{ "info": { "author": "Ryan Nelson", "author_email": "rnelsonchem@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3" ], "description": "odenlls\n=======\n\n*odenlls* is a Python3 library for simulating and fitting chemical\nkinetics data. These two pieces are accomplished as follows:\n\n1. Kinetic models are simulated using numerical simulations of the\n ordinary differential equations (ODE) for an arbitrary set of\n chemical reactions. Rate constants and starting concentrations can be\n varied arbitrarily to observe the predicted changes in concentration\n with time.\n\n2. These ODE simulations are fit to experimental kinetic data using\n non-linear least squares (nlls) methods. These fits yield the\n best-fit rate constant and concentration parameters for a given set\n of kinetic data.\n\nDependencies\n------------\n\nThis package consists of a single Python module file that was developed\nusing Python 3.6; however, it should work on most other Python 3\nversions with the appropriate external dependencies listed below.\n\n- Numpy >= 1.13.3\n- scipy>=1.0.0\n- pandas>=0.21.1\n- matplotlib>=2.1.1\n\nThe package versions above were used during development. Older/newer\nversions should work as well. Older versions of these modules may work\nas well, but you may want to run the\n`py.test `__ unit tests (*coming\nsoon*) to ensure they work properly.\n\nInstallation\n------------\n\n*odenlls* is installable using either Python's ``pip`` package manager\nor `conda `__, the package manager for the\n`Anaconda Python distribution `__.\n\nTo get the latest release using ``pip``, use the following command:\n\n::\n\n $ pip install odenlls\n\nOr to install from the latest GitHub commit:\n\n::\n\n $ pip install git+https://github.com/rnelsonchem/odenlls.git\n\nUsing ``conda``, the following command will install the latest release\nof this package.\n\n::\n\n $ conda install -c rnelsonchem odenlls\n\nUsage\n-----\n\nThe *odenlls* module capabilities are demonstrated in several\n`Jupyter `__ notebooks, which are located in the\n\"examples\" directory on the `GitHub project\npage `__. A summary of these\nnotebooks is as follows:\n\n- The `TLDR\n Notebook `__\n is a very brief overview of *odenlls* functionality with very little\n explanatory text.\n\n- `Notebook\n 1 `__\n demonstrates simulation of a simple first-order irreversible\n reaction.\n\n- In `Notebook\n 2 `__,\n reaction data fitting is shown for a user-generated set of\n first-order irreversible reaction data.\n\n- `Notebook\n 3 `__\n highlights fitting of a real-world data set using a series of\n reversible first-order reactions.\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/rnelsonchem/ODEnlls", "keywords": "non-linear fitting chemical kinetics ordinary differential equations ode", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "odenlls", "package_url": "https://pypi.org/project/odenlls/", "platform": "", "project_url": "https://pypi.org/project/odenlls/", "project_urls": { "Homepage": "https://github.com/rnelsonchem/ODEnlls" }, "release_url": "https://pypi.org/project/odenlls/0.1.0/", "requires_dist": [ "numpy (>=1.13.3)", "scipy (>=1.0.0)", "pandas (>=0.21.1)", "matplotlib (>=2.1.1)" ], "requires_python": "", "summary": "Non-linear least squares fitting of chemical kinetics data using ODE simulations", "version": "0.1.0" }, "last_serial": 3462750, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "2d6a1902ec34ec2b8b866cb5ebb5aa97", "sha256": "844c6154088f56587f845cfa138b869338b8aaa98afe9789e5b5e5a2cd0139e9" }, "downloads": -1, "filename": "odenlls-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "2d6a1902ec34ec2b8b866cb5ebb5aa97", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 10680, "upload_time": "2018-01-04T21:27:34", "url": "https://files.pythonhosted.org/packages/2b/51/ded120ed3129030bee0c6fa96a0f9c3a5dc950ca122f3b462f20f6386888/odenlls-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "bc348b8925aa893ffaa9be742ddd0b5e", "sha256": "fb7d5facd42f6013889e17412de4376f1bd43b15ec09496d352f1944855eaa5b" }, "downloads": -1, "filename": "odenlls-0.1.0.tar.gz", "has_sig": false, "md5_digest": "bc348b8925aa893ffaa9be742ddd0b5e", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8506, "upload_time": "2018-01-04T21:27:36", "url": "https://files.pythonhosted.org/packages/9c/2a/4d8e3bfe7889decc1844f6467a555e7da65789ac42b583e2cf3305bd0846/odenlls-0.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "2d6a1902ec34ec2b8b866cb5ebb5aa97", "sha256": "844c6154088f56587f845cfa138b869338b8aaa98afe9789e5b5e5a2cd0139e9" }, "downloads": -1, "filename": "odenlls-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "2d6a1902ec34ec2b8b866cb5ebb5aa97", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 10680, "upload_time": "2018-01-04T21:27:34", "url": "https://files.pythonhosted.org/packages/2b/51/ded120ed3129030bee0c6fa96a0f9c3a5dc950ca122f3b462f20f6386888/odenlls-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "bc348b8925aa893ffaa9be742ddd0b5e", "sha256": "fb7d5facd42f6013889e17412de4376f1bd43b15ec09496d352f1944855eaa5b" }, "downloads": -1, "filename": "odenlls-0.1.0.tar.gz", "has_sig": false, "md5_digest": "bc348b8925aa893ffaa9be742ddd0b5e", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8506, "upload_time": "2018-01-04T21:27:36", "url": "https://files.pythonhosted.org/packages/9c/2a/4d8e3bfe7889decc1844f6467a555e7da65789ac42b583e2cf3305bd0846/odenlls-0.1.0.tar.gz" } ] }