Source code for astropy.visualization.interval

# 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
from .zscale import zscale

__all__ = ['BaseInterval', 'ManualInterval', 'MinMaxInterval',
           'PercentileInterval', 'AsymmetricPercentileInterval',
           'ZScaleInterval']


[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: values = np.random.choice(values, 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)
[docs]class ZScaleInterval(BaseInterval): """ Interval based on IRAF's zscale. http://iraf.net/forum/viewtopic.php?showtopic=134139 Parameters ---------- image : array_like Input array. nsamples : int, optional Number of points in array to sample for determining scaling factors. Default to 1000. contrast : float, optional Scaling factor (between 0 and 1) for determining min and max. Larger values increase the difference between min and max values used for display. Default to 0.25. max_reject : float, optional If more than ``max_reject * npixels`` pixels are rejected, then the returned values are the min and max of the data. Default to 0.5. min_npixels : int, optional If less than ``min_npixels`` pixels are rejected, then the returned values are the min and max of the data. Default to 5. krej : float, optional Number of sigma used for the rejection. Default to 2.5. max_iterations : int, optional Maximum number of iterations for the rejection. Default to 5. """ def __init__(self, nsamples=1000, contrast=0.25, max_reject=0.5, min_npixels=5, krej=2.5, max_iterations=5): self.nsamples = nsamples self.contrast = contrast self.max_reject = max_reject self.min_npixels = min_npixels self.krej = krej self.max_iterations = max_iterations
[docs] def get_limits(self, values): return zscale(values, nsamples=self.nsamples, contrast=self.contrast, max_reject=self.max_reject, min_npixels=self.min_npixels, krej=self.krej, max_iterations=self.max_iterations)