class BaseFitter(object):
__doc__ = _COMMON_DOC + """
Parameters
----------
data : array-like
model : lmfit.Model
optional initial Model to use, maybe be set or changed later
""" + _COMMON_EXAMPLES_DOC
def __init__(self, data, model=None, **kwargs):
self._data = data
self.kwargs = kwargs
# GUI-based subclasses need a default value for the menu of models,
# and so an arbitrary default is applied here, for uniformity
# among the subclasses.
if model is None:
model = ExponentialModel
self.model = model
def _on_model_value_change(self, name, value):
self.model = value
def _on_fit_button_click(self, b):
self.fit()
def _on_guess_button_click(self, b):
self.guess()
@property
def data(self):
return self._data
@data.setter
def data(self, value):
self._data = value
@property
def model(self):
return self._model
@model.setter
def model(self, value):
if callable(value):
model = value()
else:
model = value
self._model = model
self.current_result = None
self._current_params = model.make_params()
# Use these to evaluate any Parameters that use expressions.
self.asteval = Interpreter()
self.namefinder = NameFinder()
self._finalize_model(value)
self.guess()
def _finalize_model(self, value):
# subclasses optionally override to update display here
pass
@property
def current_params(self):
"""Each time fit() is called, these will be updated to reflect
the latest best params. They will be used as the initial guess
for the next fit, unless overridden by arguments to fit()."""
return self._current_params
@current_params.setter
def current_params(self, new_params):
# Copy contents, but retain original params objects.
for name, par in new_params.items():
self._current_params[name].value = par.value
self._current_params[name].expr = par.expr
self._current_params[name].vary = par.vary
self._current_params[name].min = par.min
self._current_params[name].max = par.max
# Compute values for expression-based Parameters.
self.__assign_deps(self._current_params)
for _, par in self._current_params.items():
if par.value is None:
self.__update_paramval(self._current_params, par.name)
self._finalize_params()
def _finalize_params(self):
# subclasses can override this to pass params to display
pass
def guess(self):
count_indep_vars = len(self.model.independent_vars)
guessing_successful = True
try:
if count_indep_vars == 0:
guess = self.model.guess(self._data)
elif count_indep_vars == 1:
key = self.model.independent_vars[0]
val = self.kwargs[key]
d = {key: val}
guess = self.model.guess(self._data, **d)
self.current_params = guess
except NotImplementedError:
guessing_successful = False
return guessing_successful
def __assign_deps(self, params):
# N.B. This does not use self.current_params but rather
# new Parameters that are being built by self.guess().
for name, par in params.items():
if par.expr is not None:
par.ast = self.asteval.parse(par.expr)
check_ast_errors(self.asteval.error)
par.deps = []
self.namefinder.names = []
self.namefinder.generic_visit(par.ast)
for symname in self.namefinder.names:
if (symname in self.current_params
and symname not in par.deps):
par.deps.append(symname)
self.asteval.symtable[name] = par.value
if par.name is None:
par.name = name
def __update_paramval(self, params, name):
# N.B. This does not use self.current_params but rather
# new Parameters that are being built by self.guess().
par = params[name]
if getattr(par, 'expr', None) is not None:
if getattr(par, 'ast', None) is None:
par.ast = self.asteval.parse(par.expr)
if par.deps is not None:
for dep in par.deps:
self.__update_paramval(params, dep)
par.value = self.asteval.run(par.ast)
out = check_ast_errors(self.asteval.error)
if out is not None:
self.asteval.raise_exception(None)
self.asteval.symtable[name] = par.value
def fit(self, *args, **kwargs):
"Use current_params unless overridden by arguments passed here."
guess = dict(self.current_params)
guess.update(self.kwargs) # from __init__, e.g. x=x
guess.update(kwargs)
self.current_result = self.model.fit(self._data, *args, **guess)
self.current_params = self.current_result.params
class BaseFitter(object):
__doc__ = _COMMON_DOC + """
Parameters
----------
data : array-like
model : lmfit.Model
optional initial Model to use, maybe be set or changed later
""" + _COMMON_EXAMPLES_DOC
def __init__(self, data, model=None, **kwargs):
self._data = data
self.kwargs = kwargs
# GUI-based subclasses need a default value for the menu of models,
# and so an arbitrary default is applied here, for uniformity
# among the subclasses.
if model is None:
model = ExponentialModel
self.model = model
def _on_model_value_change(self, name, value):
self.model = value
def _on_fit_button_click(self, b):
self.fit()
def _on_guess_button_click(self, b):
self.guess()
@property
def data(self):
return self._data
@data.setter
def data(self, value):
self._data = value
@property
def model(self):
return self._model
@model.setter
def model(self, value):
if callable(value):
model = value()
else:
model = value
self._model = model
self.current_result = None
self._current_params = model.make_params()
# Use these to evaluate any Parameters that use expressions.
self.asteval = Interpreter()
self.namefinder = NameFinder()
self._finalize_model(value)
self.guess()
def _finalize_model(self, value):
# subclasses optionally override to update display here
pass
@property
def current_params(self):
"""Each time fit() is called, these will be updated to reflect
the latest best params. They will be used as the initial guess
for the next fit, unless overridden by arguments to fit()."""
return self._current_params
@current_params.setter
def current_params(self, new_params):
# Copy contents, but retain original params objects.
for name, par in new_params.items():
self._current_params[name].value = par.value
self._current_params[name].expr = par.expr
self._current_params[name].vary = par.vary
self._current_params[name].min = par.min
self._current_params[name].max = par.max
# Compute values for expression-based Parameters.
self.__assign_deps(self._current_params)
for _, par in self._current_params.items():
if par.value is None:
self.__update_paramval(self._current_params, par.name)
self._finalize_params()
def _finalize_params(self):
# subclasses can override this to pass params to display
pass
def guess(self):
count_indep_vars = len(self.model.independent_vars)
guessing_successful = True
try:
if count_indep_vars == 0:
guess = self.model.guess(self._data)
elif count_indep_vars == 1:
key = self.model.independent_vars[0]
val = self.kwargs[key]
d = {key: val}
guess = self.model.guess(self._data, **d)
self.current_params = guess
except NotImplementedError:
guessing_successful = False
return guessing_successful
def __assign_deps(self, params):
# N.B. This does not use self.current_params but rather
# new Parameters that are being built by self.guess().
for name, par in params.items():
if par.expr is not None:
par.ast = self.asteval.parse(par.expr)
check_ast_errors(self.asteval.error)
par.deps = []
self.namefinder.names = []
self.namefinder.generic_visit(par.ast)
for symname in self.namefinder.names:
if (symname in self.current_params and symname not in
par.deps):
par.deps.append(symname)
self.asteval.symtable[name] = par.value
if par.name is None:
par.name = name
def __update_paramval(self, params, name):
# N.B. This does not use self.current_params but rather
# new Parameters that are being built by self.guess().
par = params[name]
if getattr(par, 'expr', None) is not None:
if getattr(par, 'ast', None) is None:
par.ast = self.asteval.parse(par.expr)
if par.deps is not None:
for dep in par.deps:
self.__update_paramval(params, dep)
par.value = self.asteval.run(par.ast)
out = check_ast_errors(self.asteval.error)
if out is not None:
self.asteval.raise_exception(None)
self.asteval.symtable[name] = par.value
def fit(self, *args, **kwargs):
"Use current_params unless overridden by arguments passed here."
guess = dict(self.current_params)
guess.update(self.kwargs) # from __init__, e.g. x=x
guess.update(kwargs)
self.current_result = self.model.fit(self._data, *args, **guess)
self.current_params = self.current_result.params
