353 lines
16 KiB
YAML
353 lines
16 KiB
YAML
# Participants to include in the analysis
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# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically
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PIDS: [test03, test04,test05, test06]
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# Global var with common day segments
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DAY_SEGMENTS: &day_segments
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TYPE: FREQUENCY # FREQUENCY, PERIODIC, EVENT
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FILE: "data/external/daysegments_frequency.csv"
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INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider day segments back enough in the past as to include the first day of data
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# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time.
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TIMEZONE: &timezone
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America/New_York
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DATABASE_GROUP: &database_group
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MY_GROUP
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# config section for the script that creates participant files automatically
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PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files
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PHONE_SECTION:
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ADD: FALSE
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PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE
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PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional)
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IGNORED_DEVICE_IDS: []
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FITBIT_SECTION:
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ADD: FALSE
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SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored
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PARSED_FROM: CSV_FILE
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PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional)
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SENSOR_DATA:
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PHONE:
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SOURCE:
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TYPE: DATABASE # Phone only supports DATABASE for now
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DATABASE_GROUP: *database_group
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DEVICE_ID_COLUMN: device_id # column name
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TIMEZONE:
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TYPE: SINGLE # SINGLE or MULTIPLE
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VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
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FITBIT:
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SOURCE:
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TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path)
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DATABASE_GROUP: *database_group
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DEVICE_ID_COLUMN: device_id # column name
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TIMEZONE:
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TYPE: SINGLE # Fitbit only supports SINGLE timezones
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VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones)
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PHONE_VALID_SENSED_BINS:
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COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features
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BIN_SIZE: &bin_size 5 # (in minutes)
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# Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS.
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# If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory.
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# You can choose any of the keys shown below, just make sure its TABLE exists in your database!
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# PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT,
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# PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION
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PHONE_SENSORS: []
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PHONE_VALID_SENSED_DAYS:
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COMPUTE: False
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MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY
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MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins)
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# Communication SMS features config, TYPES and FEATURES keys need to match
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PHONE_MESSAGES:
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TABLE: messages
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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MESSAGES_TYPES : [received, sent]
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FEATURES:
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received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids" # inside src/features/phone_messages
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# Communication call features config, TYPES and FEATURES keys need to match
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PHONE_CALLS:
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TABLE: calls
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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CALL_TYPES: [missed, incoming, outgoing]
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FEATURES:
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missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
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incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
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SRC_LANGUAGE: "r"
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SRC_FOLDER: "rapids" # inside src/features/phone_calls
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PHONE_LOCATIONS:
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TABLE: locations
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LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED
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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
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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
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PROVIDERS:
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DORYAB:
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COMPUTE: False
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FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"]
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DBSCAN_EPS: 10 # meters
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DBSCAN_MINSAMPLES: 5
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THRESHOLD_STATIC : 1 # km/h
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MAXIMUM_GAP_ALLOWED: 300
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MINUTES_DATA_USED: False
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SAMPLING_FREQUENCY: 0
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SRC_FOLDER: "doryab" # inside src/features/phone_locations
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SRC_LANGUAGE: "python"
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BARNETT:
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COMPUTE: False
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FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
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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
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TIMEZONE: *timezone
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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
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SRC_FOLDER: "barnett" # inside src/features/phone_locations
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SRC_LANGUAGE: "r"
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PHONE_BLUETOOTH:
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TABLE: bluetooth
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
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SRC_LANGUAGE: "r"
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PHONE_ACTIVITY_RECOGNITION:
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TABLE:
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ANDROID: plugin_google_activity_recognition
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IOS: plugin_ios_activity_recognition
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EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
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ACTIVITY_CLASSES:
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STATIONARY: ["still", "tilting"]
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MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
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VEHICLE: ["in_vehicle"]
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SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition
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SRC_LANGUAGE: "python"
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PHONE_BATTERY:
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TABLE: battery
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EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode.
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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SRC_FOLDER: "rapids" # inside src/features/phone_battery
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SRC_LANGUAGE: "python"
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PHONE_SCREEN:
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TABLE: screen
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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REFERENCE_HOUR_FIRST_USE: 0
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IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
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IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable
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FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
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EPISODE_TYPES: ["unlock"]
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SRC_FOLDER: "rapids" # inside src/features/phone_screen
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SRC_LANGUAGE: "python"
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PHONE_LIGHT:
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TABLE: light
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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SRC_FOLDER: "rapids" # inside src/features/phone_light
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SRC_LANGUAGE: "python"
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PHONE_ACCELEROMETER:
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TABLE: accelerometer
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer
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SRC_LANGUAGE: "python"
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PANDA:
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COMPUTE: False
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VALID_SENSED_MINUTES: False
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FEATURES:
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exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
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SRC_FOLDER: "panda" # inside src/features/phone_accelerometer
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SRC_LANGUAGE: "python"
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PHONE_APPLICATIONS_FOREGROUND:
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TABLE: applications_foreground
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APPLICATION_CATEGORIES:
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CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
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CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
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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
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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
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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SINGLE_CATEGORIES: ["all", "email"]
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MULTIPLE_CATEGORIES:
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social: ["socialnetworks", "socialmediatools"]
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entertainment: ["entertainment", "gamingstrategy"]
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SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps
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EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"]
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EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
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FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
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SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground
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SRC_LANGUAGE: "python"
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PHONE_WIFI_VISIBLE:
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TABLE: "wifi"
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible
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SRC_LANGUAGE: "r"
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PHONE_WIFI_CONNECTED:
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TABLE: "sensor_wifi"
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected
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SRC_LANGUAGE: "r"
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PHONE_CONVERSATION:
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TABLE:
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ANDROID: plugin_studentlife_audio_android
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IOS: plugin_studentlife_audio
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
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"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
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"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
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"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
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"unknownexpectedfraction","countconversation"]
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RECORDING_MINUTES: 1
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PAUSED_MINUTES : 3
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SRC_FOLDER: "rapids" # inside src/features/phone_conversation
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SRC_LANGUAGE: "python"
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############## FITBIT ##########################################################
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################################################################################
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FITBIT_HEARTRATE:
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TABLE_FORMAT: JSON # JSON or CSV
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TABLE:
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JSON: fitbit_heartrate
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CSV:
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SUMMARY: heartrate_summary.csv
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INTRADAY: heartrate_intraday.csv
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"]
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INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
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FITBIT_STEPS:
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TABLE_FORMAT: JSON # JSON or CSV
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TABLE:
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JSON: fitbit_steps
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CSV:
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SUMMARY: steps_summary.csv
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INTRADAY: steps_intraday.csv
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EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation
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EXCLUDE: False
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TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section)
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FIXED:
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START: "23:00"
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END: "07:00"
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES:
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ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
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SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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THRESHOLD_ACTIVE_BOUT: 10 # steps
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INCLUDE_ZERO_STEP_ROWS: False
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FITBIT_SLEEP:
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TABLE_FORMAT: JSON # JSON or CSV
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TABLE:
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JSON: fitbit_sleep
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CSV:
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SUMMARY: sleep_summary.csv
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INTRADAY: sleep_intraday.csv
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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SLEEP_TYPES: ["main", "nap", "all"]
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SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
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FITBIT_CALORIES:
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TABLE_FORMAT: JSON # JSON or CSV
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TABLE:
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JSON: fitbit_calories
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CSV:
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SUMMARY: calories_summary.csv
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INTRADAY: calories_intraday.csv
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES: []
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### Visualizations #############################################################
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################################################################################
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HEATMAP_FEATURES_CORRELATIONS:
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PLOT: False
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MIN_ROWS_RATIO: 0.5
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep]
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CORR_THRESHOLD: 0.1
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CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}
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HISTOGRAM_VALID_SENSED_HOURS:
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PLOT: False
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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HEATMAP_DAYS_BY_SENSORS:
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PLOT: False
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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EXPECTED_NUM_OF_DAYS: -1
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DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio]
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HEATMAP_SENSED_BINS:
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PLOT: False
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BIN_SIZE: *bin_size
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OVERALL_COMPLIANCE_HEATMAP:
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PLOT: False
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ONLY_SHOW_VALID_DAYS: False
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EXPECTED_NUM_OF_DAYS: -1
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BIN_SIZE: *bin_size
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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