fit_volumeEdits_lifespan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "%pylab inline\n",
    "matplotlib.style.use('bmh')\n",
    "from lmfit import Model\n",
    "import matplotlib.mlab as mlab\n",
    "from lmfit.models import LinearModel, QuadraticModel, LognormalModel, GaussianModel, ExponentialGaussianModel, ConstantModel\n",
    "from scipy import optimize\n",
    "import os\n",
    "import os.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing data and create log differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_items = pd.read_csv('userdata_2404_notoolsitemsdata.csv', usecols=[0,1,2,3,4,5])#names = ['revContributor','editCount','lifespan','start','end','gone'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_items['logEditCount'] = users_items['editCount'].apply(lambda x: np.log(1+x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>editCount</th>\n",
       "      <th>logEditCount</th>\n",
       "      <th>lifespan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.378410e+05</td>\n",
       "      <td>137841.000000</td>\n",
       "      <td>137841.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.508435e+02</td>\n",
       "      <td>1.845402</td>\n",
       "      <td>6.194855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.446734e+03</td>\n",
       "      <td>1.646456</td>\n",
       "      <td>11.211313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.059108e+06</td>\n",
       "      <td>14.537784</td>\n",
       "      <td>44.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          editCount   logEditCount       lifespan\n",
       "count  1.378410e+05  137841.000000  137841.000000\n",
       "mean   2.508435e+02       1.845402       6.194855\n",
       "std    7.446734e+03       1.646456      11.211313\n",
       "min    1.000000e+00       0.693147       0.000000\n",
       "25%    1.000000e+00       0.693147       0.000000\n",
       "50%    2.000000e+00       1.098612       0.000000\n",
       "75%    9.000000e+00       2.302585       7.200000\n",
       "max    2.059108e+06      14.537784      44.700000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_items[['editCount','logEditCount','lifespan']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "seconds_scale = np.array([0,50,100,250,500,1000])\n",
    "labels=['0','50','100','250','500','1000']\n",
    "seconds_scale_log = np.log(1+seconds_scale)\n",
    "def plot_loghist(dataframe,bins=50,scale=seconds_scale_log,label=labels):\n",
    "    dataframe.hist(column='logEditCount',bins=bins)\n",
    "    plt.xticks(scale, label,rotation='horizontal');\n",
    "    yl = plt.gca().get_ylim()\n",
    "    plt.xlim(scale[0],scale[-1])\n",
    "    plt.ylim(yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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g2Vnk55H6pHg0piICKmBEREQkh1TAiIiISO6ogElZvXp1sOza2tpg2Vnk55H6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61acd952b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loghist(users_items,bins=50,scale=seconds_scale,label=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import scipy.stats\n",
    "size = 20000\n",
    "x = scipy.arange(size)\n",
    "# creating the dummy sample (using beta distribution)\n",
    "y = scipy.int_(scipy.round_(scipy.stats.beta.rvs(6,2,size=size)*47))\n",
    "# creating the histogram\n",
    "h = plt.hist(y, bins=range(48))\n",
    "\n",
    "dist_names = ['alpha', 'beta', 'arcsine',\n",
    "              'weibull_min', 'weibull_max', 'rayleigh']\n",
    "\n",
    "for dist_name in dist_names:\n",
    "    dist = getattr(scipy.stats, dist_name)\n",
    "    param = dist.fit(y)\n",
    "    pdf_fitted = dist.pdf(x, *param[:-2], loc=param[-2], scale=param[-1]) * size\n",
    "    plt.plot(pdf_fitted, label=dist_name)\n",
    "    plt.xlim(0,47)\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the range and scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2059108\n"
     ]
    }
   ],
   "source": [
    "min_editCount_notools = users_items['editCount'].min()\n",
    "max_editCount_notools = users_items['editCount'].max()\n",
    "\n",
    "print(min_editCount_notools,max_editCount_notools)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we choose the scale based on the computation just made\n",
    "counts_scale = np.array([1, 5, 10, 25, 100, 250, 1000, 2500, 10000, 25000, 100000, 250000, 1000000, 2500000])\n",
    "labels=['1', '5', '10', '25', '100', '250', '1000', '2500', '10000', '25000', '100000', '250000', '1000000', '2500000']\n",
    "#seconds_scale_log = np.log(seconds_scale) #np.log(1+seconds_scale)\n",
    "def plot_loghist(dataframe,bins=50,scale=seconds_scale,label=labels):\n",
    "    dataframe.hist(column='editCount',bins=bins)\n",
    "    plt.xticks(scale, label,rotation='horizontal');\n",
    "    yl = plt.gca().get_ylim()\n",
    "    plt.xlim(scale[0],scale[-1])\n",
    "    plt.ylim(yl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot histograms - tools\n",
    "The majority of edits are made in less than 5 seconds, but there is a tiny peak at around 1 day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.           1.60943791   2.30258509   3.21887582   4.60517019\n",
      "   5.52146092   6.90775528   7.82404601   9.21034037  10.1266311\n",
      "  11.51292546  12.4292162   13.81551056  14.73180129]\n"
     ]
    }
   ],
   "source": [
    "print(seconds_scale_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qWVMLZWPHjg1qK7ReqGL3J6VLY0VEskh3b6X079+fHeflWLe9bvLyuHHjgtoK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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61f4ececc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot_loghist(users_items,bins=42,scale=seconds_scale_log[0:4],label=labels[0:4])\n",
    "plot_loghist(users_items,bins=100,scale=seconds_scale,label=labels)\n",
    "\n",
    "\n",
    "#plot_loghist(users_items,bins=100,scale=seconds_scale_log,label=labels)\n",
    "#there is a third peak away that is several order of magnitude smaller (so we can't study it together)\n",
    "#datasmall = data_tools[data_tools['difference']>60]\n",
    "#plot_loghist(datasmall,bins=50,scale=seconds_scale_log[2:],label=labels[2:])\n",
    "#we keep the whole dataset now (alex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "SPINE_COLOR = 'gray'\n",
    "\n",
    "def latexify(fig_width=None, fig_height=None, columns=1):\n",
    "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
    "    Call this before plotting a figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig_width : float, optional, inches\n",
    "    fig_height : float,  optional, inches\n",
    "    columns : {1, 2}\n",
    "    \"\"\"\n",
    "\n",
    "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
    "\n",
    "    # Width and max height in inches for IEEE journals taken from\n",
    "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
    "\n",
    "    assert(columns in [1,2])\n",
    "\n",
    "    if fig_width is None:\n",
    "        fig_width = 3.39 if columns==1 else 6.9 # width in inches\n",
    "\n",
    "    if fig_height is None:\n",
    "        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
    "        fig_height = fig_width*golden_mean # height in inches\n",
    "\n",
    "    MAX_HEIGHT_INCHES = 8.0\n",
    "    if fig_height > MAX_HEIGHT_INCHES:\n",
    "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
    "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
    "        fig_height = MAX_HEIGHT_INCHES\n",
    "\n",
    "    params = {'backend': 'ps',\n",
    "              'text.latex.preamble': [r'\\usepackage{gensymb}'],\n",
    "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
    "              'axes.titlesize': 8,\n",
    "              'text.fontsize': 8, # was 10\n",
    "              'legend.fontsize': 8, # was 10\n",
    "              'xtick.labelsize': 8,\n",
    "              'ytick.labelsize': 8,\n",
    "              'text.usetex': True,\n",
    "              'figure.figsize': [fig_width,fig_height],\n",
    "              'font.family': 'serif'\n",
    "    }\n",
    "\n",
    "    matplotlib.rcParams.update(params)\n",
    "\n",
    "\n",
    "def format_axes(ax):\n",
    "\n",
    "    for spine in ['top', 'right']:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    for spine in ['left', 'bottom']:\n",
    "        ax.spines[spine].set_color(SPINE_COLOR)\n",
    "        ax.spines[spine].set_linewidth(0.5)\n",
    "\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "\n",
    "    for axis in [ax.xaxis, ax.yaxis]:\n",
    "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
    "\n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit tools for the third peak (1 day)\n",
    "Let's compute the best fit for the second plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    (Model(lognormal, prefix='a') + Model(lognormal, prefix='b'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 14008\n",
      "    # data points      = 50\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.001\n",
      "    reduced chi-square = 0.000\n",
      "    Akaike info crit   = -522.818\n",
      "    Bayesian info crit = -511.345\n",
      "[[Variables]]\n",
      "    acenter:      2.40576319 +/- 0.007562 (0.31%) (init= 0)\n",
      "    asigma:       0.07170974 +/- 0.008317 (11.60%) (init= 1)\n",
      "    aamplitude:   0.06099367 +/- 0.007197 (11.80%) (init= 1)\n",
      "    bcenter:     -20.9996439 +/- 58.24085 (277.34%) (init= 0)\n",
      "    bsigma:       2.65315789 +/- 3.409298 (128.50%) (init= 1)\n",
      "    bamplitude:   6.3968e+16 +/- 6.00e+18 (9376.38%) (init= 1)\n",
      "[[Correlations]] (unreported correlations are <  0.100)\n",
      "    C(bcenter, bamplitude)       = -1.000 \n",
      "    C(bcenter, bsigma)           = -1.000 \n",
      "    C(bsigma, bamplitude)        =  1.000 \n",
      "    C(asigma, aamplitude)        =  0.674 \n",
      "    C(aamplitude, bsigma)        = -0.595 \n",
      "    C(aamplitude, bcenter)       =  0.594 \n",
      "    C(aamplitude, bamplitude)    = -0.593 \n",
      "    C(asigma, bsigma)            = -0.394 \n",
      "    C(asigma, bcenter)           =  0.393 \n",
      "    C(asigma, bamplitude)        = -0.392 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f72f47b9240>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gy3SNbupzKlbYBYDW+mXgTuAB4AtgEHCh1npfaZckapkQ2FhmTr8rIuMUJXu/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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f4da90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cCOHcJPVuANh5553Zdddd01OwSKlfv37odQTTM2yYnuEiKnpCtHTFXK7yQtEb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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f4880c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normd = True\n",
    "plt.figure(1)\n",
    "bins=50\n",
    "hist = np.histogram(datasmall.logdifference,bins=bins,normed=normd)\n",
    "x = 0.5*(hist[1][1:]+hist[1][:-1])\n",
    "y = hist[0]\n",
    "mod = LognormalModel(prefix='a')+LognormalModel(prefix='b')#+LognormalModel(prefix='c')+LognormalModel(prefix='d')\n",
    "result = mod.fit(y, x=x)#,params=pars)\n",
    "print(result.fit_report())\n",
    "pyplot.hold(True)\n",
    "plt.hist(datasmall.logdifference, normed=normd,bins=bins)\n",
    "plt.plot(x, result.best_fit, 'r-')\n",
    "plt.xticks(seconds_scale_log[2:], labels[2:],rotation='horizontal');\n",
    "plt.figure(2)\n",
    "result.plot_residuals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compute the minimum between the two peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold in seconds:  25875.2422809\n",
      "Threshold in hours:  7.18756730024\n"
     ]
    },
    {
     "data": {
      "image/png": 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wVU6qwnnvDiIroRIANX5cygfdntaVQCIiUmbUqIgjdU86jmNfepK86GgAkj/7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\nilJKKaX8lnZUlFJKKeW3/h+22/aOPd/IDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f4728710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = linspace(8,11,200)\n",
    "torefit = result.eval_components(x=x)\n",
    "twoandthree = np.array(torefit['a'])-np.array(torefit['b'])\n",
    "plt.plot(x,twoandthree**2)\n",
    "x2 = linspace(8,11,2000)\n",
    "torefit2 = result.eval_components(x=x2)\n",
    "twoandthree2 = np.array(torefit2['a'])-np.array(torefit2['b'])\n",
    "plt.plot(x2,twoandthree2**2)\n",
    "print(\"Threshold in seconds: \", (np.exp(x2[np.argmin(twoandthree2**2)])-1))\n",
    "print(\"Threshold in hours: \", (np.exp(x2[np.argmin(twoandthree2**2)])-1)/3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_notools.to_csv('michael_datanotools_differences.csv', header=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xxhhfpGpoVPXSIuuWAaeUsY+lwPdKjHmB6NbsYmPuBe4tsn4TMDj+ZM5ijDHG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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f46dada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loghist(data_notools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    (((Model(lognormal, prefix='a') + Model(lognormal, prefix='b')) + Model(lognormal, prefix='c')) + Model(lognormal, prefix='d'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 10137\n",
      "    # data points      = 50\n",
      "    # variables        = 12\n",
      "    chi-square         = 0.004\n",
      "    reduced chi-square = 0.000\n",
      "    Akaike info crit   = -453.776\n",
      "    Bayesian info crit = -430.831\n",
      "[[Variables]]\n",
      "    acenter:      1.18539313 +/- 264.2053 (22288.41%) (init=-0.8)\n",
      "    asigma:       1.08011053 +/- 45.75291 (4235.95%) (init= 0.36)\n",
      "    aamplitude:   2.99225138 +/- 1.21e+03 (40412.99%) (init= 0.2)\n",
      "    bcenter:      1.11981079 +/- 1.391526 (124.26%) (init= 1.2)\n",
      "    bsigma:       0.50980767 +/- 1.626108 (318.97%) (init= 0.5)\n",
      "    bamplitude:   2.60710614 +/- 92.34074 (3541.89%) (init= 0.8)\n",
      "    ccenter:      1.86310923 +/- 21.89191 (1175.02%) (init= 2.43)\n",
      "    csigma:       0.76138847 +/- 12.95852 (1701.96%) (init= 0.2)\n",
      "    camplitude:   39.7425273 +/- 1.31e+04 (33051.76%) (init= 0.2)\n",
      "    dcenter:      1.79921239 +/- 26.50301 (1473.03%) (init= 0)\n",
      "    dsigma:       0.79556807 +/- 15.64256 (1966.21%) (init= 1)\n",
      "    damplitude:  -44.4334737 +/- 1.35e+04 (30287.96%) (init= 1)\n",
      "[[Correlations]] (unreported correlations are <  0.100)\n",
      "    C(acenter, aamplitude)       =  1.000 \n",
      "    C(acenter, asigma)           =  1.000 \n",
      "    C(asigma, aamplitude)        =  0.999 \n",
      "    C(csigma, dcenter)           =  0.999 \n",
      "    C(bsigma, bamplitude)        =  0.998 \n",
      "    C(ccenter, dsigma)           =  0.997 \n",
      "    C(camplitude, damplitude)    = -0.997 \n",
      "    C(bcenter, bamplitude)       =  0.989 \n",
      "    C(bcenter, bsigma)           =  0.982 \n",
      "    C(aamplitude, bamplitude)    = -0.971 \n",
      "    C(aamplitude, bcenter)       = -0.971 \n",
      "    C(bsigma, dsigma)            = -0.970 \n",
      "    C(acenter, bcenter)          = -0.969 \n",
      "    C(bamplitude, dsigma)        = -0.968 \n",
      "    C(acenter, bamplitude)       = -0.967 \n",
      "    C(asigma, bcenter)           = -0.966 \n",
      "    C(asigma, bamplitude)        = -0.961 \n",
      "    C(aamplitude, bsigma)        = -0.953 \n",
      "    C(bsigma, ccenter)           = -0.949 \n",
      "    C(acenter, bsigma)           = -0.948 \n",
      "    C(bamplitude, ccenter)       = -0.946 \n",
      "    C(asigma, bsigma)            = -0.941 \n",
      "    C(aamplitude, dsigma)        =  0.938 \n",
      "    C(asigma, csigma)            =  0.937 \n",
      "    C(asigma, dcenter)           =  0.933 \n",
      "    C(acenter, dsigma)           =  0.931 \n",
      "    C(acenter, csigma)           =  0.929 \n",
      "    C(acenter, dcenter)          =  0.925 \n",
      "    C(aamplitude, csigma)        =  0.923 \n",
      "    C(bcenter, dsigma)           = -0.922 \n",
      "    C(asigma, dsigma)            =  0.922 \n",
      "    C(aamplitude, dcenter)       =  0.918 \n",
      "    C(aamplitude, ccenter)       =  0.914 \n",
      "    C(bcenter, csigma)           = -0.911 \n",
      "    C(acenter, ccenter)          =  0.906 \n",
      "    C(asigma, ccenter)           =  0.896 \n",
      "    C(bcenter, dcenter)          = -0.895 \n",
      "    C(bcenter, ccenter)          = -0.889 \n",
      "    C(bamplitude, csigma)        = -0.856 \n",
      "    C(bamplitude, dcenter)       = -0.840 \n",
      "    C(bsigma, csigma)            = -0.824 \n",
      "    C(bsigma, dcenter)           = -0.805 \n",
      "    C(csigma, dsigma)            =  0.738 \n",
      "    C(dcenter, dsigma)           =  0.726 \n",
      "    C(ccenter, csigma)           =  0.690 \n",
      "    C(ccenter, dcenter)          =  0.678 \n",
      "    C(dcenter, damplitude)       = -0.667 \n",
      "    C(csigma, damplitude)        = -0.653 \n",
      "    C(camplitude, dcenter)       =  0.606 \n",
      "    C(csigma, camplitude)        =  0.591 \n",
      "    C(asigma, damplitude)        = -0.357 \n",
      "    C(acenter, damplitude)       = -0.335 \n",
      "    C(aamplitude, damplitude)    = -0.317 \n",
      "    C(bcenter, damplitude)       =  0.308 \n",
      "    C(asigma, camplitude)        =  0.281 \n",
      "    C(acenter, camplitude)       =  0.258 \n",
      "    C(aamplitude, camplitude)    =  0.241 \n",
      "    C(bcenter, camplitude)       = -0.234 \n",
      "    C(bamplitude, damplitude)    =  0.178 \n",
      "    C(ccenter, camplitude)       = -0.174 \n",
      "    C(bsigma, damplitude)        =  0.127 \n",
      "    C(camplitude, dsigma)        = -0.107 \n",
      "    C(bamplitude, camplitude)    = -0.100 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f72f4785f98>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkP3UUxAIJCVu/ePmWyuu6IwHVzzZ1mk7vw1c82xbb7rm9ypuvAXM9SIyTUSm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\nHf8HY5BPZDQCNW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f48315c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0m0fWS/29acL24r7inPrN+tGeJQVqyPlIP7440eNLXZxJx5e0vFCFnveUxjx8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\ne7TnL6fLO34raUGK+/i9pDu638egM/SO8yfqyFP+WgB4/ec/0a6tT+v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f47ecba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normd = True\n",
    "plt.figure(1)\n",
    "bins=50\n",
    "hist = np.histogram(data_notools.logdifference,bins=bins,normed=normd)\n",
    "x = 0.5*(hist[1][1:]+hist[1][:-1])\n",
    "y = hist[0]\n",
    "mod = LognormalModel(prefix='a')+LognormalModel(prefix='b')+LognormalModel(prefix='c')+LognormalModel(prefix='d')\n",
    "pars  = mod.make_params(acenter=-0.8, asigma=0.36, aamplitude=0.2, bcenter=1.2,bsigma=0.5,bamplitude=0.8,ccenter=2.43,csigma=0.2,camplitude=0.2)\n",
    "\n",
    "result = mod.fit(y, x=x,params=pars)\n",
    "print(result.fit_report())\n",
    "#latexify(columns=1)\n",
    "pyplot.hold(True)\n",
    "plt.hist(data_notools.logdifference, normed=normd,bins=bins)\n",
    "plt.plot(x, result.best_fit, 'r-')\n",
    "plt.xticks(seconds_scale_log[2:], labels[2:],rotation='horizontal');\n",
    "plt.ylabel('Empirical PDF')\n",
    "plt.xlabel('Difference in time')\n",
    "#plt.tight_layout()\n",
    "#format_axes(plt.gca())\n",
    "#plt.savefig(\"fit.pdf\")\n",
    "plt.figure(2)\n",
    "result.plot_residuals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, it's a good fit but we want to have 3 distributions, let's try with a expnorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    ((Model(lognormal, prefix='a') + Model(lognormal, prefix='c')) + Model(expgaussian, prefix='d'))\n",
      "[[Fit Statistics]]\n",
      "    # function evals   = 201\n",
      "    # data points      = 50\n",
      "    # variables        = 10\n",
      "    chi-square         = 0.004\n",
      "    reduced chi-square = 0.000\n",
      "    Akaike info crit   = -457.831\n",
      "    Bayesian info crit = -438.711\n",
      "[[Variables]]\n",
      "    acenter:     -0.64086225 +/- 0.050881 (7.94%) (init=-0.6366354)\n",
      "    asigma:       0.56211525 +/- 0.033182 (5.90%) (init= 0.5932666)\n",
      "    aamplitude:   0.23555652 +/- 0.011151 (4.73%) (init= 0.2504402)\n",
      "    ccenter:      2.43982097 +/- 0.040615 (1.66%) (init= 2.417159)\n",
      "    csigma:       0.12886442 +/- 0.041092 (31.89%) (init= 0.02351624)\n",
      "    camplitude:   0.06138375 +/- 0.017681 (28.81%) (init= 0.0167078)\n",
      "    dcenter:      1.85034472 +/- 0.045022 (2.43%) (init= 1.87)\n",
      "    damplitude:   0.70184519 +/- 0.021419 (3.05%) (init= 0.68)\n",
      "    dsigma:       0.53914464 +/- 0.064495 (11.96%) (init= 0.43)\n",
      "    dgamma:       0.49591287 +/- 0.031471 (6.35%) (init= 0.48)\n",
      "[[Correlations]] (unreported correlations are <  0.100)\n",
      "    C(acenter, asigma)           =  0.941 \n",
      "    C(acenter, aamplitude)       =  0.795 \n",
      "    C(asigma, aamplitude)        =  0.756 \n",
      "    C(acenter, dsigma)           = -0.688 \n",
      "    C(aamplitude, dsigma)        = -0.676 \n",
      "    C(asigma, dsigma)            = -0.649 \n",
      "    C(csigma, camplitude)        =  0.612 \n",
      "    C(dsigma, dgamma)            =  0.540 \n",
      "    C(dcenter, damplitude)       = -0.539 \n",
      "    C(acenter, dcenter)          =  0.509 \n",
      "    C(asigma, dcenter)           =  0.502 \n",
      "    C(damplitude, dgamma)        = -0.448 \n",
      "    C(aamplitude, dcenter)       =  0.433 \n",
      "    C(acenter, damplitude)       = -0.412 \n",
      "    C(dcenter, dgamma)           =  0.408 \n",
      "    C(asigma, damplitude)        = -0.404 \n",
      "    C(aamplitude, damplitude)    = -0.362 \n",
      "    C(camplitude, dgamma)        =  0.351 \n",
      "    C(damplitude, dsigma)        =  0.264 \n",
      "    C(aamplitude, dgamma)        = -0.259 \n",
      "    C(acenter, dgamma)           = -0.252 \n",
      "    C(csigma, dgamma)            =  0.241 \n",
      "    C(asigma, dgamma)            = -0.235 \n",
      "    C(camplitude, damplitude)    = -0.197 \n",
      "    C(camplitude, dsigma)        =  0.165 \n",
      "    C(dcenter, dsigma)           = -0.158 \n",
      "    C(ccenter, dgamma)           = -0.145 \n",
      "    C(csigma, damplitude)        = -0.141 \n",
      "    C(camplitude, dcenter)       =  0.135 \n",
      "    C(csigma, dsigma)            =  0.109 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f72f550b9e8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wetJ8pChRRqkbgJuA07XWn8Rd+pqwa0Yx//znPzNs2DBKSkpir4umTuUE4DkS\n12jaUfoeqx6+rVmMtc9OZ972PYWW2rWLpUuXUlJS0myp9zvuuIPp06cnnNuwYQMlJSWUlpYmnL//\n/vuZPHlywrnKykpKSkpYsmRJwvm5c+cyfvz4hHMFjz/OhZE8ai69NDahPVUeTSeJL126lB898QRb\nIrfk8ufPh8bGrOYBMHbsWObNm5dwbtGiRZSUNB89u/HGG5tNErTh5yF5SB6Sh+QheZjL46abbmLw\n4MEJ/55ffvnlLFq0qJlvKtKe06SUagQu0Vo/GTnuDmwBTtNaZ/atrX/XEuBtrfV1kWMFrANmaK2n\nJfnMjcAtwEit9bstXN8ITNNa/zFy3JlwwTRGa/100/YLFy48dtKkSe8//fTTCXOaCmbMoGjKFAB2\nP/wwdeee2+qcpnELHuGqV8O/MLvmzKH+tNPS6AWD1NSw1xFHECorQ+fns+PTT1kXKuKKZ1aw6uHb\nGHD5b5J+NGFOU4Tiiy4if8ECAHa++ioNx3obtcqUkpISnnzyyax8l1+46OwFV/O0ydsmlyBxuR9s\nc7fJx6RLurGNzmnKMncD4yJzpgYCfwWKgEcAlFKPKaV+F22slLoJmEr46bp1SqlekVdxXMw/Abcp\npc5RSg0GHgM2AM8nkzj33HObnctks94oFQV2zWnKmz8/9gRg3TnnoLt1i13reVLShwmTUjdqz4KY\neUmWaTDBlVe2PnHdNlx09oKredrkbZNLkLjcD7a52+Rj0sVk7NwM249RSh0feV9IeD7QBKVUS//S\n6uhIUaZoredE1mS6nfAttY+AM7TW0VnYfYD4Z/d/BuQBzzQJ9etIDLTWdyqlioCZQBfgX8CoVPOe\nhg4d2uxcRquBR6i0rGgqiFsBvObSSxOu7XVI85xbo+7MM9G/+AVKa/LnzaP61lvb7JgOw4cPz8r3\n+ImLzl5wNU+bvG1yCRKX+8E2d5t8TLqYjJ1p0TQy8oon2dCEJrw2kie01vcCLS41rbUe3uS4X5ox\npwBTvDpBZpv1Rkl4ei7grVRCa9eSt3gxAA19+1J/8sltjql79aJh6FBy332XnJUrCX35JY39+7c5\nriAIgiDYRNq357TWoQxfOSbFgyK2hUpBAXTqlNZnKgr23CUMeqQp/29/i72vvfRSCPlzh7ZWnqIT\nBEEQ2jm2zmmygrfeeqvZuUy3UAFiG/aCBUVT5GkHnZNDzUUXNbu+/WNvay0lLD2QpXlNTZ/ccAEX\nnb3gap6mAkZYAAAgAElEQVQ2edvkEiQu94Nt7jb5mHQxGdtz0aSUGqSU+r5S6uLIfwf6KWYDzR5F\nrK9HRbdQSfPWHFg0p6m+ntDnnwPQcOih6N69mzXZ9tFrnkI3DhhAw4ABAOS+/TZq61bvnmkyd+5c\n49/hNy46e8HVPG3ytsklSFzuB9vcbfIx6WIydsZFk1LqaqXUOuBjwk+ePRH57ydKqdVKKXum57eR\nSZMmJRyrsjJUZImGdCeBQ5ORpgDnNIXWrkXV1QHhIqclDrpkUovn0yE62qQaG8l7+WXPcdLloYce\nMv4dfuOisxdczdMmb5tcgsTlfrDN3SYfky4mY2dUNCml/gDcR/jx/4eAXwDjIv99mPAq4TOVUr/3\n2dMKvGyhAvYsOZATtwFxQ5KiqS3Uxi89IPOaBEEQhHZG2k/PKaWGAdcDzxJeELKihTbXER55ukEp\n9bTW+j3fTC0gYQuVXulvuWdL0RSKW0214ZBDPMXICSnWl1e3fPHgIxjUY2/ytm4h97XFUFkJcQuD\nCoIgCILLZLLkwBXAJqBEa13TUgOtdYVS6mLgy0j7dlU0eR1pqs/NoyY3j4L6ukD3nosfaUp2e641\nauobufofK5Nev6X/MEZvnUeouoq8116j7uyzPX2PIAiCINhGJrfnTgCeTlYwRdFaVwNPAye1RcwG\npk1L3LHFy2a9UaLLDgQ5pynh9txBB7XYZvWcO9v0HYsP3/NjN32LrqX9kGzHRWcvuJqnTd42uQSJ\ny/1gm7tNPiZdTMbOpGjaH0i+wVoin0baO82QIUMSjr1soRIlOhk8sNtzWsduzzXutx907Nhis84D\nMl8RPJ73Dj6Gyvzw3nR5L78M9fWtfMI7Nq1umy4uOnvB1Txt8rbJJUhc7gfb3G3ycXVF8EyKps5A\nuv/i7yY8Kdxpmna8ly1UokTnNalduyDNTZL9RJWVESovB6Dh4IOTtut+TNt+2Wrz8vn3ocMACG3b\nRu4777QpXirOP/98Y7FN4aKzF1zN0yZvm1yCxOV+sM3dJh+TLiZjZ1I0KcJbo2TSvl3RlpGm6FpN\nqr4ealLe4TRCwq05j5PA0+WNw0+Mvc+zaDE1QRAEQWgLme49d0Nkondr7OdFxnaiI006Px+9114Z\nfbbpquC6sNBXt9aIf3LO6yTwdHlz4PHonBxUQwN58+dT9ZvfpL16uiAIgiDYSiYjTeuAbsDgNF7d\nIu2dZvny5QnH0afndI8eGRcBQe8/lxNZCRxSr9G0a/XypNfSZVdRJ3YPOyH8vWvWEFqR7lS4zFiy\nZImRuCZx0dkLruZpk7dNLkHicj/Y5m6Tj0kXk7Ez2bC3r9a6XyYvY9ZZYs6cOXsOGhpiW4M0Znhr\nDmB3wPvPhdJc2PLrxX/35ft2jjwj9j7f0FN0M2bMMBLXJC46e8HVPG3ytsklSFzuB9vcbfIx6WIy\ntmzYm4Jbb7019l5t24ZqbAS8FU2VBR32xApg2YHonCbdsWOLe85F6f+T23z5vp2n7SmaTC09MGvW\nLCNxTeKisxdczdMmb5tcgsTlfrDN3SYfky4mY2e6jcqZSqn5SqmVSqm3IiuAt1sKCwspr6pja0Ut\nO9ZujJ2v6tqdrRW1sVc6s+MrCgO8PVddTWjtWiAyypTi1mJOvj9zrer260P9kUcCkPvRR6ivvvIl\nbjxFDq427qKzF1zN0yZvm1yCxOV+sM3dJh+TLiZjZ7KNyneBeYSfitsKHAQcr5TaT2t9kyG/wLnl\npS/YWKUYtup97omc+8c3cO9TnwBwWM9irv9O60tSBbmVSujLL2OjZCb2nEtG3ahR5C5bBkD+Sy9R\nc8UVWftuQRAEQfCbTEaabgE2A0dqrXsCPYHXgPFKqQ4pP9kOWH7AYVx67X1cd/nvmH/sabHzOs1V\nGOKfnsv2Vip+bJ/ihfgtVGTpAUEQBMF1MimajgDu1Vp/DKC13k64kOoAHG7ALXBmzpwZe19V0IGV\nfQ7h34P+H6t79c04VsJIU5bnNCWs0ZRiYUuA9S/MTHk9ExoOP5yG/cOjcLlvvonascO32ACTJ0/2\nNV42cNHZC67maZO3TS5B4nI/2OZuk49JF5OxMyma9gFWNzn3ZeS/zq/+3RI9PUz4Tkagt+fSfHIO\nIL+rfzmjFHVnnRV+W19P7quv+hcb6NOnj6/xsoGLzl5wNU+bvG1yCRKX+8E2d5t8TLqYjN3WFcF1\n3LV2x+jRo32LFeRE8NiTc6EQjf37p2zb6yT/cgZiRRP4v/TAuHHjfI2XDVx09oKredrkbZNLkLjc\nD7a52+Rj0sVk7ExXBB+jlDo+7riQcOE0QSl1XpO2Wmvdrp+uy4SEJQeyWTRpHVvYsvHAAyFLK5Hn\nhBTry6th0DEctlcXcneUk/PKK2zYvANdUBBrV5yfQ7eivKw4CYIgCEJbyLRoGhl5NaVpwQThYkqK\npghBrQiuNm2KzaHK5pNzNfWNXP2PlQBMOeg4zv7gFXJ27+b+u/7OkkOPi7V78IJBUjQJgiAITpDJ\niuChDF85JsWzwbp1/u0E03TvuWyR6ZNzVd/4v/vN64efFHt/6sdv+ha3NG4/PVdw0dkLruZpk7dN\nLkHicj/Y5m6Tj0kXk7FlRfAUPPDAA77FqswP5vZcTgaTwAE2zLvfd4f/HDqU6tx8AE759D+xNaPa\nypQpU3yJk01cdPaCq3na5G2TS5C43A+2udvkY9LFZGwpmlIwYcIE32LpUIiG4vAtumwWTQlPzh1y\nSKvtDzjvGt8dqvM78M6AIQDsvauMwzZ85kvcO++805c42cRFZy+4mqdN3ja5BInL/WCbu00+Jl1M\nxpaiKQW9evXyNV5jx/DKDNlcpyknbpgyndtzBV39zTnK4iP23KL7nk+36Gx6fDZdXHT2gqt52uRt\nk0uQuNwPtrnb5PNtWHJAaCMNHTsCwdyea+zaFd29e9a+tyn/GnQCDSr86/bdT94KzEMQBEEQvCJF\nUxZJGGnyaV5PSnbtIrQxvNFwNrdPaYnyjl1Y2vcIAPpuWc+BBiacC4IgCIJJpGhKwezZs32NF53T\nBEAWbtHlfPHFnu9Os2ja9NpTpnRYHP8UnQ+jTdOnT29zjGzjorMXXM3TJm+bXILE5X6wzd0mH5Mu\nJmNL0ZSCmpoaX+NFR5ogO/OaMn1yDqCxzt+c43nj8BNj7/1YeqCysrLNMbKNi85ecDVPm7xtcgkS\nl/vBNnebfEy6mIyd1uKWSqmHPMTWWusrPHzOGi677DJmrPAvXkOnuKJp165me9JkyrbKOipqG5Je\n7/XxCqJjW41pPDkHsN/I/2qjVXK+6r4vpb37c8imLzli/Ur23rEFGOQ53s033+yfXJZw0dkLruZp\nk7dNLkHicj/Y5m6Tj0kXk7HTXRF8OM33nWuNttYE7Y7GyERw8GcyeEVtA1c8k7yq+93rH3J65H02\nVwNPxeLDT+aQTeF9nk/59D/AKcEKCYIgCEKapFU0aa37Gvb4VtBQ7G/R1Bp9I5OtG/PywvvOWcDr\nR5zEuFcfA+QpOkEQBMEtZE5TCnbs2OFrvISRJsNzmkKNDRywdQMAtX37QW56g4p1Ff7m3JTS3gex\nMbIW1HGff0hop/fvKysr80sra7jo7AVX87TJ2yaXIHG5H2xzt8nHpIvJ2FI0peCuu+7yNV5Dx8Q5\nTSbpvX0zBfV1ANT0Oyjtz62ZM82UUhilYnvR5TY20Pm1hZ5DXXON/6uXm8ZFZy+4mqdN3ja5BInL\n/WCbu00+Jl1MxvZcNCmlRimlXlFKlSml6pVSDU1ffooGwZgxY3yN15jFounAb9bH3tcefDDry6tT\nvhoaw1PQ9j3d35xbYvHhJ8fed37lZc9xJk6c6IdOVnHR2Quu5mmTt00uQeJyP9jmbpOPSReTsdOd\nCJ6AUup8YA7wCTAb+BnwJKCAc4FVwHM+OQbGgAEDmO/j03ONHfes02T69lzfLXsWj6zod1DKCeMA\nM384EIDiPuk9ZdcWlvY9gvKiznSp3Emn1xexs7oaCgszjnPUUUcZsDOLi85ecDVPm7xtcgkSl/vB\nNnebfEy6mIztdaTpZuAd4BjgV5FzD2mtfwIcAfQGVrddr32RzdtzfeNGmqr7H2z0uzKlISeHfx12\nAgA5FRXk/utfARsJgiAIQut4LZoOA2ZrrRuA+si5PACt9RrgXsCecUBL8HvJgVQcGDfSVN0//TlN\n2SJ+dfD8F14I0EQQBEEQ0sNr0VQJ1AJorcuBGsKjS1E2A/3aphY88+fP9zVe/EgTWRpp2tKpO42d\nOqf9uS3vvGhKKYG3DxlKVV74llzeSy9BQ+ZT4B5//HG/tYzjorMXXM3TJm+bXILE5X6wzd0mH5Mu\nJmN7LZo+IzzaFOUj4FKlVK5SqhAoAZzfkXVV3DYkftBYnJ0lBzpX7qRbRTkAa3run9FnK7/yN+dk\n1OQVsOTQoQCEtmwh5913M46xbNkyv7WM46KzF1zN0yZvm1yCxOV+sM3dJh+TLiZjey2angXOVUoV\nRI5/C5wKlANbgO8A/9sWMaXUeKXUaqVUlVJqiVLquBRtD1NKPRNp36iUuraFNr+KXIt/fZrK4dpr\nm4VpEw2dsnN77sAte+Yzre15QGafHX2d3zpJSbhF92LmI1zTphleHsEALjp7wdU8bfK2ySVIXO4H\n29xt8jHpYjK2p6JJa32X1voArXVN5PgFwkXTA8BMYLjW+hGvUkqpC4E/EJ5kfgywFHhZKdUjyUeK\ngC8Iz6PalCL0x0AvYJ/I6+QUbX1HFxSiI4tMmiya4ieBr9k7s5GmbPLmoOPROTkA5M2bB1p23hEE\nQRDsxbfFLbXW/9JaX6+1vkFrvbiN4a4HZmqtH9NarwR+Snge1dgk3/2e1nqi1noOkblWSajXWm/R\nWn8TeW1ro2dmKIWObNprdqRpz53RtRYXTTuLOrN7WOQputWrCa3wcX0HQRAEQfAZT0WTUqqfUuqc\nFNfPUUr19Rg7DxgCxJaK1lpr4FXgBC8x4xiglPpKKfWFUuoJpVTWKwodeYLO5JymhJGmDG/PZZud\nI8+Ivfdyi04QBEEQsoXXkaa7gFQTfsbjfU5TDyCH8BN48WwmfEvNK0uA/wLOIDxy1Q94QylVnOwD\nkyZNasPXtUw2RpqiG/VW5xWwea+9M/rsqodvM6GUlJ2n7Sma8jIsmkpKSvzWMY6Lzl5wNU+bvG1y\nCRKX+8E2d5t8TLqYjO21aDoBeCXF9YWEJ4P7iQI8T3rRWr+stZ6rtf5Ya/0KcBbQFfhxss+ce+65\nXr8uOdGiqboaalPdSfRGbn0d+23bCIRvzelQZj/inied57tTKur260N9ZPXW3I8+Qm3YkPZnr7zy\nSlNaxnDR2Quu5mmTt00uQeJyP9jmbpOPSReTsb0WTV2BVEMlu4HuHmNvBRoIT9iOpyfNR588o7Xe\nAZQCSZfL/s9//sOCSRez6uHbEl4r/jyB7R+/mdB2R+l7LY7SrH12esLaR7pjRz4AfgBsW5e4KsMd\nd9zB9OnTE85t2LCBkpISSktLE87ff//93H3H7QnnGmqrWTvrlyxpbAT2TAJ/6Z/PsXrOnc3cvnhi\narM8oOXRpqZ5AFRsKGXVw7dRV7Ej4fxXCx7hkZn3JJyr2b6ZVQ/fRtU3iTk/+eiD3Bi3hUr+iy9S\nWVlJSUkJS5YsSWg7d+5cxo8fHzsePnw4AGPHjmXevHkJbRctWtTi/9u48cYbm63hsXTpUkpKSprt\njJ3pz2Py5MkJ51rKY/jw4c3yiOJSHtD85xGfR1VVlZN5RH+nonkE+fMYPny4bz8Pl3+vXPw7j+Yx\nd+7cZm5B5hHtSxt+r7p3727s5zFr1qxmedx0000MHjyYkpKS2Ovyyy9n0aJFzXxTobSHJ5aUUp8B\n72qtL0ly/UlgmNba0/4dSqklwNta6+six4rwuk8ztNYpnyVUSq0G/qi1ntFKu47AWuBXWuu/NL2+\ncOHCY4H3Z6yAjVUqaZxBPYv4xXcO4Kq5K1tLiwcvGMTAX/yc/OfC2/Lt+OgjGg/wPudofXl1sz3l\nTv34TaY9Ft7ZZubplzHr9DHM/OFArv5Har902pho9+AFgzhw45fsdXL4Qca6U05h93POb1soCIIg\nWE5lZSUrV64EGDJixIgP0vmM15Gmp4CLlVLXKqViMZRSOUqp64ALCW/g65W7gXFKqTFKqYHAXwkv\nK/BI5HseU0r9Lu5785RSRymljgbygf0ixwfFtZmmlDpFKXWgUupEwmtN1UdyyRrROU1gZl5T32/c\neHIunsZBg2joF15APvett1DbtwdsJAiCIAjN8Vo03QG8BvwJ2KSUekMp9QawEfgj8DrhBS89EVk6\n4H+A24EPgSOBM7TWWyJN+pA4KXzfSLv3I+dvAD4gvG4UcZ95ElgJzCa8COfxWuvEscE43nrrLa8p\nJCW+aDKxlUr8wpZenpxr6XadcZSi7qyzwm8bGshbsCCtjzUdGnYBF5294GqeNnnb5BIkLveDbe42\n+Zh0MRnb6+KWNcBI4ArgHcJPvO0deT8WOC268KVXtNb3aq37aq07aK1P0Fq/F3dtuNZ6bNzxWq11\nSGud0+Q1PK7NxVrrPpF4B2itS7TWq1M5ZHqvM628DG/aGy2aGpViXY/9Mv78to9e81spLWrPPjv2\nPi/NX/iW5gvYjovOXnA1T5u8bXIJEpf7wTZ3m3xMupiMnev1g1rrRuDhyKtdMmnSJGb4vN6i0dtz\nWsduz33dpSc1+YWtfKA5B13i/zIL6dBw3HE07r03oS1byFu4ECoroago5WceeuihLNn5h4vOXnA1\nT5u8bXIJEpf7wTZ3m3xMupiM7duK4EJ6mCyauu/eTqfqCgDW7m33opbNyMmh7swzAVBVVeQtXhys\njyAIgiA0Ia2RJqXUa0Aj4XlF9UqpdO5baa31iDbZtUNMFk0Hxk0CX9PTjUngOSHF+vJqADqdOpJ+\nkcdS65/8O+tP3PP4d3F+Dt2K8gJxFARBEARI//acInFUKkTrC00mf07/W4zJOU2JG/W6MdJUU98Y\nW5ogp6EnLxZ3oVtFOR0WvMR/P/YOu4rCReaDFwySokkQBEEIlLRuz2mtT9Vaf09rXd/kOOXLrLp5\npk1LuSSUJxJGmnzef67vlraPNLW0CGa2aMjJ5aVjwoOTBfV1nLbs9ZTtW1pwzXZcdPaCq3na5G2T\nS5C43A+2udvkY9LFZOyM5zQppToope5OtWFve2HIkCG+x9SdO8femxxpWutxo97OA4b6peOJeUNO\nj70/+/3USw/Er97sCi46e8HVPG3ytsklSFzuB9vcbfIx6WIydsZFk9a6Cria5tuctDuMdLzJOU2R\nkaZdhcWUdezqKUb3Y4L9oyrd92A+3ye80OVRaz+hz9avkrY9//zzs6XlGy46e8HVPG3ytsklSFzu\nB9vcbfIx6WIytten594HjvBT5NuCqTlNBbXV7Ls9vDXfmp4HgHJ0SplSzBsyMnbY2miTIAiCIGQL\nr0XTfwMXKaWuVEp5Xuvp20hC0eTjnKYD4kZkXNk+JRnzjxlBQ2R3nlEfvIqKbEAsCIIgCEHitWh6\nhPASBDOBnUqpVUqpZU1eS32zDIjly5f7HzQvD92hA+DvSJNfe87tWm0g5wwp69ydtw8Jzyfbb/vX\nHJPEqeku1i7gorMXXM3TJm+bXILE5X6wzd0mH5MuJmN7LZq2AZ8BbwBvAxuAsiavbX4IBsmcOXOM\nxI09Qedj0dTWPeeifL34737otJl5x+65RXfWBy3fopsxY0a2dHzDRWcvuJqnTd42uQSJy/1gm7tN\nPiZdTMb2dGtNa32qzx5Wcuutt3J/yt3pvKE7doRvvvH19ly/b9bG3q9pw0hT/5/c5odOm3n98BPZ\nXVBEx5pKRix7g9VVldAlcVuYWbNmBWTnHRedveBqnjZ52+QSJC73g23uNvmYdDEZW7ZRSUFhYeZ7\nt6VDdKRJ7doFurU1QtPj4E1fAlCXk8uG7vt6jpPjYb86E9TkF/Lqkd8FoGNNJXsteKlZm6JW9qaz\nERedveBqnjZ52+QSJC73g23uNvmYdDEZO91tVE4B0Fq/EX/cGtH2QiKxoqmhAaqqWt2YtjUKaqs5\ncMsGAL7o1Zf63PaxcvaLQ0Zy3rvzAej67DPUX1YSsJEgCILwbSbd23OLAa2U6qC1ro0ep2ivItdz\n2mTXTmm6/5xuY9F08NerydHhJ8xK9z24TbFs4qO+R/BVt97st20THd98gx2bNqF79w5aSxAEQfiW\nku7tue8BwyMFU+w4xSt63WlmzpxpJK7fyw4csvHz2PvP9mtb0bT+BTM5e0GHQrx47GkAqMZG8p9+\nOuH65MmTg9BqEy46e8HVPG3ytsklSFzuB9vcbfIx6WIydlojTVrr11Mdt1d69uzJ+tabZUzTkaa2\ncsjGL2LvS/c9qE2x8rv2bKuOr7x47Olc9erjABTMnk3NNdfEFu7s06dPkGqecNHZC67maZO3TS5B\n4nI/2OZuk49JF5Ox2zwRXCnVUyl1XORl17+4bWT06NFmAvtcNB0aN9K0qnfbiqZeJxnK2SMbeuzH\nR33Di8/nrFxJzrJlsWvjxo0LSsszLjp7wdU8bfK2ySVIXO4H29xt8jHpYjK256JJKTVCKfUesAlY\nEnltUkq9p5Q6zS/B9oifW6mEGhsYEHlybn33fakoLG5TPBt58dg9m/jmz54doIkgCILwbcZT0aSU\nGg28DPQG7gSuBK4CpkXOzY+0EVog4fZcG+c0HbBlA4V1NUDbb83ZyitHnUpjfgEA+XPnQl1dwEaC\nIAjCtxGvI02/AT4GBmmtb9ZaP6y1fkhr/UtgELAC+K1fkkGxbt261ht5wM85TfHzmT7bd0CbYgFU\nfWMm57awu0NHdp4eXiE8tHUreQsXAlBaWhqklidcdPaCq3na5G2TS5C43A+2udvkY9LFZGyvRVN/\n4GGt9c6mFyLnHgT6tUXMBh544AEjceOLprZupXLIpj3zmfwYadow7/42xzDB9tE/ir3Pf+opAKZM\nmRKQjXdcdPaCq3na5G2TS5C43A+2udvkY9LFZGyvRdNKINWk716E96ZzmgkTJhiJ6+ecpkO/iltu\nwIc1mg4475o2xzDBrlNOpXHvvQHIe/ll1Pbt3HnnnQFbZY6Lzl5wNU+bvG1yCRKX+8E2d5t8TLqY\njO21aLoJ+KlS6tymFyJzma4GbmiLmA306tXLSFzf5jRpHbs9t624C1s7d2+rGgVdzeTcZnJzqb3g\nAgBUbS15zz1n1eOz6eKisxdczdMmb5tcgsTlfrDN3SYfV5cc8LRhL3ANsAX4h1JqIxAd7jgY2Bco\nBa5VSl0b9xmttW5WZH0b8WtOU+7mr+lWUQ5Ebs1F1i9qj+SEFOu+/0MOue++8IknnmT96IubtSvO\nz6FbUfvYRkYQBEGwC69F05GEt0mJzhruG/lvfeRcITC4yWf82Zm2HeBX0dTh009i79u6Erjt1NQ3\n8l+fhPhb7/4csulLij98n1/ft4B1e++f0O7BCwZJ0SQIgiAYwdPtOa11X611vwxf/f2WN81sQ2sC\n+TWnqcOKPUVTaW9/iqZNrz3lSxxTxK/ZpP7xpwBNvDF9+vSgFbKCq3na5G2TS5C43A+2udvkY9LF\nZOw2rwjenqmpqTETuLgYHbmV1pY5TYWffBx779dIU2OdoZx94qVjT6NBhX9tD9hQimpsDNgoMyor\nK4NWyAqu5mmTt00uQeJyP9jmbpOPSReTsdtUNCml8pRSfZVSxyiljm368ksyKC677DIzgUMhiIw2\n+THSVJ1XwPoe+/mitt/I//IljinKOnVjySFDAbi7ppLjvvgwYKPMuPnmm4NWyAqu5mmTt00uQeJy\nP9jmbpOPSReTsb2uCN5FKTUL2Al8AbwHvBv3ih4LSYjOa/JcNO3cScHaNQCs6t2fxlCOT2b283/H\nnRl7f/G/5gZoIgiCIHyb8DoR/BHgHGA28Dawwy+h9kxOSLG+vBqADkXFFAJ6587YuSjpPAGW+0nc\nJHAf1mdyidcPP5lNXXrSu/wbTl75Nn03r2VNrwOD1hIEQRDaOV6LppHADK319X7K2MaOHTuAvXyL\nV1PfyNX/WAnAQ7W5DAZyKiu5as7HCSNF6TwBlrN8eey9n3vO1VXsIK/Yv5xN0JCTw+yTf8ilL/yV\nHsDFb87ljvN/EbRWWpSVldG9e9vX07IdV/O0ydsmlyBxuR9sc7fJx6SLydhe5zSVsWdtpnbLXXfd\nZSx2RUFR7H2HmqqMP5+zbFnsvR97zkVZM2eab7FM8vywsxgTKTTPev8VuuwuD9goPa65xs4V1/3G\n1Txt8rbJJUhc7gfb3G3yMeliMrbXoul+4CKlVLt++m7MmDHGYlcU7imaOtZkPtM/5+Pwk3MNKsQX\n+/T1S4t9TzeXs59UFBZz4lGnAlBYX8sF//m/YIXSZOLEiUErZAVX87TJ2yaXIHG5H2xzt8nHpIvJ\n2J5uz2mtpyqlCoD3lFKPAxuAhhba/aONfoEyYMAA5q8wE7sybqSpuDrDoqm2lpwVYbG1e/ehJr/Q\nN6/iPof4Fss0b595BQ0fvUaObuSC/zzPY6deFLRSqxx11FFBK2QFV/O0ydsmlyBxuR9sc7fJx6SL\nydieiial1H7AcODoyKslNPDteaQrQ+JvzxVlONKUU1qKqqsDoNTHW3Ou8XXXXiwafAqnL1tM993l\nnPHRQrjYnv9REARBENoXXieCPwQcC9yBPD3niYrC4tj74kyLpoT5TP5NAneRJ075EacvWwzAT954\nhor2/WyCIAiCECBe5ySdBPxea32r1vr/tNavt/TyUzQI5s+fbyz2jg579p/r+826FC2bY+rJOYAt\n77zoazyTbHnnRT49YCAf9T0CgIM2r6Hjv+z+tXv88ceDVsgKruZpk7dNLkHicj/Y5m6Tj0kXk7G9\nFk2bgW1+itjIqlWrjMV+55AhsfdnffBqRp+NTgIHKPV5jabKr8zl7DdR1ye/c0Hs3N4PzgxKJy2W\nxUZ52J4AACAASURBVI0StmdczdMmb5tcgsTlfrDN3SYfky4mY3stmv4AXKmU6thqS4e59tprjcX+\nYp9+rIwUPIdt+Ix+m9ek90GtYyNNtfv0prxjF1+9Dhx9na/xTBJ1ff3wE9nQrTcAnf71OqFPPw1S\nKyXTprmxpENbcTVPm7xtcgkSl/vBNnebfEy6mIzttWgqBOqAz5VSM5RSNyqlftHkJZNLWmHe0JGx\n92e//0panwmtW0do504Aqg873IiXazSGcnjqO+fHjgvvuy9AG0EQBKG94rVouovwU3M9gQnA7yPn\nmr6EFLx09AjqIws0jvrgFUKNzVZtaEb8JPCqw44w5uYa/xx6Jrsik+vzn34atXlzwEaCIAhCe8Nr\n0dQvjVf/togppcYrpVYrpaqUUkuUUselaHuYUuqZSPtGpVSL99UyiZkNyjt24a2B/w+AnjvLOO7z\nD1v9TPwk8KpBMtIUpaqgA/84/vsAqNpaCh58MGAjQRAEob3hqWjSWq9N5+VVSil1IeF5U78CjgGW\nAi8rpXok+UgR8AUwEdjkU0wmTZrkNYW0mTfk9Nj7s99f0Gr7+Eng1Yf7P9K06uHbfI9piqauc04c\njc4Nr6JR8NBDUJX59jSmKSkpCVohK7iap03eNrkEicv9YJu7TT4mXUzGTrtoUkoNU0p1S7NtP6VU\nW/bjuB6YqbV+TGu9EvgpUAmMbamx1vo9rfVErfUcoNaPmADnnntuG1JIjzcHHR9bfuB7H79JcXVF\nyva5kdtzulMnavvs77tPz5PO8z2mKZq6ftNlb8rP/gEAoW3byP/734PQSsmVV14ZtEJWcDVPm7xt\ncgkSl/vBNnebfEy6mIydyUjTf4AzowdKqW5KqUql1HdbaHsi8LAXIaVUHjAEWBg9p7XWwKvACdmM\nOXToUC9flxF1ufksOPp7ABTW1TB8+RtJ26qyMkIbNwJQP3gwhPzf+m+vQ8zn7BctuW4dOy72vvC+\n+6CxMZtKrTJ8+PCgFbKCq3na5G2TS5C43A+2udvkY9LFZOxM/tVVLRwX4v9WKT0iMZvO5N0M7GNR\nTN+YNyS9p+ji5zM1HCGTwFuiavCR1J10EgA5q1aR+2pma2AJgiAIQjL8H6owhyK8n53tMTPmk/0H\nsmbv8K22IV8uJW99yyuEJxRNgwdnxc1FasaPj70vvPfeAE0EQRCE9oSNRdNWoAHo1eR8T5qPFBmN\necstt7Bg0sWsevi2hNeKP09g+8dvJrTdUfpei5Oo1z47vdnWJBUbSln18G3UVUS27FOKeUNG8ivC\nazd0ffaZWNsNGzZQUlJCaWlpwiTwe1et4u47bk+I21BbzaqHb2PX6uUJ51/653OsnnNnM7cvnpja\nLI+vFjziPY+4GI/MvCfhXM32zax6+DaqmmwZM/uxh1j/QuIq3snyKPtwUUIeUfemedSNHMn83r35\nAZD3xhsJxeaNN97YbIn9pUuXUlJSQllZWcL5O+64g+nTpyeci/95xHP//fczefLkhHOVlZWUlJSw\nZMmS2Ll58+Yxd+5cxscVdlHGjh3LvHnzEs4tWrSoxUmNQecBpMzj17/+tZN5xPd/0D+PefPm+fbz\ncPn3KuruYh4tzYsNMo/o99rwe3XPPfcY+3mMGDGiWR433XQTgwcPpqSkJPa6/PLLWbRoUTPfVKjw\n1J40GirVCFyitX4yctwd2AKcprVe1KTtT4DHtNaebt0ppZYAb2utr4scK2AdMENrnXKpT6XUauCP\nWusZbYm5cOHCY6dOnfr+XhdMYmNV0zuTexjUs4hffOcArpq7stW8Zv5wIFf/o+V2vcq/4f/uKCGk\nNTUH9qXyg/dBJX5v5xNOIOezz9B5eZSvX8/6ykaueGZFm763aZsvnpjKQZckf2ownVjZateS64MX\nDGL/LoXkP/QQxTfcAEDNhRdSacmCl2PHjuWhhx4KWsM4ruZpk7dNLkHicj/Y5m6Tj0mXdGNXVlay\ncuVKgCEjRoz4IJ3YmY409VVKHauUOhY4MnJuQPRc3LV+GcZtyt3AOKXUGKXUQOCvhJcVeARAKfWY\nUup30cZKqTyl1FFKqaOBfGC/yPFB6cZsiWwsORBlc5eevHfQMQAUrF1DzttvJzaoqiIU2QuvYeBA\nyM834pGqYLKNllxzQor15dV8Oeo86rt0BSB/7ly+/uRz1pdXx17bKuuyrQtgzf9gmcbVPG3ytskl\nSFzuB9vcbfIx6WIydm6G7adGXvG0NGmkTXOFtNZzIusn3U74ltpHwBla6y2RJn2A+riP7At8GPed\nN0RerwPD04wZOC8MGcmwz8PFbsHs2VQef3zsWs6nn6IiT4LJJPDk1NQ3xkakfjrkbK5Y+ASqvp4v\n/vs2fn3hxFi7By8YRLeivKA0BUEQBAfJpGi63JhFC2it76Xlggyt9fAmx2tJY9QsVUwbWHzEyVQ+\nW0hRbTV5zz0Hd9wBHToAMgncC3/7zgVc8O/n2atqF2d98Ap/O+UCPu99UOsfFARBEIQWSLto0lo/\nalJECG8FsmjwKXz//QWEdu4kb/586n74QyBxJfCGI49MFkKIY1dRJx4eXsJ/z5tJSGsmvDiL/77i\njqC1BEEQBEex8ek5a5g2LeWccyO8ELdmU0Hcita5cRv11hu8PdfSU3a2ko7r0yeex6YuPQE46bN3\nGJLG/n4maekplPaIq3na5G2TS5C43A+2udvkY9LFZGwpmlIwZMiQrH/nB/2Ponbf/QDIXbQItXkz\nNDSQ8+mnADT07QudOxv7/s4D3FkRPB3X2rx8/nrGnjvL18x/ANJ8YtQENq3IaxJX87TJ2yaXIHG5\nH2xzt8nn27Ai+LeOIH7BdCjE9tEXAKAaGsh/+mlCX3yBqqwEzE8C736MPX9UrZGu60vHjKC0d38A\nDl//Gacte92kVkrOP//8wL47m7iap03eNrkEicv9YJu7TT4mXUzGlqLJQqJFE0D+3/8uk8DbSGMo\nh3tG7dnA8ecvPYiqTbavsyAIgiC0jBRNFlLb/yDqI5sF537yCQVPPRW7JpPAvfHvQ4fx7kFHA7B/\n2Ua6zf5bwEaCIAiCa0jRlILly5e33sgQNRdfHHufF7fMu8lJ4ECzrUtsJiNXpfjLWVfFDnv9+W7Y\ntcuAVWqaLu3fXnE1T5u8bXIJEpf7wTZ3m3xMupiMLUVTCubMmRPYd9eNHo1usup3Y/fu6H33Nfq9\nXy/+e+uNLCFT10/3H8grR54KQG5ZGYV/+YsBq9TMmDGj9UbtAFfztMnbJpcgcbkfbHO3yceki8nY\nUjSl4NZbbw3su3WXLtSdeWbCuYYjjmi2H53f9P9J8816bcWL671njqU+FN4SsfDee8NPJ2aRWbNm\nZfX7gsLVPG3ytsklSFzuB9vcbfIx6WIythRNKSgsLAz0+2vjbtFBdiaB5+QHm3MmeHHd0GM/5h5/\nDgCqooLCLK/FVVRUlNXvCwpX87TJ2yaXIHG5H2xzt8nHpIvJ2FI0WUzd8OE07r137LheJoH7woOn\nXUJDcTEABY8+SujzzwM2EgRBEFxAiiabycujtqQEAJ2TQ8OwYQELtQ+2d+zKlqt+BoTXwuowteke\n1IIgCILQHCmaUjBz5sygFaiaOJHKKVPY/eSTNB5wgPHvW/9C8DmnS1tct15xNY09w9ur5P/zn+S8\n+65fWimZPHlyVr4naFzN0yZvm1yCxOV+sM3dJh+TLiZjS9GUgp6Rf1QDpbCQmmuvpf7007Pydfld\nLcg5Tdri2lhcTNVNN8WOO0yZkpXtVfr06WP8O2zA1Txt8rbJJUhc7gfb3G3yMeliMrYUTSkYPXp0\n0ApZp9dJ7uTcVtfaSy+l4eCDAcj7z3/IW7DAD62UjBs3zvh32ICredrkbZNLkLjcD7a52+Rj0sVk\nbCmahG8veXlU3bZn2YIOU6aAbK8iCIIgJEGKJuFbTd0551A/ZAgAOZ99RuH06QEbCYIgCLYiRVMK\n1q1bF7RC1qn6xp2cfXFVispp09A5kQUv77qL0Keftj1uEkpLS43FtglX87TJ2yaXIHG5H2xzt8nH\npIvJ2FI0peCBBx4IWiHrbJh3f9AKadMW15yQYn15NevLq1nTdyBbxv0cAFVXR97PxrN+627Wl1ez\nrbLOL10ApkyZ4ms8W3E1T5u8bXIJEpf7wTZ3m3xMupiMLUVTCiZMmBC0QtY54LxrglZIm7a41tQ3\ncsUzK2Kv0X3O4sue4SUdipYv5fXrbueKZ1ZQUdvgly4Ad955p6/xbMXVPG3ytsklSFzuB9vcbfIx\n6WIythRNKejVq1fQClmnoKs7OfvpWpuXz9Qf3UhjZG+/ca88yoEGblXa9MivSVzN0yZvm1yCxOV+\nsM3dJh9ZckAQHOfjAw/jqZPPB6Cgvo7JT0+DBn9HmgRBEAR3kaJJEOK474zLWdd9PwCOXPspPR59\nKGAjQRAEwRakaErB7Nmzg1bIOpteeypohbQx4VqTX8hvfnRD7Hifu+4gtHq1b/Gnf0uWNHA1T5u8\nbXIJEpf7wTZ3m3xMupiMLUVTCmpqaoJWyDqNde7kbMr1w/5HMufEcwEIVVdTdO210NjoS+zKykpf\n4tiOq3na5G2TS5C43A+2udvkY9LFZGwpmlJw2WWXBa2QdfYb+V9BK6SNSde/jLqKjZGJ5nlvvUX+\nI4/4Evfmm2/2JY7tuJqnTd42uQSJy/1gm7tNPiZdTMaWokkQWqCqoAO/veB/YsdFU6YQWr8+QCNB\nEAQhaKRoEoQkvDNgCGUX/QQAtXs3RdddB1oHbCUIgiAEhRRNKdixY0fQClmnrsKdnLPhuumXk2js\n3RuAvMWLyX/iiTbFKysr80PLelzN0yZvm1yCxOV+sM3dJh+TLiZjS9GUgrvuuitohayzZs60oBXS\nJhuujZ07U/GnP8WOi267DbVxo+d411zjzorrbcHVPG3ytsklSFzuB9vcbfIx6WIythRNKRgzZkzQ\nClln39PdyTlbrvWnn07NRRcBoHbtorgNT9NNnDjRTzVrcTVPm7xtcgkSl/vBNnebfEy6mIwtRVMK\nBgwYELRC1inuc0jQCmmTTdeq3/6Wxsi2OnmLFlH4+997inPUUUf5qWUtruZpk7dNLkHicj/Y5m6T\nj0kXk7GlaBKENNBdu1Jx333oUPhPpsO0aeTNmxewlSAIgpBNpGgShDSpP/VUqn71q9hx8c9+Ruiz\nzwI0EgRBELKJFE0pmD9/fiDfmxNSrC+vbvXV0Oj/4+9b3nnR95imCMK1ZsIEakePBsLLEHQcMwZ2\n7kz7848//rgpNatwNU+bvG1yCRKX+8E2d5t8TLqYjJ1rLHI7YNWqVdB3VNa/t6a+kav/sbLVdjN/\nOND37678apXvMU0RiKtSVMyYQai0lNxPPiFn1SqKf/pTKp54AkKt/3+QZcuWZUEyeFzN0yZvm1yC\nxOV+sM3dJh+TLiZjy0hTCq699tqgFbLOgaOvC1ohbbLh2uKoX10On98zi/q9ugCQ/9JLFE5Lb/mD\naWm2cx1X87TJ2yaXIHG5H2xzt8nHpIvJ2DLSJAgpSDXq9/9+dDPTH7yZHN1Ih9//noYjj6RuVPZH\nJgVBEITsICNNguCRtw8Z+v/bO+8wKap0cb9f9yRnhhmSgIiCKBIElbBmEAHT/lQQwyq6BhTYa3bv\nrq7XuGvYNS/qesUEyl51UcyKCRAFVEQQVJAgOUuGydN9fn+cauhpemZ6hu6uKuZ7n6eemTp16tR7\nqrqnvjl16hz+deZVu9bzRowgsHChi0aKoihKKtGgSVH2grEn/46tvz0bcDqG//73deoYriiKovgH\nDZpq4M4773RbIe0sGn2H2woJ4wlXEVY9+BiVXboA2I7h11xT7YjhQ4YMSaeda/i1nl7y9pKLm/j5\nPHjN3Us+qXRJZdkaNNXAwIED3VZIOy1OHOS2QsJ4xTWcl0fR2LGECwsByPrwQ3IefTRu3quvvjqd\naq7h13p6ydtLLm7i5/PgNXcv+aTSJZVla9BUA7169XJbIe0UHu6fOnvJNXzIIRQ99xxGBICcf/yD\nzDjjfPXr1y/daq7g13p6ydtLLm7i5/PgNXcv+aTSJZVlezZoEpFrRWSpiJSIyNci8pta8l8gIvOd\n/HNE5MyY7aNFJByz+GckR8XzVA4YQInzSFeMIe+qq8j48kuXrRRFUZRk4cmgSUR+BzwK3A10B+YA\nH4tI82ryHw+8AjwHHA28DbwtIl1isk4AWgKtnOXilFRAabCU3Xgj5YMHAyClpeRffDHBr7922UpR\nFEVJBp4MmoCbgVHGmJeNMT8DfwCKgaHV5L8RmGCMecwYs8AYczcwC7guJl+ZMeZXY8wGZ9lWk8S0\nadP2shr+Y8uPU91WSBhPuopQ9PTTlJ9xhl0tLqbRhRcSnDkTgA8ayCS/fq2nl7y95OImfj4PXnP3\nkk8qXVJZtueCJhHJBHoCEyNpxhgDfAYcX81uxzvbo/k4Tv6+IrJeRH4WkadFpGlNLpMmTaqT+77A\n5u8nu62QMJ51zcqiaPRoKvr3B5yhCM4/n+CcOYwfP95lufTg13p6ydtLLm7i5/PgNXcv+aTSJZVl\ney5oApoDQWB9TPp67CO1eLRKIP8E4DKgH3ALcDLwoYjTczcODXHIgUMv9U+dPe2anc3Ol1+mondv\nAALbt5N/3nmM+dOfXBZLDy+++KLbCvXCS95ecnETP58Hr7l7ySeVLqks24tBU3UIYOqb3xgzzhjz\nvjHmJ2PMu8BZwDFA3+oKePLJJ/nkzotZNPqOKsv8J6/b49HQtoUz444btPytkfw6o2p/86JVC1k0\n+g4qiqo+HVz9yRjWTn61SlrZlvUsGn0HJRtWVElfP+0t/vmPe6ukhcpLWTT6DnYs/aFK+kfvvc3S\ncQ/t4fbLv+9NWT3GjPpXQvV47eUXWfn+qITqsWn2JE/WIxgQHhz5L26+9X+qzFG3cGsJpwYz+bRj\nZwACmzfT6NxzefOpp7j22mv3cBs6dOgezcqTJk2KO+bIn//85z1m8p4zZw5Dhgxh06ZNVdL//ve/\nM3LkyCppq1atYsiQISyMGcH82Wef5a677qqSVlxczJAhQ/g6pm/W+PHjtR5aD62H1sMX9bjlllvo\n1q0bQ4YM2bVceeWVdX6iJPbJl3dwHs8VA+c5wU0kfQxQaIw5N84+y4FHjTFPRKXdAww0xnSv4Vgb\ngNuNMc/Fbps4cWIP4Lsn5sOakmobo+jcIpc/9j6YYePjz08WzajBnaqdx8ztfF5283q+2vLklRbx\n1HO30HWlzRNu1Yod779PuH37Wo+vKIqipIbi4mJ+/vlngJ79+/eflcg+nmtpMsZUAN8B/SNpziO0\n/sD0anb7Kjq/w6lOelxEpA3QDFi7N76KUhtFOXnccNU/KDmiKwCBdevIHzSIwMqVLpspiqIodcFz\nQZPDY8BwEblMRDoBzwC5wBgAEXlZRB6Iyj8SOFNE/igiHZ1Wpp7AU07+PBF5SESOFZG2ItIfOyzB\nQmyH8bg8/PDDKaiat4n3+Mur+Ml1R24jlrz0Klc4o4YHV60if+BAZPVql81SQ7zmfD/gJW8vubiJ\nn8+D19y95JNKl1SW7cmgyRgzDvhv4G/AbOBI4HRjzK9OljZEdfI2xnyFHXNpOPA9MBj7aG6ekyXk\nlPEOsAA7ntO3QB+nZSsuPXv2TGKt/EFBB++Msl0bfnIFCDVtxkl33kmoQwcAgsuW0ejcc5F161w2\nSz5eGnm4LnjJ20subuLn8+A1dy/5+HVE8IyUlbyXGGOeBp6uZtseZ8QYMx6I+56hMaYUOKOuDv36\n9ePH+XXdy9806+6dL1Vt+Mk1wnlDh7LjzDNpdNZZBJcuJbh4MY1OO42dr71GuEvsWKz+5bzzznNb\noV54ydtLLm7i5/PgNXcv+aTSJZVle7KlSVH2ZcwBB7DjnXcIHXwwYB/VFZxxBhmfxQ41piiKongJ\nDZoUxQVMmzbs+OgjKnv0AJwBMC+6iOznn3fZTFEURakODZpq4Icffqg90z5G7NhIXsZPrhGixw4x\nrVqx4913KT/nHAAkHCb3llvY7y9/gcpKtxSTQuwYKX7BS95ecnETP58Hr7l7ySeVLqksW4OmGhg3\nbpzbCmln3ef/cVshYfzkCs4gmI8+XmUAzJXlAX5+7Gk2/Nf1u/LlPPss+ZdcAjt2uGi7dzzxxBO1\nZ/IgXvL2koub+Pk8eM3dSz6pdEll2Z7tCO4Fbr/9dp5d6rZFeml/yZ6jaHsVP7kClFWG2X7KTVz1\nRpy3Cw4ZxNkX5HDbm4+TGaok89NPaXTmmex87TVMmzbpl91LnvfpY0YveXvJxU38fB685u4ln1S6\npLJsbWmqgZycHLcV0k4wyz919pNrhJqc3/vNGVx39YNUFjYGIGPePAoGDCD43Xfp0ksaubm5bivU\nCy95e8nFTfx8Hrzm7iWfVLqksmwNmhTFQ8w69GgWj3+PkDPFSmDDBhqdfTaZ77zjspmiKIqiQZOi\neIzy9oey45NPqDjhBACktJT8K69kv7vugrIyl+0URVEaLho01cCoUaPcVkg7K9/3T5395BohUWfT\ntCk7x4+n7OKLd6XlPPUUjQYMIDBvXg17eoPYmcf9gpe8veTiJn4+D15z95JPKl1SWbYGTTXQokUL\ntxXSTlYT/9TZT64REnEOBsS+WVdiWHDfI6y5/R7CWVkAZPz0E4369aPkkZFs3undVqc2Puy8Dt7y\n9pKLm/j5PHjN3Us+qXRJZdkaNNXAueee67ZC2ml5on/q7CfXCIk4l1WGueqN+XYZ/zMDC3tzybVP\nsajVIQAEystp/cBfafG7C5BVq1KtXC+GDx/utkK98JK3l1zcxM/nwWvuXvJJpUsqy9agSVF8wOID\nDuWK65/m330uICwCQKOvplLQuzeZ4+NOuagoiqIkGQ2aFMUnlGdmMfKsP3DNsIdZ19g+5gts20b+\nsGHkDRuGbN3qsqGiKMq+jQZNNbBixQq3FdJOyQb/1NlPrhGS4fzdYd25+Obn2HLO7kd9WePHU3DS\nSWR88cVel58MFi5c6LZCvfCSt5dc3MTP58Fr7l7ySaVLKsvWoKkGnnvuObcV0s6qD551WyFh/OQa\nIVnOO/fLZ80TT7N85NNUFhQCEFizhkaDBsGVV7Nu/pJdU7VsLq5IyjHrwj333JP2YyYDL3l7ycVN\n/HwevObuJZ9UuqSybA2aauC6665zWyHtHDzo+tozeQQ/uUZIpnNZZZjzyzoy6Lpn+Paw7rvSm7zz\nJu36nsRXI27jmldmU1QeStoxE+Whhx5K+zGTgZe8veTiJn4+D15z95JPKl1SWbYGTTXQsmVLtxXS\nTnYT/9TZT64RUuG8vnELrr36IR4cdANbcxsBsF9FKSM+fYk3HrmSxu++BcYk/bg14aVXm+uCl7y9\n5OImfj4PXnP3ko9fhxzQCXsVZR/ABAK8ccJAPjn6FIZ9+jLnf/UOGeEwrbZugJuupfKVlyh+4AFC\nPXq4rdqg2VxckVDLX15WkKa5mWkwUhSlLmjQpCj7ENtzC3h04HWMP+5sbnr/GU5cMAOAjBkzKBgw\ngM2DL2Ddn2+jsmWrPfbVG3XqKSoPcdUb82vN98L5nfVaKIoH0cdzNfDaa6+5rZB21k5+1W2FhPGT\na4R0OS9r2Zabrvo7Nw59gNJDD9uV3vTN12l38gl8NeI2bhz77e5BNN+Yn9S+TyNHjkxaWenES95e\ncnETP58Hr7l7ySeVLqksW4OmGihrgJOjhiv8U2c/uUZIt/P0Tscy7/3PeOSca9m+Xz4AueW2v9P7\n91/Eje8/Q4utvyb9uMXFxUkvMx14ydtLLm7i5/PgNXcv+aTSJZVla9BUA5dffrnbCmnnwNOucFsh\nYfzkGsEV58xM/nPSYAbf8jLjThhIZcB+7fPKS7j0i9d5+8FLuWvcQ2QvSt7YJrfddlvSykonXvL2\nkoub+Pk8eM3dSz6pdEll2dqnSVEaCNvyCnl40A28etJ5XPLF65w98yOyKyvIDFVy9syP4fSP2d7/\nVDaMuJbiXsdUW472fVIUpaGiQZOiNDBWNT+QBwffxHOnXsaF097mgq/eoaBkJwAFEz+lYOKnzGl7\nBC/3vYgvOx+HCVRtkNZOyoqiNFQ0aKqBbdu2AYVua6SViqJtZOb5o85+co3gJefNjZryzBlDebnv\nRQyc8SHXzXybrHVrAThq+U88+tKdrGp6ABN6DOCj7v1Zsf9BCZe9adMmmjVrlir1lJE07/JypKgI\nSkqQ0lIoLUVKS8nduINjFy4ku6KM7IpyMkMVFGfnsiWvkK15jdmSX8iOnPzkuvgcP58Hr7l7ySeV\nLqksW4OmGnjkkUfYf8i9bmuklWXjHqbDlfe5rZEQfnKN4EXn4pxcXu1zPqc8dAtv//UZLvv8Pxy6\nfhkAbTavZdhnYxn22VjmtenIhB79ye47gpXsX2OZN/zhGl546d++a5G6/vrreeWVV2rPaAyycSOB\nZcsILF9OcNmy3b8vXYqsXYvEGVC0AHiqlqIrAwHMI80YVFLCf448isqmTals1pyyQztQ0rkLpZ26\nEM7PbzCPSRO+Jh7Ea+5e8kmlSyrL1qCpBi677DImVLptkV5an3qZ2woJ4yfXCF52NllZfNjzNCZ0\nH8AJC2Zw8dTx/GbxbALOzb/LqgV0WbUA8/4zfH1YDyZ0H8CUI06kOCd3j7KKjhhMUXnIdzf1W2+9\ntWqCMcjatWT88ANBZwksWUJw+XJk586UOGSEw7DxV+4F8r+aFjfPymatKeh1NFk9jiLUtSuhrl0J\nt2kDIilxcpM9romP8Jq7l3xS6ZLKsjVoqoEOHTowofZx6PYp8toc7rZCwvjJNYIfnE0gwLTOxzGt\n83E037aR0+ZM5ozZE+m8ehEAEg5z/MKZHL9wJqWZ2UzpcgJTjjiBWe2PYlOBbRLPa3M4wYCwcmtp\njcfyVGtJKET3nByCb7xhg6S5cwn++COBTZvqVExl06aUH9SWysZNMDk5hHNyMNnZhHNyCGVlyTym\njAAAHihJREFU897SnZRlZlOWkUVFRib5pUU0LtpKk6JtNNm5lSY7t9I+XMQRGzdCZfzJlg/atAY+\nXgMff7grLVxYSKhbNyqPO47KPn2o7NULcnL26pR4gaOOOspthXrjNXcv+aTSJZVla9CkKEq1bCxs\nzit9LuCVPhfQbv1yzpg9kUsXfkH2qpUA5FSUcfqcyZw+ZzIAy5sfyOxDjmRW+6MIH9eIq77eUWP5\nbnUql23bCM6bR/DHH+3y008E589HSkpq3ddkZhI++GDCbdsSateOcLt2bNj/QP5nQYg1TVtRlJNX\n7b6jBndi1Js/13qMUYM7MWL8fHLLSmhStJX9t2/ksLVL6bD2Fw5f8wuHrVtKTsyYX4Ft2whMnUrm\n1KnwyCOYnBwqjzmGyt69qTjpJDuFTqZHAlRF8SkaNCmKkhDLWrblmTOG0n3Ug/zr8fGcOXsiA+ZM\npnHx7sCo7cbVtN24mkHfToD//IN3mrRkVnsbRM0+5EhWNz2gytt4ibRGwV60SIVCBJYt2x0Y/fST\n/X3lyoR2DzdvTqhbN0JHHkll166EunUjfOihEAxWybd9aymLdiS5WVqE4pxcinNyWd2sNd8fcuSu\nTYFwiJd75XDgsoUEf/qJDCf4C6xdu3v30lIyv/iCzC++YD/A5OVRefzxVJx0EpV9+hA68kgI6FB9\nilIXNGiqgQkTJkC7M93WSCu/zviQ/Y/5rdsaCeEn1wh+dN4DEea268rcdl159Oxr6LlkDj2WzKHH\nkrkcsfJnMkOVvABcBbTesp7W333KWd99CkB5MJP1jfdnbZNWrG3Sgiaru/Ls2gBrm7RkbZNWbChs\nTii455+luC1SxiCbNxNYvdouq1ZV+V1Wryawdi0SSmx6mFD79jxfUMBlZ51FZbduhLp1w7Rs6Vo/\noZo+K+FAkMrDOrCk/WHQb3eezLVryPt6OvlfTSN/+lSy1qzetU2Kisj87DMyP/vMlrH//lQMGEDF\ngAFU9uuHKfTGW52xjB07lt///vdua9QLr7l7ySeVLqksW4OmGli0aFGDC5qKnX4rfsBPrhH86FwT\nlRmZfHN4L745vBcA2eWldFsxn/kfPseFBzYl87vvyKks35U/K1TBQZvW2D45AN9+xD1R5YUkwOb8\nJoQDAcAQCBsEQ+FDQRskmTAYAwYCpSUESmtvpYrF5OcT6tLFthx17UqoSxdCXbpAfj7f/vnPXPjH\nP9b/hCSR2j4rZZVhRsR91HcEHHME/GYYB25ey1ON19Fi5tdkTJ1KYP36XbkCv/5K9quvkv3qq5hg\nkMpjj6Xi1FOpOPVUwp07e6ZT+dy5c91WqDdec/eSTypdUlm2Bk01cMMNN/BEA+sI3vbcG91WSBg/\nuUbwo3NdKMvKYeZh3eGGp1k0uBPXjZtLl5UL6LFkLl1XzOeALes4YMt68svizw0VNGH23xGn43XN\nXaPiUtm0KRUHtKa8zcGUdOpMaaculHbuQnmbg8jLyYz7uO/hhx+usr65uCKhiYxD4T2HF9hb9vqz\nIsLqZq3ZfH5/8kZcBcYQWLiQzC+/JGPyZDKnTEGcObokFCJz+nQyp0+Hv/6VcOvWuwKoij59ID8/\nCTWqH7HXxE94zd1LPql0SWXZGjQpipIyKjKymHNIN+Yc0m13ojE0KtnJAVvX87eOmbw14TsnmNpA\nqy3rae4ETUYCGBEM0DQvi43FlXZdBINQmZHB+sL97dK4Besbt+Dis3vylx9KWV+4P2VZMW+O7QBm\nFMOMBQl3QC8qD3HVG7X/5zRqcKc6nBWXECHcsSNlHTtSdvXVUFpKxldfkfnpp2R+9hnBxYt3ZQ2s\nWUP2Sy+R/dJLmMxM2xfKeZQX7tjRM61QipJuNGhSFCW9iLAjtxE7chux7dRO/GdH21p3GTW4UzWP\noqpy1gmdWLGu9nyJdkBPRQtSuqmxrt2Pt8std1G4egX7T/uczE8/JWPqVKTMvp0nFRW7OpRz112E\n2rShcsAA2wrVu7errVCKkm40aFIUpcFRfX+gqviiBakWEq3rC+d3pmD4cMqGD4fiYvsYz+k4Hly+\nfFe+4KpVBMeMIXvMGExWlm2F6tePypNPJtS1q76Rp+zT6Ke7Bu688063FdLOotF3uK2QMH5yjeBH\n5/rg13p6ydtVl9xcKk4/nZKHH2b7rFls++Ybiu+7j4q+fTFZWbuySXk5mVOmkHv33RT07Uthhw7k\nXX452S+8QGDhQttpfy8ZMmTIXpfhFl5z95JPKl1SWba2NNXAwIEDme62RJppceIgtxUSxk+uEfzo\nXB/8Wk8veafbpcbHePsfBEOGwpChZJcVkzV1KgWTJ9FoyiSyVq/alS2wZQtZ771H1nvvARBu1YqK\n3r3tCOV9+hA+KPFJnyNcffXV9aqPF/Cau5d8UumSyrI1aKqBXr16Mb2BvT1X6Lw67gf85BrBj871\nwa/19JJ3ul3q8sjy0i0HwdGXw1GX0W7DCn6zeDa/WTyLnkvmUFCye06+wLp1ZL/+Otmvvw5A6KCD\nCPXqRWXPnlT26mUH2Kxlqpd+/frtXcVcxGvuXvJJpUsqy9agSVEURakfIixr2ZZlLdvy+omDCIRD\nHL7mF3otns3QokXkzfiaQNTUNMGVKwmuXEnWW28BEM7MpLzLEXDsMTaI6tmTcLt2+nae4lk0aFIU\nRVGSQjgQ5Oc2h/Nzm8PpPbgT1477gS6rFnDMoln0+uV7uqxcwH4Vux8BBioqyJnzPcz5Hp591pbR\nrBmhI48k1Lnz7qVjR8irfk4/RUkX2hG8BqZNm+a2QtrZ8uNUtxUSxk+uEfzoXB/8Wk8veXvJpb5U\nZmQyt11Xnj/1Mv7wh8c45W/vcslNo3hg8E281+t0lrY4eI99Aps2kTl5MjlPP03e9dczacAAmhx0\nEPndu5N3ySXk3HcfmW+8QfCnn6A4/iCpXuGDDz5wW6EKXvJJpUsqy/Zs0CQi14rIUhEpEZGvReQ3\nteS/QETmO/nniMge85+IyN9EZI2IFIvIpyJyWE1lvvbaa3tbDd+xbrJ/6uwn1wh+dK4Pfq2nl7y9\n5JIsQsEgC1sfxlvHnc3fLryFC/80mjmz5nHd1Q/yzGlXMLXTsWzNLaiyz4POz8zly8maMIH9HnuM\n/OHDKejdmyZt2lDYqRONTj+d3OHDybn/frLGjiXjiy8IrFgBlZXpr2QUI0eOdPX4sXjJJ5UuqSzb\nk4/nROR3wKPAcGAGcDPwsYgcbozZGCf/8cArwK3AB8AQ4G0R6W6MmefkuRW4DrgcWArc55TZ2RhT\nHlsmQOPGjZNeN6+Tke+fOvvJNYIfneuDX+vpJW8vuaSSUEFhlfkLMYZmOzZz6LqlHLpuGaGpb/Bj\no2Z03rSCYJyWpcCGDQQ2bCDj22/32GaCQcJt2mBatSLcogXhli0xLVoQbtFi18/I70QNpZAsmjdv\nnvQy9wYv+aTSJZVlezJowgZJo4wxLwOIyB+A/wcMBR6Kk/9GYIIx5jFn/W4ROQ0bJF0TledeY8x7\nTpmXAeuBQcC4VFVEURRFqQMibCpoxqaCZsw4vBeLfvmeK6+8j1GDDuevL07ZFUy1X7+MAzevpfXm\ndTTfsTl+UaGQHZgzanDO6gg3boxp0gRTWGiXxo3tUlhotzm/m4ICTF4e5OdjcnMxeXmY3Fzb50oH\n9tzn8VzQJCKZQE/ggUiaMcaIyGfA8dXsdjy2ZSqaj4GBTpntgVbAxKgyt4vIN86+GjQpiqJ4mUCA\nNU0PYE3TA/iyywlVNmVXlHHA5nW03ryO/z7Y8MXk73cFVK22rKewpPYZnwNbt8LWrXulaPbbb3cQ\nlZtLxpo1NDrjDExODmRl2Z/Z2Zjs7N0/s7IwmZmQkQGZmfZ3ZzFOGpmZmGAQgkGbLxjcve4sJiPD\nBm3BYJWfJhCw64EAFBURWLp017oRsW8qiti0eD+dxUTeaIzep7olXr5Ims/xXNAENAeC2FagaNYD\nHavZp1U1+Vs5v7cETC15FEVRFB9Slpm9a+iDywd3YmRe1fGmsirKabJzC812bKH5js3c0CGLj6bM\no9nOLTTbsZmmO7bQdOcWGpXsoFFJEUETrpeHlJQgUUMsCJAxY8beVC2pZAKFPXu6rYERIdMYGkce\no8ULrOIFWnF+L77/fsqvuCK1wlF4MWiqDsEGPsnMX1OenMWLF/N4n9ZkZGVXW0BQhFBZCa33q12t\n0sP5Inl+XLu4xrxeqkM8VzfPXSJlldVyfpPt5la+H9cuTvq5S0e+2M+Um361fVa8du5SlS9yTfaq\nvP0yoaAFW2nBVmB9v3Z8WBg/eBBjyCkvJbe8hBu7FfLql4vJKysmt7yEvNIi8spKyKooI7uynJzy\nMrIry+iUH2DN+q3kVJaTU1FGdnkZWZXlzFq0gO3t2tXqnC5mLV3K9kMOcVsDsC47kuDyazjA1vVb\ndq3vlxlk7ty5FCfwZmVp6a7hL2oeYTUKMUmYGyiZOI/nioHzjDHvRqWPAQqNMefG2Wc58Kgx5omo\ntHuAgcaY7iJyCPALcLQxZm5Uns+B2caYm2PLnDhx4pAnn3zy/zZs2LCH4ymnnOKpkVWTyaRJk3xT\nNz+5RvCjc33waz295O0lFzfx83nwmruXfFLpEq/sSZMmMXny5D3yOvfzS/r37/9KImV7LmgCEJGv\ngW+MMTc66wKsAJ4wxjwcJ/9rwH7GmIFRadOAOcaYa5z1NcDDxpjHnfUC7OO5y4wxr8eWOXHixGbA\n6cAyoJoJmRRFURRF8Sk5QDvg4/79+29KZAevBk0XAi8BI9g95MD5QCdjzK8i8jKwyhjzP07+44Ep\nwF+wQw5c7PzeI2rIgVuwQxJcgQ2E7gWOAI6obsgBRVEURVGUCJ7s02SMGScizYG/YTtxfw+cboz5\n1cnSBqiMyv+ViFwM3O8si7CP5uZF5XlIRHKBUUBj4EvgTA2YFEVRFEVJBE+2NCmKoiiKongNHYlL\nUZSU4EyDdIPbHnXFy95edlOUhoAGTQ51nevOT4hIbxF5V0RWi0hYRM6Jk6dO8/KlmkScvYaItBaR\nsSKy0TmPc0Skh9teqcKP1wi8c538ev6SiYjcJiIzRGS7iKwXkbdE5HC3vWpDRP7gfG62Oct0ETnD\nbS/w7ufKudZhEXms9txx9/fEfUyDJqrMdXc30B2Yg52XzjsT9ewdedh+YdcSZ1yqqHn5RgDHAEXY\n+id/MqbEqdHZa4hIY2AaUIZ967Iz8N/Alpr28zm+ukbguevku/OXAnoDTwLHAgOw4y9+IiL7uWpV\nOyuxLxb1dJZJwDsi0tlVK4vnPldOI8Qw7L21vnjjPmaMafAL8DUwMmpdgFXALc76PcBy7NADq4B/\nuu28F3UNA+fEpK0Bbo5aLwBKgAud9UzgKSdfCbAEuNVl56XA7di3LHdg34g8Gzui/NtO2hygZ5oc\n/wFMqSWPp5zTdI1uA14AtjvfoWEueyZ6ndLqXc352x94Dztu3S/YiciXAje4fb1TeB6aO+fiJGf9\nEeDdqO03OdtPjUpbBFzpAfdNEQ/Hcbhz/YqAecBxwKHAZGAnNng/JJ2fK+BOYG6cfN8D96TIIR9Y\nAPRz6v6Yk/4C8F5M3gxgA3BFXerlpNV2H5sIPBnn81YG9E20Pg2+pSlqrrvoeekM8BlwvIich/2i\nDgMOw07w+4MLqinBGfhzj3n5gMi8fGAnOz4LO+zD4cCl2Bu+29yEfQvyaOB9YCw2IBmLbTH8xVlP\nB2cDM0VknPOYYZaIXO1x53TwR+BbbH2fBv7X5ccviV4nL3i/BBwInIz97l2DDaT2ZRpjWxEiM/B+\nDpwUtb0P8CvQF0BEDgTaO/lcQUQCInIRkAtMj9p0BzAGOAqYD7wCPIN9w7sn9p/zp9IqCy8CnUVk\n13DoItId6AqMTtEx/4UNjibFpD8PnC4iLaPSzsaOnVSn+WATvI89D1zs3PMj/B47fNHnCR/M7ejc\n7QU4ABu1HhuT/iDwFfYmNx8Iuu2apPrG/udxPBACWsbk+w/wqvP7SOBTrzg7aUuBMVHrLZ18d0el\nHevUrUUaHEuwLQL3Yv9IDnfWL/Wqc7qvkZO2Dhjuomedr1M6vON8Lzs4aT2i0jo6aftkSxM2iHif\nqJZAoBA7vEx3Z/1X4BZgmrN+CbDCJd+u2NbhCmyQd0bM9bwnav1YJ+3yqLTfAUUpdoz3vfwAeCpq\n/QlgYoqOfxG29TzTWd/V0uSs/wj8KWr9HeCFutaLxO5jWcBG4Pyo7d8Dd9SlTg2+pakGIvPSvY79\nD2KpiDwrIoNEJOiuWlqInpdvDNBdRBaIyEgROdU9rSrsavEzxkQmY/4xavt6bD1apMElAHxnjLnT\nGDPHGPMs8BzwXzH5vOScDmJbZdfhbt3qfJ0c0u3dGagwxsyKJBhjFgBb0+iQbp4GumBvtAAYY7Zh\nb7p9RaQbUA48C/Rwxt3rgx3Y2A1+xgbexwL/C7wsIp2itkd/hqr7rueISH5KLffkOWyLS5bT6nIx\n9lFZUhGRNsA/sf+QVFST7XngSid/S+DMJLvsuo8ZOybjv4GhzvF6YAPfOrXsa9BkI88Q9r/+aFoA\n640xq7GPpK7B/kf6L2DKPhQ4rcN+sOLWH8AYMxs71PwdOE2nIlKn5tMUEe+LGJ0WCfrS8Tlfi22R\njGY+cHBMmpec00FsfQ3u1q2+1ynd3lJ7ln0HEXkK+C22b8namM1TgFOwjyk/N8ZsxQYsJ0XS0qi6\nC2NMpTFmiTFmljHmdmxwd2NUlnjfay9819/D9uM5F/s4LAN4MwXH6Yl9nPydiFSISAX2et0oIuUi\nIsDLQHsRORbb7WOJMWZ69UVWS633MYfngVNFpDU2WJtojFlZlwPtK3+Y640TAX8H9I+kORezP87z\naWNMmTHmfWPMTdgv7wlANxd0k44xZin2Axdd/wLsf0/To/LtNMa8bowZgW1WPs95E8nrpOvtkWnY\nxyfRdMR2Iq4rnnjjZR8lmdcplcwHMmL6nnTE9vnZp3ACpoHAKcaYFXGyfI59y+4UdgdIU7AtJB1w\nr6UplgCQXcN2T3yvjTEhbLAyFBs4vGaMScX8qp9h75NHY1vkjgJmYlt7jjKWzdiXYIYCl1PPflV1\nuI/96DgMp54tbJ6cRsUFHgNeEpHv2D3XXS4wRkQuB4LYDmXF2I5jxXjvj2y1iEgethN75L/X9iJy\nFLDZibL/CdwhIovZPS/fauzzZUTkJux/6N9jv/gXAuuc//jcck64qKTLxedxYJqI3IbtxHgscDX2\nBYK64otWhiReo3SSzOu0V9Ry/haKyMfAsyLyX9jW8Mexf3v2GUTkaezN6xygKKpT8LaoG/kXQCNs\nq8hfnLTPsV0n1hpjFqfP2CIi9wMTsEMPNML2rToZOK2m3RJM21u3RL6Xz2MDcwOcmGwHAGNM5K3B\naLciYJMxJrq19wVsX7YANTwqq+d9bBXOfSzmeE9h32p8uz4V08V2CLvGOdEl2A7gvZz0gc76Fuzr\nx9Oow+uJXliwX+Yw9g9v9PJiVJ57sK9sFgMfA4dFbbsamOXUfwvwCfY/BdecscMe3BCzT4iqnQPb\nOmlHpuk8/xaY65zDn4ChMds95+zCNZoF3OWyd32uU9K9Ezh/LYF3Hc+l2BvzHm5+Xqqpfwi4LCbf\nbOxbTpH1JtgO4v92yft551qUYFs4PgH6RW2v9XvtXP8QUJDOz1VUvinAD2k+b5OI6ggelb6UqKEl\n6lsvariPReXJww758ER96qBzzymKoihKA0NEFmHfohvpskcuNtC53BgT2yqUiuO1AxZjx8Or82Cb\n+nhOURRFURoIzkwXF2NbMse46CHYjuKREfnfS/HxMrCDWd4HfFWfgAk0aFIURVGUhsQG7HhXw4wd\n0sEtDsY+lluJbWUKp/h4J2LHifoZuKC+hejjOUVRFEVRlARo8EMOKIqiKIqiJIIGTYqiKIqiKAmg\nQZOiKIqiKEoCaNCkKIqiKIqSABo0KYqiKIqiJIAGTYqiKIqiKAmgQZOiKHERkXtEJByTFhSRh0Rk\nhYiERORNJz1PRJ4XkbUiEhaRx9yx9gYiMkZElqbpWJ+LyOR0HEtRGjo6uKWiNACciaejZxAvAzYD\nPwAfAKONMTtjdjPYuZ6iuQr4E3aS69lAZFb624HLgL9h5+SaT8Mm3rmrNyLSGTtR9mhjzIqYzUk9\nlqIo1aODWypKA8AJml4E7sROTJ0JtAL6YmdmX4GdYPSHqH0CQIYxpjwq7VXgRGPMwTHlfwVUGGP6\npLYm/kBEgkDAGFORpPLOA17HThb+Rcy2DABjTGUyjqUoSvVoS5OiNCw+MsbMilp/UET6Ylub3hGR\nzsaYMgBnWoPymP1bAFvjlNsC+ClZks68VFkRF79hjInMwJ4sBNuiFO9YGiwpSprQPk2K0sAxxnwO\n3Au0BS6NpEf3aRKRts7vfYGuTr+lkIic7KS3A86KSj/Y2S9LRP4qIotEpNTpC/WgiGRFOzj7PSEi\nQ0TkR6AUON3ZJiJyk4j8KCIlIrJORJ4RkcYxZSwTkXdF5EQR+cbJ+4uI/D62ziJSKCKPi8hSx2ul\niLwkIk2j8iTkHo/YPk2R8ycifxSRYSKy2Clzhoj0qqWsy4FxzurnUee4j7P9cxGZFJX/ZCfPBSJy\nt4isEpHtIvK6iDRy6vVPEVkvIjtE5EURyYxz3EtFZKaIFIvIJhF5VUTa1FZ3RdmX0ZYmRVEAxgIP\nYB/VveCkGXa3bvyKDajuAPKAv2BbP+Y56f/ETrz5aCS/01r0HnACMAo7UWY34GagAzA4xqE/diLN\nfwEbsY8RAZ7F9pd6ERgJHAJcDxwtIic6rToR3w7Yx1gvYGdwHwqMFpGZxpj5YDutA1OBjk6+2djZ\nz88B2gCb6+EeS/S5i+YSIB94xtl+KzBeRNpH1SOWKcATTp3vc1xgd7+x6vpY3AYUA38HDnP2r8D2\nf2oM3A0cB1yO7Yd2X2RHEbkd2z/tNeA57Gz0NwBTRKS7MWZ7DXVXlH0XY4wuuuiyjy/YG2MI6FFD\nni3AzKj1u4FQTJ7JwNw4+y4F3o1JuxR7kz4+Jn2443JcVFrYydsxJu9JzrbfxaSf6qRfFOMQAk6I\nSmsOlAAPRaX91cl3Tg3nImH3avYfDSyJWm/r+G4ACqLSz3bK+20t5Z3n5OsTZ9tkYFLU+snOseYA\nwaj0/3PKeD9m/2kxrgc7db81Jl8X7OPav7j9edZFF7cWfTynKEqEnUCjJJZ3PrY1ZKGINIss2Ju8\nAKfE5P/cGLMgThlbgYkxZcx2fGPLmGeMmR5ZMcZsBBYA7aPyDAbmGGPeTaJ7orxmqrbSfOmU176a\n/HvDS6Zq69U3zs8XY/J9AxzkdPwHG6AJ8HpM3TcAi6h/3RXF9+jjOUVRIuQD65NYXgegE/bRXiwG\n23k8mmXVlNEYe8NOpIzY1/HBtqA1iVo/FHgjTr7Y49bFPVFWVinImK32SWAVv2SxMmZ9Ww3pAaAQ\ne64Oc9YXxynTsOfLAYrSYNCgSVEURORA7E0z3o2yvgSw40DdjG25iCX25l1STRnrgSHVlBEb1FTX\nLyjevjVRV/dESZbf3hyrNocA9vHeGcQf/yl2PC9FaTBo0KQoCtiO1gb4KIll/gIcaYzZm9Gqf8F2\nEJ9ukjf8wC9A1wTy7K17MknngHq/YAOoZcaYZAbRiuJ7tE+TojRwRKQf9q24JcArSSx6HNBGRIbF\nOWaOiOQmWEYGcFecMoIiUlgPr/HAUSIysJbj7q17MinCBjKNa8uYBN7EtjDdHW9j9LAMitLQ0JYm\nRWk4CPBbsVNyZAAtgX7YN9GWYt8mS2Z/lbHYqT/+V0ROwb6lFQQ6Y4cWOA2YVf3uYIz5QkRGAX8R\nkaOBT7Bvdh2O7ax9A/YmXxcedvZ9XURGA98BzbBvso0wdlT0vXZPMt9jH6vd6oxPVQZMdDq614Va\nHwMaY5aIyB3AAyJyCPA2sAPbWX0QdgiGBj23oNJw0aBJURoOBvu6PdjOvJG5524AxhhjiqrZJ9G0\nKunGGOO05tyMffw3CDtu0BLgcWBhTftHlfNfIjITGAHcD1RiO42/jA1mai0jOt0YUyQiJ2HPxbmO\n2wbgM2BVPdyrI9alOr+avCPO60VkBHbspeexAdwpQGRKlXjHSsSpuuM9KCILsPWPtPKtxD6+remt\nQ0XZp9G55xRFURRFURJA+zQpiqIoiqIkgAZNiqIoiqIoCaBBk6IoiqIoSgJo0KQoiqIoipIAGjQp\niqIoiqIkgAZNiqIoiqIoCaBBk6IoiqIoSgJo0KQoiqIoipIAGjQpiqIoiqIkgAZNiqIoiqIoCaBB\nk6IoiqIoSgJo0KQoiqIoipIAGjQpiqIoiqIkwP8HbEBp7WJucVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f4831a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qJDTzvJe8nxZ4Gf8W+Hgzex4/wN3W+IE65rJu4Lhi9p+5OfONbRDlWeD74aH5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\niIiIiIiIRERFuYiIiIiIiEhE/j8z1gLDF2wHkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f47e7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list = data_notools.logdifference\n",
    "normd = True\n",
    "plt.figure(1)\n",
    "hist = np.histogram(list,bins=50,normed=normd)\n",
    "x = 0.5*(hist[1][1:]+hist[1][:-1])\n",
    "y = hist[0]\n",
    "\n",
    "\n",
    "mod = LognormalModel(prefix='a')+LognormalModel(prefix='c')+ExponentialGaussianModel(prefix='d')     \n",
    "pars  = mod.make_params(acenter=-0.63663540, asigma=0.59326659, aamplitude=0.25044023,ccenter=2.41715948,csigma=0.02351624,camplitude=0.01670780,dcenter=1.87,damplitude=0.68,dsigma=0.43,dgamma=0.48)\n",
    "#pars  = mod.make_params(acenter=-0.63663540, asigma=0.59326659, aamplitude=0.25044023,ccenter=2.41715948,csigma=0.02351624,camplitude=0.01670780)\n",
    "result = mod.fit(y, x=x, params=pars)\n",
    "print(result.fit_report())\n",
    "#latexify(columns=1)\n",
    "pyplot.hold(True)\n",
    "plt.hist(list, normed=normd,bins=50)\n",
    "#plt.plot(x, result.init_fit, 'k--')\n",
    "plt.plot(x, result.best_fit, 'r-')\n",
    "plt.xticks(seconds_scale_log, labels,rotation='horizontal');\n",
    "plt.ylabel('Empirical PDF')\n",
    "plt.xlabel('Difference in time')\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"fit.pdf\")\n",
    "#format_axes(plt.gca())\n",
    "#plt.savefig(\"fit.pdf\")\n",
    "plt.figure(2)\n",
    "result.plot_residuals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold in seconds:  14901.8038536\n",
      "Threshold in hours:  4.13938995934\n"
     ]
    },
    {
     "data": {
      "image/png": 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mJALUqh9P7D235T/ePGYih3MOu1iRiIhI2VEzEiEuu/8m9jRtBkDt1K3Mf26G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\n9sP8dL5U91wSkfKlq2lEPKxd9/b43x1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72f4328cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = linspace(6,11,200)\n",
    "torefit = result.eval_components(x=x)\n",
    "twoandthree = np.array(torefit['d'])-np.array(torefit['c'])\n",
    "plt.plot(x,twoandthree**2)\n",
    "x2 = linspace(6,11,2000)\n",
    "torefit2 = result.eval_components(x=x2)\n",
    "twoandthree2 = np.array(torefit2['d'])-np.array(torefit2['c'])\n",
    "plt.plot(x2,twoandthree2**2)\n",
    "print(\"Threshold in seconds: \", (np.exp(x2[np.argmin(twoandthree2**2)])-1))\n",
    "print(\"Threshold in hours: \", (np.exp(x2[np.argmin(twoandthree2**2)])-1)/3600)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#ignore this\n",
    "top100 = data3.groupby('Id')['delta'].count().sort_values(ascending=False).head(100).axes[0]\n",
    "data3[data3['Id'].isin(top100)].hist(by='Id',column='logdifference',log=True,figsize=(40,30),sharey=True,sharex=True,layout=(10,10));\n",
    "plt.xticks(seconds_scale_log, labels,rotation='horizontal');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "118px",
    "width": "251px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}