def create_matrix_from_file(coh_file, channels):
"""
Creates coherence matrix from data that's in a file.
Used typically as helper function for plotting
Parameters:
-----------
coh_file : str
File containing coherence data
channels : list (str)
channels to plot
Returns:
--------
coh_matrix : Spectrogram object
coherence matrix in form of spectrogram object
returns automatically in terms of coherence SNR:
coherence * N.
(not actually a spectrogram, though)
frequencies : numpy array
numpy array of frequencies associated with coherence matrix
labels : list (str)
labels for coherence matrix
N : int
Number of time segment used to create coherence spectra
"""
labels = []
counter = 0
if not os.path.exists(coh_file):
return None, None, None, None
f = h5py.File(coh_file, 'r')
# get number of averages
N = f['info'].value
channels = f['psd2s'].keys()
First = 1
s = 0
for channel in channels:
data = Spectrum.from_hdf5(f['coherences'][channel])
s_temp = data.size
if s_temp > s:
s = s_temp
for channel in channels:
if First:
# initialize matrix!
darm_psd = Spectrum.from_hdf5(f['psd1'][f['psd1'].keys()[0]])
First = 0
s = max(s, darm_psd.size)
coh_matrix = np.zeros((s, len(channels)))
data = Spectrum.from_hdf5(f['coherences'][channel])
labels.append(channel[3:-3].replace('_', '-'))
coh_matrix[:data.size, counter] = data
counter += 1
coh_matrix = Spectrogram(coh_matrix)
frequencies = (np.arange(s)+1) * (darm_psd.frequencies.value[1] - darm_psd.frequencies.value[0])
return coh_matrix, frequencies, labels, N
