Minimal Working Example¶

This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for 24 hr (00:00:00 to 23:59:59) and night (00:00:00 to 05:59:59) time segments of every day of data of one participant that was monitored on the US East coast with an Android smartphone.

Install RAPIDS and make sure your conda environment is active (see Installation)
Download this CSV file and save it as data/external/aware_csv/calls.csv
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 (click to expand)
1. Supported data streams.
  
  Based on the docs, we decided to use the aware_csv data stream because we are processing aware data saved in a CSV file. We will use this label in a later step; there’s no need to type it or save it anywhere yet.
2. Create your participants file.
  
  Since we are processing data from a single participant, you only need to create a single participant file called p01.yaml in data/external/participant_files. This participant file only has a PHONE section because this hypothetical participant was only monitored with a smartphone. Note that for a real analysis, you can do this automatically with a CSV file
  1. Add p01 to [PIDS] in config.yaml
  2. Create a file in data/external/participant_files/p01.yaml with the following content:
    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
3. Select what time segments you want to extract features on.
  1. Set [TIME_SEGMENTS][FILE] to data/external/timesegments_periodic.csv
  2. Create a file in data/external/timesegments_periodic.csv with the following content
    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
4. Choose the timezone of your study.
  
  We will use the default time zone settings since this example is processing data collected on the US East Coast (America/New_York)
```
TIMEZONE: 
    TYPE: SINGLE
    SINGLE:
        TZCODE: America/New_York
```
5. Modify your device data stream configuration
  1. Set [PHONE_DATA_STREAMS][USE] to aware_csv.
  2. We will use the default value for [PHONE_DATA_STREAMS][aware_csv][FOLDER] since we already stored the test calls CSV file there.
6. Select what sensors and features you want to process.
  1. Set [PHONE_CALLS][CONTAINER] to calls.csv in the config.yaml file.
  2. Set [PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE] to True in the config.yaml file.
Example of the config.yaml sections after the changes outlined above

This will be your config.yaml after following the instructions above. Click on the numbered markers to know more.
```
PIDS: [p01] # 

TIMEZONE:
    TYPE: SINGLE # 
    SINGLE:
        TZCODE: America/New_York

# ... other irrelevant sections

TIME_SEGMENTS: &time_segments
    TYPE: PERIODIC # 
    FILE: "data/external/timesegments_periodic.csv" # 
    INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE

PHONE_DATA_STREAMS:
    USE: aware_csv # 

    aware_csv:
        FOLDER: data/external/aware_csv # 

# ... other irrelevant sections

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

# ... other irrelevant sections

# Communication call features config, TYPES and FEATURES keys need to match
PHONE_CALLS:
    CONTAINER: calls.csv  # 
 
    PROVIDERS:
        RAPIDS:
            COMPUTE: True # 
            CALL_TYPES: ...
```
Run RAPIDS
```
./rapids -j1
```

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

data/processed/features/all_participants/all_sensor_features.csv