) import mpl_toolkits.axes_grid1.mpl_axes import pytest import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal def test_divider_append_axes(): fig, ax = plt.subplots() divider = make_axes_locatable(ax) axs = { "main": ax, "top": divider.append_axes("top", 1.2, pad=0.1, sharex=ax), "bottom": divider.append_axes("bottom", 1.2, pad=0.1, sharex=ax), "left": divider.append_axes("left", 1.2, pad=0.1, sharey=ax), "right": divider.append_axes("right", 1.2, pad=0.1, sharey=ax), } fig.canvas.draw() renderer = fig.canvas.get_renderer() bboxes = {k: axs[k].get_window_extent() for k in axs} dpi = fig.dpi assert bboxes["top"].height == pytest.approx(1.2 * dpi) assert bboxes["bottom"].height == pytest.approx(1.2 * dpi) assert bboxes["left"].width == pytest.approx(1.2 * dpi) assert bboxes["right"].width == pytest.approx(1.2 * dpi) assert bboxes["top"].y0 - bboxes["main"].y1 == pytest.approx(0.1 * dpi) assert bboxes["main"].y0 - bboxes["bottom"].y1 == pytest.approx(0.1 * dpi) assert bboxes["main"].x0 - bboxes["left"].x1 == pytest.approx(0.1 * dpi) assert bboxes["right"].x0 - bboxes["main"].x1 == pytest.approx(0.1 * dpi) assert bboxes["left"].y0 == bboxes["main"].y0 == bboxes["right"].y0 assert bboxes["left"].y1 == bboxes["main"].y1 == bboxes["right"].y1 assert bboxes["top"].x0 == bboxes["main"].x0 == bboxes["bottom"].x0 assert bboxes["top"].x1 == bboxes["main"].x1 == bboxes["bottom"].x1 @image_comparison(['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) @pytest.mark.parametrize("legacy_colorbar", [False, True]) def test_axesgrid_colorbar_log_smoketest(legacy_colorbar): matplotlib.rcParams["mpl_toolkits.legacy_colorbar"] = legacy_colorbar fig = plt.figure() grid = AxesGrid(fig, 111, # modified to be only subplot nrows_ncols=(1, 1), ngrids=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()) if legacy_colorbar: with pytest.warns(MatplotlibDeprecationWarning): grid.cbar_axes[0].colorbar(im) else: grid.cbar_axes[0].colorbar(im) @image_comparison(['inset_locator.png'], style='default', remove_text=True) def test_inset_locator(): fig, ax = plt.subplots(figsize=[5, 4]) # prepare the demo image # Z is a 15x15 array Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True) extent = (-3, 4, -4, 3) Z2 = np.zeros((150, 150)) 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(['inset_axes.png'], style='default', remove_text=True) def test_inset_axes(): fig, ax = plt.subplots(figsize=[5, 4]) # prepare the demo image # Z is a 15x15 array Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True) extent = (-3, 4, -4, 3) Z2 = np.zeros((150, 150)) 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(['fill_facecolor.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(['zoomed_axes.png', 'inverted_zoomed_axes.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(['anchored_direction_arrows.png'], tol=0 if platform.machine() == 'x86_64' else 0.01) def test_anchored_direction_arrows(): fig, ax = plt.subplots() ax.imshow(np.zeros((10, 10)), interpolation='nearest') simple_arrow = AnchoredDirectionArrows(ax.transAxes, 'X', 'Y') ax.add_artist(simple_arrow) @image_comparison(['anchored_direction_arrows_many_args.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(['image_grid.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).reshape((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, interpolation='nearest') 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') remove_ticks_and_titles(fig) bbox = fig.get_tightbbox(fig.canvas.get_renderer()) np.testing.assert_array_almost_equal(bbox.extents, [-17.7, -13.9, 7.2, 5.4]) @pytest.mark.parametrize("click_on", ["big", "small"]) @pytest.mark.parametrize("big_on_axes,small_on_axes", [ ("gca", "gca"), ("host", "host"), ("host", "parasite"), ("parasite", "host"), ("parasite", "parasite") ]) def test_picking_callbacks_overlap(big_on_axes, small_on_axes, click_on): """Test pick events on normal, host or parasite axes.""" # Two rectangles are drawn and "clicked on", a small one and a big one # enclosing the small one. The axis on which they are drawn as well as the # rectangle that is clicked on are varied. # In each case we expect that both rectangles are picked if we click on the # small one and only the big one is picked if we click on the big one. # Also tests picking on normal axes ("gca") as a control. big = plt.Rectangle((0.25, 0.25), 0.5, 0.5, picker=5) small = plt.Rectangle((0.4, 0.4), 0.2, 0.2, facecolor="r", picker=5) # Machinery for "receiving" events received_events = [] def on_pick(event): received_events.append(event) plt.gcf().canvas.mpl_connect('pick_event', on_pick) # Shortcut rectangles_on_axes = (big_on_axes, small_on_axes) # Axes setup axes = {"gca": None, "host": None, "parasite": None} if "gca" in rectangles_on_axes: axes["gca"] = plt.gca() if "host" in rectangles_on_axes or "parasite" in rectangles_on_axes: axes["host"] = host_subplot(111) axes["parasite"] = axes["host"].twin() # Add rectangles to axes axes[big_on_axes].add_patch(big) axes[small_on_axes].add_patch(small) # Simulate picking with click mouse event if click_on == "big": click_axes = axes[big_on_axes] axes_coords = (0.3, 0.3) else: click_axes = axes[small_on_axes] axes_coords = (0.5, 0.5) # In reality mouse events never happen on parasite axes, only host axes if click_axes is axes["parasite"]: click_axes = axes["host"] (x, y) = click_axes.transAxes.transform(axes_coords) m = MouseEvent("button_press_event", click_axes.figure.canvas, x, y, button=1) click_axes.pick(m) # Checks expected_n_events = 2 if click_on == "small" else 1 assert len(received_events) == expected_n_events event_rects = [event.artist for event in received_events] assert big in event_rects if click_on == "small": assert small in event_rects def test_hbox_divider(): arr1 = np.arange(20).reshape((4, 5)) arr2 = np.arange(20).reshape((5, 4)) fig, (ax1, ax2) = plt.subplots(1, 2) ax1.imshow(arr1) ax2.imshow(arr2) pad = 0.5 # inches. divider = HBoxDivider( fig, 111, # Position of combined axes. horizontal=[Size.AxesX(ax1), Size.Fixed(pad), Size.AxesX(ax2)], vertical=[Size.AxesY(ax1), Size.Scaled(1), Size.AxesY(ax2)]) ax1.set_axes_locator(divider.new_locator(0)) ax2.set_axes_locator(divider.new_locator(2)) fig.canvas.draw() p1 = ax1.get_position() p2 = ax2.get_position() assert p1.height == p2.height assert p2.width / p1.width == pytest.approx((4 / 5) ** 2) def test_axes_class_tuple(): fig = plt.figure() axes_class = (mpl_toolkits.axes_grid1.mpl_axes.Axes, {}) gr = AxesGrid(fig, 111, nrows_ncols=(1, 1), axes_class=axes_class)