PEP extraction algorithms: updated tests to work with new implementation

src/biopsykit/signals/icg/event_extraction/_b_point_forouzanfar2018.py

@@ -241,7 +241,7 @@ def _get_most_prominent_monotonic_increasing_segment(icg_segment: pd.Series, hei
         ].index
 
         end_index_drop_rule_b = end_index_drop_rule_b.union(end_index_drop_rule_b - 1)
-        monotony_df = monotony_df.drop(index=end_index_drop_rule_b)
+        monotony_df = monotony_df.drop(index=monotony_df.iloc[end_index_drop_rule_b].index)
 
         # Select the monotonic segment with the highest amplitude difference
         start_sample = 0

tests/test_algorithms/test_b_point_extraction_arbol2017.py

@@ -80,7 +80,7 @@ def test_regression_extract_series(self):
     @staticmethod
     def _get_regression_reference():
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_b_point_reference_arbol2017.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_b_point_extraction_debski1993.py

@@ -80,7 +80,7 @@ def test_regression_extract_series(self):
     @staticmethod
     def _get_regression_reference():
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_b_point_reference_debski1993.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_b_point_extraction_drost2022.py

@@ -80,7 +80,7 @@ def test_regression_extract_series(self):
     @staticmethod
     def _get_regression_reference():
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_b_point_reference_drost2022.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_b_point_extraction_forouzanfar2018.py

@@ -80,7 +80,7 @@ def test_regression_extract_series(self):
     @staticmethod
     def _get_regression_reference():
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_b_point_reference_forouzanfar2018.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_c_point_extraction_scipy_findpeaks.py

@@ -66,7 +66,7 @@ def test_regression_extract_series(self):
     @staticmethod
     def _get_regression_reference():
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_c_point_reference_scipy_findpeaks.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"c_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod
@@ -78,7 +78,6 @@ class TestCPointExtractionSciPyFindpeaksParameters:
     def setup(
         self,
         window_c_correction: Optional[int] = 3,
-        save_candidates: Optional[bool] = False,
     ):
         # Sample ECG data
         self.ecg_data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_ecg.csv"), index_col=0)
@@ -88,9 +87,7 @@ def setup(
         self.heartbeats = self.segmenter.extract(
             ecg=self.ecg_data, sampling_rate_hz=self.sampling_rate_hz
         ).heartbeat_list_
-        self.extract_algo = CPointExtractionScipyFindPeaks(
-            window_c_correction=window_c_correction, save_candidates=save_candidates
-        )
+        self.extract_algo = CPointExtractionScipyFindPeaks(window_c_correction=window_c_correction)
         self.test_case = unittest.TestCase()
 
     @pytest.mark.parametrize(
@@ -102,20 +99,6 @@ def test_extract_window_c_correction(self, window_c_correction):
 
         self.extract_algo.extract(icg=self.icg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
 
-        print(self.extract_algo.points_)
-
         assert isinstance(self.extract_algo.points_, pd.DataFrame)
         assert "c_point_sample" in self.extract_algo.points_.columns
         assert "nan_reason" in self.extract_algo.points_.columns
-
-    @pytest.mark.parametrize(
-        ("save_candidates", "expected_columns"),
-        [(True, ["c_point_sample", "nan_reason", "c_point_candidates"]), (False, ["c_point_sample", "nan_reason"])],
-    )
-    def test_extract_window_(self, save_candidates, expected_columns):
-        self.setup(save_candidates=save_candidates)
-
-        self.extract_algo.extract(icg=self.icg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
-
-        assert isinstance(self.extract_algo.points_, pd.DataFrame)
-        self.test_case.assertListEqual(expected_columns, self.extract_algo.points_.columns.tolist())

tests/test_algorithms/test_heatbeat_segmentation.py

@@ -62,10 +62,6 @@ def test_regression_extract_variable_length_dataframe(self):
         _assert_is_dtype(ecg_data, pd.DataFrame)
 
         self.segmenter.extract(ecg=ecg_data, sampling_rate_hz=self.sampling_rate_hz)
-
-        # print(self.segmenter.heartbeat_list_["start_time"].dtype)
-        # print(reference_heartbeats["start_time"].dtype)
-
         # check if the extraction is equal
         self._check_heartbeats_equal(reference_heartbeats, self.segmenter.heartbeat_list_)
 
@@ -136,7 +132,7 @@ def _check_heartbeats_equal(reference_heartbeats, extracted_heartbeats):
         ("data", "expected"),
         [
             (None, pytest.raises(ValueError)),
-            (pd.Series([]), pytest.raises(ValueError)),
+            (pd.Series([], dtype="Float64"), pytest.raises(ValueError)),
             (pd.DataFrame(), pytest.raises(ValidationError)),
         ],
     )

tests/test_algorithms/test_outlier_correction_forouzanfar2018.py

@@ -86,7 +86,7 @@ def _get_b_point_outlier_middle(self):
 
     def _get_regression_reference(self):
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_icg_outlier_correction_forouzanfar2018.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_outlier_correction_interpolation.py

@@ -69,8 +69,6 @@ def test_regression_correct_outlier(self, outlier_type):
             b_points=b_points, c_points=self.c_points, sampling_rate_hz=self.sampling_rate_hz
         )
 
-        print(self.outlier_algo.points_)
-
         corrected_beats = (self.b_points - self.outlier_algo.points_)["b_point_sample"] != 0
         corrected_beats = self.b_points.index[corrected_beats]
 
@@ -87,7 +85,7 @@ def _get_b_point_outlier_middle(self):
 
     def _get_regression_reference(self):
         data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_icg_outlier_correction_interpolation.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = data.astype({"b_point_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod

tests/test_algorithms/test_q_wave_onset_extraction.py → tests/test_algorithms/test_q_peak_extraction.py

@@ -2,6 +2,7 @@
 from contextlib import contextmanager
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -35,44 +36,44 @@ def test_extract(self):
         self.extract_algo.extract(ecg=self.ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
 
         assert isinstance(self.extract_algo.points_, pd.DataFrame)
-        assert "q_wave_onset_sample" in self.extract_algo.points_.columns
+        assert "q_peak_sample" in self.extract_algo.points_.columns
         assert "nan_reason" in self.extract_algo.points_.columns
 
-    # add regression test to check if the extracted q-wave onsets match with the saved reference
+    # add regression test to check if the extracted q-peaks match with the saved reference
     def test_regression_extract_dataframe(self):
         self.setup()
 
         ecg_data = self.ecg_data
         _assert_is_dtype(ecg_data, pd.DataFrame)
 
-        reference_q_wave_onsets = self._get_regression_reference()
+        reference_q_peaks = self._get_regression_reference()
         self.extract_algo.extract(ecg=ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
 
-        self._check_q_wave_onset_equal(reference_q_wave_onsets, self.extract_algo.points_)
+        self._check_q_peaks_equal(reference_q_peaks, self.extract_algo.points_)
 
     def test_regression_extract_series(self):
         self.setup()
 
         ecg_data = self.ecg_data.squeeze()
         _assert_is_dtype(ecg_data, pd.Series)
 
-        reference_q_wave_onsets = self._get_regression_reference()
+        reference_q_peaks = self._get_regression_reference()
         self.extract_algo.extract(ecg=ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
 
-        self._check_q_wave_onset_equal(reference_q_wave_onsets, self.extract_algo.points_)
+        self._check_q_peaks_equal(reference_q_peaks, self.extract_algo.points_)
 
     @staticmethod
     def _get_regression_reference():
-        data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_q_wave_onset_reference_neurokit_dwt.csv"), index_col=0)
-        data = data.convert_dtypes(infer_objects=True)
+        data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_q_peak_reference_neurokit_dwt.csv"), index_col=0)
+        data = data.astype({"q_peak_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod
-    def _check_q_wave_onset_equal(reference_heartbeats, extracted_heartbeats):
+    def _check_q_peaks_equal(reference_heartbeats, extracted_heartbeats):
         pd.testing.assert_frame_equal(reference_heartbeats, extracted_heartbeats)
 
 
-class TestQWaveOnsetExtractionVanLien2013:
+class TestQPeakExtractionVanLien2013:
     def setup(self, time_interval_ms: int = 40):
         # Sample ECG data
         self.ecg_data = pd.read_csv(TEST_FILE_PATH.joinpath("pep_test_ecg.csv"), index_col=0)
@@ -98,9 +99,9 @@ def test_extract(self):
         self.extract_algo.extract(ecg=self.ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
 
         assert isinstance(self.extract_algo.points_, pd.DataFrame)
-        assert "q_wave_onset_sample" in self.extract_algo.points_.columns
+        assert "q_peak_sample" in self.extract_algo.points_.columns
 
-    # add regression test to check if the extracted q-wave onsets match with the saved reference
+    # add regression test to check if the extracted q-peaks match with the saved reference
     @pytest.mark.parametrize(
         ("time_interval_ms"),
         [34, 36, 38, 40],
@@ -111,9 +112,9 @@ def test_regression_extract_dataframe(self, time_interval_ms):
         ecg_data = self.ecg_data
         _assert_is_dtype(ecg_data, pd.DataFrame)
 
-        reference_q_wave_onsets = self._get_regression_reference(time_interval_ms)
+        reference_q_peaks = self._get_regression_reference(time_interval_ms)
         self.extract_algo.extract(ecg=ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
-        self._check_q_wave_onset_equal(reference_q_wave_onsets, self.extract_algo.points_)
+        self._check_q_peaks_equal(reference_q_peaks, self.extract_algo.points_)
 
     @pytest.mark.parametrize(
         ("time_interval_ms"),
@@ -125,20 +126,20 @@ def test_regression_extract_series(self, time_interval_ms):
         ecg_data = self.ecg_data.squeeze()
         _assert_is_dtype(ecg_data, pd.Series)
 
-        reference_q_wave_onsets = self._get_regression_reference(time_interval_ms)
+        reference_q_peaks = self._get_regression_reference(time_interval_ms)
         self.extract_algo.extract(ecg=ecg_data, heartbeats=self.heartbeats, sampling_rate_hz=self.sampling_rate_hz)
-        self._check_q_wave_onset_equal(reference_q_wave_onsets, self.extract_algo.points_)
+        self._check_q_peaks_equal(reference_q_peaks, self.extract_algo.points_)
 
     def _get_regression_reference(self, time_interval_ms: int = 40):
         data = pd.read_csv(
             TEST_FILE_PATH.joinpath("pep_test_heartbeat_reference_variable_length.csv"), index_col=0, parse_dates=True
         )
-        data = data.convert_dtypes(infer_objects=True)
         data = data[["r_peak_sample"]] - int((time_interval_ms / self.sampling_rate_hz) * 1000)
-        data.columns = ["q_wave_onset_sample"]
-
+        data = data.assign(nan_reason=np.NAN)
+        data.columns = ["q_peak_sample", "nan_reason"]
+        data = data.astype({"q_peak_sample": "Int64", "nan_reason": "object"})
         return data
 
     @staticmethod
-    def _check_q_wave_onset_equal(reference_heartbeats, extracted_heartbeats):
+    def _check_q_peaks_equal(reference_heartbeats, extracted_heartbeats):
         pd.testing.assert_frame_equal(reference_heartbeats, extracted_heartbeats)

tests/test_data/pep/pep_test_q_wave_onset_reference_neurokit_dwt.csv → tests/test_data/pep/pep_test_q_peak_reference_neurokit_dwt.csv

@@ -1,4 +1,4 @@
-heartbeat_id,q_wave_onset_sample,nan_reason
+heartbeat_id,q_peak_sample,nan_reason
 0,423,
 1,1012,
 2,1614,