Metadata-Version: 2.1
Name: pastawrap
Version: 0.1.0
Summary: The Python wrapper for ratPASTA - R-based Awesome Toolkit for PASTA.
Home-page: https://github.com/davorvr/pastawrap
Author: Davor Virag, Ivan Kodvanj, Jan Homolak
Author-email: davor.virag@gmail.com
Maintainer: Davor Virag
Maintainer-email: davor.virag@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: R
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: rpy2 (>=3.3.0)
Requires-Dist: pandas (>=1.0.0)

pastaWRAP
=========

**pastaWRAP** is a Python wrapper for **R-based Awesome Toolkit for PASTA**,
more commonly known as [**ratPASTA**](https://github.com/ikodvanj/ratPASTA) - an
R package used for processing and visualising data from startle experiments in
rodents or experiments measuring grip strength in rodents. Currently, pastaWRAP only
supports ratPASTA functionality for startle experiments, with plans for adding
griPASTA (grip strength test) functionality wrapping at a later date. The input data
for this package is created with a **PASTA** solution (**Platform for Acoustic STArtle**),
described in detail here:

*Virag, D., Homolak, J., Kodvanj, I., Babic Perhoc, A., Knezovic, A.,
Osmanovic Barilar, J., & Salkovic-Petrisic, M. (2020). Repurposing a
digital kitchen scale for neuroscience research: a complete hardware and
software cookbook for PASTA. BioRxiv, 2020.04.10.035766. 
https://doi.org/10.1101/2020.04.10.035766*

Using the same platform for measuring grip strength in rodents is
described here:

*Homolak, J., Virag, D., Kodvanj, I., Matak, I., Babic Perhoc, A.,
Knezovic, A., Osmanovic Barilar, J., Salkovic-Petrisic, M. (2020).
griPASTA: A hacked kitchen scale for quantification of grip strength in
rodents. BioRxiv, 2020.07.23.217737.
https://doi.org/10.1101/2020.07.23.217737*


