diff --git a/tests/settings/event_config.yaml b/tests/settings/event_config.yaml new file mode 100644 index 00000000..96406fa2 --- /dev/null +++ b/tests/settings/event_config.yaml @@ -0,0 +1,543 @@ +######################################################################################################################## +# GLOBAL CONFIGURATION # +######################################################################################################################## + +# See https://www.rapids.science/latest/setup/configuration/#participant-files +PIDS: [test01, test03, test04] + +# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files +CREATE_PARTICIPANT_FILES: + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format + PHONE_SECTION: + ADD: True + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: True + DEVICE_ID_COLUMN: fitbit_id # column name + IGNORED_DEVICE_IDS: [] + EMPATICA_SECTION: + ADD: True + DEVICE_ID_COLUMN: empatica_id # column name + 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/timesegments_event.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs + +# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study +TIMEZONE: + TYPE: SINGLE + SINGLE: + TZCODE: America/New_York + MULTIPLE: + TZCODES_FILE: data/external/multiple_timezones_example.csv + IF_MISSING_TZCODE: STOP + DEFAULT_TZCODE: America/New_York + 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_csv + + # AVAILABLE: + aware_mysql: + DATABASE_GROUP: MY_GROUP + + aware_csv: + FOLDER: tests/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: 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_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_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_SCRIPT: src/features/phone_applications_foreground/rapids/main.py + +# See https://www.rapids.science/latest/features/phone-applications-notifications/ +PHONE_APPLICATIONS_NOTIFICATIONS: + CONTAINER: 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-battery/ +PHONE_BATTERY: + CONTAINER: phone_battery_raw.csv + 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: phone_bluetooth_raw.csv + PROVIDERS: + RAPIDS: + COMPUTE: FALSE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.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_SCRIPT: src/features/phone_bluetooth/doryab/main.py + +# See https://www.rapids.science/latest/features/phone-calls/ +PHONE_CALLS: + CONTAINER: phone_calls_raw.csv + 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_SCRIPT: src/features/phone_calls/rapids/main.R + +# See https://www.rapids.science/latest/features/phone-conversation/ +PHONE_CONVERSATION: + CONTAINER: + ANDROID: phone_conversation_raw.csv + IOS: phone_conversation_raw.csv + 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: [] + 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/phone_data_yield/rapids/main.R + +# See https://www.rapids.science/latest/features/phone-keyboard/ +PHONE_KEYBOARD: + CONTAINER: 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: + CONTAINER: phone_light_raw.csv + PROVIDERS: + RAPIDS: + COMPUTE: FALSE + 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 + 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: 100 # 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_SCRIPT: src/features/phone_locations/doryab/main.py + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 100 # 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 + 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: phone_messages_raw.csv + PROVIDERS: + RAPIDS: + COMPUTE: FALSE + 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: 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_SCRIPT: src/features/phone_screen/rapids/main.py + +# See https://www.rapids.science/latest/features/phone-wifi-connected/ +PHONE_WIFI_CONNECTED: + CONTAINER: phone_wifi_connected_raw.csv + 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: phone_wifi_visible_raw.csv + PROVIDERS: + RAPIDS: + COMPUTE: FALSE + 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_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + + fitbitparsed_mysql: + DATABASE_GROUP: MY_GROUP + SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + + fitbitjson_csv: + FOLDER: data/external/fitbit_csv + SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + + fitbitparsed_csv: + FOLDER: data/external/fitbit_csv + SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + +# 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_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: ["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_COMBINING_ALL: True + LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration] + RATIOS_TYPE: [count, duration] + RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES] + ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap] + SLEEP_LEVELS: + CLASSIC: [awake, restless, asleep] + STAGES: [wake, deep, light, rem] + UNIFIED: [awake, asleep] + SLEEP_TYPES: [main, nap] + INCLUDE_SLEEP_LATER_THAN: 0 # a number ranged from 0 (midnight) to 1439 (23:59) + REFERENCE_TIME: MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT" + SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py + + PRICE: + COMPUTE: False + FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain] + SLEEP_LEVELS: + CLASSIC: [awake, restless, asleep] + STAGES: [wake, deep, light, rem] + UNIFIED: [awake, asleep] + DAY_TYPES: [WEEKEND, WEEK, ALL] + GROUP_EPISODES_WITHIN: # by default: today's 6pm to tomorrow's noon + START_TIME: 1080 # number of minutes after the midnight (18:00) 18*60 + LENGTH: 1080 # in minutes (18 hours) 18*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 + 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_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 + +# 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 + +# 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 + +# 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 + +# 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 + +# 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 + +# 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: [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 ------ + +# 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"} +