XPS_peak_fitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit.models import LinearModel, VoigtModel\n",
    "from lmfit import minimize, Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    (Model(voigt, prefix='vo1_') + Model(linear, prefix='lin_'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 101\n",
      "    # data points      = 190\n",
      "    # variables        = 5\n",
      "    chi-square         = 64418185.249\n",
      "    reduced chi-square = 348206.407\n",
      "[[Variables]]\n",
      "    lin_slope:       57.5318499 +/- 0.826551 (1.44%) (init= 57.40641)\n",
      "    lin_intercept:   13218.7154 +/- 177.9060 (1.35%) (init= 21876.16)\n",
      "    vo1_amplitude:   1.7925e+06 +/- 1.41e+04 (0.79%) (init= 40000)\n",
      "    vo1_sigma:       12.5243071 +/- 0.094618 (0.76%) (init= 50)\n",
      "    vo1_center:      194.451697 +/- 0.104731 (0.05%) (init= 160)\n",
      "    vo1_gamma:       12.5243071 +/- 0.094618 (0.76%)  == 'vo1_sigma'\n",
      "    vo1_fwhm:        45.1039127 +/- 0.340750 (0.76%)  == '3.6013100*vo1_sigma'\n",
      "[[Correlations]] (unreported correlations are <  0.500)\n",
      "    C(lin_slope, lin_intercept)  = -0.904 \n",
      "    C(vo1_amplitude, vo1_sigma)  =  0.814 \n",
      "\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093aaad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[8,4])\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "with open(\"/Users/stanpol/Dropbox/Stan-Marina/Mo3p.txt\") as f:\n",
    "\tdata = np.loadtxt(f, unpack=True)\n",
    "x = np.array(range(len(data))[100:290])\n",
    "y = np.array(data[::-1][100:290])\n",
    "\n",
    "background  = LinearModel(prefix='lin_')\n",
    "pars = background.guess(y, x=x)\n",
    "\n",
    "voigt1  = VoigtModel(prefix='vo1_')\n",
    "pars.update(voigt1.make_params())\n",
    "\n",
    "pars['vo1_center'].set(160, min=100, max=250)\n",
    "pars['vo1_sigma'].set(50, min=3)\n",
    "pars['vo1_amplitude'].set(40000, min=20000)\n",
    "\n",
    "mod = voigt1 + background\n",
    "\n",
    "init = mod.eval(pars, x=x)\n",
    "plt.plot(x, y)\n",
    "\n",
    "out = mod.fit(y, pars, x=x)\n",
    "\n",
    "print(out.fit_report(min_correl=0.5))\n",
    "\n",
    "plt.plot(x, out.best_fit, 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    ((Model(voigt, prefix='vo1_') + Model(voigt, prefix='vo2_')) + Model(linear, prefix='lin_'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 136\n",
      "    # data points      = 350\n",
      "    # variables        = 8\n",
      "    chi-square         = 211169839.476\n",
      "    reduced chi-square = 617455.671\n",
      "[[Variables]]\n",
      "    lin_slope:       43.2349827 +/- 0.638470 (1.48%) (init= 28.83002)\n",
      "    lin_intercept:   15452.3657 +/- 186.3205 (1.21%) (init= 26992.33)\n",
      "    vo1_amplitude:   1.8563e+06 +/- 1.83e+04 (0.98%) (init= 40000)\n",
      "    vo1_sigma:       12.8674132 +/- 0.123878 (0.96%) (init= 50)\n",
      "    vo1_center:      195.022866 +/- 0.135062 (0.07%) (init= 160)\n",
      "    vo1_gamma:       12.8674132 +/- 0.123878 (0.96%)  == 'vo1_sigma'\n",
      "    vo1_fwhm:        46.3395438 +/- 0.446123 (0.96%)  == '3.6013100*vo1_sigma'\n",
      "    vo2_amplitude:   9.7433e+05 +/- 2.02e+04 (2.07%) (init= 40000)\n",
      "    vo2_sigma:       12.9907242 +/- 0.251892 (1.94%) (init= 50)\n",
      "    vo2_center:      367.516533 +/- 0.260926 (0.07%) (init= 360)\n",
      "    vo2_gamma:       12.9907242 +/- 0.251892 (1.94%)  == 'vo2_sigma'\n",
      "    vo2_fwhm:        46.7836251 +/- 0.907141 (1.94%)  == '3.6013100*vo2_sigma'\n",
      "[[Correlations]] (unreported correlations are <  0.500)\n",
      "    C(lin_slope, lin_intercept)  = -0.885 \n",
      "    C(vo2_amplitude, vo2_sigma)  =  0.825 \n",
      "    C(vo1_amplitude, vo1_sigma)  =  0.805 \n",
      "    C(lin_intercept, vo1_amplitude)  = -0.661 \n",
      "    C(lin_slope, vo2_amplitude)  = -0.542 \n",
      "    C(lin_intercept, vo1_sigma)  = -0.507 \n",
      "\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1098b6890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[8,4])\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "with open(\"/Users/stanpol/Dropbox/Stan-Marina/Mo3p.txt\") as f:\n",
    "\tdata = np.loadtxt(f, unpack=True)\n",
    "x = np.array(range(len(data))[100:450])\n",
    "y = np.array(data[::-1][100:450])\n",
    "\n",
    "background  = LinearModel(prefix='lin_')\n",
    "pars = background.guess(y, x=x)\n",
    "\n",
    "voigt1  = VoigtModel(prefix='vo1_')\n",
    "pars.update(voigt1.make_params())\n",
    "\n",
    "pars['vo1_center'].set(160, min=100, max=250)\n",
    "pars['vo1_sigma'].set(50, min=3)\n",
    "pars['vo1_amplitude'].set(40000, min=20000)\n",
    "\n",
    "\n",
    "voigt2  = VoigtModel(prefix='vo2_')\n",
    "pars.update(voigt2.make_params())\n",
    "\n",
    "pars['vo2_center'].set(360, min=320, max=400)\n",
    "pars['vo2_sigma'].set(50, min=3)\n",
    "pars['vo2_amplitude'].set(40000, min=20000)\n",
    "\n",
    "\n",
    "\n",
    "mod = voigt1 + voigt2 + background\n",
    "\n",
    "init = mod.eval(pars, x=x)\n",
    "plt.plot(x, y)\n",
    "\n",
    "out = mod.fit(y, pars, x=x)\n",
    "\n",
    "print(out.fit_report(min_correl=0.5))\n",
    "\n",
    "plt.plot(x, out.best_fit, 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    (Model(skewed_gaussian, prefix='sg1_') + Model(linear, prefix='lin_'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 81\n",
      "    # data points      = 70\n",
      "    # variables        = 6\n",
      "    chi-square         = 164653410.033\n",
      "    reduced chi-square = 2572709.532\n",
      "[[Variables]]\n",
      "    lin_slope:       133.476982 +/- 11.05987 (8.29%) (init=-106.6406)\n",
      "    lin_intercept:   33745.3738 +/- 874.7279 (2.59%) (init= 79522.97)\n",
      "    sg1_amplitude:   1.9526e+06 +/- 1.75e+04 (0.90%) (init= 140000)\n",
      "    sg1_sigma:       13.1068909 +/- 0.151416 (1.16%) (init= 10)\n",
      "    sg1_center:      61.0443796 +/- 0.078778 (0.13%) (init= 70)\n",
      "    sg1_gamma:       3.09001255 +/- 0.103694 (3.36%) (init= 4)\n",
      "[[Correlations]] (unreported correlations are <  0.500)\n",
      "    C(lin_slope, lin_intercept)  = -0.933 \n",
      "    C(sg1_sigma, sg1_center)     = -0.776 \n",
      "    C(sg1_sigma, sg1_gamma)      =  0.709 \n",
      "    C(sg1_center, sg1_gamma)     = -0.619 \n",
      "    C(sg1_amplitude, sg1_sigma)  =  0.602 \n",
      "\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093daad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lmfit.models import LinearModel, VoigtModel, SkewedGaussianModel\n",
    "\n",
    "fig = plt.figure(figsize=[8,4])\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "with open(\"/Users/stanpol/Dropbox/Stan-Marina/O1s.txt\") as f:\n",
    "\tdata = np.loadtxt(f, unpack=True)\n",
    "x = np.array(range(len(data))[40:110])\n",
    "y = np.array(data[::-1][40:110])\n",
    "\n",
    "background  = LinearModel(prefix='lin_')\n",
    "pars = background.guess(y, x=x)\n",
    "\n",
    "sg1  = SkewedGaussianModel(prefix='sg1_')\n",
    "pars.update(sg1.make_params())\n",
    "\n",
    "pars['sg1_center'].set(70, min=50, max=100)\n",
    "pars['sg1_sigma'].set(10, min=3)\n",
    "pars['sg1_gamma'].set(4, min=1)\n",
    "pars['sg1_amplitude'].set(140000, min=50000)\n",
    "\n",
    "mod = sg1 + background\n",
    "\n",
    "init = mod.eval(pars, x=x)\n",
    "plt.plot(x, y)\n",
    "\n",
    "out = mod.fit(y, pars, x=x)\n",
    "\n",
    "print(out.fit_report(min_correl=0.5))\n",
    "\n",
    "plt.plot(x, out.best_fit, 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}