sc3x3-dark-tls-fitting.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.analysis.kid_eqns\n",
      "reload(kid_readout.analysis.kid_eqns)\n",
      "import lmfit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.analysis.archive"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "get_temperature_at: updated cache\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sc3x3 = kid_readout.analysis.archive.load_archive('/home/data/archive/StarCryo_3x3_0813f5_HPD_Dark_1.npy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r0 = sc3x3[(sc3x3.ridx==3) & (sc3x3.atten == 33.5) & (sc3x3.Q_err < 6000) & (sc3x3.Q_i>1000)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for m in np.unique(sc3x3.atten):\n",
      "    print m, len(sc3x3[sc3x3.ridx==0][sc3x3.atten == m])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "21.5 1\n",
        "23.5 1\n",
        "25.5 1\n",
        "27.5 1\n",
        "29.5 1\n",
        "31.5 1\n",
        "33.5 40\n",
        "35.5 40\n",
        "37.5 39\n",
        "40.5 39\n",
        "43.5 39\n",
        "46.5 39\n",
        "49.5 1\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/local/lib/python2.7/site-packages/pandas/core/frame.py:2021: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
        "  \"DataFrame index.\", UserWarning)\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(r0.start_temp,r0.f_0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1471d0d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc3cce10>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km = kid_readout.analysis.kid_eqns.DarkKIDModel(f0_nom=r0.f_0.max()*1e6,cap = 20.55, Lg = 28,\n",
      "                                                sigman=1.25e5,Tc=1.56,Qc=2e5)\n",
      "km2 = kid_readout.analysis.kid_eqns.DarkKIDModel(f0_nom=r0.f_0.max()*1e6,cap = 28.55e-12, Lg = 29e-9)\n",
      "km.params['F_TLS'].vary = False\n",
      "km.params['sigman'].vary=False\n",
      "km.params['Tc'].vary=False\n",
      "km.params['Lg'].vary=False\n",
      "#km.params['cap'].vary = False\n",
      "km.params['nqp0'].value = 0\n",
      "#km.params['nqp0'].vary = False\n",
      "km.params['delta_loss'].value = 0\n",
      "km.params['delta_loss'].vary = False\n",
      "km.params['delta_0'].value =  0\n",
      "#km.params['delta_0'].vary = False\n",
      "km.params['T_star'].vary = False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 544
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r0.f_0.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 545,
       "text": [
        "111.5999587529176"
       ]
      }
     ],
     "prompt_number": 545
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "msk = r0.start_temp > 0.15\n",
      "km.fit_f0_qi(r0.start_temp[msk],r0.f_0[msk]*1e6,r0.Q_i[msk],r0.f_0_err[msk]*1e6*2,r0.Q_err[msk])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 546
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#km.fit_f0(r0.start_temp,r0.f_0*1e6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 547
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.f_res(0.1)/km.f0_nom"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 548,
       "text": [
        "1.000019395676391"
       ]
      }
     ],
     "prompt_number": 548
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.f0_nom"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 549,
       "text": [
        "111599958.7529176"
       ]
      }
     ],
     "prompt_number": 549
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.Lk(0.1)/1e-9, km.alpha(0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 550,
       "text": [
        "(83.767040897144355, 0.74947891815649392)"
       ]
      }
     ],
     "prompt_number": 550
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lmfit.report_fit(km.params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[Variables]]\n",
        "     F_TLS:         fixed\n",
        "     Lg:            fixed\n",
        "     T_star:        fixed\n",
        "     Tc:            fixed\n",
        "     cap:            18.196237 +/- 0.004837 (0.03%) initial =  20.550000\n",
        "     delta_0:        0.000000 +/- 0.000186 (941039128.35%) initial =  0.000000\n",
        "     delta_loss:    fixed\n",
        "     nqp0:           357.191144 +/- 195.746755 (54.80%) initial =  0.000000\n",
        "     sigman:        fixed\n",
        "[[Correlations]] (unreported correlations are <  0.100)\n",
        "    C(delta_0, nqp0)             =  0.998 \n",
        "    C(cap, delta_0)              = -0.993 \n",
        "    C(cap, nqp0)                 = -0.992 \n"
       ]
      }
     ],
     "prompt_number": 551
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#km.params['delta_0'].value = 1e-4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 552
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = np.linspace(0.001,0.6,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 553
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(T,(km.f_res(T)*(1+km.tls_shift(T)))/(km.f0_nom))\n",
      "plot(r0.start_temp,r0.f_0*1e6/(r0.f_0.max()*1e6),'.')\n",
      "ylim(0.999,1.0001)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 554,
       "text": [
        "(0.999, 1.0001)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD7CAYAAAB0d9PAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIlvk4A8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x165fda10>"
       ]
      }
     ],
     "prompt_number": 554
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "semilogx(km.nqp(T),(km.f_res(T)*(1+km.tls_shift(T)))/(km.f0_nom))\n",
      "semilogx(km.nqp(r0.start_temp),r0.f_0*1e6/(r0.f_0.max()*1e6),'.')\n",
      "ylim(0.999,1.001)\n",
      "#xlim(300,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 563,
       "text": [
        "(0.999, 1.001)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEFCAYAAADwhtBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8v8BK5XZNqGtzt8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1748f1d0>"
       ]
      }
     ],
     "prompt_number": 563
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "msk = r0.start_temp < 0.25\n",
      "pp = np.polyfit(r0.start_temp[msk],r0.Q_i[msk],1)\n",
      "print pp\n",
      "pp2 = np.polyfit(r0.start_temp[~msk],np.log(r0.Q_i[~msk]),1)\n",
      "print pp2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 770818.17413392   83174.52621096]\n",
        "[-14.81903286  16.39684106]\n"
       ]
      }
     ],
     "prompt_number": 486
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km2.params['nqp0'].value = 0\n",
      "km2.params['delta_0'].value = 0\n",
      "km2.params['delta_loss'].value = 0\n",
      "km2.params['sigman'].value = km.params['sigman'].value\n",
      "km2.params['Tc'].value = 1.5\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 487
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#km.params['delta_0'].value = 3e-4\n",
      "#km.params['nqp0'].value = 3\n",
      "#km.params['delta_0'].value=5e-4\n",
      "semilogy(T,km.total_Qi(T))\n",
      "semilogy(T,km.Qi(T))\n",
      "#semilogy(T,np.polyval(pp,T),'--')\n",
      "semilogy(T,1/km.delta_tls(T),'k')\n",
      "#semilogy(T,1/(6e-4*np.tanh(km.xi(T))),lw=2)\n",
      "#semilogy(T,km2.total_Qi(T))\n",
      "#semilogy(T,1/km.params['delta_loss']*np.ones_like(T))\n",
      "semilogy(r0.start_temp,r0.Q_i,'.')\n",
      "#semilogy(T,1/(1/km2.Qi(T)+6e-4*np.tanh(km.xi(T))))\n",
      "#semilogy(T,1/(1/km2.Qi(T)+1/np.polyval(pp,T)))\n",
      "#semilogy(T,np.exp(np.polyval(pp2,T)))\n",
      "#semilogy(T,1/(1/(np.exp(np.polyval(pp2,T)))+1/np.polyval(pp,T)))\n",
      "yscale('linear')\n",
      "ylim(1000,5e5)\n",
      "#xlim(0,0.3)\n",
      "grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT8O0N65olBkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x157ebf90>"
       ]
      }
     ],
     "prompt_number": 524
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(km.nqp(T),km.f_res(T)/km.f0_nom,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 537,
       "text": [
        "[<matplotlib.lines.Line2D at 0x171ba2d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEACAYAAACkvpHUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1YlGW+B/DvIIQZdRANX2ZYR0WRGQaDYEVEG1EiDLIk\noyLOpp0rait1O8ft8tAVWru2pGe39dI6utWu9nI6BEunIzmKAmrtiVQsFzfoxTdmTAEDQwIZmd/5\nY2ICYQZHYZgZvp/rmiuY+57neW4fm5+/++1RiIiAiIjoCvkM9gUQEZFnYeAgIiKnMHAQEZFTGDiI\niMgpDBxEROQUBg4iInJKn4Fj6dKlGDNmDHQ6nd06y5Ytg1arRXR0NA4fPmx7f+vWrdBqtdBqtdi2\nbRsA4IcffkBycjK0Wi3CwsLw9NNPgzOCiYg8R5+BY8mSJTAYDHbLCwsLcerUKRw9ehSvv/46lixZ\nAgD49ttv8cILL6CiogIVFRV4/vnncfbsWSgUCjz33HM4evQoqqqqcPDgQXzwwQf91yIiIhpQfQaO\n2bNnY+TIkXbLP/zwQ2RlZQEAoqKicOnSJRiNRpSUlCAlJQUBAQEICAjAHXfcgZKSElx//fWYNWsW\nAMDPzw8///nPcfr06X5qDhERDTTfaz2A0WhESEiI7XeVSgWj0QiTyQSVStXj/a6amppQVFSE3bt3\nX+tlEBGRi/TL4PjVjFFcunQJDz74IJYvX46JEyf2x2UQEZELXHPGoVKpUFtbixkzZgD4KQNRqVSo\nqKiw1autrUV8fLzt90cffRRTpkzBsmXL7B47NDQU33zzzbVeIhHRkDF58mR8/fXXA3qOa844FixY\ngLfffhsAUFlZiWHDhkGpVGLevHkwGAxobm5Gc3MzDAYD5s+fDwB49tlncf78ebz88ssOj/3NN99A\nRLzylZubO+jXwPaxfWyf971c8Y/tPjOOBx54AHv37kVDQwNCQkKwZs0amM1mAEB2djbS09NRVlYG\nrVYLf39//PnPfwYAjB8/Hjk5ObZM5LnnnsOYMWNgNBqxdu1ahIeHIzo6GgDw1FNPYenSpQPVRiIi\n6kd9Bo7/+q//6vMgGzdu7PX9JUuW2KbndlKpVLBYLFd4eURE5G64cnyQ6PX6wb6EAcX2eTa2jxxR\niIjbLttWKBRw48sjInI7rvjeZMZBREROYeAgIiKnMHAQEZFTGDiIiMgpDBxEROQUBg4iInIKAwcR\nETmFgYOIiJzCwEFERE5h4CAiIqcwcBARkVMYOIiIyCkMHERE5BQGDiIicgoDBxEROYWBg4iInMLA\nQURETukzcCxduhRjxoyBTqezW2fZsmXQarWIjo7G4cOHbe9v3boVWq0WWq0W27Zts71/6NAhREVF\nQavVYvny5dfYBCIicqU+A8eSJUtgMBjslhcWFuLUqVM4evQoXn/9dSxZsgQA8O233+KFF15ARUUF\nKioq8Pzzz6Ours52zDfeeANHjx7FyZMnUVRU1E/NISKigdZn4Jg9ezZGjhxpt/zDDz9EVlYWACAq\nKgqXLl2C0WhESUkJUlJSEBAQgICAANxxxx3YtWsXTp06BYvFgqioKADAQw89hOLiYrvHVyisLyIi\ncg/XPMZhNBoREhJi+12lUsFoNMJkMkGlUvX6ftf6SqUSRqOxz/OMH3+tV0pERP2hXwbHRaQ/DuPQ\nt99aM4+PPhrwUxERkQO+13oAlUqF2tpazJgxA8BPGYhKpUJFRYWtXm1tLeLj4231OxmNxm6ZSU+r\nu/ysx+zZemzaBPzyl9d65UREnq+8vBzl5eWuPalcgePHj0tERESvZQUFBXL33XeLiMihQ4ckMjJS\nRERMJpNMnjxZvv/+e/n+++9l0qRJcubMGRER0el0UllZKSIiCxculMLCwl6PDUAA6fW1atWVXDkR\n0dByhV/r10Tx44nseuCBB7B37140NDRgzJgxWLNmDcxmMwAgOzsbAPDkk0+irKwM/v7+eO211xAd\nHQ0A+POf/4x169YBAJ555hn84he/AGCdjvsv//IvaG9vx7x587Bhw4Zez61QKKyxww4fH+DwYSAy\n0plQSUTkvRQKxYAPH/QZOAZT5x/AL38JvPqq/Xr5+cDixa67LiIid8XA0eUP4L33gPvus19XpwP2\n7QMCA110cUREboiB47I/gKYmID4e+OIL+5/Zvx9ISHDBxRERuSFXBA6P2qsqMBD4xz+sXVP2zJ5t\nzU6IiGhgeFTG0dWRI8Att1jnWPXmxhuBv/8dmDBhAC+QiMjNMONwIDISsFjsZx/NzYBazQWDRET9\nzWMzjq6OHAGioqyBpDe33grs3s2BcyLyfsw4rlBkJNDRYT/7OHQIGDnSGmCIiOjaeEXg6LR4sXVW\nlT3TpwM7drjueoiIvJFXdFVdrqkJSE0FPv649/J/+ifg8885cE5E3oddVVcpMNA6KG4v+zh/ngPn\nRERXyysDR6eEBKCxEZg1q/fy2bOtQebkSddeFxGRJ/PKrqrefPSRNVDY8/nn3CyRiDwfu6r6UUIC\ncOIEMHp07+XTp1vrNDW59LKIiDzOkMk4unKUfSgUwGefMfsgIs/EjGOAdGYffn49y0SYfRAROTIk\nAwdgnYpbVwckJfVe/vHHQFAQFw0SEV1uSHZVXe7kSWDKFODHBxv2MGsWsH07tywhIvfHrioXYfZB\nRHTlGDh+FBgI7NplHfsIDu5ZzrEPIiKrPgOHwWCATqeDRqNBXl5ej/Jjx44hISEBERERmDt3Lkwm\nk61sxYoVCAsLg06nQ36XHQiLioqg0WgQERGBuLg41NTU9FNzrt2ECcDZs9Z1Hb35+GNrYOGiQSIa\nssSBtrY2UavVYjQaxWw2S0xMjFRWVnark5qaKtu2bRMRkdLSUklPTxcRkYKCAklKSpKOjg5paWmR\n6Ohoqa+vFxERlUol1dXVIiLyyiuvyEMPPdTr+fu4vAF34oRIcLCINd/o+brtNpHGxkG9RCKiblzx\nvekw46ioqIBWq4VSqYSvry8yMjJQXFzcrU5NTQ0SExMBAHq9Hjt37oTFYkFNTQ3mzJkDHx8fjBgx\nAlFRUdi1axcAQKVS4fz58wCApqYmTHDT3Qb7yj727mX2QURDj8PAYTQaERISYvtdpVLBaDR2q6PT\n6VBYWAjA2gXV0tKC+vp66HQ6GAwGtLa2oqGhAWVlZaitrQUAbNy4ESkpKQgJCcFbb72FZ555pr/b\n1a8iI61jH9dd17PMbLZumKjXc+yDiIYGX0eFCoWizwNs2LAB2dnZ2Lx5M+Lj46FWq6FQKJCWloaD\nBw8iNjYWQUFBmDFjhm2aWFZWFgwGA2JjY7F+/Xo8/fTT+NOf/tTr8VevXm37Wa/XQ6/XO9XA/tKZ\nfWRmWp/pcflst87s46uvuF07EblOeXk5ysvLXXpOh+s49u/fj7y8PGzfvh0AsG7dOrS3tyMnJ6fX\n+m1tbZg0aRJOnz7do2zJkiW45557EBsbi9mzZ+Prr78GAJw6dQrJycn44osvel6ci9ZxOOvkSWDq\nVKC9vfdybphIRINl0NdxxMbGoqqqCiaTCWazGfn5+UhJSelWp7Gx0XaR69evR2ZmJgDAYrGg6ce+\nm+rqalRUVCA5ORmjR49Gc3MzvvrqKwBASUkJQkND+71hA6kz+1iwwLq31eWmT7eu++DYBxF5I4dd\nVcOHD8err76K5ORkWCwWZGVlITo6Grm5uYiJiUFaWhr27NmDnJwc+Pv7Iy4uDps2bQIAtLe3Y9as\nWfDz80NAQADeffdd+Pv7AwC2bNmChQsXQqFQIDAwEFu3bh34lvazwECguNgaHOLigDNnupc3NlrH\nPrjqnIi8Dbcc6SdHjlgzjd74+XHsg4hcY9C7qujKdc68Gju2Z1nnzCtuWUJE3oAZxwA4cgS45Zae\nM68AYORI4PBhZh9ENDCYcXioyEjgu++sg+eX6xz74LoPIvJUzDgGGMc+iMiVmHF4ga5jH5dP3eWq\ncyLyRMw4XOjkSSAsDLh4sWcZsw8i6g/MOLzMhAnW9R69LRxk9kFEnoIZxyBh9kFEA4EZhxdj9kFE\nnooZhxtg9kFE/YUZxxDB7IOIPAkzDjfjKPvw9bUGkPfe46aJRNQ7ZhxDkKPs49IlYPdua2Bh9kFE\ng4UZhxvj2AcROYsZxxDXmX0sXAjcfHP3ss6xj9Gj+cAoInItZhweoqnJmn3U1fUsUyiA48eZfRAR\nMw7qIjAQqKmxjn38+CBFGxHOvCIi12HG4YGamoDMTGDHjt6f+XHbbcD773PmFdFQ5IrvTQYOD3by\nJDBxYu/B47rrgC+/ZPcV0VDjFl1VBoMBOp0OGo0GeXl5PcqPHTuGhIQEREREYO7cuTCZTLayFStW\nICwsDDqdDvn5+d0+l5OTg8jISGi1WmzYsKEfmjL0TJhgHdsYP946y6qr9nZ2XxHRABEH2traRK1W\ni9FoFLPZLDExMVJZWdmtTmpqqmzbtk1EREpLSyU9PV1ERAoKCiQpKUk6OjqkpaVFoqOjpa6uTkRE\nNm7cKI899pjtGA0NDb2ev4/Loy5OnBAZP17Ez0/EmoP89LruOms5EXk/V3xvOsw4KioqoNVqoVQq\n4evri4yMDBQXF3erU1NTg8TERACAXq/Hzp07YbFYUFNTgzlz5sDHxwcjRoxAVFQUSkpKAACvvfYa\n/v3f/912jFGjRvVvNByCJkwATCbr2o7LFw52Zh+cuktE/cFh4DAajQgJCbH9rlKpYDQau9XR6XQo\nLCwEABQVFaGlpQX19fXQ6XQwGAxobW1FQ0MDysrKUFtbC8DavbVlyxZERkYiMTER1dXV/d2uIctR\n99W5c9YxEQYPIroWvo4KFZf/07UXGzZsQHZ2NjZv3oz4+Hio1WooFAqkpaXh4MGDiI2NRVBQEGbM\nmGE7XltbG4KCgnDkyBEUFRXhF7/4BSoqKno9/urVq20/6/V66PX6K2/dENWZffS28lzEGjzmzeOe\nV0TeoLy8HOXl5S49p8NZVfv370deXh62b98OAFi3bh3a29uRk5PTa/22tjZMmjQJp0+f7lH28MMP\nY9GiRbjrrrsQGhqKPXv2YMKECRARBAQEoKWlpefFcVbVNXM0dZebJhJ5n0GfVRUbG4uqqiqYTCaY\nzWbk5+cjJSWlW53GxkbbRa5fvx6ZmZkAAIvFgqYfp/NUV1fj008/RXJyMgDgzjvvRGlpKQBg7969\nmDJlSv+2imwCA4HiYmv31eULB7lpIhFdjT7XcezYsQMrV66ExWJBVlYWVq1ahdzcXMTExCAtLQ0F\nBQXIycmBv78/4uLisGnTJvj5+aGtrQ233nor/Pz8EBAQgFdeeQWRkZEAgPPnzyMzMxMnTpyAv78/\n/vSnPyE6OrrnxTHj6FeOti3hug8i78AFgAwc/a6pCXj4YeBvfwPq63uWjxoFHDrEAELkqQa9q4q8\nT2CgdTuSL7+07rprb+ZVUhK7r4iod8w4hjhHz/xg9xWR52HGQQOu6xMHLx88b29n9kFEPTHjIJvO\n8Y8PP7Q+KKorTt0l8gwcHGfgGBTsviLyXOyqokHR2X0VHNyzjN1XRMSMg+zq7Loym4GSkp7dV8HB\n1qcSsuuKyH2wq4qBw23Y677y87M+cZBjH0TugYGDgcOtOFp5zuyDyD1wjIPcSmCgNTgsXNhz/KOu\nzvoexz6IvB8zDroqzD6I3BMzDnJbzD6Ihi5mHHTNuOsukftgxkEewVH2wXUfRN6HGQf1K0fblnDq\nLtHA43RcBg6P5WjbEg6eEw0cdlWRx+rctoSD50TehxkHDTgOnhO5DjMO8gocPCfyLn0GDoPBAJ1O\nB41Gg7y8vB7lx44dQ0JCAiIiIjB37lyYTCZb2YoVKxAWFgadTof8/Pwen122bBluvPHGa2wCeYLO\nR9Z2BpCuj6wVAXbvZvcVkccQB9ra2kStVovRaBSz2SwxMTFSWVnZrU5qaqps27ZNRERKS0slPT1d\nREQKCgokKSlJOjo6pKWlRaKjo6Wurs72uQMHDkhWVpbceOONds/fx+WRBztxQsTfX8QaNrq/rrvO\nWk5EznPF96bDjKOiogJarRZKpRK+vr7IyMhAcXFxtzo1NTVITEwEAOj1euzcuRMWiwU1NTWYM2cO\nfHx8MGLECERFRaGkpAQA0NHRgV//+td46aWXOIYxRDkaPGf3FZF7cxg4jEYjQkJCbL+rVCoYjcZu\ndXQ6HQoLCwEARUVFaGlpQX19PXQ6HQwGA1pbW9HQ0ICysjLU1tYCADZu3IiFCxdi7Nix/d0e8iDs\nviLyTL6OChUKRZ8H2LBhA7Kzs7F582bEx8dDrVZDoVAgLS0NBw8eRGxsLIKCgjBjxgwoFAqcPn0a\nBQUFKC8vv6JsY/Xq1baf9Xo99Hp9n58hz9IZQHpb+2E2WwNIeDjwxRdc+0F0ufLycpSXl7v0nA6n\n4+7fvx95eXnYvn07AGDdunVob29HTk5Or/Xb2towadIknD59ukfZkiVLcM8998DX1xePPPIIhg8f\nDgA4deoUJk+ejC+//LLnxXE67pDTufL8//6v5/RdTt0l6tugrxxva2vDtGnT8PHHHyM4OBjx8fHY\nvHkzoqOjbXUaGxsRGBgIhUKB3/zmNzh//jzWrVsHi8WC77//HoGBgaiursaiRYtw+PBh+Pv7dzvH\njTfeiObm5t4vjoFjyLIXQBQKYN48bltCZI8rvjcddlUNHz4cr776KpKTk2GxWJCVlYXo6Gjk5uYi\nJiYGaWlp2LNnD3JycuDv74+4uDhs2rQJANDe3o5Zs2bBz88PAQEBePfdd3sEDeDKusNo6Onsvmpq\nAsaMsQ6YA93HPrjvFdHg4MpxcnuO9r3ixolE3Q16V9VgY+CgTo7GPgBg7FgOnhMBDBwMHNQDB8+J\nHONeVUSXuXztR9fFg1w4SOQazDjIo10+eN6JYx80VLGrioGDroCjwXOOfdBQw64qoivgaN+rM2e4\nbQlRf2PGQV7F0eA5u69oKGBXFQMHXaWmJuv+VmfO9Cxj9xV5M3ZVEV2lwEBrcGD3FVH/Y8ZBXo9r\nP2goYVcVAwf1I3vdV/7+1vfYdUXegF1VRP2oa/dV14dGXbzIrisiZzDjoCHJ3toPzrwiT8euKgYO\nGkCceUXeiF1VRAOIM6+Irg4zDiJw4SB5D3ZVMXCQi7H7ijwdu6qIXIzdV0R9Y8ZBZIej7itmH+Su\n3CbjMBgM0Ol00Gg0yMvL61F+7NgxJCQkICIiAnPnzoXJZLKVrVixAmFhYdDpdMjPz7e9f//990Oj\n0WDatGnIysrCxd72xCYaRF0fGjV2bPcyZh80pEkf2traRK1Wi9FoFLPZLDExMVJZWdmtTmpqqmzb\ntk1EREpLSyU9PV1ERAoKCiQpKUk6OjqkpaVFoqOjpa6uTkRESkpKbJ9/4IEH5OWXX+5x7iu4PCKX\naGwUWbhQJDhYBOj+8vMTmT/fWodosLnie7PPjKOiogJarRZKpRK+vr7IyMhAcXFxtzo1NTVITEwE\nAOj1euzcuRMWiwU1NTWYM2cOfHx8MGLECERFRaGkpAQAMH/+fNvnZ86c2S1LIXI3jrIPsxnYvRuY\nMgVYsIAZCHm/PgOH0WhESEiI7XeVSgWj0ditjk6nQ2FhIQCgqKgILS0tqK+vh06ng8FgQGtrKxoa\nGlBWVoba2tpunzWbzfjLX/6ChQsX9kd7iAaUvcHzG24AGhqAHTvYhUXez7evCgqFos+DbNiwAdnZ\n2di8eTPi4+OhVquhUCiQlpaGgwcPIjY2FkFBQZgxY0aPzz7xxBO47bbbMGvWrF6PvXr1atvPer0e\ner2+z+shGkid2Ufn4LlCAVy4YM06gJ8ykPBwDqDTwCsvL0d5eblLz9nnrKr9+/cjLy8P27dvBwCs\nW7cO7e3tyMnJ6bV+W1sbJk2ahNOnT/coW7JkCe655x7cddddAIA1a9bg888/x1//+tfeL46zqshD\n2Fv/wcWD5GpuMasqNjYWVVVVMJlMMJvNyM/PR0pKSrc6jY2Ntgtdv349MjMzAQAWiwVNP+br1dXV\nqKioQHJyMgDgtddew65du/DOO+/0a4OIBoO9Lqyu2Qe7rshbXNE6jh07dmDlypWwWCzIysrCqlWr\nkJubi5iYGKSlpaGgoAA5OTnw9/dHXFwcNm3aBD8/P7S1teHWW2+Fn58fAgIC8MorryAyMhIA4Ofn\nB7VajYCAAABAeno6nn322e4Xx4yDPBCzDxpM3HKEgYM8FBcP0mBh4GDgIA/H7INcjYGDgYO8ALMP\nciUGDgYO8iLMPsgV3GJWFRH1j75mXnHlOXkKZhxEg+Dy7OOGG4CWFuvPzEDoWrCrioGDvJi9leed\nOP5BV4OBg4GDhgiOf1B/YeBg4KAhhLOvqD8wcDBw0BBkL/tg8KArwVlVREOQvdlXZ85Yg0dQELdt\np8HFjIPIjXXNPgICrIPonZiBUG+YcRANcZ3Zx+LFQFxc9zI+95wGCzMOIg/BwXO6EhwcZ+Ag6oGD\n5+QIu6qIqAcOntNgY8ZB5ME4eE6XY8ZBRA5x8JwGAzMOIi/BwXMCODjOwEF0FewNno8eDcTGAu+8\nwwDizdyiq8pgMECn00Gj0SAvL69H+bFjx5CQkICIiAjMnTsXJpPJVrZixQqEhYVBp9MhPz/f9v7x\n48cxc+ZM6HQ63H///TCbzf3UHCLqbfA8IABoaAB27GD3FfUDcaCtrU3UarUYjUYxm80SExMjlZWV\n3eqkpqbKtm3bRESktLRU0tPTRUSkoKBAkpKSpKOjQ1paWiQ6Olrq6+ttnykqKhIRkeXLl8vvf//7\nXs/fx+URUR8aG0UWLxaZP18E6P4aO9ZaTt7FFd+bDjOOiooKaLVaKJVK+Pr6IiMjA8XFxd3q1NTU\nIDExEQCg1+uxc+dOWCwW1NTUYM6cOfDx8cGIESMQFRWFXbt24dKlS/jkk09w9913AwAeeuihHsck\nov4RGAjk51u3ZR87tnvZmTPWLi1mHuQsh4HDaDQiJCTE9rtKpYLRaOxWR6fTobCwEABQVFSElpYW\n1NfXQ6fTwWAwoLW1FQ0NDSgrK0NtbS3q6uowevRo2+eVSmWPYxJR/+LaD+pPvo4KFQpFnwfYsGED\nsrOzsXnzZsTHx0OtVkOhUCAtLQ0HDx5EbGwsgoKCMGPGDCgUiis6ZlerV6+2/azX66HX6536PBFZ\nBQYC77/f+9qPixetTyAMD+fsK09TXl6O8vJyl57T4ayq/fv3Iy8vD9u3bwcArFu3Du3t7cjJyem1\nfltbGyZNmoTTp0/3KHv44YexaNEiLFiwAOPGjUN9fT0A4MCBA1i1ahV2X/7cTHBWFdFAaWoCHn0U\naGzkI2u9zaDPqoqNjUVVVRVMJhPMZjPy8/ORkpLSrU5jY6PtItevX4/MzEwAgMViQdOPeW91dTU+\n/fRTJCcnw9fXF3FxcXj//fcBAG+99RYWLFjQ7w0jIvu6jn2w+4qc1ec6jh07dmDlypWwWCzIysrC\nqlWrkJubi5iYGKSlpaGgoAA5OTnw9/dHXFwcNm3aBD8/P7S1teHWW2+Fn58fAgIC8MorryAyMhKA\ndTrugw8+iAsXLkCr1eLNN9+En59fz4tjxkHkEty6xHtwASADB5HLsPvKOzBwMHAQuZy9rUuuvx6I\njgZuuomrz90ZAwcDB9Ggsbd1CcAMxJ0N+uA4EQ1dnWs/OhcO3nTTT2VcPDi0MXAQkV1dt20/cqT7\n6vMzZ4Dx44GEBGDBAgaRoYRdVUR0xdh95f7YVUVEboXdVwQwcBCRk/rqvpoyhV1X3o5dVUR0Tewt\nHvTzA267zbo6nd1XrsPpuAwcRB6BiwfdBwMHAweRR+FjawcfB8eJyKPwsbVDAzMOIhoQjrqvmIEM\nHHZVMXAQebzLu69uuAFoabH+zPGP/sfAwcBB5BU6N05UKKyzrrpmIAwe/YuBg4GDyOv0NoDOrqv+\nw8DBwEHklbj2Y+AwcDBwEHktDp4PDAYOBg4ir8fB8/7FwMHAQTQkcPC8/7jFAkCDwQCdTgeNRoO8\nvLwe5ceOHUNCQgIiIiIwd+5cmEwmAEBHRweys7MRFhaGqVOn4rHHHoPFYgEA/Od//ifCwsKg1Wox\nf/58nD17tp+bRUSeJDAQeP99oKjIOr5x+caJY8cCQUFcPOg2xIG2tjZRq9ViNBrFbDZLTEyMVFZW\ndquTmpoq27ZtExGR0tJSSU9PFxGRkpISmTVrllgsFuno6JCZM2fKnj175IcffpDAwEA5d+6ciIj8\n+te/lmeffbbX8/dxeUTkpRobRcaOFQFEAgKs/+18jR1rLafeueJ702HGUVFRAa1WC6VSCV9fX2Rk\nZKC4uLhbnZqaGiQmJgIA9Ho9du7cCRHB+PHj0d7ejosXL6K1tRVmsxlKpRL+/v4YNWoULly4ABHB\n+fPnMWHChIGKi0Tkgbpu3R4X172MW7cPPoeBw2g0IiQkxPa7SqWC0WjsVken06GwsBAAUFRUhJaW\nFtTV1UGj0eD222/HuHHjoFQqcccddyAsLAw+Pj744x//iIiICCiVSlRXV2Pp0qUD0DQi8mSBgUB+\nvrXryt7eV3xw1ODwdVSoUCj6PMCGDRuQnZ2NzZs3Iz4+Hmq1GgqFAvv27UNZWRlMJhNEBElJSUhO\nTkZkZCSWLVuGzz//HBMnTsRTTz2FF198ETk5Ob0ef/Xq1baf9Xo99Hq9Uw0kIs/WOf7R2/TdzqcO\nDuXB8/LycpSXl7v0nA4Dh0qlQm1tre332trabhkIACiVSmzfvh0A0NbWhkmTJiE4OBivv/46UlJS\nMGLECABASkoKPv74Y/j6+mLixImYOHEiAGDx4sV48cUX7V5D18BBRENXZwZy+fTdzsHzESOAW28d\neosHL/8H9Zo1awb8nA67qmJjY1FVVQWTyQSz2Yz8/HykpKR0q9PY2Gib+rV+/XpkZmYCAEJDQ7F3\n7150dHTAbDZj7969CA0NhVqtxj/+8Q80NDQAAEpKShAaGjoQbSMiL3T5c88DAoCLF3/KRDj+MfAc\nBo7hw4fj1VdfRXJyMqZPn45FixYhOjoaubm5+N///V8AwJ49ezBt2jRERkbi1KlTWLt2LQAgPT0d\noaGhmDaGK/qZAAALW0lEQVRtGjQaDaZNm4b09HSMHTsWq1evRnx8PMLDw3HgwAHk5uYOfEuJyGvY\nGzy/4QaOf7gCFwASkUfj4sHuuHKcgYOInMCddxk4GDiIyGn2dt69/nogOhq46SbvDiIMHAwcRHQV\nHO28C3h3FxYDBwMHEV2DrtnHTTcB33//U5m3Bg+32OSQiMhTdZ19deRIz80TOXX36jDjIKIhw974\nhzdlH8w4iIj6kb31H8w+nMOMg4iGJG/NPphxEBENEGYfV48ZBxENefayj8WLrRsrehJOx2XgICIX\nuXztR0wMUFLieV1WDBwMHETkYp0BZMsWzwsaAAMHAwcRkZM4OE5ERG6HgYOIiJzCwEFERE5h4CAi\nIqcwcBARkVP6DBwGgwE6nQ4ajQZ5eXk9yo8dO4aEhARERERg7ty5MJlMAICOjg5kZ2cjLCwMU6dO\nxWOPPQaLxQIAaG9vx6OPPorIyEiEh4ejsLCwn5tFREQDxWHguHjxIh5//HEYDAYcOXIEBQUFOHz4\ncLc6y5cvR3Z2NqqqqvDcc89h+fLlAICysjIcPXoU1dXVqK6uxpEjR1BeXg4AyMnJwcSJE3HkyBF8\n8cUXSExMHJjWubHOPwtvxfZ5NraPHHEYOCoqKqDVaqFUKuHr64uMjAwUFxd3q1NTU2P74tfr9di5\ncydEBOPHj0d7ezsuXryI1tZWmM1mKJVKiAjeeecd/Nu//ZvtGCNHjhyAprk3b/+Ly/Z5NraPHHEY\nOIxGI0JCQmy/q1QqGI3GbnV0Op2tq6moqAgtLS2oq6uDRqPB7bffjnHjxkGpVOKOO+5AWFgYzp49\ni+HDh+Opp55CREQE7rrrLpzp+mR5IiJyaw4Dh0Kh6PMAGzZswK5du6DVarFjxw6o1WooFArs27cP\nZWVlMJlMMJlM2LNnDz766CNYLBYcP34ct99+O6qqqjB37lxb9xYREXkAcWDfvn1y55132n5/6aWX\n5De/+Y3d+q2trTJu3DgREVm7dq288MILtrLnn39efve738mlS5fk+uuvt71/6tQpmTp1aq/Hmzx5\nsgDgiy+++OLrCl+TJ0929LXeL3zhQGxsLKqqqmAymRAcHIz8/Hxs3ry5W53GxkYEBgZCoVBg/fr1\nyMzMBACEhoZiy5YtWLVqFSwWC/bu3YvHH38cw4YNQ1JSEsrKyjB37lzs2bMH4eHhvZ7/66+/dnR5\nREQ0CPrc5HDHjh1YuXIlLBYLsrKysGrVKuTm5iImJgZpaWkoKChATk4O/P39ERcXh02bNsHPzw8W\niwVPPPEEdu/eDQBITk7Gxo0bAQCnTp1CVlYWzp07h5EjR+LNN9+EWq0e8MYSEdG1c+vdcYmIyP24\n5crxvhYduova2lrMmTMHOp0OYWFheOmllwAA3333HZKSkhAZGYnk5GQ0dXn+5LJly6DVahEdHd1t\nTczWrVuh1Wqh1Wqxbds22/uHDh1CVFQUtFrtoE0i6OjoQFRUFNLS0gAAx48fx8yZM6HT6XD//ffD\nbDYDsK77ycjIgE6nw6xZs3Dy5EnbMV588UVoNBrodDrs2rXL9v5g3+umpiYsXrwY06dPR3h4OD75\n5BOvun+5ubmYOnUqpk2bhnvvvRc//PCDR9+/pUuXYsyYMdDpdLb3XHG/HJ1jINv29NNPQ6PRQKPR\nIDU1FefOnbOVOXtPrua+2zXgoyhOamtrE7VaLUajUcxms8TExEhlZeVgX1avzpw5I3//+99FRKS5\nuVmmTJkin332mTz55JPyhz/8QURE/vCHP8iyZctERKSgoEAWLlwoIiKVlZUyffp0ERE5ffq0TJ48\nWZqbm6W5uVkmT54sZ8+eFRERnU5na//ChQvlr3/9q0vbKCLyH//xH/Lggw9KWlqaiIikpqZKUVGR\niIgsX75cfv/734uIyPr162X58uUiIlJUVCR33XWXiIgcPHhQYmJi5NKlS2I0GkWtVkt7e7tb3Ot7\n771X3nnnHRER6ejokPPnz3vN/fvqq69k4sSJcvHiRRERue++++S1117z6Pu3b98+qayslIiICNt7\nrrhf9s4x0G0rLS2Vjo4OERF55plnZMWKFSJydffE2fvuiNsFjr1793abybVu3bpus7PcWXp6uhQX\nF8ukSZOkoaFBRETq6+ttsxyWLFkiBQUFtvparVZqa2tl69at8uSTT9ref+KJJ+TNN9+UkydPilar\ntb3/3nvvySOPPOKi1ljV1tbKvHnzpLS0VFJTU+XSpUsyevRoW/mBAwdk3rx5IiKSmJgoBw8eFBHr\nl/Do0aOlo6ND1qxZI+vXr7d95s4775T9+/cP+r1uaGiQ0NDQHu97y/07d+6cTJ06Vb777jsxm82S\nmpoqu3bt8vj7d/z48W5frq64X/bOMdBt6+qDDz6QxYsXi4g4fU/MZrNT991isTi8TrfrqrqSRYfu\n6MSJEzhw4AASEhJQX1+PUaNGAQBGjx6Nuro6AIDJZOq1bSaTCSqVqtf3u9ZXKpUu/7P41a9+hXXr\n1sHHx/pXpa6uDqNHj+71mrreOx8fH4waNQp1dXVX3D5X3+uvvvoKN998M+677z5ERETgn//5n9Hc\n3Ow19y8oKAj/+q//ip/97GcYP348AgMDERER4TX3r5Mr7pe9c7jSli1bsHDhQgBw+p7U19c7fd8d\ncbvAcSWLDt3NhQsXcO+99+KPf/wjbrrpJod1xYPmImzfvh3BwcGIioqyXbcnXX9fLBYLDhw4gJUr\nV6KqqgpBQUF44YUXHH7Gk9r/zTff4OWXX8aJEydw+vRpXLhwASUlJYN9WS7lSffLkd/+9re47rrr\nbMsdBpvbBQ6VSoXa2lrb77W1td0iqLsxm81IT09HZmYm7r77bgDAzTffjIaGBgDWf6kEBwcD6Nm2\nzkhvr8291e/6r4yB9re//Q0ffPABJk6ciAceeAClpaV45plnbG27/JpUKhVOnToFwPqlfO7cOdx8\n881Ot9tVQkJCoFQqERsbCwC499578dlnnyE4ONgr7t+nn36K+Ph4jBo1Cr6+vli0aBH27dvnNfev\n00D+/9bZHnvncIWtW7eiuLgYb7/9tu09Z9vW9e90Z/2+7rsjbhc4ui46NJvNyM/PR0pKymBfVq9E\nBI888gg0Gg1+9atf2d5fsGAB3nrrLQDAW2+9hQULFtje77z5lZWVGDZsGJRKJebNmweDwYDm5mY0\nNzfDYDBg/vz5CAkJgY+Pj202yNtvv207liusXbsWtbW1OH78ON59910kJibizTffRFxcHN5///1e\n29fZ7v/5n//BzJkzMWzYMCxYsAD//d//jUuXLsFoNKKqqgo///nPB/1eh4SEYPTo0fjyyy8BALt3\n70Z4eDhSUlK84v6Fhobik08+QWtrK0QEu3fvxrRp07zm/nUayP/fOttj7xwDzWAw4KWXXsIHH3yA\n4cOHd2uzM/dk2LBhTt33zq5pu5wcu3GJDz/8ULRarYSHh8vatWsH+3Ls2r9/vygUCpk+fbrccsst\ncsstt8iOHTvk3LlzMn/+fNHpdJKUlCSNjY22zzzxxBOi0WgkKipKDh06ZHv/jTfekPDwcAkPD5e/\n/OUvtvcPHjwot9xyi2g0Gnnqqadc2r6uysvLbbOqjh07JnFxcRIRESEZGRnS3t4uItYZcYsXL5aI\niAiZOXOmHD9+3Pb53/72txIeHi5arVYMBoPt/cG+15999pnExMSIRqORlJQU+e6777zq/uXm5kpo\naKhMnTpVMjIypLW11aPv3/333y/jxo0TPz8/UalU8sYbb7jkfjk6x0C17fXXX5fQ0FD52c9+Zvt+\nefzxx231nb0nV3Pf7eECQCIicorbdVUREZF7Y+AgIiKnMHAQEZFTGDiIiMgpDBxEROQUBg4iInIK\nAwcRETmFgYOIiJzy/07f8IpRxxBuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x16b2ffd0>"
       ]
      }
     ],
     "prompt_number": 537
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "semilogy(r0.start_temp,1/r0.Q_i,'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "[<matplotlib.lines.Line2D at 0x44f7090>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEDCAYAAADZUdTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x14728490>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(r0.Q_i,r0.Q)\n",
      "plot(r0.Q_i,r0.Q_e_real)\n",
      "plot(r0.Q_i,r0.Q_i)\n",
      "ylim(0,40000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 532,
       "text": [
        "(0, 40000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEACAYAAAByG0uxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x166059d0>"
       ]
      }
     ],
     "prompt_number": 532
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lmfit.report_fit(km.params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[Variables]]\n",
        "     F_TLS:         fixed\n",
        "     T_star:        fixed\n",
        "     Tc:             1.546948 +/- 0.006413 (0.41%) initial =  1.460000\n",
        "     delta_0:       fixed\n",
        "     delta_loss:    fixed\n",
        "     nqp0:           452.653018 +/- 11.606270 (2.56%) initial =  5000.000000\n",
        "     sigman:         249971.453017 +/- 1045.876351 (0.42%) initial =  500000.000000\n",
        "[[Correlations]] (unreported correlations are <  0.100)\n",
        "    C(Tc, sigman)                = -1.000 \n",
        "    C(Tc, nqp0)                  =  0.309 \n",
        "    C(nqp0, sigman)              = -0.308 \n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1/(2*np.pi*np.sqrt(km.cap*km.Lg))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "174911384.08590943"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km.Lk(0.1)/1e-9"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "42.244306972488886"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "errorbar(r0.start_temp,r0.Q_i,r0.Q_err)\n",
      "ylim(1000,5e5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(1000, 500000.0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBGRJgwMIiLS5P8DDvn+Cq4u6ngAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9a83310>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(r0.start_temp,r0.Q_err)\n",
      "ylim(0,10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "(0, 10000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAxERKTAYCAiIgUGAxERKfw/7rg+h0FdNb4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4a13650>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plot(T,km.xi(T))\n",
      "T2 = np.logspace(-4,0,1000)\n",
      "plot(T2,1/np.tanh(km.xi(T2)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1471dd90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44ddad0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(T,1e4/np.tanh(km.xi(T)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "[<matplotlib.lines.Line2D at 0x44e98d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEACAYAAACZLPCyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5kAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44f0490>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(T,km.tls_shift(T))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "[<matplotlib.lines.Line2D at 0xc3cb650>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFpRJREFUeJzt239M1df9x/HXpTi1g62OH0q4tlo6p1wvFYo2lGhuqwzB\nie0fy12mdyYu21Kr1TmtXdqK2rrWWadbTK1tMqsykxF/skCZdBtrWtLrClrENkvsLNzLVBiGTrOK\nuHu+f2weZViF+0HgS5+P5Cb3cs+9980R75PPvVyXMcYIAABJMQM9AABg8CAKAACLKAAALKIAALCI\nAgDAIgoAAMtxFCorK+X1epWenq6NGzd2u76jo0N+v19er1e5ublqbGy019XX12v69OnKzMyU1+tV\nR0eH03EAAA64nHxOoaOjQxMnTtQ777yj0aNHKycnR6+99poyMzPtms2bNysUCmnr1q06dOiQdu7c\nqcOHD+vSpUvKzs7WgQMHNGHCBH366aeKj49XTAwHLwAwUBw9AweDQXk8HqWmpio2NlZ+v1/l5eVd\n1lRUVCgQCEiSioqKVFNTo0gkosrKSk2bNk0TJkyQJH31q18lCAAwwBw9C4fDYY0dO9ZedrvdCofD\nn7smJiZGCQkJamlp0V//+lddvnxZPp9PXq9XL7zwgpNRAAB9INbJjV0uV9S3/fe//62amhq9//77\nGjlypGbOnKkHHnhABQUFTkYCADjgKAput1uhUMheDoVCXY4crq5pampScnKyIpGI2tralJycrLvv\nvlszZszQ1772NUlSYWGhjh8/3i0K9913nz7++GMnYwLAF05aWppOnTrV69s5evlo6tSpamhoUHNz\nszo7O1VaWtrtSb2wsFAlJSWSpMOHDysnJ0cxMTGaOXOmPvjgA3322We6cuWK/vznP2vSpEndHuPj\njz+WMYaTMSouLh7wGQbLib1gL9iLm5+i/WXa0ZHCiBEjtH37duXn5ysSiSgQCCgrK0vFxcXKzs7W\n3LlztWTJEgUCAXm9XsXHx2vv3r2SpJSUFK1cuVJTp05VZ2enCgsL9eijjzoZBwDgkKMoSFJBQUG3\no4N169bZ88OHD1dpaekNbzt//nzNnz/f6QgAgD7C34D+P+Lz+QZ6hEGDvbiGvbiGvXDO0YfX+oPL\n5dIgHxEABp1onzs5UgAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUA\ngEUUAAAWUQAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAW\nUQAAWEQBAGA5jkJlZaW8Xq/S09O1cePGbtd3dHTI7/fL6/UqNzdXjY2NXa5vampSXFycNm/e7HQU\nAIBDjqLQ0dGhxx9/XJWVlaqvr9e+fft07NixLmu2bdumlJQUnThxQqtWrdKTTz7Z5foVK1Zozpw5\nTsYAAPQRR1EIBoPyeDxKTU1VbGys/H6/ysvLu6ypqKhQIBCQJBUVFammpkbGGEnSoUOHdO+99yo9\nPd3JGACAPuIoCuFwWGPHjrWX3W63wuHw566JiYlRQkKCWlpadPHiRf385z/X2rVrnYwAAOhDjqLg\ncrmiup0xRmvXrtWPf/xj3XnnnfbIAQAwsGKd3NjtdisUCtnLoVCoy5HD1TVNTU1KTk5WJBJRW1ub\nkpKSdPToUe3fv19PPfWU2tvbFRMTo5EjR2rx4sXdHuf6owmfzyefz+dkbAAYcqqrq1VdXe34flzG\nwa/ply5d0sSJE/Xuu+8qOTlZDz30kHbs2KGsrCy7ZvPmzQqFQtq6dasOHjyonTt3qqysrMv9rFu3\nTvHx8VqxYkX3AV0ujiQAoJeife50dKQwYsQIbd++Xfn5+YpEIgoEAsrKylJxcbGys7M1d+5cLVmy\nRIFAQF6vV/Hx8dq7d6+ThwQA3EaOjhT6A0cKANB70T538olmAIBFFAAAFlEAAFhEAQBgEQUAgEUU\nAAAWUQAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAWUQAA\nWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgOU4CpWVlfJ6vUpPT9fG\njRu7Xd/R0SG/3y+v16vc3Fw1NjZKko4cOaKsrCxlZGTI6/Xq97//vdNRAAAOuYwxJtobd3R0aOLE\niXrnnXc0evRo5eTk6LXXXlNmZqZds3nzZoVCIW3dulWHDh3Szp07dfjwYdXX1yslJUVJSUk6efKk\nZs6cqTNnzsjlcnUd0OWSgxEB4Asp2udOR0cKwWBQHo9Hqampio2Nld/vV3l5eZc1FRUVCgQCkqSi\noiLV1NTIGKOMjAwlJSVJkjwejyKRiC5duuRkHACAQ46iEA6HNXbsWHvZ7XYrHA5/7pqYmBglJCSo\npaWly5p9+/bp/vvv18iRI52MAwBwKNbJjf/3pZ5ofPjhh3r66adVVVX1uWvWrl1rz/t8Pvl8PseP\nCwBDSXV1taqrqx3fj6MouN1uhUIhezkUCnU5cri6pqmpScnJyYpEImpra7MvG4XDYT322GPas2eP\nxo8f/7mPc30UAADd/e8vzOvWrYvqfhy9fDR16lQ1NDSoublZnZ2dKi0tVUFBQZc1hYWFKikpkSQd\nPnxYOTk5iomJUXt7u+bMmaOXXnpJOTk5TsYAAPQRR399JElvvvmmVq1apUgkokAgoJ/+9KcqLi5W\ndna25s6dq46ODgUCAX300UeKj4/X3r17NW7cOL3wwgt66aWX9PWvf93eV1VVlRITE7sOyF8fAUCv\nRfvc6TgKtxtRAIDeG5A/SQUADC1EAQBgEQUAgEUUAAAWUQAAWEQBAGARBQCARRQAABZRAABYRAEA\nYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAWUQAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBF\nFAAAFlEAAFhEAQBgEQUAgEUUAACW4yhUVlbK6/UqPT1dGzdu7HZ9R0eH/H6/vF6vcnNz1djYaK97\n8cUXlZ6eLq/XqyNHjjgdBQDgkKModHR06PHHH1dlZaXq6+u1b98+HTt2rMuabdu2KSUlRSdOnNCq\nVav05JNPSpJqa2t14MABnThxQpWVlfrRj36ky5cvOxkHAOCQoygEg0F5PB6lpqYqNjZWfr9f5eXl\nXdZUVFQoEAhIkoqKilRTU6NIJKLy8nJ95zvf0R133KHU1FR5PB4dPXrUyTgAAIccRSEcDmvs2LH2\nstvtVjgc/tw1MTExSkhIUEtLi5qbm+V2u296WwBA/4p1cmOXy9VXc9zicdZed8n33xMA4Jrq/56c\ncRQFt9utUChkL4dCoS5HDlfXNDU1KTk5WZFIRG1tbUpKSup22/896rieMWudjAkAXwA+Xf8Ls8u1\nLqp7cfTy0dSpU9XQ0KDm5mZ1dnaqtLRUBQUFXdYUFhaqpKREknT48GHl5OTojjvuUGFhoX7729/q\nypUrCofDamho0LRp05yMAwBwyNGRwogRI7R9+3bl5+crEokoEAgoKytLxcXFys7O1ty5c7VkyRIF\nAgF5vV7Fx8dr7969kqQHHnhAjz32mDIyMhQTE6MdO3Zo2LBhffJNAQCi4zLGmIEe4mZcLpcG+YgA\nMOhE+9zJJ5oBABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAWUQAAWEQBAGARBQCA\nRRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAWUQAAWEQBAGARBQCARRQAABZR\nAABYRAEAYBEFAIDlKArnz59XXl6eMjIylJ+fr/b29huu27Vrlzwejzwej3bv3i1J+te//qX8/Hx5\nPB594xvf0IoVK2SMcTIOAMAhl3HwTLx06VKlpaVp+fLl2rp1q06fPq1f/vKXXdacOXNG06dP1/Hj\nxyVJU6ZM0bvvvquvfOUrqqurU25urjo7OzVz5kz95Cc/0bx587oO6HIRCwDopWifOx0dKVRUVCgQ\nCEiSFixYoPLy8m5rqqqqVFBQoLi4OMXFxWn27NmqqqrSyJEjlZubK0kaNmyYpk2bpr///e9OxgEA\nOBTr5Matra1KSEiQJCUmJqqlpaXbmubmZrndbnvZ7XYrHA53WdPe3q6DBw/qrbfecjIOAMChW0Yh\nLy9PZ8+e7fb1DRs29MkAV65c0Xe/+10tW7ZM48ePv+GatWvX2vM+n08+n69PHhsAhorq6mpVV1c7\nvh9H7ymkpaUpGAwqMTFRra2tysnJ0alTp7qs2bNnj4LBoLZt2yZJeuKJJ/TQQw9p/vz5kqRFixYp\nPj6+23sRdkDeUwCAXhuQ9xQKCwtVUlIiSSopKVFhYWG3NbNmzVJlZaUuXLigCxcuqLKyUrNmzZIk\nPfvss/r000+1detWJ2MAAPqIoyOF8+fPy+/369y5cxozZoxKS0t11113qba2Vq+++qpef/11SdLO\nnTu1adMmSdLq1au1cOFChcNh3X333Zo0aZK+9KUvSfrPXzMtWrSo64AcKQBAr0X73OkoCv2BKABA\n7w3Iy0cAgKGFKAAALKIAALCIAgDAIgoAAIsoAAAsogAAsIgCAMAiCgAAiygAACyiAACwiAIAwCIK\nAACLKAAALKIAALCIAgDAIgoAAIsoAAAsogAAsIgCAMAiCgAAiygAACyiAACwiAIAwCIKAACLKAAA\nLKIAALCIAgDAijoK58+fV15enjIyMpSfn6/29vYbrtu1a5c8Ho88Ho92797d7fqioiJ5vd5oxwAA\n9KGoo1BcXKw5c+aovr5eBQUFKi4u7rbmzJkzev755xUMBhUMBrV+/XqdO3fOXn/gwAHFx8fL5XJF\nOwYAoA9FHYWKigoFAgFJ0oIFC1ReXt5tTVVVlQoKChQXF6e4uDjNnj1bVVVVkqSLFy9qy5YtevbZ\nZ2WMiXYMAEAfijoKra2tSkhIkCQlJiaqpaWl25rm5ma53W572e12KxwOS5Kee+45rVy5UnfeeWe0\nIwAA+ljsza7My8vT2bNnu319w4YNjh70+PHj+tvf/qYtW7bok08+cXRfAIC+c9MoXH2p50aSkpL0\nj3/8Q4mJiWptbVVycnK3NW63W8Fg0F4OhULKycnRe++9p/fff1/jx4/XlStX1NLSokceeUR//OMf\nb/hYa9euted9Pp98Pt8tvi0A+GKprq5WdXW14/txmShf0F+6dKnS0tK0fPlybdmyRadPn9avfvWr\nLmvOnDmj6dOn69ixY5KkKVOmqKamRqNHj7ZrGhsb9a1vfUsnTpy48YAuF+85AEAvRfvcGfV7CuvW\nrVN5ebkyMjL05ptvav369ZKk2tpa/eAHP5AkpaSk6JlnntGDDz6oBx98UGvWrOkSBEkyxvDXRwAw\nSER9pNBfOFIAgN7r9yMFAMDQQxQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAW\nUQAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAIBFFAAAFlEAAFhEAQBgEQUAgEUUAAAWUQAAWEQB\nAGARBQCARRQAABZRAABYRAEAYEUdhfPnzysvL08ZGRnKz89Xe3v7Ddft2rVLHo9HHo9Hu3fvtl+/\nfPmyfvjDHyojI0OTJk3S/v37ox0FANBHoo5CcXGx5syZo/r6ehUUFKi4uLjbmjNnzuj5559XMBhU\nMBjU+vXr1dLSIkl65plnNH78eNXX1+ujjz7SI488Ev138QVRXV090CMMGuzFNezFNeyFc1FHoaKi\nQoFAQJK0YMEClZeXd1tTVVWlgoICxcXFKS4uTrNnz9aRI0dkjNHevXu1cuVKu3bUqFHRjvKFwQ/8\nNezFNezFNeyFc1FHobW1VQkJCZKkxMREewRwvebmZrndbnvZ7XYrHA6rpaVFI0aM0NKlSzV58mQV\nFRXp7Nmz0Y4CAOgjN41CXl6evF5vt1NZWVnUD+hyuRSJRHT69Gl985vfVENDgx5++GEtW7Ys6vsE\nAPQRE6V7773XtLa2GmOMaWlpMWlpad3W7N692zzxxBP28uLFi01JSYnp7Ow0I0eOtF9vamoyEyZM\nuOHjpKWlGUmcOHHixKkXpxs9J/dErKJUWFiokpISLV++XCUlJSosLOy2ZtasWVq3bp0uXLggSaqs\nrNSaNWsUGxurvLw8/elPf9LDDz+sP/zhD5o0adINH+fUqVPRjggA6CWXMcZEc8Pz58/L7/fr3Llz\nGjNmjEpLS3XXXXeptrZWr776ql5//XVJ0s6dO7Vp0yZJ0urVq7Vw4UJJUlNTkwKBgNra2jRq1Cjt\n2bNH48aN65vvCgAQlaijAAAYegbNJ5orKyvl9XqVnp6ujRs3dru+o6NDfr9fXq9Xubm5amxsHIAp\n+8et9uLtt99WVlaWhg0bNuQ/9Hervdi0aZM8Ho8mT56sGTNm6PTp0wMwZf+41V688soruv/++5WR\nkaHs7GzV1tYOwJT941Z7cdX+/fsVExOjurq6fpyuf91qL9544w0lJSUpMzNTmZmZ+vWvf33zO4zq\nnYg+dunSJTNu3DgTDodNZ2enyc7ONnV1dV3WvPzyy2bZsmXGGGMOHjxoioqKBmLU264ne/HJJ5+Y\n+vp6873vfc/s27dvgCa9/XqyF2+//ba5dOmSMcaY7du3m0cffXQgRr3terIXFy5csOfLysrMjBkz\n+nvMftGTvTDGmH/+859m+vTpJicnx9TW1g7ApLdfT/bijTfeMEuXLu3xfQ6KI4VgMCiPx6PU1FTF\nxsbK7/d3+zDc9R+WKyoqUk1NjcwQfOWrJ3txzz33yOv1KiZmUPzz3TY92Yvp06dr+PDhkqTc3Fw1\nNzcPxKi3XU/2Ii4uzp6/ePGiUlJS+nvMftGTvZCk5557Tk8//bSGDx8+JJ8rpJ7thTGmV9//oHhW\nCYfDGjt2rL189UNun7cmJiZGCQkJN/zA3P93PdmLL4re7sWOHTs0b968/hit3/V0L1555RXdd999\nWrFihV588cX+HLHf9GQv6urq1NzcbP8q0uVy9euM/aUne+FyuXTgwAF5PB4VFRXd8qX3QRGFofoP\nFg324pre7MVvfvMb1dXV6amnnrqNEw2cnu7F4sWLderUKf3iF7/QokWLbvNUA+NWexGJRLRixQq9\n/PLL9mtD9UihJz8XV0Nw8uRJzZs3T/Pnz7/p+kERBbfbrVAoZC+HQqEu9bu6pqmpSdJ//tHb2tqU\nlJTUr3P2h57sxfWGckR6uhdvvfWWNmzYoLKyMg0bNqw/R+w3vf258Pv9+stf/tIfo/W7W+3FhQsX\ndPLkSfl8Po0fP17vvfeeioqKhuSbzT35uRg1apRiY//zkbTvf//7+uCDD25+p334nkfUPvvsM3PP\nPfeYcDhsLl++bLKzs7u9MXT9G80HDhwwc+fOHYhRb7ue7MVVCxcuHNJvNPdkL+rq6kxaWpo5derU\nAE3ZP3qyF6dPn7bny8rKTHZ2dj9P2T9683/EGGN8Pt+QfaO5J3vR0tJiz5eVlZnMzMyb3uegiIIx\nxlRUVBiPx2MmTZpkfvaznxljjFmzZo0pKyszxvznXfZvf/vbZvLkySYnJ6fLf4Ch5lZ7cfToUeN2\nu82Xv/xlk5CQYCZPnjyQ495Wn7cXv/vd74wxxsyaNcuMGTPGTJkyxUyZMsXMmzdvIMe9rW71c7Fk\nyRLj9XqNx+MxM2bMMB9++OFAjntb3WovrjeUo2DMrfdi9erVxuv1mvT0dJObm2saGhpuen98eA0A\nYA2K9xQAAIMDUQAAWEQBAGARBQCARRQAABZRAABYRAEAYBEFAID1f0TCrFFBe1zmAAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd7efc90>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "6.626e-34*200e6/1.38e-23"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "0.009602898550724638"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "deltaf = (r0.f_0-r0.f_0.max())/r0.f_0.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(2*deltaf,1/r0.Q_i,c=r0.start_temp)\n",
      "ylim(0,0.0002)\n",
      "xlim(-0.015,0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "(-0.015, 0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEACAYAAACQx1DIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVPe9LvB3Zrh4I8YiRMOgk2jaMMNw0cE4IWlI1FhR\nUZNY6KPsXmzaZnvBJtumzcXIbtUYrbl5mmPSGG3UUy+RqAHRmEi0oiSApofYY7aKwIxXMMYbMDPM\n9/xhmIrAoPKDGeD9PI/Pw5r5rd96h4T1staaNWhEREBERKSI1tcBiIioc2GxEBGRUiwWIiJSisVC\nRERKsViIiEgpFgsRESnVYrHk5ubCbDbDaDRi0aJFjZ6vra1FamoqzGYzEhMTUVZW5nlu4cKFMBqN\nMJvN2LFjR4tzpqWlwWg04t5770V6ejpqa2s9z82aNQsmkwlDhgzBgQMHbvkFExFRGxMvampqxGAw\niM1mE6fTKRaLRYqLixuMWbJkiWRkZIiISFZWlqSkpIiISGFhoVgsFnG5XGKz2cRgMIjD4fA658cf\nf+yZ9yc/+Ym89tprIiKyceNGmTBhgoiIFBcXS2xsrLfYRETkQ16PWAoKCmAymRAREYGAgACkpqYi\nOzu7wZicnBykp6cDAFJSUpCfnw+3243s7GykpaVBp9MhIiICJpMJBQUFXuccOXKkZ16r1YoTJ04A\nALKzsz3biI+Ph8vlgs1mU9euRESkjNdisdlsiIyM9Czr9fpGO/Rrx2i1WoSGhuLMmTOw2+3Q6/WN\n1rXb7S3O6XQ6sXLlSqSkpADADa1DRET+wWuxaDSa9srRwPTp0/HQQw8hMTHR85hc98kzvspGRETe\nBXh7Uq/Xo6KiwrNcUVHR4Mihfkx5eTnCw8PhdrtRVVWFsLCwRuvWH9m43W6vc2ZmZqKyshJvv/12\noxz33XefZ65rj4bqxcXF4csvv7zR105ERAAGDRqEI0eOqJvQ2wWY6upqGThwoNhsNnE4HGKxWKSo\nqKjBmGsv3m/atEnGjx8vIv++eO90OqWiokIGDhwoDofD65zvvPOO3H///VJdXd1gGxs3bpSJEyeK\niEhRUZHExMQ0mbeFl3PLXnrppTaZty11tMwdLa8IM7eHjpZXpGNmVr3v9HrE0q1bN7z11lsYPXo0\n3G430tPTMWTIELz00kuwWCwYP348ZsyYgfT0dJjNZoSEhGDt2rUAgKFDh2LSpEmIiYmBVqvF8uXL\nERgYiMDAwCbnBICnnnoKBoMBVqsVAPD444/jhRdewOOPP45du3bBZDIhODgY7733nrpmJSIipbwW\nCwCMGTMGY8aMafBYZmam5+vg4GCsX7++yXWfe+45PPfcczc0J3D1on1zli1b1lJUIiLyA7zz/gYk\nJSX5OsJN62iZO1pegJnbQ0fLC3TMzKppvju/1iloNJpG7x4jIiLvVO87ecRCRERKsViIiEgpFgsR\nESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLERE\npBSLhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGR\nUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlOpSxeJ0OnHs2DGcP3/e11GI\niDqtLlMs//rXv3B3ZCSGx8Qg4o47sOSVV3wdiYioU9KIiPg6hCoajQbNvZyowYMx+OhRWAB8C+Dd\nwEB89MknePDBB9s1IxGRv/G277yl+bpCsdTV1SEoMBAviHgO0TYBCB46FJ8XFrZrRiIif6O6WLrE\nqTCdToc+ISE4+t1yLYATAA4cOIArV674MBkRUefTJYoFAKbPno31AJYDeA2Azsd5iIg6qwBfB2gv\np+12RAK4D0A3ANsAxMXGokePHr4NRkTUyXSZYtm/dy+SAAz4bnkYgG4DB/ouEBFRJ9VlToVFDhiA\nCu3VlysATgYFwTB4sG9DERF1Ql3iXWEAcOTIETwwfDjCHA44RKC94w7kf/EF+vTp084piYj8C99u\n7EVL35yqqirs2rULgYGBGDVqFK+vEBGBxeKV6m8OEVFXwPtYiIjIr7FYiIhIKRYLEREp1WKx5Obm\nwmw2w2g0YtGiRY2er62tRWpqKsxmMxITE1FWVuZ5buHChTAajTCbzdixY0eLcy5btgyDBw+GVqvF\nuXPnPI/n5eWhd+/eiI+PR3x8PP70pz/d8gsmIqI2Jl7U1NSIwWAQm80mTqdTLBaLFBcXNxizZMkS\nycjIEBGRrKwsSUlJERGRwsJCsVgs4nK5xGazicFgEIfD4XXOAwcOyPHjx8VgMEhVVZVnG7t27ZLx\n48d7iyrfvQmhxTFERNSQ6n2n1yOWgoICmEwmREREICAgAKmpqcjOzm4wJicnB+np6QCAlJQU5Ofn\nw+12Izs7G2lpadDpdIiIiIDJZEJBQYHXOePi4jCwmbvhhe/2IiLqELwWi81mQ2RkpGdZr9fDZrM1\nO0ar1SI0NBRnzpyB3W6HXq9vtK7dbm9xzutpNBrs27cPZrMZI0aMwJdffnnjr5CIiNqV188K02g0\n7ZXDq6FDh8Jms6Fbt27YsWMHJk6ciNLS0ibHzps3z/N1UlISkpKS2ickEVEHkZeXh7y8vDab32ux\n6PV6VFRUeJYrKioaHG3UjykvL0d4eDjcbjeqqqoQFhbWaN36Ixu3293inNfr1auX5+tHH30UQUFB\nOHXqFPr169do7LXFQkREjV3/S3dmZqbS+b2eCktISEBJSQnsdjucTifWr1+PMWPGNBiTnJyM1atX\nAwA2b94Mq9UKnU6H5ORkrFu3Di6XCzabDSUlJRg2bNgNzQk0vKZSWVnp+bqoqAiXL19GeHh4q144\nERG1Da9HLN26dcNbb72F0aNHw+12Iz09HUOGDMFLL70Ei8WC8ePHY8aMGUhPT4fZbEZISAjWrl0L\n4Orpq0mTJiEmJgZarRbLly9HYGAgAgMDm5wTAN544w0sXrwYp0+fRkxMDMaOHYu3334ba9euxTvv\nvAMACAoKwtq1a6HV8hYcIiJ/xM8KIyLq4vhZYURE5NdYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVC\nRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsR\nESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLERE\npBSLhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGR\nUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVCRERK\nsViIiEipFoslNzcXZrMZRqMRixYtavR8bW0tUlNTYTabkZiYiLKyMs9zCxcuhNFohNlsxo4dO1qc\nc9myZRg8eDC0Wi3OnTvXYDuzZs2CyWTCkCFDcODAgVt6sURE1A7Ei5qaGjEYDGKz2cTpdIrFYpHi\n4uIGY5YsWSIZGRkiIpKVlSUpKSkiIlJYWCgWi0VcLpfYbDYxGAzicDi8znngwAE5fvy4GAwGqaqq\n8mxj48aNMmHCBBERKS4ultjY2CbztvByiIioCar3nV6PWAoKCmAymRAREYGAgACkpqYiOzu7wZic\nnBykp6cDAFJSUpCfnw+3243s7GykpaVBp9MhIiICJpMJBQUFXueMi4vDwIEDG+W4dhvx8fFwuVyw\n2WwKapWIiFTzWiw2mw2RkZGeZb1e32iHfu0YrVaL0NBQnDlzBna7HXq9vtG6dru9xTlvJQcREfmH\nAG9PajSa9srRoqtHa//WXLZ58+Z5vk5KSkJSUlIbpiIi6njy8vKQl5fXZvN7LRa9Xo+KigrPckVF\nRYMjh/ox5eXlCA8Ph9vtRlVVFcLCwhqtW3/U4Xa7W5yzuRz33XefZ65rj4audW2xEBFRY9f/0p2Z\nmal0fq+nwhISElBSUgK73Q6n04n169djzJgxDcYkJydj9erVAIDNmzfDarVCp9MhOTkZ69at81wP\nKSkpwbBhw25oTqDhEUpycjLWrFkDACguLvZctyEiIj/U0tX9nJwcMZlMEhUVJQsWLBARkblz58qW\nLVtE5Oo7xyZPnizR0dFitVqltLTUs+78+fMlKipKTCaT5Obmep1TROT1118XvV4vgYGBcuedd8qT\nTz7peW769OliNBolPj5eioqKmsx6Ay+HiDoYl8sl8158TmLvvUvuHxot27Zt83WkTkf1vlPz3aSd\ngkajaXQthog6trnP/x6f/P1NvDbqCk5cAH61rQe2bt+FYcOG+Tpap6F63+n1GgsRka/9fc0qfDDu\nCsx3AIgADp66gk0bN7BY/Bg/0oXID5SXlyMvL49vo29C927dUHn538uV1Tp079HDd4GoRSwWIh/7\n6/LlGGK8Fy+mTUTcvd/Hmvff93Ukv/LcvIWYuqUH/rwXeGa7DpuO3YZpv3zS17HIC15jIfIhu92O\nmO8PRoG+BoODgUM1wAP27vif8gqEhob6Op7f+OSTT7D5g/XoGXIb/nPGrBZvUaCbw2ssRJ3I8ePH\ncU+vYAwOrgEAGLsBEd0DUVHBYrnWiBEjMGLECF/HoBvEU2FEPnTPPffgyGUnDlRfXd5/BThZW4e7\n7rrLt8GIWoHFQuRD4eHhWP7eSow42R0/sIVg7OkeWPV//o7evXv7OhrRLeM1FiI/cOHCBc/HHoWE\nhPg6DnUxqvedPGIh8gNlZWWw2Wy4fPlyy4OJ/ByLhcjHfpcxCz+6/z4s+umPYf7+YOzcudPXkYha\nhafCiHxoz549+Pn4MSiMuIzbdUDeJSDtm944ee4bv/qzFdS58VQYUSdy7NgxDO+pwe26q8sP9QTO\nX7qEK1eu+DYYUSuwWIh8KCYmBp9ecOO44+ry2vPAgH790LNnT98GI2oFFguRD8XHx+OFBQsRUxYM\nQ3lPPOfoiw0fZfs6FlGr8BoLkR/49ttvUVlZicjISAQFBfk6DnUxqvedLBYioi6OF++JiMivsViI\niEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWImrWmTNnMPFHI9Hve72REH0vPv/8c19H\nog6AN0gSUZNEBA9Y4mGt/gq/HeTCnrNAxuEQHDx0GP379/d1PFKIN0gSUYu2b9+OR+4biuHmKLy6\nePEt7TTOnz+P/3voX3jF5EJEdyBtADA8VIP8/Pw2SEydSYCvAxCRWvv27cN/TH4M/2vgFYQFAbNf\nmYe6ujr81+9/f1Pz9OjRAy634FQNcGd3oE6Asstu9O7du42SU2fBIxaiTubv7/8NT4dfwRN3AA/1\nAf5iuII1K96+6XmCg4Px4gvPI2lfT8z9CvjR5z3Q7944PPzww22QmjoTHrEQdTLB3bvjvFsD4Orp\nr29dQFBQ8C3N9YcXX0Ls0AQU7N+PH0dG4mc/+xl0Op3CtNQZ8eI9USdz5MgRJCYMwa+/dwlhAYKX\nT/XA639diScmT/Z1NPJT/Nh8L1gsRFd9/fXX+Mvrr+LKpYuYPPWnGDVqlK8jkR9jsXjBYiEiunl8\nuzEREfk1FgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVC5CdqampQVVXFe7Gow2Ox\nEPmBBZmZ+N5tt+HuiDuROCQOp0+f9nUkolvGYiHysZycHKx8dTGODnLi/D0OPHjqEJ6c8hNfxyK6\nZfx0YyIfK9i/H2ndLqN/4NXlWbe7MLSwyLehiFqBRyxEPhY5YAD21vWA67tLK3suA5F38k//UsfF\nD6Ek8jGn04kJj47EiX8WY0CwFgVXBB99/AkSEhJ8HY26CH66sRcsFuqo6urqkJeXh2+//RZWqxX9\n+/OIhdoPi8ULFgsR0c1r94/Nz83NhdlshtFoxKJFixo9X1tbi9TUVJjNZiQmJqKsrMzz3MKFC2E0\nGmE2m7Fjx44W5ywtLYXVaoXZbEZaWhqcTicAYOXKlQgLC0N8fDzi4+OxYsWKVr1oIiJqQ+JFTU2N\nGAwGsdls4nQ6xWKxSHFxcYMxS5YskYyMDBERycrKkpSUFBERKSwsFIvFIi6XS2w2mxgMBnE4HF7n\nHDdunGRlZYmISEZGhixdulRERFauXCkzZ870FlW+O/JqcQwRdSxFRUXy4H1xMnjAHfIfP3lCzp8/\n7+tInY7qfafXI5aCggKYTCZEREQgICAAqampyM7ObjAmJycH6enpAICUlBTk5+fD7XYjOzsbaWlp\n0Ol0iIiIgMlkQkFBQbNzulwu7N+/HxMnTgQATJ061bMtEeEpLqIuyG63Y8yoJEy78yC2TjgN3ZEt\nmDJ5oq9jUQu8FovNZkNkZKRnWa/Xw2azNTtGq9UiNDQUZ86cgd1uh16vb7Su3W5vcs6zZ8+ib9++\nnscjIiI829JoNNi0aRNMJhNSUlIanG4jos4rLy8PDw0U/DQeuDcMWJ7swM68PaiurvZ1NPLC6w2S\nGo2mvXJ4lZKSgilTpiAgIADvvvsupkyZgn/84x9Njp03b57n66SkJCQlJbVPSCJSrmfPnjh5CRAB\nNBrg7OWr+6WgoKAW1xURfP3113A4HIiKikJAAO8Hr5eXl4e8vLw2m9/rd1qv16OiosKzXFFR0eBo\no35MeXk5wsPD4Xa7UVVVhbCwsEbr1h/ZuN3uJucMDw9HZWVlg/H1Rzx9+vTxPD5t2jTMnj272czX\nFgsRdWxjxozBy380YPIHRzDsjhq891UPvPD8HOh0Oq/r1dbWYvLEsThQuA/dgrS4PSwS23bubnBW\npCu7/pfuzMxMpfN7PRWWkJCAkpIS2O12OJ1OrF+/HmPGjGkwJjk5GatXrwYAbN68GVarFTqdDsnJ\nyVi3bh1cLhdsNhtKSkowbNiwZufU6XQYPnw4PvzwQwDA6tWrkZycDAA4c+aMZ3tbt27FPffco/Sb\nQET+KTg4GJ/s3g9r+h9x2jgT8998H8/Pndfieq/+eQncJ/Nx7MUr+PoPl2ANPYo5v53e9oHpqpau\n7ufk5IjJZJKoqChZsGCBiIjMnTtXtmzZIiJX3zk2efJkiY6OFqvVKqWlpZ5158+fL1FRUWIymSQ3\nN9frnCIix44dk+HDh0t0dLSkpqaKw+EQEZFnn31WzGazGI1GSUxMlJKSkiaz3sDLIaIu4MeTkmXF\nFIgsu/rvH7+FDIv7ga9j+S3V+07eIElEnc4P7o7E3d1t2PprQKcFZm4ATtw2Gpu25vo6ml9Sve/k\n1Swi6lRcLheOltlx1w+Ae/4b6B4IfFOtwX+9OMrX0boMFgsRdSo6nQ4hvbpj8aQr0ACocQIzN/eA\nwWDwdbQug6fCiKjTWbliBZ7/3Uykxjlx8GQQNKHRyP1kDwIDA30dzS/xQyi9YLEQUb19+/Zhz549\n6NevH9LS0m7o3peuisXiBYuFiOjmtfunGxMREd0MFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGR\nUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUiwWIiJSisVCRERK\nsViIiEgpFgsRESnFYiEiIqVYLEREpBSLhYiIlGKxEBGRUgG+DkBE1FUdOnQIRUVFiIyMxEMPPQSN\nRuPrSErwiIWIyAfWrFmNh5MSsG3zf+I3vxqHX//6pxARX8dSQiOd5ZUA0Gg0neY/DBF1Xi6XC9/7\nXgj2bqtBdBRw+TIQn9QT763KxQMPPNDueVTvO3nEQkTUzi5cuACNxo3oqKvLPXsCMSYtTpw44dtg\nirBYiIjaWZ8+fdC/3x14a4UGIkDRQWDPvjoMHTrU19GU4KkwIiIfOHz4MB6bNBrHSu3o3j0If/3r\n+3jsscd8kkX1vpPFQkTkQ5cuXUKPHj2g1fruBBKLxQsWCxF1ZnV1dXhz2esoLNqLuww/wO/m/AEh\nISGtnpfF4gWLhYg6s59Pm4J/Hc1Byk91KPhUcPJ/9PjH7i8QFBTUqnlZLF6wWIios/rmm28wYGB/\n5J0IR89eWogIUhMu4bVXNuCRRx5p1dx8uzERURfkcDgQEKBBcLerd+drNBr06KWFw+HwcbLGeMRC\nRNQBiAge/dEP0avfIUz+VSD2f+LER6uCcfDA/8Ntt93Wqrl5xEJE1AVpNBps2piD/iGT8NozoTh1\nKBF5u/a3ulTaAo9YiIi6mNTUVGzduh3duwdh9epVSE5O5sX75rBYiIi8GzrUguLiEgC13z0SBMCh\ndN/Jj80nIuoCqqur8e6776K4uBhAMIDuAOoAqP9lnEcsRESdXE1NDRKT7ocr/DLC4npj//8uwZVz\ntdAE6OCurQPg5sV7IiK6cVu2bMHlbucxZfOjGP3fwzE9/wnogrQwvfxjdI/orXx7LBYiok7uwoUL\n6HlnsOcvVN4eGQK3SzB4xqN4aM+LyrfXYrHk5ubCbDbDaDRi0aJFjZ6vra1FamoqzGYzEhMTUVZW\n5nlu4cKFMBqNMJvN2LFjR4tzlpaWwmq1wmw2Iy0tDU6ns8VtEBGRdw8//DAOfVSKkg+P4pvyi9j4\n5Kfo/6NoaAN0COrb+s8aa0S8qKmpEYPBIDabTZxOp1gsFikuLm4wZsmSJZKRkSEiIllZWZKSkiIi\nIoWFhWKxWMTlconNZhODwSAOh8PrnOPGjZOsrCwREcnIyJClS5d63cb1Wng5t2zXrl1tMm9b6miZ\nO1peEWZuDx0tr4j/Zn76mWekW2gvCQy/XYJ6d5O4v/xMRhyYL/0nJSjfd3o9YikoKIDJZEJERAQC\nAgKQmpqK7OzsBmNycnKQnp4OAEhJSUF+fj7cbjeys7ORlpYGnU6HiIgImEwmFBQUNDuny+XC/v37\nMXHiRADA1KlTPdtqahvSjhfp8/Ly2m1bqnS0zB0tL8DM7aGj5QX8N/OSxYsx89ezoLnshOtKHQ49\nvwG7H1mAC1+fUb4tr8Vis9kQGRnpWdbr9bDZbM2O0Wq1CA0NxZkzZ2C326HX6xuta7fbm5zz7Nmz\n6Nu3r+fxiIgIz7aa2wYREd0YjUaDV+bPR83Fi3DV1qKm6iKWzFuAUffEK9+W12Kpv9BDRESdg0aj\n8fybNWsWsrKylG/D6w2Ser0eFRUVnuWKiooGRxv1Y8rLyxEeHg63242qqiqEhYU1Wrf+qMPtdjc5\nZ3h4OCorKxuMrz/iaW4b14uNjW2zMszMzGyTedtSR8vc0fICzNweOlpeoONlHjRokNL5vBZLQkIC\nSkpKYLfbER4ejvXr12P58uUNxiQnJ2P16tWwWCzYvHkzrFYrdDodkpOT8Zvf/AazZ8/GqVOnUFJS\ngmHDhqGurq7JOXU6HYYPH44PP/wQEydOxOrVq5GcnNzsNpr6M54HDx5U+K0hIqJb0tLV/ZycHDGZ\nTBIVFSULFiwQEZG5c+fKli1bROTqO8cmT54s0dHRYrVapbS01LPu/PnzJSoqSkwmk+Tm5nqdU0Tk\n2LFjMnz4cImOjpbU1FRxOBwtboOIiPxLp/pIFyIi8r0ufef9uXPnMGrUKMTExGD06NE4f/58k+NW\nrVoFk8kEk8mEv/3tb57Hn3/+eQwYMAAhIU3fYPTBBx9Aq9V+96Fv/pt38eLFMJlMiI6Oxg9/+EOU\nlpYqyduWmdvyptnWZi4qKkJ8fDxMJhMyMjI8j+/duxdxcXGIjo5GbGws8vPz/TovALz55puIjY2F\n2WzGnDlzlORt68wA8Oc//xlarRbnzp3z+8xPP/00jEYjjEYjxo0bh6qqqlblbM+b2pvl60MmX5ox\nY4a8+uqrIiLy6quvyqxZsxqNOXHihAwaNEguXrwoFy9elEGDBsmpU6dERGT//v1y8uRJ6dWrV6P1\nLly4IA8++KBYrVYpKiry67y7d++WmpoaERF56623ZOLEiUrytmXmG71ptj0znz59WkREzGaz56bf\nCRMmyKZNm0REJDEx0XNKOCcnRx544AG/zvvRRx/J2LFjxel0iohIZWWlkrxtmVlEpLy8XEaPHi0G\ng0Gqqqr8PvOnn34qdXV1IiLy7LPPyuzZs285Y3vf1N6cLl0sd999t+eH5ezZszJo0KBGY1atWiUz\nZszwLE+fPl3ef//9BmOaKpaMjAzJzs6WpKQkKSws9Pu89f75z39KQkKCkrwibZf5kUce8Xxf6+rq\npG/fvuJ2u32euaysTEwmk+fxDRs2yLRp00REJC0tTdatWyciImvXrpUpU6b4dd4JEybI7t27lWRs\nr8wiIk888YR8+eWXyoulLTPX27Jli0yePPmWM3722WcyduxYz/LixYvlj3/8Y4MxTf3s1NXVSWZm\npixZssQzbuzYsbJnz54bmvN6XfpU2NmzZxEaGgoA6Nu3b5M3XTZ3o6c3xcXFsNvtnne1qXoLdFvl\nvdby5csxYcKE1of9TltlbsubZluT+fobgK+90ffll1/GM888gwEDBmDOnDlYuHChX+c9fPgwtm/f\njri4OFitVmWn7toy8+bNm6HX6xETE6Msa1tnvtbbb7/dqp+/9ryp3ZtO/4e+Ro0ahVOnTjV6fP78\n+W2yPbfbjaeffhqrVq3yPCY38f6I9s57rTVr1qC4uBifffbZTa3ny8y3yheZp02bhjfeeAOTJk3C\nhg0b8Itf/AIff/zxDa3ri7xutxsXL17EwYMH8cUXX+Dxxx9HWVnZDf+i1N6Zq6ursWDBggbf05v5\n2QN8+//y/PnzERQUhClTptzyHP5yU3unLxZvP7hhYWGorKxE3759cfbsWYSHhzcao9frUVBQ4Fmu\nqKjA/fff3+ycFy9exFdffYWkpCQAwKlTp5CSkoKtW7diyJAhfpe33s6dOzF//nzs3r0bgYGBLY73\ndeYbvWm2vTM3d2MwAOzfvx87d+4EADzxxBP4+c9/7td5IyMj8dhjjwG4ek9bUFAQTp8+jX79+vld\nZr1ej6NHj+L48eOIjY31PD506FB8/vnnTc7vD5nrrVq1CtnZ2fj0009vKGdz2vOmdq9u+WReJ3Dt\nxbilS5eRmXqZAAABqklEQVTKzJkzG42pvxh34cIFuXDhgtx9992eC8v1vF2zSEpKapOL9yrzFhcX\ny6BBg+TIkSNKcrZH5msvQG7atEnGjx/vN5mvv0j7wQcfiIiI0WiUvLw8ERHZuXOnREdH+3XepUuX\nyty5c0VE5PDhw9K/f3/PRWZ/zXyttrx4rzLztm3bxGg0ytmzZ1udsbq6WgYOHCg2m00cDodYLJZG\n+5/mfnbqL947nU6pqKiQgQMHisPhuKE5r9eli6WqqkpGjhwpZrNZRo0aJd98842IXP0G//KXv/SM\nW7FihURFRUlUVJSsXLnS8/icOXNEr9eLTqcTvV4vmZmZjbahsljaKu/IkSOlX79+EhcXJ3FxcTJh\nwgQledsyc1veNNvazIWFhRIXFydGo7HBzmfv3r0SGxsrRqNR4uPjpaCgwK/zOhwOmTp1qphMJjGZ\nTLJ9+3Yledsy87XuuusupcXSVpkHDx4sAwYM8Pz8PfXUU63K2Z43tTeHN0gSEZFSXfpdYUREpB6L\nhYiIlGKxEBGRUiwWIiJSisVCRERKsViIiEgpFgsRESnFYiEiIqX+P9TpbR/YkO43AAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad09850>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}