Refactor geometry and table adapters to use 'result' instead of specific geometry/table attributes; enhance HTML conversion and display preferences handling in adapters.

Author: PierreRaybaut

datalab/adapters_metadata/base_adapter.py

@@ -9,12 +9,13 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, ClassVar, Generator, Union
+from typing import TYPE_CHECKING, Any, ClassVar, Generator
 
 from sigima.objects import ImageObj, SignalObj
 
 if TYPE_CHECKING:
     import pandas as pd
+    from sigima.objects.scalar import GeometryResult, TableResult
 
 
 class BaseResultAdapter(ABC):
@@ -31,7 +32,7 @@ class BaseResultAdapter(ABC):
     META_PREFIX: ClassVar[str] = ""
     SUFFIX: ClassVar[str] = ""
 
-    def __init__(self, result: Any) -> None:
+    def __init__(self, result: TableResult | GeometryResult) -> None:
         self.result = result
 
     def set_applicative_attr(self, key: str, value: Any) -> None:
@@ -58,22 +59,14 @@ def get_applicative_attr(self, key: str, default: Any = None) -> Any:
             self.result.attrs[key] = default
         return self.result.attrs.get(key, default)
 
-    @abstractmethod
-    def to_dataframe(self) -> "pd.DataFrame":
-        """Convert the result to a pandas DataFrame.
-
-        Returns:
-            DataFrame with columns as in self.headers, and optional 'roi_index' column.
-        """
-
     @property
-    @abstractmethod
     def title(self) -> str:
         """Get the title.
 
         Returns:
             Title
         """
+        return self.result.title
 
     @property
     @abstractmethod
@@ -143,15 +136,15 @@ def label_contents(self) -> tuple[tuple[int, str], ...]:
         """
         return tuple(enumerate(self.headers))
 
-    def set_obj_metadata(self, obj: Union[SignalObj, ImageObj]) -> None:
+    def set_obj_metadata(self, obj: SignalObj | ImageObj) -> None:
         """Set object metadata from result (alias for add_to).
 
         Args:
             obj: Signal or image object
         """
         self.add_to(obj)
 
-    def add_to(self, obj: Union[SignalObj, ImageObj]) -> None:
+    def add_to(self, obj: SignalObj | ImageObj) -> None:
         """Add result to object metadata.
 
         Args:
@@ -161,7 +154,7 @@ def add_to(self, obj: Union[SignalObj, ImageObj]) -> None:
         metadata_key = f"{self.META_PREFIX}{self.title}{self.SUFFIX}"
         obj.metadata[metadata_key] = self.result.to_dict()
 
-    def remove_from(self, obj: Union[SignalObj, ImageObj]) -> None:
+    def remove_from(self, obj: SignalObj | ImageObj) -> None:
         """Remove result from object metadata.
 
         Args:
@@ -172,7 +165,7 @@ def remove_from(self, obj: Union[SignalObj, ImageObj]) -> None:
         obj.metadata.pop(metadata_key, None)
 
     @classmethod
-    def remove_all_from(cls, obj: Union[SignalObj, ImageObj]) -> None:
+    def remove_all_from(cls, obj: SignalObj | ImageObj) -> None:
         """Remove all results of this type from object metadata.
 
         Args:
@@ -197,7 +190,7 @@ def match(cls, key: str, _value: Any) -> bool:
 
     @classmethod
     @abstractmethod
-    def from_metadata_entry(cls, obj: Union[SignalObj, ImageObj], key: str):
+    def from_metadata_entry(cls, obj: SignalObj | ImageObj, key: str):
         """Create a result adapter from a metadata entry.
 
         Args:
@@ -212,7 +205,9 @@ def from_metadata_entry(cls, obj: Union[SignalObj, ImageObj], key: str):
         """
 
     @classmethod
-    def iterate_from_obj(cls, obj: Union[SignalObj, ImageObj]) -> Generator:
+    def iterate_from_obj(
+        cls, obj: SignalObj | ImageObj
+    ) -> Generator["BaseResultAdapter", None, None]:
         """Iterate over results stored in an object's metadata.
 
         Args:
@@ -228,3 +223,64 @@ def iterate_from_obj(cls, obj: Union[SignalObj, ImageObj]) -> Generator:
                 except (ValueError, TypeError):
                     # Skip invalid entries
                     pass
+
+    def to_dataframe(self) -> "pd.DataFrame":
+        """Convert the geometry result to a pandas DataFrame.
+
+        Returns:
+            DataFrame with columns as in self.headers, and optional 'roi_index' column.
+        """
+        return self.result.to_dataframe()
+
+    def to_html(
+        self,
+        obj=None,
+        visible_headers: list[str] = None,
+        transpose_single_row: bool = True,
+        **kwargs,
+    ) -> str:
+        """Convert the result to HTML format.
+
+        Args:
+            obj: Optional SignalObj or ImageObj for ROI title extraction
+            visible_headers: Optional list of headers to show (filters columns)
+            transpose_single_row: If True, transpose the table when there's only one row
+            **kwargs: Additional arguments passed to DataFrame.to_html()
+
+        Returns:
+            HTML representation of the result
+        """
+        # Use visible headers from display preferences if not specified
+        if visible_headers is None:
+            visible_headers = self.result.get_visible_headers()
+
+        return self.result.to_html(
+            obj=obj,
+            visible_headers=visible_headers,
+            transpose_single_row=transpose_single_row,
+            **kwargs,
+        )
+
+    def get_display_preferences(self) -> dict[str, bool]:
+        """Get display preferences.
+
+        Returns:
+            Dictionary mapping header names to visibility (True=visible, False=hidden)
+        """
+        return self.result.get_display_preferences()
+
+    def set_display_preferences(self, preferences: dict[str, bool]) -> None:
+        """Set display preferences.
+
+        Args:
+            preferences: Dictionary mapping header names to visibility
+        """
+        self.result.set_display_preferences(preferences)
+
+    def get_visible_headers(self) -> list[str]:
+        """Get list of currently visible headers based on display preferences.
+
+        Returns:
+            List of header names that should be displayed
+        """
+        return self.result.get_visible_headers()

datalab/adapters_metadata/common.py

@@ -23,7 +23,6 @@
 
 if TYPE_CHECKING:
     from qtpy.QtWidgets import QWidget
-    from sigima.objects import TypeROI
 
 
 @dataclasses.dataclass
@@ -142,29 +141,25 @@ def show_resultdata(parent: QWidget, rdata: ResultData, object_name: str = "") -
 def resultadapter_to_html(
     adapter: BaseResultAdapter,
     obj: SignalObj | ImageObj,
+    visible_headers: list[str] = None,
+    transpose_single_row: bool = True,
+    **kwargs,
 ) -> str:
     """Convert a result adapter to HTML format
 
     Args:
         adapter: Adapter to convert
         obj: Object associated to the adapter
+        visible_headers: Optional list of headers to show (filters columns)
+        transpose_single_row: If True, transpose the table when there's only one row
+        **kwargs: Additional arguments passed to DataFrame.to_html()
 
     Returns:
         HTML representation of the adapter
     """
-    df = adapter.to_dataframe()
-    if "roi_index" in df.columns:
-        df = df.drop(columns=["roi_index"])
-    text = f'<u><b style="color: blue">{adapter.title}</b></u>:'
-    row_headers = []
-    for i_row in range(df.shape[0]):
-        i_roi = i_row - 1
-        header = ""
-        if i_roi >= 0:
-            roi: TypeROI = obj.roi
-            assert obj.roi is not None, "Expected ROI to be defined"
-            header = roi.get_single_roi_title(i_roi)
-        row_headers.append(header)
-    df.set_index(pd.Index(row_headers), inplace=True)
-    text += df.to_html(float_format="%.3g", border=0)
-    return text
+    return adapter.to_html(
+        obj=obj,
+        visible_headers=visible_headers,
+        transpose_single_row=transpose_single_row,
+        **kwargs,
+    )

datalab/adapters_metadata/geometry_adapter.py

@@ -8,15 +8,12 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, ClassVar
+from typing import ClassVar
 
 from sigima.objects import GeometryResult, ImageObj, SignalObj
 
 from datalab.adapters_metadata.base_adapter import BaseResultAdapter
 
-if TYPE_CHECKING:
-    import pandas as pd
-
 
 class GeometryAdapter(BaseResultAdapter):
     """Adapter for GeometryResult objects.
@@ -34,7 +31,6 @@ class GeometryAdapter(BaseResultAdapter):
 
     def __init__(self, geometry: GeometryResult) -> None:
         super().__init__(geometry)
-        self.geometry = geometry  # Keep for backwards compatibility
 
     @classmethod
     def from_geometry_result(cls, geometry: GeometryResult) -> GeometryAdapter:
@@ -48,13 +44,17 @@ def from_geometry_result(cls, geometry: GeometryResult) -> GeometryAdapter:
         """
         return cls(geometry)
 
-    def to_dataframe(self) -> "pd.DataFrame":
-        """Convert the geometry result to a pandas DataFrame.
+    @property
+    def headers(self) -> list[str]:
+        """Get column headers for the coordinates.
 
         Returns:
-            DataFrame with columns as in self.headers, and optional 'roi_index' column.
+            List of column headers
         """
-        return self.geometry.to_dataframe()
+        # Get headers directly from the DataFrame
+        df = self.result.to_dataframe()
+        # Return coordinate columns (exclude 'roi_index' if present)
+        return [col for col in df.columns if col != "roi_index"]
 
     @property
     def category(self) -> str:
@@ -63,28 +63,7 @@ def category(self) -> str:
         Returns:
             Category
         """
-        return f"shape_{self.geometry.kind.value}"
-
-    @property
-    def title(self) -> str:
-        """Get the title.
-
-        Returns:
-            Title
-        """
-        return self.geometry.title
-
-    @property
-    def headers(self) -> list[str]:
-        """Get column headers for the coordinates.
-
-        Returns:
-            List of column headers
-        """
-        # Get headers directly from the DataFrame
-        df = self.geometry.to_dataframe()
-        # Return coordinate columns (exclude 'roi_index' if present)
-        return [col for col in df.columns if col != "roi_index"]
+        return f"shape_{self.result.kind.value}"
 
     @staticmethod
     def add_geometry_result_to_obj(

datalab/adapters_metadata/table_adapter.py

@@ -8,16 +8,13 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, ClassVar, Union
+from typing import ClassVar, Union
 
 from sigima.objects import ImageObj, SignalObj
 from sigima.objects.scalar import NO_ROI, TableResult
 
 from datalab.adapters_metadata.base_adapter import BaseResultAdapter
 
-if TYPE_CHECKING:
-    import pandas as pd
-
 
 class TableAdapter(BaseResultAdapter):
     """Adapter for TableResult objects.
@@ -35,7 +32,6 @@ class TableAdapter(BaseResultAdapter):
 
     def __init__(self, table: TableResult) -> None:
         super().__init__(table)
-        self.table = table  # Keep for backwards compatibility
 
     @classmethod
     def from_table_result(cls, table: TableResult) -> TableAdapter:
@@ -49,31 +45,14 @@ def from_table_result(cls, table: TableResult) -> TableAdapter:
         """
         return cls(table)
 
-    def to_dataframe(self) -> "pd.DataFrame":
-        """Convert the table result to a pandas DataFrame.
-
-        Returns:
-            DataFrame with columns as in self.headers, and optional 'roi_index' column.
-        """
-        return self.table.to_dataframe()
-
-    @property
-    def title(self) -> str:
-        """Get the title.
-
-        Returns:
-            Title
-        """
-        return self.table.title
-
     @property
     def headers(self) -> list[str]:
         """Get the column headers.
 
         Returns:
             Column headers
         """
-        return list(self.table.headers)
+        return list(self.result.headers)
 
     @property
     def category(self) -> str:
@@ -82,7 +61,7 @@ def category(self) -> str:
         Returns:
             Category (uses the title for backward compatibility)
         """
-        return self.table.title
+        return self.result.title
 
     @property
     def labels(self) -> list[str]:
@@ -91,7 +70,7 @@ def labels(self) -> list[str]:
         Returns:
             Column labels
         """
-        return list(self.table.labels)
+        return list(self.result.labels)
 
     def get_unique_roi_indices(self) -> list[int]:
         """Get unique ROI indices present in the data.