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FIX: LogNorm masks non-positive values when vmin and vmax are equal #32601

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Merged
story645 merged 1 commit into from
Nov 15, 2026
Merged

FIX: LogNorm masks non-positive values when vmin and vmax are equal #32601

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5 changes: 3 additions & 2 deletions lib/matplotlib/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2945,13 +2945,14 @@ def __call__(self, value, clip=None):
self.autoscale_None(value)
if self.vmin > self.vmax:
raise ValueError("vmin must be less or equal to vmax")
if self.vmin == self.vmax:
return np.full_like(value, 0)
if clip is None:
clip = self.clip
if clip:
value = np.clip(value, self.vmin, self.vmax)
t_value = self._trf.transform(value).reshape(np.shape(value))
if self.vmin == self.vmax:
t_value.fill(0)
return t_value
t_vmin, t_vmax = self._trf.transform([self.vmin, self.vmax])
if not np.isfinite([t_vmin, t_vmax]).all():
raise ValueError("Invalid vmin or vmax")
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15 changes: 15 additions & 0 deletions lib/matplotlib/tests/test_colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -527,6 +527,21 @@ def test_LogNorm_inverse():
assert_array_almost_equal([0.4], norm.inverse([0.30103]))


def test_LogNorm_single_valid_value():
"""
Test that non-positive values map to masked result when vmin and vmax are equal.
"""
norm = mcolors.LogNorm()
result = norm([0, 1])

# vmin and vmax are autoscaled to 1, since that is the only valid value.
assert norm.vmin == 1
assert norm.vmax == 1

np.testing.assert_array_equal(result.mask, [True, False])
assert result[1] == 0


def test_PowerNorm():
# Check an exponent of 1 gives same results as a normal linear
# normalization. Also implicitly checks that vmin/vmax are
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