waveformtools 2023.6.27

Tools for working with numerical relativity and waveforms data

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Waveformtools

This is a python module for the handling and analysis of waveforms and data from Numerical Relativity codes, and for carrying out gravitational wave data analysis.

waveformtools is a numerical relativity data handling package that was written to aid the handling and analysis of numerical relativity data.

This package contains implementations of customized algorithms and techniques. Some of these contain the usage of existing python based library functions from pycbc, scipy, etc but effort has been made to keep these to a minimum.

• Handling of numerical relativity data, and retreiving specific information about the physical system. Presently, this supports the EinsteinToolkit data.

  The class container and methods "sim" can load NR data into convenient lists and dictionaries, which can be used to retrieve specific data/ information about the numerical simulation.

  This offers the following functionality, like retreiving:

  • Horizon masses, mass-ratios, and areas.

  • Horizon multipole moments.

  • Merger time/ formation time of common horizon.

  • The strain waveform.

  • The shear data of the dynamical horizons.

  • And computing/ extracting the Frequency, amplitude and phase of waveforms, etc.

• Handling of waveforms.

  A basic class container for handling numerical waveforms.

  This offers functionality like:

  • loading data from hdf5 files.

  • basic information such as time step, time axis, data axis, etc.

  • extrapolating a numerical waveform recorded at a finite radius to null infinity (to be added soon).

  • integrating and differentiating waveforms in the frequency domain (to be added soon).

• Tools for preparation/handling of numerical relativity data, like:

  • checking for discontinuity, removal of duplicated rows, and interpolating for missing rows in the data.

  • integration, differentiation of numerical data.

  • equalizing lengths of waveforms.

  • resampling

  • computing the norm, shifting/ rolling the data, etc.

  • Smoothening, tapering.

  • Extrapolation of waveforms to null infinity.

  • Center of Mass corrections to waveforms.

  • BMS Supertranslations of waveforms.

• Tools for basic data analysis.

  • A match algorithms for matching two waveforms.

  • Binning and interpolation of data.

  • Smooth derivatives: spectral and finite difference derivatives.

• Miscellaneous tools:

  • Progress bar to display progress of loops.

  • A custom print function with message prioritization.

  • Saving data to disk with protocol support (binary, text, etc.)

Citing this code

Please cite the latest version of this code if used in your work. This code was developed for use in the following works:

1. News from Horizons in Binary Black Hole Mergers
2. Tidal deformation of dynamical horizons in binary black hole mergers

We request you to also cite these. Thanks!

Installing this package

Dependencies

This module has the following dependencies:

• Standard packages (come with full anaconda installation)
  • numpy
  • scipy
  • statistics
  • matplotlib
  • h5py
  • termcolor
• Optional dependencies (labelled [EXT])
  • For use with PyCBC data analysis packages.
    • pyCBC
    • lalsuite
    • ligo-common
  • gmpy2

Recommended method

I recommend installing this module through pypi:

pip install waveformtools

Alternate method

Manual install directly from gitlab:

pip install git+https://gitlab.com/vaishakp/waveformtools@main

Or from a clone:

• First, clone this repository:

git clone https://gitlab.com/vaishakp/waveformtools.git

• Second, run python setup from the waveformtools directory:

cd waveformtools
python setup.py install --prefix="<path to your preferred installation dir>"

Manually setup conda environment

• To create an environment with automatic dependency resolution and activate it, run

conda create env -f docs/environment.yml
conda activate wftools

Using this code

>>> from waveformtools.waveforms import modes_array
>>> fdir = "<path to data dir>"
>>> fname = 'ExtrapStrain_RIT-BBH-0001-n100.h5'
>>> wf1 = modes_array(label='RIT001', spin_weight=-2)
>>> wf1.file_name = fname
>>> wf1.data_dir = fdir
>>> wf1.load_modes(ftype='RIT', var_type='Strain', ell_max='auto', resam_type='auto')
>>> wf1.get_metadata()
{'label': 'RIT001',
 'data_dir': '/home/vaishakprasad/Downloads/',
 'file_name': 'ExtrapStrain_RIT-BBH-0001-n100.h5',
 'key_format': None,
 'ell_max': 4,
 'modes_list': [[2, [-2, -1, 0, 1, 2]],
  [3, [-3, -2, -1, 0, 1, 2, 3]],
  [4, [-4, -3, -2, -1, 0, 1, 2, 3, 4]]],
 'r_ext': 500,
 'frequency_axis': None,
 'out_file_name': None,
 'maxtime': None,
 'date': '2023-04-26',
 'time': '17:37:20',
 'key_ex': None,
 'spin_weight': -2}

To access the individual modes, use the :math:\ell, m notation.

>>> ell = 2
>>> emm = 2
>>> wf1.mode(ell, emm)
array([-5.82188926e-17-2.09534621e-18j, -5.96136537e-17-2.06759459e-18j,
       -6.10007697e-17-2.04743540e-18j, ...,
       -7.23538504e-18+4.65415462e-33j, -3.62788145e-18-4.11078069e-33j,
        0.00000000e+00+0.00000000e+00j])

To plot the modes

>>> import matplotlib.pyplot as plt
>>> plt.plot(wf1.time_axis, wf1.mode(ell, emm).real, label='+')
>>> plt.plot(wf1.time_axis, wf1.mode(ell, emm).imag, label='X')
>>> plt.legend()
>>> plt.grid(alpha=0.4)
>>> plt.xlabel(r'$t/M$')
>>> plt.ylabel(f'$rh/M$')
>>> plt.show()

{: .shadow}

Documentation

The documentation for this module is available at Link to the Documentation. This was built automatically using Read the Docs.

In some case where the repo has run out of gitlab CI minutes, the documentation is not automatically built. In such cases, we request the user to access the documentation through the index.html file in docs directory.

Bug tracker

If you run into any issues while using this package, please report the issue on the issue tracker.

Acknowledgements

This project has been hosted, as you can see, on gitlab. Several gitlab tools are used in the deployment of the code, its testing, version control.

The work of this was developed in aiding my PhD work at Inter-University Centre for Astronomy and Astrophysics (IUCAA, Pune, India)](https://www.iucaa.in/). The PhD is in part supported by the Shyama Prasad Nukherjee Fellowship awarded to me by the Council of Scientific and Industrial Research (CSIR, India). Resources of the Inter-University Centre for Astronomy and Astrophysics (IUCAA, Pune, India) were are used in part.