Metadata-Version: 1.1
Name: odenlls
Version: 0.1.0
Summary: Non-linear least squares fitting of chemical kinetics data using ODE simulations
Home-page: https://github.com/rnelsonchem/ODEnlls
Author: Ryan Nelson
Author-email: rnelsonchem@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: odenlls
        =======
        
        *odenlls* is a Python3 library for simulating and fitting chemical
        kinetics data. These two pieces are accomplished as follows:
        
        1. Kinetic models are simulated using numerical simulations of the
           ordinary differential equations (ODE) for an arbitrary set of
           chemical reactions. Rate constants and starting concentrations can be
           varied arbitrarily to observe the predicted changes in concentration
           with time.
        
        2. These ODE simulations are fit to experimental kinetic data using
           non-linear least squares (nlls) methods. These fits yield the
           best-fit rate constant and concentration parameters for a given set
           of kinetic data.
        
        Dependencies
        ------------
        
        This package consists of a single Python module file that was developed
        using Python 3.6; however, it should work on most other Python 3
        versions with the appropriate external dependencies listed below.
        
        -  Numpy >= 1.13.3
        -  scipy>=1.0.0
        -  pandas>=0.21.1
        -  matplotlib>=2.1.1
        
        The package versions above were used during development. Older/newer
        versions should work as well. Older versions of these modules may work
        as well, but you may want to run the
        `py.test <https://docs.pytest.org/en/latest/>`__ unit tests (*coming
        soon*) to ensure they work properly.
        
        Installation
        ------------
        
        *odenlls* is installable using either Python's ``pip`` package manager
        or `conda <https://conda.io/docs/>`__, the package manager for the
        `Anaconda Python distribution <https://www.anaconda.com/download/>`__.
        
        To get the latest release using ``pip``, use the following command:
        
        ::
        
            $ pip install odenlls
        
        Or to install from the latest GitHub commit:
        
        ::
        
            $ pip install git+https://github.com/rnelsonchem/odenlls.git
        
        Using ``conda``, the following command will install the latest release
        of this package.
        
        ::
        
            $ conda install -c rnelsonchem odenlls
        
        Usage
        -----
        
        The *odenlls* module capabilities are demonstrated in several
        `Jupyter <http://jupyter.org/>`__ notebooks, which are located in the
        "examples" directory on the `GitHub project
        page <https://github.com/rnelsonchem/odenlls>`__. A summary of these
        notebooks is as follows:
        
        -  The `TLDR
           Notebook <https://github.com/rnelsonchem/odenlls/blob/master/examples/TLDR.ipynb>`__
           is a very brief overview of *odenlls* functionality with very little
           explanatory text.
        
        -  `Notebook
           1 <https://github.com/rnelsonchem/odenlls/blob/master/examples/1.%20First%20order%20irreversible%20kinetics%20simulation.ipynb>`__
           demonstrates simulation of a simple first-order irreversible
           reaction.
        
        -  In `Notebook
           2 <https://github.com/rnelsonchem/odenlls/blob/master/examples/2.%20First%20order%20irreversible%20kinetics%20fitting.ipynb>`__,
           reaction data fitting is shown for a user-generated set of
           first-order irreversible reaction data.
        
        -  `Notebook
           3 <https://github.com/rnelsonchem/odenlls/blob/master/examples/3.%20First%20order%20reversible%20kinetics%20simulation%20and%20fitting.ipynb>`__
           highlights fitting of a real-world data set using a series of
           reversible first-order reactions.
        
Keywords: non-linear fitting chemical kinetics ordinary differential equations ode
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
