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    "info": {
        "author": "Jose Borreguero",
        "author_email": "jose@borreguero.com",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "dsfinterp\n=========\n\nSummary\n=======\nCubic Spline Interpolationf of Dynamics Structure Factors S(Q,E,f) as a function of a parameter \"f\".\n\nDetails\n=======\n\nGiven a discrete series of Dynamics Structure Factors S_i(Q,E) that\ndepends on an extra parameter \"f\", S_i(Q,E) = S(Q,E,f_i), construct\nand object that returns S(Q,E) for any value of f in the range \n[min({f_i} }, max( {f_i} )].\n\nParameter \"f\" can be an environment variable such as Temperature or an\n\"internal\" parameter, such as a component of a force-field.\n\nThis interpolator is geared towards determination of S(Q,E,f) using a\nseries { S_i(Q,E) } computed from molecular dynamics simulations,\nalthough the series could be generated from experiments, too.\n\nIf the { S_i(Q,E) } do not have associated errors, as it is the case\nwhen computed from simulations, an error will be estimated by a\nrunning regression of the S_i(Q,E) versus f. Thus, the object will\nreturn S(Q,E) and and associated error(Q,E) any value of f in the\nallowed range.",
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        "keywords": "AMBER,mdend,energy,molecular dynamics",
        "license": "UNKNOWN",
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        "summary": "Cubic Spline Interpolation of Dynamics Structure Factors",
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