update test for linear transient to plateau

tests/expected/linear_transient_to_plateau.csv

@@ -0,0 +1,2 @@
+flux,metadata
+"{'mean': 4.953589400293961, 'mean_uncertainty': 0.0033180530700721244, 'confidence_interval': (4.94708601627662, 4.960092784311302), 'pm_std': (4.950271347223889, 4.956907453364033), 'effective_sample_size': 897, 'window_size': 5, 'sss_start': np.float64(104.0), 'metadata': {'status': 'Regular', 'mitigation': 'None'}, 'start_time': 0.0}","[{'operation': 'effective_sample_size', 'options': {'column_names': 'flux', 'alpha': 0.05}}, {'operation': 'compute_statistics', 'options': {'column_name': 'flux', 'ddof': 1, 'method': 'non-overlapping', 'window_size': None}}]"

tests/output/linear_transient_to_plateau.csv

@@ -0,0 +1,2 @@
+flux,metadata
+"{'mean': 4.953589400293961, 'mean_uncertainty': 0.0033180530700721244, 'confidence_interval': (4.94708601627662, 4.960092784311302), 'pm_std': (4.950271347223889, 4.956907453364033), 'effective_sample_size': 897, 'window_size': 5, 'sss_start': np.float64(104.0), 'metadata': {'status': 'Regular', 'mitigation': 'None'}, 'start_time': 0.0}","[{'operation': 'effective_sample_size', 'options': {'column_names': 'flux', 'alpha': 0.05}}, {'operation': 'compute_statistics', 'options': {'column_name': 'flux', 'ddof': 1, 'method': 'non-overlapping', 'window_size': None}}]"

tests/test_robust_workflow.py

@@ -3,13 +3,63 @@
 import warnings
 from pathlib import Path
 
+import nbformat
 import pandas as pd
 import pandas.testing as pdt
 import papermill as pm
 
 os.chdir("examples/notebooks")
 
 
+def test_linear_transient_to_plateau():
+    # Suppress warnings from Papermill
+    warnings.filterwarnings(
+        "ignore", category=UserWarning, module="papermill.translators"
+    )
+
+    # Define notebook paths
+    input_nb = Path("robust_workflow.ipynb")
+
+    # Create a temporary directory for the executed notebook
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        output_nb = Path(tmpdirname) / "executed_notebook.ipynb"
+
+        # Execute the notebook with Papermill
+        pm.execute_notebook(str(input_nb), str(output_nb), kernel_name="python3")
+
+        # Verify the notebook file itself exists
+        assert output_nb.exists(), f"Executed notebook was not created at {output_nb}"
+
+        executed_nb = nbformat.read(output_nb, as_version=4)
+        assert (
+            "papermill" in executed_nb.metadata
+        ), "Notebook metadata missing Papermill info."
+        assert any(
+            cell.get("execution_count") for cell in executed_nb.cells
+        ), "No cells executed."
+
+        print("Papermill execution verified successfully.")
+
+        # Load the newly generated stats CSV from the notebook
+        current_csv_path = Path("../../tests/output/linear_transient_to_plateau.csv")
+        if not current_csv_path.exists():
+            raise ValueError(f"Output CSV not found at {current_csv_path}")
+
+        current = pd.read_csv(current_csv_path)
+
+        # Load the expected baseline CSV
+        expected_csv_path = Path("../../tests/expected/linear_transient_to_plateau.csv")
+        if not expected_csv_path.exists():
+            raise ValueError(f"Expected CSV not found at {expected_csv_path}")
+
+        expected = pd.read_csv(expected_csv_path)
+
+        # Compare the two DataFrames
+        pdt.assert_frame_equal(current, expected, atol=1e-8, check_dtype=False)
+
+        print("Regression test passed: current results match expected baseline.")
+
+
 def test_slope_to_sine_regression():
     """
     Runs the Robust Workflow notebook, generates output stats CSV,
@@ -33,9 +83,6 @@ def test_slope_to_sine_regression():
         # Verify the notebook file itself exists
         assert output_nb.exists(), f"Executed notebook was not created at {output_nb}"
 
-        # Load and verify Papermill metadata
-        import nbformat
-
         executed_nb = nbformat.read(output_nb, as_version=4)
         assert (
             "papermill" in executed_nb.metadata