def prepare_fit(self):
"""prepare parameters for fit
determine which parameters are actually variables
and which are defined expressions.
"""
if self.__prepared:
return
if not isgroup(self.paramgroup):
return 'param group is not a Larch Group'
self.nfev_calls = 0
self.var_names = []
self.defvars = []
self.vars = []
for name in dir(self.paramgroup):
par = getattr(self.paramgroup, name)
if not isinstance(par, Parameter):
continue
if par.expr is not None:
par.defvar = DefinedVariable(par.expr, _larch=self._larch)
par.vary = False
self.defvars.append(name)
elif par.vary:
self.var_names.append(name)
self.vars.append(par.value)
if par.name is None:
par.name = name
self.nvarys = len(self.vars)
# now evaluate make sure initial values are set
# are used to set values of the defined expressions.
# this also acts as a check of expression syntax.
self.__prepared = True
def __init__(self,
name=None,
value=None,
vary=True,
min=None,
max=None,
expr=None,
_larch=None,
**kws):
self.name = name
self.value = value
self.init_value = value
self.min = min
self.max = max
self.vary = vary
self.expr = expr
self.stderr = None
self.correl = None
self.defvar = None
if self.expr is not None and _larch is not None:
self.defvar = DefinedVariable(self.expr, _larch=_larch)
self.vary = False
self.value = self.defvar.evaluate()
def __init__(self, name=None, value=None, vary=True,
min=None, max=None, expr=None, _larch=None, **kws):
self.name = name
self.value = value
self.init_value = value
self.min = min
self.max = max
self.vary = vary
self.expr = expr
self.stderr = None
self.correl = None
self.defvar = None
if self.expr is not None and _larch is not None:
self.defvar = DefinedVariable(self.expr, _larch=_larch)
self.vary = False
self.value = self.defvar.evaluate()
class Parameter(object):
"""A Parameter is the basic Parameter going
into Fit Model. The Parameter holds many attributes:
value, vary, max_value, min_value.
Note that constraints are set elsewhere (with Larch DefinedVariables)
The value and min/max values will be set to floats.
"""
def __init__(self,
name=None,
value=None,
vary=True,
min=None,
max=None,
expr=None,
_larch=None,
**kws):
self.name = name
self.value = value
self.init_value = value
self.min = min
self.max = max
self.vary = vary
self.expr = expr
self.stderr = None
self.correl = None
self.defvar = None
if self.expr is not None and _larch is not None:
self.defvar = DefinedVariable(self.expr, _larch=_larch)
self.vary = False
self.value = self.defvar.evaluate()
def __repr__(self):
s = []
if self.name is not None:
s.append("'%s'" % self.name)
val = repr(self.value)
if self.vary and self.stderr is not None:
val = "value=%s +/- %.3g" % (repr(self.value), self.stderr)
elif not self.vary:
val = "value=%s (fixed)" % (repr(self.value))
s.append(val)
s.append("bounds=[%s:%s]" % (repr(self.min), repr(self.max)))
if self.expr is not None:
s.append("expr='%s'" % (self.expr))
return "<Parameter %s>" % ', '.join(s)
class Parameter(object):
"""A Parameter is the basic Parameter going
into Fit Model. The Parameter holds many attributes:
value, vary, max_value, min_value.
Note that constraints are set elsewhere (with Larch DefinedVariables)
The value and min/max values will be set to floats.
"""
def __init__(self, name=None, value=None, vary=True,
min=None, max=None, expr=None, _larch=None, **kws):
self.name = name
self.value = value
self.init_value = value
self.min = min
self.max = max
self.vary = vary
self.expr = expr
self.stderr = None
self.correl = None
self.defvar = None
if self.expr is not None and _larch is not None:
self.defvar = DefinedVariable(self.expr, _larch=_larch)
self.vary = False
self.value = self.defvar.evaluate()
def __repr__(self):
s = []
if self.name is not None:
s.append("'%s'" % self.name)
val = repr(self.value)
if self.vary and self.stderr is not None:
val = "value=%s +/- %.3g" % (repr(self.value), self.stderr)
elif not self.vary:
val = "value=%s (fixed)" % (repr(self.value))
s.append(val)
s.append("bounds=[%s:%s]" % (repr(self.min), repr(self.max)))
if self.expr is not None:
s.append("expr='%s'" % (self.expr))
return "<Parameter %s>" % ', '.join(s)
