GROMACS provides a framework for implementing flexible energy data analysis routines. It consists of a few components that can also be used individually, but in most cases it is desirable to use features from all of them to get most out of the framework. The main features are:

Support for per-frame parallelization. The framework is designed to support running the analysis in parallel for multiple frames for cases where different frames can be analyzed (mostly) independently. At this time, the actual parallelization is not implemented, but tools written to use the framework should be able to take advantage of it as soon as it materializes with no or minimal changes.
Access to a library of basic analysis routines. Things such as computing averages and histogramming are provided as reusable modules.
Tool code can focus on the actual analysis. Tools are implemented by subclassing an abstract class and providing an implementation for selected pure virtual methods. The framework takes care of initialization tasks, loading the trajectory and looping over it, evaluating selections, and also provides basic features like making molecules whole before passing the frame to the analysis code. This approach also makes it possible to reuse the same tool code from a scripting language such as Python simply by implementing general support for such language bindings in the framework (no such integration is implemented at this time, though).

High-level framework

The Framework for Energy Analysis (energyanalysis) module provides the high-level framework that integrates all the pieces together. It provides the abstract base class for analysis tool modules (gmx::EnergyAnalysisModule), and the code that runs such a module as a command-line tool (gmx::EnergyAnalysisCommandLineRunner). See energyanalysis module documentation for more details.

Output data handling

The Parallelizable Handling of Output Data (analysisdata) module provides two things:

Support for uniformly providing output data from analysis tools. Tools compute their output values and place them into a data object for further processing. This allows two things:
- Reusable data modules can be applied across different tools to do common post-processing.
- The data object provides parallelization support.
Set of reusable data modules for post-processing the data. These include functionality like averaging data, computing histograms, and plotting the data into a file. Many of these modules also provide their output as a data object, allowing further data modules to be attached to them.

The general concept is explained in more detail on a separate page: Analysis output data handling. The analysisdata module documentation provides more technical details.

Input options

To declare input data for the tool (typically, command-line options, including input files and selections), Extensible Handling of Options (options) module is used. The analysis tool code receives an instance of gmx::IOptionsContainer for one of its initialization methods, and uses it to provide its input options. Basic options are declared in basicoptions.h. For each option, the tool declares a local variable that will receive the value for that option. After the options are parsed from the command line (by the framework), the tool code can read the values from these variables. The option declarations filled into the gmx::IOptionsContainer object are also used to provide help to the user (also handled by the framework). See the documentation for gmx::EnergyAnalysisModule and the options module documentation for more details.