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# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from numpy import ma
from numpy.testing import assert_allclose
from ...tests.helper import pytest
from ..mpl_normalize import ImageNormalize, simple_norm
from ..interval import ManualInterval
from ..stretch import SqrtStretch
try:
import matplotlib # pylint: disable=W0611
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
DATA = np.linspace(0., 15., 6)
DATA2 = np.arange(3)
DATA2SCL = 0.5 * DATA2
@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(object):
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_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_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_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_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_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))
@pytest.mark.skipif('not HAS_MATPLOTLIB')
class TestImageScaling(object):
def test_linear(self):
"""Test linear scaling."""
norm = simple_norm(DATA2, stretch='linear')
assert_allclose(norm(DATA2), DATA2SCL, atol=0, rtol=1.e-5)
def test_sqrt(self):
"""Test sqrt scaling."""
norm = simple_norm(DATA2, stretch='sqrt')
assert_allclose(norm(DATA2), np.sqrt(DATA2SCL), atol=0, rtol=1.e-5)
def test_power(self):
"""Test power scaling."""
power = 3.0
norm = simple_norm(DATA2, stretch='power', power=power)
assert_allclose(norm(DATA2), DATA2SCL ** power, atol=0, rtol=1.e-5)
def test_log(self):
"""Test log10 scaling."""
norm = simple_norm(DATA2, stretch='log')
ref = np.log10(1000 * DATA2SCL + 1.0) / np.log10(1001.0)
assert_allclose(norm(DATA2), ref, atol=0, rtol=1.e-5)
def test_asinh(self):
"""Test asinh scaling."""
a = 0.1
norm = simple_norm(DATA2, stretch='asinh', asinh_a=a)
ref = np.arcsinh(DATA2SCL / a) / np.arcsinh(1. / a)
assert_allclose(norm(DATA2), ref, atol=0, rtol=1.e-5)
def test_min(self):
"""Test linear scaling."""
norm = simple_norm(DATA2, stretch='linear', min_cut=1.)
assert_allclose(norm(DATA2), [0., 0., 1.], atol=0, rtol=1.e-5)
def test_percent(self):
"""Test percent keywords."""
norm = simple_norm(DATA2, stretch='linear', percent=99.)
assert_allclose(norm(DATA2), DATA2SCL, atol=0, rtol=1.e-5)
norm2 = simple_norm(DATA2, stretch='linear', min_percent=0.5,
max_percent=99.5)
assert_allclose(norm(DATA2), norm2(DATA2), atol=0, rtol=1.e-5)
def test_invalid_stretch(self):
"""Test invalid stretch keyword."""
with pytest.raises(ValueError):
simple_norm(DATA2, stretch='invalid')
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