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945 lines (799 loc) 路 33.1 KB
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#!/usr/bin/env python3
"""
Various axis `~matplotlib.scale.ScaleBase` classes.
"""
import copy
import matplotlib.scale as mscale
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
import numpy as np
import numpy.ma as ma
from . import ticker as pticker
from .internals import ic # noqa: F401
from .internals import _not_none, _version_mpl, warnings
__all__ = [
'CutoffScale',
'ExpScale',
'FuncScale',
'InverseScale',
'LinearScale',
'LogitScale',
'LogScale',
'MercatorLatitudeScale',
'PowerScale',
'SineLatitudeScale',
'SymmetricalLogScale',
]
def _parse_logscale_args(*keys, **kwargs):
"""
Parse arguments for `LogScale` and `SymmetricalLogScale` that
inexplicably require ``x`` and ``y`` suffixes by default. Also
change the default `linthresh` to ``1``.
"""
# NOTE: Scale classes ignore unused arguments with warnings, but matplotlib 3.3
# version changes the keyword args. Since we can't do a try except clause, only
# way to avoid warnings with 3.3 upgrade is to test version string.
kwsuffix = '' if _version_mpl >= '3.3' else 'x'
for key in keys:
# Remove duplicates
opts = {
key: kwargs.pop(key, None),
key + 'x': kwargs.pop(key + 'x', None),
key + 'y': kwargs.pop(key + 'y', None),
}
value = _not_none(**opts) # issues warning if multiple values passed
# Apply defaults and adjust
# NOTE: If linthresh is *exactly* on a power of the base, can end
# up with additional log-locator step inside the threshold, e.g. major
# ticks on -10, -1, -0.1, 0.1, 1, 10 for linthresh of 1. Adding slight
# offset to *desired* linthresh prevents this.
if key == 'subs':
if value is None:
value = np.arange(1, 10)
if key == 'linthresh':
if value is None:
value = 1
power = np.log10(value)
if power % 1 == 0: # exact power of 10
value = value + 10 ** (power - 10)
if value is not None: # dummy axis_name is 'x'
kwargs[key + kwsuffix] = value
return kwargs
class _Scale(object):
"""
Mix-in class that standardizes the behavior of
`~matplotlib.scale.ScaleBase.set_default_locators_and_formatters`
and `~matplotlib.scale.ScaleBase.get_transform`. Also overrides
`__init__` so you no longer have to instantiate scales with an
`~matplotlib.axis.Axis` instance.
"""
def __init__(self, *args, **kwargs):
# Pass a dummy axis to the superclass
axis = type('Axis', (object,), {'axis_name': 'x'})()
super().__init__(axis, *args, **kwargs)
self._default_major_locator = mticker.AutoLocator()
self._default_minor_locator = mticker.AutoMinorLocator()
self._default_major_formatter = pticker.AutoFormatter()
self._default_minor_formatter = mticker.NullFormatter()
def set_default_locators_and_formatters(self, axis, only_if_default=False):
"""
Apply all locators and formatters defined as attributes on
initialization and define defaults for all scales.
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis.
only_if_default : bool, optional
Whether to refrain from updating the locators and formatters if the
axis is currently using non-default versions. Useful if we want to
avoid overwriting user customization when the scale is changed.
"""
# TODO: Always use only_if_default=True? Used only for dual axes right now
# NOTE: We set isDefault_minloc to True when simply toggling minor ticks
# on and off with CartesianAxes format command.
from .config import rc
if not only_if_default or axis.isDefault_majloc:
locator = copy.copy(self._default_major_locator)
axis.set_major_locator(locator)
axis.isDefault_majloc = True
if not only_if_default or axis.isDefault_minloc:
x = axis.axis_name if axis.axis_name in 'xy' else 'x'
if rc[x + 'tick.minor.visible']:
locator = copy.copy(self._default_minor_locator)
else:
locator = mticker.NullLocator()
axis.set_minor_locator(locator)
axis.isDefault_minloc = True
if not only_if_default or axis.isDefault_majfmt:
formatter = copy.copy(self._default_major_formatter)
axis.set_major_formatter(formatter)
axis.isDefault_majfmt = True
if not only_if_default or axis.isDefault_minfmt:
formatter = copy.copy(self._default_minor_formatter)
axis.set_minor_formatter(formatter)
axis.isDefault_minfmt = True
def get_transform(self):
"""
Return the scale transform.
"""
return self._transform
class LinearScale(_Scale, mscale.LinearScale):
"""
As with `~matplotlib.scale.LinearScale` but with
`~proplot.ticker.AutoFormatter` as the default major formatter.
"""
#: The registered scale name
name = 'linear'
def __init__(self, **kwargs):
"""
See also
--------
proplot.constructor.Scale
"""
super().__init__(**kwargs)
self._transform = mtransforms.IdentityTransform()
class LogitScale(_Scale, mscale.LogitScale):
"""
As with `~matplotlib.scale.LogitScale` but with `~proplot.ticker.AutoFormatter`
as the default major formatter.
"""
#: The registered scale name
name = 'logit'
def __init__(self, **kwargs):
"""
Parameters
----------
nonpos : {'mask', 'clip'}
Values outside of (0, 1) can be masked as invalid, or clipped to a
number very close to 0 or 1.
See also
--------
proplot.constructor.Scale
"""
super().__init__(**kwargs)
# self._default_major_formatter = mticker.LogitFormatter()
self._default_major_locator = mticker.LogitLocator()
self._default_minor_locator = mticker.LogitLocator(minor=True)
class LogScale(_Scale, mscale.LogScale):
"""
As with `~matplotlib.scale.LogScale` but with `~proplot.ticker.AutoFormatter`
as the default major formatter. ``x`` and ``y`` versions of each keyword
argument are no longer required.
"""
#: The registered scale name
name = 'log'
def __init__(self, **kwargs):
"""
Parameters
----------
base : float, default: 10
The base of the logarithm.
nonpos : {'mask', 'clip'}, optional
Non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number.
subs : sequence of int, default: ``[1 2 3 4 5 6 7 8 9]``
Default *minor* tick locations are on these multiples of each power
of the base. For example, ``subs=(1, 2, 5)`` draws ticks on 1, 2,
5, 10, 20, 50, 100, 200, 500, etc.
basex, basey, nonposx, nonposy, subsx, subsy
Aliases for the above keywords. These used to be conditional
on the *name* of the axis.
See also
--------
proplot.constructor.Scale
"""
keys = ('base', 'nonpos', 'subs')
super().__init__(**_parse_logscale_args(*keys, **kwargs))
self._default_major_locator = mticker.LogLocator(self.base)
self._default_minor_locator = mticker.LogLocator(self.base, self.subs)
class SymmetricalLogScale(_Scale, mscale.SymmetricalLogScale):
"""
As with `~matplotlib.scale.SymmetricalLogScale` but with
`~proplot.ticker.AutoFormatter` as the default major formatter.
``x`` and ``y`` versions of each keyword argument are no longer
required.
"""
#: The registered scale name
name = 'symlog'
def __init__(self, **kwargs):
"""
Parameters
----------
base : float, default: 10
The base of the logarithm.
linthresh : float, default: 1
Defines the range ``(-linthresh, linthresh)``, within which the plot
is linear. This avoids having the plot go to infinity around zero.
linscale : float, default: 1
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` is ``1`` (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.
subs : sequence of int, default: ``[1 2 3 4 5 6 7 8 9]``
Default *minor* tick locations are on these multiples of each power
of the base. For example, ``subs=(1, 2, 5)`` draws ticks on 1, 2,
5, 10, 20, 50, 100, 200, 500, etc.
basex, basey, linthreshx, linthreshy, linscalex, linscaley, subsx, subsy
Aliases for the above keywords. These keywords used to be
conditional on the name of the axis.
See also
--------
proplot.constructor.Scale
"""
keys = ('base', 'linthresh', 'linscale', 'subs')
super().__init__(**_parse_logscale_args(*keys, **kwargs))
transform = self.get_transform()
self._default_major_locator = mticker.SymmetricalLogLocator(transform)
self._default_minor_locator = mticker.SymmetricalLogLocator(transform, self.subs) # noqa: E501
class FuncScale(_Scale, mscale.ScaleBase):
"""
Axis scale composed of arbitrary forward and inverse transformations.
"""
#: The registered scale name
name = 'function'
def __init__(self, transform=None, invert=False, parent_scale=None, **kwargs):
"""
Parameters
----------
transform : callable, 2-tuple of callable, or scale-spec
The transform used to translate units from the parent axis to
the secondary axis. Input can be as follows:
* A single `linear <https://en.wikipedia.org/wiki/Linear_function>`__ or
`involutory <https://en.wikipedia.org/wiki/Involution_(mathematics)>`__
function that accepts a number and returns some transformation of
that number. For example, to convert Kelvin to Celsius, use
``ax.dualx(lambda x: x - 273.15)``. To convert kilometers to
meters, use ``ax.dualx(lambda x: x * 1e3)``.
* A 2-tuple of arbitrary functions. This should only be used if your
functions are non-linear and non-involutory. The second function must
be the inverse of the first. For example, to apply the square, use
``ax.dualx((lambda x: x ** 2, lambda x: x ** 0.5))``.
* A scale specification passed to the `~proplot.constructor.Scale`
constructor function. The transform and default locators and formatters
are borrowed from the resulting `~matplotlib.scale.ScaleBase` instance.
For example, to apply the inverse, use ``ax.dualx('inverse')``.
To apply the base-10 exponential, use ``ax.dualx(('exp', 10))``.
invert : bool, optional
If ``True``, the forward and inverse functions are *swapped*.
Used when drawing dual axes.
parent_scale : `~matplotlib.scale.ScaleBase`, default: `LinearScale`
The axis scale of the "parent" axis. Its forward transform
is applied to the `FuncTransform`.
major_locator, minor_locator : locator-spec, optional
The default major and minor locator. Passed to the
`~proplot.constructor.Locator` constructor function. By default, these are
the same as the default locators on the input transform. If the input
transform was not an axis scale, these are borrowed from `parent_scale`.
major_formatter, minor_formatter : formatter-spec, optional
The default major and minor formatter. Passed to the
`~proplot.constructor.Formatter` constructor function. By default, these are
the same as the default formatters on the input transform. If the input
transform was not an axis scale, these are borrowed from `parent_scale`.
See also
--------
proplot.constructor.Scale
proplot.axes.CartesianAxes.dualx
proplot.axes.CartesianAxes.dualy
"""
# Parse input args
# NOTE: Permit *arbitrary* parent axis scales and infer default locators and
# formatters from the input scale (if it was passed) or the parent scale. Use
# case for latter is e.g. logarithmic scale with linear transformation.
if 'functions' in kwargs: # matplotlib compatibility (critical for >= 3.5)
functions = kwargs.pop('functions', None)
if transform is None:
transform = functions
else:
warnings._warn_proplot("Ignoring keyword argument 'functions'.")
from .constructor import Formatter, Locator, Scale
super().__init__()
if callable(transform):
forward, inverse, inherit_scale = transform, transform, None
elif np.iterable(transform) and len(transform) == 2 and all(map(callable, transform)): # noqa: E501
forward, inverse, inherit_scale = *transform, None
else:
try:
inherit_scale = Scale(transform)
except ValueError:
raise ValueError(
'Expected a function, 2-tuple of forward and inverse functions, '
f'or an axis scale specification. Got {transform!r}.'
)
transform = inherit_scale.get_transform()
forward, inverse = transform.transform, transform.inverted().transform
# Create the transform
# NOTE: Linear scale is always identity transform (no-op).
# NOTE: Must transform parent scale cutoff arguments as well. Use inverse
# function because we are converting from some *other* axis to this one.
if invert: # used for dualx and dualy
forward, inverse = inverse, forward
parent_scale = _not_none(parent_scale, LinearScale())
if not isinstance(parent_scale, mscale.ScaleBase):
raise ValueError(f'Parent scale must be ScaleBase. Got {parent_scale!r}.')
if isinstance(parent_scale, CutoffScale):
args = list(parent_scale.args) # mutable copy
args[::2] = (inverse(arg) for arg in args[::2]) # transform cutoffs
parent_scale = CutoffScale(*args)
if isinstance(parent_scale, mscale.SymmetricalLogScale):
keys = ('base', 'linthresh', 'linscale', 'subs')
kwsym = {key: getattr(parent_scale, key) for key in keys}
kwsym['linthresh'] = inverse(kwsym['linthresh'])
parent_scale = SymmetricalLogScale(**kwsym)
self.functions = (forward, inverse)
self._transform = parent_scale.get_transform() + FuncTransform(forward, inverse)
# Apply default locators and formatters
# NOTE: We pass these through contructor functions
scale = inherit_scale or parent_scale
for which in ('major', 'minor'):
for type_, parser in (('locator', Locator), ('formatter', Formatter)):
key = which + '_' + type_
attr = '_default_' + key
ticker = kwargs.pop(key, None)
if ticker is None:
ticker = getattr(scale, attr, None)
if ticker is None: # e.g. someone used a matplotlib scale
continue # revert to defaults
ticker = parser(ticker)
setattr(self, attr, copy.copy(ticker))
if kwargs:
raise TypeError(f'FuncScale got unexpected arguments: {kwargs}')
class FuncTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self, forward, inverse):
super().__init__()
if callable(forward) and callable(inverse):
self._forward = forward
self._inverse = inverse
else:
raise ValueError('arguments to FuncTransform must be functions')
def inverted(self):
return FuncTransform(self._inverse, self._forward)
def transform_non_affine(self, values):
with np.errstate(divide='ignore', invalid='ignore'):
return self._forward(values)
class PowerScale(_Scale, mscale.ScaleBase):
r"""
"Power scale" that performs the transformation
.. math::
x^{c}
"""
#: The registered scale name
name = 'power'
def __init__(self, power=1, inverse=False):
"""
Parameters
----------
power : float, optional
The power :math:`c` to which :math:`x` is raised.
inverse : bool, optional
If ``True`` this performs the inverse operation :math:`x^{1/c}`.
"""
super().__init__()
if not inverse:
self._transform = PowerTransform(power)
else:
self._transform = InvertedPowerTransform(power)
def limit_range_for_scale(self, vmin, vmax, minpos):
"""
Return the range *vmin* and *vmax* limited to positive numbers.
"""
if not np.isfinite(minpos):
minpos = 1e-300
return (
minpos if vmin <= 0 else vmin,
minpos if vmax <= 0 else vmax,
)
class PowerTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
has_inverse = True
is_separable = True
def __init__(self, power):
super().__init__()
self._power = power
def inverted(self):
return InvertedPowerTransform(self._power)
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return np.power(a, self._power)
class InvertedPowerTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
has_inverse = True
is_separable = True
def __init__(self, power):
super().__init__()
self._power = power
def inverted(self):
return PowerTransform(self._power)
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return np.power(a, 1 / self._power)
class ExpScale(_Scale, mscale.ScaleBase):
r"""
"Exponential scale" that performs either of two transformations. When
`inverse` is ``False`` (the default), performs the transformation
.. math::
Ca^{bx}
where the constants :math:`a`, :math:`b`, and :math:`C` are set by the
input (see below). When `inverse` is ``True``, this performs the inverse
transformation
.. math::
(\log_a(x) - \log_a(C))/b
which in appearance is equivalent to `LogScale` since it is just a linear
transformation of the logarithm.
"""
#: The registered scale name
name = 'exp'
def __init__(self, a=np.e, b=1, c=1, inverse=False):
"""
Parameters
----------
a : float, optional
The base of the exponential, i.e. the :math:`a` in :math:`Ca^{bx}`.
b : float, optional
The scale for the exponent, i.e. the :math:`b` in :math:`Ca^{bx}`.
c : float, optional
The coefficient of the exponential, i.e. the :math:`C` in :math:`Ca^{bx}`.
inverse : bool, optional
If ``True``, the "forward" direction performs the inverse operation.
See also
--------
proplot.constructor.Scale
"""
super().__init__()
if not inverse:
self._transform = ExpTransform(a, b, c)
else:
self._transform = InvertedExpTransform(a, b, c)
def limit_range_for_scale(self, vmin, vmax, minpos):
"""
Return the range *vmin* and *vmax* limited to positive numbers.
"""
if not np.isfinite(minpos):
minpos = 1e-300
return (
minpos if vmin <= 0 else vmin,
minpos if vmax <= 0 else vmax,
)
class ExpTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
has_inverse = True
is_separable = True
def __init__(self, a, b, c):
super().__init__()
self._a = a
self._b = b
self._c = c
def inverted(self):
return InvertedExpTransform(self._a, self._b, self._c)
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return self._c * np.power(self._a, self._b * np.array(a))
class InvertedExpTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
has_inverse = True
is_separable = True
def __init__(self, a, b, c):
super().__init__()
self._a = a
self._b = b
self._c = c
def inverted(self):
return ExpTransform(self._a, self._b, self._c)
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return np.log(a / self._c) / (self._b * np.log(self._a))
class MercatorLatitudeScale(_Scale, mscale.ScaleBase):
"""
Axis scale that is linear in the `Mercator projection latitude \
<http://en.wikipedia.org/wiki/Mercator_projection>`__. Adapted from `this example \
<https://matplotlib.org/2.0.2/examples/api/custom_scale_example.html>`__.
The scale function is as follows:
.. math::
y = \\ln(\\tan(\\pi x \\,/\\, 180) + \\sec(\\pi x \\,/\\, 180))
The inverse scale function is as follows:
.. math::
x = 180\\,\\arctan(\\sinh(y)) \\,/\\, \\pi
"""
#: The registered scale name
name = 'mercator'
def __init__(self, thresh=85.0):
"""
Parameters
----------
thresh : float, optional
Threshold between 0 and 90, used to constrain axis limits
between ``-thresh`` and ``+thresh``.
See also
--------
proplot.constructor.Scale
"""
super().__init__()
if thresh >= 90:
raise ValueError("Mercator scale 'thresh' must be <= 90.")
self._thresh = thresh
self._transform = MercatorLatitudeTransform(thresh)
self._default_major_formatter = pticker.AutoFormatter(suffix='\N{DEGREE SIGN}')
def limit_range_for_scale(self, vmin, vmax, minpos): # noqa: U100
"""
Return the range *vmin* and *vmax* limited to within +/-90 degrees
(exclusive).
"""
return max(vmin, -self._thresh), min(vmax, self._thresh)
class MercatorLatitudeTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self, thresh):
super().__init__()
self._thresh = thresh
def inverted(self):
return InvertedMercatorLatitudeTransform(self._thresh)
def transform_non_affine(self, a):
# NOTE: Critical to truncate valid range inside transform *and*
# in limit_range_for_scale or get weird duplicate tick labels. This
# is not necessary for positive-only scales because it is harder to
# run up right against the scale boundaries.
with np.errstate(divide='ignore', invalid='ignore'):
m = ma.masked_where((a <= -90) | (a >= 90), a)
if m.mask.any():
m = np.deg2rad(m)
return ma.log(ma.abs(ma.tan(m) + 1 / ma.cos(m)))
else:
a = np.deg2rad(a)
return np.log(np.abs(np.tan(a) + 1 / np.cos(a)))
class InvertedMercatorLatitudeTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self, thresh):
super().__init__()
self._thresh = thresh
def inverted(self):
return MercatorLatitudeTransform(self._thresh)
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return np.rad2deg(np.arctan2(1, np.sinh(a)))
class SineLatitudeScale(_Scale, mscale.ScaleBase):
r"""
Axis scale that is linear in the sine transformation of *x*. The axis
limits are constrained to fall between ``-90`` and ``+90`` degrees.
The scale function is as follows:
.. math::
y = \sin(\pi x/180)
The inverse scale function is as follows:
.. math::
x = 180\arcsin(y)/\pi
"""
#: The registered scale name
name = 'sine'
def __init__(self):
"""
See also
--------
proplot.constructor.Scale
"""
super().__init__()
self._transform = SineLatitudeTransform()
self._default_major_formatter = pticker.AutoFormatter(suffix='\N{DEGREE SIGN}')
def limit_range_for_scale(self, vmin, vmax, minpos): # noqa: U100
"""
Return the range *vmin* and *vmax* limited to within +/-90 degrees
(inclusive).
"""
return max(vmin, -90), min(vmax, 90)
class SineLatitudeTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self):
super().__init__()
def inverted(self):
return InvertedSineLatitudeTransform()
def transform_non_affine(self, a):
# NOTE: Critical to truncate valid range inside transform *and*
# in limit_range_for_scale or get weird duplicate tick labels. This
# is not necessary for positive-only scales because it is harder to
# run up right against the scale boundaries.
with np.errstate(divide='ignore', invalid='ignore'):
m = ma.masked_where((a < -90) | (a > 90), a)
if m.mask.any():
return ma.sin(np.deg2rad(m))
else:
return np.sin(np.deg2rad(a))
class InvertedSineLatitudeTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self):
super().__init__()
def inverted(self):
return SineLatitudeTransform()
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return np.rad2deg(np.arcsin(a))
class CutoffScale(_Scale, mscale.ScaleBase):
"""
Axis scale composed of arbitrary piecewise linear transformations.
The axis can undergo discrete jumps, "accelerations", or "decelerations"
between successive thresholds.
"""
#: The registered scale name
name = 'cutoff'
def __init__(self, *args):
"""
Parameters
----------
*args : thresh_1, scale_1, ..., thresh_N, [scale_N], optional
Sequence of "thresholds" and "scales". If the final scale is
omitted (i.e. you passed an odd number of arguments) it is set
to ``1``. Each ``scale_i`` in the sequence can be interpreted
as follows:
* If ``scale_i < 1``, the axis is decelerated from ``thresh_i`` to
``thresh_i+1``. For ``scale_N``, the axis is decelerated
everywhere above ``thresh_N``.
* If ``scale_i > 1``, the axis is accelerated from ``thresh_i`` to
``thresh_i+1``. For ``scale_N``, the axis is accelerated
everywhere above ``thresh_N``.
* If ``scale_i == numpy.inf``, the axis *discretely jumps* from
``thresh_i`` to ``thresh_i+1``. The final scale ``scale_N``
*cannot* be ``numpy.inf``.
See also
--------
proplot.constructor.Scale
Example
-------
>>> import proplot as pplt
>>> import numpy as np
>>> scale = pplt.CutoffScale(10, 0.5) # move slower above 10
>>> scale = pplt.CutoffScale(10, 2, 20) # move faster between 10 and 20
>>> scale = pplt.CutoffScale(10, np.inf, 20) # jump from 10 to 20
"""
# NOTE: See https://stackoverflow.com/a/5669301/4970632
super().__init__()
args = list(args)
if len(args) % 2 == 1:
args.append(1)
self.args = args
self.threshs = args[::2]
self.scales = args[1::2]
self._transform = CutoffTransform(self.threshs, self.scales)
class CutoffTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
has_inverse = True
is_separable = True
def __init__(self, threshs, scales, zero_dists=None):
# The zero_dists array is used to fill in distances where scales and
# threshold steps are zero. Used for inverting discrete transorms.
super().__init__()
dists = np.diff(threshs)
scales = np.asarray(scales)
threshs = np.asarray(threshs)
if len(scales) != len(threshs):
raise ValueError(f'Got {len(threshs)} but {len(scales)} scales.')
if any(scales < 0):
raise ValueError('Scales must be non negative.')
if scales[-1] in (0, np.inf):
raise ValueError('Final scale must be finite.')
if any(dists < 0):
raise ValueError('Thresholds must be monotonically increasing.')
if any((dists == 0) | (scales == 0)):
if zero_dists is None:
raise ValueError('Keyword zero_dists is required for discrete steps.')
if any((dists == 0) != (scales == 0)):
raise ValueError('Input scales disagree with discrete step locations.')
self._scales = scales
self._threshs = threshs
with np.errstate(divide='ignore', invalid='ignore'):
dists = np.concatenate((threshs[:1], dists / scales[:-1]))
if zero_dists is not None:
dists[scales[:-1] == 0] = zero_dists
self._dists = dists
def inverted(self):
# Use same algorithm for inversion!
threshs = np.cumsum(self._dists) # thresholds in transformed space
with np.errstate(divide='ignore', invalid='ignore'):
scales = 1.0 / self._scales # new scales are inverse
zero_dists = np.diff(self._threshs)[scales[:-1] == 0]
return CutoffTransform(threshs, scales, zero_dists=zero_dists)
def transform_non_affine(self, a):
# Cannot do list comprehension because this method sometimes
# received non-1D arrays
dists = self._dists
scales = self._scales
threshs = self._threshs
aa = np.array(a) # copy
with np.errstate(divide='ignore', invalid='ignore'):
for i, ai in np.ndenumerate(a):
j = np.searchsorted(threshs, ai)
if j > 0:
aa[i] = dists[:j].sum() + (ai - threshs[j - 1]) / scales[j - 1]
return aa
class InverseScale(_Scale, mscale.ScaleBase):
r"""
Axis scale that is linear in the *inverse* of *x*. The forward and inverse
scale functions are as follows:
.. math::
y = x^{-1}
"""
#: The registered scale name
name = 'inverse'
def __init__(self):
"""
See also
--------
proplot.constructor.Scale
"""
super().__init__()
self._transform = InverseTransform()
self._default_major_locator = mticker.LogLocator(10)
self._default_minor_locator = mticker.LogLocator(10, np.arange(1, 10))
def limit_range_for_scale(self, vmin, vmax, minpos):
"""
Return the range *vmin* and *vmax* limited to positive numbers.
"""
# Unlike log-scale, we can't just warp the space between
# the axis limits -- have to actually change axis limits. Also this
# scale will invert and swap the limits you provide.
if not np.isfinite(minpos):
minpos = 1e-300
return (
minpos if vmin <= 0 else vmin,
minpos if vmax <= 0 else vmax,
)
class InverseTransform(mtransforms.Transform):
# Create transform object
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self):
super().__init__()
def inverted(self):
return InverseTransform()
def transform_non_affine(self, a):
with np.errstate(divide='ignore', invalid='ignore'):
return 1.0 / a
def _scale_factory(scale, axis, *args, **kwargs): # noqa: U100
"""
Generate an axis scale.
Parameters
----------
scale : str or `~matplotlib.scale.ScaleBase`
The axis scale name or scale instance.
axis : `~matplotlib.axis.Axis`
The axis instance.
*args, **kwargs
Passed to `~matplotlib.scale.ScaleBase` if `scale` is a string.
"""
mapping = mscale._scale_mapping
if isinstance(scale, mscale.ScaleBase):
if args or kwargs:
warnings._warn_proplot(f'Ignoring args {args} and keyword args {kwargs}.')
return scale # do nothing
else:
scale = scale.lower()
if scale not in mapping:
raise ValueError(
f'Unknown axis scale {scale!r}. Options are '
+ ', '.join(map(repr, mapping)) + '.'
)
return mapping[scale](*args, **kwargs)
# Monkey patch matplotlib scale factory with version that accepts ScaleBase instances.
# This lets set_xscale and set_yscale accept axis scales returned by Scale constructor
# and makes things constistent with the other constructor functions.
if mscale.scale_factory is not _scale_factory:
mscale.scale_factory = _scale_factory