| |
|
|
| import math |
| import numpy as np |
| import pytest |
|
|
| from astropy.convolution.convolve import convolve, convolve_fft, convolve_models |
| from astropy.modeling import models, fitting |
| from astropy.utils.misc import NumpyRNGContext |
| from numpy.testing import assert_allclose, assert_almost_equal |
|
|
| try: |
| import scipy |
| except ImportError: |
| HAS_SCIPY = False |
| else: |
| HAS_SCIPY = True |
|
|
|
|
| class TestConvolve1DModels: |
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| @pytest.mark.skipif('not HAS_SCIPY') |
| def test_is_consistency_with_astropy_convolution(self, mode): |
| kernel = models.Gaussian1D(1, 0, 1) |
| model = models.Gaussian1D(1, 0, 1) |
| model_conv = convolve_models(model, kernel, mode=mode) |
| x = np.arange(-5, 6) |
| ans = eval("{}(model(x), kernel(x))".format(mode)) |
|
|
| assert_allclose(ans, model_conv(x), atol=1e-5) |
|
|
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| @pytest.mark.skipif('not HAS_SCIPY') |
| def test_against_scipy(self, mode): |
| from scipy.signal import fftconvolve |
|
|
| kernel = models.Gaussian1D(1, 0, 1) |
| model = models.Gaussian1D(1, 0, 1) |
| model_conv = convolve_models(model, kernel, mode=mode) |
| x = np.arange(-5, 6) |
| ans = fftconvolve(kernel(x), model(x), mode='same') |
|
|
| assert_allclose(ans, model_conv(x) * kernel(x).sum(), atol=1e-5) |
|
|
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| @pytest.mark.skipif('not HAS_SCIPY') |
| def test_against_scipy_with_additional_keywords(self, mode): |
| from scipy.signal import fftconvolve |
|
|
| kernel = models.Gaussian1D(1, 0, 1) |
| model = models.Gaussian1D(1, 0, 1) |
| model_conv = convolve_models(model, kernel, mode=mode, |
| normalize_kernel=False) |
| x = np.arange(-5, 6) |
| ans = fftconvolve(kernel(x), model(x), mode='same') |
|
|
| assert_allclose(ans, model_conv(x), atol=1e-5) |
|
|
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| def test_sum_of_gaussians(self, mode): |
| """ |
| Test that convolving N(a, b) with N(c, d) gives N(a + c, b + d), |
| where N(., .) stands for Gaussian probability density function, |
| in which a and c are their means and b and d are their variances. |
| """ |
|
|
| kernel = models.Gaussian1D(1 / math.sqrt(2 * np.pi), 1, 1) |
| model = models.Gaussian1D(1 / math.sqrt(2 * np.pi), 3, 1) |
| model_conv = convolve_models(model, kernel, mode=mode, |
| normalize_kernel=False) |
| ans = models.Gaussian1D(1 / (2 * math.sqrt(np.pi)), 4, np.sqrt(2)) |
| x = np.arange(-5, 6) |
|
|
| assert_allclose(ans(x), model_conv(x), atol=1e-3) |
|
|
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| def test_convolve_box_models(self, mode): |
| kernel = models.Box1D() |
| model = models.Box1D() |
| model_conv = convolve_models(model, kernel, mode=mode) |
| x = np.linspace(-1, 1, 99) |
| ans = (x + 1) * (x < 0) + (-x + 1) * (x >= 0) |
|
|
| assert_allclose(ans, model_conv(x), atol=1e-3) |
|
|
| @pytest.mark.parametrize('mode', ['convolve_fft', 'convolve']) |
| @pytest.mark.skipif('not HAS_SCIPY') |
| def test_fitting_convolve_models(self, mode): |
| """ |
| test that a convolve model can be fitted |
| """ |
| b1 = models.Box1D() |
| g1 = models.Gaussian1D() |
|
|
| x = np.linspace(-5, 5, 99) |
| fake_model = models.Gaussian1D(amplitude=10) |
| with NumpyRNGContext(123): |
| fake_data = fake_model(x) + np.random.normal(size=len(x)) |
|
|
| init_model = convolve_models(b1, g1, mode=mode, normalize_kernel=False) |
| fitter = fitting.LevMarLSQFitter() |
| fitted_model = fitter(init_model, x, fake_data) |
|
|
| me = np.mean(fitted_model(x) - fake_data) |
| assert_almost_equal(me, 0.0, decimal=2) |
|
|
