statistical_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Statistical analysis of the job data\n",
    "\n",
    "from matplotlib.ticker import MultipleLocator, FixedLocator, LogLocator, NullFormatter, ScalarFormatter\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "from datetime import date, datetime, time, timedelta\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "import seaborn as sns\n",
    "import time, datetime, pytz\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'libertine'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (17,18,19,20,21,22) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>JobID</th>\n",
       "      <th>GID</th>\n",
       "      <th>UID</th>\n",
       "      <th>Partition</th>\n",
       "      <th>Submit</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>ElapsedRaw</th>\n",
       "      <th>CPUTimeRAW</th>\n",
       "      <th>...</th>\n",
       "      <th>ReqMem</th>\n",
       "      <th>is_gpu</th>\n",
       "      <th>ave_cpu_freq</th>\n",
       "      <th>req_mem</th>\n",
       "      <th>state</th>\n",
       "      <th>node_list</th>\n",
       "      <th>submit_hour_of_day</th>\n",
       "      <th>submit_day_of_week</th>\n",
       "      <th>submit_date</th>\n",
       "      <th>waiting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8619864</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 01:55:55</td>\n",
       "      <td>2022-01-01 01:55:55</td>\n",
       "      <td>2022-01-01 02:02:51</td>\n",
       "      <td>416</td>\n",
       "      <td>9984</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>8619865</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 03:49:53</td>\n",
       "      <td>2022-01-01 03:50:05</td>\n",
       "      <td>2022-01-01 03:56:51</td>\n",
       "      <td>406</td>\n",
       "      <td>9744</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>8619866</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:19:20</td>\n",
       "      <td>2022-01-01 04:19:20</td>\n",
       "      <td>2022-01-01 04:29:50</td>\n",
       "      <td>630</td>\n",
       "      <td>15120</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>8619867</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:32:24</td>\n",
       "      <td>2022-01-01 04:32:24</td>\n",
       "      <td>2022-01-01 04:42:22</td>\n",
       "      <td>598</td>\n",
       "      <td>14352</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>CANCELLED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8619868</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:47:23</td>\n",
       "      <td>2022-01-01 04:47:23</td>\n",
       "      <td>2022-01-01 04:50:11</td>\n",
       "      <td>168</td>\n",
       "      <td>4032</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    JobID      GID      UID   Partition               Submit  \\\n",
       "0           2  8619864  54655.0  55101.0  gpu_titan+  2022-01-01 01:55:55   \n",
       "1           3  8619865  54655.0  55101.0  gpu_titan+  2022-01-01 03:49:53   \n",
       "2           4  8619866  54655.0  55101.0  gpu_titan+  2022-01-01 04:19:20   \n",
       "3           5  8619867  54655.0  55101.0  gpu_titan+  2022-01-01 04:32:24   \n",
       "4           6  8619868  54655.0  55101.0  gpu_titan+  2022-01-01 04:47:23   \n",
       "\n",
       "                 Start                  End  ElapsedRaw  CPUTimeRAW  ...  \\\n",
       "0  2022-01-01 01:55:55  2022-01-01 02:02:51         416        9984  ...   \n",
       "1  2022-01-01 03:50:05  2022-01-01 03:56:51         406        9744  ...   \n",
       "2  2022-01-01 04:19:20  2022-01-01 04:29:50         630       15120  ...   \n",
       "3  2022-01-01 04:32:24  2022-01-01 04:42:22         598       14352  ...   \n",
       "4  2022-01-01 04:47:23  2022-01-01 04:50:11         168        4032  ...   \n",
       "\n",
       "   ReqMem  is_gpu ave_cpu_freq   req_mem      state  node_list  \\\n",
       "0    186G    True          NaN  190464.0  COMPLETED  {'r28n1'}   \n",
       "1    186G    True          NaN  190464.0  COMPLETED  {'r28n1'}   \n",
       "2    186G    True          NaN  190464.0    TIMEOUT  {'r28n1'}   \n",
       "3    186G    True          NaN  190464.0  CANCELLED  {'r28n1'}   \n",
       "4    186G    True          NaN  190464.0  COMPLETED  {'r28n1'}   \n",
       "\n",
       "   submit_hour_of_day submit_day_of_week submit_date waiting_time  \n",
       "0                   1                  5  2022-01-01          0.0  \n",
       "1                   3                  5  2022-01-01         12.0  \n",
       "2                   4                  5  2022-01-01          0.0  \n",
       "3                   4                  5  2022-01-01          0.0  \n",
       "4                   4                  5  2022-01-01          0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"slurm_data_2022_cleaned.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"{'r28n1'}\", \"{'r28n3'}\", \"{'r26n3'}\", ...,\n",
       "       \"{'r10n13', 'r11n23', '+'}\", \"{'r12n27', 'r13n', 'r13n2'}\",\n",
       "       \"{'r13n27', 'r14n25', '+'}\"], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['node_list'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gpu_titan+', 'normal', 'shared', 'fat_soil_+', 'gpu_shared',\n",
       "       'gpu_share+', 'gpu', 'hared_ju+', 'short', 'gpu_short', 'sw',\n",
       "       'fat', 'hared_52+', nan], dtype=object)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Partition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal        1165321\n",
       "shared         520491\n",
       "gpu_titan+      94703\n",
       "gpu_shared      93966\n",
       "gpu_share+      51544\n",
       "gpu             10605\n",
       "fat_soil_+       6420\n",
       "gpu_short        5568\n",
       "hared_52+        4822\n",
       "sw               3852\n",
       "hared_ju+        3079\n",
       "fat              2199\n",
       "short            1447\n",
       "Name: Partition, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Partition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generic jobs:\n",
      " COMPLETED     1538685\n",
      "FAILED         293684\n",
      "CANCELLED      109030\n",
      "TIMEOUT         42246\n",
      "OUT_OF_ME+      10824\n",
      "NODE_FAIL          33\n",
      "REQUEUED            3\n",
      "Name: state, dtype: int64 \n",
      "\n",
      "ML jobs:\n",
      " COMPLETED     190632\n",
      "FAILED         70693\n",
      "CANCELLED      31113\n",
      "TIMEOUT        13438\n",
      "OUT_OF_ME+       732\n",
      "NODE_FAIL         13\n",
      "REQUEUED           2\n",
      "Name: state, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# filtered_jobs = data[(~data[\"NodeList\"].str.contains(\"None\")) & (~data[\"NodeList\"].str.contains(\"software\")) & (~data[\"NodeList\"].str.contains(\"login\"))]\n",
    "# print(len(filtered_jobs))\n",
    "\n",
    "# [x]tab.datasets overview\n",
    "\n",
    "ge = data.loc[data[\"is_gpu\"] == False]\n",
    "ml = data.loc[data[\"is_gpu\"] == True]\n",
    "\n",
    "print('Generic jobs:\\n', ge['state'].value_counts(), '\\n')\n",
    "print('ML jobs:\\n', ml['state'].value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'OUT_OF_ME+', 'NODE_FAIL'] \n",
      " [0.6217146137113002, 0.2305534809847924, 0.10146988321163122, 0.0438258056310192, 0.002387296451994795, 4.23973413605633e-05]\n",
      "['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'OUT_OF_ME+', 'NODE_FAIL'] \n",
      " [0.7714620920980394, 0.1472465599233895, 0.054665192616714424, 0.021181195334180662, 0.005426910436424075, 1.6545458647634376e-05]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc3f0cb6d60>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig.Generic and ML jobs distribution per execution state type\n",
    "\n",
    "ge = data.loc[data[\"is_gpu\"] == False]\n",
    "ml = data.loc[data[\"is_gpu\"] == True]\n",
    "\n",
    "ml_state_l, ml_count_l = [], []\n",
    "for idx, name in enumerate(ml['state'].value_counts().index.tolist()):\n",
    "    ml_state_l.append(name)\n",
    "    ml_count_l.append(ml['state'].value_counts(normalize=True)[idx])\n",
    "print(ml_state_l[:6],'\\n',ml_count_l[:6])\n",
    "    \n",
    "ge_state_l, ge_count_l = [], []\n",
    "for idx, name in enumerate(ge['state'].value_counts().index.tolist()):\n",
    "    ge_state_l.append(name)\n",
    "    ge_count_l.append(ge['state'].value_counts(normalize=True)[idx])\n",
    "print(ge_state_l[:6],'\\n',ge_count_l[:6])\n",
    "\n",
    "index = ['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'OUT OF\\nMEMORY', 'NODE_FAIL']\n",
    "\n",
    "df = pd.DataFrame({'Generic Jobs': ge_count_l[:6], 'ML Jobs': ml_count_l[:6]}, index=index)\n",
    "ax = df.plot.bar(figsize=(9,6), rot=0, ylabel='percent', xlabel='state type', color = ['lightcoral','steelblue'])\n",
    "\n",
    "ax.set_ylabel('rate',fontdict={'fontsize':16})\n",
    "ax.set_xlabel('state type',fontdict={'fontsize':16})\n",
    "ax.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'OUT_OF_ME+', 'NODE_FAIL'] \n",
      " [0.6217146137113002, 0.2305534809847924, 0.10146988321163122, 0.0438258056310192, 0.002387296451994795, 4.23973413605633e-05]\n",
      "['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'OUT_OF_ME+', 'NODE_FAIL'] \n",
      " [0.7714620920980394, 0.1472465599233895, 0.054665192616714424, 0.021181195334180662, 0.005426910436424075, 1.6545458647634376e-05]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['libertine'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [horizonal] fig.Generic and ML jobs distribution per execution state type\n",
    "ge = data.loc[data[\"is_gpu\"] == False]\n",
    "ml = data.loc[data[\"is_gpu\"] == True]\n",
    "\n",
    "ml_state_l, ml_count_l = [], []\n",
    "for idx, name in enumerate(ml['state'].value_counts().index.tolist()):\n",
    "    ml_state_l.append(name)\n",
    "    ml_count_l.append(ml['state'].value_counts(normalize=True)[idx])\n",
    "print(ml_state_l[:6],'\\n',ml_count_l[:6])\n",
    "    \n",
    "ge_state_l, ge_count_l = [], []\n",
    "for idx, name in enumerate(ge['state'].value_counts().index.tolist()):\n",
    "    ge_state_l.append(name)\n",
    "    ge_count_l.append(ge['state'].value_counts(normalize=True)[idx])\n",
    "print(ge_state_l[:6],'\\n',ge_count_l[:6])\n",
    "\n",
    "ge_count_l = ge_count_l[:6][::-1]\n",
    "ml_count_l = ml_count_l[:6][::-1]\n",
    "labels = [\"COMPLETED\", \"FAILED\", \"CANCELLED\", \"TIMEOUT\", \"OUT OF\\nMEMORY\", \"NODE FAIL\"][::-1]\n",
    "\n",
    "y_axis = np.arange(len(labels))\n",
    "\n",
    "f = plt.figure()\n",
    "f.set_figwidth(9)\n",
    "f.set_figheight(6)\n",
    "\n",
    "# plt.barh(y_axis + 0.2, ml_count_l, 0.4, label = 'ML jobs', color = 'steelblue')\n",
    "# plt.barh(y_axis - 0.2, ge_count_l, 0.4, label = 'Generic jobs', color = 'lightcoral')\n",
    "plt.barh(y_axis + 0.2, ge_count_l, 0.4, label = 'Generic jobs', color = 'lightcoral')\n",
    "plt.barh(y_axis - 0.2, ml_count_l, 0.4, label = 'ML jobs', color = 'steelblue')\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.yticks(y_axis, labels)\n",
    "plt.xlabel(\"Ratio to all jobs\", fontsize = 24)\n",
    "plt.ylabel(\"Job type, by exit state\", fontsize = 24)\n",
    "plt.legend(loc = \"lower center\", prop={'size': 20})\n",
    "plt.show()\n",
    "f.savefig('fig-state-type.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSource:SURFLisa, DataTimespan:12 months, Year:2022, numNodes:348, numUsers:2662, numJobs:2301128\n",
      "%ML Jobs:13.32%, %Generic Jobs:86.68%\n",
      "%ML Completed Jobs:62.17%, %ML Uncompleted Jobs:37.83%\n",
      "%Generic Completed Jobs:77.15%, %Generic Uncompleted Jobs:22.85%\n"
     ]
    }
   ],
   "source": [
    "# tab.overview of our job traces dataset\n",
    "# Data Source, Data Timespan, #Nodes, #Users, #Scheduled Jobs, \n",
    "# ML: %Completed Jobs, %Uncompleted Jobs, Generic: %Completed Jobs, %Uncompleted Jobs\n",
    "def computeNumNodes(df):\n",
    "    node_l = []\n",
    "    for i in df[\"node_list\"]:\n",
    "        r = re.findall(r\"r[0-9]+n[0-9]+\",i)\n",
    "        for j in r:\n",
    "            node_l.append(j)\n",
    "    node_l = set(node_l)\n",
    "    return len(node_l)\n",
    "\n",
    "DataSource = \"SURFLisa\"\n",
    "DataTimespan = '12 months'\n",
    "Year = 2022\n",
    "numNodes = computeNumNodes(data)\n",
    "numUsers = len(data['UID'].unique())\n",
    "numJobs = len(data)\n",
    "\n",
    "pctMLJobs = len(ml)*1.0/numJobs*1.0\n",
    "pctGEJobs = len(ge)*1.0/numJobs*1.0\n",
    "\n",
    "pctMLCompletedJobs = len(ml[ml[\"state\"]=='COMPLETED'])*1.0/len(ml)\n",
    "pctMLUncompletedJobs = len(ml[ml[\"state\"]!='COMPLETED'])*1.0/len(ml)\n",
    "pctGECompletedJobs = len(ge[ge[\"state\"]=='COMPLETED'])*1.0/len(ge)\n",
    "pctGEUncompletedJobs = len(ge[ge[\"state\"]!='COMPLETED'])*1.0/len(ge)\n",
    "\n",
    "print(\"DataSource:{}, DataTimespan:{}, Year:{}, numNodes:{}, numUsers:{}, numJobs:{}\".format(DataSource, DataTimespan, Year, numNodes, numUsers, numJobs))\n",
    "print(\"%ML Jobs:{:.2%}, %Generic Jobs:{:.2%}\".format(pctMLJobs, pctGEJobs))\n",
    "print(\"%ML Completed Jobs:{:.2%}, %ML Uncompleted Jobs:{:.2%}\".format(pctMLCompletedJobs, pctMLUncompletedJobs))\n",
    "print(\"%Generic Completed Jobs:{:.2%}, %Generic Uncompleted Jobs:{:.2%}\".format(pctGECompletedJobs, pctGEUncompletedJobs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-14 163786\n",
      "163,786\n"
     ]
    }
   ],
   "source": [
    "# fig.Failed jobs in the period of one year\n",
    "group_date = data.copy()\n",
    "group_date[\"Submit\"] = pd.to_datetime(group_date[\"Submit\"], utc=True)\n",
    "group_date = group_date.set_index(\"Submit\")\n",
    "group_date = group_date.groupby([group_date.index.date, \"is_gpu\"]).count().unstack(1)\n",
    "datelist = list(group_date.index.values) \n",
    "submissionlist = group_date[\"JobID\"].fillna(0)  # just grab one column as all columns have the same counts\n",
    "submissionlist.columns = ['generic', 'ml']\n",
    "\n",
    "date_max_value = submissionlist.sum(axis=1).idxmax()\n",
    "max_val = submissionlist.loc[date_max_value].sum()\n",
    "print(date_max_value, max_val)\n",
    "print(\"{:,}\".format(max_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-443f928e3f59>:2: MatplotlibDeprecationWarning: The 'subsy' parameter of __init__() has been renamed 'subs' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      "  plt.yscale(\"symlog\", subsy = [1,2,3,4,5,6,7,8,9])\n",
      "<ipython-input-7-443f928e3f59>:3: UserWarning: Attempting to set identical bottom == top == 1 results in singular transformations; automatically expanding.\n",
      "  plt.ylim(bottom=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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xy5NgTsOGDdNLL72k1NRUNW3aVBMmTNCPP/6otWvXavLkyWratKmstXruuec0btw4t3X07dvX1bnWqFEjTZs2TWvXrtX8+fN1++23a/369br33nuzedQOycnJqlWrlh577DHNnDlTK1as0Jo1a/TZZ5/pvvvuU2RkpI4ePaoePXro0CHvA6ds3LhRd9xxh8qXL6833nhDy5cv16pVq/Tyyy+rdOnSkqRHH31UW7duzXa8zuejcePG2a7D6dChQ9qyZYsk6fLLL89WHSVKlHAtZ/X/n34udec0Fb6Ii4vT+vXr9cgjj+j++++XJEVGRrqWM7rvvvskyXXi1Z1Ro0a5locOHep1/84O55SUFP30008+xw0EAu1z6LTP6YWFhbnminf3uv3888+u6YN8TVzJbWvWrHGN3OF8z/ni6aefVo0aNRQVFaWyZcvqyiuv1AMPPKCNGzf6tf+BAweqSpUqioyMVPny5dW4cWM9+eST+uuvv/yqJ6Pp06fr3Llzknx/rmn3AcTGxrqWO3fu7Fq+4IILFBMTI8nx2bJz5868Di3bxowZ40p+aN++vWbOnKl77rlHN9xwg2699Va99dZbGjFihMLCwmSt1YsvvpjlSEuLFy9WxYoVNW3aND3++OO66aab1KVLFz333HP68MMPVbx4cUmO7ysZv/8fO3ZMixcvliTVq1dPn376qe677z7ddNNNat++vbp166YHH3xQEydO1Ndff51l0jIAAAAAAAVVqCZABIS19ltJDSS9ImmLpFOSEiT9ImmkpAZp02Xkxr5jrbVDnJ0ICD3OToEZM2a4OgqcnFep9u7dWxEREVnW9f333+vtt9+WJL311ltauXKlBg8erLZt26pRo0bq16+fli1bpjvuuEOS9J///Oe8znHJMV/3119/Lclx1dWKFSvUp08fNWrUSDfeeKNmzJih559//rwpGLJj1KhR+uOPPzR27Fj16tVLzZo1U0xMjG655Ra99957Wrt2rUqUKKHDhw+7jsmT9evXq27duvr55581bNgwNW/eXE2aNNETTzzhOpZz587p/fffz1ashw4d0o4dOyRJ11xzTbbqSO+ll15ScnKyJOn222/PVh3pEyecJzDdSUxM1OrVq11/796922u9t956q2to/HLlyqlRo0Z64403dO7cORUvXlwzZszwOMXF8OHD1alTJ0lSz5499cgjj2j+/PmujtOuXbvq9ddflySNGDFCbdu29RpLo0aNXMvr1uVKEwp4Rfuc/9tnd5yv22+//ab169eft875ujVo0EANGzYM2D5zwjn6g+TfZ8zy5cu1a9cunT17VsePH9fGjRs1btw4XXnllRo2bFim96wnixYt0v79+3Xu3DkdO3ZMa9eu1SuvvKLLLrtM//d//+f38Tg5RxExxvicAEG7DxRuJ0+edI26VqVKFV111VXnrU8/7ducOXPyNLbs2r59u6udr1y5skaNGuUa1SK9rl27uqbZO3XqlGbNmpVl3c8995yqVauW6fEGDRrowQcflOSYUmTatGnnrd+3b5+stZKkjh07qmjRoh73UalSJVWqVCnLWAAAAAAAKIhCMgHCWjvSWmv8uC3yUtdea+2T1tp61toS1trS1tqG1tpR1tq4PDys3DVypGRMYG5ZCdR+Ro70/ZiyKpsLnJ1nR48e1fz5812P//bbb9qwYYMk36+cfPHFFyU5Tg46T3plFB4ernHjxikqKkonTpzQ7Nmzz1v/7rvvSpKKFCmiDz/80G3H3tNPP6369ev7FJMnl1xyicf55yXHibtBgwZJcgyfnpWPP/5YZcuWzfR469atXVeUeksU8Gbv3r2u5aymm8jKsmXL9NZbb0lyDDfraTSFrFx33XUqV66cJGnixIn6/fff3ZZ78cUXz+tEPXHiRLb2169fP23dulU9evTwWKZ48eKaO3euPv74Y9WsWVNvvPGGOnfurCZNmui2227T3Llz1b59e/3www8a6cP/2gUXXOBa9jatBqSRI5fJmLEBuWUlUPsZOXKZz8eUVdncQvvsXn5qn9254oorXIkE6UeBSElJ0aeffiop/4z+IPn/GXPRRRfpX//6lz799FOtXLlS69atU2xsrB544AEVK1ZMkiPJZvDgwV7rqVmzph577DF99tlnWr16tdauXavPP/9cd911lyIiIpScnKzhw4f71F5ntHnzZlcCQ5s2bVS9enWftqPdBwq3BQsWuEbEufHGGzN9FrVv397VWT9//nylpKTkeYz++vHHH13LvXr1UnR0tMeyd911l+uYnYkgntSqVUvNmzf3uL579+4qVaqUJMdIdumfq/Qx0NYCAAAAAOBZSCZAAPlB+fLlXVczpe+ocV45efnll7uGe/UmISFBixYtkiTddtttXsuWLVvW1UG2YsUK1+MpKSmuOtq2beuxwyIsLEx33XVXljH5Iy4uTjt27NBvv/2mTZs2adOmTa65vzdv3uz1Stb69et7vZLX+fxl9wTf4cOHXcvOpIPs2Ldvn2699VYlJycrLCxMkydP9nrFlTdFixbVM888I8lxlVjr1q01adIkHTlyROfOndOWLVs0dOjQTFeZnTlzxmu9r7/+ujZu3KiNGzdq2bJlevfdd3X11VdrypQpuvPOO/XHH3943X7NmjWaOnWqtm3b5nb9smXL9NFHH2n//qxnBCpatKjrBK1zLmggL9E+O+Tn9tmTfv36SXJMw+Ds9FmwYIEOHjyo8PBw9enTJ6D7ywnnZ0x4eLirs8qTxo0ba/fu3Ro3bpzuuOMONW3aVNdcc426dOmit99+W+vXr9fFF18syfE+9TQdUY8ePbRjxw6NHTtWt9xyixo3bqxGjRqpZ8+emjRpkhYvXuyKZfTo0X5Pq+H8H5Hk1/uRdh8oGJwjnfkr/agO6Ud7cCpatKjatGkjSTpy5Mh5I+jkV+mnn2jWrJnXshdddJFq1KghSdq5c6dOnjzpsWxWUyZFRES4RtA4c+bMedMa1apVSxUrVpQkff3113r++ee1ceNGpaameq0TAAAAAIDChgQIIAecV6LOnTtXcXFxSklJcQ1V6uzEycqGDRtcnTwDBw50TWPg6ea8MjN9B8OOHTt0+vRpSY5OFm/8mafck40bN+ruu+9WpUqVVK5cOdWuXVv169dXgwYN1KBBA9dVp6mpqa55c91JPx2EO86kheyOfnD06FHXsrPTz19xcXHq1KmT6/keO3as6wRudj3yyCO67777JEkHDx7UwIEDVbFiRUVGRqpevXp69913Vb58eY0ZM8a1TcmSJb3WWbVqVdWvX1/169fXtddeq/vvv19r1qzRvffeqyVLlqhJkyb65Zdf3G772WefqU2bNvrhhx9Ur149ffbZZzp8+LDOnj2rXbt26a233lJ0dLQ+/fRTNW7cONN8xO44X7tTp075+rQAAUX7nL/bZ0/69OmjIkWK6ODBg1qwYIGkfzrl27dvr8qVKwd0fznh/IwpXbq015E3JMdIO96mXKlTp46mTp3q+ts54lBGWe2refPmrm1TU1P1zjvveI0rvZSUFFcMxYsXdw3p7ivafSB0JScn691339VPP/3k97Z//vmn67thgwYN3E7tIEmdO3d2LYfCNBhHjhxxLfsyGo7zuK215/0GyciZ7OZN+jLpE7rDw8M1fPhw1+fJnDlzNHDgQLVv314PPfSQJk6cqC1btmRZPwAAABBM8aNGZboBQKCRABEkxpiuxpgJ8fHxwQ4FOdClSxeVK1dOSUlJmjVrlr7//nvt379fYWFh6tu3r091HDp0KFv7dnaoSdKxY8dcy+mHoXYnp1NBfPTRR7rmmmv08ccf+3SVp7eRC5xDfnsSFuZoorJ7VVP6TqLExES/tz958qRuvPFGbdq0SZL0zDPP6JFHHslWLBm99957+uqrr9SqVSsVKVLE9XixYsXUv39//frrr6patarrcXfD0GclPDxcb731lqpWraq4uDi303YcPHhQAwYMUFJSkurVq6eVK1fqlltuUYUKFRQREaFq1arpwQcf1OLFixUVFaX9+/f7dFWw83X31uEH5Cba5/zdPntywQUXqGPHjpIco3fEx8e7Osry0/QX0j+fMdn5fHGndevWrsSTJUuWZPu5vfPOO11Jc87RR3zh/B+RpFtuuUUlSpTwa7+0+0DosdZq3rx5atCggT7//HM1atTI7zpiY2Ndy+5Gf3Bq0qSJ63Nw6dKl5031lh85P8vDw8N9atfSf26m/x6QkbepNJzSjzSXsa7rrrtOn3zyidq0aeP6DXHixAktX75c7777rvr166fbb789JEbZAAAAAAAgtxTJughyg7U2VlJsTEyM94meka9FRkbq9ttv13vvvafJkye7hj5t06bNeZ3X3qSf1/Wtt95S27ZtfdquePHibh/P6irUnNi6davuu+8+JScn64ILLtATTzyhdu3aqUaNGipZsqTr5ODEiRN1zz33SHKcWA2W9KM+pO+E9MWZM2fUtWtXrVq1SpI0bNgwjR49OpDhqXv37urevbsSExP1999/KywsTJUrV3Y9j9u3b3eVveKKK7K1j8jISHXq1EkffPCBVqxYoX379qlKlSqu9TNmzHBdrTt8+HCPHV5XXnml+vbtq48++kjr1q3TL7/84hqeN6PU1FQ5k7uyO/IGkFO0z/m7ffamf//+mjdvnr7++mvFxMQoMTFRJUuWVI8ePYId2nmc7dvp06eVlJSkqKioHNd5xRVXaMuWLUpMTNTRo0ddQ537o0iRIqpTp47Wrl2rvXv3+rzd5MmTXcsDBgzwa5+0+0Do+eWXX/TYY49p3759euWVV9S5c2e/R35JTk7W/PnzXX+/9NJLeumll3za7ptvvtEdd9zhd9x5xZnQkJKSonPnzmWZBJE+UcFbEqEvSXPpExTd1XXZZZdp7NixOnXqlH755Rf9+uuvWr9+vX799VclJydrx44dGjZsmJ5//nndeOONWe4PAAAAAICChgSIwmLkSMctL+RVh0peHpMX/fv313v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      "text/plain": [
       "<Figure size 2592x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(36, 10))\n",
    "plt.yscale(\"symlog\", subsy = [1,2,3,4,5,6,7,8,9])\n",
    "plt.ylim(bottom=1)\n",
    "plt.ylim(top=5.5 * 10**5)\n",
    "plt.locator_params(axis='y', numticks=12)\n",
    "ax.bar(datelist, submissionlist.sum(axis=1), width=1, color=\"lightcoral\")\n",
    "ax.bar(datelist, submissionlist['ml'], width=1, color=\"steelblue\")  # Plot ML jobs overlapping\n",
    "plt.xlabel(\"Date\", fontsize=30)\n",
    "plt.ylabel(\"Job Submissions\", fontsize=30)\n",
    "ax.tick_params(axis='both', which='major', labelsize=28)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "\n",
    "# date_max_value = submissionlist.sum(axis=1).idxmax()\n",
    "# max_value = submissionlist.loc[date_max_value].sum()\n",
    "# ax.annotate(\"{:,}\".format(int(max_value)),\n",
    "#             xy=(date_max_value, max_value), xycoords='data',\n",
    "#             xytext=(20, 0), textcoords='offset points',\n",
    "#             fontsize=10,\n",
    "#             arrowprops=dict(arrowstyle=\"->\",\n",
    "#                             connectionstyle=\"arc\"\n",
    "#                            )\n",
    "#             )\n",
    "\n",
    "annotate_date = submissionlist.sum(axis=1).sort_values().index[-3]\n",
    "annotate_value_all_jobs = submissionlist.sum(axis=1).loc[annotate_date]\n",
    "annotate_value_ml_jobs = submissionlist['ml'].loc[annotate_date]\n",
    "bbox = dict(boxstyle =\"round\", fc =\"0.8\")\n",
    "ax.annotate(\"All jobs\",\n",
    "            xy=(annotate_date, annotate_value_all_jobs), xycoords='data',\n",
    "            xytext=(20, -5), textcoords='offset points',\n",
    "            fontsize=30,bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "ax.annotate(\"ML jobs\",\n",
    "            xy=(annotate_date, annotate_value_ml_jobs), xycoords='data',\n",
    "            xytext=(20, -30), textcoords='offset points',\n",
    "            fontsize=30, color=\"black\",bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\",\n",
    "                            color=\"white\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "combined = submissionlist.sum(axis=1)\n",
    "median = combined.median()\n",
    "median_zeroes_filtered = combined[combined > 0].median()\n",
    "median_ml_jobs = submissionlist['ml'].median()\n",
    "avg_ml_jobs = submissionlist['ml'].mean()\n",
    "avg = combined.mean()\n",
    "ax.axhline(avg, label=\"Average ({:,.0f})\".format(avg), color=\"red\", linestyle=\"solid\", linewidth = 5)\n",
    "ax.axhline(median, label=\"Median ({:,})\".format(int(median)), color=\"red\", linestyle=\"dashed\", linewidth = 5)\n",
    "# ax.axhline(median_zeroes_filtered, label=\"Median zeros filtered ({:,})\".format(int(median_zeroes_filtered)), color=\"gold\", linestyle=\"dotted\")\n",
    "ax.axhline(avg_ml_jobs, label=\"Average ML ({:,.0f})\".format(avg_ml_jobs), color=\"darkblue\", linestyle=\"solid\", linewidth = 5)\n",
    "ax.axhline(median_ml_jobs, label=\"Median ML ({:,})\".format(int(median_ml_jobs)), color=\"darkblue\", linestyle=\"dashed\", linewidth = 5)\n",
    "ax.legend(ncol=2, prop={\"size\": 28}, loc=\"upper left\")\n",
    "plt.savefig('fig-job-by-date.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['libertine'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(36, 10))\n",
    "plt.yscale(\"symlog\", subsy = [1,2,3,4,5,6,7,8,9])\n",
    "plt.ylim(bottom=1)\n",
    "plt.ylim(top=5.5 * 10**5)\n",
    "plt.locator_params(axis='y', numticks=12)\n",
    "ax.plot(datelist, submissionlist.sum(axis=1), color=\"lightcoral\", linewidth='8')\n",
    "ax.plot(datelist, submissionlist['ml'], color=\"steelblue\",linewidth='8')  # Plot ML jobs overlapping\n",
    "plt.xlabel(\"Date\", fontsize=30)\n",
    "plt.ylabel(\"Job Submissions\", fontsize=30)\n",
    "ax.tick_params(axis='both', which='major', labelsize=28)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "\n",
    "# date_max_value = submissionlist.sum(axis=1).idxmax()\n",
    "# max_value = submissionlist.loc[date_max_value].sum()\n",
    "# ax.annotate(\"{:,}\".format(int(max_value)),\n",
    "#             xy=(date_max_value, max_value), xycoords='data',\n",
    "#             xytext=(20, 0), textcoords='offset points',\n",
    "#             fontsize=10,\n",
    "#             arrowprops=dict(arrowstyle=\"->\",\n",
    "#                             connectionstyle=\"arc\"\n",
    "#                            )\n",
    "#             )\n",
    "\n",
    "annotate_date = submissionlist.sum(axis=1).sort_values().index[-3]\n",
    "annotate_value_all_jobs = submissionlist.sum(axis=1).loc[annotate_date]\n",
    "annotate_value_ml_jobs = submissionlist['ml'].loc[annotate_date]\n",
    "bbox = dict(boxstyle =\"round\", fc =\"0.8\")\n",
    "ax.annotate(\"All jobs\",\n",
    "            xy=(annotate_date, annotate_value_all_jobs), xycoords='data',\n",
    "            xytext=(20, -3), textcoords='offset points',\n",
    "            fontsize=30,bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "ax.annotate(\"ML jobs\",\n",
    "            xy=(annotate_date, annotate_value_ml_jobs), xycoords='data',\n",
    "            xytext=(20, -100), textcoords='offset points',\n",
    "            fontsize=30, color=\"black\",bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\",\n",
    "                            color=\"white\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "combined = submissionlist.sum(axis=1)\n",
    "median = combined.median()\n",
    "median_zeroes_filtered = combined[combined > 0].median()\n",
    "median_ml_jobs = submissionlist['ml'].median()\n",
    "avg_ml_jobs = submissionlist['ml'].mean()\n",
    "avg = combined.mean()\n",
    "ax.axhline(avg, label=\"Average ({:,.0f})\".format(avg), color=\"orchid\", linestyle=\"solid\", linewidth = 5)\n",
    "ax.axhline(median, label=\"Median ({:,})\".format(int(median)), color=\"orchid\", linestyle=\"dashed\", linewidth = 5)\n",
    "# ax.axhline(median_zeroes_filtered, label=\"Median zeros filtered ({:,})\".format(int(median_zeroes_filtered)), color=\"gold\", linestyle=\"dotted\")\n",
    "ax.axhline(avg_ml_jobs, label=\"Average ML ({:,.0f})\".format(avg_ml_jobs), color=\"deepskyblue\", linestyle=\"solid\", linewidth = 5)\n",
    "ax.axhline(median_ml_jobs, label=\"Median ML ({:,})\".format(int(median_ml_jobs)), color=\"deepskyblue\", linestyle=\"dashed\", linewidth = 5)\n",
    "ax.legend(ncol=2, prop={\"size\": 28}, loc=\"upper left\")\n",
    "plt.savefig('fig-job-by-date.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 163786, 6304.460273972602)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = submissionlist.sum(axis=1)\n",
    "combined.min(), combined.max(), combined.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# divide jobs into: generic completed, generic failed, ml completed, ml failed\n",
    "\n",
    "data['elapsedraw'] = data['ElapsedRaw']/3600.0\n",
    "ge = data.loc[data[\"is_gpu\"] == False]\n",
    "ml = data.loc[data[\"is_gpu\"] == True]\n",
    "\n",
    "ge_completed = ge[ge['state'] == 'COMPLETED']\n",
    "ge_failed = ge[ge['state'] == 'FAILED']\n",
    "ml_completed = ml[ml['state'] == 'COMPLETED']\n",
    "ml_failed = ml[ml['state'] == 'FAILED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['libertine'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Job duration CDF\n",
    "\n",
    "def normalize(df):\n",
    "    df = df['target'].value_counts(sort=False, normalize=True).rename_axis('target').reset_index(name='pdf')\n",
    "    df[\"cdf\"] = df[\"pdf\"].cumsum()\n",
    "    return df\n",
    "\n",
    "\n",
    "def cdf(df_elapsed_values):\n",
    "    all_values_df = pd.DataFrame({\"target\": df_elapsed_values[\"elapsedraw\"].values})\n",
    "    count_df = normalize(all_values_df)\n",
    "    counts = np.unique(df_elapsed_values[\"elapsedraw\"].values, return_counts=True)\n",
    "    pdf = counts[1] / np.sum(counts[1])\n",
    "    cdf = np.cumsum(pdf)\n",
    "    return counts, cdf\n",
    "\n",
    "ge_completed_counts, ge_completed_cdf = cdf(ge_completed)\n",
    "ge_failed_counts, ge_failed_cdf = cdf(ge_failed)\n",
    "ml_completed_counts, ml_completed_cdf = cdf(ml_completed)\n",
    "ml_failed_counts, ml_failed_cdf = cdf(ml_failed)\n",
    "\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "plt.ylim(0,1.05)\n",
    "plt.plot(ge_completed_counts[0], ge_completed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'solid', label = 'Generic completed jobs', linewidth=3)\n",
    "plt.plot(ge_failed_counts[0], ge_failed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'dashed', label = 'Generic failed jobs', linewidth=3)\n",
    "plt.plot(ml_completed_counts[0], ml_completed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'solid', label = 'ML completed jobs', linewidth=3)\n",
    "plt.plot(ml_failed_counts[0], ml_failed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'dashed', label = 'ML failed jobs', linewidth=3)\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(\"Job duration [h]\", fontsize=24)\n",
    "plt.ylabel(\"Fraction of total jobs\", fontsize=24)\n",
    "plt.legend(loc = \"lower right\", prop={'size': 18})\n",
    "\n",
    "# plt.arrow(0.1, 0.5, 0, 0.35, head_width=0.05, head_length=0.03, linewidth=8, color='lime', length_includes_head=True)\n",
    "# plt.arrow(0.1, 0.57, 0, -0.09, head_width=0.05, head_length=0.03, linewidth=8, color='lime', length_includes_head=True)\n",
    "# bbox = dict(boxstyle =\"round\", fc =\"0.8\")\n",
    "# plt.annotate(\"difference:\\n0.42\",\n",
    "#             xy=(0.1, 0.7), xycoords='data',\n",
    "#             xytext=(20, -30), textcoords='offset points',\n",
    "#             fontsize=16, color=\"black\",bbox=bbox,\n",
    "#             arrowprops=dict(arrowstyle=\"->\",\n",
    "#                             connectionstyle=\"arc\",\n",
    "#                             color=\"black\"\n",
    "#                            )\n",
    "#             )\n",
    "\n",
    "plt.arrow(0.003, 0.5, -0.002, 0, head_width=0.02, head_length=0.0001, linewidth=6, color='lime')\n",
    "plt.arrow(0.001, 0.5, 0.0045, 0, head_width=0.02, head_length=0.0005, linewidth=6, color='lime', length_includes_head=True)\n",
    "bbox = dict(boxstyle =\"round\", fc =\"0.8\")\n",
    "plt.annotate(\"18 [s]\",\n",
    "            xy=(0.0017, 0.5), xycoords='data',\n",
    "            xytext=(20, 30), textcoords='offset points',\n",
    "            fontsize=16, color=\"black\",bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\",\n",
    "                            color=\"black\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "plt.arrow(0.3, 0.9, -0.001, 0, head_width=0.005, head_length=0.01, linewidth=8, color='lime')\n",
    "plt.arrow(0.3, 0.9, 3.591639, 0, head_width=0.005, head_length=0.1, linewidth=8, color='lime', length_includes_head=True)\n",
    "bbox = dict(boxstyle =\"round\", fc =\"0.8\")\n",
    "plt.annotate(\"3.59 [h]\",\n",
    "            xy=(1, 0.9), xycoords='data',\n",
    "            xytext=(20, -30), textcoords='offset points',\n",
    "            fontsize=16, color=\"black\",bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\",\n",
    "                            color=\"black\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "plt.arrow(0.1, 0.8695, 0, 0, head_width=0.01, head_length=0.01, linewidth=8, color='lime')\n",
    "\n",
    "plt.annotate(\"86.95%\",\n",
    "            xy=(0.1, 0.86), xycoords='data',\n",
    "            xytext=(-50, 30), textcoords='offset points',\n",
    "            fontsize=16, color=\"black\",bbox=bbox,\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\",\n",
    "                            color=\"black\"\n",
    "                           )\n",
    "            )\n",
    "\n",
    "plt.axhline(0.8695, color=\"lime\", linestyle=\"dotted\", linewidth = 4)\n",
    "# plt.axvline(0.1, color=\"lime\", linestyle=\"dashed\", linewidth = 2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig-job-cdf.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CPUTimeRAW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.591639"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3.805250-0.213611)*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      1.0\n",
       "0.10      3.0\n",
       "0.25      3.0\n",
       "0.50      3.0\n",
       "0.75      6.0\n",
       "0.90     20.0\n",
       "1.00    192.0\n",
       "Name: NCPUS, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_completed['NCPUS'].quantile([0, 0.1, 0.25, 0.5, 0.75, 0.9, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00      0.000000\n",
       "0.10      0.001111\n",
       "0.25      0.002500\n",
       "0.50      0.006111\n",
       "0.75      0.021389\n",
       "0.90      0.213611\n",
       "1.00    120.007500\n",
       "Name: elapsedraw, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_failed['elapsedraw'].quantile([0, 0.1, 0.25, 0.5, 0.75, 0.9, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normalize(df):\n",
    "    df = df['target'].value_counts(sort=False, normalize=True).rename_axis('target').reset_index(name='pdf')\n",
    "    df[\"cdf\"] = df[\"pdf\"].cumsum()\n",
    "    return df\n",
    "\n",
    "\n",
    "def cdf(df_elapsed_values):\n",
    "    all_values_df = pd.DataFrame({\"target\": df_elapsed_values[\"NCPUS\"].values})\n",
    "    count_df = normalize(all_values_df)\n",
    "    counts = np.unique(df_elapsed_values[\"NCPUS\"].values, return_counts=True)\n",
    "    pdf = counts[1] / np.sum(counts[1])\n",
    "    cdf = np.cumsum(pdf)\n",
    "    return counts, cdf\n",
    "\n",
    "ge_completed_counts, ge_completed_cdf = cdf(ge_completed)\n",
    "ge_failed_counts, ge_failed_cdf = cdf(ge_failed)\n",
    "ml_completed_counts, ml_completed_cdf = cdf(ml_completed)\n",
    "ml_failed_counts, ml_failed_cdf = cdf(ml_failed)\n",
    "\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "plt.ylim(0,1.05)\n",
    "plt.plot(ge_completed_counts[0], ge_completed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'solid', label = 'Generic completed jobs', linewidth=3)\n",
    "plt.plot(ge_failed_counts[0], ge_failed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'dashed', label = 'Generic failed jobs', linewidth=3)\n",
    "plt.plot(ml_completed_counts[0], ml_completed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'solid', label = 'ML completed jobs', linewidth=3)\n",
    "plt.plot(ml_failed_counts[0], ml_failed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'dashed', label = 'ML failed jobs', linewidth=3)\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(\"NCPUs\", fontsize=24)\n",
    "plt.ylabel(\"Fraction of total jobs\", fontsize=24)\n",
    "plt.legend(loc = \"lower right\", prop={'size': 18})\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig-cpu-cdf.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normalize(df):\n",
    "    df = df.value_counts(sort=False, normalize=True).rename_axis('target').reset_index(name='pdf')\n",
    "    df[\"cdf\"] = df[\"pdf\"].cumsum()\n",
    "    return df\n",
    "\n",
    "\n",
    "def cdf(df_elapsed_values):\n",
    "    all_values_df = pd.DataFrame({\"target\": df_elapsed_values[\"CPUTimeRAW\"].values})\n",
    "    count_df = normalize(all_values_df)\n",
    "    counts = np.unique(df_elapsed_values[\"CPUTimeRAW\"].values, return_counts=True)\n",
    "    pdf = counts[1] / np.sum(counts[1])\n",
    "    cdf = np.cumsum(pdf)\n",
    "    return counts, cdf\n",
    "\n",
    "ge_completed_counts, ge_completed_cdf = cdf(ge_completed)\n",
    "ge_failed_counts, ge_failed_cdf = cdf(ge_failed)\n",
    "ml_completed_counts, ml_completed_cdf = cdf(ml_completed)\n",
    "ml_failed_counts, ml_failed_cdf = cdf(ml_failed)\n",
    "\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "plt.tick_params(axis='both', which='major', labelsize=18)\n",
    "plt.ylim(0,1.05)\n",
    "plt.plot(ge_completed_counts[0], ge_completed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'solid', label = 'Generic completed jobs', linewidth=3)\n",
    "plt.plot(ge_failed_counts[0], ge_failed_cdf, drawstyle='steps', color = 'lightcoral', linestyle = 'dashed', label = 'Generic failed jobs', linewidth=3)\n",
    "plt.plot(ml_completed_counts[0], ml_completed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'solid', label = 'ML completed jobs', linewidth=3)\n",
    "plt.plot(ml_failed_counts[0], ml_failed_cdf, drawstyle='steps', color = 'steelblue', linestyle = 'dashed', label = 'ML failed jobs', linewidth=3)\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(\"CPU Time [CPU-s]\", fontsize=24)\n",
    "plt.ylabel(\"Fraction of total jobs\", fontsize=24)\n",
    "plt.legend(loc = \"lower right\", prop={'size': 18})\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('fig-cpu-cdf.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GE completed CPU=16 0.786468315477177\n"
     ]
    }
   ],
   "source": [
    "# compute cpus\n",
    "count = 0\n",
    "for i in ge_completed['NCPUS']:\n",
    "    if i == 16:\n",
    "        count +=1\n",
    "ratio_failed = count/len(ge_completed)\n",
    "\n",
    "print('GE completed CPU=16', ratio_failed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.927316038099903, 27.40325161850065)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ge_failed['elapsedraw'].mean()*60, ml_failed['elapsedraw'].mean()*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ML completed Jobs <= 360 seconds 0.46208401527550463\n",
      "ML failed Jobs <= 360 seconds 0.8695061745858854\n",
      "difference: 0.4074221593103808\n"
     ]
    }
   ],
   "source": [
    "# compute jobs <= 300 seconds\n",
    "count = 0\n",
    "for i in ml_completed['ElapsedRaw']:\n",
    "    if i <= 360:\n",
    "        count +=1\n",
    "ratio_completed = count/len(ml_completed)\n",
    "print('ML completed Jobs <= 360 seconds', ratio_completed)\n",
    "\n",
    "count = 0\n",
    "for i in ml_failed['ElapsedRaw']:\n",
    "    if i <= 360:\n",
    "        count +=1\n",
    "ratio_failed = count/len(ml_failed)\n",
    "print('ML failed Jobs <= 360 seconds', ratio_failed)\n",
    "print('difference:', ratio_failed - ratio_completed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>JobID</th>\n",
       "      <th>GID</th>\n",
       "      <th>UID</th>\n",
       "      <th>Partition</th>\n",
       "      <th>Submit</th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "      <th>ElapsedRaw</th>\n",
       "      <th>CPUTimeRAW</th>\n",
       "      <th>...</th>\n",
       "      <th>ReqMem</th>\n",
       "      <th>is_gpu</th>\n",
       "      <th>ave_cpu_freq</th>\n",
       "      <th>req_mem</th>\n",
       "      <th>state</th>\n",
       "      <th>node_list</th>\n",
       "      <th>submit_hour_of_day</th>\n",
       "      <th>submit_day_of_week</th>\n",
       "      <th>submit_date</th>\n",
       "      <th>waiting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8619864</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 01:55:55</td>\n",
       "      <td>2022-01-01 01:55:55</td>\n",
       "      <td>2022-01-01 02:02:51</td>\n",
       "      <td>416</td>\n",
       "      <td>9984</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>8619865</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 03:49:53</td>\n",
       "      <td>2022-01-01 03:50:05</td>\n",
       "      <td>2022-01-01 03:56:51</td>\n",
       "      <td>406</td>\n",
       "      <td>9744</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>8619866</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:19:20</td>\n",
       "      <td>2022-01-01 04:19:20</td>\n",
       "      <td>2022-01-01 04:29:50</td>\n",
       "      <td>630</td>\n",
       "      <td>15120</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>TIMEOUT</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>8619867</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:32:24</td>\n",
       "      <td>2022-01-01 04:32:24</td>\n",
       "      <td>2022-01-01 04:42:22</td>\n",
       "      <td>598</td>\n",
       "      <td>14352</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>CANCELLED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8619868</td>\n",
       "      <td>54655.0</td>\n",
       "      <td>55101.0</td>\n",
       "      <td>gpu_titan+</td>\n",
       "      <td>2022-01-01 04:47:23</td>\n",
       "      <td>2022-01-01 04:47:23</td>\n",
       "      <td>2022-01-01 04:50:11</td>\n",
       "      <td>168</td>\n",
       "      <td>4032</td>\n",
       "      <td>...</td>\n",
       "      <td>186G</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190464.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r28n1'}</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.0</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301123</th>\n",
       "      <td>2301705</td>\n",
       "      <td>10587253</td>\n",
       "      <td>47200.0</td>\n",
       "      <td>47539.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>2022-12-31 22:44:37</td>\n",
       "      <td>2023-01-01 04:16:28</td>\n",
       "      <td>2023-01-01 04:19:54</td>\n",
       "      <td>206</td>\n",
       "      <td>3296</td>\n",
       "      <td>...</td>\n",
       "      <td>5760M</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r10n16'}</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>19911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301124</th>\n",
       "      <td>2301706</td>\n",
       "      <td>10587254</td>\n",
       "      <td>47200.0</td>\n",
       "      <td>47539.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>2022-12-31 22:44:37</td>\n",
       "      <td>2023-01-01 04:17:59</td>\n",
       "      <td>2023-01-01 04:22:00</td>\n",
       "      <td>241</td>\n",
       "      <td>3856</td>\n",
       "      <td>...</td>\n",
       "      <td>5760M</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r25n7'}</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>20002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301125</th>\n",
       "      <td>2301707</td>\n",
       "      <td>10587255</td>\n",
       "      <td>59552.0</td>\n",
       "      <td>60028.0</td>\n",
       "      <td>shared</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:12:55</td>\n",
       "      <td>433</td>\n",
       "      <td>866</td>\n",
       "      <td>...</td>\n",
       "      <td>8610M</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8610.0</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r10n21'}</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301126</th>\n",
       "      <td>2301708</td>\n",
       "      <td>10587255.ba+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:12:55</td>\n",
       "      <td>433</td>\n",
       "      <td>866</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r10n21'}</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301127</th>\n",
       "      <td>2301709</td>\n",
       "      <td>10587255.ex+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:05:42</td>\n",
       "      <td>2022-12-31 23:12:55</td>\n",
       "      <td>433</td>\n",
       "      <td>866</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>{'r10n21'}</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2301128 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         JobID      GID      UID   Partition  \\\n",
       "0                 2       8619864  54655.0  55101.0  gpu_titan+   \n",
       "1                 3       8619865  54655.0  55101.0  gpu_titan+   \n",
       "2                 4       8619866  54655.0  55101.0  gpu_titan+   \n",
       "3                 5       8619867  54655.0  55101.0  gpu_titan+   \n",
       "4                 6       8619868  54655.0  55101.0  gpu_titan+   \n",
       "...             ...           ...      ...      ...         ...   \n",
       "2301123     2301705      10587253  47200.0  47539.0      normal   \n",
       "2301124     2301706      10587254  47200.0  47539.0      normal   \n",
       "2301125     2301707      10587255  59552.0  60028.0      shared   \n",
       "2301126     2301708  10587255.ba+      NaN      NaN         NaN   \n",
       "2301127     2301709  10587255.ex+      NaN      NaN         NaN   \n",
       "\n",
       "                      Submit                Start                  End  \\\n",
       "0        2022-01-01 01:55:55  2022-01-01 01:55:55  2022-01-01 02:02:51   \n",
       "1        2022-01-01 03:49:53  2022-01-01 03:50:05  2022-01-01 03:56:51   \n",
       "2        2022-01-01 04:19:20  2022-01-01 04:19:20  2022-01-01 04:29:50   \n",
       "3        2022-01-01 04:32:24  2022-01-01 04:32:24  2022-01-01 04:42:22   \n",
       "4        2022-01-01 04:47:23  2022-01-01 04:47:23  2022-01-01 04:50:11   \n",
       "...                      ...                  ...                  ...   \n",
       "2301123  2022-12-31 22:44:37  2023-01-01 04:16:28  2023-01-01 04:19:54   \n",
       "2301124  2022-12-31 22:44:37  2023-01-01 04:17:59  2023-01-01 04:22:00   \n",
       "2301125  2022-12-31 23:05:42  2022-12-31 23:05:42  2022-12-31 23:12:55   \n",
       "2301126  2022-12-31 23:05:42  2022-12-31 23:05:42  2022-12-31 23:12:55   \n",
       "2301127  2022-12-31 23:05:42  2022-12-31 23:05:42  2022-12-31 23:12:55   \n",
       "\n",
       "         ElapsedRaw  CPUTimeRAW  ...  ReqMem  is_gpu ave_cpu_freq   req_mem  \\\n",
       "0               416        9984  ...    186G    True          NaN  190464.0   \n",
       "1               406        9744  ...    186G    True          NaN  190464.0   \n",
       "2               630       15120  ...    186G    True          NaN  190464.0   \n",
       "3               598       14352  ...    186G    True          NaN  190464.0   \n",
       "4               168        4032  ...    186G    True          NaN  190464.0   \n",
       "...             ...         ...  ...     ...     ...          ...       ...   \n",
       "2301123         206        3296  ...   5760M   False          NaN    5760.0   \n",
       "2301124         241        3856  ...   5760M   False          NaN    5760.0   \n",
       "2301125         433         866  ...   8610M   False          NaN    8610.0   \n",
       "2301126         433         866  ...     NaN   False          1.0       NaN   \n",
       "2301127         433         866  ...     NaN   False          1.0       NaN   \n",
       "\n",
       "             state   node_list  submit_hour_of_day submit_day_of_week  \\\n",
       "0        COMPLETED   {'r28n1'}                   1                  5   \n",
       "1        COMPLETED   {'r28n1'}                   3                  5   \n",
       "2          TIMEOUT   {'r28n1'}                   4                  5   \n",
       "3        CANCELLED   {'r28n1'}                   4                  5   \n",
       "4        COMPLETED   {'r28n1'}                   4                  5   \n",
       "...            ...         ...                 ...                ...   \n",
       "2301123  COMPLETED  {'r10n16'}                  22                  5   \n",
       "2301124  COMPLETED   {'r25n7'}                  22                  5   \n",
       "2301125  COMPLETED  {'r10n21'}                  23                  5   \n",
       "2301126  COMPLETED  {'r10n21'}                  23                  5   \n",
       "2301127  COMPLETED  {'r10n21'}                  23                  5   \n",
       "\n",
       "        submit_date waiting_time  \n",
       "0        2022-01-01          0.0  \n",
       "1        2022-01-01         12.0  \n",
       "2        2022-01-01          0.0  \n",
       "3        2022-01-01          0.0  \n",
       "4        2022-01-01          0.0  \n",
       "...             ...          ...  \n",
       "2301123  2022-12-31      19911.0  \n",
       "2301124  2022-12-31      20002.0  \n",
       "2301125  2022-12-31          0.0  \n",
       "2301126  2022-12-31          0.0  \n",
       "2301127  2022-12-31          0.0  \n",
       "\n",
       "[2301128 rows x 35 columns]"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove Outlier in Resource data\n",
    "data2 = data.copy()\n",
    "def three_sigma(ser1): \n",
    "    # 求平均值\n",
    "    mean_value = ser1.mean()\n",
    "    # 求标准差\n",
    "    std_value = ser1.std()\n",
    "    # 位于(μ-3σ,μ+3σ)区间的数据是正常的，不在这个区间的数据为异常的\n",
    "    # ser1中的数值小于μ-3σ或大于μ+3σ均为异常值\n",
    "    # 一旦发现有异常值，就标注为True，否则标注为False\n",
    "    rule = (mean_value - 3 * std_value > ser1) | \\\n",
    "                                                   (ser1.mean() + 3 * ser1.std() < ser1)\n",
    "    # 返回异常值的位置索引\n",
    "    index = np.arange(ser1.shape[0])[rule]\n",
    "    # 获取异常数据\n",
    "    outrange = ser1.iloc[index]\n",
    "    return outrange\n",
    "\n",
    "df1 = three_sigma(data2['CPUTimeRAW'])\n",
    "data2.replace(to_replace=df1, value=np.nan)\n",
    "df2 = three_sigma(data2['NCPUS'])\n",
    "data2.replace(to_replace=df2, value=np.nan)\n",
    "df3 = three_sigma(data2['ReqCPUS'])\n",
    "data2.replace(to_replace=df3, value=np.nan)\n",
    "# print(df1, df2, df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# fill NaN values use mean()\n",
    "data2['CPUTimeRAW'].fillna(data2['CPUTimeRAW'].mean(), inplace=True)\n",
    "data2['NCPUS'].fillna(data2['NCPUS'].mean(), inplace=True)\n",
    "data2['ReqCPUS'].fillna(data2['ReqCPUS'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "# divide jobs into: generic completed, generic failed, ml completed, ml failed\n",
    "\n",
    "data2['elapsedraw'] = data2['ElapsedRaw']/3600.0\n",
    "ge = data2.loc[data2[\"is_gpu\"] == False]\n",
    "ml = data2.loc[data2[\"is_gpu\"] == True]\n",
    "\n",
    "ge_completed = ge[ge['state'] == 'COMPLETED']\n",
    "ge_failed = ge[ge['state'] == 'FAILED']\n",
    "ml_completed = ml[ml['state'] == 'COMPLETED']\n",
    "ml_failed = ml[ml['state'] == 'FAILED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CPUTimeRAW, NCPUS, ReqCPUS\n",
    "\n",
    "fig, axes = plt.subplots(figsize = (24, 6), nrows = 1, ncols = 3)\n",
    "box_1, box_2, box_3, box_4 = ge_completed['CPUTimeRAW'], ge_failed['CPUTimeRAW'], ml_completed['CPUTimeRAW'], ml_failed['CPUTimeRAW']\n",
    "axes[0].boxplot([box_1, box_2, box_3, box_4], patch_artist = True, showfliers = False, labels = ['Generic\\ncompleted', 'Generic\\nfailed', 'ML completed', 'ML failed'])\n",
    "axes[0].set_ylabel(\"CPU Time RAW\", fontsize = 24)\n",
    "\n",
    "box_1, box_2, box_3, box_4 = ge_completed['NCPUS'], ge_failed['NCPUS'], ml_completed['NCPUS'], ml_failed['NCPUS']\n",
    "axes[1].boxplot([box_1, box_2, box_3, box_4], patch_artist = True, showfliers = False, labels = ['Generic\\ncompleted', 'Generic\\nfailed', 'ML completed', 'ML failed'])\n",
    "axes[1].set_ylabel(\"NCPUS\", fontsize = 24)\n",
    "\n",
    "box_1, box_2, box_3, box_4 = ge_completed['ReqCPUS'], ge_failed['ReqCPUS'], ml_completed['ReqCPUS'], ml_failed['ReqCPUS']\n",
    "axes[2].boxplot([box_1, box_2, box_3, box_4], patch_artist = True, showfliers = False, labels = ['Generic\\ncompleted', 'Generic\\nfailed', 'ML completed', 'ML failed'])\n",
    "axes[2].set_ylabel(\"ReqCPUS\", fontsize = 24)\n",
    "\n",
    "# box_1, box_2, box_3, box_4 = ge_completed['req_mem'], ge_failed['req_mem'], ml_completed['req_mem'], ml_failed['req_mem']\n",
    "# axes[4].boxplot([box_1, box_2, box_3, box_4], patch_artist = True, labels = ['Generic\\ncompleted', 'Generic\\nfailed', 'ML completed', 'ML failed'])\n",
    "# axes[4].set_ylabel(\"Request Memory\", fontsize = 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate array containing counts of cpu core allocation for jobs \n",
    "counts_cpus = np.unique(ge_failed[\"NCPUS\"].values, return_counts=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,5))\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim(bottom=1)\n",
    "ax.set_ylim(top=10**6.5)\n",
    "ax.set_xlim((0,800))\n",
    "ax.bar(counts_cpus[0], counts_cpus[1], width=1, color=\"lightcoral\")\n",
    "ax.set_xlabel(\"NCPUS in generic failed jobs\", fontsize=20)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax.xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "\n",
    "plt.ylabel(\"Jobs\", fontsize=20)\n",
    "plt.tight_layout()\n",
    "\n",
    "a = plt.axes([.505, .54, .45, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:30], counts_cpus[1][0:30], width=0.3, color=\"lightcoral\")\n",
    "a.set_title('Zoomed-in view', fontsize=18)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=15)\n",
    "a.tick_params(axis='both', which='minor', labelsize=15)\n",
    "\n",
    "mark_inset(ax, a, loc1=3, loc2=4, fc=\"none\", ec=\"steelblue\")\n",
    "\n",
    "\n",
    "fig.savefig(\"fig-ge-failed-job-ncpu.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<mpl_toolkits.axes_grid1.inset_locator.BboxPatch at 0x7fc2c9c666a0>,\n",
       " <mpl_toolkits.axes_grid1.inset_locator.BboxConnector at 0x7fc2c9d092e0>,\n",
       " <mpl_toolkits.axes_grid1.inset_locator.BboxConnector at 0x7fc2c9d09670>)"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate array containing counts of cpu core allocation for jobs \n",
    "counts_cpus = np.unique(ml_failed[\"NCPUS\"].values, return_counts=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,5))\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_ylim(bottom=1)\n",
    "ax.set_ylim(top=10**6.5)\n",
    "ax.set_xlim((0,800))\n",
    "ax.bar(counts_cpus[0], counts_cpus[1], width=1, color=\"steelblue\")\n",
    "ax.set_xlabel(\"NCPUS in ML failed Job\", fontsize=20)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax.xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "\n",
    "plt.ylabel(\"Jobs\", fontsize=20)\n",
    "plt.tight_layout()\n",
    "\n",
    "a = plt.axes([.505, .54, .45, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:23], counts_cpus[1][0:23], width=0.3, color=\"steelblue\")\n",
    "a.set_title('Zoomed-in view', fontsize=18)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=15)\n",
    "a.tick_params(axis='both', which='minor', labelsize=15)\n",
    "\n",
    "mark_inset(ax, a, loc1=3, loc2=4, fc=\"none\", ec=\"lightcoral\")\n",
    "\n",
    "\n",
    "# fig.savefig(f\"job_cpus_count_zoom_{date_time}.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-616-3dfe5cb00e35>:64: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (22, 5), nrows = 1, ncols = 2)\n",
    "\n",
    "# CPU of ML completed jobs\n",
    "counts_cpus = np.unique(ml_completed[\"NCPUS\"].values, return_counts=True)\n",
    "ax[0].set_yscale(\"log\")\n",
    "ax[0].set_ylim(bottom=1)\n",
    "ax[0].set_ylim(top=10**6.5)\n",
    "ax[0].set_xlim((0,800))\n",
    "ax[0].bar(counts_cpus[0], counts_cpus[1], width=1, color=\"steelblue\")\n",
    "ax[0].set_xlabel(\"NCPUS in ML completed Job\", fontsize=26)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax[0].xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax[0].xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax[0].tick_params(axis='both', which='major', labelsize=26)\n",
    "ax[0].tick_params(axis='both', which='minor', labelsize=26)\n",
    "\n",
    "ax[0].set_ylabel(\"Jobs\", fontsize=26)\n",
    "\n",
    "a = plt.axes([.125, .54, .35, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:22], counts_cpus[1][0:22], width=0.5, color=\"steelblue\")\n",
    "a.set_title('Zoomed-in view', fontsize=26)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=25)\n",
    "a.tick_params(axis='both', which='minor', labelsize=25)\n",
    "\n",
    "mark_inset(ax[0], a, loc1=3, loc2=4, fc=\"none\", ec=\"lightcoral\")\n",
    "\n",
    "# CPU of ML failed jobs\n",
    "counts_cpus = np.unique(ml_failed[\"NCPUS\"].values, return_counts=True)\n",
    "ax[1].set_yscale(\"log\")\n",
    "ax[1].set_ylim(bottom=1)\n",
    "ax[1].set_ylim(top=10**6.5)\n",
    "ax[1].set_xlim((0,800))\n",
    "ax[1].bar(counts_cpus[0], counts_cpus[1], width=1, color=\"steelblue\")\n",
    "ax[1].set_xlabel(\"NCPUS in ML failed Job\", fontsize=26)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax[1].xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax[1].xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax[1].tick_params(axis='both', which='major', labelsize=26)\n",
    "ax[1].tick_params(axis='both', which='minor', labelsize=26)\n",
    "\n",
    "ax[1].set_ylabel(\"Jobs\", fontsize=26)\n",
    "\n",
    "a = plt.axes([.620, .54, .35, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:23], counts_cpus[1][0:23], width=0.5, color=\"steelblue\", hatch='/')\n",
    "a.set_title('Zoomed-in view', fontsize=26)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=25)\n",
    "a.tick_params(axis='both', which='minor', labelsize=25)\n",
    "\n",
    "mark_inset(ax[1], a, loc1=3, loc2=4, fc=\"none\", ec=\"lightcoral\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('fig-ml-cpu.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-621-4c1a7df1e2b3>:64: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (22, 5), nrows = 1, ncols = 2)\n",
    "\n",
    "# CPU of ML completed jobs\n",
    "counts_cpus = np.unique(ge_completed[\"NCPUS\"].values, return_counts=True)\n",
    "ax[0].set_yscale(\"log\")\n",
    "ax[0].set_ylim(bottom=1)\n",
    "ax[0].set_ylim(top=10**6.5)\n",
    "ax[0].set_xlim((0,800))\n",
    "ax[0].bar(counts_cpus[0], counts_cpus[1], width=1, color=\"lightcoral\")\n",
    "ax[0].set_xlabel(\"NCPUS in generic completed Job\", fontsize=26)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax[0].xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax[0].xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax[0].tick_params(axis='both', which='major', labelsize=26)\n",
    "ax[0].tick_params(axis='both', which='minor', labelsize=26)\n",
    "\n",
    "ax[0].set_ylabel(\"Jobs\", fontsize=26)\n",
    "\n",
    "a = plt.axes([.125, .54, .35, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:25], counts_cpus[1][0:25], width=0.3, color=\"lightcoral\")\n",
    "a.set_title('Zoomed-in view', fontsize=26)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=25)\n",
    "a.tick_params(axis='both', which='minor', labelsize=25)\n",
    "\n",
    "mark_inset(ax[0], a, loc1=3, loc2=4, fc=\"none\", ec=\"steelblue\")\n",
    "\n",
    "# CPU of ML failed jobs\n",
    "counts_cpus = np.unique(ge_failed[\"NCPUS\"].values, return_counts=True)\n",
    "ax[1].set_yscale(\"log\")\n",
    "ax[1].set_ylim(bottom=1)\n",
    "ax[1].set_ylim(top=10**6.5)\n",
    "ax[1].set_xlim((0,800))\n",
    "ax[1].bar(counts_cpus[0], counts_cpus[1], width=1, color=\"lightcoral\")\n",
    "ax[1].set_xlabel(\"NCPUS in generic failed Job\", fontsize=26)\n",
    "\n",
    "majors = [0, 100, 200, 300, 400, 500, 600, 700, 800]\n",
    "ax[1].xaxis.set_major_locator(FixedLocator(majors))\n",
    "ax[1].xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 800, 5))))\n",
    "ax[1].tick_params(axis='both', which='major', labelsize=26)\n",
    "ax[1].tick_params(axis='both', which='minor', labelsize=26)\n",
    "\n",
    "ax[1].set_ylabel(\"Jobs\", fontsize=26)\n",
    "\n",
    "a = plt.axes([.620, .54, .35, .35], facecolor='lightgrey')\n",
    "a.set_ylim(bottom=1, top=10**6.45)\n",
    "a.bar(counts_cpus[0][0:28], counts_cpus[1][0:28], width=0.3, color=\"lightcoral\", hatch='/')\n",
    "a.set_title('Zoomed-in view', fontsize=26)\n",
    "a.set_yscale(\"log\")\n",
    "majors_2 = [0, 10, 20, 30, 40, 50]\n",
    "a.xaxis.set_major_locator(FixedLocator(majors_2))\n",
    "a.xaxis.set_minor_locator(FixedLocator((np.arange(min(counts_cpus[0]-1), 50, 1))))\n",
    "a.tick_params(axis='both', which='major', labelsize=25)\n",
    "a.tick_params(axis='both', which='minor', labelsize=25)\n",
    "\n",
    "mark_inset(ax[1], a, loc1=3, loc2=4, fc=\"none\", ec=\"steelblue\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "fig.savefig('fig-generic-cpu.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stack bar chart: CPUTimeRAW\n",
    "def jobType(x):\n",
    "    if(x < 300):\n",
    "        return '$<$1 day'\n",
    "    elif(x < 3600):\n",
    "        return '$<$5 day'\n",
    "    elif (x < 86100):\n",
    "        return '$<$10 day'\n",
    "    else:\n",
    "        return '$\\geq$10 day'\n",
    "\n",
    "df['type'] = df['CPUTimeRAW'].apply(lambda x: jobType(x))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     1.0\n",
       "0.25     3.0\n",
       "0.50     3.0\n",
       "0.75     6.0\n",
       "0.90    16.0\n",
       "1.00    96.0\n",
       "Name: NCPUS, dtype: float64"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_failed['NCPUS'].quantile([0, 0.25, 0.5, 0.75, 0.9, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00       1.0\n",
       "0.25       2.0\n",
       "0.50       2.0\n",
       "0.75      16.0\n",
       "0.99     160.0\n",
       "1.00    2048.0\n",
       "Name: NCPUS, dtype: float64"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ge_failed['NCPUS'].quantile([0, 0.25, 0.5, 0.75, 0.99, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352.0"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ge_completed['CPUTimeRAW'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25       1.0\n",
       "0.50       1.0\n",
       "0.75       1.0\n",
       "1.00    2048.0\n",
       "Name: ReqCPUS, dtype: float64"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ge_failed['ReqCPUS'].quantile([0.25, 0.5, 0.75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig-generic-failures-week-and-day\n",
    "fig, axes = plt.subplots(figsize = (24, 5), nrows = 1, ncols = 2)\n",
    "\n",
    "x_vals = np.arange(ge_failed[\"submit_hour_of_day\"].nunique())\n",
    "y_vals = ge_failed[\"submit_hour_of_day\"].value_counts(sort=False)\n",
    "axes[1].bar(x_vals, y_vals, edgecolor='black', capsize=5, width = 0.6, color = 'lightcoral')\n",
    "axes[1].set_ylim(bottom=0, top=None)\n",
    "axes[1].set_xlim(-0.5,23.5)\n",
    "axes[1].set_xlabel(\"Hour of Day\",  fontsize=28)\n",
    "axes[1].set_ylabel(\"Number of failed jobs\", fontsize=28)\n",
    "axes[1].tick_params(axis='both', which='major', labelsize=20)\n",
    "axes[1].tick_params(axis='both', which='minor', labelsize=20)\n",
    "axes[1].xaxis.set_major_locator(MultipleLocator(1))\n",
    "\n",
    "x_vals = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "y_vals = ge_failed[\"submit_day_of_week\"].value_counts(sort=False)\n",
    "axes[0].bar(x_vals, y_vals, edgecolor='black', capsize=3, width = 0.5, color = 'lightcoral')\n",
    "axes[0].set_ylim(bottom=0, top=None)\n",
    "axes[0].set_xlim(-0.5,6.5)\n",
    "axes[0].set_xlabel(\"Day of Week\", fontsize=28)\n",
    "axes[0].set_ylabel(\"Number of failed jobs\", fontsize=28)\n",
    "axes[0].tick_params(axis='both', which='major', labelsize=26)\n",
    "axes[0].tick_params(axis='both', which='minor', labelsize=26)\n",
    "axes[0].xaxis.set_major_locator(MultipleLocator(1))\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('fig-generic-failures-week-and-day.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig-ml-failures-week-and-day\n",
    "fig, axes = plt.subplots(figsize = (24, 5), nrows = 1, ncols = 2)\n",
    "\n",
    "x_vals = np.arange(ml_failed[\"submit_hour_of_day\"].nunique())\n",
    "y_vals = ml_failed[\"submit_hour_of_day\"].value_counts(sort=False)\n",
    "axes[1].bar(x_vals, y_vals, edgecolor='black', capsize=2, width = 0.6, color = 'steelblue')\n",
    "axes[1].set_ylim(bottom=0, top=None)\n",
    "axes[1].set_xlim(-0.5,23.5)\n",
    "axes[1].set_xlabel(\"Hour of Day\",  fontsize=28)\n",
    "axes[1].set_ylabel(\"Number of failed jobs\", fontsize=28)\n",
    "axes[1].tick_params(axis='both', which='major', labelsize=20)\n",
    "axes[1].tick_params(axis='both', which='minor', labelsize=20)\n",
    "axes[1].xaxis.set_major_locator(MultipleLocator(1))\n",
    "\n",
    "x_vals = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "y_vals = ml_failed[\"submit_day_of_week\"].value_counts(sort=False)\n",
    "axes[0].bar(x_vals, y_vals, edgecolor='black', capsize=2, width = 0.5, color = 'steelblue')\n",
    "axes[0].set_ylim(bottom=0, top=None)\n",
    "axes[0].set_xlim(-0.5,6.5)\n",
    "axes[0].set_xlabel(\"Day of Week\", fontsize=28)\n",
    "axes[0].set_ylabel(\"Number of failed jobs\", fontsize=28)\n",
    "axes[0].tick_params(axis='both', which='major', labelsize=26)\n",
    "axes[0].tick_params(axis='both', which='minor', labelsize=26)\n",
    "axes[0].xaxis.set_major_locator(MultipleLocator(1))\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('fig-ml-failures-week-and-day.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "ml_failed['Submit'] = pd.to_datetime(ml_failed['Submit'], format = '%Y-%m-%d %H:%M:%S')\n",
    "ml_failed['day'] = ml_failed['Submit'].dt.date\n",
    "ml_failed['month'] = ml_failed['day'].apply(lambda x: str(x)[:7])\n",
    "ml_failed[\"hour_of_day\"] = ml_failed[\"Submit\"].dt.hour\n",
    "\n",
    "def dt_to_week(df):\n",
    "    l = []\n",
    "    for i in df['Submit']:\n",
    "        j = i.strftime(\"%W\")\n",
    "        l.append(j)\n",
    "    df['week'] = l\n",
    "    return df\n",
    "def dt_to_day(df):\n",
    "    l = []\n",
    "    for i in df['Submit']:\n",
    "        j = i.strftime(\"%j\")\n",
    "        j = int(j)\n",
    "        l.append(j)\n",
    "    df['day'] = l\n",
    "    return df\n",
    "\n",
    "ml_failed = dt_to_week(ml_failed)\n",
    "ml_failed = dt_to_day(ml_failed)\n",
    "ml_failed[\"hour\"] = (ml_failed[\"day\"]-1)*24 + ml_failed[\"hour_of_day\"]\n",
    "\n",
    "# df_month\n",
    "group = ml_failed.groupby([\"month\"])\n",
    "failL = []\n",
    "for i in group:\n",
    "    a = i[1]['state'].value_counts().to_frame()\n",
    "    fail = a.loc['FAILED','state']\n",
    "    failL.append(fail)\n",
    "df_month = pd.DataFrame({\n",
    "    \"month\": [1,2,3,4,5,6,7,8,9,10,11,12],\n",
    "    \"failed\": failL\n",
    "})\n",
    "\n",
    "# df_week\n",
    "group = ml_failed.groupby([\"week\"])\n",
    "failL, iL = [], []\n",
    "for i in group:\n",
    "    a = i[1]['state'].value_counts().to_frame()\n",
    "    fail = a.loc['FAILED','state']\n",
    "    failL.append(fail)\n",
    "    iL.append(i[0])\n",
    "\n",
    "df_week = pd.DataFrame({\n",
    "    \"week\": iL,\n",
    "    \"failed\": failL\n",
    "})\n",
    "\n",
    "# df_day\n",
    "group = ml_failed.groupby([\"day\"])\n",
    "failL, iL = [], []\n",
    "for i in group:\n",
    "    a = i[1]['state'].value_counts().to_frame()\n",
    "    fail = a.loc['FAILED','state']\n",
    "    failL.append(fail)\n",
    "    iL.append(i[0])\n",
    "\n",
    "df_day = pd.DataFrame({\n",
    "    \"day\": iL,\n",
    "    \"failed\": failL\n",
    "})\n",
    "\n",
    "# df_hour\n",
    "group = ml_failed.groupby([\"hour\"])\n",
    "failL, iL = [], []\n",
    "for i in group:\n",
    "    a = i[1]['state'].value_counts().to_frame()\n",
    "    fail = a.loc['FAILED','state']\n",
    "    failL.append(fail)\n",
    "    iL.append(i[0])\n",
    "\n",
    "df_hour = pd.DataFrame({\n",
    "    \"hour\": iL,\n",
    "    \"failed\": failL\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chuxiaoyu/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "116          16\n",
       "117          16\n",
       "125          16\n",
       "133          16\n",
       "136          16\n",
       "           ... \n",
       "2300247    8726\n",
       "2300249    8726\n",
       "2300835    8748\n",
       "2300836    8748\n",
       "2300838    8748\n",
       "Name: hour, Length: 70693, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_hour\n",
    "ml_failed[\"hour\"] = (ml_failed[\"day\"]-1)*24 + ml_failed[\"hour_of_day\"]\n",
    "ml_failed[\"hour\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "x = range(1,len(df_week)+1)\n",
    "y = df_week['failed']\n",
    "plt.xlabel('Week [w]', fontsize=30)\n",
    "plt.ylabel('Failures per week', fontsize=30)\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "plt.yscale(\"log\")\n",
    "plt.bar(x,y,color='steelblue',width=0.3)\n",
    "\n",
    "# fig.savefig('fig-failures-per-week.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>week</th>\n",
       "      <th>failed</th>\n",
       "      <th>normalize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00</td>\n",
       "      <td>22</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>691</td>\n",
       "      <td>0.086748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>892</td>\n",
       "      <td>0.112811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03</td>\n",
       "      <td>1051</td>\n",
       "      <td>0.133428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04</td>\n",
       "      <td>1284</td>\n",
       "      <td>0.163641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  week  failed  normalize\n",
       "0   00      22   0.000000\n",
       "1   01     691   0.086748\n",
       "2   02     892   0.112811\n",
       "3   03    1051   0.133428\n",
       "4   04    1284   0.163641"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week['normalize'] = (df_week['failed']-df_week['failed'].min())/(df_week['failed'].max()-df_week['failed'].min())\n",
    "df_week.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "# Plot autocorrelation\n",
    "plt.rc(\"figure\", figsize=(8,6))\n",
    "plot_acf(df_week['failed'], lags=30)\n",
    "plt.ylim(0,1)\n",
    "# plt.xlim((-1,500))\n",
    "plt.xlabel('Lags [w]', fontsize=28)\n",
    "plt.ylabel('Autocorrelation', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title('',fontsize=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig-lags-week.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "x = range(1,len(df_day)+1)\n",
    "y = df_day['failed']\n",
    "plt.xlabel('Day [d]', fontsize=30)\n",
    "plt.ylabel('Failures per day', fontsize=30)\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "plt.yscale(\"log\")\n",
    "plt.bar(x,y,color='steelblue',width=1)\n",
    "# fig.savefig('fig-failures-per-day.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(8,6))\n",
    "plot_acf(df_day['failed'], lags=30)\n",
    "plt.ylim(0,1)\n",
    "# plt.xlim((-1,500))\n",
    "plt.xlabel('Lags [d]', fontsize=28)\n",
    "plt.ylabel('Autocorrelation', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title('',fontsize=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig-lags-day.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "x = range(1,len(df_hour)+1)\n",
    "y = df_hour['failed']\n",
    "plt.xlabel('Hour [h]', fontsize=30)\n",
    "plt.ylabel('Failures per hour', fontsize=30)\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "plt.yscale(\"log\")\n",
    "plt.bar(x,y,color='steelblue',width=1)\n",
    "#fig.savefig('fig-failures-per-hour.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(8,6))\n",
    "plot_acf(df_hour['failed'], lags=30,color='steelblue')\n",
    "plt.ylim(0,1)\n",
    "# plt.xlim((-1,500))\n",
    "plt.xlabel('Lags [h]', fontsize=28)\n",
    "plt.ylabel('Autocorrelation', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title('',fontsize=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig-lags-hour.pdf', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}