169 lines
6.1 KiB
Python
169 lines
6.1 KiB
Python
"""
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Testing that skewed axes properly work.
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"""
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from contextlib import ExitStack
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import itertools
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import matplotlib.pyplot as plt
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from matplotlib.testing.decorators import image_comparison
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from matplotlib.axes import Axes
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import matplotlib.transforms as transforms
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import matplotlib.axis as maxis
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import matplotlib.spines as mspines
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import matplotlib.patches as mpatch
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from matplotlib.projections import register_projection
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# The sole purpose of this class is to look at the upper, lower, or total
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# interval as appropriate and see what parts of the tick to draw, if any.
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class SkewXTick(maxis.XTick):
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def draw(self, renderer):
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with ExitStack() as stack:
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for artist in [self.gridline, self.tick1line, self.tick2line,
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self.label1, self.label2]:
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stack.callback(artist.set_visible, artist.get_visible())
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needs_lower = transforms.interval_contains(
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self.axes.lower_xlim, self.get_loc())
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needs_upper = transforms.interval_contains(
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self.axes.upper_xlim, self.get_loc())
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self.tick1line.set_visible(
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self.tick1line.get_visible() and needs_lower)
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self.label1.set_visible(
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self.label1.get_visible() and needs_lower)
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self.tick2line.set_visible(
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self.tick2line.get_visible() and needs_upper)
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self.label2.set_visible(
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self.label2.get_visible() and needs_upper)
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super().draw(renderer)
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def get_view_interval(self):
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return self.axes.xaxis.get_view_interval()
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# This class exists to provide two separate sets of intervals to the tick,
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# as well as create instances of the custom tick
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class SkewXAxis(maxis.XAxis):
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def _get_tick(self, major):
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return SkewXTick(self.axes, None, major=major)
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def get_view_interval(self):
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return self.axes.upper_xlim[0], self.axes.lower_xlim[1]
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# This class exists to calculate the separate data range of the
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# upper X-axis and draw the spine there. It also provides this range
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# to the X-axis artist for ticking and gridlines
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class SkewSpine(mspines.Spine):
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def _adjust_location(self):
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pts = self._path.vertices
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if self.spine_type == 'top':
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pts[:, 0] = self.axes.upper_xlim
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else:
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pts[:, 0] = self.axes.lower_xlim
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# This class handles registration of the skew-xaxes as a projection as well
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# as setting up the appropriate transformations. It also overrides standard
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# spines and axes instances as appropriate.
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class SkewXAxes(Axes):
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# The projection must specify a name. This will be used be the
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# user to select the projection, i.e. ``subplot(projection='skewx')``.
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name = 'skewx'
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def _init_axis(self):
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# Taken from Axes and modified to use our modified X-axis
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self.xaxis = SkewXAxis(self)
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self.spines.top.register_axis(self.xaxis)
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self.spines.bottom.register_axis(self.xaxis)
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self.yaxis = maxis.YAxis(self)
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self.spines.left.register_axis(self.yaxis)
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self.spines.right.register_axis(self.yaxis)
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def _gen_axes_spines(self):
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spines = {'top': SkewSpine.linear_spine(self, 'top'),
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'bottom': mspines.Spine.linear_spine(self, 'bottom'),
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'left': mspines.Spine.linear_spine(self, 'left'),
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'right': mspines.Spine.linear_spine(self, 'right')}
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return spines
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def _set_lim_and_transforms(self):
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"""
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This is called once when the plot is created to set up all the
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transforms for the data, text and grids.
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"""
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rot = 30
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# Get the standard transform setup from the Axes base class
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super()._set_lim_and_transforms()
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# Need to put the skew in the middle, after the scale and limits,
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# but before the transAxes. This way, the skew is done in Axes
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# coordinates thus performing the transform around the proper origin
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# We keep the pre-transAxes transform around for other users, like the
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# spines for finding bounds
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self.transDataToAxes = (self.transScale +
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(self.transLimits +
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transforms.Affine2D().skew_deg(rot, 0)))
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# Create the full transform from Data to Pixels
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self.transData = self.transDataToAxes + self.transAxes
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# Blended transforms like this need to have the skewing applied using
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# both axes, in axes coords like before.
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self._xaxis_transform = (transforms.blended_transform_factory(
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self.transScale + self.transLimits,
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transforms.IdentityTransform()) +
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transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
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@property
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def lower_xlim(self):
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return self.axes.viewLim.intervalx
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@property
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def upper_xlim(self):
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pts = [[0., 1.], [1., 1.]]
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return self.transDataToAxes.inverted().transform(pts)[:, 0]
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# Now register the projection with matplotlib so the user can select
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# it.
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register_projection(SkewXAxes)
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@image_comparison(['skew_axes'], remove_text=True)
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def test_set_line_coll_dash_image():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1, projection='skewx')
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ax.set_xlim(-50, 50)
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ax.set_ylim(50, -50)
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ax.grid(True)
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# An example of a slanted line at constant X
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ax.axvline(0, color='b')
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@image_comparison(['skew_rects'], remove_text=True)
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def test_skew_rectangle():
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fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(8, 8))
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axes = axes.flat
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rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2))
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axes[0].set_xlim([-3, 3])
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axes[0].set_ylim([-3, 3])
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axes[0].set_aspect('equal', share=True)
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for ax, (xrots, yrots) in zip(axes, rotations):
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xdeg, ydeg = 45 * xrots, 45 * yrots
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t = transforms.Affine2D().skew_deg(xdeg, ydeg)
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ax.set_title('Skew of {0} in X and {1} in Y'.format(xdeg, ydeg))
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ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2,
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transform=t + ax.transData,
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alpha=0.5, facecolor='coral'))
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plt.subplots_adjust(wspace=0, left=0.01, right=0.99, bottom=0.01, top=0.99)
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