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fb8868b77d
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8721b944ca
88
config.yaml
88
config.yaml
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@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
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INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
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INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
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TAILORED_EVENTS: # Only relevant if TYPE=EVENT
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TAILORED_EVENTS: # Only relevant if TYPE=EVENT
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COMPUTE: True
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COMPUTE: True
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SEGMENTING_METHOD: "10_before" # 30_before, 90_before, stress_event
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SEGMENTING_METHOD: "stress_event" # 30_before, 90_before, stress_event
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INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
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INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
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IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
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IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
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@ -91,7 +91,7 @@ PHONE_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 AR episode.
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EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same AR episode.
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
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FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"]
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ACTIVITY_CLASSES:
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ACTIVITY_CLASSES:
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STATIONARY: ["still", "tilting"]
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STATIONARY: ["still", "tilting"]
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@ -120,7 +120,7 @@ PHONE_APPLICATIONS_FOREGROUND:
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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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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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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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INCLUDE_EPISODE_FEATURES: True
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INCLUDE_EPISODE_FEATURES: True
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SINGLE_CATEGORIES: ["all", "email"]
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SINGLE_CATEGORIES: ["all", "email"]
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MULTIPLE_CATEGORIES:
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MULTIPLE_CATEGORIES:
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@ -155,7 +155,7 @@ PHONE_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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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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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
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SRC_SCRIPT: src/features/phone_battery/rapids/main.py
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SRC_SCRIPT: src/features/phone_battery/rapids/main.py
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@ -169,7 +169,7 @@ PHONE_BLUETOOTH:
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SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
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SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
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DORYAB:
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DORYAB:
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COMPUTE: False
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COMPUTE: True
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FEATURES:
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FEATURES:
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ALL:
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ALL:
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DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
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DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
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@ -190,7 +190,7 @@ PHONE_CALLS:
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CONTAINER: call
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CONTAINER: call
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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FEATURES_TYPE: EPISODES # EVENTS or EPISODES
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FEATURES_TYPE: EPISODES # EVENTS or EPISODES
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CALL_TYPES: [missed, incoming, outgoing]
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CALL_TYPES: [missed, incoming, outgoing]
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FEATURES:
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FEATURES:
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@ -220,18 +220,17 @@ PHONE_CONVERSATION: # TODO Adapt for speech
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# See https://www.rapids.science/latest/features/phone-data-yield/
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# See https://www.rapids.science/latest/features/phone-data-yield/
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PHONE_DATA_YIELD:
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PHONE_DATA_YIELD:
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SENSORS: [#PHONE_ACCELEROMETER,
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SENSORS: [#PHONE_ACCELEROMETER,
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#PHONE_ACTIVITY_RECOGNITION,
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PHONE_ACTIVITY_RECOGNITION,
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#PHONE_APPLICATIONS_FOREGROUND,
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PHONE_APPLICATIONS_FOREGROUND,
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#PHONE_APPLICATIONS_NOTIFICATIONS,
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PHONE_APPLICATIONS_NOTIFICATIONS,
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#PHONE_BATTERY,
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PHONE_BATTERY,
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PHONE_BLUETOOTH #,
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PHONE_BLUETOOTH,
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#PHONE_CALLS,
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PHONE_CALLS,
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#PHONE_LIGHT,
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PHONE_LIGHT,
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#PHONE_LOCATIONS,
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PHONE_LOCATIONS,
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#PHONE_MESSAGES,
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PHONE_MESSAGES,
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#PHONE_SCREEN,
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PHONE_SCREEN,
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#PHONE_WIFI_VISIBLE
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PHONE_WIFI_VISIBLE]
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]
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: True
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COMPUTE: True
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@ -244,8 +243,9 @@ PHONE_ESM:
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PROVIDERS:
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PROVIDERS:
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STRAW:
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STRAW:
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COMPUTE: True
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COMPUTE: True
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SCALES: ["activities"]
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SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
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FEATURES: [activities_n_others, activities_inperson, activities_formal]
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"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
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FEATURES: [mean]
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SRC_SCRIPT: src/features/phone_esm/straw/main.py
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SRC_SCRIPT: src/features/phone_esm/straw/main.py
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# See https://www.rapids.science/latest/features/phone-keyboard/
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# See https://www.rapids.science/latest/features/phone-keyboard/
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@ -262,7 +262,7 @@ PHONE_LIGHT:
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CONTAINER: light_sensor
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CONTAINER: light_sensor
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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SRC_SCRIPT: src/features/phone_light/rapids/main.py
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SRC_SCRIPT: src/features/phone_light/rapids/main.py
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@ -276,7 +276,7 @@ PHONE_LOCATIONS:
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PROVIDERS:
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PROVIDERS:
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DORYAB:
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DORYAB:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"]
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FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"]
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DBSCAN_EPS: 100 # meters
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DBSCAN_EPS: 100 # meters
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DBSCAN_MINSAMPLES: 5
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DBSCAN_MINSAMPLES: 5
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@ -291,7 +291,7 @@ PHONE_LOCATIONS:
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SRC_SCRIPT: src/features/phone_locations/doryab/main.py
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SRC_SCRIPT: src/features/phone_locations/doryab/main.py
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BARNETT:
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BARNETT:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
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FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
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IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
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IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
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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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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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@ -309,7 +309,7 @@ PHONE_MESSAGES:
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CONTAINER: sms
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CONTAINER: sms
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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MESSAGES_TYPES : [received, sent]
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MESSAGES_TYPES : [received, sent]
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FEATURES:
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FEATURES:
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received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
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@ -321,7 +321,7 @@ PHONE_SCREEN:
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CONTAINER: screen
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CONTAINER: screen
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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REFERENCE_HOUR_FIRST_USE: 0
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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_SHORTER_THAN: 0 # in minutes, set to 0 to disable
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IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
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IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
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@ -334,7 +334,7 @@ PHONE_SPEECH:
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CONTAINER: speech
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CONTAINER: speech
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PROVIDERS:
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PROVIDERS:
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STRAW:
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STRAW:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
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FEATURES: ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
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SRC_SCRIPT: src/features/phone_speech/straw/main.py
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SRC_SCRIPT: src/features/phone_speech/straw/main.py
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@ -352,7 +352,7 @@ PHONE_WIFI_VISIBLE:
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CONTAINER: wifi
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CONTAINER: wifi
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PROVIDERS:
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PROVIDERS:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
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SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
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SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
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@ -521,10 +521,10 @@ EMPATICA_ACCELEROMETER:
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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CR:
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CR:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
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FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
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WINDOWS:
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 15 # specify window length in seconds
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WINDOW_LENGTH: 15 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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@ -548,11 +548,11 @@ EMPATICA_TEMPERATURE:
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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CR:
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CR:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
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FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
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"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
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"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
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WINDOWS:
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 300 # specify window length in seconds
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WINDOW_LENGTH: 300 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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@ -566,14 +566,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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CR:
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CR:
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COMPUTE: False
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COMPUTE: True
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease',
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'significantDecrease']
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'significantDecrease']
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WINDOWS:
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WINDOWS:
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COMPUTE: False
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COMPUTE: True
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WINDOW_LENGTH: 60 # specify window length in seconds
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WINDOW_LENGTH: 60 # specify window length in seconds
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
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SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
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IMPUTE_NANS: True
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IMPUTE_NANS: True
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@ -592,7 +592,7 @@ EMPATICA_BLOOD_VOLUME_PULSE:
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||||||
FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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||||||
'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
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||||||
WINDOWS:
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WINDOWS:
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||||||
COMPUTE: False
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COMPUTE: True
|
||||||
WINDOW_LENGTH: 300 # specify window length in seconds
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WINDOW_LENGTH: 300 # specify window length in seconds
|
||||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
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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
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
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||||||
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@ -606,12 +606,12 @@ EMPATICA_INTER_BEAT_INTERVAL:
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||||||
FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
|
FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
|
||||||
SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
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SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
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||||||
CR:
|
CR:
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||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
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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
|
'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
|
||||||
PATCH_WITH_BVP: True
|
PATCH_WITH_BVP: True
|
||||||
WINDOWS:
|
WINDOWS:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
WINDOW_LENGTH: 300 # specify window length in seconds
|
WINDOW_LENGTH: 300 # specify window length in seconds
|
||||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
|
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
|
SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
|
||||||
|
@ -673,12 +673,12 @@ ALL_CLEANING_INDIVIDUAL:
|
||||||
DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
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
|
DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
|
||||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||||
CORR_THRESHOLD: 0.95
|
CORR_THRESHOLD: 0.95
|
||||||
SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
|
SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
|
||||||
STRAW:
|
STRAW:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
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
|
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||||
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||||
|
@ -686,7 +686,7 @@ ALL_CLEANING_INDIVIDUAL:
|
||||||
COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
|
COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
|
||||||
COLS_VAR_THRESHOLD: True
|
COLS_VAR_THRESHOLD: True
|
||||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||||
CORR_THRESHOLD: 0.95
|
CORR_THRESHOLD: 0.95
|
||||||
STANDARDIZATION: True
|
STANDARDIZATION: True
|
||||||
|
@ -705,12 +705,12 @@ ALL_CLEANING_OVERALL:
|
||||||
DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
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
|
DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
|
||||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||||
CORR_THRESHOLD: 0.95
|
CORR_THRESHOLD: 0.95
|
||||||
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
|
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
|
||||||
STRAW:
|
STRAW:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
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
|
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||||
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||||
|
@ -718,7 +718,7 @@ ALL_CLEANING_OVERALL:
|
||||||
COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
|
COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
|
||||||
COLS_VAR_THRESHOLD: True
|
COLS_VAR_THRESHOLD: True
|
||||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||||
CORR_THRESHOLD: 0.95
|
CORR_THRESHOLD: 0.95
|
||||||
STANDARDIZATION: True
|
STANDARDIZATION: True
|
||||||
|
@ -732,7 +732,7 @@ ALL_CLEANING_OVERALL:
|
||||||
|
|
||||||
PARAMS_FOR_ANALYSIS:
|
PARAMS_FOR_ANALYSIS:
|
||||||
BASELINE:
|
BASELINE:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
FOLDER: data/external/baseline
|
FOLDER: data/external/baseline
|
||||||
CONTAINER: [results-survey637813_final.csv, # Slovenia
|
CONTAINER: [results-survey637813_final.csv, # Slovenia
|
||||||
results-survey358134_final.csv, # Belgium 1
|
results-survey358134_final.csv, # Belgium 1
|
||||||
|
@ -743,7 +743,7 @@ PARAMS_FOR_ANALYSIS:
|
||||||
CATEGORICAL_FEATURES: [gender]
|
CATEGORICAL_FEATURES: [gender]
|
||||||
|
|
||||||
TARGET:
|
TARGET:
|
||||||
COMPUTE: False
|
COMPUTE: True
|
||||||
LABEL: appraisal_stressfulness_event_mean
|
LABEL: appraisal_stressfulness_event_mean
|
||||||
ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
|
ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
|
||||||
# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
|
# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
|
||||||
|
|
334
renv.lock
334
renv.lock
|
@ -1,6 +1,6 @@
|
||||||
{
|
{
|
||||||
"R": {
|
"R": {
|
||||||
"Version": "4.2.3",
|
"Version": "4.1.2",
|
||||||
"Repositories": [
|
"Repositories": [
|
||||||
{
|
{
|
||||||
"Name": "CRAN",
|
"Name": "CRAN",
|
||||||
|
@ -46,10 +46,10 @@
|
||||||
},
|
},
|
||||||
"Hmisc": {
|
"Hmisc": {
|
||||||
"Package": "Hmisc",
|
"Package": "Hmisc",
|
||||||
"Version": "5.0-1",
|
"Version": "4.4-2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "bf9fe82c010a468fb32f913ff56d65e1"
|
"Hash": "66458e906b2112a8b1639964efd77d7c"
|
||||||
},
|
},
|
||||||
"KernSmooth": {
|
"KernSmooth": {
|
||||||
"Package": "KernSmooth",
|
"Package": "KernSmooth",
|
||||||
|
@ -104,7 +104,7 @@
|
||||||
"Package": "RPostgres",
|
"Package": "RPostgres",
|
||||||
"Version": "1.4.4",
|
"Version": "1.4.4",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "c593ecb8dbca9faf3906431be610ca28"
|
"Hash": "c593ecb8dbca9faf3906431be610ca28"
|
||||||
},
|
},
|
||||||
"Rcpp": {
|
"Rcpp": {
|
||||||
|
@ -181,7 +181,7 @@
|
||||||
"Package": "base64enc",
|
"Package": "base64enc",
|
||||||
"Version": "0.1-3",
|
"Version": "0.1-3",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "543776ae6848fde2f48ff3816d0628bc"
|
"Hash": "543776ae6848fde2f48ff3816d0628bc"
|
||||||
},
|
},
|
||||||
"bit": {
|
"bit": {
|
||||||
|
@ -221,24 +221,17 @@
|
||||||
},
|
},
|
||||||
"broom": {
|
"broom": {
|
||||||
"Package": "broom",
|
"Package": "broom",
|
||||||
"Version": "1.0.4",
|
"Version": "0.7.3",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "f62b2504021369a2449c54bbda362d30"
|
"Hash": "5581a5ddc8fe2ac5e0d092ec2de4c4ae"
|
||||||
},
|
|
||||||
"cachem": {
|
|
||||||
"Package": "cachem",
|
|
||||||
"Version": "1.0.7",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "cda74447c42f529de601fe4d4050daef"
|
|
||||||
},
|
},
|
||||||
"callr": {
|
"callr": {
|
||||||
"Package": "callr",
|
"Package": "callr",
|
||||||
"Version": "3.7.3",
|
"Version": "3.5.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "9b2191ede20fa29828139b9900922e51"
|
"Hash": "b7d7f1e926dfcd57c74ce93f5c048e80"
|
||||||
},
|
},
|
||||||
"caret": {
|
"caret": {
|
||||||
"Package": "caret",
|
"Package": "caret",
|
||||||
|
@ -270,10 +263,10 @@
|
||||||
},
|
},
|
||||||
"cli": {
|
"cli": {
|
||||||
"Package": "cli",
|
"Package": "cli",
|
||||||
"Version": "3.6.1",
|
"Version": "2.2.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "89e6d8219950eac806ae0c489052048a"
|
"Hash": "3ef298932294b775fa0a3eeaa3a645b0"
|
||||||
},
|
},
|
||||||
"clipr": {
|
"clipr": {
|
||||||
"Package": "clipr",
|
"Package": "clipr",
|
||||||
|
@ -293,7 +286,7 @@
|
||||||
"Package": "codetools",
|
"Package": "codetools",
|
||||||
"Version": "0.2-18",
|
"Version": "0.2-18",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "019388fc48e48b3da0d3a76ff94608a8"
|
"Hash": "019388fc48e48b3da0d3a76ff94608a8"
|
||||||
},
|
},
|
||||||
"colorspace": {
|
"colorspace": {
|
||||||
|
@ -310,13 +303,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "0f22be39ec1d141fd03683c06f3a6e67"
|
"Hash": "0f22be39ec1d141fd03683c06f3a6e67"
|
||||||
},
|
},
|
||||||
"conflicted": {
|
|
||||||
"Package": "conflicted",
|
|
||||||
"Version": "1.2.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "bb097fccb22d156624fd07cd2894ddb6"
|
|
||||||
},
|
|
||||||
"corpcor": {
|
"corpcor": {
|
||||||
"Package": "corpcor",
|
"Package": "corpcor",
|
||||||
"Version": "1.6.9",
|
"Version": "1.6.9",
|
||||||
|
@ -333,10 +319,10 @@
|
||||||
},
|
},
|
||||||
"cpp11": {
|
"cpp11": {
|
||||||
"Package": "cpp11",
|
"Package": "cpp11",
|
||||||
"Version": "0.4.3",
|
"Version": "0.2.4",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "ed588261931ee3be2c700d22e94a29ab"
|
"Hash": "ba66e5a750d39067d888aa7af797fed2"
|
||||||
},
|
},
|
||||||
"crayon": {
|
"crayon": {
|
||||||
"Package": "crayon",
|
"Package": "crayon",
|
||||||
|
@ -368,10 +354,10 @@
|
||||||
},
|
},
|
||||||
"dbplyr": {
|
"dbplyr": {
|
||||||
"Package": "dbplyr",
|
"Package": "dbplyr",
|
||||||
"Version": "2.3.2",
|
"Version": "2.1.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "CRAN",
|
||||||
"Hash": "d24305b92db333726aed162a2c23a147"
|
"Hash": "1f37fa4ab2f5f7eded42f78b9a887182"
|
||||||
},
|
},
|
||||||
"desc": {
|
"desc": {
|
||||||
"Package": "desc",
|
"Package": "desc",
|
||||||
|
@ -396,17 +382,17 @@
|
||||||
},
|
},
|
||||||
"dplyr": {
|
"dplyr": {
|
||||||
"Package": "dplyr",
|
"Package": "dplyr",
|
||||||
"Version": "1.1.1",
|
"Version": "1.0.5",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "eb5742d256a0d9306d85ea68756d8187"
|
"Hash": "d0d76c11ec807eb3f000eba4e3eb0f68"
|
||||||
},
|
},
|
||||||
"dtplyr": {
|
"dtplyr": {
|
||||||
"Package": "dtplyr",
|
"Package": "dtplyr",
|
||||||
"Version": "1.3.1",
|
"Version": "1.1.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "54ed3ea01b11e81a86544faaecfef8e2"
|
"Hash": "1e14e4c5b2814de5225312394bc316da"
|
||||||
},
|
},
|
||||||
"e1071": {
|
"e1071": {
|
||||||
"Package": "e1071",
|
"Package": "e1071",
|
||||||
|
@ -433,7 +419,7 @@
|
||||||
"Package": "evaluate",
|
"Package": "evaluate",
|
||||||
"Version": "0.14",
|
"Version": "0.14",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "ec8ca05cffcc70569eaaad8469d2a3a7"
|
"Hash": "ec8ca05cffcc70569eaaad8469d2a3a7"
|
||||||
},
|
},
|
||||||
"fansi": {
|
"fansi": {
|
||||||
|
@ -466,10 +452,10 @@
|
||||||
},
|
},
|
||||||
"forcats": {
|
"forcats": {
|
||||||
"Package": "forcats",
|
"Package": "forcats",
|
||||||
"Version": "1.0.0",
|
"Version": "0.5.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "1a0a9a3d5083d0d573c4214576f1e690"
|
"Hash": "1cb4279e697650f0bd78cd3601ee7576"
|
||||||
},
|
},
|
||||||
"foreach": {
|
"foreach": {
|
||||||
"Package": "foreach",
|
"Package": "foreach",
|
||||||
|
@ -506,13 +492,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "f568ce73d3d59582b0f7babd0eb33d07"
|
"Hash": "f568ce73d3d59582b0f7babd0eb33d07"
|
||||||
},
|
},
|
||||||
"gargle": {
|
|
||||||
"Package": "gargle",
|
|
||||||
"Version": "1.3.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "bb3208dcdfeb2e68bf33c87601b3cbe3"
|
|
||||||
},
|
|
||||||
"gclus": {
|
"gclus": {
|
||||||
"Package": "gclus",
|
"Package": "gclus",
|
||||||
"Version": "1.3.2",
|
"Version": "1.3.2",
|
||||||
|
@ -536,10 +515,10 @@
|
||||||
},
|
},
|
||||||
"ggplot2": {
|
"ggplot2": {
|
||||||
"Package": "ggplot2",
|
"Package": "ggplot2",
|
||||||
"Version": "3.4.1",
|
"Version": "3.3.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "d494daf77c4aa7f084dbbe6ca5dcaca7"
|
"Hash": "4ded8b439797f7b1693bd3d238d0106b"
|
||||||
},
|
},
|
||||||
"ggraph": {
|
"ggraph": {
|
||||||
"Package": "ggraph",
|
"Package": "ggraph",
|
||||||
|
@ -578,30 +557,16 @@
|
||||||
},
|
},
|
||||||
"glue": {
|
"glue": {
|
||||||
"Package": "glue",
|
"Package": "glue",
|
||||||
"Version": "1.6.2",
|
"Version": "1.4.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "4f2596dfb05dac67b9dc558e5c6fba2e"
|
"Hash": "6efd734b14c6471cfe443345f3e35e29"
|
||||||
},
|
|
||||||
"googledrive": {
|
|
||||||
"Package": "googledrive",
|
|
||||||
"Version": "2.1.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "e88ba642951bc8d1898ba0d12581850b"
|
|
||||||
},
|
|
||||||
"googlesheets4": {
|
|
||||||
"Package": "googlesheets4",
|
|
||||||
"Version": "1.1.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "fd7b97bd862a14297b0bb7ed28a3dada"
|
|
||||||
},
|
},
|
||||||
"gower": {
|
"gower": {
|
||||||
"Package": "gower",
|
"Package": "gower",
|
||||||
"Version": "0.2.2",
|
"Version": "0.2.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "be6a2b3529928bd803d1c437d1d43152"
|
"Hash": "be6a2b3529928bd803d1c437d1d43152"
|
||||||
},
|
},
|
||||||
"graphlayouts": {
|
"graphlayouts": {
|
||||||
|
@ -634,10 +599,10 @@
|
||||||
},
|
},
|
||||||
"haven": {
|
"haven": {
|
||||||
"Package": "haven",
|
"Package": "haven",
|
||||||
"Version": "2.5.2",
|
"Version": "2.3.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "8b331e659e67d757db0fcc28e689c501"
|
"Hash": "221d0ad75dfa03ebf17b1a4cc5c31dfc"
|
||||||
},
|
},
|
||||||
"highr": {
|
"highr": {
|
||||||
"Package": "highr",
|
"Package": "highr",
|
||||||
|
@ -648,10 +613,10 @@
|
||||||
},
|
},
|
||||||
"hms": {
|
"hms": {
|
||||||
"Package": "hms",
|
"Package": "hms",
|
||||||
"Version": "1.1.3",
|
"Version": "1.1.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "CRAN",
|
||||||
"Hash": "b59377caa7ed00fa41808342002138f9"
|
"Hash": "5b8a2dd0fdbe2ab4f6081e6c7be6dfca"
|
||||||
},
|
},
|
||||||
"htmlTable": {
|
"htmlTable": {
|
||||||
"Package": "htmlTable",
|
"Package": "htmlTable",
|
||||||
|
@ -683,10 +648,10 @@
|
||||||
},
|
},
|
||||||
"httr": {
|
"httr": {
|
||||||
"Package": "httr",
|
"Package": "httr",
|
||||||
"Version": "1.4.5",
|
"Version": "1.4.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "f6844033201269bec3ca0097bc6c97b3"
|
"Hash": "a525aba14184fec243f9eaec62fbed43"
|
||||||
},
|
},
|
||||||
"huge": {
|
"huge": {
|
||||||
"Package": "huge",
|
"Package": "huge",
|
||||||
|
@ -695,13 +660,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "a4cde4dd1d2551edb99a3273a4ad34ea"
|
"Hash": "a4cde4dd1d2551edb99a3273a4ad34ea"
|
||||||
},
|
},
|
||||||
"ids": {
|
|
||||||
"Package": "ids",
|
|
||||||
"Version": "1.0.1",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "99df65cfef20e525ed38c3d2577f7190"
|
|
||||||
},
|
|
||||||
"igraph": {
|
"igraph": {
|
||||||
"Package": "igraph",
|
"Package": "igraph",
|
||||||
"Version": "1.2.6",
|
"Version": "1.2.6",
|
||||||
|
@ -746,10 +704,10 @@
|
||||||
},
|
},
|
||||||
"jsonlite": {
|
"jsonlite": {
|
||||||
"Package": "jsonlite",
|
"Package": "jsonlite",
|
||||||
"Version": "1.8.4",
|
"Version": "1.7.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "a4269a09a9b865579b2635c77e572374"
|
"Hash": "98138e0994d41508c7a6b84a0600cfcb"
|
||||||
},
|
},
|
||||||
"knitr": {
|
"knitr": {
|
||||||
"Package": "knitr",
|
"Package": "knitr",
|
||||||
|
@ -802,10 +760,10 @@
|
||||||
},
|
},
|
||||||
"lifecycle": {
|
"lifecycle": {
|
||||||
"Package": "lifecycle",
|
"Package": "lifecycle",
|
||||||
"Version": "1.0.3",
|
"Version": "1.0.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "001cecbeac1cff9301bdc3775ee46a86"
|
"Hash": "3471fb65971f1a7b2d4ae7848cf2db8d"
|
||||||
},
|
},
|
||||||
"listenv": {
|
"listenv": {
|
||||||
"Package": "listenv",
|
"Package": "listenv",
|
||||||
|
@ -816,17 +774,17 @@
|
||||||
},
|
},
|
||||||
"lubridate": {
|
"lubridate": {
|
||||||
"Package": "lubridate",
|
"Package": "lubridate",
|
||||||
"Version": "1.9.2",
|
"Version": "1.7.9.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "e25f18436e3efd42c7c590a1c4c15390"
|
"Hash": "5b5b02f621d39a499def7923a5aee746"
|
||||||
},
|
},
|
||||||
"magrittr": {
|
"magrittr": {
|
||||||
"Package": "magrittr",
|
"Package": "magrittr",
|
||||||
"Version": "2.0.3",
|
"Version": "2.0.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "7ce2733a9826b3aeb1775d56fd305472"
|
"Hash": "41287f1ac7d28a92f0a286ed507928d3"
|
||||||
},
|
},
|
||||||
"markdown": {
|
"markdown": {
|
||||||
"Package": "markdown",
|
"Package": "markdown",
|
||||||
|
@ -842,13 +800,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "67101e7448dfd9add4ac418623060262"
|
"Hash": "67101e7448dfd9add4ac418623060262"
|
||||||
},
|
},
|
||||||
"memoise": {
|
|
||||||
"Package": "memoise",
|
|
||||||
"Version": "2.0.1",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "e2817ccf4a065c5d9d7f2cfbe7c1d78c"
|
|
||||||
},
|
|
||||||
"mgcv": {
|
"mgcv": {
|
||||||
"Package": "mgcv",
|
"Package": "mgcv",
|
||||||
"Version": "1.8-33",
|
"Version": "1.8-33",
|
||||||
|
@ -879,10 +830,10 @@
|
||||||
},
|
},
|
||||||
"modelr": {
|
"modelr": {
|
||||||
"Package": "modelr",
|
"Package": "modelr",
|
||||||
"Version": "0.1.11",
|
"Version": "0.1.8",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "4f50122dc256b1b6996a4703fecea821"
|
"Hash": "9fd59716311ee82cba83dc2826fc5577"
|
||||||
},
|
},
|
||||||
"munsell": {
|
"munsell": {
|
||||||
"Package": "munsell",
|
"Package": "munsell",
|
||||||
|
@ -937,7 +888,7 @@
|
||||||
"Package": "parallelly",
|
"Package": "parallelly",
|
||||||
"Version": "1.29.0",
|
"Version": "1.29.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "b5f399c9ce96977e22ef32c20b6cfe87"
|
"Hash": "b5f399c9ce96977e22ef32c20b6cfe87"
|
||||||
},
|
},
|
||||||
"pbapply": {
|
"pbapply": {
|
||||||
|
@ -956,10 +907,10 @@
|
||||||
},
|
},
|
||||||
"pillar": {
|
"pillar": {
|
||||||
"Package": "pillar",
|
"Package": "pillar",
|
||||||
"Version": "1.9.0",
|
"Version": "1.4.7",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "15da5a8412f317beeee6175fbc76f4bb"
|
"Hash": "3b3dd89b2ee115a8b54e93a34cd546b4"
|
||||||
},
|
},
|
||||||
"pkgbuild": {
|
"pkgbuild": {
|
||||||
"Package": "pkgbuild",
|
"Package": "pkgbuild",
|
||||||
|
@ -1026,10 +977,10 @@
|
||||||
},
|
},
|
||||||
"processx": {
|
"processx": {
|
||||||
"Package": "processx",
|
"Package": "processx",
|
||||||
"Version": "3.8.0",
|
"Version": "3.4.5",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "a33ee2d9bf07564efb888ad98410da84"
|
"Hash": "22aab6098cb14edd0a5973a8438b569b"
|
||||||
},
|
},
|
||||||
"prodlim": {
|
"prodlim": {
|
||||||
"Package": "prodlim",
|
"Package": "prodlim",
|
||||||
|
@ -1049,7 +1000,7 @@
|
||||||
"Package": "progressr",
|
"Package": "progressr",
|
||||||
"Version": "0.9.0",
|
"Version": "0.9.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "RSPM",
|
"Repository": "CRAN",
|
||||||
"Hash": "ca0d80ecc29903f7579edbabd91f4199"
|
"Hash": "ca0d80ecc29903f7579edbabd91f4199"
|
||||||
},
|
},
|
||||||
"promises": {
|
"promises": {
|
||||||
|
@ -1082,10 +1033,10 @@
|
||||||
},
|
},
|
||||||
"purrr": {
|
"purrr": {
|
||||||
"Package": "purrr",
|
"Package": "purrr",
|
||||||
"Version": "1.0.1",
|
"Version": "0.3.4",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "d71c815267c640f17ddbf7f16144b4bb"
|
"Hash": "97def703420c8ab10d8f0e6c72101e02"
|
||||||
},
|
},
|
||||||
"qap": {
|
"qap": {
|
||||||
"Package": "qap",
|
"Package": "qap",
|
||||||
|
@ -1101,13 +1052,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "d35964686307333a7121eb41c7dcd4e0"
|
"Hash": "d35964686307333a7121eb41c7dcd4e0"
|
||||||
},
|
},
|
||||||
"ragg": {
|
|
||||||
"Package": "ragg",
|
|
||||||
"Version": "1.2.5",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "690bc058ea2b1b8a407d3cfe3dce3ef9"
|
|
||||||
},
|
|
||||||
"rappdirs": {
|
"rappdirs": {
|
||||||
"Package": "rappdirs",
|
"Package": "rappdirs",
|
||||||
"Version": "0.3.3",
|
"Version": "0.3.3",
|
||||||
|
@ -1117,17 +1061,17 @@
|
||||||
},
|
},
|
||||||
"readr": {
|
"readr": {
|
||||||
"Package": "readr",
|
"Package": "readr",
|
||||||
"Version": "2.1.4",
|
"Version": "1.4.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "b5047343b3825f37ad9d3b5d89aa1078"
|
"Hash": "2639976851f71f330264a9c9c3d43a61"
|
||||||
},
|
},
|
||||||
"readxl": {
|
"readxl": {
|
||||||
"Package": "readxl",
|
"Package": "readxl",
|
||||||
"Version": "1.4.2",
|
"Version": "1.3.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "2e6020b1399d95f947ed867045e9ca17"
|
"Hash": "63537c483c2dbec8d9e3183b3735254a"
|
||||||
},
|
},
|
||||||
"recipes": {
|
"recipes": {
|
||||||
"Package": "recipes",
|
"Package": "recipes",
|
||||||
|
@ -1166,10 +1110,10 @@
|
||||||
},
|
},
|
||||||
"reprex": {
|
"reprex": {
|
||||||
"Package": "reprex",
|
"Package": "reprex",
|
||||||
"Version": "2.0.2",
|
"Version": "0.3.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "d66fe009d4c20b7ab1927eb405db9ee2"
|
"Hash": "b06bfb3504cc8a4579fd5567646f745b"
|
||||||
},
|
},
|
||||||
"reshape2": {
|
"reshape2": {
|
||||||
"Package": "reshape2",
|
"Package": "reshape2",
|
||||||
|
@ -1194,10 +1138,10 @@
|
||||||
},
|
},
|
||||||
"rlang": {
|
"rlang": {
|
||||||
"Package": "rlang",
|
"Package": "rlang",
|
||||||
"Version": "1.1.0",
|
"Version": "0.4.10",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "dc079ccd156cde8647360f473c1fa718"
|
"Hash": "599df23c40a4fce9c7b4764f28c37857"
|
||||||
},
|
},
|
||||||
"rmarkdown": {
|
"rmarkdown": {
|
||||||
"Package": "rmarkdown",
|
"Package": "rmarkdown",
|
||||||
|
@ -1229,24 +1173,24 @@
|
||||||
},
|
},
|
||||||
"rstudioapi": {
|
"rstudioapi": {
|
||||||
"Package": "rstudioapi",
|
"Package": "rstudioapi",
|
||||||
"Version": "0.14",
|
"Version": "0.13",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "690bd2acc42a9166ce34845884459320"
|
"Hash": "06c85365a03fdaf699966cc1d3cf53ea"
|
||||||
},
|
},
|
||||||
"rvest": {
|
"rvest": {
|
||||||
"Package": "rvest",
|
"Package": "rvest",
|
||||||
"Version": "1.0.3",
|
"Version": "0.3.6",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "a4a5ac819a467808c60e36e92ddf195e"
|
"Hash": "a9795ccb2d608330e841998b67156764"
|
||||||
},
|
},
|
||||||
"scales": {
|
"scales": {
|
||||||
"Package": "scales",
|
"Package": "scales",
|
||||||
"Version": "1.2.1",
|
"Version": "1.1.1",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "906cb23d2f1c5680b8ce439b44c6fa63"
|
"Hash": "6f76f71042411426ec8df6c54f34e6dd"
|
||||||
},
|
},
|
||||||
"selectr": {
|
"selectr": {
|
||||||
"Package": "selectr",
|
"Package": "selectr",
|
||||||
|
@ -1292,17 +1236,17 @@
|
||||||
},
|
},
|
||||||
"stringi": {
|
"stringi": {
|
||||||
"Package": "stringi",
|
"Package": "stringi",
|
||||||
"Version": "1.7.12",
|
"Version": "1.5.3",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "ca8bd84263c77310739d2cf64d84d7c9"
|
"Hash": "a063ebea753c92910a4cca7b18bc1f05"
|
||||||
},
|
},
|
||||||
"stringr": {
|
"stringr": {
|
||||||
"Package": "stringr",
|
"Package": "stringr",
|
||||||
"Version": "1.5.0",
|
"Version": "1.4.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "CRAN",
|
||||||
"Hash": "671a4d384ae9d32fc47a14e98bfa3dc8"
|
"Hash": "0759e6b6c0957edb1311028a49a35e76"
|
||||||
},
|
},
|
||||||
"survival": {
|
"survival": {
|
||||||
"Package": "survival",
|
"Package": "survival",
|
||||||
|
@ -1318,13 +1262,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "b227d13e29222b4574486cfcbde077fa"
|
"Hash": "b227d13e29222b4574486cfcbde077fa"
|
||||||
},
|
},
|
||||||
"systemfonts": {
|
|
||||||
"Package": "systemfonts",
|
|
||||||
"Version": "1.0.4",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "90b28393209827327de889f49935140a"
|
|
||||||
},
|
|
||||||
"testthat": {
|
"testthat": {
|
||||||
"Package": "testthat",
|
"Package": "testthat",
|
||||||
"Version": "3.0.1",
|
"Version": "3.0.1",
|
||||||
|
@ -1332,19 +1269,12 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "17826764cb92d8b5aae6619896e5a161"
|
"Hash": "17826764cb92d8b5aae6619896e5a161"
|
||||||
},
|
},
|
||||||
"textshaping": {
|
|
||||||
"Package": "textshaping",
|
|
||||||
"Version": "0.3.6",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "1ab6223d3670fac7143202cb6a2d43d5"
|
|
||||||
},
|
|
||||||
"tibble": {
|
"tibble": {
|
||||||
"Package": "tibble",
|
"Package": "tibble",
|
||||||
"Version": "3.2.1",
|
"Version": "3.0.4",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "a84e2cc86d07289b3b6f5069df7a004c"
|
"Hash": "71dffd8544691c520dd8e41ed2d7e070"
|
||||||
},
|
},
|
||||||
"tidygraph": {
|
"tidygraph": {
|
||||||
"Package": "tidygraph",
|
"Package": "tidygraph",
|
||||||
|
@ -1355,24 +1285,24 @@
|
||||||
},
|
},
|
||||||
"tidyr": {
|
"tidyr": {
|
||||||
"Package": "tidyr",
|
"Package": "tidyr",
|
||||||
"Version": "1.3.0",
|
"Version": "1.1.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "e47debdc7ce599b070c8e78e8ac0cfcf"
|
"Hash": "c40b2d5824d829190f4b825f4496dfae"
|
||||||
},
|
},
|
||||||
"tidyselect": {
|
"tidyselect": {
|
||||||
"Package": "tidyselect",
|
"Package": "tidyselect",
|
||||||
"Version": "1.2.0",
|
"Version": "1.1.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "79540e5fcd9e0435af547d885f184fd5"
|
"Hash": "6ea435c354e8448819627cf686f66e0a"
|
||||||
},
|
},
|
||||||
"tidyverse": {
|
"tidyverse": {
|
||||||
"Package": "tidyverse",
|
"Package": "tidyverse",
|
||||||
"Version": "2.0.0",
|
"Version": "1.3.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "c328568cd14ea89a83bd4ca7f54ae07e"
|
"Hash": "bd51be662f359fa99021f3d51e911490"
|
||||||
},
|
},
|
||||||
"timeDate": {
|
"timeDate": {
|
||||||
"Package": "timeDate",
|
"Package": "timeDate",
|
||||||
|
@ -1381,13 +1311,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "fde4fc571f5f61978652c229d4713845"
|
"Hash": "fde4fc571f5f61978652c229d4713845"
|
||||||
},
|
},
|
||||||
"timechange": {
|
|
||||||
"Package": "timechange",
|
|
||||||
"Version": "0.2.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "8548b44f79a35ba1791308b61e6012d7"
|
|
||||||
},
|
|
||||||
"tinytex": {
|
"tinytex": {
|
||||||
"Package": "tinytex",
|
"Package": "tinytex",
|
||||||
"Version": "0.28",
|
"Version": "0.28",
|
||||||
|
@ -1409,13 +1332,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "fc77eb5297507cccfa3349a606061030"
|
"Hash": "fc77eb5297507cccfa3349a606061030"
|
||||||
},
|
},
|
||||||
"tzdb": {
|
|
||||||
"Package": "tzdb",
|
|
||||||
"Version": "0.3.0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "b2e1cbce7c903eaf23ec05c58e59fb5e"
|
|
||||||
},
|
|
||||||
"utf8": {
|
"utf8": {
|
||||||
"Package": "utf8",
|
"Package": "utf8",
|
||||||
"Version": "1.1.4",
|
"Version": "1.1.4",
|
||||||
|
@ -1423,19 +1339,12 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "4a5081acfb7b81a572e4384a7aaf2af1"
|
"Hash": "4a5081acfb7b81a572e4384a7aaf2af1"
|
||||||
},
|
},
|
||||||
"uuid": {
|
|
||||||
"Package": "uuid",
|
|
||||||
"Version": "1.1-0",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "f1cb46c157d080b729159d407be83496"
|
|
||||||
},
|
|
||||||
"vctrs": {
|
"vctrs": {
|
||||||
"Package": "vctrs",
|
"Package": "vctrs",
|
||||||
"Version": "0.6.1",
|
"Version": "0.3.8",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "CRAN",
|
||||||
"Hash": "06eceb3a5d716fd0654cc23ca3d71a99"
|
"Hash": "ecf749a1b39ea72bd9b51b76292261f1"
|
||||||
},
|
},
|
||||||
"viridis": {
|
"viridis": {
|
||||||
"Package": "viridis",
|
"Package": "viridis",
|
||||||
|
@ -1451,13 +1360,6 @@
|
||||||
"Repository": "RSPM",
|
"Repository": "RSPM",
|
||||||
"Hash": "ce4f6271baa94776db692f1cb2055bee"
|
"Hash": "ce4f6271baa94776db692f1cb2055bee"
|
||||||
},
|
},
|
||||||
"vroom": {
|
|
||||||
"Package": "vroom",
|
|
||||||
"Version": "1.6.1",
|
|
||||||
"Source": "Repository",
|
|
||||||
"Repository": "CRAN",
|
|
||||||
"Hash": "7015a74373b83ffaef64023f4a0f5033"
|
|
||||||
},
|
|
||||||
"waldo": {
|
"waldo": {
|
||||||
"Package": "waldo",
|
"Package": "waldo",
|
||||||
"Version": "0.2.3",
|
"Version": "0.2.3",
|
||||||
|
@ -1474,10 +1376,10 @@
|
||||||
},
|
},
|
||||||
"withr": {
|
"withr": {
|
||||||
"Package": "withr",
|
"Package": "withr",
|
||||||
"Version": "2.5.0",
|
"Version": "2.3.0",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "c0e49a9760983e81e55cdd9be92e7182"
|
"Hash": "7307d79f58d1885b38c4f4f1a8cb19dd"
|
||||||
},
|
},
|
||||||
"xfun": {
|
"xfun": {
|
||||||
"Package": "xfun",
|
"Package": "xfun",
|
||||||
|
@ -1488,10 +1390,10 @@
|
||||||
},
|
},
|
||||||
"xml2": {
|
"xml2": {
|
||||||
"Package": "xml2",
|
"Package": "xml2",
|
||||||
"Version": "1.3.3",
|
"Version": "1.3.2",
|
||||||
"Source": "Repository",
|
"Source": "Repository",
|
||||||
"Repository": "CRAN",
|
"Repository": "RSPM",
|
||||||
"Hash": "40682ed6a969ea5abfd351eb67833adc"
|
"Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2"
|
||||||
},
|
},
|
||||||
"xtable": {
|
"xtable": {
|
||||||
"Package": "xtable",
|
"Package": "xtable",
|
||||||
|
|
|
@ -1,292 +0,0 @@
|
||||||
import pandas as pd
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
id2qc = { 44:["What have you mainly been doing within the last 10 minutes?",
|
|
||||||
"Waar ben je voornamelijk mee bezig geweest de laatste 10 minuten?",
|
|
||||||
"Kaj ste v glavnem počeli v zadnjih 10 minutah?"],
|
|
||||||
45:["What type of individual work?",
|
|
||||||
"Wat voor soort individueel werk?",
|
|
||||||
"Kakšno vrsto samostojnega dela ste opravljali?"],
|
|
||||||
46:["How did you work with others?",
|
|
||||||
"Hoe heb je met anderen gewerkt?",
|
|
||||||
"Kako ste sodelovali z drugimi?"],
|
|
||||||
47:["What type of break?",
|
|
||||||
"Wat voor soort pauze?",
|
|
||||||
"Kakšno vrsto odmora ste imeli?"],
|
|
||||||
48:["Where did you travel between?",
|
|
||||||
"Waar heb je tussen gereisd?",
|
|
||||||
"Kam ste potovali?"],
|
|
||||||
49:["Did you use a computer or phone for that?",
|
|
||||||
"Heb je daarvoor een computer of telefoon gebruikt?",
|
|
||||||
"Ste za to uporabljali računalnik ali telefon?"],
|
|
||||||
50:["What kind of an interaction was that?",
|
|
||||||
"Wat voor interactie was dat?",
|
|
||||||
"Kakšne vrste sodelovanja je bilo to?"],
|
|
||||||
51:["How many people were involved besides yourself?",
|
|
||||||
"Hoeveel mensen waren er behalve jezelf betrokken?",
|
|
||||||
"Koliko oseb je bilo poleg vas še vpletenih?"],
|
|
||||||
# 52:["What have you mainly been doing within the last 10 minutes?",
|
|
||||||
# "Waar ben je voornamelijk mee bezig geweest de laatste 10 minuten?",
|
|
||||||
# "Kaj ste v glavnem počeli v zadnjih 10 minutah?"]
|
|
||||||
}
|
|
||||||
qc2id = {v:k for k,values in id2qc.items() for v in values}
|
|
||||||
|
|
||||||
next_questions = { 44: [45,46,47,48],
|
|
||||||
45:[49,49],
|
|
||||||
46:[50,50],
|
|
||||||
47:[],
|
|
||||||
48:[],
|
|
||||||
49:[],
|
|
||||||
50:[51,51],
|
|
||||||
51:[]
|
|
||||||
#52:[45,46,47,48],
|
|
||||||
}
|
|
||||||
|
|
||||||
def esm_activities_LTM_features(
|
|
||||||
df_esm_activities_cleaned: pd.DataFrame,
|
|
||||||
) -> pd.DataFrame:
|
|
||||||
""" Function for calculating LTM(Last 10 minutes) features of questionnaire answers. It first corrects the question ids according
|
|
||||||
to esm_instructions and the updated corpus of question_ids. It then processes each LTM question chain to
|
|
||||||
find relevant social properties given by the answers such as the number of people interacted with, the formality and whether the socializing was done in person.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
df_esm_activities_cleaned: pd.DataFrame
|
|
||||||
A cleaned up dataframe, which must include esm_instructions, esm_user_answer_numeric.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
df_esm_activities_cleaned: pd.DataFrame
|
|
||||||
The same dataframe with columns which contain:
|
|
||||||
["correct_ids"] - Corrected question_ids
|
|
||||||
["ans_seq"] - For each LTM question, the sequence of numerical user answers pertaining to this chain of questions.
|
|
||||||
["n_others","inperson","formal"]- Properties of known potential social encounters as given by process_answers().
|
|
||||||
"""
|
|
||||||
#TODO: preprocess questionaires
|
|
||||||
#DONE: correct ids
|
|
||||||
correct_id_df = correct_activity_qids(df_esm_activities_cleaned)
|
|
||||||
#DONE: process subquestions
|
|
||||||
ids = correct_id_df["correct_ids"]
|
|
||||||
main_q_indices = ids[ids==44].index
|
|
||||||
q_group = []
|
|
||||||
i=-1
|
|
||||||
for id in ids:
|
|
||||||
if(id==44):
|
|
||||||
i=i+1
|
|
||||||
q_group.append(i)
|
|
||||||
correct_id_df["q_group"] = q_group
|
|
||||||
ans_seq = correct_id_df.groupby("q_group").agg({"esm_user_answer_numeric":lambda group: list(group)}).rename(columns={"esm_user_answer_numeric":"ans_seq"})
|
|
||||||
ans_seq.set_index(main_q_indices,inplace=True)
|
|
||||||
# correct_id_df["ans_seq"] = [[] for i in range(len(correct_id_df))]
|
|
||||||
# correct_id_df["ans_seq"].loc[main_q_indices] = correct_id_df.groupby("q_group").agg({"esm_user_answer_numeric":lambda group: list(group)}).values.reshape(-1)
|
|
||||||
#DONE: find types of status for each main question: socializing:[none,irl,online,unknown], num_people:[0,1,2,>2,unknown]
|
|
||||||
processed_ans_df = process_answers(ans_seq)
|
|
||||||
# df_out = df_esm_activities_cleaned.join(test)
|
|
||||||
return df_esm_activities_cleaned.join(processed_ans_df)
|
|
||||||
|
|
||||||
|
|
||||||
"""
|
|
||||||
possible answer sequences for LTM question chains
|
|
||||||
|
|
||||||
#alone
|
|
||||||
0,0,0 not social
|
|
||||||
0,0,1 not social
|
|
||||||
0,1,0 not social
|
|
||||||
0,1,1 not social
|
|
||||||
0,2 not social
|
|
||||||
0,3 not social
|
|
||||||
0,4 not social
|
|
||||||
0,5 not social
|
|
||||||
0,6 not social
|
|
||||||
#w/ others
|
|
||||||
1,0,0,0 1 irl
|
|
||||||
1,0,0,1 2 irl
|
|
||||||
1,0,0,2 3+ irl
|
|
||||||
1,0,1,0 1 irl
|
|
||||||
1,0,1,1 2 irl
|
|
||||||
1,0,1,2 3+ irl
|
|
||||||
1,1,0,0 1 online
|
|
||||||
1,1,0,1 2 online
|
|
||||||
1,1,0,2 3+ online
|
|
||||||
1,1,1,0 1 online
|
|
||||||
1,1,1,1 2 online
|
|
||||||
1,1,1,2 3+ online
|
|
||||||
1,2 positive likely to be more than 2
|
|
||||||
1,3 positive
|
|
||||||
#break
|
|
||||||
2,0 ambiguous
|
|
||||||
2,1 positive irl
|
|
||||||
2,2 ambiguous
|
|
||||||
2,3 ambiguous
|
|
||||||
#transit
|
|
||||||
3,0 ambiguous
|
|
||||||
3,1 ambiguous
|
|
||||||
3,2 ambiguous
|
|
||||||
"""
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#TODO: docstring
|
|
||||||
def process_answers(df:pd.DataFrame)-> pd.DataFrame:
|
|
||||||
""" Function to process answer sequences for LTM question chains. It checks the chain of subquestion answers and extracts the following attributes:
|
|
||||||
> n_others: Number of other people interacted with in the last 10 minutes
|
|
||||||
- -1: Number is positive but unknown exactly
|
|
||||||
- 0: No people/alone
|
|
||||||
- 1: One extra person
|
|
||||||
- 2: Two extra people
|
|
||||||
- 3: More than two extra people
|
|
||||||
- NaN : Can't say anything with enough certainty.
|
|
||||||
> inperson:
|
|
||||||
- True/False: The interaction in question was/wasn't in person.
|
|
||||||
- None: Can't say anything with enough certainty.
|
|
||||||
> formal:
|
|
||||||
- True/False: The interaction in question was/wasn't formal.
|
|
||||||
- None: Can't say anything with enough certainty.
|
|
||||||
Args:
|
|
||||||
df (pd.DataFrame): _description_
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
pd.DataFrame: _description_
|
|
||||||
"""
|
|
||||||
properties = {"n_others":[],
|
|
||||||
"inperson":[],
|
|
||||||
"formal":[]}
|
|
||||||
for ans_seq in df["ans_seq"]:
|
|
||||||
n_other = None
|
|
||||||
inperson = None
|
|
||||||
formal = None
|
|
||||||
if(ans_seq[0]==0):
|
|
||||||
n_other = 0
|
|
||||||
elif(ans_seq[0]==1):
|
|
||||||
if(ans_seq[1]==3):
|
|
||||||
n_other = -1 # anwsered "Other" but did work with other people
|
|
||||||
elif(ans_seq[1]==2):
|
|
||||||
n_other = 3 #assuming more than 2 people participated in the lecture or presentation
|
|
||||||
elif(ans_seq[1] in [0,1]):
|
|
||||||
inperson = ans_seq[1]==0 #ans[1]==0, means irl interaction, ==1 means online or phone
|
|
||||||
formal = ans_seq[2]==0#0 means formal
|
|
||||||
n_other = ans_seq[3]+1 #ans3 is on [0,2] so we add 1 to make it [1,3]
|
|
||||||
elif(ans_seq[0]==2):
|
|
||||||
formal = False#assuming one does not have a formal meeting during break time
|
|
||||||
if(ans_seq[1]==1):
|
|
||||||
n_other = -1
|
|
||||||
inperson = True
|
|
||||||
#if not 1 then we dont know anythong for sure
|
|
||||||
elif(ans_seq[0]==3):
|
|
||||||
#we cant say whether the persion was carpooling or driving alone.
|
|
||||||
pass
|
|
||||||
properties["n_others"].append(n_other)
|
|
||||||
properties["inperson"].append(inperson)
|
|
||||||
properties["formal"].append(formal)
|
|
||||||
|
|
||||||
|
|
||||||
#df = df.join(pd.DataFrame(properties,index=df.index))
|
|
||||||
return pd.DataFrame(properties,index=df.index)
|
|
||||||
|
|
||||||
def correct_activity_qids(df:pd.DataFrame)->pd.DataFrame:
|
|
||||||
"""_summary_
|
|
||||||
|
|
||||||
Args:
|
|
||||||
df (pd.DataFrame): _description_
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
pd.DataFrame: Input dataframe with added column "correct_ids"
|
|
||||||
"""
|
|
||||||
df["correct_ids"] = df["esm_instructions"].apply(lambda x: qc2id[x])
|
|
||||||
return df
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def process_answers_aggregation(df:pd.core.groupby.generic.DataFrameGroupBy)-> pd.core.groupby.generic.DataFrameGroupBy:
|
|
||||||
""" Function to process answer sequences for LTM question chains. It checks the chain of subquestion answers and extracts the following attributes:
|
|
||||||
> n_others: Number of other people interacted with in the last 10 minutes
|
|
||||||
- -1: Number is positive but unknown exactly
|
|
||||||
- 0: No people/alone
|
|
||||||
- 1: One extra person
|
|
||||||
- 2: Two extra people
|
|
||||||
- 3: More than two extra people
|
|
||||||
- NaN : Can't say anything with enough certainty.
|
|
||||||
> inperson:
|
|
||||||
- True/False: The interaction in question was/wasn't in person.
|
|
||||||
- None: Can't say anything with enough certainty.
|
|
||||||
> formal:
|
|
||||||
- True/False: The interaction in question was/wasn't formal.
|
|
||||||
- None: Can't say anything with enough certainty.
|
|
||||||
Args:
|
|
||||||
df (pd.DataFrame): _description_
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
pd.DataFrame: _description_
|
|
||||||
"""
|
|
||||||
|
|
||||||
#print("=======================\nAPPLY START:\ndf=",df.columns,df.local_segment)
|
|
||||||
properties = {"n_others":[],
|
|
||||||
"inperson":[],
|
|
||||||
"formal":[]}
|
|
||||||
ans_seq = df["esm_user_answer_numeric"].values
|
|
||||||
n_other = None
|
|
||||||
inperson = None
|
|
||||||
formal = None
|
|
||||||
if(ans_seq[0]==0):
|
|
||||||
n_other = 0
|
|
||||||
elif(ans_seq[0]==1):
|
|
||||||
if(ans_seq[1]==3):
|
|
||||||
n_other = -1 # anwsered "Other" but did work with other people
|
|
||||||
elif(ans_seq[1]==2):
|
|
||||||
n_other = 3 #assuming more than 2 people participated in the lecture or presentation
|
|
||||||
elif(ans_seq[1] in [0,1]):
|
|
||||||
inperson = ans_seq[1]==0 #ans[1]==0, means irl interaction, ==1 means online or phone
|
|
||||||
formal = ans_seq[2]==0#0 means formal
|
|
||||||
n_other = ans_seq[3]+1 #ans3 is on [0,2] so we add 1 to make it [1,3]
|
|
||||||
elif(ans_seq[0]==2):
|
|
||||||
formal = False#assuming one does not have a formal meeting during break time
|
|
||||||
if(ans_seq[1]==1):
|
|
||||||
n_other = -1
|
|
||||||
inperson = True
|
|
||||||
#if not 1 then we dont know anythong for sure
|
|
||||||
elif(ans_seq[0]==3):
|
|
||||||
#we cant say whether the persion was carpooling or driving alone.
|
|
||||||
pass
|
|
||||||
properties["n_others"].append(n_other)
|
|
||||||
properties["inperson"].append(inperson)
|
|
||||||
properties["formal"].append(formal)
|
|
||||||
|
|
||||||
|
|
||||||
df = df.join(pd.DataFrame(properties,index=df.index))
|
|
||||||
#print("APPLY END:\ndf=",df[["n_others","inperson","formal"]])
|
|
||||||
|
|
||||||
return df
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#test stuff
|
|
||||||
def test():
|
|
||||||
from esm_preprocess import preprocess_esm,clean_up_esm
|
|
||||||
df = pd.read_csv("data/raw/p031/phone_esm_with_datetime.csv")
|
|
||||||
df = preprocess_esm(df)
|
|
||||||
df = clean_up_esm(df)
|
|
||||||
df = df[df["questionnaire_id"]==97]
|
|
||||||
original = esm_activities_LTM_features(df)
|
|
||||||
df["local_segment"] = [str(i)+":"+j for i,j in df[["esm_session","device_id"]].values]
|
|
||||||
temp = df.groupby("local_segment")
|
|
||||||
temp2 = temp.apply(process_answers_aggregation)
|
|
||||||
|
|
||||||
#compare with original function results
|
|
||||||
selection = original[original["correct_ids"]==44][["n_others", "inperson", "formal"]]
|
|
||||||
temp_selection = temp2.loc[selection.index]
|
|
||||||
temp_selection.compare(selection,keep_shape=True,keep_equal =True)
|
|
||||||
|
|
||||||
#print out ans_seq processing results
|
|
||||||
# import json
|
|
||||||
# i = 0
|
|
||||||
# for j,ans in correct_id_df[["esm_json","esm_user_answer"]].values:
|
|
||||||
# obj = json.loads(j)
|
|
||||||
# text = obj["esm_instructions"]
|
|
||||||
# if ("10 minut" in text):
|
|
||||||
# print("---\n",test.ans_seq.iloc[i])
|
|
||||||
# print(test[["n_others","inperson","formal"]].values[i])
|
|
||||||
# i = i+1
|
|
||||||
# print(text,ans)
|
|
||||||
|
|
||||||
#test()
|
|
|
@ -1,8 +1,4 @@
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import sys
|
|
||||||
import warnings
|
|
||||||
sys.path.append('src/features/phone_esm/straw')
|
|
||||||
from esm_activities import esm_activities_LTM_features,process_answers_aggregation
|
|
||||||
|
|
||||||
QUESTIONNAIRE_IDS = {
|
QUESTIONNAIRE_IDS = {
|
||||||
"sleep_quality": 1,
|
"sleep_quality": 1,
|
||||||
|
@ -43,49 +39,23 @@ QUESTIONNAIRE_IDS = {
|
||||||
|
|
||||||
def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||||
esm_data = pd.read_csv(sensor_data_files["sensor_data"])
|
esm_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||||
|
|
||||||
requested_features = provider["FEATURES"]
|
requested_features = provider["FEATURES"]
|
||||||
# name of the features this function can compute
|
# name of the features this function can compute
|
||||||
requested_scales = provider["SCALES"]
|
requested_scales = provider["SCALES"]
|
||||||
base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
|
base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
|
||||||
"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge","activities_n_others","activities_inperson","activities_formal"]
|
"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
|
||||||
#TODO Check valid questionnaire and feature names.
|
#TODO Check valid questionnaire and feature names.
|
||||||
# the subset of requested features this function can compute
|
# the subset of requested features this function can compute
|
||||||
features_to_compute = list(set(requested_features) & set(base_features_names))
|
features_to_compute = list(set(requested_features) & set(base_features_names))
|
||||||
esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
|
esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
|
||||||
if not esm_data.empty:
|
if not esm_data.empty:
|
||||||
esm_data = filter_data_by_segment(esm_data, time_segment)
|
esm_data = filter_data_by_segment(esm_data, time_segment)
|
||||||
|
|
||||||
if not esm_data.empty:
|
if not esm_data.empty:
|
||||||
esm_features = pd.DataFrame()
|
esm_features = pd.DataFrame()
|
||||||
for scale in requested_scales:
|
for scale in requested_scales:
|
||||||
questionnaire_id = QUESTIONNAIRE_IDS[scale]
|
questionnaire_id = QUESTIONNAIRE_IDS[scale]
|
||||||
mask = esm_data["questionnaire_id"] == questionnaire_id
|
mask = esm_data["questionnaire_id"] == questionnaire_id
|
||||||
#print(esm_data.loc[mask].head())
|
|
||||||
#print(time_segment)
|
|
||||||
if not mask.any():
|
|
||||||
temp = sensor_data_files["sensor_data"]
|
|
||||||
warnings.warn(f"Warning........... No relevant questions for scale {scale} in {temp}-{time_segment}",RuntimeWarning)
|
|
||||||
continue
|
|
||||||
#TODO: calculation of LTM features
|
|
||||||
if scale=="activities":
|
|
||||||
requested_subset = [req for req in requested_features if req.startswith("activities")]
|
|
||||||
if not bool(requested_subset):
|
|
||||||
continue
|
|
||||||
# ltm_features = esm_activities_LTM_features(esm_data.loc[mask])
|
|
||||||
# print(esm_data["esm_json"].values)
|
|
||||||
# print(mask)
|
|
||||||
# print(esm_data.loc[mask])
|
|
||||||
# #ltm_features = ltm_features[ltm_features["correct_ids"==44]]
|
|
||||||
#print(esm_data.loc[mask]["local_segment"])
|
|
||||||
ltm_features = esm_data.loc[mask].groupby(["local_segment"]).apply(process_answers_aggregation)
|
|
||||||
#print("PRINTING ltm_features:\n",ltm_features)
|
|
||||||
ltm_features.rename(columns={"n_others":"activities_n_others","inperson":"activities_inperson","formal":"activities_formal"},inplace=True)
|
|
||||||
esm_features[requested_subset] = ltm_features.groupby("local_segment").first()[requested_subset]
|
|
||||||
#print(esm_features.columns)
|
|
||||||
#print("PRINTING esm_features after rename:\n",ltm_features)
|
|
||||||
#FIXME: it might be an issue that im calculating for whole time segment and not grouping by "local segment"
|
|
||||||
#print("~~~~~~~~~~~~~~~~~~~~~~~~===============================~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n LTM FEATURES STORED... AFTER RETURN:\n",ltm_features,esm_features[["activities_"+req for req in requested_subset]])
|
|
||||||
if("mean" in features_to_compute):
|
|
||||||
esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
|
esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
|
||||||
#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
|
#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
|
||||||
|
|
||||||
|
@ -94,15 +64,3 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
||||||
esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
|
esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
|
||||||
|
|
||||||
return esm_features
|
return esm_features
|
||||||
|
|
||||||
def test_main():
|
|
||||||
import temp_help
|
|
||||||
provider = {
|
|
||||||
"FEATURES":["mean","activities_n_others","activities_inperson","activities_formal"],
|
|
||||||
"SCALES":['activities']
|
|
||||||
}
|
|
||||||
sensor_data_files = {"sensor_data":"data/interim/p069/phone_esm_clean.csv"}
|
|
||||||
s_feat = straw_features(sensor_data_files,"straw_event_stress_event_p069_110",provider,temp_help.filter_data_by_segment)
|
|
||||||
print(s_feat)
|
|
||||||
|
|
||||||
#test_main()
|
|
|
@ -67,7 +67,7 @@ def extract_ers(esm_df):
|
||||||
|
|
||||||
segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
|
segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
|
||||||
|
|
||||||
if segmenting_method in ["10_before", "30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
|
if segmenting_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
|
||||||
""" '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
|
""" '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
|
||||||
Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
|
Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
|
||||||
All questionnaire durations over 15 minutes are excluded from the querying.
|
All questionnaire durations over 15 minutes are excluded from the querying.
|
||||||
|
@ -79,18 +79,7 @@ def extract_ers(esm_df):
|
||||||
extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
|
extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
|
||||||
extracted_ers["shift_direction"] = -1
|
extracted_ers["shift_direction"] = -1
|
||||||
|
|
||||||
if segmenting_method == "10_before":
|
if segmenting_method == "30_before":
|
||||||
"""The method 10-minutes before simply takes 10 minutes before the questionnaire and sums it with the questionnaire duration.
|
|
||||||
The timestamps are formatted with the help of format_timestamp() method.
|
|
||||||
"""
|
|
||||||
time_before_questionnaire = 10 * 60 # in seconds (10 minutes)
|
|
||||||
#TODO: split into small segments with manipulating lenght and shift
|
|
||||||
extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
|
|
||||||
extracted_ers["shift"] = time_before_questionnaire
|
|
||||||
extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
|
|
||||||
|
|
||||||
|
|
||||||
elif segmenting_method == "30_before":
|
|
||||||
"""The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
|
"""The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
|
||||||
The timestamps are formatted with the help of format_timestamp() method.
|
The timestamps are formatted with the help of format_timestamp() method.
|
||||||
"""
|
"""
|
||||||
|
|
|
@ -1,70 +0,0 @@
|
||||||
"""This file is TEMPORARY and intended for testing main.py
|
|
||||||
"""
|
|
||||||
|
|
||||||
def filter_data_by_segment(data, time_segment):
|
|
||||||
data.dropna(subset=["assigned_segments"], inplace=True)
|
|
||||||
if(data.shape[0] == 0): # data is empty
|
|
||||||
data["local_segment"] = data["timestamps_segment"] = None
|
|
||||||
return data
|
|
||||||
|
|
||||||
datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
|
|
||||||
timestamps_regex = "[0-9]{13}"
|
|
||||||
segment_regex = "\[({}#{},{};{},{})\]".format(time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex)
|
|
||||||
data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True)
|
|
||||||
data = data.drop(columns=["assigned_segments"])
|
|
||||||
data = data.dropna(subset = ["local_segment"])
|
|
||||||
if(data.shape[0] == 0): # there are no rows belonging to time_segment after droping na
|
|
||||||
data["timestamps_segment"] = None
|
|
||||||
else:
|
|
||||||
data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True)
|
|
||||||
|
|
||||||
# chunk episodes
|
|
||||||
if (not data.empty) and ("start_timestamp" in data.columns) and ("end_timestamp" in data.columns):
|
|
||||||
data = chunk_episodes(data)
|
|
||||||
|
|
||||||
return data
|
|
||||||
|
|
||||||
def chunk_episodes(sensor_episodes):
|
|
||||||
import copy
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
# Deduplicate episodes
|
|
||||||
# Drop rows where segments of start_timestamp and end_timestamp are the same
|
|
||||||
sensor_episodes = sensor_episodes.drop_duplicates(subset=["start_timestamp", "end_timestamp", "local_segment"], keep="first")
|
|
||||||
|
|
||||||
# Delete useless columns
|
|
||||||
for drop_col in ["local_date_time", "local_date", "local_time", "local_hour", "local_minute"]:
|
|
||||||
del sensor_episodes[drop_col]
|
|
||||||
|
|
||||||
# Avoid SettingWithCopyWarning
|
|
||||||
sensor_episodes = sensor_episodes.copy()
|
|
||||||
|
|
||||||
# Unix timestamp for current segment in milliseconds
|
|
||||||
sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True).astype(int)
|
|
||||||
|
|
||||||
# Compute chunked timestamp
|
|
||||||
sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1)
|
|
||||||
sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["end_timestamp", "segment_end_timestamp"]].min(axis=1)
|
|
||||||
|
|
||||||
# Compute duration: intersection of current row and segment
|
|
||||||
sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60)
|
|
||||||
|
|
||||||
# Merge episodes
|
|
||||||
cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "chunked_start_timestamp", "chunked_end_timestamp"]]
|
|
||||||
|
|
||||||
sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby, sort=False, dropna=False)
|
|
||||||
merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum()
|
|
||||||
|
|
||||||
merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first()
|
|
||||||
merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last()
|
|
||||||
|
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||||||
merged_sensor_episodes.reset_index(inplace=True)
|
|
||||||
|
|
||||||
# Compute datetime
|
|
||||||
merged_sensor_episodes["local_start_date_time"] = pd.to_datetime(merged_sensor_episodes["start_timestamp"], unit="ms", utc=True)
|
|
||||||
merged_sensor_episodes["local_start_date_time"] = pd.concat([data["local_start_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).apply(lambda x: x.tz_localize(None).replace(microsecond=0))
|
|
||||||
|
|
||||||
merged_sensor_episodes["local_end_date_time"] = pd.to_datetime(merged_sensor_episodes["end_timestamp"], unit="ms", utc=True)
|
|
||||||
merged_sensor_episodes["local_end_date_time"] = pd.concat([data["local_end_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).apply(lambda x: x.tz_localize(None).replace(microsecond=0))
|
|
||||||
|
|
||||||
return merged_sensor_episodes
|
|
|
@ -14,7 +14,6 @@ def import_path(path):
|
||||||
sys.modules[module_name] = module
|
sys.modules[module_name] = module
|
||||||
return module
|
return module
|
||||||
|
|
||||||
#TODO:check why segments change to int
|
|
||||||
def filter_data_by_segment(data, time_segment):
|
def filter_data_by_segment(data, time_segment):
|
||||||
data.dropna(subset=["assigned_segments"], inplace=True)
|
data.dropna(subset=["assigned_segments"], inplace=True)
|
||||||
if(data.shape[0] == 0): # data is empty
|
if(data.shape[0] == 0): # data is empty
|
||||||
|
@ -152,7 +151,6 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
||||||
|
|
||||||
else:
|
else:
|
||||||
segment_colums = pd.DataFrame()
|
segment_colums = pd.DataFrame()
|
||||||
print(sensor_features,sensor_features['local_segment'])
|
|
||||||
sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||||
split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||||
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
||||||
|
|
Loading…
Reference in New Issue