def test_gaussian_kernel():
"""Test the gaussian kernel.
"""
ker = gaussian(1111, 200, 50, 10)
assert_allclose(np.linalg.norm(ker), 1)
assert_equal(ker >= 0., True)
def high_gamma_estimation(block_path, bands_vals, new_file=''):
"""
Takes preprocessed LFP data and calculates High-Gamma power from the
averaged power of standard Hilbert transform on 70~150 Hz bands.
Parameters
----------
block_path : str
subject file path
bands_vals : [2,nBands] numpy array with Gaussian filter parameters, where:
bands_vals[0,:] = filter centers [Hz]
bands_vals[1,:] = filter sigmas [Hz]
new_file : str
if this argument is of form 'path/to/new_file.nwb', High Gamma power
will be saved in a new file. If it is an empty string, '', High Gamma
power will be saved in the current NWB file.
Returns
-------
Saves High Gamma power (TimeSeries) in the current or new NWB file.
Only if container for this data do not exist in the file.
"""
# Get filter parameters
band_param_0 = bands_vals[0, :]
band_param_1 = bands_vals[1, :]
with NWBHDF5IO(block_path, 'r+', load_namespaces=True) as io:
nwb = io.read()
lfp = nwb.processing['ecephys'].data_interfaces[
'LFP'].electrical_series['preprocessed']
rate = lfp.rate
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)
HG = np.mean(Xp, 2) # average of high gamma bands
# Storage of High Gamma on NWB file -----------------------------
if new_file == '' or new_file is None: # on current file
# make electrodes table
nElecs = HG.shape[1]
ecephys_module = nwb.processing['ecephys']
# first check for a table among the file's data_interfaces
####
if lfp.electrodes.table.name in ecephys_module.data_interfaces:
LFP_dynamic_table = ecephys_module.data_interfaces[
lfp.electrodes.table.name]
else:
# othewise use the electrodes as the table
LFP_dynamic_table = nwb.electrodes
####
elecs_region = LFP_dynamic_table.create_region(
name='electrodes',
region=[i for i in range(nChannels)],
description='all electrodes')
hg = ElectricalSeries(name='high_gamma',
data=HG,
electrodes=elecs_region,
rate=rate,
description='')
ecephys_module.add_data_interface(hg)
io.write(nwb)
print('High Gamma power saved in ' + block_path)
else: # on new file
with NWBHDF5IO(new_file, 'r+', load_namespaces=True) as io_new:
nwb_new = io_new.read()
# make electrodes table
nElecs = HG.shape[1]
elecs_region = nwb_new.electrodes.create_region(
name='electrodes',
region=np.arange(nElecs).tolist(),
description='all electrodes')
hg = ElectricalSeries(name='high_gamma',
data=HG,
electrodes=elecs_region,
rate=rate,
description='')
try: # if ecephys module already exists
ecephys_module = nwb_new.processing['ecephys']
except: # creates ecephys ProcessingModule
ecephys_module = ProcessingModule(
name='ecephys',
description='Extracellular electrophysiology data.')
nwb_new.add_processing_module(ecephys_module)
ecephys_module.add_data_interface(hg)
io_new.write(nwb_new)
print('High Gamma power saved in ' + new_file)
def test_hilbert_return(self):
filters = [
gaussian(50, self.rate, 10, 2),
hamming(50, self.rate, 10, 15)
]
Xh = hilbert_transform(self.X, self.rate, filters)
Xh_expected = (np.array(
[
[
-0.21311015 - 0.0607549j, 0.19600701 - 0.01539358j,
-0.173067 + 0.05698152j, 0.16533213 - 0.11073435j,
-0.11528449 + 0.19558509j, -0.01099088 - 0.23956958j,
0.14327258 + 0.17653984j, -0.18458968 - 0.04880616j,
0.13415315 - 0.03647032j, -0.0869473 + 0.04084354j,
0.10334343 - 0.03818917j, -0.12906154 + 0.10043456j,
0.08385944 - 0.19477458j, 0.03056594 + 0.24082505j,
-0.15400577 - 0.2042584j, 0.22998364 + 0.10362084j,
-0.23235176 + 0.0114163j, 0.18106783 - 0.09160866j,
-0.11846912 + 0.12803714j, 0.0616374 - 0.14137947j,
-0.00263204 + 0.13730891j, -0.05106666 - 0.10123634j,
0.06800086 + 0.04061233j, -0.04404788 + 0.00171742j,
0.0226942 - 0.0115417j, -0.0187925 + 0.03051893j,
-0.01429313 - 0.07107013j, 0.09650135 + 0.07795065j,
-0.174921 - 0.01024875j, 0.18714607 - 0.10299498j,
-0.12381878 + 0.19594172j, 0.0300773 - 0.22737868j,
0.04435296 + 0.21050902j, -0.08900898 - 0.18207646j,
0.12224477 + 0.15960843j, -0.15896489 - 0.13540482j,
0.19442573 + 0.09691784j, -0.21726567 - 0.04694222j,
0.22820716 - 0.00885319j, -0.22209987 + 0.07472441j,
0.18310959 - 0.13794065j, -0.12027016 + 0.17057613j,
0.06293843 - 0.16911559j, -0.02503065 + 0.15016524j,
0.01000077 - 0.12699406j, -0.0166137 + 0.12012645j,
0.01741438 - 0.1460833j, 0.02050994 + 0.18394571j,
-0.09605629 - 0.19316342j, 0.17592968 + 0.14807635j
],
[
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0. + 0.j,
0. + 0.j, 0. + 0.j
]
]),
np.array([[
12.66153466 + 0.j, -8.52937982 + 2.82208427j,
14.7942873 + 7.52162635j, 11.10045364 + 3.52566249j,
-4.45306874 + 1.51630448j,
-15.9713513 + 6.17519069j,
-12.79444213 - 3.10589728j,
7.48901625 + 4.52892871j,
-26.31533498 - 14.97259445j,
-13.34218938 - 5.91763703j,
-6.37207362 - 7.78666016j, 9.73243576 + 0.81983001j,
-7.31044034 - 10.82940129j, -13.95317074 +
6.17574407j, -13.04640635 - 4.2499415j,
-8.50946101 + 23.85947587j,
3.63491644 + 8.31427148j, 10.54954233 + 5.55684696j,
-7.73593253 - 7.06994243j,
-3.63069263 - 4.01064809j,
-0.58244679 - 0.45476267j, 0.3190497 - 17.87031979j,
-16.3983279 + 10.94541315j, -15.21443532 +
3.27680235j, 0.55694424 - 9.41396278j, 0. + 0.j,
0. + 0.j, 0. - 0.j, 0. - 0.j, 0. + 0.j, -0. + 0.j,
-0. + 0.j, -0. + 0.j, 0. + 0.j, 0. + 0.j, 0. - 0.j,
-0. + 0.j, 0. - 0.j, -0. + 0.j, 0. + 0.j, -0. + 0.j,
-0. + 0.j, -0. + 0.j, 0. + 0.j, -0. + 0.j, 0. - 0.j,
0. - 0.j, 0. + 0.j, 0. + 0.j, 0. - 0.j
]]))
np.testing.assert_almost_equal(Xh[0], Xh_expected[0])
np.testing.assert_almost_equal(Xh[1], Xh_expected[1])
Xh = hilbert_transform(self.X, self.rate)
Xh_expected = (
np.array(
[
-2.06641947 - 0.01287173j, 0.51412168 - 1.48267793j,
1.09878248 - 0.2770345j, 1.51753214 + 1.04348296j,
-1.17911981 + 1.40558571j, 0.14574649 - 1.35839109j,
1.24439672 + 1.55083418j, -1.71511502 + 0.64355861j,
0.08780536 - 1.05022613j, 0.68712687 + 0.47911231j,
-0.49986941 + 1.32336045j, -2.4298324 - 0.32441863j,
0.02200754 - 2.43445411j, 0.53061521 - 0.23437869j,
0.25228739 - 1.73350628j, 2.02750313 + 0.27984872j,
-0.62058197 + 0.45810456j, 0.55911732 - 0.78389594j,
0.11008321 - 0.06264994j, 0.72111797 - 0.74545203j,
0.38719571 + 0.13653827j, 0.59216716 - 1.05822436j,
1.88259492 + 0.22982255j, 0.45529182 + 1.40341331j,
-0.87420952 + 0.45050791j, -0.46801216 - 1.17055015j,
1.41085796 - 1.23975681j, 1.54165835 + 0.78468877j,
0.01297562 + 0.11350984j, 1.75234722 + 0.23771566j,
-0.32490902 + 1.84850866j, -0.40531575 - 0.56893904j,
0.2552225 + 0.44197517j, -0.01341803 - 0.68464856j,
0.8537156 + 0.27623658j, 0.43782839 - 0.38555419j,
1.48900852 + 1.13057857j, -0.60367646 + 1.05556027j,
-0.04352692 + 0.77890928j, -1.61591018 - 0.01930377j,
0.62662236 - 1.11621127j, -0.04878076 + 0.60438919j,
-0.07048588 - 0.4217956j, -0.52055125 + 0.12783525j,
-0.3065798 - 1.47275369j, 0.87571683 - 1.19296222j,
2.02126317 - 0.75806081j, 1.38610296 + 1.35508274j,
0.56164995 + 0.43484911j, 0.40738592 + 1.99470883j
]),
np.array([[
12.66153466 + 0.j,
-8.52937982 + 2.82208427j, 14.7942873 + 7.52162635j,
11.10045364 + 3.52566249j, -4.45306874 + 1.51630448j,
-15.9713513 + 6.17519069j, -12.79444213 - 3.10589728j,
7.48901625 + 4.52892871j, -26.31533498 -
14.97259445j, -13.34218938 - 5.91763703j,
-6.37207362 - 7.78666016j, 9.73243576 + 0.81983001j,
-7.31044034 - 10.82940129j, -13.95317074 +
6.17574407j, -13.04640635 - 4.2499415j,
-8.50946101 + 23.85947587j, 3.63491644 + 8.31427148j,
10.54954233 + 5.55684696j, -7.73593253 - 7.06994243j,
-3.63069263 - 4.01064809j, -0.58244679 - 0.45476267j,
0.3190497 - 17.87031979j, -16.3983279 + 10.94541315j,
-15.21443532 + 3.27680235j, 0.55694424 - 9.41396278j, 0. + 0.j,
0. + 0.j, 0. - 0.j, 0. - 0.j, 0. + 0.j, -0. + 0.j, -0. + 0.j,
-0. + 0.j, 0. + 0.j, 0. + 0.j, 0. - 0.j, -0. + 0.j, 0. - 0.j,
-0. + 0.j, 0. + 0.j, -0. + 0.j, -0. + 0.j, -0. + 0.j, 0. + 0.j,
-0. + 0.j, 0. - 0.j, 0. - 0.j, 0. + 0.j, 0. + 0.j, 0. - 0.j
]]))
np.testing.assert_almost_equal(Xh[0], Xh_expected[0])
np.testing.assert_almost_equal(Xh[1], Xh_expected[1])
def spectral_decomposition(block_path, bands_vals):
"""
Takes preprocessed LFP data and does the standard Hilbert transform on
different bands. Takes about 20 minutes to run on 1 10-min block.
Parameters
----------
block_path : str
subject file path
bands_vals : [2,nBands] numpy array with Gaussian filter parameters, where:
bands_vals[0,:] = filter centers [Hz]
bands_vals[1,:] = filter sigmas [Hz]
Returns
-------
Saves spectral power (DecompositionSeries) in the current NWB file.
Only if container for this data do not exist in the file.
"""
# Get filter parameters
band_param_0 = bands_vals[0, :]
band_param_1 = bands_vals[1, :]
with NWBHDF5IO(block_path, 'r+', load_namespaces=True) as io:
nwb = io.read()
lfp = nwb.processing['ecephys'].data_interfaces[
'LFP'].electrical_series['preprocessed']
rate = lfp.rate
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)
# Spectral band power
# bands: (DynamicTable) frequency bands that signal was decomposed into
band_param_0V = VectorData(
name='filter_param_0',
description='frequencies for bandpass filters',
data=band_param_0)
band_param_1V = VectorData(
name='filter_param_1',
description='frequencies for bandpass filters',
data=band_param_1)
bandsTable = DynamicTable(
name='bands',
description='Series of filters used for Hilbert transform.',
columns=[band_param_0V, band_param_1V],
colnames=['filter_param_0', 'filter_param_1'])
decs = DecompositionSeries(
name='DecompositionSeries',
data=Xp,
description='Analytic amplitude estimated with Hilbert transform.',
metric='amplitude',
unit='V',
bands=bandsTable,
rate=rate,
source_timeseries=lfp)
# Storage of spectral decomposition on NWB file ------------------------
ecephys_module = nwb.processing['ecephys']
ecephys_module.add_data_interface(decs)
io.write(nwb)
print('Spectral decomposition saved in ' + block_path)