rapids/config.yaml

# See https://www.rapids.science/latest/setup/configuration/#database-credentials
DATABASE_GROUP: &database_group
  MY_GROUP

# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
TIMEZONE: &timezone
  America/New_York

# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: [test01]

# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
  SOURCE:
    TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE
    DATABASE_GROUP: *database_group
    CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format
    TIMEZONE: *timezone
  PHONE_SECTION:
    ADD: TRUE
    DEVICE_ID_COLUMN: device_id # column name
    IGNORED_DEVICE_IDS: []
  FITBIT_SECTION:
    ADD: FALSE
    DEVICE_ID_COLUMN: device_id # column name
    IGNORED_DEVICE_IDS: []
  EMPATICA_SECTION:
    ADD: FALSE

# See https://www.rapids.science/latest/setup/configuration/#time-segments
TIME_SEGMENTS: &time_segments
  TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
  FILE: "data/external/timesegments_periodic.csv"
  INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs



########################################################################################################################
#                                                 PHONE                                                                #
########################################################################################################################

# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration
PHONE_DATA_CONFIGURATION:
  SOURCE: 
    TYPE: DATABASE
    DATABASE_GROUP: *database_group
    DEVICE_ID_COLUMN: device_id # column name
  TIMEZONE: 
    TYPE: SINGLE
    VALUE: *timezone

# Sensors ------

# https://www.rapids.science/latest/features/phone-accelerometer/
PHONE_ACCELEROMETER:
  TABLE: accelerometer
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
      SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer
      SRC_LANGUAGE: "python"
    
    PANDA:
      COMPUTE: False
      VALID_SENSED_MINUTES: False
      FEATURES:
        exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
        nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
      SRC_FOLDER: "panda" # inside src/features/phone_accelerometer
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-activity-recognition/
PHONE_ACTIVITY_RECOGNITION:
  TABLE: 
    ANDROID: plugin_google_activity_recognition
    IOS: plugin_ios_activity_recognition
  EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same AR episode.
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
      ACTIVITY_CLASSES:
        STATIONARY: ["still", "tilting"]
        MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
        VEHICLE: ["in_vehicle"]
      SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-applications-crashes/
PHONE_APPLICATIONS_CRASHES:
  TABLE: applications_crashes
  APPLICATION_CATEGORIES:
    CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
    CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
    SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
  PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD

# See https://www.rapids.science/latest/features/phone-applications-foreground/
PHONE_APPLICATIONS_FOREGROUND:
  TABLE: applications_foreground
  APPLICATION_CATEGORIES:
    CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
    CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
    SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      SINGLE_CATEGORIES: ["all", "email"]
      MULTIPLE_CATEGORIES:
        social: ["socialnetworks", "socialmediatools"]
        entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
      SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
      EXCLUDED_CATEGORIES: []
      EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
      FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
      SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-applications-notifications/
PHONE_APPLICATIONS_NOTIFICATIONS:
  TABLE: applications_notifications
  APPLICATION_CATEGORIES:
    CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
    CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
    UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
    SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
  PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD

# See https://www.rapids.science/latest/features/phone-aware-log/
PHONE_AWARE_LOG:
  TABLE: aware_log
  PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD

# See https://www.rapids.science/latest/features/phone-battery/
PHONE_BATTERY:
  TABLE: battery
  EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
      SRC_FOLDER: "rapids" # inside src/features/phone_battery
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-bluetooth/
PHONE_BLUETOOTH:
  TABLE: bluetooth
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
      SRC_LANGUAGE: "r"
    DORYAB:
      COMPUTE: False
      FEATURES: 
        ALL: 
            DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
            SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
            SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
        OWN: 
            DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
            SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
            SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
        OTHERS:
            DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
            SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
            SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
      SRC_FOLDER: "doryab" # inside src/features/phone_bluetooth
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-calls/
PHONE_CALLS:
  TABLE: calls
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      CALL_TYPES: [missed, incoming, outgoing]
      FEATURES:
        missed:  [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
        incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
        outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
      SRC_LANGUAGE: "r"
      SRC_FOLDER: "rapids" # inside src/features/phone_calls

# See https://www.rapids.science/latest/features/phone-conversation/
PHONE_CONVERSATION:
  TABLE: 
    ANDROID: plugin_studentlife_audio_android
    IOS: plugin_studentlife_audio
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
    "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy",
    "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy",
    "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction",
    "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
    "unknownexpectedfraction","countconversation"]
      RECORDING_MINUTES: 1
      PAUSED_MINUTES : 3
      SRC_FOLDER: "rapids" # inside src/features/phone_conversation
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-data-yield/
PHONE_DATA_YIELD:
  SENSORS: []
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
      MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
      SRC_LANGUAGE: "r"
      SRC_FOLDER: "rapids" # inside src/features/phone_data_yield

# See https://www.rapids.science/latest/features/phone-keyboard/
PHONE_KEYBOARD:
  TABLE: keyboard
  PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD

# See https://www.rapids.science/latest/features/phone-light/
PHONE_LIGHT:
  TABLE: light
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
      SRC_FOLDER: "rapids" # inside src/features/phone_light
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-locations/
PHONE_LOCATIONS:
  TABLE: locations
  LOCATIONS_TO_USE: ALL_RESAMPLED # ALL, GPS, ALL_RESAMPLED, OR FUSED_RESAMPLED
  FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold
  FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row
  HOME_INFERENCE:
    DBSCAN_EPS: 10 # meters
    DBSCAN_MINSAMPLES: 5
    THRESHOLD_STATIC : 1 # km/h
    CLUSTERING_ALGORITHM: DBSCAN #DBSCAN,OPTICS
  
  PROVIDERS:
    DORYAB:
      COMPUTE: False
      FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome"]
      ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius
      DBSCAN_EPS: 10 # meters
      DBSCAN_MINSAMPLES: 5
      THRESHOLD_STATIC : 1 # km/h
      MAXIMUM_ROW_GAP: 300
      MAXIMUM_ROW_DURATION: 60
      MINUTES_DATA_USED: False
      CLUSTER_ON: PARTICIPANT_DATASET # PARTICIPANT_DATASET,TIME_SEGMENT
      CLUSTERING_ALGORITHM: DBSCAN #DBSCAN,OPTICS
      RADIUS_FOR_HOME: 100  
      SRC_FOLDER: "doryab" # inside src/features/phone_locations
      SRC_LANGUAGE: "python"

    BARNETT:
      COMPUTE: False
      FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
      ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius
      TIMEZONE: *timezone
      MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
      SRC_FOLDER: "barnett" # inside src/features/phone_locations
      SRC_LANGUAGE: "r"

# See https://www.rapids.science/latest/features/phone-messages/
PHONE_MESSAGES:
  TABLE: messages
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      MESSAGES_TYPES : [received, sent]
      FEATURES: 
        received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
        sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
      SRC_LANGUAGE: "r"
      SRC_FOLDER: "rapids" # inside src/features/phone_messages

# See https://www.rapids.science/latest/features/phone-screen/
PHONE_SCREEN:
  TABLE: screen
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      REFERENCE_HOUR_FIRST_USE: 0
      IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
      IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable
      FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
      EPISODE_TYPES: ["unlock"]
      SRC_FOLDER: "rapids" # inside src/features/phone_screen
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/phone-wifi-connected/
PHONE_WIFI_CONNECTED:
  TABLE: "sensor_wifi"
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected
      SRC_LANGUAGE: "r"

# See https://www.rapids.science/latest/features/phone-wifi-visible/
PHONE_WIFI_VISIBLE:
  TABLE: "wifi"
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible
      SRC_LANGUAGE: "r"



########################################################################################################################
#                                                 FITBIT                                                               #
########################################################################################################################

# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration
FITBIT_DATA_CONFIGURATION:
  SOURCE: 
    TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly)
    COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT
    DATABASE_GROUP: *database_group
    DEVICE_ID_COLUMN: device_id # column name
  TIMEZONE: 
    TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE 
    VALUE: *timezone

# Sensors ------

# See https://www.rapids.science/latest/features/fitbit-data-yield/
FITBIT_DATA_YIELD:
  SENSOR: FITBIT_HEARTRATE_INTRADAY
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
      MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 #  0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
      SRC_LANGUAGE: "r"
      SRC_FOLDER: "rapids" # inside src/features/fitbit_data_yield


# See https://www.rapids.science/latest/features/fitbit-heartrate-summary/
FITBIT_HEARTRATE_SUMMARY:
  TABLE: heartrate_summary
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      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"]
      SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/
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"

# See https://www.rapids.science/latest/features/fitbit-sleep-summary/
FITBIT_SLEEP_SUMMARY:
  TABLE: sleep_summary
  SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
      SLEEP_TYPES: ["main", "nap", "all"]
      SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/fitbit-steps-summary/
FITBIT_STEPS_SUMMARY:
  TABLE: steps_summary
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
      SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary
      SRC_LANGUAGE: "python"

# See https://www.rapids.science/latest/features/fitbit-steps-intraday/
FITBIT_STEPS_INTRADAY:
  TABLE: steps_intraday
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES:
        STEPS: ["sum", "max", "min", "avg", "std"]
        SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
        ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
      THRESHOLD_ACTIVE_BOUT: 10 # steps
      INCLUDE_ZERO_STEP_ROWS: False
      SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday
      SRC_LANGUAGE: "python"

# FITBIT_CALORIES:
#   TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES
#   TABLE:
#     JSON: fitbit_calories
#     CSV:
#       SUMMARY: calories_summary
#       INTRADAY: calories_intraday
#   PROVIDERS:
#     RAPIDS:
#       COMPUTE: False
#       FEATURES: []


########################################################################################################################
#                                                 EMPATICA                                                             #
########################################################################################################################

EMPATICA_DATA_CONFIGURATION:
  SOURCE: 
    TYPE: ZIP_FILE
    FOLDER: data/external/empatica
  TIMEZONE: 
    TYPE: SINGLE # Empatica devices don't support time zones so we read this data in the timezone indicated by VALUE 
    VALUE: *timezone

# Sensors ------

EMPATICA_ACCELEROMETER:
  TABLE: ACC
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
      SRC_FOLDER: "dbdp" # inside src/features/empatica_accelerometer
      SRC_LANGUAGE: "python"

EMPATICA_HEARTRATE:
  TABLE: HR
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
      SRC_FOLDER: "dbdp" # inside src/features/empatica_heartrate
      SRC_LANGUAGE: "python"

EMPATICA_TEMPERATURE:
  TABLE: TEMP
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
      SRC_FOLDER: "dbdp" # inside src/features/empatica_heartrate
      SRC_LANGUAGE: "python"

EMPATICA_ELECTRODERMAL_ACTIVITY:
  TABLE: EDA
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
      SRC_FOLDER: "dbdp" # inside src/features/empatica_electrodermal_activity
      SRC_LANGUAGE: "python"

EMPATICA_BLOOD_VOLUME_PULSE:
  TABLE: BVP
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
      SRC_FOLDER: "dbdp" # inside src/features/empatica_blood_volume_pulse
      SRC_LANGUAGE: "python"

EMPATICA_INTER_BEAT_INTERVAL:
  TABLE: IBI
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
      SRC_FOLDER: "dbdp" # inside src/features/inter_beat_interval
      SRC_LANGUAGE: "python"

EMPATICA_TAGS:
  TABLE: TAGS
  PROVIDERS:  # None implemented yet


########################################################################################################################
#                                                 PLOTS                                                                #
########################################################################################################################

# Data quality
HISTOGRAM_PHONE_DATA_YIELD:
  PLOT: False

HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
  PLOT: False

HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT:
  PLOT: False

HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
  PLOT: False
  SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE]

# Features
HEATMAP_FEATURE_CORRELATION_MATRIX:
  PLOT: False
  MIN_ROWS_RATIO: 0.5
  CORR_THRESHOLD: 0.1
  CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}