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ENH: expose make_norm_from_scale #20405

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Jul 19, 2021
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ENH: expose make_norm_from_scale #20405

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1 change: 1 addition & 0 deletions doc/api/colors_api.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -52,3 +52,4 @@ Functions
is_color_like
same_color
get_named_colors_mapping
make_norm_from_scale
8 changes: 8 additions & 0 deletions doc/api/next_api_changes/behavior/20405-JMK.rst
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@@ -0,0 +1,8 @@
Make norm from scale now public
===============================

Downstream libraries can take advantage of `.colors.make_norm_from_scale`
to create a `~.colors.Normalize` subclass directly from an existing scale.
Usually norms have a scale, and the advantage of having a `~.scale.ScaleBase`
attached to a norm is to provide a scale, and associated tick locators and
formatters, for the colorbar.
17 changes: 9 additions & 8 deletions lib/matplotlib/colors.py
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Expand Up @@ -1415,13 +1415,14 @@ def __call__(self, value, clip=None):
return super().__call__(value, clip=clip)


def _make_norm_from_scale(scale_cls, base_norm_cls=None, *, init=None):
def make_norm_from_scale(scale_cls, base_norm_cls=None, *, init=None):
"""
Decorator for building a `.Normalize` subclass from a `.Scale` subclass.
Decorator for building a `.Normalize` subclass from a `~.scale.ScaleBase`
subclass.

After ::

@_make_norm_from_scale(scale_cls)
@make_norm_from_scale(scale_cls)
class norm_cls(Normalize):
...

Expand All @@ -1436,7 +1437,7 @@ class norm_cls(Normalize):
a dummy axis).

If the *scale_cls* constructor takes additional parameters, then *init*
should be passed to `_make_norm_from_scale`. It is a callable which is
should be passed to `make_norm_from_scale`. It is a callable which is
*only* used for its signature. First, this signature will become the
signature of *norm_cls*. Second, the *norm_cls* constructor will bind the
parameters passed to it using this signature, extract the bound *vmin*,
Expand All @@ -1446,7 +1447,7 @@ class norm_cls(Normalize):
"""

if base_norm_cls is None:
return functools.partial(_make_norm_from_scale, scale_cls, init=init)
return functools.partial(make_norm_from_scale, scale_cls, init=init)

if init is None:
def init(vmin=None, vmax=None, clip=False): pass
Expand Down Expand Up @@ -1509,7 +1510,7 @@ def inverse(self, value):
return Norm


@_make_norm_from_scale(
@make_norm_from_scale(
scale.FuncScale,
init=lambda functions, vmin=None, vmax=None, clip=False: None)
class FuncNorm(Normalize):
Expand Down Expand Up @@ -1542,7 +1543,7 @@ def forward(values: array-like) -> array-like
"""


@_make_norm_from_scale(functools.partial(scale.LogScale, nonpositive="mask"))
@make_norm_from_scale(functools.partial(scale.LogScale, nonpositive="mask"))
class LogNorm(Normalize):
"""Normalize a given value to the 0-1 range on a log scale."""

Expand All @@ -1555,7 +1556,7 @@ def autoscale_None(self, A):
super().autoscale_None(np.ma.array(A, mask=(A <= 0)))


@_make_norm_from_scale(
@make_norm_from_scale(
scale.SymmetricalLogScale,
init=lambda linthresh, linscale=1., vmin=None, vmax=None, clip=False, *,
base=10: None)
Expand Down