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- import numpy as np
- from numpy.testing import assert_array_equal, assert_array_almost_equal
- import scipy.signal._wavelets as wavelets
- class TestWavelets:
- def test_ricker(self):
- w = wavelets._ricker(1.0, 1)
- expected = 2 / (np.sqrt(3 * 1.0) * (np.pi ** 0.25))
- assert_array_equal(w, expected)
- lengths = [5, 11, 15, 51, 101]
- for length in lengths:
- w = wavelets._ricker(length, 1.0)
- assert len(w) == length
- max_loc = np.argmax(w)
- assert max_loc == (length // 2)
- points = 100
- w = wavelets._ricker(points, 2.0)
- half_vec = np.arange(0, points // 2)
- # Wavelet should be symmetric
- assert_array_almost_equal(w[half_vec], w[-(half_vec + 1)])
- # Check zeros
- aas = [5, 10, 15, 20, 30]
- points = 99
- for a in aas:
- w = wavelets._ricker(points, a)
- vec = np.arange(0, points) - (points - 1.0) / 2
- exp_zero1 = np.argmin(np.abs(vec - a))
- exp_zero2 = np.argmin(np.abs(vec + a))
- assert_array_almost_equal(w[exp_zero1], 0)
- assert_array_almost_equal(w[exp_zero2], 0)
- def test_cwt(self):
- widths = [1.0]
- def delta_wavelet(s, t):
- return np.array([1])
- len_data = 100
- test_data = np.sin(np.pi * np.arange(0, len_data) / 10.0)
- # Test delta function input gives same data as output
- cwt_dat = wavelets._cwt(test_data, delta_wavelet, widths)
- assert cwt_dat.shape == (len(widths), len_data)
- assert_array_almost_equal(test_data, cwt_dat.flatten())
- # Check proper shape on output
- widths = [1, 3, 4, 5, 10]
- cwt_dat = wavelets._cwt(test_data, wavelets._ricker, widths)
- assert cwt_dat.shape == (len(widths), len_data)
- widths = [len_data * 10]
- # Note: this wavelet isn't defined quite right, but is fine for this test
- def flat_wavelet(l, w):
- return np.full(w, 1 / w)
- cwt_dat = wavelets._cwt(test_data, flat_wavelet, widths)
- assert_array_almost_equal(cwt_dat, np.mean(test_data))
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