Metadata-Version: 2.1
Name: CoordinationMetricsToolbox
Version: 0.1.3
Summary: A toolbox for measuring and analyzing coordination metrics.
Home-page: https://github.com/oceanedbs/CoordinationMetricsToolbox
Author: Océane Dubois
Author-email: 
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# Coordination Metrics Toolbox

The Coordination Metrics Toolbox is a comprehensive suite of tools designed to measure and analyze coordination metrics in various projects.

## Installation
Download this repository
At the root of this repository, run 
    `pip install .`


Or 
    `pip install CoordinationMetricsToolbox`


# Informations

This work comes from the article : Dubois, O., Roby-Brami, A., Parry, R. et al. A guide to inter-joint coordination characterization for discrete movements: a comparative study. J NeuroEngineering Rehabil 20, 132 (2023). https://doi.org/10.1186/s12984-023-01252-2

Not all metrics have been implemented since some of them needs additional informations suche as joints position over time. 

2 more metrics have been added : Joints contribution variation based on Principal Component Analysis (JcvPCA) and Joint Synchronization Variation based on Continuous Relative Phase (JsvCRP). These metrics are under publication for PLOS-One

# Tutorial
An exemple with simulated data is available in test_coord_metric.py. 

Csv files containing a time column and joint angles trajectories can also be loaded to build a CoodinationMetric object form which different metrics can be computed. 


The format expected for the data is the following one : 
| time | joint_i | joint_j | joint_k | _ee_x_ | _ee_y_ | _ee_z_ |
|------|---------|---------|---------|--------|--------|--------|
|      |         |         |         |        |        |        |
|      |         |         |         |        |        |        |
|      |         |         |         |        |        |        |

Columns in italic (ee_x, ee_y, ee_z) which are the position of the end-effector are optional. However, without thoses columns, the number of metrics that can be computed is limited.

The file generate_testing_dataset.py can also be used to generate different datasets based on sinusoids. You can personalize this file to generate your own datasets. 

The full documentation of the toolbox can be found [here](https://oceanedbs.github.io/CoordinationMetricsToolbox/index.html)


# Generate local documentation 

Go to the doc file and execute __make html__ to generate documentation locally


