Update minimal workflow
JulioV
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@ -3,62 +3,75 @@ Minimal Working Example
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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.
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!!! hint
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If you don't have `call` data that you can use to try this example you can restore this [CSV file](../img/calls.csv) as a table in a MySQL database.
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1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation))
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3. Download this [CSV file](../img/calls.csv) and save it as `data/external/aware_csv/calls.csv`
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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)
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??? info "Required configuration changes"
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1. **Add your [database credentials](../../setup/configuration#database-credentials).**
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1. **Supported [data streams](../../setup/configuration#supported-data-streams).**
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Setup your database connection credentials in `.env`, we assume your credentials group in the `.env` file is called `MY_GROUP`.
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We identified that we will 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.
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2. **Choose the [timezone of your study](../../setup/configuration#timezone-of-your-study).**
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3. **Create your [participants file](../../setup/configuration#participant-files).**
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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.
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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 a smartphone. Note that for a real analysis, you can do this [automatically with a CSV file](../../setup/configuration##automatic-creation-of-participant-files)
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1. Add `p01` to `[PIDS]` in `config.yaml`
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3. **Create your [participants files](../../setup/configuration#participant-files).**
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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:
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```yaml
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PHONE:
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DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
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PLATFORMS: [android] # or ios
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LABEL: MyTestP01 # any string
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START_DATE: 2020-01-01 # this can also be empty
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END_DATE: 2021-01-01 # this can also be empty
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```
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1. Create a file in `data/external/participant_files/p01.yaml` with the following content:
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```yaml
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PHONE:
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DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
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PLATFORMS: [android] # or ios
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LABEL: MyTestP01 # any string
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START_DATE: 2020-01-01 # this can also be empty
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END_DATE: 2021-01-01 # this can also be empty
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```
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4. **Select what [time segments](../../setup/configuration#time-segments) you want to extract features on.**
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`[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:
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```csv
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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night,00:00:00,5H 59M 59S,every_day,0
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```
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1. Set `[TIME_SEGMENTS][FILE]` to `data/external/timesegments_periodic.csv`
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5. **Modify your [device data source configuration](../../setup/configuration#device-data-source-configuration)**
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1. Create a file in `data/external/timesegments_periodic.csv` with the following content
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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.
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```csv
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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night,00:00:00,5H 59M 59S,every_day,0
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```
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2. **Choose the [timezone of your study](../../setup/configuration#timezone-of-your-study).**
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We will use the default time zone settings since this example is processing data collected on the US East Coast (`America/New_York`)
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```yaml
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TIMEZONE:
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TYPE: SINGLE
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SINGLE:
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TZCODE: America/New_York
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```
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5. **Modify your [device data stream configuration](../../setup/configuration#data-stream-configuration)**
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Set `[PHONE_DATA_STREAMS][USE]` to `aware_csv`.
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6. **Select what [sensors and features](../../setup/configuration#sensor-and-features-to-process) you want to process.**
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Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` in the `config.yaml` file.
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1. Set `[PHONE_CALLS][CONTAINER]` to `calls.csv` in the `config.yaml` file.
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1. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` in the `config.yaml` file.
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??? example "Example of the `config.yaml` sections after the changes outlined above"
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Highlighted lines are related to the configuration steps above.
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``` yaml hl_lines="1 4 7 12 13 38"
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``` yaml hl_lines="1 4 6 12 16 27 30"
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PIDS: [p01]
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TIMEZONE: &timezone
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America/New_York
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DATABASE_GROUP: &database_group
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MY_GROUP
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TIMEZONE:
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TYPE: SINGLE
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SINGLE:
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TZCODE: America/New_York
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# ... other irrelevant sections
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@ -67,17 +80,10 @@ This is a quick guide for creating and running a simple pipeline to extract miss
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FILE: "data/external/timesegments_periodic.csv" # make sure the three lines specified above are in the file
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INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE
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# No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env`
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DEVICE_DATA:
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PHONE:
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SOURCE:
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TYPE: DATABASE
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DATABASE_GROUP: *database_group
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DEVICE_ID_COLUMN: device_id # column name
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TIMEZONE:
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TYPE: SINGLE # SINGLE or MULTIPLE
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VALUE: *timezone
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PHONE_DATA_STREAMS:
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USE: aware_csv
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# ... other irrelevant sections
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############## PHONE ###########################################################
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################################################################################
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@ -86,10 +92,10 @@ This is a quick guide for creating and running a simple pipeline to extract miss
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# Communication call features config, TYPES and FEATURES keys need to match
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PHONE_CALLS:
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TABLE: calls # change if your calls table has a different name
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CONTAINER: calls.csv
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PROVIDERS:
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RAPIDS:
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COMPUTE: True # set this to True!
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COMPUTE: True
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CALL_TYPES: ...
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```
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@ -99,7 +105,7 @@ This is a quick guide for creating and running a simple pipeline to extract miss
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```
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4. The call features for daily and morning time segments will be in
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```
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/data/processed/features/p01/phone_calls.csv
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data/processed/features/all_participants/all_sensor_features.csv
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```
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