def _get_fit(self, errors=None):
'''convenience'''
errors = errors or [dict(axis='y', err_val=1.0)]
_fit = XYFit(xy_data=self._ref_xy_data,
model_function=simple_xy_model,
cost_function=XYCostFunction_Chi2(
axes_to_use='xy', errors_to_use='covariance'),
minimizer=self.MINIMIZER)
for _err in errors:
_fit.add_error(**_err)
return _fit
def _get_fit(self, model_function=None, cost_function=None, error=None):
'''convenience'''
model_function = model_function or simple_xy_model
# TODO: fix default
cost_function = cost_function or XYCostFunction_Chi2(
axes_to_use='xy', errors_to_use='covariance')
error = error or 1.0
_fit = XYFit(xy_data=self._ref_xy_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER)
_fit.add_error(axis='y', err_val=error)
return _fit
def _get_fit(self, errors=None):
'''convenience'''
errors = errors or [dict(axis='y', err_val=1.0)]
_fit = XYFit(
xy_data=self._ref_xy_data,
model_function=line_xy_model,
cost_function=self._default_cost_function,
minimizer=self.MINIMIZER,
#x_error_algorithm='nonlinear', # TODO: test other algorithms
)
for _err in errors:
_fit.add_error(**_err)
_fit.set_all_parameter_values(self._ref_initial_pars)
return _fit
def _get_fit(self, errors=None):
'''convenience'''
_fit = XYFit(xy_data=self._ref_xy_data,
model_function=simple_xy_model,
cost_function=XYCostFunction_Chi2(
axes_to_use='xy', errors_to_use='covariance'),
minimizer=self.MINIMIZER)
if errors is None:
_fit.add_matrix_error(axis='y',
err_matrix=np.eye(self._n_points),
matrix_type='cov')
else:
for _err in errors:
if 'err_matrix' in _err:
_fit.add_matrix_error(**_err)
else:
_fit.add_error(**_err)
return _fit
def _get_fit(self,
model_function=None,
cost_function=None,
errors=None,
dynamic_error_algorithm=None):
'''convenience'''
model_function = model_function or simple_xy_model
# TODO: fix default
cost_function = cost_function or XYCostFunction_Chi2(
axes_to_use='xy', errors_to_use='covariance')
errors = errors or [dict(axis='y', err_val=1.0)]
dynamic_error_algorithm = dynamic_error_algorithm or "nonlinear"
_fit = XYFit(xy_data=self._ref_xy_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER,
dynamic_error_algorithm=dynamic_error_algorithm)
for _err in errors:
_fit.add_error(**_err)
return _fit
def _get_fit(self,
errors=None,
constraints=None,
dynamic_error_algorithm=None):
'''convenience'''
errors = errors or [dict(axis='y', err_val=1.0)]
constraints = constraints or []
dynamic_error_algorithm = dynamic_error_algorithm or "nonlinear"
_fit = XYFit(xy_data=self._ref_xy_data,
model_function=line_xy_model,
cost_function=self._default_cost_function,
minimizer=self.MINIMIZER,
dynamic_error_algorithm=dynamic_error_algorithm)
for _err in errors:
_fit.add_error(**_err)
for _constraint in constraints:
_fit.add_parameter_constraint(**_constraint)
_fit.set_all_parameter_values(self._ref_initial_pars)
return _fit
import numpy as np
import matplotlib.pyplot as plt
KAFE_PLOT = False
_x = [-1.0, 1.0]
_y = [-1.0, 1.0]
_x_err = np.sqrt(2.0)
_y_err = np.sqrt(2.0)
def model_function(x, a):
return x * a
_fit_iminuit = XYFit(xy_data=[_x, _y], model_function=model_function, minimizer='iminuit')
_fit_iminuit.add_error('x', _x_err)
_fit_iminuit.add_error('y', _y_err)
_fit_iminuit.do_fit()
#_plot = _fit_iminuit.generate_plot()
#_plot.plot(with_fit_info=True)
_profiler_iminuit = ContoursProfiler(_fit_iminuit)
_a_profile_iminuit = _profiler_iminuit.get_profile('a')
_fit_scipy = XYFit(xy_data=[_x, _y], model_function=model_function, minimizer='scipy')
_fit_scipy.add_error('x', _x_err)
_fit_scipy.add_error('y', _y_err)
