def get_localizer_epochs(filename, reject=dict(mag=4e-12)):
import locale
locale.setlocale(locale.LC_ALL, "en_US")
raw = mne.io.read_raw_ctf(filename, system_clock='ignore')
sf = float(raw.info['sfreq'])
mapping = {
50: ('con_change', 0),
64: ('stim_onset', 0),
160: ('start', 0),
161: ('end', 0)
}
meta, timing = preprocessing.get_meta(raw, mapping, {}, 160, 161)
if len(meta) == 0:
return None
tmin, tmax = (timing.min().min() / sf) - \
5, (max(timing.max().max()) / sf) + 5
raw = raw.crop(tmin=max(0, tmin), tmax=min(tmax, raw.times[-1]))
raw.load_data()
raw.notch_filter(np.arange(50, 251, 50), n_jobs=4)
meta, timing = preprocessing.get_meta(raw, mapping, {}, 160, 161)
l = len(timing)
events = np.vstack([timing.stim_onset_time, [0] * l, [1] * l]).T
e = mne.Epochs(
raw,
events=events.astype(int),
tmin=-1.25,
tmax=1.75,
reject=reject,
)
del raw
return e
def get_preprocessed_block(raw, block):
start, end = block.start * 60, block.end * 60 # To seconds
r = raw.copy().crop(start, end)
print("Processing ", r)
block_meta, block_timing = get_meta(r, mapping, {}, 41, 41)
r, ants, artdef = preprocess_block(r)
return r, ants, artdef
def get_preprocessed_block(raw, block):
start, end = block.start*60, block.end*60 # To seconds
r = raw.copy().crop(start, end)
print('Processing ',r)
block_meta, block_timing = get_meta(r, mapping, {}, 41, 41)
# For subject 10 there is a spurious trigger in coherent_motion on
# Filter this one out
try:
if len(block_meta.loc[10, 'coherence_on']) == 2:
block_meta.loc[10, 'coherence_on'] = 1
block_timing.loc[10, 'coherence_on_time'] = block_timing.loc[10, 'coherence_on_time'][0]
except TypeError:
pass
r, ants, artdef = preprocess_block(r)
return r, ants, artdef, block_meta, block_timing
def get_blocks(raw):
meta, timing = get_meta(raw, mapping, {}, 41, 41)
# meta.loc[:, 'hash'] = np.arange(len(meta))
# meta.loc[:, 'timing'] = np.arange(len(meta))
return blocks_from_marker(raw)
def preprocess(subject, session):
columns_meta = [
u"baseline_start",
u"decision_start",
u"dot_onset",
u"feedback",
u"noise",
u"response",
u"rest_delay",
u"trial_end",
u"trial_num",
u"trial_start",
u"wait_fix",
u"session_number",
u"block_start",
]
columns_timing = [
"baseline_start_time",
"decision_start_time",
"dot_onset_time",
"feedback_time",
"noise_time",
"response_time",
"rest_delay_time",
"trial_end_time",
"trial_start_time",
"wait_fix_time",
]
path = "/mnt/homes/home024/gortega/megdata/"
path_cluster = "/home/gortega/preprocessed_megdata/sensor_space"
path_megdata = "/home/gortega/megdata/"
for file_idx, filename in enumerate(
glob.glob(
os.path.join(path_megdata,
"*S%i-%i_Att*" % (subject, session)))):
date = filename[-14:-6]
raw = mne.io.read_raw_ctf(filename)
#raw._first_samps = np.cumsum(raw._raw_lengths) - raw._raw_lengths[0]
#raw._last_samps = np.cumsum(raw._last_samps)
##pins and mapping
other_pins = {100: "session_number", 101: "block_start"}
trial_pins = {150: "trial_num"}
mapping = {
("noise", 0): 111,
("noise", 1): 112,
("noise", 2): 113,
("start_button", 0): 89,
("start_button", 1): 91,
("trial_start", 0): 150,
("trial_end", 0): 151,
("wait_fix", 0): 30,
("baseline_start", 0): 40,
("dot_onset", 0): 50,
("decision_start", 0): 60,
("response", -1): 61,
("response", 1): 62,
("no_decisions", 0): 68,
("feedback", 0): 70,
("rest_delay", 0): 80,
}
mapping = dict((v, k) for k, v in mapping.items())
# I get metadata and timing of the raw data.
meta, timing = preprocessing.get_meta(raw, mapping, trial_pins, 150,
151, other_pins)
index = meta.block_start
block_times, block_idx = get_blocks(meta, timing)
for bnum in block_times.keys():
print(
"******************************** SESSION",
session,
"BLOCK",
bnum,
"******************************** ",
)
mb2 = meta.loc[block_idx[bnum][0]:block_idx[bnum][1]]
tb2 = timing.loc[block_idx[bnum][0]:block_idx[bnum][1]]
tb2 = tb2.dropna(subset=["dot_onset_time"])
mb2 = mb2.dropna(subset=["dot_onset"])
index = []
for idx in tb2.index:
try:
if len(tb2.loc[idx, "dot_onset_time"]) == 10:
index.append(idx)
tb2.loc[idx, "first_dot_time"] = tb2.loc[
idx, "dot_onset_time"][0]
mb2.loc[idx, "first_dot"] = mb2.loc[idx,
"dot_onset"][0]
except TypeError:
pass
tb2 = tb2.loc[index]
mb2 = mb2.loc[index]
r = raw.copy() # created a copy to do not change raw
r.crop(
tmin=block_times[bnum][0] /
1200, #[tb2.trial_start_time.min() / 1200.0 - 1,
tmax=block_times[bnum][1] /
1200 #1 + (tb2.feedback_time.max() / 1200.0),
) # crop for each block
r = interpolate_bad_channels(subject, session, r)
mb, tb = meg.preprocessing.get_meta(
r, mapping, trial_pins, 150, 151,
other_pins) # get new metadata for each block
mb = eliminate_spurious_columns(
mb, columns_meta) # sometime we need to drop some columns
tb = eliminate_spurious_columns(tb, columns_timing)
tb = tb.dropna()
mb = mb.dropna()
r, ants, artdef = meg.preprocessing.preprocess_block(
r,
blinks=True) # preprocess of each block looking for artifacts
print("Notch filtering")
midx = np.where([x.startswith("M") for x in r.ch_names])[0]
r.load_data()
r.notch_filter(np.arange(50, 251, 50), picks=midx)
bad_channels = r.info["bads"]
if len(r.info["bads"]) > 0:
r.load_data()
r.interpolate_bads(reset_bads=False)
r.info["bads"] = []
trial_repeat = []
mb.loc[:, "hash"] = hash(subject, mb.session_number, bnum,
mb.trial_num)
# Create a colum for onset of first dot
tb.loc[:, 'first_dot_time'] = np.array(
[x[0] for x in tb.dot_onset_time])
stimmeta, stimlock = preprocessing.get_epoch(
r,
mb.dropna(),
tb.dropna(),
event="first_dot_time",
epoch_time=(-1, 3.5),
epoch_label="hash",
reject_time=(0, 2),
)
if len(stimmeta) > 0:
stim_filename = join(
path_cluster,
"down_sample_stim_meta_sub%i_sess%i_block%i_offset%i" %
(subject, session, bnum, file_idx),
)
stimlock.resample(600, npad="auto")
stimmeta.to_hdf(stim_filename + ".hdf", "meta")
stimlock.save(stim_filename + "-epo.fif.gz")
rlmeta, resplock = preprocessing.get_epoch(
r,
mb.dropna(),
tb.dropna(),
event="response_time",
epoch_time=(-2.5, 0.5),
epoch_label="hash",
reject_time=(-1, 0.4),
)
if len(rlmeta) > 0:
resp_filename = join(
path_cluster,
"down_sample_resp_meta_sub%i_sess%i_block%i_offset%i" %
(subject, session, bnum, file_idx),
)
resplock.resample(600, npad="auto")
rlmeta.to_hdf(resp_filename + ".hdf", "meta")
resplock.save(resp_filename + "-epo.fif.gz")
print("done")
('wait_fix', 0): 30,
('baseline_start', 0): 40,
('dot_onset', 0): 50,
('decision_start', 0): 60,
('response', -1): 61,
('response', 1): 62,
('no_decisions', 0): 68,
('feedback', 0): 70,
('rest_delay', 0): 80
}
mapping = dict((v, k) for k, v in mapping.items())
# I created the key_map to give a number to each trial.
#I get metadata and timing of the raw data.
meta, timing = preprocessing.get_meta(raw, mapping, trial_pins, 150,
151, other_pins)
index = meta.block_start
#I separate the raw data by blocks
#time where the block start
events = mne.find_events(raw, 'UPPT001', shortest_event=1)
index_b_start = np.where(
events[:, 2] == 100) #block start on every trigger 100
time_block = []
time_block2 = []
for i in index_b_start[0]:
time_block.append(events[i][0])
if len(meta.groupby('block_start')) == len(time_block):
pass
else:
if filename == path_megdata + 'S17-7_Attractor_20161130_01.ds': ####here I solve the problem with subject17