rapids/config.yaml

########################################################################################################################
#                                              GLOBAL CONFIGURATION                                                    #
########################################################################################################################

# See https://www.rapids.science/latest/setup/configuration/#participant-files
PIDS: ['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']

# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
CREATE_PARTICIPANT_FILES:
  USERNAMES_CSV: "data/external/main_study_usernames.csv"
  CSV_FILE_PATH: "data/external/main_study_participants.csv" # see docs for required format
  PHONE_SECTION:
    ADD: True
    IGNORED_DEVICE_IDS: []
  FITBIT_SECTION:
    ADD: False
    IGNORED_DEVICE_IDS: []
  EMPATICA_SECTION:
    ADD: True
    IGNORED_DEVICE_IDS: []

# See https://www.rapids.science/latest/setup/configuration/#time-segments
TIME_SEGMENTS: &time_segments
  TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
  FILE: "data/external/straw_events.csv"
  INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
  TAILORED_EVENTS: # Only relevant if TYPE=EVENT
    COMPUTE: True
    PARAMETER_ONE: "something"

# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
TIMEZONE: 
    TYPE: MULTIPLE
    SINGLE:
      TZCODE: Europe/Ljubljana
    MULTIPLE:
      TZ_FILE: data/external/timezone.csv
      TZCODES_FILE: data/external/multiple_timezones.csv
      IF_MISSING_TZCODE: USE_DEFAULT
      DEFAULT_TZCODE: Europe/Ljubljana
      FITBIT: 
        ALLOW_MULTIPLE_TZ_PER_DEVICE: False
        INFER_FROM_SMARTPHONE_TZ: False

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

# See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration
PHONE_DATA_STREAMS:
  USE: aware_postgresql
  
  # AVAILABLE:
  aware_mysql: 
    DATABASE_GROUP: MY_GROUP

  aware_postgresql:
    DATABASE_GROUP: PSQL_STRAW
  
  aware_csv:
    FOLDER: data/external/aware_csv
  
  aware_influxdb: 
    DATABASE_GROUP: MY_GROUP

# Sensors ------

# https://www.rapids.science/latest/features/phone-accelerometer/
PHONE_ACCELEROMETER:
  CONTAINER: accelerometer
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
      SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py
    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_SCRIPT: src/features/phone_accelerometer/panda/main.py

# See https://www.rapids.science/latest/features/phone-activity-recognition/
PHONE_ACTIVITY_RECOGNITION:
  CONTAINER: 
    ANDROID: google_ar
    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: True
      FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
      ACTIVITY_CLASSES:
        STATIONARY: ["still", "tilting"]
        MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
        VEHICLE: ["in_vehicle"]
      SRC_SCRIPT: src/features/phone_activity_recognition/rapids/main.py

# See https://www.rapids.science/latest/features/phone-applications-crashes/
PHONE_APPLICATIONS_CRASHES:
  CONTAINER: 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:
  CONTAINER: applications
  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"
    PACKAGE_NAMES_HASHED: True
    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: True
      INCLUDE_EPISODE_FEATURES: True
      SINGLE_CATEGORIES: ["all", "email"]
      MULTIPLE_CATEGORIES:
        social: ["socialnetworks", "socialmediatools"]
        entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
      CUSTOM_CATEGORIES:
        social_media: ["com.google.android.youtube", "com.snapchat.android", "com.instagram.android", "com.zhiliaoapp.musically", "com.facebook.katana"]
        dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"]
      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"] # TODO list system apps?
      FEATURES: 
        APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
        APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
      IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
      IGNORE_EPISODES_LONGER_THAN: 300 # in minutes, set to 0 to disable
      SRC_SCRIPT: src/features/phone_applications_foreground/rapids/main.py

# See https://www.rapids.science/latest/features/phone-applications-notifications/
PHONE_APPLICATIONS_NOTIFICATIONS:
  CONTAINER: 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-battery/
PHONE_BATTERY:
  CONTAINER: 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: True
      FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
      SRC_SCRIPT: src/features/phone_battery/rapids/main.py

# See https://www.rapids.science/latest/features/phone-bluetooth/
PHONE_BLUETOOTH:
  CONTAINER: bluetooth
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R

    DORYAB:
      COMPUTE: True
      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_SCRIPT: src/features/phone_bluetooth/doryab/main.py

# See https://www.rapids.science/latest/features/phone-calls/
PHONE_CALLS:
  CONTAINER: call
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      FEATURES_TYPE: EPISODES # EVENTS or EPISODES
      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_SCRIPT: src/features/phone_calls/rapids/main.R

# See https://www.rapids.science/latest/features/phone-conversation/
PHONE_CONVERSATION: # TODO Adapt for speech
  CONTAINER: 
    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_SCRIPT: src/features/phone_conversation/rapids/main.py

# See https://www.rapids.science/latest/features/phone-data-yield/
PHONE_DATA_YIELD:
  SENSORS: [#PHONE_ACCELEROMETER,
            PHONE_ACTIVITY_RECOGNITION,
            PHONE_APPLICATIONS_FOREGROUND,
            PHONE_APPLICATIONS_NOTIFICATIONS,
            PHONE_BATTERY,
            PHONE_BLUETOOTH,
            PHONE_CALLS,
            PHONE_LIGHT,
            PHONE_LOCATIONS,
            PHONE_MESSAGES,
            PHONE_SCREEN,
            PHONE_WIFI_VISIBLE]
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      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_SCRIPT: src/features/phone_data_yield/rapids/main.R

PHONE_ESM:
  CONTAINER: esm
  PROVIDERS:
    STRAW:
      COMPUTE: True
      SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
      FEATURES: [mean]
      SRC_SCRIPT: src/features/phone_esm/straw/main.py

# See https://www.rapids.science/latest/features/phone-keyboard/
PHONE_KEYBOARD:
  CONTAINER: keyboard
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["sessioncount","averageinterkeydelay","averagesessionlength","changeintextlengthlessthanminusone","changeintextlengthequaltominusone","changeintextlengthequaltoone","changeintextlengthmorethanone","maxtextlength","lastmessagelength","totalkeyboardtouches"]
      SRC_SCRIPT: src/features/phone_keyboard/rapids/main.py

# See https://www.rapids.science/latest/features/phone-light/
PHONE_LIGHT:
  CONTAINER: light_sensor
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
      SRC_SCRIPT: src/features/phone_light/rapids/main.py

# See https://www.rapids.science/latest/features/phone-locations/
PHONE_LOCATIONS:
  CONTAINER: 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
  ACCURACY_LIMIT: 100 # meters, drops location coordinates with an accuracy equal or higher than this. This number means there's a 68% probability the true location is within this radius

  PROVIDERS:
    DORYAB:
      COMPUTE: True
      FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"]
      DBSCAN_EPS: 100 # meters
      DBSCAN_MINSAMPLES: 5
      THRESHOLD_STATIC : 1 # km/h
      MAXIMUM_ROW_GAP: 300 # seconds
      MINUTES_DATA_USED: False
      CLUSTER_ON: PARTICIPANT_DATASET # PARTICIPANT_DATASET, TIME_SEGMENT, TIME_SEGMENT_INSTANCE
      INFER_HOME_LOCATION_STRATEGY: DORYAB_STRATEGY # DORYAB_STRATEGY, SUN_LI_VEGA_STRATEGY
      MINIMUM_DAYS_TO_DETECT_HOME_CHANGES: 3
      CLUSTERING_ALGORITHM: DBSCAN # DBSCAN, OPTICS
      RADIUS_FOR_HOME: 100
      SRC_SCRIPT: src/features/phone_locations/doryab/main.py

    BARNETT:
      COMPUTE: True
      FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
      IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
      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_SCRIPT: src/features/phone_locations/barnett/main.R

# See https://www.rapids.science/latest/features/phone-log/
PHONE_LOG:
  CONTAINER: 
    ANDROID: aware_log
    IOS: ios_aware_log
  PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD

# See https://www.rapids.science/latest/features/phone-messages/
PHONE_MESSAGES:
  CONTAINER: sms
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      MESSAGES_TYPES : [received, sent]
      FEATURES: 
        received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
        sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
      SRC_SCRIPT: src/features/phone_messages/rapids/main.R

# See https://www.rapids.science/latest/features/phone-screen/
PHONE_SCREEN:
  CONTAINER: screen
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      REFERENCE_HOUR_FIRST_USE: 0
      IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
      IGNORE_EPISODES_LONGER_THAN: 360 # 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_SCRIPT: src/features/phone_screen/rapids/main.py

# See https://www.rapids.science/latest/features/phone-wifi-connected/
PHONE_WIFI_CONNECTED:
  CONTAINER: sensor_wifi
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_SCRIPT: src/features/phone_wifi_connected/rapids/main.R

# See https://www.rapids.science/latest/features/phone-wifi-visible/
PHONE_WIFI_VISIBLE:
  CONTAINER: wifi
  PROVIDERS:
    RAPIDS:
      COMPUTE: True
      FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
      SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R



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

# See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration
FITBIT_DATA_STREAMS:
  USE: fitbitjson_mysql
  
  # AVAILABLE:
  fitbitjson_mysql: 
    DATABASE_GROUP: MY_GROUP
    SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00).
  
  fitbitparsed_mysql: 
    DATABASE_GROUP: MY_GROUP
    SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00).
  
  fitbitjson_csv: 
    FOLDER: data/external/fitbit_csv
    SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00).

  fitbitparsed_csv: 
    FOLDER: data/external/fitbit_csv
    SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after midnight. By default, 660 (11:00).

# Sensors ------

# See https://www.rapids.science/latest/features/fitbit-calories-intraday/
FITBIT_CALORIES_INTRADAY:
  CONTAINER: fitbit_data
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      EPISODE_TYPE: [sedentary, lightlyactive, fairlyactive, veryactive, mvpa, lowmet, highmet]
      EPISODE_TIME_THRESHOLD: 5 # minutes
      EPISODE_MET_THRESHOLD: 3
      EPISODE_MVPA_CATEGORIES: [fairlyactive, veryactive]
      EPISODE_REFERENCE_TIME: MIDNIGHT # or START_OF_THE_SEGMENT
      FEATURES: [count, sumduration, avgduration, minduration, maxduration, stdduration, starttimefirst, endtimefirst, starttimelast, endtimelast, starttimelongest, endtimelongest, summet, avgmet, maxmet, minmet, stdmet, sumcalories, avgcalories, maxcalories, mincalories, stdcalories]
      SRC_SCRIPT: src/features/fitbit_calories_intraday/rapids/main.R

# 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_SCRIPT: src/features/fitbit_data_yield/rapids/main.R


# See https://www.rapids.science/latest/features/fitbit-heartrate-summary/
FITBIT_HEARTRATE_SUMMARY:
  CONTAINER: 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_SCRIPT: src/features/fitbit_heartrate_summary/rapids/main.py

# See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/
FITBIT_HEARTRATE_INTRADAY:
  CONTAINER: heartrate_intraday
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
      SRC_SCRIPT: src/features/fitbit_heartrate_intraday/rapids/main.py

# See https://www.rapids.science/latest/features/fitbit-sleep-summary/
FITBIT_SLEEP_SUMMARY:
  CONTAINER: sleep_summary
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
      SLEEP_TYPES: ["main", "nap", "all"]
      SRC_SCRIPT: src/features/fitbit_sleep_summary/rapids/main.py

# See https://www.rapids.science/latest/features/fitbit-sleep-intraday/
FITBIT_SLEEP_INTRADAY:
  CONTAINER: sleep_intraday
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES:
        LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
        RATIOS_TYPE: [count, duration]
        RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
      SLEEP_LEVELS:
        INCLUDE_ALL_GROUPS: True
        CLASSIC: [awake, restless, asleep]
        STAGES: [wake, deep, light, rem]
        UNIFIED: [awake, asleep]
      SLEEP_TYPES: [main, nap, all]
      SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
    PRICE:
      COMPUTE: False
      FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
      SLEEP_LEVELS:
        INCLUDE_ALL_GROUPS: True
        CLASSIC: [awake, restless, asleep]
        STAGES: [wake, deep, light, rem]
        UNIFIED: [awake, asleep]
      DAY_TYPES: [WEEKEND, WEEK, ALL]
      LAST_NIGHT_END: 660 # number of minutes after midnight (11:00) 11*60
      SRC_SCRIPT: src/features/fitbit_sleep_intraday/price/main.py

# See https://www.rapids.science/latest/features/fitbit-steps-summary/
FITBIT_STEPS_SUMMARY:
  CONTAINER: steps_summary
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"]
      SRC_SCRIPT: src/features/fitbit_steps_summary/rapids/main.py

# See https://www.rapids.science/latest/features/fitbit-steps-intraday/
FITBIT_STEPS_INTRADAY:
  CONTAINER: steps_intraday
  EXCLUDE_SLEEP: # you can exclude step data that was logged during sleep periods
    TIME_BASED:
      EXCLUDE: False
      START_TIME: "23:00"
      END_TIME: "07:00"
    FITBIT_BASED:
      EXCLUDE: False
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      FEATURES:
        STEPS: ["sum", "max", "min", "avg", "std", "firststeptime", "laststeptime"]
        SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
        ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
      REFERENCE_HOUR: 0
      THRESHOLD_ACTIVE_BOUT: 10 # steps
      INCLUDE_ZERO_STEP_ROWS: False
      SRC_SCRIPT: src/features/fitbit_steps_intraday/rapids/main.py


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

EMPATICA_DATA_STREAMS:
  USE: empatica_zip
  
  # AVAILABLE:
  empatica_zip: 
    FOLDER: data/external/empatica

# Sensors ------

# See https://www.rapids.science/latest/features/empatica-accelerometer/
EMPATICA_ACCELEROMETER:
  CONTAINER: ACC
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
      SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
    CR:
      COMPUTE: True
      FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
      WINDOWS:
        COMPUTE: True
        WINDOW_LENGTH: 15 # specify window length in seconds
        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
      SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py


# See https://www.rapids.science/latest/features/empatica-heartrate/
EMPATICA_HEARTRATE:
  CONTAINER: HR
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"]
      SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py

# See https://www.rapids.science/latest/features/empatica-temperature/
EMPATICA_TEMPERATURE:
  CONTAINER: TEMP
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
      SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
    CR:
      COMPUTE: True
      FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", 
                  "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
      WINDOWS:
        COMPUTE: True
        WINDOW_LENGTH: 300 # specify window length in seconds
        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
      SRC_SCRIPT: src/features/empatica_temperature/cr/main.py

# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
EMPATICA_ELECTRODERMAL_ACTIVITY:
  CONTAINER: EDA
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
      SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
    CR:
      COMPUTE: True
      FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 
                  'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 
                  'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
                  'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 
                  'significantDecrease']
      WINDOWS:
        COMPUTE: True
        WINDOW_LENGTH: 60 # specify window length in seconds
        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
        IMPUTE_NANS: True
      SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py

# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
EMPATICA_BLOOD_VOLUME_PULSE:
  CONTAINER: BVP
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
      SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
    CR:
      COMPUTE: False
      FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
                  'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
      WINDOWS:
        COMPUTE: True
        WINDOW_LENGTH: 300 # specify window length in seconds
        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
      SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py

# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
EMPATICA_INTER_BEAT_INTERVAL:
  CONTAINER: IBI
  PROVIDERS:
    DBDP:
      COMPUTE: False
      FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
      SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
    CR:
      COMPUTE: True
      FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
                  'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features            
      PATCH_WITH_BVP: True
      WINDOWS:
        COMPUTE: True
        WINDOW_LENGTH: 300 # specify window length in seconds
        SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
      SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py

# See https://www.rapids.science/latest/features/empatica-tags/
EMPATICA_TAGS:
  CONTAINER: TAGS
  PROVIDERS:  # None implemented yet


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

# Data quality ------

# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield
HISTOGRAM_PHONE_DATA_YIELD:
  PLOT: False

# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield
HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
  PLOT: False
  TIME: RELATIVE_TIME # ABSOLUTE_TIME or RELATIVE_TIME

# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors
HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT:
  PLOT: False

# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count
HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
  PLOT: False
  SENSORS: []

# Features ------

# See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix
HEATMAP_FEATURE_CORRELATION_MATRIX:
  PLOT: False
  MIN_ROWS_RATIO: 0.5
  CORR_THRESHOLD: 0.1
  CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}


########################################################################################################################
#                                                    Data Cleaning                                                     #
########################################################################################################################

ALL_CLEANING_INDIVIDUAL:
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      IMPUTE_SELECTED_EVENT_FEATURES:
        COMPUTE: False
        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
      COLS_VAR_THRESHOLD: True
      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
      DROP_HIGHLY_CORRELATED_FEATURES:
        COMPUTE: True
        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
        CORR_THRESHOLD: 0.95
      SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
    STRAW:
      COMPUTE: True
      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
      COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
      COLS_VAR_THRESHOLD: True
      DROP_HIGHLY_CORRELATED_FEATURES:
        COMPUTE: True
        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
        CORR_THRESHOLD: 0.95
      STANDARDIZATION: True
      SRC_SCRIPT: src/features/all_cleaning_individual/straw/main.py

ALL_CLEANING_OVERALL:
  PROVIDERS:
    RAPIDS:
      COMPUTE: False
      IMPUTE_SELECTED_EVENT_FEATURES:
        COMPUTE: False
        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
      COLS_VAR_THRESHOLD: True
      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
      DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
      DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
      DROP_HIGHLY_CORRELATED_FEATURES:
        COMPUTE: True
        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
        CORR_THRESHOLD: 0.95
      SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
    STRAW:
      COMPUTE: True
      PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
      PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
      EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
      ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
      COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
      COLS_VAR_THRESHOLD: True
      DROP_HIGHLY_CORRELATED_FEATURES:
        COMPUTE: True
        MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
        CORR_THRESHOLD: 0.95
      STANDARDIZATION: True
      SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py


########################################################################################################################
#                                                      Baseline                                                        #
########################################################################################################################

PARAMS_FOR_ANALYSIS:
  BASELINE:
    COMPUTE: True
    FOLDER: data/external/baseline
    CONTAINER: [results-survey637813_final.csv,  # Slovenia
                results-survey358134_final.csv,  # Belgium 1
                results-survey413767_final.csv  # Belgium 2
    ]
    QUESTION_LIST: survey637813+question_text.csv
    FEATURES: [age, gender, startlanguage, limesurvey_demand, limesurvey_control, limesurvey_demand_control_ratio, limesurvey_demand_control_ratio_quartile]
    CATEGORICAL_FEATURES: [gender]

  TARGET:
    COMPUTE: True
    LABEL: PANAS_negative_affect_mean
    ALL_LABELS: [PANAS_positive_affect_mean, PANAS_negative_affect_mean, "JCQ_job_demand_mean", "JCQ_job_control_mean", "JCQ_supervisor_support_mean", "JCQ_coworker_support_mean"]