Source code for probscale.transforms
import numpy
from matplotlib.transforms import Transform
def _mask_out_of_bounds(a):
"""
Return a Numpy array where all values outside ]0, 1[ are
replaced with NaNs. If all values are inside ]0, 1[, the original
array is returned.
"""
a = numpy.array(a, float)
mask = (a <= 0.0) | (a >= 1.0)
if mask.any():
return numpy.where(mask, numpy.nan, a)
return a
def _clip_out_of_bounds(a):
"""
Return a Numpy array where all values outside ]0, 1[ are
replaced with eps or 1 - eps. If all values are inside ]0, 1[
the original array is returned. (eps = 1e-300)
"""
a = numpy.array(a, float)
a[a <= 0.0] = 1e-300
a[a >= 1.0] = 1 - 1e-300
return a
class _ProbTransformMixin(Transform):
"""
Mixin for MPL axes transform for quantiles/probabilities or
percentages.
"""
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
def __init__(self, dist, as_pct=True, out_of_bounds='mask'):
Transform.__init__(self)
self.dist = dist
self.as_pct = as_pct
self.out_of_bounds = out_of_bounds
if self.as_pct:
self.factor = 100.0
else:
self.factor = 1.0
if self.out_of_bounds == 'mask':
self._handle_out_of_bounds = _mask_out_of_bounds
elif self.out_of_bounds == 'clip':
self._handle_out_of_bounds = _clip_out_of_bounds
else:
raise ValueError("`out_of_bounds` muse be either 'mask' or 'clip'")
[docs]class ProbTransform(_ProbTransformMixin):
"""
MPL axes tranform class to convert quantiles to probabilities
or percents.
Parameters
----------
dist : scipy.stats distribution
The distribution whose ``cdf`` and ``pdf`` methods wiil set the
scale of the axis.
as_pct : bool, optional (True)
Toggles the formatting of the probabilities associated with the
tick labels as percentanges (0 - 100) or fractions (0 - 1).
out_of_bounds : string, optionals ('mask' or 'clip')
Determines how data outside the range of valid values is
handled. The default behavior is to mask the data.
Alternatively, the data can be clipped to values arbitrarily
close to the limits of the scale.
"""
[docs] def transform_non_affine(self, prob):
prob = self._handle_out_of_bounds(numpy.asarray(prob) / self.factor)
q = self.dist.ppf(prob)
return q
[docs] def inverted(self):
return QuantileTransform(self.dist, as_pct=self.as_pct, out_of_bounds=self.out_of_bounds)
[docs]class QuantileTransform(_ProbTransformMixin):
"""
MPL axes tranform class to convert probabilities or percents to
quantiles.
Parameters
----------
dist : scipy.stats distribution
The distribution whose ``cdf`` and ``pdf`` methods wiil set the
scale of the axis.
as_pct : bool, optional (True)
Toggles the formatting of the probabilities associated with the
tick labels as percentanges (0 - 100) or fractions (0 - 1).
out_of_bounds : string, optionals ('mask' or 'clip')
Determines how data outside the range of valid values is
handled. The default behavior is to mask the data.
Alternatively, the data can be clipped to values arbitrarily
close to the limits of the scale.
"""
[docs] def inverted(self):
return ProbTransform(self.dist, as_pct=self.as_pct, out_of_bounds=self.out_of_bounds)