def residual(params, mod_pars, photo_data, rv_data, ncores, *args):
# mod_pars[1] = params['epoch'].value
params = utilfuncs.get_lmfit_parameters(mod_pars, params)
mod_flux, mod_rv = utilfuncs.model(mod_pars, params, photo_data[0], rv_data[0], ncores)
weighted = ((mod_flux - photo_data[1]) / photo_data[2])
return np.append(weighted, ((mod_rv - rv_data[1]) / rv_data[2]))
def lnlike(mod_pars, params, photo_data, rv_data):
# lnf = np.log(1.0e-10) # Natural log of the underestimation fraction
# inv_sigma2 = 1.0 / (yerr ** 2 + model ** 2 * np.exp(2 * lnf))
# return -0.5 * (np.sum((y - model) ** 2 * inv_sigma2 - np.log(inv_sigma2)))
mod_flux, mod_rv = utilfuncs.model(mod_pars, params, photo_data[0], rv_data[0])
flnl = np.sum((-0.5 * ((mod_flux - photo_data[1]) / photo_data[2]) ** 2))
rvlnl = np.sum((-0.5 * ((mod_rv - rv_data[1]) / rv_data[2])**2))
return flnl + rvlnl
def lnlike(cube, ndim, nparams):
theta = np.array([cube[i] for i in range(ndim)])
params = utilfuncs.split_parameters(theta, mod_pars[0])
mod_flux, mod_rv = utilfuncs.model(mod_pars, params, photo_data[0], rv_data[0], ncores)
flnl = np.sum((-0.5 * ((mod_flux - photo_data[1]) / photo_data[2]) ** 2))
rvlnl = np.sum((-0.5 * ((mod_rv - rv_data[1]) / rv_data[2])**2))
per_iteration(mod_pars, theta, flnl, mod_flux)
return flnl + rvlnl
def generate(lmod_pars, lparams, lphoto_data, lrv_data, lncores, lfname):
global mod_pars, params, photo_data, rv_data, ncores, fname
mod_pars, params, photo_data, rv_data, ncores, fname = \
lmod_pars, lparams, lphoto_data, lrv_data, lncores, lfname
# number of dimensions our problem has
parameters = ["{0}".format(i) for i in range(mod_pars[0] * 5 + (mod_pars[0] - 1) * 6)]
nparams = len(parameters)
# make sure the output directories exist
if not os.path.exists("./output/{0}/multinest".format(fname)):
os.makedirs(os.path.join("./", "output", "{0}".format(fname), "multinest"))
if not os.path.exists("./output/{0}/plots".format(fname)):
os.makedirs(os.path.join("./", "output", "{0}".format(fname), "plots"))
if not os.path.exists("chains"): os.makedirs("chains")
# we want to see some output while it is running
progress_plot = pymultinest.ProgressPlotter(n_params=nparams,
outputfiles_basename='output/{0}/multinest/'.format(fname))
progress_plot.start()
# progress_print = pymultinest.ProgressPrinter(n_params=nparams, outputfiles_basename='output/{0}/multinest/'.format(fname))
# progress_print.start()
# run MultiNest
pymultinest.run(lnlike, lnprior, nparams, outputfiles_basename=u'./output/{0}/multinest/'.format(fname),
resume=True, verbose=True,
sampling_efficiency='parameter', n_live_points=1000)
# run has completed
progress_plot.stop()
# progress_print.stop()
json.dump(parameters, open('./output/{0}/multinest/params.json'.format(fname), 'w')) # save parameter names
# plot the distribution of a posteriori possible models
plt.figure()
plt.plot(photo_data[0], photo_data[1], '+ ', color='red', label='data')
a = pymultinest.Analyzer(outputfiles_basename="./output/{0}/reports/".format(fname), n_params=nparams)
for theta in a.get_equal_weighted_posterior()[::100, :-1]:
params = utilfuncs.split_parameters(theta, mod_pars[0])
mod_flux, mod_rv = utilfuncs.model(mod_pars, params, photo_data[0], rv_data[0])
plt.plot(photo_data[0], mod_flux, '-', color='blue', alpha=0.3, label='data')
utilfuncs.report_as_input(params, fname)
plt.savefig('./output/{0}/plots/posterior.pdf'.format(fname))
plt.close()
def plot_model(mod_pars, body_pars, photo_data, rv_data):
mod_flux, mod_rv = utilfuncs.model(mod_pars, body_pars, photo_data[0], photo_data[0])
# print("Reduced chi-square:",
# np.sum(((photo_data[1] - mod_flux) / photo_data[2]) ** 2) /
# (photo_data[2].size - 1 - (mod_pars[0] * 5 + (mod_pars[0] - 1) * 6)))
#
# inv_sigma2 = 1.0 / (photo_data[2] ** 2 + mod_flux ** 2 * np.exp(2.0 * np.log(1.0e-10)))
# print("Custom optimized value:",
# -0.5 * (np.sum((photo_data[1] - mod_flux) ** 2 * inv_sigma2 - np.log(inv_sigma2))))
pylab.plot(photo_data[0], photo_data[1], 'k+')
pylab.plot(photo_data[0], mod_flux, 'r')
pylab.show()
pylab.plot(rv_data[0], rv_data[1], 'o')
pylab.plot(photo_data[0], mod_rv, 'r')
pylab.show()
