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

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

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

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

"""

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`

"""

<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 :class:`~matplotlib.ticker.Locator` and

:class:`~matplotlib.ticker.Formatter` objects on the given

axis to match 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):

"""

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</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, **kwargs):

pass

<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):

"""

Set the locators and formatters to reasonable defaults for

linear scaling.

"""

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())</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):

"""

The transform for linear scaling is just the

:class:`~matplotlib.transforms.IdentityTransform`.

"""

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

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

input_dims = 1

output_dims = 1

is_separable = True

has_inverse = True

def __init__(self, nonpos='clip'):

Transform.__init__(self)

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

<div class="viewcode-block" id="LogTransformBase.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransformBase.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"):

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>

def __str__(self):

type(self).__name__, "clip" if self._clip else "mask")</div>

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

input_dims = 1

output_dims = 1

is_separable = True

has_inverse = True

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

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

def __str__(self):

return "{}()".format(type(self).__name__)</div>

<div class="viewcode-block" id="Log10Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log10Transform">[docs]</a>class Log10Transform(LogTransformBase):

base = 10.0

<div class="viewcode-block" id="Log10Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log10Transform.inverted">[docs]</a> def inverted(self):

return InvertedLog10Transform()</div></div>

<div class="viewcode-block" id="InvertedLog10Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog10Transform">[docs]</a>class InvertedLog10Transform(InvertedLogTransformBase):

base = 10.0

<div class="viewcode-block" id="InvertedLog10Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog10Transform.inverted">[docs]</a> def inverted(self):

return Log10Transform()</div></div>

<div class="viewcode-block" id="Log2Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log2Transform">[docs]</a>class Log2Transform(LogTransformBase):

base = 2.0

<div class="viewcode-block" id="Log2Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log2Transform.inverted">[docs]</a> def inverted(self):

return InvertedLog2Transform()</div></div>

<div class="viewcode-block" id="InvertedLog2Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog2Transform">[docs]</a>class InvertedLog2Transform(InvertedLogTransformBase):

base = 2.0

<div class="viewcode-block" id="InvertedLog2Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog2Transform.inverted">[docs]</a> def inverted(self):

return Log2Transform()</div></div>

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

base = np.e

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

return InvertedNaturalLogTransform()</div></div>

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

base = np.e

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

return NaturalLogTransform()</div></div>

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

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

LogTransformBase.__init__(self, nonpos)

self.base = base

<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(InvertedLogTransformBase):

def __init__(self, base):

InvertedLogTransformBase.__init__(self)

self.base = base

<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 so non-positive

values are not plotted.

For computational efficiency (to push as much as possible to Numpy

C code in the common cases), this scale provides different

transforms depending on the base of the logarithm:

- base 10 (:class:`Log10Transform`)

- base 2 (:class:`Log2Transform`)

- base e (:class:`NaturalLogTransform`)

- arbitrary base (:class:`LogTransform`)

"""

name = 'log'

# compatibility shim

LogTransformBase = LogTransformBase

Log10Transform = Log10Transform

InvertedLog10Transform = InvertedLog10Transform

Log2Transform = Log2Transform

InvertedLog2Transform = InvertedLog2Transform

NaturalLogTransform = NaturalLogTransform

InvertedNaturalLogTransform = InvertedNaturalLogTransform

LogTransform = LogTransform

InvertedLogTransform = InvertedLogTransform

"""

*basex*/*basey*:

The base of the logarithm

*nonposx*/*nonposy*: {'mask', 'clip'}

non-positive values in *x* or *y* can be masked as

invalid, or clipped to a very small positive number

*subsx*/*subsy*:

Where to place the subticks between each major tick.

Should be a sequence of integers. 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')

else:

base = kwargs.pop('basey', 10.0)

subs = kwargs.pop('subsy', None)

nonpos = kwargs.pop('nonposy', 'clip')

if len(kwargs):

raise ValueError(("provided too many kwargs, can only pass "

"{'basex', 'subsx', nonposx'} or "

"{'basey', 'subsy', nonposy'}. You passed ") +

"{!r}".format(kwargs))

if nonpos not in ['mask', 'clip']:

raise ValueError("nonposx, nonposy kwarg must be 'mask' or 'clip'")

if base <= 0 or base == 1:

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

if base == 10.0:

self._transform = self.Log10Transform(nonpos)

elif base == 2.0:

self._transform = self.Log2Transform(nonpos)

elif base == np.e:

self._transform = self.NaturalLogTransform(nonpos)

else:

self._transform = self.LogTransform(base, nonpos)

self.base = base

self.subs = subs

<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):

"""

Set the locators and formatters to specialized versions for

log scaling.

"""

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 a :class:`~matplotlib.transforms.Transform` instance

appropriate for the given logarithm base.

"""

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 # This value should rarely if ever

# end up with a visible effect.

return (minpos if vmin <= 0 else vmin,

minpos if vmax <= 0 else vmax)</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 = 1

output_dims = 1

is_separable = True

has_inverse = True

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)

<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):

sign = np.sign(a)

-self.linthresh,

self.linthresh,

copy=False)

log = sign * self.linthresh * (

self._linscale_adj +

ma.log(np.abs(masked) / self.linthresh) / self._log_base)

if masked.mask.any():

return ma.where(masked.mask, a * self._linscale_adj, log)

else:

return log</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 = 1

output_dims = 1

is_separable = True

has_inverse = True

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):

masked = ma.masked_inside(a, -self.invlinthresh,

self.invlinthresh, copy=False)

exp = sign * self.linthresh * (

ma.power(self.base, (sign * (masked / self.linthresh))

- self._linscale_adj))

if masked.mask.any():

return ma.where(masked.mask, a / self._linscale_adj, exp)

else:

return exp</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*).

"""

name = 'symlog'

# compatibility shim

SymmetricalLogTransform = SymmetricalLogTransform

InvertedSymmetricalLogTransform = InvertedSymmetricalLogTransform

"""

*basex*/*basey*:

The base of the logarithm

*linthreshx*/*linthreshy*:

A single float which defines the range (-*x*, *x*), within

which the plot is linear. This avoids having the plot go to

infinity around zero.

*subsx*/*subsy*:

Where to place the subticks between each major tick.

Should be a sequence of integers. 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*:

This allows the linear range (-*linthresh* to *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.

"""

if axis.axis_name == 'x':

linthresh = kwargs.pop('linthreshx', 2.0)

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 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 = self.SymmetricalLogTransform(base,

linthresh,

linscale)

self.base = base

self.linthresh = linthresh

self.linscale = linscale

self.subs = subs

<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):

"""

Set the locators and formatters to specialized versions for

symmetrical log scaling.

"""

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 a :class:`SymmetricalLogTransform` instance.

"""

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 = 1

output_dims = 1

is_separable = True

has_inverse = True

Transform.__init__(self)

self._nonpos = nonpos

<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._nonpos)</div>

def __str__(self):

return "{}({!r})".format(type(self).__name__,

"clip" if self._clip else "mask")</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 = 1

output_dims = 1

is_separable = True

has_inverse = True

Transform.__init__(self)

self._nonpos = nonpos

<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._nonpos)</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, nonpos='mask'):

"""

*nonpos*: {'mask', 'clip'}

values beyond ]0, 1[ can be masked as invalid, or clipped to a number

very close to 0 or 1

"""

raise ValueError("nonposx, nonposy kwarg must be 'mask' or 'clip'")

self._transform = LogitTransform(nonpos)

<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 a :class:`LogitTransform` instance.

"""

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):

# ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...

axis.set_major_locator(LogitLocator())

axis.set_major_formatter(LogitFormatter())

axis.set_minor_locator(LogitLocator(minor=True))

axis.set_minor_formatter(LogitFormatter())</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 # This value should rarely if ever

# end up with a visible effect.

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

_scale_mapping = {

'linear': LinearScale,

'log': LogScale,

'symlog': SymmetricalLogScale,

'logit': LogitScale,

}

<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 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.

ACCEPTS: [ %(names)s ]

"""

scale = scale.lower()

if scale is None:

scale = 'linear'

if scale not in _scale_mapping:

raise ValueError("Unknown scale type '%s'" % scale)

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

scale_factory.__doc__ = cbook.dedent(scale_factory.__doc__) % \

{'names': " | ".join(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.

*scale_class* must be a subclass of :class:`ScaleBase`.

"""

_scale_mapping[scale_class.name] = scale_class</div>

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

"""

Helper function for generating docstrings related to scales.

"""

docs = []

for name in get_scale_names():

scale_class = _scale_mapping[name]

docs.append(" '%s'" % name)

docs.append("")

class_docs = cbook.dedent(scale_class.__init__.__doc__)

class_docs = "".join([" %s\n" %

x for x in class_docs.split("\n")])

docs.append(class_docs)

docs.append("")

return "\n".join(docs)</div>

docstring.interpd.update(

scale=' | '.join([repr(x) for x in get_scale_names()]),

scale_docs=get_scale_docs().rstrip(),

)

</pre></div>

</div>

</div>

Last updated on Feb 28, 2019.

Created using

<a href="http://sphinx-doc.org/">Sphinx</a> 1.8.4.

Doc version v3.0.3-2-gfc177a9cd.

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