qnmfits is an open-source Python code for multimode ringdown modeling. It
allows you to fit for the quasinormal mode amplitudes by minimizing the
mismatch between a numerical relativity waveform and an analytical ringdown
model. Additionally, it is equipped with a 'greedy' algorithm that picks the
most important modes to model based on their power contribution to the residual
between numerical and model waveforms. This multimode modeling code includes
overtones, retrograde modes, and spherical-spheroidal mixing coefficients.
qnmfits uses the parent qnm package to
access quasinormal mode frequencies and spherical-spheroidal mixing
coefficients.
This code can handle both extrapolated and CCE waveforms from the SXS catalog,
and it comes equipped with scri based
function that allows you to map the CCE waveform to the superrest frame of the
remnant for a high-precision ringdown analysis.
This packages uses scri, which should be installed with conda (see the scri quickstart here). It is recommended to install scri first, before installing this package:
conda install -c conda-forge scriqnmfits is available on PyPI:
pip install qnmfitsqnm is available on conda-forge:
conda install -c conda-forge qnmClone and install this code locally:
git clone [email protected]:sxs-collaboration/qnmfits.git
cd qnmfits
pip install .This package uses the following dependencies:
Dependencies should be installed automatically by using the instructions above.
Automatically-generated API documentation is available on Read the Docs: qnmfits.
Contributions are welcome! There are at least two ways to contribute to this codebase:
- If you find a bug or want to suggest an enhancement, use the issue tracker on GitHub. It's a good idea to look through past issues, too, to see if anybody has run into the same problem or made the same suggestion before.
- If you will write or edit the python code, we use the fork and pull request model.
You are also allowed to make use of this code for other purposes, as detailed in the MIT license. For any type of contribution, please follow the code of conduct.
If this package contributes to a project that leads to a publication,
please acknowledge this by citing the qnmfits article in.
The code is developed and maintained by Lorena Magaña Zertuche and Eliot Finch.