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"""

Testing that skewed Axes properly work.

"""

from contextlib import ExitStack

import itertools

import platform

import matplotlib.pyplot as plt

from matplotlib.testing.decorators import image_comparison

from matplotlib.axes import Axes

import matplotlib.transforms as transforms

import matplotlib.axis as maxis

import matplotlib.spines as mspines

import matplotlib.patches as mpatch

from matplotlib.projections import register_projection

# The sole purpose of this class is to look at the upper, lower, or total

# interval as appropriate and see what parts of the tick to draw, if any.

class SkewXTick(maxis.XTick):

def draw(self, renderer):

with ExitStack() as stack:

for artist in [self.gridline, self.tick1line, self.tick2line,

self.label1, self.label2]:

stack.callback(artist.set_visible, artist.get_visible())

needs_lower = transforms.interval_contains(

self.axes.lower_xlim, self.get_loc())

needs_upper = transforms.interval_contains(

self.axes.upper_xlim, self.get_loc())

self.tick1line.set_visible(

self.tick1line.get_visible() and needs_lower)

self.label1.set_visible(

self.label1.get_visible() and needs_lower)

self.tick2line.set_visible(

self.tick2line.get_visible() and needs_upper)

self.label2.set_visible(

self.label2.get_visible() and needs_upper)

super().draw(renderer)

def get_view_interval(self):

return self.axes.xaxis.get_view_interval()

# This class exists to provide two separate sets of intervals to the tick,

# as well as create instances of the custom tick

class SkewXAxis(maxis.XAxis):

def _get_tick(self, major):

return SkewXTick(self.axes, None, major=major)

def get_view_interval(self):

return self.axes.upper_xlim[0], self.axes.lower_xlim[1]

# This class exists to calculate the separate data range of the

# upper X-axis and draw the spine there. It also provides this range

# to the X-axis artist for ticking and gridlines

class SkewSpine(mspines.Spine):

def _adjust_location(self):

pts = self._path.vertices

if self.spine_type == 'top':

pts[:, 0] = self.axes.upper_xlim

else:

pts[:, 0] = self.axes.lower_xlim

# This class handles registration of the skew-xaxes as a projection as well

# as setting up the appropriate transformations. It also overrides standard

# spines and axes instances as appropriate.

class SkewXAxes(Axes):

# The projection must specify a name. This will be used be the

# user to select the projection, i.e. ``subplot(projection='skewx')``.

name = 'skewx'

def _init_axis(self):

# Taken from Axes and modified to use our modified X-axis

self.xaxis = SkewXAxis(self)

self.spines.top.register_axis(self.xaxis)

self.spines.bottom.register_axis(self.xaxis)

self.yaxis = maxis.YAxis(self)

self.spines.left.register_axis(self.yaxis)

self.spines.right.register_axis(self.yaxis)

def _gen_axes_spines(self):

spines = {'top': SkewSpine.linear_spine(self, 'top'),

'bottom': mspines.Spine.linear_spine(self, 'bottom'),

'left': mspines.Spine.linear_spine(self, 'left'),

'right': mspines.Spine.linear_spine(self, 'right')}

return spines

def _set_lim_and_transforms(self):

"""

This is called once when the plot is created to set up all the

transforms for the data, text and grids.

"""

rot = 30

# Get the standard transform setup from the Axes base class

super()._set_lim_and_transforms()

# Need to put the skew in the middle, after the scale and limits,

# but before the transAxes. This way, the skew is done in Axes

# coordinates thus performing the transform around the proper origin

# We keep the pre-transAxes transform around for other users, like the

# spines for finding bounds

self.transDataToAxes = (self.transScale +

(self.transLimits +

transforms.Affine2D().skew_deg(rot, 0)))

# Create the full transform from Data to Pixels

self.transData = self.transDataToAxes + self.transAxes

# Blended transforms like this need to have the skewing applied using

# both axes, in axes coords like before.

self._xaxis_transform = (transforms.blended_transform_factory(

self.transScale + self.transLimits,

transforms.IdentityTransform()) +

transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes

@property

def lower_xlim(self):

return self.axes.viewLim.intervalx

@property

def upper_xlim(self):

pts = [[0., 1.], [1., 1.]]

return self.transDataToAxes.inverted().transform(pts)[:, 0]

# Now register the projection with matplotlib so the user can select

# it.

register_projection(SkewXAxes)

@image_comparison(['skew_axes.png'], remove_text=True)

def test_set_line_coll_dash_image():

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection='skewx')

ax.set_xlim(-50, 50)

ax.set_ylim(50, -50)

ax.grid(True)

# An example of a slanted line at constant X

ax.axvline(0, color='b')

@image_comparison(['skew_rects.png'], remove_text=True,

tol=0 if platform.machine() == 'x86_64' else 0.009)

def test_skew_rectangle():

fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(8, 8))

axes = axes.flat

rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2))

axes[0].set_xlim([-3, 3])

axes[0].set_ylim([-3, 3])

axes[0].set_aspect('equal', share=True)

for ax, (xrots, yrots) in zip(axes, rotations):

xdeg, ydeg = 45 * xrots, 45 * yrots

t = transforms.Affine2D().skew_deg(xdeg, ydeg)

ax.set_title(f'Skew of {xdeg} in X and {ydeg} in Y')

ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2,

transform=t + ax.transData,

alpha=0.5, facecolor='coral'))

plt.subplots_adjust(wspace=0, left=0.01, right=0.99, bottom=0.01, top=0.99)

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