def chisquared(a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb):
""" Wrapper for likelihood.
Arguments:
a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb - nine elements of cube.
"""
ndim = 9 # Model parameters - 9 for CMSSM.
nparams = 100 # Approximate number of items in cube.
cube = [0] * nparams # Initialise cube as empty list.
# Copy arguments to cube.
for i, arg in enumerate([a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb]):
cube[i] = arg
# ndim and nparams are irrelavant.
chisquared = -2 * Likelihood.myloglike(cube, ndim, nparams)
return chisquared
def chisquared(a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb):
""" Wrapper for likelihood.
Arguments:
a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb - nine elements of cube.
"""
ndim = 9 # Model parameters - 9 for CMSSM.
nparams = 100 # Approximate number of items in cube.
cube = [0] * nparams # Initialise cube as empty list.
# Copy arguments to cube.
for i, arg in enumerate(
[a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb]):
cube[i] = arg
# ndim and nparams are irrelavant.
chisquared = -2 * Likelihood.myloglike(cube, ndim, nparams)
return chisquared
# Tests an input point, run with
# python Tester.py a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb
# Superpy modules.
import Debug as DB # Debug options.
import Likelihood # Constraints and likelihood functions.
# External modules.
import sys
# Initialise the cube.
cols = 100
nparams = cols - 2 # Subract 2 for chi-squared and posterior weight.
ndim = 10 # For CNMSSM.
# Read the input parameters into the cube.
cube = [0] * nparams # Initialise cube to an empty list.
# Plust one to ignore first argument is name of file.
if len(sys.argv) != ndim + 1:
sys.exit("You should supply a0, alphas, invalpha, lambda, m0, m12, mb, mt, signmu, tanb")
for i in range(len(sys.argv)):
if i == 0:
continue # Ignore name of file.
# Minus one to ignore name of file, which is zeroth argument, and convert
# string to float.
cube[i - 1] = float(sys.argv[i])
# Call likelihood function.
loglike = Likelihood.myloglike(cube, ndim, nparams)
import Likelihood # Constraints and likelihood functions.
import PlotMod as PM # To access this, export PYTHONPATH=$PWD/SuperPlot
# External modules.
import sys
# Check that line number was supplied.
if len(sys.argv) != 2:
sys.exit('You should supply line number.')
# Open the chain with a GUI.
labels, data = PM.OpenData()
ndim = 10 # Number of model parameters - 10 for CNMSSM.
nparams = 100 # Approximate number of items in cube.
# Read line to re-calculate.
# Plust one to ignore first argument is name of file, e.g. python
# LineProcess.py arg1 etc
linenumber = int(sys.argv[1]) # We need to convert from string to integer.
# Initialise the cube to an empty list - it must be a list, not a dictionary.
cube = [0] * nparams
# Copy model parameters from the *.txt file to the cube.
for i in range(ndim):
# Plus 2 for chi-squared and posterior weight.
cube[i] = data[i + 2][linenumber]
# Re-calculate the cube etc with this likelihood function.
loglike = Likelihood.myloglike(cube, ndim, nparams)
# parameters, rather than a list.
def chisquared(a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb, lambda):
""" Wrapper for likelihood.
Arguments:
a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb, lambda - ten elements of cube.
"""
ndim = 10 # Model parameters - 10 for CNMSSM.
nparams = 100 # Approximate number of items in cube.
cube = [0] * nparams # Initialise cube as empty list.
# Copy arguments to cube.
for i, arg in enumerate([a0, alphas, invalpha, m0, m12, mb, mt, signmu, tanb, lambda]):
cube[i] = arg
# ndim and nparams are irrelavant.
chisquared = -2 * Likelihood.myloglike(cube, ndim, nparams)
return chisquared
# Setup initial values, errors etc.
kwdarg = dict(
a0=-3000,
error_a0=100,
alphas=1.18400000e-01,
fix_alphas=True,
invalpha=1.27944000e+02,
fix_invalpha=True,
m0=400,
error_m0=100,
m12=900,
error_m12=100,
mb=4.18000000e+00,
