{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![feature](img/feature.jpg)\n",
    "\n",
    "# Pengenalan dan Panduan Jupyter Notebook untuk Pemula\n",
    "\n",
    "[Jupyter notebook](https://jupyter.org) adalah software sangat sangat populer beberapa tahun terakhir. Mungkin sebagian besar dari pembaca sudah menggunakannya sehari-hari. Tapi mungkin ada beberapa yang baru mengenal nama saja dan belum terlalu mengerti. Atau bahkan mungkin baru mendengar. Jangan kuatir, tulisan ini akan mengenalkan Anda kepada Jupyter notebook dari nol sampai sedikit menguasainya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apakah Jupyter Notebook dan Kenapa Dia Begitu Populer\n",
    "\n",
    "***Jupyter*** (https://jupyter.org/) adalah organisasi non-profit untuk mengembangkan software interaktif dalam berbagai bahasa pemrograman. ***Notebook*** adalah satu software buatan Jupyter, adalah aplikasi web open-source yang memungkinkan Anda membuat dan berbagi dokumen interaktif yang berisi kode *live*, persamaan, visualisasi, dan teks naratif yang kaya.\n",
    "\n",
    "Mungkin penjelasan di atas kurang jelas. Ilustrasinya begini. Dulu, biasanya kita membagikan kode dan dokumen secara terpisah. Kode-kode kita satukan dalam sebuah librari/aplikasi/proyek (Visual Studio, Eclipse, dsb), dan dokumen kita buat dengan penyunting kata. Dalam dokumen bisa tampilkan cuplikan kode, tampilan hasil, dan visualisasi lainnya dari program kita.\n",
    "\n",
    "Nah Jupyter Notebook menyatukan semua ini, baik itu teks/narasi, kode hidup, persamaan, tampilan hasil, gambar statis, dan visualisasi grafis, dalam satu file interaktif. Dan, kelebihan lainnya, notebook dapat dijalankan ulang oleh siapapun yang membukanya, untuk mereproduksi eksekusi kode di dalamnya.\n",
    "\n",
    "Contohnya adalah dokumen ini sendiri. Dokumen ini aslinya adalah sebuah Jupyter Notebook. Mungkin Anda membacanya di blog IndoML, karena notebook ini telah dikonversi menjadi blog Wordpress dengan menggunakan utilitas [nb2wp](https://github.com/bennylp/nb2wp). Anda bisa melihat file aslinya [di GitHub](https://github.com/stosia/indoml/blob/master/notebooks/00-pengenalan-jupyter/pengenalan-jupyter-01.ipynb), dan akan tampak keluaran yang sama.\n",
    "\n",
    "Nah keistimewaan dari dokumen ini adalah kemampuannya membawa kode hidup (*live code*). Seperti di bawah ini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Halo teman. Waktu sekarang adalah 2019-09-26 07:58:06.107879\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "now = datetime.datetime.now()\n",
    "print('Halo teman. Waktu sekarang adalah {}'.format(now))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kode di atas bukan sekedar cuplikan tulisan kode di dokumentasi, tapi adalah kode hidup. Kalau Anda melihat tulisan ini di blog IndoML atau di GitHub, memang yang Anda lihat adalah tampilan statis dari eksekusi terakhir ketika notebook ini disimpan. Ibaratnya Anda sedang melihat sebuah dokumen. Tampilannya tidak akan berubah. Tapi kalau Anda menjalankan file notebook ini di instalasi Jupyter Anda, maka Anda bisa menjalankan kode di atas dan nanti akan keluar tampilan waktu yang berbeda tentunya!\n",
    "\n",
    "Jenis keluaran kode yang didukung juga tidak sebatas teks sederhana seperti di atas. Bisa juga grafik seperti ini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGeCAYAAACJsaAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XmcXHWZ6P/Pc07tXb0vWTvdSQiEYFAgLAECSNwAFUaUiLnKMmMYHMflOupo1PHHNagz6vU1jHe8eB1xtK/IGFQIiBcSIRIJEmQLW9buTmfpfa/9nO/vj6qGTnpJJ6nu08vzfr2KdJ86y1PVdFc99X2+z1eMMSillFJKKaWUOnWW1wEopZRSSiml1HShCZZSSimllFJK5YkmWEoppZRSSimVJ5pgKaWUUkoppVSeaIKllFJKKaWUUnmiCZZSSimllFJK5YkmWEopNU2JyD0i8o0JutbXReTnE3GtqUJEbheRZhHpE5Hy3L+LvI4LQERqRcSIiM/rWJRSarrRBEsppfJMRD4iIjtyb6gPi8jvROTSPJx33BKmQW+4+3K3ZhH5XyLiH4/rTSaS9UkReVFEYiJyREQeF5EPn8I5/cD3gHcZY6LGmPbcv/tO8nxX5H4+XzjZmJRSSk0MTbCUUiqPROS/A98H7gRmAQuA/wVc62VcJ6DEGBMFlgMrgb+b6AA8GFX5V+AzwOeAcmAe8BXgPcPtnEvIjvf6OQsIAS/nKcabgI7cv0oppSYxTbCUUipPRKQYuAP4O2PM/caYfmNM2hjzoDHm87l9giLyfRE5lLt9X0SCufuuEJEmEfmciLTkRr9uyd23DlgLfCE3wvRgbvuZudGWLhF5WUTef0xYpSLykIj0isjTIrJ4LI/FGNMCPAosG/T45orIRhFpFZH9IvKpYw4Licgvc9f6i4i8dSzH5soLfyUiPxeRHuBmEblARJ7KPa7DIvJvIhIYdIwRkU+JyD4RaRORfxlIekRksYhsEZH23H11IlIyws/sdOATwIeNMY8aY+LGGMcY86Qx5uZB+z0uIhtEZBsQAxaJyC0i8mru8e4TkdsGnfP13KFdIrJlUMyn5b6+WkReyR17UET+YaSfhYhEgA+STXaXiMiKY+6/VET+lHuuDojIzbnt14jIcyLSk9v+9VGucb2I1IvIW0baRyml1NhogqWUUvmzkuyoxa9H2Wc9cBHwNuCtwAVkR0sGzAaKyY6i/DXwAxEpNcbcDdQB/5wrNXtfrgztQeD/AVXA3wN1InLGoPPdCPx/QCmwB9gwlgciInOBdwPbc99buWu9kIttNfAZEXn3oMOuBf4LKAP+L/AbEfGfwLG/Akpyj9MBPgtUkH1eV5NNhAb7K2AFcG7u+FsHwge+CcwFzgSqga+P8FCvBA4YY3aM4Wn5KLAOKAQagBbgvUARcAvwP0XkXGPMLuCs3DElxpgrhznXj4HbjDGFwFuALaNc93qgj+xz+3vgYwN3iMgC4HfAXUAl2f+vns/d3Z/btwS4BrhdRK479uS5JP7bwDuMMTuP8xwopZQ6Dk2wlFIqf8qBNmNMZpR91gJ3GGNajDGtZJOfjw66P527P22MeZjsG+szhjkPZBO1KPAtY0zKGLMF2EQ2qRpwvzHmz7mY6si+AR9Nm4h0AQfJvkH/VW77+UClMeaO3LX2AT8CBs9TetYY8ytjTJrs/KNQLsaxHPuUMeY3xhg3N4r0rDFmuzEmY4ypB/43cPkxsX7bGNNhjGkkW5Z5I4AxZk9uNCqZe46/N8yxAyqAI4M35EYRu0QkISI1g+66xxjzci6mtDHmIWPMXpP1BNlEd9Woz+6b0sAyESkyxnQaY/4yyr43Ab80xjhkE9cb5c25cWuBx4wxv8jF1G6MeT73PDxujHkp95y+CPximOfhM8DngSuMMXvGGLtSSqlRaIKllFL50w5UHGcO0Vyyox8DGnLb3jjHMQlajGwSNdK5Dhhj3GPON2/Q94OTh9HONaDCGFMCRIBtwCO57TXA3Fzi0ZVLwr5Mdq7RgAMDX+RiasrFeELHQrbMTkQ2SbbhRA/ZOW0Vx8Q6+Jg3nkcRqRKRe3Oldz3Az4c5dkA7MGfwBmPM/Nz+QbKjYSPFeJWIbBeRjtxjunqU6xzr+tz+DSLyhIisHG4nEakG3k42OQb4LdnE9Zrc99XA3hGOvVBE/pAry+wG/naY+D4P/MAY0zTGuJVSSh2HJlhKKZU/TwEJYEgZ1iCHyCYcAxbkto2FGeZc1XJ0w4UFZEefTokxJg7cA6wUkQqyycV+Y0zJoFuhMebqQYdVD3yRi2l+LsaxHHvsY/t34DVgiTGmiGxCJsfsUz3o68HP4zdz5zs7d+x/G+bYAVuA+cfOaxrBGzFKdt7cRuA7wKxcUvrwKNc5+kTGPGOMuZZsaedvgPtG2PWjZF+rHxSRI8A+sgnWQJngAWCkeXX/F3gAqDbGFAM/HCa+dwFfEZHrxxK3Ukqp49MESyml8sQY0w18jey8qetEJJKbg3SViPxzbrdfkH1DW5lLXL5GdoRlLJqBwesoPU22jO8LuetcAbwPuPdUH0sugfgo2RGwduDPQI+IfFFEwiJii8hbROT8QYedJyIfyI3gfQZIkp3DNZZjj1UI9AB9IrIUuH2YfT4vIqW5UZ5PA78cdGwf2QYT88iO0gzLGPM62fLDe0XknQPxARcf5ykKkB3hagUyInIV2WTluEQkICJrRaQ4V07ZQ3bO2XA+RraM9G2DbtcD14hIOdmRrXeIyA0i4pPselsDZaCFQIcxJiEiFwAfGeb8L5PtlvgDGdogRSml1EnQBEsppfLIGPM94L+TbVzRSnaE4ZNkRykAvgHsAF4EXgL+kts2Fj8mO2+nS0R+Y4xJAe8HrgLayLaD/5gx5rVTeAhdItJHNplbCbw/N8fIIZu8vQ3Yn7ve/yHbkGPAb4E1QCfZ5OwDuXlBYzn2WP9ANiHoJTtf65fD7PNb4FmyTR0eIvv8QDYhORfozm2//ziP+e/Itmr/HtlW6E3A/8g9lsbhDjDG9AKfIjvy1JmL9YHjXGewjwL1uRLGvyU7ynYUEbkIqCVbwndk0O0Bsg1LbszNP7uabIv5DrLPxUD3xk8Ad4hIL9lEfthRMmPMC2SbdfwolygqpZQ6BWLMsVUZSiml1OQmIoZs+aA2ZlBKKTWp6AiWUkoppZRSSuWJJlhKKaWUUkoplSdaIqiUUkoppZRSeaIjWEoppZRSSimVJ5pgKaWUUkoppVSeaIKllFJKKaWUUnmiCZZSSimllFJK5YkmWEoppZRSSimVJz6vAzgRFRUVpra21uswlFJKnYRnn322zRhT6XUcXtLXMaWUmrrG+jo2pRKs2tpaduzY4XUYSimlToKINHgdg9f0dUwppaausb6OaYmgUkoppZRSSuWJJlhKKaWUUkoplSeaYCmllFJKKaVUnmiCpZRSSimllFJ5ogmWUkoppZRSSuWJJlhKKaWUUkoplSeaYCmllFJKKaVUnmiCpZRSSimllFJ5ogmWUkoppZRSSuWJJlhKjaC7P8HL+w/z9GuNPL/3IG3d/RhjvA5LKZVHIvIfItIiIjtHuF9E5F9FZI+IvCgi5w667yYR2Z273TRxUSullJrMfF4HoNRkk8447Nh1gAMtXfhsm4DfIuO47Gpqo7wwwkXLaigIBbwOUymVH/cA/wb85wj3XwUsyd0uBP4duFBEyoB/AlYABnhWRB4wxnSOe8RKKaUmNR3BUmoQ1zX8+fUDHGztpqo0SnlxhMJIiNLCCLNKo/QnUvzxxX0k0xmvQ1VK5YExZivQMcou1wL/abK2AyUiMgd4N/CoMaYjl1Q9Crxn/CNWSqmR1dXVUVtbi2VZ1NbWUldX53VIM5ImWEoN0t7TT1NrF5WlUURkyP3F0RC98SSNLV0eRKeU8sA84MCg75ty20bark6RvkFU6uTU1dWxbt06GhoaMMbQ0NDAunXr9HfIA5pgKTXI3sPthIP+UfcpjoZ47UCLzsdSamYY+klLtiRwpO1DTyCyTkR2iMiO1tbWvAY3FZxIwqRvEJU6eV/60peIxWJHbYvFYqxfv96jiGYuTbCUGqSrL04oMPrUxKDfRzKZJuO6ExSVUspDTUD1oO/nA4dG2T6EMeZuY8wKY8yKysrKcQt0MjpewhSLxdi/fz/bt2/nt7/9LZ/61Kf0DaJSJ6i5uZmvfvWrHDhwYNj7GxsbJzgipU0ulBrEtixcM3ri5BqDAaxhSgiVUtPOA8AnReResk0uuo0xh0Xk98CdIlKa2+9dwJe8CnKyWr9+/bAJ080338ztt99Ob2/vmM6jbxCVGuqVV17he9/7Hj/72c9Ip9OEw2Hi8fiQ/RYsWOBBdDObJlhKDbKgqoSd+48QCoxcJtgbSzK3vAjb8n4AOJXO0NEdo7s3TmtnH4lkGoCA30dFaQGlRQWUFodHfTxKzWQi8gvgCqBCRJrIdgb0Axhjfgg8DFwN7AFiwC25+zpE5H8Az+ROdYcxZrRmGTPSSIlRJpPh1ltvZdasWUfdrrvuOg4ePDhkf32DqGaquro61q9fT2NjIwsWLGDDhg3MnTuX7373uzz00EOEQiFuvfVWPvvZz/LMM8+wbt26IR9qvOc92n9noslUmkeyYsUKs2PHDq/DUNNYPJnm4adfpaggSMA/9PMH1zW0dPVx5dtOo7Ik6kGEWX2xJPua2th3oA3HNdiWRSjox2dnkz7HdUkk0ziui4hQO7ecRfPLKS4MexazUiLyrDFmhddxeGmmvY7V1tbS0NAwZHtNTQ319fVDtg+UFA5+g+j3+/nJT37C2rVrxzNUpSad4X4fRARjDJWVlXzyk5/k9ttvZ3Dp8eCErLq6mmg0yu7du3nssce47LLLvHgY08pYX8c0wVLqGE2tXfzp5QbCIR+F4eAb3QT7Eym6+xOcvXAOZ9XO9iQ21zXsa2rjpV2HsCyhOBrGtkcfSXNdQ3dfnHTaYdlps1myoBKfz56giJV6kyZYM+917Oc//zk33XQT7qA5q5FIhLvvvnvEhGnwG8RwOEwymeTFF19k2bJlExW2UpPCSB9QlJWV0dTURDh8/A9NOzs7ufjii2lpaWH79u0sWbJkPEKdMcb6OuZ9jZNSk8z8yhJWn3sapQURWrv6ae3qo6WzF79tc9nyhZ4lV6l0hm3P7eX515ooKQxTVlxw3OQKwLKE0qII5aUFvLq3ma3P7iGeSE9AxEqpmS6dTuO6LuXl5YgINTU1oyZXAGvXrqW+vh7Xddm/fz8lJSXceuutOI4zgZEr5b2RSmw7OzvHlFwBlJaW8tBDD2FZFtdccw3t7e35DFGNQBMspYZRXlTApcsX8t6LlvGOc0/nqgvO5B3nLmFeRYkn8aTSGf70/H46umPMKi86qREo27KoLIvSH0/xR02ylFLjrKuriy9+8YtvfHruui719fUnVOpXVVXFXXfdxdNPP833v//9cYxWqclnpLmHJzoncdGiRfz2t7+lsbGRD3zgAySTyXyEp0ahCZZSowgH/ZREw0QHlQpONGMMz7/aRGd3jLLiglM+X0lhhGQqw9Mv1eNoq3ml1Dj5+te/TltbG3fddRfWKTQF+vCHP8y1117LV77yFXbt2pXHCJWa3DZs2IBtH/2BaiQSYcOGDSd8rosvvpif/OQnbN26lY9//OO6luc40wRLqUmuqbmLhiOdlJecenI1oKQoQntnP/uatFRAKZV/O3fu5N/+7d+47bbbOPfcc0/pXCLCv//7vxMKhfjrv/7ro+ZzKTWdrVixAsdxKCoqGnOJ7WhuvPFG7rjjDn72s5+dVJKmxk4TLKUmsVQ6w/OvNlFWFMn7CFpZSQE7dx2iP57K63mVUjObMYa///u/p7i4mG984xt5OeecOXP4/ve/z5NPPskPfvCDvJxTqcluw4YNhMNhdu/efVIltsP5yle+wkc/+lG++tWv8otf/CJPkapjaYKl1CR2uLWHdMYZtmX8qfLZFiJw4Ehn3s+tlJq57rvvPh5//HE2bNhAeXl53s77sY99jKuuuop//Md/ZP/+/Xk7r1KT0Z49e6irq+P222+nqqoqb+cVEX70ox+xatUqbrnlFr7+9a9TW1uLZVnU1tZSV1eXt2vNZNqmXalJyhjDY9tfx5jsXLDxkM449MdTXLVq2RtraCk1XrRN+/R/Hevr6+PMM8+ksrKSZ555Zsj8kVN14MABzjrrLM4//3wee+wxz+bGKjXebrnlFu69917279/P7Nn5717c3t7OsmXLaGlpOWr78ZZRmOm0TbtSU1wimaG3PzFuyRWA32eTzmTo60+M2zWUUjPHnXfeSVNTE3fddVfekyuA6upqvvOd77BlyxZ+9KMf5f38Sk0G+/bt42c/+xm33XbbuCRXAOXl5fh8Q6tjYrEY69evH5drziSaYCk1SfXGEjABA8yC0BvTlq1KqVOze/duvvvd7/LRj36USy65ZNyu8/GPf5zVq1fzD//wDyOuE6TUVHbnnXfi8/n4whe+MK7XOXz48LDb9ffq1GmCpdQk1R9LTUj5i8+26OqJjft1lFLT22c+8xmCwSDf/va3x/U6A3NIXNfltttu03bTalqpr6/npz/9KevWrWPu3Lnjeq18rbOlhtIES6lJKp3JTEiCZVkWqbQz7tdRSk1fmzZt4uGHH+af/umfmDNnzrhfb+HChXzrW9/ikUce4ac//em4X0+pifLNb34Ty7LGffQKsl0KI5HIUdtOdp0tdTRNsJSapCZq8rbBIJZOFFdKnZxEIsGnP/1pzjzzTD71qU9N2HU/8YlPsGrVKj7xiU8wf/587YKmprzGxkZ+8pOf8Dd/8zfMnz9/3K+3du1a7r77bqqrqwGIRqPa4CJPNMFSapIKhwK4ZvwX1MxkXKLhwLhfRyk1PX3nO99h3759/Ou//it+//g15TmWZVlce+21xONxDh48iDGGhoYG1q1bp0mWmpK+9a1vAfDFL35xwq65du1aGhsb+chHPoLP5+P666+fsGtPZ5pgKXWKjDHEnQR9mX76Mv2knLEv3GuMoas3RuPhDp577QB//Mtentixm23P7eVgcyc9fQmSqcw4Rp+NobgwPK7XUEpNT42Njdx5551cf/31vOMd75jw6991111DtmkXNDUVNTU18eMf/5hbb73VkzlQt9xyC11dXTzwwAMTfu3pKP+rlyqVB8a4uKYX4/ZicAALS8JYVhEi3o+2ZNwMrckODsWb6Up34xoHQ7Yjn8EQtAKUB0qZG5lNib8IS47+LCOdcTjU0s1r+5vpiycRhIDfxu+3ESBuIJZIsq+pjeb2HkqLC5hbUUxxYQjIXzmfMQaDIRoJ5e2cSqnpr66ujvXr19PQ0ICIsGrVKk/iGKnbmXZBU1PNt7/9bVzX5Utf+pIn13/7299OdXU199xzDzfccIMnMUwnmmCpScV1e0ll9pBKv4YxaQb3KRfAIPh81QT9Z2JbsyZ8kUnXuDT2H2Rvfz2OcQlbIYp80SEJVMbN0Jbs4GDiCAV2hDOLllAeLAWgtbOPHTsbiSVTFBWEqCorHPZaBeEAi+dX0NrVT388xct7D1NWHGHhvAqCgaN/dTOpDIlYknh/kkw6g3ENts8mGA4QjAQIRQKIDB2w7o+nqCiJUqAlgkqpMaqrq2PdunXEYtnuo8YYvvzlL1NRUTHhczcWLFhAQ0PDsNuVmioOHTrEj370I26++WZqamo8icG2bT72sY/xzW9+k4MHDzJv3jxP4pguZCq1N12xYoXZsWOH12GocWCMQyr9KonkXxCxsKxSRIbW8mdHtroxJobfriEUvBDLKpiQGPsy/ezsep2eTA8l/mJsGdsimgknSa/Tz/zgHJKHQ7y66wghv49QwAci+HwWwXAAn3/o+fr6k7y46yDFhSFEhL5YEuPC6bWVFEVCdLX1cKShnXhfdqFgQRBbQATjuhg3+/stllA1v4yKuaWEC94crWpp7+WScxcxu6IoD8+QUqMTkWeNMSu8jsNL0+F1rLa2dtikpqamhvr6+gmN5dhkD7Jd0HSivppKPv3pT/ODH/yA3bt3s3DhQs/i2LNnD0uWLOFb3/rWhM4Dm0rG+jqmCZbynDEpYoknyDhNuVGp4w+sGmNw3XZAKAi/E9uuGNcYe9K97Oh4AVt8RH2R4x8wSDqdoa2ll+f27KG7OcWc2DxsfEdX+hkIhP2UVRVRPquIUCQ7omSM4fX6Frp74xQWBHPnczjS2E447RIO+glHgvhDo08sdx2XWF8cN+NSXFFIzRlzSbnZ4y87/zRsS6djqvGnCdb0eB2zLGvYtadEBNcd/8Y8x6qrq+PLX/4yjY2NmlypKefw4cMsWrSIG2+8kf/4j//wOhxWrVpFW1sbr7zyyoRXCU0FY30d8+xdlYhUi8gfRORVEXlZRD7tVSzKO8Y4xBJP4GQO47PnjSm5guwLuW1XIBKkP/57HLdz3GKMZeLs6HiRgBU4oeQqlUpTv6+Z557Zz0svNtLf4VJQ6SM1v5doaYjCksibt9IItm3RfKCDnU/vZ/dLTcT7k4gIC+eVA9l5W6l4mubdzcRae2jvS+CPBPCHfBgrhrG7cO12XLsDY/cAb65tZdkW0eICisoLifXEeWHbLg42tvG2pfM0uVJKnZDJtjjp2rVraWho4G//9m8BuO666zyJQ6mT8S//8i+k0+lJ05jl5ptv5rXXXuPpp5/2OpQpzct3Vhngc8aYM4GLgL8TkWUexqM8kEq/SsY5iO2bfVLHW1YUJEA88STG5L/bnmtcXu5+HQshbI+tEYTB0NHey87nGmlr7sEfsOnPpCmKhgm5ERK+GL2B7iHH+fw20eIwhaVhYj0JXn6mniONHfh9FqfVVNLe2kvjziYyyTSFJQUEQxYt3QdIB18jE3qdTHA/TvAATrCRTHAv6cjLuP7DIMmjrhMsCJHxCVZrPwd2HsRxJv4TZ/UmYxxcpwM3U08mtYtMehdu5iDG7fU6NKWGtWHDBqxjPpiZDIuTrlmzhlgsxqZNmzyNQ6mxam5u5oc//CFr165l8eLFXocDwA033EAkEuGee+7xOpQpzbMEyxhz2Bjzl9zXvcCrgM6om0Ect5tE6llsq+qUzmNbxThuO6n0a3mK7E2H4810pLso9EfHtL9rXBr3tbLr1UMEgj4KCkMcae8l4LffWMw34ITojrSRtoZv5y4ihKNBCopCNO1tYc+LB7GTDtLSS1rACvgQX5JoVQN2URN9sTiWW5i7RXO3QsQN4vhbSIdfwfU1A4aM49IdS7B4ThnLls6j4fVDvPzUbk/KemY6YxJkUjtJxzaSTjxMJvEkTvrPOKk/k0n+gXTsN6TiD+OkGzATsB6aUmO1atUqXNelpKQEEaGmpmZSlOWtWrWK2bNn88tf/tLTOJQ6nrq6Ompra5k9ezbxeJyzzz7b65DeUFhYyPXXX8+9995LPB73Opwpa1LUBolILXAOoOORM0gqvQvwjbkscDS2VUEy/VJeR7Fc47Knr54i3/Bd/obbv2FfK4cPdVJcUoDPbxOLp0ikMvj9bz5GCwtxhb5hRrEGs22LorICutr7eGrT85QXRVg4qwTHipMO1WMw2G4xvX0urjPMfAhsLLcAcQvI+JvocQ4QS6U5Y24588uLEEuomFtK/euH2f/KwRN7ctQpcTJNpGMP4KSfByuKZc9CfLOw7OxN7FmIbzaYJJnk42SSWzBun9dhKwXA/fffD8Cf//xnXNelvr7e8+QKsl3QPvShD/Hwww/T26sjwGpyGmjMMrhRzNe+9rVJtTj2zTffTHd3N7/5zW+8DmXK8jzBEpEosBH4jDGmZ5j714nIDhHZ0draOvEBqnFhTIZU+nVsqzQv5xMJYNwkjtOcl/MBdKS6SDhJAtboDSQGHDnYSfPhLopLChiYF9rRE8PvG/pr5neD9Ad7cMUZct9gxhgSnf2kEhk6O2IURhzOOquR4ohNf3+QWBIyjiGeTA97vOMYevuht6eAaHErZy/JUFX8ZtdFEaF8djGvPrOP3s7+MT1OdWoy6dfJJB4DK5RNpobpljlArCiWby6u0046/jDG7ZrASJUa3saNG1m+fDlLlizxOpQh1qxZQzKZ1MVS1aS1fv36o7pewuRbHPuKK66gpqZGywRPgacJlmTfWWwE6owx9w+3jzHmbmPMCmPMisrKyokNUI0b1+0B3LyMXg0Q8ZNxW/J2vtZkB0FrbOtD9fUlaGpop6g48kZy5Tgu/bEUgWHarwsWrrik7OSQ+wbr74zR29FHcXkB/X1J+lNN+P0O88sDnFENs0oBbFo6U3T3ufTkbt25WzJtmFNu87YlIZbOL8VfvBfD0aN8ts8mFAnywpOva6ngOHMyTTjJ7dkRKgmP+TjLLgOxScc3Y0xiHCNUanRHjhxh27ZtXH/99V6HMqyVK1cyb948LRNUk9ZUWBzbsixuuukmHn30UZqamrwOZ0rysougAD8GXjXGfM+rOJQ3XNML5PfNvEgEx8lfgtWZ7CRoB4+7n+O67N/dTDDkx7LebGmaSjsDgQ17nBghZY2cYBlj6DzUSTAcABFCYYv2tlYSvdkRqIAfqkphWa1F7ew0Zy30s7TGzxk1fs5aGOC8M4KsWBqiZrafSMhC8IE4uP62IdeKlkTobO2lq0XLasaLceM4yT8hdtlJfbAgVhGGJE7yL+MQnVJj8+tf/xpjzKRNsCzL4oYbbuCRRx6hq0tHfNXkM9m6cI7kYx/7GMYY/vM//9PrUKYkL0ewLgE+ClwpIs/nbld7GI+aQMakOXohqFMn4svbp/uucelzYvjH8Ea4u7OfeCxJ8Ji1qJLp0eeDWcYm5Rs5wUrGkiRjKXzBbAy+SAyxDJ0tR8+3sizwCYT8hpJCm9JCm6ICi4B/6PMrTgFOcD+GoXO2gmE/9a8dGjVmdfKczC4MKUTG1o1yOGKV42b24I7jsgRKjWbjxo2cfvrpnHXWWV6HMqI1a9aQTqd1/oialDZs2IDPd/R7i8nQhfNYixcv5rLLLuOee+4Zdt07NTovuwg+aYwRY8zZxpi35W4PexWPmmj5X7zO4JKv/6XdXNe2sSyyd+RgJ8HQ0FLCTMZhtCWmxMioc7B62/qw7TdPYAV7CIZsutsdnMzRf+yMGFxlJNVAAAAgAElEQVT3+H8AxYQwdt+Q1u0AhSUFHN7fQjI+fHdDdfKMSeOkX0WsslM6j4iFET9uek+eIlNq7Nrb23n88ce5/vrrJ/UCpBdccAG1tbXcd999Xoei1BBr165l1qxZhEKhSdWFczi33HILu3fv5qmnnvI6lCnH8yYXamayrAjDDKKcEmOSeWuaMdYEMBZL0tebIBgaYaRrtDchAjLKdeLd8TdGrwAscbCwcR2hr/vo8kpBhh2VGvnaQ0fXxBIQoa87NswB6lQYtxtMetSGFmMlVjFupv7Ug1LqBD3wwAM4jjNpywMHiAg33HADjz76KO3t7V6Ho9RR9u/fz8GDB/nmN785qbpwDueDH/wgBQUF2uziJGiCpTxhSXH+B7HcOJY1Ky+nssXCb/lxzOhd/mL9yRGTKNuyMKOMKrni4HeGf8PtZlxSyTT24AYZkj2XLwD9PUPPO3j+10kTtJvgODBuz+jJ9gnIJmkJjKvrk6iJtXHjRmprazn33HO9DuW4brjhBjKZDL/+9a+9DkWpowwshP3e977X40iOLxqN8sEPfpB77713SOdDNTpNsJQnLCuCbVXg5mltH2MMRgx+X34SLBGh1F9E0hm9XK63O47fP/yv0XDdAwcz4hJwhu8kl05lhoxuGdcHlovPD7G+N0ewjMk+/oBv9OsdfbLhR9yCoQBdbdroIt+MiZHPP7cGAUbvQKlUPvX09PDoo4/ygQ98YFKXBw4499xzWbx4sXYTVJPOpk2bWLp0KaeddprXoYzJLbfcQm9vr35YcYI0wVKeCfjfguuOvtjuWLmmC589H8sqysv5AMqDZcTd0Ztm9PbG8fuHH4UK+H2MNC/U5Ar6/M7wbeDNMO3S3WQhWBlsW8ikzRvzsBzXJRDwIaNN+Bo4ryQRtwDM8N0RLVtIJ/O3WLMaHzLov+rUich7ROR1EdkjIv84zP3/c1Azpl0i0jXoPmfQfdN28aVNmzaRSqX4wAc+4HUoYyIirFmzhi1bttDSkr/uskqdit7eXh5//PEpMXo1YNWqVSxcuFDLBE+QJljKM37ffGy7CucUF081JoMxcUKB/JatVAbLEeSNhhfDyaQdLHv4N7p+n43fb+Nkhh7vWGlCmTB+d4R1toY5pZMqBCNkxy8EN1e9mEo7FBUcv508gLH7sBI1o879ykupoTqKZRUio/x/dCKMMdmOTiewjpYamYjYwA+Aq4BlwI0ismzwPsaYzw40YwLuAgav2xgf1Kjp/RMW+ATbuHEjc+bMYeXKlV6HMmZr1qzBdV3uv3/YZTaVmnCPPvooqVRqSiVYA2tibd68eVKt1TXZaYKlPCNiEw5djHETGHNy5U7GuDhOM6HACmz71Dq0HStkB5kbrqInM0rJ3Gh9JQTKSwqGbdeesdIUJkZuyGHJML+axsZJlCC5xYkN2fJA1zUUR4//ZtvggLGxM1Uj7uNkXHyB/C3+rLLEKs7mxnmRRKwiRMa2CLY6rguAPcaYfcaYFHAvcO0o+98I/GJCIpskYrEYv/vd7/irv/orrDGMlE8Wy5cvZ+nSpVomqCaNTZs2UVJSwsUXX+x1KCdE18Q6cVPnL6WalmyrlEjoShy3Ddc9seYKxmRwnEME/EsJ+Jcd/4CTsDhaCwhpNz3s/ZZtjbo+RGEkiCXgOm/uk7YShNNRQpnIiMf5gr5sEeEx53biZdlmF1YaEUim0hQWhPD7R0+KDAbj68ROLEbMyJ3sUok0pVX5K7NUOVKMSBRjTr0xhXF7sHyn5yEolTMPODDo+6bctiFEpAZYCGwZtDkkIjtEZLuIXDd+YXrnkUceIR6PT/rugcca6Cb4xBNPcPjwYa/DUTOc67o89NBDXHXVVSNOLZisFi5cyNvf/nZdE+sEaIKlPOf3z6cgfDWGNI7TjDlO5z5jDI7bheM2EwqeRyh4ITLciE8ehOwQy4qW0JXuGbZUMBIJkEmPHK9tW8wqLyKezDbLcCSDESiNV45epmdbBMOBIeWFxg2Q6qzG8mWw/AlcF2aVRUd9DAYX42vDTi7ATtWMui/GEC0eOfFTJ0dEsP3LwTm1BYKzC3QbbN9xfo7qRAz3izjSO4gPA78yR/+RWmCMWQF8BPi+iCwecgGRdbkkbEdra+upRzzBNm7cSHl5OZdddpnXoZywNWvWYIzhV7/6ldehqBnumWeeoaWlZUqVBw528803s3fvXp588kmvQ5kSNMFSk4LPnkU08n78/qW4bisZ5zCO04FrErk5Vilct4eM04zjHsaWIqLh9xEMvHXckqsBs0NVnBatpT3VOSTJKioJkzpOU4jiaJhoJER/MkbGSlHRPxefe/xPryIlEdKJoedOJUJY6VpSToLZczP4AsPP7TG4uHY3xteBnTgNO3HGqEmd67iIJURLNMEaD5avFuzK7JpYJ8m4rdj+8xBLf0Z51ARUD/p+PnBohH0/zDHlgcaYQ7l/9wGPA+cce5Ax5m5jzApjzIrKysp8xDxhkskkmzZt4rrrrsPnm3rlw8uWLeMtb3mLlgkqz23atAnbtnnPe97jdSgn5frrrycajWqzizHSBEtNGpaECAfPJ1pwA+HgKvy+BQgurunDmCSWXUYocC7RyLUURN6DbVdMSFwiwuJoLacXLqIj1UXcebOzYKQgdNzlfY0YSiptrAAUtFYRyoytOUFhWRTXdYeUCabTDplMkEh8FSWcA5LB9bXj+jpw7S5cuxPX147xdWOn5uLvXYkveRpynF/3ns5+qpfMJhCcWqULkO2kmExnSCTTJNMZ3FHWH/OKiI0/eDHGpDEnsTyB67Rg2fOw/WeMQ3Qz2jPAEhFZKNmJbR8GhnQDFJEzgFLgqUHbSkUkmPu6ArgEeGVCop4gjz32GD09PVOuPHCwNWvWsG3bNpqamrwORc1gmzZt4pJLLqGsLL/zxSdKQUEB5513Hj/5yU+wLIva2lrq6uq8DmvSmnofR6lpz5IQAf8i8C/yOpQ3iAiLojWUBUrY2f06bckOCnwRCgpD+HwWTsbF9h2dwBgMCRJkyFBil3Ba2Rns7W6nuzdOUUEIOU63vkAkQLgwRDqZwR/KJj2ZjEMsnuKMpbOpnT0byQimdx7G7sa1YiApwIdlAkimFDFja4RgjCGdTFN9+pyTen4mWsZxaevqo6Wzj9bOPrr7E7nOetkpaiJCeXEBVaVRKkujlBVFJsXaPWKV4A+9k3RiM8ZpR6yy48ZlTAacFizfPHzBy8Z9xHamMcZkROSTwO8BG/gPY8zLInIHsMMYM5Bs3Qjca46egHAm8L9FxCX7geW3jDHTKsHauHEjxcXFrF692utQTtoNN9zAV7/6Vf7rv/6Lz372s16Ho2agAwcO8Pzzz/PP//zPXody0urq6ti+ffsbc7AaGhpYt24dAGvXrvUytElJptJktRUrVpgdO3Z4HYaa4TKuQ1uynfr+A/Rm+mg50k3LoW4KCyOYbGHeG6V4xRRTbsopoABBcByHA0e6ONjSRSDgJxz0jfoGO96T4OCrBwkVhUmmMqRTGarnlnHBioWQx4Shq62H8tklLF+5hP0vNnBw92ECkQCLzq5h3mlzJkVyApBIpmk40sHrja0kUxkCfptw0E8g4MMaFKPjuiRTGeLJNI7jUlgQ4owFVcyvKsFne5+gGLefTOoZ3EwDYoVyTTCOXijamCQmt4SB7T8H2790yD5TjYg8m5uvNGNNpdexdDrN7Nmzufrqq/nZz37mdTin5JxzziEYDLJ9+3avQ1Ez0A9/+ENuv/12XnnlFc4880yvwzkptbW1NDQ0DNleU1NDfX39xAfkkbG+jukIllInyGfZzA5XMStUSdxJ0BHq4g+NLxJK+/H5LAIECBIkRAjfMb9itm1TO6+cspICmo500tUbxxLB77cJ+H1HrUHlZFxMwMZfHKa7rY9Zc0rwY3HG6bPzmlylk2lw4czzFvLnh/9CT3svReWFZFIOO37/Asl4isVn1+bteifDGMPBlm6efb0Jx3EoLgxTUjhyqaVtWURCASKh7AhePJlmx6uN7Gps4fwzF1Ba5O0cJrEK8AUvx/hbcdO7cZ3G7FzDgSE4BCGC7X8btm8hYhV4Gq+ambZu3UpHR8eUWVx4NGvWrOFLX/oS9fX11NbWeh2OmmE2bdrEokWLWLp0qdehnLSR1sDStbGG5/1HuUpNUSJCxBdmfskcLnvLOQRbC5jDHMopJ0p0SHI1WFFBiGWL53DO0vlUzy4lHPQTi6fo7k3kbnHSjkNJNMQ5KxazZNEsfA7MnltCcR67/LmOS2dLD8svWUJ/d4yulh7K55bhD/oJR0OUzy1l1zN7yQyzltdESaUzPP1yA9te2k8k7KeiNIrfd2IjOeGgn6qyQjKOy2PP7OK1+mbP52mJCJZdhS90Cf7IGvyR6/CHr8Yffi+ByAcJFHwAX+Atmlwpz2zcuJFIJMK73/1ur0M5ZTfccAMA9913n8eRqJkmFouxefNm3ve+902aapCTsWDBghPaPtPpCJZSeVBdW8Ghpg46O/ooPU7b9MHCoQDzQgHmzSrJtZ93MS5YVna0a0DItnnt2f2UFo2tQcZYOBmH9sNdLF2xiHmLZrH3hfoh88h8fh9OxiGVSOM7zlpb4yGZzrDthf109caYXV54yi9O0UiQcMjPi3sOEU+meeuSeUeNGnpFxAIpHKXHo1ITy3Vdfv3rX3P11VcTiUz9rpWLFi3i/PPP57777uMLX/iC1+GoGWTz5s0kEokp2559wIYNG1i3bh2xWOyNbZFIhA0bNngY1eSlI1hK5YFlWZxz/iKCQT/dXbHjHzAMEcFn2/j99lHJVSKewnUNH/qbKwgEfXQ2d2NOcfQlEUvScaSbZRcsZslbs58+FZUX4mSOXtMrlUzjD/gJRsbWLCOfMhmHp16sp7s/TkVpNG+f/NmWRVV5IbsPtPLSvsO6aKJSw/jTn/7EkSNHpnT3wGOtWbOGZ599lj179ngdippBNm3aRGFh4ZRcR26wtWvXcvfdd1NTk12H0e/3c/fdd2uDixFogqVUnoTCAS667AwCAZuOtr68vHHv643T35fkosuXMn9hJZe+9xzmLKyk9WAHiVjyhM/nOi7th7twUhlWXv1WTjt7wRuJS/ncUiqrK2g50EasN05vRx9dzd0su/j0oxK+ifJyfTNtPf2UF+e/RM4Soaq8kNcbmjnUdvLrUik1XW3cuJFgMMg111zjdSh586EPfQjQMkE1cYwxbNq0iXe/+90EAhP/QWW+rV27lvr6ejZs2EA6nead73yn1yFNWppgKZVHkYIgKy9fyqy5xbQ2dx93EeKROI5L65FuAgEfq96xjPLKQgCC4QBvW7WUC999NsYYWg920tPRh+sMv9gwZP/AJ2Mp2g510tXaw8Jl81h17Qoq5pQetZ9lWZz/nrfx1suXvTH/6pK/uoDqM+ad1GM4FW1dfbxe30zFOCRXAywRSgsj7Hi1iUQyPW7XUWqqMcZw//338653vYvCwkKvw8mbBQsWsHLlSl10WE2Y5557jkOHDk358sBjDSzb8Ic//MHjSCYvnYOlVJ6FwgHOvXAxc+eX8eKz9XR39RMtDBMeQ5ldKpmhtztbYnjm8vksXDILe5iGDrOqy6maX0ZnSw8Nrx+iuaEdx3FgYBaPMblOg9lRtMLSApZffDpzaioIhEZeSNjn91F71gJqz/Ju0qrjujzz6gGKoqFxnx8VDPjoiyd5ef8RzltaPa7XUmqq2LFjB42Njdxxxx1eh5J3ixcv5uc//zmWZbFgwQI2bNigJU5q3GzatAkR4aqrrvI6lLw677zzKCoqYvPmzaxZs8brcCYlTbCUGgciwpz5ZVTOKqblSBd7XjtCa3M3YgmWWASCNiKCMdm5RpmUAwLBkI9lZ1czp7qMUHj0hExEKJtVTNmsYowxxPoSxHoTZFIZjAHbFoKRINHiCD7/iZX4GWNIOBlcY/BZFkF74v5UtHb20R9PUlU2MZ+clxZF2H+og2ULZxMOjpx8KjVT3H///fh8Pt7//vd7HUpe1dXVsXHjRiD7N04XSlXj7cEHH+TCCy+kqqrK61DyyufzccUVV7BlyxavQ5m0NMFSahz5/DZzq8uZM7+MWH+S/t4E3V399PYkcBwXyxLCkSBl5VEi0SDRwhCWdeKVuyJCQWGYglHWhjoex3Vpjvexu6eV5lgvGdfNjX8ZQrafuQVFnFZUQUWoYFxbze5qbCUcmrhadUsEETjQ3MXpCyon7LpKTUbGGDZu3MiVV15JaWnp8Q+YQtavX088Hj9qWywWY/369Zpgqbw7fPgwO3bsmLZd9q688koeeOABGhoa3mh8od6kCZZSE0BEKIiGKIiGqJpT4nU4Q7TG+3iquYGeVIKIP0BxIIxvUKKXdh2a+rvZ29NOVTjKhVULKA7kr2X8gP54itbOPipKJ3btp+JoiN0HWjXBUjPezp072b17N5/73Oe8DiXvdKFUNZEefvhhgGk3/2rAwDyszZs3c+utt3oczeSjTS6UmuFe6TzCIwdew2CYU1BEcSB0VHIF4LdsyoIR5kSK6E0leajhVRp7O/MeS28siREmfDHGgN9HPJXWZhdqxqqrq6O2tpazzz4bIDenc3rRhVLVRHrwwQeprq5m+fLlXocyLs466yxmzZrF5s2bvQ5lUtIES6kZ7NXOZna0NlEVLiTqD47pmJJgmJJgmCcO7+VgX1de4+nqjWF7tPCvkE3wlJpp6urqWLduHQ0NDW9s+/znP09dXZ2HUeXfhg0bhiyarAulqvGQSCR49NFHed/73jfhHxhOFBHhyiuvZMuWLbqe5DA0wVJqhmpP9LOjrYlZ4cIhI1bHE7R9lIcKeLK5nv50Kn8x9cQIBryrXO6Pa4KlZp7169cTix29QPrA3KTpRBdKVRPl8ccfJxaLTdvywAGrV6/myJEjvPLKK16HMulogqXUDGSM4emWRgp9wRNOrgYEbR/GwPPtB/MWVzqTwT7JeE6VZQmpzMjriSk1Xc2kuUkDC6XeeeedpNNp3vWud3kdkpqGNm3aRCQS4e1vf7vXoYyrgXlY2k1wKE2wlJqB2hL9dCRjFAbGVhY4kvJQhPrezryOYnlKyxzUDDQT5yZdccUVAGzdutXbQNS0Y4zhwQcf5B3veAehUMjrcMZVbW0tCxcu1HlYw9AES6kZaH9vB0Hr1EvxrFxt+aFYzymfC8Bn27iuN0mO6xp8wyzqrNR0NxPnJq1YsYJIJMITTzzhdShqmtm5cyeNjY28733v8zqUCbF69Woef/xxMpmM16FMKppgKTUDHYn1UuDPz1pTEZ+f5jwlWKWFYRIp7zr5RSMTt/6WUpPF2rVr+drXvvbG9zU1NdN+bpLf7+eSSy7h8ccf9zoUNc1s2rQJgKuvvtrjSCbG6tWr6e7u5i9/+YvXoUwqmmCpactxk8Qz7cQyLcQyraScPozROTauMfSmkwSs/IzWBG0f7cn48Xccg7KiAs9GsDAQDZ9ayaRSU1VhYSEA+/fvp76+flonVwMuv/xyXnrpJdrb270ORU0jDz74IOeddx5z5871OpQJceWVVwJomeAxNMFS04rjJulK7md/7/9jT88mGvue4EDfNg70/TG37QGOxJ4jkcn/Gk5ThZubZ5Sv1rGWCG6eEtfCSNCTdq/pjIPfbxMO+if82kpNBlu3bqW6uvqNDnszgc7DUvnW2trK9u3bZ0x5IEBVVRXLly/XBOsYmmCpacEYQ0+qif29v6cl/hwYIeKrIuKrJOKryP1bScAqoTd1gPrezRyO7SDjJrwOfcLZIrmkKD+JTMZ1Cdn5aa0ejQQpK4oQS0xs04yevgSL55VP2/VKlBqNMYatW7dy2WWXzajfgfPPP59wOKzzsFTe/O53v8MYM+3bsx9r9erVbNu2jURi5r2nGokmWGrKc41Dc/w5DsW245MCwr5KfNbwnXsssQn5Son4quhNH6S+bzNJp3uCI/aWiFAejBDP5GeuUzyTpjIczcu5AE5fUEXfBC74a4wh47jUzCmbsGsqNZns3buXw4cPc9lll3kdyoQKBAJcfPHFOg9LnbK6ujpqa2u56aabsCyLV1991euQJtSVV15JIpHgqaee8jqUSUMTLDWlGWNoiT9PV3IfEbsS2xpbkwIRIWyXIdgc6HuCpJOfJg1TxfxoMX2Z/CQxSTfD7HBhXs4FMLuskKDfN2HNLrp7E8yvKtb5V2rGGiiRm2kJFmTnYb344ot0dHR4HYqaourq6li3bh0NDQ0AuK7LbbfdRl1dnceRTZzLL78c27a1THAQTbDUpNefSXAo3s7Orv080fwCjxz6Mw8d3M4jh/7M7w9t4S/tL9DvFJB0T3weUMAqAGwOx57BNTOnxeiCaCmuMadcJph2HQKWzaxI/hIsn8/mvKXVdPUmxn0+VjrjkHEdlp82MyYjKzWcrVu3UllZyRlnnOF1KBPu8ssvxxjDH//4R69DUVPU+vXricViR22LxWKsX7/eo4gmXlFREeeff74mWINogqUmJde4tCa6+FPbyzx25FmeaX+dxlgLDi4F/jDFgQKCtk17ch9taYuXulvY1naAnV0tdKVO7I150C4i6XTRldw3jo9ocon6g5xeXElrom/Y+41rSMXTpBOjJ50t8T7OLp+LP08dCQfMrSxm4ZwyOrpjx9/5JBljaO/q55zT5+volZrRZuL8qwEXXHABoVBI52Gpk9bY2HhC26er1atX88wzz9DTM7MqgkaiCZaadHrSMZ5s3cm2tpfpS8epCBZTGSqmJBAlZAewxcISi5TTTsASSvwRygJhSv1BOtNxdnQe4oWuZuLO2EvMgnYp7cnXZtQo1lvL5xK2/fSm3ywVNMbQ3tjFK3/Yy8uP7WXno3vY81Qjsa6hE1fbEzFmhaMsKaoYl/iWnzaHSChAd+/4TJpt7eynZnYZNbN17pWauQ4cOMD+/ftnZHkgQCgU4qKLLtJ5WOqkLViw4IS2T1erV6/GcRz9sCJHEyw1aRhj2Nd7iD80P0/MSVIVKiHqDw/7qaoxhr70Ifx25I1tIkLUF6A8EKY7nWR7exOH4r1jurYtflyTJpZpzdvjmeyCto8r5i4m6WToSWWTmJa9HTQ8dxjbtimsKCBaHiHRm2LXtgZi3W8mOm2JfoK2zaWzF2Fb4/NnJBTwc+lbF+H3WXT25GedLci2qW/p6GVeRRHnnVmNZc28T+2VGjBQGjdTEyzItmt//vnn6erq8joUNQVt2LCBSCRy1LZIJMKGDRs8isgbK1euJBQKaZlgjiZYalJwjctLXft5oWsfpYEoRf7IqPs7bhLXZLAYvj14kT9A1A7wcncL+/o6x1QyaIl/RiVYAKXBCO+efwY+sTjQ2UHTqy1EyyP4gtnnVUQIFQbx+W0Ov9ZG0slwqL+bimAB75x/OgX+sTUVOVkF4QCXn3saRQVBmtt7yTintt5WIpmmtaOXhXPLOf+sGny2/glUM9vWrVspLi5m+fLlXofimYF5WE8++aTXoagpaO3atXzhC18Asq+ZNTU13H333TNise7BQqEQl1xyCVu2bPE6lElB310ozxljeLm7gb39h6gMleAbw3yejDl+2ZjPsigLhNnb10F9//E/mfRJmFimbUwxTyclwTDvWbCURZQQz6ToziToSSVIOGmSbpp4Jk0i4NDU1EZfPMnKWbW8fd5pRHzjm1wNCAf9XHbOYt52+jw6umN09MRwTrChSTrj0NrZRzKV4dK3LuK8pdWaXCkFPPHEE1x66aXYdn7nUU4lF110EcFgUMsE1UkrKCgA4NChQ9TX18+45GrA6tWreemll2hpafE6FM/lZ3VQpU7BgVgLe3oPUhkqwRrjJGszxrlSlghlgTB7+joo9AWoCBWMuK+IjWMmbv2lycRv2ZxWVEFn+Tx8pQF6Uwl60kkc18Xvt5lbUITjpnlv9ZlEIhPfEMK2LJZUVzKrrJA9TW3sP9SOASJBP6Ggf9hkKZXOkEhliCfTBP0+li2cxcK55YQC/gmPX6nJqKWlhddee41bbrnF61A8FQqFuPDCC3XuiDppTz75JKeddhqzZ8/2OhRPrV69GoAtW7bw4Q9/2ONovKUJlvJUfybBC137KAsWjjm5AuAE9rVEKPQFeKW3jYsCIQKjjJAJM3c+TmFxBItsQloePLpEMxFPQTmEwxMzajWSooIQ554xn2ULZ3GorZsjbb20dvWRybgM/OgEMAYiYT+zygqZV1lMVWmhjlgpdQydf/WmK664gm984xt0d3dTXFzsdThqCjHGsG3bNq655hqvQ/HceeedR3FxMZs3b9YEy+sA1Mz2Utc+fGLjt07sf0VLTmwUImj7iKcT7OvrYGlR5bD7uCZFwI6e0Hmnk8LiMHMXlHOkqYOyysI3motkMg7dnTFWXLpk0rRxDgX8LJpbwaK5FRhjSKQypDMOrjFYIgQDPoJ+/fOm1Gi2bt1KJBLh3HPP9ToUz11++eXccccdbNu2jauvvtrrcNQUsmvXLtra2rj00ku9DsVztm1zxRVXaKMLdA6W8lB3qo8jiU6KAyOX7Y3EJ6ETPqbYF+RgvHfE9u0ZN07Erjrh804nZ5+/kNnVZbS39NLW3ENbSw89nTHeesFC5tXkvx276xoyjntKCwqLCOGgn6KCECXRMEUFIU2u1AkRkfeIyOsiskdE/nGY+28WkVYReT53+5tB990kIrtzt5smNvJTs3XrVlauXEkg4O3I9GRw0UUXEQgEdB6WOmH/P3tvFiTXmd35/b67577UjgIKO0CCW5NAYyGbDTbZasmjCMlhP2hkuhUOW+KD1GGFHHqZYMSM1BaipR4pwp6IeTA9o7AfENbY4wd1h6VWS+puogESAAEuAAESS6GAQqG2rMzKPe/Nu3x+yAKIpXZUVRYK9xeBpTLvcnKt73/POf9z1xwlFFgt3nrrLYaGhhgaGmp3KG0lXIWEtI1b9cllD6hVFQNdjeNKB10sridICLYTCmwAACAASURBVIEiBBN2jW2x9CP3SwIi2urMdHpS0A2N/a/upvp8g0qpgaIKMh1xDHNl+paklBRLdUbHikzmq5QrDZASoQhSiQhdnQk29aZJp+Z3kQwJWSmEECrw74FfAUaAj4QQP5JSXn5o0/8kpfzeQ/tmgX8DHAAkcH5m3+k1CP2xKBaLfPbZZ/zJn/xJu0NZF0SjUQ4ePBj2YYUsmVOnTtHR0cHevXvbHcq64M033wTgn//5n/nd3/3dBbbeuIQZrJC24AYet2oTJPWlZ6/uktD68YOlDaGNqwbD9RLBQxkTN6hjqiksNbPseDYS8WSEvi1ZejZlVkxcTRdrvP/BFX5x6ipDt/MEMqAjG6OzM0EmE8MLAgZv5vj5ySuc+PAqpRWcfRUSMg8HgetSyhtSyibwN8BvLnLfXwX+UUpZmBFV/wj82irFuaKcOnUKKWXYf3Ufb7zxBufPn6dSWdz8xJAQaGWwXnvttXVTQt9u9u3bR29v71Nv1x4KrJC2UPEaSCSqWP5b0NTSGGoMN1j8QlxTFNwgwPa/ciGUMqDpl+mOvPjEfEF6gc+EXWSkPsWdep6cXcKXjzcjarUIAsmVa+P84tRVnKZPd1eCTDqKZer3nm9FCCxTJ5uJ0d2VoN5o8vOTX3L9xsRjlQ+GhCyCfuD2fT+PzNz2MP+1EOKCEOI/CyG2LHHfdceJEycwDINDhw61O5R1w9GjR/F9n1OnTrU7lJAnhImJCa5du8Zrr73W7lDWDUII3nzzTX72s5891b+/Q4EV0haqboPH/dwpQiFj7CKQLgGLs22/S/2+PqyGP0XG3E1UW//9V3XP5nJpmL8fPc8HuS84V7jG2fxVTk5d4iej57lWHsX2m+0O8x5SSi59cYfLV0bp6IgRjy2unDMRt8hmYly4fIcvro091V/SIavObFdVHn7D/RjYJqV8Efgn4P9cwr4IId4RQpwTQpzL5dbHMPMTJ05w8OBBIpFIu0NZNxw5cgRN08I+rJBF88EHHwBh/9XDvPXWW0xMTHDp0qV2h9I2QoEV0hYKzTKm+vilZ7oaI2vuoelX8eXs5hUPowqouA5SSupejpjWQ1fk+ceOZbWZcsr888RnXK+OkdAjdFkpOs0UXVaKLjNNVDO5VL7FLyYuUnbr7Q4XgFu381y9MUlnVwJVWdrXjaoqdHUm+PLKGHfGFh4UHRKyTEaALff9vBkYvX8DKWVeyntD8v53YP9i953Z/z0p5QEp5YGurtldTNeSWq3GuXPnwvLAh4jFYmEfVsiSOHnyJKZpsn///oU3foq4Ow/raXYTDAVWSFtwAhftMcoD7yeid9Bp7iOQDs2ginz0AvIDqEKh4Teo+xMkjQE2xQ6jiPXt9zLdrHIyd5mIapI1EmizmIPoikanmQIh+OXkJWre0vrTVppa3eHCpTt0dMSWNuPsPhRFkMnE+Ozz2zTs9ZOZ22j4nk95ukZ+osTUeInCZJlauUEQrM+y0xXmI2C3EGK7EMIA/iXwo/s3EEL03ffjbwBfzPz/H4DvCCEyQogM8J2Z29Y1p0+fxvO8UGDNwtGjRzl37hzVarXdoYQ8AZw8eZKvf/3rmObiqjOeFrZu3crOnTufaoG1vleVIRuWVsnX4/c7SSlxmj6OYyGd3ZTtO9h+AVUVRCyTqGlh6CoSCKSLL5s0fRufFJuj3yRu9C14jnYTyIBz+WtEVANLXdhOOa5ZFJs1Ppse4tWuZ9cgwtm5MZRDUQS6tjynyLsYhkalanNruMAze3pXKLqQRs1h9OYUd27mqJRm3BxFa0gzQiClRFUV0h1xtuzspmdzFt3YeL8ypJSeEOJ7tISRCvy1lPKSEOL7wDkp5Y+A/1EI8RuABxSA/25m34IQ4n+mJdIAvi+lLKz5g1giJ06cQFEUXn311XaHsu544403+MEPfsAHH3zAd77znXaHE7KOqdfrfPzxx/zxH/9xu0NZl7z11lv8zd/8DZ7noWkb73fHQjx9jzhkXaAIBcnyr4437Ca5Qo2JqTKe/9VxFCWBH1g0gxpe0MDz8+iGpKtToyOdIGZ2oqsRNke3L1pc1coNpqfKTE9WKEyUqFcdgiBAURSicZNsT4qOnhSpzjixxMr3MxSaFaqeTZeVWvQ+KT3KuD1NxW2Q0Ne+x6LpegzdzpNOrcy5U8kIgzcn2bWjC+0xBdvTTrXc4Opnw4wN50EI4kmLbHdiVoOXIAho1B0+/fA6qqqwfW8fO/ZtWjFnyfWClPLvgL976LZ/fd///xXwr+bY96+Bv17VAFeYEydO8Morr5BIJNodyrrj1VdfRVVV3n///VBghczL2bNn8Twv7L+agzfffJP33nuP8+fPP5VmOqHACmkLST3GpFNkqSbttuMyNJInX6oQ4GGaoOitkkAFgSoMIoqFKmKImRLEputRz7vU8tDXlcTKigWHGwdBQH68xM0vRpm4U0Ag0C0NK2KQ6owhZq7we02fseEphq+OI6WkZ0uW7c/209GbWjFHwhuViSX3qwkh0BSVkfoUz6a2LLzDClMqNwiCVgZkJdA0Fc8PKFdsspnlW/s/zQRBwPC1CS6dv4mmK2S7kwhl/vdo6yKCRTRu4XsBg1+Mcmcox0uv7qazd/GCP2T94DgOp0+f5vd///fbHcq6JB6P8/Wvfz00ughZkLtuk0eOHGlzJOuT++dhPY0CK+zBCmkLaT2GH/iL3j4IJHdyeT64+AU3C4M0tHGaWp6KX6DqTVP1pim5BQrNMSadW0zYN6m4ebzAwdA1kokIybjF+FSFLwcnaFbnzp7VKg0++qdLfPjTi5Smq3T0pujoS5HMxDAsHUVRWkOLFQXD0klmYnT0tbYpFap8+A8X+Ohnl6lXV6YHatIpEtOsJe8XVU0m7dKKxLBUSuXGsvuu5kRKqrX29pU9qbhNj/MnrnDx7A1S2RipbHxBcfUwqqbQ0ZNE1VU++OlFrl4YDt0dn0DOnTuHbdth/9U8HD16lI8++ohardbuUELWMSdPnuS5554jm822O5R1SVdXF1u2bOH73/8+iqKwbds2jh8/3u6w1owwgxXSFuJLKFtruk0+uXGFsXyeSFQjqpkoCxhkBFJS9YpU3AIRNUZC70JTdBIxE7dpc/7TUYLdKnu2dz+Qabo9OMHFD6+j6Srd/UsbOiyEIJ6KEk9Fmc6Vef9vP+bFI7vo3/F49u++DBDL6FdTEHhy8SJ2JalWHXSj9RrV7Sblmk25ZlOq2bhuKyZFUUhEDJLxCMmYSSJmzSvKNF2lWg+NLpaK2/Q49/6XTE9V6NqUfuzjWVEDw9S48ultfC/gmZe3PjHz40Ja5YEQ2krPxxtvvMFf/MVf8OGHH/Ltb3+73eGErEN83+eDDz7gt3/7t9sdyrrl+PHjjI2N4XmtMTq3bt3inXfeAeDtt99uZ2hrQiiwQtpCTLWI61FsvzmvcUPFmeb89S8pV5pkk/FFL+QUITCE1TLBCGxse5iU0YlQDLJGir5IiotXRwmk5NmdLeOEwc9HuPTRDbI9CTT98T4aqY54K2vw/pc4tsuOfcufPaorGoEMUMTSeo98AkylPb0yUkpK1QbDk0VK1QZCERiahqFpRIzWgOEgkDiuz8hkkSCQGIZKf1eK7kwCba7SwnXiaud4HsGMKYShaiufrVshpJRcOD3IdK5Ctie5YsdVVIWOviTXPh8hEjXY9symFTt2yOry/vvv8/zzz9PR0dHuUNYtr7322r0+rFBghczGpUuXKJfL4YDheXj33Xfviau71Ot13n333VBghYSsFkIIdsc38cn04KwCS0rJdHOCC0PXqVYEmUR82efRhUkgA6abk0jVYH/k5daMpY44n18dI2LpUHG4dHaQjr40qrYylbO6odHRm+LzM4OomsLWPctzLOy10ow2pkkv0Df2MHXPZmty7YcnN2yXwTt5Lg9N0pGJkYpHZhXGiiIwDQ1zxpnO9XxujhYYzZXZvaWTVPzBLKfnBVjWwi6Kq4Hjedwplxkpl5ms1bA9j7sVdkIIslaETckEA6k0KWvp5ZyrxV2XwJXIXD2MorRKBi+dv0lHX5pEKrri5whZWTzP49SpU/zO7/xOu0NZ1yQSCfbv3x/2YYXMycmTJ4EwEzwfw8PDS7p9oxH2YIW0jd5IB6pQcAPvkfumm2PczA1RnVZJxh9/voQiFFRh4Pl16naOQAaoikJnNsaHZ69z7pdX6OhLrZi4uouqKXT0Jrl4+jrFfGVZx9ge76UZLG6I8l0CKQkkbI52Luucy2Vquso/nv4S23WJRwwipr7orKOuqaTiERQhuDg4xvD49EM9PpLkKrg0zoftuZy7c4f/9/JlPrx9m3y9TlzX6YvH6Ym1/nRGojiex+cTk/zoyy/56fXr5NZB70aj5nDxzA0ynbM7BK4EqqZiWDoXTg8+LTOznmg+/fRTqtVq2H+1CI4ePcrZs2ep19fH0PaQ9cXJkyfZtGkT27Zta3co65aBgYEl3b7RCAVWSNswFI3n09uYdh4UHpVmnvHabXLjCrGotiKLQykljnAZUHqp+wUKzijQ6lO6c2OSCdtGrJDj3cOomkokZnLh1DV8b+k9UWk9RkaPU3EX/4u+5NbYHO0gqq3d8MPcdJUT568TMXV6u1L3nBaXimlopOIRhiemuTlWQEpJELTmpiUSa5cdGimV+PGVK1zL5+mMROiNx0maJrraKtWUgaRcbJCfrFCdaqDWApLoVG2bn1y7xidjo7h+e3rgAG5dG0fKAN1c3UKFRDpKYbJMfry8qucJeXzu9l+9/vrrbY5k/fPGG2/QbDY5ffp0u0MJWYecOnWK1157Lew/nYdjx44RjT5Y2RCNRjl27FibIlpb2iqwhBB/LYSYFEJ83s44QtrHQLSbbitDqdm64u/6DhP2LeolnSAAbYUySg1c0iJGSo1hqUkKzTEaXpXJkQJqAA0/oFx3VuRcsxFLRihP17h1dXzJ+woh2N+xC08G1LyFXfRKzRqmovNCetsyIl0e5ZrNqU8GScQsLFPHNDWymRgNe3mmFIoQpOMR7kyWGMuXKVcabN2SvVdOuJpIKfl8YoKf3bhBVNPojsVQla/eh57nMzlW4sL521y9NMat61OM3CwwdC3HF5+NcudKHr0h+Xx8gp8PDeF4j2ZoVxu36XHzy3GS2bWxtI/ETW58Obom5wpZPidOnGDXrl1s2hT2zC3EN77xDRRF4f333293KCHrjOHhYYaHh8PywAV4++23ee+99+71e/b19fHee+89Ff1X0P4M1v8B/FqbYwhpI4pQeCmzE4mk5trkmrdBCgp5l4i1MgNlHemiodAj0jPnFBhKhLHaDUZvTRJLWpi6ylhheSV8iyXVGef6xdv4/tJLqZJ6lG92PUcgA3J2Ccd/tGSw4Tvk7CKWqvONrmfnNQ9ZSfwg4OPLt9E1Deu+AbSbelO4TX/ZVt5CCJJxixsjU1SqNju2dq1UyPNyOTfJx2Oj9MbjWNqDJiGO7XLl4hjDg1NomkIiFSGRihBPWiRSEZLpCJ4XMHQlR2O0wWSlyi+GhmiucSYrN1bE83zUNRrKHEtY5EaL1MqNNTlfyNIJgoBf/vKXYXngIkkmk7zyyiuhwAp5hLvzr0KBtTBvv/32vX61P/uzP3tqxBW0WWBJKU8AhXbGENJ+YprFq53PUXZL5O0p3IaB70lU9fFT7450kUi2Kp3o97nw6YpBsVCh5lRRVYWIoVGoNLCbq5dt0A2Npu2SHy8ua/+UEePNnpd4KbMdV3rk7BJTTpkpp8SkXURBcCC7m6PdLxBdxtys5TI0kmeqWCMZf/Cc8ZjFpr4M5fLyZ1epioJje/ha63irzUS1yvnRUXpi8QeyVgBu0+fqpXGaTY9EOoqmzy5eTEsnmY4wna9SH62Tq9b4dGxs1WO/n6mxImZk7RwkhRAIAeVi2K+yXrl8+TKFQiEUWEvg6NGjnD59GtsO5++FfMWpU6eIxWK8+OKL7Q7liWDPnj2k0+mnrty23RmskBAA0kacXYkYoDJarKA+5tpQSkldOigItis9mOLRA9YnAjy9VZrYqqMWVJdZ0rZYrJjB8NWJJe/nBwHlpkPNdUlrSQ5lnuW1rn282vkMr3Xu462el3iz5yW2xLrQlLXJWgB4fsAXNybIzuEg19+XJpWKUK4sPbMhpaRUbtDfm0E3NAql1TWOcDyPD4aHSZsWmvLoV+PocIGm4xKNLdzXJoQgnoyQz1XR6pIvp3KMV1c3Q3o/+cnymgosaM0pK05V1/ScIYvnbv9VKLAWz9GjR3EchzNnzrQ7lJB1xMmTJzly5AiaFhpxLwZFUTh06FAosNYbQoh3hBDnhBDncrlcu8MJWSWagY0rCxzp3I1a12nqHu4yh+Q2pUcVm4yIsUPpwRSPfglKKXHKAVLzaQat3itNFavahwUQiZnkJ4qLKpsrOTYXcuP8ZOgq/+nKRX48+AV/P3SVv79xlb8buspPbwxx5s4Yt4oVlpN4k9IjCKYJvFt4zat4zS/x3UECfxIpF3fFdrJQoel56HOUoqmqwp4dPaSSEUqlOt4iyyM936dUbtCZjbFzexfRiMGNkalFP7blMFgoUHddYsajpZVu0yM3USEaX3wWTQhBJGYwcadEyjA5OzKy7HLJpeC5PvWKjWGurcAyIwb5ydKanjNk8Zw4cYLNmzeHrmdL4PXXX0cIEdq1h9yjVCpx4cKFsDxwiRw5coTPP/+cSmXtLjS2m3Uvv6WU7wHvARw4cGD1VychbaHhVUCCIlWydpKuVIwJWaYqbTQUDPR5h7kGMsDBIyDAwmC70k1czL0Ydm2fIJDoqqDpNzAUE0NTKddWtxRE1VQ818euN4nMkQmZqFW5MDXOeK2CKhTiuk5nJPrI45dS4vg+g8UCX+RzpCyLFzp72ZpMz/9cBdME7nV87xoQIKREzmTwQIIEhERRelH1fQi1FzHHkOObI3miC8ymUjWFvTt7yeUr3BzOI5FYpo6uqw84MEkpcV0P2/ZQVYXdO3royMYQQpCIWYxMlHhpr7doowspJeVSA8fxkFKiagqpVBR9ltI+Pwj4IpcjG5ndBn4638qeKcrSylYNQ6NcbIAjKSlNpup1umKrazzhuR60wdlK1RSa9tLGCYSsDVJKTpw4wbe+9a3Q9WwJpNNpBgYG+MEPfsCf/umfMjAwwLFjx56qPpKQBzl9+jRSynDA8BI5fPgwUkrOnj3LW2+91e5w1oR1L7BCng5qXhEhFNxmAAiSSpSEjNCgSUHWqMgGwczV/5Zht0QiELRuU1FIiSgZEcNi4dlLruODAEXoNIMGkEZTFGrNtVkgNmrOIwLL9jwu5Mb4opAjrhv0RuPzPg4hBJamYc2UKdRdl1+ODDEYT3KobwsJ48HjS2njO58QeNeQQkcoGcRMdu/hs0gpkbKMa/8zQkmjma+hqB2PbJMrVkktYjaVUATdXUkymRjF6TrjuRKVig1iRta1XlSiUZMd2zJk0lG0+7JiykyPT7XuLCiwXNdnfLTI9WtjVCtOyy5+RjhqmsK2Hd1sGeggfp/l+2StRsPzSM8xJHg6X8dYpt25oghqFZtI1uRqfmrVBRYAbbgUFa7b1y+Dg4OMjY1x9OjRdofyRHH8+HHu3LmDN+MEeuvWLd555x2AUGQ9pZw8eRJVVTl06FC7Q3miOHjwINASqKHAWgOEEP8X8AbQKYQYAf6NlPI/tjOmkPZQ8abRhYUnJYjW6lAIQRSTqDBb2Q18mnj4BDMiqyWsTDQ01CVdmZVBa8GtCpXmTDmcEMzMW1ocfhDcK3nTVOURU4T5CB4qlZu2G/x8+Aa259IXS8ybgZqLqK4T1XUKdp0fX/+Sb2zeykAyPXO+HJ79CyQuQu1BEfPHKoQAkUQoSWRQwWv8fyjGK6j6c/ee57rt4vtySY9b11S6uhJ0dSXw/YBm0yMIJIoiMExt3mNJ2bKD70jPLVBqVZszH16nWnVIJCw6uxIP3O95ATeuT3D92jhfe2UbWwZaonGqXkOb5zn3PR9lCY/zfoQicN2ArGEwVllcj5KUctmZBkVVaIfCCgK5Zq6FIUsj7L9aHu++++49cXWXer3Ou+++Gwqsp5RTp07x0ksvkUgkFt445B6ZTIZnnnnmqerDaqvAklL+djvPH7J+aAYNVKHh3ytVexAhBAYaxkq9ZcXdfxSkDJBIpFxcCVjNcZko1RgrVVuZEUAVgr50gp5klIixcO/L/YvnfKPOT29ew9I0umPx5T2e+8haURzf4+e3b/B6/za2JTxc+6cIEUdR0ks+nlASSBHBb54Hmqj6ywghsJvuY2UtVFUhElm8lbyuKVTnKeGs1x1OnriKENDVNfsvP01TyHbEcV2f8x8NAbBloIOJao2oPvfrpqgK/jKt1qVsOWLqqkqz0aDuuo+cywt8Ju0ig9VRim6NQAboisamSAfbYj2k9NiiBZduaGiGhr+GNu0ATsOlqy+zZucLWTwnTpygq6uLvXv3tjuUJ4rh4eEl3R6ysXFdl9OnT/N7v/d77Q7lieTIkSP8+Mc/fqwLiE8S697kIuTp4G7zv64rrIEPQCsbcd/nW9LKSBkLLEhHixU+vjXOeKlK3DRIRSxSEYuIoTM6XeH8zXEmyvO73Ula/SrQMrL4p1vXiej6IyV9j4OpanRHYvxy5EtuTv0EIRIIZfmlaUJoCLUXv/kZgXetdaNc2zyJEIK5EoxSSj7+aAikJJFY2IhC11Wy2RiffnyTSrlBybYx53GEisYM3GVa+Ad+gGnNCCoBDffBMtTbtRz/MHaes4UrOH6TjB6j00gSVy3u1Kf4xeQFTuY+X9SQaWg9Tx3dSezG2vZDNW2XbPfjXyAIWXlOnDjBN7/5zadiUbOSDAwMLOn2kI3NJ598QqPRCA0ulsnhw4eZmppicHCw3aGsCaHAClkXtErWJLquYJkKnrf0YbxLQbfUB9SBAJqeT3oeC+7xYpXrE9MkLINExHwg26UqComIScwyuDKWJ1eZZx6QhGjcIpCSD0eHUYQgrq/8UGBNUUipd/hw3MUOHn+GlBAKQu3Ba36EDMr3+qfWCinlnBnGYrFOoVAlmVq4H+wuuq6iKgojwwV8Gcz7WDq7E/j+0uWk7weoikIq85WNfXDfFYTByigfFa4Q10y6zRRRzUIRCkIINEUlY8TpNlNUvQYnJi9QcRc3Z6qjJ4ldW11HzIeREhKpNegvC1kSt2/fZmhoKCwPXAbHjh0jGn1wBEU0GuXYsWNtiiikndwdMBwaXCyPw4cPAzw1ZYKhwApZF1hKDE+2rrin0waOs7xyrMWimyqqoeB5HopQEQjcICARmV2IuL7PjVyRZMSct09IUxTilsH1yWn84FGR6Doe0biJYelcnZ5isl4lYy1eFMyF3XDJT1W5dTPHxYsjfPrxLT75+CLXL49z+47CTy7lKRabj20TLoSOEBpe8yMsQ1+TbONdXC8gEZ399RkemkLXl14+mkhGGBqaBH/+bFw0bhKLmzhzuOQpwsXSykT1aaL6NJZWRhUujZpD96bkvYwlknv9daP1PJ8Vb9BlptCV+WNP6TFUofDh1Bc4/sKZqe7+LFKyJrbw0MpeRWImqY5QYK0njh8/ziuvvALAn//5n3P8+PE2R/Rk8fbbb/Pee++RybRKX/v7+3nvvffC/qunlJMnT7J9+3Y2bdrU7lCeSJ577jlisdhTI7BCF8GQdUFC76Dk5kCN0dFhMT7eYKXM1qSUNPFw8XCkS4BEAEqPpDJZI6unZ1zzBPE5hrPmqy0Xw8UYOuiqSs1pMl2z6Uw8ePWzXrXp396F7bl8PHGHrujyH6SUUCrVGRudplxuOfJpmoJhaGi6SuDnCQILWQ04U6hQv+myJWOyc2eUnh4TTVvm9RWRQXp3iERqaJrSytKoa3GtRpKIP5phlFIycrtAOjP7sOP50DQF3wvQm+AYHtosM7DuMrCjgy8+u4OqKWiaiqlWSVkTJK0chtKYifArfM/HiZlkNu/FQ8MlhQQiuo6Uki/Kw6RnhNNiiGsRck6J0Uae7fHeebeNJSx6tmQo5avE5xgCvZJUinWe+/qOZRuBhKw8x48f55133qFeb2U9x8bGQge8ZfD222/z7LPPsn//fv7qr/6K3/qt32p3SCFtQErJyZMn+dVf/dV2h/LEoqoqBw8eDAVWSMhaEtES9wwjOrImqibw/ADtMRbunvQpyxoFKrgEMGPsLmbmPdndHrlmETcuaNRVOpPpOQ0qxks1rCVkSExNY7Jcf0RguU2PzTt7GC4XkRJ0ZXkmBHbD5caNScqVBpapt0rj7q9x8xsI6aCqCXQdsqagrgl8Hz7+uEw8rvHyy0nS6aUPoxVCEAiNwB+iM5OmWG6QiD1+CeJ8BDM+7onoowLL94N5ywcXREDGtBh3G7MOGb5LPGmx85keRm8M0p8aJhUr4wUqjhfF9tIotJ4bKSVNp9WvtXkgTswaQ+E2DT9JXd9DVNfJO2UqXp1uc2mmI3EtwqXiIHFNUPfLNPwakgCBgqlGSGgpImqMiBpj57P9nPrJBWKJCGK5z80iaDouqqbSN9Cx8MYha8a77757T1zdJXTAWx4vvPAClmVx5syZUGA9pQwODjI5ORmWBz4mR44c4Yc//CH1ev2R8tuNRiiwQtYFMTWJIhR86aNpKlv6YwyP1Einlt6bJKWkKGtMMN0aaouOIR4VElbEoCprGE6EglbG6Ai4KTT6ZSf6Qx+NpuejL8GRTVUUmg+5zjVqDslMjEQ2xs8Gh0mZyxMlk5NlhoZy6LpKKj37F5SUde6vAI4pChOuy+6kSVfEpFbzOXGiwN69MXbvji1ZnAglSeDdYkf/Lk5N3lh1gVWp2mzpSWPMInIF4rFL4TpjUUYq85uTCHw2dd4hpQ9yfdTj/GQMO1BQVIEQrRpDUwQkVEl30mDX1iSGpeHK1mvU9Is8m/yYoBnjdi2CIRb/9etJj4pbotCcIOeUfPlDJgAAIABJREFU0dUCaSOGJvSZaXCSkjvNmGy5m2lCpz+xjf5nOhm7Pk1HT3L5T848SCkpTlXZ/829Xxl5hKwLQge8lUPXdfbv38+ZM2faHUpImzh58iRAaHDxmBw+fBjP8zh//jyvv/56u8NZVcJ6jpB1garodJtbaXhlAPr7YwgBrrs0swtP+tyWk4yRx0QnJixUMbsw8nHJ9qZpVH3SaoxuM0mOIpfEEFUaD8W3tEV8y4b0wZ8r03X2fG0r5aZD3XXvDQheCqN3phkcnCQeN4lE5xGfsgr3LeDvuoeVvJboi8VUOjo0vvyyyMWLOTyvhpTNRcchhImUVTozOqah4bqr2zPnND12bOmc9T5VUzDN5ccgJfSmkkR0Hceb3SnQdutMTX/AuVtX+KSQpBHpItMVpyuhkjQgrkgSOlhRAz8ZZ1Sx+OBOk2t5l/rMe7jhmXTGt4N3mUrjLMYivn0DJNPNKa5XLzFuDyNQiKoxImqSuJbCUqOYagRLjRLXkqT0LCk9i6lYDNeuUxu4iZ3KU53PdOUxKOYq9G/rYtPW2V+bkPYROuCtLIcOHeLjjz+m2Vz892TIxuHkyZNkMhmeffbZdofyRHN3QPPTUCYYCqyQdUOHtZkAn0AGmKbKM3vSlMqLN2bwpM+wnKSBS1xEFuxt8QOPjnQnVtwkpeuoKCSJoqDwhXKTCl8tSjPRCPYSbLodzyMb+8q8ojRVZfPObno2Zyk37WXZm0+Ml7k1nCeZjMwMk50biQ08KCx1ISh4DXx/AtcdxPcvk0oPMjh4hYuff4Rt/xLbPoXTvITvTyHlQoJFoAibfbt6KZTmz/48DqVKg85MjOw8vUQ7dvVQLjfmvH8uHMclGjHIZuPs6+5m2nnQCj2QktFinsu3TzNaKmIZSTqiOqmIQiKqk+mM092bpKc/RXdfks6OKNm4TkdUJWYo3Cy6nLxpcz3fsoFPWhGE0ov060j3CyRzG1Y0A4fb9euM2cOYIkJMS6IprSzRQu8fTdFJ6hnSVgfp52FEv0ypMb3k52c+SlNVYqkILxzaEdp/r0OOHTuG/tC8tdABb/kcOnQI27a5cOFCu0MJaQOnTp3i1VdfDftMH5Pu7m527NgRCqyQkLUkosbpsbZR84oAdHdb9PZEKJYWdk0LpOSOnMLFJyIWLit0AwdDieA0FF7+2lYMXaXptM5jomNJgytiGJvW1cqeVAx/riFMD9EyzICumf6rRs1BURT2fb21EM3V65hL7C2r15vcvJWbEVeLWMxKnwc/3g6azDFl38bzJ1oLexFBVeN0dkW4cSNBYToDwiAIpnHcz7CdU7juTaScQ1hKQAZs6+ugKxunVFm6wFkIz/NxXZ9Xnt0y7yK+f3MWGUiCRb5Gd6mUbXbt7UVRBDuzWaKaTm3mCrXjeVwYHed2/jKZSI24lURdQimlpggylkrCVPhs3CFXi2C7rfgiehovqCLdq7OKJdtvcLN2Fcd3iGsp1Pt69SSgLdIYQxEKvek+djzfx7B+mYny2KLjnwsZSPLjZaIJi4Pf2odhhqWB65G3336bffv2oes6Qgi2bt0aOuA9BnevvIdlgk8fuVyOL7/8MiwPXCGOHDnChx9+uGYut+0iFFgh64r+yG4MxcTxGwgheGZvmnRKZ7o0f1nGtKxQx1mUuApk0LJQd5Js7kmzpT/LnhcHsOtN3GZLZBm0Mlo3xTgBkpip05GwKDcWHvZabjj0pOJYuoZdd2hUHQ5++zmsSCu2otPAUBdfHhgEkqEbk+i6tjhxBbQ+2hIICIJpXO8OmnSpSwtFic3YrbeOJYQgmZRc+EziuiqKEkNVsggRwfNu4DgfEQTlR08hWn8piuCVZ7fg+QENZ+WG2waBZKpY46Vn+hfs8YpEDbbt6KKQry76+LWqQyRisGlTy4LZUFVeGxig5NjUmk0+uzOO05xic3waRGLZj8PxXfZ2xHBchX8aLFFr+myOxLFlFIJJpD/50PY2t+rXUISKpT5o4e/LAFUIEtrSet460h288MIeyh23GZkYwVtmOaVdbzI1VmLLri4OvfXVezpk/SGlZGRkhO9+97sEQcDNmzdDcfUYDAwM0NvbGwqsp5APPvgACPuvVorDhw8zNjbG7du32x3KqhIKrJB1haro7Ih/DSeo4QVNNE3h+eeyZFIG+YIz67DXpnSZpEiEhRd7Ukoabh3hJNna28nApiwCQSIV5ZmXt+E0XBr11oDWKBYlUaVAS1zs7ukgbhqUavasM668IKBYb5COWuzoSlEp1mlUmxz+zgukO79aoPuBXNIHb3q6RrViE4kuIVMgLJAOvj9BEBQRwkJRNQQwW5LHsgROUzJy+6s7hdBQ1AwSid08i+eNPrSXRCitLF0iZvH6Kzup1Rzq9uP3KPhBwGShwr4dvWzvX5w73b7nNtPTlyKXKy+YyapUbFzP5+CruzDMr8RudzzO8909nBy6RdN36IvcmTGpWF4JXM11iekG3fEEHTGNpi/5xVCZpKpjCBVXJsC7eq9U0Jc+I40bKELFUB51TKy5Dv2RzKLGBTxMIpbgued3E3mxRqFYJD9ennOu1/1IKalVbHKjRTzX5/CvPMeLhx583kLWH0NDQ+TzeQ4ePNjuUDYEQggOHToUCqynkFOnTmEYBgcOHGh3KBuCp2XgcCiwQtYdcT3Drvh+6n4ZN2ii6wovPJ9l544ExVKTWv3BkrVpWUUgUBbquZIB09Uqpszwyu6dDGzK3Bv6CpBIR9l3YDuGoVGeruIHATFpMSqmCJDoqsJz/V1s7kjQaLoU6zblhkOp4VCs29iux9aONHu600yPl4nETL7x618j2/2gg5uiLNxDcxcpYWy0iDmfocVsCAvPH0dKB6FY97JVLbPz2Uml4MYQj4hYRYmgiBSuewnXuzMTl4sgghBfZVI60jG++fVduK5PoVhbdvq/VneYKtR4ac8m9u3sXXR/j6op7D+wg+07uikUquSnKg8YXwSBpFxqkJusYBgarx99hmTy0SHPjaZLXzyOKooE0kGy9BI4KSXVZpOopjOQTt0rLcxEVCrNgM/GGuyJJ5n2fKT0kX4OgKnmBG7QxFQezVC5gY8Ukh5r+dk0U43Q2Zdix69Eef7gNgIvID9eIj9WYjpXoVKsUy3VqRTrFCbKrfsmykSiJofe3Me3fvMVuvqWZi0f0h7uCoG7pW0hj8+hQ4e4evUq09Mr288Ysr45efIkBw4cwLJW1y33aeHFF1/EsqwNL7DCS5Ah65KM2cNucYDByse4gUZUS7Jta4KOrMXVa0UKBQdNE0TiKkVqWPMsgn1fUq3b+NJna9cm9m3diTGH5XokZvHMK9uZGClw58YEUoKX8KipDRJE0VSFbZ1pNmeSlBoODddFQWDpGpYQ1Ms21ek6zx3cwcCevlkH8MY0g5LjLOp5qNcdajWHZPpRITAnEoKgiJQuivqVuPMkmArMpVc0TdB0JPm8oLv7wfuE0EBJ47pfoChRhPRRtR2PHKMjFePbh/dy8dooN0cLRCMGiai5KJFkOy6lqk0iZvKtQ7vpSC19CLOqKbzw0gA7d/dwZ6TA4LVJXNebmXzW6tXatr2LTDY2a0wjxRKD+QIv9PZSrQ4xWjFwgyYRXX9AjM+FlND0PZpBQE8sRmc09kjfVmdUZXDaYXMyzhYrzu2GS6e4hUOSvDNBVI0/clw38Kl6Ns8lNxHRHq8sL6amKHl5+ga28Maul6lXbGoVm1KhRqPuEHgBiqaQSEVJZqLEEhbWLPPHQtY3Z86cIRqN8vzzz7c7lA3DXbF69uzZcODsU0Kj0eDcuXP80R/9UbtD2TAYhsH+/ftDgRUS0i7SRjfPp7/JrdrnFJuTRLUUiYTJKy93Uqm6jI3VuTFRpKY08QIFRQ1QhJjxXpBIKQlkgFRcenri7O7ZRTqSWvC8iiLoG+igszdFIVdh8PZthqcn6PWyqJqCpqsIITClRPUFnhsgnSbETF44vIveLR2Y8/SmdEfjDJWmYRFr1lrNYZF+BvcIgmmktBEiipQBYuYAzQDSC4zyMkyYmpJ0dz8qJoTQUJQYbvMyujaAou2a9RiWqfP157eydVOW68M5RnNlFAGGoRExdVRVQSAIggC76WE7Ln4QkIia7N+3hS09abQlzBybjWjUZPeePnbt7sX3AwJfounqvPO+vCDg7O0RspEoApu44bGzo49crUa+0UBKia4o6KryQLZUSokbBLh+gESSMA0GYnGi+uyiXxEt84tzo3X+xd4suqIwVL1D3m71Xd1/7GbgUfMcVKHwfGoTWfNR8bVUhBBElDi364NkjW5iyQixZITu/sxjHztk/XDmzBn279+PtoxxECGzc+DAAYQQnDlzJhRYTwnnzp3Ddd1wwPAKc+TIEf7dv/t3OI6DaW7MC3jhN2/IusZUo+xOHKDgjDFqX6fs5tCESSweZe+eNPF+iVm0MX0T2/ZmnP4CUHxU0ydqWnTHdpA2u1GWWBGrGxo9/RniPRFwJM8rOyhOValXG/eu8kcTEdKdCWIJi2jCWpSFa9I0F10iWC7V0ZeyQApcAu8OQokhUAiCqVY/FuBISC2gW0wTCvm57xfCwg/G8IMaQsnOe6zubILubIJa3SFfqpGbrpEvVqnVmwRSomsqmVSEnVs66UjFyCSjSx54vBBCiJZYW8RTOF6u0HA9UpYFM6YemqLQl0jQFYtRcZpUHJu66+IGX2XFhBBEdZ1MJELKNDEX8XpFdIWi3WSq5vFSKkufUeafputU/RTFZh1mhgdHVI3diW46zQS68nii835M1aLkTlH1yiT0hS86hDxZNJtNPvnkE773ve+1O5QNRTKZZN++fRv+yntIi+PHj9/7DP3BH/wBlUolNIpZIQ4fPsxf/uVf8umnn27YMuZQYIWse4RQ6LD6yZp9VL0iU84IZTdHPWiSa+ZR9Sa6JdHjAqREUVSiSpKk3kVESyxZWD1MRDUo6lW6+7L0bFmc4cJ8pM0IiiLwggBtAUFWqTjoxuIX1kFQBAIEKlKJQVBGBh5C0ZASkgt84g0DCoVWWaU6i2OhlD5C6khhAA6wcE16LGoSi5oM9M0vyNrN5YkccWNmzlRQ5v4WVU1RyEQsMpHW4/UDiZyRyaqiLMsCI26ofDHZoD9p4CPZFoW4uZ1m4BMgUYWCqWiLKk1cDqowmHRGQ4EFCCF+DfhfaQ2P+w9Syj9/6P7/CfhdwANywH8vpbw1c58PXJzZdFhK+RtrFvgcXLhwAcdxNuzCpZ0cOnSIv/3bv50ZJh/Of9uoHD9+nHfeeYd6vTUPc2RkhHfeeQcgFFkrwP1GFxv1eyo0uQh5YhBCIaFn2R5/kZcyb/FS5k36InvYFNlFf3Q3myO72R57gR2xl+iL7CKmpR5bXEFrllAgA4JljQd+FENV2ZvupGgvbPnuef7iBxtKSRDkQGn1awkEitYJeDT8gIQKiVkOJSVUPcntesClSsDFksffjrj8w4TL+WmPkbqPN1NyiSyi6rtbJhrexrFYrTddcrUa8bulCtKGefr6VEWgKQraMsUVQNxQmKy51F2fkudhCB9NUYlqBnHNJKIuru9ruZiKRcktrNrxnxSEECrw74H/AtgH/LYQYt9Dm30CHJBSvgj8Z+CH993XkFJ+beZP28UVhAYXq8nhw4fJ5/MMDg62O5SQVeTdd9+9J67uUq/Xeffdd9sU0caiv7+fzZs3b+hscCiwQp5YdMUkoiWIqHGiapKImkBTDMSyl7xzI8VyjbpnZ2c6iyv9BZ32JHJOU4pHtg3qSOkh+CrjJTBQlG7qvscW3X/kWDVPcqnsc7HsM2YHeAFYqiClCXQFJhzJ2aLP3427DFamQWxC0QZQRBLPvbLUh71uKTvOQ6/v8uZELQUhBEJAxQmo+DaGWLkSwMWgCR3br+EFcwySfno4CFyXUt6QUjaBvwF+8/4NpJQ/l1LeXW2dBjavcYxL4syZM/T09LBly5Z2h7LhCAcOPx0MDw8v6faQpXN34PBGJRRYIU80UdXEk6u7QHQDD1PRF7SBXwppK8LOVJapRn3e7VRFWXCm012kdGYVYzYWCb2TDs1BBo2ZbWGsEXCh5Ld6s3SFuK5gqAIhBKoqMBRBUhd0GT5xtcqFSoZTxS4cP0CICH4wjVzl536tKDYaD5X7rI3YEQjydQc38FCVta3YFkIgEDgz74mnmH7g/nTsyMxtc/E/AH9/38+WEOKcEOK0EOK/XI0Al8rZs2c5dOhQWMK2Cjz33HPEYrFQYG1wBgYGlnR7yNI5fPgwt27dYmxsrN2hrAqhwAp5oskaSZqrvMh3giYdxsr3qbzSswlNUbC9uYe9RmMmrre4bIqUVR5uqwykpO4HPBfNYBp7EEoUGVQZrTe4UfNJ6AJDCKZtwXBZcL2gMOYqjBQFVdtDBlWQHqaxg77YACXX45dTBZxAIoREysrjPAXrhlLDwVDvE1UiCiw8hPdx0VUoOj7ggXh8h8DlEMjVz9atc2ZTIbNe1RBC/LfAAeDf3nfzgJTyAPDfAP+LEGLnLPu9MyPCzuVyuZWIeU6mp6e5cuVKWB64SqiqyoEDB0KBtcE5duzYIw6c0WiUY8eOtSmijcfdPqyN+lkKBVbIE01MiyBWpjVqTmzfpdNceYFlaTpHNg0wZTfwgmDWbZLJCG5zcQJS0gTx1S8EKSXTns8OyySpqa1yQXUbFTnALdsgqjSZqLp8WQgYqQTUXR/bcRFmk9FCk0+GNC6O9FF1d6GI1nDbrKFT9Tw+LZaQUiLlwn1k6xEpJVP1OiPlEmXHJpAB9xsYCiUBzP6arCSKYOa19xFKcsHtQ1aFEeD+WrrNwOjDGwkhvg28C/yGlPLeIDsp5ejMvzeAXwAvP7yvlPI9KeUBKeWBrq6ulY3+IT766CMg7L9aTQ4dOsQnn3yCvYg+2pAnk7fffpuBgQFMszXHcevWrbz33nuhwcUK8sorr6Dr+obtwwpdBEOeaBJalIQeo+E7RNSVn6UQSIkUkm5rdRzwNidSHOnbwoejt+mJxR6x4o7HTQJ/sQryq+3uiqt+Q2eb+dVMrkBKBmtgap3crnjYnkdUD1BES8Q1HEE6Y5KMm4CO3Qy4cMtjT5+gN90yfejQdW7XG/SbPjus1RchK43je/xy+Cbj1SrMmK0HriSu3ze7TLSMQqScezDzSiABTQgE8t451xpljXu/1iEfAbuFENuBO8C/pJWNuocQ4mXgfwN+TUo5ed/tGaAupXSEEJ3AazxogLHmnDlzBiEEBw4caGcYG5pDhw7hui6ffvrpvavwIRsLx3EYGRnhD//wD/nhD9v6kd6wWJbFyy+/vGEFVpjBCnmiEUKwK76Zirc6fSRVr06/1bkq4u0ue7NdvLZpgFyjRtVtPnBfIhFB1xV8bzFCRgEpcaUkPyOu9kasB5zoppsedddntBLgBoK4YaIoERAJIIEQEeKpGAIdAUQMhYQluDLaJF9piTAhBElN54uKg3wCv0Iu5yYZr1bpjSfojcfpjsW5UymTq93XDycsUFIgV7c/yfUlCSNAU+L4i5k8vYJI2bKaN5X2CLv1gmw1En4P+AfgC+D/llJeEkJ8Xwhx1xXw3wJx4P8RQnwqhPjRzO3PAueEEJ8BPwf+XEp5eY0fwgOcPXuWZ555hlQqtN9fLTZ6aVNIa9RBs9nk4MGD7Q5lQ3P48GE++ugjPG9j9HPfz5O3OgoJeYgeK0tCi1DzVrZcw5cBTuCyK7H6Tly7s5382rY9IGGiVsWfKRlUFEHfpjT1urPAEUBIk7LnUvcDno9aj4grgLGGje1Bw5dEtAc//k4jIJHVULUH99FUQSKicHWsOTPIGaKaStH1KXtP3lfItUKezmjs3s+KEHTFY0xWH+wnE+oWYHVLgHwJWatBwnyGZtBceIcVxJMuUTWOtsbmGusRKeXfSSn3SCl3SimPzdz2r6WUP5r5/7ellD0P27FLKT+QUr4gpXxp5t//2ObHwZkzZ8LywFVm06ZNbN68ORRYG5izZ88ChAJrlTl8+DD1ep2LFy8uvPETxpO3OgoJeQhNUXk5s4e6b+PLlStZyzfLPJvcRlKPLbzxCtAdjfMvduzl2Y5u8nad8VqVhufS0ZFEVRQ8d/bH5smAomszHehkNcnhRIw+w3jEQcyXkrLnUrIlkYeGCMsAAl+S7pp9sa2rAs+XTFdbhggSiYKg8gQKLCHEI/b4pq7yQBMWgJIGEUUGqyiypEvCjJI2d9NczfPMgu03SBmPPzg7ZP1w8+ZNcrlcKLDWgEOHDoUCawNz9uxZent7w1EHq8z9A4c3Gk/e6igkZBbSRoLnktuZcoorIrLyToleK8P2+KYViG7xGKrKKz2b+K92P8/hvi0EUlJw68T6otwpFSm6dktMuTYFt8G0a2P7HtutNK+lBng+qmLNMZjYCXzqrsSTrUG592PXfDo26ejm3F8JliEYKbSc9aRsYKgpCs35bebXI3s7Opmq1+6JLD8ICKRkSzKF80CZgoLQ9gL1BeeVLYeG6xPVGsQTr5I1NyEQBCt4gWAhAly6zL41O1/I6nN3wR9edV99Dh06xI0bN1htV8iQ9nDmzBkOHjwYjjpYZbZt20Z3d/eGFFhPTW2IF/iUXZuqa1OdKSWzVJ2kHiGpRzDUp+apWDRSSmSQJ/CuE3jDSNyWE52+A1XbiVDS7Q7xAbbHNxEguVQeIqPHMRR9ycfwZUC+WaLHzPJyZi/qHLOvgiCgOFWlmK9g15sIRWBaOtmuJKmO+GN/KVuaxq5MB7syHdieR9mxORMMMjpeIpmJoAmFiKoTVXRMRW2dT0qaMjYzD+vRfh4poeHJR8oGnXqAFVdJdc7/fFm6QrEe4AcSgYOubcULnjyL72c7uig7DjeL0zNDqSX7+/oxhMq523foSdxnl64kQd0G/i0kmRU1vCg1ihwY2IWq9aECnWYfhWaOuLb6boKO3yCmpYipiVU/V8jacfbsWSzL4oUXXmh3KBueu1nCs2fP8uu//uttjiZkJSkWi1y5coXvfve77Q5lwyOE2LADhze8qmh4TYaqU1wpj+MGPlK2hrcKWotpCagIdiS62JXsJqk/3Q3fd5FBA9f5JTIYBwyEkkKgAD6+e5WgeQmhDaCZR2ZdzLcDIQS7EpuJqRafla5T82xSemzRA4KrXoOG77A3sZVdiX7UWdzVmo7L+O08g5/foVaxUVQFVVP+f/beNDjO9Lr3+z3v2vuKHSRAcB9ySA45JEDZI8mSRrryEsvl+Po6obeUyuMqJ66yXfdD4qlKJU6x7HyIXUn5puTJUnZu6Kur5FYsOZYVybY8lkciQA5nhvtOAsS+o/fud3nyoQESJLE0gG40AL6/KlR1v/0uB/129/Oc55zzPyDBcVwcxyESD7LvSDvNuxLoxsa/Yj5Nw6eF+Mmzr3Op7yETE2kaGpeYGAuBqnZiWTdRl7gnQpRrfhY7f8Wci24IWjqNCp0HiePaaKoGIoKmVF+BzrIdipYN87aahoamVi/Yrqsqb+3u5HhTCwXbImyY+HWdvGXx4eAwjuuiLooCCq0TKfPgjlfFyZISHGcGocToaHimQNbm72C8ODQvGV+75AIpJXknw2vBN73V2R1Gb28vb775Jrq+9sUlj7Xx5ptvoqoqFy9e9BysHcbly5cBLxK8WZw9e5ZvfvObTE1NkUzunLT1He1gDWan6Zt8jC0d4sbLEtgLOK7Lo8wk91JjnEjs5kCkednIxauAdPNYhe8iZR5FfTGFSEGoDQC4zjBW4R/QfV9ACOPlE9WJ1kADcTPMnfQTnmTHymp4qolPNZ6buEopKbkWObeI7TokjAinE4eJGUuv6s9OZbj0/ZsUCxbhWICG1qUjeIVckY9/eI9g2MeZzx0hFKmO065pKme693Llw8cMD88QT4TQ9ec/06raiG37kLKAEL7nXjMVtVxmJCXSFRSyDr6ASsse4yVhi+UQgCCFqh7CsiFpbrw5ruO6TM5kGZqYZWImSzZfLOuXL5gkIRr20xgP0t4UIxEJVMUxiJgmEfOZI+rXdY61NvPJ8Cgti6NYCIR+CGmp4A4jiQAaOcvFdiVCCAK6QHuxhmsJpHRAzjGej3Gq4yx+49k9CmoRdvv3MZh/SFSv3SCTceZo9LUTNxpqdg2PzceyLK5cucJv/dZv1duUV4JgMMixY8e8OqwdyILAxZkzZ+psyavBYlXOn/qpn6qzNdVjRzpYUkpuzg3xyfQQSTOIqa68mqcqCkkzhO06XJkaYKqYpbuha1mHbKdjl3qRMoeirjwBU9RGXGcMp/Qxmrm1Vnp8qsmJ2H4OhXczUphiJD/FXCmDgzs/eZcgBRE9wJ5gK+3+xhXFLGYm0vzoe9fwB32EYyuLXvgCJr6ASWYuxwffucqPfen1VY+pFE1TOX1mL08Gprh2dQChKERjAZSFyb3Q0PUjlEqXUYQxH3UsowpBXFO5P2eBIkm06sQadCpdSyjZElMvoaoxNG0X0k5tKOJrOy6Ph6e583iMfNHCZ2r4TJ2GWPA5B8qVklLJ5tHwNHcHJogGfRze08yu5tiaHS0pLaSbApnlWSNhHaGEQIQ53NTIk5k5UoUiEd/iKKCC0A9iW1EmUrd5krLJ2kY53VKCUKA9ZNAWNvDrL7+hUkqQGcAlZe8lGW7lcPPL9U/tgT1Ml8bJ2RkC2sad1xcpOgUUFPYED3rRqx3G1atXKRQK3qr7JtLT08PXv/51XNdFWab21WP70dfXx6FDh4jFtlYZxE7l9OnTKIrCxYsXPQdrq3M3NcbV6SGa/ZE1RaI0RaXVH2UwN40yJTjbsPeVm4RIdw5pDyDUlor2F0oDjn0X1Tj+UsRkK+BTTbqCbXQF23DnZddd6SIQGIpeUYpbNl2g9x9uEAj58AUqT4cMRQNkU3n6/uEmP/6TJ/D5qxPlUxRB554GGhrD3L83ysDAFFJK/H4Tn19HVeNoWie2/QRFiSNdKBRc8nmmuTeVAAAgAElEQVSHRtfgod+mfbeJL7i2BYRcqcS+ZolhvEbOKRE3gkTX6WDNpHJcvjlAKlMgGvETCS3/2VGEwGfq+MzyQkm+YHHx2mNaR6OcPLSL4Crvq5QFXHsQx76FdGYBEAikcMuPJCAEEgVFbeVsxx6+czeHYan49Gc/kbbrcnNSMpVrI6iniGuz86/ouFJlOFNiKFPi9cYACb+GlC7IEk+l3pUWCrKNkhR8rqvzuTTEBVShcSjyBtfn+sg7Wfxq9RQsS26Ropvj9Wg3hrI10no9qsfCqrunILh59PT08Gd/9mfcvXuXw4cP19scjyqw0Orgi1/8Yr1NeWUIhUIcP358x9Vh7TgHa6aY5aPpAZp8a3OuFhBC0GRGeJyZpM0fpTP0aqXROPYjpNBeEkJYDiHUcv2RPYCmH6yxdRtDEcq6GgY/vDkIkjU5VwsEI36mxuYYuDfGwePVlXsNBk1OvNHJ4dfaGBmZZWx0junpDJblIGjGsrO4zjiaFiUeN9izx8+nm3Xy98eYmHNYizvsuCWQJVrip0EEmCvN8emm9UVBHgxO8tHtQYJ+g6bk2kUW/D4dv09nZi7H9y7e5lPHu2he4jxSlnCs6zjWzbITpYQRavNTm1+yXLpId4qAHOQLHYIfDiaQdOHXDSRwc2KC2UKBZLDclFnKZqSbBplGIUtYd7FcybWxPKdaDEKGXhbJUHYj1CR5S5Aqlvj8gb3E/cs7pn41wOvRM9yYu0zGniWoRje80JOzMzhYHI2eIax7DWh3Ir29vTQ1NdHZ2VlvU14ZFpzZ3t5ez8HaIQwODjI6OupFgjeZs2fPcuHCBRzHQVV3RvbYjnKwpJRcnnpMQDOWXB2uFCEESSPE5al+WvzRVVMMdxLSGUOINa6aKz5wJmCLO1jroVgo8eT+OLGlRCUqJJoI8uj2EPuOtKFq1f/hME2d3R1JQokA7ZZFoWAhEET8B9DFNRT1LroWQYjypP6nupL8L1dGyVkugSXS2RYjASmzzGYFh3YdxTQTTBUydAaTtAfia7b1bv84H98dojEe2rBwRSzip1Cy+MFHD3jrjb20NDxT33OdMezCB0hyCKWhvBCwCkIoIKIIJUpMLfJW+yCfjI0zmzuKxMd0LkdikWMkhIZQ40B8XsbdwZAuJUoM5HwcDe8Cyr9LU7kCSMnbB/fSFFo99c+vBjkW7eFR9jaTpVHCagxtXaqYDml7hrAWY3/ozZqkHXpsDTxZ6c3n8OHDRCIRent7+bVf+7V6m+NRBbwGw/Xh7NmzfO1rX+P27dscPXq03uZUhR3lYE2XskwVs7T4N75Ca6gadsllKDfL3nBjFazbHkgs1i6TpgD2qnttR0YGpstNdTfgsGu6hlVymBiZpWV3dcULckWLJ1Oz3B4ep1Cyy6lvwLwEH3saOtiTbEFRLiGYQ1ESdEb8fOW1OH91axrH1Qkv0fuqfI4ill0kU4ywt2UvuxubmC5mCWomp5J71jyRezI6w8d3h2iKh1CrpAroM3REWPDDq4/4/JmDxMJ+7NJtnFIvQomiKM3rOq8QJpHgHs7sGufxTC/fftAGLC+fXn4vNBAQNHUmc0UKto2Ukplcns54nDd3txE0Kk8TNVUfh8InSBabeZS9TdaZw6cEMdXV0zItt0jOySCEQlfwMC2+3TVVJvSoL7Ozs9y+fZtz587V25RXCkVROHPmzI7s4fOq0tfXh2EYnDhxot6mvFIsbjjsOVhbkIHMFIZSvX8povu4mxp9pRwsIQJId3aJ/KmVsGAL1l9Vgyf3xwiGAxs+jz9gMvhgvKoO1tD0HD+6048LxAImUf/z98B1JcPTKR6N23Q1neHYrhyuexPcIieTKuqRIN9+kGU0pRDQFfyGRFFskBZFC0p2EF1v4cjuNhpjAcbyKZr8YT7VuB/fGqO6uUKJK7cHSUYDVXOuFjANDb+tc+lmP5854YDdh1BbEGJjvwVCCPxmMwcaApyYu8Gt2RNM58rNoH3a8tLxrivJlko8nJqmK5HgM/u66IitL81PCEGjr5WE2chMcZKhwmPmrCnKVWQCTegIIZBS4kgbFweBwFR87A0eIWk2oStbR+HTozYsyEp79Vebz9mzZ/mjP/ojcrkcgcDGxwqP+tLX18cbb7yBaXp1qpvJgQMHiMfjXLx4ka9+9av1Nqcq7CgHazSfIqhVbzLhU3XGC2ks10avouO2lVHUvTj2D0CpPCVOukUUc2fm/RdyRQIrCDBUiqarFPKlKlhUZmBilg/uPCYR8mPqS382FUUQD/mRUvJ4IkvRDvOpAz+PwgSOO87JpjE6I5IPxlLcn7SZyWmoBDC1BkL+CHsTcXx+FUvapK0CbyY72RduWnMkRErJ1bvDABjL2LpRQgGTVOoBY2O3aWk6sGHnajFSBPGZUT7f0c/j3I8xkrKZyedJF4tl52Z+P4FESvDpGnG/j0/t6eDN9raqpGypQqPB10LSbKboFig4ObJOhrydxsVFQcFUAoT0CD41gKn4vIjVK8SCVLgnK7359PT04DgOV65c4a233qq3OR4bwHEcLl++zK//+q/X25RXDkVROHv27I4SutgxXoPtOqTtAo3m+mtlXkQIgRCQsYrEzR3zVq2IorVhlzSQdkWT1HK/pSBCadoE6zYfx3GrMkEWisBx3NV3rIDpTI4f3esnGQ5gVFDTJYSgORpiZCbNtScTnOpqR6UdgHY//ELUZbYrz1BumolCmlkrj5QSn6rRaIZpDcRo8cfW3bZgNp1ncHyWpkTt6n8ERXY33GZgXKEhoVDNPquqEDiuH8jTFrhP2PcmILAch7xl40pZ7g8mBH5dQ1dVRtNpmkLBqtfDCCHwqX58qp8YO6cho8fG6O3t9WSl68RioQvPwdre3Lp1i0wm49Vf1YmzZ8/yne98h7m5OaLR7S/GtGO8BlfKcnujGhT4OlKuvtMOQQgDVT+BU+oDtbVc+L8MUtpIZxLN/NyK+21nDFMv9zjZYFqb67gYZnVm/R8PDDJjzzGRHUOgkDSjJIzIqumxjZEg90YmOdzWRGCRLYqikDCDJMzqSYIv5vHwNLqu1rT4PqDfRFFcHNfPTDpXVWdOUQQNQT9zBUHcfEJRtFOSreiqir6E2tHCb1HCSxfy2AQWZKW//OUv19uUV5Kmpib27NnjNRzeAXgCF/Xl7NmzSCm5dOkSb7/9dr3N2TDLzhqFEN8WQuzZPFM2hphv+Fl1JBVLlu8UVP01VOMY0hlByvyS+0g3i3TGUI1uVH3P5hq4icQbQuQyxQ2fJ58rkmjaeHR1MD3J9x9fJ00KJLjS5Ul2jBtzD8k5K9upCIEQgieTsyvuV01KVrlBcLQKaZbLoYgMpjqA4yYI+DSGxqv//7VHopRsF1tGCYqbrPRjM1co0BmLEVqDoIXH8my3sWizGRgYYHx83Ku/qiM9PT2eg7UD6OvrIxqNcuDAgXqb8krS398PwBe/+EX27NnDhQsX6mzRxlhpWf7Pge8KId4VQmx5nXJdUQloBiW3ump2EghUsa5rOyCEQNVPoZmfBeni2iO4zjiuM4nrjOE6IyA0NN/baMbOUHtZjs6DrRTz1obOIaXEtlzauzYmlmK7Dt99+AmmahDRg+iKjqHoRPRyKtqDzOCqawyxgMnt4XFcd3OisulcEYncUNuE1TDVJ0hUQGBoGvmijWU5Vb1G1OejIRRgOg+ayKAxs+R+ecvCclyONa9PvdBjSf6cbTQWbTYLE3vPwaofPT09DAwMMDIyUm9TPDZAX18f3d3dG1IN9lgfFy5c4Hd+53eePu/v7+edd97Z1k7Wsp8iKeU3gJOUtYkvCyH+tRDi9xb+Ns3CNdDkC5O3qyckUHJsApq+ZsW0nUDZyepC938F3f9lVP0kqn4YVT+N7vtpdN9Po2q76m1mzYk1hAhFfBQ3IFCRyxRINkcIxzaWgjdRmGE2UyC0hLqRXzHJ2yUy9tIRxwUMTaNo2ZSc6jogy5HKFGp8BQef9gjHfSahLoBCsboLLULA4cZG4gE/0zkH4dx/7nXbdZnIZsmWLL6wby+xFRoJe6yN7TgWbSa9vb2YpsmxY8fqbcory4LEtBfF2r7k83muXr3qpQfWiXfffZdcLvfctlwux7vvvlsnizbOajVYFpAFTCAMVKdKv0Z0hpI8ykxSrdK4OSvP0Vhblc5WOwr5EnMzWeZmskyOTpPOzGBbWVAs/AGNhuZGEskGEslWAsG1TfyEEAi1CUXdmSIWq6EoCvuP7eKjD+7S6IuvuY5IupJsKs+x7n0btiXj5JGuWLZNmQBKrgWsco8F2I4DNVL0W8xMKoep164ruyoyCGGz+KdMCiiULMJUV2ZXUxReb2piMmsynBrhSTqDoBzl1hSFw42N7EskCHvyvrVgW41Fm0lvby+nTp3C8FJS68bJkyfRdZ3e3l5+7ud+rt7meKyDjz76CMdxPAerTgwMDKxp+3Zg2RmWEOLLwB8D3wJOSSlzy+27VWj0hQloBnnHwr/BqJPjurhS0hnamkpdUkpmp7L0Pxhj6PEkrizhMIHQx9B0F2EKpFRI5ySTNx/gOoDU2b3nIIcOv0GiMe6FwSukvauRqdE5Bh9NkGyuvJ+RdCUTI7Psf30XjW0bV/cyFA2hgFwh+KSKCpwZSU1T9hZTsp2afs5UJQsviNAogF0lxcYXURRBUzhGgz/PobbdlKQPVQhChrGk4IXHxtmOY9FmYVkWV65c4Td/8zfrbcorjc/n48SJE14EaxvjtTqoLx0dHU9rsF7cvl1ZaQn7XeBfSilvbJYxG0UVCqcTnfzj2F1Mf3RD4hSTxTSvx9sJaltvNTqfK3L9w8eMDs1gmBrBeIGScxeJgyrCiMWTbBNC85lpjlNidOQmTx7fp739BKd63iAY3pkNgquJoii83r0Px5EMP54g0RRBXUUe3bZspsdTdL3WxuGTnVVR0Gs04wQCKrOTNv4X2gbYsuzIhLSVo1eW7aBrakXy7tsBVcwi5fOLKRJQaq5LoxAybBTVUwrcBLbdWLRZXL9+nXw+7626bwF6enr4i7/4CxzHQfUWW7YdfX197N69m9bW1nqb8kpy/vx53nnnnefSBAOBAOfPn6+jVRtjpRqsT2/HAa0tGGd/pImJQnrd55gpZokZQQ5FWqpoWXUYGpjkH799lamJNA3NEXzhaYrONYQw0ZTo887VC6iqQSzeQLLJYGziEt/76/fpfzCGfIVk6NeLpqucfOsAh092kp7NMTkySz77smpfLl1gYmSGbLrAsZ79vH5mb9UiOAHNR8+u/aSsHCXnWU1Y0bXI2Hk6A81oq8jlz+YKHGxt3LQIlmlouG7tsrkUUeLFnzGJ3LCs/mqU/eXq1nl5LM12HYs2A0/gYuvQ09NDJpPh1q1b9TbFYx0sCFx41Idz587x3nvv0dnZCYBpmrz33nucO3euzpatnx2ZI3Yq0UF7IMZIfg5HVj65k1IyWcxgahqfbj6Ats7GqrVASsn9m8Nc/ud7BCM+YokgljNJwbqLKmIoaxDXUhQf0XgUPfSAKz+6zq1PBmo6Cd4pKIrCgWO7+cLPn+bUpw+iagpTo7NMjc3N/83iC+h0f+4Ib//8aboOt1a999MbDXs53dpFpmiTsrOkrCyKEBwKd9BgrpyG6EpZTntt3LxmpIlIgKJVS0dkicUBKfBXqeeYh8dWpre3l4aGBrq6uuptyivP4obDHtuLyclJHj586C1U1Jlz587x+PFjfvd3fxchBL/4i79Yb5M2xI5pNLwYTVE527iX6OwIN2aHCWgGYd234mQ3Z5eYLeXoDCV5M9m55ZQDH9we4ebH/TQ0R1BVBemWKFh3UERkxajVcihCR9dNjMQQ9276yippxztq2gx2p6AbGm17GmntbMAq2diWU1Zd1FQMs7ZfKSEEn9l3iHxWEA0aGJqGrmhUctcm0zm6muKEfJuX9hoJ+hCydp8pV+q8rHcg8Rm1/f6W3boduT7lsY3o6+ujp6fH+93eAhw4cIB4PM7Fixf56le/Wm9zPNbApUuXAK/B8Fahu7ubP/mTP+H69eucPHmy3uasmx07Q9AUlWOJXXyx/QhRw89YIcVYPsVsKUfOLpGzS6RKeSYKaUbzc6hC8JnmA/xY474t51yNj8xw8+N+kvPOFUDRGULirCly9SKqEsAVaaINNvduDDPUP1ktk18JhBAYpk4g5MMfNGvuXC3QEAnSs3836WwJ4YqKnKuJVJZkOMDJrvaa27eYcMBEVZWaiU44MjavIlimaNkE/SaaVuMUQQlChGp6DQ+PlUilUty6dcubFG4RhBBew+FtSm9vL4qi8Oabb9bbFA+eObp9fX11tmRjrDoLEUKkhRCp+b+CEMIRQqQ2w7hqkDRDfLblED+96zg9DV3sDsTxazqmotHoD3Mq0cGX2o7ypbaj7AomttxKYKlo8XHvIyKxwFPnSkqHkj2IWoUJnkIAWz4h1hDi2uXH5HMv1xV5bD32tzRw9kAn09k8k+nskg6MlJJUvsDITJqWWJhPH96z6Up3mqayd1eSuXRt+mE5bojFHma+YLGrKbL8AVVAShuEBmJjfc081sZ2H4uqzaVLl5BSemlNW4ienh5u3LhBJpOptykea6Cvr48jR44QCnmLZluBrq4uksnktnewVl1yl1KGFz8XQvwcsO2WzMK6j7DuY0+4od6mrIk71waxLYdI7Jk6nCszSOx1pQa+iKL4sN0ZdFMiFMH1Dx9z5tOHNnxej5WRUmJbDlJKNF1dlxBGV3OCZCTA4/EZ7o1OPnWyyr2ZBBJJayxM9/4OmiIhlNpL6y1JZ2uCu4/HcaXckLLnUjgyVG58hYPtClRNIRausbKfzKKoLVtuMWans1PGomqxECnxIlhbh56eHlzX5fLly/zET/xEvc3xqAApJX19fXzlK1+ptyke8wgh6O7u3vkO1otIKf9KCPFf1sKYVxXbcpidzpJO5UjP5ZFS4vMbmD6d+7eGaW6PP7e/4+agosSwShG4ZInGo4wNzZCazRGJefLT1UZKycxEmv47www/WpyOKYk3R9h3dDcNLdFVJeAXE/H7ON7ZymvtTUyms5RsB8d10TWVaMBHxF9/Gf5I0EfXriT9I9M0xKq9QqhRtDswtUFmUj727U4+jfTWCunmUExvEaLevOpjUV9fHwcPHiQej6++s8emsNAU9XOf+xydnZ2cP39+W6ugvQo8evSIqakpLxK8xeju7uY73/kO6XSacDi8+gFbkFUdLCHEzy96qgCnWVK6y2OtFAsWj+6N8fjeKLbtogiBZqgIBLbtMDo0zdjQLNlMkdZdCYLhsjiBK4tUu3zOlTaqItB0lSePJjh6srOq53/VGRuc4tblx2TmchimRqIpgpiPKEkpyaUL9P3ddUy/wYHju+k81LqmqJauqbTGa5satxGO7m1lZCJFvmhVXeGv4OzBde4Sj8RpTtQ6PbCIEAGE0lzT63i8jDcWPUNKSW9vL2+//Xa9TfGY58KFC/ze7/3e0+f9/f288847AJ6TtYVZiJJ4keCtRXd3N1JKrly5wmc/+9l6m7MuKpnB/UeL/v4FkAa8WOoGGR+Z5f3vXOPB7WFCET8NTRESjWEi0QDhqJ9oPEg+VyLZHCGdynHjo36G+qdw3VrNJ8qT/XAswMCDcayaSmu/Wjy6OUTv964D0NAaI5IIPXWuoBwOD0b8NLbF8QdNrl28z7Uf3cexnXqZXHVMQ6P79U5S6QJWlf+vdN5PodREV5vCKm3ANox0JlGNE4haX8hjKbyxaJ4nT54wOjrqrbpvId59993nmqQC5HI53n333TpZ5FEJfX19+P1+jh49Wm9TPBZx5swZYHsLXVRSg/WfbYYhrxIDjyb4+OIDIvHgsql4xYKFazsYQRNDV3F9kqH+KfK5Erv3+YDqTlIX1AhVVcFxXbKpArGkV/C5UQbujXKt9z7J5hhqBcp2uqHR2BZn4P4oQlU41rPvuVofx3UZn8vwcGyadL6IKyWGrtKeiNKRjBH0GbX8dzZEYzxEz7FOeq/3E4v4MfWNqy5m80UKRZvuUz+DqX0PKUsIUZv3QLrTKFo7iravJuf3WBlvLHqG12B467GQHljpdo+tQW9vL6dOnULXt5Z69KtOY2Mje/fu3dYOViUqgnuFEH8thJgQQowLIb4phNi7GcbtRCbG5vj44gMSjWF8vuW/0MVCCbkoWKUogkgswPRkmuGB3LLHrRU5fxFFPBPREEA6la/aNV5V0jNZrv7wHommaEXO1QJCCBqaYzy+NcTwowmg7FjdGR7nry/f4v0bj5hKZxECNFVQshyu9Y/y/354ix/efsxcrjaKfdVgd0ucH3+ji3S2yOwGPmOulEzMZHAl/MTpA8SjTWjGWaQzgVxDc/FKkTKPlBaa0eNFr+qENxY9o6+vD8MwOHHiRL1N8Zino6NjTds96o9lWVy5csVLD9yidHd3b+u2B5XMFP4S+AbQCrQB/xfw72pp1E6lVLT4aD5ypa0iZJBNF15SfRMCwtEAE8MlijlwpbVhm1yZR1eSiEX9tHRTY3oyveFzVxPbdZgoTnE39YCLU1f4/tgH/P3YB7w/cZGPZ28wkB0iY2XrbeZzDNwfRdNVNH3tao9CEUTiQe5fH6Rk21y8O8CVh8METI2WeIhIwIepaxiaRsDUaYoGaYwGGUtl+N4nd5lIbV2Z4NaGKF86e5h4NMDoZJpsvvLWAK6UpDIFxqfSdLUlebv7ILFweXFA0faiGq8jndGqOllSFsCZRTM/j1C2bp3bK0DNxiIhxJeFEHeEEPeXEs4QQphCiH8//3qvEGLPotf+q/ntd4QQ/6Ia9qxGb28vJ0+exDC2bsT6VeP8+fMEAs9npAQCAc6fP18nizxW4/r16xQKBS8SvEXp7u7myZMnjIyM1NuUdVGJgyWklP9WSmnP//2fvKKFxRtl4OEEpaK9YuRqAavkoCyhhqYI8Ad8jA0GcNyNOUFSSqQsYGi7n9uuqgqlwtaowbJciwfpx/zTxI/4aOY6Q/lRXNclqAWIaEF8wiBVSnMn84APpi5xafpjposz9TabUsGi/84o4dj6eyX5AiZz0xm+f+kOg1NztMRCGNryaXWKEMSDfoI+g3+8/pCZTFmRsuTY5O0SRcfCrUF0Zz0E/QY/fqKLH3+jC5+hMzaVZnImSyZXfKmnl2U7pLIFxqczTM5kSUYDfP7MQU4e3oWxKM1QCIGqn5p3skaQcuM93aQ7h3TnUH1fQNXaNnw+jw1Rk7FIlPtd/BvgJ4EjwH8ihDjywm5fBWaklPuBPwH++/ljjwC/BBwFvgz8z6Ia/TOW4cKFC3R2dvKDH/yAmzdvcuHChVpdymONnDt3jvfee4/OzrJAlGmavPfee57AxRbGE7jY2izcl0uXLtXZkvWx7GxNCJGYf/j9+RW9r1MezP4V8DebYNuOwrEdHtwZJRqvbMItpctybXYMU2NuLkKpUET1F1CU9UlxuzKNrjWhKrHntgshcN36T8SnizNcm7tNybWIaGE05eV5iyIUNEUjSHnlMGfn6Zv5hN3+Ng6EuzCU+uRVjw1NIV13TamBS5Eplbj/ST8n3zpYcd8lv6FTdGy+ef0qje0mObfIQncsXagcjLbQEWggpNdXwl0IQVtjlLbGKHPpPGMzGSZnMkzNZijZ5c+/lOA3dZriIZLREM3JMEH/8qv2Qiio+imESGCXLiJJI5TkmntWSWmDMwlKHN3/BRTFk8KuF5swFnUD96WUD+ev93XK4hk3F+3zFeC/mX/8fwN/Ksofqq8AX5dlb/6REOL+/Pl+VAW7nuPChQu88847T4UU0um0p1K3xTh37hznzp3jt3/7t/nzP/9zfumXfqneJnmsQF9fHw0NDezZs6fepngswcmTJ1FVlb6+Pn72Z3+23uasmZWqzD+kPIgtzEx+c9FrEvjvamXUTiQ9l8cq2egV9pdSVOW5GqwX0VSdYroD0/8IpPpUpKJSXDePQMGnHXjJkZOu3LBjsFEGckPcnLtLWAsRMiqPAgU0P37pYyQ/yqw1x5vxY/jUzXckUtNZdGOjIg6SiUIBRcqKHQRXSgZLkwxa08zl8wSLu2kKR5++brkOt+ZGuD47xO5AglOJPZhq/Yt7o2E/0bCfgx2NSClxXImUEkURqGtswlyOZHWhqE3YpUtIewBXaAgljhAr3xMp8+DMIYWCapxE1Q+veoxHzan1WNQOPFn0fBB4MWfo6T5SSlsIMQck57dffOHY9g3asyQrqdR5DtbWoqenhz/90z/l1q1bvP766/U2x2MZ+vr66O7u9prGb1ECgQDHjh3btkIXy84cpJRdtb64EOLLwP8IqMD/KqX8o1pfs16k0/k1fYn9ARPbnlv2dd3QSM8otLSfIGddRUoDVVndeZNS4so0AoWAeRJFMV/ap1SyCUfr12h4KDfCzbm7JIwY6jqybYQQxI0YKTvNh9NXOZ14A1Pd3FqFUt5aMsVzLaTyRQq2jb/CnmeudLlXGGXCmiOq+tF0jam5PE3hZ3VDuqLSYIaRUjKSn+X98dt8uvEQfm3r1HIIIdDUjQ94Qgmi+34C6c7h2A9xrTvlyJSQSBSeZUiXFTkFEkQE1fwUirYLIerfpNljU8aipT5sLy5vLbdPJccihHgHeAfWL3rgqdRtHxZSm3p7ez0Ha4uSTqe5ceMGv/ALv1BvUzxWoLu7m2984xu4rrum3qBbgUoaDavATwN7Fu8vpfzjjVx4Ud77Fymv+l0SQnxLSnlz5SO3J7l0AXUNE25/wFixukDTVPL5ErqWIKScIW/dwnZnUPAvmTJYdqzySFlA15rwaQeWdK4AHNt9msq4oDK4WSs8aSvDjdRd4vr6nKvFRLQws9Yct1P3OR57bVNXqVRdXTECWQnTmTyKEBV9bqSUPCqMM2nNEVMDCCHwmwpTmRyO4750DiEESTPMdDHDjybu8enmw+hLpGDuBIQSRTNOIvUTIOs+IrsAACAASURBVDNIN43rzgEWoIAwUZQIQkQRin+103nUiVqNRZTHn8WFqLuA4WX2GRTlkGYUmK7wWKSU7wHvAZw+fXpdvwwdHR309/cvud1ja7F//35isRh9fX189atfrbc5Hkvw4YcfIqX06q+2ON3d3bz33nvcv3+fgwcP1tucNVFJ7stfAwXgGlDNwpxK8t53DGvtD2z6DVat355/WVWCBI1TWM4UJWcA253h2cLqs0VWXUliaEdQldiy9V1WyWJmdIaHVx5w/ftZ8pmy5Ldu6iRa4zS0JWjubCAYXb94w3K40uX63B18irlkvdV6iGoRRorjtBQaafY3VuWcleALmFiljQmFFCwb4browdUjKRm3wIg989S5grIgCghs11nWSUuYIUYLswxkp9gXbtqQvVsZKSW5vEU2C6WSH1f6UITAMDRCQRO/3/DSRLY+tRqLLgEHhBBdwBBl0Yr/9IV9vgX8GuXaql8A/kFKKYUQ3wL+Ugjxx5SVDQ8ANclnOX/+/HM1WOCp1G1VFEWhu7t726Y2vQp4Ahfbg4X709fXtyMdrF1SyuM1uHYlee87Bn/AwHEqnxMYhkYw4qNYKGEu0TzWcVx0c7GCmoKhNWJojUjXwiU3L+MuUISOIgIr1pKUChbDD0YZfjiGlNAUM/EHfU8dKdtySE2mGe+f4MYHt2nqbOC1noNEkuHK34RVmCrNkLbTJI3E6jtXiBCCiBbibuYhjb4kyib1MGreneDuxy+vNq8FKSVWziZ5dPX3Y6w0i466hJOwumcf14PcTg3TFWrYtPdnM5BSMjOb43H/JMOjs899/17M7VI1hV1tcfZ0NBCN+D1na2tSk7FovqbqvwD+P8rp6v+7lPKGEOIPgMtSym8B/xvwb+dFLKYpO2HM7/cNyguDNvCfSymr2wV+noU6q3fffZeBgQE6Ojo4f/68V3+1Renu7uYP//APyeVyL8m3e9Sfvr4+9u3bRzKZrLcpHitw5MgRgsEgfX19/PIv/3K9zVkTlThYfyuE+JKU8rtVvvam5a5vBcIRP2tVFG7dleTejaElHSyrZJNoXNq5EYqOSpRKY0DTo7M8ujaAlBLVNNi7r4lI4vlza7qKFg0QjAaQUjI7nuL9b/yQ1z51kL3HO6uSG/s4+wR/DVK0TMVgsjTDrJUiYcRWP6AKRBMhYo1hcpkCgdD6ank0IZBIos0r914quTbj9hzhF947KQHJqvfGVHVmC1mmihkafTujz9PEZJqrNwZJpwvohkok6l9RLMNxXIZGZnnUP0k8GuDY0V0kE6FNtNijAmo1FiGl/Dbw7Re2/deLHheAf7nMseeBTQkjLajUeWx9enp6cByHK1eu8NZbb9XbHI8X6Ovr8+7LNkBVVd58881tGQ2uZFZ8Efh/hBB5IURKCJEWQqSqcO2Kc9ellKellKcbGzcvxavahGPl1K21RLEisQCaoWJbLy+I2pZbseT7Sow8HOPu5QeYfgN/2I+qKsTiK6+2CSGIJMPEW2Lc+OA2H/3DdRxnY4u2BafATGmOgFabGhhT6Izkx2ty7uXYd3QX2VR+3cfrJUmoPYa2ihphxi2UFfdeiLoULItIwIeuru5q60JjJL+8qMp2oViy+eTaE37ww3u4UtLYGCYWDayqRKiqCrFogKbGCJbt8P4Hd7l2cxBrie+eR92o1Vjk4VF1FgtdeGwtRkZGePLkiZceuE3o7u7mo48+olQq1duUNVGJg/U/AJ8CAlLKiJQyLKWsxjL307x3IYRBOeXiW1U475bEMDQ69jWRms2tvvM8qqrQubeJbKb4VGwCwLZdVF0hEt2YMzLeP8HAzUEiyTCaoZHNFNi1O4GmVxb7UjWVxt0NDN0b4eo/3XrOxrWStfNLhjSrhamaTJc2twFxU3ucprY4MxNrnwNmU3kS0RCNHQkGH0/w5P4Y44MzlIov13U50mGpgHDRctiVjL60fSk0RaXgbK8frxfJZIv80w/v0v9kksamMIEV+mWtRCBg0tgQ5uGjSf7pgzvkchtvWOxRFWo1Fnl4VJ2mpib27NmzLVfedzpe/dX2oru7m1KpxNWrV+ttypqoxMG6B1yXG5k9L4GU0gYW8t5vAd+QUt6o5jW2Gl0HmnFdd02r4onGMInGENnMs0leNl1gV2fDhmTAc+k8j28NEkqGUVSFfK5EOOKnaZV0tBcRQtDQnmDg5iAjj9YfIcrYGUSFcuTrwVB08k4B292Y8MRaUDWVk585TCgaWJOTlZnLYVkOLZ1Jsg+muX17mInhWfrvjnD1h/eYGJ594QgB4vmvp+U4aKpCLFB5eqKoqYtbWzLZIv/8o3vYtkMyGX4pmrdWFEXQ0BCiZDn888X7npO1NajJWOThUSu6u7u9CNYWpK+vD03TOHnyZL1N8aiAxUIX24lKarBGgH8UQvwt8HSWUQVp3CXz3ncywZCP10/t4ZO+hzQ0R1GU1SeBQgg69jZx46N+ioUSti2JJgI0rtERWoyU8Oj6ALqho6oKpZKN60j2dDUgKrBpKRtjTRE++f51kq0xTP/S8u8rUXIs1BoLLAgEtnTQKvrYVwfDp9Pz9utc+cFtJgYHUY1pgqFZUBwUTBS1FUVtQQg/uXSBbDpPKBpg75F2rvc94EB7I6VphaxlEwn7cGyXhzcGMX06kUQ5RVQTCshn9812XNL5Esc6WiqujbNdB/+ihsMlyyaVK5LKFZjN5LFcFwUIBUxiQT9hv0loHfe5FhRLNj+69ACJJBKuboppJOInlcpx8fJDPv2pg+gVRnc9akLNxiIPj1qw0MNnbGyM5ubmepvjMU9fXx/Hjx/H7/facmwHOjo6aGpqoq+vj9/6rd+qtzkVU8lM89H8nzH/57EBOvY2kk7leXh7lGRTuKIeR4apc+jYbj784T0CQYOugy3rcoQWyMxkyc5miSQjlIo2paLNoSOt+NaZUgVg+AxSUxmGH4zR9fpWFSOpz8K3brqc+PEJpscGGbhnMTagIFERwkbKe0h5G1XZRUPraxztPkqyJcqPvnOVcCyIaWgcbWjg2sQEc8UiEcPAFzAZ6Z986mCF1LLkuCtdLFuSK5Y41N5IPFS5cpXl2rQG4kynczwanebR6DRSSgSgayqKIpASrIlZXBdAEg8FOLy7keZEuKI6r1px+84I+VyRZBUVLRcTiQSYnEpz5/4or7/WXpNreFSENxZ5bCt6esrCyJcuXeJnfuZn6myNx4ULF/j93/99BgYGCIVCXLhwwRON2QYIIbZl24NVHSwp5X+7GYa8KgghOHJiN6ZP4/bVQXx+Y15hcHlKRZtMKs/ptw5SyBbJpPLEk6F1S0mPD0yg6uWaK4Tg0JFWQuH1Kd0tJpIMcf/KIzqP7FqzqqBPM7GpnaDAQlaRtoJUfW2ua1Eqvo+UYzS0dtLQCvmszdxUEavkIl2JpgsCoSkSzQ6anqBUsJibztLQWlY8NDWVE81NPJiZZTSbBQHZqQy25aDpKqpUCTp+hnOzNPhCHOtsJRasfGUub5fwKwb3H07xeHwGQ1NJhFdW3QPIFkr88NZjgqbBmUMdNMWWV91LFYsMpubIlEqYmkpbOEKDP7BhOfSJyTQPHk3Q2FQb52qBRDzE3ftjtDZHPXXBOuGNRR7bjVOnTqGqKr29vZ6DVWcuXLjwXB+5TCbDO++8A+A5WduA7u5u/uZv/oa5uTmi0cpqy+vNqrNNIcT3WWLpX0r5+ZpY9AqgKAoHXmunsTnGrU8GmBibQ1EUfH4d3dAQlIUsCvkStu3g8xucPLuP9s4kVsnh1icD9D8YJxoPrDnqJF3JxNAUqBrJxgAdnUkMszpOx0IUKzuXIxxf2yQ0qAVrGmAqSYugFqxaA+NKsa17uPYwqtb2dJs/qOEPPv+eSxnEtq6hqh3Ay42gdUXhcDJBVzTKeDbL7cw4E+kcqipQhMLheBshRWVPNIm6xv/xydw0YtLEUuZojlXuuAd9BkGfQa5Y4vuf3OfQriaOdbU855hZjsOl4SEezk6jCIGuqtiuy9XxURr8Qd7q6CRsrC/VUErJtZuDhCO+DddcrYaiCIJBkxu3h/nMj22vZoc7BW8s8thuBAIBjh07tu1W3nci77777nNNugFyuRzvvvuu52BtA7q7u5FS8uGHH/L5z2+Pn/xKZtb/etFjH/AfU26o6LFBYokgn/rca6TnckyOp5gaS5FOFZDSxfQbtO2Ok2iKkGh4lkpo+hTe6NlH6+4ENz4aYGJ0DtOnE4r4Vowaua4kmykwN5nBslyOHGkhngzWpKHquhwsNQDIclpaDWwqOEXa/S1VP+9KSOlgWzcQ6uqNDIVQkCKAZd3B8L1FJBEinyngf6GHlqmpJITOl14/QPdbR5GSp7V8N2aDXJsdpNkXrdjhuD81zsRonpPhJgLm+hydgGng03XuPBmnULI4c2g3qqLgSskHgwMMpeZoDr7suM0W8nzv4QO+vO8AAV1f5uzLMzObI5Uq0LhMP7hqEwqaTEykmEvlia4SdfaoCd5Y5LHtWKjDcl23Kv0iPdbHwMDAmrZ7bC3OnDkDlOvndoyDJaX88IVNHwgh3q+RPa8k4WiAcDRA14HKHYDmtjhNrTFmpjL03xtjZGgG13Xn13fLTsr8QwQgFEFjS4yOjgRGKU+ioTZpToqqkJnNrvk4UzVoMJOkrDQhbeP9vV7EljYt/s3to+a6k0iZRxGVhbOFiOI6j4AzHHyjg97v3cDw6ajas4iUbdnk0gXeeOsQQojnIl2vRdsouQ53U8MkzQj6CpEsR7o8np1kdCTNqWgXgXVGkRZQFEFzPMTA+CyaqvDmgV2MZNIMzM3SFl5akCXm8zOezXBrcpw3W9de2/S4fxLd2NyIpK6rDDyZ4tjRXZt6XQ9vLPLYnnR3d/Pee+9x//59Dh70ot/1oqOjg/7+/iW3e2x9EokE+/fv31bR4EpSBBOLnirAm8DmhgI8lkQIQaIhTKIhzAnXJZ8tkUnnKRVtHMdBVRR0UycY8hEImaiqwvjARE3TqRRF4NgvN1PO2SWyVglbll8zFJWw7sNYJI7QGWjn0swnhKiug5V3CoS1EBFtcyIdT5HFNQmfC6EgEUhZpHlXkuOf2s/NSw+RUqJqKrbtoCiCk585RLLlZadNEQpvxDsIayY35oYoOTYB3SSommWHW0qKrkXKKuA6LoVJyYnwxp2rZ/YLmmJBHoxM0ZoIcys1SWiVcyf8Ae5OT/J6UzOmWnmqqpSS4dHZDfeCWyuhkI/BkRnPwaoD3ljksR1ZELro7e31HKw6cv78eX7jN36DfD7/dFsgEOD8+fN1tMpjLXR3d/P++9tnTa2SGc2HlOMignI6xiPgq7U0ymPtKIpCMOwjuIpYhaIqG2oIvBquK1G1chrEdDHHw9QUj9MzFB1r0V7ljksSSVj3cTDaSEc4RtyI0WgmmbVSRKvkDLnSJWNn6Umeqknq4cooLNUAeCXKt6b8/u053EZLZwMTQ9PkM0X8YZPm9iSGb/l0OiEE+yMt7Ak1Ml5McXdulKlSev68kpDu53RyD2PDOVSZIuyrrtS6EIJ4yM+lu4PMBi3awivfR01RkC7kShamv3IHK5cv4bhyVSGOaqNpKsWiTaFg4VvhPnjUBG8s8th2vPbaa4RCIfr6+viVX/mVepvzynLu3Dnu3r3LH/zBH5Tb33R0cP78ea/+ahvR3d3NX/7lXzI0NER7+9ZX9K0kRbBrMwzx2BzKNT21czRcx8UNaXx38A7juQy6qhI1fMTNlyMN5YiKw5XJIS5PDHIo1sjBWBeXpj+i5FoYysYnsDOlObqCu4kbm686I5QgkpejecshpYUQGkI8e698foPd+9e+SK8pKm3+OG3+OFJKHOmiCgUhBJl8kUujIzRGq5+KCWDqGqlckelUdlUHa71ks8UFb3TTEQIy2YLnYG0y3ljksR1RVZXTp09vq9SmnUokUk5XHx0dpampqc7WeKyVhYbDly5d2hYO1rLLv0KIM0KIlkXPf1UI8U0hxP/0QqqGxzbCH/ajaAquU/nEv1JcKRlSsvxzdpB0qUhrMEKDL7hsLZAQAp+q0RwI0RwIcT81yd8PPqTN7CRtpym51pLHVcpMaZakGWdfqD7zMkWJo6pNuG66ov1ddwZNP4KospS8EAJNUZ9G8AbGZ1GFqGmqaDToo5ApMVvIr7if7booiiBkLK2GWXQsxgqz3EkN8cHELf5+9BP+buRj/mn4Jv3ZSSYKc2SsQk2jsi8hwbJq11LA43m8schju9Pd3c3HH39MsVhcfWePmnHx4kW6uro852qb8sYbb6Bp2rZZrFgpv+bPgBKAEOIzwB8B/wcwB7xXe9M8aoGiKLTtayE9k6nqeV0puZ6bYCBQoDUWI7ZExGpFu4SgyR9CV1U+nJigUesgY2dJ22sXzHCkw2RxmrgZ43jstU2XZl+MZhwHN4WUK0/IpSwgAFXbW1N7XFdyd2iCWGjjfc9WwtBUGvQg46nMis7PdD7H4WTjc42KpZTMlDJ8OP2Avx35kB9O3OZ2apCMXXbWFCGwXYe0leVBepTrs4/5eOYR4/lZLLf2onKS8ufdY9PwxiKPbU1PTw+lUolPPvmk3qa80vT29j6tifPYfvj9fo4fP75tHKyVlspVKeX0/ON/BbwnpfwPwH8QQnxce9M8asWeI7sYuDVY1XPec2Z5kJ3i+Gt7MbT1R2ACmoEmVK5PT3O2+QAz9iiTpWlCagCfurJT4EqXtJ3Blg6HwvvoCLajiPrK4qpqO5pxCqv0IYraiBAv1zy5bgYp0xjm2yjK0il1jusync4xly0yMZcmlSviSokiBJGASWM0RCTgIxEOoKnL/8/ZYgnLcdDU2judcdOHpUtGMhlaQqGXImbTuRwhw+BwQ8PTbQWnxLXZfp5kJzBUnYQRWvIe+jQdn2oQMcqOvOXaPMyMomZV9oVbSBghXmomViUE1LzvlsdzeGORx7ZmIbWpt7f36WOPzWV4eJgnT55w9uzZepvisQEW6rC2Q9uDFR0sIYQmpbSBLwDvVHicxxYn1hSlcVeS1FSaSHLjNTITTo4HhRkadD8NrRvP2DFUlYQvyJXJMX664zXSdorH2SdMlWYQgK4Y6KLckNlFUnSK2NJFFYJ2fyu7g201kXpfL7pxAiFCWKUruHIaIQwQCkgLcBBKAtP8SRT15bSFfNGif2KGO4MTFC0bAfgMDVPX0Oal+CdTWYYmU7hITE3l4K4mOptiBMyX0+4y+WJNGzovJmga+DSNSEOAO1MTIASaUHCkiyMlraEQP7arE59WrmUazc9weeo+Li6NvuiKoiSarj7nQOmKhm5oWK7N7dQgjWaUrlBzbaKXAgzD+wncRLyxyGNb097eTmtr67ZZed+J9Pb2AngO1janp6eHr33ta9y9e5fDhw/X25wVWWlw+nfA+0KISSAP/ABACLGfcmqGxzZFCMHxzxzhH//9B1glG30Dk8WSdLhhT6OkbfaePrChcy3Gp2pkSkWuTA7z6ZYuWnyNpO0sGSvLjDVH1s4BEk1otPmbiehhInq4KsIYtUDT96Fqe3DdUVxnFKQNwoeqtiOU5EvOhJSS/rEZPnwwiCshFvQRCy4dwTN1DQLlx5btcKN/lBv9o5za386epsTTRsQAuYJVQ4mTF+1SyeZLfL7tAEcamxhOp8jZJXRFpTUUJuZ7lkY6kB3n0tR9YkYQU139HvoD+pIiF7qiEdODTBXTFN0ShyK70JUqz8GlIBiorvqix4p4Y5HHtkYIQU9Pz9NJvsfmc/HiRQzD4I033qi3KR4bYCEC3NfXt30dLCnleSHE3wOtwHfls0IKBfjtzTDOo3aEYkGOf/YIV/7uGsm2RDkisA6e2BlmZ9Mc2NNGvDlWVRuTvgD96WmOxJtp8AWJ6CEieog2mqt6nc1CCBVVbUdVV1a/KVo2fXcHGJpMkYwEMLTK742uqTRGg1i2Q9+dJzyZmKPnUAe+ecfXdt1Nk6sXCBy3/LMR0HX2J5JL7jeUm+LS9H0SZnjF5siLMXwqiloWa1FeTIkUgogRIGPluJMa5LXIbtQqRbIs28EwVU9BcBPxxiKPnUB3dzd/9Vd/xfT0NImEp82y2Vy8eJGTJ09imt7i2Hbm0KFDhMNh+vr6+NVf/dV6m7MiKy7tSikvLrHtbu3M8dhMOg7vwrFcrr5/g0hjBN8aV+WLpRI3p0fYs6uZzteq33hVCIGp6tyfm6TBt3VS/pbCdlwmpzNMzWSYnM6SzReRUmIaGsl4iIZ4kKZkGNNceWJeKNn84PpD5nIFWhPrT9/UNZXWRJipdJb3rz3gM6/vxW/q5b5TmyTQIJGoysrOXNb+/9l7sxi5zvTu7/eetU7tVV29s5v7roUaUS0NKY5HHtvjIEYSG4kxhmDPd5HRXNgGDDiZTKAgEwQh7Cvf2ECQQeA4Xz4ZsBN74gST8TIzmkXUNEVJo4UUSXHrjb13VXXtZ31zUc29l+pmN6tJnh9AqFV16pznVJ+uev/neZ7/0+Bc/goZI96yuILmtZHrjbEwXSWWXP66jetRSk6VidoCO+Ob4xpVKTfYNbi8UAzZOsLvopDHnVvmCufOneOrX/1qm6N5uvA8j/fff59vfOMb7Q4l5CF5nMYebO8OsZAtZ/ezg7zynxzHqTssTBXwvbXtp4NAUpwrMb6Qp+tAN/ue2bllzYYZ0+J6eQHb33p3uI3geT6f35jhn396gV98eJ3r4ws4nk80ahKLRRCKwuTsIu9/OsYPfnqBX14Yp1Z3lt2X6/u889kNyg1702ZUdSSi1G2Xdy7cwPV8LPPRtazYrk/CWlm0BzLgo8INNKFibKCMr6s3juetPm4goVvcrC9Qcmrr3v/9SClxPZ/BgVBghYSErI8XX3wRIcRjsTB80jh//jy1Wi10EHxCeFzGHoQNwiF0D3by2tdOcu2jEa5/Oobv+RgRnUgsgmY0zSQ8z8eu2jRqNooQ9O3voWNfnOvu4pY6uShCICWUXRtT3V6Xa2GxyvufjFGp2aRTUfRlSvk0VcFcKs/zg4DxqQLjUwWOHdnBQG/mnnK9y+Oz5Es1ujPxTY0zk7CYXazy2dgMe3qyyEfUhdVwXHZ2ZVZ8frpeYKpeoMfaWGlpNGEQSxg06i4Ra/nMoBAKlmpyvTLN85ndD1UeWanadHYkSCbWN4IgJCQkJJVKcejQoVBgtYHh4WYCPDS4eDIYGhrCdV0+/vjjbe3KGWawQgAwIgaHXznAb3z9V3jpq8fo398LUlJeKFNaKOPZLp0DHRx77Shf+f0v8YWvPEdJ9Yhqyw+I3WxK9uoDax81U7OL/OQXnxNISWdHYllxdT+qopBNx4jHTN77eISLV6dul+vlyzXOj82QS0W3JN5cIsrFiVnqjoeqCvwtGDR9P14gySZXPp/Py5Mk9I3P4xJCsPNAlkbVJQhWLns0VZ26b1N2N57F8oOAes3h6OG+De8jJCTk6eaW0cUjHYwewtmzZ+nq6mLXrl3tDiVkE7jb6GI7s71SAiFtRzd0evd007tnbSOJkmsT1ba+2V9XVMre8mV17WC+UOEXH14nnYyuy647CALq5Qae5xNB4cOPx1AVhYN7e/j4xhQxU0fdomygoggSEYNPbkyxry/H1cl5OhJbI+YAPN9HVxVyK5Q6LjpV8k6FrkjqoY6TTEfo25VierxMMrOyWDMUnalGkaSxsdLLfL7KoYO9ZNLbuxcwJCRk+zI0NMTf/M3fMDo6Gi72HyHDw8O8/PLLj8zgKWRreVzGHoQCK2TDBFIiHkG5maCZQdgO2I7Hex+NkIhbLYsrp+GSnykyPTKH6/hL45sEnu/zT9enee1XjjJdLdGbezixsRZxy2SmUObAjhye5yOl3LIvnGKlwcGBTvQVBhrPNhY3bQh0/+40iwt1ahWHaHz5jGpENSjYFfwgWLeILRZrpJMW+/dsjlFGSEjI08mtHqCzZ8+GAusRUSgUuHTpEr//+7/f7lBCNgkhBENDQ9teYIUlgiEbRhUC+Qim1jbd6LbHpXrp2jSO52O1aNNdzlf49BefM3FlBt3USWbjJDJxEpkYmVwSTSj8wz8OM3b2Bs4K5hebiaapLJRq7OzOki9vTdml4/kgYE/PymYQc/YikRbmXbWCpikcfL4bRRXUKsu/h00hKan763uPi8Uapqnx8kt70NZhlx8SEhJyP88++yyRSGTbLwyfJM6dOweE/VdPGkNDQ1y+fJlisdjuUFZke6xaQx5LMqZFw9t6dz838Ekb7TcWaNgu18fmybbo8FcuVLj4wQ0MUyeRiaHpD2a8UskoDSHwXJ/P37my5SIrYZlMLpR4bk8viiKw3c3//eVLNV7ct4NoZPlskpSSvFPBUjevf8+0NI680INuKJQK9RV7suygtffXDwLm5svEogYnX9mHtcK5hISEhLSKrut84QtfCAcOP0KGh4cRQnD8+PF2hxKyidzqw3r//ffbHMnKhAIrZMN0WwnqvrvlxxFCkDDaPxxwYrp5p0RZYbaTF/i3m5c91+PKR2NYMRNjldlXgQzwZYCjCHzXZ+yj8c0P/C4MTaVqOyhCMHRwgHy5juevbc3fKrOLFQa60uzsXtk90JcBbuChblKJ4C1MS+PIi730DCQpF+o0avdem4oQ2Gtcr1JKypUGC/MVDu7v4dUv7g/FVUhIyKYxNDTEhx9+iOtu/XdnSFNgHT16lGQy2e5QQjaRW4J5O2eDQ4EVsmE6IjG8Le6N8oIAVSgkH8Jt7m4cf5GifZmF+kcU7cs4fqnl107NFoktM4zZD3w+XZjmnckRzs2OU3NdinOlpt39GoOFHdfH0BSqDZdIIsLi9CL1cmPd57UeBFBtOPR1pHjp4ABzi9VmWd9DIKVkrlilK5XgpYMDq/Z2BVtYVqppCjv3Zzl6vBcjorK4UKNSsvG9ACEEwQruXa7nUyhUmZsvE4+afPnUQQ4f6A3LAkNCQjaVoaEh6vU6Fy5ctt4drwAAIABJREFUaHcoTzxSSs6ePRvOv3oCSafTHDx4cFsLrNDkImTD5CIx4rpBw3OJbJGbYN6ucTjdhfaQPVheUGeufo6aN41AQREagfRYkB8R1fvotI6jKSuXIQaBpLBYJ72M7fhsrcpcrUJHJErZtblWnIeRMpHY2lk3eZdRiBcEKJpCfmyB/qP9Gz/ZFvCWbNr39nagqyrnLo+haSrp2PqFrO165Mt1dvdk+cL+/hWNLW6hPAJjlEQ6wtEXe6lVHGYnK+RnqpTqNtFIHbNevr2dECAlREydXYMdDA50hHOuQkJCtoy7jS6OHTvW5miebK5evUo+nw/7r55QhoaG+Ld/+7ctNex6GEKBFbJhFCE4munh7OwYfVsgsAIp8WXAnuTKZgmt4AV1Jqs/IZA2ltp1zx+ilJKGt8BU9Sf0xV5DVZYXGI7r4fvBsuWBngxQl/apKQq1hoNStUlmWxgYfFcsrh9gJSLkx/NbKrDkvYdlsCtNNmHxwZWbTOVLxCI6Cctc8wPLdj1KVRtNU3j16C76c6mWPuRUoaArGl7goylbmyGKxg12Hciy60CWyVKBw5FBcmqCQEoUITAMjXjMxFwj0xgSEhKyGezevZtcLsd7773HN7/5zXaH80Rzq9ctFFhPJkIIpqenUVWVwcFBTp8+zeuvv97usG4TCqyQh2J3Msul4ixl1yauGTjSxvUbNIIa3pKhgCo0ImoUQ4mgK5GW7bnn6hWOZLpJGg9XHli0L+IFNSwt98BzQggiWoa6N0fRvkyH9fyy+2jeIVl+/51WjInKIgW7jkCw30ozKcrLb3wft/WabP6gqAqes/XGIfe7MsYtk1PP7GZuscLnN+eZypcQgKoITF1DURSklLiej+35CMAydV7Y10d/Lk1kHfPAhBDkzCRFt0p8iwXW3RiGykBnlvQGZ2E9DQRSUvMcnMBDyuZNlIiqYz2CeXchIU8DtyymQ6OLrWd4eJh4PM7hw4fbHUrIJvPWW2/x93//90BzfTY6Osobb7wBsG1EViiwQh4KXVF5pXsH/3jjA1SxiIuNAIRQbwspKQMKTrPHR1MMMkYPCS2Npqy8aCu7NjHN4JlM70PF5wc2Jec6ETW76nammqXkXCMTOYIiHoyrKTCWf21E03mxewc118ZUdIQTMNlifLqmgrhTKiilRNnCvp9bJhwJ68HyRUURdGcSdGcSVBsOi9UGhXKNfKWG5zWzd3HLJJeMkohGSMesFQ0/1qLTTDJdLxDXNqe3bi0CKZHIR3a8x4ma5zBeLXKzVmC+UcGX8p4iTgmYikaXlWBXLEtPNPXQJbvbBSFEFvg7YBcwAvyulLJw3zbHgP8ZSAI+cFpK+XdLz/0N8CvA4tLm/05K+dGjiD3k8WVoaIgf/OAHlMtlEolEu8N5YhkeHmZoaAh1jbL1kMePN998k0bj3n71Wq3Gm2++GQqskCeDiltkyv6EnFXk80WHHiuBusqi2wtc5htjLIibdEd2EdceLCurug4Nz+U3Bg5iPOQHoxM01z1CrNEXJFQkPo6/SGSZTJdpaJiGhuv5TVF0H4aiYpjN/iwPDwQEQYCyxkJUEQqGplGzHQxNwbWbZhdbheP5JCxz2XO4m1jEIBYx6OvYGuelzkiKAPnIaqfLbo1+q2PLSxIfJ4pOnYvFKUYqeQQQ0wyyZnTZDLMb+Mw1yoxX8+iKxqFUDweSnRjqY/8V8m3gR1LKPxdCfHvp//+b+7apAX8gpbwihOgDPhBC/IuU8tYAlv9aSvl/PcKYQx5zhoaGkFLy/vvv89prr7U7nCeSer3Oxx9/zLe+9a12hxKyBYyNja3r8Xbw2H87hrQHKSVT9euM1S4TUWM80zFATCtxvlAkqRtEVljAa4qOpuh4gcvN+hVSeiddkcHblt35Ro0Aya/tOEDWfNBQYt1xEkDLrnViafvlyWXjLBSq6PHVF+martHZm2FhpkhsGVOM+0lYJqW6jaYqVKrOlvZflWsOB3Y8KCBXwvMDposlxuaL2K5PEAQYukYuGWNnLk3U3JiFeVKPkjOTVH37kWSVbN9lT6Jny4/zOODLgM8XZ/koP4GuqHRG4ihriFxdUUkZFiks3MDnfOEmV0uzfLFrN93WY21//J8CX176+X8HfsJ9AktK+fldP08KIWaBTmD7TrgM2dbcmuHz3nvvhQJri/jwww/xPC90EHxCGRwcZHR0dNnHtwuhwApZN1JKbtauMlG/QlLvuH3He3cqScIw+OX8ArWGS8owV8xmaYpOTKQouwsE0iOp7aBo1+mPpXmlZycJfXPmXqmi9cW7RK66/WBfhompIon4vdu4QZ2KO0/dX8SXHioqolOlMV4lKiOINXrONFUhEdHxvQBFV0l2bc2CVUqJF/js7Fp5RtUtarbLyGyeyzfnsD2fqKmhqQpCCOquy3ShzKcjUwzk0uzvzZFLrr+v6UCijzNzF4mpaxtqPAxVr0HSiJE1WjAdecKpey5nZq4x2yiTi8Q2lNHTFZUuK0HNc/jh5CWOpvt4Ltu/pkjbpnRLKacApJRTQoiu1TYWQgwBBnDtrodPCyH+e+BHwLellPaWRRvyRNDR0cHevXu3tcX0487w8DBAKLCeUE6fPs0bb7xBrVa7/Vg0GuX06dNtjOpeQoEVsm7m7ZtM1D8nqeceKCfKWRF+pa+Hq4tlRstlAiCqapiqeo/YqjoehbrDQi1gtjZGTF/gUHIXmlflx/XrdMXi9MRi9MaTRLSNX6aGksJQ0rhBFV1ZXgRIKal4eXyps+hVwKuiC4OIGsVUIrcX/7lsAtNQcVwPQ9dwghp5e4y6v4iCgi4iqEIDCdKqQ7bI2MwCPV39mMrydfZSShRFYaAjwfx0gcNf3I+6RT1YpZpNTzpBMrq66MxXavzssxu4nkc6ZpFZJp54xCSQkpnFMiOzBY7t6eNQX+e6hFJ3JE2flWXeLpExt0b8+DKg6jb4cvezLZurPKnUPIcfT31Ow3PoiT68iI9qBhFV50JxEjfweTE3uC1FlhDih8By6cs317mfXuD/AL4upbyV6v5vgWmaouu7NLNf/+Myr30DeAO21x3WkPbx8ssv89Of/rTdYTyxnD17lt27d9Pd3d3uUEK2gFt9Vt/+9reZmJggnU7zV3/1V9um/wpCgRWyThp+jZHqZ8S1zIoLVkNVOZJNsy+VYKZWZ7pep9BwcAKfcsNltuJQc31MRSFjmXyhsxtDbzAYjWOpcRzfZ6K8yNXCAqqA/ZkcBzpypMz1l5IJIchEjjJd+xmqMFHEnUveCzwKbpHZxjR1f5GkcYCCf775uqaZOTEtQV9kN1mzE01VeeZgP+c+GSWdFczUL6GgYinJe33PBZho7DpkMebPcHP6Bp0d/STMB+3my3WHjGUQCyTs6ya9Y3Uzjo3i+wF1x+VLz+xZdbt8pcaPP72K4gXYhTqXL880M2uqQixh0dWXJpaKIkTTYS4ds0hYAR9dv0ngBxwdbL0MTwjB85nd/HD6Y2zfxVQ336luwS5zMLWDrPl0N5I7gc/Ppq/S8F2ykc1zUVSEoNtK8nlpBl1ROdaxY9P2vVlIKX9tpeeEEDNCiN6l7FUvMLvCdkng+8B/J6UcvmvfU0s/2kKI/w34r1aI4bs0BRjHjx/fuknbIY8NQ0ND/O3f/i03b96kv39r5x4+jQwPD3Py5Ml2hxGyhbz++uu8/vrr7Nu3j+eee25biSsIBVbIOhmrXUYVyqoOgLcwVJWBRJyBRJyq4/LL6SKNAA6kLBKmdo8TmRsIZhqj7IodxtQ0zKWslR8EXC3muZyf53hvPweyuXXfJY/pfeQix5mvf4CqmBhKipJbYbw+hh1UMRSVXus5ovqD4sD2G3xe+Yh4I8W++DMM9mW4MTnJxcmPyKZjqMrKPUiKprDzmW6sUYvp0SkaikMinkXVFJBQrdk06i6Hu3t59pX9VE2FX169SU9288XAXKnGc7t7ScdXHqJbs13+9f3LzIzOY5dsVFUhYunohoaUksV8hfnpRayowY69XWQ6m3GqikJXOs4no9MkrAiDnemW44pqJkMd+zkzd5GMGcdQNu8jaaFRpiuS4mAiXLx8Vpii6NTosjb/2mqKrAQXilP0RlN0b8ExtpD/B/g68OdL//2n+zcQQhjA94B/L6X8P+977pY4E8B/Bpzf+pBDngRula699957/PZv/3abo3mymJycZHx8PJx/9ZRw4sQJ/uVf/mXbDRx+umtmQtZFw69SdKax1PWVF81WG/xsdI6q47EjGSVjGQ/YPOuKgRvY1LzqPY+rikJnNEaHFeW9yQl+PHKNhrf+OVEpcx87Er9OTB9gvHaVy5VPkdKmwxyk23pxWXEFYKoR0noOx7f5pPgLFt083XsWMSM69eraf8hCVejek+bIqztJ7fcxrWYJoesH6FGT3/3aCX7jayfo39PN3t4O+nMpZouVdZ/fLQIpqXsuZcem4jrUPZfZYoWeTIL9fZ2rvvbClZtc/HgE2fBIZqLEkxaarqGqKpqmEY1FSGViCAFXPhlndiJ/+7WqopBNWHw8MkkQrO8GfY+V4ZXcQYpOhbrvbOi87yaQkrnGIlkzzlDHgafeOXCuUeFCcZLcJmau7kcRCikjwvDcdRx/6+e4bSJ/Dvy6EOIK8OtL/48Q4rgQ4n9d2uZ3gS8B/04I8dHSv2NLz70lhPgU+BTIAf/Tow0/5HHl2LFjaJoW9mFtAeGA4aeLkydPMjs7y/Xr19sdyj2EGayQlpm3JxGo67pDMFOp897NPElTw1yjt0hTDIruNDH9QQGnqyp9iSRz1Qo/GrnGV3btXXdvlqlm8aniin52J46iCg3R4j2GqBbHDWw+KZ7BVBq8/EIfH39aoJC3SaWNNedB6bpBotugYyCJU4vS8KsceiZKIzbKh4WLzVlDaoSu/hQlx2e6UKIrnWgpW+cFAQuNGpPVMouuTSCDpZlaUGnYJC2TVE+UyXKJ3kQCfRnr+8VClX/+8cekoxbWMjOy7jkXQyeRVhm5PI1maGSXTDlMXaNYaTBXqtKdXl9PVX+0g1PKM5zLf07VtcmaazvbLUfDdyg6NfYmengmNYi+iRmxxxEpJefmRkjorQ/43ihRzWC2XuZKaY6jDzm/7lEhpVwAvrLM4+8D/+XSz/8B+A8rvP5XtzTAkCeWSCTC888/Hw4c3gKGh4cxDINjx46tvXHIY8+tUtAzZ86wd+/eNkdzhzCDFdIyBWeGiNr6XfBiw+H9yQKpiL6muALQhUnVKxNIf8VtOmNxSnaDn43doFRvULPdluOpeRVGqhdJ6Vk0YbQsrm7Hp5h4QY2ZxgxGRHD8Cx3s3BmnWLAplZxVMzdSSuyaxsXJi5T06/QezeNa8wQEWFqCqJYABOVgjlTfNF5slCuzI1TtlQ3JAikZLy9yZnqMC/lZGr5LUjfImhYxVQdXsr8zx0t7Bqh5Lj8bG+F7lz7jysI8wX1Tk987dwVPSqJriKtbKIpCLBlh9PI0gX/H2t4yNT6fnGtpH/fTGUnyle7n2RnvZN4uUbAr+HJl2/y7qXk2s40iTuBxqusIL2T2PPXiCmDBrlJ06sQ3yZVzLTJmlMuLM3hBa7+3kJCnmUwmw09+8hMURWHXrl289dZb7Q7pieDs2bO88MILmOaj+dwLaS9HjhwhlUpx5syZdodyD+EKJKQlfOlR9ysktNZMGLwg4OPpIhFNwVBbEzK3EhZOYBNRl58fJQOJ3fD41xtXuHh9hl4rQS4Z4/ldfXQkVp85NVq9jCr0ptPfBgmw8SUUnTwdZif79ibo7o4wOVnj5mSdpm6RKKpAIAj8AAn4gY+aXKRrYJ6j/buJaA9meBRhoCsGUS1Bap/H+PwcI2NXSFZ6ycYT98ycqnkuF/NzFJ0GKcO8XXJpuz4120XXVI7u7KYj0RTEpqaRME0c32d4YoKxxSIv7xgkbhg0ajYXr02RTKzcn7UcmqZRc21K+SrppX6shGUyVSzh+v6ymbK1MFWdFzJ72BPrZqw2z/Xy1O0pZqaqoykqgqa4tAMXL/ABQUqPMpQ9QLeVDoXVXVwtzT3SYcC6otLwXabrJXbEWu/FCwl52njrrbf42c9+hly62TU6Osobb7wBsO2a9R8nPM/j3LlzfOMb32h3KCGPCEVR+OIXv8i7777b7lDuIVyJhLSE7dcRiJbLA6/nq5Qdl1x0vXeQBF7gwAoC68r0PBPFOeKWx6g/RsKKUHUk1y6c50v7jtCf7iamJh+Is+5XKbrzJFsUiCsRSA9LizJnz5IxmjPAEnGdgwdS7N2ToFrzqFY9Go1mFs7QFcyoJC9GUDQPQaIlgxBVaOzq7CWbLJEvzuMvWswUHQRgS59LpTkUBFFdp267eIEECbGIwcEdnWQT0Qf63KBpPNKXSLBQr/Gv167wa3v2sTA6j+P7GxImZkRnenzhtsBqvu8Czw82JLBukTJiPGvEOJjsZ9GpUfJqzDdK2L6DpPmB2mdlyRhxErpFQrO2VXPrdsCXAaOVPB2Rhx/YvR4sTWe8mg8FVkjIKrz55ps4zr09p7VajTfffDMUWA/B+fPnqdVq4fyrp4wTJ07wne98h2KxSDq9Pb57QoEV0hKSgFatCxw/4GqhTCayssPeakeSPFheJKXPdHmWq6VLmBY4CAJfMFUN2J2y8Knxi5vnOEiOqJpgR3QvWaPr9qK76MyvSyCuhEBBBVxsGn6dqHanZFLTFFJJg1TyznlLAsZrV1ADH1ONYfuLCFqPIWkm0XNVRMcCu4zjzFfqfP/zz0lYESKqhiIEUVMnETGxTB3L1Fvaf4cVZbHR4Ec3rtE9GaCZG/soMCydSrGGlHc71cvbd2UfFkPR6Iwk6STJ3njrFvAhUHFtAuQjn/9lqToz9fIjPWZIyOPG2NjYuh4PaY3Q4OLp5OTJk0gpGR4e5jd/8zfbHQ4QCqyQFhHrkAXTlTqBlLcHC0vZnMN0q1dH01QUdbW93fuc7deZbowxUZ5FURQM0SxlS+iS+ZrHzqRGwjAoVhsYJAmkz6XyL+kwutgdO4ypWiy6eQxl/XO07sdUY9S8YjOuwCbK6j1pRWeOulchqjfjUtDumcXVCpYao+TkWWSMiXqEvo4kHdbDZyVSkQhztSqXZuYxVYXGBkTRrasi8IOm/TyAFGgrlIXKoAKyhPTzIMuAD2igZBFKGpQUQoR185tB2W205biGqlFw6jR8l8gWzDYLCXkSGBwcZHR0dNnHQzbO8PAwnZ2d7Nq1q92hhDxChoaGUFWVM2fOhAIr5PFCU0xkizms64UqcV2jXnNYLFSpVmyCQCLg9h4MQyWZipJIR9G0O4txgUC7S4DUvQrjtasoKOhEUcUd0wdFCAKg2HDpjJoIIQgCiaVHMNUIJbfIJ8VhjqaOU/PKaOLhF3txvZOKO4eKQcOvr7qtLz3m7cnb/VZuUCWl9yM2kFGI62l+Ofs5hcVedqZyG4p9OXJWlPcbNZK+hqNIoqzvPbp1TShLgqruuMQi+j3lgVL6EMwg3c/An6F5JSggDLh1VXg3QDSHO0t1L0Lfh1C2Zujy00LVc9aRK91cFCFo+F4osEJCVuD06dO88cYb1Gq1249Fo1FOnz7dxqgef4aHh3nllVfCkvGnjHg8zvPPP7+tjC5CF8GQljAUE00x8OXyM25cz2ex3mB8ociVqQUuX77JpxfGmZxbxEOiGiqmpRONGlhWc2E9P1fmxpUZCvMV5JIDn0SiL2WabL/GeO0KmjAwVQvLMAjucyfTFYWy6+MHEkU0rcJvEdeSKELhwuL7uIGzKR+4phJHV6L40lu2lPFuKt4icqlES0qJRBLVNiqOBNfzNpq2uqhb916FoLM7RXGxioQH3AXXwmm4xJLW7fLAUs3myI7u2++1DIrIxg8JGj9CBhWk0o1QuxFqJ0JJIZRk879qFyjdSNGB9EeQ9f+PwDmHlCu7KIasji+DDVndbwZSSoIWHSBDQp5GXn/9db773e/S3d0NQGdnJ9/97nfD/quHoFgscunSpbA88Cnl5MmTnD17Fm8Ds1K3glBghbRMSsth35W1kVJSajT4fGqO4etjfDI+xQdXbjI5nqdat0FXcAjIV2vMLJa5WSxRrDfwgwBNU4hGDSKWztxsiYnRBWzHQVdMNEUnkD5T9VEUoaMvmUIkLANFUXD9OzbuhipYtF3KdZv+bPIBYwdLjSGR5J2ZFcXhajiBTcUrYQfNciuBIGMM4ATVNXtbSm4eTWn2Y9n+IgmtG22DZYrFuoPn6TTk4oZevxp9O3M0XJekplF1W7e9B7DrLj0DzUyTHwSoiqC/ozkXK3CvIuvfR8oqQu1DKIk1Ra4QKkLpQCrdSO8asvEDZJBf9TUhy6OgtNw3uRWsp9cwJORp5PXXX2d0dBTTNPmDP/iDUFw9JLeGNocGF08nJ0+epFar8fHHH7c7FCAUWCHroDOyA3cpo1B3XM5PTPPL0UkWqjWSkQi6C6XpCmZEJxIx0FQFQ1UxdQ1T19AVhVKtwWSxRL7W7NNShCAWM3Ecj/HJKRJKM8NTdOapB3VM5U4/jioUdubS+IGk2nCwXQ/P8yhUbbKJKDs6MsvGHdeSuNKh4Myv63zLbolrlYuM165zrXKRktvsvYpqaVJ6H1I28OXygkQS0PCrqGg0vCKW1kHa2Lmu49/NTKWBqWl4gYO3AaG4GkbUINWfIqi5uIGP3+IMI9/z0XSFZLZZArlQqrOvJ4ehaQTuRXB+gVQ6EEpq3TEJoSCUTqQUyMa/IoOFde/jacfS9E0zG9kIurJxF8mQkKcF0zQZGhri5z//ebtDeewZHh5GCMFLL73U7lBC2sCJEycAto1deyiwQlomrqUxlShjhTneH7lJxXbIRi3ipoHv+cyM5NEi+oplSUKIptjSVCp1m6limcZSKteIqLi2z+JNDyl9FuwZLOVBIwfL0NnX00F/NkksYpKJRdmRS3GgL3fbVGM5MnoX843JlvvIAGbtmxiKSVSLE1EsZuybQDNzF9U72Bt/BduvUPbmcIN7S/fcwMb2SziyQkLvIWfu21Dv1S0Wag4RvTkDaiVR9zAMHO7FdV12RhMUHRt/jYV5EARUSjUGD/SgagrzpSqdqShHB7sJvDFw3m+WAz5k35tQ4kiiyMaPkUH1ofb1tJF4RMOF78cPAjShEtM24iIaEvL08eqrr/Lhhx9SrYafcQ/D2bNnOXr0KMlkst2hhLSBgYEBBgYGtk0fViiwQlpGIKgVc1yeHSdmasTvGny7MFFECO44ya26o6bQEgJmFyvUHRdX1OnQe5mfrjJTmMfDQxXL3wHXFIV0zGJHNklPOkFE11HWuJTTRg5XulT91u2jJcFdZU7idk9J3a+S1nP0RPdxOP3rDEZfQCCoePO3/zl+jZjeQ5/1Ahlz90OJq0BKyraLqSpIwbpEYqukMjEyL/SQChT6tQiLjQbOXaWYd+M6LuVijcH9vaQ7E8wUK3Qkopw4uAtN2OAMNzNXK/z+1otQYs0eNudcWzMyjxtxPYKER/6e1X2XXCQeNpmHhLTIqVOn8DyP4eHhdofy2HLLojssD3y6OXHiRCiwQh4/Pp2Y5sa0Q19kF764c6fNsT2qi3UMS0dF3O+yviKqoqBrKrO1Ar6tEyWLaRpMTE2i0tri3A8kuqKwSvKqeSyhkjE6KTRmWwsO6DR7aQQ1al6ZRlCly+xFSokd1NkR3bO0X52MOcD+5Jc5kvoqh1K/xpHUb3A49evE9W405eGzCIGUS3OmBEjWFJMbQVMU9JTJl37rGPt7c/RhUC7VmKtUKDVsPNejXrUpFar4vmTHoR6UlEGx0uBwfxevHt6NqWtI98OlWDc5e6Jkkf440nvQ1jhkeQxFpSsSp+o5a2+8idRch/7Y+stCQ0KeVk6cOIEQIiwTfAiuXr1KPp8PDS6eck6ePMnExATj4+PtDiW0aQ9pjZuFRT6dmKYnmQARxZM1bFnGFAkqhRpCUZolgOtc+wvFQ0OlMGeRywVELJ2JWpGEl6UVjdXwfDKW0dLd8g6jh4I7R82rEF2yTl+NlJ7FUEycwEZTDGJqnEU3T5+1i6R+b7+XEAJN3FsSZSpRvMC5bXSxUW5l0aQMUITy0PtbDiklQhEk0jFOfPU5ni1WGb06zdVrM0wslsg7DSKWQcdgjlgiQjJqcai/k/6OJIbW/BiRQRm8EVA2fyCwEAIp0uBdQGo7w+xIixxM9fDz6avEH1G5YCADEDAQW74fMiQk5EFSqRTPP/8877zzTrtDeWwJBwyHQFNgAZw5c4avfe1rbY0lFFgha9JwPc5enyATtVAUAWh0cJgFLtOQRaplG01vqiFdgCq4bWCxMpJAcRBSwar3YgcK04tlBrJpAsXHc4AW1oQNz2dXZvVhv7fQFYNOsxdfetT9Kpa69ussNXZ7u5JbIKGnGYjua+l4aaOTOfsm8YcURKoi0FWFhmcT1eNbksFyfJ+4fifORDrGM8f38szxvUCzr8bzAwIp0VQFbUlQ3430RpBoWyZ+hBIFfxqCBVA3bxbYk0yPlcRQVRzfw1C3/uO+YNfZm+gM51+FhKyTV199lb/+67/GdV10Pfz7WS/Dw8PE43EOHz7c7lBC2shzzz1HLBbj3XffbbvACksEnwJqdYfZhTJXRmb55NJNPrl0k0vXppmeK1GuNtbs0fjs5gye72MZdz70VaGTE4dJsAM7WEREHCQSIQQZVaURrLxPKTwCrYHiRzFqvSiBgaWrlOo2lYaNUMCxWzNy8CV0WK3dnRdCYCgRjqaGUFAoufmWZvV4gUvBnSdtdHAo8QKa0tqXX6fZhx9sjiFFNmpQcRqk9c5N2d/91D2XrtjKWT1VUTB1DctoDhFeVkT512ADjoHrQaIh/ZtbeownCU1ROJbdwYK99c3zbuDjyYBD6e4tP1ZIyJPGqVOnqNVq/PKXv2x3KI8lZ8+eZWhoCFUN3UufZjRN4+WXX94WfVhhBusJJQgz4LQVAAAgAElEQVQkc/mmqJpbqAACoYCmqggBvh/g+01xkYhHOLC7i97OFLp+74eT7XpcmZmnI/6go58iVBIMoM8UUPpK+EYZJKRNjTkv4I5+l0jhIxUPhETxTbRaN6pn3WMiYWgKc5UqZlrH89e2Iq+5HhnLIBVpLUPkS4+oFieqxXk2/Qo36zeYrI8AEFEsdMW8PdvKlz5O0MAJGqhCY3/8WXJm75qzr+4mqiZJ6R3UvDJRLdHy65YjaUpulrSH3s9KNDyf7lhrmcDlkNKGoIpQtya+2wgL/LmtPcYTxu5EjtFqgYJdJWM++He8Wcw3Kgx17iKhb2zWW0jI08ypU6cA+PnPf87Q0FCbo3m8qNfrfPTRR3zrW99qdygh24ATJ07wZ3/2Z1QqFeLxtdtBtopQYD2BlKsNPjw/znyhSswyyGVXd/SqNxze/3SMaMTgxWcH6czeuSDH84tImhmM5RBCoHgWVimLVB08vYymV4jpJWpBnYgqQAoUP4LmxlE8CxEYyw4hNVSNqu1g2DqBWN7B7hZSSiqOx0t9rWdMHOmQ1Jq9IZqiszN2gN7IIAvOLHl7hoq/eLuHREMjoWfYaRwkY+ZQxfr/VIQQ7Iof5dPCO/iBh6ps7M8tkAHJqEenuQNk6yYireIHAUJAX+IhrG2DMjyKvihhgZxv9oyFfVgtoQjBUG4nP5i4QM1ziG6Bffp8vUJvNM3exNZkWENCnnR6e3vZu3cv77zzDn/6p3/a7nAeKz788EM8zwsdBEOAZh+W7/u89957/Oqv/mrb4ggF1hPG+GSB98+PYeoq3bnWsglWxMCKGNQaDj977wqH9vRweF8PiiK4PpcnEVm5BE8oAjNm4Ls+mjAxbBPsHPulx2fOAobQUdeR9RECnJqCZq5etrhou/TEI3THW79bLgOf2H0ZIEON0GsN0msNEsiAQPpN0cgKZXDrxFJj7I4f5WrlExJ6Zt1CLZABJXeBfclD6I7OzXKJDmtzsxALjTr7sx2Y2sN8HLhIuZxs3lyEUCDwAQ8I+xRaJa6bfLn3AD+avASwqSJroVElYVic7NqzRt9lSEjIarz66qt8//vfD28grYO33nqLP/7jPwbgD//wDymXy7z++uttjiqknbzyyisIIThz5kxbBVbYg/UEMTKxwHsfj5BOWiQT1rpfH40YdGYTXLo+zfnLN/F8n0KtTkRffeGdyERxGveW9FlCY0BNUJHuuubwaKqCXVcxdZ1ALp/Fsj0fKeFoV7rlLyE3sLHUGDFt5YxX06FPRxWba9TQGdnBnvgzVNwitl9r+XVOYFNyF9hh7aff2stz3T14QYC7wnyqjWB7HooQHO0K+2aedDojcb7Sd4i651KwW78OV8KXATP1EinD4rXe/ZiPwEQjJORJ5tSpU8zPz3Pp0qV2h/JY8NZbb/HGG29QKBQAmJiY4I033uCtt95qc2Qh7SSdTnP06NG292GFAusJYb5Q4cPz43RkYujaxps8FUXQ2ZHg85E5LlybhjXdACHTk8RzH1z0d6oWOdWiTOsiSwTgI+iO99EI6g887/gBJcfjxb4s0TWE391U/TJ90d3r6qHaTLojgzyT+iICwaIzj+0/eG63uCWs/MDhUOIlBmL7EUKQME1e6utntlYl2IThsYGUzNdrvNy/g+hDu1bpCLH1A22lDECohMn3jdEZifMf7ThK1owxWV3EaaHXcTnKboPZeoVn0n281nsgdA0MCdkE7u7DClmbN998k1rt3ptFtVqNN998s00RhWwXTp48yS9+8QuCYG0js60iFFhPAK7r8/4nY8RjJtpDiKtbKEKQy8T4+OIEtr32AiyWihBNmNj1ex3zhBAMqgk6lAgl6bYkCryGR6wjSlLrRBM6bnBnSGrV8SjZLkN9WTpjrZcG1rwyCT1Dp9nb8mu2grie5pn0CfYnjqEKjZKzQMnJU3IXlv41f0YG7I49w3PpU2TMe3ta9mY7OJTrZKpSfiiRFUjJVKXMM13d7EylH/bUQEnAJoi+NZF1ELmwfOYhiOsmv9KznxPde6h5LtO1EmV3bTdRXwYs2FWm6yUiis5v9h/h2Ww/uhK6doWEbAb79++nq6srFFgtMjY2tq7HQ54eTp48SalU4sKFC22LIbwN/ARwbWyOhu2Sy26eW4qmqWiayujNIgOdqy/AhRAMHunh8tlRdFNFucsQQxGCnWqCKBrjfgUdBWuFPiS74WJGDax0BEUo9Fm7GKleJpCCkh2QNDVe7ets2TUQwPYb+PjsjR1FEe1fCKpCo8PspcPsxQ1s6n4VT7ogJaqiY6kxdGGuKCAUITje148i4LO5OTqsKJF19k7VXZd8o86zXd0c6+l94Fj1SoPCXAnHdlEVBcMy6OhJoa2SMRTCRCoxpHQQwsCXDm5QRy6VeQqhoisWqnjI3h9ZB23nw+0jBEUI9iRyDMayzDRKXCrOMNcoIwGW+j+EEEgpkYCQzez27niOvckcGSMaityQkE1GCMGpU6fCgcMtMjg4yOjo6LKPhzzdnDhxAoB3332XZ599ti0xhALrMcfzfK6MzJFOrr/nai0SMZPKRJ163cGyVl8Yx1IWvXtz3Lw2RzITQyh3Fl9CCLq0KAnVYNyrUJI2qmwKrVvlh57rIQNJ184MNekDEilNDNnDbH2CQx0ZDuU6VnQzXI6meHE4kjxOVGufVedK6IqJrrQ2w+tuFCF4sbef7licX0yMs2g3yEYs9DXmf9i+R6HRwFRVvrJ7L/3Je10D8zOLjHw2weSNpg26EAKJRAYS3dDYdbifgQO9xFa41jzRT7XxLpWghi9d4O6MiAAkqjBIaP1EtRzaBs7dcxzq1RRBUGy+F6pCNBHBWIfoDrmDpij0R9P0R9O4gU/FtSm7DWzfw5cSTVGIagYJ3SSmmaGJRUjIFvPqq6/yD//wD0xMTLBjx452h7OtOX36NF//+tfx7+pLjkajnD59uo1RhWwH9uzZQ3d3N2fOnOGb3/xmW2Joi8ASQvwXwP8AHAaGpJTvtyOOJ4G5fAXX8zelNPB+DL0pgObnKwwMZNfcvmdPB4EvmbqxQCwVQbtvppYlNA7oaWqBx0JQZyFoEEiJ0/CQSLoG05RxqbsuC/U6aSvCV3YeJR05zHj9ElW/SEwk13Ti8wKXil8iriY4nPzCls2OWo1Aus3sjVBQ0Jrud5uIEIKBVJpcNMb1Qp7P5uaa/TRCENU0tCUh6gUBNc8FCRFN5ws9vezJZO9xDJRScu2TMT577xqRmEm2J42i3LuQ9jyf6xfGuXFhgqHfeI6O3jtZzUB6lJ3PKdqfId0xNLWHiLq802EgPRbdEYruDVL6IAm9f9XMopSSwmyDyWuLzN0sUqu4CO0aiCXHQtHcJhKL0NGbYcf+Hjp60/dkUUNaQ1dUMmZ0S2dlhYSErM7dfVi/93u/1+Zotjevv/46f/Inf0K1WqXRaDA4OMjp06dDF8EQhBCcOHGirUYX7cpgnQd+B/hf2nT8J4bZhQrGOswe1kNE1zAiOvlCtSWBJYSgb3+OaNJk7OIMjYqNFTdR7xNaUUXDEnFytkG1YWNkInTvzaJHdOqOSy4W5atHDmLclZFJm2mm6mNMNUbwpYcqdAzFRF1anHuBhyttPOmhC4NdsYN0RwZuP/8o8KVDzZ2kaF/CDcp3PaOQMHaRNHZjKJlNLa2ydJ2jXd0czHWyUKtRbNSZrVapey5CCCxNozuWIB2JkLWs2xlA2/FYKFRwHI/xi5Pc+HSMgT1dWNbyWSVNU8l2p2nUHIb/+SNO/MdfINOVxA0qzNfO4PgFTG0HUEX6C8DyGSVFaJhqCikDiu4oVX+eTvMQunLvoj4IJFM3ylz5aJ7KooNuKFjRGh39z6JoD16LruMxN7HAxOdTROIm+4/tYuBAL+oW3HgICQkJ2Sqef/554vF4KLBaYGRkhPn5ef7yL/+SP/qjP2p3OCHbjJMnT/K9732P6elpenp6Hvnx2yKwpJQXgbCGfxOYz5exzK35NaqKQtIyKZUb+H6Aqq6dFRBCkOlJEs9EyU+VmBnN41ds4N6CMSTEMxY7j/SQzN4pKZzzq+zN5e4RV9AcDDwQ20tfdCeLbp6SW6Dk5rGDBgKBqUTI6l2k9CxJPfNI+60C6VJofEbJuYokQFcSWFrXnVOVAVV3nJJzFVPJkI0cI6p3rbLH9aMpCt3xON3xOAdzKw97LZXr3BhfYGR8niCQNEp1rp29ipWOUfjsJrmOBN2dSeKx5YVWJGogpeTcDz/lS//5UebtnyKRRPTmh5dU9yKCBaR0EWJlZzkhFCw1gxvUmGl8Snfk2dsiq1pyOP/uDDMTFZLZCLm+WHOQsdKDUJd/33RDQ8/GIQtOw+XTdy4x8tlNXvjyEVItzoMLCQkJaTeapnHixImwD6sF3n77bQBee+21NkcSsh05efIk0OzD+p3f+Z1HfvywB+sxRkpJqdogl9m6/qLeTJK5fDPTsVYf1t3opkb3rixdgxnsuotdc/C9AKEIdEPFjBnoxoOXnx8E9KZWXhCrQiNrdJE1NlegbBQ/aDBde5eGv0BE7VhW2AmhYKoZANygylT1bbqiQySM3Y801vHJAh98PIKqqWRSUVRFYWSiQDodJ5aOEkhJoVhldr7E7sEcPV3LzwyzYiaVYpnLV/+VjgETQ71TLigUE6kdQrjnkaTXLI3UlShuUGO2cYFe6wWmR2p89LMpNE2ha8fSdS0bIEBo+1q6KWNEdDp3dFBZrPHTfzzLM1/cT/+RLqpeEU86CBR0xSShZ9c9+DkkJCRkq3n11Vf5zne+Q6FQIJPJtDucbcvbb79NV1cXR44caXcoIduQF154AdM0nzyBJYT4IbBcTu5NKeU/rWM/bwBvQOgMcz9SAnJrM4G5eAwA39+YBbdQBJGYQSS2tjir2A4diRiZ2OYbdmwFgXSZrp3B8ReJaq0JPl2JoQqDmfpZBBpxY2CLo2wyMVngvV/eoCMdQ18q2XQbLvMTC8SXBLoiBLGoiR8EXB+dB1hRZMnIPDfOz9Gz65kHnhNqJ1LuQ3hXkKQQa4gYXYli+4t89tlFrr+rkem20I0loSrrgIPQjyGU1q35AeKpKCLi85Mfv012TqX/uSxCqNC07kAVOt2RXXSaOzDU7XfNuY5HfqpAfrpIfqqAXW+OLIgmLTr6MmS600v9cmG/WUjIk8SpU6eQUnLmzBl+67d+q93hbEuklLz99tt8+ctfDquhQpbFNE1eeumltvVhbZnAklL+2ibt57vAdwGOHz/+CAbtPD4IwW0r5a36gDE0lVwiRrFeJx5fv+vbeig3bF4YbO+sqvWQb5zH9vJY6yz3U4RORO1gtn4WU8ugK1vrcFit2Xzwyeg94gqguliDJfvtu1EVhWQiwsj4Asl4hGj03t+7F1TAmqU2a+DUPQzrwY8RoQ0gURHe50gMhIitGmNxwuTDn44y2L+vKa5kAJQAE6G/gFDWX+a36Mwxa4+gd6rMfuKSigr6jtzp3/Klx3TjGrONEQ4kXyKmbcI8sE3AaThc+3iUkfNjeI6HZuiYURNN15BAtVhnYbKI7/lYcZP9X9jDwKH+lkp4Q0JCtj8vv/wyuq7z85//PBRYK3Dt2jUmJibC8sCQVTl58iR/8Rd/Qb1ex7Ie7Y3U8Bv5MUYIQSIewXHWHga8UaSU9KUTmKZOw9264xRqdfozSfozy2dMtht+YFNyrhHROjb0+uY8KEHZGdnUuJZjfLIAgnvEFUDgrzzhXFUUFEUwM19+4Lm6O4UQTWdE31v5nofQ+sB4CSEiyCCPlLVlh9k2qj4Xz1RI5QxcUYCgBLIISh/CeHFD4qrkLDDTGMFU41hGnHRPnGtnJ6ks1O+co9CIa1k0xeDS4lnq3oPn+qiZGZvj7b97l2sfjRLPJMjtyJHuSmHFI+imjmHqRJMW2Z40nTs6MCIGn/z0M979v9+jlK+0O/yQkJBNwLIsjh8/HvZhrULYfxXSCidOnMB1XT744INHfuy2CCwhxG8LISaALwLfF0L8SzvieBLozMap2+6W7d9xPDpSMU7sHyRfrREEm59EtD0PLwg4vmvHY5Pqr7oTSORSydnGMNU0JfsKgdy635/nB1wdmSWVePDOjRCiOZ5qBWJRg9n5Eq53Z8ZIID3q/gzaUkZqLfd5ocRAP4bQn0MICyGLyKCIDBaRskoQVLl8dh6oEjED7GCGQOlAGMdR9P2rGmWseM6Bw2xjhIgav90Tp2gKZsLgyrsT+N69wtJQLDRF50b1k2UF4KNi5PwYw//vBxgRg46+zANjDpbDiBh0DuSoV21+/g/DLEwVHkGkISEhW82pU6c4d+4c9Xp97Y2fQt5++216e3s5cOBAu0MJ2cbcGjjcjjLBtggsKeX3pJQ7pJSmlLJbSvnVdsTxJNDVkcB1/bU33CCVmkNvd4r+TIqjfd1MlysEm7gIdTyffLXOyX07iZmPblis43ncmMtz7sYEZ6+PcXl6jpqzttDxg4BCo8bFwieMlFQu5EtcyJe4UqwyU7Opul7Li3RFaAS41NyZhz2dFVks1XBdH30Zu3LN0JYa+VaKT0FKqFQatx/zgxoAMhAIBTRjbREghIJQOxDGMTBeRujPINT/v707D5LrLA9//33O6X2ZfdNIs2jfbckWtmXJZrH9YzFGIr8QTJSUb5lcFZVLLiSXBLCqckkKUUCSH6SSX5KrhBAqnrAUAcskGDAgjLGFjcCrLFubNVpnX3p6X857/5jRMtJIs/XMmel5PqWpmT7dfc7TRz3T/fT7vs/TilhV9F8I0n3aS1ltE+JZBp7lOHbjlEatLorn+jHCNQVHgmV+hnrSdJ24NgkJ2BES+UGShcEpH3c6zrxxnhefeo2qRVUEQpOfihutjBAqC3Hwe4cY7InNQISlSUSqRORJETk28n3MigIiUhCRF0e+Hr9i+1IReW7k/t8UEe14rYpi+/bt5HI5nn/+ebdDmXMurr96+9vfPm8+lFXuqKmpYdWqVa4kWFpCa56rqYrg9XrI5cd+Ez0dxhgKBYfmRVWICDc1LcIxhiMXuqmNhvDa0zteKptjIJVm+8rWWZsaaIzhtQvdHD7fgeMY/B4PIvBm7wAvnD7P8rpqNjUtGvXYjDH0ZVIcH+zhRKyXgpOlJ91DyC5DZHjapGMMDgYQfJawsjzM4nCQ8DijEJb4yBYGgCVFeXyFgsNAV4zB3iH6u2N0d8c4c7IL01pLsCxIpDKMNbJWJ1IZxhfwjazzGftPgQEKV4xa5k0CMMT7MyxeVY7HO7nPaMQKAkGwh6dWth9pJ1QZxfIMvy+VQm44ibOnth7KMQ59uQ78MvZc60h1gDMvd1O/suratWfipTt9hnBkdtdiJQaTvPSzw1Q1TGzU6noCIT+FXIFfP/kKd//27XhmqD9eifkU8BNjzOdF5FMjlz85xu1SxphNY2z/AvAlY8w3ROSfgA8D/zhz4aqF4mKJ6aeffpq3vvWtLkczt7zxxht0dHTo9EA1Idu2bePxxx+f0XoFY9FX4HnOY1usWlrL4aMXqK0ubr+fwaEUjQ3ll3oiWZawuaWRSMDPC6fP4bM9VIQmV9kNhpORnngSv8fDvetWUFc2s0UeLjLG8NKZCxw+30l9WeRS012A8pG4jnf1ksxm2b6iFduy6M8keb7zDN3pOF7LpjoQAjyIePFbY39YnXMcXuuP82pfnJZokA1VEfzXSUYFiwLpMa+bjELB4czRCxx7qZ10IoNlW/iDPtKJDKmhFGePnMcYB4/fS+PKBmqaqrFsi4bl9Zw5fIZoTdl14rvqOE4aS2zyWYfGFdNLihMDGfo7klQ1Xm4yLOIh70x9SkzeZCk4eXyesRMsb8BDoi9NrDNBxaLRz7uAHWYw1zXlY0/Vyz8/gsfnHbNtwWSFy0N0n+vl5MunWXXrsiJEV/J2AG8b+flrwM8YO8G6hgy/Ur8D+N0r7v8ZNMFSRVBVVcWGDRt4+umn3Q5lztH1V2oytm3bxle/+lWOHj3K6tWrZ+24WuSiBCxrqiES8pNIZoq2z1y+QL7gsHFV46jtIsKqhhretXE10YCPC4NDDCRTE5o2mC849MSTdMXiLK+t4t0bV81acgXQE0/y2oUuGsqio5KriywRGsoinO2PcaK7l8N9Hfx3+xHi+SwNoTKqA2FssRjv18ZrWdQEfNQEvJyJp3jybA8XEmMnUQaDML2RwEQsycHvv8grzxzFH/RRu7iK6oYKIuUhIuVBfCE/0eoIZTVl+PxeTr9yhjcOHiOdyFDZWIE3MJyIjR0fV42MGgY6M9S1RolUTq+qZM/ZOJZHrvpESbiqJfWkGOPccF0ZDCdZXScGrtluYZM3M1fIZSyDPTG6zvQUtRlyZV0FJ158k9wMFr8pIfXGmAsAI9+vVxI0ICKHROSXIrJzZFs1MGDMpSfNWWDxzIarFpK77rqLZ599lnxef5ev9NOf/pSmpiaWLdMPkdT4Lq7DevbZZ2f1uJpglQCPx+bWjc0kUtmirMdyHENvf4JNa5YQvs56kPJggHesXcF961ZQXx6lJ56gMxaneyjBYCpNPJ0hns4wkEzTGRu+biCVZlV9De+5eQ1bli7BP8tTmI529hD0eq6ZGna1aMDHd469yq+7zlITiFDuGz1KZ40kRGacRMASoSbgI2BbPNPZz5H+oWvWZxmTx55kf6crxQeTPPNfL5AcSlG7pAqff3RRiHDQj9/nuVSowuPzUFZbRjaZ5fVn3iCfLbDituXkc3kG++MMJTN098c50zXAqfO9dPUN0dkfo7M3Riyeou9Cmmi1l7Vbp9/ouf9CkkDo6ueAM27frBsRkXHzM3/Ey0DHtRX3HBzsaRQtmYr2I+fw+iZfyONGPF6bXLZA95meou53vhKRH4vIq2N87ZjEbpqNMVsYHq36sogsZ+xUfsxnn4jsHknQDnV3d0/hUaiFaPv27cTjcV5++WW3Q5kzHMfhZz/7ma6/UhO2evVqqqqqZn0dlk4RLBFVFWFuu6mF5148RWV5CN8UpxsVHIfu3jhrV9TTsqTqhre1LKG2LEJtWYRUtpGhdIaBZIreRIp8oQAIAa+HmkiIsmCAsqB/2uu2pipXKHCmf5DaSOiGt8sbh+OpHjqTQ6z11uEZY6RLxMZvVZF3hvBYN94fQMC2qQ1YHO6L4xhYVxm59MJgKBDy1E/tMWXzPP+jl7Fsi0j52HGIJTTWl3PqTO+oSoLBsiDpeJrXnztGw8Zm8vVRzr16hlwig+3zEoj6yWTyVFWGGRhMcfpUN6bgUNlkcdN6IVvI45nGyJsxhoHO5DWjYMYp4PVOfVTTIz4ssXBM4ZoiF5du47OJ96bIpfN4A5d/T7JOkrBnzBoHM6bjzS4ilTfuETYVgZCfztM9NC4fq9f7wnKjnowi0ikii4wxF0RkETDmHFFjzPmR7ydF5GfAZuA/gQoR8YyMYi0Bzl/n/trPUU3aXXfdBQyvw7rllltcjmZuOHz4MD09PTo9UE2YZVls3bp11hMsHcEqIUsWVbL1lmUMJTIMxJKTvn8imaG3L8HG1Y2sW7FoUp8OBX1e6soirGqoZevyZu5atZS7VrXylqVLWFpbRXVk+kUxpmO4UIPBusFjMsbwxlAXA7kUZZ4ABXP9PlFhbyMFshM+vi1CXdDHkYE4p+LDa4xyTgKfVYnPmtqb+mMvniI5lL5ucnVRdWUYj8cic0WVRAOkjeH4yU5eeO4NvCEfK7euYtntK6isLycXy1BIZPEbIFdgxcZm7njgFtbfuZ7BZJoX3zjH0fZOslPsjZZNFXDyDrbn2j9BtjX1ZoCW2FT46siOs45LEDKJ0VUjc06G+kDrlI89WelkhkwqU5S1V1fzh/30ndeS7RPwOPDQyM8PAfuvvoGIVIqIf+TnGmAb8JoZHo4+APz2je6v1FQ1NTXR0tKi67CuoOuv1FRs27aN119/nd7e3lk7po5glZjG+nLu3baaF4+co6N7kKDfSyQSuGFikUhmSKQyREMB3r51FZXjvGGfj2xLAMEx10+yujMJOtJDVHlDDObSI+utxuazyrAJ4Jgs1gQrM1siVPu9vNQToy7gQyROfXDrlKY5pOJpTr56lqqG8QtNeL0eVq9o4LXXhz9cF8uiozdGPJGhrDpCti+FFAziFYLlITwhP76GMlYuraOyMoztsRHr4oibn7CnCuN36I+lGIidY3lTNdUVkxt1chwDVz1ux8liWwFsufHzL5nKkM0WcIzBY1mEI/5Ra+qi3ir6MhduXDFIzKhGy1knjd8KEp3FEaxsKouMt2Bsinx+L7GeGI7jYI0xCqsu+TzwLRH5MHAa+ACAiGwBPmKM+QNgLfD/iYjD8IeSnzfGvDZy/08C3xCRzwIvAF+Z7QegSttdd93Fk08+OesV0OaqAwcOsHTpUlpaWtwORc0jF6tyHjx4kPe+972zckxNsEpQOOTnzluW0juQ4MTpHs53DgBCPpcnk87hFBwKBQcRwRfwsnhRJTetWUxtdRSPXZpvxry2zZKKMrqHEmNWPsw4eV6Pd1HmCZBzHPxeD6EbrY0RIeprZSB7BK94sCY4GOy1LLyW8HzXBbY31BHyTm0K1/k3uxBLJvzmORoOsH5NIy+9do6znf14PDaRkfV1IpDojhFuqCCVzmJZwrq1i6kYI9EWBMtZRsHzMtFQNblCgSNvdrK0MUdjXcW03gDknQRR36ox92GMYWAwybmOAeKJNIKMrIETbNtiUV05dbVRfF4PPitItb+Rnsw5QnbZNYnc8A65tD3v5EgX4qwpux0Zr3NyEc18U2O5UZszBRhjeoF7xth+CPiDkZ+fBTZe5/4ngdtmMka1sG3fvp1HH32U48ePs3LlSrfDcZXjODz11FO8//3vdzsUNc+cOHECgAceeICWlhb27t3Lrl27ZvSYmmCVKBGhpjJCTWWEnu4Ybxw5z4kzfaQzBRDweCwCXg++PNixLGTy4BimWdBuTltVX2/7P80AACAASURBVEN77wBlQf81o1gXUjEc4+C1bPqSSVbW1YybLAQ8NUTNUoayb+K1yy4VvxhPyJOlO1XAtjZhydQKHHSc6iEUndxUOtu2sPwW9XVlxONpEsksIlAAus/0Eqgro7W5hqrKMN4b9GOynHocE8SQwmsHKY8EOXW+DwEa6yc2AmTbgrmiv1bByWBbfvyemmtu6ziGU2d76OiMEQx4KbvqcRcKDucu9NPVM8SalfWEgn4qfQ0UTIH+bAc+K4jn6pL6MryGMFUYIudkWBHZTNRbPaHYi8Uuct+6KzkFB8uWcQu6KKXmtivXYS30BOull16iv79fpweqSWlra+OjH/3opcvt7e3s3r0bYEaTLE2wSpjjOLzxyhmOH7mA12uzvLUOe4wRqmQiw6+fPU44GuC2u1YTjk69qt1cVhsNs2ZRLa9f6Ka+LHxpWlneOJxODhCy/AwkU1SFQ9SXTaxsdti7BMEmljuBhY3HCiPXGc0qmCx5k8QrYRrCyzgRi7M4MvlqfI7jMNgbp6Ju7N5VYx674HDkVCdej01ZdYDa6iiZbA6nYDDGkBpMsXHdYjwTeNMveLDzN5P3HgTHh23ZlEWCnDzfRzDoo7Js/KINvqAHX8BDPudge4ZHryoDN2ONUUHw9Lk+OjpjlJcFxxyMsm2LaDRAOp3nyLEONqxuxO/3UuNfQsAO05+5QCo/iIiNJTaO45AuJMgGYtR4GmkMriDsmZ1G11cKRoMgwwlksROhTCpLRV2ZTilSap5bu3Yt1dXVPP300zz88MNuh+MqXX+lpmLPnj0kk6PrEiSTSfbs2aMJlpo8x3F45VenaD/ZSU192Q2nkoXCfkJhP0OxFM/85DBb376OaPnUCw3MVSLC5qZG/B6bV893ghECXpu+TIKBdJqobagvi7CirnpkzdbEhLyL8NplpPIdJPMdgMHGB2KBcXAoYEwBjxWi3LeKgF2NweL00ACJXJawd2JruC7Kj6w/msyb8rNdA2Qyecoil5Nn/xVTIAup7PAI5gRZphIrv4aC5wiWU41tWUSCfo6d7mHzaj/eCZTgL68PEutO4YkkiPha8dkVl67L5QokUln6BuK8dvQC4ZCfwXgKv9eD3zd2qf1AwMNQPENHV4yWpmpEhKi3ioinkoyTIJGPkXey5HMO0Zoabqm7B7/t3npD27Yorykjk8wQjBT3Q41UPE3jiqlVp1RKzR0iwvbt27XQBcMJ1sqVK1m8WNvNqYk7ffr0pLYXiyZYJerYkfO0n+yitqF8wp9iR8uCxGMpnv/562y/bwP+QHH788wFliVsWNzAiroazvQN0JtI0jOQoLW6kuUVVQSm2JPIa4Xx+pYT8TaTzveRN0M4Tg6xbGzx47cr8VrRS+t+BEAM/ZnkpBMsBGQSzXiHkhnOdg5QHrlB0mwmP9JhO0shX6DgOYrllOHz+klnc5zu6Gd5U+24969o8HL+zbMsrlxNyNMMQCqT43znIF29QwD0DSRIpHMUjMEZcjAGbMuisjxEZVnwmhHZcMhHZ3eMxYsqLo3GiQgBO0LAHi7EMRCPsWTlIleTq4ta1y/hhZ++WvQEq5DPs2jp9HuVKaXcd9ddd7F//34uXLjAokWL3A7HFfl8np///Oc8+OCDboei5pnm5mba29vH3D6TSrOiwQKXTmU5/uo5quuik54iFCkLkk5lOd9e2k1KA14PK+truGNZM3VVYVZUV085ubqSJV5C3nrKfCuoCKyl3L+KiK8F7xjFFjyWTW968uX0vT4PHp+HfH5iTaXPdw3g89pjTq+D4fU6ttfCvsG6q7EIgsdZiSf3FoxkcKSPcNimszdOOpO77v0ckyeT76JiiRDxrCBoD/+R6+iO8dJrZ+kZiBON+IlG/ORyBcoifgI+L6GAn3DQj9dr09uf4OTZPpKp0aXyLUsoOA6x+PXLtOcyeZasmBv9oepb67A9NvkplrsfS2IwSWV9BeU1E59CqpSau7Zv3w7AL37xC5cjcc8LL7xALBbT6YFq0vbu3UsoNPoD1VAoxN69e2f0uJpglaALZ/owMOZ6q4mIVoQ4/voFCoXr94EqFdlCgVg2g8+a/eoeIY+XjuTQpO8nIlTXV5BOZMa9bTqTp3cwSTBw/VGyXDpH+Irmx5Nlmzq8ubuwCssRSWJ5++gcOEPeSeKYLI7JkneSZAq9pPMd5JxBor41LKt7gKWr1hAfSNB+ro8Tp7sJhXxEQ8NFSJyCwTgO1lWV/WzLIhT04bGF9gv9DA6NTqYsEXLZsZ+7qUSasuow5bUTW2M303x+L+vuWEXfheL0rHIKDolYkvV3ri7K/pRS7rvlllsIhUILeprgxfVXb3vb29wNRM07u3btYt++fbS0tCAitLS0sG/fPq0iqCbHcRxOvH6esoqpT3/y+TwM9iXo645R21Ax/h3msayTR8CVYgBeyyaRn3iz4istXlHPhfbucZsM9w7GR6YUXl82mWXxuunNaRf8eJyVGGcpEauHzo5ummr9OCYNgC0BQp4mAnY1fk/tpeqJreuX8JvnjtKfL1BZHhpV3XG8/xKPbRP0Cxe6Y3i9NqGRJNJgrvuAY71xtty3cU4Vf2hZv4TzJzsZ7B6kvHZ6xTZ6zvex6palVJX4761SC4nX6+WOO+5Y8AnW2rVraWiYG7MP1Pyya9euGU+orqYjWCUmMZQhk8rh808vd/b5PXSdHyxSVHOXgRtnHzNIRhofT0Xt4kp8AS/Z9PWn4gEMDKUI+K7/XMhn89g+m/L64kwnEzz4rAZyyaVEZBuLI/ezOHI/DeF7qAxsJOhtHFWa3hPxkw75sLOFa0rniyVYtkXBuf5Iqm1Z+LweLnTHLjUONgb8YzzmgZ4Y9c01c25tkmVZ3HLvRnxBH4M9sSntwxhDz7leGpfXs2rL8iJHqJRy21133cXLL7/M4GDpvy5fLZfL8fTTT+v0QDWvaIJVYvK5wvgf/U+A7bFJp6Y2ujKfWAiTqBdRVAaDZ4KNgq/m8XrYsHUlA92x6zasNY5hKJHBe53S68YYkgNJmm9qLnpPJgPEJ/D8OXysg6b1SwgEfWSSo6c8WiJUV4TJ3GA9F4DXY5PPFxiIpynkHbwem+hVrQaymRxO3mHj9jUTbs48m4LhAFvft4VQWZDuMz0UJri+DiCbztJ9poclqxq55Z6NM9pfSynlju3bt+M4DgcPHnQ7lFl36NAhEomEJlhqXpl77zSUmkV+e3ik43pJykzKFgpEvP4p379xaR1NqxbRe75/zPgzuZFy7mMk3MYY4r1xqpurqGws/nQy2xb6Yzcu4JFIZrjQPUhVZYRlty0nm8ySu2pELjpSXc+5wSgWDJec7xtMMpTI0NhQcanHGUAum6e/a5DN71hPuGzuth8IhgNs2/EW1t25ioGuQXrP95O7QXKZTmToOddLcijFW969mU1vX6/JlVIl6mJJ6Xe/+920trbS1tbmckSzR9dfqflI12CVGI/XHp4jNU2FfIFAcJLlw+chj2VR4Q+SKeQJeGa3LH0yn2VZWfWU7y8i3LRtFU7B4dzJTqoaKkY1CnacwpiDmU7eYagvTtXiSppvap6R9Uge2yI7TmW8/lgSQRARIlURVm1bzdFn3sBxHPyh4cTT67Wpry2joytGMOi9puDFRbZlMTiUorGunLorqudlUlkGemLces9GFrXOramBY7E9Nis2LWXxikWcO36Bky+1ExspWX/xt1oYTpAjlWE2vX099a11+Pyl11JBKTWsra2NP/qjP7p0ub29nd27dwPM+roSNxw4cICbbrqJmpoat0NRasI0wSox4ah/eG1OJj+tdVi5bIG6xuktuJ8v6oMRTsb6Zj3BKhhDVWB6Iyq2x2bTW9dSVV/G4edPYllQVh0dlWhd5OQdUrEUjnFo2rCEuqW1WFOsNDkR46X53f1xfL7LcUZroqy5ey0nnj9OvHdouLKhJVSUhzBAZ3cMSwS/33Mp0TIYcrkCuXwBv8/DkkUVeDwWxhgGumOIwB3v3kx98/x6YQ5GAqzYtJTlN7eSTmRIDCbJ5/KICF6/l0hFCN8NKkMqpUrHnj17SCZHzwhIJpPs2bOn5BOsTCbDM888cymhVGq+0ASrxFiWxYo1i3jtpdNU102tcEE2m8cf8FJVuzD66CyOlHNkoHtWj+kYgwDV/uk3u7Vti6Xrm6hrqubM0Q5OvXaOQr5AKpMj0RNHUjkQwbKEumW1VDdXEyhyY9urOY4Zt01AIpm9Zn1YuDLMurev5/yRc3Se6MQb8BKMBqksDxEO+hiMpekfTGLM5alzwaCPhroy8gWHguMQH0iQHErRuLyBDVtXEQhPfRqm20SEYCRQ9EbESqn54+L0wIluLyXPPfccqVRK11+peUcTrBK0qKma1148TaHgTKkX1tBAkrU3N0+5j9Z8Ux+MEvJ4yBTyl9ZkzbT+TIoV5TVFHTULl4VYs2UZKze3khxKMdSfYOjg69RUhvGHA/hDvkmNWOUdh/50iv50moFMmnRhuKS93/ZQ4Q9QFQhSEQiMWagjVyhQHp5aUuDxeWi+uYWqpmo6T3TSf64PEHwBL1UVQaqrwuTzDmAQS7BFyKayxAYSDNo2S25q4aa711K7uGpOlWNXSqmpaG5upr29fcztpe7AgQOICHfffbfboSg1KZpglaBAyMeK9Yt545Wz1DaUTepNZjyWIhD0sbhlfk2pmg5LhHWVDfy6+wwNoZkftTPGkHXyrCifmXNs2xbRijDRijBNXf0YDAHfxBO5guNwdihGe2yQvOPgtW38tk3EOzwlLe84dCYTnI3H8IhFa3kFiyPRUYUlxAhl4yRYoYDvmibBV4pURYhURciub2Kwc5BYT4x4zxD5bH5k/qHBiODx2ISrI3hromx/2zpWrZpeTy+llJpL9u7dy+7du0dNEwyFQuzdu9fFqGbHgQMH2Lx5M5WVlW6HotSkaIJVolauaySdyNJ+soua+uiESlMPxVKYgmHrO9biDyysRfPLy6o50t9JIpcl7J3ZtS096STLotVUB6Y/PXA8dZURTl3om3CCFc9mOdzTTTKXJer3jzk65bNtfPbw1L6843B8oI/ORJy11bVEfL7h3l4CkdCNp+bVVIY5db533Jh8IR+1S2upXVo7fMxM/lLPK7EFj8+DiNDdO0Rdg74IK6VKy8V1Vnv27Lk0kvXJT36y5NdfpVIpDh48OKrAh1LzxcKYA7YAWZbFxre0snLdInq7hhjojVMojF3qOpnI0NMZw+Ox2XbveqLlc7eU9Uzx2TbbGloZzKan3Px3ItL5HLYl3Fo7O6Msi+sqyOYm1lMplsnw687zFIxDZTA4oR5dHsuiKhAk5zj8pvM8sWyGoUSaJfUV1+2/dVFVRRhjJl8i3+P34Av58IV8eP1eRIRcroDX67lU1l0ppUrJrl27OHXqFL29vdi2TSaTGf9O89zBgwfJZrO6/krNS5pglTDLslh7cwtvfddNLFlay2B/gp6OGD2dMXq7YvR0xOjuGMTn83LrnSu4674NhKML9w1qfSjKhqoGOpJDM9IXK+cU6MskubO+ZdYqFlaVhygLB0iN06w3lc/zYncHfttDyDv52EJeLz7bw0tdHQwm0yxfPP70x0jIz6KaKEOJ6b9RGBhKsqq1dtQ0RaWUKjVVVVXcfffdPPbYY26HMuMOHDiAbdvcddddboei1KTpFMEFoKwixIZbWlm1fjF9PXHyuQKO4+Dx2oQjAcorw26HCIBxYphCJyI+sBcjMvtPz001jaQLOU7EeqkPRsds0jsVOadAVyrOnfUtLIkUv7Hv9YgIq1vr+dXhdoLX6ZVkjOFoXw8YCHimfs4DHg9D8TTddorKCTb0XbtiET/95VHCQd+Ui6qkMzl8Xg8ti6feU0wppeaLHTt28PGPf5zjx4+zYsUKt8OZMQcOHODWW2+lrGxhVDRWpUU/7l1AfH4vDYsrWdJaQ/OyOhqbqudQcjVAIf19nOzzOJmncDI/x5ixpzTOJEuE2+uaWVVey4VkjEzhxs1yJyKWTdOTTrC9YSkrK2qLEOXkNNVXUFMZYTA+dkGJvnSKnlSSMv/0ypk7jsHjWATL/XTGExO6T2VZiPUrGujui09p1DBfcBiIpbh1fTN+n35epJQqfTt27ABg//79LkcycxKJBM8//7xOD1TzliZYak5wcsfBWIjdAPZiTOE8OP2uxGJbFrfVNfG2xuXEcxl60okprcvKOQUuJGP4bQ/3N69lebk7IyyWJdy6ZgnZ7HBD3qudjg0SLMKUxVgiTVNDJbVlYd7o6pnw/Va11rOsqYaunqHrrhMcSyabp6dviM3rmmhYID3blFKqtbWVm2++uaSnCT7zzDPkcjlNsNS8pQmWmhvEAobfXF8ayXCxh5GI0BKt5IHWdbRGKulOxelMDpEeZ0TLMYZ4LkNHMkYsm+aWmsW8s2k1VbNQMfBGouEAt65roncgQf6KJCadz9OfSROcxtRAGE6uysMBltSWU+b3cz4WI5W78bqviyxL2LR2CRtXN9I7kGBgKHXDhLZQcOjpT5BMZblz8zKWNy+clgJKKQXDo1jPPvssXV1dbocyIw4cOIDH42Hbtm1uh6LUlOicGjUnWJ4VOPmTULgAOIi9DGT21ipdT8jj446GFm6qWUT70ABHB7vpTA5dut5gALn4DzFCdTDE5prFNIbL8Fo3rqQ3m1oWVZEvOPzmyBmqK8J4PTbJfA5BptyQ1xhDLJEhEvSzurX+ciNjEYYyGYITLJhhWcKqpfU01Jbx+slOznYMIDLc08s7UhI+mytQMAZbYHlzDSua6wgusHYCSikFwwnWX/7lX/Jf//VfPPzww26HU3QHDhzgtttuIxKJuB2KUlOiCZaaE8Qqwwq8B5weEA9YDYjMnQHWkMfH2so61lbWkSnkGcplSOayFEZGWryWRcTrJ+Idu3fUXLF8SQ0+j82h107j8dgkyTHVccJcvkA8maGmMsLyJTV4rixSYQxD6Qx1k3xxLIsEue2mVjasyjIYS9E3mCSVGY4xGvZTURaioiyIz6t/upRSC9fmzZtpampi//79JZVgtbW18elPf5ozZ85QVlZGW1tbyff7UqVJ36WoOUOsEFjNbocxLr/twW97IDA3CoRMVlNDJZXlIV44cpYTp/vI5vMwier8+YJDIpXFEmF1Sz01FWGuztIsEbKTWE91tVDARyjgY1Fd+ZT3UYoyqSxD/Qks26K8JjrlyotKqflNRNixYwdf+cpXSCaThELuTkMvhra2Nnbv3k0ymQQgFouxe/duAE2y1Lyjr85KLUCRoJ9tm5Zxy5oleDw2A0MpYvE06UwOxxm9/skYQzaXJ5HKMjCUIpPJ09xQweY1S6ipvDa5guG1aHN5JG++McZw9IVT/Pjrz/DLJ17g2e/9mgPf+iV9nYNuh6aUcsnOnTtJpVL86Ec/cjuUotizZ8+l5OqiZDLJnj17XIpIqanTESylFijLEpYvquFscoio18fAUJrYUIqhZJp8wYAwvMLMQCjopbYiQnk0SHkkgGXdeGKhABG/b1Yex0Jw5lgHR54/Ts3iqkujVqlEhud+8CJv+5+3E4ws3AbhSi1Ud999NxUVFezfv5+dO3e6Hc60nT59elLblZrLNMFSagGLjiRBF6fkNdaWgRmeBmiMQUSwLBk3obqaAaLT7KulhhljOP7Cm1TUlo2aEhgM+0nGkpx/s5PlG1tcjFAp5Qav18t73vMevve975HP5/FMsxqs25qbm2lvbx9zu1Lzjc7hUWoBi/r9lAUCo0uqC3g8Fl6vjcdjTTq5SmSzVAaDhH1a4a8YnIJDMp7BH7x2RNAX9DHUO7Gmzkqp0rNz5056e3t59tln3Q5l2vbu3XtNkhgKhdi7d69LESk1dZpgKbWAiQjr6msZSGWKts+hdIZ19bVTLv2uRrNsi2DYTyadvea6bCpLpGp+FltRSk3fu971Lnw+H/v373c7lGn7wAc+gN/vJxQKDfeibGlh3759WuBCzUuaYCm1wDVVlFMe8BPPTD/JiqUzVAaDLK7Q6n/FIiKs3NzKYFcMx7miSXQyg4jQuLTOxeiUUm6KRqPcc889PPbYY5gbNGifD37wgx+QSCT49re/jeM4nDp1SpMrNW9pgqXUAue1be5sbSaeyZErFKa8n2yhQDKb447WZq0gWGRNqxax6pZl9HcM0HO+j55zfWRTOW5/182EokG3w5u3RKRKRJ4UkWMj3yvHuM3bReTFK77SIrJz5Lp/E5E3r7hu0+w/CrXQ7dixg5MnT3L48GG3Q5mWRx99lNraWu677z63Q1Fq2vRdkFKK6nCIO5c20x1PDvfFmqRMPk9PPMn2ZS1UhfQNf7GJCGvesox7HtzGbf/jZrbev5l7HtxK9aJr8gE1OZ8CfmKMWQn8ZOTyKMaYA8aYTcaYTcA7gCRwZV3sP714vTHmxVmJWqkrPPDAAwA89thjLkcydbFYjO9973t88IMfnPfFOpQCTbCUUiOWVlVy9/JWBtMZ+pOpCd3HGENfIsVQOsPbViylpbJihqNc2AJhP3VN1VQvqsT22G6HUwp2AF8b+flrwHi1rn8beMIYkxzndkrNmsbGRm6//fZ5vQ7rO9/5Dul0WqcEqpKhCZZS6pKWygruX7eamnCIjlicnniSTD4/am6/MYb0yIhV51CcumiY+9etoUnXXan5p94YcwFg5Pt4C9oeBL5+1ba9IvKyiHxJRLQ3gXLFjh07OHToEGfPnnU7lClpa2tj+fLl3H777W6HolRRaIKllBol6vfzthXLuH/dalbV1lBwDF3xBF1DI1/xBMYxrK6r4b3r1vDW5Uu1qbCas0TkxyLy6hhfOya5n0XARuCHV2z+NLAGeAtQBXzyOvfdLSKHRORQd3f3FB+JUtd3sdHw448/7nIkk3f+/Hl+8pOfsGvXLq0+q0qGTnRVSo2pIhhg85JFbF6yiGyhQDZfQAR8to3X1ulpan4wxtx7vetEpFNEFhljLowkUF032NXvAN81xlxqGndx9AvIiMhXgU9cJ4Z9wD6ALVu2zO9Sb2pOWrNmDStXrmT//v384R/+odvhTMo3vvENjDE6PVCVFB3BUkqNy2fbRPw+wj6fJleqlDwOPDTy80PAjRaxfIirpgeOJGXI8MfuO4FXZyBGpcYlIuzYsYMDBw4wODjodjiT0tbWxpYtW1i1apXboShVNJpgKaWUWqg+D9wnIseA+0YuIyJbRORfLt5IRFqBJuCpq+7fJiKvAK8ANcBnZyFmpca0c+dOcrkcTzzxhNuhTNiRI0f4zW9+w+/93u+5HYpSRaVTBJVSSi1Ixphe4J4xth8C/uCKy6eAxWPc7h0zGZ9Sk3HHHXdQW1vL/v37efDBB90OZ0La2tqwLIsPfvCDboeiVFHpCJZSSiml1Dxn2zYPPPAA3//+98lms26HMy5jDP/xH//BvffeS0NDg9vhKFVUmmAppZRSSpWAnTt3EovF+NnPfuZ2KOM6ePAgb775pha3UCVJEyyllFJKqRJw7733EgqFeOyxx9wOZVxtbW0Eg0He//73ux2KUkWnCZZSSimlVAkIBoO8853v5PHHHx/VIH6uyeVyfPOb32THjh1Eo1G3w1Gq6DTBUkoppZQqETt27ODcuXP8+te/djuU6/rhD39Ib2+vTg9UJUsTLKWUUkqpEnH//fdjWdacnibY1tZGdXU173znO90ORakZoQmWUkoppVSJqKmpYdWqVXzxi1/EsixaW1tpa2tzO6xLhoaG2L9/P7/zO7+D1+t1OxylZoT2wVJKKaWUKhFtbW2cOHGCXC4HQHt7O7t37waYE1Pyvvvd75JKpbS5sCppOoKllFJKKVUi9uzZcym5uiiZTLJnzx6XIhqtra2NpUuXsnXrVrdDUWrGaIKllFJKKVUiTp8+Pants6mjo4Mf//jH/O7v/i4i4nY4Ss0YTbCUUkoppUpEc3PzpLbPpm984xs4jjMnpioqNZM0wVJKKaWUKhF79+4lFAqN2hYMBtm7d69LEV3W1tbGLbfcwtq1a90ORakZ5UqCJSJ/JSKvi8jLIvJdEalwIw6llFJKqVKya9cu9u3bR0tLy6VpePfff7/ro0ZHjx7l0KFDrseh1GxwawTrSWCDMeYm4CjwaZfiUEoppZQqKbt27eLUqVM4jsPOnTv54Q9/SHd3t6sxtbW1ISI8+OCDrsah1GxwJcEyxvzIGJMfufhLYIkbcSillFJKlbLPfe5zJBIJV6cIGmN49NFHecc73kFjY6NrcSg1W+bCGqyHgSfcDkIN/wFUSimlVOlYu3YtDz/8MP/wD//Am2++OevHb2tro7GxkZMnT/Liiy/OqabHSs2UGWs0LCI/BhrGuGqPMWb/yG32AHngur9tIrIb2A1zowJOqUinc/T1xuntjdPTGycRT+M4BssSQmE/tTVRqqrDVFdHCQS007pSSik1X33mM5/h0Ucf5c///M/593//91k7bltbG7t37yaZTALQ29s7p5oeKzVTxK1RCxF5CPgIcI8xJjmR+2zZssUcOnRoZgMrcYODSd58s5szZ/pwHAefz0Mg4MXr9WBZguMYcrkC6XSWXDYPIjQtqWLpsjoqKkLjH0Appa5DRH5tjNnidhxu0tcx5ZZPf/rTfOELX+A3v/kNmzZtmpVjtra20t7efs32lpYWTp06NSsxKFVME30dcyXBEpF3Af8LeKsxZsKrLvWFaery+QLHjnVw9GgHXq+HsrIgljV+kz/HMQzFUmSzeVaubGDV6gY8HnsWIlZKlRpNsPR1TLlnYGCAZcuWcfvtt/PEE7OzMsOyrDGXH4gIjuPMSgxKFdNEX8fcWoP190AUeFJEXhSRf3IpjgUhHk/z86de5/jRTqqqIlRUhCaUXAFYllBeEaK6JsrxE5089dTrDA2lZjhipZRSShVTRUUFjzzyCD/4wQ84cODArByzrq5uzO265EOVOreqCK4wxjQZYzaN32d1xgAAD2pJREFUfH3EjTgWgng8zS9+cZRcrkB1bRTbntp/uWUJNTVRCgWHX/ziKLGYJllKKaXUfPLRj36UpqYmPvnJT854Yavu7m4ymcylXlwXhUKhOdH0WKmZNBeqCKoZkk7nOPjsMSxLiJYFi7LPSCSAx2Nz8NljpFLZouxTKaWUUjMvEAjwl3/5l/zqV7/iP//zP2fsOI7j8NBDD5FKpfjsZz97qelxS0sL+/bt0wIXquRpglWijDG8dvgs2WyBSCRQ1H2Hw37yhQKvvnJGS7srpZRS88jv//7vs379eh555BFyudyMHOOv//qveeKJJ/jSl77EI488cqnp8alTpzS5UguCJlglqrNjkPbTfVRUzkzlv4qKMOfO9nP+3MCM7F8ppZRSxWfbNp///Oc5duwYX/nKV4q+/2effZZHHnmED3zgA3zkI7oCRC1MmmCVIMcxHH7tHOXlwWvmPheLiFBeGea1187iODqKpZRSSs0X999/P9u3b+cv/uIvSCQSRdtvX18fH/rQh2hubuaf//mfZ+w9iFJznSZYJai/P8HQUHrGGwT7/R6SyRy9vfEZPY5SSimlikdE+MIXvkBHRwdf/vKXi7JPYwwPP/wwFy5c4Jvf/Cbl5eVF2a9S85EmWCXo1KluAv6ZTa4uCgS9nHqza1aOpZRSSqniuPPOO9m5cydf+MIX6Onpmfb+/u7v/o79+/fzxS9+kbe85S1FiFCp+UsTrBJjjKGrM0Yo7J+V44VCPrq7h7TYhVJKKTXPfO5znyORSEy7bPqhQ4f4xCc+wfve9z4+9rGPFSk6peYvTbBKTDqdI58v4PHMzn+tbVvkCw7JpJZsV0oppeaTtWvXcvfdd/PlL38Zy7JobW2lra1tUvsYHBzkgx/8IA0NDXz1q1/VdVdKAR63A1DFNZzozO4fN0FIJrOEZ2nUTCmllFLT19bWxnPPPQcMz4Bpb29n9+7dABMqp26MYffu3bS3t/PUU09RVVU1o/EqNV/oCFaJcRwHmN3pegaDU3Bm9ZhKKaWUmp49e/aQSqVGbUsmk3ziE58YeT9xY/v27eNb3/oWn/3sZ9m2bdtMhanUvKMJlioOnRGglJpnROQDInJYRBwR2XKD271LRN4QkeMi8qkrti8VkedE5JiIfFNEfLMTuVLFcfr06TG3d3R0UF9fz4MPPsi//uu/cubMmUvXtbW10draimVZfOQjH2Hjxo382Z/92WyFrNS8oAlWibFte/YPasDjceG4Sik1Pa8CvwX8/Ho3EBEb+N/Au4F1wIdEZN3I1V8AvmSMWQn0Ax+e2XCVKq7m5uYxt1dXV/Oe97yHp556ig9/+MM0Nzezdu1a3vnOd/LhD3+Y9vb2S8Wtjh8/zte//vXZDFupOU8TrBITifiZ9YJ+gq6/UkrNO8aYI8aYN8a52W3AcWPMSWNMFvgGsEOGV/K/A/j2yO2+BuycuWiVKr69e/cSCoVGbQuFQvzt3/4tX/va1zh//jyvvPIKf/M3f0NraytPPvkkmUxm1O1TqRR79uyZzbCVmvM0wSoxPp+HYNBLLleYlePl8wV8Ps+MNzVWSimXLAbOXHH57Mi2amDAGJO/artS88auXbvYt28fLS0tiAgtLS3s27fvUoELEWHDhg38yZ/8CU888cR193O9qYZKLVRaRbAENTZWcqq9m8rKyIwfa2goTdMSrRqklJqbROTHQMMYV+0xxuyfyC7G2GZusH2sGHYDu+H6U7KUcsuuXbsmVDEQhp+/7e3tY25XSl2mI1glqKm5mnzOmZXmv/lcgZbWmhk/jlJKTYUx5l5jzIYxviaSXMHwyFTTFZeXAOeBHqBCRDxXbR8rhn3GmC3GmC21tbVTfShKue56Uwqn26hYqVKjCVYJKisLUlsbJR7PjH/jaUgkMlRVhSkvD41/Y6WUmp9+BawcqRjoAx4EHjfDn2AdAH575HYPARNN2pSal8abUqiUGqYJVolav2EJ6VSWwgz1pyoUHBKJDBs2No1/Y6WUmoNE5P0ichbYCvy3iPxwZHujiHwfYGSN1UeBHwJHgG8ZYw6P7OKTwJ+IyHGG12R9ZbYfg1KzbdeuXZw6dQrHcTh16pQmV0qNQddglajy8hBr1y3myGvnqK0rK/r++/sSrF7dQGVluOj7Vkqp2WCM+S7w3TG2nwfec8Xl7wPfH+N2JxmuMqiUUkpdoiNYJWz58jpq68ro640Xdb99vXGqqyOsXDnWunGllFJKKaUWLk2wSphtW2zZspTKyjA9PUM4zvSKXhhj6O0ZorwixFtuW6bNhZVSSimllLqKJlglzufzcNvty2luqqane4h0Ojel/WQyObq6hli8uJLbb1+Oz6ezS5VSSimllLqavkteALxem02bW2hYVM6LL54mPpQmHPETDPrGvW86nSM+lMLn87L1juU0LKqYhYiVUkoppZSanzTBWkAaGiq4554onZ2DHDvWQU/3EGDweGy8Pg8igjGGXK5APlcADJFIkE2bW2loKNdRK6WUUkoppcah75gXGK/XZsmSKhYvriQeT5NIZOjvTzA0lMYpGCxLiET8VFZFCIf9RKMBRMTtsJVSSimllJoXNMFaoESEaDRINBqkoUGn/SmllFJKKVUMWuRCKaWUUkoppYpEEyyllFJKKaWUKhJNsJRSSimllFKqSMSY6TWfnU0i0g20ux3HDKsBetwOYg7R8zGano9r6TkZbS6fjxZjTK3bQbipSK9jc/n/eLbpubhMz8Vlei4u03NxWTHOxYRex+ZVgrUQiMghY8wWt+OYK/R8jKbn41p6TkbT81H69P/4Mj0Xl+m5uEzPxWV6Li6bzXOhUwSVUkoppZRSqkg0wVJKKaWUUkqpItEEa+7Z53YAc4yej9H0fFxLz8loej5Kn/4fX6bn4jI9F5fpubhMz8Vls3YudA2WUkoppZRSShWJjmAppZRSSimlVJFogjUHichficjrIvKyiHxXRCrcjslNIvIBETksIo6ILNhKOCLyLhF5Q0SOi8in3I7HbSLyryLSJSKvuh2L20SkSUQOiMiRkd+Vj7kdk5oZ+ndgmD7nryUitoi8ICL/5XYsbhKRChH59sj7qCMistXtmNwiIn888vvxqoh8XUQCbsc0W8Z6jyAiVSLypIgcG/leOVPH1wRrbnoS2GCMuQk4Cnza5Xjc9irwW8DP3Q7ELSJiA/8beDewDviQiKxzNyrX/RvwLreDmCPywP9jjFkL3AH8X/r8KD36d2AUfc5f62PAEbeDmAP+FviBMWYNcDML9JyIyGLg/wa2GGM2ADbwoLtRzap/49r3CJ8CfmKMWQn8ZOTyjNAEaw4yxvzIGJMfufhLYImb8bjNGHPEGPOG23G47DbguDHmpDEmC3wD2OFyTK4yxvwc6HM7jrnAGHPBGPObkZ+HGH5DsdjdqNQM0L8DI/Q5P5qILAHuB/7F7VjcJCJlwN3AVwCMMVljzIC7UbnKAwRFxAOEgPMuxzNrrvMeYQfwtZGfvwbsnKnja4I19z0MPOF2EMp1i4EzV1w+ywJ+M6GuT0Ragc3Ac+5GomaA/h0Ygz7nAfgy8GeA43YgLlsGdANfHZku+S8iEnY7KDcYY84Bfw2cBi4Ag8aYH7kblevqjTEXYPhDGqBupg6kCZZLROTHI3Nir/7accVt9jA8DaLNvUhnx0TOxwInY2zTEqBqFBGJAP8JfNwYE3M7HlV0+nfgKvqcBxF5L9BljPm127HMAR7gFuAfjTGbgQQzOA1sLhtZX7QDWAo0AmER+T13o1o4PG4HsFAZY+690fUi8hDwXuAeswBq6Y93PhRngaYrLi9hAQ31q/GJiJfhN5ptxpjvuB2PmhH6d+AK+py/ZBvwPhF5DxAAykTkUWPMQnwzfRY4a4y5OJr5bRZoggXcC7xpjOkGEJHvAHcCj7oalbs6RWSRMeaCiCwCumbqQDqCNQeJyLuATwLvM8Yk3Y5HzQm/AlaKyFIR8TG8UPVxl2NSc4SICMNrDo4YY/6X2/GoGaN/B0boc/4yY8ynjTFLjDGtDD8nfrpAkyuMMR3AGRFZPbLpHuA1F0Ny02ngDhEJjfy+3MMCLfhxhceBh0Z+fgjYP1MH0gRrbvp7IAo8KSIvisg/uR2Qm0Tk/SJyFtgK/LeI/NDtmGbbSNGTjwI/ZPgP5LeMMYfdjcpdIvJ14CCwWkTOisiH3Y7JRduA3wfeMfI348WRT7NVCdG/A6Poc15dzx8BbSLyMrAJ+JzL8bhiZBTv28BvgFcYfs+/z9WgZtF13iN8HrhPRI4B941cnpnjL4DZZ0oppZRSSik1K3QESymllFJKKaWKRBMspZRSSimllCoSTbCUUkoppZRSqkg0wVJKKaWUUkqpItEESymllFJKKaWKRBMspaZARPaIyGEReXmkPPDtRdrvKRGpmeJ9m0TkTRGpGrlcOXK5pRixKaWUKh36OqbUzPG4HYBS842IbAXeC9xijMmMvJD4XA4LY8wZEflHhvs67B75vs8Y0+5uZEoppeYSfR1TambpCJZSk7cI6DHGZACMMT3GmPMw+pM7EdkiIj8b+fkzIvI1EfnRyG1+S0S+KCKviMgPRMR7xf7/VESeH/laMXL/fxOR3754AxGJXye2LzHcuf3jwHbgb4r82JVSSs1/+jqm1AzSBEupyfsR0CQiR0XkH0TkrRO833LgfmAH8ChwwBizEUiNbL8oZoy5Dfh74MuTCcwYkwP+lOEXqI8bY7KTub9SSqkFQV/HlJpBmmApNUnGmDhwK8PTF7qBb4rI/zGBuz4x8sLxCmADPxjZ/grQesXtvn7F961TCPHdwAVgwxTuq5RSqsTp65hSM0sTLKWmwBhTMMb8zBjz/wIfBf7nyFV5Lv9eBa6628WpGA6QM8aYke0Oo9dDmjF+vrRfERGuM1deRDYB9wF3AH8sIosm+dCUUkotAPo6ptTM0QRLqUkSkdUisvKKTZuAiwtwTzH8qSBcfrGarA9e8f3gGPvdAXivus/FF6x/ZHhKxWngr4C/nmIMSimlSpS+jik1szTBUmryIsDXROQ1EXkZWAd8ZuS6vwD+VkSeBgpT3L9fRJ4DPgb88ci2fwbeKiLPA7cDiTHu938Cp40xT45c/gdgzSTm1iullFoY9HVMqRkkl0d3lVJKKaWUUkpNh45gKaWUUkoppVSRaIKllFJKKaWUUkWiCZZSSimllFJKFYkmWEoppZRSSilVJJpgKaWUUkoppVSRaIKllFJKKaWUUkWiCZZSSimllFJKFYkmWEoppZRSSilVJP8/OyT6wVJyoY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "fig.suptitle('Contoh Beberapa Grafis Acak')\n",
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100)\n",
    "\n",
    "ax1.scatter(x, y, c=colors, s=sizes, alpha=0.3, cmap='viridis')\n",
    "ax1.set_xlabel('Sumbu X')\n",
    "ax1.set_ylabel('Sumbu Y')\n",
    "\n",
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "ax2.plot(x, y, '-ok')\n",
    "ax2.set_xlabel('Sumbu X')\n",
    "ax2.set_ylabel('Sumbu Y')\n",
    "\n",
    "fig.tight_layout(rect=[0, 0.03, 1, 0.97])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bisa juga menampilkan tabel/DataFrame pandas misalnya seperti ini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama</th>\n",
       "      <th>propinsi</th>\n",
       "      <th>umur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>joni</td>\n",
       "      <td>Sumatera Selatan</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>meri</td>\n",
       "      <td>Jatim</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>DKI</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>japri</td>\n",
       "      <td>Bali</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>budi</td>\n",
       "      <td>Papua</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lisa</td>\n",
       "      <td>NTT</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nama          propinsi  umur\n",
       "0   joni  Sumatera Selatan    23\n",
       "1   meri             Jatim    78\n",
       "2  peter               DKI    22\n",
       "3  japri              Bali    19\n",
       "4   budi             Papua    12\n",
       "5   lisa               NTT    33"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "        'nama':['joni','meri','peter','japri','budi','lisa'],\n",
    "        'umur':[23,78,22,19,12,33],\n",
    "        'propinsi':['Sumatera Selatan','Jatim','DKI','Bali','Papua','NTT']\n",
    "     })\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Banyak jenis-jenis media lain yang bisa ditampilkan secara hidup, misalnya Markdown, HTML, audio, video, Javascript, dan sebagainya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dengan begitu kayanya interativitas yang disediakan Jupyter notebook, maka Anda bisa melihat potensi penggunaannya, terutama untuk riset dan kolaborasi. Saya sendiri sering sekali menemukan penggunaan Jupyter notebook, misalnya di kursus-kursus online (MOOC) sebagai platform bagi murid untuk melakukan pemrograman, di Kaggle, dan banyak periset-periset yang membagikan karyanya dalam bentuk notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apakah Jupyter Notebook Hanya Untuk Python?\n",
    "\n",
    "Tidak. Walaupun Jupyter notebook ditulis dalam bahasa Python, namun kemampuan untuk mendukung suatu bahasa pemrograman di dalam notebook diimplementasikan secara modular dalam bentuk ***kernel***. Saat ini ada lebih dari 130 kernel yang mendukung hampir 100 bahasa pemrograman misalnya:\n",
    "- bash, \n",
    "- C, \n",
    "- C++, \n",
    "- C#, \n",
    "- Clojure, \n",
    "- Common LISP, \n",
    "- Erlang, \n",
    "- F#, \n",
    "- Go, \n",
    "- J, \n",
    "- Java, \n",
    "- Julia, \n",
    "- livescript, \n",
    "- Lua, \n",
    "- Matlab, \n",
    "- NodeJS, \n",
    "- Perl, \n",
    "- PHP, \n",
    "- Prolog, \n",
    "- Python, \n",
    "- Q, \n",
    "- R, \n",
    "- Ruby, \n",
    "- Scala, \n",
    "- SQL, \n",
    "- Tcl, dan \n",
    "- Zsh. \n",
    "\n",
    "Untuk daftar lengkapnya silakan lihat laman [Jupyter Kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels).\n",
    "\n",
    "Nah sekarang mudah-mudahan Anda semakin tertarik untuk mencoba Jupyter notebook. Mari kita install.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Petunjuk Instalasi Jupyter Notebook\n",
    "\n",
    "Sebelumnya, semestinya Anda harus mempunyai instalasi Python. Kalau belum, maka Anda perlu menginstalasinya terlebih dahulu dari https://www.python.org/downloads/. Pilih Python versi 3 terakhir.\n",
    "\n",
    "Ada beberapa cara untuk menginstall Jupyter, tapi menurut saya yang paling praktis adalah menggunakan `pip`. Cukup jalankan perintah ini di Terminal (Mac/Linux) atau Command Prompt (Windows):\n",
    "\n",
    "```sh\n",
    "pip3 install jupyter\n",
    "```\n",
    "\n",
    "Alternatifnya, kalau Anda mengalami kesulitan menginstall dengan cara di atas, mungkin bisa dicoba cara ini:\n",
    "\n",
    "```sh\n",
    "python3 -m pip install --upgrade pip\n",
    "python3 -m pip install jupyter\n",
    "```\n",
    "\n",
    "Setelah terinstall, maka siap dijalankan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menjalankan Jupyter Notebook\n",
    "\n",
    "Untuk menjalankan Jupyter Notebook, jalankan perintah di bawah ini di Terminal (Mac/Linux) atau Command Prompt (Windows) dari direktori yang menaungi semua direktori proyek-proyek Anda (misalnya Anda mempunyai dua proyek yaitu di `C:\\project\\project1` dan `C:\\project\\project2`, maka jalankan dari direktori `C:\\project` agar Anda bisa mengakses kedua proyek tersebut dari Jupyter).\n",
    "\n",
    "```sh\n",
    "jupyter notebook\n",
    "```\n",
    "\n",
    "Setelah beberapa saat, jupyter akan membuka laman http://localhost:8888/ yang merupakan laman depan dari instalasi Jupyter notebook Anda (disebut laman ***Notebook Dashboard***).\n",
    "\n",
    "### Notebook Dashboard\n",
    "\n",
    "Laman ini menampilkan semua direktori dan file yang ada di direktori di mana Jupyter dijalankan. Di tempat saya tampilannya seperti ini:\n",
    "\n",
    "![Notebook Dashboard](img/005NotebookDashboard.png)\n",
    "\n",
    "Sekarang kita siap membuat notebook baru."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Membuat Notebook Baru\n",
    "\n",
    "Klik tombol ***New***, lalu pilih ***Python 3*** seperti pada gambar di bawah ini:\n",
    "\n",
    "![New Notebook](img/010NewNotebook.png)\n",
    "\n",
    "Sebuah tab (atau window) baru akan muncul di browser Anda, berisi notebook baru yang kosong. Setelah beberapa saat (tunggu sampai tulisan \"*Starting*\" di judul tab hilang), kita siap menyunting notebook tersebut."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menyunting Notebook\n",
    "\n",
    "Mari kita kenali bagian-bagian utama dari tampilan notebook.\n",
    "\n",
    "![Gambar 020](img/020BagianUtamaNotebook.png)\n",
    "\n",
    "\n",
    "### Judul\n",
    "\n",
    "Yang pertama harus Anda sunting adalah judul. Masukkan judul dengan mengklik tulisan *Untitled* di atas. Harap diperhatikan bahwa judul ini akan menjadi nama file notebooknya juga."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sel dan Jenis Sel\n",
    "\n",
    "Setelah itu, bagian penting yang Anda harus ketahui adalah ***sel*** (Inggris: ***cell***) (lihat gambar di atas). *Sel* adalah tempat Anda menuliskan karya Anda, baik berupa narasi ataupun kode. Nantinya notebook akan berisi banyak sel. Saat ini notebook hanya mempunyai satu sel. Jenis *sel* Anda pilih dengan memilih pada *dropdown* jenis sel seperti yang ditunjukkan pada gambar. Pada awalnya, jenis *sel* adalah kode.\n",
    "\n",
    "Untuk sel pertama, sebaiknya kita isi dengan narasi awal tentang karya kita, misalnya judul, pendahuluan, dsb. Ikuti petunjuk berikut."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menulis Narasi dengan Markdown\n",
    "\n",
    "Untuk menulis narasi, gantilah jenis sel menjadi ***Markdown*** dari pilihan dropdown jenis cell di atas. Lalu tulislah judul dan narasi awal untuk karya Anda, misalnya seperti ini:\n",
    "\n",
    "![040](img/040EditingFirstMarkdownCell.png)\n",
    "\n",
    "Sintaks Markdown akan dijelaskan di bawah, tapi kalau Anda sekarang penasaran Anda bisa melihat detilnya di laman [Markdown cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).\n",
    "\n",
    "Ketika kita menyunting sel seperti ini, maka sel disebut sedang dalam ***mode edit***. Perhatikan garis kotak hijau yang mengelilingi sel. Pada mode ini, untuk sel Markdown kode sumber Markdownnya akan ditampilkan.\n",
    "\n",
    "Untuk menampilkan hasil tulisan Markdown kita itu, sel harus dijalankan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Menjalankan Sel\n",
    "\n",
    "Untuk melihat tampilan sebuah sel, maka sel itu harus kita jalankan, dengan salah satu cara berikut (kalau selnya masih belum mendapatkan fokus, maka kliklah sekali agar dia fokus):\n",
    "\n",
    "| Cara Menjalankan dari Menu | Kunci Pintas | Keterangan |\n",
    "|-----------------------|:------------:|------------|\n",
    "| Menu *Cell* --> *Run Cells* | `Ctrl+Enter` <BR>(atau `Cmd+Enter` untuk Mac) | Menjalankan sel |\n",
    "| Menu *Cell* --> *Run Cells and Select Below* | `Shift+Enter` | Menjalankan sel lalu memindahkan fokus ke sel berikutnya |\n",
    "| Menu *Cell* --> *Run Cells and Insert Below* | `Alt+Enter` <BR>(atau `Option+Enter` untuk Mac) | Menjalankan sel lalu membuat sel baru di bawahnya |\n",
    "\n",
    "Menjalankan sel mempunyai makna yang berbeda tergantung jenis selnya. Untuk jenis sel Markdown, maka ini akan merubah kode Markdown itu menjadi tampilan yang diharapkan (dirender). Untuk jenis sel kode, maka kodenya akan dijalankan oleh kernel, perubahan *state* akan disimpan oleh kernel, dan hasilnya ditampilkan di notebook.\n",
    "\n",
    "Sebagai tambahan informasi, ada cara untuk menjalankan semua sel yang bisa Anda lihat di menu *Cell*:\n",
    "- Run All\n",
    "- Run All Above\n",
    "- Run All Below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menjalankan Sel Markdown\n",
    "\n",
    "Mari kita eksekusi sel Markdown kita di atas, dan hasilnya akan seperti ini:\n",
    "\n",
    "![Gambar 045](img/045RenderingFirstMarkdownCell.png)\n",
    "\n",
    "Setelah sel dieksekusi, maka sel disebut ada dalam ***mode command***. Perhatikan garis kotak biru yang mengelilingi sel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menyunting Kembali\n",
    "\n",
    "Ketika sel dalam ***mode command***, untuk menyunting kembali di ***mode edit*** Anda bisa mengklik ganda sel itu atau mengklik sekali untuk menjadikan sel itu fokus lalu menekan `Enter`.\n",
    "\n",
    "Selanjutnya mari kita membuat sel baru bertipe kode. Tambahkan sel baru dengan memilih menu ***Insert*** --> ***Insert Cell Below***, atau dengan menekan tombol **`+`** dari toolbar. Secara default, jenis sel baru adalah kode jadi kita tidak perlu merubah jenisnya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menulis dan Menjalankan Kode\n",
    "\n",
    "Mari kita sunting sel kode seperti di bawah ini:\n",
    "\n",
    "![Gambar 050](img/050EditingCodeCell.png)\n",
    "\n",
    "Perhatikan juga bahwa untuk jenis sel kode, ada braket kotak di sebelah kiri sel, bertuliskan \"**`In [ ]:`**\". Braket kotak ini nanti akan diisi angka urutan eksekusi setelah selnya dieksekusi.\n",
    "\n",
    "Mari kita jalankan. Hasilnya akan seperti ini:\n",
    "\n",
    "![Gmabar 055](img/055RunningCellCode.png) \n",
    "\n",
    "Yeyy.. kodenya dijalankan. Perhatikan braket kotaknya sekarang terisi nomor urutan eksekusi (\"**`In [1]:`**\"). Nomor ini berguna karena urutan eksekusi tidak harus sama dengan urutan sel dalam notebook. Misalnya Anda punya lima sel, katakanlah sel 1 2 3 4 5, maka bisa saja Anda mengeksekusinya dengan urutan 1 4 2 5 3. Hal ini mungkin terdengar aneh sekarang. Tapi pada prakteknya ini merupakan hal yang sangat biasa Anda lakukan nantinya. Seperti kalau kita menyunting dokumen atau kode, maka sering kita menyunting bagian-bagian secara melompat-lompat. Makanya nomor urutan eksekusi ini sangat berguna untuk diperhatikan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lingkup (Scope) Variabel\n",
    "\n",
    "Variabel-variabel dan fungsi-fungsi yang Anda deklarasikan di sel akan mempunyai lingkup global, dan bisa diakses dari sel lainnya. Misalnya, mari kita membuat sel baru yang mengakses variabel `msg` yang kita deklarasikan di sel kedua tadi (ingat, sel pertama kita adalah Markdown):\n",
    "\n",
    "![Gambar 060](img/060SecondCodeCell.png)\n",
    "\n",
    "Anda bisa mengakses variabel global yang didefinisikan di sel lain, asalkan sel tempat variabel tersebut didefinisikan telah dieksekusi sebelumnya. Cara melihatnya adalah dengan melihat nomor eksekusi dalam braket kotak di sebelah kiri sel itu. Karena nomor eksekusinya lebih kecil dari nomor sekarang (nomor eksekusi sel itu 1, dan nomor sekarang 2), dan kernelnya sama, maka berarti sel tersebut telah tereksekusi sebelumnya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Membuat Narasi yang Lebih Kaya dengan Markdown\n",
    "\n",
    "Dengan Markdown kita bisa membuat teks narasi yang cukup kaya. Semua fitur-fitur dasar yang dibutuhkan dalam penyuntingan teks didukung oleh Markdown, misalnya heading/judul, mulai heading 1 sampai heading 6, tipografi (huruf **tebal**, *miring*, `huruf tetap`, ***tebal miring***, ~~tercoret~~, dsb.), list:\n",
    "\n",
    "- item 1\n",
    "- item 2\n",
    "  - item 2a\n",
    "  - item 2b\n",
    "- item 3\n",
    "\n",
    "List bernomor:\n",
    "1. item 1\n",
    "2. item 2\n",
    "   1. item 2a\n",
    "   1. item 2b\n",
    "3. item 3\n",
    "\n",
    "Dan juga tautan, gambar, tabel, kode, video, dan sebagainya. Banyak fitur-fitur ini yang sudah Anda lihat sendiri dalam notebook yang sedang Anda baca ini! Kalau dibutuhkan fitur yang belum ada, bisa kita tuliskan dengan markup HTML.\n",
    "\n",
    "Referensi lengkap dari Markdown dapat Anda lihat di laman [**Markdown Cheatsheet**](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).\n",
    "\n",
    "Harap dibedakan kode yang merupakan bagian dari narasi (di dalam sel Markdown), dengan kode hidup yang ada di dalam sel kode. Cuplikan kode dalam sel Markdown juga diwarnai sesuai sintaks (*syntax coloring*), sehingga terlihat bagus seperti pada cuplikan berikut:\n",
    "\n",
    "```python\n",
    "def katakan(berita):\n",
    "    \"\"\"Fungsi untuk mengatakan berita\"\"\"\n",
    "    print(berita)\n",
    "```\n",
    "\n",
    "Jangan salah, cuplikan kode di atas hanyalah berupa teks Markdown dan tidak dapat dieksekusi, karena bukan dalam sel kode.\n",
    "\n",
    "Cuplikan kode di atas ditulis dengan membungkus kodenya dengan tiga backtick (` ``` `), lalu tuliskan bahasa pemrograman yang diinginkan untuk mengaktifkan pewarnaan sintaks, seperti ini:\n",
    "\n",
    "````\n",
    "```python\n",
    "def katakan(berita):\n",
    "    \"\"\"Fungsi untuk mengatakan berita\"\"\"\n",
    "    print(berita)\n",
    "```\n",
    "````\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Persamaan Latex\n",
    "\n",
    "Markdown mendukung Latex untuk menampilkan ekspresi matematika, yang bisa ditampilkan dalam baris yang sama misalnya: $e^{i\\pi} + 1 = 0$ atau dalam baris tersendiri:\n",
    "\n",
    "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Menyimpan dan Membuka Notebook\n",
    "\n",
    "### Menyimpan Notebook\n",
    "\n",
    "Untuk menyimpan notebook, Anda bisa melakukan salah satu di bawah ini:\n",
    "- tekan kunci `Ctrl+S` (atau `Cmd+S` di Mac)\n",
    "- pilih dari menu **File** --> **Save and Checkpoint**\n",
    "- tekan tombol disket dari toolbar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menutup Notebook\n",
    "\n",
    "Untuk menutup notebook, pilih dari menu ***File*** --> ***Close and Halt***.\n",
    "\n",
    "Kadang-kadang window/tab browser tidak tertutup setelah Anda melakukan itu. Anda bisa menutup tab/window browser secara manual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Membuka Kembali Notebook\n",
    "\n",
    "Anda bisa membuka notebook dari *Notebook Dashboard* dengan mengklik notebooknya:\n",
    "\n",
    "![Gambar 070](img/070MembukaNotebook.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latihan\n",
    "\n",
    "Sekarang karena Anda sudah mengerti operasi-operasi dasar notebook, mari kita berlatih. Caranya adalah dengan mengunduh contoh-contoh notebook berikut ini ke direktori di mana `jupyter` anda jalankan, lalu buka notebooknya dari *Notebook Dashboard*. Silakan Anda oprek-oprek notebook itu, lalu kembali ke sini.\n",
    "\n",
    "- [Introduction to Python oleh Bryn Mawr College](https://jupyter.brynmawr.edu/services/public/dblank/CS245%20Programming%20Languages/2016-Fall/Labs/Chapter%2002%20-%20Introduction%20to%20Python.ipynb) - dasar-dasar Python [**[Unduh]**](https://jupyter.brynmawr.edu/services/public/dblank/CS245%20Programming%20Languages/2016-Fall/Labs/Chapter%2002%20-%20Introduction%20to%20Python.ipynb)\n",
    "- [Introduction to Python oleh Ondrej Lexa](https://github.com/ondrolexa/r-python/blob/master/01-Introduction-to-Python.ipynb) - dasar-dasar Python juga [**[Unduh]**](https://raw.githubusercontent.com/ondrolexa/r-python/master/01-Introduction-to-Python.ipynb) (Save-As ke file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Membagikan Notebook\n",
    "\n",
    "Kini saatnya untuk membagikan karya gemilang notebook Anda pada orang-orang! Ada banyak cara untuk membagikan notebook.\n",
    "\n",
    "### Secara Statis dengan Export File\n",
    "\n",
    "Anda bisa membagikan notebook Anda sebagai file PDF, HTML, Latex, Rst, atau Markdown dengan cara memilih menu ***File*** --> ***Download as***. Cara ini cocok kalau kalau penerimanya kurang mengerti teknis, misalnya bos Anda hehe.\n",
    "\n",
    "\n",
    "### Secara Statis/Dinamis dengan GitHub/GitLab\n",
    "\n",
    "![Github dan Gitlab](img/github-gitlab.png) Kalau pekerjaan Anda tersimpan di GitHub atau GitLab, maka Anda cukup bagikan link ke file notebook di GitHub/GitLab itu. Sebagai contoh Anda bisa melihat file notebook ini di [tautan ini](https://github.com/stosia/indoml/blob/master/notebooks/00-pengenalan-jupyter/pengenalan-jupyter-01.ipynb).  Penerima bisa melihat tampilan dari notebook Anda dari situ, atau mengunduhnya  dan menjalankannya sendiri di komputernya. \n",
    "\n",
    "Menurut saya cara ini adalah cara yang paling praktis untuk membagikan notebook ke kolega Anda.\n",
    "\n",
    "### Secara Dinamis dengan Google Colab\n",
    "\n",
    "Google Colab (https://colab.research.google.com) adalah layanan gratis untuk kolaborasi Jupyter notebook. Seperti Google Doc dipakai untuk berkolaborasi dalam penyuntingan dokumen, Google Colab dipakai untuk berkolaborasi dalam penyuntingan Jupyter notebook.\n",
    "\n",
    "Colab juga menyediakan fitur eksekusi di GPU (gratis) bagi notebook Anda, sehingga sangat berguna untuk melatih model pembelajaran mesin Anda.\n",
    "\n",
    "Colab bisa membuka notebook Anda dari GitHub, tapi kalau notebook Anda membutuhkan file lain (misalnya file gambar, atau file Python yang diimport oleh notebook Anda) maka mungkin harus ada langkah-langkah lain yang harus Anda lakukan (saya belum terlalu mencoba).\n",
    "\n",
    "### Secara Statis dengan Nbviewer\n",
    "\n",
    "Nbviewer (https://nbviewer.jupyter.org/) adalah layanan untuk melihat notebook secara statis. Kalau notebook Anda dapat terakses dengan sebuah URL, maka cukup berikan URLnya ke Nbviewer untuk melihatnya. Sebagai contoh, Anda bisa melihat notebook ini di nbviewer [di tautan ini](https://nbviewer.jupyter.org/github/stosia/indoml/blob/master/notebooks/00-pengenalan-jupyter/pengenalan-jupyter-01.ipynb).\n",
    "\n",
    "Cara ini tidak banyak berbeda dengan melihat notebook dengan GitHub/GitLab. Tapi GitHub kadang bermasalah dalam menampilkan notebook, jadi cara ini bisa menjadi alternatif.\n",
    "\n",
    "\n",
    "### Bagikan File Notebook Beserta Kebutuhannya\n",
    "\n",
    "Anda juga bisa bagikan saja file notebooknya kepada kolega Anda. Jika Anda ingin kolega Anda bisa menjalankan notebook itu, jangan lupa untuk mengikutsertakan file-file yang dibutuhkan notebook itu (misalnya file gambar, atau file Python yang diimport oleh notebook Anda). Tapi kalau kolega Anda hanya ingin melihat tampilannya, maka cukup dibagikan notebooknya saja.\n",
    "\n",
    "Cara ini hanya efektif kalau notebook Anda tidak tergantung pada file-file lain.\n",
    "\n",
    "\n",
    "### Tampilkan di Wordpress dengan Utilitas nb2wp\n",
    "\n",
    "Kalau blog Anda di Wordpress, cara yang paling tepat untuk mempublikasikan notebook Anda menurut saya adalah dengan mengkonversinya ke HTML sederhana dengan utilitas [nb2wp](https://indoml.com/2019/09/24/converting-jupyter-notebook-to-wordpress-com-html/). Silakan Anda baca tautan [nb2wp](https://indoml.com/2019/09/24/converting-jupyter-notebook-to-wordpress-com-html/) untuk informasi lebih lanjut.\n",
    "\n",
    "Wordpress.com (bedakan dengan self-hosted Wordpress) memberikan banyak keterbatasan dalam penyuntingan blog, misalnya tidak bisa menginstall plugin, tidak bisa mengeksekusi skrip dari postingan, tidak bisa memakai CSS, dan tidak memperbolehkan `data:` URI. Utilitas `nb2wp` menghasilkan HTML yang mengatasi keterbatasan-keterbatasan ini.\n",
    "\n",
    "Kalau Anda membaca ini di IndoML, maka tulisan ini dibuat dengan `nb2wp`.\n",
    "\n",
    "\n",
    "### Tampilkan Hasil Export HTML di Web\n",
    "\n",
    "Unggah file HTML dari hasil export notebook ke web atau blog Anda. Kalau Anda membutuhkan kustomisasi, Anda bisa edit file HTML-nya, atau rubah template `nbconvert` (agak panjang ceritanya).\n",
    "\n",
    "Cara ini lumayan, tapi membutuhkan layanan web atau blog yang memungkinkan Anda mengunggah file HTML secara utuh. Wordpress.com, misalnya, tidak memperbolehkan ini.\n",
    "\n",
    "\n",
    "### Tampilkan Cuplikan di Web/Blog\n",
    "\n",
    "Cara lain yang mudah adalah meng-embded [Gist](https://gist.github.com/) di laman postingan blog. Caranya:\n",
    "1. Buka file notebook (yang berekstensi `.ipynb`) di teks editor\n",
    "2. Select All --> Copy\n",
    "3. Buat Gist baru di https://gist.github.com/\n",
    "4. Paste isi notebook tadi di gist baru ini\n",
    "5. Beri nama file gist dengan ekstensi `.ipynb` agar gist ini ditampilkan sebagai Jupyter notebook\n",
    "6. Embed-kan URL gist ini di web atau blog Anda. Wordpress mempunyai fasilitas ini, dan semestinya penyedia layanan blog lainnya juga (saya belum mencoba yang lain sih).\n",
    "\n",
    "Dengan cara ini, notebook akan tampil dalam sebuah *iframe* kecil yang bisa di-*scroll*.\n",
    "\n",
    "Cara ini mungkin cocok kalau Anda berniat untuk membuat narasi tersendiri di blog Anda, dan hanya ingin mereferensi notebook Anda. \n",
    "\n",
    "Kelebihan cara ini adalah mudah, sedangkan kekurangannya menurut saya:\n",
    "- Anda harus membuat narasi lagi di postingan blog\n",
    "- notebook hanya tampil dalam *iframe* kecil yang lebih sulit dilihat\n",
    "- mengkopi-paste notebook ke gist sangat tidak praktis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Belajar dari Contoh Hebat\n",
    "\n",
    "Berikut adalah contoh-contoh notebook bisa Anda pelajari:\n",
    "- [Python for Exploratory Computing](http://mbakker7.github.io/exploratory_computing_with_python/) - buku pengenalan Python yang dibuat secara interaktif dengan Jupyter notebook\n",
    "- [Data analysis in R and Python](https://github.com/ondrolexa/r-python) - tutorial Python, Numpy, Pandas, Matplotlib, dsb.\n",
    "- [Tutorial Matplotlib](https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb) - indah untuk dilihat\n",
    "- [Contoh-contoh dari Jupyter sendiri](https://nbviewer.jupyter.org/github/jupyter/notebook/tree/master/docs/source/examples/Notebook/) - agak-agak kurang bagus untuk diikuti karena kurang terstruktur\n",
    "- [Kumpulan notebook-notebook menarik](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks) - ini adalah direktori besar yang berisi tautan ke notebook-notebook menarik dan juga ke direktori notebook lain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Penutup\n",
    "\n",
    "Sebenarnya masih banyak aspek-aspek dan fitur-fitur Jupyter notebook yang menarik untuk dibahas, namun tulisan ini rasanya sudah cukup panjang. \n",
    "\n",
    "Mudah-mudahan artikel ini menginspirasi Anda untuk menggunakan Jupyter notebook. Kalau Anda ada pertanyaan atau membutuhkan bantuan, silakan komentari di blog IndoML."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### (Bagian Ini Khusus buat Saya)\n",
    "\n",
    "Bagian ini saya pergunakan untuk secara otomatis mengkonversi notebook ini menjadi HTML untuk diunggah ke Wordpress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using template: full\n",
      "Using CSS file ../../../nb2wp/style.css\n",
      "html\\pengenalan-jupyter-01.html: 204572 bytes written in 35.976s\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'../../../nb2wp')\n",
    "from nb2wp import nb2wp\n",
    "\n",
    "img_prefix = 'https://raw.githubusercontent.com/' + \\\n",
    "             'stosia/indoml/master/notebooks/' + \\\n",
    "             '00-pengenalan-jupyter/html/img'\n",
    "\n",
    "nb2wp('pengenalan-jupyter-01.ipynb', \n",
    "      css_file='../../../nb2wp/style.css',\n",
    "      out_dir='html', \n",
    "      img_url_prefix=img_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python [root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}