[Fix] Fixed memory baviour in hrv calc

neurokit2/ppg/ppg_intervalrelated.py

@@ -61,7 +61,7 @@ def ppg_intervalrelated(data, sampling_rate=1000):
         rate_cols = [col for col in data.columns if "PPG_Rate" in col]
         if len(rate_cols) == 1:
             intervals.update(_ppg_intervalrelated_formatinput(data))
-            intervals.update(_ppg_intervalrelated_hrv(data, sampling_rate))
+            intervals.update(*_ppg_intervalrelated_hrv(data, sampling_rate))
         else:
             raise ValueError(
                 "NeuroKit error: ppg_intervalrelated(): Wrong input,"
@@ -71,20 +71,18 @@ def ppg_intervalrelated(data, sampling_rate=1000):
         ppg_intervals = pd.DataFrame.from_dict(intervals, orient="index").T
 
     elif isinstance(data, dict):
-        for index in data:
-            intervals[index] = {}  # Initialize empty container
+        for epoch in data:
+            intervals[epoch] = {}  # Initialize empty container
 
             # Add label info
-            intervals[index]["Label"] = data[index]["Label"].iloc[0]
+            intervals[epoch]["Label"] = data[epoch]["Label"].iloc[0]
 
             # Rate
-            intervals[index] = _ppg_intervalrelated_formatinput(
-                data[index], intervals[index]
-            )
+            intervals[epoch].update(_ppg_intervalrelated_formatinput(data[epoch]))
 
             # HRV
-            intervals[index] = _ppg_intervalrelated_hrv(
-                data[index], sampling_rate, intervals[index]
+            intervals[epoch].update(
+                *_ppg_intervalrelated_hrv(data[epoch], sampling_rate)
             )
 
         ppg_intervals = pd.DataFrame.from_dict(intervals, orient="index")
@@ -97,27 +95,28 @@ def ppg_intervalrelated(data, sampling_rate=1000):
 # =============================================================================
 
 
-def _ppg_intervalrelated_formatinput(data, output={}):
-
+def _ppg_intervalrelated_formatinput(data):
     # Sanitize input
     colnames = data.columns.values
-    if len([i for i in colnames if "PPG_Rate" in i]) == 0:
+
+    if "PPG_Rate" not in colnames:
         raise ValueError(
             "NeuroKit error: ppg_intervalrelated(): Wrong input,"
             "we couldn't extract heart rate. Please make sure"
             "your DataFrame contains a `PPG_Rate` column."
         )
     signal = data["PPG_Rate"].values
-    output["PPG_Rate_Mean"] = np.mean(signal)
 
-    return output
+    PPG_Rate_Mean = np.mean(signal)
 
+    return {"PPG_Rate_Mean": PPG_Rate_Mean}
 
-def _ppg_intervalrelated_hrv(data, sampling_rate, output={}):
 
+def _ppg_intervalrelated_hrv(data, sampling_rate):
     # Sanitize input
     colnames = data.columns.values
-    if len([i for i in colnames if "PPG_Peaks" in i]) == 0:
+
+    if "PPG_Peaks" not in colnames:
         raise ValueError(
             "NeuroKit error: ppg_intervalrelated(): Wrong input,"
             "we couldn't extract peaks. Please make sure"
@@ -129,7 +128,5 @@ def _ppg_intervalrelated_hrv(data, sampling_rate, output={}):
     peaks = {"PPG_Peaks": peaks}
 
     results = hrv(peaks, sampling_rate=sampling_rate)
-    for column in results.columns:
-        output[column] = results[column].values.astype("float")
 
-    return output
+    return results.astype("float").to_dict("records")

tests/tests_ppg.py

@@ -18,9 +18,10 @@
 params_combis = list(itertools.product(*params))
 
 
-@pytest.mark.parametrize("duration, sampling_rate, heart_rate, freq_modulation", params_combis)
+@pytest.mark.parametrize(
+    "duration, sampling_rate, heart_rate, freq_modulation", params_combis
+)
 def test_ppg_simulate(duration, sampling_rate, heart_rate, freq_modulation):
-
     ppg = nk.ppg_simulate(
         duration=duration,
         sampling_rate=sampling_rate,
@@ -42,7 +43,6 @@ def test_ppg_simulate(duration, sampling_rate, heart_rate, freq_modulation):
     signals, _ = nk.ppg_process(ppg, sampling_rate=sampling_rate)
     if sampling_rate > 25:
         assert np.allclose(signals["PPG_Rate"].mean(), heart_rate, atol=1)
-
         # Ensure that the heart rate fluctuates in the requested range.
         groundtruth_range = freq_modulation * heart_rate
         observed_range = np.percentile(signals["PPG_Rate"], 90) - np.percentile(signals["PPG_Rate"], 10)
@@ -56,7 +56,6 @@ def test_ppg_simulate(duration, sampling_rate, heart_rate, freq_modulation):
     [(0.1, 3), (0.2, 5), (0.3, 8), (0.4, 11), (0.5, 14), (0.6, 19)],
 )
 def test_ppg_simulate_ibi(ibi_randomness, std_heart_rate):
-
     ppg = nk.ppg_simulate(
         duration=20,
         sampling_rate=50,
@@ -109,7 +108,6 @@ def test_ppg_simulate_legacy_rng():
 
 
 def test_ppg_clean():
-
     sampling_rate = 500
 
     ppg = nk.ppg_simulate(
@@ -126,7 +124,9 @@ def test_ppg_clean():
         random_state=42,
         show=False,
     )
-    ppg_cleaned_elgendi = nk.ppg_clean(ppg, sampling_rate=sampling_rate, method="elgendi")
+    ppg_cleaned_elgendi = nk.ppg_clean(
+        ppg, sampling_rate=sampling_rate, method="elgendi"
+    )
 
     assert ppg.size == ppg_cleaned_elgendi.size
 
@@ -141,7 +141,6 @@ def test_ppg_clean():
 
 
 def test_ppg_findpeaks():
-
     sampling_rate = 500
 
     # Test Elgendi method
@@ -159,17 +158,23 @@ def test_ppg_findpeaks():
         random_state=42,
         show=True,
     )
-    ppg_cleaned_elgendi = nk.ppg_clean(ppg, sampling_rate=sampling_rate, method="elgendi")
+    ppg_cleaned_elgendi = nk.ppg_clean(
+        ppg, sampling_rate=sampling_rate, method="elgendi"
+    )
 
-    info_elgendi = nk.ppg_findpeaks(ppg_cleaned_elgendi, sampling_rate=sampling_rate, show=True)
+    info_elgendi = nk.ppg_findpeaks(
+        ppg_cleaned_elgendi, sampling_rate=sampling_rate, show=True
+    )
 
     peaks = info_elgendi["PPG_Peaks"]
 
     assert peaks.size == 29
     assert np.abs(peaks.sum() - 219764) < 5  # off by no more than 5 samples in total
 
     # Test MSPTD method
-    info_msptd = nk.ppg_findpeaks(ppg, sampling_rate=sampling_rate, method="bishop", show=True)
+    info_msptd = nk.ppg_findpeaks(
+        ppg, sampling_rate=sampling_rate, method="bishop", show=True
+    )
 
     peaks = info_msptd["PPG_Peaks"]
 
@@ -182,7 +187,6 @@ def test_ppg_findpeaks():
     [("elgendi", "elgendi"), ("nabian2018", "elgendi"), ("elgendi", "bishop")],
 )
 def test_ppg_report(tmp_path, method_cleaning, method_peaks):
-
     sampling_rate = 100
 
     ppg = nk.ppg_simulate(
@@ -212,3 +216,32 @@ def test_ppg_report(tmp_path, method_cleaning, method_peaks):
         method_peaks=method_peaks,
     )
     assert p.is_file()
+
+
+def test_ppg_intervalrelated():
+    sampling_rate = 100
+
+    ppg = nk.ppg_simulate(
+        duration=500,
+        sampling_rate=sampling_rate,
+        heart_rate=70,
+        frequency_modulation=0.025,
+        ibi_randomness=0.15,
+        drift=0.5,
+        motion_amplitude=0.25,
+        powerline_amplitude=0.25,
+        burst_amplitude=0.5,
+        burst_number=3,
+        random_state=0,
+        show=True,
+    )
+    # Process the data
+    df, info = nk.ppg_process(ppg, sampling_rate=sampling_rate)
+    epochs = nk.epochs_create(
+        df, events=[0, 15000], sampling_rate=sampling_rate, epochs_end=150
+    )
+    epochs_ppg_intervals = nk.ppg_intervalrelated(epochs)
+    assert "PPG_Rate_Mean" in epochs_ppg_intervals.columns
+
+    ppg_intervals = nk.ppg_intervalrelated(df)
+    assert "PPG_Rate_Mean" in ppg_intervals.columns