def clean_trials(trialevents):
resampled_dict = {}
for trial in trialevents.Trial.unique():
starttime, stoptime = trialevents.loc[trialevents.Trial == trial,
'TETTime'].iloc[[0, -1]]
rawtrial = trialevents.loc[(trialevents.TETTime >= starttime)
& (trialevents.TETTime <= stoptime)]
cleantrial = pupil_utils.deblink(rawtrial)
string_cols = ['Load', 'Trial', 'Condition']
trial_resamp = pupil_utils.resamp_filt_data(cleantrial,
filt_type='low',
string_cols=string_cols)
baseline = trial_resamp.loc[trial_resamp.Condition == 'Ready',
'DiameterPupilLRFilt'].last(
'250ms').mean()
baseline_blinks = trial_resamp.loc[trial_resamp.Condition == 'Ready',
'BlinksLR'].last('250ms').mean()
if baseline_blinks > .5:
baseline = np.nan
trial_resamp['Baseline'] = baseline
trial_resamp['Dilation'] = trial_resamp[
'DiameterPupilLRFilt'] - trial_resamp['Baseline']
trial_resamp = trial_resamp[trial_resamp.Condition == 'Record']
trial_resamp.index = pd.DatetimeIndex(
(trial_resamp.index - trial_resamp.index[0]).astype(np.int64))
resampled_dict[trial] = trial_resamp
dfresamp = pd.concat(resampled_dict, names=['Trial', 'Timestamp'])
return dfresamp
def clean_trials(trialevents):
resampled_dict = {}
for trial in trialevents.Trial.unique():
rawtrial = trialevents.loc[trialevents.Trial == trial]
rawtrial = rawtrial.loc[(rawtrial.CurrentObject == 'Ready') | (
rawtrial.CurrentObject.str.contains('PlayWord'))]
cleantrial = pupil_utils.deblink(rawtrial,
pupilthresh_hi=4.,
pupilthresh_lo=1.5)
cleantrial.loc[:, 'Trial'] = cleantrial.Trial.astype('str')
string_cols = ['Trial', 'CurrentObject']
trial_resamp = pupil_utils.resamp_filt_data(cleantrial,
filt_type='low',
string_cols=string_cols)
baseline = trial_resamp.loc[trial_resamp.CurrentObject == "Ready",
"DiameterPupilLRFilt"].last(
"1000ms").mean()
trial_resamp['Baseline'] = baseline
trial_resamp['Dilation'] = trial_resamp[
'DiameterPupilLRFilt'] - trial_resamp['Baseline']
trial_resamp = trial_resamp[trial_resamp.CurrentObject.str.match(
"PlayWord")]
trial_resamp.index = pd.DatetimeIndex(
(trial_resamp.index - trial_resamp.index[0]).astype(np.int64))
resampled_dict[trial] = trial_resamp
dfresamp = pd.concat(resampled_dict, names=['Trial', 'Timestamp'])
dfresamp = dfresamp.reset_index(level='Trial', drop=True).reset_index()
return dfresamp
def clean_trials(df):
dfresamp = pupil_utils.resamp_filt_data(df, filt_type='low', string_cols=['CurrentObject'])
baseline = dfresamp.loc[dfresamp['CurrentObject']=='Recallinstructions','DiameterPupilLRFilt'].last('1000ms').mean()
dfresamp['Baseline'] = baseline
dfresamp['Dilation'] = dfresamp['DiameterPupilLRFilt'] - dfresamp['Baseline']
dfresamp = dfresamp[dfresamp.CurrentObject=='Recall']
dfresamp.index = pd.to_datetime((dfresamp.index - dfresamp.index[0]).total_seconds(), unit='s')
return dfresamp
def proc_subject(filelist):
"""Given an infile of raw pupil data, saves out:
1. Session level data with dilation data summarized for each trial
2. Dataframe of average peristumulus timecourse for each condition
3. Plot of average peristumulus timecourse for each condition
4. Percent of samples with blinks """
tpre = 0.5
tpost = 2.5
samp_rate = 30.
for fname in filelist:
print('Processing {}'.format(fname))
if (os.path.splitext(fname)[-1]
== ".gazedata") | (os.path.splitext(fname)[-1] == ".csv"):
df = pd.read_csv(fname, sep="\t")
elif os.path.splitext(fname)[-1] == ".xlsx":
df = pd.read_excel(fname, parse_dates=False)
else:
raise IOError('Could not open {}'.format(fname))
subid = pupil_utils.get_subid(df['Subject'], fname)
timepoint = pupil_utils.get_timepoint(df['Session'], fname)
oddball_sess = get_oddball_session(fname)
df = pupil_utils.deblink(df)
dfresamp = pupil_utils.resamp_filt_data(df)
dfresamp['Condition'] = np.where(dfresamp.CRESP == 5, 'Standard',
'Target')
pupil_utils.plot_qc(dfresamp, fname)
sessdf = get_sessdf(dfresamp)
sessdf['BlinkPct'] = get_blink_pct(dfresamp, fname)
dfresamp['zDiameterPupilLRFilt'] = pupil_utils.zscore(
dfresamp['DiameterPupilLRFilt'])
targdf, standdf = proc_all_trials(sessdf,
dfresamp['zDiameterPupilLRFilt'],
tpre, tpost, samp_rate)
targdf_long = reshape_df(targdf)
standdf_long = reshape_df(standdf)
glm_results = ts_glm(
dfresamp.zDiameterPupilLRFilt,
sessdf.loc[sessdf.Condition == 'Target', 'Timestamp'],
sessdf.loc[sessdf.Condition == 'Standard',
'Timestamp'], dfresamp.BlinksLR)
# Set subject ID and session as (as type string)
glm_results['Subject'] = subid
glm_results['Session'] = timepoint
glm_results['OddballSession'] = oddball_sess
save_glm_results(glm_results, fname)
allconddf = standdf_long.append(targdf_long).reset_index(drop=True)
# Set subject ID and session as (as type string)
allconddf['Subject'] = subid
allconddf['Session'] = timepoint
allconddf['OddballSession'] = oddball_sess
plot_pstc(allconddf, fname)
save_pstc(allconddf, fname)
# Set subject ID and session as (as type string)
sessdf['Subject'] = subid
sessdf['Session'] = timepoint
sessdf['OddballSession'] = oddball_sess
sessout = pupil_utils.get_outfile(fname, '_SessionData.csv')
sessdf.to_csv(sessout, index=False)
def proc_subject(filelist):
"""
Given an infile of raw pupil data, saves out:
1) Session level data with dilation data summarized for each trial
2) Dataframe of average peristumulus timecourse for each condition
3) Plot of average peristumulus timecourse for each condition
4) Percent of samples with blinks
"""
for fname in filelist:
print('Processing {}'.format(fname))
if (os.path.splitext(fname)[-1]
== ".gazedata") | (os.path.splitext(fname)[-1] == ".csv"):
df = pd.read_csv(fname, sep="\t")
elif os.path.splitext(fname)[-1] == ".xlsx":
df = pd.read_excel(fname)
else:
raise IOError('Could not open {}'.format(fname))
subid = pupil_utils.get_subid(df['Subject'], fname)
timepoint = pupil_utils.get_timepoint(df['Session'], fname)
# Keep only samples after last sample of Recall
df = df[df[df.CurrentObject == "Recall"].index[-1] + 1:]
df = pupil_utils.deblink(df)
dfresamp = pupil_utils.resamp_filt_data(df,
filt_type='low',
string_cols=['CurrentObject'])
# Resampling fills forward fills Current Object when missing. This
# results in values of "Response" at beginning of trials. Reaplce these
# by backfilling from first occurrence of "Fixation" in every trial.
for i in dfresamp.TrialId.unique():
trialstartidx = (dfresamp.TrialId == i).idxmax()
fixstartidx = (
dfresamp.loc[dfresamp.TrialId == i,
"CurrentObject"] == "Fixation").idxmax()
dfresamp.loc[trialstartidx:fixstartidx,
"CurrentObject"] = "Fixation"
dfresamp = clean_trials(df)
pupildf = proc_all_trials(dfresamp)
pupildf['Subject'] = subid
pupildf['Session'] = timepoint
pupildf = pupildf.rename(columns={
'DiameterPupilLRFilt': 'Diameter',
'BlinksLR': 'BlinkPct'
})
# Reorder columns
cols = [
'Subject', 'Session', 'TrialId', 'Baseline', 'Diameter',
'Dilation', 'BlinkPct', 'Duration', 'Condition'
]
pupildf = pupildf[cols]
pupil_outname = pupil_utils.get_proc_outfile(fname,
'_ProcessedPupil.csv')
pupil_outname = pupil_outname.replace("-Delay", "-Recognition")
pupildf.to_csv(pupil_outname, index=False)
print('Writing processed data to {0}'.format(pupil_outname))
def clean_trials(df):
dfresamp = pupil_utils.resamp_filt_data(df,
filt_type='low',
string_cols=['CurrentObject'])
# Resampling fills forward fills Current Object when missing. This
# results in values of "Response" at beginning of trials. Reaplce these
# by backfilling from first occurrence of "Fixation" in every trial.
for i in dfresamp.TrialId.unique():
trialstartidx = (dfresamp.TrialId == i).idxmax()
fixstartidx = (dfresamp.loc[dfresamp.TrialId == i,
"CurrentObject"] == "Fixation").idxmax()
dfresamp.loc[trialstartidx:fixstartidx, "CurrentObject"] = "Fixation"
dfresamp.groupby("TrialId").apply(lambda x: x.index[-1] - x.index[0]).sum()
return dfresamp
def clean_trials(df, trialevents):
resampled_dict = {}
for trialnum in trialevents.Trial.unique():
basestart, basestop, respstart, respstop = trialevents.loc[trialevents.Trial==trialnum,'TETTime']
condition = trialevents.loc[trialevents.Trial==trialnum,'Condition'].iat[0]
rawtrial = df.loc[(df.TETTime>=basestart) & (df.TETTime<=respstop)]
rawtrial.loc[(rawtrial.TETTime>=basestart) & (rawtrial.TETTime<=basestop),'Phase'] = 'Baseline'
rawtrial.loc[(rawtrial.TETTime>=respstart) & (rawtrial.TETTime<=respstop),'Phase'] = 'Response'
# rawtrial = rawtrial[rawtrial.Condition=='Response']
cleantrial = pupil_utils.deblink(rawtrial)
trial_resamp = pupil_utils.resamp_filt_data(cleantrial, filt_type='low', string_cols=['CurrentObject', 'Phase'])
baseline = trial_resamp['DiameterPupilLRFilt'].first('1000ms').mean()
# baseline = trial_resamp.DiameterPupilLRFilt.iat[0]
trial_resamp['Baseline'] = baseline
trial_resamp['Dilation'] = trial_resamp['DiameterPupilLRFilt'] - trial_resamp['Baseline']
trial_resamp = trial_resamp[trial_resamp.Phase=='Response']
trial_resamp['Condition'] = condition
resampled_dict[trialnum] = trial_resamp
dfresamp = pd.concat(resampled_dict, names=['Trial','Timestamp'], sort=True)
return dfresamp
def proc_subject(filelist):
"""Given an infile of raw pupil data, saves out:
1. Session level data with dilation data summarized for each trial
2. Dataframe of average peristumulus timecourse for each condition
3. Plot of average peristumulus timecourse for each condition
4. Percent of samples with blinks """
tpre = 0.250
tpost = 2.5
samp_rate = 30.
for pupil_fname in filelist:
print('Processing {}'.format(pupil_fname))
if (os.path.splitext(pupil_fname)[-1] == ".gazedata") | (
os.path.splitext(pupil_fname)[-1] == ".csv"):
df = pd.read_csv(pupil_fname, sep="\t")
elif os.path.splitext(pupil_fname)[-1] == ".xlsx":
df = pd.read_excel(pupil_fname, parse_dates=False)
else:
raise IOError('Could not open {}'.format(pupil_fname))
subid = pupil_utils.get_subid(df['Subject'], pupil_fname)
timepoint = pupil_utils.get_timepoint(df['Session'], pupil_fname)
df = pupil_utils.deblink(df)
df.CurrentObject.replace('StimulusRecord', 'Stimulus', inplace=True)
dfresamp = pupil_utils.resamp_filt_data(
df, filt_type='band', string_cols=['TrialId', 'CurrentObject'])
dfresamp = dfresamp.drop(columns='TrialId_x').rename(
columns={'TrialId_y': 'TrialId'})
eprime_fname = get_eprime_fname(pupil_fname)
eprime = pd.read_csv(eprime_fname,
sep='\t',
encoding='utf-16',
skiprows=0)
if not np.array_equal(eprime.columns[:3],
['ExperimentName', 'Subject', 'Session']):
eprime = pd.read_csv(eprime_fname,
sep='\t',
encoding='utf-16',
skiprows=1)
eprime = eprime.rename(columns={"Congruency": "Condition"})
pupil_utils.plot_qc(dfresamp, pupil_fname)
sessdf = get_sessdf(dfresamp, eprime)
sessdf['BlinkPct'] = get_blink_pct(dfresamp, pupil_fname)
dfresamp['zDiameterPupilLRFilt'] = pupil_utils.zscore(
dfresamp['DiameterPupilLRFilt'])
condf, incondf, neutraldf = proc_all_trials(
sessdf, dfresamp['zDiameterPupilLRFilt'], tpre, tpost, samp_rate)
condf_long = reshape_df(condf)
incondf_long = reshape_df(incondf)
neutraldf_long = reshape_df(neutraldf)
glm_results = ts_glm(dfresamp.zDiameterPupilLRFilt,
sessdf.loc[sessdf.Condition == 'C', 'Timestamp'],
sessdf.loc[sessdf.Condition == 'I', 'Timestamp'],
sessdf.loc[sessdf.Condition == 'N',
'Timestamp'], dfresamp.BlinksLR)
# Set subject ID and session as (as type string)
glm_results['Subject'] = subid
glm_results['Session'] = timepoint
save_glm_results(glm_results, pupil_fname)
allconddf = condf_long.append(incondf_long).reset_index(drop=True)
allconddf = allconddf.append(neutraldf_long).reset_index(drop=True)
# Set subject ID and session as (as type string)
allconddf['Subject'] = subid
allconddf['Session'] = timepoint
allconddf = allconddf[allconddf.Timepoint < 3.0]
plot_pstc(allconddf, pupil_fname)
save_pstc(allconddf, pupil_fname)
sessdf['Subject'] = subid
sessdf['Session'] = timepoint
sessout = pupil_utils.get_proc_outfile(pupil_fname, '_SessionData.csv')
sessdf.to_csv(sessout, index=False)