Merge branch 'feature/fitbit_sleep_intraday' into develop

Snakefile

@@ -257,11 +257,16 @@ for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
-# for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys():
+for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys():
-#     if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
+    if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
-#         files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"]))
-#         files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"]))
-#         files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed_with_datetime.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_intraday.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
+        files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
 for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys():
     if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]:

config.yaml

@@ -379,6 +379,42 @@ FITBIT_SLEEP_SUMMARY:
       SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary
       SRC_LANGUAGE: "python"
 
+# See https://www.rapids.science/latest/features/fitbit-sleep-intraday/
+FITBIT_SLEEP_INTRADAY:
+  TABLE: fitbit_data
+  PROVIDERS:
+    RAPIDS:
+      COMPUTE: False
+      FEATURES:
+        LEVELS_AND_TYPES_COMBINING_ALL: True
+        LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
+        RATIOS_TYPE: [count, duration]
+        RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
+        ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
+      SLEEP_LEVELS:
+        CLASSIC: [awake, restless, asleep]
+        STAGES: [wake, deep, light, rem]
+        UNIFIED: [awake, asleep]
+      SLEEP_TYPES: [main, nap]
+      INCLUDE_SLEEP_LATER_THAN: 0 # a number ranged from 0 (midnight) to 1439 (23:59)
+      REFERENCE_TIME: MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT"
+      SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_intraday
+      SRC_LANGUAGE: "python"
+  
+    PRICE:
+      COMPUTE: False
+      FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain]
+      SLEEP_LEVELS:
+        CLASSIC: [awake, restless, asleep]
+        STAGES: [wake, deep, light, rem]
+        UNIFIED: [awake, asleep]
+      DAY_TYPES: [WEEKEND, WEEK, ALL]
+      GROUP_EPISODES_WITHIN: # by default: today's 6pm to tomorrow's noon
+        START_TIME: 1080 # number of minutes after the midnight (18:00) 18*60
+        LENGTH: 1080 # in minutes (18 hours) 18*60
+      SRC_FOLDER: "price" # inside src/features/fitbit_sleep_intraday
+      SRC_LANGUAGE: "python"
+
 # See https://www.rapids.science/latest/features/fitbit-steps-summary/
 FITBIT_STEPS_SUMMARY:
   TABLE: steps_summary

docs/features/add-new-features.md

@@ -41,6 +41,7 @@ As a tutorial, we will add a new provider for `PHONE_ACCELEROMETER` called `VEGA
     - Fitbit Heart Rate Summary
     - Fitbit Heart Rate Intraday
     - Fitbit Sleep Summary
+    - Fitbit Sleep Intraday
     - Fitbit Steps Summary
     - Fitbit Steps Intraday
 

docs/features/fitbit-sleep-intraday.md

@@ -0,0 +1,260 @@
+# Fitbit Sleep Intraday
+
+Sensor parameters description for `[FITBIT_SLEEP_INTRADAY]`:
+
+|Key                                         | Description |
+|----------------|-----------------------------------------------------------------------------------------------------------------------------------
+|`[TABLE]`| Database table name or file path where the sleep intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file.
+
+
+The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. 
+
+We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity).
+
+??? example "Example of the structure of source data with Fitbit’s sleep API Version 1"
+
+    === "JSON"
+
+        |device_id                                |fitbit_data                                               |
+        |---------------------------------------- |--------------------------------------------------------- |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep": [{"awakeCount": 2, "awakeDuration": 3, "awakeningsCount": 10, "dateOfSleep": "2020-10-07", "duration": 8100000, "efficiency": 91, "endTime": "2020-10-07T18:10:00.000", "isMainSleep": true, "logId": 14147921940, "minuteData": [{"dateTime": "15:55:00", "value": "3"}, {"dateTime": "15:56:00", "value": "3"}, {"dateTime": "15:57:00", "value": "2"},...], "minutesAfterWakeup": 0, "minutesAsleep": 123, "minutesAwake": 12, "minutesToFallAsleep": 0, "restlessCount": 8, "restlessDuration": 9, "startTime": "2020-10-07T15:55:00.000", "timeInBed": 135}, {"awakeCount": 0, "awakeDuration": 0, "awakeningsCount": 1, "dateOfSleep": "2020-10-07", "duration": 3780000, "efficiency": 100, "endTime": "2020-10-07T10:52:30.000", "isMainSleep": false, "logId": 14144903977, "minuteData": [{"dateTime": "09:49:00", "value": "1"}, {"dateTime": "09:50:00", "value": "1"}, {"dateTime": "09:51:00", "value": "1"},...], "minutesAfterWakeup": 1, "minutesAsleep": 62, "minutesAwake": 0, "minutesToFallAsleep": 0, "restlessCount": 1, "restlessDuration": 1, "startTime": "2020-10-07T09:49:00.000", "timeInBed": 63}], "summary": {"totalMinutesAsleep": 185, "totalSleepRecords": 2, "totalTimeInBed": 198}}
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep": [{"awakeCount": 3, "awakeDuration": 21, "awakeningsCount": 16, "dateOfSleep": "2020-10-08", "duration": 19260000, "efficiency": 89, "endTime": "2020-10-08T06:01:30.000", "isMainSleep": true, "logId": 14150613895, "minuteData": [{"dateTime": "00:40:00", "value": "3"}, {"dateTime": "00:41:00", "value": "3"}, {"dateTime": "00:42:00", "value": "3"},...], "minutesAfterWakeup": 0, "minutesAsleep": 275, "minutesAwake": 33, "minutesToFallAsleep": 0, "restlessCount": 13, "restlessDuration": 25, "startTime": "2020-10-08T00:40:00.000", "timeInBed": 321}], "summary": {"totalMinutesAsleep": 275, "totalSleepRecords": 1, "totalTimeInBed": 321}}
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep": [{"awakeCount": 1, "awakeDuration": 3, "awakeningsCount": 8, "dateOfSleep": "2020-10-09", "duration": 19320000, "efficiency": 96, "endTime": "2020-10-09T05:57:30.000", "isMainSleep": true, "logId": 14161136803, "minuteData": [{"dateTime": "00:35:30", "value": "2"}, {"dateTime": "00:36:30", "value": "1"}, {"dateTime": "00:37:30", "value": "1"},...], "minutesAfterWakeup": 0, "minutesAsleep": 309, "minutesAwake": 13, "minutesToFallAsleep": 0, "restlessCount": 7, "restlessDuration": 10, "startTime": "2020-10-09T00:35:30.000", "timeInBed": 322}], "summary": {"totalMinutesAsleep": 309, "totalSleepRecords": 1, "totalTimeInBed": 322}}
+    
+    === "PLAIN_TEXT"
+
+        All columns are mandatory, however, all except `device_id`, `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "minutesData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1. Actually, you only need to make sure rows extracted from the same "minutesData" block are assigned with the same unique `type_episode_id` value.
+
+        |device_id                             |type_episode_id  |local_date_time     |duration  |level      |is_main_sleep  |type           |
+        |------------------------------------  |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524  |0                |2020-10-07 15:55:00 |60        |awake      |0              |classic        |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524  |0                |2020-10-07 15:56:00 |60        |awake      |0              |classic        |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524  |0                |2020-10-07 15:57:00 |60        |restless   |0              |classic        |
+
+??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2"
+
+    === "JSON"
+
+        |device_id                                |fitbit_data                                               |
+        |---------------------------------------- |--------------------------------------------------------- |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep":[{"dateOfSleep":"2020-10-10","duration":3600000,"efficiency":92,"endTime":"2020-10-10T16:37:00.000","infoCode":2,"isMainSleep":false,"levels":{"data":[{"dateTime":"2020-10-10T15:36:30.000","level":"restless","seconds":60},{"dateTime":"2020-10-10T15:37:30.000","level":"asleep","seconds":660},{"dateTime":"2020-10-10T15:48:30.000","level":"restless","seconds":60},...], "summary":{"asleep":{"count":0,"minutes":56},"awake":{"count":0,"minutes":0},"restless":{"count":3,"minutes":4}}},"logId":26315914306,"minutesAfterWakeup":0,"minutesAsleep":55,"minutesAwake":5,"minutesToFallAsleep":0,"startTime":"2020-10-10T15:36:30.000","timeInBed":60,"type":"classic"},{"dateOfSleep":"2020-10-10","duration":22980000,"efficiency":88,"endTime":"2020-10-10T08:10:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-10T01:46:30.000","level":"light","seconds":420},{"dateTime":"2020-10-10T01:53:30.000","level":"deep","seconds":1230},{"dateTime":"2020-10-10T02:14:00.000","level":"light","seconds":360},...], "summary":{"deep":{"count":3,"minutes":92,"thirtyDayAvgMinutes":0},"light":{"count":29,"minutes":193,"thirtyDayAvgMinutes":0},"rem":{"count":4,"minutes":33,"thirtyDayAvgMinutes":0},"wake":{"count":28,"minutes":65,"thirtyDayAvgMinutes":0}}},"logId":26311786557,"minutesAfterWakeup":0,"minutesAsleep":318,"minutesAwake":65,"minutesToFallAsleep":0,"startTime":"2020-10-10T01:46:30.000","timeInBed":383,"type":"stages"}],"summary":{"stages":{"deep":92,"light":193,"rem":33,"wake":65},"totalMinutesAsleep":373,"totalSleepRecords":2,"totalTimeInBed":443}}
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep":[{"dateOfSleep":"2020-10-11","duration":41640000,"efficiency":89,"endTime":"2020-10-11T11:47:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-11T00:12:30.000","level":"wake","seconds":450},{"dateTime":"2020-10-11T00:20:00.000","level":"light","seconds":870},{"dateTime":"2020-10-11T00:34:30.000","level":"wake","seconds":780},...], "summary":{"deep":{"count":4,"minutes":52,"thirtyDayAvgMinutes":62},"light":{"count":32,"minutes":442,"thirtyDayAvgMinutes":364},"rem":{"count":6,"minutes":68,"thirtyDayAvgMinutes":58},"wake":{"count":29,"minutes":132,"thirtyDayAvgMinutes":94}}},"logId":26589710670,"minutesAfterWakeup":1,"minutesAsleep":562,"minutesAwake":132,"minutesToFallAsleep":0,"startTime":"2020-10-11T00:12:30.000","timeInBed":694,"type":"stages"}],"summary":{"stages":{"deep":52,"light":442,"rem":68,"wake":132},"totalMinutesAsleep":562,"totalSleepRecords":1,"totalTimeInBed":694}}
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524     |{"sleep":[{"dateOfSleep":"2020-10-12","duration":28980000,"efficiency":93,"endTime":"2020-10-12T09:34:30.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-12T01:31:00.000","level":"wake","seconds":600},{"dateTime":"2020-10-12T01:41:00.000","level":"light","seconds":60},{"dateTime":"2020-10-12T01:42:00.000","level":"deep","seconds":2340},...], "summary":{"deep":{"count":4,"minutes":63,"thirtyDayAvgMinutes":59},"light":{"count":27,"minutes":257,"thirtyDayAvgMinutes":364},"rem":{"count":5,"minutes":94,"thirtyDayAvgMinutes":58},"wake":{"count":24,"minutes":69,"thirtyDayAvgMinutes":95}}},"logId":26589710673,"minutesAfterWakeup":0,"minutesAsleep":415,"minutesAwake":68,"minutesToFallAsleep":0,"startTime":"2020-10-12T01:31:00.000","timeInBed":483,"type":"stages"}],"summary":{"stages":{"deep":63,"light":257,"rem":94,"wake":69},"totalMinutesAsleep":415,"totalSleepRecords":1,"totalTimeInBed":483}}
+    
+    === "PLAIN_TEXT"
+
+        All columns are mandatory, however, all except `device_id`, `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "data" and "shortData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1. Actually, you only need to make sure rows extracted from the same "minutesData" block are assigned with the same unique `type_episode_id` value.
+
+        |device_id                              |type_episode_id  |local_date_time     |duration  |level      |is_main_sleep  |type           |
+        |------------------------------------   |---------------- |------------------- |--------- |---------- |-------------- |-------------- |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |0                |2020-10-10 15:36:30 |60        |restless   |0              |classic        |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |0                |2020-10-10 15:37:30 |660       |asleep     |0              |classic        |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |0                |2020-10-10 15:48:30 |60        |restless   |0              |classic        |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |...              |...                 |...       |...        |...            |...            |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |1                |2020-10-10 01:46:30 |420       |light      |1              |stages         |
+        |a748ee1a-1d0b-4ae9-9074-279a2b6ba524   |1                |2020-10-10 01:53:30 |1230      |deep       |1              |stages         |
+
+## RAPIDS provider
+
+!!! info "Available time segments"
+    - Available for all time segments
+
+!!! info "File Sequence"
+    ```bash
+    - data/raw/{pid}/fitbit_sleep_intraday_raw.csv
+    - data/raw/{pid}/fitbit_sleep_intraday_parsed.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv
+    - data/processed/features/{pid}/fitbit_sleep_intraday.csv
+    ```
+
+
+Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]`:
+
+|Key                                         | Description |
+|----------------|-----------------------------------------------------------------------------------------------------------------------------------
+|`[COMPUTE]`                                  | Set to `True` to extract `FITBIT_SLEEP_INTRADAY` features from the `RAPIDS` provider|
+|`[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_TYPES]`                              | Types of sleep to be included in the feature extraction computation. Fitbit provides 2 types of sleep: `main`, `nap`.
+|`[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.
+|`[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`.
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]`:
+
+|Feature                                                              |Units          |Description                                                  |
+|------------------------------- |-------------- |-------------------------------------------------------------|
+|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.
+|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.
+|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.
+|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.
+|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.
+|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.
+|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.
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_LEVELS]`:
+
+|Feature                                               |Units |Description                                                        |
+|-------------------------- |-------------- |-------------------------------------------------------------|
+|ratiocount`[LEVEL]`         |-|Ratio between the **count** of episodes of a single sleep `[LEVEL]` and the **count** of all episodes of all levels during both `main` and `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` episodes were `rem`? (e.g., $countepisode[remstages][all] / countepisode[all][all]$)
+|ratioduration`[LEVEL]`      |-|Ratio between the **duration** of episodes of a single sleep `[LEVEL]` and the **duration** of all episodes of all levels during both `main` and `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` time was `rem`? (e.g., $sumduration[remstages][all] / sumduration[all][all]$)
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_TYPES]`:
+
+|Feature                                               |Units          |Description                                                  |
+|-------------------------- |-------------- |-------------------------------------------------------------|
+|ratiocountmain             |-              |Ratio between the **count** of all `main` episodes (independently of the levels inside) divided by the **count** of all `main` and `nap` episodes. This answers the question: what percentage of all sleep episodes (`main` and `nap`) were `main`? We do not provide the ratio for `nap` because is complementary. ($countepisode[all][main] / countepisode[all][all]$)
+|ratiodurationmain          |-              |Ratio between the **duration** of all `main` episodes (independently of the levels inside) divided by the **duration** of all `main` and `nap` episodes. This answers the question: what percentage of all sleep time (`main` and `nap`) was `main`? We do not provide the ratio for `nap` because is complementary. ($sumduration[all][main] / sumduration[all][all]$)
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[WITHIN_LEVELS]`:
+
+|Feature                                                      |Units          |Description                                                  |
+|--------------------------------- |-------------- |-------------------------------------------------------------|
+|ratiocount`[TYPE]`within`[LEVEL]`    |-              |Ratio between the **count** of episodes of a single sleep `[LEVEL]` during `main` sleep divided by the **count** of episodes of a single sleep `[LEVEL]` during `main` **and** `nap`. This answers the question: are `rem` episodes more frequent during `main` than `nap` sleep? We do not provide the ratio for `nap` because is complementary. ($countepisode[remstages][main] / countepisode[remstages][all]$)
+|ratioduration`[TYPE]`within`[LEVEL]` |-              |Ratio between the **duration** of episodes of a single sleep `[LEVEL]` during `main` sleep divided by the **duration** of episodes of a single sleep `[LEVEL]` during `main` **and** `nap`. This answers the question: is `rem` time more frequent during `main` than `nap` sleep? We do not provide the ratio for `nap` because is complementary. ($countepisode[remstages][main] / countepisode[remstages][all]$)
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[WITHIN_TYPES]`:
+
+|Feature                                                   |Units|Description|
+| - |- | - |
+|ratiocount`[LEVEL]`within`[TYPE]`    |-|Ratio between the **count** of episodes of a single sleep `[LEVEL]` and the **count** of all episodes of all levels during either `main` or `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` episodes were `rem` during `main`/`nap` sleep time? (e.g., $countepisode[remstages][main] / countepisode[all][main]$)
+|ratioduration`[LEVEL]`within`[TYPE]` |-|Ratio between the **duration** of episodes of a single sleep `[LEVEL]` and the **duration** of all episodes of all levels during either `main` or `nap` sleep types. This answers the question: what percentage of all `wake`, `deep`, `light`, and `rem` time was `rem` during `main`/`nap` sleep time? (e.g., $sumduration[remstages][main] / sumduration[all][main]$)
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][ROUTINE]`:
+
+|Feature                           |Units          |Description                                                  |
+|--------------------------------- |-------------- |-------------------------------------------------------------|
+|starttimefirstmainsleep           |minutes        |Start time (in minutes since `REFERENCE_TIME`) of the first main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
+|endtimelastmainsleep              |minutes        |End time (in minutes since `REFERENCE_TIME`) of the last main sleep episode after `INCLUDE_EPISODES_LATER_THAN`.
+|starttimefirstnap                 |minutes        |Start time (in minutes since `REFERENCE_TIME`) of the first nap episode after `INCLUDE_EPISODES_LATER_THAN`.
+|endtimelastnap                    |minutes        |End time (in minutes since `REFERENCE_TIME`) of the last nap episode after `INCLUDE_EPISODES_LATER_THAN`.
+
+
+
+
+
+!!! note "Assumptions/Observations"
+    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.
+    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`.
+    
+
+## PRICE provider
+
+!!! info "Available time segments"
+    - Available for any time segments larger or equal to one day
+
+!!! info "File Sequence"
+    ```bash
+    - data/raw/{pid}/fitbit_sleep_intraday_raw.csv
+    - data/raw/{pid}/fitbit_sleep_intraday_parsed.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv
+    - data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv
+    - data/processed/features/{pid}/fitbit_sleep_intraday.csv
+    ```
+
+
+Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE]`:
+
+|Key                                                                             | Description |
+|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+|`[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   
+|`[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`).
+|`[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).  |
+
+
+Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][PRICE]`:
+
+|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.
+|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.
+|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.
+|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.
+|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.
+|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.
+|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.
+|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.
+|socialjetlag                          |minutes           | Difference in minutes between the avgstarttimeofepisodemain (bed time) of weekends and weekdays. 
+|meanssdstarttimeofepisodemain         |minutes squared   | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive start times.
+|meanssdendtimeofepisodemain           |minutes squared   | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive end times.
+|meanssdmidpointofepisodemain          |minutes squared   | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive mid times.
+|medianssdstarttimeofepisodemain       |minutes squared   | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive start times.
+|medianssdendtimeofepisodemain         |minutes squared   | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive end times.
+|medianssdmidpointofepisodemain        |minutes squared   | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the median is computed over the squared differences of each pair of consecutive mid times.
+
+
+
+!!! 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).
+    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?
+    
+        `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:
+
+        <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` 
+
+
+    
+
+
+

docs/stylesheets/extra.css

@@ -25,4 +25,8 @@ div[data-md-component=announce]>div#announce-msg{
 div[data-md-component=announce]>div#announce-msg>a{
     color: var(--md-typeset-a-color);
     text-decoration: underline;
+}
+
+.md-typeset table:not([class]) th {
+    min-width: 0rem;
 }

mkdocs.yml

@@ -105,6 +105,7 @@ nav:
         - Fitbit Heart Rate Summary: features/fitbit-heartrate-summary.md
         - Fitbit Heart Rate Intraday: features/fitbit-heartrate-intraday.md
         - Fitbit Sleep Summary: features/fitbit-sleep-summary.md
+        - Fitbit Sleep Intraday: features/fitbit-sleep-intraday.md
         - Fitbit Steps Summary: features/fitbit-steps-summary.md
         - Fitbit Steps Intraday: features/fitbit-steps-intraday.md
       - Empatica:

rules/features.smk

@@ -660,6 +660,40 @@ rule fitbit_sleep_summary_r_features:
     script:
         "../src/features/entry.R"
 
+rule resample_sleep_episodes:
+    input:
+        "data/raw/{pid}/fitbit_sleep_intraday_parsed.csv"
+    output:
+        "data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv"
+    script:
+        "../src/features/utils/resample_episodes.R"
+
+rule fitbit_sleep_intraday_python_features:
+    input:
+        sensor_data = "data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv",
+        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
+    params:
+        provider = lambda wildcards: config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        sensor_key = "fitbit_sleep_intraday"
+    output:
+        "data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_python_{provider_key}.csv"
+    script:
+        "../src/features/entry.py"
+
+rule fitbit_sleep_intraday_r_features:
+    input:
+        sensor_data = "data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv",
+        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
+    params:
+        provider = lambda wildcards: config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
+        provider_key = "{provider_key}",
+        sensor_key = "fitbit_sleep_intraday"
+    output:
+        "data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_r_{provider_key}.csv"
+    script:
+        "../src/features/entry.R"
+
 rule merge_sensor_features_for_individual_participants:
     input:
         feature_files = input_merge_sensor_features_for_individual_participants

src/data/fitbit_parse_sleep.py

@@ -14,66 +14,53 @@ SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency",
                                 "timestamp")
 SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless")
 
-SLEEP_INTRADAY_COLUMNS = ("device_id",
+SLEEP_INTRADAY_COLUMNS = (# Extract "type_episode_id" field based on summary data: start from 0
+                            "type_episode_id",
+                            "duration",
                             # For "classic" type, original_level is one of {"awake", "restless", "asleep"}
                             # For "stages" type, original_level is one of {"wake", "deep", "light", "rem"}
                             "level",
                             # For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"}
                             # For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"}
                             "unified_level",
-                            # one of {0, 1} where 0: nap, 1: main sleep
+                            # One of {0, 1} where 0: nap, 1: main sleep
                             "is_main_sleep", 
-                            # one of {"classic", "stages"}
+                            # One of {"classic", "stages"}
                             "type",
                             "local_date_time",
-                            "timestamp")
+                            "start_timestamp",
+                            "end_timestamp")
 
-def mergeLongAndShortData(data_summary):
-    longData = pd.DataFrame(columns=['dateTime', 'level', 'seconds'])
-    shortData = pd.DataFrame(columns=['dateTime','level', 'seconds'])
 
-    windowLength = 30
+def mergeLongAndShortData(data_intraday):
+    long_data = pd.DataFrame(columns=["dateTime", "level"])
+    short_data = pd.DataFrame(columns=["dateTime", "level"])
 
-    for data in data_summary['data']:
+    window_length = 30
-        origEntry = data
+
+    for data in data_intraday["data"]:
         counter = 0
-        numberOfSplits = origEntry['seconds']//windowLength
+        for times in range(data["seconds"] // window_length):
-        for times in range(numberOfSplits):
+            row = {"dateTime": dateutil.parser.parse(data["dateTime"])+timedelta(seconds=counter*window_length), "level": data["level"]}
-            newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
+            long_data = long_data.append(row, ignore_index = True)
-            longData = longData.append(newRow, ignore_index = True)
             counter = counter + 1
 
-    for data in data_summary['shortData']:
+    for data in data_intraday["shortData"]:
-        origEntry = data
         counter = 0
-        numberOfSplits = origEntry['seconds']//windowLength
+        for times in range(data["seconds"] // window_length):
-        for times in range(numberOfSplits):
+            row = {"dateTime": dateutil.parser.parse(data["dateTime"])+timedelta(seconds=counter*window_length), "level": data["level"]}
-            newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
+            short_data = short_data.append(row, ignore_index = True)
-            shortData = shortData.append(newRow,ignore_index = True)
             counter = counter + 1
-    longData.set_index('dateTime',inplace=True)
+    long_data.set_index("dateTime",inplace=True)
-    shortData.set_index('dateTime',inplace=True)
+    short_data.set_index("dateTime",inplace=True)
-    longData['level'] = np.where(longData.index.isin(shortData.index) == True,'wake',longData['level'])
+    long_data["level"] = np.where(long_data.index.isin(short_data.index) == True, "wake", long_data["level"])
     
-    longData.reset_index(inplace=True)
+    long_data.reset_index(inplace=True)
     
-    return longData.values.tolist()
+    return long_data.values.tolist()
-
-def classicData1min(data_summary):
-    dataList = list()
-    for data in data_summary['data']:
-        origEntry = data
-        counter = 0
-        timeDuration = 60
-        numberOfSplits = origEntry['seconds']//timeDuration
-        for times in range(numberOfSplits):
-            newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*timeDuration),'level':origEntry['level'],'seconds':timeDuration}
-            dataList.append(newRow)
-            counter = counter + 1
-    return dataList
 
 # Parse one record for sleep API version 1
-def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
+def parseOneRecordForV1(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
 
     sleep_record_type = "classic"
 
@@ -110,16 +97,16 @@ def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, rec
             d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1]
 
 
-            row_intraday = (device_id,
+            row_intraday = (type_episode_id, 60,
                             d_original_level, -1, d_is_main_sleep, sleep_record_type,
-                            d_datetime, 0)
+                            d_datetime, 0, 0)
 
             records_intraday.append(row_intraday)
 
     return records_summary, records_intraday
 
 # Parse one record for sleep API version 1.2
-def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
+def parseOneRecordForV12(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
     
     sleep_record_type = record['type']
 
@@ -138,52 +125,24 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re
     
     # Intraday data
     if fitbit_data_type == "intraday":
-        if sleep_record_type == 'classic':
+        if sleep_record_type == "classic":
-            start_date = d_start_datetime.date()
+            for data in record["levels"]["data"]:
-            end_date = d_end_datetime.date()
+                d_datetime = dateutil.parser.parse(data["dateTime"])
-            is_before_midnight = True
-            curr_date = start_date
-            data_summary = record['levels']
-            dataSplitted = classicData1min(data_summary)  ##Calling the function to split the data in regular 60 seconds interval
-            for data in dataSplitted:
-                # For overnight episodes, use end_date once we are over midnight
-                d_time = data["dateTime"].time()
-                if is_before_midnight and d_time.hour == 0:
-                    curr_date = end_date
-                d_datetime = datetime.combine(curr_date, d_time)
 
-                d_original_level = data["level"]
+                row_intraday = (type_episode_id, data["seconds"],
-
+                    data["level"], -1, d_is_main_sleep, sleep_record_type,
-                row_intraday = (device_id,
+                    d_datetime, 0, 0)
-                    d_original_level, -1, d_is_main_sleep, sleep_record_type,
-                    d_datetime, 0)
                 records_intraday.append(row_intraday)
         else:
             # For sleep type "stages"
-            start_date = d_start_datetime.date()
+            for data in mergeLongAndShortData(record["levels"]):
-            end_date = d_end_datetime.date()
+                row_intraday = (type_episode_id, 30,
-            is_before_midnight = True
+                    data[1], -1, d_is_main_sleep, sleep_record_type,
-            curr_date = start_date
+                    data[0], 0, 0)
-            data_summary = record['levels']
-            dataList = mergeLongAndShortData(data_summary)
-            for data in dataList:
-
-                d_time = data[0].time()
-                if is_before_midnight and d_time.hour == 0:
-                    curr_date = end_date
-                d_datetime = datetime.combine(curr_date, d_time)
-
-                d_original_level = data[1]
-
-                row_intraday = (device_id,
-                    d_original_level, -1, d_is_main_sleep, sleep_record_type,
-                    d_datetime, 0)
 
                 records_intraday.append(row_intraday)
     
     return records_summary, records_intraday
-    
-
 
 def parseSleepData(sleep_data, fitbit_data_type):
     SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
@@ -194,6 +153,7 @@ def parseSleepData(sleep_data, fitbit_data_type):
             return pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS)
     device_id = sleep_data["device_id"].iloc[0]
     records_summary, records_intraday = [], []
+    type_episode_id = 0
     # Parse JSON into individual records
     for multi_record in sleep_data.fitbit_data:
         for record in json.loads(multi_record)["sleep"]:
@@ -203,11 +163,13 @@ def parseSleepData(sleep_data, fitbit_data_type):
             # For sleep API version 1
             if "awakeCount" in record:
                 SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1
-                records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
+                records_summary, records_intraday = parseOneRecordForV1(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
             # For sleep API version 1.2
             else:
                 SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
-                records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
+                records_summary, records_intraday = parseOneRecordForV12(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
+            
+            type_episode_id = type_episode_id + 1
 
     if fitbit_data_type == "summary":
         parsed_data = pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS)
@@ -216,6 +178,19 @@ def parseSleepData(sleep_data, fitbit_data_type):
 
     return parsed_data
 
+def mergeSleepEpisodes(sleep_data, cols_for_groupby):
+    sleep_episodes = pd.DataFrame(columns=["type_episode_id", "level_episode_id", "level", "unified_level", "is_main_sleep", "type", "start_timestamp", "end_timestamp"])
+    if not sleep_data.empty:
+        sleep_data = sleep_data.groupby(by=cols_for_groupby)
+        sleep_episodes = sleep_data[["start_timestamp"]].first()
+        sleep_episodes["end_timestamp"] = sleep_data["end_timestamp"].last()
+    
+        sleep_episodes.reset_index(inplace=True, drop=False)
+
+    return sleep_episodes
+
+
+
 timezone = snakemake.params["timezone"]
 column_format = snakemake.params["column_format"]
 fitbit_data_type = snakemake.params["fitbit_data_type"]
@@ -237,6 +212,9 @@ elif column_format == "PLAIN_TEXT":
 else:
     raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
 
+# Drop duplicates
+parsed_data.drop_duplicates(inplace=True)
+
 if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
     if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end":
         raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].")
@@ -245,6 +223,10 @@ if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
     
     if not pd.isnull(local_start_date) and not pd.isnull(local_end_date):
         parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)]
+    
+    # Sort by "local_start_date_time" column
+    parsed_data.sort_values(by="local_start_date_time", ascending=True, inplace=True)
+
     parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
     parsed_data.dropna(subset=['timestamp'], inplace=True)
     parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True)
@@ -252,8 +234,18 @@ if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
 if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday":
     if not pd.isnull(local_start_date) and not pd.isnull(local_end_date):
         parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
-    parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
+    
-    parsed_data.dropna(subset=['timestamp'], inplace=True)
+    # Sort by "local_date_time" column
-    parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1)
+    parsed_data.sort_values(by="local_date_time", ascending=True, inplace=True)
+
+    parsed_data["start_timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
+    parsed_data.dropna(subset=['start_timestamp'], inplace=True)
+    parsed_data["end_timestamp"] = parsed_data["start_timestamp"] + ((parsed_data["duration"] - 1) * 1000) + 999
+    parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "restless", "wake"]), 0, 1)
+    
+    # Put consecutive rows with the same "level" field together and merge episodes
+    parsed_data.insert(2, "level_episode_id", (parsed_data[["type_episode_id", "level"]] != parsed_data[["type_episode_id", "level"]].shift()).any(axis=1).cumsum())
+    parsed_data = mergeSleepEpisodes(parsed_data, ["type_episode_id", "level_episode_id", "level", "unified_level", "is_main_sleep", "type"])
+
 
 parsed_data.to_csv(snakemake.output[0], index=False)

src/features/fitbit_sleep_intraday/price/main.py

@@ -0,0 +1,245 @@
+import pandas as pd
+import itertools
+
+
+
+def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute):
+
+    features_fullnames = ["local_segment"]
+
+    sleep_level_with_group = []
+    for sleep_level_group in sleep_levels_to_compute:
+        for sleep_level in sleep_levels_to_compute[sleep_level_group]:
+            sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
+
+    for feature in intraday_features_to_compute:
+        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)])
+        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)])
+        elif feature in ["avgstarttimeofepisodemain", "avgendtimeofepisodemain", "avgmidpointofepisodemain", "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain"]:
+            features_fullnames.extend([feature + x.lower() for x in day_types_to_compute])
+        else:
+            features_fullnames.append(feature)
+    
+    return features_fullnames
+
+def mergeSleepEpisodes(sleep_data, cols_for_groupby, base_sleep_levels):
+
+    sleep_level_with_group = []
+    for sleep_level_group in base_sleep_levels:
+        for sleep_level in base_sleep_levels[sleep_level_group]:
+            sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
+    
+    sleep_episodes = pd.DataFrame(columns=["local_segment", "durationinbed", "start_timestamp", "end_timestamp", "local_start_date_time", "local_end_date_time"] + ["duration" + x for x in sleep_level_with_group])
+
+    if cols_for_groupby and (not sleep_data.empty):
+        sleep_data = sleep_data.groupby(by=cols_for_groupby)
+        sleep_episodes = sleep_data[["duration"]].sum().rename(columns={"duration": "durationinbed"})
+
+        sleep_episodes["start_timestamp"] = sleep_data["start_timestamp"].first()
+        sleep_episodes["end_timestamp"] = sleep_data["end_timestamp"].last()
+
+        sleep_episodes["local_start_date_time"] = sleep_data["local_start_date_time"].first()
+        sleep_episodes["local_end_date_time"] = sleep_data["local_end_date_time"].last()
+
+        for sleep_level in sleep_level_with_group:
+            sleep_episodes["duration" + sleep_level] = sleep_data.apply(lambda group: group[group["level"] == sleep_level.replace("classic", "").replace("stages", "").replace("unified", "")]["duration"].sum())
+
+        sleep_episodes.reset_index(inplace=True, drop=False)
+        del sleep_episodes["type_episode_id"]
+    
+    return sleep_episodes
+
+def extractDailyFeatures(sleep_data):
+    daily_grouped = sleep_data.groupby(["local_segment", "fake_date"])
+    daily_features = daily_grouped[["start_minutes"]].first().rename(columns={"start_minutes": "starttimeofepisodemain"})
+    daily_features["endtimeofepisodemain"] = daily_grouped["end_minutes"].last()
+    daily_features["midpointofepisodemain"] = (daily_features["starttimeofepisodemain"] + daily_features["endtimeofepisodemain"]) / 2
+    daily_features["durationinbedmain"] = daily_grouped["durationinbed"].sum()
+
+    for col in sleep_data.columns:
+        if col.startswith("duration") and col != "durationinbed":
+            daily_features[col + "main"] = daily_grouped[col].sum().fillna(0)
+            daily_features["ratio" + col + "withinmain"] = daily_features[col + "main"] / daily_features["durationinbedmain"]
+    daily_features.reset_index(inplace=True)
+
+    # The day of the week with Monday=0, Sunday=6. Set Friday and Saturday as Weekend, others as Weekday.
+    daily_features["is_weekend"] = pd.to_datetime(daily_features["fake_date"]).dt.dayofweek.apply(lambda x: 1 if (x == 4 or x == 5) else 0)
+    
+    return daily_features
+
+def statsOfDailyFeatures(daily_features, day_type, sleep_levels, intraday_features_to_compute, sleep_intraday_features):
+    if day_type == "WEEKEND":
+        daily_features = daily_features[daily_features["is_weekend"] == 0]
+    elif day_type == "WEEK":
+        daily_features = daily_features[daily_features["is_weekend"] == 1]
+    elif day_type == "ALL":
+        pass
+    else:
+        raise ValueError("Please make sure the [FITBIT_SLEEP_INTRADAY][PROVIDERS][PRICE][DAY_TYPES] parameter in config.yaml file only contains the subset of [WEEKEND, WEEK, ALL].")
+
+    if daily_features.empty:
+        return sleep_intraday_features
+    
+    if sleep_intraday_features.empty:
+        sleep_intraday_features = pd.DataFrame()
+    
+    # Average of time related features
+    if "avgstarttimeofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].mean().to_frame().rename(columns={"starttimeofepisodemain": "avgstarttimeofepisodemain" + day_type.lower()})], axis=1)
+    if "avgendtimeofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain"]].groupby("local_segment")["endtimeofepisodemain"].mean().to_frame().rename(columns={"endtimeofepisodemain": "avgendtimeofepisodemain" + day_type.lower()})], axis=1)
+    if "avgmidpointofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain"]].groupby("local_segment")["midpointofepisodemain"].mean().to_frame().rename(columns={"midpointofepisodemain": "avgmidpointofepisodemain" + day_type.lower()})], axis=1)
+    
+    # Std of time related features
+    if "stdstarttimeofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].std().to_frame().rename(columns={"starttimeofepisodemain": "stdstarttimeofepisodemain" + day_type.lower()})], axis=1)
+    if "stdendtimeofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","endtimeofepisodemain"]].groupby("local_segment")["endtimeofepisodemain"].std().to_frame().rename(columns={"endtimeofepisodemain": "stdendtimeofepisodemain" + day_type.lower()})], axis=1)
+    if "stdmidpointofepisodemain" in intraday_features_to_compute:
+        sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features[["local_segment","midpointofepisodemain"]].groupby("local_segment")["midpointofepisodemain"].std().to_frame().rename(columns={"midpointofepisodemain": "stdmidpointofepisodemain" + day_type.lower()})], axis=1)
+
+    # Duration & Ratio features
+    for sleep_level_group in sleep_levels:
+        for sleep_level in sleep_levels[sleep_level_group]:
+            if "avgduration" in intraday_features_to_compute:
+                col = "duration" + sleep_level + sleep_level_group.lower() + "main"
+                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 "avgratioduration" in intraday_features_to_compute:
+                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)
+
+    return sleep_intraday_features
+
+def socialJetLagFeature(daily_features, sleep_intraday_features): 
+    daily_features_weekend = daily_features[daily_features["is_weekend"] == 1]
+    sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekend[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].mean().to_frame().rename(columns={"starttimeofepisodemain": "helper_weekend"})], axis=1)
+    
+    daily_features_weekday = daily_features[daily_features["is_weekend"] == 0]
+    sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekday[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].mean().to_frame().rename(columns={"starttimeofepisodemain": "helper_weekday"})], axis=1)
+
+    sleep_intraday_features["socialjetlag"] = sleep_intraday_features["helper_weekend"] - sleep_intraday_features["helper_weekday"]
+
+    for col in ["helper_weekend", "helper_weekday"]:
+        del sleep_intraday_features[col]
+    
+    return sleep_intraday_features
+    
+def MSSDFeatures(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["fake_date"] = date_idx["fake_date"].dt.date
+    daily_features = daily_features.merge(date_idx, on="fake_date", how="right")
+
+    for col in ["starttimeofepisodemain", "endtimeofepisodemain", "midpointofepisodemain"]:
+        daily_features[col + "_diff"] = daily_features[col].diff()
+
+    if "meanssdstarttimeofepisodemain" 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)
+    if "meanssdendtimeofepisodemain" 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)
+    if "meanssdmidpointofepisodemain" 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)
+
+    if "medianssdstarttimeofepisodemain" 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)
+    if "medianssdendtimeofepisodemain" 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)
+    if "medianssdmidpointofepisodemain" 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)
+
+    return sleep_intraday_features
+
+
+
+
+def price_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+
+    daily_start_time = provider["GROUP_EPISODES_WITHIN"]["START_TIME"]
+    daily_end_time = daily_start_time + provider["GROUP_EPISODES_WITHIN"]["LENGTH"]
+
+    sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+    requested_intraday_features = provider["FEATURES"]
+    requested_sleep_levels = provider["SLEEP_LEVELS"]
+    requested_day_types = provider["DAY_TYPES"]
+
+    # 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_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
+                        "STAGES": ["wake", "deep", "light", "rem"],
+                        "UNIFIED": ["awake", "asleep"]}
+    base_day_types = ["WEEKEND", "WEEK", "ALL"]
+
+    # The subset of requested features this function can compute
+    intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features))
+    sleep_levels_to_compute = {key: list(set(requested_sleep_levels[key]) & set(base_sleep_levels[key])) for key in requested_sleep_levels if key in base_sleep_levels}
+    day_types_to_compute = list(set(requested_day_types) & set(base_day_types))
+
+    # Full names
+    features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, day_types_to_compute)
+    sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
+
+    if sleep_intraday_data.empty:
+        return sleep_intraday_features
+    
+    # Filter by segemnts and chunk episodes
+    sleep_intraday_data = filter_data_by_segment(sleep_intraday_data, time_segment)
+
+    # Discard segments shorter than one day
+    sleep_intraday_data["segment_length"] = (sleep_intraday_data["segment_end_timestamp"] - sleep_intraday_data["segment_start_timestamp"]) / 1000 # in seconds
+    sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["segment_length"] >= 24 * 60 * 60 - 1]
+    del sleep_intraday_data["segment_length"]
+
+    # Select main sleep records
+    sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["is_main_sleep"] == 1]
+
+    if sleep_intraday_data.empty:
+        return sleep_intraday_features
+
+    # Merge rows to get sleep episodes
+    main_sleep_episodes = mergeSleepEpisodes(sleep_intraday_data, ["local_segment", "type_episode_id"], base_sleep_levels)
+
+    # Extract number of minutes after midnight as start time; add duration to get the end time
+    main_sleep_episodes["start_minutes"] = main_sleep_episodes["local_start_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
+    main_sleep_episodes["end_minutes"] = main_sleep_episodes["start_minutes"] + main_sleep_episodes["durationinbed"]
+    # Extract fake date
+    """ The rule used for fake date extraction
+    set DS = daily_start_time, DE = daily_end_time
+    set start = start_minutes, end = end_minutes
+
+    if (DS <= start < DE) or (DS < end <= DE) or (start <= DS and end >= DE):
+        assign today
+    elif if end <= DS:
+        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"] = (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
+    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"]
+    
+    # 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
+    main_sleep_episodes = main_sleep_episodes.sort_values(["fake_date", "start_minutes"])
+    # Extract daily features
+    daily_features = extractDailyFeatures(main_sleep_episodes)
+    
+    # Extract features per segment based on daily features
+    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)
+    if "socialjetlag" in intraday_features_to_compute:
+        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.index.name = "local_segment"
+    sleep_intraday_features.reset_index(inplace=True)
+    
+
+    return sleep_intraday_features

src/features/fitbit_sleep_intraday/rapids/main.py

@@ -0,0 +1,265 @@
+import pandas as pd
+from datetime import datetime
+import itertools
+
+def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all):
+    
+    features_fullname = ["local_segment"]
+
+    sleep_level_with_group = []
+    for sleep_level_group in sleep_levels_to_compute:
+        for sleep_level in sleep_levels_to_compute[sleep_level_group]:
+            sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
+    
+    if consider_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 + ["all"])])
+    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)])
+    if "ACROSS_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
+        features_fullname.extend(["ratio" + x[0] + x[1] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_level_with_group)])
+    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"]])
+    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)])
+    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(intraday_features_to_compute["ROUTINE"])
+    return features_fullname
+
+def mergeSleepEpisodes(sleep_data, cols_for_groupby):
+    
+    sleep_episodes = pd.DataFrame(columns=["local_segment", "duration", "start_timestamp", "end_timestamp", "local_start_date_time", "local_end_date_time"])
+
+    if cols_for_groupby and (not sleep_data.empty):
+        sleep_data = sleep_data.groupby(by=cols_for_groupby)
+        sleep_episodes = sleep_data[["duration"]].sum()
+        sleep_episodes["start_timestamp"] = sleep_data["start_timestamp"].first()
+        sleep_episodes["end_timestamp"] = sleep_data["end_timestamp"].last()
+        sleep_episodes["local_start_date_time"] = sleep_data["local_start_date_time"].first()
+        sleep_episodes["local_end_date_time"] = sleep_data["local_end_date_time"].last()
+    
+        sleep_episodes.reset_index(inplace=True, drop=False)
+
+    return sleep_episodes
+
+def statsFeatures(sleep_episodes, features, episode_type):
+    
+    episode_features = pd.DataFrame(columns=[feature + episode_type for feature in features])
+    if sleep_episodes.empty:
+        return episode_features
+
+    if "countepisode" in features:
+        episode_features["countepisode" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].count()
+    if "sumduration" in features:
+        episode_features["sumduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].sum()
+    if "maxduration" in features:
+        episode_features["maxduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].max()
+    if "minduration" in features:
+        episode_features["minduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].min()
+    if "avgduration" in features:
+        episode_features["avgduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].mean()
+    if "medianduration" in features:
+        episode_features["medianduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].median()
+    if "stdduration" in features:
+        episode_features["stdduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].std()
+    
+    return episode_features
+
+def allStatsFeatures(sleep_data, base_sleep_levels, base_sleep_types, features, sleep_intraday_features):
+
+    # For CLASSIC
+    for sleep_level, sleep_type in itertools.product(base_sleep_levels["CLASSIC"] + ["all"], base_sleep_types + ["all"]):
+        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_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)
+    
+    # For STAGES
+    for sleep_level, sleep_type in itertools.product(base_sleep_levels["STAGES"] + ["all"], base_sleep_types + ["all"]):
+        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_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)
+    
+    # For UNIFIED
+    for sleep_level, sleep_type in itertools.product(base_sleep_levels["UNIFIED"] + ["all"], base_sleep_types + ["all"]):
+        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 = 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)
+    
+    # Ignore the levels (e.g. countepisode[all][main])
+    for sleep_type in base_sleep_types + ["all"]:
+        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_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_none, features, "all" + sleep_type)], axis=1)
+
+    return sleep_intraday_features
+
+
+# Since all the stats features have been computed no matter they are requested or not,
+# we can pick the related features to calculate the RATIOS features directly.
+# Take ACROSS_LEVELS RATIOS features as an example:
+# ratiocount[remstages] = countepisode[remstages][all] / countepisode[all][all]
+def ratiosFeatures(sleep_intraday_features, ratios_types, ratios_scopes, sleep_levels, sleep_types):
+    
+    # Put sleep_level_group and sleep_level together.
+    # For example:
+    # input (sleep_levels): {"CLASSIC": ["awake", "restless", "asleep"], "UNIFIED": ["awake", "asleep"]}
+    # output (sleep_level_with_group): [("classic", "awake"), ("classic", "restless"), ("classic", "asleep"), ("unified", "awake"), ("unified", "asleep")]
+    sleep_level_with_group = []
+    for sleep_level_group in sleep_levels:
+        for sleep_level in sleep_levels[sleep_level_group]:
+            sleep_level_with_group.append((sleep_level_group.lower(), sleep_level))
+
+    # ACROSS LEVELS
+    if "ACROSS_LEVELS" in ratios_scopes:
+        # Get the cross product of ratios_types and sleep_level_with_group.
+        # For example:
+        # input: ratios_types is ["count", "duration"], sleep_level_with_group is [("classic", "awake"), ("classic", "restless"), ("unified", "asleep")]
+        # output: 
+        # 1) ratios_type: "count", sleep_levels_combined: ("classic", "awake")
+        # 2) ratios_type: "count", sleep_levels_combined: ("classic", "restless")
+        # 3) ratios_type: "count", sleep_levels_combined: ("unified", "asleep")
+        # 4) ratios_type: "duration", sleep_levels_combined: ("classic", "awake")
+        # 5) ratios_type: "duration", sleep_levels_combined: ("classic", "restless")
+        # 6) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep")
+        for ratios_type, sleep_levels_combined in itertools.product(ratios_types, sleep_level_with_group):
+            sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
+            agg_func = "countepisode" if ratios_type == "count" else "sumduration"
+            across_levels = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + "all"] / sleep_intraday_features[agg_func + "all" + sleep_level_group + "all"]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_level + sleep_level_group})
+            sleep_intraday_features = pd.concat([sleep_intraday_features, across_levels], axis=1)
+    
+    # ACROSS TYPES
+    if "ACROSS_TYPES" in ratios_scopes:
+        for ratios_type in ratios_types:
+            agg_func = "countepisode" if ratios_type == "count" else "sumduration"
+            across_types = (sleep_intraday_features[agg_func + "allmain"] / sleep_intraday_features[agg_func + "allall"]).to_frame().rename(columns={0: "ratio" + ratios_type + "main"})
+            sleep_intraday_features = pd.concat([sleep_intraday_features, across_types], axis=1)
+    
+    # Get the cross product of ratios_types, sleep_level_with_group, and sleep_types.
+    # For example:
+    # input:
+    # ratios_types is ["count", "duration"]
+    # sleep_level_with_group is [("classic", "awake"), ("unified", "asleep")]
+    # sleep_types is ["main", "nap"]
+    # output:
+    # 1) ratios_type: "count", sleep_levels_combined: ("classic", "awake"), sleep_type: "main"
+    # 2) ratios_type: "count", sleep_levels_combined: ("classic", "awake"), sleep_type: "nap"
+    # 3) ratios_type: "count", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
+    # 4) ratios_type: "count", sleep_levels_combined: ("unified", "asleep"), sleep_type: "nap"
+    # 5) ratios_type: "duration", sleep_levels_combined: ("classic", "awake"), sleep_type: "main"
+    # 6) ratios_type: "duration", sleep_levels_combined: ("classic", "awake"), sleep_type: "nap"
+    # 7) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
+    # 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):
+        sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
+        agg_func = "countepisode" if ratios_type == "count" else "sumduration"
+
+        # WITHIN LEVELS
+        if "WITHIN_LEVELS" in ratios_scopes:
+            within_levels = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + sleep_type] / sleep_intraday_features[agg_func + sleep_level + sleep_level_group + "all"]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_type + "within" + sleep_level + sleep_level_group})
+            sleep_intraday_features = pd.concat([sleep_intraday_features, within_levels], axis=1)
+
+        # WITHIN TYPES
+        if "WITHIN_TYPES" in ratios_scopes:
+            within_types = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + sleep_type] / sleep_intraday_features[agg_func + "all" + sleep_level_group + sleep_type]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_level + sleep_level_group + "within" + sleep_type})
+            sleep_intraday_features = pd.concat([sleep_intraday_features, within_types], axis=1)
+
+    return sleep_intraday_features
+
+
+def singleSleepTypeRoutineFeatures(sleep_intraday_data, 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)]
+    if "starttimefirst" + sleep_type in routine:
+        grouped_first = sleep_intraday_data.groupby(["local_segment"]).first()
+        if 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)
+        elif reference_time == "START_OF_THE_SEGMENT":
+            sleep_intraday_features["starttimefirst" + sleep_type] = (grouped_first["start_timestamp"] - grouped_first["segment_start_timestamp"]) / (60 * 1000)
+        else:
+            raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
+    
+    if "endtimelast" + sleep_type in routine:
+        grouped_last = sleep_intraday_data.groupby(["local_segment"]).last()
+        if 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)
+        elif reference_time == "START_OF_THE_SEGMENT":
+            sleep_intraday_features["endtimelast" + sleep_type] = (grouped_last["end_timestamp"] - grouped_last["segment_start_timestamp"]) / (60 * 1000)
+        else:
+            raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
+
+    return sleep_intraday_features
+
+def routineFeatures(sleep_intraday_data, routine, reference_time, sleep_type, sleep_intraday_features):        
+    
+    if "starttimefirstmainsleep" in routine or "endtimelastmainsleep" in routine:
+        sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "mainsleep", sleep_intraday_features)
+    
+    if "starttimefirstnap" in routine or "endtimelastnap" in routine:
+        sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "nap", sleep_intraday_features)
+    
+    return sleep_intraday_features
+
+
+def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
+
+    sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    consider_all = provider["FEATURES"]["LEVELS_AND_TYPES_COMBINING_ALL"]
+    include_sleep_later_than = provider["INCLUDE_SLEEP_LATER_THAN"]
+    reference_time = provider["REFERENCE_TIME"]
+
+    requested_intraday_features = provider["FEATURES"]
+    requested_sleep_levels = provider["SLEEP_LEVELS"]
+    requested_sleep_types = provider["SLEEP_TYPES"]
+
+    # Name of the features this function can compute
+    base_intraday_features = {"LEVELS_AND_TYPES": ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"],
+                                "RATIOS_TYPE": ["count", "duration"],
+                                "RATIOS_SCOPE": ["ACROSS_LEVELS", "ACROSS_TYPES", "WITHIN_LEVELS", "WITHIN_TYPES"],
+                                "ROUTINE": ["starttimefirstmainsleep", "endtimelastmainsleep", "starttimefirstnap", "endtimelastnap"]}
+    base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
+                            "STAGES": ["wake", "deep", "light", "rem"],
+                            "UNIFIED": ["awake", "asleep"]}
+    base_sleep_types = ["main", "nap"]
+
+    # 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}
+    sleep_levels_to_compute = {key: list(set(requested_sleep_levels[key]) & set(base_sleep_levels[key])) for key in requested_sleep_levels if key in base_sleep_levels}
+    sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types))
+
+    # Full names
+    features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all)
+    sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
+    
+    # Include sleep later than
+    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[sleep_intraday_data["start_minutes"] >= include_sleep_later_than]
+    del sleep_intraday_data["start_minutes"]
+
+    sleep_intraday_data = filter_data_by_segment(sleep_intraday_data, time_segment)
+
+    # While level_episode_id is based on levels provided by Fitbit (classic & stages), unified_level_episode_id is based on unified_level.
+    sleep_intraday_data.insert(3, "unified_level_episode_id", (sleep_intraday_data[["type_episode_id", "unified_level"]] != sleep_intraday_data[["type_episode_id", "unified_level"]].shift()).any(axis=1).cumsum())
+
+    if not sleep_intraday_data.empty:
+
+        sleep_intraday_features = pd.DataFrame()
+
+        # ALL LEVELS AND TYPES: compute all stats features no matter they are requested or not
+        sleep_intraday_features = allStatsFeatures(sleep_intraday_data, base_sleep_levels, base_sleep_types, base_intraday_features["LEVELS_AND_TYPES"], sleep_intraday_features)
+
+        # RATIOS: only compute requested features
+        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
+        sleep_intraday_features = routineFeatures(sleep_intraday_data, intraday_features_to_compute["ROUTINE"], reference_time, sleep_types_to_compute, sleep_intraday_features)
+
+        # Reset index and discard features which are not requested by user
+        sleep_intraday_features.index.name = "local_segment"
+        sleep_intraday_features.reset_index(inplace=True)
+        sleep_intraday_features = sleep_intraday_features[features_fullnames]
+
+
+    return sleep_intraday_features

src/features/utils/utils.py

@@ -55,7 +55,7 @@ def chunk_episodes(sensor_episodes):
     sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60)
 
     # Merge episodes
-    cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp"]]
+    cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "chunked_start_timestamp", "chunked_end_timestamp"]]
 
     sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby)
     merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum()

tools/config.schema.yaml

@@ -29,6 +29,7 @@ required:
   - FITBIT_HEARTRATE_SUMMARY
   - FITBIT_HEARTRATE_INTRADAY
   - FITBIT_SLEEP_SUMMARY
+  - FITBIT_SLEEP_INTRADAY
   - FITBIT_STEPS_SUMMARY
   - FITBIT_STEPS_INTRADAY
   - HISTOGRAM_PHONE_DATA_YIELD
@@ -896,6 +897,136 @@ properties:
         additionalProperties: 
           $ref: "#/definitions/PROVIDER"
   
+  FITBIT_SLEEP_INTRADAY:
+    type: object
+    required: [TABLE, PROVIDERS]
+    properties:
+      TABLE:
+        type: string
+      PROVIDERS:
+        type: ["null", object]
+        properties:
+          RAPIDS:
+            allOf:
+              - $ref: "#/definitions/PROVIDER"
+              - properties:
+                  FEATURES:
+                    type: object
+                    required: [LEVELS_AND_TYPES_COMBINING_ALL, LEVELS_AND_TYPES, RATIOS_TYPE, RATIOS_SCOPE, ROUTINE]
+                    properties:
+                      LEVELS_AND_TYPES_COMBINING_ALL:
+                        type: boolean
+                      LEVELS_AND_TYPES:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
+                      RATIOS_TYPE:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [count, duration]
+                      RATIOS_SCOPE:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
+                      ROUTINE:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
+                  SLEEP_LEVELS:
+                    type: object
+                    required: [CLASSIC, STAGES, UNIFIED]
+                    properties:
+                      CLASSIC:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [awake, restless, asleep]
+                      STAGES:
+                        type: array
+                        uniqueItems: True
+                        items:  
+                          type: string
+                          enum: [wake, deep, light, rem]
+                      UNIFIED:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [awake, asleep]
+                  SLEEP_TYPES:
+                    type: array
+                    uniqueItems: True
+                    items:
+                      type: string
+                      enum: [main, nap]
+                  INCLUDE_SLEEP_LATER_THAN:
+                      type: number
+                      minimum: 0
+                      maximum: 1439
+                  REFERENCE_TIME:
+                    type: string
+                    enum: [MIDNIGHT, START_OF_THE_SEGMENT]
+          PRICE:
+            allOf:
+              - $ref: "#/definitions/PROVIDER"
+              - properties:
+                  FEATURES:
+                    uniqueItems: True
+                    items:
+                      type: string
+                      enum: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain]
+                  SLEEP_LEVELS:
+                    type: object
+                    required: [CLASSIC, STAGES, UNIFIED]
+                    properties:
+                      CLASSIC:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [awake, restless, asleep]
+                      STAGES:
+                        type: array
+                        uniqueItems: True
+                        items:  
+                          type: string
+                          enum: [wake, deep, light, rem]
+                      UNIFIED:
+                        type: array
+                        uniqueItems: True
+                        items:
+                          type: string
+                          enum: [awake, asleep]
+                  DAY_TYPES:
+                    type: array
+                    uniqueItems: True
+                    items:
+                      type: string
+                      enum: [WEEKEND, WEEK, ALL]
+                  GROUP_EPISODES_WITHIN:
+                    type: object
+                    required: [START_TIME, LENGTH]
+                    properties:
+                      START_TIME:
+                        type: number
+                        minimum: 0
+                        maximum: 1439
+                      LENGTH:
+                        type: number
+                        minimum: 0
+                        maximum: 1440
+        additionalProperties:
+          $ref: "#/definitions/PROVIDER"
+  
   FITBIT_STEPS_SUMMARY:
     type: object
     required: [TABLE, PROVIDERS]