plt.close()
print
print '*************************************************************'
print
if args.cc != 'False':
cc = ChainConsumer()
for nd in xrange(0, mc.ndim): # (0,ndim):
cc.add_chain(chain[:, :, nd].flatten(), walkers=mc.nwalkers)
#print(cc.get_latex_table())
print cc.get_summary()
print cc.diagnostic_gelman_rubin(threshold=0.05)
print cc.diagnostic_geweke()
print
print '*************************************************************'
print
x0 = 1. / 150
M_star1_rand = np.random.normal(M_star1, M_star1_err, n_kept)
if 'kepler' in mc.model_list:
if args.p != 'False' or args.v != 'False':
plot_dir = dir_output + '/files_plot/'
if not os.path.exists(plot_dir):
plt.close()
print
print '*************************************************************'
print
if args.cc != 'False':
cc = ChainConsumer()
for nd in xrange(0, mc.ndim): # (0,ndim):
cc.add_chain(chain[:, :, nd].flatten(), walkers=mc.nwalkers)
#print(cc.get_latex_table())
print cc.get_summary()
print cc.diagnostic_gelman_rubin(threshold=0.05)
print cc.diagnostic_geweke()
print
print '*************************************************************'
print
x0 = 1. / 150
M_star1_rand = np.random.normal(M_star1, M_star1_err, n_kept)
if 'kepler' in mc.model_list:
if args.p != 'False' or args.v != 'False':
plot_dir = dir_output + '/files_plot/'
if not os.path.exists(plot_dir):
