Welcome to qnm

Python implementation of Cook-Zalutskiy spectral approach to computing Kerr QNM frequencies.

TODO basic info

Installation

PyPI

qnm is available through PyPI:

pip install qnm

From source

git clone https://github.com/duetosymmetry/qnm.git
cd qnm
python setup.py install

If you do not have root permissions, replace the last step with python setup.py install --user

Dependencies

All of these can be installed through pip or conda.

• numpy
• scipy
• numba

Documentation

Automatically-generated API documentation is available on Read the Docs: qnm.

Usage

The highest-level interface is via qnm.cached.KerrSeqCache, which load cached spin sequences from disk. A spin sequence is just a mode labeled by (s,l,m,n), with the spin a ranging from a=0 to some maximum, e.g. 0.9995. A large number of low-lying spin sequences have been precomputed and are available online. The first time you use the package, download the precomputed sequences:

>>> import qnm

>>> qnm.download_data() # Only need to do this once
Trying to fetch https://duetosymmetry.com/files/qnm/data.tar.bz2
Trying to decompress file /<something>/qnm/data.tar.bz2
Data directory /<something>/qnm/data contains 860 pickle files

Then, use qnm.cached.KerrSeqCache to load a qnm.spinsequence.KerrSpinSeq of interest. If the mode is not available, it will try to compute it (see detailed documentation for how to control that calculation).

>>> ksc = qnm.cached.KerrSeqCache(init_schw=True) # Only need init_schw once
>>> mode_seq = ksc(s=-2,l=2,m=2,n=0)
>>> omega, A, C = mode_seq(a=0.68)
>>> print(omega)
(0.5239751042900845-0.08151262363119974j)

Calling a spin sequence with mode_seq(a) will return the complex quasinormal mode frequency omega, the complex angular separation constant A, and a vector C of coefficients for decomposing the associated spin-weighted spheroidal harmonics as a sum of spin-weighted spherical harmonics.

Credits

The code is developed and maintained by Leo C. Stein.