{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - FRET histogram fitting\n",
    "\n",
    "*This notebook is part of smFRET burst analysis software [FRETBursts](http://opensmfs.github.io/FRETBursts/).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In this notebook shows how to fit a FRET histogram.\n",
    "> For a complete tutorial on burst analysis see \n",
    "> [FRETBursts - us-ALEX smFRET burst analysis](FRETBursts - us-ALEX smFRET burst analysis.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Optimized (cython) burst search loaded.\n",
      " - Optimized (cython) photon counting loaded.\n",
      "--------------------------------------------------------------\n",
      " You are running FRETBursts (version 0.6.5+5.g5243bc2).\n",
      "\n",
      " If you use this software please cite the following paper:\n",
      "\n",
      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
      "\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from fretbursts import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lmfit version: 0.9.7\n"
     ]
    }
   ],
   "source": [
    "sns = init_notebook(apionly=True)\n",
    "import lmfit\n",
    "print('lmfit version:', lmfit.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tweak here matplotlib style\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'].insert(0, 'Arial')\n",
    "mpl.rcParams['font.size'] = 12\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL:  http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
      "File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n",
      " \n",
      "File already on disk: /Users/anto/src/FRETBursts/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 \n",
      "Delete it to re-download.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Total photons (after ALEX selection):     2,259,522\n",
      "#  D  photons in D+A excitation periods:      721,537\n",
      "#  A  photons in D+A excitation periods:    1,537,985\n",
      "# D+A photons in  D  excitation period:     1,434,842\n",
      "# D+A photons in  A  excitation period:       824,680\n",
      "\n",
      " - Calculating BG rates ... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DONE]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Performing burst search (verbose=False) ..."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DONE]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Calculating burst periods ..."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DONE]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Counting D and A ph and calculating FRET ... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   - Applying background correction.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   [DONE Counting D/A]\n"
     ]
    }
   ],
   "source": [
    "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n",
    "download_file(url, save_dir='./data')\n",
    "full_fname = \"./data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\"\n",
    "\n",
    "d = loader.photon_hdf5(full_fname)\n",
    "loader.alex_apply_period(d)\n",
    "d.calc_bg(bg.exp_fit, time_s=1000, tail_min_us=(800, 4000, 1500, 1000, 3000))\n",
    "d.burst_search(L=10, m=10, F=6)\n",
    "ds = d.select_bursts(select_bursts.size, add_naa=True, th1=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the FRET histogram\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start defining the model. Here we choose a 3-Gaussian model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name             Value      Min      Max     Vary    Expr    \n",
      "p1_amplitude         1     0.01      inf     True    \n",
      "p1_center            0        0        1     True    \n",
      "p1_fwhm            nan     -inf      inf     True    2.3548200*p1_sigma\n",
      "p1_height          nan     -inf      inf     True    0.3989423*p1_amplitude/max(1.e-15, p1_sigma)\n",
      "p1_sigma          0.05     0.02      0.2     True    \n",
      "p2_amplitude         1     0.01      inf     True    \n",
      "p2_center          0.5        0        1     True    \n",
      "p2_fwhm            nan     -inf      inf     True    2.3548200*p2_sigma\n",
      "p2_height          nan     -inf      inf     True    0.3989423*p2_amplitude/max(1.e-15, p2_sigma)\n",
      "p2_sigma          0.05     0.02      0.2     True    \n",
      "p3_amplitude         1     0.01      inf     True    \n",
      "p3_center            1        0        1     True    \n",
      "p3_fwhm            nan     -inf      inf     True    2.3548200*p3_sigma\n",
      "p3_height          nan     -inf      inf     True    0.3989423*p3_amplitude/max(1.e-15, p3_sigma)\n",
      "p3_sigma          0.05     0.02      0.2     True    \n"
     ]
    }
   ],
   "source": [
    "model = mfit.factory_three_gaussians()\n",
    "model.print_param_hints()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previsou cell prints all the model parameters. \n",
    "Each parameters has an initial value and bounds (min, max).\n",
    "The column `vary` tells if a parameter is varied during the fit\n",
    "(if False the parameter is fixed).\n",
    "Parameters with an expression (`Expr` column) are not free but\n",
    "the are computed as a function of other parameters.\n",
    "\n",
    "We can modify the paramenters constrains as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_param_hint('p1_center', value=0.1, min=-0.1, max=0.3)\n",
    "model.set_param_hint('p2_center', value=0.4, min=0.3, max=0.7)\n",
    "model.set_param_hint('p2_sigma', value=0.04, min=0.02, max=0.18)\n",
    "model.set_param_hint('p3_center', value=0.85, min=0.7, max=1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we fit and plot the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rUkaT5K1v7LvoW8+/q5fCds5KY9motmF5+D4sVubuDPNK4nnGH/u+DvPuBprG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NnjCNC27+AtWlJax77jGCAT/g3Cmafd4VcQvANNdWcWDHRip2bOBQ6Q5sKEhnextujxdPUlJk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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1237da940>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 308,
       "width": 560
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E_fitter = bext.bursts_fitter(ds, 'E', binwidth=0.03)\n",
    "E_fitter.fit_histogram(model=model, pdf=False, method='nelder')\n",
    "E_fitter.fit_histogram(model=model, pdf=False, method='leastsq')\n",
    "dplot(ds, hist_fret, show_model=True, pdf=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dplot(ds, hist_fret, show_model=True, pdf=False, figsize=(6, 4.5));\n",
    "# plt.xlim(-0.1, 1.1)\n",
    "# plt.savefig('fret_hist_fit.png', bbox_inches='tight', dpi=200, transparent=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are in `E_fitter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name             Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "p1_amplitude     26.43     0.01      inf    2.665     True     None     None\n",
      "p1_center       0.1304     -0.1      0.3 0.003665     True     None     None\n",
      "p1_fwhm         0.1247     -inf      inf 0.009604    False 2.3548200*p1_sigma     None\n",
      "p1_height          199     -inf      inf     12.7    False 0.3989423*p1_amplitude/max(1.e-15, p1_sigma)     None\n",
      "p1_sigma       0.05296     0.02      0.2 0.004079     True     None     None\n",
      "p2_amplitude     97.73     0.01      inf    5.766     True     None     None\n",
      "p2_center         0.42      0.3      0.7 0.006083     True     None     None\n",
      "p2_fwhm         0.3394     -inf      inf  0.02357    False 2.3548200*p2_sigma     None\n",
      "p2_height        270.5     -inf      inf    6.868    False 0.3989423*p2_amplitude/max(1.e-15, p2_sigma)     None\n",
      "p2_sigma        0.1441     0.02     0.18  0.01001     True     None     None\n",
      "p3_amplitude     46.61     0.01      inf      4.2     True     None     None\n",
      "p3_center       0.8268      0.7      1.1  0.01187     True     None     None\n",
      "p3_fwhm         0.2946     -inf      inf  0.02508    False 2.3548200*p3_sigma     None\n",
      "p3_height        148.6     -inf      inf    6.991    False 0.3989423*p3_amplitude/max(1.e-15, p3_sigma)     None\n",
      "p3_sigma        0.1251     0.02      0.2  0.01065     True     None     None\n"
     ]
    }
   ],
   "source": [
    "res = E_fitter.fit_res[0]\n",
    "res.params.pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a dictionary of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p1_amplitude': 26.425112546882755,\n",
       " 'p1_center': 0.1304211780456301,\n",
       " 'p1_fwhm': 0.12471768267083956,\n",
       " 'p1_height': 199.04744887476406,\n",
       " 'p1_sigma': 0.05296272439967367,\n",
       " 'p2_amplitude': 97.725109792777,\n",
       " 'p2_center': 0.419974100092639,\n",
       " 'p2_fwhm': 0.3394332126358552,\n",
       " 'p2_height': 270.47033272302514,\n",
       " 'p2_sigma': 0.14414401637316449,\n",
       " 'p3_amplitude': 46.61031803037159,\n",
       " 'p3_center': 0.8267695396211263,\n",
       " 'p3_fwhm': 0.2945718036320869,\n",
       " 'p3_height': 148.6478716009145,\n",
       " 'p3_sigma': 0.12509312967958777}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the startndard lmfit's fit report:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    ((Model(gaussian, prefix='p1_') + Model(gaussian, prefix='p2_')) + Model(gaussian, prefix='p3_'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 156\n",
      "    # data points      = 46\n",
      "    # variables        = 9\n",
      "    chi-square         = 7950.001\n",
      "    reduced chi-square = 214.865\n",
      "    Akaike info crit   = 255.005\n",
      "    Bayesian info crit = 271.463\n",
      "[[Variables]]\n",
      "    p1_sigma:       0.05296272 +/- 0.004078 (7.70%) (init= 0.05)\n",
      "    p1_center:      0.13042117 +/- 0.003665 (2.81%) (init= 0.1)\n",
      "    p1_amplitude:   26.4251125 +/- 2.664757 (10.08%) (init= 1)\n",
      "    p2_sigma:       0.14414401 +/- 0.010011 (6.95%) (init= 0.04)\n",
      "    p2_center:      0.41997410 +/- 0.006082 (1.45%) (init= 0.4)\n",
      "    p2_amplitude:   97.7251097 +/- 5.766403 (5.90%) (init= 1)\n",
      "    p3_sigma:       0.12509312 +/- 0.010649 (8.51%) (init= 0.05)\n",
      "    p3_center:      0.82676953 +/- 0.011870 (1.44%) (init= 0.85)\n",
      "    p3_amplitude:   46.6103180 +/- 4.200092 (9.01%) (init= 1)\n",
      "    p1_fwhm:        0.12471768 +/- 0.009604 (7.70%)  == '2.3548200*p1_sigma'\n",
      "    p1_height:      199.047448 +/- 12.70087 (6.38%)  == '0.3989423*p1_amplitude/max(1.e-15, p1_sigma)'\n",
      "    p2_fwhm:        0.33943321 +/- 0.023574 (6.95%)  == '2.3548200*p2_sigma'\n",
      "    p2_height:      270.470332 +/- 6.868062 (2.54%)  == '0.3989423*p2_amplitude/max(1.e-15, p2_sigma)'\n",
      "    p3_fwhm:        0.29457180 +/- 0.025077 (8.51%)  == '2.3548200*p3_sigma'\n",
      "    p3_height:      148.647871 +/- 6.991347 (4.70%)  == '0.3989423*p3_amplitude/max(1.e-15, p3_sigma)'\n",
      "[[Correlations]] (unreported correlations are <  0.500)\n",
      "    C(p2_sigma, p2_amplitude)    =  0.935 \n",
      "    C(p3_sigma, p3_amplitude)    =  0.857 \n",
      "    C(p2_amplitude, p3_amplitude)  = -0.806 \n",
      "    C(p1_amplitude, p2_sigma)    = -0.792 \n",
      "    C(p2_sigma, p3_amplitude)    = -0.789 \n",
      "    C(p2_amplitude, p3_center)   =  0.779 \n",
      "    C(p1_sigma, p1_amplitude)    =  0.774 \n",
      "    C(p2_sigma, p3_center)       =  0.768 \n",
      "    C(p1_amplitude, p2_amplitude)  = -0.762 \n",
      "    C(p3_center, p3_amplitude)   = -0.731 \n",
      "    C(p2_amplitude, p3_sigma)    = -0.713 \n",
      "    C(p2_sigma, p3_sigma)        = -0.665 \n",
      "    C(p3_sigma, p3_center)       = -0.660 \n",
      "    C(p1_sigma, p2_sigma)        = -0.554 \n",
      "    C(p2_center, p3_sigma)       = -0.553 \n",
      "    C(p1_sigma, p2_amplitude)    = -0.547 \n",
      "    C(p2_center, p3_amplitude)   = -0.534 \n",
      "    C(p1_amplitude, p3_amplitude)  =  0.531 \n",
      "    C(p1_amplitude, p3_center)   = -0.518 \n",
      "    C(p2_center, p3_center)      =  0.511 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(res.fit_report(min_correl=0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell reports error ranges computed from the covariance matrix.\n",
    "[More accurare confidence intervals](https://lmfit.github.io/lmfit-py/confidence.html) \n",
    "can be obtained with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anto/miniconda3/envs/fretbursts-dev/lib/python3.6/site-packages/lmfit/confidence.py:303: UserWarning: Warning, rel_change=0.0 < 0.01  at iteration 5 and prob(p2_sigma=0.19420068222167178) = 0.9928120659904571 < max(sigmas).\n",
      "  warn(errmsg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 99.73%    95.45%    68.27%    _BEST_    68.27%    95.45%    99.73%\n",
      " p1_sigma    :  -0.01374  -0.00894  -0.00442   0.05296  +0.00456  +0.00953  +0.01531\n",
      " p1_center   :  -0.01193  -0.00771  -0.00382   0.13042  +0.00397  +0.00835  +0.01354\n",
      " p1_amplitude:  -9.30991  -6.10472  -3.00234  26.42511  +2.97612  +6.05976  +9.43949\n",
      " p2_sigma    :  -0.03326  -0.02232  -0.01138   0.14414  +0.01224  +0.02572      +inf\n",
      " p2_center   :  -0.02278  -0.01400  -0.00664   0.41997  +0.00641  +0.01298  +0.02009\n",
      " p2_amplitude: -22.75290 -14.16482  -6.83120  97.72511  +6.80352 +13.90510 +21.30745\n",
      " p3_sigma    :  -0.03201  -0.02169  -0.01125   0.12509  +0.01292  +0.02877  +0.04959\n",
      " p3_center   :  -0.05671  -0.03303  -0.01497   0.82677  +0.01330  +0.02587  +0.03840\n",
      " p3_amplitude: -13.74698  -9.15839  -4.65290  46.61032  +5.09443 +11.07792 +18.71141\n"
     ]
    }
   ],
   "source": [
    "ci = res.conf_interval()\n",
    "lmfit.report_ci(ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy fit results\n",
    "\n",
    "It is convenient to put the fit results in a DataFrame for further analysis.\n",
    "A dataframe of fitted parameters is already in `E_fitter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p1_amplitude</th>\n",
       "      <th>p1_center</th>\n",
       "      <th>p1_sigma</th>\n",
       "      <th>p2_amplitude</th>\n",
       "      <th>p2_center</th>\n",
       "      <th>p2_sigma</th>\n",
       "      <th>p3_amplitude</th>\n",
       "      <th>p3_center</th>\n",
       "      <th>p3_sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.4251</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>0.0529627</td>\n",
       "      <td>97.7251</td>\n",
       "      <td>0.419974</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>46.6103</td>\n",
       "      <td>0.82677</td>\n",
       "      <td>0.125093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  p1_amplitude p1_center   p1_sigma p2_amplitude p2_center  p2_sigma  \\\n",
       "0      26.4251  0.130421  0.0529627      97.7251  0.419974  0.144144   \n",
       "\n",
       "  p3_amplitude p3_center  p3_sigma  \n",
       "0      46.6103   0.82677  0.125093  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_fitter.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With [`pybroom`](http://pybroom.readthedocs.io/) we can get a \"tidy\" DataFrame\n",
    "with more complete fit results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybroom as br"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>vary</th>\n",
       "      <th>expr</th>\n",
       "      <th>stderr</th>\n",
       "      <th>init_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1_amplitude</td>\n",
       "      <td>26.425113</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>inf</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>2.664758</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p1_center</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.003665</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p1_fwhm</td>\n",
       "      <td>0.124718</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3548200*p1_sigma</td>\n",
       "      <td>0.009604</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p1_height</td>\n",
       "      <td>199.047449</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3989423*p1_amplitude/max(1.e-15, p1_sigma)</td>\n",
       "      <td>12.700874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p1_sigma</td>\n",
       "      <td>0.052963</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p3_amplitude</td>\n",
       "      <td>46.610318</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>inf</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>4.200093</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>p3_center</td>\n",
       "      <td>0.826770</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.011871</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>p3_fwhm</td>\n",
       "      <td>0.294572</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>2.3548200*p3_sigma</td>\n",
       "      <td>0.025078</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>p3_height</td>\n",
       "      <td>148.647872</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>False</td>\n",
       "      <td>0.3989423*p3_amplitude/max(1.e-15, p3_sigma)</td>\n",
       "      <td>6.991347</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>p3_sigma</td>\n",
       "      <td>0.125093</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name       value       min       max   vary  \\\n",
       "0   p1_amplitude   26.425113  0.010000       inf   True   \n",
       "1      p1_center    0.130421 -0.100000  0.300000   True   \n",
       "2        p1_fwhm    0.124718      -inf       inf  False   \n",
       "3      p1_height  199.047449      -inf       inf  False   \n",
       "4       p1_sigma    0.052963  0.020000  0.200000   True   \n",
       "..           ...         ...       ...       ...    ...   \n",
       "10  p3_amplitude   46.610318  0.010000       inf   True   \n",
       "11     p3_center    0.826770  0.700000  1.100000   True   \n",
       "12       p3_fwhm    0.294572      -inf       inf  False   \n",
       "13     p3_height  148.647872      -inf       inf  False   \n",
       "14      p3_sigma    0.125093  0.020000  0.200000   True   \n",
       "\n",
       "                                            expr     stderr  init_value  \n",
       "0                                           None   2.664758        1.00  \n",
       "1                                           None   0.003665        0.10  \n",
       "2                             2.3548200*p1_sigma   0.009604         NaN  \n",
       "3   0.3989423*p1_amplitude/max(1.e-15, p1_sigma)  12.700874         NaN  \n",
       "4                                           None   0.004079        0.05  \n",
       "..                                           ...        ...         ...  \n",
       "10                                          None   4.200093        1.00  \n",
       "11                                          None   0.011871        0.85  \n",
       "12                            2.3548200*p3_sigma   0.025078         NaN  \n",
       "13  0.3989423*p3_amplitude/max(1.e-15, p3_sigma)   6.991347         NaN  \n",
       "14                                          None   0.010650        0.05  \n",
       "\n",
       "[15 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = br.tidy(res)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for example, we can easily select parameters by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p1_center</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.003665</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>p2_center</td>\n",
       "      <td>0.419974</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.006083</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>p3_center</td>\n",
       "      <td>0.826770</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.011871</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name     value  min  max  vary  expr    stderr  init_value\n",
       "1   p1_center  0.130421 -0.1  0.3  True  None  0.003665        0.10\n",
       "6   p2_center  0.419974  0.3  0.7  True  None  0.006083        0.40\n",
       "11  p3_center  0.826770  0.7  1.1  True  None  0.011871        0.85"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.name.str.contains('center')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>vary</th>\n",
       "      <th>expr</th>\n",
       "      <th>stderr</th>\n",
       "      <th>init_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p1_sigma</td>\n",
       "      <td>0.052963</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.20</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>p2_sigma</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.18</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>p3_sigma</td>\n",
       "      <td>0.125093</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.20</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name     value   min   max  vary  expr    stderr  init_value\n",
       "4   p1_sigma  0.052963  0.02  0.20  True  None  0.004079        0.05\n",
       "9   p2_sigma  0.144144  0.02  0.18  True  None  0.010011        0.04\n",
       "14  p3_sigma  0.125093  0.02  0.20  True  None  0.010650        0.05"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.name.str.contains('sigma')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Executed:** Sat Nov 18 15:59:51 2017\n",
    "\n",
    "**Duration:** 22 seconds.\n",
    "\n",
    "**Autogenerated from:** [Example - FRET histogram fitting.ipynb](out/Example - FRET histogram fitting.ipynb)"
   ]
  }
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