def ets(self):
"""The event-triggered standard error of the mean """
#Make a list for the output
h = [0] * self._len_h
if self._is_ts:
# Loop over channels
for i in xrange(self._len_h):
data = self.data[i]
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i]) != 0]
h[i] = np.empty((event_types.shape[0], self.len_et),
dtype=complex)
# This offset is used to pull the event indices below, but we
# have to broadcast it so the shape of the resulting idx+offset
# operation below gives us the (nevents, len_et) array we want,
# per channel.
offset = np.arange(self.offset,
self.offset + self.len_et)[:, np.newaxis]
# Loop over event types
for e_idx in xrange(event_types.shape[0]):
idx = np.where(self.events[i] == event_types[e_idx])[0]
event_trig = data[idx + offset]
#Correct baseline by removing the first point in the series
#for each channel:
if self._correct_baseline:
event_trig -= event_trig[0]
h[i][e_idx] = stats.sem(event_trig, -1)
#In case the input events are an Events:
else:
#Get the indices necessary for extraction of the eta:
add_offset = np.arange(self.offset,
self.offset + self.len_et)[:, np.newaxis]
idx = (self.events.time / self.sampling_interval).astype(int)
#Make a list for the output
h = [0] * self._len_h
# Loop over channels
for i in xrange(self._len_h):
#If this is a list with one element:
if self._len_h == 1:
event_trig = self.data[0][idx + add_offset]
#Otherwise, you need to index straight into the underlying data
#array:
else:
event_trig = self.data.data[i][idx + add_offset]
h[i] = stats.sem(event_trig, -1)
h = np.array(h).squeeze()
return ts.TimeSeries(data=h,
sampling_interval=self.sampling_interval,
t0=self.offset * self.sampling_interval,
time_unit=self.time_unit)
def ets(self):
"""The event-triggered standard error of the mean """
#Make a list for the output
h = [0] * self._len_h
if self._is_ts:
# Loop over channels
for i in range(self._len_h):
data = self.data[i]
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i]) != 0]
h[i] = np.empty((event_types.shape[0], self.len_et),
dtype=complex)
# This offset is used to pull the event indices below, but we
# have to broadcast it so the shape of the resulting idx+offset
# operation below gives us the (nevents, len_et) array we want,
# per channel.
offset = np.arange(self.offset,
self.offset + self.len_et)[:, np.newaxis]
# Loop over event types
for e_idx in range(event_types.shape[0]):
idx = np.where(self.events[i] == event_types[e_idx])[0]
event_trig = data[idx + offset]
#Correct baseline by removing the first point in the series
#for each channel:
if self._correct_baseline:
event_trig -= event_trig[0]
h[i][e_idx] = stats.sem(event_trig, -1)
#In case the input events are an Events:
else:
#Get the indices necessary for extraction of the eta:
add_offset = np.arange(self.offset,
self.offset + self.len_et)[:, np.newaxis]
idx = (self.events.time / self.sampling_interval).astype(int)
#Make a list for the output
h = [0] * self._len_h
# Loop over channels
for i in range(self._len_h):
#If this is a list with one element:
if self._len_h == 1:
event_trig = self.data[0][idx + add_offset]
#Otherwise, you need to index straight into the underlying data
#array:
else:
event_trig = self.data.data[i][idx + add_offset]
h[i] = stats.sem(event_trig, -1)
h = np.array(h).squeeze()
return ts.TimeSeries(data=h,
sampling_interval=self.sampling_interval,
t0=self.offset * self.sampling_interval,
time_unit=self.time_unit)
def ets(self):
"""The event-triggered standard error of the mean """
#Make a list for the output
h = [0] * self._len_h
for i in xrange(self._len_h):
data = self.data[i]
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i]) != 0]
h[i] = np.empty((event_types.shape[0], self.len_et), dtype=complex)
for e_idx in xrange(event_types.shape[0]):
idx = np.where(self.events[i] == event_types[e_idx])
idx_w_len = np.array([idx[0] + count + self.offset for count
in range(self.len_et)])
event_trig = data[idx_w_len]
#Correct baseline by removing the first point in the series for
#each channel:
if self._correct_baseline:
event_trig -= event_trig[0]
h[i][e_idx] = stats.sem(event_trig, axis=-1)
h = np.array(h).squeeze()
return ts.TimeSeries(data=h,
sampling_interval=self.sampling_interval,
t0=self.offset * self.sampling_interval,
time_unit=self.time_unit)
def ets(self):
"""The event-triggered standard error of the mean """
#Make a list for the output
h = [0] * self._len_h
for i in xrange(self._len_h):
data = self.data[i]
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i]) != 0]
h[i] = np.empty((event_types.shape[0], self.len_et), dtype=complex)
for e_idx in xrange(event_types.shape[0]):
idx = np.where(self.events[i] == event_types[e_idx])
idx_w_len = np.array([
idx[0] + count + self.offset
for count in range(self.len_et)
])
event_trig = data[idx_w_len]
#Correct baseline by removing the first point in the series for
#each channel:
if self._correct_baseline:
event_trig -= event_trig[0]
h[i][e_idx] = stats.sem(event_trig, axis=-1)
h = np.array(h).squeeze()
return ts.TimeSeries(data=h,
sampling_interval=self.sampling_interval,
t0=self.offset * self.sampling_interval,
time_unit=self.time_unit)
def correlation(self):
"""
The correlation between all combinations of trials
Returns
-------
(r,e) : tuple
r is the mean correlation and e is the mean error of the correlation
(with df = n_trials - 1)
"""
c = np.corrcoef(self.input.data)
c = c[tril_indices_from(c, -1)]
return np.mean(c), stats.sem(c)
def correlation(self):
"""
The correlation between all combinations of trials
Returns
-------
(r,e) : tuple
r is the mean correlation and e is the mean error of the correlation
(with df = n_trials - 1)
"""
c = np.corrcoef(self.input.data)
c = c[tril_indices_from(c, -1)]
return np.mean(c), stats.sem(c)
