[BUGFIX] Fix stuck sensor detection only applying to last column (#177)

* Append index faults by column to avoid only returning last columns faults

* Add test for find_sensor_stuck_faults

* Ruff format.

* Option for returning stuck sensors by column.

* Rerun smarteole examples in case.

* Rerun artificial data examples.

flasc/turbine_analysis/find_sensor_faults.py

@@ -25,26 +25,36 @@ def find_sensor_stuck_faults(
     n_consecutive_measurements=3,
     plot_figures=True,
     verbose=False,
+    return_by_column=False,
 ):
     # Settings which indicate a sensor-stuck type of fault: the standard
     # deviation between the [no_consecutive_measurements] number of
     # consecutive measurements is less than [stddev_threshold].
+
+    # TODO: remove unused argument 'ti'
+
+    index_faults = {c: np.array([]) for c in columns}
     for c in columns:
         if verbose:
             print("Processing column %s" % c)
         measurement_array = np.array(df[c])
 
-        index_faults = _find_sensor_stuck_single_timearray(
+        column_index_faults = _find_sensor_stuck_single_timearray(
             measurement_array=measurement_array,
             no_consecutive_measurements=n_consecutive_measurements,
             stddev_threshold=stddev_threshold,
             index_array=df.index,
         )
 
-        if (plot_figures) & (len(index_faults) > 0):
-            _plot_top_sensor_faults(df, c, index_faults)
+        if (plot_figures) & (len(column_index_faults) > 0):
+            _plot_top_sensor_faults(df, c, column_index_faults)
+
+        index_faults[c] = column_index_faults
 
-    return index_faults
+    if return_by_column:
+        return index_faults
+    else:
+        return np.unique(np.concatenate([v for v in index_faults.values()]))
 
 
 def _plot_top_sensor_faults(

tests/turbine_analysis_test.py

@@ -0,0 +1,48 @@
+import numpy as np
+import pandas as pd
+
+from flasc.turbine_analysis.find_sensor_faults import find_sensor_stuck_faults
+
+
+def test_find_sensor_stuck_faults():
+    df_test = pd.DataFrame({"a": [0, 0, 0, 1, 2, 3, 4], "b": [4, 5, 6, 6, 7, 7, 7]})
+
+    # Test default behavior
+    results_a = find_sensor_stuck_faults(df_test, columns=["a"], ti=0, plot_figures=False)
+    assert (results_a == np.array([0, 1, 2])).all()
+
+    results_b = find_sensor_stuck_faults(df_test, columns=["b"], ti=0, plot_figures=False)
+    assert (results_b == np.array([4, 5, 6])).all()
+
+    results_ab = find_sensor_stuck_faults(df_test, columns=["a", "b"], ti=0, plot_figures=False)
+    assert (results_ab == np.array([0, 1, 2, 4, 5, 6])).all()
+
+    results_ba = find_sensor_stuck_faults(df_test, columns=["b", "a"], ti=0, plot_figures=False)
+    assert (results_ab == results_ba).all()
+
+    results_ab2 = find_sensor_stuck_faults(
+        df_test, columns=["a", "b"], ti=0, n_consecutive_measurements=2, plot_figures=False
+    )
+    assert (results_ab2 == np.array([0, 1, 2, 3, 4, 5, 6])).all()
+
+    # Test returning by column
+    results = find_sensor_stuck_faults(
+        df_test, columns=["a", "b"], ti=0, plot_figures=False, return_by_column=True
+    )
+    test_dict = {"a": np.array([0, 1, 2]), "b": np.array([4, 5, 6])}
+    assert results["a"].size == 3
+    assert results["b"].size == 3
+    assert results.keys() == test_dict.keys()
+    assert all((results[k] == test_dict[k]).all() for k in test_dict)
+
+    # Test stddev_threshold
+    df_test = pd.DataFrame({"a": [0, 0.1, -0.1, 0.05, 1]})
+    std_true = np.std(df_test["a"][:-1])
+
+    results = find_sensor_stuck_faults(df_test, columns=["a"], ti=0, plot_figures=False)
+    assert results.size == 0  # Empty array, no fault detected
+
+    results = find_sensor_stuck_faults(
+        df_test, columns=["a"], ti=0, stddev_threshold=std_true * 2, plot_figures=False
+    )
+    assert (results == np.array([0, 1, 2, 3])).all()