bio-rtd 0.7.2

Description:

biortd 0.7.2

bio-rtd library
bio-rtd library is a python library for modeling
residence time distributions (RTD)
of integrated continuous biomanufacturing processes.
Version
Current version: 0.7.1
Requirements
Python 3.7.+
Core dependencies:
numpy
scipy

Packages for visual representation in examples:
bokeh
xlrd

Packages for importing data from Excel in examples:
pandas

Set up
Using package manager:
pip install bio_rtd

To run examples, download or clone the github repo:
git clone https://github.com/open-biotech/bio-rtd.git

and set local destination (/path_to/bio-rtd) as a working directory
or add it to the python path.
Getting started
Examples can be run as scripts:
python examples/models/single_pcc.py

Model examples ending with _gui.py
can be run via bokeh serve command:
bokeh serve examples/models/integrated_mab_gui.py

or
python bokeh serve examples/models/integrated_mab_gui.py

For more information see the Documentation.
Documentation
The documentation can be accessed at https://open-biotech.github.io/bio-rtd-docs/ or by building a local version.
To build a local version of documentation install the following packages:
pip install sphinx sphinx_autodoc_typehints sphinx_rtd_theme

run command:
make html

and open docs/build/html/index.html with a web browser.
Meta information
Distributed under the MIT license. See LICENSE for more information.
For technical issues, bug reports and feature request use
issue tracker.
If you want to contribute to the code, see
Developers guide.
If you are using the library in your projects please let
us know.
This way we know how much interest there is, what is the scope of usage,
the needs, etc. This information influences the future development of the project.
E-mail: [email protected]
University of Natural Resources and Life Sciences (BOKU), Vienna
Referencing the library
If you are using this package for a scientific publication,
please add the following reference:

Senčar, J., (2020) GitHub Repository,
https://github.com/open-biotech/bio-rtd.

Acknowledgements
This work was supported by:

The Federal Ministry for Digital and
Economic Affairs (bmwd), the Federal Ministry for Transport,
Innovation and Technology (bmvit),
the Styrian Business Promotion Agency SFG,
the Standortagentur Tirol,
Government of Lower Austria,
and ZIT - Technology Agency of the City of Vienna
through the COMET-Funding Program managed
by the Austrian Research Promotion Agency FFG
Baxalta Innovations GmbH (now part of Takeda)
Bilfinger Industrietechnik Salzburg GmbH