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.

import abc
import numpy as np

from astropy.utils.misc import InheritDocstrings
from .transform import BaseTransform

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

[docs]class BaseInterval(BaseTransform, metaclass=InheritDocstrings): """ Base class for the interval classes, which, when called with an array of values, return an interval computed following different algorithms. """
[docs] @abc.abstractmethod def get_limits(self, values): """ Return the minimum and maximum value in the interval based on the values provided. Parameters ---------- values : `~numpy.ndarray` The image values. Returns ------- vmin, vmax : float The mininium and maximum image value in the interval. """
[docs] def __call__(self, values, clip=True, out=None): """ Transform values using this interval. Parameters ---------- values : array-like The input values. clip : bool, optional If `True` (default), values outside the [0:1] range are clipped to the [0:1] range. out : `~numpy.ndarray`, optional If specified, the output values will be placed in this array (typically used for in-place calculations). Returns ------- result : `~numpy.ndarray` The transformed values. """ 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, optional The minimum value in the scaling. Defaults to the image minimum (ignoring NaNs) vmax : float, optional The maximum value in the scaling. Defaults to the image maximum (ignoring NaNs) """ def __init__(self, vmin=None, vmax=None): self.vmin = vmin self.vmax = vmax
[docs] def get_limits(self, values): vmin = np.nanmin(values) if self.vmin is None else self.vmin vmax = np.nanmax(values) if self.vmax is None else self.vmax return vmin, vmax
[docs]class MinMaxInterval(BaseInterval): """ Interval based on the minimum and maximum values in the data. """
[docs] def get_limits(self, values): return np.nanmin(values), np.nanmax(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.nanpercentile(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().__init__( lower_percentile, upper_percentile, n_samples=n_samples)
[docs]class ZScaleInterval(BaseInterval): """ Interval based on IRAF's zscale. Original implementation: Licensed under a 3-clause BSD style license (see AURA_LICENSE.rst). Parameters ---------- nsamples : int, optional The number of points in the array to sample for determining scaling factors. Defaults to 1000. contrast : float, optional The scaling factor (between 0 and 1) for determining the minimum and maximum value. Larger values increase the difference between the minimum and maximum values used for display. Defaults to 0.25. max_reject : float, optional If more than ``max_reject * npixels`` pixels are rejected, then the returned values are the minimum and maximum of the data. Defaults to 0.5. min_npixels : int, optional If less than ``min_npixels`` pixels are rejected, then the returned values are the minimum and maximum of the data. Defaults to 5. krej : float, optional The number of sigma used for the rejection. Defaults to 2.5. max_iterations : int, optional The maximum number of iterations for the rejection. Defaults 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): # Sample the image values = np.asarray(values) values = values[np.isfinite(values)] stride = int(max(1.0, values.size / self.nsamples)) samples = values[::stride][:self.nsamples] samples.sort() npix = len(samples) vmin = samples[0] vmax = samples[-1] # Fit a line to the sorted array of samples minpix = max(self.min_npixels, int(npix * self.max_reject)) x = np.arange(npix) ngoodpix = npix last_ngoodpix = npix + 1 # Bad pixels mask used in k-sigma clipping badpix = np.zeros(npix, dtype=bool) # Kernel used to dilate the bad pixels mask ngrow = max(1, int(npix * 0.01)) kernel = np.ones(ngrow, dtype=bool) for niter in range(self.max_iterations): if ngoodpix >= last_ngoodpix or ngoodpix < minpix: break fit = np.polyfit(x, samples, deg=1, w=(~badpix).astype(int)) fitted = np.poly1d(fit)(x) # Subtract fitted line from the data array flat = samples - fitted # Compute the k-sigma rejection threshold threshold = self.krej * flat[~badpix].std() # Detect and reject pixels further than k*sigma from the # fitted line badpix[(flat < - threshold) | (flat > threshold)] = True # Convolve with a kernel of length ngrow badpix = np.convolve(badpix, kernel, mode='same') last_ngoodpix = ngoodpix ngoodpix = np.sum(~badpix) slope, intercept = fit if ngoodpix >= minpix: if self.contrast > 0: slope = slope / self.contrast center_pixel = (npix - 1) // 2 median = np.median(samples) vmin = max(vmin, median - (center_pixel - 1) * slope) vmax = min(vmax, median + (npix - center_pixel) * slope) return vmin, vmax