{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "Fitting SciKit\n==============\n\nA framework for fitting functions to data with SciPy which unifies the various\navailable interpolation methods and provides a common interface to them based\non the following simple methods:\n\n- ``Fitter.__init__(p)``: set parameters of interpolation function, e.g. polynomial degree\n- ``Fitter.fit(x, y)``: fit given input-output data\n- ``Fitter.__call__(x)`` or ``Fitter.eval(x)``: evaluate function on new input data\n\nEach interpolation routine falls in one of two categories: scatter fitting or\ngrid fitting. They share the same interface, only differing in the definition\nof input data ``x``.\n\nScatter-fitters operate on unstructured scattered input data (i.e. not on a\ngrid). The input data consists of a sequence of ``x`` coordinates and a sequence\nof corresponding ``y`` data, where the order of the ``x`` coordinates does not\nmatter and their location can be arbitrary. The ``x`` coordinates can have an\narbritrary dimension (although most classes are specialised for 1-D or 2-D\ndata). If the dimension is bigger than 1, the coordinates are provided as an\narray of column vectors. These fitters have ``ScatterFit`` as base class.\n\nGrid-fitters operate on input data that lie on a grid. The input data consists\nof a sequence of x-axis tick sequences and the corresponding array of ``y``\ndata. These fitters have ``GridFit`` as base class.\n\nThe module is organised as follows:\n\nScatter fitters\n---------------\n\n- ``ScatterFit``: Abstract base class for scatter fitters\n- ``LinearLeastSquaresFit``: Fit linear regression model to data using SVD\n- ``Polynomial1DFit``: Fit polynomial to 1-D data\n- ``Polynomial2DFit``: Fit polynomial to 2-D data\n- ``PiecewisePolynomial1DFit``: Fit piecewise polynomial to 1-D data\n- ``Independent1DFit``: Interpolate N-dimensional matrix along given axis\n- ``Delaunay2DScatterFit``: Interpolate scalar function of 2-D data, based on\n Delaunay triangulation and cubic / linear interpolation\n- ``NonLinearLeastSquaresFit``: Fit a generic function to data, based on\n non-linear least squares optimisation\n- ``GaussianFit``: Fit Gaussian curve to multi-dimensional data\n- ``Spline1DFit``: Fit a B-spline to 1-D data\n- ``Spline2DScatterFit``: Fit a B-spline to scattered 2-D data\n- ``RbfScatterFit``: Do radial basis function (RBF) interpolation\n\nGrid fitters\n------------\n\n- ``GridFit``: Abstract base class for grid fitters\n- ``Spline2DGridFit``: Fit a B-spline to 2-D data on a rectangular grid\n\nHelper functions\n----------------\n\n- ``squash``: Flatten array, but not necessarily all the way to a 1-D array\n- ``unsquash``: Restore an array that was reshaped by ``squash``\n- ``sort_grid``: Ensure that the coordinates of a rectangular grid are in\n ascending order\n- ``desort_grid``: Undo the effect of ``sort_grid``\n- ``vectorize_fit_func``: Factory that creates vectorised version of\n function to be fitted to data\n- ``randomise``: Randomise fitted function parameters by resampling residuals\n\nSource\n------\nhttps://github.com/ska-sa/scikits.fitting\n\nContact\n-------\nLudwig Schwardt \n\n\nHistory\n=======\n\n0.7 (2018-09-20)\n----------------\n\n* Python 3 support\n* Clean up tests and more flake8 (line lengths)\n\n0.6 (2016-12-05)\n----------------\n\n* Fix pip installation, clean up setup procedure, flake8 and add README\n* PiecewisePolynomial1DFit updated to work with scipy 0.18.0\n* Delaunay2DScatterFit now based on scipy.interpolate.griddata, which is\n orders of magnitude faster, more robust and smoother. Its default\n interpolation changed from 'nn' (natural neighbours - no longer available)\n to 'cubic'.\n* Delaunay2DGridFit removed as there is no equivalent anymore\n\n0.5.1 (2012-10-29)\n------------------\n\n* Use proper name for np.linalg.LinAlgError\n\n0.5 (2011-09-26)\n----------------\n\n* Initial release of scikits.fitting", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/ska-sa/scikits.fitting", "keywords": "", "license": "Modified BSD", "maintainer": "Ludwig Schwardt", "maintainer_email": "ludwig@ska.ac.za", "name": "scikits.fitting", "package_url": "https://pypi.org/project/scikits.fitting/", "platform": "OS Independent", "project_url": "https://pypi.org/project/scikits.fitting/", "project_urls": { "Homepage": "https://github.com/ska-sa/scikits.fitting" }, "release_url": "https://pypi.org/project/scikits.fitting/0.7/", "requires_dist": null, "requires_python": ">=2.7,!=3.0,!=3.1,!=3.2", "summary": "Framework for fitting functions to data with SciPy", "version": "0.7" }, "last_serial": 4776646, "releases": { "0.5": [ { "comment_text": "", "digests": { "md5": "f998863bb264166fbb270b44aa928798", "sha256": "dc97336d3a694b0974c2123a303c331bd94659db1a03c73b31e0c7d55653d00a" }, "downloads": -1, "filename": "scikits.fitting-0.5.tar.gz", "has_sig": true, "md5_digest": "f998863bb264166fbb270b44aa928798", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 32961, "upload_time": "2011-09-26T12:19:46", "url": "https://files.pythonhosted.org/packages/ec/44/3fe34149ab78b36244c9e8aaedd42284c54d3076707de2942ab5b2b56de3/scikits.fitting-0.5.tar.gz" } ], "0.5.1": [ { "comment_text": "", "digests": { "md5": "fbe50d41b1e31ef6542129d405448150", "sha256": "4be8f15f7d77f4c8aa16d7e7cfa344f96ad3d5393ea8d8ff1fa4ae2ae7ea362d" }, "downloads": -1, "filename": "scikits.fitting-0.5.1.tar.gz", "has_sig": true, "md5_digest": "fbe50d41b1e31ef6542129d405448150", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 32982, "upload_time": "2012-10-29T08:21:52", "url": "https://files.pythonhosted.org/packages/1d/16/821c1a51a3138333cb8f2372513a597e2ecea3dec4aeb2d9275b0b9494de/scikits.fitting-0.5.1.tar.gz" } ], "0.6": [ { "comment_text": "", "digests": { "md5": "badf0dbc9c9982038f7be9cd597c39c4", "sha256": "f764ca61530de5b3a012ff0f20553847708c545f05d5632bf9f27be230c79f48" }, "downloads": -1, "filename": "scikits.fitting-0.6-py2-none-any.whl", "has_sig": true, "md5_digest": "badf0dbc9c9982038f7be9cd597c39c4", "packagetype": "bdist_wheel", "python_version": "py2", "requires_python": null, "size": 54079, "upload_time": "2016-12-05T14:39:17", "url": "https://files.pythonhosted.org/packages/80/49/e830b61648a3d9ee32a52db9260ae27dd33367d4fd55076dda141854e100/scikits.fitting-0.6-py2-none-any.whl" }, { "comment_text": "", "digests": { "md5": "5168eb4a8d53cd9fc091bd5996fd9a4b", "sha256": "6d2f2c400a7ea3fac6338c2675bfded47ece984fb903f8c256ec0dcac2c4613b" }, "downloads": -1, "filename": "scikits.fitting-0.6.tar.gz", "has_sig": true, "md5_digest": "5168eb4a8d53cd9fc091bd5996fd9a4b", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 34013, "upload_time": "2016-12-05T14:39:28", "url": "https://files.pythonhosted.org/packages/7b/66/0ddf2e8976ddd8dbac94b03464a511de0bb5b7caf1cee264bcffe909bbf9/scikits.fitting-0.6.tar.gz" } ], "0.7": [ { "comment_text": "", "digests": { "md5": "4789e9f83d0ffcb294f275f36280f6cc", "sha256": "a16b80726be5393d291e7c6244471b185b90a9d3d75aab3422f1e34ec89dfebe" }, "downloads": -1, "filename": "scikits.fitting-0.7-py2.py3-none-any.whl", "has_sig": true, "md5_digest": "4789e9f83d0ffcb294f275f36280f6cc", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": ">=2.7,!=3.0,!=3.1,!=3.2", "size": 52266, "upload_time": "2018-09-20T12:23:12", "url": "https://files.pythonhosted.org/packages/0a/6f/110af352e1b7771518c1e0a3b2e3cbd965fc648b2e004e1b859047815464/scikits.fitting-0.7-py2.py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "cf97f3e706856c81636d6eb02d0badbe", "sha256": "caf68be8016891cf78766585e5ed86513aa718cdcf89a710fae43ccac1ed253c" }, "downloads": -1, "filename": "scikits.fitting-0.7.tar.gz", "has_sig": true, "md5_digest": "cf97f3e706856c81636d6eb02d0badbe", "packagetype": "sdist", "python_version": "source", "requires_python": ">=2.7,!=3.0,!=3.1,!=3.2", "size": 35085, "upload_time": "2018-09-20T12:22:56", "url": "https://files.pythonhosted.org/packages/62/47/a540f4cf6a7e1f6014121e86a53e9d3d15938f85ffcf13781aeb0368546d/scikits.fitting-0.7.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "4789e9f83d0ffcb294f275f36280f6cc", "sha256": "a16b80726be5393d291e7c6244471b185b90a9d3d75aab3422f1e34ec89dfebe" }, "downloads": -1, "filename": "scikits.fitting-0.7-py2.py3-none-any.whl", "has_sig": true, "md5_digest": "4789e9f83d0ffcb294f275f36280f6cc", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": ">=2.7,!=3.0,!=3.1,!=3.2", "size": 52266, "upload_time": "2018-09-20T12:23:12", "url": "https://files.pythonhosted.org/packages/0a/6f/110af352e1b7771518c1e0a3b2e3cbd965fc648b2e004e1b859047815464/scikits.fitting-0.7-py2.py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "cf97f3e706856c81636d6eb02d0badbe", "sha256": "caf68be8016891cf78766585e5ed86513aa718cdcf89a710fae43ccac1ed253c" }, "downloads": -1, "filename": "scikits.fitting-0.7.tar.gz", "has_sig": true, "md5_digest": "cf97f3e706856c81636d6eb02d0badbe", "packagetype": "sdist", "python_version": "source", "requires_python": ">=2.7,!=3.0,!=3.1,!=3.2", "size": 35085, "upload_time": "2018-09-20T12:22:56", "url": "https://files.pythonhosted.org/packages/62/47/a540f4cf6a7e1f6014121e86a53e9d3d15938f85ffcf13781aeb0368546d/scikits.fitting-0.7.tar.gz" } ] }