This library is for processing data that is obtained from the Pupil Labs eye tracker working in conjunction with LSL that retrieves the event markers needed for this processing library. These event markers can be created anywhere i.e. a different computer and sent over network to the Lab Recorder.

One way is to use it is in a script with calls that resemble the main() function in pupil_lib.py. yaml_path must be defined in the get_build_config(yaml_path=<PATH/TO/YAML>) call. Or if you don't need much control, script_run(yaml_path=<PATH/TO/YAML>) in the same file can be used to do everything and return an PupilLibRunner object that contains the data in the field .data_store.

See docs/data_container.md for more information on the data container .data_store.

You can also use it through the command prompt as well with something like: python pupil_lib.py -D C:\Recordings\CurrentStudy\subj4\block__old41.xdf --data-names gaze_x gaze_y --trigger-pre-processing "{name: default}" {'name':'get_sums','config':[4]} -t S11 S12 --max-workers 1 --tr -2 0 --logger stdout --test --testingdepth deep

Or with only this to get the arguments from a YAML configuration file (defined in the docs/ folder): python pupil_lib.py --run-config C:\Users\Gregory\PycharmProjects\pupil_lib_parallel_exp\resources\test_yaml1.yml

data_container.py shows the general structure of the data once it's finished processing, with docs in docs/data_container.md. Generally speaking, accessing data will be similar in all cases to what is done in simple_script.py.

Using the Pupil Labs LSL plugin, you can create and send markers from a stimulus script in the same way that is done here: https://github.com/sccn/labstreaminglayer/blob/master/LSL/liblsl-Python/examples/SendStringMarkers.py

The stream can/will be saved by the Lab Recorder software and that data can then be used for processing in this library. (For the stimulus scripts, they can be in any language that LSL offers so that markers can be created and sent).

These images below are from processing a dataset where a subject was looking at these stimuli:

Gaze data (gaze_x, and gaze_y fields):

All trials across all triggers:

Mean of all trials for each trigger overlaid: