def create_piezosensor_events(event_channel, threshold, sampling_freq, filt_cutoff_freq, minduration, pickOutOrIn):
if filt_cutoff_freq is not None:
event_channel = filt.low_pass_filter(event_channel, sampling_freq, filt_cutoff_freq)
event_channel = event_channel - np.mean(event_channel[0:10])
event_transitions = np.diff(np.int32(event_channel < threshold))
event_samples = np.nonzero(event_transitions)
event_samples = np.squeeze(event_samples)
event_values = event_transitions[event_samples]
event_durations = event_samples[event_values < 0] - event_samples[event_values > 0]
valid_indices = np.squeeze(np.nonzero(np.abs(event_durations) > minduration))
if pickOutOrIn:
event_samples = event_samples[event_values > 0][valid_indices]
event_values = event_values[event_values > 0][valid_indices]
else:
event_samples = event_samples[event_values < 0][valid_indices]
event_values = event_values[event_values < 0][valid_indices]
return np.array([event_samples, event_values])
def create_piezosensor_events(event_channel, threshold, sampling_freq,
filt_cutoff_freq, minduration, pickOutOrIn):
if filt_cutoff_freq is not None:
event_channel = filt.low_pass_filter(event_channel, sampling_freq,
filt_cutoff_freq)
event_channel = event_channel - np.mean(event_channel[0:10])
event_transitions = np.diff(np.int32(event_channel < threshold))
event_samples = np.nonzero(event_transitions)
event_samples = np.squeeze(event_samples)
event_values = event_transitions[event_samples]
event_durations = event_samples[event_values < 0] - event_samples[
event_values > 0]
valid_indices = np.squeeze(
np.nonzero(np.abs(event_durations) > minduration))
if pickOutOrIn:
event_samples = event_samples[event_values > 0][valid_indices]
event_values = event_values[event_values > 0][valid_indices]
else:
event_samples = event_samples[event_values < 0][valid_indices]
event_values = event_values[event_values < 0][valid_indices]
return np.array([event_samples, event_values])
data_ecog = data[:64, :]
data_probe = data[64:, :]
data_probe_hp = pl.memmap(os.path.join(memap_folder, 'data_probe_hp.dat'),
dtype='int16',
mode='w+',
shape=pl.shape(data_probe))
for i in pl.arange(0, pl.shape(data_probe)[0]):
data_probe_hp[i, :] = filters.high_pass_filter(data_probe[i, :],
Fsampling=f_sampling,
Fcutoff=f_hp_cutoff)
data_probe_hp.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_probe_hp.npy'), data_probe_hp)
shape_data_ss = (pl.shape(data_ecog)[0],
pl.shape(data_ecog)[1] / int(f_sampling / f_subsample))
data_ecog_lp_ss = pl.memmap(os.path.join(memap_folder, 'data_ecog_lp_ss.dat'),
dtype='int16',
mode='w+',
shape=shape_data_ss)
for i in pl.arange(0, pl.shape(data_ecog)[0]):
data_ecog_lp_ss[i, :] = signal.decimate(
filters.low_pass_filter(data_ecog[i, :],
Fsampling=f_sampling,
Fcutoff=f_lp_cutoff),
int(f_sampling / f_subsample))
data_ecog_lp_ss.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_ecog_lp_ss.npy'), data_ecog_lp_ss)
data_ecog = data[:64,:]
data_probe = data[64:,:]
data_probe_hp = pl.memmap(os.path.join(memap_folder,'data_probe_hp.dat'), dtype='int16', mode='w+', shape=pl.shape(data_probe))
for i in pl.arange(0, pl.shape(data_probe)[0]):
data_probe_hp[i,:] = filters.high_pass_filter(data_probe[i,:], Fsampling=f_sampling, Fcutoff=f_hp_cutoff)
data_probe_hp.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_probe_hp.npy'), data_probe_hp)
shape_data_ss = (pl.shape(data_ecog)[0], pl.shape(data_ecog)[1]/int(f_sampling/f_subsample))
data_ecog_lp_ss = pl.memmap(os.path.join(memap_folder, 'data_ecog_lp_ss.dat'), dtype='int16', mode='w+', shape=shape_data_ss)
for i in pl.arange(0, pl.shape(data_ecog)[0]):
data_ecog_lp_ss[i,:] = signal.decimate(filters.low_pass_filter(data_ecog[i,:], Fsampling=f_sampling, Fcutoff=f_lp_cutoff), int(f_sampling/f_subsample))
data_ecog_lp_ss.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_ecog_lp_ss.npy'), data_ecog_lp_ss)
spike_samples = tf.spikedetect(data_probe_hp, threshold_multiplier=6.5, bad_channels=probe_bad_channels)
pl.save(os.path.join(memap_folder, 'spike_samples.npy'), spike_samples)
spike_samples_clean = spike_samples
for i in pl.arange(pl.size(spike_samples_clean)-1,-1,-1):
data = data_probe_hp[:, spike_samples[i]-60:spike_samples[i]+60]
stdevs = sp.std(data,1)
if np.max(data) > 3000 or pl.any(stdevs>600):
data_probe = data[64:,:]
data_probe_hp = pl.memmap(os.path.join(memap_folder,'data_probe_hp.dat'), dtype='int16', mode='w+', shape=pl.shape(data_probe))
for i in pl.arange(0, pl.shape(data_probe)[0]):
data_probe_hp[i,:] = filters.high_pass_filter(data_probe[i, :], Fsampling=f_sampling, Fcutoff=f_hp_cutoff)
data_probe_hp.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_probe_hp.npy'), data_probe_hp)
shape_data_ss = (pl.shape(data_ecog)[0], pl.shape(data_ecog)[1]/int(f_sampling/f_subsample))
data_ecog_lp_ss = pl.memmap(os.path.join(memap_folder, 'data_ecog_lp_ss.dat'), dtype='int16', mode='w+', shape=shape_data_ss)
for i in pl.arange(0, pl.shape(data_ecog)[0]):
data_ecog_lp_ss[i,:] = signal.decimate(
filters.low_pass_filter(data_ecog[i, :], Fsampling=f_sampling, Fcutoff=f_lp_cutoff), int(f_sampling / f_subsample))
data_ecog_lp_ss.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_ecog_lp_ss.npy'), data_ecog_lp_ss)
spike_samples = tf.spikedetect(data_probe_hp, threshold_multiplier=6.5, bad_channels=probe_bad_channels)
pl.save(os.path.join(memap_folder, 'spike_samples.npy'), spike_samples)
spike_samples_clean = spike_samples
for i in pl.arange(pl.size(spike_samples_clean)-1,-1,-1):
data = data_probe_hp[:, spike_samples[i]-60:spike_samples[i]+60]
stdevs = sp.std(data,1)
if np.max(data) > 3000 or pl.any(stdevs>600):
spike_samples_clean = pl.delete(spike_samples_clean, i)
def time_lock_raw_data(data, events, times_to_cut, sampling_freq, baseline_time=None, sub_sample_freq=None,
high_pass_cutoff=None, rectify=False, low_pass_cutoff=None, avg_reref=False, keep_trials=False):
"""
Time locks, baselines, high or low passes, and sub samples the data (in that order)
"""
if np.ndim(events) == 2:
events = events[0, :]
number_of_trials = np.size(events, np.ndim(events) - 1)
times_to_cut = np.array(times_to_cut)
samples_to_cut = (times_to_cut * sampling_freq).astype(int)
if np.size(np.shape(data)) > 1:
number_of_channels = np.shape(data)[0]
else:
number_of_channels = 1
number_of_samples = samples_to_cut[1] - samples_to_cut[0]
time_axis = np.arange(times_to_cut[0], times_to_cut[1], (times_to_cut[1] - times_to_cut[0]) / number_of_samples)
if sub_sample_freq:
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data = np.zeros(
[number_of_channels, math.ceil(number_of_samples * (sub_sample_freq / sampling_freq)),
number_of_trials])
else:
tl_singletrial_data = np.zeros(
[math.ceil(number_of_samples * (sub_sample_freq / sampling_freq)), number_of_trials])
tl_avgtrials_data = np.empty(
[number_of_channels, math.ceil(number_of_samples * (sub_sample_freq / sampling_freq))])
else:
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data = np.zeros([number_of_channels, number_of_samples, number_of_trials])
else:
tl_singletrial_data = np.zeros([number_of_samples, number_of_trials])
tl_avgtrials_data = np.zeros([number_of_channels, number_of_samples])
for index in range(number_of_trials):
temp_samples_to_cut = samples_to_cut + events[index]
breakpoint = False
# if there aren't enough points at the begining of the data set then disregard the event and move to the next one
while np.min(temp_samples_to_cut) < 0:
if temp_samples_to_cut[0] < 0:
index = index + 1
temp_samples_to_cut = samples_to_cut + events[index]
elif temp_samples_to_cut[1] < 0:
breakpoint = True
if breakpoint:
break
if np.size(np.shape(data)) > 1:
temp_data = data[:, int(temp_samples_to_cut[0]): int(temp_samples_to_cut[1])]
else:
temp_data = data[int(temp_samples_to_cut[0]): int(temp_samples_to_cut[1])]
if avg_reref: # rereference with mean over all channels
temp_data = temp_data - np.mean(temp_data, 0)
if high_pass_cutoff:
temp_data = filt.high_pass_filter(temp_data, sampling_freq, high_pass_cutoff)
elif low_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq, low_pass_cutoff)
if rectify:
temp_data = np.abs(temp_data)
if low_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq, low_pass_cutoff)
else:
temp_data = filt.low_pass_filter(temp_data, sampling_freq, high_pass_cutoff / 2)
elif not rectify and high_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq, high_pass_cutoff / 2)
if sub_sample_freq:
temp_data, sub_time_axis = subsample_data(temp_data, time_axis, sampling_freq, sub_sample_freq,
filterType='fir', filterOrder=30)
if baseline_time is not None:
if sub_sample_freq:
temp_data = baseline_correct(temp_data, sub_sample_freq, time_axis, baseline_time[0], baseline_time[1])
else:
temp_data = baseline_correct(temp_data, sampling_freq, time_axis, baseline_time[0], baseline_time[1])
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data[:, :, index] = temp_data
else:
tl_singletrial_data[:, index] = temp_data
if index == 0:
tl_avgtrials_data = temp_data
elif index > 0:
tl_avgtrials_data += temp_data
if index % 10 == 0:
print(index)
tl_avgtrials_data /= number_of_trials
if sub_sample_freq:
time_axis = sub_time_axis
returned_tuple = [tl_avgtrials_data, time_axis]
if keep_trials:
returned_tuple = [tl_singletrial_data, tl_avgtrials_data, time_axis]
return returned_tuple
def time_lock_raw_data(data,
events,
times_to_cut,
sampling_freq,
baseline_time=None,
sub_sample_freq=None,
high_pass_cutoff=None,
rectify=False,
low_pass_cutoff=None,
avg_reref=False,
keep_trials=False):
"""
Time locks, baselines, high or low passes, and sub samples the data (in that order)
"""
if np.ndim(events) == 2:
events = events[0, :]
number_of_trials = np.size(events, np.ndim(events) - 1)
times_to_cut = np.array(times_to_cut)
samples_to_cut = (times_to_cut * sampling_freq).astype(int)
if np.size(np.shape(data)) > 1:
number_of_channels = np.shape(data)[0]
else:
number_of_channels = 1
number_of_samples = samples_to_cut[1] - samples_to_cut[0]
time_axis = np.arange(times_to_cut[0], times_to_cut[1],
(times_to_cut[1] - times_to_cut[0]) /
number_of_samples)
if sub_sample_freq:
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data = np.zeros([
number_of_channels,
math.ceil(number_of_samples *
(sub_sample_freq / sampling_freq)),
number_of_trials
])
else:
tl_singletrial_data = np.zeros([
math.ceil(number_of_samples *
(sub_sample_freq / sampling_freq)),
number_of_trials
])
tl_avgtrials_data = np.empty([
number_of_channels,
math.ceil(number_of_samples * (sub_sample_freq / sampling_freq))
])
else:
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data = np.zeros(
[number_of_channels, number_of_samples, number_of_trials])
else:
tl_singletrial_data = np.zeros(
[number_of_samples, number_of_trials])
tl_avgtrials_data = np.zeros([number_of_channels, number_of_samples])
for index in range(number_of_trials):
temp_samples_to_cut = samples_to_cut + events[index]
breakpoint = False
# if there aren't enough points at the begining of the data set then disregard the event and move to the next one
while np.min(temp_samples_to_cut) < 0:
if temp_samples_to_cut[0] < 0:
index = index + 1
temp_samples_to_cut = samples_to_cut + events[index]
elif temp_samples_to_cut[1] < 0:
breakpoint = True
if breakpoint:
break
if np.size(np.shape(data)) > 1:
temp_data = data[:,
int(temp_samples_to_cut[0]
):int(temp_samples_to_cut[1])]
else:
temp_data = data[int(temp_samples_to_cut[0]
):int(temp_samples_to_cut[1])]
if avg_reref: # rereference with mean over all channels
temp_data = temp_data - np.mean(temp_data, 0)
if high_pass_cutoff:
temp_data = filt.high_pass_filter(temp_data, sampling_freq,
high_pass_cutoff)
elif low_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq,
low_pass_cutoff)
if rectify:
temp_data = np.abs(temp_data)
if low_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq,
low_pass_cutoff)
else:
temp_data = filt.low_pass_filter(temp_data, sampling_freq,
high_pass_cutoff / 2)
elif not rectify and high_pass_cutoff:
temp_data = filt.low_pass_filter(temp_data, sampling_freq,
high_pass_cutoff / 2)
if sub_sample_freq:
temp_data, sub_time_axis = subsample_data(temp_data,
time_axis,
sampling_freq,
sub_sample_freq,
filterType='fir',
filterOrder=30)
if baseline_time is not None:
if sub_sample_freq:
temp_data = baseline_correct(temp_data, sub_sample_freq,
time_axis, baseline_time[0],
baseline_time[1])
else:
temp_data = baseline_correct(temp_data, sampling_freq,
time_axis, baseline_time[0],
baseline_time[1])
if keep_trials:
if np.size(np.shape(data)) > 1:
tl_singletrial_data[:, :, index] = temp_data
else:
tl_singletrial_data[:, index] = temp_data
if index == 0:
tl_avgtrials_data = temp_data
elif index > 0:
tl_avgtrials_data += temp_data
if index % 10 == 0:
print(index)
tl_avgtrials_data /= number_of_trials
if sub_sample_freq:
time_axis = sub_time_axis
returned_tuple = [tl_avgtrials_data, time_axis]
if keep_trials:
returned_tuple = [tl_singletrial_data, tl_avgtrials_data, time_axis]
return returned_tuple
