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2c3c408 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
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)
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