matplotlib · QuLogic · Mar 7, 2023 · Jan 1, 2023 · Jan 1, 2023 · Jan 1, 2023
diff --git a/doc/api/transformations.rst b/doc/api/transformations.rst
@@ -14,4 +14,4 @@
        BboxTransformFrom, ScaledTranslation, TransformedPath, nonsingular,
        interval_contains, interval_contains_open
    :show-inheritance:
-   :special-members:
+   :special-members: __add__, __sub__
diff --git a/lib/matplotlib/backend_bases.py b/lib/matplotlib/backend_bases.py
@@ -473,10 +473,10 @@ def draw_image(self, gc, x, y, im, transform=None):
             The distance in physical units (i.e., dots or pixels) from the
             bottom side of the canvas.
 
-        im : (N, M, 4) array-like of np.uint8
+        im : (N, M, 4) array of `numpy.uint8`
             An array of RGBA pixels.
 
-        transform : `matplotlib.transforms.Affine2DBase`
+        transform : `~matplotlib.transforms.Affine2DBase`
             If and only if the concrete backend is written such that
             `option_scale_image` returns ``True``, an affine transformation
             (i.e., an `.Affine2DBase`) *may* be passed to `draw_image`.  The

diff --git a/lib/matplotlib/cm.py b/lib/matplotlib/cm.py
@@ -426,18 +426,18 @@ def _scale_norm(self, norm, vmin, vmax):
 
     def to_rgba(self, x, alpha=None, bytes=False, norm=True):
         """
-        Return a normalized rgba array corresponding to *x*.
+        Return a normalized RGBA array corresponding to *x*.
 
         In the normal case, *x* is a 1D or 2D sequence of scalars, and
-        the corresponding `~numpy.ndarray` of rgba values will be returned,
+        the corresponding `~numpy.ndarray` of RGBA values will be returned,
         based on the norm and colormap set for this ScalarMappable.
 
         There is one special case, for handling images that are already
-        rgb or rgba, such as might have been read from an image file.
+        RGB or RGBA, such as might have been read from an image file.
         If *x* is an `~numpy.ndarray` with 3 dimensions,
         and the last dimension is either 3 or 4, then it will be
-        treated as an rgb or rgba array, and no mapping will be done.
-        The array can be uint8, or it can be floating point with
+        treated as an RGB or RGBA array, and no mapping will be done.
+        The array can be `~numpy.uint8`, or it can be floats with
         values in the 0-1 range; otherwise a ValueError will be raised.
         If it is a masked array, the mask will be ignored.
         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):
         replace the preexisting alpha.  A ValueError will be raised
         if the third dimension is other than 3 or 4.
 
-        In either case, if *bytes* is *False* (default), the rgba
+        In either case, if *bytes* is *False* (default), the RGBA
         array will be floats in the 0-1 range; if it is *True*,
-        the returned rgba array will be uint8 in the 0 to 255 range.
+        the returned RGBA array will be `~numpy.uint8` in the 0 to 255 range.
 
         If norm is False, no normalization of the input data is
         performed, and it is assumed to be in the range (0-1).

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

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

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

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

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

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

diff --git a/lib/mpl_toolkits/mplot3d/axes3d.py b/lib/mpl_toolkits/mplot3d/axes3d.py
@@ -2676,12 +2676,12 @@ def voxels(self, *args, facecolors=None, edgecolors=None, shade=True,
             These parameters can be:
 
             - A single color value, to color all voxels the same color. This
-              can be either a string, or a 1D rgb/rgba array
+              can be either a string, or a 1D RGB/RGBA array
             - ``None``, the default, to use a single color for the faces, and
               the style default for the edges.
             - A 3D `~numpy.ndarray` of color names, with each item the color
               for the corresponding voxel. The size must match the voxels.
-            - A 4D `~numpy.ndarray` of rgb/rgba data, with the components
+            - A 4D `~numpy.ndarray` of RGB/RGBA data, with the components
               along the last axis.
 
         shade : bool, default: True

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