Analysis of ODE models with focus on model selection and parameter estimation.
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.
- 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.
S-timator supports Python versions 2.7 and 3.3+.
S-timator depends on the “scientific python stack”. The mandatory requirements for S-timator are the following libraries:
- Python (2.7 or 3.3+)
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.
- Jupyter and all its dependencies: some S-timator examples are provided as Jupyter notebooks.
After installing the required libraries, (Python, numpy, scipy, matplotlib) the easiest way to install S-timator is with pip:
$ pip install stimator
or, in a Anaconda/Miniconda installation, install from the aeferreira channel:
$ conda install -c aeferreira stimator