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[BUG] - Technical Audit: Time Frequency edits #199

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merged 10 commits into from
Jul 6, 2020
Merged

[BUG] - Technical Audit: Time Frequency edits #199

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0da8c6a
Transpose wavelet transform output. Time is now plotted on the x-axis…
elybrand Jun 25, 2020
7296b0b
use np.unwrap to wrap negative phases in [0, 2pi]
elybrand Jun 25, 2020
61f4b26
Average wavelet transform array entries within rows, since each row c…
elybrand Jun 25, 2020
cabc436
Add norm kwarg for compute_wavelet_transform
elybrand Jun 26, 2020
51a631a
Merge remote-tracking branch 'upstream/master' into issue193_tf
elybrand Jun 29, 2020
cee912f
Update documentation to reflect return of analytic signal, not hilber…
elybrand Jun 29, 2020
2443269
Add docstring for norm kwarg.
elybrand Jul 2, 2020
b48fc10
Take modulus squared of morelet wavelet transform, then average.
elybrand Jul 2, 2020
2201fd8
Update docstring of compute_wavelet_transform to reflect that you can…
elybrand Jul 3, 2020
1233711
updates & makes i_f handle nans
TomDonoghue Jul 6, 2020
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2 changes: 1 addition & 1 deletion neurodsp/spectral/power.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,7 @@ def compute_spectrum_wavelet(sig, fs, freqs, avg_type='mean', **kwargs):
freqs = create_freqs(*freqs)

mwt = compute_wavelet_transform(sig, fs, freqs, **kwargs)
spectrum = get_avg_func(avg_type)(mwt, axis=0)
spectrum = get_avg_func(avg_type)(mwt, axis=1)

return freqs, spectrum

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8 changes: 3 additions & 5 deletions neurodsp/timefrequency/hilbert.py
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Expand Up @@ -26,7 +26,7 @@ def robust_hilbert(sig, increase_n=False):
Returns
-------
sig_hilb : 1d array
The Hilbert transform of the input signal.
The analytic signal, of which the imaginary part is the Hilbert transform of the input signal.

Examples
--------
Expand Down Expand Up @@ -193,12 +193,10 @@ def freq_by_time(sig, fs, f_range=None, hilbert_increase_n=False,
... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> instant_freq = freq_by_time(sig, fs=500, f_range=(8, 12))
"""

pha = phase_by_time(sig, fs, f_range, hilbert_increase_n,
remove_edges, **filter_kwargs)
pha = np.unwrap(phase_by_time(sig, fs, f_range, hilbert_increase_n,
remove_edges, **filter_kwargs))

phadiff = np.diff(pha)
phadiff[phadiff < 0] = phadiff[phadiff < 0] + 2 * np.pi

i_f = fs * phadiff / (2 * np.pi)
i_f = np.insert(i_f, 0, np.nan)
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6 changes: 3 additions & 3 deletions neurodsp/timefrequency/wavelets.py
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Expand Up @@ -11,7 +11,7 @@
###################################################################################################

@multidim()
def compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5):
def compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5, norm='sss'):
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"""Compute the time-frequency representation of a signal using morlet wavelets.

Parameters
Expand Down Expand Up @@ -48,9 +48,9 @@ def compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5):
freqs = create_freqs(*freqs)
n_cycles = check_n_cycles(n_cycles, len(freqs))

mwt = np.zeros([len(sig), len(freqs)], dtype=complex)
mwt = np.zeros([len(freqs), len(sig)], dtype=complex)
for ind, (freq, n_cycle) in enumerate(zip(freqs, n_cycles)):
mwt[:, ind] = convolve_wavelet(sig, fs, freq, n_cycle, scaling)
mwt[ind, :] = convolve_wavelet(sig, fs, freq, n_cycle, scaling, norm=norm)

return mwt

Expand Down