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Update docs for add new data stream

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JulioV 2021-03-10 08:56:52 -05:00
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docs/datastreams/add-new-data-streams.md
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# Add New Data Streams
A data stream is a set of sensor data collected using a specific type of **device** with a specific **format** and stored in a specific **container**. RAPIDS is agnostic to data streams' formats and containers (see the [Data Streams Introduction](../data-streams-introduction) for a list of supported data streams).
A data stream is a set of sensor data collected using a specific type of **device** with a specific **format** and stored in a specific **container**. RAPIDS is agnostic to data streams' formats and container; see the [Data Streams Introduction](../data-streams-introduction) for a list of supported streams.
In short, a format describes how raw data logged by a device maps to the data expected by RAPIDS, and a container is a script that connects to the database or file where that data is stored.
**A container** is queried with an R or Python script that connects to the database, API or file where your stream's raw data is stored.
The most common cases when you would want to implement a new data stream are:
- You collected data with a mobile sensing app RAPIDS does not support yet. For example, [Beiwe](https://www.beiwe.org/) data stored in MySQL. You will need to define a new format and a new container.
- You collected data with a mobile sensing app RAPIDS supports but this data is stored in a container that RAPIDS can't connect to yet. For example, AWARE data stored in PostgreSQL. In this case, you can reuse the format of the`aware_mysql` stream but you will need to implement a new container script.
!!! hint
RAPIDS supports smartphones, Fitbit, and Empatica devices, you can add a new data stream for the first two.
## Formats and Containers in RAPIDS
**CONTAINER**. The container of a data stream is queried using a `container.[R|py]` script. This script implements functions that will pull data from a database, file, etc.
**FORMAT**. The format of a data stream is described using a `format.yaml` file. A format file describes the mapping between your stream's raw data and the data that RAPIDS needs.
**A format** is described using a `format.yaml` file that specifies how to map and mutate your stream's raw data to match the data and format RAPIDS needs.
Both the `container.[R|py]` and the `format.yaml` are saved under `src/data/streams/[stream_name]` where `[stream_name]` can be
`aware_mysql` for example.
The most common cases when you would want to implement a new data stream are:
- You collected data with a mobile sensing app RAPIDS does not support yet. For example, [Beiwe](https://www.beiwe.org/) data stored in MySQL. You will need to define a new format file and a new container script.
- You collected data with a mobile sensing app RAPIDS supports, but this data is stored in a container that RAPIDS can't connect to yet. For example, AWARE data stored in PostgreSQL. In this case, you can reuse the format file of the `aware_mysql` stream, but you will need to implement a new container script.
## Implement a Container
The `container` script of a data stream can be implemented in R (strongly recommended) or python. This script must have two functions if you are implementing a stream for phone data or one function otherwise. The script can contain any other auxiliary functions that your data stream might need.
The `container` script of a data stream can be implemented in R (strongly recommended) or python. This script must have two functions if you are implementing a stream for phone data or one function otherwise. The script can contain other auxiliary functions.
First of all, add any parameters your script might need in `config.yaml` under `(device)_DATA_STREAMS`. These parameters will be available in the `stream_parameters` argument of the one or two functions you implement. For example, if you are adding support for `Beiwe` data stored in `PostgreSQL` and your container needs a set of credentials to connect to a database, your new data stream configuration would be:
@ -48,7 +41,7 @@ Then implement one or both of the following functions:
|--------------------|-------------------------------------------------------------------------------------------------------|
| stream_parameters | Any parameters (keys/values) set by the user in any `[DEVICE_DATA_STREAMS][stream_name]` key of `config.yaml`. For example, `[DATABASE_GROUP]` inside `[FITBIT_DATA_STREAMS][fitbitjson_mysql]` |
| sensor_container | The value set by the user in any `[DEVICE_SENSOR][CONTAINER]` key of `config.yaml`. It can be a table, file path, or whatever data source you want to support that contains the **data from a single sensor for all participants**. For example, `[PHONE_ACCELEROMETER][CONTAINER]`|
| device | The device id that you need to get the data for (this is set by the user in the [participant files](../../setup/configuration/#participant-files)). For example, in AWARE this device is a uuid|
| device | The device id that you need to get the data for (this is set by the user in the [participant files](../../setup/configuration/#participant-files)). For example, in AWARE this device id is a uuid|
| columns | A list of the columns that you need to get from `sensor_container`. You specify these columns in your stream's `format.yaml`|
@ -89,7 +82,7 @@ Then implement one or both of the following functions:
| Param | Description |
|--------------------|-------------------------------------------------------------------------------------------------------|
| stream_parameters | Any parameters (keys/values) set by the user in any `[DEVICE_DATA_STREAMS][stream_name]` key of `config.yaml`. For example, `[DATABASE_GROUP]` inside `[FITBIT_DATA_STREAMS][fitbitjson_mysql]` |
| device | The device id that you need to infer the OS for (this is set by the user in the [participant files](../../setup/configuration/#participant-files)). For example, in AWARE this device is a uuid|
| device | The device id that you need to infer the OS for (this is set by the user in the [participant files](../../setup/configuration/#participant-files)). For example, in AWARE this device id is a uuid|
!!! example
@ -117,13 +110,13 @@ Then implement one or both of the following functions:
## Implement a Format
A format describes the mapping between your stream's raw data and the data that RAPIDS needs. This file has a section per sensor (e.g. `PHONE_ACCELEROMETER`), and each section has two attributes (keys):
A format file `format.yaml` describes the mapping between your stream's raw data and the data that RAPIDS needs. This file has a section per sensor (e.g. `PHONE_ACCELEROMETER`), and each section has two attributes (keys):
1. `RAPIDS_COLUMN_MAPPINGS` are mappings between the columns RAPIDS needs and the columns your raw data already has.
1. The reserved keyword `FLAG_TO_MUTATE` flags columns that RAPIDS requires but that are not initially present in your container (database, CSV file). These columns have to be created by your mutation scripts.
2. `MUTATION`. Columns marked as `FLAG_TO_MUTATE` need to be created before RAPIDS can process data from a sensor
2. `MUTATION`. Sometimes your raw data needs to be transformed to match the format RAPIDS can handle (including creating columns marked as `FLAG_TO_MUTATE`)
2. `COLUMN_MAPPINGS` are mappings between the columns a mutation `SCRIPT` needs and the columns your raw data has.
@ -132,7 +125,7 @@ A format describes the mapping between your stream's raw data and the data that
!!! hint
`[RAPIDS_COLUMN_MAPPINGS]` and `[MUTATE][COLUMN_MAPPINGS]` have a `key` (left-hand side string) and a `value` (right-hand side string). The `values` are the names used to pulled columns from a container (e.g., columns in a database table). All `values` are renamed to their `keys` in lower case. The renamed columns are sent to every mutation script within the `data` argument, and the final output is the input RAPIDS process further.
For example, let's assume we are using `aware_mysql` and defining the following format for `PHONE_FAKESENSOR`:
For example, let's assume we are implementing `beiwe_mysql` and defining the following format for `PHONE_FAKESENSOR`:
```yaml
PHONE_FAKESENSOR:
@ -150,7 +143,7 @@ A format describes the mapping between your stream's raw data and the data that
RAPIDS will:
1. Download `beiwe_timestamp`, `beiwe_deviceID`, and `beiwe_value` from the container of `aware_mysql` (MySQL DB)
1. Download `beiwe_timestamp`, `beiwe_deviceID`, and `beiwe_value` from the container of `beiwe_mysql` (MySQL DB)
2. Rename these columns to `timestamp`, `device_id`, and `magnitude`, respectively.
3. Execute `square_magnitude.py` with a data frame as an argument containing the renamed columns. This script will square `magnitude` and rename it to `magnitude_squared`
4. Verify the data frame returned by `square_magnitude.py` has the columns RAPIDS needs `timestamp`, `device_id`, and `magnitude_squared`.
@ -159,7 +152,7 @@ A format describes the mapping between your stream's raw data and the data that
Note that although `RAPIDS_COLUMN_MAPPINGS` and `[MUTATE][COLUMN_MAPPINGS]` keys are in capital letters for readability (e.g. `MAGNITUDE_SQUARED`), the names of the final columns you mutate in your scripts should be lower case.
Let's explain this column mapping further with examples.
Let's explain in more depth this column mapping with examples.
### Name mapping