def _set_indexed_fits(self, minimizer):
_data = TestMultiFit._get_indexed_data()
self._fit_indexed_all = TestMultiFit._get_indexed_fit(
_data, TestMultiFit.quadratic_model_indexed_all, minimizer)
_split_data_1 = _data[:50]
_split_data_2 = _data[50:]
self._fit_indexed_all_multi = MultiFit(
TestMultiFit._get_indexed_fit(
_data, TestMultiFit.quadratic_model_indexed_all, minimizer))
self._fit_indexed_split_multi = MultiFit(fit_list=[
TestMultiFit._get_indexed_fit(
_split_data_1, TestMultiFit.quadratic_model_indexed_split_1,
minimizer),
TestMultiFit._get_indexed_fit(
_split_data_2, TestMultiFit.quadratic_model_indexed_split_2,
minimizer)
])
self._fit_indexed_split_1 = TestMultiFit._get_indexed_fit(
_split_data_1, TestMultiFit.quadratic_model_indexed_split_1,
minimizer)
self._fit_indexed_split_1_multi = MultiFit(
TestMultiFit._get_indexed_fit(
_split_data_1, TestMultiFit.quadratic_model_indexed_split_1,
minimizer))
self._fit_indexed_split_2 = TestMultiFit._get_indexed_fit(
_split_data_2, TestMultiFit.quadratic_model_indexed_split_2,
minimizer)
self._fit_indexed_split_2_multi = MultiFit(
TestMultiFit._get_indexed_fit(
_split_data_2, TestMultiFit.quadratic_model_indexed_split_2,
minimizer))
def _set_indexed_fits(self):
_data = TestMultiFit._get_indexed_data()
self._fit_indexed_all = self._get_indexed_fit(
_data, TestMultiFit.quadratic_model_indexed_all)
_split_data_1 = _data[:50]
_split_data_2 = _data[50:]
self._fit_indexed_all_multi = MultiFit([
self._get_indexed_fit(_data,
TestMultiFit.quadratic_model_indexed_all)
])
self._fit_indexed_split_multi = MultiFit(fit_list=[
self._get_indexed_fit(
_split_data_1, TestMultiFit.quadratic_model_indexed_split_1),
self._get_indexed_fit(_split_data_2,
TestMultiFit.quadratic_model_indexed_split_2)
])
self._fit_indexed_split_1 = self._get_indexed_fit(
_split_data_1, TestMultiFit.quadratic_model_indexed_split_1)
self._fit_indexed_split_1_multi = MultiFit([
self._get_indexed_fit(_split_data_1,
TestMultiFit.quadratic_model_indexed_split_1)
])
self._fit_indexed_split_2 = self._get_indexed_fit(
_split_data_2, TestMultiFit.quadratic_model_indexed_split_2)
self._fit_indexed_split_2_multi = MultiFit([
self._get_indexed_fit(_split_data_2,
TestMultiFit.quadratic_model_indexed_split_2)
])
def _set_hist_fits(self):
_raw_data = TestMultiFit._get_hist_data()
self._fit_hist_all = self._get_hist_fit(_raw_data)
_split_data_1, _split_data_2 = self._split_data(_raw_data)
self._fit_hist_all_multi = MultiFit([self._get_hist_fit(_raw_data)])
self._fit_hist_split_multi = MultiFit(fit_list=[
self._get_hist_fit(_split_data_1),
self._get_hist_fit(_split_data_2),
])
self._fit_hist_split_1 = self._get_hist_fit(_split_data_1)
self._fit_hist_split_1_multi = MultiFit(
[self._get_hist_fit(_split_data_1)])
self._fit_hist_split_2 = self._get_hist_fit(_split_data_2)
self._fit_hist_split_2_multi = MultiFit(
[self._get_hist_fit(_split_data_2)])
def _set_xy_fits(self, minimizer):
_xy_data = TestMultiFit._get_xy_data()
self._fit_xy_all = TestMultiFit._get_xy_fit(_xy_data, minimizer)
_split_data_1, _split_data_2 = TestMultiFit._split_data(_xy_data,
axis=1)
self._fit_xy_all_multi = MultiFit(
TestMultiFit._get_xy_fit(_xy_data, minimizer))
self._fit_xy_split_multi = MultiFit(fit_list=[
TestMultiFit._get_xy_fit(_split_data_1, minimizer),
TestMultiFit._get_xy_fit(_split_data_2, minimizer)
])
self._fit_xy_split_1 = TestMultiFit._get_xy_fit(
_split_data_1, minimizer)
self._fit_xy_split_1_multi = MultiFit(
TestMultiFit._get_xy_fit(_split_data_1, minimizer))
self._fit_xy_split_2 = TestMultiFit._get_xy_fit(
_split_data_2, minimizer)
self._fit_xy_split_2_multi = MultiFit(
TestMultiFit._get_xy_fit(_split_data_2, minimizer))
self._fit_xy_all_double = MultiFit([
TestMultiFit._get_xy_fit(_xy_data, minimizer),
TestMultiFit._get_xy_fit(_xy_data, minimizer)
])
self._expected_parameters_all = calculate_expected_fit_parameters_xy(
x_data=_xy_data[0],
y_data=_xy_data[1],
model_function=quadratic_model,
y_error=0.01,
initial_parameter_values=[1.0, 1.0, 1.0],
x_error=0.01,
model_function_derivative=quadratic_model_derivative)
self._expected_parameters_split_1 = calculate_expected_fit_parameters_xy(
x_data=_split_data_1[0],
y_data=_split_data_1[1],
model_function=quadratic_model,
y_error=0.01,
initial_parameter_values=[1.0, 1.0, 1.0],
x_error=0.01,
model_function_derivative=quadratic_model_derivative)
self._expected_parameters_split_2 = calculate_expected_fit_parameters_xy(
x_data=_split_data_2[0],
y_data=_split_data_2[1],
model_function=quadratic_model,
y_error=0.01,
initial_parameter_values=[1.0, 1.0, 1.0],
x_error=0.01,
model_function_derivative=quadratic_model_derivative)
def construct_multi_fit(shared_x_error):
fit_1 = XYFit(xy_data=[U, T], model_function=empirical_T_U_model)
fit_1.add_error(axis='y', err_val=sigT) # declare errors on T
fit_2 = XYFit(xy_data=[U, I], model_function=I_U_model)
fit_2.add_error(axis='y', err_val=sigI) # declare errors on I
multi_fit = MultiFit(fit_list=[fit_1, fit_2], minimizer='iminuit')
if shared_x_error:
multi_fit.add_error(axis='x', err_val=sigU, fits='all')
else:
fit_1.add_error(axis='x', err_val=sigU)
fit_2.add_error(axis='x', err_val=sigU)
return multi_fit
def _set_hist_fits(self, minimizer):
_raw_data = TestMultiFit._get_hist_data()
self._fit_hist_all = TestMultiFit._get_hist_fit(_raw_data, minimizer)
_split_data_1, _split_data_2 = TestMultiFit._split_data(_raw_data)
self._fit_hist_all_multi = MultiFit(
TestMultiFit._get_hist_fit(_raw_data, minimizer))
self._fit_hist_split_multi = MultiFit(fit_list=[
TestMultiFit._get_hist_fit(_split_data_1, minimizer),
TestMultiFit._get_hist_fit(_split_data_2, minimizer),
])
self._fit_hist_split_1 = TestMultiFit._get_hist_fit(
_split_data_1, minimizer)
self._fit_hist_split_1_multi = MultiFit(
TestMultiFit._get_hist_fit(_split_data_1, minimizer))
self._fit_hist_split_2 = TestMultiFit._get_hist_fit(
_split_data_2, minimizer)
self._fit_hist_split_2_multi = MultiFit(
TestMultiFit._get_hist_fit(_split_data_2, minimizer))
def _set_xy_fits(self):
self._xy_data = TestMultiFit._get_xy_data()
self._fit_xy_all = self._get_xy_fit(self._xy_data)
self._split_data_1, self._split_data_2 = TestMultiFit._split_data(
self._xy_data, axis=1)
self._fit_xy_all_multi = MultiFit([self._get_xy_fit(self._xy_data)])
self._fit_xy_split_multi = MultiFit(fit_list=[
self._get_xy_fit(self._split_data_1),
self._get_xy_fit(self._split_data_2)
])
self._fit_xy_split_1 = self._get_xy_fit(self._split_data_1)
self._fit_xy_split_1_multi = MultiFit(
[self._get_xy_fit(self._split_data_1)])
self._fit_xy_split_2 = self._get_xy_fit(self._split_data_2)
self._fit_xy_split_2_multi = MultiFit(
[self._get_xy_fit(self._split_data_2)])
self._fit_xy_all_double = MultiFit(
[self._get_xy_fit(self._xy_data),
self._get_xy_fit(self._xy_data)])
def _get_multifit(
self, hist_fit=True, xy_fit_1=True, indexed_fit=True, xy_fit_2=True, reverse=False):
_fits = []
if hist_fit:
_hist_container = HistContainer(
n_bins=10, bin_range=(-5, 5), fill_data=self._hist_data)
_hist_fit = HistFit(_hist_container)
_fits.append(_hist_fit)
if xy_fit_1:
_xy_fit_1 = XYFit(xy_data=[self._x_data, self._y_data_1])
_xy_fit_1.add_error("y", 1.0)
_fits.append(_xy_fit_1)
if indexed_fit:
_indexed_fit = IndexedFit(
self._indexed_data, TestSharedErrorLogic.indexed_model_function)
_indexed_fit.add_error(1.0)
_fits.append(_indexed_fit)
if xy_fit_2:
_xy_fit_2 = XYFit(xy_data=[self._x_data, self._y_data_2])
_xy_fit_2.add_error("y", 1.0)
_fits.append(_xy_fit_2)
if reverse:
_fits = reversed(_fits)
return MultiFit(_fits)
xy_d1.axis_labels = ['x', 'y (1)']
xy_d2.label = 'Beispieldaten (2)'
xy_d2.axis_labels = ['x', 'y(2) & f(x)']
# 3. create the Fit objects
xyFit1 = Fit(xy_d1, model1)
xyFit2 = Fit(xy_d2, model2)
# set meaningful names for model
xyFit1.model_label = 'Lineares Modell'
xyFit2.model_label = 'Lineares Modell'
# add the parameter constraints
xyFit1.add_parameter_constraint(name='g1', value=c1, uncertainty=ec1)
xyFit2.add_parameter_constraint(name='g2', value=c2, uncertainty=ec2)
# combine the two fit objects to form a MultiFit
multiFit = MultiFit(fit_list=[xyFit1, xyFit2])
# 4. perform the fit
multiFit.do_fit()
# 5. report fit results
multiFit.report()
# 6. create and draw plots
multiPlot = Plot(multiFit)
##multiPlot = Plot(multiFit, separate_figures=True)
multiPlot.plot(figsize=(13., 7.))
# 7. show or save plots #
##for i, fig in enumerate(multiPlot.figures):
## fig.savefig("MultiFit-"+str(i)+".pdf")
sigU, sigI, sigT = 0.1, 0.1, 0.1 # uncertainties
T0 = 273.15 # 0 degrees C as absolute Temperature (in Kelvin)
T -= T0 # Measurements are in Kelvin, convert to °C
# -- Finally, go through the fitting procedure
# Step 1: construct the singular fit objects
fit_1 = XYFit(xy_data=[U, T], model_function=empirical_T_U_model)
fit_1.add_error(axis='y', err_val=sigT) # declare errors on T
fit_2 = XYFit(xy_data=[U, I], model_function=I_U_model)
fit_2.add_error(axis='y', err_val=sigI) # declare errors on I
# Step 2: construct a MultiFit object
multi_fit = MultiFit(fit_list=[fit_1, fit_2], minimizer='iminuit')
# Step 3: Add a shared error error for the x axis.
multi_fit.add_error(axis='x', err_val=sigU, fits='all')
# (Optional): assign names for models and parameters
multi_fit.assign_parameter_latex_names(x='U',
p2='p_2',
p1='p_1',
p0='p_0',
R0='R_0',
alph=r'\alpha_\mathrm{T}')
multi_fit.assign_model_function_expression('{1}*{x}^2 + {2}*{x} + {3}',
fit_index=0)
multi_fit.assign_model_function_latex_expression(