def read_edf(subj_data_file_raw):
"""Convert the EDF to pd.DataFrames containing pupil samples, events, and messages."""
samples, events, messages = edf.pread(subj_data_file_raw)
assert not samples.empty, 'Error in EDF to pd.DataFrame conversion. Check input.'
return samples, events, messages
def raw_el_data(subject, datapath='/net/store/nbp/projects/etcomp/'):
# Input: subjectname, datapath
# Output: Returns pupillabs dictionary
filename = os.path.join(datapath,subject,'raw')
from pyedfread import edf # parses SR research EDF data files into pandas df
elsamples, elevents, elnotes = edf.pread(os.path.join(filename,findFile(filename,'.EDF')[0]), trial_marker=b'')
return (elsamples,elevents,elnotes)
def load_pupil(self):
'''
Load pupil, events, messages from raw directory.
'''
directory = join(self.flex_dir, 'raw', 'bids_mr_v1.2', self.subject,
self.session, 'func')
files = glob(join(directory, '*{}*.edf'.format(self.run)))
if len(files) > 1:
raise RuntimeError(
'More than one log file found for this block: %s' % files)
elif len(files) == 0:
raise RuntimeError('No log file found for this block: %s, %s, %s' %
(self.subject, self.session, self.run))
if self.type == 'inference':
return edf.pread(
files[0], trial_marker=b'trial_id'
) # 'inference' and 'instructed' run files use different trial_markers
elif self.type == 'instructed':
return edf.pread(files[0], trial_marker=b'TRIALID')
def load_pupil(self, directory=None):
'''
Concatenates path and file name and loads sa, ev, and m file.
Recquires subject code, session, phase and block.
'''
directory = join(self.flex_dir, 'raw', 'bids_mr_v1.2', self.subject,
self.session, 'func')
files = glob(join(directory, '*{}*.edf'.format(self.run)))
if len(files) > 1:
raise RuntimeError(
'More than one log file found for this block: %s' % files)
elif len(files) == 0:
raise RuntimeError('No log file found for this block: %s, %s, %s' %
(self.subject, self.session, self.run))
if self.type == 'inference':
return edf.pread(files[0], trial_marker=b'trial_id')
elif self.type == 'instructed':
return edf.pread(files[0], trial_marker=b'TRIALID')
def load_edf(filename):
'''
Loads one EDF file and returns a clean DataFrame.
'''
events, messages = edf.pread(filename,
properties_filter=[
'gx', 'gy', 'pa', 'sttime', 'start',
'type', 'gavx', 'gavy'
],
filter='all')
#events = edf.trials2events(events, messages)
#events['stime'] = pd.to_datetime(events.sample_time, unit='ms')
events['type'] = events.type == 'fixation'
if all((events.right_pa == -32768) | np.isnan(events.right_pa)):
del events['right_pa']
events['pa'] = events.left_pa
del events['left_pa']
elif all((events.left_pa == -32768) | np.isnan(events.left_pa)):
del events['left_pa']
events['pa'] = events.right_pa
del events['right_pa']
else:
raise RuntimeError(
'Recorded both eyes? So unusual that I\'ll stop here')
# In some cases the decision variable still contains ['second', 'conf', 'high'], 21.0 -> Fix this
# In these cases the decision_time variable has 2 time stamps as well...
if messages.decision.dtype == np.dtype(object):
messages['decision'] = np.array([
x[-1] if isinstance(x, collections.Sequence) else x
for x in messages.decision.values
])
messages['decision_time'] = np.array([
x[-1] if isinstance(x, collections.Sequence) else x
for x in messages.decision_time.values
])
return events, messages
import numpy as np
import matplotlib.pyplot as plt
from pyedfread import edf
# 3b
samples, events, messages = edf.pread('181105.edf',
trial_marker=b'Start Trial')
# 3f
esr = int(messages['RECCFG'][0][1])
samples['time'] = samples['time'] / esr
events[['start', 'end']] = events[['start', 'end']] / esr
messages[['trialid_time', 'Cue_time', 'End_time'
]] = messages[['trialid_time', 'Cue_time', 'End_time']] / esr
# 3g
eind = np.arange(15000)
ex = samples['gx_left'][eind]
ey = samples['gy_left'][eind]
et = samples['time'][eind]
ex[ex > 1920] = np.nan
ex[ex < 0] = np.nan
ey[ey > 1080] = np.nan
ey[ey < 0] = np.nan
# 3n NEED TO WRITE AND SUBMIT
plt.figure()
plt.subplot(2, 1, 1)
plt.plot(et, ex)
plt.xlabel('Time (s)')
plt.ylabel('Eye Postion X')
def get_df(filelist, filter_fun=None, new_rect=None):
'''
Reads all EDF files in the filelist (see get_filelist) tuple with pyedfread
and returns a merged dataframe where each row is one fixation.
The output dataframe consists of Event and Message dataframes (outputs of
pyedfread module, see pyedfread).
Metadata is used to label each row with participant and run id.
(see get_filelist).
If no FILTER_FUN is provided, Message DataFrame is directly appended to
Event DataFrame. In some cases one might desire to preprocess Message
DataFrame before appending. In that case, the result of FILTER_FUN(messages)
is appended.
Specify NEWRECT to discard pixels outside of a square central region. NEWRECT
of 500 would discard all data outside of a square of 500x500 pixels.
See get_filelist to know more about filelist format.
See pyedfread for more information on the output dataframes.
'''
total_file = len(filelist)
print("Receivied {} EDF files".format(total_file))
#init 3 variables that will accumulate data frames as a list
e = [None] * total_file
m = [None] * total_file
#will be used to index fixation ranks below in df.groupby.apply combo
def addfix(df):
df["fix"] = range(df.shape[0])
return df
#Call pyedfread and concat different data frames as lists.
for i, file in enumerate(filelist):
filename = file[0]
_, E, M = edf.pread(filename,
meta=file[1],
ignore_samples=True,
filter='all')
#just to be sure not to have any spaces on column names (this could be moved to pyedfread)
E.rename(columns=str.strip, inplace=True)
M.rename(columns=str.strip, inplace=True)
#remove saccade events.
E = E.loc[E["type"] == "fixation"]
#take only useful columns, include the meta data columns as well.
E = E[list(file[1].keys()) + ['trial', 'gavx', 'gavy', 'start', 'end']]
#get SYNCTIME (i.e. stim onsets)
E = E.merge(M.loc[:, ['SYNCTIME', 'py_trial_marker']],
right_on='py_trial_marker',
left_on='trial',
how="left")
#correct time stamps with SYNCTIME
E.start = E.start - E.SYNCTIME
E.end = E.end - E.SYNCTIME
#remove prestimulus fixations
E = E[E.start > 0]
#index fixations
E = E.groupby("trial").apply(addfix)
#get display coordinates and calibration values
E = E.append(M.loc[M.py_trial_marker == -1,
['validation_result:', 'subject', 'run']])
#add the display coordinates.
rect = M.loc[M.py_trial_marker == -1, ['DISPLAY_COORDS']].values
E['DISPLAY_COORDS'] = np.repeat(rect, E.shape[0], axis=0)
#drop useless columns
E = E.drop(['SYNCTIME', 'py_trial_marker'], axis=1)
#get what we want from messages using filter_fun or simply discard messages.
if filter_fun != None:
m = filter_fun(M)
e[i] = E.merge(m,
left_on='trial',
right_on='py_trial_marker',
how='left')
else:
e[i] = E
#progress report
sys.stdout.write('\r')
sys.stdout.write("Reading file {}\n".format(filename))
sys.stdout.write("[%-20s] %d%%\n" % ('=' * round(
(i + 1) / total_file * 20), round((i + 1) / total_file * 100)))
sys.stdout.flush()
#convert lists to data frame.
events = pd.concat(e, ignore_index=True)
#add fixation weight (todo: add it only to trials > 0)
events.loc[:, 'weight'] = 1
#Check whether all data has same DISPLAY_COORDINATES
#Overwrite DISPLAY_COORDINATES if required.
events = set_rect(events, new_rect)
rect = get_rect(events)
# #Remove out of range fixation data
valid_fix = (events.gavx >= rect[0]) & \
(events.gavx <= rect[2]) & \
(events.gavy >= rect[1]) & \
(events.gavy <= rect[3]) | events.trial.isnull()
events = events.loc[valid_fix, :]
#
return events
def load_pupil(self):
'''
Loads sa, ev & m of edf file
'''
return edf.pread(self.file)
# if the variance is not distinct enough from the 1 percentile,
# signal has to be found manually, indicated as 666 in the first item
# of the list.
r_hr = [
ds.hamilton_detector(ica_data[i]) for i in range(ica_data.shape[0])
]
r_hr = [np.var(np.diff(i)) for i in r_hr]
ecg_out[ica_key] = r_hr
r_hr = np.array(r_hr)
if (np.percentile(r_hr, 1) - np.min(r_hr)) > 500:
hr = list(np.where(r_hr < np.percentile(r_hr, 1))[0])
else:
hr = [666]
samples, events, messages = edf.pread(edf_path)
eye = ["left", "right"][events.eye.unique()[0]]
del events
del messages
# cleaning the eyetracking datas
samples = samples.loc[samples.time != 0.0]
start = samples.loc[samples.input == 252.]
end = samples.loc[samples.input == 253.]
start_ix = start.index[0] - 100
end_ix = end.index[-1] + 100
samples = samples.iloc[start_ix:end_ix]
samples.reset_index(inplace=True)
samples.time = samples.time - samples.time.iloc[0]
out = [edf for edf in os.listdir(path) if edf.endswith(ftype)]
return(out)
#%% EYELINK
subject = 'inga_3'
datapath='/net/store/nbp/projects/etcomp/pilot'
# filepath for preprocessed folder
preprocessed_path = os.path.join(datapath, subject, 'preprocessed')
# Load edf
filename = os.path.join(datapath,subject,'raw')
# elsamples: contains individual EL samples
# elevents: contains fixation and saccade definitions
# elnotes: contains notes (meta data) associated with each trial
elsamples, elevents, elnotes = edf.pread(os.path.join(filename,findFile(filename,'.EDF')[0]), trial_marker=b'')
elevents.time.unique()
#%% Checking blink_id
# Probably not very interesting
elevents = events.el_make_events(subject)
plt.figure()
plt.plot(elevents.index, elevents.start, 'x', color='b')
plt.plot(elevents.index, elevents.end, 'x', color='b')
def create(self, *args, **kwargs):
if not os.path.isfile(self.args["edfFile"]):
return self
edffile = self.args["edfFile"]
trialStart = self.args["trialStartMarker"]
trialEnd = self.args["trialEndMarker"]
messages, timestamps = edfread.read_messages(edffile)
trialStarts = []
trialEnds = []
for ts, message in zip(timestamps, messages):
m = message.decode().strip("\x00").replace(" ", "")
if m == trialEnd:
trialEnds.append(ts)
elif m == trialStart:
trialStarts.append(ts)
trialEnds = np.array(trialEnds)
trialStarts = np.array(trialStarts)
D = trialEnds[None, :] - trialStarts[:, None]
D[D < 0] = D.max()
if len(trialStarts) < len(trialEnds):
# match every trial start with its closest trial end
idx = D.argmin(1)
trialEnds = trialEnds[idx]
elif len(trialStarts) > len(trialEnds):
idx = D.argmin(0)
trialStarts = trialStarts[idx]
sample_size = ((trialEnds - trialStarts).astype(np.uint64)).sum()
samples, events, messages = edf.pread(edffile)
sample_time = samples["time"]
# get sampling rate by comparing distance between timestamps
sr = sample_time[1] - sample_time[0]
if sr == 0:
# 2 kHz, i.e. 2 timestamps per second
sample_size = np.uint64(2 * sample_size)
gazex = np.zeros((sample_size, ))
gazey = np.zeros((sample_size, ))
trialidx = np.zeros((sample_size, ), dtype=np.uint64)
ssidx = 0
offset = 0
for (i, (start, end)) in enumerate(zip(trialStarts, trialEnds)):
sidx = np.searchsorted(sample_time[ssidx:], start, side='let')
eidx = np.searchsorted(sample_time[ssidx:], end, side='right')
sidx += ssidx
eidx += ssidx
w = eidx - sidx
ssidx = eidx + 1
gazex[offset:offset + w] = samples["gx_left"][sidx:eidx]
gazey[offset:offset + w] = samples["gy_left"][sidx:eidx]
trialidx[offset:offset + w] = i
offset += w
self.gazeX = gazex
self.gazeY = gazey
self.trialStart = trialStarts
self.trialEnd = trialEnds
self.trialIdx = trialidx
self.setidx = [0 for i in range(len(self.trialIdx))]
def test_ignore_samples(self):
samples, events, messages = edf.pread('../SUB001.EDF',
ignore_samples=True)
self.assertEqual(samples.shape[0], 0)
def setUp(self):
samples, events, messages = edf.pread('../SUB001.EDF')
self.samples = samples
self.events = events
self.messages = messages
