Metadata-Version: 2.1
Name: rsatoolbox
Version: 0.0.2
Summary: Representational Similarity Analysis (RSA) in Python
Home-page: https://github.com/rsagroup/rsatoolbox
Author: rsatoolbox authors
Author-email: 
License: UNKNOWN
Keywords: neuroscience
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: coverage
Requires-Dist: h5py
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: tqdm

# Representational Similarity Analysis 3.0

[![Documentation Status](https://readthedocs.org/projects/rsatoolbox/badge/?version=latest)](https://rsatoolbox.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/rsatoolbox.svg)](https://badge.fury.io/py/rsatoolbox)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/626ca9ec9f75485a9f73783c02710b1f)](https://www.codacy.com/gh/rsagroup/rsatoolbox?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=rsagroup/rsatoolbox&amp;utm_campaign=Badge_Grade)
[![CodeFactor](https://www.codefactor.io/repository/github/rsagroup/rsatoolbox/badge)](https://www.codefactor.io/repository/github/rsagroup/rsatoolbox)
[![codecov](https://codecov.io/gh/rsagroup/rsatoolbox/branch/master/graph/badge.svg)](https://codecov.io/gh/rsagroup/rsatoolbox)


Conceived during the RSA retreat 2019 in Blue Mountains.

[Documentation](https://rsatoolbox.readthedocs.io/)


#### Getting Started

The easiest way to install rsatoolbox is with pip:

```sh
pip install rsatoolbox
```

here is a simple code sample:

```python
import numpy, rsatoolbox
data = rsatoolbox.data.Dataset(numpy.random.rand(10, 5))
rdms = rsatoolbox.rdm.calc_rdm(data)
rsatoolbox.vis.show_rdm(rdms)
```

