778 lines
25 KiB
Python
778 lines
25 KiB
Python
"""
|
|
Scales define the distribution of data values on an axis, e.g. a log scaling.
|
|
|
|
They are attached to an `~.axis.Axis` and hold a `.Transform`, which is
|
|
responsible for the actual data transformation.
|
|
|
|
See also `.axes.Axes.set_xscale` and the scales examples in the documentation.
|
|
"""
|
|
|
|
import inspect
|
|
import textwrap
|
|
|
|
import numpy as np
|
|
from numpy import ma
|
|
|
|
from matplotlib import cbook, docstring, rcParams
|
|
from matplotlib.ticker import (
|
|
NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter,
|
|
NullLocator, LogLocator, AutoLocator, AutoMinorLocator,
|
|
SymmetricalLogLocator, LogitLocator)
|
|
from matplotlib.transforms import Transform, IdentityTransform
|
|
from matplotlib.cbook import warn_deprecated
|
|
|
|
|
|
class ScaleBase:
|
|
"""
|
|
The base class for all scales.
|
|
|
|
Scales are separable transformations, working on a single dimension.
|
|
|
|
Any subclasses will want to override:
|
|
|
|
- :attr:`name`
|
|
- :meth:`get_transform`
|
|
- :meth:`set_default_locators_and_formatters`
|
|
|
|
And optionally:
|
|
|
|
- :meth:`limit_range_for_scale`
|
|
|
|
"""
|
|
|
|
def __init__(self, axis, **kwargs):
|
|
r"""
|
|
Construct a new scale.
|
|
|
|
Notes
|
|
-----
|
|
The following note is for scale implementors.
|
|
|
|
For back-compatibility reasons, scales take an `~matplotlib.axis.Axis`
|
|
object as first argument. However, this argument should not
|
|
be used: a single scale object should be usable by multiple
|
|
`~matplotlib.axis.Axis`\es at the same time.
|
|
"""
|
|
if kwargs:
|
|
warn_deprecated(
|
|
'3.2.0',
|
|
message=(
|
|
f"ScaleBase got an unexpected keyword "
|
|
f"argument {next(iter(kwargs))!r}. "
|
|
'In the future this will raise TypeError')
|
|
)
|
|
|
|
def get_transform(self):
|
|
"""
|
|
Return the :class:`~matplotlib.transforms.Transform` object
|
|
associated with this scale.
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
"""
|
|
Set the locators and formatters of *axis* to instances suitable for
|
|
this scale.
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def limit_range_for_scale(self, vmin, vmax, minpos):
|
|
"""
|
|
Returns the range *vmin*, *vmax*, possibly limited to the
|
|
domain supported by this scale.
|
|
|
|
*minpos* should be the minimum positive value in the data.
|
|
This is used by log scales to determine a minimum value.
|
|
"""
|
|
return vmin, vmax
|
|
|
|
|
|
class LinearScale(ScaleBase):
|
|
"""
|
|
The default linear scale.
|
|
"""
|
|
|
|
name = 'linear'
|
|
|
|
def __init__(self, axis, **kwargs):
|
|
# This method is present only to prevent inheritance of the base class'
|
|
# constructor docstring, which would otherwise end up interpolated into
|
|
# the docstring of Axis.set_scale.
|
|
"""
|
|
"""
|
|
super().__init__(axis, **kwargs)
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
# docstring inherited
|
|
axis.set_major_locator(AutoLocator())
|
|
axis.set_major_formatter(ScalarFormatter())
|
|
axis.set_minor_formatter(NullFormatter())
|
|
# update the minor locator for x and y axis based on rcParams
|
|
if (axis.axis_name == 'x' and rcParams['xtick.minor.visible']
|
|
or axis.axis_name == 'y' and rcParams['ytick.minor.visible']):
|
|
axis.set_minor_locator(AutoMinorLocator())
|
|
else:
|
|
axis.set_minor_locator(NullLocator())
|
|
|
|
def get_transform(self):
|
|
"""
|
|
Return the transform for linear scaling, which is just the
|
|
`~matplotlib.transforms.IdentityTransform`.
|
|
"""
|
|
return IdentityTransform()
|
|
|
|
|
|
class FuncTransform(Transform):
|
|
"""
|
|
A simple transform that takes and arbitrary function for the
|
|
forward and inverse transform.
|
|
"""
|
|
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, forward, inverse):
|
|
"""
|
|
Parameters
|
|
----------
|
|
forward : callable
|
|
The forward function for the transform. This function must have
|
|
an inverse and, for best behavior, be monotonic.
|
|
It must have the signature::
|
|
|
|
def forward(values: array-like) -> array-like
|
|
|
|
inverse : callable
|
|
The inverse of the forward function. Signature as ``forward``.
|
|
"""
|
|
super().__init__()
|
|
if callable(forward) and callable(inverse):
|
|
self._forward = forward
|
|
self._inverse = inverse
|
|
else:
|
|
raise ValueError('arguments to FuncTransform must be functions')
|
|
|
|
def transform_non_affine(self, values):
|
|
return self._forward(values)
|
|
|
|
def inverted(self):
|
|
return FuncTransform(self._inverse, self._forward)
|
|
|
|
|
|
class FuncScale(ScaleBase):
|
|
"""
|
|
Provide an arbitrary scale with user-supplied function for the axis.
|
|
"""
|
|
|
|
name = 'function'
|
|
|
|
def __init__(self, axis, functions):
|
|
"""
|
|
Parameters
|
|
----------
|
|
axis : `~matplotlib.axis.Axis`
|
|
The axis for the scale.
|
|
functions : (callable, callable)
|
|
two-tuple of the forward and inverse functions for the scale.
|
|
The forward function must be monotonic.
|
|
|
|
Both functions must have the signature::
|
|
|
|
def forward(values: array-like) -> array-like
|
|
"""
|
|
forward, inverse = functions
|
|
transform = FuncTransform(forward, inverse)
|
|
self._transform = transform
|
|
|
|
def get_transform(self):
|
|
"""Return the `.FuncTransform` associated with this scale."""
|
|
return self._transform
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
# docstring inherited
|
|
axis.set_major_locator(AutoLocator())
|
|
axis.set_major_formatter(ScalarFormatter())
|
|
axis.set_minor_formatter(NullFormatter())
|
|
# update the minor locator for x and y axis based on rcParams
|
|
if (axis.axis_name == 'x' and rcParams['xtick.minor.visible']
|
|
or axis.axis_name == 'y' and rcParams['ytick.minor.visible']):
|
|
axis.set_minor_locator(AutoMinorLocator())
|
|
else:
|
|
axis.set_minor_locator(NullLocator())
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="LogTransform")
|
|
class LogTransformBase(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, nonpos='clip'):
|
|
Transform.__init__(self)
|
|
self._clip = {"clip": True, "mask": False}[nonpos]
|
|
|
|
def transform_non_affine(self, a):
|
|
return LogTransform.transform_non_affine(self, a)
|
|
|
|
def __str__(self):
|
|
return "{}({!r})".format(
|
|
type(self).__name__, "clip" if self._clip else "mask")
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="InvertedLogTransform")
|
|
class InvertedLogTransformBase(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def transform_non_affine(self, a):
|
|
return ma.power(self.base, a)
|
|
|
|
def __str__(self):
|
|
return "{}()".format(type(self).__name__)
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="LogTransform")
|
|
class Log10Transform(LogTransformBase):
|
|
base = 10.0
|
|
|
|
def inverted(self):
|
|
return InvertedLog10Transform()
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="InvertedLogTransform")
|
|
class InvertedLog10Transform(InvertedLogTransformBase):
|
|
base = 10.0
|
|
|
|
def inverted(self):
|
|
return Log10Transform()
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="LogTransform")
|
|
class Log2Transform(LogTransformBase):
|
|
base = 2.0
|
|
|
|
def inverted(self):
|
|
return InvertedLog2Transform()
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="InvertedLogTransform")
|
|
class InvertedLog2Transform(InvertedLogTransformBase):
|
|
base = 2.0
|
|
|
|
def inverted(self):
|
|
return Log2Transform()
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="LogTransform")
|
|
class NaturalLogTransform(LogTransformBase):
|
|
base = np.e
|
|
|
|
def inverted(self):
|
|
return InvertedNaturalLogTransform()
|
|
|
|
|
|
@cbook.deprecated("3.1", alternative="InvertedLogTransform")
|
|
class InvertedNaturalLogTransform(InvertedLogTransformBase):
|
|
base = np.e
|
|
|
|
def inverted(self):
|
|
return NaturalLogTransform()
|
|
|
|
|
|
class LogTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, base, nonpos='clip'):
|
|
Transform.__init__(self)
|
|
self.base = base
|
|
self._clip = {"clip": True, "mask": False}[nonpos]
|
|
|
|
def __str__(self):
|
|
return "{}(base={}, nonpos={!r})".format(
|
|
type(self).__name__, self.base, "clip" if self._clip else "mask")
|
|
|
|
def transform_non_affine(self, a):
|
|
# Ignore invalid values due to nans being passed to the transform.
|
|
with np.errstate(divide="ignore", invalid="ignore"):
|
|
log = {np.e: np.log, 2: np.log2, 10: np.log10}.get(self.base)
|
|
if log: # If possible, do everything in a single call to NumPy.
|
|
out = log(a)
|
|
else:
|
|
out = np.log(a)
|
|
out /= np.log(self.base)
|
|
if self._clip:
|
|
# SVG spec says that conforming viewers must support values up
|
|
# to 3.4e38 (C float); however experiments suggest that
|
|
# Inkscape (which uses cairo for rendering) runs into cairo's
|
|
# 24-bit limit (which is apparently shared by Agg).
|
|
# Ghostscript (used for pdf rendering appears to overflow even
|
|
# earlier, with the max value around 2 ** 15 for the tests to
|
|
# pass. On the other hand, in practice, we want to clip beyond
|
|
# np.log10(np.nextafter(0, 1)) ~ -323
|
|
# so 1000 seems safe.
|
|
out[a <= 0] = -1000
|
|
return out
|
|
|
|
def inverted(self):
|
|
return InvertedLogTransform(self.base)
|
|
|
|
|
|
class InvertedLogTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, base):
|
|
Transform.__init__(self)
|
|
self.base = base
|
|
|
|
def __str__(self):
|
|
return "{}(base={})".format(type(self).__name__, self.base)
|
|
|
|
def transform_non_affine(self, a):
|
|
return ma.power(self.base, a)
|
|
|
|
def inverted(self):
|
|
return LogTransform(self.base)
|
|
|
|
|
|
class LogScale(ScaleBase):
|
|
"""
|
|
A standard logarithmic scale. Care is taken to only plot positive values.
|
|
"""
|
|
name = 'log'
|
|
|
|
# compatibility shim
|
|
LogTransformBase = LogTransformBase
|
|
Log10Transform = Log10Transform
|
|
InvertedLog10Transform = InvertedLog10Transform
|
|
Log2Transform = Log2Transform
|
|
InvertedLog2Transform = InvertedLog2Transform
|
|
NaturalLogTransform = NaturalLogTransform
|
|
InvertedNaturalLogTransform = InvertedNaturalLogTransform
|
|
LogTransform = LogTransform
|
|
InvertedLogTransform = InvertedLogTransform
|
|
|
|
def __init__(self, axis, **kwargs):
|
|
"""
|
|
Parameters
|
|
----------
|
|
axis : `~matplotlib.axis.Axis`
|
|
The axis for the scale.
|
|
basex, basey : float, default: 10
|
|
The base of the logarithm.
|
|
nonposx, nonposy : {'clip', 'mask'}, default: 'clip'
|
|
Determines the behavior for non-positive values. They can either
|
|
be masked as invalid, or clipped to a very small positive number.
|
|
subsx, subsy : sequence of int, default: None
|
|
Where to place the subticks between each major tick.
|
|
For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``
|
|
will place 8 logarithmically spaced minor ticks between
|
|
each major tick.
|
|
"""
|
|
if axis.axis_name == 'x':
|
|
base = kwargs.pop('basex', 10.0)
|
|
subs = kwargs.pop('subsx', None)
|
|
nonpos = kwargs.pop('nonposx', 'clip')
|
|
cbook._check_in_list(['mask', 'clip'], nonposx=nonpos)
|
|
else:
|
|
base = kwargs.pop('basey', 10.0)
|
|
subs = kwargs.pop('subsy', None)
|
|
nonpos = kwargs.pop('nonposy', 'clip')
|
|
cbook._check_in_list(['mask', 'clip'], nonposy=nonpos)
|
|
|
|
if kwargs:
|
|
raise TypeError(f"LogScale got an unexpected keyword "
|
|
f"argument {next(iter(kwargs))!r}")
|
|
|
|
if base <= 0 or base == 1:
|
|
raise ValueError('The log base cannot be <= 0 or == 1')
|
|
|
|
self._transform = LogTransform(base, nonpos)
|
|
self.subs = subs
|
|
|
|
@property
|
|
def base(self):
|
|
return self._transform.base
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
# docstring inherited
|
|
axis.set_major_locator(LogLocator(self.base))
|
|
axis.set_major_formatter(LogFormatterSciNotation(self.base))
|
|
axis.set_minor_locator(LogLocator(self.base, self.subs))
|
|
axis.set_minor_formatter(
|
|
LogFormatterSciNotation(self.base,
|
|
labelOnlyBase=(self.subs is not None)))
|
|
|
|
def get_transform(self):
|
|
"""Return the `.LogTransform` associated with this scale."""
|
|
return self._transform
|
|
|
|
def limit_range_for_scale(self, vmin, vmax, minpos):
|
|
"""Limit the domain to positive values."""
|
|
if not np.isfinite(minpos):
|
|
minpos = 1e-300 # Should rarely (if ever) have a visible effect.
|
|
|
|
return (minpos if vmin <= 0 else vmin,
|
|
minpos if vmax <= 0 else vmax)
|
|
|
|
|
|
class FuncScaleLog(LogScale):
|
|
"""
|
|
Provide an arbitrary scale with user-supplied function for the axis and
|
|
then put on a logarithmic axes.
|
|
"""
|
|
|
|
name = 'functionlog'
|
|
|
|
def __init__(self, axis, functions, base=10):
|
|
"""
|
|
Parameters
|
|
----------
|
|
axis : `matplotlib.axis.Axis`
|
|
The axis for the scale.
|
|
functions : (callable, callable)
|
|
two-tuple of the forward and inverse functions for the scale.
|
|
The forward function must be monotonic.
|
|
|
|
Both functions must have the signature::
|
|
|
|
def forward(values: array-like) -> array-like
|
|
|
|
base : float
|
|
logarithmic base of the scale (default = 10)
|
|
|
|
"""
|
|
forward, inverse = functions
|
|
self.subs = None
|
|
self._transform = FuncTransform(forward, inverse) + LogTransform(base)
|
|
|
|
@property
|
|
def base(self):
|
|
return self._transform._b.base # Base of the LogTransform.
|
|
|
|
def get_transform(self):
|
|
"""Return the `.Transform` associated with this scale."""
|
|
return self._transform
|
|
|
|
|
|
class SymmetricalLogTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, base, linthresh, linscale):
|
|
Transform.__init__(self)
|
|
self.base = base
|
|
self.linthresh = linthresh
|
|
self.linscale = linscale
|
|
self._linscale_adj = (linscale / (1.0 - self.base ** -1))
|
|
self._log_base = np.log(base)
|
|
|
|
def transform_non_affine(self, a):
|
|
abs_a = np.abs(a)
|
|
with np.errstate(divide="ignore", invalid="ignore"):
|
|
out = np.sign(a) * self.linthresh * (
|
|
self._linscale_adj +
|
|
np.log(abs_a / self.linthresh) / self._log_base)
|
|
inside = abs_a <= self.linthresh
|
|
out[inside] = a[inside] * self._linscale_adj
|
|
return out
|
|
|
|
def inverted(self):
|
|
return InvertedSymmetricalLogTransform(self.base, self.linthresh,
|
|
self.linscale)
|
|
|
|
|
|
class InvertedSymmetricalLogTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, base, linthresh, linscale):
|
|
Transform.__init__(self)
|
|
symlog = SymmetricalLogTransform(base, linthresh, linscale)
|
|
self.base = base
|
|
self.linthresh = linthresh
|
|
self.invlinthresh = symlog.transform(linthresh)
|
|
self.linscale = linscale
|
|
self._linscale_adj = (linscale / (1.0 - self.base ** -1))
|
|
|
|
def transform_non_affine(self, a):
|
|
abs_a = np.abs(a)
|
|
with np.errstate(divide="ignore", invalid="ignore"):
|
|
out = np.sign(a) * self.linthresh * (
|
|
np.power(self.base,
|
|
abs_a / self.linthresh - self._linscale_adj))
|
|
inside = abs_a <= self.invlinthresh
|
|
out[inside] = a[inside] / self._linscale_adj
|
|
return out
|
|
|
|
def inverted(self):
|
|
return SymmetricalLogTransform(self.base,
|
|
self.linthresh, self.linscale)
|
|
|
|
|
|
class SymmetricalLogScale(ScaleBase):
|
|
"""
|
|
The symmetrical logarithmic scale is logarithmic in both the
|
|
positive and negative directions from the origin.
|
|
|
|
Since the values close to zero tend toward infinity, there is a
|
|
need to have a range around zero that is linear. The parameter
|
|
*linthresh* allows the user to specify the size of this range
|
|
(-*linthresh*, *linthresh*).
|
|
|
|
Parameters
|
|
----------
|
|
basex, basey : float
|
|
The base of the logarithm. Defaults to 10.
|
|
|
|
linthreshx, linthreshy : float
|
|
Defines the range ``(-x, x)``, within which the plot is linear.
|
|
This avoids having the plot go to infinity around zero. Defaults to 2.
|
|
|
|
subsx, subsy : sequence of int
|
|
Where to place the subticks between each major tick.
|
|
For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place
|
|
8 logarithmically spaced minor ticks between each major tick.
|
|
|
|
linscalex, linscaley : float, optional
|
|
This allows the linear range ``(-linthresh, linthresh)`` to be
|
|
stretched relative to the logarithmic range. Its value is the number of
|
|
decades to use for each half of the linear range. For example, when
|
|
*linscale* == 1.0 (the default), the space used for the positive and
|
|
negative halves of the linear range will be equal to one decade in
|
|
the logarithmic range.
|
|
"""
|
|
name = 'symlog'
|
|
# compatibility shim
|
|
SymmetricalLogTransform = SymmetricalLogTransform
|
|
InvertedSymmetricalLogTransform = InvertedSymmetricalLogTransform
|
|
|
|
def __init__(self, axis, **kwargs):
|
|
if axis.axis_name == 'x':
|
|
base = kwargs.pop('basex', 10.0)
|
|
linthresh = kwargs.pop('linthreshx', 2.0)
|
|
subs = kwargs.pop('subsx', None)
|
|
linscale = kwargs.pop('linscalex', 1.0)
|
|
else:
|
|
base = kwargs.pop('basey', 10.0)
|
|
linthresh = kwargs.pop('linthreshy', 2.0)
|
|
subs = kwargs.pop('subsy', None)
|
|
linscale = kwargs.pop('linscaley', 1.0)
|
|
if kwargs:
|
|
warn_deprecated(
|
|
'3.2.0',
|
|
message=(
|
|
f"SymmetricalLogScale got an unexpected keyword "
|
|
f"argument {next(iter(kwargs))!r}. "
|
|
'In the future this will raise TypeError')
|
|
)
|
|
# raise TypeError(f"SymmetricalLogScale got an unexpected keyword "
|
|
# f"argument {next(iter(kwargs))!r}")
|
|
|
|
if base <= 1.0:
|
|
raise ValueError("'basex/basey' must be larger than 1")
|
|
if linthresh <= 0.0:
|
|
raise ValueError("'linthreshx/linthreshy' must be positive")
|
|
if linscale <= 0.0:
|
|
raise ValueError("'linscalex/linthreshy' must be positive")
|
|
|
|
self._transform = SymmetricalLogTransform(base, linthresh, linscale)
|
|
self.base = base
|
|
self.linthresh = linthresh
|
|
self.linscale = linscale
|
|
self.subs = subs
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
# docstring inherited
|
|
axis.set_major_locator(SymmetricalLogLocator(self.get_transform()))
|
|
axis.set_major_formatter(LogFormatterSciNotation(self.base))
|
|
axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(),
|
|
self.subs))
|
|
axis.set_minor_formatter(NullFormatter())
|
|
|
|
def get_transform(self):
|
|
"""Return the `.SymmetricalLogTransform` associated with this scale."""
|
|
return self._transform
|
|
|
|
|
|
class LogitTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, nonpos='mask'):
|
|
Transform.__init__(self)
|
|
cbook._check_in_list(['mask', 'clip'], nonpos=nonpos)
|
|
self._nonpos = nonpos
|
|
self._clip = {"clip": True, "mask": False}[nonpos]
|
|
|
|
def transform_non_affine(self, a):
|
|
"""logit transform (base 10), masked or clipped"""
|
|
with np.errstate(divide="ignore", invalid="ignore"):
|
|
out = np.log10(a / (1 - a))
|
|
if self._clip: # See LogTransform for choice of clip value.
|
|
out[a <= 0] = -1000
|
|
out[1 <= a] = 1000
|
|
return out
|
|
|
|
def inverted(self):
|
|
return LogisticTransform(self._nonpos)
|
|
|
|
def __str__(self):
|
|
return "{}({!r})".format(type(self).__name__, self._nonpos)
|
|
|
|
|
|
class LogisticTransform(Transform):
|
|
input_dims = output_dims = 1
|
|
|
|
def __init__(self, nonpos='mask'):
|
|
Transform.__init__(self)
|
|
self._nonpos = nonpos
|
|
|
|
def transform_non_affine(self, a):
|
|
"""logistic transform (base 10)"""
|
|
return 1.0 / (1 + 10**(-a))
|
|
|
|
def inverted(self):
|
|
return LogitTransform(self._nonpos)
|
|
|
|
def __str__(self):
|
|
return "{}({!r})".format(type(self).__name__, self._nonpos)
|
|
|
|
|
|
class LogitScale(ScaleBase):
|
|
"""
|
|
Logit scale for data between zero and one, both excluded.
|
|
|
|
This scale is similar to a log scale close to zero and to one, and almost
|
|
linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.
|
|
"""
|
|
name = 'logit'
|
|
|
|
def __init__(
|
|
self,
|
|
axis,
|
|
nonpos='mask',
|
|
*,
|
|
one_half=r"\frac{1}{2}",
|
|
use_overline=False,
|
|
):
|
|
r"""
|
|
Parameters
|
|
----------
|
|
axis : `matplotlib.axis.Axis`
|
|
Currently unused.
|
|
nonpos : {'mask', 'clip'}
|
|
Determines the behavior for values beyond the open interval ]0, 1[.
|
|
They can either be masked as invalid, or clipped to a number very
|
|
close to 0 or 1.
|
|
use_overline : bool, default: False
|
|
Indicate the usage of survival notation (\overline{x}) in place of
|
|
standard notation (1-x) for probability close to one.
|
|
one_half : str, default: r"\frac{1}{2}"
|
|
The string used for ticks formatter to represent 1/2.
|
|
"""
|
|
self._transform = LogitTransform(nonpos)
|
|
self._use_overline = use_overline
|
|
self._one_half = one_half
|
|
|
|
def get_transform(self):
|
|
"""Return the `.LogitTransform` associated with this scale."""
|
|
return self._transform
|
|
|
|
def set_default_locators_and_formatters(self, axis):
|
|
# docstring inherited
|
|
# ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...
|
|
axis.set_major_locator(LogitLocator())
|
|
axis.set_major_formatter(
|
|
LogitFormatter(
|
|
one_half=self._one_half,
|
|
use_overline=self._use_overline
|
|
)
|
|
)
|
|
axis.set_minor_locator(LogitLocator(minor=True))
|
|
axis.set_minor_formatter(
|
|
LogitFormatter(
|
|
minor=True,
|
|
one_half=self._one_half,
|
|
use_overline=self._use_overline
|
|
)
|
|
)
|
|
|
|
def limit_range_for_scale(self, vmin, vmax, minpos):
|
|
"""
|
|
Limit the domain to values between 0 and 1 (excluded).
|
|
"""
|
|
if not np.isfinite(minpos):
|
|
minpos = 1e-7 # Should rarely (if ever) have a visible effect.
|
|
return (minpos if vmin <= 0 else vmin,
|
|
1 - minpos if vmax >= 1 else vmax)
|
|
|
|
|
|
_scale_mapping = {
|
|
'linear': LinearScale,
|
|
'log': LogScale,
|
|
'symlog': SymmetricalLogScale,
|
|
'logit': LogitScale,
|
|
'function': FuncScale,
|
|
'functionlog': FuncScaleLog,
|
|
}
|
|
|
|
|
|
def get_scale_names():
|
|
"""Return the names of the available scales."""
|
|
return sorted(_scale_mapping)
|
|
|
|
|
|
def scale_factory(scale, axis, **kwargs):
|
|
"""
|
|
Return a scale class by name.
|
|
|
|
Parameters
|
|
----------
|
|
scale : {%(names)s}
|
|
axis : `matplotlib.axis.Axis`
|
|
"""
|
|
scale = scale.lower()
|
|
cbook._check_in_list(_scale_mapping, scale=scale)
|
|
return _scale_mapping[scale](axis, **kwargs)
|
|
|
|
|
|
if scale_factory.__doc__:
|
|
scale_factory.__doc__ = scale_factory.__doc__ % {
|
|
"names": ", ".join(map(repr, get_scale_names()))}
|
|
|
|
|
|
def register_scale(scale_class):
|
|
"""
|
|
Register a new kind of scale.
|
|
|
|
Parameters
|
|
----------
|
|
scale_class : subclass of `ScaleBase`
|
|
The scale to register.
|
|
"""
|
|
_scale_mapping[scale_class.name] = scale_class
|
|
|
|
|
|
@cbook.deprecated(
|
|
'3.1', message='get_scale_docs() is considered private API since '
|
|
'3.1 and will be removed from the public API in 3.3.')
|
|
def get_scale_docs():
|
|
"""
|
|
Helper function for generating docstrings related to scales.
|
|
"""
|
|
return _get_scale_docs()
|
|
|
|
|
|
def _get_scale_docs():
|
|
"""
|
|
Helper function for generating docstrings related to scales.
|
|
"""
|
|
docs = []
|
|
for name, scale_class in _scale_mapping.items():
|
|
docs.extend([
|
|
f" {name!r}",
|
|
"",
|
|
textwrap.indent(inspect.getdoc(scale_class.__init__), " " * 8),
|
|
""
|
|
])
|
|
return "\n".join(docs)
|
|
|
|
|
|
docstring.interpd.update(
|
|
scale_type='{%s}' % ', '.join([repr(x) for x in get_scale_names()]),
|
|
scale_docs=_get_scale_docs().rstrip(),
|
|
)
|