Python Packages

etformat is a Python package designed to simplify the extraction, conversion, and analysis of eye-tracking data from EDF files. It provides tools for exporting data, analyzing fixations and saccades, validating calibration, and visualizing gaze movements.

etformat is a Python package designed to simplify the extraction, conversion, and analysis of eye-tracking data from EDF files. It provides tools for exporting data, analyzing fixations and saccades, validating calibration, and visualizing gaze movements. Whether you're conducting research in psychology, neuroscience, or usability testing, etformat makes handling eye-tracking data efficient and accessible.

scia is a robust Python package dedicated to single-case data analysis. Designed for researchers and practitioners working with single-case experimental designs.

scia is a robust Python package dedicated to single-case data analysis. Designed for researchers and practitioners working with single-case experimental designs. scia provides a comprehensive suite of statistical methods to analyze, interpret, and report data effectively. The package includes functionality to create and preprocess single-case data frames, fill in missing values, filter data, and compute key non-overlap and effect size metrics. Advanced methods for evaluating treatment effects and trends are available through functions such as autocorr for autocorrelation analysis and bayesplm for Bayesian piecewise linear regression. Additional tools like pnd, pem, pet, nap, pand, ird, tau_u, and corrected_tau offer further analytical capabilities to support rigorous, data-driven decision making.

A Python package for processing academic references from plaintext files. RefCatch extracts references from text files (markdown, txt, etc.).

A Python package for processing academic references from plaintext files. RefCatch extracts references from text files (markdown, txt, etc.), attempts to find their DOIs using the CrossRef API, and outputs the results.

A Python package for creating diffeomorphically transformed images and videos for psychological experiments. Implements diffeomorphic transformations for image and video morphing.

A Python package for creating diffeomorphically transformed images and videos for psychological experiments. Diffeomorphic implements diffeomorphic transformations for image and video morphing, as described in: Stojanoski, B., & Cusack, R. (2014). Time to wave good-bye to phase scrambling: Creating controlled scrambled images using diffeomorphic transformations. Journal of Vision, 14(12), 6. doi:10.1167/14.12.6. This Python package was developed by Mohammad Ahsan Khodami, based on the original algorithms described in the paper by Stojanoski & Cusack.

asc2csv is a Python package developed for converting EyeLink .asc files into structured .csv files, facilitating the analysis of eye-tracking data.

asc2csv is a Python package developed for converting EyeLink .asc files into structured .csv files, facilitating the analysis of eye-tracking data. It extracts gaze data and event details such as blinks, saccades, and fixations, organizing them into an easy-to-use format.

Reconstruct pupil size during blinks in eye-tracking data with a physiologically inspired approach. Automatically detects blink intervals and reconstructs pupil size.

Reconstruct pupil size during blinks in eye-tracking data with a physiologically inspired approach. Features: Automatically detects blink intervals in eye-tracking data. Reconstructs pupil size during blinks using logarithmic recovery with dynamic adjustment of the recovery time constant (τ). Incorporates Gaussian noise to mimic natural variability in pupil size measurements. Validates input data to ensure compatibility and provides clear error messages for invalid inputs. Supports multiple input formats: CSV, Excel, JSON, Parquet, and TXT. Flexible output: Add a new column for reconstructed data or replace the original pupil size column with reconstructed values.