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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.scale — Matplotlib 3.4.2 documentation</title>

title="Search within Matplotlib 3.4.2 documentation"

href="../../_static/opensearch.xml"/>

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<li class="nav-item nav-item-this"><a href="">matplotlib.scale</a></li>

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</div>

<h1>Source code for matplotlib.scale</h1><div class="highlight"><pre>

"""

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

import matplotlib as mpl

from matplotlib import _api, docstring

from matplotlib.ticker import (

NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter,

NullLocator, LogLocator, AutoLocator, AutoMinorLocator,

SymmetricalLogLocator, LogitLocator)

from matplotlib.transforms import Transform, IdentityTransform

<div class="viewcode-block" id="ScaleBase"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase">[docs]</a>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):

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.

"""

<div class="viewcode-block" id="ScaleBase.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.get_transform">[docs]</a> def get_transform(self):

"""

Return the :class:`~matplotlib.transforms.Transform` object

associated with this scale.

"""

raise NotImplementedError()</div>

<div class="viewcode-block" id="ScaleBase.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.set_default_locators_and_formatters">[docs]</a> def set_default_locators_and_formatters(self, axis):

"""

Set the locators and formatters of *axis* to instances suitable for

this scale.

"""

raise NotImplementedError()</div>

<div class="viewcode-block" id="ScaleBase.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.limit_range_for_scale">[docs]</a> def limit_range_for_scale(self, vmin, vmax, minpos):

"""

Return the range *vmin*, *vmax*, restricted to the

domain supported by this scale (if any).

*minpos* should be the minimum positive value in the data.

This is used by log scales to determine a minimum value.

"""

return vmin, vmax</div></div>

<div class="viewcode-block" id="LinearScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale">[docs]</a>class LinearScale(ScaleBase):

"""

The default linear scale.

"""

name = 'linear'

def __init__(self, axis):

# 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.

"""

"""

<div class="viewcode-block" id="LinearScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.set_default_locators_and_formatters">[docs]</a> 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 mpl.rcParams['xtick.minor.visible'] or

axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']):

axis.set_minor_locator(AutoMinorLocator())

else:

axis.set_minor_locator(NullLocator())</div>

<div class="viewcode-block" id="LinearScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.get_transform">[docs]</a> def get_transform(self):

"""

Return the transform for linear scaling, which is just the

`~matplotlib.transforms.IdentityTransform`.

"""

return IdentityTransform()</div></div>

<div class="viewcode-block" id="FuncTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform">[docs]</a>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')

<div class="viewcode-block" id="FuncTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, values):

return self._forward(values)</div>

<div class="viewcode-block" id="FuncTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.inverted">[docs]</a> def inverted(self):

return FuncTransform(self._inverse, self._forward)</div></div>

<div class="viewcode-block" id="FuncScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale">[docs]</a>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

<div class="viewcode-block" id="FuncScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.get_transform">[docs]</a> def get_transform(self):

"""Return the `.FuncTransform` associated with this scale."""

return self._transform</div>

<div class="viewcode-block" id="FuncScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.set_default_locators_and_formatters">[docs]</a> 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

axis.set_minor_locator(AutoMinorLocator())

else:

axis.set_minor_locator(NullLocator())</div></div>

<div class="viewcode-block" id="LogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform">[docs]</a>class LogTransform(Transform):

input_dims = output_dims = 1

def __init__(self, base, nonpositive='clip'):

super().__init__()

if base <= 0 or base == 1:

raise ValueError('The log base cannot be <= 0 or == 1')

self.base = base

self._clip = _api.check_getitem(

{"clip": True, "mask": False}, nonpositive=nonpositive)

def __str__(self):

return "{}(base={}, nonpositive={!r})".format(

type(self).__name__, self.base, "clip" if self._clip else "mask")

<div class="viewcode-block" id="LogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.transform_non_affine">[docs]</a> 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</div>

<div class="viewcode-block" id="LogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.inverted">[docs]</a> def inverted(self):

return InvertedLogTransform(self.base)</div></div>

<div class="viewcode-block" id="InvertedLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform">[docs]</a>class InvertedLogTransform(Transform):

input_dims = output_dims = 1

def __init__(self, base):

super().__init__()

self.base = base

def __str__(self):

return "{}(base={})".format(type(self).__name__, self.base)

<div class="viewcode-block" id="InvertedLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, a):

return ma.power(self.base, a)</div>

<div class="viewcode-block" id="InvertedLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.inverted">[docs]</a> def inverted(self):

return LogTransform(self.base)</div></div>

<div class="viewcode-block" id="LogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale">[docs]</a>class LogScale(ScaleBase):

"""

A standard logarithmic scale. Care is taken to only plot positive values.

"""

name = 'log'

@_api.deprecated("3.3", alternative="scale.LogTransform")

@property

def LogTransform(self):

return LogTransform

@property

def InvertedLogTransform(self):

return InvertedLogTransform

"""

Parameters

----------

axis : `~matplotlib.axis.Axis`

The axis for the scale.

base : float, default: 10

The base of the logarithm.

nonpositive : {'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.

subs : 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.

"""

# After the deprecation, the whole (outer) __init__ can be replaced by

# def __init__(self, axis, *, base=10, subs=None, nonpositive="clip")

# The following is to emit the right warnings depending on the axis

# used, as the *old* kwarg names depended on the axis.

axis_name = getattr(axis, "axis_name", "x")

@_api.rename_parameter("3.3", f"base{axis_name}", "base")

@_api.rename_parameter("3.3", f"subs{axis_name}", "subs")

@_api.rename_parameter("3.3", f"nonpos{axis_name}", "nonpositive")

def __init__(*, base=10, subs=None, nonpositive="clip"):

return base, subs, nonpositive

base, subs, nonpositive = __init__(**kwargs)

self._transform = LogTransform(base, nonpositive)

self.subs = subs

base = property(lambda self: self._transform.base)

<div class="viewcode-block" id="LogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.set_default_locators_and_formatters">[docs]</a> 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)))</div>

<div class="viewcode-block" id="LogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.get_transform">[docs]</a> def get_transform(self):

"""Return the `.LogTransform` associated with this scale."""

return self._transform</div>

<div class="viewcode-block" id="LogScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.limit_range_for_scale">[docs]</a> 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)</div></div>

<div class="viewcode-block" id="FuncScaleLog"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog">[docs]</a>class FuncScaleLog(LogScale):

"""

Provide an arbitrary scale with user-supplied function for the axis and

then put on a logarithmic axes.

"""

name = 'functionlog'

"""

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, default: 10

Logarithmic base of the scale.

"""

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.

<div class="viewcode-block" id="FuncScaleLog.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog.get_transform">[docs]</a> def get_transform(self):

"""Return the `.Transform` associated with this scale."""

return self._transform</div></div>

<div class="viewcode-block" id="SymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform">[docs]</a>class SymmetricalLogTransform(Transform):

input_dims = output_dims = 1

def __init__(self, base, linthresh, linscale):

super().__init__()

if base <= 1.0:

raise ValueError("'base' must be larger than 1")

if linthresh <= 0.0:

raise ValueError("'linthresh' must be positive")

if linscale <= 0.0:

raise ValueError("'linscale' must be positive")

self.base = base

self.linthresh = linthresh

self.linscale = linscale

self._linscale_adj = (linscale / (1.0 - self.base ** -1))

self._log_base = np.log(base)

<div class="viewcode-block" id="SymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, a):

abs_a = np.abs(a)

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</div>

<div class="viewcode-block" id="SymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.inverted">[docs]</a> def inverted(self):

return InvertedSymmetricalLogTransform(self.base, self.linthresh,

self.linscale)</div></div>

<div class="viewcode-block" id="InvertedSymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform">[docs]</a>class InvertedSymmetricalLogTransform(Transform):

input_dims = output_dims = 1

def __init__(self, base, linthresh, linscale):

super().__init__()

symlog = SymmetricalLogTransform(base, linthresh, linscale)

self.base = base

self.linthresh = linthresh

self.invlinthresh = symlog.transform(linthresh)

self.linscale = linscale

<div class="viewcode-block" id="InvertedSymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, a):

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</div>

<div class="viewcode-block" id="InvertedSymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.inverted">[docs]</a> def inverted(self):

return SymmetricalLogTransform(self.base,

self.linthresh, self.linscale)</div></div>

<div class="viewcode-block" id="SymmetricalLogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale">[docs]</a>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

----------

base : float, default: 10

The base of the logarithm.

linthresh : float, default: 2

Defines the range ``(-x, x)``, within which the plot is linear.

This avoids having the plot go to infinity around zero.

subs : 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.

linscale : 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'

@property

def SymmetricalLogTransform(self):

return SymmetricalLogTransform

@_api.deprecated(

"3.3", alternative="scale.InvertedSymmetricalLogTransform")

@property

def InvertedSymmetricalLogTransform(self):

return InvertedSymmetricalLogTransform

# See explanation in LogScale.__init__.

@_api.rename_parameter("3.3", f"linthresh{axis_name}", "linthresh")

@_api.rename_parameter("3.3", f"subs{axis_name}", "subs")

@_api.rename_parameter("3.3", f"linscale{axis_name}", "linscale")

def __init__(*, base=10, linthresh=2, subs=None, linscale=1):

return base, linthresh, subs, linscale

base, linthresh, subs, linscale = __init__(**kwargs)

self._transform = SymmetricalLogTransform(base, linthresh, linscale)

self.subs = subs

linthresh = property(lambda self: self._transform.linthresh)

linscale = property(lambda self: self._transform.linscale)

<div class="viewcode-block" id="SymmetricalLogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.set_default_locators_and_formatters">[docs]</a> def set_default_locators_and_formatters(self, axis):

# docstring inherited

axis.set_major_locator(SymmetricalLogLocator(self.get_transform()))

axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(),

self.subs))

axis.set_minor_formatter(NullFormatter())</div>

<div class="viewcode-block" id="SymmetricalLogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.get_transform">[docs]</a> def get_transform(self):

"""Return the `.SymmetricalLogTransform` associated with this scale."""

return self._transform</div></div>

<div class="viewcode-block" id="LogitTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform">[docs]</a>class LogitTransform(Transform):

input_dims = output_dims = 1

def __init__(self, nonpositive='mask'):

super().__init__()

_api.check_in_list(['mask', 'clip'], nonpositive=nonpositive)

self._nonpositive = nonpositive

self._clip = {"clip": True, "mask": False}[nonpositive]

<div class="viewcode-block" id="LogitTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, a):

"""logit transform (base 10), masked or clipped"""

out = np.log10(a / (1 - a))

if self._clip: # See LogTransform for choice of clip value.

out[1 <= a] = 1000

return out</div>

<div class="viewcode-block" id="LogitTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.inverted">[docs]</a> def inverted(self):

return LogisticTransform(self._nonpositive)</div>

def __str__(self):

return "{}({!r})".format(type(self).__name__, self._nonpositive)</div>

<div class="viewcode-block" id="LogisticTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform">[docs]</a>class LogisticTransform(Transform):

input_dims = output_dims = 1

super().__init__()

self._nonpositive = nonpositive

<div class="viewcode-block" id="LogisticTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.transform_non_affine">[docs]</a> def transform_non_affine(self, a):

"""logistic transform (base 10)"""

return 1.0 / (1 + 10**(-a))</div>

<div class="viewcode-block" id="LogisticTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.inverted">[docs]</a> def inverted(self):

return LogitTransform(self._nonpositive)</div>

def __str__(self):

<div class="viewcode-block" id="LogitScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale">[docs]</a>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'

one_half=r"\frac{1}{2}", use_overline=False):

r"""

Parameters

----------

axis : `matplotlib.axis.Axis`

Currently unused.

nonpositive : {'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(nonpositive)

self._use_overline = use_overline

self._one_half = one_half

<div class="viewcode-block" id="LogitScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.get_transform">[docs]</a> def get_transform(self):

"""Return the `.LogitTransform` associated with this scale."""

return self._transform</div>

<div class="viewcode-block" id="LogitScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.set_default_locators_and_formatters">[docs]</a> 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

)

)</div>

<div class="viewcode-block" id="LogitScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.limit_range_for_scale">[docs]</a> def limit_range_for_scale(self, vmin, vmax, minpos):

"""

Limit the domain to values between 0 and 1 (excluded).

"""

minpos = 1e-7 # Should rarely (if ever) have a visible effect.

1 - minpos if vmax >= 1 else vmax)</div></div>

_scale_mapping = {

'linear': LinearScale,

'log': LogScale,

'symlog': SymmetricalLogScale,

'logit': LogitScale,

'function': FuncScale,

'functionlog': FuncScaleLog,

}

<div class="viewcode-block" id="get_scale_names"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.get_scale_names">[docs]</a>def get_scale_names():

"""Return the names of the available scales."""

return sorted(_scale_mapping)</div>

<div class="viewcode-block" id="scale_factory"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.scale_factory">[docs]</a>def scale_factory(scale, axis, **kwargs):

"""

Return a scale class by name.

Parameters

----------

scale : {%(names)s}

axis : `matplotlib.axis.Axis`

"""

scale = scale.lower()

_api.check_in_list(_scale_mapping, scale=scale)

return _scale_mapping[scale](axis, **kwargs)</div>

if scale_factory.__doc__:

scale_factory.__doc__ = scale_factory.__doc__ % {

"names": ", ".join(map(repr, get_scale_names()))}

<div class="viewcode-block" id="register_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.register_scale">[docs]</a>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</div>

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(),

)

</pre></div>

</div>

</div>

Last updated on May 08, 2021.

Created using

<a href="http://sphinx-doc.org/">Sphinx</a> 3.2.1.

Doc version v3.4.2-2-gf801f04d09-dirty.

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