MNT: Better workaround for format_cursor_data on ScalarMappables

By introducing an explicit override function, we can move the
formatting code to ScalarMappable, where it logically belongs.
Also, artist.py does no longer depend on colors.py and cm.py,
which simplifies module dependencies.

Author: timhoffm

lib/matplotlib/artist.py

@@ -13,8 +13,6 @@
 
 import matplotlib as mpl
 from . import _api, cbook
-from .colors import BoundaryNorm
-from .cm import ScalarMappable
 from .path import Path
 from .transforms import (BboxBase, Bbox, IdentityTransform, Transform, TransformedBbox,
                          TransformedPatchPath, TransformedPath)
@@ -1346,37 +1344,11 @@ def format_cursor_data(self, data):
         --------
         get_cursor_data
         """
-        if np.ndim(data) == 0 and isinstance(self, ScalarMappable):
-            # This block logically belongs to ScalarMappable, but can't be
-            # implemented in it because most ScalarMappable subclasses inherit
-            # from Artist first and from ScalarMappable second, so
-            # Artist.format_cursor_data would always have precedence over
-            # ScalarMappable.format_cursor_data.
-            n = self.cmap.N
-            if np.ma.getmask(data):
-                return "[]"
-            normed = self.norm(data)
-            if np.isfinite(normed):
-                if isinstance(self.norm, BoundaryNorm):
-                    # not an invertible normalization mapping
-                    cur_idx = np.argmin(np.abs(self.norm.boundaries - data))
-                    neigh_idx = max(0, cur_idx - 1)
-                    # use max diff to prevent delta == 0
-                    delta = np.diff(
-                        self.norm.boundaries[neigh_idx:cur_idx + 2]
-                    ).max()
-                elif self.norm.vmin == self.norm.vmax:
-                    # singular norms, use delta of 10% of only value
-                    delta = np.abs(self.norm.vmin * .1)
-                else:
-                    # Midpoints of neighboring color intervals.
-                    neighbors = self.norm.inverse(
-                        (int(normed * n) + np.array([0, 1])) / n)
-                    delta = abs(neighbors - data).max()
-                g_sig_digits = cbook._g_sig_digits(data, delta)
-            else:
-                g_sig_digits = 3  # Consistent with default below.
-            return f"[{data:-#.{g_sig_digits}g}]"
+        if np.ndim(data) == 0 and hasattr(self, "_format_cursor_data_override"):
+            # workaround for ScalarMappable to be able to define its own
+            # format_cursor_data(). See ScalarMappable._format_cursor_data_override
+            # for details.
+            return self._format_cursor_data_override(data)
         else:
             try:
                 data[0]

lib/matplotlib/cm.py

@@ -611,6 +611,38 @@ def changed(self):
         self.callbacks.process('changed', self)
         self.stale = True
 
+    def _format_cursor_data_override(self, data):
+        # This function overwrites Artist.format_cursor_data(). We cannot
+        # implement ScalarMappable.format_cursor_data() directly, because
+        # most ScalarMappable subclasses inherit from Artist first and from
+        # ScalarMappable second, so Artist.format_cursor_data would always
+        # have precedence over ScalarMappable.format_cursor_data.
+        n = self.cmap.N
+        if np.ma.getmask(data):
+            return "[]"
+        normed = self.norm(data)
+        if np.isfinite(normed):
+            if isinstance(self.norm, colors.BoundaryNorm):
+                # not an invertible normalization mapping
+                cur_idx = np.argmin(np.abs(self.norm.boundaries - data))
+                neigh_idx = max(0, cur_idx - 1)
+                # use max diff to prevent delta == 0
+                delta = np.diff(
+                    self.norm.boundaries[neigh_idx:cur_idx + 2]
+                ).max()
+            elif self.norm.vmin == self.norm.vmax:
+                # singular norms, use delta of 10% of only value
+                delta = np.abs(self.norm.vmin * .1)
+            else:
+                # Midpoints of neighboring color intervals.
+                neighbors = self.norm.inverse(
+                    (int(normed * n) + np.array([0, 1])) / n)
+                delta = abs(neighbors - data).max()
+            g_sig_digits = cbook._g_sig_digits(data, delta)
+        else:
+            g_sig_digits = 3  # Consistent with default below.
+        return f"[{data:-#.{g_sig_digits}g}]"
+
 
 # The docstrings here must be generic enough to apply to all relevant methods.
 mpl._docstring.interpd.update(