def myprior(cube, ndim, nparams):
""" MultiNest callback function. Sets the model parameters
from the unit hypercube.
Arguments:
cube -- Unit hypercube from which model parameters are mapped.
ndim -- Number of model paramers.
nparams -- Total number of parameters in the cube .
Returns:
"""
# Set-up the paramters - CMSSM model.
Model = CMSSMModelTracker()
# Set the model parameters from the cube.
Model.SetParams(cube)
# Copy them to the cube so that they are printed, in alphabetical order.
for name in sorted(Model.param.keys(), key=str.lower):
Cube.AddCube(cube, Model.param[name].value, Cube.label, name)
# Print-out cube model parameters for debugging.
print '*****************************************************'
print 'Parameters:'
for i in range(Cube.AddCube.count() + 1): # Count begins at 0 - need +1.
print Cube.label[i], cube[i]
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
