The multimatch-gaze command is the standalone equivalent of the MultiMatch toolbox and is easiest executed directly from the command line.

Command line

The computation of the similarity between two scan paths doesn’t involve anything beyond the command line keyword multimatch-gaze followed by two input files, corresponding to tab-separated files with a fixation vector, and the screensize in pixel, supplied as two consecutive integers corresponding to the x and y dimension of the screen:

multimatch-gaze path/to/scanpath_one path/to/scanpath_two x_dim y_dim

The input files will be read with numpys recfromcsv() and should contain one fixation per line. The first three columns of the input file are relevant. One column should contain x-coordinates of the fixation in px (start_x), one column should contain y-coordinates in px (start_y), and one column should contain contain the fixation duration (duration) in seconds. Example files with this structure can be found here. Note that the input data needs to have a header with the correct column names (x_start, y_start, duration).

An examplary command line call that you could execute if you cloned the repository looks like this:

multimatch-gaze data/fixvectors/segment_0_sub-01.tsv data/fixvectors/segment_0_sub-19.tsv 1280 720

Scanpath simplification

Optionally, scan paths can be simplified to reduce their complexity. To simplify scan paths, specify the following arguments:

  • --direction-threshold: If two consecutive saccades have a small angle, they will be combined. Should be in degrees, such as 45.0 for 45°
  • --amplitude-threshold: If two consecutive saccades are short, they will be combined. Should be in pixel, such as 100.0 for 100px.
  • --duration-threshold: Only if the intermediate fixation’s durations are shorter than this threshold the above simplification will be performed. Should be in seconds, such as 0.1 for 100ms.

Note: If either direction- or amplitude threshold is specified as 0, no grouping will be performed!

A commandline call of the module with simplification would hence look like this:

multimatch-gaze data/fixvectors/segment_0_sub-01.tsv data/fixvectors/segment_0_sub-19.tsv 1280 720
--direction-threshold 45.0 --amplitude-threshold 100.0 --duration-threshold 0.1

There are no guidelines whether and if so, how much, simplification is appropriate, and it is strongly dependent on individual use case. The original Matlab toolbox implements a default amplitude threshold of 10% of the screen diagonal as amplitude, 45° as angle, and 300ms as duration thresholds. multimatch-gaze has defaults of 0 for simplification parameters (i.e. simplification is not performed by default).

Output configuration

The way results are displayed in the command line can be configured with the -o/--output-type parameter. Three different formats are possible:

  • hr (default): Results are returned row-wise, with dimension name. This is the most human readable format, and good for a quick glance at results:
Vector similarity = <value>
Direction similarity = <value>
  • single-row: Results are returned in a single row, delimited with tabs, and without dimension name. Makes it easy to collate results in a table:
  • single-del: Results are returned row-wise, with tabs seperating dimension name and value. This makes it easy to pick out a selection of scores:

REMoDNaV helper

REMoDNaV is a velocity-based event detection algorithm for eye movement classification. It detects and labels saccades, fixations, post-saccadic oscillations, and smooth pursuit movements, and it was specifically developed to work with dynamic stimulation. REMoDNaV is an open-source Python package, and its outputs, BIDS-compliant TSV files, can be read natively by multimatch-gaze. The conversion of data to a fixation vector is then handled internally.

Should you have data produced by REMoDNaV, you can to supply the --remodnav parameter:

multimatch-gaze data/remodnav_samples/sub-01_task-movie_run-1_events.tsv
data/remodnav_samples/sub-01_task-movie_run-2_events.tsv 1280 720 --remodnav

As REMoDNaV classifies pursuits, which can be seen as a “visual intake” category such as fixations, you can decide whether to include or discard any pursuit events. Using pursuits would be useful for example in the case of moving stimuli: Visual intake of a moving target would appear as a pursuit in eye tracking data. Setting this function is handled with the --pursuit parameter. Chose between options "discard" and "keep".

  • discard (default) will disregard pursuit events.
  • keep will turn a pursuit movement into two fixations - the start and ending point of the pursuit movement.

Specify to keep pursuit movements (i.e. inclusion into the scan path) like this:

multimatch-gaze data/remodnav_samples/sub-01_task-movie_run-1_events.tsv
data/remodnav_samples/sub-01_task-movie_run-2_events.tsv 1280 720 --remodnav --pursuit 'keep'


If you wish to use the functionality of multimatch-gaze within a running Python instance such as IPython, you can import the module and use the function docomparison. Here is an example:

import multimatch_gaze as m
import numpy as np

# read in data
fix_vector1 = np.recfromcsv('data/fixvectors/segment_0_sub-01.tsv',
delimiter='\t', dtype={'names': ('start_x', 'start_y', 'duration'),
'formats': ('f8', 'f8', 'f8')})
fix_vector2 = np.recfromcsv('data/fixvectors/segment_0_sub-19.tsv',
delimiter='\t', dtype={'names': ('start_x', 'start_y', 'duration'),
'formats': ('f8', 'f8', 'f8')})

# Optional - if the input data are produced by REMoDNaV
# pursuits = True is the equivalent of --pursuits 'keep', else specify False
fix_vector1 = m.remodnav_reader('data/remodnav_samples/sub-01_task-movie_run-1_events.tsv',
screensize = [1280, 720], pursuits = True)

# execution with multimatch-gaze's docomparison() function without grouping
m.docomparison(fix_vector1, fix_vector2, screensize=[1280, 720])

# execution with multimatch-gaze's docomparison() function with grouping
m.docomparison(fix_vector1, fix_vector2, screensize=[1280, 720], grouping=True, TDir=30.0,
TDur=0.1, TAmp=100.1)

The results will be returned as an array, such as [0.98, 0.87, 0.45, 0.78, 0.80].