def run(slhadata, inputs=None, update=False) :
SIout = SuperISOPrecObs()
fname = "/tmp/mc-{u}".format(u=tools.unique_str())
slhadata.write(fname)
SIlib.run_superiso(c_char_p(fname.encode('ascii')), byref(SIout))
tools.rm(fname)
return tools.ctypes_field_values(SIout, name)
def run(slhadata, inputs=None, update=False) :
MOout = MicromegasPrecObs()
reader = lambda f: MOlib.run_micromegas(c_char_p(f.encode('ascii')), byref(MOout))
writer = lambda f: slhadata.write(f)
fname = "/tmp/mc-{u}".format(u=unique_str())
writer(fname)
reader(fname)
rm(fname)
return ctypes_field_values(MOout, name)
def myloglike(cube, ndim, nparams):
obs,params=get_obs(cube,ndim)
if obs:
chi2=get_chi2(obs)
#RESULT ORIENTED: for sampling set error to default if error in one of the predictors
for name in ['FeynHiggs','Micromegas','BPhysics','SUSY-POPE']:
if obs[(name,'error')]:
chi2=default_chi
obs[('tot_X2', 'all')]=chi2
else:
chi2=default_chi
obs=params
# write everything to root files
if args.root_out:
rootstore.write_point_to_root(obs)
if args.pickle_out:
with open('{}/{}.pkl'.format(args.multinest_dir, unique_str()),'wb') as pickle_file:
pickle.dump(obs,pickle_file)
if 'X' in args.verbose:
print("X^2={}".format(chi2))
return -chi2
def myloglike(cube, ndim, nparams):
obs, params = get_obs(cube, ndim)
if obs:
chi2 = get_chi2(obs)
# RESULT ORIENTED: for sampling set error to default if error in one of the predictors
for name in ["FeynHiggs", "Micromegas", "BPhysics", "SUSY-POPE"]:
if obs[(name, "error")]:
chi2 = default_chi
obs[("tot_X2", "all")] = chi2
else:
chi2 = default_chi
obs = params
# write everything to root files
if args.root_out:
VARS = rootstore.get_VARS(obs, args.model)
root.root_write(VARS)
if args.pickle_out:
with open("{}/{}.pkl".format(args.multinest_dir, unique_str()), "wb") as pickle_file:
pickle.dump(obs, pickle_file)
if "X" in args.verbose:
print("X^2={}".format(chi2))
return -chi2
def myloglike(cube, ndim, nparams):
obs,params=get_obs(cube,ndim)
if obs is not None:
chi2=get_chi2(obs)
#RESULT ORIENTED: for sampling set error to default if error in one of the predictors
#FIXME: consider to not set Micromegas error to infinity, since it crashes on neutralino!=lsp
for name in ['FeynHiggs','Micromegas','BPhysics','SUSY-POPE']:
if obs[(name,'error')]:
chi2=default_chi()
if numpy.isnan(chi2):
chi2=default_chi()
obs[('tot_X2', 'all')]=chi2
else:
obs={}
chi2=default_chi()
# obs=params
obs[('tot_X2', 'all')]=chi2
# write everything to root files
if args.root_out:
if args.storage_dict is not None:
vars=len(storage_dict)*[0.]
for oids, val in obs.items():
try:
vars[storage_dict[oids]]=val
except KeyError:
print('WARNING: not saving {}'.format(oids))
continue
else:
vars=rootstore.get_VARS(obs, args.model)
root.root_write(vars)
if args.pickle_out:
with open('{}/{}.pkl'.format(args.multinest_dir, unique_str()),'wb') as pickle_file:
pickle.dump(obs,pickle_file)
if 'X' in args.verbose:
print("X^2={}".format(chi2))
return -chi2
def __str__(self):
tmp_name = "/tmp/mc-{u}".format(u=unique_str())
self.write(tmp_name)
txt=open(tmp_name).read()
rm(tmp_name)
return txt
