{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:21:48.421000Z",
     "start_time": "2019-02-19T03:21:40.234000Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import os\n",
    "import subprocess\n",
    "import time\n",
    "import pandas\n",
    "import pickle\n",
    "import math\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC, SVR, LinearSVC\n",
    "from sklearn.metrics import accuracy_score, silhouette_score, adjusted_rand_score, silhouette_samples\n",
    "from sklearn.cluster import AgglomerativeClustering, SpectralClustering, KMeans\n",
    "from sklearn.model_selection import (StratifiedKFold, train_test_split,\n",
    "                                     cross_val_score, LeaveOneOut, GridSearchCV)\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn import linear_model\n",
    "from sklearn.manifold import TSNE\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import (ModelDesc, EvalEnvironment, Term, EvalFactor, LookupFactor, dmatrices, INTERCEPT)\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.colorbar as colorbar\n",
    "import sys\n",
    "import re\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:21:48.568000Z",
     "start_time": "2019-02-19T03:21:42.011Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('ticks')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['axes.titlesize'] = 16\n",
    "mpl.rcParams['axes.labelsize'] = 12\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['legend.fontsize'] = 12\n",
    "mpl.rcParams['legend.labelspacing'] = 0.2\n",
    "mpl.rcParams['axes.labelpad'] = 2\n",
    "mpl.rcParams['xtick.major.size'] = 2\n",
    "mpl.rcParams['xtick.major.width'] = 0.5\n",
    "mpl.rcParams['xtick.major.pad'] = 1\n",
    "mpl.rcParams['ytick.major.size'] = 2\n",
    "mpl.rcParams['ytick.major.width'] = 0.5\n",
    "mpl.rcParams['ytick.major.pad'] = 1\n",
    "mpl.rcParams['lines.scale_dashes'] = False\n",
    "mpl.rcParams['lines.dashed_pattern'] = (2, 1)\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['text.color'] = 'k'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:21:48.573000Z",
     "start_time": "2019-02-19T03:21:43.563Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def binaryclassifier(y, X, cv, algorithm='GaussianNB'):\n",
    "    if cv=='loo':\n",
    "        cv = LeaveOneOut()\n",
    "    if algorithm == 'GaussianNB':\n",
    "        clf = GaussianNB()\n",
    "        accuracies = cross_val_score(clf, X, y, cv=cv)  \n",
    "    elif algorithm == 'SVC':\n",
    "        hyperparameters = {'kernel': ['linear', 'rbf'],\n",
    "                           'gamma': [1e-2, 1e-1, 1e0, 1e1, 1e2],\n",
    "                           'C': [1e-2, 1e-1, 1e0, 1e1, 1e2]}\n",
    "        clf = GridSearchCV(SVC(), hyperparameters, cv=cv)\n",
    "        if np.all(np.isnan(X)):\n",
    "            accuracy=np.nan\n",
    "        else:\n",
    "            clf.fit(X, y)\n",
    "\n",
    "        accuracies = clf.best_score_\n",
    "    \n",
    "    return accuracies\n",
    "\n",
    "def run_binaryclassifier(y, X, filename, numshuffles=0, cv=5):    \n",
    "    \n",
    "#     start_time = time.time()\n",
    "    accuracies = binaryclassifier(y, X, cv)\n",
    "#     print 'Took %f seconds'%(time.time()-start_time)\n",
    "    \n",
    "    if type(accuracies)==np.ndarray:\n",
    "        accuracy = np.mean(accuracies)\n",
    "        if cv=='loo':\n",
    "            tempcv = len(y)\n",
    "        else:\n",
    "            tempcv = cv\n",
    "        shuffledresults = np.nan*np.ones((numshuffles, tempcv))\n",
    "        for shuffleid in range(numshuffles):\n",
    "            shuffled_y = np.random.permutation(y)\n",
    "            shuffledresults[shuffleid,:] = binaryclassifier(shuffled_y, X, cv)\n",
    "\n",
    "        \n",
    "        shuffledresults = np.mean(shuffledresults, axis=1)\n",
    "    else:\n",
    "        accuracy = accuracies\n",
    "        shuffledresults = np.nan*np.ones((numshuffles,))\n",
    "        for shuffleid in range(numshuffles):\n",
    "            shuffled_y = np.random.permutation(y)\n",
    "            shuffledresults[shuffleid] = binaryclassifier(shuffled_y, X, cv)\n",
    "        \n",
    "\n",
    "    if filename:\n",
    "        results = {}\n",
    "        results['accuracies'] = accuracies\n",
    "        results['shuffledresults'] = shuffledresults\n",
    "        with open(filename, 'wb') as f:\n",
    "            pickle.dump(results, f)    \n",
    "    \n",
    "    \n",
    "    return accuracy, shuffledresults\n",
    "\n",
    "def standardize_plot_graphics(ax):\n",
    "    [i.set_linewidth(0.5) for i in ax.spines.itervalues()]\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    return ax\n",
    "    \n",
    "def CDFplot(x, ax, color=None, label='', linetype='-'):\n",
    "    x = np.array(x)\n",
    "    ix=np.argsort(x)\n",
    "    ax.plot(x[ix], ECDF(x)(x)[ix], linetype, color=color, label=label)\n",
    "    return ax\n",
    "\n",
    "def do_PCA(X, basedir='', condition='', show_plots=False):\n",
    "    pca = PCA(n_components=X.shape[1], whiten=True)\n",
    "    pca.fit(X) \n",
    "\n",
    "    transformed_data = pca.transform(X)\n",
    "    pca_vectors = pca.components_\n",
    "    print 'Number of PCs = %d'%(pca_vectors.shape[0])\n",
    "\n",
    "    x = 100*pca.explained_variance_ratio_\n",
    "    xprime = x - (x[0] + (x[-1]-x[0])/(x.size-1)*np.arange(x.size))\n",
    "    numpcs_tokeep = np.argmin(xprime)\n",
    "    # Number of PCs to be kept is defined as the number at which the \n",
    "    # scree plot bends. This is done by simply bending the scree plot\n",
    "    # around the line joining (1, variance explained by first PC) and\n",
    "    # (num of PCs, variance explained by the last PC) and finding the \n",
    "    # number of components just below the minimum of this rotated plot\n",
    "    print 'Number of PCs to keep = %d'%(numpcs_tokeep)\n",
    "    \n",
    "    if show_plots:\n",
    "        fig, ax = plt.subplots(figsize=(2,2))\n",
    "        ax.plot(np.arange(pca.explained_variance_ratio_.shape[0]).astype(int)+1, x, 'k')\n",
    "        ax.set_ylabel('Percentage of\\nvariance explained')\n",
    "        ax.set_xlabel('PC number')\n",
    "        ax.axvline(numpcs_tokeep, linestyle='--', color='k', linewidth=0.5)\n",
    "        ax.set_title('Scree plot')\n",
    "        # ax.set_xlim([0,50])\n",
    "        standardize_plot_graphics(ax)\n",
    "\n",
    "        fig.subplots_adjust(left=0.3, right=0.98, bottom=0.25, top=0.9)\n",
    "        fig.savefig(os.path.join(basedir, '%s Scree plot.pdf'%condition),\n",
    "                    format='pdf')\n",
    "\n",
    "\n",
    "        numcols = 3.0\n",
    "        fig, axs = plt.subplots(int(np.ceil(numpcs_tokeep/numcols)), int(numcols),\n",
    "                                sharey='all', sharex='all',\n",
    "                                figsize=(1*numcols, 1*int(np.ceil(numpcs_tokeep/numcols))))\n",
    "        for pc in range(numpcs_tokeep):\n",
    "            ax = axs.flat[pc]\n",
    "            ax.plot(pca_vectors[pc,:])\n",
    "            ax.annotate(s='PC %d'%(pc+1), xy=(0.45, 0.06), xytext=(0.45, 0.06), xycoords='axes fraction',\n",
    "                        textcoords='axes fraction', multialignment='center', size='large')\n",
    "            standardize_plot_graphics(ax)\n",
    "\n",
    "\n",
    "        fig.text(0.5, 0.05, 'Features', horizontalalignment='center', rotation='horizontal')\n",
    "        fig.text(0.02, 0.6, 'PCA weights', verticalalignment='center', rotation='vertical')\n",
    "        fig.tight_layout()\n",
    "        for ax in axs.flat[numpcs_tokeep:]:\n",
    "            ax.set_visible(False)\n",
    "\n",
    "        fig.subplots_adjust(wspace=0.08, hspace=0.08, left=0.19, right=0.98,\n",
    "                            bottom=0.1, top=0.96)\n",
    "        fig.savefig(os.path.join(basedir, '%s PCA weights.pdf'%condition),\n",
    "                    format='pdf')\n",
    "    return transformed_data, numpcs_tokeep\n",
    "\n",
    "\n",
    "def Benjamini_Hochberg_pvalcorrection(vector_of_pvals):\n",
    "    # This function implements the BH FDR correction\n",
    "    \n",
    "    # Parameters:\n",
    "    # Vector of p values from the different tests\n",
    "    \n",
    "    # Returns: Corrected p values.\n",
    "    \n",
    "    sortedpvals = np.sort(vector_of_pvals)\n",
    "    orderofpvals = np.argsort(vector_of_pvals)\n",
    "    m = sortedpvals[np.isfinite(sortedpvals)].size #Total number of hypotheses\n",
    "    corrected_sortedpvals = np.nan*np.ones((sortedpvals.size,))\n",
    "    corrected_sortedpvals[m-1] = sortedpvals[m-1]\n",
    "    for i in range(m-2, -1, -1):\n",
    "        corrected_sortedpvals[i] = np.amin([corrected_sortedpvals[i+1], sortedpvals[i]*m/(i+1)])\n",
    "    correctedpvals = np.nan*np.ones((vector_of_pvals.size,))\n",
    "    correctedpvals[orderofpvals] = corrected_sortedpvals\n",
    "    return correctedpvals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First, make example figure from one animal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "basedir = '/home/stuberlab/Dropbox (Stuber Lab)/lecca and mameli/figures'\n",
    "\n",
    "df1 = pd.read_excel(os.path.join(basedir, 'Example z-score Fig3.xlsx'),\n",
    "                    sheetname='AL', header=1, index_col=0)\n",
    "df2 = pd.read_excel(os.path.join(basedir, 'Example z-score Fig3.xlsx'),\n",
    "                    sheetname='Runaway', header=1, index_col=0)\n",
    "\n",
    "colors_for_key = {}\n",
    "colors_for_key[trial_types[0]] = (0,0.5,1)\n",
    "colors_for_key[trial_types[1]] = (1,0.5,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rY6wiMQpjFY4WklTRjhTGgOdmMfilghCnik4IqRE6oZAkgusqqr0apaDZFsrF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\nI4My/g8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1e990ed90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actionlocking = df1.values.astype(float).T\n",
    "runaway = df2.values.astype(float).T\n",
    "\n",
    "pre_window_size = np.where(timepoints==0)[0][0]\n",
    "window_size = len(timepoints)\n",
    "framerate = 20.\n",
    "cmax = 4 # Maximum colormap value. \n",
    "\n",
    "data = np.stack((runaway, actionlocking), axis=2)\n",
    "timepoints = df1.index.values.astype(float)\n",
    "trial_types = ['Runaway', 'Action-locking']\n",
    "\n",
    "fig, axs = plt.subplots(2,2,figsize=(3*2, 3*2), sharex='all', sharey='row')\n",
    "\n",
    "cbar_ax = fig.add_axes([0.77, .35, .01, .3])\n",
    "cbar_ax.tick_params(width=0.5) \n",
    "\n",
    "for t, trial_type in enumerate(trial_types):\n",
    "    ax = axs[0, t]\n",
    "    ax.set_title(trial_type)\n",
    "    sns.heatmap(data[:, :, t],\n",
    "                ax=ax,\n",
    "                cmap=plt.get_cmap('coolwarm'),\n",
    "                vmin=-cmax,\n",
    "                vmax=cmax,\n",
    "                cbar=(t==0),\n",
    "                cbar_ax=cbar_ax if (t==0) else None,\n",
    "                cbar_kws={'label': 'z-score'})\n",
    "    ax.grid(False)\n",
    "    ax.tick_params(width=0.5)\n",
    "    ax.set_xticks([0, pre_window_size, window_size]) \n",
    "    ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "                                     for a in [0, pre_window_size,\n",
    "                                               window_size]],\n",
    "                       rotation=0)\n",
    "    ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "#     ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5)     \n",
    "#     ax.set_xlabel('Time from action (s)')\n",
    "    \n",
    "        \n",
    "    ax = axs[-1, t]\n",
    "    sns.tsplot(data[:, :, t],\n",
    "               ax=ax, color=colors_for_key[trial_type])\n",
    "    ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "    ax.set_xlabel('Time from action (s)')\n",
    "    standardize_plot_graphics(ax)\n",
    "    ax.set_xticks([0, pre_window_size, window_size]) \n",
    "    ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "                                     for a in [0, pre_window_size,\n",
    "                                               window_size]],\n",
    "                       rotation=0)\n",
    "    ax.axhline(0, linestyle='--', color='k', linewidth=0.5)  \n",
    "    \n",
    "    \n",
    "    \n",
    "axs[-1,0].set_ylabel('Mean z-score')\n",
    "ax = axs[0, 0]\n",
    "ax.set_ylabel('Neurons')\n",
    "ax.set_yticks([0, 14, 45]) \n",
    "ax.set_yticklabels([str(int(a+1)) for a in [0, 14, 45]])\n",
    "\n",
    "# axs[0,1].set_yticks([])\n",
    "\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(right=0.72)\n",
    "\n",
    "fig.savefig(os.path.join(basedir, 'Example figure.png'), format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir, 'Example figure.pdf'), format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now do the full analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "basedir = '/home/stuberlab/Dropbox (Stuber Lab)/lecca and mameli/reaction time corrected'\n",
    "\n",
    "df1 = pd.read_excel(os.path.join(basedir, 'Decoding analysis.xlsx'),\n",
    "                    sheetname='action locking', header=1, index_col=1)\n",
    "df2 = pd.read_excel(os.path.join(basedir, 'Decoding analysis.xlsx'),\n",
    "                    sheetname='Runaway', header=1, index_col=1)\n",
    "\n",
    "if 'reaction time corrected' in basedir:\n",
    "    df1 = df1.drop(labels=['trial n'], axis=1)\n",
    "    df2 = df2.drop(labels=['trial n'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:22: RuntimeWarning: Mean of empty slice\n",
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:23: RuntimeWarning: Mean of empty slice\n"
     ]
    }
   ],
   "source": [
    "numneurons = 248\n",
    "numtrials = 20 # initialize to a max of numtrials possible trials\n",
    "timepoints = np.array(list(df1)).astype(float)\n",
    "if 'corrected baseline' in basedir:\n",
    "    end_looming_baseline = np.where(timepoints==0)[0][0]\n",
    "else:\n",
    "    end_looming_baseline = 0\n",
    "timepoints = timepoints[end_looming_baseline:]\n",
    "numtimepoints = len(timepoints)\n",
    "\n",
    "actionlocking = np.nan*np.ones((numtrials, numtimepoints, numneurons))\n",
    "runaway = np.nan*np.ones((numtrials, numtimepoints, numneurons))\n",
    "\n",
    "for neuron in range(numneurons):\n",
    "    temp = df1.loc[neuron+1, :].values.astype(float)\n",
    "#     temp = (temp-np.mean(temp[:,60:120], axis=1)[:,None])/np.std(temp[:,60:120], axis=1)[:,None]\n",
    "    actionlocking[:temp.shape[0], :, neuron] = temp[:,end_looming_baseline:]\n",
    "    temp = df2.loc[neuron+1, :].values.astype(float)\n",
    "#     temp = (temp-np.mean(temp[:,60:120], axis=1)[:,None])/np.std(temp[:,60:120], axis=1)[:,None]\n",
    "    runaway[:temp.shape[0], :, neuron] = temp[:,end_looming_baseline:]\n",
    "    \n",
    "meandata = np.stack((np.nanmean(runaway, axis=0).T,\n",
    "                     np.nanmean(actionlocking, axis=0).T), axis=2)\n",
    "np.save(os.path.join(basedir, 'mean responses.npy'), meandata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:36:58.269000Z",
     "start_time": "2019-02-19T03:36:58.259000Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((248, 201, 2), (248, 400), (248, 300))\n"
     ]
    }
   ],
   "source": [
    "temp = np.load(os.path.join(basedir, 'mean responses.npy'))\n",
    "\n",
    "if 'reaction time corrected' in basedir:\n",
    "    temp1 = temp[:,np.where(np.abs(timepoints-(-0.5))<1E-5)[0][0]:,:]\n",
    "    # Only count data from 0.5 s prior to action so as to not run into issues with missing\n",
    "    # data for PCA\n",
    "\n",
    "populationdata = np.concatenate((temp[:,:-1,0], temp[:,:-1,1]), axis=1)\n",
    "# Remove last frame since original data counts 201 frames for 10 s.\n",
    "populationdata_allpresent = np.concatenate((temp1[:,:-1,0], temp1[:,:-1,1]), axis=1)\n",
    "# Remove last frame since original data counts 201 frames for 10 s.\n",
    "print(temp.shape, populationdata.shape, populationdata_allpresent.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will visualize the full dataset to get a sense of what they look like. As mentioned previously, the first 100 features correspond to CS+ responses and the last 100 features correspond to CS- responses. So I will plot both trial types separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:31:40.229000Z",
     "start_time": "2019-02-19T03:31:40.221000Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "framerate = 20\n",
    "pre_window_size = int(3*framerate) # 3 seconds. Corresponds to baseline prior to actions.\n",
    "window_size = int(10*framerate) # Total number of frames plotted around action\n",
    "pre_window_size_allpresent = int(0.5*framerate) # Frames in which no neuron's data are missing\n",
    "window_size_allpresent = int(7.5*framerate) # Total number of frames plotted around action\n",
    "sort_end_period = int(7*framerate) # Sort by activity during this period\n",
    "\n",
    "sortwindow = [pre_window_size_allpresent, pre_window_size_allpresent + sort_end_period] # Sort responses\n",
    "sortresponse = np.argsort(np.mean(populationdata_allpresent[:,sortwindow[0]:sortwindow[1]], axis=1))[::-1]\n",
    "# sortresponse corresponds to an ordering of the neurons based on their average response in the sortwindow\n",
    "\n",
    "cmax = 4 # Maximum colormap value. \n",
    "\n",
    "trial_types = ['Runaway', 'Action-locking']\n",
    "\n",
    "colors_for_key = {}\n",
    "colors_for_key[trial_types[0]] = (0,0.5,1)\n",
    "colors_for_key[trial_types[1]] = (1,0.5,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-18T23:14:58.804000Z",
     "start_time": "2019-02-18T23:11:36.795000Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p0X188IwgaCqRJFu9QlDUDNc4WjwzUTIQfL3MMxESZKO5mntI/+h0WMleZbVJ\nrAwY6iZD2aBwAUPjmQup8SGcFKxpiwhVgcDhEP7/UHYnGxI1YmP/R9TmHvbOpkOcjrFhTBHWMTpm\nFDYZqBa502ROEwhDIApiN0C5giRdOaS4dgdlRTrR/SejequkA8WMbELuApQwdF2bZT1JbjUpGmME\nRe4h6MKKyomU8BeFlA4tDVNRFyUMoUgrh4nSjoens5KL5iyiKJDpwJMzixysQc3McJuRXPjaX4Uw\nxsUJyKB6TfVYFocrmSqTe7JnnmHn95HPL2Lz3CvpWIuMwqqIOjahFCIKPSO7VsdNzOK0F1p0qnxP\na8qmO4cFXNlaL4wBa5DpENFd9i0oWUaQjnB5TtwfUF9awaQZ/d1e6KXYucJw15CiU/xMdkXjyISJ\nw1pQj+hvbGFnJlBJhG41/CkoDAoIAkUcaCYD5Vc76Vc9IWVFLxI6hEPIkQ7oivTQQw9x22238YY3\nvIF3vetd3HfffXz4wx/mzDPPXLPd4h03VvWcXNcZ6TpGajIiLJJeUcc44Yt70hCrlEAU1G2HVm+3\nJ6M6g0r7/qJamvfMZYAwXL17GwNZiisZzaI14e+wgz753Dx5p4ezDpNmXHfPI7y4XmfxR4s/sZEv\n2RphC4dKZMWZU7rUmNOq1J/zcHXWG2GNQypBMul16XQ9IZps+r6hkqXt8gKbZpjRiGKY+j6iwnjI\nO5AgJbpR84hao+ZDWucZ32NdOhGGyFrdsxCSOi4IcGOem7Pe2Z3zdbMwrqSiq1zJZJXTynSATIf+\n2JTGlsyGMVKKMbggQDiHCROMrmGCkFHY9HqD+zULCmfXG/uerr3lLW/hTW96E41Gg7//+7/n3e9+\nN5deeilf/OIX12x37R1DcqsYZIpaaMiMrFYYjyRZNtRWmBBLKFcwkE1SG2ER1KRfXTIXVqGJxKKF\nr7Jrl5KKhIFN/N+iqChBxim0yAnIvUIRIc4JIjnijv/4Bue8+DjCwdIqKxyQw653yNHQr0rGtxG4\nLMP0+7g0q4iyQkgPh49NCn/xj0+2DjyVZ6xOGmiPwpUrTUUYNca/Vkgocoq5fdhRSt7xDIQgicoi\nqacKhRMtgnYTNTXt9+ksZBmuyHF57qHpetPnjZE/L04q7zTj9gljsMMBttej6A3oPrqb7u5lhJRE\nzQhdi8pang93bWEI65HXcohDhCpXaSl9n1Sg2Pw/v3CgLq3nvB3Q0C5NU173utfxwQ9+kAsuuIAz\nzjiDPH8yUfT03jcwUR0Thqhi5NkFKsSoEGkN3XiKwmk6dgIlDDX6TBT7aCw9inr8Qexo5FupVdm2\nkNQr7phTGoT0PD1ZspqtQeVPJ7MDAAAgAElEQVQjRJ4hesvYvbuxo1F1Ydq84IZ/+w4nH7+DzhMr\nLD+2wnB39rQUen6aJVsjahtj2tva1Uo15tQJKbHFKmtb1xNfiI3CskYj0K0Gcpw31T0PESE8kzuM\nsLUWLgiwYQ0TxFgVYFToa2sqJJWJbyV3ksIF2JIdEoocIVZbyyWlWIzTpE7Ts5rcekZJYSWBtMQq\nI5D+/BinyEwJpJT7LKxk8wE9e89tO6COlGUZ8/PzXHfddfzd3/0d8/PzpOmTka1/E6/DjATOQaQd\n1qcUjDLJMIUsd8SRQJXXiQ4ckXa06yegjrNoaZgI++QuIDMBqfEabNZJ4iAjMwGjQtMIU2KVMSv2\nEpgMIwMKGcIxoK3PpRyCLEg4bccrkKefyqa8z2zZVKhsTmBSwrL5TaYDfwcP9OpFbAoYDWA49P1E\nWUaxuES6uMLCfbvIhzlRM6K9fdY33yURKomRceQdIo5xeYEzRUkatchmyzuHlDDo+4kc454q49vI\ns7kFRmU7iNQB8WQTPeGbALEOs7CEsw6X5yh8XWei3fKUnpIJ4azFjVKKbq9aSYUUVegJns0ta3WQ\nwgublAXoMf0JKQjGK2Gzja01vbBKkQNvPJCX13PaDqgjXXzxxZx33nlccMEFHHnkkZx77rm8853v\nfNJ2p888AKxqeY/vYjkhigKFITRDchmtQZr2b/KLigFh3kcWGdLmhPM7/YVY5E/unwGvQlSiXmPd\nahslVV9Ue+Uxphbaa5oBZT6CYR/SkVfKSUeYwZCi08MaQ94dkK54PTeTW6QSVaijIs3U0Vt8jlPK\n+gqtEUp5/e0g8L1SmWfBC7UfXSdNEUXh8716AzE545E6pXFBADJASUVdqlUGNx788DW2Al1q9pFn\nMOhXyj/j/q1x6CoChZ6Z8r+HISKK/XlKvNjKmD/o+l3/GucqJ3LG4EY5w4WHcM4ihEQlETIKEfU6\nvOIAXVjPAzvg8Le1Fll2ky4tLTE5+eRqQu+WK8iSCa/2oyJ60STGBaWyqqZwiryEbR2iaoMwTpSc\nLk/1AZ9KKGGYDpdRoiA0I6QzhMV+2uC2WO2KHaNeY/mpUuPgQ1/4V9736xf6i3dc+HTWX4hjUXoh\nQAWgAu+MrCJp5JnPNfZrA0Hth/yNZaycXc2byj4hpFojeu93vF8T3RgZ8yfYd+CmI1yRe83tsh/I\n5gU2z8m7A4YLHUxWkHZHlQzzWIwfQNciz9mLvTCkVAo9PUEwOekLvPUGTocgA/8IfuDBWGBTSO/E\nY4SxzO9cEPhHpamd/aZf9HJ63tgBXZHm5ub44he/yPLy2t6d973vfWv+7re20A8nMHjnCciJ3JBU\nlImwE1i8HPHI+AvWOslE1OPw/l0EI6+3Jo3Pv0Q2wgXhmovNjZPuErKWRYZVGhPVSevTXvSjyMBZ\npM15+bld7MZtyH4H8rTsFi18UbQEFuxgWPb+iGp1EVJg84LR3CLdx+ewxhFEXrUnmmggQ99Yly51\nMWnuuXpJVCF44+5VoBIpwTpfhDXGd7yWiOT+4dTYGcekVacDVKnVEG9W1PPch4FpVhFdRVnAHb+f\n0NqvQGFY8uNibL2F0TE2iKouV+Gsl9uS2lOOhCTOOqh8hCyZ9k5p3ws2zkuB5wbh6tmxA+pIf/AH\nf0Cz2eS4445DiP9aILD9+A8JNx5BrzaLdIb24sP+rgu+USyss1Tfwlw2jRSwNdpNYDOifEAWNsjD\nGjobEK7s8SuLkLhA46Ri2NwIztOLlMmqlcgGITiHSvvozvzqiiQkNq4DAqNjzFQdmQ0QxsuEjcNA\nBSj8BVM0StrNcAW55zHcoI9NM5Jpr1swlgDOB0PSpS55Oc2hP98n62f09vQJYkUylbDx+K1E7Yan\nBg2GqEYN1WhSzC9RdHqky12AiiQrpCRd6dHZuchgcUDazZg8bILGxhatF24haHmSbLBxM0QJRXMK\nGyY4wAjlGymF9LB4uTrLPY9R7NlNvrDsc7hmA71hE2bDNkxUpwh8cVdgvbgJsFzbzMj5G18iBkTF\noKoL2rIgvs5seJp2wQUX8PWvf/1nbvfIg/fjhGTzXd/gvr/5Iju/tfenbn/0m3YQ1kNUqEk7AwaL\nAxYfWvqJoh+TJ7WoTfkvvkgLipGh+8jgp7aZq0Typdoy/8dCi6AV0D6y7leRbva0ZIlrh8c0NiZM\nHDZFMtVA1yLvWP2h15ordbKHyyOvy9CMmTpyE+FE06uV5jlI6cdIJhEi0L4dfIxSjm2sqRAnfvWN\na9ggpDd7JCvRLJkLyZwmL8EYJU0lvjkOmT2rxIfJhS1bJaQhkpkHYqwmM0El5DkuOQTC+mKxE+RW\neVTPilJS2j/+76cfOiOKD+iKtGXLFgaDAbXaT1/U//2Ro7EWzjquTvzXr+MYkdMcLdJYeBi55zGo\nN8lntnqxQud8W0Mp1mG1b0HYYb3ULs63QYthH9IU6g2QinxiA2nipXp12kPl3unUqO9BhCL34h1l\nnC+/fTNnH32YF2TprFCsdJi7/QGyF2UIKWhtm/ZSyNOTqFbTH8twSLqn5L8V5cU4TNl9++Psu2Xp\nv2SdCy2YfckEUSumvW2CIIkoRhmiNyBIIsLpSS9YPxqRLix5lE8vEU5NImtJCU54p7IzmzH1CawM\nKmAmHi4QjZZxMsCqgEzXMSJgpOoUThOqsU56gSvFNQ0BShalVpOsepbCsgt2nKeOnS6UOZFICcgJ\njT+30lkCk6FsVgJDhw6z4YA60oYNG/i1X/s1Tj/9dOI4rp7/8RzpzfoffZL60MjXPpTCxE0Gk9vI\nZ48iMGklgWuFZDT5okp4MLb9SmJXOEuuYj+RwuaExYgw66GKEUaFKJNhCCl0Qha1kDYnEsI7UjqC\nhX0IYzDdHoP7HmBhbh9Z118Uo5Uhu27Zsx/f7tEDeaoQJcI37j5VUYgZZbjCIJQi2ji7tlYkpV95\ndORrRWX+YsOYPGqQ6Tr9sE3uypYKk+CcoF9EFHnpBKlvmdh/oFtmAwaZprCCtPDbjbGSLPcjdMbK\nYFkOkY+QadUtoXLE2qClIVQehCicpHCSfqoPqca+A+pIW7duZevWnz2C9x/Nr/OiTT2aekAoMwYm\noaH6bFy5n8auuwEwzUkQkjyZoJ0/hjAFauCpO5Vghyn8T6BxYYywhqI+gbAFLhLgDNLk6P4Sctir\nuHVIhdm7m9HufeRdH7rdvHeJV591ik/2pQRreeHFJVm00cImTZ9rhZGHpoVALu5ldO+9ZMsdst4I\nIQVRu1G1cqQrfaJ2HRWFBPUEVfM/stHwDhKX7AZrSO+9F5vnqCRGz8xQLC2RLyxiC+OnhU9N+4nu\nQuCiGhQe6pb5iGDf49TCiIki9/vrd3HpyIeJwwGm7z+j3jCLaHtUzjQnsUqT1qYQ2iGsIcoWUAu7\nPdASJ+STm6qoACDoLeFKxDIPJ3x+KRRBNsAJ5YVoyrYXEoAzDuTl9Zy2Aw5/9/t97rrrLoqi4MQT\nT6TRaDxpmy/fYlHCEQWWMLAo4XtdAmlxZYweBxlN1UOVw62sU+ROY1AUTlXxvCs5eWOovKrOC0iC\nFCksgTDEYkRkB0x0dxL0l/1Ix7iBK1WHvnPzLZxz0osQxuCUwsnAQ+eDbjlSc9mHcrv30nn4CQYL\nfXp7u+X0Og8r28JgcusLnaX+nAwUSkva2ya9zkHoW8aDJPLSVY0aItCIOKq6TF1JlB1Tjmy/72Hu\nLPN1qFqCTJJVkqjz7wlUaCL1pr/BxGU9yBb+s43rUTr0SkAy8CBLKX7PGNLGUajIdx2XjYNOSIzw\nSKsQrupp2t8KvKqTQXHsjkNnrvkBXZF++MMf8s53vpOZmRmMMezdu5fLL7+cl7zkJWu2S87yZEYH\n7M97OPmdJ6NrkR9Jn8QkRx/pazE1T/dxOsSFcTUo2CpdjSgZxZNemLDsCB0LqPgJCQHGBfRli07r\nBIqmWlPcVcIy31pg18ZTidyQ+mgRnfWRvUW/4hV5BTcHtYTGtg3UNuRMHpFXclj5IMWaEpIulVl1\notH1mCAOCSea6FYDEYaoqWnszGZsXGfY2IBRIaOgjhMS6QxRMSAeLnnhloFnVah0hOt1ytm4fUzH\no3lCCg9EBHpV725cmwKwa2lOoruMSEfYbgdpDMIY1JjVEOhVIZPxc0r5EHPMC4xrngAbhLjAC3u6\nICQLG1ipvdNVReJDx5EO6Ir05je/md/93d+t2N433XQTH//4x/nSl760ZrvRtz6DqbVQvWU611zD\n9/7y6c0lGkvpbjpxA7XpOjOnHY+amKTYfjTB0l7c3B5Mt8e+797JvV96cI2+9tbzNjCxfYrmtllk\nFPI/79rJ+37jQly97etS2o+HEc4h+ivkDz7gtRuWuzxy/d1P0hvf9qsbqU37OUWTOzYTz06hX3gE\ndnoTIhshussw7GN7PeTkNHZ2C53ZI9mntlZTHFqqg8JghWRg63TzGmkRMB13OWLxFsx/XM1ofplk\n8yzhr5xIb/OLyIOkquus1DahbE6Sd4n//Ys8ft3tPHzlTsDz/E5/7/kEW7dRbDrcr8oL+9YMK3jx\n//USpn71bMzURgYT20gDDxoJHFYoumLCa7HTZaL3RKX1XQQJg7DFSNTomxqhzNEi55gd257W9/p8\ntAPqSK9//ev553/+5zXPXXTRRVxxxRVrnvvtjy6y9QUtWk3FREuwoZUzmQyZDDtEjBDOEtiMsBjh\nhKhCi7GN5/AMXa0iX/Zy/6WnRlVIFPhmtUBagjHapEo2uHDVTNRQZNx28/W8/PRTCIshOh+gipRw\nZS/usYfW8NFsnnvF05I3J+vlUOcohvYktjlJb/qFjHSdkaxj8McjhSViRGAzauky4aizSqbNRoh+\nx+c2xuCGQ4puz7dYpFmllBrUEq+SKoXvAgZE7Iu6CFmJjiD2kxvOMxgOoCgwvW5VVC4GQ2yWV2zt\n8VQ+GYWVqKSaaPuCbWsS05jABiEmiFdZF85WeVEW1LDljWe8Im150YkH6Mp67tsBDe2klOzatasC\nHHbu3Omr7T9m/+N1IZHqIoTDOkkni1keJTw41yIvfBRVjx2xtgTKj8jMCklWCKbqOYURWASRMgTS\nei5nSR+qhxlBWTOpqwEOQcssIq3BlM16Y8QPB0ZqnBOEZkR9uEC8tMu3FziLjeuIw45Cmhy1suRD\nPGM806B0KpdmnoUQhr6/JxvS3HMPrSyFcrSKUMoTWkcj36yXZpVksNy42dd/WtPQnqlytyL0oZ6w\nBp12EdYQrMxDr0Oxexfp3LxH9zbMQHsKF9cpGr5T1iqNGvcZWeMBAuHzPhOs5jvgw2vhPIonnOco\nGiER+QDVXYRBl+KBeyvlWe/YkrBdRzdq6FqCatSpT06v0pnA/77uSE/P3vWud3HxxRfz0pf6+sG3\nv/1t3v/+9z9pu63hExgZsHnXf9L5l3+mGGU89M37OXZDQn2mgQoVcbtGkEQkGyaxaVaJEQJgLUVv\nQN7r0ys7QefuXcAWjiN+7QRqWzf4i9cYT40JNGpy0g8V7vbo79pHNNlEKEU45evv//lvN3LuY8cw\ntI5wso2qJZjBkPnv38fKziVMbgnrIe1tk0gdoMIAZx2jpS79+T62MMhAMbVjA7UNUyRH7YCJaQR4\nuDqu42RA0F1ELi9QzM35xr7HH0XV62AtQgW4IkcrBXPz2NwPPFYbZhBJgh2NMP0Bql4jbjSQbY+c\nkY5we5+AThfSjOSYY7CTG+hPbKMbz5ASMzAJWhSVgElmA+p6WPV3jSHzQBgSOSQxXZKorJcdfjIO\nCSokGDudzSmco4BKxainJzEolrIWS8OECw/kxfUctwMa2s3Pz9PpdLj55ptxznHmmWeyY8eOJ233\n3XtXWE4TXppfg57fSe87N1VhRXzYC3zYMuZ/GcPghSdVHC5pco8eKb8CKJOhipGn/ZSDskSewqDn\nYdxyLlBFsOx3cJ1l32rQKTl7ccSNu5c5+6RjcLWGX4nyDDm/m85tPyBb6bPze4+itCSe8ONMlh9b\nrjppg1bAzIkT1KZqRM2I2uwEuh4TzU6h2hNVAm+HQx9aZRn5codimJJ1B6gopHnENoKJtqc7pSPy\npRWy5Q7dnXOlVp7yUHoYEE21ibZs8u0QcQ1qDZyOcCrwsss6YZRMkquInmzTL2qVHkMgDJnVVaE1\nFJlnQFjNsIgQwlELRsTSF1szF2FQDIsYC1XXcl0NSMSA0AwpZEgmYjIXMjQxmQ3oZyGvPunQmUhx\nQB3p/PPP56qrrvqZ28297+1k3QF53zOTdT1G1yJaF1zodQukxAYhK5OHk8uIVCSVGL6fhu6IGaLN\nqKqm50HkgYH9whTpjL+T4mWnjAzIVYwpi7sGzwbQZNz6nRt4+emnEGcd9Kjj2zNGfc+YyDPs0oJf\nEbo9bFZUxFFrDFIpZOjhb5t6ZSCbr6JlfkK4qGYsqagUmNyP+IpSXuR+7BzgWy1U4BG48XwnIbBx\nHRvGHj3bLy/xM3N9O7pwzg9rk4GfGWVy1LC3ygTZ72t3+/EixVjwMkwoai1MEK+GgXJ1ptUYDV0N\nEUU1h9YhSInX4e+na1u3buW2227j5JNPrlopfpL13/b/kAt/tzIolm1ManTlKOBBgVDmHp7G1zUC\nUXgdNScYiYSR8tV7qyS5C0pVId/JmVtV1ZnGJoRD4lDSAw9aGpRwhCrjxu99n5edcSqyyFCjPiIf\nIborfjhYnpc/BTYryDqrg8JUFPo7dd/z46TWvh+n/PwyCpFhiKzXfINcqbTq4gQXRBU66ISkwOcr\nVQ4Hq31D+zuA8WRThMCVkyEEEmFSr29eFL5FvmwReZIZg+11PbtdKQ+UBAHUm7ha0+dUgUaYgsD2\ny7lHZbPjfj1e+0/o21+vPQ8SQjlkHf5+mnbuueeyZ88egiAgDEOccwghuO2229ZsN7r6057rFie4\nhx9g9/X/ydKjixWkLLRAJYr6tghd0xz362eRd3vsuvl+Hr9m95PGRO5vG87wOY9Q+zmQlKTdjNG8\n1wjPuwUqUahEEk9rkqmEH4z6nLFhChUGJJO1qoenGK0lu6bdEf25Plk/J+t5ZR6Xu/KYJS73Aim1\n6ZipI6Zpbd+AikOCWuILqePWhXERtfArl7MWO0p9rqS1b6lotrAzm8nq01ipCPYbN2nl2hE3whYE\naa+qraVxGycVuYrJlb9pjetqBZqeqTMoYkx5owmEXf1dWpIgRWHJXbBmLM7YxrJnY9IqUIFH4NHT\nXz0hftLrflntgDrSrl27fuLzP04buvoHmWcYBwUn9L+N+uFNmG4P1agjdhyL6K9gpjZRRA2yuMVQ\nN5HOkGQdwsESLvD8OSM10hlS3aCQmlxFDEvN79SGKOGLiqHIieSIwmliNyCwGXHWrfqZnFTcfOMN\nnHvcC5HdJQ8XO4cd9CmWV5i77T5+9I1Hn8QgD6c00awmiAPqMwlhPaK5ecLrKpSTHGSoMWlGMfAt\nFroWebEU/GoWTrZR9Rpy42Zsc8qvUGPZL8DoBFHCzHo4pkj5v6XJvUPKABsmZGGjWiF8C4mreokA\nBtEEqayRO40Qruz7Wp0fJYRf9ceDlUUpSFM4XT6uZY1rkVfk13Fj5phdUriA44/c9AtcTc8vO6Ch\n3Y839I3txx3p6PojSGfY8PDNFA/eh8VTWxACHn8Iohgx/0Pcnn1kT8wRNxKKQYpr1giOeiGiVicc\nq4UC9XEYOeh7FoQxkNQpWtPY0CN9QX8ZmQ2hX4Y0pUaCK/zYzG9ffT3nRq/AOofp9X3+UkLUMyce\nycwJRwCe3Z2WYvJCCtKVPsOlPnvv2sdw9wLwBBNHN1ChJOsXxO2IxoYGm047hnDrFg+g1Js+HArL\nO7az0FnC3v19TG9Af9c+n2MJQX3TNMFEkyAMUe0JDyvXm4jmpBdqrK/KLEtXKhwJ5W8u+1F7xrmL\nLXPGpaxFL4sw1lOztLK0wgGBKko5aQnOr2K51WRWrwEsEjkgNH6FTGVCVs5GKpx/H2OfXPb4ZbYD\nuiK98pWvrH7P85y5uTmOP/54vvzlL6/Z7tt396p4WgofRjgnKtQIfKjR1L2q/wWoCptApTcty8kS\nA1uv+mX8XdIQMaqY4coWlZ54mHZ8/D/o+Aa/YZ9rr7mBM2cn6D66h5WdS+TDnN7ePtlygcsd0axG\nKkEQBwSxnx80fdRGJo47EtVqwYbNfvp3SZ+xUiNsgXDG5zX75TxVZ681q4m+kH4lUgFG17BqNZFX\n+Qhpc2Tu5zyNSbM45xv0iryaasFo4AtxY9QzTlZbxveb92pVVE3yKKSuhmLvb+N8dQzgjLexQlXa\n7WPHlFjGZ388v+pXjjx0dIQO6Ip0zTXXrPn79ttvf5ITAUxHyySuX7U+yNSHCIvJVgqCSn54Vauh\nhL6FrcADKSyFCwCv0Kr3AyakMNXE8+pi2A/JM0GMFDlFve0h8aSBOPxoopOPIz4zZ0M6hKwUD4G1\nmgolzw+3mt/gLKwsESwvrGoqjG1cpIy8IL7ToRddUbqaJuGEIg+iNfw/XaTVkDTpjEcRy7E2Ik9X\nme+wqg3RaOFakxUp1UlVDUmuirDlsQtboLMcnfVLgUhbKrd68RRsUaGB+5swZnU6vPLiKy7yDYVW\n+xV2lYWy7kgHxE4++WQ+8IEPPOl54xS7i00YqyicxDovaDLjVvwX7sSaSXkCj7alNiQ1nolgnMBY\nWQmhbIwXURREdkhgMqK8V8HYqrPg1YDyHNvveQg7zShKwZDRwgpX33QHxz9wvNeUayRrD9jux3KW\n0quljqeFl0KOzphKD0HoEtqOEy9aH2jQ4epFLQOM9u8xRufCYiyr7FfNscjI+P82CHF1XaqnFsh8\nle47Zqu7/WDy8etU2q/+N2Y4VB9LlGpKQQxCVD1gptRmGFtgsioysKVKqxFBtc3++VHhAoyT5FZz\n2tO5aJ6ndkAd6a677qp+d85x5513MhqNnrTdI8ee+xNff/hbj8FZx//P3ptHW1KW9/6fd6iqPZ6p\nuw89MDTI2IACQtuMIohR0cAFxWjM/SU//WUlS2PUa8QYEJNlNBAwMdwbjboWGoltQEYRMUKgwaCA\nucoVryIyytBND2feu3bVO/z+eKtq78Og3aabw3Cetc46e9euXfXuvd/nfZ/h+3yfuF1n+KB90Hvu\nHSZgHCiibFQrwsDFLuNMRW7SZTxMBhlg/HO1MUQyAoBZtibkPLwg88F8jMhomBm0zWjkc5x88F0M\nH3ZQCB/nAV4jyvBxQaJSkte7WrNy6quxCFE0jWZg1e8XIAYTLFT0Spsji8BBbPLK9BPlvUpW14IE\nBVmUmMe1ipuiLPgLLURrAXmtk0D04kzYXWyOshky6yCztBpTyQCEVAXFVlGCMfC4RHeXwQzhTPW9\nPI0HXfVrkaodG4DX/xdn1AtHdpmPJIRgbGyMD33oQ7zqVfMLvNLrPhsYa+otekPjAamgk2olnJJL\nqsa+pXOrhEVhUcIw1N2McoG8Q2dzIe9TmiMlZbAtWCfLkgJn8VFc5WGYnSZ/7FHM7Byzv9zEbQ88\nzsFEzG3p4nJLNmXQLYW3nqgZoSIZ/KMkrOhRvYhk9SydrR1sZkmGYoZWDlMfbVakjTKJiUaHw+5V\nlimUnfuiOIwlz0Jex/vALVfQC6N0QcxChVhwOiaN+w0Iyt3AeUnmQy4ucxrrVNWdo6SDLpEJxsl5\nfqf3oRmBLvgbYmVoqC6Sony+uIcpcnRAFUxIrca6wM1aU6bqcqGl4bD9lu2sqfW8lwVp6/KTXzzB\nnGlgvCSWhrpKSURa7RACF0j1VS3AT0j6HN9kaBdMja5skfuo4BeQyCJpWxb/hSJBQyJ6xD6lmU2i\nzEAFlBDBkTc9zr/4C5zztt/qk/EX7EFEcbUr+ijud7PodfGTE9ipyVCPND1LunUK7wKJoox0gPTU\na6hagl6yBDFUND9Lav1WKSW0R/dzLiVaoVr5hehX+mZdmJmEAZJHX0QvcT4oqpDQCmytvdGVTLdX\nksommY8r7nPliw4XA43dPCI0xC4K+FJfp+djjNOBPKVIJ0g8NZkSkQU/tPA9BR4rdPV/9b7779qJ\n9DySnapIc3NzXHTRRdx///185jOf4dOf/jRnn302zaLUoJS77p1kid5KYjuhbKE3i87m6LbGq51G\n2X5Gvlw9Vd4NJkrZmbwkLhSSXmtpgK7IqHKQXVH9Wl1ngJU0fPgQjVIu53vf+z4nvPLl87qfy7wX\nJm7x3sHyAVGyug4Q7gcqYzFvXF6GHdHrKHRw0DXyqEmuE4yMq4rTMgI2WC4CVFHIwaBJ5HskpoO2\nPaJsLnQxzHuhnL4kaywQ6t7kFeq8LJeYJwOIbaF0gCgJEbpbFBg+0xzGqZg8blb1T/Mu4fJ+hNGF\n7yPKu4wcduKzT5YXmexUH+kTn/gE4+PjbN26lSRJmJ2d5WMf+xgXXXTRvPMOmrwt3PyB/8svr9vA\n1l9sIWpE1Edq6FqEihTZXEZzWRvvHOPveBsuTnBxA3w3wGNmJvGducC1PTOLBnS7hdxzH2xzqLLr\nZdYLPYzKCFwvDRG5KApRtKSOS+rEnQlqmx8KjnvSCBzcMxOYB39BPj3Ltp8+jJCC1ooxkiWj4B2m\n2wsEJsaSTsww9cgWbO6wWVjt28uHiBoJUbOGzQzeWmxmiJo1pFZEziO6PWqjLdoHHwi77x0ibXEt\n+CgyLAxWhyBA3JspkNchbO5khMq7mKSFjOv4ZuBhyOIW0/FSMh8zlbfommCGalmEsX0gwh+k4vJe\nkJoI4yT1yNCKOiQyC60vXcRsXsM5gctD+UpNZZUZOZkmpLkMNBpOkBuB8/D7O3NyPc9lpyrST3/6\nUz71qU+xYcMG6vU6F154IW9605uedp6weSBa3GcNK/7kIJYXk8aqeL4TXyC2s4J2WOZpcGzrLagV\nvYCkRkhFrzFGRoDOOKkwMqoCD1bqirQQAtEhhNyH9BblDTfe+22OOPlUlMuCuectMq4hh5eQOMeK\nY/rtWspdLYbKOW8AYyDIOjwAACAASURBVNUJfZBnyQDkpargO6ZsoCwVSiisUEwUJtxgCLy8l/QW\n6Sx53Kz6D1VmXWEGlnFFaXNq3QnqnS0hVG5zpAlN1WSvO59FtiTuL0PccRJMWRV6Kfm4Fthp40b4\nX4wZANOPDBKDr6lA5VVE9ZyQwCE7OoVesLLTC/sGxVr7jODVB8aPLfI9hVFT8KtNmhGU7Od+EplV\nOSWASOREZNUPWE4qJxQ1M1cpDYSCvVzE5MSkNqnyT5WZKGwoFRA5ketx/CtfjjZdomwO1ZsLEapy\nJ3NFI2QZWFlNVIMCCW2LSJkTqmihElXQpfJ+VkYYAnHLYH6slPLzOmSVTC7NuSpxXSQ8ywRo9Z0P\nJELLx4PvU5jwHXuDdhmy2Jm1CxE4nXeDyWx6Ae1edDIUc7OQdpBpip+ZDeUf07Nks6EzRndiruLy\nC21pAro9bsaBm69Vh7/+0nbPnRe67FRFOuqoo/jbv/1b0jTltttu49JLL2Xt2qe3iF+RPoDRNZxQ\npLoZEn8IaqpXQFLCsGZNUcY94OjmRGgRJgeirxixSEMbFhMwZk5qosJfaglFpkOEq+eTKvrUsXWc\nbzLBKI+rvfhF/HJ0zRSYs9ATNRY9IttDuzzUPzmDcsF/UyZDmR5eyIGCNxvQEt6jujOhm6DJAy3W\n5BQ27ZFumaQ3NUs61aU30yu69Al0otG1iMaSFrWxIXS9Fojtl41DrY4dHccmTdL6ErpxO+ALi+jm\noBKWUbyQyE6q71BKR6x6AerjDMobRNyuUB/SWZTL+h05oCjHCN39IiAuntdUHCi4it1/EBmRC0Uq\nNeM7c3I9z2WnBhvyPOfzn/88t9xyC845jjvuON7znvcQx/P7qG6553vkuk43auOFwHqNJIS3DVEV\niVPCVryfmpx2d/M8kvZSnChyJ94R96YDH17cDKu0MyHBabJ+PqfoIFHldiY281eXXsdH33BMSKgO\n9kYt+xJJidBR6ADRKUrRcxPaVeZ5RXjvvUMV5Jglcb2s10M1ayNUnJbBCC9VULrwQwREglTYxhC9\n2nDY4YokaVDgvJrYPd2o8HMQdjOLrlAg1XdTBDC8F+ReVztzLHO0MPPeX1IZCzyJ7Ad7rFdVU7JB\ngKvCVeDXWGbhN8q2hdyVM4y9/KXT12WnKNKf//mfP/sNhOCTn/zkvGPffcURDO8xjIokUSOp8i5j\nx60tGmypypTKh5ZWPoeJ6pioXmXfjYxCHY+MiGwvrLCFTyG8C36Vt/2uE95WE1earF9LoyI2/Ohn\nHLP2lRhdJ9dJlb0PuDI9z68qw++quH7/y3y6yemqVTuuVu0yWhhwgKaPBzS9fhI1LxKoZV5sUPGz\ntGrBWcGXSr+y7A1VilT9cHtJX1b0iA1+W2gEXZqmg+MtxxrZXrUTD36XQD+ZW/ShpUhMm6jxklKk\nnWLa7bfffk87NjExwZe//OVnZF6dvuQObr5X8PtH/pxaNoNyOUbV+LHat+q+tyzexqqNPyB6/AHM\nbnsGZiFA5128VMQTT4QInHe41kiFfMjrwzih6CbD1LKZQqFyplsr6Ko2E/kIM3lCbiVSQE3n1HXO\nY3HOQ7U1JDJj2G1FeEczn0TZLPRZytPAbDq9FfPwQ6H9Zq02vy/sst1w9TabVh3BNjvGRNpES0cz\n6tGkw3j3YeJsFmGyCthqdIJ0hiibI3rwJ2SPPcb/+fIGJu/pFw+ufuMqdC0im8vozaTzqMBWvWac\nPY47iHi4TbRiReCea7RDe5a4HsLeOsboGtON3ejQwiFJbUJq46r40XpB4oNV4K2g19N080DCOTGr\nyYq4xJKhgBSPlKOTadIswLuSKNAhp7nE5oLutOAPdsbkeoHILknI3n777Zx99tmccMIJnHPOOdTr\n87FrndsuJ2svwylN48G7uenMf6heW3rkCEv3W0pj2TA/u/YndB56OsToV8mh7zqYZccdSe/gdfx7\nfiIbvjfHf94YCgtfdvgB6Ehx0MFLWDomOaP9beKND+I2b+Kvr/p3PnLSkWz+4b1svGcTE3dPP+s9\n9JBm+VFL2O+s1yDHl+MntvK9cy+j+9jT23w+m6i6ZM+TVzKyepxktE1z7Voe2u/1TGRD3L+lTZoJ\nrIVIw7Jhg5bhZ5rqav73/0nZujkoWr0Zs2w81GDFsWTjEx2sdcxMdhFSsN+BSxlfqmjVPcYKennI\n5W7eavDeE8eSobYiieHJLZZt23p476k3InZbFpHlsGRE0MvCxqcVjLYstciSKEuv6F+V5grvYTZV\ngQog9rzlVS+dbhQ7VZGMMVx00UVcddVVfPzjH+f1r39mrFX6nS+BNUxvuJXO5ilmNk6jE42QgqlH\np5j8+ey8RsjHfer1gUduqA0mMOv4sgOeC+aMyzJUo47ee9+QZDR56E5Rgjudx2VF6Nf7sIOUKAAd\ncevdP+P4A/Yk37gpRKWeDJ0kbMH9BiC1orn7buix0VDl2mjiGy1MewkmDkEToxOMrtHTDeYIPpEW\nhuFsM0lvBl1Qazkd98Gn3mOaw4EfQSqMqhX8EskAat1VwQ7pii6EgM4Dr7ewJjRbK+qRrE6wKqYb\nD1VRxNh0q+uVvpYTqjKFvZAVKWSZKJa4kLMq6owGSyWM11gkU70muZX0bGjtYh0YJ3jrupeOIu20\nqN3DDz/MBz7wARqNBldddRUrVjw7hP7vJn4PAH/YH3Dovp69hrahCwqoWNUZQ5P6Gj0b7PQnddgd\nUl8jcxGRMORek5qY3KkKH5aovCJ9zL3GOF0hyJWwFQOo8ZqsqPpUhIjg493/4MG1x+CQZC7CFJHD\n0pnOC+xaLA0tPUcsejRMoA0u8z+xSRHehiZnLkdErgiEWDJdJ9N1bGtlBafxiIpEsmPr1eexVlAX\nOZGzDOkZEt8ltilJNhNMwKknQ4FinuG7XXxWLBZFk2SAqECgt5vt0CQ5rmGbIxUUSdpeyLnpGKNq\n1WfQRUSymW2r6rZUOocooFW2MVzllbyQIdJnUqKtj0F3Dj87W3CPt4D37JS59UKQnaJIX//617ng\nggv4gz/4A/74j//4157/x/vfhjIhR+NFjJ8JtTLTrRVErkfsu9SZwaqCTdS5AgiZY1RIssZIGqpb\n5ZnKnFQpscjwUhSt7vuvSW+pexuSnMUqH5kuP/3uNzn94KVIl88jUCyd6pJsMaCni9eLJHKJ9pYu\nR1hTBR0a7vH5yOpSrK3CyyUKu0J1M0hg6YviwLDrlt0D8/YSGAqVsb5MlApJHjVC/kwlpKJRhcFL\nwpjc67AgFAuD8xLvwZrwP1KuWHQ8sTZESajxikVegIZDGbpyeRWAkC4Eb+z43lUQpYRQvXQYG3aS\naXfggQcipSRJknktL5+N/OS7/3euItqAfkKyrnOMk4XZEFDKzShUzyrh50NaBtHLCFp6rkCsmUpp\nBk2SakwDCU4pbNVR4c7v/QfHrT0C4X0FnC0z9eGLCrtLic0rw+3VdQcmv3Q2lHgAwtuChScijxtV\nxNEL8YxVqYOl4YPUzdLllVkGIeGMCJVaolT2gfE4GSEI+SBtekjbC0pe5odKBYWKxaiMYFoVaqfC\nOPu5osGSdSiiksW3PhhGL0Psa/Zd+WxT5kUnO2VHuummm3bo/M2zNVqJQQhoRj1qOiMRGXXRCZO0\nCAubOK6Yb8owdJndD7mNsjFW4BWwyKp8AIKCKmmJhCEWGUqYKrmqbUaUz4Wo3Nwkoxt/ypIH8oDD\nK+qGcA6fZ/i88JOUCh0fGu0AT4pq2KRRhXxLWuA8bpCrGk7IatcrkQQKi7a9QG9VjF/g+hTDhUib\nV0roEaE4r7qPLNrRlN02Avdc4E5Iqu+ooiJLCPtykaQtF7GS/iySAWVSkz0ikSNFKI9/aql5+VgW\njwfBq3ZgwXoq8PalIDtFkbanudigvHL059WP0BGt6kcIiOaMWndrQF8XOQsXBwfYSU08+QSiM4uf\nnMBMTGBm53jiznurrgsQImIve/Pe7HbUgah6HbXHXrjWSL+kOs9Ct/KpCdzMLDMPPMJ1N3yfvQ/Z\nDxVJWivG0M068dIxZBSB1oihkRBalgpK+FBTQr0dEqdRPZSxqxjhLLGbA6h2LmnzShmsiuflwaS3\neN1A+uDYGxnTE/XCmVcFjMoSE8rOI9cjKnyWwYBEw8wQyxQrozD5RShnyImLeiJNQsiBRSKft+DI\nAjnivML4qPIjjdPM5Um/IpnAxa6EI1ZBASNpq0rmsm9Vz86nC3uxy4LUI938427IPaicROXVSlhG\nicofNdBF9Xce6FNClRS75eqaqBwJaGkq869cOUt4EVCUQ6uiwK0fUPjRXRtYt25dUdYeJqHGULcz\nRLZHnM2GXWLABwCC31SWg0e1MLakjZOaTNfJZYDoZCSkLsF6RWpiMquwRYNjScjBKBGaAkQyBE1K\nv69EG5T+nsIS2bSC9ciCYEXbNHA7eIvMumG8eYZI5wJANc/6dUxlvyMd4XWIXg5yPZikTS9pB4oz\n1cYUa66ijyEsYUmDUqIoYNG02+WycbpWVGXWGK4bGnFQqFgaImlQ3lYEhINcaaqolHVehokWuQKe\nklc/aAkrKovXlMvRNg8woQEfY5AZVHrLE9kjrEjHq2ZZQL9gzXt6yVDFWVAiHnKRzAOilgWGpoDa\neCuQzlXMSLHMiUXOUDKDwlTohjI0rW0PYdw8GuBy9ypD32VNVdlUrYIKiTCBByc6hN1Gu6wyl8t6\nodJcGwysOKkrSFIuE2wxPQYZmxQ28ALms6HJtc0QJgvMSEUQpar72vftz8V0el7IgijS2hUPBjOm\nCLeWk3LWNUltQs9GVQ8jJTx1lVYBhBpdnJTBzynQzMKFlblk4ql4EvCFqTiByjoB2zZAKuJUVAE0\n77jtVl673/J+yXpJ9WttWL0LWA0lHRbg6i1srdlnFSoK+criQldEHT2CLGqSimY1OXPK4j9QwtCL\n6wx2FBfVs7AwOGRAHSCqIIoodk7lTOVveQTSW7Q3AYxa5IeMjEjjJj1Xm4cWH2RHLVMCzksiZwo8\nXQDvlpzrxkdkMmEyXkJPlYBZkICSA9Ak4JW7ZPY8P2VBFGnplZ8hGh1G1OvBeY8CB5trDPVprwjM\nOUDftynApuHgAOmGjpkc3hMrNJkIJPk9F5O7KMTwahJR9wWKvF9pWlM9JI6a6HL4a17PzNJ9iLNZ\ndN7tk4CogGwunXwn9TxMWhnFKn0d5UyI+vlQ5VuFwvMuDbEt7CQFctoXZC22IKa3QiOLnaWMhBmi\narc1FIpZwHp8gL9X/6EIIIgw6YUaoCIrer4GdlRXhbGF96HUvDBXy0jfYFTS6FpFnl+ONRGKlpY4\nIYNfNUCjthhseI7ksdPPrlrUK4qyBZeTikZlbjwVxQz0ywOK6FzJOZ07RZsuwheIZEJf2Eh1kdpV\n6zxQ5ZT6Zk1An8+qYbZEK8l0RGriipY3UXlI+Iq8WqGV6INjI9dDOkuSDyjgYCl2yRdnsvksPGWo\nel4RYFgcbFHv5KUij5uYgkchVwlWaIyIsEL3kQa+H3qOZVaZtqUpW5puwQyVGCIynxRKIMmlLnJK\nAXPnlaBo2FEtPmVCu8/bUPhuPjAyxT5AuQYjfLBIWbxLZfjiDzD35BTpVMrEg5PM/iLwIowc0mL2\nkXQePKixuoaZtqi6pL17g/aKIaJ6RH3JEMlws+r+oOpJ6KxQCxxtslaDKAl0Vjqq+BSAisrKl+0h\nveO+f7uS31neCSHvMjla/A+ccFFltpUsqOUqDdCLWqTxELlKQnGf12Q+rkjoM6tJTYRDkNuQI3NO\nlD2ekYJiJwkl4eUx6TzKh66E0rpwXrHDlP/LXVYJi5ci8HcLg3SW2KZEphuUPpsLQYi8F8pKXGAw\nKim0XFLHFeF8F9UwuoZVMbku2+oo8FT5tEGUOxSwIiEro/SlJAsStbvwSken60gSiTGel+/naSc5\nq+pPssf9/x5C3hsfQy5fBXnGzD6vRDqDlTFp1GRODuG8ZNhtJcnniPIu083dqOVzzCajgVhfBD5q\n7wU9F1eNsZZt+Sn84DayiSlMt0dt2Ri2m3LNN7/PK0eG0YlmyYGrkEoRjw2jR0eQ9QY+Cy0uzeYt\nRCtXVuQgtjlEXh8hrY/S043K78tUnZyYiXwIVUz6tg5AU4Vh0oyQO0XPBp6ESFmMC1wKdZVX1FlB\nARWRcuRW0o4zEpVXyISJtMZIrUdqdMXD0NAZSgYas4lei1aUVpFRi8L6AKsqm4K5gs2ppedCK1LT\nZDarYZygHplqh5FFpHWZepKRmUdRJqXT2o00atEVzRBWJ0N5Q2y6rDjwsOd6ai2YLIgidf7jCnCO\n7ugqkrmtqPvvQQgZ6mZGxkLXvaKbeBWyhQJ+HEEvxWzbytyDj5J3evSm5pjbMofpGcbXrETXYqJm\nDVWvhV5FSoXGxmlobBYPt5GNOqrVDhzZtTq3/uxhTjjsoMDImmXz+QxkQfVbkjWWx/MM35nDpSk+\nN6FtS6MZiPzjGmZ4Kd3WOLmuY2QUyr1tVjV7Dj/AAASpbCSmYrLaEEbGdOKhkGT1oirEy3won4ei\nd25hlsUqhMxD4rWIJHo5r0G1caHX7nSqMRayXFCLPXHkiQufyroiaFHysKuw6ynpiZWtSJNSozEu\n7LBaemSBUHdOYJzgjLUvnV1pQUw70euA1NS3PhKSrrutCs2vCjLEily+OYxpDPXxbWUFaUHCoY+T\nRN7RAEYGm3MRIDTuGW8uSIt7SB/CtcJZbr7muxxz7NGBB9wUUBrXNzHLojibNAIZfUFCH4IOIWxs\nhZ4HoylD0kAVPLBSYaOQQ7IDhIv986ieCzwyo0/bLPqhdC1N0VRAIOhXunov0NKQ0IdTVamDghdd\nOYNs28o8E94Hf+kpdMaDEKDSVBukByuLHXPigmY68An2/deh33SKvOBkYRQp7UJSY2L3w5DeMrzx\nZ6Fn66Zf4uZmybdN0HliC+nEDHG7wdiJx0JSww6N4eIG2syGThKzk9BLcZMTZFu24Z2j/rLQfsVn\nGd7koRtdsxVubIqWlSXERwe/ysZ1jjvyMFyUIE3hR1kRHOciDC6MwWuNztP5bK6duTCGrBf8Mght\nW3SET/p1WFUwAUKpQxTArjYOJPomblT5Mle06Cz9rdyH6GWpdKlLKpOsbCwghK/gUdb0o2bOiSrx\nO3isZxVpHoobI1VE52zYScrap9ApHpwXKFn6b77Y1cL5zoddrQCdh3OKWXX0Qf/FifICkgVRpGtH\n3x1uPhVWOFt/BZkW7Lt6GltMilgahvUUPe940uWBt6BgB3VCkS+Jq3BwQkpsu0hn6QpZUEFR7Q4l\nALRcgateQUX3OoCtzVmeGFnTB18W9TbGB58itOYE62QVLYQ+4LYMECgZolyDwQAAVZRjQJ86OCDa\n3TxMoCx2kjKyqH1OzXf6iVVnSbKZimg/cFAUvA9F/1cvVT9dICU2aQbljJtV6L5kVK1QE0ViuMI1\nCo2lT45fStmyBagS0dV3IXwFZwqJ4b120ox5/suCKNKb0/XV45I7zSeaGbE7TobJb2RM7gNoVRVI\n5lwkFWI78hkRIZmbkdBTNaxU/SiZ02QFL3XuFD0jMVYU3SvCj65lmMiJNnz3+//JYWtPJJYZCSnK\n59TzGeJ0OihEQZ5SogLKSB4DCd6yDMKpktxk4OstwKtla5USVV4S//df6zcCMCrGyBgjIhyKjmph\npcKoVaFfrlMVnbDAY71Cy7DYDKLly3RACN2HpG7uo77fNNB3N3O62sWMC6apcaKihCjfY10/CKpk\nf5GQ0lfQrKcTELx4ZUEU6Yk911UwksFSh9xHFWONMbqi5+q4evVDl470YP5ECE8rSpHCFTRalobq\nIqIBc6ZAQw+uruVu4hEcve5VFfyoSwMEdJI2Mlk+D082mHAU+D4CvVjZB88t814lvq/suVqu4oP+\nUPm58OBt8dkM80yyZxJR3FkIHwobZY4SrmphqQkIkLLjeyklQLaCF0mJ1bpC2FtUP1pX7I5SWPQA\nOn+Q9/uZykrgmO2aDy8GWRBFal/yCeLhFrJWQ7VboQODjmDJeFCwotu2bQxVeRygKsUW3iNtr+pg\nJ7Nev/mWK5KdJaMOQJzga80QqKg1q4I8E9WrhsVNP02L6SJg0HfcAbQPk1G5kmTRzNudYKAeCXAq\nqkCrPdWokr6llP1ZA3auX0CoXB8zWNUKiX4ZyTwFFvORDw4ZdhnCbpIVi09mNV0T4bwgs7LwawRZ\n4UcZC7kJ5eHegyosNevCYymhFnu0ClG9SDmUDKUXNR0wkg3VZchsIzJdat2JQBTTnWFRkXaxPPH/\nnM/Gzgjb5iJaNcuK1jSjaqLK/8yYFmXV65CaCeadMOQ+JnMxHZuQWQ214JsMR7PFCmwqjoHcxwhC\nU+BEpBV6YobhCoQ55LYR2R71dIK7vvcfvOaINWQ6BAhKOqoSPiO9xcqI6dpSOq5J5iKsC6ZUJA2z\neZ25PME4QeQdynmyrmK2p+nlgQ+7FjsaiWPvoc2M5ZsqEKoXkva2B1HT28AaXGsEW0QrnY5J60tI\no4DpS0wHbVJyXaenG8y6EaZ6jSIX5UiNqpK2Wjpaca8KiSupIQpmn5YO4+YHIYyTJMqipaGp02on\nKn2fEs0wWBrfMTUmek0eYykST9IMead8SHHCQkyuBZKFySPddnkAhw4215Iq8LWV4j0UzYpLaD8l\nEsHkyHQOJrdip6fpbdpMNDzUzxUZi+n2MGkWyFGAuFVDKEUyNoweHgrkJ/V61cDr1nvu44SXHxB2\nNWMCecpAYZ/rdvG5wfWyQCDpHEiJjEOeStQSZFwgKZLQGI2kHpqSFZ0onEpwxQ771OrYgPLWlX9Y\nRfAKLFswD4sSksK/GSRrrKBVTlX1QNYLurlmNlVkuaDbg27q6aaO2VnD3FxO3jP0eqaiHy5FallV\nO0spqsdCCrzzVfWzkAIdSbSWSClIEo3WgiRR/MXvvHQaMi/IjnT30t+qisEiYaqJ0GaqikxpF6iH\n0zgw8UhviUwv1NxYg2qmiJFx8J7kEFfRBmuYh8T2QuJ13Oc1kArjXbhGwRshTQ+XPIavNUMEzJWA\n1RL/FiEKgkUfN8niBkbGpFGLjATjdRXkGJzMZTVqiWGT+MpBL12pqisEjggLHqRzFYtpeV6Zd7JF\nQCC3sgoIOCfC3uH7PJHW9X3BRuxoxDDa6q+ZSkiESICkSrBKGQIwZY1Uya5aRRu9wLpwn6ferwo8\nFIlbgaEC7L0EZEEUaWX0BENzGwOr6EBbxu6SPfu8BwXjaNKb7nepoCx/0OTRaIWg9kIyFS3FelV1\nrTNOkzmNccGJ17IPvhTCo2JPJHO0sEQi51v338KBp76S2KYVent+ObeeR8wfEAMxqY3DPVyY2N4z\nj4/CDUzoUoTosxM91R4oX1NV4tVVk7ScsEIEFIHCUNMD6G4RQh/lwtS/Zn/HKgMuxs1HHfRrvkr8\nXr+uaTBIExYEquRwmcMabJJdgnxhsbBvl8qyx+9GmB5ichu9Bx6gMzGFijTxklFUu9VPopb0xd05\n0BFmbDkmaaGzORqbH8U8+Atst0d301aa3R7eOVp77IZuNcN1lu0WgKtKhbqikuo37YSEbZ4H82zp\nOKfuUWf5A/8RePAKFDZpF3ppwNlBoJkCvHfhcXsE32jji67ipjmC0TU69dFgnglJFjAG8yA+PZdg\nkcQipya75D5mOm/TMTFCeDKjmEojnIPMhB0nKyzOEBTwJHGYuM1aUfotPXNpiZIIxJLGQi+DTuqD\ngltPnnukLHctjymex7GkXpPVdZ0PZJCRDn+5CdeyzhNpwVDTU4uCzzQ5KysCSVeExXPjWbPvcz2z\nFk4WRJHuHHszuZX4JYIlh8zO62tarq5xUTJe5j8AGsyyxS4L0aimxu4VJllqAk9AZmS18go8kfYk\nyjKcpNWKXSZCB1dpKRyPTH6Xn+99TGXOQOAlKPkNynIPgNimVbWpF7JqJVO2TAnkJHKe2ZfZwFln\nClxc2Llk+B6KHq6l5FaS6GBeDtUD4rtM9EJQghI/N2hmaRUUwFiBsaCkII6CaRaUR6A1xBriKChX\neW6ZXyv7P8dRP1IHQYEh4O5K8y8zisxCo+apxeVuOd/Ue6nIgijSuns/h1u2CmFzsv+8k+/++Q07\nfA09pNnzxJUk7YT27stoHbgfYsXu/XC5kMwu3Ydu3EZ6y+i2+/FCorozuKRB1hwLDKe6Tice4o7v\nf5/9Dz+FSIYS91gGxRnW01WUL8k7NLrbUCatWmF262PMqFEe6yxFCc9wMsewnKoihgCxyGlFs1WF\na9c3eHxujKk04t4HPb2e5cmNc0SJQgqBtY60m9Od7WFyw+77LGXp0hpLxxRCwN0/nubRXzzK9JbA\nBjs8PkYUR0RJRHukwdBwwr771Fk6ZDloyRPE9DBE3Du5konZAFYdajhG6lnlozkv2Txb47EtEmvD\nhtxqSJRknqKtHO0xknRo6g4agxQW6zUdVye1cWXiBfKT+rP9fC86WZCo3R133PG0TucLLc/HMb3Q\n5aX0nS4Izv3OO+9ciNv+Snk+jumFLi+l73RBFOmZuvgttDwfx/RCl5fSd7ogpt2iLMqLTZ7zYMOl\nl17K+vXrEUKwxx578IlPfIIlS5Y818Oo5JZbbuGiiy4iyzIOOOAAPvnJT9JqtRZsPC90ufrqq7nk\nkkuq5zMzM2zatIkNGzawdOnSBRzZLhb/HMqPf/xj/5rXvMZPT097773/m7/5G3/uuec+l0OYJ1u3\nbvXr1q3zDz74oPfe+wsuuMCfd955CzaeF5tkWebPOussv379+oUeyi6X59RHOuSQQ/j2t79Nu92m\n1+uxadMmRkZGnsshzJPvfve7HHrooaxevRqAt7/97XzjG9/AL1q7O0W+8IUvMDY2xu/8zu8s9FB2\nuTznwYYoirjxxhs54YQTuOuuuzjjjDOe6yFUsnHjRpYv73OvLV++nNnZWebm5hZsTC8W2bZtG5dc\ncgkf/ehHF3oo+07L+gAAIABJREFUz4ksSNTuta99LXfccQd/8id/wrve9S6cc7/+TbtAnu2+Ui7I\n1/Kikssuu4yTTz6ZPfbYY6GH8pzILp8xn/nMZzjttNM47bTT+B//43/wgx/8oHrtzDPP5PHHH2dq\nampXD+MZZcWKFWzevLl6vmnTJoaHh2k0GgsynheTXH/99QtqbTzn8lw6ZHfddZc//vjj/datW733\n3l911VX+zW9+83M5hHmyZcsWf/TRR1fBhgsvvNB/5CMfWbDxvFhkcnLSv+IVr/BZli30UJ4zeU7D\n30ceeSR/9Ed/xH//7/8dpRTj4+P8r//1v57LIcyTJUuW8KlPfYr3ve995HnOnnvuyfnnn79g43mx\nyMMPP8yyZcuIopdOs7HFhOyiLMpOkEWvelEWZSfIoiItyqLsBFlUpEVZlJ0gi4q0KIuyE2RRkRZl\nUXaCLCrSoizKTpBFRVqURdkJsqhIi7IoO0EWFWlRFmUnyKIiLcqi7ARZVKRFWZSdIC9JRVqEFy7K\nzpYXpCIdfvjhCCF2mDet1+vxp3/6p1xzzTXVsdWrV/Pe9753Zw/xaXLLLbcghJhXj/VflRNPPJE3\nvelNv/Hri7LzZEEoi/8rcs8993D33XezZs0avvjFL+4Qd9oTTzzBP/zDP3D88cdXx6666ipGR0d3\nxVAXXP7xH/8RpV46rVUWUl5wO9KXv/xlXvGKV/Dud7+br33ta/9lfoXDDz+8Ij95scmaNWs44IAD\nFnoYLwl5QSmStZavfvWrvP71r+dtb3sbc3Nz/Ou//uu8cx5++GHOOussxsbGGBsb48wzz+SRRx7h\noYceYu+99wbgrW99KyeeeCLwdNPuoYce4qyzzmJ8fJx2u81pp53GfffdV73+8Y9/nCOPPJL169ez\n//77U6vVOOqoo7j99tt3+PPceuutnHDCCQwNDbHbbrvx3ve+l9nZ2XnnXHnllRx55JE0Gg323ntv\nPvnJTz6rj3fxxRcjpeRLX/oSMN+0K03L2267jWOPPZZarcY+++zDF7/4xXnXuPvuuznppJNoNpvs\ns88+XHrppey77758/OMf3+HP95KShS3Q3TG54YYbPOB//OMfe++9P+WUU/zRRx9dvT41NeV33313\nf8ABB/j169f7a6+91q9Zs8avWbPGp2nqr7zySg/4T37yk/4nP/mJ9977vfbay7/nPe/x3nv/y1/+\n0i9btswfdthh/utf/7q//PLL/aGHHurHx8f9Y4895r33/rzzzvPtdtvvu+++/l/+5V/8dddd5w8+\n+GC/cuVKn+f5s4795ptv9oC/6667vPfeX3/99V5K6c866yx//fXX+89+9rN+dHTUn3DCCd5a6733\n/utf/7oH/O///u/7G264wX/605/2URT5T33qU95771/96lf7U0891Xvv/fr1672U0l988cXVPQdf\nL++/cuVKf9FFF/mbbrrJn3HGGR6ovouNGzf60dFRv27dOn/ttdf6z33uc350dNTHcbzL+f6u0/v7\n6/T+u/Qeu1JeUIr0jne8wx9++OHV86985SvzJsLf//3fe621f+CBB6pzfvjDH/rVq1f7e+65xz/4\n4IMe8Jdffnn1+qAiffCDH/StVstv3ry5en3z5s2+3W77D37wg977oEiAv+OOO6pzrrnmGg/4H/zg\nB8869qcq0hFHHOHXrVs375xyobj22mu9994fdthh/qSTTpp3zoc//OGK56JUlH/7t3/zcRxXClbK\nMynS+eefX70+MTHhhRD+wgsv9N57/9GPftQPDw/7iYmJ6pxSmXe1In2zfoD/Zv2AXXqPXyff+ta3\n/Kc//Wnf6XT8N77xjR167wvGtJuZmeHqq6/mjDPOYHJyksnJSU466SQajUZlntx+++0cfPDBlQkH\ncNhhh/Hggw9y8MEH/9p73HrrrbzmNa+ZR627dOlSTj75ZDZs2FAd01pz5JFHVs933313gMpfM8bM\n+/NPMcVmZ2f54Q9/yFvf+tZ5x3/rt36L0dFRNmzYQLfb5Uc/+hFvfvOb551z/vnnc+2111bPf/7z\nn3PGGWew1157cfbZZ//az7hu3brq8cjICK1Wqxr3LbfcwoknnjiPtPP0009H610fk1J1haovXGDk\n85//POvXr+eGG24gTVP+5//8nzvEJ/KCUaSvf/3rdDodzj33XEZHRxkdHWXVqlV0Oh2+8pWvkGUZ\n27ZtY3x8/De+x8TEBLvtttvTju+2225MT09Xz5Mkmcd9Vz52zvHQQw8RRdG8v0ElBJicnMR7/4z3\nGh8fZ3p6mm3btlXPf5Xcd999HHfccdx3333zOLefTZ5KNSalrPj9tmzZwrJly+a9rpR6Tji7VV2i\n6gs3Hb/5zW/yhS98gXq9zujoKJdddhnXXXfddr//BRP+/ud//meOOuooLrjggnnHf/KTn/De976X\nq6++muHhYe6///6nvfdb3/oWRxxxxK+9x9jYGJs2bXra8Y0bN2430f/KlSu566675h074IAD+M//\n/M/q+cjICEKIX3mvoaEhgHm8ewCPPvoov/jFL6oQ/tq1a7n++ut5xzvewYc//GF++7d/+zee+KtW\nrXra/ZxzbN269Te63o7IQioRBCsjjuPq+dDQ0A7txC+IHemRRx5hw4YN/N7v/R4nnnjivL8/+qM/\nYvny5Xzxi1/kmGOO4Z577uHhhx+u3vvTn/6UN77xjdx9992/Nqdy3HHHcfPNN7Nly5bq2JYtW7jp\npps49thjt2uscRxz5JFHzvtrt9vzzmm1Whx22GFcfvnl845/+9vfZmpqimOPPZZ2u82hhx76tFXx\n4osv5nd/93erXXDZsmUIIbjwwgvp9Xr82Z/92XaN85nk+OOP55Zbbpm3+37rW98iz/Pf+JrbKyqR\nqGThpuOKFSuqyGaWZXz2s59l1apV2/3+F4QifeUrX0EIwVve8panvaaU4m1vexs33XQTZ555JsuX\nL+fUU0/liiuu4Oqrr+ass85i7dq1nHTSSQwPDwNw4403cvfddz/tWh/4wAeIoohTTjmFK664giuu\nuIJTTjmFOI55//vfv1M/01/+5V9yxx138La3vY0bbriBz3/+8/zu7/4uRx99NG94wxsA+NjHPsaN\nN97IH/7hH/Kd73yHT3/603zmM5/hIx/5COIp3Y5XrVrFxz72Mb70pS9x6623/kZjet/73oeUklNP\nPZXrrruOSy65hHe/+93ArqdxVpFERQs3Hc8991wuueQS7r33Xg477DBuvfVWzj333O2/wC4Jf+xk\nOeCAA/zxxx//rK/feeedHvDnnnuuf+CBB/zpp5/uW62WX7JkiX/nO9/pN23aVJ179tln+0aj4Q89\n9FDv/fyonffe33PPPf6Nb3yjbzabfnh42J9++un+3nvvrV4/77zzfLPZnHf/H/7whx7wN99887OO\n8alRO+9DtO+II47wcRz75cuX+/e+971Vy5tSLrvsMn/ooYf6OI79vvvu+6zhbe9DG5UDDzzQH3TQ\nQb7X6z1j1G7w/t57Pzw8PC8i94Mf/MAfc8wxPkkSv/fee/v169d7wF900UXP+tl2hnxv7VH+e2uP\n2qX3+HUyOzvrO52On5mZ8Vu2bNmh974gFGlRnhu5/fbb/Y033jjv2L333usBf8011+zSe39/3Vr/\n/XVrd+k9fpV885vf9Keccor33vsHHnjAr1u3zt90003b/f4XhGm3KM+N3H///bzhDW/goosu4tZb\nb+Xyyy/nrLPOYv/99+d1r3vdLr23iiUqXrjp+LnPfY5//ud/BmDvvffmyiuv5OKLL97u979gonaL\nsuvlne98J1u2bOGf/umfOOecc2i327zuda/jggsuoFar7dJ7S72w4Frn3LxeWStWrNihdkOLirQo\n8+T973//Tg+sbI8sZKABQurja1/7Gm95y1sQQnDVVVftUBphkUR/UZ4X8uM3vQaAQ6+7eUHu/9BD\nD/HBD36Qn/3sZwghOPjgg7nwwgvZc889t+v9izvSojwv5FftSJdeeinr169HCMEee+zBJz7xiacl\nyP+r3elXr17NlVdeydTUFEqpHe5svyA7Unr95+kt34esNsTWZCXTeRvjJVo4YpXhEXgv6JoEj6Cb\na4QA76EV90hUjkcwkTaZ7Wm6mSQ3Aq08vVxgLBgDvcxjjGd6xqC1JIoEjbpEa0Gee6wDYzxCwCM/\n28Dqg16NVOE1YzzOe7wDpQRZ5vA+HNc65HCUEszNGnJjmZ3u4b3HmWBXO+/RkSKKNXGsmJro0Ov0\nyHsG5x02t1hjybMMHWm880RJjHMOKSVCCKy1pLMd0tkO3jmSZp1aq8HQ2BDWOvJejskNzlqiOMZ7\nT1yLSTspNjdk3R7eO6RSxPWEuJZQb9WxxqIjjbOWyScnyXsZQgr0QD8jFWniesj0e+fJ0h4mN+hI\no6Oo8h+UUkhdQKSMo94KvpRUin/51PYnNH/21hDMOPDyf5t3/J577uF973sf11xzDe12m/PPP5+5\nuTn+6q/+qjpn27ZtnHrqqaxfv57Vq1fzt3/7t8zNze1Q6cfmzZv513/9VyYnJ+cdP+ecc7br/Quy\nI5kHf0GSdklaQyRLpxhq7oZREblKsF7jkBiviWKD94KGVnjC5G2oLhKH9Yo5ldCMQQpN3LQ4J7Be\n0M0k1gnmUoExgiSJaDcFjcTRrhm0dEylEcYGhR2uG9RkzstflmOcJM0lHoEeWCS3zkRkORgLcTHf\nshyGh3RQuhUNrPVkmSM3HmfD+uScRylBkmiy3GJyi5ACpSTWOkzu8MU5tUaE1kGJup2cLAvnA9X5\ncRycciEFJncYY3HGVYqrlMS5IbpzPbwLx+JaVF2jlLSb4Z1ibEVY2b33mNzgnUcqSVKPC2UO42u0\nG0RJUHiAPDMASCVRSiJk+H2kEESJxtod6wss9TPvSIcccgjf/va3iaKIXq/Hpk2bKpBwKc/Unf60\n007jvPPOe1ri+tnkAx/4AO12mzVr1mz3ewZlYUy7I4/H9bq4pI5HUMum0XmXaHoLYtuT0Euh0YRa\nHVdrMrdsH3q6Qa7CaucRGBrEytCMesTNnKXmCbTthdcLFHF7072I6Y2kDzzI5H2/ZHjvlcw9sYWo\nWUMlMVG7ge1l9CZmmP3Foxz0wy/gnSNuN6qJUe4wUkuccTR33w0RReAdva2TuCxHN+sk40uRQ8MQ\ne6grcA7iJHwWAKWgVocowdVb+AKulNdHMFGdNG4T2R7SB8XBe5TNiHozCGvwKvxUMi+uJyR5bQjh\nHVYnCO8wukYnGcEJxZxvIXFkXmG9wHmJ94KWniMSOVJYMp/gvEQLQ0RG6ut0bJ3cKdpRh1gE6yD3\nEbnXOC9RIowvEh6PQOIQwqKwCHz1+1h2LAqnomc/P4oibrzxRv7iL/6COI553/veN+/1X9WdfntN\ntK1bt3LppZfu0JgHZUEUacNxH9rp13xk4PH+Z72M1vIRzOpVyDgmWbWS5fu+DFpD1HWE0zEIidca\nJTWRjvnfX/gqp/x/78JLhRMKL4uVXyi8kFgETipmvEf4oFzlxLFAisCoOJilQiK8Q9A/V3qHcjnC\ne4S3KJcHZTE9omyOOOtXxpa7rxeSPGmDCNf0CKhTXM9CORYhsDIGIajls3ghqTMTJrTUxeeRWBF2\ne4/Eeo3CooUJykKMFI6m7uC8RAqHRyDwRCInFhmCoDzl+Mrnojhajr08tiNShr/vuOMO7rzzTtau\nXcurXvWq6vXXvva1vPa1r+Wyyy7jXe96F9/5znfmoe6f8Zo7AGtauXIlnU7nN27E/aIINrzmc28D\nrcNO4TxCKVy3A97j0pT0l4/iehnJsjFcL8PlBpfneGPxzuPynFfNbCS/7BJUpFFRhGrWUfU6ItKI\nWg0RxaA0aB12lyjBxzW8EPi4hlNRmPBSBSVF4FSEFxKEwIkwUcrnVhVI48L503kHnc4ishTR64I1\nkGeQB4XDWrx3UJhWItKgI2i2IYpxtSY2ruNVhNE1nNRBofEYCQ4QwiNxhGd9ma/wYcfxyEoRS3Pb\neYlFYb3EOE3mwi5lnGTQ0xYCpHAcuAO/YWnavepVr5qnQA8//DCbN2+u6r/OPPNMzjvvPKampirS\nmhUrVszDTv4m3enHx8c5/fTTWbt27byc2fPaR4rHIsbWDOOdY++TD62OP37nfWz60RZ87rFdiygi\nOQe/fQ1xu453nid/8igTD01iZi21pQmt8SaP3PB9xvZfCc4RtZoAeO+YeWQT049PseXebXQeSjnw\n7fuSDDVorlqGbtSRjTpCSLx3NGTM0Mv3B6lwc7PIerH015sgZTDVlMJPTSAaTej18MtamMYQCBnM\n0l4nTP5uB9eZw0xMIiONrNeR48txo6G2KK8PI7wFIVG9ubDb6JisuYR8pI6yWfieupPV7mbiJtLl\nmKiBNmkwq2yGNBk2aWKiOt1kmDjvgBBkuk5HDZH5mK6tIfC0maWdb0O7jJ5u0FFDOCQdW0cLi0WS\niAwpwm5qvQIHDklddnAomn4GBLgBv1Xgkd4ivEN5gxOqUMxDtntOPFtCdvPmzXzwgx/k6quvZmxs\njG984xvst99+85ifjjvuOM4//3weeughVq9ezde+9jVOPvnk7b43BNDvjqC9nyoLErV78P/9bcYO\n2otobBSxzwGkY7tjojq+AKNLlyPwwSQRim7cRnhHR7bJfYQtkE1KOIxXpCZmMq1jvSDNJVKAdWEh\nN1ZgbIjoCQGRLoMAYeU0VtDLBZd94TxOfedfIkU4DpDEUE88sfZkRpCbEBHMiqqCbhoif1nmcA66\nqUVJgTGOOJZYG4IIQgiEhDxzWOur495DkkjiWCKloFEXSCnQhYsFYKwny2FuziIkqMJ3S1OLMf2f\nTutwH2McxoT7ABjjUCq8p96IiAbOyzJHnluyzCKFwA1MBaUkrXaMkgKlBGlq6fVMETzpm0zee6JI\nYYyjVtdEWhBFITL64TO337R69E/OAmD3iy972mtf/epX+epXv4pSivHxcT72sY8xOTnJOeecU3EU\nbtiwgYsuumhed/rBSt/tkTRNefjhh9lvv/3IsmyH0BwLoki3H3kUS142xsjqcdqnvpl0ZAVeKrrx\nED3doOdqOCTDfhvCO4Zmn0DYHD2zFTE7DUrhWiO4WhMX1cJrsxMA2MZQcMKdRWdzSBNW9+7wCjyi\ncsaNiBDCY33YlO/4/vc4dt2RNPJp6p2tqLyLsDlehte9jvFCYOImc7UxUtnEoKnRpZbPkmQzROk0\nCEFeG8KoYPbF2SzCWZyOMaqGwKFMjzQZJlM1pv0IqU2YyRM6WbhXpBz1yBBLQyvqUhMpsnDyjY9C\nsMVrPILUJvP8kY6JyZ3CucLPQmCLx5FyxCpELeu6R89GWKeYyWKkACk8zofzcyvR0iEEKNn3gSLl\nEMLT0GHnKqUuU4TwaAwWhcKy38v22u458fgH3g7Ayr9bv4OzaefI3XffzXve8x601nzta1/jtNNO\n47Of/ex2FYTCApl2R/zZW3FpCsaQ33Ebdmo25ECAyFgaxpLPpXSnOpg057GNs+SdnNkHu9ju0x3L\nvd+0O6JwLL1zrDr2EIQUiHarmmJJdgd6bAlN74I5Zy0+K6J81rL0pw8xPvMjsLa6rofgD7lgxEil\n0ElMDRBKIXQRB5cCP/A+PXgNV2yN5f/imrVi2xsHhNKIOA4+jxQgB8yc0qws3ytEOCYV+OK4VOFY\nFONVFPy24rHTMVbX8ELipAYP3ivIgx8kcLgkBFicVFgRfCsr+lPDFZaCJShv6SuVQYYywGC9ChZD\n4U/tiCw01u7888/nS1/6Eh/60IdYvnw5F1xwAX/913/NFVdcsV3vXxBF+qfGn9ErTKw3vvpJei4m\ns5pW1CVzEZnV3PfkEI1akfQTntwKtk3LKonqPSwvoFD/20K3JxACkgjuVh5jBbkBrWCfZXNYL8it\nIjWKmrYo4RipzaGFJXMRl//oQprH/wUA1kkcAucEkQpj8B6MC451pBzehzk9k+oQXnZV3ABjBZH2\naOkxTtDthXFDGE8SOepxOFCPLEJ4mlEP7wVCeEaiGWq+g/SWrmzhkOQuoufCbjObJRgbFDHWjpo2\nKGGD4+8l3VxjXNhR2nFGI0rRwmK8wjqF8yHkYJ0idwrjJDVt0IRd0LkQtcucxjpFasM0GY67DOtp\nWm4SJxQzYgQpHArLstkHEd6jezN4FdGrDQNHb/eceLY80nMlaZqy7777Vs9f/epX83d/93fb/f4F\nUaTfv/f9qHqCrNWQ9yzFbHwCOzPLzMMbMWmGzQzNLXN0J7rUR+t458i7OQeP1KtrzD45h1QCoQRJ\nu4aKJM1l7RCFM5aokRA160TNGraXEY8MUdt7NYwsCbtML8WOLMXW2sw1lvG6Yw5idfwIqWyiyclI\nyH3EbF5HCI/3gprOME4jhcO6cgWt4bygZ4KCKeFpRDmxMiQyI7UJXRsx0UlIc0kjDmZbPTJhHRce\nKRyTaZ2ZVGOsQMk2xgYTS4jgo2kFNW2JlCOSFikkmZEB2ZErlAgL03SqmekIZuY8caSIdEyz3sT7\n4CdG2hMrTzPpl4/PpJpZoZECtHQ4gl/ZSgw1bYhk2GE3zrV51A6RmaDEmRGoEIQk0avwCHpGEOFR\nHc9/24E5IRaYWllrzdTUVJWMfeCBB3bs/btiUL9OZBKjDj4c0xhirrmU7sFD5Cqh54Jz55DgJQkC\n42W1Mm7JYlpxjhKWpk6ZyRvkTjExYM8bJ3FekOaKuZ6klwt6WdAdgFbdh4CBEExuDpCfJBY88Ogk\nW8cOqsbonKfT9aSpxVpPraaIoxZJEnY+IfpQoiz3hTMeHPc8syil6MwpwBDHHiFzhBR0Znr00pws\nDdCgWj0uUAk5tXpEoxFjnWNuJiNLc7pzaYWGqDdr6EjRS3PyXo6ONO2RBlGiyFKDVP8/dW8eZdtV\n1/t+ZrO63dSu5vRdzkkvpIEkhKiJEtDLiyKgIjwgD3g040ICMmyGAjaBqwR5mIi+qyI+dCiioAgi\nVwRpk5AQG0JPGhII6U5/qtvNambz/phrrao6DanDPfcd3xyjRu3ae661194155q/+f19v9+fpCoN\n+bhk+cgyUknSTsLUbA9TWaSSFJOyZSw469CRYrg4QgqJqQxxFqOjOvmrJGVeEqdx+35lXhIlEVG8\nwl7wzpN2ApyfZhFCCrzz/PTl69/sn+7Q7jWveU0rI/nFX/xFbr/99jU0pMdrp2Ui3ftf3sQ9B6aR\nJdhc8L/N/Ssb7vwnPvvqtfbDMxdPsf2S7fS2baAa5Tx214PsvTW43CxNaXb/2A42X3IO8eaNyG4P\nt7yE7HaxS0u48YTH7vg6/W2zdLbMkWzfhl1c4J6/vY0D/x6AiWtecwnF4ojv3PIg//rAIzwz3cie\na3ay6cIz0L0uetDHLC4DUC4skcwOkEmM3hhgbDcZU+47gC1KZKRRSYyIIjwVeAkd8MYy3nsQqRU6\nS8h2bcflBdX8Ige//h2iLA48ukE3XOf0RsSec7FZH1kVyPESjIaY7WdSdOewMqa78BAyHyHGQ9xg\nA8XUJkZZoPoIPJ3JEaJ8CVnm2KTDaGobhe4wESE1oKmwaA6X03U42WNzcpDUFERmCScV6WSeKu7i\npGaYzCC8p0SxbALs7L1AS8PYpAzLCO8FWWTYnh1AYRhM9gM/tO4xIU7zRLr66qs588wzuf3223HO\ncf3113PWWWet+/jTgtot/9s/cXDufCa+Q+U1E5NQ2IgDywkHFyR54SkKx9yMYqrruWDzAQAkji7L\nxDZH2zLkYghJRGkDa6CMe1Q6oZJJywZQ3jCiT+FjJiahciv/NONCbP7lf7+Vy556JZmq6OgJSgTK\nS+liLBJFQKsAChtj6qSkcbJ93nuBcZJJtXL+RDucD68137RWnrxSWAejQuKcwPnwvFbQTSz9JISH\nsQyctiZnUzlFaXVN5PUt2gZQWUlhVuD/BuoHqIxoYf1YOaT0qJrmE0mL9bJF9KQIIar1gYuo6vBT\nSUsiSpSweASlizE+5JOUsEg8ldeUVjM2Mc+4cP3w8fzbrgNg5o1/tO5jTmXbt28f73rXu3jzm9/M\nt7/9bX73d3+Xt7zlLcf4/J2onZ7QzlZseewu8B6Zj2AyCmiZqSCOAwrVC8gSixIeXQLvcKMR3tqA\nmDVsAyn59P/+rrUfakpjlgzTF/TwziOkIJ1O6W/pk+QVtnI4Y9GJRsWabKbD4vKICx76W3Svi0xi\nhBTYSYHQKuSBtMJVhvLIIsPHDlGOCg7ec5DR14fHfr5Vj49nZFUBOhJ0ZiM2zkXoVJMOErKZDjrR\n6DRCxREqiRBSMto/T744YXhgRHEoZ/xg3p7LHnXuhr999P29NxvR2ZaQTqdEWUTcjXHGUk2q9nsA\nULFGJ5pk0EVqhYw1tt63lstjVKyRWiGkpBoXeOcoRyXjI2OqwmArhwAGqYJbvvD4g6H5zk7zivSG\nN7yBpz/96UBIzl5++eW86U1v4k//9E/Xdfzp4do94w2oTBH1Fec+6zyKpTHlqOTw/YepxhY7cfS2\nB2Bhetc06SA7hkQqpEBIQb44YebiKZa/M8ZOLL7ymKVwF19YM8iHqKsl4yM5+aGC/EDJ7IVTxL0Y\nqSW33vcwl89NIxdH6DRqYWeTV7jKIJRExRFRv8PcxeeiOhm7nt2FJA03AB2F30rhxyNcnuPGEwBU\nv4ecng2k1STFRSl4hywmcPhAuDkkCcxuDDxAvSJnwDmmpFyB5b1DeI9XGhclmKQX6ElAEfcDo0H0\nKFyM9YrCRi2ooYRHSoMSFV448IJYNKuWx3hN6SWliwJBVVpm1RF6xRFiZ0hqxoJVccjF6Q5eKKzv\n0BGWKXOEpBrihKKMTo6zdrrBhvn5eV7ykpcAwUn3ZS97Gf/wD/+w7uNPy0TaeMk0nbkOKtZM5kfo\nJKwKu36wz+jgMpOFCYsPLTN+MOfwXYuPe77dP7Gd6Z3BmdQUltkzN2Lykt62DYFTV/PTsq3BTNGV\nJa4MfDvviozXAAAgAElEQVRbGWxe8sN5uMvbosQWIYkrlEKnMSrWRP0OKk2JZgbI6RmIYuj2Q1JY\nx9gow8QdnFDtQKtU0hJFHaomjNaJUl+TP88Mn6Gh5QgCH06sCiVDX9k+J7xH+ZWQLzBAQhgGEIuC\nVIZJ7NWKJMDXsZ3wgdKjvEE6i1gV3a8h21rAhnyTaxLTQtV0IE+nDEaSfVacWK0MoIOuaU7rbad7\nIllr2b9/f2sjfejQoZMqkXpaJtL573wzshwjqhI5GQapgamgrL/8JimZ1DF2kYOzuOEQs7iEGU+Y\nHFzAFhXOWPq7Vjy0ZaRJtm1Bxglu19k4HVN2ZhilsyzJLqWPw4D2AodA4klkgf/CpxGXX0psCqSr\nwr7LlIGu5CzChRXBAVWUYaOUSTKgkglWaiofU7qYymsqp/BO4GzI6zR7p+b/0kwm61b2TeHc4XoC\nKhj2P7DCMm+e19K1cPfq15vzAu3+5kRjoXkvWbMWZE00laJOv4oVamvzfTXv0SRhVzMbVjdBWP2O\ndTY/cTvdYMPLXvYynvvc53LVVVchhOCOO+7gV37lV9Z9/GmZSP7Wj6POOhuyLsu7LsLImCN6Mx0x\nYmbpIUyUYXSKlZqsWERVOV5qjE4o4h4HxFaM06SqIBEF81hSOyIrlxCm5GBvK0ZGOK/o2CUqmTCi\nz6iGyw+PM76zN+xkpnqCjQPD3332O4x3vAzvYX7JMzsQdFPH5v4E5yWVDYnOxbFitACVCfC4kpCm\ngrKCycQxGhnSVJGmYdM/KRxxJANMXjoOHRjVfDvH0pEho8URWS/jzB/YwuxswlRPIiXkRYDll4aO\nPHccPDhGa4kxDq0l/X7M7EwI6YzxjCeBUzc9UGgFRxYskRYsLRuyTDGZWDZuiNg86xnlEmNhccmx\nbZMgix1VneDNS4kQkJegJFQGulnIZfVTy/xIc2QJHn4kRwjF/OExcaqZnc1IasvhNJVsmoXLT4b+\nfZpXpOc973lccMEF3HnnnSileMUrXsG555677uNPy0Ta/5xfILPLKGcwKiayBZvzB7Eqpkym0GZC\nb/ER1HgJ05tBVjkuShlnswzVNCkFsVpi4joUPiETY6yMOJCdwch0iH0FFqY5jHSWLV/6Bw7f8UW2\nbZqlGo54wlSPq3tdoj1n4pliFJ9J/qO7eNqGL5AOD6In+6CKsfEUY7GDMsqQzmI6MWm2zHy6FYck\ndwmxqNq7s/WKwsWt+K0nhxwoNzAxEaVRDNKcVFn6KrCwlS2ZxFNUyrM3dzg/oXKKolIcWg6TRGtJ\nrCW7tofQVdesDSVrDpyASIMUkqXxCmK3Z4dEK0+iJeAZFRHjXLD/CJRVOG4wFV6rrKCoAvtiNAkK\n4EhDGnuco5bvC/JS4jxM9z3JmRlagT8rpZc5IuXRymNdIL8aC6xaIR+vne7QDqDb7fKyl72MT3zi\nE/zLv/wLW7duPca3/UTttMDf/xSduK5pPBsxfU6Pwc7pFnHb8pTzkWkKUiDjuEXu5M66DpL3uLSL\nj2JsnIVQzJpaGyQQ1iCrAmFKxKSuOauDypXJCD8ecdvd3+FHLjovqFrTLGiMkgxZFoiqCPqgKKbq\nzzHpbcaoGCdkqzNqZPIhTLIoF+QEjZBOEKg02pVkxSLSGYzOcDJs4NPJPKqaIE0ZCK5xvfeSEV4o\nKh32W41MwQpNJRIcQXOkqciqcHMSOPKoRyUTJr7TcuUUtn0ciK+qVc4K4YllhfWBJ9eADasJsT05\nZJAfICrDd+hUFHRVQlCpFCMjrIxwIpzz7LNW6lQ9Xhv/2Q0AdF7+lnUfcyrbb/7mbwLw0pe+lJe+\n9KVcddVVjEYj/uAP/mBdx5+WFekZH6590xqtgI7C0i5VIKt5t4JcORcGsTWrkKsgdGPhMLig65E2\n5JqUFPjNO/FSYTtTYRMuFS7OcDLCbdDt4G9AAScVH//CH/OEi58ZyJ2NCK/eyDd6GyCoXAkb/laH\ng0dXVSuOA1oCqGsVtgonJKVKqbIEWLX38WHgK1eteb65BqAV6TVkUuUN2oU9ZfMepc5ahW74LYgp\n1wAXDbCgvKHW/QZwwzmECyCD8L4GLyTKVShToExRH2/b8yiTEwkJ3pE2al3vw3ctFLD+iXS6Q7uv\nf/3rfPCDH+Td7343P/3TP80v/dIv8bM/+7PrPv60TKSD5/1oO0gLEWDu5s5qallDJCo6bjkkXmsU\nKckX0cUQ4QxearxUeB1joqxVnDZ3xmYCaFdiZNzumQAEDovGeoXxgTt38RVXsyA3UHlNJnJmyv0M\n7vsCbv4wstOlPPtiinTAcjKHIcIh24SkEOG6LQLrQoLS2kCSVcK1BNjcqJbQKlkJhbRckXVXNmB2\nqxOoEO4dTX+AYS5bqUSkA4fOuUCKLc0KyLDag8Q50RJr48jX7+GZFCFUtPXxzftKAVLWQIRcue81\n1+B9eL25jtKEfZXIIdaeF53EmBDq9Iq1vfdIKbn99tt59atfDcBkMln38afl6r/z0y9msrdsk6Yb\nz98IpaWcVDz8yb1r+vbP6zDY0SdfLDj0HwsnOGNoO56xGe88pjAIGZxtdKKZ2jZgZvcWOhc8EbIQ\nAgprcZ0+Tse4KOW7w+9wxpEOKh8FhM4Z/NQMUinc4YPIOz9NRym6SdzSvF2eh+SwlMhOF7IMOv1W\ngi6cbeHxIpvBxjHSGYR3rSeEq+FkZcswMiOBlREidkhv0bUuSjiDGo/wQiBNienNBNk6QStlVUwV\ndcPrzoSQ1lmcDCu7kwrZII+1L0Xj56BcRWQLhPfE5RAvBMrkAeo2BarKwYNNMrxUCO+DEFNIpK1I\nFveDCyRcLyKErXAqA37uuP+n47XTvUfatWsXr3rVq3jkkUe4/PLL+aVf+iXOP3/9aMlpmUhPeuXV\nfPEPP41ZMlz6K8+HDZvBe6rZrfT+73M4bOYobIRxEikd3xkFhrUUnkFaEqtG5CYpjGZcKpbGisNA\nL3N0YsMgyVtRXFLNk8w/iC8miKrAdqcps2msilGuwgvJ5756P5df+aOkQhAdeGhF5wPIXh8ZJxDH\nlHsu4NDgTHKfEYuiXo0iSh/TFUO6ZZjsk6jP3moLYxOT6QopHINoiMaw5cg3iOb3Q5mHSZl2wupa\nT0Cb9sME8C78QBjAtkIsL1B861vYokQlMcm551FtOxMhFJ2lx9CjBcjHUIdcbv9eyoOHAwN+0Edv\n3IDo9jGbdzHpb0Z4h5MRlU4YqmnG0W60sEgcUz5wErUtmUT9EDEQtSt64zBUdJ5MbmOcpzVOUcKz\nvtJsdfseE8l7zxvf+EbOOeccXvGKVxzz+u/8zu/w8Y9/vK1/tWfPHt75zneezLvztre9jU9+8pNc\neumlRFHEZZddxnOf+9x1H39aJpJQiu2XbeVR9vLtv/0XNj/5LHSWIKKIwcIHmeuGcE9EESrLsJf9\nCHk2x0Kyia5dJDIFcbGEiTKG3TmOZHMcjnsYJxmVivlRxKHliE1TKdPJBBspFjZtpHJBdDYxCRMT\n4UzwwZMCZi+ecGd+KWlsiXdbtHCtfMJ4WXvmSZRwVENFaevnHDVq59FqjlTvQAhPUu8lgodeinGC\nx2yfSDm+4nbiAwhHFjviWhohRMj/aOnIVIWWhkzlmBrEGE9lOMCfJ4hVWAFyGzMsE0ormSAZKYlJ\naRnv1WZPdkYtIhxYYm3R0jMsNPmixLmAzqXa1voq1XoCKrkVY1d0VlqFH2NDCAchrEwiHyaRCyFg\nrEJ+6qTaCSbSAw88wFve8ha+8pWvcM455xy3z5e+9CVuvvnmdatZj9c6nQ579uzh85//PD/zMz/D\n+eefT5Zlj39g3U7LRHI//Ey2XnE126wJd9DhEpgKP5mgpwchLJGyvav6Oz+N2HuQ6mvf5f77jmAn\nDhHVhoRacNYzzmGTEIFhnUZ0d+9EpAnsPheXdjFJH7c6Bq9tsAQ+IHymxJZf5kfzhRU1aq1E9UKE\nDbpdNaLqf7pfpWT1MhiFeBusvLxQgekgNSQhhGtAAS+ODws3wELTN1zfCtNgUI/eFfaBR1EhtUEo\nh1IlZLSGKat/r77O1tYrW7H1aluv/oxTYg3w0mycPMdef2MxtvbaHHDl+gfFCSbS+973Pn7mZ36G\nbdu2Hff1siz55je/yZ/92Z/x5je/mTPOOIM3vvGNJ+x/ovahD32I97znPRRFwY//+I9z3XXX8Qu/\n8As8//nPX9fxp3QiPfDAA9x1110873nP4/rrr+fee+/lrW9965qS9ACff+r139f5VSbZcsUGkn7K\nzJmb6e3ZgYg03lrceMLo0QN897b7GP/DV9l2yTam93yHZHaAG08wowlTFz0RbypEp4tbmMc7F2D0\nXp/b7/g3rt41F+Dv5UXc0iLD+x8EIJrqkWzbAtYiu0GKQNqBrIsZbAioXNylijvBpy5fIp5/hMRU\n2JlN2KTLuLupHpgSLyRGxkGuLTzKGUqRoqmIXEFWLhFVk7AvKsdIW4G1uCRD2IqqM4twhjKdQlcr\nG2KjUyqdoWpmRqVSKpUwZKrNbUkcDknqxwxGe1Emx0QdrE5YSDYjcBgiUj8mMeMWrTQqRnjfQv5O\nKJQ3JNUYbcI1CO9QtkQ4u2Jkud4mj+9r18DSd95553EP279/P1dccQW/+Iu/yJ49e3jPe97Ddddd\nx4c//OGTckx973vfywc+8AGuvfZa5ubm+NCHPsQrX/nK0zORbrjhBp7//Ofz2c9+lvn5eW688UZu\nvvlmPvCBDzz+wetoduJ49LMH6r8eQmVfpLcnY/ulO9h4+RPpn7mDHzhrFyLSqF4fO1zGW0s6O4Pc\nsIlq0y5slCFrmDlaOhTySt5x1eVPxmwKsDmzW5GmoHdObSclBHIyxGsN1mJ70whTYZMOJglOnroc\nkSzsxcUJwnvs1FxgtguJsAZlSxSBq5bkC+h8CTUMPEIfp1T9WcpkCicjJvEUw3QO7UqUMyTVECtj\ntM1ruD5qV8oy7hGZScjf1Hy4UTzNyPcoXURGzqbiIZJiKZi5qGCe4qUOkzGZIi6HRMUy2w9+O0yW\nOMFFKXl3Q2Doe4u2BZM4xKORLRgc/BaynAT6VlW2hF3609jO1Mn/c+sV6Whfu8drO3fuXMPQfsUr\nXsEf/dEf8cgjj7Bz5851n0dKucaVdevWrY9bvHt1O6UTqSgKnv3sZ/Nbv/VbXHPNNTz1qU89bkXs\nPc/awcLDS6hItvKBzlyf7vaNSK1wpWG07zCTI0Ocsex85hXIwTRM1aIypfBRilcKF3eo4i7GWZzS\ngR/nLeTLqPkDIUwZLcNoOTgLFYER7qKEcrAZNxvhVMT4oGBpZjeTqId2FbGZ0Ft+rB14443nYKWm\nVCFubkiiQgTyZq+aYLMeTsdMOhsodRZ4eI1hSE1Y7bolrNSodBrdz4mKYQsoKFuiTUE23I8qxsjl\neRgt48Yj3GjcooRqeoDsD/DTs/ikg1MRJulRJT2E9XS8pesXarekmFEywzCdC8CCUFTEVD7C1OmA\nXMd4LxincQtlx9ria2he1q5DA7lI5ApKnfHItqeGPZpP632kqkEGh/USLexJuNoRzDe/j3bPPfdw\nzz33rAEGgkVY9D2OOrZNT09z9913t6vYP/7jP7bgxXraKZ1IZVly6NAhPve5z/Enf/InHDp0iKIo\njun36Vd+iqIIrAUhYNMsdBJPpBxaht+z6TC40jjFtyZZG8bPZGFvE0lLR0+IREXqxwjvKGWKqhU6\nZhCRbwrGiM5LlqouxsnWZio3muVcUVSSSHv+6c6PMfeUa7Gl5PA4wTlw/mJ6yhIrh8uhNIpxKet8\nTcjpdJMg3OvET2DkFKYS+IWw6U60I9W2lcFXViLlHKVRjMqQUyqrIOozNmzkIeRgosjDLIg5WvIq\nhFxPoj2xtigRqD6pDgJAKRzDKiUvVZsz6sRVm+DV0jETLxGLgr6ZDy6qQlNFCb1qnqnhg8jJMj5K\nKaY2hdXZEtIBZdCRSWfAWap0ijLu0a2Z5xBchpaZZmgy5ifZyU0k+f3B31JK3vrWt3LppZeyc+dO\n/vqv/5rzzjtvjRf4etqb3vQmXv/61/PQQw9x5ZVXkiQJf/RH6xcZntKJ9IIXvICrr76aa665hrPP\nPpunPe1pXHfddcf0e/GXXs14/zzl8pjlfUssPrLM+KEcO3E4oABWZ5NE/dM7OyPb0kUnimymQ3fT\nAJ3GDC6p3VpFrdup4XSv9crz3uGjmk3e0rBdGCTGMXu24fLH/i4wJVZbX01CGIXU9W+F18GK2Mca\nXyd/rUpxXdXaEx8NLEhnkTVsL5VFRIERoWrfPeHMGtYA1LKHVUBBqDETwkPvJQ0BQjQardX+2/X1\neqdWmA5OQiFaI05tS7wQpCJQfobTO/Ezx7r5NIyNBnzwQrQMiub9ms/bYUSaTJiLNbC+Il3ASTEb\nvva1r7XmkOeeey6//uu/zmte8xqstWzZsoWbb755/e9btzzP+chHPsKDDz6ItZY9e/ac1Kp2yrl2\nTX0fCGKp1dayTRvf9nftYISQUHRSY3VCFXUwKibXXUqf1F5pug2jmmadCrC0l1z4qf+GWRpSjSc8\nfMe36G7o8uAnHj6uB96J2lfdmIvk92egvt6WbI6J+op0OiHpx23CWEjRKlNVXP/UKl2Vxu1jGUXB\nOCbLEEntHe4cvjJ4U+GKkuLIImZSUC6PccZRTUqccS1vUWqJihRRJ0FnCVIrktmwpxk9doilRw5T\njkoWH15meP/6M/vxbEQ8q+lv65FNZ8TdmLP+8p/Wffzks+8DILv6xev/Qk9hu+aaa/jnf/7n7/v4\nU7oirbdYU3XnrSFH1O2gZucY330PC/c/yj1/ez++OnZe6ylNPKsZ7OrTme20g663bQ6dpaitm4jO\nOY80TnjCjz8TgN3/5zIszuPKAqE0bjJGDWpXm043wNxJGhgKnSnu+LMPcPXLX4BLMmyU4aVGF8tI\nUwZje2fBOVzSwcUpJumhqklQqqqEIum3TARpQ5J3ks0yjtduvIX3FKQk5FTUhbwQjF3KxCSUVuM9\ndKOSSFUM5CKdaikwxaMOHsGymmkFgACFTyhdRCQMuU2QwmG8JFEVmcwpXUzpInIbU1nZUoA6OqyG\nSloqFzzvlGyY65ZuXQ5GCkcsK1rpoRdkKifzIyJXkJRDkkntdBt1qKLspBWy/vsM7U5VO++88/jo\nRz/KpZdeusZ8f722x6d0Rbr22muPW6zpta997Zp+Dft77pIBT/zvb0Y4i4m7VHGXJF8gWjzA/Ec/\nxtf+8quYJUPv7Ix0OmH7ZWew4Zofw6cZ1fQW9GgB05miyGYYxwOUq0iqEUmxRLR0CB57EJwPrkJ5\nTnFkkWJhSDzoEnUyku1bEHGMrypuueubXHn2TmQco2ZnIU7w/QEu6zMa7OBgvB2HZFhlgaO3yvRE\n4ImVQULrYed84M71EkOig6lIqkqE8EQiGDoqLJqKie9QuJilsoP3UFhVy8J9MKv0gSfXeNFp4bB+\nxQxlXAY/vCa5HOuQ5DVWIKWnF1ckKtiYlU4TySDlUMIQ25xlMU3pIyYmITcRjlAl0TlBGlmmkwk9\nPSKRAdIuXMqS6TKqEiQeW+u1CiMpV3EEf+6K9Zs+jm/7OwA6V62fVnQq24UXXngMMCaE4O67717X\n8ad0Iq13eVz64icQ1iCcRU2WEVVIw/vGhhdCaRSpVux4AblwEPPdBykOHeGxf/sWD39mL77ybP6h\nWZa+O6I8UmEnjk1PnaFYLtn51F2oOCLuZ6SbNxDv3BmqS5i6VEqR48Yjlr7xLT7yyS9zzoJk8uix\n4Mj3av3zOnTmUmb3zNHbOovuZsQzg1CBotsN7xcn+CTD62DW4lQUEqP1auakaqHro+sKOamDlIKV\nukurm1Fxm5Oy6Jb029oMe4n1KiiCa4dVSVDaNl4OKwrclRvDaiUsQGWj+nx10bLaOajxgmhakJGE\na7zk3LV1Xr9XG98erIE7P7x+xvV/pnZKQ7v1Fmv69tQlKOFIREHuUjbZRwEYR1PkPiMTYzYe/Cb6\n0QdAKUZnX4ZRCdXG8+jsugBd5ez+CcP2G7tIWxEvH0KYAoTEZn28jsmzGbxQaBvuosvJgJEaMHZZ\nUAit9ki4yvGN4h38xOv+K5VM0K7ECYV2JRMVKmB4BJEId6zET1qy50hOYbxm6AUHbIJxmmEZMSlV\n7UDq6CeBwd7YA0fKUhjdsr2Fh6qUpJFFSUeqDKkuiYQhkQWlj9uqG7aGmSGsTMJ5hAvIZG6DW6rz\nAaFr3GGVsGhpiURF38yjbUk6OYxv2BOmDHZmZR6KECR9yrgXvgMbZBi0AEjQRhkZU4isZr0rJEGm\nPjIdxiZluYhPauyc7tDOOcd73vMebr31Vowx/PAP/zCvfvWr0Xp9U+SUTqT1Fmt69AlrqSMPP855\nt1+9iaSfMti1AbFhGpEliCSmMzsHUwPy7ecH9ayeZmi7WB/8sRv5QqINXQqckQz0ErHP6Rbzawib\nVz3lYiJbhIp3tRDPI4lNTqWTQNlpChC7YDyiXUnPHm7zU42vg0l62E7aOu54JJVOAnLnLVE1RuAC\nK6H2g/A6phJdjMqwUqOroq3q52QtGKz3XsK7IB0REYXqYGSE9A4bKQxRkIbU+6dGvSvwpH5MWctM\nRsk0wju0a2QqvqX3KFehTd6yGoSzbYE0oxSRKVDSoGQdarqKyISby0Bn2EgTiwlw6brHzumeSDfd\ndBP33HMPL33pS3HO8YEPfIC3v/3t/Nqv/dq6jj+lE+n7Kdb05NdegneOg3c/1rIWNlw2za4fOgtn\nLPn8kA0XnY2rKsxogqsqdK+zwndbnCcV9xEP5phSEV7pMKC9x0UJTiWoSY5cytuwUawSCPooxgvJ\nYN+9TH/b4A7sDRD2ZIKenQuUoigO1CGl8N2plbKY3iOGC20OxE5vwEuNdBVqkmPiLmWSUapA26l0\n0g7OuBxSplMsZptbn/GmfEtixoyiAQ5F6eOwcnlNLCoyEag7Y90n9xmFi+mKMRJH5SMWyn4ADmry\na6Iq0rpS/BEzYFgmraFkJwqlWVJVgoDKRcHoUbl25Wvqxk5MQul0MMqXDu+C5gpC0YFhGbVaq1g5\nYmVPyvzkdE+k2267jb//+79vIe+nPe1pPPvZz1738ad0Ir32ta9lNBrxjW98A2MMF1100XGL4V5w\n9z+SlcuUOmWiO/TzI5w1OsC53/wPyn0H0P0eemYGZjeAkJRz2xAqQQsRdDu2CvSbcoKAoM2RClnl\nyGIc9DBpFz0Mj8XSPFQVfm4T7oF7GT+8F2ct2aa5QEytKj7ziTv4oY5HDaYRUwPkBhVyUos5djQP\nziO7XYS1AUiREpKMavMZLUlVmgKvdMjzKIVwlv5wH8JZrE4D88IaoskC6sF7MfPzZA88RC+N6Wzb\nFKyQkzQUoe5MtQTZoPLVgeA7GWH3PspMmoZSMGlnRVEcxSuUnayL7/QppzaGpLSMUOUI4V2wO7YV\nSI3pDqAu+SLwQVpiy8DbizqBeoTE66DuNT5qXYWakE4Ji+5USBwFae3QJGkL3q6jne6JdDQbIo7j\nk8ojndKJ9NWvfpXrrruODRs2tD5h73rXu46htzuhGCXT7UZ2MdnIfLIZd+XFre1Te4HCkLkhsZmQ\nTY6gh/MIUyDKAsqCUH/1WA811Qj34hTXmwXAxl3U1EbSiyrUeAkxWoKyxI+W+dEnnYfQEXZxAT8f\noFyhVPCJiCJEVg9cIaDbb/VD0pTBoFFLnE7wUuOUbpOgVoUVry2KjEB15hCzZ6JtSfwjZWBleI+x\ngWwqbF2oucoD9O4dSI2LM3zWw2/cgREyrLxiJTHaMr1XJXKlM7XYr6JKp3BSUw12UqkQrlYirIYO\nSeV1W2XjaDBC4lHOtmGiFI6IqpWqN5L7jNGq/8L62QVenN6JdP7553PjjTdy7bXXAoF1fjIuQqcU\ntXvxi1/M6173upbt/YUvfIHf+73f42//dm05w9XmJ3OXDLClZfhQ3jqkHq/1z+uw5cLNTO3YgM4S\nbFFSLo+pxgVSK6bP2YnqZETbd6w90FSUjz3Gdz/1JbzzVJOK6Z3TJIMu/TO2Es1OI7ds55Z7HuRH\nz9+N2/sIy/d9h/HBRfZ/Y99xDSqz7Qlz508zd/bmlkHuKoMtw/WHhGpEOtMn3boZtX0H1aZdgQQq\nggI2KZbaO7/RGXnUpZCdVnKfuhHaVShXkU7mW6WqjTPyzlwYuM4inWXUmcMJRS67a7ztCp9waDLF\noVGCENBLDFqGyWGdRApPbhRaOoyT9OKw50l1SVfntfRfYZwmrvdDS1U3QN1WtVUFM10wLeaJXEFc\nM8GdUGz+gfXvkRa+/DkApp/0tHUfcyrbcDjkt37rt7jttttwznHllVfy67/+66cnj/Sc5zynrenZ\ntJ/6qZ/iox/96Jrn9t/9xVZv0zSPoFIBDfJCUPlArKxc8FZo7oKrYdlm9UpU2T52iLaolmmqbtcM\nCCkCkmWPWvU8gj//w7fysusCKBKMEtdCuYHX5tfckZWojUPqv5sNPhCMRVY5pTYiwaa52p27IbMG\naHrFiNHWE2r1xFj9fcl6tWiMLo/uJ1f1XVlVXFscQLRU2pVrXu3WuppFAivurC1vr2amC++QzrR7\nv+b7BNhwwfoLjR356m0AzF501bqP+c/UTmloJ6Xk0UcfbQGHRx555LhU9P+4aMUWY+6SAXNnbwhF\nw6Tg25/6LsX+k7O7/V5t7pIBvbkO2UyHDU84A2cDQ2FyeCmUY9GK/hlb+Tn2cfkX34GME7x3iCTF\nLczjqopi30F0N0PGoWyLTOLg0RDHLY8PwA2XcaMxSIHeuh0/mMP0ZnBS41RUh2Khv/RBsxOEhRWj\nuTOYRH3Gsk8kSoT3VMQoDKkdkZbLWBUHmy0R41AUPqHymmGV1mVWXKs7atC6jiooXYj1SxuY6JG0\nRLJC1itOYaO6eJkjUzkTmxLL4NfXFSv+6aVI2/qwQjicUkxkD0swkWnAktSMiMzJ5eNO9x6pMTw5\nupGZl/YAACAASURBVL3rXe867vNHt1M6ka6//npe8IIX8IM/GO5Et99+OzfccMMx/Z7xV6/Al+Xa\n+qpKIdOUbc9NIYpxvWlMbwarU4xO8bVtFgQCqLY50hqkq/iJG06cs8imesxsmWPT9hm27+gxPaXo\ndz1p7BmkVRh80vHtf7+VwVN+BOdX2NaTKpRPKW1w5bEuuPs0xoxNpXQpgolicNVZMW70HlLhyLRl\nc2eBjeYxIjMJe58aKhd1cri37176TU3bJMN0pzFJj8XuVoZqmqV0tk2yljaisMHo3npBLENVvY6a\nsDl/kHRpH3zti+R793PwGw+xdN+hE9bfPZk2d8mALRduJRl0iQc9cI60rvdbLo+pJhXGWJadZ3Ro\nxKbP3r7uc7vTvEd65jOf2T6uqorPfOYznHfeif0Xj26nNLQ7dOgQS0tL3HnnnXjvueKKK45brCn/\nH39Mcfc3MZOC5L88C710GPIxfmkR0etB2qHcuDMUEZaKzr778UpRDTax1N+OkTGpGdFd3htyHCqi\nTKewMqbUKUYGv7qSBI9AY+oiwar1XVjNVYtExX///Zt4/etfjyH0a0KcJpwE2ox+E16GJGgSqgpa\n3YaMWjq0CD4MpdNUNkyAXlwQyQpb13I9MO6yMAoDaNCx7B4cJhNjtF+hqiRmTKUSxrLP2GZtjmxU\nRUhCCcumNGVpNblRSAGdqKIXjenKMYkbt5Xcl+xUeP96AsYyyCyasDKVYW8U+SBX0a4kMgWidjpa\nvXI0ezQvRNjrqYRRNKDwKYWLT4rZcOCb/wHApidctu5j/lc2YwzXXnst73//+9fV/5SuSNdeey0f\n//jHOfPMM79nP3dgH3p6gOpb3B2fYVJXf8B5vHd19Yh/QyqF1IoyiZGRRmcZs6tPtCpszIzBFSXJ\neEI0M8AOR4gkxhclrizxzhPNTuMrA1IghAy/pQSl+MlBzrYvfjiEa1EdsskaAZMKHwVTxzUhiHdr\nZQ51C6ibC9SnZsVt+q/pWB8rJZQxfkm1/cTq45o+7Xl8TXNyAaI/+l7oXaA3rL5GWSN5QoTvTUdt\n/ssrFShZKloDt1Oba3oVtapcT0AenYywdda/RSSFRGHpiBGZGgPrn0juNId2RzfnHAcOHHj8jnU7\n5QnZu+66iyc96UmtlOJ4rbzkapTJEbZCmxJtqjV7DaTCpl2cioN5/io4GYJhx2pDj0k81WbntS0Z\n6gTlDJVK2v7CewrdWWPO0Wbu8SzLf2X+osvW8NkEvg05Gq+FFfdUgfMqrF6I2mxStSDIMeVb6sfN\nqnV0vyakbCDn5v2F8K2jUVMVrwUMVgEY7XlWcew8oq0q2ISBTd/GFSm8t2jfv5l/qq56sdr2YDUv\nTxKkWwBytQllfbxcqVizrna6Q7uj90j33Xcfl19++bqPP+XmJy960YvQWhPHMd57hBDcdddda/rd\n/pT/elLnFZEg3RQTDzQyCl94ZzblrJ+8HO89cV1RT2/fAXEKzuIX5hH9KdAan3URhw/g8wnl/oN4\na4lmp1HTM6A0Ph9z14f+hZ+QB0LBL6VB67Z4mB8uhRUsSbFbdlJ25zA6RXqLqSdrI8aLy7A5F85g\nddI6wGqTo8sRsspRo6XgaTcagrVhFc7zsHIWJSKJQ0I6SaHTX1l5qxKUxnX6lJ2QhG4AjNXOPrqa\nUMXdYMksNZGZ4IVknEyzLKbJXeAEGi/JdEFHTtCiatFF6zVKBChf+hDaWjSNf7nwDiMiSh9C56YW\nU6gzm9TooOFkppI7zsretM997nPcdNNNlGXJeeedx4033nhMon89fb5XW71HEkLwwhe+kCuvXL8L\n0indIz366KPHff5o2lD+978H/WlwFjs1GwaWNWFQR0lwy3EWWytax/0ttZn8il1VLrtMXIYl+AP0\nWQz+1zXdRuKYckeIbEFUjTE6YzHZiCFA4gCFi9t9z13/ditPveIHiWtiqkcwseH9jZfoGgZvyKGV\nC3Lupu6RVh4pPKru19RoNU62VBug7X80XN1I0RsnsFhbvBeURpKbAP9bL3AuWBI3NsJK+hDpWVH/\nBMl6s8JotfLvLaoViUPjS6dV8KXrxKa9PlODK6qNPD1lLZHIy3ANzXm0CvJ3JcOK1UvrdIH0/NSl\n679PP/StIFfYdc4PrHn+yJEj/ORP/iR/8zd/w+7du3nHO97BaDTizW9+80n1OVE7Wjt3dFtvHumU\nrkgnuqijJ1JT81VEgqv/x69h+rN11Yja5UYqymyaPO6T62B/pZxhMNpLMjyEXDxM9cC3ag88idi6\ng3xuF0YnAeFDkOULqGrc7h+0GpONDlAlfaxOWnOSJp+1ye7lnEO347/yr4y+8wi6m5FsmEV2MnxR\nYpaHeGvDXm1uFpl1oNuDOJiwiMkIf+RQa1Aiu91ws4AQAy0v4U2FryrMwiJmPME7TzJX91GKpXsf\nxEwKqnFBf+dGkul+gNsjHVYl55G1MlZu3NzKS9yhA+Ac+WP7yA8vYSYF/Z2biWYG6G3boDsVjGLS\nLjbth/I5ca8Nj9PJPKqcoIpxYIJEaahoXodbylWgPdmhBxEH9+ImE0QcI5I0hORFjrcWO1zGjSd4\na1GdDC79v9Y9dk4U2n3+85/nwgsvZPfu3QC88IUv5DnPeQ433HBDq3lbT58TtSuuuOK4fZpoar16\npFM6kV73ute1j6uq4uDBg1xwwQV88IMfXNNvy5UbiLsxKpJ894/fS2/zAJ0lpBumUWlwXO1MTdGN\nY5iaxmU9fBQHCo4QuP4M+oInBX0PsG/bk4EQHjSJw6qbtKUdm41soYK8o9nfNPsJh+RTd93HuVc+\nm+LpV2FcQOAadKuy9T7Jr11FGiFd8zc7wt1b15sG71eq2jVIn6zdVBu3ngYmt17CD4XHlZWU9epk\n3Ar0vgLBhwJgUoIx4DbXq09tkdCcs2mqxhgoQFX1almvZsaK9txCQOIc2kCMqyucQ6JN0BxtuQS1\nta7KIVboQ03Su5GJOCcYlZr1G/6uTKSjfe327du3xshky5YtDIdDRqNRG7qtp8+J2m/8xm/w4he/\nmIcffvik7LuObqd0In3mM59Z8/eXv/zlYyYRwNT/89c0pUYSM8YLybLssohEiwrtKioRU/qEWBRU\nBLuoiUtJZci1hDL0MbEq6TEkdjmRt8gaKBjpQbCcEprKayYmYXkUh/dUtnYsMngfEpTnX/JjHC6m\nqJxqS0vmRpNqg1Ah7Gk27I3WJxae0gSAITeKXlwRKddOlFEVtWGSVsEW2HpBFhmUDAN1XMVo6YLr\nTz05e0lJpquW2dCI6VbnjbR0a5gPzYCGoJrNbYDdjZPt5+3ospWSAyhhSWTZMkcWyn4LpXf1GIds\nayhVTrWgR/O+jT+7dRLV2CxHwc1oYwdg47rHTnNTO9rXzrnj575Wg1nr6XOi9t73vpcXvehF/PzP\n/zwf/vCH1329R7f/pZbFT3rSk3jLW44tHLXw0hew/MiYatmekF/XOztj+owpRDcJVcaBRAl0GqHi\nCJ0loUByEpPu3gVRsgLx9qaYURE2q8MXWaNsMgoWwDbw1FQ+CsXHxsscefArPEV/Cbu4QHHwCMXi\niMWHDrH4yCLFUsnou/lx/SRUJtFTmi27u/S3ThF3Y7K5KVQaE/U6RIMpRKQDczxJA4Ch9Voft4Zk\nusomuYXGj2dxLGQLU1Ojie156tY+J1WArK3Am9pZaNXGfrWl8bZVpFdnVowo274u0IpWNy8kKMLz\nDoSxq149mYl0/NBu69atfOUrX2n/3r9/P4PBYI14dD19TtT27NnDxRdfjLV2Dbn6REDZidopnUjf\n+MY31lzI17/+dfL8WOva5M//EV0nAJuEpxZ2Dc+rgVkLrygIoZGirjXkV2ygBJ4jea8Nu5yHVFuM\nC30yXbV39V6UhzDE1VB6j/pOD3/9Tw/Tef7rmYpGRLKqvd/GDLxD2xJtJgEytxWihtObHE2RDiij\nbmvnO6kre682h2zl3KyyzKph8+ZzrG4tn7D5WcWpcy1sLVtI3Xq1BnpWwpKoimm1QGwndIqFtkSM\nrHJkWYRSLI1LqjH45VClXHS6kCRh0jfaLWdDP2gnMkLg+rOY7iDklaIUK2OcVBS6w4aTGDvNinR0\nu/LKK3n729/Ogw8+yO7du3n/+9/PM57xjJPuc6L2h3/4h+zbt49XvepVvPvd7z6JK17bTilq9/Sn\nP33lxEIwOzvLL//yLx9jQfvofV9jonp4JFJYNh25j0lnA0vJHAtmmkSVaGFIyMl9RiLzFl4e+R6V\nC/N/Z3U/EBxK4/E8vnZaFc4iDz5K9fDDmOGIcmGZeLpPNNVDxDFqdi4MkqwLSuGilFu//E2uvPwS\nXJRQJf2ADMZ9CpkhcZQk5C7hcB5qiko8iTaUdQg1KjXGibaGa3MjT7QjUo5EWWIVQrtRGTMsVP16\n2FMtTlbuaWm0Qs7VytNPKmJlKIzGeslSHhFr1+5TjJPklQzhaBX2Vm0+1osWVcuScMxqJG9UKJT0\nRCrUkl0eSyoDdVFB0tjRSYJcvrKq3rdJUl0z3aVrP8/iSLI0DChiNxO8egVRftz2zfsfA+AJZx9r\nfn/LLbdw0003UVUVu3bt4u1vfzsPP/xw6213oj7rRdwARqMR3cbX/ftop6WGbP6BGs1JUvzUTNAW\nLS8yuvtelh/aT74YEK3uxj7dbRvonLkbsWETZnoTeW9jKGUpFNoWROUIWdaG86MlmIwgThjteAKl\n7mClZmq4l/jheyFJMQ/cR7WwDEC8YRaRJsjN27j16/dz1aUXhqJdSQenkzYhK12Fq/NFVdyh1Flb\nHrJQHQwRShhmRo8hTZjUcvEw/vABvHPI/gC79Qzy/iasjFGunuy1/qgpEmZX5XwqnSG9JS6HLQ0n\nJLHr/ZpOkM6glw4ixsMgMpzehI06CGeQpsDGXYadDSzJWbQwaFb2XY7AQlCuQnlDrruMbI+RSelF\nEwb+CLNH7g83pmIcBktVhlCxtoyWS4fD/7EowgpVFmBMa4GWXfumdY+Jr9+/D4ALzj45h9T/LO2U\nhnaj0YibbrqJBx54gN///d/n5ptv5ld/9VePmel+0/aWjmKjFBt3cdsU/gd+iEzFJEJR6oxSxOQo\n9nu9hj3QbryVwKWSMgocOtcNdYo6UaAcyTpuV90d6CcEZE+fbVujjiZw8gj+8WM3s/unrwqs6Xof\nYOrymKuZCcar1pXHOElZBuZAaSTWB9jMJwKzQWBmQn4HwFYCjoTPH+QYNVtnVR0hrVZCvyaHE4p3\nrYStUJNq662l6HnU1ApaGCmHVJ6ozucIs/Y+2XyWxr7ZIyhrLmCD3B2edNByBpmeFa6nt4IEtnko\n4WDVDd81TI0adXQInrXOcRM+2/qtu/4ztlM6kX77t3+bTZs2cfjwYZIkYTgc8pu/+ZvcdNNNa/qJ\n8TJiPIKsAzObaol2yB9VSZ8i7rPfbAkImyw588i/hjKOURqy9SqiKe3Y7lWg3sOUUAROXLOKUJea\nNDocL51Fuoq4WEJWBV4qrnniFjYtfzsUUa7NTsbxACOiViYQdEqOSIwD0iVWat6WLmplCrBC7zE1\nabVyKhQjk65FxnKrGZfh9SwKRcASZYmloavHJCJvy7A0RFDrFUvlimxiOhmSyHDjWK56LFcJk0qz\nXATfvSyydKISLRyl0+QmvJ+SIYEshSOtE8mRCmb5Uji0sFS1nquBt3X9+ZW0WKcYm5iqVtQq4RG1\nQDDCtUTd9TZ7gj3S/1/aKZ1Id999N29729u45ZZbyLKM3/3d3+VZzzr2vvTFPddia55ZZVV7x7VO\nBBP6cqUSnsDzXX1Na5gIIP0KJQcB/TQ/hnO22uJYEFgGTbl70ewRkpVrerRj+HbnwrUXatf+2YAg\njYXyar6cPUq817wmhQtm+lRrzgPQl5ZutKLbaY41XrJY9YBee/2rhYypWkE6xyZlzIpjU6arFjpv\n3qfxnUtV2RqhrH5PxwonsKj961bzAJtV0iDBS6SXyPo6YmXWfGYtHF4JYi2A9XsenK4V6Y1vfOP3\nfP1tb3vbus5zyoV9q5u19rhY/p4/fRXJTJ+o18H8+PPwTYZeK6yKA5dLxZQqw6KIXU5ixnSW9xEd\nfBiGSxQPP0q8eWMQ4G3ewWjDHiqdtcTUpghWVIW6RXJ5PqBOUYzPunid4LUOHgtRwt/d8n7+jx1D\nxIFH8dYikhQ/u6kl04qqxGuN7QyossEaCDnYVKW10WMocgz1ZKrJtsobsmIx2BqXoyAREZI8GbSm\n9J1iIayyzpJnM1QqaQ1HmvM1v009mRNZkPjAdWv8+JxQ5KIT+iBqIZ7HehVoUTWiqaUhVhVdMSSt\nhmR13SaEwOmYMpkKBZe9Jc6Xgv9dMQ7FmLuh5IlwFjVeCqF6U5QAwt+sr0gXnL6J1JTTvOuuu3js\nscd49rOfjVKKj33sYyeVoD2lE+kpT3kK73jHO8jznNtuu42/+qu/Oi6D9u6X/3lgMddhTrzqDls5\nReEiilK3CcosCvVSO4MzSWYDoicvc3TcMmk1pP/oN+l//RZ8WWKXlpBpiitKZLcTYF1jsYDsZIHa\nYgyinlAIiXCGK5/yZIrprVSbz6FS6ZrKdE1rWOOrzUZgpdTk6ibwaxjrVmhG6WyoYB5PhcnuDHE1\nCmrZWqQobIWwhn6+1A7KphynqEEHhAyVNoQMVdlrljysVC9vZQl1dYyjP0MzYVdekDipKLuz7eeT\nzoQQWWomnQ2hDIzUbVUKK/QaebpjZT8pcRy/4uvx29Hy//+v2stf/nIAPvnJT/K+972vrRv7/Oc/\nn5e85CXrPs8pnUi//Mu/zLvf/W76/T7vfOc7ufLKK7n++mPLXD7xL18ZKjF0M7oXX4QfLWPn57n3\ng7dz+POHjunfiJ2zp87QOXOOdKaHt46omwZFZpaguxnpzh3oM/aAc0hjcMuLyJm5gCg1FldVTmOH\n7Ffxypa7y4w7G5De0l9+LMjBqzzs3VQdotR7LS9U618ny0nYZzWlXnSMLMfttdu03xqcOLky0LQt\nWgcgIyOkqLCq36KBjQ+CtFX9U7b5HxdpXJS216WqSbt/NDpD4NqbQaUCS72ZTI1xvxIG4T2xnSBr\nSckoDquM9I6sWsbIKNCaamNLbQtiM67L2MSUKiUxY6JqTFQs15Muao1Z4Ix1j53TDTYcPnyYOF5R\nWgshmK/dpNbTTgn8/b3iTCEEN95445rnRnd8CHVoL8W995BcfEkoZ5+EON/tfYRy/0HueMunWibB\npqfOUOWW+a8s/c9eKgC7f2I73Y19ets24KzFG8vbP3cXv/IjTw7Fz7RCZwnxhll0U8Gi24Mkpdy4\nizydwUlFXA7RJhhPOpWgylFI2NZ3ehul9Z3foCbDtug0qvbMcw6fT0CpkATt9EO4qZNQ/U/FlEkg\nmTbiOeEdST2YIXh/W6Fr/29NZpeJTUiCO6moVBKKiYkEV1OwpA91YgsfNonRKsb7QtUPuTGnSFTw\ncUhURSQMujb/16LCeh3MabymchHeC5S0xLKqoXbDOWetfyJ9/pvBxuvKJ3z/uZz/mXbDDTfw0EMP\n8axnPQvvPR/5yEd44hOfyBve8IZ1HX9KVqTjlW2fn5/nL/7iL47rvKr2BZPi5KInMdn8/7J33mFS\nVFkb/1XqNHmYIWeJSjYAi6IgCoIB1BVd3VUx57QuurrKGlgFUVdc06rofgJmwIgRMIsJwYAISJI4\nDJN6OlXV/f64XTXdw8A0MEMj9vs8/UyHqurbNXXq3HvOe97TEVsz8FaVoIWDRHsfQcSXR+FJd7K+\nIoeqkEqFLtxQsYiHgzVVUJQlF+panFwZNnWipmxb4jfkdNERebeEQsTUCUY0fomrgaqKwLQlL65V\n13msGPAHfHo0HuaWbVEKla0EImV4w2XYqoGnehuBqGznEvEXUJHdAlP1EFKy8CIvYAsdDROPFcIX\nlTkrkaeSVfIL6qZ1rH5+LiXLSvAX+ukwtCd6dgAtP0b0p2WYwRCR8ir8xfkYOdnktGuHXdwKy/Bh\nlG2WdUyAyM5HiYYxf/6R0iXLMcurUVQV3wEt8DbJx2jXTlb6ajpUByEWIbZxE5GtZdiW5dKX9OYt\nQFUQVVXESkpopWmoPm9yrN0W2NEokS2lmKEIZjiKGYoQWrcNTdeIbK2moH0BulenoGtbedxWreGA\n5C4kO0O6pnYO/vGPfzB9+nTeeecdFEXhuOOO4/TTT095/0ZJyH7yySeMHz+ewYMHc/PNN7vzTge3\nTTcpbqLj9UA0JpkAHgPys20Ks6Loqk3E1ImYKtuqpK1HYuA1ZItIW8ici9eIZ/59Jn5DCoA4dUI2\niiSUCgibGpGYQjCsEAoLVFVBU8EXj9rZNnz35Xy69D4Kn1ceVwjI9kliqd8w4yIoKjFLIRJT3Fof\nhzltWgoBn41HixungGgsHoWzFHRN4DHiUce4WIpDaPV5bHL9Jl7NwhIqoZj0Nj5dhsRjlkZlWCcW\nZ2rn+C0K/SECephsVWqVW2jxMLWOoggqY5Jn5kTvIpZBRdhTw7hwugfGW8coCAKGpFN5VIe1IGlb\n1aZH/t64yIpDYnWOH41X4QYcPbx49LJX56YpXzPzlsiAyZCeqauzNjQ2btzITz/9xKBBg9iyZQst\nWrRIed8GXSOZpsmUKVOYNWsWEyZMYMSIEXVud/4hPwA1hElL1eOl3zULdtuIkzFziS9q46XTcXGS\niO0hankwRU3hnC0khd+jyShVlhFx+Wggw8qOkTnJQwfzVr7LiOF/AEB1NOziBXeKIsj1SbF5TbXc\ntijJOnW4mRDns8QWKFBTbCeTrKpbPuEU+llC5n5yvLEkPT2PYZJlRLbT1rOFSoW1fQdxEWeI13D0\nwKvFaJYVS0oJJI7VeZ7I+XMSz7oqz68UV/EknTfnXAIEY16CMe92rV5SgZXmNdL8+fOZMGECqqry\n7LPPMnLkSCZPnsywYcNS2r/BDGn16tVcc801BAIBZs2atVNrzgpulklY3UPEm4swauqEVNtCt6P4\nKzZKdSEzhtmkBbbuietnG25XBiMalN0chI1l+FGtaFzSOIYwvETymhHzZBE1AtiKhscM4Y1V4i1d\nixIOYW/egIjFsMMR7KIyBq94WHbzc/o0VVVgbtpErLwCNd4pWQv4UbOzUX0+RHELKcivqlIFqXwb\nVkUFVqUMjxiFBSh+vxThNwxEboEbdtcqSiFcTWz1aoRlySK/Ll0gK0dqmccjgVF/vtsOVLNNFNum\nylsgQ992CI8ZQrVNTM2Lx6zG1Jxkci66HUO34ywPW+bQjFgIW9WJ6AG30tgh1cZETd7Hp4TiunoW\nKhbZsTJ84XI3YV3tyXP1L7yxapdV7gQodCvKrnSjsNM8tXvwwQd5/vnnufDCC2natCkzZsxg/Pjx\ne9eQXnzxRSZNmsS5557LJZdcUu/2ygdvYMdi2KaFZll4wlE0nwdFUVE9OqrHg5qfB0VNwRdAjYYR\niopux3Wl47dERdgyXByLIjQDxZLhWhHwgWXh27AcXzQM0Sh2qBpMEzU3T+4vbBTdQA1koeUrKOVR\nyXhWNWx/NrbhRRS2QHTogRLX1LM0D1FVk7rcdgzVMtHMMNgWmi8L1eNDLyxGryiTYpHRKFbpNsxQ\nxFUAUnQNzefDdnrE5ueh5uXL/FYgB9uXFefiyLC6Ea6Q3D0rJgMUikKOcw4SmrKhKAjdwNY9CM3A\nHy7D1L0IVcNSDTdYETJyXA0GhwKlICRJWImgI2WSVdsiy5Z5L1VYqLYpQ+vxKGJuLCRby6gGtqph\nqoYbFleEja3XXRaxI1h2eg3Jtm2aNq2Zinbv3r3e6tpENIgh3XzzzaiqymOPPcZ///tf9/0d1XR8\neH3qTXr3FqZbJcS0XSH+1w3FUOqsW/I281DYVYaXA4UBrKiJqmtoHg3N0PDmZWEEvNLQjPi/RVXx\nFORhx2IoOdnESsswCvOxq0OoPh9mRQV21HRvPlpeLkZhkWS05xfjsW0sX5YknpoyP+V0ZlejIRmu\n9vplSYWwUaJh6Y0rtmGVbpVl7oVFiOz49FHVwTYR3gC2ZmAafvzVW/FUbEHZ/KuMvHp98byXDZ0P\nTPm8pTvY4Pf7Wb9+vWs8X375JV6vt569atAghvTee+/t0vYtvv/ELeMWCesIp6JToMhIHJIW5FHN\nuPC7Rsg0iFlyQV4dkRG3I1v95Mr4mkInbMtePtm6XMA6Mr4qdlINENSsTYZ8/gktDjvcHVdiRSrg\nloknUm+k3ncyarePTBSaVFUTFYGtmCiArsTQ4uzTKBCjxtO63SziNU1OMMFGdTlwiWsgBw41SqVm\n3ZY0XqVGasz5/XXJhyWulWpTihJh+RWEX8EsduS/VHf9eUyK1wPglt6nC9dddx3jxo1jy5YtjB07\nllWrVjF16tSU928QQ9rV5mLdf53rsr9t3eN2Ebc1HTveS9ZUPTVUm/h6wRARdDWCKmJoWhjFL8C2\nCEeKpEAHoJpRYp4sNDuGHVffUeLrKKFqqJaJEu+4p9qmvBNbJhvKvqXblnihWwKEqm0vguBUsjoC\ni/HxKbGonGpGw3EhhjhjwxF7dPmBcQkgVZOlJJoR16TwJFX0JopP2ooWTwY7AgzyuLLQMM5kj7eD\nSRKjSxi30HRXvdY9ZsLxFFuqqTo8RsWWjBKE7SahTd0rK40T2mECbndBxRG3BOColK8Ja8/UlPcY\n/fr14/nnn+ebb77Btm169+5NYWFh/TvG0ail5jtCZbOuBCo3oEZC6NvWoVWUIcJSiUYxJLNb9Xjl\nNEFVZD7E8GJl52F7AtiqjmkEXJZ2RA8kNCxWCNt+bFRZWiFUV8wkauvYKKDJaJMDFcHzP3xG7tDh\neDQTryr7rhpKDBVZdqEJ010vgCTNJtKIYoqXqPAkfW9dXhdk1M6yHeGV7SOIiR7AgS1A2LWIsXGb\nsZzttZrfs93+zjYWCKcEQ0ne3pEQU+LSWonV304E0/3tCdFPM0GWTI5Lko7H1H8puNjdqZ0Qs8Ak\nfAAAIABJREFUghtvvJHOnTtz3nnn1bnNXXfdxdy5c8nLk1PrDh06cP/99ydtM378eMaPH8+RRx7p\nvnfRRRfx6KOPpjSOtBiSEQtSndMCkatgN+3iZucdvW5bqESFgSW0eMjVcFvQV0U0TEvqq0Vj8kZf\nss3EtgTRqI1p2jRp4kUIyM+tuaNrGmT7hdv2EZJv3G0PGsav5TJ6qGvESwxqPndUR5WE95X41NO5\nEO0dTIESnURiuxTbTlZadQzKEjXKPhBXRhXJx5KhcPDEq2911carS+ERQ61RYtXiIjO1x2U5XfeE\n5pblOzQdJwye1K289jG0mu101XT1/JwaL4nU8zA70C/ZKVasWME///lPvv322zpJAQ6++eYb7r33\n3u0a3iXi7bffZtGiRTzyyCN06NABkNoPqSIthrREP4TqqIGqQJYRJWZqbqGZ3zARAiojHjaVaUSi\nAq9HwYzLT3kMyAnYGLog1ydvrd1bW8QsFUOzEQIMLUTE0gjFdKojKk2yo/TlS4xwBca2Ta5nCwWK\nXBqNyFvDoU1XymbG6OiYhIXPrTPSlJpeSV5N5pR8SghdyCrXKiUPG5WobbjlCI7qDkiBe8Bdf6mK\njaFb5BiSlxeyfIRMA49mosfzYk6OzBGnVBSZOwrGvJi26ua65HFVQqZBJOZ03Eu+yzvKSIqC21vW\nGUfUlgThiCnHaGg2hd5KvHHFppDtI2p53NIXXZEKTLoi28NIRaUINho6ToPmKLtiSLsTtZs+fTon\nn3wyLVtuX57uIBqN8sMPP/Dkk08yYcIE2rVrx4033rjdPu3ateOqq67inHPOYcqUKRxyyK6J+afF\nkMpCPiKmvOtujHlctoJpghBG/M4PXo/UDgh4ZdGZT5eyUqpiuwVyTvMw1VOjH+dRY1hCxfTokCUX\n2euULqh+G7UgWVLKuUMvWLiYngOPwel/aiLLFDTFwqfb7iLcFipBUxbWVRJIIlsmeQ4UTFQ3QOGM\nFUiSxDKFvPB11STbsNzvcJqjxWyNYMICXiAZEc6UERICHK6nEuiqwBNPjBqqTFAbqiV1w2t5KkdY\nxTI0N4FdbXkJmr5ax6/xNqatYykaUdtAUQSheE2Ucz4sodB2F66JHfFrFixYUGdKZeLEidxyyy0A\nfPbZZzs87qZNmxgwYADXXnstHTp04IknnuDSSy9l1qxZSeFtRVEYMmQIBQUFXHnllYwfPz59PWRT\nRTAi+XCqIpKmW149LkroFPDFSy2kUKEt9dziF0Jihz5TyCiW8w+vivlRFEFAi7jKO1HbwBQqlu2I\n3tdEmGyh0KbHSFaUN5Nzf4F70UKywL2m4gpAJk7XakezasO5uEGWacvfbyexFbSEC1tXbGzFRhMK\nhppcYOccTwW3YlXmgmqaAjhG66CmQbI0XgsVkTC1dKAqNiig1xG1qx3Rqz3dS4wi7jKzIe6RagtE\nHnnkkfzwww+7dKxEtGnTJiklc9555/HQQw+xbt26pHojhynXp08fnn76aS688EJKS0tT/p60GNJJ\n9ouo0RDYNlZWLooQMuJVvhV721YUTUPEk4+YJmqrtghfFrbXj+XxyzII558bj2yF/QVSs04IFGGh\nm+F4/54Yno2/UP3NN6iGwcYvl1GyrAQNyGuWheE3yGmRT3Wwmj9sfgFPQR6KbkhRFJ9Pyg4btRqZ\nOdpzgO3PwTZ8siu4SC59j3lzpDQwiiz6i9fwONFIzTYx7IgMVqhel+zqwKn1cURKHDFNW5HFg6qI\nezBklDMs/E5r5LhX0zDijG1FERhILp1XiSR9j2Ngbphc2K6ovSV093PZe0MGXUzFwEZDU0y3DAMg\nEC1HtU3JOtmFALgTtastELmnWLp0KUuXLmX06Brd19odzAEuv7yGYNuhQwdmzpzJAw88kPL3pMWQ\nPs49Ea8u5aXaZG9BJ0ZWrBy1mYWlGkT0AEGRzcbqAhRFcFj4fbRQFbbhc41ItaJU5bQkaORRZWez\nNZyDV7MI6GFytQryIlsQqER1H5GCzjQtboMWqqRdq5a0N3QwvJgtO2B6s6nKasrH//43Rx1/Bmrl\nFiy/7Foe88jmxkLV2OptSaWZTVXUm1SQ6FMjRGwP5ZGAnI5qMZp4ajTQdWL4Y5X4w2VSMixer2T5\nclDMKELTMT1ZhLx5mKoHzY6RXV1So1seH4eleQh7ctCtqBstjGo+Kq0cwpYHYSo08ZShE8NnBono\nAdfYnSbJ/uoS9GC5lCpbt04ywWMxUFWM7ACewgK0ggKpUR4OY1ZUohq6S4ki3iOKQBZmQTNs3YMi\nhKyQdQoNDR+KGUUP7lycvjZ2J9iQClRV5c477+Tggw+mTZs2zJgxg65du7oSx59++ikDBw7Etm3e\nfvvtpH13pRtFWgwpekgvnGxN4LyD8OQECLRqiqIomFXV+IRN847t6eSVHiHUrCMxXy6m5iNsZLn8\nMBUbgyj5yjbyAuVU2rmELS9lIp+QJ+BqJhRFNxDz5WKsXkq0pAQRi+Fp0QK9dCO6buAt38TRXVvh\n27oGQkHUrRvA8ODxySierXvIUldLgXnd44ohauEwqrAwKkpkDse2Xd6dC18A2+NHiYWl7Fg0AuEQ\nSnWQ0Kq1hErKQVHwZPsIZGdhNMlHy8pC8fkl78/wSuWDaJgchwNoeBGGB8ufQ1NNlxJdioIaDaOY\n8szaXj+27kWvrkCJRUBVMbMLMLMLiBa2wTrQg6Ua0tNFKmUbGgQxRcPWDNmORlEwVQ9R3Y+pyjYu\nptBxdCsUBB4lRiBfcgtjihevXY3HCuPJKkxQkqgfDZlHWrJkiat516VLF26++WYuueQSLMuiefPm\n3Hvvve62r7/+OgMHDuT//u//tjuOoigce+yxKX1nWnTtfl6x2p0iaMJEt6LygoxVuwlG4VSxKqqr\n6yZUjWhc/y2sZxGyA0Rtw12cO/N0Q7VcRR9b1GTvExfokLy2WbRwAb0PPTIpYJAY/nbC06ZdE5p2\nomKJ6yeH+eyu85SaMLmj150kvJ+ARLGRxDWOpogk9kIi+1xVkoMGznESQ9oO691hWDtjr72uS5QG\nc36zc1zTrhGoseN5orquHEeiQ1MEYw5LnW/3yFvy766ISu5LSItHarP0TcS2UmIlJXg6dYZgJcKy\nsINBRFTeUfWiIihqjpldQFVea0BSZ3QzjMc2ya/6GW3LeimzK2x5B8/KQWzdjBUMEtm8FX/rFqj5\nhYj8wjipNYayrQRRHZQtWvILwOPDKmjK+58+y8AeYckk98iaGNWMopeXuKIptjeAFcjF0j2ynNqK\nuc2+jOVLqFj8PWY4RlbLIrSsLPTiYsjJk56qqiKJZBpdt47w1jJWL1hK6Y+VmBUmml+ts2GyYii0\nGdqC4u6t8Obn4G3dArW4uev97NKtiJiJ7Zy7/DyU5q2xfVnSE5qm/A2xKHbJZqyqIGZ1iFhVNdHy\nIKFtkgysGarL+bNNG1VXUTSNrA5tULw+FL9fEmd1Q7YItSz5/cEgqKpcWwayXDlj2F5mYEdIF7Nh\nR93MHaSlq3mqMItaoeY2QW/TEWFbkC3LubVYRKp46l7COUVUB4owVcMto5bNlGU7FsvfjViTeDeE\nOIMZQOtoyUhYraiTE+5WWm2fZBRCoe2ROj/kHwHUyrLn1hzD2ba2pxMC7B5DEQcpSXd7547tJGpV\nN9oFjjKI8kf5nsfJ8dQxNoBN8UftzzTFRu0stvNuiUgM84uOcjy2rVBXktgZo1c38WoxV+MOakUN\n2f4cOx1CnNB96isMsB33VwerozGR2KlvT5AWQ3p80/EYhoLHAEOvWWiq8QpYMwTV2wThcE2Y2dAV\nfD4Fnzde3eoRGLrAo8nwsaHFda1VzRU6LNC2YdgRfLEqjGg1tqajx0K4zY7jIvi2qrPe/IUOdlO3\nTsdWNJcW5PRcUoQMDSvxMgcABct9bqs6puYhogWksVNLMDKBKOpE1ixRM/2xccTwZf7IJaAmFPk5\nxYXOsVScIsR4sZ98J85MkMUSGpL1rYhkQ3B/UxxR1UdY+OMJZU98DDUdDhOnm0743XnuTDNVxcYX\nJxlD6voLllX/No2BMWPqJjIJIVi9enXKx0mLIZ3aTXatUBAYZkRqYSfKWQmBbobRIkFJOI0TSZVY\nRHbYskwoDcnpii2gsAhheEDVEYaHYH5rYsKPHonK1vWqh1BWDlHVh43mXmyJIpJvLXqTToNPiV/u\n8aZgCV2G3TwJSpJs8nZiizY4Cxonl+KseZwcmPseNoYac587iPs8oKbdiWN8iUxtd9uEBLMWV7UU\nca9oKtKTI5yEcfKYHaNUsfGIMAV2ldvUWosXBSb+bxyiq4iH4O04wdhWNHeM4Hi51MtS0k1affbZ\nZ5k0aRKhUMh9r7CwkI8//jil/dNiSEXfvQc5ebJfrMfrElEt3Yut6qBA2FeA8Ev2rUsUtS1XQN/J\npwhFIaRlY6HHG2Pp+JVqYnioVvyukAkWeESNIIoDRUi53oP7D3IrRZ2Hc9ElTmFUbHQFFMVMSkw6\nHgFwGxkrInn64xbJCQvVTp4uScJtckd1wN3OCWW7zGwcJofq5qhs95yo7vGgptGxIuLeK+6J9Dhj\nPrHk31Y0UIhPqQvieSnNPSuJLXWckvfaECjsZKZZJywrPVM7B4899hjTpk3j4Ycf5uqrr2bevHls\n3Lgx5f3TYkiisCnKprXYZeVo7TqgVa9FhKv56u4XKI/LMtUFfysvoV8jZHfyY5uC6lVh9/3up/RA\n93uxInGul9+Lr3kxasCPmpOLXVUpu0Js3YLqD8iFc04+wuNDKAqrg7/StjyLsL8JuiUrcr3BrbL0\nwoxix9tsOrp2IOW2nB60ESMbw5JJYD0m72q2ZsjgheahyteEiOJHU008Vhhbkxd/VPHFGePJntJQ\nY3gJE4hV4IkF0WJhVCuaJNGl2THZQE2pKedQhYUNBGKVRPQAMc3nJnVVYVGJZECjgEePJtU5OVG9\nLLWavMgWApWyG4VavrVGRswXkOUe8SS1YlmooSr5ubDdTvAAHLDzhXwiagwpPcjPz6d37950796d\nrVu3cskll3DyySenvH9aDGlh8UkoxQl36niI2PPcpbSIK9jUrAtsl48mhEI+NXPy/ATPsil+C3SI\nmJbQiNkalq25ofEsI+IWDjoNxpx9Xlx0B9l/GIyw4qUNtoLti4fCDZKCBy4L3MbtZK7Guz5oqkDx\nCjcs7RJX7TidB5tqJavG8wmHOR73GggsVCK2hwgeKrRcVE1g+xKmnNQUFyaKhtQUSErvJEwFYdYE\nRhLhTDsdUqxb8KcIquwsgkYArUkbudZqbjl8CZcRkeRd48wG1bbcljWaGdmlPFJjJWRTha7rlJeX\n065dOxYvXsygQYMIBnd8U99u/0Yc2w6RY4Rc+n7U1gnFNLdZVsSMq+u4LefjF6cSbzis1pAyDU1e\npMW+iqQSBofSH0OTBFFbTo0cI6otdG8JhYP6HUXYlFJeNTmYGs5dXVMYZ0xO7igxClZXrmhnUbLa\nx3VuFkIkR/2ciJtjGJZds85zxpBYtlH7uXP8qLtmSv7uxHE759cJciQam/Oe4nAFVdutX3LOV5/t\nftmOkW6PdNppp3HRRRfxyCOPMHr0aN555x06duyY8v7pCX/butRI02PkKNXoPhMNC0NE3LucYUVk\nBSvI6kxFIaZ5MVVZRWqhERXepOiX09YyWwuiKIIcrXK7dUqiXrdTCCiEwq/+bbTL2YIpZFc8geJ6\nM0io1XGjZCQlQ3cEl1wrasIJtQMGiduBY4jJkTql1l818b34Re54DLkOst3K2cTfnrh+shUVS+iS\n9BtvvOyMK5Ed7q4FEUnjdvpMqe56rWaFuatItyGdeuqpjBw5kkAgwHPPPceSJUs44ogjUt4/LYbU\ntfoLLN0LisJHkYFYtpN7ifNU44V7zh3UsuPeSJNeQlOlOGOLPClG6OREHI+yLeTHEgrhmEosXq6h\nKAKfpyZcLlnfYMZZCq/Pf4rC7ifjN0y3yzjIqtGagruEJsauFxKuaKKjV+ewwx1jcHT3ADmNUi00\n1cK0dUKmgWXLUIWjWREz5Xd7dRtdk8f36THZTd1WasQZE6eOyAYE0XjPpXC8zD7ba5LnlefJtGsi\njY4nilo6dvxcGJqNTzO3Y0s4HsbRsBBxryhrosDQZO5OamnorgftvgvXhFXbNe5lhMNh5s2bR1lZ\nDUfwxRdf5Mwzz0xp/7QYknfFt0RWrWLT1z9zaMtCzFCEnHYt0PNkf1a7OoQViqD5vaheD3qHTuD1\nEclpiR6pwvJmxRnT2eixkHxUbEFoBmpVGegGdnY+1XktiXiyCWp5FEQ2EjJyyA6VuK1jFAS2qlPt\nySN81IF0zttAXqwEzYqiKhZhTw6eWDUqFp5wGULVqM5qSlTzubmmkAjgVcJkxcrxRisJe/MI65IL\nWG1nETR9GKpFQKsiIKpkaTw+KcSiKrRiG2FdrpnK7Hy8SpQqM4tsPYiFhopNLN7MzKtG3RopaXo2\nVXa2Oz3M10NUWDlkGVLM0WkaZgm5VoxYBlErrtcgIN8XIqBHKdRL0YSJYUUI61luzsnJNVWQj1eJ\nYKGhYRG2fXjUKIYSRbNNdDuKYUXw2FVU5khJqyolD9hxwV1t2Gn2SBdffDEVFRW0bt3afU9RlH3b\nkIb97xAgXoG4LP7mWvBm+cktKsDjkxGh6jXVmJEYsY+iCGETCYaAgoQjOTF/P7hlZIlVmVFkv8nS\nWtsndlqP0aR1HiW/Rpj1eYRATjM8Hh3Lsqksq0LYPgyvQSDnAHRDwzZt7Lir1A0nmWpgm1lEoybC\nFuiGhhbvXRkOVWDGTMyYiS+QhbBtbNsiHAyjKAqK6kfTFBRVJSffxp8VwOPTqK7yEYuYRMI2mm6i\nKAr+rLiQphDouoovYGAYGpXlYSIRE13PjY9LIRKOxMdR8y9W1BiqYqKoCj6fjqr6sG1BeVkTbMsm\nFIyg65psJBBfLAlboGoqQsjfZMYs+bniRzdkIzRVAdO0sW2BbdlSr09TeTQ1/XkArDQnkjZt2sQb\nb7zBrmjZJSIthnT33QdTEfFSHtJRFUGOz8JvmFhCYf02H5YNVdWC8gqbioooFeURVAX8WR4CATnk\nZk09aCrkZQtCEYWYKfcB+c/X9ZqivMI8qb0d8AqCYRW/V2p05/qibh/Vpx6azlnnnIGuCSKmimkp\nxKymGJrAZ9h4dAu/HqM65qEyLHXJY6biBkK8hmwzaQnF7RsLkO2VpfMBI4ZXi2EKlYAWISZy8Kgx\nIpaHLL0agygqlcTwJFGa5FRM5o0UpRqPEnFZ2DHbwlDNhCmniV8N4xPVbisXp4eRzwzijVbhr9iA\nrXsJZTdjk6cNEctDxDKI2Ro+XUNTBD5NlpgrCKK2QVXM52p/q2iukIxXi6Epsj2ps76K2U7bmF0s\n7EuzR+rSpQslJSUUFxfv1v5pMSS98nuOc4u3FCSfuXZZr4IMA/njjx1BqeN5XXcVJeF958KrOW7e\nuFH077ej0mItPr66ArqJ31mXfrXjtXYmNliwk88aBznA9hL3DStg//nnn6dcpJdujzRixAiOO+44\nunTpgq7XmMX//ve/lPZPTx5p4cIGrYJsCOyLY/qtY1fOabrXSJMnT+aiiy6ibdtdUZqoQVoMqa52\nmOnGvjim3zp25Zzujkd65plnmDlzJoqi0KZNG+644w6aNGmy3Xbz589nypQpRKNRunbtysSJE8nO\nzk7aJisriwsuuGCXx+Bgrxf2pfrj9xZSOckZpI7Zs2czbdo093VlZSWbNm1iwYIFFBXtmMR6yWQZ\ndn74+vyUvue7777jyiuvZM6cOeTk5HD33XcTDAa57bbbkrYrLS1l1KhRzJw5k/bt2zN58mSCwSAT\nJkxI2u7ee++lWbNmHHPMMUktMPPzUxsPYi9iyZIlYsiQIaKiokIIIcRdd90l/vGPf+zNISRh69at\nYsCAAeKXX34RQggxadIkceutt6ZtPPsbotGoOO2008TMmTPr3fbCf20VF/5r6y4fXwghwuGwuOaa\na8SUKVO222bOnDniggsucF+vXbtW9OvXT9i2nbRdz549RdeuXZMe3bp1S3kse7W7U48ePXjrrbfI\nyckhEomwadOm1C2+EfDRRx/Rs2dP2rdvD8AZZ5zBq6++6kozZbBn+O9//0thYWFKLSQty65zerdg\nwQIOPPDA7R6zZ8/GMAzeffddBg8ezBdffFEnyXTjxo2u0AlA8+bNqaqq2o5HN2PGDFdxyHn8+OOP\nKf/Wvb5Gcn78TTfdhMfj4corr9zbQ3Cxs5Ocmd7tGUpLS5k2bRovv/xyStvbpjSiXdW1GzZsGMOG\nDeP555/nvPPO45133kFVE4i8O2DDJm4DcP311/Pmm2+mNNY6j7fbe+4Bhg0bxueff84VV1zBeeed\nt8Mf29hI9SRnsOt4/vnnOfroo5NEGHcGxyP179+fK664ot5o3+rVq/nyyy/d16eccgrr16+nvLw8\nabsWLVqwZcsW9/WmTZvIy8sjEAgkbde1a1deffVV1q9fT1lZmftIFY1+xfz73//mpJNO4qSTTuK6\n665L6cfvLaR6kjPYdbzxxhu7VM+zo6ndjrBlyxauvfZaVw311VdfpXPnzhQUJOfkDj/8cL799ltW\nrVoFyErYo48+ervjvffee1x//fUMHTqUAQMGMGDAAAYOHJjyePZqsOGLL74QRxxxhNi6VS4qZ82a\nJU444YS9OYQklJSUiIEDB7rBhnvuuUfccMMNaRvP/oKysjLRu3dvNxiQCs4Yv1acMX7tLn3P9OnT\nxahRo8SJJ54ozj//fLFmzRohhBCLFy8WJ554orvd/PnzxQknnCBGjBghLrzwQrFt27Zd+p5UsNfD\n3zNmzGDGjBlomkbTpk255ZZbUnb/jYEFCxYwZcoUYrEYbdu25e67705rAGR/wOLFi7nuuut45513\nUt5n7F+l0Mhz97RrrGHtFLZt88QTT/DBBx9gmiaDBg3i4osvTmI57AxpEYjMIIPa+OM1vwDwwn0d\n0vL9kydPZunSpZxxxhnYts1zzz1Hx44duemmm1LaPy3MhgwyqA0rXXpccXz44Ye89NJLrrj+UUcd\nxYknnpjy/hlDymCfgBP+ThdErQ4VHo9n3++PlEEGtZFuj9StWzcmTpzIWWedBchugF26dEl5/0zC\nJIN9ArZlYafRmG699VbKy8s5/fTTOe200ygtLeUf//hHyvtnPFIG+wTSPbXLzs7m7rvv3u39M4aU\nwT6BdE3tbrzxxh1+pigKEydOTOk4GUPKYJ+AbabHkDp37rzde9u2bePpp5+mVatWKR8nY0gZ7BNI\nl0caN25c0utPPvmE8ePHc8IJJ3DzzTenfJyMIWWwTyBdHsmBaZpMmTKFWbNmMWHCBEaMGLFL+2cM\nKYN9AumM2K1evZprrrmGQCDArFmzaNGiRf071UKGIpTB7xovvvgikyZN4txzz+WSSy7Z7eNkDCmD\n3zW6deuGqqp4vd4kcUghBIqi8PXXX6d0nIwhZfC7xq+//rrTz1ON3GUMKYMMGgAZilAGGTQAMoaU\nQQYNgIwhZZBBAyBjSBlk0ADIGFIGGTQAMoaUQQYNgIwhZZBBAyBjSBlk0ADIGFIGGTQAMoaUQQYN\ngIwhZZBBAyBjSBlk0ADIGFIGGTQAMoaUQQYNgIwhZZBBAyBjSBlk0ABIiyGl2ld0b+L+++9P9xAy\n+A0jLYZUX3lvBhn81rBbhpTYdzWDDDLYTUO68MILG3ocacfs2bPTPYQMfsPYLUN69NFHG3ocacfo\n0aN3e9+oBcu2QnWsAQeUwW8K9RrS3/72t+3ea9q0aaMM5rcIIWBFKVRG5F87o8n0u0S9hrR06VJ+\nD4pduzu12xSs8URRCzZVNeCgMvjNoF7t7+LiYkaNGkXv3r3Jyspy398Vpf7fAnZnamfasLGW4WwK\nQtMs0DIZut8V6jWkvn370rdv370xlt8cNlWBZUNJNXy0Bvq3ghY5sDko/2bw+0G9hnT55ZcTDAb5\n/vvvMU2TXr16kZ2dvTfGtlcxe/Zsrr766h1+bgtQa6ShiVmwpRp+2QYXvAZlYcj3weMnSG+U8Uq/\nL9T7r168eDHDhw9n4sSJ/Otf/2Lo0KEpC4v/lrCzqV3MgsWbYG25DC4ArKuAqgiMfxcU4K6j5d+/\nvw9RU3qlDH4/qNcj3X333dxzzz0MGDAAgE8//ZS77rqL559/vtEHt69gQ3wKtzkIIRMMFUpD8OhX\n8EsZPDhSTussATe9D6/9DKd0h2bZyV4sg/0X9Xqkqqoq14gABg4cSCgUatRBpQOzZ89GURR69uxJ\nnz596Nu3L127duXQQw/lw0+/dLerjEgj2lAJz30Px3eRRgRwbEfo1RQe+kJul4ng/X5QryGpqprE\njVu3bh2apjXqoNIBZ2o3b948Fi1axDfffMNPP/3E8WPGcvdNV1AaklM8B1O/AEWBiw6ueU9R4KoB\nsDUEz34vI3qx9HZ0zGAvod6p3WWXXcbYsWMZOHAgAB9//DG33nprg3z5/76FJ79pkEPtEOP6wl96\n796+pmmyYtUaKrVCjn0G8j6dwGEFJRx92YO8vQL6LZ3A0/8qYcKkB7ng5KPo1ncg3yz8GO/yNTza\n5gjGvPg0m4MqTz84kdmzZxMOhwkGg9xzzz0MHjyYtm3bsnnzZrKysrj44ov54Ycf+OCDDwDZbXvO\nnDmsXLmSiRMnEo1G2bx5M2effTa33347F1xwAcXFxW77+unTp/Piiy8ya9ashjp1GewC6jWkYcOG\n0bFjRz777DOEEFx88cUccMABe2NsexVOQnbIkCGoqsqWLVvw+XwcNOh41g2ZRod82GjBOyvhvfeh\nexH0bQGhcujaBAwNyjes4PGX5/PtmiDjju3G5BkLuGpER959910WLFiA3+/n2Wef5ZZbbmHJkiX0\n79+fefPmcfzxxzNv3jzKy8upqqpizZo1GIZB9+7dueyyy3j66afp3Lkz69evp23btlxduOmLAAAg\nAElEQVR11VVcdtlljBw5kttuuw1d13n00Ue56aab0nwWf7+o15C+//57AHr3lrf1cDjM999/z0EH\nHbTHX/6X3rvvLRoao0ePZsGCBcybN4+ioiK++eYbRow4jtKiPyCymvLgcfDsCvjsZ+h3IJzbB15+\nGHJ9NWHuk048gbb5KoIcClp24r0fSrnmjCHc98jTTJ8+neXLl/PZZ59RVSUXT2PGjOHNN9+kU6dO\ntGrVih49erBgwQIWL17MKaecgqIovPrqq7z22mvMmDGDH3/8ESEEwWCQPn360KFDB15//XW6dOnC\n+vXrOfbYY9N4Bn/fqNeQrrjiCvd5LBZjy5Yt9OjRgxdffLFRB5Zu9O3bl7/edh9/u/J8jrhrAM2y\n2+P3KPRpJrj+D3IbrxLFTojK+f1+igKwJQgtcxV+tAX3vPQ1Sx44ieuvvYZjjz2WI4880m36O2bM\nGAYPHkyXLl045phjKCgo4O2332bhwoU8/PDDBINB+vbty5gxYzjiiCMYN24cs2fPdilbl112GU8+\n+SRdunThwgsvTOqBmsHeRb2G9P777ye9XrRo0X5pRLW5dqYNSs8zoPWTlM2+GsbOpqCwmE/efxMh\nBCJazSfz306KaDpokwdeHQ5tCe/P/4BDux/CmHHX0ibH4vLLLsWyZASidevWFBUV8cgjj/DMM89Q\nUFDAHXfcQSAQoE+fPixatIiKigruuOMOPB4PzzzzDJFIxN3/1FNP5YYbbmDJkiUsXLiw8U9SBjvE\nLufe+/Tp40739ifUTsiWVMMbP0P+yQ+y9LM3+enzt/jTmWdS0KSYMYd35tqzR7oBmNrI9oCuwtEd\nQe99Bj+vK2FY/wPp1edgAoFsSktLqaysBKRX2rJlC3379qVjx474/X7GjBkDQK9evTj++OPp1q0b\n/fr145VXXuHAAw9k+fLlAHg8Hk499VQGDhxIUVFRI56dDOqFqAffffed+1iyZImYOXOmGDFiRH27\n7RQPPPDAHu3fGLjvvvvc56YlxPxfhDBuE+KMF4VYWVqzXTgmH/UhFBPiq/VCnPmSEOo/hXj5ByG+\n/FWIJZuEiFkNM+aqqirRr18/8dlnnzXMATPYbezSGklRFAoLC5kwYUJj2nZakMi121gFb6+AmA2j\nOicTUL31njEJnw7FAfhLL3jxB3jkK7hzCERMmahtlbtn433rrbc444wzGDduHP3799+zg2Wwx9jl\nNdL+CmdqF4vXFM1aCh0LJGvBl6Lx1EbLHNgWlpHJ/34NLbPh0kMl2bVFzp7Rh4YPH05paenuHyCD\nBsUOL5Hbb799p1Gg/a0eycGmICz8FX4sgRsGQdM9ILprKrTIhgv6yTXXU9/C6nK4fYhkixf6G27c\nGaQXOww2+P1+8vPzd/jY3zB79mxZqFcJ934cQ723JfPvGEGut/59jz32WEpKSgAYOXIkP/zwg/tZ\nUUB6tL8fDtcOgPmr4Ob3YXOKPDxFUdxj7yqOOuqoOiOs559/Pu++++5uHTODurFDj/T555/zwgsv\nMHnyZK6//vq9Oaa0YPTo0WwJwqvL4OePZ9G5Wy+WffcVP/74I927d9/pvu+88477/I033kj6TFHk\nNG51GfyppwyrP7AQ3lohw+RZnkb5OTvF448/vve/dD/HDj3S1q1beeSRR3jttdeYNm3ado/9EZuC\n8PjXEFj0EKeMGc0fTxubpMD65JNPctBBB9GrVy+GDh3K2rVrOffccwFJLVq7di3t27fnyy8lW/yx\nxx6jR48eHD2wN1edeSyrVyzjTz3B/9o5TPz7lRx99BA6derE8ccf77Iddobbb7+dAw88kF69enHq\nqaeyceNGADZu3Mjo0aPp1q0bBx54IA888EDSfqZpctppp3HmmWdimqbrqVatWsUBBxzAFVdcwWGH\nHUanTp147rnnAKiuruYvf/kLXbp04bDDDuOcc87hnHPOaYjTvF9ip2uk119/nXA4zLJlyxrn27/9\nH3zzZOMc20HfcdD7L/Vu9vKs2ZT0upr1K39AX/MZJ5/6Mmw7mCOPPJKJEyeybt06xo8fz9dff02b\nNm24//77ufPOO5k2bRpPPfWUSy1y8P777zNp0iQ+/fRTiouLeXLaU/zt/NE8+/73tMuDpcu+4v45\n73NcF5WhR/TnhRdecI2yLkybNo0333yTL774gqysLCZMmMA555zD3LlzufTSS+nSpQuzZ8+mvLyc\nQYMGMXLkSACi0Sh//OMfadWqFVOnTt1u3bty5UqGDx/O1KlTeemll7j22msZO3Yst99+O6ZpsnTp\nUqqqqjjiiCMykgM7wQ4NadCgQQwaNIgnnniC8847b2+OKS0YOmI0j34Dhd8/TO9ho+jRrhB/p0I6\ndOjAo48+is/nY/jw4bRp0wZgp2XpAHPnzmXs2LEUFxcDMO7cc7jm6quIlKyiTR4s6zyCWcu99G0D\nPXr2rDcC9+abb3Luuee6AjRXXXUVd955J9FolHfffZdJkyYBkJeXx3fffefud91111FZWcmKFSvq\nDB4ZhuEaXb9+/dxxvPHGG9x7772oqkpubi5nn302ixcvTuVU/i5Rb2C3UY2o919S8hZ7A99ugo1Z\nQXxf/o8lAR/dO7cHoKKigv/85z/87W9/S7oQQ6EQq1evplu3bnUez7bt7d4TQlDsj5Htge4t/Mz5\nCc7uDeURhagpeOWVV7jlllsAaNmyZdJ6q/bxbNvGNE2EEOi6njS2lStXut7xz3/+M0IILrjgAl55\n5ZXtxuTxeFBVOcNXFMXl8em6niTDtj/WoDUkMvIcyDLyBW/NJvDjdJoUFfHrr+tZtWoVq1atYuXK\nlVRVVVFWVsa7777Lhg0bAKk264hnappGLJYsszp8+HCee+45Vyd92rRpNGnShE6dOpFlyPyUEDK/\nZNsytzRk+IksWrSIRYsWbRe0GD58ONOmTSMYlGIQDzzwAIMHD8br9TJs2DB33VpeXs7RRx/Nzz//\nDMBhhx3G7bffzvLly/nvf/+b8jkZNWoU06ZNw7ZtqqurmTFjRoYUuxPsZqpx/8LmIFR1HI3/64e5\n7Mpr8Rg1d9/8/HyuvPJKXnvtNSZPnsyIESMAaNGiBU8+Kdd3J598Mocffjhz5sxx9zvmmGO45ppr\nGDp0KLZtU1xczGuvvYaqqmiqZD2ceqAsVx8clSpFy0uhe3HdCeDzzjuPtWvXcthhh2HbNp06dWL6\n9OkAPPjgg1xyySX06tUL27a58cYbOfjgmtJdn8/HU089xbHHHsvQoUNTOic33ngjl19+OT179iQv\nL4+mTZsSCAR2+dz+bpAOXtK+xrV7/CshGH6fuP9TISx773xnWUiIt5cLkXWnEO3uE+KpbyQXb/nW\nvfP99WHmzJni9ddfF0IIYVmWGD16tHjooYfSPKp9F7s1tXPYyfsDysKS5a38NJsTuu491Z88H7TO\nhfuGS2Wi81+FV36S4wntA2L8PXr04M4776RPnz706NGDli1bcv7556d7WPssdmtqd/vtt+/08zlz\n5vDEE0+gKAp+v5+bbrqJnj177tYAGxN2XAD/3V+g66DRtN5DIumuomUO9GsBz54CN7wHt30guXnX\nDIAOBXt3LLXRo0cPPv744/QO4jeE3TKkHj167PCzlStXMnnyZF5++WWaNm3KggULuOKKK5g/f/7u\njrHRsLoM5vwEFREY2Rw8ezkwleeTtUsAD4yAf8yD/3wB/VtLdvjeHk8Gu496Denzzz/nscceo7y8\nPOn9HVXJejwe7rjjDrf1S48ePSgpKSEajeLxpIEPswOETfi1QjIZujSBVe/MBnaeG2oMtM2Dn7bK\n5zcdIQmz/1kIg9tKClEGvw3Ua0g333wzf/7zn2nbtm1KB2zdujWtW7cGZN7EkTn2eDx8/vnnLFy4\nkHXr1u3ZqBsApSFZI7ShSrKxSyO732hsT+A34MBiqSEOcE4f+Pfn8NZyOLuPVCfKYN9HvYbUpEkT\n/vKXXU+aVldXc8MNN7Bx40aXJNm/f3/69+/P1KlTd32kDYzZS2H6EiktfEQ7eCONF6xHk15xQxX8\n8UCY+R1MXQhHtofOTdI3rgxSR71Ru6FDhzJ9+nTWrFnD+vXr3cfOsH79ek4//XQ0TeN///sfubl7\neRVfD0wL7v1UFu5dOwCaZaW/h6yiyOBD2zw4vy8s3gyvLZPSyBns+6jXI5WWlnLvvffi99dUoSmK\nssOOFGVlZZx11lmcfPLJXH755Q030gbEh2tgxTb4x2BoEpCL/j3pIduQaJkDY7rB/y2Gh76Ew9vK\n8vZMEeC+jXoNae7cuXz00Ucpq9TMnDmTDRs28M477yTV6Tz11FMUFKQ5phvH2yvk36PaQbt9rEZR\nU+WYLjoYbp4nZZ3P6SNbxhTEjak6BttCEIxBE7+8GWSQXqS0RiosLEz5gJdccokrgLivYv4quSbp\n26KGjlNfo7G9iaIAnNgVPlgjw+FO9C5QBYLkhG1lRPauzXQITC/qNaSePXvypz/9iSFDhiSFr3dW\nO7MvIxiFrzbAWb1q7vCw70ztQK6XWuXCLYNl6fvf34OJR8PRHeTnlg0rt8lmZ/1bw/rKmi6BGaQH\n9RpSJBKhQ4cOrFq1ai8Mp/Hx7kopszWiU7pHsnMU+qE4Cx44Dq6cK41p+AFSeHLeKqiMyu1yvdLg\njmovP8uspdKDeg2pqKiI6667bm+MpVFh2vJCe22ZDDePqNVQY1+a2jnokC8Tx1NHwD2fyobPUQsG\nt4OBraXRPPSlpBfdP1zuUx1jr1OdMkjBkObPn79fGNKmKrn2eGsFDGglu0gkYl+a2jkwNOhcCMu2\nwq1H1r3NQcVw0Wvw13fgweOgT3Pw65kAxN5GvYbUunVrxo0bR79+/dwyZ/jtrZE+XivFH9dWwMWH\npHs0qcNhPiwvld4G5BrKo0kPCzD1ONlZ/eq3pGdyPs9JQUosg4ZBvYbkaNgltr9sVEQqoWKdvBps\nC7KbQ2DP0vubqmBsAjXw5Dqqw/fFqZ0DQ4NuRTLcbdmS6Or0ZApG4edSeGik9EwXvSYDE7oqvZWW\nqYHeK6jXkP71r38B0pBM06Rdu3aNO6Ly1WBGal6Htu6xIT2R0F6zcyF0K95+m31xapcIRalhiici\nyyONTFVg+slw5ZswYT60zZWBiPb7WJ5sf0W9hrR69WouvfRSNm/ejG3bFBQU8OijjzZO+8toEH6e\nC2s/hqxiMALQvA806bJHh531Y83zkZ33cIz7IHw6dCqEn0rgrmFw9my45A2YPgbyffKRQeOiXsd/\n2223cf755/PFF1/w1Vdfcckll/DPf/6zcUbzwwvw7vWw4i34+nH4/N8w51xYNX+3D7k5KPNGFx0M\nE46CW3awaE83125PETCkMbXMgYdHya4XN8+TrPIEMaAMGgn1GtLWrVuTSstPOeUUtm3b1vAjsU1Y\n+CDktII/vwvjPobT4/JRP75UczXYFpStgm0rUzrs3J8lG+CU7jCuz47zLPv61C4V5HihaxEcUAg3\nHg7fbIRHv5I3kwwaF/UakmVZlJWVua8brZXIqg9gw1fQbTToXtA8kNMS8jvAxm+hcr00tm0roXor\nhLZBLFTvYeeugByPTFj+HvIrPh0OKIATusJxnaTc19srZJAig8ZDvWuks846i7Fjx3LccccBUvHz\n7LPPbthR2CZ8MkmuiQ6+CJp0hlg1CBua9YJf3oeNi2QEL1IBP78B0Uo48FRoPRA8dXNjhJC8ukFt\n6i+Q25ejdruKHK8sERk/CL7eAHd+KG8kmYrbxkO9hjR27Fjatm3LRx99hG3b3HrrrfzhD39ouBGE\ny2H1AljxNvQ+G5oeBIoKenyFPGg8rHwX5t8CQ+6EV8+HyngoPrgZ/E2gaQ/QjO0OvbxUFstdV3er\n1yTsD1O7ROR6ZVHglf3hpvflFO/WIzMVt42FlLIMbdu2ZdSoURx//PHk5eU1bDNmKwJf/EdO5wZe\nJ40oEa0OhQHXwIavYcZxUF0Cxz8KXU6QnmndZ9J71YG5smcxo/Ys6PebRcscOLU79G0ODy6ExZvS\nPaL9F/V6pMmTJ/PMM8/QpElNLkdRFN577709/3YzAve3BzME/c6H4jr6EKk69DwDfPlyDdV5FBR1\ng5zWsOV7ePMKuf/Aa7fbde5yaJUDXVNIQ+1PU7tEdIhP8f70Mkz+BP49AprtQRfCDOpGvYb05ptv\n8vbbb9OsWbNG+HYvHDtFRuH6XwHqDuYdBR2hw1Bof5ScwuW0goIOcOI0mHsV4oPbqehzDUJR3JxJ\n2JT1PKO7yWRmfdjfpnYODE1qP5zcTTaFHtMNhnaQzPIMGg71Tu1atGjROEbk4KA/Qq8zIbvFjrfR\nfdJwvLlQ2FkyHfyFkNsSu+MwlHAZPy5bxhcJLKaXfoSqKJxxUOMN/beCZllw9QAZhJiwQJJgV26T\n3MMMGgb1GtLAgQOZNGkSX331Fd9//737aDB4c6FJF0ykN6qOQXm4ju18+TKaZ9QkgkT+AawOHArA\n7a+sYsR0qQ4UMeHfn8kL6Fin7qh6531Yf+sJ2Z1BUaB3c6lR8VMJ/O0dyT/8bjOsKpONojNGtWeo\nd2r38ssvA1K7wUGDrZEANA9RC1Ztg+bZ8k5p2bLkoXm2FP6oC0LA8m0K1/80hNnAJPUm3jGHMOUD\niwW/ePlivcodR1noqiaJsGs/gZaHyNxUHdhfp3YOsj1S6mtbCO74EM5/Bf42CHo0ha3VkuR6QGHd\nfL4M6ke9hvT+++83+iA2VcG8X+CwVjBjiRSVP7W7LGKrS9etOgZryuCW+TBnXTEfFV/E4Vse5b62\ns7h8zVg+2gADiiu4vus62OaH9V/BnLOh03Fwwn+hYq2cLua1rQmz/w7QKhfO6ClD43d/DOfMgT+0\nhj+0kWpFlpDJ3LzfzylpMChC7H0m1tSpU7niiivc1yOnw5vLZc+gLfFIdoFP3kEvPVQmF53amrIw\n/LxVRqBe+AHO6gl3H/ILLf7XHUvzsSZayAcM5rRBHQj4/dCkK7xxmcw9FRwAw++Teauc1tBuMBQe\nAL48jjrqqH1Sn7yhEbXk9G5bSEp+vbVC1mgpwIUHwwX9ZOK2OJBakCYDibQ3GttQKY0IpBH9qYdk\naN/1MTz2tVTHObePNKRQTHquuz+Gz3+FP/eCCUdCy5xm0PMM9GWvk4XKOdGnIbGRgicH2hwO6z6F\nl06vyTsVdYPh90N+e0afcPxe/+3pgEeTfLxVZbLA8eJDpIjKo1/Jx08l8K+j5Tq1U2HGmFJF2g3p\nuXjc4uze0gv9pbf85z1xAox9Cf65AKYtgmWXS4H5c+dIEuo1A+DCftC+AFACMOxuOPRycqtDsOFD\nGd1b8wFEq6D7KVC+FtZ+JI3qpKdg6zL48E545Tw44u+Sy7flRxmSt6KAIpPDvjwIFO9XV5QjkVwW\nho1VkoN421HQvQju/UxW2t50hNw2Y0ypISVD+vXXXykvL09qznvQQXseVxYCnvga2uTCHUOgNCyn\ncQFDLoCfOgnOfBnWlEvO3D8XSPWc6WOgTwtZpOc2BvPlQ2gb/kARZMWVQAoPkHy9nBbQ8mD49ino\nfzW06CuTv7mt4d3x8MZlLHs/H47rKGufQlvlft5cyGomI35Nusjk8H4Ep1bJtOVU76xesrT93k/h\ntBfl/+TUA6WMcsaYdo60Mhs+XgvfbYE7h0LLXCjKqhFsLM6CfrqsH7roNRg5Q75/0xEwqK2kvySV\nUWseKOoqn+e2khd9LATCAk88lX/qczIH5bz2F0JhJ/jmSU5d9RTMvYLt4G8Ch98IqiHD7/shdFWe\n7zyfPP8DW8Pf3oW/vy+NbHQ3uW4KbE9nzCCOtDIbnlokpxln95aepXYT4hwvjIzngWwBfx0oC/Ra\n1VcO4XgOo1bxUV6bZC5fTksZtRtwFUu+DjB0RFdZ6u5vIhkU4W3ww4vwzl8lPWn4fZDdiMnpNMOj\nyShpwJB6etfOlQnc5tlSLyJgQJ5XGl7IlKHyjI6eRL2G1FjMBtOGOUthSPudG0brPLi6v3x+8SHS\nE+02ahNiQXolXz6z3vuCqy67SPL/NENO7YQtuX2f3Q/fzZRTwaMn7ndTvEToce3xQr8sWz//Fbjg\nVTi/n6QZBRMMZ0tQrrM65GemfvVeEQ6z4eijj8bnq0kw7Oka6bN1UBKfl9eHmwfL7HvHgkb6hykq\no089XZZjCJH8JZFKOPJWCG6CT++FdkdCl1GNMIh9CzlemVuafrLsbfvwl/LRrUhKI68tl9O96wfK\n0Hm6e96mG2ljNnRtItc7Y1OwR2dRvFfuerW/xJsjAxPHTIJnhsP7N0HrAXusbPRbQI4XejaDe46F\nbzfCl+slEXhdhYz0fbVessonHAWjOkst9Sb+36cEWFoTslFr32k4XG9C1jbhw4kw/1YYdAMMvX2/\nnuIloioqjScYTX5/XQXc+J4kwU4+Rkopq3HZsDZ5269592ek1GjslVdeIRgMIoTAtm1Wr17NlClT\n9vjL9xUjghS4dqoOh10uK3k/mSwreQ8aW2dl7v6G7Lh2XtSSHeBtIf93xVnwyCgp/XXt23LqfWIX\nWdbu0fa93lONiXqd8NVXX80nn3zCSy+9xMaNG5k9ezaq+jv03SADE8c9IOuh5l4lZcJSEGDZX+DR\nJJm4aZacarfNkz2mHh4Flx8qRWbu/xxGPydTFr8n1GsR69ev57HHHmPw4MGcddZZzJw5kzVr1uyN\nse1VpFxG0bQnHPdvGd177WJZ7l62WsqEgQxWRCqhaiOEy/Z7UbkcryxlH9cXnjgRnjsFLj4YPlmb\n7pHtXdRrSE7Ly/bt27Ns2TKaNWuGaZqNPrC9jZTLKDQD2g+Rlb3hbfDymTI8vnGRpB1t+hY2fwer\nP4SSZVDyI1ixuo8Vq4ZQ6W/e2HK80KuZ5PAd1loSjZfs200bGxwptb58/PHH6dOnD1OnTiU7O5uq\nqqqd7jN//nymTJlCNBqla9euTJw4kezs/UgowJcn9fcK2sOC2+Dz+2H5mzI0Xvoz/LoQYkGZ2O04\nTEqMtT08eT1V8av0WgCezXE+nyqVkewYZDWV+++o/H4fg6bKtZRTzxTawb1jf0VKksUej4dDDjmE\nHj168MADD/DXv/51h9uXlpZy4403MnXqVN566y3atGnDPffc06CDbgzscoVsdjNoMwhGPQzH3CMN\n4OvHpFfqeAwc/nepW/7jyzDrz3I9VbVRbleyFL79P3jhj/DUkTIauPFbqZS0cKrMVy2fK8Vdqkvi\nVKffltfy7/8xmCSkFP4Oh8OsXr2azp07E41GkxKztfHKK6/w2muv8dhjjwGwbt06TjrpJL788kuU\neI6mdj3SvoD7779/91SEhC0NpGKdbALgzZVexMgCMyyN441LZaBiwDWS57d0Dix/Q9ZKZbeA1fNB\niXseYcnnwpLrscOugGa9Jd1J1cCbJwm6OxDFzCA9qHdqt2jRIi6//HJ0XefZZ5/lpJNO4uGHH6Zf\nv351br9x40aaN2/uvm7evDlVVVUEg0G+//57Fi5cyP+3d+4xUV1bGP/mzACCPEdRUUBQK2IdbK9c\nAWFaeRQvJDKITUuxBYSi3lsxxvqKt4SGggq2tFRqatt7aQI3WOrEgtQGtIoBi1RCWgwSqvKoyqMK\nyqMwMziz7x9bjkxlQGEeDJxfQiaHmTlnDcw6a++11/7W7du3tfcJDA2PoTV7M+zonMfEnCrGDmHt\nSB9/3A+UPup8yAiAFzbTIR8joBGq+QJ1ysX/oIqyvxUBtXlA8RYAPMDWhZ5r/t9pJFzgSYd/02iH\n72RmTEfKyMjA119/jd27d2PevHnIyMhAWloapFLpiK9XqUYWmWYYBl5eXvDy8sLRo0cnZrUOmLCu\nnYn5k0WyAGBuB7hHAPbLqW4EUQHzXgRsHAFrJ1q1bjkPmLeSOqWFPXUu6/mAmwRo+pHKld2qpLrn\nv5cDlzOpQKbnP6mDWcymEU9X86mHcmq3QEN5ieoh1WKX91AbTC3pjcVI5nfaYExHkslkWLLkcQvw\nl19+GR9//LHG1zs4OODXX39ljzs6OmBjYwMLi8nd1FSn4idmVtRRrOY/2udkRYdnQ1gvoD/DMbej\njmbjTDcaDv5J50ndt2h3jqv/A34rpk65OBhwW0/Xt8zt6PBSGzyU0Q2PA4+6j/AYGm35pvSmweNT\ncc7+e0DnoySLYAbdzuLkS/eDTRPGdCSBQIDu7m52ftPYOHo7FT8/P6Snp6O5uRkuLi44ceIEAgMD\ntWOtMcM3fdJZxkJgBtg+6pA4NJWd9RzdmOgeQTOFN88Clw4D1ceARcGPi2rtXMdvKyHUOe7WAdek\nwG+nqf02ToBwKb0J2Cykkaj/LlB3giZZhiNcAiReH78NRsaYyYYLFy4gMzMT9+7dg4+PDy5duoSU\nlBSsW7dO43suXryIjz76CIODg3B2dkZ6ejrbixaYnMkGoxM/GRygX2JZN9BaDdTmUh30hwOAgycQ\nkfd4o+MQij66WCzvoVGOb0qHkTyGptwFMwC+GV0fu1FC6wrl3YDD32jy5H7j4wYGw7F2BkRRtJgX\nPKDjF5qt3PaLXv4Uk4Gnytq1tLTg0qVLUKlU8Pb2VhvqjYfJ6EjjztpNBuQ9wJ93aWq9oZDWAs59\nAZD8h0YG1UPaU6rxHNDZANypok5l60qHmxb29Bx2rnTt6pf/0lY6wucA8b+BuaLHEVGpoNfpa6fr\nYgILGiXNhY+HlIq+R/O8Z4zARozGod3w5mI2NjYIDQ1Ve254hOEwMGbW9MfWhWpLzBAC5w8A0ijA\neyed4zQUUhUlgGb/zGcDv1dQfYq/wggAz38BL8TSOdpMewA8Omd6KKPXeCijDiqYQZ1o+GKzxSyj\nW/eaKBodydvbm50XAQAhBDwej32sr6/X9FajZEp0o2D49EvsvYN+wcuSgOKt9DmL2cDftwNuYTRt\nzmNodJH30qEc34wO2/7sAOZ4APM8qMMN3yqiKTM5EtNsy6xGR9qwYQNqamoQEK2H10oAAAkXSURB\nVBCAjRs3Tng4N9mZUpLFghnAqgTAcTXQ+OOj9PqLtKTJ1JLOjQBaAzjYT6MLw6ePykHqdGYT2dM/\n/dDoSIcOHcLAwABKS0uRlpaG/v5+hIWFYf369bC2ngbNWI0dE3NgwWo6z+GbjuwYfBOAbwOA64k5\nUUattTM3N4dEIkFOTg6ysrLQ19eH6Oho4x8CjcCU7EbBY+hQj4suOuepd+h1dXWhq6sL9+/fR29v\nry5tMghTamjHoXdGXZBta2tDUVERioqKwDAMwsLCUFBQoNvGYxwcRohGR3rrrbfQ1NSE0NBQHDly\nBMuXL9enXXpnSmTtOAyGxgXZZcuWwczMDAzDjJgGr6mpGfdFt2zZApFINO7364Lbt2/D0dHR0GZM\nKeRy+ah716YSGiOS1jryjYBIJJp0lQ2TsdrC2JmMVf66QqMjLVigu/KO1atX6+zc42Uy2mTsTKe/\nqd4l/PLy8pCfnw8ejwcnJyekpqaqdbrQN1NeX0LPfPfdd8jJyWGPe3t70dHRgYsXL7JCOlMSokeu\nXr1K/P39SU9PDyGEkMOHD5OkpCR9mqBGZ2cn8fb2Jk1NTYQQQjIyMkhycrLB7JlqKBQK8tprr5H8\n/HxDm6Jz9Kr0uGLFCpSUlMDKygpyuRwdHR0GLX6tqKiASCSCi4sLAOCNN97A6dOn1RqqcYyfL7/8\nEkKhEJGRkYY2RefoXTLVxMQE586dw0svvYQrV64gIiJC3yawjKYvwTExurq6kJOTgwMHDhjaFL1g\nEO3hoKAgVFVVITExEfHx8Rp1HnTNaPoSHBOjoKAAgYGBcHJyMrQpekHn35isrCxIJBJIJBK8++67\nqK6uZp/buHEjWltb0d3drWszRsTBwQF3795lj41FX8IYOHPmjEFHG3pHnxOyK1euELFYTDo7Owkh\nhJw6dYqsX79enyaoce/ePeLj48MmGz788EOyf/9+g9kzVXjw4AFZuXIlUSgUhjZFb+g1/e3p6Ylt\n27YhOjoafD4fc+bMwWeffaZPE9SYNWsWDh06hB07dqjpS3BMjJaWFtjb28PEZPrIrRqk0RgHx1SD\nm1VzcGgBzpE4OLQA50gcHFpgUjpSamoqmzJfsWIF1q1bxx7LZDJIJBL09PTo5NrJyckICAgYVZbZ\nkMTFxaGrqwsAkJCQgBs3bmjt3H19fYiPj4dMJtP4mnPnziE7O1tr15wyGDptOBb+/v6ktrZWb9dz\nc3MjbW1terves7J06VJ2+UDbJCcnkx9++GHM18XGxpJr167pxAZjxSgbuLu5uaGyshJlZWUoLS2F\nTCbDnTt34ODggE2bNiEvLw/Nzc3YvHkz4uLiAADffvst8vPzoVKpYGtri6SkJCxerC7yHhUVBUII\nEhISkJycjL1798LDwwMNDQ3YtWsXXFxckJKSggcPHoDH4yEuLg7h4eGoqqpCZmYm5syZg+vXr8Pc\n3ByJiYnIzc1FU1MTgoODRyyVuXDhAo4fPw6FQoGuri6Eh4ezu3RPnjyJnJwcMAwDOzs7pKen49NP\nPwUAxMTE4IsvvsCmTZuQlZUFkUiEb775Brm5uWAYBrNnz0ZSUhJcXV2xf/9+WFpaoqGhAe3t7Vi0\naBEyMzMxc6Z6f6W2tjaUlZXhvffeAwBUV1fj8OHDbPXH1q1bWZnqV199FdnZ2QZduph0GNqTx2Kk\niDR0V5ZKpWTVqlWktbWVKJVKEhoaShITE4lSqST19fVEJBIRpVJJqqqqSFRUFOnv7yeEEFJeXk5C\nQkJGvN7wO76/vz/Jzs4mhBAyODhIAgMDSUlJCSGEkPb2diIWi0lNTQ25fPkycXd3J3V1dYQQQuLj\n48nrr79O5HI56ezsJM8//zxpb29Xu45KpSJvvvkmuxjc3t5O3N3dSWdnJ6mvrydeXl6ktbWVEEJI\nTk4OWyX/V/tqa2vJTz/9RIKCgtjfS6VSEhISQlQqFdm3bx9ri0KhIOHh4eTkyZNPfO7c3Fyyb98+\n9jg6OpoUFxcTQgipr68n77//Pvtcb28v8fDwIAMDA5r/cdMMo4xIwxGJRHBwcAAAODo6ws/PDwzD\nwMnJCXK5HAMDAygrK0NLS4taFXJ3d/dTSS97enoCAJqbmyGXyxEcHAwAmDt3LoKDg1FeXg4vLy84\nOjqyuhbOzs6wsrKCqakphEIhZs6cie7ubjXRGB6Ph88//xxlZWUoLi7GzZs3QQjBwMAAKisr4efn\nx36u2NjYUW0sLy9HaGgohEIhACAiIgJpaWlsQzexWAxTUyoKuXTp0hFLshobG+Hs7Mweh4SEICUl\nBefPn8eaNWuwa9cu9jlLS0tYWlrizp07T0T16cqkTDY8C0NfkCEEgifvDSqVChKJBIWFhSgsLMSp\nU6cglUphYzO2MOJQ3d1IBa6EELbD+9PYMZz+/n5s2LABdXV1WL58Ofbu3QuBQABCCPh8vppOhkwm\nw82bNzWei4ywpj7ctuGtSodkp/8KwzBqnzEyMhJFRUXw9fVFRUUFwsLC1GTYlEol+Pzp00hsLIze\nkZ4GX19ffP/99/jjjz8AAPn5+YiJiXmmc7i6usLExASlpaUAaIFrSUkJ1qxZMy6bWlpa0NfXh507\ndyIgIAA///wzFAoFVCoVvLy8UFlZydp74sQJHDlyBADA5/NZBxnCz88PZ86cYbN5UqkUtra2WLhw\n4VPb4+Liglu3brHHkZGRqK+vR0REBD744AP09PSwkay3txdyuRzz588f12efihj90O5pEIvFSEhI\nQFxcHHg8HiwtLZGdna121x8LExMTHDt2DKmpqTh69CiUSiXeeecdeHt7o6qq6pltcnNzw9q1axES\nEgJra2s4OztjyZIlaGlpgVgsxp49e/D2228DAOzt7XHw4EEAwCuvvIKoqCgcO3aMPZevry9iY2MR\nExMDlUoFoVCI48ePP9N2kKCgIHz11VdspNm9ezcOHjyITz75BAzDYPv27azKUkVFBdauXftEFJ7O\ncLV2HCxJSUnw8fFRa+EzEtHR0Thw4ACWLVumJ8smP9NiaMfxdOzZswcFBQWjLsiePXsWnp6enBP9\nBS4icXBoAS4icXBoAc6RODi0AOdIHBxagHMkDg4twDkSB4cW4ByJg0ML/B/xoR6dm8vT+QAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1e941e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,1,figsize=(3*1,3*2), sharex='all', sharey='row')\n",
    "cbar_ax = fig.add_axes([0.77, .35, .01, .3])\n",
    "cbar_ax.tick_params(width=0.5) \n",
    "\n",
    "for t in range(len(trial_types)):\n",
    "    ax = axs[t]\n",
    "    ax.set_title(trial_types[t])\n",
    "    sns.heatmap(populationdata[sortresponse, t*window_size: (t+1)*window_size],\n",
    "                ax=ax,\n",
    "                cmap=plt.get_cmap('coolwarm'),\n",
    "                vmin=-cmax,\n",
    "                vmax=cmax,\n",
    "                cbar=(t==0),\n",
    "                cbar_ax=cbar_ax if (t==0) else None,\n",
    "                cbar_kws={'label': 'Normalized fluorescence'})\n",
    "    ax.grid(False)\n",
    "    ax.tick_params(width=0.5)   \n",
    "#     ax.set_xticks([0, pre_window_size, window_size]) \n",
    "#     ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "#                                      for a in [0, pre_window_size,\n",
    "#                                                window_size]])\n",
    "    ax.set_yticks([])\n",
    "    ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "#     ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5)     \n",
    "#     ax.set_xlabel('Time from action (s)')\n",
    "    ax.set_ylabel('Neurons')\n",
    "        \n",
    "    ax = axs[-1]\n",
    "    sns.tsplot(populationdata[sortresponse, t*window_size:(t+1)*window_size],\n",
    "               ax=ax, color=colors_for_key[trial_types[t]],\n",
    "               condition=trial_types[t])\n",
    "    ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "#     ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "axs[-1].set_ylabel('Mean norm. fluor.')\n",
    "axs[-1].legend()       \n",
    "axs[-1].set_xlabel('Time from action (s)')\n",
    "standardize_plot_graphics(axs[-1])\n",
    "\n",
    "for ax in axs:\n",
    "    ax.set_xticks([0, pre_window_size, window_size]) \n",
    "    ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "                                     for a in [0, pre_window_size,\n",
    "                                               window_size]],\n",
    "                       rotation=0)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(right=0.72)\n",
    "\n",
    "fig.savefig(os.path.join(basedir, 'All neuron PSTH.png'), format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir, 'All neuron PSTH.pdf'), format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Including all response features per neuron makes the feature space quite large. So, let us first reduce the dimensionality of this space using Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:31:51.958000Z",
     "start_time": "2019-02-19T03:31:50.903000Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of PCs = 248\n",
      "Number of PCs to keep = 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc19584c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yDx4QJNGUW6cAA0o2lOfSI/nz8KzpAtcWz5qmuRhYfNfT14F6WexjNy4N3wSF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\ngzPH2woAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc195f02990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=populationdata_allpresent.shape[1], whiten=True)\n",
    "pca.fit(populationdata_allpresent) \n",
    "with open(os.path.join(basedir, 'pcaresults.pickle'), 'wb') as f:\n",
    "    pickle.dump(pca, f)\n",
    "    \n",
    "transformed_data = pca.transform(populationdata_allpresent)\n",
    "\n",
    "pca_vectors = pca.components_\n",
    "print 'Number of PCs = %d'%(pca_vectors.shape[0])\n",
    "\n",
    "x = 100*pca.explained_variance_ratio_\n",
    "xprime = x - (x[0] + (x[-1]-x[0])/(x.size-1)*np.arange(x.size))\n",
    "num_retained_pcs = np.argmin(xprime)\n",
    "# Number of PCs to be kept is defined as the number at which the \n",
    "# scree plot bends. This is done by simply bending the scree plot\n",
    "# around the line joining (1, variance explained by first PC) and\n",
    "# (num of PCs, variance explained by the last PC) and finding the \n",
    "# number of components just below the minimum of this rotated plot\n",
    "print 'Number of PCs to keep = %d'%(num_retained_pcs)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(2,2))\n",
    "ax.plot(np.arange(pca.explained_variance_ratio_.shape[0]).astype(int)+1, x, 'k')\n",
    "ax.set_ylabel('Percentage of\\nvariance explained')\n",
    "ax.set_xlabel('PC number')\n",
    "ax.axvline(num_retained_pcs, linestyle='--', color='k', linewidth=0.5)\n",
    "ax.set_title('Scree plot')\n",
    "# ax.set_xlim([0,50])\n",
    "[i.set_linewidth(0.5) for i in ax.spines.itervalues()]\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "fig.subplots_adjust(left=0.3)\n",
    "fig.subplots_adjust(right=0.98)\n",
    "fig.subplots_adjust(bottom=0.25)\n",
    "fig.subplots_adjust(top=0.8)\n",
    "fig.savefig(os.path.join(basedir, 'scree plot.pdf'), format='pdf')\n",
    "\n",
    "\n",
    "numcols = 3.0\n",
    "fig, axs = plt.subplots(int(np.ceil(num_retained_pcs/numcols)), int(numcols), sharey='all',\n",
    "                        figsize=(2*numcols, 2*int(np.ceil(num_retained_pcs/numcols))))\n",
    "for pc in range(num_retained_pcs):\n",
    "    ax = axs.flat[pc]\n",
    "    for k, tempkey in enumerate(trial_types):\n",
    "        ax.plot(pca_vectors[pc, k*window_size_allpresent:(k+1)*window_size_allpresent],\n",
    "                color=colors_for_key[tempkey],\n",
    "                label='PC %d: %s'%(pc+1, tempkey))\n",
    "    ax.axvline(pre_window_size_allpresent, linestyle='--', color='k', linewidth=1)\n",
    "    ax.annotate(s='PC %d'%(pc+1), xy=(0.45, 0.06), xytext=(0.45, 0.06), xycoords='axes fraction',\n",
    "                textcoords='axes fraction', multialignment='center', size='large')\n",
    "    if pc >= num_retained_pcs-numcols:\n",
    "        ax.set_xticks([0, pre_window_size_allpresent,\n",
    "                       window_size_allpresent])\n",
    "        ax.set_xticklabels([str(int((a-pre_window_size_allpresent+0.0)/framerate))\n",
    "                             for a in [0, pre_window_size_allpresent,\n",
    "                                       window_size_allpresent]])\n",
    "    else:\n",
    "        ax.set_xticks([])\n",
    "        ax.xaxis.set_ticks_position('none')\n",
    "    if pc%numcols:\n",
    "        ax.yaxis.set_ticks_position('none')\n",
    "    [i.set_linewidth(0.5) for i in ax.spines.itervalues()]\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "fig.text(0.5, 0.05, 'Time from action (s)', horizontalalignment='center', rotation='horizontal')\n",
    "fig.text(0.02, 0.6, 'PCA weights', verticalalignment='center', rotation='vertical')\n",
    "fig.tight_layout()\n",
    "for ax in axs.flat[num_retained_pcs:]:\n",
    "    ax.set_visible(False)\n",
    "\n",
    "fig.subplots_adjust(wspace=0.08, hspace=0.08)\n",
    "fig.subplots_adjust(bottom=0.13)\n",
    "fig.savefig(os.path.join(basedir, 'principal components.pdf'), format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have reduced the full dataset into a reduced dimensionality corresponding to the PCA subspace. \n",
    "\n",
    "Next, we will start the clustering. The approach we will use is Spectral Clustering. This is a pretty good method for dealing with high dimensional data. Note that even after PCA dimensionality reduction, there are 8 dimensions in this dataset. The choice of the method can affect the exact clustering results and there really is no \"the best\" algorithm. Thus, clustering is almost always the beginning of an investigation, rather than the end. In other words, the point of this clustering approach is not to say \"There are 9 subpopulations of neurons in OFC for sure\", but rather to test if these identified clusters can be mapped onto interesting features either in terms of their responses or in terms of their biological features (genes, anatomy etc).\n",
    "\n",
    "The one issue with Spectral Clustering is that you have to pre-specify the number of clusters. However, there are ways to optimize this number by using metrics of \"clusteredness\". I use a common method: <a href=\"https://en.wikipedia.org/wiki/Silhouette_(clustering)\">silhouette score</a>. The one caveat of this method you need to specify a minimum of two clusters. In my experience, when the data truly do not contain any clusters, the minimum of two clusters identified will be quite obviously similar to each other. When data visualization clearly shows that the identified \"clusters\" are similar in terms of responses, one should conclude that there are no clusters.\n",
    "\n",
    "This is the fundamental issue with approaches such as clustering. In the end, there are strong subjective elements to it. However, when there truly are clusters in the data, it can be a powerful approach to uncovering such structure. In my case, I validated these identified clusters using two major approaches:\n",
    "\n",
    "1. I found that the responses of neurons that were clustered on one day are stable on another day when the behavior is stable. Thus, the difference between clusters remains even on a cross-validation day.\n",
    "\n",
    "2. Subpopulations of the output population studied here (e.g. OFC neurons projecting to VTA or NAc) comprise of only select clusters. This show that specific types of responses are absent in specific downstream projections. Such a clear mapping of identified clusters to biology is a good indication that the clustering results are real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T02:11:29.894000Z",
     "start_time": "2019-02-19T01:53:51.863000Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with numclusters = 2, num nearest neighbors = 10: score = 0.181\n",
      "Done with numclusters = 2, num nearest neighbors = 30: score = 0.237\n",
      "Done with numclusters = 2, num nearest neighbors = 50: score = 0.241\n",
      "Done with numclusters = 2, num nearest neighbors = 100: score = 0.229\n",
      "Done with numclusters = 2, num nearest neighbors = 200: score = 0.186\n",
      "Done with numclusters = 3, num nearest neighbors = 10: score = 0.209\n",
      "Done with numclusters = 3, num nearest neighbors = 30: score = 0.236\n",
      "Done with numclusters = 3, num nearest neighbors = 50: score = 0.252\n",
      "Done with numclusters = 3, num nearest neighbors = 100: score = 0.250\n",
      "Done with numclusters = 3, num nearest neighbors = 200: score = 0.128\n",
      "Done with numclusters = 4, num nearest neighbors = 10: score = 0.234\n",
      "Done with numclusters = 4, num nearest neighbors = 30: score = 0.247\n",
      "Done with numclusters = 4, num nearest neighbors = 50: score = 0.263\n",
      "Done with numclusters = 4, num nearest neighbors = 100: score = 0.256\n",
      "Done with numclusters = 4, num nearest neighbors = 200: score = 0.176\n",
      "Done with numclusters = 5, num nearest neighbors = 10: score = 0.219\n",
      "Done with numclusters = 5, num nearest neighbors = 30: score = 0.259\n",
      "Done with numclusters = 5, num nearest neighbors = 50: score = 0.243\n",
      "Done with numclusters = 5, num nearest neighbors = 100: score = 0.248\n",
      "Done with numclusters = 5, num nearest neighbors = 200: score = 0.166\n",
      "Done with numclusters = 6, num nearest neighbors = 10: score = 0.247\n",
      "Done with numclusters = 6, num nearest neighbors = 30: score = 0.270\n",
      "Done with numclusters = 6, num nearest neighbors = 50: score = 0.270\n",
      "Done with numclusters = 6, num nearest neighbors = 100: score = 0.275\n",
      "Done with numclusters = 6, num nearest neighbors = 200: score = 0.165\n",
      "Done with numclusters = 7, num nearest neighbors = 10: score = 0.220\n",
      "Done with numclusters = 7, num nearest neighbors = 30: score = 0.239\n",
      "Done with numclusters = 7, num nearest neighbors = 50: score = 0.275\n",
      "Done with numclusters = 7, num nearest neighbors = 100: score = 0.278\n",
      "Done with numclusters = 7, num nearest neighbors = 200: score = 0.193\n",
      "Done with numclusters = 8, num nearest neighbors = 10: score = 0.180\n",
      "Done with numclusters = 8, num nearest neighbors = 30: score = 0.237\n",
      "Done with numclusters = 8, num nearest neighbors = 50: score = 0.279\n",
      "Done with numclusters = 8, num nearest neighbors = 100: score = 0.266\n",
      "Done with numclusters = 8, num nearest neighbors = 200: score = 0.156\n",
      "Done with numclusters = 9, num nearest neighbors = 10: score = 0.175\n",
      "Done with numclusters = 9, num nearest neighbors = 30: score = 0.246\n",
      "Done with numclusters = 9, num nearest neighbors = 50: score = 0.244\n",
      "Done with numclusters = 9, num nearest neighbors = 100: score = 0.201\n",
      "Done with numclusters = 9, num nearest neighbors = 200: score = 0.020\n",
      "Done with numclusters = 10, num nearest neighbors = 10: score = 0.172\n",
      "Done with numclusters = 10, num nearest neighbors = 30: score = 0.225\n",
      "Done with numclusters = 10, num nearest neighbors = 50: score = 0.248\n",
      "Done with numclusters = 10, num nearest neighbors = 100: score = 0.217\n",
      "Done with numclusters = 10, num nearest neighbors = 200: score = 0.037\n",
      "Done with numclusters = 11, num nearest neighbors = 10: score = 0.155\n",
      "Done with numclusters = 11, num nearest neighbors = 30: score = 0.205\n",
      "Done with numclusters = 11, num nearest neighbors = 50: score = 0.224\n",
      "Done with numclusters = 11, num nearest neighbors = 100: score = 0.150\n",
      "Done with numclusters = 11, num nearest neighbors = 200: score = -0.030\n",
      "Done with model fitting\n"
     ]
    }
   ],
   "source": [
    "max_n_clusters = 11 # Maximum number of clusters expected. I already ran this with up to 20 clusters and know\n",
    "# that the optimal number is 9. So, I am leaving this at 11. In your data, might be worth increasing this, but\n",
    "# it will take more time to run.\n",
    "\n",
    "possible_n_clusters = np.arange(2, max_n_clusters+1) #This requires a minimum of 2 clusters.\n",
    "# When the data contain no clusters at all, it will be quite visible when inspecting the two obtained clusters, \n",
    "# as the responses of the clusters will be quite similar. This will also be visible when plotting the data in\n",
    "# the reduced dimensionality PC space (done below).\n",
    "\n",
    "\n",
    "possible_n_nearest_neighbors = np.array([10, 30, 50, 100, 200]) # This should be selected for each dataset\n",
    "# appropriately. When 4813 neurons are present, the above number of nearest neighbors provides a good sweep of the\n",
    "# parameter space. But it will need to be changed for other data.\n",
    "    \n",
    "silhouette_scores = np.nan*np.ones((possible_n_clusters.size,\n",
    "                                    possible_n_nearest_neighbors.size))\n",
    "\n",
    "for n_clustersidx, n_clusters in enumerate(possible_n_clusters):\n",
    "    for nnidx, nn in enumerate(possible_n_nearest_neighbors):\n",
    "        model = SpectralClustering(n_clusters=n_clusters, affinity='nearest_neighbors', n_neighbors=nn)\n",
    "        model.fit(transformed_data[:,:num_retained_pcs])\n",
    "        silhouette_scores[n_clustersidx, nnidx] = silhouette_score(transformed_data[:,:num_retained_pcs],\n",
    "                                                                   model.labels_,\n",
    "                                                                   metric='cosine')\n",
    "        print 'Done with numclusters = %d, num nearest neighbors = %d: score = %.3f'%(n_clusters,\n",
    "                                                                                      nn,\n",
    "                                                                                      silhouette_scores[n_clustersidx,                                                                           \n",
    "                                                                                                        nnidx])\n",
    "\n",
    "print 'Done with model fitting'\n",
    "\n",
    "temp = {}\n",
    "temp['possible_n_clusters'] = possible_n_clusters\n",
    "temp['possible_n_nearest_neighbors'] = possible_n_nearest_neighbors\n",
    "temp['silhouette_scores'] = silhouette_scores\n",
    "temp['shape'] = 'cluster_nn'\n",
    "with open(os.path.join(basedir, 'silhouette_scores.pickle'), 'wb') as f:\n",
    "    pickle.dump(temp, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will reorder the cluster labels such that there is a fixed order for naming the clusters. This is important because if you run the previous code multiple times, it will return different orders for the cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:37:24.663000Z",
     "start_time": "2019-02-19T03:37:04.713000Z"
    },
    "code_folding": [],
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 50\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'transformed_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-79a830ea27c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m#                                 linkage='average')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformed_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_retained_pcs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformed_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnum_retained_pcs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cosine'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'transformed_data' is not defined"
     ]
    }
   ],
   "source": [
    "with open(os.path.join(basedir, 'silhouette_scores.pickle'), 'rb') as f:\n",
    "    silhouette_scores = pickle.load(f)\n",
    "    \n",
    "# transformed_data = np.load(os.path.join(basedir, 'OFCCaMKII_transformed_data.npy'))\n",
    "\n",
    "# Identify optimal parameters from the above parameter space\n",
    "temp = np.where(silhouette_scores['silhouette_scores']==np.nanmax(silhouette_scores['silhouette_scores']))\n",
    "n_clusters = silhouette_scores['possible_n_clusters'][temp[0][0]]\n",
    "n_nearest_neighbors = silhouette_scores['possible_n_nearest_neighbors'][temp[1][0]]\n",
    "\n",
    "print n_clusters, n_nearest_neighbors\n",
    "\n",
    "np.random.seed(1)\n",
    "# Redo clustering with these optimal parameters\n",
    "model = SpectralClustering(n_clusters=n_clusters,\n",
    "                           affinity='nearest_neighbors',\n",
    "                           n_neighbors=n_nearest_neighbors)\n",
    "\n",
    "# model = KMeans(n_clusters=n_clusters)\n",
    "\n",
    "# model = AgglomerativeClustering(n_clusters=2,\n",
    "#                                 affinity='l2',\n",
    "#                                 linkage='average')\n",
    "\n",
    "model.fit(transformed_data[:,:num_retained_pcs])\n",
    "\n",
    "temp = silhouette_score(transformed_data[:,:num_retained_pcs], model.labels_, metric='cosine')\n",
    "\n",
    "print 'Number of clusters = %d, average silhouette = %.3f'%(len(set(model.labels_)), temp)\n",
    "\n",
    "# Save this optimal clustering model.\n",
    "# with open(os.path.join(basedir, 'clusteringmodel.pickle'), 'wb') as f:\n",
    "#     pickle.dump(model, f)\n",
    "\n",
    "          \n",
    "# Since the clustering labels are arbitrary, I rename the clusters so that the first cluster will have the most\n",
    "# positive response and the last cluster will have the most negative response.\n",
    "def reorder_clusters(rawlabels):\n",
    "    uniquelabels = list(set(rawlabels))\n",
    "    responses = np.nan*np.ones((len(uniquelabels),))\n",
    "    for l, label in enumerate(uniquelabels):\n",
    "        responses[l] = np.mean(populationdata[rawlabels==label, pre_window_size:2*pre_window_size])\n",
    "    temp = np.argsort(responses).astype(int)[::-1]\n",
    "    temp = np.array([np.where(temp==a)[0][0] for a in uniquelabels])\n",
    "    outputlabels = np.array([temp[a] for a in list(np.digitize(rawlabels, uniquelabels)-1)])\n",
    "    return outputlabels\n",
    "newlabels = reorder_clusters(model.labels_)\n",
    "\n",
    "# Create a new variable containing all unique cluster labels\n",
    "uniquelabels = list(set(newlabels))\n",
    "\n",
    "# np.save(os.path.join(basedir, 'OFCCaMKII_clusterlabels.npy'), newlabels)\n",
    "\n",
    "colors_for_cluster = [[0.933, 0.250, 0.211],\n",
    "                      [0.941, 0.352, 0.156],\n",
    "                      [0.964, 0.572, 0.117],\n",
    "                      [0.980, 0.686, 0.250],\n",
    "                      [0.545, 0.772, 0.247],\n",
    "                      [0.215, 0.701, 0.290],\n",
    "                      [0, 0.576, 0.270],\n",
    "                      [0, 0.650, 0.611],\n",
    "                      [0.145, 0.662, 0.878]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a silhouette plot to visualize clustering quality. The average silhouette score above is the mean of all sample silhouettes. There are two other clustering methods commented out above (KMeans and agglomerative). One can see that if you apply those methods, the average silhouette score is lower, demonstrating that those methods are worse for these data. This also then shows that the high dimensional geometry for these data is unlikely to be a simple Gaussian structure, as these other methods are specialized for picking up Gaussian clusters. Spectral clustering is effective as it can uncover peculiarly shaped clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_silhouette_plot(X, cluster_labels):\n",
    "    \n",
    "    n_clusters = len(set(cluster_labels))\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    fig.set_size_inches(4, 4)\n",
    "\n",
    "    # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax.set_xlim([-0.4, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "    silhouette_avg = silhouette_score(X, cluster_labels, metric='cosine')\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X, cluster_labels, metric='cosine')\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = colors_for_cluster[i]\n",
    "        ax.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.9)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i+1))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax.set_xticks([-0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tpk2DLMsFYryu0pzgu94FCt+J2MKe7/F4qGXLlpSUlESLFy+mzz//nMaOHVvoCW0f38n5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\nXRDBThAEXfgOBNZxR2yVbiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1b499b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_silhouette_plot(transformed_data[:,:num_retained_pcs], model.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average silhouette coefficient is about 0.2. As a general rule, this is a fairly low clustering quality index. However, if the range of spread of the data in the high dimensional space is fixed, silhouette score will necessarily be lower when there are many clusters in the data. Thus, this silhouette cannot by itself be used to argue against clustering in these data. This is why the clustering quality in this case needs to be verified through other means, such as projection specificity or response stability for clusters.\n",
    "\n",
    "The neurons with negative silhouette scores are assigned to the wrong cluster. So one possible approach could be to run clustering multiple times (it's stochastic) and only select neurons that are reliably assigned to their corresponding cluster. This will improve cluster-wise inference for other analyses. However, I wanted to keep the analysis simpler and hence, decided not to do such filtering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the identified clusters. This plot is a good comparison to the original plot of the data, which did not cluster the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T03:39:00.214000Z",
     "start_time": "2019-02-19T03:37:53.323000Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aLLpEnXnM3EHIM8hzP+aIqjFLWqeY3oyLa/RqMyiGbQ971CnpYOFz71XEgCuJ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eYJrctj7cLcSAiPOmtO022n+9+fJ7n2dV+TpSJlKhY1S94u+ouT6eG4LWcXlbyd2DKwY5/d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CEJ+H+UJKDC46ffhcGu19Ah5Z2DK197KjhhOm5OcpM/BnV8AvvQI+fOnelTUbOpIIOu8E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\nH+M/GkRLR0vaNwR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6CtSQtsaEQq8ULXb/Wat2i/vK5P4/ubfUIimRAUludCjcPlg1/acK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\naQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc19583ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmax = 6\n",
    "sortwindow = [pre_window_size, window_size]\n",
    "\n",
    "fig, axs = plt.subplots(len(trial_types)+1,len(uniquelabels),\n",
    "                        figsize=(2*len(uniquelabels),2*(len(trial_types)+1)))\n",
    "cbar_ax = fig.add_axes([.94, .3, .01, .4])\n",
    "cbar_ax.tick_params(width=0.5) \n",
    "\n",
    "numroisincluster = np.nan*np.ones((len(uniquelabels),))\n",
    "\n",
    "for c, cluster in enumerate(uniquelabels):\n",
    "    for k, tempkey in enumerate(trial_types):\n",
    "        temp = populationdata[np.where(newlabels==cluster)[0], k*window_size:(k+1)*window_size]\n",
    "        numroisincluster[c] = temp.shape[0]\n",
    "        sortresponse = np.argsort(np.mean(temp[:,sortwindow[0]:sortwindow[1]], axis=1))[::-1]\n",
    "        sns.heatmap(temp[sortresponse],\n",
    "                    ax=axs[k, cluster],\n",
    "                    cmap=plt.get_cmap('coolwarm'),\n",
    "                    vmin=-cmax,\n",
    "                    vmax=cmax,\n",
    "                    cbar=(cluster==0),\n",
    "                    cbar_ax=cbar_ax if (cluster==0) else None,\n",
    "                    cbar_kws={'label': 'Normalized fluorescence'})\n",
    "        axs[k, cluster].grid(False)\n",
    "        axs[k, cluster].set_xticks([])\n",
    "        axs[k, cluster].set_yticks([])\n",
    "        axs[k, cluster].axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)\n",
    "        axs[k, cluster].axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5) \n",
    "        if cluster==0:\n",
    "            axs[k, 0].set_ylabel('%s\\nNeurons'%(tempkey))\n",
    "        ax = axs[-1, cluster]\n",
    "        if cluster==0:\n",
    "            sns.tsplot(temp, ax=ax, color=colors_for_key[tempkey],\n",
    "                       condition=tempkey)\n",
    "            ax.set_ylabel('Mean per cluster')\n",
    "        else:\n",
    "            sns.tsplot(temp, ax=ax, color=colors_for_key[tempkey])\n",
    "            ax.set_yticklabels([])\n",
    "        ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)\n",
    "        ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5) \n",
    "        ax.set_ylim([-2, 10])\n",
    "        standardize_plot_graphics(ax)\n",
    "        ax.set_xticks([0, pre_window_size, window_size]) \n",
    "        ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "                                         for a in [0, pre_window_size,\n",
    "                                                   window_size]])\n",
    "    axs[0, cluster].set_title('Cluster %d\\n(n=%d)'%(cluster+1, numroisincluster[c]))\n",
    "axs[-1, 0].legend(loc='lower right')\n",
    "    \n",
    "fig.text(0.5, 0.01, 'Time from action (s)', fontsize=12,\n",
    "         horizontalalignment='center', verticalalignment='center', rotation='horizontal')\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.1)\n",
    "# fig.subplots_adjust(left=0.03)\n",
    "fig.subplots_adjust(right=0.93)\n",
    "# fig.subplots_adjust(bottom=0.2)\n",
    "# fig.subplots_adjust(top=0.83)\n",
    "\n",
    "fig.savefig(os.path.join(basedir, 'Clustering results.png'), format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir, 'Clustering results.pdf'), format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Clus 1  Clus 2  Clus 3  Clus 4  Clus 5  Clus 6  Clus 7  Clus 8\n",
      "Mouse 1      16      10       0       7       3       6       7       3\n",
      "Mouse 2       4       4      12       8       9      22       5      10\n",
      "Mouse 3       4       9       0       8       1       1       0       4\n",
      "Mouse 4       2       7      10      10      15       6      17      28\n"
     ]
    }
   ],
   "source": [
    "numcells_per_animal = np.array([52, 74, 27, 95])\n",
    "animals = ['Mouse '+str(a+1) for a in range(len(numcells_per_animal))]\n",
    "cum_numcells_per_animal = np.concatenate((np.array([0]),np.cumsum(numcells_per_animal)))\n",
    "\n",
    "uniquelabels = list(set(newlabels))\n",
    "    \n",
    "clusterlabels = {}\n",
    "\n",
    "numneuronsperlabel = np.nan*np.ones((len(animals), len(uniquelabels)))\n",
    "for a, animal in enumerate(animals):\n",
    "    clusterlabels[animal] = newlabels[cum_numcells_per_animal[a]:cum_numcells_per_animal[a+1]]\n",
    "    for l, label in enumerate(uniquelabels):\n",
    "        numneuronsperlabel[a, l] = np.sum(clusterlabels[animal]==label)\n",
    "\n",
    "\n",
    "df = pandas.DataFrame(data=numneuronsperlabel.astype(int),\n",
    "                      columns=['Clus '+str(l+1) for l in uniquelabels],\n",
    "                      index=animals).sort_index()\n",
    "print df\n",
    "df.to_csv(os.path.join(basedir, \"Num neurons per cluster and animal.csv\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save(os.path.join(basedir, \"newlabels.npy\"), newlabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newlabels = np.load(os.path.join(basedir, \"newlabels.npy\"))\n",
    "np.savetxt(os.path.join(basedir, \"clusterlabelsforcell.txt\"),\n",
    "           np.stack((np.arange(len(newlabels))+1, newlabels+1), axis=1),\n",
    "           header='Cell number     Cluster ID', fmt='%d',\n",
    "           delimiter='                      ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newlabels = np.load(os.path.join(basedir, \"newlabels.npy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:31: RuntimeWarning: Mean of empty slice\n",
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:32: RuntimeWarning: Mean of empty slice\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-3, 248, 0.50705972049826331, 0.46586649776113764)\n",
      "(0, 248, 0.57366796104803608, 0.4671836825577092)\n",
      "(3, 248, 0.55201033034910951, 0.46704083660823786)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "binsize = 3 # The size of a time bin over which decoding is performed\n",
    "if 'corrected baseline' in basedir:\n",
    "    window_start = 0\n",
    "    window_end = 10\n",
    "else:\n",
    "    window_start = -3\n",
    "    window_end = 7\n",
    "starttimes = np.arange(window_start, window_end, binsize)\n",
    "if np.abs(window_end-starttimes[-1]-binsize)>1E-5:\n",
    "    starttimes = starttimes[:-1]\n",
    "\n",
    "decoding_accuracies = np.nan*np.ones((numneurons, len(starttimes)))\n",
    "decoding_nullaccuracies = np.nan*np.ones((numneurons, len(starttimes)))\n",
    "ttestresults_all = np.nan*np.ones((len(starttimes), 2)) # t score, p value\n",
    "\n",
    "mean_or_allframes = 'slope' # Take mean per bin or use all frames per bin\n",
    "min_num_good_trials = 2 # Minimum number of trials with data for the given time bin and neuron\n",
    "# If there are fewer trials than this for each action, set accuracy to nan for the neuron\n",
    "\n",
    "\n",
    "# fig, axs = plt.subplots(1, len(starttimes), figsize=(3*len(starttimes), 3))\n",
    "\n",
    "for s, starttime in enumerate(starttimes):\n",
    "    endtime = starttime + binsize # in s\n",
    "    startindex = int(np.where(np.abs(timepoints-starttime)<1E-5)[0][0])\n",
    "    endindex = int(np.where(np.abs(timepoints-endtime)<1E-5)[0][0])\n",
    "    for neuron in range(numneurons):\n",
    "        tempnumtrials_actionlocking = np.sum(np.isfinite(np.nanmean(actionlocking[:,:,neuron],\n",
    "                                                                    axis=1)))\n",
    "        tempnumtrials_runaway = np.sum(np.isfinite(np.nanmean(runaway[:,:,neuron], axis=1)))\n",
    "#         print(startindex, endindex, tempnumtrials_actionlocking, tempnumtrials_runaway)\n",
    "    #     if np.amin([tempnumtrials_runaway, tempnumtrials_actionlocking])<6:\n",
    "    #         continue\n",
    "        data = np.concatenate((actionlocking[:tempnumtrials_actionlocking,\n",
    "                                             startindex:endindex,neuron],\n",
    "                               runaway[:tempnumtrials_runaway,\n",
    "                                       startindex:endindex,neuron]))\n",
    "        \n",
    "#         tempnumtrials_actionlocking = 3\n",
    "#         tempnumtrials_runaway = 3\n",
    "#         data = np.random.normal(size=(tempnumtrials_actionlocking + tempnumtrials_runaway, 1))\n",
    "        labels = np.concatenate((np.zeros((tempnumtrials_actionlocking)),\n",
    "                                 np.ones((tempnumtrials_runaway))))\n",
    "    \n",
    "        labels = labels[np.isfinite(np.nanmean(data, axis=1))]\n",
    "        data = data[np.isfinite(np.nanmean(data, axis=1)),:]\n",
    "        \n",
    "        if mean_or_allframes == 'mean':\n",
    "            data = np.mean(data, axis=1)[:,None]\n",
    "        elif mean_or_allframes == 'pca':\n",
    "            pca = PCA(n_components=data.shape[1], whiten=False)\n",
    "            pca.fit(data)\n",
    "\n",
    "            data = pca.transform(data)[:,:1]\n",
    "        elif mean_or_allframes == 'slope':\n",
    "            \n",
    "            temp = np.nan*np.ones((data.shape[0], 3)) # mean, slope and y-intercept\n",
    "            for i in range(data.shape[0]):\n",
    "                temp_idx = np.where(np.isfinite(data[i,:])==1)[0][0]\n",
    "                lm = sm.OLS(data[i,temp_idx:],\n",
    "                            sm.add_constant(np.arange(data[i,temp_idx:].shape[0]))).fit()\n",
    "#                 temp[i, 0] = np.mean(data[i,temp_idx:])\n",
    "                temp[i, 0] = lm.params[1]\n",
    "                temp[i, 1] = lm.params[0]\n",
    "                temp[i, 2] = np.std(data[i,temp_idx:])\n",
    "                \n",
    "            data = temp\n",
    "        elif mean_or_allframes == 'all':\n",
    "            data = data\n",
    "         \n",
    "        if np.amin([np.sum(labels==0), np.sum(labels==1)]) >= min_num_good_trials:\n",
    "            decoding_accuracies[neuron, s], temp = \\\n",
    "            run_binaryclassifier(labels, data, '', cv='loo', numshuffles=10)\n",
    "            decoding_nullaccuracies[neuron, s] = np.mean(temp)\n",
    "        else:\n",
    "            decoding_nullaccuracies[neuron, s] = np.nan\n",
    "\n",
    "        \n",
    "\n",
    "    print(starttime, np.sum(np.isfinite(decoding_accuracies[:,s])),\n",
    "          np.mean(decoding_accuracies[np.isfinite(decoding_accuracies[:,s]), s]),\n",
    "          np.mean(decoding_nullaccuracies[np.isfinite(decoding_nullaccuracies[:,s]), s]))\n",
    "#     fig, ax = plt.subplots()\n",
    "#     CDFplot(decoding_accuracies[:, s], axs[s], color='r', label='data')\n",
    "#     CDFplot(decoding_nullaccuracies[:, s], axs[s], color='k', label='shuffled')\n",
    "    ttestresults_all[s,:] = stats.ttest_ind(decoding_accuracies[np.isfinite(decoding_accuracies[:,s]), s],\n",
    "                                            decoding_nullaccuracies[np.isfinite(decoding_accuracies[:,s]), s])\n",
    "    ttestresults_all[s,1] *= 2 # One sided t test\n",
    "#     print(np.mean(decoding_accuracies, axis=0))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ttestresults_all[:,1] = Benjamini_Hochberg_pvalcorrection(ttestresults_all[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.22640765e+00,   2.67340707e-03],\n",
       "       [  9.36923892e+00,   1.56148623e-18],\n",
       "       [  7.53926598e+00,   6.83943808e-13]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttestresults_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if 'corrected baseline' in basedir:\n",
    "    starttimes -= 3 # Label axes ticks wrt action times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, (248,))\n",
      "(1, (248,))\n",
      "(2, (248,))\n"
     ]
    },
    {
     "data": {
      "image/png": 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l5+dj9+7d8PHxwd27dxESEoJ169aJXO+tizEmUjmiVatWVEmCtHgNJihfX19M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\nhUQJihBCiEL6PwAAA6kp2fUJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1cdb1c210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(6, 3))    \n",
    "\n",
    "for s in range(len(starttimes)):\n",
    "    if (ttestresults_all[s,1]<0.05) and (ttestresults_all[s,0]>0):\n",
    "        color = 'r'\n",
    "        marker = '*'\n",
    "    else:\n",
    "        color = 'k'\n",
    "        marker = 'o'\n",
    "        \n",
    "    ax = axs[0]    \n",
    "    temp = decoding_accuracies[np.isfinite(decoding_accuracies[:,s]), s]\n",
    "    temp1 = decoding_nullaccuracies[np.isfinite(decoding_nullaccuracies[:,s]), s]\n",
    "    print(s, temp.shape)\n",
    "    ax.errorbar(starttimes[s], np.mean(temp),\n",
    "                stats.sem(temp), color=color, marker=marker)\n",
    "    ax.errorbar(starttimes[s], np.mean(temp1),\n",
    "                stats.sem(temp1), color=0.3*np.ones((3,)),\n",
    "                marker='o')\n",
    "    \n",
    "#     if s==0 or s==3:\n",
    "#         ax.axvline(s, linestyle='--', color='k', linewidth=0.5) \n",
    "    \n",
    "    \n",
    "    ax = axs[1]\n",
    "    sem_diff = np.sqrt(np.var(temp)/temp.shape[0]+\n",
    "                       np.var(temp1)/temp1.shape[0])\n",
    "    \n",
    "    ax.errorbar(starttimes[s], \n",
    "                np.mean(temp)-np.mean(temp1),\n",
    "                sem_diff, color=color, marker=marker)\n",
    "#     if s==0 or s==3:\n",
    "#         ax.axvline(s, linestyle='--', color='k', linewidth=0.5) \n",
    "    \n",
    "axs[0].set_ylabel('Mean single cell\\ndecoding accuracy')\n",
    "axs[1].set_ylabel('Mean single cell decoding\\naccuracy above chance')\n",
    "axs[1].set_ylim([-0.02, 0.15])\n",
    "axs[1].axhline(0, color='k', ls='--', lw=0.5)\n",
    "\n",
    "for ax in axs:\n",
    "    ax.set_xlabel('Bin start wrt action (s)')\n",
    "    ax.set_xlim([-4, 7])\n",
    "    ax.set_xticks(starttimes)\n",
    "    standardize_plot_graphics(ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies for %s fluor per %ds bin.png'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies for %s fluor per %ds bin.pdf'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='pdf')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoding accuracies per cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newlabels = np.load(os.path.join(basedir, 'newlabels.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clusters = list(set(newlabels))\n",
    "ttestresults = np.nan*np.ones((len(starttimes), len(clusters), 2)) # t score, p value\n",
    "for s in range(len(starttimes)):\n",
    "    for c, cluster in enumerate(clusters):\n",
    "        temp = np.where(newlabels==cluster)[0]\n",
    "        temp1 = decoding_accuracies[temp, s]\n",
    "        temp1 = temp1[np.isfinite(temp1)]\n",
    "        temp2 = decoding_nullaccuracies[temp, s]\n",
    "        temp2 = temp2[np.isfinite(temp2)]\n",
    "        ttestresults[s,c,:] = stats.ttest_ind(temp1, temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = Benjamini_Hochberg_pvalcorrection(np.ravel(ttestresults[:,:,1]))\n",
    "ttestresults[:,:,1] = temp.reshape(ttestresults[:,:,1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  4.41294352e-01,   7.55317033e-01],\n",
       "        [  1.17934985e-01,   9.06526654e-01],\n",
       "        [  2.53793886e+00,   2.98977204e-02],\n",
       "        [  1.20102658e+00,   2.95789882e-01],\n",
       "        [  3.45731063e-01,   7.97332527e-01],\n",
       "        [  2.58025747e+00,   2.62617516e-02],\n",
       "        [  7.77863912e-01,   5.27908004e-01],\n",
       "        [  1.93662679e+00,   7.90584497e-02]],\n",
       "\n",
       "       [[  6.61599802e+00,   2.87281671e-07],\n",
       "        [  5.02740309e+00,   4.06983622e-05],\n",
       "        [  4.15506350e+00,   5.35487602e-04],\n",
       "        [  4.19779001e+00,   3.39458817e-04],\n",
       "        [  2.06664195e+00,   6.53656098e-02],\n",
       "        [ -1.91909385e-01,   8.85271425e-01],\n",
       "        [  2.25020563e+00,   4.54124711e-02],\n",
       "        [  4.32964094e+00,   1.89202148e-04]],\n",
       "\n",
       "       [[  3.12994200e+00,   7.77913835e-03],\n",
       "        [  8.65645523e+00,   1.17909978e-10],\n",
       "        [  2.98904420e+00,   1.11883311e-02],\n",
       "        [  4.42880490e+00,   1.89202148e-04],\n",
       "        [  3.26762859e+00,   5.66615231e-03],\n",
       "        [ -2.44438976e+00,   3.15832005e-02],\n",
       "        [  2.41983834e+00,   3.22257141e-02],\n",
       "        [  1.67128178e+00,   1.30958291e-01]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttestresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1e9d41a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, len(clusters), figsize=(3*len(clusters), 3*2),\n",
    "                        sharey='row', sharex='all')    \n",
    "\n",
    "for s in range(len(starttimes)):\n",
    "    for c, cluster in enumerate(clusters):\n",
    "        temp = np.where(newlabels==cluster)[0]\n",
    "        temp1 = decoding_accuracies[temp, s]\n",
    "        temp1 = temp1[np.isfinite(temp1)]\n",
    "        temp2 = decoding_nullaccuracies[temp, s]\n",
    "        temp2 = temp2[np.isfinite(temp2)]\n",
    "        \n",
    "#         print(s, cluster+1, temp1.shape, temp2.shape)\n",
    "        if (ttestresults[s,c,1]<0.05) and (ttestresults[s,c,0]>0):\n",
    "            color = 'r'\n",
    "            marker = '*'\n",
    "        else:\n",
    "            color = 'k'\n",
    "            marker = 'o'\n",
    "\n",
    "        ax = axs[0,c]    \n",
    "        ax.errorbar(starttimes[s], np.mean(temp1),\n",
    "                    stats.sem(temp1), color=color, marker=marker)\n",
    "        ax.errorbar(starttimes[s], np.mean(temp2),\n",
    "                    stats.sem(temp2), color=0.3*np.ones((3,)),\n",
    "                    marker='o')\n",
    "        if s==0:\n",
    "            ax.axvline(s, linestyle='--', color='k', linewidth=0.5) \n",
    "        standardize_plot_graphics(ax)\n",
    "        ax.set_title('Cluster %d'%(cluster+1))\n",
    "\n",
    "        ax = axs[1,c]\n",
    "        sem_diff = np.sqrt(np.var(temp1)/temp1.shape[0]+\n",
    "                           np.var(temp2)/temp2.shape[0])\n",
    "\n",
    "        ax.errorbar(starttimes[s], \n",
    "                    np.mean(temp1)-np.mean(temp2),\n",
    "                    sem_diff, color=color, marker=marker)\n",
    "        ax.set_xlim([-4, 7])\n",
    "        ax.axhline(0, color='k', ls='--', lw=0.5)\n",
    "        ax.set_xticks(starttimes)\n",
    "        if s==0:\n",
    "            ax.axvline(s, linestyle='--', color='k', linewidth=0.5) \n",
    "        standardize_plot_graphics(ax)\n",
    "    \n",
    "axs[0,0].set_ylabel('Mean single cell\\ndecoding accuracy')\n",
    "axs[1,0].set_ylabel('Mean single cell decoding\\naccuracy above chance')\n",
    "\n",
    "# for ax in axs:\n",
    "#     ax.set_xlabel('Bin start (s)')\n",
    "#     ax.set_xlim([-4, 7])\n",
    "#     ax.set_xticks(starttimes)\n",
    "fig.text(0.5, 0.01, 'Bin start wrt action (s)', fontsize=12,\n",
    "         horizontalalignment='center', verticalalignment='center', rotation='horizontal')\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies per cluster for %s fluor per %ds bin.png'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies per cluster for %s fluor per %ds bin.pdf'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='pdf')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KyiCNBeD//d//AQBmzZqVrw5fvnyJBg0aKNsNGjRAamrqa4ZHRERUSoSGAiNH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\nAMwIIADAjAACAMwIIADAjAACAMwEnALPF0vByxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1e96743d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, len(starttimes), figsize=(3*len(starttimes), 3),\n",
    "                        sharey='row', sharex='all')    \n",
    "\n",
    "axes_titles = ['Pre\\nbehavior onset', 'Immediately after\\nbehavior onset',\n",
    "               'Later after\\nbehavior onset']\n",
    "\n",
    "for s in range(len(starttimes)):\n",
    "    ax = axs[s]\n",
    "    for c, cluster in enumerate(clusters):\n",
    "        temp = np.where(newlabels==cluster)[0]\n",
    "        temp1 = decoding_accuracies[temp, s]\n",
    "        temp1 = temp1[np.isfinite(temp1)]\n",
    "        temp2 = decoding_nullaccuracies[temp, s]\n",
    "        temp2 = temp2[np.isfinite(temp2)]\n",
    "        \n",
    "#         print(s, cluster+1, temp1.shape, temp2.shape)\n",
    "        if (ttestresults[s,c,1]<0.05) and (ttestresults[s,c,0]>0):\n",
    "            color = 'r'\n",
    "        else:\n",
    "            color = 'k'\n",
    "        marker = 'o'\n",
    "\n",
    "        \n",
    "        sem_diff = np.sqrt(np.var(temp1)/temp1.shape[0]+\n",
    "                           np.var(temp2)/temp2.shape[0])\n",
    "\n",
    "        ax.errorbar(cluster+1, \n",
    "                    np.mean(temp1)-np.mean(temp2),\n",
    "                    sem_diff, color=color, marker=marker)\n",
    "\n",
    "    ax.axhline(0, color='k', ls='--', lw=0.5)\n",
    "    ax.set_title(axes_titles[s])\n",
    "    ax.set_xticks([clust+1 for clust in clusters])\n",
    "    ax.set_xlabel('Cluster ID')\n",
    "    standardize_plot_graphics(ax)\n",
    "    \n",
    "# axs[0,0].set_ylabel('Mean single cell\\ndecoding accuracy')\n",
    "axs[0].set_ylabel('Mean single cell decoding\\naccuracy above chance')\n",
    "\n",
    "# for ax in axs:\n",
    "#     ax.set_xlabel('Bin start (s)')\n",
    "#     ax.set_xlim([-4, 7])\n",
    "#     ax.set_xticks(starttimes)\n",
    "# fig.text(0.5, 0.01, 'Cluster ID', fontsize=12,\n",
    "#          horizontalalignment='center', verticalalignment='center', rotation='horizontal')\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies per epoch for %s fluor per %ds bin.png'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='png', dpi=300)\n",
    "fig.savefig(os.path.join(basedir,\n",
    "                         'decoding accuracies per epoch for %s fluor per %ds bin.pdf'%(mean_or_allframes,\n",
    "                                                                             binsize)),\n",
    "            format='pdf')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot trials from neurons that had high decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stuberlab/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:23: RuntimeWarning: Mean of empty slice\n"
     ]
    },
    {
     "data": {
      "image/png": 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tt9tFamqqSEpKEps3bxa3b98Wly9fFmlpaY9t42vUHGd1dbVISUkRSUlJIisr\nS3R1dU1jxVOTn5+vjPbMlPdT1Tg/0Uzk/Z9KiHTC8JO0GH6SFsNP0mL4SVoMP0mL4SdpMfwkrf8H\n7/HoUONSlloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1e918d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "starttime_of_interest = 2 \n",
    "# Set above to 0 for pre behavior onset, 1 for immediately after behavior onset\n",
    "# and 2 for later after behavior onset.\n",
    "temp = decoding_accuracies[:,starttime_of_interest]-\\\n",
    "       decoding_nullaccuracies[:, starttime_of_interest]\n",
    "neurons = np.where(temp>0.3)[0]\n",
    "\n",
    "cmax = 8\n",
    "\n",
    "fig, axs = plt.subplots(3,1,figsize=(3*1,3*2), sharex='all', sharey='row')\n",
    "cbar_ax = fig.add_axes([0.77, .35, .01, .3])\n",
    "cbar_ax.tick_params(width=0.5)\n",
    "for neuron in neurons:\n",
    "    temp1 = runaway[:,:,neuron]\n",
    "    temp2 = actionlocking[:,:,neuron]\n",
    "\n",
    "    trialdata = np.stack((temp1, temp2), axis=2)\n",
    "\n",
    "     \n",
    "\n",
    "    for t in range(len(trial_types)):\n",
    "        ax = axs[t]\n",
    "        ax.set_title(trial_types[t])\n",
    "        tempidx = np.where(np.isnan(np.nanmean(trialdata[:, :, t], axis=1)))[0][0]\n",
    "        sns.heatmap(trialdata[:tempidx, :, t],\n",
    "                    ax=ax,\n",
    "                    cmap=plt.get_cmap('coolwarm'),\n",
    "                    vmin=-cmax,\n",
    "                    vmax=cmax,\n",
    "                    cbar=(t==0),\n",
    "                    cbar_ax=cbar_ax if (t==0) else None,\n",
    "                    cbar_kws={'label': 'z-score'})\n",
    "        ax.grid(False)\n",
    "        ax.tick_params(width=0.5)   \n",
    "    #     ax.set_xticks([0, pre_window_size, window_size]) \n",
    "    #     ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "    #                                      for a in [0, pre_window_size,\n",
    "    #                                                window_size]])\n",
    "        ax.set_yticks(range(tempidx))\n",
    "        ax.set_yticklabels([a+1 for a in range(tempidx)])\n",
    "        ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "    #     ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5)     \n",
    "    #     ax.set_xlabel('Time from action (s)')\n",
    "        ax.set_ylabel('Trial number')\n",
    "\n",
    "        ax = axs[-1]\n",
    "        sns.tsplot(trialdata[:tempidx, :, t],\n",
    "                   ax=ax, color=colors_for_key[trial_types[t]],\n",
    "                   condition=trial_types[t],\n",
    "                   estimator=np.nanmean)\n",
    "        ax.axvline(pre_window_size, linestyle='--', color='k', linewidth=0.5)   \n",
    "    #     ax.axvline(np.where(timepoints==0)[0][0], linestyle='--', color='k', linewidth=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    axs[-1].set_ylabel('Mean norm. fluor.')\n",
    "    axs[-1].legend()       \n",
    "    axs[-1].set_xlabel('Time from action (s)')\n",
    "    standardize_plot_graphics(axs[-1])\n",
    "\n",
    "    for ax in axs:\n",
    "        ax.set_xticks([0, pre_window_size, window_size]) \n",
    "        ax.set_xticklabels([str(int((a-pre_window_size+0.0)/framerate))\n",
    "                                         for a in [0, pre_window_size,\n",
    "                                                   window_size]],\n",
    "                           rotation=0)\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.subplots_adjust(right=0.72)\n",
    "\n",
    "    fig.savefig(os.path.join(basedir,'exampletrials',\n",
    "                             'Trials from neuron %d.png'%(neuron+1)),\n",
    "                format='png', dpi=300)\n",
    "    fig.savefig(os.path.join(basedir, 'exampletrials',\n",
    "                             'Trials from neuron %d.pdf'%(neuron+1)), format='pdf')\n",
    "    for ax in axs:\n",
    "        ax.clear()"
   ]
  }
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