Merge release_dev into main

CHANGELOG.md

@@ -1,8 +1,26 @@
 # CHANGELOG
 
 
+## v0.9.3 (2026-03-18)
+
+### Bug Fixes
+
+- Refactor hierarchical_heatmap_template and align test with template changes
+  ([`459aa95`](https://github.com/FNLCR-DMAP/SCSAWorkflow/commit/459aa95100922f16d1ebd58205a874882bc3f421))
+
+- Update template to return (clustergrid, mean_intensity) tuple for in-memory mode - Fix param names
+  in test: Feature_s_, Standard_Scale_ - Remove unused params: Method, Metric - Add all template
+  params to test for full coverage - Add in-memory mode test (save_results_flag=False) - Add figure
+  title and CSV output assertions
+
+
 ## v0.9.2 (2026-03-03)
 
+### Continuous Integration
+
+- **version**: Automatic development release
+  ([`97ead81`](https://github.com/FNLCR-DMAP/SCSAWorkflow/commit/97ead819ef09c101a6cc59af7e98f979548abcb6))
+
 
 ## v0.9.1 (2026-02-27)
 

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='spac',
-    version="0.9.2",
+    version="0.9.3",
     description=(
         'SPatial Analysis for single-Cell analysis (SPAC)'
         'is a Scalable Python package for single-cell spatial protein data '

src/spac/__init__.py

@@ -22,7 +22,7 @@
     functions.extend(module_functions)
 
 # Define the package version before using it in __all__
-__version__ = "0.9.2"
+__version__ = "0.9.3"
 
 # Define a __all__ list to specify which functions should be considered public
 __all__ = functions

src/spac/templates/hierarchical_heatmap_template.py

@@ -42,18 +42,20 @@ def run_from_json(
     json_path : str, Path, or dict
         Path to JSON file, JSON string, or parameter dictionary
     save_results_flag : bool, optional
-        Whether to save results to file. If False, returns the figure and
-        dataframe directly for in-memory workflows. Default is True.
+        Whether to save results to file. If False, returns a tuple of
+        (clustergrid_figure, mean_intensity_dataframe) for in-memory
+        workflows (e.g., Shiny server). Default is True.
     show_plot : bool, optional
         Whether to display the plot. Default is True.
     output_dir : str or Path, optional
         Directory for outputs. If None, uses params['Output_Directory'] or '.'
 
     Returns
     -------
-    dict or DataFrame
+    dict or tuple
         If save_results_flag=True: Dictionary of saved file paths
-        If save_results_flag=False: The mean intensity dataframe
+        If save_results_flag=False: Tuple of (figure, mean_intensity_df)
+            where figure is the matplotlib Figure from the ClusterGrid
     """
     # Parse parameters from JSON
     params = parse_params(json_path)
@@ -185,9 +187,11 @@ def run_from_json(
         print("Hierarchical Heatmap completed successfully.")
         return saved_files
     else:
-        # Return the dataframe directly for in-memory workflows
-        print("Returning mean intensity dataframe (not saving to file)")
-        return mean_intensity
+        # Return the ClusterGrid and dataframe for in-memory workflows
+        # Returns ClusterGrid (not .fig) so consumers can access
+        # .ax_heatmap for post-processing (e.g. label rotation).
+        print("Returning (clustergrid, mean_intensity_df) for in-memory use")
+        return clustergrid, mean_intensity
 
 
 # CLI interface

tests/templates/test_hierarchical_heatmap_template.py

@@ -2,7 +2,11 @@
 """
 Real (non-mocked) unit test for the Hierarchical Heatmap template.
 
-Validates template I/O behaviour only.
+Snowball seed test — validates template I/O behaviour only:
+  • Expected output files are produced on disk
+  • Filenames follow the convention
+  • Output artifacts are non-empty
+
 No mocking. Uses real data, real filesystem, and tempfile.
 """
 
@@ -15,7 +19,7 @@
 from pathlib import Path
 
 import matplotlib
-matplotlib.use("Agg")
+matplotlib.use("Agg")  # Headless backend for CI
 
 import anndata as ad
 import numpy as np
@@ -53,14 +57,24 @@ def setUp(self) -> None:
             "Upstream_Analysis": self.in_file,
             "Annotation": "cell_type",
             "Table_to_Visualize": "Original",
-            "Features_to_Visualize": ["All"],
-            "Standard_Scale": "None",
-            "Method": "average",
-            "Metric": "euclidean",
+            "Feature_s_": ["All"],
+            "Standard_Scale_": "None",
+            "Z_Score": "None",
+            "Feature_Dendrogram": True,
+            "Annotation_Dendrogram": True,
+            "Figure_Title": "Test Hierarchical Heatmap",
             "Figure_Width": 6,
             "Figure_Height": 4,
             "Figure_DPI": 72,
             "Font_Size": 8,
+            "Matrix_Plot_Ratio": 0.8,
+            "Swap_Axes": False,
+            "Rotate_Label_": False,
+            "Horizontal_Dendrogram_Display_Ratio": 0.2,
+            "Vertical_Dendrogram_Display_Ratio": 0.2,
+            "Value_Min": "None",
+            "Value_Max": "None",
+            "Color_Map": "seismic",
             "Output_Directory": self.tmp_dir.name,
             "outputs": {
                 "figures": {"type": "directory", "name": "figures_dir"},
@@ -82,24 +96,80 @@ def test_hierarchical_heatmap_produces_expected_outputs(self) -> None:
         Validates:
         1. saved_files dict has 'figures' and 'dataframe' keys
         2. Figures directory contains non-empty PNG(s)
-        3. Summary CSV exists
+        3. Summary CSV exists and is non-empty
+        4. Figure title matches the parameter
         """
+        # -- Act (save_results_flag=True): write outputs to disk -------
         saved_files = run_from_json(
             self.json_file,
             save_results_flag=True,
             show_plot=False,
             output_dir=self.tmp_dir.name,
         )
 
-        self.assertIsInstance(saved_files, dict)
-        self.assertIn("figures", saved_files)
+        # -- Act (save_results_flag=False): get objects in memory ------
+        clustergrid, mean_intensity_df = run_from_json(
+            self.json_file,
+            save_results_flag=False,
+            show_plot=False,
+        )
+
+        # -- Assert: return type ---------------------------------------
+        self.assertIsInstance(
+            saved_files, dict,
+            f"Expected dict from run_from_json, got {type(saved_files)}"
+        )
 
+        # -- Assert: figures directory contains at least one PNG -------
+        self.assertIn("figures", saved_files,
+                       "Missing 'figures' key in saved_files")
         figure_paths = saved_files["figures"]
-        self.assertGreaterEqual(len(figure_paths), 1)
+        self.assertGreaterEqual(
+            len(figure_paths), 1, "No figure files were saved"
+        )
+
         for fig_path in figure_paths:
             fig_file = Path(fig_path)
-            self.assertTrue(fig_file.exists())
-            self.assertGreater(fig_file.stat().st_size, 0)
+            self.assertTrue(
+                fig_file.exists(), f"Figure not found: {fig_path}"
+            )
+            self.assertGreater(
+                fig_file.stat().st_size, 0,
+                f"Figure file is empty: {fig_path}"
+            )
+            self.assertEqual(
+                fig_file.suffix, ".png",
+                f"Expected .png extension, got {fig_file.suffix}"
+            )
+
+        # -- Assert: figure has the correct title ----------------------
+        axes_title = clustergrid.ax_heatmap.get_title()
+        self.assertEqual(
+            axes_title, "Test Hierarchical Heatmap",
+            f"Expected 'Test Hierarchical Heatmap', got '{axes_title}'"
+        )
+
+        # -- Assert: in-memory mean_intensity_df is a DataFrame --------
+        self.assertIsInstance(
+            mean_intensity_df, pd.DataFrame,
+            f"Expected DataFrame, got {type(mean_intensity_df)}"
+        )
+        self.assertIn(
+            "cell_type", mean_intensity_df.columns,
+            "Annotation column 'cell_type' missing from mean_intensity_df"
+        )
+
+        # -- Assert: summary CSV exists and is non-empty ---------------
+        self.assertIn("dataframe", saved_files,
+                       "Missing 'dataframe' key in saved_files")
+        csv_path = Path(saved_files["dataframe"])
+        self.assertTrue(
+            csv_path.exists(), f"Summary CSV not found: {csv_path}"
+        )
+        self.assertGreater(
+            csv_path.stat().st_size, 0,
+            f"Summary CSV is empty: {csv_path}"
+        )
 
 
 if __name__ == "__main__":