A python package for solving problems in black hole perturbation theory
pybhpt is a collection of numerical tools for analyzing perturbations of Kerr spacetime, particularly the self-forces experienced by small bodies moving in a Kerr background. Subpackages include:
pybhpt.geodesic: a module that generates bound timelike geodesics in Kerr spacetimepybhpt.radial: a module that calculates homogeneous solutions of the radial Teukolsky equationpybhpt.swsh: a module that constructs the spin-weighted spheroidal harmonicspybhpt.teuk: a module that evaluates the inhomogeneous solutions (Teukolsky amplitudes) of the radial Teukolsky equation due to a point-particle on a bound timelike Kerr geodesicpybhpt.flux: a module that produces the gravitational wave fluxes sourced by a point-particle on a generic bound timelike Kerr geodesicpybhpt.hertz: a module that solves for the Hertz potentials for the CCK and AAB metric reconstruction procedurespybhpt.metric: a module that produces the coefficients needed to reconstruct the metric from the Hertz potentialspybhpt.redshift: a module that computes the generalized Detweiler redshift invariant in a variety of gauges
Warning This project is still under heavy active development. Please use at your own risk. If you plan on using this code for research purposes, please reach out to the authors first.
PyBHPT relies on several dependencies to install and run, namely a C/C++ compiler (e.g., g++), gsl, boost, Cython, numpy, scipy, and python >= 3.7.
To reduce package conflicts and ensure that the proper dependencies are installed,
we recommend using Anaconda and its virtual environments.
Create a conda environment pybhpt-env (or whatever name you would like)
with the necessary dependencies to install pybhpt:
conda create -n pybhpt-env -c conda-forge gsl boost-cpp Cython numpy scipy python=3.7
conda activate pybhpt-env
To include the necessary compiler on MACOSX run:
conda install clang_osx-64 clangxx_osx-64
To include the necessary compiler on linux run:
conda install gcc_linux-64 gxx_linux-64
Next clone the pybhpt repository from GitHub:
git clone https://github.com/znasipak/pybhpt.git
cd pybhpt
Finally, we recommend installing the package via pip:
pip install .
To run the code in a Jupyter notebook, we recommend pip installing the following dependencies:
pip install ipykernel matplotlib
One can then make the environment accessible within Jupyter by running
python -m ipykernel install --user --name=pybhpt-env
If the package is installed using pip, then one can easily uninstall the package by executing
pip uninstall pybhpt
To clean the repository, one will also need to remove the directories build/ and pybhpt.egg.info/ from the main repository
Zachary Nasipak