rapids/docs/workflow-examples/minimal.md

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.

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

2. 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)

   ??? info "Required configuration changes" 1. Add your database credentials.

        Setup your database connection credentials in `.env`, we assume your credentials group in the `.env` file is called `MY_GROUP`.
   
    2. **Choose the [timezone of your study](../../setup/configuration#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.
   
    3. **Create your [participants files](../../setup/configuration#participant-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:
        ```yaml
        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
        ```
   
    4. **Select what [time segments](../../setup/configuration#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:
         ```csv
         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
         ```
   
     5. **Modify your [device data source configuration](../../setup/configuration#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.
   
     6. **Select what [sensors and features](../../setup/configuration#sensor-and-features-to-process) you want to process.** 
   
        Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` in the `config.yaml` file.
   

   ??? example "Example of the config.yaml sections after the changes outlined above" Highlighted lines are related to the configuration steps above. ``` yaml hl_lines="1 4 7 12 13 38" 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: ...
    ```
   

3. Run RAPIDS

   ./rapids -j1
   

4. The call features for daily and morning time segments will be in

   /data/processed/features/p01/phone_calls.csv