stimator 0.9.120

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.

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 2.7 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+)

• numpy

• scipy

• matplotlib

• pip

One of the following “scientific python” distributions is recommended, as they all provide an easy installation of all requirements:

• Anaconda (or Miniconda followed by the necessary conda install’s)

• Python (x,y)

• Enthought Canopy

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.

Installation

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