def test_classifier(self):
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_eegs = m2b.filter()
wf = MorletWaveletFilter(time_series=bp_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
frequency_dim_pos=0,
verbose=True
)
pow_wavelet, phase_wavelet = wf.filter()
pow_wavelet = pow_wavelet.remove_buffer(duration=1.0)
print pow_wavelet
np.log10(pow_wavelet.data, out=pow_wavelet.data)
print pow_wavelet
mean_powers = pow_wavelet.mean(dim='time')
print mean_powers
# sess_pow_mat_post[ev,i,:] = np.nanmean(pow_ev_stripped, axis=1)
print
def get_bp_data(evs):
eeg_reader = EEGReader(events=evs, channels=monopolar_channels,
start_time=start_time, end_time=end_time, buffer_time=buffer_time)
base_eegs = eeg_reader.read()
m2b = MonopolarToBipolarMapper(time_series=base_eegs, bipolar_pairs=bipolar_pairs)
bp_eegs = m2b.filter()
return bp_eegs
def test_monopolar_to_bipolar_filter(self):
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_base_eegs = m2b.filter()
bipolar_pairs = bp_base_eegs['bipolar_pairs'].data
for i, bp in enumerate(bipolar_pairs):
e0 = self.base_eegs.sel(channels=bp[0])
e1 = self.base_eegs.sel(channels=bp[1])
# res = e0.__sub__(e1)
assert_array_equal(e0 - e1, bp_base_eegs[i])
def test_monopolar_to_bipolar_filter(self):
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_base_eegs = m2b.filter()
bipolar_pairs = bp_base_eegs['bipolar_pairs'].data
for i, bp in enumerate(bipolar_pairs):
e0 = self.base_eegs.sel(channels=bp[0])
e1 = self.base_eegs.sel(channels=bp[1])
# res = e0.__sub__(e1)
assert_array_equal(e0 - e1, bp_base_eegs[i])
def compute_continuous_wavelets(dataroot):
session_reader = EEGReader(session_dataroot=dataroot, channels=monopolar_channels)
session_eegs = session_reader.read()
m2b = MonopolarToBipolarMapper(time_series=session_eegs, bipolar_pairs=bipolar_pairs)
session_bp_eegs = m2b.filter()
wf = MorletWaveletFilter(time_series=session_bp_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
frequency_dim_pos=0,
verbose=True
)
pow_wavelet_session, phase_wavelet_session = wf.filter()
return pow_wavelet_session
def test_monopolar_to_bipolar_filter_and_data_chopper(self):
dataroot = self.base_events[0].eegfile
session_reader = EEGReader(session_dataroot=dataroot, channels=self.monopolar_channels)
session_eegs = session_reader.read()
m2b = MonopolarToBipolarMapper(time_series=session_eegs, bipolar_pairs=self.bipolar_pairs)
bp_session_eegs = m2b.filter()
sedc = DataChopper(events=self.base_events, session_data=bp_session_eegs, start_time=self.start_time,
end_time=self.end_time, buffer_time=self.buffer_time)
bp_session_eegs_chopped = sedc.filter()
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_base_eegs = m2b.filter()
assert_array_equal(bp_session_eegs_chopped, bp_base_eegs)
def test_classifier(self):
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_eegs = m2b.filter()
wf = MorletWaveletFilter(time_series=bp_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
frequency_dim_pos=0,
verbose=True
)
pow_wavelet, phase_wavelet = wf.filter()
pow_wavelet = pow_wavelet.remove_buffer(duration=1.0)
print(pow_wavelet)
np.log10(pow_wavelet.data, out=pow_wavelet.data)
print(pow_wavelet)
mean_powers = pow_wavelet.mean(dim='time')
print(mean_powers)
def test_classifier(self):
m2b = MonopolarToBipolarMapper(timeseries=self.base_eegs,
bipolar_pairs=self.bipolar_pairs)
bp_eegs = m2b.filter()
wf = MorletWaveletFilter(timeseries=bp_eegs,
freqs=np.logspace(np.log10(3), np.log10(180),
8),
output='power',
verbose=True)
pow_wavelet = wf.filter()['power']
pow_wavelet = pow_wavelet.remove_buffer(duration=1.0)
print(pow_wavelet)
np.log10(pow_wavelet.data, out=pow_wavelet.data)
print(pow_wavelet)
mean_powers = pow_wavelet.mean(dim='time')
print(mean_powers)
def get_stim_eeg(evs_on,
tal_struct,
bipolar_pairs,
monopolar_channels,
chans=np.array([]),
start_time=-1.0,
end_time=1.5):
from ptsa.data.readers import EEGReader
from ptsa.data.filters import MonopolarToBipolarMapper
#Get EEG clips
eeg_reader = EEGReader(events=evs_on,
channels=chans,
start_time=start_time,
end_time=end_time,
buffer_time=0.0)
eegs = eeg_reader.read()
eegs = eegs.baseline_corrected((-1.0, 2.5))
if tal_struct is np.nan:
return eegs, np.nan
if ('bipolar_pairs'
in list(eegs.coords)): #Gotta do this for bipolar ENS subjects
bps_eeg = [list(i) for i in np.array(eegs['bipolar_pairs'])]
bps_tal = [list(i) for i in np.array(tal_struct['channel'])]
bps_eeg_good = []
bps_tal_good = []
for iidx, i in enumerate(bps_eeg):
for jidx, j in enumerate(bps_tal):
if i == j:
bps_eeg_good.append(iidx)
bps_tal_good.append(jidx)
bps_eeg_good = np.array(bps_eeg_good)
bps_tal_good = np.array(bps_tal_good)
eegs_raw = eegs[bps_eeg_good]
tal_struct = tal_struct[bps_tal_good]
else:
try:
m2b = MonopolarToBipolarMapper(time_series=eegs,
bipolar_pairs=bipolar_pairs)
eegs_raw = m2b.filter()
except KeyError: #we've got to do this subject with specified channels
eeg_reader = EEGReader(events=evs_on,
channels=monopolar_channels,
start_time=start_time,
end_time=end_time,
buffer_time=0.0)
eegs = eeg_reader.read()
eegs = eegs.baseline_corrected((-1.0, 2.5))
#Bipolar rereference
m2b = MonopolarToBipolarMapper(time_series=eegs,
bipolar_pairs=bipolar_pairs)
eegs_raw = m2b.filter()
return eegs_raw, tal_struct
def test_monopolar_to_bipolar_filter_and_data_chopper(self):
dataroot = self.base_events[0].eegfile
session_reader = EEGReader(session_dataroot=dataroot, channels=self.monopolar_channels)
session_eegs = session_reader.read()
m2b = MonopolarToBipolarMapper(time_series=session_eegs, bipolar_pairs=self.bipolar_pairs)
bp_session_eegs = m2b.filter()
sedc = DataChopper(events=self.base_events, session_data=bp_session_eegs, start_time=self.start_time,
end_time=self.end_time, buffer_time=self.buffer_time)
bp_session_eegs_chopped = sedc.filter()
m2b = MonopolarToBipolarMapper(time_series=self.base_eegs, bipolar_pairs=self.bipolar_pairs)
bp_base_eegs = m2b.filter()
assert_array_equal(bp_session_eegs_chopped, bp_base_eegs)
def test_monopolar_to_bipolar_filter_norhino():
data = np.random.random((20, 10, 5))
rate = 1000
dims = ('time', 'channels', 'events')
coords = {
'time': np.linspace(0, 1, 20),
'channels': range(10),
'events': ['A', 'B', 'C', 'D', 'E']
}
ts = timeseries.TimeSeries.create(data,
rate,
coords=coords,
dims=dims,
name="test",
attrs={'test_attr': 1})
bipolar_pairs1 = np.array([range(9), range(1, 10)])
m2b1 = MonopolarToBipolarMapper(timeseries=ts,
bipolar_pairs=bipolar_pairs1)
ts_m2b1 = m2b1.filter()
bipolar_pairs2 = np.array([(i, j) for i, j in zip(range(9), range(1, 10))],
dtype=[('ch0', '<i8'), ('ch1', '<i8')])
m2b2 = MonopolarToBipolarMapper(timeseries=ts,
bipolar_pairs=bipolar_pairs2)
ts_m2b2 = m2b2.filter()
assert np.all(ts_m2b1 == ts_m2b2)
# checking each coord is probably redundant (mismatching coords
# should cause failure in the above assertion), but won't hurt
for coord in ts_m2b1.coords:
assert np.all(ts_m2b1[coord] == ts_m2b2[coord])
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b1[coord])
# sanity check that we haven't lost any coords:
for coord in ts.coords:
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b1[coord])
for attr in ts.attrs:
assert np.all(ts_m2b1.attrs[attr] == ts_m2b2.attrs[attr])
assert np.all(ts.attrs[attr] == ts_m2b1.attrs[attr])
assert ts.name == ts_m2b1.name
assert ts.name == ts_m2b2.name
assert np.all(ts_m2b1['channels'] == bipolar_pairs2)
assert np.all(ts_m2b1 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
assert np.all(ts_m2b2 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
dims2 = ('time', 'electrodes', 'events')
coords2 = {
'time': np.linspace(0, 1, 20),
'electrodes': range(10),
'events': ['A', 'B', 'C', 'D', 'E']
}
ts2 = timeseries.TimeSeries.create(data,
rate,
coords=coords2,
dims=dims2,
name="test",
attrs={'test_attr': 1})
m2b3 = MonopolarToBipolarMapper(timeseries=ts2,
channels_dim='electrodes',
bipolar_pairs=bipolar_pairs1)
ts_m2b3 = m2b3.filter()
assert np.all(ts_m2b3.values == ts_m2b1.values)
# checking each coord is probably redundant (mismatching coords
# should cause failure in the above assertion), but won't hurt
for coord in ts_m2b3.coords:
if coord != 'electrodes':
assert np.all(ts[coord] == ts_m2b3[coord])
assert np.all(ts_m2b3['electrodes'].values == ts_m2b1['channels'].values)
# sanity check that we haven't lost any coords:
for coord in ts.coords:
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b3[coord])
for attr in ts.attrs:
assert np.all(ts_m2b3.attrs[attr] == ts_m2b1.attrs[attr])
assert np.all(ts.attrs[attr] == ts_m2b3.attrs[attr])
assert ts.name == ts_m2b3.name
assert np.all(ts_m2b3['electrodes'] == bipolar_pairs2)
assert np.all(ts_m2b3 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
m2b4 = MonopolarToBipolarMapper(timeseries=ts2,
channels_dim='electrodes',
bipolar_pairs=bipolar_pairs2)
ts_m2b4 = m2b4.filter()
assert np.all(ts_m2b4 == ts_m2b3)
# checking each coord is probably redundant (mismatching coords
# should cause failure in the above assertion), but won't hurt
for coord in ts_m2b4.coords:
assert np.all(ts_m2b3[coord] == ts_m2b4[coord])
# sanity check that we haven't lost any coords:
for coord in ts.coords:
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b4[coord])
for attr in ts.attrs:
assert np.all(ts_m2b4.attrs[attr] == ts_m2b1.attrs[attr])
assert np.all(ts.attrs[attr] == ts_m2b4.attrs[attr])
assert ts.name == ts_m2b4.name
assert np.all(ts_m2b4['electrodes'] == bipolar_pairs2)
assert np.all(ts_m2b4 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
bipolar_pairs3 = np.array([(i, j) for i, j in zip(range(9), range(1, 10))],
dtype=[('channel0', '<i8'), ('channel1', '<i8')])
m2b5 = MonopolarToBipolarMapper(timeseries=ts2,
channels_dim='electrodes',
bipolar_pairs=bipolar_pairs1,
chan_names=['channel0', 'channel1'])
ts_m2b5 = m2b5.filter()
assert np.all(ts_m2b5.values == ts_m2b1.values)
# checking each coord is probably redundant (mismatching coords
# should cause failure in the above assertion), but won't hurt
for coord in ts_m2b5.coords:
if coord != 'electrodes':
assert np.all(ts[coord] == ts_m2b5[coord])
for a, b in zip(ts_m2b5['electrodes'], ts_m2b1['channels']):
assert np.all(
np.array(a.values.tolist()) == np.array(b.values.tolist()))
# sanity check that we haven't lost any coords:
for coord in ts.coords:
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b5[coord])
for attr in ts.attrs:
assert np.all(ts_m2b5.attrs[attr] == ts_m2b1.attrs[attr])
assert np.all(ts.attrs[attr] == ts_m2b5.attrs[attr])
assert ts.name == ts_m2b5.name
assert np.all(ts_m2b5['electrodes'] == bipolar_pairs3)
assert np.all(ts_m2b5 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
m2b6 = MonopolarToBipolarMapper(timeseries=ts2,
channels_dim='electrodes',
chan_names=['channel0', 'channel1'],
bipolar_pairs=bipolar_pairs3)
ts_m2b6 = m2b6.filter()
assert np.all(ts_m2b6 == ts_m2b5)
# checking each coord is probably redundant (mismatching coords
# should cause failure in the above assertion), but won't hurt
for coord in ts_m2b6.coords:
assert np.all(ts_m2b6[coord] == ts_m2b5[coord])
# sanity check that we haven't lost any coords:
for coord in ts.coords:
if coord != 'channels':
assert np.all(ts[coord] == ts_m2b6[coord])
for attr in ts.attrs:
assert np.all(ts_m2b6.attrs[attr] == ts_m2b1.attrs[attr])
assert np.all(ts.attrs[attr] == ts_m2b6.attrs[attr])
assert ts.name == ts_m2b6.name
assert np.all(ts_m2b6['electrodes'] == bipolar_pairs3)
assert np.all(ts_m2b6 == (ts.sel(channels=range(9)).values -
ts.sel(channels=range(1, 10)).values))
