Python package to analyse, validate, fill and visualise data acquired in the context of (waste) water treatment
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README.rst

wwdata

https://travis-ci.org/UGentBiomath/wwdata.svg?branch=master Documentation Status Updates

Data analysis package aimed at data obtained in the context of (waste)water

Structure

The package contains one class and three subclasses, all in separate .py files. Division in subclasses is based on the type of data:

  • online data from full scale installations (OnlineSensorBased)
  • online data from lab experiments (LabSensorBased)
  • offline data obtained from lab experiments (LabExperimentBased).

Jupyter notbeook files (.ipynb) illustrate the use of the available functions. The most developed class is the OnlineSensorBased one. The workflow of this class is shown in below Figure, where OSB represents an OnlineSensorBased object. Main premises are to never delete data but to tag it and to be able to check the reliability when gaps in datasets are filled.

./figs/packagestructure_rel.png

Examples

For the workflow with code and more specific examples, check out the Showcase Jupyter Notebook(s) included as documentation of the package.

Credits

This package was created with support from Cookiecutter and the audreyr/cookiecutter-pypackage project template, as well as this GitHub page, provided by Daler and explaining how to use sphinx documentation generation in combination with GitHub Pages.