Fix validation error while using deprecated include/exclude Chart2D

flow360/plugins/report/report_items.py

@@ -46,6 +46,7 @@
     is_flow360_unit,
     unyt_quantity,
 )
+from flow360.exceptions import Flow360ValidationError
 from flow360.log import log
 from flow360.plugins.report.report_context import ReportContext
 from flow360.plugins.report.utils import (
@@ -1417,32 +1418,37 @@ class Chart2D(BaseChart2D):
 
     @pd.model_validator(mode="after")
     def _handle_deprecated_include_exclude(self):
-        if (self.include is not None) or (self.exclude is not None):
-            if not isinstance(self.x, DataItem):
-                self.x = DataItem(data=self.x, include=self.include, exclude=self.exclude)
-            else:
-                self.x.include = self.include
-                self.x.exclude = self.exclude
+        include = self.include
+        exclude = self.exclude
+        if (include is not None) or (exclude is not None):
+            self.include = None
+            self.exclude = None
+            self.x = self._overload_include_exclude(include, exclude, self.x)
             if isinstance(self.y, List):
-                new_y = []
-                for y in self.y:
-                    if not isinstance(self.y, DataItem):
-                        new_y.append(
-                            DataItem(data=self.y, include=self.include, exclude=self.exclude)
-                        )
-                    else:
-                        y.include = self.include
-                        y.exclude = self.exclude
-                        new_y.append(y)
-                self.y = new_y
+                new_value = []
+                for data_variable in self.y:
+                    new_value.append(
+                        self._overload_include_exclude(include, exclude, data_variable)
+                    )
+                self.y = new_value
             else:
-                if not isinstance(self.y, DataItem):
-                    self.y = DataItem(data=self.y, include=self.include, exclude=self.exclude)
-                else:
-                    self.y.include = self.include
-                    self.y.exclude = self.exclude
+                self.y = self._overload_include_exclude(include, exclude, self.y)
         return self
 
+    @classmethod
+    def _overload_include_exclude(cls, include, exclude, data_variable):
+        if isinstance(data_variable, Delta):
+            raise Flow360ValidationError(
+                "Delta can not be used with exclude/include options. "
+                + "Specify the Delta data using DataItem."
+            )
+        if not isinstance(data_variable, DataItem):
+            data_variable = DataItem(data=data_variable, include=include, exclude=exclude)
+        else:
+            data_variable.include = include
+            data_variable.exclude = exclude
+        return data_variable
+
     def get_requirements(self):
         """
         Returns requirements for this item.

flow360/plugins/report/utils.py

@@ -709,7 +709,7 @@ def _validate_operations(cls, values):
     def _check_for_averages(self):
         components = split_path(self.data)
         if "averages" in components:
-            self.operations.insert(0, Average(fraction=0.1))
+            self.operations.append(Average(fraction=0.1))
             components.remove("averages")
             self.data = "/".join(components)
         return self
@@ -830,7 +830,9 @@ def __str__(self):
 
 # pylint: disable=too-few-public-methods
 class Tabulary(Tabular):
-    """The `tabulary` package works better than the existing pylatex implementations so this includes it in pylatex"""
+    """
+    The `tabulary` package works better than the existing pylatex implementations so this includes it in pylatex
+    """
 
     packages = [Package("tabulary")]
 
@@ -854,41 +856,41 @@ def generate_colorbar_from_image(
     is_log_scale=False,
 ):  # pylint: disable=too-many-arguments,too-many-locals
     """
-        Generate a color bar image from an existing PNG file with ticks and labels.
+    Generate a color bar image from an existing PNG file with ticks and labels.
 
-        This function reads a colormap from a provided PNG file (a horizontal strip of
-        colors), and overlays ticks and labels according to the specified value limits
-        and scale type (linear or logarithmic).
-    '
-        For a linear scale, matplotlib automatically chooses the number and format of
-        ticks. For a log scale, a twin axis is used for proper tick placement without
-        distorting the color distribution.
+    This function reads a colormap from a provided PNG file (a horizontal strip of
+    colors), and overlays ticks and labels according to the specified value limits
+    and scale type (linear or logarithmic).
 
-        Parameters
-        ----------
-        image_filename : str
-            Path to the colormap PNG file (horizontal strip).
-        limits : tuple of float
-            A tuple (min_value, max_value) specifying the data range.
-            If `is_log_scale` is True, `max_value` must be greater than 0.
-        field_name : str
-            The field name for the label.
-        output_filename : str
-            The output filename for the resulting image with ticks.
-        height_px : int
-            The height in pixels for the color bar image.
-        is_log_scale : bool
-            If True, use a log scale axis for ticks and minor ticks.
+    For a linear scale, matplotlib automatically chooses the number and format of
+    ticks. For a log scale, a twin axis is used for proper tick placement without
+    distorting the color distribution.
 
-        Returns
-        -------
-        None
-            The resulting image is saved to `output_filename`.
+    Parameters
+    ----------
+    image_filename : str
+        Path to the colormap PNG file (horizontal strip).
+    limits : tuple of float
+        A tuple (min_value, max_value) specifying the data range.
+        If `is_log_scale` is True, `max_value` must be greater than 0.
+    field_name : str
+        The field name for the label.
+    output_filename : str
+        The output filename for the resulting image with ticks.
+    height_px : int
+        The height in pixels for the color bar image.
+    is_log_scale : bool
+        If True, use a log scale axis for ticks and minor ticks.
+
+    Returns
+    -------
+    None
+        The resulting image is saved to `output_filename`.
 
-        Notes
-        -----
-        - On a log scale, the main colorbar axis remains linear to avoid deforming
-          the color distribution. A twin axis is used solely for log-scale labeling.
+    Notes
+    -----
+    - On a log scale, the main colorbar axis remains linear to avoid deforming
+        the color distribution. A twin axis is used solely for log-scale labeling.
     """
 
     img = Image.open(image_filename)

tests/report/test_report_items.py

@@ -14,6 +14,7 @@
 from flow360.component.resource_base import local_metadata_builder
 from flow360.component.utils import LocalResourceCache
 from flow360.component.volume_mesh import VolumeMeshMetaV2, VolumeMeshV2
+from flow360.exceptions import Flow360ValidationError
 from flow360.plugins.report.report import ReportTemplate
 from flow360.plugins.report.report_context import ReportContext
 from flow360.plugins.report.report_doc import ReportDoc
@@ -1471,3 +1472,38 @@ def test_include_exclude(here, cases):
 
     with pytest.raises(AttributeError):
         plot_model = chart.get_data(cases=cases[:2], context=context)
+
+    with pytest.raises(Flow360ValidationError):
+        chart = Chart2D(
+            x=Delta(data="surface_forces/averages/totalCD"),
+            y="surface_forces/averages/totalCL",
+            section_title="CL/CD",
+            fig_name="clcd",
+            include=["blk-1/BODY"],
+        )
+
+
+def test_in_path_averages(here, cases):
+    dataitem = DataItem(
+        data="total_forces/averages/CL",
+        operations=[Expression(expr="CL*beta")],
+        variables=[Variable(name="beta", data="params/operating_condition/beta")],
+    )
+
+    assert dataitem.operations[0] == Expression(expr="CL*beta")
+    assert dataitem.operations[1] == Average(fraction=0.1)
+
+    cl_beta = dataitem.calculate(case=cases[1], cases=cases)
+
+    load_data = pd.read_csv(
+        os.path.join(here, "..", "data", cases[1].id, "results", "total_forces_v2.csv"),
+        skipinitialspace=True,
+    )
+    to_avg = round(len(load_data) * 0.1)
+
+    cl_beta_expected = np.average(load_data["CL"].iloc[-to_avg:]) * 2
+
+    assert dataitem.operations[2] == Average(fraction=0.1)
+    assert dataitem.operations[1] == Expression(expr="CL*beta")
+
+    assert np.allclose(cl_beta, cl_beta_expected)