RAPIDS configuration settings are defined in ``config.yaml`` (See `config.yaml`_). This is the only file that you need to understand in order to compute the features that RAPIDS ships with.
It has global settings like ``PIDS``, ``DAY_SEGMENTS``, among others (see :ref:`global-sensor-doc` for more information). As well as per sensor settings, for example, for the :ref:`messages-sensor-doc`::
The ``Snakefile`` file (see the actual `Snakefile`_) pulls the entire system together. The first line in this file identifies the configuration file. Next are a list of included directives that import the rules used to pull, clean, process, analyze and report data. It compiles the list of ``files_to_compute`` by scaning the config file looking for the sensors with a ``COMPUTE`` flag equal to ``True``. Then, the ``all`` rule is called with this list which prompts Snakemake to exectue the pipeline (raw files, intermediate files, feature files, reports, etc).
In RAPIDS the ``all`` rule lists the output files we expect the pipeline to compute. Before the ``all`` rule is called snakemake checks the ``config.yaml`` and adds all the rules for which the sensors ``COMPUTE`` parameter is ``True``. The ``expand`` function allows us to generate a list of file paths that have a common structure except for PIDS or other parameters. Consider the following::
Thus, this allows us to define all the desired output files without having to manually list each path for every participant and every sensor. The way Snakemake works is that it looks for the rule that produces the desired output files and then executes that rule. For more information on ``expand`` see `The Expand Function`_
The ``rules`` directory contains the ``snakefiles`` that were included in the main ``Snakefile`` file. A short description of these files are given in the :ref:`includes-section` section.
A Snakemake workflow is defined by rules (See the features_ snakefile as an actual example). Rules decompose the workflow into small steps by specifying what output files should be created by running a script on a set of input files. Snakemake automatically determines the dependencies between the rules by matching file names. Thus, a rule can consist of a name, input files, output files, and a command to generate the output from the input. The following is the basic structure of a Snakemake rule::
The ``rule`` directive specifies the name of the rule that is being defined. ``params`` defines additional parameters for the rule's script. In the example above, the parameters are passed to the ``messages_features.R`` script as an dictionary. Instead of ``script`` a ``shell`` command call can also be called by replacing the ``script`` directive of the rule and replacing it with::
It should be noted that rules can be defined without input and output as seen in the ``renv.snakemake``. For more information see `Rules documentation`_ and for an actual example see the `renv`_ snakefile.
There are times when the same rule should be applied to different participants and day segments. For this we use wildcards ``{my_wildcard}``. All wildcards are inferred from the files listed in the ``all` rule of the ``Snakefile`` file and therefore from the output of any rule::
If the rule’s output matches a requested file, the substrings matched by the wildcards are propagated to the input and params directives. For example, if another rule in the workflow requires the file ``data/processed/p01/messages_sent_daily.csv``, Snakemake recognizes that the above rule is able to produce it by setting ``pid=p01``, ``messages_type=sent`` and ``day_segment=daily``. Thus, it requests the input file ``data/raw/p01/messages_with_datetime.csv`` as input, sets ``messages_type=sent``, ``day_segment=daily`` in the ``params`` directive and executes the script. ``../src/features/messages_features.R``. See the preprocessing_ snakefile for an actual example.
The ``src`` directory holds all the scripts used by the pipeline for data manipulation. These scripts can be in any programming language including but not limited to Python_, R_ and Julia_. This directory is organized into the following directories:
