{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\ndoc_parameters_valuesdict.py\n============================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "##\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n##\n# <examples/doc_parameters_valuesdict.py>\nimport numpy as np\n\nfrom lmfit import Minimizer, Parameters, report_fit\n\n# create data to be fitted\nx = np.linspace(0, 15, 301)\ndata = (5.0 * np.sin(2.0*x - 0.1) * np.exp(-x*x*0.025) +\n        np.random.normal(size=x.size, scale=0.2))\n\n\n# define objective function: returns the array to be minimized\ndef fcn2min(params, x, data):\n    \"\"\"Model a decaying sine wave and subtract data.\"\"\"\n    v = params.valuesdict()\n\n    model = v['amp'] * np.sin(x * v['omega'] + v['shift']) * np.exp(-x*x*v['decay'])\n    return model - data\n\n\n# create a set of Parameters\nparams = Parameters()\nparams.add('amp', value=10, min=0)\nparams.add('decay', value=0.1)\nparams.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2)\nparams.add('omega', value=3.0)\n\n# do fit, here with the default leastsq algorithm\nminner = Minimizer(fcn2min, params, fcn_args=(x, data))\nresult = minner.minimize()\n\n# calculate final result\nfinal = data + result.residual\n\n# write error report\nreport_fit(result)\n\n# try to plot results\ntry:\n    import matplotlib.pyplot as plt\n    plt.plot(x, data, 'k+')\n    plt.plot(x, final, 'r')\n    plt.show()\nexcept ImportError:\n    pass\n# <end of examples/doc_parameters_valuesdict.py>"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}