def test_implicit_autoscale(self):
norm = ImageNormalize(vmin=None,
vmax=10.,
stretch=SqrtStretch(),
clip=False)
norm2 = ImageNormalize(DATA,
interval=ManualInterval(None, 10),
stretch=SqrtStretch(),
clip=False)
output = norm(DATA)
assert norm.vmin == np.min(DATA)
assert norm.vmax == 10.
assert_allclose(output, norm2(DATA))
norm = ImageNormalize(vmin=2.,
vmax=None,
stretch=SqrtStretch(),
clip=False)
norm2 = ImageNormalize(DATA,
interval=ManualInterval(2, None),
stretch=SqrtStretch(),
clip=False)
output = norm(DATA)
assert norm.vmin == 2.
assert norm.vmax == np.max(DATA)
assert_allclose(output, norm2(DATA))
def test_scalar(self):
norm = ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch(),
clip=True)
norm2 = ImageNormalize(data=6, interval=ManualInterval(2, 10),
stretch=SqrtStretch(), clip=True)
assert_allclose(norm(6), 0.70710678)
assert_allclose(norm(6), norm2(6))
def test_chaining(self):
stretch_1 = SqrtStretch() + SqrtStretch()
stretch_2 = PowerStretch(0.25)
stretch_3 = PowerStretch(4.)
np.testing.assert_allclose(stretch_1(DATA), stretch_2(DATA))
np.testing.assert_allclose(stretch_1.inverse(DATA), stretch_3(DATA))
def test_clip(self):
norm = ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch(),
clip=True)
norm2 = ImageNormalize(DATA, interval=ManualInterval(2, 10),
stretch=SqrtStretch(), clip=True)
output = norm(DATA)
expected = [0., 0.35355339, 0.70710678, 0.93541435, 1., 1.]
assert_allclose(output, expected)
assert_allclose(output.mask, [0, 0, 0, 0, 0, 0])
assert_allclose(output, norm2(DATA))
def test_chaining(self):
stretch_1 = SqrtStretch() + SqrtStretch()
stretch_2 = PowerStretch(0.25)
stretch_3 = PowerStretch(4.)
np.testing.assert_allclose(stretch_1(DATA),
stretch_2(DATA))
np.testing.assert_allclose(stretch_1.inverse(DATA),
stretch_3(DATA))
def test_masked_clip(self):
mdata = ma.array(DATA, mask=[0, 0, 1, 0, 0, 0])
norm = ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch(),
clip=True)
norm2 = ImageNormalize(mdata, interval=ManualInterval(2, 10),
stretch=SqrtStretch(), clip=True)
output = norm(mdata)
expected = [0., 0.35355339, 1., 0.93541435, 1., 1.]
assert_allclose(output.filled(-10), expected)
assert_allclose(output.mask, [0, 0, 0, 0, 0, 0])
assert_allclose(output, norm2(mdata))
def test_noclip(self):
norm = ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch(),
clip=False)
norm2 = ImageNormalize(DATA, interval=ManualInterval(2, 10),
stretch=SqrtStretch(), clip=False)
output = norm(DATA)
expected = [np.nan, 0.35355339, 0.70710678, 0.93541435, 1.11803399,
1.27475488]
assert_allclose(output, expected)
assert_allclose(output.mask, [0, 0, 0, 0, 0, 0])
assert_allclose(norm.inverse(norm(DATA))[1:], DATA[1:])
assert_allclose(output, norm2(DATA))
def test_masked_noclip(self):
mdata = ma.array(DATA, mask=[0, 0, 1, 0, 0, 0])
norm = ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch(),
clip=False)
norm2 = ImageNormalize(mdata, interval=ManualInterval(2, 10),
stretch=SqrtStretch(), clip=False)
output = norm(mdata)
expected = [np.nan, 0.35355339, -10, 0.93541435, 1.11803399,
1.27475488]
assert_allclose(output.filled(-10), expected)
assert_allclose(output.mask, [0, 0, 1, 0, 0, 0])
assert_allclose(norm.inverse(norm(DATA))[1:], DATA[1:])
assert_allclose(output, norm2(mdata))
def test_sqrt_invalid_kw(self, invalid):
stretch = SqrtStretch()
norm1 = simple_norm(DATA3, stretch='sqrt', min_cut=-1, max_cut=1,
clip=False, invalid=invalid)
norm2 = ImageNormalize(stretch=stretch, vmin=-1, vmax=1, clip=False,
invalid=invalid)
assert_equal(norm1(DATA3), norm2(DATA3))
def test_clip_invalid():
stretch = SqrtStretch()
values = stretch([-1., 0., 0.5, 1., 1.5])
np.testing.assert_allclose(values, [0., 0., 0.70710678, 1., 1.])
values = stretch([-1., 0., 0.5, 1., 1.5], clip=False)
np.testing.assert_allclose(values, [np.nan, 0., 0.70710678, 1., 1.2247448])
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)
from astropy.utils.exceptions import AstropyDeprecationWarning
from astropy.visualization.mpl_normalize import ImageNormalize, simple_norm, imshow_norm
from astropy.visualization.interval import ManualInterval, PercentileInterval
from astropy.visualization.stretch import LogStretch, PowerStretch, SqrtStretch
from astropy.utils.compat.optional_deps import HAS_MATPLOTLIB, HAS_PLT # noqa
if HAS_MATPLOTLIB:
import matplotlib
MATPLOTLIB_LT_32 = Version(matplotlib.__version__) < Version('3.2')
DATA = np.linspace(0., 15., 6)
DATA2 = np.arange(3)
DATA2SCL = 0.5 * DATA2
DATA3 = np.linspace(-3., 3., 7)
STRETCHES = (SqrtStretch(), PowerStretch(0.5), LogStretch())
INVALID = (None, -np.inf, -1)
@pytest.mark.skipif('HAS_MATPLOTLIB')
def test_normalize_error_message():
with pytest.raises(ImportError) as exc:
ImageNormalize()
assert (exc.value.args[0] == "matplotlib is required in order to use "
"this class.")
@pytest.mark.skipif('not HAS_MATPLOTLIB')
class TestNormalize:
def test_invalid_interval(self):
with pytest.raises(TypeError):
def test_inverted(self):
stretch_1 = SqrtStretch().inverse
stretch_2 = PowerStretch(2)
np.testing.assert_allclose(stretch_1(DATA), stretch_2(DATA))
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.])
class TestStretch:
@pytest.mark.parametrize('stretch', RESULTS.keys())
