Skeleton file main.py for EDA CalcFt. integration.

config.yaml

@@ -180,7 +180,7 @@ PHONE_CALLS:
   CONTAINER: calls.csv
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES_TYPE: EPISODES # EVENTS or EPISODES
       CALL_TYPES: [missed, incoming, outgoing]
       FEATURES:
@@ -477,7 +477,7 @@ EMPATICA_ACCELEROMETER:
   CONTAINER: ACC
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
 
@@ -486,7 +486,7 @@ EMPATICA_HEARTRATE:
   CONTAINER: HR
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
       SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py
 
@@ -495,7 +495,7 @@ EMPATICA_TEMPERATURE:
   CONTAINER: TEMP
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
       SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
 
@@ -504,16 +504,20 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
   CONTAINER: EDA
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
+    CF:
+      COMPUTE: True
+      FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
+      SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
 
 # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
 EMPATICA_BLOOD_VOLUME_PULSE:
   CONTAINER: BVP
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
 
@@ -522,7 +526,7 @@ EMPATICA_INTER_BEAT_INTERVAL:
   CONTAINER: IBI
   PROVIDERS:
     DBDP:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
 

src/features/empatica_electrodermal_activity/cf/main.py

@@ -0,0 +1,76 @@
+import pandas as pd
+from scipy.stats import entropy
+
+
+def statsFeatures(eda_data, features, eda_features):
+    col_name = "electrodermal_activity"
+    if "sumeda" in features:
+        eda_features["sumeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].sum()
+    if "maxeda" in features:
+        eda_features["maxeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].max()
+    if "mineda" in features:
+        eda_features["mineda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].min()
+    if "avgeda" in features:
+        eda_features["avgeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].mean()
+    if "medianeda" in features:
+        eda_features["medianeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].median()
+    if "modeeda" in features:
+        eda_features["modeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].agg(lambda x: pd.Series.mode(x)[0])
+    if "stdeda" in features:
+        eda_features["stdeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].std()
+    if "diffmaxmodeeda" in features:
+        eda_features["diffmaxmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+                                                  col_name].max() - \
+                                              eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+                                                  col_name].agg(lambda x: pd.Series.mode(x)[0])
+    if "diffminmodeeda" in features:
+        eda_features["diffminmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+                                                  col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
+                                              eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+                                                  col_name].min()
+    if "entropyeda" in features:
+        eda_features["entropyeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[
+            col_name].agg(entropy)
+
+    return eda_features
+
+
+def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
+    eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
+    if not eda_intraday_data.empty:
+        eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
+
+        if not eda_intraday_data.empty:
+            eda_intraday_features = pd.DataFrame()
+
+            # get stats of eda
+            eda_intraday_features = statsFeatures(eda_intraday_data, features, eda_intraday_features)
+
+            eda_intraday_features.reset_index(inplace=True)
+
+    return eda_intraday_features
+
+
+def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    requested_intraday_features = provider["FEATURES"]
+    # name of the features this function can compute
+    base_intraday_features_names = ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda",
+                                    "diffminmodeeda", "entropyeda"]
+    # the subset of requested features this function can compute
+    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
+
+    # extract features from intraday data
+    eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data,
+                                                                    intraday_features_to_compute, time_segment,
+                                                                    filter_data_by_segment)
+
+    return eda_intraday_features