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Summarizing pupillometric data

import sys
sys.path.insert(0,"..")
import pypillometry as pp

The pypillometry package provides several functions for summarizing datasets. Simply print()ing a PupilData object gives a readable summary of the main properties of the dataset and also prints the complete history of operations applied to the dataset.

d=pp.create_fake_pupildata(ntrials=100)
print(d)

PupilData(fake_nutuheho, 13.9MiB):
 n                 : 113453
 nmiss             : 0
 perc_miss         : 0.0
 nevents           : 200
 nblinks           : 0
 ninterpolated     : 0
 blinks_per_min    : 0.0
 fs                : 1000.0
 duration_minutes  : 1.8908833333333335
 start_min         : 0.0
 end_min           : 1.8908780800185903
 baseline_estimated: False
 response_estimated: False
 History:
 *

By calling the function summary(), summary data is returned in dict form:

d.summary()

{'name': 'fake_nutuheho',
 'n': 113453,
 'nmiss': 0,
 'perc_miss': 0.0,
 'nevents': 200,
 'nblinks': 0,
 'ninterpolated': 0,
 'blinks_per_min': 0.0,
 'fs': 1000.0,
 'duration_minutes': 1.8908833333333335,
 'start_min': 0.0,
 'end_min': 1.8908780800185903,
 'baseline_estimated': False,
 'response_estimated': False}

Using the package pandas, this allows to rapidly summarize properties of many datasets in a table.

To illustrate, we create a list of datasets, each corresponding to one individual subject:

nsubjs=10
datasets=[pp.create_fake_pupildata(ntrials=100) for _ in range(nsubjs)]

We can now call summary() for each of the objects and merge the resulting dict into a table form:

import pandas as pd
pd.DataFrame([d.summary() for d in datasets])

	name	n	nmiss	perc_miss	nevents	nblinks	ninterpolated	blinks_per_min	fs	duration_minutes	start_min	end_min	baseline_estimated	response_estimated
0	fake_tamofase	113832	0	0.0	200	0	0	0.0	1000.0	1.897200	0.0	1.897196	False	False
1	fake_lobavifu	114425	0	0.0	200	0	0	0.0	1000.0	1.907083	0.0	1.907083	False	False
2	fake_paketomu	112977	0	0.0	200	0	0	0.0	1000.0	1.882950	0.0	1.882941	False	False
3	fake_hiligovi	114345	0	0.0	200	0	0	0.0	1000.0	1.905750	0.0	1.905735	False	False
4	fake_kolavebo	113732	0	0.0	200	0	0	0.0	1000.0	1.895533	0.0	1.895517	False	False
5	fake_ropopodi	113661	0	0.0	200	0	0	0.0	1000.0	1.894350	0.0	1.894346	False	False
6	fake_rilimolo	114402	0	0.0	200	0	0	0.0	1000.0	1.906700	0.0	1.906698	False	False
7	fake_gapudome	113891	0	0.0	200	0	0	0.0	1000.0	1.898183	0.0	1.898178	False	False
8	fake_belobeva	113932	0	0.0	200	0	0	0.0	1000.0	1.898867	0.0	1.898865	False	False
9	fake_rihatofi	113304	0	0.0	200	0	0	0.0	1000.0	1.888400	0.0	1.888393	False	False

This file was created from the following Jupyter-notebook: docs/summary.ipynb
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