def val_db(pkey, p, value):
"""
Returns the scenario value of the value argument, already casted according to p=glb.params[pkey]
"""
isdistrib = 'distrib' in p
if isdistrib:
dist_name = p['distrib'].lower().title()
if prs.isscalar(value):
#value is scalar, define how to set db value:
if dist_name == "Uniform" :
return [value, value]
elif dist_name == "Normal":
return [value, 0]
else:
raise Exception("{0} distribution not implemented in cast function (scenario.py). Please contact the administrator".format(dist_name))
return value
def val_db(pkey, p, value):
"""
Returns the scenario value of the value argument, already casted according to p=glb.params[pkey]
"""
isdistrib = 'distrib' in p
if isdistrib:
dist_name = p['distrib'].lower().title()
if prs.isscalar(value):
#value is scalar, define how to set db value:
if dist_name == "Uniform":
return [value, value]
elif dist_name == "Normal":
return [value, 0]
else:
raise Exception(
"{0} distribution not implemented in cast function (scenario.py). Please contact the administrator"
.format(dist_name))
return value
def val_sc(pkey, p, value):
"""
Returns the scenario value of the value argument, already casted according to p=glb.params[pkey]
"""
if pkey == gk.IPE:
return glb.def_gmpes[value]
if pkey == gk.SOF:
return glb.sof[value]
isdistrib = 'distrib' in p
if isdistrib:
dist_name = p['distrib'].lower().title()
if not prs.isscalar(value): #note that UncertainFunction is NOT scalar,
#but here we should have only numbers or array of numbers (according to params in glb)
#try build a distribution with the given value(s)
return mcerp.__dict__[dist_name](*value)
return value
def val_sc(pkey, p, value):
"""
Returns the scenario value of the value argument, already casted according to p=glb.params[pkey]
"""
if pkey == gk.IPE:
return glb.def_gmpes[value]
if pkey == gk.SOF:
return glb.sof[value]
isdistrib = 'distrib' in p
if isdistrib:
dist_name = p['distrib'].lower().title()
if not prs.isscalar(
value): #note that UncertainFunction is NOT scalar,
#but here we should have only numbers or array of numbers (according to params in glb)
#try build a distribution with the given value(s)
return mcerp.__dict__[dist_name](*value)
return value