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Add option to return complex valued wavelet transform #2

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Add option to return complex valued wavelet transform #2

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12 changes: 8 additions & 4 deletions behavelet/morlet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,7 @@ def _morlet_conj_ft(omegas, omega0=5.0, gpu=False):
return ft_wavelet


def _morlet_fft_convolution(X, freqs, scales, dtime, omega0=5.0, gpu=False):
def _morlet_fft_convolution(X, freqs, scales, dtime, omega0=5.0, return_complex=False, gpu=False):
"""
Calculates a Morlet continuous wavelet transform
for a given signal across a range of frequencies
Expand Down Expand Up @@ -163,7 +163,8 @@ def _morlet_fft_convolution(X, freqs, scales, dtime, omega0=5.0, gpu=False):
convolved *= backend.sqrt(scale)

convolved = convolved[idx0:idx1] # remove zero padding
convolved = backend.abs(convolved) # use the norm of the complex values
if not return_complex:
convolved = backend.abs(convolved) # use the norm of the complex values

# scale power to account for disproportionally
# large wavelet response at low frequencies
Expand All @@ -183,7 +184,7 @@ def _morlet_fft_convolution_parallel(feed_dict):

def wavelet_transform(X, n_freqs, fsample, fmin, fmax,
prob=True, omega0=5.0, log_scale=True,
n_jobs=1, gpu=False):
return_complex=False, n_jobs=1, gpu=False):
"""
Applies a Morlet continuous wavelet transform to a data set
across a range of frequencies.
Expand Down Expand Up @@ -215,6 +216,8 @@ def wavelet_transform(X, n_freqs, fsample, fmin, fmax,
Whether to sample the frequencies on a log scale.
omega0 : float (default = 5.0)
Dimensionless omega0 parameter for wavelet transform.
return_complex: bool (default = False)
Whether to return complex wavelet transform.
n_jobs : int (default = 1)
Number of jobs to use for performing the wavelet transform.
If -1, all CPUs are used. If 1 is given, no parallel computing is
Expand Down Expand Up @@ -284,6 +287,7 @@ def wavelet_transform(X, n_freqs, fsample, fmin, fmax,
"scales": scales,
"dtime": dtime,
"omega0": omega0,
"return_complex": return_complex,
"gpu": gpu}
for feature in X.T]

Expand All @@ -299,7 +303,7 @@ def wavelet_transform(X, n_freqs, fsample, fmin, fmax,
# for idx, conv in enumerate(convolved):
# X_new[:, (n_freqs * idx):(n_freqs * (idx + 1))] = conv.T

power = X_new.sum(axis=1, keepdims=True)
power = np.abs(X_new).sum(axis=1, keepdims=True)

if prob:
X_new /= power
Expand Down