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Autoinfer norm bounds. #21989

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Merged
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Dec 17, 2021
Merged

Autoinfer norm bounds. #21989

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23 changes: 13 additions & 10 deletions lib/matplotlib/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1229,7 +1229,8 @@ def __call__(self, value, clip=None):

result, is_scalar = self.process_value(value)

self.autoscale_None(result)
if self.vmin is None or self.vmax is None:
self.autoscale_None(result)
# Convert at least to float, without losing precision.
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
Expand Down Expand Up @@ -1520,7 +1521,8 @@ def __init__(self, *args, **kwargs):

def __call__(self, value, clip=None):
value, is_scalar = self.process_value(value)
self.autoscale_None(value)
if self.vmin is None or self.vmax is 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:
Expand Down Expand Up @@ -1555,6 +1557,15 @@ def inverse(self, value):
.reshape(np.shape(value)))
return value[0] if is_scalar else value

def autoscale(self, A):
# i.e. A[np.isfinite(...)], but also for non-array A's
in_trf_domain = np.extract(np.isfinite(self._trf.transform(A)), A)
return super().autoscale(in_trf_domain)

def autoscale_None(self, A):
in_trf_domain = np.extract(np.isfinite(self._trf.transform(A)), A)
return super().autoscale_None(in_trf_domain)

Norm.__name__ = (f"{scale_cls.__name__}Norm" if base_norm_cls is Normalize
else base_norm_cls.__name__)
Norm.__qualname__ = base_norm_cls.__qualname__
Expand Down Expand Up @@ -1603,14 +1614,6 @@ def forward(values: array-like) -> array-like
class LogNorm(Normalize):
"""Normalize a given value to the 0-1 range on a log scale."""

def autoscale(self, A):
# docstring inherited.
super().autoscale(np.ma.array(A, mask=(A <= 0)))

def autoscale_None(self, A):
# docstring inherited.
super().autoscale_None(np.ma.array(A, mask=(A <= 0)))


@make_norm_from_scale(
scale.SymmetricalLogScale,
Expand Down