Modules

Data analysis and reading from different formats is supported via modules. This allows to extend the functionality of the program via defined APIs to cover specific experimental protocols. Here, we cover all the types of the modules and show, using an artificial example, how to write new ones.

Before proceeding with the tutorial and description of module details, few general notes that have to be taken into account. Modules are written in Python and are loaded recursively from iocbio.kinetics.modules and any other folder specified in program settings. This allows you to structure modules according to your experiments and put all modules dealing with the same type of experiment together in the same folder.

When modules are loaded, all iocbio.kinetics objects have to be imported using absolute path. For example,

from iocbio.kinetics.handler.experiment_generic import ExperimentGeneric

All module Python files, have to be loaded using relative path, as in

from .experiment_spectro import ABC

By following this simple rule, you will ensure that the correct code gets loaded and your modules will not depend on internal implementation used to load them.

Module types

Modules can be of several types to cover different type of processing done by them. In practice, module types define how the application accesses them and at which part of the analysis. Each Python file can contain multiple APIs and cover several types as well. Its also possible to have Python files that are just dependency of some module file and, in that case, the type of that dependency file should be left unspecified.

Module type is defined using global variable in that module - IocbioKineticsModule as

IocbioKineticsModule = ["args", "database_schema"]

where IocbioKineticsModule is a list with elements describing a type. For each module type declared in Python file, we have to define specific function with the given API that will be called by the application. Below, is the table summarizing available modules and the functions called by application.

Type	API Function	Comment
analyzer	analyzer	Analysis of experimental data
args	args	Parameters for protocols given in addition to raw data
database_info	database_info	Info field in the data overview
database_schema	database_schema	Definition of database tables
database_processor	database_processor	Additional database processing after import of experiment
reader	create_data	Import of experimental data

Modules API is described in a formal way and via tutorial. The tutorial describes in details how to write new modules using an artificial dataset as an example.