gwnrtools / nr-catalog-tools

A unified interface to various catalogs of Numerical Relativity simulations of compact binary mergers.

Interface to Numerical Relativity Catalogs

The nrcatalogtools python package provides a unified high-level interface to multiple catalogs of data products from Numerical Relativity simulations of compact-object binary mergers. At the moment of writing, different research groups have separate formats of data and/or tools to interface with them. This package will be a convenience layer atop those for downstream research applications. This package derives where it can from the sxs package of the Simulating eXtreme Spacetimes collaboration, as that has much of the capabilities for handling Numerical Relativity data that we need in general for all catalogs. Much of the interface here is deliberately identical to that in the sxs package to facilitate their interuse.

We currently support the following catalogs:

• Simulating eXtreme Spacetimes Waveforms Catalog
• Georgia Tech Binary Black Hole Simulations
• RIT Waveform Catalog

Usage

>>> from nrcatalogtools import RITCatalog
>>> rcatalog = RITCatalog.load()
>>> print(rcatalog.simulations_dataframe.index)
Index(['RIT:BBH:0001-n100-id3', 'RIT:BBH:0002-n100-id0',
       'RIT:BBH:0003-n100-id0', 'RIT:BBH:0004-n100-id0',
       'RIT:BBH:0005-n100-id0', 'RIT:BBH:0006-n100-id3',
       'RIT:BBH:0007-n100-id0', 'RIT:BBH:0008-n100-id0',
       'RIT:BBH:0009-n100-id0', 'RIT:BBH:0010-n100-id0',
       ...
       'RIT:BBH:1914-n144-id1', 'RIT:BBH:1915-n144-id1',
       'RIT:BBH:1916-n100-id1', 'RIT:BBH:1917-n100-id1',
       'RIT:BBH:1918-n100-id1', 'RIT:BBH:1919-n100-id1',
       'RIT:BBH:1920-n100-id1', 'RIT:BBH:1921-n100-id1',
       'RIT:BBH:1922-n100-id1', 'RIT:BBH:1923-n100-id1'],
      dtype='object', length=1879)

Now, if one needs a particular simulation, they can do:

>>> rwf = rcatalog.get('RIT:BBH:0003-n100-id0')

To check which modes are available for this simulation:

>>> print(rwf.LM)
[[ 2 -2]
 [ 2 -1]
 [ 2  0]
 [ 2  1]
 [ 2  2]
 [ 3 -3]
 [ 3 -2]
 [ 3 -1]
 [ 3  0]
 [ 3  1]
 [ 3  2]
 [ 3  3]
 [ 4 -4]
 [ 4 -3]
 [ 4 -2]
 [ 4 -1]
 [ 4  0]
 [ 4  1]
 [ 4  2]
 [ 4  3]
 [ 4  4]]

To extract a single mode from this:

>>> rwf.get_mode(2, 2)
array([[-1.18175000e+03,  8.41055081e-02,  6.60652456e-04],
       [-1.18150000e+03,  8.41034759e-02, -7.94687302e-04],
       [-1.18125000e+03,  8.40763695e-02, -2.25019642e-03],
       ...,
       [ 3.61000000e+02,  2.56889323e-12, -3.97799029e-25],
       [ 3.61250000e+02,  1.30444912e-12, -1.64922275e-25],
       [ 3.61500000e+02,  0.00000000e+00,  0.00000000e+00]])

To get polarizations for the same simulation:

>>> pols = rwf.get_td_waveform(total_mass = 40, # solar masses
                               distance = 100., # Megaparsecs
                               inclination = 0.2, # radians
                               coa_phase = 0.3) # radians
>>> hp, hc = pols.real(), -1 * pols.imag()

which can subsequently be plotted easily:

>>> import matplotlib.pyplot as plt
>>> plt.plot(hp.sample_times, hp, label='+')
>>> plt.plot(hc.sample_times, hc, label='x')
>>> plt.legend()
>>> plt.show()

which should give the following figure: