Skip to content
MiraPy: A Python package for Deep Learning in Astronomy
Branch: master
Clone or download
swapsha96 Documentation! (#34)
Documentation!
Latest commit 28a4fac May 19, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
astropy_helpers @ 5876103 add Axes3D import May 15, 2019
docs
logos added logos Apr 22, 2019
mirapy small fix May 15, 2019
.gitignore Added package requirements Apr 3, 2019
.gitmodules Initialize astropy_helpers at version v3.1 Apr 3, 2019
.rtd-environment.yml minor fix May 19, 2019
.travis.yml modified tutorials.rst May 19, 2019
LICENSE.rst added train to models May 2, 2019
MANIFEST.in Creation of MiraPy from astropy package template Apr 3, 2019
README.rst
ah_bootstrap.py Initialize astropy_helpers at version v3.1 Apr 3, 2019
appveyor.yml Creation of MiraPy from astropy package template Apr 3, 2019
code-of-conduct.md added code of conduct May 7, 2019
readthedocs.yml minor fix May 19, 2019
requirements.txt add tutorials May 19, 2019
setup.cfg
setup.py minor fix May 19, 2019

README.rst

MiraPy: Python Package for Deep Learning in Astronomy

Powered by Astropy Badge Travis CI Documentation Status Slack PyPI LICENSE Zenodo DOI

MiraPy is a Python package for Deep Learning in Astronomy. It is built using Keras for developing ML models to run on CPU and GPU seamlessly. The aim is to make applying machine learning techniques on astronomical data easy for astronomers, researchers and students.

The documentation is available here.

Applications

MiraPy can be used for problem solving using ML techniques and will continue to grow to tackle new problems in Astronomy. Following are some of the experiments that you can perform right now:

  • Classification of X-Ray Binaries using neural network
  • Astronomical Image Reconstruction using Autoencoder
  • Classification of the first catalog of variable stars by ATLAS
  • HTRU1 Pulsar Dataset Image Classification using Convolutional Neural Network
  • OGLE Catalogue Variable Star Classification using Recurrent Neural Network (RNN)
  • 2D and 3D visualization of feature sets using Principal Component Analysis (PCA)
  • Curve Fitting using Autograd (basic implementation)

There are more projects that we will add soon and some of them are as following:

  • Feature Engineering (Selection, Reduction and Visualization)
  • Classification of different states of GRS1905+105 X-Ray Binaries using Recurrent Neural Network (RNN)
  • Feature extraction from Images using Autoencoders and its applications in Astronomy

You can find the applications MiraPy in our tutorial repository.

Installation

You can download the package using pip package installer:

pip install mirapy

You can also build from source code:

git clone --recursive https://github.com/mirapy-org/mirapy.git
cd mirapy
pip install -r requirements.txt
python setup.py install

Contributing

MiraPy is far from perfect and we would love to see your contributions to open source community! In future, it will be able to do more and in better ways and we need your suggestions! Tell us what you would like to see as a part of this package on Slack.

About Us

MiraPy is developed by Swapnil Sharma and Akhil Singhal as their final year 'Major Technical Project' under the guidance of Dr. Arnav Bhavsar at Indian Institute of Technology, Mandi.

License

This project is Copyright (c) Swapnil Sharma, Akhil Singhal and licensed under the terms of the MIT license.

You can’t perform that action at this time.