EinsteinPy is an open source pure Python package dedicated to problems arising in General Relativity and gravitational physics, such as geodesics plotting for Schwarzschild, Kerr and Kerr Newman space-time model, calculation of Schwarzschild radius, calculation of Event Horizon and Ergosphere for Kerr space-time. Symbolic Manipulations of various tensors like Metric, Riemann, Ricci, Ricci Scalar, Weyl, Schouten, Stress-Energy-Momentum, Einstein and Christoffel Symbols is also possible using the library. EinsteinPy also features Hypersurface Embedding of Schwarzschild space-time, which will soon lead to modelling of Gravitational Lensing! It is released under the MIT license.
Complete documentation, including a user guide and an API reference, can be read on the wonderful Read the Docs.
In the examples directory, you can find several Jupyter notebooks with specific applications of einsteinpy. You can consider theses Jupyter Notebooks as tutorials for einsteinpy. You can launch a cloud Jupyter server using binder to edit the notebooks without installing anything. Try it out!
EinsteinPy requires the following Python packages:
- NumPy, for basic numerical routines
- Astropy, for physical units and time handling
- Matplotlib, for static geodesics plotting and visualizations.
- Plotly, for interactive geodesics plotting and visualizations.
- SciPy, for solving ordinary differential equations.
- SymPy, for symbolic calculations related to GR.
- Numba, for accelerating the code
EinsteinPy is usually tested on Linux, Windows and OS X on Python 3.5, 3.6, 3.7 and 3.8 against latest NumPy.
|OS X||Github Actions|
The easiest and fastest way to get the package up and running is to install EinsteinPy using conda:
$ conda install einsteinpy --channel conda-forge
Or you can simply install it from PyPI:
$ pip install einsteinpy
Or for Debian/Ubuntu/Mint users, the package is installable (Ubuntu 19.04 onwards) from apt:
$ sudo apt install python3-einsteinpy
Please note that the package version in Debian Repositories might not be the latest. But it will be definitely the most stable version of EinsteinPy available till date.
Please check out the guide for alternative installation methods.
If installed correctly, the tests can be run using pytest:
$ python -c "import einsteinpy.testing; einsteinpy.testing.test()" ============================= test session starts ============================== platform linux -- Python 3.7.1, pytest-4.3.1, py-1.8.0, pluggy-0.9.0 rootdir: /home/shreyas/Local Forks/einsteinpy, inifile: setup.cfg plugins: remotedata-0.3.1, openfiles-0.3.1, doctestplus-0.3.0, cov-2.5.1, arraydiff-0.3 collected 56 items [...] ==================== 56 passed, 1 warnings in 28.19 seconds ==================== $
If the installation fails or you find something that doesn't work as expected, please open an issue in the issue tracker.
EinsteinPy is a community project, hence all contributions are more than welcome! For more information, head to CONTRIBUTING.rst.
Developers Documentation can be found here.
Release announcements and general discussion take place on our mailing list. Feel free to join!
If you still have a doubt, write a mail directly to email@example.com.
If you use EinsteinPy on your project, please drop us a line.
You can also use the DOI to cite it in your publications. This is the latest one:
And this is an example citation format:
Shreyas Bapat et al.. (2019). EinsteinPy: einsteinpy 0.1.0. Zenodo. 10.5281/zenodo.2582388
EinsteinPy is released under the MIT license, hence allowing commercial use of the library. Please refer to COPYING.
EinsteinPy comes from the name of the famous physicist, Nobel laureate, revolutionary person, Prof. Albert Einstein. This is a small tribute from our part for the amazing work he did for the humanity!
Can I do <insert nerdy thing> with EinsteinPy?
EinsteinPy is focused on general relativity. One can always discuss probable features on the mailing list and try to implement it. We welcome every contribution and will be happy to include it in EinsteinPy.
What's the future of the project?
EinsteinPy is actively maintained and we hope to receive an influx of new contributors. The best way to get an idea of the roadmap is to see the Milestones of the project.
The whole documentation and code structure is shamelessly inspired by poliastro . We really thank the poliastro developers to make this possible. EinsteinPy is nothing without it's supporters.