From fc94ae174d2547eb736a77cd3e9bab065a0d5574 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Trygve=20Magnus=20R=C3=A6der?= <trygvrad@gmail.com>
Date: Sun, 15 Jun 2025 19:04:58 +0200
Subject: [PATCH] Abstract base class for Normalize

---
 lib/matplotlib/colorizer.py                   |   2 +-
 lib/matplotlib/colorizer.pyi                  |  14 +--
 lib/matplotlib/colors.py                      | 106 ++++++++++++++++--
 lib/matplotlib/colors.pyi                     |  26 ++++-
 .../test_colors/test_norm_abc.png             | Bin 0 -> 16129 bytes
 lib/matplotlib/tests/test_colors.py           |  47 ++++++++
 6 files changed, 177 insertions(+), 18 deletions(-)
 create mode 100644 lib/matplotlib/tests/baseline_images/test_colors/test_norm_abc.png

diff --git a/lib/matplotlib/colorizer.py b/lib/matplotlib/colorizer.py
index b4223f389804..92a6e4ea4c4f 100644
--- a/lib/matplotlib/colorizer.py
+++ b/lib/matplotlib/colorizer.py
@@ -90,7 +90,7 @@ def norm(self):
 
     @norm.setter
     def norm(self, norm):
-        _api.check_isinstance((colors.Normalize, str, None), norm=norm)
+        _api.check_isinstance((colors.Norm, str, None), norm=norm)
         if norm is None:
             norm = colors.Normalize()
         elif isinstance(norm, str):
diff --git a/lib/matplotlib/colorizer.pyi b/lib/matplotlib/colorizer.pyi
index f35ebe5295e4..9a5a73415d83 100644
--- a/lib/matplotlib/colorizer.pyi
+++ b/lib/matplotlib/colorizer.pyi
@@ -10,12 +10,12 @@ class Colorizer:
     def __init__(
         self,
         cmap: str | colors.Colormap | None = ...,
-        norm: str | colors.Normalize | None = ...,
+        norm: str | colors.Norm | None = ...,
     ) -> None: ...
     @property
-    def norm(self) -> colors.Normalize: ...
+    def norm(self) -> colors.Norm: ...
     @norm.setter
-    def norm(self, norm: colors.Normalize | str | None) -> None: ...
+    def norm(self, norm: colors.Norm | str | None) -> None: ...
     def to_rgba(
         self,
         x: np.ndarray,
@@ -63,10 +63,10 @@ class _ColorizerInterface:
     def get_cmap(self) -> colors.Colormap: ...
     def set_cmap(self, cmap: str | colors.Colormap) -> None: ...
     @property
-    def norm(self) -> colors.Normalize: ...
+    def norm(self) -> colors.Norm: ...
     @norm.setter
-    def norm(self, norm: colors.Normalize | str | None) -> None: ...
-    def set_norm(self, norm: colors.Normalize | str | None) -> None: ...
+    def norm(self, norm: colors.Norm | str | None) -> None: ...
+    def set_norm(self, norm: colors.Norm | str | None) -> None: ...
     def autoscale(self) -> None: ...
     def autoscale_None(self) -> None: ...
 
@@ -74,7 +74,7 @@ class _ColorizerInterface:
 class _ScalarMappable(_ColorizerInterface):
     def __init__(
         self,
-        norm: colors.Normalize | None = ...,
+        norm: colors.Norm | None = ...,
         cmap: str | colors.Colormap | None = ...,
         *,
         colorizer: Colorizer | None = ...,
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
index 254e2c1a203b..b82e6bbc425c 100644
--- a/lib/matplotlib/colors.py
+++ b/lib/matplotlib/colors.py
@@ -41,6 +41,7 @@
 
 import base64
 from collections.abc import Sequence, Mapping
+from abc import ABC, abstractmethod
 import functools
 import importlib
 import inspect
@@ -2257,7 +2258,101 @@ def _init(self):
         self._isinit = True
 
 
-class Normalize:
+class Norm(ABC):
+
+    def __init__(self):
+        """
+        Abstract base class for normalizations.
+
+        Subclasses include `colors.Normalize` which maps from a scalar to
+        a scalar. However, this class makes no such requirement, and subclasses may
+        support the normalization of multiple variates simultaneously, with
+        separate normalization for each variate.
+        """
+        self.callbacks = cbook.CallbackRegistry(signals=["changed"])
+
+    @property
+    @abstractmethod
+    def vmin(self):
+        """Lower limit of the input data interval; maps to 0."""
+        pass
+
+    @property
+    @abstractmethod
+    def vmax(self):
+        """Upper limit of the input data interval; maps to 1."""
+        pass
+
+    @property
+    @abstractmethod
+    def clip(self):
+        """
+        Determines the behavior for mapping values outside the range ``[vmin, vmax]``.
+
+        See the *clip* parameter in `.Normalize`.
+        """
+        pass
+
+    @abstractmethod
+    def __call__(self, value, clip=None):
+        """
+        Normalize the data and return the normalized data.
+
+        Parameters
+        ----------
+        value
+            Data to normalize.
+        clip : bool, optional
+            See the description of the parameter *clip* in `.Normalize`.
+
+            If ``None``, defaults to ``self.clip`` (which defaults to
+            ``False``).
+
+        Notes
+        -----
+        If not already initialized, ``self.vmin`` and ``self.vmax`` are
+
+        initialized using ``self.autoscale_None(value)``.
+        """
+        pass
+
+    @abstractmethod
+    def inverse(self, value):
+        """
+        Maps the normalized value (i.e., index in the colormap) back to image
+        data value.
+
+        Parameters
+        ----------
+        value
+            Normalized value.
+        """
+        pass
+
+    @abstractmethod
+    def autoscale(self, A):
+        """Set *vmin*, *vmax* to min, max of *A*."""
+        pass
+
+    @abstractmethod
+    def autoscale_None(self, A):
+        """If *vmin* or *vmax* are not set, use the min/max of *A* to set them."""
+        pass
+
+    @abstractmethod
+    def scaled(self):
+        """Return whether *vmin* and *vmax* are both set."""
+        pass
+
+    def _changed(self):
+        """
+        Call this whenever the norm is changed to notify all the
+        callback listeners to the 'changed' signal.
+        """
+        self.callbacks.process('changed')
+
+
+class Normalize(Norm):
     """
     A class which, when called, maps values within the interval
     ``[vmin, vmax]`` linearly to the interval ``[0.0, 1.0]``. The mapping of
@@ -2307,11 +2402,11 @@ def __init__(self, vmin=None, vmax=None, clip=False):
         -----
         If ``vmin == vmax``, input data will be mapped to 0.
         """
+        super().__init__()
         self._vmin = _sanitize_extrema(vmin)
         self._vmax = _sanitize_extrema(vmax)
         self._clip = clip
         self._scale = None
-        self.callbacks = cbook.CallbackRegistry(signals=["changed"])
 
     @property
     def vmin(self):
@@ -2352,13 +2447,6 @@ def clip(self, value):
             self._clip = value
             self._changed()
 
-    def _changed(self):
-        """
-        Call this whenever the norm is changed to notify all the
-        callback listeners to the 'changed' signal.
-        """
-        self.callbacks.process('changed')
-
     @staticmethod
     def process_value(value):
         """
diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi
index eadd759bcaa3..02864408d4d0 100644
--- a/lib/matplotlib/colors.pyi
+++ b/lib/matplotlib/colors.pyi
@@ -1,4 +1,5 @@
 from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
+from abc import ABC, abstractmethod
 from matplotlib import cbook, scale
 import re
 
@@ -249,8 +250,31 @@ class BivarColormapFromImage(BivarColormap):
         origin: Sequence[float] = ..., name: str = ...
     ) -> None: ...
 
-class Normalize:
+class Norm(ABC):
     callbacks: cbook.CallbackRegistry
+    def __init__(self) -> None: ...
+    @property
+    @abstractmethod
+    def vmin(self) -> float | tuple[float] | None: ...
+    @property
+    @abstractmethod
+    def vmax(self) -> float | tuple[float] | None: ...
+    @property
+    @abstractmethod
+    def clip(self) -> bool | tuple[bool]: ...
+    @abstractmethod
+    def __call__(self, value: np.ndarray, clip: bool | None = ...) -> ArrayLike: ...
+    @abstractmethod
+    def inverse(self, value: ArrayLike) -> ArrayLike: ...
+    @abstractmethod
+    def autoscale(self, A: ArrayLike) -> None: ...
+    @abstractmethod
+    def autoscale_None(self, A: ArrayLike) -> None: ...
+    @abstractmethod
+    def scaled(self) -> bool: ...
+
+
+class Normalize(Norm):
     def __init__(
         self, vmin: float | None = ..., vmax: float | None = ..., clip: bool = ...
     ) -> None: ...
diff --git a/lib/matplotlib/tests/baseline_images/test_colors/test_norm_abc.png b/lib/matplotlib/tests/baseline_images/test_colors/test_norm_abc.png
new file mode 100644
index 0000000000000000000000000000000000000000..077365674ac27803460ddb533d5efa09ea7ef9c7
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
index df3f65bdb2dc..217cb3349b65 100644
--- a/lib/matplotlib/tests/test_colors.py
+++ b/lib/matplotlib/tests/test_colors.py
@@ -7,6 +7,7 @@
 from PIL import Image
 import pytest
 import base64
+import platform
 
 from numpy.testing import assert_array_equal, assert_array_almost_equal
 
@@ -1829,3 +1830,49 @@ def test_LinearSegmentedColormap_from_list_value_color_tuple():
         cmap([value for value, _ in value_color_tuples]),
         to_rgba_array([color for _, color in value_color_tuples]),
     )
+
+
+@image_comparison(['test_norm_abc.png'], remove_text=True,
+                   tol=0 if platform.machine() == 'x86_64' else 0.05)
+def test_norm_abc():
+
+    class CustomHalfNorm(mcolors.Norm):
+        def __init__(self):
+            super().__init__()
+
+        @property
+        def vmin(self):
+            return 0
+
+        @property
+        def vmax(self):
+            return 1
+
+        @property
+        def clip(self):
+            return False
+
+        def __call__(self, value, clip=None):
+            return value / 2
+
+        def inverse(self, value):
+            return 2 * value
+
+        def autoscale(self, A):
+            pass
+
+        def autoscale_None(self, A):
+            pass
+
+        def scaled(self):
+            return True
+
+    fig, axes = plt.subplots(2,2)
+
+    r = np.linspace(-1, 3, 16*16).reshape((16,16))
+    norm = CustomHalfNorm()
+    colorizer = mpl.colorizer.Colorizer(cmap='viridis', norm=norm)
+    c = axes[0,0].imshow(r, colorizer=colorizer)
+    axes[0,1].pcolor(r, colorizer=colorizer)
+    axes[1,0].contour(r, colorizer=colorizer)
+    axes[1,1].contourf(r, colorizer=colorizer)