Matplotlib渲染所有内部体素（带有Alpha）

Question

我想在matplotlib中渲染一个卷。该体积是一个简单的7x7x7多维数据集，并且我希望能够看到所有内部体素（即使我知道它看起来像一团糟）。

我已经能够渲染透明的体素，但是似乎从未绘制过表面上没有的任何体素。

该卷的每个7x7切片应如下所示：

我汇集了MWE

以下代码使用5x5的红色，绿色，蓝色，黄色和青色层创建5x5x5的体积。每层的Alpha设置为.5，因此整个过程应该是透明的。

然后，我将所有非表面体素的颜色更改为带有alpha 1的黑色，因此如果显示出来，我们应该能够在中心看到一个黑框。

单独渲染它会产生左侧的图形，但是如果我们从青色层中删除填充，我们可以看到黑框确实存在，只是没有显示它，因为即使那些遮盖了体素的遮盖物也被100％遮盖了alpha小于1。

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D  # NOQA

spatial_axes = [5, 5, 5]
filled = np.ones(spatial_axes, dtype=np.bool)

colors = np.empty(spatial_axes + [4], dtype=np.float32)
alpha = .5
colors[0] = [1, 0, 0, alpha]
colors[1] = [0, 1, 0, alpha]
colors[2] = [0, 0, 1, alpha]
colors[3] = [1, 1, 0, alpha]
colors[4] = [0, 1, 1, alpha]

# set all internal colors to black with alpha=1
colors[1:-1, 1:-1, 1:-1, 0:3] = 0
colors[1:-1, 1:-1, 1:-1, 3] = 1

fig = plt.figure()

ax = fig.add_subplot('111', projection='3d')
ax.voxels(filled, facecolors=colors, edgecolors='k')

fig = plt.figure()
ax = fig.add_subplot('111', projection='3d')
filled[-1] = False
ax.voxels(filled, facecolors=colors, edgecolors='k')

有什么办法渲染所有被遮挡的体素？

Answer 1

要将以上我的评论变成答案：

• 您可能总是像这样绘制所有体素
  • 用matplotlib表示体素
  • matplotlib中的3D离散热图
• 该负责人举例通过offsettingt体素的一个位面解决了这个问题，这样他们都绘制。
• 这个matplotlib问题讨论了内部多维数据集上缺少的面。有一个拉取请求仍然存在一些问题，因此尚未合并。

尽管存在一些小问题，但您仍可以在代码中插入pull请求的当前状态：

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D, art3d  # NOQA
from matplotlib.cbook import _backports
from collections import defaultdict
import types

def voxels(self, *args, **kwargs):

    if len(args) >= 3:
        # underscores indicate position only
        def voxels(__x, __y, __z, filled, **kwargs):
            return (__x, __y, __z), filled, kwargs
    else:
        def voxels(filled, **kwargs):
            return None, filled, kwargs

    xyz, filled, kwargs = voxels(*args, **kwargs)

    # check dimensions
    if filled.ndim != 3:
        raise ValueError("Argument filled must be 3-dimensional")
    size = np.array(filled.shape, dtype=np.intp)

    # check xyz coordinates, which are one larger than the filled shape
    coord_shape = tuple(size + 1)
    if xyz is None:
        x, y, z = np.indices(coord_shape)
    else:
        x, y, z = (_backports.broadcast_to(c, coord_shape) for c in xyz)

    def _broadcast_color_arg(color, name):
        if np.ndim(color) in (0, 1):
            # single color, like "red" or [1, 0, 0]
            return _backports.broadcast_to(
                color, filled.shape + np.shape(color))
        elif np.ndim(color) in (3, 4):
            # 3D array of strings, or 4D array with last axis rgb
            if np.shape(color)[:3] != filled.shape:
                raise ValueError(
                    "When multidimensional, {} must match the shape of "
                    "filled".format(name))
            return color
        else:
            raise ValueError("Invalid {} argument".format(name))

    # intercept the facecolors, handling defaults and broacasting
    facecolors = kwargs.pop('facecolors', None)
    if facecolors is None:
        facecolors = self._get_patches_for_fill.get_next_color()
    facecolors = _broadcast_color_arg(facecolors, 'facecolors')

    # broadcast but no default on edgecolors
    edgecolors = kwargs.pop('edgecolors', None)
    edgecolors = _broadcast_color_arg(edgecolors, 'edgecolors')

    # include possibly occluded internal faces or not
    internal_faces = kwargs.pop('internal_faces', False)

    # always scale to the full array, even if the data is only in the center
    self.auto_scale_xyz(x, y, z)

    # points lying on corners of a square
    square = np.array([
        [0, 0, 0],
        [0, 1, 0],
        [1, 1, 0],
        [1, 0, 0]
    ], dtype=np.intp)

    voxel_faces = defaultdict(list)

    def permutation_matrices(n):
        """ Generator of cyclic permutation matices """
        mat = np.eye(n, dtype=np.intp)
        for i in range(n):
            yield mat
            mat = np.roll(mat, 1, axis=0)

    for permute in permutation_matrices(3):
        pc, qc, rc = permute.T.dot(size)
        pinds = np.arange(pc)
        qinds = np.arange(qc)
        rinds = np.arange(rc)

        square_rot = square.dot(permute.T)

        for p in pinds:
            for q in qinds:
                p0 = permute.dot([p, q, 0])
                i0 = tuple(p0)
                if filled[i0]:
                    voxel_faces[i0].append(p0 + square_rot)

                # draw middle faces
                for r1, r2 in zip(rinds[:-1], rinds[1:]):
                    p1 = permute.dot([p, q, r1])
                    p2 = permute.dot([p, q, r2])
                    i1 = tuple(p1)
                    i2 = tuple(p2)
                    if filled[i1] and (internal_faces or not filled[i2]):
                        voxel_faces[i1].append(p2 + square_rot)
                    elif (internal_faces or not filled[i1]) and filled[i2]:
                        voxel_faces[i2].append(p2 + square_rot)

                # draw upper faces
                pk = permute.dot([p, q, rc-1])
                pk2 = permute.dot([p, q, rc])
                ik = tuple(pk)
                if filled[ik]:
                    voxel_faces[ik].append(pk2 + square_rot)

    # iterate over the faces, and generate a Poly3DCollection for each voxel
    polygons = {}
    for coord, faces_inds in voxel_faces.items():
        # convert indices into 3D positions
        if xyz is None:
            faces = faces_inds
        else:
            faces = []
            for face_inds in faces_inds:
                ind = face_inds[:, 0], face_inds[:, 1], face_inds[:, 2]
                face = np.empty(face_inds.shape)
                face[:, 0] = x[ind]
                face[:, 1] = y[ind]
                face[:, 2] = z[ind]
                faces.append(face)

        poly = art3d.Poly3DCollection(faces,
            facecolors=facecolors[coord],
            edgecolors=edgecolors[coord],
            **kwargs
        )
        self.add_collection3d(poly)
        polygons[coord] = poly

    return polygons



spatial_axes = [5, 5, 5]
filled = np.ones(spatial_axes, dtype=np.bool)

colors = np.empty(spatial_axes + [4], dtype=np.float32)
alpha = .5
colors[0] = [1, 0, 0, alpha]
colors[1] = [0, 1, 0, alpha]
colors[2] = [0, 0, 1, alpha]
colors[3] = [1, 1, 0, alpha]
colors[4] = [0, 1, 1, alpha]

# set all internal colors to black with alpha=1
colors[1:-1, 1:-1, 1:-1, 0:3] = 0
colors[1:-1, 1:-1, 1:-1, 3] = 1

fig = plt.figure()

ax = fig.add_subplot('111', projection='3d')
ax.voxels = types.MethodType(voxels, ax)
ax.voxels(filled, facecolors=colors, edgecolors='k',internal_faces=True)

fig = plt.figure()
ax = fig.add_subplot('111', projection='3d')
ax.voxels = types.MethodType(voxels, ax)
filled[-1] = False
ax.voxels(filled, facecolors=colors, edgecolors='k',internal_faces=True)

plt.show()