def test_centrfreq():
# db1 is Haar function, frequency=1
w = pywt.Wavelet('db1')
expected = 1
result = pywt.centfrq(w, precision=12)
assert_almost_equal(result, expected, decimal=3)
# db2, frequency=2/3
w = pywt.Wavelet('db2')
expected = 2/3.
result = pywt.centfrq(w, precision=12)
assert_almost_equal(result, expected)
def test_centrfreq():
# db1 is Haar function, frequency=1
w = pywt.Wavelet('db1')
expected = 1
result = pywt.centfrq(w, precision=12)
assert_almost_equal(result, expected, decimal=3)
# db2, frequency=2/3
w = pywt.Wavelet('db2')
expected = 2/3.
result = pywt.centfrq(w, precision=12)
assert_almost_equal(result, expected)
def get_tf_info(self, Fs):
""" returns energy centroids and spread in the time frequency plane """
# This is some pretty ugly hack and is only intended to allow basic
# plotting features
C = np.cumsum(self.waveform**2)
center = np.min(np.nonzero(C>np.max(C)/2))
start = np.min(np.nonzero(C>0.00001*np.max(C)))
end = np.min(np.nonzero(C>(0.99999*np.max(C))))
scale = 2**(np.round(np.log2(end-start)))
L = scale
K = L / 4
freq = centfrq(self.nature, precision=self.level)
f = float(freq) * float(Fs) / float(L)
bw = (float(Fs) / scale) # / 2
p = center / float(Fs)
l = float(L + K) / float(Fs)
return f, bw, p, l
