Metadata-Version: 2.1
Name: pyroved
Version: 0.1.1
Summary: Variational encoder-decoder models in Pyro probabilistic programming language
Home-page: https://github.com/ziatdinovmax/pyroVED/
Author: Maxim Ziatdinov
Author-email: maxim.ziatdinov@ai4microcopy.com
License: MIT license
Description: # pyroVED
        
        ---
        [![build](https://github.com/ziatdinovmax/pyroVED/actions/workflows/actions.yml/badge.svg)](https://github.com/ziatdinovmax/pyroVED/actions/workflows/actions.yml)
        [![Documentation Status](https://readthedocs.org/projects/pyroved/badge/?version=latest)](https://pyroved.readthedocs.io/en/latest/README.html)
        [![PyPI version](https://badge.fury.io/py/pyroved.svg)](https://badge.fury.io/py/pyroved)
        
        pyroVED is an open-source package built on top of the Pyro probabilistic programming language for applications of variational encoder-decoder models in spectral and image analyses. The currently available models include variational autoencoders with translational and/or rotational invariance for unsupervised, class-conditioned, and semi-supervised learning, as well as *im2spec*-type models for predicting spectra from images and vice versa.
        More models to come!
        
        ## Examples
        The easiest way to start using pyroVED is via [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb), which is a free research tool from Google for machine learning education and research built on top of Jupyter Notebook. The following notebooks can be executed in Google Colab by simply clicking on the "Open in Colab" icon:
        
        *   Shift-VAE: Mastering the 1D shifts in spectral data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/master/examples/shiftVAE.ipynb)
        
        *   r-VAE: Disentangling image content from rotations [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/master/examples/rVAE.ipynb)
        
        *   j(r)-VAE: Learning (jointly) discrete and continuous representations of data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/main/examples/jrVAE.ipynb)
        
        *   ss(r)-VAE: Semi-supervised learning from data with orientational disorder [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/main/examples/ssrVAE.ipynb)
        
        ## Installation
        
        #### Requirements
        *   python >= 3.6
        *   pyro-ppl >= 1.6
        
        Install pyroVED using pip:
        
        ```bash
        pip install pyroved
        ```
        
        #### Latest (unstable) version
        
        To upgrade to the latest (unstable) version, run
        
        ```bash
        pip install --upgrade git+https://github.com/ziatdinovmax/pyroved.git
        ```
        
        ## Development
        
        To run the unit tests, you'll need to have a pytest framework installed:
        
        ```bash
        python3 -m pip install pytest
        ```
        
        Then run tests as:
        
        ```bash
        pytest tests
        ```
        
        If this is your first time contributing to an open-source project, we highly recommend starting by familiarizing yourself with these very nice and detailed contribution [guidelines](https://github.com/firstcontributions/first-contributions).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
