def tear_down(self):
MetropolisMH.tear_down(self)
fit = self._fit
# Restore ARF to original state
for specresp, arf in zip(self.backup_arfs, self.arfs):
arf.specresp = specresp
def tear_down(self):
MetropolisMH.tear_down(self)
fit = self._fit
# Restore ARF to original state
for specresp, arf in zip(self.backup_arfs, self.arfs):
arf.specresp = specresp
def __init__(self, fcn, sigma, mu, dof, fit, *args):
MetropolisMH.__init__(self, fcn, sigma, mu, dof, *args)
self._fit = fit
if hasattr(fit.model, 'teardown'):
fit.model.teardown()
self.srcarfs, self.bkgarfs = search_arfs(fit)
# Save a copy of original ARF
self.arfs = flatten_arfs(self.srcarfs, self.bkgarfs)
self.backup_arfs = [arf.specresp.copy() for arf in self.arfs]
self.simarf = None
def __init__(self, fcn, sigma, mu, dof, fit, *args):
MetropolisMH.__init__(self, fcn, sigma, mu, dof, *args)
self._fit = fit
if hasattr(fit.model, 'teardown'):
fit.model.teardown()
self.srcarfs, self.bkgarfs = search_arfs(fit)
# Save a copy of original ARF
self.arfs = flatten_arfs(self.srcarfs, self.bkgarfs)
self.backup_arfs = [arf.specresp.copy() for arf in self.arfs]
self.simarf = None
def init(self,
log=False,
inv=False,
defaultprior=True,
priorshape=False,
priors=(),
originalscale=True,
scale=1,
sigma_m=False,
p_M=.5,
simarf=None,
p_M_arf=.5,
sigma_arf=0.1):
# nsubiters is missing from init() to indicate that nubiters=1 for
# full bayes
if isinstance(simarf, PCA1DAdd):
self.simarf = simarf
else:
self.simarf = ARFSIMFactory()(simarf)
if not isinstance(self.simarf, PCA1DAdd):
raise TypeError("Simulation ARF must be PCA for FullBayes" +
" not %s" % type(self.simarf).__name__)
self.accept_arfs = [0]
self.p_M_arf = p_M_arf
self.rrsig = sigma_arf
return MetropolisMH.init(self, log, inv, defaultprior, priorshape,
priors, originalscale, scale, sigma_m, p_M)
def init(self, log=False, inv=False, defaultprior=True, priorshape=False,
priors=(), originalscale=True, scale=1, sigma_m=False, p_M=.5,
simarf=None, nsubiters=10):
# Note that nsubiters is used as a dummy parameter to indicate the default
# value. See the function WalkWithSubIters.__call__()
if isinstance(simarf, (PCA1DAdd, SIM1DAdd)):
self.simarf = simarf
else:
self.simarf = ARFSIMFactory()(simarf)
return MetropolisMH.init(self, log, inv, defaultprior, priorshape,
priors, originalscale, scale, sigma_m, p_M)
def init(self, log=False, inv=False, defaultprior=True, priorshape=False,
priors=(), originalscale=True, scale=1, sigma_m=False, p_M=.5,
simarf=None, p_M_arf=.5, sigma_arf=0.1):
# nsubiters is missing from init() to indicate that nubiters=1 for
# full bayes
if isinstance(simarf, (PCA1DAdd,)):
self.simarf = simarf
else:
self.simarf = ARFSIMFactory()(simarf)
if not isinstance(self.simarf, (PCA1DAdd,)):
raise TypeError("Simulation ARF must be PCA for FullBayes" +
" not %s" % type(self.simarf).__name__)
self.accept_arfs = [0]
self.p_M_arf = p_M_arf
self.rrsig = sigma_arf
return MetropolisMH.init(self, log, inv, defaultprior, priorshape,
priors, originalscale, scale, sigma_m, p_M)
def init(self,
log=False,
inv=False,
defaultprior=True,
priorshape=False,
priors=(),
originalscale=True,
scale=1,
sigma_m=False,
p_M=.5,
simarf=None,
nsubiters=10):
# Note that nsubiters is used as a dummy parameter to indicate the default
# value. See the function WalkWithSubIters.__call__()
if isinstance(simarf, (PCA1DAdd, SIM1DAdd)):
self.simarf = simarf
else:
self.simarf = ARFSIMFactory()(simarf)
return MetropolisMH.init(self, log, inv, defaultprior, priorshape,
priors, originalscale, scale, sigma_m, p_M)
