Multidimensional scanpath comparison
Project description
Build Status codecov Documentation PyPIversion License: MIT Build status DOI
multimatch-gaze
Reimplementation of MultiMatch toolbox (Dewhurst et al., 2012) in Python.
The MultiMatch method proposed by Jarodzka, Holmqvist and Nyström (2010), implemented in Matlab as the MultiMatch toolbox and validated by Dewhurst and colleagues (2012) is a vector-based, multi-dimensional approach to compute scanpath similarity.
For a complete overview of this software, please take a look at the Documentation
The method represents scanpaths as geometrical vectors in a two-dimensional space: Any scanpath is build up of a vector sequence in which the vectors represent saccades, and the start and end position of saccade vectors represent fixations. Two such sequences (which can differ in length) are compared on the five dimensions ‘vector shape’, ‘vector length’ (saccadic amplitude), ‘vector position’, ‘vector direction’ and ‘fixation duration’ for a multidimensional similarity evaluation (all in range [0, 1] with 0 denoting maximal dissimilarity and 1 denoting identical scanpaths on the given measure). The original Matlab toolbox was kindly provided via email by Dr. Richard Dewhurst and the method was ported into Python with the intent of providing an open source alternative to the matlab toolbox.
Installation instructions
It is recommended to use a dedicated virtualenv:
# create and enter a new virtual environment (optional) virtualenv --python=python3 ~/env/multimatch . ~/env/multimatch/bin/activate
multimatch-gaze can be installed via pip. To automatically install multimatch-gaze with all dependencies, use:
# install from pyPi pip install multimatch-gaze
Support/Contributing
Bug reports, feedback, or any other contribution are always appreciated. To report a bug, request a feature, or ask a question, please open an issue. Pull requests are always welcome.
Examplary usage of multimatch-gaze in a terminal
required inputs: - two tab-separated files with nx3 fixation vectors (x coordinate in px, y coordinate in px, duration) - screensize in px (x dimension, y dimension)
multimatch data/fixvectors/segment_10_sub-19.tsv data/fixvectors/segment_10_sub-01.tsv 1280 720
optional inputs:
if scanpath simplification should be performed, please specify in addition - –amplitude-threshold (-am) in px - –direction-threshold (-di) in degree - –duration-threshold (-du) in seconds
Example usage with grouping:
multimatch data/fixvectors/segment_10_sub-19.tsv data/fixvectors/segment_10_sub-01.tsv 1280 720 --direction-threshold 45.0 --duration-threshold 0.3 --amplitude-threshold 147.0
REMoDNaV helper:
Eye movement event detection results produced by REMoDNaV can be read in natively by multimatch-gaze. To indicate that datafiles are REMoDNaV outputs, supply the --remodnav parameter.
multimatch 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
REMoDNaV can classify smooth pursuit movements. As a consequence, when using REMoDNaV output, users need to indicate how these events should be treated. By default, multimatch-gaze will discard pursuits. In some circumstances, however, it can be useful to include pursuit information. Moving stimuli for example would evoke a pursuit movement during visual intake. When specifying the --pursuit keep parameter, the start and end points of pursuits will be included in the scanpath.
multimatch 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
References:
Dewhurst, R., Nyström, M., Jarodzka, H., Foulsham, T., Johansson, R. & Holmqvist, K. (2012). It depends on how you look at it: scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach. Behaviour Research Methods, 44(4), 1079-1100. doi: 10.3758/s13428-012-0212-2.
Dijkstra, E. W. (1959). A note on two problems in connexion withgraphs. Numerische Mathematik, 1, 269–271. https://doi.org/10.1007/BF01386390
Jarodzka, H., Holmqvist, K., & Nyström, M. (eds.) (2010). A vector-based, multidimensional scanpath similarity measure. In Proceedings of the 2010 symposium on eye-tracking research & applications (pp. 211-218). ACM. doi: 10.1145/1743666.1743718
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file multimatch_gaze-0.1.0.tar.gz
.
File metadata
- Download URL: multimatch_gaze-0.1.0.tar.gz
- Upload date:
- Size: 24.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/2.7.16
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ea624b78c116dcda850d7b2011e49ad78033e261b9002f97acc7ad3cef33b7bf |
|
MD5 | 9e54de2968c3af094c336ae96b1934df |
|
BLAKE2b-256 | a187bdb2c6fb3dc0dd5f56f30db89ceab3fb107880e3524d91d9364f8b0f0523 |
File details
Details for the file multimatch_gaze-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: multimatch_gaze-0.1.0-py3-none-any.whl
- Upload date:
- Size: 42.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/2.7.16
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 02b06f1765e8a34c5aed95c6128400df90f9d9b1b77487b6f308b53724e4c9a0 |
|
MD5 | 911f80fa6f40b986dc563642224fc666 |
|
BLAKE2b-256 | 2fba7b25256f945adc19082562aeab903d23446467259ee33b0b71469e679bf8 |