{ "info": { "author": "Exneyder A. Montoya-Araque & Ludger O. Suarez-Burgoa", "author_email": "eamontoyaa@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Programming Language :: Python :: 3.6" ], "description": "===============\n``jelinekstat``\n===============\n\n\n.. image:: https://img.shields.io/pypi/v/jelinekstat.svg\n :target: https://pypi.python.org/pypi/jelinekstat\n\n.. image:: https://readthedocs.org/projects/jelinekstat/badge/?version=latest\n :target: https://jelinekstat.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\nApplication software in **Python 3** to apply the statistical proposal of\n`Jel\u00ednek (1978) `_ for a sample of several\nsecond-order tensors in order to obtain the mean tensor of the sample, its\nprincipal values with their confidence intervals, and the principal directions\nwith their confidence regions.\n\nThis application program is able to plot the summary of the statistical model\ndescribed above in a stereographic projection for a better understanding of the\noutcomes. Provided that, the next picture represents the aim of ``jelinekstat``.\n\n.. figure:: https://rawgit.com/eamontoyaa/jelinekstat/master/docs/otherFiles/my_image.svg\n :alt: Outcome plot example\n\nFeatures\n--------\n\n* `Documentation `_\n* `PyPI `_\n* `GitHub `_\n* Open source and free software: `BSD-2-Clause `_.\n\n\nRequirements\n------------\n\nThe code was written in Python 3. The packages `numpy `_,\n`scipy `_, `matplotlib `_\nand `mplstereonet `_ are\nrequired for using ``jelinekstat``. All of them are\ndownloadable from the PyPI repository by opening a terminal and typing the\nfollowing code lines:\n\n\n::\n\n pip install numpy\n pip install scipy\n pip install matplotlib\n pip install mplstereonet\n\n\nInstallation\n------------\n\n\nTo install ``jelinekstat`` open a terminal and type:\n\n::\n\n pip install jelinekstat\n\n\nExample\n-------\n\nTo produce the plot shown above execute the following script\n\n::\n\n from jelinekstat.jelinekstat import tensorStat\n\n # Input data\n sample = [[1.02327, 1.02946, 0.94727, -0.01495, -0.03599, -0.05574],\n [1.02315, 1.01803, 0.95882, -0.00924, -0.02058, -0.03151],\n [1.02801, 1.03572, 0.93627, -0.03029, -0.03491, -0.06088],\n [1.02775, 1.00633, 0.96591, -0.01635, -0.04148, -0.02006],\n [1.02143, 1.01775, 0.96082, -0.02798, -0.04727, -0.02384],\n [1.01823, 1.01203, 0.96975, -0.01126, -0.02833, -0.03649],\n [1.01486, 1.02067, 0.96446, -0.01046, -0.01913, -0.03864],\n [1.04596, 1.01133, 0.94271, -0.01660, -0.04711, -0.03636]]\n confLevel = 0.95\n\n # Performing the calculation all in one function.\n jelinekStatsSummary, stereonetPlot = tensorStat(\n sample, confLevel=0.95, want2plot=True, plotName='tes', ext='pdf')\n stereonetPlot.show()\n\n\nReferences\n----------\nJel\u00ednek, V (1978). Statistical processing of anisotropy of magnetic\nsusceptibility measured on group of specimens. Studia Geophysica et Geodaetica,\n22 (1), pp. 50-62.\n\n\n\n\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-07-08)\n------------------\n\n* First release on PyPI.\n\n0.1.1 (2018-07-12)\n------------------\n\n* Documentation and the examples directory were completely adjusted.", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/eamontoyaa/jelinekstat", "keywords": "Anisotropy of Magnetic Susceptibility,Jel\u00ednek,tensor,statistics,Python,application software", "license": "BSD 2-Clause License", "maintainer": "", "maintainer_email": "", "name": "jelinekstat", "package_url": "https://pypi.org/project/jelinekstat/", "platform": "", "project_url": "https://pypi.org/project/jelinekstat/", "project_urls": { "Homepage": "https://github.com/eamontoyaa/jelinekstat" }, "release_url": "https://pypi.org/project/jelinekstat/0.1.1/", "requires_dist": null, "requires_python": "", "summary": "Application software for applying the Second-order tensor statistical proposal of Jel\u00ednek (1978).", "version": "0.1.1" }, "last_serial": 4056610, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "89c35829d468baf320d4f85da1b4c76d", "sha256": "035ec0c0b3f0bcffc046e10a2efac4e6ca718c9c8936a92f462b3f58a07d37be" }, "downloads": -1, "filename": "jelinekstat-0.1.0.tar.gz", "has_sig": false, "md5_digest": "89c35829d468baf320d4f85da1b4c76d", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 20636, "upload_time": "2018-07-09T02:36:57", "url": "https://files.pythonhosted.org/packages/ab/b4/a1d493824fd0d758ca9cf7a78efb7ae6e2d6f99e3c758c7db5bef7c685cc/jelinekstat-0.1.0.tar.gz" } ], "0.1.1": [ { "comment_text": "", "digests": { "md5": "eb7e335e4252c21bda4c057c706e73ec", "sha256": "ec2de4b2aa91f928ebcb9c86cdfff94f9d3922b001ec9cb8f17322ae67a0af5b" }, "downloads": -1, "filename": "jelinekstat-0.1.1.tar.gz", "has_sig": false, "md5_digest": "eb7e335e4252c21bda4c057c706e73ec", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 20511, "upload_time": "2018-07-13T04:04:31", "url": "https://files.pythonhosted.org/packages/dc/5d/1a3f0798e6485222d743541602e125f8a4b3fbb3e034b3db35c3a3a0d333/jelinekstat-0.1.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "eb7e335e4252c21bda4c057c706e73ec", "sha256": "ec2de4b2aa91f928ebcb9c86cdfff94f9d3922b001ec9cb8f17322ae67a0af5b" }, "downloads": -1, "filename": "jelinekstat-0.1.1.tar.gz", "has_sig": false, "md5_digest": "eb7e335e4252c21bda4c057c706e73ec", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 20511, "upload_time": "2018-07-13T04:04:31", "url": "https://files.pythonhosted.org/packages/dc/5d/1a3f0798e6485222d743541602e125f8a4b3fbb3e034b3db35c3a3a0d333/jelinekstat-0.1.1.tar.gz" } ] }