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Merge pull request #30980 from scottshambaugh/log3d
Fix 3D axes to properly support non-linear scales (log, symlog, etc.)
2 parents d3f5e11 + 3050aae commit 829a9cc

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‎doc/release/next_whats_new/3d_scales.rst‎

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Non-linear scales on 3D axes
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----------------------------
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Resolving a long-standing issue, 3D axes now support non-linear axis scales
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such as 'log', 'symlog', 'logit', 'asinh', and custom 'function' scales, just
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like 2D axes. Use `~.Axes3D.set_xscale`, `~.Axes3D.set_yscale`, and
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`~.Axes3D.set_zscale` to set the scale for each axis independently.
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.. plot::
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:include-source: true
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:alt: A 3D plot with a linear x-axis, logarithmic y-axis, and symlog z-axis.
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import matplotlib.pyplot as plt
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import numpy as np
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# A sine chirp with increasing frequency and amplitude
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x = np.linspace(0, 1, 400) # time
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y = 10 ** (2 * x) # frequency, growing exponentially from 1 to 100 Hz
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phase = 2 * np.pi * (10 ** (2 * x) - 1) / (2 * np.log(10))
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z = np.sin(phase) * x ** 2 * 10 # amplitude, growing quadratically
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fig = plt.figure()
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ax = fig.add_subplot(projection='3d')
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ax.plot(x, y, z)
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ax.set_xlabel('Time (linear)')
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ax.set_ylabel('Frequency, Hz (log)')
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ax.set_zlabel('Amplitude (symlog)')
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ax.set_yscale('log')
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ax.set_zscale('symlog')
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plt.show()
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See `matplotlib.scale` for details on all available scales and their parameters.

‎galleries/examples/mplot3d/scales3d.py‎

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"""
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================================
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Scales on 3D (Log, Symlog, etc.)
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================================
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Demonstrate how to use non-linear scales such as logarithmic scales on 3D axes.
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3D axes support the same axis scales as 2D plots: 'linear', 'log', 'symlog',
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'logit', 'asinh', and custom 'function' scales. This example shows a mix of
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scales: linear on X, log on Y, and symlog on Z.
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For a complete list of built-in scales, see `matplotlib.scale`. For an overview
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of scale transformations, see :doc:`/gallery/scales/scales`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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# A sine chirp with increasing frequency and amplitude
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x = np.linspace(0, 1, 400) # time
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y = 10 ** (2 * x) # frequency, growing exponentially from 1 to 100 Hz
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phase = 2 * np.pi * (10 ** (2 * x) - 1) / (2 * np.log(10))
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z = np.sin(phase) * x **2 * 10 # amplitude, growing quadratically
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fig = plt.figure()
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ax = fig.add_subplot(projection='3d')
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ax.plot(x, y, z)
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ax.set_xlabel('Time (linear)')
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ax.set_ylabel('Frequency, Hz (log)')
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ax.set_zlabel('Amplitude (symlog)')
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ax.set_yscale('log')
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ax.set_zscale('symlog')
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plt.show()
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# %%
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#
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# .. admonition:: References
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#
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# The use of the following functions, methods, classes and modules is shown
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# in this example:
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#
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# - `mpl_toolkits.mplot3d.axes3d.Axes3D.set_xscale`
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# - `mpl_toolkits.mplot3d.axes3d.Axes3D.set_yscale`
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# - `mpl_toolkits.mplot3d.axes3d.Axes3D.set_zscale`
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# - `matplotlib.scale`
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#
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# .. tags::
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# plot-type: 3D,
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# level: beginner

‎galleries/examples/scales/scales.py‎

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Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.
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See `matplotlib.scale` for a full list of built-in scales, and
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:doc:`/gallery/scales/custom_scale` for how to create your own scale.
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See `matplotlib.scale` for a full list of built-in scales,
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:doc:`/gallery/scales/custom_scale` for how to create your own scale, and
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:doc:`/gallery/mplot3d/scales3d` for using scales on 3D axes.
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"""
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import matplotlib.pyplot as plt

‎lib/mpl_toolkits/mplot3d/art3d.py‎

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@@ -79,7 +79,7 @@ def _viewlim_mask(xs, ys, zs, axes):
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Parameters
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----------
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xs, ys, zs : array-like
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The points to mask.
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The points to mask. These should be in data coordinates.
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axes : Axes3D
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The axes to use for the view limits.
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@@ -198,7 +198,10 @@ def draw(self, renderer):
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else:
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pos3d = np.array([self._x, self._y, self._z], dtype=float)
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proj = proj3d._proj_trans_points([pos3d, pos3d + self._dir_vec], self.axes.M)
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dir_end = pos3d + self._dir_vec
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points = np.asarray([pos3d, dir_end])
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proj = proj3d._scale_proj_transform(
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points[:, 0], points[:, 1], points[:, 2], self.axes)
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dx = proj[0][1] - proj[0][0]
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dy = proj[1][1] - proj[1][0]
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angle = math.degrees(math.atan2(dy, dx))
@@ -334,9 +337,7 @@ def draw(self, renderer):
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dtype=float, mask=mask).filled(np.nan)
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else:
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xs3d, ys3d, zs3d = self._verts3d
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xs, ys, zs, tis = proj3d._proj_transform_clip(xs3d, ys3d, zs3d,
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self.axes.M,
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self.axes._focal_length)
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xs, ys, zs, tis = proj3d._scale_proj_transform_clip(xs3d, ys3d, zs3d, self.axes)
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self.set_data(xs, ys)
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super().draw(renderer)
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self.stale = False
@@ -427,7 +428,8 @@ def do_3d_projection(self):
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vs_list = [np.ma.array(vs, mask=np.broadcast_to(
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_viewlim_mask(*vs.T, self.axes), vs.shape))
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for vs in vs_list]
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xyzs_list = [proj3d.proj_transform(*vs.T, self.axes.M) for vs in vs_list]
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xyzs_list = [proj3d._scale_proj_transform(
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vs[:, 0], vs[:, 1], vs[:, 2], self.axes) for vs in vs_list]
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self._paths = [mpath.Path(np.ma.column_stack([xs, ys]), cs)
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for (xs, ys, _), (_, cs) in zip(xyzs_list, self._3dverts_codes)]
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zs = np.concatenate([zs for _, _, zs in xyzs_list])
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"""
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segments = np.asanyarray(self._segments3d)
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# Handle empty segments
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if segments.size == 0:
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LineCollection.set_segments(self, [])
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return np.nan
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mask = False
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if np.ma.isMA(segments):
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mask = segments.mask
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viewlim_mask = np.broadcast_to(viewlim_mask[..., np.newaxis],
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(*viewlim_mask.shape, 3))
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mask = mask | viewlim_mask
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xyzs = np.ma.array(proj3d._proj_transform_vectors(segments, self.axes.M),
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mask=mask)
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xyzs = np.ma.array(
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proj3d._scale_proj_transform_vectors(segments, self.axes), mask=mask)
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segments_2d = xyzs[..., 0:2]
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LineCollection.set_segments(self, segments_2d)
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dtype=float, mask=mask).filled(np.nan)
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else:
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xs, ys, zs = zip(*s)
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vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs,
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self.axes.M,
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self.axes._focal_length)
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vxs, vys, vzs, vis = proj3d._scale_proj_transform_clip(xs, ys, zs, self.axes)
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self._path2d = mpath.Path(np.ma.column_stack([vxs, vys]))
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return min(vzs)
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dtype=float, mask=mask).filled(np.nan)
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else:
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xs, ys, zs = zip(*s)
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vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs,
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self.axes.M,
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self.axes._focal_length)
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vxs, vys, vzs, vis = proj3d._scale_proj_transform_clip(xs, ys, zs, self.axes)
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self._path2d = mpath.Path(np.ma.column_stack([vxs, vys]), self._code3d)
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return min(vzs)
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xs, ys, zs = np.ma.array(self._offsets3d, mask=mask)
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else:
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xs, ys, zs = self._offsets3d
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vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs,
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self.axes.M,
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self.axes._focal_length)
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vxs, vys, vzs, vis = proj3d._scale_proj_transform_clip(xs, ys, zs, self.axes)
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self._vzs = vzs
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if np.ma.isMA(vxs):
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super().set_offsets(np.ma.column_stack([vxs, vys]))
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xyzs = np.ma.array(self._offsets3d, mask=mask)
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else:
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xyzs = self._offsets3d
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vxs, vys, vzs, vis = proj3d._proj_transform_clip(*xyzs,
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self.axes.M,
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self.axes._focal_length)
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vxs, vys, vzs, vis = proj3d._scale_proj_transform_clip(*xyzs, self.axes)
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self._data_scale = _get_data_scale(vxs, vys, vzs)
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# Sort the points based on z coordinates
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# Performance optimization: Create a sorted index array and reorder
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# Some faces might contain masked vertices, so we want to ignore any
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# errors that those might cause
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with np.errstate(invalid='ignore', divide='ignore'):
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pfaces = proj3d._proj_transform_vectors(self._faces, self.axes.M)
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pfaces = proj3d._scale_proj_transform_vectors(self._faces, self.axes)
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if self._axlim_clip:
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viewlim_mask = _viewlim_mask(self._faces[..., 0], self._faces[..., 1],

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