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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 3.3.0 documentation</title>

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<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 cbook, docstring

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

<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, **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', removal='3.4',

message=(

f"ScaleBase got an unexpected keyword argument "

f"{next(iter(kwargs))!r}. This will become an error "

"%(removal)s.")

)

<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'

# 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)

<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'

"""

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

@cbook._rename_parameter("3.3", "nonpos", "nonpositive")

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

Transform.__init__(self)

if base <= 0 or base == 1:

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

self.base = base

self._clip = cbook._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):

Transform.__init__(self)

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'

@cbook.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")

@cbook._rename_parameter("3.3", f"base{axis_name}", "base")

@cbook._rename_parameter("3.3", f"subs{axis_name}", "subs")

@cbook._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'

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, 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):

Transform.__init__(self)

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):

Transform.__init__(self)

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

@cbook.deprecated(

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

@property

def InvertedSymmetricalLogTransform(self):

return InvertedSymmetricalLogTransform

# See explanation in LogScale.__init__.

@cbook._rename_parameter("3.3", f"base{axis_name}", "base")

@cbook._rename_parameter("3.3", f"linthresh{axis_name}", "linthresh")

@cbook._rename_parameter("3.3", f"subs{axis_name}", "subs")

@cbook._rename_parameter("3.3", f"linscale{axis_name}", "linscale")

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

if kwargs:

warn_deprecated(

'3.2', removal='3.4',

message=(

f"SymmetricalLogScale got an unexpected keyword "

f"argument {next(iter(kwargs))!r}. This will become "

"an error %(removal)s.")

)

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'):

Transform.__init__(self)

cbook._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

Transform.__init__(self)

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'

def __init__(self, axis, nonpositive='mask', *,

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()

cbook._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 Jul 17, 2020.

Created using

<a href="http://sphinx-doc.org/">Sphinx</a> 2.2.2.

Doc version v3.3.0-3-g8994f1b60.

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