Current File : //proc/self/root/lib/python3/dist-packages/matplotlib/scale.py
"""
Scales define the distribution of data values on an axis, e.g. a log scaling.
They are defined as subclasses of `ScaleBase`.
See also `.axes.Axes.set_xscale` and the scales examples in the documentation.
See :doc:`/gallery/scales/custom_scale` for a full example of defining a custom
scale.
Matplotlib also supports non-separable transformations that operate on both
`~.axis.Axis` at the same time. They are known as projections, and defined in
`matplotlib.projections`.
"""
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
class ScaleBase:
"""
The base class for all scales.
Scales are separable transformations, working on a single dimension.
Subclasses should override
:attr:`name`
The scale's name.
:meth:`get_transform`
A method returning a `.Transform`, which converts data coordinates to
scaled coordinates. This transform should be invertible, so that e.g.
mouse positions can be converted back to data coordinates.
:meth:`set_default_locators_and_formatters`
A method that sets default locators and formatters for an `~.axis.Axis`
that uses this scale.
:meth:`limit_range_for_scale`
An optional method that "fixes" the axis range to acceptable values,
e.g. restricting log-scaled axes to positive values.
"""
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.
"""
def get_transform(self):
"""
Return the `.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):
"""
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
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.
"""
"""
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())
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 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())
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")
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):
super().__init__()
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'
def __init__(self, axis, *, base=10, subs=None, nonpositive="clip"):
"""
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.
"""
self._transform = LogTransform(base, nonpositive)
self.subs = subs
base = property(lambda self: 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, 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.
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):
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)
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):
super().__init__()
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
----------
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'
def __init__(self, axis, *, base=10, linthresh=2, subs=None, linscale=1):
self._transform = SymmetricalLogTransform(base, linthresh, linscale)
self.subs = subs
base = property(lambda self: self._transform.base)
linthresh = property(lambda self: self._transform.linthresh)
linscale = property(lambda self: self._transform.linscale)
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, nonpositive='mask'):
super().__init__()
_api.check_in_list(['mask', 'clip'], nonpositive=nonpositive)
self._nonpositive = nonpositive
self._clip = {"clip": True, "mask": False}[nonpositive]
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._nonpositive)
def __str__(self):
return "{}({!r})".format(type(self).__name__, self._nonpositive)
class LogisticTransform(Transform):
input_dims = output_dims = 1
def __init__(self, nonpositive='mask'):
super().__init__()
self._nonpositive = nonpositive
def transform_non_affine(self, a):
"""logistic transform (base 10)"""
return 1.0 / (1 + 10**(-a))
def inverted(self):
return LogitTransform(self._nonpositive)
def __str__(self):
return "{}({!r})".format(type(self).__name__, self._nonpositive)
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
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`
"""
if scale != scale.lower():
_api.warn_deprecated(
"3.5", message="Support for case-insensitive scales is deprecated "
"since %(since)s and support will be removed %(removal)s.")
scale = scale.lower()
scale_cls = _api.check_getitem(_scale_mapping, scale=scale)
return scale_cls(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
def _get_scale_docs():
"""
Helper function for generating docstrings related to scales.
"""
docs = []
for name, scale_class in _scale_mapping.items():
docstring = inspect.getdoc(scale_class.__init__) or ""
docs.extend([
f" {name!r}",
"",
textwrap.indent(docstring, " " * 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(),
)
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