Manipulate gravitational waveforms—changing frames, and so on.

The code in this project extends code written for the paper "Angular velocity of gravitational radiation from precessing binaries and the corotating frame", giving an explicit implementation of the methods discussed in it. The first commit of this project is the last commit of the paper's project.

The license for using this software is basically open (see the LICENSE file in this directory), though citations to the original paper are appreciated, where relevant. Also, if your work depends on features found in this module that have not been described in a separate publication of mine, I would appreciate the opportunity to be a coauthor.

Note that this code is not optimized. In many cases, obvious optimizations were rejected in favor of clearer or simpler code, or code that simply reflected the discussion in the paper more directly. The code is generally more than fast enough for interactive use on individual waveforms, but there is plenty of room for improvement. In particular, rotations may be painfully slow for large data sets.

The best way to get GWFrames running is to use anaconda. The first step is to install anaconda. On Linux and Mac, the next step should be as easy as running

conda create -y -n GWFrames
conda activate GWFrames
# conda config --env --set subdir osx-64  # If using an ARM Mac, uncomment this line
conda install -c conda-forge -y gwframes ipython jupyter  # these last two are optional

For most use cases, that should be enough. On Windows (or if that doesn't work), you'll need to compile GWFrames yourself, which is actually easiest again with conda, to get the build requirements. Instead of the last line of the instructions above, run the following

# Create a new conda env just for GWFrames
# You may want to add "matplotlib jupyter" to the end of the next line
conda install python=2 swig==3.0.10 numpy gsl hdf5 fftw h5py

## If using a Mac, you may need uncomment the following line
# conda install -y clang_osx-64 clangxx_osx-64 gfortran_osx-64

# Get the code
git clone --recursive https://github.com/moble/GWFrames.git

# Compile and install the code into this conda env
cd GWFrames/Code
python setup.py install

In either case, the commands above give you a separate GWFrames environment with working code, and without polluting other parts of your more modern python installation. After those commands have finished, you can check that that worked with

# Test that it works by constructing an equal-mass aligned-spin PN waveform
cd ~
python -c 'import GWFrames; W = GWFrames.PNWaveform("TaylorT4", 0.0, [0., 0., 0.9], [0.0, 0.0, 0.9], 0.01); print("That worked!")'

In the future, every time you want to use GWFrames in a new shell, just run

conda activate GWFrames

and python/ipython will automatically know where to find this module. You can install more packages (like matplotlib, ipython, jupyter, etc.) in this environment. Or if you want to stop using this environment and go back to your default environment, just run

conda deactivate

If any of the above fails, you may have an old version of conda with screwed up compilers. It maybe sufficient to just run

conda update -y -n base -c defaults conda

Otherwise, the surest way to solve that is just to reinstall with the current version of conda.

While conda is the scientific python community's prescribed way of running all things python, the community may have moved on by the time you try to run this code, or you may just not want to use a sensible way of running python. In that case, a good alternative to compiling everything yourself (described below) is to use docker.

You can download and run this image using the following commands:

docker pull moble/gwframes
docker run -i -t moble/gwframes bash

You'll probably want to attach some local directory to the container as a "volume", by adding such a flag to the command line:

docker run -i -t moble/gwframes --volume=/path/to/data:/data bash

This uses the directory found on your local machine in /path/to/data, and makes it available in the docker container as /data. Note that this allows the directory on your local machine to be both read from and written to, so that your output will survive even after you exit the container.

A convenient way to use this code is to start a Jupyter Notebook server and interact with it via your browser:

docker run -i -t -p 8888:8888 moble/gwframes --volume=/path/to/data:/data \
bash -c "/opt/conda/bin/jupyter notebook --notebook-dir=/data --ip='*' --port=8888 --no-browser"

You can then view the Jupyter Notebook by opening http://localhost:8888 in your browser.

It is easy to recompile the code in docker. To do so, you'll probably want to edit the code, which will require apt-get install emacs (or nano or vim or whatever). Then, you'll need to tell the compiler where GSL_HOME is. In this case, it's /opt/conda, so you can compile the code in GWFrames/Code by running

GSL_HOME=/opt/conda python setup.py install

As mentioned above, installing the build requirements is easiest with conda. Otherwise, you're on your own with all of the following:

To build just the C++ code:

• C++ compiler
• GNU Scientific Library, built as a shared library

To use the optional—but highly recommended—Python interface:

• SWIG v3.0.10
• HDF5 v1.8.10 or greater, built as a shared library with development headers (libhdf5-dev or similar)
• FFTW v3.2 or greater, built as a shared library

And, of course, python and a few of its goodies, for which I cannot recommend anaconda highly enough -- it makes installation and maintenance of the python software stack vastly easier.

• Python v2.7.4 or greater (but less than v3), with development headers (python-dev or similar)
• NumPy v1.7 or greater (conda install numpy)
• Matplotlib v1.2 or greater (conda install matplotlib)
• h5py v2.1 or greater (conda install h5py)
• IPython v2.0 or greater, with notebook (conda install ipython)

All of the above are reasonably standard, and can be installed easily through package managers such as apt and homebrew. The notebook interface for IPython provides an environment much like the Mathematica notebook interface. The examples are provided in a notebook (.ipynb) file.

The first step is to clone the git repo and its submodules:

git clone https://github.com/moble/GWFrames.git
cd GWFrames
git submodule init
git submodule update

The code is mostly in the form of C++, for speed. All functions are provided in the GWFrames namespace. The interface is reasonably straightforward, and can easily be included into and compiled with other code. Look for examples of how to do this in the C++Example directory. (See below for a few more details.)

However, it is mostly intended to be used through the interface to python. With the above-listed requirements, build the code and install to the user directory. To do this, run make or

python setup.py install --user

After this, you should be able to start a new python session (in any directory) and import GWFrames, which is the basic module provided by this code. The primary objects are Quaternion and Waveform objects, with various methods defined for each. An IPython notebook in the Docs directory contains extensive examples, following the outline of the paper. To use it, run

ipython notebook Docs/AngVelOfGravRadFromPrecessingBinaries_CorotatingFrame.ipynb

Alternatively, this code may be used directly through its C++ interface. A simple example is provided in the C++Example directory, along with the Makefiles needed to build all the necessary code. If it does not compile easily, make sure the various paths in both Makefiles are set properly.

Detailed documentation of most functions may be found through python's help function, or by running make in the Docs subdirectory, and reading Docs/html/index.html.

The code is primarily written by me (Mike Boyle). Dan Hemberger helped by porting some of my older code from Triton to perform noise-weighted overlap calculations, and with numerous bug reports and helpful suggestions. Serguei Ossokine also helped substantially by cross-checking the post-Newtonian formulas and results. Dante Iozzo has done his best to keep things working as this code enters its dotage.

Other contributions are entirely welcome. The preferred method is via github's excellent interface. If you have a bug report, just go to the issues page for this repo, check for related issues, and if this is new click "New Issue". If you want to contribute code, it is easiest for everyone if you use the [fork & pull method described here] (https://help.github.com/articles/using-pull-requests).