def write_to_HDF5(data, file_name, condition, sampling_rate, \
window='blackmanharris', taps=513, filter_type='FIR',\
group_name='', species='', location='', number_in_group='', name='', date='',\
amplitude=False, displacement_aucs=False, amplitude_aucs=False,\
overwrite=False,\
bands = ('raw', 'delta', 'theta', 'alpha', 'beta', 'gamma', 'high-gamma', 'broad'),
downsample='nyquist'):
import h5py
from neuroscience import neuro_band_filter
from avalanches import area_under_the_curve, fast_amplitude
from time import gmtime, strftime, clock
if downsample==False:
downsample=sampling_rate
version = 'filter_'+filter_type+'_'+str(taps)+'_'+window+'_ds-'+str(downsample)
f = h5py.File(file_name+'.hdf5')
try:
versions = list(f[condition])
if version not in versions:
f.create_group(condition+'/'+version)
except KeyError:
f.create_group(condition+'/'+version)
pass
for band in bands:
print 'Processing '+band
if band=='raw':
if 'raw' not in list(f[condition]):
f.create_group(condition+'/raw')
tic = clock()
if 'displacement' not in list(f[condition+'/raw']):
f.create_dataset(condition+'/raw/displacement', data=data)
if amplitude and 'amplitude' not in list(f[condition+'/raw']):
data_amplitude = fast_amplitude(data)
f.create_dataset(condition+'/raw/amplitude', data=data_amplitude)
if displacement_aucs and 'displacement_aucs' not in list(f[condition+'/raw']):
data_displacement_aucs = area_under_the_curve(data)
f.create_dataset(condition+'/raw/displacement_aucs', data=data_displacement_aucs)
if amplitude_aucs and 'amplitude_aucs' not in list(f[condition+'/raw']):
data_amplitude_aucs = area_under_the_curve(data_amplitude)
f.create_dataset(condition+'/raw/amplitude_aucs', data=data_amplitude_aucs)
toc = clock()
print toc-tic
continue
if band not in list(f[condition+'/'+version]):
f.create_group(condition+'/'+version+'/'+band)
if 'displacement' not in list(f[condition+'/'+version+'/'+band]):
print 'Filtering, '+str(data.shape[-1])+' time points'
filtered_data, frequency_range, downsampled_rate = neuro_band_filter(data, band, sampling_rate=sampling_rate, taps=taps, window_type=window, downsample=downsample)
f.create_dataset(condition+'/'+version+'/'+band+'/displacement', data=filtered_data)
elif overwrite:
print 'Filtering, '+str(data.shape[-1])+' time points'
filtered_data, frequency_range, downsampled_rate = neuro_band_filter(data, band, sampling_rate=sampling_rate, taps=taps, window_type=window, downsample=downsample)
f[condition+'/'+version+'/'+band+'/displacement']=filtered_data
elif amplitude_aucs or amplitude or displacement_aucs:
filtered_data = f[condition+'/'+version+'/'+band+'/displacement'][:,:]
else:
continue
if amplitude and 'amplitude' not in list(f[condition+'/'+version+'/'+band]):
print 'Fast amplitude, '+str(filtered_data.shape[-1])+' time points'
tic = clock()
data_amplitude = fast_amplitude(filtered_data)
f.create_dataset(condition+'/'+version+'/'+band+'/amplitude', data=data_amplitude)
toc = clock()
print toc-tic
elif amplitude:
data_amplitude = f[condition+'/'+version+'/'+band+'/amplitude'][:,:]
if displacement_aucs and 'displacement_aucs' not in list(f[condition+'/'+version+'/'+band]):
print 'Area under the curve, displacement'
tic = clock()
data_displacement_aucs = area_under_the_curve(filtered_data)
f.create_dataset(condition+'/'+version+'/'+band+'/displacement_aucs', data=data_displacement_aucs)
toc = clock()
print toc-tic
if amplitude_aucs and 'amplitude_aucs' not in list(f[condition+'/'+version+'/'+band]):
print 'Area under the curve, amplitude'
tic = clock()
data_amplitude_aucs = area_under_the_curve(data_amplitude)
f.create_dataset(condition+'/'+version+'/'+band+'/amplitude_aucs', data=data_amplitude_aucs)
toc = clock()
print toc-tic
f[condition+'/'+version+'/'+band].attrs['frequency_range'] = frequency_range
f[condition+'/'+version+'/'+band].attrs['downsampled_rate'] = downsampled_rate
f[condition+'/'+version+'/'+band].attrs['processing_date'] = strftime("%Y-%m-%d", gmtime())
f[condition+'/'+version].attrs['filter_type'] = filter_type
f[condition+'/'+version].attrs['window'] = window
f[condition+'/'+version].attrs['taps'] = taps
f.attrs['group_name']=group_name
f.attrs['number_in_group']=number_in_group
f.attrs['species'] = species
f.attrs['location']=location
f.attrs['name']=name
f[condition].attrs['date']=date
f.close()
return
def HDF5_filter(file,\
window='hamming', taps=25, filter_type='FIR',\
amplitude=False, displacement_aucs=False, amplitude_aucs=False,\
overwrite=False,\
bands = ('raw', 'delta', 'theta', 'alpha', 'beta', 'gamma', 'high-gamma', 'broad'),
downsample='nyquist'):
from neuroscience import neuro_band_filter
from avalanches import area_under_the_curve, fast_amplitude
from time import gmtime, strftime, clock
import h5py
if type(file)!=h5py._hl.group.Group:
return
for i in file.keys():
if i.startswith('filter'):
continue
elif not i.startswith('raw'):
HDF5_filter(file[i])
else:
if 'displacement' not in file[i].keys():
return
else:
#At this point we know there is a 'raw' directory with a 'displacement' in it,
# so we can filter!
if downsample==False:
downsample=sampling_rate
version = 'filter_'+filter_type+'_'+str(taps)+'_'+window+'_ds-'+str(downsample)
if version not in file.keys():
file.create_group(version)
file[version].attrs['filter_type'] = filter_type
file[version].attrs['window'] = window
file[version].attrs['taps'] = taps
data = file['raw/displacement'][:,:]
for band in bands:
print 'Processing '+band
if band=='raw':
if amplitude and 'amplitude' not in file['raw'].keys():
data_amplitude = fast_amplitude(data)
file.create_dataset('/raw/amplitude', data=data_amplitude)
if displacement_aucs and 'displacement_aucs' not in file['raw'].keys():
data_displacement_aucs = area_under_the_curve(data)
file.create_dataset('/raw/displacement_aucs', data=data_displacement_aucs)
if amplitude_aucs and 'amplitude_aucs' not in file['raw'].keys():
data_amplitude_aucs = area_under_the_curve(data_amplitude)
file.create_dataset('/raw/amplitude_aucs', data=data_amplitude_aucs)
continue
if band not in file[version].keys():
file.create_group(version+'/'+band)
if 'displacement' not in file[version+'/'+band].keys():
print 'Filtering, '+str(data.shape[-1])+' time points'
filtered_data, frequency_range, downsampled_rate = neuro_band_filter(data, band, sampling_rate=sampling_rate, taps=taps, window_type=window, downsample=downsample)
file.create_dataset(version+'/'+band+'/displacement', data=filtered_data)
elif overwrite:
print 'Filtering, '+str(data.shape[-1])+' time points'
filtered_data, frequency_range, downsampled_rate = neuro_band_filter(data, band, sampling_rate=sampling_rate, taps=taps, window_type=window, downsample=downsample)
file.create_dataset(version+'/'+band+'/displacement', data=filtered_data)
elif amplitude_aucs or amplitude or displacement_aucs:
filtered_data = file[version+'/'+band+'/displacement'][:,:]
else:
continue
if amplitude and 'amplitude' not in file[version+'/'+band].keys():
print 'Fast amplitude, '+str(filtered_data.shape[-1])+' time points'
tic = clock()
data_amplitude = fast_amplitude(filtered_data)
file.create_dataset(version+'/'+band+'/amplitude', data=data_amplitude)
toc = clock()
print toc-tic
elif amplitude_aucs:
data_amplitude = file[version+'/'+band+'/amplitude'][:,:]
if displacement_aucs and 'displacement_aucs' not in file[version+'/'+band].keys():
print 'Area under the curve, displacement'
tic = clock()
data_displacement_aucs = area_under_the_curve(filtered_data)
file.create_dataset(version+'/'+band+'/displacement_aucs', data=data_displacement_aucs)
toc = clock()
print toc-tic
if amplitude_aucs and 'amplitude_aucs' not in file[version+'/'+band].keys():
print 'Area under the curve, amplitude'
tic = clock()
data_amplitude_aucs = area_under_the_curve(data_amplitude)
file.create_dataset(version+'/'+band+'/amplitude_aucs', data=data_amplitude_aucs)
toc = clock()
print toc-tic
file[version+'/'+band].attrs['frequency_range'] = frequency_range
file[version+'/'+band].attrs['downsampled_rate'] = downsampled_rate
file[version+'/'+band].attrs['processing_date'] = strftime("%Y-%m-%d", gmtime())
return