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)