ggk_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software Setup, Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -r requirements.txt > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import importlib\n",
    "\n",
    "import salduba.simulation.ggk as ggk\n",
    "importlib.reload(ggk)\n",
    "from salduba.simulation.ggk import exponential, parametrized_gamma\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "probs = np.random.binomial(1, 0.0, 100)\n",
    "\n",
    "probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inter_arrival_time = 1.0\n",
    "op_time = inter_arrival_time/1000\n",
    "target_utilization = 0.8\n",
    "k_servers: int = 10\n",
    "processing_time = inter_arrival_time * k_servers * target_utilization\n",
    "gamma_shape = 9.0\n",
    "\n",
    "n_jobs: int = 10000\n",
    "sampling_target: float = 5.0\n",
    "simulation_factor: float = 100.0\n",
    "\n",
    "sampling_period = int(inter_arrival_time * sampling_target)\n",
    "time_limit = int(inter_arrival_time * n_jobs + target_utilization/(1-target_utilization)*inter_arrival_time*simulation_factor)\n",
    "jobs_limit: int = round(time_limit/inter_arrival_time)\n",
    "\n",
    "time_unit: float = sampling_period*10\n",
    "\n",
    "arrival_distribution = exponential(inter_arrival_time, time_unit)\n",
    "processing_distribution = parametrized_gamma(gamma_shape, processing_time, time_unit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrival Times & Jobs Data Frame\n",
    "\n",
    "*jobs := [arrival time, processing time, start, complete, arrived, pending]*\n",
    "\n",
    "- **Id** A unique Id for the job, corresponding to its arrival order\n",
    "- **Arrival Time**: When the job arrives to the system\n",
    "- **Processing Time**: The time the system takes to process the job once started\n",
    "- **Start**: The time when the system starts processing the job.\n",
    "- **Complete**: The time when the system completes processing of the job. It is always the case that $Complete = ArrivalTime + ProcessingTime$\n",
    "- **Arrived**: How many jobs have arrived up until the time of this arrival, including this one.\n",
    "- **Pending**: How many jobs are in the system or \"pending\" at the time of arrival.\n",
    "- **WIP**: How many jobs are currently being actively worked on.\n",
    "- **DONE**: How many jobs are currently Complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs = ggk.initialize_jobs(n_jobs, arrival_distribution, processing_distribution)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill-In Dynamic values:\n",
    "\n",
    "- Start\n",
    "- Complete\n",
    "- Arrived\n",
    "- Pending\n",
    "- WIP\n",
    "- DONE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Arrival</th>\n",
       "      <th>Processing</th>\n",
       "      <th>Start</th>\n",
       "      <th>Complete</th>\n",
       "      <th>Arrived</th>\n",
       "      <th>Pending</th>\n",
       "      <th>WIP</th>\n",
       "      <th>DONE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>527</td>\n",
       "      <td>1</td>\n",
       "      <td>528</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>432</td>\n",
       "      <td>43</td>\n",
       "      <td>475</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>89</td>\n",
       "      <td>483</td>\n",
       "      <td>90</td>\n",
       "      <td>573</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>573</td>\n",
       "      <td>151</td>\n",
       "      <td>724</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>176</td>\n",
       "      <td>433</td>\n",
       "      <td>177</td>\n",
       "      <td>610</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>492346</td>\n",
       "      <td>371</td>\n",
       "      <td>492347</td>\n",
       "      <td>492718</td>\n",
       "      <td>9996</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>492355</td>\n",
       "      <td>289</td>\n",
       "      <td>492356</td>\n",
       "      <td>492645</td>\n",
       "      <td>9997</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>492428</td>\n",
       "      <td>605</td>\n",
       "      <td>492429</td>\n",
       "      <td>493034</td>\n",
       "      <td>9998</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>492446</td>\n",
       "      <td>589</td>\n",
       "      <td>492447</td>\n",
       "      <td>493036</td>\n",
       "      <td>9999</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>492545</td>\n",
       "      <td>340</td>\n",
       "      <td>492546</td>\n",
       "      <td>492886</td>\n",
       "      <td>10000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  Arrival  Processing   Start  Complete  Arrived  Pending  WIP  DONE\n",
       "0        0        0         527       1       528        1        0    0     0\n",
       "1        1       42         432      43       475        2        1    1     0\n",
       "2        2       89         483      90       573        3        2    2     0\n",
       "3        3      150         573     151       724        4        3    3     0\n",
       "4        4      176         433     177       610        5        4    4     0\n",
       "...    ...      ...         ...     ...       ...      ...      ...  ...   ...\n",
       "9995  9995   492346         371  492347    492718     9996        6    6  9989\n",
       "9996  9996   492355         289  492356    492645     9997        7    7  9989\n",
       "9997  9997   492428         605  492429    493034     9998        6    6  9991\n",
       "9998  9998   492446         589  492447    493036     9999        7    7  9991\n",
       "9999  9999   492545         340  492546    492886    10000        6    6  9993\n",
       "\n",
       "[10000 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ggk.fill_in_dynamic_values(jobs, k_servers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the Event Logs\n",
    "\n",
    "- Arrivals: Arrival events\n",
    "- Starts: Start Events\n",
    "- Completes: Complete Events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arrivals':         Id  Timestamp    Event\n",
       " 0        0          0  ARRIVAL\n",
       " 1        1         42  ARRIVAL\n",
       " 2        2         89  ARRIVAL\n",
       " 3        3        150  ARRIVAL\n",
       " 4        4        176  ARRIVAL\n",
       " ...    ...        ...      ...\n",
       " 9995  9995     492346  ARRIVAL\n",
       " 9996  9996     492355  ARRIVAL\n",
       " 9997  9997     492428  ARRIVAL\n",
       " 9998  9998     492446  ARRIVAL\n",
       " 9999  9999     492545  ARRIVAL\n",
       " \n",
       " [10000 rows x 3 columns],\n",
       " 'starts':         Id  Timestamp  Event\n",
       " 0        0          1  START\n",
       " 1        1         43  START\n",
       " 2        2         90  START\n",
       " 3        3        151  START\n",
       " 4        4        177  START\n",
       " ...    ...        ...    ...\n",
       " 9995  9995     492347  START\n",
       " 9996  9996     492356  START\n",
       " 9997  9997     492429  START\n",
       " 9998  9998     492447  START\n",
       " 9999  9999     492546  START\n",
       " \n",
       " [10000 rows x 3 columns],\n",
       " 'completes':         Id  Timestamp     Event\n",
       " 0        0        528  COMPLETE\n",
       " 1        1        475  COMPLETE\n",
       " 2        2        573  COMPLETE\n",
       " 3        3        724  COMPLETE\n",
       " 4        4        610  COMPLETE\n",
       " ...    ...        ...       ...\n",
       " 9995  9995     492718  COMPLETE\n",
       " 9996  9996     492645  COMPLETE\n",
       " 9997  9997     493034  COMPLETE\n",
       " 9998  9998     493036  COMPLETE\n",
       " 9999  9999     492886  COMPLETE\n",
       " \n",
       " [10000 rows x 3 columns],\n",
       " 'all_events':          Id  Timestamp     Event\n",
       " 0         0          0   ARRIVAL\n",
       " 1         0          1     START\n",
       " 2         1         42   ARRIVAL\n",
       " 3         1         43     START\n",
       " 4         2         89   ARRIVAL\n",
       " ...     ...        ...       ...\n",
       " 29995  9993     492678  COMPLETE\n",
       " 29996  9995     492718  COMPLETE\n",
       " 29997  9999     492886  COMPLETE\n",
       " 29998  9997     493034  COMPLETE\n",
       " 29999  9998     493036  COMPLETE\n",
       " \n",
       " [30000 rows x 3 columns]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logs = ggk.event_logs(jobs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Some derived statistics for control\n",
    "\n",
    "- Busy Time: Should be equivalent to Processing Times\n",
    "- Lead Time: Total Wait + Processing time\n",
    "- Wait Time: Time waiting for a processor (+1 as artificially introduced scheduling delay)\n",
    "- Total Time to complete simulation and the equivalent utilization. Note that this needs to be improved by removing ramp-up and ramp-down times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398.7872, 130.3154454686503)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_time = completes['Timestamp'] - starts['Timestamp']\n",
    "(busy_time.mean(), busy_time.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455.4562, 158.6210904848238)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_time = completes['Timestamp'] - arrivals['Timestamp']\n",
    "(lead_time.mean(), lead_time.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56.669, 91.22930185829416)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wait_time = starts['Timestamp'] - arrivals['Timestamp']\n",
    "(wait_time.mean(), wait_time.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(498175, 0.8004962111707733)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_time = max(completes['Timestamp'])\n",
    "\n",
    "utilization = sum(busy_time)/(total_time*k_servers)\n",
    "\n",
    "(total_time, utilization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_bins = [idx*sampling_target for idx in range(0, round(total_time/sampling_target)+2)]\n",
    "\n",
    "arrival_counts = pd.DataFrame(arrivals.groupby(pd.cut(arrivals['Timestamp'], time_bins), observed=True).size(), columns=['count'])\n",
    "completion_counts = pd.DataFrame(completes.groupby(pd.cut(completes['Timestamp'], time_bins), observed=True).size(), columns=['count'])\n",
    "start_counts =  pd.DataFrame(starts.groupby(pd.cut(starts['Timestamp'], time_bins), observed=True).size(), columns=['count'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "events['wip_change'] = events['Event'].apply(lambda ev: 1 if ev == 'ARRIVAL' else -1 if ev == 'COMPLETE' else 0)\n",
    "events['busy_change'] = events['Event'].apply(lambda ev: 1 if ev == 'START' else -1 if ev == 'COMPLETE' else 0)\n",
    "events['wip'] = events['wip_change'].cumsum()\n",
    "events['busy'] = events['busy_change'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = events[100:-100]\n",
    "markersize = 0.1\n",
    "linewidth = 0.2\n",
    "bins = 25\n",
    "\n",
    "fig, ((tl, tr), (bl, br)) = plot.subplots(nrows=2, ncols=2, figsize=(15, 5))\n",
    "plot.subplots_adjust(top = 1.0, bottom=0.01, hspace=0.3, wspace=0.3)\n",
    "\n",
    "\n",
    "#customer_counts.plot.bar(title=\"Unique Active Customers\", ax = axes[0][0])\n",
    "\n",
    "#events[['Timestamp', 'wip', 'busy']].plot.bar(x='Timestamp')\n",
    "tl.hist(subset['wip'], bins=bins)\n",
    "tl.set_xlabel(\"Number of jobs\")\n",
    "tl.set_ylabel(\"Frequency\")\n",
    "bl.plot(subset['Timestamp'], subset['wip'], markersize=markersize, linewidth=linewidth)\n",
    "bl.set_xlabel(\"Time\")\n",
    "bl.set_ylabel(\"Number of Jobs\")\n",
    "tl.set_title(\"WIP\")\n",
    "tr.hist(subset['busy'], bins=k_servers)\n",
    "tr.set_title(\"Busy Servers\")\n",
    "tr.set_xlabel(\"Number of Busy Servers\")\n",
    "tr.set_ylabel(\"Frequency\")\n",
    "br.plot(subset['Timestamp'], subset['busy'], 'o', markersize=markersize, linewidth=linewidth)\n",
    "br.set_xlabel(\"Time\")\n",
    "br.set_ylabel(\"Number of Jobs\")\n",
    "\n",
    "\n",
    "plot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = events\n",
    "markersize = 0.1\n",
    "linewidth = 0.2\n",
    "bins = 30\n",
    "\n",
    "fig, ((tl, tr), (ml, mr), (bl, br)) = plot.subplots(nrows=3, ncols=2, figsize=(15, 12))\n",
    "plot.subplots_adjust(top = 1.0, bottom=0.01, hspace=0.3, wspace=0.3)\n",
    "\n",
    "tl.hist(lead_time, bins=bins)\n",
    "tl.set_title('Lead Time Distribution')\n",
    "tl.set_xlabel('Time')\n",
    "tl.set_ylabel('Job Frequency')\n",
    "\n",
    "tr.hist(subset['wip'], bins=bins)\n",
    "tr.set_title('WIP Distribution')\n",
    "tr.set_xlabel('WIP')\n",
    "tr.set_ylabel('Frequency')\n",
    "\n",
    "ml.hist(wait_time, bins=bins)\n",
    "ml.set_title('Wait Time Distribution')\n",
    "ml.set_xlabel('Time')\n",
    "ml.set_ylabel('Job Frequency')\n",
    "\n",
    "mr.hist(subset['wip']-subset['busy'], bins=bins)\n",
    "mr.set_title('Queue Size Distribution')\n",
    "mr.set_xlabel('Queue Size')\n",
    "mr.set_ylabel('Frequency')\n",
    "\n",
    "bl.hist(busy_time, bins=bins)\n",
    "bl.set_title('Processing Time Distribution')\n",
    "bl.set_xlabel('Time')\n",
    "bl.set_ylabel('Job Frequency')\n",
    "\n",
    "br.hist(subset['busy'], bins=k_servers)\n",
    "br.set_title('Busy Servers Distribution')\n",
    "br.set_xlabel('Busy Servers')\n",
    "br.set_ylabel('Frequency')\n",
    "\n",
    "\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Event</th>\n",
       "      <th>wip_change</th>\n",
       "      <th>busy_change</th>\n",
       "      <th>wip</th>\n",
       "      <th>busy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ARRIVAL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>START</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.143954</td>\n",
       "      <td>ARRIVAL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.144954</td>\n",
       "      <td>START</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3.260838</td>\n",
       "      <td>ARRIVAL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>9995</td>\n",
       "      <td>9950.312004</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>9992</td>\n",
       "      <td>9950.937170</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>9997</td>\n",
       "      <td>9952.446144</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>9998</td>\n",
       "      <td>9953.826383</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>9999</td>\n",
       "      <td>9954.378135</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id    Timestamp     Event  wip_change  busy_change  wip  busy\n",
       "0         0     0.000000   ARRIVAL           1            0    1     0\n",
       "1         0     0.001000     START           0            1    1     1\n",
       "2         1     0.143954   ARRIVAL           1            0    2     1\n",
       "3         1     0.144954     START           0            1    2     2\n",
       "4         2     3.260838   ARRIVAL           1            0    3     2\n",
       "...     ...          ...       ...         ...          ...  ...   ...\n",
       "29995  9995  9950.312004  COMPLETE          -1           -1    4     4\n",
       "29996  9992  9950.937170  COMPLETE          -1           -1    3     3\n",
       "29997  9997  9952.446144  COMPLETE          -1           -1    2     2\n",
       "29998  9998  9953.826383  COMPLETE          -1           -1    1     1\n",
       "29999  9999  9954.378135  COMPLETE          -1           -1    0     0\n",
       "\n",
       "[30000 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count per ~10 time units')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 1000\n",
    "arrivals.hist('Timestamp', bins=bins)\n",
    "plot.title(\"Arrivals\")\n",
    "plot.xlabel(\"Time\")\n",
    "plot.ylabel(f\"Count per ~{round(total_time/bins)} time units\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "symbolic-notebooks",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}