The impact framework helps scientists and engineers analyze data for characterizing microbial physiology. Data can be imported from analytical equipment such as HPLC and plate reader using native data formats. From here, data is parsed into a hierarchical data structure based on the logical organization of trials in an experiment and its identifying metadata. Then, this data can be used to generate visualizations using a plotting package, there are many options but we rely on plotly because it is mostly open-source, is written in js, provides interactivity within a browser, and provides numerous interfaces to other languages such as matlab and python.
This documentation describes the use of the core impact module, which does most of the heavy-lifting. Use of the module is demonstrated using the jupyter notebook, which is a web-based user interface allowing scientists to store prose describing experiments and code used to analyze data and generate visualizations together, greatly increasing the transparency of data analysis.
For first time users, visit quick_install to install the package. Then, head over to the Quickstart tutorial to begin importing and analyzing data.
To learn more about the features which are automatically extracted from the data and visualization of these features, follow the Analyzing Features tutorial.
Finally, all of the classes and methods which compose the module are documented impact package, and can be used as a guide to extend the framework to include more raw data formats and features.
Commits including appropriate tests which extend functionality are welcomed.