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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib.pyplot as plt
from matplotlib.testing.decorators import image_comparison, cleanup
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1 import AxesGrid
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from matplotlib.colors import LogNorm
import numpy as np
@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 = np.max([np.max(np.fabs(x)), np.max(np.fabs(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(())
@cleanup
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, 6, loc=1) # zoom = 6
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=8,
pad=0.1, borderpad=0.5, sep=5,
frameon=False)
ax.add_artist(asb)
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
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