def _zscale(self, image, vmin, vmax, stretch, contrast=0.25):
"""Normalization object using Zscale algorithm
See :mod:`astropy.visualization.ZScaleInterval`
:param image: the image object
:type image: :mod:`astropy.io.fits` HDU or CCDData
:param contrast: 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.
:type contrast: float
:returns: :mod:`astropy.visualization.normalization` object
"""
# clip=False required or NaNs get max color value, see https://github.com/astropy/astropy/issues/8165
if stretch == 'linear':
s = LinearStretch()
elif stretch == 'sqrt':
s = SqrtStretch()
elif stretch == 'power':
s = PowerStretch(2)
elif stretch == 'log':
s = LogStretch(1000)
elif stretch == 'asinh':
s = AsinhStretch(0.1)
else:
raise ValueError(f'Unknown stretch: {stretch}.')
norm = ImageNormalize(data=image,
vmin=vmin,
vmax=vmax,
interval=ZScaleInterval(contrast=contrast),
stretch=s,
clip=False)
return norm
def _zscale(self, image, contrast=0.25):
"""Normalization object using Zscale algorithm
See :mod:`astropy.visualization.ZScaleInterval`
:param image: the image object
:type image: :mod:`astropy.io.fits` HDU or CCDData
:param contrast: 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.
:type contrast: float
:returns: :mod:`astropy.visualization.normalization` object
"""
# clip=False required or NaNs get max color value, see https://github.com/astropy/astropy/issues/8165
norm = ImageNormalize(data=image,
interval=ZScaleInterval(contrast=contrast),
stretch=LinearStretch(),
clip=False)
return norm
def simple_norm(data,
stretch='linear',
power=1.0,
asinh_a=0.1,
log_a=1000,
min_cut=None,
max_cut=None,
min_percent=None,
max_percent=None,
percent=None,
clip=True):
if percent is not None:
interval = PercentileInterval(percent)
elif min_percent is not None or max_percent is not None:
interval = AsymmetricPercentileInterval(min_percent or 0., max_percent
or 100.)
elif min_cut is not None or max_cut is not None:
interval = ManualInterval(min_cut, max_cut)
else:
interval = MinMaxInterval()
if stretch == 'linear':
stretch = LinearStretch()
elif stretch == 'sqrt':
stretch = SqrtStretch()
elif stretch == 'power':
stretch = PowerStretch(power)
elif stretch == 'log':
stretch = LogStretch(log_a)
elif stretch == 'asinh':
stretch = AsinhStretch(asinh_a)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
vmin, vmax = interval.get_limits(data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip)
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
from numpy.testing import assert_equal
from astropy.visualization.stretch import (
LinearStretch, SqrtStretch, PowerStretch, PowerDistStretch,
InvertedPowerDistStretch, SquaredStretch, LogStretch, InvertedLogStretch,
AsinhStretch, SinhStretch, HistEqStretch, InvertedHistEqStretch,
ContrastBiasStretch)
DATA = np.array([0.00, 0.25, 0.50, 0.75, 1.00])
RESULTS = {}
RESULTS[LinearStretch()] = np.array([0.00, 0.25, 0.50, 0.75, 1.00])
RESULTS[LinearStretch(intercept=0.5) + LinearStretch(slope=0.5)] = \
np.array([0.5, 0.625, 0.75, 0.875, 1.])
RESULTS[SqrtStretch()] = np.array([0., 0.5, 0.70710678, 0.8660254, 1.])
RESULTS[SquaredStretch()] = np.array([0., 0.0625, 0.25, 0.5625, 1.])
RESULTS[PowerStretch(0.5)] = np.array([0., 0.5, 0.70710678, 0.8660254, 1.])
RESULTS[PowerDistStretch()] = np.array([0., 0.004628, 0.030653, 0.177005, 1.])
RESULTS[LogStretch()] = np.array([0., 0.799776, 0.899816, 0.958408, 1.])
RESULTS[AsinhStretch()] = np.array([0., 0.549402, 0.77127, 0.904691, 1.])
RESULTS[SinhStretch()] = np.array([0., 0.082085, 0.212548, 0.46828, 1.])
RESULTS[ContrastBiasStretch(contrast=2.,
bias=0.4)] = np.array([-0.3, 0.2, 0.7, 1.2, 1.7])
RESULTS[HistEqStretch(DATA)] = DATA
RESULTS[HistEqStretch(DATA[::-1])] = DATA
RESULTS[HistEqStretch(DATA**0.5)] = np.array([0., 0.125, 0.25, 0.5674767, 1.])
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
from astropy.visualization.stretch import (LinearStretch, SqrtStretch,
PowerStretch, PowerDistStretch,
SquaredStretch, LogStretch,
AsinhStretch, SinhStretch,
HistEqStretch, ContrastBiasStretch)
DATA = np.array([0.00, 0.25, 0.50, 0.75, 1.00])
RESULTS = {}
RESULTS[LinearStretch()] = np.array([0.00, 0.25, 0.50, 0.75, 1.00])
RESULTS[SqrtStretch()] = np.array([0., 0.5, 0.70710678, 0.8660254, 1.])
RESULTS[SquaredStretch()] = np.array([0., 0.0625, 0.25, 0.5625, 1.])
RESULTS[PowerStretch(0.5)] = np.array([0., 0.5, 0.70710678, 0.8660254, 1.])
RESULTS[PowerDistStretch()] = np.array([0., 0.004628, 0.030653, 0.177005, 1.])
RESULTS[LogStretch()] = np.array([0., 0.799776, 0.899816, 0.958408, 1.])
RESULTS[AsinhStretch()] = np.array([0., 0.549402, 0.77127, 0.904691, 1.])
RESULTS[SinhStretch()] = np.array([0., 0.082085, 0.212548, 0.46828, 1.])
RESULTS[ContrastBiasStretch(contrast=2.,
bias=0.4)] = np.array([-0.3, 0.2, 0.7, 1.2, 1.7])
RESULTS[HistEqStretch(DATA)] = DATA
RESULTS[HistEqStretch(DATA[::-1])] = DATA
RESULTS[HistEqStretch(DATA**0.5)] = np.array([0., 0.125, 0.25, 0.5674767, 1.])
class TestStretch:
@pytest.mark.parametrize('stretch', RESULTS.keys())