Oct 27, 2025
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
        self._scale.linear_width = value


 class CenteredAsinhNorm(AsinhNorm):
    def __init__(self, vcenter=0, halfrange=None, linear_width=1, clip=False):
        # Initialize with AsinhNorm, vmin/vmax set via halfrange setter
        super().__init__(linear_width=linear_width, vmin=None, vmax=None, clip=clip)
        self._vcenter = vcenter
        self.halfrange = halfrange

    def autoscale(self, A):
        """Set *halfrange* to ``max(abs(A-vcenter))``, then set *vmin* and *vmax*."""
        A = np.asanyarray(A)
        self.halfrange = max(self._vcenter - A.min(),
                             A.max() - self._vcenter)

    def autoscale_None(self, A):
        """Set *vmin* and *vmax* if not already set."""
        A = np.asanyarray(A)
        if self.halfrange is None and A.size:
            self.autoscale(A)

    @property
    def vcenter(self):
        """The center value of the normalization."""
        return self._vcenter

    @vcenter.setter
    def vcenter(self, vcenter):
        """Set center value and update vmin/vmax accordingly."""
        if vcenter != self._vcenter:
            self._vcenter = vcenter
            self.halfrange = self.halfrange  # Recalculate vmin/vmax
            self._changed()

    @property
    def halfrange(self):
        """The half-range of the normalization. Returns None if vmin or vmax is not set."""
        if self.vmin is None or self.vmax is None:
            return None
        return (self.vmax - self.vmin) / 2

    @halfrange.setter
    def halfrange(self, halfrange):
        """Set half-range and update vmin/vmax symmetrically around vcenter."""
        if halfrange is None:
            self.vmin = None
            self.vmax = None
        else:
            self.vmin = self.vcenter - abs(halfrange)
            self.vmax = self.vcenter + abs(halfrange)


 class PowerNorm(Normalize):
    r"""
    Linearly map a given value to the 0-1 range and then apply
diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi
    @linear_width.setter
    def linear_width(self, value: float) -> None: ...

 class CenteredAsinhNorm(AsinhNorm):
    """Type stub for CenteredAsinhNorm - symmetric asinh normalization."""
    def __init__(
        self,
        vcenter: float = ...,
        halfrange: float | None = ...,
        linear_width: float = ...,
        clip: bool = ...,
    ) -> None: ...
    @property
    def vcenter(self) -> float: ...
    @vcenter.setter
    def vcenter(self, vcenter: float) -> None: ...
    @property
    def halfrange(self) -> float | None: ...
    @halfrange.setter
    def halfrange(self, halfrange: float | None) -> None: ...
    def autoscale(self, A: ArrayLike) -> None: ...
    def autoscale_None(self, A: ArrayLike) -> None: ...

 class PowerNorm(Normalize):
    gamma: float
    def __init__(
diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
    assert norm.vcenter == 1


 def test_CenteredAsinhNorm():
    """Test CenteredAsinhNorm - symmetric asinh normalization."""
    np.random.seed(0)

    # Test 1: Basic centering - vcenter should be in middle of vmin/vmax
    norm = mcolors.CenteredAsinhNorm(vcenter=0)
    norm.autoscale_None([-10, 50])  # Asymmetric data
    assert norm.vmax + norm.vmin == 2 * norm.vcenter
    assert norm.vmin == -50  # Symmetric around 0
    assert norm.vmax == 50

    # Test 2: halfrange is preserved when set explicitly
    norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
    norm.autoscale_None([1, 3000])
    assert norm.halfrange == 10
    assert norm.vmin == -10
    assert norm.vmax == 10

    # Test 3: Different vcenter values
    vcenter = 5
    norm = mcolors.CenteredAsinhNorm(vcenter=vcenter)
    norm.autoscale_None([1, 10])
    assert norm.vmax + norm.vmin == 2 * vcenter
    assert norm.halfrange == 5  # max(5-1, 10-5) = 5
    assert norm.vmin == 0
    assert norm.vmax == 10

    # Test 4: Center maps to 0.5
    norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10, linear_width=1)
    result = norm(0)
    assert np.isclose(result, 0.5), f"Center should map to 0.5, got {result}"

    # Test 5: Symmetry - equal distances from center
    norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
    val_pos = norm(5)
    val_neg = norm(-5)
    assert np.isclose(val_pos, 1 - val_neg), \
        f"Symmetric values should map symmetrically: {val_pos} vs {1-val_neg}"

    # Test 6: vcenter setter preserves halfrange
    norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
    assert norm.vmin == -10
    assert norm.vmax == 10
    norm.vcenter = 5
    assert norm.halfrange == 10
    assert norm.vmin == -5
    assert norm.vmax == 15

    # Test 7: halfrange setter
    norm = mcolors.CenteredAsinhNorm(vcenter=0)
    norm.halfrange = 20
    assert norm.vmin == -20
    assert norm.vmax == 20
    assert norm.vcenter == 0

    # Test 8: linear_width parameter (inherited from AsinhNorm)
    norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10, linear_width=2)
    assert norm.linear_width == 2
    norm.linear_width = 5
    assert norm.linear_width == 5

    # Test 9: Negative vcenter
    norm = mcolors.CenteredAsinhNorm(vcenter=-10, halfrange=5)
    assert norm.vmin == -15
    assert norm.vmax == -5
    assert norm.vcenter == -10

    # Test 10: autoscale with symmetric data
    norm = mcolors.CenteredAsinhNorm(vcenter=0)
    data = np.array([-5, -3, 0, 3, 5])
    norm.autoscale(data)
    assert norm.halfrange == 5
    assert norm.vmin == -5
    assert norm.vmax == 5

    # Test 11: Main use case from issue #30679 - diverging colormaps
    norm = mcolors.CenteredAsinhNorm(vcenter=0)
    data = np.array([-10, -5, 0, 5, 50])  # Asymmetric data
    normalized = norm(data)
    center_idx = np.where(data == 0)[0][0]
    assert np.isclose(normalized[center_idx], 0.5), \
        "Center value should map to 0.5 for diverging colormaps"


 @pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]])
 def test_lognorm_invalid(vmin, vmax):
    # Check that invalid limits in LogNorm error
Original file line number	Diff line number	Diff line change
Expand Up		@@ -3083,6 +3083,56 @@
		self._scale.linear_width = value


		class CenteredAsinhNorm(AsinhNorm):
		def __init__(self, vcenter=0, halfrange=None, linear_width=1, clip=False):
		# Initialize with AsinhNorm, vmin/vmax set via halfrange setter
		super().__init__(linear_width=linear_width, vmin=None, vmax=None, clip=clip)
		self._vcenter = vcenter
		self.halfrange = halfrange

		def autoscale(self, A):
		"""Set halfrange to ``max(abs(A-vcenter))``, then set vmin and vmax."""
		A = np.asanyarray(A)
		self.halfrange = max(self._vcenter - A.min(),
		A.max() - self._vcenter)

		def autoscale_None(self, A):
		"""Set vmin and vmax if not already set."""
		A = np.asanyarray(A)
		if self.halfrange is None and A.size:
		self.autoscale(A)

		@property
		def vcenter(self):
		"""The center value of the normalization."""
		return self._vcenter

		@vcenter.setter
		def vcenter(self, vcenter):
		"""Set center value and update vmin/vmax accordingly."""
		if vcenter != self._vcenter:
		self._vcenter = vcenter
		self.halfrange = self.halfrange # Recalculate vmin/vmax
		self._changed()

		@property
		def halfrange(self):
		"""The half-range of the normalization. Returns None if vmin or vmax is not set."""
Check warning on line 3120 in lib/matplotlib/colors.py View workflow job for this annotation GitHub Actions/ ruff `[rdjson] reported by reviewdog 🐶Line too long (91 > 88)Raw Output:message:"Line too long (91 > 88)" location:{path:"/home/runner/work/matplotlib/matplotlib/lib/matplotlib/colors.py" range:{start:{line:3120 column:89} end:{line:3120 column:92}}} severity:WARNING source:{name:"ruff" url:"https://docs.astral.sh/ruff"} code:{value:"E501" url:"https://docs.astral.sh/ruff/rules/line-too-long"}`
		if self.vmin is None or self.vmax is None:
		return None
		return (self.vmax - self.vmin) / 2

		@halfrange.setter
		def halfrange(self, halfrange):
		"""Set half-range and update vmin/vmax symmetrically around vcenter."""
		if halfrange is None:
		self.vmin = None
		self.vmax = None
		else:
		self.vmin = self.vcenter - abs(halfrange)
		self.vmax = self.vcenter + abs(halfrange)


		class PowerNorm(Normalize):
		r"""
		Linearly map a given value to the 0-1 range and then apply
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -388,6 +388,26 @@ class AsinhNorm(Normalize):
		@linear_width.setter
		def linear_width(self, value: float) -> None: ...

		class CenteredAsinhNorm(AsinhNorm):
		"""Type stub for CenteredAsinhNorm - symmetric asinh normalization."""
		def __init__(
		self,
		vcenter: float = ...,
		halfrange: float \| None = ...,
		linear_width: float = ...,
		clip: bool = ...,
		) -> None: ...
		@property
		def vcenter(self) -> float: ...
		@vcenter.setter
		def vcenter(self, vcenter: float) -> None: ...
		@property
		def halfrange(self) -> float \| None: ...
		@halfrange.setter
		def halfrange(self, halfrange: float \| None) -> None: ...
		def autoscale(self, A: ArrayLike) -> None: ...
		def autoscale_None(self, A: ArrayLike) -> None: ...

		class PowerNorm(Normalize):
		gamma: float
		def __init__(
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -496,6 +496,90 @@ def test_CenteredNorm():
		assert norm.vcenter == 1


		def test_CenteredAsinhNorm():
		"""Test CenteredAsinhNorm - symmetric asinh normalization."""
		np.random.seed(0)

		# Test 1: Basic centering - vcenter should be in middle of vmin/vmax
		norm = mcolors.CenteredAsinhNorm(vcenter=0)
		norm.autoscale_None([-10, 50]) # Asymmetric data
		assert norm.vmax + norm.vmin == 2 * norm.vcenter
		assert norm.vmin == -50 # Symmetric around 0
		assert norm.vmax == 50

		# Test 2: halfrange is preserved when set explicitly
		norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
		norm.autoscale_None([1, 3000])
		assert norm.halfrange == 10
		assert norm.vmin == -10
		assert norm.vmax == 10

		# Test 3: Different vcenter values
		vcenter = 5
		norm = mcolors.CenteredAsinhNorm(vcenter=vcenter)
		norm.autoscale_None([1, 10])
		assert norm.vmax + norm.vmin == 2 * vcenter
		assert norm.halfrange == 5 # max(5-1, 10-5) = 5
		assert norm.vmin == 0
		assert norm.vmax == 10

		# Test 4: Center maps to 0.5
		norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10, linear_width=1)
		result = norm(0)
		assert np.isclose(result, 0.5), f"Center should map to 0.5, got {result}"

		# Test 5: Symmetry - equal distances from center
		norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
		val_pos = norm(5)
		val_neg = norm(-5)
		assert np.isclose(val_pos, 1 - val_neg), \
		f"Symmetric values should map symmetrically: {val_pos} vs {1-val_neg}"

		# Test 6: vcenter setter preserves halfrange
		norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10)
		assert norm.vmin == -10
		assert norm.vmax == 10
		norm.vcenter = 5
		assert norm.halfrange == 10
		assert norm.vmin == -5
		assert norm.vmax == 15

		# Test 7: halfrange setter
		norm = mcolors.CenteredAsinhNorm(vcenter=0)
		norm.halfrange = 20
		assert norm.vmin == -20
		assert norm.vmax == 20
		assert norm.vcenter == 0

		# Test 8: linear_width parameter (inherited from AsinhNorm)
		norm = mcolors.CenteredAsinhNorm(vcenter=0, halfrange=10, linear_width=2)
		assert norm.linear_width == 2
		norm.linear_width = 5
		assert norm.linear_width == 5

		# Test 9: Negative vcenter
		norm = mcolors.CenteredAsinhNorm(vcenter=-10, halfrange=5)
		assert norm.vmin == -15
		assert norm.vmax == -5
		assert norm.vcenter == -10

		# Test 10: autoscale with symmetric data
		norm = mcolors.CenteredAsinhNorm(vcenter=0)
		data = np.array([-5, -3, 0, 3, 5])
		norm.autoscale(data)
		assert norm.halfrange == 5
		assert norm.vmin == -5
		assert norm.vmax == 5

		# Test 11: Main use case from issue #30679 - diverging colormaps
		norm = mcolors.CenteredAsinhNorm(vcenter=0)
		data = np.array([-10, -5, 0, 5, 50]) # Asymmetric data
		normalized = norm(data)
		center_idx = np.where(data == 0)[0][0]
		assert np.isclose(normalized[center_idx], 0.5), \
		"Center value should map to 0.5 for diverging colormaps"


		@pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]])
		def test_lognorm_invalid(vmin, vmax):
		# Check that invalid limits in LogNorm error
Expand Down