{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram example\n",
    "Example's idea taken from https://github.com/rdp/ruby_gnuplot/blob/master/examples/histogram.rb ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "#<GnuplotRB::Plot:0x000000033679e8 @datasets=Hamster::Vector[#<GnuplotRB::Dataset:0x00000003367740 @type=:datablock, @data=#<GnuplotRB::Datablock:0x0000000336b4a8 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-dsb15x-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Austria\"]>, #<GnuplotRB::Dataset:0x00000003367448 @type=:datablock, @data=#<GnuplotRB::Datablock:0x0000000336a0a8 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-12k6uss-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Hungary\"]>, #<GnuplotRB::Dataset:0x00000003367128 @type=:datablock, @data=#<GnuplotRB::Datablock:0x00000003368b68 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-1jj9iuv-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Belgium\"]>], @options=Hamster::Hash[:title => \"Histogram example\", :style => \"data histograms\", :term => [\"pngcairo\", {:size=>[600, 600]}], :xtics => \"nomirror rotate by -45\"], @already_plotted=true, @cmd=\"plot \", @terminal=#<GnuplotRB::Terminal:0x00000003366bd8 @cmd=\"gnuplot 2>&1\", @current_datablock=0, @err_array=[], @in=#<IO:fd 23>>>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "require 'gnuplotrb'\n",
    "include GnuplotRB\n",
    "\n",
    "titles = %w{decade Austria Hungary  Belgium}\n",
    "\n",
    "data = [\n",
    "    ['1891-1900',  234081,  181288,  18167],\n",
    "    ['1901-1910',  668209,  808511,  41635],\n",
    "    ['1911-1920',  453649,  442693,  33746],\n",
    "    ['1921-1930',  32868,   30680,   15846],\n",
    "    ['1931-1940',  3563,    7861,    4817],\n",
    "    ['1941-1950',  24860,   3469,    12189],\n",
    "    ['1951-1960',  67106,   36637,   18575],\n",
    "    ['1961-1970',  20621,   5401,    9192],\n",
    "]\n",
    "\n",
    "x = data.map(&:first)\n",
    "datasets = (1..3).map do |col|\n",
    "  y = data.map { |row| row[col] }\n",
    "  Dataset.new([x, y], using: '2:xtic(1)', title: titles[col], file: true)\n",
    "end\n",
    "\n",
    "histogramm = Plot.new(\n",
    "  *datasets,\n",
    "  title: 'Histogram example',\n",
    "  style: 'data histograms',\n",
    "  xtics: 'nomirror rotate by -45',\n",
    "  term: ['pngcairo', size: [600, 600]]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same data may be also plotted in another form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37gwZMqTbr586dSolJeW9995bs2aNSCR6+eWXX3nllc4OFggETGanfSbWrVu3e/furVu3jh8/3snJSS6XT5w4sbm5uds6RKdQkz27GPmrdd1kOfQZOssAgIkiu8Zs2bKF/187d+4kCOLrr78mD2Cz2e0GVNTV1ek/C4XCPXv2VFRU5OXlLVy4MDk5+bvvvutbTQ4cOPDiiy+uWbMmOjo6NDSUzWb38U8Ck4QgBABTpFarDx06NHbs2PN/NXz48P/85z/kSgj+/v4SiUSfhVVVVbdu3Xr4VOHh4R988AGLxcrPzycIgsVi9eRhTk+n0zU2NvJ4PP2eI0eO9Otv670xG9CkaUQIQgAwRT/++KNcLl+1alXsXz377LMSieTChQsEQSxatKi2tnb9+vUymSw3N/fxxx+3s7Mjv15cXBwTE/Pxxx+fOXPm3Llzzz77rEajiY+PJwgiLCyspaVl586dmZmZZN/RrjEYjKlTp+7Zs+fmzZsqlerLL7/UP5IOGLzbMyoEIQCYov379zs5OS1cuLDd/iVLlnA4HLLVdOjQoUeOHDl16pSfn9/ixYuXL18+YsQI8jAejxcSEvLxxx/PmzdvwYIFYrH4+PHjEydOJAhizpw5K1as2LRp07hx42bOnNmTyuzevXvUqFExMTE+Pj6pqampqamU/q1AM0tea7H/LHstSgCAHrLsmyGeCAEAwKohCAEAwKohCAEAwKohCAEAwKohCAEAwKohCAEAwKohCAGABh9//DGDwWg3EUxJSQmDwejJ8vEAFEIQAgCAVUMQAgB05eF1esHCIAgBwEQ9+eSTo0ePNtwTGxs7d+5c8vPatWu9vLwuXbo0btw4DocTHBz84YcfGh585MgRcqWI8PDw48ePx8fHz5o1iyzKz89fsmSJv78/m80OCgp64YUXamtr9V8kz5yenj527FgOh5OcnPz9998zGIxr164Znj8+Pr5d9Sj3Qf7Fy5VF+s1LlUUf5F806hWtE9YjBADa1NfX69caJP66iFJP1NTUvPjii//+979DQkIOHz68Zs2awYMHz5gxgyCI8+fPL168OCkpaefOndXV1a+++mpjY+PIkSPJLxYXFw8aNGjhwoUCgaCoqGjr1q3Z2dmXL182PPPq1at37NgRGRnZ0tISEBDg6+u7e/fuzz//nDzg3r17GRkZ+k0jifMOnn1u7/5Ji6d4Dz5Xdvfvl749Gf+0Ua9opXTQOfw+AEby0UcfdXZT2rp1K3nME088MWrUKMNvTZ48ec6cOeTn5ORkgiAyMzP1pVFRUcuWLSM/T5w4cfjw4fqinJwcgiASExM7rEx2djZBELm5uYZnTk9PNzxm06ZNXC63rq6O3Hz11VednZ1VKlWff4EeulZdIvz23X/mnRd+++616hJjX64zln0zxBMhANBm//79fn5++s2qqqpFixb1/OsODg5jxozRbwYFBUkkEoIgdDpdZmbm+vXr9UWRkZGDBw/Wb2o0mo8++ujgwYMSiaS+vp7cefv27YiICPKzra1tbGys4bWWL1/+7rvvHjx4cOXKlWq1et++fU8++aSjo2PXNUzJPrPxxpme/0WdWZd1iiCIUSf/3Z+TbBiekDIiof+VsTwIQgCgTXR0dEhIiH6zpKSkV193cnIy3LSzsyNX3FUoFC0tLR4ef1nDz9PTU/953bp1u3fv3rp16/jx452cnORy+cSJEw1X6xUIBEwms93X58+fv3v37pUrV6amplZVVa1cubLbGqaM6G/2kC2i/xwza13WKbKNtD9ngw4hCAHARLHZbP3q86S6ujrDleI7w+fzWSyWTCYz3FlZWeni4kJ+PnDgwIsvvrhmzRpy8+rVqz2pD7lK8B9//LF79+7x48frHx+NR/9ecKRAGOLioX9faOzrWhv0GgUAE+Xv7y+RSPRZWFVV1W4AfmeYTGZ0dPSxY8f0e/Ly8u7evUt+1ul0jY2NhoF65MiRnpx28uTJYWFh69atu3jxYk8eB/svR1H2beyTIwVCgiBGCoSHYp+8IS8bgOtaGwQhAJioRYsW1dbWrl+/XiaT5ebmPv7443Z2dj387saNG3NychYtWnTmzJlDhw7NmzfPy8uLbO1kMBhTp07ds2fPzZs3VSrVl19++fXXX/fwtKtWrfrll1/4fH6v3mX2WXL45Ameg/SbEz0HJYdPHoDrWhsEIQCYqKFDhx45cuTUqVN+fn6LFy9evnz5iBEjevjduLi4Q4cO5ebmPvbYY5s2bdq2bZtIJNI3je7evXvUqFExMTE+Pj6pqampqak9PO3ChQsJgvjb3/7GZrP78BeBaWLodDq662C6GAz8PgCWoLy8PDg4ePPmzS+//HJ/zvPVV189/fTTBQUFhn18rIFl3wwt+W/rP8v+twewYCqV6o033oiPj3dzcysqKnr//fdlMtnNmzcFAkHfTlhQUHD//v2VK1eOHDny5MmT1NbW9Fn2zRC9Ri3KB/kXx7r76V8qXKosyqyS4KUCWCFbW1upVLpy5Uq5XM7lcmNjY48ePdrnFCQI4oUXXvjll1/GjRv3ySefUFhPMAXUvyO8ePHilClT3NzcnJ2dx4wZc+jQIcNSmUy2bNkyV1dXLpc7ffr0goICUyi1GHHewYsvHEgvv0sQxLmyu0suHIjzDqa7UgA0YLPZJ06cKC8vV6vVCoXi2LFjQ4cO7c8Jz507p1arf/nlF5FIRFUlwVRQO1FNVlaWvb396NGjDx48mJqa+thjjxEEcfDgQbJUrVZHRUV5e3t/+eWXhw8fjoyM9PDwqKiooLe0C5T/PgPARCZkAgBLYo43w56j+G9bu3Ytg8GorKwkN1tbW319fadPn05u7tu3jyCIjIwMclMqldrb2ycnJ9Nb2gUz/bdfeuEgY+/aT2/9SndFAMBCmOnNsIcobhrVarU2Njb6Psrk57a2NnLz5MmTQqEwLi6O3BSJRFOmTDlx4gS9pRbmXNndE5J8giA2Zp8h20gBAKALFAfh8uXLHRwcnn/+ealUKpPJ3n///Vu3bunnMRKLxWFhYYbHh4eHFxYWklP80VVqScgJmXwdeaE8jyeDRv3tl2+RhWDiPv74Y8Z/2djYiESiJUuW3Lt3r4df37x5M5fLpfZIsDYUB2FoaGh6evrp06f9/Pw8PT3ffffdQ4cOzZw5kyxVKBR8Pt/weD6fr9PplEoljaWWJEdR9p9JS6QNys0jZ/wqe4AJmcBcfPrppz/88MOxY8fWrFmTnp4eHx+vXxSCKh4eHsOGDaP2nGAZKA7CnJycGTNmDBs27MSJEz/99NOiRYueeOIJs26EZHQkJSWF7np1LDl8sos9J9BJMMs39HatzJ/Lx9gJMAuxsbGzZs2aM2fOq6++umPHjuLi4t9//53aS6xYsSIzM5Pac1qklJSUh296dFfKuCgOwjfeeIPD4Zw6dWrOnDnTp0//6quvJk2atHr1arKUz+cbrkZNEIRSqWQwGOTst3SVdq3DN6smG4QEQWTLS0e4Cu2YNrN8hx0vzqO7OgC95urqShCERqPR78nNzZ09ezaPx+NwODExMZcuXersu0eOHAkNDWWz2eHh4cePH4+Pj581axZZZNg0+uSTT44ePdrwi7GxsXPnziU/r1271svLKzs7e8KECRwOJzg4mJyD7bPPPgsKCnJyckpISJBKpZT+0R07Vdh4S6EhCOK7Ow0EQdxSaE4VNhr7oikpKQ/f9Ix9UXpRHIQ3b94MDw9nsVj6PaNHjy4rKyNzKCwsTCwWGx6fn58fGBjI4XBoLLUw2YrSEQIhQRBJAZGpD3Lprg5AjzQ2NqpUqtra2qysrA0bNgiFwsmT/2zMyMnJeeSRRyorKz///PNjx455eHjEx8dfu3bt4ZOcP39+8eLF4eHhJ0+efPPNN1999dWbN2/2rT4NDQ1Llix5/PHHDx8+HBwcvHjx4tdee+3AgQPvvffeJ598kpeX99RTT/X9r+2xMIH9zuza/Gr10bsNedXqndm1YQL7Abiu1aG2E+r48eOFQmFTU5N+T2xsrJOTU2trq06n++qrrwiCuHDhAllEDmN45ZVXyE26SrtA+e8zAB459dGF8ns6na65VcM/8FZpQy3dNQLoykcffdTupjRo0KDc3Fz9AQkJCf7+/iqVitzUarWRkZFz584lNzdt2uTo6Eh+njhx4vDhw/VfzMnJIQgiMTHx4SOfeOKJUaNGGVZj8uTJc+bMIT8nJycTBJGWlkZuymQyJpMpFAobGxvJPbt27SIIoidjkfuvsEaz8lzVolOVK89VFdZoBuCKHTLHm2HPUTzF2osvvrhkyZKEhISVK1eyWKzvvvvuwoUL69evt7GxIQjiiSee2LFjx9KlSzdv3uzo6LhlyxYXF5e1a9eS36Wr1JJodW25ynLyiZBlYzvTN/R4cd7q0Bi66wXQjYMHD/r5+el0utLS0p07d06bNu2XX34JDg5Wq9Xnz59/4YUXHB0dySOZTOasWbM+//zzdmfQ6XSZmZnr16/X74mMjBw8uI9r2LJYrPj4ePKzu7u7h4fHo48+qm9DInuhS6VSw1XvO/TdnYajdxv6Vod2FM1tr19W9OcMCwY7LhziSEllLA3l0Xrq1KmJEycKBAInJ6cRI0bs2bNHq9XqSysqKpYuXcrj8RwcHBISEsRiseF36SrtjDF+H6MqqKkM/O49/ebx4rzY05/QWB+AbpFPhAUFBfo9SqWSw+E89dRTOp2uoqKCIAhbW1uWAVtbW3IOaJ3Bc151dTVBEJ999pnhySdMmNC3J0JPT0/DUn9//zVr1ug3f/vtN4IgLl26RMUP0I3cqhb9E2FeVcsAXLFDZncz7BXqJ91OTExMTEzsrNTT0/PgwYOmVmoxsuV/viAkTReG/L9Lh8sb67wdnGmsFUCv8Hg8kUiUm5tLEISLi4uNjc2qVauee+65rr/F5/NZLJZMJjPcWVlZqZ/fwxCbzdYvfE+qq6vrSe+5AZZXrf4kp27daN7rlxXrRvP+ebVmdZRzuBteE1IMC/NalGxF6QhXH/0m28Z2unDo9xJxF18BMDVVVVUSicTd3Z0gCDabHRsbe+HChUGDBoX8VbtvMZnM6OjoY8eO6ffk5eXdvdvxhBL+/v4SiUSfhVVVVbdu3TLOX9MvxXWta0a4DHKxXTDYcZCL7ZoRLg/qWrv/GvQSgtCitHsiJAhiQUDkUfQdBZN34cKFU6dO/fDDD7t3746Pj1er1c8//zxZ9MEHHxQVFU2YMGHfvn3p6elHjhxZt27dunXrHj7Jxo0bc3JyFi1adObMmUOHDs2bN8/Ly4vJ7OAut2jRotra2vXr18tkstzc3Mcff9zOzs64f2GfzAp0CHG1IwiCfLcX4mo3K9CB7kpZIKxHaFGy5aUjBX9ZI2aGKPSZK0dkzSoPNiaXAtO1atUq8oOHh0dkZOTZs2enTJlC7omKisrKykpJSVm3bl1tba27u/vo0aM7bCmNi4s7dOjQhg0bHnvssaCgoG3btr3//vsdNo0OHTr0yJEj77zzzs6dOwMDA99++239lMhghSx50eH+M69FmSUNNWN/2Fm+eEO7/YvO/2eqcMjyIWNpqRUAXcrLy4ODgzdv3vzyyy/TXRezZ143w95C06jleLhdlJQUEIGR9WANVCrVCy+88P3331+5cuXAgQMJCQlcLndgRr6DWUPTqOUgJ1d7eP9jvmHP/npU3tIoYOHtAlgyW1tbqVS6cuVKuVzO5XJjY2OPHj0qEAjorheYOgSh5chWlC4LGvXwfgdbuyneg7+X5D89OHrgawUwYNhstllP8Q90QdOo5eisaZT4c95RTMANANABBKGFqGpW1WtaAp1cOyx9zHfY5coipbppgGsFAGD6EIQW4oaiLMrVh0F0vGyYkx0r1jvoB4ysBwB4CILQQmTLS0cIfLo4ACPrAQA6hCC0EJ11GdWb7Rt2saKwVt08YFUCADALCEILoV+PtzMu9uyJXoNOSfu4TikAgKVCEFoClaalpKE21KWbpdGS/NE6CgDQHtHF6a8AACAASURBVILQEuQoy4fxPG07mlzY0Dz/8Izye3UatI4CAPwPgtASdDGC0BDPnvOIR8DpElNcbgYAgC4IQkvQbU8ZPcw7CgDQDoLQEnTbU0Zvnn/EmdI7Da1qY1cJAMBcIAjNnrpNe6tGFunq3ZODBSyHaHff0yUFxq4VAIC5QBCavZs1lYFOAo5NT9fXXoB5RwEADCAIzd51eUkP20VJSf6RaaW3m7Qa41UJAMCMIAjNXreTq7XjxnYcIRD+XHLbeFUCADAjCEKz1/Muo3oLAiKPPsgxUn0AAMwLgtC8tel0OcryXjWNEgSR5B/xY0lBs7bVSLUCADAjCELzdq++2o3lyLPn9OpbnhynKFefM6VoHQUAQBCaud6+INRL8o9MLUbfUQAABKGZ68MLQlJSQMQPEnELWkcBwOohCM1btqJ0pEDUhy8KHVyG8TzPld2lvEoAAOYFQWjerlX3bhChoaSAyNRizDsKANYOQWjGShpqbZk2Pg7Offv6woCo7yVidZuW2loBAJgXBKEZy1aUDnftS08ZksjRZbCz2/nyexRWCQDA7CAIzVgPlyHswoIArFkPANYOQWjG+txlVG9BQOQJSX5rWxtVVQIAMDsIQjOWrSgd2b8nwgCuawDX9ULFfaqqBABgdhCE5qq6uaFG3RTkLOjnedA6CgBWDkFornKUZZF8bwbB6Od5FgVEHSvOQ+soAFgtBKG56n9PGdIgJ1eRo8ulysL+nwoAwBwhCM1V/3vK6CX5R2DeUQCwWhQH4dy5cxkPiY6O1h8gk8mWLVvm6urK5XKnT59eUFBg+HW6Ss1RtqKMkidCgiAWDopKfZCr1aF1FACskS21p3vttdeefPJJ/eb9+/dff/31mTNnkpsajSYhIUEmk23fvp3L5W7ZsiU2NjY3N9fT05PGUnPU0KqWqJRhPC9KzjbE2d2dzb1S+WCSVyAlJwQAMCc6Y1q/fj2DwSgsLCQ39+3bRxBERkYGuSmVSu3t7ZOTk+kt7YKxf58++7Xywajv/03hCTdmn3nx9+MUnhAALInJ3gwpYcS/TavV+vn5TZo0Sb9n/vz5QqHQ8JgZM2YEBQXRW9oFk/2331Vw5R+Xj1B4QrGyQvjtu9q2NgrPCQAWw2RvhpQwYmeZ8+fPSySSv//97/o9YrE4LCzM8Jjw8PDCwsLm5mYaS80RVV1G9YbxPF3s2b9XFVN4TgAAs2DEINy3b5+jo+PChQv1exQKBZ/PNzyGz+frdDqlUkljqTmisMuoHkbWA4B1MlYQ1tfXHzt2bP78+Vwu10iXGBgPd4JlMBgpKSk0VknTpr1VK4vqx7oTHVoQEPndgxwdoaP2tABgXlJSUh6+6dFdKeMyVhB+9913jY2Nhu2iBEHw+fyamhrDPUqlksFg8Hg8Gku71mGDMr1BeLOm0o/Ld7C1o/a0EXxvri0rs0pK7WkBwLykpKQ8fNOju1LGZawg3Ldvn7+/f1xcnOHOsLAwsVhsuCc/Pz8wMJDD4dBYanay5aUjqH4cJCWhdRQArI9RgrCwsPDy5cvLli1r90A9e/bskpKSixcvkpslJSXp6elz5syht9TsZCso7imjR74mROsoAFgXY3RFfeeddwiCuHfvXrv9arU6IiLCx8dn7969hw8fjoyMdHd3Lysro7e0C0b6ffpp4o8fnyu7Y6STBx/dmlUlMdLJAcBMmebNkCrU/21tbW0BAQETJkzosLSiomLp0qU8Hs/BwSEhIUEsFptCaWdM8N9e29bm/J/11c0NRjr/61d/fP3qj0Y6OQCYKRO8GVKIobP0t6D9wWCY3O9zr646Pm33g4XrjXT+q9XSxy8cuL/gDSOdHwDMkQneDCmE1SfMzHUjjCA0NNrNl0EQNxRlxrsEAIBJQRCaGeP1lNGbHxCBvqMAYD0QhGaG8snVHpbkH/ldUY5RLwEAYDoQhGbGGJOrtRPt7tvS1pqnLDfqVQAATASC0JyUNdbpCELk6GLUqzAIxnx/tI4CgLVAEJqTbHnpCIFR5pRpBxNwA4D1QBCak+vyEmO3i5LGe/jXa1pu1lQOwLUAAOiFIDQnPe8ympki68+FGARjnn84HgoBwBogCM1JtrysJ0HY1qrL2tivICQIYkFAFIIQAKwBgtBsKFoa5S0NwU5u3R5Z8Vtj/y8X4xEgb2m4VdvfQAUAMHEIQrORoyiL5Hszu1whMzNFtouRf3xSEUEQuxj5uxj5fW4jZTIY8/wjUvFQCACWDkFoNrIVpSMFoq6PiU7xWK0L94zmEAQR9ZJgtS48OsWjz1dM8o9ILc7r89cBAMwCgtBs9PAFYZOstfa+miCIu4dqFeKW/lxxkldgWWPdvbrq/pwEAMDEIQjNxnV5SU+CsCS9QRjrOGaDx8jX3X97vaI/V7RhMNF3FAAsHoLQPDS2ah6oFMN4nt0eKUmr95vmFJ3iEbHateaOWnpW1Z/rJvlHonUUACwbgtA85CnLh7p42DNtujlOR0jSVH4zuARBMO0Y47d5/vpqhU7b91XE4ryDJCplYb28z2cAADBxCELz0MO5tuV5zSyeDVdkR24GznNmu9kW7Kvp83VtGMzZfmHH8FAIAJYLQWgeejinTPHPKr9pXMM9j/zL64+3KzX1bX2+NOYdBQDLhiA0D9ny0pE9CEJpWr3vX4PQfQTbN56bvb3vPT8f9Q6+V1ddrFL2+QwAAKYMQWgGNG1acU1FlGs3605oVG2yq03CyY7t9o/f6pn3ibxeounb1e2YNmgdBQALhiA0A7dqZb6OPEdb+64PK7vY4BntYOvQ/t/UUWgXvkrwx9t9X0oiyR+towBgsRCEZqCHPWUkaap27aJ6I9e5laQ3yK429a0CCcIht2pl0oa+d7oBADBZCEIz0MOeMpK09j1l9Oy4zOgUjytr+zi+3o5pM8t32PHi/L59HQDAlCEIzUBPJlerK1RrVFpBOLuzA0Kf5mvq2wpP1PWtDkn+EanFaB0FAAuEIDR1OkJ3owfTbUvPqnwTnIjOl6ZgMImY7V6/rq3QtvRlfP004dBcRXlZYx9zFADAZCEITV1hvcLZji1gOXR9WBftonrCOEdeCCv/U0UfqsGysZ0pCjmOvqMAYHEQhKYuW979C8I2ja70fIPv1G6CkCCImO1e196rapZr+1CTBQFRmHcUACwPgtDU9aTLaOUfTS6D7dmC7mYiJQh+CCtoofPVLX1ZrXeGKOSGvFTW3K9ZvAEATA2C0NT1pMuoJK2+23ZRvegUjzsHamvu9HqpQraNbYJwKFpHAcDCIAhNXU+aRiVpKr9pTj08Icfddvhat99e78v4esw7CgCWB0Fo0sob61p1bX6OvC6Oaapqrb2n9hzH6flpo14SyPOay35p6G19ZopCs6qlVWgdBQALgiA0admK0uHdTTFKLknPtO185MRDbOwZYzd7Xn6pXNfLRSkcbO2m+gz5XiLu3dcAAEwYgtCk9bhdtKcvCPUGP+5i68C8c7DXs6YlBUSmPsBrQgCwHAhCk5atKOumy6iOkPzci54yhmI+8P79zUpNQ++eCmeJQn+VPVC0NPbhigAAJghBaNK6fSKU5zfbO9s4BXSzMEWHPMdyvGMccnb0bqlCrh1rik8wWkcBwGIgCE1Xjbqpqlk1xNm9i2P61i6qN36bZ86H8sby1l59K8k/EvOOAoDFQBCarhuKsgi+N5PRVS8Yaf+C0CnAftgzrn+807uhFLP9wi5VFCnVfVzUCQDApCAITVe37aKahrbKzEaf2PZL0vfK6PXuD36sr7rei1RzsmNN9go8Jb3Zn+sCAJgIowTh6dOnJ02axOVyXVxcxo8ff/HiRX2RTCZbtmyZq6srl8udPn16QUGB4RfpKjVN3U6uVvZLg8cYBzvHfv0j2jkxx7zV66UKMbIeACwG9UG4e/fuxMRENpu9efPmf/3rX6NGjaqo+PMmq9FoEhIS0tPTt2/fvnfv3vLy8tjY2MrKSnpLTVa3k6v1s11UL+xZflOVtvjH+p5/ZbZf2IXy+3Wa5v5fHQCAZjpKFRUVsdnsNWvWdFi6b98+giAyMjLITalUam9vn5ycTG9pFyj/fXqusVXt8PXrLdrWLo45GHKnOqeJkss9+LHuYMgdrbqt51+ZeeaLg/evU3J1ADBxNN4MBwDFf9vbb79tb2+vVCp1Op1Wq21XOn/+fKFQaLhnxowZQUFB9JZ2gcZ/+8wqyfATH3RxQN0D9Vc+BbpeJFc3vk8oytsl7/nxe+9kzkv/irLLA4AJs+wgpLhp9PLly5GRkQcPHvT19bWxsQkICNixY4dO9+eS6GKxOCwszPD48PDwwsLC5uZmGktN03V5STftomdUvlO5XSxJ31sx//LK2iRT1/Z0qcJ5/uHpZffqNb1exQIAwKRQHIRlZWW3b99OSUl5++2309LSHn300eTk5G3btpGlCoWCz+cbHs/n83U6nVKppLHUNGXLy7oOQklafc9XnOgJQSQ7YJbTtfeqeng8z54z3sP/dIkZdDsCAOgCxUHY1tZWX1+/Z8+eFStWJCQk7N27Ny4u7p///KdW25cl0U0BoyMpKSnGvm62oqsuo22tPV2SvlfGbfG8uVdZe1/dw+Mx7yiA5UlJSXn4pkd3pYyL4iAUCAQEQcTHx+v3TJ06taampri4mCAIPp9fU/OXWZ6VSiWDweDxeDSWdq3DBmVjB2FrW5tYWRHV+boTlX80OQfas926X5K+VzgetlFr3P5Y39P+tPP8wtNKbze09jQ4AcD0paSkPHzTo7tSxkVxEJKv4gx/NfIzk8kkS8Xiv8xRmZ+fHxgYyOFwaCw1QbfrZD4Ozk52rM4O6OfMal0Yniyo+L2x/HKP5tR2YzuOcfP9qeSWMWoCADAwKA7CefPmEQTx008/6fecPn3aw8PDz8+PIIjZs2eXlJTox9eXlJSkp6fPmTOH3KSr1ARly0tHCkRdHCBNq/c1ThDacphjN3leWVtO9Oy/gAsCIlMxsh4AzBq1nVDb2tri4uJcXFx27NiRmpq6aNEigiA+/fRTslStVkdERPj4+Ozdu/fw4cORkZHu7u5lZWX0lnaB8t+nh1754+S23IzOSpuqW/fwb2o11I2caKdNd2TMvTuHanpybEVjncuB9Y2tamNVBgBMAF03w4FB/d9WW1u7evVqT09Pe3v78PDw/fv3G5ZWVFQsXbqUx+M5ODgkJCSIxWJTKO0MXf/2sac/SSu53VnpnW9rTs8tNmoFyq80fD3odmtTj7I29vQnx4vzjFofAKCXZQchQ2fpb0H7g8Gg4ffRETrXg+/cSXrNnd1x42fG06Ue0Zzwla5GrcZP8yWeYzkjX+tqESjSroIrv1UVH5i01Kj1AQAa0XIzHDBYfcLkPKhXOtrad5aChI6QnFH5TzfKC0JDMdu9srdXN1Z0v1RhUkDkj9KCZm3vFjUEADARCEKT0/Vc2wpxs50Do29L0veKc6D90GW8rHdl3R7pxXGK4HudLbtj7CoBABgDgtDkZMtLR3YehJI0lS+lE8p0YczbHvdT6xT53U9El4RVmQDAbCEITU7XT4TGG0H4MBbfZtQb7r++1v34+gUBkT9IxOo2c50/CACsGYLQ5FzvfD3e1sa2yswmYVy/lqTvlYjVrrX31JI0VdeHCR1cQnme59A6CgBmCEFoWiqa6lu0rf5cfoelZb80uo9k93NJ+l5h2jHGv+955ZXyttZuOowlBURg3lEAMEcIQtNyQ1E6vPMpRiVp9QPWLqoXONfZwdvu1lc1XR+2MCDqpFSsQesoAJgbBKFp6Xr1JUmaitqll3ooZrvXH+9UquvaujjG15EX6CQ4X35/wGoFAEAJBKFpye78BWG9RNNSo3WLYg9wlQiCcBvO9k3gZv+zm6UKF/hHHn2QMzBVAgCgCoLQtGQrOh07IT2j8o2nckn6Xhn/nmf+Z4r6Yk0XxywcFHlckt/a1tWDIwCAqUEQmpBadXNFU/0Ql45nNaPlBaGeo9Au/DnBH291NZQigOvq78i/WInWUQAwJwhCE5KjKAvnedkwOvhHaWvVlWY00PKCUG/kOreS8w2yrKYujlmAkfUAYG4QhCaki6H0sqwmpwDql6TvFTsuM3qjx6U1XS1VuGhQ1PHifK0OraMAYDYQhCYkW95pEA7khDJdCP1/fG2L7v6xus4OCHQSeHOcLlUWDWStAAD6A0FoQrLlpSM6GURoIkHIYBIx271+XVehben0qTAJa9YDgFlBEJqKZm3r3brqCL53B0UKbc3tFq9HHAa+Vg8TxjnyQ1l5u+SdHbBoUNTRB7loHQUAc4EgNBX5yvLBzm4sG9uHi0rSVT4THZh2NI2ceEjMdq/r26qbqzueRGaIs7s7m/urrHiAawUA0DcIQlORreh0ThkpTRPKdIYfwgpe5Jy1qdOlCpMCItA6CgDmAkFoKrruKeNrAi8IDUWneN49VFtzp6XDUnIQha6L3qUAACYDQWgqOptcTXGzxYbFcAky+pL0vcJ2sxm+1u3XdR2Prw/jebnYs3+XSQa4VgAAfYAgNAlaXVuesny4oIMuo5Kf603tcZAU9ZJAIW6Wnut4qUKMrAcAc4EgNAm3a6u8HZyd7TqYUJuuFSe6ZWPPGLfF89dXKzrsH5rkH/ndgxy0jgKA6UMQmoTO2kVbG9sqf28UDeCS9L0SvMjFjsu8/bXy4aJIV29HW/usKunA1woAoFcQhCahs8nVyi41uo3g2DmZ7j9TzHbv39+SaRo6eCqc7x9xtBitowBg6kz3DmtVsuWlIzp8QUjrihM94TmW4zPR4cb26oeLFgREfleEvqMAYOoQhPTTEbpseelIgejhIqlpzKzWtfFbPXN2ylXS9ksVjhAIbZnM6/JSWmoFANBDCEL6SVQ1HFs7D3b7wKuXaJqVWvcRHFpq1XNOAfZhK1wzN3Qwvj7JHyPrAcDUIQjpd72TofTSsyrfKbQtSd8ro9e7F/9cX3Wt/VKFSQGRh4tyaKkSAEAPIQjpl63ouMuo1PQmlOmMnRNzzNseV9ZWtNs/xs2XIIgbijI6KgUA0CMIQvp1OLmaTqsrSVf5TjWPICQIImwFv6la++CH+nb756N1FABMG4KQfh0OIqzManLyt3Pw7GAxCtPEsGE88r7nlVcr2jR/6SaaFBBxBK2jAGDCEIQ0q2yqb2xVBzjx2+2Xpql8TXJCmS74z3Ry8rfL/0xhuHOsu1+zVpOvbN9qCgBgIhCENMtRlEe5+jAe6hJjIkvS99aEf3tf3VTVrPjfUoUMgjE/IALzjgKAyUIQ0ixb0cEIwmaFVnHTVJak7xXXYaxBc52ub60y3LkgIApBCAAmC0FIsw7nlCnNaPCZ5GBjbw4jJx4ybrNnwT5lzV21fs94d/8adVNBTcdrNgEA0AtBSLPr8pKHu4xK0upNc8WJnuB42Eatcfv9zf+9FGQyGPP90ToKACYKQUinOk1zWWNdiItHu/2Sn83yBaHe8GSBLKup7FKDfg+WJwQAk4UgpFOuojyM72XD+Mu/grKgxYbFcAk2rSXpe8WWwxy72fPXtRX6CbcneA6qamm4VdvBNGwAAPSiOAjPnTvH+Cs3NzfDA2Qy2bJly1xdXblc7vTp0wsKCkyhlC4dzikjSVP5Jpjx4yBp6BM8nY64800NuclkMOb5hR8rzqO3VgAADzPKeO0dO3b4+vqSn1ksln6/RqNJSEiQyWTbt2/ncrlbtmyJjY3Nzc319PSksZRG2fLSse5+7XZK0urDV7rSUh8qMYiJO73TFkkD5znbOjAJgkgKiHw164c3I6fQXTMAgL/SUers2bMEQeTl5XVYum/fPoIgMjIyyE2pVGpvb5+cnExvaRco/33aGX7ig8wqieEeTaP2c2exuk5r1OsOmNPzi69ukZGfW9u0nodS7tZW0VslAOgDY98M6WWsd4SNjY06XfsVWU+ePCkUCuPi4shNkUg0ZcqUEydO0FtKlxZt6526qgi+t+HO8suNbsNNekn6Xon5l9eNHdWN5a0EQdgwmHP9wlPROgoAJsYoN9wJEyY4Ojpyudz58+ffv39fv18sFoeFhRkeGR4eXlhY2NzcTGMpXcQ1lYFOArbNX1qnzXRCmc44B9oPfYqXmfJnH5mkAEzADQAmh+IgdHZ2fv755z///PNTp06tW7cuPT39kUceKS8vJ0sVCgWf/5dJNfl8vk6nUyqVNJbSpcO5ti0sCAmCGPO2R+GJOnleM0EQcV7BD1TKwno53ZUCAPgfioMwOjr6o48+WrRoUWJi4oYNG3744QeZTLZz505qr2IZshXtV19SSTVNslY3k1+SvldYfJtRb7pffqmcIAhbJnO237Djxfl0VwoA4H+M+y5q0qRJfn5+WVlZ5Cafz6+pqTE8QKlUMhgMHo9HY2nXGB1JSUnp6U/QuYeXIZSeU/nGcxkW8n7wfyKec1WVtEp+VhEYWQ9g8lJSUh6+6dFdKeMy+k23tbVV/yOGhYWJxWLD0vz8/MDAQA6HQ2Np1zrsYtT/INTq2vKU5SPbBaHFtYuSmHaM8e97Xn6lvK1VN8V78N26KklDTfdfAwA6pKSkPHzTo7tSxkVxELa2thpu/vjjj2VlZePGjSM3Z8+eXVJScvHiRXKzpKQkPT19zpw59JbS4m5dtTub62zH1u/RaXXSs5YwlL5DgXOdHX3sbn6htGPaPOYXduwB+o4CgKlgUBv106dP9/HxiYqKcnJyunbt2hdffOHp6Xnt2jV3d3eCIDQazahRo+Ry+ebNmx0dHbds2VJeXp6Tk+Pt7U1jaVe/DoPi30fvUGF2anHe0bin9Hsq/2i68Gzp4zeCjXE5U1B9o/mHGQ+euDX4bN2drbnplxOfp7tGANBTxrsZmgRqhyVu37591KhRPB7P1tbW19d3xYoV5eXlhgdUVFQsXbqUx+M5ODgkJCSIxWJTKO0M5b+P3qtZP2y+cdZwT+bGyl/XlXd2vGU49zfpb29UqLWtgoNvlzTU0F0dAOgp490MTYFFh3y/Ge8/QVPTdr8cNmmmKFS/JzWmcOwmT9Gjjsa4nIloKNV8G3Vv0dWg1ZLUMe6+L4ROoLtGANAjlv1EaHE9FM1Eu0GEzQqtQtziPcH8lqTvFUehXcRqwW9vViYFRKbiNSEAmAYEIQ0kDTV2TBtvB2f9ntLzDd4TzHVJ+l4Z8apb2cWG4RLfHEVZWWMd3dUBAEAQ0uHhEYSStHqLHDjxMDsuc+wmz6xXq2eKQk5IMLIeAOiHIKTBw5OrSX6yzBGEHQr5O79No4spC8a8owBgChCENGg3uZryVgvTnsEbwuriK5aEwSRi/uXFSnG6Ji+RNavorg4AWDsEIQ3aNY1K0lS+U63lcZAkjHP0GuIYrQo4gXlHAYBuCMKBVt3cUK9pCXT63xr0ljqzWtditnsFHfE5cjeH7ooAgLVDEA60bEVplKsPg/izg6i2WVf+a6NoitUFIW8oa15Y+O8Vkiq0jgIArRCEAy1bXjpC4KPfLLvcIIhg2Ttb4z/EpHeEITd9Dv2OLjMAQCdrvP/Sq12XUWmaym+aE431oRHbzSYpIOLr367RXREAsGoIwoHWrsuo5S1J3yvPPRN9k1+el1ZFd0UAwHohCAeUStMibagJdfEkNxvKWhsrW91HWdSS9L3i4sie4DDo032ZOq3FTmMIACYOQTigcpXlYTwvW+afP7v0TL1oiqPlLUnfK0/FjMwKLbq1H0v1AgA9rPsePOCuPzSC0GpfEOrN8Qu/E1B+fpNUU99Gd10AwBohCAeUYU8ZXRshPWfVLwhJTnasST5BxfMrsrdXZ6bI6K4OAFgdBOGAMuwpU3WtydHHzsHLlt4qmYIFAZHiOGneLnnWRgQhAAw0BOHAUbdpb9XIIl29yU0r7y9qaI5f2KWaIv9nLXlRYgAwWQjCgXOzpnKQkyvHxo7clJ5V+cYjCAmCIO68Vy+6LjiYfoMgiF2M/F2MfLSRAsCAQbvcwMmWl44UiMjP6lqtPLfZJxbPQARBENEpHsl3Y36cVUBMIcZu8hz9ljvdNQIAK4InwoFjOLlaSUaD1yNWsSR9D83xCz9XdreFpSn+qZ7uugCAdUEQDpzr8hJ9l1G8IDT0Qf7FgprKcR5+zSn18rzm9Lv3P8i/SHelAMBaIAgHSJtOl6Ms13cZRRAaivMOXnzhQBjPK2dcsSxJ8cSVA3HewXRXCgCsBd4RDpB79dVuLEeePYcgiJrbLQwGwRtqLUvSd2ukQHgy/ulZZ79UqJvOT7y/8erskX8Xdv81AAAq4IlwgNyQlw13/fMFoRUuSd+tkQLh28OnqrWtc0ThrCMOOkwyAwADBUE4QK7LS0aiXbRz58rubsk5tzRoxKHK6/eGl1ddb6K7RgBgLRCEAyRb8efYCW2LrvxKowgjCA2cK7v790vfnox/+q2oeI6t/RcLLqaez6e7UgBgLRCEA+RadQnZU6b8coMg3EqXpO9MjqLs29gnRwqEIS4ePHv2q+5Tfr8npbtSAGAt0FlmIJQ01NoybXwcnAm0i3YkOXyy/vMMYUiTXXPMoZDm97RsgQ2NtQIAK4HnkoGQrSj9S08Zq196qQuJvqE/ld/ymeQoPaOiuy4AYBUQhAMh+7/LEDaUtTaUaTxGsemukemK9Qq6WVPpNNMGU8wAwMBAEA4E/TKE0rMq3ylchg1mVusUy8Y21jvozshSyc8qDKIAgAGAIBwI2YpScuyEJK3eFy8IuzNTFJLRfJfFt8EgCgAYAAhCo6tubqhRNwU5C3RthPSsyi8BQdiNRNGwM2V3RDMdik+jdRQAjA5BaHQ5yrJIvjeDYFRdb3LwsnMU2tFdI1MncnQRObhUT6kt/gn9ZQDA6BCERqfvKYOBEz2X6Bua6V6kvNncLNfSXRcAsHAIQqP7X08ZBGGPJfoOIwdRSNLQOgoAxoUgNLpsRdkIgVBdq63OafKZiCXpe2Scu195U53dzDYJWkcBwMgQhMbV0KqWTxNm/QAAIABJREFUqJRhPK+S8w1ejzjYsDFwokdsGMwEnyE3I0okaRhEAQDGhSA0rlxFeYiLhy2TKU1T+WFCmd6YKQpNb77LFthUXcMgCgAwIiMGYVJSEoPB+Pvf/264UyaTLVu2zNXVlcvlTp8+vaCgwBRKjSdb8WdPmeKf8YKwd2aIQi5W3PdIZGMQBQAYlbGC8OTJkxcuXLC3tzfcqdFoEhIS0tPTt2/fvnfv3vLy8tjY2MrKSnpLjYrsMlpzV0206fihWJK+F1xZDhF877LJ1ZhrDQCMS2cE9fX1vr6+e/bscXR0/Nvf/qbfv2/fPoIgMjIyyE2pVGpvb5+cnExvaRf6//uM+v7fv1Y+yP2oOuMfJf08lRXaknPuucvHPncWN1Zq6K4LgFUzUliYCKM8Eb711lu+vr7PPPNMu/0nT54UCoVxcXHkpkgkmjJlyokTJ+gtNR5Nm/ZWrSzK1UeCF4R9kigK/am8wGeyo/Qc+o4CgLFQH4RXr1797LPPPv30UwajfQ9JsVgcFhZmuCc8PLywsLC5uZnGUuO5WVPpx+WztLZllxpF8Rg40WuRrt6aNq12ZgsGUQCA8VAchFqtdsWKFc8991xkZOTDpQqFgs/nG+7h8/k6nU6pVNJYajzZ8tIRrj4Vvza6DmOxeFhjttcYBGOGMCQvTFr8cz0GUQCAkVAchP/+979lMtnGjRupPS2NGB1JSUnpyXfJLqOYWa0/En2HnVPd4bjbyq5iEAXAQEhJSXn4pkd3pYyLyiAsLy/fsGHDxo0btVptTU1NTU0NQRBqtbqmpqa1tZUgCD6fT+7UUyqVDAaDx+PRWNq1Dt+s9jQI5WQQYumlvov3GXxVXiKYZS9B31GAAZGSkvLwTY/uShkXlUFYXFzc2Nj4j3/8g/9fDQ0Nhw4d4vP5586dIwgiLCxMLBYbfiU/Pz8wMJDD4dBYaiRtOl2OonyoxlMl1XiOMeKFLJujrf0jHv5FEyswiAIAjITKIBw2bNj5v2Kz2dOmTTt//nx0dDRBELNnzy4pKbl48SJ5fElJSXp6+pw5c8hNukqNpLBezmdxGi62iR7FkvT9kiga9rvLA2VBS5Osle66AIAlMt7IDJ1O124coVqtjoiI8PHx2bt37+HDhyMjI93d3cvKyugt7UJ/fp/DhTfmnvvqzBNS8ReKPp8EdDrd/bpqj282nJpddPuAku66AFgpY4cFvQY0CHU6XUVFxdKlS3k8noODQ0JCglgsNoXSzvTn3/71qz+mXD/zpXtBvVTd55MAaWjqtkO78888IaW7IgBWyrKDkKGz9Leg/cFg9P33mX5mz5N2Y2xecFySP5jaWlmhVzJPspvsgxb4P10RgnZmgIHXn5uh6cPqE8aSLS8V/OGMCWUokegberbutoOHreyqcedAAAArhCA0irLGOh1BNP/EwAhCSkz0DLxbV8V9jIm+owBAOQShUWTLS6NcvKtvNPlMwsxqFLBn2kzxHnx3fDlGEwIA5RCERnFdXhJc6+41HkvSUybRN/RXbqHythqDKACAWghCo8hWlHqI+WgXpdBMUei5ijuej3IkZzABNwBQCUFoFNnyMm4a1xc9ZajjxXEKdnKTT1diJQoAoJYt3RWwQIqWxuqmBo8qJ9dhWJKeSjN9Q29wJW1vsHVaHQZRAABV8ERIvRxF2WC1u188HgcpligKPVNz28HbtjILK1EAAGUQhNTLVpSKHgiw4gTlxrj5KtVNNnNa0ToKABRCEFLvuqzMNdPFNx5BSDEmgzFNOPR2dDlGEwIAhRCE1MsqlYTbe7H4WJKeeomi0Cuswpo76sZKDKIAAGogCCnW2KqRqJXjx/jRXRHLNE049NfqIrep9lIMogAAiiAIKZanLPeq5gUlONNdEcvkYs8eKRCVTatG6ygAUAVBSLE/CqWiIlfPaCxJbywzRSHX/SXSNJVOa7Fz4QPAQEIQUuzKreIIRx+McjOeRN/QM8pbHKFtZSYGUQAABRCEFMupKZ0Q6k93LSxZGM/LlmHTOqe5GIMoAIAKCEIqabTaIk51fHwg3RWxcDNFIbdGlWIlCgCgBIKQSr9lSgX1XA9/LL1kXIm+oZfs7tfexyAKAKAAgpBKF/4oGsb0orsWli/OOzhHWeaUYCNNQ+soAPQXgpBKV8tKon0xgtDoODZ2k7wCpfEyDKIAgP5DEFJGU9922142aVQA3RWxCjNFoVdFD6RnMIgCAPoLQUiZkguqEj/5GB9fuitiFWb5DjuruMMR2VT+gUEUANAvCELK/HGh1NmGLWA50F0Rq+DnyPPmOKvm1aN1FAD6CUFImV/vSIYLhHTXwook+oaKo8owmhAA+glBSI3a++oHblVj/dEuOnASRaG/MO7WFaobKzCIAgD6DkFIDWmaqjKiZgSeCAfQIx4BxQ1K1gydBIMoAKAfEITUkKTVF7lWIwgHki2TmSAcWjSlElPMAEB/IAgpoFXrCrKr2+x1fo48uutiXWaKQrI8i6RnVW2tGEQBAH2EIKRA5W+N8rG1IwQ+dFfE6swQhfyiLLT3Z2IQBQD0GYKQApI0lXJ8PdpFB547mxvK81DMq0HrKAD0GYKQApI0ldRPMcIVQUiDRFFofpgUgygAoM8QhP3VJGutK1IX6CrwREiLRN9hGdq7dUXqxnIMogCAvkAQ9pf0rIqXYFvVohri7E53XazRCIFPc5uGMVstSUPrKAD0BYKwvyRpqoYpqgi+N5PBoLsu1ohBMKYLQ+5OqkDrKAD0DYKwf3SE9IyqPBRD6emU6Bv6h6Co5BwGUQBAXyAI+6U6p9meZ3OzrQI9ZWg01WfI1XqpTRBR+TsGUQBAryEI+0WSVu83jZutKMUTIY2c7Fhj3fwq5sixEgUA9AGCsF8kaSqPaazCenk434vuuli1RN/Q3BAJRhMCQB9QHIRXrlyZNWuWSCRis9ne3t6zZ8/OysoyPEAmky1btszV1ZXL5U6fPr2goMAUSvtGo2qrutakjKwf7Oxuz7Tp/wmhzxJ9Qy+03qstVjeUYRAFAPQOxUH44MEDDoezZs2a3bt3v/TSS2KxeMKECdnZ2WSpRqNJSEhIT0/fvn373r17y8vLY2NjKysr6S3ts9ILDZ7RDrkN5SPRLkq3Ic7ujrb2LfOaMIgCAHpNZ0wPHjwgCOLll18mN/ft20cQREZGBrkplUrt7e2Tk5PpLe1C17/PxefLrv+rauWvRz+8ean73wKM7MXfj7/w5fc/L5TQXREAC2TssKCXcd8Renl52dra2tj82Wx48uRJoVAYFxdHbopEoilTppw4cYLe0j6TptX7TeNel5eiy6gpSBQN+4P/QIpBFADQS0YJwqampvr6+tu3by9fvtzBweGZZ54h94vF4rCwMMMjw8PDCwsLm5ubaSztm7pCtaahzWWYvVhZEeWKdSfoN9kr8FaDTBfaikEUANArRgnCqVOnOjs7h4SEnD17Ni0tLSQkhNyvUCj4fL7hkXw+X6fTKZVKGkv7RnJG5ZvgdLte5uPg7GTH6vN5gCosG9s476CSx6owiAIAesUoQfjpp59evHjxP//5T0BAwMyZM9t1HDUvjI6kpKRI01R+07jZcowgNCGJvsNuBEmKTyMIAfouJSXl4Zse3ZUyLqMEYURExKRJk5588smMjAxHR8c333yT3M/n82tqagyPVCqVDAaDx+PRWNq1Dt+svrN+Q+mFBt+p3Gx5GV4Qmo5EUehlTWFdKQZRAPRdSkrKwzc9uitlXMbtLMPhcIYNG3b37l1yMywsTCwWGx6Qn58fGBjI4XBoLO2Dit8bXQbbswU22YrSkQJR304ClPNxcPZz5NXOq8MgCgDoOYqDUKvVGm5WVlZevXo1ODiY3Jw9e3ZJScnFixfJzZKSkvT09Dlz5tBb2gdku6iO0GXLS0cI0FPGhMwUhd4eVyrBShQA0GMMap95ExIS/Pz8IiMjXVxcioqK9u7dW1lZeebMmcmTJxMEodFoRo0aJZfLN2/e7OjouGXLlvLy8pycHG9vbxpLu/p1GB3/Pt+NuT/h397NUc0TT+8qefxtCn9A6KdfZQ+W//Ldy8tnPS0LYdpZ+IsNgAHT2c3QQlA7LPGTTz6JiYkRCAQsFisoKOiJJ57Iy8szPKCiomLp0qU8Hs/BwSEhIUEsFptCaWc6/H0aKzV7+De1mrbUB7mzzn7Zk/PAgGlt07p9887Hk6+X/qKiuy4AloPysDApFh3y/dbhf4LuHqq9913tjP/f3p3HN1Xl/+O/N2uztE2abmmSQkv3FmQriFQE2UQ2gVFBBwZ1BnUYxXV+Xx39UGZwXMCxCC4oIMvgjjCAFGQTBQpl7Qpd6N6mC22Tttlz7/n9cTHELmlC29x70/fz4cNHcu5t+2oS7rv33HPO/SHyzcuHcRz/56iZtGQDPfnjL19GnAtaqBt197/D6M4CgI/w7TNCuPuEx6hbL2EYdqUF1pRhotnqxCtDYRIFAMBdUAg9hLCqIx23CmFzLSy3zUAPqBPOWytbGsyGWhvdWQAALACF0DM3c82CAK7/UEG9qd1M2IdI5b1/DfAuuUB0V1BE86KWqiMwdhQA0DsohJ5xnA5ebakdCUuMMtWD6sRrY+tgrTUAgDugEHqm2nGBsLkOFldjrNnqxLOiG9VHDaTNZy/vAwD6CxRCD9g6yIYLpojJEgzDrsDdlxhsRJAScbD2VEN9lpHuLAAApoNC6IG6U4awVBFfwsGoIaNwRshgD6gSymfWV8ISMwCA3kAh9EDVkQ7NTH8Mw/RWc72pPT4whO5EoEezNYmX1JVVcJkQANAbKIQecIyUyWmpS5GFc3F49ZjrfmVMnr2usdkAkygAAK7BodxdbWVWazsRPMIPg35RNpDwBGmhUQ2LmisPQ+8oAMAVKITuqj7aoZkuxXAMo0bKQCFkvAc1CQUja6B3FADgGhRCdzn6RbFbQ0ZhEiHTzVEnneaXVZ/ogEkUAAAXoBC6hbSh2pOGyBn+GIaZCXtJ283h8l5u4QRoF+UfFCwSN92tqz8LkygAAD2CQuiWhvOmgGECv2AuhmH5rdrYgGAhl0d3KNC72ZrEsmkwiQIA4AoUQrc47jiBYdiVFlhThjVmqxMvhFfAWmsAABegELql8wVCKIQskRYWVYVaa9v17VUwiQIA0D0ohL0zNdn1Jdbwe8TUU1hcjUX4HO7UiNi6hU3VP0HvKACge1AIe1dz3BBxn4TDwzEMIxCZ16odqYAho6wxW52Yn1ILvaMAgJ5AIeydc79osb5JKQ4I4PvRGwm4b5Y64RyvvPKXdsIKkygAAN2AQtgbhFUdbo984FYhvAz9omwTLvKPCwzRpjXDJAoAQLegEPaiOd/Ml3ICogTUU1hcjY1mqxNL7tPCEjMAgG5BIeyFc78odmvIKFwgZJnZmsTskDKYTQgA6BYUwl5UH+mInOlPPUYYutJcO1qhpjcS8NQYhbqDay23NsMkCgBAV1AIe1F/3qi6X0I9rurQ+fH4oX5S118CmIaD4w+o4mseaqw+AieFAIDOoBD2InTsrVvSYxh2paV2NFwgZKfZmsTchBqYRAEA6AoKYS86XyCEIaPsNFMVf5VbU3ZGB5MoAACdQCHsheMCIUbNnYAzQnYK4PuNDVZX3ddUfwYmUQAAfgcKYTey0xs/wvM/wvMxDPtmVOlHeH52eiMGZ4QsN1uTWJxWVwVrrQEAfg9HCHqKeoTjt1+fBlN74g/vNT/+T5y6Sz1gm2u6hqkHN69/b8ljubF0ZwGAZZwPhr4HzgjdldOivSsoAqogeyXKwsR+/FK8qb0SJlEAAG6DQuiuKy0wg5D1HlAnVM5pqDoCY0cBALdBIXQXrCnjA2arE6/GVlbBEjMAACdQCN11ubkGhoyy3RRlTAm3qehCC0yiAAA4QCF0S5vNXGdsSwgMpTsI6BM/Lu8+5bCKyfXa0zCJAgBwCxRCt+S2aJPl4VwcXi7Wm61OvH53LdyJAgDgAEd2t1xpgRmEPmK2JvF8QEX5YT3dQQAATAGF0C0wUsZnaCQyTYCsQFQPkygAABQohG6Buy/5kgfVCRWzGqoOQ+8oAADD+r0Qnjx58oknnoiNjRWLxcOGDVu5cmVjY6PzDo2NjUuXLg0KCpJKpQ888MC1a9eYsNU1C2EvbmsaLle6/yWAyWZrkq5EVcJ9egEAlH5eNWfy5Ml6vX7hwoWRkZGFhYWbNm0KCwvLycnx9/fHMMxms6WmpjY2Nq5du1Yqlb711lv19fW5ublhYWE0bnX16uA4Quhyc+2ffv0q76FX+vGFAjQiEBn6Zfr/99q8l0vGcIWwVBAAvfPtJdYw1K+uX7/u/HT37t0Yhm3dupV6un37dgzDTpw4QT2trq4WCAQvv/wyvVtdoF6fLUXnl5760tOXAjDZ4pO7nl6+v/pYO91BAGCHfi8WjNLPXaPx8fHOTydPnoxhWG1tLfV0//79KpVqypQp1FO1Wj116tR9+/bRu7VXV1rg7ku+ZrYm8XpqLfSOAgCwgR4sc+bMGQzDhg8fTj0tKChITk523iElJaWsrMxsNtO4tVdX4DaEPucBVcJlSc2Nozq6gwAA6DeAhbClpeXVV18dNWrU3LlzHS1yudx5H7lcjhBqbW2lcatrBCLzWrWjoRD6lmA/SUpwWG5ALUyiAAAMVCE0mUwLFy40GAzffvstl8sdoJ/iBTxVSHtdY6BAhDtJT0+nOxfoqwfViWUztJWwxAwAv5eeno53QXeogTUghdBisSxYsODq1as//fRTTEyMo10ul+t0v+uMam1txXFcJpPRuNW1L08fW5R6b6crq1AIfcBsdeJFTSWstQZAJ+np6V2Hk9AdamD1fyG0Wq1/+MMfzpw5k5mZOWrUKOdNycnJBQUFzi35+fnR0dEikYjGra5daakdFQRryvigkYoIQkheKqwjLD7+jxwA4Fo/F0K73b548eLjx48fPHhwwoQJnbbOmzevpqbm1KlT1NOamprjx4/Pnz+f3q2uwUgZX4Vj+IORCTfu12pPG+jOAgCgUz/Pkfzzn/+8devWFStWTJ8+3dEYExMzcuRIDMNsNtuYMWOam5vXrl0rkUjeeustrVabk5OjVCpp3Orq1cFxxe438x56RSkO6MdXCTDEvqr8f/948oMbD09cH053FgAYDSbUe6DTLAXKypUrHTvU19c/9thjMplMLBbPmDGjoKDA+cvp2toTDMPCv+qmuxz4hg6bRbr99a13ufVhAGAw6/diwSg+XeT7DMfxWT99fmj6n+kOAgbK9MOb49cPeXvL/f5DBXRnAYC5fPuMEO4+0Y3380+dbiinHo8KUv3aUP5+/il6I4EBMluTdON+LSwxA8BgBoWwG1OUMYt//u9xbQmGYVwOZ8nP/52ijOn1qwAbzdYkXoqorDjcRncQAABtoBB2Y7RCtX/ak3/65WsMwz4rOrd/2pOwsoyvig0IDpT4nbtRBZMoABi0oBB2b7RC9Y+7pmEYtnrkdKiCvu3ByISy++rrfoVJFAAMUlAIu3esruStnGMYhr2Vc5zqIwW+aq4mOW9EdRVcJgRgsIJC2I1jdSXLf/16/7QnMQyj+kihFvqwSeHRVaKW3J8b6A4CAKAHFMJu5LTUfT35j1SP6GiF6qvJf7zaXEd3KDBQBBzu/arYy2FVbWVWurMAAGjgy1ND+s63p84Ahy3F5/+7L2eT/x9Sng2iOwsATOTbB0M4IwQAm61OvBJadeOwnu4gAAAaQCEEAFOKA6IDg07XlBFmn/2bFwDQEyiEAGAYhs0ZklQ6CSZRADAYQSEEAMMwbLYmMTexGm5YD8AgBIUQAAzDsHHBkW0i06WzWrqDAAC8DQohABiGYRwcf2BIwsWISv0NmEQBwOAChRCAW2ZrEksm1lUfgSVmABhcoBACcMtMVXy+orb4p1a6gwAAvAoKIQC3yAWikUERPzeUwiQKAAYVKIQA3DY3KrnkXm3dLzCJAoBBBAohALfNVidejauCSRQADCpQCAG4LUUezhNxzlyoojsIAMB7oBAC8DtzohMvqWESBQCDCBRCAH5ntjqxaEJt1WGYRAHAYAGFEIDfmRoRWxZws/DYTbqDAAC8BAohAL8j4vInhg093lJiN5F0ZwEAeAMUQgA6mxuVVJKmrfvFSHcQAIA3QCEEoLMH1Yk50VUVmW10BwEAeAMUQgA6i/IPChVLf84ppzsIAMAboBAC0I25MUmXIiv0pTCJAgDfB4UQgG7M1iReH1tXdRiWmAHA90EhBKAbaaFRDVL9lZMNdAcBAAw4KIQAdIPH4UxTxZ3oKLYbYRIFAD4OCiEA3ZsbnVQ0oQ4mUQDg86AQAtC9B9UJBZq60sN6uoMAAAYWFEIAuhfiJ42RKo4VFtMdBAAwsKAQAtCj+XHJl4ZUwSQKAHwbFEIAejRbk3htVA3cpxcA3waFEIAejQlWm8W287/W0B0EADCAoBAC0CMcwx8cknDCVAKTKADwYf1cCOvq6p5//vkJEyaIRCIcxysqKjrt0NjYuHTp0qCgIKlU+sADD1y7do0JWwHoydyopOt319aeMtAdBAAwUPq5EJaVlX3zzTcKhWLChAldt9psthkzZhw/fnz9+vXbtm3TarWTJ09uaGigdysALkxXxRUrG64faaE7CABgwKB+RRAE9eCDDz7AMKy8vNx56/bt2zEMO3HiBPW0urpaIBC8/PLL9G51od9fH8BGad999MKDP9GdAgA6+fbBsJ/PCDkcV99w//79KpVqypQp1FO1Wj116tR9+/bRuxUA1x6KT746tFJXApMoAPBNXh0sU1BQkJyc7NySkpJSVlZmNptp3AqAa7MjEwtG1FTCfXoB8FFeLYQtLS1yudy5RS6XI4RaW1tp3AqAawmBoWI/wamsCrqDAAAGBEyf6AXenfT0dLpzAa+aHZV40gaTKMCgkJ6e3vWgR3eogeXVQiiXy3U6nXNLa2srjuMymYzGra51e2UVCuFgM29Y0vVxtbU/wyQK4PvS09O7HvToDjWwvFoIk5OTCwoKnFvy8/Ojo6NFIhGNWwHo1eTwYTXBrblHm+gOAgDof14thPPmzaupqTl16hT1tKam5vjx4/Pnz6d3KwC9EnJ5E2VRh25cpzsIAKD/4f17zosQ2rNnD4ZhP/744/bt2z/++OOQkBClUjlx4kQMw2w225gxY5qbm9euXSuRSN566y2tVpuTk6NUKmnc6urVwfv59QHstbkoa8fmq4eefUoWK6A7CwDe5uMHw/6dlmiz2br+iNmzZzt2qK+vf+yxx2QymVgsnjFjRkFBgfOX07W1J/3++gD2qu7Q+X/2j0sZjXQHAYAGvn0w9Oki32c+/kcQ8FDijveeOHbv33d1s3wgAL7Ntw+GMH0CAHfNj0v+mYRJFAD4GiiEALhr7rDka2Nqa0/CJAoAfAoUQgDcdXdIZGugMft4Ld1BAAD9CQohAO7i4pxpIbGZ1TCJAgCfAoUQAA8sSE65OqxaV2yhOwgAoN9AIQTAA7PUCUUx9cWZerqDAAD6DRRCADwQJBQnCEMzr0DvKAC+AwohAJ55KDH5F7zU1gGTKMCdyE5vpDsC6AwKIQCemRedVDCmpu4UTKIAd+LCGiiEjAOFEADPjAhSYiL068lKuoMAAPoHFEIAPINj+IzQ+Mx6uEwIPHD+/xo+wvM/wvMxDKMeQB8pc0AhBMBji0YMvxJVpSux0h0EMB7CtKeNp1bWFWxuDb9HPGlTBIZhKX8NEofz9CXWmhMGzGfX72QTX15Hte98e51ZcMcMdmvIjvSjHX+buCqC7iyAkRCmPWMs2qW78YNeHi+MXyYbtijQT8HFMOwjPH8lSkEEqjlpKPystf6cMe4xWdKf5YExjL69l28fDHl0BwCAfSQ8wVih+sDZwokYFELwO01XzNe3t5bva+OJOQnLZY9cjPEfwnfeIXV1KIZhOBfXTJNqpkkNdfaiXa0HZlUIArnJK+Rxj8v4Euio8zZfLvJ959t/BIG++ODqr3s+LTy5/i98KRy2AKa/YS3apbvxnd5uQvHLZDEPBwYlCz36Dk2XTAWftZYfaIuaGxC/VKZMEw9Q1Dvj2wdDX/7d+s6333vQF2XtzWN3bbgU+UrUnAC6swDaGBvsJV/ri3bpOqpssUsCYx4ODL9HjPfhTyOrnij5Rl+0U2duIRL+JEt8Ui4KYUS/nW8fDH35d+s7337vQR9Fbfn3a0UPrFg3mu4gwNts7WTZ3raiXbqGC6aoef7xy2TqKRKci/fjj2gptBTt1BX9Vxcyyi9+mSx6QQCH15/f31O+fTD05d+t73z7vQd99NfMvfVfW37YsZjuIMBLCCsq/19b0U5d7SmD6j5J/DLZ0Dn+PNEA9o0TFlS+v61op6451xy7RJa8Qh4QTc+YGt8+GPry79Z3vv3egz46ri1ZsW3vxUWr5AmeXQ0C7ELaUVVmR+l3+sof20NGi+KXyobM8fcL4nozg6HWVvRfXeHnreJwXvwyWfwfZTyxVy9O+/bB0Jd/t77z7fce9JGVJIK2vbm/4+n7XxhCdxYwABCmPWMs/U5/Y0+bOJwXv1Q2bGGAVMPv/QsHLhGJ1ZzoKNqpqz7WETU3IPlpechokXd+tG8fDBlxGRb0l4NlxhgZPyGI/12x4eE4yfUWW6nONieaWcPPfIaAw00TDdt/4dr9GBRCn6Irslz7QlfyjZ7Dw+Iel807MtTTIaADBOdg1KQLi44o/VZ/8i91hJmMXyZLeirIL9irZ6g+BgqhT0lWCN67qFt5V8D3JYaEIP7HOW1/HyujO5QvW3RXyubsi7YOEiZR+IC2cuv1Hbob3+nNzUTMo4GzvteEjPHS+ZanhDJu8oqg5BVBLQWWol26r+8qUU6UJK2Qa6ZKMTqH1LCVL5/t9h0bewPK9fb3LupazGSQH+fvY2VRgfC3zgCqN7XH7HznasSrMXPhDw62MjXZi7/U3/hO31JgiXkkMH6pLHyCqH9o1ukWAAAgAElEQVSHgA40akxN4WetumJL7BJZyjNy/6H9PKaGjQdD9/ny79Z37HrvG43El9cNWVqzc2NUAD8lmC/h4xI+R8rHxXyOlI9L+ByqhVX/2Bkqacu6Z0omPf/ueLqDAM/YOsiyH9qKdum0Z41DZ/vHL5Vppku5fgP1T+L9/FPjQyLTwqKop782lGc3Vb2ccl///pSOalvxl7qCz1oDowVJK+RR8wO4gv75jdh1MPSUL/9ufcfk9x4hrM5AlLfZKvT2cr29vM0m5OJDA/hSPn65ydJhRf4C/IEh4iARx2hDBhsy2EiDDRnspMHpKY+DiX+rkbeqI48j6dTy2wM+Z6COEay+tPn3o4cKvr354+fL6A4C3ELaUNXhjqJdusrD7ar7JDEPBw6d5y+UDfgFtsvNtfOObdsxafFUZeyxupLlv369f9qToxWqgfhZ1Jiaws9aa08Zoh8KSHk2KHikXx+/J5MPhn3ny79b3zHqvbcQqFxvL9PbyvX2Mr29yUSopFy1lBcVyIsO5A8J4Il4eN5NK3Vd8P+dbnknLYi6XpgS7KqTxEoggw11UGXSRnZ5/FsFtZEdNoRhmPMJpdSpTEp/a6QeBwg9ON2kunNX3hXwr/O6N8bLqF+BLZ26ZxsrFn/x5X+rn5i0SUl3FtAzhFUf7yjaqav8sV2eKBz2cGDMw4GSCK9+xi43184+uvUPUSN+qMg7MGBV0JmllSj9Tp//cQvOw+OXyuKXyu541gejDob9zpd/t76j971vt5LFrbYyvb2mw17TTjSZiKEBvOhAXnQgPyqQp5RweV1O0Qb61KpT1bSRmJXosXAa7UjMc6tqSgV4TTvBxkub7+efSg1Wzzuw/ZXX5rxxc/wA9XeBvtCeNpZ+py/7oY0n4SQ+IYt9NLDfr5+5ZibspxvKf6orOlJTVNrebLJbhVz+5PBh9ytj7o+IGRWk4uADfomCWsi0bG+bavIdjqmBQjh4efm9N9lRRZu9XG8rb7NX6O31RkIp4UYF8qIC+FGBvCH+PD9a11jylMmOjLf6Y29Xx1v/tyPnp+1WZCN/9zqnhgnnx4iVEq6Uz+jRmFR/V5RdEbEt5PH1KX8t/n7g+ruARwy1tpJv9MVf6tsrrDEPB8Y+FqicKOnLKqAeIRE621hxsLrwWF1JUVvT5PBhczVJIi7/tUuH3kud8+qFAy8kT2o2G043lOfr6lODNdMi4iaGDr07ZAiPM4ARLTqi5Et94bZW0oaSnpLHPS6j7gzlDiiEg9dAv/ftVrKijbrCZy/X21rMpMafFxXIGxrAiw7ka/y5A3dZjmmuNlk+yWnXWUh/AT5B6YdhWJ2B0BrsVgJTSri//cdTSrjhEq6QMYN8stMbf9h+feMrRyQdQqvQ/syGaSMDI0b8TRExSSyLZ8TMs8HG3EKU7Wkr/lLXeMkUNTcg7rFAzQwph++lD0yzxXisrvin2uKf6oqEHN4MVfwMVdz9ypgAvp/zdUHn64VaY9sJbSn1n4W036+Mof4bKg0auJw3c8zXtraWfq9XTZIkPilXT5P2+icCFMLBq3/fe4Swmg47dZ2P6vDkcfBhgfzowFsdnnI/Rp/9DBwXlzYJhN00EY1GosFI1LTbazqIRiNhtKMQESdUzA0Tc9VSntqfFyHh0ni6/H7+qVeyDwi4XI2fLN4aFlEepMj2D74qi0tUhE8UK9PEEZMk/TV4D1Cy0xvHpYc6nppvEkW7dTe+0zfnmaPmB8Q8HKiZIeUKvfGa20kyq6nyp9qin+qKS/RN94UPm6GKm6GKH+avcN7NnVGjZe3NjqIo4QmoijhFGRMu8h+I5M43B45eEJD0Z7lieI9jaqAQDl59fO9JhNU6Vb6qdrvCj6v256qlg73ydeLppU0bieoNRG0H0WAkGo1Edbu9ut2O45haytP486gCGSrmeueUmvpLf3rGyKMvXP3P+HkiLv/SzZpLzTUFzfVNRkNUh0JdplAUBiRIQ9PioqKnBkSkSQZujP7gQd3n3WYgy/a0lX6nr/3ZoJrsjVWwKQhDl5trj9WVHKsrvnizZmywelpE3BxNYrIsvL9+RFl78+mG8jONFYdqrhEIpYVGTYuInaVO0Ej6f8aqsd5e8o3+2tZWjgBPXiGPe0zWdYEIKISDl6fvvY1EN3S3B3Y2GIkwMTc68NbATo0/T+KtLppByGBDjrpY02FvNBL1BsJfcOvEUSXlUjUyVMTtx6EJjv4u+0Y+7zmbo7+L2qqzmvJb6y8115yrrLrYUF1JtoY2B2jKFcP5ylGRqvtSo+LvDoIzxTtgayc/CyhMeTao9Du9VM2Pe0wWuzjQC6uA1hnbqMt+vzSUqcWB0yJip0XE3RM6VMwb2B9d1t58rK6EqrtyoXhaROy0iNipytggYX9OMUIkVnOso3Bra92vhpg/BCY+JQ++6/YJIhTCQcdxgkK99y5OUDpNaehU+agpDd7PDxxazWRNh/23AknUdthbLaRcyFFJeRp/7q3OVX+eXHiH5xAezZK2kkRJW1PWjcpfr1Veaa0p9mv0Mwpi20JHSlUT44dMHKuJDQ25w9/Tp5F2pCu2NueZm3PNN/a06YoszltTV4c695H2Owth/7Wh/Fhd8bG6kkpD65TwmDmaxBmqeE+7K/tlRDeByKstdcfqSk43lJ9uKB/mr5gYNjQtLGqmKj6A39eZgg7mZqLov7pr21o5fDzpSXnsY4FCGRcK4aDjmNY2PESY22RxntbmYkoD7VeqgDvsJGo2k47LjdSlRyuJqN5UTy869uXoRiCyuObmmezqrNLKHFNdcVA9EqBoMnhMsOrepKhxak1CYCjXa8McmaSjxtaSb7mZa27JNzfnmVuvWSQRfMVwYVCKn2KEnyLFTxYv+IRfsBKlDFyGS801nXo+p0XEjgyKuON3pN8ny9pJMqe1jgp5trEiMTCMOkO9NyxKyO2f2UeNF03XtraW7WvTTJNO/68GkchXFzKFQtg96lP76bSQp481PRonNdlJakqD1vDblIZAflQAD875fEOzidAaCW0HoTUQdQa71kC0mEmFH0cp4UVIueFirlLKVYq5ClHnseb9eHSztZNXf2k4lVOeU6stIZtqY5sbZG1DeUGpSs04lWaMQj1aoR7oLjhaWNvIlgIzdcLXnG9pzjXjOKYY4ReU4qcYLlSM8FMk+/H9O5cf6hph/ybRWU1H64qP1ZUcrSsWcflzNUnTIuImhA6R8Ppn3uHArQNssFuzGiup09aitqa7QyKp+RjjQyL5nD4tmpOd3nhhTSOGYX/Dhm/C8rCBPwWnBRTCbnxXbPi+xIBh2Ldzwh452IBhWHQgf2qk39AA3pAA3uCZ0jCYEQhrMBJ1HXatgbj1X4fdaEfU/I0ICU8p4Sql3HAx96aJ7PejGyLQzavmkjOt5wqqLzRUN0TqG6P1ZYFNkSL5iDBlkixsTLB6fEhkqJ+07z/Ly2wG8uYVc0uhuaXA0nTJ1FJowblYyEiRPEkYOkYUlCwMjBEIAns8djtOwTM+a3xhRWjfl4ywkcSp+jKqhFR0tNyvjKWuwEX/fsxnVwYbMttJM4FMdmSyI6Mdme3ITCCzHRlspNmOTAQyO21qMRMm++8OtkF+3KEBPH8B7i/g+As4AQLcn8/xF3ButfDvcJ59u81yvqmK+o2K25rGh0RSp7OjFSq8Dyd00DU6GFED+j+dFvLMsaZeVykDg4TJjrQGot5grzMQ2g6izmCvaifsv18KYFy4cOZQUaCAEyjkBAj6oWMTkVjrNYv2jKH6TMfl/Lry8KamVF29Sl8sbPAT8sco1MmyMKo0JsnC+nKkGwiIQK3XrVTBaykwN14yWVqJoGS/oCRhyBhRULIwKMnPo3XO+usUvLKj9Uht0cHq6+cba0cGDZkQMmyMIlIjDjYTmNFOOpW02+XNaCedq52Ej/vxcD8ux4+Hi3m4iIf78XARF/fj4RI+x4+H+3FxapOYR+2Jl+nt2wraW8ykXMh5LEEaLuG2W8l2G2q3ku1Wss1KtltRu5XssJFtVtRhJaUCjj//VpmkqmMA9dip0fWKEw2m9l8ayo7VlfxUW2y0W+8LHzYtInZi2NA7GN0KhXDQcUxri5bxy3Q2d1bsZJr09PT09HS6U3iMjbGztJZt+W1tViTm4cODBTwO3moh9Bakt5AmOxko5MiEHJmQEyDkyIXcQCEnUIDL/bgBAlwm5N7BKOK2Mqv2tFF7xlj1U0czv0M3ua1+hK5C2ZRn1xrt1hRZ+JhgdbIsPEkWlhqscXGtyDHMh3rN+2txOKPW3njR5Djh09+wBg4TBCX7yZOEVNmTxQs4fbua0LWD0Uog6szMaLv1wGwnjXbqwe2ztHYbUW80tlqsJjvCMT4fF3Aw3I+PS/lcqmj9VrdulzEJH3faxBH9VtLu4IKIp+sAI4S125xLI9lmvVUy262ow3ardprsiDp9lArwgFtnlhz/359cBgg4Ih5ODT093VB+QltKYu7Ox3B/5CCrDZZC2NjY+PLLL//4449WqzUtLe2DDz5ITEzsaWcfeO9Z+ucb62K7PrrZSNRmRa1mos1K6iykzkLqLbcetFnIVgtpJ7EAIf5bgeTI/TgBAo7cj0OdUMqFHNcDdgx1du0ZQ80xg/a0oaPGJknjmqcaa+NaiqWNl1trbrQ3D/NXjFGoxwSrxyjUI4MipPzbi904FjeZFhF3tLb4zm6GQPVzNl4ytRZaWgrMLYUWDh8PHuF3u58zVigI8Oy0GCHMcY8U463V+EjqDipGO1nYbKtqtzvvj2MYj4M7TssctYo6LRNysRZr+432hqstlVWG5rsUYeNDIu5TRsYFykW8AbyhSleOo0rK43/P3/1efx1VCIQ5V8e2384sO6xkm5XssCG9heywkTYSo84jpQJOgADHcGuDubWsXZuvqxby0Zjg0LRw1UxVlFIs6fT9XYwc9CUsO+7cGZvNlpqa2tjYuHbtWqlU+tZbb9XX1+fm5oaFhbn+QtYdlx1Ympx1sft4dLMSSG8lW82ko1JSBVJvIal2DMOoc8oAASfIjxPwW4EMFHKodsdqc8Z6e92vBupkUVdsCR8vDkzjGcYZG6P0V/S1l27WXG6ulQlFYxRqqjS2G8OlAvNfs7fKCv11Se0fj3+KQ8pcJ+/az2nVkSGj/ZxP+Lr2cyIMM9pQB1XM7KTjpmBGR4W7/YA02pDZjsR8XMrniPm4hMcR/7ZWO7WAu5iP3zQSmRXGNiuSCTl/SfEfFdbNinu1Rv2P1dccE/7maJLmRibdJY8Y0JU83UTLh9xGog7qhNJG6i23+mOpYllnNNUbrAY7iUgBjuE8rt1fwIkQi+R+vAABntdaHSYKLG4WbJsZ+syxpunRhnJDhe8tK8+y486d2bFjx/Lly0+cODFlyhQMw2pqaoYNG/bcc8+tX7/e9Rey7rjswNLkLI2NDVhyC4FazaTeSuotZKuFbHOcUFpJqp2DY7fq4m8nlDIhR2xGtmKbOdvc9ouh9YpZMdxPmSYJniq4majPNWivttRdba6raiPGB8xvIM+puVONvLOBKDU+tCZMggQcroQvEHH5flw+1oibykl7GeootpsvoY4yW7hSGpTsJx4hlNzlJ4zhIwXHSGJOFY402JHzA6ONNNmR2KmSSfi4U4XjSHi48wMxnyN2eRK8+uLFmlbNG+MU1Cn42uxmtbx6zdixGIYRiMxuqv6pruhIbVFpW/Pk35Y6ixyApVj6grEf8nab5URd+cm6ynNNdQ1Ga7JMEx+gMpmDmk1CzGnk4EQ1+fxd/baADkMw9C3pX4sWLTp//nxNTY2j5cEHHywuLi4tLXX9hYz9yPaKpclZGhujL7nJjroWyFsPLESbBQk4mITABG2IqyXIG1Z/AUcZJlDFCqJSRQdvlp6tkQg5EgtpEEmKFTyersVmaMPMVsxmx+2Ii/vxuX58nM/n8QQ4h8/HhVxMwOcIbchMICuJ2UjcjuM2DmbHOQSfQ3Jwu4iPczmEP5/H5xABQp4fD5MJeFRx9ePyRVy+hCcQcLn+fCEP5wQKRBwclwtE7v++m/O1G64d3Jg2edUrn2es+8uqs6eWR0+VSRuP1Badqr+REBg6IyJuhip+bLCasVMwWfEhv2k2nKwvPaEtPVFXarUHjhDP2D9v2EMHypYlCRdER9Cdrv+x4C3pu4SEhCFDhhw5csTR8ve//339+vVGo9HPz9VyDKz4yHaLpclZGhtjcHKDDekshN6CWi1Es85eV2ZtaLC1thMN4Rjp/7tuTE67Xd6ISwR4gIQTKOMFhfKCQnhiAUfCwyV8XEydzPE4Qi5qt1ushN1gt5oJu4mwGe02C2HvsFtsJKG3mkmEWq0mEiG91WRHZLvNYiUJg81qImxmwmawW60E0W6z2BGpt5qonbk4J4Av5HO4Ur5QyOWJuXwRj+/H5Ul4AgGHFyAQcnGOTCDi4LhMIKo3tW8vuaDLypWOT1H4iXk4x/kmD3S9zu5j7EelW3k3rRuv6sy8C7umzHkv51JZUyTrRg66w9eueXarpaVl5MiRzi1yuRwh1NraqlTCXcWBL5PwcQmfp5JiGMbHIjAs6Vb78+8dJH4JaX8k0qLgCpsJ/2+reJObNrwyx41v6dk5nDtIhPQ2s40kOmwWC2E3EjaT3WYm7Aa71Ura220WO0nqqKppMfJwToo8/PRo09whSf8a9cCw3ib8gb6obLNPijT8vyuXMQzbcO3Hd0f/saJNDIVw0MEH/ubRA4SlyVkaG2Np8h9/9/jDV2kLcge+wrCv6M5wZ1j5UcGw/dOepEYaY1gs3Vn62aAohHK5XKfTObe0trbiOC6T9XIVnUU9GAB4xDHe9e+Pl7+3O4otc4R6ur0t3bl8Vqdl5b+a/Mfspirfe8HZ1Ft9xxYuXHjhwoXq6mpHi5uDZQDweZ3ucMtwHt3uAwA3DYpCuH379ieeeOLnn3++7777sN+mT/ztb397//336Y4GAACAZoOiENpstjFjxjQ3N69du1Yikbz11ltarTYnJwdGygAAABgUhRDDsIaGhpdeeunQoUOOJdaSkpJ6/zIAAAC+brAUQgAAAKBbDF18AQAAAPAOKIQeIAji8uXLdKfwGEtjY5Dc61gaG2NtcpbGxticvFtQCD1w7dq1pUuXjhs37vvvv6c7iwdYGhuD5F7H0tgYa5OzNDbG5uTdQ8ATBEF89dVXQUFBH330Ed1ZPMDS2AiSex1LYyPWJmdpbMTm5F3BGaFnOBxOfX19QEDAgw8+SHcWD7A0NgbJvY6lsTHWJmdpbIzNybtBdyVmmQ8++GDo0KHl5eV0B/EMS2MjSO51LI2NWJucpbERm5N3BYXQAyx941kaG0Fyr2NpbMTa5CyNjdicvFtQCN3F0jeepbERJPc6lsZGrE3O0tiIzcl7AoWwe3a7vaWlxfG0pze+oaEhIyNj48aNNTU1Xs3XAzdjI4SKiorOnTtnNBq9F84lSO5lLI2NWJucpbERm5O7Dwph9/bs2TN8+PDGxkbU8xtfVVWlVCofeuihRx99NCgoaPPmzTQE/T13YhcXF48bN04ul8fExMhksi1bttAQtAtI7mUsjY1Ym5ylsRGbk7sPCmGPVq9ePXz48DVr1vT0589f//rX1157jXp8/fr1mJiYTz75xKsRu+M6dnV1tVKpfPfddwmCQAhdvnw5Ojp69+7dNATtApJ7GUtjI9YmZ2lsxObkboJC6Mrq1asxDMvOzu5265IlS7777jvH0+Li4uDg4LKyMm+l65GL2MuWLXvxxRedW3Jzc2NjY70VrReQ3MtYGhuxNjlLYyM2J3cHFMJeUH8KUd0ClMrKyocffnjo0KHR0dELFy503nnFihVvv/221zN2o2tsikqlys/P79SYmprKnG59SO5lLI2NWJucpbERm5P3ikffDEZ2SE9PxzBs7ty5WVlZOI5XVFTcc889S5YsWbFiRU5Oztq1a9evX//KK69QO/N4PKPRSGfc33SK7WiXSqUdHR2ddhaJRCKRyJvxXIDkXsbS2Bhrk7M0Nsbm5L2juxKzg6PD8+GHH3ZcF0QIFRcXy+XyxYsXHzly5OOPP1YoFEVFRTRl7EbXfto1a9bMnTvXueX7779/9dVXvRjKLYMnOXVZhXYsjY1Y+1G5s9gkSQ54st6w9AV3DQqhZ5RKZade8k8++SQsLGz27NmPPPJITk4O1ci0YcQvvviizWZDCJlMpokTJy5YsCA/P7++vj4jIyM2NtZ5bLTdbqcvZjfcTG632y9dukRr0s7cSZ6bm6tUKufOnXv+/Hm6897iTmyTyTRmzJhHHnmka4cYjdz/kDOKR/82H3/8cYZcfEGsfcG7BYXQM2lpadu3b3duuXLlikwmczxl5jDi+fPnHzp0iHpsNBpffvnlkJAQDMOkUilV100m0zvvvDNs2DAej8eoEV+9Jqfk5eUlJSWlpqY6D1+ilzvJMzMzN2zYsHTpUn9//w0bNtAX9jY3X3Cr1bpp06aQkJAPP/yQpqSduZmcadyMTVXB6dOnJyQkMKQW9pqcIIi9e/cuXbp0ypQpy5cvz8rKojWvK1AIPXPw4EGVSqXVah0tGRkZ9913H/WYscOIDx06FB0dXVtb62jZtm2bSqX69ddfEUKlpaXDhw9fsGBBbm4uSZLZ2dkxMTGd6j1dXCd3xrS18N1PjhAqLy/XaDRnz571YsDueRS7oqIiJiZmx44dXgzYI4+SM4c7sakqOHPmTJPJpNVqGVILXScvLy+fMGHCokWLzp4929TUtHfv3qioqE8//ZS+vK5AIfTY22+/rdFovvnmm9LS0k8++SQkJOTKlSvUJiYPI960aZNGo1m1atWbb76Zmpq6YMGCqqoqhFBhYWFERMTGjRudd87JyQkPD2fCBQnUc/KumLbyk/vJEUJffPHF8uXLvRmvJx7FvnbtWmho6M2bN72ZsCe9JtdqtcePH9+4ceMzzzwzadIktVrNhIriOjZVBSMiItrb26kW5tRCF0eV8PDwSZMmXbx40bFzeXl5cHBwfX09fXl7BIXwTuzdu3fUqFEikWjSpEnOl6YYPoy4paXlxx9/3L9/v+OPuIaGhsjIyC+++KLrzkFBQQw5uqHuknfFtCpIcSc55ejRo7Nnz/ZOql65iH306NFOf9f/6U9/YsglANRd8ry8vKeffjotLU2tVo8aNeqPf/zjv//973379hUXF69YsSItLc1qtdKbGbl8wfV6/caNG1NSUu6//36TyUQ1arXaJ554gpnJGxoaNBrNiBEjHn74YbVavXDhwo6ODmrTCy+88M0339AXtkdQCPtTfHz8uXPnOjVOmjSJljDumDdv3uuvv961vb6+XqFQMG3gjAvMrILuIwjioYce+s9//kN3kN6Vl5dHRUVlZGQ4WlasWLFu3ToaI7lgNps1Gs0bb7xx48YN5x4OkiSfffbZtLQ0x2kWw5nN5mXLlj311FN0B+ndggULHBe8jUbjkiVLHPOtCwsLmfmC4wghGidv+Jh//vOfFy9e3L9/v6Nlz54958+ff++992hM1ZOLFy8++uij169f5/P5nTZRHbwZGRm0BPNURkbGhg0bTp48OXToULqzuFJZWVlYWGiz2QiCIEkSx3GxWIzjeFFR0e7du6VSaWZmpkAgoDtmZ3l5eUKhMC4uztFSUlKSmpo6a9asFStWlJSUvP7669nZ2dHR0TSGdOHw4cPLly//3//+N378eKoFIbRy5cq8vLzMzEypVEpvvG7l5eUhhEaMGOHcaLFYEhISjh8/ztiXGsOwq1evZmRkbN++3dFiNptTUlJ27do1YcIE+nL1gkvNkQR98dJLL02dOpXD4YwfP37z5s0HDx5MSUlBCG3duvXtt9/esWMHNbGUIIirV68qlUq6896ybdu2+Pj4GTNmdGo/dOjQunXrvvrqK8d8WEYlJwhCr9c7srmogoyK3djYOGrUqMbGxpaWFq1WW19fX1NTc+PGjdLS0qampjlz5qxbt85RBRmVfN++fadOnZo6daqjRaFQiESirVu3lpeXV1VVbdmyJSkpCWNYbIeYmJiUlJTFixffe++9arW62yrItOT79u07ePDgnDlznBt5PF5eXp5AIBg+fLijkWnJd+3adc899yQmJjpaeDxeYWGh1Wq9++67HY1Miw1do/2ApUP8V69e/Y9//KNT48mTJ0NDQzsNN2dUcnfWwqcwKjbqeYWqrhiVXKfThYWFUecoDpcuXeLz+Z32ZFTsTjIzM8PCwrKysrrtEWVacr1eHxYWVlJS0ql91qxZBw4ccG5hWvJ33nnHuducMnPmzE6D55kWGwphP2DpEP9Lly6pVCrnQVxbtmwJCws7depU150ZlbzXG4M4MCo28qQWMir5jh074uLinGcNffjhh+PGjeu6J6Nid3Lo0CE+nz927NhuL1MxLfnOnTsTEhLq6uocLUePHlWpVAaDodOejEqen5+vVCqdYx88eDA8PLytra3TnoyKDWuN9oNZs2a99NJLd99998KFCwMCAg4fPqxWq7OysjQaTac9ORxOfX19QEDAgw8+SEtUZ6NHj37hhRfGjRu3cuVKgUDw7bff2u32X375xflqkAOjklP9+atXr87OznZ9XZBRsbHfkk+dOvX48eNUt0FPGJV82bJlN2/eHD9+/L/+9a8JEyacOnVq7dq1mZmZXfdkVGxnCKEDBw4kJydXV1cXFBQ4rhc6MC350qVLm5ubx40bt2rVquTk5KysrM2bN//www9isbjTnoxKnpyc/NJLL40dOzY9PT0uLu7YsWOffPLJvn37/P39O+3JqNhwRthvWDrE//Tp088///xzzz23f/9+FxMHGZjcnbMrBsZGrE1+8eLFxYsXJycnL1q0qFNPqQMDY1McPaKZmZmrVq3qugMzk+fl5T377LNTp05duXJlT7d4Y2DyAwcOzJw586677nryySdLS0u73YdRsaEQeg+j3niPMDb56qK3JkYAAAw7SURBVNWrx48f31P9ZmxshNDHH3/sohAyObkLTI69bds2FwP3mZzcNZYmZ1psKIRewrQ33n0MT86iP5PdxNLkLI2NILnXMTA2FMIBYbfbnRdfZ+Ab3xOWJmdpbMTa5CyNjSC517EiNhTCAeH+EH+mYWlylsZGrE3O0tgIknsdK2LDhPoBkZiY2NDQ8NprrzU1NX366afMX/TEgaXJWRobY21ylsbGILnXsSM23ZXYl61evRrDMCbfC60nLE3O0tiItclZGhtBcq9jeGwohAOr60D5wsLC//u//+t2Z0bdZp2lyVkaG7E2uUexEWuTMyc2Ym1yJn9UoBAOuE5D/HU63fjx41944YWuezJt2SGWJmdpbMTa5O7HRqxNzqjYiLXJGftRgULoDZ2G+Lt4+xm17BBibXKWxkasTe5+bMTa5IyKjVibnJkfFQ59VycHkaioKOengYGBR44c6dRIYdayQ6xNztLYGGuTux8bY21yRsXGWJucoR+VAS2zwFOMHV7cK5YmZ2lsBMm9jqWxESR3AxRCBoHPq5exNDaC5F7H0tgIkrsHCiFTwOfVy1gaG0Fyr2NpbATJ3QaFkB7uLztUVFR07tw5o9HovXAusTS5R+s8sTQ5o2Ij1iZnaWwEyfsACiE93Fl2qLi4eNy4cXK5PCYmRiaTbdmyhYagXbA0uZvrPLE0OQNjI9YmZ2lsBMn7AAohbVzfZr26ulqpVL777rsEQSCELl++HB0dvXv3bhqCdsHS5L3e156lyRkbG7E2OUtjI0h+p6AQ0snFskPLli178cUXnVtyc3NjY2O9Fa0XLE3uep0nliZncmzE2uQsjY0g+R2BQkiznm5WrlKp8vPzOzWmpqYyp1ufpcld3B2epckZHhuxNjlLYyNI7jnewM5SBL2h7v4xd+7crKwsHMcd7VKptKOjo9POIpFIJBJ5M54LLE3eU2yMtckZHhtjbXKWxsYg+R3ol3IK+qjrbdbXrFkzd+5c55bvv//+1Vdf9WIot7A0ebf3tXednLo4QTtPX3CGxEY+9FHpNbZjLU16sfQFR54nz8/Pz8jI6MtPhELILC+++KLNZkMImUymiRMnLliwID8/v76+PiMjIzY21nmEsd1upy9mN9xMzpy18B3cSZ6bm6tUKufOnXv+/Hm6897iTmyTyTRmzJhHHnmka7cSjdz/kDOK+5/wxx9//O2336Y17O+w9AVHbifXarUJCQl9ec2hEDLL/PnzDx06RD02Go0vv/xySEgI1TNAXUM2mUzvvPPOsGHDeDwec0Z8ITeSUxi1Fj7FneSZmZkbNmxYunSpv7//hg0b6At7m5svuNVq3bRpU0hIyIcffkhT0s7cTM407sSmquD06dP7eFzuX70mJwhi7969S5cunTJlyvLly7OysmjNe5v7H5U+1kIohMxy6NCh6Ojo2tpaR8u2bdtUKtWvv/6KECotLR0+fPiCBQtyc3NJkszOzo6Jidm+fTt9eW9zndwZo9bCR54kRwiVl5drNJqzZ896MWD3PIpdUVERExOzY8cOLwbskUfJmaPX2FQVnDlzpslk6vs5Sj9ynby8vHzChAmLFi06e/ZsU1PT3r17o6KiPv30U/ry3ubRR6UvrzkUQsbZtGmTRqNZtWrVm2++mZqaumDBgqqqKoRQYWFhRETExo0bnXfOyckJDw9nyDWJnpJ3xbSVn9xPjhD64osvli9f7s14PfEo9rVr10JDQ2/evOnNhD3pNblWqz1+/PjGjRufeeaZSZMmqdVqJhQVF7GpKhgREdHe3k61MKoWujiqhIeHT5o06eLFi46dy8vLg4OD6+vr6ct7m0cf8jt+zXGEUP+MugH9p7W1NSsriyCIMWPGREREYBjW2NiYmpq6Zs2a5cuXd9pZoVAUFxcrFAoagnbRNXlXGRkZGzZsOHny5NChQ72bzhV3klOOHTuWkZFx8OBBr2VzwUXsY8eO3bhx4+mnn3a0LF++/N57733qqae8HrMbXZPn5+dv2rSpoKCgoqIiJCQkOTk56Tfr168vLCw8ceIEn89nWmxKW1vbzp07N2/eHBoa+uOPP/r5+WEYVl9f//rrr2/evJn22FgPR5WxY8fK5fL4+PisrKxx48bt3LlTIpFgGPbiiy9OmDDhkUceoTs1hnnybxPDsPLy8pEjR7755puvvPKKBz+jT8UaeMu8efNef/31ru319fUKhYJpA2dcYNq5oKcIgnjooYf+85//0B2kd+Xl5VFRUc6j6VasWLFu3ToaI7lgNps1Gs0bb7xx48YN5x4OkiSfffbZtLQ0x5kWk5nN5mXLlj311FN0B3HLggULHBe8jUbjkiVLFi5cSD0tLCxkxQvuLCcn529/+5tCoZgzZ46nlznhjJAFLl68+Oijj16/fr3r35XUagsZGRm0BPMUM88Fu6qsrCwsLLTZbARBkCSJ47hYLMZxvKioaPfu3VKpNDMzUyAQ0B2zs7y8PKFQGBcX52gpKSlJTU2dNWvWihUrSkpKXn/99ezs7OjoaBpDunD48OHly5f/73//Gz9+PNWCEFq5cmVeXl5mZqZUKqU3Xld5eXkIoREjRjg3WiyWhISE48ePM/Z1ply9ejUjI2P79u2OFrPZnJKSsmvXrgkTJtCXy2MdHR1ff/31559/3tDQ8NRTTz355JMqlcrTbwIT6lng8OHDjz76aNcqeOjQoT179ly9etXRQhAEl8v1broeEQTR1tYml8upp66rIHOSU73Q99xzz5AhQ4RCIZfLJUmSqog2m+3pp59eunSp83vBnORnz56trKz897//7WiJjY1ds2bNmjVrWlpaJBLJ0aNHHUdn5sR2eOCBB7Zv3z5//nyqFvZUBZmT/OzZs1evXv3kk0+cG4VC4f3333/u3LmuhZA5yTEMO3r06Pz5851b/Pz8pk2blp2d3akQMiq2s4sXL3722Wd79uyZPHnyP//5z+nTp3M4HOcdPEje/yeooL+tXr36H//4R6fGkydPhoaGMnlOhTsryjMzuYtl2BwYmFyn04WFhVGnKQ6XLl3i8/mOpwyM3UlmZmZYWFhWVlanHlEGJtfr9WFhYSUlJZ3aZ82adeDAAcdTBiZHCL3zzjtdJ6HPnDnTEY+ZsRFCOp3uo48+GjlyZExMzDvvvNN1UM8dJIdCyAKXLl1SqVTO7/eWLVvCwsJOnTqFmD2nwvWK8sxP3lMtZGzyHTt2xMXFabVaR8uHH344btw46jFjY3dy6NAhPp8/duxYRxVkbPKdO3cmJCTU1dU5Wo4ePapSqQwGA/WUscnz8/OVSqVz8oMHD4aHh7e1tSEGx0YIvfvuu0uWLDlx4kS3o+XvLDkUQnZYt25dZGTku++++8EHH0yYMCE1NbWoqAixYU5FTyvKsyJ5t7WQ4cnff//9yMjIHTt2FBcXf/7556GhodRSPgyP7UCNjhk5cmRYWNi5c+cQ45N/8MEHarV63bp1hw4devPNN0NDQ0+fPk1tYnjydevWRUREfPbZZz///PMbb7yhUCio+XkMj+3CHSeHQsgap0+ffv7555977rn9+/dTb2pDQ0NkZOQXX3zRdeegoCCGzBhD3VUUSD6gLl68uHjx4uTk5EWLFlE9payITXH0iGZmZq5atYoVyfPy8p599tmpU6euXLnSsU4mK5IfOHBg5syZd91115NPPllaWopYErtbfUkOhZDFWDSnYvXq1ePHj3f8Ucai5B9//LFzIWRRcmcsir1t2zbngfssSt4JS5OzNDbqW3IohGx14cKF6Ohoq9XaddMLL7ywatUq70dyzfGXMuuSO7A0OUtjI0judSyNjfqcHKZPsJX7cyoYIioqinrAuuQOLE3O0tgYJPc6lsbG+pwcCiFb2e32TpNmMAz7+eefn3jiiYMHDwYFBdGSyh2Q3MtYGhuD5F7H0thY35MPzHkqGHCu51QwGST3MpbGRpDc61gaG/U5OTc9PX2ASjQYUEqlkiTJv/zlLzab7cKFCy+99NKFCxcOHDgwevRouqP1ApJ7GUtjY5Dc61gaG+tzclhrlN3OnDnz7bffIoSmT58+Z84cHMfpTuQuSO5lLI2NQXKvY2lsrA/JoRACAAAY1DpfXQQAAAAGFSiEAAAABjUohAAAAAY1KIQAAAAGNSiEAAAABjUohAAAAAY1KIQAAAAGNSiEAAAABjUohAAAAAa1/x9K3RvVJ157FwAAAABJRU5ErkJggg==",
      "text/plain": [
       "#<GnuplotRB::Plot:0x00000003337e00 @datasets=Hamster::Vector[#<GnuplotRB::Dataset:0x00000003367740 @type=:datablock, @data=#<GnuplotRB::Datablock:0x0000000336b4a8 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-dsb15x-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Austria\"]>, #<GnuplotRB::Dataset:0x00000003367448 @type=:datablock, @data=#<GnuplotRB::Datablock:0x0000000336a0a8 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-12k6uss-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Hungary\"]>, #<GnuplotRB::Dataset:0x00000003367128 @type=:datablock, @data=#<GnuplotRB::Datablock:0x00000003368b68 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-1jj9iuv-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Belgium\"]>], @options=Hamster::Hash[:title => \"Histogram example\", :style => [\"data linespoints\"], :term => [\"pngcairo\", {:size=>[600, 600]}], :xtics => \"nomirror rotate by -45\"], @already_plotted=true, @cmd=\"plot \", @terminal=#<GnuplotRB::Terminal:0x00000003337ab8 @cmd=\"gnuplot 2>&1\", @current_datablock=0, @err_array=[], @in=#<IO:fd 27>>>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "histogramm.style('data linespoints')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set fill with multi word option key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       "<svg \n",
       " width=\"600\" height=\"480\"\n",
       " viewBox=\"0 0 600 480\"\n",
       " xmlns=\"http://www.w3.org/2000/svg\"\n",
       " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       ">\n",
       "\n",
       "<title>Gnuplot</title>\n",
       "<desc>Produced by GNUPLOT 5.0 patchlevel rc2 </desc>\n",
       "\n",
       "<g id=\"gnuplot_canvas\">\n",
       "\n",
       "<rect x=\"0\" y=\"0\" width=\"600\" height=\"480\" fill=\"none\"/>\n",
       "<defs>\n",
       "\n",
       "\t<circle id='gpDot' r='0.5' stroke-width='0.5'/>\n",
       "\t<path id='gpPt0' stroke-width='0.222' stroke='currentColor' d='M-1,0 h2 M0,-1 v2'/>\n",
       "\t<path id='gpPt1' stroke-width='0.222' stroke='currentColor' d='M-1,-1 L1,1 M1,-1 L-1,1'/>\n",
       "\t<path id='gpPt2' stroke-width='0.222' stroke='currentColor' d='M-1,0 L1,0 M0,-1 L0,1 M-1,-1 L1,1 M-1,1 L1,-1'/>\n",
       "\t<rect id='gpPt3' stroke-width='0.222' stroke='currentColor' x='-1' y='-1' width='2' height='2'/>\n",
       "\t<rect id='gpPt4' stroke-width='0.222' stroke='currentColor' fill='currentColor' x='-1' y='-1' width='2' height='2'/>\n",
       "\t<circle id='gpPt5' stroke-width='0.222' stroke='currentColor' cx='0' cy='0' r='1'/>\n",
       "\t<use xlink:href='#gpPt5' id='gpPt6' fill='currentColor' stroke='none'/>\n",
       "\t<path id='gpPt7' stroke-width='0.222' stroke='currentColor' d='M0,-1.33 L-1.33,0.67 L1.33,0.67 z'/>\n",
       "\t<use xlink:href='#gpPt7' id='gpPt8' fill='currentColor' stroke='none'/>\n",
       "\t<use xlink:href='#gpPt7' id='gpPt9' stroke='currentColor' transform='rotate(180)'/>\n",
       "\t<use xlink:href='#gpPt9' id='gpPt10' fill='currentColor' stroke='none'/>\n",
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       "\t  <feComposite in='SourceGraphic' in2='bgnd' operator='atop'/>\n",
       "\t</filter>\n",
       "\t<filter id='greybox' filterUnits='objectBoundingBox' x='0' y='0' height='1' width='1'>\n",
       "\t  <feFlood flood-color='lightgrey' flood-opacity='1' result='grey'/>\n",
       "\t  <feComposite in='SourceGraphic' in2='grey' operator='atop'/>\n",
       "\t</filter>\n",
       "</defs>\n",
       "<g fill=\"none\" color=\"white\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
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       "\t\t<text><tspan font-family=\"Arial\" > 0</tspan></text>\n",
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       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M80.2,338.7 L89.2,338.7 M575.0,338.7 L566.0,338.7  '/>\t<g transform=\"translate(71.9,343.2)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" > 500</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M80.2,255.0 L89.2,255.0 M575.0,255.0 L566.0,255.0  '/>\t<g transform=\"translate(71.9,259.5)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" > 1000</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M80.2,171.3 L89.2,171.3 M575.0,171.3 L566.0,171.3  '/>\t<g transform=\"translate(71.9,175.8)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" > 1500</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M80.2,87.6 L89.2,87.6 M575.0,87.6 L566.0,87.6  '/>\t<g transform=\"translate(71.9,92.1)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" > 2000</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M179.2,422.4 L179.2,413.4 M179.2,54.1 L179.2,63.1  '/>\t<g transform=\"translate(179.2,444.9)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >1</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M278.1,422.4 L278.1,413.4 M278.1,54.1 L278.1,63.1  '/>\t<g transform=\"translate(278.1,444.9)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >2</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M377.1,422.4 L377.1,413.4 M377.1,54.1 L377.1,63.1  '/>\t<g transform=\"translate(377.1,444.9)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >3</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M476.0,422.4 L476.0,413.4 M476.0,54.1 L476.0,63.1  '/>\t<g transform=\"translate(476.0,444.9)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >4</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<path stroke='black'  d='M80.2,54.1 L80.2,422.4 L575.0,422.4 L575.0,54.1 L80.2,54.1 Z  '/></g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<g transform=\"translate(17.6,238.3) rotate(270)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Time, s</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<g transform=\"translate(327.6,471.9)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Number of test</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<g transform=\"translate(327.6,31.6)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"middle\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Time spent to run deploy pipeline</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "\t<g id=\"gnuplot_plot_1\" ><title>Build</title>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<g transform=\"translate(507.9,76.6)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Build</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
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       "\t\t</pattern>\n",
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       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<defs>\n",
       "\t\t<pattern id='gpPat2' patternUnits='userSpaceOnUse' x='0' y='0' width='8' height='8'>\n",
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       "\t</g>\n",
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       "\t</g>\n",
       "\t<path stroke='rgb(148,   0, 211)'  d='M451.3,422.4 L451.3,370.2 L467.8,370.2 L467.8,422.4 L451.3,422.4 Z  '/></g>\n",
       "\t</g>\n",
       "\t<g id=\"gnuplot_plot_2\" ><title>Test</title>\n",
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       "\t<g transform=\"translate(507.9,94.6)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Test</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<defs>\n",
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       "\t\t\t<path style='fill:none; stroke:rgb(0,158,115)' d='M0,0 L8,8 M0,8 L8,0'/>\n",
       "\t\t</pattern>\n",
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       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<defs>\n",
       "\t\t<pattern id='gpPat4' patternUnits='userSpaceOnUse' x='0' y='0' width='8' height='8'>\n",
       "\t\t\t<path style='fill:none; stroke:rgb(0,158,115)' d='M0,0 L8,8 M0,8 L8,0'/>\n",
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       "\t<path stroke='rgb(  0, 158, 115)'  d='M467.8,422.4 L467.8,336.4 L484.3,336.4 L484.3,422.4 L467.8,422.4 Z  '/></g>\n",
       "\t</g>\n",
       "\t<g id=\"gnuplot_plot_3\" ><title>Deploy</title>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<g transform=\"translate(507.9,112.6)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Deploy</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<defs>\n",
       "\t\t<pattern id='gpPat5' patternUnits='userSpaceOnUse' x='0' y='0' width='8' height='8'>\n",
       "\t\t\t<path style='fill:none; stroke:rgb(86,180,233)' d='M0,0 L8,8 M0,8 L8,0 M0,4 L4,8 L8,4 L4,0 L0,4'/>\n",
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       "<g fill=\"none\" color=\"black\" stroke=\"currentColor\" stroke-width=\"1.00\" stroke-linecap=\"butt\" stroke-linejoin=\"miter\">\n",
       "\t<defs>\n",
       "\t\t<pattern id='gpPat6' patternUnits='userSpaceOnUse' x='0' y='0' width='8' height='8'>\n",
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       "\t</g>\n",
       "\t<g id=\"gnuplot_plot_4\" ><title>Overall</title>\n",
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       "\t<g transform=\"translate(507.9,130.6)\" stroke=\"none\" fill=\"black\" font-family=\"Arial\" font-size=\"12.00\"  text-anchor=\"end\">\n",
       "\t\t<text><tspan font-family=\"Arial\" >Overall</tspan></text>\n",
       "\t</g>\n",
       "</g>\n",
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       "\t<defs>\n",
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      ],
      "text/plain": [
       "#<GnuplotRB::Plot:0x000000032f9ee8 @datasets=Hamster::Vector[#<GnuplotRB::Dataset:0x000000032f9bf0 @type=:datablock, @data=#<GnuplotRB::Datablock:0x000000032fd5c0 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-cnd4fa-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Build\"]>, #<GnuplotRB::Dataset:0x000000032f9920 @type=:datablock, @data=#<GnuplotRB::Datablock:0x000000032fc3a0 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-1xm6obk-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Test\"]>, #<GnuplotRB::Dataset:0x000000032f9628 @type=:datablock, @data=#<GnuplotRB::Datablock:0x000000032fb7c0 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-1v4esw2-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Deploy\"]>, #<GnuplotRB::Dataset:0x000000032f9308 @type=:datablock, @data=#<GnuplotRB::Datablock:0x000000032fab68 @stored_in_file=true, @file_name=\"tmp_data20150703-10464-5quofm-0\">, @options=Hamster::Hash[:using => \"2:xtic(1)\", :title => \"Overall\"]>], @options=Hamster::Hash[:style_data => \"histograms\", :style_fill => \"pattern border\", :xlabel => \"Number of test\", :yrange => 0..2200, :title => \"Time spent to run deploy pipeline\", :ylabel => \"Time, s\"], @already_plotted=true, @cmd=\"plot \", @terminal=#<GnuplotRB::Terminal:0x000000032f8c00 @cmd=\"gnuplot 2>&1\", @current_datablock=0, @err_array=[], @in=#<IO:fd 31>>>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = %w{decade Build Test Deploy Overall}\n",
    "data = [\n",
    "    [1,  312, 525,  215, 1052],\n",
    "    [2,  630, 1050, 441, 2121],\n",
    "    [3,  315, 701,  370, 1386],\n",
    "    [4,  312, 514,  220, 1046]\n",
    "]\n",
    "x = data.map(&:first)\n",
    "datasets = (1..4).map do |col|\n",
    "    y = data.map { |row| row[col] }\n",
    "    Dataset.new([x, y], using: '2:xtic(1)', title: titles[col], file: true)\n",
    "end\n",
    "\n",
    "Plot.new(\n",
    "    *datasets,\n",
    "    style_data: 'histograms',\n",
    "    style_fill: 'pattern border',\n",
    "    yrange: 0..2200,\n",
    "    xlabel: 'Number of test',\n",
    "    ylabel: 'Time, s',\n",
    "    title: 'Time spent to run deploy pipeline'\n",
    ")"
   ]
  }
 ],
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   "file_extension": "rb",
   "mimetype": "application/x-ruby",
   "name": "ruby",
   "version": "2.1.2"
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