Spectral decomposition and high gamma duplicate code and processing

[emilyps14]

1. processing_data.high_gamma_estimation recomputes the spectral decomposition bands, even if they have already been computed in processing_data.spectral_decomposition. It would be nice if the code would check to see if they have been run already, and use the precomputed bands if possible. If it is computing new bands, it would make sense to append these to the existing decomposition table (or make a new decomposition table?) to save the processing.

2. The spectral decomposition code is copy and pasted between processing_data.spectral_decomposition and processing_data.high_gamma_estimation:
   

   ecogVIS/ecogvis/signal_processing/processing_data.py

   Lines 377 to 397 in a5eda73

   	nBands = len(band_param_0)
   	nSamples = lfp.data.shape[0]
   	nChannels = lfp.data.shape[1]
   	Xp = np.zeros((nBands, nChannels, nSamples)) # power (nBands,nChannels,nSamples)
   	# Apply Hilbert transform ---------------------------------------------
   	print('Running Spectral Decomposition...')
   	start = time.time()
   	for ch in np.arange(nChannels):
   	Xch = lfp.data[:, ch] * 1e6 # 1e6 scaling helps with numerical accuracy
   	Xch = Xch.reshape(1, -1)
   	Xch = Xch.astype('float32') # signal (nChannels,nSamples)
   	X_fft_h = None
   	for ii, (bp0, bp1) in enumerate(zip(band_param_0, band_param_1)):
   	kernel = gaussian(Xch.shape[-1], rate, bp0, bp1)
   	X_analytic, X_fft_h = hilbert_transform(Xch, rate, kernel, phase=None, X_fft_h=X_fft_h)
   	Xp[ii, ch, :] = abs(X_analytic).astype('float32')
   	print('Spectral Decomposition finished in {} seconds'.format(time.time() - start))
   	# data: (ndarray) dims: num_times * num_channels * num_bands
   	Xp = np.swapaxes(Xp, 0, 2)

   
   

   ecogVIS/ecogvis/signal_processing/processing_data.py

   Lines 467 to 488 in a5eda73

   	nBands = len(band_param_0)
   	nSamples = lfp.data.shape[0]
   	nChannels = lfp.data.shape[1]
   	Xp = np.zeros((nBands, nChannels, nSamples)) # power (nBands,nChannels,nSamples)
   	# Apply Hilbert transform ---------------------------------------------
   	print('Running High Gamma estimation...')
   	start = time.time()
   	for ch in np.arange(nChannels):
   	Xch = lfp.data[:, ch] * 1e6 # 1e6 scaling helps with numerical accuracy
   	Xch = Xch.reshape(1, -1)
   	Xch = Xch.astype('float32') # signal (nChannels,nSamples)
   	X_fft_h = None
   	for ii, (bp0, bp1) in enumerate(zip(band_param_0, band_param_1)):
   	kernel = gaussian(Xch.shape[-1], rate, bp0, bp1)
   	X_analytic, X_fft_h = hilbert_transform(
   	Xch, rate, kernel, phase=None, X_fft_h=X_fft_h)
   	Xp[ii, ch, :] = abs(X_analytic).astype('float32')
   	print('High Gamma estimation finished in {} seconds'.format(time.time() - start))
   	# data: (ndarray) dims: num_times * num_channels * num_bands
   	Xp = np.swapaxes(Xp, 0, 2)

(I can look into writing a PR for this when I have some time)