Analysis of ODE models with focus on model selection and parameter estimation.
Project description
S-timator is a Python library to analyse ODE-based models (also known as dynamic or kinetic models). These models are often found in many scientific fields, particularly in Physics, Chemistry, Biology and Engineering.
Features include:
A mini language used to describe models: models can be input as plain text following a very simple and human-readable language.
Basic analysis: numerical solution of ODE’s, parameter scanning.
Parameter estimation and model selection: given experimental data in the form of time series and constrains on model operating ranges, built-in numerical optimizers can find parameter values and assist you in the experimental design for model selection.
S-timator is in an alpha stage: many new features will be available soon.
Requirements
S-timator supports Python versions 2.6 and up, but support of 3.x is coming soon.
S-timator depends on the “scientific python stack”. The mandatory requirements for S-timator are the following libraries:
Python (2.6 or 2.7)
numpy
scipy
matplotlib
pip
pandas
seaborn
One of the following “scientific python” distributions is recommended, as they all provide an easy installation of all requirements:
The installation of these Python libraries is optional, but strongly recommended:
sympy: necessary to compute dynamic sensitivities, error estimates of parameters and other symbolic computations.
IPython and all its dependencies: some S-timator examples are provided as IPython notebooks.
wxPython: although S-timator is a python library meant to be used for scripting or in IPython literate programming interface, a simple GUI is included. This interface requires wxPython.
Installation
After installing the required libraries, (Python, numpy, scipy, matplotlib, pandas and pip) the easiest way to install S-timator is with pip:
$ pip install stimator
The classical way also works, but is not recommended:
$ python setup.py install
