# Licensed under a 3-clause BSD style license - see LICENSE.rst import pytest import numpy as np from numpy import ma from numpy.testing import assert_allclose from astropy.visualization.mpl_normalize import ImageNormalize, simple_norm, imshow_norm from astropy.visualization.interval import ManualInterval from astropy.visualization.stretch import SqrtStretch try: import matplotlib # pylint: disable=W0611 from matplotlib import pyplot as plt 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: def test_invalid_interval(self): with pytest.raises(TypeError): ImageNormalize(vmin=2., vmax=10., interval=ManualInterval, clip=True) def test_invalid_stretch(self): with pytest.raises(TypeError): ImageNormalize(vmin=2., vmax=10., stretch=SqrtStretch, clip=True) 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: 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_log_with_log_a(self): """Test log10 scaling with a custom log_a.""" log_a = 100 norm = simple_norm(DATA2, stretch='log', log_a=log_a) ref = np.log10(log_a * DATA2SCL + 1.0) / np.log10(log_a + 1) assert_allclose(norm(DATA2), ref, atol=0, rtol=1.e-5) def test_asinh(self): """Test asinh scaling.""" norm = simple_norm(DATA2, stretch='asinh') ref = np.arcsinh(10 * DATA2SCL) / np.arcsinh(10) assert_allclose(norm(DATA2), ref, atol=0, rtol=1.e-5) def test_asinh_with_asinh_a(self): """Test asinh scaling with a custom asinh_a.""" asinh_a = 0.5 norm = simple_norm(DATA2, stretch='asinh', asinh_a=asinh_a) ref = np.arcsinh(DATA2SCL / asinh_a) / np.arcsinh(1. / asinh_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') @pytest.mark.skipif('not HAS_MATPLOTLIB') def test_imshow_norm(): image = np.random.randn(10, 10) ax = plt.subplot() imshow_norm(image, ax=ax) with pytest.raises(ValueError): # X and data are the same, can't give both imshow_norm(image, X=image, ax=ax) with pytest.raises(ValueError): # illegal to manually pass in normalization since that defeats the point imshow_norm(image, ax=ax, norm=ImageNormalize()) imshow_norm(image, ax=ax, vmin=0, vmax=1) # vmin/vmax "shadow" the MPL versions, so imshow_only_kwargs allows direct-setting imshow_norm(image, ax=ax, imshow_only_kwargs=dict(vmin=0, vmax=1)) # but it should fail for an argument that is not in ImageNormalize with pytest.raises(ValueError): imshow_norm(image, ax=ax, imshow_only_kwargs=dict(cmap='jet')) # make sure the pyplot version works imres, norm = imshow_norm(image, ax=None) assert isinstance(norm, ImageNormalize)