Clean up access to data item (only xdata, data_metadata).

nionswift_plugin/nion_eels_analysis/BackgroundSubtraction.py

@@ -229,9 +229,9 @@ def subtract_background(api: Facade.API_1, window: Facade.DocumentWindow) -> Non
                     background_model = api._new_api_object(background_model)
                     source_data_items = api.library._document_model.get_source_data_items(eels_spectrum_data_item._data_item)
                     if len(source_data_items) == 1:
-                        source_xdata = source_data_items[0].xdata
-                        assert source_xdata
-                        if source_xdata.is_navigable and source_data_items[0].datum_dimension_count == 1:
+                        source_data_metadata = source_data_items[0].data_metadata
+                        assert source_data_metadata
+                        if source_data_metadata.is_navigable and source_data_metadata.datum_dimension_count == 1:
                             spectrum_image = api._new_api_object(source_data_items[0])
                             subtracted = api.library.create_data_item_from_data(numpy.zeros(spectrum_image._data_item.xdata.navigation_dimension_shape))
                             api.library.create_computation(
@@ -266,9 +266,9 @@ def use_signal_for_map(api: Facade.API_1, window: Facade.DocumentWindow) -> None
                     background_model = api._new_api_object(background_model)
                     source_data_items = api.library._document_model.get_source_data_items(eels_spectrum_data_item._data_item)
                     if len(source_data_items) == 1:
-                        source_xdata = source_data_items[0].xdata
-                        assert source_xdata
-                        if source_xdata.is_navigable and source_data_items[0].datum_dimension_count == 1:
+                        source_data_metadata = source_data_items[0].data_metadata
+                        assert source_data_metadata
+                        if source_data_metadata.is_navigable and source_data_metadata.datum_dimension_count == 1:
                             spectrum_image = api._new_api_object(source_data_items[0])
                             map = api.library.create_data_item_from_data(numpy.zeros(spectrum_image._data_item.xdata.navigation_dimension_shape))
                             signal_interval_graphic = target_interval

nionswift_plugin/nion_eels_analysis/ElementalMappingController.py

@@ -73,8 +73,11 @@ async def pick_new_edge(document_controller: DocumentController.DocumentControll
     model_display_item = document_model.get_display_item_for_data_item(model_data_item)
     assert model_display_item
 
+    model_data_metadata = model_data_item.data_metadata
+    assert model_data_metadata
+
     pick_region = Graphics.RectangleGraphic()
-    pick_region.size = Geometry.FloatSize(min(1 / 16, 16 / model_data_item.dimensional_shape[0]), min(1 / 16, 16 / model_data_item.dimensional_shape[1]))
+    pick_region.size = Geometry.FloatSize(min(1 / 16, 16 / model_data_metadata.dimensional_shape[0]), min(1 / 16, 16 / model_data_metadata.dimensional_shape[1]))
     pick_region.label = "{} {}".format(_("Pick"), str(edge.electron_shell))
     model_display_item.add_graphic(pick_region)
 
@@ -482,12 +485,14 @@ def explorer_interval(self) -> typing.Optional[typing.Tuple[float, float]]:
 
     def __is_model(self, data_item: typing.Optional[DataItem.DataItem]) -> bool:
         if isinstance(data_item, DataItem.DataItem):
-            return data_item.is_data_3d
+            data_metadata = data_item.data_metadata
+            return data_metadata is not None and data_metadata.is_data_3d
         return False
 
     def __is_explorer(self, document_model: DocumentModel.DocumentModel, data_item: typing.Optional[DataItem.DataItem]) -> bool:
         if isinstance(data_item, DataItem.DataItem):
-            if data_item.is_data_1d:
+            data_metadata = data_item.data_metadata
+            if data_metadata and data_metadata.is_data_1d:
                 for display_item in document_model.get_display_items_for_data_item(data_item):
                     for graphic in display_item.graphics:
                         if isinstance(graphic, Graphics.IntervalGraphic) and graphic.graphic_id == "explore":
@@ -496,8 +501,9 @@ def __is_explorer(self, document_model: DocumentModel.DocumentModel, data_item:
 
     def __is_calibrated_map(self, data_item: DataItem.DataItem) -> bool:
         if isinstance(data_item, DataItem.DataItem):
-            if data_item.is_data_2d and data_item.intensity_calibration:
-                return data_item.title.startswith("Map") and data_item.intensity_calibration.units.startswith("~")
+            data_metadata = data_item.data_metadata
+            if data_metadata and data_metadata.is_data_2d and data_metadata.intensity_calibration:
+                return data_item.title.startswith("Map") and data_metadata.intensity_calibration.units.startswith("~")
         return False
 
     async def explore_edges(self, document_controller: DocumentController.DocumentController) -> None:
@@ -507,8 +513,10 @@ async def explore_edges(self, document_controller: DocumentController.DocumentCo
         model_display_item = document_model.get_display_item_for_data_item(model_data_item)
         assert model_display_item
         assert model_display_item.data_item
+        model_data_metadata = model_data_item.data_metadata
+        assert model_data_metadata
         pick_region = Graphics.RectangleGraphic()
-        pick_region.size = Geometry.FloatSize(min(1 / 16, 16 / model_data_item.dimensional_shape[0]), min(1 / 16, 16 / model_data_item.dimensional_shape[1]))
+        pick_region.size = Geometry.FloatSize(min(1 / 16, 16 / model_data_metadata.dimensional_shape[0]), min(1 / 16, 16 / model_data_metadata.dimensional_shape[1]))
         pick_region.label = _("Explore")
         model_display_item.add_graphic(pick_region)
         pick_data_item = document_model.get_pick_region_new(model_display_item, model_display_item.data_item, pick_region=pick_region)
@@ -553,12 +561,13 @@ def normalized_interval(interval: tuple[float, float]) -> tuple[float, float]:
             self.__explorer_interval_changed(data_item, (s, e))
 
     def __connect_explorer_interval(self, document_model: DocumentModel.DocumentModel, data_item: DataItem.DataItem) -> None:
-        if data_item.is_data_1d:
+        data_metadata = data_item.data_metadata
+        if data_metadata and data_metadata.is_data_1d:
             for display_item in document_model.get_display_items_for_data_item(data_item):
                 for graphic in display_item.graphics:
                     if isinstance(graphic, Graphics.IntervalGraphic) and graphic.graphic_id == "explore":
-                        dimensional_shape = data_item.dimensional_shape
-                        dimensional_calibrations = data_item.dimensional_calibrations
+                        dimensional_shape = data_metadata.dimensional_shape
+                        dimensional_calibrations = data_metadata.dimensional_calibrations
                         self.__explorer_property_changed_listeners[data_item.uuid] = graphic.property_changed_event.listen(functools.partial(self.graphic_property_changed, graphic, data_item, dimensional_shape, dimensional_calibrations))
                         self.graphic_property_changed(graphic, data_item, dimensional_shape, dimensional_calibrations, "interval")
 
@@ -570,12 +579,13 @@ def __disconnect_explorer_interval(self, data_item: DataItem.DataItem) -> None:
 
     def add_edge(self, electron_shell: PeriodicTable.ElectronShell) -> typing.Optional[ElementalMappingEdge]:
         model_data_item = self.__model_data_item
-        if model_data_item:
+        model_data_metadata = model_data_item.data_metadata if model_data_item else None
+        if model_data_item and model_data_metadata:
             binding_energy_eV = PeriodicTable.PeriodicTable().nominal_binding_energy_ev(electron_shell)
             signal_interval_eV = binding_energy_eV, binding_energy_eV * 1.10
             fit_interval_eV = binding_energy_eV * 0.93, binding_energy_eV * 0.98
-            dimensional_shape = model_data_item.dimensional_shape
-            dimensional_calibrations = model_data_item.dimensional_calibrations
+            dimensional_shape = model_data_metadata.dimensional_shape
+            dimensional_calibrations = model_data_metadata.dimensional_calibrations
             if dimensional_shape is not None and dimensional_calibrations is not None and len(dimensional_calibrations) > 0:
                 calibration = dimensional_calibrations[-1]
                 if calibration.units == "eV":

nionswift_plugin/nion_eels_analysis/Thermometry.py

@@ -43,14 +43,14 @@ def __init__(self, computation: Facade.Computation, **kwargs: typing.Any) -> Non
         self.__fit: typing.Optional[DataArrayType] = None
 
     def execute(self, near_data_item: DataItem.DataItem, far_data_item: DataItem.DataItem, fit_interval_graphic: Graphics.IntervalGraphic, **kwargs: typing.Any) -> None:
-        assert near_data_item.xdata
-        assert near_data_item.xdata.is_data_1d
-        assert far_data_item.xdata
-        assert far_data_item.xdata.is_data_1d
-        # Only allow data of same shape for now. A future version could crop to the smaller size of the two.
-        assert near_data_item.data is not None and far_data_item.data is not None and len(near_data_item.data) == len(far_data_item.data)
         near_xdata = near_data_item.xdata
         far_xdata = far_data_item.xdata
+        assert near_xdata
+        assert near_xdata.is_data_1d
+        assert far_xdata
+        assert far_xdata.is_data_1d
+        # Only allow data of same shape for now. A future version could crop to the smaller size of the two.
+        assert len(near_xdata.data) == len(far_xdata.data)
         # For now only allow near and far having the same calibration. A future version could allow different
         # offsets and shift the data accordingly
         assert near_xdata.dimensional_calibrations == far_xdata.dimensional_calibrations
@@ -111,13 +111,13 @@ def measure_temperature(api: Facade.API_1, window: Facade.DocumentWindow, *, dis
     assert selected_display_items[1][0] is not None, error_msg
     assert selected_display_items[0][0].data_item is not None, error_msg
     assert selected_display_items[1][0].data_item is not None, error_msg
-    assert selected_display_items[0][0].data_item.data is not None, error_msg
-    assert selected_display_items[1][0].data_item.data is not None, error_msg
-    assert selected_display_items[0][0].data_item.is_data_1d, error_msg
-    assert selected_display_items[1][0].data_item.is_data_1d, error_msg
+    assert selected_display_items[0][0].data_item.xdata, error_msg
+    assert selected_display_items[1][0].data_item.xdata, error_msg
+    assert selected_display_items[0][0].data_item.xdata.is_data_1d, error_msg
+    assert selected_display_items[1][0].data_item.xdata.is_data_1d, error_msg
 
     # First find out which data item is near and which is far. Far should have the higher maximum.
-    if np.amax(selected_display_items[0][0].data_item.data) > np.amax(selected_display_items[1][0].data_item.data):
+    if np.amax(selected_display_items[0][0].data_item.xdata.data) > np.amax(selected_display_items[1][0].data_item.xdata.data):
         far_data_item = selected_display_items[0][0].data_item
         near_data_item = selected_display_items[1][0].data_item
     else: