def approximate_planetary_params(self, nrun=500, nwalkers=30):
"""
Approximate the planetary mass and semi-major axis from the estimated planetary parameters implemented in
keplerian_analysis. The mass, semi-major axis, and period are stored in a csv file labeled
_PlanetaryProperties in the directory specified, and the rest of the orbital parameters are stored in a
seperate csv labeled _MCMCAnalysisPlanetaryParameters.csv in the directory specified.
:param nrun: the MCMC number of iterations
:param nwalkers: the number of MCMC walkers
"""
df = radvel.mcmc(self.post,
nwalkers=100,
nrun=nrun,
savename='rawchains.h5') # Start MCMC analysis
# Some cool plots
Corner = mcmc_plots.CornerPlot(self.post,
df,
saveplot=self.dir + "TestCornerPlot")
Corner.plot() # A corner plot
trendplot = radvel.plot.mcmc_plots.TrendPlot(self.post,
df,
nwalkers,
outfile=self.dir +
'TestTrendFile')
trendplot.plot() # A trend plot
multipanel = radvel.plot.orbit_plots.MultipanelPlot(self.post,
saveplot=self.dir +
'TestMultipanel')
multipanel.plot_multipanel(
) # A multipanel plot with the estimated parameters
# Make the planetary properties pandas DataFrame.
mass = []
a = []
for per in list(self.periods):
mass_ = radvel.utils.Msini(np.average(self.y.values),
per,
self.smass,
e=0.0)
mass.append(mass_)
a_ = radvel.utils.semi_major_axis(per, self.smass)
a.append(a_)
params_df = pd.DataFrame({
"Plantary Period (JD)": list(self.periods),
"Semi Major Axis (AU)": list(a),
"Planetary Mass (Earth masses)": list(mass)
})
print("Saving the computed planetary properties at the directory " +
self.dir + "...")
params_df.to_csv(self.dir + self.name + '_PlanetaryProperties.csv')
print("Done!")
self.paramsdf = params_df
print("Saving the computed orbital parameters at the directory " +
self.dir + "...")
df.to_csv(self.dir + self.name +
'_MCMCAnalysisPlanetaryParameters.csv')
print("Done!")
def plots(args):
"""
Generate plots
Args:
args (ArgumentParser): command line arguments
"""
config_file = args.setupfn
conf_base = os.path.basename(config_file).split('.')[0]
statfile = os.path.join(
args.outputdir, "{}_radvel.stat".format(conf_base)
)
status = load_status(statfile)
P, post = radvel.utils.initialize_posterior(config_file,
decorr=args.decorr)
assert status.getboolean('fit', 'run'), \
"Must perform max-liklihood fit before plotting"
post = radvel.posterior.load(status.get('fit', 'postfile'))
for ptype in args.type:
print("Creating {} plot for {}".format(ptype, conf_base))
if ptype == 'rv':
args.plotkw['uparams'] = post.uparams
args.plotkw['status'] = status
if 'saveplot' not in args.plotkw:
saveto = os.path.join(
args.outputdir, conf_base+'_rv_multipanel.pdf'
)
else:
saveto = args.plotkw['saveplot']
args.plotkw.pop('saveplot')
P, _ = radvel.utils.initialize_posterior(config_file)
if hasattr(P, 'bjd0'):
args.plotkw['epoch'] = P.bjd0
if args.gp:
GPPlot = orbit_plots.GPMultipanelPlot(
post, saveplot=saveto, **args.plotkw
)
GPPlot.plot_multipanel()
else:
RVPlot = orbit_plots.MultipanelPlot(
post, saveplot=saveto, **args.plotkw
)
RVPlot.plot_multipanel()
# check to make sure that Posterior is not GP, print warning if it is
if isinstance(post.likelihood, radvel.likelihood.CompositeLikelihood):
like_list = post.likelihood.like_list
else:
like_list = [post.likelihood]
for like in like_list:
if isinstance(like, radvel.likelihood.GPLikelihood):
print("WARNING: GP Likelihood(s) detected. \
You may want to use the '--gp' flag when making these plots.")
break
if ptype == 'corner' or ptype == 'auto' or ptype == 'trend':
assert status.getboolean('mcmc', 'run'), \
"Must run MCMC before making corner, auto, or trend plots"
chains = pd.read_csv(status.get('mcmc', 'chainfile'))
autocorr = pd.read_csv(status.get('mcmc', 'autocorrfile'))
if ptype == 'auto':
saveto = os.path.join(args.outputdir, conf_base+'_auto.pdf')
Auto = mcmc_plots.AutoPlot(autocorr, saveplot=saveto)
Auto.plot()
if ptype == 'corner':
saveto = os.path.join(args.outputdir, conf_base+'_corner.pdf')
Corner = mcmc_plots.CornerPlot(post, chains, saveplot=saveto)
Corner.plot()
if ptype == 'trend':
nwalkers = status.getint('mcmc', 'nwalkers')
nensembles = status.getint('mcmc', 'nensembles')
saveto = os.path.join(args.outputdir, conf_base+'_trends.pdf')
Trend = mcmc_plots.TrendPlot(post, chains, nwalkers, nensembles, saveto)
Trend.plot()
if ptype == 'derived':
assert status.has_section('derive'), \
"Must run `radvel derive` before plotting derived parameters"
P, _ = radvel.utils.initialize_posterior(config_file)
chains = pd.read_csv(status.get('derive', 'chainfile'))
saveto = os.path.join(
args.outputdir, conf_base+'_corner_derived_pars.pdf'
)
Derived = mcmc_plots.DerivedPlot(chains, P, saveplot=saveto)
Derived.plot()
savestate = {'{}_plot'.format(ptype): os.path.relpath(saveto)}
save_status(statfile, 'plot', savestate)
