Update params & docs of sleep features
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config.yaml
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config.yaml
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@ -321,19 +321,19 @@ FITBIT_DATA_STREAMS:
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# AVAILABLE:
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# AVAILABLE:
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fitbitjson_mysql:
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fitbitjson_mysql:
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DATABASE_GROUP: MY_GROUP
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DATABASE_GROUP: MY_GROUP
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SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
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SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after the midnight. By default, 660 (11:00).
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fitbitparsed_mysql:
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fitbitparsed_mysql:
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DATABASE_GROUP: MY_GROUP
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DATABASE_GROUP: MY_GROUP
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SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
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SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after the midnight. By default, 660 (11:00).
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fitbitjson_csv:
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fitbitjson_csv:
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FOLDER: data/external/fitbit_csv
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FOLDER: data/external/fitbit_csv
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SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
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SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after the midnight. By default, 660 (11:00).
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fitbitparsed_csv:
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fitbitparsed_csv:
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FOLDER: data/external/fitbit_csv
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FOLDER: data/external/fitbit_csv
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SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp.
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SLEEP_SUMMARY_LAST_NIGHT_END: 660 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after the midnight. By default, 660 (11:00).
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# Sensors ------
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# Sensors ------
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@ -386,7 +386,7 @@ FITBIT_SLEEP_SUMMARY:
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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: False
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FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
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FEATURES: ["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
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SLEEP_TYPES: ["main", "nap", "all"]
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SLEEP_TYPES: ["main", "nap", "all"]
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SRC_SCRIPT: src/features/fitbit_sleep_summary/rapids/main.py
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SRC_SCRIPT: src/features/fitbit_sleep_summary/rapids/main.py
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@ -397,31 +397,30 @@ FITBIT_SLEEP_INTRADAY:
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RAPIDS:
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RAPIDS:
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COMPUTE: False
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COMPUTE: False
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FEATURES:
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FEATURES:
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LEVELS_AND_TYPES_COMBINING_ALL: True
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LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
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LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
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RATIOS_TYPE: [count, duration]
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RATIOS_TYPE: [count, duration]
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RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
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RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
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ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
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ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
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SLEEP_LEVELS:
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SLEEP_LEVELS:
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INCLUDE_ALL_GROUPS: True
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CLASSIC: [awake, restless, asleep]
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CLASSIC: [awake, restless, asleep]
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STAGES: [wake, deep, light, rem]
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STAGES: [wake, deep, light, rem]
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UNIFIED: [awake, asleep]
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UNIFIED: [awake, asleep]
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SLEEP_TYPES: [main, nap]
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SLEEP_TYPES: [main, nap, all]
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INCLUDE_SLEEP_LATER_THAN: 0 # a number ranged from 0 (midnight) to 1439 (23:59)
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LAST_NIGHT_END: 0 # a number ranged from 0 (midnight) to 1439 (23:59) which denotes number of minutes after the midnight
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REFERENCE_TIME: MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT"
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ROUTINE_REFERENCE_TIME: MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT"
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SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
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SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
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PRICE:
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PRICE:
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COMPUTE: False
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COMPUTE: False
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FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain]
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FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
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SLEEP_LEVELS:
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SLEEP_LEVELS:
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INCLUDE_ALL_GROUPS: True
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CLASSIC: [awake, restless, asleep]
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CLASSIC: [awake, restless, asleep]
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STAGES: [wake, deep, light, rem]
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STAGES: [wake, deep, light, rem]
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UNIFIED: [awake, asleep]
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UNIFIED: [awake, asleep]
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DAY_TYPES: [WEEKEND, WEEK, ALL]
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DAY_TYPES: [WEEKEND, WEEK, ALL]
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GROUP_EPISODES_WITHIN: # by default: today's 6pm to tomorrow's noon
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LAST_NIGHT_END: 660 # number of minutes after the midnight (11:00) 11*60
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START_TIME: 1080 # number of minutes after the midnight (18:00) 18*60
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LENGTH: 1080 # in minutes (18 hours) 18*60
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SRC_SCRIPT: src/features/fitbit_sleep_intraday/price/main.py
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SRC_SCRIPT: src/features/fitbit_sleep_intraday/price/main.py
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# See https://www.rapids.science/latest/features/fitbit-steps-summary/
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# See https://www.rapids.science/latest/features/fitbit-steps-summary/
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@ -1,5 +1,9 @@
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# Change Log
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# Change Log
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## v1.2.0
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- Sleep summary and intraday features are more consistent.
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- Add wake and bedtime features for sleep summary data.
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- Fix bugs with sleep PRICE features.
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## v1.1.1
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## v1.1.1
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- Fix length of periodic segments on days with DLS
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- Fix length of periodic segments on days with DLS
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- Fix crash when scraping data for an app that does not exist
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- Fix crash when scraping data for an app that does not exist
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@ -8,6 +8,10 @@ Sensor parameters description for `[FITBIT_SLEEP_INTRADAY]`:
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## RAPIDS provider
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## RAPIDS provider
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!!! hint "Understanding RAPIDS features"
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[This diagram](../../img/sleep_intraday_rapids.png) will help you understand how sleep episodes are chunked and grouped within time segments and `LNE-LNE` intervals for the RAPIDS provider.
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!!! info "Available time segments"
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!!! info "Available time segments"
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- Available for all time segments
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- Available for all time segments
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@ -29,23 +33,23 @@ Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]`:
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `RAPIDS` provider|
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|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `RAPIDS` provider|
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|`[FEATURES]` | Features to be computed from sleep intraday data, see table below |
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|`[FEATURES]` | Features to be computed from sleep intraday data, see table below |
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|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS grouped them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`).
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|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS groups them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`). In this section, there is a boolean flag named `INCLUDE_ALL_GROUPS` that if set to TRUE, computes LEVELS_AND_TYPES features grouping all levels together in a single `all` category.
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|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. Fitbit provides 2 types of sleep: `main`, `nap`.
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|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. There are three sleep types: `main`, `nap`, and `all`. The `all` type means both main sleep and naps are considered.
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|`[INCLUDE_SLEEP_LATER_THAN]`| All resampled sleep rows (bin interval: one minute) that started after this time will be included in the feature computation. It is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. If a segment is longer than one day, this value is for every day.
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|`[LAST_NIGHT_END]`| All resampled sleep rows (bin interval: one minute) that started after this time will be included in the feature computation. It ranges from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. If a segment is longer than one day, this value is applied every day.
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|`[REFERENCE_TIME]`| The reference point from which the `[ROUTINE]` features are to be computed. Chosen from `MIDNIGHT` and `START_OF_THE_SEGMENT`, default is `MIDNIGHT`. If you have multiple time segments per day it might be more informative to set this flag to `START_OF_THE_SEGMENT`.
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|`[ROUTINE_REFERENCE_TIME]`| The reference point from which the `[ROUTINE]` features are computed, it can be `MIDNIGHT` or `START_OF_THE_SEGMENT`, default is `MIDNIGHT`. If you have multiple time segments per day it might be more informative to set this flag to `START_OF_THE_SEGMENT`.
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Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]`:
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Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]`:
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|Feature |Units |Description |
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|Feature |Units |Description |
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|------------------------------- |-------------- |-------------------------------------------------------------|
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|------------------------------- |-------------- |-------------------------------------------------------------|
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|countepisode`[LEVEL][TYPE]` |episodes |Number of `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]` can also be `all` when ``LEVELS_AND_TYPES_COMBINING_ALL`` is True, which ignores the levels and groups by sleep types.
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|countepisode`[LEVEL][TYPE]` |episodes |Number of `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|sumduration`[LEVEL][TYPE]` |minutes |Total duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|sumduration`[LEVEL][TYPE]` |minutes |Total duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|maxduration`[LEVEL][TYPE]` |minutes | Longest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|maxduration`[LEVEL][TYPE]` |minutes | Longest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|minduration`[LEVEL][TYPE]` |minutes | Shortest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|minduration`[LEVEL][TYPE]` |minutes | Shortest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|avgduration`[LEVEL][TYPE]` |minutes | Average duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|avgduration`[LEVEL][TYPE]` |minutes | Average duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|medianduration`[LEVEL][TYPE]` |minutes | Median duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|medianduration`[LEVEL][TYPE]` |minutes | Median duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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|stdduration`[LEVEL][TYPE]` |minutes | Standard deviation duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|stdduration`[LEVEL][TYPE]` |minutes | Standard deviation duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). `[LEVEL]` can also be `all` when `INCLUDE_ALL_GROUPS` is True, which ignores the levels and groups by sleep types.
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Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_LEVELS]`:
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Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_LEVELS]`:
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@ -84,22 +88,29 @@ Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS][ROUTINE]`:
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|Feature |Units |Description |
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|Feature |Units |Description |
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|--------------------------------- |-------------- |-------------------------------------------------------------|
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|--------------------------------- |-------------- |-------------------------------------------------------------|
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|starttimefirstmainsleep |minutes |Start time (in minutes since `REFERENCE_TIME`) of the first main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|starttimefirstmainsleep |minutes |Start time (in minutes since `ROUTINE_REFERENCE_TIME`) of the first main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastmainsleep |minutes |End time (in minutes since `REFERENCE_TIME`) of the last main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastmainsleep |minutes |End time (in minutes since `ROUTINE_REFERENCE_TIME`) of the last main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
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|starttimefirstnap |minutes |Start time (in minutes since `REFERENCE_TIME`) of the first nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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|starttimefirstnap |minutes |Start time (in minutes since `ROUTINE_REFERENCE_TIME`) of the first nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastnap |minutes |End time (in minutes since `REFERENCE_TIME`) of the last nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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|endtimelastnap |minutes |End time (in minutes since `ROUTINE_REFERENCE_TIME`) of the last nap episode after `INCLUDE_EPISODES_LATER_THAN`.
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!!! note "Assumptions/Observations"
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!!! note "Assumptions/Observations"
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1. Deleting values from `[SLEEP_LEVELS]` or `[SLEEP_TYPES]` will only change the features you receive from `[LEVELS_AND_TYPES]`. For example if `STAGES` only contains `[rem, light]` you will not receive `countepisode[wake|deep][TYPE]` or sum, max, min, avg, median, or std `duration`. These values will not influence `RATIOS` or `ROUTINE` features.
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1. [This diagram](../../img/sleep_intraday_rapids.png) will help you understand how sleep episodes are chunked and grouped within time segments and `LNE-LNE` intervals for the RAPIDS provider.
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2. Any `[LEVEL]` grouping is done within the elements of each class `CLASSIC`, `STAGES`, and `UNIFIED`. That is, we never combine `CLASSIC` or `STAGES` types to compute features when `LEVELS_AND_TYPES_COMBINING_ALL` is True or when computing `RATIOS`.
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1. Features listed in `[LEVELS_AND_TYPES]` are computed for any levels and types listed in `[SLEEP_LEVELS]` or `[SLEEP_TYPES]`. For example if `STAGES` only contains `[rem, light]` you will not get `countepisode[wake|deep][TYPE]` or sum, max, min, avg, median, or std `duration`. Levels or types in these lists do not influence `RATIOS` or `ROUTINE` features.
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2. Any `[LEVEL]` grouping is done within the elements of each class `CLASSIC`, `STAGES`, and `UNIFIED`. That is, we never combine `CLASSIC` or `STAGES` types to compute features.
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3. The categories for `all` levels (when `INCLUDE_ALL_GROUPS` is `True`) and `all` `SLEEP_TYPES` are not considered for `RATIOS` features as they are always 1.
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3. These features can be computed in time segments of any length, but only the 1-minute sleep chunks within each segment instance will be used.
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4. Within any time segment instance, any chunks with a local time before `LAST_NIGHT_END` will be discarded. The default `LNE` is 00:00 so no chunks are ignored.
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5. `ROUTINE_REFERENCE_TIME` influences all the `[ROUTINE]` features. If `MIDNIGHT`, the reference for these times is 00:00, if `START_OF_THE_SEGMENT`, the reference time is the start of each segment instance.
|
||||||
|
|
||||||
## PRICE provider
|
## PRICE provider
|
||||||
|
|
||||||
|
!!! hint "Understanding PRICE features"
|
||||||
|
[This diagram](../../img/sleep_intraday_price.png) will help you understand how sleep episodes are chunked and grouped within time segments and `LNE-LNE` intervals for the PRICE provider.
|
||||||
|
|
||||||
!!! info "Available time segments"
|
!!! info "Available time segments"
|
||||||
- Available for any time segments larger or equal to one day
|
- Available for any time segments larger or equal to one day
|
||||||
|
|
||||||
|
@ -120,94 +131,40 @@ Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE]`:
|
||||||
|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||||
|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `PRICE` provider |
|
|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `PRICE` provider |
|
||||||
|`[FEATURES]` | Features to be computed from sleep intraday data, see table below
|
|`[FEATURES]` | Features to be computed from sleep intraday data, see table below
|
||||||
|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS grouped them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`).
|
|`[SLEEP_LEVELS]` | Fitbit’s sleep API Version 1 only provides `CLASSIC` records. However, Version 1.2 provides 2 types of records: `CLASSIC` and `STAGES`. `STAGES` is only available in devices with a heart rate sensor and even those devices will fail to report it if the battery is low or the device is not tight enough. While `CLASSIC` contains 3 sleep levels (`awake`, `restless`, and `asleep`), `STAGES` contains 4 sleep levels (`wake`, `deep`, `light`, `rem`). To make it consistent, RAPIDS groups them into 2 `UNIFIED` sleep levels: `awake` (`CLASSIC`: `awake` and `restless`; `STAGES`: `wake`) and `asleep` (`CLASSIC`: `asleep`; `STAGES`: `deep`, `light`, and `rem`). In this section, there is a boolean flag named `INCLUDE_ALL_GROUPS` that if set to TRUE, computes avgdurationallmain`[DAY_TYPE]` features grouping all levels together in a single `all` category.
|
||||||
|`[DAY_TYPE]` | The features of this provider can be computed using daily averages/standard deviations that were extracted on `WEEKEND` days only, `WEEK` days only, or `ALL` days|
|
|`[DAY_TYPE]` | The features of this provider can be computed using daily averages/standard deviations that were extracted on `WEEKEND` days only, `WEEK` days only, or `ALL` days|
|
||||||
|`[GROUP_EPISODES_WITHIN]` | This parameter contains 2 values: `[START_TIME]` and `[LENGTH]`. Only `main` sleep episodes that intersect or contain the period between [`START_TIME`, `START_TIME` + `LENGTH`] are taken into account to compute the features described below. Both `[START_TIME]` and `[LENGTH]` are in minutes. `[START_TIME]` is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. `[LENGTH]` is a number smaller than 1440 (24 hours). |
|
|`[LAST_NIGHT_END]` | Only `main` sleep episodes that start within the `LNE-LNE` interval [`LAST_NIGHT_END`, `LAST_NIGHT_END` + 23H 59M 59S] are taken into account to compute the features described below. `[LAST_NIGHT_END]` is a number ranging from 0 (midnight) to 1439 (23:59). |
|
||||||
|
|
||||||
|
|
||||||
Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE]`:
|
Features description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE]`:
|
||||||
|
|
||||||
|Feature |Units |Description |
|
|Feature |Units |Description |
|
||||||
|------------------------------------- |----------------- |-------------------------------------------------------------|
|
|------------------------------------- |----------------- |-------------------------------------------------------------|
|
||||||
|avgduration`[LEVEL]`main`[DAY_TYPE]` |minutes | Average duration of daily `LEVEL` sleep episodes. You can include daily average that were computed on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|avgduration`[LEVEL]`main`[DAY_TYPE]` |minutes | Average duration of daily sleep chunks of a `LEVEL`. Use the `DAY_TYPE` flag to include daily durations from weekend days only, weekdays, or both. Use `[LEVEL]` to group all levels in a single `all` category.
|
||||||
|avgratioduration`[LEVEL]`withinmain`[DAY_TYPE]` |- | Average ratio between daily `LEVEL` time and in-bed time inferred from `main` sleep episodes. `LEVEL` is one of `SLEEP_LEVELS` (e.g. awake-classic or rem-stages). In-bed time is the total duration of all `main` sleep episodes for each day. You can include daily ratios that were computed on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|avgratioduration`[LEVEL]`withinmain`[DAY_TYPE]` |- | Average of the daily ratio between the duration of sleep chunks of a `LEVEL` and total duration of all `main` sleep episodes in a day. When `INCLUDE_ALL_GROUPS` is `True` the `all` `LEVEL` is ignored since this feature is always 1. Use the `DAY_TYPE` flag to include start times from weekend days only, weekdays, or both.
|
||||||
|avgstarttimeofepisodemain`[DAY_TYPE]` |minutes | Average start time of the first `main` sleep episode of each day in a time segment. You can include daily start times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|avgstarttimeofepisodemain`[DAY_TYPE]` |minutes | Average of all start times of the first `main` sleep episode within each `LNE-LNE` interval in a time segment. Use the `DAY_TYPE` flag to include start times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|avgendtimeofepisodemain`[DAY_TYPE]` |minutes | Average end time of the last `main` sleep episode of each day in a time segment. You can include daily end times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|avgendtimeofepisodemain`[DAY_TYPE]` |minutes | Average of all end times of the last `main` sleep episode within each `LNE-LNE` interval in a time segment. Use the `DAY_TYPE` flag to include end times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|avgmidpointofepisodemain`[DAY_TYPE]` |minutes | Average mid time between the start of the first `main` sleep episode and the end of the last `main` sleep episode of each day in a time segment. You can include episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|avgmidpointofepisodemain`[DAY_TYPE]` |minutes | Average of all the differences between `avgendtime...` and `avgstarttime..` in a time segment. Use the `DAY_TYPE` flag to include end times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|stdstarttimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of start time of the first `main` sleep episode of each day in a time segment. You can include daily start times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|stdstarttimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of all start times of the first `main` sleep episode within each `LNE-LNE` interval in a time segment. Use the `DAY_TYPE` flag to include start times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|stdendtimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of end time of the last `main` sleep episode of each day in a time segment. You can include daily end times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|stdendtimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of all end times of the last `main` sleep episode within each `LNE-LNE` interval in a time segment. Use the `DAY_TYPE` flag to include end times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|stdmidpointofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of mid time between the start of the first `main` sleep episode and the end of the last `main` sleep episode of each day in a time segment. You can include episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|
|stdmidpointofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of all the differences between `avgendtime...` and `avgstarttime..` in a time segment. Use the `DAY_TYPE` flag to include end times from `LNE-LNE` intervals that start on weekend days only, weekdays, or both.
|
||||||
|socialjetlag |minutes | Difference in minutes between the avgmidpointofepisodemain (average mid time between bedtime and wake time) of weekends and weekdays.
|
|socialjetlag |minutes | Difference in minutes between the avgmidpointofepisodemain of weekends and weekdays that belong to each time segment instance. If your time segment does not contain at least one week day and one weekend day this feature will be NA.
|
||||||
|meanssdstarttimeofepisodemain |minutes squared | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive start times.
|
|rmssdmeanstarttimeofepisodemain |minutes | Square root of the **mean** squared successive difference (RMSSD) between today's and yesterday's `starttimeofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the mean of how someone's `starttimeofepisodemain` (bedtime) changed from night to night.
|
||||||
|meanssdendtimeofepisodemain |minutes squared | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive end times.
|
|rmssdmeanendtimeofepisodemain |minutes | Square root of the **mean** squared successive difference (RMSSD) between today's and yesterday's `endtimeofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the mean of how someone's `endtimeofepisodemain` (wake time) changed from night to night.
|
||||||
|meanssdmidpointofepisodemain |minutes squared | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive mid times.
|
|rmssdmeanmidpointofepisodemain |minutes | Square root of the **mean** squared successive difference (RMSSD) between today's and yesterday's `midpointofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the mean of how someone's `midpointofepisodemain` (mid time between bedtime and wake time) changed from night to night.
|
||||||
|medianssdstarttimeofepisodemain |minutes squared | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive start times.
|
|rmssdmedianstarttimeofepisodemain |minutes | Square root of the **median** squared successive difference (RMSSD) between today's and yesterday's `starttimeofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the median of how someone's `starttimeofepisodemain` (bedtime) changed from night to night.
|
||||||
|medianssdendtimeofepisodemain |minutes squared | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive end times.
|
|rmssdmedianendtimeofepisodemain |minutes | Square root of the **median** squared successive difference (RMSSD) between today's and yesterday's `endtimeofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the median of how someone's `endtimeofepisodemain` (wake time) changed from night to night.
|
||||||
|medianssdmidpointofepisodemain |minutes squared | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive mid times.
|
|rmssdmedianmidpointofepisodemain |minutes | Square root of the **median** squared successive difference (RMSSD) between today's and yesterday's `midpointofepisodemain` values across the entire participant's sleep data grouped per time segment instance. It represents the median of how someone's `midpointofepisodemain` (average mid time between bedtime and wake time) changed from night to night.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
!!! note "Assumptions/Observations"
|
!!! note "Assumptions/Observations"
|
||||||
1. These features are based on descriptive statistics computed across daily values (start/end/mid times of sleep episodes). This is the reason why they are only available on time segments that are longer than 24 hours (we need at least 1 day to get the average).
|
1. [This diagram](../../img/sleep_intraday_price.png) will help you understand how sleep episodes are chunked and grouped within time segments and `LNE-LNE` intervals for the PRICE provider.
|
||||||
|
1. We recommend you use periodic segments that start in the morning so RAPIDS can chunk and group sleep episodes overnight. Shifted segments (as any other segments) are labelled based on their start and end date times.
|
||||||
|
5. `avgstarttime...` and `avgendtime...` are roughly equivalent to an average bed and awake time only if you are using shifted segments.
|
||||||
|
1. The features of this provider are only available on time segments that are longer than 24 hours because they are based on descriptive statistics computed across daily values.
|
||||||
2. Even though Fitbit provides 2 types of sleep episodes (`main` and `nap`), only `main` sleep episodes are considered.
|
2. Even though Fitbit provides 2 types of sleep episodes (`main` and `nap`), only `main` sleep episodes are considered.
|
||||||
3. How do we assign sleep episodes to specific dates?
|
4. The reference point for all times is 00:00 of the first day in the LNE-LNE interval.
|
||||||
|
5. Sleep episodes are formed by 1-minute chunks that we group overnight starting from today’s LNE and ending on tomorrow’s LNE or the end of that segment (whatever is first).
|
||||||
`START_TIME` and `LENGTH` control the dates that sleep episodes belong to. For a pair of `[START_TIME]` and `[LENGTH]`, sleep episodes (blue boxes) can only be placed at the following places:
|
5. The features `avgstarttime...` and `avgendtime...` are the average of the first and last sleep episode across every LNE-LNE interval within a segment (`avgmidtime...` is the mid point between start and end). Therefore, only segments longer than 24hrs will be averaged across more than one LNE-LNE interval.
|
||||||
|
5. `socialjetlag` is only available on segment instances equal or longer than 48hrs that contain at least one weekday day and one weekend day, for example seven-day (weekly) segments.
|
||||||
<figure>
|
|
||||||
<img src="../../img/features_fitbit_sleep_intraday.png" max-width="100%" />
|
|
||||||
<figcaption>Relationship between sleep episodes and the given times`([START_TIME], [LENGTH])`</figcaption>
|
|
||||||
</figure>
|
|
||||||
|
|
||||||
- If the end time of a sleep episode is before `[START_TIME]`, it will belong to the day before its start date (e.g. sleep episode #1).
|
|
||||||
|
|
||||||
- if (1) the start time or the end time of a sleep episode are between (overlap) `[START_TIME]` and `[START_TIME] + [LENGTH]` or (2) the start time is before `[START_TIME]` and the end time is after `[START_TIME] + [LENGTH]`, it will belong to its start date (e.g. sleep episode #2, #3, #4, #5).
|
|
||||||
|
|
||||||
- If the start time of a sleep episode is after `START_TIME] + [LENGTH]`, it will belong to the day after its start date (e.g. sleep episode #6).
|
|
||||||
|
|
||||||
Only `main` sleep episodes that intersect or contain the period between `[START_TIME]` and `[START_TIME] + [LENGTH]` will be included in the feature computation. If we process the following `main` sleep episodes:
|
|
||||||
|
|
||||||
| episode |start|end|
|
|
||||||
|-|-|-|
|
|
||||||
|1|2021-02-01 12:00|2021-02-01 15:00|
|
|
||||||
|2|2021-02-01 21:00|2021-02-02 03:00|02-01
|
|
||||||
|3|2021-02-02 05:00|2021-02-02 08:00|02-01
|
|
||||||
|4|2021-02-02 11:00|2021-02-02 14:00|
|
|
||||||
|5|2021-02-02 19:00|2021-02-03 06:00|02-02
|
|
||||||
|
|
||||||
And our parameters:
|
|
||||||
|
|
||||||
- `[INCLUDE_EPISODES_INTERSECTING][START_TIME]` = 1320 (today's 22:00)
|
|
||||||
|
|
||||||
- `[INCLUDE_EPISODES_INTERSECTING][LENGTH]` = 720 (tomorrow's 10:00, or 22:00 + 12 hours)
|
|
||||||
|
|
||||||
Only sleep episodes 2, 3,and 5 would be considered.
|
|
||||||
|
|
||||||
4. Time related features represent the number of minutes between the start/end/midpoint of sleep episodes and the assigned day's midnight.
|
|
||||||
|
|
||||||
5. All `main` sleep episodes are chunked within the requested [time segments](../../setup/configuration/#time-segments) which need to be at least 24 hours or more long (1, 2, 3, 7 days, etc.). Then, daily features will be extracted and averaged across the length of the time segment, for example:
|
|
||||||
|
|
||||||
The daily features extracted on 2021-02-01 will be:
|
|
||||||
|
|
||||||
- starttimeofepisodemain (bedtime) is `21 * 60` (episode 2 start time 2021-02-01 21:00)
|
|
||||||
|
|
||||||
- endtimeofepisodemain (wake time) is `32 * 60 `(episode 3 end time 2021-02-02 08:00 + 24)
|
|
||||||
|
|
||||||
- midpointofepisodemain (midpoint sleep) is `[(21 * 60) + (32 * 60)] / 2`
|
|
||||||
|
|
||||||
|
|
||||||
The daily features extracted on 2021-02-02 will be:
|
|
||||||
|
|
||||||
- starttimeofepisodemain (bedtime) is `19 * 60` (episode 5 start time 2021-02-01 19:00)
|
|
||||||
|
|
||||||
- endtimeofepisodemain (wake time) is `30 * 60 `(episode 5 end time 2021-02-03 06:00 + 24)
|
|
||||||
|
|
||||||
- midpointofepisodemain (midpoint sleep) is `[(19 * 60) + (30 * 60)] / 2`
|
|
||||||
|
|
||||||
And `avgstarttimeofepisodemain[DAY_TYPE]` will be `([21 * 60] + [19 * 60]) / 2`
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -9,6 +9,9 @@ Sensor parameters description for `[FITBIT_SLEEP_SUMMARY]`:
|
||||||
|
|
||||||
## RAPIDS provider
|
## RAPIDS provider
|
||||||
|
|
||||||
|
!!! hint "Understanding RAPIDS features"
|
||||||
|
[This diagram](../../img/sleep_summary_rapids.png) will help you understand how sleep episodes are chunked and grouped within time segments using `SLEEP_SUMMARY_LAST_NIGHT_END` for the RAPIDS provider.
|
||||||
|
|
||||||
!!! info "Available time segments"
|
!!! info "Available time segments"
|
||||||
- Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59)
|
- Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59)
|
||||||
|
|
||||||
|
@ -26,14 +29,19 @@ Parameters description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`:
|
||||||
|Key | Description |
|
|Key | Description |
|
||||||
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|
||||||
|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_SUMMARY` features from the `RAPIDS` provider |
|
|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_SUMMARY` features from the `RAPIDS` provider |
|
||||||
|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. Fitbit provides 3 types of sleep: `main`, `nap`, `all`. |
|
|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. There are three sleep types: `main`, `nap`, and `all`. The `all` type means both main sleep and naps are considered. |
|
||||||
|`[FEATURES]` | Features to be computed from sleep summary data, see table below |
|
|`[FEATURES]` | Features to be computed from sleep summary data, see table below |
|
||||||
|
|`[FITBIT_DATA_STREAMS][data stream][SLEEP_SUMMARY_LAST_NIGHT_END]` | As an exception, the `LAST_NIGHT_END` parameter for this provider is in the data stream configuration section. This parameter controls how sleep episodes are assigned to different days and affects wake and bedtimes.|
|
||||||
|
|
||||||
|
|
||||||
Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`:
|
Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`:
|
||||||
|
|
||||||
|Feature |Units |Description |
|
|Feature |Units |Description |
|
||||||
|------------------------------ |---------- |-------------------------------------------- |
|
|------------------------------ |---------- |-------------------------------------------- |
|
||||||
|
|firstwaketimeTYPE |minutes |First wake time for a certain sleep type during a time segment. Wake time is number of minutes after midnight of a sleep episode's end time.
|
||||||
|
|lastwaketimeTYPE |minutes |Last wake time for a certain sleep type during a time segment. Wake time is number of minutes after midnight of a sleep episode's end time.
|
||||||
|
|firstbedtimeTYPE |minutes |First bedtime for a certain sleep type during a time segment. Bedtime is number of minutes after midnight of a sleep episode's start time.
|
||||||
|
|lastbedtimeTYPE |minutes |Last bedtime for a certain sleep type during a time segment. Bedtime is number of minutes after midnight of a sleep episode's start time.
|
||||||
|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a time segment.
|
|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a time segment.
|
||||||
|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a time segment.
|
|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a time segment.
|
||||||
|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a time segment.
|
|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a time segment.
|
||||||
|
@ -50,10 +58,13 @@ Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`:
|
||||||
|
|
||||||
|
|
||||||
!!! note "Assumptions/Observations"
|
!!! note "Assumptions/Observations"
|
||||||
|
1. [This diagram](../../img/sleep_summary_rapids.png) will help you understand how sleep episodes are chunked and grouped within time segments using `LNE` for the RAPIDS provider.
|
||||||
1. There are three sleep types (TYPE): `main`, `nap`, `all`. The `all` type contains both main sleep and naps.
|
1. There are three sleep types (TYPE): `main`, `nap`, `all`. The `all` type groups both `main` sleep and `naps`. All types are based on Fitbit's labels.
|
||||||
|
|
||||||
2. There are two versions of Fitbit’s sleep API ([version 1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and [version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), and each provides raw sleep data in a different format:
|
2. There are two versions of Fitbit’s sleep API ([version 1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and [version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), and each provides raw sleep data in a different format:
|
||||||
- _Count & duration summaries_. `v1` contains `count_awake`, `duration_awake`, `count_awakenings`, `count_restless`, and `duration_restless` fields for every sleep record but `v1.2` does not.
|
- _Count & duration summaries_. `v1` contains `count_awake`, `duration_awake`, `count_awakenings`, `count_restless`, and `duration_restless` fields for every sleep record but `v1.2` does not.
|
||||||
|
3. _API columns_. Most features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`.
|
||||||
3. _API columns_. Features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`.
|
4. Bed time and sleep duration are based on episodes that started between today’s LNE and tomorrow’s LNE while awake time is based on the episodes that started between yesterday’s LNE and today’s LNE
|
||||||
|
5. The reference point for bed/awake times is today’s 00:00. You can have bedtimes larger than 24 and awake times smaller than 0
|
||||||
|
6. These features are only available for time segments that span midnight to midnight of the same or different day.
|
||||||
|
7. We include first and last wake and bedtimes because, when `LAST_NIGHT_END` is 10 am, the first bedtime could match a nap at 2 pm, and the last bedtime could match a main overnight sleep episode that starts at 10pm.
|
||||||
|
5. Set the value for `SLEEP_SUMMARY_LAST_NIGHT_END` int the config parameter [FITBIT_DATA_STREAMS][data stream][SLEEP_SUMMARY_LAST_NIGHT_END].
|
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|
@ -500,19 +500,19 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
|
||||||
# AVAILABLE:
|
# AVAILABLE:
|
||||||
fitbitjson_mysql:
|
fitbitjson_mysql:
|
||||||
DATABASE_GROUP: MY_GROUP
|
DATABASE_GROUP: MY_GROUP
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
|
SLEEP_SUMMARY_LAST_NIGHT_END: 660
|
||||||
|
|
||||||
fitbitjson_csv:
|
fitbitjson_csv:
|
||||||
FOLDER: data/external/fitbit_csv
|
FOLDER: data/external/fitbit_csv
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
|
SLEEP_SUMMARY_LAST_NIGHT_END: 660
|
||||||
|
|
||||||
fitbitparsed_mysql:
|
fitbitparsed_mysql:
|
||||||
DATABASE_GROUP: MY_GROUP
|
DATABASE_GROUP: MY_GROUP
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
|
SLEEP_SUMMARY_LAST_NIGHT_END: 660
|
||||||
|
|
||||||
fitbitparsed_csv:
|
fitbitparsed_csv:
|
||||||
FOLDER: data/external/fitbit_csv
|
FOLDER: data/external/fitbit_csv
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: False
|
SLEEP_SUMMARY_LAST_NIGHT_END: 660
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
|
@ -524,7 +524,7 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
|
||||||
| Key | Description |
|
| Key | Description |
|
||||||
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
||||||
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
|
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
|
||||||
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
|
| `[SLEEP_SUMMARY_LAST_NIGHT_END]` | Segments are assigned based on this parameter. Any sleep episodes starts between today's SLEEP_SUMMARY_LAST_NIGHT_END (LNE) and tomorrow's LNE is regarded as today's sleep episode. While today's bedtime is based on today's sleep episodes, today's wake time is based on yesterday's sleep episodes. |
|
||||||
|
|
||||||
--8<---- "docs/snippets/database.md"
|
--8<---- "docs/snippets/database.md"
|
||||||
|
|
||||||
|
@ -535,7 +535,7 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
|
||||||
| Key | Description |
|
| Key | Description |
|
||||||
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
||||||
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
|
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
|
||||||
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
|
| `[SLEEP_SUMMARY_LAST_NIGHT_END]` | Segments are assigned based on this parameter. Any sleep episodes starts between today's SLEEP_SUMMARY_LAST_NIGHT_END (LNE) and tomorrow's LNE is regarded as today's sleep episode. While today's bedtime is based on today's sleep episodes, today's wake time is based on yesterday's sleep episodes. |
|
||||||
|
|
||||||
|
|
||||||
=== "fitbitparsed_mysql"
|
=== "fitbitparsed_mysql"
|
||||||
|
@ -546,7 +546,7 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
|
||||||
| Key | Description |
|
| Key | Description |
|
||||||
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
||||||
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
|
| `[DATABASE_GROUP]` | A database credentials group. Read the instructions below to set it up |
|
||||||
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
|
| `[SLEEP_SUMMARY_LAST_NIGHT_END]` | Segments are assigned based on this parameter. Any sleep episodes starts between today's SLEEP_SUMMARY_LAST_NIGHT_END (LNE) and tomorrow's LNE is regarded as today's sleep episode. While today's bedtime is based on today's sleep episodes, today's wake time is based on yesterday's sleep episodes. |
|
||||||
|
|
||||||
--8<---- "docs/snippets/database.md"
|
--8<---- "docs/snippets/database.md"
|
||||||
|
|
||||||
|
@ -557,7 +557,7 @@ Modify the following keys in your `config.yaml` depending on the [data stream](.
|
||||||
| Key | Description |
|
| Key | Description |
|
||||||
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
|---------------------|----------------------------------------------------------------------------------------------------------------------------|
|
||||||
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
|
| `[FOLDER]` | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process. |
|
||||||
| `[SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]` | One of `start` or `end`. Summary sleep episodes are considered as events based on either the start timestamp or end timestamp (they will belong to the day where they start or end). |
|
| `[SLEEP_SUMMARY_LAST_NIGHT_END]` | Segments are assigned based on this parameter. Any sleep episodes starts between today's SLEEP_SUMMARY_LAST_NIGHT_END (LNE) and tomorrow's LNE is regarded as today's sleep episode. While today's bedtime is based on today's sleep episodes, today's wake time is based on yesterday's sleep episodes. |
|
||||||
|
|
||||||
=== "Empatica"
|
=== "Empatica"
|
||||||
|
|
||||||
|
|
|
@ -58,15 +58,18 @@ def parseSleepData(sleep_data):
|
||||||
|
|
||||||
def main(json_raw, stream_parameters):
|
def main(json_raw, stream_parameters):
|
||||||
parsed_data = parseSleepData(json_raw)
|
parsed_data = parseSleepData(json_raw)
|
||||||
|
parsed_data["local_date_time"] = (parsed_data["local_start_date_time"] - pd.Timedelta(minutes=stream_parameters["SLEEP_SUMMARY_LAST_NIGHT_END"])).dt.strftime('%Y-%m-%d 00:00:00')
|
||||||
|
|
||||||
|
# complete missing dates
|
||||||
|
missed_dates = list(set([x.strftime('%Y-%m-%d 00:00:00') for x in pd.date_range(parsed_data["local_date_time"].min(), parsed_data["local_date_time"].max()).to_pydatetime()]) - set(parsed_data["local_date_time"]))
|
||||||
|
parsed_data = pd.concat([parsed_data, pd.DataFrame({"local_date_time": missed_dates})], axis=0)
|
||||||
|
parsed_data.sort_values(by=["local_date_time", "local_start_date_time"], inplace=True)
|
||||||
|
parsed_data["device_id"] = parsed_data["device_id"].interpolate(method="pad")
|
||||||
|
|
||||||
parsed_data["timestamp"] = 0 # this column is added at readable_datetime.R because we neeed to take into account multiple timezones
|
parsed_data["timestamp"] = 0 # this column is added at readable_datetime.R because we neeed to take into account multiple timezones
|
||||||
if pd.api.types.is_datetime64_any_dtype( parsed_data['local_start_date_time']):
|
if pd.api.types.is_datetime64_any_dtype( parsed_data['local_start_date_time']):
|
||||||
parsed_data['local_start_date_time'] = parsed_data['local_start_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
|
parsed_data['local_start_date_time'] = parsed_data['local_start_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
|
||||||
if pd.api.types.is_datetime64_any_dtype( parsed_data['local_end_date_time']):
|
if pd.api.types.is_datetime64_any_dtype( parsed_data['local_end_date_time']):
|
||||||
parsed_data['local_end_date_time'] = parsed_data['local_end_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
|
parsed_data['local_end_date_time'] = parsed_data['local_end_date_time'].dt.strftime('%Y-%m-%d %H:%M:%S')
|
||||||
|
|
||||||
if stream_parameters["SLEEP_SUMMARY_EPISODE_DAY_ANCHOR"] == "start":
|
|
||||||
parsed_data["local_date_time"] = parsed_data['local_start_date_time']
|
|
||||||
else:
|
|
||||||
parsed_data["local_date_time"] = parsed_data['local_end_date_time']
|
|
||||||
|
|
||||||
return(parsed_data)
|
return(parsed_data)
|
||||||
|
|
|
@ -3,6 +3,15 @@ source("renv/activate.R")
|
||||||
library(yaml)
|
library(yaml)
|
||||||
library(dplyr)
|
library(dplyr)
|
||||||
library(readr)
|
library(readr)
|
||||||
|
|
||||||
|
fix_pandas_nan_in_string_columns <- function(column){
|
||||||
|
return(vapply(column, function(value) {
|
||||||
|
if(!is.character(value) && !is.nan(value))
|
||||||
|
stop("The reticulate conversion from the python mutation script to r failed. One or more returned columns are a list with unsupported mixed types. We only handle string columns with np.nan values. Open a GitHub issue or fix the mutation script")
|
||||||
|
return(ifelse(is.nan(value), NA_character_, value))
|
||||||
|
}, FUN.VALUE = character(1)))
|
||||||
|
}
|
||||||
|
|
||||||
# we use reticulate but only load it if we are going to use it to minimize the case when old RAPIDS deployments need to update ther renv
|
# we use reticulate but only load it if we are going to use it to minimize the case when old RAPIDS deployments need to update ther renv
|
||||||
mutate_data <- function(scripts, data, data_configuration){
|
mutate_data <- function(scripts, data, data_configuration){
|
||||||
for(script in scripts){
|
for(script in scripts){
|
||||||
|
@ -25,6 +34,7 @@ mutate_data <- function(scripts, data, data_configuration){
|
||||||
if(py_has_attr(script_functions, "main")){
|
if(py_has_attr(script_functions, "main")){
|
||||||
message(paste("Applying mutation script", script))
|
message(paste("Applying mutation script", script))
|
||||||
data <- script_functions$main(data, data_configuration)
|
data <- script_functions$main(data, data_configuration)
|
||||||
|
data <- data %>% mutate(across(where(is.list), fix_pandas_nan_in_string_columns))
|
||||||
} else{
|
} else{
|
||||||
stop(paste0("The following mutation script does not have a main function: ", script))
|
stop(paste0("The following mutation script does not have a main function: ", script))
|
||||||
}
|
}
|
||||||
|
|
|
@ -3,7 +3,7 @@ import itertools
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute):
|
def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute, levels_include_all_groups):
|
||||||
|
|
||||||
features_fullnames = ["local_segment"]
|
features_fullnames = ["local_segment"]
|
||||||
|
|
||||||
|
@ -14,7 +14,7 @@ def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day
|
||||||
|
|
||||||
for feature in intraday_features_to_compute:
|
for feature in intraday_features_to_compute:
|
||||||
if feature == "avgduration":
|
if feature == "avgduration":
|
||||||
features_fullnames.extend(["avgduration" + x[0] + "main" + x[1].lower() for x in itertools.product(sleep_level_with_group, day_types_to_compute)])
|
features_fullnames.extend(["avgduration" + x[0] + "main" + x[1].lower() for x in itertools.product(sleep_level_with_group + (["all"] if levels_include_all_groups else []), day_types_to_compute)])
|
||||||
elif feature == "avgratioduration":
|
elif feature == "avgratioduration":
|
||||||
features_fullnames.extend(["avgratioduration" + x[0] + "withinmain" + x[1].lower() for x in itertools.product(sleep_level_with_group, day_types_to_compute)])
|
features_fullnames.extend(["avgratioduration" + x[0] + "withinmain" + x[1].lower() for x in itertools.product(sleep_level_with_group, day_types_to_compute)])
|
||||||
elif feature in ["avgstarttimeofepisodemain", "avgendtimeofepisodemain", "avgmidpointofepisodemain", "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain"]:
|
elif feature in ["avgstarttimeofepisodemain", "avgendtimeofepisodemain", "avgmidpointofepisodemain", "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain"]:
|
||||||
|
@ -69,7 +69,7 @@ def extractDailyFeatures(sleep_data):
|
||||||
|
|
||||||
return daily_features
|
return daily_features
|
||||||
|
|
||||||
def statsOfDailyFeatures(daily_features, day_type, sleep_levels, intraday_features_to_compute, sleep_intraday_features):
|
def statsOfDailyFeatures(daily_features, day_type, sleep_levels, intraday_features_to_compute, sleep_intraday_features, levels_include_all_groups):
|
||||||
if day_type == "WEEKEND":
|
if day_type == "WEEKEND":
|
||||||
daily_features = daily_features[daily_features["is_weekend"] == 0]
|
daily_features = daily_features[daily_features["is_weekend"] == 0]
|
||||||
elif day_type == "WEEK":
|
elif day_type == "WEEK":
|
||||||
|
@ -110,6 +110,8 @@ def statsOfDailyFeatures(daily_features, day_type, sleep_levels, intraday_featur
|
||||||
if "avgratioduration" in intraday_features_to_compute:
|
if "avgratioduration" in intraday_features_to_compute:
|
||||||
col = "ratioduration" + sleep_level + sleep_level_group.lower() + "withinmain"
|
col = "ratioduration" + sleep_level + sleep_level_group.lower() + "withinmain"
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment", col]].groupby("local_segment")[col].mean().to_frame().rename(columns={col: "avg" + col + day_type.lower()})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment", col]].groupby("local_segment")[col].mean().to_frame().rename(columns={col: "avg" + col + day_type.lower()})], axis=1)
|
||||||
|
if levels_include_all_groups and ("avgduration" in intraday_features_to_compute):
|
||||||
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment", "durationinbedmain"]].groupby("local_segment")["durationinbedmain"].mean().to_frame().rename(columns={"durationinbedmain": "avgdurationallmain" + day_type.lower()})], axis=1)
|
||||||
|
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
|
@ -127,28 +129,28 @@ def socialJetLagFeature(daily_features, sleep_intraday_features):
|
||||||
|
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
def MSSDFeatures(daily_features, intraday_features_to_compute, sleep_intraday_features):
|
def RMSSDFeatures(daily_features, intraday_features_to_compute, sleep_intraday_features):
|
||||||
|
|
||||||
date_idx = pd.DataFrame(pd.date_range(start=daily_features["fake_date"].min(), end=daily_features["fake_date"].max(), freq="D"), columns=["fake_date"])
|
date_idx = pd.DataFrame(pd.date_range(start=daily_features["fake_date"].min(), end=daily_features["fake_date"].max(), freq="D"), columns=["fake_date"])
|
||||||
date_idx["fake_date"] = date_idx["fake_date"].dt.date
|
date_idx["fake_date"] = date_idx["fake_date"].dt.date
|
||||||
daily_features = daily_features.merge(date_idx, on="fake_date", how="right")
|
daily_features = daily_features.merge(date_idx, on="fake_date", how="right")
|
||||||
|
|
||||||
for col in ["starttimeofepisodemain", "endtimeofepisodemain", "midpointofepisodemain"]:
|
for col in ["starttimeofepisodemain", "endtimeofepisodemain", "midpointofepisodemain"]:
|
||||||
daily_features[col + "_diff"] = daily_features[col].diff()
|
daily_features[col + "_diff"] = daily_features[col].diff().pow(2)
|
||||||
|
|
||||||
if "meanssdstarttimeofepisodemain" in intraday_features_to_compute:
|
if "rmssdmeanstarttimeofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain_diff"]].groupby("local_segment")["starttimeofepisodemain_diff"].mean().to_frame().rename(columns={"starttimeofepisodemain_diff": "meanssdstarttimeofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain_diff"]].groupby("local_segment")["starttimeofepisodemain_diff"].mean().pow(0.5).to_frame().rename(columns={"starttimeofepisodemain_diff": "rmssdmeanstarttimeofepisodemain"})], axis=1)
|
||||||
if "meanssdendtimeofepisodemain" in intraday_features_to_compute:
|
if "rmssdmeanendtimeofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain_diff"]].groupby("local_segment")["endtimeofepisodemain_diff"].mean().to_frame().rename(columns={"endtimeofepisodemain_diff": "meanssdendtimeofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain_diff"]].groupby("local_segment")["endtimeofepisodemain_diff"].mean().pow(0.5).to_frame().rename(columns={"endtimeofepisodemain_diff": "rmssdmeanendtimeofepisodemain"})], axis=1)
|
||||||
if "meanssdmidpointofepisodemain" in intraday_features_to_compute:
|
if "rmssdmeanmidpointofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain_diff"]].groupby("local_segment")["midpointofepisodemain_diff"].mean().to_frame().rename(columns={"midpointofepisodemain_diff": "meanssdmidpointofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain_diff"]].groupby("local_segment")["midpointofepisodemain_diff"].mean().pow(0.5).to_frame().rename(columns={"midpointofepisodemain_diff": "rmssdmeanmidpointofepisodemain"})], axis=1)
|
||||||
|
|
||||||
if "medianssdstarttimeofepisodemain" in intraday_features_to_compute:
|
if "rmssdmedianstarttimeofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain_diff"]].groupby("local_segment")["starttimeofepisodemain_diff"].median().to_frame().rename(columns={"starttimeofepisodemain_diff": "medianssdstarttimeofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain_diff"]].groupby("local_segment")["starttimeofepisodemain_diff"].median().pow(0.5).to_frame().rename(columns={"starttimeofepisodemain_diff": "rmssdmedianstarttimeofepisodemain"})], axis=1)
|
||||||
if "medianssdendtimeofepisodemain" in intraday_features_to_compute:
|
if "rmssdmedianendtimeofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain_diff"]].groupby("local_segment")["endtimeofepisodemain_diff"].median().to_frame().rename(columns={"endtimeofepisodemain_diff": "medianssdendtimeofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain_diff"]].groupby("local_segment")["endtimeofepisodemain_diff"].median().pow(0.5).to_frame().rename(columns={"endtimeofepisodemain_diff": "rmssdmedianendtimeofepisodemain"})], axis=1)
|
||||||
if "medianssdmidpointofepisodemain" in intraday_features_to_compute:
|
if "rmssdmedianmidpointofepisodemain" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain_diff"]].groupby("local_segment")["midpointofepisodemain_diff"].median().to_frame().rename(columns={"midpointofepisodemain_diff": "medianssdmidpointofepisodemain"})], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain_diff"]].groupby("local_segment")["midpointofepisodemain_diff"].median().pow(0.5).to_frame().rename(columns={"midpointofepisodemain_diff": "rmssdmedianmidpointofepisodemain"})], axis=1)
|
||||||
|
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
|
@ -157,16 +159,16 @@ def MSSDFeatures(daily_features, intraday_features_to_compute, sleep_intraday_fe
|
||||||
|
|
||||||
def price_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
def price_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||||
|
|
||||||
daily_start_time = provider["GROUP_EPISODES_WITHIN"]["START_TIME"]
|
last_night_end = provider["LAST_NIGHT_END"]
|
||||||
daily_end_time = daily_start_time + provider["GROUP_EPISODES_WITHIN"]["LENGTH"]
|
|
||||||
|
|
||||||
sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||||
requested_intraday_features = provider["FEATURES"]
|
requested_intraday_features = provider["FEATURES"]
|
||||||
|
levels_include_all_groups = provider["SLEEP_LEVELS"]["INCLUDE_ALL_GROUPS"]
|
||||||
requested_sleep_levels = provider["SLEEP_LEVELS"]
|
requested_sleep_levels = provider["SLEEP_LEVELS"]
|
||||||
requested_day_types = provider["DAY_TYPES"]
|
requested_day_types = provider["DAY_TYPES"]
|
||||||
|
|
||||||
# Name of the features this function can compute
|
# Name of the features this function can compute
|
||||||
base_intraday_features = ["avgduration", "avgratioduration", "avgstarttimeofepisodemain", "avgendtimeofepisodemain", "avgmidpointofepisodemain", "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", "socialjetlag", "meanssdstarttimeofepisodemain", "meanssdendtimeofepisodemain", "meanssdmidpointofepisodemain", "medianssdstarttimeofepisodemain", "medianssdendtimeofepisodemain", "medianssdmidpointofepisodemain"]
|
base_intraday_features = ["avgduration", "avgratioduration", "avgstarttimeofepisodemain", "avgendtimeofepisodemain", "avgmidpointofepisodemain", "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", "socialjetlag", "rmssdmeanstarttimeofepisodemain", "rmssdmeanendtimeofepisodemain", "rmssdmeanmidpointofepisodemain", "rmssdmedianstarttimeofepisodemain", "rmssdmedianendtimeofepisodemain", "rmssdmedianmidpointofepisodemain"]
|
||||||
base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
|
base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
|
||||||
"STAGES": ["wake", "deep", "light", "rem"],
|
"STAGES": ["wake", "deep", "light", "rem"],
|
||||||
"UNIFIED": ["awake", "asleep"]}
|
"UNIFIED": ["awake", "asleep"]}
|
||||||
|
@ -178,7 +180,7 @@ def price_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
||||||
day_types_to_compute = list(set(requested_day_types) & set(base_day_types))
|
day_types_to_compute = list(set(requested_day_types) & set(base_day_types))
|
||||||
|
|
||||||
# Full names
|
# Full names
|
||||||
features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute)
|
features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute, levels_include_all_groups)
|
||||||
sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
|
sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
|
||||||
|
|
||||||
# Filter by segemnts and chunk episodes
|
# Filter by segemnts and chunk episodes
|
||||||
|
@ -206,26 +208,18 @@ def price_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
||||||
main_sleep_episodes["end_minutes"] = main_sleep_episodes["start_minutes"] + main_sleep_episodes["durationinbed"]
|
main_sleep_episodes["end_minutes"] = main_sleep_episodes["start_minutes"] + main_sleep_episodes["durationinbed"]
|
||||||
# Extract fake date
|
# Extract fake date
|
||||||
""" The rule used for fake date extraction
|
""" The rule used for fake date extraction
|
||||||
set DS = daily_start_time, DE = daily_end_time
|
if start_minutes >= last_night_end
|
||||||
set start = start_minutes, end = end_minutes
|
|
||||||
|
|
||||||
if (DS <= start < DE) or (DS < end <= DE) or (start <= DS and end >= DE):
|
|
||||||
assign today
|
assign today
|
||||||
elif if end <= DS:
|
else:
|
||||||
assign yesterday
|
assign yesterday
|
||||||
else: (same as start >=DE)
|
|
||||||
assign tomorrow
|
|
||||||
"""
|
"""
|
||||||
main_sleep_episodes["fake_date_delta"] = main_sleep_episodes[["start_minutes", "end_minutes"]].apply(lambda row: 0 if ((row["start_minutes"] >= daily_start_time and row["start_minutes"] < daily_end_time) or (row["end_minutes"] > daily_start_time and row["end_minutes"] <= daily_end_time) or (row["start_minutes"] <= daily_start_time and row["end_minutes"] >= daily_end_time)) else -1 if (row["end_minutes"] <= daily_start_time) else 1, axis=1)
|
main_sleep_episodes["fake_date_delta"] = main_sleep_episodes[["start_minutes"]].apply(lambda row: 0 if row["start_minutes"] >= last_night_end else -1, axis=1)
|
||||||
main_sleep_episodes["fake_date"] = (main_sleep_episodes["local_start_date_time"] + pd.to_timedelta(main_sleep_episodes["fake_date_delta"], unit="d")).dt.date
|
main_sleep_episodes["fake_date"] = (main_sleep_episodes["local_start_date_time"] + pd.to_timedelta(main_sleep_episodes["fake_date_delta"], unit="d")).dt.date
|
||||||
|
|
||||||
# Update "start_minutes" column based on START_TIME
|
# Update "start_minutes" column based on START_TIME
|
||||||
main_sleep_episodes["start_minutes"] = main_sleep_episodes[["start_minutes", "fake_date_delta"]].apply(lambda row: row["start_minutes"] - 24 * 60 * row["fake_date_delta"], axis=1)
|
main_sleep_episodes["start_minutes"] = main_sleep_episodes[["start_minutes", "fake_date_delta"]].apply(lambda row: row["start_minutes"] - 24 * 60 * row["fake_date_delta"], axis=1)
|
||||||
main_sleep_episodes["end_minutes"] = main_sleep_episodes["start_minutes"] + main_sleep_episodes["durationinbed"]
|
main_sleep_episodes["end_minutes"] = main_sleep_episodes["start_minutes"] + main_sleep_episodes["durationinbed"]
|
||||||
|
|
||||||
# We keep a sleep episode that intersects or contains the period between [START_TIME, START_TIME + LENGTH], aka [daily_start_time, daily_end_time].
|
|
||||||
main_sleep_episodes = main_sleep_episodes.query("(start_minutes >= @daily_start_time and start_minutes < @daily_end_time) or (end_minutes > @daily_start_time and end_minutes <= @daily_end_time) or (start_minutes <= @daily_start_time and end_minutes >= @daily_end_time)")
|
|
||||||
|
|
||||||
# Sort main sleep episodes based on fake_date and start_minutes
|
# Sort main sleep episodes based on fake_date and start_minutes
|
||||||
main_sleep_episodes = main_sleep_episodes.sort_values(["fake_date", "start_minutes"])
|
main_sleep_episodes = main_sleep_episodes.sort_values(["fake_date", "start_minutes"])
|
||||||
# Extract daily features
|
# Extract daily features
|
||||||
|
@ -233,10 +227,10 @@ def price_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
||||||
|
|
||||||
# Extract features per segment based on daily features
|
# Extract features per segment based on daily features
|
||||||
for day_type in day_types_to_compute:
|
for day_type in day_types_to_compute:
|
||||||
sleep_intraday_features = statsOfDailyFeatures(daily_features, day_type, sleep_levels_to_compute, intraday_features_to_compute, sleep_intraday_features)
|
sleep_intraday_features = statsOfDailyFeatures(daily_features, day_type, sleep_levels_to_compute, intraday_features_to_compute, sleep_intraday_features, levels_include_all_groups)
|
||||||
if "socialjetlag" in intraday_features_to_compute:
|
if "socialjetlag" in intraday_features_to_compute:
|
||||||
sleep_intraday_features = socialJetLagFeature(daily_features, sleep_intraday_features)
|
sleep_intraday_features = socialJetLagFeature(daily_features, sleep_intraday_features)
|
||||||
sleep_intraday_features = MSSDFeatures(daily_features, intraday_features_to_compute, sleep_intraday_features)
|
sleep_intraday_features = RMSSDFeatures(daily_features, intraday_features_to_compute, sleep_intraday_features)
|
||||||
|
|
||||||
sleep_intraday_features.index.name = "local_segment"
|
sleep_intraday_features.index.name = "local_segment"
|
||||||
sleep_intraday_features.reset_index(inplace=True)
|
sleep_intraday_features.reset_index(inplace=True)
|
||||||
|
|
|
@ -2,7 +2,7 @@ import pandas as pd
|
||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
import itertools
|
import itertools
|
||||||
|
|
||||||
def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all):
|
def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, levels_include_all_groups):
|
||||||
|
|
||||||
features_fullname = ["local_segment"]
|
features_fullname = ["local_segment"]
|
||||||
|
|
||||||
|
@ -11,8 +11,8 @@ def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sle
|
||||||
for sleep_level in sleep_levels_to_compute[sleep_level_group]:
|
for sleep_level in sleep_levels_to_compute[sleep_level_group]:
|
||||||
sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
|
sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
|
||||||
|
|
||||||
if consider_all:
|
if levels_include_all_groups:
|
||||||
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group + ["all"], sleep_types_to_compute + ["all"])])
|
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group + ["all"], sleep_types_to_compute)])
|
||||||
else:
|
else:
|
||||||
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group, sleep_types_to_compute)])
|
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group, sleep_types_to_compute)])
|
||||||
if "ACROSS_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
if "ACROSS_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||||
|
@ -20,9 +20,9 @@ def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sle
|
||||||
if "ACROSS_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"] and "main" in sleep_types_to_compute:
|
if "ACROSS_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"] and "main" in sleep_types_to_compute:
|
||||||
features_fullname.extend(["ratio" + x + "main" for x in intraday_features_to_compute["RATIOS_TYPE"]])
|
features_fullname.extend(["ratio" + x + "main" for x in intraday_features_to_compute["RATIOS_TYPE"]])
|
||||||
if "WITHIN_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
if "WITHIN_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||||
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_types_to_compute, sleep_level_with_group)])
|
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], set(sleep_types_to_compute) & set(["main", "nap"]), sleep_level_with_group)])
|
||||||
if "WITHIN_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
if "WITHIN_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||||
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_level_with_group, sleep_types_to_compute)])
|
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_level_with_group, set(sleep_types_to_compute) & set(["main", "nap"]))])
|
||||||
features_fullname.extend(intraday_features_to_compute["ROUTINE"])
|
features_fullname.extend(intraday_features_to_compute["ROUTINE"])
|
||||||
return features_fullname
|
return features_fullname
|
||||||
|
|
||||||
|
@ -68,26 +68,28 @@ def statsFeatures(sleep_episodes, features, episode_type):
|
||||||
def allStatsFeatures(sleep_data, base_sleep_levels, base_sleep_types, features, sleep_intraday_features):
|
def allStatsFeatures(sleep_data, base_sleep_levels, base_sleep_types, features, sleep_intraday_features):
|
||||||
|
|
||||||
# For CLASSIC
|
# For CLASSIC
|
||||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["CLASSIC"] + ["all"], base_sleep_types + ["all"]):
|
for sleep_level, sleep_type in itertools.product(base_sleep_levels["CLASSIC"] + ["all"], base_sleep_types):
|
||||||
sleep_episodes_classic = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
sleep_episodes_classic = sleep_data[sleep_data["type"] == "classic"]
|
||||||
|
sleep_episodes_classic = sleep_episodes_classic[sleep_episodes_classic["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_episodes_classic
|
||||||
sleep_episodes_classic = sleep_episodes_classic[sleep_episodes_classic["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_classic
|
sleep_episodes_classic = sleep_episodes_classic[sleep_episodes_classic["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_classic
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_classic, features, sleep_level + "classic" + sleep_type)], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_classic, features, sleep_level + "classic" + sleep_type)], axis=1)
|
||||||
|
|
||||||
# For STAGES
|
# For STAGES
|
||||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["STAGES"] + ["all"], base_sleep_types + ["all"]):
|
for sleep_level, sleep_type in itertools.product(base_sleep_levels["STAGES"] + ["all"], base_sleep_types):
|
||||||
sleep_episodes_stages = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
sleep_episodes_stages = sleep_data[sleep_data["type"] == "stages"]
|
||||||
|
sleep_episodes_stages = sleep_episodes_stages[sleep_episodes_stages["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_episodes_stages
|
||||||
sleep_episodes_stages = sleep_episodes_stages[sleep_episodes_stages["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_stages
|
sleep_episodes_stages = sleep_episodes_stages[sleep_episodes_stages["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_stages
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_stages, features, sleep_level + "stages" + sleep_type)], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_stages, features, sleep_level + "stages" + sleep_type)], axis=1)
|
||||||
|
|
||||||
# For UNIFIED
|
# For UNIFIED
|
||||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["UNIFIED"] + ["all"], base_sleep_types + ["all"]):
|
for sleep_level, sleep_type in itertools.product(base_sleep_levels["UNIFIED"] + ["all"], base_sleep_types):
|
||||||
sleep_episodes_unified = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
sleep_episodes_unified = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||||
sleep_episodes_unified = sleep_episodes_unified[sleep_episodes_unified["unified_level"] == (0 if sleep_level == "awake" else 1)] if sleep_level != "all" else sleep_episodes_unified
|
sleep_episodes_unified = sleep_episodes_unified[sleep_episodes_unified["unified_level"] == (0 if sleep_level == "awake" else 1)] if sleep_level != "all" else sleep_episodes_unified
|
||||||
sleep_episodes_unified = mergeSleepEpisodes(sleep_episodes_unified, ["local_segment", "unified_level_episode_id"])
|
sleep_episodes_unified = mergeSleepEpisodes(sleep_episodes_unified, ["local_segment", "unified_level_episode_id"])
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_unified, features, sleep_level + "unified" + sleep_type)], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_unified, features, sleep_level + "unified" + sleep_type)], axis=1)
|
||||||
|
|
||||||
# Ignore the levels (e.g. countepisode[all][main])
|
# Ignore the levels (e.g. countepisode[all][main])
|
||||||
for sleep_type in base_sleep_types + ["all"]:
|
for sleep_type in base_sleep_types:
|
||||||
sleep_episodes_none = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
sleep_episodes_none = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||||
sleep_episodes_none = mergeSleepEpisodes(sleep_episodes_none, ["local_segment", "type_episode_id"])
|
sleep_episodes_none = mergeSleepEpisodes(sleep_episodes_none, ["local_segment", "type_episode_id"])
|
||||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_none, features, "all" + sleep_type)], axis=1)
|
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_none, features, "all" + sleep_type)], axis=1)
|
||||||
|
@ -151,6 +153,11 @@ def ratiosFeatures(sleep_intraday_features, ratios_types, ratios_scopes, sleep_l
|
||||||
# 7) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
|
# 7) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
|
||||||
# 8) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "nap"
|
# 8) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "nap"
|
||||||
for ratios_type, sleep_levels_combined, sleep_type in itertools.product(ratios_types, sleep_level_with_group, sleep_types):
|
for ratios_type, sleep_levels_combined, sleep_type in itertools.product(ratios_types, sleep_level_with_group, sleep_types):
|
||||||
|
|
||||||
|
# "all" sleep type will not be cosidered for any ratios features since it will be 1 all the time
|
||||||
|
if sleep_type == "all":
|
||||||
|
continue
|
||||||
|
|
||||||
sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
|
sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
|
||||||
agg_func = "countepisode" if ratios_type == "count" else "sumduration"
|
agg_func = "countepisode" if ratios_type == "count" else "sumduration"
|
||||||
|
|
||||||
|
@ -167,36 +174,36 @@ def ratiosFeatures(sleep_intraday_features, ratios_types, ratios_scopes, sleep_l
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
|
|
||||||
def singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, sleep_type, sleep_intraday_features):
|
def singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, routine_reference_time, sleep_type, sleep_intraday_features):
|
||||||
|
|
||||||
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["is_main_sleep"] == (1 if sleep_type == "mainsleep" else 0)]
|
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["is_main_sleep"] == (1 if sleep_type == "mainsleep" else 0)]
|
||||||
if "starttimefirst" + sleep_type in routine:
|
if "starttimefirst" + sleep_type in routine:
|
||||||
grouped_first = sleep_intraday_data.groupby(["local_segment"]).first()
|
grouped_first = sleep_intraday_data.groupby(["local_segment"]).first()
|
||||||
if reference_time == "MIDNIGHT":
|
if routine_reference_time == "MIDNIGHT":
|
||||||
sleep_intraday_features["starttimefirst" + sleep_type] = grouped_first["local_start_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
|
sleep_intraday_features["starttimefirst" + sleep_type] = grouped_first["local_start_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
|
||||||
elif reference_time == "START_OF_THE_SEGMENT":
|
elif routine_reference_time == "START_OF_THE_SEGMENT":
|
||||||
sleep_intraday_features["starttimefirst" + sleep_type] = (grouped_first["start_timestamp"] - grouped_first["segment_start_timestamp"]) / (60 * 1000)
|
sleep_intraday_features["starttimefirst" + sleep_type] = (grouped_first["start_timestamp"] - grouped_first["segment_start_timestamp"]) / (60 * 1000)
|
||||||
else:
|
else:
|
||||||
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: ROUTINE_REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
||||||
|
|
||||||
if "endtimelast" + sleep_type in routine:
|
if "endtimelast" + sleep_type in routine:
|
||||||
grouped_last = sleep_intraday_data.groupby(["local_segment"]).last()
|
grouped_last = sleep_intraday_data.groupby(["local_segment"]).last()
|
||||||
if reference_time == "MIDNIGHT":
|
if routine_reference_time == "MIDNIGHT":
|
||||||
sleep_intraday_features["endtimelast" + sleep_type] = grouped_last["local_end_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
|
sleep_intraday_features["endtimelast" + sleep_type] = grouped_last["local_end_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
|
||||||
elif reference_time == "START_OF_THE_SEGMENT":
|
elif routine_reference_time == "START_OF_THE_SEGMENT":
|
||||||
sleep_intraday_features["endtimelast" + sleep_type] = (grouped_last["end_timestamp"] - grouped_last["segment_start_timestamp"]) / (60 * 1000)
|
sleep_intraday_features["endtimelast" + sleep_type] = (grouped_last["end_timestamp"] - grouped_last["segment_start_timestamp"]) / (60 * 1000)
|
||||||
else:
|
else:
|
||||||
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: ROUTINE_REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
||||||
|
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
def routineFeatures(sleep_intraday_data, routine, reference_time, sleep_type, sleep_intraday_features):
|
def routineFeatures(sleep_intraday_data, routine, routine_reference_time, sleep_type, sleep_intraday_features):
|
||||||
|
|
||||||
if "starttimefirstmainsleep" in routine or "endtimelastmainsleep" in routine:
|
if "starttimefirstmainsleep" in routine or "endtimelastmainsleep" in routine:
|
||||||
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "mainsleep", sleep_intraday_features)
|
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, routine_reference_time, "mainsleep", sleep_intraday_features)
|
||||||
|
|
||||||
if "starttimefirstnap" in routine or "endtimelastnap" in routine:
|
if "starttimefirstnap" in routine or "endtimelastnap" in routine:
|
||||||
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "nap", sleep_intraday_features)
|
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, routine_reference_time, "nap", sleep_intraday_features)
|
||||||
|
|
||||||
return sleep_intraday_features
|
return sleep_intraday_features
|
||||||
|
|
||||||
|
@ -205,11 +212,11 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
|
|
||||||
sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||||
|
|
||||||
consider_all = provider["FEATURES"]["LEVELS_AND_TYPES_COMBINING_ALL"]
|
last_night_end = provider["LAST_NIGHT_END"]
|
||||||
include_sleep_later_than = provider["INCLUDE_SLEEP_LATER_THAN"]
|
routine_reference_time = provider["ROUTINE_REFERENCE_TIME"]
|
||||||
reference_time = provider["REFERENCE_TIME"]
|
|
||||||
|
|
||||||
requested_intraday_features = provider["FEATURES"]
|
requested_intraday_features = provider["FEATURES"]
|
||||||
|
levels_include_all_groups = provider["SLEEP_LEVELS"]["INCLUDE_ALL_GROUPS"]
|
||||||
requested_sleep_levels = provider["SLEEP_LEVELS"]
|
requested_sleep_levels = provider["SLEEP_LEVELS"]
|
||||||
requested_sleep_types = provider["SLEEP_TYPES"]
|
requested_sleep_types = provider["SLEEP_TYPES"]
|
||||||
|
|
||||||
|
@ -221,7 +228,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
|
base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
|
||||||
"STAGES": ["wake", "deep", "light", "rem"],
|
"STAGES": ["wake", "deep", "light", "rem"],
|
||||||
"UNIFIED": ["awake", "asleep"]}
|
"UNIFIED": ["awake", "asleep"]}
|
||||||
base_sleep_types = ["main", "nap"]
|
base_sleep_types = ["main", "nap", "all"]
|
||||||
|
|
||||||
# The subset of requested features this function can compute
|
# The subset of requested features this function can compute
|
||||||
intraday_features_to_compute = {key: list(set(requested_intraday_features[key]) & set(base_intraday_features[key])) for key in requested_intraday_features if key in base_intraday_features}
|
intraday_features_to_compute = {key: list(set(requested_intraday_features[key]) & set(base_intraday_features[key])) for key in requested_intraday_features if key in base_intraday_features}
|
||||||
|
@ -229,13 +236,13 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types))
|
sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types))
|
||||||
|
|
||||||
# Full names
|
# Full names
|
||||||
features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all)
|
features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, levels_include_all_groups)
|
||||||
sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
|
sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
|
||||||
|
|
||||||
# Include sleep later than
|
# Any 1-minute sleep chuncks with a local time before LAST_NIGHT_END will be discarded.
|
||||||
start_minutes = sleep_intraday_data.groupby("start_timestamp").first()["local_time"].apply(lambda x: int(x.split(":")[0]) * 60 + int(x.split(":")[1]) + int(x.split(":")[2]) / 60).to_frame().rename(columns={"local_time": "start_minutes"}).reset_index()
|
start_minutes = sleep_intraday_data.groupby("start_timestamp").first()["local_time"].apply(lambda x: int(x.split(":")[0]) * 60 + int(x.split(":")[1]) + int(x.split(":")[2]) / 60).to_frame().rename(columns={"local_time": "start_minutes"}).reset_index()
|
||||||
sleep_intraday_data = sleep_intraday_data.merge(start_minutes, on="start_timestamp", how="left")
|
sleep_intraday_data = sleep_intraday_data.merge(start_minutes, on="start_timestamp", how="left")
|
||||||
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["start_minutes"] >= include_sleep_later_than]
|
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["start_minutes"] >= last_night_end]
|
||||||
del sleep_intraday_data["start_minutes"]
|
del sleep_intraday_data["start_minutes"]
|
||||||
|
|
||||||
sleep_intraday_data = filter_data_by_segment(sleep_intraday_data, time_segment)
|
sleep_intraday_data = filter_data_by_segment(sleep_intraday_data, time_segment)
|
||||||
|
@ -254,7 +261,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
sleep_intraday_features = ratiosFeatures(sleep_intraday_features, intraday_features_to_compute["RATIOS_TYPE"], intraday_features_to_compute["RATIOS_SCOPE"], sleep_levels_to_compute, sleep_types_to_compute)
|
sleep_intraday_features = ratiosFeatures(sleep_intraday_features, intraday_features_to_compute["RATIOS_TYPE"], intraday_features_to_compute["RATIOS_SCOPE"], sleep_levels_to_compute, sleep_types_to_compute)
|
||||||
|
|
||||||
# ROUTINE: only compute requested features
|
# ROUTINE: only compute requested features
|
||||||
sleep_intraday_features = routineFeatures(sleep_intraday_data, intraday_features_to_compute["ROUTINE"], reference_time, sleep_types_to_compute, sleep_intraday_features)
|
sleep_intraday_features = routineFeatures(sleep_intraday_data, intraday_features_to_compute["ROUTINE"], routine_reference_time, sleep_types_to_compute, sleep_intraday_features)
|
||||||
|
|
||||||
# Reset index and discard features which are not requested by user
|
# Reset index and discard features which are not requested by user
|
||||||
sleep_intraday_features.index.name = "local_segment"
|
sleep_intraday_features.index.name = "local_segment"
|
||||||
|
|
|
@ -44,6 +44,21 @@ def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sl
|
||||||
if "countepisode" in summary_features:
|
if "countepisode" in summary_features:
|
||||||
sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "countepisode" + sleep_type})
|
sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "countepisode" + sleep_type})
|
||||||
|
|
||||||
|
features_first = sleep_summary_data[["local_segment", "minutes_start_episode", "minutes_end_episode"]].groupby(["local_segment"]).first()
|
||||||
|
|
||||||
|
if "firstwaketime" in summary_features:
|
||||||
|
sleep_summary_features = sleep_summary_features.join(features_first[["minutes_end_episode"]].shift(), how="outer").rename(columns={"minutes_end_episode": "firstwaketime" + sleep_type})
|
||||||
|
if "firstbedtime" in summary_features:
|
||||||
|
sleep_summary_features = sleep_summary_features.join(features_first[["minutes_start_episode"]], how="outer").rename(columns={"minutes_start_episode": "firstbedtime" + sleep_type})
|
||||||
|
|
||||||
|
features_last = sleep_summary_data[["local_segment", "minutes_start_episode", "minutes_end_episode"]].groupby(["local_segment"]).last()
|
||||||
|
|
||||||
|
if "lastwaketime" in summary_features:
|
||||||
|
sleep_summary_features = sleep_summary_features.join(features_last[["minutes_end_episode"]].shift(), how="outer").rename(columns={"minutes_end_episode": "lastwaketime" + sleep_type})
|
||||||
|
if "lastbedtime" in summary_features:
|
||||||
|
sleep_summary_features = sleep_summary_features.join(features_last[["minutes_start_episode"]], how="outer").rename(columns={"minutes_start_episode": "lastbedtime" + sleep_type})
|
||||||
|
|
||||||
|
|
||||||
return sleep_summary_features
|
return sleep_summary_features
|
||||||
|
|
||||||
|
|
||||||
|
@ -55,7 +70,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
requested_sleep_types = provider["SLEEP_TYPES"]
|
requested_sleep_types = provider["SLEEP_TYPES"]
|
||||||
|
|
||||||
# name of the features this function can compute
|
# name of the features this function can compute
|
||||||
base_summary_features = ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
|
base_summary_features = ["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
|
||||||
base_sleep_types = ["main", "nap", "all"]
|
base_sleep_types = ["main", "nap", "all"]
|
||||||
# the subset of requested features this function can compute
|
# the subset of requested features this function can compute
|
||||||
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
|
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features))
|
||||||
|
@ -63,13 +78,15 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
# full names
|
# full names
|
||||||
features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)]
|
features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)]
|
||||||
|
|
||||||
colnames_can_be_zero = [col for col in features_fullnames_to_compute if "avgefficiency" not in col]
|
colnames_can_be_zero = ["".join(feature) for feature in itertools.product(set(summary_features_to_compute) - set(["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "avgefficiency"]), sleep_types_to_compute)]
|
||||||
|
|
||||||
# extract features from summary data
|
# extract features from summary data
|
||||||
sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute)
|
sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute)
|
||||||
if not sleep_summary_data.empty:
|
if not sleep_summary_data.empty:
|
||||||
sleep_summary_data = filter_data_by_segment(sleep_summary_data, time_segment)
|
sleep_summary_data = filter_data_by_segment(sleep_summary_data, time_segment)
|
||||||
|
|
||||||
|
notna_segments = sleep_summary_data[sleep_summary_data["type"].notna()]["local_segment"].unique()
|
||||||
|
|
||||||
if not sleep_summary_data.empty:
|
if not sleep_summary_data.empty:
|
||||||
# only keep the segments start at 00:00:00 and end at 23:59:59
|
# only keep the segments start at 00:00:00 and end at 23:59:59
|
||||||
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
|
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
|
||||||
|
@ -78,13 +95,19 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
||||||
segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex)
|
segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex)
|
||||||
sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)]
|
sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)]
|
||||||
|
|
||||||
|
# calculate number of minutes after segment's start date time
|
||||||
|
dt_cols = ["local_start_date_time", "local_end_date_time", "local_date_time"]
|
||||||
|
sleep_summary_data[dt_cols] = sleep_summary_data[dt_cols].apply(pd.to_datetime)
|
||||||
|
sleep_summary_data["minutes_start_episode"] = (sleep_summary_data["local_start_date_time"] - sleep_summary_data["local_date_time"]) / pd.Timedelta(minutes=1)
|
||||||
|
sleep_summary_data["minutes_end_episode"] = (sleep_summary_data["local_end_date_time"] - (sleep_summary_data["local_date_time"] + pd.Timedelta(days=1))) / pd.Timedelta(minutes=1)
|
||||||
|
|
||||||
if not sleep_summary_data.empty:
|
if not sleep_summary_data.empty:
|
||||||
sleep_summary_features = pd.DataFrame()
|
sleep_summary_features = pd.DataFrame()
|
||||||
|
|
||||||
for sleep_type in sleep_types_to_compute:
|
for sleep_type in sleep_types_to_compute:
|
||||||
sleep_summary_features = extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features_to_compute, sleep_type, sleep_summary_features)
|
sleep_summary_features = extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features_to_compute, sleep_type, sleep_summary_features)
|
||||||
|
|
||||||
sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0)
|
sleep_summary_features.loc[notna_segments, colnames_can_be_zero] = sleep_summary_features.loc[notna_segments, colnames_can_be_zero].fillna(0)
|
||||||
|
|
||||||
sleep_summary_features = sleep_summary_features.reset_index()
|
sleep_summary_features = sleep_summary_features.reset_index()
|
||||||
|
|
||||||
|
|
|
@ -827,22 +827,44 @@ properties:
|
||||||
type: string
|
type: string
|
||||||
fitbitjson_mysql:
|
fitbitjson_mysql:
|
||||||
type: object
|
type: object
|
||||||
required: [DATABASE_GROUP, SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]
|
required: [DATABASE_GROUP, SLEEP_SUMMARY_LAST_NIGHT_END]
|
||||||
properties:
|
properties:
|
||||||
DATABASE_GROUP:
|
DATABASE_GROUP:
|
||||||
type: string
|
type: string
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR:
|
SLEEP_SUMMARY_LAST_NIGHT_END:
|
||||||
type: string
|
type: number
|
||||||
enum: ["start", "end"]
|
minimum: 0
|
||||||
|
maximum: 1439
|
||||||
fitbitparsed_mysql:
|
fitbitparsed_mysql:
|
||||||
type: object
|
type: object
|
||||||
required: [DATABASE_GROUP, SLEEP_SUMMARY_EPISODE_DAY_ANCHOR]
|
required: [DATABASE_GROUP, SLEEP_SUMMARY_LAST_NIGHT_END]
|
||||||
properties:
|
properties:
|
||||||
DATABASE_GROUP:
|
DATABASE_GROUP:
|
||||||
type: string
|
type: string
|
||||||
SLEEP_SUMMARY_EPISODE_DAY_ANCHOR:
|
SLEEP_SUMMARY_LAST_NIGHT_END:
|
||||||
|
type: number
|
||||||
|
minimum: 0
|
||||||
|
maximum: 1439
|
||||||
|
fitbitjson_csv:
|
||||||
|
type: object
|
||||||
|
required: [FOLDER, SLEEP_SUMMARY_LAST_NIGHT_END]
|
||||||
|
properties:
|
||||||
|
FOLDER:
|
||||||
type: string
|
type: string
|
||||||
enum: ["start", "end"]
|
SLEEP_SUMMARY_LAST_NIGHT_END:
|
||||||
|
type: number
|
||||||
|
minimum: 0
|
||||||
|
maximum: 1439
|
||||||
|
fitbitparsed_csv:
|
||||||
|
type: object
|
||||||
|
required: [FOLDER, SLEEP_SUMMARY_LAST_NIGHT_END]
|
||||||
|
properties:
|
||||||
|
FOLDER:
|
||||||
|
type: string
|
||||||
|
SLEEP_SUMMARY_LAST_NIGHT_END:
|
||||||
|
type: number
|
||||||
|
minimum: 0
|
||||||
|
maximum: 1439
|
||||||
|
|
||||||
FITBIT_DATA_YIELD:
|
FITBIT_DATA_YIELD:
|
||||||
type: object
|
type: object
|
||||||
|
@ -926,7 +948,7 @@ properties:
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
items:
|
items:
|
||||||
type: string
|
type: string
|
||||||
enum: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
|
enum: ["firstwaketime", "lastwaketime", "firstbedtime", "lastbedtime", "countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"]
|
||||||
SLEEP_TYPES:
|
SLEEP_TYPES:
|
||||||
type: array
|
type: array
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
|
@ -951,10 +973,8 @@ properties:
|
||||||
- properties:
|
- properties:
|
||||||
FEATURES:
|
FEATURES:
|
||||||
type: object
|
type: object
|
||||||
required: [LEVELS_AND_TYPES_COMBINING_ALL, LEVELS_AND_TYPES, RATIOS_TYPE, RATIOS_SCOPE, ROUTINE]
|
required: [LEVELS_AND_TYPES, RATIOS_TYPE, RATIOS_SCOPE, ROUTINE]
|
||||||
properties:
|
properties:
|
||||||
LEVELS_AND_TYPES_COMBINING_ALL:
|
|
||||||
type: boolean
|
|
||||||
LEVELS_AND_TYPES:
|
LEVELS_AND_TYPES:
|
||||||
type: array
|
type: array
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
|
@ -981,8 +1001,10 @@ properties:
|
||||||
enum: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
|
enum: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
|
||||||
SLEEP_LEVELS:
|
SLEEP_LEVELS:
|
||||||
type: object
|
type: object
|
||||||
required: [CLASSIC, STAGES, UNIFIED]
|
required: [INCLUDE_ALL_GROUPS, CLASSIC, STAGES, UNIFIED]
|
||||||
properties:
|
properties:
|
||||||
|
INCLUDE_ALL_GROUPS:
|
||||||
|
type: boolean
|
||||||
CLASSIC:
|
CLASSIC:
|
||||||
type: array
|
type: array
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
|
@ -1006,12 +1028,12 @@ properties:
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
items:
|
items:
|
||||||
type: string
|
type: string
|
||||||
enum: [main, nap]
|
enum: [main, nap, all]
|
||||||
INCLUDE_SLEEP_LATER_THAN:
|
LAST_NIGHT_END:
|
||||||
type: number
|
type: number
|
||||||
minimum: 0
|
minimum: 0
|
||||||
maximum: 1439
|
maximum: 1439
|
||||||
REFERENCE_TIME:
|
ROUTINE_REFERENCE_TIME:
|
||||||
type: string
|
type: string
|
||||||
enum: [MIDNIGHT, START_OF_THE_SEGMENT]
|
enum: [MIDNIGHT, START_OF_THE_SEGMENT]
|
||||||
PRICE:
|
PRICE:
|
||||||
|
@ -1022,11 +1044,13 @@ properties:
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
items:
|
items:
|
||||||
type: string
|
type: string
|
||||||
enum: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain]
|
enum: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
|
||||||
SLEEP_LEVELS:
|
SLEEP_LEVELS:
|
||||||
type: object
|
type: object
|
||||||
required: [CLASSIC, STAGES, UNIFIED]
|
required: [INCLUDE_ALL_GROUPS, CLASSIC, STAGES, UNIFIED]
|
||||||
properties:
|
properties:
|
||||||
|
INCLUDE_ALL_GROUPS:
|
||||||
|
type: boolean
|
||||||
CLASSIC:
|
CLASSIC:
|
||||||
type: array
|
type: array
|
||||||
uniqueItems: True
|
uniqueItems: True
|
||||||
|
@ -1051,18 +1075,10 @@ properties:
|
||||||
items:
|
items:
|
||||||
type: string
|
type: string
|
||||||
enum: [WEEKEND, WEEK, ALL]
|
enum: [WEEKEND, WEEK, ALL]
|
||||||
GROUP_EPISODES_WITHIN:
|
LAST_NIGHT_END:
|
||||||
type: object
|
|
||||||
required: [START_TIME, LENGTH]
|
|
||||||
properties:
|
|
||||||
START_TIME:
|
|
||||||
type: number
|
type: number
|
||||||
minimum: 0
|
minimum: 0
|
||||||
maximum: 1439
|
maximum: 1439
|
||||||
LENGTH:
|
|
||||||
type: number
|
|
||||||
minimum: 0
|
|
||||||
maximum: 1440
|
|
||||||
additionalProperties:
|
additionalProperties:
|
||||||
$ref: "#/definitions/PROVIDER"
|
$ref: "#/definitions/PROVIDER"
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue