0.2.7

neurokit2/eda/eda_intervalrelated.py

@@ -75,9 +75,7 @@ def eda_intervalrelated(data, sampling_rate=1000, **kwargs):
             # Add label info
             results[index]["Label"] = data[index]["Label"].iloc[0]
 
-            results[index] = _eda_intervalrelated(
-                data[index], results[index], sampling_rate=sampling_rate, **kwargs
-            )
+            results[index] = _eda_intervalrelated(data[index], results[index], sampling_rate=sampling_rate, **kwargs)
 
         results = pd.DataFrame.from_dict(results, orient="index")
 
@@ -89,9 +87,7 @@ def eda_intervalrelated(data, sampling_rate=1000, **kwargs):
 # =============================================================================
 
 
-def _eda_intervalrelated(
-    data, output={}, sampling_rate=1000, method_sympathetic="posada", **kwargs
-):
+def _eda_intervalrelated(data, output={}, sampling_rate=1000, method_sympathetic="posada", **kwargs):
     """Format input for dictionary."""
     # Sanitize input
     colnames = data.columns.values
@@ -114,7 +110,12 @@ def _eda_intervalrelated(
         )
         output["SCR_Peaks_Amplitude_Mean"] = np.nan
     else:
-        output["SCR_Peaks_Amplitude_Mean"] = np.nanmean(data["SCR_Amplitude"].values)
+        peaks_idx = data["SCR_Peaks"] == 1
+        # Mean amplitude is only computed over peaks. If no peaks, return NaN
+        if peaks_idx.sum() > 0:
+            output["SCR_Peaks_Amplitude_Mean"] = np.nanmean(data[peaks_idx]["SCR_Amplitude"].values)
+        else:
+            output["SCR_Peaks_Amplitude_Mean"] = np.nan
 
     # Get variability of tonic
     if "EDA_Tonic" in colnames:
@@ -126,28 +127,28 @@ def _eda_intervalrelated(
         if "EDA_Clean" in colnames:
             output.update(
                 eda_sympathetic(
-                    data["EDA_Clean"], sampling_rate=sampling_rate, method=method_sympathetic
+                    data["EDA_Clean"],
+                    sampling_rate=sampling_rate,
+                    method=method_sympathetic,
                 )
             )
         elif "EDA_Raw" in colnames:
             # If not clean signal, use raw
             output.update(
                 eda_sympathetic(
-                    data["EDA_Raw"], sampling_rate=sampling_rate, method=method_sympathetic
+                    data["EDA_Raw"],
+                    sampling_rate=sampling_rate,
+                    method=method_sympathetic,
                 )
             )
 
     # EDA autocorrelation
     output.update({"EDA_Autocorrelation": np.nan})  # Default values
     if len(data) > sampling_rate * 30:  # 30 seconds minimum (NOTE: somewhat arbitrary)
         if "EDA_Clean" in colnames:
-            output["EDA_Autocorrelation"] = eda_autocor(
-                data["EDA_Clean"], sampling_rate=sampling_rate, **kwargs
-            )
+            output["EDA_Autocorrelation"] = eda_autocor(data["EDA_Clean"], sampling_rate=sampling_rate, **kwargs)
         elif "EDA_Raw" in colnames:
             # If not clean signal, use raw
-            output["EDA_Autocorrelation"] = eda_autocor(
-                data["EDA_Raw"], sampling_rate=sampling_rate, **kwargs
-            )
+            output["EDA_Autocorrelation"] = eda_autocor(data["EDA_Raw"], sampling_rate=sampling_rate, **kwargs)
 
     return output