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rapids/docs/setup/configuration.md

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Configuration

You need to follow these steps to configure your RAPIDS deployment before you can extract behavioral features

  1. Verify RAPIDS can process your data streams
  2. Create your participants files
  3. Select what time segments you want to extract features on
  4. Choose the timezone of your study
  5. Configure your data streams
  6. Select what sensors and features you want to process

When you are done with this configuration, go to executing RAPIDS.

!!! hint Every time you see config["KEY"] or [KEY] in these docs we are referring to the corresponding key in the config.yaml file.

Supported data streams

A data stream refers to sensor data collected using a specific type of device with a specific format and stored in a specific container. For example, the aware_mysql data stream handles smartphone data (device) collected with the AWARE Framework (format) stored in a MySQL database (container).

Check the table in introduction to data streams to know what data streams we support. If your data stream is supported, continue to the next configuration section, you will use its label later in this guide (e.g. aware_mysql). If your steam is not supported but you want to implement it, follow this tutorial to add support for new data streams and get in touch by email or in Slack if you have any questions.

Participant files

Participant files link together multiple devices (smartphones and wearables) to specific participants and identify them throughout RAPIDS. You can create these files manually or automatically. Participant files are stored in data/external/participant_files/pxx.yaml and follow a unified structure.

??? important "Remember to modify the config.yaml file with your PIDS" The list PIDS in config.yaml needs to have the participant file names of the people you want to process. For example, if you created p01.yaml, p02.yaml and p03.yaml files in /data/external/participant_files/ , then PIDS should be: yaml PIDS: [p01, p02, p03]

??? info "Optional: Migrating participants files with the old format" If you were using the pre-release version of RAPIDS with participant files in plain text (as opposed to yaml), you can run the following command and your old files will be converted into yaml files stored in data/external/participant_files/

```bash
python tools/update_format_participant_files.py
```

Structure of participants files

??? example "Example of the structure of a participant file"

In this example, the participant used an android phone, an ios phone, a fitbit device, and a Empatica device throughout the study between Apr 23rd 2020 and Oct 28th 2020

 If your participants didn't use a `[PHONE]`, `[FITBIT]` or `[EMPATICA]` device, it is not necessary to include that section in their participant file. In other words, you can analyse data from 1 or more devices per participant.

```yaml
PHONE:
  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524, dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43]
  PLATFORMS: [android,ios]
  LABEL: test01
  START_DATE: 2020-04-23
  END_DATE: 2020-10-28
FITBIT:
  DEVICE_IDS: [fitbit1]
  LABEL: test01
  START_DATE: 2020-04-23
  END_DATE: 2020-10-28
EMPATICA: # Empatica doesn't have a device_id because the devices produce zip files per participant
  LABEL: test01
  START_DATE: 2020-04-23
  END_DATE: 2020-10-28
```

=== "[PHONE]"

| Key                                 | Description                                                                                                                                                                                                                                                                                                                                |
|-------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `[DEVICE_IDS]` | An array of the strings that uniquely identify each smartphone, you can have more than one for when participants changed phones in the middle of the study, in this case, data from all their devices will be joined and relabeled with the last 1 on this list.                                                                           |
| `[PLATFORMS]`  | An array that specifies the OS of each smartphone in  `[DEVICE_IDS]` , use a combination of  `android`  or  `ios`  (we support participants that changed platforms in the middle of your study!). You can set `[PLATFORMS]: [infer]`  and RAPIDS will infer them automatically (each phone data stream infer this differently, e.g. `aware_mysql` uses the `aware_device` table). |
| `[LABEL]`      | A string that is used in reports and visualizations.        |
| `[START_DATE]` | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *after*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                               |
| `[END_DATE]`   | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *before*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                              |

=== "[FITBIT]"

| Key                                   | Description                                                                                               |
|------------------|-----------------------------------------------------------------------------------------------------------|
| `[DEVICE_IDS]`   | An array of the strings that uniquely identify each Fitbit, you can have more than one in case the participant changed devices in the middle of the study, in this case, data from all devices will be joined and relabeled with the last  `device_id`  on this list. |
| `[LABEL]`        | A string that is used in reports and visualizations.                                              |
| `[START_DATE]`   | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *after*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                               |
| `[END_DATE]`     | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *before*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                              |

=== "[EMPATICA]"

| Key                                   | Description                                                                                               |
|------------------|-----------------------------------------------------------------------------------------------------------|
| `[LABEL]`        | A string that is used in reports and visualizations.                                                                                              |
| `[START_DATE]`   | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *after*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                               |
| `[END_DATE]`     | A string with format `YYYY-MM-DD` or `YYYY-MM-DD HH:MM:SS`. Only data collected  *before*  this date time will be included in the analysis. By default, `YYYY-MM-DD` is interpreted as `YYYY-MM-DD 00:00:00`.                              |

Automatic creation of participant files

You can use a CSV file with a row per participant to automatically create participant files.

??? "AWARE_DEVICE_TABLE was deprecated" In previous versions of RAPIDS, you could create participant files automatically using the aware_device table. We deprecated this option but you can still achieve the same results if you export the output of the following SQL query as a CSV file and follow the instructions below:

```sql
SELECT device_id, device_id as fitbit_id, CONCAT("p", _id) as pid, if(brand = "iPhone", "ios", "android") as platform, CONCAT("p", _id)  as label, DATE_FORMAT(FROM_UNIXTIME((timestamp/1000)- 86400), "%Y-%m-%d") as start_date, CURRENT_DATE as end_date from aware_device order by _id;
```

In your config.yaml:

  1. Set CSV_FILE_PATH to a CSV file path that complies with the specs described below
  2. Set the devices (PHONE, FITBIT, EMPATICA) [ADD] flag to TRUE depending on what devices you used in your study.
  3. Set [DEVICE_ID_COLUMN] to the name of the column in your CSV file that uniquely identifies each device (only for PHONE and FITBIT).
CREATE_PARTICIPANT_FILES:
  CSV_FILE_PATH: "your_path/to_your.csv"
  PHONE_SECTION:
    ADD: TRUE # or FALSE
    DEVICE_ID_COLUMN: device_id # column name
    IGNORED_DEVICE_IDS: []
  FITBIT_SECTION:
    ADD: FALSE # or FALSE
    DEVICE_ID_COLUMN: fitbit_id # column name
    IGNORED_DEVICE_IDS: []
  EMPATICA_SECTION: # Empatica doesn't have a device_id column because the devices produce zip files per participant
    ADD: FALSE # or FALSE

Your CSV file ([CSV_FILE_PATH]) should have the following columns (headers) but the values within each column can be empty:

Column Description
phone device id The name of this column has to match [PHONE_SECTION][DEVICE_ID_COLUMN]. Separate multiple ids with ;
fitbit device id The name of this column has to match [FITBIT_SECTION][DEVICE_ID_COLUMN]. Separate multiple ids with ;
pid Unique identifiers with the format pXXX (your participant files will be named with this string)
platform Use android, ios or infer as explained above, separate values with ;
label A human readable string that is used in reports and visualizations.
start_date A string with format YYY-MM-DD.
end_date A string with format YYY-MM-DD.

!!! example We added white spaces to this example to make it easy to read but you don't have to.

```csv
device_id                                                                ,fitbit_id ,pid ,label ,platform    ,start_date ,end_date
a748ee1a-1d0b-4ae9-9074-279a2b6ba524;dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43 ,fitbit1   ,p01 ,julio ,android;ios ,2020-01-01 ,2021-01-01
4c4cf7a1-0340-44bc-be0f-d5053bf7390c                                     ,fitbit2   ,p02 ,meng  ,ios         ,2021-01-01 ,2022-01-01
```

Then run

snakemake -j1 create_participants_files

Time Segments

Time segments (or epochs) are the time windows on which you want to extract behavioral features. For example, you might want to process data on every day, every morning, or only during weekends. RAPIDS offers three categories of time segments that are flexible enough to cover most use cases: frequency (short time windows every day), periodic (arbitrary time windows on any day), and event (arbitrary time windows around events of interest). See also our examples.

=== "Frequency Segments"

These segments are computed on every day and all have the same duration (for example 30 minutes). Set the following keys in your `config.yaml`

```yaml
TIME_SEGMENTS: &time_segments
  TYPE: FREQUENCY
  FILE: "data/external/your_frequency_segments.csv"
  INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE
```

The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can only have 1 row.

| Column | Description                                                          |
|--------|----------------------------------------------------------------------|
| label  | A string that is used as a prefix in the name of your time segments   |
| length | An integer representing the duration of your time segments in minutes |

!!! example

    ```csv
    label,length
    thirtyminutes,30
    ```
    
    This configuration will compute 48 time segments for every day when any data from any participant was sensed. For example:

    ```csv
    start_time,length,label
    00:00,30,thirtyminutes0000
    00:30,30,thirtyminutes0001
    01:00,30,thirtyminutes0002
    01:30,30,thirtyminutes0003
    ...
    ```

=== "Periodic Segments"

These segments can be computed every day, or on specific days of the week, month, quarter, and year. Their minimum duration is 1 minute but they can be as long as you want. Set the following keys in your `config.yaml`.

```yaml
TIME_SEGMENTS: &time_segments
  TYPE: PERIODIC
  FILE: "data/external/your_periodic_segments.csv"
  INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE
```

If `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is set to `TRUE`, RAPIDS will consider instances of your segments back enough in the past as to include the first row of data of each participant. For example, if the first row of data from a participant happened on Saturday March 7th 2020 and the requested segment duration is 7 days starting on every Sunday, the first segment to be considered would start on Sunday March 1st if `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is `TRUE` or on Sunday March 8th if `FALSE`.

The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows.

| Column        | Description                                                                                                                                                                                                   |
|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| label         | A string that is used as a prefix in the name of your time segments. It has to be **unique** between rows                                                                                                      |
| start_time    | A string with format `HH:MM:SS` representing the starting time of this segment on any day                                                                                                                                 |
| length        | A string representing the length of this segment.It can have one or more of the following strings **`XXD XXH XXM XXS`** to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S`                        |
| repeats_on    | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day                                                                        |
| repeats_value | An integer complementing `repeats_on`. If you set `repeats_on` to `every_day` set this to `0`, otherwise `1-7` represent a `wday` starting from Mondays, `1-31` represent a `mday`, `1-91` represent a `qday`, and `1-366` represent a `yday` |

!!! example

    ```csv
    label,start_time,length,repeats_on,repeats_value
    daily,00:00:00,23H 59M 59S,every_day,0
    morning,06:00:00,5H 59M 59S,every_day,0
    afternoon,12:00:00,5H 59M 59S,every_day,0
    evening,18:00:00,5H 59M 59S,every_day,0
    night,00:00:00,5H 59M 59S,every_day,0
    ```

    This configuration will create five segments instances (`daily`, `morning`, `afternoon`, `evening`, `night`) on any given day (`every_day` set to 0). The `daily` segment will start at midnight and will last `23:59:59`, the other four segments will start at 6am, 12pm, 6pm, and 12am respectively and last for `05:59:59`.

=== "Event segments"

These segments can be computed before or after an event of interest (defined as any UNIX timestamp). Their minimum duration is 1 minute but they can be as long as you want. The start of each segment can be shifted backwards or forwards from the specified timestamp. Set the following keys in your `config.yaml`.

```yaml
TIME_SEGMENTS: &time_segments
  TYPE: EVENT
  FILE: "data/external/your_event_segments.csv"
  INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE
```

The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows.

| Column        | Description                                                                                                                                                                                                   |
|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| label         | A string that is used as a prefix in the name of your time segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing auxiliary data for features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments). There cannot be two *overlaping* event segments with the same label (RAPIDS will throw an error)                                                                                                      |
| event_timestamp    | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding time segment will be computed around this moment using `length`, `shift`, and `shift_direction`                                                                                            |
| length        | A string representing the length of this segment. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes, and seconds. For example `7D 23H 59M 59S`                        |
| shift    | A string representing the time shift from `event_timestamp`. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes and seconds. For example `7D 23H 59M 59S`. Use this value to  change the start of a segment with respect to its `event_timestamp`. For example, set this variable to `1H` to create a segment that starts 1 hour from an event of interest (`shift_direction` determines if it's before or after).                                                                        |
| shift_direction | An integer representing whether the `shift` is before (`-1`) or after (`1`) an `event_timestamp` |
|device_id| The device id (smartphone or fitbit) to whom this segment belongs to. You have to create a line in this event segment file for each event of a participant that you want to analyse. If you have participants with multiple device ids you can choose any of them|

!!! example
    ```csv
    label,event_timestamp,length,shift,shift_direction,device_id
    stress1,1587661220000,1H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    stress2,1587747620000,4H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    stress3,1587906020000,3H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    stress4,1584291600000,7H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    stress5,1588172420000,9H,5M,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    mood,1587661220000,1H,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    mood,1587747620000,1D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524
    ```
    
    This example will create eight segments for a single participant (`a748ee1a...`), five independent `stressX` segments with various lengths (1,4,3,7, and 9 hours). Segments `stress1`, `stress3`, and `stress5` are shifted forwards by 5 minutes and `stress2` and `stress4` are shifted backwards by 4 hours (that is, if the `stress4` event happened on March 15th at 1pm EST (`1584291600000`), the time segment will start on that day at 9am and end at 4pm). 
    
    The three `mood` segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these `mood` segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant.

Segment Examples

=== "5-minutes" Use the following Frequency segment file to create 288 (12 * 60 * 24) 5-minute segments starting from midnight of every day in your study csv label,length fiveminutes,5 === "Daily" Use the following Periodic segment file to create daily segments starting from midnight of every day in your study csv label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 === "Morning" Use the following Periodic segment file to create morning segments starting at 06:00:00 and ending at 11:59:59 of every day in your study csv label,start_time,length,repeats_on,repeats_value morning,06:00:00,5H 59M 59S,every_day,0 === "Overnight" Use the following Periodic segment file to create overnight segments starting at 20:00:00 and ending at 07:59:59 (next day) of every day in your study csv label,start_time,length,repeats_on,repeats_value morning,20:00:00,11H 59M 59S,every_day,0 === "Weekly" Use the following Periodic segment file to create non-overlapping weekly segments starting at midnight of every Monday in your study csv label,start_time,length,repeats_on,repeats_value weekly,00:00:00,6D 23H 59M 59S,wday,1 Use the following Periodic segment file to create overlapping weekly segments starting at midnight of every day in your study csv label,start_time,length,repeats_on,repeats_value weekly,00:00:00,6D 23H 59M 59S,every_day,0 === "Week-ends" Use the following Periodic segment file to create week-end segments starting at midnight of every Saturday in your study csv label,start_time,length,repeats_on,repeats_value weekend,00:00:00,1D 23H 59M 59S,wday,6 === "Around surveys" Use the following Event segment file to create two 2-hour segments that start 1 hour before surveys answered by 3 participants csv label,event_timestamp,length,shift,shift_direction,device_id survey1,1587661220000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 survey2,1587747620000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 survey1,1587906020000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr survey2,1584291600000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr survey1,1588172420000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3 survey2,1584291600000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3

Timezone of your study

Single timezone

If your study only happened in a single time zone or you want to ignore short trips of your participants to different time zones, select the appropriate code form this list and change the following config key. Double-check your timezone code pick, for example, US Eastern Time is America/New_York not EST

TIMEZONE: 
    TYPE: SINGLE
    TZCODE: America/New_York

Multiple timezones

If your participants lived on different time zones or they travelled across time zones, and you know when participants' devices were in a specific time zone, RAPIDS can use this data to process your data streams with the correct date-time. You need to provide RAPIDS with the time zone data in a CSV file ([TZCODES_FILE]) in the format described below.

TIMEZONE: 
    TYPE: MULTIPLE
    SINGLE:
      TZCODE: America/New_York
    MULTIPLE:
      TZCODES_FILE: path_to/time_zones_csv.file
      IF_MISSING_TZCODE: STOP
      DEFAULT_TZCODE: America/New_York
      FITBIT: 
        ALLOW_MULTIPLE_TZ_PER_DEVICE: False
        INFER_FROM_SMARTPHONE_TZ: False

Parameters for [TIMEZONE]

Parameter                                                            Description
[TYPE] Either SINGLE or MULTIPLE as explained above
[SINGLE][TZCODE] The time zone code from this list to be used across all devices
[MULTIPLE][TZCODES_FILE] A CSV file containing the time and code from this list visited by each device in the study. Multiple devices can be linked to the same person, read more in Participants Files
[MULTIPLE][IF_MISSING_TZCODE] When a device is missing from [TZCODES_FILE] Set this flag to STOP to stop RAPIDS execution and show an error, or to USE_DEFAULT to assign the time zone specified in [DEFAULT_TZCODE] to any such devices
[MULTIPLE][FITBIT][ALLOW_MULTIPLE_TZ_PER_DEVICE] You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zone, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below.
[MULTIPLE][FITBIT][INFER_FROM_SMARTPHONE_TZ] You only need to care about this flag if one or more Fitbit devices sensed data in one or more time zone, and you want RAPIDS to take into account this in its feature computation. Read more in "How does RAPIDS handle Fitbit devices?" below.

??? info "Format of TZCODES_FILE" TZCODES_FILE has three columns and a row for each time zone a device visited (a device can be a smartphone or wearable (Fitbit/Empatica)):

|Column | Description |
|--|--|
|`device_id`|A string that uniquely identifies a smartphone or wearable|
|`tzcode`| A string with the appropriate code from this [list](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) that represents the time zone where the `device` sensed data|
|`timestamp`| A UNIX timestamp indicating when was the first time this `device_id` sensed data in `tzcode`|

```csv
device_id,                            tzcode,              timestamp
13dbc8a3-dae3-4834-823a-4bc96a7d459d, America/New_York,     1587500000000
13dbc8a3-dae3-4834-823a-4bc96a7d459d, America/Mexico_City,  1587600000000
13dbc8a3-dae3-4834-823a-4bc96a7d459d, America/Los_Angeles,  1587700000000
65sa66a5-2d2d-4524-946v-44ascbv4sad7, Europe/Amsterdam,     1587100000000
65sa66a5-2d2d-4524-946v-44ascbv4sad7, Europe/Berlin,        1587200000000
65sa66a5-2d2d-4524-946v-44ascbv4sad7, Europe/Amsterdam,     1587300000000
```

Using this file, RAPDIS will create time zone intervals per device, for example for `13dbc8a3-dae3-4834-823a-4bc96a7d459d`:

-  Interval 1 `[1587500000000, 1587599999999]` for `America/New_York`
-  Interval 2 `[1587600000000, 1587699999999]` for `America/Mexico_City`
-  Interval 3 `[1587700000000, now]` for `America/Los_Angeles`

Any sensor data row from a device will be assigned a timezone if it falls within that interval, for example:

- A screen row sensed at `1587533333333` will be assigned to `America/New_York` because it falls within Interval 1
- A screen row sensed at `1587400000000` will be discarded because it was logged outside any interval.

??? note "What happens if participant X lives in Los Angeles but participant Y lives in Amsterdam and they both stayed there during my study?" Add a row per participant and set timestamp to 0: csv device_id, tzcode, timestamp 13dbc8a3-dae3-4834-823a-4bc96a7d459d, America/Los_Angeles, 0 65sa66a5-2d2d-4524-946v-44ascbv4sad7, Europe/Amsterdam, 0

??? note "What happens if I forget to add a timezone for one or more devices?" It depends on [IF_MISSING_TZCODE].

If `[IF_MISSING_TZCODE]` is set to `STOP`, RAPIDS will stop its execution and show you an error message.

If `[IF_MISSING_TZCODE]` is set to `USE_DEFAULT`, it will assign the time zone specified in `[DEFAULT_TZCODE]` to any devices with missing time zone information in `[TZCODES_FILE]`. This is helpful if only a few of your participants had multiple timezones and you don't want to specify the same time zone for the rest.

??? note "How does RAPIDS handle Fitbit devices?" Fitbit devices are not time zone aware and they always log data with a local date-time string.

- When none of the Fitbit devices in your study changed time zones (e.g., `p01` was always in New York and `p02` as always in Amsterdam), you can set a single time zone per Fitbit device id along with a timestamp 0 (you can still assign multiple time zones to smartphone device ids)
```csv
device_id, tzcode,              timestamp
fitbit123, America/New_York,     0
fitbit999, Europe/Amsterdam,     0
```

- On the other hand, when at least one of your Fitbit devices changed time zones **AND** you want RAPIDS to take into account these changes, you need to set `[ALLOW_MULTIPLE_TZ_PER_DEVICE]` to `True`. **You have to manually allow this option because you need to be aware it can produce inaccurate features around the times when time zones changed**. This is because we cannot know exactly when the Fitbit device detected and processed the time zone change.

    If you want to `ALLOW_MULTIPLE_TZ_PER_DEVICE` you will need to add any time zone changes per device in the `TZCODES_FILE` as explained above. You could obtain this data by hand but if your participants also used a smartphone during your study, you can use their time zone logs. Recall that in RAPIDS every participant is represented with a participant file `pXX.yaml`, this file links together multiple devices and we will use it to know what smartphone time zone data should be applied to Fitbit devices. Thus set `INFER_FROM_SMARTPHONE_TZ` to `TRUE`, if you have included smartphone time zone data in your `TZCODE_FILE` and you want to make a participant's Fitbit data time zone aware with their respective smartphone data.

??? note "How does RAPIDS handle Empatica devices?" Empatica devices do not have a device id, since the raw data can only be exported in zip files per device that are saved in a folder per participant (e.g. data/external/empatica/{pid}).

Therefore, in your `TZCODES_FILE`, use the participant's ids (PIDs) instead of the device's ids. Remember a person could have used one or more devices with different device ids, but every person only gets a single PID (e.g. `p01`, a.k.a the name of their participant file `p01.yaml`).

Data Stream Configuration

Modify the following keys in your config.yaml depending on the data stream you want to process.

=== "Phone"

Set `[PHONE_DATA_STREAMS][TYPE]` to the smartphone data stream you want to process (e.g. `aware_mysql`) and configure its parameters (e.g. `[DATABASE_GROUP]`). Ignore the parameters of streams you are not using (e.g. `[FOLDER]` of `aware_csv`).

```yaml
PHONE_DATA_STREAMS:
  TYPE: aware_mysql
  aware_mysql:
    DATABASE_GROUP: MY_GROUP
  aware_csv:
    FOLDER: data/external/aware_csv
```

=== "aware_mysql"

    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[DATABASE_GROUP]`   | A database credentials group. Read the instructions below to set it up    |

    --8<---- "docs/snippets/database.md"

=== "aware_csv"

    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[FOLDER]`   | Folder where you have to place a CSV file **per** phone sensor. Each file has to contain all the data from every participant you want to process.     |

=== "Fitbit"

Set `[FITBIT_DATA_STREAMS][TYPE]` to the Fitbit data stream you want to process (e.g. `fitbitjson_mysql`) and configure its parameters (e.g. `[DATABASE_GROUP]`). 

Ignore the parameters of streams you are not using (e.g. `[FOLDER]` of `aware_csv`).


```yaml
FITBIT_DATA_STREAMS:
  TYPE: fitbitjson_mysql

  fitbitjson_mysql:
    DATABASE_GROUP: MY_GROUP
    COLUMN_MAPPINGS_READY: False

  fitbitjson_csv:
    FOLDER: data/external/fitbit_csv
    COLUMN_MAPPINGS_READY: False

  fitbitparsed_mysql:
    DATABASE_GROUP: MY_GROUP
    COLUMN_MAPPINGS_READY: False
    
  fitbitparsed_csv:
    FOLDER: data/external/fitbit_csv
    COLUMN_MAPPINGS_READY: False

```

=== "fitbitjson_mysql"

    This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a MySQL database.


    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[DATABASE_GROUP]`   | A database credentials group. Read the instructions below to set it up    |
    | `[COLUMN_MAPPINGS_READY]`   | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitjson_mysql`](../../datastreams/fitbitjson-mysql#format)   |

    --8<---- "docs/snippets/database.md"

=== "fitbitjson_csv"

    This data stream process Fitbit data inside a JSON column as obtained from the Fitbit API and stored in a CSV file.

    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[FOLDER]`   | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process.     |
    | `[COLUMN_MAPPINGS_READY]`   | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitjson_csv`](../../datastreams/fitbitjson-csv#format)   |


=== "fitbitparsed_mysql"

    This data stream process Fitbit data stored in multiple columns after being parsed from the JSON column returned by Fitbit API and stored in a MySQL database.
    

    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[DATABASE_GROUP]`   | A database credentials group. Read the instructions below to set it up    |
    | `[COLUMN_MAPPINGS_READY]`   | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitparsed_mysql`](../../datastreams/fitbitparsed-mysql#format)   |

    --8<---- "docs/snippets/database.md"
    
=== "fitbitparsed_csv"

    This data stream process Fitbit data stored in multiple columns (plain text) after being parsed from the JSON column returned by Fitbit API and stored in a CSV file.

    | Key                  | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[FOLDER]`   | Folder where you have to place a CSV file **per** Fitbit sensor. Each file has to contain all the data from every participant you want to process.     |
    | `[COLUMN_MAPPINGS_READY]`   | Set this to `True` after you have modified this stream's `format.yaml` column mappings to match your raw data column names: [`fitbitparsed_csv`](../../datastreams/fitbitparsed-csv#format)   |

=== "Empatica"

Set `[USE]` to the Empatica data stream you want to use, see the table in [introduction to data streams](../../datastreams/data-streams-introduction). Configure any parameters as inidicated below.

```yaml
EMPATICA_DATA_STREAMS:
  USE: empatica_zipfiles
  
  # AVAILABLE:
  empatica_zipfiles: 
    FOLDER: data/external/empatica

```

=== "empatica_zipfiles"

    | Key             | Description                                                                                                                |
    |---------------------|----------------------------------------------------------------------------------------------------------------------------|
    | `[FOLDER]` | The relative path to a folder containing one subfolder per participant. The name of a participant folder should match their pid in `config[PIDS]`, for example `p01`. Each participant folder can have one or more zip files with any name; in other words, the sensor data contained in those zip files belongs to a single participant. The zip files are [automatically](https://support.empatica.com/hc/en-us/articles/201608896-Data-export-and-formatting-from-E4-connect-) generated by Empatica and have a CSV file per sensor (`ACC`, `HR`, `TEMP`, `EDA`, `BVP`, `TAGS`). All CSV files of the same type contained in one or more zip files are uncompressed, parsed, sorted by timestamp, and joinned together.|

    ??? example "Example of an EMPATICA FOLDER"
        In the file tree below, we want to process the data of three participants: `p01`, `p02`, and `p03`. `p01` has two zip files, `p02` has only one zip file, and `p03` has three zip files. Each zip will have a CSV file per sensor that are joinned together and process by RAPIDS. These zip files are generated by Empatica.
        ```bash
        data/ # this folder exists in the root RAPIDS folder
          external/
            empatica/
              p01/
                file1.zip
                file2.zip
              p02/
                aaaa.zip
              p03/
                t1.zip
                t2.zip
                t3.zip
        ```

Sensor and Features to Process

Finally, you need to modify the config.yaml section of the sensors you want to extract behavioral features from. All sensors follow the same naming nomenclature (DEVICE_SENSOR) and parameter structure which we explain in the Behavioral Features Introduction.

!!! done Head over to Execution to learn how to execute RAPIDS.