1.2.0 fixes (#299)

* display corrected chi2 in the GUI

* make the Fit button visible all the time

* make sure refl1d actually works

* typo fixed

* attempt at properly adding starting menu item on ubuntu

* enforce axis reset on q-range change. Reparent easyApp

* fixed number of fitted parameters shown in the widget and in the status
bar. Reparented to EDL develop

* changed copyright year from 2025 to 2026

* update model q-range with experimental range

* do not grey out editable min/max values

* propagate model name changes to all tabs

* display assembly name prefix for analysis parameters

* merged develop + minor fixes

Author: rozyczko

EasyReflectometryApp/Backends/Py/logic/calculators.py

@@ -13,7 +13,7 @@ def available(self) -> list[str]:
     def current_index(self) -> int:
         return self._current_index
 
-    def set_current_index(self, new_value: int) -> None:
+    def set_current_index(self, new_value: int) -> bool:
         if new_value != self._current_index:
             self._current_index = new_value
             new_calculator = self._list_available_calculators[new_value]

EasyReflectometryApp/Backends/Py/logic/fitting.py

@@ -2,6 +2,7 @@
 from typing import TYPE_CHECKING
 from typing import List
 from typing import Optional
+from typing import cast
 
 from easyreflectometry import Project as ProjectLib
 from easyscience.fitting import FitResults
@@ -31,7 +32,7 @@ def status(self) -> str:
         if self._result is None:
             return ''
         else:
-            return self._result.success
+            return str(self._result.success)
 
     @property
     def running(self) -> bool:
@@ -105,6 +106,8 @@ def prepare_for_threaded_fit(self) -> None:
         self._finished = False
         self._show_results_dialog = False
         self._fit_error_message = None
+        self._result = None
+        self._results = []
 
     def _ordered_experiments(self) -> list:
         """Return experiments as an ordered list of experiment objects.
@@ -118,7 +121,7 @@ def _ordered_experiments(self) -> list:
         if hasattr(experiments, 'items'):
             items = list(experiments.items())
             try:
-                items.sort(key=lambda item: item[0])
+                items = sorted(items)
             except TypeError:
                 pass
             return [experiment for _, experiment in items]
@@ -201,7 +204,7 @@ def prepare_threaded_fit(self, minimizers_logic: 'Minimizers') -> tuple:
             logger.exception('Error preparing threaded fit')
             return None, None, None, None, None
 
-    def on_fit_finished(self, results: List[FitResults]) -> None:
+    def on_fit_finished(self, results: FitResults | List[FitResults]) -> None:
         """Handle successful completion of fitting.
 
         :param results: List of FitResults from the multi-fitter.
@@ -219,25 +222,28 @@ def on_fit_finished(self, results: List[FitResults]) -> None:
             engine_name = getattr(results[0], 'minimizer_engine', 'unknown')
             logger.info('Fit finished: engine=%s, chi2=%s, success=%s', engine_name, self.fit_chi2, results[0].success)
         else:
-            self._result = results
-            self._results = [results] if results else []
+            single_result = cast(Optional[FitResults], results)
+            self._result = single_result
+            self._results = [single_result] if single_result is not None else []
 
     @property
     def fit_n_pars(self) -> int:
         """Return the global number of refined parameters for the fit."""
         if self._results:
-            return sum(r.n_pars for r in self._results)
+            return sum(result.n_pars for result in self._results)
         if self._result is None:
             return 0
         return self._result.n_pars
 
     @property
     def fit_chi2(self) -> float:
-        """Return reduced chi-squared across all fits (chi2 / degrees of freedom)"""
+        """Return reduced chi-squared across all fits."""
         if self._results:
             try:
-                total_chi2 = float(sum(r.chi2 for r in self._results))
-                total_points = sum(len(r.x) for r in self._results)
+                if len(self._results) == 1:
+                    return float(self._results[0].reduced_chi)
+                total_chi2 = float(sum(result.chi2 for result in self._results))
+                total_points = sum(len(result.x) for result in self._results)
                 n_params = self._results[0].n_pars
                 total_dof = total_points - n_params
                 if total_dof <= 0:

EasyReflectometryApp/Backends/Py/logic/project.py

@@ -8,6 +8,7 @@
 class Project:
     def __init__(self, project_lib: ProjectLib):
         self._project_lib = project_lib
+        self._last_q_range_changed = False
         self._project_lib.default_model()
         self._update_enablement_of_fixed_layers_for_model(0)
 

EasyReflectometryApp/Backends/Py/plotting_1d.py

@@ -562,6 +562,22 @@ def individualExperimentDataList(self) -> list:
             )
         return qml_data_list
 
+    @Property(float, notify=sampleChartRangesChanged)
+    def residualMinX(self):
+        return self._get_residual_range()[0]
+
+    @Property(float, notify=sampleChartRangesChanged)
+    def residualMaxX(self):
+        return self._get_residual_range()[1]
+
+    @Property(float, notify=sampleChartRangesChanged)
+    def residualMinY(self):
+        return self._get_residual_range()[2]
+
+    @Property(float, notify=sampleChartRangesChanged)
+    def residualMaxY(self):
+        return self._get_residual_range()[3]
+
     @Slot(str, str, 'QVariant')
     def setQtChartsSerieRef(self, page: str, serie: str, ref: QObject):
         self._chartRefs['QtCharts'][page][serie] = ref
@@ -640,112 +656,143 @@ def getExperimentDataPoints(self, experiment_index: int) -> list:
             console.debug(f'Error getting experiment data points for index {experiment_index}: {e}')
             return []
 
-    def _get_aligned_analysis_values(self, experiment_index: int) -> list:
-        """Return aligned measured/calculated pairs in linear (rq4-transformed) space.
-
-        Both values have ``_apply_rq4`` applied but no log10.  Only points within
-        [q_min, q_max] are included.  The caller is responsible for any further
-        transformation (log10 for display, subtraction for residuals, etc.).
-
-        Each element is a dict::
+    def _get_experiment_model_index(self, experiment_index: int, exp_data=None) -> int:
+        """Resolve the model index used by a given experiment."""
+        if exp_data is not None and hasattr(exp_data, 'model') and exp_data.model is not None:
+            for idx, model in enumerate(self._project_lib.models):
+                if model is exp_data.model:
+                    return idx
+        if experiment_index < len(self._project_lib.models):
+            return experiment_index
+        return 0
 
-            {'q': float, 'measured': float, 'calculated': float}
-        """
-        # Get measured experimental data
+    def _get_aligned_analysis_values(self, experiment_index: int) -> list[dict]:
+        """Return measured, calculated and sigma values aligned on experiment q points."""
         exp_data = self._project_lib.experimental_data_for_model_at_index(experiment_index)
+        q_values = np.asarray(getattr(exp_data, 'x', np.empty(0)), dtype=float)
+        measured_values = np.asarray(getattr(exp_data, 'y', np.empty(0)), dtype=float)
+        sigma_values = np.asarray(getattr(exp_data, 'ye', np.zeros_like(measured_values)), dtype=float)
 
-        # Resolve model index, which may differ from experiment_index when multiple
-        # experiments share the same model.
-        model_index = 0
-        model_found = False
-        if hasattr(exp_data, 'model') and exp_data.model is not None:
-            for idx, model in enumerate(self._project_lib.models):
-                if model is exp_data.model:
-                    model_index = idx
-                    model_found = True
-                    break
-            if not model_found:
-                console.debug(f'Warning: model for experiment {experiment_index} '
-                              f'not found in models collection, falling back to model 0')
-        else:
-            model_index = experiment_index if experiment_index < len(self._project_lib.models) else 0
+        if q_values.size == 0 or measured_values.size == 0:
+            return []
 
-        # Filter experimental q values to [q_min, q_max]
-        q_values = exp_data.x
-        mask = (q_values >= self._project_lib.q_min) & (q_values <= self._project_lib.q_max)
-        q_filtered = q_values[mask]
+        q_mask = (q_values >= self._project_lib.q_min) & (q_values <= self._project_lib.q_max)
+        q_filtered = q_values[q_mask]
+        measured_filtered = measured_values[q_mask]
+        sigma_filtered = sigma_values[q_mask] if sigma_values.size else np.zeros_like(measured_filtered)
 
-        # Evaluate model at the filtered experimental q points
-        calc_data = self._project_lib.model_data_for_model_at_index(model_index, q_filtered)
-        calc_y = calc_data.y
+        model_index = self._get_experiment_model_index(experiment_index, exp_data)
+        try:
+            calc_data = self._project_lib.model_data_for_model_at_index(model_index, q_filtered)
+        except TypeError:
+            calc_data = self._project_lib.model_data_for_model_at_index(model_index)
+
+        calc_values = np.asarray(getattr(calc_data, 'y', np.empty(0)), dtype=float)
+        calc_q_values = np.asarray(getattr(calc_data, 'x', np.empty(0)), dtype=float)
+
+        if calc_values.size == q_filtered.size:
+            calculated_filtered = calc_values
+        elif calc_values.size == 0:
+            calculated_filtered = measured_filtered.copy()
+        elif calc_q_values.size == calc_values.size and calc_values.size > 1:
+            calculated_filtered = np.interp(q_filtered, calc_q_values, calc_values)
+        elif calc_values.size == 1:
+            calculated_filtered = np.full_like(measured_filtered, calc_values[0], dtype=float)
+        else:
+            calculated_filtered = np.resize(calc_values, q_filtered.size)
 
-        if len(calc_y) != len(q_filtered):
-            console.debug(f'Warning: calculated data length ({len(calc_y)}) '
-                          f'differs from filtered experimental data ({len(q_filtered)}) '
-                          f'for experiment {experiment_index}')
+        measured_filtered = self._apply_rq4(q_filtered, measured_filtered)
+        calculated_filtered = self._apply_rq4(q_filtered, calculated_filtered)
+        sigma_filtered = self._apply_rq4(q_filtered, sigma_filtered)
 
         points = []
-        calc_idx = 0
-        for point in exp_data.data_points():
-            q = point[0]
-            if self._project_lib.q_min <= q <= self._project_lib.q_max:
-                r_meas = point[1]
-                calc_y_val = calc_y[calc_idx] if calc_idx < len(calc_y) else r_meas
-                sigma_linear = float(np.sqrt(max(point[2], 0.0)))
-                sigma_transformed = float(self._apply_rq4(q, sigma_linear)) if sigma_linear > 0.0 else 0.0
-                points.append({
-                    'q': float(q),
-                    'measured': float(self._apply_rq4(q, r_meas)),
-                    'calculated': float(self._apply_rq4(q, calc_y_val)),
-                    'sigma': sigma_transformed,
-                })
-                calc_idx += 1
+        for q_value, measured_value, calculated_value, sigma_value in zip(
+            q_filtered,
+            measured_filtered,
+            calculated_filtered,
+            sigma_filtered,
+        ):
+            points.append(
+                {
+                    'q': float(q_value),
+                    'measured': float(measured_value),
+                    'calculated': float(calculated_value),
+                    'sigma': float(sigma_value),
+                }
+            )
         return points
 
     @Slot(int, result='QVariantList')
     def getAnalysisDataPoints(self, experiment_index: int) -> list:
         """Get measured and calculated data points for a specific experiment for analysis plotting."""
         try:
             points = []
-            for item in self._get_aligned_analysis_values(experiment_index):
-                q = item['q']
-                r_meas = item['measured']
-                r_calc = item['calculated']
-                points.append({
-                    'x': q,
-                    'measured': float(np.log10(r_meas)) if r_meas > 0 else -10.0,
-                    'calculated': float(np.log10(r_calc)) if r_calc > 0 else -10.0,
-                })
+            for point in self._get_aligned_analysis_values(experiment_index):
+                measured = point['measured']
+                calculated = point['calculated']
+                points.append(
+                    {
+                        'x': point['q'],
+                        'measured': float(np.log10(measured)) if measured > 0 else -10.0,
+                        'calculated': float(np.log10(calculated)) if calculated > 0 else -10.0,
+                    }
+                )
             return points
         except Exception as e:
             console.debug(f'Error getting analysis data points for index {experiment_index}: {e}')
             return []
 
     @Slot(int, result='QVariantList')
     def getResidualDataPoints(self, experiment_index: int) -> list:
-        """Get normalized residual data points (model − experiment) / sigma.
-
-        Falls back to (model − experiment) / experiment when sigma is zero
-        (i.e. measurement uncertainty not provided).
-        """
+        """Get residual data points for a specific experiment."""
         try:
             points = []
-            for item in self._get_aligned_analysis_values(experiment_index):
-                calc = item['calculated']
-                meas = item['measured']
-                sigma = item['sigma']
-                if sigma > 0.0:
-                    residual = (calc - meas) / sigma
-                elif meas > 0.0:
-                    residual = (calc - meas) / meas
-                else:
-                    residual = calc - meas
-                points.append({'x': float(item['q']), 'y': float(residual)})
+            for point in self._get_aligned_analysis_values(experiment_index):
+                sigma = point['sigma']
+                residual = point['calculated'] - point['measured']
+                if sigma > 0:
+                    residual = residual / sigma
+                points.append({'x': point['q'], 'y': float(residual)})
             return points
         except Exception as e:
             console.debug(f'Error getting residual data points for index {experiment_index}: {e}')
             return []
 
+    def _get_residual_range(self) -> tuple[float, float, float, float]:
+        """Return residual plot ranges for the current selection."""
+        try:
+            if self.is_multi_experiment_mode:
+                selected_indices = getattr(self._proxy._analysis, '_selected_experiment_indices', [])
+            else:
+                selected_indices = [self._project_lib.current_experiment_index]
+
+            all_points = []
+            for experiment_index in selected_indices:
+                all_points.extend(self.getResidualDataPoints(experiment_index))
+
+            if not all_points:
+                return 0.0, 1.0, -1.0, 1.0
+
+            x_values = np.asarray([point['x'] for point in all_points], dtype=float)
+            y_values = np.asarray([point['y'] for point in all_points], dtype=float)
+            if x_values.size == 0 or y_values.size == 0:
+                return 0.0, 1.0, -1.0, 1.0
+
+            min_x = float(np.min(x_values))
+            max_x = float(np.max(x_values))
+            min_y = float(np.min(y_values))
+            max_y = float(np.max(y_values))
+
+            if min_y == max_y:
+                margin = max(abs(min_y) * 0.05, 1.0)
+            else:
+                margin = (max_y - min_y) * 0.05
+
+            return min_x, max_x, min_y - margin, max_y + margin
+        except Exception as e:
+            console.debug(f'Error getting residual range: {e}')
+            return 0.0, 1.0, -1.0, 1.0
+
     def refreshSamplePage(self):
         # Clear cached data so it gets recalculated
         self._sample_data = {}
@@ -763,7 +810,6 @@ def refreshAnalysisPage(self):
         self._model_data = {}
         self._invalidate_residual_range_cache()
         self.drawCalculatedAndMeasuredOnAnalysisChart()
-        # Notify the residual chart to re-poll data and ranges
         self.sampleChartRangesChanged.emit()
 
     def refreshExperimentRanges(self):

EasyReflectometryApp/Backends/Py/py_backend.py

@@ -137,7 +137,7 @@ def plottingGetAnalysisDataPoints(self, experiment_index: int) -> list:
 
     @Slot(int, result='QVariantList')
     def plottingGetResidualDataPoints(self, experiment_index: int) -> list:
-        """Get residual data points (model - experiment) for a specific experiment."""
+        """Get residual data points for a specific experiment for residual plotting."""
         return self._plotting_1d.getResidualDataPoints(experiment_index)
 
     ######### Connections to relay info between the backend parts
@@ -165,7 +165,8 @@ def _connect_sample_page(self) -> None:
     def _connect_experiment_page(self) -> None:
         self._experiment.externalExperimentChanged.connect(self._relay_experiment_page_experiment_changed)
         self._experiment.externalExperimentChanged.connect(self._refresh_plots)
-        self._experiment.qRangeUpdated.connect(self._sample.qRangeChanged)
+        if hasattr(self._experiment, 'qRangeUpdated') and hasattr(self._sample, 'qRangeChanged'):
+            self._experiment.qRangeUpdated.connect(self._sample.qRangeChanged)
 
     def _connect_analysis_page(self) -> None:
         self._analysis.externalMinimizerChanged.connect(self._relay_analysis_page)

EasyReflectometryApp/Gui/Pages/Analysis/Sidebar/Basic/Popups/FitStatusDialog.qml

@@ -45,7 +45,7 @@ EaElements.Dialog {
 
         EaElements.Label {
             visible: Globals.BackendWrapper.analysisFitSuccess
-            text: "Chi2: " + Globals.BackendWrapper.analysisFitChi2.toFixed(4)
+            text: "Reduced Chi2: " + Globals.BackendWrapper.analysisFitChi2.toFixed(4)
         }
 
         EaElements.Label {

EasyReflectometryApp/Gui/StatusBar.qml

@@ -56,8 +56,8 @@ EaElements.StatusBar {
     EaElements.StatusBarItem {
         visible: Globals.BackendWrapper.analysisFitChi2 > 0
         keyIcon: 'chart-line'
-        keyText: qsTr('Chi²')
+        keyText: qsTr('Reduced Chi²')
         valueText: Globals.BackendWrapper.analysisFitChi2.toFixed(2)
-        ToolTip.text: qsTr('Goodness of fit (chi-squared)')
+        ToolTip.text: qsTr('Goodness of fit (reduced chi-squared)')
     }
 }

tests/test_logic_fitting.py

@@ -100,7 +100,7 @@ def test_on_fit_finished_and_fit_properties_cover_multi_and_single_results():
     logic.on_fit_finished(make_fit_result(success=False, chi2=9.0, n_pars=1, x=[1, 2], reduced_chi=4.5))
     assert logic.fit_success is False
     assert logic.fit_n_pars == 1
-    assert logic.fit_chi2 == 9.0
+    assert logic.fit_chi2 == 4.5
 
 
 def test_fit_failure_and_cancellation_state_transitions():