def add_rv(self,
variable,
distribution='uniform',
loc=0.,
scale=1.,
shape=1.,
n_int=30):
'''Declare a variable as random
'''
if variable not in self.rf.param_keys:
raise AssertionError('parameter %s not defined by the response function' \
% variable)
params_with_distr = self.rf.traits(
distr=lambda x: type(x) == ListType and distribution in x)
if variable not in params_with_distr:
raise AssertionError('distribution type %s not allowed for parameter %s' \
% ( distribution, variable ))
# @todo - let the RV take care of PDistrib specification.
# isolate the dirty two-step definition of the distrib from spirrid
#
pd = PDistrib(distr_choice=distribution, n_segments=n_int)
pd.distr_type.set(scale=scale, shape=shape, loc=loc)
self.rv_dict[variable] = RV(name=variable, pd=pd, n_int=n_int)
def set_random( self, distribution = 'uniform', discr_type = 'T grid',
loc = 0., scale = 1., shape = 1., n_int = 30 ):
possible_distr = self.source_trait.distr
if distribution and distribution not in possible_distr:
raise AssertionError, 'distribution type %s not allowed for parameter %s' \
% ( distribution, self.name )
self.pd = PDistrib( distr_choice = distribution, n_segments = n_int )
self.pd.distr_type.set( scale = scale, shape = shape, loc = loc )
self.n_int = n_int
self.discr_type = discr_type
self.random = True
def _get_pd( self ):
if self.random:
tr = self.rf.trait( self.varname )
pd = PDistrib( distr_choice = tr.distr[0], n_segments = self.n_int )
trait = self.rf.trait( self.varname )
# get the distribution parameters from the metadata
#
distr_params = {'scale' : trait.scale, 'loc' : trait.loc, 'shape' : trait.shape }
dparams = {}
for key, val in distr_params.items():
if val:
dparams[key] = val
pd.distr_type.set( **dparams )
return pd
else:
return None
def _pdf_xi_default(self):
pd = PDistrib(distr_choice='weibull_min', n_segments=30)
pd.distr_type.set(shape=4.54, scale=0.017)
return pd
