Minimal Working Example¶

This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for daily (00:00:00 to 23:59:59) and night (00:00:00 to 05:59:59) epochs of every day of data of one participant monitored on the US East coast with an Android smartphone.

Hint

If you don’t have call data that you can use to try this example you can restore this CSV file as a table in a MySQL database.

Install RAPIDS and make sure your conda environment is active (see Installation)

Make the changes listed below for the corresponding Configuration step (we provide an example of what the relevant sections in your config.yml will look like after you are done)

Required configuration changes

Add your database credentials.

Setup your database connection credentials in .env, we assume your credentials group in the .env file is called MY_GROUP.
Choose the timezone of your study.

Since this example is processing data collected on the US East cost, America/New_York should be the configured timezone, change this according to your data.
Create your participants files.

Since we are processing data from a single participant, you only need to create a single participant file called p01.yaml. This participant file only has a PHONE section because this hypothetical participant was only monitored with an smartphone. You also need to add p01 to [PIDS] in config.yaml. The following would be the content of your p01.yaml participant file:
```
PHONE:
    DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
    PLATFORMS: [android] # or ios
    LABEL: MyTestP01 # any string
    START_DATE: 2020-01-01 # this can also be empty
    END_DATE: 2021-01-01 # this can also be empty
```
Select what time segments you want to extract features on.

[TIME_SEGMENTS][TYPE] should be the default PERIODIC. Change [TIME_SEGMENTS][FILE] with the path (for example data/external/timesegments_periodic.csv) of a file containing the following lines:
```
label,start_time,length,repeats_on,repeats_value
daily,00:00:00,23H 59M 59S,every_day,0
night,00:00:00,5H 59M 59S,every_day,0
```
Modify your device data source configuration

In this example we do not need to modify this section because we are using smartphone data collected with AWARE stored on a MySQL database.
Select what sensors and features you want to process.

Set [PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE] to True in the config.yaml file.

Example of the config.yaml sections after the changes outlined above

Highlighted lines are related to the configuration steps above.

PIDS: [p01]

TIMEZONE: &timezone
America/New_York

DATABASE_GROUP: &database_group
MY_GROUP

# ... other irrelevant sections

TIME_SEGMENTS: &time_segments
    TYPE: PERIODIC
    FILE: "data/external/timesegments_periodic.csv" # make sure the three lines specified above are in the file
    INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE

# No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env`
DEVICE_DATA:
    PHONE:
        SOURCE: 
            TYPE: DATABASE
            DATABASE_GROUP: *database_group
            DEVICE_ID_COLUMN: device_id # column name
        TIMEZONE: 
            TYPE: SINGLE # SINGLE or MULTIPLE
            VALUE: *timezone 


############## PHONE ###########################################################
################################################################################

# ... other irrelevant sections

# Communication call features config, TYPES and FEATURES keys need to match
PHONE_CALLS:
    TABLE: calls # change if your calls table has a different name
    PROVIDERS:
        RAPIDS:
            COMPUTE: True # set this to True!
            CALL_TYPES: ...

Run RAPIDS
```
./rapids -j1
```
The call features for daily and morning time segments will be in
```
/data/processed/features/p01/phone_calls.csv
```