def PrintInfo():
""" Function to write an info file, for use with plotting,
and for saving Multinest options, likelihood functions and
priors.
"""
# Import variable inside function, to avoid circular module
# imports.
import MNOptions as MN
import Priors
import Likelihood
infofile = MN.outputfiles_basename + '.info'
info = open(infofile, 'w')
info.write('''# SuperPy info file, with cube indicies.
# This info file can be used for plot labels.\n''')
for key, value in label.iteritems():
# Add 1 to the keys, so that labelling begins at 1, rather than 0.
# This is identical to the SuperBayeS info file convention.
key = str(int(key) + 1)
info.write('''lab%s=%s\n''' % (key, value))
# Write the prior types and arguements.
info.write('''# Priors.\n''')
for key in Priors.CNMSSMModelTracker().param:
info.write('''%s %s %s\n''' %
(key, Priors.CNMSSMModelTracker().param[key].type,
Priors.CNMSSMModelTracker().param[key].arg))
# Write the likelihoods types and arguments.
info.write('''# Likelihoods.\n''')
for key in Likelihood.CNMSSMConstraintTracker().constraint:
info.write(
'''%s %s %s\n''' %
(key, Likelihood.CNMSSMConstraintTracker().constraint[key].type,
Likelihood.CNMSSMConstraintTracker().constraint[key].arg))
# Write the MN parameters.
info.write('''# MultiNest options.\n''')
for vars in dir(MN):
if not vars.startswith("__"):
info.write('''%s %s\n''' % (vars, getattr(MN, vars)))
info.close()
outputfiles_basename = os.path.abspath('./chains/CNMSSM')
# These are the settings that are passed to MultiNest.
# Loglike callback function.
LogLikelihood = Likelihood.myloglike
# Prior callback function.
Prior = Priors.myprior
# Number of model parameters.
n_dims = len(Priors.CNMSSMModelTracker().param)
# Total number of parameters in cube.
# This is model parameters + constraints + chi2 from constraints + 33
# sparticle/particle masses + mu + neutralino mixing.
# For NMSSM, neutralino mixing matrix is 5 by 5.
# For NMSSM, 2 extra masses for singlet and singlino.
n_params = n_dims + 2 * \
len(Likelihood.CNMSSMConstraintTracker().constraint) + 33 + 2 + 1 + 25
# Which parameters to mode cluster, first n.
n_clustering_params = 2
# Should be a vector of 1s and 0s - 1 = wrapped, 0 = unwrapped.
wrapped_params = None
# Whether to separate modes.
multimodal = True
const_efficiency_mode = False
n_live_points = 10
evidence_tolerance = 1.0
sampling_efficiency = 2.0
n_iter_before_update = 1
null_log_evidence = -1e+90
# For memory allocation only, if memory exceeded, MultiNest halts.
max_modes = 5
# Seed the random number generator, if -1 seeded from system clock.
