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        "author": "Martin Roelfs",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": ".. image:: https://zenodo.org/badge/24005390.svg\n   :target: https://zenodo.org/badge/latestdoi/24005390\n.. image:: https://coveralls.io/repos/github/tBuLi/symfit/badge.svg?branch=master\n   :target: https://coveralls.io/github/tBuLi/symfit?branch=master\n\n\nPlease cite this DOI if ``symfit`` benefited your publication. Building this has been a lot of work, and as young researchers your citation means a lot to us.\nMartin Roelfs & Peter C Kroon, symfit. doi:10.5281/zenodo.1133336\n\nDocumentation\n=============\nhttp://symfit.readthedocs.org\n\nProject Goals\n=============\n\nThe goal of this project is simple: to make fitting in Python pythonic.\nWhat does pythonic fitting look like? Well, there's a simple test. If I can\ngive you pieces of example code and don't have to use any additional words to\nexplain what it does, it's pythonic.\n\n.. code-block:: python\n\n  from symfit import parameters, variables, Fit, Model\n  import numpy as np\n   \n  xdata = np.array([1.0, 2.0, 3.0, 4.0, 5.0])\n  ydata = np.array([2.3, 3.3, 4.1, 5.5, 6.7])\n  yerr = np.array([0.1, 0.1, 0.1, 0.1, 0.1])\n  \n  a, b = parameters('a, b')\n  x, y = variables('x, y')\n  model = Model({y: a * x + b})\n  \n  fit = Fit(model, x=xdata, y=ydata, sigma_y=yerr)\n  fit_result = fit.execute()\n\nCool right? So now that we have done a fit, how do we use the results?\n\n.. code-block:: python\n\n  import matplotlib.pyplot as plt\n  \n  yfit = model(x=xdata, **fit_result.params)[y]\n  plt.plot(xdata, yfit)\n  plt.show()\n\n.. figure:: http://symfit.readthedocs.org/en/latest/_images/linear_model_fit.png\n  :width: 600px\n  :alt: Linear Fit\n\nNeed I say more? How about I let another code example do the talking?\n\n.. code-block:: python\n\n  from symfit import parameters, Fit, Equality, GreaterThan\n  \n  x, y = parameters('x, y')\n  model = 2 * x * y + 2 * x - x**2 - 2 * y**2\n  constraints = [\n      Equality(x**3, y),\n      GreaterThan(y, 1),\n  ]\n  \n  fit = Fit(- model, constraints=constraints)\n  fit_result = fit.execute()\n\nI know what you are thinking. \"What if I need to fit to a system of Ordinary Differential Equations?\"\n\n.. code-block:: python\n\n  from symfit import variables, Parameter, ODEModel, Fit, D\n  import numpy as np\n  \n  tdata = np.array([10, 26, 44, 70, 120])\n  adata = 10e-4 * np.array([44, 34, 27, 20, 14])\n          \n  a, b, t = variables('a, b, t')\n  k = Parameter('k', 0.1)\n  \n  model_dict = {\n      D(a, t): - k * a**2,\n      D(b, t): k * a**2,\n  }\n  \n  ode_model = ODEModel(model_dict, initial={t: 0.0, a: 54 * 10e-4, b: 0.0})\n  \n  fit = Fit(ode_model, t=tdata, a=adata, b=None)\n  fit_result = fit.execute()\n\nFor more fitting delight, check the docs at http://symfit.readthedocs.org.",
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