{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\nFit with Data in a pandas DataFrame\n===================================\n\nSimple example demonstrating how to read in the data using pandas and supply\nthe elements of the DataFrame from lmfit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom lmfit.models import LorentzianModel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "read the data into a pandas DataFrame, and use the 'x' and 'y' columns:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dframe = pd.read_csv('peak.csv')\n\nmodel = LorentzianModel()\nparams = model.guess(dframe['y'], x=dframe['x'])\n\nresult = model.fit(dframe['y'], params, x=dframe['x'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "and gives the plot and fitting results below:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "result.plot_fit()\nplt.show()\n\nprint(result.fit_report())"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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