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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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