8000 Merge pull request #24861 from oscargus/docfixes · matplotlib/matplotlib@fb356ed · GitHub
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Merge pull request #24861 from oscargus/docfixes
Documentation fixes
2 parents 84e5f02 + 2de8489 commit fb356ed

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‎doc/api/transformations.rst

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@@ -14,4 +14,4 @@
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BboxTransformFrom, ScaledTranslation, TransformedPath, nonsingular,
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interval_contains, interval_contains_open
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:show-inheritance:
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:special-members:
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:special-members: __add__, __sub__

‎lib/matplotlib/backend_bases.py

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@@ -473,10 +473,10 @@ def draw_image(self, gc, x, y, im, transform=None):
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The distance in physical units (i.e., dots or pixels) from the
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bottom side of the canvas.
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im : (N, M, 4) array-like of np.uint8
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im : (N, M, 4) array of `numpy.uint8`
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An array of RGBA pixels.
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transform : `matplotlib.transforms.Affine2DBase`
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transform : `~matplotlib.transforms.Affine2DBase`
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If and only if the concrete backend is written such that
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`option_scale_image` returns ``True``, an affine transformation
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(i.e., an `.Affine2DBase`) *may* be passed to `draw_image`. The

‎lib/matplotlib/cm.py

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< 57AE td data-grid-cell-id="diff-76cc27a01fed07ec3907a9ba6bc11abf6895968032ba148d366e6f13f8b4fccd-448-448-2" data-line-anchor="diff-76cc27a01fed07ec3907a9ba6bc11abf6895968032ba148d366e6f13f8b4fccdR448" data-selected="false" role="gridcell" style="background-color:var(--bgColor-default);padding-right:24px" tabindex="-1" valign="top" class="focusable-grid-cell diff-text-cell right-side-diff-cell left-side">
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@@ -426,18 +426,18 @@ def _scale_norm(self, norm, vmin, vmax):
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def to_rgba(self, x, alpha=None, bytes=False, norm=True):
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"""
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Return a normalized rgba array corresponding to *x*.
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Return a normalized RGBA array corresponding to *x*.
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In the normal case, *x* is a 1D or 2D sequence of scalars, and
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the corresponding `~numpy.ndarray` of rgba values will be returned,
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the corresponding `~numpy.ndarray` of RGBA values will be returned,
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based on the norm and colormap set for this ScalarMappable.
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There is one special case, for handling images that are already
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rgb or rgba, such as might have been read from an image file.
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RGB or RGBA, such as might have been read from an image file.
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If *x* is an `~numpy.ndarray` with 3 dimensions,
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and the last dimension is either 3 or 4, then it will be
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treated as an rgb or rgba array, and no mapping will be done.
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The array can be uint8, or it can be floating point with
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treated as an RGB or RGBA array, and no mapping will be done.
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The array can be `~numpy.uint8`, or it can be floats with
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values in the 0-1 range; otherwise a ValueError will be raised.
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If it is a masked array, the mask will be ignored.
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If the last dimension is 3, the *alpha* kwarg (defaulting to 1)
@@ -446,9 +446,9 @@ def to_rgba(self, x, alpha=None, bytes=False, norm=True):
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replace the preexisting alpha. A ValueError will be raised
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if the third dimension is other than 3 or 4.
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In either case, if *bytes* is *False* (default), the rgba
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In either case, if *bytes* is *False* (default), the RGBA
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array will be floats in the 0-1 range; if it is *True*,
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the returned rgba array will be uint8 in the 0 to 255 range.
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the returned RGBA array will be `~numpy.uint8` in the 0 to 255 range.
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If norm is False, no normalization of the input data is
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performed, and it is assumed to be in the range (0-1).

‎lib/matplotlib/colors.py

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@@ -681,7 +681,7 @@ def __init__(self, name, N=256):
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self.colorbar_extend = False
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def __call__(self, X, alpha=None, bytes=False):
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"""
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r"""
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Parameters
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----------
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X : float or int, `~numpy.ndarray` or scalar
@@ -695,8 +695,8 @@ def __call__(self, X, alpha=None, bytes=False):
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floats with shape matching X, or None.
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bytes : bool
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If False (default), the returned RGBA values will be floats in the
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interval ``[0, 1]`` otherwise they will be uint8s in the interval
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``[0, 255]``.
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interval ``[0, 1]`` otherwise they will be `numpy.uint8`\s in the
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interval ``[0, 255]``.
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Returns
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-------

‎lib/matplotlib/contour.py

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@@ -107,7 +107,7 @@ def clabel(self, levels=None, *,
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- If one string color, e.g., *colors* = 'r' or *colors* =
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'red', all labels will be plotted in this color.
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- If a tuple of colors (string, float, rgb, etc), different labels
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- If a tuple of colors (string, float, RGB, etc), different labels
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will be plotted in different colors in the order specified.
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inline : bool, default: True

‎lib/matplotlib/image.py

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@@ -74,7 +74,7 @@ def composite_images(images, renderer, magnification=1.0):
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Returns
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-------
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image : uint8 array (M, N, 4)
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image : (M, N, 4) `numpy.uint8` array
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The composited RGBA image.
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offset_x, offset_y : float
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The (left, bottom) offset where the composited image should be placed
@@ -333,9 +333,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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the given *clip_bbox* (also in pixel space), and magnified by the
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*magnification* factor.
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*A* may be a greyscale image (M, N) with a dtype of float32, float64,
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float128, uint16 or uint8, or an (M, N, 4) RGBA image with a dtype of
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float32, float64, float128, or uint8.
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*A* may be a greyscale image (M, N) with a dtype of `~numpy.float32`,
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`~numpy.float64`, `~numpy.float128`, `~numpy.uint16` or `~numpy.uint8`,
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or an (M, N, 4) RGBA image with a dtype of `~numpy.float32`,
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`~numpy.float64`, `~numpy.float128`, or `~numpy.uint8`.
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If *unsampled* is True, the image will not be scaled, but an
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appropriate affine transformation will be returned instead.
@@ -347,12 +348,12 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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Returns
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-------
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image : (M, N, 4) uint8 array
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image : (M, N, 4) `numpy.uint8` array
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The RGBA image, resampled unless *unsampled* is True.
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x, y : float
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The upper left corner where the image should be drawn, in pixel
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space.
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trans : Affine2D
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trans : `~matplotlib.transforms.Affine2D`
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The affine transformation from image to pixel space.
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"""
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if A is None:
@@ -596,12 +597,12 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
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Returns
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-------
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image : (M, N, 4) uint8 array
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image : (M, N, 4) `numpy.uint8` array
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The RGBA image, resampled unless *unsampled* is True.
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x, y : float
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The upper left corner where the image should be drawn, in pixel
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space.
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trans : Affine2D
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trans : `~matplotlib.transforms.Affine2D`
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The affine transformation from image to pixel space.
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"""
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raise NotImplementedError('The make_image method must be overridden')
@@ -781,7 +782,7 @@ def set_interpolation_stage(self, s):
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Parameters
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----------
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s : {'data', 'rgba'} or None
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Whether to apply up/downsampling interpolation in data or rgba
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Whether to apply up/downsampling interpolation in data or RGBA
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space.
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"""
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if s is None:

‎lib/matplotlib/markers.py

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@@ -418,7 +418,7 @@ def transformed(self, transform: Affine2D):
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Parameters
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----------
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transform : Affine2D, default: None
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transform : `~matplotlib.transforms.Affine2D`, default: None
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Transform will be combined with current user supplied transform.
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"""
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new_marker = MarkerStyle(self)

‎lib/matplotlib/path.py

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@@ -29,7 +29,7 @@ class Path:
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The underlying storage is made up of two parallel numpy arrays:
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- *vertices*: an (N, 2) float array of vertices
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- *codes*: an N-length uint8 array of path codes, or None
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- *codes*: an N-length `numpy.uint8` array of path codes, or None
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These two arrays always have the same length in the first
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dimension. For example, to represent a cubic curve, you must
@@ -1051,9 +1051,9 @@ def get_path_collection_extents(
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master_transform : `.Transform`
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Global transformation applied to all paths.
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paths : list of `Path`
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transforms : list of `.Affine2D`
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transforms : list of `~matplotlib.transforms.Affine2DBase`
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offsets : (N, 2) array-like
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offset_transform : `.Affine2D`
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offset_transform : `~matplotlib.transforms.Affine2DBase`
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Transform applied to the offsets before offsetting the path.
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Notes

‎lib/matplotlib/texmanager.py

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@@ -346,7 +346,7 @@ def get_grey(cls, tex, fontsize=None, dpi=None):
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@classmethod
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def get_rgba(cls, tex, fontsize=None, dpi=None, rgb=(0, 0, 0)):
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r"""
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Return latex's rendering of the tex string as an rgba array.
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Return latex's rendering of the tex string as an RGBA array.
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Examples
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--------

‎lib/mpl_toolkits/mplot3d/axes3d.py

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@@ -2676,12 +2676,12 @@ def voxels(self, *args, facecolors=None, edgecolors=None, shade=True,
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These parameters can be:
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- A single color value, to color all voxels the same color. This
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can be either a string, or a 1D rgb/rgba array
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can be either a string, or a 1D RGB/RGBA array
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- ``None``, the default, to use a single color for the faces, and
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the style default for the edges.
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- A 3D `~numpy.ndarray` of color names, with each item the color
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for the corresponding voxel. The size must match the voxels.
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- A 4D `~numpy.ndarray` of rgb/rgba data, with the components
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- A 4D `~numpy.ndarray` of RGB/RGBA data, with the components
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along the last axis.
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shade : bool, default: True

‎src/_image_wrapper.cpp

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@@ -17,12 +17,12 @@ const char* image_resample__doc__ =
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"Parameters\n"
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"----------\n"
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"input_array : 2-d or 3-d array-like of float, double or uint8\n"
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"input_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`\n"
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" If 2-d, the image is grayscale. If 3-d, the image must be of size\n"
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" 4 in the last dimension and represents RGBA data.\n\n"
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"output_array : 2-d or 3-d numpy array of float, double or uint8\n"
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" The dtype and number of dimensions must match *input_array*.\n\n"
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"output_array : 2-d or 3-d NumPy array of float, double or `numpy.uint8`\n"
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" The dtype and number of dimensions must match `input_array`.\n\n"
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"transform : matplotlib.transforms.Transform instance\n"
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" The transformation from the input array to the output array.\n\n"

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