# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Classes that deal with computing intervals from arrays of values based on
various criteria.
"""
from __future__ import division, print_function
import abc
import numpy as np
from .transform import BaseTransform
__all__ = ['BaseInterval', 'ManualInterval', 'MinMaxInterval',
'PercentileInterval', 'AsymmetricPercentileInterval']
[docs]class BaseInterval(BaseTransform):
"""
Base class for the interval classes, which, when called with an array of
values, return an interval computed following different algorithms.
"""
@abc.abstractmethod
[docs] def get_limits(self, values):
"""
Return the minimum and maximum value in the interval based on the values provided.
"""
[docs] def __call__(self, values, clip=True, out=None):
vmin, vmax = self.get_limits(values)
if out is None:
values = np.subtract(values, float(vmin))
else:
if out.dtype.kind != 'f':
raise TypeError("Can only do in-place scaling for floating-point arrays")
values = np.subtract(values, float(vmin), out=out)
if (vmax - vmin) != 0:
np.true_divide(values, vmax - vmin, out=values)
if clip:
np.clip(values, 0., 1., out=values)
return values
[docs]class ManualInterval(BaseInterval):
"""
Interval based on user-specified values.
Parameters
----------
vmin : float
The minimum value in the scaling
vmax : float
The maximum value in the scaling
"""
def __init__(self, vmin, vmax):
self.vmin = vmin
self.vmax = vmax
[docs] def get_limits(self, values):
return self.vmin, self.vmax
[docs]class MinMaxInterval(BaseInterval):
"""
Interval based on the minimum and maximum values in the data.
"""
[docs] def get_limits(self, values):
return np.min(values), np.max(values)
[docs]class AsymmetricPercentileInterval(BaseInterval):
"""
Interval based on a keeping a specified fraction of pixels (can be asymmetric).
Parameters
----------
lower_percentile : float
The lower percentile below which to ignore pixels.
upper_percentile : float
The upper percentile above which to ignore pixels.
n_samples : int, optional
Maximum number of values to use. If this is specified, and there are
more values in the dataset as this, then values are randomly sampled
from the array (with replacement)
"""
def __init__(self, lower_percentile, upper_percentile, n_samples=None):
self.lower_percentile = lower_percentile
self.upper_percentile = upper_percentile
self.n_samples = n_samples
[docs] def get_limits(self, values):
# Make sure values is a Numpy array
values = np.asarray(values).ravel()
# If needed, limit the number of samples. We sample with replacement
# since this is much faster.
if self.n_samples is not None and values.size > self.n_samples:
try:
values = np.random.choice(values, self.n_samples)
except AttributeError: # Numpy 1.6.x
values = values[np.random.randint(0, values.size, self.n_samples)]
# Filter out invalid values (inf, nan)
values = values[np.isfinite(values)]
# Determine values at percentiles
vmin, vmax = np.percentile(values, (self.lower_percentile,
self.upper_percentile))
return vmin, vmax
[docs]class PercentileInterval(AsymmetricPercentileInterval):
"""
Interval based on a keeping a specified fraction of pixels.
Parameters
----------
percentile : float
The fraction of pixels to keep. The same fraction of pixels is
eliminated from both ends.
n_samples : int, optional
Maximum number of values to use. If this is specified, and there are
more values in the dataset as this, then values are randomly sampled
from the array (with replacement)
"""
def __init__(self, percentile, n_samples=None):
lower_percentile = (100 - percentile) * 0.5
upper_percentile = 100 - lower_percentile
super(PercentileInterval, self).__init__(lower_percentile, upper_percentile, n_samples=n_samples)