Welcome to qnmfits

qnmfits is an open-source Python code for least-squares fitting of quasinormal modes to ringdown waveforms. The code is designed to interface with the sxs and scri packages, which allows for easy loading and transformations of SXS waveforms (including CCE waveforms) - but any waveform data can be used. Quasinormal-mode frequencies and spherical-spheroidal mixing coefficients are loaded via the qnm package, and multimode fitting across multiple spherical-harmonic modes with mode mixing is handled automatically.

Installation

qnmfits is available on conda-forge:

conda install conda-forge::qnmfits

and PyPI:

pip install qnmfits

From source

Clone and install this code locally:

git clone git@github.com:sxs-collaboration/qnmfits.git
cd qnmfits
pip install .

Usage

Perform a seven-overtone fit to the (2,2) mode of SXS:BBH_ExtCCE:0001:

import numpy as np
import qnmfits

# Download SXS:BBH_ExtCCE:0001
abd = qnmfits.cce.load(1)

# Transform to the superrest frame. First, shift the times for convenience:
h22 = abd.h.data[:, abd.h.index(2, 2)]
abd.t -= abd.t[np.argmax(np.abs(h22))]

# Map to the superrest frame
abd_prime = qnmfits.utils.to_superrest_frame(abd, t0=300)

# Get the strain and remnant mass and spin
h = abd_prime.h
Mf = abd_prime.bondi_rest_mass()[-1]
chif = np.linalg.norm(abd_prime.bondi_dimensionless_spin()[-1])

# QNMs we want to fit for. The format is (ell, m, n, sign), where sign is +1
# for "regular" (positive real part) modes, and -1 is for "mirror" (negative
# real part) modes.
qnms = [(2, 2, n, 1) for n in range(7+1)]

# Spherical modes we want to fit to. The format is (ell, m).
spherical_modes = [(2, 2)]

# Ringdown start time
t0 = 0.

# Perform the fit
best_fit = qnmfits.fit(
    data=h,
    chif=chif,
    Mf=Mf,
    qnms=qnms,
    spherical_modes=spherical_modes,
    t0=t0
)

print(f"Mismatch = {best_fit['mismatch']}")

The mismatch should be of order 1E-7. Please see the notebooks in examples for additional code usage.

Contributing

Contributions are welcome! There are at least two ways to contribute to this codebase:

1. 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.
2. 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.

Credits

The code is developed and maintained by Lorena Magaña Zertuche and Eliot Finch.