def compute_coherence(time_windows, TR, f_lb, f_ub, roi_names):
n_timewindows = time_windows.shape[0]
n_samples = time_windows.shape[1]
n_rois = time_windows.shape[2]
coherence_3Darray = np.zeros((n_timewindows, n_rois, n_rois))
if n_rois == len(roi_names):
for time_index in range(n_timewindows):
ts = time_windows[time_index, :, :]
data = np.zeros((n_rois, n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = ts[:, n_idx]
data = percent_change(data)
T = TimeSeries(data, sampling_interval=TR)
C = CoherenceAnalyzer(T)
freq_idx = np.where(
(C.frequencies > f_lb) * (C.frequencies < f_ub))[0]
coh = np.mean(C.coherence[:, :, freq_idx], -1)
coherence_3Darray[time_index] = coh
else:
raise Exception(
"Number of ROIs in 3D Array do not match number of ROI names provided."
)
return coherence_3Darray
# Get roi correlations and coherence
for runNum in range(allROIS.shape[1]):
#need to load timeseries by run
fixTS=ts.TimeSeries(allROIS[:,runNum,:], sampling_interval=TR)
fixTS.metadata['roi'] = roi_names
# Get plot and correlations
C=CorrelationAnalyzer(fixTS)
fig01 = drawmatrix_channels(C.corrcoef, roi_names, size=[10., 10.], color_anchor=0, title='Correlation Results Run %i' % runNum)
plt.show()
# Save correlation
corr_all[subject][runNum]=C.corrcoef
# Get coherence
Coh = CoherenceAnalyzer(fixTS)
Coh.method['NFFT'] = NFFT
Coh.method['n_overlap']=n_overlap
# Get the index for the frequencies inside the ub and lb
freq_idx = np.where((Coh.frequencies > f_lb) * (Coh.frequencies < f_ub))[0]
# Extract coherence
# Coher[0]= correlations for first ROI in list with others
coher = np.mean(Coh.coherence[:, :, freq_idx], -1) # Averaging on the last dimension
fig03 = drawmatrix_channels(coher, roi_names, size=[10., 10.], color_anchor=0, title='Coherence Results Run %i' % runNum)
# Save coherence (coher is the average of the coherence over the specified frequency)
coh_all[subject][runNum]=coher
file=open(saveFile, 'w') # write mode
color_anchor=0,
title='MTCoherenceAnalyzer')
"""
.. image:: fig/multi_taper_coh_02.png
For comparison, we also perform the analysis using the standard
CoherenceAnalyzer object, which does the analysis using Welch's windowed
periodogram, instead of the multi-taper spectral estimation method (see
:ref:`resting_state` for a more thorough analysis of this data using this
method):
"""
C3 = CoherenceAnalyzer(T)
freq_idx = np.where((C3.frequencies > f_lb) * (C3.frequencies < f_ub))[0]
#Extract the coherence and average across these frequency bands:
coh = np.mean(C3.coherence[:, :, freq_idx],
-1) # Averaging on the last dimension
fig03 = drawmatrix_channels(coh,
roi_names,
size=[10., 10.],
color_anchor=0,
title='CoherenceAnalyzer')
"""
.. image:: fig/multi_taper_coh_03.png
fixTS.metadata['roi'] = roi_names
# Get plot and correlations
C = CorrelationAnalyzer(fixTS)
fig01 = drawmatrix_channels(C.corrcoef,
roi_names,
size=[10., 10.],
color_anchor=0,
title='Correlation Results Run %i' %
runNum)
plt.show()
# Save correlation
corr_all[subject][runNum] = C.corrcoef
# Get coherence
Coh = CoherenceAnalyzer(fixTS)
Coh.method['NFFT'] = NFFT
Coh.method['n_overlap'] = n_overlap
# Get the index for the frequencies inside the ub and lb
freq_idx = np.where(
(Coh.frequencies > f_lb) * (Coh.frequencies < f_ub))[0]
# Extract coherence
# Coher[0]= correlations for first ROI in list with others
coher = np.mean(Coh.coherence[:, :, freq_idx],
-1) # Averaging on the last dimension
fig03 = drawmatrix_channels(coher,
roi_names,
size=[10., 10.],