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()
def myloglike(cube, ndim, nparams):
""" MultiNest callback function.
Calculate the model's predictions (by calling external programs),
saves the extra parameters in the cube, and returns log likelihood to Multinest.
Arguments:
cube -- Unit hypercube from which model parameters are mapped.
ndim -- Number of model paramers.
nparams -- Total number of parameters in the cube.
Returns: The total log likelihood for the model parameter
point under consideration.
"""
# Set up constraints class.
Constraints = CNMSSMConstraintTracker()
# Copy cube to constraints, so it can work out predictions etc.
for i, name in enumerate(
sorted(Priors.CNMSSMModelTracker().param.keys(), key=str.lower)):
Constraints.param[name] = cube[i]
# Set predictions and loglikes.
Constraints.SetPredictions()
Constraints.SetLogLike()
# Copy constraints to cube.
for name in sorted(Constraints.constraint.keys(), key=str.lower):
Cube.AddCube(cube, Constraints.constraint[name].theory, Cube.label,
name)
# Copy associated chi2s to cube. Better to print chi2 than loglike,
# beceause MultiNest prints chi2 and they can be treated in the
# same way when plotting.
for name in sorted(Constraints.constraint.keys(), key=str.lower):
Cube.AddCube(cube, -2 * Constraints.constraint[name].loglike,
Cube.label, 'chi2:' + name)
# Copy SLHA masses to the cube.
for key in Constraints.masses:
Cube.AddCube(cube, Constraints.masses[key], Cube.label,
'Mass:' + str(key))
# Copy mu-parameter to the cube.
Cube.AddCube(cube, Constraints.mu, Cube.label, 'mu')
# Copy neutralino mixing to the cube.
for key in Constraints.neutralino:
Cube.AddCube(cube, Constraints.neutralino[key], Cube.label,
'NMIX:' + str(key))
# Print-out cube for debugging.
print 'Predictions:'
for label, param in zip(Cube.label.itervalues(), cube):
print label, param
print 'Total loglike', Constraints.loglike
# Start cube count from 0 again.
Cube.AddCube.reset()
# Print an info file for the cube.
# Decorator insures this is printed only once.
# Point must be physical, else the info will be incomplete.
if Constraints.physical:
Cube.PrintInfo()
# Return the log likelihood to MultiNest.
return Constraints.loglike
import Likelihood # For setting callback function and number of constraints.
# Make chains directory for MultiNest.
if not os.path.exists("chains"):
os.mkdir("chains")
# File names - best to convert to absolute path here.
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