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        "%matplotlib inline"
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      "source": [
        "\nFit Using differential_evolution Algorithm\n==========================================\n\nThis example compares the \"leastsq\" and \"differential_evolution\" algorithms on\na fairly simple problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport lmfit\n\nnp.random.seed(2)\nx = np.linspace(0, 10, 101)\n\n# Setup example\ndecay = 5\noffset = 1.0\namp = 2.0\nomega = 4.0\n\ny = offset + amp*np.sin(omega*x) * np.exp(-x/decay)\nyn = y + np.random.normal(size=y.size, scale=0.450)\n\n\ndef resid(params, x, ydata):\n    decay = params['decay'].value\n    offset = params['offset'].value\n    omega = params['omega'].value\n    amp = params['amp'].value\n\n    y_model = offset + amp * np.sin(x*omega) * np.exp(-x/decay)\n    return y_model - ydata\n\n\nparams = lmfit.Parameters()\nparams.add('offset', 2.0, min=0, max=10.0)\nparams.add('omega', 3.3, min=0, max=10.0)\nparams.add('amp', 2.5, min=0, max=10.0)\nparams.add('decay', 1.0, min=0, max=10.0)\n\no1 = lmfit.minimize(resid, params, args=(x, yn), method='leastsq')\nprint(\"# Fit using leastsq:\")\nlmfit.report_fit(o1)\n\no2 = lmfit.minimize(resid, params, args=(x, yn), method='differential_evolution')\nprint(\"\\n\\n# Fit using differential_evolution:\")\nlmfit.report_fit(o2)\n\nplt.plot(x, yn, 'ko', lw=2)\nplt.plot(x, yn+o1.residual, 'r-', lw=2)\nplt.plot(x, yn+o2.residual, 'b--', lw=2)\nplt.legend(['data', 'leastsq', 'diffev'], loc='upper left')\nplt.show()"
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