Split FITBIT_HEARTRATE into FITBIT_HEARTRATE_SUMMARY and FITBIT_HEARTRATE_INTRADAY

Snakefile

@@ -144,9 +144,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
 
-if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
-    raise ValueError("config['FITBIT_HEARTRATE']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_HEARTRATE"]["TABLE_FORMAT"])
-
 if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
     raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"])
 
@@ -156,13 +153,22 @@ if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
 if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]:
     raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"])
 
-for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys():
+
-    if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
+for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys():
-        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])))
+    if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"]))
+
+for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys():
+    if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_raw.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"]))
 
 for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys():
     if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]:

config.yaml

@@ -44,6 +44,7 @@ DEVICE_DATA:
   FITBIT:
     SOURCE: 
       TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path)
+      COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT
       DATABASE_GROUP: *database_group
       DEVICE_ID_COLUMN: device_id # column name
     TIMEZONE: 
@@ -258,20 +259,22 @@ PHONE_CONVERSATION:
 ############## FITBIT ##########################################################
 ################################################################################
 
-FITBIT_HEARTRATE:
+FITBIT_HEARTRATE_SUMMARY:
-  TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES
+  TABLE: heartrate_summary
-  TABLE:
-    JSON: fitbit_heartrate
-    CSV:
-      SUMMARY: heartrate_summary
-      INTRADAY: heartrate_intraday
   PROVIDERS:
     RAPIDS:
       COMPUTE: False
-      FEATURES:
+      FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
-        SUMMARY: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
+      SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary
-        INTRADAY: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
+      SRC_LANGUAGE: "python"
-      SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate
+
+FITBIT_HEARTRATE_INTRADAY:
+  TABLE: heartrate_intraday
+  PROVIDERS:
+    RAPIDS:
+      COMPUTE: False
+      FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
+      SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday
       SRC_LANGUAGE: "python"
 
 FITBIT_STEPS:

rules/features.smk

@@ -372,29 +372,55 @@ rule phone_wifi_visible_r_features:
     script:
         "../src/features/entry.R"
 
-rule fitbit_heartrate_python_features:
+rule fitbit_heartrate_summary_python_features:
     input:
-        sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]),
+        sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv",
         day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
     params:
-        provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
-        sensor_key = "fitbit_heartrate"
+        sensor_key = "fitbit_heartrate_summary"
     output:
-        "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_python_{provider_key}.csv"
+        "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_python_{provider_key}.csv"
     script:
         "../src/features/entry.py"
 
-rule fitbit_heartrate_r_features:
+rule fitbit_heartrate_summary_r_features:
     input:
-        sensor_data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", fitbit_data_type=["summary", "intraday"]),
+        sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv",
         day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
     params:
-        provider = lambda wildcards: config["FITBIT_HEARTRATE"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key = "{provider_key}",
-        sensor_key = "fitbit_heartrate"
+        sensor_key = "fitbit_heartrate_summary"
     output:
-        "data/interim/{pid}/fitbit_heartrate_features/fitbit_heartrate_r_{provider_key}.csv"
+        "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_r_{provider_key}.csv"
+    script:
+        "../src/features/entry.R"
+
+rule fitbit_heartrate_intraday_python_features:
+    input:
+        sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv",
+        day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
+    params:
+        provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        sensor_key = "fitbit_heartrate_intraday"
+    output:
+        "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_python_{provider_key}.csv"
+    script:
+        "../src/features/entry.py"
+
+rule fitbit_heartrate_intraday_r_features:
+    input:
+        sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv",
+        day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
+    params:
+        provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        sensor_key = "fitbit_heartrate_intraday"
+    output:
+        "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_r_{provider_key}.csv"
     script:
         "../src/features/entry.R"
 
@@ -424,19 +450,6 @@ rule fitbit_steps_r_features:
     script:
         "../src/features/entry.R"
 
-# rule fitbit_heartrate_features:
-#     input:
-#         heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv",
-#         heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv"
-#     params:
-#         day_segment = "{day_segment}",
-#         summary_features = config["HEARTRATE"]["SUMMARY_FEATURES"],
-#         intraday_features = config["HEARTRATE"]["INTRADAY_FEATURES"]
-#     output:
-#         "data/processed/{pid}/fitbit_heartrate_{day_segment}.csv"
-#     script:
-#         "../src/features/fitbit_heartrate_features.py"
-
 # rule fitbit_step_features:
 #     input:
 #         step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv",

rules/preprocessing.smk

@@ -40,14 +40,13 @@ rule download_phone_data:
 rule download_fitbit_data:
     input:
         participant_file = "data/external/participant_files/{pid}.yaml",
-        input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]["CSV"][str(wildcards.fitbit_data_type).upper()] 
+        input_file = [] if config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]
     params:
         source = config["DEVICE_DATA"]["FITBIT"]["SOURCE"],
         sensor = "fitbit_" + "{sensor}",
-        fitbit_data_type = "{fitbit_data_type}",
         table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"],
     output:
-        "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_raw.csv"
+        "data/raw/{pid}/fitbit_{sensor}_raw.csv"
     script:
         "../src/data/download_fitbit_data.R"
 
@@ -183,14 +182,14 @@ rule phone_application_categories:
 
 rule fitbit_parse_heartrate:
     input:
-        data = expand("data/raw/{{pid}}/fitbit_heartrate_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))
+        "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv"
     params:
         timezone = config["DEVICE_DATA"]["PHONE"]["TIMEZONE"]["VALUE"],
         table = config["FITBIT_HEARTRATE"]["TABLE"],
-        table_format = config["FITBIT_HEARTRATE"]["TABLE_FORMAT"]
+        column_format = config["DEVICE_DATA"]["FITBIT"]["SOURCE"]["COLUMN_FORMAT"],
+        fitbit_data_type = "{fitbit_data_type}"
     output:
-        summary_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed.csv",
+        "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv"
-        intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv"
     script:
         "../src/data/fitbit_parse_heartrate.py"
 

src/data/fitbit_parse_heartrate.py

@@ -96,7 +96,7 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date,
 # def append_timestamp(data):
 
 
-def parseHeartrateData(heartrate_data):
+def parseHeartrateData(heartrate_data, fitbit_data_type):
     if heartrate_data.empty:
         return pd.DataFrame(columns=HR_SUMMARY_COLUMNS), pd.DataFrame(columns=HR_INTRADAY_COLUMNS)
     device_id = heartrate_data["device_id"].iloc[0]
@@ -109,29 +109,36 @@ def parseHeartrateData(heartrate_data):
         record = json.loads(record)  # Parse text into JSON
         curr_date = datetime.strptime(record["activities-heart"][0]["dateTime"], "%Y-%m-%d")
 
+        if fitbit_data_type == "summary":
             record_summary = record["activities-heart"][0]
             row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date)
             records_summary.append(row_summary)
 
+        if fitbit_data_type == "intraday":
             dataset = record["activities-heart-intraday"]["dataset"]
             records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range)
     
-    return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS)
+    if fitbit_data_type == "summary":
+        parsed_data = pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS)
+    elif fitbit_data_type == "intraday":
+        parsed_data = pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS)
+    else:
+        raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].")
+    return parsed_data
+    
+
 
-table_format = snakemake.params["table_format"]
 timezone = snakemake.params["timezone"]
+column_format = snakemake.params["column_format"]
+fitbit_data_type = snakemake.params["fitbit_data_type"]
 
-if table_format == "JSON":
+if column_format == "JSON":
     json_raw = pd.read_csv(snakemake.input[0])
-    summary, intraday = parseHeartrateData(json_raw)
+    parsed_data = parseHeartrateData(json_raw, fitbit_data_type)
-elif table_format == "CSV":
+elif column_format == "PLAIN_TEXT":
-    summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
+    parsed_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
-    intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None))
 
-if summary.shape[0] > 0:
+if parsed_data.shape[0] > 0:
-    summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
+    parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
-if intraday.shape[0] > 0:
-    intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6
 
-summary.to_csv(snakemake.output["summary_data"], index=False)
+parsed_data.to_csv(snakemake.output[0], index=False)
-intraday.to_csv(snakemake.output["intraday_data"], index=False)

src/features/fitbit_heartrate_intraday/rapids/main.py

@@ -0,0 +1,79 @@
+import pandas as pd
+from scipy.stats import entropy
+
+def statsFeatures(heartrate_data, features, features_type, heartrate_features):
+
+    if features_type == "hr":
+        col_name = "heartrate"
+    elif features_type == "restinghr":
+        col_name = "heartrate_daily_restinghr"
+    elif features_type == "caloriesoutofrange":
+        col_name = "heartrate_daily_caloriesoutofrange"
+    elif features_type == "caloriesfatburn":
+        col_name = "heartrate_daily_caloriesfatburn"
+    elif features_type == "caloriescardio":
+        col_name = "heartrate_daily_caloriescardio"
+    elif features_type == "caloriespeak":
+        col_name = "heartrate_daily_caloriespeak"
+    else:
+        raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].")
+
+    if "sum" + features_type in features:
+        heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum()
+    if "max" + features_type in features:
+        heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max()
+    if "min" + features_type in features:
+        heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
+    if "avg" + features_type in features:
+        heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean()
+    if "median" + features_type in features:
+        heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median()
+    if "mode" + features_type in features:
+        heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
+    if "std" + features_type in features:
+        heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std()
+    if "diffmaxmode" + features_type in features:
+        heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
+    if "diffminmode" + features_type in features:
+        heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
+    if "entropy" + features_type in features:
+        heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy)
+    
+    return heartrate_features
+
+def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment):
+    heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features])
+    if not heartrate_intraday_data.empty:
+        num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"]
+        heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment)
+
+        if not heartrate_intraday_data.empty:
+            heartrate_intraday_features = pd.DataFrame()
+        
+            # get stats of heartrate
+            heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features)
+
+            # get number of minutes in each heart rate zone
+            for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)):
+                heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]]
+                heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute
+                heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True)
+        heartrate_intraday_features.reset_index(inplace=True)
+
+    return heartrate_intraday_features
+
+
+def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
+
+    heartrate_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 = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
+    # 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
+    heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment)
+    
+    return heartrate_intraday_features

src/features/fitbit_heartrate_summary/rapids/main.py

@@ -58,41 +58,16 @@ def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features):
     
     return heartrate_summary_features
 
-def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment, filter_data_by_segment):
-    heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in features])
-    if not heartrate_intraday_data.empty:
-        num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"]
-        heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, day_segment)
-
-        if not heartrate_intraday_data.empty:
-            heartrate_intraday_features = pd.DataFrame()
-        
-            # get stats of heartrate
-            heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features)
-
-            # get number of minutes in each heart rate zone
-            for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)):
-                heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]]
-                heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute
-                heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True)
-        heartrate_intraday_features.reset_index(inplace=True)
-
-    return heartrate_intraday_features
-
 
 def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_segment, *args, **kwargs):
 
-    heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"][0])
+    heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
-    heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"][1])
 
-    requested_summary_features = provider["FEATURES"]["SUMMARY"]
+    requested_summary_features = provider["FEATURES"]
-    requested_intraday_features = provider["FEATURES"]["INTRADAY"]
     # name of the features this function can compute
     base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
-    base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
     # the subset of requested features this function can compute
     summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))
-    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
 
     # extract features from summary data
     heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in summary_features_to_compute])
@@ -110,10 +85,4 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
             if not heartrate_summary_data.empty:
                 heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute)
     
-    # extract features from intraday data
+    return heartrate_summary_features
-    heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment)
-    
-    # merge summary features and intraday features
-    heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer")
-    
-    return heartrate_features