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. 1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) 3. Download this [CSV file](../img/calls.csv) and save it as `data/external/aware_csv/calls.csv` 2. Make the changes listed below for the corresponding [Configuration](../../setup/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 (*click to expand*)" 1. **Supported [data streams](../../setup/configuration#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. 3. **Create your [participants file](../../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` 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](../../setup/configuration##automatic-creation-of-participant-files) 1. Add `p01` to `[PIDS]` in `config.yaml` 1. Create a file in `data/external/participant_files/p01.yaml` with the following content: ```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.** 1. Set `[TIME_SEGMENTS][FILE]` to `data/external/timesegments_periodic.csv` 1. Create a file in `data/external/timesegments_periodic.csv` with the following content ```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 ``` 2. **Choose the [timezone of your study](../../setup/configuration#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`) ```yaml TIMEZONE: TYPE: SINGLE SINGLE: TZCODE: America/New_York ``` 5. **Modify your [device data stream configuration](../../setup/configuration#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](../../setup/configuration#sensor-and-features-to-process) you want to process.** 1. Set `[PHONE_CALLS][CONTAINER]` to `calls.csv` in the `config.yaml` file. 1. 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" This will be your `config.yaml` after following the instructions above. Click on the numbered markers to know more. ``` { .yaml .annotate } PIDS: [p01] # (1) TIMEZONE: TYPE: SINGLE # (2) SINGLE: TZCODE: America/New_York # ... other irrelevant sections TIME_SEGMENTS: &time_segments TYPE: PERIODIC # (3) FILE: "data/external/timesegments_periodic.csv" # (4) INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE PHONE_DATA_STREAMS: USE: aware_csv # (5) aware_csv: FOLDER: data/external/aware_csv # (6) # ... other irrelevant sections ############## PHONE ########################################################### ################################################################################ # ... other irrelevant sections # Communication call features config, TYPES and FEATURES keys need to match PHONE_CALLS: CONTAINER: calls.csv # (7) PROVIDERS: RAPIDS: COMPUTE: True # (8) CALL_TYPES: ... ``` 1. We added `p01` to PIDS after creating the participant file: ```bash data/external/participant_files/p01.yaml ``` With the following content: ```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 ``` 2. We use the default `SINGLE` time zone. 3. We use the default `PERIODIC` time segment `[TYPE]` 4. We created this time segments file with these lines: ```csv label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 night,001:00:00,5H 59M 59S,every_day,0 ``` 5. We set `[USE]` to `aware_device` to tell RAPIDS to process sensor data collected with the AWARE Framework stored in CSV files. 6. We used the default `[FOLDER]` for `awre_csv` since we already stored our test `calls.csv` file there 7. We changed `[CONTAINER]` to `calls.csv` to process our test call data. 8. We flipped `[COMPUTE]` to `True` to extract call behavioral features using the `RAPIDS` feature provider. 3. Run RAPIDS ```bash ./rapids -j1 ``` 4. The call features for daily and morning time segments will be in ``` data/processed/features/all_participants/all_sensor_features.csv ```