A typical BEAT experiment is composed of several building blocks. Datasets that provide data to the system, algorithms that handles the functions introduced by user, analyzers that is in charge of interpreting the output result and producing the appropriate results and figures, and toolchains that determines the data flow between the blocks from datasets to the final results. In addition, each block accepts specific data formats and the data is synchronized between blocks neatly without users need to interfere. These basic functionalities that are introduced in the “Getting Started with BEAT” section in BEAT documentation are all defined and managed by beat.core. For example, as it is explained in the “Algorithms” section of “Getting Started with BEAT” in BEAT documentation, algorithm objects should be derived from the class Algorithm when using Python or in case of C++, they should be derived from IAlgorithmLagacy, IAlgorithmSequential, or IAlgorithmAutonomous depending of the algorithm type. All these parent classes are defined in beat.core package.

The rest of this document includes information about the backend api used to handle data through the BEAT ecosystem. For developers and advanced user there is information for local development of the package.