def session1(bread, data_path, chan, fs, nperseg, noverlap):
"""
storing session 1 into bread (the 3D array) for a monkey.
Parameters
----------
bread : array-like 3d
data into a 3d array.
data_path : String
set the path.
chan : integer
grab the particular channel.
fs : array-like 1d
frequency.
nperseg : integer
desired window per segment.
noverlap : integer
desired overlapping of window.
Returns
-------
bread : array-like 3d
return the session 1 stored into bread data- frequency x channel x condition.
"""
# session 1
for i in range(1, chan):
#grabbing session 1 channel i
data = getECoGdata(data_path, 1, i)
#grabbing TimeFile from session 1 to set the indices for each condition
tf1 = getTimeFile(data_path, 1)
# AwakeEyesOpened
s1start1 = int(getStart(tf1, 0) * fs)
s1end1 = int(getEnd(tf1, 1) * fs)
bread[0][i - 1][:] = spectral.scv(data[s1start1:s1end1],
fs,
nperseg=int(fs),
noverlap=noverlap,
outlierpct=5.)[1]
#print(fs, s1start1, s1end1)
#print(len(f_), f_)
#print(len(scv_), scv_)
#print(len(spectral.scv(data[s1start1:s1end1],fs,nperseg=int(fs),noverlap=noverlap)[0]))
#print(len(spectral.scv(data[s1start1:s1end1],fs,nperseg=int(fs),noverlap=noverlap)[1]))
#print(bread.shape)
#AwakeEyesClosed
s1start2 = int(getStart(tf1, 2) * fs)
s1end2 = int(getEnd(tf1, 3) * fs)
#s1SP2 = getSP(data, s1start2, s1end2, fs, nperseg, noverlap)
bread[1][i - 1][:] = spectral.scv(data[s1start2:s1end2],
fs,
nperseg=int(fs),
noverlap=noverlap,
outlierpct=5.)[1]
return bread
def session2(bread, data_path, chan, fs, nperseg, noverlap):
"""
storing session 2 into bread (the 3D array) for a monkey.
Parameters
----------
bread : array-like 3d
data into a 3d array.
data_path : String
set the path.
chan : integer
grab the particular channel.
fs : array-like 1d
frequency.
nperseg : integer
desired window per segment.
noverlap : integer
desired overlapping of window.
Returns
-------
bread : array-like 3d
return the session 2 stored into bread data- frequency x channel x condition.
"""
# session 2
for i in range(1, chan):
if ('0730PF' in data_path and i == 63):
bread[2][i - 1][:] = "Nan"
bread[3][i - 1][:] = "Nan"
else:
#grabbing session 2 channel i
data = getECoGdata(data_path, 2, i)
#grabbing TimeFile from session 2 to set the indices for each condition
tf2 = getTimeFile(data_path, 2)
#Anesthetized-Start
s2start1 = int(getStart(tf2, 1) * fs)
s2end1 = int(getEnd(tf2, 2) * fs)
bread[2][i - 1][:] = spectral.scv(data[s2start1:s2end1],
fs,
nperseg=int(fs),
noverlap=noverlap,
outlierpct=5.)[1]
#RecoveryEyesClosed
s2start2 = int(getStart(tf2, 3) * fs)
s2end2 = int(getEnd(tf2, 4) * fs)
#s2SP2 = getSP(data, s2start2, s2end2, fs, nperseg, noverlap)
bread[3][i - 1][:] = spectral.scv(data[s2start2:s2end2],
fs,
nperseg=int(fs),
noverlap=noverlap,
outlierpct=5.)[1]
return bread
def test_scv():
"""
Confirm SCV calculation
"""
# Load data
data_idx = 1
x = _load_example_data(data_idx=data_idx)
fs = 1000.
# load ground truth scv
gt_scv = np.load(os.path.dirname(neurodsp.__file__) +
'/tests/data/sample_data_' + str(data_idx) + '_scv.npz')
# compute SCV
freq, SCV = spectral.scv(x, fs)
assert np.allclose(np.sum(np.abs(gt_scv['freq'] - freq)), 0, atol=10 ** -5)
assert np.allclose(np.sum(np.abs(gt_scv['SCV'] - SCV)), 0, atol=10 ** -5)
def session3(bread, data_path, chan, fs, nperseg, noverlap):
"""
storing session 3 into bread (the 3D array) for a monkey.
Parameters
----------
bread : array-like 3d
data into a 3d array.
data_path : String
set the path.
chan : integer
grab the particular channel.
fs : array-like 1d
frequency.
nperseg : integer
desired window per segment.
noverlap : integer
desired overlapping of window.
Returns
-------
bread : array-like 3d
return the session 3 stored into bread data- frequency x channel x condition.
"""
# session 3
for i in range(1, chan):
#grabbing session 3 channel i
data = getECoGdata(data_path, 3, i)
#grabbing TimeFile from session 3 to set the indices for each condition
tf3 = getTimeFile(data_path, 3)
# RecoveryEyesOpened
s3start = int(getStart(tf3, 0) * fs)
s3end = int(getEnd(tf3, 1) * fs)
#s3SP = getSP(data, s3start, s3end, fs, nperseg, noverlap)
bread[4][i - 1][:] = spectral.scv(data[s3start:s3end],
fs,
nperseg=int(fs),
noverlap=noverlap,
outlierpct=5.)[1]
return bread