def _perform_fit(self):
layer_data_item = self.viewer.data_dock.wgt_data_tree.current_item
# we have to guard against all kinds of stuff that can
# be throw at us by events generated by the GUI. In this
# particular case, the 'current_item' item may not be the
# one appropriate to provide the correct data to feed into
# a fitting calculation.
if not isinstance(layer_data_item, LayerDataTreeItem):
return
cmodel = layer_data_item.model
if not type(cmodel) in all_models.values() and \
not type(cmodel.__class__) == core._CompoundModelMeta:
return
fitter_name = self.viewer.model_editor_dock.wgt_fit_selector.currentText()
fitter = get_fitter(str(fitter_name))
init_model = layer_data_item.model
x, y = layer_data_item.item.x.data, layer_data_item.item.y.data
fit_model = fitter(init_model, x, y)
new_y = fit_model(x)
self._update_parameter_values(fit_model, layer_data_item)
self._update_model_plot(layer_data_item, new_y)
def _perform_fit(self):
layer_data_item = self.layer_dock.current_item
if len(layer_data_item._model_items) == 0:
return
fitter = model_fitting.get_fitter(
str(self.model_editor_dock.wgt_fit_selector.currentText()))
init_model = layer_data_item.model
# x, y, x_unit, y_unit = self.sub_window.graph.get_roi_data(
# layer_data_item)
x, y = layer_data_item.item.dispersion, layer_data_item.item.flux
mask = self.sub_window.graph.get_roi_mask(layer_data_item)
print(x.shape, y.shape, layer_data_item.item.dispersion.shape,
layer_data_item.item.flux.shape)
fit_model = fitter(init_model, x.value[~mask], y.value[~mask])
new_y = fit_model(x.value[~mask])
# Create new data object
fit_spec_data = SpectrumData(new_y, unit=layer_data_item.item.unit,
mask=layer_data_item.item.mask,
wcs=layer_data_item.item.wcs,
meta=layer_data_item.item.meta,
uncertainty=layer_data_item.item.uncertainty)
# Add data object to model
# spec_data_item = self.model.create_data_item(
# fit_spec_data, name="Model Fit ({}: {})".format(
# layer_data_item.parent.text(), layer_data_item.text()))
# Display
# self.display_graph(spec_data_item)
self.client.add_layer(fit_spec_data, mask=mask,
name="Model Fit ({}: {})".format(
layer_data_item.parent.text(), layer_data_item.text()))
# Update using model approach
for model_idx in range(layer_data_item.rowCount()):
model_data_item = layer_data_item.child(model_idx)
for param_idx in range(model_data_item.rowCount()):
parameter_data_item = model_data_item.child(param_idx, 1)
if layer_data_item.rowCount() > 1:
value = fit_model[model_idx].parameters[param_idx]
else:
value = fit_model.parameters[param_idx]
parameter_data_item.setData(value)
parameter_data_item.setText(str(value))
def _perform_fit(self):
layer_data_item = self.viewer.data_dock.wgt_data_tree.current_item
if not isinstance(layer_data_item, LayerDataTreeItem):
return
fitter_name = self.viewer.model_editor_dock.wgt_fit_selector.currentText(
)
fitter = get_fitter(str(fitter_name))
init_model = layer_data_item.model
x, y = layer_data_item.item.x.data, layer_data_item.item.y.data
fit_model = fitter(init_model, x, y)
new_y = fit_model(x)
# Update using model approach
for model_idx in range(layer_data_item.rowCount()):
model_data_item = layer_data_item.child(model_idx)
for param_idx in range(model_data_item.rowCount()):
parameter_data_item = model_data_item.child(param_idx, 1)
if layer_data_item.rowCount() > 1:
value = fit_model[model_idx].parameters[param_idx]
else:
value = fit_model.parameters[param_idx]
parameter_data_item.setData(value)
parameter_data_item.setText(str(value))
fit_spec_data = SpectrumData(x=layer_data_item.item.x)
fit_spec_data.set_y(new_y,
wcs=layer_data_item.item.y.wcs,
unit=layer_data_item.item.y.unit)
spec_data_item = self.add_data_set(fit_spec_data,
name="Model Fit ({}: {})".format(
layer_data_item.parent.text(),
layer_data_item.text()))
self.display_graph(spec_data_item)
def _perform_fit(self):
layer_data_item = self.viewer.data_dock.wgt_data_tree.current_item
if not isinstance(layer_data_item, LayerDataTreeItem):
return
fitter_name = self.viewer.model_editor_dock.wgt_fit_selector.currentText()
fitter = get_fitter(str(fitter_name))
init_model = layer_data_item.model
x, y = layer_data_item.item.x.data, layer_data_item.item.y.data
fit_model = fitter(init_model, x, y)
new_y = fit_model(x)
# Update using model approach
for model_idx in range(layer_data_item.rowCount()):
model_data_item = layer_data_item.child(model_idx)
for param_idx in range(model_data_item.rowCount()):
parameter_data_item = model_data_item.child(param_idx, 1)
if layer_data_item.rowCount() > 1:
value = fit_model[model_idx].parameters[param_idx]
else:
value = fit_model.parameters[param_idx]
parameter_data_item.setData(value)
parameter_data_item.setText(str(value))
fit_spec_data = SpectrumData(x=layer_data_item.item.x)
fit_spec_data.set_y(new_y, wcs=layer_data_item.item.y.wcs,
unit=layer_data_item.item.y.unit)
spec_data_item = self.add_data_set(fit_spec_data,
name="Model Fit ({}: {})".format(
layer_data_item.parent.text(),
layer_data_item.text()))
self.display_graph(spec_data_item)
