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Merge pull request matplotlib#6404 from eric-wieser/patch-1
ENH: Add a ax.voxels(bool3d) function
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‎doc/users/whats_new/axes3d_voxels.rst

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``voxels`` function for mplot3d
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-------------------------------
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:class:`~mpl_toolkits.mplot3d.axes3d.Axes3D` now has a ``voxels`` method, for
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visualizing boolean 3d data. Uses could include plotting a sparse 3D heat map,
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or visualizing a volumetric model.

‎examples/mplot3d/voxels.py

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'''
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==========================
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3D voxel / volumetric plot
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==========================
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Demonstrates plotting 3D volumetric objects with ``ax.voxels``
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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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from mpl_toolkits.mplot3d import Axes3D
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# prepare some coordinates
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x, y, z = np.indices((8, 8, 8))
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# draw cuboids in the top left and bottom right corners, and a link between them
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cube1 = (x < 3) & (y < 3) & (z < 3)
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cube2 = (x >= 5) & (y >= 5) & (z >= 5)
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link = abs(x - y) + abs(y - z) + abs(z - x) <= 2
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# combine the objects into a single boolean array
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voxels = cube1 | cube2 | link
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# set the colors of each object
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colors = np.empty(voxels.shape, dtype=object)
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colors[link] = 'red'
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colors[cube1] = 'blue'
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colors[cube2] = 'green'
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# and plot everything
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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ax.voxels(voxels, facecolors=colors, edgecolor='k')
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plt.show()

‎examples/mplot3d/voxels_numpy_logo.py

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'''
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===============================
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3D voxel plot of the numpy logo
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===============================
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Demonstrates using ``ax.voxels`` with uneven coordinates
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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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from mpl_toolkits.mplot3d import Axes3D
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def explode(data):
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size = np.array(data.shape)*2
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data_e = np.zeros(size - 1, dtype=data.dtype)
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data_e[::2, ::2, ::2] = data
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return data_e
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# build up the numpy logo
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n_voxels = np.zeros((4, 3, 4), dtype=bool)
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n_voxels[0, 0, :] = True
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n_voxels[-1, 0, :] = True
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n_voxels[1, 0, 2] = True
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n_voxels[2, 0, 1] = True
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facecolors = np.where(n_voxels, '#FFD65DC0', '#7A88CCC0')
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edgecolors = np.where(n_voxels, '#BFAB6E', '#7D84A6')
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filled = np.ones(n_voxels.shape)
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# upscale the above voxel image, leaving gaps
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filled_2 = explode(filled)
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fcolors_2 = explode(facecolors)
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ecolors_2 = explode(edgecolors)
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# Shrink the gaps
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x, y, z = np.indices(np.array(filled_2.shape) + 1).astype(float) // 2
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x[0::2, :, :] += 0.05
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y[:, 0::2, :] += 0.05
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z[:, :, 0::2] += 0.05
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x[1::2, :, :] += 0.95
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y[:, 1::2, :] += 0.95
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z[:, :, 1::2] += 0.95
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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ax.voxels(x, y, z, filled_2, facecolors=fcolors_2, edgecolors=ecolors_2)
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plt.show()

‎examples/mplot3d/voxels_rgb.py

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'''
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==========================================
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3D voxel / volumetric plot with rgb colors
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==========================================
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Demonstrates using ``ax.voxels`` to visualize parts of a color space
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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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from mpl_toolkits.mplot3d import Axes3D
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def midpoints(x):
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sl = ()
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for i in range(x.ndim):
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x = (x[sl + np.index_exp[:-1]] + x[sl + np.index_exp[1:]]) / 2.0
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sl += np.index_exp[:]
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return x
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# prepare some coordinates, and attach rgb values to each
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r, g, b = np.indices((17, 17, 17)) / 16.0
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rc = midpoints(r)
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gc = midpoints(g)
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bc = midpoints(b)
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# define a sphere about [0.5, 0.5, 0.5]
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sphere = (rc - 0.5)**2 + (gc - 0.5)**2 + (bc - 0.5)**2 < 0.5**2
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# combine the color components
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colors = np.zeros(sphere.shape + (3,))
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colors[..., 0] = rc
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colors[..., 1] = gc
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colors[..., 2] = bc
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# and plot everything
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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ax.voxels(r, g, b, sphere,
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facecolors=colors,
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edgecolors=np.clip(2*colors - 0.5, 0, 1), # brighter
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linewidth=0.5)
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ax.set(xlabel='r', ylabel='g', zlabel='b')
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plt.show()

‎examples/mplot3d/voxels_torus.py

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'''
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=======================================================
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3D voxel / volumetric plot with cylindrical coordinates
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=======================================================
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Demonstrates using the ``x, y, z`` arguments of ``ax.voxels``.
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'''
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import matplotlib.pyplot as plt
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import matplotlib.colors
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D
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def midpoints(x):
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sl = ()
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for i in range(x.ndim):
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x = (x[sl + np.index_exp[:-1]] + x[sl + np.index_exp[1:]]) / 2.0
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sl += np.index_exp[:]
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return x
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# prepare some coordinates, and attach rgb values to each
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r, theta, z = np.mgrid[0:1:11j, 0:np.pi*2:25j, -0.5:0.5:11j]
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x = r*np.cos(theta)
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y = r*np.sin(theta)
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rc, thetac, zc = midpoints(r), midpoints(theta), midpoints(z)
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# define a wobbly torus about [0.7, *, 0]
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sphere = (rc - 0.7)**2 + (zc + 0.2*np.cos(thetac*2))**2 < 0.2**2
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# combine the color components
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hsv = np.zeros(sphere.shape + (3,))
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hsv[..., 0] = thetac / (np.pi*2)
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hsv[..., 1] = rc
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hsv[..., 2] = zc + 0.5
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colors = matplotlib.colors.hsv_to_rgb(hsv)
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# and plot everything
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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ax.voxels(x, y, z, sphere,
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facecolors=colors,
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edgecolors=np.clip(2*colors - 0.5, 0, 1), # brighter
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linewidth=0.5)
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plt.show()

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