remodnav - Increase precision in output

This release contains a feature request from #47: it increases the precision of reported x and y start and end points to 5 decimal points.

- Python
Published by adswa about 3 years ago

remodnav - Even more safeguards

Includes #36 - a safeguard against events without any valid velocity values.

- Python
Published by adswa over 4 years ago

remodnav - Maintenance release

What's Changed

• Specified expected CSV-format in README.md by @jliebers in https://github.com/psychoinformatics-de/remodnav/pull/16
• BF: prevent conversion of lower y-axis limit to -inf by @adswa in https://github.com/psychoinformatics-de/remodnav/pull/15
• BF-19: Safeguard inappropriate savgol filter set up by @adswa in https://github.com/psychoinformatics-de/remodnav/pull/20
• BF: Loosen savgol-filter warning to allow disabling the filter by @adswa in https://github.com/psychoinformatics-de/remodnav/pull/22
• Switch to appveyor w/ cross-platform tests by @mih in https://github.com/psychoinformatics-de/remodnav/pull/21
• Fix CIs by @adswa in https://github.com/psychoinformatics-de/remodnav/pull/32
• Improve input validation by @adswa in https://github.com/psychoinformatics-de/remodnav/pull/29

New Contributors

• @jliebers made their first contribution in https://github.com/psychoinformatics-de/remodnav/pull/16

Full Changelog: https://github.com/psychoinformatics-de/remodnav/compare/1.0...v1.1

- Python
Published by adswa over 4 years ago

remodnav - Submission time

• Improve program help

- Python
Published by mih about 7 years ago

remodnav - Go publish

• Ability to distinguish any number of fixation and pursuit events within a single inter-saccade-period
• No longer use a maximum amplitude parameter to distinguish pursuits from fixations, but use a single velocity threshold instead. The threshold is evaluated against a heavily low-pass filtered gaze trajectory, to only reflect "smooth" eye movement components (and thereby suppress the impact of measurement noise).
• New parameter noise_factor that influences the adaptive saccade velocity threshold. The saccade onset velocity threshold is the median of all sub-threshold velocities plus noise_factor times the MAD of these velocities. The saccade peak velocity threshold is computed in the same fashion, but uses 2x noise_factor. The default value should work for noisy data. Reducing this factor can boost saccade detection sensitivity for clean data (e.g. Nyström et al., 2010 use the equivalent of a factor of 3.0)

- Python
Published by mih about 7 years ago

remodnav - New beginnings

- Python
Published by mih over 7 years ago