{ "info": { "author": "Ant\u00f3nio Ferreira", "author_email": "aeferreira@fc.ul.pt", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Life", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Physics" ], "description": "*S-timator*: analysis of ODE models with focus on model selection and parameter estimation.\n===========================================================================================\n\n*S-timator* is a Python library to analyse ODE-based models\n(also known as *dynamic* or *kinetic* models). These models are often found\nin many scientific fields, particularly in Physics, Chemistry, Biology and\nEngineering.\n\nFeatures include:\n\n- **A mini language used to describe models**: models can be input as plain text \n following a very simple and human-readable language.\n- **Basic analysis**: numerical solution of ODE's, parameter scanning.\n- **Parameter estimation** and **model selection**: given experimental data in\n the form of time series and constrains on model operating ranges,\n built-in numerical optimizers can find parameter values and assist you in the\n experimental design for model selection.\n\n*S-timator* is in an alpha stage: many new features will be available soon.\n\nRequirements\n------------\n\n*S-timator* supports Python versions 2.7 and 3.3+.\n\n*S-timator* depends on the \"scientific python stack\". The **mandatory**\nrequirements for *S-timator* are the following libraries:\n\n- ``Python (2.7 or 3.3+)``\n- ``numpy``\n- ``scipy``\n- ``matplotlib``\n- ``pip``\n\nOne of the following \"scientific python\" distributions is recommended, **as they all provide \nan easy installation of all requirements**:\n\n- `Anaconda `_ (or `Miniconda `_ followed by the necessary ``conda install``'s)\n- `Python (x,y) `_\n- `Enthought Canopy `_\n\nThe installation of these Python libraries is optional, but strongly recommended:\n\n- ``sympy``: necessary to compute dynamic sensitivities, error estimates of\n parameters and other symbolic computations.\n- ``Jupyter`` and all its dependencies: some *S-timator* examples are provided\n as Jupyter notebooks.\n\n\nInstallation\n------------\n\nAfter installing the required libraries, (``Python``, ``numpy``, ``scipy``,\n``matplotlib``) the easiest way to install *S-timator* is\nwith ``pip``::\n\n $ pip install stimator\n\nor, in a Anaconda/Miniconda 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