usALEX - Burst search comparison.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:e7a6d4dee3031a82bd2bcc348e5afcceb62c5d8aa76c46f77b51a41b090df65e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# usALEX - Corrections - Direct excitation physical parameter\n",
      "\n",
      "> *This compares different bursts searches.*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Library imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import os\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "#import lmfit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Load data RAW PR data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = {}\n",
      "\n",
      "for bsearch_ph_sel in ['all-ph', 'Dex', 'AND-gate']:\n",
      "    data_file = 'results/usALEX-5samples-PR-raw-%s.txt' % bsearch_ph_sel\n",
      "    d[bsearch_ph_sel] = pd.read_csv(data_file, sep=\"\\s+\").set_index('sample')[['E_pr_fret_kde']]\n",
      "    #d[bsearch_ph_sel].columns = [bsearch_ph_sel]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "\n",
      "for bs, data in d.items():\n",
      "    data.plot(label=bs, ax=ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBCCCGEEEIIIYQQQgghhPX6P+6lgmmps+SFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc877518>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(d['AND-gate'] - d['all-ph']).abs().max()*100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "E_pr_fret_kde    0.8\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(d['AND-gate'] - d['Dex']).abs()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>E_pr_fret_kde</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7d</th>\n",
        "      <td> 0.0020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12d</th>\n",
        "      <td> 0.0016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17d</th>\n",
        "      <td> 0.0002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22d</th>\n",
        "      <td> 0.0006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27d</th>\n",
        "      <td> 0.0000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "        E_pr_fret_kde\n",
        "sample               \n",
        "7d             0.0020\n",
        "12d            0.0016\n",
        "17d            0.0002\n",
        "22d            0.0006\n",
        "27d            0.0000"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(d['all-ph'] - d['Dex']).abs().max()*100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "E_pr_fret_kde    0.96\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d['Dex']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>E_pr_fret_kde</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7d</th>\n",
        "      <td> 0.9336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12d</th>\n",
        "      <td> 0.7510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17d</th>\n",
        "      <td> 0.4908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22d</th>\n",
        "      <td> 0.2752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27d</th>\n",
        "      <td> 0.1944</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "        E_pr_fret_kde\n",
        "sample               \n",
        "7d             0.9336\n",
        "12d            0.7510\n",
        "17d            0.4908\n",
        "22d            0.2752\n",
        "27d            0.1944"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#bsearch_ph_sel = 'all=ph'\n",
      "#bsearch_ph_sel = 'AND-gate'\n",
      "bsearch_ph_sel = 'Dex'\n",
      "\n",
      "data_file = 'results/usALEX-5samples-PR-raw-%s.txt' % bsearch_ph_sel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These are the **RAW proximity ratios** for the 5 samples (only background correction, no leakage nor direct excitation):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_raw = pd.read_csv(data_file, sep=\"\\s+\").set_index('sample')\n",
      "data_raw[['E_pr_fret', 'E_pr_fret_kde']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>E_pr_fret</th>\n",
        "      <th>E_pr_fret_kde</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7d</th>\n",
        "      <td> 0.927586</td>\n",
        "      <td> 0.9336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12d</th>\n",
        "      <td> 0.749365</td>\n",
        "      <td> 0.7510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17d</th>\n",
        "      <td> 0.480290</td>\n",
        "      <td> 0.4908</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22d</th>\n",
        "      <td> 0.280477</td>\n",
        "      <td> 0.2752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27d</th>\n",
        "      <td> 0.194521</td>\n",
        "      <td> 0.1944</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "        E_pr_fret  E_pr_fret_kde\n",
        "sample                          \n",
        "7d       0.927586         0.9336\n",
        "12d      0.749365         0.7510\n",
        "17d      0.480290         0.4908\n",
        "22d      0.280477         0.2752\n",
        "27d      0.194521         0.1944"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Load \u03bcs-ALEX corrections"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "leakage_coeff_fname = 'results/usALEX - leakage coefficient DexDem.txt'\n",
      "leakage = np.loadtxt(leakage_coeff_fname)\n",
      "\n",
      "print 'Leakage coefficient:', leakage"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Leakage coefficient: 0.0999978359136\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir_ex_t_datasheet_fname = 'results/Dyes - ATT0647N-ATTO550 abs X-section ratio at 532nm.txt'\n",
      "dir_ex_t_datasheet = np.loadtxt(dir_ex_t_datasheet_fname)\n",
      "\n",
      "print 'Direct excitation (dir_ex_t) from datasheet:', dir_ex_t_datasheet"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Direct excitation (dir_ex_t) from datasheet: 0.106516290727\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamma_coeff_fname = 'results/usALEX - gamma factor - all-ph.txt'\n",
      "gamma = np.loadtxt(gamma_coeff_fname)\n",
      "\n",
      "print 'Gamma factor:', gamma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Gamma factor: 1.02500832027\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Load E data from corrected FRET bursts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And these are the **FRET efficiencies** fitted from corrected histograms for the same 5 samples: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_file = 'results/usALEX-5samples-E-corrected-all-ph.txt'\n",
      "data_corr = pd.read_csv(data_file, sep=\"\\s+\").set_index('sample')\n",
      "data_corr[['E_pr_fret', 'E_pr_fret_kde']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>E_pr_fret</th>\n",
        "      <th>E_pr_fret_kde</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7d</th>\n",
        "      <td> 0.926023</td>\n",
        "      <td> 0.9300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12d</th>\n",
        "      <td> 0.730013</td>\n",
        "      <td> 0.7416</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17d</th>\n",
        "      <td> 0.430222</td>\n",
        "      <td> 0.4370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22d</th>\n",
        "      <td> 0.191358</td>\n",
        "      <td> 0.1874</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27d</th>\n",
        "      <td> 0.093747</td>\n",
        "      <td> 0.0972</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "        E_pr_fret  E_pr_fret_kde\n",
        "sample                          \n",
        "7d       0.926023         0.9300\n",
        "12d      0.730013         0.7416\n",
        "17d      0.430222         0.4370\n",
        "22d      0.191358         0.1874\n",
        "27d      0.093747         0.0972"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Proximity Ratio correction formula\n",
      "\n",
      "The expression to convert RAW PR values to corrected FRET efficiencies:\n",
      "\n",
      "$$ E = \\frac{E_{R} \\left(L_{k} + d_{exT} \\gamma + 1\\right) - L_{k} - d_{exT} \\gamma}{E_{R} \\left(L_{k} - \\gamma + 1\\right) - L_{k} + \\gamma}$$\n",
      "\n",
      "*See [Derivation of FRET and S correction formulas](Derivation of FRET and S correction formulas.ipynb) for the derivation*.\n",
      "\n",
      "We load [FRETBursts software](https://github.com/tritemio/FRETBursts/) that provides this function (as `fretmath.correct_E_gamma_leak_dir`):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run load_fretbursts.py --nogui"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " - Optimized (cython) burst search loaded.\n",
        " - Optimized (cython) photon counting loaded.\n",
        "\n",
        "FRETBursts revision:\n",
        " 2014-08-16 68e0ade Better KDE normalization in hist_fret_kde() plot\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Comparison of datasheet-based *vs* \u03bcs-ALEX-based direct excitation\n",
      "\n",
      "Using [datasheet values](dyes/Absorption ATTO550-ATTO647N.ipynb) for $d_{exT}$ we the following FRET efficiencies:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "E_datasheet = fretmath.correct_E_gamma_leak_dir(data_raw.E_pr_fret_kde, \n",
      "                                                leakage=leakage, \n",
      "                                                dir_ex_t=dir_ex_t_datasheet,\n",
      "                                                gamma=gamma)*100\n",
      "E_datasheet"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([ 92.43130215,  71.22134243,  39.95430179,  13.06912254,   2.75506448])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out = data_corr[['E_pr_fret_kde']].copy()*100\n",
      "out.columns = ['E_alex']\n",
      "out['E_datasheet'] = E_datasheet\n",
      "out"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>E_alex</th>\n",
        "      <th>E_datasheet</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sample</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7d</th>\n",
        "      <td> 93.00</td>\n",
        "      <td> 92.431302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12d</th>\n",
        "      <td> 74.16</td>\n",
        "      <td> 71.221342</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17d</th>\n",
        "      <td> 43.70</td>\n",
        "      <td> 39.954302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22d</th>\n",
        "      <td> 18.74</td>\n",
        "      <td> 13.069123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27d</th>\n",
        "      <td>  9.72</td>\n",
        "      <td>  2.755064</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "        E_alex  E_datasheet\n",
        "sample                     \n",
        "7d       93.00    92.431302\n",
        "12d      74.16    71.221342\n",
        "17d      43.70    39.954302\n",
        "22d      18.74    13.069123\n",
        "27d       9.72     2.755064"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out.plot(alpha=0.4, lw=3, style=dict(E_alex='-ob', E_datasheet='-sr'));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEPCAYAAABBUX+lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1488f9e8>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> **NOTE:** The corrected FRET efficiencies using the datasheet and \n",
      "> \u03bcs-ALEX-based direct excitation do not match well."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Fitting of physical direct excitation coefficient $d_{exT}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import lmfit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def residuals_absolute(params, E_raw, E_ref):\n",
      "    dir_ex_t = params['dir_ex_t'].value\n",
      "    return E_ref - fretmath.correct_E_gamma_leak_dir(E_raw, \n",
      "                                                     leakage=leakage, \n",
      "                                                     gamma=gamma, \n",
      "                                                     dir_ex_t=dir_ex_t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def residuals_relative(params, E_raw, E_ref):\n",
      "    dir_ex_t = params['dir_ex_t'].value\n",
      "    return (E_ref - fretmath.correct_E_gamma_leak_dir(E_raw, \n",
      "                                                      leakage=leakage, \n",
      "                                                      gamma=gamma, \n",
      "                                                      dir_ex_t=dir_ex_t))/E_ref"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "params = lmfit.Parameters()\n",
      "params.add('dir_ex_t', value=0.05) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = lmfit.minimize(residuals_absolute, params, args=(data_raw.E_pr_fret_kde, data_corr.E_pr_fret_kde))\n",
      "lmfit.report_fit(m.params, show_correl=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[Variables]]\n",
        "     dir_ex_t:     0.0301536 +/- 0.004201888 (13.93%) initial =  0.05\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = lmfit.minimize(residuals_relative, params, args=(data_raw.E_pr_fret_kde, data_corr.E_pr_fret_kde))\n",
      "lmfit.report_fit(m.params, show_correl=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[Variables]]\n",
        "     dir_ex_t:     0.02860964 +/- 0.001570824 (5.49%) initial =  0.0301536\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> **NOTE:** The fitted `dir_ex_t` is XX% as opposed to 10.6% as expected from the [absorption spectra of ATTO550 and ATTO647](dyes/Absorption ATTO550-ATTO647N.ipynb) at 532nm."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.params['dir_ex_t'].value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0.028609637544006607"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('results/usALEX - leakage coefficient dir_ex_t.txt', 'w') as f:\n",
      "    f.write(str(m.params['dir_ex_t'].value))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}