def test_settings(self):
lmfit_new = LMFitModel(TestLMFitModel.sliced_data,
TestLMFitModel.orbitals)
lmfit_new.set_settings(self.lmfit.get_settings())
class LMFitResultTab(LMFitBaseTab, LMFitTab_UI):
open_plot_tab = pyqtSignal(list, list, Axis)
def __init__(self, data, results, settings):
self.results = results
self.current_result = self.results[0]
self.model = LMFitModel(*data)
self.model.set_settings(settings)
# Setup GUI
super(LMFitResultTab, self).__init__()
self.setupUi(self)
self._setup()
self._connect()
self.refresh_sliced_plot()
self.refresh_selected_plot()
kmap = self.refresh_sum_plot()
self.refresh_residual_plot(weight_sum_data=kmap)
def get_title(self):
return 'Results'
def change_slice(self):
index = self.slider.get_index()
self.current_result = self.results[0] if len(
self.results) == 1 else self.results[index]
self.update_tree()
self.result.result = self.current_result[1]
self.refresh_sliced_plot()
self._refresh_orbital_plots()
def refresh_selected_plot(self):
params = self.current_result[1].params
super().refresh_selected_plot(params)
def refresh_sum_plot(self):
params = self.current_result[1].params
return super().refresh_sum_plot(params)
def refresh_residual_plot(self, weight_sum_data=None):
params = self.current_result[1].params
super().refresh_residual_plot(params, weight_sum_data)
def update_tree(self):
params = self.current_result[1].params
self.tree.update_result(params)
def print_result(self):
report = self.result.get_fit_report()
print(report)
def print_covariance_matrix(self):
cov_matrix = self.result.get_covariance_matrix()
print(cov_matrix)
def plot(self):
results = [result[1] for result in self.results]
orbitals = self.model.orbitals
axis = self.model.sliced_data.axes[self.model.slice_policy[0]]
self.open_plot_tab.emit(results, orbitals, axis)
def _setup(self):
LMFitBaseTab._setup(self)
self.result = LMFitResult(self.current_result[1], self.model)
self.tree = LMFitResultTree(self.model.orbitals,
self.current_result[1].params,
self.model.background_equation[1])
self.crosshair._set_model(self.model.crosshair)
layout = QVBoxLayout()
self.scroll_area.widget().setLayout(layout)
layout.insertWidget(0, self.slider)
layout.insertWidget(1, self.result)
layout.insertWidget(3, self.colormap)
layout.insertWidget(4, self.crosshair)
self.layout.insertWidget(1, self.tree)
def _connect(self):
self.result.print_triggered.connect(self.print_result)
self.result.cov_matrix_requested.connect(self.print_covariance_matrix)
self.result.plot_requested.connect(self.plot)
LMFitBaseTab._connect(self)
class LMFitResultTab(LMFitBaseTab, LMFitTab_UI):
open_plot_tab = pyqtSignal(list, list, Axis, list, list)
def __init__(self, lmfit_tab, results, settings):
self.results = results
self.lmfit_tab = lmfit_tab
self.current_result = self.results[0]
self.settings = settings
self.model = LMFitModel(*self.lmfit_tab.get_data())
self.model.set_settings(settings)
# Setup GUI
super(LMFitResultTab, self).__init__()
self.setupUi(self)
self._setup()
self._connect()
self.refresh_all()
@classmethod
def init_from_save(cls, save, dependencies, tab_widget):
results = save['results']
settings = save['settings']
tab = tab_widget.get_tab_by_ID(dependencies['lmfittab'])
self = LMFitResultTab(tab, results, settings)
self.slider.restore_state(save['slider'])
self.crosshair.restore_state(save['crosshair'])
self.colormap.restore_state(save['colormap'])
self.sliced_plot.set_colormap(save['colorscales']['sliced'])
self.sliced_plot.set_levels(save['levels']['sliced'])
self.sum_plot.set_colormap(save['colorscales']['sum'])
self.sum_plot.set_levels(save['levels']['sum'])
self.residual_plot.set_levels(save['levels']['residual'])
self.residual_plot.set_colormap(save['colorscales']['residual'])
self.selected_plot.set_levels(save['levels']['selected'])
self.selected_plot.set_colormap(save['colorscales']['selected'])
self.locked_tabs = [tab]
return self
def save_state(self):
save, dependencies = super().save_state()
save.update({'title': self.title,
'crosshair': self.crosshair.save_state(),
'slider': self.slider.save_state(),
'colormap': self.colormap.save_state(),
'results': self.results,
'settings': self.settings})
dependencies.update({'lmfittab': self.lmfit_tab.ID})
return save, dependencies
def get_title(self):
return 'Results'
def change_slice(self):
index = self.slider.get_index()
slices = [result[0] for result in self.results]
if len(self.results) == 1:
self.current_result = self.results[-1]
elif index <= slices[0]:
self.current_result = self.results[0]
elif index >= slices[-1]:
self.current_result = self.results[-1]
else:
self.current_result = self.results[index-slices[0]]
self.update_tree()
self.result.result = self.current_result[1]
self.refresh_sliced_plot()
self._refresh_orbital_plots()
def refresh_selected_plot(self):
params = self.current_result[1].params
super().refresh_selected_plot(params)
def refresh_sum_plot(self):
params = self.current_result[1].params
return super().refresh_sum_plot(params)
def refresh_residual_plot(self, weight_sum_data=None):
params = self.current_result[1].params
super().refresh_residual_plot(params, weight_sum_data)
def update_tree(self):
params = self.current_result[1].params
self.tree.update_result(params)
def print_result(self):
report = self.result.get_fit_report()
print(report)
def print_covariance_matrix(self):
cov_matrix = self.result.get_covariance_matrix()
print(cov_matrix)
def get_orbitals(self):
return self.model.orbitals
def plot(self):
indices = [result[0] for result in self.results]
results = [result[1] for result in self.results]
orbitals = self.model.orbitals
axis = self.model.sliced_data.axes[self.model.slice_policy[0]]
axis = axis.sublist(indices)
kmaps = abs(self.get_residual_kmaps())
residuals = list(np.nansum(np.nansum(kmaps, axis=1), axis=1))
self.open_plot_tab.emit(results, orbitals, axis, residuals,
self.model.background_equation[1])
def export_to_hdf5(self):
path = config.get_key('paths', 'hdf5_export_start')
if path == 'None':
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)')
else:
start_path = str(__directory__ / path)
file_name, _ = QFileDialog.getSaveFileName(
None, 'Save .hdf5 File (*.hdf5)', str(start_path))
if not file_name:
return
else:
h5file = h5py.File(file_name, 'w')
kmaps = self.get_residual_kmaps()
slice_axis = self.slider.data.axes[0]
x_axis = self.slider.data.axes[1]
y_axis = self.slider.data.axes[2]
h5file.create_dataset('name', data='Residual')
h5file.create_dataset('axis_1_label', data=slice_axis.label)
h5file.create_dataset('axis_2_label', data=x_axis.label)
h5file.create_dataset('axis_3_label', data=y_axis.label)
h5file.create_dataset('axis_1_units', data=slice_axis.units)
h5file.create_dataset('axis_2_units', data=x_axis.units)
h5file.create_dataset('axis_3_units', data=y_axis.units)
h5file.create_dataset('axis_1_range', data=slice_axis.range)
h5file.create_dataset('axis_2_range', data=x_axis.range)
h5file.create_dataset('axis_3_range', data=y_axis.range)
h5file.create_dataset('data', data=kmaps, dtype='f8',
compression='gzip', compression_opts=9)
h5file.close()
def get_residual_kmaps(self):
kmaps = []
for i, result in enumerate(self.results):
residual = self.model.get_residual(i, result[1].params)
kmaps.append(residual.data)
kmaps = np.array(kmaps)
return kmaps
def _setup(self):
LMFitBaseTab._setup(self)
self.result = LMFitResult(self.current_result[1], self.model)
self.tree = LMFitResultTree(
self.model.orbitals, self.current_result[1].params,
self.model.background_equation[1])
self.crosshair._set_model(self.model.crosshair)
layout = QVBoxLayout()
self.scroll_area.widget().setLayout(layout)
layout.insertWidget(0, self.slider)
layout.insertWidget(1, self.result)
layout.insertWidget(3, self.colormap)
layout.insertWidget(4, self.crosshair)
self.layout.insertWidget(1, self.tree)
def _connect(self):
self.result.print_triggered.connect(self.print_result)
self.result.cov_matrix_requested.connect(self.print_covariance_matrix)
self.result.plot_requested.connect(self.plot)
LMFitBaseTab._connect(self)