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    "info": {
        "author": "Geish Miladinovic",
        "author_email": "npdata@bigpond.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "===========\nScikit-Qfit\n===========\n\n\nScikit-Qfit is a package that supports fitting gradient orthogonal Q-polynomials to 2D data.\n\n\nDescription\n===========\n\nThis package implements the algorithm described in:\n\n* G W Fobes, `Fitting freeform shapes with orthogonal bases <https://www.osapublishing.org/oe/abstract.cfm?uri=oe-21-16-19061>`_, Opt. Express 21, 19061-19081 (2013)\n\nAdditional project documentation and references for Q- freeform polynomials can be found at:\n`<http://scikit-qfit.readthedocs.org/>`_.\n\nInstallation\n============\n\nThe package can be installed through pip:\n\n  > pip install scikit-qfit\n\nUsage\n=====\n\nTo generate a Q-freeform spectrum from a data map, pass the coordinate arrays x and y and 2-D array of\ndata with shape (x.size,y.size) as arguments to the method qspec(). The azimuthal and radial spectrum\nlimits are set by m_max and n_max respectively.\n\n  >>> import skqfit.qspectre as qf\n  >>> ...\n  >>> qspec = qf.qspec(x, y, zmap, m_max=500, n_max=500)\n\nTo observe the contribution of spectrum components it is necessary to use the sine and cosine elements in the forward processing step.\nAfter modifying the spectrum, an inverse process creates a data map along with the x and y derivatives.\n\n  >>> qs = qf.QSpectrum()\n  >>> qs.data_map(x, y, zmap)\n  >>> a_nm, b_nm = qs.q_fit(mmax, nmax)\n  >>> ... # modify spectrum\n  >>> nmap, dfdx, dfdy = qs.build_map(x, y, a_nm, b_nm, inc_deriv=True)\n\nA 1D trace across the fitted data can also be generated by passing a set of xv, yv arrays that represent the\n(x, y) coordinates.\n\n  >>> zv, dfdx, dfdy = qs.build_profile(xv, yv, a_nm, b_nm, inc_deriv=True)\n\n\nLimitations\n===========\n\nThe Jacobian polynomial calculation required by the algorithm can generate very large numbers which limits spectral resolution\nto a maximum of 1500 for the radial and azimuthal terms (n, m). Using values greater than this can lead to an overflow.\nIf the nominal spectral resolution for a datamap is greater than this limit the data should be filtered prior to processing\nto avoid aliasing.\n\n\nNote that the process is an N^2 algorithm, so doubling the number of radial and azimuthal terms takes four times as long.\n\n\nDependencies\n============\n\nThe package requires numpy and scipy and was tested on Linux with:\n\n * Python 2.7.6\n * numpy 1.14.5\n * scipy 1.0.0\n\nThese python, numpy and scipy versions were available on the Ubuntu 18.04 Linux release at the time of testing.\nThe package has been informally tested with python 3.6 successfully and I am not aware of reason it should not work with\nlater releases of these packages.\n\nAcknowledge\n===========\n\n* Greg Forbes for support with the implementation and validation of the algorithm.\n* Andreas Beutler, `Mahr GmbH <http://www.mahr.com/>`_, for choosing to make this work available as open source.\n\nNote\n====\n\n\nThis project has been set up using PyScaffold 3.2.1. For details and usage\ninformation on PyScaffold see https://pyscaffold.org/.\n\n\n",
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        "license": "mit",
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