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    "info": {
        "author": "Simon Reinhardt",
        "author_email": "simon.reinhardt@physik.uni-regensburg.de",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "curve_fit.annealing\n===============================\n\nMost curve fitting algorithms rely on local optimization routines. These demand good estimates of the fit parameters.\n\nInstead, this module allows to use  **global optimization** routines of\nscipy.optimize_ to minimize the squared deviation function.\n\nInstallation\n------------------------\n\nThis module can be installed from PyPI ::\n\n    pip3 install curve_fit.annealing\n\nExample\n---------------\n\nLet us fit a beat signal with two sinus functions, with a total of 6 free parameters.\n\nBy default, the ``curve_fit`` function of this module will use the scipy.optimize.dual_annealing_ method to find the global optimum of the curve fitting problem. The dual annealing algorithm requires bounds for the fitting parameters.\nOther global optimization methods like scipy.optimize.basinhopping_ require an initial guess of the parameters instead.\n\n\n::\n\n import numpy as np\n from matplotlib import pyplot as plt\n from curve_fit import annealing\n\n def f(x,p):\n       # Sum of two sinus functions\n       return p[0]*np.sin(p[1]*x + p[2]) + p[3]*np.sin(p[4]*x+p[5])\n\n\n   xdata = np.linspace(-100,100,1000)\n   ydata = f(xdata, [1, 1, 0, 1, 0.9, 0])\n\n   plt.plot(xdata, ydata, label='data')\n   bounds=[[0,2],[0,2],[0,2*np.pi],[0,2],[0,2],[0,2*np.pi]]\n\n   result = annealing.curve_fit(f, xdata, ydata, bounds=bounds)\n\n   p_opt = result.x # optimal fit parameters\n   ydata_res = f(xdata, p_opt)\n   plt.plot(xdata, ydata_res, label='fit')\n   plt.legend()\n   plt.grid()\n\n   plt.show()\n\n\nOr use scipy.optimize.basinhopping_ ::\n\n result = annealing.curve_fit(f, xdata, ydata, method='basinhopping', x0=np.zeros(6))\n\n\nAPI\n-----\n\n``curve_fit(f, xdata, ydata, [method='dual_annealing', args, kwargs])``\n\nFit function ``f`` to data with selectable optimization method\nfrom ``scipy.optimize``.\n\nParameters:\n f: callable\n  The model function, ``f(xdata, p)``. The second argument holds the\n  fitting parameters.\n xdata : array_like or object\n  The independent variable where the data is measured.\n  Should usually be an M-length sequence or an (k,M)-shaped array for\n  functions with k predictors, but can actually be any object.\n ydata : array_like\n  The dependent data, a length M array - nominally ``f(xdata, ...)``.\n method : str\n  scipy.optimize method to use for non-linear least squares minimization.\n  Default is 'dual_annealing'.\n args, kwargs : tuple and dict, optional\n  Additional arguments passed to the optimization method.\n\nReturns:\n Return ``OptimizeResult`` object. The ``x`` attribute holds the fitting\n parameters. \n\n\n.. _scipy.optimize: https://docs.scipy.org/doc/scipy/reference/optimize.html\n.. _scipy.optimize.dual_annealing: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.dual_annealing.html#scipy.optimize.dual_annealing\n.. _scipy.optimize.basinhopping: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.basinhopping.html#scipy.optimize.basinhopping\n\n\n",
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