eeg_my.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANALISI DATI EEG con algoritmo di Sinkhorn e algoritmo FreeEnergy\n",
    "\n",
    "autore: Miriam Zara, Università degli studi di Padova, ultima modifica 09/03/2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "necessario importare il file \"my.sinkhorn.py\", che contiene l'algoritmo di Sinkhorn, e il file \"freeot.py\", che contiene l'algoritmo free energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vk/kftm8379123bsmwrdp8l0xr00000gn/T/ipykernel_1569/2197354630.py:5: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pl\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import statsmodels as sm\n",
    "from statsmodels.stats.anova import AnovaRM\n",
    "import freeot\n",
    "import mysinkhorn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPUT: best_maps.csv ogni riga contiene le 64 coordinate di un centroide. i centroidi sono 9.\n",
    "\n",
    "OUTPUT:  cost_sqeuclidean= np.array((9,9)). Le entrate sono normalizzate ad 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data= np.loadtxt('best_maps.csv', delimiter= ',')\n",
    "centroidi=np.array(data)\n",
    "n_cluster=centroidi.shape[0]\n",
    "n_channels=centroidi.shape[1]\n",
    "cost_euclidean=np.zeros((9,9))\n",
    "cost_sqeuclidean=np.zeros((9,9))\n",
    "for i in range(n_cluster):\n",
    "    for j in range(n_cluster):\n",
    "        cost_euclidean[i,j]=np.linalg.norm(centroidi[i]-centroidi[j])\n",
    "        cost_sqeuclidean[i,j]=np.linalg.norm(centroidi[i]-centroidi[j])**2\n",
    "cost_euclidean/=cost_euclidean.max()\n",
    "cost_sqeuclidean/=cost_sqeuclidean.max()\n",
    "fig, ax2 = plt.subplots(figsize = (5,5))\n",
    "im2=ax2.imshow(cost_sqeuclidean, cmap='Greys')\n",
    "positions=np.arange(9)\n",
    "ax2.set_xlabel(\"cluster\", fontsize=14)\n",
    "ax2.set_ylabel(\"cluster\", fontsize=14)\n",
    "ax2.set_xticks(positions)\n",
    "ax2.set_xticklabels(['A','B','C','D','E','F','G','H','I'], fontsize=14)\n",
    "ax2.set_yticks(positions)\n",
    "ax2.set_yticklabels(['A','B','C','D','E','F','G','H','I'], fontsize=14)\n",
    "\n",
    "#cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7])  # Define position and size of the colorbar axis\n",
    "cbar = fig.colorbar(im2)\n",
    "plt.suptitle(r'Matrice di costo $C_{i,\\,j}=||x_{i}-x_j||^{2}$', fontsize=16)\n",
    "plt.savefig(\"EEG_matrice.svg\")\n",
    "plt.savefig(\"EEG_matrice.eps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analisi con Sinkhorn\n",
    "\n",
    "Imput: 'prob_states.csv', colonne= [Subject,Cond,Int,State,Prob]\n",
    "\n",
    "Output: panda DataFrame 'results_df', colonne= ['Soggetto', 'C/I', 'PC', 'Costo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of iterations reached (1000), convergence criterion NOT reached\n",
      "Maximum number of iterations reached (1000), convergence criterion NOT reached\n",
      "Maximum number of iterations reached (1000), convergence criterion NOT reached\n",
      "    Subject Cond Int      Cost\n",
      "0         1    C   H  0.025178\n",
      "1         1    C   M   0.02208\n",
      "2         1    C   L  0.023266\n",
      "3         1    I   H  0.051705\n",
      "4         1    I   M  0.043137\n",
      "..      ...  ...  ..       ...\n",
      "259      44    C   M  0.022259\n",
      "260      44    C   L  0.020402\n",
      "261      44    I   H  0.022364\n",
      "262      44    I   M  0.019625\n",
      "263      44    I   L  0.025793\n",
      "\n",
      "[264 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "dati_df=pd.read_csv('prob_states.csv')\n",
    "n_soggetti=44\n",
    "n_task=7\n",
    "n_task_effettive=6\n",
    "n_cluster=9\n",
    "nomi_colonne_results=['Subject', 'Cond', 'Int', 'Cost' ]\n",
    "results_df=pd.DataFrame(index=range(n_soggetti*6), columns=nomi_colonne_results)\n",
    "lunghezza_blocco_soggetto=n_cluster*n_task\n",
    "resting_temp=np.zeros(n_cluster)\n",
    "task_temp=np.zeros(n_cluster)\n",
    "\n",
    "epsilon = 0.01\n",
    "\n",
    "for n in np.arange(n_soggetti):\n",
    "    for k in np.arange(n_cluster):\n",
    "            resting_temp[k]=dati_df['Prob'][(lunghezza_blocco_soggetto*n)+k]\n",
    "    for m in np.arange(1, 7):\n",
    "        for p in np.arange(n_cluster):\n",
    "            task_temp[p]=dati_df['Prob'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)+p]\n",
    "        #######################################################\n",
    "        G_sinkhorn=mysinkhorn.sinkhorn_solver(resting_temp, task_temp, epsilon, cost_sqeuclidean)\n",
    "        matbis=np.multiply(cost_sqeuclidean,G_sinkhorn)\n",
    "        ######################################################\n",
    "        costo=np.sum(matbis)\n",
    "        results_df.loc[n*6+m-1, 'Subject']=n+1 \n",
    "        results_df.loc[n*6+m-1,'Cond']=dati_df['Cond'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)] #condizione C /I\n",
    "        results_df.loc[n*6+m-1,'Int' ]=dati_df['Int'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)] #int H,M,L\n",
    "        results_df.loc[n*6+m-1,'Cost']=costo\n",
    "\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_values = ['C', 'I']\n",
    "b_values = ['H','M','L']\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,5))\n",
    "\n",
    "counter = -1\n",
    "\n",
    "medie = []\n",
    "errore_medie = []\n",
    "\n",
    "for a in a_values:\n",
    "    for b in b_values:\n",
    "        counter += 1\n",
    "        prova = results_df[results_df['Cond']==a]\n",
    "        prova = prova[prova['Int']==b]\n",
    "        prova_cost=prova['Cost'].values\n",
    "        media = prova_cost.mean(); medie += [media]\n",
    "        errore_media = prova_cost.std()/np.sqrt(prova_cost.size); errore_medie += [errore_media]\n",
    "        ax1.scatter(np.random.uniform(counter-0.1, counter+0.1, prova_cost.size), prova_cost , color='lightgrey')\n",
    "\n",
    "\n",
    "ax1.errorbar([0,1,2], [medie[0], medie[1], medie[2]], yerr= [errore_medie[0],errore_medie[1], errore_medie[2]], color='blue')\n",
    "ax1.plot([0, 1, 2], [medie[0], medie[1], medie[2]], marker='o', markersize=6, color='blue', linestyle='None')\n",
    "ax1.errorbar([3,4,5], [medie[3],medie[4], medie[5]], yerr= [errore_medie[3],errore_medie[4],errore_medie[5]], color='red')\n",
    "ax1.plot([3, 4, 5], [medie[3], medie[4], medie[5]], marker='o', markersize=6, color='red', linestyle='None')\n",
    "\n",
    "xpositions=np.arange(n_task-1)\n",
    "ax1.set_xlabel(\"task\", fontsize=14)\n",
    "ax1.set_ylabel(\"Costo ottimale\", fontsize=14)\n",
    "ax1.set_xticks(xpositions)\n",
    "ax1.set_xticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "\n",
    "#################### test T di student #########################################\n",
    "pvalues=np.zeros((n_task-1, n_task-1))\n",
    "counter_rows = -1\n",
    "\n",
    "for a in a_values:\n",
    "    for b in b_values:\n",
    "        counter_rows += 1\n",
    "        counter_cols = -1\n",
    "        for a_bis in a_values:\n",
    "            for b_bis in b_values:\n",
    "                counter_cols += 1\n",
    "                prova1 = results_df[results_df['Cond']==a]\n",
    "                prova1 = prova1[prova1['Int']==b]\n",
    "                prova1_cost=np.array(prova1['Cost'].values)\n",
    "\n",
    "                prova2 = results_df[results_df['Cond']==a_bis]\n",
    "                prova2 = prova2[prova2['Int']==b_bis]\n",
    "                prova2_cost=np.array(prova2['Cost'].values)\n",
    "\n",
    "\n",
    "                ttest=stats.ttest_ind(prova1_cost.tolist(), prova2_cost.tolist())\n",
    "                pvalues[counter_rows,counter_cols]=ttest.pvalue\n",
    "\n",
    "ax2.set_title(\"p-values del t-test\", fontsize = 14)\n",
    "im=ax2.imshow(pvalues, cmap='Grays')\n",
    "ax2.set_xticks(xpositions)\n",
    "ax2.set_xticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "ax2.set_yticks(xpositions)\n",
    "ax2.set_yticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "\n",
    "# Display the values within each cell\n",
    "for i in range(pvalues.shape[0]):\n",
    "    for j in range(pvalues.shape[1]):\n",
    "        text = ax2.text(j, i, f'{pvalues[i, j]:.2f}', ha='center', va='center', color='black', fontsize=12)\n",
    "\n",
    "# Show the plot  \n",
    "plt.suptitle(fr\"Algoritmo Sinkhorn ad $\\epsilon= {epsilon}$\", fontsize = 16)\n",
    "plt.savefig(f\"EEG_risultati_epsilon={epsilon}.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TWO WAY ANOVA TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Anova\n",
      "======================================\n",
      "         F Value Num DF  Den DF Pr > F\n",
      "--------------------------------------\n",
      "Cond      9.9233 1.0000 43.0000 0.0030\n",
      "Int       1.5727 2.0000 86.0000 0.2134\n",
      "Cond:Int  1.1263 2.0000 86.0000 0.3290\n",
      "======================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "arm = AnovaRM(data=results_df, depvar='Cost', subject='Subject', within=['Cond','Int'])\n",
    "arm = arm.fit()\n",
    "print(arm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analisi con FreeEnergyOT\n",
    "\n",
    "Imput: panda DataFrame 'dati_df'\n",
    "\n",
    "Output: panda DataFrame 'results_free_df',  colonne= ['Soggetto', 'C/I', 'PC', 'Costo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35981            0.00014       0.00017\n",
      "             316.22777        0.20539            0.00150       0.00150\n",
      "            1000.00000        0.08994            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35852            0.00015       0.00021\n",
      "             316.22777        0.20268            0.00100       0.00101\n",
      "            1000.00000        0.08644            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35860            0.00016       0.00022\n",
      "             316.22777        0.20079            0.00030       0.00030\n",
      "            1000.00000        0.08470            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36543            0.00019       0.00025\n",
      "             316.22777        0.21949            0.00019       0.00019\n",
      "            1000.00000        0.11281            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36239            0.00017       0.00023\n",
      "             316.22777        0.21226            0.00047       0.00047\n",
      "            1000.00000        0.10297            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36292            0.00023       0.00031\n",
      "             316.22777        0.21318            0.00061       0.00061\n",
      "            1000.00000        0.10444            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36504            0.00032       0.00047\n",
      "             316.22777        0.20829            0.00007       0.00007\n",
      "            1000.00000        0.09497            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35898            0.00037       0.00055\n",
      "             316.22777        0.20489            0.00004       0.00004\n",
      "            1000.00000        0.09258            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36085            0.00057       0.00104\n",
      "             316.22777        0.20440            0.00008       0.00008\n",
      "            1000.00000        0.09262            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36440            0.00042       0.00070\n",
      "             316.22777        0.20638            0.00011       0.00011\n",
      "            1000.00000        0.09494            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35748            0.00034       0.00052\n",
      "             316.22777        0.19964            0.00104       0.00104\n",
      "            1000.00000        0.08584            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35806            0.00043       0.00079\n",
      "             316.22777        0.19932            0.00004       0.00004\n",
      "            1000.00000        0.08592            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36328            0.00009       0.00010\n",
      "             316.22777        0.19972            0.00059       0.00059\n",
      "            1000.00000        0.08221            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36121            0.00008       0.00008\n",
      "             316.22777        0.19968            0.00018       0.00019\n",
      "            1000.00000        0.08234            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36206            0.00011       0.00013\n",
      "             316.22777        0.19524            0.00051       0.00051\n",
      "            1000.00000        0.07342            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36192            0.00009       0.00009\n",
      "             316.22777        0.20053            0.00014       0.00014\n",
      "            1000.00000        0.08323            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36331            0.00012       0.00012\n",
      "             316.22777        0.19991            0.00058       0.00058\n",
      "            1000.00000        0.08263            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36411            0.00013       0.00014\n",
      "             316.22777        0.20201            0.00025       0.00025\n",
      "            1000.00000        0.08760            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35875            0.00065       0.00109\n",
      "             316.22777        0.20121            0.00008       0.00008\n",
      "            1000.00000        0.09523            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36254            0.00021       0.00032\n",
      "             316.22777        0.20631            0.00109       0.00109\n",
      "            1000.00000        0.09718            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36074            0.00041       0.00067\n",
      "             316.22777        0.20122            0.00006       0.00007\n",
      "            1000.00000        0.09120            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36888            0.00043       0.00073\n",
      "             316.22777        0.21310            0.00034       0.00034\n",
      "            1000.00000        0.10809            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37486            0.00046       0.00078\n",
      "             316.22777        0.22488            0.00032       0.00032\n",
      "            1000.00000        0.12024            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37428            0.00048       0.00084\n",
      "             316.22777        0.21927            0.00036       0.00036\n",
      "            1000.00000        0.11452            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35366            0.00023       0.00035\n",
      "             316.22777        0.19805            0.00070       0.00070\n",
      "            1000.00000        0.08150            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35614            0.00018       0.00018\n",
      "             316.22777        0.20087            0.00017       0.00017\n",
      "            1000.00000        0.08555            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35494            0.00017       0.00020\n",
      "             316.22777        0.19873            0.00031       0.00031\n",
      "            1000.00000        0.08143            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35255            0.00022       0.00031\n",
      "             316.22777        0.19563            0.00059       0.00059\n",
      "            1000.00000        0.07681            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35541            0.00024       0.00027\n",
      "             316.22777        0.20173            0.00037       0.00037\n",
      "            1000.00000        0.08920            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35612            0.00026       0.00034\n",
      "             316.22777        0.19926            0.00016       0.00016\n",
      "            1000.00000        0.08327            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34943            0.00018       0.00022\n",
      "             316.22777        0.20336            0.00017       0.00017\n",
      "            1000.00000        0.09207            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35872            0.00012       0.00012\n",
      "             316.22777        0.20942            0.00056       0.00056\n",
      "            1000.00000        0.10297            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35355            0.00013       0.00016\n",
      "             316.22777        0.20576            0.00006       0.00007\n",
      "            1000.00000        0.09600            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35296            0.00015       0.00020\n",
      "             316.22777        0.20578            0.00017       0.00017\n",
      "            1000.00000        0.09562            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35954            0.00016       0.00017\n",
      "             316.22777        0.21148            0.00012       0.00012\n",
      "            1000.00000        0.10542            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35800            0.00024       0.00035\n",
      "             316.22777        0.21547            0.00133       0.00133\n",
      "            1000.00000        0.11275            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34962            0.00094       0.00187\n",
      "             316.22777        0.22020            0.00004       0.00005\n",
      "            1000.00000        0.12131            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35868            0.00030       0.00037\n",
      "             316.22777        0.21459            0.00025       0.00025\n",
      "            1000.00000        0.11062            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35866            0.00036       0.00057\n",
      "             316.22777        0.22186            0.00030       0.00029\n",
      "            1000.00000        0.12115            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35623            0.00085       0.00152\n",
      "             316.22777        0.23115            0.00004       0.00004\n",
      "            1000.00000        0.13006            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34988            0.00040       0.00071\n",
      "             316.22777        0.21422            0.00070       0.00071\n",
      "            1000.00000        0.10949            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35510            0.00056       0.00092\n",
      "             316.22777        0.22370            0.00091       0.00086\n",
      "            1000.00000        0.11985            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36729            0.00013       0.00013\n",
      "             316.22777        0.20268            0.00083       0.00084\n",
      "            1000.00000        0.08685            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36477            0.00011       0.00011\n",
      "             316.22777        0.19685            0.00024       0.00024\n",
      "            1000.00000        0.07630            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36528            0.00011       0.00010\n",
      "             316.22777        0.19528            0.00004       0.00004\n",
      "            1000.00000        0.07305            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36580            0.00016       0.00021\n",
      "             316.22777        0.20021            0.00012       0.00012\n",
      "            1000.00000        0.08373            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36502            0.00008       0.00008\n",
      "             316.22777        0.19855            0.00074       0.00073\n",
      "            1000.00000        0.07957            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36526            0.00018       0.00025\n",
      "             316.22777        0.19629            0.00050       0.00050\n",
      "            1000.00000        0.07654            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36598            0.00012       0.00011\n",
      "             316.22777        0.19842            0.00007       0.00007\n",
      "            1000.00000        0.08012            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37050            0.00012       0.00011\n",
      "             316.22777        0.21113            0.00033       0.00033\n",
      "            1000.00000        0.10020            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36694            0.00015       0.00015\n",
      "             316.22777        0.20120            0.00039       0.00040\n",
      "            1000.00000        0.08626            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36814            0.00015       0.00014\n",
      "             316.22777        0.20445            0.00042       0.00042\n",
      "            1000.00000        0.09146            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37164            0.00023       0.00025\n",
      "             316.22777        0.21415            0.00050       0.00049\n",
      "            1000.00000        0.10649            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36732            0.00016       0.00017\n",
      "             316.22777        0.19901            0.00030       0.00030\n",
      "            1000.00000        0.08112            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36209            0.00008       0.00008\n",
      "             316.22777        0.20178            0.00036       0.00036\n",
      "            1000.00000        0.08519            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35982            0.00010       0.00013\n",
      "             316.22777        0.19688            0.00003       0.00003\n",
      "            1000.00000        0.07895            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36333            0.00017       0.00029\n",
      "             316.22777        0.20072            0.00010       0.00010\n",
      "            1000.00000        0.08395            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36518            0.00013       0.00019\n",
      "             316.22777        0.20316            0.00013       0.00013\n",
      "            1000.00000        0.08687            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36703            0.00009       0.00011\n",
      "             316.22777        0.20383            0.00007       0.00007\n",
      "            1000.00000        0.08858            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36576            0.00012       0.00015\n",
      "             316.22777        0.20183            0.00017       0.00017\n",
      "            1000.00000        0.08529            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33703            0.00076       0.00109\n",
      "             316.22777        0.19602            0.00061       0.00065\n",
      "            1000.00000        0.09331            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33574            0.00071       0.00105\n",
      "             316.22777        0.19047            0.00075       0.00076\n",
      "            1000.00000        0.08504            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33622            0.00066       0.00089\n",
      "             316.22777        0.19391            0.00002       0.00002\n",
      "            1000.00000        0.08956            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33219            0.00097       0.00142\n",
      "             316.22777        0.19320            0.00002       0.00002\n",
      "            1000.00000        0.09374            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.32585            0.00128       0.00197\n",
      "             316.22777        0.18962            0.00071       0.00075\n",
      "            1000.00000        0.09315            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33818            0.00077       0.00099\n",
      "             316.22777        0.19802            0.00003       0.00003\n",
      "            1000.00000        0.09703            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36432            0.00019       0.00023\n",
      "             316.22777        0.22553            0.00011       0.00011\n",
      "            1000.00000        0.12444            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36377            0.00019       0.00024\n",
      "             316.22777        0.22438            0.00014       0.00014\n",
      "            1000.00000        0.12188            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35998            0.00016       0.00021\n",
      "             316.22777        0.22062            0.00005       0.00005\n",
      "            1000.00000        0.11783            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36368            0.00015       0.00017\n",
      "             316.22777        0.21842            0.00009       0.00009\n",
      "            1000.00000        0.11253            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36039            0.00010       0.00011\n",
      "             316.22777        0.20762            0.00004       0.00004\n",
      "            1000.00000        0.09758            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35646            0.00013       0.00016\n",
      "             316.22777        0.21058            0.00011       0.00011\n",
      "            1000.00000        0.10192            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35130            0.00035       0.00045\n",
      "             316.22777        0.20245            0.00020       0.00020\n",
      "            1000.00000        0.08882            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35588            0.00057       0.00077\n",
      "             316.22777        0.21489            0.00084       0.00082\n",
      "            1000.00000        0.10610            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35735            0.00044       0.00048\n",
      "             316.22777        0.20904            0.00009       0.00009\n",
      "            1000.00000        0.09826            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35758            0.00052       0.00073\n",
      "             316.22777        0.21041            0.00062       0.00059\n",
      "            1000.00000        0.09948            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35624            0.00062       0.00106\n",
      "             316.22777        0.20745            0.00052       0.00051\n",
      "            1000.00000        0.09649            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35122            0.00076       0.00120\n",
      "             316.22777        0.20142            0.00037       0.00037\n",
      "            1000.00000        0.09022            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34647            0.00045       0.00059\n",
      "             316.22777        0.19472            0.00121       0.00120\n",
      "            1000.00000        0.07837            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35023            0.00068       0.00084\n",
      "             316.22777        0.19480            0.00014       0.00014\n",
      "            1000.00000        0.08004            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35181            0.00036       0.00042\n",
      "             316.22777        0.19257            0.00028       0.00028\n",
      "            1000.00000        0.07224            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35087            0.00037       0.00045\n",
      "             316.22777        0.19215            0.00015       0.00015\n",
      "            1000.00000        0.07171            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34987            0.00035       0.00036\n",
      "             316.22777        0.19540            0.00021       0.00021\n",
      "            1000.00000        0.07757            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35052            0.00044       0.00049\n",
      "             316.22777        0.19383            0.00059       0.00059\n",
      "            1000.00000        0.07534            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36424            0.00011       0.00009\n",
      "             316.22777        0.20677            0.00023       0.00023\n",
      "            1000.00000        0.09385            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36642            0.00009       0.00008\n",
      "             316.22777        0.20218            0.00042       0.00043\n",
      "            1000.00000        0.08514            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36557            0.00011       0.00010\n",
      "             316.22777        0.20272            0.00041       0.00040\n",
      "            1000.00000        0.08730            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36101            0.00011       0.00013\n",
      "             316.22777        0.20753            0.00087       0.00087\n",
      "            1000.00000        0.09487            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36636            0.00018       0.00018\n",
      "             316.22777        0.21156            0.00013       0.00013\n",
      "            1000.00000        0.10375            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36829            0.00030       0.00042\n",
      "             316.22777        0.22281            0.00006       0.00006\n",
      "            1000.00000        0.12057            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36785            0.00023       0.00023\n",
      "             316.22777        0.21063            0.00129       0.00129\n",
      "            1000.00000        0.10517            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36537            0.00025       0.00028\n",
      "             316.22777        0.21341            0.00005       0.00005\n",
      "            1000.00000        0.10992            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36245            0.00024       0.00026\n",
      "             316.22777        0.20005            0.00006       0.00006\n",
      "            1000.00000        0.08863            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36640            0.00034       0.00051\n",
      "             316.22777        0.20798            0.00006       0.00006\n",
      "            1000.00000        0.10008            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37422            0.00065       0.00095\n",
      "             316.22777        0.23102            0.00128       0.00128\n",
      "            1000.00000        0.13021            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36447            0.00039       0.00051\n",
      "             316.22777        0.21454            0.00011       0.00011\n",
      "            1000.00000        0.10970            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35113            0.00067       0.00063\n",
      "             316.22777        0.19082            0.00007       0.00007\n",
      "            1000.00000        0.07444            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35386            0.00081       0.00092\n",
      "             316.22777        0.19393            0.00007       0.00007\n",
      "            1000.00000        0.08118            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34723            0.00090       0.00105\n",
      "             316.22777        0.18802            0.00007       0.00007\n",
      "            1000.00000        0.07355            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35154            0.00066       0.00067\n",
      "             316.22777        0.19108            0.00007       0.00007\n",
      "            1000.00000        0.07368            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34862            0.00082       0.00085\n",
      "             316.22777        0.18885            0.00007       0.00007\n",
      "            1000.00000        0.07316            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34731            0.00082       0.00092\n",
      "             316.22777        0.18766            0.00007       0.00007\n",
      "            1000.00000        0.07140            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34709            0.00012       0.00013\n",
      "             316.22777        0.19942            0.00049       0.00049\n",
      "            1000.00000        0.08372            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34635            0.00021       0.00030\n",
      "             316.22777        0.19761            0.00078       0.00076\n",
      "            1000.00000        0.08474            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34232            0.00018       0.00020\n",
      "             316.22777        0.19387            0.00062       0.00062\n",
      "            1000.00000        0.07816            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35336            0.00011       0.00011\n",
      "             316.22777        0.19641            0.00046       0.00047\n",
      "            1000.00000        0.07946            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35296            0.00013       0.00014\n",
      "             316.22777        0.19864            0.00070       0.00071\n",
      "            1000.00000        0.08308            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34714            0.00023       0.00028\n",
      "             316.22777        0.19965            0.00059       0.00059\n",
      "            1000.00000        0.08911            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36271            0.00007       0.00007\n",
      "             316.22777        0.20658            0.00047       0.00047\n",
      "            1000.00000        0.09537            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36290            0.00017       0.00021\n",
      "             316.22777        0.21494            0.00082       0.00082\n",
      "            1000.00000        0.10997            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35471            0.00013       0.00016\n",
      "             316.22777        0.19927            0.00143       0.00143\n",
      "            1000.00000        0.08475            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36357            0.00012       0.00015\n",
      "             316.22777        0.20977            0.00001       0.00001\n",
      "            1000.00000        0.10167            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35965            0.00012       0.00015\n",
      "             316.22777        0.20241            0.00084       0.00084\n",
      "            1000.00000        0.08987            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35318            0.00008       0.00008\n",
      "             316.22777        0.19521            0.00150       0.00150\n",
      "            1000.00000        0.07444            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36677            0.00065       0.00083\n",
      "             316.22777        0.22074            0.00011       0.00012\n",
      "            1000.00000        0.11809            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36086            0.00072       0.00088\n",
      "             316.22777        0.21303            0.00037       0.00037\n",
      "            1000.00000        0.10827            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36318            0.00055       0.00066\n",
      "             316.22777        0.20449            0.00006       0.00006\n",
      "            1000.00000        0.09610            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36871            0.00081       0.00105\n",
      "             316.22777        0.22671            0.00003       0.00003\n",
      "            1000.00000        0.12580            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36609            0.00063       0.00082\n",
      "             316.22777        0.22135            0.00010       0.00010\n",
      "            1000.00000        0.11926            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37516            0.00099       0.00132\n",
      "             316.22777        0.23331            0.00052       0.00052\n",
      "            1000.00000        0.13335            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34691            0.00065       0.00086\n",
      "             316.22777        0.20216            0.00004       0.00004\n",
      "            1000.00000        0.10135            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34715            0.00050       0.00065\n",
      "             316.22777        0.19191            0.00070       0.00069\n",
      "            1000.00000        0.08170            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34881            0.00057       0.00064\n",
      "             316.22777        0.19847            0.00070       0.00069\n",
      "            1000.00000        0.09256            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35000            0.00059       0.00067\n",
      "             316.22777        0.20154            0.00072       0.00066\n",
      "            1000.00000        0.09549            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34969            0.00062       0.00080\n",
      "             316.22777        0.20457            0.00003       0.00003\n",
      "            1000.00000        0.10192            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34801            0.00074       0.00102\n",
      "             316.22777        0.20081            0.00001       0.00001\n",
      "            1000.00000        0.09908            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35666            0.00027       0.00026\n",
      "             316.22777        0.19321            0.00038       0.00038\n",
      "            1000.00000        0.07230            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35715            0.00017       0.00016\n",
      "             316.22777        0.19451            0.00025       0.00025\n",
      "            1000.00000        0.07338            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35711            0.00020       0.00020\n",
      "             316.22777        0.19399            0.00036       0.00036\n",
      "            1000.00000        0.07389            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35463            0.00025       0.00026\n",
      "             316.22777        0.19392            0.00022       0.00023\n",
      "            1000.00000        0.07422            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35479            0.00024       0.00026\n",
      "             316.22777        0.19388            0.00027       0.00028\n",
      "            1000.00000        0.07388            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35675            0.00025       0.00030\n",
      "             316.22777        0.19307            0.00048       0.00047\n",
      "            1000.00000        0.07213            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35828            0.00052       0.00085\n",
      "             316.22777        0.19930            0.00006       0.00006\n",
      "            1000.00000        0.08464            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36074            0.00028       0.00027\n",
      "             316.22777        0.19773            0.00001       0.00001\n",
      "            1000.00000        0.08001            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36233            0.00027       0.00024\n",
      "             316.22777        0.19626            0.00004       0.00004\n",
      "            1000.00000        0.07635            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35920            0.00041       0.00047\n",
      "             316.22777        0.20170            0.00144       0.00145\n",
      "            1000.00000        0.08856            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35893            0.00090       0.00173\n",
      "             316.22777        0.20434            0.00020       0.00020\n",
      "            1000.00000        0.09682            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36004            0.00061       0.00089\n",
      "             316.22777        0.20265            0.00092       0.00092\n",
      "            1000.00000        0.09187            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35691            0.00016       0.00014\n",
      "             316.22777        0.19630            0.00042       0.00042\n",
      "            1000.00000        0.07801            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35215            0.00021       0.00025\n",
      "             316.22777        0.19371            0.00055       0.00056\n",
      "            1000.00000        0.07454            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35419            0.00016       0.00014\n",
      "             316.22777        0.19788            0.00035       0.00035\n",
      "            1000.00000        0.08204            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35799            0.00014       0.00012\n",
      "             316.22777        0.19827            0.00042       0.00042\n",
      "            1000.00000        0.08152            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35230            0.00016       0.00013\n",
      "             316.22777        0.19499            0.00052       0.00052\n",
      "            1000.00000        0.07741            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36005            0.00010       0.00010\n",
      "             316.22777        0.20614            0.00035       0.00033\n",
      "            1000.00000        0.09605            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34821            0.00025       0.00037\n",
      "             316.22777        0.21699            0.00078       0.00078\n",
      "            1000.00000        0.11216            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35669            0.00031       0.00037\n",
      "             316.22777        0.22011            0.00038       0.00039\n",
      "            1000.00000        0.11549            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34850            0.00033       0.00048\n",
      "             316.22777        0.21477            0.00083       0.00083\n",
      "            1000.00000        0.10823            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35310            0.00031       0.00047\n",
      "             316.22777        0.21898            0.00058       0.00056\n",
      "            1000.00000        0.11296            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35635            0.00043       0.00064\n",
      "             316.22777        0.22726            0.00081       0.00074\n",
      "            1000.00000        0.12364            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34777            0.00049       0.00076\n",
      "             316.22777        0.21521            0.00002       0.00002\n",
      "            1000.00000        0.11070            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36330            0.00008       0.00009\n",
      "             316.22777        0.20649            0.00048       0.00049\n",
      "            1000.00000        0.09317            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36693            0.00005       0.00005\n",
      "             316.22777        0.21067            0.00094       0.00095\n",
      "            1000.00000        0.09811            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36292            0.00007       0.00007\n",
      "             316.22777        0.20443            0.00066       0.00066\n",
      "            1000.00000        0.08959            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36293            0.00008       0.00009\n",
      "             316.22777        0.20676            0.00025       0.00026\n",
      "            1000.00000        0.09291            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36811            0.00014       0.00018\n",
      "             316.22777        0.21528            0.00028       0.00028\n",
      "            1000.00000        0.10597            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36404            0.00017       0.00024\n",
      "             316.22777        0.21542            0.00029       0.00028\n",
      "            1000.00000        0.10717            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34347            0.00026       0.00029\n",
      "             316.22777        0.20416            0.00067       0.00067\n",
      "            1000.00000        0.09780            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35373            0.00018       0.00016\n",
      "             316.22777        0.20842            0.00079       0.00079\n",
      "            1000.00000        0.09877            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34703            0.00020       0.00018\n",
      "             316.22777        0.20015            0.00138       0.00138\n",
      "            1000.00000        0.08729            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33727            0.00043       0.00053\n",
      "             316.22777        0.20331            0.00026       0.00026\n",
      "            1000.00000        0.09918            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34839            0.00025       0.00024\n",
      "             316.22777        0.20593            0.00027       0.00027\n",
      "            1000.00000        0.09922            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35865            0.00031       0.00034\n",
      "             316.22777        0.22192            0.00023       0.00023\n",
      "            1000.00000        0.12042            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35635            0.00037       0.00057\n",
      "             316.22777        0.20739            0.00131       0.00128\n",
      "            1000.00000        0.09460            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35350            0.00020       0.00020\n",
      "             316.22777        0.19654            0.00016       0.00017\n",
      "            1000.00000        0.07834            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35693            0.00029       0.00035\n",
      "             316.22777        0.20444            0.00045       0.00044\n",
      "            1000.00000        0.08963            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35483            0.00042       0.00056\n",
      "             316.22777        0.20669            0.00114       0.00112\n",
      "            1000.00000        0.09275            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35630            0.00034       0.00040\n",
      "             316.22777        0.20218            0.00023       0.00024\n",
      "            1000.00000        0.08801            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35450            0.00029       0.00031\n",
      "             316.22777        0.20225            0.00021       0.00021\n",
      "            1000.00000        0.08560            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35854            0.00023       0.00021\n",
      "             316.22777        0.20442            0.00006       0.00006\n",
      "            1000.00000        0.09209            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35107            0.00018       0.00019\n",
      "             316.22777        0.19826            0.00012       0.00012\n",
      "            1000.00000        0.08187            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35355            0.00019       0.00017\n",
      "             316.22777        0.19754            0.00006       0.00006\n",
      "            1000.00000        0.07964            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35588            0.00023       0.00025\n",
      "             316.22777        0.20098            0.00006       0.00006\n",
      "            1000.00000        0.08711            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35964            0.00012       0.00011\n",
      "             316.22777        0.20462            0.00006       0.00006\n",
      "            1000.00000        0.09240            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35708            0.00025       0.00027\n",
      "             316.22777        0.20432            0.00006       0.00006\n",
      "            1000.00000        0.09326            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36116            0.00020       0.00024\n",
      "             316.22777        0.20830            0.00137       0.00136\n",
      "            1000.00000        0.09567            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36078            0.00018       0.00027\n",
      "             316.22777        0.19617            0.00146       0.00146\n",
      "            1000.00000        0.07825            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36046            0.00013       0.00015\n",
      "             316.22777        0.19620            0.00006       0.00006\n",
      "            1000.00000        0.07724            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36584            0.00024       0.00032\n",
      "             316.22777        0.20943            0.00007       0.00007\n",
      "            1000.00000        0.09860            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36053            0.00019       0.00027\n",
      "             316.22777        0.20204            0.00006       0.00006\n",
      "            1000.00000        0.08796            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36048            0.00027       0.00043\n",
      "             316.22777        0.20012            0.00006       0.00006\n",
      "            1000.00000        0.08677            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35296            0.00018       0.00022\n",
      "             316.22777        0.19967            0.00080       0.00080\n",
      "            1000.00000        0.08433            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35446            0.00019       0.00026\n",
      "             316.22777        0.20248            0.00101       0.00101\n",
      "            1000.00000        0.08792            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35635            0.00023       0.00029\n",
      "             316.22777        0.20401            0.00058       0.00058\n",
      "            1000.00000        0.09095            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35398            0.00011       0.00012\n",
      "             316.22777        0.19964            0.00003       0.00003\n",
      "            1000.00000        0.08197            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35405            0.00018       0.00021\n",
      "             316.22777        0.20040            0.00056       0.00056\n",
      "            1000.00000        0.08349            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35557            0.00012       0.00014\n",
      "             316.22777        0.19689            0.00012       0.00012\n",
      "            1000.00000        0.07870            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35032            0.00044       0.00041\n",
      "             316.22777        0.19729            0.00016       0.00016\n",
      "            1000.00000        0.08344            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35476            0.00037       0.00030\n",
      "             316.22777        0.19858            0.00049       0.00048\n",
      "            1000.00000        0.08547            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34717            0.00047       0.00049\n",
      "             316.22777        0.19778            0.00005       0.00005\n",
      "            1000.00000        0.08805            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35475            0.00039       0.00031\n",
      "             316.22777        0.20581            0.00184       0.00178\n",
      "            1000.00000        0.09769            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35726            0.00036       0.00029\n",
      "             316.22777        0.20522            0.00012       0.00011\n",
      "            1000.00000        0.09454            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35121            0.00051       0.00056\n",
      "             316.22777        0.20321            0.00002       0.00002\n",
      "            1000.00000        0.09651            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36002            0.00011       0.00014\n",
      "             316.22777        0.20301            0.00055       0.00056\n",
      "            1000.00000        0.08591            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36076            0.00010       0.00013\n",
      "             316.22777        0.20535            0.00077       0.00076\n",
      "            1000.00000        0.09151            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36225            0.00005       0.00005\n",
      "             316.22777        0.19918            0.00030       0.00030\n",
      "            1000.00000        0.07995            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36192            0.00007       0.00008\n",
      "             316.22777        0.19887            0.00038       0.00037\n",
      "            1000.00000        0.07973            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36377            0.00005       0.00005\n",
      "             316.22777        0.20208            0.00057       0.00057\n",
      "            1000.00000        0.08600            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35898            0.00007       0.00008\n",
      "             316.22777        0.19823            0.00024       0.00024\n",
      "            1000.00000        0.07825            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35597            0.00015       0.00015\n",
      "             316.22777        0.20358            0.00147       0.00147\n",
      "            1000.00000        0.08887            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35704            0.00015       0.00016\n",
      "             316.22777        0.19897            0.00116       0.00116\n",
      "            1000.00000        0.08308            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35455            0.00017       0.00023\n",
      "             316.22777        0.20796            0.00009       0.00009\n",
      "            1000.00000        0.09589            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36352            0.00020       0.00025\n",
      "             316.22777        0.21840            0.00068       0.00067\n",
      "            1000.00000        0.10974            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36582            0.00029       0.00039\n",
      "             316.22777        0.22081            0.00023       0.00023\n",
      "            1000.00000        0.11247            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35895            0.00038       0.00044\n",
      "             316.22777        0.22608            0.00034       0.00035\n",
      "            1000.00000        0.12629            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36528            0.00007       0.00009\n",
      "             316.22777        0.19726            0.00061       0.00061\n",
      "            1000.00000        0.07707            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36809            0.00006       0.00008\n",
      "             316.22777        0.20469            0.00026       0.00026\n",
      "            1000.00000        0.09272            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36283            0.00005       0.00005\n",
      "             316.22777        0.19501            0.00021       0.00021\n",
      "            1000.00000        0.07232            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36594            0.00006       0.00006\n",
      "             316.22777        0.19708            0.00050       0.00050\n",
      "            1000.00000        0.07669            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36817            0.00007       0.00007\n",
      "             316.22777        0.20347            0.00079       0.00079\n",
      "            1000.00000        0.08862            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36026            0.00004       0.00004\n",
      "             316.22777        0.19645            0.00004       0.00004\n",
      "            1000.00000        0.07459            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36944            0.00006       0.00007\n",
      "             316.22777        0.20537            0.00063       0.00063\n",
      "            1000.00000        0.08995            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36738            0.00008       0.00010\n",
      "             316.22777        0.20243            0.00062       0.00062\n",
      "            1000.00000        0.08638            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36686            0.00005       0.00005\n",
      "             316.22777        0.20238            0.00006       0.00006\n",
      "            1000.00000        0.08591            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36599            0.00009       0.00010\n",
      "             316.22777        0.20054            0.00125       0.00125\n",
      "            1000.00000        0.08299            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36747            0.00005       0.00006\n",
      "             316.22777        0.20088            0.00140       0.00140\n",
      "            1000.00000        0.08209            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36693            0.00007       0.00006\n",
      "             316.22777        0.20349            0.00005       0.00005\n",
      "            1000.00000        0.08794            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35451            0.00030       0.00049\n",
      "             316.22777        0.19820            0.00005       0.00006\n",
      "            1000.00000        0.08527            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35725            0.00015       0.00016\n",
      "             316.22777        0.19784            0.00077       0.00077\n",
      "            1000.00000        0.08144            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35732            0.00017       0.00018\n",
      "             316.22777        0.20128            0.00166       0.00165\n",
      "            1000.00000        0.08544            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35866            0.00014       0.00014\n",
      "             316.22777        0.20324            0.00121       0.00121\n",
      "            1000.00000        0.08975            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35983            0.00013       0.00015\n",
      "             316.22777        0.20061            0.00005       0.00005\n",
      "            1000.00000        0.08481            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35891            0.00027       0.00039\n",
      "             316.22777        0.20329            0.00073       0.00073\n",
      "            1000.00000        0.09033            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35848            0.00015       0.00019\n",
      "             316.22777        0.20060            0.00005       0.00005\n",
      "            1000.00000        0.08430            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35861            0.00018       0.00023\n",
      "             316.22777        0.20998            0.00116       0.00116\n",
      "            1000.00000        0.09952            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35739            0.00014       0.00019\n",
      "             316.22777        0.19866            0.00065       0.00065\n",
      "            1000.00000        0.08175            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35908            0.00023       0.00030\n",
      "             316.22777        0.20397            0.00131       0.00131\n",
      "            1000.00000        0.09118            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35307            0.00027       0.00038\n",
      "             316.22777        0.20410            0.00109       0.00109\n",
      "            1000.00000        0.09416            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35842            0.00015       0.00022\n",
      "             316.22777        0.19718            0.00006       0.00006\n",
      "            1000.00000        0.08012            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33662            0.00063       0.00085\n",
      "             316.22777        0.20279            0.00007       0.00007\n",
      "            1000.00000        0.09916            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33264            0.00049       0.00059\n",
      "             316.22777        0.19049            0.00010       0.00010\n",
      "            1000.00000        0.07506            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33259            0.00037       0.00049\n",
      "             316.22777        0.19317            0.00008       0.00008\n",
      "            1000.00000        0.08238            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33191            0.00085       0.00111\n",
      "             316.22777        0.20418            0.00005       0.00005\n",
      "            1000.00000        0.10287            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33800            0.00067       0.00081\n",
      "             316.22777        0.20554            0.00119       0.00121\n",
      "            1000.00000        0.10224            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33014            0.00063       0.00083\n",
      "             316.22777        0.19675            0.00131       0.00132\n",
      "            1000.00000        0.08885            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34503            0.00017       0.00020\n",
      "             316.22777        0.19514            0.00057       0.00057\n",
      "            1000.00000        0.07804            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34698            0.00012       0.00014\n",
      "             316.22777        0.19362            0.00030       0.00030\n",
      "            1000.00000        0.07306            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34994            0.00018       0.00022\n",
      "             316.22777        0.19996            0.00070       0.00071\n",
      "            1000.00000        0.08676            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35712            0.00008       0.00008\n",
      "             316.22777        0.20127            0.00049       0.00049\n",
      "            1000.00000        0.08276            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35033            0.00009       0.00008\n",
      "             316.22777        0.19420            0.00029       0.00029\n",
      "            1000.00000        0.07246            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35133            0.00009       0.00010\n",
      "             316.22777        0.19736            0.00038       0.00038\n",
      "            1000.00000        0.07766            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35060            0.00028       0.00035\n",
      "             316.22777        0.20191            0.00014       0.00014\n",
      "            1000.00000        0.09130            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35243            0.00022       0.00026\n",
      "             316.22777        0.19742            0.00006       0.00006\n",
      "            1000.00000        0.08180            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.34782            0.00029       0.00035\n",
      "             316.22777        0.19780            0.00006       0.00007\n",
      "            1000.00000        0.08387            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35283            0.00050       0.00059\n",
      "             316.22777        0.20598            0.00015       0.00015\n",
      "            1000.00000        0.09986            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35544            0.00042       0.00048\n",
      "             316.22777        0.20845            0.00007       0.00007\n",
      "            1000.00000        0.10155            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35659            0.00038       0.00047\n",
      "             316.22777        0.22042            0.00033       0.00032\n",
      "            1000.00000        0.11832            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35869            0.00023       0.00035\n",
      "             316.22777        0.20897            0.00059       0.00055\n",
      "            1000.00000        0.09548            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36201            0.00018       0.00024\n",
      "             316.22777        0.20846            0.00013       0.00013\n",
      "            1000.00000        0.09246            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36559            0.00014       0.00019\n",
      "             316.22777        0.21362            0.00016       0.00016\n",
      "            1000.00000        0.09929            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36032            0.00015       0.00019\n",
      "             316.22777        0.20156            0.00058       0.00058\n",
      "            1000.00000        0.08539            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36335            0.00013       0.00014\n",
      "             316.22777        0.20651            0.00017       0.00017\n",
      "            1000.00000        0.08916            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.35850            0.00015       0.00016\n",
      "             316.22777        0.20071            0.00025       0.00025\n",
      "            1000.00000        0.08455            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37171            0.00086       0.00059\n",
      "             316.22777        0.24168            0.00061       0.00070\n",
      "            1000.00000        0.14123            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36115            0.00080       0.00054\n",
      "             316.22777        0.22278            0.00061       0.00063\n",
      "            1000.00000        0.11953            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36344            0.00076       0.00055\n",
      "             316.22777        0.22876            0.00045       0.00049\n",
      "            1000.00000        0.12465            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.37336            0.00097       0.00065\n",
      "             316.22777        0.24227            0.00054       0.00059\n",
      "            1000.00000        0.14128            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36306            0.00087       0.00071\n",
      "             316.22777        0.23467            0.00044       0.00044\n",
      "            1000.00000        0.13191            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.36978            0.00081       0.00053\n",
      "             316.22777        0.22941            0.00040       0.00043\n",
      "            1000.00000        0.12527            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33927            0.00039       0.00039\n",
      "             316.22777        0.19866            0.00060       0.00059\n",
      "            1000.00000        0.09060            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33993            0.00046       0.00047\n",
      "             316.22777        0.19390            0.00003       0.00003\n",
      "            1000.00000        0.08370            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33856            0.00039       0.00039\n",
      "             316.22777        0.19299            0.00003       0.00003\n",
      "            1000.00000        0.08176            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33760            0.00046       0.00048\n",
      "             316.22777        0.19440            0.00009       0.00009\n",
      "            1000.00000        0.08431            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33559            0.00072       0.00096\n",
      "             316.22777        0.19361            0.00003       0.00003\n",
      "            1000.00000        0.08558            0.00000       0.00000\n",
      "===============================================================================\n",
      " \n",
      "===============================================================================\n",
      "                Beta             U               Err_row        Err_col  \n",
      "===============================================================================\n",
      "             100.00000        0.33821            0.00037       0.00039\n",
      "             316.22777        0.19750            0.00021       0.00021\n",
      "            1000.00000        0.08741            0.00000       0.00000\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "from freeot import FreeOT\n",
    "free_instance = FreeOT()\n",
    "\n",
    "results_free_df=pd.DataFrame(index=range(n_soggetti*6), columns=nomi_colonne_results)\n",
    "lunghezza_blocco_soggetto=n_cluster*n_task\n",
    "resting_temp=np.zeros(n_cluster)\n",
    "task_temp=np.zeros(n_cluster)\n",
    "\n",
    "temperatura_iniziale = 0.01\n",
    "temperatura_finale = 0.001\n",
    "beta = 1/temperatura_finale\n",
    "\n",
    "\n",
    "for n in np.arange(n_soggetti):\n",
    "    for k in np.arange(n_cluster):\n",
    "            resting_temp[k]=dati_df['Prob'][(lunghezza_blocco_soggetto*n)+k]\n",
    "    for m in np.arange(1, 7):\n",
    "        for p in np.arange(n_cluster):\n",
    "            task_temp[p]=dati_df['Prob'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)+p]\n",
    "        #######################################################\n",
    "        G_free, cost_free=free_instance.solve(resting_temp, task_temp, cost_sqeuclidean, temperatura_iniziale, temperatura_finale)\n",
    "        #print(G_free)\n",
    "        #matbis=np.multiply(cost_sqeuclidean,G_free)\n",
    "        ######################################################\n",
    "        #costo=np.sum(matbis)\n",
    "        results_free_df.loc[n*6+m-1, 'Subject']=n+1 \n",
    "        results_free_df.loc[n*6+m-1,'Cond']=dati_df['Cond'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)] #condizione C /I\n",
    "        results_free_df.loc[n*6+m-1,'Int' ]=dati_df['Int'][(lunghezza_blocco_soggetto*n)+(n_cluster*m)] #int H,M,L\n",
    "        results_free_df.loc[n*6+m-1,'Cost']=cost_free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3l5mdrbi4mFvXavr06RIZ416/fs00bdpUbqp09nyKyMtG2Lt3bwYAc/r0abnHKjr/mzdvuGx0sly9elUqQ2B12qpIddLCyyMWi5lOnToxAJgJEyZIXH9WVlYWs23bNol2qHpcddPCy9OzZ0+ZqcVVrWdlr6fqz8G7wsPDJTL/LVu2TO7rKbJhwwYGANO+fXuJtPdFRUXMpEmTuPNPmDBBqfN9+umnDADGxMSECQsLk9j25MkTblmDgwcPqlRfVSmTpZBhGObUqVMMAMbMzIyJjIzkyjMyMrj12ObNm6fWY1U9bsGCBUzr1q2ZBQsW1Mg5CSHqRQEXITouLCyMAcAYGRlJLFwqD7tI6rp167gyeUFMaGgot4BvixYtmNGjRzP9+vVjDA0NmREjRnDBRlJSksRx7y4G7OLiwowaNYoZNGiQ0gsfKyKvrmx6bjMzM2bYsGHM5MmTmcmTJzPPnj1T6vyqBFzVaasybVRnwMUwFQsDe3l5MQAYU1NTplu3bszo0aOZYcOGMV5eXoy+vj4DQCqFvSrHsW3w9fVVmCL8/Zt5VQMuVeupzPum6s/Bu9q3b8897EhJSVH4evJkZ2dz76uNjQ0zZMgQJjAwkGnUqBFjbm7OzJ49u0oBV/Pmzbn283g87v3q1q0b9179+OOPKtW1KiIjIxkfHx/uy9bWlgHAODk5SZTLWn+MXaOKz+czAwYMYAIDAxlLS0vusydvsffqHKvKcWwQKe/aaOKchBD1oYCLEB03btw4BlB+UcxNmzYxAJgPPviAK1O0htWjR4+YoUOHMtbW1oyxsTHTtm1bZu3atYxIJGIMDQ0ZPT09mWtMxcfHM19//TXTrFkzxtDQkDE3N2e6du0q1cPwruoEXGKxmAkKCmLatWvH3Ry/f2OuiYBL1bYq00Z1B1wMU9Ert337dqZ3796MjY0NY2BgwDRq1Ijx8vJivv76a+bSpUtqOU7Zdbhmz55dpXYoCrhUqaey75uqPwcsdo28Tz/9tNLXUiQ9PZ356quvmObNmzNGRkaMo6Mj89lnnzEvXrxg9u7dq/QNeGZmJgNULDD966+/Mm5uboyRkRFjaGjIODs7M6NHj5ZaP0xT2J+tyr7k9SAeOXKE6dGjB2NhYcGYmJgwbm5uzKpVq5R60KHqsVU9TpngSBPnJISoB49h6vCgekKI1goPD0fPnj3h7u6Ox48f13Z1CKkVyvwciMViNG/eHHFxcbh16xa6du1aw7WUFhISgoEDB+Kjjz7C33//XdvVIYSQOo2SZhBCNCY9PZ3LsveuJ0+eYMqUKQCASZMm1XS1CKlR1f052LlzJ+Li4tC1a9c6EWwBkgseE0IIUYzSwhNCNCY6Ohq9e/dG27Zt0axZM5iYmODNmzd48OABysvL8dFHH2HmzJm1XU1CNEqVn4OYmBisXbsWqampCAkJgZ6eHtatW1dLLZD2/oLHhBBC5KMhhYQQjUlOTsYvv/yCa9euISkpCXl5eTA3N0e7du0wZswYTJkyBQYG9NyHaDdVfg7CwsLQu3dvGBoaok2bNli6dCmGDh1aSy2QZmdnh7S0NKSmpsLOzq62q0MIIXUaBVyEEEIIIYQQoiE0h4sQQgghhBBCNIQCLkIIIYQQQgjREAq4CCGEEEIIIURDKOAihBBCCCGEEA2hgIsQQgghhBBCNIQCLkIIIYQQQgjREAq4CCGEEEIIIURDKOAihBBCCCGEEA2hgIsQQgghhBBCNIQCLkIIIYQQQgjREAq4CCGEEEIIIURDKOAihBBCCCGEEA2hgIsQQgghhBBCNIQCLkIIIYQQQgjREAq4CCGEEEIIIURDKOAihBBCCCGEEA2hgIsQQgghhBBCNIQCLkIIIYQQQgjREAq4CCGEEEIIIURDKOAihBBCCCGEEA2hgIsQQgghREv06tULPB6vtquhVhMnTgSPx0NsbKzK5wgLCwOPx8PSpUvVVi9ClEUBFyGEEEIIIe+oTpDn6uoKV1dXtddJkdjYWPB4PEycOLFGX5cohwIuQgghhBBCCNEQCrgIIYQQQgghREMo4CKEEEIIUeDd+T83btxAr169YG5uDktLSwQGBuLly5dKnef69evg8Xj4/PPPZW5PS0sDn8+Hr68vVxYZGYkZM2bAzc0NAoEAJiYmcHd3x6pVq1BaWqrU6y5duhQ8Hg9hYWFS24KDg8Hj8RAcHCy17fHjxxg9ejQcHBxgaGgIFxcXzJw5E5mZmVL7Xr16FQMHDoSjoyOMjIxgZ2eHDz/8EDt37lSqjgAQHR2NTz75BObm5hAIBBg0aBCePHmi8JgzZ86gT58+sLKygrGxMdzc3LBu3TqIxWKlX/d9rq6u2LdvHwCgadOm4PF44PF46NWrl8Lj2GF9cXFxiIuL446TNXcsPDwc/v7+sLW1hZGREVq2bImffvoJhYWFUuc9ceIEevbsiUaNGsHY2BiOjo7o27cvTpw4AaDiGjZt2hQAsG/fPonXlXXNSc0zqO0KEEIIIYTUB3fu3EFQUBAGDBiAmTNnIjo6GqdOncL169dx584dNGvWTOHx3bt3h6urK06cOIHffvsNxsbGEtsPHTqEsrIyjBs3jivbtWsXzp07hx49emDQoEEoLCxEWFgYFi5ciHv37nE33ep29uxZjBw5Enp6ehg8eDCcnZ3x9OlTbNmyBZcuXcLdu3dhZWUFALhw4QL8/f1haWmJwYMHw8HBAenp6Xj06BH++OMPfPnll5W+3pMnT+Dr64v8/HwMGzYMLVu2REREBHx9feHp6SnzmIULF2LVqlVo3Lgxhg0bBoFAgOvXr2P+/Pm4e/cujh07plLb58yZg+DgYDx69AizZ8+GpaUlAFQ6L8vS0hJLlizBpk2buPOw3g3Wtm3bhq+//hqWlpbw9/dHo0aNcP/+ffz888+4evUqrl69CkNDQ27fr776Cg4ODhg6dChsbGyQmpqKiIgInDp1CoGBgfDy8sLs2bOxefNmeHp6YsiQIdxr1fRcMiIbBVxKKi8vR3JyMszNzbUu+w8hhNRlDMMgLy8Pjo6O0NOjgRmk9ly6dAnbt2/H1KlTubIdO3Zg2rRpmD17Ns6dO6fweB6Ph88++wwrV67kApp3/fHHHzA0NJQo/+GHH7B161bo6+tzZQzD4IsvvsCePXtw8+ZNiR4xdcjMzMS4ceNga2uLmzdvwsXFhdt2+PBhfPrpp1i8eDF+/fVXAMCePXvAMAyuXr0qFRzJ6g2TZcaMGcjNzcWBAwcwduxYrvyHH35AUFCQ1P7//PMPVq1ahf79++PEiRMwNTUFUPHefPXVV9i+fTtOnDiBwMDAKrd/zpw5iIqKwqNHjzBnzhylgxZLS0ssXbqU6y2UlRHx6dOnmDVrFjw8PBAaGgobGxtu26pVq7Bw4UL8+uuvmDdvHgBg9+7dMDQ0RFRUFBo1aiRxLva99fLywpw5c7B582Z4eXlRJsa6iCFKSUhIYADQF33RF33RVy19JSQk1PafAqKjrl69ygBgWrVqxYjFYoltYrGYadmyJcPj8Zi0tLRKzxUTE8MAYPz9/SXKnz59ygBghgwZolSdIiMjGQDM0qVLJcp79uzJAJK3d0uWLGEAMFevXpU6z969exkAzN69e7myDRs2MACY/fv3y3ztDh06MLa2ttz3w4YNYwAwMTExStX9fXFxcQwAxsPDQ2pbXl4eY2lpyQBg3rx5w5UHBAQwAJi4uDipY3Jychgej8cEBgZyZew1XLJkiVJ1mjBhgtRrKsvFxYVxcXGRuW3WrFkMACY8PFxqm1gsZho2bMh07NiRK+vQoQNjamrKZGVlKXzNN2/eMACYCRMmVLm+RPOoh0tJ5ubmAICEhARYWFjUcm0IIUR35ObmwtnZmfs9TEht8fX1lepl1dPTg6+vL168eIFHjx6hb9++OH36NKKioiT269WrF3r16oVWrVqhc+fOCAkJQUZGBmxtbQEABw4cAACJ4YQAUFJSgi1btuDw4cN49uwZ8vPzwTAMtz05OVnt7bxz5w4A4O7du3j16pXU9uLiYmRkZHD1Hz16NE6ePIkuXbpgzJgx6NOnDz788EOubZV59OgRgIohl+8zMzODl5eX1FykO3fuwNTUFHv27JF5ThMTEzx79kyp16+q4OBgqXTxQ4YMgZeXV6XHsu/tpUuXEBoaKrWdz+dL1Hv06NH47rvv4ObmhjFjxqB3797o3r073YvWMxRwKYkdRmhhYUEfckIIqQU0nJvUNjs7O4XlQqEQAHD69Gku6cK72Hk848aNQ0REBI4cOYKvv/4aDMPg4MGDsLKywscffyxxzPDhw3Hu3Dm0atUKo0aNQqNGjcDn85GTk4PNmzdDJBKpsYUVsrKyAABbt25VuF9BQQFsbW0xYsQInD59Ghs2bMD27duxdetW8Hg89O7dG+vXr680EGHft/eHzLFkve9ZWVkoKyvDsmXLFNZPE4KDg3Ht2jWJMldXV6UCLva9/fnnn5V6rW+//RY2NjbYtm0b1q9fj3Xr1sHAwAAff/wxNm7cyCXLIHUbBVyEEEIIIUp4+/atwnKBQACg4oZcVtY/1ujRozF37lwcOHAAX3/9NcLDwxEXF4epU6fCyMiI2+/evXs4d+4c+vfvjwsXLkjM47pz5w42b96sVL3ZXrmysjKpbWyw8y72wfK///4LNzc3pV5j8ODBGDx4MPLy8nDz5k2cPHkSv//+OwYMGIBnz55xiSdkYd+3tLQ0mdtlve8WFhbg8XjIyMhQqn7qVJ3Mf+x7m5ubq1SvPZvV8vPPP0dmZiauX7+OQ4cO4ejRo3jx4gUeP34s8bkgdRPNPiaEEEIIUcLNmzdRXl4uUVZeXo5bt26Bx+PJzab3PltbWwwYMAB37tzBy5cvueGEn332mcR+7HC+jz/+WOqm+vr160rXm80mmJSUJLXt4cOHUmU+Pj4AgNu3byv9Gixzc3MMGDAAO3fuxMSJE/H27VvcvXtX4THs+3bjxg2pbfn5+VLDM9k6ZmZm4sWLF1WuozLY91uV9PL6+vpyj2PfW3ZoYVXY2NhgyJAhOHLkCPz8/PD06VNuSYLq1JdoHgVchBBCCCFKeP78OXbt2iVRtmvXLjx//hwff/wxGjZsqPS52Llau3fvxrFjx9C0aVOpbINsdsD3A5Ho6GiZmfvk6dSpEwBg//79EgHj7du3cfDgQan9J02aBHNzc/z444+Ijo6W2l5YWCgRMISHh8u80Wd7rN5Pf/++Jk2aoEePHnj8+LFUfX755Rfk5ORIHTNr1iwA4Hp+3peamor//vtP4esqYm1tDaBi7r4qx2ZkZKC4uFhq21dffQUDAwPMnDkT8fHxUttzcnIkguCwsDCJOXsAUFpayg1NZN9bKysr8Hg8lepLNI+GFBJCCCGEKKF///6YNWsWLl68iHbt2iE6Ohrnzp2Dra2t0sP7WP7+/hAIBNiwYQNKS0sxa9YsqXmKnTt3RufOnXH06FGkpKSgS5cuiI+Px9mzZ/Hxxx/j+PHjSr1Wly5d4OvriytXrqBr167o0aMH4uLicObMGfj7++PUqVMS+zds2BCHDh3CiBEj4OnpiQEDBqBNmzYQiUSIjY3FtWvX0K1bN4SEhACoCH6Sk5O5dcZ4PB5u3LiBiIgIdOnSRWYyjPdt3boVvr6+GD9+PE6fPs2tw3Xv3j18+OGHUj16AwYMwKJFi7BixQq0aNECAwYMgIuLCzIzM/Hy5Utcv34dK1euxAcffKDUe/Q+Pz8/rFu3Dl9++SUCAwNhamoKFxcXqaQm8o69f/8+Bg4ciA8//BCGhobo0aMHevToATc3N/z222+YPn06WrdujUGDBqF58+bIy8vD69evce3aNUycOBHbt28HUJGMw8LCAl26dIGLiwtKS0vxzz//4OnTpxg+fDgXlJuZmaFTp04IDw/HuHHj0LJlS+jp6WHcuHESaf1JLanlLIn1hlAoZAAwQqGwtqtCCCE6hX7/ktr2bkrx69evMz179mRMTU0ZCwsLZujQocyLFy9UOu8XX3zBLXsgL6V6Wloa8/nnnzOOjo6MsbEx4+7uzmzdupV5/fq1zDTgstLCMwzDZGRkMOPHj2esra0ZExMTpkuXLsylS5dkpoVnPXv2jJk8eTLj4uLCGBoaMlZWVoy7uzsza9YsJiIigtvv8OHDzMiRI5nmzZszDRo0YAQCAePp6cmsXr2aycvLU/r9+Pfff5lBgwYxZmZmjLm5OTNw4EDm33//VZii/Z9//mH8/f2Zhg0bMnw+n7G3t2e6du3KrFixgomPj+f2q2paeIZhmDVr1jAtW7Zk+Hw+A4Dp2bOnUsfl5eUxU6ZMYRwcHBh9fX2ZrxsREcGMHj2acXR0ZPh8PmNra8t06NCBWbBgAfPff/9x+/32229MQEAA4+LiwhgbGzM2NjZM586dmW3btjElJSUS54yJiWEGDRrEWFpaMjweT+5SAKTm8RjmvX5KIlNubi4EAgGEQiFlKSSEkBpEv39JbQsLC0Pv3r2xZMkSWlSWEFJlNIeLEEIIIYQQQjSEAi5CCCGEEEII0RAKuAghhBBCCCFEQyhLISFEbRiGQUFBAcrKymBgYABTU1OprFuEEFLf9OrVSyo1NyGEKIsCrhpEN6NEmwmFQqSkpKCsrIwrMzAwgIODAwQCQS3WjBBCCCGk9lDAVUPoZpRoM6FQKHOxxbKyMq6cPueEEEII0UUUcNUAuhkl2oxhGKSkpCjcJzU1FRYWFtSjS4ialZeXIzk5Gebm5vTzRQghNYhhGOTl5cHR0RF6eorTYlDApWF0M0q0HTtMVpHS0lIUFBTAzMyshmpFiG5ITk6Gs7NzbVeDEEJ0VkJCApycnBTuQwGXhtHNKNF2lX2+q7ofIUR55ubmtV2FGrVmzZrarkKNGTt2bG1XoUYEBwfXdhVqjI+PT21XocbEx8fXdhU0rqioCF9//bVSv4cp4NIwuhkl2s7AQLlfI8ruRwhRnq6NjDAxMantKtQYCwuL2q5CjTA2Nq7tKtQYU1PT2q5CjWnQoEFtV6HGKPN7uM6uw7V161a4urrC2NgYPj4+iIiIkLtvdHQ0AgMD4erqCh6Ph02bNik896pVq8Dj8TBnzhz1VloGuhkl2s7U1LTSzy+fz9epPzSEEEIIIaw6GXAdOXIEc+fOxZIlS/DgwQN4enqif//+SEtLk7l/YWEhmjVrhlWrVsHe3l7hue/du4cdO3bAw8NDE1WXQjejRNvxeDw4ODgo3Mfe3l7nnsQTQgghhAB1NODasGEDpkyZgkmTJqFt27bYvn07GjRogD179sjcv1OnTli7di1Gjx4NIyMjuefNz8/H2LFjsWvXLlhZWWmq+hLoZpToAoFAAGdnZ6mHC3w+H87OzpSFkxBCCCE6q84FXCUlJYiMjETfvn25Mj09PfTt2xe3b9+u1rm//vprfPzxxxLnrgl0M0p0gUAgQOvWreHq6gonJye4urqiVatW9PkmhBBCiE6rcxOHMjIyIBaLYWdnJ1FuZ2eHZ8+eqXzew4cP48GDB7h3755S+4tEIohEIu773NxclV8bqLgZtbCw4LIWGhgYwNTUlHq2iFbh8XiUbZMQQggh5B11rodLExISEjB79mwcPHhQ6Ww4QUFBEAgE3Jc61jlhb0YtLS1hZmZGwRYhhBBCCCFars4FXLa2ttDX18fbt28lyt++fVtpQgx5IiMjkZaWhg4dOsDAwAAGBga4du0a/ve//8HAwABisVjqmIULF0IoFHJfCQkJKr02IYQQQgghRHfVuYDL0NAQHTt2RGhoKFdWXl6O0NBQdO3aVaVz9unTB//++y+ioqK4L29vb4wdOxZRUVHQ19eXOsbIyAgWFhYSX4QQQgghhBBSFXVuDhcAzJ07FxMmTIC3tzc6d+6MTZs2oaCgAJMmTQIAjB8/Ho0bN0ZQUBCAikQbT58+5f6flJSEqKgomJmZoUWLFjA3N4ebm5vEa5iamsLGxkaqnBBCCCGEEELUpU4GXKNGjUJ6ejoWL16M1NRUeHl5ISQkhEukER8fDz29/+ucS05ORvv27bnv161bh3Xr1qFnz54ICwur6eoTQgghhBBCCIA6GnABwIwZMzBjxgyZ294PolxdXcEwTJXOT4EYIYQQQgghRNPq3BwuQgghhBBCCNEWFHARQgghhBBCiIZQwEUIIYQQQgghGkIBFyGEEEIIIYRoCAVchBBCCCGEEKIhFHARQgghhBBCiIZQwEUIIYQQQgghGkIBFyGEEEIIIYRoSJ1d+JgQQgghpLpKS0tx8eJFREREoKioCI6Ojvjkk0/Qpk0bhcddvHgRf/31l1S5gYEBNm7cyH2fnZ2N27dvIzo6Gunp6dDT04ODgwP69+9f6Wuok0gkwsqVK3H48GHk5OTAzc0NixYtgp+fn8Ljnj9/jj179uDevXt49OgRRCIRnjx5AhcXF6l9T5w4gb/++gv379/Hq1ev0L17d5nvkaaVlZUhJCQEkZGRKCwshKOjIwYMGIDWrVsrPO7SpUv4+++/pcoNDAywevVq7vvS0lKcPHkS8fHxyMnJQXl5OWxsbNC5c2f4+vpCX19f7W2Sp6SkBLt27UJISAhyc3PRokULTJ06FZ07d1Z4XFhYGE6fPo1Xr15BKBTC0tISbm5umDx5Mpo3by73uMTERIwdOxYlJSXYs2cPPvjgA3U3Sa7S0lIcPXoUN27cQH5+Ppo0aYJRo0bBw8OjSuf5+eef8e+//6Jfv374/PPP5e737NkzLF26FACwc+dOWFhYVKf6ClEPFyGEEKKCyMhITJ48GS1btoSpqSlMTEzQvHlzjBs3Dv/88w+339KlS8Hj8XD48GG555o4cSJ4PB7u3LlTE1XXKQcOHMCVK1fQqVMnBAYGQk9PD9u2bcOrV6+UOn7UqFEYP3489zV27FiJ7Y8fP8bly5fRsGFDfPLJJxgwYABEIhG2bt1ao9dz2rRp2LJlC0aOHInVq1dDT08PgYGBuHXrlsLjIiIisG3bNuTn51casOzevRsXLlxA48aNYWVlpc7qV8mhQ4dw7do1dOjQAUOGDAGPx8Pu3bvx+vVrpY4PDAzEmDFjuK9Ro0ZJbC8tLUVqairatGmDQYMGwd/fH46Ojjh79iwOHTqkiSbJtXLlShw6dAj9+vXDN998Az09PcydOxePHj1SeNyrV69gbm6OkSNH4ttvv8WwYcPw/PlzTJ48GS9evJB73ObNm2s0oHzXtm3bcPHiRfj6+mLChAnQ09PD6tWr8ezZM6XPERERgefPn1e6X3l5OYKDg2FkZFSdKiuNergIIYSQKigvL8e3336LjRs3wsDAAH5+fggICACfz8fr169x4cIFHDhwAMuXL8eiRYtqu7o6LTY2Fg8ePMCQIUPQp08fAEDnzp3xyy+/4MyZM5g7d26l5/Dy8oKZmZnc7a1atcLy5csl9vH19cXq1atx4cIFdOnSpfoNqcT9+/dx/PhxrFy5ErNnzwYAjBkzBj4+Pli0aBFCQ0PlHjto0CAkJibC3NwcmzdvxuPHj+Xuu2vXLjg6OkJPT6/SHhZNiY+PR1RUFD755BP07t0bAODt7Y21a9fi/PnzmDVrVqXn8PDwUHhNGzRowL2PrG7dusHY2Bg3b95EQECARntDWNHR0fjnn38wY8YMLtAfOHAgxo4diy1btmDXrl1yj508ebJUWUBAAAICAnDy5El8//33Utvv3LmDu3fvYuzYsQgODlZbO5Tx8uVL3Lp1C2PHjoW/vz8AoEePHpg/fz4OHjyIFStWVHqOkpIS/PHHHwgICMCxY8cU7hsaGorMzEz4+fnVSC8t9XARQgghVfDTTz9h48aN8PLywrNnz3Dp0iWsX78eq1atwtGjR5GUlIQ1a9agoKCgtquq86KioqCnp4du3bpxZXw+H127dsWbN2+QnZ1d6TkYhkFRUREYhpG53cHBQermnc/no23btsjJyUFxcXH1GqGE06dPQ19fH5MmTeLKjI2NMW7cOERERCAxMVHusdbW1jA3N1fqdZycnKCnV7u3jo8ePYKenh66du3KlfH5fPj4+CAuLk6pawoAxcXFcq+pPNbW1gCAoqKiKh2nqqtXr0JfXx9DhgzhyoyMjODv748nT57g7du3VTqflZUVjI2NkZ+fL7WtrKwMGzduxMiRI+Hk5FTdqlfZ3bt3oaenxz0YAQBDQ0P07t0bL168QEZGRqXnOHfuHBiG4QI2efLz83H06FGMGDECDRo0qHbdlUE9XIQQQoiSXr58iTVr1sDGxgYhISGws7OT2sfExATz58+HSCSqhRqSdyUmJqJRo0YwMTGRKGfnJyUmJlY6NG7ZsmUQiUQwNDSEh4cHhg4dqlTvRl5eHgwNDWFoaKh6A5T0+PFjtGjRQqpe3t7e3PbauInWhKSkJDRs2BDGxsYS5c7OzgCA5OTkSq/pL7/8wl1TNzc3BAQEyAw6y8rKUFxcjNLSUiQmJiIsLAxWVlawtbVVX4MUeP78OZydnWFqaipR3rZtW267rN9B78rLy0NZWRkyMzNx5MgRFBQUcJ+Ldx0+fBh5eXmYNGkSwsLC1NYGZcXGxsLBwUEqAGLnm8XFxSl83zMyMnDmzBlMmzat0p+5o0ePQiAQoG/fvjhx4kT1K68ECrgIIYQQJQUHB0MsFmPq1KmV3ujU1NwAIl9ubq7M4IgtEwqFco9t0KABevTogaZNm8LAwACvXr1CeHg44uLiMH/+fKkg7l3p6el49OgRvLy8aqRHKDU1Ffb29lLlbFlqaqrG61BT8vLyZAZH7DXNzc2Ve6yJiQl8fX3h6uoKAwMDvH79Gjdv3kR8fDy++eYbqSDu33//xYEDB7jvnZ2dMWrUqBqb45SZmQkbGxupcjbwUKbX54svvkB8fDyAis/0xIkTpXqAMjMzsXfvXsycOVMquKsp2dnZsLS0lCpng+fKei7/+OMPuLq6SvRmyxIXF4fLly/j+++/r9HeWgq4CCGEECXdvHkTACrN/CbL8ePH5U7+joqKUuocIpFIoudM0c0lqUh+YGAgfavD5/O57fL06tVL4nsvLy+4uLhg3759uH79Ovr16yfzODa7G5/Px+DBg1WvfBUUFxfLDPDZspoaAlcTqnNNe/ToIfG9h4cHmjRpgoMHD+LmzZsSw9kAcBkBi4qK8OLFCyQnJ6OkpEQNrVAO2wv3PrZMmV70n376CQUFBUhOTsb58+chEolQXl4uEWxs3boVjRs3RkBAgPoqX0UlJSXcNXwXW6bofY+OjkZERARWrlxZ6esEBwfDy8sLnp6eqldWBRRwEUIIIUpiewpUGZ514sSJag9fCQoKwrJly6p1Dl3C5/NRVlYmVc7elMu6wVPE29sbp06dQkxMjMyAq7y8HHv37kVqaiqmTZsGgUCgWsWryNjYWObNN1umqDeuvlH3Ne3QoQPOnj2LFy9eSAVc5ubmXG+ap6cnLl++jB07dmDBggU1kjTDyMhIZqDBlinTi+7u7s79v2/fvvj0008BgEsu8uTJE4SEhODXX3+t1fl5hoaGMoNltkzeMEGxWIzg4GB8+OGHCtPdA8CtW7fw/PlzrF27tvoVriJKmkEIIYTUgEOHDoFhGJlfEyZMUOocCxcuhFAo5L4SEhI0XOv6zcLCQmYvIFumSkBkaWmJwsJCmdsOHTqE6OhojB07ttIU6+pkb28vc9ggWyZruGF9ZW5ujry8PKly9pqqEggpuqbv8vT0hEgkQnR0dJVfQxU2NjbIzMyUKmeHElZ1LpmFhQU6duwosRbZ1q1b4enpCUdHR6SkpCAlJQU5OTnc69TUcFQrKyvudd/FDiWUNy8vPDwcycnJ6NOnD9LS0rgvoKLnNy0tjXvwcPDgQXTp0gUGBgbcfux1z8zMRFZWlgZaVoF6uAghhBAl2dvb49mzZ0hKSqrRG2qWkZERzQ2rAicnJ7x48QJFRUUSvTyxsbHc9qpgGAZZWVkyjzt9+jTu3LmDwMBAmUkJNMnd3R3h4eFSc9bu3bsHAFVeOLYua9y4MV69eoXi4mKJOVfsPCVHR8cqnY9hGGRnZyt1HNvbUlNDNFu2bIkHDx6goKBAYm4VG/C1atWqyucUiUQSWQpTU1ORmpqKYcOGSe373XffwczMTGJdQU1xcXFBdHQ0CgsLJRJnvHz5ktsuS0ZGBsRiMZYsWSK1LTw8HOHh4Zg3bx46deqEzMxM3Lx5kxsa/q6FCxfCxcVFYgFsdaKAixBCCFGSr68vwsLCEBoaqtI8LlKzvLy8EBoailu3bnHDxUpLS3H37l24urpyT82zsrJQUlIi0RMkKznD9evXkZ+fjw8++ECi/PLlywgNDUW/fv2k5n7VhCFDhuB///sf9u7dy60fJRKJcPDgQXh7e3MBYkJCAgoLC2vlYYG6eHh4ICwsDLdv3+bW4SorK8O9e/fQpEkTiSQLJSUlEslt8vPzpVL437p1C/n5+WjTpo3EfqampuDxeBL7sgtZsxkRNc3Pzw9//vknTp8+za3DVVJSggsXLqBdu3Zc21JTU1FcXAxXV1fu2KysLC6NPSslJQX379+X+PwuWLBAaumCyMhIHDt2DDNnzpQb6Kibj48Pzp8/j9DQUC6pR2lpKa5du4YWLVpIJAoRiURo3LgxgIr10d5tN2v9+vVo3749/Pz80KJFCwDAvHnzpPa7desWbt++ja+++kpmghJ1oYCLEEIIUdLEiROxatUq7Ny5E3PmzEHDhg3l7isSiag3qpa5urqiffv2OHv2LPLy8tCwYUPcvXsXmZmZGDNmDLffH3/8gZcvX+LXX3/lypYsWYIOHTrA0dGRy2j34MEDODk5oXv37tx+jx49wpkzZ9CwYUPY29tzvUqs1q1ba3y+T6dOnTB06FAsXboU6enpaNasGf7880/ExcVhy5Yt3H5ffvklbty4ITEkTygUYseOHQD+L6DYsWMHLC0tIRAIMHXqVG7fGzdu4NatWwAqbnwLCwuxZs0aABU3vu++L5ri4uICT09PXLx4Efn5+bC1tcX9+/eRlZWFkSNHcvsdOnQIr169wvr167mylStXwsvLCw4ODjAwMMCbN28QFRUFR0dHiXW9Hjx4gFu3bsHNzQ02NjYQiUSIiYnB8+fP0bZtW7Rs2VLj7QSAdu3awc/PD9u2bUN2djacnJxw8eJFpKSk4IcffuD2W758OR4+fIjbt29zZZ999hm8vb3RqlUrmJubIyEhAefOnUNZWRmmT5/O7efj4yP1umwPWPv27aUeLmhKy5Yt0aVLFxw+fBi5ubmws7NDeHg40tPTJT6DW7duxX///YfDhw8DqOjxZIOv9zVs2BCdOnXivn/3/yy2t9vLy0ujP6cUcBFCCCFKatGiBb777jsEBQVh4MCBOHbsGJo2bSqxT3FxMX777Tekp6cjKCiolmpKWOPGjYO1tTXu3buHwsJCODo6Ytq0adxTb3m8vb3x5s0bPHr0CKWlpbC2tkafPn3Qv39/iQn8SUlJACpSwe/fv1/qPLNmzaqRBAs7d+7EihUrcPjwYeTk5MDNzQ3Hjh2rNAjKycnBihUrJMrYwLNJkyYSN7vh4eFSn2n22IULF9ZIwAUAn376KaysrBAZGYmioiI4ODhg8uTJlSZN6NChA2JjY/H48WOUlZXBysoKvXr1Qt++fSWuadOmTREbG4uoqCjk5eVBT08PjRo1QkBAQI21kbV48WLs3LkTISEhyMvLQ/PmzbFu3Tq0b99e4XHDhg3DrVu3cOfOHRQWFsLKygo+Pj4YP358pZ/92vLVV1/h6NGjuH79OgoKCtCkSRN89913NRb0aRKPqeoy2zoqNzcXAoEAQqGwRn5xEkIIqVDXfv+Wl5fj22+/xcaNG8Hn8+Hn5wc3Nzfw+Xy8efMGly9fRmZmJlauXIkff/wRS5cuxbJly3Do0CGMHj1a5jknTpyIffv24fbt2+jSpYvSdWHfG13xbg+Utps4cWJtV6FG7Ny5s7arUGMqWyNKm8TFxdV2FTSusLAQn3/+uVJ/m6iHixBCSLUxDIOCggKUlZXBwMBA5vwHbaGnp4cNGzZgzJgx2LZtGzcxu7y8HA4ODujfvz8mTZqEvn371nZVCSGE1AEUcBFCCKkWoVCIlJQUibVxDAwM4ODgoNW9L97e3vj9998r3W/p0qVYunSpwn2Cg4MRHBysnooRQgipUyjgIoQQojJ5a0GVlZVx5docdBFCCCGVoYWPCSGEqIRhGKSkpCjcJzU1FTRVmBBCiC6jgIsQQohK2DlbipSWlqKgoKCGakQIIYTUPRRwEUIIUUllwVZV9yOEEEK0EQVchBBCVGJgoNw0YGX3I4QQQrQR/RUkhJAaoI1p001NTWFgYKCwB4vP58PU1LQGa0UIIYTULRRwEUKIhmlr2nQejwcHBweZWQpZ9vb29T6wJIQQQqqDAi5CCNEgbU+bztb9/YCSz+fD3t6+XreNEEIIUQcKuAghREOUTZtuYWFRr3uBBAIBLCwstG7IJCGEEKIOFHARQoiGVCVtupmZWQ3VSjN4PF69bwMhhBCiCZSlkBBCNITSphNCCCGEAi5CCNEQSptOCCGEEAq4CCFEQ9i06YpQ2nRCCCFEu1HARQghGsKmTVeE0qYTQggh2o3GsRBCNE4bF/2VRVY7KW06IYQQotso4CKEaJS2Lvr7vsraSWnTCSGEEN1UZ4cUbt26Fa6urjA2NoaPjw8iIiLk7hsdHY3AwEC4urqCx+Nh06ZNUvts27YNHh4esLCwgIWFBbp27Yq//vpLgy0ghLCL/r6fhY9d9FcoFNZSzdRLmXayadMtLS1hZmZGwRYhhBCiI+pkwHXkyBHMnTsXS5YswYMHD+Dp6Yn+/fsjLS1N5v6FhYVo1qwZVq1aBXt7e5n7ODk5YdWqVYiMjMT9+/fh5+eHwYMHIzo6WpNNIURnKbvoL8MwNVQjzdCVdhJCCCFENXUy4NqwYQOmTJmCSZMmoW3btti+fTsaNGiAPXv2yNy/U6dOWLt2LUaPHg0jIyOZ+/j7+2PQoEFo2bIlWrVqhZ9//hlmZma4c+eOJptCiM6qyqK/9ZmutJMQQgghqqlzAVdJSQkiIyPRt29frkxPTw99+/bF7du31fIaYrEYhw8fRkFBAbp27SpzH5FIhNzcXIkvQojydGXRX11pJyGEEEJUU+eSZmRkZEAsFsPOzk6i3M7ODs+ePavWuf/991907doVxcXFMDMzw6lTp9C2bVuZ+wYFBWHZsmXVej1CdJmuLPqrK+0kdduaNWtgYmJS29XQuJkzZ9Z2FWqMt7d3bVehRowYMaK2q1BjrKysarsKNaZp06a1XQWNy8vLU3rfOtfDpUmtW7dGVFQU7t69i+nTp2PChAl4+vSpzH0XLlwIoVDIfSUkJNRwbQmp33Rl0V9daSchhBBCVFPnHrna2tpCX18fb9++lSh/+/at3IQYyjI0NESLFi0AAB07dsS9e/ewefNm7NixQ2pfIyMjufPBCCGVYxf9VfSwQhsW/dWVdhJCCCFENXWuh8vQ0BAdO3ZEaGgoV1ZeXo7Q0FC5861UVV5eDpFIpNZzEkL+j0AggLOzs1QPEJ/Ph7Ozs9asw6Ur7SSEEEJI1dW5Hi4AmDt3LiZMmABvb2907twZmzZtQkFBASZNmgQAGD9+PBo3boygoCAAFYk22KGBJSUlSEpKQlRUFMzMzLgerYULF2LgwIFo0qQJ8vLy8OeffyIsLAyXLl2qnUYSoiN0ZdFfXWknIYQQQqqmTgZco0aNQnp6OhYvXozU1FR4eXkhJCSES6QRHx8PPb3/65xLTk5G+/btue/XrVuHdevWoWfPnggLCwMApKWlYfz48UhJSYFAIICHhwcuXbqEjz76qEbbRoguYhf91Xa60k5CCCGEKK9OBlwAMGPGDMyYMUPmNjaIYrm6ula6qOjvv/+urqoRQgghhBBCiFLq3BwuQgghhBBCCNEWFHARQgghhBBCiIZQwEUIIYQQQgghGkIBFyGEEEIIIYRoCAVchBBCCCGEEKIhFHARQgghhBBCiIbU2bTwhBBC6jaGYWihZ0IIIaQSFHARQgipMqFQiJSUFJSVlXFlBgYGcHBwgEAgqMWaEUIIIXULBVyEEEKqRCgUIiEhQaq8rKyMK6egixBCCKlAc7gIITWOYRjk5+cjJycH+fn5YBimtqtElMQwDFJSUhTuk5qaSteUEEII+f+oh4sQUqNoKFr9xs7ZUqS0tBQFBQUwMzOroVoRQgghdRcFXISQGkND0eq/yoKtqu5HCCGEaDsaUkgIqRE0FE07GBgo95xO2f0IIYQQbUcBFyGkRlRlKBqpu8RicaX78Pl8mJqa1kBtCCGEkLqPAi5CSI2goWj1nzK9lABgZ2dH63ERQggh/x+N+SCklujaorE0FK3+U6aXEqBrWB+Ulpbi4sWLiIiIQFFRERwdHfHJJ5+gTZs2Co+7ePEi/vrrL6lyAwMDbNy4kfs+Ozsbt2/fRnR0NNLT06GnpwcHBwf079+/0tcgqikpKcGuXbsQEhKC3NxctGjRAlOnTkXnzp0VHhcWFobTp0/j1atXEAqFsLS0hJubGyZPnozmzZvLPS4xMRFjx45FSUkJ9uzZgw8++EDdTZJLJBJh/fr1OHnyJIRCIT744AN8++236NGjh8Lj/vrrL5w7dw6PHj1Ceno6HB0d0adPH8yaNUtq/vDZs2cRGhqKhw8fIjY2Fl26dMHRo0c12SyZRCIRVq5cicOHDyMnJwdubm5YtGgR/Pz8FB73/Plz7NmzB/fu3cOjR48gEonw5MkTuLi4SO174sQJ/PXXX7h//z5evXqF7t27y/w51ySRSIQ1a9bg+PHj3DVdsGABevbsqfC4ly9fYv/+/Xjw4AH+/fdfiEQiREREoEmTJlL7FhcXY+fOnTh27BgSExMhEAjg7e2Nb7/9VuO/l+ivYh2hazffuk4XM/WZmprCwMBA4Q07DUWr26iXUnscOHAAUVFR6N27Nxo2bIi7d+9i27ZtmDVrlsKbbNaoUaNgZGTEff/+36vHjx/j8uXL8PDwgI+PD8rLyxEREYGtW7di7Nix6NKli9rbpOtWrlyJK1euYNSoUXB2dsaFCxcwd+5cbN26FZ6ennKPe/XqFczNzTFy5EgIBAJkZWXh/PnzmDx5Mnbt2oWWLVvKPG7z5s3Q19fXVHMUmjdvHi5evIjJkyfD1dUVx48fx8SJE3H48GGFAeaCBQtgZ2eHYcOGwdHREc+ePcO+fftw9epVXLx4EcbGxty+Bw4cwL///gtPT09kZ2fXRLNkmjZtGk6fPo2vvvoKzZs3x8GDBxEYGIgLFy6gW7duco+LiIjAtm3b0KZNG7Ru3RqPHz+Wu+/u3bsRFRWFDh06ICsrSxPNqNTs2bNx/vx5TJkyBc2aNcORI0cwduxYnDhxAj4+PnKPu3//Pnbv3o1WrVqhZcuWePLkidx9v/76a1y6dAljx46Fh4cHUlNTsXfvXnzyySe4evUqnJ2dNdE0ABRw1Qm6ePOty3Q1Ux+Px4ODg4PMtrPs7e3pQUMdRr2UFSIjI/Hbb78hPDwcycnJKC8vh6OjI7p164bx48fjo48+AgAsXboUy5YtA1Bxg7hu3TqZ5/v++++xZs0aAMCSJUuwdOlSjdY/NjYWDx48wJAhQ9CnTx8AQOfOnfHLL7/gzJkzmDt3bqXn8PLyUpj2v1WrVli+fLnEPr6+vli9ejUuXLhAAZeaRUdH459//sGMGTMwduxYAMDAgQMxduxYbNmyBbt27ZJ77OTJk6XKAgICEBAQgJMnT+L777+X2n7nzh3cvXsXY8eORXBwsNraoYyoqCicPXsWP/74I6ZOnQoACAwMxEcffYSgoCCcOnVK7rHbt29H165dJcrc3d0xd+5cnDp1Cp9++ilXvmnTJtjb20NPTw99+/bVTGMqcf/+fRw/fhwrV67E7NmzAQBjxoyBj48PFi1ahNDQULnHDho0CImJiTA3N8fmzZsVBly7du2Co6Mj9PT0Ku0R1YQHDx7g9OnTWLx4Mb766isAwIgRI9CrVy+sWLEC58+fl3ts//798fz5c5iZmeG3336TG3ClpKTgwoULmD59OpYsWcKV+/j4YPjw4bh48SL3edIEmsNVy9ib7/efCLM330KhsJZqRjRB1zP1CQQCODs7S92Q8/l8ODs7a2WgqU3YXkpFtLmXsry8HHPnzoW3tzf279+PZs2aYdq0aZg9ezY6duyICxcuoF+/flixYoXEcQYGBjhw4IDMnr+ysjLs37+/RoPUqKgo6OnpSTwd5/P56Nq1K968eaPU03yGYVBUVCT3d5WDg4NUQMbn89G2bVvk5OSguLi4eo0gEq5evQp9fX0MGTKEKzMyMoK/vz+ePHmCt2/fVul8VlZWMDY2Rn5+vtS2srIybNy4ESNHjoSTk1N1q15lFy5cgL6+PsaMGcOVGRsbY9SoUYiMjERycrLcY98PtgBgwIABACqGpr2LDUBq0+nTp6Gvr49JkyZxZcbGxhg3bhwiIiKQmJgo91hra2uYm5sr9TpOTk612tbz589DX18f48aN48qMjY0xZswY3L9/H0lJSXKPtbKyUmrNR/az3LBhQ4lyOzs77vU0SbsfQ9Zxyt58W1hY0FN/LUGLxlYEXRYWFjSEth7S9V7Kn376CRs3boSXlxeOHz8uNfSuqKgIW7ZsQWZmpkT5wIEDce7cOZw/f17ihhiomBOVmpqKgIAAnD17VtNNAFAx96ZRo0YwMTGRKGfndiQmJsLKykrhOZYtWwaRSARDQ0N4eHhg6NChsLCwqPS18/LyYGhoCENDQ9UbQKQ8f/4czs7OUg872rZty21nbyzlycvLQ1lZGTIzM3HkyBEUFBTA29tbar/Dhw8jLy8PkyZNQlhYmNraoKzo6Gg0bdpUKpjw8vLitjs6Oip9vvT0dACo9DNfGx4/fowWLVpI/Wyx1+Xx48e1EvSq25MnT9CsWTOpa9q+fXsAFde0cePG1XoNV1dXODo6Yvv27WjRogXc3NyQmpqKFStWoEmTJlK/m9WNAq5aRDffuofmwFTg8Xha9ZnWpTmYbC/k+8Og+Xw+7O3ttbaX8uXLl1izZg1sbGwQEhIi8+bVxMQE8+fPh0gkkigfNmwYrl+/jj179kj9Ud+zZw+srKwwdOjQGgu4cnNzZQZHbJmikRUNGjRAjx490LRpUxgYGODVq1cIDw9HXFwc5s+fLxXEvSs9PR2PHj2Cl5dXrfccaJvMzEzY2NhIldva2gIAMjIyKj3HF198gfj4eAAV13nixInw9/eXep29e/di5syZtdaTnZaWhkaNGkmVs2VV7c3btm0b9PX18fHHH6ulfuqUmpoKe3t7qXK2LDU1taarpBFv376V+TuVvabqaCefz8fu3bvx1VdfYfz48Vy5h4cHzp8/r/G/XRRw1SK6+dY9NAdG++jiHExd7KUMDg6GWCzG1KlTK+0peDeZBFAxVOXTTz/Frl27JG4s3r59iwsXLuDLL7/U+HCWd5WWlsr8HcPn87nt8vTq1Uviey8vL7i4uGDfvn24fv06+vXrJ/M4NpMdn8/H4MGDVa88kYntbXwfW/b+QwBZfvrpJxQUFCA5ORnnz5+HSCRCeXm5RHC8detWNG7cGAEBAeqrfBUVFxdL/YwB//dzV5XhqqdPn8bhw4cxbdo0NG3aVG11VJfK2lpUVFTTVdKI4uJimZ9f9veiuoYgW1paol27dvD390fHjh3x5s0b/Prrr5gyZQqOHDmi0d/D9IipFtHNt+7R9Tkw2kaX52CyvZSWlpYwMzPT6mALAG7evAkAlaZilmfy5MncfC3W/v37UVZWJjNpgTwikQi5ubkSX1XF5/NlPshjAy028FKWt7c3LCwsEBMTI3N7eXk59u7di9TUVHz++eda+yCiNhkZGaGkpESqnC2TddP+Pnd3d3Tp0gXDhg3Dpk2bcOnSJfz222/c9idPniAkJASzZ8+u1R5KY2NjmQEkW6bsTfPdu3cxf/589OzZE999951a66gulbVVUY9yfWJsbCzz88sGWuoIhHJzczF48GB4e3vjxx9/xIABAzB9+nTs3r0bd+/exeHDh6v9GopQwFWL6OZb97BzYBTR5jkw2kTXE6DoGnZIi6rzJTp27AgPDw/s3buXK9u7dy88PT3RoUMHpc8TFBQEgUDAfamSxtjCwkJmoMaWqRIQWVpaorCwUOa2Q4cOITo6GmPHjkXr1q2rfG5SORsbG6m5g8D/DSVkhxYqy8LCAh07dsTff//NlbHp5R0dHZGSkoKUlBTk5ORwr1NTw9saNWqEtLQ0qXK2rLIeaAB4+vQpJk+ejNatW2P79u119sG2vb29zPeVLZM13LA+srOzkzkUlL2m6mjn+fPnkZ6ejv79+0uUd+vWDebm5rh37161X0MRCrhqEd186ybK1KcdqjIHkxAA+Pzzz/Hff//h9u3buH37Nv777z98/vnnVTrHwoULIRQKuS9FCUzkcXJyQlpamtRwpNjYWG57VTAMg6ysLJnzMk+fPo07d+5g2LBhMhMwEPVo2bIlEhISpH7fREdHA6hI019VIpFIIkthamoqoqKiMGzYMO5ry5YtAIDvvvtOIsOcJrVr1w5v3rxBXl6eRHlUVBS3XZHY2FiMGzcOtra22LdvX51+qO3u7o6XL19KPSBhgwMPD4/aqJbatWvXDq9fv5a6pg8ePOC2Vxf78EEsFkuUMwwDsVis8ek7FHDVMrr51k0CgQCtW7eGq6srnJyc4OrqilatWtH1rkdoDqZuYZ+wKkpPXJnPPvsMhoaG2LNnD/bs2QNDQ0NuzSRlGRkZwcLCQuKrqry8vFBeXo5bt25xZaWlpbh79y5cXV25bG1ZWVlST9ffvyECgOvXryM/Px8ffPCBRPnly5cRGhqKfv36Sc39Iurl5+cHsViM06dPc2UlJSW4cOEC2rVrx/X6pKamcoE1S9ZCtykpKbh//77ENV2wYAFWrVol8TVixAgAwMyZMzW+fhxr0KBBEIvF+PPPP7kykUiEo0ePon379lyGwqSkJKlU72lpafjss8+gp6eHP/74Q2aikbpkyJAhEIvFEj3jIpEIBw8ehLe3N/dwJCEhQe6Q3vrA398fYrEYf/zxB1cmEolw+PBhdOjQgctQmJiYiBcvXqj0Gs2aNQMAiZ8RALh06RIKCwvh5uamWuWVVDf7UHWMLk5AJ9qXqU/X0BxM3eLr64uwsDCEhoaqPI/LxsYGgwcPxpEjRwBU3EzVxg2fq6sr2rdvj7NnzyIvLw8NGzbE3bt3kZmZKbG20R9//IGXL1/i119/5cqWLFmCDh06wNHREQYGBnj9+jUePHgAJycndO/endvv0aNHOHPmDBo2bAh7e3up4TqtW7dWKVgksrVr1w5+fn7Ytm0bsrOz4eTkhIsXLyIlJQU//PADt9/y5cvx8OFD3L59myv77LPP4O3tjVatWsHc3BwJCQk4d+4cysrKMH36dG4/Hx8fqddle8Dat28vFXBrSvv27fHxxx9j9erVyMjIgKurK44fP47ExESsXbuW2++bb77BnTt3uMyLADB+/HjEx8dj2rRpuHfvnsTn0tbWFj169OC+v3v3Lu7evQugIigtKirC//73PwAV74Ws90PdOnXqhKFDh2Lp0qVIT09Hs2bN8OeffyIuLo7rXQSAL7/8Ejdu3JB4ICIUCrFjxw4AFQtVA8COHTtgaWkJgUAgscjvjRs3uAcwGRkZKCws5BZj79atm8TPtiZ06NAB/v7++OWXX5CRkYGmTZvi6NGjSEhIwIYNG7j9Zs6cidu3b0s8CMrNzcXvv/8OAIiIiABQkf2Vvbdm58j269cPrVu3xoYNG5CYmMglzdi7dy/s7OwkfvdpAt0J1BF0801I/cLOwVTUg0VzMLXHxIkTsWrVKuzcuRNz5syRWjzzXSKRSG6Sgs8//xzHjh3j/l9bxo0bB2tra9y7dw+FhYVwdHTEtGnT0KJFC4XHeXt7482bN3j06BFKS0thbW2NPn36oH///hJZxtiewPT0dIlEIaxZs2ZRwKVmixcvxs6dOxESEoK8vDw0b94c69at49YykmfYsGG4desW7ty5g8LCQlhZWcHHxwfjx4+v9PNQWzZu3Ij169fj5MmTyM3NRZs2bbB3795Kg6CnT58CALZv3y61rUuXLhIB182bN7Fp0yaJfdatWwcAmDNnTo0EXACwc+dOrFixAocPH0ZOTg7c3Nxw7NixSoOgnJwcqUXY2YcnTZo0kQi4wsPDERQUJLEve+zChQs1HnCxdVu9ejWOHz8OoVCIDz74AH/88YfMxarflZOTg9WrV0uUsdfXycmJC7gMDQ1x5swZbNy4EZcvX8bp06dhamqKAQMGYOHChRp/+MVjaEa3UnJzcyEQCCAUCumPhBro0rpFRHtVNoeGhgWrR135/fvDDz8gKCgIHTt2xLFjx6TSSBcXF+O3335Deno6goKCsHTpUixbtgyHDh3C6NGjAVRk7Dt37hyAimE0bLa3w4cP49NPP8WSJUuqNDSLfW/WrFmjNRnLFJk5c2ZtV6HGvNsLpc2qu6BtfVIXF1fWFF2Yv5yXl4eWLVsq9beJerhIjdPFdYuIdtLVRYB11cqVK1FcXIyNGzeidevW8PPzg5ubG/h8Pt68eYPLly8jMzMTK1eulHsOPT09WoeKEEJ0DAVcpEbJ6xFg1y0CVEtJTEhtoTmYukNPTw8bNmzAmDFjsG3bNoSHhyM8PBzl5eVwcHBA//79MWnSJPTt27e2q0oIIaQOoYCrBun6MDpl1y2ysLDQqfeF1H80B1O3eHt7c5O0FVm6dKnSwwNHjx7NDTskhBCiXSjgqiE0jK5q6xbV55tXXQ+sCSGEEELI/6GAqwbQMLoKurBuEQXWhBBCCCHkXbTwsYYpO4xOF5JFavu6RWxg/X7AyAbWQqGwlmpGCCGEEEJqCwVcGlaVYXTajl23SJH6um4RBdaEVGAYBvn5+cjJyUF+fj595gkhhOi8+tmVUI/owjA6ZfF4PDg4OChct8je3r5eznfSlflphMjDMAzS0tKQmZmJ8vJyrpyG1BJCCNF1FHBpmLYPo6sqbV23iAJrAuhuwhShUIjk5GSIxWKpbbo2V5UQQgh5n27c5dcidhidohvt+jqMTlXauG4RBdZEVxOmyEsK9D5a8oEQQoiuqrNzuLZu3QpXV1cYGxvDx8cHERERcveNjo5GYGAgXF1dwePxsGnTJql9goKC0KlTJ5ibm6NRo0YYMmQIYmJiNNiCCuwwOkXq6zC66mDXLbK0tISZmVm9b782z08jldO1hCnsPK3s7GwkJycrdYyuzFUlhBBC3lcnA64jR45g7ty5WLJkCR48eABPT0/0798faWlpMvcvLCxEs2bNsGrVKtjb28vc59q1a/j6669x584d/PPPPygtLUW/fv1q5AZAIBDA2dlZ6oacz+fD2dlZq59+K6JNk+spsNZdupYwRSgUIiYmBrGxsUhKSpI5jFAeGlJLCCFEF9XJ8U0bNmzAlClTMGnSJADA9u3bceHCBezZswcLFiyQ2r9Tp07o1KkTAMjcDgAhISES3wcHB6NRo0aIjIxEjx491NwCado4jK46tHH4lbbOTyOK6VLCFGWHD8pDQ2oJIYToomr99UtNTcXJkyfx7NkzFBYWYvfu3QCA9PR0vHnzBu7u7jAxManSOUtKShAZGYmFCxdyZXp6eujbty9u375dnepKYIf4WFtby9wuEokgEom473Nzc6v9muwwOl2nzQtBU2Cte5TttSktLdVwTTRLmZ48RWhILSGEEF2l8pDC3377DU2bNsWMGTOwZcsW7N27l9uWlpaGrl274sCBA1U+b0ZGBsRiMezs7CTK7ezskJqaqmp1JZSXl2POnDnw9fWFm5ubzH2CgoIgEAi4L2dnZ7W8tq7TheFX2jY/jSimbK9NampqvZ7LpUxPniI0pJYQQoiuUingOnfuHGbMmAF3d3ecPXsW06dPl9jerl07eHh44PTp0+qoo9p9/fXXePLkCQ4fPix3n4ULF0IoFHJf1RlGQ/4PLQRNtI0yCVMAQCwW1+sEGqoGW/r6+jo9V5UQQghRaUjh2rVr0aRJE1y9ehWmpqaIjIyU2sfd3R3Xr1+v8rltbW2hr6+Pt2/fSpS/fftWbkKMqpgxYwbOnz+P8PBwODk5yd3PyMgIRkZG1X49IonWqyLaRpkFvd9VX9OjV3X+lb6+PqytrdGoUaN611ZCCCFEnVTq4YqKisLHH3+scDx+48aNpYImZRgaGqJjx44IDQ3lysrLyxEaGoquXbuqUl0AFUPZZsyYgVOnTuHKlSto2rSpyuciqqP1qog2Yocd6+lV/iu1vvbgKtOTp6+vDycnJ7i4uMDZ2RlGRkYoKCio10OECSGEkOpS6a62vLwcfD5f4T5paWkq9xDNnTsXEyZMgLe3Nzp37oxNmzahoKCAy1o4fvx4NG7cGEFBQQAqEm08ffqU+39SUhKioqJgZmaGFi1aAKgYRvjnn3/izJkzMDc35+aDCQSCKif2qA6GYXQ6oQItBE20lUAgAMMwSExMrHTf+tiDq0xPnqOjIwAgKSlJqzKQEkIIIdWhUsDVunVrhcMFy8rKEB4eDnd3d5UqNWrUKKSnp2Px4sVITU2Fl5cXQkJCuEQa8fHxEk+Sk5OT0b59e+77devWYd26dejZsyfCwsIAANu2bQMA9OrVS+K19u7di4kTJ6pUz6rSxlToVaXMTRtNrif1lbb34Fa29AEArc1ASgghhKhKpb/6Y8eOxbfffotly5ZhyZIlEtvEYjG+/fZbvH79Gt9//73KFZsxYwZmzJghcxsbRLFcXV0rHbJS20NatDkVelVp+3pVut6Lqct0oQdX3tIHABATE6Pw2Po6f40QQgipDpUCrpkzZ+LcuXNYvnw5Dh48CGNjYwDAyJEjcf/+fcTGxqJfv36YPHmyWitbXymbCl2XbkS0db0q6sXUbbrSgytrTcH8/HydWQCaEEIIqQqVkmbw+XxcunQJCxYsQGZmJp48eQKGYXD8+HFkZWXh+++/x9mzZ+v9TYW6UCp02bRtvSq2F/P9a832YtbXdOCkatgEGu8PG+Tz+VqdHp0ykBJCCCGyqTyRwNDQED///DNWrlyJmJgYZGVlwcLCAh988AH09fXVWcd6j25EtB/1YpJ3aWsPriLaPn+trhs7diwsLCxquxoa5+3tXdtVqDHVycxcn+jSvY8u3R83aNCgtqugcVVJDljtv3w8Hg9t2rSp7mm0Gt2IaL+q9GLScCrdIGvYnTbThflrhBBCiCpUGlJIqkaZ9WvoRqR+o15MouvY+WuKaMP8NUIIIaSqlOpS8fPzU+nkPB5PYgFjXaUrE+l1GfViEqL9GUgJIYQQVSh19/d+GnZlUQDxf+hGRLvRcCpCKuji/DVCCCFEEaUCrvLyck3XQyfQjYj2ol5MQv6Prs1fI4QQQhSh8U01jG5EtBf1YhJCCCGEkPdRwEWIGlEvJiGEEEIIeVe1Aq7i4mLcu3cPycnJEIlEMvcZP358dV6C6BCGYbQiUKFeTEIIIYQQwlI54Nq6dSsWLVoEoVAoczvDMODxeBRwEaUIhUKpoXgGBgZwcHCgoXiEEEIIIaTeUmkdrpMnT2LmzJlwdnbGunXrwDAMBg8ejF9++QUDBgwAwzAIDAzEnj171F1fooWEQiESEhKkMvyVlZUhISFBblBPCCGEEEJIXadSwLVp0yY0atQIt2/fxjfffAMA8PLywvfff48LFy7gwIEDOH36NFxcXNRaWaJ9GIZBSkqKwn1SU1PBMEwN1YgQQgghhBD1USngevz4MQICAtCgQQOuTCwWc/8fM2YM/Pz8sHz58urXkGg1ds6WIqWlpSgoKKihGhFCCCGEEKI+KgVcpaWlaNiwIfe9iYkJcnJyJPbx9PTEgwcPqlU5ov0qC7aquh8hpPYwDIP8/Hzk5OQgPz+feqYJIYQQqJg0w9HRUWIYmIuLCx4+fCixT1xcHAwMKOs8UUzZzwh9lgip2yjxDSGEECKbSj1cnTp1kui9GjBgAG7evImgoCBER0djx44dOHnyJDp16qS2ihLtZGpqWmkwxefzYWpqWkM1IoRUFSW+IYQQQuRTKeAaMWIERCIRYmNjAQALFy6Ek5MTfvrpJ3h4eGD69OkwMzPDmjVr1FlXooV4PB4cHBwU7mNvb18v1+MiRBdQ4htCCCFEMZXGaQ0dOhRDhw7lvm/YsCGioqKwe/duvH79Gi4uLhg3bhwaN26stooS7cUON3p/OBKfz4e9vT0NRyKkDqtK4htaEJwQQoguUtvEGCsrK8yfP19dpyM6RiAQwMLCgrt5MzAwgKmpKfVsEVLHUeIbQgghRDHKREDqDB6PR0/ACalnKPENIYQQopjKfwEZhsGZM2fw6NEjJCcno7S0VGofHo+H33//vVoVJIQQUnexiW8U9WBR4htCCCG6TKWA6+XLl/jkk0/w4sULhROhKeAiRPcwDENDQ3UIm/gmISFB7j6U+IYQQoguUylL4ddff43nz59j2rRpCA8Px4sXL/DmzRupr9evX6u7voSQOkwoFCImJgaxsbFITExEbGwsnj17hpSUFFoIV4u8v8CxhYUFnJ2dpYYN8vl8ODs7U+KbOkgkEmHRokVo2bIlGjZsiN69e+PKlSuVHvf8+XMsWLAAffr0ga2tLczNzREXFydz3xMnTuCLL76Al5cXzM3NMXDgQHU3QyklJSXYunUr/P390bNnT0yePBkRERGVHhcWFoY5c+bA398fPXr0QEBAAH744Qe8evVK4XGJiYno2bMnunbtiv/++09dzSDvEIlEWLBgAZydnWFmZoauXbvin3/+UerYpKQkjB49GjY2NrCyssLQoUOl7leLioowZcoUeHp6wtraGgKBAB06dMD//vc/mSO6NEkkEuH777+Ho6MjTExM4OPjU6W2jhw5EpaWlrCwsMDgwYNl3ptv27YNI0aMQJMmTcDj8TBx4kQ1t6Jy7DV1cnKCqalpla/pqFGjYG1tDUtLSwwZMkTmNf3iiy/g4eEBKysrWFhYoH379jV2TVXq4bp+/ToCAgKwdetWddeHEFJPsWsxvU8sFiMzMxOZmZnQ09PjfvFTz1f9pGiB49atW1PvZj0xbdo0nD59Gl999RWaN2+OgwcPIjAwEBcuXEC3bt3kHhcREYFt27ahTZs2aN26NR4/fix33927dyMqKgodOnRAVlaWJpqhlJUrV+LKlSsYNWoUnJ2dceHCBcydOxdbt26Fp6en3ONevXoFc3NzjBw5EgKBAFlZWTh//jwmT56MXbt2oWXLljKP27x5M/T19TXVHALg888/x4kTJzBr1iy0bNkS+/btg7+/Py5fvozu3bvLPS4/Px99+/aFUCjEggULwOfzsXnzZvj5+SEyMhI2NjYAKm7Oo6OjMWDAALi6ukJPTw+3b9/GvHnzEBERgQMHDtRUUzFx4kQcP34cc+bMQcuWLREcHIxBgwbh6tWrlba1d+/eEAqF+OGHH8Dn87Fx40b07NkTUVFRXFsBYPXq1cjLy0Pnzp0rXeZDUyZNmoQTJ05g9uzZaNGiBfbv349PPvkEoaGhlbazT58+EAqFWLhwIfh8PjZt2oTevXvjwYMHEtf06dOnGDhwIFxdXcHj8XD79m3MnTsXd+/excGDBzXaPh6jwiNnOzs7jBs3DuvWrdNEneqk3NxcCAQCCIVCWFhY1HZ1CKlTGIZBTExMlTLRsTfp2tj7oa3DKuUF1SxN9WbVl9+/kZGR+O233xAeHo7k5GSUl5fD0dER3bp1w/jx4/HRRx8BAJYuXYply5bh0KFDGD16dLVek31vkpKSlH5v7t+/j969e2PlypWYPXs2AKC4uBg+Pj6wtbVFaGio3GOzsrLA5/Nhbm6OzZs346effsKTJ0/g4uIitW9iYiIcHR2hp6eHzp07w8bGBn/99ZdqDf3/njx5UqX9o6Oj8cUXX2DGjBkYO3YsgIon6WPHjoWVlRV27dpVpfNlZWUhICAA/v7++P7776W237lzB9999x3Gjh2L4OBg7NmzBx988EGVXoPVtWtXlY6rb6qawTQiIgLdunXD6tWrMW/ePAAVn19PT080bNgQN27ckHvs2rVrsXDhQty+fRudOnUCADx79gyenp749ttv8fPPPyt87dmzZ2Pr1q1ITEyEvb19leoNoMqBeEREBHx8fLB27Vp8++23ACra6ubmhkaNGuHWrVtyj12zZg2+//57RERESLTVzc0N3333HX755Rdu37i4OK53y8zMDMOHD0dwcHCV2/eu8vJypfeNiIhA165dsWbNGolr6uHhgUaNGlV6TRcsWIA7d+5ItNPDwwPz58+v9JrOmjULW7duRVJSUpWvaW5uLqysrJT626TSkMKPPvpI4UUmVff+EB0aekXqE2XWYnpfWVkZEhISIBQKNVSr2iFrWGVMTEy9byctcCxfeXk55s6dC29vb+zfvx/NmjXDtGnTMHv2bHTs2BEXLlxAv379sGLFitquKgDg9OnT0NfXx6RJk7gyY2NjjBs3DhEREUhMTJR7rLW1NczNzZV6HScnJ+jpqXSboTZXr16Fvr4+hgwZwpUZGRnB398fT548wdu3b6t0PisrKxgbGyM/P19qW1lZGTZu3IiRI0fCycmpulUncpw4cQL6+vqYMmUKV2ZsbIxJkybhzp07Ch8KnThxAt7e3tyNOQC0adMGfn5+OH78eKWvzT5YyMnJUb0BVXD8+HHo6+vjyy+/5MqMjY0xefJk3L59W2Fbjx8/jk6dOkm1tU+fPjh69KjEvi4uLrX6UFDeNf38888rbeeJEydkttPPzw/Hjh2r9LVdXV0BaP6aqvSbcO3atUhOTsb8+fNRXFys7jrpHG29QSO6ozprLGnTTTrbA/T++6ENwWVVFjjWNT/99BM2btwILy8vPHv2DJcuXcL69euxatUqHD16FElJSVizZk2deW8eP36MFi1aSD2R9fb25rZri+fPn8PZ2VkqS2bbtm257ZXJy8tDdnY2Xr58iV9++QUFBQXce/Wuw4cPIy8vTyKQJeoXFRWFVq1aSX1+2RvuqKgomceVl5fj33//lXntOnXqhFevXiEvL0+ivKSkBBkZGUhISMDp06exYcMGuLi4oEWLFuppTCUePnwos62dO3cGoLitjx8/ltnWzp07y2xrbZLXTmWu6ePHj9GxY0epbfLa+e41PXXqFNavX18j11SlOVwODg64dOkSunbtip07d6Jly5Yyu9J4PJ7CoQlE/hAd9gYNgFYOuSLapTprLLE36fV9DTZle4AsLCzq5fBCWuBYtpcvX2LNmjWwsbFBSEgI7OzspPYxMTHB/PnzIRKJaqGG0lJTU2UOnWHLUlNTa7pKGpOZmSkxV4Vla2sLAMjIyKj0HF988QXi4+MBAA0aNMDEiRPh7+8v9Tp79+7FzJkzaQkEDZP3+XVwcAAAub+Hs7KyIBKJFB6bnJyM1q1bc+WnTp3ihqICFQ8ldu3aVWPrCqakpHB1e9e79ZWFbWtlx77b1tqUmppaY+08efKk1DXdvXu3xq+pSmd/+PAhPvroI6777cGDBzL3q483FTVJ22/QiO5QZi0mRbThJr0qPUD1MbikBY5lCw4OhlgsxtSpU2UGW+8yMjKqoVopVlxcLLMubFlRUVFNV0ljRCIRDA0NpcrZMmWC4J9++gkFBQVITk7G+fPnIRKJUF5eLjFccuvWrWjcuDECAgLUV3kiU1FRkczPr7GxMbdd3nGA7J9Decf26tULISEhEAqFuHLlCh49elSjPdU12dbaVFRUJPPnlK2rvNF0itop7/dZ7969cenSJeTk5ODKlSt4/PhxjVxTlf4yzpkzBzk5OVi9ejU+/fRTODg4UEYeFWj7DRrRHcqsxaSINtyka3sPEC1wLNvNmzcBAH5+fjXyeiKRSCJIyM3NrfI5jI2NZQYabJmJiYnqFaxjjIyMUFJSIlXOlikTBLu7u3P/79u3Lz799FMAFZPtgYpEHiEhIfj1119rfc6aLjAxMZH5+WVvyuV9ftnyqhxrZ2fHPUgJDAxEUFAQBgwYgGfPnqmUNKOqarKttcnExETmzylbVzbwknUcILud8n6fvXtNhw8fjqCgIPTv3x8xMTEavaYq/WaIjIzEqFGjMH/+fDg5OVGwpSJtv0EjukUgEMhci6ky2nKTru09QGxQrYguLnDMDr+rqSQJQUFBEAgE3Jezs3OVz2Fvby9z2CBbVhM3kjXFxsYGmZmZUuXsUEJ2aKGyLCws0LFjR/z9999cGZte3tHRESkpKUhJSeFGAGVkZGjVEM26QN7nlx0xJO/3lLW1NYyMjBQe6+joqPC1AwMDkZ+fj7Nnz1a12ipxcHCQORKqsvqybVXl2Npgb29fa+1kr+mZM2eqWu0qUekvv4WFRaVDJ0jltP0GjegegUAACwsLFBQUIDc3Fzk5OZWmhtWWm3Rd6AFi55O+vw4Xn8+Hvb09zTetAQsXLsTcuXO573Nzc6scdLm7uyM8PBy5ubkS86/v3bsHAPDw8FBPZeuAli1b4sGDBygoKJD42YuOjgYAtGrVqsrnFIlEElkKU1NTkZqaimHDhknt+91338HMzEzpBVxJ5Tw9PREWFib1+WUXs/by8pJ5nJ6eHtzd3XH//n2pbREREWjWrFmlGTjZ4Wk1lQDJy8sLV69elWrr3bt3ue2yKGrr3bt3lWprTfLy8qrWNY2MjJTapmw72WuqymiBqlCph2vw4MG4cuVKlXLsE2nsDZoi9f0Gjegedh0PR0dHfPDBB3B1dYWNjY1UTzifz9fYuk21QVd6gAQCAVq3bg1XV1c4OTnB1dWVyy6li0tbsL1BSUlJNfJ6RkZGsLCwkPiqqiFDhkAsFmPv3r1cmUgkwsGDB+Ht7c311iUkJCAmJkZtda8Nfn5+EIvFOH36NFdWUlKCCxcuoF27dtzD49TUVMTGxkocK2ux5pSUFNy/f19iba0FCxZg1apVEl8jRowAAMycORNLly5Ve7t0WWBgIMRiscQaaiKRCPv27UPnzp25BxDx8fF49uyZxLHDhg3D/fv3JQKRmJgYXL16FYGBgVxZRkaGzN9he/bsAQCZWfE0Yfjw4RCLxdi5cydXJhKJsHfvXvj4+Chs6/Dhw3Hv3j2ptl65coX7fNYV8q5pcHBwpe0MDAyU2c6rV69i+PDhXJm8a/r7778D0Pw1VanrZPXq1fjoo48wduxYrFu3Do0bN1Z3vXSCMvNetOEGjaWti8ES+djgy8zMDPb29lp//XWlB4i9riyhUCjVZm1e2Ppdvr6+CAsLQ2hoaI3N46quTp06YejQoVi6dCnS09PRrFkz/Pnnn4iLi8OWLVu4/b788kvcuHFDIq2yUCjEjh07AFQs8gsAO3bsgKWlJQQCAaZOncrte+PGDW7NzoyMDBQWFmLNmjUAgG7duqF79+4ab2u7du3g5+eHbdu2ITs7G05OTrh48SJSUlLwww8/cPstX74cDx8+xO3bt7myzz77DN7e3mjVqhXMzc2RkJCAc+fOoaysDNOnT+f28/HxkXpdtgesffv2Ki98TGTz8fHB8OHD8eOPPyItLQ0tWrTA/v37ERsbKxGYTJw4EeHh4RK/l6ZPn47ff/8dAQEBmDt3Lvh8PjZt2gQ7OzuJnuODBw9i586dCAgIQLNmzZCXl4e///4bly9fxieffFJjP+s+Pj4YMWIEFi5cyLV13759iI2N5QIFABg/fjyuXbsmEVB89dVX2LVrFz7++GN8++234PP52LBhA+zs7LjFhVnnzp3Do0ePAFTkDnj8+DFWrlwJAAgICNB4rzd7TX/44QekpaWhefPm+OOPPxAbGysRhE2cOBHXrl2DWCzmyqZPn47du3fD398f8+bNA5/Px8aNG2Ve0x07dmDw4MFo2rQp8vPzcenSpRq7pioFXF5eXigpKcH9+/dx9OhRWFlZyU0L/+rVq2pXUpvpyg2aLt+QkQrv36Rrq3eHVWpzcMnS9aUtJk6ciFWrVmHnzp2YM2cOGjZsKHdfkUhUZzIV7ty5EytWrMDhw4eRk5MDNzc3HDt2rNIgKCcnR2oB519//RUA0KRJE4mAKzw8HEFBQRL7sscuXLiwRgIuAFi8eDF27tyJkJAQ5OXloXnz5li3bh3at2+v8Lhhw4bh1q1buHPnDgoLC2FlZQUfHx+MHz++xtZhIrIFBwdj8eLFOHjwILKzs+Hu7o4zZ86gR48eCo8zNzdHaGgo5s2bh19++QXl5eXo2bMn1q9fL/Gz6+vri9u3b+PIkSN4+/YtDAwM0Lp1a6xbtw4zZszQdPMk7N+/H4sWLcIff/yB7OxseHh44Pz580q1NSwsDN988w1WrlyJ8vJy9OrVCxs3bpT6PXXixAns27eP+/7hw4d4+PAhgIr5qTUxzHjfvn1YvHgxDhw4wLXz7NmzSrXzypUrmDt3Ln7++Wfumm7YsEHqmt66dQuHDx+WuKbr16+vkWvKY1QY9+Hq6qr0zcObN2+qXKm6KDc3FwKBAEKhUKUhHJXR5t4feTdkLG0aVkaILmEYBjExMZXOW2vVqlW1fp9p+vdvdf3www8ICgpCx44dcezYMTRt2lRie3FxMX777Tekp6cjKCgIS5cuxbJly3Do0CGMHj26Wq/NvjdJSUl18r1RtydPntR2FWpM165da7sKNUKXEoPpUpI5XZh2lJubCysrK6X+NqnUw/X+OGdSfdr69L8qa40B0NqgkxBtREtbVFi5ciWKi4uxceNGtG7dGn5+fnBzcwOfz8ebN29w+fJlZGZmckN0WNu2bUNISIjMc37xxRc11gNECCFEs+pk+rutW7di7dq1SE1NhaenJ3799Vd07txZ5r7R0dFYvHgxIiMjERcXh40bN2LOnDkS+4SHh2Pt2rWIjIxESkoKTp06hSFDhmi+IUTpG7K0tDRkZ2fTkEMtoc09tuT/0NIWFfT09LBhwwaMGTMG27ZtQ3h4OMLDw1FeXg4HBwf0798fkyZNQt++fSWOY/eTpVevXhRwEUKIlqhzAdeRI0cwd+5cbN++HT4+Pti0aRO3IFmjRo2k9i8sLESzZs0wYsQIfPPNNzLPWVBQAE9PT3z++ecy07YSzVH2Ris9PV3msbowB0Tb0Hw92bQxCJW12KQsurK0hbe3t8REdnmWLl1KmesIIUSHKPVXcPny5eDxePj6669hbW2N5cuXK3VyHo+HRYsWValCGzZswJQpUzBp0iQAwPbt23HhwgXs2bMHCxYskNq/U6dO6NSpEwDI3A4AAwcOxMCBA6tUD6Ie6rjRYocc1vebU12g6wkU5NHGIFQoFMp8UPI+WtqCEEKIrlPqbnjp0qXg8XgYNWoUrK2tlX4yV9WAq6SkBJGRkVi4cCFXpqenh759+0qkaq0JIpFI4umtphdE01ampqbQ09Or1uRJXZgDog2qMl9Pl4JnbQxClbnWLG1a2oIQQghRhVIB19WrVwFUpHx993t1y8jIgFgs5hYiZNnZ2UktdKZpQUFBWLZsWY2+pjbi8XiwtLSUuYBkVWj7HBBtQAkUpGlrEKrMtQaAhg0b1rtgkhBCCFE3pQKunj17KvxeGy1cuFBiwbTc3FxupWtSNRYWFtUOuHRlDkh9RgkUpGlrEKrsNawra04RQgghtUlPlYP279+Px48fK9znyZMn2L9/f5XOa2trC319fbx9+1ai/O3bt7C3t69yPavDyMgIFhYWEl9ENaamptUKmGgOSP2g7DXWpeBZW4NQutaEEEKI8lQKuCZOnIjTp08r3OfMmTNc4gtlGRoaomPHjggNDeXKysvLERoaqjMLAGojHo8HBwcHlY+nOSD1gzKBta4Fz9oamNC1JoQQQpSnsb/yYrEYenpVj+fmzp2LCRMmwNvbG507d8amTZtQUFDABW/jx49H48aNERQUBKAi0cbTp0+5/yclJSEqKgpmZmZo0aIFACA/Px8vX77kXuPNmzeIioqCtbU1Ny+NaAabCpthGDRs2BBZWVkQi8Xcdj6fz/Vevp/Fjd1WH+eAaGMK8MqwgbWsBBEsXQue2cBEUQ9WfQxM6FoTQgghytNYwPXw4UNYW1tX+bhRo0YhPT0dixcvRmpqKry8vBASEsIl0oiPj5cI5JKTk9G+fXvu+3Xr1mHdunXo2bMnwsLCAAD3799H7969uX3YuVkTJkxAcHCwCq0jypCXCrthw4YwMjKSCkQsLCy0IkjRxhTgyhIIBCgsLERmZqbUNhsbG61v//u0OTBhr6U2PSghhBBCNIHHMAyjzI5+fn7c/8PCwuDq6gpXV1ep/cRiMRITExEbG4uRI0fi0KFDaqtsbcrNzYVAIIBQKKT5XEqQlwqb5ezsrJU3ZLrabpaut18eWUG4tgQmNdGbS79/5WPfm6SkJJ14b548eVLbVagxujKVor7NYa0OfX392q5CjanOckD1RW5uLqysrJT626R0DxfbWwRUPLWNjY1FbGys1H56enqwtrbGiBEjsGnTJmVPT7SItqbCroyutpul6+1XRCAQaE0P7vt4PF69yrBICCGE1DSlA653I1U9PT0sXboUixcv1kilSP2mramwK6Or7WbpevsrQ4EJIYQQoptUmsN19epVNG3aVN11IVpCW1NhV0ZX283S9fbrGl1MDEMIIYSoQqW08MuWLZMYYijLgQMHJOZ9Ed2hramwK6Or7Wbpevt1iVAoRExMDGJjY7k5uzExMRAKhbVdNUIIIaTOUSngCgsLkzl/611xcXG4du2aKqcn9ZyurtGjq+1m6Xr7dQWbGOX9nsqysjIkJCRQ0EUIIYS8R6WASxkFBQXg8/maOj2pw5RZ6Li+psJWRFfbzdLF9jMMg/z8fOTk5CA/Px9KJn2tt5RNjKLt7wMhhBBSFUqP7YmPj5f4PicnR6oMqEgLn5CQgBMnTshMG090g66u0aOr7WbpUvt1cb01SoxCCCGEVJ3SAZerqyv3ZJrH42Hz5s3YvHmz3P0ZhsHatWurX0NSb8lKhd2gQQMUFhYiJydHayfaa3MKcGXoQvvlrTfGDqsDoJVBFyVGIYQQQqpO6YBr/Pjx4PF4YBgG+/fvh6enJ7y8vKT209fXh7W1Nfz8/DBgwAB11pXUQ++mwhYKhXj+/LlO9AjoegpwbW5/VdYbA6BVgSclRiGEEEKqTum/isHBwdz/r127hkmTJmHWrFmaqBPRQrraI0C0j7LD6tLS0pCdna1VDxjYxCiK2k+JUQghhBBJKj2GfPPmjbrrQbRYVXoE6vPTf6IblB0ul56eLvPY+vyAgU2MIuvhCUvbEqPUF8HBwTA2Nq7tamjciBEjarsKNUZXhubqUo/4sGHDarsKNea///6r7SponFgsVnrfan/Kb968iaioKOTm5sLCwgJeXl7w9fWt7mmJFqGJ9kSbqOPmoD4/YNClxCiEEEKIOqh853Dr1i1MmjQJL1++BFDRi8HePLRs2RJ79+5F165d1VNLHcEwjFbN92DRRHuiTZQZVleZ+v6AQRcSoxBCCCHqolLAFR0djX79+qGwsBAfffQRevfuDQcHB6SmpuLq1av4+++/0b9/f9y5cwdt27ZVd521kjanmKaJ9kSbKDOsThm5ubn1NuACtDsxCiGEEKJOKt3hLl++HCUlJbh48aJUJsLvv/8eISEhCAgIwPLly3H48GG1VFSbaXtCCZpoT7SNomF1lpaWMudvvU8oFMLBwYF6hQghhBAtp1LAFRYWhuHDh8tN+z5gwAAMHz4coaGh1aqcLtCFhBI00Z5oI3nD6gAgKyur0sm0YrG4Xg8rJIQQQohyVAq4hEIhmjZtqnCfpk2bQigUqlQpXaIrCSVooj3RRvKG1VlaWiIzM7PS47Vl3qK2zj8lhBBC1EGlgMvR0RF37txRuM/du3fh6OioUqV0iS4llKCJ9kRXmJubKxVwacO8RW2ef0oIIYSog54qBwUEBCAsLAyLFi1CcXGxxLbi4mIsWbIEV69exeDBg9VSSW2mawkl2B4BS0tLmJmZUbBFtBI7b1ERbZi3yM4/ff+BEDv/lEY5EEIIISr2cC1atAjnz5/HL7/8gh07dqBz586ws7PD27dvce/ePaSnp6NZs2ZYtGiRuuurdSihBCHaR5l5i5aWljVXIQ3QhfmnhBBCiDqo1MNlY2ODO3fuYMKECcjPz8fFixexd+9eXLx4EXl5eZg0aRLu3LkDa2trdddX67A3ZopQQglC6h+BQABnZ2e5PV3p6emIiYmpt71AVZl/SgghhOgylcep2draYs+ePdixYweePXuG3NxcWFhYoE2bNuDz+eqso9ajhBKEaCd23mJaWprMVPH1eekHXZp/SgghhFRHtScG8fl8uLu7q6MuOo0SShCivbKzsxVur49D73Rt/ikhhBCiKvpLWIfISzGtDShtNNFGynyutXXpB5p/SgghhCiHAi6icZQ2mrxLW4JvZT/X2jr0jhY0J4QQQpRDARfRKDZt9Pvq89wVojptCb6r8rnW5qF3NP+UEEIIqVz9+wtP6g1KG03epS3Bd1U/19o+9I7mnxJCCCGKqZQWnhBlUNpowlI2SGEYpoZqpLqqfq51YekHWtCcEEIIkY8CLqIx2jp3hVSdNgXfVf1cMwyD4uJimUGIvr4+nJ2d60XP3vsYhkF+fj5ycnKQn59fL4JlQgghpDZUe0hhWVkZYmJiuHW4WrduXS/nIhD10+a5K6RqtCn4rsrnWigUIikpCeXl5TL3EYvF6qxajdGWuXiEEEJITVC5hysrKwtTpkyBQCCAh4cHunfvDg8PD1haWuLLL79EZmamOutJ6iF27ooi9XnuClGeNgXfynyu9fX1ublp8oItVn0ZSsli5+K9Hxyz7RUKhbVUM0IIIaRuUingysrKQpcuXfD777/DxMQEH330EcaPH49+/frBxMQEu3fvRrdu3ZCVlaXu+uoEbRmqowtzV4hyTE1Noa+vr3Cf+hJ8K/O5FovFSExMVOp89WUoJaBdc/EIIYSQmqLS4+QVK1bg5cuXmD9/PhYvXixxk1RYWIgVK1Zg9erV+Pnnn7F+/Xq1VVYXaNtQHUobTQAgNze30uFz9Sn4lve5VlV9GEoJaO8izoQQQogmqRRwnTlzBr169cLq1aultjVo0ABBQUG4e/cuTp06RQFXFWhL2uz3Udpo3aZMr4i+vj4sLCxqqEbqIRAIYG5ujpiYmGrPxaoPQykB7ZqLp+vKysoQEhKCyMhIFBYWwtHREQMGDEDr1q0VHnfp0iX8/fffUuUGBgYS9wSlpaU4efIk4uPjkZOTg/LyctjY2KBz587w9fWttMdbnUQiEdavX4+TJ09CKBTigw8+wLfffosePXooPO6vv/7CuXPn8OjRI6Snp8PR0RF9+vTBrFmzpP4Wnz17FqGhoXj48CFiY2PRpUsXHD16VJPNkiISibBkyRIcPHgQ2dnZcHd3x/Lly/HRRx9VemxSUhLmzZuHf/75B+Xl5ejVqxfWr1+PZs2acfsUFRVh1qxZiIiIQEJCAsRiMZo3b46JEydi+vTp4PP5mmyezhKLxfjvv/8QHx+PkpISCAQCtG3bFnZ2dgqPCwkJQWFhocxtpqam6N+/P/f9yZMnZe7Xrl27Sn8nqFN5eTkyMjIgFAohFothZGSEhg0bKvUAr6CgABkZGRCJRGAYBoaGhrC2tpZ531xWVob09HTk5+dDLBbDwMAADRo0gKOjoyaaBUDFgCs5ORmffvqpwn26du2KW7duqVQpXaTta1axaaOJ7lGmV0QsFtfLXpHCwsJqB1v1ZSgloF1z8TQpNjYWTZs2Rf/+/RESEgIAWLp0KZYtW4ZDhw5h9OjRMo+bOHEi9u3bh9u3b6NLly4areOhQ4fw+PFj9OjRA7a2trh37x52796N6dOnS9xkyxMYGAgjIyPu+/f/LpWWliI1NRVt2rSBtbU1eDweYmNjcfbsWcTHx+Ozzz5Te5vkmTdvHi5evIjJkyfD1dUVx48fx8SJE3H48GF07txZ7nELFiyAnZ0dhg0bBkdHRzx79gz79u3D1atXcfHiRRgbG3P7HjhwAP/++y88PT2RnZ1dE82S8vnnn+PEiROYNWsWWrZsiX379sHf3x+XL19G9+7d5R6Xn5+Pvn37QigUYsGCBeDz+di8eTP8/PwQGRkJGxsbABUBV3R0NAYMGABXV1fo6enh9u3bmDdvHiIiInDgwIGaaqpOiYyMRFJSElq0aAEzMzPExcXh1q1b+PDDD2Frayv3OA8PD6m/vYWFhXj69KnMYK1Ro0Zo0qSJRJmlpaVa2qCslJQU5ObmwtraGoaGhlxHhIuLCxo0aCD3uLy8PCQmJsLExIR7T/Ly8pCcnIyysjLuMwxU/G6KjY0FUNE+Pp+PsrIyFBUVabRtKv1VFAgEiIuLU7hPXFxcveyNqS00VIdoK23uFVFHnevTUEptX8RZV8THxyMqKgqffPIJevfuDQDw9vbG2rVrcf78ecyaNavSc3h4eCj8W9SgQQPMnj1boqxbt24wNjbGzZs3ERAQUCO92lFRUTh79ix+/PFHTJ06FUBFsPjRRx8hKCgIp06dknvs9u3b0bVrV4kyd3d3zJ07F6dOnZJ48Lxp0ybY29tDT08Pffv21UxjFIiIiMCRI0ewevVqzJs3DwAwbtw4eHp6YsGCBbhx44bcY7dt24YXL17g9u3b6NSpEwBgwIAB8PT0xIYNG/Dzzz8DAKytraUepE+dOhUCgQBbt27FunXrYG9vr6EW6qasrCwkJibCzc0NrVq1AgA0adIEly9fxpMnT9CrVy+5x8rqrXn27BkAwNnZWWqbmZmZVMBVk4qKipCbm4tGjRpxAZJAIMDr16+RlpYGV1dXucdmZ2fDwMAATZo0gZ5eRXoKKysrvHr1CkKhUCLgSklJAY/Hg6ura40+HFQpaUbPnj1x7NgxXL58Web20NBQHDt2TOEHgUjS5ptSotu0uVekOnWuj2twUSIc7fDo0SPo6elJBBN8Ph8+Pj6Ii4tTuoemuLi4yglSrK2tAUDjT5NZFy5cgL6+PsaMGcOVGRsbY9SoUYiMjERycrLcY98PtoCKQAQAXr58KVHu6OjI3ejVhhMnTkBfXx9TpkzhyoyNjTFp0iTcuXNH5nSFd4/19vbmgi0AaNOmDfz8/HD8+PFKX9vFxQUAkJOTo3oDiExJSUng8Xho2rQpV6avrw9XV1dkZWXJHTIoT0JCAho0aCARgLxLLBbX2nIlubm5ACR71fT09GBpaYmioiKUlpbKPVYsFkNfX1/iZ5DH40FfX1/i75FIJEJBQQFsbGxgYGCA8vLyGkvypNLdwpIlS3DhwgX0798fgwYNQs+ePWFnZ4e3b98iLCwMf/31Fxo0aIDFixeru75aS1tvShmGoblbOk6be0WUadv79PX1YW1tjUaNGtXLnwVKhFP/JSUloWHDhhJD4oD/e+qdnJwMKysrhef45ZdfIBKJYGhoCDc3NwQEBMDc3Fxqv7KyMhQXF6O0tBSJiYkICwuDlZWVwqFQ6hQdHY2mTZtK1c3Ly4vbXpV5G+np6QBQ6ftT06KiotCqVSupXkM2iIqKipLZq1FeXo5///0XkyZNktrWqVMn/PPPP8jLy5N4/0pKSpCbm4uioiJERkZiw4YNcHFxQYsWLdTcKiIUCmFmZiY1P479/AmFQoVD7d6Vk5ODvLw8uXOy4uLi8Pr1awCAubk52rRpI/Mzoyns75P353eamJgAqHjAI2+eoKmpKTIzM5GWlsYFbEKhEMXFxWjcuDG3H5sRWF9fH3FxcVzAampqCnt7exgaGqq7WRyV7t7btWuHS5cuYeLEibhw4QIuXLgAHo/HRYnNmzdHcHAw2rVrp9bKajNtvCnVtoyLRDVsr4iiJ6z1tVdEmbY5Oztz63Jpy0MHSoRTv71/A81ib9bZJ82ymJiYwNfXlxuO8/r1a9y8eRPx8fH45ptvpIK4f//9V2Juj7OzM0aNGlVjSTPS0tLQqFEjqXK27O3bt1U637Zt26Cvr4+PP/5YLfVTl9TUVJnD+dgeaXlzxLOysiASiRQem5ycLHGTfurUKYwdO5b73tvbG7t27ap3D4Trg+LiYqmfKQBcWVV6itm/U7KCKGtrazg5OaFBgwYoLi7G69evce/ePZSWlio1p1Md2L8l72PLFN0f29raoqSkBJmZmdw6wDweD05OTlIPC4CKnxdjY2M0btwYpaWlyMjIQHx8PJo1a6axnmqVfzq6d++OFy9e4ObNm3j48CFyc3NhYWGB9u3bw9fXt9p/eLdu3Yq1a9ciNTUVnp6e+PXXX+VObo2OjsbixYsRGRmJuLg4bNy4EXPmzKnWOWuatt2UamvGRVI1bA9nQUGBxEMZlr6+PhwdHev1Z0FXe3woEY5qjh8/zs2jeF9UVFSN1KG0tFTmjQ379FjR0J33M/t5eHigSZMmOHjwIG7evIk+ffpIbG/RogWmTp2KoqIivHjxAsnJydxNT00oLi6WSO7BYsuKi4uVPtfp06dx+PBhTJs2TWKIV11QVFQks52V3Ziz5VU5tlevXggJCYFQKMSVK1fw6NGjerOWYH0jFotlBgDsA4vy8nKlzsMwDBISEriHZe97fwqQq6srrly5gujoaLi4uNTIA5Ly8nKZv5fY+15FQ/94PB4MDQ1hbm7OBVg5OTlITk5GkyZNuF4y9v0yMDCAs7Mzd24DAwMkJydDKBRqrPe6Wo8jeDweunfvrjD7jSqOHDmCuXPnYvv27fDx8cGmTZvQv39/xMTEyHxSVVhYiGbNmmHEiBH45ptv1HLO2qAtN27annGRKEdWD+f7amusuLpRjw9R1okTJ3DixAmVjxeJRBCJRNz3inqj5GGzcr2PDbSqmt67Q4cOOHv2LF68eCEVcL17A+Tp6YnLly9jx44dWLBgQY0kzTA2NpZ4v1hsmazeA1nu3r2L+fPno2fPnvjuu+/UWkd1MDExkdlONqBkbzhlHQegSsfa2dlxWe4CAwMRFBSEAQMG4NmzZ5Q0Q8309fVlBlXs305le2MyMjJQXFyMli1bKrW/np4emjVrhqioKGRnZ9fIEGA9PT2ZQRVbpujvaWpqKoqKitC0aVNuPwsLC7x+/RqpqancAxL2/TI3N5c4n4WFBZKTk1FUVKSxgEulfrNmzZrhf//7n8J9tm7dqnI35IYNGzBlyhRMmjQJbdu2xfbt29GgQQPs2bNH5v6dOnXC2rVrMXr0aJlPaVQ5Z20RCARo3bo1XF1d4eTkBFdXV7Rq1areBFtA1TIuEu3BMAzy8/ORk5ODt2/fIiEhQam5TampqTU2aVWT2B4fS0tLmJmZUbBFZDp06BAYhpH5NWHChEqPDwoKgkAg4L5UmWNhbm6OvLw8qXI2eFMlELK0tFRqAr+npydEIhGio6Or/BqqaNSoEdLS0qTK2bLK1jICgKdPn2Ly5Mlo3bo1tm/fXieHztnb2yM1NVWqnH34KS/ZjbW1NYyMjBQeW9kct8DAQOTn5+Ps2bNVrTaphLGxscxe2MoC6ffFx8cDAJycnJR+bXZuWE31SMubVsOWyfu5YxgGOTk5Un93eTweTE1NJZL7sOd4/1xsgg1NPgRWKeCKjY2tNBtNTk5OpanjZSkpKUFkZKREWlU2zert27erfD5VzykSiZCbmyvxpU7v3pzm5+dL3HDW9xs3yrgoSdG11hZCoRDPnj1DbGwsEhMTuYnlytD24FsXrj+pOQsXLoRQKOS+FA1Dl6dx48ZIT0+XupFjb8qquvgnwzDIzs5Wao4x24tWU1kK27Vrhzdv3kgFmOzwzcrmmsfGxmLcuHGwtbXFvn376uw8ak9PTzx//lzqXiUiIgLA/yUJeZ+enh7c3d1x//59qW0RERFo1qyZzPl+72KvpVAoVKHmRBGBQID8/HypYb5ZWVnc9sqIxWIkJyejYcOGSgdowP8lmJDXkaFuRkZGKCkpkQp62M+XvN5oRfeS7N9b9l/2HO8fwzAMtwCypmgsh6lQKFTpImVkZEAsFks9dbKzs5P5BEZT51THU0R5hEIhYmJiuJvT2NhYxMTEaM0vK2XH+tbUpOnapO3XGvi/+XrVeTKkrcG3Llz/d1FwqXlGRkawsLCQ+KoqDw8PlJeXSzxwLCsrw71799CkSRNuSE12drZUUon8/Hyp8926dQv5+flo06aNxH6yrv+dO3cAyJ64rwmDBg2CWCzGn3/+yZWJRCIcPXoU7du354LLpKQkqVTvaWlp+Oyzz6Cnp4c//vhDbirtuiAwMBBisRi7du3iykQiEfbt24fOnTtz73d8fLzUHMJhw4bh/v37EkFXTEwMrl69isDAQK4sIyND5jVlRwp17NhRrW0iFQ9HGIbBmzdvuDKxWIy4uDhYWVlxvVCFhYUye62BilEkpaWlcn/mZA0nLS0txcuXL2FoaFhjGTnZ32XvduiUl5cjJycHxsbGEnNM362zgYEB9PT0kJeXJ/H5LC8vR35+PgwNDbmhhA0aNIC+vj6EQqHEUE32NTX5QEXpUC48PFzi+9jYWKkyoOKDkJCQgIMHD3KLtNVHCxcuxNy5c7nvc3Nz1fIHQheSSSjbI8cmUdDWeS+6cK0ZhkFSUlK1z1MXh+hUly5c/3dRVtL6w8XFBZ6enrh48SLy8/Nha2uL+/fvIysrCyNHjuT2O3ToEF69eoX169dzZStXroSXlxccHBxgYGCAN2/eICoqCo6OjhLrVj148AC3bt2Cm5sbbGxsIBKJEBMTg+fPn6Nt27ZKzyWprvbt2+Pjjz/G6tWrkZGRAVdXVxw/fhyJiYlYu3Ytt98333yDO3fucL18ADB+/HjEx8dj2rRpuHfvHu7du8dts7W1lUggcvfuXdy9exdARe9DUVERN/XCx8cHPj4+Gm2nj48Phg8fjh9//BFpaWlo0aIF9u/fj9jYWOzcuZPbb+LEiQgPD5f4OZ0+fTp+//13BAQEYO7cueDz+di0aRPs7Owk7oMOHjyInTt3IiAgAM2aNUNeXh7+/vtvXL58GZ988gn8/Pw02kZdZG1tjcaNGyM6OhoikQhmZmZcOvN3A9z79+8jIyMDw4YNkzpHQkIC9PT0JNKjv+vVq1dISUmBvb09l6WQfQ1vb+8aW1/OxMQE5ubmSEtLQ1lZGQwNDSEUClFaWirR656cnIzCwkJ88MEHACruJW1sbJCeno43b97A0tKSG2ZYVlYmcayenh4aNWqElJQUxMXFQSAQoLS0FFlZWdzra4rSdzm9evXiboR5PB727duHffv2ydyXYRjweDysWrWqyhWytbWFvr6+1FO1t2/fqjwZU5VzGhkZqb0bVVeSSSjbW5Gbm4vExEStvEHTlWudn5+vdJYkeerbcgfK0JXrz9K14FIbfPrpp7CyskJkZCSKiorg4OCAyZMno3nz5gqP69ChA2JjY/H48WOUlZXBysoKvXr1Qt++fSXWsGnatCliY2MRFRWFvLw87kYnICBA7Ym2KrNx40asX78eJ0+eRG5uLtq0aYO9e/dWGgQ9ffoUALB9+3apbV26dJEIuG7evIlNmzZJ7LNu3ToAwJw5czQecAFAcHAwFi9ejIMHDyI7Oxvu7u44c+aMVGbJ95mbmyM0NBTz5s3DL7/8gvLycvTs2RPr169Hw4YNuf18fX1x+/ZtHDlyBG/fvoWBgQFat26NdevWYcaMGZpuns7y9vbG06dPER8fj9LSUggEAnTr1k2pRBalpaXckgHykuHY2NggKysLsbGxKCkpgYGBAaysrNChQ4caTyrn6OiI9PR0rgfKyMgIzs7Ola41ZmtrCz6fj6ysLKSnp4NhGBgZGaFx48ZSowAsLS3B4/G4dbv09PRgZWWFhg0bavTvsdIB1+LFi7keieXLl6Nnz55SaSSB/1vUs3fv3lz0WRWGhobo2LEjQkNDMWTIEAAV3YKhoaEq/0Br4pyqqEoyifqcblnZ3gp2DPK7tOUGTVeutTrmXmlL0PEuXbn+gO4Fl9qCz+fD398f/v7+cvf56quvpMre7QFTxNnZGePHj1e5fupkbGyMH3/8ET/++KPcfY4ePSpV9m5vV2Xmzp0r0RtUG4yNjbFmzRqsWbNG7j5XrlyRWe7k5IQjR44oPL+3tzcOHz5crTqSqtPX14e7uzvc3d3l7iMvqObz+dx9rzzvZp2sbXp6epXWx8XFRWY5OwVIGVXZV12UDriWLl3K/f/atWuYNGmSxn6Zzp07FxMmTIC3tzc6d+6MTZs2oaCggFsJffz48WjcuDGCgoIAVCTFYJ9ElZSUICkpCVFRUTAzM+NWPq/snDVBV5JJKLOIc2Xq+w2arlxrdcjMzESDBg3qdYD9Pl26/roUXBJCCCGqUGnixNWrV9VdDwmjRo1Ceno6Fi9ejNTUVHh5eSEkJISLeOPj4yXGlCYnJ6N9+/bc9+vWrcO6devQs2dPhIWFKXXOmqBsz099n8+izCLOlanvN2i6cq3NzMyQkZFR7fPU9wD7fbpy/QHdCi6V5erqKpVcYOnSpRIPLmUJDg5GcHCw5ipGCCGkVqj01z4hIQEvXrxAly5duHGV5eXlWLt2Lc6ePQsTExN88803+Pjjj1Wu2IwZM+QO92ODKJasP25VPWdNUKbnx8DAgJvsp21JJKqqPt+gKVN3bZi7ZGpqWunaFexQZEXqe4D9PmV+1rXh+gO6FVwSQgghqlDpL+CiRYtw7tw5iZTqP//8M5YsWcJ9f+3aNdy6dQudOnWqfi21hDI9P+Xl5RLrl9XHJBLKzOlQRn29QWMYRqklDOzt7et9MM3j8eDo6KjwM21paYns7OxKz1WfA+z3KfOzrg3XH9Ct4JIQQghRhUq5Hm/evIm+fftyGU8YhsGWLVvQpk0bxMfH/7/27jy8qSr9A/j3Jk33Nl2A7lB2RQRksQMqAlYWF0DQEcSFxV1mcKpsjmAZUQb9ATro4DYFcSsKDCoiCiigsgnIKAgFlEKhTfcmTZe0Te7vj3gv2XOztT037+d58ig396bnNLfJec/yHhw6dAhRUVFWKVeJmbCnl20wIexJZZvxTUgiwdK+PVLWdLjDcgNNav3lsg+Zs3s6JCQEiYmJkjcNZzXAdkb4vTh6n1srzW5rEIJLV+QSXBJCCCHe8KqFU1ZWZpUl5NixYygvL0dubi7S09ORnp6OiRMnYs+ePX4rqJyo1WrExsZa7T/lbr0TS2tc/DFSwXIDLRjXtDi6p4U9+aRgOcB2x9F0S5PJJItsnAKhDrb7cKlUKiQnJ8uijoQQQoi3vAq4TCaT1UjM7t27wXGc1aZ3aWlpkqZVBSuO48T1Knq93uUaGICtNS6+jFTIoYEWrGtaLO9pnudRUFAg+VqWA2xngi1duqOgWwii9Xq9LDc3J4QQQqTwqsXXuXNnHDp0SPz3li1bkJKSgt69e4vHNBoN4uLifC5gMJDbiIgvaeGTkpKYDrYAWtMCeDatMjU1lfn33BLP86irqxODDFdY6kiRwjLoBswbItuOerG4LpUQQgjxhVcLCSZPnowffvgBd955J+699158//33mDx5stU5v/76K7p16+aXQsqd3EZEpKzpcKa0tFRSxsn2jNa0SO8ckFvDW6vVoqCgAIWFhZLT5bPSkeIKz/PQ6/WoqamBXq8Hz/PQarUoKiqyqx+L61IJIYQQX3jVgn/66afx9ddfY/PmzQCAfv36We0vcv78eRw6dAgLFizwSyHlTo4jIs7WdLgjlx7/YF/TIrdOBCmEAAMAuPp6XJWVBQA4cfAg+D+2z3CE9d+B5SiWZb2Lf/wRCA93ep2cplMSQgghrnj1TR8bG4sDBw7g+PHjAIArr7zSLhPX5s2bMXjwYN9LGATkmkLadk1HY2OjpF5/OfT4A87XtLD2PnpDjp0Irni7FQLrvwPLINOWbcZVW3LpXCGEEELc8alrtW/fvg6Pd+nSxSqLIXFPriMitslBpARcrPf4C2t4nAVZ7p6XA7l2Ijjj7VYILP4OhPu3ubnZ58RIculcIYQQQlzxuWX7ww8/4NixY9DpdIiNjcWAAQNw3XXX+aNsQUfuIyLBMOrhLkmAo+cVCgVSU1Nll2RGrp0IjngTOCiVSsTGxgagNIHj6P71BeudK4QQQogUXn/b7du3DzNmzMDZs2cBmHs9hcCgZ8+eWLt2LYYOHeqfUsqA1FEN2yxfciL3UQ9n06uEJAH19fWorKy0e95kMuHixYtoaGjwOtlIe8TzPJRKJZKSktDS0gKVSiW7TgSBN4GD0Whkakqdq+mD3mC9c4UQQgiRyquA68SJExg9ejTq6+tx8803Y+TIkUhJSYFGo8G3336Lr7/+GmPGjMGBAwfQp08ff5eZOa5GPYQRLb1eD8A8ChQdHS27BqlArqMeUtbwOAq2bJ+PjIxk9ndgqaamBsXFxeD1ejGJQsGRI0ju3l2W97a3WyGwMqXO2zVqrrDcuUIIIYR4wquA6x//+Aeampqwbds2jB071uq5+fPnY/v27Rg/fjz+8Y9/ID8/3y8FZZW7UQ+FQmG1uLyiogIKhQKJiYno1KmTLBskcpw66e0aHlvFxcXMZ24rKSkRg0vLWgj3PABZBJWW7EZvLTYyjzxyBHXDhgE2iYUAdqbUeXt/C8mULDd2Z71zhRBCCPGUV9/2u3fvxp133mkXbAnGjh2LO++8E7t27fKpcKyT0ivsKJOXyWRCeXk5KisrkZaWJsuGidymTvprpIK1aWaWeJ5HWVmZ25E8uaYDF/5Oa999F0kvvCAe7/r442hKSoJmwQLosrPF4yxNqfP2/k5NTZVd5wohhBDiKa82PtZqtejatavLc7p27Rr0G1v6OuphMplog1BG+HOkgpVpZpa0Wi1OnTqF8vJyt+c2NzejoqKC+Q2uHVHv2oW0J59ESFmZ1XFVWRkycnIQu3OneIylKXWS72+LkawuFy5A/cf06OjoaMTFxcl6ujQhhBDijFcBV2pqKg4cOODynIMHDyI1NdWrQsmFvxrOGo1Glo1TORHW8PgDK9PMBMK0WctpY+6UlpaioKBAXp0JRiPw17+C43nYhhQczwM8j+QXX0S4VouMhASoGRrFlHJ/x33zDa6cPFn8d/RddwGZmcDmzQEuHSGEENK+edWyGz9+PFavXo1Fixbh73//O8LDw8XnGhsbsWzZMnz77bf461//6reCsshfDWfaILT9k5KBMSEhAVVVVS5fh6VpZoBvyRSYWNNlNAJVVUB5ueNHWdnl/790CaiudvpSHIDQ8nL0uP76ywdVKiAi4vIjPNz634E45mAtmTvu7u/YnTuRlpNjDiwtXboE3HknsHEjMGmSxz+XEEIIkQOvIoJFixZh69atePHFF/Hmm2/i2muvRVJSEkpLS/Hjjz+ivLwc3bp1w6JFi/xdXqZ4m7nMERanmQUbKRkYOY5zucaJpWlmgIRpsxKSR7Tqmq6WFqCy0nHA5OhRWQk4WGfpN83N5odOF7ifYSskxKtgTR0RgVAAuqYmtKhUMIWFgQ8PhyI8HKlLl9oHWwDA8wDHAU8+CUyY4FWwRwghhLDOq4ArMTERBw4cwLx585Cfn49t27aJz4WHh2PGjBlYvnw5EhIS/FZQFkkZ9ZCKtWlmwcpdBsaUlBRERkaiuLhYFpnbXAVbsTt3ImXZMvHfzpJH+DSC29QEVFRIG4EqLzePQHkzPTc+HujYEejUyfxfm4c+Kgq1v/yCFAmdTPyOHeCGDgUaG4GGBuuHL8dcnWswXC5ASwtQW2t+eCjij4dHeB4oKgK++w4YMcLjn0kIIYSwzutWfIcOHZCXl4c333wTp06dgk6nQ2xsLK644gqoVCp/lpFparUajY2NkpIJOKNQKJiaZhbs3GVglFNafGcdAbE7dyIjJ8cuuBGSRxStXGkVdImBm8HgPmiyfNTUeF5ojgMSEx0GTg6DqsRE89Q/J8StHzIzkfjaa1CVlTkc7eE5Ds1JSWgaMgTRUVFAa/5Nm0zWwZg/gjjh/y9dAs6dc18GP+/jRaxlZWUFxfdEfHx8Wxeh1SiDZER4UhBNN94cRGtaMzIy2roIAeco07gzPg+bqFQqXH311b6+jKx16tQJVVVVHiUVsMTzPHQ6HXOjH8Q5uaTFdzht1mhE8j//CThJHsEDSHv2WcTs2QNlTQ1CqqoQUl0NvqYGnBejLlAogA4d3AdOlgGUnxoyVmvYlEpoFixARk4OeFjvQcb/EUxr5s9HbFskwFEogMhI88Pfdu8GRo50f15Kiv9/NiGEEMIAjwKuF154AXV1dViyZInTUaympibk5uYiNjYWCxYs8EshWcdxHFJTU+2mFtbXc8jKugoAcPDgCURGOm6I8Tzf/pMLkKDkaNps1NGjCC0tdX4NAGVdHeK3bHF8QkiIfQDlLHjq2BFISDAHFG3Adg2bLjsbRStXImXZMqgsUsM3JyVBM38+dNnZSJDb9OAbbgDS080jXY6CSY4zP3/DDa1fNkIIIaQdkPzNv3PnTixevBgvvfSSyymDoaGh6NChA+bOnYusrCyMlNLzGQScJVTwhFw3jCVss723QyROn60ZMwZ1WVloSUiAMT4eLQkJ4Dp1Qo/Bg8G1UQDlKUd/y7rsbNRmZeGqYcMAAOf+/W8xWYhSqURzczP0ej2z00gBcyeQ1ZTYV14Bd9dd5uDKMugS6vfKK5QwgxBCSNCSHHCtX78e8fHxmD17tttzn3jiCSxbtgxr166lgMuC7dodg8Gznm5KD0/aK8t7G336SLqm+u67UTdkiN3xuvp6Zu5xp8lsLIKL+kGDxH8bjUZcunRJvDYlJYW5UWutVmvXcRRy1VXIePddRC1caB7pEqSnm4OtIFqjQQghhNiS3OLft28fsrOzERYW5vbcsLAwZGdn44cffvCpcHJkuXZHr/d8LQeL6eHtesMZ7tknzon39rhxQHo6+EuXXCaPqBs40OHrsHSP+7L1AxP7kNkQE4TYaGlpwblrrkHG/v1Qd+5sPrhtGzB6NI1sEUIICXqS5+0UFxejW7dukl+4a9euXm+IGizq6uo8voa1rEVarRYFBQUoLCzExYsXUVhYiIKCAmi1WrtzeZ6HXq9HTU0N9Ho9+LZILhAAzuol1/pCqUTdiy8CAGxrZJk8wllDnKUtEIQ1bO7OcUWj0TDx3kvZ5Lq0ouLyP4YPp2CLEEIIgQcjXAqFAs3NzZJfuLm5GQpG1mG0FYPhcq/4kSORGDaszm37hKWRIVe94bY9+w6nKTE65cqSs3qp1WpotVrZ1Rf4I8nLkCGIdJM8whGVSsVcamt36zPdBVOsTBV2u8k14NF3BCGEEBIsJAdcqampOH78uOQXPn78ONLS0rwqVDDYvBmYPTtW/Pfjj3dFUlITFizQIDtb5/Q6VqZb8TwvrlVxRkgCotPpJAdmLHEVcFZWVjo8znJ9BULD3FXyCGeSk5OZ6lQQ2O2tZrnRsAQs/F2zUEZCCCGkPZI8BHXDDTfgm2++QWFhodtzCwsL8c0332D48OG+lE1WLKePffBBA+68k0dJiXXDsqxMhZycDOzcGevkVdiZblVWVuZ2QzihZ9/dNCVWplxZkjL9yhkW62vJqmHuJHmELZVKhYyMDKYDTWENW1xcHKKTkgCeh762FryEva8MHgZobYGVzx5CCCGkvZEccD3xxBNobm7GnXfeiQrLefo2Kisrcdddd6GlpQWPPfaYXwrJOst1TOfPX8RTTyn/yJxsHXDxvPnfy5cnw9EeyaxMt+J5HlVVVZLO1ev1kqYpebPerS1JmX7lDIv1teRJwzwhIQGZmZno1asX08GWM1FRUZLWXVZXV7f7IFtIEOIKa2tMCSGEkNYgOeAaOHAgnnzySRw9ehR9+vTB4sWL8e233+LMmTM4c+YMdu/ejUWLFqFPnz44cuQI/va3v2GgkyxkwUSYViY0vo8ejUJpaShsgy0Bz3PQaEJx9Kh9YMXKdKu6ujoYHUWMPmBtOpOv5WWtvpaioqIkr9+MjY1FdHQ0E/e1NziOQ0JCgtvzWlpa2n2QLSVBiL//7gkhhBA58GiOyIoVKxAeHo6XX34ZL7zwAl544QWr53meh1KpxMKFC7F06VK/FpRFjqaVlZdL+5U/91wqBgyoR2ZmE7p3b8GQIbEIDY0JRDH9zl2wUF/PISvrKgDAjz+eRHi4+9dkbTqTr+Vlrb6WOI5DamoqLl686PI8VkZsfSVlKw2AjSDbbYKQyEgc/+UXAEBGSwvkN2ZJCCGEeM6jVh3HcXjxxRcxa9YsrF27Fvv27YNGowFgHn257rrrMH36dHTv3j0ghWWNo2llHTtKa1QVFYWhqMi6ocZxQOfOQO/e1o9evcz7i7aXpJCeBAvu1nkBbDbMfdmficX62pIyYsXKiK2vpP49sBJkq9VqxMTE4NSpUy7/foWkOMHwHhNCCCGuePUN3717dxrBksBRY3vgwDokJTWhrEwlrtmyxiMxsQULFpTgwoUwnD8fjkuXonD2bAi0WuD8efPj66+tr4qMBHr2tA/EevcGYp3n4AgIX4INR1hsmHMcB7Va7TAboTss1teSZXZGyxEPgVKpRGpqqizXbDki5e+BtSC7vr5eclKc9p7unhBCCAk0NrpUGeWox1qpBBYs0CAnJwPmbWEvN6w5zrxo/tlnS+xSw6enZ6CpSY2CAlg9Tp8GfvsNqK8H/vc/88NWcrLjQKxrVyAQnerCWg9HKdFdUSgUVo04lUqF5ORkJhvmWq3W42CL5foKpGwHwPM8Ylu7F6ANSfl7YC3IltqZwsI0SUIIISTQKOAKIGc929nZOqxcWYRly1JQVqYSjyclNWP+fMf7cJWWatCrVyw6deJwww3WzzU3A+fOWQdhwv+XlgIajfmxZ4/1dSoV0L27fSDWuzfQoYN5CqO33K31cCQ1NVX8fYWEhCAqKoqpRqjAm5TwHTt2RKdOnZisryW9Xu925MNkMkGv1yMmho01if7g7O+B1SBbbtMkCSGEkECib8MActWznZ2tQ1ZWLYYNMyeP+Pe/z2HYsDqne8K6mp6jUpmDpV69gNtvt36upuZyAGYZiJ0+DTQ2AqdOmR+24uMdB2I9ekBSkgvg8mawFRUVKC0tFY9bJjI7ciRSrHdISIgsph95kxK+pqYGnTp1ClCJWo/UTHt1dXVBFXABDjZHZrhTwZNpkjzPy6LOhBBCiLco4AowVyM9lsHVoEH1ToMtgTfTc+LigGuvNT8smUxAUZF9IFZQAFy4AFRXAwcOmB+WOA7IzLQOwoRHWpr9qBjHcejQoQMqKyvR0tKCnTtjsWzZ5dTSjz/eFUlJTfj738sxezY7a1hc8eZ9ovUuwUHYHJl1UqZJxsbGoqysDNXV1VZ/EyEhIUhJSWFuVI8QQgjxFgVcrcCyZ7u5uRktLS0wGo3wdNsdf07PUSiALl3Mj9GjrZ+rrwfOnoXderGCAkCnM09fPHcO+Oor6+siIx0HYr16mRtna9dqkZOTAdv9XcvKVJgzJxVpaRwmTfJbFduMt++THNa7REdHu9wY3fI8wjZ304adrWFsaWkRAzUKugghhASDdhtwvf7663j55Zeh0WjQv39/rF69GtfaDtNY+OSTT7Bo0SIUFhaiZ8+eWL58OW655Rbx+dLSUsyfPx9ff/01ampqMHz4cKxevRo9e/ZsjeqA4zgYjUaUlpaKjZP6eg5AsqTrWzOLWWQk0K+f+WGJ54GyMvsgzDJxx7Fj5oet1FQ1Kipi/wi2OJvX5cBxwJNPAhMmwO1IX3vnbZZGOax3iYqKglKpdLkBrlKpBM/zqKmpCaopZnKcWmfZmaTT6VBVVSX5WkobTwghJFi0yxbehg0bkJOTgzfeeANZWVl45ZVXMGbMGBQUFDhc57Jv3z5MnToVy5Ytw2233YYPP/wQEydOxNGjR9G3b1/wPI+JEydCpVLh008/RWxsLFauXIns7Gz8+uuvrRLIWKbK9kZ7yGLGcUBSkvkxfLj1c83NwO+/2wdiBQXmIK24GLANtCzxvHmK47Zt9uvQWONNlkbW0oI7I2x67KruPM/j/Pnz4r+DYYqZVqu1GwmSS705jkNUVJTbja5t0TRaQgghwaKdbJVrbeXKlXjooYcwY8YM9OnTB2+88QYiIyORl5fn8PxXX30VY8eOxdy5c3HllVfi+eefx8CBA/Haa68BAM6cOYMDBw5gzZo1GDJkCHr37o01a9agoaEBH330UcDr403WOksdO3Zs940ylco8fXD8eGDuXOCdd4C9e81ZEqurgX/8Q9rrjB9vnpZ4773Av/4F7N8PNDQEtuyBoFarPWpItoeA2l/UajUyMjLsRuyUfwxd2mYxFKaYabXaVitjaxI6W2xHPOVUb28SxQDymEZLCCGEuNPuRriamppw5MgRLFy4UDymUCiQnZ2N/fv3O7xm//79yMnJsTo2ZswYbNmyBQBgMBgAAOEW6fUUCgXCwsLw/fff48EHH7R7TYPBIF4HADqdfap2qZw1RiIjefzyy3GX14aEhDCfvS4uDnap7F05c8b8+OAD879DQszTG4XkH9deC1xxRfueelhSUgK9Xt/WxWgzjjLyuRvxk8MUM9tpg5GRkW47W+RQb28DJzlMo2VVU1MT3n77bWzfvh06nQ49evTAI4884nLqPgDs3r0bW7ZswW+//QatVou4uDj07dsXs2bNQvfu3Z1ed/HiRUybNg1NTU3Iy8vDlVde6e8qOWUwGLB06VLk5+ejpqYGffv2xaJFizBq1CiX150+fRp5eXn48ccf8b///Q8GgwHHjx9Hly5d7M7dtGkTvvzySxw+fBi//fYbrr/+enz55ZeBqpJDBoMBixcvxnvvvYfq6mr069cPS5cuxc033+z22kuXLuFvf/sbvv76a5hMJowcORKrVq1Ct27drM5bs2YNvvnmGxw8eBBFRUV44IEHsG7dugDVyDmj0YiTJ0/iwoULaGpqglqtRp8+fZCUlOTyuu3bt6O+vt7hc1FRURgzZoz4782bNzs876qrrkLv3r29Lzxxiud5aLVa1NXVged5qFQqqNVqq/a7M/X19dDpdGhuboZCoUBERATUarXY2WtJr9ejtrZW/K6Ojo4OeObkdjfCVVFRAaPRaPdHk5SUBI1G4/AajUbj8vwrrrgCnTt3xsKFC1FdXY2mpiYsX74cFy9edNoYWrZsGdRqtfjIyMjwuk6+9OKmpKQw3RAT3HADkJ7ufG8vjgMyMswjYtu3m0fEbrsN6NQJaGkBjh4F3ngDmDkT6NvXHMSNGAHMmwds3GjOrGibjKOtmEwmjzc91mg04NtLBfxEyMgXFxcHAC7XdQGXp5ixSqvVoqCgAIWFhbh48SIKCwtx6tQpt3//rNcbgMMvNHfkMo3WUmFhITiOw9ixY8Vjubm54DgOHMfh6aefdnrt/PnzxfNyc3MDXtalS5fio48+wujRo/G3v/0NCoUCOTk5+N///ufyut9++w0xMTH485//jKeffhqTJk3C6dOnMWvWLJw5c8bpda+++qpX94k/PProo3jttdfw5z//GcuXL4dCocDkyZOxb98+l9cdOnQIa9asgV6vd9vAfuedd/DFF18gLS0N8fHx/iy+ZNOnT8fKlSsxbdo08fd9yy234Pvvv3d5nV6vx8iRI7Fnzx4888wzWLJkCX766SfceOONdt9ly5cvxzfffIOrrrqqTTtMjhw5gjNnziAjIwP9+/cHx3HYt2+f26RN/fr1w+DBg60effr0AQCHwVqnTp3szk9JSbE7j/hHVVUVamtrERUVJbYdysvLrQZAHNHr9aisrIRCoUBcXByioqJQX1+P8vJyu7aVXq9HdXU1VCoV4uPjERoaipqaGp8GVqQIiu5FlUqFzZs3Y9asWUhISIBSqUR2djbGjRvntJG7cOFCq1EznU7nddDlzYcSx3FIS0tr91MJpVIqgVdfBe680xxcWf7ahSDslVfMAdaYMeYHcHlt16FDlx+HDwN6vXkjZ8vNnDt1sh4FGzIESEhotSqKPA22APmvZ5Ha6cDqFDNnazTdbQItYLXewOX1aZ6S0zRaKUJCQvD+++/jn//8p913QktLC9avX+9Vsh1vnDhxAjt27MDs2bMxbdo0AMC4ceMwbdo0vPbaa3j77bedXjtr1iy7Y+PHj8f48eOxefNmzJ8/3+75AwcO4ODBg5g2bVqrj4YcPnwYGzduxNKlSzFnzhwAwD333IOsrCwsWrQIu3btcnrtLbfcgosXLyImJgavvvoqfv75Z6fnvv3220hNTYVCoXA7ShgIhw4dQn5+Pl5++WUxsL///vvRt29fzJs3z2Vw+e9//xtnzpzBoUOHMGTIEADm+6Fv375YsWIFXnzxRfHcPXv2oHPnzm26xUVVVRUuXryIvn37olevXgCAzp07Y+fOnTh+/DhGjBjh9NrU1FS7Y6f+2IzUURsvOjoanTt39k/BiUsGgwH19fXiDBnAPOqo0WhQU1PjdPRSSMIVFhaGjh07it8rYWFhqKiogF6vF0evTCYTtFotwsPD0aFDBwCXsybrdDpER0dDoQjMWFS7G+Hq0KEDlEql1Ua5gDnLYHKy44x+ycnJbs8fNGgQjh07hpqaGpSUlGD79u2orKy0Gy4XhIWFITY21urhLSFrnSd4nnc5AseiSZPMo1G2n3fp6ebjjlLCcxzQubM5UHvpJWD3bkCrBX75BcjLAx59FBg40DztsKwM2LoVWLwYGDsWSEwEevYE7rnHHMzt29c668GampocHq+v53D11X1x9dV9/8hQaY3lRrc7Uu9/FqeY+bpGE2Cz3oDz9WmuqFQqZGRkyKYzSapx48ahtLQUW7dutXtu27Zt0Gg0Vpl1A+nbb7+FUqnExIkTxWNhYWG4/fbbcfz4cbvvU3fi4+MRHh7ucBp1S0sLVq1ahT//+c9IT0/3tege27JlC5RKJWbMmCEeCw8Px3333YdDhw65TPaSkJAgeZpRenp6wBpqUmzcuBFKpRIPP/yweCw8PByzZs3C/v37XU7p3rhxI4YMGSIGW4B5ZtBNN92Ejz/+2OrcLl26tHlHyaVLl8BxHLp27SoeUyqVyMzMRFVVldMpg84UFRUhMjISiYmJDp83Go1uZ2gQ3zX80UCzDOSFpExNTU1Ov2eam5vB8zwiIiKs7k3h35b3g8FggMlksussiI6OBs/zYhkCod0FXKGhoRg0aJBVr5PJZMKuXbswdOhQh9cMHTrUrpdqx44dDs9Xq9Xo2LEjzpw5g8OHD2PChAn+rYADQtY6b1RWVsou6Pr118v/3rbNvKeXJ/tvKZXmaYUzZgBr1gBHjpj3B9u3zzyKNm2aOdACzPuJffQR8Le/AdddB8TEmAO0Rx8F/vMfc+Dmz89RrVbrdRIEVhvdUkhpkLM6xczbhBECVuvtSaAZGxuL9PR0ZGZmolevXkEXbAHApEmTEBcX5zD5U15eHuLj43HHHXe0SllOnz6NjIwMu/tOmFp1+vRpt69RW1uL6upqnD17Fi+++CLq6uowePBgu/Py8/NRW1trFfC0pp9//hk9evSw6zQVyupq1IolP/30E3r16mVXT2G07Zij/Vpgbl/9/PPPDt+7a6+9Fr/99htqa2v9Xl5faLVaREdHQ6VSWR0XpnJ68h1cU1OD2tpapzOYzp8/j08//RSffvopduzY4VO2aeJaU1MTQkJC7DouQkNDAZgDK0eEmWqOOgI4jhMDMsvXEF7T9mc46zD3h3bZwsvJycEDDzyAwYMH49prr8Urr7yCuro68QP7/vvvR1paGpYtWwYAmDNnDm688UasWLECt956K/Lz83H48GG89dZb4mt+8skn6NixIzp37oxffvkFc+bMwcSJEzHadtffAHG3SagrlZWVSEpKatPeM3+ynMY/fLh/kl9ERABDh5ofgqoq8/TDQ4eAH38EDh40rxH76Sfz4803zedFRQGDBl2ehnjtteYNoT3txPMl9T+rjW4ptFqtpJThrE4x83XeN6sJMzwJNBsaGpCRkcFkPf0lPDwcU6dOxdtvv43S0lJxekxpaSm++OILPPzww5IWhvtDZWWlw958YYqNlM3LH3zwQVy4cAEAEBkZienTp+N2mz09KisrsXbtWvzlL39ps883jUbjcHaMcMzZ2nDWlJSUOOzYFY4Vm/dmsVNVVQWDweD22vaUJKKxsdHh34pwzJNRCuE721HAlZCQgPT0dERGRqKxsRG///47fvzxRzQ3NzudHUW8ZzQaHa7zFI45G2UUOqttg6Xm5mZxWr/JZLLaI9T253AcB4VCIXkZgDfaZcB19913o7y8HIsXL4ZGo8GAAQOwfft28QvqwoULVsHHsGHD8OGHH+LZZ5/FM888g549e2LLli3o27eveE5JSQlycnJQWlqKlJQU3H///Vi0aFGr1stR5raamhrU1NS4vbayshIdO3YMfCFbGc/z0OsDsxlsQgIwerT5Yf5ZwMWLjteD7d1rfgg6drRfD+ZktoFYD19GIlkNNtyR+nthdYqZMHfcFzqdjsn335OOI7mvUZRq1qxZWLNmDdavX4+5c+cCANavX4+WlhbMmjVL0siSPzLoGgwGux5e4HIvr7sF6gDw7LPPoq6uDsXFxdi6das4Vcfyu/n1119HWloaxo8f73EZ/aWxsRFhYWF2x4VjgZxC1JoaGhoc1tNdECIc9+batmI0Gh12QDvbesQZnudRVFRktWbIku1asMzMTHzzzTc4ceIEunTp0mZJYOSK53mno1TC844olUpERESgrq5OzA5sNBpRXV1t9dqufobwcwKZvKxdBlwAMHv2bMyePdvhc7t377Y7dtddd+Guu+5y+np//etf8de//tVfxfOa7UJTqV+WzoZSWXf69GmEhl6uWyA3gxUyIWZkAJMnm48ZjebNmS2DsJ9/BsrLgS++MD8E3bpZB2HXXANERpqfk9Lbb9k5c+RIJIYNq0N4uArJyclMBhtSSB0FYfWLq6yszOceMVaDESmNcktyXqMo1aBBg9CvXz+sXbtWDLjWrl2L/v37Y+DAgZICrmXLlmHJkiU+lSMsLMzh1BnhmKPGt62rr75a/P/s7GxMnToVAMTv2ePHj2P79u1YvXp1m87OCA8Pd3ivCsciIiJau0gBERER4bCejY2N4vPOrgMc/z27u7atKJVKh5+7wuiF1PutoqICjY2N6CmsQXBDoVCgW7duOHbsGKqrq8URYeIfzgIeV1MGBQkJCaisrLRa1hEZGYmQkBA0NDSI94SroMpVMOYP7TbgCha2c5B9PY81LS0tsOxoFTaDBdAqQYhSCfTpY35Mn24+1tgI/O9/1kHY6dPA77+bH/n5l6/t29ccfPXtq0BaWhi6dzfA0VKsnTtjsWzZ5Skbjz/eFUlJTXj55Wbcd588pxIC8s5OqNVqUV5e7pfXYq3+3tRdzmsUPTFz5kw8+eST4r6SJ0+exKuvvir5en9k0E1MTHT4/glTCT1tSMbGxmLQoEH4+uuvxYDr9ddfR//+/ZGamiqOcgujwRUVFU6n+vlbcnKyw+l0wlTC1ihDa0hJScGlS5fsjgu/e0fZ+QBzQzUsLMzhTAR317aV8PBwh6NungaIwpRYT5K5RP7RyxrItT7BynLKnyVn0wAtKRQKdOzYES0tLeKMqZCQEJSWlkKhUIgBl+X0RMvX43neboTe3+gbsI0lJiZKygjlLHsOiyIjeZw8WeCykdmWm8GGhwNZWeaHoLraPP3wxx/NAdjBg4BGYw7MzNvWRALoiYgIE668sgF9+zbg6qvr0bdvA06ejMBTT2XY7RNWVqbCAw+oEBnJY/JktqaTSSXX7IT+yExoiaX6e1v3oqIipKamynY0V6p7770X8+bNE5NnhIaGiqnZpQgLC5M0AuVKz549cfToUdTV1VmtrTpx4gQAiKm2PWEwGKyyFGo0Gmg0GkxykBFp3rx5iI6Oxo4dO7wovWeuvvpq7N27Fzqdzmra2I8//gjAvC+THAwYMADffvutXT0PHjwoPu+IQqHA1VdfjcOHD9s9d/DgQXTr1i3gG8J6Sq1Wo7y8HM3NzVad0VVVVeLz7hiNRhQXF6Njx44ejeAJeyb6+jdI7IWGhqK2ttYu8BGCWykDD0KgBZinljY1NYlBsuVrNDU1Wb3vws9wNNXaX+SRhYFhCoXCbTCVmJgom4QZgLRpZu1tM9j4eODmm4FnngG2bAGKi837g23aBCxYAIwaxSMqyoiGBgWOHo3C+vUdMHduZ4wb19si2LIOqnje/O85c3i/ZkpsT6RsicBiwhBfMxNaYq3+3tbdaDSiqKjI6yyecpGYmIgJEyZgw4YN2LBhAyZOnNjqHWqjRo2C0WjEli1bxGNNTU344osvcNVVV4nrpTUaDQoLC62uFRq1lkpKSnD48GFceeWV4rEFCxbgn//8p9VDmPb/l7/8pVU2dwaAiRMnwmg0Yu3ateIxg8GADz74AIMHDxZHN4qKilBQUNAqZQqEO++8E0aj0SpZmMFgwNq1a5GVlSWOgl64cEHcd8ry2h9//NEq6CooKMA333zjcqlGW0lLSwPP8zh37px4zGg04vz584iPjxcb2PX19U4zLGo0GjQ3NzsdHXY0xbK5uRlnz55FaGhom21uLWdCAGTZccPzPOrq6hAaGiq2JVpaWiQtsxFG1C2n64eFhUGhUNhtYaHX68FxXEATF7HTrSpjQiYgRxvmJiYmym5XczlMM+M48/5h6elCSnsO1dV6fPddGX75JQK//BKBEycicPJkBIxG56NXPM/h0iUO330HuNirkTnCh2RLSwvi4+NdTj+Te8IId1irv691b8vR6/Zi5syZ+OSTT8T/b21XXXUVRo0ahTVr1qC6uhrp6enYtm0bSkpK8Mwzz4jn/eMf/8BPP/0kTn8EzCN0gwcPRq9evRATE4OioiJ8/vnnaGlpwWOPPSael2U5ReAPQiPnmmuusQrOAmnIkCG44447kJubi/LycnTr1g0ffvghzp8/j9dee0087+GHH8b3339v1UDXarV48490tgcOHAAAvPnmm4iLi4NarcYjjzwinvv999+LmwtXVFSgvr4eL730EgBzYq/rr78+oPXMysrCXXfdhYULF6KsrAw9evTAu+++i8LCQvznP/8Rz7v//vuxZ88eq3Usjz/+ON5++23ceuutePrpp6FSqbBy5UokJSXhqaeesvo5n3/+Of5nntaB5uZm/Pzzz1i6dCkA8wbYrTFimJCQgLS0NJw4cQIGgwHR0dE4f/486uvrMWjQIPG8w4cPo6KiwuEoa1FRERQKBdLS0hz+jN9++w0lJSVITk4WsxQKP2Pw4MGy6gRvL8LCwhAREQGtVguTyYSQkBCxHWGZNE7IrGkZLOt0OjQ3NyM0NBQcx6GhoQGNjY1Qq9VWo5EKhQKxsbGoqalBRUWFuMZT2HA5kOvJKeBqA5aNUSEzX0pKCpKSklBZWSkOk8ttZEsg12lmCgXQrZsB3boZMGFCDQDgs8/U+Pvf3a+vOHu2DoMH837N0thWtFqt3fYHSqVSnCNteSwhIcGnTcXbij/uTaVSyeQUO1/rzmqSEH8aPXq0OLp08803t0kZFi9ejLfeegvbt29HbW0tunfvjv/7v//DNddc4/K6SZMmYd++fThw4ADq6+sRHx+PrKws3H///ejRo0crld4zb731Fp5//nnk5+ejpqYGffv2xSeffOI2CKqpqcHzzz9vdWz16tUAgM6dO1sFXHv37hW3qhEI1y5cuDDgARdgzni5aNEivPfee6iurka/fv2wdetWDB8+3OV1MTEx2L17N/72t79h6dKlMJlMGDFiBFatWmWXHXnTpk149913xX//9NNP+OmnnwCY10K11hTNwYMH49dff8WFCxfQ3NwMtVqNYcOGSVp/2NzcLK4hdDZNLTExEVVVVSgsLBT3h4qPj8fAgQPRqVMnf1eH/CExMRFarRZ1dXUwmUwIDQ1Fhw4d3I48qVQqNDQ0iGv7hDa05XRCQUxMDDiOQ21tLRoaGhASEoK4uLiAfydxfCBzIMqITqeDWq2GVqv1qYHoqDEayMx87RHP8ygocL2GS6VSoVevXswEH87q9OOPUZg5s6vb6/PyzmHIkDrm7wV3e5HFxsZCr9dbBV4s1lnKPeyKUqlE7969mexQ8bXugLlhFhcXJ/l8f33+BlphYSG6du2KMWPGYPv27QCA3NxcLFmyBB999BGmTJni8vr8/HxMnToVzz33nOQpd8LvZufOnUxNTfWW5XYvchcsnRKThbTBQWDz5s1tXYRW42kyHxaZTCZcunRJ0ncTe9/2DBMao7YNFSEzn5zXNpj329KjpqYGdXV1bqdJsjbNytm6loED65CU1ASOc9yvwXE8kpObMHCgeb0ay/eClGQKOp3OLp0vi3XmOM6nqb6pqalMBluA73UH2Bu9JoQQQnxB33qtREpjVK5rG5yN6iUmJqK6utquAc7inkzOevuVSmDBAg1ycjIA8LBMnCEEYfPna2BbZRbvBV8TSbBWZ2FEzvbeVqlUYqppZ8+xNJrniFB+V6OZzrCWJMQTmZmZdnu85ObmSh6tmjJlittRMEIIIeyhgKuVeJKZT07TCJxNMWtpaXGYJAS4nM0MaJ29uPzBVY99drYOK1cWYdmyFJSVXZ4vnpTUjPnzNcjOtt/8msV7wddkCizWWa1WIzY21m5NJmD+m09OTkZzczNCQkLEQIOVgNIdbztGWBu9JoQQQnxFAVcrkUNmPk/5ulcRSyMeQvpzZ+9fdrYOWVm1GDbsKgDAv/99DsOG1dmNbFli7V7wxzQx1uoMmKfYRUVFiUFXWVkZqqurHa7TZOFelsrT94rVJCGEEEKIryjgaiVyzczniq9TzFga8RDWtbiaYmUZXA0aVO8y2ALYuxfcBZ1SsFZnwPGUWVvCOjWAnVFbdxztU+MIx3GIjY1FWloas+vWCCGEEF/Qt18rkbIBLGCeTicX/hitYGnEQ61WIyMjw+599qaRyeI6F1+TKbBYZ2eJcJzRaDR2a3xYpNVqXe6tZonneWi1Wpw+fZqpxCiEEEKIv7DXncwojuOQnJyMixcvujzv0qVLMJlMsljv4Y/RCtZGPGzX9CiVSvE99QQrUyltqdVq1NfXO1yfFx0dbbe7uyXW1vZ4M2WWpVFbZ7ydKizHUT5CCCFECrZas4yTEjwIOf2F81nbn8iSow3nPMHiiAdgDq6FBrVerxdHPyIjefzyy3FJr1FZWYnIyEjm3nutVus0GYperxc3NZRD5j5vp8w2NzcHoDStJ9iyURJCCCG+ooCrFXnaSGG9R7i+vt6n61lvlAl7j3mLtYap1H24evXqhfr6equsfqzU0ZK3QQdL02QdCcZslIQQQogvKOBqRd5Oj2Ot4S3wtWFWU1PD3DQzgVarRXFxsU9r8lhrmErd+qC+vp6ZOrni7d+zSqVyf1I7FqzZKAkhhBBvUdKMViCMdDQ3N3u1d43Q8GaNrw0zo9HIZL1LSkpQVFTklwQoLDVMg23rA6mJcGyxti7RVlRUlM/ZBlmfVkkIIYR4gu1vfgZISRkthU6nY25UwB9pwllrnNfU1Dhdw+QNlhrnnmx9wPO83WbBrI1kStkKwBar6xIt6XQ6j5PA2GLpviaEEEJ8Rd96ASSkjPbXa7G2cao3DVJbLDXMeJ5HcXGx316Ptca5lABbpVLBaDSioKDA4cbArK1VVKvVaGxslJwindUpsgJfNzMXsD6tkhBCCPEETSkMEH81TASsTq9ztjeVFEqlkqmAo66uTnLPv5RGN2uNcyn7cMXGxjrct0pIEMPiPk1hYWGSzktMTGQuoLTla4ZCgL2OBEIIIcRX7AwfMMYfDRNbrE2vE1juTdXc3IySkhJJgUlCQgJTAYcn74+rzW9ZTZMOXM6maTuNVqVSISYmxu10SxYTxEjtTIiJiQlwSQLPH59BrHUkEEIIIb6igCtAAhEcsTS9zpbl3lQcx7ndABoAwsPDA10sv/LH+6NUKtGzZ0+fkxK0JdvNn4VphlLec9YyMwLm/eaUSqXLJClyGdWReo937NgR1dXVsthvjRBCCPEVuy34ds7fwZFcGmwAEBcXh4aGBrejHaztQRYVFeW24e2O0WhkMm26oyQYQh14nkdBQYHk12JpJFdIiuPuPZfLqI6UdXpKpRIdO3ZEp06dmE+MQgghhPgDBVwB4o8MfZbk0mATpKSkICIiwu2oB0tTzDiOQ0JCguQECs6wFHAAjjNxWibB8HR6LSsjuVKS4rCaDMQZKYlwjEYjTp8+Lat6E0IIIb5gd95SOyclgYAUKpUKGRkZsmy4SGlYs7YHmdQECq6wEnAAl4MOZ0kwampqPAq2WBnJlZoUx9f06e2RlEQ4LCdBIYQQQvyNnZYdgzxNGW2pQ4cOiI6OlvU0HJ1OJ+k8lkZ8fA2WWAk4AGlBx8WLF9GxY0fJr8nKSK7UUTuTycTc1Fgp1Go1YmJicOrUKZdBJUsj1IQQQkig0AhXgHXq1AlKpdLj68LDwxEdHS3bhorJZEJNTY2kc1ka8RGmknqLlYADkB50SO1wSE9PZyYo8bQTQKPRuMxMyaL6+nq3I3isjVATQgghgUABV4BxHIfU1FSPr2MpyPCUVqt12zMuYG0vLilTSRMTE+3eXxanjvp75JGle97Tssox8JD6/rM0Qk0IIYQEAjstHIY525vIGdaCDE9ISTRgKS4ujpkRH6kiIyORnJzMfAY3fwdILDXMvUmKw1L9pJD6/rMUSLPswoULiIyMbOtiBFzXrl3bugitJhjeTwA4efJkWxeh1WRkZLR1EVqNJ229YEAjXK1ErVajd+/eyMzMRGJiostzjUaj5PVNLJGaaMASa5vFSqljSUkJ9Ho908EW4Pv0SVssNcy9SYrDUv2kkPL+s7QmkRBCCAkUCrhakbD5b3JystuNbeW45kMIMqRicaRPyrqmlpYWnD9/HhcvXkRhYSEKCgqYzObmr0ycAJsNc7Va7bbzRKBQKJirnztS3n+W1iQSQgghgUIBVxuoq6sLisXmPM9Dr9ejpqYGpaWlHg8vszjSV1tb6/E1Qgrt0tJS5oJstVqN9PR0n1+H1YY5ayOw/uYsRTyLaxIJIYSQQJHXHBdGBMNic0eb4XqDpbTSPM+jurra6+vLy8tRVVWF1NRUphqqcXFxMBgMkrIRKhQKu84Gd6O97VlUVJTDOtkymUyoq6tDdHR0K5Ws9ajVasTGxjK/JpEQQggJFAq42oDcF5t7mhjDFWGkj4WGqpSRS3eMRiOT+zZJ3fDZ0e+H5b2qOI5DXFwcqqqq3J7LcgeKO8J0acDc8UDBFyGEEHIZmy16xknNPMRihiJvEmO4w0pD1Z/lZGlkD/BP5wBrdRbExsZKCrhY7UDxhKOR7ZCQEKSkpDAXTBNCCCH+wu5cHobV19f79bz2ROpmuJ5gpaHqz3KytobPHxkLWauzgLL1mQkj27Z//8IaRRYTwxBCCCH+QAFXG5DzGi5/l5mlhqq/06Sz9P77K2MhS3UWULY+aSPbcsy8SgghhEhBAVcbkPMaLn+XmaWGqj/TpAPsvf+uMtZ17NhR0muwVmdBsGfrkzKyzeoIJiGEEOIrNls3DLJdSK5UKmE0Gp2ez9LIjiVhlMfXkQqlUslctj7gctIH23UsQn0cPecIq++/s4x1AFBdXe2y3qzWWRDM2frkPGpPCCGE+KrdjnC9/vrryMzMRHh4OLKysnDo0CGX53/yySe44oorEB4ejquvvhrbtm2zel6v12P27NlIT09HREQE+vTpgzfeeCOQVRBptVoUFBSgsLBQ3OzWHZZGdiz5a5THaDSipKSEyXUfarUavXv3RmZmJtLT05GZmYkrrrhCDMakTKti9f0HLmesi4uLQ3R0NDiOC5ppd47qHgzkPGpPCCGE+KpdBlwbNmxATk4OnnvuORw9ehT9+/fHmDFjUFZW5vD8ffv2YerUqZg1axZ++uknTJw4ERMnTsTx48fFc3JycrB9+3a8//77OHnyJJ588knMnj0bn332WUDr4mwhuTC6ZbsHkRymIDmbXuUplhfbO2p4C/eCu5FN1t9/Z4RNkuV0z1tu7q3X64N2jZKwH5krSqWS6RFMQgghxFvtMuBauXIlHnroIcyYMUMciYqMjEReXp7D81999VWMHTsWc+fOxZVXXonnn38eAwcOxGuvvSaes2/fPjzwwAMYMWIEMjMz8fDDD6N///5uR858IWUhuUKhQJcuXcSRkF69ejHZ8LSlVqvRq1cvv2xqK4fF9lLvhZ49e8ri/XdEq9VCo9FY7cWlUCgQFxeH2NjYNiyZdxyNXBcUFDDZQeAPwTKaRwghhHiq3QVcTU1NOHLkCLKzs8VjCoUC2dnZ2L9/v8Nr9u/fb3U+AIwZM8bq/GHDhuGzzz7DpUuXwPM8vv32W5w+fRqjR492+JoGgwE6nc7q4SkpC8lbWlrEzVPlNgWpvr7e542AAXkstpdyL5hMJpSXl7dSiVqXs5Feoc4nT55kKlChFOjW6urqXI7cAuZRfdb/jgkhhBBvtLuAq6KiAkajEUlJSVbHk5KSoNFoHF6j0Wjcnr969Wr06dMH6enpCA0NxdixY/H6669j+PDhDl9z2bJlUKvV4iMjI8PjugT7QnJ/1ov135HU8ldVVTE/mmdLyuieyWRiJlChFOj2gv2zjhBCCHGl3QVcgbJ69WocOHAAn332GY4cOYIVK1bgiSeewM6dOx2ev3DhQmi1WvFRVFTk8c8M9oXk/qwX678jqeWX4yiAJ5thsxCoUAp0e8H+WUcIIYS40u6+/Tp06AClUonS0lKr46WlpUhOTnZ4TXJyssvzGxoa8Mwzz+C///0vbr31VgBAv379cOzYMfzf//2f3XREAAgLC0NYWJhPdZGSIp31VNiu+CtFvBx+R1FRUW63AhDIbRTAk/oIgUp0dHQAS+QbGs2xF+yfdYQQQogr7W6EKzQ0FIMGDcKuXbvEYyaTCbt27cLQoUMdXjN06FCr8wFgx44d4vnNzc1obm62S+CgVCr9ssbIGW9TYcsl8xnHcU6DZE/IJV14QkKCpHPlNgrgaX3ae6BCozn2OI5zm+xFDn/HhBBCiDfaZYsgJycHDzzwAAYPHoxrr70Wr7zyCurq6jBjxgwAwP3334+0tDQsW7YMADBnzhzceOONWLFiBW699Vbk5+fj8OHDeOuttwAAsbGxuPHGGzF37lxERESgS5cu2LNnD9avX4+VK1cGtC7ONsJVqVRITk62a6RotVq7c0NCQpCSksJc9johK523nP2OWNWpUydUVla6DPLlOArg6Uhnew9UaDTHnlarRWVlpdPno6OjoVQqwfM8BV2EEEKCTrts2dx9990oLy/H4sWLodFoMGDAAGzfvl1MjHHhwgWr0aphw4bhww8/xLPPPotnnnkGPXv2xJYtW9C3b1/xnPz8fCxcuBDTpk1DVVUVunTpghdeeAGPPvpowOujVqsRGxsrrv0ICQlBVFSUXcPD2VoxIfOZ8Fos8HbdmyAmJgadO3eWVeOM4zikpaW5/L3ExsbKqs7A5ZFeKfcDC4GKlPoE02iOlCQier0eer2e2c4jQgghxBccz+p8tVam0+mgVquh1WoDsmcQz/M4efKk29GPXr16tfuGHM/zKCgo8HlqGKub4bpTUlLicjRArvXWarUoLi52uY6Npbo7Go2W26isFHq9HoWFhR5d4+n7HOjPX5YJv5u8vDxERka2dXECbsSIEW1dhFbTsWPHti5Cq7DsHJc7vV7f1kVoNb50urNGyndTu1vDFazKysrcridjJfOZJ1npXGEhY52neJ53m/pcjvUGzKOzV1xxBTp27Gi3nlKlUjEVbAHm+vTu3RuZmZlWG5fHxsbKYg2mK7brTD0l13ucJc3Nzfjggw/w2GOP4b777sPf//53/Pzzzx6/zgsvvIApU6YgLy/P5XmnTp3ClClTMGXKFK/2tfSFwWDA888/j/79+yMzMxPjxo3Dnj173F539uxZLF68GLfddhu6dOmC5ORkXLhwweG5jY2N+Ne//oUbbrgBXbt2xYABA/Dggw/i1KlT/q6OUwaDAQsWLEB6ejqioqIwdOhQ7NixQ9K1ly5dwt13342EhATExcVh4sSJ+P33363OaWhowIMPPoh+/fohPj4esbGxuOaaa/Cvf/0Lzc3NgaiSUyaTCWVlZThz5gxOnTqFc+fOSf4sqqurw/nz53H69GkUFBTg3LlzTr+XW1paUFJSIv6cs2fPori42J9VcYvnedTU1ODSpUu4ePEiSktL0djYKOna+vp6aDQaFBUV4dKlS6iqqnLa6anX61FSUoKioiKUlJSgtrbWn9UIeu1ySmGw4Xne5YiHpdb+UPOGv5IesJCxzlOepBSXU70FHMchKSkJnTp1cjvFlgUcx1m9T67WYEqZVswCR3X0lJzvcVasWbMGBw8exLhx45CcnIw9e/Zg+fLlWLRoEa644gpJr3Ho0CGcPn3a7Xkmkwnr1q1DWFgYDAaDr0X32Jw5c7B161Y89NBD6NatGzZs2IBp06Zh06ZNyMrKcnrd4cOH8c4776BXr17o2bMnjh8/7vTcJ554Al999RWmTZuGfv36QaPRYO3atbjtttvw7bfferWXp6dmzJiBTZs2Yc6cOejRowfWr1+P2267Dbt27cL111/v9Dq9Xo+bbroJWq0WCxcuhEqlwiuvvIKRI0fi6NGjSExMBGAOuH799VeMGzcOmZmZ4DgO+/fvR05ODg4ePIgPPvgg4HUUlJSUQKfTISEhAaGhoeIyhi5durgc6a2trcXFixcRERGBDh06iMeKi4vR0tIi1hUwf04Jo/dxcXFQqVRoaWlBQ0NDQOtmq6qqCvX19YiJiUFISAjq6upQXl6OTp06ucymrdfrUV1djbCwMMTFxcFoNEKv16OpqQlJSUlW3z/CuREREYiJiYHBYEBNTQ14nqdZBX5CAVc7UFdXJzlbYnvP4Ab4N+kBC/V1hed5q0a21ICZ9Xq7YxuoyIG7NZi22wKwuJ7J17WZluR+j1sqLCxE165dMWbMGGzfvh0AkJubiyVLluCjjz7ClClTWrU8Z8+exb59+zBt2jTcfvvtAIDhw4dj7ty5+OCDD/D888+7fY2mpia89957GD9+PD755BOX5+7atQuVlZUYNWoUvvzyS7/UQaqjR49iy5YtWLx4MR5//HEAwF133YURI0bg+eefx9atW51eO2bMGJw+fRrR0dH497//7TTgKikpwRdffIHHHnsMzz33nHg8KysLd955J7Zt24ZHHnnEvxWzcejQIWzYsAEvvfQSnnrqKQDmBGP9+vXDggUL8P333zu9ds2aNThz5gwOHDiAIUOGAADGjh2Lfv36YeXKlXjhhRcAAAkJCdi3b5/VtY8++ijUajVef/11rFixwi+Zid1paGiATqdDp06dxABJrVbj999/R1lZGTIzM51eW11djZCQEHTu3FmcaREfH4/ffvsNWq3WKuAqKSkBx3HIzMxss2ROBoMB9fX1Yi4AwJy4SaPRoKamRsxtYEsYFQsLC0PHjh3F4CosLAwVFRXQ6/WIiYkBYO4Q0Wq1CA8PF4NQ4ftZp9MhOjrablYK8Rz9BluRs3TvnjQ8VCpVoIrnN0IWN1eUSqWk12rvGetc0Wq1KCgoQGFhIS5evIjCwkLJWRtZrncwkpI4wnYahxCIuZti2l5IqaMn6B5vOwcPHoRCocBNN90kHgsNDcXIkSNx5swZVFRUuH2Nzz//HDzPiwGbM3q9Hh9//DHuuuuuNlljtnXrViiVStx3333isfDwcNxzzz04fPgwLl265PTa+Ph4SR1DwlQ22zVXQmM4PDzcm6J7ZNOmTVAqlXjooYfEY+Hh4Zg5cyb279/vsqNk06ZNGDJkiBhsAcAVV1yBUaNGuQ2mAYgBTk1Njdfl94QwJTUuLk48plAoEBcXh4aGBpcdm0ajEUql0iqA4DgOSqXSasTHYDCgrq4OiYmJCAkJgclkapNp0MJomuV9yHEcoqKi0NTU5LT92NzcDJ7nERERYVUv4d/19fXiMYPBAJPJZHevR0dHg+f5Vh/RkysKuFqJo8Z3QUEBtFqtRw0PFhopUvYfS0lJcVsXFjLWOSOMBNh+GErZ+Jjleksll73mBL6sW2RlPZO/1mYCwXGPt2eFhYVISUmxC4C6d+8OADh//rzL6ysqKvDpp5/innvuQWhoqMtzP/74Y6jVamRnZ/tWaC8dP34c3bp1E3vzBddccw0A4MSJEz7/jMzMTKSmpuKNN97A119/jeLiYhw9ehTz5s1D586dMXHiRJ9/hjs//fSTuIbUkhBEHTt2zOF1JpMJP//8MwYNGmT33LXXXovffvvNbi1PU1MTKioqUFRUhP/+979YsWIFunTpgh49evinMm4YDAaEhobaddxGREQAgMv1TVFRUTAYDCgrK0NTUxOamppQXl6OxsZGq9EtYb28UqnE+fPnUVBQgFOnTuHChQtoamoKQK0ca2pqQkhIiN0Ik/B35yy4FL5THE1b5zhODMgsX8P2b1n4d2vWV87af+tdBtxNNUpPT5e0T5Gw7oMFUvYf4zhOlqm1fR0JYLXetmynUwprluS015wgGNYzSa1jbGwsVCqVy3WpcrnHWVVdXW01OiCIj48Xn3flvffeQ2ZmJoYNG+byvPPnz2Pnzp2YP39+m01JKi0tdTjtqlOnTgDg016RApVKhXfeeQePP/447r//fvF4v379sHXr1lb5XNNoNA47OoVjzhI9VFVVwWAwuL22d+/e4vHNmzdj2rRp4r8HDx6Md955p9U6hIXvFFvCMVefVR06dEBTUxMqKyvFzyiO45Cenm4VlAtBhkajQXh4ONLS0tDc3IyKigpcuHAB3bp1a5V7WhiRsyUcc9aJK/wubIOl5uZmcQmLyWSymupu+3M4joNCoZC85IW4RgFXgElpfJeWlkrapyglJYWpRoq7/cc83RSaFVJHAjiOsxrZYL3elpwFVWq12mFDnMW95iz52tBgYT2T1DrqdDqEhIQgMTERWq1WVn/bctHU1ORwerpwzFWP9okTJ3Do0CEsXbrU7c9Zt24dBgwYgP79+3tfWB81NjY6HIUTpvlJzfbmTlxcHK666ircfvvtGDRoEM6dO4fVq1fjoYcewoYNGwI+rbChocGregrTxRwlXxCO2U4pGzlyJL766ivU1NTgm2++wc8//9yqGZRNJpPDzyOhbeFqxgDHcQgNDUVMTIwYYNXU1KC4uBidO3cWR8mEICMkJAQZGRnia4eEhKC4uBharVbsoAgkZ5vFu6urUqlEREQE6urqEBISgsjISBiNRqvOFOFaVxvS27ZTiPco4AowqVnplEolMjIyHO5TpFQqkZqaymQjxV1yBLVajZiYGFRWVqK5uRkqlQqJiYlML9CU2njmeR5KpRJxcXGIiYlhNmudLVcjuu6ycWo0GiY3fxbWLXobOLEwVdiTOgrvteXoPcuZGdsTg8FglenPmxTroaGhDqciOZtaJDAajVi3bh1uuOEGcfqhM/v27cPp06fx8ssve1w+fwoPD3cYQAoBiD8CIZ1OhwkTJuDxxx/HY489Jh7v378/Jk2ahPz8fEyfPt3nn+NKRESEV/UUAgxH2SOFY8I5gqSkJHHU8M4778SyZcswZswYFBQUtErSDIVC4TAIcDWNTqDRaNDQ0ICuXbuK58XGxuL333+HRqNB165dxZ8BADExMVavFxsbi+LiYjQ0NLRKwOUs4JFS14SEBFRWVkKr1YprhSMjIxESEoKGhgaxjq6CKlfBGPEMu61aRkhtgLW0tIj7FGVmZqJDhw7o0KEDunTpgiuuuILJYEsKrVaL06dPo7S0FFVVVSgtLcXp06eZSSTgiCeNZ6PRiMrKShiNRll8qPk6nZKVveZsSVm36Awr65m8qWNpaSmioqIQFxeH6OhoWdzjbW3ZsmVQq9Xiw5t04/Hx8Q4THAi9384aknv37kVxcTFuuukmlJWViQ/A3LAvKysTG+kffPAB/vSnPyEkJEQ8T1ioX1lZiaqqKo/L7Y2kpCSUlpbaHRfK7Y8AYevWrSgvL8eYMWOsjg8bNgwxMTH48ccfff4Z7iQnJzv87BWOpaamOrwuISEBYWFhXl0rmDx5MvR6PT799FNPi+0VZx0/wjFn38FC5j7bzyIhCUVjY6MYeAivYftaQoINKeux/cHZz3I2DdCSQqFAx44dkZKSIv43MTERRqMRCoVCDLicTU/keR4mk4npDvD2pP13qzJOauNbOE8YEWrv6zn8wd3aNoDN6WXejHawOrJjyx+JFViYXueIsymy7r6cWVrP5KyOzrCyPo0lCxcuRE5OjvhvnU7ncdDVpUsXnDhxAvX19VaJM86ePSs+70hFRQWMRqNV6nPB3r17sXfvXjz11FMYMmQIKisr8cMPP+CHH35wWIcuXbpg+fLlHpXbG1dddRV++OEH1NbWWq3ROXr0qPi8r4Ssjo4arEajsVU+0wYMGIDdu3dDp9NZJc44dOiQ+LwjCoUCV199NY4cOWL33MGDBx0mHLElTDlsrQ2tw8LCUFdXZ7e+SSiHs9E8V++D7fQ64TVsrxHe09aalRAaGora2lq7wEcYzZSSuTokJEQsr8lkQlNTk9XfveVUYsvRTOFnuEuMQ6ShgCvApDS+Wenh9icpIyGsBiHCSIAn+xXJpWHqj4YFC9PrnHG2blGn08lmraJQx9LSUknpw1kNoNursLAwl5udSpGVlYWtW7di165dYlr35uZm7NmzBz169BD34qmoqIDBYEBaWhoA84iNoz2OVqxYgWuuuQajRo0SM9UJe0FZ2rdvH/bv34/HH3/cKiNcIN1+++1Ys2YN3nvvPXEfLoPBgPz8fAwcOFCs28WLF9HQ0ICePXt6/DO6desGANiyZQvmzp0rHv/qq69QX1+Pvn37+qEmrk2ePBkrVqzA22+/Lf7uDQYD1q1bh6ysLDEov3DhAurr6602t548eTIWLlyIw4cPY/DgwQCAgoICfPvtt1bvY0VFBRITE+2+k//zn/8AgMNMh4EQGxuLqqoq1NTUiPeRyWRCTU0NwsPDxQBCSBAh/L0I2f5qa2ut9qYymUzQ6/UIDQ0Vg5rIyEgolUpxby7huDAy3FpttoiICNTW1kKv14uBtJCQKjQ01CpRCM/zbgMwofyWbY2wsDAoFAro9XqrgEuv11sFn8Q37LZsGCGl8c1SD7e/SF3bxmoQ4ulIACCPhqmvwZIcOh8crVt0l0CGNUIdpQRcLAfQctWzZ0/86U9/Qn5+PnQ6HZKSkrB3716Ul5dbbdD7+uuv4+TJk8jPzwcApKWliQGKrY4dO1rt42T5/4LCwkIA5tEW2/TlgTJw4EDcfvvtePHFF1FRUYGuXbvi448/RlFREVauXCme95e//AX79++3ylqo0+nEYEIYKcrLyxP/nmfNmgUAGD16NHr37o2VK1fi4sWLYtKMtWvXIikpCffcc0/A6ylssvzMM8+grKwM3bt3x3vvvYfCwkK8/fbb4nnTp0/Hnj17rEbjHnvsMbzzzju4/fbb8dRTT0GlUmHVqlVISkqyGk394IMP8Oabb2LChAno2rUr9Ho9vvrqK+zcuRO33XYbRo0aFfB6AuYgJCYmBmVlZWhpaUFoaCi0Wi2am5utpj8WFxejvr4eV155JQDz51ZiYiLKy8tx7tw5xMXFidMMW1parK5VKBTo1KkTSkpKcP78eajVajQ3N6Oqqkr8+a0hLCwMERER0Gq1YrIQ4XvEct83Iduk5Wi3TqdDc3MzQkNDwXEcGhoa0NjYCLVabdVpo1AoEBsbi5qaGlRUVCA8PNxqw2Wp+6YS1+ibsBXINRufL4IhCBG+lCsqKhyuIbAlh4apr8kj5Nz54C6BDGukvtettdaBeObxxx/Hxx9/jO+++w51dXXo3Lkz5s2bJzZO5WT16tVYvnw5Nm7cCK1WiyuvvBLvvfcehg4d6vK6mpoau2mPb7zxBgAgPT1dDLhCQ0Px6aefYtWqVdi5cye2bNmCqKgojB07FgsXLmy10bx3330Xixcvxvvvv4/q6mr069cPn332GYYPH+7yupiYGHzzzTfIycnBCy+8AJPJhBtvvBErV660atRfd9112LdvH/Lz81FaWoqQkBD07t0bK1aswOzZswNdPSupqakoLy8XA5GwsDBkZGS43Vy7Q4cOUKlUqKqqQnl5OXieR1hYGNLS0uw6AeLi4sBxHCorK1FWVgaFQoH4+Hir0bHWIGR8raurg8lkQmhoKDp06OB25EmlUqGhoUGcaikkJXP0OxKSg9TW1qKhoQEhISHi2lviHxxP+R4l0el0UKvV0Gq1XvfMOduXKFhY1r+5uVlSEJKZmcn8HzzP8ygoKHA7rbRXr16yuB+crc0TULpw+XD3XgsyMjJ8em/98fnblgoLC9G1a1eMGTMG27dvBwDk5uZiyZIl+OijjzBlyhSvX1v43eTl5bltbMrBiBEj2roIrcYy2JGz1phy2V7o9fq2LkKr8WRZBeukfDex36XOELn1cHvC0b5M7shhehkQfNNKpYzoJicnB3Xng1yo1WrwPI+LFy+6PI/V9ZitYc2aNWIQZuvBBx/E9ddf38olIoQQ4m8UcJGAk9oLbivYghA5cbdmKZg7H+SE53mH+znZYnk9ZqAJ2f0cGTFiBAVchBAiAxRwtSNynHLozb5MwRqEyA0FVfLm6ag1y+sxfZWZmWm3sWhubi5yc3PbpkCEEEJaFQVc7YSjxktISAhSUlKYDjw82ZcpMTERMTExFITIjBw7EoKdN6PWwoa4hBBCSLChgKsdkOsGwIBnvdo6nU5W0wiJfDsSgpk3o9YAUF5ejvDwcHrfCSGEBB2F+1NIIEndAJjVZJKepDoX1nnIEc/z0Ov1qKmpgV6vZ/b99ITQkWAbdAsdCVqtto1KFhjB8h57Mmpti+XPMkIIIcRbNMLVxuS+AbCn+zLJcZ2Ho1EepVKJ1NRU2fb2S+1IkEvmumAayfPlb5TlzzJCCCHEWzTC1cbkHogIKdGlksPmv5acjfIYjUYUFRXhwoULshwR8aQjgXXBNpLn698oq59lhBBCiLfk1bplkNTGC8uBiNDD726RvVz23RJIGeXR6XTQ6XQA5DUiItTJHdYb38E2kgd4Pmpti+XPMkIIIcQbNMLVxoTGiytyCETUajUSEhJcnhMREQGtViub0R5P17rIZUSE53nU1NRIOpf1xndZWVnQjOQJPB21tiSHzzJCCCHEU2y3dmRAaLy4Gv2RQ+Y+nufdjnrIabRHq9Xi0qVLXl3L+ohIXV0dTCaT2/OUSiXTjW+tVovy8nJJ57I+kmfL2UbeSqUSRqPR6XVy+CwjhBBCPEUBVzvgrPEipw2AvR3tAdhLie/NHkWWWE8sIPV9jouLY7bx7WlqdNZH8hxxtpG3TqeT9WcZIYQQ4in5tQIY5azxwmqD1JYvaaRZGu3xdo8iWyyPiEgNLmJiYgJcksDxpANB+FuWI0cbecv9s4wQQgjxFAVcbYzn+aBomHjbw8/aaI8vexRZYnlEREpSBdbX8njyHgvTaYNpdMdRIEYIIYQEK3ZbdTIQTHv3+JLZjKXRHn+UlfVgJBjWJXoSEAtbAADsTY/1VrB0JBFCCCFSUMDVRpyt82F57ZIrUhrhzrA02uOPsrIejADyX5foTQcCa9NjvRVMHUmEEEKIFOy0ZGUkGPfuAZw3wl1hbbQnKirKbaY2Z1gORhyNaMh5LY83HQisTY/1RrB1JBFCCCFSUMDVBqSs85Fr48y2EW4wGFym1mZttIfjOCQkJEhOFy5ISkpChw4dmKqrwN2IhtzuYYE3HQgsTY/1lMlkQnFxsctz5NiRRAghhLhDAVcbCPbGme2C+vDwcFlNPQsLC/P4GpVKxWQjNNhHNIQOhIqKCpSWlro9n6XpsZ4Q9p1zt/+aXDuSCCGEEFfk+e3fzkltdMm1cWZLblPPvHnfWHyvg3VqrC2O49ChQwdUVlbKOjOjM57uOyfXjiRCCCHEGUVbFyAYCQvuXZFr48wZYdQrLi4O0dHRTDfQpby/llh9rz2ZGit3wpouV1ibHiuFN/vOsdi5QAghhPiCAq42EKyNs2Ah5f21xOp7HexTY22p1WpkZGTYBRQqlQoZGRmynFrp6b5zrHYuEEIIIb6grsY2Ive02cFOSkIF1t9rmhprT27TY93xNJhmtXOhPeN5HgDQ0NDQxiVpHbW1tW1dhFbjzXpgFnmT1ZdV7ta5EjYJn8OuBE9LqB0KtsZZsLF9f5VKJTiOk817LWUvqmAc0bBNCiNnUoNppVKJ1NRUZjsX2jMhAHniiSfauCSEEBKcamtr3X6/UcDVxoKpcRaM5Pz+StmLikY05E1K0K1UKtG7d28oFDSDPRBSU1NRVFSEmJiYVvtb0+l0yMjIQFFREWJjY1vlZ7YVqqv8BEs9AaproPE8j9raWqSmpro9t90GXK+//jpefvllaDQa9O/fH6tXr8a1117r9PxPPvkEixYtQmFhIXr27Inly5fjlltuEZ939kX00ksvYe7cuX4vPyHBgKbGBjcpQXdqaioFWwGkUCiQnp7eJj87NjZW9o04AdVVfoKlngDVNZCktnPa5bfghg0bkJOTg+eeew5Hjx5F//79MWbMGJSVlTk8f9++fZg6dSpmzZqFn376CRMnTsTEiRNx/Phx8ZySkhKrR15eHjiOw+TJk1urWoTIklqtRu/evZGZmYn09HRkZmaiV69eFGwFiWBMFkIIIYR4guOlrPRqZVlZWRgyZAhee+01AOZFhhkZGfjLX/6CBQsW2J1/9913o66uDlu3bhWP/elPf8KAAQPwxhtvOPwZEydORG1tLXbt2iWpTDqdDmq1GlqtNmh6CQghRCqe5wO2HpU+f9uXYHo/qK7yEyz1BKiu7Um7G+FqamrCkSNHkJ2dLR5TKBTIzs7G/v37HV6zf/9+q/MBYMyYMU7PLy0txRdffIFZs2Y5LYfBYIBOp7N6EEIIcUxOe+kR18LCwvDcc88FRRY9qqv8BEs9Aapre9LuAq6KigoYjUYkJSVZHU9KSoJGo3F4jUaj8ej8d999FzExMZg0aZLTcixbtgxqtVp8ZGRkeFgTQgghRH7CwsKQm5vbbhs2/kR1lZ9gqSdAdW1P2l3A1Rry8vIwbdo0hIeHOz1n4cKF0Gq14sPVonBCCCGEEEIIcaTdZSns0KEDlEolSktLrY6XlpYiOTnZ4TXJycmSz//uu+9QUFCADRs2uCxHWFhYu42SCSGEEEIIIWxodyNcoaGhGDRokFUyC5PJhF27dmHo0KEOrxk6dKhd8osdO3Y4PP8///kPBg0ahP79+/u34IQQQgghhBBio92NcAFATk4OHnjgAQwePBjXXnstXnnlFdTV1WHGjBkAgPvvvx9paWlYtmwZAGDOnDm48cYbsWLFCtx6663Iz8/H4cOH8dZbb1m9rk6nwyeffIIVK1a0ep0IIYQQQgghwafdjXAB5jTv//d//4fFixdjwIABOHbsGLZv3y4mxrhw4QJKSkrE84cNG4YPP/wQb731Fvr374+NGzdiy5Yt6Nu3r9Xr5ufng+d5TJ06tVXrQwghhLDgyJEjmDVrFnr27ImoqChERESge/fuuO+++7Bjxw7xvNzcXHAch/z8fKevNX36dHAchwMHDrRG0T3iaT05jsPTTz/t9PXmz58vnpebm9sKNfCOP9/f9qywsBAcx2Hs2LHiMdbvWUdc1VMu96wtb9/bttYuAy4AmD17Ns6fPw+DwYCDBw8iKytLfG737t1Yt26d1fl33XUXCgoKYDAYcPz4cdxyyy12r/nwww+jvr6eNuIkhBBCLJhMJuTk5GDw4MFYv349unXrhkcffRRz5szBoEGD8MUXX2D06NF4/vnn27qoPvG2niEhIXj//ffR0tJi95otLS1Yv3693ebf7UmwvL/kMtbvWbmh3zQhhBAS5J599lmsWrUKAwYMwMaNG9G9e3er5xsaGvDaa6+hsrKyjUroH97Wc9y4cfj888+xdetWTJw40eq5bdu2QaPRYPz48fjss88CXQWvBMv7Sy5j/Z6Vm3Y7wkUIIYSQwDt79ixeeuklJCYmYvv27XaNcQCIiIjA3LlzsWTJkjYooX/4Us9JkyYhLi4OeXl5dtfk5eUhPj4ed9xxR8DK7otgeX+JNZbvWTmigIsQQggJYuvWrYPRaMQjjzwirpV2huXtUnypZ3h4OKZOnYovv/zSahua0tJSfPHFF5g6darLvT3bUrC8v8Qay/esHNGUQol4ngdgznRICCGk9Qifu8LnMPGvH374AQAwatQoj6/duHEjTp065fC5Y8eO+VIsv/OlngAwa9YsrFmzBuvXr8fcuXMBAOvXr0dLSwtmzZqF06dP+62s/uRrveWGpXvWV6zes3JEAZdEtbW1AICMjIw2LgkhhASn2tpaSnoUABqNBgCQnp7u8bWbNm3Cpk2b/F2kgPClngAwaNAg9OvXD2vXrhUbr2vXrkX//v0xcODAdtt49bXecsPSPesrVu9ZOaKAS6LU1FQUFRUhJiYGHMf57XV1Oh0yMjJQVFSE2NhYv71ue0f1Dp56B2OdAaq3P+vN8zxqa2uRmprql9cj/vPRRx9hypQpDp+bPn063n333VYuUWDNnDkTTz75JPbv3w8AOHnyJF599dU2LhXxBN2zdM+2BQq4JFIoFAHtHYqNjQ2qRpmA6h08grHOANXbX2hkK3CSk5Nx6tQpXLp0Cb17927r4gSMP+p57733Yt68eWIigtDQUEybNs2fxfS7YHl/iWMs3rNyREkzCCGEkCB23XXXAQB27drVxiUJLH/UMzExERMmTMCGDRuwYcMGTJw4EYmJif4qYkAEy/tLHGPxnpUjCrgIIYSQIDZ9+nQolUq89dZbKC8vd3muwWBopVL5n7/qOXPmTNTW1qK2thYzZ870dzH9LljeX+Ica/esHFHA1cbCwsLw3HPPBV0qVqp38NQ7GOsMUL2Drd4s69GjB+bNm4eKigqMGzcO586dszunsbERK1euRG5ubusX0E/8Vc/Ro0djy5Yt2LJlC26++eYAltg/guX9Jc6xds/KEa3hamNhYWFB+QFH9Q4ewVhngOpN2LJ06VI0NjZi1apV6N27N0aNGoW+fftCpVLh3Llz2LlzJyorK7F06dK2LqpP/FFPhUKBCRMmtGKpfedtvdesWYPt27c7fM0HH3wQ119/fWsUn/iIxXvWG+35fqWAixBCCAlyCoUCK1euxD333IM1a9Zg79692Lt3L0wmE1JSUjBmzBjMmDED2dnZbV1UnwRLPW15W2/hPEdGjBhBARdpV9rz/crxtJMkIYQQQgghhAQEreEihBBCCCGEkAChgIsQQgghhBBCAoQCrgA6cuQIZs2ahZ49eyIqKgoRERHo3r077rvvPuzYsUM8Lzc3FxzHIT8/3+lrTZ8+HRzH4cCBA61RdK95WmeO4/D00087fb358+eL57GwEN+f7zlLCgsLwXEcxo4dKx6T033tiKs6y+2+dsTb95wQQggJNhRwBYDJZEJOTg4GDx6M9evXo1u3bnj00UcxZ84cDBo0CF988QVGjx6N559/vq2L6jfe1jkkJATvv/8+Wlpa7F6zpaUF69evR0hI+8/tEozvOXFOLvc1IYQQQnxH3/gB8Oyzz2LVqlUYMGAANm7ciO7du1s939DQgNdeew2VlZVtVEL/87bO48aNw+eff46tW7di4sSJVs9t27YNGo0G48ePx2effRboKvgkGN9z4pxc7mtCCCGE+I5GuPzs7NmzeOmll5CYmIjt27fbNbwBICIiAnPnzsWSJUvaoIT+50udJ02ahLi4OOTl5dldk5eXh/j4eNxxxx0BK7s/BON7TlyTw31NCCGEEP+ggMvP1q1bB6PRiEceeQRJSUkuzw0LC2ulUgWWL3UODw/H1KlT8eWXX6K0tFQ8Xlpaii+++AJTp05FeHh4QMrtL8H4nhPX5HBfE0IIIcQ/aEqhn/3www8AgFGjRnl87caNG3Hq1CmHzx07dsyXYgWUL3UGgFmzZmHNmjVYv3495s6dCwBYv349WlpaMGvWLJw+fdpvZQ0EX+svd6ze175i/b4mhBBCiH9QwOVnGo0GAJCenu7xtZs2bcKmTZv8XaSA86XOADBo0CD069cPa9euFRuma9euRf/+/TFw4MB23zD1tf5yx+p97SvW72tCCCGE+AdNKWxHPvroI/A87/DxwAMPtHXxAmrmzJk4efIk9u/fj/379+PkyZOYOXNmWxeL+AHd13RfE0IIIcGMAi4/S05OBgBcunSpjUvSevxR53vvvRehoaHIy8tDXl4eQkNDMW3aNH8VMaCC8T0n0rB8XxNCCCHEPyjg8rPrrrsOALBr1642Lknr8UedExMTMWHCBGzYsAEbNmzAxIkTkZiY6K8iBlQwvudEGpbva0IIIYT4BwVcfjZ9+nQolUq89dZbKC8vd3muwWBopVIFlr/qPHPmTNTW1qK2tpapaVfB+J4T6Vi9rwkhhBDiHxRw+VmPHj0wb948VFRUYNy4cTh37pzdOY2NjVi5ciVyc3Nbv4AB4K86jx49Glu2bMGWLVtw8803B7DE/hWM7zmRjtX7mhBCCCH+QVkKA2Dp0qVobGzEqlWr0Lt3b4waNQp9+/aFSqXCuXPnsHPnTlRWVmLp0qVtXVS/8UedFQoFJkyY0Iql9h9v679mzRps377d4Ws++OCDuP7661uj+CSAWL6vvUX3NSGEEHIZBVwBoFAosHLlStxzzz1Ys2YN9u7di71798JkMiElJQVjxozBjBkzkJ2d3dZF9ZtgrLMlb+svnOfIiBEjqGFKmET3NSGEEHIZx/M839aFIIQQQgghhBA5ojVchBBCCCGEEBIgFHARQgghhBBCSIBQwEUIIYQQQgghAUIBFyGEEEIIIYQECAVchBBCCCGEEBIgFHARQgghhBBCSIBQwEUIIYQQQgghAUIBFyGEEEIIIYQECAVchBAr06dPB8dxKCwsbOuiEEIIIYQwjwIuQtqx3bt3g+M45ObmtnVRCCGEEEKIFyjgIoQQQgghhJAAoYCLEEIIIYQQQgKEAi5C2qnc3FyMHDkSALBkyRJwHCc+CgsLcfr0acybNw8DBw5EYmIiwsPD0atXLyxYsAB6vd7u9UpKSjBnzhz07NkTERERiIuLw5VXXolHH30UWq3WbXl2796NuLg4dO7cGadOnfJ7fQkhhBBC5CikrQtACHFsxIgRKCwsxLvvvosbb7wRI0aMEJ+Li4vDG2+8gf/85z8YOXIkRowYAZPJhAMHDmD58uXYs2cP9u7dC5VKBQCor6/Hddddh8LCQowePRp33HEHmpqacO7cObz33nt4+umnoVarnZZl06ZNmDZtGrp3746vvvoK6enpga4+IYQQQogsUMBFSDslBFjvvvsuRowYYZc447777kNOTg5CQ0Otjv/jH//Ac889h48//hjTpk0DAOzatQvnzp3Dk08+iVWrVlmdr9frxcDMkTfeeANPPPEE/vSnP2Hr1q2Ij4/3vXKEEEIIIUGCphQSwqi0tDS7YAsAZs+eDQDYuXOn3XMRERF2x6KjoxEWFubwZyxZsgSPPfYYbrnlFuzcuZOCLUIIIYQQD1HARQijeJ5HXl4ehg8fjoSEBCiVSnAch8TERABAcXGxeO7w4cORkpKCf/7zn7j11luxZs0a/Prrr+B53unrP/nkk8jNzcX06dPx3//+12GwRgghhBBCXKMphYQw6q9//Stee+01ZGRkYPz48UhJSRFHqpYsWQKDwSCeq1arceDAASxevBiff/45tm3bBgDIyMjAggUL8Pjjj9u9/t69ewEAt99+O0JC6KOCEEIIIcQb1IoihEFlZWV4/fXX0a9fP+zfvx+RkZHicxqNBkuWLLG7pnPnzli3bh1MJhN+/vlnfP311/jXv/6FJ554AvHx8Zg6darV+f/9738xY8YMTJkyBfn5+Zg0aVLA60UIIYQQIjc0pZCQdkypVAIAjEaj1fHff/8dPM8jOzvbKtgCgO+++87layoUCgwYMADz5s3DRx99BAD47LPP7M7r0qULdu/ejYyMDNx9993YtGmTL1UhhBBCCAlKFHAR0o4lJCQAAIqKiqyOd+nSBQCwb98+mEwm8fjFixexcOFCu9c5ceIESktL7Y4Lx8LDwx3+/M6dO2P37t3o0qULpkyZgo0bN3pXEUIIIYSQIEVTCglpx6644gqkpqYiPz8fYWFhSE9PB8dx+Mtf/oLJkydj06ZNGDx4MG666SaUlpZi69atuOmmm/Dbb79Zvc6OHTswd+5cXHfddejVqxcSExPx+++/47PPPkN4eDieeOIJp2XIyMjA7t27MXLkSEydOhU8z+Ouu+4KdNUJIYQQQmSBAi5C2jGlUonNmzdj/vz5+Oijj1BbWwsAuPfee7Fu3TpkZmZi06ZNWL16NTp37oycnBzMnz/fbiRqzJgxKCwsxN69e7F582bo9XqkpaXh7rvvxrx589CnTx+X5UhPTxeDrnvuuQc8z+PPf/5zwOpNCCGEECIXHO8qLzQhhBBCCCGEEK/RGi5CCCGEEEIICRAKuAghhBBCCCEkQCjgIoQQQgghhJAAoYCLEEIIIYQQQgKEAi5CCCGEEEIICRAKuAghhBBCCCEkQCjgIoQQQgghhJAAoYCLEEIIIYQQQgKEAi5CCCGEEEIICRAKuAghhBBCCCEkQCjgIoQQQgghhJAAoYCLEEIIIYQQQgKEAi5CCCGEEEIICZD/B3rdB1GkeFZpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_values = ['C', 'I']\n",
    "b_values = ['H','M','L']\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,5))\n",
    "\n",
    "counter = -1\n",
    "\n",
    "medie = []\n",
    "errore_medie = []\n",
    "\n",
    "for a in a_values:\n",
    "    for b in b_values:\n",
    "        counter += 1\n",
    "        prova = results_free_df[results_free_df['Cond']==a]\n",
    "        prova = prova[prova['Int']==b]\n",
    "        prova_cost=prova['Cost'].values\n",
    "        media = prova_cost.mean(); medie += [media]\n",
    "        errore_media = prova_cost.std()/np.sqrt(prova_cost.size); errore_medie += [errore_media]\n",
    "        ax1.scatter(np.random.uniform(counter-0.1, counter+0.1, prova_cost.size), prova_cost , color='lightgrey')\n",
    "\n",
    "\n",
    "ax1.errorbar([0,1,2], [medie[0], medie[1], medie[2]], yerr= [errore_medie[0],errore_medie[1], errore_medie[2]], color='blue')\n",
    "ax1.plot([0, 1, 2], [medie[0], medie[1], medie[2]], marker='o', markersize=6, color='blue', linestyle='None')\n",
    "ax1.errorbar([3,4,5], [medie[3],medie[4], medie[5]], yerr= [errore_medie[3],errore_medie[4],errore_medie[5]], color='red')\n",
    "ax1.plot([3, 4, 5], [medie[3], medie[4], medie[5]], marker='o', markersize=6, color='red', linestyle='None')\n",
    "\n",
    "xpositions=np.arange(n_task-1)\n",
    "ax1.set_xlabel(\"task\", fontsize = 14)\n",
    "ax1.set_ylabel(\"Costo ottimale\", fontsize = 14)\n",
    "ax1.set_xticks(xpositions)\n",
    "ax1.set_xticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "\n",
    "#################### test T di student #########################################\n",
    "pvalues=np.zeros((n_task-1, n_task-1))\n",
    "counter_rows = -1\n",
    "\n",
    "for a in a_values:\n",
    "    for b in b_values:\n",
    "        counter_rows += 1\n",
    "        counter_cols = -1\n",
    "        for a_bis in a_values:\n",
    "            for b_bis in b_values:\n",
    "                counter_cols += 1\n",
    "                prova1 = results_free_df[results_free_df['Cond']==a]\n",
    "                prova1 = prova1[prova1['Int']==b]\n",
    "                prova1_cost=np.array(prova1['Cost'].values)\n",
    "\n",
    "                prova2 = results_free_df[results_free_df['Cond']==a_bis]\n",
    "                prova2 = prova2[prova2['Int']==b_bis]\n",
    "                prova2_cost=np.array(prova2['Cost'].values)\n",
    "\n",
    "\n",
    "                ttest=stats.ttest_ind(prova1_cost.tolist(), prova2_cost.tolist())\n",
    "                pvalues[counter_rows,counter_cols]=ttest.pvalue\n",
    "\n",
    "ax2.set_title(\"p-values del t-test\", fontsize = 14)\n",
    "im=ax2.imshow(pvalues, cmap='Grays')\n",
    "ax2.set_xticks(xpositions)\n",
    "ax2.set_xticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "ax2.set_yticks(xpositions)\n",
    "ax2.set_yticklabels(['CH','CM','CL','IH','IM','IL'], fontsize = 14)\n",
    "\n",
    "# Display the values within each cell\n",
    "for i in range(pvalues.shape[0]):\n",
    "    for j in range(pvalues.shape[1]):\n",
    "        text = ax2.text(j, i, f'{pvalues[i, j]:.2f}', ha='center', va='center', color='black', fontsize=12)\n",
    "\n",
    "# Show the plot  \n",
    "plt.suptitle(fr\"Algoritmo FreeEnergy a $\\beta$={beta}.\", fontsize = 16)\n",
    "plt.savefig(f\"EEG_risultati_beta={beta}.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOT delle OCCORRENZE\n",
    "\n",
    "Imput: 'prob_states.csv', colonne= [Subject,Cond,Int,State,Prob]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x3000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dati_df = pd.read_csv('prob_states.csv')\n",
    "dati_resting_df = dati_df[dati_df['Cond']== 'R']\n",
    "\n",
    "nomi_cluster = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']\n",
    "cond_values = ['C', 'I']\n",
    "int_values = ['H','M','L']\n",
    "fig = plt.figure(figsize=(20,30))\n",
    "plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)\n",
    "gs=fig.add_gridspec(4,3, hspace=0.3, wspace=0.15)\n",
    "ax0 = fig.add_subplot (gs [0, 0:])\n",
    "\n",
    "medie = []\n",
    "error = []\n",
    "counter = -1\n",
    "for a in nomi_cluster:\n",
    "    counter += 1\n",
    "    prova = dati_resting_df[dati_resting_df['State']==a]\n",
    "    prova_cost=prova['Prob'].values\n",
    "    media = prova_cost.mean(); medie += [media]\n",
    "    errore_media = prova_cost.std()/np.sqrt(prova_cost.size); error += [errore_media]\n",
    "    ax0.scatter(np.random.uniform(counter-0.1, counter+0.1, prova_cost.size), prova_cost , color='lightgrey')\n",
    "\n",
    "ax0.errorbar(nomi_cluster, medie, yerr=error, marker='o', markersize=4, color='black', capsize=5, elinewidth=2, linestyle='None')\n",
    "ax0.bar(nomi_cluster, medie, color= 'gray', alpha= 0.3)\n",
    "ax0.set_xticks(range(len(nomi_cluster)))\n",
    "ax0.set_xticklabels(nomi_cluster, fontsize=18)\n",
    "ax0.set_ylim(0, 0.20)\n",
    "ax0.tick_params(axis='y', labelsize=12)\n",
    "ax0.set_title(\"Resting State\", fontsize = 20)\n",
    "\n",
    "####### riga 1 ###############\n",
    "axes_counter = -1\n",
    "for a in nomi_cluster[:3]:\n",
    "    axes_counter += 1\n",
    "    ax = fig.add_subplot (gs [1, axes_counter])\n",
    "    temp_df = dati_df[dati_df['State'] == a]\n",
    "    resting_temporaneo_df = dati_df[(dati_df['State'] == a) & (dati_df['Cond'] == 'R')]\n",
    "    resting_temporaneo_values = resting_temporaneo_df['Prob'].values\n",
    "    counter = -1\n",
    "    medie = []\n",
    "    error = []\n",
    "    xvalues = []\n",
    "    for c in cond_values:\n",
    "        values_df = temp_df[temp_df['Cond'] == c]\n",
    "        for i in int_values:\n",
    "            counter += 1\n",
    "            xvalues.append(c+i)\n",
    "            values_fin_df = values_df[values_df['Int'] == i]\n",
    "            values = values_fin_df['Prob'].values - resting_temporaneo_values\n",
    "            media = values.mean(); medie += [media]\n",
    "            errore_media = values.std()/np.sqrt(values.size); error += [errore_media]\n",
    "            ax.scatter(np.random.uniform(counter-0.1, counter+0.1, values.size), values , color='lightgrey')\n",
    "\n",
    "    ax.errorbar(xvalues[:3], medie[:3], yerr=error[:3], marker='o', markersize=4, color='blue', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[:3], medie[:3], color= 'lightsteelblue', alpha= 0.3)\n",
    "    ax.errorbar(xvalues[3:], medie[3:], yerr=error[3:], marker='o', markersize=4, color='red', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[3:], medie[3:], color= 'lightcoral', alpha= 0.3)\n",
    "    ax.set_xticks(range(len(xvalues)))\n",
    "    ax.set_xticklabels(xvalues, fontsize = 18)\n",
    "    ax.axhline(y=0, color='black', linewidth=1)\n",
    "    ax.set_ylim(-0.1, +0.1)\n",
    "    ax.set_title(f\"Cluster {a}\", fontsize = 20)\n",
    "    ax.tick_params(axis='y', labelsize=12)\n",
    "    if(axes_counter != 0):\n",
    "        ax.set_yticks([])\n",
    "\n",
    "####### riga 2 ###############\n",
    "axes_counter = -1\n",
    "for a in nomi_cluster[3:6]:\n",
    "    axes_counter += 1\n",
    "    ax = fig.add_subplot (gs [2, axes_counter])\n",
    "    temp_df = dati_df[dati_df['State'] == a]\n",
    "    resting_temporaneo_df = dati_df[(dati_df['State'] == a) & (dati_df['Cond'] == 'R')]\n",
    "    resting_temporaneo_values = resting_temporaneo_df['Prob'].values\n",
    "    counter = -1\n",
    "    medie = []\n",
    "    error = []\n",
    "    xvalues = []\n",
    "    for c in cond_values:\n",
    "        values_df = temp_df[temp_df['Cond'] == c]\n",
    "        for i in int_values:\n",
    "            counter += 1\n",
    "            xvalues.append(c+i)\n",
    "            values_fin_df = values_df[values_df['Int'] == i]\n",
    "            values = values_fin_df['Prob'].values - resting_temporaneo_values\n",
    "            media = values.mean(); medie += [media]\n",
    "            errore_media = values.std()/np.sqrt(values.size); error += [errore_media]\n",
    "            ax.scatter(np.random.uniform(counter-0.1, counter+0.1, values.size), values , color='lightgrey')\n",
    "\n",
    "    ax.errorbar(xvalues[:3], medie[:3], yerr=error[:3], marker='o', markersize=4, color='blue', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[:3], medie[:3], color= 'lightsteelblue', alpha= 0.3)\n",
    "    ax.errorbar(xvalues[3:], medie[3:], yerr=error[3:], marker='o', markersize=4, color='red', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[3:], medie[3:], color= 'lightcoral', alpha= 0.3)\n",
    "    ax.set_xticks(range(len(xvalues)))\n",
    "    ax.set_xticklabels(xvalues, fontsize = 18)\n",
    "    ax.axhline(y=0, color='black', linewidth=1)\n",
    "    ax.set_ylim(-0.1, +0.1)\n",
    "    ax.set_title(f\"Cluster {a}\", fontsize = 20)\n",
    "    ax.tick_params(axis='y', labelsize=12)\n",
    "    if(axes_counter != 0):\n",
    "        ax.set_yticks([])\n",
    "\n",
    "####### riga 3 ###############\n",
    "axes_counter = -1\n",
    "for a in nomi_cluster[6:9]:\n",
    "    axes_counter += 1\n",
    "    ax = fig.add_subplot (gs [3, axes_counter])\n",
    "    temp_df = dati_df[dati_df['State'] == a]\n",
    "    resting_temporaneo_df = dati_df[(dati_df['State'] == a) & (dati_df['Cond'] == 'R')]\n",
    "    resting_temporaneo_values = resting_temporaneo_df['Prob'].values\n",
    "    counter = -1\n",
    "    medie = []\n",
    "    error = []\n",
    "    xvalues = []\n",
    "    for c in cond_values:\n",
    "        values_df = temp_df[temp_df['Cond'] == c]\n",
    "        for i in int_values:\n",
    "            counter += 1\n",
    "            xvalues.append(c+i)\n",
    "            values_fin_df = values_df[values_df['Int'] == i]\n",
    "            values = values_fin_df['Prob'].values - resting_temporaneo_values\n",
    "            media = values.mean(); medie += [media]\n",
    "            errore_media = values.std()/np.sqrt(values.size); error += [errore_media]\n",
    "            ax.scatter(np.random.uniform(counter-0.1, counter+0.1, values.size), values , color='lightgrey')\n",
    "\n",
    "    ax.errorbar(xvalues[:3], medie[:3], yerr=error[:3], marker='o', markersize=4, color='blue', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[:3], medie[:3], color= 'lightsteelblue', alpha= 0.3)\n",
    "    ax.errorbar(xvalues[3:], medie[3:], yerr=error[3:], marker='o', markersize=4, color='red', capsize=5, elinewidth=2, linestyle='None')\n",
    "    ax.bar(xvalues[3:], medie[3:], color= 'lightcoral', alpha= 0.3)\n",
    "    ax.set_xticks(range(len(xvalues)))\n",
    "    ax.set_xticklabels(xvalues, fontsize = 18)\n",
    "    ax.axhline(y=0, color='black', linewidth=1)\n",
    "    ax.set_ylim(-0.1, +0.1)\n",
    "    ax.set_title(f\"Cluster {a}\", fontsize = 20)\n",
    "    ax.tick_params(axis='y', labelsize=12)\n",
    "    if(axes_counter != 0):\n",
    "        ax.set_yticks([])\n",
    "\n",
    "\n",
    "#plt.suptitle(r\"Occorrenze dei cluster nelle varie task, rispetto alle occorrenze al resting.\", fontsize = 20,  y=0.95)\n",
    "plt.savefig(f\"EEG_cluster_occorrenze.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.set_title(r\"$U_{opt}(CL)<U_{opt}(IH,IM,IL) \\quad (pvalue \\leq 0.03)$\")\n",
    "plt.savefig(\"testo.svg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}