queue-simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb8cfe27",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D/D/1: deterministic interarrival/deterministic service/one server\n",
      "Initial jobs:\n",
      "\n",
      "    T: interarrival time\n",
      "    A: arrival time\n",
      "    S: service time\n",
      "    W: waiting time\n",
      "    E: exit time\n"
     ]
    },
    {
     "data": {
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       "<table id=\"T_336d9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_336d9_level0_col0\" class=\"col_heading level0 col0\" >id</th>\n",
       "      <th id=\"T_336d9_level0_col1\" class=\"col_heading level0 col1\" >T</th>\n",
       "      <th id=\"T_336d9_level0_col2\" class=\"col_heading level0 col2\" >A</th>\n",
       "      <th id=\"T_336d9_level0_col3\" class=\"col_heading level0 col3\" >S</th>\n",
       "      <th id=\"T_336d9_level0_col4\" class=\"col_heading level0 col4\" >W</th>\n",
       "      <th id=\"T_336d9_level0_col5\" class=\"col_heading level0 col5\" >E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_336d9_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_336d9_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row0_col3\" class=\"data row0 col3\" >0.666667</td>\n",
       "      <td id=\"T_336d9_row0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "      <td id=\"T_336d9_row0_col5\" class=\"data row0 col5\" >1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_336d9_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_336d9_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row1_col2\" class=\"data row1 col2\" >2.000000</td>\n",
       "      <td id=\"T_336d9_row1_col3\" class=\"data row1 col3\" >0.666667</td>\n",
       "      <td id=\"T_336d9_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "      <td id=\"T_336d9_row1_col5\" class=\"data row1 col5\" >2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_336d9_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_336d9_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row2_col2\" class=\"data row2 col2\" >3.000000</td>\n",
       "      <td id=\"T_336d9_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "      <td id=\"T_336d9_row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "      <td id=\"T_336d9_row2_col5\" class=\"data row2 col5\" >3.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_336d9_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_336d9_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n",
       "      <td id=\"T_336d9_row3_col3\" class=\"data row3 col3\" >0.666667</td>\n",
       "      <td id=\"T_336d9_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "      <td id=\"T_336d9_row3_col5\" class=\"data row3 col5\" >4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_336d9_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_336d9_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_336d9_row4_col2\" class=\"data row4 col2\" >5.000000</td>\n",
       "      <td id=\"T_336d9_row4_col3\" class=\"data row4 col3\" >0.666667</td>\n",
       "      <td id=\"T_336d9_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "      <td id=\"T_336d9_row4_col5\" class=\"data row4 col5\" >5.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_eb6b8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_eb6b8_level0_col0\" class=\"col_heading level0 col0\" >label</th>\n",
       "      <th id=\"T_eb6b8_level0_col1\" class=\"col_heading level0 col1\" >def</th>\n",
       "      <th id=\"T_eb6b8_level0_col2\" class=\"col_heading level0 col2\" >stat_sim</th>\n",
       "      <th id=\"T_eb6b8_level0_col3\" class=\"col_heading level0 col3\" >stat_ana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row0_col0\" class=\"data row0 col0\" >lambda</td>\n",
       "      <td id=\"T_eb6b8_row0_col1\" class=\"data row0 col1\" >mean arrival rate</td>\n",
       "      <td id=\"T_eb6b8_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_eb6b8_row0_col3\" class=\"data row0 col3\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row1_col0\" class=\"data row1 col0\" >mu</td>\n",
       "      <td id=\"T_eb6b8_row1_col1\" class=\"data row1 col1\" >mean service rate</td>\n",
       "      <td id=\"T_eb6b8_row1_col2\" class=\"data row1 col2\" >1.500000</td>\n",
       "      <td id=\"T_eb6b8_row1_col3\" class=\"data row1 col3\" >1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row2_col0\" class=\"data row2 col0\" >rho</td>\n",
       "      <td id=\"T_eb6b8_row2_col1\" class=\"data row2 col1\" >(mean arrival rate)/(mean service rate)</td>\n",
       "      <td id=\"T_eb6b8_row2_col2\" class=\"data row2 col2\" >0.666667</td>\n",
       "      <td id=\"T_eb6b8_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row3_col0\" class=\"data row3 col0\" >sigma^2</td>\n",
       "      <td id=\"T_eb6b8_row3_col1\" class=\"data row3 col1\" >variance of the service dist. G</td>\n",
       "      <td id=\"T_eb6b8_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "      <td id=\"T_eb6b8_row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row4_col0\" class=\"data row4 col0\" >p_N_s</td>\n",
       "      <td id=\"T_eb6b8_row4_col1\" class=\"data row4 col1\" >prob. of having N jobs in the system</td>\n",
       "      <td id=\"T_eb6b8_row4_col2\" class=\"data row4 col2\" >[0.311 0.689]</td>\n",
       "      <td id=\"T_eb6b8_row4_col3\" class=\"data row4 col3\" >[0.333 0.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row5_col0\" class=\"data row5 col0\" >p_N_q</td>\n",
       "      <td id=\"T_eb6b8_row5_col1\" class=\"data row5 col1\" >prob. of having N jobs in the queue</td>\n",
       "      <td id=\"T_eb6b8_row5_col2\" class=\"data row5 col2\" >[1.]</td>\n",
       "      <td id=\"T_eb6b8_row5_col3\" class=\"data row5 col3\" >[1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row6_col0\" class=\"data row6 col0\" >p_0_s</td>\n",
       "      <td id=\"T_eb6b8_row6_col1\" class=\"data row6 col1\" >prob. of having 0 jobs in the system</td>\n",
       "      <td id=\"T_eb6b8_row6_col2\" class=\"data row6 col2\" >0.311000</td>\n",
       "      <td id=\"T_eb6b8_row6_col3\" class=\"data row6 col3\" >0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row7_col0\" class=\"data row7 col0\" >p_0_q</td>\n",
       "      <td id=\"T_eb6b8_row7_col1\" class=\"data row7 col1\" >prob. of having 0 jobs in the queue</td>\n",
       "      <td id=\"T_eb6b8_row7_col2\" class=\"data row7 col2\" >1.000000</td>\n",
       "      <td id=\"T_eb6b8_row7_col3\" class=\"data row7 col3\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row8_col0\" class=\"data row8 col0\" >L_s</td>\n",
       "      <td id=\"T_eb6b8_row8_col1\" class=\"data row8 col1\" >expected number of jobs in the system</td>\n",
       "      <td id=\"T_eb6b8_row8_col2\" class=\"data row8 col2\" >0.689400</td>\n",
       "      <td id=\"T_eb6b8_row8_col3\" class=\"data row8 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row9_col0\" class=\"data row9 col0\" >L_q</td>\n",
       "      <td id=\"T_eb6b8_row9_col1\" class=\"data row9 col1\" >expected number of jobs in the queue</td>\n",
       "      <td id=\"T_eb6b8_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
       "      <td id=\"T_eb6b8_row9_col3\" class=\"data row9 col3\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row10_col0\" class=\"data row10 col0\" >W_s</td>\n",
       "      <td id=\"T_eb6b8_row10_col1\" class=\"data row10 col1\" >expected time spent in the system</td>\n",
       "      <td id=\"T_eb6b8_row10_col2\" class=\"data row10 col2\" >0.666667</td>\n",
       "      <td id=\"T_eb6b8_row10_col3\" class=\"data row10 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_eb6b8_row11_col0\" class=\"data row11 col0\" >W_q</td>\n",
       "      <td id=\"T_eb6b8_row11_col1\" class=\"data row11 col1\" >expected time spent in the queue</td>\n",
       "      <td id=\"T_eb6b8_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
       "      <td id=\"T_eb6b8_row11_col3\" class=\"data row11 col3\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M/D/1: exponential interarrival/deterministic service/one server\n",
      "Initial jobs:\n",
      "\n",
      "    T: interarrival time\n",
      "    A: arrival time\n",
      "    S: service time\n",
      "    W: waiting time\n",
      "    E: exit time\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8042d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_8042d_level0_col0\" class=\"col_heading level0 col0\" >id</th>\n",
       "      <th id=\"T_8042d_level0_col1\" class=\"col_heading level0 col1\" >T</th>\n",
       "      <th id=\"T_8042d_level0_col2\" class=\"col_heading level0 col2\" >A</th>\n",
       "      <th id=\"T_8042d_level0_col3\" class=\"col_heading level0 col3\" >S</th>\n",
       "      <th id=\"T_8042d_level0_col4\" class=\"col_heading level0 col4\" >W</th>\n",
       "      <th id=\"T_8042d_level0_col5\" class=\"col_heading level0 col5\" >E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_8042d_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_8042d_row0_col1\" class=\"data row0 col1\" >0.795875</td>\n",
       "      <td id=\"T_8042d_row0_col2\" class=\"data row0 col2\" >0.795875</td>\n",
       "      <td id=\"T_8042d_row0_col3\" class=\"data row0 col3\" >0.666667</td>\n",
       "      <td id=\"T_8042d_row0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "      <td id=\"T_8042d_row0_col5\" class=\"data row0 col5\" >1.462541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8042d_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_8042d_row1_col1\" class=\"data row1 col1\" >1.255931</td>\n",
       "      <td id=\"T_8042d_row1_col2\" class=\"data row1 col2\" >2.051805</td>\n",
       "      <td id=\"T_8042d_row1_col3\" class=\"data row1 col3\" >0.666667</td>\n",
       "      <td id=\"T_8042d_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "      <td id=\"T_8042d_row1_col5\" class=\"data row1 col5\" >2.718472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8042d_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_8042d_row2_col1\" class=\"data row2 col1\" >0.923223</td>\n",
       "      <td id=\"T_8042d_row2_col2\" class=\"data row2 col2\" >2.975028</td>\n",
       "      <td id=\"T_8042d_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "      <td id=\"T_8042d_row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "      <td id=\"T_8042d_row2_col5\" class=\"data row2 col5\" >3.641695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8042d_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_8042d_row3_col1\" class=\"data row3 col1\" >0.787201</td>\n",
       "      <td id=\"T_8042d_row3_col2\" class=\"data row3 col2\" >3.762230</td>\n",
       "      <td id=\"T_8042d_row3_col3\" class=\"data row3 col3\" >0.666667</td>\n",
       "      <td id=\"T_8042d_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "      <td id=\"T_8042d_row3_col5\" class=\"data row3 col5\" >4.428896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8042d_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_8042d_row4_col1\" class=\"data row4 col1\" >0.551048</td>\n",
       "      <td id=\"T_8042d_row4_col2\" class=\"data row4 col2\" >4.313278</td>\n",
       "      <td id=\"T_8042d_row4_col3\" class=\"data row4 col3\" >0.666667</td>\n",
       "      <td id=\"T_8042d_row4_col4\" class=\"data row4 col4\" >0.115618</td>\n",
       "      <td id=\"T_8042d_row4_col5\" class=\"data row4 col5\" >5.095563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11f956610>"
      ]
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      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_3135b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_3135b_level0_col0\" class=\"col_heading level0 col0\" >label</th>\n",
       "      <th id=\"T_3135b_level0_col1\" class=\"col_heading level0 col1\" >def</th>\n",
       "      <th id=\"T_3135b_level0_col2\" class=\"col_heading level0 col2\" >stat_sim</th>\n",
       "      <th id=\"T_3135b_level0_col3\" class=\"col_heading level0 col3\" >stat_ana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row0_col0\" class=\"data row0 col0\" >lambda</td>\n",
       "      <td id=\"T_3135b_row0_col1\" class=\"data row0 col1\" >mean arrival rate</td>\n",
       "      <td id=\"T_3135b_row0_col2\" class=\"data row0 col2\" >1.001408</td>\n",
       "      <td id=\"T_3135b_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row1_col0\" class=\"data row1 col0\" >mu</td>\n",
       "      <td id=\"T_3135b_row1_col1\" class=\"data row1 col1\" >mean service rate</td>\n",
       "      <td id=\"T_3135b_row1_col2\" class=\"data row1 col2\" >1.500000</td>\n",
       "      <td id=\"T_3135b_row1_col3\" class=\"data row1 col3\" >1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row2_col0\" class=\"data row2 col0\" >rho</td>\n",
       "      <td id=\"T_3135b_row2_col1\" class=\"data row2 col1\" >(mean arrival rate)/(mean service rate)</td>\n",
       "      <td id=\"T_3135b_row2_col2\" class=\"data row2 col2\" >0.667605</td>\n",
       "      <td id=\"T_3135b_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row3_col0\" class=\"data row3 col0\" >sigma^2</td>\n",
       "      <td id=\"T_3135b_row3_col1\" class=\"data row3 col1\" >variance of the service dist. G</td>\n",
       "      <td id=\"T_3135b_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "      <td id=\"T_3135b_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row4_col0\" class=\"data row4 col0\" >p_N_s</td>\n",
       "      <td id=\"T_3135b_row4_col1\" class=\"data row4 col1\" >prob. of having N jobs in the system</td>\n",
       "      <td id=\"T_3135b_row4_col2\" class=\"data row4 col2\" >[0.33  0.318 0.18  0.091 0.045 0.021 0.008 0.004 0.002 0.001 0.    0.\n",
       " 0.    0.    0.    0.   ]</td>\n",
       "      <td id=\"T_3135b_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row5_col0\" class=\"data row5 col0\" >p_N_q</td>\n",
       "      <td id=\"T_3135b_row5_col1\" class=\"data row5 col1\" >prob. of having N jobs in the queue</td>\n",
       "      <td id=\"T_3135b_row5_col2\" class=\"data row5 col2\" >[0.647 0.18  0.091 0.045 0.021 0.008 0.004 0.002 0.001 0.    0.    0.\n",
       " 0.    0.    0.   ]</td>\n",
       "      <td id=\"T_3135b_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row6_col0\" class=\"data row6 col0\" >p_0_s</td>\n",
       "      <td id=\"T_3135b_row6_col1\" class=\"data row6 col1\" >prob. of having 0 jobs in the system</td>\n",
       "      <td id=\"T_3135b_row6_col2\" class=\"data row6 col2\" >0.330000</td>\n",
       "      <td id=\"T_3135b_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row7_col0\" class=\"data row7 col0\" >p_0_q</td>\n",
       "      <td id=\"T_3135b_row7_col1\" class=\"data row7 col1\" >prob. of having 0 jobs in the queue</td>\n",
       "      <td id=\"T_3135b_row7_col2\" class=\"data row7 col2\" >0.647000</td>\n",
       "      <td id=\"T_3135b_row7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row8_col0\" class=\"data row8 col0\" >L_s</td>\n",
       "      <td id=\"T_3135b_row8_col1\" class=\"data row8 col1\" >expected number of jobs in the system</td>\n",
       "      <td id=\"T_3135b_row8_col2\" class=\"data row8 col2\" >1.346100</td>\n",
       "      <td id=\"T_3135b_row8_col3\" class=\"data row8 col3\" >1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row9_col0\" class=\"data row9 col0\" >L_q</td>\n",
       "      <td id=\"T_3135b_row9_col1\" class=\"data row9 col1\" >expected number of jobs in the queue</td>\n",
       "      <td id=\"T_3135b_row9_col2\" class=\"data row9 col2\" >0.675600</td>\n",
       "      <td id=\"T_3135b_row9_col3\" class=\"data row9 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row10_col0\" class=\"data row10 col0\" >W_s</td>\n",
       "      <td id=\"T_3135b_row10_col1\" class=\"data row10 col1\" >expected time spent in the system</td>\n",
       "      <td id=\"T_3135b_row10_col2\" class=\"data row10 col2\" >1.343257</td>\n",
       "      <td id=\"T_3135b_row10_col3\" class=\"data row10 col3\" >1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_3135b_row11_col0\" class=\"data row11 col0\" >W_q</td>\n",
       "      <td id=\"T_3135b_row11_col1\" class=\"data row11 col1\" >expected time spent in the queue</td>\n",
       "      <td id=\"T_3135b_row11_col2\" class=\"data row11 col2\" >0.676590</td>\n",
       "      <td id=\"T_3135b_row11_col3\" class=\"data row11 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11f5052d0>"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M/M/1: exponential interarrival/exponential service/one server\n",
      "Initial jobs:\n",
      "\n",
      "    T: interarrival time\n",
      "    A: arrival time\n",
      "    S: service time\n",
      "    W: waiting time\n",
      "    E: exit time\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4d314\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_4d314_level0_col0\" class=\"col_heading level0 col0\" >id</th>\n",
       "      <th id=\"T_4d314_level0_col1\" class=\"col_heading level0 col1\" >T</th>\n",
       "      <th id=\"T_4d314_level0_col2\" class=\"col_heading level0 col2\" >A</th>\n",
       "      <th id=\"T_4d314_level0_col3\" class=\"col_heading level0 col3\" >S</th>\n",
       "      <th id=\"T_4d314_level0_col4\" class=\"col_heading level0 col4\" >W</th>\n",
       "      <th id=\"T_4d314_level0_col5\" class=\"col_heading level0 col5\" >E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_4d314_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_4d314_row0_col1\" class=\"data row0 col1\" >0.212110</td>\n",
       "      <td id=\"T_4d314_row0_col2\" class=\"data row0 col2\" >0.212110</td>\n",
       "      <td id=\"T_4d314_row0_col3\" class=\"data row0 col3\" >0.153505</td>\n",
       "      <td id=\"T_4d314_row0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "      <td id=\"T_4d314_row0_col5\" class=\"data row0 col5\" >0.365614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4d314_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_4d314_row1_col1\" class=\"data row1 col1\" >0.060332</td>\n",
       "      <td id=\"T_4d314_row1_col2\" class=\"data row1 col2\" >0.272441</td>\n",
       "      <td id=\"T_4d314_row1_col3\" class=\"data row1 col3\" >1.966660</td>\n",
       "      <td id=\"T_4d314_row1_col4\" class=\"data row1 col4\" >0.093173</td>\n",
       "      <td id=\"T_4d314_row1_col5\" class=\"data row1 col5\" >2.332274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4d314_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_4d314_row2_col1\" class=\"data row2 col1\" >0.574139</td>\n",
       "      <td id=\"T_4d314_row2_col2\" class=\"data row2 col2\" >0.846580</td>\n",
       "      <td id=\"T_4d314_row2_col3\" class=\"data row2 col3\" >0.068410</td>\n",
       "      <td id=\"T_4d314_row2_col4\" class=\"data row2 col4\" >1.485694</td>\n",
       "      <td id=\"T_4d314_row2_col5\" class=\"data row2 col5\" >2.400684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4d314_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_4d314_row3_col1\" class=\"data row3 col1\" >1.274026</td>\n",
       "      <td id=\"T_4d314_row3_col2\" class=\"data row3 col2\" >2.120606</td>\n",
       "      <td id=\"T_4d314_row3_col3\" class=\"data row3 col3\" >0.848464</td>\n",
       "      <td id=\"T_4d314_row3_col4\" class=\"data row3 col4\" >0.280078</td>\n",
       "      <td id=\"T_4d314_row3_col5\" class=\"data row3 col5\" >3.249148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4d314_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_4d314_row4_col1\" class=\"data row4 col1\" >1.739613</td>\n",
       "      <td id=\"T_4d314_row4_col2\" class=\"data row4 col2\" >3.860219</td>\n",
       "      <td id=\"T_4d314_row4_col3\" class=\"data row4 col3\" >1.226519</td>\n",
       "      <td id=\"T_4d314_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "      <td id=\"T_4d314_row4_col5\" class=\"data row4 col5\" >5.086738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11f998190>"
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     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d5306\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_d5306_level0_col0\" class=\"col_heading level0 col0\" >label</th>\n",
       "      <th id=\"T_d5306_level0_col1\" class=\"col_heading level0 col1\" >def</th>\n",
       "      <th id=\"T_d5306_level0_col2\" class=\"col_heading level0 col2\" >stat_sim</th>\n",
       "      <th id=\"T_d5306_level0_col3\" class=\"col_heading level0 col3\" >stat_ana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row0_col0\" class=\"data row0 col0\" >lambda</td>\n",
       "      <td id=\"T_d5306_row0_col1\" class=\"data row0 col1\" >mean arrival rate</td>\n",
       "      <td id=\"T_d5306_row0_col2\" class=\"data row0 col2\" >0.995039</td>\n",
       "      <td id=\"T_d5306_row0_col3\" class=\"data row0 col3\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row1_col0\" class=\"data row1 col0\" >mu</td>\n",
       "      <td id=\"T_d5306_row1_col1\" class=\"data row1 col1\" >mean service rate</td>\n",
       "      <td id=\"T_d5306_row1_col2\" class=\"data row1 col2\" >1.505241</td>\n",
       "      <td id=\"T_d5306_row1_col3\" class=\"data row1 col3\" >1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row2_col0\" class=\"data row2 col0\" >rho</td>\n",
       "      <td id=\"T_d5306_row2_col1\" class=\"data row2 col1\" >(mean arrival rate)/(mean service rate)</td>\n",
       "      <td id=\"T_d5306_row2_col2\" class=\"data row2 col2\" >0.661050</td>\n",
       "      <td id=\"T_d5306_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row3_col0\" class=\"data row3 col0\" >sigma^2</td>\n",
       "      <td id=\"T_d5306_row3_col1\" class=\"data row3 col1\" >variance of the service dist. G</td>\n",
       "      <td id=\"T_d5306_row3_col2\" class=\"data row3 col2\" >0.446146</td>\n",
       "      <td id=\"T_d5306_row3_col3\" class=\"data row3 col3\" >0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row4_col0\" class=\"data row4 col0\" >p_N_s</td>\n",
       "      <td id=\"T_d5306_row4_col1\" class=\"data row4 col1\" >prob. of having N jobs in the system</td>\n",
       "      <td id=\"T_d5306_row4_col2\" class=\"data row4 col2\" >[0.338 0.233 0.145 0.097 0.059 0.046 0.026 0.019 0.012 0.009 0.006 0.003\n",
       " 0.002 0.001 0.001 0.    0.    0.    0.    0.    0.   ]</td>\n",
       "      <td id=\"T_d5306_row4_col3\" class=\"data row4 col3\" >[0.333 0.222 0.148 0.099]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row5_col0\" class=\"data row5 col0\" >p_N_q</td>\n",
       "      <td id=\"T_d5306_row5_col1\" class=\"data row5 col1\" >prob. of having N jobs in the queue</td>\n",
       "      <td id=\"T_d5306_row5_col2\" class=\"data row5 col2\" >[0.571 0.145 0.097 0.059 0.046 0.026 0.019 0.012 0.009 0.006 0.003 0.002\n",
       " 0.001 0.001 0.    0.    0.    0.    0.    0.   ]</td>\n",
       "      <td id=\"T_d5306_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row6_col0\" class=\"data row6 col0\" >p_0_s</td>\n",
       "      <td id=\"T_d5306_row6_col1\" class=\"data row6 col1\" >prob. of having 0 jobs in the system</td>\n",
       "      <td id=\"T_d5306_row6_col2\" class=\"data row6 col2\" >0.338000</td>\n",
       "      <td id=\"T_d5306_row6_col3\" class=\"data row6 col3\" >0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row7_col0\" class=\"data row7 col0\" >p_0_q</td>\n",
       "      <td id=\"T_d5306_row7_col1\" class=\"data row7 col1\" >prob. of having 0 jobs in the queue</td>\n",
       "      <td id=\"T_d5306_row7_col2\" class=\"data row7 col2\" >0.571000</td>\n",
       "      <td id=\"T_d5306_row7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row8_col0\" class=\"data row8 col0\" >L_s</td>\n",
       "      <td id=\"T_d5306_row8_col1\" class=\"data row8 col1\" >expected number of jobs in the system</td>\n",
       "      <td id=\"T_d5306_row8_col2\" class=\"data row8 col2\" >1.932200</td>\n",
       "      <td id=\"T_d5306_row8_col3\" class=\"data row8 col3\" >2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row9_col0\" class=\"data row9 col0\" >L_q</td>\n",
       "      <td id=\"T_d5306_row9_col1\" class=\"data row9 col1\" >expected number of jobs in the queue</td>\n",
       "      <td id=\"T_d5306_row9_col2\" class=\"data row9 col2\" >1.270000</td>\n",
       "      <td id=\"T_d5306_row9_col3\" class=\"data row9 col3\" >1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row10_col0\" class=\"data row10 col0\" >W_s</td>\n",
       "      <td id=\"T_d5306_row10_col1\" class=\"data row10 col1\" >expected time spent in the system</td>\n",
       "      <td id=\"T_d5306_row10_col2\" class=\"data row10 col2\" >1.943669</td>\n",
       "      <td id=\"T_d5306_row10_col3\" class=\"data row10 col3\" >2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d5306_row11_col0\" class=\"data row11 col0\" >W_q</td>\n",
       "      <td id=\"T_d5306_row11_col1\" class=\"data row11 col1\" >expected time spent in the queue</td>\n",
       "      <td id=\"T_d5306_row11_col2\" class=\"data row11 col2\" >1.279323</td>\n",
       "      <td id=\"T_d5306_row11_col3\" class=\"data row11 col3\" >1.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vvKK7AUOpVGLs2LFYsWIFZDIZQkNDsWfPHpSWlhrUZ07fXn/9dd08ic8//zw6deqEtWvXora2FkuWLGnR9hDZpDa9J5dIAupPT9KY+tOTlJeXi06dOont27e3en3asgCMPgoLC022UVZWJmbMmCF69+4tXFxchFKpFNHR0Sbb0U7hUX+qEa3MzEwxevRoERAQIBwdHUVAQICYOHGi+O233/TK7dq1S4SFhYlOnToZTH9x7Ngx8be//U3cddddQi6Xi6CgIDFu3DiRmZmpK6Od7qThtCrJycnCxcXFoF/Dhg0Tffv2NbkPtBpOJSKEEKdPnxZPPPGE8PDwEAqFQkRFRYk9e/boldFO77FlyxYxd+5c4ePjI5ycnERiYqLeFCaNOXLkiIiMjBSOjo6ie/fuYs2aNbrtbKyPr7/+uoiKihIeHh7CyclJ9O7dW7zxxhuirq5OV+bmzZti1qxZwtvbW8hkMl2d2ulHjE11Y2q6ExcXF3H69GkRFxcnnJ2dha+vr1CpVLqpX7QuX74sxowZI5ydnUXnzp3Fc889J/Ly8gzqNNU3IQynOxFCiNzcXBEfHy9cXV2Fs7OzeOihh8R3332nV8bU58vUNCxEtkYmBK8UJbK27du3Y9KkSSgrKzM5WtaW9bXUrl27kJSUhK+++ko3wbBUBAYGIj4+3qwbD4iIrI3X2BG1AQ8PD7z33nsWC2GWrq+lPvjgA3Tv3t3g59Fs3Y0bN3DlyhWDqVKIiNobXmNH1Abi4uLadX3m2rp1K37++Wekp6fj3Xffteidlm1t//792Lp1K65fv667AYCIqL3iqVgiumMymQyurq4YP3481qxZY7HfqW0PHnroIfz++++YPn06XnnllbbuDhFRoxjsiIiIiCSC19gRERERSYRNnC/RaDS4dOkS3NzcJHXtDhEREVFThBCoqqpCQECAwRyZDdlEsLt06RJ/DoaIiIg6tAsXLqBr166NlrGJYOfm5gbg1gbxZ2GIiIioI6msrERgYKAuDzXGJoKd9vSru7s7gx0RERF1SM25HM0mgp01qDUCOYVXUVpVAx83BaJCPGFvx+v5iIiIyHYw2AHIyCvCwt35KKqo0S3zVyqgGhWGhHD/NuwZERERUfN1+OlOMvKKMH1Trl6oA4DiihpM35SLjLyiNuoZERERkXk6dLBTawQW7s6HsRmatcsW7s6HWsM5nImIiKj969DBLqfwqsFIXX0CQFFFDXIKr1qvU0REREQt1KGDXWmV6VDXknJEREREbalDBzsfN4VFyxERERG1pQ4d7KJCPOGvVMDUpCYy3Lo7NirE05rdIiIiImqRDh3s7O1kUI0KAwCDcKd9rhoVxvnsiIiIyCZ06GAHAAnh/lg9eSB83OV6y/2UCqyePJDz2BEREZHN4ATFuBXu7r/bC/0WfAkA2JAyGEN7eHOkjoiIiGxKhx+x06of4vhzYkRERGSLGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJKJFwW7VqlUIDg6GQqFAdHQ0cnJyTJb9/PPPMWjQIHh4eMDFxQUDBgzAxo0bW9xhIiIiIjLO7GC3bds2pKamQqVSITc3FxEREYiPj0dpaanR8p6envi///f/Ijs7Gz///DNSUlKQkpKC/fv333HniYiIiOg2s4PdsmXLMG3aNKSkpCAsLAxr1qyBs7Mz1q1bZ7T8gw8+iMcffxx9+vRBaGgo/vGPf6B///745ptv7rjzRERERHSbWcGurq4OR48eRWxs7O0K7OwQGxuL7OzsJtcXQiAzMxMFBQV44IEHTJarra1FZWWl3oOIiIiIGmdWsCsrK4NarYavr6/ecl9fXxQXF5tcr6KiAq6urnB0dERiYiJWrFiBRx55xGT5tLQ0KJVK3SMwMNCcbhIRERF1SFa5K9bNzQ3Hjx/Hjz/+iDfeeAOpqanIysoyWX7u3LmoqKjQPS5cuGCNbhIRERHZtE7mFPby8oK9vT1KSkr0lpeUlMDPz8/kenZ2drj77rsBAAMGDMDJkyeRlpaGBx980Gh5uVwOuVxuTteIiIiIOjyzRuwcHR0RGRmJzMxM3TKNRoPMzEzExMQ0ux6NRoPa2lpzmiYiIiKiJpg1YgcAqampSE5OxqBBgxAVFYXly5fj2rVrSElJAQBMmTIFXbp0QVpaGoBb18sNGjQIoaGhqK2txd69e7Fx40asXr3asltCRERE1MGZHezGjx+Py5cvY/78+SguLsaAAQOQkZGhu6Hi/PnzsLO7PRB47do1PP/887h48SKcnJzQu3dvbNq0CePHj7fcVhARERERZEII0dadaEplZSWUSiUqKirg7u7eKm1U191E2PxbkybnL4qHs6PZmZeIiIjI4szJQfytWCIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJ6NTWHeho1BqBnMKrKK2qgY+bAlEhnrC3k7V1t4iIiEgCGOysKCOvCAt356Oooka3zF+pgGpUGBLC/duwZ0RERCQFPBVrJRl5RZi+KVcv1AFAcUUNpm/KRUZeURv1jIiIiKSCwc4K1BqBhbvzIYy8pl22cHc+1BpjJYiIiIiah8HOCnIKrxqM1NUnABRV1CCn8Kr1OkVERESSw2BnBaVVpkNdS8oRERERGcNgZwU+bgqLliMiIiIyhsHOCqJCPOGvVMDUpCYy3Lo7NirE05rdIiIiIolhsLMCezsZVKPCAMAg3Gmfq0aFcT47IiIiuiMMdlaSEO6P1ZMHwsddrrfcT6nA6skDOY8dERER3TFOUGxFCeH+uP9uL/Rb8CUAYEPKYAzt4c2ROiIiIrIIjthZWf0Qx58TIyIiIktisCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIoloUbBbtWoVgoODoVAoEB0djZycHJNlP/jgAwwdOhSdO3dG586dERsb22h5IiIiImoZs4Pdtm3bkJqaCpVKhdzcXERERCA+Ph6lpaVGy2dlZWHixIk4fPgwsrOzERgYiLi4OPzxxx933HkiIiIius3sYLds2TJMmzYNKSkpCAsLw5o1a+Ds7Ix169YZLf/pp5/i+eefx4ABA9C7d298+OGH0Gg0yMzMvOPOExEREdFtZgW7uro6HD16FLGxsbcrsLNDbGwssrOzm1VHdXU1bty4AU9P07+LWltbi8rKSr0HERERETXOrGBXVlYGtVoNX19fveW+vr4oLi5uVh1z5sxBQECAXjhsKC0tDUqlUvcIDAw0p5tEREREHZJV74pdvHgxtm7dip07d0KhUJgsN3fuXFRUVOgeFy5csGIviYiIiGyTWb8V6+XlBXt7e5SUlOgtLykpgZ+fX6Prvv3221i8eDEOHjyI/v37N1pWLpdDLpeb0zUiIiKiDs+sETtHR0dERkbq3figvREiJibG5HpLlizBa6+9hoyMDAwaNKjlvSUiIiIik8wasQOA1NRUJCcnY9CgQYiKisLy5ctx7do1pKSkAACmTJmCLl26IC0tDQDw1ltvYf78+di8eTOCg4N11+K5urrC1dXVgptCRERE1LGZHezGjx+Py5cvY/78+SguLsaAAQOQkZGhu6Hi/PnzsLO7PRC4evVq1NXV4YknntCrR6VSYcGCBXfWeyIiIiLSMTvYAcDMmTMxc+ZMo69lZWXpPT979mxLmiAiIiIiM/G3YomIiIgkgsGOiIiISCIY7IiIiIgkgsGOiIiISCIY7IiIiIgkgsGOiIiISCIY7IiIiIgkgsGOiIiISCIY7IiIiIgkgsGOiIiISCIY7IiIiIgkgsGOiIiISCIY7IiIiIgkolNbd4Baj1ojkFN4FaVVNfBxUyAqxBP2drK27hYRERG1EgY7icrIK8LC3fkoqqjRLfNXKqAaFYaEcP827BkRERG1Fp6KlaCMvCJM35SrF+oAoLiiBtM35SIjr6iNekZERESticFOYtQagYW78yGMvKZdtnB3PtQaYyWIiIjIljHYSUxO4VWDkbr6BICiihrkFF61XqeIiIjIKhjsJKa0ynSoa0k5IiIish0MdhLj46awaDkiIiKyHQx2EhMV4gl/pQKmJjWR4dbdsVEhntbsFhEREVkBg53E2NvJoBoVBgAG4U77XDUqjPPZERERSRCDnQQlhPtj9eSB8HGX6y33UyqwevJAzmNHREQkUZygWKISwv1x/91e6LfgSwDAhpTBGNrDmyN1REREEsYROwmrH+L4c2JERETSx2BHREREJBEMdkREREQSwWBHREREJBEMdkREREQSwWBHREREJBEMdkREREQSwWBHREREJBEMdkREREQSwWBHREREJBEMdkREREQS0aJgt2rVKgQHB0OhUCA6Oho5OTkmy544cQJjxoxBcHAwZDIZli9f3tK+Ujul1ghkn76CXcf/QPbpK1BrRFt3iYiIqEPqZO4K27ZtQ2pqKtasWYPo6GgsX74c8fHxKCgogI+Pj0H56upqdO/eHWPHjsU///lPi3Sa2o+MvCIs3J2Poooa3TJ/pQKqUWFICPdvw54RERF1PGaP2C1btgzTpk1DSkoKwsLCsGbNGjg7O2PdunVGyw8ePBhLly7FhAkTIJfL77jD1H5k5BVh+qZcvVAHAMUVNZi+KRcZeUVt1DMiIqKOyaxgV1dXh6NHjyI2NvZ2BXZ2iI2NRXZ2tsU6VVtbi8rKSr0HtS9qjcDC3fkwdtJVu2zh7nyeliUiIrIis4JdWVkZ1Go1fH199Zb7+vqiuLjYYp1KS0uDUqnUPQIDAy1WN1lGTuFVg5G6+gSAoooa5BRetV6niIiIOrh2eVfs3LlzUVFRoXtcuHChrbtEDZRWmQ51LSlHREREd86smye8vLxgb2+PkpISveUlJSXw8/OzWKfkcjmvx2vnfNwUFi1HREREd86sETtHR0dERkYiMzNTt0yj0SAzMxMxMTEW7xy1X1EhnvBXKiAz8boMt+6OjQrxtGa3iIiIOjSzT8Wmpqbigw8+wMcff4yTJ09i+vTpuHbtGlJSUgAAU6ZMwdy5c3Xl6+rqcPz4cRw/fhx1dXX4448/cPz4cfz++++W2wqyOns7GVSjwgDAINxpn6tGhcHezlT0IyIiIkszex678ePH4/Lly5g/fz6Ki4sxYMAAZGRk6G6oOH/+POzsbufFS5cu4Z577tE9f/vtt/H2229j2LBhyMrKuvMtoDaTEO6P1ZMHQvWfEyiprNUt9+M8dkRERG3C7GAHADNnzsTMmTONvtYwrAUHB0MITnkhVQnh/rj/bi/0W/AlAGBDymAM7eHNkToiIqI20C7viiXbUj/ERYV4MtQRERG1EQY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSiBbNY0fUFtQagZzCqyitqoGPm4JTqxARETXAYEc2ISOvCAt356Oooka3zJ+/cEFERKSHp2Kp3cvIK8L0Tbl6oQ4AiitqMH1TLjLyitqoZ0RERO0Lgx21a2qNwMLd+TD2o3TaZQt350Ot4c/WERERMdhRu5ZTeNVgpK4+AaCoogY5hVet1ykiIqJ2isGO2rXSKtOhriXliIiIpIw3T1C75uOmsGi55uDdt0REZKsY7KhdiwrxhL9SgeKKGqPX2ckA+ClvhS9L4N23RERky3gqlto1ezsZVKPCANwKcfVpn6tGhVlkRI133xIRka1jsKN2LyHcH6snD4SPu1xvuZ9SgdWTB1pkJI133xIRkRTwVCzZhIRwf9x/txf6LfgSALAhZTCG9vC22LVv5tx9GxN6l0XaJCIisjSO2JHNqB/iLH1DA+++JSIiKWCwI0Lb3H1LRERkaQx2RLh9962pMUAZbt0da6m7b4mIiFoDgx0RrHv3LRERUWthsCP6X9a4+7YhtUYg+/QV7Dr+B7JPX+Fdt0REdEd4VyxRPa199219nAyZiIgsjSN2RA205t23WpwMmYiIWgODHZGVtcVkyDzlS0TUMfBULJGVWXsyZJ7yJSLqODhiR2Rl1pwMmad8iYg6FgY7Iiuz1mTIPOVLRNTx8FQskZVpJ0MurqgxGrpkuDXFyp1OhsxTvkREHQ9H7IiszFqTIUv9lC9HB4mIDHHEjqgNaCdDVv3nBEoqa3XL/Sw4wtVeTvnKcOuU7yNhfhabOsaao4NqjUBO4VWUVtXAx03RalPgEBFZAoMdURtp7cmQpXzKd/qmXINt0o4OWvJXQnh6mYhsDU/FErWh1pwMWYqnfK15Q4i1Ty9b69QyT2ETSRtH7IgkTEqnfAHrjQ5a+/SytUYGrT0Caa3T2FJrx5ptsR3baMscLQp2q1atwtKlS1FcXIyIiAisWLECUVFRJsvv2LED8+bNw9mzZ9GjRw+89dZbGDlyZIs7TUTNJ5VTvoD1RgeteXrZWqeWrXkKW9uelMKqNUOx1LZJau1Yuy1zmX0qdtu2bUhNTYVKpUJubi4iIiIQHx+P0tJSo+W/++47TJw4EU8//TSOHTuGpKQkJCUlIS8v7447T0TNI4VTvoD1RgetFSCtdWrZ2nMaWus0ttTasWZbbMc22moJs0fsli1bhmnTpiElJQUAsGbNGqSnp2PdunV4+eWXDcq/++67SEhIwL/+9S8AwGuvvYYDBw5g5cqVWLNmzR1233KEEJDfvHWqSlNdDc3N1jlLram7aZV2rNkW22n/bbV2O3HdlVjzRB+8nn4SpVX1Tvm6K/DKyN54pLsSmurqO25nkK8CQc4ylFSaHh30dVdgkK/ijtrzcbj9fdBUuTtpJ6fwKq5eqYC8kTJXr9Qi5+QfiL6DEU9rtQPcCpFpnx+Do4n9JwOQ9vkxDA92v6OwL7V2rNkW22k5U23V2jtCyGStMguAuWRCiGb/F62urg7Ozs747LPPkJSUpFuenJyM8vJy7Nq1y2Cdbt26ITU1FbNnz9YtU6lU+OKLL/A///M/Rtupra1Fbe3tnVZZWYnAwEBUVFTA3d29ud01y1/llbhwb3Sr1E1ERETSlfToG6jtdPu/Tlum3WuRWQC0KisroVQqm5WDzDoVW1ZWBrVaDV9fX73lvr6+KC4uNrpOcXGxWeUBIC0tDUqlUvcIDAw0p5st4uzI+0iIiIjozlliFoCWapdpZu7cuUhNTdU9147YtSaZkxN65R5t1TaIyHLUGoGfzv2Jy1U18HZTYFBQZ4uf+jiQX4w39/6K4srbX9K608thfndcv1ojELvsSJOnlg+mDrvjU1XWaAcAfii8iqnrc5ostyEl6o5O+0qtHWu2xXZazlRbtfaOes8tMQtAS5kV7Ly8vGBvb4+SkhK95SUlJfDzM/4l5+fnZ1Z5AJDL5ZDLG7saxPJkMhlkzs5WbZOIWs4OQExfl1ZtI35Qd8QODGm1KQ3sAMz92z2YvikXAPRCl7aFuX+7Bw6ud7ad1moHAKL6OMHzLmXTd0n36QK7O9iPUmvHmm2xnZZrdlsWmAWgpcw6Fevo6IjIyEhkZmbqlmk0GmRmZiImJsboOjExMXrlAeDAgQMmyxMRtSf2djLEhN6F0QO6ICb0LouPCmrnGvRT6v8P30+psOgUJNZqx1p3SUutHWu2xXZso62WMuvmCeDWdCfJyclYu3YtoqKisHz5cmzfvh2//vorfH19MWXKFHTp0gVpaWkAbk13MmzYMCxevBiJiYnYunUr3nzzTeTm5iI8PLxZbZpz0SARkS2S2gSuUpu7TIpzpLEd22gLMC8HmR3sAGDlypW6CYoHDBiA9957D9HRt+4offDBBxEcHIwNGzboyu/YsQOvvvqqboLiJUuWmDVBMYMdEZHtkVpYleKvGrAd22ir1YOdtTHYERERUUdlTg5ql3fFNqTNnpWVlW3cEyIiIiLr0uaf5ozF2USwq6qqAgCrzGdHRERE1B5VVVVBqVQ2WsYmTsVqNBpcunQJbm5ukMla704T7Xx5Fy5c6NCnfLkfuA+0uB9u4X64hfuB+0CL++EWa+0HIQSqqqoQEBAAO7vGJzSxiRE7Ozs7dO3a1Wrtubu7d+gDVYv7gftAi/vhFu6HW7gfuA+0uB9uscZ+aGqkTsuseeyIiIiIqP1isCMiIiKSCAa7euRyOVQqldV/zqy94X7gPtDifriF++EW7gfuAy3uh1va436wiZsniIiIiKhpHLEjIiIikggGOyIiIiKJYLAjIiIikggGOyIiIiKJ6HDBbtWqVQgODoZCoUB0dDRycnIaLb9jxw707t0bCoUC/fr1w969e63U09aRlpaGwYMHw83NDT4+PkhKSkJBQUGj62zYsAEymUzvoVAorNTj1rFgwQKDberdu3ej60jtWAgODjbYBzKZDDNmzDBaXirHwVdffYVRo0YhICAAMpkMX3zxhd7rQgjMnz8f/v7+cHJyQmxsLE6dOtVkveZ+t7S1xvbDjRs3MGfOHPTr1w8uLi4ICAjAlClTcOnSpUbrbMnnqi01dSxMnTrVYHsSEhKarFdKxwIAo98TMpkMS5cuNVmnrR0LzfnbWFNTgxkzZuCuu+6Cq6srxowZg5KSkkbrben3yZ3oUMFu27ZtSE1NhUqlQm5uLiIiIhAfH4/S0lKj5b/77jtMnDgRTz/9NI4dO4akpCQkJSUhLy/Pyj23nCNHjmDGjBn4/vvvceDAAdy4cQNxcXG4du1ao+u5u7ujqKhI9zh37pyVetx6+vbtq7dN33zzjcmyUjwWfvzxR73tP3DgAABg7NixJteRwnFw7do1REREYNWqVUZfX7JkCd577z2sWbMGP/zwA1xcXBAfH4+amhqTdZr73dIeNLYfqqurkZubi3nz5iE3Nxeff/45CgoK8NhjjzVZrzmfq7bW1LEAAAkJCXrbs2XLlkbrlNqxAEBv+4uKirBu3TrIZDKMGTOm0Xpt6Vhozt/Gf/7zn9i9ezd27NiBI0eO4NKlS/jb3/7WaL0t+T65Y6IDiYqKEjNmzNA9V6vVIiAgQKSlpRktP27cOJGYmKi3LDo6Wjz33HOt2k9rKi0tFQDEkSNHTJZZv369UCqV1uuUFahUKhEREdHs8h3hWPjHP/4hQkNDhUajMfq6FI8DAGLnzp265xqNRvj5+YmlS5fqlpWXlwu5XC62bNlish5zv1vam4b7wZicnBwBQJw7d85kGXM/V+2JsX2QnJwsRo8ebVY9HeFYGD16tHj44YcbLWPLx4IQhn8by8vLhYODg9ixY4euzMmTJwUAkZ2dbbSOln6f3KkOM2JXV1eHo0ePIjY2VrfMzs4OsbGxyM7ONrpOdna2XnkAiI+PN1neFlVUVAAAPD09Gy33119/ISgoCIGBgRg9ejROnDhhje61qlOnTiEgIADdu3fHpEmTcP78eZNlpX4s1NXVYdOmTfj73/8OmUxmspwUj4P6CgsLUVxcrPdeK5VKREdHm3yvW/LdYosqKiogk8ng4eHRaDlzPle2ICsrCz4+PujVqxemT5+OK1eumCzbEY6FkpISpKen4+mnn26yrC0fCw3/Nh49ehQ3btzQe2979+6Nbt26mXxvW/J9YgkdJtiVlZVBrVbD19dXb7mvry+Ki4uNrlNcXGxWeVuj0Wgwe/Zs3H///QgPDzdZrlevXli3bh127dqFTZs2QaPR4L777sPFixet2FvLio6OxoYNG5CRkYHVq1ejsLAQQ4cORVVVldHyUj8WvvjiC5SXl2Pq1Kkmy0jxOGhI+36a81635LvF1tTU1GDOnDmYOHFioz90bu7nqr1LSEjAJ598gszMTLz11ls4cuQIRowYAbVabbR8RzgWPv74Y7i5uTV5CtKWjwVjfxuLi4vh6Oho8B+bpjKEtkxz17GETq1WM7V7M2bMQF5eXpPXPcTExCAmJkb3/L777kOfPn2wdu1avPbaa63dzVYxYsQI3b/79++P6OhoBAUFYfv27c36n6jUfPTRRxgxYgQCAgJMlpHicUBNu3HjBsaNGwchBFavXt1oWal9riZMmKD7d79+/dC/f3+EhoYiKysLw4cPb8OetZ1169Zh0qRJTd44ZcvHQnP/NrZXHWbEzsvLC/b29gZ3sJSUlMDPz8/oOn5+fmaVtyUzZ87Enj17cPjwYXTt2tWsdR0cHHDPPffg999/b6XeWZ+Hhwd69uxpcpukfCycO3cOBw8exDPPPGPWelI8DrTvpznvdUu+W2yFNtSdO3cOBw4caHS0zpimPle2pnv37vDy8jK5PVI+FgDg66+/RkFBgdnfFYDtHAum/jb6+fmhrq4O5eXleuWbyhDaMs1dxxI6TLBzdHREZGQkMjMzdcs0Gg0yMzP1RiHqi4mJ0SsPAAcOHDBZ3hYIITBz5kzs3LkThw4dQkhIiNl1qNVq/PLLL/D392+FHraNv/76C6dPnza5TVI8FrTWr18PHx8fJCYmmrWeFI+DkJAQ+Pn56b3XlZWV+OGHH0y+1y35brEF2lB36tQpHDx4EHfddZfZdTT1ubI1Fy9exJUrV0xuj1SPBa2PPvoIkZGRiIiIMHvd9n4sNPW3MTIyEg4ODnrvbUFBAc6fP2/yvW3J94lFtNptGe3Q1q1bhVwuFxs2bBD5+fni2WefFR4eHqK4uFgIIcRTTz0lXn75ZV35b7/9VnTq1Em8/fbb4uTJk0KlUgkHBwfxyy+/tNUm3LHp06cLpVIpsrKyRFFRke5RXV2tK9NwPyxcuFDs379fnD59Whw9elRMmDBBKBQKceLEibbYBIt48cUXRVZWligsLBTffvutiI2NFV5eXqK0tFQI0TGOBSFu3bHXrVs3MWfOHIPXpHocVFVViWPHjoljx44JAGLZsmXi2LFjurs9Fy9eLDw8PMSuXbvEzz//LEaPHi1CQkLE9evXdXU8/PDDYsWKFbrnTX23tEeN7Ye6ujrx2GOPia5du4rjx4/rfVfU1tbq6mi4H5r6XLU3je2Dqqoq8dJLL4ns7GxRWFgoDh48KAYOHCh69OghampqdHVI/VjQqqioEM7OzmL16tVG67D1Y6E5fxv/67/+S3Tr1k0cOnRI/PTTTyImJkbExMTo1dOrVy/x+eef65435/vE0jpUsBNCiBUrVohu3boJR0dHERUVJb7//nvda8OGDRPJycl65bdv3y569uwpHB0dRd++fUV6erqVe2xZAIw+1q9fryvTcD/Mnj1bt898fX3FyJEjRW5urvU7b0Hjx48X/v7+wtHRUXTp0kWMHz9e/P7777rXO8KxIIQQ+/fvFwBEQUGBwWtSPQ4OHz5s9DOg3VaNRiPmzZsnfH19hVwuF8OHDzfYP0FBQUKlUukta+y7pT1qbD8UFhaa/K44fPiwro6G+6Gpz1V709g+qK6uFnFxccLb21s4ODiIoKAgMW3aNIOAJvVjQWvt2rXCyclJlJeXG63D1o+F5vxtvH79unj++edF586dhbOzs3j88cdFUVGRQT3112nO94mlyf63I0RERERk4zrMNXZEREREUsdgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0TUQFZWFmQyGbKyssxed+rUqXB1dbV4n4KDgzF16lTd8zvpo7kWLFgAmUymt0wmk2HmzJmt3jYAbNiwATKZDGfPnrVKe0S2jMGOyMK0f4RkMhm++eYbg9eFEAgMDIRMJsOjjz5qtI4XX3wRYWFhrVLfX3/9BZVKhYSEBHh6ekImk2HDhg0t3FqyNW+++Sa++OKLtu6GUe25b0S2gsGOqJUoFAps3rzZYPmRI0dw8eJFyOVyk+ump6cjMTGxVeorKyvDokWLcPLkSURERDR3czqUBx54ANevX8cDDzzQ1l0xqaV9bEl4evXVV3H9+nWz1mkJU3176qmncP36dQQFBbV6H4hsHYMdUSsZOXIkduzYgZs3b+ot37x5MyIjI+Hn52d0vTNnzqCgoMAg2FmqPn9/fxQVFeHcuXNYunRpSzdP0uzs7KBQKGBn136/Iq3Rx2vXrgEAOnXqBIVC0WrtNMXe3h4KhcLgdDARGWq/31pENm7ixIm4cuUKDhw4oFtWV1eHzz77DE8++aTJ9dLT06FUKjFkyJBWqU8ul5sMgc31xRdfIDw8HAqFAuHh4di5cyemTp2K4OBgXRlT14CdPXvW6OnfX3/9FU888QQ8PT2hUCgwaNAg/Oc//9ErY+xaL8D0NVj79u3D0KFD4eLiAjc3NyQmJuLEiRNNbp+pvu/YsQORkZFwcnKCl5cXJk+ejD/++MNoHWfOnEF8fDxcXFwQEBCARYsWQQjRZNtCCLz++uvo2rUrnJ2d8dBDDxnts7E+njp1CmPGjIGfnx8UCgW6du2KCRMmoKKiAsCt6+KuXbuGjz/+WHd6X3vdnnbf5ufn48knn0Tnzp11x4yp/Q4An376KXr16gWFQoHIyEh89dVXeq83PC60GtbZWN9Mvb/vv/8++vbtC7lcjoCAAMyYMQPl5eV6ZR588EGEh4cjPz8fDz30EJydndGlSxcsWbLE6PYQ2ToGO6JWEhwcjJiYGGzZskW3bN++faioqMCECRNMrrd371488sgj6NSpU6vW11JffvklxowZA5lMhrS0NCQlJSElJQU//fRTi+s8ceIE7r33Xpw8eRIvv/wy3nnnHbi4uCApKQk7d+5sUZ0bN25EYmIiXF1d8dZbb2HevHnIz8/HkCFDWnQR/oYNGzBu3DjY29sjLS0N06ZNw+eff44hQ4YYhAm1Wo2EhAT4+vpiyZIliIyMhEqlgkqlarKd+fPnY968eYiIiMDSpUvRvXt3xMXF6UbPTKmrq0N8fDy+//57zJo1C6tWrcKzzz6LM2fO6Pq3ceNGyOVyDB06FBs3bsTGjRvx3HPP6dUzduxYVFdX480338S0adMabfPIkSOYPXs2Jk+ejEWLFuHKlStISEhAXl5ek9vZUHP6Vt+CBQswY8YMBAQE4J133sGYMWOwdu1axMXF4caNG3pl//zzTyQkJCAiIgLvvPMOevfujTlz5mDfvn1m95Oo3RNEZFHr168XAMSPP/4oVq5cKdzc3ER1dbUQQoixY8eKhx56SAghRFBQkEhMTNRb99q1a0KhUIj169e3Wn31/fjjjwKAydeNGTBggPD39xfl5eW6ZV9++aUAIIKCgnTLDh8+LACIw4cP661fWFho0Obw4cNFv379RE1NjW6ZRqMR9913n+jRo4dumUqlEsa+trT7qLCwUAghRFVVlfDw8BDTpk3TK1dcXCyUSqXB8oYa9r2urk74+PiI8PBwcf36dV25PXv2CABi/vz5umXJyckCgJg1a5betiQmJgpHR0dx+fJlk+2WlpYKR0dHkZiYKDQajW75K6+8IgCI5ORkk308duyYACB27NjR6La5uLjo1aOl3bcTJ040+Vp9AAQA8dNPP+mWnTt3TigUCvH444/rliUnJ+sdF43VaapvDd9f7X6Ki4sTarVaV27lypUCgFi3bp1u2bBhwwQA8cknn+iW1dbWCj8/PzFmzBiDtohsHUfsiFrRuHHjcP36dezZswdVVVXYs2dPo6dNDx06hNraWowYMcIq9ZmrqKgIx48fR3JyMpRKpW75I488orvr1lxXr17FoUOHMG7cOFRVVaGsrAxlZWW4cuUK4uPjcerUKZOnO005cOAAysvLMXHiRF19ZWVlsLe3R3R0NA4fPmxWfT/99BNKS0vx/PPP611rlpiYiN69eyM9Pd1gnfpTgWinBqmrq8PBgwdNtnPw4EHU1dVh1qxZeqcpZ8+e3WQfte/H/v37UV1d3ZzNMuq//uu/ml02JiYGkZGRuufdunXD6NGjsX//fqjV6hb3oSna/TR79my9awynTZsGd3d3g/fD1dUVkydP1j13dHREVFQUzpw502p9JGorljk3Q0RGeXt7IzY2Fps3b0Z1dTXUajWeeOIJk+XT09MxaNAg+Pr6WqU+c507dw4A0KNHD4PXevXqhdzcXLPr/P333yGEwLx58zBv3jyjZUpLS9GlS5dm13nq1CkAwMMPP2z0dXd3d7P6qN3uXr16GbzWu3dvg2lo7Ozs0L17d71lPXv2BIBGTwOb2r/e3t7o3Llzo30MCQlBamoqli1bhk8//RRDhw7FY489hsmTJ+uF8KaEhIQ0u6yx46Bnz56orq7G5cuX7/haTlNMvR+Ojo7o3r277nWtrl27Glwj2LlzZ/z888+t0j+itsRgR9TKnnzySUybNg3FxcUYMWIEPDw8TJbdu3cvUlJSrFpfazF1sX3DkRyNRgMAeOmllxAfH290nbvvvrtFdW7cuNFouLDU9YbtzTvvvIOpU6di165d+PLLL/HCCy8gLS0N33//Pbp27dqsOpycnCzap+a+Z63J3t7e6HLRjJtZiGyNNL/diNqRxx9/HM899xy+//57bNu2zWS5vLw8nD9/3mCak9auzxzaecS0I2L1FRQU6D3XjjA1vLGg4WiKdmTLwcEBsbGxjbZfv876gbZhnaGhoQAAHx+fJutsDu12FxQUGIwCFhQUGMyvptFocObMGd0oHQD89ttvAGD0DtGG7Zw6dUpvxO/y5cv4888/m9XXfv36oV+/fnj11Vfx3Xff4f7778eaNWvw+uuvAzAdtFrC2HHw22+/wdnZGd7e3gBuvWcNjwHA8D0zp2/134/6+6murg6FhYUWec+JbBWvsSNqZa6urli9ejUWLFiAUaNGmSy3d+9e+Pr6YtCgQVatzxz+/v4YMGAAPv74Y90UGsCta9ry8/P1ygYFBcHe3t5g+ov3339f77mPjw8efPBBrF27FkVFRQZtXr58WfdvbWCrX6d2ioz64uPj4e7ujjfffNPgDsmGdTbHoEGD4OPjgzVr1qC2tla3fN++fTh58qTR8Lxy5Urdv4UQWLlyJRwcHDB8+HCT7cTGxsLBwQErVqzQG01avnx5k32srKw0mOOwX79+sLOz0+uzi4uL0aDVEtnZ2Xqn3y9cuIBdu3YhLi5ON0oWGhqKiooKvdOeRUVFRu92bm7fYmNj4ejoiPfee09vP3300UeoqKiw6H9miGwNR+yIrCA5ObnJMunp6RgxYkSzRi3utL6VK1eivLwcly5dAgDs3r0bFy9eBADMmjWr0Wuy0tLSkJiYiCFDhuDvf/87rl69ihUrVqBv377466+/dOWUSiXGjh2LFStWQCaTITQ0FHv27EFpaalBnatWrcKQIUPQr18/TJs2Dd27d0dJSQmys7Nx8eJF/M///A8AIC4uDt26dcPTTz+Nf/3rX7C3t8e6devg7e2N8+fP6+pzd3fH6tWr8dRTT2HgwIGYMGGCrkx6ejruv/9+veDVFAcHB7z11ltISUnBsGHDMHHiRJSUlODdd99FcHAw/vnPf+qVVygUyMjIQHJyMqKjo7Fv3z6kp6fjlVde0Y1kGePt7Y2XXnoJaWlpePTRRzFy5EgcO3YM+/btg5eXV6N9PHToEGbOnImxY8eiZ8+euHnzJjZu3Ah7e3uMGTNGVy4yMhIHDx7EsmXLEBAQgJCQEERHRzd7X9QXHh6O+Ph4vPDCC5DL5brQvnDhQl2ZCRMmYM6cOXj88cfxwgsvoLq6GqtXr0bPnj0Nrslsbt+8vb0xd+5cLFy4EAkJCXjsscdQUFCA999/H4MHD9a7UYKow2nTe3KJJKj+9CSNqT89SXl5uejUqZPYvn17q9enLYv/na6i4UM7pURj/v3vf4s+ffoIuVwuwsLCxOeff250WovLly+LMWPGCGdnZ9G5c2fx3HPPiby8PKNTrJw+fVpMmTJF+Pn5CQcHB9GlSxfx6KOPis8++0yv3NGjR0V0dLRwdHQU3bp1E8uWLTOYDkPr8OHDIj4+XiiVSqFQKERoaKiYOnWq3hQdxpiaqmXbtm3innvuEXK5XHh6eopJkyaJixcv6pVJTk4WLi4u4vTp0yIuLk44OzsLX19foVKp9KbmMEWtVouFCxcKf39/4eTkJB588EGRl5cngoKCGp3u5MyZM+Lvf/+7CA0NFQqFQnh6eoqHHnpIHDx4UK/+X3/9VTzwwAPCyclJbwoV7fQjxqZjMTXdyYwZM8SmTZtEjx49hFwuF/fcc4/BPhPi1nQ44eHhwtHRUfTq1Uts2rTJaJ2m+mbq/V25cqXo3bu3cHBwEL6+vmL69Onizz//1CszbNgw0bdvX4M+mZqGhcjWyYTg1aNEbW379u2YNGkSysrKzLqD0Vr1NcfUqVORlZXVosl/25vMzEzExsbi66+/NvgFECKi9ozX2BG1Ax4eHnjvvfcsFsIsXV9Ho73Wr6nTn0RE7Q2vsSNqB+Li4tp1fR3FtWvX8Omnn+Ldd99F165d9e5qJSKyBRyxIyL6X5cvX8asWbPg5OSEf//733q/akBEZAt4jR0RERGRRPC/o0REREQSwWBHREREJBE2cfOERqPBpUuX4ObmZtGfwyEiIiJq74QQqKqqQkBAQJPX/tpEsLt06RICAwPbuhtEREREbebChQvo2rVro2VsIti5ubkBuLVB7u7ubdwbIiIiIuuprKxEYGCgLg81xiaCnfb0q7u7O4MdERERdUjNuRzNJoKdNag1AjmFV1FaVQMfNwWiQjxhb8fr+YiIiMh2MNgByMgrwsLd+SiqqNEt81cqoBoVhoRw/zbsGREREVHzdfjpTjLyijB9U65eqAOA4ooaTN+Ui4y8ojbqGREREZF5OnSwU2sEFu7Oh7Gf3tAuW7g7H2oNf5yDiIiI2r8OHexyCq8ajNTVJwAUVdQgp/Cq9TpFRERE1EIdOtiVVpkOdS0pR0RERNSWOnSw83FTWLQcERERUVvq0MEuKsQT/koFTE1qIsOtu2OjQjyt2S0iIiKiFunQwc7eTgbVqDAAMAh32ueqUWGcz46IiIhsQocOdgCQEO6P1ZMHwsddrrfcT6nA6skDOY8dERER2QxOUIxb4e7+u73Qb8GXAIANKYMxtIc3R+qIiIjIpnT4ETut+iGOPydGREREtojBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJKJFwW7VqlUIDg6GQqFAdHQ0cnJymrXe1q1bIZPJkJSU1JJmiYiIiKgRZge7bdu2ITU1FSqVCrm5uYiIiEB8fDxKS0sbXe/s2bN46aWXMHTo0BZ3loiIiIhMMzvYLVu2DNOmTUNKSgrCwsKwZs0aODs7Y926dSbXUavVmDRpEhYuXIju3bs32UZtbS0qKyv1HkRERETUOLOCXV1dHY4ePYrY2NjbFdjZITY2FtnZ2SbXW7RoEXx8fPD00083q520tDQolUrdIzAw0JxuEhEREXVIZgW7srIyqNVq+Pr66i339fVFcXGx0XW++eYbfPTRR/jggw+a3c7cuXNRUVGhe1y4cMGcbhIRERF1SJ1as/Kqqio89dRT+OCDD+Dl5dXs9eRyOeRyeSv2jIiIiEh6zAp2Xl5esLe3R0lJid7ykpIS+Pn5GZQ/ffo0zp49i1GjRumWaTSaWw136oSCggKEhoa2pN9ERERE1IBZp2IdHR0RGRmJzMxM3TKNRoPMzEzExMQYlO/duzd++eUXHD9+XPd47LHH8NBDD+H48eO8do6IiIjIgsw+FZuamork5GQMGjQIUVFRWL58Oa5du4aUlBQAwJQpU9ClSxekpaVBoVAgPDxcb30PDw8AMFhORERERHfG7GA3fvx4XL58GfPnz0dxcTEGDBiAjIwM3Q0V58+fh50df9CCiIiIyNpkQgjR1p1oSmVlJZRKJSoqKuDu7t4qbVTX3UTY/P0AgPxF8XB2bNX7SoiIiIiaxZwcxKE1IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIolgsCMiIiKSCAY7IiIiIoloUbBbtWoVgoODoVAoEB0djZycHJNlP/jgAwwdOhSdO3dG586dERsb22h5IiIiImoZs4Pdtm3bkJqaCpVKhdzcXERERCA+Ph6lpaVGy2dlZWHixIk4fPgwsrOzERgYiLi4OPzxxx933HkiIiIiuk0mhBDmrBAdHY3Bgwdj5cqVAACNRoPAwEDMmjULL7/8cpPrq9VqdO7cGStXrsSUKVOa1WZlZSWUSiUqKirg7u5uTnebrbruJsLm7wcA5C+Kh7Njp1Zph4iIiMgc5uQgs0bs6urqcPToUcTGxt6uwM4OsbGxyM7OblYd1dXVuHHjBjw9PU2Wqa2tRWVlpd6DiIiIiBpnVrArKyuDWq2Gr6+v3nJfX18UFxc3q445c+YgICBALxw2lJaWBqVSqXsEBgaa000iIiKiDsmqd8UuXrwYW7duxc6dO6FQKEyWmzt3LioqKnSPCxcuWLGXRERERLbJrAvJvLy8YG9vj5KSEr3lJSUl8PPza3Tdt99+G4sXL8bBgwfRv3//RsvK5XLI5XJzukZERETU4Zk1Yufo6IjIyEhkZmbqlmk0GmRmZiImJsbkekuWLMFrr72GjIwMDBo0qOW9JSIiIiKTzL71MzU1FcnJyRg0aBCioqKwfPlyXLt2DSkpKQCAKVOmoEuXLkhLSwMAvPXWW5g/fz42b96M4OBg3bV4rq6ucHV1teCmEBEREXVsZge78ePH4/Lly5g/fz6Ki4sxYMAAZGRk6G6oOH/+POzsbg8Erl69GnV1dXjiiSf06lGpVFiwYMGd9Z6IiIiIdMyex64tcB47IiIi6qhabR47IiIiImq/GOyIiIiIJILBjoiIiEgieCFZG1FrBHIKr6K0qgY+bgpEhXjC3k7W1t0iIiIiG8Zg1wYy8oqwcHc+iipqdMv8lQqoRoUhIdy/DXtGREREtoynYq0sI68I0zfl6oU6ACiuqMH0TbnIyCtqo54RERGRrWOwsyK1RmDh7nwYm19Gu2zh7nyoNe1+BhoiIiJqhxjsrCin8KrBSF19AkBRRQ1yCq9ar1NEREQkGQx2VlRaZTrUtaQcERERUX0Mdlbk46awaDkiIiKi+hjsrCgqxBP+SgVMTWoiw627Y6NCPK3ZLSIiIpIIBjsrsreTQTUqDAAMwp32uWpUGOezIyIiohZhsLOyhHB/rJ48ED7ucr3lfkoFVk8eyHnsiIiIqMU4QXEbSAj3x/13e6Hfgi8BABtSBmNoD2+O1BEREdEd4YhdG6kf4vhzYkRERGQJDHZEREREEsFTsR2AWiOQU3gVpVU18HFTcISQiIhIohjsJC4jrwgLd+fr/eKFv1IB1agw3qhBREQkMTwVK2EZeUWYvinX4GfMiitqMH1TLjLyitqoZ0RERNQaGOwkSq0RWLg7H8LIa9plC3fnQ60xVoKIiIhsEYOdROUUXjUYqatPACiqqEFO4VXrdYqIiIhaFYOdRJVWmQ51LSlHRERE7R+DnUT5uCksWo6IiIjaPwY7iYoK8YS/UmHwm7RaMty6OzYqxNOa3SIiIqJWxGAnUfZ2MqhGhQGAQbjTPleNCuN8dkRERBLCYCdhCeH+WD15IHzc5XrL/ZQKrJ48kPPYERERSQwnKJa4hHB/3H+3F/ot+BIAsCFlMIb28G7VkTr+0gUREVHbYLDrAOqHqtYOWfylCyIiorbDU7FkMfylCyIiorbFYEcWwV+6ICIiansMdmQRbflLF2qNQPbpK9h1/A9kn77C8EhERB0Wr7Eji2irX7rgNX1ERES3ccSOLKItfumC1/QRERHpY7Aji7D2L13wmj4iIiJDDHZkEdb+pYu2vKaPiIiovWKwI4ux5i9dtNU1fbxRg4iI2jPePEEWZa1fumira/p4owYREbVnHLEji7PGL11Y+5q+trpRgyOERERkDo7YkU3SXtM3fVMuZIDeTRSWvqavqRs1ZLh1o8YjYX4WDbEcISQiInNxxI5slrWu6WuLGzU4QkhERC3BETuyada4ps/aN2p0lBFCtUYgp/AqSqtq4OOmaLXT9kREHQmDHdm81r6mz9o3apgzQhgTepdF2tSOEDYMk9oRQkvf1dwWp5kZJImoI2CwI2qC9kaN4ooao6NoMtw6/WupGzWkPkJo7RCpbVPKQdLaoVXK7Ul526zdnpS3rS3aay4GO6ImWPNGDUDaI4RtcZpZ6kHS2qFVyu1Jedus3Z6Ut60t2jNHi26eWLVqFYKDg6FQKBAdHY2cnJxGy+/YsQO9e/eGQqFAv379sHfv3hZ1lqitWHPyZWtP5WLNEUJr34jSFj89Z80bX6x9k42U25Pytlm7PSlvW1u0Zy6zR+y2bduG1NRUrFmzBtHR0Vi+fDni4+NRUFAAHx8fg/LfffcdJk6ciLS0NDz66KPYvHkzkpKSkJubi/DwcItshCUIISC/WQsA0FRXQ3OzdQczNXU3rdaeNduScntx3ZWImT4YUW9kAgDWPhWJ+0O9YG8ng6a62mLtyAAseCQE/9h6HIDxEcIFj/SBrOY6NBZoz8fh9rHfVLk73c7Ll/9sVluXL/8Jjb/THbUF3AqSV69UQN5ImatXapFz8g9EWyAoqzUCaZ8fg6OJbZQBSPv8GIYHu9/xiKQ125J6e1LeNmu3J+Vta6y9WntHCJms1W5uM4dMCGHWf1Wjo6MxePBgrFy5EgCg0WgQGBiIWbNm4eWXXzYoP378eFy7dg179uzRLbv33nsxYMAArFmzxmgbtbW1qK29vdMqKysRGBiIiooKuLu7m9PdZvurvBIX7o1ulbqJiIhIupIefQO1nW7/F3LLtHstdnMbcCsHKZXKZuUgs07F1tXV4ejRo4iNjb1dgZ0dYmNjkZ2dbXSd7OxsvfIAEB8fb7I8AKSlpUGpVOoegYGB5nSzRZwdebkhERER3TlL/065OcxKM2VlZVCr1fD19dVb7uvri19//dXoOsXFxUbLFxcXm2xn7ty5SE1N1T3Xjti1JpmTE3rlHm3VNojaswP5xXhz768orrz9heTnrsArI3vjkTA/i7bT2GnmdycMsFh7ao1A7LIjKKk0fUezr7sCB1OHWeS0yQ+FVzF1fePXHAPAhpSoOz71a822pN6elLfN2u1Jedsaa6/W3lHvuSV/p9xc7XKYSi6XQy5v7KoYy5PJZJA5O1u1TaL2JH5Qd8QODGn12/fjB3XHcoWTyTvK4i14I4odgLl/uwfTN+UCMB4k5/7tHji4ulikvag+TvC8S9n01Dh9usDuDverNduSentS3jZrtyflbTOrPQvd3NYSZp2K9fLygr29PUpKSvSWl5SUwM/P+P+w/fz8zCpPRG3H3k6GmNC7MHpAF8SE3tVqF/8mhPvjmzkPY8u0e/HuhAHYMu1efDPn4VaZJkB7R7OfUv9/0K1xR7N2ahwABnc1W3pqHGu2JfX2pLxt1m5PytvWFu21RItunoiKisKKFSsA3Lp5olu3bpg5c6bJmyeqq6uxe/du3bL77rsP/fv3N3nzREPmXDRIRGSMNScT5XxhttmelLfN2u1Jedvaoj1zcpDZwW7btm1ITk7G2rVrERUVheXLl2P79u349ddf4evriylTpqBLly5IS0sDcGu6k2HDhmHx4sVITEzE1q1b8eabb5o13QmDHRHZGs7wb5vtSXnbrN2elLfN2u21arADgJUrV2Lp0qUoLi7GgAED8N577yE6+tZUIQ8++CCCg4OxYcMGXfkdO3bg1VdfxdmzZ9GjRw8sWbIEI0eObJUNIiIiIpKSVg921lZRUQEPDw9cuHCBwY6IiIg6FO3sIOXl5VAqlY2WbZd3xTZUVVUFAFaZz46IiIioPaqqqmoy2NnEiJ1Go8GlS5fg5uYGmaz1zpdrEzFHBm/jPjHEfWIc94sh7hND3CeGuE8McZ/oE0KgqqoKAQEBsLNrfEITmxixs7OzQ9euXa3Wnru7Ow+kBrhPDHGfGMf9Yoj7xBD3iSHuE0PcJ7c1NVKnZdY8dkRERETUfjHYEREREUkEg109crkcKpXK6j9n1p5xnxjiPjGO+8UQ94kh7hND3CeGuE9aziZuniAiIiKipnHEjoiIiEgiGOyIiIiIJILBjoiIiEgiGOyIiIiIJILBjoiIiEgiOlywW7VqFYKDg6FQKBAdHY2cnJxGy+/YsQO9e/eGQqFAv379sHfvXiv1tPWlpaVh8ODBcHNzg4+PD5KSklBQUNDoOhs2bIBMJtN7KBQKK/W49S1YsMBg+3r37t3oOlI+RrSCg4MN9otMJsOMGTOMlpficfLVV19h1KhRCAgIgEwmwxdffKH3uhAC8+fPh7+/P5ycnBAbG4tTp041Wa+530ntSWP75MaNG5gzZw769esHFxcXBAQEYMqUKbh06VKjdbbkM9ieNHWcTJ061WD7EhISmqxXqscJAKPfLTKZDEuXLjVZp60fJ62pQwW7bdu2ITU1FSqVCrm5uYiIiEB8fDxKS0uNlv/uu+8wceJEPP300zh27BiSkpKQlJSEvLw8K/e8dRw5cgQzZszA999/jwMHDuDGjRuIi4vDtWvXGl3P3d0dRUVFuse5c+es1GPr6Nu3r972ffPNNybLSv0Y0frxxx/19smBAwcAAGPHjjW5jtSOk2vXriEiIgKrVq0y+vqSJUvw3nvvYc2aNfjhhx/g4uKC+Ph41NTUmKzT3O+k9qaxfVJdXY3c3FzMmzcPubm5+Pzzz1FQUIDHHnusyXrN+Qy2N00dJwCQkJCgt31btmxptE4pHycA9PZFUVER1q1bB5lMhjFjxjRary0fJ61KdCBRUVFixowZuudqtVoEBASItLQ0o+XHjRsnEhMT9ZZFR0eL5557rlX72VZKS0sFAHHkyBGTZdavXy+USqX1OmVlKpVKRERENLt8RztGtP7xj3+I0NBQodFojL4u9eMEgNi5c6fuuUajEX5+fmLp0qW6ZeXl5UIul4stW7aYrMfc76T2rOE+MSYnJ0cAEOfOnTNZxtzPYHtmbJ8kJyeL0aNHm1VPRztORo8eLR5++OFGy0jpOLG0DjNiV1dXh6NHjyI2Nla3zM7ODrGxscjOzja6TnZ2tl55AIiPjzdZ3tZVVFQAADw9PRst99dffyEoKAiBgYEYPXo0Tpw4YY3uWc2pU6cQEBCA7t27Y9KkSTh//rzJsh3tGAFufZY2bdqEv//975DJZCbLSf04qa+wsBDFxcV6x4JSqUR0dLTJY6El30m2rqKiAjKZDB4eHo2WM+czaIuysrLg4+ODXr16Yfr06bhy5YrJsh3tOCkpKUF6ejqefvrpJstK/ThpqQ4T7MrKyqBWq+Hr66u33NfXF8XFxUbXKS4uNqu8LdNoNJg9ezbuv/9+hIeHmyzXq1cvrFu3Drt27cKmTZug0Whw33334eLFi1bsbeuJjo7Ghg0bkJGRgdWrV6OwsBBDhw5FVVWV0fId6RjR+uKLL1BeXo6pU6eaLCP146Qh7fttzrHQku8kW1ZTU4M5c+Zg4sSJcHd3N1nO3M+grUlISMAnn3yCzMxMvPXWWzhy5AhGjBgBtVpttHxHO04+/vhjuLm54W9/+1uj5aR+nNyJTm3dAWofZsyYgby8vCavUYiJiUFMTIzu+X333Yc+ffpg7dq1eO2111q7m61uxIgRun/3798f0dHRCAoKwvbt25v1P8iO4KOPPsKIESMQEBBgsozUjxMyz40bNzBu3DgIIbB69epGy0r9MzhhwgTdv/v164f+/fsjNDQUWVlZGD58eBv2rH1Yt24dJk2a1OTNVlI/Tu5Ehxmx8/Lygr29PUpKSvSWl5SUwM/Pz+g6fn5+ZpW3VTNnzsSePXtw+PBhdO3a1ax1HRwccM899+D3339vpd61LQ8PD/Ts2dPk9nWUY0Tr3LlzOHjwIJ555hmz1pP6caJ9v805FlrynWSLtKHu3LlzOHDgQKOjdcY09Rm0dd27d4eXl5fJ7esoxwkAfP311ygoKDD7+wWQ/nFijg4T7BwdHREZGYnMzEzdMo1Gg8zMTL2RhfpiYmL0ygPAgQMHTJa3NUIIzJw5Ezt37sShQ4cQEhJidh1qtRq//PIL/P39W6GHbe+vv/7C6dOnTW6f1I+RhtavXw8fHx8kJiaatZ7Uj5OQkBD4+fnpHQuVlZX44YcfTB4LLflOsjXaUHfq1CkcPHgQd911l9l1NPUZtHUXL17ElStXTG5fRzhOtD766CNERkYiIiLC7HWlfpyYpa3v3rCmrVu3CrlcLjZs2CDy8/PFs88+Kzw8PERxcbEQQoinnnpKvPzyy7ry3377rejUqZN4++23xcmTJ4VKpRIODg7il19+aatNsKjp06cLpVIpsrKyRFFRke5RXV2tK9NwnyxcuFDs379fnD59Whw9elRMmDBBKBQKceLEibbYBIt78cUXRVZWligsLBTffvutiI2NFV5eXqK0tFQI0fGOkfrUarXo1q2bmDNnjsFrHeE4qaqqEseOHRPHjh0TAMSyZcvEsWPHdHd4Ll68WHh4eIhdu3aJn3/+WYwePVqEhISI69ev6+p4+OGHxYoVK3TPm/pOau8a2yd1dXXiscceE127dhXHjx/X+46pra3V1dFwnzT1GWzvGtsnVVVV4qWXXhLZ2dmisLBQHDx4UAwcOFD06NFD1NTU6OroSMeJVkVFhXB2dharV682WofUjpPW1KGCnRBCrFixQnTr1k04OjqKqKgo8f333+teGzZsmEhOTtYrv337dtGzZ0/h6Ogo+vbtK9LT063c49YDwOhj/fr1ujIN98ns2bN1+8/X11eMHDlS5ObmWr/zrWT8+PHC399fODo6ii5duojx48eL33//Xfd6RztG6tu/f78AIAoKCgxe6wjHyeHDh41+XrTbrdFoxLx584Svr6+Qy+Vi+PDhBvsqKChIqFQqvWWNfSe1d43tk8LCQpPfMYcPH9bV0XCfNPUZbO8a2yfV1dUiLi5OeHt7CwcHBxEUFCSmTZtmENA60nGitXbtWuHk5CTKy8uN1iG146Q1yYQQolWHBImIiIjIKjrMNXZEREREUsdgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEsFgR0RERCQRDHZEREREEvH/AZINqTJhyVz4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M/G/1 (G=Uniform): exponential interarrival/uniform service/one server\n",
      "Initial jobs:\n",
      "\n",
      "    T: interarrival time\n",
      "    A: arrival time\n",
      "    S: service time\n",
      "    W: waiting time\n",
      "    E: exit time\n"
     ]
    },
    {
     "data": {
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       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4952b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_4952b_level0_col0\" class=\"col_heading level0 col0\" >id</th>\n",
       "      <th id=\"T_4952b_level0_col1\" class=\"col_heading level0 col1\" >T</th>\n",
       "      <th id=\"T_4952b_level0_col2\" class=\"col_heading level0 col2\" >A</th>\n",
       "      <th id=\"T_4952b_level0_col3\" class=\"col_heading level0 col3\" >S</th>\n",
       "      <th id=\"T_4952b_level0_col4\" class=\"col_heading level0 col4\" >W</th>\n",
       "      <th id=\"T_4952b_level0_col5\" class=\"col_heading level0 col5\" >E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_4952b_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_4952b_row0_col1\" class=\"data row0 col1\" >3.393308</td>\n",
       "      <td id=\"T_4952b_row0_col2\" class=\"data row0 col2\" >3.393308</td>\n",
       "      <td id=\"T_4952b_row0_col3\" class=\"data row0 col3\" >1.225983</td>\n",
       "      <td id=\"T_4952b_row0_col4\" class=\"data row0 col4\" >0.000000</td>\n",
       "      <td id=\"T_4952b_row0_col5\" class=\"data row0 col5\" >4.619291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4952b_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_4952b_row1_col1\" class=\"data row1 col1\" >3.673071</td>\n",
       "      <td id=\"T_4952b_row1_col2\" class=\"data row1 col2\" >7.066380</td>\n",
       "      <td id=\"T_4952b_row1_col3\" class=\"data row1 col3\" >1.173061</td>\n",
       "      <td id=\"T_4952b_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "      <td id=\"T_4952b_row1_col5\" class=\"data row1 col5\" >8.239440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4952b_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_4952b_row2_col1\" class=\"data row2 col1\" >0.139742</td>\n",
       "      <td id=\"T_4952b_row2_col2\" class=\"data row2 col2\" >7.206122</td>\n",
       "      <td id=\"T_4952b_row2_col3\" class=\"data row2 col3\" >1.057084</td>\n",
       "      <td id=\"T_4952b_row2_col4\" class=\"data row2 col4\" >1.033318</td>\n",
       "      <td id=\"T_4952b_row2_col5\" class=\"data row2 col5\" >9.296524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4952b_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_4952b_row3_col1\" class=\"data row3 col1\" >0.713618</td>\n",
       "      <td id=\"T_4952b_row3_col2\" class=\"data row3 col2\" >7.919741</td>\n",
       "      <td id=\"T_4952b_row3_col3\" class=\"data row3 col3\" >0.232364</td>\n",
       "      <td id=\"T_4952b_row3_col4\" class=\"data row3 col4\" >1.376784</td>\n",
       "      <td id=\"T_4952b_row3_col5\" class=\"data row3 col5\" >9.528888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4952b_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_4952b_row4_col1\" class=\"data row4 col1\" >0.120858</td>\n",
       "      <td id=\"T_4952b_row4_col2\" class=\"data row4 col2\" >8.040599</td>\n",
       "      <td id=\"T_4952b_row4_col3\" class=\"data row4 col3\" >0.961064</td>\n",
       "      <td id=\"T_4952b_row4_col4\" class=\"data row4 col4\" >1.488290</td>\n",
       "      <td id=\"T_4952b_row4_col5\" class=\"data row4 col5\" >10.489952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x11f172c50>"
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      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_68415\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_68415_level0_col0\" class=\"col_heading level0 col0\" >label</th>\n",
       "      <th id=\"T_68415_level0_col1\" class=\"col_heading level0 col1\" >def</th>\n",
       "      <th id=\"T_68415_level0_col2\" class=\"col_heading level0 col2\" >stat_sim</th>\n",
       "      <th id=\"T_68415_level0_col3\" class=\"col_heading level0 col3\" >stat_ana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row0_col0\" class=\"data row0 col0\" >lambda</td>\n",
       "      <td id=\"T_68415_row0_col1\" class=\"data row0 col1\" >mean arrival rate</td>\n",
       "      <td id=\"T_68415_row0_col2\" class=\"data row0 col2\" >1.002423</td>\n",
       "      <td id=\"T_68415_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row1_col0\" class=\"data row1 col0\" >mu</td>\n",
       "      <td id=\"T_68415_row1_col1\" class=\"data row1 col1\" >mean service rate</td>\n",
       "      <td id=\"T_68415_row1_col2\" class=\"data row1 col2\" >1.500944</td>\n",
       "      <td id=\"T_68415_row1_col3\" class=\"data row1 col3\" >1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row2_col0\" class=\"data row2 col0\" >rho</td>\n",
       "      <td id=\"T_68415_row2_col1\" class=\"data row2 col1\" >(mean arrival rate)/(mean service rate)</td>\n",
       "      <td id=\"T_68415_row2_col2\" class=\"data row2 col2\" >0.667862</td>\n",
       "      <td id=\"T_68415_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row3_col0\" class=\"data row3 col0\" >sigma^2</td>\n",
       "      <td id=\"T_68415_row3_col1\" class=\"data row3 col1\" >variance of the service dist. G</td>\n",
       "      <td id=\"T_68415_row3_col2\" class=\"data row3 col2\" >0.147994</td>\n",
       "      <td id=\"T_68415_row3_col3\" class=\"data row3 col3\" >0.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row4_col0\" class=\"data row4 col0\" >p_N_s</td>\n",
       "      <td id=\"T_68415_row4_col1\" class=\"data row4 col1\" >prob. of having N jobs in the system</td>\n",
       "      <td id=\"T_68415_row4_col2\" class=\"data row4 col2\" >[0.335 0.26  0.178 0.105 0.051 0.031 0.018 0.009 0.006 0.003 0.002 0.001\n",
       " 0.001 0.    0.    0.    0.    0.    0.    0.   ]</td>\n",
       "      <td id=\"T_68415_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row5_col0\" class=\"data row5 col0\" >p_N_q</td>\n",
       "      <td id=\"T_68415_row5_col1\" class=\"data row5 col1\" >prob. of having N jobs in the queue</td>\n",
       "      <td id=\"T_68415_row5_col2\" class=\"data row5 col2\" >[0.595 0.178 0.105 0.051 0.031 0.018 0.009 0.006 0.003 0.002 0.001 0.001\n",
       " 0.    0.    0.    0.    0.    0.    0.   ]</td>\n",
       "      <td id=\"T_68415_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row6_col0\" class=\"data row6 col0\" >p_0_s</td>\n",
       "      <td id=\"T_68415_row6_col1\" class=\"data row6 col1\" >prob. of having 0 jobs in the system</td>\n",
       "      <td id=\"T_68415_row6_col2\" class=\"data row6 col2\" >0.335000</td>\n",
       "      <td id=\"T_68415_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row7_col0\" class=\"data row7 col0\" >p_0_q</td>\n",
       "      <td id=\"T_68415_row7_col1\" class=\"data row7 col1\" >prob. of having 0 jobs in the queue</td>\n",
       "      <td id=\"T_68415_row7_col2\" class=\"data row7 col2\" >0.595000</td>\n",
       "      <td id=\"T_68415_row7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row8_col0\" class=\"data row8 col0\" >L_s</td>\n",
       "      <td id=\"T_68415_row8_col1\" class=\"data row8 col1\" >expected number of jobs in the system</td>\n",
       "      <td id=\"T_68415_row8_col2\" class=\"data row8 col2\" >1.580000</td>\n",
       "      <td id=\"T_68415_row8_col3\" class=\"data row8 col3\" >1.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row9_col0\" class=\"data row9 col0\" >L_q</td>\n",
       "      <td id=\"T_68415_row9_col1\" class=\"data row9 col1\" >expected number of jobs in the queue</td>\n",
       "      <td id=\"T_68415_row9_col2\" class=\"data row9 col2\" >0.915300</td>\n",
       "      <td id=\"T_68415_row9_col3\" class=\"data row9 col3\" >0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row10_col0\" class=\"data row10 col0\" >W_s</td>\n",
       "      <td id=\"T_68415_row10_col1\" class=\"data row10 col1\" >expected time spent in the system</td>\n",
       "      <td id=\"T_68415_row10_col2\" class=\"data row10 col2\" >1.566108</td>\n",
       "      <td id=\"T_68415_row10_col3\" class=\"data row10 col3\" >1.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_68415_row11_col0\" class=\"data row11 col0\" >W_q</td>\n",
       "      <td id=\"T_68415_row11_col1\" class=\"data row11 col1\" >expected time spent in the queue</td>\n",
       "      <td id=\"T_68415_row11_col2\" class=\"data row11 col2\" >0.899861</td>\n",
       "      <td id=\"T_68415_row11_col3\" class=\"data row11 col3\" >0.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x11f172c50>"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import exponential, uniform, poisson\n",
    "import pandas as pd\n",
    "import scipy\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "N_DIGITS = 3   # rounding\n",
    "N_DIST = 4 # number of distribution values to show\n",
    "\n",
    "def make_job_table(T, S):\n",
    "    '''Construct a table (DataFrame) of job data,\n",
    "    given interarrival times T and service times S\n",
    "    (each length-n vectors).\n",
    "    \n",
    "    We want to see the following for each job:\n",
    "    \n",
    "    id: job ID (from 0 to n-1)\n",
    "    T: interarrival time\n",
    "    A: arrival time\n",
    "    S: service time\n",
    "    W: waiting time\n",
    "    E: exit time'''\n",
    "    assert(len(T) == len(S))\n",
    "    n = len(T)\n",
    "    job_ids = np.arange(n)\n",
    "    # arrival times\n",
    "    A = np.cumsum(T)\n",
    "    # waiting times (to be filled in)\n",
    "    W = np.zeros(n)\n",
    "    # exit times (to be filled in):\n",
    "    E = np.zeros(n)\n",
    "    # the job exits after\n",
    "    # it arrived, waited (maybe), and got served\n",
    "    E[0] = A[0] + W[0] + S[0]\n",
    "    for i in range(1, n):\n",
    "        if A[i] > E[i-1]:\n",
    "            # then this job arrived\n",
    "            # after the previous job finished,\n",
    "            # so there's no wait\n",
    "            W[i] = 0\n",
    "        else:\n",
    "            # then this job will have to wait\n",
    "            # until the previous job finishes\n",
    "            W[i] = E[i-1] - A[i]\n",
    "        E[i] = A[i] + W[i] + S[i]\n",
    "\n",
    "    d = {\n",
    "        'id': job_ids,\n",
    "        'T': T,\n",
    "        'A': A,\n",
    "        'S': S,\n",
    "        'W': W,\n",
    "        'E': E\n",
    "    }\n",
    "\n",
    "    df_jobs = pd.DataFrame(d)\n",
    "    return df_jobs\n",
    "\n",
    "def job_table_to_stats(df_jobs, nt=10000):\n",
    "    '''Construct a table of system and queue statistics.\n",
    "    \"System\" statistics includes the service time,\n",
    "    while \"queue\" statistics do not.\n",
    "    \n",
    "    df_jobs: a table generated by make_job_table()\n",
    "    nt: number of time steps\n",
    "    '''\n",
    "    \n",
    "    # grab job data from the table\n",
    "    job_ids, T, A, S, W, E = df_jobs.to_numpy().T\n",
    "    \n",
    "    n, ncols = df_jobs.shape\n",
    "    \n",
    "    # make a time grid\n",
    "    t_max = df_jobs['E'].iloc[-1]  # the last exit time\n",
    "    nt = 10000                     # number of time slices\n",
    "    t = np.linspace(0, t_max, nt)\n",
    "\n",
    "    # construct matrices indicating whether\n",
    "    # each job j is in the system/queue or not at time t[i], i.e.\n",
    "    # system_mat = [1 if job j is in the system at time t[i], else 0],\n",
    "    # queue_mat = [1 if job j is in the queue at time t[i], else 0],\n",
    "    system_mat = np.zeros((nt, n))\n",
    "    queue_mat = np.zeros((nt, n))\n",
    "    for j in job_ids.astype('int'):\n",
    "        # for the system matrix,\n",
    "        # just check if t is between job j's arrival and exit times\n",
    "        system_mat[:, j] = np.where((A[j] <= t) & (t < E[j]), 1, 0)\n",
    "        # similarly for the queue matrix, but don't count the service time\n",
    "        queue_mat[:, j] = np.where((A[j] <= t) & (t < E[j] - S[j]), 1, 0)\n",
    "\n",
    "    # gather statistics:\n",
    "    lamb = 1/np.mean(T) # mean arrival rate\n",
    "    mu = 1/np.mean(S)   # mean service rate\n",
    "    rho = lamb/mu\n",
    "    s2 = np.var(S)    # variance of service rate\n",
    "\n",
    "    # probability of having N jobs in the system/queue (distributions):\n",
    "    n_jobs_s = np.sum(system_mat, axis=1).astype('int')\n",
    "    n_jobs_q = np.sum(queue_mat, axis=1).astype('int')\n",
    "    # count bins and normalize by the number of time steps\n",
    "    p_N_s = (np.bincount(n_jobs_s) / nt).round(N_DIGITS)\n",
    "    p_N_q = (np.bincount(n_jobs_q) / nt).round(N_DIGITS)\n",
    "\n",
    "    # probability of having zero jobs in the system/queue\n",
    "    p_0_s = p_N_s[0]\n",
    "    p_0_q = p_N_q[0]\n",
    "\n",
    "    # mean number of jobs in the system/queue\n",
    "    L_s = np.mean(n_jobs_s)\n",
    "    L_q = np.mean(n_jobs_q)\n",
    "\n",
    "    # mean time in the system/queue\n",
    "    W_s = np.mean(W + S)\n",
    "    W_q = np.mean(W)\n",
    "\n",
    "    labels = ['lambda', 'mu', 'rho', 'sigma^2', 'p_N_s', 'p_N_q', 'p_0_s', 'p_0_q', 'L_s', 'L_q', 'W_s', 'W_q']\n",
    "    stats  = [ lamb,     mu,   rho,         s2,  p_N_s,   p_N_q,   p_0_s,   p_0_q,   L_s,   L_q,   W_s,   W_q]\n",
    "    defs = [\n",
    "        'mean arrival rate',\n",
    "        'mean service rate',\n",
    "        '(mean arrival rate)/(mean service rate)',\n",
    "        'variance of the service dist. G',\n",
    "        'prob. of having N jobs in the system',\n",
    "        'prob. of having N jobs in the queue',\n",
    "        'prob. of having 0 jobs in the system',\n",
    "        'prob. of having 0 jobs in the queue',\n",
    "        'expected number of jobs in the system',\n",
    "        'expected number of jobs in the queue',\n",
    "        'expected time spent in the system',\n",
    "        'expected time spent in the queue',\n",
    "    ]\n",
    "\n",
    "    d = {\n",
    "        'label': labels,\n",
    "        'def': defs,\n",
    "        'stat_sim': stats\n",
    "    }\n",
    "    df_stats = pd.DataFrame(d)\n",
    "\n",
    "    return (df_stats, system_mat, queue_mat)\n",
    "\n",
    "def plot_jobs(df_jobs, title, ax):\n",
    "    job_ids, T, A, S, W, E = df_jobs.to_numpy().T\n",
    "    for job_id, A_i, E_i in zip(job_ids, A, E):\n",
    "        ax.broken_barh([(A_i, E_i-A_i)], (job_id-0.25, 0.5))\n",
    "    ax.set_xlabel('time')\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "def plot_time_slices(time_slice_mat, title, ax):\n",
    "    '''Plot a matrix of time slices.\n",
    "    System matrix plots should look similar to the job plot,\n",
    "    but with a different time scale.\n",
    "    Queue matrix plots should have shorter bars\n",
    "    (or missing bars, in the case of no wait),\n",
    "    because the service times don't count.'''\n",
    "    ax.imshow(time_slice_mat.T, origin='lower', interpolation='none', aspect='auto')\n",
    "    ax.set_title(title)\n",
    "\n",
    "def plot_distribution(p_N, title, ax):\n",
    "    ax.stem(p_N)\n",
    "    ax.set_title(title)\n",
    "\n",
    "def D_D_1_analytic(lamb, mu):\n",
    "    # from \"Auditing of Queueing Systems under Various Frame-Works\"\n",
    "    rho = lamb/mu\n",
    "    sigma2 = 0\n",
    "    p_0_s = 1 - rho\n",
    "    p_N_s = (\n",
    "        np.array([1 - rho, rho])\n",
    "    ).round(N_DIGITS)\n",
    "    p_0_q = 1\n",
    "    p_N_q = np.array([1])\n",
    "    L_s = rho\n",
    "    L_q = 0\n",
    "    W_s = 1/mu\n",
    "    W_q = 0\n",
    "    labels = ['lambda', 'mu', 'rho', 'sigma^2', 'p_N_s', 'p_0_s', 'L_s', 'L_q', 'W_s', 'W_q', 'p_0_q', 'p_N_q']\n",
    "    stats = [    lamb,   mu ,  rho ,  sigma2 ,  p_N_s ,  p_0_s ,  L_s ,  L_q ,  W_s ,  W_q ,  p_0_q ,  p_N_q ]\n",
    "    d = {\n",
    "        'label': labels,\n",
    "        'stat_ana': stats\n",
    "    }\n",
    "    df_ana = pd.DataFrame(d)\n",
    "    return df_ana\n",
    "\n",
    "def M_D_1_analytic(lamb, mu):\n",
    "    # from \"Auditing of Queueing Systems under Various Frame-Works\"\n",
    "    rho = lamb/mu\n",
    "    sigma2 = 0\n",
    "    L_s = rho*(2-rho)/(2*(1-rho))\n",
    "    L_q = rho**2/(2*(1-rho))\n",
    "    W_s = (2-rho)/(2*mu*(1-rho))\n",
    "    W_q = rho/(2*mu*(1-rho))\n",
    "    labels = ['lambda', 'mu', 'rho', 'sigma^2', 'L_s', 'L_q', 'W_s', 'W_q']\n",
    "    stats = [    lamb,   mu ,  rho ,  sigma2 ,   L_s ,  L_q ,  W_s ,  W_q ]\n",
    "    d = {\n",
    "        'label': labels,\n",
    "        'stat_ana': stats\n",
    "    }\n",
    "    df_ana = pd.DataFrame(d)\n",
    "    return df_ana\n",
    "\n",
    "\n",
    "\n",
    "def M_M_1_analytic(lamb, mu):\n",
    "    # from \"Auditing of Queueing Systems under Various Frame-Works\"\n",
    "    rho = lamb/mu\n",
    "    sigma2 = 1/mu**2\n",
    "    p_0_s = 1 - rho\n",
    "    p_N_s = (\n",
    "        (1-rho)*rho**(np.arange(N_DIST))\n",
    "    ).round(N_DIGITS)\n",
    "    L_s = rho/(1-rho)        # = lamb / (mu-lamb)\n",
    "    L_q = rho**2/(1-rho)     # = lamb**2 / (mu*(mu-lamb))\n",
    "    W_s = rho/(lamb*(1-rho)) # = 1 / (mu-lamb)\n",
    "    W_q = rho/(mu*(1-rho))   # = lamb / (mu*(mu-lamb))\n",
    "    labels = ['lambda', 'mu', 'rho', 'sigma^2', 'p_N_s', 'p_0_s', 'L_s', 'L_q', 'W_s', 'W_q']\n",
    "    stats = [    lamb,   mu ,  rho ,  sigma2 ,  p_N_s ,  p_0_s ,  L_s ,  L_q ,  W_s ,  W_q ]\n",
    "\n",
    "    d = {\n",
    "        'label': labels,\n",
    "        'stat_ana': stats\n",
    "    }\n",
    "    df_ana = pd.DataFrame(d)\n",
    "    return df_ana\n",
    "\n",
    "def M_G_1_analytic(lamb, mu, sigma2):\n",
    "    # from \"Auditing of Queueing Systems under Various Frame-Works\"\n",
    "    rho = lamb/mu\n",
    "    L_s = (rho**2 + lamb**2*sigma2)/(2*(1-rho)) + rho\n",
    "    L_q = (rho**2 + lamb**2*sigma2)/(2*(1-rho))\n",
    "    W_s = (rho**2/lamb + lamb*sigma2)/(2*(1-rho)) + 1/mu\n",
    "    W_q = (rho**2/lamb + lamb*sigma2)/(2*(1-rho))\n",
    "    labels = ['lambda', 'mu', 'rho', 'sigma^2', 'L_s', 'L_q', 'W_s', 'W_q']\n",
    "    stats = [    lamb,   mu ,  rho ,  sigma2 ,  L_s ,  L_q ,  W_s ,  W_q ]\n",
    "    d = {\n",
    "        'label': labels,\n",
    "        'stat_ana': stats\n",
    "    }\n",
    "    df_ana = pd.DataFrame(d)\n",
    "    return df_ana\n",
    "\n",
    "def queue_demo(label, T, S, df_ana):\n",
    "    # tables\n",
    "    df_jobs = make_job_table(T, S)\n",
    "    df_stats, system_mat, queue_mat = job_table_to_stats(df_jobs)\n",
    "    df_all = df_stats.merge(df_ana, 'outer', on='label')\n",
    "    print('Initial jobs:')\n",
    "    print('''\n",
    "    T: interarrival time\n",
    "    A: arrival time\n",
    "    S: service time\n",
    "    W: waiting time\n",
    "    E: exit time''')\n",
    "    display(\n",
    "        df_jobs\n",
    "        .head(5)\n",
    "        .style.hide(axis='index')\n",
    "    )    \n",
    "    display(\n",
    "        df_all\n",
    "        .style.hide(axis='index')\n",
    "    )\n",
    "    \n",
    "    # plots:\n",
    "    # jobs\n",
    "    fig, ax = plt.subplots()\n",
    "    max_jobs = 20\n",
    "    df_jobs_head = df_jobs.head(max_jobs)\n",
    "    plot_jobs(df_jobs_head, f'Jobs in {label} system', ax)\n",
    "    plt.show()\n",
    "    \n",
    "    # jobs and wait times\n",
    "    fig, axs = plt.subplots(2, 1)\n",
    "    df_stats_head, system_mat_head, queue_mat_head = job_table_to_stats(df_jobs_head)\n",
    "    plot_time_slices(system_mat_head, f'Jobs in discretized {label} system', axs[0])\n",
    "    plot_time_slices(queue_mat_head, f'Jobs in discretized {label} queue', axs[1])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # number of jobs in system/queue distributions\n",
    "    fig, axs = plt.subplots(2, 1)\n",
    "    plot_distribution(\n",
    "        df_stats[df_stats['label'] == 'p_N_s']['stat_sim'].iloc[-1],\n",
    "        f'{label} system job distribution',\n",
    "        axs[0]\n",
    "    )\n",
    "    plot_distribution(\n",
    "        df_stats[df_stats['label'] == 'p_N_q']['stat_sim'].iloc[-1],\n",
    "        f'{label} queue job distribution',\n",
    "        axs[1])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "def all_queue_demos():\n",
    "    np.random.seed(0)\n",
    "    n = 90000\n",
    "    lamb = 1\n",
    "    mu = 1.5\n",
    "    labels = [\n",
    "        'D/D/1',\n",
    "        'M/D/1',\n",
    "        'M/M/1',\n",
    "        'M/G/1 (G=Uniform)'\n",
    "    ]\n",
    "    long_labels = [\n",
    "        'deterministic interarrival/deterministic service/one server',\n",
    "        'exponential interarrival/deterministic service/one server',\n",
    "        'exponential interarrival/exponential service/one server',\n",
    "        'exponential interarrival/uniform service/one server'\n",
    "    ]\n",
    "    T = [\n",
    "        np.ones(n) / lamb,\n",
    "        exponential(scale=1/lamb, size=n),\n",
    "        exponential(scale=1/lamb, size=n),\n",
    "        exponential(scale=1/lamb, size=n)\n",
    "    ]\n",
    "    S = [\n",
    "        np.ones(n) / mu,\n",
    "        np.ones(n) / mu,\n",
    "        exponential(scale=1/mu, size=n),\n",
    "        uniform(low=0, high=2/mu, size=n)\n",
    "    ]\n",
    "    \n",
    "    df_ana = [\n",
    "        D_D_1_analytic(lamb, mu),\n",
    "        M_D_1_analytic(lamb, mu),\n",
    "        M_M_1_analytic(lamb, mu),\n",
    "        M_G_1_analytic(lamb, mu, (2/mu)**2/12)\n",
    "    ]\n",
    "    for label, long_label, T_i, S_i, df_ana_i in zip(labels, long_labels, T, S, df_ana):\n",
    "        print(f'{label}: {long_label}')\n",
    "        queue_demo(label, T_i, S_i, df_ana_i)\n",
    "\n",
    "all_queue_demos()"
   ]
  }
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