Incorporating feedback from @story645

trygvrad · trygvrad · commit 9c26a1e0a038 · 2025-02-08T10:50:06.000+01:00
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -5775,12 +5775,12 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
               colors using normalization and a colormap. See parameters *norm*,
               *cmap*, *vmin*, *vmax*.
             - (K, M, N): multiple images with scalar data. Must be used
-              with a multivariate or bivariate colormap. See *cmap*.
+              with a multivariate or bivariate colormap that supports K channels.
             - (M, N, 3): an image with RGB values (0-1 float or 0-255 int).
             - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),
               i.e. including transparency.
 
-            Here M and N define the rows and columns of the image.
+            The dimensions M and N are the number of rows and columns of the image.
 
             Out-of-range RGB(A) values are clipped.
 
@@ -6268,12 +6268,17 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
         # we need to get the colorizer object to know the number of
         # n_variates that should exist in the array, we therefore get the
         # colorizer here.
-        colorizer = mcolorizer.ColorizingArtist._get_colorizer(norm=norm,
-                                                               cmap=cmap,
-                                                               colorizer=colorizer)
-        if colorizer.norm.n_input > 1:
-            data = mcolorizer._ensure_multivariate_data(colorizer.norm.n_input, args[0])
-            args = (data, *args[1:])
+        colorizer_obj = mcolorizer.ColorizingArtist._get_colorizer(norm=norm,
+                                                                   cmap=cmap,
+                                                                   colorizer=colorizer)
+        if colorizer_obj.norm.n_input > 1:
+            # Valid call signatures for pcolor are with args = (C) or args = (X, Y, C)
+            # If provided, _pcolorargs will check that X, Y and C have the same shape.
+            # Before this check, we need to convert C from shape (K, N, M), where K is
+            # the number of variates, to (N, M) with a data type with K fields.
+            data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
+                                                        args[-1])
+            args = (*args[:-1], data)
 
         X, Y, C, shading = self._pcolorargs('pcolor', *args, shading=shading,
                                             kwargs=kwargs)
@@ -6311,7 +6316,7 @@ def pcolor(self, *args, shading=None, alpha=None, norm=None, cmap=None,
         coords = stack([X, Y], axis=-1)
 
         collection = mcoll.PolyQuadMesh(
-            coords, array=C, colorizer=colorizer,
+            coords, array=C, colorizer=colorizer_obj,
             alpha=alpha, **kwargs)
         collection._scale_norm(norm, vmin, vmax)
 
@@ -6546,6 +6551,10 @@ def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None,
                                                                    cmap=cmap,
                                                                    colorizer=colorizer)
         if colorizer_obj.norm.n_input > 1:
+            # Valid call signatures for pcolor are with args = (C) or args = (X, Y, C)
+            # If provided, _pcolorargs will check that X, Y and C have the same shape.
+            # Before this check, we need to convert C from shape (K, N, M), where K is
+            # the number of variates, to (N, M) with a data type with K fields.
             data = mcolorizer._ensure_multivariate_data(colorizer_obj.norm.n_input,
                                                         args[-1])
             args = (*args[:-1], data)
diff --git a/lib/matplotlib/colorizer.py b/lib/matplotlib/colorizer.py
@@ -226,7 +226,7 @@ def _set_cmap(self, cmap):
             if self.norm.n_output != cmap_obj.n_variates:
                 raise ValueError(f"The colormap {cmap} does not support "
                                  f"{self.norm.n_output} variates as required by "
-                                 "the norm on this Colorizer.")
+                                 f"the {type(self.norm)} on this Colorizer.")
         self._cmap = cmap_obj
         if not in_init:
             self.changed()  # Things are not set up properly yet.
@@ -738,13 +738,7 @@ def _ensure_norm(norm, n_variates=1):
         if norm is None:
             norm = colors.Normalize()
         elif isinstance(norm, str):
-            try:
-                scale_cls = scale._scale_mapping[norm]
-            except KeyError:
-                raise ValueError(
-                    "Invalid norm str name; the following values are "
-                    f"supported: {', '.join(scale._scale_mapping)}"
-                ) from None
+            scale_cls = scale._get_scale_cls_from_str(norm)
             norm = _auto_norm_from_scale(scale_cls)()
         return norm
     else:  # n_variates > 1
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -3260,13 +3260,7 @@ def __init__(self, norms, vmin=None, vmax=None, clip=False):
             if n is None:
                 norms[i] = Normalize()
             elif isinstance(n, str):
-                try:
-                    scale_cls = scale._scale_mapping[n]
-                except KeyError:
-                    raise ValueError(
-                        "Invalid norm str name; the following values are "
-                        f"supported: {', '.join(scale._scale_mapping)}"
-                    ) from None
+                scale_cls = scale._get_scale_cls_from_str(n)
                 norms[i] = mpl.colorizer._auto_norm_from_scale(scale_cls)()
 
         # Convert the list of norms to a tuple to make it immutable.
@@ -3377,9 +3371,8 @@ def __call__(self, value, clip=None):
         """
         if clip is None:
             clip = self.clip
-        else:
-            if not np.iterable(clip):
-                clip = [clip]*self.n_input
+        elif not np.iterable(clip):
+            clip = [clip]*self.n_input
 
         value = self._iterable_variates_in_data(value, self.n_input)
         result = [n(v, clip=c) for n, v, c in zip(self.norms, value, clip)]
diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -715,6 +715,35 @@ def get_scale_names():
     return sorted(_scale_mapping)
 
 
+def _get_scale_cls_from_str(scale_as_str):
+    """
+    Returns the scale class from a string.
+
+    Used in the creation of norms from a string to ensure a reasonable error
+    in the case where an invalid string is used. This cannot use
+    `_api.check_getitem()`, because the norm keyword accepts arguments
+    other than strings.
+
+    Parameters
+    ----------
+    scale_as_str : string
+        A string corresponding to a scale
+
+    Returns
+    -------
+    A subclass of ScaleBase.
+
+    """
+    try:
+        scale_cls = _scale_mapping[scale_as_str]
+    except KeyError:
+        raise ValueError(
+            "Invalid norm str name; the following values are "
+            f"supported: {', '.join(_scale_mapping)}"
+        ) from None
+    return scale_cls
+
+
 def scale_factory(scale, axis, **kwargs):
     """
     Return a scale class by name.
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_visualizations.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/bivariate_visualizations.png
diff --git a/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_visualizations.png b/lib/matplotlib/tests/baseline_images/test_multivariate_axes/multivariate_visualizations.png
diff --git a/lib/matplotlib/tests/test_multivariate_axes.py b/lib/matplotlib/tests/test_multivariate_axes.py
@@ -4,6 +4,7 @@
 from matplotlib.testing.decorators import (image_comparison,
                                            remove_ticks_and_titles)
 import matplotlib.colors as mcolors
+from matplotlib import cbook
 import matplotlib as mpl
 import pytest
 import re
@@ -14,7 +15,7 @@ def test_bivariate_visualizations():
     x_0 = np.arange(25, dtype='float32').reshape(5, 5) % 5
     x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
 
-    fig, axes = plt.subplots(1, 6, figsize=(10, 2))
+    fig, axes = plt.subplots(1, 7, figsize=(12, 2))
 
     axes[0].imshow((x_0, x_1), cmap='BiPeak', interpolation='nearest')
     axes[1].matshow((x_0, x_1), cmap='BiPeak')
@@ -24,7 +25,8 @@ def test_bivariate_visualizations():
     x = np.arange(5)
     y = np.arange(5)
     X, Y = np.meshgrid(x, y)
-    axes[4].pcolormesh(X, Y, (x_0, x_1), cmap='BiPeak')
+    axes[4].pcolor(X, Y, (x_0, x_1), cmap='BiPeak')
+    axes[5].pcolormesh(X, Y, (x_0, x_1), cmap='BiPeak')
 
     patches = [
         mpl.patches.Wedge((.3, .7), .1, 0, 360),             # Full circle
@@ -36,7 +38,7 @@ def test_bivariate_visualizations():
     colors_1 = np.arange(len(patches)) % 2
     p = mpl.collections.PatchCollection(patches, cmap='BiPeak', alpha=0.5)
     p.set_array((colors_0, colors_1))
-    axes[5].add_collection(p)
+    axes[6].add_collection(p)
 
     remove_ticks_and_titles(fig)
 
@@ -47,7 +49,7 @@ def test_multivariate_visualizations():
     x_1 = np.arange(25, dtype='float32').reshape(5, 5).T % 5
     x_2 = np.arange(25, dtype='float32').reshape(5, 5) % 6
 
-    fig, axes = plt.subplots(1, 6, figsize=(10, 2))
+    fig, axes = plt.subplots(1, 7, figsize=(12, 2))
 
     axes[0].imshow((x_0, x_1, x_2), cmap='3VarAddA', interpolation='nearest')
     axes[1].matshow((x_0, x_1, x_2), cmap='3VarAddA')
@@ -57,7 +59,8 @@ def test_multivariate_visualizations():
     x = np.arange(5)
     y = np.arange(5)
     X, Y = np.meshgrid(x, y)
-    axes[4].pcolormesh(X, Y, (x_0, x_1, x_2), cmap='3VarAddA')
+    axes[4].pcolor(X, Y, (x_0, x_1, x_2), cmap='3VarAddA')
+    axes[5].pcolormesh(X, Y, (x_0, x_1, x_2), cmap='3VarAddA')
 
     patches = [
         mpl.patches.Wedge((.3, .7), .1, 0, 360),             # Full circle
@@ -70,7 +73,7 @@ def test_multivariate_visualizations():
     colors_2 = np.arange(len(patches)) % 3
     p = mpl.collections.PatchCollection(patches, cmap='3VarAddA', alpha=0.5)
     p.set_array((colors_0, colors_1, colors_2))
-    axes[5].add_collection(p)
+    axes[6].add_collection(p)
 
     remove_ticks_and_titles(fig)
 
@@ -625,52 +628,8 @@ def test_cmap_error():
                                                    )):
         mpl.collections.PatchCollection([], cmap='not_a_cmap')
 
-if 0:
-    def test_artist_format_cursor_data_multivar():
-
-        X = np.zeros((3, 3))
-        X[0, 0] = 0.9
-        X[0, 1] = 0.99
-        X[0, 2] = 0.999
-        X[1, 0] = -1
-        X[1, 1] = 0
-        X[1, 2] = 1
-        X[2, 0] = 0.09
-        X[2, 1] = 0.009
-        X[2, 2] = 0.0009
-
-        labels_list = [
-            "[0.9, 0.0]",
-            "[1., 0.0]",
-            "[1., 0.0]",
-            "[-1.0, 0.0]",
-            "[0.0, 0.0]",
-            "[1.0, 0.0]",
-            "[0.09, 0.0]",
-            "[0.009, 0.0]",
-            "[0.0009, 0.0]",
-        ]
-
-        pos = [[0, 0], [1, 0], [2, 0],
-               [0, 1], [1, 1], [2, 1],
-               [0, 2], [1, 2], [2, 2]]
-
-        from matplotlib.backend_bases import MouseEvent
-
-        for cmap in ['BiOrangeBlue', '2VarAddA']:
-            fig, ax = plt.subplots()
-            norm = mpl.colors.BoundaryNorm(np.linspace(-1, 1, 20), 256)
-            data = (X, np.zeros(X.shape))
-            im = ax.imshow(data, cmap=cmap, norm=(norm, None))
-
-            for v, text in zip(pos, labels_list):
-                xdisp, ydisp = ax.transData.transform(v)
-                event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
-                assert im.format_cursor_data(im.get_cursor_data(event)) == text
-
 
 def test_multivariate_safe_masked_invalid():
-    from matplotlib import cbook
     dt = np.dtype('float32, float32').newbyteorder('>')
     x = np.zeros(2, dtype=dt)
     x['f0'][0] = np.nan
