cogwheel is a code for parameter estimation of gravitational wave sources.
It implements a convenient system of coordinates for sampling, a "folding" algorithm to reduce the multimodality of posteriors, and the relative binning algorithm for fast likelihood evaluation (generalized to waveforms with higher modes).
It supports likelihood marginalization over distance.
It interfaces with third-party routines for downloading public data (GWOSC, GWpy), generating waveforms (lalsuite) and sampling distributions (PyMultiNest, dynesty).
The coordinate system and folding algorithm are described in an accompanying article https://arxiv.org/abs/2207.03508
git clone git@github.com:jroulet/cogwheel.git
cd cogwheel
conda create --name <environment_name> --file requirements.txt(replace <environment_name> by a name of your choice).
Example: how to sample a gravitational wave source posterior using PyMultiNest:
path_to_cogwheel = '' # Edit as appropriate
import sys
sys.path.append(path_to_cogwheel)
from cogwheel.posterior import Posterior
from cogwheel import sampling
parentdir = 'example' # Directory that will contain parameter estimation runs
eventname, mchirp_guess = 'GW150914', 20
approximant = 'IMRPhenomXPHM'
prior_class = 'IASPrior'
post = Posterior.from_event(eventname, mchirp_guess, approximant, prior_class)
pym = sampling.PyMultiNest(post, n_live_points=512)
rundir = pym.get_rundir(parentdir)
pym.run(rundir) # Will take a whileLoad and plot the samples:
import pandas as pd
from cogwheel import gw_plotting
samples = pd.read_feather(rundir/sampling.SAMPLES_FILENAME)
gw_plotting.CornerPlot(samples).plot()Transform the samples to a standard system of coordinates:
post.prior.transform_samples(samples)
gw_plotting.CornerPlot(samples[['ra', 'dec']]).plot()