Welcome to qnm

Python implementation of the Cook-Zalutskiy spectral approach to computing Kerr quasinormal frequencies (QNMs).

With this python package, you can compute the QNMs labeled by different (s,l,m,n), at a desired dimensionless spin parameter 0≤a<1. The angular sector is treated as a spectral decomposition of spin-weighted spheroidal harmonics into spin-weighted spherical harmonics. Therefore you get the spherical-spheroidal decomposition coefficients for free when solving for ω and A (see below for details).

We have precomputed a large number of low-lying modes (s=-2 and s=-1, all l<8, all n<7). These can be automatically installed with a single function call, and interpolated for good initial guesses for root-finding at some value of a.

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
• tqdm (just for qnm.download_data() progress)

Documentation

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

Usage

The highest-level interface is via qnm.cached.KerrSeqCache, which loads 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 (see below for details).

Visual inspections of modes are very useful to check if the solver is behaving well. This is easily accomplished with matplotlib. Here are some simple examples:

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('text', usetex = True)

s, l, m = (-2, 2, 2)
mode_list = [(s, l, m, n) for n in np.arange(0,7)]
modes = {}
for ind in mode_list:
    modes[ind] = ksc(*ind)

plt.figure(figsize=(16,8))

plt.subplot(1, 2, 1)
for mode, seq in modes.items():
    plt.plot(np.real(seq.omega),np.imag(seq.omega))


modestr = "{},{},{},n".format(s,l,m)
plt.xlabel(r'$\textrm{Re}[\omega_{' + modestr + r'}]$', fontsize=16)
plt.ylabel(r'$\textrm{Im}[\omega_{' + modestr + r'}]$', fontsize=16)
plt.gca().tick_params(labelsize=16)
plt.gca().invert_yaxis()

plt.subplot(1, 2, 2)
for mode, seq in modes.items():
    plt.plot(np.real(seq.A),np.imag(seq.A))

plt.xlabel(r'$\textrm{Re}[A_{' + modestr + r'}]$', fontsize=16)
plt.ylabel(r'$\textrm{Im}[A_{' + modestr + r'}]$', fontsize=16)
plt.gca().tick_params(labelsize=16)

plt.show()

Which results in the following figure:

s, l, n = (-2, 2, 0)
mode_list = [(s, l, m, n) for m in np.arange(-l,l+1)]
modes = {}
for ind in mode_list:
    modes[ind] = ksc(*ind)

plt.figure(figsize=(16,8))

plt.subplot(1, 2, 1)
for mode, seq in modes.items():
    plt.plot(np.real(seq.omega),np.imag(seq.omega))


modestr = "{},{},m,0".format(s,l)
plt.xlabel(r'$\textrm{Re}[\omega_{' + modestr + r'}]$', fontsize=16)
plt.ylabel(r'$\textrm{Im}[\omega_{' + modestr + r'}]$', fontsize=16)
plt.gca().tick_params(labelsize=16)
plt.gca().invert_yaxis()

plt.subplot(1, 2, 2)
for mode, seq in modes.items():
    plt.plot(np.real(seq.A),np.imag(seq.A))

plt.xlabel(r'$\textrm{Re}[A_{' + modestr + r'}]$', fontsize=16)
plt.ylabel(r'$\textrm{Im}[A_{' + modestr + r'}]$', fontsize=16)
plt.gca().tick_params(labelsize=16)

plt.show()

Which results in the following figure:

Spherical-spheroidal decomposition

The angular dependence of QNMs are naturally spin-weighted spheroidal harmonics. The spheroidals are not actually a complete orthogonal basis set. Meanwhile spin-weighted spherical harmonics are complete and orthonormal, and are used much more commonly. Therefore you typically want to express a spheroidal (on the left hand side) in terms of sphericals (on the right hand side),

Here ℓmin=max(|m|,|s|) and ℓmax can be chosen at run time. The C coefficients are returned as a complex ndarray, with the zeroth element corresponding to ℓmin.

Credits

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