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| import pytest |
| import numpy as np |
| from numpy.testing import assert_allclose, assert_array_equal, assert_array_less |
|
|
| from astropy.modeling import models, InputParameterError |
| from astropy.coordinates import Angle |
| from astropy.modeling import fitting |
| from astropy.tests.helper import catch_warnings |
| from astropy.utils.exceptions import AstropyDeprecationWarning |
|
|
| try: |
| from scipy import optimize |
| HAS_SCIPY = True |
| except ImportError: |
| HAS_SCIPY = False |
|
|
|
|
| def test_sigma_constant(): |
| """ |
| Test that the GAUSSIAN_SIGMA_TO_FWHM constant matches the |
| gaussian_sigma_to_fwhm constant in astropy.stats. We define |
| it manually in astropy.modeling to avoid importing from |
| astropy.stats. |
| """ |
| from astropy.stats.funcs import gaussian_sigma_to_fwhm |
| from astropy.modeling.functional_models import GAUSSIAN_SIGMA_TO_FWHM |
| assert gaussian_sigma_to_fwhm == GAUSSIAN_SIGMA_TO_FWHM |
|
|
|
|
| def test_Trapezoid1D(): |
| """Regression test for https://github.com/astropy/astropy/issues/1721""" |
|
|
| model = models.Trapezoid1D(amplitude=4.2, x_0=2.0, width=1.0, slope=3) |
| xx = np.linspace(0, 4, 8) |
| yy = model(xx) |
| yy_ref = [0., 1.41428571, 3.12857143, 4.2, 4.2, 3.12857143, 1.41428571, 0.] |
| assert_allclose(yy, yy_ref, rtol=0, atol=1e-6) |
|
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|
|
| def test_Gaussian2D(): |
| """ |
| Test rotated elliptical Gaussian2D model. |
| https://github.com/astropy/astropy/pull/2038 |
| """ |
|
|
| model = models.Gaussian2D(4.2, 1.7, 3.1, x_stddev=5.1, y_stddev=3.3, |
| theta=np.pi/6.) |
| y, x = np.mgrid[0:5, 0:5] |
| g = model(x, y) |
| g_ref = [[3.01907812, 2.99051889, 2.81271552, 2.5119566, 2.13012709], |
| [3.55982239, 3.6086023, 3.4734158, 3.17454575, 2.75494838], |
| [3.88059142, 4.0257528, 3.96554926, 3.70908389, 3.29410187], |
| [3.91095768, 4.15212857, 4.18567526, 4.00652015, 3.64146544], |
| [3.6440466, 3.95922417, 4.08454159, 4.00113878, 3.72161094]] |
| assert_allclose(g, g_ref, rtol=0, atol=1e-6) |
| assert_allclose([model.x_fwhm, model.y_fwhm], |
| [12.009582229657841, 7.7709061486021325]) |
|
|
|
|
| def test_Gaussian2DCovariance(): |
| """ |
| Test rotated elliptical Gaussian2D model when cov_matrix is input. |
| https://github.com/astropy/astropy/pull/2199 |
| """ |
|
|
| cov_matrix = [[49., -16.], [-16., 9.]] |
| model = models.Gaussian2D(17., 2.0, 2.5, cov_matrix=cov_matrix) |
| y, x = np.mgrid[0:5, 0:5] |
| g = model(x, y) |
| g_ref = [[4.3744505, 5.8413977, 7.42988694, 9.00160175, 10.38794269], |
| [8.83290201, 10.81772851, 12.61946384, 14.02225593, 14.84113227], |
| [13.68528889, 15.37184621, 16.44637743, 16.76048705, 16.26953638], |
| [16.26953638, 16.76048705, 16.44637743, 15.37184621, 13.68528889], |
| [14.84113227, 14.02225593, 12.61946384, 10.81772851, 8.83290201]] |
| assert_allclose(g, g_ref, rtol=0, atol=1e-6) |
|
|
|
|
| def test_Gaussian2DRotation(): |
| amplitude = 42 |
| x_mean, y_mean = 0, 0 |
| x_stddev, y_stddev = 2, 3 |
| theta = Angle(10, 'deg') |
| pars = dict(amplitude=amplitude, x_mean=x_mean, y_mean=y_mean, |
| x_stddev=x_stddev, y_stddev=y_stddev) |
| rotation = models.Rotation2D(angle=theta.degree) |
| point1 = (x_mean + 2 * x_stddev, y_mean + 2 * y_stddev) |
| point2 = rotation(*point1) |
| g1 = models.Gaussian2D(theta=0, **pars) |
| g2 = models.Gaussian2D(theta=theta.radian, **pars) |
| value1 = g1(*point1) |
| value2 = g2(*point2) |
| assert_allclose(value1, value2) |
|
|
|
|
| def test_Gaussian2D_invalid_inputs(): |
| x_stddev = 5.1 |
| y_stddev = 3.3 |
| theta = 10 |
| cov_matrix = [[49., -16.], [-16., 9.]] |
|
|
| |
| models.Gaussian2D() |
| models.Gaussian2D(x_stddev=x_stddev, y_stddev=y_stddev, theta=theta) |
| models.Gaussian2D(x_stddev=None, y_stddev=y_stddev, theta=theta) |
| models.Gaussian2D(x_stddev=x_stddev, y_stddev=None, theta=theta) |
| models.Gaussian2D(x_stddev=x_stddev, y_stddev=y_stddev, theta=None) |
| models.Gaussian2D(cov_matrix=cov_matrix) |
|
|
| with pytest.raises(InputParameterError): |
| models.Gaussian2D(x_stddev=0, cov_matrix=cov_matrix) |
| with pytest.raises(InputParameterError): |
| models.Gaussian2D(y_stddev=0, cov_matrix=cov_matrix) |
| with pytest.raises(InputParameterError): |
| models.Gaussian2D(theta=0, cov_matrix=cov_matrix) |
|
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|
|
| @pytest.mark.parametrize('gamma', (10, -10)) |
| def test_moffat_fwhm(gamma): |
| ans = 34.641016151377542 |
| kwargs = {'gamma': gamma, 'alpha': 0.5} |
| m1 = models.Moffat1D(**kwargs) |
| m2 = models.Moffat2D(**kwargs) |
| assert_allclose([m1.fwhm, m2.fwhm], ans) |
| assert_array_less(0, [m1.fwhm, m2.fwhm]) |
|
|
|
|
| def test_RedshiftScaleFactor(): |
| """Like ``test_ScaleModel()``.""" |
|
|
| |
| m = models.RedshiftScaleFactor(0.4) |
| assert m(0) == 0 |
| assert_array_equal(m([1, 2]), [1.4, 2.8]) |
|
|
| assert_allclose(m.inverse(m([1, 2])), [1, 2]) |
|
|
| |
| m = models.RedshiftScaleFactor([-0.5, 0, 0.5], n_models=3) |
| assert_array_equal(m(0), 0) |
| assert_array_equal(m([1, 2], model_set_axis=False), |
| [[0.5, 1], [1, 2], [1.5, 3]]) |
|
|
| assert_allclose(m.inverse(m([1, 2], model_set_axis=False)), |
| [[1, 2], [1, 2], [1, 2]]) |
|
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|
|
| def test_Ellipse2D(): |
| """Test Ellipse2D model.""" |
| amplitude = 7.5 |
| x0, y0 = 15, 15 |
| theta = Angle(45, 'deg') |
| em = models.Ellipse2D(amplitude, x0, y0, 7, 3, theta.radian) |
| y, x = np.mgrid[0:30, 0:30] |
| e = em(x, y) |
| assert np.all(e[e > 0] == amplitude) |
| assert e[y0, x0] == amplitude |
|
|
| rotation = models.Rotation2D(angle=theta.degree) |
| point1 = [2, 0] |
| point2 = rotation(*point1) |
| point1 = np.array(point1) + [x0, y0] |
| point2 = np.array(point2) + [x0, y0] |
| e1 = models.Ellipse2D(amplitude, x0, y0, 7, 3, theta=0.) |
| e2 = models.Ellipse2D(amplitude, x0, y0, 7, 3, theta=theta.radian) |
| assert e1(*point1) == e2(*point2) |
|
|
|
|
| def test_Ellipse2D_circular(): |
| """Test that circular Ellipse2D agrees with Disk2D [3736].""" |
| amplitude = 7.5 |
| radius = 10 |
| size = (radius * 2) + 1 |
| y, x = np.mgrid[0:size, 0:size] |
| ellipse = models.Ellipse2D(amplitude, radius, radius, radius, radius, |
| theta=0)(x, y) |
| disk = models.Disk2D(amplitude, radius, radius, radius)(x, y) |
| assert np.all(ellipse == disk) |
|
|
|
|
| def test_Scale_inverse(): |
| m = models.Scale(1.2345) |
| assert_allclose(m.inverse(m(6.789)), 6.789) |
|
|
|
|
| def test_Multiply_inverse(): |
| m = models.Multiply(1.2345) |
| assert_allclose(m.inverse(m(6.789)), 6.789) |
|
|
|
|
| def test_Shift_inverse(): |
| m = models.Shift(1.2345) |
| assert_allclose(m.inverse(m(6.789)), 6.789) |
|
|
|
|
| @pytest.mark.skipif('not HAS_SCIPY') |
| def test_Shift_model_levmar_fit(): |
| """Test fitting Shift model with LevMarLSQFitter (issue #6103).""" |
|
|
| init_model = models.Shift() |
|
|
| x = np.arange(10) |
| y = x+0.1 |
|
|
| fitter = fitting.LevMarLSQFitter() |
| fitted_model = fitter(init_model, x, y) |
|
|
| assert_allclose(fitted_model.parameters, [0.1], atol=1e-15) |
|
|
|
|
| def test_Shift_model_set_linear_fit(): |
| """Test linear fitting of Shift model (issue #6103).""" |
|
|
| init_model = models.Shift(offset=[0, 0], n_models=2) |
|
|
| x = np.arange(10) |
| yy = np.array([x+0.1, x-0.2]) |
|
|
| fitter = fitting.LinearLSQFitter() |
| fitted_model = fitter(init_model, x, yy) |
|
|
| assert_allclose(fitted_model.parameters, [0.1, -0.2], atol=1e-15) |
|
|
|
|
| @pytest.mark.parametrize('Model', (models.Scale, models.Multiply)) |
| def test_Scale_model_set_linear_fit(Model): |
| """Test linear fitting of Scale model (#6103).""" |
|
|
| init_model = Model(factor=[0, 0], n_models=2) |
|
|
| x = np.arange(-3, 7) |
| yy = np.array([1.15*x, 0.96*x]) |
|
|
| fitter = fitting.LinearLSQFitter() |
| fitted_model = fitter(init_model, x, yy) |
|
|
| assert_allclose(fitted_model.parameters, [1.15, 0.96], atol=1e-15) |
|
|
|
|
| |
| def test_Ring2D_rout(): |
| m = models.Ring2D(amplitude=1, x_0=1, y_0=1, r_in=2, r_out=5) |
| assert m.width.value == 3 |
|
|
|
|
| @pytest.mark.skipif("not HAS_SCIPY") |
| def test_Voigt1D(): |
| voi = models.Voigt1D(amplitude_L=-0.5, x_0=1.0, fwhm_L=5.0, fwhm_G=5.0) |
| xarr = np.linspace(-5.0, 5.0, num=40) |
| yarr = voi(xarr) |
| voi_init = models.Voigt1D(amplitude_L=-1.0, x_0=1.0, fwhm_L=5.0, fwhm_G=5.0) |
| fitter = fitting.LevMarLSQFitter() |
| voi_fit = fitter(voi_init, xarr, yarr) |
| assert_allclose(voi_fit.param_sets, voi.param_sets) |
|
|
|
|
| @pytest.mark.skipif("not HAS_SCIPY") |
| def test_compound_models_with_class_variables(): |
| models_2d = [models.AiryDisk2D, models.Sersic2D] |
| models_1d = [models.Sersic1D] |
|
|
| for model_2d in models_2d: |
| class CompoundModel2D(models.Const2D + model_2d): |
| pass |
| x, y = np.mgrid[:10, :10] |
| f = CompoundModel2D()(x, y) |
| assert f.shape == (10, 10) |
|
|
| for model_1d in models_1d: |
| class CompoundModel1D(models.Const1D + model_1d): |
| pass |
| x = np.arange(10) |
| f = CompoundModel1D()(x) |
| assert f.shape == (10,) |
|
|
